Handbook of Climate Change Mitigation and Adaptation 9783319144092, 9783319144085, 9783319144108

Table of contents :
Front Matter....Pages i-xxix
Front Matter....Pages 1-1
Front Matter....Pages 3-15
Front Matter....Pages 17-45
Front Matter....Pages 47-60
Front Matter....Pages 61-91
Front Matter....Pages 93-125
Front Matter....Pages 127-165
Front Matter....Pages 167-219
Back Matter....Pages 221-255
....Pages 257-312

Citation preview

Wei-Yin Chen Toshio Suzuki Maximilian Lackner Editors

Handbook of Climate Change Mitigation and Adaptation Second Edition

Handbook of Climate Change Mitigation and Adaptation

Wei-Yin Chen • Toshio Suzuki Maximilian Lackner Editors

Handbook of Climate Change Mitigation and Adaptation Second Edition

With 1108 Figures and 352 Tables

Editors Wei-Yin Chen Department of Chemical Engineering University of Mississippi Oxford, MS, USA

Toshio Suzuki National Institute of Advanced Industrial Science and Technology (AIST) Nagoya, Japan

Maximilian Lackner Institute of Advanced Engineering Technologies University of Applied Sciences FH Technikum Wien Vienna, Austria

ISBN 978-3-319-14408-5 ISBN 978-3-319-14409-2 (eBook) ISBN 978-3-319-14410-8 (print and electronic bundle) DOI 10.1007/978-3-319-14409-2 Library of Congress Control Number: 2016946080 1st edition: # Springer Science+Business Media, LLC 2012 # Springer International Publishing Switzerland 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

Scientific evidence is mounting that human activities have begun to change global climate. As a consequence, attention is increasingly turning in the public, private, and nonprofit sectors to the options available for dealing with that change. As we gradually begin to realize, actually only three options are available to us: mitigation, adaptation, and suffering. Mitigating climate change means cutting and sequestering emissions of greenhouse gases to prevent further increases in their atmospheric concentrations and perhaps even reducing concentrations to levels deemed less unsafe than the ones to which they have been driven since the start of the industrial revolution. Adaptation means finding ways that can help reduce the impacts of climate change on society, the various sectors of its economy, and the places in which we live – be those small rural villages or the cities and towns that by now house the majority of the human population, that account for the bulk of infrastructure investments, and that contribute most to energy consumption and carbon emissions. To the extent that mitigation and adaptation efforts are too timid, suffering will inevitably result. What are safe concentrations of greenhouse gases is a topic of vigorous debate because we do not yet fully understand the coupled earth system and human system dynamics that will play themselves out in a world of unprecedented greenhouse gas concentrations. How high is too high will be known well after the point of no return has been reached, that is when developments in the global biogeochemical system are kicked off to move in a direction and rate that cannot be undone. Ice sheets may melt and free the methane and carbon dioxide long locked up in the soils underneath, thus further accelerating climate change. Major ocean currents, which move waters and nutrients to support the biological activity in the seas, may abruptly change direction or entirely cease. The established precipitation and temperature patterns, which are so central to agriculture, may be altered in ways that further challenge our abilities to feed a growing human population. And so, the extent to which we embrace mitigation, in part, reflects our aversion – or desire – to take risks in matters pertaining not just to the stability of global climate conditions but the global human condition and that of other species more broadly. It is against this backdrop that this volume illuminates humanity’s mitigation options. From its coverage it is quite obvious that there is no magic knife with which to cut emissions, because the sources of emissions are varied and intricately woven v

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Foreword

into the very fabric of our society and economy. Movement from oil, coal, and natural gas, for example, to biological feedstocks for the production of fuels and chemicals is an essential strategy to decarbonize our economies, but the effect of this strategy hinges on the extent to which production of biomass can decouple itself from fossil-based generation of fertilizers and pesticides, minimize land conversation, and prevent the associated release of carbon from soils and impacts on biodiversity. Other, at least equally daunting, challenges surround the conversion of syngas to fuel; the deployment of geothermal, solar, and fusion technology; and the various means to sequester greenhouse gases. Policy and investment decision makers who wish to navigate and shape the resultant dynamics are further challenged in their abilities to understand and project resource and emissions trends into the future because of the rapid changes in technologies and markets, as well as the emergence of new, major players in the game, such as has happened in recent years with the proliferation of shale oil and shale gas developments. Even if the production of new sources of energy and materials can occur with lower emissions, the end use technologies and infrastructures need to be in place to take full advantage of these improvements. This will require changes in our built environment – from houses to transportation networks to energy storage to power grids and beyond. These changes, in turn, will, at least for the foreseeable future, require continued use of existing infrastructures that have developed around the use of conventional fuels and land use practices. Decarbonization at the process level, even when combined with the most aggressive efficiency improvements in the end uses of materials and energy, however only translates to net reductions in emissions if “other influences” do not overwhelm the rates at which these improvements are realized. Among these other influences are economic growth that comes from generating ever larger production of output, population growth that leads to ever more demand for goods and services, and climate change itself that is triggering a need for surplus production to built up our safety nets – from personal insurance to large-scale flood control systems – that can help us weather adverse climate conditions. In the final analysis, it is the interplay of technology change, behavioral change, institutional change, and environmental change that must be managed for mitigation to become effective. How well that interplay is orchestrated will in large part depend on our ability to provide the right incentives for climate mitigation – be it through international agreements or through unilateral action, through market-based approaches, direct government intervention, or a mix of them all. Our success will be indicative not just of the technological prowess of our age, but also of the values and institutions that guide our actions. Despite all efforts to stabilize and perhaps even reduce, in the long run, atmospheric greenhouse gas concentrations, humans have already committed themselves to decades of temperature changes and centuries of sea level rise. And, to worsen the outlook, rising global temperatures and sea levels will be accompanied by many other changes in our biophysical and socioeconomic environment. Since the heat budget of the globe will be disturbed, the frequency and severity of extreme weather events will likely increase. Disruptions in biophysical conditions will trigger, and be

Foreword

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triggered by, changes in ecosystems – including changes in the productivity of managed forests and croplands, as well as changes in the distribution of pests and diseases. The associated tightening of resource constraints will undermine the livelihoods of people, displace populations, and inflict pain and death. There are unlikely to be long-term winners from climate change. None of the places already suffering from shortages in water and food, for example, or flooding and crumbling infrastructures will, in the long run, be better off because of climate change. Even if they do feel like “winners” temporarily – perhaps because the length of growing seasons increases with rising temperatures or a melting of sea ice improves shipping and boosts their economy – those benefits are fleeting. Climate will not stop changing once optimal conditions are reached, and benefits in one sector may already be overwhelmed by costs imposed on other parts of the economy and society. Clearly, some form of adaptation will need to take place. Ideally, adaptation strategies are implemented not just as climate change unfolds, but in anticipation of any further climate change so that people, economic sectors, cities and their infrastructures, as well as natural systems such as wetlands and forests, are better prepared for, and perhaps even protected from, further disruptions. But even if there were no further climate change, there already is considerable variability in the weather conditions with which people, economic sectors, cities, and natural systems must cope. Maintaining vital wetlands well before flooding events will help provide natural buffers for coastal communities. Creating redundancies in lifeline infrastructures, such as the different ways of powering businesses and homes from centralized power plants and small-scale generators, will allow for switching across electricity sources during extreme weather events, for example. And promoting more efficient energy use in the first place will reduce the reliance on some of that energy. To the extent that adaptation helps reduce already existing inefficiencies, it can make good social, economic, and environmental sense irrespective of the details with which future climate conditions manifest themselves. The conclusion one may draw that “less mitigation today can be balanced by more adaptation in the future,” however, is misleading. It suggests that the two strategies are, at some abstract level, substitutable. In reality, though, less mitigation today means not just a need for more adaptation in the future. Rather, less mitigation today means more adaptation over more of our future, because even reduced emissions continue to add to atmospheric greenhouse gas concentrations, and because the damages that result will be cumulative in nature – heat waves, droughts, and flooding events, for example, will continuously undermine our wealth and welfare and require ever larger diversion of resources to address the causes and effects of climate change. Understanding the role of mitigation and choosing the proper mitigation strategies is, therefore, an essential forebear to anything else we may be doing about climate change. Recognizing the urgency for preparedness, given the extent to which humanity has already committed itself to a changing climate, is central to motivating investment in new technologies, changes in behaviors, and deployment of infrastructures that can better withstand the vagaries of the climate.

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Foreword

A worldview that is consistent with this understanding sees mitigation and adaptation as complements, offers them as strategies to address persistent and nascent inefficiencies, and treats them as a package that substitutes for the only other option available, namely suffering. It is in this sense that this Handbook of Climate Change Mitigation and Adaptation provides valuable insights into the preconditions for a prosperous future. School of Public Policy and Urban Affairs Northeastern University Boston, MA, USA

Matthias Ruth Director and Professor

Contents

Volume 1 Part I Scientific Evidences of Climate Change and Societal Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

Introduction to Climate Change Mitigation . . . . . . . . . . . . . . . . . . . . . . Maximilian Lackner, Wei-Yin Chen, and Toshio Suzuki

3

........

17

Paleoclimate Changes and Significance of Present Global Warming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asadullah Kazi

47

Loss and Damage Associated with Climate Change Impacts Linta M. Mathew and Sonia Akter

Life Cycle Assessment of Greenhouse Gas Emissions . . . . . . . . . . . . . . L. Reijnders

61

Some Economics of International Climate Policy . . . . . . . . . . . . . . . . . Karen Pittel, Dirk R€ubbelke, Martin Altemeyer-Bartscher, and Sebastian Otte

93

Ethics and Environmental Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . David J. Rutherford and Eric Thomas Weber

127

Mass Media Roles in Climate Change Mitigation . . . . . . . . . . . . . . . . . Kristen Alley Swain

167

Economics for a Sustainable Planet . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arif S. Malik

221

Emissions Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roger Raufer, Paula Coussy, Carla Freeman, and Sudha Iyer

257

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Contents

Carbon Markets: Linking the International Emission Trading Under the United Nations Framework Convention on Climate Change (UNFCCC) and the European Union Emission Trading Scheme (EU ETS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Itziar Martínez de Alegría, Gonzalo Molina, and Belén del Río European Union (EU) Strategy to Face the Climate Change Challenge in the Framework of the International Commitments Itziar Martínez de Alegría, María-Azucena Vicente-Molina, and Cristian Moore

313

.....

341

Implications of Climate Change for the Petrochemical Industry: Mitigation Measures and Feedstock Transitions . . . . . . . . . . . . . . . . . . Simon J. Bennett and Holly A. Page

383

Venture Capital Investment and Trend in Clean Technologies . . . . . . . John C. P. Huang

427

..........

477

The Role of Aviation in Climate Change Mitigation . . . . . . . . . . . . . . . Katsuya Hihara

489

Part II

525

Analysis of the Co-benefits of Climate Change Mitigation Douglas Crawford-Brown

Impact of Climate Change and Adaptation . . . . . . . . . . . . .

Carbon Liability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yoshihiro Fujii

527

....

555

......................

595

Sea-Level Rise and Hazardous Storms: Impact Assessment on Coasts and Estuaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Ding

621

Climate Change and Carbon Sequestration in Forest Ecosystems Dafeng Hui, Qi Deng, Hanqin Tian, and Yiqi Luo Impact of Climate Change on Biodiversity David H. Reed

Projected Impacts of Climatic Changes on Cisco Oxythermal Habitat in Minnesota Lakes and Management Strategies . . . . . . . . . . . . . . . . . . Xing Fang, Heinz G. Stefan, Liping Jiang, Peter C. Jacobson, and Donald L. Pereira Impact of Climate Change on Crop Production Gamal El Afandi

..................

657

723

Contents

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Volume 2 Climate Change Impacts, Vulnerability, and Adaptation in East Africa (EA) and South America (SA) . . . . . . . . . . . . . . . . . . . . . . . . . . . Anne Nyatichi Omambia, Ceven Shemsanga, and Ivonne Andrea Sanchez Hernandez

749

Statistics in Climate Variability, Dry Spells, and Implications for Local Livelihoods in Semiarid Regions of Tanzania: The Way Forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ceven Shemsanga, A. N. N. Muzuka, L. Martz, H. Komakech, and Anne Nyatichi Omambia

801

Climate Change Adaptation, Mitigation, and the Attainment of Food Security in the Sudano-Sahelian Belt of Nigeria . . . . . . . . . . . . . . . . . . Aishetu Abdulkadir

849

Understanding Climate Change Adaptation Needs and Practices of Households in Southeast Asia: Lessons from Five Years of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Herminia A. Francisco and Noor Aini Zakaria

863

Impact of Climate Change, Adaptation, and Potential Mitigation to Vietnam Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trinh Van Mai and Jenny Lovell

899

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case Study on the City of Dhaka, Bangladesh . . . . . . . . . . . . . . . . . . A. K. M. Azad Hossain and Greg Easson

925

Potential of Solid Waste and Agricultural Biomass as Energy Source and Effect on Environment in Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . S. R. Samo, K. C. Mukwana, and A. A. Sohu

953

The Advanced Recycling Technology for Realizing Urban Mines Contributing to Climate Change Mitigation . . . . . . . . . . . . . . . . . . . . . 1007 Tatsuya Oki and Toshio Suzuki An Introductory Course on Climate Change . . . . . . . . . . . . . . . . . . . . . 1037 Wei-Yin Chen Reducing Personal Mobility for Climate Change Mitigation . . . . . . . . . 1071 Patrick Moriarty and Damon Honnery Nontechnical Aspects of Household Energy Reductions Patrick Moriarty and Damon Honnery

. . . . . . . . . . . . 1107

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Contents

Bringing Global Climate Change Education to Middle School Classrooms: An Example from Alabama . . . . . . . . . . . . . . . . . . . . . . . . 1127 Ming-Kuo Lee, Chandana Mitra, Amy Thomas, Tyaunnaka Lucy, Elizabeth Hickman, Jennifer Cox, and Chris Rodger Climate Change: Outreaching to School Students and Teachers . . . . . . 1149 Dudley E. Shallcross, Timothy G. Harrison, Alison C. Rivett, and Jauyah Tuah Geoengineering for Climate Stabilization Maximilian Lackner

. . . . . . . . . . . . . . . . . . . . . . . 1201

Social Efficiency in Energy Conservation Patrick Moriarty and Damon Honnery

. . . . . . . . . . . . . . . . . . . . . . . 1235

Measuring Household Vulnerability to Climate Change . . . . . . . . . . . . 1251 Sofie Waage Skjeflo Fracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1265 Qingmin Meng Transport Through Porous Media: Case Studies of CO2 Sequestration, CO2-Oxygen Reaction in Oxy-Combustion, and Oxygen Transport in Membrane at High Temperatures . . . . . . . . . . . . 1279 Aishuang Xiang Part III Climate Change Mitigation: Energy Conversation, Efficiency, and Sustainable Energies . . . . . . . . . . . . . . . . . . . . . . . . .

1307

Energy Efficiency: Comparison of Different Systems and Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1309 Maximilian Lackner Fuel Efficiency in Transportation Systems . . . . . . . . . . . . . . . . . . . . . . . 1385 Maximilian Lackner, John M. Seiner, and Wei-Yin Chen Thermal Insulation for Energy Conservation . . . . . . . . . . . . . . . . . . . . 1413 David W. Yarbrough Thermal Energy Storage and Transport . . . . . . . . . . . . . . . . . . . . . . . . 1433 Satoshi Hirano Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1465 Dawood Al Abri, Arif S. Malik, Mohammed Albadi, Yassine Charabi, and Nasser Hosseinzadeh Concentrated Solar Thermal Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1503 Anjaneyulu Krothapalli and Brenton Greska

Contents

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Volume 3 Harvesting Solar Energy Using Inexpensive and Benign Materials Susannah Lee, Melissa Vandiver, Balasubramanian Viswanathan, and Vaidyanathan (Ravi) Subramanian

. . . 1537

Greenhouse Gas Emission Reduction Using Advanced Heat Integration Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1581 Kailiang Zheng, Helen H. Lou, and Yinlun Huang Modern Power Plant Control for Energy Conservation, Efficiency Increase, and Financial Benefit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1631 Pal Szentannai Mobile and Area Sources of Greenhouse Gases and Abatement Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1657 Waheed Uddin Biomass as Feedstock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1723 Debalina Sengupta Biochemical Conversion of Biomass to Fuels . . . . . . . . . . . . . . . . . . . . . 1777 Swetha Mahalaxmi and Clint Williford Thermal Conversion of Biomass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1813 Zhongyang Luo and Jingsong Zhou Chemicals from Biomass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1855 Debalina Sengupta and Ralph W. Pike Hydrodeoxygenation (HDO) of Bio-Oil Model Compounds with Synthesis Gas Using a Water Gas Shift Catalyst with a Mo/Co/K Catalyst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1903 Rangana Wijayapala, Akila G. Karunanayake, Damion Proctor, Fei Yu, Charles U. Pittman, and Todd E. Mlsna Biochar from Biomass: A Strategy for Carbon Dioxide Sequestration, Soil Amendment, Power Generation, and CO2 Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1937 Vanisree Mulabagal, David A. Baah, Nosa O. Egiebor, and Wei-Yin Chen Wind Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1975 Manfred Lenzen and Olivier Baboulet Wave Power: Climate Change Mitigation and Adaptation . . . . . . . . . . 2007 Gregorio Iglesias and Javier Abanades Geothermal Energy Hirofumi Muraoka

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2057

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Contents

Hydropower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2085 Jingsheng Jia, Petras Punys, and Jing Ma Nuclear Energy and Environmental Impact K. S. Raja, B. Pesic, and M. Misra

. . . . . . . . . . . . . . . . . . . . . 2133

Part IV Climate Change Mitigation: Advanced Carbon Conversion Sciences and Technologies . . . . . . . . . . . . . . . . . . . . . . .

2195

Reducing Greenhouse Gas Emissions with CO2 Capture and Geological Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2197 J. Marcelo Ketzer, Rodrigo S. Iglesias, and Sandra Einloft Chemical Absorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2239 Mengxiang Fang and Dechen Zhu CO2 Capture Using Solid Sorbents Yao Shi, Qing Liu, and Yi He

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 2349

Volume 4 CO2 Capture by Membrane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2405 Teruhiko Kai and Shuhong Duan CO2 Geological Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2433 Masao Sorai, Xing Lei, Yuji Nishi, Tsuneo Ishido, and Shinsuke Nakao Conversion of CO2 to Value Added Chemicals: Opportunities and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2487 Arun S. Agarwal, Edward Rode, Narasi Sridhar, and Davion Hill Oxy-Fuel Firing Technology for Power Generation . . . . . . . . . . . . . . . . 2527 Edward John (Ben) Anthony Gasification Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2557 Lawrence J. Shadle, Ronald W. Breault, and James Bennett Conversion of Syngas to Fuels and Chemicals . . . . . . . . . . . . . . . . . . . . 2629 Steven S. C. Chuang and Long Zhang Chemical Looping Combustion Edward John (Ben) Anthony

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2647

High Temperature Oxygen Separation Using Dense Ceramic Membranes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2681 Jaka Sunarso, Kun Zhang, and Shaomin Liu

Contents

Part V

xv

Climate Change Mitigation: Advanced Technologies . . . . .

2707

Photocatalytic Water Splitting and Carbon Dioxide Reduction . . . . . . 2709 Nathan I. Hammer, Sarah Sutton, Jared Delcamp, and Jacob D. Graham Simultaneous CO2 and H2S Sequestration by Electrocatalytic Conversion for Chemical Feedstock Synthesis . . . . . . . . . . . . . . . . . . . . 2757 Nosa O. Egiebor and Jonathan Mbah Power-to-Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2775 Michael Sterner Reduction of Greenhouse Gas Emissions by Catalytic Processes Gabriele Centi and Siglinda Perathoner Integrated Systems to Reduce Global Warming Preben Maegaard and Anna Krenz

. . . . . 2827

. . . . . . . . . . . . . . . . . . 2881

Thermoacoustics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2967 Matthew E. Poese Hydrogen Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2995 Qinhui Wang Low-Temperature Fuel Cell Technology for Green Energy . . . . . . . . . . 3039 Scott A. Gold Solid Oxide Fuel Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3087 Nigel M. Sammes, Kevin Galloway, Mustafa F. Serincan, Toshio Suzuki, Toshiaki Yamaguchi, Masanobu Awano, and Whitney Colella Molten Carbonate Fuel Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3113 Takao Watanabe Fusion Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3139 Hiroshi Yamada 3rd-Generation Biofuels: Bacteria and Algae as Sustainable Producers and Converters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3173 Maximilian Lackner Biopolymers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3211 Maximilian Lackner Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3231 Maximilian Lackner Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3297

About the Editors

Wei-Yin Chen is a Chemical Engineering professor with degrees in Chemical Engineering and Applied Mathematics and Statistics. He initiated the Sustainable Energy and Environment (SEE) group, which was formed at the University of Mississippi in 2007 and now has over 200 collaborators around the world. The SEE group has been offering new courses on climate change and sustainable energy, edited this Handbook, been developing experimental modules for outreach with several nationally recognized awards, and been developing a multidisciplinary research program leading to efficient power generation, CO2 utilization, CO2 capture, and carbon activation. He has served as a panelist, reviewer, or advisor for research organization in the USA, China, Romania, India, Jordan, Malaysia, etc. He is an adjunct or visiting faculty of five universities in China and Taiwan. He is currently on the editorial board of five journals. He has reviewed manuscripts and book proposals for over 50 journals and publishers. He has received the outstanding research, teaching, and service awards from the School of Engineering of the University of Mississippi. His publications on pedagogy have been cited as the “Best Practice” by a review of Chemical Engineering Education in both the thermodynamics and chemical reaction engineering areas.

Toshio Suzuki is Group Leader of Functional Integration Technology Group, at Japan’s National Institute of Advanced Industrial Science and Technology (AIST), working in the fields of materials science, electrochemistry, and nanotechnology, especially on the development of next generation electrochemical devices such as solid oxide fuel cells (SOFCs). He is specializing in the design of advanced materials for energy conversion, with R&D experience in electrical, structural, and optical properties of novel ion conducting materials, correlating with microstructure. Dr. Suzuki received his xvii

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About the Editors

Ph.D. in Ceramic Engineering from University of Missouri-Rolla, USA, in 2001 and has published over 140 research articles, book chapters, and patents.

Maximilian Lackner received his Ph.D. in Technical Chemistry from Vienna University of Technology, Austria, in 2003, and his habilitation in Chemical Engineering in 2009. His research interests include: climate change mitigation, material science, lasers in chemistry, combustion, biofuels, and biobased plastics. Dr. Lackner was visiting researcher at Munich University of Technology (Germany), Darmstadt University (Germany), and Lund Institute of Technology (Sweden). He held several senior leadership positions in the petrochemical industry in Austria and China and founded five companies. Dr. Lackner has published over 100 research articles, book chapters, and patents. He is lecturer at Vienna University of Technology, the University of Applied Sciences FH Technikum Wien, and Johannes Kepler University (Austria).

Contributors

Javier Abanades School of Marine Science and Engineering, University of Plymouth, Plymouth, UK Aishetu Abdulkadir Centre for Disaster Risk Reduction and Development Studies (CDRM & DS), Federal University of Technology, Minna, Nigeria Dawood Al Abri Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat, Oman Arun S. Agarwal Materials Program, Strategic Research and Innovation, DNV GL, Dublin, OH, USA Sonia Akter Social Sciences Division, International Rice Research Institute, Los Baños, Laguna, Philippines Mohammed Albadi Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat, Oman Martin Altemeyer-Bartscher Faculty of Law and Economics, Martin-Luther University Halle-Wittenberg, Halle Institute for Economic Research, Halle (Saale), Germany Edward John (Ben) Anthony CanmetENERGY, Natural Resources Canada, Ottawa, ON, USA Masanobu Awano National Institute of Advanced Industrial Science and Technology (AIST), Nagoya, Japan David A. Baah Department of Chemical Engineering, Tuskegee University, Tuskegee, AL, USA Olivier Baboulet ISA, School of Physics-A28, The University of Sydney, Sydney, NSW, Australia James Bennett U. S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, USA xix

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Contributors

Simon J. Bennett Imperial Centre for Energy Policy and Technology, Imperial College, London, UK International Energy Agency, Paris, France Ronald W. Breault U. S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, USA Gabriele Centi Dip. Ingegneria Elettronica, Chimica ed Ingegneria Industriale (DIECII), University of Messina, ERIC aisbl and CASPE-INSTM, Messina, Italy Yassine Charabi Department of Geography, Sultan Qaboos University, Muscat, Oman Wei-Yin Chen Department of Chemical Engineering, The University of Mississippi, Oxford, MS, USA Steven S. C. Chuang First Energy Advanced Energy Research Center, Department of Chemical and Biomolecular Engineering, The University of Akron, Akron, OH, USA Whitney Colella Sandia National Laboratories, Albuquerque, NM, USA Paula Coussy Economics and Environmental Evaluation Department, CO2 Market Expert, IFP Energies nouvelles, Rueil-Malmaison, France Jennifer Cox Alabama Science in Motion Program, Alabama State University, Montgomery, AL, USA Douglas Crawford-Brown Cambridge Centre for Climate Change Mitigation Research, Department of Land Economy, University of Cambridge, Cambridge, UK Belén del Río Chair in International Studies, University of the Basque Country (UPV/EHU), Bilbao, Spain Jared Delcamp Department of Chemistry and Biochemistry, The University of Mississippi University, Oxford, MS, USA Qi Deng Department of Biological Sciences, Tennessee State University, Nashville, TN, USA Yan Ding National Center for Computational Hydroscience and Engineering, The University of Mississippi, Oxford, MS, USA Shuhong Duan Research Institute of Innovative Technology for the Earth (RITE), Kizugawa-shi, Kyoto, Japan Greg Easson Mississippi Mineral Resources Institute, The University of Mississippi, Oxford, MS, USA Nosa O. Egiebor Department of Chemical Engineering and Division of Global Engagement, The University of Mississippi, Oxford, MS, USA

Contributors

xxi

Sandra Einloft FAQUI – Faculty of Chemistry, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil Gamal El Afandi Department of Agricultural and Environmental Sciences, College of Agriculture, Environment and Nutrition Sciences, Tuskegee University, Tuskegee, AL, USA Department of Astronomy and Meteorology, Faculty of Science, Al Azhar University, Cairo, Egypt Mengxiang Fang Institute for Thermal Power Engineering, Zhejiang University, Hangzhou, Zhejiang, China Xing Fang Department of Civil Engineering, Auburn University, Auburn, AL, USA Herminia A. Francisco Economy and Environment Program for Southeast Asia (EEPSEA), Los Baños, Laguna, Philippines Carla Freeman School of Advanced International Studies, Johns Hopkins University, Washington, DC, USA Yoshihiro Fujii Graduate School of Global Environmental Studies, Sophia University, Tokyo, Japan Kevin Galloway Department of Metallurgical and Materials Engineering, Colorado School of Mines, Golden, CO, USA Scott A. Gold Department of Chemical and Materials Engineering, University of Dayton, Dayton, OH, USA Jacob D. Graham Johns Hopkins University, Baltimore, MD, USA Brenton Greska Cameron International, Houston, TX, USA Nathan I. Hammer Department of Chemistry and Biochemistry, The University of Mississippi University, Oxford, MS, USA Timothy G. Harrison Bristol ChemLabS, School of Chemistry, University of Bristol, Bristol, UK Yi He Department of Chemical and Biological Engineering, Institute of Industrial Ecology and Environment, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China Elizabeth Hickman Alabama Mathematics and Science Technology Initiative, Auburn University, Auburn, AL, USA Katsuya Hihara Graduate School of Public Policy, the University of Tokyo, Hongo Bunkyo-ku, Tokyo, Japan Davion Hill Energy and Materials, DNV GL, Dublin, OH, USA

xxii

Contributors

Satoshi Hirano Thermal and Fluids Systems Group, Energy Technology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan Damon Honnery Department of Mechanical and Aerospace Engineering, Monash University, Melbourne, VIC, Australia A. K. M. Azad Hossain National Center for Computational Hydroscience and Engineering (NCCHE), The University of Mississippi, Oxford, MS, USA Nasser Hosseinzadeh Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat, Oman John C. P. Huang Focus Capital Group, Cupertino, CA, USA Yinlun Huang Lab for Multiscale Complex Systems Science and Engineering, Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, MI, USA Dafeng Hui Department of Biological Sciences, Tennessee State University, Nashville, TN, USA Gregorio Iglesias School of Marine Science and Engineering, University of Plymouth, Plymouth, UK Rodrigo S. Iglesias FENG – Engineering Faculty, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil Tsuneo Ishido National Institute of Advanced Industrial Science and Technology (AIST), Geological Survey of Japan, Tsukuba, Ibaraki, Japan Sudha Iyer Cerebronics, LLC, Hoboken, NJ, USA Peter C. Jacobson Minnesota Department of Natural Resources, Park Rapids, MN, USA Jingsheng Jia International Commission on Large Dams (ICOLD), Paris, France Liping Jiang Department of Civil Engineering, Auburn University, Auburn, AL, USA Teruhiko Kai Research Institute of Innovative Technology for the Earth (RITE), Kizugawa-shi, Kyoto, Japan Akila G. Karunanayake Department of Chemistry, Mississippi State University, Starkville, MS, USA Asadullah Kazi Isra University, Hyderabad, Sindh, Pakistan J. Marcelo Ketzer IPR – Institute of Petroleum and Natural Resources, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil

Contributors

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H. Komakech Department of Water and Environmental Sciences and Engineering, Nelson Mandela Institution of Science and Technology-Tengeru, Tengeru, Arusha, Tanzania Anna Krenz Nordic Folkecenter for Renewable Energy, Hurup Thy, Denmark Anjaneyulu Krothapalli Department of Mechanical Engineering, Florida State University, Tallahassee, FL, USA Maximilian Lackner Institute of Advanced Engineering Technologies, University of Applied Sciences FH Technikum Wien, Vienna, Austria Ming-Kuo Lee Department of Geology and Geography, Auburn University, Auburn, AL, USA Susannah Lee Department of Chemical and Metallurgical Engineering, Chemical and Materials Engineering Department, LME 310, MS 388, University of Nevada, Reno, NV, USA Xing Lei National Institute of Advanced Industrial Science and Technology (AIST), Geological Survey of Japan, Tsukuba, Ibaraki, Japan Manfred Lenzen ISA, School of Physics-A28, The University of Sydney, Sydney, NSW, Australia Qing Liu Department of Chemical and Biological Engineering, Institute of Industrial Ecology and Environment, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China Shaomin Liu Department of Chemical Engineering, Curtin University, Perth, WA, Australia Helen H. Lou Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, TX, USA Jenny Lovell Environmental Studies Department, University of California Santa Cruz, Santa Cruz, CA, USA Tyaunnaka Lucy Alabama Mathematics and Science Technology Initiative, Auburn University, Auburn, AL, USA Yiqi Luo Department of Microbiology and Plant Sciences, University of Oklahoma, Norman, OK, USA Zhongyang Luo State Key Laboratory of Clean Energy Utilization, College of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China Jing Ma China Institute of Water Resources and Hydropower Research, Beijing, China Preben Maegaard Nordic Folkecenter for Renewable Energy, Hurup Thy, Denmark

xxiv

Contributors

Swetha Mahalaxmi Department of Chemical Engineering, The University of Mississippi, Oxford, MS, USA Trinh Van Mai Institute for Agricultural Environment, Vietnam Academy of Agricultural Sciences, Phudo, South Tu Liem, Hanoi, Vietnam Arif S. Malik Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman Itziar Martínez de Alegría Engineering School of Bilbao, University of the Basque Country (UPV/EHU), Bilbao, Spain L. Martz Department of Geography, University of Saskatchewan, Saskatoon, SK, Canada Linta M. Mathew Social Sciences Division, International Rice Research Institute, Los Baños, Laguna, Philippines Jonathan Mbah Department of Chemical Engineering, Florida Institute of Technology, Melbourne, FL, USA Qingmin Meng Department of Geosciences, Mississippi State University, Starkville, MS, USA M. Misra Department of Metallurgical Engineering, University of Utah, Salt Lake City, UT, USA Chandana Mitra Department of Geology and Geography, Auburn University, Auburn, AL, USA Todd E. Mlsna Department of Chemistry, Mississippi State University, Starkville, MS, USA Gonzalo Molina University of the Basque Country (UPV/EHU), Bilbao, Spain Cristian Moore Alcoa Inc., Alcoa, TN, USA Patrick Moriarty Department of Design, Monash University, Melbourne, VIC, Australia K. C. Mukwana Energy and Environment Engineering Department, Quaid-EAwam University of Engineering, Science and Technology (QUEST), Nawabshah, Sindh, Pakistan Vanisree Mulabagal Department of Chemical Engineering, Tuskegee University, Tuskegee, AL, USA Hirofumi Muraoka North Japan Research Institute for Sustainable Energy, Hirosaki University, Aomori, Japan

Contributors

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A. N. N. Muzuka Department of Water and Environmental Sciences and Engineering, Nelson Mandela Institution of Science and Technology-Tengeru, Tengeru, Arusha, Tanzania Shinsuke Nakao National Institute of Advanced Industrial Science and Technology (AIST), Geological Survey of Japan, Tsukuba, Ibaraki, Japan Yuji Nishi National Institute of Advanced Industrial Science and Technology (AIST), Geological Survey of Japan, Tsukuba, Ibaraki, Japan Tatsuya Oki National Institute of Advanced Industrial Science and Technology (AIST), Onogawa Tsukuba, Ibaragi, Japan Anne Nyatichi Omambia National Environment Management Authority, Nairobi, Kenya Sebastian Otte Technische Universität Bergakademie Freiberg, Freiberg, Germany Holly A. Page Imperial College, London, UK Siglinda Perathoner Dip. Ingegneria Elettronica, Chimica ed Ingegneria Industriale (DIECII), University of Messina, ERIC aisbl and CASPE-INSTM, Messina, Italy Donald L. Pereira Minnesota Department of Natural Resources, St. Paul, MN, USA B. Pesic Chemical and Materials Engineering, University of Idaho, Moscow, ID, USA Ralph W. Pike Minerals Processing Research Institute, Louisiana State University, Baton Rouge, LA, USA Karen Pittel Ifo Institute – Leibniz Institute for Economic Research and University of Munich, Munich, Germany Charles U. Pittman Department of Chemistry, Mississippi State University, Starkville, MS, USA Matthew E. Poese Applied Research Laboratory, State College, PA, USA Damion Proctor Department of Chemistry, Mississippi State University, Starkville, MS, USA Petras Punys Water Management Department, Water and Land Management Faculty, Lithuanian University of Agriculture, Kaunas-Akademija, Lithuania Dirk Rübbelke Technische Universität Bergakademie Freiberg, Freiberg, Germany K. S. Raja Chemical and Materials Engineering, University of Idaho, Moscow, ID, USA

xxvi

Contributors

Roger Raufer Hopkins Nanjing Center, Nanjing University, Nanjing, Jiangsu Province, China David H. Reed Department of Biology, University of Louisville, Louisville, KY, USA L. Reijnders Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands Alison C. Rivett Bristol ChemLabS, School of Chemistry, University of Bristol, Bristol, UK Edward Rode Materials Program, Strategic Research and Innovation, DNV GL, Dublin, OH, USA Chris Rodger Department of Mathematics and Statistics, Auburn University, Auburn, AL, USA David J. Rutherford Department of Public Policy Leadership, The University of Mississippi, Oxford, MS, USA Nigel M. Sammes Department of Metallurgical and Materials Engineering, Colorado School of Mines, Golden, CO, USA S. R. Samo Quaid-E-Awam University of Engineering, Science and Technology (QUEST), Nawabshah, Sindh, Pakistan Ivonne Andrea Sanchez Hernandez Sustainability Development, AB Origen Fundación, Armenia, Quindio, Colombia John M. Seiner Debalina Sengupta Texas A&M University, College Station, TX, USA Mustafa F. Serincan Department of Mechanical Engineering, University of Connecticut, Storrs, CT, USA Lawrence J. Shadle U. S. Department of Energy, National Energy Technology Laboratory, Morgantown, WV, USA Dudley E. Shallcross Bristol ChemLabS, School of Chemistry, University of Bristol, Bristol, UK Ceven Shemsanga Department of Water and Environmental Sciences and Engineering, Nelson Mandela Institution of Science and Technology-Tengeru, Tengeru, Arusha, Tanzania Department of Environmental Engineering and Management, University of Dodoma, Dodoma, Tanzania David H. Reed: deceased. John M. Seiner: deceased.

Contributors

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Yao Shi Department of Chemical and Biological Engineering, Institute of Industrial Ecology and Environment, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China Sofie Waage Skjeflo UMB School of Economics and Business, Norwegian University of Life Sciences, Ås, Norway A. A. Sohu Mechanical Engineering Department, QUCEST, Larkano, Sindh, Pakistan Masao Sorai National Institute of Advanced Industrial Science and Technology (AIST), Geological Survey of Japan, Tsukuba, Ibaraki, Japan Narasi Sridhar Materials Program, Strategic Research and Innovation, DNV GL, Dublin, OH, USA Heinz G. Stefan St. Anthony Falls Laboratory, Department of Civil Engineering, University of Minnesota, Minneapolis, MN, USA Michael Sterner Forschungsstelle Energienetze und Energiespeicher (FENES), Fakultät für Elektro- und Informationstechnik, OTH Regensburg, Regensburg, Germany Vaidyanathan (Ravi) Subramanian Department of Chemical and Metallurgical Engineering, Chemical and Materials Engineering Department, LME 310, MS 388, University of Nevada, Reno, NV, USA Jaka Sunarso Department of Chemistry, University of Waterloo, Waterloo, ON, Canada Sarah Sutton Department of Chemistry and Biochemistry, The University of Mississippi University, Oxford, MS, USA Toshio Suzuki National Institute of Advanced Industrial Science and Technology (AIST), Nagoya, Japan Kristen Alley Swain Meek School of Journalism and New Media, The University of Mississippi, Oxford, MS, USA Pal Szentannai Department of Energy Engineering, Budapest University of Technology and Economics, Budapest, Hungary Amy Thomas Outreach Program, College of Sciences and Mathematics, Auburn University, Auburn, AL, USA Hanqin Tian International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA Jauyah Tuah Secretariat of Brunei Darussalam Technical and Vocational Education Council, Permanent Secretary Office (Higher Education), Ministry of Education, Bandar Seri Begawan, Brunei Darussalam

xxviii

Contributors

Waheed Uddin Department of Civil Engineering, The University of Mississippi, Oxford, MS, USA Melissa Vandiver Department of Chemical and Metallurgical Engineering, Chemical and Materials Engineering Department, LME 310, MS 388, University of Nevada, Reno, NV, USA María-Azucena Vicente-Molina Economics and Business Administration College, University of the Basque Country, Bilbao, Spain Balasubramanian Viswanathan National Center for Catalysis Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India Qinhui Wang Institute for Thermal Power Engineering, Zhejiang University, Hangzhou, Zhejiang, China Takao Watanabe Central Research Institute of Electric Power Industry, Yokosuka, Kanagawa, Japan Eric Thomas Weber Department of Public Policy Leadership, University of Mississippi, Oxford, MS, USA Rangana Wijayapala Department of Chemistry, Mississippi State University, Starkville, MS, USA Clint Williford Department of Chemical Engineering, The University of Mississippi, Oxford, MS, USA Aishuang Xiang Chemical Engineering Department, Massachusetts Institute of Technology, Cambridge, MA, USA Hiroshi Yamada Department of Helical Plasma Research, National Institute for Fusion Science, Toki, Gifu, Japan Toshiaki Yamaguchi National Institute of Advanced Industrial Science and Technology (AIST), Nagoya, Japan David W. Yarbrough R&D Services, Inc., Cookeville, TN, USA Fei Yu Agricultural and Biological Engineering, Mississippi State University, Starkville, MS, USA Noor Aini Zakaria Economy and Environment Program for Southeast Asia (EEPSEA), Los Baños, Laguna, Philippines Kun Zhang Department of Chemical Engineering, Curtin University, Perth, WA, Australia Long Zhang Department of Polymer Science, The University of Akron, Akron, OH, USA Kailiang Zheng Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, TX, USA

Contributors

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Jingsong Zhou State Key Laboratory of Clean Energy Utilization, College of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China Dechen Zhu Institute for Thermal Power Engineering, Zhejiang University, Hangzhou, Zhejiang, China

Part I Scientific Evidences of Climate Change and Societal Issues

Introduction to Climate Change Mitigation Maximilian Lackner, Wei-Yin Chen, and Toshio Suzuki

Contents Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Greenhouse Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anthropogenic Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change: What Will Change? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of Climate Change Mitigation Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change Adaptation Versus Climate Change Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Handbook of Climate Change Mitigation and Adaption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Why This Book Is Needed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Audience of the Handbook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Since the first edition of the Handbook, important new research findings on climate change have been gathered. The handbook was extended to also cover, apart from climate change mitigation, climate change adaptation as one can witness increasing initiatives to cope with the phenomenon. Instrumental M. Lackner (*) Institute of Advanced Engineering Technologies, University of Applied Sciences FH Technikum Wien, Vienna, Austria e-mail: [email protected] W.-Y. Chen Department of Chemical Engineering, The University of Mississippi, Oxford, MS, USA e-mail: [email protected] T. Suzuki National Institute of Advanced Industrial Science and Technology (AIST), Nagoya, Japan e-mail: [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_1

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recording shows a temperature increase of 0.5  C Le Houérou (J Arid Environ 34:133–185, 1996) with rather different regional patterns and trends (Folland CK, Karl TR, Nicholls N, Nyenzi BS, Parker DE, Vinnikov KYA (1992) Observed climate variability and change. In: Houghton JT, Callander BA, Varney SDK (eds) Climate change, the supplementary report to the IPCC scientific assessment. Cambridge University Press, Cambridge, pp 135–170). Over the last several million years, there have been warmer and colder periods on Earth, and the climate fluctuates for a variety of natural reasons as data from tree rings, pollen, and ice core samples have shown. However, human activities on Earth have reached an extent that they impact the globe in potentially catastrophic ways. This chapter is an introduction to climate change.

Climate Change There has been a heated discussion on climate change in recent years, with a particular focus on global warming. Over the last several million years, there have been warmer and colder periods on Earth, and the climate fluctuates for a variety of natural reasons as data from tree rings, pollen, and ice core samples have shown. For instance, in the Pleistocene, the geological epoch which lasted from about 2,588,000 to 11,700 years ago, the world saw repeated glaciations (“ice age”). More recently, “Little Ice Age” and the “Medieval Warm Period” (IPCC) occurred. Several causes have been suggested such as cyclical lows in solar radiation, heightened volcanic activity, changes in the ocean circulation, and an inherent variability in global climate. Also on Mars, climate change was inferred from orbiting spacecraft images of fluvial landforms on its ancient surfaces and layered terrains in its polar regions (Haberle et al. 2012). Spin axis/orbital variations, which are more pronounced on Mars compared to Earth, are seen as main reasons. As to recent climate change on Earth, there is evidence that it is brought about by human activity and that its magnitude and effects are of strong concern. Instrumental recording of temperatures has been available for less than 200 years. Over the last 100 years, a temperature increase of 0.5  C could be measured (Le Houérou 1996) with rather different regional patterns and trends (Folland et al. 1992). In (Ehrlich 2000), Bruce D. Smith is quoted as saying, “The changes brought over the past 10,000 years as agricultural landscapes replaced wild plant and animal communities, while not so abrupt as those caused by the impact of an asteroid as the Cretaceous-Tertiary boundary some 65 Ma ago or so massive as those caused by advancing glacial ice in the Pleistocene, are nonetheless comparable to these other forces of global change.” At the Earth Summit in Rio de Janeiro in 1992, over 159 countries signed the United Nations Framework Convention on Climate Change (FCCC, also called “Climate Convention”) in order to achieve “stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system” (United Nations (UN) 1992). In 2001, the Intergovernmental Panel on Climate Change (IPCC) (Intergovernmental Panel on Climate Change (IPCC) 2007) wrote, “An increasing body of

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observations gives a collective picture of a warming world and other changes in the climate system. . . There is new and stronger evidence that most of the warming observed over the last 50 years is attributable to human activities.” In its fourth assessment report of 2007, the IPCC stated that human actions are “very likely” the cause of global warming. More specifically, there is a 90 % probability that the burning of fossil fuels and other anthropogenic factors such as deforestation and the use of certain chemicals have already led to an increase of 0.75 in average global temperatures over the last 100 years and that the increase in hurricane and tropical cyclone strength since 1970 also results from man-made climate change. In its fifth assessment report of 2013, the IPCC confirms their findings as “Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia. The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, sea level has risen, and the concentrations of greenhouse gases have increased” (IPCC 2013). Figures 1 and 2 show some details of IPCC’s findings. In Fig. 2, natural and man-made (anthropogenic) radiative forcings (RF) are depicted. RF, or climate forcing, expressed in W/m2, is a change in energy flux, viz., the difference of incoming energy (sunlight) absorbed by Earth and outgoing energy (that radiated back into space). A positive forcing warms up the system, while negative forcing cools it down. (Anthropogenic) CO2 emissions, which have been accumulating in the atmosphere at an increasing rate since the Industrial Revolution, were identified as the main driver. The position of the IPCC has been adopted by several renowned scientific societies, and a consensus has emerged on the causes and partially on the consequences of climate change. The history of climate change science is reviewed in (Miller et al. 2009). There are researchers who oppose the scientific mainstream’s assessment of global warming (Linden 1993). However, the public seems to be unaware of the high degree of consensus that has been achieved in the scientific community, as elaborated in a 2009 World Bank report (Worldbank 2009). In (Antilla 2005), there is a treatment of the mass media’s coverage of the climate change discussion with a focus on rhetoric that emphasizes uncertainty, controversy, and climate scepticism. Climate change skeptic films were found to have a strong influence on the general public’s environmental concern (Greitemeyer 2013).

The Greenhouse Effect A greenhouse, also called a glass house, is a structure enclosed by glass or plastic which allows the penetration of radiation to warm it. Gases capable of absorbing the radiant energy are called the greenhouse gases (GHG). Greenhouses are used to grow flowers, vegetables, fruits, and tobacco throughout the year in a warm, agreeable climate. On Earth, there is a phenomenon called the “natural greenhouse” effect, or the Milankovitch cycles.

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Without the greenhouse gas effect, which is chiefly based on water vapor in the atmosphere (Linden 2005) (i.e., clouds that trap infrared radiation), the average surface temperature on Earth would be 33  C colder (Karl and Trenberth 2003). The natural greenhouse effect renders Earth habitable since the temperature which would be expected from the thermal equilibrium of the irradiation from the sun and radiative losses into space (radiation balance in the blackbody model) is approximately 18  C. On the moon, for instance, where there is hardly any atmosphere, extreme surface temperatures range from 233  C to 133  C (Winter 1967). On Venus, by contrast, the greenhouse effect in the dense CO2 laden atmosphere results in an average surface temperature in excess of 450  C (Sonnabend et al. 2008; Zasova et al. 2007). The current discussion about global warming and climate change is centered on the anthropogenic greenhouse effect. This is caused by the emission and accumulation of greenhouse gases in the atmosphere. These gases (water vapor, CO2, CH4, N2O, O3, and others) act by absorbing and emitting infrared radiation. The combustion of fossil fuels (oil, coal, and natural gas) has led mainly to an increase in the CO2 concentration in the atmosphere. Preindustrial levels of CO2 (i.e., before the start of the Industrial Revolution) were approximately 280 ppm, whereas today, they are above 380 ppm with an annual increase of approximately 2 ppm. According to the IPCC Special Report on Emission Scenarios (SRES) (IPCC 2010a), by the end of the twenty-first century, the CO2 concentration could reach levels between 490 and 1,260 ppm, which are between 75 % and 350 % above the preindustrial levels, respectively. CO2 is the most important anthropogenic greenhouse gas because of its comparatively high concentration in the atmosphere. The effect of other greenhouse-active gases depends on their molecular structure and their lifetime in the atmosphere, which can be expressed by their greenhouse warming potential (GWP). GWP is a relative measure of how much heat a greenhouse gas traps in the atmosphere. It compares the amount of heat trapped by a certain mass of the gas in question to the amount of heat trapped by a similar mass of CO2. With a time horizon of 100 years, the GWP of CH4, N2O, and SF6 with respect to CO2 is 25, 298, and 22,800, respectively (IPCC 2010b). But CO2 has a much higher concentration than other GHGs, and it is increasing at a higher rate due to burning of fossil fuels. Thus, while the major mitigating emphasis has mainly been placed on CO2, efforts on mitigating CH4, N2O, and SF6 have also been active.

ä Fig. 1 (a) Observed global mean combined land and ocean surface temperature anomalies, from 1850 to 2012 from three data sets. Top panel: annual mean values. Bottom panel: decadal mean values including the estimate of uncertainty for one dataset (black). Anomalies are relative to the mean of 1961–1990. (b) Map of the observed surface temperature change from 1901 to 2012 derived from temperature trends determined by linear regression from one dataset (orange line in panel a). Trends have been calculated where data availability permits a robust estimate (i.e., only for grid boxes with greater than 70 % complete records and more than 20 % data availability in the first and last 10 % of the time period). Other areas are white. Grid boxes where the trend is significant at the 10 % level are indicated by a + sign (Source: IPCC (IPCC 2013))

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Fig. 2 Radiative forcing estimates in 2011 relative to 1750 and aggregated uncertainties for the main drivers of climate change. Values are global average radiative forcing (RF), partitioned according to the emitted compounds or processes that result in a combination of drivers. The best estimates of the net radiative forcing are shown as black diamonds with corresponding uncertainty intervals; the numerical values are provided on the right of the figure, together with the confidence level in the net forcing (VH very high, H high, M medium, L low, VL very low). Albedo forcing due to black carbon on snow and ice is included in the black carbon aerosol bar. Small forcings due to contrails (0.05 W m 2, including contrail induced cirrus), and HFCs, PFCs and SF6 (total 0.03 W m 2) are not shown. Concentration-based RFs for gases can be obtained by summing the likecoloured bars. Volcanic forcing is not included as its episodic nature makes is difficult to compare to other forcing mechanisms. Total anthropogenic radiative forcing is provided for three different years relative to 1750 (Source: IPCC (IPCC 2013))

Anthropogenic Climate Change The climate is governed by natural influences, yet human activities have an impact on it as well. The main impact that humans exert on the climate is via the emission of greenhouse gases. Deforestation is another example of an activity that influences the climate (McMichael et al. 2007). Figure 3 shows the share of greenhouse gas emissions from various sectors taken from (Quadrelli and Peterson 2007). The energy sector is the dominant source of GHG emissions.

Introduction to Climate Change Mitigation Fig. 3 Shares of global anthropogenic greenhouse gas emissions (Reprinted with permission from (Quadrelli and Peterson 2007))

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Waste 2.5% Agriculture 8%

Energy* 84%

CO2 95%

Industrial processes 5.5% CH4 4% N2O 1%

According to the International Energy Agency (IEA), if no action toward climate change mitigation is taken, global warming could reach an increase of up to 6 in average temperature (International Energy Association IEA 2009). This temperature rise could cause devastating consequences on Earth, which will be discussed briefly below.

Effects of Climate Change Paleoclimatological data show that 100–200 Ma ago, almost all carbon was in the atmosphere as CO2, with global temperatures being 10  C warmer and sea levels 50–100 m higher than today. Photosynthesis and CO2 uptake into the oceans took almost 200 Ma. Since the Industrial Revolution, i.e., during the last 200 years, this carbon is being put back into the atmosphere to a significant extent. This is a rate which is 107 times faster, so there is a risk of a possible “runaway” reaction greenhouse effect. Figure 4 shows the timescales of several different effects of climate change for the future. Due to the long lifetime of CO2 in the atmosphere, the effects of climate change until a new equilibrium has been reached will prove long term. A global temperature increase of 6  C would be severe, so the IEA has developed a scenario which would limit the temperature increase to 2  C (International Energy Association IEA 2009) to minimize the effects. Sea level rise will indeed be the most direct impact. Other impacts including those on weather, flooding, biodiversity, water resources, and diseases are discussed here.

Climate Change: What Will Change? An overall higher temperature on Earth, depending on the magnitude of the effect and the rate at which it manifests itself, will change the sea level, local climatic conditions, and the proliferation of animal and plant species, to name but a few of the most obvious examples. The debate on the actual consequences of global warming is the most heated part of the climate change discussion.

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Sea-level rise due to ice melting: Several millennia Sea-level rise due to thermal expansion: centuries to millennia

CO2 emissions peak: 0 to 100 years

Temperature stabilisation: a few centuries CO2 stabilisation: 100 to 300 years

CO2 emissions Today 100 years

1000 years

Fig. 4 Time scales of climate change effects based on a stabilization of CO2 concentration levels between 450 and 1,000 ppm after today’s emissions (Reprinted with permission from (Quadrelli and Peterson 2007))

Apart from changes in the environment, there will be various impacts on human activity. One example is the threats to tourism revenue in winter ski resorts (Hoffmann et al. 2009) and low-elevation tropical islands (Becken 2005). Insurance companies will need to devise completely new business models, to cite just one example of businesses being forced to react to climate change.

Impact of Climate Change Mitigation Actions The purpose of climate change mitigation is to enact measures to limit the extent of climate change. Climate change mitigation can make a difference. In the IEA reference scenario (International Energy Association IEA 2009), the world is headed for a CO2 concentration in the atmosphere above 1,000 ppm, whereas that level is limited to 450 ppm in the proposed “mitigation action” scenario. In the first case, the global temperature increase will be 6  C, whereas it is limited to 2  C in the latter (International Energy Association IEA 2009). The Intergovernmental Panel on Climate Change has projected that the financial effect of compliance through trading within the Kyoto commitment period will be limited at between 0.1 % and 1.1 % of GDP. By comparison, the Stern report estimated that the cost of mitigating climate change would be 1 % of global GDP and the costs of doing nothing would be 5–20 times higher (IPCC 2010b; Stern 2007).

Climate Change Adaptation Versus Climate Change Mitigation Individuals (Grothmann and Patt 2005), municipalities (Laukkonen et al. 2009; van Aalst et al. 2008), businesses (Hoffmann et al. 2009), and nations (Næss et al. 2005; Stringer et al. 2009) have started to adapt to the ongoing and expected state of

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Fig. 5 Conceptual framework for developing a climate change adaptation strategy. OUV Outstanding Universal Values (each World Heritage (WH) site has one or more such OUV. According to UNESCO, WH represent society’s highest conservation designation (Source: Jim Perry (2015))

climate change. Climate change adaptation and climate change mitigation face similar barriers (Hamin and Gurran 2009). To best deal with the situation, there needs to be a balanced approach between climate change mitigation and climate change adaptation (Becken 2005; Laukkonen et al. 2009; Hamin and Gurran 2009). This will prove to be one of mankind’s largest modern challenges. Figure 5 shows a conceptual framework for developing a climate change adaptation strategy. Details are presented in this Handbook.

Handbook of Climate Change Mitigation and Adaption Motivation The struggle in mitigating climate change is not only to create a sustainable environment but also to build a sustainable economy through renewable energy resources. “Sustainability” has turned into a household phrase as people become increasingly aware of the severity and scope of future climate change. A survey of

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the current literature on climate change suggests that there is an urgent need for a comprehensive handbook introducing the mitigation of climate change to a broad audience. The burning of fossil fuels such as coal, oil, and gas and the clearing of forests has been identified as the major source of greenhouse gas emissions. Reducing the 24 billion metric tons of carbon dioxide emissions per year generated from stationary and mobile sources is an enormous task that involves both technological challenges and monumental financial and societal costs with benefits that will only surface decades later. The Stern Report (2007) provided a detailed analysis of the economic impacts of climate change and the ethical ground of policy responses for mitigation and adaptation. The decline in the supply of high-quality crude oil has further increased the urgency to identify alternative energy resources and develop energy conversion technologies that are both environmentally sound and economically viable. Various routes for converting renewable energies have emerged – including energy conservation and energy-efficient technologies. The energy industry currently lacks an infrastructure that can completely replace fossil fuels in the near future. At the same time, energy consumption in developing countries like China and India is rapidly increasing as a result of their economic growth. It is generally recognized that the burning of fossil fuels will continue until an infrastructure for sustainable energy is established. Therefore, there is now a high demand for reducing greenhouse gas emissions from fossil fuel–based power plants. Adaptation is a pragmatic approach to deal with the facts of climate change so that life, property, and income of individuals can be protected. The pursuit of sustainable energy resources has become a complex issue across the globe. The Handbook on Climate Change Mitigation and Adaptation is a valuable resource for a wide audience who would like to quickly and comprehensively learn the issues surrounding climate change mitigation.

Why This Book Is Needed There is a mounting consensus that human behaviors are changing the global climate and that its consequence, if left unchecked, could be catastrophic. The fourth climate change report by the Intergovernmental Panel on Climate Change (IPCC 2007) has provided the most detailed assessment ever on climate change’s causes, impacts, and solutions. A consortium of experts from 13 US government science agencies, universities, and research institutions released the report Global Climate Change Impacts in the United States (2009), which verifies that global warming is primarily human induced and climate changes are underway in the USA and are only expected to worsen. From its causes and impacts to its solutions, the issues surrounding climate change involve multidisciplinary sciences and technologies. The complexity and scope of these issues warrants a single comprehensive survey of a broad array of topics, something which the Handbook on Climate Change Mitigation and Adaptation achieves by providing readers with all the necessary background information on

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the mitigation of climate change. The handbook introduces the fundamental issues of climate change mitigation in independent chapters rather than directly giving the detailed advanced analysis presented by the IPCC and others. Therefore, the handbook will be an indispensable companion reference to the complex analysis presented in the IPCC reports. For instance, while the IPCC reports give large amounts of data concerning the impacts of different greenhouse gases, they contain little discussion about the science behind the analysis. Similarly, while the IPCC reports present large amounts of information concerning the impacts of different alternative energies, the reports rarely discuss the science behind the technology. There is currently not a single comprehensive source that enables the readers to learn the science and technology associated with climate change mitigation.

Audience of the Handbook Since the handbook covers a wide range of topics, it will find broad use as a major reference book in environmental, industrial, and analytical chemistry. Scientists, engineers, and technical managers in the energy and environmental fields are expected to be the primary users. They are likely to have an undergraduate degree in science or engineering with an interest in understanding the science and technology used in addressing climate change and its mitigation.

Scope This multivolume handbook offers a comprehensive collection of information on climate change and how to minimize its impact. The chapters in this handbook were written by internationally renowned experts from industry and academia. The purpose of this book is to provide the reader with an authoritative reference work toward the goal of understanding climate change, its effects, and the available mitigation and adaptation strategies with which it may be tackled: • • • • • • •

Scientific evidence of climate change and related societal issues The impact of climate change Energy conservation Alternative energy sources Advanced combustion techniques Advanced technologies Education and outreach

This handbook presents information on how climate change is intimately involved with two critical issues: available energy resources and environmental policy. Readers will learn that these issues may not be viewed in isolation but are mediated by global economics, politics, and media attention. The focus of these presentations will be current scientific technological development although societal impacts will not be neglected.

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References Antilla L (2005) Climate of scepticism: US newspaper coverage of the science of climate change. Global Environ Change Part A 15(4):338–352 Becken S (2005) Harmonising climate change adaptation and mitigation: the case of tourist resorts in Fiji. Global Environ Change Part A 15(4):381–393 Ehrlich PR (2000) Human natures: genes cultures and the human prospect B&T. Island Press, Washington, DC. ISBN 978-1559637794 Folland CK, Karl TR, Nicholls N, Nyenzi BS, Parker DE, Vinnikov KYA (1992) Observed climate variability and change. In: Houghton JT, Callander BA, Varney SDK (eds) Climate change, the supplementary report to the IPCC scientific assessment. Cambridge University Press, Cambridge, pp 135–170 Greitemeyer T (2013) Beware of climate change skeptic films. J Environ Psychol 35:105–109 Grothmann T, Patt A (2005) Adaptive capacity and human cognition: the process of individual adaptation to climate change. Global Environ Change Part A 15(3):199–213 Haberle RM, Forget F, Head J, Kahre MA, Kreslavsky M, Owen SJ (2012) Summary of the Mars recent climate change workshop NASA/Ames Research Center. Icarus 222(1):415–418 Hamin EM, Gurran N (2009) Urban form and climate change: balancing adaptation and mitigation in the U.S. and Australia. Habitat Int 33(3):238–245 Hoffmann VH, Sprengel DC, Ziegler A, Kolb M, Abegg B (2009) Determinants of corporate adaptation to climate change in winter tourism: an econometric analysis. Global Environ Change 19(2):256–264 Intergovernmental Panel on Climate Change (IPCC) (2007) IPCC fourth assessment report: climate change 2007 (AR4), vol 3. Cambridge University Press, Cambridge International Energy Association IEA (2009) World energy outlook 2009. International Energy Association (IEA), Paris. ISBN 9789264061309 IPCC (2010) Special Report on Emission Scenarios (SRES). http://www.grida.no/climate/ipcc/ emission/ IPCC (2010) Intergovernmental panel on climate change. http://www.ipcc.ch/ IPCC (2013) Climate change 2013: the physical science basis, summary for policymakers. http:// www.ipcc.ch/report/ar5/wg1/ IPCC IPCC third assessment report, chap 2.3.3 was there a “Little ice age” and a “Medieval warm period”? http://www.grida.no/publications/other/ipcc_tar/?src=/climate/ipcc_tar/wg1/070.htm Jim Perry (2015) Climate change adaptation in the world’s best places: A wicked problem in need of immediate attention, Landscape and Urban Planning, 133:1–11 Karl TR, Trenberth KE (2003) Modern global climate change. Science 302(5651):1719–1723 Laukkonen J, Blanco PK, Lenhart J, Keiner M, Cavric B, Kinuthia-Njenga C (2009) Combining climate change adaptation and mitigation measures at the local level. Habitat Int 33(3):287–292 Le Houérou HN (1996) Climate change, drought and desertification. J Arid Environ 34:133–185 Linden HR (1993) A dissenting view on global climate change. Electron J 6(6):62–69 Linden HR (2005) How to justify a pragmatic position on anthropogenic climate change. Ind Eng Chem Res 44(5):1209–1219 McMichael AJ, Powles JW, Butler CD, Uauy R (2007) Food, livestock production, energy, climate change, and health. Lancet 370:1253–1263 Miller FP, Vandome AF, McBrewster J (eds) (2009) History of climate change science. Alphascript, Mauritius. ISBN 978-6130229597 Næss LO, Bang G, Eriksen S, Vevatne J (2005) Institutional adaptation to climate change: flood responses at the municipal level in Norway. Global Environ Change Part A 15(2):125–138 Quadrelli R, Peterson S (2007) The energy-climate challenge: recent trends in CO2 emissions from fuel combustion. Energy Policy 35(11):5938–5952 Sonnabend G, Sornig M, Schieder R, Kostiuk T, Delgado J (2008) Temperatures in Venus upper atmosphere from mid-infrared heterodyne spectroscopy of CO2 around 10 μm wavelength. Planet Space Sci 56(10):1407–1413

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Stern N (2007) The economics of climate change: the stern review. Cambridge University Press, Cambridge. ISBN 978-0521700801 Stringer LC, Dyer JC, Reed MS, Dougill AJ, Twyman C, Mkwambisi D (2009) Adaptations to climate change, drought and desertification: local insights to enhance policy in southern Africa. Environ Sci Policy 12(7):748–765 United Nations (UN) (1992) United framework convention on climate change. United Nations, Geneva van Aalst MK, Cannon T, Burton I (2008) Community level adaptation to climate change: the potential role of participatory community risk assessment. Global Environ Change 18(1): 165–179 Winter DF (1967) Transient radiative heat exchange at the surface of the moon. Icarus 6 (1–3):229–235 Worldbank (2009) Attitudes toward climate change: findings from a multi-country poll. http:// siteresources.worldbank.org/INTWDR2010/Resources/Background-report.pdf Zasova LV, Ignatiev N, Khatuntsev I, Linkin V (2007) Structure of the Venus atmosphere. Planet Space Sci 55(12):1712–1728

Loss and Damage Associated with Climate Change Impacts Linta M. Mathew and Sonia Akter

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definition of Loss and Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conventions and Treaties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Loss and Damage in Vulnerable Countries Initiative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Warsaw International Mechanism for Loss and Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Approaches to Address Loss and Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monetary Versus Nonmonetary Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insurance Versus Compensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Empirical Evidence of Loss and Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Global Estimate of Loss and Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Country-Specific Evidence of Loss and Damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The impacts of climate change that are not mitigated, or appropriately adapted or coped with, are referred to as “loss and damage.” The global community has recently recognized that addressing and financing the “residual” loss and damage from climate change requires a different approach as such costs cannot or have not been appropriately mitigated or adapted to. Although international pressures to weigh a country’s contribution to climate change financing against their contribution to climate change has been proposed, no such legally binding climate change L.M. Mathew • S. Akter (*) Social Sciences Division, International Rice Research Institute, Los Baños, Laguna, Philippines e-mail: [email protected]; [email protected]; [email protected]; [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_55

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deals have been fashioned. Most parties have only agreed to nonbinding actions to either reduce emissions or finance loss and damage in low-income, vulnerable countries. This is because the concept of loss and damage and the approaches to address the concept have been widely contested and debated. Additionally, the lack of a global consensus on an appropriate mechanism to attribute gradual and extreme natural calamities to climate change has further intensified the debate. Given this background, this chapter seeks to synthesize the key issues surrounding this debate. The objectives of this chapter are to review the definitions of loss and damage, examine the evolution of its significance in the international climate politics, present a comparative analysis of the approaches to address climate change-induced loss and damage, and outline empirical evidence of loss and damage in geographically and economically vulnerable nations.

Introduction In its effort to combat climate change, the global community focused on rapid reduction of greenhouse gases (GHGs) by implementing enhanced mitigation efforts from the early 1990s to the mid-2000s. By the mid-2000s, scientific evidence indicated the likelihood of global temperature rising between 3  C and 4  C above the preindustrial level within this century (IPCC 2007a). This evidence suggested that mitigation efforts alone will not be sufficient to avoid climate change as some of the climate change impacts may already have started to take effect. Although steep cuts in global GHGs could stabilize atmospheric GHG concentrations at lower levels than under the status quo, they likely would be above the current levels, thus resulting in further rises in global temperatures. The projected impacts of a 3–4  C temperature rise would lead to serious consequences for humans and ecosystems due to dangerous sea-level rise, unprecedented heat waves, severe drought, and major floods in many parts of the world (IPCC 2007a). Once it became clear that mitigation efforts would be insufficient to avoid all climate change impacts, adaptation became a necessary complement to mitigation (Ott et al. 2008). Adaptation was defined by the Intergovernmental Panel for Climate Change (IPCC) (2007b) as “adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities.” As of 2007, global adaptation cost estimates ranged from $4 billion to well over $100 billion a year (Parry et al. 2009). These estimates led to the establishment of the Green Climate Fund (GFC) in Durban, South Africa, in 2011 during the 16th session of the Conference of Parties (COP), with the objective of raising a minimum of $100 billion/year by 2020 to support sustainable and climate-resilient development (Institute for Policy Studies 2014; Green Climate Fund 2014). This came to be known as the “adaptation fund.” However, adaptation also appeared to have its limit. It became increasingly apparent that adaptation cannot successfully contain all the adversities invoked by climate change. Such remnants of the adverse effects of climate change came to be

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known as “residual loss and damage.” Widespread international understanding and agreement on the distinction between adaptation and loss and damage was deemed essential in recognizing that not all adversities of climate change can be successfully mitigated or adapted to. Such remnants of the ill effects of climate change impacts were forecasted to account for two-thirds of all potential impacts across all sectors over the longer term (Parry et al. 2009). This recognition highlighted the need to allocate adequate compensation and relief efforts, above and beyond the GCF, to help the victims of loss and damage in geographically and economically vulnerable countries. The term loss and damage appeared in the United Nations Framework Convention on Climate Change (UNFCCC) negotiations in 2007 at COP 13, where the Bali Action Plan called for enhanced action on adaptation including the consideration of “disaster risk reduction strategies and means to address loss and damage associated with climate change impacts in vulnerable countries” (Roberts 2012). Loss and damage was recognized as a separate concept from adaptation in 2008, when the Alliance of Small Island States (AOSIS) proposed a Multi-Window Mechanism to address and finance the distinct concept of loss and damage due to climate change impacts. This was followed by the establishment of the UNFCCC Work Program on Loss and Damage in 2010 and the Warsaw International Mechanism on Loss and Damage in 2013. In addition, the Loss and Damage in Vulnerable Countries Initiative was formed in 2012, with the aim of understanding both the national context and the range of accessible implementation options for addressing loss and damage (Roberts 2012). However, no official lifetime commitment by developed countries to provide funds to the vulnerable communities has been undertaken as yet. Hence, the initiatives could be seen as weak attempts by the rich countries to admit liability for their contributions to climate change. In this chapter, we synthesize the debates surrounding the classification of loss and damage and also uncover the issues around an appropriate compensation mechanism. The purpose of the chapter is to not add to the already substantive literature on loss and damage but to provide a review of the concept, historical treaties and conventions that finally led up to increased international focus on the issue, and the empirical heterogeneity in its estimate and impact across a multicountry sample. The remainder of this chapter is structured as follows: section “Definition of Loss and Damage” provides an in-depth examination of the definition and debates surrounding the concept of loss and damage. Section “History” accommodates a study of the international conventions and treaties on climate change and examines the gradual recognition of the need to address loss and damage in the international climate politics. This is followed by a discussion in section “Approaches to Address Loss and Damage” of the different approaches in addressing loss and damage such as monetary versus nonmonetary costs and insurance versus compensation. Finally, section “Empirical Evidence of Loss and Damage” provides empirical evidence of the global and international estimates of loss and damage as well as multi-country evidence of loss and damage experienced by some of the most geographically and economically vulnerable countries in the world.

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Definition of Loss and Damage A widely accepted definition of loss and damage does not exist. The framing of the definition and its conceptual discussion continue to evolve within the UNFCCC and the academic literature with different groups displaying heterogeneous understanding of the terminology and concept. The UNFCCC defined the concept as one of the “impacts associated with climate change in developing countries that negatively affects human and natural systems” (UNFCCC 2012). However, the definition offered by the UNFCCC was found to be at its nascent stages and was therefore found to lack much clarity. This led to the formation of the Loss and Damage for Vulnerable Countries Initiative in 2012, headed by the Government of Bangladesh, to understand the meaning of the concept and how it can be approached in vulnerable countries (Roberts 2012). The UNFCCC and Warsaw International Mechanism for Loss and Damage act as guides to the initiative. Loss was defined by the Loss and Damage in Vulnerable Countries Initiative, as the “negative impacts that cannot be repaired or restored (such as loss of geological freshwater sources related to glacial melt or desertification),” whereas damage was defined as the “negative impacts that can be repaired or restored (such as windstorm damage to the roof of a building).” Therefore, the Loss and Damage in Vulnerable Countries Initiative views loss and damage as the avoidable and the unavoidable costs associated with climate change impacts. The Loss and Damage in Vulnerable Countries Initiative definition also identified the need to include “the full range of climate change related impacts from (changes in) extreme events to slow onset process and combinations thereof.” This definition included a continuum of climate change events and not only the extreme calamities resulting from climate change. The UNFCCC’s Working Program on Loss and Damage called for a similar attempt to investigate a range of tools and approaches to address all forms of loss and damage resulting from climate change, ranging from slow onset to extreme weather conditions (UNFCCC 2012). However, the convention itself does not define loss and damage as the Work Program, which again indicates the lack of consistency and clarity of the concept. Another working definition of loss and damage, compiled by Action Aid (2010), characterized loss and damage as consequences of the adverse effects of climate change that cannot be (or have not been) adapted to. This gave rise to the ideology of “residual” loss, i.e., unavoidable and unavoided loss and damage and recognized that certain aspects of climate change cannot be appropriately adapted to, given the limited resources available by many of the vulnerable nations affected by climate change. Action Aid (2010) summarized different categories of loss and damage, of which unavoided and unavoidable loss and damage were regarded as residual loss and damage (Table 1). Unavoided costs can also be classified as the “avoidable costs of loss and damage,” i.e., the costs of climate change impacts that can be avoided through appropriate mitigation and/or adaptation. However, such costs are not always avoided due to limited capacity or resource. It is very important to regionally and internationally allocate appropriate resources, such that the resulting loss and

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Table 1 Avoided, unavoided, and unavoidable damage Avoided damage Avoidable damage avoided Damage prevented through mitigation and/or adaptation measures

Unavoided damage Avoidable damage and loss not avoided Where the avoidance of further damage was possible through adequate mitigation and/or adaptation, but where adaptation measures were not implemented due to financial or technical constraints

Unavoidable damage Unavoidable damage and loss Damage that could not be avoided through mitigation and/or adaptation measures, e.g., coral bleaching, sea-level rise, damage due to extreme events where no adaptation efforts would have helped prevent physical damage

Source: Action Aid (2010)

damage can be reduced or mitigated completely. In least developed countries (LDCs), this often implies that such resulting loss and damage will only be adapted to if national benefits from adaptation exceed national losses and damages. Therefore, the loss and damage resulting from slow onset events and its victims events are often ignored1. This remains to be one of the major, but often sidelined, issues in the international climate change debate. Additionally, even in the case of unavoidable loss and damage, appropriate financing/funding still remains to be a problem. This is due to the “attribution problem” in climate change science, which can be briefly described as the inability to completely underpin the loss and damage due to weather-related events to climate change2. A technical representation of residual (unavoidable and unavoided) loss and damage was compiled by Rothman et al. (2003). This is represented in Fig. 1, where residual unavoidable and unavoided impacts of climate change with adaptation are demonstrated by the dotted line. Unavoidable and unavoided residual loss and damage reflects ill effects that have not been mitigated and which cannot/have not been adapted to. One must also note that for stakeholders to undertake adaptation measure, the benefits from adaptation (“effect of adaptation”) must be greater than adaptation costs. Although the diagram above seems straightforward enough, the effects of adaptation and the impact of climate change (its cost) are quite hard to calculate and reproduce in such a simple two-dimensional linear frame. Another schematic representation of the residual damage as compiled by Parry et al. (2009) is presented in Fig. 2. Figure 2a represents the short-term nonlinearity of climate change impacts. Lower adaptation costs are associated with higher avoided damage, therefore giving it a low incremental adaptation cost to avoided damage ratio. This curve is estimated to fluctuate greatly across sectors and gives one a clearer picture of the variability and nonlinearity of such a concept. Figure 2b represents a longer time period of adaptation to damage, which illustrates that over the longer term, all damages will

For more information on this, refer to section “Empirical Evidence of Loss and Damage.” This “attribution problem” and the resulting financing issue will be examined in greater detail in section “Approaches to Address Loss and Damage.”

1 2

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Fig. 1 Traditional representation of climate impacts and adaptation (Source: Rothman et al. (2003))

not be adapted to, due to its lack of economic viability or structural feasibility (Parry et al. 2009). The above representation also considers the trend in damage due to asset growth, therefore normalizing asset damage, such that the increase in damage is not associated with an asset growth. The impact of loss and damage should be narrowed down to the ones that can be attributed to climate change. This can be accomplished by attempting to distinguish between bad weather and natural calamities that can be attributed to climate change. However, the lack of traceability of such events to climate change has induced reluctances by many stakeholders to officially commit to any binding financial agreements. This is commonly referred to as the “attribution problem” in climate science. Various methods of calculating the odds of relating extreme natural calamities to climate change have been devised to aid the allocation of climate changerelated funds (Hulme et al. 2011). One such event attribution, termed as the probabilistic event attribution, compiled by Stone and Allen (2005), seeks to differentiate between weather changes caused as a result of human interference and “bad luck” (Hulme et al. 2011).

History This section presents a history of evolution of the concept of loss and damage including the formation of the Loss and Damage in Vulnerable Countries Initiative and the Warsaw International Mechanism for Loss and Damage.

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Fig. 2 Avoided damages and residual costs over the short term and long term (Source: Parry et al. (2009))

Conventions and Treaties Various conventions and treaties were established over the years starting from 1979, which led up to the formation of the IPCC in 1988, followed by the creation of the UNFCCC in 1992. The role of the IPCC is to “assess on a comprehensive, objective, open and transparent basis the scientific, technical and socio-economic information relevant to understanding the scientific basis of risk of human-induced climate change, its potential impacts and options for adaptation and migration” (IPCC 2007b). The scientific evidence on climate change, gathered by the IPCC, underlined the severity of the issue and played a major role in the creation of the UNFCCC (IPCC 1995). The UNFCCC was formed to work to limit average global temperature increases and the resulting climate change and to cope with the unavoidable loss and

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damage (UNFCCC 2014e). A summary of the major climate change conventions and treaties, post the initiation of the UNFCCC, is presented in Table 2.

Loss and Damage in Vulnerable Countries Initiative The Loss and Damage in Vulnerable Countries Initiative was commenced by the Government of Bangladesh and expanded with the help of the Climate and Development Knowledge Network (CDKN), which appointed a consortium of specialists including Germanwatch, United Nations University Institute for Environmental and Human security (UNU EHS), International Centre for Climate Change and Development (ICCAD), and Munich Climate Insurance Initiative (MCII) and was implemented from 2012 (Loss and Damage in Vulnerable Countries Initiative 2014). The Loss and Damage in Vulnerable Countries Initiatives are to: Understand the scope and significance of loss and damage associated with the adverse impacts of climate change; Developing and cocreating an approach and vision for loss and damage among decision makers and relevant stakeholders; Assisting least developed countries and other vulnerable countries to develop a coherent approach to the loss and damage debate; Identifying and beginning to take necessary steps to support a paradigm shift on loss and damage in the coming years; Source: Loss and Damage in Vulnerable Countries Initiative (2014)

The activities of the UNFCCC and Warsaw International Mechanism for Loss and Damage guide the initiative. The initiative follows four main activity areas to support less developed nations in their plight to reduce and mitigate loss and damage due to climate change impact and to “create momentum in the climate change debate” (Loss and Damage in Vulnerable Countries Initiative 2014). These activities include supporting and strengthening the position of LDCs in loss and damage negotiations in the UNFCCC Work Program on loss and damage, conceptually framing loss and damage and providing policy assistance, providing country-specific insights on the adverse effects of loss and damage, and imparting the cumulative results of mitigation and adaptation efforts in Bangladesh as an analytical tool for other vulnerable countries.

Warsaw International Mechanism for Loss and Damage The Warsaw International Mechanism for Loss and Damage was established to address the loss and damage due to climate change, including “extreme and slow onset events” in economically and geographically vulnerable countries (UNFCCC 2013). Two-year work plans were drawn up for the initiative during the resumed initial meeting in September 2014, which involved understanding the concept of loss and damage of extreme and slow onset events, risk management, comprehending the

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Table 2 Precedent conventions and treaties Year 1992

Key event(s) AOSIS proposal for an insurance scheme

1995

The first Conference of Parties (COP 1): Berlin Mandate

1997

COP 3: Kyoto Protocol Adoption

2001

COP 7: Marrakesh Accords

2005

Meeting of the Parties to the Kyoto Protocol (MOP 1)

Description Proposal for an insurance scheme was put forward by the members of the Alliance of Small Island States (AOSIS), the principle objective of which was to create an International Climate Fund and an International Insurance Pool to finance measures and to provide appropriate financial insurance respectively to counter the adverse effects of climate change. However, the parties only agreed to the insurance pool 10 years onward, provided that over the 10-year period, the “rate of global mean sea-level rise will have reached an agreed figure” (Hayes and Smith 1993). The COP1 held in Germany, where the Berlin mandate established the need for developed countries to “take the lead in combating climate change” and for developing countries to achieve sustainable economic growth (UNFCCC 1995). Adoption of the Kyoto Protocol is undertaken in Kyoto, Japan, setting legally binding emission reduction targets. The summit recognized the greater role of developed countries in having historically contributed significantly to greenhouse emissions (through their previously active roles in industrial activity), and therefore placed a’heavier burden’(UNFCCC 2014) on developed nations under the notion of’common but differentiated responsibilities’(UNFCCC 1998). Formation of the Marrakesh Accords, which laid out the rules and details for the implementation of the Kyoto Protocol, set up adaptation methodologies, and formed a technology transfer framework (UNFCCC 2014a). The Kyoto Protocol entered into force as the Russian Federation submitted its compliance (United Nations 2014). Negotiations for the next phase of the protocol under the Ad Hoc Working Group on Further Commitments for Annex Parties under the Kyoto Protocol (AWG-KP), later known as the “Nairobi Work Program,” were also agreed upon. (continued)

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Table 2 (continued) Year 2007

Key event(s) COP 13: Bali Road Map

2008

COP 14: Joint Implementation Mechanism for the Kyoto Protocol

AOSIS proposal of a Multi-Window Mechanism

2009

COP 15: Copenhagen Accord

2010

COP 16: Cancun Adaptation Framework

UNFCCC Work program to address loss and damage

Description Introduction of the Bali Road Map in Bali, Indonesia, which included the “Bali Action Plan.” This plan was envisioned to charter the way toward a post-2012 outcome (UNFCCC 2014e). The Bali Action Plan is divided into categories such as shared vision, mitigation, adaptation, technology, and financing. However, it is to be noted that no significant effort was made to differentiate between adaptation and loss and damage in this stage. A joint Implementation Mechanism for the Kyoto Protocol was initiated. This was described by UNFCCC (2014d) as an initiative that “allows a country with an emission reduction or limitation commitment under the Protocol to earn emission reduction units from an emission reduction or emission removal project in another country with similar commitments”. The Alliance of Small Island States (AOSIS) proposed a Multi-Window Mechanism to address and finance loss and damage from climate change impacts (Alliance of Small Island States 2008). The Copenhagen Accord was developed at COP15 in Copenhagen, Denmark, where developed countries undertook emission reduction and mitigation and adaptation action plan for the period of 2010–2012, pledging $30 billion as start-up finance (UNFCCC 2014e; United Nations 2014). The Cancun Adaptation Framework was formed at COP16, where governments of developed countries pledged comprehensive packages to assist developing countries to deal with climate change (UNFCCC 2014e). The agreements also made the reduction pledges of the countries official, which formed the “largest collective effort to reduce emission in a manually accountable way” (United Nations 2014). The Cancun Adaptation Framework also established a work program to address the loss and damage impacts of climate change in LDCs vulnerable to the adverse effects of climate change (Roberts 2012). (continued)

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Table 2 (continued) Year 2011

Key event(s) COP 17: Durban Platform for Enhanced Action

Green Climate Fund

2012

COP 18: Doha Amendments to the Kyoto Protocol

Loss and Damage Initiative in Vulnerable Countries Initiative

2013

COP 19: Warsaw Outcomes

Description Plans to draw up a new universal climate change agreement by 2015, to deal with the adverse effects of climate change beyond 2020, were formed in Durban, South Africa. This led to the formation of the Durban Platform for Enhanced Action or the Ad Hoc Working Group on the Durban Platform for Enhanced Action (ADP) (UNFCCC 2014e). COP 17 also led to the formation of the Green Climate Fund (GCF), with an aim of raising $100 billion per year in climate financing by 2020 (Institute for Policy Studies 2014). The Doha Amendments to the Kyoto Protocol commenced. This includes new commitments for a second commitment period from January 2010 until December 2020, a revised list of greenhouse gases to be reported by the Parties, and amendments to several articles of the Kyoto Protocol to issues pertaining to the first commitment period (UNFCCC 2014b). Governments also agreed to work speedily toward drafting a universal climate change agreement by 2015 (UNFCCC 2014b). The Doha Convention further addressed international efforts and strengthened international cooperation on loss and damage as a result of climate change (European Commission 2013). Loss and Damage Initiative was implemented in February 2012, with the objective of partnering with vulnerable LDCs and other parties to better understand loss and damage (Roberts 2012). The Decision to progress on the ADP Platform was agreed upon. A rulebook for reducing emissions from deforestation and forest degradation, enhancing the conservation and sustainable management of forests and forest carbon stocks in developing countries (REDD+), establishing a mechanism to address loss and damage from long-term climate change impact, and agreeing on capitalizing the GCF in the second half of 2014, as part of the Warsaw Outcome was undertaken (UNFCCC 2014g).

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current coping and adaptation mechanisms, and drawing up socioeconomically appropriate policies to adapt to monetary and nonmonetary residual losses as a result of climate change (UNFCCC 2014f). The introduction of the mechanism was seen as a “notable step forward” as it allowed to address and implement socioeconomically appropriate policies to deal with the adversities of climate change in vulnerable communities (Warner 2013). The primary roles of the Warsaw International Mechanism are to: Facilitate support of actions to address loss and damage; Improve coordination of the relevant work of existing bodies under the Convention; Convene meetings of relevant experts and stakeholders; Promote the development of, and compile, analyze, synthesize, and review information; Provide technical guidance and support; Make recommendations, as appropriate, on how to enhance engagement, actions, and coherence under and outside the Convention, including on how to mobilize resources and expertise at different levels. Source: UNFCCC (2014f)

Approaches to Address Loss and Damage This section summarizes the different approaches and their challenges for assessing and addressing loss and damage. Monetary and nonmonetary nature of loss and damage is discussed first followed by a range of economic instruments that can be used to address these costs. Finally, the attribution problem which lies at the center of the loss and damage debate is discussed in detail.

Monetary Versus Nonmonetary Costs Climate change invokes both monetary and nonmonetary loss and damage in vulnerable countries. These categories are also known as economic and noneconomic loss and damage. Monetary or economic loss and damage refer to the costs for which economic or monetary estimates are readily available, such as structural damage and crop failure due to flooding3. Nonmonetary losses are those that cannot be measured in monetary or economic terms, such as loss of biodiversity, loss of livelihoods, or number of deaths caused by flooding. As these goods are not traded in the market, the monetary estimates of loss and damage caused to these goods are not readily available, and hence, these items are generally ignored by the loss and damage accounting (Morrissey and Smith 2013). The concept of nonmonetary 3

A wide range of the estimates of the monetary costs from climate change have been estimated in previous studies, the details of which have been covered in section “Global Estimate of Loss and Damage.”

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costs was also highlighted in COP16 in Copenhagen where the parties recognized that all social and environmental loss and damage cannot be adequately captured by monetary measures. However, as such costs are difficult to quantify and monetize, it can be quite problematic to analyze the costs for inaction for such costs. Nonmarket valuation techniques are often used to assign monetary values to the goods and services that are not traded in the market. Many studies, such as “valuing the ocean” study by the Stockholm Environment Institute (SEI), instead of employing market values to decipher loss and damage, monetized the costs of climate change to the ocean by focusing on five areas, namely, fisheries, sea-level rise, storms, tourism, and the ocean carbon sink (Stockholm Environmental Institute 2012). They pinned monetary values on such components by employing two scenarios: low climate change impact scenario, where emissions are reduced quickly, and a high climate change impact scenario, where the global emissions continue to rise for the next few decades (Stockholm Environmental Institute 2012). However, the results of the study were criticized as it only considers variables that can be “realistically altered by humans and can be monetized” (The Guardian 2012). Thus, the study only took into account the avoidable costs of loss and damage from climate change. Therefore, even though some studies have tried to monetize the marketed and nonmarketed goods affected by climate change, not all nonmarketed costs were effectively captured.

Insurance Versus Compensation The concrete proposal put forth by the AOSIS in 2008 highlighting the need to finance a “Multi-Window Mechanism to Address Loss and Damage from Climate Change Impacts” placed the issue of financing loss and damage under the limelight. The proposed mechanism suggested three interdependent components for compensation: (1) insurance, (2) rehabilitation/compensation, and (3) risk management (AOSIS 2008). The insurance component was proposed to manage financial risk from extreme weather events and to provide insurance to countries who cannot find access to insurance. The rehabilitation/compensatory component addressed the progressive unavoidable climate change impacts, such as sea-level rise and ocean acidification. Finally, the risk management component was incorporated for risk assessment and management and to inform the insurance and the rehabilitation/ compensatory component (AOSIS 2008). The insurance option is one where regular payment by an individual to a private or public insurance entity subsists, such that the entity insures against any loss and damage that may be accidentally incurred by the individual. Munich Climate Insurance Initiative (MCII) (2012) stated that “insurance options can support adaptation and risk resilience for extreme weather, but are not appropriate for many, usually slower-onset, climate-induced impacts.” Therefore, insurance was suggested to be an appropriate adaptation measure against unpredictable extreme events and not for predictable, slow onset events (Warner et al. 2012). This is because insurance companies will only be prepared to provide insurance payouts if the loss and damage

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is entirely uncontrolled for and unforeseen. Insurance was suggested to be an adaptation, as opposed to a coping measure, as it reduces the impact of loss and damage and helps a timely recovery in the aftermath of extreme unforeseen calamities (Warner et al. 2012). Insurance policies were found to be an unpopular method of financing loss and damage among the poorer households in geographically and economically vulnerable countries (Gine et al. 2008; Akter 2012). This was attributed to the lack of knowledge and affordability of insurance premiums in such countries. In some cases, coupling microcredit with insurance schemes was seen as a viable option to extend insurance services to low-income households. For instance, in a study conducted by OECD (2005), the Grameen Bima insurance programs in Bangladesh were found to offer insurance with microcredit, where no premiums were required to be a member of the fund, but payments to the fund were bundled with the interest paid on loans. However, the program was seen to be taken up by the middle class, as opposed to the low-income households, as the poor could not afford the premium (OECD 2005). Compensation schemes in the context of loss and damage financing are funds provided by states or institutions to reduce the impact of loss and damage. The compensation option is perceived to be more appropriate than insurance schemes in funding the loss and damage from the gradual and predictable impacts of climate change. Therefore, the loss and damage from gradually occurring predictable events such as rising sea levels and desertification are best funded by states or institutions. However, individuals not insured against unpredictable extreme calamities should be considered for compensation schemes. This includes individuals in the poorer counterpart of the society, who are not able to afford the insurance premiums. Additionally, the lack of sufficient resources in low-income vulnerable countries does not enable appropriate compensation for all. The effectiveness in reducing the impact of loss and damage of such compensation packages depends on the efficiency of state policies and their outreach approach.

Attribution Attributing weather-related range of slow set to extreme calamities to climate change was found to be quite difficult and operates as one of the major limitations in climate change financing. The lack of good traceability measures also provides a good justification for many developed countries to reject liability and therefore fail to make any firm commitments to financing loss and damage in low-income countries. Hence, the following paragraphs examine the effectiveness and limitations of such attribution mechanisms and their potential role in aiding the global community with financing loss and damage from negative climate change impacts. The most popular method in climate change attribution is the examination of another related variable, which is linked to the characteristics of the extreme natural event. This is done as it is difficult to gain insights from examining trends of extremely rare natural calamities (Huggel et al. 2013). However, such studies have

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confirmed the link between some natural calamities and climate change, but not all. Increasingly, studies have identified that the increase in economic damages from extreme events has been attributed to increased “exposed asset values” rather than an increased intensity of extreme natural calamities (Huggel et al. 2013). To this end, Neumayer and Barthel (2011) calculated an actual-to-potential-loss ratio (APLR), which provided a normalization method to measure the economic loss after the onset of a severe natural disaster. Even though no upward trend in normalized loss and damage was found, the authors did not account for mitigation measures, which may have compromised the findings. Additionally, even if the increased loss and damage is accounted to increased asset value, this does not imply that the resulting loss and damage must not be compensated for. Such a finding, if anything, calls for increased insurance or compensation schemes to be implemented by regional and international bodies. However, many limitations in relying on such attribution methods to allocate any loss and damage funds were found such as the unreliability of such methodologies as they are based on climate estimates without climate change, which cannot be logically verified; the inability to accurately predict the percentage of overall risk attributable to human actions; and the undesirable shift in international climate change initiative from adaptation to compensation, if such methodologies are extensively used to allocate the international adaptation finance (Hulme et al. 2011). The lack of good quality data may also affect the accuracy of such measures. However, even though many such objections to attribution measures exist, formulating attribution mechanisms should be encouraged by the international community as it helps to reduce (to some extent) the moral hazard related to adverse events, where individuals will take greater risks (for instance, building houses in flood-prone areas) in the hope of being compensated. However, care must be taken in order to not get carried away by such measures. Successfully implemented techniques can help eradicate such uncertainty, which can aid the international community to identify the victims of climate change and to allocate funds to communities who are essentially negatively affected from climate change. The global community should allocate sufficient funds such that until a clear measure of attribution is found, the civilians experiencing loss and damage, especially in geographically and economically vulnerable countries, as a result of climate change impacts, do not suffer substantially. This was highlighted by the Philippines Senate Present Juan Ponce Enrile who stated that “developing countries like the Philippines should be receiving compensation. . . Instead, however, we are accepting, or worse, being “forced” to avail of loans that are, in the long run, more disadvantageous for the country” (Climate Justice Now 2010).

Empirical Evidence of Loss and Damage This section presents monetary estimates of loss and damage due to climate change impacts both at the global and local level. The first subsection summarizes the global estimates of loss and damage available in the literature. The second subsection

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presents country-specific local estimates of loss and damage from eleven most economically and geographically vulnerable countries in the world. It also outlines the existing loss and damage-coping strategies used by the households in these countries.

Global Estimate of Loss and Damage Various global estimates of loss and damage have been produced over the years. Global monetary estimates of loss and damage can be measured in terms of the social costs of carbon, which is defined as the “net economic costs of damages from climate change aggregated across the globe and discounted to the present” (IPCC 2007). IPCC’s Fourth Assessment Report (AR4) disclosed that the peer-reviewed estimate of the social cost of carbon in 2005 has an average value of US$12/t of carbon dioxide. However, the range from 100 estimates was found to be large ($3–$95/t of carbon dioxide), which demonstrates a substantial degree of disagreement on its measurement (IPCC 2007). Natural disasters are estimated to have doubled from an average of 200/year in 1998 to an average of 400/year in 2008, whereas costs of natural disasters in monetary terms have increased sevenfold (United Nations 2009; cited in Action Aid 2010). Therefore, future estimates of climate change have painted a dull portrait of an impending catastrophe. Monetary values of loss and damage can also be calculated from the overall loss and damage caused by climate change after accounting for certain scenarios of mitigation and adaptation (Action Aid 2010). One such probabilistic estimation method, known as the Policy Analysis for the Greenhouse Effect (PAGE), calculates the regional and global impacts of climate change, social costs of greenhouse gases, and also the cost of abatement and adaptation (UNFCCC 2014d). This model helps one to calculate the economic loss from such climate change adversities. Action Aid (2010) put together a table for global loss and damage under a scenario of no mitigation and the lowest emission scenario proposed by UNFCCC. Adaptation costs were also derived from UNFCCC reports. The costs are accrued over the years 2000–2200 and presented in discounted net present values (NPV). From the analysis presented in Table 3, it was inferred that with respect to the cost of impact, the optimal action is to combine mitigation and adaptation. However, even with successful mitigation, a residual loss of US$275 trillion was found. However, this method of calculation was found to be more appropriate to predict global, as opposed to regional, loss and damage. Regional calculations of loss and damage are mostly obtainable from local insurance estimates. However, such estimates in low-income developing countries may only adumbrate monetary loss and damage to important sectors such as energy and infrastructure and neglect or overlook the loss and damage to most households. In such circumstances, national statistics are ones’ best gamble in obtaining regional loss and damage statistics. Although the calculation of residual loss and damage has been highly debated, as demonstrated by the noteworthy range of the formulated estimates, a common underlying theme of globally increasing loss and damage was found. Additionally, the calculations of monetary loss and damage also suggested that if appropriate

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Table 3 Monetary estimates under “no mitigation” and “mitigation and lowest emission” scenarios

Cost of impact (without adaptation) Cost of impacts (with adaptation) Adaptation costs Mitigation costs

Trillion US$ No mitigation Lower end Mean 270 1,240

Higher end 3,290

170

890

2,340

60

275

760

4

6

9

4 50

6 110

9 170

Lowest emission scenario Lower Higher end Mean end 100 410 1,070

Source: Action Aid (2010)

measures are not taken to constantly curb global emissions, loss and damage, particularly to low-income vulnerable countries, would only increase exponentially over time. Therefore, communities with a higher exposure to the risks of climate change and with lower adaptive capacity would experience a greater burden of loss and damage in comparison to others. Such vulnerable countries include the Alliance of Small Island States (AOSIS), threatened by the rise in sea level, and low-income developing countries, where a large proportion of the population relies on agricultural income, particularly susceptible to climatic fluctuations. Although developed rich nations may experience greater monetary losses from extreme events due to a higher proportion of exposed assets, the loss and damage as a percentage of GDP is peripheral in comparison to the low-income vulnerable nations. This is demonstrated in the figure below which compares the monetary damage to the monetary damage as a percentage of GDP in both developing and developed countries. Figure 3 shows the damages as a percentage of GDP are higher for low-income developing countries than for the developed rich nations. On average, the agricultural sector contributes substantially to a poor developing nations’ GDP, which is particularly vulnerable to the weather changes that have resulted from climate change. Additionally, poorly built infrastructure and households in low-income developing countries are often unable to withstand extreme weather disruptions, causing greater damage as a proportion of GDP in such countries.

Country-Specific Evidence of Loss and Damage Country-level evidence of loss and damage occurring due to climate change impacts in vulnerable countries is crucial in assessing the future risks of climate change in such countries. Table 4 summarizes the environmental and economic vulnerability facing eleven low-income countries due to climate change impacts. The specific nature of the vulnerability, monetary estimate of loss, and damage resulting from unavoidable climate change impacts and coping strategies are summarized in the following paragraphs.

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Fig. 3 Disaster losses, total and as a share of GDP, in the richest and poorest nations, 1985–1999 (Source: United Nations Inter-Agency Secretariat for the International Strategy for Disaster Risk Reduction (2003))

Bangladesh In the case of Bangladesh, it was found that climatic susceptibility along with increased climate change has adverse consequences, especially in the coastal region. Frequent cyclones, such as Sidr in 2007 and Aila in 2009, caused massive loss and damage to the coastal population. Cyclone Sidr claimed 4,234 lives, injured 55,282 people, and damaged 8.9 million people’s livelihood (Ministry of Disaster Management and Relief 2014). The economic damage caused by Cyclone Sidr was equivalent to US$1.67 billion (Ministry of Disaster Management and Relief 2014). Eleven out of the 19 coastal districts were severely affected by Cyclone Aila. It claimed 190 lives, injured 7,000 people, killed 100,000 livestock, and caused US$170 million worth of economic damage (United Nations Development Fund 2010 cited in Akter and Mallick 2013). The loss and damage experienced by a cyclone as powerful as Cyclone Sidr are expected to rise nearly fivefold to over $9 billion by 2050, accounting for 0.6 % of GDP (World Bank 2010). These cyclones forced saline water into the agricultural lands (Rabbani et al. 2013). The rise in sea level, also attributed to global climate change, is expected to push saline water further inland, therefore severely affecting the agricultural productivity and the quality of drinking water in the coastal districts of

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Table 4 Climatic susceptibility, long-term climate change threat, and livelihood impact in the nine disaster-prone areas Climatic susceptibility Cyclones

Country Bangladesh

Region Sathkira

Bhutan

Punakha

Burkina Faso

Sahel

Glacial lake outburst floods Drought

Ethiopia

Gambella

Floods

Gambia

North Bank

Drought

Kenya

Budalangi

Floods

Micronesia

Kosrae

Storms

Mozambique

South/ Central

Floods/ droughts

Nepal

Udayapur

Floods

Pakistan

Baluchistan

Philippines

Tacloban

Flood, glacial lake outbursts Cyclones

Long-term threats Sea-level rise, salinity intrusion Changing monsoon Changing rainfall patterns Changing rainfall patterns Changing rainfall patterns Changing rainfall patterns Sea-level rise, coastal erosion Changing rainfall patterns Changing rainfall patterns Changing rainfall patterns Changing storm intensity

Impacts Rice, drinking water

Rice

Livestock, crops

Habitability, crops, livestock

Agriculture

Livestock, crops, property, disruption of social and economic activities Crops, livestock, fish

Housing, livelihood

Staple crops

Agriculture, transport and communication Lives, agriculture, livestock, and property

Source: Warner and van der Geest (2013) and (Roberts et al. 2014)

Bangladesh (Rabbani et al. 2013). High yielding rice varieties were unable to withstand the increase in soil salinity (Rabbani et al. 2013). New varieties, such as BINA 8 and BRRI 47, henceforth developed after 2009, to resist high-salinity levels, were however found to be inappropriate for the chosen region (Rabbani et al. 2013). It is also estimated that the region incurred a decrease in its rice production by 0.1 million tons between 2008 and 2010. The total cost of loss to rice production due to salinity was estimated to be US $1.9 million from 2009 to 2011. The dangers of massive rural–urban and coastal-central migration looms in the near future if the region continues to experience such frequent calamities.

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Bhutan The district of Punakha is referred to as Bhutan’s “rice bowl,” where a substantial proportion of the population engages in small-scale farming (Kusters and Wangdi 2013). Kusters and Wangdi (2013) conducted a study on this region. A large proportion of the research participants recognized a pattern of unreliable monsoon and overall annual precipitation. This observation was confirmed by rainfall data collected over 1990–2008. This changing water availability was reported to have a negative effect on crop production. Coping measures adopted by the households include ritual performance (costing households between US$700 and US$900 per year), developing or modifying water-sharing arrangements, maintaining irrigation channels, changing cropping pattern, buying irrigation water from upstream villages, and using water pumps. Improved availability of fertilizers and modern technology was found to greatly enhance agricultural productivity for many farmers. Nevertheless, most adaptation measures were not without costs, some of which are monetary and some are nonmonetary. For instance, unsuccessful watersharing arrangements led to local conflicts, disrupting social cohesion. Maintenance of irrigation canals required a substantial contribution, which was typically found to be unaffordable by the poor households, and therefore they were excluded from such water-sharing arrangements. Some farmers changed the cropping pattern from rice to maize resulted in an economic loss equivalent to US$2,000/acre. Even though improved seed varieties and the availability of fertilizers and pesticides led to an overall increase in rice production in the district between 2002 and 2010, this improvement was not uniform across the whole region as poorer households failed to access these inputs. Therefore, the need to promote equal access to agricultural inputs is identified in the study. Additionally, the local officials often perceive the issue of glacial lake outburst floods due to the melting of glaciers and the threat of destabilizing ice-cored dams as a policy priority in comparison to changes in precipitation levels. This allowed them to overlook the problem of gradual changes in water availability, as the effects were less visible and less severe in relation to the impact of floods. Burkina Faso Burkina Faso is a semiarid, landlocked country in western Africa. Ninety percent of its population is engaged in agriculture and livestock sectors (Traore and Owiyo 2013). The high reliance on the agricultural and livestock by a large proportion of the population in the Sahel region of Burkina Faso implied that a substantial proportion of the population is engaged in activities that are weather sensitive. Therefore, their livelihoods depend significantly on climatic conditions. Traore and Owiyo (2013) found draught to be the main climatic stressor in the region. The occurrence of draught was confirmed by rainfall data, which indicated a high variation of rainfall and also a recent history of draught in the Sahel region. Severe negative impact on crop and livestock rearing was reported by a large percentage of the sampled households. Coping measures included reducing food consumption, selling property and livestock, cutting expenditure, receiving external support, migrating, earning extra income, transhumance, and a small proportion of the sample reported resorting

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to begging. Modifying food consumption and selling property were found to be the most popular coping mechanisms. However, from the households that reported to undertake coping mechanism, 71 % indicated that they were still experiencing negative effects of the drought. The destruction caused by the onset of draughts, such as the lack of water for crop yields, led to the unavailability of water for the local people and their livestock, which further limited their future coping and adaptation ability. The range of average crop production loss was reported to be between US$577 and US$636 per household, whereas the range of average livestock loss was found to be between US$1,922 and US$8,759 per herder in the region.

Ethiopia Ethiopia is heavily dependent on rain-fed agriculture. Historically, the country is prone to extreme weather events mostly characterized by highly variable rainfall pattern. Using spatially explicit analyses of climate change effects on selected key sectors of Ethiopia’s economy, Robinson et al. (2013) found that the residual loss and damage might cost an annual average of US$0.4–3.0 billion. A case study was conducted by Haile et al. (2013) in the lowlands of Gambella, Ethiopia. The area experienced frequent river flooding that severely affected its people and their livelihoods. The main source of livelihood of the participants was crop cultivation and livestock rearing. The 2007 extreme flooding severely damaged the crops of three quarters of the respondents of the study and damaged the household properties of a quarter of the respondents. Most of the participants described the effect of the flood as either “very severe” or “disastrous” (Haile et al. 2013). However, unlike in the case of Sahel in Burkina Faso, the ability to relocate livestock ensured a better source of livelihood for the livestock owners as opposed to the farmers, most of who reported that the yield of their next cropping season severely suffered as a result of the floods. Coping mechanisms included relying on assistance from NGOs, social networks, government support, and religious organizations. NGOs and social networks provided support to the largest proportion of the affected households. Nevertheless, the erosive quality of some coping measures is highlighted, where the respondents believed that the goodwill and resources of their reliable contacts will gradually diminish, inhibiting their future coping ability. Hence, the reliance on social networks was not perceived to ensure a long-term adaptation solution. Moreover, a majority of the households who had undertaken preventive measures such as increasing the floor height, harvesting premature crops, and constructing a high stage for livestock were unable to fully evade the negative effects of the 2007 flood. Additionally, as voluntary government resettlement plans are underway, the villagers are questioning its habitability as the new villages are lacking essential services such as health services and potential security. Gambia A study by Njie et al. (2007) estimated the residual damages from climate change in Gambia to range between US$123 million and US$130 million/year in the near term. For the more distant 2070–2099 period, residual damage cost estimated to

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range from US$955 million to US$1.0 billion (Njie et al. 2007). A case study conducted by Yaffa (2013) in severely drought-prone regions of Gambia found that the varying level of rainfall and shorter duration of the rainy season along with rising temperatures implied severe calamity for its community that was mostly reliant on agriculture for their livelihoods. The prominent ill effects incurred by the community included food shortage, rise in food prices and reduction in crop production, and livestock ownership. Similar coping measures, as seen in the previous case studies, were adopted, where most of the measures were seen to aid short-term relief.

Kenya Climate change poses a serious threat to Kenya’s economy. Currently climate change accounts for an approximate monetary loss of approximately US$0.5 billion/year which is equivalent to 2 % of the country’s GDP (Stockholm Environment Institute 2009). This cost is expected to rise and eventually claim 3 % of Kenya’s GDP by 2030 (Stockholm Environment Institute 2009). A forecasted increase in rainfall in Kenya, due to climate change, along with human activities such as deforestation and overgrazing, is speculated to have increased the severity of flooding in the low-lying coastal regions of Kenya (Opondo 2013). The main sources of livelihood in the flood-prone regions are crop cultivation, livestock rearing, and other nonagricultural activities such as fishing, small-scale trade, and manual labor. It was found that more than three fourths of the farmers in the affected region reported that their livelihood had been severely affected by the flooding. Additionally, almost three fourths of all participants from all the occupational and income categories had reported that they were severely affected. The most common coping strategies included reducing food consumption and receiving help from local governments, NGOs, and religious organizations. However, most coping strategies, as in the case of the previous case studies, were found to be short-term solutions and most of such coping mechanisms implied “long term negative effects on the household economy” (Van der Geest and Dietz 2004 cited in Opondo 2013). For instance, undertaking the sale of property implied a reduced household asset base, unfavorable for a longer-term sustainable means of adaptation. Micronesia The case study examined below demonstrates a principal environmental concern of Micronesia as well as other small island states of the Pacific Ocean. Monnereau and Abraham (2013) confirmed that the rising sea level (attributed to climate change) has led to severe coastal erosion in the coastal region of Kosrae and has threatened the livelihood and habitability of many of its inhabitants. A rise in the sea level and coastal erosion is particularly dangerous to such island territories as it leads to a reduction in island size. The study revealed that the households who had adopted coping measures such as building seawalls, reinforcing their homes, and planting trees provided only temporary protection for the local inhabitants and had adverse long-term environmental effects. For instance, the building of sea walls and the

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planting of trees only provided short-term solutions and only protected small sections of coastline. This highlighted the requirement of a large-scale or even state-level investment to provide sufficient barriers for the coastline. However, no initiatives have been successfully implemented to date as previous studies had indicated that the building of sea walls was found to have caused current changes and beach loss. The majority of the participants had indicated that they suffered from the effect of coastal erosion and that the coping strategies pursued was not sufficient to counter its adverse effects.

Mozambique With a large coastline, Mozambique was found to experience severe floods in the lowlands (central), which adversely affected the livelihood of the rural farmers. In the year 2007 itself, Mozambique experienced a total economic loss and damage of $71 million from severe flooding (United Nations Office for Disaster Risk Reduction 2014). Brida et al. (2013) provided an account of the struggle of the community and the coping and adoption measures adopted and their effectiveness. The government of the country undertook resettlement projects, relocating communities to the uplands (south). However, this turned out to be as disastrous to the community as the uplands experienced frequent draughts, forcing many to go back to the lowlands and endure the negative effects of the floods. Crop cultivation, livestock rearing, and fishing were the most prominent sources of income in decreasing order of importance. Overall, a “double blow” from both the floods and the droughts was found to affect the entire sample interviewed, where the greatest ill effect was experienced by the farmers (Brida et al. 2013). As a result of food shortage, food prices increased, therefore further intensifying the adversity. The most prominent coping mechanisms included looking for other sources of income that includes laboring for the better-off households and selling property. However, as seen in previous case studies, such measures did not provide any long-term solutions. Moreover, the government resettlement initiative was found to worsen the situation for many. Nepal Frequent floods are one of the recurrent natural disasters that affect Nepal. Between 1971 and 2007, a staggering amount of 2,500 floods were recorded, which claimed more than 3,000 lives and damaged at least 150,000 buildings. The region of Udayapur in Nepal was found to be particularly susceptible to increasingly severe floods and vulnerable to the impact of climate change (Bauer 2013). The two main rivers in Udaypur reported increased rate of flooding. This was worsened by man-made obstructions such as roads and bridge piers, along with other activities such as deforestation which made the rivers shallower and accelerated sedimentation. Agriculture constituted the largest source of livelihood for many. More than 4/5 of all households reported that their agricultural output has decreased over the past years. Prevention and coping mechanisms undertaken by the farmers such as constructing stonewalls and seeking help from institutions such as NGOs was inadequate to avoid the recurrent loss and damage. Another frequent coping

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mechanism was labor migration to cities and overseas. The relatives of the migrants often relied on their remittances as an extra source of income, but often male migration was associated with increased work load for the women.

Pakistan Flooding and overflowing rivers caused substantial damage to 14 districts, particularly to the southern and northern parts of the district in 2010. It directly affected an estimated 14-20 million people, and killed over 1,700 (Kirsch et al. 2012). The floods also severely affected crops and livestock, where the crops were either partially or completely submerged and the livestock suffered from a lack of fodder availability. A total country-wide loss of US$9.7 billion was expected to have occurred in the agricultural sector (Kirsch et al. 2012). Food insecurity and malnutrition were also reported to have occurred in poorer societies. The average reported household monthly income of the affected community decreased by 50% (Kirsch et al. 2012). National response mechanisms included the use of military-affiliated rescue and aid operations, civil society relief operations included aid and establishment of social welfare infrastructure, and international donor aid and assistance was provided to affected areas (Asian Development Bank 2011). Rebuilding projects are being undertaken with the aim of constructing a flood-resilient society. However, the lack of proper pre-disaster awareness techniques prevents adequate preparation procedures. Therefore, loss and damage due to extreme flooding can almost be perceived as an unavoidable consequence. Additionally, even though civilians were requested to not reside in low-lying areas or near rivers, such a request is unfeasible as most of the rural poor reside in such vulnerable areas. Philippines According to National Disaster Reduction and Management Council (NDRRMC) (2014) 6,201 persons were killed, 28,626 were injured and 1,785 are missing over the entire Philippines in the aftermath of Typhoon Haiyan. The typhoon was the most powerful typhoon to make landfall to date which also caused significant economic damage to infrastructure and property. The damage to infrastructure and agriculture damages were estimated at US$802 million (Mori et al. 2014). Most of the residents were recorded to have taken sufficient coping and adopting mechanisms to frequent storms that hit the country. Local residents were reported to have never experienced a typhoon even remotely as brutal as Haiyan and were therefore defenseless. National and international relief efforts were mobilized post disaster, although the collapse of the local airport slowed down the process. Local inhabitants, with little or no socioeconomic assets and connections, are still known to be suffering from the adversities of the typhoon and were soon after subject to the adversities. After the onset of such a calamity, the Philippines hosted the Conference of United Nations Risk Reduction and Management in Manila to emphasize the importance of an available, accessible, and affordable disaster risk information system as part of the “Post-Haiyan Tacloban Declaration” (The United Nations Office for Disaster Risk Reduction 2014). During the 2013 Warsaw Conference, the Philippine Climate Change Commissioner, Naderev Yeb Sano, fought back tears while warning the

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international community that his country is particularly suffering as a result of climate change, reflecting on the recently acquired news of his family’s safe residence after Typhoon Haiyan (Galarraga and Roman 2013).

Conclusions Loss and damage was recognized as a separate concept from adaptation in 2008, when the AOSIS proposed a Multi-Window Mechanism to address and finance the distinct concept of loss and damage arising due to climate change impacts. This was followed by the establishment of the UNFCCC Work Program on Loss and Damage in 2010 and the Warsaw International Mechanism on Loss and Damage in 2013 to further comprehend and address the issue. The Loss and Damage in Vulnerable Countries Initiative, formed in 2012, was the largest independent entity solely dedicated to building a common understanding of loss and damage. However, despite current global efforts in understanding the concept of loss and damage, the exact definition is still as elusive as ever and is still widely contested among the stakeholders. Additional issues such as distinguishing between avoidable and unavoidable loss and damage, slow onset and extreme events, and monetary and nonmonetary loss and damage were also highlighted in this chapter. Discriminating between such categories of loss and damage from climate change adversities is essential as each category would require a different approach. For instance, in the case of avoidable and unavoidable loss and damage, it was pointed out previously that institutions and individuals must dedicate resources such that avoidable losses and damages can be successfully mitigated or adapted to and unavoidable losses and damages can be appropriately financed. Additionally, the debate regarding the constituents of loss and damage impacts from climate change makes it quite difficult to converge on a global estimate, therefore impeding a concrete commitment to tackle such an adversity. However, a global climate deal is being furnished and will be executed in 2015, where parties have agreed to adhere to a legally binding international climate change deal. Nevertheless, this agreement was only concurred by the EU, some other European nations, and Australia. Although this can be seen as a significant step forward, the lack of commitment by all developed countries still poses a great obstacle in obtaining an ideal climate change deal. The biggest limitation in forming a concrete deal to address loss and damage was found to be the attribution problem. This can be described as lack of solid traceability of adverse weather impacts to climate change, which was found to impede any solid commitments by countries. To resolve the attribution problem, many studies have devised mechanisms to examine the extent to which adverse weather impacts can be attributed to climate change. However, as of now, no globally agreed upon mechanism has been fashioned. Additionally, the degree of impact of loss and damage due to climate change on livelihoods differed substantially across developing and developed countries. On the one hand, civilians in developed countries were mostly insured against the loss and damage from natural calamities or their losses and damages were mostly compensated for, where insurance or compensation policies

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depended on country-specific requirements and regional policies. On the other hand, the poor farmers and livestock owners in vulnerable low-income countries were found to suffer substantially as a result of such climatic changes. Country-specific loss and damage estimates and coping strategies from some of the most economically vulnerable countries have been analyzed in this chapter. The degree to which such adversities affected households depended on their socioeconomic status and geographical location. The livelihood of the poorer farmers and livestock owners was generally seen to be affected the most due to their restricted mobility and limited livelihood options (after a partial/complete destruction of their farms and livestock from extreme natural calamities). Common coping measures for predictable events included modifying food consumption, selling property and livestock, cutting expenditure, receiving external support, and finding extra income sources. However, many of the coping strategies adopted by the locals were seen as temporary and some measures even eroded their long-term coping capacity. Additionally, extreme and unexpected events, such as typhoon Haiyan in the Philippines, addressed the need to identify disaster identification technologies to reduce the loss and damage from such natural calamities. Overall, the case studies of economically and geographically vulnerable countries highlighted the need to identify and implement long-term measures to mitigate loss and damage and the need for active collaboration between international organizations, NGOs, and local governments to draw up cost-effective and feasible policies to combat such residual loss and damage.

Future Directions Future directions for research include extended research work on regional- or countryspecific insurance or compensation schemes for low-income countries, such that financing options that are best suited to address the environmental and social vulnerability of the region can be devised. Additionally, it was found that one of the biggest limitations in the climate change debate was the “attribution problem.” Therefore, such a problem must be appropriately conceptualized and addressed, where better attribution techniques should be thoroughly examined and critiqued, and its applicability to the entire range of slow on set to extreme climatic conditions should be studied.

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Paleoclimate Changes and Significance of Present Global Warming Asadullah Kazi

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paleoclimate Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ice Core Records of Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changing Atmospheric Chemical Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Current Climate Change and Global Warming Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Counteracting Present-Day Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Awareness and Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adaption and Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reversal of Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Earth’s climate has been changing since the conceivable beginning of the geological history of Earth. This is reflected by paleoclimate occurrences of ice ages, followed by consequent warmer interglacial episodes. The most recent ice age has been tentatively traced back to some three million years ago. However, the onslaught of industrial revolution has greatly affected the framework of climate change. Atmospheric carbon dioxide levels are now 40 % higher than before the industrial revolution. This, in turn, has given rise to increase in temperature during the past couple of centuries. Glaciers have recently started melting, and the global average sea level has risen by more than 25 cm. Study of core records from Antarctica and Greenland disclose that paleoclimate ice cores dating back to 800,000 years revealed that the current concentrations of greenhouse gases exceeded the concentration of these gases, preserved in those A. Kazi (*) Isra University, Hyderabad, Sindh, Pakistan e-mail: [email protected]; [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_56

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ice cores. Currently, global warming has emerged as the most serious environmental threat to mankind, and unless a drastic cut is made in the emission of greenhouse gases, the world would be heading toward an unretractable disaster. Consequently, this requires a global approach for development to combat the situation. To start with, there has to be awareness and preparedness, followed by capacity building through community education and training, as well as enforcement of regulations. This approach supports strategy of adaptation to vulnerability reduction and readiness to policy-supporting development, as the future course of action.

Introduction Earth’s climate is a global challenge. It is an ever-changing long-term phenomenon, whose incidence is manifested by repeated ice ages and the corresponding warmer interglacial periods, during paleoclimate episodes, in the geological history of Earth. Incidentally, such glacial and interglacial events occur in a cyclic pattern in conformity with average global temperature. A number of ice ages have occurred throughout Earth’s history; the earliest was over two billion years ago, and the most recent one began approximately three million years ago and is followed by an interglacial stage of events. Earth was warmer than at present for most of this time interrupted by sporadic ice ages (Fig. 1). Carbon dioxide is the primary greenhouse gas resulting from human activities (such as burning fossil fuels for energy and transportation). Incidentally, human activity-derived greenhouse gases are the largest potential cause of global warming. It is reported that there is a direct relationship between the concentration of greenhouse gases, particularly carbon dioxide (CO2) in the atmosphere, and the temperature of Earth, during the past 400,000 years (NOAA 2008) (Fig. 2). It is also important to recognize that CO2, CH4, and NO2 show high concentration during interglacial times and lower concentrations during the glacial episode. The atmosphere already contains over 25 % more CO2 than it had done for the last 160,000 years (Leggett 1990). In his lecture, “Changing Planet: Past, Present, Future – Earth’s Climate back to the Future,” Schrag (2012) portrayed a history of how the Earth’s climate changed over since its birth. Table 1 shades light on sources and buildup of common greenhouse gases and their relative contribution in the atmosphere. Currently, we are in a warm interglacial episode that began about 11,000 years ago. The main question that worries us is what if the continuing buildup of greenhouse gases in the atmosphere, resulting in the warm-up of Earth, would lead to abundant melting of ice? And if so, what needs to be done to circumvent this impending devastation? NASA (2014) is preparing to launch a satellite to measure atmospheric concentrations of CO2, a greenhouse gas that contributes almost 55 % to global warming (Table 1). It is stated that CO2 levels have reached their highest concentration in the

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Fig. 1 Time history of five major ice ages of Earth’s history (Adapted from Saltzman (2002), and Eldredge and Biely (2010))

Fig. 2 Temperature change and carbon dioxide change observed, in ice core records (Adapted from NOAA (2008))

past 800,000 years or so. In the northern hemisphere, it has reached 400 parts per million by volume (ppmv), for the first time in human history. This level is 40 % up since the wide use of fossil fuels began with the industrial revolution. The World Meteorological Organization expects the global average concentration to be above 400 ppmv in 2015–2016. Rising concentration of this heat-trapping gas raises risks of more heat waves, droughts, and rising sea level. It may be mentioned that during the last 800,000 years, the level of atmospheric CO2 fluctuated between 180 and 280 ppmv and has probably not been above 400 ppmv. The UN panel of experts, on greenhouse gas, suggested that the concentration of CO2 gas would have to be kept below 450 ppmv to give a good chance of achieving less than 2  C increase in global temperature, before the end of this century.

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Table 1 The common greenhouse gases, their origins, rates of buildup in the atmosphere, and their contribution to global warming in the 1980s (Source: Leggett 1990)

Gas Carbon dioxide (CO2)

Chlorofluorocarbons (CFCs) and related gases (HFCs and HCFCs) Methane (CH4)

Nitrous oxide (N2O)

Principal sources Fossil fuel burning (77 %), deforestation (23 %) Various industrial uses: refrigerants, foamblowing solvents Rice paddies, enteric fermentation, gas leakage Biomass burning, fertilizer use, fossil fuel combustion

Current rate of annual increase and concentrationa 0.5 % (353 ppmv)

Contribution to global warming (%) 55

4 % (764 pptv)

24

0.9 % (1.72 ppmv)

15

0.8 % (310 ppbv)

6

a

ppmv parts per million by volume, ppbv parts per billion by volume, pptv parts per trillion by volume

Human activities are altering the carbon cycle, both by adding more CO2 to the atmosphere and by influencing the ability of natural sinks, like forests and oceans, which absorb and trap (capture) CO2 and keep the temperature rise within permissible limits. Slightly different values of greenhouse gas contribution to global warming are quoted by the UN Framework Convention on Climate change (UNFCCC) (1992). Nevertheless, estimated contribution of greenhouse gases to global warming, during the last 100 years, as reported by the UNFCCC is as follows: CO2

66%; CH4

23%; CFC’s

8%; N2 O

3%:

Comparing these concentrations with those in 1980s, by UNFCCC, reveals that in terms of contributions to global warming, CO2 has increased by 17 %, CH4 has decreased by 5 %, HCFCs increased by 6 %, and NO2 increased by 3 %. The increase in the magnitude of CO2 can obviously be attributed to higher consumption of fossil fuels in the energy mix; United States, China, and India, among others, are the countries that make the maximum use of fossil fuels in the energy sector of their strong economies.

Paleoclimate Perspective Carbon dioxide, as described above, is a major greenhouse gas, which contributes to Earth’s global warming leading to climate change. Paleoclimate sources of information, leading to an assessment of conditions that prevailed during the life history of Earth, are important in predicting likely climatic changes in the future (Fig. 3). Research in the study of ice cores drilled in Greenland and Antarctica provides much-needed information about variations in past temperatures and composition of

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Fig. 3 Climatic changes during glacial and interglacial episodes of the history of Earth

atmospheric gases such as CO2, NO2, and CH4, as well as many other aspects of the global environment. It is also understood that, among other things, the warming of glacial periods is essentially synchronized by gradual shift in Earth’s rotation (USEPA 2014a; Hansen and Sato 2011). However, changes in climate change are accompanied mainly by changes in CO2, NO2 and CH4, leading to changes in the temperature.

Ice Core Records of Temperature Crucially, the ice cores enclose small bubbles of air in the atmosphere, and from these, it is possible to measure the past concentration of gases. According to briefings of British Antarctic Survey (BAS), pertaining to “ice cores and climate change,” the oldest ice core records extend to 123,000 years in Greenland and 800,000 years in Antarctica. The longest ice cores lengthen to 3 km in depth. Analysis of these ice core records reveals details of Earth’s past climate and is, therefore, useful in recreating long-term records of temperature as well as other environmental counterparts that allow us to explore the past climate. It may be pointed out that in the instance of temperature, no direct measurement of temperature is available (www.climatedata.info, 2010). However, the ratio of heavy oxygen (18O) to light oxygen (16O) is helpful in elucidating climate changes, which occurred in the past. Water molecules, containing light oxygen, evaporate easily as compared to water molecules containing heavy oxygen atom. At the same time, water vapor molecules, containing the heavy oxygen, condense more easily. Furthermore, the concentration of 18O in precipitation decreases with temperature. As shown in Fig. 4, the difference in 18O concentrations in annual precipitation, compared to

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Fig. 4 Concentration of 18O decreases with temperature (Adapted from Jonzel et al. (1994))

the average annual temperature, at the present-day ice-capped locations is fairly obvious. As explained in the feature articles of NASA (paleontology: the oxygen balance), air cools by moving toward the poles, and the moisture begins to condense and falls as precipitation. Initially, the rain contains a higher ratio of water made of heavy oxygen molecules, which condense more easily than water vapor containing light oxygen. The remaining moisture in the air becomes depleted in of heavy oxygen, as the air continues to move toward colder upper latitudes. Consequently, the falling rain or snow now is made up of more water-containing light oxygen. Less heavy oxygen in the frozen water means that temperatures were cooler. According to NASA (2010), the Earth moved out of ice ages, over the past millions of years; the glacial temperature rose by a total of 4–7  C, over approximately 5,000 years. The stage of changes in temperature and CO2 across glacial–interglacial episodes in the past is consistent with the proposition that CO2 acts as an important amplifier of climate changes in the natural system (Wolff 2011). In the past century alone, the temperature in response to increase in CO2 has climbed 0.7  C, which is roughly 10 times faster than the average rate of ice age recovery warming. This leaves many questions to answers in the context of the present global warming.

Changing Atmospheric Chemical Composition Unlike temperature, the ice cores allow direct measurements of atmospheric gases, like carbon dioxide and methane. The fastest natural increase in carbon dioxide (CO2) and methane (CH4), measured in older ice cores, is 20 parts per million by

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volume, in 1000 years. This is seen as the order of magnitude during Earth’s emergence from the last ice age, around 12,000 years ago. The concentration of carbon dioxide (CO2) increased in the last two centuries by the same amount. Similarly, methane (CH4) also shows unprecedented increase in concentration over the last 200 years. Its concentration is now much more than double its preindustrial levels. Nitrous oxide (NO2) is yet another constituent which increased from a preindustrial concentration of about 265 ppb to the present-day value of 319 ppb (Forster et al. 2007). Atmospheric CO2 levels are now 40 % higher than before the industrial revolution (British Antarctic Survey 2014). The increase in the magnitude of global temperature is crucially important as it is proportional to increase in CO2. UN Secretary-General, Ban Ki-moon (2013), pointed out, “We must limit global temperature rise to 2 . We are far from there, and even that is enough to cause dire consequences. If we continue along current path, we are close to 6 increase.” At the end of most interglacial episodes, NO2 remains considerably longer on interglacial levels than methane. This is substantiated by studies on glacial–interglacial and millennial scale variations in the atmospheric NO2 concentrations during the last 800,000 years (Schilt et al. 2010). Furthermore, it is noted that increase in the nitrous (NO2) concentration starts before the onset of the warming period (Fl€uckiger et al. 2004).

Current Climate Change and Global Warming Perspective Global warming, resulting from climate change, has emerged as the most serious environmental threat, suggesting that the mankind is heading for deep trouble unless a drastic cut is made in emission of greenhouse gases into the atmosphere. In a report by the Intergovernmental Panel on Climate (IPCC), a body set by the UN General Assembly in 1988, emissions resulting from human activities are substantially increasing the atmospheric concentration of greenhouse gases. If this increase in the greenhouse gas continued at the present rates, the world average temperature will rise by one degree centigrade or thereabouts within just 30 years.

Counteracting Present-Day Climate Change From the human point of view, “if you change climate, you change everything.” It is, therefore, pertinent to build a future in which humans live in harmony with nature. Table 2 presents a summary of the potential of greenhouse gases and the effects that occur over a period of about 100 years, after a particular mass of a gas is emitted (USEPA 2014c). Some gases stay longer in the atmosphere than others. Indeed, there is no doubt that there is a climate change, and part of this change is caused by human activities (Cook et al 2013). But, is the part played by humans significant enough to bring about change in the natural glacial–interglacial cycle of episodes, prior to the existence of humans on the Earth?

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Table 2 Major long-lived greenhouse gases and their characteristics (USEPA 2014b)

Greenhouse gas Carbon dioxide

Methane

Nitrous oxide

Fluorinated gases

How it is produced Emitted primarily through the burning of fossil fuels (oil, natural gas, and coal), solid waste, and trees and wood products. Changes in land use also play a role. Deforestation and soil degradation add carbon dioxide to the atmosphere, while forest regrowth takes it out of the atmosphere Emitted during the production and transport of coal, natural gas, and oil. Methane emissions also result from livestock and agricultural practices and from the anaerobic decay of organic waste in municipal solid waste landfills Emitted during agricultural and industrial activities, as well as during combustion of fossil fuels and solid waste A group of gases that contain fluorine, including hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride, among other chemicals. These gases are emitted from a variety of industrial processes and commercial and household uses and do not occur naturally. Sometimes used as substitutes for ozone-depleting substances such as chlorofluorocarbons (CFCs)

Average lifetime in the atmosphere

100-year global warming potential 1

12 years

28

121 years

265

A few weeks to thousands of years

Varies (the highest is sulfur hexafluoride at 23,500)

Figure 5 shows the impact of climate change in terms of projected increase in temperature by the year 2100. Five types of risk factors are identified together with the likely future temperature. There is a range of scenarios and uncertainties accompanied with each risk factor. Under worst conditions, if the future temperature rises above 3  C, there will be a risk of irreversible large-scale and abrupt transition. Knowing that the present-day anthropological factors have accelerated the climate change, efforts must be to stop, or adapt or mitigate, the arrival of the advent of the natural cycle of events, which can be nothing less than disastrous. It may not be humanly possible to stop; nevertheless, efforts must be made to adapt or mitigate and continue to make developments in the fast-moving socioeconomic setup of knowledge-based society.

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Fig. 5 Risks and impact of climate change (Source: Smith et al. 2009)

For a sustainable development, holistic approach integrating climate change in policy making for resource development is presented, in a guideline entitled “Training Manual on Climate Change Adaptation and Development,” compiled by Geene et al. (2010). In 1997, the Kyoto Protocol was concluded and established legally binding obligations for developed countries to reduce their greenhouse gas emissions. Furthermore, Millennium Development Goals (MDG) of the United Nations (2014) also put emphasis on efforts to ensure environmental sustainability (goal number 7) and to develop a global partnership for development (goal number 8). Figure 5 shows the impact of climate change in terms of projected increase in temperature by the year 2100. In order to control and reverse the process of global warming, it is essential to look into adaption and development together with awareness and mitigation measures.

Awareness and Mitigation Climate change is a global challenge. This challenge will continue to affect all forms of life. Apparently, there was little awareness of this calamity prior to preindustrial period (prior to 1760 AD), but at the present time, if nothing is done to curb this menace, irreparable damage may jeopardize the entire ecosystem and the human development process. Management of greenhouse gases includes measures, which are necessary before a catastrophic situation appears. Emphasis is placed on awareness and

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Control of Climate Change

Awareness and Preparedness

Capacity Building through Community Education & Training

Regulation and Enforcement

Fig. 6 Steps to control climate change

preparedness, capacity building through community education and training, as well as enforcement of regulations (Fig. 6), meant to reduce or capture emissions. This requires a global partnership, for development to combat the situation. The principal cause of climate change as noted in Fig. 3 is attributed to three principal greenhouse houses, namely, CO2, NO2, and CH4. The former is the utmost contributor of change in temperature, which has affected the climate, both in the paleoclimate and the present scenario. The IPCC (2001, 2007) has clearly spelled out that “adaptation will be necessary to address impacts resulting from warming, which is already unavoidable due to past emissions.” This supports the idea that adaptation and development should be treated as a complimentary response strategy to awareness and mitigation.

Adaption and Development Adaption to climate change can either be planned or automatic. Plants and animals have no plan to control over environment. For them, adaptation in response to environmental changes is necessary for their survival else they will disappear. For humans, in spite of being aware of the effects of climate change, it is necessary to take measures which may not stop but at least reduce the impact of environmental changes through specific policy framework (F€ussel and Klein 2006). Adaption can, therefore, be taken as an option after mitigation. It implies reduction of impacts rather than vulnerability for development (Fig. 7). Indeed, climate change is a reality, and there is a general agreement that “we must stop and reverse this process now or face a devastating cascade of natural disasters that will change life on earth, as we know it.”

Reversal of Climate Change Climate change can be looked into paleoclimate and preindustrial perspectives. It is only in the postindustrial period that the effects of climate change were noticed. Nevertheless, there is growing and convincing evidence that climate change has

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Fig. 7 Development preceded by adaptation

been occurring throughout the geological history of Earth due to natural causes. However, the anthropogenic activities have added to causes of climate change manifested by a growing increase in temperature in the postindustrial instance. It is, therefore, pertinent to control as well as reverse the process of global warming by awareness, mitigation, and adaptation to bring about the much-needed development (Fig. 8). Reducing deforestation and encouraging reforestation can reduce emission from fossil fuels by trapping CO2 and thus play a significant role in the long run to curtail global warming (Union of Concerned Scientists 2013). But, this may not be the only solution. At the global community level, it may be useful to reverse the global warming process and follow adaptation and development together with awareness and mitigation. This is well and good, but research is being conducted to curb the release of CO2, which acts as cover to stop it from escaping into the upper layers of atmosphere and as a result raises the temperature of Earth. Today, the power sector alone represents global CO2 emissions approximating 40 %. As reported by Curry (2004), researchers in the MIT Laboratory for Energy and the Environment have been studying a global climate change mitigation technology called “carbon dioxide capture and storage,” since 1989, under the auspices of a program currently called the “Carbon Capture and Sequestration Technologies Program.” Similar research is being conducted at centers and institutes of research and development elsewhere. Basically, there are three steps involved in this venture. The first step is to trap and separate CO2 from other gases. In the second step, this gas is taken to a far-flung storage location, away from the atmosphere, and lastly, the gas is carefully stored deep inside the Earth’s crust or deep in oceans. Furthermore, there are three methods of capture, namely, post-combustion carbon capture, pre-combustion capture, and oxyfuel carbon capture (GreenFacts 2014).

Future Directions Paleoclimate sources of information, leading to an assessment of conditions that prevailed during the life history of Earth, are important in predicting likely climatic changes in the future. The Earth’s climate is constantly changing. There are a number of processes that can influence this fluctuation. Increase in CO2, among

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Fig. 8 Historic perspective of climate change and reversal approach of combating climate change

other variables, is the main ingredient of this variation. Comparison with prehistoric records reveals that the concentration of CO2 has been on the rise since postindustrial times. This has led to fears that the consequent rise in the temperature of atmosphere may be extraordinarily difficult to handle. The UN panel of experts on greenhouse gases, led by CO2, suggested that the concentration of this gas would have to be kept below 450 ppmv to give a good chance of achieving less than 2  C increase (2014) in global temperature, before the end of this century. Failure to control the rising concentration of CO2 can be nothing less than disastrous in this regard. The best way of control would be to reduce emission from the fossil fuel (oil, gas, and coal)-based power plants, industrial units, or the transport sector in this situation. Reducing deforestation and encouraging reforestation can reduce emission from fossil fuels by trapping CO2 and thus play a significant role in the long run to curtail global warming. The concern is that anthropogenic emissions of greenhouse gases may be driving average global temperature higher than previously recorded or estimated. At the global community level, it may be useful to reverse the global warming process and follow adaptation and development together with awareness and mitigation. Research is being conducted in some developed countries to find a technically feasible and economically affordable solution to capture CO2 at source and store the same at depth either in the sea or in the Earth’s crust.

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References British Antarctic Survey (2014) Ice cores and climate change. www.antarctica.ac.uk/bas-research/ science-briefings/icecorebriefing.php Climate Data Information (2010) www.Climatedata.info Cook J, Nuccitelli D, Green SA, Richardson M, Winkler B, Pointing R, Way R, Jacobs G, Skuce A (2013) Quantifying the consensus on anthropogenic global warming in the scientific literature. Environ Res Lett 8(2):024024, 7pp Curry TE (2004) Public awareness of carbon capture and storage: a survey of attitudes towards climate change mitigation: a thesis submitted to the Engineering Systems Division in partial fulfillment of the requirements for the degree of Master of Science in Technology & Policy at the Massachusetts Institute of Technology Eldredge S, Biely B (2010) Ice Ages- what are they and what causes them? Utah Geological survey. Surv Notes 42(3):2 Fl€uckiger J, Blunier T, Stauffer B, Chappellaz J, Spahni KR, Schwander J, Stocker TF, Jensen D (2004) N2O and CH4 variations during the last glacial epoch: insight into global processes. Global Biogeochem Cycles 18:GB1020 Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts R, Fahey DW, Haywood J, Lean J, Lowe DC, Myhre G, Nganga J, Prinn R, Raga G, Schulz M, Van Dorland R (2007) Changes in atmospheric constituents and in radiative forcing. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of Working Group 1 to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK/New York F€ussel H-M, Klein RJT (2006) Climate change vulnerability assessments: an evolution of conceptual thinking. Clim Change 75(3):301–329 Geene J. van, Terwisscha Van Scheltinga CTHM, Gordijn F, Jaspers AMJ, Argaw M (2010) Trainer’s manual on climate change adaptation and development: integrating climate change in policy making for sustainable development in agriculture and natural resources management. Wageningen UR GreenFacts (2014) CO2 capture and storage. www.greenfacts.org/en/co2-capture-storage/ Hansen J, Sato M (2011) Paleoclimate implications for human-made climate change. In Climate Change at the Eve of the Second Decade of the Century: Inferences from Paleoclimate and Regional Aspects: Proceedings of the Milutin Milankovitch 130th Anniversary Symposium. In: Berger A, Mesinger F, Sˇijaci D (eds). Springer, (in press) IPCC (2001) Third assessment report of the IPCC. Cambridge University Press, Cambridge IPCC (2007) Working Group II, summary for policy makers. Cambridge University Press, Cambridge Jonzel J, Koster RD, Snozzo RJ (1994) Stable Water isotope behavior during the last glacial maximum: a general circulation model analysis. J Geophys Res 99:25791–25802 Leggett J (1990) The nature of the greenhouse threat, Chapter 1, pp 14–43. In: Global warming, the greenpeace report, Introduction. Oxford University Press, Oxford, pp 1–9 NASA (2014) NASA readies satellite to measure atmosphere CO2. The Necos International, 20 June 2014 NASA Earth Observation (2010) Global Warming Goddard Space Flight Center, USA NOAA (2008) A Paleo perspective on global warming. Temperature change and carbon dioxide, National Oceanic and Atmospheric Administration, paleo@ hoaa.gov Saltzman B (2002) Dynamical paleoclimatology: generalized theory of climate change. Academic/Elsevier Science, Santiago, 354 p Schilt A, Baumgartner M, Blunier T, Schwander J, Spahni R, Fischer H, Stocker TF (2010) Glacial–interglacial and millennial-scale variations in the atmospheric nitrous oxide concentration during the last 800,000 years. Quat Sci Rev 29(1-2): 182–192 Schrag D (2012) Changing planet: past, present, future – earth’s climate back to the future, Lecture 3. Harvard University

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Smith, JB, Schneider, SH, Oppenheimer M, Yohe GW, Hare W, Mastrandrea MD, Patwardhan A, Burton I, Corfee-Morlot J, Magadza CHD, Fussel HM, Pittock AB, Rahman A, Suarez A, van Ypersele JP (2009) Assessing dangerous climate change through an update of the Intergovernmental Panel on Climate Change (IPCC) ‘reasons for concern’. In: Proceedings of the National Academy of Sciences 106(11):4133–4137. doi:10.1073/pnas.0812355106. PMC 2648893. UN Secretary General Ban Ki-moon (2013) United Nations global issues: climate change. www. un.org/en/globalissues/climatechange/. 3p UNFCCC (1992) United Nation framework convention on climate change. United Nations Publisher, New York. Union of Concerned Scientists (2013) Global warming. www.ucsusa.org/gobal_warming/solu tions/stop-deforestation/ United Nations (2014) United Nations Millennium Development Goals. Last retrieved: 01 July 2014. http://www.un.org/millenniumgoals/bkgd.shtml USEPA (2014a) Climate change indicators in the United States. United States Environment Protection Agency. EPA Headquarters, Washington, DC, 99p USEPA (2014b) A student’s guide to climate change, United States Environmental Agency. EPA Headquarters, Washington, DC USEPA (2014c) Climate change indicators in the United States. Green House Gases. United Agency, EPA’s Office of Research and Development, EPA Headquarters, Washington, DC, 4pp Wolff EW (2011) Greenhouse gases in the earth system: a paleoclimate perspective. Philos Trans R Soc A Math Phys Eng Sci 369(1943):2133–2147

Life Cycle Assessment of Greenhouse Gas Emissions L. Reijnders

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is Life Cycle Assessment and How Does It Work? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Goal and Scope Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inventory Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Cycle Assessments Focusing on Greenhouse Gas Emissions or a Part Thereof . . . . . . Simplified Life Cycle Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Published Life Cycle Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Main Findings from Life Cycle Studies of Greenhouse Gas Emissions . . . . . . . . . . . . . . . . . . . . . . . Energy Conversion Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Products Consuming Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conventional and Unconventional Fossil Fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Green Energy Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biofuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polymeric Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Crop-Based Lubricants and Solvents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recycling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nanotechnology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reduction of Life Cycle Greenhouse Gas Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Change in Carbon Stocks of Recent Biogenic Origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indirect Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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L. Reijnders (*) Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands e-mail: [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_2

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Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comprehensives of Dealing with Climate Warming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Consequential Life Cycle Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Life cycle assessments of greenhouse gas emissions have been developed for analyzing products “from cradle to grave”: from resource extraction to waste disposal. Life cycle assessment methodology has also been applied to economies, trade between countries, aspects of production, and waste management, including CO2 capture and sequestration. Life cycle assessments of greenhouse gas emissions are often part of wider environmental assessments, which also cover other environmental impacts. Such wider-ranging assessments allow for considering “trade-offs” between (reduction of) greenhouse gas emissions and other environmental impacts and co-benefits of reduced greenhouse gas emissions. Databases exist which contain estimates of current greenhouse gas emissions linked to fossil fuel use and to many current agricultural and industrial activities. However, these databases do allow for substantial uncertainties in emission estimates. Assessments of greenhouse gas emissions linked to new processes and products are subject to even greater data-linked uncertainty. Variability in outcomes of life cycle assessments of greenhouse gas emissions may furthermore originate in different choices regarding functional units, system boundaries, time horizons, and the allocation of greenhouse gas emissions to outputs in multi-output processes. Life cycle assessments may be useful in the identification of life cycle stages that are major contributors to greenhouse gas emissions and of major reduction options, in the verification of alleged climate benefits, and to establish major differences between competing products. They may also be helpful in the analysis and development of options, policies, and innovations aimed at mitigation of climate change. The main findings from available life cycle assessments of greenhouse gas emissions are summarized, offering guidance in mitigating climate change. Future directions in developing life cycle assessment and its application are indicated. These include better handling of indirect effects, of uncertainty, and of changes in carbon stock of recent biogenic origin and improved comprehensiveness in dealing with climate warming.

Introduction This handbook is about climate change mitigation. In decision-making about climate change mitigation, question marks about proper choices regularly emerge. Is going for electric cars a good thing, when power production is largely coal based? Do the extra inputs in car production invalidate the energy efficiency gains of

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hybrid cars? Should a company focus its greenhouse gas management on its own operations or on those of raw material suppliers? Is material recycling better or worse for climate change mitigation than incineration in the case of milk cartons? And what about biofuels: should their use be encouraged or not? Regarding all these questions, assessment of the life cycle emission of greenhouse gases, or more in general the environmental burden, is important for giving proper answers. Life cycle assessments may lead to anti-intuitive results. This can be illustrated by the case of liquid biofuels (Hertwich 2009). It has been argued that biofuels are “climate neutral” (e.g., Sann et al. 2006; De Gorter and Just 2010). The CO2 which emerges from burning biofuels has been recently fixed by photosynthesis, so, it has been argued, there should be no net effect of burning biofuels on the atmospheric concentration of CO2. However, if one looks at the “seed-to-wheel” life cycle of biofuels, a different picture may emerge. Consider, e.g., corn ethanol used as a transport biofuel in the USA. In the actual production thereof, there are substantial inputs of fossil fuels (Fargione et al. 2008; Searchinger et al. 2008). Corn cultivation also leads to emissions of the major greenhouse gas N2O (Crutzen et al. 2007). And corn cultivation is associated with changes in carbon stocks of agroecosystems (Searchinger et al. 2008). Considering the life cycle emissions of greenhouse gases leads to the conclusion that bioethanol from the US corn is far from “climate neutral” but is rather associated with larger greenhouse gas emissions than conventional gasoline (Searchinger et al. 2008; Reijnders and Huijbregts 2009). This has clearly implications for making good decisions about mitigating climate change linked to fuel choice (Hertwich 2009). Against this background, this chapter will consider current life cycle assessment, with a focus on the life cycle emission of greenhouse gases. First, it will be discussed what life cycle assessment is and how it is done. It will appear that such assessment may give rise to substantial uncertainty. Notwithstanding such uncertainty, life cycle assessments can be helpful in making proper choices about climate change mitigation. To illustrate this, main findings from available peerreviewed life cycle assessments of greenhouse gas emissions will be summarized.

What Is Life Cycle Assessment and How Does It Work? Life cycle assessment has been developed for analyzing current products from resource extraction to final waste disposal, or from cradle to grave. Apart from analyzing the status quo, life cycle assessments may also deal with changes in demand for, and supply of, products and with novel products. The latter type of assessment has been called consequential, as distinguished from the analysis of status quo products, which has been called attributional (Sanden and Kalstro¨m 2007; Frischknecht et al. 2009). The assessment of novel products has also occasionally been called: prospective attributional (Hospido et al. 2010; Song and Lee 2010). Different data may be needed in attributional and consequential life cycle assessment. Whereas in attributional life cycle assessment one, e.g., uses electricity data reflecting current power production, in consequential life, one needs data regarding

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changes in electricity supply. For the short term, assessing a marginal change in capacity of current electricity supply may suffice to deal with changes in electricity supply. When the longer term is at stake, major changes in energy supply, including complex sets of energy supply technologies, should be assessed (Lund et al. 2010). When novel products go beyond existing components, materials, and processes, knowledge often partly or fully relates to the research and development stage or to the limited production stage. These stages reflect immature technologies. Comparing these with products of much more mature technologies may be unfair, as maturing technologies are optimized and tend to allow for better resource efficiency and a lower environmental impact (Wernet et al. 2010; Mohr et al. 2009). Also, novel products may be subject to currently uncommon environmental improvement options and may have to operate under conditions that diverge from those that are currently common (Sanden and Kalstro¨m 2007; Frischknecht et al. 2009). The latter conditions may, e.g., include constraints on resource availability which currently do not exist, new infrastructures, budget constraints, higher resource costs which are conducive to resource efficiency, and strict caps on greenhouse gas emissions. A solution to such divergence from “business as usual” may be found in assuming technological trajectories and/or constructing scenarios which include assumptions about the environmental performance of future mature technologies under particular conditions (Frischknecht et al. 2009; Mohr et al. 2009; Jorquera et al. 2010; Spatari et al. 2010). It should be realized that the assumptions involved lead to considerable uncertainty regarding the outcomes of consequential life cycle assessments, as these assumptions may be at variance with “real life” in the future. Life cycle assessment is generally divided in four stages (Guinee 2002; Rebitzer et al. 2004): – – – –

Goal and scope definition Inventory analysis Impact assessment Interpretation

Goal and Scope Definition In the goal and scope definition stage, the aim and the subject of life cycle assessment are determined. This implies the establishment of “system boundaries” and usually the definition of a “functional unit.” A functional unit is a quantitative description of service performance of the product(s) under investigation. It may, for instance, be the production of a megawatt hour (MWh) of electricity. This allows for comparing different products having the same output: e.g., photovoltaic cells, a coal-fired power plant, a gas-fired power plant, and a wind turbine. It should be noted though that the functional unit may cover only a part of the service performance, because products may have special properties. For instance, in the case of power generation, the production of a MWh of electricity as a functional unit does not take account of the

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phenomenon that a coal-fired power plant is most suitable for base load and a gas-fired power plant for peak load. In the goal and scope definition stage, a number of questions have to be answered. For instance, the life cycle of products usually includes a transport stage. As to transport the question arises what to include into the assessment: production of the transport vehicle? road building? building storage facilities for products? Similarly, in the life cycle assessment of fishery products, questions arise such as: should one include the bycatch of fish which is currently discarded? the energy input in shipbuilding and ship maintenance? and/or the energy input in building harbor facilities? In the goal and scope definition stage, one should also consider the matter of significant indirect effects of products. A well-known example thereof is the rebound effect in the case of more energy-efficient products with lowered costs of ownership. Such products may, for instance, increase use of the product and may lead to spending of money saved by the energy-efficient product, which in turn may impact energy consumption, and associated greenhouse gas emissions (Schipper and Grubb 2000; Thiesen et al. 2008; Greene 2011). Another case in point concerns biofuels from crops that currently serve as source for food or feed. When carbohydrates or lipids from such crops are diverted to biofuel production, this diversion may give rise to additional food and/or feed production elsewhere, because demand for food and feed is highly inelastic (Searchinger et al. 2008). This, in turn, may have a substantial impact on estimated greenhouse gas emissions. Similarly, the use of waste fat for biodiesel production may have the indirect effect of reducing the amount of fat available for feed production, which in turn might lead to an increased use of virgin fat, which will impact land use and may thus change carbon stocks of recent biogenic origin. However, indirect effects of decisions about biofuels do not end with the consideration of indirect effects on land use. It may, for instance, be argued that not expanding biofuel production may increase dependency on mineral oil and that this may increase military activities to safeguard oil installations and shipping and associated emissions of greenhouse gases (De Gorter and Just 2010). Still another example of indirect effects regards wood products. These may have the indirect effect of substituting for non-wood products, and including such substitution has a significant effect on estimated greenhouse gas emissions (Sathre and O’Connor 2010). Decision-making about significant indirect effects is not straightforward. This has led some to the conclusion that including indirect effects is futile (e.g., De Gorter and Just 2010), whereas others have argued that including at least some indirect effects is conducive to good decision-making (e.g., Searchinger et al. 2008; Sathre and O’Connor 2010). System boundaries refer to what is included in life cycle assessment. In general, system boundaries are drawn between technical systems and the environment, between relevant and irrelevant processes, between significant and insignificant processes, and between technological systems. An example of the latter is, for instance, a boundary between the motorcar life cycle and the life cycle of the building in which the car is produced. The choice of system boundaries may have a substantial effect on the outcomes of life cycle assessments (also: Finnveden et al. 2009; Gandreault et al. 2010).

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Inventory Analysis The inventory analysis gathers the necessary data for all processes involved in the product life cycle. This is a difficult matter when one is very specific about a product: for instance, the apples which I bought last Saturday in my local supermarket. However, databases have been developed, such as Ecoinvent (Frischknecht et al. 2005), the Chinese National Database (Gong et al. 2008), Spine (www. globalspine.com), JEMAI (Narita et al. 2004), and the European Reference Life Cycle Data System (ELCD 2008), which give estimates about resource extraction and emissions that are common in Europe, China, the USA, and Japan for specified processes (for instance, the production and use of phosphate fertilizer). Also, there are databases which extend to economic input–output analyses and give resource extraction and emission data at a higher level of aggregation than the process level (Tukker et al. 2006). A study of De Eicker et al. (2010), which also gives a fuller survey of available databases, suggests that among available databases the Ecoinvent database is preferable for relatively demanding LCA studies. If only greenhouse gas emissions are considered, the 2006 guidelines for national greenhouse gas inventories of the IPCC (Intergovernmental Panel on Climate Change; www.ipcc.ch/) were found to be useful (De Eicker et al. 2010). Available databases do not always give the same emissions for the same functional units. For instance, according to a study of Fruergaard et al. (2009), data about the average emission of greenhouse gases linked to 1 kWh electricity production in 25 EU countries varied between databases by up to 20 %. For similar estimates in the USA, an even greater between-database uncertainty (on average 40 %) was found (Weber et al. 2010). Though such uncertainties are substantial, they should not detract from using databases such as Ecoinvent, Spine, and JEMAI, if only because between-process differences often exceed uncertainty. This may be illustrated by the geographical variability in greenhouse gas emissions linked to electricity production. For instance, country-specific average emissions of greenhouse gases per kWh of electricity in such databases vary by a factor of 160 (Fruergaard et al. 2009). For marginal emissions of greenhouse gases per kWh of electricity (which are used to assess changes in supply or demand as needed for consequential life cycle assessment), variations were even larger: up to 400–750 times (Fruergaard et al. 2009). In the inventory stage of life cycle assessments of greenhouse gas emissions, the focus is evidently on the latter emissions. In wider-ranging life cycle assessments, the inventory may comprise all extractions of resources and emissions of substances causally linked to the functional unit for each product under consideration, within the system boundaries that were established in the stage of goal and scope definition. Such wider-ranging life cycle assessments have a benefit over life cycle assessments, which only focus on greenhouse gas emissions. First, they give a better picture of the overall environmental impact, for which life cycle greenhouse gas emissions may well be a poor indicator (Huijbregts et al. 2006, 2010; Laurent et al. 2010). Also, such wider-ranging LCAs allow for considering “trade-offs”

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between environmental impacts and the occurrence of co-benefits linked to reducing greenhouse gas emissions (Nishioka et al. 2006; Haines et al. 2009; Markandya et al. 2009; Chester and Horvath 2010; Walmsley and Godbold 2010). For instance, Walmsley and Godbold (2010) concluded that stump harvesting for bioenergy may not only impact greenhouse gas emissions but may have the co-benefit of reducing fungal infections and may have negative co-impacts linked to erosion, nutrient depletion and loss, increased soil compaction, increased herbicide use, and loss of valuable habitat for a variety of (non-pest) species. Many current transport biofuels have larger life cycle greenhouse gas emissions than the fossil fuel which they replace but have the benefit that dependence on mineral oil is reduced (Reijnders and Huijbregts 2009). A large part of the impacts which go beyond climate change can be covered by standard wider-ranging LCAs. Aspects of environmental impact which are, apart from the emission of greenhouse gases, often covered by such wider-ranging life cycle assessments are summarized in Box 1. In evaluating buildings, the indoor environment may also be a matter to consider (Demou et al. 2009; Hellweg et al. 2009). New operationalizations of some of the aspects of environmental impact mentioned in Box 1 and additions to the list of Box 1 are under development. The latter include ecosystem services (Koellner and de Baan 2013) and the impacts of freshwater use (Boulay et al. 2011; Verones et al. 2013). Adding to the aspects often covered in wide ranging LCAs, a proposal has been published for the inclusion into life cycle assessment of change in albedo which is relevant to climate, characterized in terms of CO2 equivalents (Munoz et al. 2010). An estimate of the contribution inclusion of black carbon emissions to climate change has also become available (IPPC Working Group I 2013). In life cycle assessments, the problem arises that many production systems have more than one output. For instance, rapeseed processing not only leads to the output oil, which may be used for biodiesel production, but also to rapeseed cake, which may be used as feed. Similarly, mineral oil refinery processes may not only generate gasoline but also kerosene, heavy fuel oil, and bitumen, and biorefineries produce a variety of product outputs too (Brehmer et al. 2009). In the case of multi-output processes, extractions of resources and emissions have to be allocated to the different outputs. There are several ways to do so. Major ways to allocate are based on physical units (e.g., energy content or weight of outputs) or on monetary value (price). There may also be allocation on the basis of substitution. In the latter case, the environmental burden of a coproduct is established on the basis of another, similar product. Different kinds of allocation may lead to different outcomes of life cycle assessment (Reijnders and Huijbregts 2009; Finnveden et al. 2009; Fruergaard et al. 2009; Sayagh et al. 2009). The usual outcome of the inventory analysis of a wide ranging life cycle assessment is a list with all extractions of resources and emissions of substances causally linked to the functional unit for the product considered and, apart from the case of nuisance, commonly disregarding place and time of the extractions and emissions.

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Box 1: Aspects of Environmental Impact Which Are Often Considered in Wide Ranging Life Cycle Assessments

Resource depletion (abiotic, biotic) Effect of land use on ecosystems and landscape Desiccation Impact on the ozone layer Acidification Photooxidant formation Eutrophication or nitrification Human toxicity Ecotoxicity Nuisance (odor, noise) Radiation Casualties Waste heat Water footprint

Impact Assessment The next stage in life cycle assessment is impact assessment. This firstly implies a step called characterization. In this step, extractions of resources and emissions are aggregated for a number of impact categories. When only greenhouse gas emissions are considered, the aggregation aims at establishing the emission of other greenhouse gases in terms of CO2 equivalents (CO2eq), which means that the emission of greenhouse gases like N2O, CH4, and CF4 are recalculated in terms of CO2 emissions. To do so, one needs to choose a time horizon (e.g., 25 years, 100 years, 104 years), because the greenhouse gas effect of emitted greenhouse gases may be different dependent on the time horizon chosen (see Table 1). The time-dependent differences in Table 1 reflect differences in atmospheric fate of greenhouse gases. For instance, the removal of CH4 from the atmosphere is much faster than the removal of CO2 (Myrhe et al. 2013). In practice, often a time horizon of 100 years is chosen and the global warming potentials (GWP) from the corresponding column of Table 1 are commonly used in life cycle assessments. Table 1 considers only direct impacts or effects of the greenhouse gases. There are however also indirect impacts. For instance, the emission of CH4 may affect the presence of ozone, which is also a greenhouse gas. There have been proposals for including such indirect effects in global warming potentials. Using a 100-year time horizon and assuming the GWP of CO2 to be 1, Brakkee et al. (2008) proposed, for instance, a GWP for CH4 of 28 and for non-methane volatile organic compounds, a GWP of 8. The latter have a direct GWP of 0. A number of estimated examples of global warming potentials calculated with and without indirect effects are in Table 2.

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Table 1 Estimated global warming potentials (GWP) in CO2eq of CH4 and N2O for time horizons of 20 and 100 years as proposed by the Intergovernmental Panel on Climate Change (IPCC) (Myrhe et al. 2013). Apart from climate–carbon interactions, only direct effects are considered Gas/time horizon CO2 CH4 N2O

20 years 1 86 268

100 years 1 34 298

Table 2 Estimated global warming potentials (GWP) with a time horizon of 100 years relative to the GWP of CO2 for a number of gases as calculated by Brakkee et al. (2008) Gas/type of GWP CH4 CO Non-methane volatile organic compounds (NMVOC) Chlorofluorocarbon (CFC) 11 Chlorofluorocarbon (CFC) 12 Chlorofluorocarbon (CFC) 113 CF4 CO2

GWP, direct effect only; time horizon 100 years 18 0 0

GWP, including indirect effects; time horizon 100 years 28 3 8

4,800 11,000 6,200

3,300 6,100 4,700

6,100 1

6,100 1

Table 3 Global warming potentials in CO2eq for a number of gases

Gas/global warming potential CH4 Chlorofluorocarbon (CFC) 11 CF4 CO2

GWP assuming 70 % removal from atmosphere (direct effect only) (Sekiya and Omamoto 2010) 10.6 2,249

GWP as in Table 1 with a time horizon of 100 years as calculated by IPCC (Myrhe et al. 2013) 34 5,350

1,560,558 1

7,350 1

One may note that Brakkee et al. (2008) give an estimate for the GWP of CH4 (direct effect only), which is different from the value in Table 1. Still another possibility is to calculate GWPs on the basis of a similar percentage of greenhouse gas remaining in, or lost from, the atmosphere. This is exemplified by Table 3, with values as calculated by Sekiya and Okamoto (2010). In the case of life cycle assessment of greenhouse gas emissions, calculating the emission in terms of CO2eq is where the impact assessment stage often ends, though there is also the option to quantify the impact in terms of damage to public

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health (e.g., Haines et al. 2009), human health, and ecosystems (De Schryver et al. 2009) and in terms of negative effects on the economy (e.g., Stern 2006). Such damage-based characterizations facilitate weighing of trade-offs and co-benefits, when a variety of environmental impacts (cf. Box 1) are included in life cycle assessment. Having CO2eq emissions as an outcome of life cycle assessment is often sufficient to guide the selection of product life cycle options, policies, and innovations aimed at mitigation of climate change, because the emission of greenhouse gases is in a first approximation directly causally linked with environmental impact (climate change). Still, it should be noted that the temporal pattern of greenhouse emissions may affect the rate of climate change, which in turn is, e.g., a major determinant of impact on ecosystems. When the temporal pattern of the emissions is important, as, for instance, in the case of land use change or capital investments in production systems, it is possible to adapt life cycle assessment by including the estimated temporal pattern of greenhouse gas emissions linked to the object of life cycle assessment (cf. Reijnders and Huijbregts 2003; Kendall et al. 2009). Also, one may note that effect of activities on climate may go beyond the emission of greenhouse gases. For instance, agricultural activities may change albedo, evaporation, and wind speed, which may in turn affect climate (Reijnders and Huijbregts 2009). Also, the greenhouse effect of air traffic may be different than expected solely on the basis of CO2, N2O, and CH4 emissions, because air traffic triggers formation of contrails and cirrus clouds (Lee et al. 2010a). A direct causal link between emission and impact for greenhouse gas emissions may be at variance with other environmental impact categories. For instance, lead emissions which do not lead to exceeding a no-effect level for exposure of organisms will have no direct environmental impact. Also, specificity as to time and place can be very important for other impacts than climate change caused by greenhouse gases, such as the impacts of the emissions of hazardous and acidifying substances (Scho¨pp et al. 1998; Hellweg et al. 2005; Pottimg and Hauschild 2005; BassetMens et al. 2006). It may be noted, however, that in such cases time and place specificity may be introduced by adaptation of life cycle assessment or combining life cycle assessment with other tools (e.g., Hellweg et al. 2005; Huijbregts et al. 2000; Rehr et al. 2010).

Interpretation The interpretation stage connects the outcome of the impact assessment to the real world. Much of the practical usefulness of life cycle assessments of greenhouse gas emissions in this respect depends upon the uncertainty of outcomes, which has a variety of sources (e.g., Finnveden et al. 2009; Huijbregts et al. 2001, 2003; Geisler et al. 2005; De Koning et al. 2010; Williams et al. 2009). These can be categorized as uncertainties due to choices, uncertainties due to modeling, and parameter

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uncertainty (Huijbregts et al. 2001, 2003). Parameter uncertainty and uncertainty due to choice (e.g., regarding time horizon, type of allocation, system boundaries, and functional unit) would seem to be the most important types of uncertainty in the case of estimating life cycle greenhouse gas emissions. Uncertainty in the outcomes of life cycle assessments of greenhouse gas emissions partly depends on the reliability of input data (categorized as parameter uncertainty). As pointed out above, databases regarding fossil fuel use in industrialized countries such as the USA, China, and Japan and EU countries allow for substantial uncertainties in this respect (Sann et al. 2006; Fruergaard et al. 2009). Similar data regarding other countries tend to be still more uncertain. Greenhouse gas emissions linked to land use, N2O emissions, and animal husbandry are also characterized by a relatively large uncertainty (Reijnders and Huijbregts 2009; Ro¨o¨s et al. 2010). Additional variability in outcomes of life cycle assessments of greenhouse gas emissions may originate in different choices regarding system boundaries. This has, for instance, been shown by Christensen et al. (2009) and Gandreault et al. (2010), who analyzed life cycle greenhouse gas emissions of forestry products. They found that different assumptions about the boundary to the forestry industry and interactions between the forestry industry on one hand and on the other hand the energy industry and the recycled paper market might lead to substantial differences in outcomes of life cycle assessments. Choices regarding time horizons and the allocation of greenhouse gas emissions to outputs in multi-output processes may also have major consequences for such outcomes (Reijnders and Huijbregts 2009). Sensitivity analysis may be part of the interpretation stage and, for instance, consider the dependence on different assumptions regarding allocation and time horizon. Similarly, uncertainty analysis may be part of the interpretation stage. Several approaches to uncertainty analysis have been proposed, using Monte Carlo techniques (Huijbregts et al. 2003; Hertwich et al. 2000), matrix perturbation (Heijungs and Suh 2002), or Taylor series expansion (Hong et al. 2010). In practice, uncertainty analysis has been applied in a limited way. Also, in the interpretation stage, conclusions can be drawn. For instance, stages or elements of the product life cycle can be identified, which are linked to relatively high greenhouse gas emissions. These can be prioritized for emission reduction options and policies. Also, it may be established that, given a functional unit and specified assumptions, one product has lower greenhouse gas emissions (in CO2eq) than another. Examples of conclusions which can be drawn from life cycle assessments are given in section “Main Findings from Life Cycle Studies of Greenhouse Gas Emissions.” Though life cycle assessment has been developed for products, in practice the methodology has been applied more widely (cf. “Published Life Cycle Assessments”). To the extent that life cycle assessment methodology, which does not focus on products, essentially assesses parts of product life cycles (e.g., the nickel industry, waste incineration, and CO2 capture and sequestration), the usefulness of assessment may be similar to the assessment of products: one may find, prioritize, and validate emission reduction options.

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Some of the applications of life cycle assessments, which go beyond products, give rise to additional problems. For instance, applying life cycle assessments to state economies and trade may give rise to double counting of emissions (Lenzen 2008). On the other hand, e.g., expansion of life cycle assessments to trade between states may give useful insights about the actual environmental impacts of imports and exports. This is a useful addition to climate regimes such as the Kyoto protocol, which focus on greenhouse gas emissions within state borders. Also, economy-wide LCAs may help in prioritizing product categories or economic sectors for policy development (Jansen and Thollier 2006).

Life Cycle Assessments Focusing on Greenhouse Gas Emissions or a Part Thereof The emergence of climate change as a major environmental concern has led to a rapid increase in life cycle assessments focusing on the emission of greenhouse gases. However, it should be pointed out that there are also life cycle assessments which cover only a part of the greenhouse gases. In this context, one may note the growing popularity of “carbon footprinting” (e.g., De Koning et al. 2010; Barber 2009; Johnson 2008; Weber and Matthews 2008; Schmidt 2009). There is no generally agreed upon definition of carbon footprinting. In practice, the focus of carbon footprinting is often on the emission of carbonaceous greenhouse gases, if the footprinting is not being “slimlined” to covering CO2 only (e.g., Schmidt 2009). Also, there is an increasing interest in life cycle assessments focusing on the cumulative input of fossil fuels, which in turn is closely related to the life cycle emission of the major greenhouse gas CO2 (Laurent et al. 2010; Nishioka et al. 2006). The focus on carbonaceous greenhouse gases may lead to outcomes which substantially deviate from overall greenhouse gas emissions. As several authors (Crutzen et al. 2007; Reijnders and Huijbregts 2009; Laurent et al. 2010; Nishioka et al. 2006) have pointed out, cumulative energy demand may be substantially at variance with overall environmental performance and life cycle emissions of greenhouse gases, in the case of agricultural commodities and in other cases in which life cycles impact land use. The same will hold in the case of a number of compounds, such as adipic acid, caprolactam, and nitric acid, when syntheses are used which generate N2O in a poorly controlled way (Fehnann 2000; PerezRamirez et al. 2003). Also, there can be a major divergence of “carbon footprinting” from overall life cycle greenhouse gas emissions when there are substantial emissions of halogenated greenhouse gases. The latter, e.g., applies to the case of halogenated refrigerant use (Ciantar and Hadfield 2000), the use of halogenated blowing agents for the production of insulation (Johnson 2004), to primary aluminum production, which is associated with the emission of potent fluorinated greenhouse gases such as CF4 (Fehnann 2000; Weston 1996), and to circuit breakers using SF6 and magnesium foundries (Fehnann 2000; Harrison et al. 2010). In the following, only assessments will be used which give an estimate of all greenhouse gas emissions, recalculated as CO2eq emissions.

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Simplified Life Cycle Assessments Full life cycle assessments require extensive data acquisition, which tends to be laborious and time-consuming, and this may well be beyond what practice in industry and policy requires (Bala et al. 2010). This has led to the emergence of simplified tools for the life cycle assessment of greenhouse gas emissions, such as screening LCAs. These tend to focus on major causes of life cycle greenhouse gas emissions (“hotspots”) and are often useful in identifying and prioritizing emission reduction options (Andersson et al. 1998; Rehbitzer and Buxmann 2005).

Published Life Cycle Assessments A wide variety of products has been the object of life cycle assessments of greenhouse gas emissions. Examples range from teddy bears to power generators, from pesticides to motorcars, from tomato ketchup to buildings, and from a cup of coffee to tablet e-newspapers. Products have not been the only objects of life cycle assessments of greenhouse gas emissions. Life cycle assessment has also been used for state economies, trade between countries, branches of industry, industrial symbiosis, aspects of production and product technologies, networks, soil and groundwater remediation, and waste management options, including CO2 capture and sequestration.

Main Findings from Life Cycle Studies of Greenhouse Gas Emissions Though, as pointed out in section “Goal and Scope Definition,” there are substantial uncertainties in assessments of life cycle greenhouse gas emissions, some outcomes of such assessments are robust to such an extent that they provide a sufficiently firm basis for conclusions. The latter are summarized here, assuming a time horizon of 100 years, using the values for global warming potentials as given by IPCC (Myrhe et al. 2013) (see Table 1), and focusing on direct effects only, unless indicated otherwise. After this summary, options for life cycle greenhouse gas emission reduction which commonly emerge from life cycle assessments will be briefly discussed.

Energy Conversion Efficiency Improvements in efficiency of the conversion of primary energy to energy services, including reduction of heat loss, often lead to lower life cycle greenhouse gas emissions for energy services (e.g., Erlandsson et al. 1997; Citherlet et al. 2000; Nakamura and Kondo 2006; Citherlet and Defaux 2007;

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Boyd et al. 2009) when only direct effects are considered. There are some exceptions. Phase change materials, which may be used in buildings to improve energy conversion efficiency, have been shown to not significantly reduce the life cycle greenhouse gas emission of buildings in a Mediterranean climate (De Gracia et al. 2010). Electric heat pumps, though generally giving rise to lower life cycle greenhouse gas emissions for space heating, may increase life cycle greenhouse gas emissions when electricity generation is coal based (Saner et al. 2010). Also, the III/V solar cells, which contain, e.g., In (indium) and Ga (gallium) and have higher conversion efficiencies for solar energy into electricity than Si (silicium)-based photovoltaic cells, do not appear to have lower life cycle greenhouse gas emissions per kWh than multicrystalline Si solar cells (Mohr et al. 2009). Noteworthy is the potential for indirect effects linked to improvements of energy efficiency. As noted before: in the case that improvements in energy conversion lead to lower costs of ownership, there may be a rebound effect on energy use because money linked to such lower costs tends to be spend on increased use of the product or elsewhere, which in turn entails additional energy consumption and emission of greenhouse gases (Schipper and Grubb 2000; Thiesen et al. 2008; Greene 2011). Lower costs may also be conducive to economic growth (Thiesen et al. 2008). When only microeconomic effects of improved energy efficiency are considered, life cycle greenhouse gas emissions tend to be still lowered, though less so than when only the effect of energy efficiency by itself is considered (Schipper and Grubb 2000; Greene 2011). Including economy-wide rebound effects in life cycle assessments of improved energy conversion efficiency has as yet no firm empirical basis (Thiesen et al. 2008).

Products Consuming Energy Life cycle greenhouse gas emissions of products which consume energy are often dominated by emissions during the use stage of the life cycle, when shares of fossil fuels in the production and consumption stages are similar (Nakamura and Kondo 2006; Boyd et al. 2009, 2010; Finkbeiner et al. 2006; Kofoworola and Gheewala 2008; Yung et al. 2008; Cullen and Allwood 2009; Duan et al. 2009; Ortiz et al. 2010; Rossello-Batle et al. 2010). There are exceptions, however, such as, for instance, a personal computer for limited household use (Choi et al. 2006), mobile phones (Andrae and Andersen 2010), and very energy-efficient dwellings (Citherlet and Defaux 2007). The latter illustrates a more general point. To the extent that energy conversion efficiency in the use stage improves, energy embodied in the product (e.g., Kakudate et al. 2002; Blengini and di Carlo 2010) and in the case of transport also energy embodied in infrastructure (e.g., Frederici et al. 2009) often become a more important factor in life cycle greenhouse gas emissions. It may be noted, though, that there are exceptions as to the growing importance of energy embodied in the product, such as CMOS chips for personal computers and other electronics (Boyd et al. 2009, 2010).

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Transport At continental distances in the order of 0). In the maximization the Nash assumption is employed, i.e., the welfare-maximizing country supposes that its choices do not affect the behavior of the other countries, i.e., it takes X~ i to be exogenous. From the first-order conditions for the welfare maximum, we get MRSi ¼

@U i =@X ¼ c: @U i =@yi

(4)

Consequently, it is optimal for the individual country to provide climate protection up to the level where the marginal rate of substitution (left-hand side of Eq. 4)

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between public good and private good becomes equal to the unit price ratio (righthand side of Eq. 4) between public and private good (i.e., 1c). To put it plainly, when deciding about allocating its income between private goods and climate protection, a country fares best when it invests in climate protection until the benefit from spending another dollar on the climate compared to the benefits from spending another dollar on private goods is equal to the relative costs of buying the two goods. While this provision level is optimal from an individual country’s point of view, it is not optimal from a global perspective. Global welfare could be raised by deviating from the provision levels associated with condition (4). In order to illustrate this, global welfare is maximized in a next step. It seems reasonable to assume that global welfare is a function of the individual countries’ welfare levels. The global welfare level attainable from the consumption of private goods and climate protection is, however, restricted by the aggregate income that the countries can spend on private goods and climate protection. Thus the global welfare maximization problem reads max UðU 1 ðy1 , XÞ, U2 ðy2 , XÞ, . . . , Un ðyn , XÞÞ

y1 , ..., yn , X

(5)

s.t. n n X X yi þ cX ¼ Ii : i¼1

(6)

i¼1

Let us – for simplicity – assume that each individual country’s welfare has an equal weight with respect to global welfare, i.e., U ðU1 ðy1 , XÞ, U 2 ðy2 , XÞ, . . . , U n ðyn , XÞÞ ¼ U1 ðy1 , XÞ þ U 2 ðy2 , XÞ þ . . . þ U n ðyn , XÞ. Then, optimization yields the so-called Samuelson condition (see Samuelson 1954, 1955) n n X X @U i =@X MRSi ¼ ¼ c: @U i =@yi i¼1 i¼1

(7)

Therefore, in order to maximize global welfare, an individual country should provide climate protection up to a level where the sum of all countries’ marginal rates of substitution between public and private good becomes equal to the unit price ratio between public and private good. Such outcomes, where no country can improve its welfare without harming another one, are called Pareto optima. Condition (7) deviates from condition (4), since – without international coordination – an individual country would only take into account its own marginal rate of substitution between public and private goods (i.e., its own benefits from the two goods) when deciding about its climate protection efforts, while Pareto efficiency requires that countries also take into account spillovers exerted on other countries (i.e., the global benefits generated by its climate protection efforts). Therefore also the other countries’ marginal rates of substitution between the public and private good have to be included in the efficiency condition.

Some Economics of International Climate Policy Fig. 4 Prisoner’s dilemma game

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B’s strategy no participation

participation

no participation

0, −1

6, −3

participation

−4, 7

5, 6

A’s strategy

On a national level, efficient public good provision can be enforced by the government, but on the global scale there is no central coercive authority which can enforce an efficient global climate protection level. Therefore, the only option is for countries to voluntarily negotiate a climate protection agreement in order to get closer to the globally efficient protection level.

International Negotiations in Normal Form Games Such international negotiations on climate change can be comfortably depicted in a game-theoretical setting. Regularly, such negotiations are described as a prisoner’s dilemma game which captures the free-rider incentives associated with the provision of public goods. A normal form game in the shape of a prisoner’s dilemma (PD) situation with two agents or countries is presented in Fig. 4. Both considered countries have the choice between “participation” in an international climate protection agreement (or climate protection efforts) and “no participation” in international climate protection efforts. In the matrix, the numbers in front of the commas represent the payoffs for country A, while the numbers behind the commas stand for the payoffs received by country B. In the prisoner’s dilemma case of Fig. 4, the dominant strategy for each agent is to choose “no participation” in an international climate protection agreement (a dominant strategy is a strategy which always yields the highest payoff for the agent choosing this strategy, regardless of the choice of the opponents. For a more detailed discussion of these and related game theoretic concepts, see, e.g., Fudenberg and Tirole (1991)). This outcome is the so-called Nash equilibrium where no country has anything to gain by changing only its own strategy unilaterally. While this equilibrium is stable, the payoffs of countries A and B are merely 0 and
1, respectively. However, “From an economic viewpoint an ideal state of cooperation has two features: It is a Paretooptimum and it is stable” (Buchholz and Peters 2003, p. 82). The Nash equilibrium in the depicted PD situation is of course not Pareto optimal. Both agents would obtain a higher payoff if they would both participate in the international agreement.

110 Fig. 5 Chicken game

K. Pittel et al. B’s strategy no participation

participation

no participation

−6, −6

6, −3

1 –p

participation

−3, 6

3, 3

p

1– q

q

A’s strategy

Alternatively, a “Chicken” game setting can be employed in order to illustrate the negotiation situation. Lipnowski and Maital (1983) provide an analysis of voluntary provision of a pure public good in general as the game of Chicken. In fact, a Chicken game tends to describe international negotiations on the provision of the specific public good “climate protection” in a more adequate way than the prisoner’s dilemma game (see Carraro and Siniscalco 1993). The case of a Chicken game, which belongs to the group of coordination games, is depicted in Fig. 5. In contrast to the PD situation, there exists no dominant strategy. There are a couple of papers investigating the differences associated with the two, PD and Chicken, games. Ecchia and Mariotti (1998) investigate coalition formation in international environmental agreements and compare different versions of the two game types using simple three-country examples. In their paper, Rapoport and Chammah (1966) stress the difference between both games with respect to the attractiveness of retaliation decisions. Snyder (1971) examines differences in the logic and social implications of PD and Chicken games in the context of international politics. Lipman (1986) and Hauert and Doebeli (2004) analyze how the evolution of cooperation differs in the two games. Rabin (1993), R€ubbelke (2011), and Pittel and R€ubbelke (2013) investigate fairness in these settings. Pittel and R€ubbelke (2012) depict negotiations on climate change in (3  3) matrices in which they integrate both Chicken and PD settings simultaneously. Hence, in their study they allow for a broader range of choices for the involved countries. The main difference between both games, i.e., between PD and Chicken games, is that the agents in the PD situation obtain the lowest payoffs when they play unilateral “participation,” while in the Chicken game, they face the lowest payoffs if they mutually play “no participation.” This outcome is the reason why the Chicken game is said to represent international negotiations better: in case of mutual non-participation, the whole world is threatened by a global warming catastrophe. This catastrophe can be prevented in the best way by means of mutual cooperation in international climate protection. However, if the other agent refuses to cooperate, then unilateral participation in international climate protection efforts would be the best choice since this is the only remaining way to prevent the global warming

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catastrophe. Yet, if the other agent provides climate protection (and thus chooses “participation”), it would be best to choose “no participation” and thus to take a free ride. Each agent hopes that the other agent provides climate protection, such that he himself can take a free ride in climate protection. As can be observed from Fig. 5, there exist multiple Nash equilibria, which are associated with pure and mixed strategies. The Nash equilibria in connection with pure strategies prevail where the payoffs (
3,6) and (6,
3) arise. Given possible uncertainties regarding the countries’ behavior, mixed strategies become germane. Agents form probabilities about the other agent’s behavior. Country A assesses the likelihood with which country B will participate (q) or not participate (1
q) and vice versa for country B ( p and 1
p). In order to determine the mixed strategies in the Chicken game situation in Fig. 5, the likelihood q (resp. p) of participation by country B (country A) has to be calculated that makes country A (country B) indifferent between playing “participation” and “no participation.” Probability q is determined by calculating the level of q, for which the expected payoffs of both strategies of A (“participation” and “no participation”) are equal. This is the case if
3ð1
qÞ þ 3q ¼
6ð1
qÞ þ 6q:

(8)

The left-hand side represents A’s expected payoff from participation, and the righthand side reflects A’s expected payoff from defection. Analogously p can be determined from solving
3ð1
pÞ þ 3p ¼
6ð1
pÞ þ 6p

(9)

for p. In this case, the mixed-strategy equilibrium requires q ¼ p ¼ 1⁄2:

(10)

If country A or country B is uncertain whether the other country participates or defects, then it should cooperate (participate) provided it expects the antagonist to play “participation” with a probability of less than ½.

Integration of Ancillary Benefits into the Negotiations Climate policies regularly generate side effects. Afforestation and reforestation, for example, do not only mitigate CO2-induced global warming by sequestering carbon; these measures also increase the habitat for endangered species. Furthermore, forests can serve as recreational areas and reduce soil erosion. As Ojea, Nunes, and Loureiro (2010) stress, forests’ “provision of goods and services plays an important role in the overall health of the planet and is of fundamental importance to human economy and welfare.” Furthermore, Sandler and Sargent (1995, p. 160) point out that tropical

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K. Pittel et al. Climate Policy (e.g., Carbon-Tax)

GHG Abatement Measures

Climate Protection

Primary (ClimateProtection Related) Benefits

Reduction in Local Air Pollution

Ancillary Benefits

Fig. 6 Climate policy generating primary and ancillary benefits, see R€ ubbelke (2002, p. 36)

forests provide a bequest value which the current generation derives from passing on the forests to future generations. Concerning the case of Brazil, Fearnside (2001, p. 180) stresses: “The environmental and social impacts of mitigation options such as large hydropower projects, mega-plantations or nuclear energy, contrast with the “ancillary” benefits of forest maintenance.” An overview of studies assessing the co-effects of afforestation is provided by Elbakidze and McCarl (2007, p. 565). Similarly, side effects arise from the implementation of more efficient technologies, the reduction of road traffic, and the substitution of carbon-intensive fuels. Ancillary or secondary benefits induced by these CO2-emission-reducing activities accrue, for example, when the emissions of other pollutants like particulate matter are reduced simultaneously (see Fig. 6). There are a number of terms which convey the idea of ancillary or secondary benefits. The others are co-benefits and spillover benefits (see IPCC 2001). The main difference is the relative emphasis given to the climate change mitigation benefits versus the other benefits (Markandya and R€ubbelke 2004, p. 489). In fuel combustion processes, CO2 emissions are accompanied by emissions of, e.g., NOX, SO2, N2O, and others. Therefore, fuel combustion reductions do not only cause a decrease in CO2 emissions but also diminish the emissions of other pollutants. In general, positive health effects of air pollution reduction that accompany climate protection measures are assessed to represent the most important category of secondary benefits. (However, Aunan et al. (2003, p. 289) annotate that “some particulate air pollution has a cooling effect on the atmosphere, reducing it may exacerbate global warming.”) Further negative impacts of air pollution, like accelerated surface corrosion, weathering of materials, and impaired visibility are mitigated by fuel combustion reductions, too. But, road traffic mitigation does not only produce ancillary benefits by reducing the emission of air pollutants, but it is also accompanied by lower noise levels and reduced frequency of accidents, less traffic congestion, and less road surface damage.

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While primary benefits accrue globally from the prevention of climate changeinduced damages, ancillary benefits are mostly local or regional (IPCC 1996, p. 217; Pearce 1992, p. 5). They represent domestic public goods for individual countries. (However, regarding the abatement of the greenhouse gases chlorofluorocarbons (CFCs), the ancillary effect of ozone layer protection and the respective ancillary benefits can be enjoyed globally.) Local air pollution mitigation generated by climate policy, for example, can be exclusively enjoyed by the protecting country. Therefore, ancillary effects can be considered to be private to the host country of a climate policy. Consequently, they differ from climate protection-related primary benefits which exhibit global publicness. Global damages arise, e.g., in the form of droughts caused by global warming. R€ubbelke and Vögele (2011, 2013) recently analyzed the effects of such droughts on the power sector. Several studies ascertaining the level of ancillary benefits found that such benefits even represent a multiple of climate protection-related primary benefits, as Pearce (2000, p. 523) illustrates in an overview. In the next stage, ancillary benefits will be explicitly introduced into our normal form game. It will be taken into account that ancillary benefits are enjoyed (mainly) privately by the host country of the climate protection activity. Ancillary benefits arise regardless of the behavior of the antagonist. In Fig. 7, ancillary benefits (ABA, ABB) are explicitly included into the matrix of the Chicken game, where it is assumed that ABA< ABB. Analogously to the procedure concerning the Chicken game situation without ancillary benefits, the mixed strategies can be investigated here. Again, probability q is determined by identifying the level of q, where the expected payoffs of both strategies of A (“participation” and “no participation”) balance. This is the case if ð
3 þ ABA Þ ð1
qÞ þ ð3 þ ABA Þq ¼
6 ð1
qÞ þ 6q:

(11)

Analogously p can be specified ð
3 þ ABB Þ ð1
pÞ þ ð3 þ ABB Þp ¼
6 ð1
pÞ þ 6p: Fig. 7 Chicken game with ancillary benefits

(12)

B’s strategy no participation

participation

no participation

−6, −6

6, −3 + ABB

participation

−3 + ABA, 6

3 + ABA, 3 + ABB

1–q

q

A’s strategy

1–p

p

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From Eqs. 11 and 12, q and p can be derived. Scientific studies largely assess that there are especially important co-benefits of local/regional air pollution reduction in developing countries; an overview of a selection of studies investigating ancillary benefits in developing countries can be found in Appendix 1. Neglecting potential differences in the primary benefits and supposing that A represents the group of industrialized countries, while B represents the developing world, we obtain: q ¼ 1⁄2 þ ABA =6 < p ¼ 1⁄2 þ ABB =6:

(13)

If country A (resp. country B) is uncertain whether the antagonist participates or defects, then it should participate provided it expects the antagonist to play “participation” with a probability of less than 1⁄2 þ ABA =6 (resp. 1⁄2 þ ABB =6). Comparison of Eqs. 10 and 13 shows that q and p rise due to the inclusion of ancillary benefits into the analysis. Consequently, for the Chicken game example illustrated above, it is found that taking ancillary benefits into account will increase the likelihood of cooperative behavior in international negotiations on climate change. According to Eq. 13, the inclusion of ancillary benefits into the reasoning brings about especially an increase in the likelihood that developing countries will participate in international climate protection efforts (for a more general analysis of the influence of ancillary benefits in international negotiations on climate change, see Pittel and R€ ubbelke 2008). Consequently, these results confirm Halsnæs and Olhoff (2005, p. 2324) who stress that “the inclusion of local benefits in developing countries in GHG emission reduction efforts will [. . .] create stronger incentives for the countries to participate in international climate change policies.” Yet, in their analysis of qualitative and strategic implications associated with ancillary benefits, Finus and R€ubbelke (2013) find a more moderate influence of co-benefits on the participation in international climate agreements and on the success of these treaties in welfare terms. They employ a setting of noncooperative coalition formation in the context of climate change. According to their results, ancillary benefits will not raise the likelihood of an efficient global agreement on climate change to come about although ancillary benefits provide additional incentives to protect the climate. The rationale behind this result is that countries taking the private ancillary benefits to a greater extent into account will undertake more emission reduction, irrespective of an international agreement. However, if we consider the high local/regional pollution levels in developing countries, it remains at least highly disputable whether developing countries conduct efficient local/regional environmental policies. Hence, the commitment in an international climate protection agreement will most likely help to raise the efficiency in local/regional environmental protection in these countries. Consequently, ancillary benefits – although not being the major impetus for immediate action – may take the role of a catalyst to climate policy (rather than that of a direct driver). Joining international climate protection efforts may become politically more feasible for

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developing countries (like China and India) which face serious local/regional pollution problems, when ancillary benefits are included in the political reasoning.

Price Ducks: An Approach to Break the Deadlock? Due to the inefficiency of the Kyoto Protocol scheme, which is a quantity duck since it stipulates emission-reduction quantity targets, there arose an intense discussion about general alternatives to such quantity ducks (which are more than just technology-focused climate policy partnerships like the APP). Nordhaus (2006, p. 31) points out: “Unless there is a dramatic breakthrough or a new design the Protocol threatens to be seen as a monument to institutional overreach.” • Price-influencing international climate protection schemes have been proposed by Nordhaus (2006) as a proper successor of the quantity approach of the Kyoto type. “This is essentially a dynamic Pigovian pollution tax for a global public good” (Nordhaus 2006, p. 32). An international carbon tax scheme where no international emission limits are dictated is considered to have several significant advantages over the Kyoto mechanism. This scheme could also contain side payments in order to motivate countries to participate: “Additionally, poor countries might receive transfers to encourage early participation,” Nordhaus (2006, p. 32). • Such a scheme is a price duck, because via the taxes, the prices of polluting activities are increased, such that there are additional incentives to mitigate the level of such polluting activities. • In contrast to taxing polluting activities in order to protect the climate, of course, prices can be influenced by subsidizing climate-protecting activities (e.g., energyefficient appliances or carbon sequestration measures could be subsidized). The subsidy will reduce the effective price of climate-protecting activities, and hence the agents receiving the subsidy will raise their provision level of climate protection. • Recently, Altemeyer-Bartscher, R€ubbelke, and Sheshinski (2010) elaborated Nordhaus’ proposal of an international carbon tax scheme. They analyze how individual countries or regions could negotiate the design of such a tax scheme in a decentralized way. In the scheme they suggest countries offer side payments to their opponents that are conditional on the level of the environmental tax rates implemented in the transfer-receiving opponent country. As can be shown, such a side-payment scheme might yield the first-best optimal tax policy and hence an efficient global climate protection regime. The scheme does not require the coercive power of a central global authority as the individual countries implement carbon taxes voluntarily. Altemeyer-Bartscher, Markandya, and R€ubbelke (2014) investigate the effects of ancillary benefits on the outcomes of this scheme.

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• Other price-influencing schemes which work in a similar way and do not require an international coercive authority are matching schemes which were first developed by Guttman (1978, 1987). Danziger and Schnytzer (1991) provide a general formulation of Guttman’s matching idea which allows for income effects, nonidentical players, and nonsymmetric equilibria. Guttman’s matching approach has been applied to the sphere of international environmental agreements by R€ ubbelke (2006) and Boadway, Song, and Tremblay (2007, 2011). Guttman’s basic scheme consists of two stages. Each agent i’s contribution xi to the public good can be written as: x i ¼ ai þ bi

n X aj

ðj 6¼ iÞ;

(14)

j¼1

where ai is the agent’s unconditional or flat contribution to the public good (in our case “climate protection”) and bi is his matching rate, which he provides for each unit of flat public good contributions by other agents. Therefore, the agent’s n X matching contribution is bi aj ðj 6¼ iÞ. The unit costs of the goods are supposed j¼1

to be equal to unity. The budget constraint of the agent in the shape of the income restriction is: yi þ ai þ bi

n X

aj ¼ I i

ðj 6¼ iÞ:

(15)

j¼1

Ii is again the monetary income of the considered agent i. In the first stage of the game, each agent makes a decision on the level of the matching rates he wants to offer to the other agents. It could be assumed that this decision is stipulated in an international agreement on matching rates, where all negotiating agents or decision makers – as representatives of their nations – agree on the matching rates their countries will provide (see R€ubbelke 2006). All the agents’ actions in both stages of the game are guided by welfare-maximizing behavior, i.e., the agents aim to maximize their individual countries’ welfare as represented by the function in Eq. 2. In the second stage, all agents will make decisions about their flat contributions. Total public good contribution of all agents then becomes equal to: X¼

n X i¼1

ai þ bi

n X

! aj

ðj 6¼ iÞ:

(16)

j¼1

Given the matching rates of the other agents, the considered agent will contribute flat contributions to the public good up to the level where the marginal rate of

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substitution between public and private good is equal to the effective price of the public good, i.e., where MRSi ¼

1þ

1 Xn

b j¼1 j

ðj 6¼ iÞ:

(17)

The decline in the effective price, from unity to the level specified on the right-hand side of Eq. 17, induces an increase in the private provision of the public good. Comparison of the right-hand sides of Eq. 4 (for which it is assumed that c = 1) and of Eq. 17 shows that in the matching scheme the considered agent or country faces a decline in the effective price of the public good “climate protection” as long as at least one other agent provides a positive matching rate bj. As Bergstrom (1989) illustrates, there are indeed incentives to announce positive matching rates. Consequently, the matching scheme has a price-influencing effect (similar to that of a subsidy) which the quantity targets stipulated by the Kyoto Protocol do not exert. Due to the decline in the effective price, the agent tends to raise the level of his public good provision. Put differently, within the matching scheme, individual countries manipulate (via their matching commitments) the effective price of climate protection from other countries’ point of view in order to influence these opponent countries to raise their public good provision levels. And as Boadway, Song, and Tremblay (2007, p. 682) point out: “the notion that countries might attempt to influence other countries’ contributions by preemptive matching commitments is not far-fetched in light of recent examples of disaster relief or international campaigns to combat the effects of infectious diseases.” In the case of identical agents, Summing (Eq.17) up over all i generates n X i¼1

MRSi ¼ n

1 ðj 6¼ iÞ 1 þ ðn
1Þbj

(18)

Hence, a Pareto optimum is attainable if each agent would choose bi ¼ 1 . As Buchholz, Cornes, and R€ubbelke (2009) demonstrate, matching may work better if there is a large number of agents/countries (than when there is a small number of agents), which is an important result if it is taken into account that international negotiations involve many countries.

Future Directions The Kyoto Protocol has been an inefficient agreement, although its flexible mechanisms (CDM, Joint Implementation, ETS) helped to mitigate this inefficiency. Efficiency would require that the cheapest GHG abatement options are abated first, which is not generally the case under the Kyoto Protocol. Furthermore, the

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emissions of large greenhouse gas emitters in the industrialized world, e.g., Russia and the USA, are not restricted under the protocol in the second commitment period. The immense threat of global warming necessitates an improved global climate protection regime, since otherwise the world might experience dramatic and lifethreatening consequences. Among the possible negative effects are the melting of glaciers, a decline in crop yields (especially in Africa), rising sea levels, sudden shifts in regional weather patterns, and an increase in worldwide deaths from malnutrition and heat stress (Stern 2007, Chap. 3). An improved future international climate protection regime has to organize climate protection more effectively, and it has to stipulate significant GHG emission reductions for all major polluters. Developing countries like China and India belong to the group of major emitter countries. Consequently, if international climate policy is to succeed in combating global warming, developing countries will also have to commit to emission reductions under an international agreement. Since there is no global coercive authority which could enforce countries to conduct an efficient climate protection in the future, mutual voluntary negotiations are the only means by which international coordination in climate protection can be accomplished. Put differently, “international treaties have to rely on voluntary participation and must be designed in a self-enforcing way” (Eyckmans and Finus 2007, p. 74). Yet, international easy- or free-rider incentives which are due to the global public good property of climate protection make the agreement on such an international treaty a difficult task. Another way to protect the global climate, which deviates from the Kyoto concept of stipulating GHG emission-reduction quantities, is the negotiation of international price-influencing regimes. These regimes manipulate effective prices via taxes, subsidies, or matching grants in order to influence the behavior of individual countries in such a way that globally efficient climate protection levels are reached. An international carbon tax, as suggested by Nordhaus (2006), might indeed yield a more efficient outcome, but due to the lack of will in the political arena to launch such a tax, it might be more promising to base the future global climate protection architecture on the already established structures associated with the Kyoto scheme. Yet, the advantages of price ducks like matching schemes are remarkable, and international price-influencing concepts like the global carbon tax or matching schemes should not be dismissed with levity. Private ancillary benefits may take the role of a catalyst to climate policy rather than a direct driver to international climate negotiations. Joining international climate protection efforts may become politically more feasible for developing countries (like China and India) which face serious local/regional pollution problems when ancillary benefits are included in the political reasoning. Not only co-effects in terms of reduced local/regional air pollution are relevant but also co-benefits in the shape of, e.g., economic development, energy security, and employment.

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Appendix 1 See Table 1 Table 1 Ancillary benefit studies regarding developing countries Study Aunan et al. (2003)

Country China

Pollutants (local/regional) PM, SO2, TSP

Aunan et al. (2004) Aunan et al. (2007) Bussolo and O’Connor (2001) Cao (2004)

China

SO2, particles

China

NOX, TSP

India

NOX, particulates, SO2 SO2, TSP

China

Cao et al. (2008)

China

Chen et al. (2007) Cifuentes et al. (2000) Cifuentes et al. (2001)

China

Dadi et al. (2000) Dessus and O’Connor (2003) Dhakal (2003)

Chile Brazil, Chile, Mexico China

NOX, particulates, SO2

CO, PM, NOX, SO2 Ozone, particulates SO2

Model/approach Comparison of studies that comprise a bottomup study, a semi-bottom-up study, and a top-down study using a CGE model Analysis and comparison of six different CO2abating options CGE model CGE model

Technology assessment, sensitivity to discount rate Integrated modeling approach combining a top-down recursive dynamic CGE model with a bottom-up electricity sector model Comparison of partial and general equilibrium MARKAL models No economic modeling Development of scenarios that estimate the cumulative public health impacts of reducing GHG emissions Linear programming model

Chile

CO, lead, NO2, ozone, PM, SO2

CGE model

Nepal

Analysis of long-range energy system scenarios

Eskeland and Xie (1998)

Chile, Mexico

Garbaccio et al. (2000) Garg (2011)

China

CO, HC, NOX, SO2, particles, lead NOX, particulates, SO2, VOCs PM, SO2

India

PM10

Gielen and Chen (2001)

China

NOX, SO2

Technology and cost-curve assessment

CGE model Health impacts (mortality and morbidity) quantified for different socioeconomic groups in Delhi MARKAL, technology assessment, and alternative policy scenarios (continued)

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Table 1 (continued) Country China

Pollutants (local/regional) SO2, TSP

Model/approach CGE model

China

Particulates

Shanghai MARKAL model

China

SO2

Li (2006) Markandya et al. (2009)

Thailand China, India

Particulates Particles

McKinley et al. (2005)

Mexico

Mestl et al. (2005) Morgenstern et al. (2004) O’Connor et al. (2003) Peng (2000)

China

CO, HC, NOX, particulates, SO2 PM, SO2

MARKAL of energy sector; base vs. advanced technology scenarios for controlling CO2 and SO2 Dynamic recursive CGE model Use of the POLES and GAINS models as well as of a model to estimate the effect of PM2.5 on mortality on the basis of the WHO’s comparative risk assessment methodology Analysis of five pollution control options in Mexico City

China

SO2

China

NOX, SO2, TSP

China

Particulates, SO2 SO2, development benefits NOX, SO2

RAINS-Asia for local and GTAP for economy-wide effects CGE model

China

SO2

Simulation model

China

SO2, TSP

China

Particulates, SO2

Synthesis of a significant body of research on co-benefits of climate policy in China No economic modeling

Mexico

CO, HC, NOX, particulates, SO2 SO2

Study Ho and Nielsen (2007) Kan et al. (2004) Larson et al. (2003)

Rive and R€ubbelke (2010) Shrestha et al. (2007) Smith and Haigler (2008) Van Vuuren et al. (2003) Vennemo et al. (2006) Wang and Smith (1999a, b) West et al. (2004) Zheng et al. (2011)

China

Thailand

China

China

Project-by-project analysis Survey of recent banning of coal burning in small boilers in downtown area of Taiyuan CGE model

Four scenarios, use of end-use-based AsiaPacific Integrated Assessment Model (AIM/Enduse) Sample calculations regarding interventions in the household energy sector

Linear programming model

Using a panel of 29 Chinese provinces over the period 1995–2007, application of panel cointegration techniques

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Ethics and Environmental Policy David J. Rutherford and Eric Thomas Weber

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Understanding Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Terminology and Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Perceptions, Communication, and Language of Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions: The State of Climate Change Knowledge and Future Predictions . . . . . Types of Mitigation Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Uncertainties and Moral Obligations Despite Them . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ethics and Reporting About Climate Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Avoiding the Fallacy of Appealing to Ignorance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Limits of Challenges About Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Traditions and New Developments in Environmental Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sources of Value in Environmental Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Persons Who Experience Benefits and Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . New Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

This chapter offers a survey of important factors for the consideration of the moral obligations involved in confronting the challenges of climate change. The first step is to identify as carefully as possible what is known about climate change science, predictions, concerns, models, and both mitigation and adaptation efforts. While the present volume is focused primarily on the mitigation side of reactions to climate change, these mitigation efforts ought to be planned in part with reference to what options and actions are available, likely, and desirable for adaptation. Section “Understanding Climate Change,” therefore, provides an D.J. Rutherford (*) • E.T. Weber Department of Public Policy Leadership, The University of Mississippi, Oxford, MS, USA e-mail: [email protected]; [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_5

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overview of the current understanding of climate change with careful definitions of terminology and concepts along with the presentation of the increasingly strong evidence that validates growing concern about climate change and its probable consequences. Section “Uncertainties and Moral Obligations Despite Them” addresses the kinds of uncertainty at issue when it comes to climate science. The fact that there are uncertainties involved in the understanding of climate change will be shown to be consistent with there being moral obligations to address climate change, obligations that include expanding the knowledge of the subject, developing plans for a variety of possible adaptation needs, and studying further the various options for mitigation and their myriad costs. Section “Traditions and New Developments in Environmental Ethics” covers a number of moral considerations for climate change mitigation, opening with an examination of the traditional approaches to environmental ethics and then presenting three pressing areas of concern for mitigation efforts: differential levels of responsibility for action that affects the whole globe, the dangers of causing greater harm than is resolved, and the motivating force of diminishing and increasingly expensive fossil fuels that will necessitate and likely speed up innovation in energy production and consumption that will be required for human beings to survive once fossil fuels are exhausted.

Introduction Few subjects are as complex and as frequently oversimplified as climate change. After big snowfalls in winters past, news outlets have featured various observers of these local events, who dismiss the idea of global warming with statements such as “so much for the global warming theory” (LaHay 2000). On the other hand, climate scientists note that Earth’s average temperature has risen over time, and as a result, they predict increases in temperature extremes and vaporization of water that, in turn, lead to an expectation of increased snowfall in some years. Problems of understanding and misunderstanding such as these are important causes of confusion in discussions about climate change, and those problems and that confusion combined with the complexity of the issues at stake add considerable challenge to addressing the topic of focus in this chapter: the ethics of climate change mitigation. This chapter will argue that despite limitations to knowledge about the complexities of the climate system, certain efforts must be undertaken to prepare for and address the developments in climate change. The science on the subject is growing increasingly compelling, showing that there is need to work toward mitigating the causal forces that are bringing about climate change along with preparing adaptations to changes in climate, some of which have already begun (Walther et al. 2002). Furthermore, the existence of uncertainties with respect to climate science calls for more study of the subject of climate change, with greater collaboration than is already at work. Calling for further study of the subject, however, does not imply the postponement of all or any particular measure of precaution and potential action. This chapter will examine the current knowledge about climate change as well as the

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moral dimensions at issue in both seeking to minimize those changes and working to prepare for the changes and their effects. When the term “mitigation” arises in this chapter, it is important to keep in mind a consistent meaning. To mitigate something generally means to make it less harsh and less severe, but in relation to climate change, mitigation carries a more precise meaning. The term refers to human actions taken to reduce the forces that are believed responsible for the increase of the average temperature of the Earth. The primary concern with climate change is the increase of global average temperature, and mitigation is aimed at decreasing the rate of growth of this global temperature and stabilizing it or even decreasing it should it rise too high. Mitigation is sometimes referred to as abatement. Generally, the idea of abatement is either to reduce the rate of growth that is or will likely be problematic or to actually reverse the trend and reduce global average temperature. In contrast to mitigation, a second category of response to climate change is to find ways of adapting life to new conditions, the method of adaptation. Adaptation refers to adjustments made in response to changing climates that moderate harm or exploit beneficial opportunities (Intergovernmental Panel on Climate Change 2007a). The interesting issue that arises in focusing on climate change mitigation – the efforts to decrease the causal forces of rising global temperatures – is that subtle changes in temperature might be the kind to which some or even many people will be able to adapt relatively easily. For instance, if people live on coastal lands that are increasingly inundated, there are ways of reclaiming land from water or places to which people can move in adaptation to the climate changes. Other adaptations might include systems of planned agricultural crop changes prepared to avoid problems that could arise in growing food for the world’s increasing population. An important consideration about adaptation is that while humans may be able to change and adjust to changing climates, natural ecosystems and habitats may not, a point that will also be addressed in this chapter. There are certainly reasons to worry about sudden, great changes, but more gradual and less severe changes raise a host of ethical issues. For instance, it is reasonable to ask whether a farmer has the moral right to grow a certain crop. If so, then it may be that people have a responsibility to avoid changing the climate. Belief in such a right, however, could be considered highly controversial. What if farmers could reasonably expect some help in adapting the crops that they raise to new conditions? This idea would lessen the moral concern over the ability to grow a certain crop in a particular region, and thus a matter of adaptation would have bearing on the moral dimensions of climate change mitigation. It is likely that the best solution to address the ill effects of climate change will require a combination of mitigation and adaptation strategies. A central claim of this chapter, therefore, is that the ethics of climate change mitigation must not be considered in isolation from the options available for adaptation. Of the two, however, the more controversial, morally speaking, are abatement efforts or mitigation. This is because when climate conditions change, there will be no choice for people but to adapt to new circumstances if presented with serious challenges for survival, at least until humans are able to exert control in a desirable way on the trends in global climate. But abatement efforts, on the other hand, require sacrifices

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early, before certainty exists about the exact nature and extent of the problems to come and whom the problems, benefits, and mitigating efforts will most affect and how. Accompanying the problem of complexity that exists in climate change is a necessary challenge of uncertainty. The approach of addressing change through adaptive measures can be started early and is also possible as some more gradual changes occur, such as in the evacuation of islands that slowly disappear under the rising level of the sea. Other problems, however, are predicted to occur swiftly, such as in the potential disruption of the ocean conveyor, a “major threshold phenomenon” that could bring “significant climatic consequences,” such as severe droughts (Gardiner 2004, pp. 562–563). The problem of knowledge, of the limits to human abilities to identify where suffering or benefits will occur, under what form, by which mechanisms, implies that preventive adaptations may be impossible in the face of sudden changes in global climates. Furthermore, if there existed no idea of changes that might occur, this limited knowledge might render the effects of changing conditions less troubling morally speaking. But the fact is that today many scientists have devised models that suggest potential outcomes of climate change and so undercut the option of ignorant dismissal or avoidance of moral obligation. Limited knowledge about climate change first and foremost calls for increasing the knowledge and study of the subject, but it also demands consideration of the kinds of problems that can be expected, weighed against the anticipated costs of alleviating the worst of the threats. This chapter will offer a survey of a number of important factors for the consideration of the moral obligations involved in confronting the challenges of climate change. The first step is to identify as carefully as possible what is known about climate change science, predictions, concerns, models, and both mitigation and adaptation efforts. While the present volume is focused primarily on the mitigation side of reactions to climate change, these mitigation efforts ought to be planned in part with reference to what options and actions are available, likely, and desirable for adaptation. Section “Understanding Climate Change,” therefore, provides an overview of current understanding of climate change with careful definitions of terminology and concepts along with the presentation of the increasingly strong evidence that validates growing concern about climate change and its probable consequences. Next, section “Uncertainties and Moral Obligations Despite Them” will address the kinds of uncertainty at issue when it comes to climate science. The fact that there are uncertainties involved in human understanding of climate change will be shown to be consistent with there being moral obligations to address climate change. As mentioned above, these are obligations to know more than is currently known, to develop plans for a variety of possible adaptation needs, and to study further the various options for mitigation and their myriad costs. Plus, Gardiner (2004) presented a convincing case for the weighing of options that concludes in accepting the consequences of a small decrease in GNP from setting limits on global greenhouse gas emissions. Gardiner’s argument is compelling even in the face of uncertainty. After all, the uncertainties involved in climate change resemble uncertainties that motivate moral precaution in so many other spheres of human conduct. Finally,

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section “Traditions and New Developments in Environmental Ethics” covers a number of moral considerations for climate change mitigation. This section opens with an examination of the traditional approaches to environmental ethics and then presents three pressing areas of concern for mitigation efforts: differential levels of responsibility for action that affects the whole globe, the dangers of causing greater harm than is resolved (with geoengineering efforts, among others), and the motivating forces of diminishing and increasingly expensive fossil fuels that will necessitate and likely speed up innovation in energy production and consumption that will be required for human beings to survive once fossil fuels are exhausted.

Understanding Climate Change Given the complexity of addressing global climate change, it is crucial to clarify the meaning of a number of key terms, forces, and strategies for mitigation, so this first section will begin with a description of central terms and concepts at issue. The section then covers perceptions and methods for describing climate change because ideologies and affective influences on discourse about climate change can be used to mislead the public about the nature and the state of climate science. After that, the section examines the state of scientific knowledge and the predictions that the scientific community has presented about the future of climate change. This is important in order to grasp the extent of concern that world leaders and publics ought to feel about the future of the world’s climates. Finally, this section will close with a brief description of the various proposals that have been considered for mitigating climate change.

Terminology and Concepts Uncertainty, confusion, and misunderstanding result from poorly or ambiguously defined terminology and concepts, and this is especially the case with the topic of climate change. Climate change is complex and often elicits heated and impassioned public discourse. To reduce such problems, this section provides definitions for terms and concepts that are essential for both an explanation of what is known about climate change and for consideration of the broader topic of ethics and climate change mitigation. Some of these definitions are contested, and in such cases, the preferred definitions presented here will be contrasted with other definitions found in the literature, along with provision of an explanation for the selections made.

Weather and Climate The term “weather” refers to short-term atmospheric conditions occurring in a specific time and place and identified by the sum of selected defining variables that can include temperature, precipitation, humidity, cloudiness, air pressure, wind (velocity and direction), storminess, and more. Weather is measured and reported at the scale of moments, hours, days, and weeks. Climate, on the other hand, is defined

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(in a narrow sense) as the aggregate of day-to-day weather conditions that have been averaged over longer periods of time such as a month, a season, a year, decades, or thousands to millions of years. Climate is a statistical description that includes not just the average or mean values of the relevant variables but also the variability of those values and the extremes (McKnight and Hess 2000; Intergovernmental Panel on Climate Change 2007b).

The Climate System Understanding climate entails more than consideration of just the aggregated day-today weather conditions averaged over longer periods of time. Those average atmospheric conditions operate within the wider context of what is called the climate system that includes not just the atmosphere but also the hydrosphere, the cryosphere, the Earth’s land surface, and the biosphere. • The atmosphere is a mixture of gasses that lie in a relatively thin envelope that surrounds the Earth and is held in place by gravity. The atmosphere also contains suspended liquid and solid particles that “can vary considerably in type and concentration and from time to time and place to place” (Kemp 2004, p. 37). On average, 50 % of the atmospheric mass lies between sea level and 5.6 km (3.48 miles or 18,372 ft) of altitude. To highlight how thin this is, consider that the peak of Mt. McKinley in Alaska is 6.19 km (20,320 ft) above sea level and, as a result, the density of air is less than 50 % of that available at sea level or that the peak of Mt. Everest at 8.85 km (29,029 ft) has less than 32 % of the air density that is available at sea level. Commercial jet airliners generally fly at about 10.5 km (35,000 ft) above sea level, and humans would lapse into unconsciousness very quickly if cabin pressure were to decrease suddenly at this altitude (Strahler and Strahler 1978). • The hydrosphere consists of liquid surface water such as the ocean, seas, lakes, and rivers, along with groundwater, soil water, and, importantly, water vapor in the atmosphere. • The cryosphere consists of all snow, ice (glaciers and ice sheets), and frozen ground (including permafrost) that lie on and beneath the surface of the Earth. • Earth’s land surface consists of the naturally occurring rock and soil along with the structures (buildings, roads, etc.) that humans have constructed. • The biosphere consists of all living organisms, both plant and animal, on land, in fresh water, and in the ocean, including derived dead organic matter such as litter, soil organic matter, and ocean detritus. The climate system functions by means of complex interactions among these five components in which flows and fluxes of energy and matter take place through myriad processes such as radiation, convection, evaporation, transpiration, chemical exchanges, and many more (Climate Change 2007c). Given this complexity, climate science is an interdisciplinary endeavor that necessarily involves the interactions and contributions of a wide range of the physical sciences such as physics, chemistry, biology, ecology, oceanography, and the atmospheric sciences. Moreover, because

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human existence involves interactions with climate, the social sciences such as psychology, political science, and sociology also play important roles in human understanding. In addition, climate operates over time and space, so the synthesizing disciplines of history and geography have much to contribute as well. Furthermore, as shown later in this chapter, the humanities contribute to the understanding of the social dimensions of climate systems when it comes to considering the moral implications of various situations and actions in response to climate change.

Climate Change The most recent definition of climate change developed by the Intergovernmental Panel on Climate Change (IPCC) will be used in this chapter: Climate change refers to a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer (Climate Change 2007c, p. 78; see also USCCSP (United States Climate Change Science Program) 2007).

Importantly, this definition is solely descriptive and includes no reference to causation, particularly no indication of the extent to which any changes in climate result from natural or human (anthropogenic) causes. Other definitions of climate change include causation, such as the United Nations Framework Convention on Climate Change: “Climate change” means a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods (UNFCCC (United Nations Framework Convention on Climate Change) 1992, p. 3).

The first definition was selected for use in this chapter because it focuses on identifying and describing observed changes in climate and specifically refrains from assigning causation to either natural or anthropogenic processes. As a result, it draws attention to the distinction between two aspects of inquiry: (1) questions related to the presence, extent, and direction of changes in climate and (2) questions about causation of any observed changes, especially determinations of natural or anthropogenic causes. Views about (2) are often disconnected from questions about presence, extent, and direction of change and also tend to generate more contentious debate, especially in public and political discourse. As means to reduce contention, it is helpful to make the clear distinction between these two aspects of inquiry, and such clarity is especially important in this chapter, considering issues of ethics, mitigation, and adaptation. Additionally, and importantly, the selected definition implies no specific type of change(s) but instead fosters recognition that changes can occur in all manner of the variables that constitute climate such as temperature, precipitation, humidity, cloud cover, etc. (this point is further elaborated below with respect to the terms “climate change” and “global warming”). An additional reason to clarify the difference between (1) and (2) is that consideration of (1) generally engenders less controversy, while the task of determining

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who should act in addressing any needs that arise from climate change will depend in part on how one addresses issue (2). As such, (2) is not to be ignored in addressing the ethics of climate change, but after untangling (1) from (2), the problems to be addressed can be recognized for what they are more easily.

Climate Variability Most definitions of climate variability found in the literature differ little from the above definitions of climate change. For example, as defined in the Synthesis Report for the IPCC Fourth Assessment (Climate Change 2007c, pp. 78–79), the two terms actually seem synonymous in that they both refer to changes occurring on timescales of multiple decades or longer and they both allow for natural and anthropogenic causes. Other definitions of climate variability retain the focus on timescales of multiple decades or longer but limit climate variability to only natural causes (Batterbee and Binney 2008; Climate Research Program 2010). In this chapter, however, the term will refer to something different from either of these uses. The term “climate variability” is used in this chapter in recognition that the longterm, statistical averages of the variables that define climates can contain substantial variation around the mean. Droughts, rainy periods, El Niño events, etc., occur in time periods of a year to as much as three decades within climates that are considered to be stable as well as within climates that are experiencing changes in the longer term. This variability is different from extreme weather events such as floods and heat waves that occur on timescales of hours, days, and weeks, and it is also different from the long-term climate changes that occur on scales that span multiple decades to millions of years (which have already been defined above as “climate change”). The reasons to differentiate climate variability from climate change in this way are twofold. First, climate variability can generate considerable “noise” in the data that can lead to erroneous conclusions about climate change. For example, Fig. 1 shows two levels of variability – interannual and multi-decadal – that are present in the observed global temperature record that extends from 1880 to 2009. Interannual Global land-ocean temperature index .6 Temperature anomaly (°c)

Fig. 1 A line plot of the global land-ocean temperature index from 1880 to 2009, with the base period 1951–1980. The dotted black line is the annual mean and the solid black line is the 5-year mean. The gray bars show uncertainty estimates (GISS (Goddard Institute for Space Studies) 2010a)

Annual mean 5-year mean

.4 .2 .0 −.2 −.4 1880

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1920

1940

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variability (variability from year to year) is as much as 0.3  C (0.54  F), a range that could be expressed as 1 year with a very hot summer and a mild winter followed by a second year with a mild summer and a very cold winter. The conditions present in either of these years could lead people to make poor judgments about climate. In particular, the long-term warming trend that the graph shows occurring across the full 119-year period is sometimes dismissed because people generally give greater weight in decision making and opinion formation to immediate affective sensory input over cognitive consideration of statistics (Weber 2010) (more will be said below about human decision making that is affect based compared to a basis on statistical description). The variability over several decades is exhibited in Fig. 1 for the time period 1940–1980, which shows a plateau within the longer-term, 119-year warming trend. During this shorter time period, media reports and even a few researchers erroneously forecast “global cooling” based on the observational record at the time that included inadequate and uncertain data from years earlier than this time period and, obviously, no data beyond 1980 (de Blij 2005, p. 85). The second important reason for distinguishing between climate variability and climate change in the way defined in this chapter is related to dynamic equilibrium in ecosystems. Dynamic equilibrium results as ecosystems adapt to dynamic, ongoing forces that are not so extreme as to produce catastrophic changes. This dynamic equilibrium occurs because the change forces are not dramatic enough (or they cancel each other out), so that relative stability in the ecosystem can be perpetuated as the organisms (plants and animals) and the physical environment respond with adjustments that are within their adaptive capacities. In general, ecosystem adaptive capacity is not exceeded (and dynamic equilibrium is maintained) as a result of climate variability as defined here, but climate change, on the other hand, often exceeds this capacity and leads to fundamental alterations of the ecosystems. Such fundamental alterations occurring in natural ecosystems include processes such as species extinction, changes in community compositions, changes in ecological interactions, changes in geographical distributions, etc. Fundamental alterations can also occur within ecosystems upon which humans depend, leading to such changes as increases/decreases in agricultural productivity and the availability of water, changes in storm patterns, etc. (Intergovernmental Panel on Climate Change 2007a). These effects on both natural and human ecosystems will be discussed in more detail in what follows, but the important point here is that climate variability rarely produces such fundamental alterations, whereas climate change frequently can.

Global Warming and Global Average Temperature Global warming is defined as an increase in the average temperature of Earth’s surface NASA (National Aeronautics and Space Administration) 2007. As Fig. 1 illustrates, this average surface temperature has increased by 0.75  C  0.3  C (1.35  F  0.54  F) between 1880 and 2009. While this change might seem small, the paleoclimate record demonstrates that even “mild heating can have dramatic consequences” such as advancing or retreating glaciers, sea level changes, and changes in precipitation patterns that can all force considerable changes in human activity and

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push natural ecosystems beyond dynamic equilibrium (Hansen 2009). The graph in Fig. 1 comes from NASA’s Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP) database which contains temperature observations from land and sea from 1880 to the present (GISS (Goddard Institute for Space Studies) 2010b). It is one of the three such large databases of Earth surface atmospheric observations that all begin in the mid- to late nineteenth century and extend to the present. The National Oceanic and Atmospheric Administration (NOAA) maintains the second database that is titled the Global Historical Climatology Network (GHCN), and while this database contains observations from land stations only, it includes precipitation and air pressure data as well as temperature (National Climatic Data Center 2008). The third database is abbreviated HadCRUT3 which reflects the source of the dataset being a collaborative project of the Met Office Hadley Center of the UK National Weather Service (“Had”) and the Climate Research Unit (“CRU”) at the University of East Anglia. The Hadley Center provides marine surface temperature data, and the Climate Research Unit provides the land surface temperature data. These three databases are not completely independent because they share some of the same observation stations, but nevertheless, some differences in the raw data exist, and the three centers work independently using different approaches to the compilation and analysis done on the datasets. As such, the comparisons of results from the different databases allow for verification. Considerable consistency is apparent across the databases, especially in the overall trend of global warming since 1880. The different centers “work independently and use different methods in the way they collect and process data to calculate the global average temperature. Despite this, the results of each are similar from month to month and year to year, and there is definite agreement on temperature trends from decade to decade. Most importantly, they all agree that global average temperature has increased over the past century and this warming has been particularly rapid since the 1970s” (Stott 2011). Figure 2 shows the temperature record for each of the three datasets superimposed upon one another, and the consistency among them is clear. In addition, research has been done to identify and quantify uncertainty in the data, and good estimates of the uncertainty indicate that the data are valid. As one such study stated: Since the mid twentieth century, the uncertainties in global and hemispheric mean temperatures are small, and the temperature increase greatly exceeds its uncertainty. In earlier periods the uncertainties are larger, but the temperature increase over the twentieth century is still significantly larger than its uncertainty (Brohan et al. 2006, p. 1).

The temperature records shown in Fig. 2 for each of the three centers are developed as each center uses its dataset to calculate a “global average temperature,” both for the past and for monthly updates, and it is these values that are displayed on the graphs in the figure. While these calculations are done differently at the three centers, all three use the following general procedure. First, they expend considerable efforts to obtain the most accurate data possible and define the uncertainty that remains in those data. Then, the monthly average temperature value for each

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Anomoly (°C) relative to 1961– 1990

0.6 HadCRUT3 NCDC GISS

0.4 0.2 0 −0.2 −0.4 −0.6 −0.8 1850

1900

1950

2000

Fig. 2 Correlation between the three global average temperature records. All three datasets show clear correlation and a marked warming trend, particularly over the past three decades. The HadCRUT3 graph shows uncertainty bands which tighten up considerably after 1945 (WMO (World Meteorological Organization) 2010)

reporting station is converted into what is called an “anomaly.” The anomaly of each reporting station is calculated by subtracting the monthly average value from the average value that the station has maintained over some relatively long-term “base period” (e.g., the HadCRUT3 uses the period 1961–1990 as its base period). The reason for using anomalies is stated as follows: For example, if the 1961–1990 average September temperature for Edinburgh in Scotland is 12  C and the recorded average temperature for that month in 2009 is 13  C, the difference of 1  C is the anomaly and this would be used in the calculation of the global average (Stott 2011).

One of the main reasons for using anomalies is that they remain fairly constant over large areas. So, for example, an anomaly in Edinburgh is likely to be the same as the anomaly further north in Fort William and at the top of Ben Nevis, the UK’s highest mountain. This is even though there may be large differences in absolute temperature at each of these locations. The anomaly method also helps to avoid biases. For example, if actual temperatures were used and information from an Arctic observation station was missing for that month, it would mean the global temperature record would seem warmer. Using anomalies means missing data such as this will not bias the temperature record (Stott 2011; see National Climatic Data Center 2010a for additional explanation of the calculation and use of anomalies as used for the National Climate Data Center’s GHCN system). Even though using anomalies produces the most accurate record of Earth’s global average temperature, it is still interesting to calculate one single absolute “global

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average temperature.” Using the GHCN dataset (National Climatic Data Center 2010b), the average value for the last 10 years, the warmest decade on record (GISS (Goddard Institute for Space Studies) 2010a; Atmospheric Administration 2009; WMO (World Meteorological Organization) 2009), produces a global average temperature for planet Earth of 14.4  C or 58  F.

Climate Forcing and Climate Feedback Climate forcing refers to the processes that produce changes in the climate. The word force is generally defined as “strength or energy that is exerted or brought to bear [and that often] causes motion or change” (Merriam-Webster 2003). With respect to Earth’s climate system, a variety of forces cause climates to change. These are called “climate forcings,” and they are all related to Earth’s “energy balance,” that is, the balance between incoming energy from the Sun and outgoing energy from the Earth. The forcings can be internal or external. “Internal forcings” occur within the climate system and include processes such as changes in atmospheric composition or changes in ice cover that cause different rates of absorption/reflection of solar radiation. “External forcings” originate from outside the climate system and include processes such as changes in Earth’s orbit around the Sun and volcanic eruptions. Forcings can be naturally occurring, such as those resulting from solar activity or volcanic eruptions, or anthropogenic in origin, for example, the emission of greenhouse gases or deforestation (Intergovernmental Panel on Climate Change 2007a, p. 9). A feedback is defined as a change that occurs within the climate system in response to a forcing mechanism. A feedback is called “positive” when it augments or intensifies the effects of the forcing mechanism or “negative” when it diminishes or reduces the effects caused by that original forcing mechanism (Intergovernmental Panel on Climate Change 2007a, p. 875). Forcing and feedback mechanisms often interact in complex ways that make it difficult to decipher the processes and dynamics of climate change. This difficulty also frequently frustrates policymakers, the media, and the public, and it can result in the dissemination of misinformation, both intentional and unintentional, into the public discourse. One example of this relates to the relationship between carbon dioxide (CO2) and temperature. While it is relatively easy to understand that increasing concentrations of atmospheric CO2 can increase the naturally occurring greenhouse effect thereby causing global warming, confusion and misinformation result when research brings to light a climate record in which changes in the atmospheric CO2 level lag behind changes in temperature by 800–1,000 years. The legitimate question arises as to how it could be possible that CO2 causes global warming if the rise in temperature occurs before the increase in the atmospheric concentration of CO2. While the question is legitimate, unfortunately, some who are disposed to doubt claims of global warming neither seek answers to the question nor pursue additional investigation. Instead, they simply assert the premise that because CO2 lags temperature, it cannot possibly be the cause of global warming. However, a more objective review of the scientific literature emphasizes the importance of distinguishing between forcings and feedbacks.

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The initial, external forcing that begins the temperature changes observed in the climate record stems from fluctuations in the orbital relations between the Sun and Earth, and these fluctuations produce rather small changes in the amount of solar radiation reaching Earth (Hays et al. 1976). This relatively weak forcing action causes small temperature changes that are then amplified by other processes (Lorius et al. 1990). One such amplifying process that appears to be quite significant occurs because ocean temperature changes also change the ocean’s capacity to retain soluble CO2. As this capacity changes, it causes CO2 to either be released from the oceans into the atmosphere (during times of warming temperatures) or removed from the atmosphere and dissolved into the oceans (during times of cooling temperatures). Consequently, CO2 operates in these situations as a positive feedback mechanism that augments the temperature change. In other words, it enhances the greenhouse effect and amplifies temperature increases during times of warming and reduces the greenhouse effect and reinforces temperature decreases during times of cooling (Martin et al. 2005). Careful analysis therefore suggests that a climate record which shows CO2 operating as a feedback mechanism neither negates nor renders less likely the potential that CO2 could operate as an initial forcing mechanism as well. Considering that the atmospheric concentration of CO2 has increased by 25 % in the last 50 years (Atmospheric Administration 2010), it is entirely possible that this increasing CO2 concentration is functioning as the forcing agent for contemporary global warming. Simply put, it is a false premise to claim that CO2 could not be causing contemporary global warming because CO2 has been observed to lag behind temperature changes in the past. This false premise has been lampooned by the analogous statement that “Chickens do not lay eggs, because they have been observed to hatch from them” (Bruno 2009).

Global Warming Versus Climate Change The terms “global warming” and “climate change” have been defined above, and those definitions will not be repeated here. But it is important to emphasize the difference between the two terms and the significance of exercising precision in use of them. While “global warming” is a useful way to refer to the increase of global average temperature that strong scientific evidence shows has occurred over the last 130 years (Fig. 2), for some people, the term carries the automatic connotation that human activity is the cause of this observed temperature increase. As stated earlier, a clear distinction should be made between questions that, on the one hand, relate to the changes in climate, if any, that are occurring and, on the other hand, the causes of any identified changes, specifically, naturally occurring or anthropogenic. Because the term “global warming” carries the more polemical and politicized connotation, it poses a higher probability of conflating the two questions than does the term “climate change” which has not yet attracted such politicized interpretations. Consequently, in general, the term “climate change” is preferable. A second deficiency with the term “global warming” is the one-dimensional and totalizing change that it implies. Although the average temperature of planet Earth is increasing, the temperature change that any particular place on the Earth might

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experience could be cooling instead of warming, or perhaps that place might be experiencing no change in temperature at all. But the term “global warming” is easily, and perhaps most naturally, understood to mean that all places on the Earth will experience warming. Moreover, even if the term is explained, it does not readily lend itself to the broader understanding that although the global average temperature is increasing, it is not necessarily the case that temperature is increasing at any given place on Earth. The term “climate change,” on the other hand, does not imply this uniform nature of change and thus possesses greater capacity to communicate the potential for different changes occurring in different places and regions. In addition, the term “global warming” implies a narrow view of the nature of changes that can occur in the climate system, namely, an exclusive focus on temperature. But the possible changes to climate are not restricted to just the climate variable of temperature, and the observed increase in global average temperature has been associated with changes in a range of other climate variables that include precipitation amounts, timing and patterns, cloudiness, humidity, wind direction and velocity, storminess, and more. While the term “global warming” places the focus on temperature, the term “climate change” offers a much richer capacity to incorporate these other types of changes as well and, as a result, is generally emerging as the preferred term.

Thresholds and Tipping Points The term “threshold” in ecology and environmental science means “a fixed value at which an abrupt change in the behavior of a system is observed” (Park 2008, p. 450). In climate science, the term “climate threshold” means the point at which some forcing of the climate system “triggers a significant climatic or environmental event which is considered unalterable, or recoverable only on very long time-scales, such as widespread bleaching of corals or a collapse of oceanic circulation systems” (Intergovernmental Panel on Climate Change 2007a, p. 872). Substantial research indicates that climate changes are prone to such thresholds, or “tipping points,” at which climate on a global scale or climates at regional scales can suddenly experience major change (Committee on Abrupt Climate Change 2002; Lenton et al. 2008). A wide number of complex systems exhibit similar threshold events – financial markets, ecosystems, and even epileptic seizures and asthma attacks – in which the system seems stable right up until the time when the sudden change occurs (Scheffer et al. 2009). Research has provided general ideas on where these thresholds or tipping points might operate with respect to climate – the loss of Arctic sea ice or Antarctic ice shelves, the release of methane into the atmosphere from the melting of Siberian permafrost, or the disruption of the “oceanic conveyor belt” – but this knowledge is rudimentary at best. Scheffer and colleagues (2009) report tentative efforts to identify “early warning signs” that precede threshold events, and with respect to climate, they state that “flickering,” “rapid alterations,” or increased weather and climate “variability” seem to have preceded sudden changes observed in the climate record. But at present, predicting these climatic thresholds is vague at best. One of the authors explained the idea of thresholds and the uncertainty about them in an interview with Time magazine, “Managing the environment is like

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driving [on] a foggy road at night by a cliff.. . .You know it’s there, but you don’t know where exactly” (Walsh 2009).

Defining and Communicating Uncertainty Clearly, climate science contains uncertainties that are endemic to the data sources used, to the understanding of processes involved, and to predictions of future trends, impacts, and outcomes. Consequently, it is essential to accompany any study of climate change with careful, explicit, and candid assessments of the levels of certainty or confidence associated with the findings or claims made. Indeed, reports or studies are suspect if they fail to include such information and/or if they make unequivocal statements about “proving” their points. To some extent, the same can be said about commentaries, news reports, or various information sources. While the politicized environment in which climate change is debated might encourage strong and definite affirmations, such statements can prove counterproductive if they are perceived or exposed as exaggerated (Weber 2010; Hodder and Martin 2009). Numerous approaches exist for defining and communicating uncertainty, and this brief discussion here does not attempt a comprehensive overview. Instead, it focuses on the approach that the IPCC has developed for its assessment reports. The main function of the IPCC is to “assess the state of our understanding and to judge the confidence with which we can make projections of climate change, its impacts, and costs and efficacy of options,” but in its first and second assessments (1990 and 1995, respectively), the IPCC gave inadequate attention to “systematizing the process of reaching collective judgments about uncertainties and levels of confidence or standardizing the terms used to convey uncertainties and levels of confidence to the decision-maker audience” (Moss 2006, p. 5 emphasis added). Consequently, the IPCC conducted a comprehensive project to rectify these inadequacies (Moss and Schneider 2000; Manning et al. 2004), and the result was the following system for defining and communicating uncertainties in the Fourth Assessment Report published in 2007. The first step is to present a general summary of the state of knowledge related to the topic being presented. This summary should include (1) the amount of evidence available in support of the findings and (2) the degree of consensus among experts on the interpretation of the evidence (Climate Change 2005). Figure 3 illustrates how these two factors form interacting continua that produce qualitative categories. The IPCC guidance notes for addressing uncertainty (Climate Change 2005, p. 3 emphasis in original) state that in cases where the level of knowledge is determined to be “high agreement, much evidence, or where otherwise appropriate,” additional information about uncertainty should be provided through specification of a level of confidence scale and a likelihood scale. The level of confidence scale addresses the degree of certainty that the results are correct, while the likelihood scale specifies a probability that the occurrence or outcome is taking place or will take place. The IPCC guidelines state that the level of confidence scale “can be used to characterize uncertainty that is based on expert judgment as to the correctness of a model, an analysis or a statement. The last two terms in the scale should be reserved for areas of major concern that need to be considered from a risk or opportunity perspective, and

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Established but Incomplete High agreement / Limited Evidence

Speculative Low agreement / Limited evidence

Well Established High agreement / Much evidence

Competing Explanations Low agreement / Much evidence

Increasing amounts of evidence (theory, observations, models)

Fig. 3 Conceptual framework for assessing the current level of understanding (Moss 2006; Climate Change 2005)

the reason for their use should be carefully explained” (Climate Change 2005, p. 4). Table 1 shows the scale. The likelihood scale is used to refer to “a probabilistic assessment of some well defined outcome having occurred or occurring in the future” (Climate Change 2005, p. 4).

Adaptation and Mitigation The terms “adaptation” and “mitigation” were briefly discussed in the introduction of this chapter, but the more detailed definition and explanation in Table 2 outline important distinctions that will be helpful for the sections of the chapter that follow.

Perceptions, Communication, and Language of Climate Change Moser (Moser 2010, p. 33) writes that “a number of challenging traits make climate change a tough issue to engage with,” and she implies that something in the nature of climate change itself makes it more challenging for people to perceive and communicate about than many other, even related issues (environmental, hazards, health). She lists the following characteristics of climate change that produce this substantial challenge: • Invisible causes: Greenhouse gasses are not visible and have no direct or immediate health implications. The same is true for other forcing agents such as Earth/ Sun relations. • Distant impacts: The lack of immediacy in temporal and geographic distance. • Insulation of modern humans from their environment: This diminishes the perception of any changes in the climate or their significance. • Delayed or absent gratification for taking action: Action taken today is not likely to reduce global average temperature within the lifetime of the person taking the action. • The lack of recognition that humans have of their technological power: This produces disbelief that humans have the capacity to alter the global climate.

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Table 1 Scales of uncertainty used in the IPCC Fourth Assessment Report, 2007. None of these are statistically significant because no tests are conducted to determine the values. Instead, they are based on expert judgment Qualitatively calibrated levels of confidence (Climate Change2005) Terminology Degree of confidence in being correct Very high confidence At least 9 out of 10 chances of being correct High confidence About 8 out of 10 chances of being correct Medium confidence About 5 out of 10 chances of being correct Low confidence About 2 out of 10 chances of being correct Very low confidence Less than 1 out of 10 chances of being correct Likelihood scale (Intergovernmental Panel on Climate Change2007b) Terminology Likelihood of the occurrence or outcome (%) Virtually certain >99 Extremely likely >95 Very likely >90 Likely >66 More likely than not >50 About as likely as not 33–66 Unlikely 20 ktCO2/year (any year of 2011–2014) New regulation: Industry >10 ktCO2/year, mandatory reporting when >5 ktCO2/year, Non industrial sectors: with > 5 ktCO2/year Transport: threshold TBD

New entrants reserve (20 Mt). New project (including capacity extension or

metals, glass and paper)

commercial) construction.

In case of closure or displacement of activity, compliance obligation is

and paper, rubber, chem. fiber); other sectors (aviat., ports, rail., comm.,etc.) 191 companies Threshold: 20 ktCO2/year (any year of 2010 or 2011) for industrial companies; 10 ktCO2e/ year for other sectors. Mandatory emissions reporting for about 600 firms. Threshold: 10 ktCO2/year

Reserve (2 % of total cap). New fixedasset projects with over ¥ 200 million

832 companies Threshold: 5 ktCO2e/ year 197 large buildings. Threshold: 20,000 m2 for public buildings and 10,000 m2 for state office buildings. Mandatory reporting. Threshold: emissions btw. 3-5 ktCO2e/year +

under consideration.

(continued)

114 entities Threshold: 20 ktCO2/ year (any year since 2009) Mandatory reporting for carbon intensive industries and civil buildings with >10 ktCO2e/year (steel, iron, power, heating, (petro) chemicals). Compliance obligation in case of closure.

civil buildings.

Emissions Trading 287

Allocation

Grandfather method (2009–2011)

allowance change.

Beijing

Grandfather (2008–2012)

Chongqing

Sources: Zhong (2014), Wu et al. (2014), Quemin and Wang (2014)

Pilots

Table 1 (continued)

Grandfather method (2010–2012)

reconstruction) with >10 ktCO2/year should purchase all quotas prior to operation. Quota reallocation for activity change, reduction and closure.

Guangdong

Comprehensive method; grandfather

Hubei

Grandfather (2009–2011) Benchmarking

due and 50 % of followingyear allowances after obligation shall be taken back.

Shanghai

invest. should submit emission eval. report. In case of closure or displacement of activity, compliance due and 50 % of followingyear allowances shall be taken back. Carbon Emission per Industrial Value Added

Shenzhen

Grandfather (base year not specific)

Tianjin

288 R. Raufer et al.

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Table 2 Economic structure of the seven carbon market pilot regions (% share of GDP, 2012) Pilots Beijing Chongqing Guangdong Hubei Shanghai Shenzhen Tianjin

Primary sector 0.9 % 8.6 % 5% 13.4 % 0.7 % 0.1 1.6

Secondary sector 24 % 55 % 50 % 48.7 % 42.1 % 47.5 % 52.4 %

Tertiary sector 75.1 % 36.4 % 45 % 37.9 % 57.2 % 52.4 % 46.0 %

Energy mix (coal) 43 % 50 % 22 % 72.5 % 30 % 59 % 71 %

Sources: PMR (2014), Liu and Xu (2012), UNDP China and Institute for Urban and Environmental Studies, CASS (2013)

Despite the variation among them, the seven pilots also share many fundamental features. All pilots include both indirect and direct emissions of carbon dioxide (ICAP 2014b). Most pilots use grandfathering as the principal method by which to allocate initial allowances (PMR 2014). Nearly all pilots distribute allowances for entities mandated to participate in the cap-and-trade system at the beginning of a compliance year without a charge. (In Shenzhen and Guangdong, however, a small number of allowances are also allocated via fixed-price sale or auction (King and Wood Mallesons 2014).) The majority of pilots allow offsets that may or may not include CCERs and other offset types (such as Hubei, which includes forest offsets from within the Province; Chongqing is also considering doing so). Finally, most, with the exception of Shenzhen, which bases its cap on a set of criteria, set their cap based on a minimum quantitative level of carbon emissions. Carbon trading transactions reached approximately USD 140 million by September 2014 (Carbon Eight Group 2014). Every pilot region has its own carbon exchange; membership in the exchange is a prerequisite for trading. Allowances are tradable only in the regional exchanges. During the first year in which trading took place, price volatility was a feature of most of the pilot regions. Prices also varied considerably from one region to the next, not surprisingly, given the variation in design and economic structures among the pilot markets (see Fig. 13). Trading volumes have also been quite low. As one study points out, Shenzhen, which has been the most active of the pilot markets, traded just 4 % of the total allowances available in its market during his first compliance year (Munnings et al. 2014). Pilots have been experimenting with ways to boost liquidity. To date, Chinese authorities have prohibited futures contracts in carbon trading out of concerns that doing so would invite destabilizing speculation in its financial markets. However, Guangdong, Tianjin, and Hubei have allowed some investors to trade permits with entities bound by emissions limits. Shanghai allows registered institutional investors to trade permits; Shenzhen plans to allow foreign investors to do so, reportedly allowing trading in foreign currency (Chen and Reklev 2014b).

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Fig. 13 Prices for Chinese ETSs, July 2013–October 2014 (Source: Bifera 2014)

China ETS Phase II The announcement by a senior climate policy official from the NDRC in late August 2014 that China would launch a national carbon market by 2016, with regulations for a national market to be sent to the State Council for approval by the end of the year, was an unequivocal commitment by China’s central authorities to scale up carbon market development (Chen and Reklev 2014b). Launching a national carbon market would be an ambitious undertaking for even the most developed economy; to implement cap-and-trade on a nationwide scale for a transitional economy the size and complexity of China’s requires authorities to tackle numerous challenges. They must not only arrive at a functional design but also construct the institutions necessary to create a national market for buying and selling carbon. Doing so requires substantial numbers of technically capable trained personnel along with regulatory institutions that can set emissions caps, support an emissions trading registry, and monitor trading and enforce compliance. Pilots have taken on these challenges at the local level. However, as will be discussed below, the development of regional schemes has also revealed the challenges of designing an effective market in a political-economy in which transparency is limited. For a market to function, an accurate accounting of carbon emissions must be made in order for legitimate transactions to take place. China’s official data collections systems are highly opaque, a feature that must be adjusted for cap-and-trade to work. Specifically, a national MRV system capable of inspiring confidence in trade for an intangible commodity must be developed (Kong and Freeman 2013). In short, on the institutional front, as China’s proposal for market readiness observes, what is required is a “reliable statistical system, effective program management system and necessary laws and/ or regulations.” (PMR 2013). The latter includes the passing by the National People’s Congress of a national environmental law that defines carbon as a commodity and explicitly enables enforcement of compliance by regulated firms (Munnings et al. 2014).

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In addition to institutional development and implementation, it is also necessary that the central government determine which specific sectors will be covered by the national carbon market, with an eye to future emissions trends, mitigation potential, and other factors such as international linkages (PMR 2013). China has already published monitoring and reporting guidelines for the national level, covering ten sectors: power generation, power transmission and distribution, aviation, cement, ceramics, flat glass, electrolytic aluminum, magnesium smelting, chemicals, and iron and steel. Among the considerations to be addressed are the development of policies to mitigate potential constrains on firm competitiveness and leakage from cap-andtrade; ways of encouraging liquidity without excessive risk to China’s fragile financial system; and management of potential new entrants to ensure that increased participation does not add to carbon emissions (Munnings et al. 2014).

China ETS Challenges and Opportunities Ahead China’s bottom-up approach to carbon market development offers numerous lessons for the NDRC as it moves forward. However, the differences among the protocols established for measuring emissions among pilots alone reflects a heterogeneity which will pose challenges to future efforts at harmonization. The seven pilots applied different rules for monitoring, reporting, and verifying emissions; however, a national market requires a single set of enforceable procedures (Kong and Freeman 2013). Chinese authorities, led by the NDRC, are in the process of drafting a National Climate Change law that could provide a legal foundation for a national trading system. The NDRC has also published guidelines for some industries to date but a national registry for greenhouse gas emissions is still under development (Song and Lei 2014). Moreover, for a cap-and-trade system to function, China must develop a system for data reporting, and for collecting greenhouse gas emissions data about industrial sources that is transparent. In addition, China’s lack of a welldeveloped legal system means that compliance by individual firms is heavily dependent on administrative enforcement, which in turn relies on the capacity and will of local authorities to do so. Currently, local officials’ (cadres’) promotion opportunities are closely linked to economic growth. China’s central authorities will have to complete the retooling of China’s “cadre evaluation system” to increase the effectiveness of local implementation, as they move ahead with legal development in the country. Other key systems structuring China’s economy also require reform and development for a national cap-and-trade system in China to function effectively. First, reforms are needed in how China manages power pricing. Currently, there are centrally-determined price caps on electricity in place that prevent power producers from passing on the cost of carbon to consumers. This explains why local pilots exclude the power sector or limit coverage to implied (i.e., emissions divided by activity) rather than direct emissions from power consumption. To fully bring the power sector– among the largest sources of carbon emissions in China – into the carbon trading system, difficult national policy changes in this area will be required (Kong and Freeman 2013). Second, China’s financial system remains undeveloped and fragile. Concerned about risk, China’s NDRC took futures trading off the table

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of options for local carbon trading pilots’ design. However, most experts see trading in derivative products as necessary for China’s carbon market to have the liquidity to be an effective tool in reducing the cost of cuts to emissions (Song and Lei 2014). China’s authorities are actively engaged in pushing reforms in the financial sector that will bring it into line with more mature economies; however, this process is a delicate one that will take time. Finally, national tools must be developed to mitigate against the potential for carbon leakage. This requires the ability to assess the risks of leakage accurately so that provisions can be made for regulated enterprises subject to this risk – something the European cap-and-trade system does through rebates in the form of allocations (Munnings et al. 2014). These are just some of the tasks ahead for China as it develops carbon trading on a national scale. Thus, while China’s pilot markets mark significant progress toward the development of cap-and-trade, the country still has a long way to go to build an effective national carbon trading system.

US Carbon Trading Programs While the US played a key role in introducing emissions trading through its ETP and acid rain regulatory programs, and also introduced the market-oriented approach into the Kyoto Protocol, the Bush Administration’s withdrawal from the Kyoto process in early 2001 led to a significantly diminished role for the country. European countries, initially quite skeptical about emissions trading, assumed the lead with the launch of the EU ETS in January 2005. The US did not pursue national carbon trading during the Bush administration, but expectations grew as the 2008 elections approached, because all three major candidates – Hillary Clinton and Barack Obama on the Democratic side and John McCain on the Republican one – espoused support for cap-and-trade legislation during the Presidential campaign. The build-up to the Copenhagen meeting thus assumed that the US would rejoin international efforts, and perhaps link its own national carbon market to ongoing EU ETS and Kyoto Protocol efforts. Such enthusiasm was enhanced when the American Clean Energy and Security Act (ACES) passed the US House of Representatives less than 6 months after Obama’s inauguration in January 2009. It contained an allowance-based program that required a 17 % CO2 reduction by 2020 (from a 2005 base year) and an 83 % reduction by 2050. Often referred to as the Waxman-Markey bill (after its two principal sponsors), ACES provided for the use of international offsets and also included an allowance price floor. Very similar legislation, entitled the American Power Act (APA), was submitted to the US Senate by Senators Kerry and Lieberman in May of 2010 – but a special election in the State Of Massachusetts earlier that year meant that the Democrats no longer had a “filibuster-proof” Senate (i.e., a Senate able to pass legislation over the objections of Republicans). The overwhelming Republican victory in mid-term elections later in 2010 ensured that such cap-and-trade legislation would not be enacted at the national level, and that party’s efforts have since then focused on rolling back existing environmental legislation (and US EPA’s budget) rather than passing new mandates. Prospects for new emissions trading legislation thus appear quite bleak; as one recent

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article in Foreign Policy noted: “Congress will never pass cap-and-trade, at least until Miami starts flooding” (Galbraith 2014). Despite such problems, market-oriented GHG control efforts continued at the state level (in California); at the regional level (in the Northeast’s Regional Greenhouse Gas Initiative [RGGI]); and even at the national level, through previous legislation initially designed for CAC regulation. These three levels of programs in the US are described below:

California’s Emissions Trading Program California’s cap-and-trade program is a result of the California Global Warming Solutions Act of 2006 (AB 32), which required the state’s Air Resources Board to develop regulations and market mechanisms to cut the state’s GHG emissions back to 1990 levels by 2020 – a reduction of approximately 25 %. Its emissions trading program is thus part of a larger regulatory effort (including a Low Carbon Fuel Standard as well as other energy efficiency standards) to achieve that target. The market-oriented program went into effect in January, 2012, with compliance obligations beginning 1 year later. The first two compliance years focus solely on electricity and industrial sectors, but the program will expand after that to include transportation and heating fuels (see Fig. 14). It is thus the first multisector carbon trading plan in the US, and given its emissions coverage, is second in size only to the EU ETS.

450 400 350 Offsets 300 Allowances

250 e2 /year 200 MMTCO 150

Narrow Scope Projected BAU Emissions Broad Scope Projected BAU Emissions

100 50 0 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Year

Fig. 14 California’s GHG Cap compared with BAU projections (Source: Center for Climate and Energy Solutions 2014; adapted from CARB 2010)

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The market covers the same six pollutants as the first commitment period of the Kyoto Protocol, as well as NF3 and other fluoridated gases. It covers approximately 350 business (with 600 facilities), and has been designed to link with similar trading programs in other states and regions. The California market has several notable features, including both cost containment and market flexibility mechanisms. There is an auction floor price (starting at $10 per allowance in 2012, rising at 5 % above inflation annually) and a strategic reserve (rising from 1 % to 7 % over time, with higher tiered prices similarly rising at 5 % above inflation). There are thus both floor and ceiling mechanisms in place to contain prices (as long as there are sufficient allowances in the reserve). There are three compliance periods: (a 2-year period [2013–2014]), followed by two 3-year periods [2015–2017 and 2018–2020]). At the end of every year, a source must provide allowances and offsets to cover 30 % of its previous year’s emissions. Then, at the end of each compliance period, it must provide the remaining allowances and offsets. This provides sources with the ability to cover any annual variation in product output. If the source does not do so and is not in compliance, then four allowances must be surrendered for every ton not covered within the compliance period. Offsets are allowed in the California program, but were initially restricted to US emission reduction projects from four targeted types: forestry; urban forestry; dairy digesters; and the destruction of ozone depleting substances. A linkage with Quebec’s emissions trading scheme began in January 2014, and linkages with other systems are ultimately expected to occur as well.

Regional Greenhouse Gas Initiative The Regional Greenhouse Gas Initiative (RGGI) was the first regulatory US capand-trade scheme addressing GHGs. It was designed to reduce CO2 emissions from power plants in ten Northeastern US states – although this was subsequently reduced to nine states when the Republican Governor of New Jersey withdrew his state from the program in 2011. RGGI is a regional program, but it is implemented through legislation adopted by each individual state. A “Model Rule” was drafted in 2006 and finalized in 2008, with requirements for individual facilities (i.e., fossil-fueled power plants greater than 25 MW generating capacity) beginning on January 1, 2009. RGGI initially sought to cap CO2 emissions at a steady rate through 2014, and then drop them annually by 2.5 % – and thus achieve a 10 % reduction one decade later. A significant fuel shift towards natural gas at power plants in the region, however, coupled with lower electricity demand and increased levels of both nuclear power and renewables led to an overallocation of allowances. Prices reflected that, and the clearing price for allowances at RGGI auctions was often less than $2. RGGI’s target was revised when New Jersey left, and was then significantly changed as a result of a 2012 Program Review. The new cap called for a reduction of 45 % by 2020 (from 2005 levels), with a 2.5 % reduction occurring annually from the revised 2014 cap levels. This new Model Rule also introduced other provisions, including a Cost Containment Reserve (CCR), and an interim compliance period requiring sources to hold specific allowance levels in time periods before final

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compliance dates (Bifera 2013). Most of the allowances in RGGI are sold through auctions, and the collected funds are dedicated for energy efficiency, renewable and clean energy, as well as bill support for low-income energy consumers. RGGI allows offsets to achieve compliance, but only from five categories: (1) Landfill methane capture and destruction; (2) Reduction in emissions of sulfur hexafluoride (SF6) in the electric power sector; (3) Sequestration of carbon due to US forest projects (reforestation, improved forest management, avoided conversion) or afforestation (for CT and NY only); (4) Reduction or avoidance of CO2 emissions from natural gas, oil, or propane end-use combustion due to end-use energy efficiency in the building sector; and (5) Avoided methane emissions from agricultural manure management operations (RGGI n.d.). Despite the significant drop in target levels in 2014, Fig. 15 shows that the actual emissions in recent years were not significantly above the new cap (i.e., 92 million short tons in 2012, just above the 91 million ton target in 2014). The cap will tighten in coming years, however, and it is not clear that the fuel shifts and other downward trends evident in recent years will continue. Thus, it is anticipated that the RGGI cap could become more binding in the future (EIA 2014).

The US EPA’s Clean Power Plan President George Bush promised to address CO2 emissions during the 2000 Presidential campaign, but reneged on this shortly after taking office. In 2003, his Administration’s EPA overturned a previous Clinton Administration decision, and declared that it did not have the authority to regulate CO2 under the Clean Air Act – and further noted that it would refrain from doing so, even if it did have the authority. The State of Massachusetts and others filed suit against EPA for its failure to act, a suit which was subsequently decided in their favor in 2007 by the US Supreme Court. The Court ruled that EPA did have such authority, but the law required EPA to determine whether or not such emissions could reasonably be anticipated to endanger public health or welfare. In 2009, under the Obama Administration, US EPA issued such an “Endangerment Finding,” and proceeded to issue new standards for light, medium and heavy duty vehicles in the following years. The Agency also proposed GHG standards for new power plants in 2012 and then revised and proposed them once again in September 2013. On June 2, 2014, it proposed standards for existing power plants, under a program called the Clean Power Plan. Utility emissions are the largest source of carbon pollution in the US, accounting for roughly one-third of all domestic GHGs (EPA 2014b). The Clean Power Plan tackled this in two ways: (1) It set state-specific goals, which were based on achieving a level of carbon intensity in the state by 2030. This would have the effect of reducing CO2 emissions from the power sector by 30 % (from a 2005 base) and (2) The EPA provided guidelines for the states in how they might achieve such goals. Under the Clean Power Plan, states would have until June 2016 to submit plans to achieve these goals, with the possibility of a 1-year extension – or 2 years if states join together in a multistate plan. The states were also required to make “reasonable progress” in achieving such goals by 2020.

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million short tons RGGI goes into effect in 2009

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Fig. 15 Regional Greenhouse Gas Initiative CO2 emissions cap vs. actual emissions (Source: EIA 2014)

Section 111(d) of the Clean Air Act requires US EPA to issue “standards of performance” reflecting the “best system of emission reduction” (BSER), and the Agency has used four “building blocks” of BSER to set the state-by-state goals: (1) heat rate improvements; (2) dispatch changes among affected units (e.g., coal to natural gas units); (3) expanded low- or zero-carbon generation (e.g., renewables and nuclear); and (4) use of demand-side energy efficiency, thereby reducing generation requirements. US EPA has offered the states considerable flexibility in determining how they might meet their goals. They are able, for example, to: • Look broadly across the power sector for strategies that get reductions • Invest in existing energy efficiency programs – or create new ones • Consider market trends toward improved energy efficiency and a greater reliance on lower-emitting power sources • Expand renewable energy generation capacity • Tap into investments already being made to upgrade aging infrastructure • Integrate their plans into existing power sector planning processes • Design plans that use innovative, cost-effective regulatory strategies • Develop a state-only plan or collaborate with each other to develop plans on a multistate basis (USEPA 2014c) Note that these last two options allow individual states to team up with other states if they choose – and also to employ market-based mechanisms to achieve their goals. Not only would this would allow them to accomplish their reductions in the most cost efficient manner – they will also get an extension on the time required to develop such an approach. The Clean Air Act of 1970 is a piece of legislation now almost 45 years old, and its principal architecture was developed within the CAC framework. It was never

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intended to tackle a problem as complicated and as comprehensive as GHG control. The failure of the political system to pass legislation (such as ACES or APA) means that it must now serve as the foundation for such control, given the fact that the problem is real (as indicated in the Endangerment Finding) and the courts have indicated that US EPA has the authority (and, indeed, the responsibility) to address it. The US EPA has developed a creative regulatory approach that will allow states to utilize emissions trading, if they so choose – and to do so on a multistate basis. This plan will surely be modified in response to public comment, and must also survive the inevitable lawsuits when it is promulgated. Opponents have already attacked the Plan, based upon media reports that environmentalists played a key role in its development (Davenport 2014; Chait 2014). The final 111(d) rule is due to be released in June 2015, and while states must begin to make reductions by 2020, full compliance with the CO2 emission performance level in the state plan must be achieved by no later than 2030.

Voluntary Carbon Market In addition to the “compliance” markets discussed above, a corollary, voluntary market has developed that provides carbon trading opportunities for companies, individuals, and other entities not subject to mandatory limitations, but still wishing to offset their GHG emissions. As the name implies, the voluntary carbon market includes all carbon offset trades that are not required by regulation. Over the past several years, this market has not only provided an opportunity for consumers to alleviate their carbon footprint, but also provided an alternative source of carbon finance. The instrument of trading is called a Voluntary Emission Reduction (VER), although it should be noted that some market participants consider this acronym to mean “Verified Emission Reduction.” While still very much smaller than the compliance market ( 5.2 m
0.5 (As in m2 and Hmax in m) were simulated to typically not support cisco oxythermal habitat under past climate conditions and the future climate scenario (MIROC 3.2). Mediumdepth lakes are projected to be most vulnerable to climate warming with most

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increase in the number of years with cisco kill. The mean daily TDO3 values over a 31-day fixed and variable benchmark periods were calculated for each of simulated years and then averaged over the simulation period for each lake type. Projected increases of the multiyear average TDO3 (called AvgATD3) under the two future climate scenarios and relative to the 47-year simulation period from 1962 to 2008 had averages from 2.6  C to 3.4  C. Isopleths of AvgATD3 were interpolated for the 30 simulated virtual lakes on a plot of Secchi depth versus lake geometry ratio used as indicators of trophic state and summer mixing conditions, respectively. Marking the 620 Minnesota lakes with identified cisco populations on the plot of AvgATD3 allowed to partition the 620 lakes into three tiers depending on where they fell between the isopleths: lakes with AvgATD3  11  C (tier 1 lakes) were selected to be most suitable for cisco; lakes with 11  C < AvgATD3  17  C (tier 2 lakes) had suitable habitat for cisco; and non-refuge lakes with AvgATD3 > 17  C (tier 3 lakes) would support cisco only at a reduced probability of occurrence or not at all. About 208 (one third) and 160 (one fourth) of the 620 lakes that are known to have cisco populations are projected to maintain viable cisco habitat under the two projected future climate scenarios using the fixed and variable benchmark periods, respectively. These selective lakes have a Secchi depth greater than 2.3 m (mesotrophic and oligotrophic lakes) and are seasonally stratified (geometry ratio less than 2.7 m
0.5). Management strategies were developed and implemented for some of the refuge lakes.

Introduction The potential significance of climate change for inland aquatic ecosystems (e.g., streams, lakes, reservoirs) caught the attention of water resource professionals and scientists in the early 1990s. An increase of carbon dioxide (CO2) and/or other greenhouse gases in the atmosphere is projected to cause climate warming (NRC 1983; IPCC 2007), which would alter water temperature (T), ice/snow cover, and dissolved oxygen (DO) characteristics in lakes (Blumberg and Di Toro 1990; Stefan et al. 1996). These changes are in turn expected to have an effect on indigenous fish populations: cold-water, cool-water, and warmwater fish species (Coutant 1990; Magnuson et al. 1990; Chang et al. 1992; Stefan et al. 1995; De Stasio et al. 1996). Chapter 16 in the previous edition of the handbook has summarized some basic information on effects of climate change on water quality (water temperature, dissolved oxygen, snow and ice covers) and fish habitat for three fish guilds (8, 7, and 14 species of cold-water, cool-water, and warmwater fish guilds, respectively) in lakes in Minnesota and the contiguous USA (Fang and Stefan 2012). This chapter summarizes simulation results and model validation of cisco oxythermal habitat in Minnesota lakes that were used to identify cisco “refuge lakes” under future climate scenarios and develop management strategies for them. A “refuge lake” is a cisco lake that is projected to provide suitable cold-water habitat under future climate scenarios.

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Cisco Coregonus artedi is the most common cold-water stenothermal fish in northern lakes in Minnesota, Wisconsin, and other northern states, and it is a common forage fish for walleye Sander vitreus and northern pike Esox lucius among other prized sport fishes. The Minnesota (MN) Department of Natural Resources (DNR) has sampled cisco from 648 lakes in netting assessments since 1946 (MN DNR files). These lakes are typically deeper and more transparent than average lakes in Minnesota (Fang et al. 2009). The lakes are scattered throughout much of the central and northern portions of the state (Fig. 1) and cross several land uses (agricultural, urban, and forested). The wide distribution suggests that ciscoes are somewhat more eurythermal than other native, lentic cold-water stenotherms such as lake whitefish Coregonus clupeaformis (sampled in 155 Minnesota lakes), lake trout Salvelinus namaycush (124 Minnesota lakes), and burbot Lota (233 Minnesota lakes). Cisco physiologically requires cold, well-oxygenated water to survive, grow, and reproduce (Cahn 1927; Frey 1955). The combination of a wide distribution (Fig. 1) and a requirement for cold, oxygenated water (Frey 1955) make cisco an excellent “canary in a mineshaft” species that is a sensitive indicator of ecological stresses such as eutrophication and climate warming. For example, 18 lakes in north-central Minnesota experienced cisco mortality in the unusually hot summer of 2006 (Jacobson et al. 2008), and one example of cisco mortality is given in Fig. 2. The climate warming is projected to warm the water and increase hypolimnetic oxygen depletion during periods of stratification in lakes (Blumberg and Di Toro 1990; Fang and Stefan 1999, 2000, 2009). Fish habitat is constrained by water temperature, available DO, food supply, human interference, and other environmental factors (Frey 1955; Fry 1971). In lakes, water temperature and DO are the two important water quality parameters that affect survival and growth of cold-water fishes (Magnuson et al. 1979; Coutant 1985, 1990; Christie and Regier 1988; Jacobson et al. 2010; Fang et al. 2012a, b; Jiang et al. 2012). Therefore, projected changes of water temperature and DO characteristics due to climate warming have the potential to reduce cold-water fish habitat (such as cisco) in lakes (Magnuson et al. 1990; Schindler et al. 1996; Stefan et al. 1996; Fang et al. 2004b). Ciscoes have been declining in recent years in Minnesota lakes, likely because of climate warming (Jacobson et al. 2012). Recently, Sharma et al. (2011) estimated that 30–70 % of the cisco population in about 170 of Wisconsin’s deepest and coldest lakes could become a climate change casualty and disappear from most of the Wisconsin cisco lakes by the year 2100. The goal of the study was to simulate daily water temperature and DO profiles in different cisco lakes to project the quality of cold-water fish habitat in 620 known cisco lakes in Minnesota under future climate scenarios and to identify potential cisco refuge lakes and impacts of climate change on cisco habitat. To make projections of water quality and fish habitat in lakes under future climate scenarios, numerical simulation models of daily temperature and DO profiles are useful. It is infeasible to simulate 620 cisco lakes in Minnesota using MINLAKE2010/MINLAKE2012 (Fang et al. 2012a). In this study, simulations of daily water temperature and DO profiles were made for the 30 virtual lakes

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Fig. 1 Geographic distribution of 620 cisco lakes grouped by latitude, three weather stations (stars), and associated grid center points (crosses) of CGCM 3.1 and MIROC 3.2 used for model simulations. Background shades identify ecoregions of Minnesota. Cisco lakes are essentially in two ecoregions: (1) Northern Lakes and Forests and (2) North Central Hardwood Forests (modified from Jiang et al. 2012)

(Fang et al. 2012b; Jiang et al. 2012) and 44 representative lakes (Fang et al. 2014) in Minnesota before cisco oxythermal habitat and lethal conditions were examined in these lakes. The overall modeling methodology for the study is discussed in detail in the next section (Fig. 3).

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Fig. 2 Lake Andrusia in Minnesota had cisco mortality in July 2006 (Photo: Peter C. Jacobson, Minnesota Department of Natural Resources)

In this study, cold-water oxythermal fish habitat was identified using three different methods (Fig. 3): (1) constant lethal limits (lethal temperature, LT and DO survival limit), (2) lethal-niche-boundary curve (Jacobson et al. 2008) (also called variable lethal limits), and (3) a single oxythermal habitat variable TDO3, temperature at 3 mg/L of DO (Jacobson et al. 2010). Depths at the good-growth temperatures and lethal limits and TDO3 values were calculated day by day from simulated daily lake water temperature and DO profiles obtained from the processoriented, one-dimensional year-round water quality model MINLAKE2010/ MINLAKE2012 (Fang et al. 2012a). The model was run in daily time steps over a continuous 48-year simulation period for past (1961–2008) climate conditions and for two projected future climate scenarios (CGCM 3.1 and MIROC 3.2). Monthly (31-day) fixed and variable benchmark periods (Fang et al. 2012b; Jiang et al. 2012; Jacobson et al. 2010) were used to identify future cold-water fish habitat in lakes based on projected future temperature and DO profiles.

Overall Modeling Methodology Figure 3 shows a flowchart of the study to project impacts of climate changes on cisco oxythermal habitat in Minnesota. Past climate conditions (1961–2008, 48 years) and two future climate scenarios at different weather stations were assembled and used as model inputs (atmospheric boundary conditions) to the deterministic, unsteady, one-dimensional (vertical) lake water quality model MINLAKE2010/MINLAKE2012 which can simulate T and DO profiles in cisco lakes continuously for 48 years over the open-water seasons and winter ice-cover periods. A cisco habitat model with three different modeling options were developed, validated, and used for the study. The first option is to use the constant lethal limits to model cisco habitat in Minnesota lakes. The constant limits for fish survival (lethal) and good growth do not change with time, and the method was previously used to

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Fig. 3 Flowchart of the study to project impacts of climate changes on cisco oxythermal habitat in Minnesota lakes

project cold-water, cool-water, and warmwater fish habitat in small lakes in Minnesota and over the contiguous USA (Stefan et al. 2001). The constant lethal limits of cisco were calculated from the lethal-niche-boundary curve of adult cisco (Jacobson et al. 2008) and then determined through model validation in 23 Minnesota lakes using cisco mortality and survival data in the summer of 2006. The second option is to use a fitted regression equation as the lethal niche boundary of adult cisco. The equation was developed by Jacobson et al. (2008) and gives DO survival limits at different temperatures, which are temperature-varying lethal limits. The third option is to use a single oxythermal variable TDO3 to identify cisco refuge lakes

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(Fang et al. 2012b; Jiang et al. 2012). TDO3 has been a useful parameter for quantifying the oxythermal niche of cold-water fish (Jacobson et al. 2010). TDO3 was calculated from simulated daily T and DO profiles for every simulated day except days in 1961 to avoid effects of initial conditions. In the third option of cisco oxythermal habitat modeling, the daily TDO3 values were averaged over either the fixed benchmark (FB) period (ATDO3FB) (Fang et al. 2012b) or the variable benchmark (VB) period (ATDO3VB) (Jiang et al. 2012) for each simulated lake and year. Using the variable benchmark periods for each simulated year gave the maximum average TDO3 over a 31-day period in different years and different lake types (Jiang et al. 2012). The oxythermal habitat options 1 and 2 determine which day lethal conditions can occur. In the first option, when the LT isotherm and the DO limit isopleth for cisco intersect in a particular day (Stefan et al. 2001; Fang et al. 2004a; Fang and Stefan 2012), the entire depth of a stratified lake is under lethal conditions on that day. The lethal conditions are because water temperature is higher than LT from the water surface to or below the intersecting depth and DO is lower than the DO limit from the lake bottom to or above the intersecting depth. In the second option, lethal conditions for cisco are assumed to occur if the simulated DO is less than the DOlethal value in all water layers (from the lake water surface to the lake bottom) on that day when DOlethal is calculated from simulated water temperature using the lethal-nicheboundary curve (Jacobson et al. 2008). To understand climatic variability, the water quality model and fish habitat model were run using the weather data from 1961 to 2008 for the past climate conditions in 30 virtual deep lakes and 44 representative lakes (Table 1) in Minnesota (Fig. 3). A number of years with cisco kill and number of cisco lethal days were determined during the simulation period for the habitat modeling options 1 and 2. The cisco kill was assumed to occur when the continuous lethal days of cisco last 3 or 7 days (Fang et al. 2014). To assess the quality of cisco habitat in a lake and identify refuge lakes, the 47-year averages of annual ATDO3FB and ATDO3VB values in the 1962–2008 simulation period (i.e., AvgATD3FB and AvgATD3VB) were calculated in 30 virtual deep lakes (Table 2) and compared to TDO3 limits (11  C and 17  C determined by the analysis of field data) to divide cisco lakes into three tiers: tiers 1 and 2 refuge lakes and tier 3 non-refuge lakes (Fang et al. 2012b; Jiang et al. 2012). To implement the above modeling approach, 44 “representative” Minnesota lake types (Table 1 and Fig. 3) and 30 “virtual” cisco lake types (Table 2 and Fig. 3) were chosen as representative of the entire set of Minnesota lakes in general and 620 cisco lakes, respectively; a similar approach using 27 “generic” lake types had been used to study climate warming impact on fish habitat in small lakes in Minnesota (Stefan et al. 1996) and in the contiguous USA (Stefan et al. 2001; Fang et al. 2004a, b), because it was not viable to run the deterministic model for all 620 cisco lakes over 47 years. To apply the oxythermal habitat results to the hundreds of cisco lakes that could not all be simulated, the virtual and representative simulated lakes had to be characterized in a generic way. Following previous practice (Stefan et al. 2001; Fang et al. 2004a, b), two parameters were chosen for this purpose: a lake geometry ratio (GR) as an indicator of a lake’s potential for strong or weak summer stratification (Gorham and Boyce 1989) and mean summer Secchi depth (SD) as an indicator of

Surface area AS (km2) 0.2 1.7 10 0.05 0.2 1.7 10 0.2 1.7 10 2.32

Secchi depth, SD (m) 1.2 2.5 LakeR01a LakeR02 LakeR04 LakeR05 LakeR07 LakeR08 LakeR37 LakeR38 LakeR10 LakeR11 LakeR13 LakeR14 LakeR16 LakeR17 LakeR19 LakeR20b LakeR22 LakeR23 LakeR25 LakeR26 LakeR41c LakeR42 4.5 LakeR03 LakeR06 LakeR09 LakeR39 LakeR12 LakeR15 LakeR18 LakeR21 LakeR24 LakeR27 LakeR43

7.0 LakeR28 LakeR29 LakeR30 LakeR40 LakeR31 LakeR32 LakeR33 LakeR34 LakeR35 LakeR36 LakeR44

Geometry ratio (GR) As0.25/Hmax 5.29 m
0.5 9.03 m
0.5 14.06 m
0.5 1.15 m
0.5 1.63 m
0.5 2.78 m
0.5 4.33 m
0.5 0.88 m
0.5 1.50 m
0.5 2.34 m
0.5 1.63 m
0.5

Note: a The first 28 shallow and medium-depth lakes were used for fish habitat modeling of the constant lethal limits method b These highlighted lakes are strongly stratified mesotrophic and oligotrophic deep lakes used for fish habitat modeling of the lethal-niche-boundary curve method c These four deep lakes (LakeR41–LakeR44) have the same geometry ratio but different surface areas as four medium-depth lakes LakeR10–LakeR12 and LakeR31 for comparison study

Hmax = 24 m (deep)

Hmax = 13 m (medium depth)

Maximum depth (m) Hmax = 4 m (shallow)

Table 1 Morphometric characteristics and “names” of the 44 representative or regional lake types in Minnesota simulated with the MINLAKE2010/ MINLAKE2012 model

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Table 2 Morphometric characteristics and “names” of the 30 virtual cisco lakes simulated with the MINLAKE2010/MINLAKE2012 model (maximum lake depth Hmax = 24 m) Surface area AS (km2) 0.1 0.5 1.5 5.0 13.0 50.0

Secchi depth SD (m) 1.2 LakeC01 LakeC06 LakeC11 LakeC16 LakeC21 LakeC26

2.5 LakeC02 LakeC07 LakeC12 LakeC17 LakeC22 LakeC27

4.5 LakeC03 LakeC08 LakeC13 LakeC18 LakeC23 LakeC28

7.0 LakeC04 LakeC09 LakeC14 LakeC19 LakeC24 LakeC29

8.5 LakeC05 LakeC10 LakeC15 LakeC20 LakeC25 LakeC30

Geometry ratio, GR =As0.25/Hmax 0.74 1.11 1.46 1.97 2.50 3.50

lake trophic state and transparency. These virtual and representative lakes are described in detail in a separate section.

Simulation Models for Year-Round Water Quality To make projections of water quality and fish habitat in small lakes under future climate scenarios, numerical simulation models of daily temperature and DO profiles are indispensable. The one-dimensional (vertical) year-round lake water quality model MINLAKE2010 was developed to run continuously over many simulation years for both the open-water season and the ice-cover period (Fang and Stefan 1996a). The model uses a stacked layer system (Fig. 4); the layers consist of lake water and lake sediments during the open-water season and additional ice cover and snow cover during the winter ice-cover period (Fang and Stefan 2009). It simulates daily water temperature profiles in a lake using daily weather data as input. Figure 4 is a schematic of a stratified lake including heat transfer components, oxygen sources and sinks for the year-round water temperature and DO models, and typical temperature and DO profiles in the summer and winter (Fang and Stefan 1996a, b, c). MINLAKE2010 uses a mixed layer approach (Chapra 1997), which considers a mechanical energy balance (kinetic and potential energy), to predict the thickness of the mixed layer (epilimnion) after the heat diffusion equation is solved and convective mixing is considered (Ford and Stefan 1980). A lake is divided into a series of well-mixed horizontal water layers (Fig. 4) because the horizontal variations of water quality parameters are typically much smaller than the vertical variations in a small stratified lake. The one-dimensional, unsteady heat transfer equation in a lake is solved for daily vertical water temperature profiles (Hondzo and Stefan 1993):   @T 1 @ @T Hw ¼ K zT A þ @t A @z @z ρCp

(1)

where T(z, t) is the water temperature ( C) in a horizontal layer, t is the time (day), A(z) is the horizontal area (m2) as a function of depth z (m) based on lake bathymetry

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Meteorological forcing T= 0°C

Ice cover season

Open water season

TA

HSN HA HBR HE

U

HSN HE

HC

HC

TA T= 0°C U

Snow Ice Water HSED

HSED Sediment

Sediment Sediment Temperature profile in summer DO = 0 mg L

Temperature profile in winter

–1

O2

DO = 0mg/l

Fs

Ua

Phytoplankton P (Chl-a)

Snow Ice R BOD SOD

Water

Phytoplankton P (Chl-a) WODR

R=0

Sediment Sediment

DO profile in summer Oxygen source

Sediment

Oxygen sink

DO profile in winter

Fig. 4 Schematic of a stratified lake showing heat transfer components, oxygen sources and sinks for the year-round water temperature and DO model MINLAKE96/MINLAKE2010/ MINLAKE2012, and typical temperature and DO profiles in summer open-water season (left) and winter ice-cover period (right) including sediment, water, ice, and snow layers (Fang and Stefan 2009)

input data, KzT is the vertical turbulent heat diffusion coefficient (m2 day
1), ρCp is the density of water (ρ) times heat capacity of water (Cp) and represents heat capacity per unit volume (J m
3  C
1), and Hw is the internal heat source strength per unit volume of water (J m
3 day
1). Solar radiation absorption in the water column is the main contributor to the heat source term during the open-water season (Stefan and Ford 1975). Heat exchange with the bottom sediment layer included in MINLAKE2010 can be important in the shallow water layers and during the winter ice-cover periods (Fang and Stefan 1996b). Heat exchange between the lake and the atmosphere is treated as a source or sink term (Fig. 4) for the topmost water layer of a lake during the open-water season due to the surface wind mixing, i.e., Hw(1) in Eq. 1 = As/V(1)  (HSN + HA– HBR– HE– HC), where As is the lake surface area and V(1) is the volume of the topmost/first water layer. It includes surface heat fluxes in J m
2 day
1 such as net incoming heat from shortwave solar radiation (HSN), long-wave radiation (HA), outgoing heat from back radiation (HBR), evaporation/condensation (HE), and conduction/convection (HC) related to wind speed (U, Fig. 4). The computation of above surface heat fluxes and the internal heat source term (Hw) using daily weather input data has been discussed by Hondzo and Stefan (1993), among others. During the ice-cover period (Fang and Stefan 1996b, c), the model first simulates snow/ice thicknesses

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and sediment temperature profiles (heat conduction equation), then determines the heat source/sink terms, and finally solves the heat transfer equation to obtain water temperature profiles below the ice. The heat budget components through the water surface are directly linked to climate parameters that are related to future climate changes. Dissolved oxygen concentration is viewed as one of the most important lake water quality parameters which indicate a lake’s overall ecological health. The vertical DO profiles in the lake are computed from a balance between oxygen sources (surface reaeration and photosynthesis, Fig. 4) and oxygen sinks (sedimentary oxygen demand (SOD), biochemical oxygen demand (BOD), and plant respiration (R)). The numerical simulation model for daily DO profiles in a lake solves the one-dimensional, unsteady transport equation: @C @t

¼

  1 @ @C Sb @A T
20 AK Z θ þ PMAX θpT
20 Min½L Chla
A @A @z A @z s 1 kr θr T
20 Chla
kb θb T
20 BOD
YCHO2

(2)

In Eq. 2, C(z, t) is the DO concentration in mg/L as a function of depth (z) and time (t), Kz(z, t) is the DO vertical turbulent diffusion coefficient in m2 day
1, and Sb is the coefficient for SOD at 20  C in mg O2 m
2 day
1. PMAX is the maximum specific oxygen production rate by aquatic plants at 20  C under saturating light conditions in mg O2 (mg Chla)
1 day
1. Min[L] is the light limitation determined by Haldane kinetics (Megard et al. 1984). Chla is the chlorophyll-a concentration in mg/L to represent the biomass of aquatic plants in a lake. YCHO2 is the yield coefficient, i.e., the ratio of mg chlorophyll-a to mg oxygen. The first-order decay rate coefficients are kb and kr for BOD and plant respiration (day
1), respectively. The temperature adjustment coefficients for SOD, photosynthesis, BOD, and plant respiration are θs, θp, θb, and θr, respectively. Typical values and ranges of temperature and DO model parameters have been summarized elsewhere (Fang and Stefan 2012). Oxygen production is related to chlorophyll-a concentration and limitation of available light determined by Haldane kinetics. In the DO model, chlorophyll-a is specified by a mean annual value which depends on the specified trophic state of a lake and a function that calculates typical seasonal chlorophyll-a cycles (Stefan and Fang 1994a) based on observational data from 56 lakes and reservoirs in Europe and North America (Marshall and Peters 1989). Annual mean chlorophyll-a concentration that represents biomass or phytoplankton in the MINLAKE2010 model was calculated from the relationship between chlorophyll-a and Secchi depth used in the Carlson trophic index (Carlson 1977) for virtual and representative lakes in Minnesota (Tables 1 and 2) or measured data for calibration lakes. In the model, the oxygen transfers through the water surface (reaeration) during the open-water season is used as an oxygen source or sink term in the topmost water (surface) layer of the lake after the reaeration is multiplied by the surface area and divided by the layer volume, and the surface oxygen transfer coefficient is calculated as a function of wind speed. SOD is treated as a sink term for each water layer

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because each water layer is in contact with lake sediments. BOD occurs in the water column along all water depths, and plant respiration for all water layers is a function of chlorophyll-a concentration. BOD (mg/L) is used to reflect biodegradable detritus in lake water column. Diffusive oxygen flux at the lake bottom is set equal to zero as a boundary condition (the flux is not explicitly modeled, but SOD is used as surrogate of the flux). For the DO simulations in a lake during the ice-cover period (Fig. 4), modifications must be made to account for the presence of an ice cover and low temperatures. For example, reaeration is zero because the lake ice cover prevents any significant gas exchange between the atmosphere and the water body. The water column oxygen demand (WOD in Fig. 4) is set at 0.01 g O2 m
3 per day after model calibration. DO concentrations are simulated after water temperature and snow/ice covers have been simulated. Equations 1 and 2 are solved numerically for time steps of 1 day and layer thicknesses from 0.02 m (near the water surface and the ice-water interface) to 1.0 m (when z > 1.0 m) for small lakes using an implicit finite difference scheme and a Gaussian elimination method. Model parameters and detailed formulations of the year-round DO model (Eq. 2) have been described elsewhere (Stefan and Fang 1994a; Fang and Stefan 1997). Several modifications and refinements were made to develop MINLAKE2010 from MINLAKE96 for relative deep cisco lakes in Minnesota and have been reported by Fang et al. (2012a). Most recent version MINLAKE2012 (Fang et al. 2014) used in a part of the study is a spreadsheet model developed from MINLAKE2010. The most important upgrades of MINLAKE2012 compared to MINLAKE2010 are the conversion to a user-friendly Excel spreadsheet (for data input and displaying basic graphic results) and the introduction of variable temporal resolution, allowing the model to run at hourly and daily time steps. The MINLAKE model was calibrated and validated against extensive Minnesota lake data: first, using 5,378 water temperature and DO measurements for 48 lake years in 9 lakes for MINLAKE96 (Fang and Stefan 1996) and then using 7,384 water temperature and DO measurements for 439 lake years in 28 lakes for MINLAKE2010 (Fang et al. 2012a). Twenty-one cisco lakes and seven non-cisco lakes were selected for model calibration of MINLAKE2010 based on multiyear data availability; more than half of these 28 lakes had maximum depths greater than 24.0 m, which is Hmax used for the 30 virtual lakes in Table 2, and 23 of the 28 lakes were either mesotrophic or oligotrophic lakes (Fang et al. 2012a). After calibration, the average standard error of estimates against measured data for all 28 lakes was 1.47  C for water temperature (range from 0.8  C to 2.06  C) and 1.50 mg/L for DO (range from 0.88 to 2.76 mg/L) (Fang et al. 2012a).

Cisco Lakes in Minnesota The modeling analysis was conducted for 620 known cisco lakes in Minnesota (Fig. 1); the MN DNR had netting assessments for these lakes since 1946. On average, Minnesota cisco lakes are deeper and more transparent (larger Secchi

670

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depth) and have lower chlorophyll-a concentrations than other lakes in Minnesota (Fang et al. 2009). The 620 cisco lakes vary in mean Secchi depth (SD) and lake geometry ratio (GR) as shown in Fig. 5. The lake GR is defined as As0.25/Hmax in m
0.5 when surface area As is in m2 and maximum depth Hmax in m. The strength of the seasonal lake stratification is related to the GR (Gorham and Boyce 1989). Polymictic lakes such as large shallow lakes have the highest GR numbers, while strongly stratified lakes have the lowest GR numbers; the transition from weakly to strongly stratified lakes occurs when GR is between 3 and 5 (Gorham and Boyce 1989). Lake geometry ratios of Minnesota cisco lakes range from 0.47 to 22.7 m
0.5 (Fig. 4), and about 73 % of these lakes have GR less than 3.0 m
0.5 (Fang et al. 2009). Only 6 % or 39 of these lakes have GR greater than 5.0 m
0.5; these are very weakly stratified or unstratified lakes during the summer. Lake of the Woods is located at the border of the USA and Canada and has the largest surface area (3847.8 km2) with the largest GR = 22.7 m
0.5 and a maximum depth of 10.97 m. Mille Lacs Lake has the second largest GR = 11.9 m
0.5 with a maximum depth of 12.8 m and surface area of 536.5 km2. Maximum depths of the 620 cisco lakes range from 3.0 to 64.9 m, and 25 % of these lakes have maximum depth greater than 24 m (Fang et al. 2009). For these 620 cisco lakes in Minnesota, there are 14 shallow lakes with Hmax < 5.0 m, 385 medium-depth lakes (Fig. 5 top) with 5.0 m  Hmax < 20.0 m, and 221 deep lakes with Hmax > 20.0 m (Fig. 5 bottom) based on regional lake classifications in Minnesota (Stefan et al. 1996). Surface areas of these lakes range from 0.04 to 3,847.8 km2 (Fang et al. 2009) and mean summer Secchi depths from 0.7 to 9.5 m. Nineteen percent and 81 % of the 620 cisco lakes (Fang et al. 2009) have mean summer Secchi depth greater than 4.5 m (oligotrophic lakes) and 2.5 m (mesotrophic lakes), respectively, based on regional lake classifications in Minnesota (Stefan et al. 1993). For modeling purposes, the 620 Minnesota cisco lakes were grouped by either shortest distance or latitude to associate with three Class I National Weather Service (NWS) weather stations in Minnesota (International Falls, Duluth, and St. Cloud; Fig. 1). Weather data from only these three Class I NWS weather stations were useful and available for cisco lake long-term simulations for the period from 1961 to 2008. Three options (methods) were used to associate each lake with one of the three weather stations: (1) association by shortest distance (Fang et al. 2012b), (2) association by latitude (Jiang et al. 2012), and (3) association of one single weather station with all lakes simulated (Fang et al. 2010b). Refuge lakes were determined using each of the three options (Fang et al. 2010b), but results were similar; simulation results by methods (1) and (2) are presented here.

Representative Lake Types in Minnesota Simulations of daily water temperature and DO profiles and oxythermal habitat parameters were made for 30 virtual cisco lakes and 44 representative lakes (lake types or classes) in Minnesota before fish habitat was examined in 620 cisco lakes. The 44 representative lake types in Table 1 were expanded from the 27 lake types

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Fig. 5 Distribution of 14 shallow and 385 medium-depth cisco lakes (top) and 221 deep cisco lakes (bottom) from 620 cisco lakes in Minnesota including 44 regional or representative lakes (Table 1) and 30 virtual deep lakes (Table 2) plotted using Secchi depth and lake geometry ratio as axes

672

X. Fang et al.

used to study fish habitat in Minnesota (Stefan et al. 1996) and in the contiguous USA (Fang et al. 2004a). Lakes were classified by lake geometry (surface area As and maximum depth Hmax) and trophic state as related to Secchi depth (SD in Tables 1 and 2). The representative surface areas chosen for the 27 lake types were 0.2, 1.7, and 10.0 km2 for small, medium-size, and large lakes (Stefan et al. 1996; Fang et al. 2004a), respectively. The representative maximum depths chosen were 4, 13, and 24 m for shallow, medium-depth, and deep lakes (Stefan et al. 1996; Fang et al. 2004a), respectively. With these numbers for As and Hmax, nine lake types were obtained ranging from relatively large and shallow lakes to relatively small and deep lakes. Secchi (disk) depth (SD) is a common limnological parameter to measure transparency of a lake (Hutchinson 1957; Horne and Goldman 1994). It was used in previous fish habitat studies (Stefan et al. 1996; Fang et al. 2004a) to represent both trophic state (primary productivity of biomass or photosynthesis of plants) and solar radiation attenuation in a lake, which is used to quantify how much solar energy reaching the water surface can penetrate through a water column to heat water and to support photosynthesis of aquatic plants (Fig. 4). Lake turbidity from suspended inorganic sediment is relatively rare in Minnesota, and total phosphorus or chlorophyll-a in most Minnesota lakes is well correlated with SD (Stefan and Fang 1994b). Therefore, SD is a representative parameter to characterize trophic state of each of the 620 cisco lakes in the database. Contours (isotherms or isopleths) on plots with SD versus GR as axes have been previously used successfully by the authors to give/present generic, but regional, patterns or variations of different characteristic parameters in lakes, e.g., maximum surface water temperatures, maximum lake bottom temperatures, minimum DO at the sediment/water interface, and various fish habitat parameters in lakes (Stefan et al. 1996; Fang and Stefan 1997, 1999). The representative Secchi depths of 1.2, 2.5, and 4.5 m were previously selected for eutrophic, mesotrophic, and oligotrophic Minnesota lakes (Stefan et al. 1996; Fang et al. 2004a), respectively, using Carlson’s trophic state index (Carlson 1977). Ten percent or 62 lakes of 620 cisco lakes have mean summer Secchi depths of 5.0–9.5 m. Therefore, the fourth Secchi depth of 7.0 m was added creating nine new representative lake types for the study. A set of virtual cisco lakes (Table 2) with SD = 7.0 m was first used before to study cisco refuge lakes in Minnesota (Jiang et al. 2012). Therefore, the first 36 representative lake types (Table 1) were characterized by a 3  3  4 matrix consisting of (a) three different lake surface areas, (b) three lake maximum depths, and (c) four Secchi depths. The first 28 shallow and medium-depth lakes in Table 1 were used for fish habitat modeling of the constant lethal limits method (Fang et al. 2014). Four medium-depth lakes LakeR37–LakeR40 were added to have a lake geometry ratio of 1.15 to reflect some of the medium-depth cisco lakes with smaller GR (Fig. 5). Four deep lakes (LakeR41–LakeR44) were added to have the same geometry ratio but different surface areas as four medium-depth lakes LakeR10–LakeR12 and LakeR31 to compare fish habitat parameters in these two groups of lakes (Fang et al. 2014). With above eight additional regional lakes, there

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are a total of 44 representative lakes (Table 1 and Fig. 5 top) used for cisco habitat modeling in Minnesota. These representative or regional values for each parameter (As, Hmax, and SD in Table 1) were selected from the data analysis of these parameters in the Minnesota Lakes Fisheries Database containing lake survey data for 3,002 lakes (Hondzo and Stefan 1993) and 620 cisco lakes (Fang et al. 2009). The lake bathymetry of these 44 lakes (the shape of the lake basin as part of model input data) is characterized by three functions A(z)/As versus z/Hmax for small, medium-size, and large lakes, which are regression equations developed from 122 Minnesota lakes by Hondzo and Stefan (1993). More important than the geometric characteristics of each lake type is the likelihood of relating a strong or weak stratification in a lake to the lake’s geometry ratio GR = As0.25/Hmax (Gorham and Boyce 1989). The above 44 lake types cover geometry ratios from 0.88 to 14.06 m
0.5 (Table 1). Polymictic lakes, i.e., large shallow lakes, have the highest geometric ratio, while strongly stratified lakes, i.e., small deep lakes, have the lowest geometry ratio. Hence, these 44 lake types selected for the study include the full range of stratification behavior. The set of 30 virtual cisco lakes (Fig. 5 bottom) comprised lakes with five different SD values (1.2, 2.5, 4.5, 7.0, and 8.5 m) and six different surface areas (0.1, 0.5, 1.5, 5.0, 13.0, and 50 km2). The maximum depth of all 30 virtual lakes was set at 24 m (Fang et al. 2009, 2012; Jiang et al. 2012). Combinations of the maximum depth and surface areas gave six different geometry ratios for the 30 virtual lakes, i.e., 0.74, 1.11, 1.46, 1.97, 2.50, and 3.50 m
0.5 (Table 2). Twenty of the 30 virtual cisco lake types were strongly stratified with GR < 2 m
0.5; the other ten lake types were weakly stratified (Table 2). The 30 virtual cisco lakes (Table 2) did not include any polymictic lakes because they likely would not provide suitable cold-water habitat in Minnesota after climate warming. For example, Fang et al. (2012a) studied the oxythermal habitat variable TDO3 in 21 study cisco lakes in Minnesota and found that two cisco lakes with GR > 4.0 m
0.5 (White Iron Lake and South Twin Lake) had high annual maximum TDO3 values indicating unfavorable conditions for cisco survival and growth. Values of TDO3 extracted from observed temperature and DO profiles were lowest in lakes with small geometry ratios (GR < 2 m
0.5); a geometry ratio of 4 m
0.5 effectively marked the transition between stratified and unstratified lakes (Jacobson et al. 2010). Even though the lake bathymetry (surface area and maximum depth) and Secchi depth of the 30 virtual cisco lakes were subjective, the selected values were representative of most of the 620 Minnesota cisco lake database (Fang et al. 2009). The 30 virtual cisco lakes were all stratified lakes based on geometry ratio and included eutrophic to oligotrophic lakes (Table 2). The 30 virtual cisco lakes were more or less uniformly distributed on the plot of SD vs. GR (Fig. 5 bottom) (Fang et al. 2012b). None of the 30 virtual cisco lakes in Table 2 have the same lake surface areas as 44 representative Minnesota lakes in Table 1. These two groups of generic lake classes (types) were used in the different fish habitat modeling options (Fig. 3): 44 representative lakes for the oxythermal model options 1 and 2 using lethal limits (Fig. 5) and 30 virtual lakes for the model option 1 (constant lethal limits) and the model option 3 using TDO3 (Fig. 3) during different study periods.

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Past Climate and Future Climate Scenarios Climate conditions control water temperature and DO distribution in a lake. Climate scenarios are model inputs of MINLAKE2010/MINLAKE2012 for producing water temperature and DO concentration scenarios for the simulated lakes (Tables 1 and 2), which are used to assess potential changes in cisco habitats in these lakes. To identify refuge lakes, we need to project whether a lake that currently has a cisco population can support cisco habitat under future climate scenarios, i.e., after climate warming. To make the projection, the model outputs from two coupled general circulation models (CGCMs) of the earth’s atmosphere and oceans (i.e., CGCM 3.1 and MIROC 3.2) were used as input to the MINLAKE2010 model to calculate a range of future water quality conditions. Forty-eight years (1961–2008) of recorded daily weather data, which were obtained from the Solar and Meteorological Surface Observation Network (SAMSON) and Midwestern Regional Climate Center, were used to describe past climate conditions for the study lakes. Weather data used for lake modeling consist of daily air temperature, dew point temperature, wind speed, solar radiation, percent sunshine, and precipitation (both rainfall and snowfall). The CGCM 3.1 (Kim et al. 2002, 2003) is the third generation of CGCMs from the Canadian Centre for Climate Modeling and Analysis (CCCma). The CCCma CGCM 3.1 uses the ocean component from the earlier second-generation CGCM (McFarlane et al. 1992) and applies a substantially updated atmospheric component – the third-generation atmospheric general circulation model. Output of the CGCM 3.1 model with a coarse global surface grid resolution of roughly 3.75 latitude and longitude or approximately 410 km in Minnesota was used for the study because it was available to be downloaded from the Intergovernmental Panel on Climate Change (IPCC) data center in 2008. When CGCM 3.1 is used for the study, there is one grid center point within Minnesota and another grid center point in Canada that is the closest grid point to International Falls weather station (Fig. 1). The MIROC 3.2 (Hasumi and Emori 2004) was developed by the Center for Climate System Research, University of Tokyo; the National Institute for Environmental Studies; and the Frontier Research Center for Global Change – Japan Agency for Marine-Earth Science and Technology. Output of the MIROC 3.2 model with a high spatial surface grid resolution of roughly 1.12 latitude and longitude or approximately 120 km in Minnesota was used. The MIROC 3.2 model has 17 grid center points in Minnesota, and Fig. 1 shows three grid center points from MIROC 3.2 that are the closest to three weather stations (St. Cloud, Duluth, and International Falls) used for the model study. At all CGCM grid center points, the differences or ratios known as “change fields” were produced and reported at a monthly interval. The 2070–2099 change field data, 30-year averages compatible with the Third Assessment Report of the IPCC (2007), were downloaded from the IPCC’s website and used in the study. These monthly climate parameter differences or ratios predicted by CGCM models were then applied to measured daily climate conditions (1961–2008) month by month to produce the projected daily future climate scenario. Monthly increments

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Table 3 Monthly changes of air temperature ( C) and solar radiation (Langley/day) projected by MIROC 3.2 and CGCM 3.1 for the three principal Minnesota weather stations Station Month January February March April May June July August September October November December Average

International Falls Taira SRADb 5.15/
20.34/ 6.89c
5.69 4.70/
25.36/ 5.07
9.51 4.64/
24.84/1.83 3.90 4.52/
3.32/ 4.31
18.77 4.37/
4.43/ 4.12
29.39 3.62/
3.82/9.63 4.59 3.53/
5.38/14.43 3.80 3.75/
0.49/7.63 3.30 3.80/ 16.57/10.69 3.49 4.46/
2.76/3.94 3.19 4.10/
4.22/ 2.89
1.82 4.18/
12.80/ 4.14
4.86 4.24/
7.60/ 4.14
1.82

Duluth Tair 4.67/ 4.84 4.67/ 8.09 4.53/ 6.25 3.89/ 3.60 4.21/ 3.47 3.59/ 3.28 3.68/ 3.25 3.82/ 3.32 3.81/ 3.34 4.29/ 3.39 3.89/ 3.06 3.99/ 2.91 4.09/ 4.07

SRAD
15.92/
7.49
23.42/
38.21
15.75/
55.05 0.78/
25.99
15.22/
19.96
3.92/2.28 10.36/14.78 0.52/3.66 19.27/0.34 3.03/
1.18 3.03/
2.16
9.24/
1.32
3.87/
10.86

St. Cloud Tair 4.35/ 4.84 4.17/ 8.09 4.08/ 6.25 3.88/ 3.60 4.33/ 3.47 3.67/ 3.28 3.67/ 3.25 3.92/ 3.32 3.87/ 3.34 4.30/ 3.39 3.87/ 3.06 3.92/ 2.91 4.00/ 4.07

SRAD
4.60/
7.49
9.13/
38.21 0.35/
55.05 9.80/
25.99 2.45/
19.96 0.36/2.28 17.08/14.78 3.64/3.66 24.14/0.34 9.87/
1.18 5.79/
2.16
3.16/
1.32 4.71/
10.86

Conversion of temperature changes, 1.0  C = 1.8  F Stands for solar radiation, 1.0 Langley/day = 0.484 W/m2 c The first value is for MIROC 3.2 and the second value is for CGCM 3.1

a

b

from the grid center point closest to a weather station were used to specify the future climate. For the MIROC 3.2 future climate scenario, each of the three Class I NWS weather stations (International Falls, Duluth, and St. Cloud) used for the study had a closest grid center point (Fig. 1); for the CGCM 3.1 future climate scenario, Duluth and St. Cloud used the grid center point in Minnesota (Fig. 1), and International Falls used a grid center point in Canada (Fig. 1). Monthly air temperature increases projected by MIROC 3.2 range from 3.53  C to 4.70  C with annual averages of 4.00–4.24  C for the three weather stations (Table 3); CGCM 3.1 projection has a range from 2.89  C to 8.09  C with annual averages of 4.07–4.14  C for the three weather stations (Fang et al. 2010b). The average monthly increases in air temperature range from 3.6  C to 4.7  C (3.6–3.8  C from July to September) in Bemidji, Minnesota, which was also used for the oxythermal habitat option 2 (lethal-niche-boundary curve).

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Simulation and Validation of Cisco Oxythermal Habitat Using the Constant Lethal Limits The physiological response of adult populations of different fish species to T and DO levels has been the subject of numerous laboratory and field studies, e.g., by Coutant (1970), McCormick et al. (1972), and Hokanson et al. (1977). These studies correlated fish survival, growth, reproduction, and other responses to chronic levels of T and DO exposure. Simulation of oxythermal fish habitat in lakes was conducted in small lakes (up to 10 km2 surface area) in Minnesota and the contiguous USA using constant survival limits (Stefan et al. 1996; Fang et al. 2004a, b). The oxythermal habitat approach commonly used in cold-water fish niche modeling (Dillon et al. 2003) defines an upper boundary for T and a lower boundary for DO, which are lethal temperature (LT) and DO survival limit (DOlethal). These oxythermal habitat models determine the water volume or layer thickness in a stratified lake between the upper temperature and lower DO bound that represent either optimal thermal habitat (Dillon et al. 2003) or nonlethal/useable habitat (Stefan et al. 2001). The “uninhabitable spaces” or “lethal conditions” for a fish species in a lake are where temperature is above or DO is below the survival limits (Stefan et al. 2001). This study uses the oxythermal habitat approach to investigate the lethal conditions and fish kill in summer for a cold-water fish species – cisco Coregonus artedi in Minnesota lakes. The goals of the study were to first validate cisco survival and lethal conditions in 23 Minnesota lakes (Tables 4 and 5) under 2006 weather conditions and then simulate daily T and DO profiles in 58 lake types (28 shallow and medium-depth lakes in Table 1 and 30 virtual lakes in Table 2) to project cisco survival and potential lethal conditions in 620 cisco lakes in Minnesota under future climate scenarios. The 28 shallow and medium-depth lakes include LakeR01–LakeR18, LakeR28–LakeR33, and LakeR37–LakeR40 in Table 1.

Prediction of Cisco Lethal Conditions Using Constant Lethal Limits Cisco habitat (survival) and lethal conditions were determined using simulated daily T and DO profiles in lakes, similar to the approach by Christie and Regier (1988). Temperature and DO limits of lethal conditions for adult cisco were applied to simulated water temperature and DO profiles day by day to examine whether lethal conditions occur or not in each specific day, as shown in Fig. 6 for Pine Mountain Lake. Simulated T and DO profiles were not averaged during the simulation period as was done in previous studies (Stefan et al. 1996; Fang et al. 2004a, b). When the LT isotherm and the DOlethal isopleth for cisco intersect in a particular day, the entire depth of a stratified lake is under lethal conditions on that day. The lethal conditions are because water temperature is higher than LT from the water surface to or below the intersecting depth and DO is lower than the DOlethal from the lake bottom to or above the intersecting depth. When the maximum daily water temperature is lower than the LT, the LT isotherm does not show up in the plot of depth-time contours of cold-water habitat (the upper and lower good-growth temperature contours used in previous

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Table 4 Lethal days simulated in the 23 Minnesota lakes using the constant value method with eight different lethal temperatures (LT) and DO survival limits Number of days with lethal conditions 22.0  C LT = 23.4  C DO = 2 mg/L 3 mg/L 4 mg/L 2 mg/L 3 mg/L 27 29 29 58 62 19 25 29 55 60 1 3 5 5 15

Lake name Little Turtle Andrusia Little Pine (Otter Tail) Cotton 37 37 37 Pine Mountain 12 23 35 Leech 24 24 24 Itasca 26 27 28 Gull 0 0 17 Woman 27 28 29 Little Pine 18 50 68 (Crow Wing) Eighth Crow 28 29 30 Wing Straight 0 0 5 Mille Lacs 36 36 36 Star 0 0 6 Bemidji 0 0 0 Seventh Crow 3 10 19 Wing Long 0 0 0 Carlos 0 0 0 Reference lakes without cisco kills in 2006 Big Trout 0 0 0 Kabekona 0 0 0 Scalp 0 0 0 Ten Mile 0 0 0 Rose 0 0 0

21.2  C 22.6  C 4 mg/L 63 66 21

2 mg/L 71 62 15

4 mg/L 48 51 15

57 35 36 48 4 55 39

58 56 36 53 22 59 54

59 72 36 55 54 61 106

72 56 56 59 29 67 59

52 55 28 41 28 46 45

53

55

47

67

47

0 49 9 0 17

5 49 15 7 20

20 49 29 16 23

2 56 16 6 22

9 43 20 8 20

0 0

4 0

12 0

1 0

7 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

studies were not used/shown in Fig. 6). Therefore, the DO lethal/survival limit becomes the only lethal criterion during the early spring, late fall, and winter ice-cover period, but the study only deals with lethal conditions of cisco during the summer months. Figure 6 shows cisco habitat results from April 15 to October 31, i.e., the day of year (DOY) 105–304, which is during the typical open-water season in Minnesota. The LT is the water temperature to which fish cannot be acclimated without causing death. In early publications by authors, it was called as the upper survival temperature limit (Stefan et al. 1996), which was the 95th percentile of the weekly mean temperatures from an updated national fish/temperature database (Stefan

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Table 5 Maximum depth (Hmax), lake geometry ratio (GR), and fish habitat validation results using constant lethal limits (LT = 22.0  C and DOlethal = 3 mg/L) for 23 Minnesota lakes. Simulated and observed days with lethal conditions or mortality for cisco are given Lake name (Hmax in m, GR in m
0.5) Little Turtle (9.1, 4.02) Andrusia (18.3, 2.75) Little Pine (Otter Tail) (23.8, 2.26) Cotton (8.5, 6.07) Pine Mountain (23.8, 2.11) Leech (13.0, 10.91) Itasca (13.7, 3.32) Gull (24.4, 3.26) Woman (16.5, 4.02) Little Pine (Crow Wing) (11.0, 2.90) Eighth Crow Wing (9.1, 4.11) Straight (19.2, 1.94) Mille Lacs (10.7, 14.14) Star (28.7, 2.26) Bemidji (23.2, 3.13) Seventh Crow Wing (12.2, 2.61) Long (39.0, 1.22) Carlos (49.7, 1.15)

Lethal conditions No. First Last of day day days 180 241 62

Simulated lethal days in 2006 180 (62)a

Observed mortality day in 2006 200 (7/19)b

Model agreement Yes (Yes)c

192

251

60

192 (59)

202 (7/21)

Yes (Yes)

202

216

15

202 (15)

203 (7/22)

Yes (Yes)

184

241

58

184 (58)

205 (7/24)

Yes (Yes)

193

250

56

193 (36); 232 (20)

207 (7/26)

Yes (Yes)

188

225

36

211 (7/30)

Yes (Yes)

188

241

53

188 (3); 193 (31); 224 (2) 188 (38); 227(15)

209 (7/28)

Yes (Yes)

206

227

22

206 (22)

210 (7/29)

Yes (Yes)

183

241

59

183 (59)

210 (7/29)

Yes (Yes)

186

250

54

214 (8/2)

Yes (Yes)

187

241

55

186 (2); 192 (34); 227 (2); 230 (7); 238 (4); 246 (5) 188 (55)

216 (8/4)

Yes (Yes)

211

215

5

211 (5)

213 (8/1)

Yes (Yes)

203

251

49

203 (49)

204 (7/23)

Yes (Yes)

203

217

15

203 (15)

200 (7/19)

Yes (No)

211

217

7

211 (7)

208 (7/27)

Yes (No)

196

215

20

196 (20)

216 (8/4)

Yes (No)

213

216

4

214 (4)

218 (8/6)

Yes (No)

0

No kill

239 (8/27)

No (continued)

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Table 5 (continued) Lethal conditions No. Lake name Last of (Hmax in m, GR First day day days in m
0.5) Reference lakes without cisco kill in 2006 Big Trout 0 (39.0, 1.24) Kabekona 0 (40.5, 1.38) Scalp (27.4, 0 1.15) Ten Mile (63.4, 0 1.06) Rose (41.8, 0 1.12)

Simulated lethal days in 2006

Observed mortality day in 2006

Model agreement

No kill

No kill

Yes

No kill

No kill

Yes

No kill

No kill

Yes

No kill

No kill

Yes

No kill

No kill

Yes

Note: Stands for a DOY in 2006 and the number of continuous cisco lethal days from the lethal day predicted by the fish habitat model b DOY followed by month and date in 2006 inside brackets c The first Yes/No gives the agreement of cisco lethal prediction and reported cisco mortality in 2006, and Yes/No inside brackets gives the agreement whether or not cisco lethal days from the model include reported date with cisco mortality a

et al. 1996) that includes observed stream temperature and fish observations. The LT values compared favorably with laboratory test results involving exposures of fish in several days (Stefan et al. 1992). The LT = 23.4  C used for the cold-water fish guild in previous studies (Stefan et al. 1996, 2001) was the mean value of LT values for nine cold-water fish species (pink salmon, sockeye salmon, chinook salmon, chum salmon, coho salmon, brown trout, rainbow trout, brook trout, and mountain whitefish), which do not include cisco. The LT of cold-water fish species ranged from 22.1  C (brook trout) to 26.6  C (brown trout) in the 1992 study (Stefan et al. 1996). Eaton et al. (1995) updated the LT values that ranged from 19.8  C (chum salmon) to 24.1  C (brown trout) with guild mean of 22.9  C. The DO concentration of 3.0 mg/L requirement for the cold-water fish guild, below which mortality is more likely to occur or growth is impaired (US EPA 1976), was developed from an available US EPA database (Chapman 1986). Jacobson et al. (2010) selected a benchmark oxygen concentration of 3 mg/L which is probably lethal or nearly so for many cold-water species. Frey (1955) also used an oxygen concentration of 3 mg/L in his definition of cisco habitat. Several benchmark DO concentrations (2, 3, 4, and 5 mg/L) were considered by Jacobson et al. (2010), and they were highly correlated (Table 4). Before appropriate LT and DO limits for adult cisco were determined/used for cisco oxythermal modeling, a sensitivity analysis using 22.0  C and 23.4  C as LT and 2, 3, and 4 mg/L as DO survival limit was performed. The lethal temperature of 22.0  C used for the sensitivity analysis was determined from the lethal-nicheboundary curve (Jacobson et al. 2008) (Eq. 3) at DOlethal = 3 mg/L. The 22.0  C

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Fig. 6 Simulated isopleths of lethal temperatures (LT) and DOlethal limits in 2006 for Pine Mountain Lake for eight LT-DO limit combinations. Selected LT are 21.2 and 22.6  C (top) 22.0  C (middle) and 23.4  C (bottom), and selected DO survival limits are 2, 3, and 4 mg/L

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LT compares favorably with lower LT values for other cold-water fish species in previous studies (Stefan et al. 1992, 1996; Eaton et al. 1995). Using the lethal-nicheboundary curve (Jacobson et al. 2008), the lethal temperatures were 21.2  C and 22.6  C for DOlethal = 2 and 4 mg/L, respectively, and these two LT-DO combinations were also used for the sensitivity analysis (Table 4). The combinations of four LTs and three DO limits resulted in eight constant LT-DO criteria for the sensitivity analysis of the fish habitat model. The final LT-DO criteria, LT = 22.0  C and DOlethal = 3 mg/L, were chosen according to fish habitat model validation based on the cisco mortality field data in 2006 (Table 5) as discussed below.

Fish Habitat Model Validation in 23 Lakes Against 2006 Observations The fish habitat model uses simulated daily temperature and DO profiles in a lake to check day by day whether cisco habitable or lethal conditions occur. When the LT isotherm and the DO limit isopleth for cisco do not intersect in a day, cisco is survivable in that day; otherwise, the entire depth of a stratified lake is under lethal conditions (too warm temperature or lower DO) in that day. A number of days with cisco lethal conditions in 2006 under eight different criteria are given in Fig. 5 for Pine Mountain Lake as an example. Pine Mountain Lake located in Cass County, Minnesota, has a maximum depth of 23.8 m (deep lake) and a surface area of 6.36 km2. Pine Mountain Lake is a mesotrophic stratified lake because of its mean Secchi depth 2.4 m, mean chlorophyll-a concentration 6.5 μg/L, and GR = 2.11. The total number of lethal days in 2006 increases from 35 to 72 days (Fig. 5b and Table 4) when LT = 22.0  C and the DO survival limit changes from 2 to 4 mg/L. The 35 lethal days were not continuous as shown in Fig. 5b and were 2 days from DOY 196 (July 15–16, 2006), 26 days from 199 (July 18–August 12), 1 day from 227 (August 15), 2 days from 235 (August 23–24), and 4 days from 238 (August 26–29). When LT increases to 23.4  C (mean LT for cold-water fish species not including cisco), the total number of lethal days in 2006 decreases to 12–35 days (Fig. 5c), respectively, when DO survival limits are 2–4 mg/L. With LT = 21.2  C and DOlethal = 2 mg/L limits (Fig. 5a) from the lethal-nicheboundary curve, Pine Mountain Lake was simulated to have 56 lethal days: 196 (37 continuous lethal days) and 230 (21); with LT = 22.6  C and DOlethal = 4 mg/L limits, Pine Mountain Lake was simulated to have 55 lethal days: 186 (2), 192 (37), 231 (13), and 248 (3). With DOlethal = 4 mg/L, lethal days started 10 days early, but prevalent lethal conditions occurred after DOY 196 (July 15) for all three LT-DO limit combinations from the lethal-niche-boundary curve. Results of the sensitivity analysis using eight LT and DO survival limits in all 23 lakes are summarized in Table 4. The period of lethal conditions (lethal days) simulated using LT = 22.0  C is typically longer than the lethal days using LT = 23.4  C. When DO survival limits increase from 2 to 4 mg/L, cisco lethal days increase also (Fig. 5 and Table 4) when LT is fixed. Using LT = 23.4  C, Star, Little Pine (Otter Tail County), Bemidji, Gull, Straight, Long, and Carlos lakes were simulated to have zero or a few days with lethal conditions in 2006, which indicates

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that LT = 23.4  C used in previous studies for the cold-water guild is little too high for cisco. Three LT-DO combinations, where LT was computed using DOlethal and the cisco lethal-niche-boundary curve, resulted very similar values on lethal days in 23 lakes (Table 4), but starting days of lethal conditions could be somewhat different as shown in Fig. 5 for Pine Mountain Lake. Detailed simulation results of LT = 22.0 and DOlethal = 3 mg/L are given in Table 5 for 23 Minnesota lakes for model validation against cisco mortality observations in 2006. Table 5 lists the first DOY, the last DOY, and the total number of days with cisco lethal conditions predicted by the model (using constant lethal limits) in 2006 (hindcast or backtesting). It also lists the DOYs and the number of continuous days from DOYs with cisco lethal conditions predicted in 2006. For example, Little Turtle Lake has “180(62)” under “simulated lethal days in 2006” in Table 5 that means lethal conditions were simulated on DOY 180 (June 29, 2006) and the number of continuous cisco lethal days is 62 (DOYs 180–241). The DOY and month/day in 2006 inside brackets when cisco mortality was reported in each lake is listed under “observed mortality date in 2006” in Table 5 and used to examine model agreement. In the last column of Table 5, the first Yes/No gives the agreement of cisco lethal prediction and reported cisco mortality in 2006, and the second Yes/No inside brackets gives the agreement whether or not cisco lethal days from the model include reported date with cisco mortality. For example, for Mille Lacs Lake, the fish habitat model predicted a total of 49 days from July 22 (DOY 203) to September 8 (DOY 251) having cisco lethal conditions, which agree with reported cisco mortality in 2006, and the period of predicted cisco lethal conditions includes the reported day with cisco mortality, i.e., July 23 or DOY 204. Therefore, the model agreement with mortality observation is “Yes (Yes)” as listed in Table 5. The fish habitat model predicted the “Yes (Yes)” agreement in 13 of the 18 lakes that experienced cisco mortality in 2006 (Table 5). For four lakes (Bemidji, Star, Seventh Crow Wing, and Long), the model predicted cisco lethal conditions, but the predicted lethal periods did not include corresponding reported cisco mortality days in 2006; these lakes have the “Yes (No)” agreement (Table 5). For two lakes (Bemidji and Star), cisco lethal conditions were predicted to occur after the reported cisco mortality days in 2006. For Seventh Crow Wing Lake, cisco lethal conditions were predicted to occur from DOY 196 to 215 (August 3) in 2006, and the cisco mortality was reported on August 4. For Long Lake, cisco lethal conditions were predicted to occur from DOY 213 to 216 (August 4) in 2006, and the cisco mortality was reported on August 6. These two cases can be considered as “Yes (Yes)” agreement because cisco mortality might be reported one or a few days after cisco mortality occurred when study lakes were not constantly monitored and observed. Long Lake was predicted with only 4 days of lethal conditions. Long Lake located in Otter Tail County, Minnesota, has a maximum depth of 39.0 m (deep lake) and a surface area of 5.1 km2. Long Lake is a mesotrophic strongly stratified lake because of its mean Secchi depth 3.0 m, mean chlorophyll-a concentration 7.2 μg/L, and GR = 1.22. There was only 1 day in 2006 with observed temperature and DO profiles in Long Lake for model calibration.

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For Lake Carlos, the model did not predict cisco lethal conditions, but they had cisco mortality in 2006; the model has the No agreement with mortality observation (Table 5). Lake Carlos located in Douglas County, Minnesota, has a maximum depth of 49.7 m and a surface area of 10.5 km2. Lake Carlos is a mesotrophic strongly stratified lake because of its mean Secchi depth 3.0 m, mean chlorophyll-a concentration 5.0 μg/L, and GR = 1.15. The late summer cisco mortality event that occurred on August 27 (DOY 239) in Lake Carlos did not fit the lethal-nicheboundary curve developed from the midsummer events in 16 lakes (Jacobson et al. 2008). Based on the lethal-niche-boundary curve and measured profiles on September 1, 2006 (Jacobson et al. 2008), cisco could exist in some surface layers with low temperatures and high DO, but could not exist in the hypolimnion with anoxic conditions. Jacobson et al. (2008) also studied the five reference lakes that did not experience cisco mortality in 2006. These five reference lakes are all deep strongly stratified lakes (GR < 1.4). The fish habitat model using constant LT and DOlethal limits predicted no lethal conditions for cisco in all five reference lakes (Table 5). Therefore, the fish habitat model has an overall good agreement in the 23 study lakes with and without cisco mortality reported in 2006. With LT = 21.2  C and DOlethal = 2 mg/L limits, Straight Lake changed the agreement from “Yes (Yes)” to “Yes (No)” and Seventh Crow Wing from “Yes (No)” to “Yes (Yes)”; with LT = 22.6  C and DOlethal = 4 mg/L limits, Star Lake changed the agreement from “Yes (No)” to “Yes (Yes)” when comparing with cisco mortality observations in 2006. Therefore, three LT and DOlethal limit combinations derived from the lethal-niche-boundary curve (Jacobson et al. 2008) had similar overall agreement with cisco mortality observations in 2006, and all of them could be used for constant lethal limits for adult cisco when there is no additional laboratory and field data support on survival limits. To be consistent with previous studies with DO survival limit (Stefan et al. 1996, 2001; Fang et al. 2004a, b) or benchmark DO (Jacobson et al. 2010; Fang et al. 2012b; Jiang et al. 2012) of 3 mg/L, LT = 22.0  C and DOlethal = 3 mg/L limits were used for remaining of the oxythermal habitat modeling with constant lethal limits.

Simulation Results of 28 Regional Lakes and 30 Virtual Lakes Both 28 regional lakes (LakeR01–LakeR18, LakeR28–LakeR33, and LakeR37– LakeR39 in Table 1) and 30 virtual lakes (Table 2) were first simulated using MINLAKE2010 to generate daily temperature and DO profiles over 48 simulation years (1962–2008 under the past climate conditions) and then simulated using the fish habitat model to determine number of days with cisco lethal conditions year by year (excluding the first year 1961) using constant lethal limits. Results for shallow or medium-depth lakes and deep lakes were grouped and analyzed separately as recommended by Fang et al. (2014). Figure 7 shows contour plots of total number of years with cisco lethal days and average cisco kill days for the years with lethal conditions under past (left frames)

Fig. 7 Contour plots of total number of years with cisco lethal days (top) and average cisco lethal days for the years with lethal conditions (bottom) under past (1962–2008) and future (MIROC 3.2) climate scenarios. Duluth weather data were used for model simulations. Contours were derived by interpolation from simulated points for 28 regional lakes

684 X. Fang et al.

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685

and future (right frames for MIROC 3.2) climate scenarios. Duluth weather data over 47 years were used for model simulation results presented in Fig. 7. Contours were derived by interpolation from simulated points for 28 regional/representative lakes (Fig. 7). Under the past climate conditions, shallow lakes with a geometry ratio >5.2 m
0.5 had 23–47 years with lethal conditions and are projected to have all 47 years with lethal days under MIROC 3.2 (Fig. 7). Average cisco lethal days for the years with lethal conditions under the past climate conditions ranged from 11 to 26 days and are projected to range from 38 to 65 days in shallow lakes under MIROC 3.2 climate scenario. Therefore, 12 shallow regional lakes simulated (Fig. 7) and 14 shallow lakes in the 620 cisco lake database (Fig. 5) do not provide suitable habitat conditions to support cisco. For 16 medium-depth lakes simulated under the past climate conditions, there were 0 (GR = 1.63 m
0.5) to 8 (GR = 4.33 m
0.5) years with lethal conditions with average lethal days ranging from 0 to 14 days (Fig. 7). Under future climate scenario (MIROC 3.2), they are projected to have 20–47 years with lethal conditions with average lethal days ranging from 12 to 52 days (Fig. 7). It seems medium-depth lakes are most vulnerable to climate change with largest increases in lethal days and the number of years with lethal conditions. When simulated under past climate conditions, none of the 30 virtual deep lakes (Hmax = 24 m) have cisco lethal conditions; therefore, deep lakes provided relatively good fish habitats compared with shallow and medium-depth lakes. The results for virtual deep lakes under past climate conditions seem to have certain disagreement with cisco mortality observations (Table 5). Minnesota lakes with Hmax < 20.0 m were classified as deep lakes (Stefan et al. 1996), and 7 of the 18 study lakes with cisco mortality in 2006 are deep lakes with GR ranging from 1.15 to 3.26 m
0.5 (Table 5). Generalized model parameters (Fang et al. 2010a) were used for simulations in the 30 virtual lake types, but for the 23 lakes (Table 5), model parameters were first calibrated against measured profiles before the cisco habitat model was applied. Differences in model parameters are one of the major reasons for disagreement in habitat projections and suggest that other oxythermal habitat parameters, e.g., TDO3 (Jacobson et al. 2010), should be used for studying cisco fish habitat in relatively deep lakes. Under the future climate scenario (MIROC 3.2), projected number of years with lethal conditions are up to 20 years with average lethal days up to 23 days (Fig. 8). Distributions of total number of years with cisco lethal days and average cisco kill days for the years with lethal conditions under MIROC 3.2 are given in Fig. 8, which shows eutrophic deep lakes are projected with more lethal days. Figure 8 was not used to divide 221 deep cisco lakes into tier 1 to tier 3 refuge lakes as it was done using TDO3 (Fang et al. 2012; Jiang et al. 2012). Under MIROC 3.2 climate scenario, those lakes with 0–2 years (Fig. 8 top) and 0–3 days (Fig. 8 bottom) with lethal conditions provide most suitable habitat for cisco, and lakes with 2–5 years (Fig. 8 top) and 3–6 days (Fig. 8 bottom) with lethal conditions also provide suitable habitat for cisco. Good-growth habitat areas and volumes used in previous studies (Stefan et al. 1996, 2001; Fang et al. 2004a, b) may be used to classify refuge lakes in the future.

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Fig. 8 Contour plots of total number of years with cisco lethal days (top) and average cisco lethal days for the years with lethal conditions (bottom) in deep lakes under future (MIROC 3.2) climate scenarios. Duluth weather data were used for model simulations. Contours were derived by interpolation from simulated points for 30 virtual lakes (Hmax = 24 m)

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Simulation and Validation of Cisco Oxythermal Habitat Using the Lethal-Niche-Boundary Curve For oxythermal habitat modeling of cisco, the second option is to use temperaturevarying lethal DO limits. The fish habitat model uses a fitted regression equation as the lethal niche boundary of adult cisco developed by Jacobson et al. (2008). The equation mapped the DO concentrations and water temperatures from the profiles measured in 16 Minnesota lakes that experienced cisco mortality in 2006 (Table 6). One profile was measured in each of the 16 lakes on the same day or a few days after reported mortality. The shifted exponential function given in Eq. 3 is a fit of the 99th quartile nonparametric regression line bracketed the lethal combinations of observed oxygen and temperature in 16 lakes with midsummer (July 19 to August 6) mortality events in 2006 (Jacobson et al. 2008). First, the fish habitat model (the option 2) was validated in 23 Minnesota lakes (Table 6) and then used to project cisco lethal conditions in 36 representative lake types under past (1992–2008) climate conditions and a future climate scenario (MIROC 3.2) at Bemidji, Minnesota. Based on the number of years with cisco kill and the number of annual cisco lethal days, lake types that most likely could not support cisco were identified, and lake types that can support cisco under both the past climate and the future climate scenarios were identified as potential cisco refuge lakes.

Fish Habitat Projection Model There were 18 Minnesota lakes with reported cisco mortality in 2006, but no temperature and DO profiles were measured in Mille Lacs Lake, which had automated temperature recorder data at 2 m below the surface (Jacobson et al. 2008). Measured temperature and DO profiles in Lake Carlos, which had late summer cisco mortality event occurred on August 27, were not used to fit the lethal-niche-boundary curve (Jacobson et al. 2008). The lethal-niche-boundary curve of adult cisco developed from measured temperature and DO profiles in 16 Minnesota lakes is given as DOlethal ¼ 0:40 þ 0:0000060 e0:59T

(3)

where DOlethal and T are the DO concentrations (in mg/L) and the water temperatures (in  C), respectively, which define the lethal niche boundary (Jacobson et al. 2008). The computed DOlethal is the required minimum DO concentration at a given water temperature T for cisco to survive. For the regression Eq. 3, the coefficients 0.40 and 0.0000060 are in mg/L, and the coefficient 0.59 for the exponent is in  C
1. Equation 3 indicates the DO survival limit for adult cisco is not constant but depends on water temperature. The lethal-niche-boundary curve for cisco (Eq. 3)

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Table 6 Fish habitat validation results using the lethal-niche-boundary curve for 23 Minnesota lakes. Simulated and observed days with lethal conditions for cisco are given as DOYs

Lake name Little Turtle

Lethal conditions No. First Last of day day days 181 222 38

Andrusia Little Pine (Otter Tail) Cotton

192 199

234 216

39 17

188

225

36

Pine Mountain Leech

192

241

49

189

217

22

Itasca Gull Woman

189 209 183

222 227 224

31 19 36

Little Pine 183 240 48 (Crow Wing) Eighth Crow 195 222 28 Wing Bemidji 212 217 6 Mille Lacs 216 241 26 Star 212 216 5 Seventh 196 215 20 Crow Wing Long 216 216 1 Carlos 0 Straight 0 Reference lakes without cisco kill in 2006 Big Trout 0 Kabekona 0 Scalp 0 Ten Mile 0 Rose 0

Observed mortality day in 2006 200 (7/19)b

Model agreement Yes (Yes)c

202 (7/21) 203 (7/22)

Yes (Yes) Yes (Yes)

188(1), 190(1), 192(34) 192(45), 238(4)

205 (7/24)

Yes (Yes)

207 (7/26)

Yes (Yes)

189(1), 195(6), 203(15) 189(1), 193(30) 209(19) 183(2), 187(1), 190(1), 193(32) 183(5), 190(39), 233(3), 240(1) 195(28)

211 (7/30)

Yes (Yes)

209 (7/28) 210 (7/29) 210 (7/29)

Yes (Yes) Yes (Yes) Yes (Yes)

214 (8/2)

Yes (Yes)

216 (8/4)

Yes (Yes)

212(6) 216(26) 212(5) 196(20)

208 (7/27) 204 (7/23) 200 (7/19) 216 (8/4)

Yes (No) Yes (No) Yes (No) Yes (No)

216(1) No kill No kill

218 (8/6) 239 (8/27) 213 (8/1)

Yes (No) No No

No kill No kill No kill No kill No kill

No kill No kill No kill No kill No kill

Yes Yes Yes Yes Yes

Simulated lethal days in 2006 182(4)a, 187(4), 193(30) 192(37), 233(2) 199(1), 201(16)

Note: a Stands for a DOY in 2006 and the number of continuous cisco lethal days from the lethal day predicted by the fish habitat model b DOY followed by month and date in 2006 inside brackets c The first Yes/No gives the agreement of cisco lethal prediction and reported cisco mortality in 2006, and Yes/No inside brackets gives the agreement whether or not cisco lethal days from the model include reported date with cisco mortality

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Fig. 9 Simulated DO versus simulated temperature for selected days to show three different types of fish habitat in Pine Mountain Lake and Lake Itasca (Table 6) and the lethal-niche-boundary curve of adult cisco (Eq. 3)

was plotted in Fig. 9 for two Minnesota lakes (Pine Mountain Lake and Lake Itasca). In comparison with the previous constant lethal temperature for cold-water fish species, i.e., 23.4  C (Eaton et al. 1995), the lethal temperature from Eq. 3 is only 22.0  C when 3.0 mg/L is used as the DO survival limit (Jacobson et al. 2008). In this study, the required DO concentration DOlethal was computed from the simulated water temperature in each water layer of a lake for each simulated day using Eq. 3. The computed DOlethal value was then compared with the simulated DO concentration in the same layer; lethal conditions for cisco were assumed to occur if the simulated DO concentrations were less than the DOlethal values in all water layers (from the lake water surface to the lake bottom) on that day. In Fig. 9, simulated DO was plotted against simulated temperature for selected days in the two lakes. In Pine Mountain

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Lake, from July 11, 2006 (DOY 192 in Table 6) to August 29, 2006 (DOY 241), there were 49 days (Table 6, one discontinuous lethal day) that all simulated T-DO data points (shown by the crosses) are located at the right side of or below the lethal-nicheboundary curve when simulated DO concentrations were below computed DOlethal values at simulated temperatures at all water depths. The same situation of cisco lethal conditions was predicted to occur on July 31, 2006 in Lake Itasca. If simulated DO concentrations are less than the DOlethal values in only some of the water layers, lethal conditions for cisco are not assumed to occur on that day because cisco can swim to other water layers having suitable T and DO condition. These days with habitat at some depths are shown as filled triangles in Fig. 9. Pine Mountain Lake on July 9, 2005 had lower DO at higher temperatures near the surface and near anoxic DO (0.2 mg/L) in the hypolimnion. Therefore, layers near the surface and in the hypolimnion could not support cisco habitat, but some intermediate layers had high enough DO at simulated temperatures to support cisco habitat. Lake Itasca on August 7, 2008 had suitable cisco habitat in the surface layers and no cisco habitat in the bottom layers (Fig. 9). Lake Itasca located in Clearwater County, Minnesota, has a maximum depth of 13.7 m and a surface area of 4.3 km2. Lake Itasca is mesotrophic due to its mean Secchi depth 2.8 m and mean chlorophyll-a concentration 10.4 μg/L and has relatively weak stratifications because of its GR = 3.32 (Gorham and Boyce 1989; Stefan et al. 1996). When simulated DO concentrations are greater than the DOlethal values in all water layers, fish can live in any depth of the lake, i.e., filled circles in Fig. 9 (e.g., October 15, 2004 in Pine Mountain Lake and September 12, 2008 in Lake Itasca).

Validation of Fish Habitat Model Figure 9 shows simulated DO versus simulated temperature during two periods (July 11, 2006–August 24, 2006 and August 26–29, 2006) in Pine Mountain Lake when cisco lethal conditions were predicted by the fish habitat model using the cisco lethal-niche-boundary curve. Surface water temperatures in Pine Mountain Lake (As = 6.36 km2, Hmax = 23.77 m, and GR = 2.11 m
0.5) reached 29.4  C on July 31, 2006. When the lethal temperature of 22.0  C for adult cisco was used, for the 49 days in Pine Mountain Lake (Fig. 9), 43 % of water depths had temperatures greater than 22.0  C, and 58 % of water depths had DO < 3 mg/L. The cisco mortality in Pine Mountain Lake was reported on July 26, 2006 (Jacobson et al. 2008) when the fish habitat model predicted continuous 16 days of lethal conditions starting from July 11, 2006. The fish habitat modeling using the cisco lethal-niche-boundary curve was validated in the 23 Minnesota lakes that Jacobson et al. (2008) studied first in 2006. Validation results are summarized in Table 6 for each lake, which shows similar information as Table 5 does but using different oxythermal lethal limits. For example, Lake Andrusia has “192(37), 233(2)” under “simulated lethal days in 2006” in Table 6 that means lethal conditions were simulated on DOY 192 (July 11, 2006) with the number of continuous cisco lethal days of 37 (DOYs 192–228)

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and on DOYs 233–234 (August 21–22, 2006). Lethal conditions were not simulated for DOYs 229–232 (August 17–20, 2006) in Lake Andrusia. In comparison to “Observed mortality day in 2006” in Table 6, the model agreement with mortality observation is “Yes (Yes)” for Lake Andrusia (Table 6). Using the lethal-niche-boundary curve, for five lakes (Bemidji, Mille Lacs, Star, Seventh Crow Wing, and Long), the model predicted cisco lethal conditions, but the predicted lethal periods did not include corresponding reported cisco mortality days in 2006; these lakes have the “Yes (No)” agreement (Table 6). For three lakes (Bemidji, Mille Lacs, and Star), cisco lethal conditions were predicted to occur after the reported cisco mortality days in 2006. In the Seventh Crow Wing Lake, cisco lethal conditions were predicted to occur from DOY 196 to 215 (August 3) in 2006, and the cisco mortality was reported on August 4. This case can be considered as “Yes (Yes)” agreement because cisco mortality might be reported one or a few days after cisco mortality occurred when study lakes were not constantly monitored and observed. Long Lake was predicted with only 1 day of lethal conditions. There are two lakes (Straight Lake and Lake Carlos) that the model did not predict cisco lethal conditions, but they had cisco mortality in 2006; the model has the No agreement with mortality observation (Table 6). Lake Carlos had a late summer cisco mortality event occurred on August 27, which might be due to the formation of the metalimnetic oxygen minimum during part of the summer (Smith et al. 2014). The five reference lakes (deep strongly stratified) that Jacobson et al. (2008) studied did not experience cisco mortality in 2006. The fish habitat model using simulated temperature and DO profiles predicted no lethal conditions for cisco in all five reference lakes (Table 6). Therefore, the fish habitat model using the lethalniche-boundary curve has overall good agreement in the 23 study lakes with and without cisco mortality reported in 2006. The overall good agreement is very similar but not exactly the same as the agreement using the constant lethal limits (LT = 22.0  C and DOlethal = 3 mg/L, Table 5).

Fish Habitat Simulations in 36 Representative Lake Types To understand cisco habitat and to determine cisco kill in 36 different lake types (LakeR01–LakeR36 in Table 1), daily water temperature and DO profiles were simulated using MINLAKE2012 under past (1991–2008) climate conditions from Bemidji weather station and the corresponding future climate scenario (MIROC 3.2). Bemidji weather station is close to most of the 23 study lakes (Tables 5 and 6) and has 18 years of weather data. The fish habitat model for cisco was applied every day year by year from 1992 to 2008 using simulated daily water temperature and DO profiles. Results for the first simulation year (1991) were not used for cisco modeling in order to remove the possible effect of initial conditions.

Total Days of Cisco Lethal Conditions in Each Year Total days of lethal conditions for cisco in each year over 17 simulation years were used to create box plots showing the maximum and minimum and 25 %, 50 %, and

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75 % quartile values simulated for each of the 36 representative lake types in Minnesota under past climate conditions and MIROC 3.2 future climate scenario (Fang et al. 2014). Lethal conditions may be continuous for many days or discontinuous for some days (Fig. 9 and Table 6). For the 12 shallow lakes with GR  5.29 m
0.5, median annual days of cisco lethal conditions ranged from 13 to 22 days under past climate conditions and are projected to range from 47 to 55 days under the future climate scenario (Fang et al. 2014). These results indicate again that shallow lakes typically cannot support cisco habitat. In the MN DNR cisco lake database, there are a total of 620 cisco lakes, of which 37 lakes have GR  5.29 m
0.5 and maximum depths less than 11.0 m. There are 14 lakes with Hmax < 5.0 m that are classified as shallow lakes in Minnesota (Stefan et al. 1996), of which 13 lakes have GR > 5.29 m
0.5 and one lake has GR = 4.4 m
0.5. These lakes had cisco observed in the past, but, most likely, they cannot sustain cisco habitat. For the 12 medium-depth lakes with Hmax = 13.0 m (LakeR10–LakeR18 and LakeR31–LakeR33), GR values are 1.63, 2.78, and 4.33 m
0.5 (Table 1). Median annual days of cisco lethal conditions ranged from 0 to 1 day under past climate conditions and are projected to range from 19 to 49 days under the future climate scenario. Under past climate conditions, cisco lethal conditions reached a maximum of 30 days for medium-depth lakes during the unusual hot summer in 2006. These lakes are vulnerable to climate warming because lethal conditions are projected up to 80 days (Fang et al. 2014). For the 12 deep lakes with Hmax = 24 m (LakeR19–LakeR27 and LakeR34–LakeR36), eutrophic deep lakes (LakeR19, LakeR22, and LakeR25) and mesotrophic large deep lake (LakeR26 with GR = 2.34 m
0.5) are projected to have some cisco lethal days under the future climate scenario (Fang et al. 2014). Cisco lethal conditions, however, were not simulated to occur under past climate conditions (1992–2008). Large deep lakes with GR = 2.34 m
0.5 seem to require Secchi depth more than 2.5 m to have nonlethal conditions under the future climate scenario. Other strongly stratified mesotrophic and oligotrophic deep lakes (LakeR20, LakeR21, and LakeR34 with GR = 0.88; LakeR23, LakeR24, and LakeR35 with GR = 1.50; and LakeR27 and LakeR36 with GR = 2.34 in Table 1) are possible to support cisco habitat under both past and future climate conditions. These deep lakes are good candidates of cisco refuge lakes (Fang et al. 2012b; Jiang et al. 2012). Annual cisco lethal days are strongly dependent on lake stratification characteristics (i.e., GR) but vary relatively weakly with trophic status (i.e., SD). The four lakes with the same GR but different Secchi depths were grouped together to compute mean and standard deviation of annual cisco lethal days under past climate conditions and the future climate scenario (Fig. 10). There are consistent patterns of average annual lethal days for each group of lakes with the same maximum depth (shallow, medium-depth, and deep). Shallow lakes (GR > 5.2 m
0.5) have large numbers of cisco lethal days, medium-depth lakes (GR = 1.63, 2.78, and 4.33 m
0.5) have cisco lethal days increasing with geometry ratio (Fig. 10), and deep lakes (GR = 0.88, 1.50, and 2.34 m
0.5) have little or no cisco lethal days. The four lakes with GR = 1.63 m
0.5 (LakeR10–LakeR12 and LakeR31) are small medium-depth lakes (As = 0.2 km2 and Hmax = 13.0 m). Cisco lethal days

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Fig. 10 Number of annual cisco lethal days (mean  standard deviation) simulated for the 36 representative lake types with nine GR values under past climate conditions and the future climate scenario (Fang et al. 2014)

projected for these four lakes (especially under the future climate scenario) are different from deep lakes with GR just less or greater than 1.63 m
0.5 (Fig. 10). These results may indicate that fish habitat modeling for deep lakes should be separated from the modeling for medium-depth lakes. To further prove the point, four deep lakes (LakeR41–LakeR44, Table 1) having GR = 1.63 m
0.5 with larger As = 2.32 km2 and deeper Hmax = 24.0 m were created, and daily temperature and DO profiles were simulated using MINLAKE2012 under past climate conditions and the future climate scenario. The simulated number of annual cisco lethal days in these four deep lakes is zero for all years (1992–2008) under past climate conditions and is projected to be zero under the future climate scenario except for LakeR41, which is an eutrophic deep lake having 1 year with 4 days of cisco lethal projection. Therefore, simulations of cisco lethal days in these deep lakes (LakeR41–LakeR44) are consistent with other deep lakes but different from medium-depth lakes with the same geometry ratio. Other fish habitat parameters, e.g., good-growth period for cold-water fish in 27 Minnesota lake types, had similar discontinuous patterns in some medium-depth and deep lakes in a previous study (Fang et al. 2004b). In the MN DNR cisco lake database, there are 385 medium-depth lakes with 5 m  Hmax < 20 m and 221 deep lakes with Hmax  20 m. Therefore, it is recommended to model cisco habitat and survival separately for medium-depth lakes and deep lakes in the future.

Number of Years with Cisco Kill It is still uncertain how many days that exceed non-survival or lethal niche limits are necessary to result fish mortality. In previous regional fish habitat projections (Stefan et al. 1996) when daily water temperature and DO concentration profiles used for fish habitat simulations were long-term (30-year) averages, fish kill was assumed to occur when the number of non-survival days (either consecutive or discontinuous) totaled at

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least seven. In this study, a sensitivity analysis on the number of continuous lethal days for determining cisco kill was performed when daily profiles were not averaged over 17 years (1992–2008), but cisco lethal conditions were checked in each day year by year. A cisco kill was assumed to occur if the number of continuous lethal days was greater than 3, 7, and 14 days for the sensitivity analysis. The 3 days are the half of 7 days used before, and 14 days are the double of 7 days. For the 11 Minnesota lakes (Table 6) in which the simulated lethal days included the reported cisco mortality days in 2006, the number of continuous lethal days to the mortality day was calculated and ranged from 2 (Gull Lake) to 25 (Little Pine Lake in Crow Wing County) days. Median value of the number of continuous lethal days to the mortality day was 14 days (mean value 13 days with a standard deviation 7 days). This result provides another reason to use 14 days for the sensitivity analysis. When 3, 7, and 14 continuous lethal days were used for determining cisco kill, Fig. 11 shows the numbers of years with cisco kill simulated for the 36 representative lake types in Minnesota for 17 simulation years under past (1992–2008) climate conditions (blue triangles) and the future climate scenario (red circles). The x axis gives lake’s geometry ratio, and the four lake types with the same geometry ratio (Table 1) were grouped together to compute mean and standard deviation of the number of years with cisco kill. Under past climate conditions, the 12 shallow lakes (LakeR01–LakeR09, LakeR28–LakeR30, GR = 5.29, 9.03, and 14.06 m
0.5) were simulated to have cisco kills on average in 14–15 years when 3 continuous lethal days were used to determine whether cisco kill happens or not. Under the future climate scenario (MIROC 3.2), the 12 shallow lakes are projected to have cisco kills in all 17 simulation years. These results provide strong evidence to indicate that shallow lakes cannot sustain cisco habitat under future warmer weather. The shallow lakes in MN cisco database are weakly stratified or polymictic with relatively high temperatures from surface to bottom during the summer which caused summer cisco kill almost every year from 1992 to 2008. Although cisco was observed in these 14 lakes in the past, whether cisco still exists in them is unknown. The projection under the future climate scenario shows they are not favorable to support cisco habitat every year. When 7 continuous lethal days were used to determine whether cisco kill happens, the 12 shallow lakes were simulated to have cisco kills on average in 11–12 years (range from 9 to 13 years) under past climate conditions and are projected to have 17 years of cisco kills under the future climate scenario. When 14 continuous lethal days were used to determine whether cisco kill happens, the 12 shallow lakes were simulated to have only 1 year (2006) with cisco kill under past climate conditions and are projected to have 11–12 years of cisco kills under the future climate scenario. It projects there are more years with cisco kills in some medium-depth lakes than in the 12 shallow lakes under the future climate scenario (Fig. 11). This finding may indicate that 14 continuous lethal days for determining cisco kill may be longer than how many lethal days would be needed for cisco mortality to occur because it gives inconsistent results on fish habitat projections.

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Fig. 11 Numbers of years with cisco kill simulated for the 36 representative lake types in Minnesota under past climate conditions (triangles) and the future climate scenario (circles) using 3, 7, and 14 continuous lethal days for determining cisco kill (Fang et al. 2014)

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Therefore, the 14 continuous lethal days for determining cisco kill are not recommended for further fish habitat study. Using 3 continuous lethal days for determining cisco kill, the 12 medium-depth lakes were simulated to have cisco kills on average in 2–7 years (range from 1 to 8 years) under past climate conditions and are projected to have 16–17 years of cisco kills under the future climate scenario. Using 7 continuous days for determining cisco kill, the 12 medium-depth lakes were simulated to have cisco kills on average in 1–5 years (range from 0 to 6 years) under past climate conditions and are projected to have 15–17 years (range from 13 to 17 years) of cisco kills under the future climate scenario. Figure 11 shows medium-depth lakes are most vulnerable to climate warming with average increase of 13 years with cisco kill (range from 9 to 15 years). The 12 deep lakes (LakeR19–LakeR27, LakeR34–LakeR36) were simulated to have no cisco kill under past climate conditions using either 3, 7, or 14 continuous lethal days for determining cisco kill (Fig. 11). The 12 deep lakes are projected to have on average 1–4 years (range from 0 to 9 years) or 0–2 years (range from 0 to 6 years) of cisco kills under the future climate scenario when 3 and 7 continuous lethal days were used for determining cisco kill, respectively. Only eutrophic deep lakes (SD = 1.2 m, LakeR19, LakeR22, and LakeR25) and large mesotrophic deep lake (As = 10 km2, SD = 2.5 m, LakeR26) are projected to have a few years with cisco kill under the future climate scenario. Figure 11 shows most mesotrophic and oligotrophic deep lakes can support cisco habitat under both past climate conditions and the future climate scenario and are good candidates for cisco refuge lakes, as supported by previous studies (Fang et al. 2012b; Jiang et al. 2012). It seems that 3 or 7 continuous lethal days for determining cisco kill provide quite reasonable results for cisco kill simulations in shallow, medium-depth, and deep lakes in Minnesota. The box plots of the numbers of annual continuous lethal days greater than or equal to 3 and 7 days simulated for the 36 lake types in Minnesota under past climate conditions (1992–2008) and the future climate scenario (MIROC 3.2) were presented elsewhere (Fang et al. 2014). Those lethal days that are not continuous for 3 or 7 days were excluded. Under past climate conditions, 1 to a few years with cooler summers did not result in cisco kills in the 12 shallow lakes, but most other years had cisco kills with annual continuous lethal days up to 36 days (Fang et al. 2014). Under the future climate scenario, projected annual continuous lethal days are up to 94 days and 40 days in shallow lakes and eutrophic deep lakes, respectively. Medium-depth lakes are projected to have relatively large change in annual continuous lethal days.

Identification of Cisco Refuge Lakes Using the Fixed Benchmark Period Cisco Habitat Criteria and Selection of Cisco Refuge Lakes The oxythermal habitat approach used in the oxythermal fish niche modeling options 1 and 2 (Fig. 3) uses lethal limits for temperature and oxygen. In the third option, the quality of oxythermal habitat for cisco was determined using a single variable,

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TDO3, i.e., water temperature at 3 mg/L of DO, proposed by Jacobson et al. (2010). A higher TDO3 value represents higher oxythermal stress for the cold-water fish. For example, if TDO3 is high, then fish must choose between well-oxygenated water that is too warm and live in hypoxic water that is of a proper temperature. The oxygen concentration of 3 mg/L is probably lethal or nearly so for many cold-water species (Frey 1955; US EPA 1986; Evans 2007) and therefore represents a desirable benchmark for a presence/absence niche model. Oxythermal habitat niche relationships developed for several cold-water fish (including cisco) by Jacobson et al. (2010) were used to identify potential refuge lakes for cisco under future climate scenarios. Niche breadth measures, i.e., central response borders used by Heegaard (2002), were used by Jacobson et al. (2010) to identify values of TDO3 measured in the period of greatest oxythermal stress in late summer (maxTDO3) useful for describing the quality of cold-water habitat for cisco. Central response borders essentially bracket the core range of a habitat variable required for a species to thrive (Heegaard 2002). The central species response borders for cisco ranged from maxTDO3 of 4.0  C to 16.9  C (Jacobson et al. 2010). TDO3 can be determined by interpolation from measured or simulated (vertical) temperature and DO profiles in a stratified lake. When non-monotonic profiles generate low oxygen concentrations with more than one TDO3 value, the coldest TDO3 was used (Jacobson et al. 2010). Temperature and DO profiles simulated for the first year (1961) were not used to compute TDO3 in order to avoid possible effects of assumed initial conditions. Figure 12 illustrates the procedure how TDO3 can be extracted from either measured or simulated temperature and DO profiles. Elk Lake (Fig. 12) has a maximum depth of 27 m and mean summer Secchi depth of 3.6 m (mesotrophic lake). Elk Lake has a lake geometry ratio of 1.16 and is a strongly stratified (dimictic) lake; it has excellent cold-water oxythermal habitat with TDO3 = 5.8  C on June 24, 2008 (using observed temperature and DO profiles) and is projected to have a TDO3 value of 6.3  C on June 24 under the future climate scenario MIROC 3.2. There were 99 measured T and DO profiles that had adequate data to extract TDO3 values for the 21 cisco lakes (Fang et al. 2010b). The standard error of TDO3 determined from simulated profiles against TDO3 from 99 measured profiles was 2.19  C with correlation coefficient R = 0.88 (Fang et al. 2010b). Through cisco habitat modeling, cisco refuge lakes were selected and identified in two categories: tier 1 refuge lakes and tier 2 refuge lakes. Tier 1 refuge lakes have TDO3 less than or equal to 11  C, and tier 2 refuge lakes have TDO3 less than or equal to 17  C but greater than 11  C. Lakes having TDO3 greater than 17  C are classified as tier 3 or non-refuge lakes. The limit of 17  C corresponds to the upper cisco central response border of TDO3, and the limit of 11  C is near the midpoint of the cisco central response borders of TDO3, as well as the upper central response border of TDO3 for lake whitefish Coregonus clupeaformis (Jacobson et al. 2010). Therefore, tier 1 refuge lakes identified for cisco in this study are also useful to the management of lake whitefish in Minnesota. The multiyear average TDO3 over a fixed benchmark (FB) period was used to identify cisco refuge lakes in Minnesota. The benchmark period is the period of greatest oxythermal stress for cold-water fish. It is defined as the month (31-day

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Fig. 12 Examples of measured (a) and simulated (b) temperature and DO profiles in Elk Lake for past (a) and a future (b) climate to show the determination of TDO3 (temperature at 3 mg/L DO) and (c) time series of simulated daily TDO3 values for Elk Lake in 2004 and for future climate scenarios CGCM 3.1 and MIROC 3.2. The fixed benchmark periods for stratified lakes are between the vertical dashed lines (Fang et al. 2012b)

period) with the highest value of TDO3 and typically occurs in late summer (Jacobson et al. 2010). From a total of 9,521 T and DO profiles measured in 1,623 Minnesota lakes in the years 1993 through 2005, associated maximum TDO3 values were computed in summer periods, and then Jacobson et al. (2010) determined that the period of greatest oxythermal stress for cold-water fish differed by stratification status of a lake. The stress occurred earlier in unstratified lakes. In this study, the fixed benchmark (FB) period from July 28 through August 27 proposed by Jacobson et al. (2010) was chosen to calculate the monthly (31-day) average of daily TDO3, called ATDO3FB, in each simulated year over the 47-year simulation period because the 30 simulated virtual cisco lakes (Table 2) are strongly stratified lakes. The overall modeling approach of the option 3 was depicted in Fig. 3 and discussed in detail by Fang et al. (2012b). To pursue the overall objective, we had to (1) review how the T and DO habitat constraints on cold-water fishes in lakes can be quantified (oxythermal fish habitat criteria), (2) develop a method to quantify

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oxythermal fish habitat under past climate and by how much climate warming will change it in existing cisco lakes, (3) validate that the methods under (1) and (2) would give results for past climate conditions that would match actual cisco observations in lakes, (4) apply the method to make projections for a representative number of lakes in the future, and (5) extrapolate those results to 620 cisco lakes of Minnesota. To implement the above modeling approach, 30 “virtual” cisco lake types (Table 2) were chosen as representative of the entire set of 620 lakes. For step (4), to assess the quality of cisco habitat in 30 virtual cisco lakes and to identify refuge lakes, the 47-year average of annual ATDO3FB values in the 1962–2008 simulation period (i.e., AvgATD3FB, Fig. 3) was calculated and compared to TDO3 limits (11  C and 17  C determined by the analysis of field data) to divide 620 Minnesota cisco lakes into three tiers. It is impossible to validate the projected number of refuge lakes for future climate scenarios, but a model validation on 23 cisco study lakes was performed (Table 7). Adult cisco mortality was reported in 18 lakes in Minnesota during the unusually hot summer of 2006, and no cisco mortality was reported in five reference lakes (Jacobson et al. 2008), which is shown in the last column “Reported 2006 cisco mortality” of Table 7. Water temperature and DO profiles in all 23 lakes were simulated under 2006 weather data from the closest weather station after necessary model calibration (Fang et al. 2014). ATDO3FB in 2006 was calculated for each lake and used to classify the lake into tier 1 and tier 2 refuge lakes or tier 3 non-refuge lake. The ATDO3FB values in 2006 range from 13.2  C (Lake Carlos) to 25.3  C (Little Turtle Lake) for 18 Minnesota lakes with cisco mortality (Table 7). Except Lake Carlos (tier 2 refuge lake), all other 17 cisco lakes with 2006 cisco mortality are classified as tier 3 non-refuge lakes because their ATDO3FB values in 2006 are greater than 17  C. The ATDO3FB values in 2006 for five reference lakes range from 6.4  C (Big Trout Lake) to 14.3  C (Rose Lake): four references are classified as tier 1 refuge lake, and only Rose Lake is classified as tier 2 non-refuge lake in 2006 (Table 7). Rose Lake has one profile for model calibration. Out of 23 cisco study lakes, there are two lakes (Lake Carlos and Rose Lake) having sort of disagreement with 2006 observations. Overall, there is a remarkable agreement between model predictions of refuge lake classification using ATDO3FB and observed adult cisco mortality and survival lakes in 2006 (Table 7). This study was to develop a method to rank the quality of fish habitat for cisco in Minnesota lakes and the identification of potential refuge lakes if and when projected future (warmer) climate scenarios become reality. When a fish species is eliminated by changes in water temperature, it is not only a loss but also opens the habitat for other, exotic invasive species. For these reasons, a method to identify potential refuge lakes is important.

Multiyear Average of Oxythermal Stress (AvgATDO3FB) From the time series of daily TDO3 for each year in the 47-year (1962 to 2008) simulation period, different TDO3 statistics can be extracted, e.g., the annual

Lake name Little Turtle Star Mille Lacs Andrusia Little Pine (Otter Tail) Cotton Pine Mountain Leech Bemidji Itasca Gull Woman Straight

ATDO3FB ( C)b 25.28

22.63 22.75 24.08 21.09

25.25 24.21

23.91 21.82 23.66 23.23 25.23 21.55

Stratified or nota No

Yes No Yes Yes

No Yes

No Yes Yes Yes No Yes

23.92 22.04 24.55 23.43 25.32 22.34

25.25 24.99

23.15 24.20 24.62 22.44

ATDO3VB ( C) 25.31

3 3 3 3 3 3

3 3

3 3 3 3

Tier of refuge lakec 3

20.77 19.37 20.71 20.45 21.36 16.69

22.00 21.83

20.02 20.76 21.10 19.49

AvgTDO3FB ( C)d 21.88

21.10 19.75 21.43 20.68 21.90 17.08

22.30 22.45

20.69 22.09 21.52 20.20

AvgTDO3VB ( C) 22.18

3 3 3 3 3 2 or 3

3 3

3 3 3 3

Tier of refuge lakee 3

26.15 23.25 26.33 24.41 27.13 24.06

26.62 26.77

24.82 24.83 25.80 24.56

TDO3AM ( C) 27.04

Table 7 Simulated habitat parameters in 23 Minnesota lakes that had cisco mortality or habitat observations in 2006

Yes Yes Yes Yes Yes Yes

Yes Yes

Yes Yes Yes Yes

Having lethal daysf Yes

Yes Yes Yes Yes Yes Yes

Yes Yes

Yes Yes Yes Yes

Reported 2006 cisco mortality Yes

700 X. Fang et al.

24.07

21.50

24.46

21.84 13.21 6.43 9.60 7.06 8.99 14.26

Yes

Yes

No

Yes Yes Yes Yes Yes Yes Yes

22.01 16.54 9.31 13.48 8.61 10.86 16.33

24.50

23.06

25.29

3 2 1 1 or 2 1 1 2

3

3

3

19.23 9.14 6.20 8.57 8.60 8.40 11.87

21.57

19.71

21.39

19.57 14.41 8.85 12.25 12.32 10.30 14.40

21.89

20.68

22.10

3 2 1 1 or 2 1 or 2 1 2

3

3

3

23.38 17.27 10.04 14.43 9.31 11.19 17.25

25.78

24.72

27.15

Yes No No No No No No

Yes

Yes

Yes

Yes Yes No No No No No

Yes

Yes

Yes

a

Note: Stratification classification based on Jacobson et al. (2008) b ATDO3FB and ATDO3VB are 31-day average TDO3 for 2006 c Tier classification of refuge lakes based on ATDO3FB and ATDO3VB d AvgTDO3FB and AvgTDO3VB are averaged ATDO3 over the simulation period based on available weather data (e.g., 1962–2012 or 1992–2008), 2006 e Tier classification of refuge lakes based on AvgTDO3FB and AvgTDO3VB f Lethal condition predicted from the constant lethal limits (LT = 22  C and DOlethal = 3 mg/L) and TDO3AM

Little Pine (Crow Wing) Seventh Crow Wing Eighth Crow Wing Long Carlos Big Trout Kabekona Scalp Ten Mile Rose

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Fig. 13 Examples of annual time series of mean daily TDO3 values for the fixed benchmark periods for past climate (1962–2008) and for the future climate scenario MIROC 3.2. Averages (AvgATDO3FB) over the 47-year simulation period are presented as dashed horizontal lines. Weather data from the closest Class I NWS weather station were used for the model simulations (Duluth for White Iron Lake and International Falls for Little Trout Lake)

maximum of daily TDO3 (TDO3AM), monthly (31-day) averages over fixed benchmark periods (ATDO3FB), multiyear averages of the above, etc. Twelve options of TDO3 characteristic values or statistics were calculated and explored (Fang et al. 2010b). Examples of daily TDO3 time series for Elk Lake are given in Fig. 12. The TDO3AM value was 15.5  C in 2004 for Elk Lake (bottom of Fig. 12); the day of occurrence of TDO3AM was DOY = 222 (August 10). The TDO3AM value is projected to increase by 3.8 or 4.3  C in Elk Lake under the future climate CGCM 3.1 and MIROC 3.2 scenarios, respectively (Fig. 12). Two of the 21 cisco study lakes (Fang et al. 2012a, b), White Iron Lake and Little Trout Lake, were selected to illustrate examples of time series of mean daily TDO3 in the 31-day FB periods, ATDO3FB (Fig. 13). Lake geometry ratios for White Iron

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Lake and Little Trout Lake are 4.27 and 1.08 m
0.5, respectively. White Iron Lake is a weakly stratified lake or a relatively shallow, large, and eutrophic lake (maximum depth Hmax = 14.3 m, surface area As = 13.9 km2, Secchi depth SD = 1.4 m). Little Trout Lake (Hmax = 29.0 m and SD = 6.3 m) is a strongly stratified and oligotrophic lake. Values of ATDO3FB ranged from 17.2  C to 22.9  C for White Iron Lake (using fixed benchmark period for unstratified lakes) and from 4.5  C to 6.9  C for Little Trout Lake under past climate conditions. ATDO3FB values in weakly stratified eutrophic White Iron Lake are much larger than ATDO3FB in strongly stratified oligotrophic Little Trout Lake. Under the future climate scenario MIROC 3.2, values of ATDO3FB are projected to range from 20.9  C to 26.2  C for White Iron Lake and from 4.6  C to 7.1  C for Little Trout Lake (Fig. 13). Mean daily TDO3 values over the fixed benchmark (ATDO3FB) period for each simulated year were averaged over the simulation period to obtain the parameter value AvgATDO3FB, which had been chosen as the TDO3 characteristic parameter for the selection of cisco refuge lakes (Fang et al. 2010b, 2012b). The AvgATDO3FB value is one value for the 47-year simulation period (1962–2008) for each lake and each climate scenario, whereas ATDO3FB has one value for each simulated year and 47 values in the simulation period. The AvgATDO3FB values were 20.0  C (standard deviation STD = 1.19  C) for White Iron Lake and 5.4  C (STD = 0.44  C) for Little Trout Lake under past climate conditions (1962–2008). AvgATDO3FB is typically higher for weakly stratified lakes (e.g., White Iron Lake) than for stratified lakes (Little Trout lakes, Fig. 13). Values of AvgATDO3FB for the future climate scenario MIROC 3.2 are projected to be 23.5  C for White Iron Lake and 5.7  C for Little Trout Lake; the projected increases of AvgATDO3FB are 3.5  C and 0.2  C for these two lakes, respectively. Simulated AvgATDO3FB values in 23 Minnesota lakes using available climate data (ranging 17–50 years) from a closest weather station are listed in Table 7. Based on AvgATDO3FB, the tier of the refuge lake is signed for each lake. Results of the refuge lake classification based on ATDO3FB and AvgATDO3FB are almost the same except Straight Lake. Straight Lake has AvgATDO3FB of 16.7  C, which is slightly less than 17  C, and is classified as tier 2 refuge lake based on AvgATDO3FB instead of tier 3 non-refuge lake based on ATDO3FB in the warmer summer of 2006. Overall agreement between refuge lake classification using AvgATDO3FB and observation of cisco mortality and suitable habitat in 2006 is very good. Table 7 also lists the maximum TDO3 in 2006 (TDO3AM) for each lake that was used to determine whether lethal days could occur or not based on the constant lethal limits (LT = 22  C and DOlethal = 3 mg/L). The projection of having lethal days based on TDO3AM in 2006 has almost perfect agreement with 2006 cisco mortality observation except Lake Carlos (Table 7). Simulated AvgATDO3FB values are affected by lake bathymetry and trophic state (Fig. 14) for stratified lakes, but they are less dependent on Secchi depth (trophic status) when a lake is weakly stratified, e.g., GR > 4. This is very similar to the findings by Jacobson et al. (2010) that lake productivity did not significantly affect TDO3 in the unstratified lakes. Simulated AvgATDO3FB values are lower in northern Minnesota (International Falls, Fig. 14) than in north-central and central

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Fig. 14 Contour plots of AvgATDO3FB values under the future climate scenario MIROC 3.2 at International Falls, Minnesota. Contours were derived by interpolation from simulated data points for 30 virtual cisco lakes

Minnesota (Duluth and St. Cloud), but they have similar relationships (patterns) as function of GR and SD at all three locations (Fang et al. 2012b). The AvgATDO3FB values ranged from 6.1  C to 19.6  C under past climate conditions and from 6.3  C to 23.3  C under two future climate scenarios at three weather stations for the 30 virtual cisco lakes (Fang et al. 2012). The projected increases of AvgATDO3FB values from past climate to future scenarios were from 0.0  C to 6.5  C in the 30 virtual lakes, and average increases are projected to be from 2.6  C to 2.9  C (Fang et al. 2012b), which is about 1.0–1.5  C less than projected annual air temperature increases under the climate scenarios MIROC 3.2 and CGCM 3.1 (Table 3), respectively.

Identified Cisco Refuge Lakes in Minnesota The selection of cisco refuge lakes was based on TDO3 parameters projected under the CGCM 3.1 and MIROC 3.2 future climate scenarios using the temperature boundaries derived from 30 simulated lakes (e.g., AvgATDO3FB in Fig. 14). Cisco refuge lakes were also determined by simulations for past climate conditions (1962–2008) because the results would be expected to match actual cisco lakes in Minnesota and would be a useful reference to gage both the reliability of the selection procedure and the impact of climate warming on cisco lakes in Minnesota.

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Fig. 15 Geographic distribution of 620 cisco lakes in Minnesota assigned by shortest distance to one of the three weather stations (International Falls, Duluth, and St. Cloud). The three weather stations (stars) and the grid center points (crosses) of MIROC 3.2 GCM are shown. Background shades identify ecoregions of Minnesota. Cisco lakes are found in two ecoregions: (1) Northern Lakes and Forests and (2) North Central Hardwood Forests

For the 620 cisco lakes, lakes were grouped by the shortest distance to one of the three Class I NWS weather stations in Minnesota; 169, 189, and 262 lakes were associated with International Falls, Duluth, and St. Cloud, respectively (shown by different symbols, Fig. 15).

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Fig. 16 Distribution of tier 1 and tier 2 refuge lakes and tier 3 non-refuge lakes on a plot of Secchi depth versus lake geometry ratio for 169 cisco lakes close to International Falls. The boundary contour lines between tiers 1, 2, and 3 are for AvgATDO3FB = 11  C and 17  C, respectively, and were determined by the fixed benchmark method for the future climate scenario MIROC 3.2

The cisco lakes assigned to International Falls, Duluth, and St. Cloud weather stations were divided into tier 1 and tier 2 refuge lakes and tier 3 non-refuge lakes by the 11  C and 17  C isotherms of AvgATDO3FB simulated using corresponding climate input. The selection of refuge lakes shown in Fig. 16 was based on contour lines of AvgATDO3FB for the fixed benchmark period simulated under the future climate scenario MIROC 3.2. It was projected that 66 (23 + 43 in Fig. 16), 89, and 56 lakes associated with International Falls, Duluth, and St. Cloud, respectively, would be tier 1 plus tier 2 refuge lakes. A total of 211 or 205 lakes of 620 cisco lakes were identified as refuge lakes (tier 1 plus tier 2) under the future climate scenarios MIROC 3.2 and CGCM 3.1, respectively (Fang et al. 2012b). It means that about one third of the 620 lakes that currently have cisco populations are projected to maintain cisco habitat under future projected warmer climate scenarios. Under past climate conditions (1962–2008), 483 lakes or 78 % of the 620 lakes with documented cisco populations were classified as refuge lakes (tier 1 plus tier 2) (Fang et al. 2012b). Mean values of gillnet catch per unit effort (CPUE, number of cisco gillnet) were determined from standard MN DNR netting assessments of cisco in 474 lakes. The CPUE is used as a measure of relative abundance. Mean CPUE values were 5.1, 4.1, and 3.6 for 49 tier 1 (out of total 474 lakes MN DNR studied), 97 tier 2, and 328 tier 3 refuge lakes, respectively. The netting assessment data show that there is a correlation between the tier and relative abundance, i.e., cisco abundance diminishes from tier 1 to tier 3 lakes. The geographic distribution or a division of the 620 Minnesota cisco lakes into 84 tier 1 refuge lakes (large green circles), 127 tier 2 refuge lakes (medium-size pink pentagons), and 409 non-refuge cisco lakes (small black hexagons) is projected for

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Fig. 17 Geographic distribution of tier 1 and tier 2 cisco refuge lakes and tier 3 non-refuge cisco lakes obtained from simulations for the future climate scenario MIROC 3.2. The boundary limits for tier 1 and tier 2 refuge lakes were contour lines of AvgATDO3FB = 11  C and 17  C, respectively. The fixed benchmark method and weather data from principal weather stations in International Falls, Duluth, and St. Cloud, Minnesota, were used

the future climate scenario MIROC 3.2 (Fig. 17). Twenty-three (23) tier 1 and 43 tier 2 cisco refuge lakes (Fig. 16 and Table 8) are associated with International Falls where there is little urban or agricultural development (protected by the Superior National Forest); 39 tier 1 and 50 tier 2 refuge lakes (Table 8) are near Duluth where more development pressure exists and more protection may be necessary; and there

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Table 8 Number (%) of lakes selected as tier 1 and tier 2 refuge lakes and tier 3 non-refuge lakes from cisco lakes partitioned by shortest distance to weather stations in International Falls, Duluth, and St. Cloud, Minnesota. The total number of lakes considered is 620 Closest weather station International Falls

Duluth

St. Cloud

All three stations

Climate scenario Past CGCM 3.1 MIROC 3.2 Past CGCM 3.1 MIROC 3.2 Past CGCM 3.1 MIROC 3.2 Past CGCM 3.1 MIROC 3.2

Tier 1 refuge lakes 49 (8) 23 (4)

Tier 2 refuge lakes 88 (14) 39 (6)

Total number of refuge lakes 137 (22) 62 (10)

Nonrefuge lakes 31 (5) 106 (17)

Total number of lakes 169 (27.2) 169 (27.2)

23 (4)

43 (7)

66 (11)

103 (17)

169 (27.2)

78 (13) 36 (6)

91 (15) 51 (8)

169 (27) 87 (14)

20 (3) 102 (16)

189 (30.5) 189 (30.5)

39 (6)

50 (8)

89 (14)

100 (16)

189 (30.5)

49 (8) 19 (3)

128 (21) 37 (6)

177 (29) 56 (9)

85 (14) 206 (33)

262 (42.3) 262 (42.3)

22 (4)

34 (5)

56 (9)

206 (33)

262 (42.3)

176 (28) 78 (13)

307 (50) 127 (20)

483 (78) 205 (33)

137 (22) 415 (67)

620 (100) 620 (100)

84 (14)

127 (20)

211 (34)

409 (66)

620 (100)

are 22 tier 1 and 34 tier 2 refuge lakes (Table 8) associated with the St. Cloud area and its moderate development pressure. It was found that 84 tier 1 refuge lakes have mean summer Secchi depths from 3.20 to 9.46 m (oligotrophic lakes), lake geometry ratios from 0.47 to 1.83 m
0.5 (strongly stratified lakes), maximum depths from 13.7 to 64.9 m, and surface areas from 0.08 to 21.27 km2 (Fang et al. 2012b). The upper 50 % of the 127 tier 2 refuge cisco lakes have a mean summer Secchi depth greater than 3.89 m, a geometry ratio from 1.56 to 2.66 m
0.5, and a maximum depth greater than 21.3 m (Fang et al. 2012b). On the other hand, the lower 50 % of the 409 tier 3 non-refuge lakes have a mean summer Secchi depth less than 2.9 m, a geometry ratio from 2.72 to 11.89 m
0.5, and a maximum depth less than 13.4 m. Tier 1 plus tier 2 refuge lakes selected under the MIROC 3.2 climate scenario have a Secchi depth greater than 2.3 m, a lake geometry ratio less than 2.7 m, and a maximum depth greater than 11.6 m (Fang et al. 2012b). The geographic distribution of the projected cisco refuge lakes in Minnesota was surprisingly uniform (Fig. 16). Refuge lakes are not exclusively found in the northern and colder region; in fact, many non-refuge lakes are in the north, and a few refuge lakes are near St. Cloud in the south. This is because stratification characteristics related to lake geometry ratio

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and trophic status play an important role in determining cold-water habitat for cisco in addition to climate conditions.

Identification of Cisco Refuge Lakes Using the Variable Benchmark Periods In the third option of the oxythermal habitat modeling for cisco, single variable TDO3 can be determined by interpolation from simulated temperature and DO profiles in a stratified lake such as Elk Lake shown in Fig. 12. To determine refuge cisco lakes, average TDO3 in each simulation year can be quantified in two different periods, i.e., fixed benchmark period (Fang et al. 2012b) and variable benchmark periods (Jiang et al. 2012). Jacobson et al. (2010) determined that the period of greatest oxythermal stress for cold-water fish differed by stratification status of a lake. The 31-day fixed benchmark period of greatest oxythermal stress went from July 13 through August 12 (DOY 194 to DOY 224) for unstratified lakes and from July 28 through August 27 (DOY 209 to DOY 239) for stratified lakes; these benchmark periods explained 65 % of the deviance in TDO3 for the stratified lakes and 68 % for the unstratified lakes (Jacobson et al. 2010). However, fixing the benchmark period in time may introduce a bias for some lakes (Jiang et al. 2012). The highest average daily TDO3 value in any 31-day sliding (variable) benchmark (VB) period, called ATDO3VB (Fig. 3), was calculated for each simulated lake and year. Multiyear average of annual maximum oxythermal stress ATDO3VB, i.e., AveATDO3VB (Fig. 3), was then used to compare with the TDO3 limits of 11  C and 17  C to identify cisco refuge lakes. Figure 18 shows time series of daily TDO3 values in Big Trout Lake under 2006 and future climate scenarios (CGCM 3.1 and MIROC 3.2). The annual maximum daily TDO3 (TDO3AM) was 9.8  C, and the day of its occurrence was at DOY = 283 (October 10) in 2006 in Big Trout Lake. The TDO3AM value is projected to increase by 1.2–1.8  C in Big Trout Lake under the future climate CGCM 3.1 and MIROC 3.2 scenarios, respectively (Fig. 18). Big Trout Lake, with a maximum depth of 39.0 m and a lake geometry ratio of 1.24, is a seasonally stratified (dimictic) lake and is projected to have a smaller increase in TDO3 under the future climate scenarios than White Iron Lake which has a maximum depth of 14.3 m and a geometry ratio of 4.27 and is a weakly stratified lake (Jiang et al. 2012). The variable benchmark periods were determined using sliding window of 31 days to find the highest average daily TDO3 (called ATDO3VB) over any 31-day period in each simulated year. The variable (sliding) 31-day benchmark period retained for each simulation year must not only have the highest mean daily ATDO3VB but must also contain the maximum daily TDO3 in that year (i.e., TDO3AM). Using time series of daily TDO3 in each simulated year, the mean daily TDO3 over each sliding benchmark period of 31 days was calculated, and only the highest mean value in any of the sliding benchmark periods of a year (i.e., ATDO3VB) was retained in the fish habitat program. For example, the beginning date of the VB period in Big Trout Lake was DOY 262 (September 19) in 2006 (Fig. 18),

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Fig. 18 Time series of simulated daily TDO3 for Big Trout Lake in 2006 and for future climate scenarios CGCM 3.1 and MIROC 3.2. The beginning dates of variable benchmark periods of highest mean daily TDO3 in 2006 for Big Trout Lake are DOY 262 (September 19)

which was quite different from the fixed benchmark period (DOYs 209 to 239). Under the future climate scenarios MIROC 3.2 and CGCM 3.1, the beginning dates for the ATDO3VB period are projected (simulated) to be DOYs 273 and 265 in Big Trout Lake, respectively; these dates are not much different from the beginning dates of the ATDO3VB for the past climate conditions. The VB period in White Iron Lake was DOY 199 (July 18) in 2006, which was about 2 months earlier than the VB period in Big Trout Lake. To further illustrate the variability of the VB periods that give the annual ATDO3VB values, the beginning dates of these VB periods for virtual cisco LakeC06 and LakeC08 were determined for past climate (1962–2008) and for the future climate scenario MIROC 3.2 (Jiang et al. 2012). Duluth weather data were used as model simulation input. The beginning dates of these VB periods ranged from DOY 192 to 226 for LakeC06 and from DOY 241 to 271 for LakeC08 under past climate conditions. Average beginning dates were DOY 210 (July 29) for LakeC06 and DOY 253 (September 10) for LakeC08 under past climate conditions (1962–2008). These two small virtual lakes have the same lake geometry but different Secchi depths (Table 2); LakeC06 is an eutrophic lake with SD = 1.2 m, and LakeC08 is an oligotrophic lake with SD = 4.5 m. Isolines that give simulated average beginning dates of the VB periods of greatest oxythermal stress over the 47-year simulation period as a function of Secchi depth and lake geometry ratio were developed and studied (Jiang et al. 2012). Averages of the beginning dates of the VB periods for ATDO3VB ranged from DOY 207 (July 26) to DOY 269 (September 26)

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for the 30 virtual lakes under past climate conditions (1962–2008). It was found that the beginning date of greatest oxythermal stress for cisco can vary considerably from year to year depending on weather, but it can also vary by lake type, i.e., stratification characteristics and trophic status (Jiang et al. 2012). Later dates occur in lakes with lower geometry ratio and higher Secchi depth, i.e., stratified oligotrophic lakes produce oxythermal stress for cisco later in the season than other lakes. The differences between the latest and earliest average beginning dates of the greatest oxythermal stress period for cold-water fish in Minnesota lakes were ranged from 59 to 89 days under past and future climates using weather data from three Class I weather stations. Therefore, variable (sliding) benchmark periods (ATDO3VB) performed better than fixed benchmark periods to quantify the maximum oxythermal stress to cisco. The differences in average beginning dates of VB periods are much smaller (nearly negligible) between projected future climate and past climate than the differences between different lake types and the differences from year to year (Jiang et al. 2012).

Oxythermal Parameters (ATDO3VB and AveATDO3VB) for VB Periods To evaluate oxythermal stress for cisco in different lake types and under different climate scenarios, the AvgATDO3VB was chosen as the TDO3 parameter to identify and select cisco refuge lakes for the study (Fang et al. 2010b). The time series of ATDO3VB for virtual cisco LakeC06 and LakeC08 (Table 2) were analyzed under past climate (1962–2008 at Duluth) and the future climate scenario MIROC 3.2 (Jiang et al. 2012). Over the simulation period (1962–2008), ATDO3VB values ranged from 7.3  C to 12.1  C for LakeC06 and from 11.8  C to 19.9  C for LakeC08 under past climate conditions. Averages of ATDO3VB over the 47-year simulation period (i.e., AvgATDO3VB) were 9.3  C with standard deviation of 1.0  C for LakeC06 and 15.6  C with standard deviation of 1.5  C for LakeC08 under past climate conditions (1962–2008). Values of AvgATDO3VB for the MIROC 3.2 future climate scenario are projected to be 12.6  C for LakeC06 and 20.7  C for LakeC08; the projected increases are 3.3  C and 5.1  C, respectively (Jiang et al. 2012). Figure 19 shows contour plots of AvgATDO3VB under the past climate conditions and the CGCM 3.1 and MIROC 3.2 future climate scenarios using Duluth weather data. Contours were derived by interpolation from simulated AvgATDO3VB data points for the 30 virtual cisco lakes (dots in top frame of Fig. 19). Statistics of AvgATDO3VB values under past climate and future scenarios for three weather stations (International Falls, Duluth, and St. Cloud) were summarized by Jiang et al. (2012). The AvgATDO3VB values ranged from 7.48  C to 19.91  C under past climate conditions and from 8.02  C to 23.28  C under two future climate scenarios for the 30 virtual cisco lakes (Jiang et al. 2012). The projected increases of AvgATDO3VB values from past climate to future scenarios are from 0.30  C to 5.11  C, and average increases are projected to be from 2.79  C to 3.40  C. These increases are crucial, when cisco refuge lakes for future climate scenarios are identified and selected. Values of AvgATDO3VB vary by lake type depending on

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30 regional cisco lakes

PAST (1962 - 2008)

8 3 25 2 23 1 2 9 1

15

11

9

4

13

6 17

21

Secchi Depth (m)

10

2 0

Location: Duluth

CGCM 3.1

8

17

21

11 13

19

7

9

4

15

6

5 27 2

25

23

23

Secchi Depth (m)

10

2 0 MIROC 3.2 8

15

25

13

17

9

23

11

4

29 27

25

6

19

21

Secchi Depth (m)

10

2 0 0.3

0.5

0.8 1.0

2.0

3.0

4.0 5.0 6.0

Geometry Ratio (m –0.5)

Fig. 19 Contour plots of averages of mean TDO3 over variable benchmark periods (AvgATDO3VB) under past (1962–2008), CGCM 3.1, and MIROC 3.2 future climate scenarios. Duluth weather data was used for past climate condition. Contours were derived by interpolation from simulated data points for 30 virtual lakes

stratification characteristics and trophic status (Fig. 19); stratified oligotrophic lakes produce lower AvgATDO3VB values or lower oxythermal stress for cisco.

Cisco Refuge Lakes in Minnesota Contour lines of 11  C and 17  C in the contour plots of AvgTDO3VB in Fig. 19 were used to identify cisco refuge lakes in the database of 620 Minnesota cisco lakes. The final selection of cisco refuge lakes was based on AvgTDO3VB projected under

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Table 9 Number (%) of lakes selected as tier 1 and tier 2 refuge lakes and tier 3 non-refuge lakes from cisco lakes grouped by latitude (Fig. 1). Weather input data from three principal weather stations (International Falls, Duluth, and St. Cloud) are each assigned to a different range of latitudes for use in the simulations (Jiang et al. 2012) Weather station by latitude Northern (International Falls)

Mid-latitude (Duluth)

Southern (St. Cloud)

All three latitudes

Climate scenario Past CGCM 3.1 MIROC 3.2 Past CGCM 3.1 MIROC 3.2 Past CGCM 3.1 MIROC 3.2 Past CGCM 3.1 MIROC 3.2

Tier 1 refuge lakes 41 (7) 24 (4)

Tier 2 refuge lakes 96 (15) 43 (7)

Total number of refuge lakes 137 (22) 67 (11)

Tier 3 non-refuge lakes 29 (5) 99 (16)

Total number of lakes 166 (27) 166 (27)

19 (3)

43 (7)

62 (10)

104 (17)

166 (27)

52 (8) 10 (2)

285 (46) 83 (13)

337 (54) 93 (15)

62 (10) 306 (49)

399 (64) 399 (64)

10 (2)

84 (14)

94 (15)

305 (49)

399 (64)

1 (X2-value) 0.00000*** % Correct prediction 74.46 No. of observations 1,711 Base category: no adaptation

Reactive measures 0.9650***

Proactive measures 0.7882***

0.0405

0.0369

0.1049**

0.0976**

0.0264

0.0214

0.1586*** 0.0981***

0.1496*** 0.0992***

0.0187 0.0185 0.1139***

0.0269 0.0279 0.1037***

0.0120* 0.0168*** 0.0020 0.0452

0.0085 0.0157*** 0.0010 0.0513

0.0600**

0.0638**

0.0597*** 0.0392 0.0031 0.0014 0.2183*** 0.3696*** 0.5946***

0.0523*** 0.0252 0.0017 0.0009 0.2400*** 0.2960*** 0.5312***

Cf: Francisco et al. (2011) Note: ***, **, * = significant at 1 %, 5 % and 10 % level, respectively a 1 = yes, 0 = otherwise b 1 = more severe than what was experienced, 0 = otherwise

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livelihood from extreme climate change disasters. In fact, everyone should also be thinking of mitigation because while it is true that our generation is already committed to climate change, there are efforts that can be done to slow down climate change for future generations.

Coastal Communities’ Vulnerability and Adaptation Practices Coastal areas in Asia face “an increasing range of stresses and shocks,” which are intensified by climate change (Cruz et al. 2007). This is supported by a 170-country assessment by Harmeling (2011) on the impacts of extreme weather-related events such as storms, flood, and extreme temperatures. The assessment showed six Asian countries to be among the most vulnerable, namely, Bangladesh (rank 1), Myanmar (rank 2), Vietnam (rank 5), the Philippines (rank 7), Mongolia (rank 9), and Tajikistan (rank 10). That coastal communities are highly vulnerable to climate change is widely recognized (IPCC 2007, 2014; ADB 2009, 2014). Their vulnerability comes from the rising sea level that accompanies the overall warming of temperature as well as the storm surges that accompanies the increased frequency and intensity of typhoons. Low physical and financial capacity for disaster preparedness also contributes, to some extent, to these areas’ vulnerability to extreme climate events (Ward and Shively 2011; Adger 1999); wealthier countries typically suffer lower social losses than poorer countries (Kahn 2005). The threat from sea level rise has been in the radar of climate discussions during the last few years, but the threat from storm surges became real only with the Philippines’ experience during super Typhoon Haiyan in November 2013. In just less than an hour, the 13 ft storm surges with strong current experienced during Haiyan left a death toll of 6,300 with 1,785 left unaccounted for. A few months after the super typhoon, more than 52,000 families are still living in tents in the danger zone in Tacloban City as the local government struggled to find a 100-hectare relocation site for these people (Lowe 2014; Stevens 2014). Given the critical situation that coastal communities face as a result of the changing climate, EEPSEA also supported several research projects on CCA in coastal areas. This effort was done in collaboration with the WorldFish Philippine Country Office (WF-PCO) and came in two sets of projects. The first project focused on understanding the adaptation practices and assessing the vulnerability of selected coastal communities in the Philippines, Vietnam, and Indonesia. The various studies part of this first project looked into the impacts and adaptation practices used in dealing with typhoons/flooding, coastal erosion, and saltwater intrusion at the household, community, and local government levels. Several planned adaptation options were then evaluated using cost-effectiveness analysis (CEA). The second project, which is still ongoing, looks into intra-household impacts of climate change, how the various members are affected, and how they can be engaged to generate a stronger household adaptation plan. Table 11 shows the

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Table 11 Summary of climate change hazards, impacts, and compounding issues in the study sites Batangas, Philippines Hazards Sea level rise Confounding environmental issues Coastal erosion Sand quarrying Illegal charcoal making from mangroves Illegal fishing using blasting and cyanide Fishing with fine mesh net and superlight Impacts Damage to property (hotels, resort, houses, and boat) during typhoon Coral bleaching and increasing number of crown of thorns Impacts to livelihood and tourism in vulnerable coastal areas House relocation due to coastal erosion Mangrove areas, coral reefs, marine protected areas, and beaches now at risk

Palawan, Philippines Hazards More frequent and intense typhoons Floods Confounding environmental issues Mangrove cutting for charcoal, housing, and fencing materials Weak enforcement of coastal management laws Illegal fishing Burning of some upland areas for rice farming (kaingin) Expansion of private beachfront property Inadequate protection of the fish sanctuary Impacts Change in the fish species caught More houses and boats destroyed by typhoons Coral bleaching Decreased land area due to coastal erosion Loss of traditionally gleaned shells along the coastline Seawater is hotter during the 3–4 pm gleaning activity Bangus fry collected for the past 5–6 years declined significantly

Jakarta, Indonesia

Ben Tre, Vietnam

Hazards Coastal erosion and seawater intrusion Coastal or tidal flooding Sea level rise Confounding environmental issues Loss of most of mangrove and coastal ecosystems Large population Pollution that affects water quality, soil erosion Absence of strong fisheries policy and overlapping jurisdictions Impacts Land subsidence, coastal inundation, and coastal abrasion Seawater intrusion has reached the National Monument Increased turbidity of water affecting photosynthesis Decreasing water quality Change in the pattern of flow, bathymetry, and coastline Sediment accumulation in the entrance of harbor lanes increases dredging costs

Hazards More frequent and intense typhoons Destructive flood and tidal surges from 1996 to 2008 Confounding environmental issues Sand mining Salinity intrusion Heavy traffic of sea vessels Impacts Loss of shelter and livelihood from typhoons Land encroachment Saltwater intrusion during the dry season leads to a shortage of freshwater for domestic and production uses

Cf: Perez et al. (2013), shown as Table 3 in the original study

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climate change impacts considered in the different study sites and the compounding environmental stresses that those communities face in addition to climate change. In particular, most communities had to deal with coastal erosion, sand quarrying, deforestation of forests and mangroves, and rampant illegal fishing, all of which could compound the impacts of climate change. What Table 11 further tells us is that efforts to address the development needs of these coastal communities need to be holistic since theirs is a complex environment that is not necessarily affected only by their coastal location. Instead, it is important to note that coastal communities live in an environment that traverses several ecosystems, some of which are linked to one another. They are also affected by an economic and governance system that influences their livelihood and hazard vulnerability, be it climate change or other hazards. Moreover, they are also assisted by the government and other development agencies in how they cope with climate change impacts. These coping measures are further discussed in the next section.

Adaptation Practices in Selected Coastal Villages In the Philippines, it is noticeable that all local governments have formed a municipal disaster risk management council (MDRMC) that is funded using 5 % of the 20 % Development Fund (Table 12). This fund is used to provide disaster victims with food, particularly those who stay in evacuation centers during disaster events. The fund is also used to undertake information, education, and communication (IEC) campaigns on disaster risk reduction (DRR). In addition, local governments conduct dredging and river widening activities to reduce the flooding threat as well as to rehabilitate mangroves, which are now widely believed to provide protection from coastal erosion and flooding. The same efforts are reported in the Indonesia and Vietnam study sites. In addition, Vietnam reports of technological support to farmers in the form of drought/flood-tolerant varieties and modified farming systems to suit the new climate (Table 12). Vietnam is also doing more structural measures, in the form of dike and pond system, to support livelihood. Indeed, as noted by Francisco (2008), there are a lot that countries in the region could learn from Vietnam on how they have been living with flooding. Adaptation practices at the community and household levels were also obtained during the study. It is worth noting that most local government initiatives involved the community. Community folk participated in mangrove replanting, dike repairs and construction, and in other activities to improve their environment and help prepare for disasters. At the household level, adaptation practices included relocating and strengthening of houses, putting up defense structures like cement dikes, engaging in alternative livelihood activities to enhance financial security, and shifting fishing/farming practices to suit new and changed environments, particularly in Vietnam.

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Table 12 CCA and disaster mitigation strategies in the study sites, 2012 Government-led initiatives San Juan, Batangas, Philippines Organized MDRMC, which is financed by 5 % of the 20 % Development Fund Gave out cash support (e.g., PhP 1,000–2,000 to affected fisher folk) River dredging and widening to prevent flooding Regular IEC campaigns Maintenance of Marine Protected Areas, mangrove replanting, and engagement in “Billions of Trees Project” in 400 ha of upland, lowland, and beach side areas Honda Bay, Palawan, Philippines Passed an ordinance to conserve, protect, and restore (CPR) the Puerto Princesa City’s sources of life Flood control project implementation and construction of breakwaters Mangrove reforestation Established a barangay disaster risk management council (BDRMC) with fund allocation

Jakarta Bay, Indonesia Implemented measures related to watershed management and coastal and marine resources protection Conducted capacity building and community empowerment activities to implement watershed and marine and coastal resources management Promoted policies that integrate environmental concerns in economic development Encouraged institutional strengthening for river basin management and coastal and marine bay management

Community-based initiatives

Autonomous household adaptation practices

Aquaculture and fish processing projects Mangrove replanting Crown-of-thorns starfish removal, as spearheaded by resort owners Typhoon warning system improvement and preparation for emergency evacuation Cleanup of drainage and flood control structures

Relocate or strengthen house structures Plant and sell mangrove seedlings Temporarily remove light structures in beach areas Join savings/credit cooperative Modify planting schedules

Establishment/ maintenance of fish sanctuary Participation in riverbank bioengineering projects (e.g., sea dike construction, mangrove reforestation) to reduce erosion and siltation Establishment of community-based early warning system and provision of temporary evacuation center

Use of indigenous materials to strengthen housing structures Use of cement and rocks to build dikes

Community involvement in various initiatives to protect watershed and coastal and marine resources Construction of permanent embankments Drainage improvement and river dredging Mangrove planting

Clean the beach fronting their houses

(continued)

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Table 12 (continued) Government-led initiatives Ben Tre Province, Vietnam Coastal zone management: road construction, dike upgrading, and mangrove protection Freshwater resources management“Acknowledgements” section has been deleted as it is not a part of the contribution structure. investment on irrigation systems for water storage, construction of dikes to prevent saltwater intrusion, and watershed management to protect water sources Supported agricultural adaptation: switch to salt-tolerant crops, investment on drought-tolerant crops, improvement of early warning system Supported CCA for aquaculture and capture fisheries: technological innovation in pond construction for improved water storage, introduction of fish-rice model in saline areas, and research to identify rich fishing grounds Information and awareness campaign on how to prepare for climate change

Community-based initiatives

Autonomous household adaptation practices

Mangrove forest protection Ensuring supply of freshwater for agriculture, aquaculture, and domestic needs (e.g., storage structure construction and provision of containers to harvest rainwater) Relocation of at-risk houses Participation in sea dike construction

Harvest rainwater Switch from black tiger shrimp to whiteleg shrimp to adapt to saline water Change cultivation schedules to avoid saltwater intrusion Use of sandbags to build dikes around the farm to prevent saltwater intrusion and seawater inflow

Source: Perez et al. (2013)

Cost-Effectiveness Analysis of Selected Adaptation Options The results of a CEA of selected adaptation options, which were identified by LGUs as priority projects, are presented in Tables 13, 14, and 15. For the analysis, the researchers selected a common denominator, such as cost per unit of area protected or per household saved or protected. However, this is a very crude comparison as multiple types of benefits may be delivered by each of the adaptation options. For instance, a mangrove protection project will produce other types of benefits than what can be “produced” by installing a dike to protect a given area. As such, the comparison should be interpreted with caution. The last column in each of the next three tables provides some additional information regarding the interpretation of results. For San Juan, Batangas, in the Philippines, sea wall construction and mangrove reforestation were compared, and results showed that it is a lot cheaper to prevent a kilometer of shoreline erosion using mangrove reforestation (Table 13). In addition,

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Table 13 Cost-effectiveness analysis results for San Juan, Batangas, Philippines

Objective Protect the coastline from erosion

Planned adaptation strategies Sea wall construction

Mangrove reforestation

Increase the number of households safe from typhoon/flooding

Zoning provisions and relocation

CE ratio USD 0.16 M/ linear km of erosion prevented USD 0.01 M/ linear km of erosion prevented USD 0.07 M/ HH saved

Notes Mangrove reforestation is not only more cost-effective but also offers other co-benefits like additional sources of income and marine biodiversity preservation

The changing zoning provisions need to be accompanied by the removal of communities from areas they currently occupy, a very costly and socially unattractive option

Source:

this option produces other forms of benefits from the mangrove resources, both in terms of provisioning and regulating functions; if valued, these benefits will make this measure even more attractive. The use of early warning system and the provision of evacuation shelter were also compared with improvement of zoning regulation and relocation. As expected, the latter was a lot more costly to implement as a way of protecting households from the negative impacts of flooding and/or typhoons. The early warning system is being put in place in many parts of the country. A similar analysis was carried out for the Palawan, Philippines, study site (Babuyan in Honda Bay). Several options were evaluated: to protect households from storm surges (i.e., breakwater construction, dike construction, and mangrove reforestation), to protect them from inland flooding (i.e., upland reforestation, IEC with provision of temporary evacuation shelter, and household relocation), and to protect production areas (i.e., dike construction, riverbank rehabilitation, and river dredging). The results show the superiority of mangrove reforestation over structural measures, the cost-effectiveness of river dredging and riverbank rehabilitation, and support for an effective early warning system supplemented by IEC as part of DRR strategies (Table 14). For the study sites in Jakarta Bay, Indonesia, several options with varying objectives were compared as shown in Table 15. River dredging1 was found to be more cost-effective than the construction of new canals or embankment and even

1

Note: The study did not indicate how often this has to be done.

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Table 14 Cost-effectiveness analysis for Honda Bay, Palawan, Philippines Objectives Protect households from storm surges and loss of property and minimize sand erosion

Planned adaptation strategies Breakwater construction Dike/levee construction Mangrove reforestation

Prevent river overflow and minimize siltation, which damage coconut plantations and fishponds

Riverbank rehabilitation using vetiver grass Riverbank rehabilitation using vetiver grass combined with mechanical method Dike construction

River dredging

Protect households from inland flooding

Upland reforestation IEC/early warning system establishment and provision of temporary evacuation area Household relocation

CE ratio USD 0.276 M/ HH USD 0.032 M/ HH USD 0.019 M/ HH USD 0.004 M/ ha USD 0.034 M/ ha

USD 0.032 M/ ha USD 0.002 M/ ha USD 926/HH USD 120/HH

Notes Mangrove reforestation is cost-effective in protecting households and properties and in minimizing sand erosion where mangroves are seen to thrive well

The discussion on the planned options and costeffectiveness (CE) ratios focused on prioritizing riverbed dredging together with riverbank rehabilitation using vetiver grass alone

IEC is cost-effective but success depends on the maturity of the residents to react accordingly

USD 2,234/ HH

Source:

mangrove rehabilitation. The high cost of mangrove rehabilitation is attributed to the need to purchase land from private landowners who already have rights over the areas previously occupied by mangroves. However, mangrove restoration is likely to make an even bigger contribution to the local economy in the face of climate change and the resulting increase in typhoon frequency and intensity (Tuan and Duc 2013). Mclvor et al. (2012) suggested that mangroves can potentially play a significant role in coastal defense and DRR. Overall, one can see that the CEA results tend to favor mangrove reforestation over structural measures and river dredging in order to increase flood control function. The scientific basis for this claim was found in the study by Mazda

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Table 15 Cost-effectiveness analysis for Jakarta, Indonesia Planned adaptation strategies Construction of East flood canal Dredging of Sunter river

Site Rorotan

Objectives Reduce the no. of HH affected by flooding

Marunda

Reduce the no. of HH affected by flooding and coastal flooding

Construction of permanent embankment Mangrove rehabilitation

Kalibaru

Reduce the no. of HH affected by flooding, coastal flooding, and saltwater intrusion

Road elevation

Kamal Muara

Reduce the no. of HH affected by flooding and coastal flooding

Dredging of Pesanggrahan river Mangrove rehabilitation

Muara Angke

Reduce the no. of HH affected by flooding and coastal flooding

Road elevation

Dredging of Cakung river

Mangrove rehabilitation

CE ratio USD 307 M/ HH USD 0.695 M/ HH USD 2.5 M/ HH USD 13.37/ HH USD 2.64 M/ HH USD 2.09 M/ HH USD 2.09 M/ HH USD 2.43 M/ HH

USD 0.311 M/ HH USD 2.07 M/ HH

Notes The cost-effective option suitable in this area is the dredging of Sunter river

The cost-effective option is planting mangroves

The more cost-effective solution is to dredge Cakung river

The more cost-effective solution is to dredge Pesanggrahan river. A large portion of the cost of planting mangroves is the value of coastal land owned by private individuals or groups The more cost-effective solution is to elevate roads. Like in Kamal Muara, a large portion of the cost of planting mangroves is the value of privately-owned coastal land

Source: Agus et al. (2013)

et al. (2006) and cited in Andrade et al. (2010). Moreover, the early warning system supplemented by evacuation shelter provision was found to be quite cost-effective compared with the other options evaluated. This finding is consistent with other studies’ findings which validate the use of an early warning system as one of the most cost-effective measures to reduce damage cost (Hallegatte 2012; Linham and Nicholls 2010). The next section describes efforts to link research with local government adaptation planning based on the experience from two cross-country projects implemented from 2011 to 2013.

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Working with Local Governments in Adaptation Planning Climate change will affect everyone. Differences on the impacts felt will depend on the locality’s hazard exposure, the people’s adaptive capacity, and the LGU’s level of preparedness. This means that adaptation planning has to be locale specific to suit the conditions and capability of different local governments. In order to bring research to the level where it can have impact, EEPSEA supported two multi-country projects to engage local government planners in adaptation planning in 2011–2013.

CBMS-EEPSEA Project The main funding source of EEPSEA, the International Development Research Centre (IDRC), also supports a Community-Based Monitoring System (CBMS), a global project with presence in the Philippines, Vietnam, and Indonesia. The CBMS-EEPSEA partnership was carried out to pilot test the application of the EEPSEA framework on climate change vulnerability assessment and mapping at the local level using CBMS data and supplemented by data from other government sources. There are two outcomes that were expected from this initiative: (1) LGU level capacity building on understanding how to assess climate change vulnerability and (2) identification of adaptation strategies based on research done on this topic. The study sites included (1) Vietnam, Kim Son district of Ninh Binh province in the North, Nghia Lo municipal of Yen Bai province in the North Mountainous Region, and Tam Ky town of Quang Nam province in the Central Region; (2) Indonesia, two villages in the province of Kota Pekalongan (Pasirsari village, Kecamatan Pekalongan Barat, and Panjang Wetan village, Kecamatan Pekalongan Utara); and (3) the Philippines, municipality of Carmona in Cavite province and Marinduque province. Experience in pilot testing the climate change vulnerability framework shows that its main advantage is its simplicity, which allows local government decisionmakers to understand what factors they should consider when doing such an assessment. The ability to map information that allows government officials to immediately see how they fare relative to their neighbors was also found attractive. Experience in using vulnerability mapping to aid in identifying suitable adaptation strategies varies across the participating country teams. The Philippine teams managed to bring the discussion to the point that they were able to identify the adaptation practices that need to be strengthened and those that need to be implemented as listed in Table 16 (Reyes 2012). The Vietnamese team shares that the process helped them understand the location and the sources of vulnerability but that these were not sufficient to assess what the community needs in order to adapt to the changing climate. In a way, identifying the adaptation practices that the community or the local government could undertake is indeed only the first step. Given limited resources

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Table 16 Adaptation strategies identified in the Philippine CBMS-EEPSEA project Study site Carmona, Cavite

Marinduque province

Adaptation strategies identified Strengthen current efforts in: river cleanup, solid waste management, and de-clogging of canals and waterways Conduct more orientations on DRR management, enhance DRR communication capability and early warning system with new equipment and strengthen flood forecasting, and upgrade evacuation and health facilities Install diversion canals, dams, and reservoirs to protect industrial and agricultural lands Review/update and enhance the provincial DRR management plan Strengthen the rehabilitation of watershed areas and reforestation projects through the National Greening Project (NGP), Bamboo Greenbelt Project, and other forest rehabilitation projects Build LGU and community capability and capacity on the various facets of DRR (i.e., warning, search and rescue, emergency relief, logistics and supply, communication and information management, emergency operation management, evacuation planning and management, health emergency education, and post disaster management) Establish/construct evacuation centers in safe areas and improve and construct roads and feeder roads, drainage facilities, footbridges, spillways, and floodways in priority areas Produce and disseminate natural hazard and geographic info system susceptibility maps and IEC materials and install Integrated Warning/ Communication and Response System Install automatic weather station in major critical areas such as Boac and Sta. Cruz

Source: Reyes (2012)

and varying capacity, an assessment of the economics of these measures and their technical and social acceptability must be carried out as well; these are not within the scope of the CBMS-EEPSEA project. In the case of the Indonesian team, the adaptation policies and programs of the national and local governments were discussed and analyzed as a separate activity from the vulnerability mapping. The analysis revealed that current adaptation planning is largely addressing the hazard component of vulnerability, which they pointed out is a major limitation on account of the findings of the project. In particular, the CBMS-EEPSEA project showed clearly that adaptive capacity and sensitivity are equally important sources of vulnerability and should therefore be addressed as well. All the project country teams recommended that similar efforts be done to assist other LGUs to better understand their vulnerability situation and to help them identify adaptation practices. To help evaluate the economic viability and acceptability of these identified strategies would require a longer time and a different set of skills, as demonstrated in EEPSEA’s project with IDRC’s Climate Change and Water program, which is discussed in the next section.

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CCW-EEPSEA Project In February 2011, EEPSEA and the IDRC program on climate change and water (CCW) awarded three 3-year research grants to institutions based in the Philippines, Vietnam, and Cambodia. The project, entitled “Building Capacity to Adapt to Climate Change in Southeast Asia,” aimed to enhance the capacity of university researchers, provincial officials, LGU representatives, and other mass organizations in selected SEA countries by equipping them with knowledge on how to assess climate change causes and impacts and how to undertake an economic analysis of selected adaptation options. Specifically, the collaborating partners were expected to undertake vulnerability analysis, prioritize adaptation options, and develop sound and feasible project proposals for funding. Based on the highly vulnerable sites identified in Yusuf and Francisco (2009), the project selected the following study sites: Kampong Speu in Cambodia (highly vulnerable to drought), Laguna in the Philippines (highly vulnerable to flooding), and Thua Thien Hue in Vietnam (exposed to flooding and typhoons). The project adopts a multidisciplinary and participatory approach. Each country research team is composed of researchers with backgrounds in natural science, sociology, and economics. Their mandate was to work with their study site’s local government officials in undertaking vulnerability assessment, in identifying and evaluating adaptation projects, and in developing the CCA proposal/plan for submission to donor agencies. During project implementation, the research team implemented a series of training courses, joint field visits, and dialogues with community members. The key skills that the training courses addressed are vulnerability assessment and mapping following the framework used in Yusuf and Francisco (2009), evaluation of climate change impacts, economic analysis of adaptation options, and proposal development for adaptation funding support. Interestingly, many of the team members are now being involved in providing consultancy services on these areas in their own countries – a sure offshoot of their engagement in this project. The engagement of the local government people in the project had brought about several benefits, namely, (1) greater awareness on climate change risks; (2) generation of risk maps, which were used to develop agricultural production plans for three subregions in Thua Thien Hue; (3) integration of climate risks in the socioeconomic development plan; and (4) improved knowledge on how to conduct vulnerability assessment and economic analysis of adaptation options as well as proposal development. They were also able to network with government people from other countries as the project hosted sharing and training meetings as part of the capacity building activities designed for the collaborators. A post project survey was implemented to assess changes in knowledge and skills of the people involved in the project. All team members across the three countries demonstrated improved understanding of the climate change problem and a higher level of knowledge on the various tools that were used by the team. The

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biggest improvement was noted among the research team members from Cambodia. In terms of concrete actions taken up, local government partners in Thua Thien Hue have developed agricultural production plans based on the results of the climate change vulnerability map that was prepared through the project. The Thua Thien Hue LGU staff developed proposals to raise funds for the construction of local CCA measures, particularly better use of water in rice production. The LGU partners in Laguna, Philippines, now have better appreciation and knowledge on how to implement vulnerability assessment and mapping and how to package proposals, but they acknowledged that they may not be able to carry these out on their own. Their reservation regarding their independent implementation/ conduct of such activities is not due to perceived capacity constraint but more as a result of their busy schedules as they are responsible for multiple projects at the provincial office. The project research findings also affirmed the findings of previous studies. First, that vulnerability to climate change is quite high and that it varies across areas. In Thua Thien Hue, Vietnam, households living in delta communes had higher adaptive capacity compared with households living in coastal and upland communes. The social capital was found to be generally high but limited infrastructure support, access to technology, and lower financial resources contributed to lower adaptive capacity for many households. In Laguna, Philippines, higher vulnerability was noted among coastal communities compared with agriculture-based households. Interestingly, it was found that a big proportion of the vulnerable households are not knowledgeable about the threats posed by climate change. The most vulnerable households are often the most poor as well and so it probably makes no difference where their poverty stresses are coming from. In terms of experience with climate-related hazards, majority identified typhoons and flooding as the hazards posing the greatest threat based on experienced damages over the years, most intensely in the last few years. The biggest losses/damages come in the form of damages to houses and furniture. About 16 % of the households regularly experience evacuation, and a tenth of those interviewed had experienced permanent relocation as supported by the local government of Sta. Cruz, Laguna. The important role played by social capital, particularly those involving women’s organizations, in accessing DRR programs was also highlighted in the Philippine study site. The study site in Kampong Speu, Cambodia, is predominantly a farming community and is threatened mostly by drought, with flash floods occurring in certain areas only. Vulnerability was found highest in the eastern and central western regions of the province on account of the highest concentration of vulnerable communes in these areas. Farming households in lowland areas have been coping with drought conditions by shifting to short-duration crop varieties. Those in mountainous areas were found less willing to shift to new varieties, but they generally have more resources to cope with the impacts of a changing climate. For both sites, other adaptation strategies included: constructing and renovating canals, selling household assets, and migrating to

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work outside the village (e.g., work in a garment factory, as household help, and in construction). The teams also packaged adaptation proposals, which the LGUs can then submit for funding support: (1) a technology-based flood early warning system for the Sta. Cruz River Watershed in Laguna, Philippines; (2) improved irrigation system for Kampong Speu, Cambodia; and (3) upgrading of the An Xuan tributary banks and river dredging in Thua Thien Hue, Vietnam. These projects were found to be the most economically efficient option for the three sites based on the economic analysis of several alternative projects.

Lessons in Action Research: Working with Local Government Units The researchers of the two projects considered working with the local governments both rewarding and productive. They are able to immediately share and discuss with local government planners their research results as the study progresses. The local government collaborators were involved more deeply in data analysis and presentation as they were engaged all throughout the project. Hence, there is joint ownership of the research reports, which was linked to adaptation planning more directly, unlike in the traditional research process. More importantly, there is improvement in the knowledge and skills of not only the researchers but also of their collaborating local government partners. All these are expected to result in a better understanding of the problem and a higher capacity to assess, evaluate, and decide on how it can be addressed. Nonetheless, it must be mentioned that the heavy workload of the local government partners prevented them from being more engaged in the research process. They are only engaged in the project part time with DRR/climate change being only one of their many other responsibilities in their capacity as local government staff. The same can be said of the university researchers who are only working part time with the research. They meet and work with their collaborators only during meetings or major activities. Perhaps, the project duration for the CCW-EEPSEA project is too long (i.e., 3 years), and that of the CBMS-EEPSEA project (i.e., 1 year) is too short. The CCW-EEPSEA project differs from the CBMS-EEPSEA project in that it is longer and thus has more resources. Its idea is to fully understand the various aspects of vulnerability by forming three teams (i.e., economic, social, and mapping teams) and to analyze and characterize who are the most vulnerable sectors in the locality. The team is comprised of university researchers, LGU representatives, and representatives from NGOs working in the community. On the second year, the potential and existing adaptation practices were identified and evaluated. An economic analysis of those options was carried out. In the last year, the team assisted the LGU in developing a proposal to seek support from donor organizations to fund their adaptation plans. This 3-year project completely contrasts with the 1-year duration of the CBMS-EEPSEA project, both in terms of depth of analysis and resources.

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From a program point of view, it will appear that having 2 years for both projects will be a Pareto improvement. The CBMS-EEPSEA project would have more time to expand its adaptation analysis. On the other hand, 3 years may have been too long for the CCW-EEPSEA team as the members managed to do multiple assignments that took them away from a more concerted, focused analysis of the problem on hand. Somewhere in between these two project durations could result in a more intensive interaction between researchers and their local government partners as well as richer data collection and analysis.

Lessons from EEPSEA-Funded Climate Change Adaptation Research in Southeast Asia: Future Directions Climate change is here to stay and is likely to get worse given that serious commitment to reduce greenhouse gas (GHG) emission will only happen starting 2020 if, and only if, the 2015 International Agreement with significant GHG reduction commitment will be signed by all countries in Paris. The prospect of having such a stronger agreement in place does not look good given how previous talks have failed on this front. We can only hope that our global leaders will come to their senses and put in place serious efforts to reduce GHG emission to slow down climate change. On the positive side, it is good to hear about various countries’ efforts to move toward a green economy by using more efficient technologies, building high energy savings, and investing on ecosystem reforestation or protection. However, these efforts cannot be done at a microscale – we need government commitment to pass stronger measures to reduce carbon emission. Surely, a carbon tax supplemented by programs to reduce impacts on the poor is a step in the right direction but is opposed in many countries because of strong lobbying pressure by those sectors who will be hit by this measure. With the exception of climate deniers, everyone agrees that climate change poses a real threat to people, with some countries facing (and in fact, already experiencing) more serious challenges than others and the poor being more vulnerable to it. The various research supported by EEPSEA on CCA in SEA proved that the impact of extreme climate events on those affected is huge with an extreme event costing households up to 44 % of their annual household income. Depending on the number of extreme climate events (which unfortunately is expected to be more frequent and intense in the future), future damage can be even bigger and will most likely drive vulnerable households to extreme poverty. Despite the severity of the situation, adaptation actions by SEA households are generally very crude and mostly in the form of reactive measures (e.g., strengthening housing units, using sandbags during flooding, storing of food, evacuation) rather than preventive ones (e.g., relocation, building multistorey and stronger housing units). Largely, this is explained by the limited resources available to most vulnerable households for investment in stronger measures. Moreover, in

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many cases, there is still the hope that extreme events will not occur that often in the future or that they are used to this condition already and will be able to manage. This complacency is now being addressed by enhanced IEC campaigns that most governments are launching toward DRR/climate change. A lot of this effort needs to be done more widely and intensely, but stronger adaptation investments need to be made if adaptation capacity or resilience is to be enhanced in areas most vulnerable to climate change. The financial resources available to these communities are undoubtedly small in relation to needs and efforts must be done to provide more resources and/or use more wisely the limited resources available. Hopefully, developing countries in the region will be able to access some of the adaptation funds available, and the economic analysis done on some of these measures was found to be economically viable. Local governments need support in adaptation planning. The two action research projects carried out with funding support from EEPSEA and the collaborating organizations (IDRC’s CBMS and CCW) showed that local government officials are receptive to such research collaborations and are very much willing to learn science-based planning. With more resources provided to free up some of their time during the conduct of such action research projects, a higher-level and more fruitful engagement can perhaps be expected from them. In addition, our experience shows that there are existing programs (e.g., CBMS, WorldFish, etc.) in most communities that researchers can tie up with so that synergy can be achieved in getting more resources and technical expertise in the action research. Finally, given the urgency of the situation and the relatively higher level of research information now available from different organizations working in SEA, it is high time to move toward research that feeds directly into concrete actions, to not simply come up with plans and proposals but with adaptation projects/measures that can be readily implemented on the ground. This calls for action research with some funding available to pilot test adaptation projects that will be evaluated as part of the research. This is not going to be easy as there seems to be a dichotomy between research and development. In most cases, research organizations just do research while development organizations focus on development projects. This is so unfortunate in this instance since concrete actions supported by science are what we need to help prepare local governments, communities, and households to better adapt to climate change. Research alone oftentimes translates to just pure talk; action research is walking the talk.

References ADB (Asian Development Bank) (2009) The economics of climate change in Southeast Asia: a regional review. Asian Development Bank, Manila ADB (Asian Development Bank) (2014) Climate change and rural communities in the greater Mekong Subregion: a framework for assessing vulnerability and adaptation options. Asian Development Bank, Bangkok Adger WN (1999) Social vulnerability to climate change and extremes in coastal Vietnam. World Dev 27(2):249–269

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Impact of Climate Change, Adaptation, and Potential Mitigation to Vietnam Agriculture Trinh Van Mai and Jenny Lovell

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change in Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extreme Events in Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change and Sea Level Rise Scenarios in Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vietnam Agricultural Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of Climate Change on Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agriculture Vulnerability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Damages in Agriculture Production Associated with Climate Change . . . . . . . . . . . . . . . . . . . . Quantify the Impacts Through Calculating Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change Adaptation for Crop Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experiences in Climate Change Adaptation in Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change Adaptation Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GHGs Emission in Crop Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Identify and Analyze Technologies to Reduce GHG Emission in Agriculture . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

900 900 901 902 902 903 903 903 910 911 911 913 916 916 918 922 922

Abstract

The Institute for Agricultural Environment of Vietnam conducted a research to study climate change impacts on agriculture, develop climate change adaptation measures, and identify mitigation options. Climate change impacts were

M. Van Trinh (*) Institute for Agricultural Environment, Vietnam Academy of Agricultural Sciences, Phu do, South Tu Liem, Hanoi, Vietnam e-mail: [email protected] J. Lovell Environmental Studies Department, University of California Santa Cruz, Santa Cruz, CA, USA e-mail: [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_87

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assessed through past, current, and future conditions. Past information showed damages due to extreme weather events. Current production and climate conditions showed potential vulnerability. Future climate change scenarios and crop growth modeling predicted long-term impacts on crop production. The impacts of many adaptation measures and mitigation options were evaluated to reduce risks and losses from climate change and to reduce greenhouse gas emissions. Results showed that climate change has caused strong impacts on agriculture in Vietnam. It has caused severe damages in the past, and it is likely to cause high vulnerability and heavy crop production losses in the future. Flat lands experience stronger impacts than highlands, with the Mekong River Delta suffering the strongest impacts, followed by the Coastal Central area and Red River Delta. As a country strongly impacted by climate change, it has suffered many extreme events and disasters. The agricultural sector has developed suitable adaptation measures to cope with the extreme events. Vietnam maintains a high agricultural productivity not only to feed more than 70 million people but also to export a high amount of food and foodstuffs. Vietnam is the leading cashew nut exporter, second highest rice exporter, and third highest coffee exporter in the world. However, with extensive areas of rice paddy production and high animal populations, Vietnam’s agriculture contributes substantial greenhouse gas (GHG) emissions. As a result, the Vietnamese agricultural sector is developing and implementing many mitigation options, such as alternative wetting and drying irrigation, biogas digestion, composting, and converting rice land to non-rice land. These policies target a 20 % GHG emission reduction by 2020 in comparison to the “business-asusual” scenario.

Introduction Climate Change in Vietnam Climate change is projected to impact human and ecosystems health at global, regional, country, and local scales. Due to human-induced greenhouse gas (GHG) emissions to the atmosphere, climate change will cause a cascade of environmental changes. These effects will particularly impact agricultural production. The main causes of climate change are the accelerated increase of GHG-emitting activities, combined with the exploitation of carbon sinks, such as forests, and the destabilization of natural carbon sequestration mechanisms, such as oceans. The Kyoto Protocol aims to prevent and stabilize the emission of six main GHGs including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrochlorofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6). Of these GHGs, the most abundant and concerning is CO2, which originates primarily from anthropogenic fossil fuel combustion of coal, oil, and gas. The second most important GHG is CH4, which is emitted from landfills, enteric fermentation, air-cooling systems, coalmines, and natural gas.

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These emissions increase GHG concentrations in the atmosphere, causing global warming and higher temperatures; glacial melting leading to the loss of lowlands and islands; shifting borders between ecological and agroecological zones that have existed for thousands of years; altering atmospheric, hydrologic, and biogeochemical cycles; and altering the functional components and quality of the hydrosphere, biosphere, and geosphere. These impacts ultimately result in profound changes in natural and human system productivity. Because Vietnam is considered one of the countries most strongly impacted by climate change Dasgypta et al. (2007), the Government of Vietnam has paid a lot of attention to this issue. Climate change and sea level rise scenarios were developed for Vietnam as a foundation for assessment of impacts on sectors and regions in order to take proper responsive actions.

Extreme Events in Vietnam Temperature Changes in annual temperature over the past 50 years in Vietnam have been inconsistent in different regions. Overall, the average annual temperature increased by approximately 0.5 degrees Celsius ( C). The maximum temperature change ranged between 3.0  C and 3.0  C, and the minimum temperature change varied between 5.0  C and 5.0  C compared with the historical average (MONRE 2012), similar to the global temperature trends. Rainfall During the past 50 years, rainfall tended to decrease in northern regions but increased in southern regions. In the northern climate zones, rainfall has changed insignificantly in the dry season (from November to April) but decreased between 5 % and 10 % in the rainy season (from May to October). In the southern climate zones, rainfall decreased dramatically in the dry season but increased from 5 % to 20 % in the rainy season (MONRE 2012). However, the overall trend of average annual rainfall is to decrease in north and to increase in the south. In particular, rainfall increased by about 20 % in the southern central region compared to other regions (Table 1). Typhoon Annually there is an average of 12 typhoons or tropical depressions that affect Vietnam. Of these storm systems, 45 % originate from the Eastern Sea and 55 % originate from the Pacific Ocean (MONRE 2012). The Central Coastal areas of Vietnam, lying between 16 N and 18 N, and the Northern regions above 20 N, have the highest frequency of typhoons and tropical depressions. Drought Drought, including monthly water scarcity and seasonal scarcity, has increased overall but inconsistently. Drought impacts shift depending on the region and weather station within the same climate zone. Overall, the number of hot days

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Table 1 Increase of temperature and changing of rainfall during 50 years in agro-climate zones of Vietnam Temperature ( C) Agro-climate zone Northern West Northern East Red River Delta North Central South Central Central Highlands Mekong River Delta

January 1.4 1.5 1.4 1.3 0.6 0.9

July 0.5 0.3 0.5 0.5 0.5 0.4

Year 0.5 0.6 0.6 0.5 0.3 0.6

Rainfall (%) Period November–April 6 0 0 4 20 19

0.8

0.4

0.6

27

Period May–October 6 9 13 5 20 9

Year 2 7 11 3 20 11

6

9

Source: IMHEN (2010a)

increased significantly across the country, especially in the Central and Southern regions.

Sea Level Rise Satellite data from 1993 to 2010 indicated that the sea level in the Eastern Sea is rising at a rate of 4.7 mm per year. This translates to an average sea level rise along Vietnam’s coastline of 2.9 mm per year. However, the coastal areas in the Central regions and the Southwestern region have higher sea level rise than other coastal areas.

Climate Change and Sea Level Rise Scenarios in Vietnam In 2009, the Ministry of Natural Resources and Environment (MONRE 2009) published the first edition of Climate Change and Sea Level Rise Scenarios. The report included low-resolution forecasting for the seven climatic zones of Vietnam. MONRE designated the Vietnam Institute of Meteorology, Hydrology and Environment (IMHEN) as the leading agency on detailing and updating climate change and sea level rise scenarios for Vietnam. In collaboration with other research institutes, government agencies, and departments, the team prepared updates in 2010 and developed climate models and selective statistical tools specialized for Vietnam. Through this collaborative work, MONRE published the second edition of higher-resolution climate change and sea level rise scenarios for Vietnam in 2012 (MONRE 2012). The report included details at regional scales and an additional climatic extreme section.

Vietnam Agricultural Production Although the agricultural sector only accounts for 20 % of Vietnam’s gross domestic product (GDP), the industry plays a crucial role in Vietnam’s economy. The sector is

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responsible for feeding more than 70 million people. While Vietnam has seen a substantial increase in industrial production, the agricultural production has continued to increase at a slower rate over the past 10 years. The average annual growth rate of agriculture remained at about 3.5 % but has decreased in recent years due to the severe effects of climate and soil conditions. For example, in 2005 it accounted for 19.3 % of the GDP, and in 2012 it had only increased to 19.67 % of the GDP. The makeup of Vietnam’s agricultural outputs shows no significant change in recent years. Food crops are the biggest proportion of Vietnam’s agricultural sector, accounting for 74.6 % of the total production. Crops are followed by livestock production, which accounts for 23.8 %, and agricultural services makes up the remaining 1.58 %. Crop production still plays the biggest role by far. There was a very slight decrease of the proportion of crop production between 2005 and 2012, from 76.39 % to 73.81 %. During this time livestock production filled that gap with an increase from 21.95 % to 24.65 %. Crop production is dominated by annual crops, which account for 83.4 % of the area under production, and perennial crops, which account for 16.6 %. Most of the annual crops are cereals such as rice, maize, and soybeans, which make up 78.5 % of the annual crop production area. The remainders are commodity and annual industrial crops, covering about 9.4 % of the annual crop area. Because Vietnam’s agricultural sector is dominated by annual crops, it is a highly important resource for food and economic security. Annual crop systems are highly vulnerable to weather and will be strongly impacted with a more variable and changing climate (Table 2).

Impact of Climate Change on Agriculture Agriculture Vulnerability According to Patnaik and Narayanan (2005) and Iyengar and Sudarshan (1982), agricultural vulnerability can be calculated using an index based on three factors: exposure to a hazard, sensitivity, and adaptability. This index approach is shown in Table 3.

Damages in Agriculture Production Associated with Climate Change Damages Due to Natural Disaster, Drought, and Flood Vietnam is located on the eastern seaboard of Southeast Asia, an area frequented by natural disasters. Climate change adds complexity to this system, increasing the frequency of typhoons and extreme hot and cold weather, droughts, landslides, and flooding. According to historical meteorological data, an average of 6.96 typhoons hit Vietnam annually between 1950 and 2008. The number of typhoons increased over the years, and, more importantly, these typhoons occurred later in the rainy season between 1990 and 2008. Between 1950 and 1990, typhoons would normally

Source: GSO (2013)

Perennial cops In which

Year Agriculture land In Which Annual crops In which Spring rice Autumn summer rice Summer rice

Industrial Fruit tree

Maize Soybean Peanut Sugarcane Annual industrial crop

Cereals crops Rice

Table 2 Area dynamics of some key crops in Vietnam 2000 12,644.3 10,540.3 8399.1 7666.3 3012.3 2292.8 2360.3 730.2 2000 244.9 302.3 778.1 2104.0 1451.3 565.0

2005 13,287.0 10,818.8 8383.4 7329.2 2942.1 2349.3 2037.8 1052.6 2005 269.6 266.3 861.5 2468.2 1633.6 767.4

2010 14,061.1 11,214.3 8615.9 7489.4 3085.9 2436.0 1967.5 1125.7 2010 231.4 269.1 797.6 2846.8 2010.5 779.7

2011 14,363.5 11,420.5 8777.6 7655.4 3096.8 2589.5 1969.1 1121.3 2011 223.8 282.2 788.2 2943.0 2079.6 772.5

2012 14,579.2 11,481.5 8872.3 7753.2 3124.3 2659.1 1977.8 1118.3 2012 220.5 297.9 727.2 3097.7 2215.0 765.9

904 T. Van Mai and J. Lovell

Sensitivity condition

Group of factor Exposure condition

Parameter Average max temp Average min temp Trend or temp increase within 50 years (1958–2007) Annual rainfall Percentage of agricultural land Water surface area Population Poor household Coastal length Jobless % Woman %

Year 2010 2010 2010

2010 2010 2010 2010 2010 2010 2010 2010

Unit  C  C  C

mm %

1000 ha head/km2 % km % %

+ + + + + +

+ +

Relationship + + +

124.900 939 8.3 267 2.61 50.72

1614.2 38.268

RRD 26.4 22.0 0.60

69.860 117 29.4 250 1.21 50.11

1450.6 14.913

NM 25.7 21.6 0.55

Table 3 Agriculture and aquaculture production from seven different agroecological zones in 2010

54.500 189 20.4 639 2.94 50.46

2785.3 15.842

NCC 29.1 23.9 0.50

25.100 289 20.5 1010 2.94 50.79

2526.5 17.769

SCC 30.8 24.1 0.30

11.100 95 22.2 0 2.15 49.33

1287.3 29.775

HL 30.5 22.3 0.60

61.700 502 2.3 447 3.91 51.25

1162.7 46.036

SE 32.1 24.3 0.55

(continued)

727.400 426 12.6 684 3.59 51.28

2244.4 63.066

MRD 31.9 25.2 0.60

Impact of Climate Change, Adaptation, and Potential Mitigation to Vietnam. . . 905

Group of factor Adaptability condition

Parameter Agricultural value Rice yield Maize yield Peanut yield Fishing production Raising aquaculture production Raised fish production Raised shrimp production Aquaculture export value

Table 3 (continued) Year 2010 2010 2010 2010 2009 2010

2010 2010 2010

Unit Bil. VND Tons/ha Tons/ha Tons/ha Tons

Tons

Tons

Tons

Bil. VND

Relationship

463.6

294

4859

50,162

RRD 4235 5.92 4.52 5.28 10,744

3843

14,511

243,818

322,146

NM 510 4.64 3.32 8.85 175,051

2538.5

13,713

62,437

89,728

NCC 2771 5.07 3.99 2.04 219,583

5080

33,073

12,325

58,198

SCC 7060 4.82 3.99 2.04 452,213

146.3

61

14,702

1502

HL 157 5.07 4.92 2.93 3412

4119.5

19,664

62,433

91,727

SE 3071 4.49 5.2 5.16 412,116

33,891.1

307,070

1,419,010

1,838,638

MRD 34,991 5.43 5.29 3.1 863,289

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occur in August. However, between 1990 and 2008, they appeared regularly in October and November, much later in the season. There are also more extreme typhoons with higher intensity affecting the whole country. Historically, the highest typhoon speed was between 118 and 133 km per hour (km/h), which is an intensity of 12 on the typhoon Beaufort scale. In recent years typhoons have reached speeds of between 134 and 183 km/h, an intensity of 13–15. As a consequence of these more extreme events, there have been stronger impacts on agriculture production. Data in Table 4 indicates that the average annual damage to agriculture during the period between 1995 and 2007 was 781.74 billion VND (equivalent to 54.9 million USD). This accounts for 0.67 % of agriculture’s GDP (comparing to the 1.24 % GDP loss of all sectors caused by natural disasters). While agricultural production is a small portion of the country’s income, the industry feeds more than 71.41 % of the population. Any damages to agriculture caused by natural disasters resulted in severe impacts on poor people, for whom it is very difficult to recover from a shock (Table 5).

Damages Due to Sea Level Rise Without major action such as dyke reinforcements and improved drainage systems, a 1 m rise in mean sea levels along the coastline of Vietnam would cause an estimated threat of inundation to 17,423 km2 (km2) or 5.3 % of Vietnam’s total land area (IMHEN 2010a). Specifically, it would threaten 39 % of the Mekong Delta, 10 % of the Red River Delta, and over 2.5 % of the Central Coast provinces. Moreover, 33 out of 63 provinces and municipalities, or 5 out of 8 economic regions, are threatened by severe inundation (IMHEN 2010a). The effects of sea level rise on saline water intrusion are significant, especially for the Mekong River Delta. In the period between 1980 and 1999, the 4 % salinity level reached 22.0 kilometers Table 4 Damages in agriculture caused by natural disasters in Vietnam (1995–2007)

Year 1995 1996 1997 1998 1999 2000 2001 2006 2007 Average damage/ year Loss in GDP (%)

Agriculture VND (million) 58,369 2,463,861 1,729,283 285,216 564,119 468,239 79,485 954,690 432,615 781,764.11

USD (million) 4.2 178.5 124.4 20.4 40.3 32.2 5.5 61.2 27.7 54.9

All sectors VND (million) 1,129,434 7,798,410 7,730,047 1,797,249 5,427,139 5,098,371 3,370,222 18,565,661 11,513,916 6,936,716.6

0.67

–

1.24

Source: MARD (1995–2007) a Percentage rate of agricultural GDP loss and total GDP loss

USD (million) 82.1 565.1 556.1 128.4 387.7 350.2 231.5 1190.1 738.1 469.9

(%)a 5.2 31.6 22.4 15.9 10.4 9.2 2.4 5.1 3.8 11.6

Ha Ha Ha Ha Ha Ha Ha Ha Ton Ton Ha Ha Ha Ha Tree Ha Ha M Piece Piece Piece Piece Piece Board Board

Total flooded rice area + Strong damaged + Lost Total flooded non-rice area + Strong damaged + Lost Flooded rice bed + Lost Cereal wetted and lost Seed wetted and lost Lost industrial crop area Damaged industrial crop Damaged sugarcane Damaged planted forest Falling tree Damaged fruit tree + Died Channel slide Brocken bridge and water inlet Brocken water resources works Damaged small water resource works Lost water plank Flooded irrigation station Sunk and lost board Damaged board 132755.15 6678 15847.8 85528 4600 3027 3158.5 302 17237 287.7 4796 48824 17296 5328 786995 51221 7 282542 1335 240 620 974 180 2033 344

2001 51025.4 2846 2669.2 43761.94 0 10434 5.1 2.5 46065 726.025 33 1655 1368 7 13984 33637.3 201 731124 638 23 451 509 29 26 0

2002

Sources: Department of Natural Disaster Management, MARD 2001–2008

Unit

Item

Table 5 Impact of extreme events on agriculture 210514.2 22987 41076 52617.9 0 5924.9 4.2 1.7 43650.1 311 831.1 6939.4 11638.5 738.2 467063 7910.8 500 73263 3 26 326 669 92 184 1

2003 433790.7 9035.3 105336.5 44093.6 195 3071.9 5327 75 1029.5 442.01 0 505 990.2 302 13975 3881.5 0 702904 7 39 148 981 19 97 122

2004 537133.3 0 30372.1 161455.3 11 1709.5 0 0 6915.2 1128.038 306 26171.2 1829 23524 4014390 66 0 166448 70 113 750 36 47 382 89

2005 139230.8 5370 21348.1 122459.8 749.2 23488.07 0.5 0.5 13345.5 2565 1825.8 68842 3064 34028.4 27549424 86433 3000 106720 140 61 117 377 20 1151 1095

2006 173830.2 4709.7 33063.8 215059.2 951.24 37767.8 2115 0 79118.1 8569 0 16293.78 33769.1 5403.88 3100042 30647.16 1761 994853 132 42 1926 1276 89 266 163

2007

146945.5 29932.35 44627.54 325614 0 189395.1 0 601.75 73392.9 902.2 0 63753.33 6302.2 3525.3 735191 5395.8 0 628230.8 450 376 1438 943 184 226 52

2008

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Impact of Climate Change, Adaptation, and Potential Mitigation to Vietnam. . .

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(km) inland on the Red River. With 1 m mean sea level rise, by the end of the twentyfirst century, the 4 % contour line will penetrate 4.5 km further inland into the Red River (IMHEN 2010b; Table 6). Based on projected impacts due to sea level rise scenarios developed in 2009 and updated in 2012 by MONRE (2009, 2012), potential loss in rice production (rice equivalent) of the Mekong Delta will be about 7.6 million tons per year, equivalent to 40.52 % of the total rice yield toward 2100. If climate change and sea level rise happen as predicted in the scenarios and if the average annual yield of rice production remained unchanged, Vietnam would face serious problems of food shortages in 2100 with a 21.39 % rice yield lost.

Damages Caused by Changes in Climate Factors on Crop Production For this damage, the impact of climate change on crop production was determined using crop growth models performed by the Institute for Agricultural Environment (Mai Van Trinh and Nguyen Hong Son 2011). The model used long-term climate factors such as maximum temperature, minimum temperature, rainfall, sunshine hour, humidity, and evaporation as the data inputs for the model. Potential rice, maize, and soybean yield in the spring and summer seasons, as well as yield changes due to altered climatic parameters, were determined in seven different ecological regions. Two provinces were selected for each region (Table 7).

Table 6 Forecast of rice yield loss according to 1 m sea level rise scenarios in MDR

Province Ben Tre Long An Tra Vinh Soc Trang Ho Chi Minh Vinh Long Bac Lieu Tien Giang Kien Giang Can Tho Total

Total area (1000 ha) 231.5 449.2

Flooded area (1000 ha) 113.1 216.9

Flooded agricultural land (1000 ha) 81.7 160.0

Average rice yield (ton/ha/ season) 4.06 4.08

Rice season (season) 2.0 2.0

Rice yield loss (1000 t) 663.7 1305.3

222.6 322.3

102.1 142.5

83.5 116.6

4.43 4.93

2.0 2.0

739.9 1150.1

209.5

86.2

39.2

3.17

2.0

248.6

147.5

60.6

49.2

4.77

2.0

468.9

252.1

96.2

80.4

4.66

2.0

749.0

236.7

78.3

60.1

4.90

2.0

588.5

626.9

175.7

112.8

4.61

2.0

1040.5

298.6 2996.8

75.8 1147.4

64.6 848.1

5.18 44.79

2.0 2.0

669.6 7597.4

Source: Tran Van The et al. 2010 MDR Mekong Delta River

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Table 7 Damages of climate change on major crops of Vietnam

Parameter 1. Rice Yield loss Spring rice Summer rice 2. Maize 3. Soybean 

2030 forecast Production (1000 t) Percentage (%)* 1.966,4 8.18 1.966,6 8.10 1.222,8 7,93 743,8 8,40 500,4 18,71 14,38 3,51

2050 forecast Production (1000 t) 3.634.7 3.634,7 2.159,3 1.475,4 880,4 37,01

Percentage (%)* 15,06 14.97 14,01 16,66 32,91 9,03

Compare to 2008 production

IAE simulated crop yield for the years 2030 and 2050 under climate change conditions compared with normal climatic conditions. The simulation showed that rice yield will be reduced by 8.18 % and 15.06 % in 2030 and 2050; maize yield will be reduced by 18.71 % and 32.91 %; and soybean yield will be reduced 3.51 % and 9.03 %, respectively (Mai Van Trinh and Nguyen Hong Son 2011).

Quantify the Impacts Through Calculating Indexes Vulnerability Index Vulnerability indices of the agricultural sector for seven ecological zones were calculated following Patnaik and Narain (2005) and Iyengar and Sudarshan (1982). Results (Fig. 1) show that agricultural production in the Mekong River Delta and the South Central region are the most impacted by climate change with the highest vulnerability score. Their vulnerability is due to high annual rainfall, high average annual temperatures, high unemployment rate, long coastal exposure, and a large proportion of agricultural land. Damage Index Table 5 shows impacts of extreme events on agriculture, and Fig. 1 shows a damage index from 2001 to 2008. From these estimates, IAE concluded that South Central, North Central, and the Mekong River Delta were the most damaged by climate change. The Central Highlands, Northern Midlands, and Mountainous Areas are less affected. Particularly, in 2010 and 2011, there were 10 and 6 typhoons, respectively. These typhoons hit the North and South Central regions, causing extreme property damage, human injuries, and deaths. Impact on Crop Production Based on climatic changes such as temperature and rainfall under the scenarios from 2020 to 2025 and crop yields under regular cultivation conditions, a crop production impact index for seven ecological zones in Vietnam was calculated and presented in Fig. 1. The Red River Delta, Mekong River Delta, and North Central areas are the top three regions that scored the highest on the impact index.

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Fig. 1 Vulnerability indices, damages, crop yield prediction, and climate change impact

Indicators for Climate Change’s Impact Assessment on Agricultural Sectors An average of three indices were used as indicators to calculate impacts of climate change on different agricultural zones: a damage index, potential impact index (vulnerability), and forecasted impact index (impact of crop production). According to these indices, climate change will impact the Mekong River Delta the most, followed by the North and South Central Coast regions, and the Red River Delta. This result is also in line with the historical documentation of climate change in the regions. It can be used as a validated model to fully understand the impact of climate change and to develop adaptation measures and mitigation options for that region.

Climate Change Adaptation for Crop Production Experiences in Climate Change Adaptation in Vietnam In Vietnam, agricultural production is widely distributed in all ecological zones with climatic, geographic, soil, and crop characteristics. Because of this diversity, it is necessary and important to develop climate change adaptation measures for sustainable agricultural production in each region. – Northern Midlands and mountainous areas: Rainfed crops that demand less water (such as sugarcane, cassava, and edible canna) are recommended to replace paddy rice. Additionally, upland rice and other crops that demand less water are

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–

–

–

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recommended for sloping land with the purpose of reducing dependence on the irrigation system and preventing soil erosion and degradation. In specific microecological zones in the high mountain areas with temperate climatic condition, Mai Van Trinh et al. (2014) recommend that farmers plant high-value medicine and herbal plants, such as black cardamom. This approach allows growers to gain the economic benefits of agricultural production while reducing impacts of extreme weathers, such as freezes and droughts, on perennial industrial crops. Farmers can also apply soil conservation methods on sloping land such as terrace farming, contour farming, windbreak, shading plants, and agro-forestry (Ha Dinh Tuan 2005). These valuable experiences can be transferred to other regions with the same conditions, facing the same climate change impacts. Red River Delta: The key crops of this region are paddy rice and annual industrial crops. Regarding climate change adaptation, changing the crop rotation, crop type, cropping calendar, and water supply are among the most popular methods to reduce risks from extreme events such as, drought, flood, and salinization. Specifically, for paddy rice production in the Red River Delta, the crop calendar can be changed with late transplanting of spring rice to avoid cold weather and increase rice yield. More importantly, changing of the crop calendar for an earlier summer season allows winter crops after the double rice crop. The farmers can achieve a better harvest with this modified crop calendar. Central Coastal areas: Adaptation measures in the Central Coast areas mainly focus on drought and desertification prevention for high water demand crops. Drought-tolerant crops are introduced to suitably grow in limited water supply conditions. New water sources, new farming methods, and modified cultivation techniques have also been introduced and implemented successfully in the region. For example, there is a new crop rotation including an annual rice, sweet potato, and cassava crop (Binh Dinh PPC 2010). Southeast region: As a semiarid ecological zone, adaptation measures in the Southeastern region mainly focus on changing the crop pattern and preventing drought for key crops such as cashew, peanut, sesame, sugarcane, and other annual industrial crops. IAE-ICRISAT (2010) showed that farmers in this region also want to reduce the rice production area and introduce other upland crops with lower water demand and higher profit values. These new crops, such as grapes and apples, improve grower’s incomes and are more sustainable in drought conditions. Central Highlands: The main crops of the Central Highlands region are coffee, pepper, rubber, tea, and fruit tree. Most of adaptation measures aim to reduce water stress for these crops. In the same time, legume cover crops, fruit trees, and intercropping between annual crops and perennial crops should be implemented. These techniques increase income while maintaining soil moisture, protecting soil from erosion, and saving irrigation water. Mekong River Delta: As the most agriculturally productive region of the country, many adaptation measures, tools, and techniques are transferred to farmers in the Mekong River Delta. For example, extension workers promote suitable crop

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selection, changing crop patterns or rotations, and integrating crop management to optimize crop production and reduce investment cost (Pham Quang Ha 2013).

Climate Change Adaptation Technologies Prioritized Climate Change Adaptation Technologies in Agriculture Plant Breeding Using annual data on key crops between 1995 and 2008, IAE notes that rice, cassava, soybean, and sugarcane yields increased significantly (Table 8). Yield increase under climate impact is a sign of strong adaptability in terms of food security. With the exception of a reduction in sweet potato production, yields from rice, maize, soybean, and peanut increased greatly between 1995 and 2008. Specifically, annual yields of rice, maize, and cassava production increased by 13.8, 3.3, and 5.0 million tons, respectively. The side benefits from yield improvements include increased photosynthetic biomass production, and therefore increased carbon sequestration in belowground biomass. Climate change also causes changes in the frequency and severity of precipitation surplus and deficits. For example, Vietnam is experiencing a high number of rainfall events during the rainy season, which can lead to flooding and flash flooding. Conversely, less rainfall is associated with drought and salinization and works to increase the effects of sea level rise and saltwater intrusion. Observed data shows that saltwater intrusion moved inland from approximately 20 to 30 km from the shoreline in the Northern coastal line and between 50 and 70 km in the Southern coastal line (Mai Van et al. 2014). For this reason, the government encourages farmers to introduce and implement flood-, drought-, and salt-tolerant varieties. Many of rice varieties were introduced and successfully implemented in practice,

Table 8 Increase of crops’ yield during 1995–2008 Unit, 1000 t Crop 1. Rice 2. Maize 3. Sweet potato 4. Peanut 5. Soybean 5. Cassava 6. Sugarcane 7. Coffee

RRD 1568.4 172.7 275.0

NMR 1228.6 1.150.6 25.8

CCR 2238.0 674.9 288.6

CH 508.3 981.5 55.8

SE 372.8 229.0 0.9

MRD 7857.5 142.6 119.2

Whole country 13,773.6 3351.3 361.9

51.0 66.8 23.1 77.5 0.0

55.1 41.0 721.7 858.5 4.4

100.3 4.4 2206.2 3605.8 10.2

7.9 33.3 2072.4 1172.3 821.9

31.1 7.8 2133.7 169.2 1.3

16.1 7.4 27.2 311.4 0.0

199.3 143.1 7184.3 5416.9 837.8

RRD Red River Delta, MRD Mekong River Delta, CH Central Highland, SE South East

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such as rice varieties of OM5464 (Tran Thi Cuc Hoa 2011), OM5629, OM5981, and OM7368 (Lang et al. 2011). Water-Saving Irrigation (WSI) One of the impacts of climate change on agriculture is reducing available irrigation water. Many reservoirs in the upland areas tend to run out of water before the rainy season comes, causing drought late in the dry season. Hence, water-saving irrigation helps to save water, better contribute water resources for the whole season, and secure crop harvesting in the dry season. System of Rice Intensification (SRI) SRI was first introduced in Vietnam in 2003 in three provinces (Hanoi, Hoa Binh, Quang Nam). SRI uses the following practices: (i) using young seedlings (2.5 leaves or 8–15 days old); (ii) transplanting one plant per hill (instead of three or four); (iii) irrigating with a minimum of water (a thin water layer of 1–2 cm deep); (iv) applying more organic fertilizers and reducing nitrogen fertilizer; and (v) hand weeding (Uphoff 2002). These practices could be modified to suite local conditions. SRI practices were highly recognized and accepted by farmers and local authorities in the Northern region. The Ministry of Agriculture and Rural Development (MARD) officially adopted SRI as a good farming package by issuing Decision Number 3062/QĐ-BNN-KHCN on October 15, 2007. In 2011, 21 of 33 provinces in the Northern region applied and implemented SRI with total area of 185,065 ha and 1,070,384 participant farmers (PPD 2011). Produce Compost from Crop Residues Previously, crop residues or by-products were mostly used as fuel for cooking, roofing material, animal fodder, or tilled back into the fields. Recently, because rural areas are urbanizing, the traditional rice straw roof is being replaced by brick and concrete; wood cooking stoves are being replaced by gas, electricity, and coal; and crop residues are becoming obsolete. Burning these residues after harvesting is becoming popular practice but causes a serious air pollution and GHG emission problem. One of the suitable solutions is to produce compost using effective microbials. Rice straw is shown to have many positive effects when used as a crop residue. Pham Thi Nhung (2006) carried out field experiments during the winter maize seasons of 2004 and 2005 with fertilizer application of 8 t manure per hectare (ha); 150 kilograms (kg) of nitrogen (N), 90 kg of phosphorus pentoxide (P2O5), and 90 kg of potassium oxide (K2O) per ha; and 5 t of dried rice straw per ha. Results indicated that rice straw increased rice yield by 6–8 % compared to the control of no rice straw application. Rice straw supplementation with 10 % N, 10 % P2O5, and 10–20 % K2O increased maize yield by 7–8 % above the control treatment. Increasing the supplement to 30–40 % K2O does not change the maize’s yield, and at 50 % the maize’s yield’s reduced 4 %. This illustrates that rice straw is a nice complement to input supplementation, increasing the efficiency and reducing the amount of nitrogen, potassium, and phosphorus needed.

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Reduced Tillage On sloping land: Tillage is one important field activity for cultivating maize, cassava, pachyrhizus, and edible canna in the central and mountainous provinces. Adoption of reduced tillage has significantly expanded with labor-saving benefits and introduction of better fertilizer and herbicide use (CYMMIT 1991). For example, in Son La Province (Northwest region), over 23 % of the area under production applied reduced tillage. This includes more than 30,000 ha out of total 130,000 ha of maize production. Farmers were interested with this farming technique, and they learned to practice it. The technique has also been widely implemented in five other mountainous provinces. On flatland: No till is mainly applied in the winter crops after a double rice crop. For example, no till is used in soybean, potato, and maize by directly sowing the seeds on soil surface after harvesting of the summer rice and then covering the seed with rice straw. This farming technique has gradually expanded and is implemented in between 25 % and 60 % of the total winter soybean and potato crops in the Red River Delta (MARD 2009). Biochar Application Biochar, or charcoal, is produced by pyrolysis of biomass under the absence of oxygen or oxygen-limited conditions. Many scientists consider biochar “black gold” in agricultural production. Biochar contains carbon in stable pools with slowed decomposition that transforms to active forms (CO2, CH4, and other GHGs) recommend to use (Feng et al. 2012; Liu et al. 2011). Therefore, biochar has high potentials in improving soil properties by increasing the soil’s water- and nutrientholding capacity and protecting soil microorganisms and in improving yields. More importantly, biochar is considered an effective approach to carbon sequestration by storing carbon permanently in the soil. The agricultural sector of Vietnam produces huge amounts of crop residues which can be pyrolyzed to become biochar. With an annual production of 61 million tons of rice straw and other annual crop residues (IAE 2012), there is a large source of inputs for biochar production in Vietnam. Soil Surface Cover Farmers cover soil surface in well-drained land (on terraces); high-evaporative, arid land; and sandy land with low water-holding capacity. The materials for covering include rice straw, tree leaves and trunks, and nylon. The technique is widely applied in the Central region of Vietnam. The area is characterized by large areas of sandy soil with low soil organic matter, nutrient content, high vulnerability to drought, and low crop yield. Surface cover helps seeds to germinate more fully and faster, prevents weeds from emerging, maintains soil moisture, and reduces nitrogen volatilization. Soil cover is most often used on peanut, soybean, and sugarcane fields (Le Duy Thanh 2004). Change Land Use from Rice to Fruit Tree In many areas, rice is not the most suitable crop for the conditions. For example, in areas where land is elevated and unfavorable for irrigation infrastructure, soil fertility

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is low, or soil is waterlogged due to a depression or sea level rise, usually rice yields are very low. One of the effective measures is converting rice to fruit tree orchards, especially in Mekong River Delta. Large areas of lowland paddy rice were shifted into fruit-fish systems, sculpting the land into alternating bunches of fruit trees and ditches for fish ponds. Change Intensive Rice to Rice and Aquaculture (i) Paddy rice and fish/shrimp system Fish/shrimp systems are a growing trend in Mekong River Delta as an option for breaking away from solely rice farming. The old systems include an annual triple rice crop (three rice seasons per year) or double rice crop (two rice seasons per year). Because of changing climate and hydrological conditions, the low-elevation areas were deeply flooded and could not support the triple crop. Rice crops that are flooded at harvest time maintain low and unstable yields, usually in the rainy season in mid-September. In the fish/shrimp systems, the flooded land is used for raising fish or shrimp instead of growing rice in the late rainy season (third or second rice season). This land use is growing quickly in Mekong River Delta. As of 2008, there were 120,000 ha under fish/shrimp production. This trend is in line with the MARD policy to expand this land use type in Mekong River Delta to adapt to climate change and diversify production. (ii) Rice/duck system The rice/duck system has been implemented for a long time in the Mekong River Delta. The duck populations in the region as of 2010 reached 20 million (GSO 2011–2014), 70 % of which was released into the rice fields to seek food left after harvesting. However, in recent years, as the severe impacts of bird flu, field-free duck breeding is not recommended. It is recommended that ducks be raised in planned areas to reduce disease outbreaks (Table 9).

Climate Change Mitigation GHGs Emission in Crop Production Because agriculture is a leading sector for GHG emission reduction, MARD issued a plan for reducing GHG emissions by the year 2020 (Decision No. 3119, 2011). The plan focuses on the following solutions: (i) Applying advanced farming techniques. This set of practices is aimed at rice production and includes water-saving irrigation such as SRI. Another part of the advanced farming techniques program is the “three reductions and three gains” program, which includes a reduction of seed, nitrogen fertilizer, and other chemical inputs and gains of crop yield, product quality, and economic

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Table 9 Potential prioritized mitigation practices in agriculture # 1

Practice Genetics

2

WSI (water-saving irrigation)

3

SRI

4

Production of organic fertilizers from agricultural residues/byproducts

5

Biogas

6

Minimum cultivation

7

Biochar

8

Soil cover

9

Conversion from rice production to fruit tree plantation Conversion from three rice season system to two combined rice/fish/ shrimp system

10

Properties Increase carbon sequestration, increase biomass and yield Increase flood tolerance, salt tolerance, and drought tolerance Water saving and reduction in irrigation Increase fertilizer use efficiency Reduce CH4 emission Reducing costs on seeds, fertilizers, pesticides, and irrigation Reduce CH4 emission Replacement of inorganic fertilizers Reduce emission of N2O (from dry land) and CH4 (from flooded land) Reduce environmental pollution from burning agricultural residues or agricultural waste Prevent environmental pollution from livestock production Reduce N2O emission Collect biogas to replace other fuels (wood, coal, gas, etc.) Reduce soil erosion, soil run-off, and carbon loss Reduce costs on tillage and plowing Recovery of 50 % carbon in crop biomass Stabilized carbon in soil for long time and reduce emission Significantly improve soil properties including fertility Increase fertilizer efficiency, reduce N2O emission Use as alternative fuel Reduce soil erosion and carbon loss Reduce N2O emission Save irrigation, fertilizers, and weeding labor Reduce using pesticides Reduce CH4 emission Increase efficiency and bring more benefit of lowlands

Prospect High

High

High

Medium

High

Medium

Medium

Low

Medium Medium

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benefit. This strategy is nicknamed the “3R3G.” Another campaign is called the “1M5R” and focuses on reducing adoption of one standard seed variety and reductions of overall seed inputs, nitrogen fertilizer, chemicals, water, and postharvest loses. Alternate wetting and drying irrigation (AWD) is also encouraged to save water and reduce GHG emissions. AWD application has been shown to reduce the global warming potential (GWP) of rice production. Pandey et al. (2014) reported that AWD irrigation reduced methane emissions by approximately 67–71 %. While there is a slight increase in nitrogen oxide, overall, there is a 62–67 % reduction in CO2 emissions. (ii) Change land use type from rice to short-duration industrial crops The total area of rice cultivation in Vietnam was 7.89 million ha (about 4.1 million ha rice land) in 2013 (GSO 2011–2014). However, according to development planning for the agricultural sector Mard (2010), rice cultivation will see a reduction to 3.8 million ha (about 7.0 million ha of cultivated rice land). Firstly, 0.266 million ha of rice cultivation will be converted to higher value annual industrial crops to reduce GHG emissions. This transition is expected to decrease emissions by 1.29 million tons of CO2e (2.27 %). (iii) Change low yield/benefit rice land to high-value aquaculture (shrimp, fish) The main advantages of rice and fish/shrimp farming systems include obtaining higher economic benefits, reducing pesticide costs by 48–56 %, and increasing income by approximately $1375 per ha. According to MARD, in the Mekong River Delta, more than 600,000 ha of rice cultivation area could be converted to rice and fish/shrimp systems in order to reduce GHGs emission by 5.37 %, equivalent to a reduction of 3.06 million tons of CO2. (iv) Effectively use rice straw Rice straw is increasingly going unused after the harvest, which poses a problem for disposal. However, the unused straw could represent a potential source for efficient reuse of waste materials and a reduction in GWP for crop production. It is reported that straw compost has also shown potential for maintaining N2O emissions at a low level (Yao et al. 2010). Pandey et al. (2014) carried field experiment in Red River Delta of Vietnam and found that the reuse of composted rice straw can reduce methane emissions by about 25–29 %, reduce nitrogen oxide by 5–20 %, and reduce CO2 emissions by 24–28 %. In addition to compost, biochar that is pyrolyzed from rice straw Yanai et al. (2007) can improve soil fertility and crop yield (Table 10).

Identify and Analyze Technologies to Reduce GHG Emission in Agriculture GHG reduction technologies are primarily applied in rice cultivation, in livestock breeding, and in altered fertilizer application on arable land.

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Table 10 Potential GHG emission reduction from some crop production (Gg of CO2e) Measure Business as usual (BAU) Compost Biochar Short-duration variety ICM 3R3G NH4SO4 SRI

Rice 9078.29 3755.06 9633.38

Maize 1.020 0.589 0.255

Crop Soybean 0.177 0.029 0.006 0.060 0.074

Peanut 0.239 0.113 0.020 0.243 0.239

Cassava 0.089 0.014 0.001 0.090 0.086

Sugarcane 1.270 0.625 0.897 1.300 1.205

7230.52 7858.27 1304.26

Source: Mai Van Trinh et al. 2012

Rice Production Rice production is the most important agricultural subsector in Vietnam. According to Mai Van et al. (2014), GHG reduction technologies in rice cultivation should meet the following requirements: (1) increase rice yield, (2) increase economic benefits for farmers, (3) decrease investment costs, and (4) be sustainable. Currently, two promising GHG reduction technologies in rice cultivation are being studied: irrigation management and nutrient management. Irrigation management includes irrigating less, draining the soil, and keeping the soil humid during rice growth stages. The key stage to keep the soil humid is after top tillering until just before flowering and after ripening. By using this combination of irrigation management, experiments have shown a decrease in CH4 emissions from rice as well as increased soil aeration and, hence, increase plant available nutrient. This option could be easily applied in lowlands with sufficient irrigation. There are several options for mitigating GHG emissions using nutrient management techniques. Results of studies on emissions from rice fields indicate that both organic manure and nitrogen fertilizers increase CH4 and N2O emissions. Thus, increasing manure application needs to be paired with draining the soil to reduce CH4 emission and increase N fertilizer use efficiency. By applying the correct N fertilizer amount to meet plant requirement in each growing stage, studies show reductions in N2O emissions. Other mitigation options include changing the land use type from rice to upland crops; introducing drought-, flood-, and salt-tolerant crops; shifting the crop calendar; and implementing SRI. Farmers can also combine N fertilizer management and crop management to practice integrated crop management (ICM). Based on the program of integrated pesticide management (IPM), ICM aims to optimize water, soil, and solar conditions to optimize potential yield, reduce yield gaps, and lower GHG emissions. Irrigation management is considered the most important approach for reducing CH4 emissions from rice fields. Approaches can be implemented by mid-season drainage, AWD irrigation, and field expose-shallow irrigation (Table 11). All these

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Table 11 Prioritized climate change mitigation technologies in agriculture No. 1

Technology Suitable nitrogen fertilization rate

2

Field water drainage during rice growth stages

3

Surface expose drying and irrigation

4

Application of short duration varieties

5

Nutrient improvement by oriented additions of animal’s feed Application of stimulants in livestock production

6

7

Biogas

8

Crop rotation to reduce soil organic carbon (SOC) loss

9

Plant cover crops on sloping land to reduce soil erosion and maintain soil moisture

10

Nutrient improvement by mechanical and chemical processes

11

Gene modification for livestocks

Description Establish nitrogen formula suitable for soil property and crop characteristics; deep fertilizing, plant winter crops for better use of nitrogen residues from previous season Drain field water out during two stages: tillering and ripening in order to reduce CH4 emission and increase rice yield Application cycle of shallow irrigation and water drain out (20–25 day/cycle) after transplanting in order to reduce CH4 emission and increase rice yield Application of short-term rice varieties (approximate 100 days of growth duration) with high yield and high disease and pest resistance to replace long duration varieties (more than 140 days of growth duration) Oriented addition of nutrients in animal’s feed (BMU cakes, urea, etc.) Applying stimulants such as bovine somatotropin (BST) and anabolic steroid to improve and increase meat and milk quality and yield as well as reduce CH4 emission Store livestocks’ wastes under anaerobic condition to generate CH4 (biogas) for fuels. By-products from biogas are used as fertilizers or animal’s feed Applying conservation farming to maintain soil’s water content. Planting legumes to increase soil’s nitrogen fixation. Applying crop rotation suitable for soil properties and climate conditions Planting different cover crops following contour line on sloping land. Digging holes to prevent soil erosion and recover soil from water run-offs Processing animal’s feed through mechanical methods (grinding and blending) and chemical methods (fermentation, compost, micro-nutrient increase, etc.) in order to increased iggestiverate and meat yield Apply gene engineering to create new livestock varieties with ability of rapid growth, high disease resistance

Prospect

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mitigation options are suitable with local conditions but prioritized following the technology needs assessment (TNA) procedure (Quach Tat Quang et al. 2012).

Fertilization The emission of N2O is related to nitrogen in the soil and N fertilizer. Nitrogen is the key element in creating and increasing crop yield, making it both essential and potentially harmful in agricultural management. Fertilizer management can include many techniques. Farmers can perform a soil quality assessment in order to identify soil potential supply. Yield modeling can be used to identify the rate and amount of fertilizers to apply, based on soil properties and crop characteristic. Simple nutrient budgets and N testing can be used to identify the rate and time of fertilizer application. Farmers can also calculate their N balance from crop uptake, absorption, run-off, and emissions. Efficient use of manure and organic wastes used as fertilizers can reduce N emissions. Growers can also use winter crops to sequestrator remaining nitrogen from previous seasons or apply deep fertilization. Finally, growers can implement proper irrigation techniques and management to avoid fertilizer loss and check the soil’s pH and acidity. Cropland, Noncultivated Land, and Bare Land Cropland On actively cultivated croplands, farmers can conserve the soil by using alley cropping, contouring, terracing, crop residues to cover soil surface, reduced tillage, or no tillage. Mulching is practiced widely in sloping land areas to protect the soil from rain and wind erosion, limit evaporation, maintain water content in the soil, and prevent organic matter degradation. It also returns organic carbon and nutrients into the soil and reduces the costs of chemical fertilizer. Fallow Land and Bare Land Fallow and bare lands usually occur where there are unstable rainfall conditions, poor irrigation regimes, unfavorable transportation conditions, and poor humus. These lands occupy about 23 % of the nation. In these unfavorable soil conditions, many pedological processes lead to continued soil degradation and GHG emissions. Improvement and rehabilitation of these lands will improve ecological conditions, control soil erosion, reduce leaching, and reduce GHG emissions. It is recommended to apply the following techniques to reduce GHG emission for noncultivated and bare lands: – Plant green manure or grasses along the contour lines to prevent surface flows and retain sediment during high rainfall. Simultaneously, these buffers make terraces and soil traps along the contour lines to trap soil and prevent sedimentation from gently sloping surfaces. – Apply intercropping, agro-forestry, and mix planting of annual crops and perennial crops. These practices have been implemented widely in hilly and mountainous areas. They were especially promoted as a key activity of the National Project

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(Project 327) of regreening noncultivated and bare lands to protect against natural disasters, flash floods, and droughts in highlands – Application of Sloping Agricultural Land Technology or SALT. SALT has certain advantages over both the traditional techniques of slash-and-burn (swidden agriculture) and conventional terrace farming. SALT enables farmers to stabilize and enrich the soil and to grow food crops economically. There is also a reduced need for expensive inputs like chemical fertilizers. In addition, SALT also conserves soil moisture and reduces pests and diseases. But most importantly, to a financially harried farmer, the technology can increase his or her annual income to almost threefold after only a period of 5 years.

Conclusion Climate change strongly impacts Vietnam’s agricultural sector. Damages have been documented in the past, the existing environment is highly vulnerable, and projections show high crop losses in the future. Flatlands are more strongly affected than highlands, in which the Mekong River Delta shows the most impacts. The Coastal Central area and Red River Delta follow the Mekong Delta. Vietnam experiences many extreme events and disasters each year. Climate change is projected to increase the frequency and severity of these weather events, damaging agricultural outputs. The agricultural sector has developed suitable adaptation measures to cope with climate change and keep production ongoing, not only feeding over 76 million people but also exporting a high amount of food and foodstuffs. Vietnam is the leading cashew nut exporter, second rice exporter, and third coffee exporter in the world. With a large area of paddy rice land and a large livestock sector, production processes in Vietnam have high GHG emissions. Thus, the agricultural sector is striving to implement many mitigation options such as AWD irrigation, composting, and converting rice land to non-rice land. With these mitigation measures, the sector plans to reach the target of reducing GHG emission by 20 % by 2020.

References Binh Dinh PPC (2010) Binh Dinh province people committee report in 2010 CIMMYT (1991) Annual report: improving the productivity of maize and wheat in developing countries – an assessment of impact Dasgypta S, Laplante B, Meisner C, Wheeler D, Yan J (2007) The impact of sea level rise on developing countries: a comparative analysis. World Bank policy research working paper 4136 Decision No. 3062 (2007) Decision No. 3062/QĐ-BNN-KHCN. In 15/10/2007 MARD issues a decision to acknowledge innovative/advance techniques and methods in rice cultivation Decision No. 3119 (2011) Decision No. 3119/QĐ-BNN-KHCN. On approving programme of Green House Gas (GHG) emission reduction in the agriculture and rural development sector upto 2020 Decision no. 899/QĐ-TTg (2013) Approving the project “Agricultural restructuring towards raising added value and sustainable development”

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Dobermann A, Walters DT, Adviento-Borbe MAA (2007) Global warming potential of highyielding continuous corn and corn-soybean systems. Better Crops 91(3):16–19 Feng Y, Xu Y, Yu Y, Xie Z, Lin X (2012) Mechanism of biochar decreasing methane emission from Chinese paddy soils. Soil Biol Biochem 46:80–88 GSO (2013) General statistical office of Vietnam 2013 GSO (2011–2014) General Statistical Office of Vietnam from 2011–2014 Ha Dinh Tuan (2005) Farming techniques on sloping land. Selected proceeding from scientific conference of Vietnam Agriculture Science Institute (VASI) IAE (2012) Potential of crop residue in “Viet Nam” agriculture, workshop proceeding on efficiency use g crop residue in Viet Nam agriculture, Hanoi 12/2013. IAE-ICRISAT (2010) To the report of ICRISAT/ADB-RETA6349 IMHEN (2010a) Sea level rise scenarios and possible disaster risk reduction in Viet Nam, Institute of Meteorology, Hydrology and Environment, pp 24–25 IMHEN (2010b) Impacts of climate change on water resources and adaptation measures, Institute of Meteorology, Hydrology and Environment, pp 70–71 Iyengar NS, Sudarshan P (1982) A method of classifying regions from multivariate data. Econ Pol Wkly 2048–2052. Special Article Le Duy Thanh (2004) Study technique of nylon covering crop to increase peanut yield on degraded soil in Viet Yen district, Bac Giang province, Hanoi University 124 pp Liu Y, Yang M, Wu Y, Wang H, Chen Y, Wu W (2011) Reducing CH4 and CO2 emissions from waterlogged paddy soil with biochar. J Soils Sediments 11:930–939 Mai Van Trinh, Nguyen Hong Son (2011) Study the impact of climate change on cereal crop production in Vietnam, Science and Technology Journal of Agriculture and Rural Development. Minist Agric Rural Dev 12:3–9 Mai Van Trinh, Tran Viet Cuong (2012) Pyrolysis of rice straw and rice husk to produce biochar, material for improving soil fertility, crop yield and reducing Green House Gas (GHG) emission. In: Nguyen Van Tuat, Bui Chi Buu, Nguyen Van Viet, Nguyen The Yen (eds) Trends in rice research to overcome stresses in a changing climate. Agricultural Publishing House, Ha Noi, pp 366–372 Mai Van Trinh, Tran Van The, Dinh Vu Thanh (2014) Climate change and crop production. Agricultural Publishing House Ha Noi, pp 153 (Vietnamese) MARD (2009) Ministry of agriculture and rural development’s Agriculture production and plan to 2020 Mariam Trinh, Tran van The, Bui Thi Phuong (2012) An estimation of GHG reduction for the agriculture sector in Viet Nam, project's report for United Nations development (UNDP). MONRE (2009) Climate change, sea level rise scenarios for Vietnam. Ha Noi, Vietnam MONRE (2012) Climate change, sea level rise scenarios for Vietnam. Ha Noi, Vietnam Lang NT, Van Tao N, Buu BC (2011) Marker-assisted backcrossing (mab) for rice submergence tolerance in Mekong delta. Omonrice 18:11–21 Patnaik U, Narayanan K (2005) Vulnerability and climate change: an analysis of the eastern coastal districts of India, Human security and climate change: an international workshop, Asker Pham Quang Ha (2013) Survey and assessment impacts of climate change on agriculture and develop adaptation and mitigation measures for crop production and aquaculture in Vietnam. Research project report. Ministry for Agriculture and Rural Development Pham Thi Nhung (2006) Study influence of burying agriculture residues to potassium pools in soil and crop yield on Eutric Fluvisols soil, Dan Phuong, Ha Tay province. MSc thesis, Hanoi Agriculture University PPD (2009) Statistical data about implementation of SRI, Plant Protection Department Quach Tat Quang, Nguyen Van Anh, Nguyen Thanh Hai (2012) Vietnam technology needs assessment for climate change mitigation and adaptation, summary report of technology needs assessment for climate change adaptation and mitigation of Vietnam, The Global Technology Needs Assessment, Ha Noi

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Tran Thi Cuc Hoa (2011) Development of transgenic rice lines resistant to insect pests using Agrobacterium tumefaciens- mediated transformation and mannose selection system. Omonrice 18:1–10 Tran van The, Pham Quang Ha, Mariam (2010) Impact of climate change on Mekong delta river, scientific workshop proceeding, institute for agricultural environment. Uphoff N (2002) Supporting food security in the 21st century through resource-conserving increases in agricultural production. Agric Food Secur 1:1–18 Yanai Y, Toyota K, Okazaki M (2007) Effects of charcoal addition on N2O emissions from soil resulting from rewetting air-dried soil in short-term laboratory experiments. Soil Sci Plant Nutr 53:181–188 Yao Z, Zhou Z, Zheng X, Xie B, Mei B, Wang R, Bal Butterbach K, Zhu KJ (2010) Effects of organic matter incorporation on nitrous oxide emissions from rice-wheat rotation ecosystems in China. Plant Soil 327:315–330

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case Study on the City of Dhaka, Bangladesh A. K. M. Azad Hossain and Greg Easson

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Normalized Difference Vegetation Index (NDVI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Land Surface Temperature (LST) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changes in Greenness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changes in the Urban Heat Island (UHI) Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Megacities with populations of more than ten million people in compact urban areas are the most vulnerable environments on the earth. The impacts of climate change on these megacities will be multi-faceted and severe, especially in developing countries, due to fast growth rate and inefficient adaptation. It is very important therefore to understand the contributions of the growth of megacities to climate change, especially in the developing countries. Dhaka, the capital of Bangladesh, is one of the fastest-growing megacities in the world; its population

A.K.M.A. Hossain (*) National Center for Computational Hydroscience and Engineering (NCCHE), The University of Mississippi, Oxford, MS, USA e-mail: [email protected] G. Easson Mississippi Mineral Resources Institute, The University of Mississippi, Oxford, MS, USA e-mail: [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_68

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increased from 6.621 million (in 1990) to 16.982 million (in 2014). Today, Dhaka is the 11th largest megacity in the world and is projected to be the 6th largest megacity in the world with a population of 27.374 million by the year 2030. Remote sensing technology has been successfully used for mapping, modeling, and assessing urban growth and associated environmental studies for many years. This research investigates how the intensity of the urban heat island (UHI) effects correlates with continuous decrease in the greenness of the city of Dhaka, as measured from satellite observations. The results of this study indicate that Landsat imagery-derived normalized difference vegetation index (NDVI) can be used to investigate the changes in greenness in the city of Dhaka from 1980 to 2014. The changes in greenness can be correlated with the increase in the intensity of UHI effects in the city of Dhaka as determined using Landsat thermal data from 1989 to 2014.

Introduction Urban populations grew rapidly throughout the nineteenth century, more by migration from the rural areas to the cities and manufacturing centers than by absolute population growth. Throughout the twentieth century, the number and sizes of cities grew, along with the percentage of the total population living in the cities (Schubel and Levi 2000). Since 1950, the worldwide urban population has grown from 746 million to 3.9 billion in 2014, 54 % of the total global population (United Nations 2014). Continued population growth and urbanization are predicted and it is projected to add 2.5 billion more people to the world’s urban population by 2050 (United Nations 2014). A large percentage of the urban growth is concentrated in the developing world, where the average urban growth rate for developing countries is 3.5 % per year, compared with a rate of less than 1 % per year for the developed countries (United Nations 1997; WRI 1998). Asia, despite its lower level of urbanization, is home to 53 % of the world’s urban population. By 2050 it is projected that 90 % of the world’s urban population will be in Asia and Africa (United Nations 2014). The past several decades have seen the emergence of megacities, a metropolitan area with a total population in excess of ten million people (New Scientist Magazine 2006). A megacity can be a single metropolitan area or two or more metropolitan areas that have converged. The concept of megacities was initiated in 1987 to combine both theory and practice in the search for successful approaches to improve urban management and the conditions of daily life in the world’s largest cities. The megacities concept was based on a collaborative effort among government, business, and community leaders of these megacities, in an attempt to shorten the time between the introduction of innovative ideas and their implementation and diffusion. The idea was coined not simply to identify, distill, and disseminate positive approaches but to strengthen the leaders and groups who are evolving the approaches and find sources of

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support to multiply their efforts. The idea promotes a dual strategy that functions simultaneously at the practical and theoretical levels: (1) sharing “best practices” among the cities and putting the lessons of experience in the hands of decision makers and the public and (2) gaining a deeper understanding of the process of innovation and the consequences of deliberate social changes in the cities. In 1950, New York and London were the world’s only megacities (Schubel and Levi 2000). In 1990, the number of megacities had increased to 10, with a population of 153 million people, representing less than 7 % of the global urban population. By 2014, the number of megacities had nearly tripled to 28. The urban population in these megacities has grown to 453 million, and these areas now account for 12 % of the world’s urban residents. The number of megacities is projected to increase to 41 by 2030 (United Nations 2014). Since most of the recent urban growth is concentrated in the developing world, the majority of the megacities are expected to be located in the developing world (Schubel and Levi 2000). Currently, 15 out of the 28 megacities are located in Asia, with the number projected to increase to 23 in Asia by 2030 (United Nations 2014). Table 1 shows the list of the current and projected megacities in the world. The most vulnerable environments on the earth are the urban areas, especially the megacities. It is increasingly recognized that airborne emissions from major urban and industrial areas influence both air quality and climate change on scales ranging from regional to continental and global. The viability of important natural and agricultural ecosystems in regions surrounding highly urbanized areas is severely affected by the deteriorating urban air quality. Megacities also influence regional atmospheric chemistry. This situation is particularly acute in the developing world where the rapid growth of megacities is producing atmospheric pollution at unprecedented severity and extent (Gurjar et al. 2014). The impacts of climate change due to urbanization are multi-faceted and severe. The impacts differ dramatically among the megacities in the developed and in the developing countries. The impacts in the developed countries are already adapted or being adapted with efficient technologies/policies/regulations, whereas in the developing countries, due to fast growth rate and inefficient adaptation, the impact is imminent and severe. There are also no signs that the governments in the developing countries will prove to be more capable in the future. These swarming, massive urban areas will continue to grow and should concern the world (Liotta and Miskel 2012). It is very important therefore to understand the impacts of the growth of megacities on climate change. Studies on megacities at different spatial and temporal scales using various models will be required to understand their local-to-global impacts and implications (Gurjar et al. 2014). Lawrence et al. (2007) employed a global model to examine the outflow characteristics of pollutants from megacities. That model demonstrated the trade-offs between pollutant buildup in the region surrounding each megacity versus export to downwind regions or to the upper troposphere. Unfortunately, the coarse resolution of global atmospheric models and source inventories still presents difficulties to capturing the details of the impact of megacity emissions temporally and spatially (Gurjar et al. 2014).

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Table 1 List of megacities (United Nations 2014)

Megacity Tokyo Delhi Shanghai Mexico City Sao Paolo Mumbai Osaka Beijing New York*** Cairo Dhaka Karachi Buenos Aires Kolkata Istanbul Chongqing Rio de Janeiro Manila Lagos Los Angeles* Moscow Guangzhou Kinshasa Tianjin Paris Shenzhen London Jakarta

Country Japan India China Mexico Brazil India Japan China USA Egypt Bangladesh Pakistan Argentina India Turkey China Brazil Philippines Nigeria USA Russian Federation China Congo** China France China UK Indonesia

Population (thousands) 2014 2030 37,833 37,190 24,953 36,060 22,991 30,751 20,843 23,865 20,831 23,444 20,741 27,797 20,123 19,976 19,520 27,706 18,591 19,885 18,419 24,502 16,982 27,374 16,126 24,838 15,024 16,956 14,766 19,092 13,954 16,694 12,916 17,380 12,825 14,174

Rank 2014 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

2030 1 2 3 10 11 4 13 5 14 8 6 7 18 15 20 17 23

Last 5 years’ average growth (2010–2015) 0.6 3.2 3.4 0.8 1.4 1.6 0.8 4.6 0.2 2.1 3.6 3.3 1.3 0.8 2.2 3.4 0.8

12,764 12,614 12,308 12,063

16,756 24,239 13,257 12,200

18 19 20 21

19 9 26 31

1.7 3.9 0.2 1.2

11,843 11,116 10,860 10,764 10,680 10,189 10,176

17,574 19,996 14,655 11,803 12,673 11,467 13,812

22 23 24 25 26 27 28

16 12 22 33 29 36 25

5.2 4.2 3.4 0.7 1 1.2 1.4

*

Los Angeles-Long Beach-Santa Ana Democratic Republic of the Congo *** New York-Newark **

Dhaka, the capital of Bangladesh, is one of the fastest-growing megacities in the world; its population increased from 6.621 million (in 1990) to 16.982 million (in 2014) (Table 1). Today, Dhaka is the 11th largest megacity in the world and is projected to be the 6th largest megacity in the world with a population of 27.374 million by the year 2030 (United Nations 2014).

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The urban heat island (UHI) effect is an important impact of urbanization. Urban and suburban areas experience elevated temperatures compared to their surrounding rural areas (EPA 2015). The annual mean air temperature of a city with one million people or more can be 1.8–5.4
F (1–3
C) warmer than the surrounding area (OKe 1997). On a clear, calm night, this temperature difference can be as much as 22
F (12
C) (OKe 1987). The UHI effect for the city of Dhaka has already been recorded by several reports and articles (Ahmed et al. 2013). It is not yet however completely understood how the intensity of the UHI effect changes with the continuous growth of the city. Remote sensing technology has been successfully used for urban growth and associated environmental studies for many years. This research investigates how the intensity of the UHI correlates with continuous decrease in the greenness of the city, as measured from satellite observations and using digital image processing techniques. The specific objectives include: (1) evaluating the changes in greenness from 1973 to 2014 using Landsat imagery-derived normalized difference vegetation index (NDVI), (2) estimating land surface temperatures (LST) using Landsat thermal imagery, and (3) investigating the potential of the Landsat-derived LST to evaluate the changes in the UHI effect.

Research Data A time series of ten Landsat images covering Dhaka, Bangladesh, acquired from 1973 to 2014 were used in this research. The time series of imagery includes data acquired by Landsat 1 and Landsat 3 Multispectral Scanner (MSS), Landsat 4 and Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). Table 2 lists the imagery acquisition dates and corresponding sensors and their characteristics. All ten data sets were used for visual analysis, but only selected imagery were used for vegetation and land surface temperature (LST) analysis. Table 3 shows the data usage matrix. The spatial distribution of vegetation in Dhaka was mapped to evaluate the changes in greenness over time. A normalized difference vegetation index (NDVI) was used to detect the changes in greenness. NDVI was calculated for the imagery acquired in 1973, 1980, 1989, 2000, 2010, and 2014. The thermal sensor of the Landsat series became available with the launch of Landsat 4. The earliest thermal data available for this region was acquired in 1989, started the time series of LST data for 1989, 2000, 2010, and 2014 (Table 3). The gradual changes in land use and land cover in and around the city of Dhaka from 1973 to 2014 are shown in Fig. 2. Figure 3 shows the net change in land cover and land use between 1973 and 2014.

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Table 2 Satellite data acquired Date Dec. 05, 1973 Feb. 20, 1980 Jan. 28, 1989 Feb. 28, 2000 Mar. 24, 2003 Jan. 16, 2005 Nov. 03, 2006 Jan. 11, 2009 Feb. 15, 2010 Jan. 25, 2014

Sensor Landsat 1 MSS Landsat 3 MSS Landsat 4 TM Landsat 7 ETM+ Landsat 7 ETM+ Landsat 5 TM Landsat 5 TM Landsat 5 TM Landsat 5 TM Landsat 8 OLI

VNIR bands 4,5,6, and 7 4,5,6, and 7 1,2,3, and 4 1,2,3, and 4 1,2,3, and 4 1,2,3, and 4 1,2,3, and 4 1,2,3, and 4 1,2,3, and 4 2,3,4, and 5

Spatial resolution (m) 60a 60a 30 30 30 30 30 30 30 30

Thermal bands NA NA 6 6 6 6 6 6 6 10, 11

Spatial resolution (m) NA NA 30b 30c 30c 30b 30b 30b 30b 30d

Original MSS pixel size was 79  57 m; production systems now resample the data to 60 m TM Band 6 was acquired at 120-m resolution but is resampled to 30-m pixels (after February 25, 2010) c ETM+ Band 6 is acquired at 60-m resolution but is resampled to 30-m pixels (after February 25, 2010) d TIRS bands are acquired at 100 m resolution but are resampled to 30 m in delivered data product a

b

Table 3 Satellite data used Date Dec. 05, 1973 Feb. 20, 1980 Jan. 28, 1989 Feb. 28, 2000 Mar. 24, 2003 Jan. 16, 2005 Nov. 03, 2006 Jan. 11, 2009 Feb. 15, 2010 Jan. 25, 2014

Sensor Landsat 1 MSS Landsat 3 MSS Landsat 4 TM Landsat 7 ETM+ Landsat 7 ETM+ Landsat 5 TM Landsat 5 TM Landsat 5 TM Landsat 5 TM Landsat 8 OLI

Visual inspection X X X X X X X X X X

NDVI X X X X

Thermal analysis

X X

X X

X X

Methods This research is based on the hypothesis that satellite observation-based normalized difference vegetation index (NDVI) and land surface temperature (LST) can monitor changes in urban greenness in a megacity and the changes in the intensity of urban heat island (UHI) effect. Data acquired by Landsat satellites would be the best available option to achieve these results due to the extensive archive of imagery and consistency of the sensors.

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Fig. 1 Location of the study site (not scaled)

A time series of Landsat 1-3 MSS, Landsat 4-5 TM, Landsat 7 ETM+, and Landsat 8 OLI imagery for a period of 41 years (1973-2014) for Dhaka City, Bangladesh Dhaka City: December, 1973

Dhaka City: March, 2003

District Boundary

0

5

Dhaka City: February, 1980

Dhaka City: January, 1989

Dhaka City: January, 2009

Dhaka City: February, 2010

Kilometers

Fig. 2 Changes in land cover in Dhaka City

Dhaka City: February, 2000

Dhaka City: January 25, 2014

False color composite of Landsat data. Green: Vegetation. Light Purple: Urban areas

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Dhaka City as Observed by Landsat 1 MSS Imagery December 05,1973

Dhaka City as Observed by Landsat 8 OLI Imagery January 25,2014

False color composite of 4 (as red), 6 (as green), 5 (as blue) band combination. False color composite of 3 (as red), 4 (as green), 2 (as blue) band combination. Green color represents vegetation. Light purple represents urban developments. Green color represents vegetation. Light purple represents urban developments.

Legend District Boundary

0

2.5

5

10 Kilometers

N

Fig. 3 Land use and land cover changes as observed by Landsat data

Normalized Difference Vegetation Index (NDVI) The normalized difference vegetation index (NDVI) is an image enhancement technique, which can be used to describe the greenness or relative density and health of vegetation in an image. It is one of the most widely accepted and widely used vegetation indices. NDVI was first attributed by Rouse et al. (1973), but the concept was discussed by Kriegler et al. (1969). NDVI is commonly used as an indicator of relative biomass and greenness (Boone et al. 2000). The calculation of NDVI is based on the nature of the variation of reflectance values obtained from vegetated surfaces in the near-infrared (NIR) and red regions of the electromagnetic spectrum (EMS). The reflectance values of vegetation in the near-infrared (NIR) region are higher than that in the red region. NDVI provides a ratio of the NIR and the red bands (Eq. 1), eliminating any discrepancies that may occur in the imagery due to sensor differences or image quality issues, such as brightness and other interference (Hossain and Easson 2011). The NDVI can be computed for a wide variety of sensors depending on the availability of measurements in the NIR and red bands. NDVI ¼

ðNIR  RÞ ðNIR þ RÞ

(1)

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where NIR and R are pixel values of NIR and R bands, respectively. Landsat data has been used for vegetation studies for many years. Since all the sensors used in Landsat data acquisitions consist of both visible and near-infrared (VNIR) channels (Table 2), it is possible to calculate NDVI using image data from all Landsat sensors. In this study, NDVI was calculated using imagery acquired by Landsat 1 MSS, Landsat 3 MSS, Landsat 4 TM, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI satellites (Table 3). The NDVI calculation equations are as follows: NDVILandsat1_MSS ¼

ðBand7  Band4Þ ðBand7 þ Band4Þ

(2)

NDVILandsat3_MSS ¼

ðBand7  Band4Þ ðBand7 þ Band4Þ

(3)

NDVILandsat4_TM ¼

ðBand4  Band3Þ ðBand4 þ Band3Þ

(4)

NDVILandsat5_TM ¼

ðBand4  Band3Þ ðBand4 þ Band3Þ

(5)

NDVILandsat7_ETMþ ¼ NDVILandsat8_OLI ¼

ðBand4  Band3Þ ðBand4 þ Band3Þ

ðBand5  Band4Þ ðBand5 þ Band4Þ

(6) (7)

Land Surface Temperature (LST) Land surface temperatures (LSTs) in and around the city of Dhaka were estimated using the Level 1 thermal data acquired by Landsat 4–5 TM, Landsat 7 ETM+, and Landsat 8 TIRS sensors. The unitless digital number (DN) values of the thermal bands were digitally processed to corresponding radiance values. The processed radiance values were then used to calculate LST.

Conversion of DN to Radiance Landsat 4–5 TM and Landsat 7 ETM+ During the generation of Level 1 data, pixel values from raw unprocessed imagery (Level 0 data) were converted to units of absolute radiance using 32-bit floatingpoint calculations. These absolute radiance values were then scaled to 8-bit values representing calibrated digital numbers (Qcal) before output to the distribution media. Conversion of these calibrated digital numbers (Qcal) in L1 products back

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to the “at-sensor spectral radiance” (Lλ) requires knowledge of the original rescaling factors. The following equation (Eq. 8) was used to perform a radiance conversion for the Level 1 Landsat 4–5 TM and Landsat 7 ETM+ imagery (Chander and Markham 2003; Chander et al. 2009). Lλ ¼

  LMAXλ LMINλ ðQcal Qcalmin Þ þ LMINλ Qcalmax Qcalmin

(8)

where Lλ = spectral radiance at the sensor’s aperture in W/(m2.sr.μm) Qcal = quantized calibrated pixel value in DNs Qcalmin = minimum quantized calibrated pixel value corresponding to LMINl (DN = 0) Qcalmax = maximum quantized calibrated pixel value corresponding to LMAXl (DN = 255) LMINλ = spectral radiance that is scaled to Qcalmin in W/(m2.sr.μm) LMAXλ = spectral radiance that is scaled to Qcalmax in W/(m2.sr.μm) The required parameters were obtained from the Level 1 product metadata to process the acquired thermal data using Eq. 8. Equation 8 was modified to Eqs. 9, 10, and 11 and was used to obtain the at-sensor radiance values for the imagery acquired in 1989, 2000, and 2010, respectively. 

 15:303  1:238 LλðL4 TM_1989Þ ¼ ðDNBand6  1Þ þ 1:2378 255  1   12:650  3:20 LλðL7 ETM_2000Þ ¼ ð DNBand62  1Þ þ 3:20 255  1   15:303  1:238 ð DNBand6 Þ þ 1:238 LλðL5 TM_2010Þ ¼ 255  1

(9) (10) (11)

Landsat 8 TIRS Landsat 8 TIRS data has two different thermal bands (Band 10 and Band 11), unlike Landsat 4–5 TM and Landsat 7 ETM+. The center wavelength and bandwidth of Band 10 are 10.9 and 0.6 μm respectively, whereas the center wavelength and bandwidth of Band 11 are 12.0 and 1.0 μm, respectively. In this study, Band 11 was used to estimate LST to be more comparable with Landsat TM and ETM+ thermal data. As proposed by USGS (2014), the conversion of DN values (Qcal) to the “at-sensor spectral radiance” (Ll) was done using different approaches (comparing to Landsat TM and ETM+). Equation 12 was used in this case. This approach was also used in several recent research projects (e.g., Sameen and Kubaisy 2014).

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case. . .

Lλ ¼ M:Qcal þ B

935

(12)

where M is the radiance multiplier B is the radiance add The values of “radiance multiplier” and “radiance add” were obtained from Landsat 8 TIRS metadata (Table 4) for Band 11. These values were used in Eq. 12 to obtain Eq. 13, which was used to estimate LST for 2014 imagery acquisition date. LλðL8 TIR2014 Þ ¼ M  DNBand11 þ B

(13)

Conversion of Radiance to LST The obtained radiance values for all Landsat thermal data were converted to land surface temperature (LST) using Eq. 14. Since the obtained radiance values are of top of the atmosphere (at-sensor radiance), Eq. 14 was modified by adding an emissivity factor (Ɛ) to minimize the influence of atmospheric distortion in the calculation (Eq. 15). Table 4 provides the values of K1 and K2 for Landsat 8 TIRS (Maher and Kubaisy 2014). Table 5 provides the values of K1 and K2 for Landsat 4–5 TM and Landsat 7 ETM + (Coll et al. 2010). K2   K1 ln þ1 Lλ K2  Tk ¼  K1 :ε ln þ1 Lλ Tk ¼

(14)

(15)

where Tk = effective at-satellite temperature in Kelvin K2 = calibrated constant 2 in Kelvin Table 4 Landsat 8 TIR parameters TIR parameters

TIR bands Band 10 Band 11

Radiance multiplier (M ) 0.0003342 0.0003342

Radiance add (B) 0.1 0.1

Thermal constants K1 W/(m2.sr.μm) 774.89 480.89

K2 Kelvin 1,321.08 1,201.14

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A.K.M.A. Hossain and G. Easson

Table 5 Landsat TM and ETM+ thermal band calibration constants

Constants K1 Units W/(m2.sr.μm) 671.62 607.76 666.09

Sensor type Landsat 4 TM Landsat 5 TM Landsat 7 ETM+

K2 Kelvin 1,284.30 1,260.56 1,282.71

K1 = calibrated constant 1 in W/(m2.sr.μm) Lλ = spectral radiance at the sensor’s aperture e = emissivity (typically 0.95) Equation 15 was then modified to form Eqs. 16–19 to calculate LST, in degrees Kelvin, for each different Landsat sensor by using corresponding values of Lλ, K1, and K2. After calculating LST in absolute temperature, the values were converted to degrees Celsius, using Eq. 20. TkðL4 TM1989 Þ ¼

1260:56   607:76  0:95 ln þ1 LλðL4 TM_1989Þ

TkðL7 ETM2000 Þ ¼

1282:71  666:09  0:95 ln þ1 LλðL7 ETM_2000Þ

TkðL5 TM2010 Þ ¼

1260:56   607:76  0:95 ln þ1 LλðL5 TM_2010Þ





TkðL8 TIR2014 Þ ¼ ln

1201:14  480:89

LλðL8 TIR_2014Þ

Tc ¼ Tk  273:15

(16)

(17)

(18)

(19) þ1 (20)

Results and Analysis The processed NDVI and LST data were subset for Dhaka metropolitan area for analysis of changes in greenness and land surface temperature. The time series of Dhaka NDVI data was used to study the changes in greenness since 1980. The time series of Dhaka LST data was used to evaluate the changes in the intensity of urban heat island (UHI) impact since 1989.

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937

Changes in Greenness On the basis of the minimum and maximum values of NDVI, the lookup table of the entire time series data was scaled from 0.5 to 0.65 to visualize the changes in greenness over time. Figure 4 shows the NDVI time series for Dhaka city from 1980 to 2014. In Fig. 4, it is clearly seen that the average NDVI values decreased continuously from 1980 to 2014. The most dramatical change occurred between 1989 and 2000. The net change in greenness from 1980 to 2014 is also substantial as seen in Fig. 5.

Changes in the Urban Heat Island (UHI) Effects The variation in the intensity of urban heat island (UHI) effects due to the changes in greenness in the city of Dhaka was evaluated by determining the changes in the nature of spatiotemporal distribution of land surface temperature (LST) over time. The Landsat satellite observed LST time series data were used in different ways to study the changes in the nature of spatiotemporal distribution of LST over time.

Fig. 4 Changes in greenness from 1980 to 2014 as observed by the Landsat data

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A.K.M.A. Hossain and G. Easson

Fig. 5 Detailed changes in greenness from 1980 to 2014 as observed by the Landsat data (Thana is a kind of local administrative boundary like county)

At first, the LST distribution was visually analyzed by stretching the data lookup table from red to green. The red and green ends represent the maximum and minimum temperatures for each date. The areas covered by yellow represents approximately mean temperature for each date. Figure 6 shows the spatiotemporal distribution of Landsat observed LST in the city of Dhaka from 1989 to 2014. The LST imagery time series in Fig. 6 clearly shows that the areas characterized by high temperature extended substantially from 1989 to 2014, with significant increase from 1989 to 2000. Since the satellite imagery used in this study were acquired in different seasons of different years, it was not found reasonable to determine the absolute changes in LST variation by detecting the net changes in LST values. The second approach focused on the variation in LST along specific cross-section profile. A cross-section line A-B was selected in the east-west direction on each LST data set to extract the temperature values along the line (Fig. 6). The extracted LST values along line A-B were plotted and compared with the mean LST value for the corresponding data acquisition dates. Figures 7, 8, 9, and 10 show the variation in LST along A-B in 1989, 2000, 2010, and 2014, respectively. This analysis supports

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case. . .

939

Fig. 6 Land surface temperature (LST) from 1989 to 2014 as observed by the Landsat data

the visual analysis performed earlier and also provided a more quantitative understanding of how the LST values changed over time in reference to the mean values. The changes in the LST distribution pattern observed along line A-B provide a good quantitative evaluation of the changes in the intensity of UHI effects over time. However, the observation is limited in a particular direction and areas. The potential of image classification techniques was therefore evaluated to extend the quantitative analysis. The classification was performed based on the statistics of the satelliteobserved LST imagery (Fig. 6) as shown in Table 6 and Figs. 11 and 12.

940

Fig. 7 Variation in LST along A-B in 1989

Fig. 8 Variation in LST along A-B in 2000

A.K.M.A. Hossain and G. Easson

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case. . .

Fig. 9 Variation in LST along A-B in 2010

Fig. 10 Variation in LST along A-B in 2014

941

942 Table 6 Temperature statistics

A.K.M.A. Hossain and G. Easson

Date 1989 2000 2010 2014

Sensor Landsat 4 TM Landsat 7 ETM+ Landsat 5 TM Landsat 8 TIR

Temperature statistics (
C) Min Max Mean 22.09 32.66 25.17 21.61 36.63 26.41 21.63 31.81 25.52 17.47 25.38 20.49

Each LST imagery was classified into five classes around the mean temperature to map the spatiotemporal distribution of the areas characterized by different levels of above mean temperature. The classes are as follows: • • • • •

Class 1: Areas with temperature equal or less than mean Class 2: Areas with temperature 1
higher than mean Class 3: Areas with temperature 2
higher than mean Class 4: Areas with temperature 3
higher than mean Class 5: Areas with temperature >3
higher than mean

Figures 13, 14, 15, and 16 show the distribution of LST pixels above mean LST in the city of Dhaka as observed in 1989, 2000, 2010, and 2014, respectively. The classified raster LST data were converted to vector data and polygons were simplified. The vector data with simplified polygons were used to calculate the areas covered by each LST regime (class). The area calculations were plotted for different classes to compare them graphically. The area comparison plots improve the understanding of the changes in the intensity of UHI effect over time. Figure 17 shows the comparison of the size of the areas characterized by total above mean temperature in the city of Dhaka from 1989 to 2014. The total size of the areas where LST remained above the mean increased continuously from 1989 to 2010 but decreased in 2014. Figure 18 shows the comparison of the size of the areas characterized by 1
above mean temperature in the city of Dhaka from 1989 to 2014. The total size of the areas where LST remained 1
above the mean decreased from 1989 to 2000, but increased since then continuously. Figure 19 shows the comparison of the size of the areas characterized by 2
above mean temperature in the city of Dhaka from 1989 to 2014. The total size of the areas where LST remained 2
above the mean increased from 1989 to 2000, but decreased since then. Figure 20 shows the comparison of the size of the areas characterized by 3
above mean temperature in the city of Dhaka from 1989 to 2014. The total size of the areas where LST remained 3
above the mean increased significantly from 1989 to 2000 and remained above 1989 since then.

Discussion and Conclusions The number and size of megacities are increasing with the majority of the growth occurring in developing countries, especially in Asia and Africa. Understanding the potential impacts of the growth of megacities on the climate in Southeast Asia will

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case. . . January 28, 1989

943

Mean

50000

Number of pixels

40000

30000

20000

10000

0 22.09

24.72

27.36 Temperature (C)

30.00

32.62

29.12

32.88

36.63

February 28, 2000 Mean 50000

Number of pixels

40000

30000

20000

10000

0 21.61

25.37

Temperature (C)

Fig. 11 LST mean for 1989 and 2000

provide insight for understanding the relationship between climate change and urban growth in the developing world. The analysis of the results obtained in this research for Dhaka, Bangladesh, shows that:

944

A.K.M.A. Hossain and G. Easson February 15, 2010 Mean 50000

Number of pixels

40000

30000

20000

10000

0 21.63

24.17

26.72

29.27

31.81

23.40

25.38

Temperature (C) January 25, 2014 Mean 50000

Number of pixels

40000

30000

20000

10000

0 17.47

19.45

21.42 Temperature (C)

Fig. 12 LST mean for 2010 and 2014

• The land use and land cover change due to urban growth and development can be mapped and quantified using time series data acquired by the Landsat satellite programs from 1973 to date (Landsat 1–3 MSS, Landsat 4–5 TM, Landsat 7 ETM+, and Landsat 8 OLI and TIRS).

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case. . .

945

Fig. 13 Distribution of above mean temperature on January 28, 1989

• Landsat imagery-derived NDVI can be used to map and monitor the changes in greenness in growing megacities. It was observed that the average NDVI values in Dhaka decreased continuously from 1980 to 2014 with a significant change between 1989 and 2000. • The changes in the land surface temperature (LST) can be used to determine the changes in the intensity of urban heat island (UHI) effect as a result of the growth

946

A.K.M.A. Hossain and G. Easson

Fig. 14 Distribution of above mean temperature on February 28, 2000

and development in a megacity. The Landsat satellite-observed thermal data can be used to estimate continuous LST at 80–30 m spatial resolution from 1980 to date. • It is possible to study the changes in the intensity of UHI effects in megacities, such as Dhaka, using the thermal data acquired by Landsat 4–5 TM, Landsat 7 ETM+, and Landsat 8 TIRS from 1989 to 2014. Visual inspection of the

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case. . .

947

Fig. 15 Distribution of above mean temperature on February 15, 2000

Landsat-derived LST estimation can be used to interpret the changes in the intensity of UHI effects. However, a quantitative assessment of the changes in the spatiotemporal distribution of the LST over time is necessary to quantify the changes in the intensity of UHI effects. Image classification technique of the LST distribution can provide a reasonable solution in this regard. A five-class image classification scheme based on mean LST and 1, 2, 3, >3
above mean LST

948

A.K.M.A. Hossain and G. Easson

Fig. 16 Distribution of above mean temperature on January 25, 2014

provided a good understanding of the spatiotemporal variation of the above mean LST in Dhaka from 1980 to 2014. • The imaging technology of LST (thermal data) by the Landsat 8 TIRS is different from that of the other Landsat sensors. The Landsat 8 TIRS data calibration approach used by NASA is also different. More research is needed to make the

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case. . .

949

Fig. 17 Comparison of the size of the areas characterized by above mean temperature in the city of Dhaka (1989–2014)

Fig. 18 Comparison of the size of the areas characterized by 1
above mean temperature in the city of Dhaka (1989–2014)

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A.K.M.A. Hossain and G. Easson

Fig. 19 Comparison of the size of the areas characterized by 2
above mean temperature in the city of Dhaka (1989–2014)

Fig. 20 Comparison of the size of the areas characterized by 3
above mean temperature in the city of Dhaka (1989–2014)

Potential Impacts of the Growth of a Mega City in Southeast Asia: A Case. . .

951

thermal data acquired by Landsat 8 TIRS and other Landsat sensors (TM and ETM+) comparable and reduce uncertainty. • The interpretation of the changes in the greenness in the city of Dhaka was qualitative in nature in this study. It is recommended to use the surface reflectance-based NDVI calculation for the quantitative change detection studies.

Future Research Directions The research presented in this chapter shows the potential of remote sensing data and image processing techniques to improve our current understanding about the impact of the growth of megacities in Southeast Asia on climate change. This study provides a good platform for future research to contribute in climate change studies following the emerging “bottom-up approach” (Hossain 2013). As part of this approach, initiatives are underway to extend the current research in the following directions. • Categorize the NDVI and LST data for specific seasons and months so that the seasonal and monthly variations in land use and land cover and LST are minimized. • Develop more statistically based methods to determine the changes in the intensity of UHI effects over time by normalizing the seasonal and monthly variations of LST. • Extend this study to selected other megacities in both developing and developed countries to investigate if the developed methods/techniques work globally to determine the changes in the intensity of UHI effects due to urban growth. Acknowledgments Thanks are due to NASA and USGS for providing all the Landsat data used in this research at free of charge. Thanks are also due to the National Center for Computational Hydroscience and Engineering (NCCHE) and Mississippi Mineral Resources Institute (MMRI) at the University of Mississippi for providing all the logistics and computing facilities for conducting this research.

References Boone RB, Galvin KA, Lynn SJ (2000) Generalizing El Nino effects upon Maasai livestock using hierarchical clusters of vegetation patterns. Photogramm Eng Remote Sens 66:737–744 Chander G, Markham BL (2003) Revised Landsat-5 TM radiometric calibration procedures, and post-calibration dynamic ranges. IEEE Trans Geosci Remote Sens 41(11):2674–2677 Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113:893–903 Coll C, Galve JM, Sánchez JM et al (2010) Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Trans Geosci Remote Sens 48(1):547–555 EPA (2015) Heat island effect. http://www.epa.gov/heatisland/. Accessed 20 Apr 2015

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Gurjar BR, Nagpure AS, Singh TP et al (2014) Air quality in Megacities. The encyclopedia of earth. http://www.eoearth.org/view/article/149934/. Accessed 26 Jan 2015 Hossain A (2013) Flood inundation and crop damage mapping: a method for modeling the impact on rural income and migration in humid deltas. In: Roger P Sr (ed) Climate vulnerability: understanding and addressing threats to essential resources, vol. 5. Elsevier, Academic Pres, p 357–374. http://store.elsevier.com/product.jsp?locale=en_US&isbn=9780123847034 Hossain A, Easson G (2011) Predicting shallow surficial failures in the Mississippi river levee system using airborne hyperspectral imagery. Geomatics Nat Hazards Risk 3(1):55–78 Kriegler FJ, Malila WA, Nalepka RF et al (1969) Preprocessing, transformations and their effects on multispectral recognition. In: Proceedings of the sixth international symposium on remote sensing of environment. University of Michigan, Ann Arbor, pp 97–131 Lawrence MG, Butler TM, Steinkamp J et al (2007) Regional pollution potentials of megacities and other major population centers. Atmos Chem Phys 7:3969–3987 Maher IS, Kubaisy MHA (2014) Automatic surface temperature mapping in ArcGIS using Landsat8 TIRS and ENVI tools, case study: Al Habbaniyah lake. J Environ Earth Sci 4(12):12–17 New Scientist Magazine (2006) How big can cities get? 17 June 2006, p 41 Oke TR (1987) Boundary layer climates. Routledge, New York Oke TR (1997) Urban climates and global environmental change. In: Thompson RD, Perry A (eds) Applied climatology: principles & practices. Routledge, New York, pp 273–287 Rouse JW, Haas RH, Schell JA et al (1973) Monitoring vegetation systems in the Great Plains with ERTS. In: Third ERTS Symposium, NASA SP-351 I, pp 309–317 Schubel JR, Levi C (2000) The emergence of megacities. Med Glob Surviv 6(2):107–110 United Nation (2014) World urbanization prospects, the 2014 revision. Department of Economic and Social Affairs, United Nations, New York United Nations (1997) The state of world population 1996: changing places: population, development and the urban future. United Nations, New York USGS (2014) Using the USGS Landsat 8 product. http://landsat.usgs.gov/Landsat8_Using_Prod uct.php. Accessed 20 Apr 2015 World Resources Institute (1998) World resources 1996–97: a guide to the global environment: the urban environment. Oxford University Press, Oxford

Potential of Solid Waste and Agricultural Biomass as Energy Source and Effect on Environment in Pakistan S. R. Samo, K. C. Mukwana, and A. A. Sohu

Contents Part A: Solid Waste and Its Management in Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Solid Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Composition of Solid Wastes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Solid Waste Issue in Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Current Status of Solid Waste Generation in Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Common Composition of Solid Wastes in Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environmental Problems Due to Solid Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Major Constraints in Solid Waste Management in Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integrated Solid Waste Management Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Solid Waste Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Waste Management Through Source Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Landfill Operations for Municipal Solid Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Infectious Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hazardous Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Changes in Solid Waste Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part B: Agricultural Biomass of Major Crops Produced in the Province of Sindh, Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Historical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

955 955 956 956 957 958 959 960 961 968 969 974 977 978 978 979 980 980 981

S.R. Samo (*) Quaid-E-Awam University of Engineering, Science and Technology (QUEST), Nawabshah, Sindh, Pakistan e-mail: [email protected] K.C. Mukwana Energy and Environment Engineering Department, Quaid-E-Awam University of Engineering, Science and Technology (QUEST), Nawabshah, Sindh, Pakistan e-mail: [email protected]; [email protected] A.A. Sohu Mechanical Engineering Department, QUCEST, Larkano, Sindh, Pakistan e-mail: [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_92

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The Irrigation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 983 Pattern of Cropping and the Yield of Major Crops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 990 The Rain-Fed (Nonirrigated) Area of Sindh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 993 Biomass Residue of Agricultural Crops in the Province of Sindh . . . . . . . . . . . . . . . . . . . . . . . . 995 Energy Content of Biomass Residue of Agricultural Crop in the Province of Sindh and Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 998 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1004 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006

Abstract

The issue of waste management is now a global problem because it is not only damaging soils but also deteriorating the natural state of air and water. This chapter focuses on two important aspects of solid waste in Pakistan, i.e., domestic solid waste and agricultural solid waste. The industrial and commercial activities are contributing heavily in large quantity of solid waste. Solid waste comprises of heterogeneous substances. The most common substances may belong to paper, aluminum, plastic, glass, ferrous materials, nonferrous waste, yard waste, construction and demolition wastes, etc. The issue of management of solid waste in Pakistan is a major environmental problem. Various research findings indicate that solid waste generation in Pakistan varies from 0.283 to 0.612 Kg/capita/day, while various studies indicate that the waste generation growth rate is 2.4 % per year. As a general practice, solid waste is commonly dumped on low-lying land or open vacant land area. Then, it is burned by sanitary staff to reduce its volume so that the life span of the dumpsite can be enhanced. However, the dumped solid waste does not burn completely but rather produces clouds of smoke that can be seen from miles away. This causes obnoxious smell and creates a breeding ground for flies and rats. Various findings indicate that currently, about 60,000 t/day of solid waste is generated in Pakistan. No weighing or segregation facilities are located at any disposal sites. The wastes generated from hospitals and industrial activities are simply treated as ordinary wastes. They are jointly collected and shifted to the dump sites. The research findings indicate that common composition of solid waste in Pakistan contains plastic, rubber, metal, paper, cardboard, textile waste, glass, food, animal waste, leaves, grass, straws and fodder, bones, wood, stones, and fines to certain extent. Out of this the food wastes are 8.4–21 % of the total solid waste; paper waste 15–25 %; leaves, grass, straw, and fodder 10.2–15.6 %; fines 29.7–47.5 %; and recyclables 13.6–23.55 %. Keeping in view this proportion of solid waste, a sustainable and viable management of solid waste may be adopted by recycling, composting, and waste to energy. In the other part of the chapter, focus is on agricultural waste which is actually agricultural biomass. Being an agricultural country, huge quantity of biomass is generated and remains unutilized or burned in the agricultural fields causing air pollution. It can be observed from Tables 6, 7, 8, and 9 the biomass residue of

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various crops such as wheat straw, rice husk, rice straw, cane trash, bagasse, and cotton stalk is in different ratios. These ratios are wheat and wheat straw ratio 1:1, 20 % rice husk found as waste in paddy, paddy and rice straw ratio 1:1, 23 % cane trash found as waste in sugarcane harvest, 30 % wet bagasse found as waste in sugarcane industry, and cotton and cotton sticks ratio 1:3. Keeping in view the availability factor, the different agricultural residue biomass is used as animal feed and also as a raw source of energy at local level; hence, we theoretically consider 40 % availability of total average amount of agricultural residue biomass, i.e., 5354.73  103 t/year. Theoretically considering average calorific value of agricultural residue biomass 3500 KCal/Kg, then the theoretical energy content = 3500 k Cal/Kg  5354.73  106 Kg = 1.91  1013KCal (heating value) if this amount of heat energy is multiplied by 4.81 for converting K Cal into KJ = 7.97  1013 KJ or KW-S. Hence, the power plants of about 10,000 MW are possible to be installed in the province of Sindh with the use of only 40 % of single source by agricultural residue biomass of only major crops. This step will help in reducing air pollution in the region as a whole, and on larger scale it will help in restoring the climatic changes occurring around the world.

Part A: Solid Waste and Its Management in Pakistan Solid Waste Solid waste can be explained as the waste that is discarded by the society as unwanted material with the understanding that it is of no any productive use and has passed through its ultimate use. Mostly it is in solid, semisolid, or liquid state and is thrown out from residential, commercial, or industrial premises. The Basel Convention defines wastes as, “substances or objects which are disposed of or are intended to be disposed of or are required to be disposed of by the provisions of the law” (Basel Convention 1989). With the advancements, solid waste generation has increased enormously and consequently has started to cause environmental problems. In the past whatever the small quantity of solid waste used to be produced by the society, it was thrown in the open environment and the nature was capable to absorb it slowly and gradually without causing any significant harm or effect to the natural environment and the valuable environmental components of it. But the situation changed over time with the generation of large quantities of solid waste, and nature turned unable to replenish the damaging environment as the quantities of waste started to be disposed of in larger quantities. The issue of waste management is now a global problem because it is not only damaging soils but also deteriorating the natural state of air and water.

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The large quantity of generation of solid waste is taking place due to changing lifestyles; increasing use of disposable items and excessive packaging are all adding to an increase in the quantity of solid waste being generated. The industrial and commercial activities are contributing heavily in large quantity of solid waste. Solid waste comprises of heterogeneous substances (APO 2004). The most common substances may belong to paper, aluminum, plastic, glass, ferrous materials, nonferrous waste, yard waste, construction and demolition wastes, etc. (UNEP 2005). The issues associated with solid waste management are complicated because of the quantity and diversity of the nature of waste as well as financial limitations in large cities. It is due to this reason that nowadays the authorities of municipal organizations, planners, and managers are concentrating on proper management of the solid waste so that it does not cause any adverse problem to the environment in general and human society in particular. The municipal solid waste management (MSWM) is generation, segregation, collection, transferring, transportation, and final dumping to the designated place. By this way it succeeds in addressing public health concerns, economics, conservation, aesthetics, and the environment. For proper management it is essential to understand sources and types of solid waste (Hoornweg and Thomas 1999). These are given in Table 1.

Composition of Solid Wastes Waste composition is used to describe the individual ingredients present in solid waste stream and their relative distribution. Knowledge of composition of solid wastes is important in evaluating and assessing the equipment needed, system and management programs, and plans needed for solid waste management. The municipal and commercial portion adds up about 50–75 % of total municipal solid waste generated in a society. The actual percentage distribution depends on the extent of the municipal services provided, location, season, economic conditions, population, social behavior, climate, market for waste materials, and other factors.

Solid Waste Issue in Pakistan Like the rest of the world’s urban areas, the issue of management of solid waste in Pakistan is a major environmental problem. Various research findings indicate that solid waste generation in Pakistan varies from 0.283 to 0.612 Kg/capita/day, while various studies indicate that the waste generation growth rate is 2.4 % per year. As a general practice solid waste is commonly dumped on low-lying land or open vacant land area. Then, it is burned by sanitary staff to reduce its volume so that the life span of the dumpsite can be enhanced. However, the dumped solid waste does not burn completely but rather produces clouds of smoke that can be seen from miles away. This causes obnoxious smell and creates a breeding ground for flies and rats.

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Table 1 Sources and types of solid waste Locations from where solid waste is generated Single or multifamily housing units

S. no. 1

Sources Domestic

2

Factories

3

Commercial

4

Institutional

Schools, hospitals, prisons, government and private offices

5

Construction or demolition

6

Municipal services

7

Agricultural

New construction sites, road repair, renovation sites, demolition of buildings, broken pavements Street cleaning, landscaping, parks, beaches, recreational areas, water and wastewater treatment plants Crops, orchards, feedlots, agricultural farms, etc.

All locations where light and heavy manufacturing takes place, construction sites, power and commercial plants Departmental stores, hotels, restaurants, markets, office buildings, etc.

Types of solid waste Food waste, paper, cardboard, plastic, textiles, leather, yard waste, wood, glass, metals, ashes, special waste bulky items, consumer electronics, goods, batteries, oil Manufacturing process waste, scrap materials, construction and demolition wastes, rubbish, ashes, and special wastes Paper, cardboard, plastics, wood, food wastes, glass, metals, special wastes, hazardous wastes Paper, cardboard, plastic, wood, food wastes, glass, metals, special waste Wood, steel, concrete, dirt, etc.

Street sweepings, landscape and tree/plant trimmings, general waste from parks, beaches, and other recreational areas, sludge Spoiled food wastes, agricultural waste, rubbish, hazardous wastes

Source: Solid Waste Management in Asia Urban Development Sector Unit

This soil may have been used for more useful purposes and most important is that the possible recyclable materials are lost (ESMF 2009). There is no proper waste collection system in any urban area at all. The solid waste is dumped on the streets. Different types of waste are not collected separately. There are no controlled sanitary landfill sites. Citizens are not aware of the relationship between ways of disposing of waste. As a result the overall natural environmental and public health problems emerged frequently. Figures 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17 given below reflect the solid waste dumped in an improper way in an open area (Pakistan’s garbage disposal 2001).

Current Status of Solid Waste Generation in Pakistan Various findings indicate that currently, about 60,000 t/day of solid waste is generated in Pakistan. No weighing or segregation facilities are located at any

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Fig. 1 Improper dumping of solid waste in the vicinity of vacant residential area

disposal sites. The wastes generated from hospitals and industrial activities are simply treated as ordinary wastes. They are jointly collected and shifted to the dump sites (Wastes in Pakistan 2006). The estimated waste generations in different cities of Pakistan are given in Table 2. Dumping of solid waste at abandoned open places and then its burning are the common practice. This thrown away waste has got lot of potential for recycling and is lost away easily. The total collection varies from 51 % to 70 %, while the rest of the solid waste stays behind on the streets. No relevant disposal facilities exist anywhere. It is unfortunate that no any city in Pakistan has acceptable solid waste management system right from generation of solid waste up to its final disposal. All such uncollected waste poses certain risk to the human health as well as results in clogging of drains and formation of stagnant pools of dirty water. This situation provides breeding ground for mosquitoes and flies that result in spreading malaria and cholera.

Common Composition of Solid Wastes in Pakistan The research findings indicate that the common composition of solid waste in Pakistan contains plastic, rubber, metal, paper, cardboard, textile waste, glass, food, animal waste, leaves, grass, straws and fodder, bones, wood, stones, and fines to certain extent. Out of this the food wastes are 8.4–21 % of the total solid

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Fig. 2 Improper dumping of solid waste in the low-lying area

waste; paper waste 15–25 %; leaves, grass, straw, and fodder 10.2–15.6 %; fines 29.7–47.5 %; and recyclables 13.6–23.55 % (UNEP 2005).

Environmental Problems Due to Solid Waste Ground Pollution As water whether surface or rain infiltrates deep down through solid waste, it causes leachate that comprises organic matter and may contain metallic substances like iron, mercury, lead, and zinc from discarded batteries and appliances. The leachate may also contain paints, chemical substances, pesticides, detergents, printing inks, etc. Such polluted water may cause serious impact on all living beings, including humans, and on an ecosystem. Air Pollution When solid waste is burned away in such an open place, heavy metals like iron and lead and harmful gases spread over the populated areas and result in air pollution. The blowing of wind also transfers finer solid waste substances, dust particles, and gases to the far-flung areas. The disintegration of solid waste in sunlight results in obnoxious smells and reduced visibility.

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Fig. 3 Scavengers searching and collecting valuable items from improperly dumped solid waste

Health Hazards The open dumping of solid waste initiates skin and eye infections and turns common in the prevailing area. Finer dust particles in the ambient air at or in the vicinity of dumpsites can cause respiratory problems in children and elders. The insects like flies breed on openly dumped waste and spread diseases like diarrhea, typhoid, all types of hepatitis, and cholera. Mosquitoes’ bites transfer many viruses and parasites that cause diseases like malaria and yellow fever. Stray animals like dogs, cats, and rats staying and wondering around the dump site turn carriers of a variety of diseases including plague and fever. Even the sanitary staff engaged in handling and transferring of solid waste suffers from intestinal and skin diseases.

Major Constraints in Solid Waste Management in Pakistan The proper management is missing in Pakistan because of numerous problems and issues. They include lack of infrastructure, inadequate budgetary allocations, lack of clear roles and responsibilities, uncontrolled disposal of solid waste (dumped in suburb and city boundaries), threat to public health and sanitation, and environmental pollution. In all such circumstances, solid waste solutions are not only with the engineers but can equally be distributed in between 50 % engineering and 50 % social – policy and institutional lack of participation and involvement (UNEP 2005).

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Fig. 4 Scavengers intentionally burning the dumped solid waste and collecting exposed metallic items

Integrated Solid Waste Management Practices Integrated solid waste management (ISWM) can be explained as a comprehensive waste prevention, recycling, composting, and disposal program through which the generated solid waste is managed in an environment-friendly way. A successful ISWM system focuses on prevention, recycling, and managing solid waste in such a way that it effectively protects human health and the natural environment. ISWM concentrates on sustainable solid waste management and involves in examining local needs and conditions. After this it selects the most suitable solid waste management methodologies for their strategies. Each and every activity needs careful collection and transportation of the waste to the final disposal point (Landfill design, U.S 1993).

Waste Prevention Waste prevention is a term used for “source reduction” under which efforts are launched to prevent waste from being produced. The methodologies for waste prevention are the use of less packaging material, designing and manufacturing such items that can stay longer, and reusing repeatedly the products and materials which are once produced. Waste prevention supports in reduced handling, reduced treatment, and minimized disposal costs and in the end reduces the generation of methane. Waste Recycling Waste recycling may be defined as a process of collecting, reprocessing, and recovering certain waste substances, e.g., metals, glass, plastics, and paper, to

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Fig. 5 Improperly dumped solid waste and its burning cause smoke and air pollution

Fig. 6 Improper dumping of solid waste causes damage to water and soil resources

produce new items. After collection, substances are segregated and forwarded to facilities that process them to manufacture new products and items and are made available as new products for their use. Recycling is the excellent option to resolve solid waste management issues. This technique of recycling exists in most cities. However, the recycling system differs from developing countries and developed countries. The advanced countries have established well-organized at-source segregation and recycling system, while in the developing countries, the system of recycling is not effective.

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Fig. 7 Solid waste skip fully filled with waste and dropping down on the ground

Recycling converts materials into valuable raw materials that otherwise would have become useless waste and initiates a host of environmental, financial, and social benefits (Environment Protection 2011). There are at least five benefits for recycling of solid waste and they are as under: (a) Economic Possible economic benefit of waste prevention includes reduced waste disposal fees as the waste is not usually disposed free of cost; rather, substantial charges are to be paid by the producer. On the other hand, revenues can be earned from recycling commodities by selling the recyclable substances. (b) Environmental The environmental advantage lies in reduced energy consumption, reduced pollution, conservation of natural resources, and extension of valuable landfill capacity, which stimulate the development of greener technologies and prevent emissions of many greenhouse gases and water pollutants. (c) Employee Morale Employees’ morale improves when they see the company taking steps to reduce waste through recycling. This enhanced morale certainly increases employee interest, productivity, and more waste prevention measures. Some companies use recycling revenues for employee recreation (i.e., picnics, holiday parties, etc.). (d) Corporate Image When customers and the surrounding neighborhoods see that the company is environmentally conscious, it creates a favorable image of the company. An enhanced corporate image might attract customers. It is noted from various surveys that more consumers now give preferences to a firm’s environmental record when making purchasing decisions.

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Fig. 8 Improperly dumped waste material alongside a wall of a residential house

(e) Compliance Reducing solid waste through recycling means compliance with local or state solid waste regulations. Some stakeholders and organizations restrict the quantities or types of waste accepted at solid waste management facilities. By implementing an extensive and sustainable recycling program, the business can help ensure compliance with these requirements.

Waste Composting Composting is the term used for degradation of organic materials under controlled conditions. This practice results in a marketable soil amendment or mulch. It is in fact a natural process that can be carried out with very simple human efforts. It can be systematically executed by reducing the composting time and space required and by minimizing the objectionable smells. The stabilized end products obtained after composting are rich in organic matter. These end products can act as a fine soil conditioner. But the concentrations of key nutrients such as nitrogen, phosphorus, and potassium are typically low in comparison to commercially manufactured fertilizers. In the simplest composting systems, yard wastes are stacked in long, outdoor piles called windrows. A typical windrow might be 2 m high, 3 or 4 m wide, and tens of meters long. Their length is determined by the rate of input of new materials, the length of time that materials need for decomposition, and the cross-sectional area of the pile. The composting process is affected by temperature, moisture, pH, nutrient supply, availability of oxygen, bacteria, and fungi. These are principal players in the decomposition process. But microorganisms such as nematodes,

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Fig. 9 Animals like dogs contract pathogens from the dumped solid waste material which spreads within the human settlements

mites, bugs, earthworms, and beetles also play a key role by physically breaking down the materials into smaller bits that are easier for microorganisms to attack (Integrated Agricultural Systems 2002). A number of nutrients must be available to the microorganisms that are attacking and degrading the compost pile. The most important nutrients needed are carbon for energy, nitrogen for protein synthesis, and phosphorus and potassium for cell reproduction and metabolism. In addition, a number of nutrients are needed in trace amounts, including calcium, cobalt, copper, magnesium, and manganese.

Disposal (Landfilling and Combustion) The landfilling and combustion activities are adopted to manage that solid waste which cannot be prevented or recycled. Under the method of landfilling, the solid waste is placed in properly designed, constructed, and managed landfills. In the landfills the dumped solid waste is safely contained permanently. The other method to handle this waste is through combustion. Combustion is the controlled burning of waste, which helps reduce its volume, and the burning process is called as incineration. The Three Rs (Reduce, Reuse, and Recycle) In today’s world, reduce, reuse, and recycle, commonly referred as the three Rs for waste management, are effective measures that serve as alternatives to disposing

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Fig. 10 Open dumping practice of solid waste in huge quantity on a large area

waste in landfills. Nowadays, various methods are available for properly managing the solid waste that is produced. The relevant municipal authority introduces an integrated approach to manage the generated solid waste along with combination of substitute options. The strategies of three Rs assist in reducing down the quantities of waste disposed away. The strategies of three Rs presently are helping out in conserving natural resources, landfill capacities, and energy. In addition to this, the three Rs save land and money. Choosing a new landfill has become complicated and more costly due to environmental bindings and opposition from the public living in the vicinity (Margaret Cunningham 2010). (a) Reduce The concept behind the option of reduce is not to produce the solid waste. This option can be practiced by purchasing carefully and being familiar with few guidelines like purchasing products in bulk quantity. Larger, economy-size items or those products that are confined in less packaging may be opted, avoiding overpackaged products, especially those which are packed with several materials such as foil, paper, or wooden boxes. Avoid disposable goods, such as paper plates, cups, napkins, razors, and lighters as their disposal contributes to the problem. Additionally, they are expensive because they are replaced again and again. Purchase long-lasting products that are durable or that carry good warranties. They will be long-lasting, save money in the long run, and save landfill capacities. At the office environments, make two-sided copies whenever possible. Maintain one master file instead of using several files.

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Fig. 11 Scavenger is busy in openly burning the solid waste

Adopt the practice of using electronic mails or maintain a bulletin board for correspondence and communication. Also, adopt the practice of usage of cloth napkins instead of paper napkins. (b) Reuse The concept behind reuse is using again and again the products that are capable to do that. By adopting this practice, the paper material and plastic bags can be saved in larger quantities. More importantly, the broken appliances like furniture and toys can be repaired and made ready for reuse. Similarly, use a coffee can to act as a lunch box. Reuse successfully plastic microwave dinner trays as picnic dishes. Sell old clothes, appliances, toys, and furniture in garage sales or donate them to charities. Use resealable containers rather than plastic wrap. Use a ceramic coffee mug instead of paper cups. Reuse grocery bags or bring your own cloth bags to the store. Avoid taking any a bag from the shops until and unless they are really needed. (c) Recycling Recycling practice may be explained as a series of steps in which the used materials are processed and converted into raw material again and new products are remanufactured. These remanufactured items are sold as it is as a new product. The concerned organizations advise to purchase products made from recycled material. The remanufactured products are identified by a symbol called a recycling symbol. The recycling symbol indicates out one of the two things –

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Fig. 12 Smoldering smoke coming out from dumped solid waste

either the product is made of recycled material or the item can be recycled. For instance, many plastic containers have a recycling symbol with a numbered code that indicates out the type of plastic resin it is made from. The users of the relevant plastic product can check out collection centers and curbside pickup facilities to see what they accept and start collecting those materials. Most commonly, the recyclable materials may include metal cans, newspapers, paper products, glass, and plastics. Preferences must be given to buying recycled materials at work when materials for office supply, office equipment, or manufacturing are needed. Speak to store management and ask for products and packaging that help in reducing down the solid waste. Prefer purchasing those items made from substances that are collected for recycling in the local area. Adopt practices for recycled paper for letterhead, copier paper, and newsletters.

Solid Waste Collection Solid waste collection is the first operation of the solid waste management stream. More significantly, three parties play an important role in this operation, i.e., the producers of the waste, the municipal authority, and the sanitary staff. In order to execute this system, all the three parties have to work in close coordination; else, it will be difficult to yield the success. The producers must be familiar with their roles

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Fig. 13 Improper dumping of solid waste and emerging smoke in the vicinity of vacant residential area

and responsibilities, and then there come the responsibilities of the municipal authorities and then the sanitary staff. For better performance the collection system can be classified in residential refuse collection systems, commercial waste collection, and recyclable material collection. In executing waste collection system, there may be some influence of prevailing climate, topography, available transportation systems, traffic, roads, types of wastes, and population density. However, they can be managed by adopting best waste collection practices.

Waste Management Through Source Reduction The solid waste that is not produced does not have to be collected is a simple concept. That is not the case in many other advanced countries in the world – especially those with modest domestic resources and limited land space for disposal, such as Germany and Japan. In such circumstances, strategies like good green design practices can be considered successful in reducing solid waste.

Green Product Design Strategies The design that reduces the environmental impacts associated with the manufacture, use, and disposal of products is an important part of any pollution prevention

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Fig. 14 Improper dumping of solid waste in the vicinity of vacant residential area

strategy. Companies that engage in such green product design are finding products that combine environmental advantages with good performance and price. The Office of Technology Assessment, USA, identifies two complementary goals of green design: (a) Waste prevention (b) Better material management Waste prevention can be achieved by reducing the weight, toxicity, and energy use of products along with increasing the useful life of products. Better material management facilitates remanufacturing, recycling, and composting along with enhanced energy recovery opportunities.

Product System Life Extension Products that do not wear out as quickly do not have to be replaced as often, which usually means that resources are saved and less waste is generated. Sometimes, products are discarded for reasons that have nothing to do with their potential lifetime. For example, computers become obsolete and clothing fashions change, but many products can continue to remain in service for extended periods of time if they are designed to be durable, reliable, reusable, remanufacturable, and repairable. Extending product life means consumers replace their products less often, which translates into decreased sales volume for manufacturers. Market share in the future may be driven to some extent by

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Fig. 15 Improper dumping of solid waste and exposure of pollutants to the passersby

consumer demand for greener products. Consumers, of course, must make that happen.

Material Life Extension Once a product has reached the end of its useful life, the materials from which it was made may still have economic value, and additional savings can result from avoiding disposal. The key design parameter for extending the life of materials is the ease with which they can be recycled. Products that have been designed to be recycled easily are becoming especially common in Germany. For example, Germany’s requirement that automakers take back and recycle old automobiles has stimulated green design in companies such as BMW and Volkswagen. BMW already sells cars with plastic body panels that have been designed for disassembly and that are labeled as to resin type so they may more easily be recycled. Material Selection A critical stage in product development is selection of appropriate materials to be used. In green design, attempts are made to evaluate the environmental impacts associated with the acquisition, processing, use, and retirement of the materials under consideration. In some cases substituting one material for another can have a modest impact on the quality and price of the resulting product but can have a considerable impact on the environmental consequences.

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Fig. 16 Large number of scavengers busy in searching valuable items from solid waste

Process Management Manufacturing products requires raw materials and energy inputs. Both of which often can be managed more efficiently. The energy required to manufacture a product is an important component of a life cycle assessment. Process improvements that take advantage of waste-heat recovery, more efficient motors and motor controls, and high-efficiency lighting are almost always very cost effective. Efficient Distribution Methods of packaging and transporting products greatly affect the overall energy and environmental impacts associated with those products. Transportation costs are affected by the quantity and type of material shipped, which is in turn affected by packaging, trip distance, and type of carrier. The type of carrier is constrained by the terrain to be covered as well as the speed required for timely arrival. In general, shipping by boat is the least energy-intensive option, followed by rail, truck, and then air, which is fastest but requires the most energy per ton-mile transported. Policy Options Germany has shifted the burden of packaging disposal from the consumer back to manufacturers and distributors. Germany’s Packaging Waste Law requires manufacturers and distributors to recover and recycle their own packaging wastes. The concept may even be extended to large durable goods, such as household appliances and automobiles. Germany’s take-back policy is one of many examples of approaches that governments can take to encourage reduction in the environmental costs of products.

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Fig. 17 Open transportation of collected solid waste and the sanitary staff involved in loading activity without relevant safety measures Table 2 Estimated waste generation in different cities of Pakistan

S. no. 1 2 3 4 5

Name of city Karachi Hyderabad Peshawar Faisalabad Quetta

Population 1,66,31,255 14,96,163 13,44,967 28,08,982 6,57,788

Waste generation rate Kg/capita/day 0.613 0.563 0.489 0.391 0.378

Daily waste generation (tons/day) 10,195 842 658 1098 248

Annual waste generation (tons/ year) 37,21,160 3,07,454 2,40,056 4,00,883 90,755

Source: Sustainable Development Policy Institute Islamabad Pakistan

Labeling Surveys have consistently shown that American consumers purchase products that are environmentally superior to competing products, even if they cost a bit more. Attempts by manufacturers to capture that environmental advantage have led to an overuse of poorly defined terms such as recyclable, recycled, eco-safe, ozone friendly, and biodegradable on product labels. Unfortunately, without a uniform and consistent standard for such terms, these labels are all too often meaningless or even misleading. For example, all soaps and detergents have been “biodegradable” since the 1960s. Aerosols “CFC-free, ozone friendly” have been the norm in the USA since the banning of CFCs for such applications in 1978.

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Landfill Operations for Municipal Solid Waste The properly designed landfills for disposal of municipal solid waste have brought a revolution in the place of traditional dumping of waste in the outskirts of residential settlements. The traditional waste dumping used to develop a variety of environmental pollution problems, viz., emergence of large number of flies, rats, airborne diseases, obnoxious smells/odors, and a black cloud of smoke. The term landfill refers to physical facilities adopted for the final placement of solid wastes in the ground. There are few more terms which are used in this exercise of solid waste dumping. For example, sanitary landfill refers to engineered mechanism for the dumping of domestic solid wastes into the ground to reduce the public health and environmental impact, while the term secure landfill refers to the engineered facility for the dumping of hazardous wastes. When the waste is placed in the landfills, biotransformation of organics takes place, and landfill gases and liquids are generated. The aerobic process starts to take place; then, it is followed by anaerobic processes. The aerobic processes produce and release carbon dioxide (CO2) and liquid (H2O), while the anaerobic process produces carbon dioxide (C2O), methane (CH4), and trace amounts of ammonia and hydrogen sulfide (Landfill manuals 1993). Nowadays, the proper dumping of municipal solid waste in the properly designed and constructed land areas is referred as municipal solid waste landfills. The term landfill refers to physical facilities adopted for the final placement of solid wastes in the ground. There are few more terms which are used in this exercise of solid waste dumping. For example “Sanitary landfill” refers to engineered mechanism for the dumping of domestic solid wastes into the ground to reduce the public health and environmental impact. While the term “Secure landfill” refers to the engineered facility for the dumping of hazardous wastes. When the waste is placed in the landfills biotransformation of organics take place and landfill gases and liquids are generated. The starting take place aerobic process, then it is followed by anaerobic processes. The aerobic processes produce and release carbon dioxide (CO2) and liquid (H2O). While the anaerobic process produces carbon dioxide (C2O), methane (CH4) and trace amounts of ammonia and hydrogen sulfide (Environment Protection 2011).

Solid Waste Management Landfill Methods There are various landfill methods which are discussed as under: (a) The trench method The trench method of landfills is used in level terrain. The trenches are dug by excavation in the ground. Solid waste is filled in the trenches and dirt is replaced on top of the buried material. After completion the trench is compacted. (b) The area method The area method is the most popular landfill method. In this method a side of a hill or a sloped area is found out. Then, refuse is dumped on the side of the slope and then covered with dirt. It continues until the entire slope is leveled.

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(c) The valley or ravine method The valley or ravine method is commonly used where large quantities of waste are generated by large cities. This method is used in an area with large depression or slope such as a valley or ravine. Usually, an area naturally developed is considered most suitable. Refuse is dumped in the depression and filled with dirt. The area is then compacted and built up.

Landfill Operation Classification There are three classifications of landfills: (a) Class I landfills or secure landfills These landfills are designed for dumping of hazardous waste. (b) Class II landfills or monofills These landfills are designed for dumping of designated wastes, which are particular types of wastes such as incinerator ash or sewage sludge that are relatively uniform in characteristics. (c) Class III landfills or sanitary landfills These landfills are designed for dumping of municipal solid waste. Though landfills remain the primary means of municipal solid waste disposal, three Rs of reduce, reuse, and recycling are beginning to have greater attraction in the waste management system.

Landfill Site Construction Requirements The construction of a landfill site requires a step-by-step approach. The landfill activity planners and designers are basically concerned with the viability of a suitable site (Pak EPA 2011). In order to construct commercially and environmentally feasible landfill site, specific requirements must be kept in consideration. The specific requirements are discussed below: (a) Location The landfill site should be in easy access to transport the solid waste by road. The transfer stations may be established if the haul distance is far away or rail network is used for solid waste transportation. In addition to this, land value (cost of land), cost of meeting government requirements, and prospects of overall community served through the operation may also be considered before making any final decision. Types of construction, i.e., more than one landfill, may be used at a single site in order to enhance the life of the planned site for a very long time. (b) Stability It is the complete study of underlying geology at the planned landfill site, by studying the nearby area for earthquake faults or any such weak strata, underground water table, and location of nearby rivers, streams, and flood plains so that the landfill site stays intact. (c) Capacity Capacity refers to the quantity of solid waste that is to be dumped in the landfill area. The calculation of solid waste capacity is based on quantity of the wastes,

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amount of intermediate and daily cover applied to it, and amount of settlement that the waste will undergo following tipping. The thickness of capping the landfill operation is considered along with construction of lining and drainage layers when it is completely filled at its end. (d) Protection of soil and water The solid waste is permanently dumped in the landfills. This permanent placement of solid waste needs installation of liner and collection systems. In addition to this arrangement for storm water control, leachate management and landfill gas migration may be seriously considered. (e) Nuisance and hazard management The overall operation of solid waste landfills is of critical nature. The solid waste is transported from one place to its final disposal. It is ensured that it does not cause any nuisance as there is a chance of it falling down from the loaded vehicle on its hauling path. The waste may possibly emit odor that is again a nuisance for the residents living on the haul-way path or near the landfill site. In some cases the solid waste may be infectious or hazardous in nature, and in that case special attention must be given while collection is performed.

Basic Features of Landfills Moisture or water content in a landfill is important if the wastes are to decompose properly. The initial moisture contained in the wastes themselves is quickly dissipated, so it is water that percolates through the surface, sides, and bottom that eventually dominates the water balance in the landfill. Water percolating through the wastes is called leachate. Its collection and treatment is essential to the protection of the local groundwater. Municipal solid waste landfills require composite liners and leachate collection system to prevent groundwater contamination. The composite liner consists of a flexible membrane liner (FML) above a compacted clay soil. Leachate is collected with perforated pipes that are situated above the FML. A final cover over the completed landfill is designed to minimize infiltration of water (BC Ministry of Environment 2013). During waste decomposition, methane gas is formed so completed landfills need collection and venting system. Sources of Methane: Landfill On a global scale the methane gas emissions from landfill activities are estimated to be in between 30 and 70 million tons each year. It is believed that major releases of this landfill methane presently come from developed countries, where the levels of waste tend to be highest and no comprehensive methane recovery mechanism is adopted. Landfills provide most suitable conditions for methanogenesis, with huge quantity dumping of organic material and prevailing anaerobic conditions. The larger quantities of waste which are dumped in landfills can produce methane for years to come after the closure of the landfills, as the waste passes through slow decay under the ground. The municipal solid waste landfill activities are the largest human-related methane emissions. This landfill gas is easily captured and redirected if relevant

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arrangement is made. Firstly, the commercial landfill gas energy (LFGE) recovery project was started in 1975 in Los Angeles. Gases are released during decomposition in landfills. When captured and converted to an alternative energy source, LFG is called biogas. The municipal solid waste decomposition releases methane ~50 %, CO2  45 %, and N2  5 % (Ahmed et al. 2013). Methane release takes place from landfills either directly to the air or by diffusion through the soil cover and causes impacts on the natural environment.

Infectious Waste Infectious waste is a waste that possesses potential risk of diseases or illness to human beings or a waste that is capable of causing infectious diseases. Segregation and labeling of infectious waste is carried out at its source of generation. When performing handling of infectious waste, it should be ensured that it must be carefully managed. The intactness of the packaging must be maintained and quicker bacterial growth and putrefaction is repressed. The containers in which sharps are placed must be impervious, rigid, puncture resistant, leak resistant, and sturdy so that they can sustain the risk. Infectious waste may include human blood, body fluids, separated or removed away body organs through surgical operation, syringes, sharps, glassware, or all items used during the treatment activity on an infected patient.

Training for Handling Infectious Waste Persons working with blood may be trained about blood-borne pathogens. The engaged staff working with animal contact may be trained with relevant training. The staff working with any waste that carries a potential risk of transmitting illness or disease to humans may be trained with infectious waste management. The staff engaged with infectious organisms having risk to cause illness or disease in healthy people, animals, or plants may be trained in biosafety. Personal Protective Equipment for Handling Infectious Waste The people who are engaged in handling infectious waste must be advised to wear relevant personal protective equipment (PPE), for example, wearing impermeable gloves for handling infectious waste. For splashing risk, wearing of eye protection or wearing shoe covers for walk-through hazards is advised. Infectious Waste Storage The noninfectious waste must be kept separate from infectious waste. If infectious waste is mixed by mistake with noninfectious waste, then it must be considered as infectious waste. Efforts should be made to minimize storage time duration. The area where infectious waste is placed must only be accessible by authorized personnel. The infectious waste storage area must be ventilated to outdoors. The area must be labeled with a warning sign in a clear and visible manner. The area must thwart exposure to common people, animals, and insects and disclosure to

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weather. Red- or orange-colored bags are generally used for putting the infectious waste and are also called biohazard bags. The colored bags must be correctly sealed when filled. For leakage or seep hazards, leak-proof bins or bags should be used.

Labels for Infectious Waste The universal biohazard symbol must be displayed at the place where infectious waste is placed. The biohazard symbol should be understandable from at least seven meters away from the placed bags containing infectious waste. A written message like “biohazard” or “biohazardous,” “infectious waste,” or “biomedical waste” must also be displayed.

Hazardous Waste Wastes containing harmful components that are too dangerous to be handled in ordinary manner and sent to the landfill, dumped into the sewer system, or released into the atmosphere are hazardous substances. These can be in a form of a solid, liquid, contained gas, or sludge. Improper release of hazardous waste may seriously threaten the environment and human health. Hazardous waste is regulated from the moment it is created through the time of final disposal. Identification of listed hazardous wastes: F-Listed Wastes • Wastes from nonspecific sources • Solvents from cleaning and degreasing operations • Wastewater treatment K-Listed Wastes • Created from specific sources • Chemical or pesticide manufacturing P-Listed Wastes • Acutely hazardous discarded commercial chemical products • Arsenic trioxide U-Listed Wastes • Less hazardous discarded commercial chemical products

Future Changes in Solid Waste Composition In terms of solid waste management planning, knowledge of future trends in the composition of solid waste and quantities is of great importance. As the time is passing, we are experiencing new lifestyles and for that we too adopt many practices; some of them are pointed as under:

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(a) Food waste The quantity of kitchen food waste collections has been changed significantly with the passage of time as a result of technological advancements and change in public health. Food processing and packaging industry and the use of kitchen food waste have affected the quantity of food waste. The proportion of food waste, by weight, has decreased as it is preserved or refrigerated for some time and used later on. (b) Paper and cardboard The proportion of paper and cardboard found in municipal solid waste stream has increased greatly over the past 50 years. All this happened due to excessive use of paper in offices and newspaper publications. This is expected to increase further in coming times. (c) Yard wastes The proportion of yard waste has also increased significantly, primarily due to passage of the relevant legislations that prohibit burning of yard wastes. By weight, yard waste currently accounts for about 16–24% of the waste stream. (d) Plastics The proportion of plastics in solid waste has increased significantly during the past 50 years. It is anticipated the use of plastic will continue to increase, but at a slower rate than during the past 25 years.

Future Directions The way in which currently the solid waste is being managed is not an environment friendly. The incomplete collection of generated waste, lack of awareness from the common person to the chain of municipal authority, and ignorance toward segregation and open burning of waste are few issues out of many which need engineering and administrative approaches. The improper disposal has started causing numerous problems. The air is being polluted by addition of air pollutants in the atmosphere due to this open burning. The sewerage system is clogged and starts malfunctioning due to fallen down solid waste material. The freshwater bodies including canals and lakes as well as ocean water are heavily polluted, and its aquatic life and ecosystem are damaged. The receiving soils lose their fertility and deteriorate the landscaping. The scattered waste causes nuisance and foul smells in the prevailing area. The solution lies in systematic approach of integrated solid waste management (ISWM). The individuals, the authorities of health-providing facilities, commercial areas, institutions, manufacturing facilities, etc. all need awareness and have to play participatory roles in order to perform their due responsibilities. The most important responsibility no doubt is on the part of municipal authorities. The municipal authorities should introduce separate waste collection system for recyclable waste for recycling, organic waste for composting, combustible waste for incineration, and the left out waste for properly maintained landfills. This will certainly help in properly managing the solid waste problem in Pakistan to a greater extent, and resultantly, the overall environment will be saved.

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Fig. 18 Mohenjo-daro: Renowned place of archeological importance (Source: http://www. mohenjodaro.net)

Part B: Agricultural Biomass of Major Crops Produced in the Province of Sindh, Pakistan Introduction The province of Sindh, Pakistan, is historically important due to one of the oldest civilizations of the world, the Indus Valley Civilization, and the archeological site of Mohenjo-daro (Fig. 18). Since histories back the Indus Valley Civilization grew agriculture with Indus River as the source, agriculture is the backbone of our economy. In Sindh the major crops of wheat, rice, sugarcane, and cotton are on average annually grown on 2,41,22,600 ha, which produce an average of 1,87,98,930 t of major crops; as a result there is 1,36,34,340 t of average agriculture residue biomass annually. If only 40 % of this biomass residue is utilized for power generation, there will be more than 10,000 MW of power plant generation capacity which is almost 81 % replacement of fossil fuel power generation and 52 % of overall generation capacity of Pakistan with Sindh province as the only source, while, if 35 % of agricultural biomass residue of only major crops of Pakistan is utilized, there would be almost double of the installed capacity of Pakistan up to 2013 (Installed Capacity of Pakistan 19,360 MW). Though there is a huge potential available in this region, if it is properly utilized, there could be double of our crop yields resulting double amount of agricultural

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biomass residue, hence more poverty reduction and more environment-friendly energy generation for the future needs and development of the country at large.

Historical Background History is evident about the emergence of the world’s oldest civilizations, most of these civilizations born and flourished on the banks of the rivers – they needed freshwater to drink, to grow grains to feed, and to sail as a way of transportation. The first ancient civilizations arose in the Middle East, the Mesopotamia (land between two rivers Tigris and Euphrates); Egypt (Nile River); modern-day Pakistan, the Indus Valley Civilization (Indus River); and China, the Huang He (Yellow River) Valley. The common factors of flourishing of these civilizations among all these were water and agriculture. The Indus Valley remained the host of ancient civilization of the world’s human history; it is learned that the cloth used for the mummification in Egypt was exported from Mohenjo-daro. After the extermination of the Indus Valley Civilization, the new settlements grew slowly in the region (especially in the “doabs,” the region lying between and reaching to the confluence of two rivers). Most of today’s irrigation systems we have were an effort of the British authorities in the middle of the nineteenth century who managed the already existing system of low irrigation occurrences which evolved much earlier before the British rule; such systems were the small dams and inundation canals (Ghar Wah, the famous inundation canal constructed by the then Kalhora rulers of Sindh before the British in the region now known as Larkano District of Sindh). The British authorities expanded and modernized the whole irrigation water supply system of the Indus Basin known as Indus Basin Irrigation System (IBIS). On the part of the British, they constructed in 1932 AC one the most famous barrages on the Indus River near Sukkur city of Sindh (known as Lord Lloyd or Sukkur barrage with seven canals taking from it) in the Sindh irrigation system (Sindh irrigation system is a part of IBIS). The Sindh irrigation system with three barrages, one pre-independence and two post-independence, and 14 main canals is one of the world’s largest irrigation systems; the agricultural area of the province is irrigated by these 14 main canals. The size of the area irrigated by the irrigation system in Sindh province is more than the area irrigated in Egypt, and it is one-and-a-half times the area irrigated in Mexico (van Steen Bergen 2014). The economy of the province is of dual nature; on the one side it has the country’s biggest metropolitan city with Bin Qasim seaport and capital of Sindh province Karachi, which accounts for about 40 % of the population of the province and is the economic hub of the country as a whole. The rural population and area on the other side is mostly agricultural. The rural Sindh is the most poverty stricken with huge manpower still with 35 % of the population below poverty line in 1999; one of the causes of this

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A I Main area A II Piedmont soil region B I Non-perennial Guddu command area

India

B II Perennial Sukkur command area C Main area Kotri command D Thar and Nara deserts E Kohistan

Balochistan

Arabian sea

Rann of Kutch

A. Rice/Wheat zone of right bank of upper Sindh B. Cotton/Wheat zone of left bank C. Rice/Wheat Sugarcane zone of lower Sindh

Fig. 19 Agricultural and ecological zones of Sindh province. Source: PARC

poverty is the great extent of inequity in land distribution. Big landlords with large farm cultivate their land through the tenants and land laborers and with mechanized equipments; mostly these equipments are tractors. Officially classified small farms are only 33 % the area of the size less 8 hectares (ha) (van Steen Bergen 2014). Almost the entire agricultural area of the Sindh province is irrigated with 14 canals except limited areas in the west with spate irrigation, and agriculture in Tharparkar desert is rain fed. Rice, wheat, cotton, and sugarcane are the major crops mostly grown in the province; Fig. 19 gives the detail of command area and crops grown and includes desert area (van Steen Bergen 2014). The yield compared to the rest of the world which is much below is graphically shown in letter pages of this write-up. Mango and banana orchards are in districts of Hyderabad and Sanghar, guava is grown in Larkano District, and date palms are grown in Khairpur District. All are limited but high-valued horticulture, while mango and date palms are exported too. The agricultural production of the province has increased more or less at the pace of population increase at a rate of 2–3 % a year, over the last 15 years (van Steen Bergen 2014).

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Table 3 Barrages on Indus River in Sindh province 1. Guddu barrage (i) Left bank canals: one 01. The Ghotki Feeder (non-perennial) canal (ii) Right bank canals: 01. Desert Pat Feeder and 02.The Begari Sindh Feeder, (both are two canals non-perennial canals) 2. Sukkur/Lord Lloyd barrage (i) Left bank canals: four 01. Khairpur West Canal, 02. Rohri Canal, 03. Khairpur East Canal, canals and 04. Eastern Nara Canal (ii) Right bank canals: 01. North West Canal, 02. Rice Canal, and 03. Dadu Canal three canals 3. Kotri barrage (i) Left bank canals: 01. The Akram Wah (Lined Canal), 02. The Fuleli Canal, and 03. three canals The Pinyari Canal (ii) Right bank canals: 01. Kalri Baghar Feeder one canal Courtesy: Sindh Irrigation Department

The Irrigation System The three gigantic barrages (namely, Guddu, Sukkur, and Kotri) on the Indus River in Sindh province (Table 3) supply irrigation water; the Sukkur barrage construction was completed in year 1932 pre-independence, while the other two barrages Kotri barrage construction completed in 1955 and Guddu barrage construction completed in 1962 both are post-independence barrages. Figures 20, 21, and 22 show the view of these three barrages. The 14 main canals are fed by the above three gigantic barrages; further, the larger canals are subdivided into branches. Through the branches, distributaries, and minors, irrigation water is supplied to the agricultural lands through water courses. In the province of Sindh, there are 109 branch canals, 509 distributaries, and 902 minors (Courtesy: Sindh Irrigation Department). Rohri Canal, and Eastern Nara Canal these off take from Indus river at Sukkur barrage on the left bank, both these serve command near about 1 million ha such is the huge size of Sindh irrigation system, these are one of the largest single Canal in the world (van Steen Bergen 2014). Out of these 14 main canals, some are non-perennial larger canals which flow seasonally (in summer season only when the river is in its high flows), and others are perennial which flow year-round (only closed in month of January for maintenance purpose). The IBIS network for agriculture of our country Pakistan (Fig. 23) provides 90 % of fiber and food needs, while 10 % is provided through rain-fed (locally called barani) area. The IBIS has three dams, of which Tarbela Dam is a mega dam of Pakistan; the system also contains 19 barrages on river, 12 inter-river link canals, 45 big canal commands, and more than 1 million tube wells, including disposal network of agricultural effluent about 18,000 km length, with one drain carrying much part of agricultural saline effluent direct into the Arabian Sea known as Left Bank Outfall Drain (LBOD). The drainage network is still not connected as the

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Fig. 20 Guddu barrage (Courtesy: Sindh Irrigation Department)

Fig. 21 Sukkur/Lord Lloyd barrage (Courtesy: Sindh Irrigation Department)

irrigation network is, resulting in such a large irrigated agricultural system that has not achieved the objective of poverty reduction. Further to the effect, the system even more deteriorated as time passes; the causes are detached management of different sectors (irrigation system, agriculture, environment, and social), as there is

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Fig. 22 Kotri barrage (Courtesy: Sindh Irrigation Department)

deficiency in coordination among various water-related stakeholders and dearth of linkage of systemic process between economic, social, and environment; deficiency in execution of modern water management technologies; deprived water polices, especially in groundwater management; and poor maintenance and operation of the system as a whole (Lashari and Mahesar 2012). As about 60 % population of the Sindh province of Pakistan is rural and their source of income is almost agriculture, so the economy of Sindh is agriculture dependent. The major crops are rice, wheat, cotton, and sugarcane; these crops make use of 68 % of area under crop, though in Sindh some horticulture crops are grown too, such as dates, bananas, and mangoes, including vegetables almost of all kinds seasonally. Total gross command area (GCA) of Sindh is 5.76 million hectare (Mha), and it is estimated that production of major crops in Sindh is reduced by 40–60 % as about 37.6 % of the total GCA is under waterlogging and salinity problems (Lashari and Mahesar 2012). On the other hand, Table 4 shows the detail of 14-year data, where the net production of major cops and net cropped area vary from year to year depending upon availability of surface water and other factors such as to some extent introduction of modern techniques of hybrid seeds. The contribution of Sindh province in overall national production of major agricultural crops from the 14-year data (i.e., from financial years 1997–1998 to

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Fig. 23 Map of Pakistan and Sindh province showing IBIS (Source: SIDA.org.pk)

2010–2011) shows an average of 14 % to maximum 17 % in 2010–2011 in wheat, average of 34 % to maximum 36 % in 2008–2009 in rice, average of 27 % to maximum 29 % in 2007–2008 in sugarcane, and average of 23 % to maximum 33 % in 2009–2010 in cotton (Pakistan Statistical Year Book of 2008 and 2011).

Production (000) ton 2659.4 2675.1 3001.3 2226.5 2101 2109.2 2172.2 2508.6 2750.4 3409.1 3411.4 3540.2 3703.1 4287.9 40,555.4 2896.81

Rice (paddy) Area Production ha. (000) (000) ton 689.3 1840.9 704.1 1930.3 690.4 2123 540.1 1682.3 461.1 1159.1 488.3 1299.7 551.2 1432.8 543.9 1499.6 593.2 1721 598.1 1761.8 594 1817.7 733.5 2537.1 707.7 2422.3 361.2 1230.3 8256.1 24,457.9 589.72 1746.99

Courtesy (Pakistan Statistical Year Book 2008 and 2011)

Cropping year 1997–1998 1998–1999 1999–2000 2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 Total Average of 14 years

Wheat Area ha. (000) 1120.2 1123.7 1144.2 810.7 875.2 863.7 878.2 887.4 933.2 982.2 989.9 1031.4 1092.3 1144.4 13,876.7 991.19

Sugarcane Area ha. (000) 261.6 270.8 230.6 238.8 240.7 258.6 259.9 214.9 183.2 214.7 308.8 263.9 233.9 226.5 3406.9 243.35

Table 4 The production of major agricultural crops in Sindh province 14-year data Production (000) ton 15,999.6 17,050.7 14,290.8 12,049.7 11,416.3 13,797.6 14,611.8 9357.4 11,243.4 12,529.2 18,793.9 13,304.3 13,505.4 13,766.4 1,91,716.5 13,694.04

Cotton Area ha. (000) 600.3 630.2 633.5 523.6 547.4 542.6 561.4 635.1 637.1 570.1 607.4 651.5 634.7 457 8231.9 587.99

Production (000) bales (375 lbs each) 2335.5 2134.1 2377.4 2141.1 2443.2 2411.8 2242.8 3016.7 2648 2398.2 2536.2 2978.3 4270.7 3536.8 37,470.8 2676.49

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Cotton-wheat Rice-wheat Mixed crops Pulses-wheat Maize-wheat-oilseed Maize-wheat Orchard-vegetables-wheat Peri-urban around Quetta

Fig. 24 Crop production regions (Source: FAO of UN)

The province of Sindh is the second largest province of Pakistan in terms of population and economy and is also known as the “Valley of Mehran.” Mehran and Sindhu are the two well-known names of the Indus River. The name of province Sindh is also derived from word Sindhu – the Indus River – and it is the third largest province in terms of area which is 54,407 sq-miles (1,40,914 sq-km). The total gross command area (GCA) of the province is 5.76 million hectares (Mha), and its culturable command area (CCA) is 2027Mha. The major crops grown are wheat, rice, sugarcane, and cotton; these major crops are cultivated on 68 % of total cropping area, and the rest is the horticulture crops. The irrigation network provides 48.76 million acre foot (MAF) of surface water to irrigate the command area, though the availability is generally 10–12% less than the above quantity of surface irrigation water. 5 MAF is the groundwater availability that is too unregulated, while the rainwater has good potential, but it is also not properly explored (Lashari and Mahesar 2012). In horticulture, Sindh produces 88 % of chilies, 34 % of mangoes, and 73 % of banana grown in Pakistan. Others are the fodder crops, pulses, oil seeds, condiments, fruits, and vegetables (Kiani 2008).

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Fig. 25 (a, b) Image of paddy crop sowing (Source: SARSO Sindh, Pakistan)

Figure 24 gives the detail of the entire Pakistan including the Sindh province region-wise major crops grown. In addition to this, in different regions various vegetables and other horticulture crops are also grown (Figs. 25, 26, 27, 28, 29, 30, 31, 32, 33, and 34).

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Fig. 26 Image of grown paddy crop (Source: SARSO Sindh, Pakistan)

Fig. 27 Image of cotton crop sowing (Source: SARSO Sindh, Pak)

Pattern of Cropping and the Yield of Major Crops Figure 35 gives the detail of cropping pattern of the entire country and Sindh province district and region wise; Table 4 shows the yield of major crops and area of each cultivated crop per year (wheat, rice, sugarcane, and cotton), while Fig. 36 and Table 5 show yield in kg/ha of different major crops for each year of Sindh province. From Table 4, Table 5, and Fig. 36, it is very clear that all major crops from last 14-year data are in increasing trend in terms of cropping area, yield, and especially the average yield per hectare; the increase of average yield per hectare predicts the

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Fig. 28 Image of grown cotton crop

future of agricultural production of the province on the positive side. For example, the cropping pattern and yield of major crops for the last 6 years (i.e., from 2004–2005 to 2010–2011) have remained worth noting as under: Wheat in 2004–2005 area under crop was 887,400 ha and yield was 2827 Kg/ha, and in year 2010–2011 area under crop was 11,44,400 ha and yield was 3747 Kg/ha; cropping area increased by 22 % and yield per ha increased by 25 %. Rice in 2004–2005 area under crop was 5,43,900 ha and yield 2757 Kg/ha, and in year 2010–2011 area under crop was 3,61,200 ha and yield 3406 kg/ha; cropping area decreased by 51 % (the reason is Sindh province was the most affected region due to floods of cropping year 2010–2011) even though yield per ha increased by 19 %, and if we compare cropping year 2008–2009 with that of 2004–2005, then the area under crop was 7,33,500 ha and yield 3459 Kg/ha; here cropping area increased by 26 % and yield per ha increased by 25 %. Cotton in 2004–2005 area under crop was 6,35,100 ha and yield 808 Kg/ha, and in year 2010–2011 area under crop was 4,57,000 ha and yield 1316 Kg/ha; cropping area decreased by 39 % (the reason is Sindh province was the most affected region due to super floods of cropping year 2010–2011) even though yield per ha increased by 39 %, and if we match cropping year 2008–2009 with 2004–2005, then the area under crop was 651,500 ha and yield 902 Kg/ha; here cropping area increased by 2.5 % and yield per ha increased by 10 %.

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Fig. 29 Image of ready cotton crop (Source: SARSO Sindh, Pakistan)

Sugarcane in 2004–2005 area under crop was 2,14,900 ha and yield was 43,543 Kg/ha, and in year 2010–2011 area under crop was 2,26,500 ha and yield 60,779 Kg/ha; cropping area increased by 5 % and yield per ha increased by 28 %. From Table 5 it is clear from colored data and from Fig. 36 that the yield of major crops has gone under speedy increase in the last 6 years. The yield of wheat was 3747 Kg/ha and the yield of rice was more than 3400 Kg/ha. However, it is also observed through data that some of the districts of Sindh Nawabshah and Khairpur were receiving the yield wheat around 4000 Kg/ha, while from field visits and meeting with progressive zamindars (land growers), the maximum was around 6000 Kg/ha. It is also observed through data and field visits that some of the paddy-growing districts of Sindh, Larkano, Shikarpur, Jacobabad, and Kandhkot, Kashmore, were getting paddy (using hybrid seed) yield around 5930 Kg/ha on average and some of the progressive zamindars (land growers) of the same area get yield maximum around 7907 Kg/ha. From Figs. 37 and 38, evaluating the yield of wheat and rice in Sindh with the rest of the world’s most advanced in yield of wheat and rice, the province of Sindh is 3747 Kg/ha, France 7280 Kg/ha, Germany 7750 Kg/ha, and the USA 8250 Kg/ha of wheat. Likewise, the yield of rice crop in Sindh is 3406 Kg/ha, the USA is more than 8000 Kg/ha, and Egypt is even more than 10,000 Kg/ha. But it is worth mentioning here that Sindh province has the capability to yield wheat of

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Fig. 30 Image of grown wheat crop (Source: SARSO Sindh, Pakistan)

4000 Kg/ha to 6000 Kg/ha and rice about 6000 Kg/ha to 8000 Kg/ha as mentioned in the above paragraph regarding progressive land growers. The province of Sindh has the capability of improving its yields as the trend shows in Fig. 36; the only requirement is the proper management of irrigation system and utilization of modern technologies and machinery while educating the farmers, tenants, and land growers.

The Rain-Fed (Nonirrigated) Area of Sindh There are three prospective areas in Sindh where rain feed can be achieved; these are Thar desert, Khirhar hills, and Ubhan Shah hills; as shown green areas are canalirrigated lands, while white areas are the rain-fed areas in Fig. 39 (Lashari and Mahesar 2012). From Fig. 36, it is clear that the yield of major crops is on increasing trend but it is still low at national levels as of some progressive land growers are getting even more than the yields shown in Fig. 20, it means there is a potential gap that should be filled to increase the yield. Another point of focus is the rain-fed areas which are almost ignored and less considered which require more consideration that will result in significant growth in the production and yield. The sustainable irrigated agriculture is under threat from a number of issues which include increasing need of

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Fig. 31 Image of wheat crop harvesting (Source: SARSO Sindh, Pak)

Fig. 32 Image of wheat grain (Source: SARSO Sindh, Pakistan)

water to meet raising population demand; poor maintenance of canal system causing inefficient service; waterlogging and salinity problems; excess use of groundwater resources resulting in exhausted groundwater aquifers, thus leaving large areas not viable for the poor farmers; insufficient field drainage system (even the existing field drainage effluent network not properly connected) which could dispose of agricultural effluent in due time; improper pricing or valuation of water; and scarce participation of consumers. Therefore, for the optimum production and crop yield, these areas need to be focused: competent and resourceful management including conservation of present water resources, equitable distribution of water in different areas and canal commands (from head to tail users), efficient drainage effluent interventions with well-connected networks for the maximization of crop

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Fig. 33 Image of grown sugarcane crop

production, and reforms to be introduced within institutions to make the managing organizations more responsive, efficient, and dynamic (Lashari and Mahesar 2012). The distribution and supply of irrigation water from head to tail has not remained reliable; people at the head get more water than the water users at the tail. As a result the set targets and objectives of irrigated agriculture are not being achieved, so the productivity is much lesser in quantity than it should have been. If reforms in irrigation sector are executed wholeheartedly, then farmers can play their important role to improve the productivity. Poor working of irrigation system since the 1960s is mainly due to water shortage which resulted into defective and unreliable supply, distribution, and finally inefficient use of irrigation water (Lashari and Mahesar 2012).

Biomass Residue of Agricultural Crops in the Province of Sindh It can be observed from Tables 6, 7, 8, and 9 in columns from a to f that the biomass residue of various crops such as wheat straw, rice husk, rice straw, cane trash, bagasse, and cotton stalk is in different ratios, which is given below. These ratios are verified on-site and from research studies (Cerqueira et al. 2010; Figs. 40, 41, 42, and 43). (a) Wheat and wheat straw ratio 1:1 (b) 20 % rice husk found as waste in paddy (c) Paddy and rice straw ratio 1:1

996 Fig. 34 Image of harvested sugarcane crop (Source: SARSO Sindh, Pakistan)

Fig. 35 Cropping pattern (Source: PARC)

(d) 23 % cane trash found as waste in sugarcane harvest (e) 30 % wet bagasse found as waste in sugarcane industry (f) Cotton and cotton sticks ratio 1:3

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Yield of major Crops in Sindh 4000

Yield Kg/ha

3500 3000 2500 2000 1500 1000 500 2010-11

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1999-00

1998-99

1997-98

0

Cotton Production Kg/ha (Sindh)

Wheat Production Kg/ha (Sindh) Rice (Paddy) Production Kg/ha (Sindh)

Fig. 36 Yield of major crops in Sindh Table 5 Yield of major crops in Kg/ha of Sindh province Cropping year 1997–1998 1998–1999 1999–2000 2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011

Wheat production Kg/ha 2374 2381 2623 2746 2401 2442 2473 2827 2947 3471 3446 3432 3390 3747

Rice (paddy) production Kg/ha 2671 2742 3075 3115 2514 2662 2599 2757 2901 2946 3060 3459 3423 3406

Cotton production Kg/ha 662 576 638 696 759 756 680 808 707 716 710 902 1145 1316

Sugarcane production Kg/ha 61,161 62,964 61,972 50,459 47,430 53,355 56,221 43,543 61,372 58,357 60,861 50,414 57,740 60,779

It may be observed from Tables 6, 7, 8, and 9 that annual average production of various agricultural major crops based on 14 years of data is a huge quantity which is given as under in Table 10. From Tables 6, 7, 8, and 9, the total biomass residue of various agricultural major crops of 14 years and its average production are given as under in Table 11.

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2010-11 Wheat 60000 Yield kg/ha

50000 40000 30000 20000 10000 0

Area (1000)ha Yield kg/ha Total Yield (1000)ton

Pak

France

Germany

U.K

1144.4

5400

3320

1940

3747

7280

7750

8250

25213.8

49300

25120

16000

Fig. 37 Wheat crop production and yield in the world

Yield kg/ha

2010-11 Rice 35000 30000 25000 20000 15000 10000 5000 0 Pak Area(1000)ha Yield kg/ha Total Yield (1000)ton

Egypt

U.S.A

Japan

China

2365.3

640

1370

1620

29800

630

3406

10100

8070

6660

6590

4930

4823.3

4200

7620

7850

13500

2050

Iran

Fig. 38 Rice crop production and yield in the world

Total average amount of agricultural residue biomass of Sindh = 13,634.34  103t/year.

Energy Content of Biomass Residue of Agricultural Crop in the Province of Sindh and Pakistan Keeping in view the availability factor, the different agricultural residue biomass is used as animal feed and also as a raw source of energy at local level; hence, we theoretically consider 40 % availability of above total average amount of agricultural residue biomass, i.e., 5354.73  103 t/year. Theoretically considering average calorific value of agricultural residue biomass 3500 KCal/Kg, then the theoretical energy content = 3500 KCal/Kg5354.73  106

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Fig. 39 Rain-fed and canal-irrigated lands (Source: Lashari and Mahesar (2012))

Kg = 1.91  1013KCal (heating value) if this amount of heat energy is multiplied by 4.81 for converting K Cal into KJ = 7.97  1013 KJ or KW-S. With this theoretical heat content if converted into electrical energy with 40 % overall power plant efficiency, then we have 3.2  1013 KW-S (el energy). Converting this for power plant to be installed, we’ll have 8.9  1010 KWh = 10,120 MWYear. Hence, the power plants of about 10,000 MW are possible to be installed in the province of Sindh with the use of only 40 % of single source by agricultural residue biomass of only major crops. By the end of the year 2013, the installed capacity of Pakistan is given in Table 12. Then, the 10,000 MW through agricultural residue biomass of major crops of Sindh province only will be 52 % of total installed capacity and 81 % of total fossil fuel installed capacity of Pakistan. As already mentioned, that the province of Sindh contributes significantly toward overall national production of major agricultural crops from the 14-year data (i.e., from financial years 1997–1998 to 2010–2011) shows an average of 14 % to maximum 17 % in 2010–2011 in wheat, average of 34 % to maximum 36 % in 2008–2009 in rice, average of 27 % to maximum 29 % in 2007–2008 in sugarcane,

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Table 6 Production of agricultural crop wheat and biomass residue in province of Sindh Wheat Cropping year 1997–1998 1998–1999 1999–2000 2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 Total Average of 14 years

Area (000) ha. 1120.2 1123.7 1144.2 810.7 875.2 863.7 878.2 887.4 933.2 982.2 989.9 1031.4 1092.3 1144.4 13,876.7 991.19

Production (000) ton 2659.4 2675.1 3001.3 2226.5 2101 2109.2 2172.2 2508.6 2750.4 3409.1 3411.4 3540.2 3703.1 4287.9 40,555.4 2896.81

a Wheat straw (000) ton 2659.4 2675.1 3001.3 2226.5 2101 2109.2 2172.2 2508.6 2750.4 3409.1 3411.4 3540.2 3703.1 4287.9 40,555.4 2896.81

Table 7 Production of agricultural crop rice (paddy) and biomass residue in province of Sindh Rice (paddy)

Cropping year 1997–1998 1998–1999 1999–2000 2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 Total Average of 14 years

Area (000) ha. 689.3 704.1 690.4 540.1 461.1 488.3 551.2 543.9 593.2 598.1 594 733.5 707.7 361.2 8256.1 589.72

Production (000) ton 1840.9 1930.3 2123 1682.3 1159.1 1299.7 1432.8 1499.6 1721 1761.8 1817.7 2537.1 2422.3 1230.3 24,457.9 1746.993

b 20 % rice husk (000) ton 368.18 386.06 424.6 336.46 231.82 259.94 286.56 299.92 344.2 352.36 363.54 507.42 484.46 246.06 4891.58 349.40

c Rice straw ratio 1:1 (000) ton 1840.9 1930.3 2123 1682.3 1159.1 1299.7 1432.8 1499.6 1721 1761.8 1817.7 2537.1 2422.3 1230.3 24,457.9 1746.99

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Table 8 Production of agricultural crop sugarcane and biomass residue in province of Sindh Sugarcane

Cropping year 1997–1998 1998–1999 1999–2000 2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 Total Average of 14 years

Area (000) ha. 261.6 270.8 230.6 238.8 240.7 258.6 259.9 214.9 183.2 214.7 308.8 263.9 233.9 226.5 3406.9 243.4

Production (000) tons 15,999.6 17,050.7 14,290.8 12,049.7 11,416.3 13,797.6 14,611.8 9357.4 11,243.4 12,529.2 18,793.9 13,304.3 13,505.4 13,766.4 1,91,716.5 13,694.0

d Cane trash 23 % (000) ton 3679.908 3921.661 3286.884 2771.431 2625.749 3173.448 3360.714 2152.202 2585.982 2881.716 4322.597 3059.989 3106.242 3166.272 44,094.795 3149.6

e Bagasse 30 % (000) ton 4799.88 5115.21 4287.24 3614.91 3424.89 4139.28 4383.54 2807.22 3373.02 3758.76 5638.17 3991.29 4051.62 4129.92 57,514.95 4108.2

Table 9 Production of agricultural crop cotton and biomass residue in province of Sindh Cotton

Cropping year 1997–1998 1998–1999 1999–2000 2000–2001 2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 2010–2011 Total Average of 14 years

Area ha. (000) 600.3 630.2 633.5 523.6 547.4 542.6 561.4 635.1 637.1 570.1 607.4 651.5 634.7 457 8231.9 587.99

Production (000) bales (375 lbs each) 2335.5 2134.1 2377.4 2141.1 2443.2 2411.8 2242.8 3016.7 2648 2398.2 2536.2 2978.3 4270.7 3536.8 37,470.8 2676.49

f Cotton stalk ratio 1:3 (000) ton 1192.2 1089.0 1212.5 1093.3 1246.4 1230.6 1145.3 1539.5 1351.3 1224.6 1293.8 1763.0 2180.2 1804.2 19,365.78 1383.3

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Sindh Province 1997-2011. 5000 4500

(000)

4000 3500 3000 2500

Wheat Area (000) ha.

2010-11

2009-10

2008-09

2007-08

2006-07

2005-06

2004-05

2003-04

2002-03

2001-02

2000-01

1999-00

1998-99

0

1997-98

2000 1500 1000 500

Wheat Production(000) Ton Wheat a wheat straw(000) Ton

Fig. 40 Wheat crop and biomass residue wheat straw

and average of 23 % to maximum 33 % in 2009–2010 in cotton (Pakistan Statistical Year Book of 2008 and 2011). Production of agricultural residue biomass in Pakistan is shown in Table 13 (by the production of major crops data of major crops Pakistan Statistical Year Book of 2008 and 2011) on same considerations as Table 11 for Sindh agricultural residue biomass. Total average amount of agricultural residue biomass of Pakistan = 60,088.05103 t/year as shown in Table 13. If the 35 % above total biomass agricultural residue produced in Pakistan could be utilized, about 39,026 MW can be produced, which is very near to about 200 % of total installed capacity of all power plants in Pakistan. This shows that there is huge potential of biomass that is available in Pakistan which could be utilized to meet the energy requirement and can overcome the energy crisis problem. At present, Alternative Energy Development Board Ministry of Water and Power Government of Pakistan has planned two power plants based on biomass waste in the province of Sindh; the details are given as under: 1. 12 MW biomass to energy power plant at Mirwah Gorchani Town, Mirpur Khas, Sindh 2. 9 MW biogas power plant at Pak Ethanol, Matli, Sindh. Letter of interest (LoI) issued to Pak Ethanol (Pvt) Ltd. for setting up power plant

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Sindh Province 1997-2011. 3000 2500

(000)

2000 1500 1000

2010-11

2009-10

2008-09

2007-08

2006-07

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2004-05

2003-04

2002-03

2001-02

2000-01

1999-00

1997-98

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1998-99

500

Rice(Paddy) Areaha. (000)

Rice(Paddy) Production (000) Ton

Rice(Paddy) b 20% Rice Husk (000) Ton

Rice(Paddy) c Rise straw ratio (000) Ton

Fig. 41 Paddy/rice crop and biomass residue rice husk and rice straw

Sindh Province 1997-2011. 20000

(000)

15000 10000 5000

2010-11

2009-10

2008-09

2007-08

2006-07

2005-06

2004-05

2003-04

2002-03

2001-02

2000-01

1999-00

1998-99

1997-98

0

Sugarcane Area ha. (000)

Sugercane Production (000) Ton

Sugercane d Cane Trash 23% (000) Ton

Sugarcane e Bagsse 30% (000) Ton

Fig. 42 Sugarcane crop and biomass cane trash and bagasse

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Sindh Province 1997-2011. 4500 4000 3500

(000)

3000 2500 2000 1500 1000

Cotton Area ha. (000)

2010-11

2009-10

2008-09

2007-08

2006-07

2005-06

2004-05

2003-04

2002-03

2001-02

2000-01

1999-00

1998-99

0

1997-98

500

Cotton Production (000)bales (375 Ibs each)

Cotton f cotton Stalk ratio 1:3(000)Ton

Fig. 43 Cotton crop and biomass residue cotton stalk

Table 10 Annual average productions based on 14 years of data of various agricultural major crops of Sindh

Crop Wheat Rice (paddy) Sugarcane Cotton

Production (000) ton 2896.81 1746.99 13,694 2676.5(000 bales, 375 lbs each)

Future Directions Pakistan is bestowed by God with such great potentials which need to be exploited properly; it has such a climate with temperatures ranging from 50  C to +50  C; though we are facing difficulties with modernizing our agricultural and irrigation systems with the world today, still it has sufficient food to feed its population and to export, but there remains huge gap to be filled. Our irrigation system is one of the largest systems of the world, and our agricultural command area is so expanded and large that it can also be compared to the huge agricultural commands of Egypt and Mexico. Our agricultural lands have capability to produce yields to the world’s best producing countries; to get such yields we have to improve all our systems related to agriculture as it is the backbone of our economy. To achieve such targets of which our agricultural lands and irrigation system are capable of, we must interlink various sectors which are helpful and beneficial to our agroeconomy. For this task, the irrigation department, agriculture department including farm water

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Table 11 Production of agricultural residue biomass for Sindh province Biomass total production in Category Agribiomass residue 14 years (million tons) a Wheat straw (000) ton 40.56 b Rice husk (000) ton 4.90 c Rice straw ratio (000) ton 24.50 d Cane trash (000) ton 44.10 e Bagasse (000) ton 57.52 f Cotton stalk ratio (000) ton 19.40 Total average amount of agricultural residue biomass of Sindh (000) ton/year

Table 12 Total power installed capacity of Pakistan by 2013

Oil and gas Coal Total fossil fuel installed Others Total installed as a whole

Average production per year (000) tons 2896.81 349.40 1746.99 3149.63 4108.21 1383.30 13,634.34

12,340 160 12,500 6860 19,360

MW MW MW MW MW

Table 13 Production of agricultural residue biomass for Pakistan Category Agribiomass residue a Wheat straw (000) ton b Rice husk (000) ton c Rice straw ratio (000) ton d Cane trash (000) ton e Bagasse (000) ton f Cotton stalk ratio (000) ton Total average amount of agricultural residue biomass of Pakistan (000) ton/year =

Average production per year (000) tons 20,947.40 1034.35 5171.77 11,780.10 15,365.33 5789.10 60,088.05

management, revenue department, and finance department should be interlinked starting from union council level by a focal person from each department to resolve related issues and to report to the higher authorities; every complaint must be recorded online and weekly progress-based reporting should be submitted, and to tackle the issue related to water with proper and well-connected agricultural drainage effluent networks, modernizing agriculture with farm water management and farm power machinery while at the same time educating the farmers, tenants, and land growers and revenue collection should be made accordingly while financing the growers and the tenants for better yields and proper pricing of the crops. As our economy is agrobased, hence research and development must be carried out in

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every sector to improve our irrigation system, effluent drainage system, and agriculture at large. Better yields will result in more production of agricultural residue biomass; consequently, we will have better economy at grassroots levels and poverty reduction, and we will be able to cater our energy requirements by utilizing some portion of agricultural residue biomass which is our own resource; this will further improve our economy.

References (1999) Solid waste management in Asia. Hoornweg, Daniel with Laura Thomas. Working paper series no. 1. Urban Development Sector Unit. East Asia and Pacific Region (2001) A comprehensive research on Pakistan’s issues concerning garbage disposal and Government & Social efforts to improve them (2002) The art and science of composting, A resource for farmers and compost producers. Center for Integrated Agricultural Systems (2006) Techno-economic disposal of hospital wastes in Pakistan. J Med Res 45(02) (2009) Environmental and Social Management Framework (ESMF), 4th edn (2010) The 3 Rs of reducing solid waste: reuse, reduce & recycle, Margaret Cunningham (2011) Characteristics of emissions from municipal waste landfills. Environ Prot Eng 37(4) (2013) Revisiting Solid Waste Management (SWM): a case study of Pakistan. Sustainable Development Policy Institute Islamabad Pakistan. Int J Sci Footprints Ahmed SI, Johari A, Hashim H, Mat R, Alkali H (2013) Landfill gas and its renewable energy potentials in Johor. Malaysia Int J Emerging Trends Eng Dev Issue 3:1 Asian Productivity Organization (2004–2005) Solid waste management: “issues and challenges in Asia”. Report of the APO, Survey on solid waste management, Asian Productivity Organization, Japan, 2004–2005 BC Ministry of Environment (2013) Landfill criteria for municipal solid waste, 2nd edn. BC Ministry of Environment, Victoria Cerqueira L, Edye LA, Wegener MK, Scarpare F, Renouf MA (2010) Report on “optimising sugarcane trash management for bio fuels production in Australia and Brazil” 2010 Director General Pak EPA, Environment, economic analysis of resources efficiency policies, Final report, August 2011 Draft guidelines for Solid Waste management, Pakistan Environmental Protection Agency, June 2005 http://en.wikipedia.org/wiki/Bagasse International Environmental Technology Centre (UNEP) (2005) Solid waste management. International Environmental Technology Centre (UNEP) Kiani A (2008) TFP and MIRR in the agricultural crop sub- sector of Sindh. Eur J Soc Sci 7 Landfill manuals, landfill design, U.S, Environmental Protection Agency, 26 July 1993 Lashari BK, Mahesar MA (2012) Potentials for improving water and agriculture productivity in Sindh, Pakistan. Sixteenth international water technology conference, IWTC 16 2012, Istanbul Rice Knowledge Bank http://www.knowledgebank.irri.org/ The Basel convention on the control of hazardous wastes and their disposal adopted on 22 March 1989 by the conference of Plenipotentiaries in Basel, Switzerland United Nations, Environment Program (2009) Developing Integrated Solid Waste Management plan, training manual van Steen Bergen F (2014) Water charging in Sindh, Pakistan – financing large canal systems. Meta Meta Research

The Advanced Recycling Technology for Realizing Urban Mines Contributing to Climate Change Mitigation Tatsuya Oki and Toshio Suzuki

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recent Development of Urban Mines in Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . From “Quantity Recycling” to “Quality Recycling” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technical Challenges of Quality Recycling: Liberation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technical Challenges of Quality Recycling: High-Quality Separation . . . . . . . . . . . . . . . . . . . . “Strategic Urban Mining” that Japan Aims for . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Missing Link of Resource Circulation and Resource Circulation Interface Function . . . . . Aiming for Establishing Strategic Urban Mines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Prospects of Strategic Urban Mines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Coping with sustainable civilization and utilization of renewable energy, climate change mitigation is one of the big challenges. Obtaining metal resources from urban mines (waste) for supporting the civilization of human races will contribute not only to support sustainable development of civilization society to the future but also to mitigate climate change. Urban mines are one of the promising resources especially for poor natural metal resource countries such as Japan. Fortunately, Japan is one of the major rare metal consumers and also is capable of smelting rare metal by its own. Japan’s urban mine will be more practical with top class recycling technology. In addition to these technological developments, it T. Oki (*) National Institute of Advanced Industrial Science and Technology (AIST), Onogawa Tsukuba, Ibaragi, Japan e-mail: [email protected] T. Suzuki National Institute of Advanced Industrial Science and Technology (AIST), Nagoya, Japan e-mail: [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_69

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is necessary to reform the society system in order to realize productive and economical urban mines which overcome the natural mine. Furthermore, in order to continue a steady development of rare metal recycling, it is necessary to conduct well-planned technology development based on the prediction of the future material usage. In this chapter, the authors show the technical subjects for realizing total circulating usage of metal resources including rare metals and an attempt currently tackled in Japan.

Introduction Huge metal resources are needed to support the civilization of mankind. Not only developed countries but also the remarkably developing Asian region has been consuming metal resources. In the past time, a lot of metal resources have been acquired from natural mines. Although a true depletion of the resources may be in the far future, the metal content of mines continues to decline gradually, and the amount of associated heavy metals and radioactive substances tend to increase. Total material requirement (TMR) which indicates the total amount of material usage to produce 1 ton of metal is usually utilized to compare the material consumption for production (Yamasue et al. 2009). For example, copper ore grade is decreasing year by year worldwide; as a result, the TMR of copper is increasing. This means that total energy consumption involved in the copper production increases. TMR increases especially for minor metals, which nowadays are very important for supporting advanced industries. Japan is being the world’s largest minor metal-consuming country (in Japan, 47 species of minor metal are called “rare metals,” as shown in Fig. 1), while the production of rare metals tends to be monopolized by certain countries and the world production volume of each rare metal is small. Therefore, the access to these metals can be easily restricted by the control of the production. In addition, environmental destruction is becoming a serious problem because the regulation for metal production is not well effective in such areas. Figure 2 shows the estimated UO (urban ore)-TMR compared with NO (natural ore)-TMR (Yamasue et al. 2009). As can be seen, utilization of urban ore is already effective for minimizing the total energy consumption for almost all metal elements, and thus utilization of UO is considered to lead to mitigate the climate change in the future. In addition, considerably not far in the future, some of the metals are suspected to fall into critical shortage, and as a result, it is considered that maintenance of the substance society could be threatened. Deterioration of metal production efficiency and increase in the metal consumption mean an increase of the energy consumption for maintaining civilized society, which easily impacts on the climate change, too. Thus, an improvement of rare metal production technology for suppressing energy consumption for the metal production will be a significant step for climate change mitigation as well.

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Fig. 1 Definition of the rare metals in Japan Fig. 2 The estimated UO-TMR compared with NO-TMR (From Ref. Yamasue et al. 2009)

On the other hand, almost all metal products remain somewhere on earth after usage. Some of them are soluble in water, but basically they exist in the solid state. According to the law of conservation of mass, most metals yielded ever from natural mines stay somewhere on earth, accumulated in the close area of human activities. Even in the narrow land of Japan, it is said that the total amount of accumulated metals in Japan is to surpass the annual metal consumption in the world. Thus, used products that are accumulated in the land have become to be called “urban mines.” Most urban mines include products that have been routinely used without harmful substances and radioactive materials and are considered to be relatively safe

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resources. Even when the metal quality of urban mines is not as high as that of natural mines where metals are concentrated by taking an enormous amount of time, for urban mine, it is possible to control of the distribution and concentration of the waste products for easy recycling. In the future, it will be possible to deliberately control the formation of urban mines, which will contribute not only to the sustainable development of civilized society but also to suppression of energy consumption due to the metal production, by extension, to contribute to climate change mitigation. Although Japan is poor in natural resources, Japan has been supplying high-tech products to the world by importing the raw materials produced from natural mines overseas over the years. In recent years, however, due to the rise of resource nationalism, one experienced the soaring of metal prices and the imminence of supply itself. From such a background, a strategy is considered to attempt to collect resources, including rare metals from urban mines, and the strategic program has been carried out in Japan for the first time in the world. The purpose is not for recycling to reduce diffusion and waste of harmful substances but for thorough resource recovery. In this chapter, the recent efforts of the strategy in Japan will be shown, and a discussion will be presented on the development of urban mines that realize sustainable civilized society and the relaxation of climate change.

Recent Development of Urban Mines in Japan From “Quantity Recycling” to “Quality Recycling” Japan relies for most of the natural metal resources to support its manufacturing industry on imports from abroad, and in recent years Japan encountered a situation that stable supply of the resources, especially “rare metals,” has been imminent by sudden export restriction and steep rises in metal prices. Since the characteristic of each rare metal is different from each element, it is difficult to be replaced by other rare metals, unlike energy resources. Therefore, even when weight-wise the usage of rare metals is negligible in the products, the shortage of the supply can lead to the stop of the production. True depletion of metal resources is considerably ahead of the future, and a sufficient amount of metals should exist somewhere on earth; however, the stable supply is not a guaranteed issue. In addition, production efficiency of natural metal resources in the process will gradually decrease toward the depletion, which will require enormous energy and cost. This will become a significant impact on the manufacturing industry and the world economy. By keeping the current situation that the society strongly depends on the natural metal resources, the world will become unsustainable at some point. On the other hand, the metals from natural mines, always, exist somewhere on earth. Most of them are already buried as wastes; some are used in the social infrastructure. Fossil energy disappears once it is used. Organic resources such as resin and paper are not possible to use repeatedly for a long period of time. However, the metals can be recycled forever in theory. Even if they lose product value, they still have industrial

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value in the element itself; the smelting process can completely restore the original raw material. The amount of recyclable metal is dependent on the degree of living standards and population in the area. Used products that are accumulated in a certain area (city) are called “urban mine” in contrast to the natural mine. In urban mine, resources can be acquired by recycling technology, not mine technology for a natural mine. Currently still good natural mines exist, and developing urban mines is not economical since recycling technologies are still costly. However, as the extraction of natural mines progresses to reduce the amount of reserves, the amount of reserves for urban mines increases. As described above, with the decline of natural mine production efficiency, one day, development of urban mines would become economically realistic. The pseudo-phenomenon actually occurred in Japan around 2010 due to import restrictions from soaring overseas metal prices. Therefore, in Japan, the development of urban mines was seriously considered in order to adapt the critical situation. In Japan, recycling has been actively carried out since 1990. At the time, recycling was encouraged due to the shortage of waste disposal sites, and the aim of recycling was to reduce the amount of wastes, the so-called quantitative recycling. Target materials are abundant materials such as resin, glass, iron, and aluminum, and a major goal is to reduce the volume of wastes, not to recover the resource to reuse. Until now, mass metals such as iron, aluminum, and precious metals have been recycled. But this time, “rare metal,” which is not used in large quantities and not expensive as precious metals, attracted attention the most. Rare metals are also called “vitamins for industry” in Japan. Even if the usage is negligible, without it, it is no longer possible to manufacture a number of high-tech products. That temporal collapse of the balance of supply and demand, the situation that would be anticipated in the future, would be completely upsetting Japan. Currently, there are few metal mines running in Japan, and raw materials have been imported and consumed by 120 million of Japanese people over the decades, and the several decades worth of materials has been accumulated in the national territory. This situation cannot still be called “urban mines” until the resource can be retrieved within a certain economy. While natural mines are formed by concentrating resources over incredibly long time, urban mines are not naturally formed by gathering a large amount of waste products. In other words, “urban mines” are not something to look for but to be intentionally developed (urban mining). Actually, urban mine development technology that realizes the reduction of the entropy of metal spread in land should be efficient and economical. When the urban mine development for retrieving rare metals was started in Japan, landfilled and reused wastes were not targeted, but the products owned by each person, “hoarding goods.” The hoarded goods refer to digital home appliances that were not in use, sleeping in a desk drawer without being discarded. In the early stage of urban mine development in Japan, where recycling infrastructure associated with the legislation in the 1990s is well established, it is thought that the rare metal can be easily recovered once the hoarded goods from the public are collected. The recovery operations of rare metal from small household appliances

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in the designated area were conducted in 2008 in Japan. However, in reality, it was not possible to recycle the rare metals utilizing the existing recycling facilities. Currently, among metals contained in the waste products, only noble metals (gold, silver, platinum, palladium, etc.) and some of base metals (iron, aluminum, copper, etc.) are recycled. Rare metals except for platinum and palladium are rarely recycled from waste products once available in the market. To realize the recovery system of rare metals from waste products, at least two problems must be overcome. First, used products widely spread to the consumers need to be stably collected when they are discarded. This requires the construction of rules and social systems. Second, it is required to develop the technology that realizes a high purity metal extraction from the collected waste products, high enough to be directly used by rare metal smelting and raw material manufacturers. Although rare metal density is relatively high in small appliance wastes, the system for collecting these product wastes systematically did not exist until recently. Therefore, Small Appliances Recycling Act was made effective in Japan in 2013. It made it possible to collect small appliance wastes widely across the administrative area, being one breakthrough for the first problem. No obligation, however, exists on rare metal recovery. Without overcoming the latter technical problem, recycling of metals will only stay on recovering precious metals and part of the base metal even for small appliances wastes. Unlike recycling a conventional iron and aluminum used in construction materials, various rare metal concentrations in the waste products are about several hundreds to several thousands ppm. Thus, there exists a technical leap for recycling rare metals from small appliances in traditional recycling infrastructure, like trying surgery by using the pickaxe. At this time, “quality recycling” was started to be more paid attention to than “quantitative recycling,” and the need for technological transformation had been widely recognized around 2010 in Japan.

Technical Challenges of Quality Recycling: Liberation Physical Sorting Techniques and the Composition of the Waste Products Because the amount of rare metals in waste products is very low compared to construction materials such as iron and aluminum, economical recovery of rare metals is difficult, and it is not practical to use hydrometallurgy processes with chemicals. Pyrometallurgy processes with high temperature, which are effective methods for recovering low concentrated copper and precious metals, are not ideal either because most of rare metals thermally dissolve and disperse into glass slug. Thus, it is of importance to separate copper and precious metals from the waste products by physical sorting before using smelting processes for rare metals. Development of low-cost physical sorting technology will be a key for realizing low concentrating rare metal recycling systems. Each waste product is thought to be consisting of various metal particles (hybrid particles) including rare metal particles. High purity sorting of rare metal strongly relies on the concentration and dispersion of the rare metal particles in the products.

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Fig. 3 Schematic image showing the existence status of rare metal in the waste product

Figure 3 is a schematic image showing the existence status of rare metal in the waste product. As can be seen, rare metal X is one of more than ten rare metals used in a smartphone and is considered to be concentrated by physical sorting. It is a great possibility of recovery if the concentration of the rare metal X is high enough after sorting. On the other hand, the possibility also depends on the dispersion of the rare metal X in the product. Here, dispersion is defined as the domain size (or distribution) of the rare metal X in the product. If the domain size is larger or the rare metal is concentrated in a particular area in the product, the dispersion of the rare metal X is considered to be small. Rectangular boxes in Fig. 3 show the rare metal X distribution in the product (smartphone). Status A is the best for physical sorting processes. The rare metal X exists in high concentration, locally in the product. In this case, it is quite easy to separate the particles with highly concentrated rare metal X from the rest of the particles by crushing or dismantling processes. This process is called “liberation – single separation.” This is a very important operation in the physical sorting process and will be discussed later. Status A is not only easy for single separation but also for the rest of sorting operation due to the fact that the concentration of the rare metal X is high in the particles. The next best status for physical sorting is Status B at the upper left in Fig. 3. Even though the total amount of the rare metal X is low, it is expected to have liberation as good as Status A. Only this will cause difficulty at the latter operations in the process due to small amount of the rare metal X. Status C, even though the concentration of the rare metal X is high, is more difficult for liberation due to high distribution of X in the product. Status D is the worst scenario for physical sorting processes. In the case of a smartphone, they

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typically have maldistribution of elements in the product, and thus it is not possible to apply physical sorting for single separation – liberation. Rather, they require very fine crushing processes to extract rare metal X. The required particle size after crushing totally depends upon the dispersion of the rare metal X. Usually, ordinal sorting processes can be applied down to several micron particle size, and it is not possible to apply physical sorting if liberation cannot be achieved still in this particle size. Even when the liberation is possible for such small particles, it requires large amounts of energy for fine grinding. In addition, as discussed later, it also requires separation processes in the wet condition for less than 0.5 mm particles, while the millimeter size of particles can be applied to dry separation processes. The wet processes require another electric power for pumping water, water treatment process unit, which adds more energy consumption and cost. The product obtained by wet process is still not as good as that by dry sorting processes in the millimeter range. There is a trade-off between the purity and the recovery of X. Thus, the combination of fine grinding and wet separation processes is the last to be chosen in the physical sorting processes. These processes, however, are cost-effective compared to chemical processes and are effective for collecting the rate metals that are considered to be difficult to recycle. In the case of electrode and fluorescent materials that are used as fine powder, no crushing process is required and can be applied to wet sorting process in the ordinary recycling facility.

Importance of Liberation Physical sorting processes for collecting metals from waste products require the decomposition of “hybrid” components into small “individual element” pieces in the first step. Large products may be decomposed by hand, and then, the pieces are sent to crushing processes. In many industrial processes, the purpose of crushing is to obtain uniform fine particles from complex particles, and the processes improve physical properties of the particles such as mobility, processability, and reactivity (Owada 2007). On the other hand, the goal of the physical sorting is to complete liberation. The ideal status is that a single element is a single particle. The element that is the target material for recycling can be atom, alloy metal or parts, etc. In the case of the particle including more than two elements, it is called “locked.” It would be no problem when the locked material includes highly concentrated target material, and crushing processes would be effective for such locked constituents. If it is not the case, it is not possible to improve the purity of the element by utilizing physical sorting only. In the course of physical sorting processes, the crushing process is considered to be a pre-sorting process to realize the status of liberation, single separation of the target particle. Figure 4 shows the 2D matrix model of liberation process, proposed by A. M. Gaudin (1939). It is a classical model, yet not enough for modeling the actual situation, but it helps in the understanding of the concept of liberation. Figure 4a shows the status before the crushing process, including target material in the matrix. “a” in the matrix is the size of locked and assuming that the product is cut into pieces

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Fig. 4 2D matrix model of liberation process

uniformly with the size of “a,” as shown in Fig. 4b, not influenced by the interface between the target material and the matrix. Some particles can be the status of liberation, but in the most of the pieces, the target material is still the status of locked. Another crushing process, the size less than “a,” may lead to liberation status for the target material. In the actual process, such random crushing does not likely occur and the status of liberation can be easily obtained. Only the efficiency can be different for each crushing method; thus, wise choice of the method is crucial in order to realize high-quality liberation. Figure 5 shows the schematic image of the relationship between progress of liberation and nonuniformity of crushed pieces. Let us consider Status A in Fig. 3 and apply crushing process in order to liberate the rare metal X. As crushing time proceeds, the particles become finer, and, eventually, particles of rare metal X are liberated. If this happens in a short period of time, which is the best scenario, the Status A shown in Fig. 5 will be realized, which leads to best pretreatment for physical sorting process. On the other hand, if longer crushing process time is required for liberating rare metal X, finer particles tend to be obtained, being Status B in Fig. 5 that is relatively difficult for physical sorting. Typically, Status B includes a wide range of particle size distribution, and it is almost impossible to collect particles under several microns by physical sorting process. Therefore, even when the nonuniformity of individual particle is realized, toward the right-hand side in Fig. 5, the status of well-mixed finer particles makes it difficult to separate the target particle. Also the same is applied to Status C in Fig. 5 where all particles are locked. To summarize, the process of liberation refers to achieving nonuniformity of individual particle by sacrificing the nonuniformity of the target material in the crushed product. The ideal situation is that nonuniformity of both individual particles and the target material is realized. Solid and broken lines in Fig. 5 show when the selective, optimized crushing process and random crushing process, respectively, are selected, which shows the idea of actual crushing process. Selective crushing is a very important technique to realize liberation at the coarse particle size. In order to realize efficient selective crushing, it is important to proceed to the breakup at the boundary

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Fig. 5 Schematic image of the relationship between progress of liberation and nonuniformity of crushed pieces

of the rare metal X domain and the matrix. Since the waste products’ properties are different, even considering culler phone, each one has different structure; strength, depending upon the model; manufacturer; and year of product; there is no all-fitted selective crushing machine. Currently, the selective crushing property of particular products is investigated by utilizing an existing crushing machine. Study of theoretical and systematic approaches is expected for realizing ideal selective crushing process.

Grinding Method Aiming at the Promotion of Liberation Mechanical Crushing Mechanical crushing is one of most realistic choices for early introduction to actual recycling plants. Mechanical crushing tends to break up products uniformly, and it is not easy to break up only at the interface as shown in Fig. 6a. It may, on the other hand, be possible to expect selective crushing as shown in Fig. 6b or c if there is a difference between mechanical properties of the target and the matrix. Figure 6b can be realized by combining thermal treatment and crushing processes, which were actually applied to the process for separating bone steel from concrete wastes (Mitsubishi Material Co, Ltd 2003; Matsumura 2003). Figure 6c can be realized by combining stirring and surface friction destruction processes.

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Fig. 6 Example of selective crushing for liberation

Not many cases are reported for metal recycling using mechanical crushing, but there is one good example, printed circuit boards. Actually, for the recycling of metal (copper) from the printed circuit board, the swing-type hammer mill process machine was applied, and it was found that the copper could be recycled as sphere particles due to its ductility (Furuyanaka et al. 1999). The selectivity of metal and nonmetal parts in the printed circuit board can be further improved by controlling the operating condition of the machine. Other new processes are also under development (Koyanaka et al. 2006; Furuyanaka 2006; Koyanaka et al. 2006). One of them is the so-called active crushing method. It controls multiple operating conditions of impact crushing simultaneously during the actual operation. Single separation of target material, control of particle size, and ejection of crushed materials are optimized timely and continuously during the crushing operation. Figure 7a shows the schematic image of the active crushing system (Furuyanaka 2006; Koyanaka et al. 2006). The system is based on the fast swing-type hammer mill, and the inverters for controlling hammer and feeder, electric air valve, and servo amplifier are connected to a PC, in which a certain operating pattern is programmed for automatic operation. In addition, the shape of the lining plate is also specially designed. Figure 7b and c shows examples of operating patterns for the speed of impact and the period of opening screen, which were actually applied to crushing circuit boards for TV after crushed using the cutter mill. As can be seen, the speed of hammer increases to 60 m/s with (b) 14 s and (c) 3 s, respectively. Using the pattern (c), one could obtain the metal separation efficiency of 50.6 %, with average particle sizes of 421 μm and 188 μm for metal and nonmetal, respectively. It was also shown that 59.6 % efficiency could be obtained by further optimization (Fig. 7b). As can be seen, accurate operation of mechanical crushing can provide realistic liberation of target materials. Not much verification so far is reported, but it is expected to increase the application of such techniques on recycling for portable devices. Electrical Crushing In order to realize the liberation of target materials without excess crushing, it is ideal to have selective separation at the boundary of target materials and the matrix.

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Fig. 7 (a) Schematic image of the active crushing system and (b, c) examples of operating patterns

Ordinal mechanical crushing methods, on the other hand, tend to result in uniform breakage, and it is difficult to crush designated area only. Therefore, electrical crushing is being considered, which allows crushing selectively along the boundary of the target material and the matrix. Mainly, there are two electrical crushing methods: electrical disintegration (ED) and electrohydraulic disintegration (EHD) methods. The ED method utilizes high voltage and large current in the liquid where particles (consisting of target material and matrix) are dispersed in a container. The particles are close to or touched at one electrode, and the other electrode is placed at

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the other side of the container. The high-voltage pulse of several 10 kV in several 10 μs is applied, so the large current only flows at the boundary in the particles that leads to crush the particles along the boundary (Fujita et al. 2002). Many studies are reported especially for rocks, coals, and concretes and, in recent years, for liberation from used products on recycling purposes. For example, the method was applied to liquid crystal panels used for cellular phones and laptop computers, and it was confirmed that the panel was separated into two glass substrates, and indium (ITO) could be collected after different treatments (Shibayama et al. 2002). The EHD method, on the other hand, utilized a shock wave generated by the large current flow in the liquid (Fujita et al. 2002). Explosives can be utilized instead of using the current flow. In either case, the shock wave generates tensile stress at the boundary and promotes selective crushes along the boundary. In the case of the cellular phone, it was reported that the shock wave propagated along the boundary between metal and resin and confirmed that metal parts were removed from the matrix (Kejun et al. 2001). As explained above, the electric crushing for liberation of used products is under development, and in the near future, it has a potential to be an innovative recycling method; however, it may also be difficult to introduce in the existing facilities due to the use of large current or explosives. Alternative Technologies for Hand Dismantling and Picking: Easy Sensing The most certain way to break up a complex product into pieces is dismantling. Dismantling is usually done by hand, while crushing is done by machines, and industrial robots may take human’s place for dismantling processes in near future. From the technical point of view, dismantling is defined as liberating operation for individual product, while crushing as liberating operation for massed products. Thus, crushing is much more efficient and also cost-effective compared to dismantling. Nevertheless, hand dismantling is still the major method for recycling because it is an easier process for liberation. Actually, even in Japan (where the labor cost is considered to be higher), hand dismantling is applied for recycling motors, which are relatively large pieces, from used appliances. Since there is no almighty method for liberation, hand dismantling is still applied in many cases, even in some that are not cost-effective. Another advantage of hand dismantling is that breaking up and separation of pieces proceed in the same time. Although this method is very useful for applying for the variety of products, there is a limitation of this method from economical point of view, especially in Japan. Thus, the development of automated dismantling machines has been carried out, and some processes are successfully automated, for example, sorting process utilizing advanced sensing technology. This technology is very useful for getting rid of impurities from the uniform particle, but it cannot be applied to widely scattered pieces from dismantled products. Under circumstances, the authors have been developing a cost-effective sorting machine utilizing “easy sensing” technology, alternative technologies for hand dismantling and picking. Instead of using expensive, high-performance sensors, this machine utilized a combination of cost-effective sensors, which were close to human sensibility, and highly controlled operation procedure based on the nature of target products.

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Fig. 8 Automatic collection of Nd magnet from HDD by HDD cutting separator (HDD hard disk drive)

For example, the authors have proposed a two-step crushing separation method for collecting neodymium magnets including rare-earth metal from hard disk drives (HDDs), “HDD cutting separator (HDD-CS)” as shown in Fig. 8. When HDD is normally crushed, the very strong neodymium magnets can be attached inside the crushing machine and cause many problems such as blockade at the screen. Even though they are luckily extracted out of the machine, they are agglomerated with metal pieces and not possible to be liberated. Thus, demagnetization process is typically applied in such case. Neodymium magnets have relatively low Curie temperature and can be demagnetized at around 350  C. However, it is not costeffective to use thermal energy only for extracting the magnet, 2 wt% of HDD, meaning that it requires 50 times more thermal energy for demagnetization. The HDD-CS solves such a problem by utilizing four magnetic sensors and location sensors that identify the leakage magnetic flux density and the position of magnets in the HDD without destruction. Then, the magnet is punched out with a nonmagnetic cutter. The accuracy of sensing is kept improving by optimizing the machine using the database of the leakage magnetic flux density for each HDD. This, small and cost-effective, machine realizes an automatic separation process of 400,000–1,000,000 HDD per year and can concentrate the magnet component ten times. After demagnetization, impact crushing, and screening processes, 94–97 % of magnetic alloy particles are successfully collected (Oki et al. 2011). Another “easy sensing” technology, called “Arena Sorter,” a sensor-based sorting technology, is also developed as an alternative hand selecting sensing technology. It

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utilizes laser 3D measurement unit and weight detector that obtain the parameters (size, weight, and so on) of waste products, and they are recorded on the database. The system is operated using discrimination algorithm that utilizes neural network and the database (Koyanaka and Kobayashi 2011). For example of recycling cellular phones, the system successfully realized 90 % accuracy of automatic separation for tantalum capacitors from the cellular phones (Koyanaka et al. 2006).

Technical Challenges of Quality Recycling: High-Quality Separation Challenges for the Optimization of Physical Sorting Processes Even when ideal liberation is realized, particles still remain in a mixed status, and thus separation is required. For example, let us consider liberated metal particles including 100 ppm of target metal. This separation means that we need to pick up a target particle from a bucket filled with 10,000 particles. In the case of particles with centimeter order, separation by hand can be applied with high accuracy; however, it is not economical at the end. As described above, one of the practical systems is sensor-based sorting system that utilizes materials’ information obtained from the sensor. This can be costeffective for such separation and is also called individual separation. Pressured air can be applied to the particle separation in the range of several mm–300 mm (Furuyanaka 2010). In addition, a variety of sensing technologies, such as color, images, transmission X-ray, and fluorescence X-ray, can be applied, and they are effective for the separation of specific particles (Owada et al. 2010). In the case of mixed particles consisting of many kinds of materials, on the other hand, accurate sensing cannot be expected. As the particle size decreases, it becomes more difficult to separate the particles. In this case, it may be more efficient to handle the particles as an aggregate. This is called “bulk separation” or “mass separation,” which usually utilizes the difference in the properties of particles, such as density, magnetism, and wettability. In addition, the process can be categorized as dry separation and wet separation (usually in water). A dry separation process allows for high throughput and easy collection after separation, and a dry process unit is easy to install and costeffective. On the other hand, a wet separation process utilizing bulk properties is expected to improve the separation efficiency compared to a dry separation process; however, it also has disadvantages that it consumes more energy for water circulation, dehydration, and drying processes. In addition, in many cases a surfactant is utilized in a wet separation process, which enlarges load for effluent treatment. After all, these two process types have an optimum range of particle sizes for separation as shown in Fig. 9. We conveniently define low-limit particle sizes shown in Fig. 9, and under certain reliability, low-limit particle sizes for dry and wet separation using bulk properties are 1 mm and 50 μm, respectively. As described above, if the liberation is achieved at the stage of coarse particles, dry separation can be applicable, and it realizes economical and highly efficient separation. Once the crushing process is applied, it always generates fine particles less than 1 mm. Some rare metals in certain products have a tendency to be concentrated in the

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Fig. 9 Category of separation technology and concept of low limit of applicable particle size

particles, and thus, such fine particles will also need to be separated and collected (Oki 2008a; Oki et al. 2008). So far, the particles below 50 μm required wet separation processes utilizing surface property, such as flotation process. A recent study, on the contrary, showed the possibility of a dry process that realizes gravity concentration up to 10 μm particles using strong centrifugal force (Oki 2009). On the contrary, one cannot achieve complete metal recycling no matter how individual elemental technology for recycling was developed. A great variety of products are manufactured and abolished in every year. Thus, construction of a flexible sorting process is necessary to cope with a change of such variations or the chronological changes. However, at the moment, the techniques to build these processes are not yet established, and one has to keep working on the development of these techniques to realize the most suitable sorting process and to derive the most suitable sorting condition. Figure 10 shows the model of the simplest separation process, combining a grinder and a sorter. The targets fed to the sorting process have a variety of constituents such as various kinds of printed circuit boards and electric parts. The grinder itself also has a variety of models operable in the different treatment conditions. In addition, there are so many options for separation of crushed particles. Thus, although the model shown in Fig. 10 has only seven items, it gives ten million ways of separation processes, supposing that each item provides ten conditions. If 20 conditions were given for each item, 1.28 billion ways will exist. Since the efficiency of the liberation is determined as multiplication of grinding efficiency and separation efficiency, both processes need to be well optimized. Even if the simple model shown in Fig. 10 has one grinding and one separation process, the combination of possible patterns will be more than 100 million. In the actual case, a couple of grinding processes and three to ten of separation processes will be applied, which is an astronomical figure. Only a small portion of

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Fig. 10 The model of the simplest separation process

these patterns can actually be ideal physical separations for urban mine resources, and what makes it more difficult is that the cycle of product release is short and the amount and kind of rare metal are different from each product. Thus, one optimum separation pattern will no longer be effective in a short period. Because of this, it is very difficult to optimize the separation pattern, and in reality, they are operated under inefficient conditions. Although the improvement of liberation processes by selecting optimum grinding and separation processes is important, it is very difficult to recognize the importance of the processes. Figure 11 shows the schematic image of a physical separation process that extracts and purifies rare metal X from waste product. For example, let us think about cellular phones as a feed. As described in Fig. 3, rare metal X can be located anywhere at the variety of status points in the cellular phone, and this affects the difficulty of liberation using a crushing process. In the crushing process, as shown in Fig. 5, the type of grinding machine and its operating conditions determine the size of particles and the degree of liberation. Even if the ideal liberation is realized such as Status A (or Status B) in Fig. 11, the results can be different depending upon the efficiency of the latter separation process. However, efficient separation after Status D does not often achieve higher purity of rare metal X than that of Status B with inefficient separation. Here, the information that can be obtained from typical recycle plants through the series of processes is threefold: (1) purity of rare metal X in the feed, (2) particle size after grinding, and (3) purity and yield of rare metal X after separation. Actually, important parameters that determine the quality of the entire process, such as the dispersion of rare metal X

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Fig. 11 The relationship between the selection of separation method and the degree of liberation

in the waste product and handy analysis method for the degree of liberation, do not exist. Important parameters in the processes from product feed to collection of separated particle are in the black box, and it is not possible to find out the source of problems such as poor purity of rare metal X after recycling. It could be due to the quality of liberation or separation. In this current situation, the concept of liberation and importance of selective grinding are not well recognized, and in not a few cases, only latter separation process is discussed without considering the degree of liberation. In addition, component analysis of separated products does not provide sufficient feedback to the process for improvement, and this makes it difficult to optimize the process of physical separation. Development of simple and easy liberation measuring equipment, and the optimization of the selective grinding and sorting systems which depends on the liberation data, are indispensable issues for success of the rare metal recycling.

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Selection of Sorting Technologies and Challenges During the crushing process, particles with various sizes are generated. The ideal particle size range for better sorting results differs depending upon each sorting technology. Typically, the particles are separated into two or three particle size groups by a screening process, and an optimum sorting process is applied. The particles in the range below several hundred microns are usually not collected; however, collection of such particle range is becoming important especially for recycling precious metals and rare metals, as well as the sorting technology for such particle range. In order to realize highly efficient, low-cost, and low-environmental impact sorting technologies, it is necessary to broaden the applicable particle range of each sorting technology, possibly of the technology in the right-hand side of the image shown in Fig. 8. For the example of improvement for the columnar pneumatic sorter, one of dry sorters, separation of 0.1 mm copper and aluminum particles is realized using model particles (Oki et al. 2007). A wet process can be utilized for finer particle separation where a dry process is no longer efficient. On the other hand, wet gravity concentration, based on particle bulk properties, still has the problem that the separation accuracy decreases as the particle size decreases due to low inertia. Typical particle size range for accurate separation is about 50 μm for conventional wet gravity concentrators such as shaking tables and spiral gravity concentrators. For the particle size below that, wet separation processes such as flotation using the surface properties of the particles can be effective. For the example of removal of ink from waste paper, flotation works very effectively. On the contrary, this process is not ideal for waste products with surface contamination that lower the separation efficiency significantly. Currently, application of wet separation techniques utilizing bulk property of particles is expected even for the particles below 50 μm. Another gravity concentration technology utilizing strong centrifugal fields has been developed since the 1980s and has shown the possibility of gravity concentration up to 10 μ particles. Figure 12 shows a modified image of applicable particle sizes for each separation technology based on the literature by F. F. Aplan (2003). Among them, detailed information for conventional sorting technologies that can be used for mineral processing and metal recycling is available in the literature (Wills 2006). In this chapter, the authors focus on the gravity concentration technology that realizes wet separation of fine particles utilizing bulk properties. As the particle size decreases to below 50 μm, it becomes difficult to separate particles using wet gravity concentration. One of the reasons is that the mobility of particles in the water decreases and separation takes a lot of time. Another reason can be that it is difficult to separate the particles using the specific gravity difference as the inertia of the particles decreases. Gravity concentration using strong centrifugal fields, on the other hand, improves not only the mobility of particles but also the efficiency of separation. Figure 13 shows the category of wet gravity concentration devices and the acceleration of gravity or centrifugal field affecting the separation (OKi 2008b).

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6mm

Sorting

Air Table

75µm 75µm

50mm 25mm

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6mm Heavy Media Separation 13mm HMS Cyclone

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Without Cyclone

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Original figure:F.F.Aplan principles of mineral processing (2003)

Fig. 12 Applicable particle sizes for each separation technology

Here, it shows the acceleration of rather small, lab-scale devices. Wet gravity concentration devices can be categorized into three types: 1. Water flow separation, method to separate utilizing particle sedimentation rate and velocity of the water stream 2. Film flow separation, method to separate utilizing the resistance between particles and water film on the slope and the friction between particles and the slope 3. Pulsatile flow separation, method to separate utilizing the upward and downward motion of water to differentiate the time reaching the bottom Among these methods, separation by gravity settling, especially shaking table and jig, has been widely used for a long time as typical wet separation method. Hydro-cyclone using rotational flow and spiral separation devices are conventional installations for fine particles utilizing wet gravity concentration. In addition, the compulsive rotational wet gravity concentration method realized 10 μ particle separation utilizing strong centrifugal forces given by a mechanical rotating force. For the device shown in Fig. 13, maximum acceleration is in the range of 30–300 G (1 G = 9.80665 m s 2). Although it is not possible to define the low-limit particle

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Fig. 13 Category of wet gravity concentration devices and settling acceleration

size or separation accuracy by the acceleration due to the difference in the particle motion or the method of particle collection, it is clear that the velocity and inertia of particles are increased by the acceleration. So far, compulsive rotational devices were utilized mainly overseas; however, the mechanism of particle separation and operability is still not clarified, and only few cases were applied to rare metal recycling. Since the wet process is promising, more application is expected in the future.

New Sorting Technology for Urban Mine Development: Smart Operation A physical sorting process is usually combined from three to ten separation stages, and the combination of separation stages yields astronomical figures. Thus, most cases are abandoned before finding out the true performance of each device. To solve this situation, the authors have examined a system that promptly derives the optimum condition using a database and computer simulation. Without relying on experienced workers, the “smart operation” system realized automated operation at optimum condition. The system has been applied to recycling of printed circuit boards, and the author succeeded in the development of a sorting process that could collect tantalum capacitors at high purity for the first time in the world and achieved practical use upon introduction to a Japanese recycling plant in 2012. Since tantalum is one of the most expensive rare metals and most of it is not recycled, the Japanese Government chose tantalum as one of five important metals (tungsten, tantalum, cobalt, neodymium, and dysprosium) that are preferentially recycled in 2012. Tantalum is mostly used in a tantalum capacitor on printed circuit

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boards. At first, the recycling process of the printed circuit board for tantalum is developed based on the conventional liberation method (see Fig. 5). Considering the tantalum atom as the species of liberation, the authors aimed at the improvement of the liberation by fine grinding of the printed circuit board. After fine grinding, the authors conducted separation process based on the physical property of tantalum oxide, resulting in the concentration of tantalum to several times. Tantalum was, however, collected with a precious metal and other heavy metals due to low weight ratio of tantalum in printed circuit boards, around 1,000 ppm. As described before, rare metals such as tantalum need to be separated from copper or precious metals before pyrometallurgical treatment, and the recycling of tantalum could not be accomplished by the method mentioned above. At first, it was considered that it was almost impossible to collect a certain electric element from a printed circuit board where various electronic elements were mixed. A phenomenon, however, was found that an electronic element was exfoliated from a printed circuit board as the original form by using a certain crushing device. Thus, we made an attempt to find out the most optimum separation pattern, considering the tantalum capacitor as the species of liberation and each electronic element has peculiar sorting properties. The authors classified over 400,000 electric elements in 320 categories according to the size and the function and built the database for their physical and sorting properties. Then, three kinds of separation methods, viz., size, specific gravity, and magnetic properties, are considered, and by numerical computation, the authors predicted the sorting result of approximately 2,055 trillion ways of patterns that include repetition use and performed narrowing of the optimum that a tantalum capacitor could concentrate afterward. As a result, the authors found the sorting process that realizes over 80 % of recovery of tantalum capacitors and purity of tantalum capacitors, from the mixture of exfoliated electric elements (Fig. 14, Oki et al. 2010). Although the optimum sorting process pattern was clarified, there was no device that could realize the process pattern. Thus, the development of such device “inclined and low-intensity magnetic-shape separator” was conducted as the next step. This small device, rather used as an auxiliary unit, was a hybrid device that collected aluminum electrolytic capacitors at the inclined conveyor and collects quartz resonators at the low-magnetic field sorter. The device could collect iron and aluminum separately, and the rest that includes tantalum capacitor is sent to a special pneumatic sorter. This “double-tube pneumatic separator” is the main device of the sorting process and can control the airflow rates in the columns precisely using single blower. In the first column, elements heavier than the tantalum capacitor are collected by gravity, and in the second column, only tantalum capacitor is collected by gravity. The flow rate of the first column is slightly faster than that of the second one based on the numeric calculation. In order to realize highly accurate gravity concentration, this device introduces new operating parameters for both software and hardware. Especially, it is possible to operate automatically from the calibration of the device to the collection of elements by selecting the target elements (not only tantalum capacitor but also other elements) on the display, by operation control using electric element database (Oki et al. 2010).

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Fig. 14 Tantalum capacitor collecting process optimized by the simulation based on the database

It used to be assumed that the maximum separation efficiency of the tantalum capacitor was around 10–30 %; however, after such device development described above, the separation efficiency of 97 % was achieved by the trial run with a recycling plant where the device was installed (Oki et al. 2010, 2011). In this way, by using product information appropriately, it is possible to derive the most suitable sorting condition quickly and to recalculate the most suitable sorting condition by substitution of the information in the case of altering product specification, without going through again from the beginning. Use of the easy sensing technology and the smart operation technology just began, and it is expected that the development of recycling technology for other resources will progress further by the innovation of such physical sorting technology.

“Strategic Urban Mining” that Japan Aims for Missing Link of Resource Circulation and Resource Circulation Interface Function In recent years, the development of sensor-based sorting technology for recycling has been active around Europe, and in most of the cases, the mineral processing technology that was applied to the natural mine is converted into the physical

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separation in the recycling. The mineral processing technology at the natural mine does not only utilize magnets for iron and gravity concentration for heavy metal but also make the most of the property of “minerals” based on the knowledge of geology and mineralogy, and minute sorting was conducted. In addition, natural mines are usually developed for several decades, and there is enough time to optimize the mineral processing technology for a specific mineral. As a result, it is possible to obtain a variety of metals including rare metals economically. On the other hand, when the technology is applied to urban mines, only the separating technology based on the element characteristic can be utilized due to less information of the waste products. As a result, except for precious metals, metals used for structural materials such as iron, aluminum, and copper became targets of the recycling. In recent years in Japan, urban mines are expected to develop for supplying rare metals enough to manufacturing products; however, the technology still requires improvement from the conventional “quantity recycling” technology. The collection of rare metals was difficult in the old urban mine concept, but it can be said that a technical breakthrough will be achieved by compiling the characteristics of the waste products into a database and utilizing it for the separation process, as shown in the example of the tantalum capacitor. On the other hand, however, the urban mining without considering infrastructure and system surrounding it does not realize efficient resource recovery as much as a natural mine can supply, even when new technology is introduced into a part of the resource circulation loop. Even if the recycling is promoted by both production and consumption sides, the resources do not circulate without considering an interface between both sides. In order to realize sustained circulation use of the strategic metals, it is important to construct a series of systems from the supply of reproduced raw materials to a product design, not only to develop resource recycling technology such as physical sorting. The authors thought that the introduction of innovative sorting technology and the eco-design functioned as a mediation technology, and the technology was named “resource circulation interface” (Fig. 15). As discussed above, even if advancement of the physical sorting technology is accomplished, it will be difficult to apply it to all product forms with that alone due to the variety of the product designs released up to now. Thus, an easy recycling design (eco-design) can be considered to compensate for the technology gap. The effective and minimum easy recycling design can be achieved by the suggestion to the products as well as the design guidance of the parts and products that realized easy to sort without spoiling products’ original function and charm. In this way, the improvement of the physical sorting technology is a key for the development of the urban mine including the interfacial function of global resource circulation of materials.

Aiming for Establishing Strategic Urban Mines In order to succeed with efficient and deliberate urban mining, it is of importance to build a society system that introduces product eco-design and physical sorting

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Fig. 15 Missing link and interfacial function of resource circulation

technology utilizing artifact databases. For this purpose, the authors have conducted a project called “Strategic Metal Resource Circulation Technology (Urban Mining)” between 2012 and 2014, aiming for the total development of urban mining in Japan. In this project, the authors specified important metals as “strategic metals” that are necessary to continue industrial activity and potentially have supply risk and evaluated the potential of urban mining and efficient collecting technologies. Venous industry (recycling industry), as shown in the upper part of Fig. 15, mainly focuses on the short-midterm technological subjects aiming for the development of urban mines that are scattered and disorderedly accumulated. Arterial industry (manufacturing industry) of the lower part of Fig. 16, on the other hand, focuses on the mid-long-term technological subjects for realizing a practical urban mining plan utilizing eco-design from manufacturer aspects. As described above, by considering the demand and supply risk of metal resources, as well as the system that realizes deliberately and efficiently collecting strategic metals, the authors named their initiative “Strategic Urban Mine” in contrast to conventional disordered urban mines. In addition, in November 2013, a new research base “Strategic Urban Mining Research Base (SURE)” was established in the National Institute of Advanced Industrial Science and Technology (AIST) for continuous research activity based

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Fig. 16 Summary of Strategic Metal Resource Circulation Technology (Urban Mining) Project

on the project’s concept. SURE holds 37 researchers from AIST (Fig. 17). And SURE maintains a laboratory for evaluation of sorting technology (SURE LATEST) at the AIST Tsukuba West site, aiming at the improvement of physical sorting technology. The laboratory has large space room and four separate rooms that hold about 60 physical sorting devices for grinding, crushing, and separation processes. Twenty of them were originally developed at the AIST (Oki 2012, 2013a, b, c, 2014a, b, c). Such open laboratory, the core of physical sorting technology, is the first attempt in Japan and expected to contribute accelerating development of urban mining development. In order to introduce the strategic urban mine into the society, the support from industry side is necessary. For this purpose, the SURE consortium was organized in October 2014, together with companies related to metal resource circulation, aiming at an early realization of strategic urban mines by extracting needs from industry and society. Currently, the members of the consortium are 45 companies and 20 industry groups and public organizations and institutes. Members of the SURE consortium discuss common subjects in the industry group or individual company’s subjects such as eco-design and utilization technologies of recycled materials, in order to promote the strategic urban mining concept from the manufacturers’ point of view. In addition, they can utilize the facility in SURE LATEST aiming at the extraction of potential problems at recycling plants for

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Fig. 17 Structure of Strategic Urban Mining Research Base

better improvement of the technology. The SURE consortium is expected to propose a variety of new ideas related to urban mine development.

Future Prospects of Strategic Urban Mines Obtaining enormous amount of metal resources from urban mines for supporting the civilization of human races will contribute not only to support sustainable development of civilization society to the future but also to mitigate climate change. While several incidents had happened in Japan, which accelerated the development of urban mining, up to now, there are still many issues that need to be solved from both technical and society system points of view. In this chapter, the authors showed the technical subjects for realizing total circulating usage of metal resources including rare metals and an attempt currently tackled in Japan. Japan has already selected five important metals that need to be recycled and conducted related research on the nation level. It is, however, not possible to realize practical resource circulation with high international competitiveness if the technologies are developed individually.

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Furthermore, even when one recycling technology has been established, the period of validity is not very long due to fast product cycle. The concentration of rare metal in the product also changes year by year, as well as characteristics of separation and crushing. In addition, the more a particular rare metal becomes important, the less material will be used in the product by promoting the use of alternative materials. It takes a lot of time to determine an ideal sorting pattern for a product from the enormous combination of crushing-sorting technologies, and when the process is ready, it is not a few cases that the target rare metal is no longer used anymore. Because of this, in the recycling process in many cases, a hand dismantling and picking process is used under inefficient conditions, and thus, the technology development does not always catch up with it. In order to continue a steady development of rare metal recycling, it is necessary to conduct well-planned technology development based on the prediction of the future. From this point of view, there are two important forecasts: One is that which rare metals will be more important in the next 5 years and 10 years, in other words, which rare metals will be necessary to be recycled from urban mines. The other one is that we have to choose products from which rare metals are recycled. Currently in Japan, for the first one, five kinds of rare metals are selected as strategic rare metals; however, it is difficult to predict their true demand and price trend. At least, for the second one, those rare metals are already used in the products, and one can easily select appropriate products. In order to proceed strategic urban mine development, the authors organized the Strategic Urban Mining Research Base (SURE) in the National Institute of Advanced Industrial Science and Technology (AIST). In this research base, the metals considered to be important in the next generation, not only rare metals, are designated to be “strategic metals,” and they are evaluated for their recycling potential. In addition, the database of physical properties for waste products is being constructed, and based on the database, automatic sorting technology for products including “strategic metal” and pre-smelting treatment technology are under development for preparing raw materials by recycling. These efforts will contribute to economical collection of strategic metal from the current “disordered urban mine” accumulated in the land. Urban mines are one of the promising resources for Japan as a poor natural metal resource country. Fortunately, Japan is one of the major rare metal consumers and also is capable of smelting rare metal by its own. Japan’s urban mine will be more practical with the world top class recycling technology. In addition to these technological developments, it is necessary to reform the society system in order to realize productive and economical urban mine which overcomes the natural mines. The number of researchers and scientists will also be expected to increase for speeding up the development of technological aspect. Coping with sustainable civilization, utilization of renewable energy, and climate change mitigation is one of big challenges. All of them are interconnected, and from developing urban mine point of view, the activity of SURE including collaboration with private companies, and cultivation of human resources, is expected to contribute the climate change mitigation in the future.

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References Aplan FF (2003) Gravity concentration principles of mineral processing. SME, Colorado, pp 185–219 Fujita T et al (2002) Liberation as pre-treatment of recycling process by electrical crushing and water explosion. Resour Treat Technol 49:187–196 Furuyanaka S (2006) Active crushing of waste products – selective crushing technology for composite waste materials. Funtai Kogyou 38:57–64 Furuyanaka S (2010) Crushing technology and eco-recycle. NGT, Tokyo, pp 110–115 Furuyanaka S et al (1999) Evaluation of liberation property for impact crushing and gravity concentration of waste printed electric circuit board. Funtai Kogyou 36:479–483 Gaudin AM (1939) Principles of mineral dressing. McGraw-Hill, New York, pp 70–91 Kejun L et al (2001) Extraction of metals from disposed fragmented portable telephones by various leaching solution. Mater Trans 42:2519–2522 Koyanaka S, Ohya H, Endoh S (2006) New grinding technique to simplify the recycling process of scrap electronic devices. Rev Automot Eng 27:353–355 Koyanaka S et al. AIST web page. https://staff.aist.go.jp/s-koyanaka/ARENNA.pdf Koyanaka S, Kobayashi K (2011) Res Conserv Recycl 55:515–523 Koyanaka S, Endoh S, Ohya H (2006) Effect of impact velocity control on selective grinding of waste printed circuit boards. Adv Powder Technol 17:113–126 Matsumura (2003) Concrete recycle technology. Consult Hokkaido 106:13–19 Mitsubishi Material Co, Ltd. (2003) Development of low environmental load type concrete from waste concrete. MITI Report of FY2003, Ministry of Economy, Trade and Industry Oki T (2008) Proceeding of 16th environmental resource engineering symposium. pp 24–30 OKi T (2008) Screening, separation and gravity concentration. Min Mater Process Inst Jpn Tech Semin Book: 31–44 Oki T (2009) Funtai Gijutu 1(5):39–48 Oki T (2011) Proceedings of the conference of metallurgists (COM2011). pp 69–77 Oki T (2012) Physical sorting technology for rare earth recycle. Automob Technol 66(11):74–79 Oki T (2013a) Physical sorting technology for strategic development of urban mine – unused, refractory resources and Japan’s resource vision. Systhesiology 6(4):238–245 Oki T (2013b) Physical sorting technology for strategic development of urban mine and future prospective. Kankyo Kanri 49(3):62–65 Oki T (2013c) Collection of electric element from waste printed circuit board based on the concept of strategic urban mining. Ceram Jpn 49(1):30–34 Oki T (2014) Urban mine development. Denki Hihyou 2014(2): 27–28 Oki T (2014b) Technical problems of rare metal recycle from waste portable appliances. Energy Resour 35(4):234–238 Oki T (2014c) Development of gas flow sorting device for the realization of strategic urban mine. Funtai Kogaku Gakkai-shi 51(7):527–531 Oki T et al (2007) Establishment of environmental friendly metal recycle system. AIST Environ Energy Symp Ser 1:20–24 Oki T et al (2008) Proc Spring Symp Min Mater Process Inst Jpn 2:91–92 Oki T et al (2010) IMPC2010. pp 3839–3844 Oki T et al (2011) Development of crushing and sorting device for collecting rare earth magnet from HDD. Kido-rui 58:34–35 Owada S (2007) Crushing/sorting technology. J Min Mater Process Inst Jpn 123:575–581 Owada S et al (2010) J Min Mater Process Inst Jpn: 153–156 Shibayama A et al (2002) Collection of materials from crushed liquid crystal panel using electrical crushing. J Min Mater Process Inst Jpn 118:490–496 Wills BA (2006) Will’s mineral processing technology, 7th edn. Butterworth-Heinemann, Oxford Yamasue E et al (2009) Novel evaluation method of elemental recyclability from urban mine – concept of urban ore TMR. Mater Trans 50(6):1536–1540

An Introductory Course on Climate Change Wei-Yin Chen

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Course Scope, Format, and Title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Textbooks and References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lectures Presented by Faculty and Scholars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction to Climate Change: Causes, by Wei-Yin Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction to Climate Change: Impacts, by Wei-Yin Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reasoning Economically About Potential Environmental Catastrophes, by Neil Mason . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atmospheric Physics and Chemistry, by Nathan Hammer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Global Climate Change: Scale and Complexities, by Charles Wax . . . . . . . . . . . . . . . . . . . . . . . Climate Change and US Laws, by David Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change and International Protocols, by David Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessment of Impact of Sea Level Rise on Costal and Estuarine Infrastructure Using Numerical Simulation Model, by Yang Ding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Climate Changes on Water Resources, by Cristiane Queiroz Surbeck . . . . . . . . . Impacts of Global Climate Change on Biodiversity, by David Reed . . . . . . . . . . . . . . . . . . . . . . Ocean and Human Health Consequences, by Deborah Gochfeld and Kristie Willett . . . . Surface Chemistry and Nanotechnology: An Approach to Green Energy, by Scott Gold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Conservation: Use of Foil Radiant Barriers to Reduce Residential/Commercial Energy Usage in Summer/Winter for Cooling/Heating, by Jeff Roux . . . . . . . . . . . . . . . . . . . . Biological Conversion of Biofuels, by Clint Williford . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chemistry of CO2, by Walter Cleland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobile and Area Source of Greenhouse Gas (GHG) and Abatement Strategies, by Waheed Uddin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nuclear Energy: Statistics, by Elizabeth Ervin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fuel Efficiency in Transportation Systems, by Jack Seiner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Photochemical Reduction of CO2 and Water Splitting, by Nathan I. Hammer . . . . . . . . . . . Carbon Sequestration, by Robert Holt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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W.-Y. Chen (*) Department of Chemical Engineering, The University of Mississippi, Oxford, MS, USA e-mail: [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_54

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Introduction to Climate Change: Solutions, by Wei-Yin Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integrated Gasification Combined Cycle (IGCC), by Robert Dahlin . . . . . . . . . . . . . . . . . . . . . . Oxy-firing and Chemical Looping, by Thomas K. Gale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fuel Cells, by Amala Dass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Computational Chemistry, by Steven Davis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Activities of the Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The University of Mississippi offered a seminar course entitled Climate Change – Causes, Impacts and Solutions twice in the last 4 years. The immediate goal of this course is to raise the public awareness of the climate change issue. The second objective is to consolidate a knowledge base for the various outreach, education, and research activities on mitigating the climate change. Junior, senior, and graduate students of science and engineering majors were encouraged to take this course. About 25 speakers from Mississippi, Alabama, and Louisiana gave lectures that covered their expertise in a wide spectrum of areas that include causes, impacts, and solutions of climate change. The slides used in these lectures are posted on the course web site for public dissemination: http://home.olemiss. edu/~cmchengs/Global%20Warming/. Students chose a specific research topic for approval in the early stage of the class. They submitted their research papers and made presentations at the end of the semester. Their overall performance is based on their classroom enthusiasm, final report, and presentation. When the course was offered for the first time, they also made recommendations to the Chancellor’s Ole Miss Green Initiative of the University of Mississippi on the reduction of carbon emissions in the community. This chapter discusses the motivation, content, and outcomes of this course in detail.

Introduction Climate change is arguably the most serious environmental issue. As discussed in the previous chapters of this handbook, from the greenhouse gas emissions to the affected areas of climate change, it is an environmental problem unprecedentedly large in quantity and space. The causes of climate change are complex. Its impacts on weather, ecology, and economy are potentially severe. Technologies for mitigating climate change are costly, and the infrastructure of green technology has just started to emerge. The decline in the supply of high-quality crude oil has further increased the urgency to identify alternative energy resources and develop energy conversion technologies that are both environmentally sound and economically acceptable. Although the concept of sustainability is not new, it has become a household phrase as people become increasingly aware of the severity and scope of future climate change. Climate change has thus become the central issue of sustainability literacy. At the same time, there has been a shortage of workforce

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in the organizations that require some, if not much, knowledge about climate change. Scientists and engineers play a pivotal role in developing green technologies leading to climate change mitigation. Therefore, there is an urgent need to equip the graduates with adequate knowledge about climate change so that they can function effectively in their careers. Under such demand, a campaign on climate change mitigation was planned in fall 2007. Personally, it was another twist in my life. I came to the USA in 1973 as a graduate student in applied mathematics, but the “energy crisis” in 1974 changed my plan. I switched back to chemical engineering in 1975 and started my research in coal hydropyrolysis. I have been maintaining activities in areas of hydrocarbon fuel conversions and pollution control since then. Coal was called “black gold” and “king coal” in the 1970s. Coal remains a major and indispensible energy source, particularly for developing countries such as China and India. However, coal suffered a severe image problem in the last decade because of the concern of its greenhouse gas emissions. As a fossil fuel researcher, offering solutions to the climate change concern becomes not only an obligation but also a serious challenge. About 30 faculty members of the University of Mississippi (UM) and scholars in the region enthusiastically joined the “climate change study group” in fall 2007. The activities of this group include offering courses, editing a handbook, collaborating on clean energy production and conservation research, sponsoring workshops, and developing outreach and public awareness activities. The climate change class is discussed herein, and this handbook has outgrown from this exercise. The engineering school at the University of Mississippi has a relatively small faculty and student population. Faculty has a fairly tight teaching arrangement. Fortunately, the administrators shared the same views and enthusiasm about the course. Students also have tight curricula with 128 credit hours requirement for engineering majors that have been imposed by the Institute of Higher Learning of the State of Mississippi. Most students of the engineering school have to take two elected courses in science and engineering before they graduate. Sixteen students enrolled the climate change class before the course application was approved by the administrator. Some of them were from Chemistry Department and School of Pharmacy. They were seniors and graduate students. This size is about the average for the engineering classes at the university. The course was offered for the first time in spring 2008 and subsequently in spring 2009.

Course Scope, Format, and Title Students signed up for the class with curiosities, more or less, in different aspects: the course content, the instruction format, as well as the instructors’ change in teaching style. The reports about global warming and climate change they heard in the news media just about every day had profoundly promoted their curiosity and interests in the issue. They were notably happy to see an introductory course on climate change. Students in sciences and engineering were used to analytical courses in rigid setting

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that include formal lectures covering relatively narrow scope of subjects with welldeveloped techniques for the analyses. At the outset of the preparation for this course, it was decided to deliver the materials in seminar style for several reasons. Naturally, the broadness of the course subject requires multiple lecturers from many different fields. The question-and-answer session at the end of each lecture allows students to express their thoughts and imaginations. Different lecture topics for different sessions inspire their thoughts about the relations among these subjects. Students are indeed eager to learn a serious subject through in much relaxed atmosphere. Most of the students had taken my engineering mathematics and chemical engineering thermodynamics courses (the second semester course in thermodynamics) prior to my offer of the climate change class. They were pleasantly surprised to know that a course of quite different delivery and learning styles was offered. The Intergovernmental Panel on Climate Change (IPCC) and Al Gore were awarded the Nobel Peace Prize when the course request was approved by the university. The gigantic three-volume IPCC’s Fourth Assessment Report (AR4) provides a comprehensive assessment of many aspects of climate change analytically written and edited by a selected group of 559 scholars. At the outset of the course planning, it was decided to cover the same three areas as those covered by AR4, and the course was entitled Climate Change: Causes, Impacts and Solutions. The detailed structure of the course is discussed later in this chapter. Speakers of the lectures were invited mainly from the UM as well as the universities in the region to cover the topics in the fields of causes, impacts, and solutions of climate change. Most of them accepted the invitations without hesitation since the invited lectures were their expertise. There were 25 formal lectures when the course was offered for the second time in 2009. About 60 % of these lectures focused on mitigation technologies. Each lecture lasts about 60 min followed by a 15-min question-and-answer session. Two sessions at the end of the semester were reserved for student presentations. Formal lecturers were asked to assign homework, which they will be asked to grade in the following week.

Textbooks and References Vanek and Albright’s Energy Systems Engineering: Evaluation and Implementation was chosen as the textbook (Vanek and Albright 2008). The authors introduce the system concept in the analysis of energy conversion technologies at its outset. It is an interesting and beneficial approach, although the “system” concept has been introduced in several engineering courses such as process control, dynamics, reaction engineering, engineering economy, etc.; this textbook applies the concept to several different aspects of the energy systems including material and energy balances, economics analysis, carbon cycle, etc. They are uniquely demonstrated in energy systems in one place. The authors introduce the science and engineering fundamentals, technological costs, and impact on natural environment of each of the selected energy systems. Major working equations associated with the technology are identified; the functions of these equations are consciously illustrated by short

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representative examples. These equations effectively link the current issues of energy systems and climate change with materials introduced in other courses, such as economics, heat transfer, thermodynamics, etc. They also provide the readers with an overview of the context within which these systems are being implemented and updated today and into the future. Their presentation centers around the utilizations of alternative energy resources and their roles in climate change mitigation. An extensive online ancillary package for instructors provides an instructor’s manual, solution files, course syllabus, MATLAB scripts, and teaching PowerPoint files. Vanek and Albright indeed made it a friendly book to both the students and the instructors. The book was published in late 2008, i.e., after the price surge of oil; therefore, the economic analysis is based on fairly reasonable data. Moreover, this textbook contains representative homework exercises that serve the pedagogical needs and broad in nature. The cost of this book is moderate. These features render it suitable for a one-semester, introductory textbook for engineering students who would like to know the alternative energy options. Specifically, it covers energy supply and demands, issues surrounding CO2 emissions, factors affecting and models of energy systems, economic tools for energy systems, climate change and climate modeling, fossil fuel resources, stationary combustion systems, carbon sequestration, nuclear energy systems, solar resource, solar photovoltaic technologies, solar thermal applications, wind energy systems, transportation energy technologies, systems prospective on transportation energy, and a conclusion chapter on creating the twenty-first-century energy system. The book, however, lacks coverage on a number of important areas including biomass conversion systems, energy efficiency for buildings, geothermal energy, and advanced combustion systems including oxy-fuel combustion and chemical looping systems. The seminar class did not cover all the topics in this handbook; however, several scholars in the regions were invited to cover most of these topics. IPCC’s AR4 (Intergovernmental Panel on Climate Change 2007a, b, c) was introduced to the class early mainly because it provides the most comprehensive and up-to-date scientific, technical, and socioeconomic information about climate change. It is available online at no cost; the paper copies are available from Cambridge University Press at moderate costs. The students were asked to read the “Summary for Policy Makers and Technical Summary” for each volume (with a total size less than 300 pages) in the first 3 weeks of the course so that they can have a general understanding of the subject. Some of the slides for the introductory sessions were extracted from the online version of these reports. While AR4 presents the most detailed advanced analysis presented by the IPCC and others, it does not contain textbook-level explanations about most of the scientific and technological terminologies. The major portions of the AR4 (other than the “Summary for Policy Makers and Technical Summary”) serve as an indispensable reference to the complex subject, which deserve frequent visits during their study. The ambitious book written by Tester et al. (2005) was adopted as a reference. This decadelong effort of five MIT professors is the first single source of the sustainable energy utilizations when it was published in 2005. It assesses technologies of converting fossil fuels (oil, gas, and coal), nuclear energy, and renewable

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energies (solar, biomass, wind, hydro-, and geothermal) and discusses energy storage, transmission, end use, and efficiency/conservation issues. These technologies are assessed in a political, social, economic, and environmental context with life cycle assessment and systems integration methods. This textbook has been used in a graduate course on sustainable energy many times at MIT. The book was recommended as an advanced reference. Dessler and Parson (2010) provide an integrated treatment of the science, technology, economics, policy, and politics of climate change. Aimed at the educated nonspecialist, and at courses in environmental policy or climate change, the book clearly lays out the scientific foundations of climate change, the issues in current policy debates, and the interactions between science and politics that make the climate change debate so contentious and confusing. This new edition is brought completely up to date to reflect the rapid movement of events related to climate change. In addition, all sections have been improved; in particular, a more thorough primer on the basic science of climate change is included. The book also now integrates the discussion of contrarian claims with the discussion of current scientific knowledge, extends the discussion of cost and benefit estimates, and provides an improved glossary. Web sites of major international and US organizations were introduced to the students: • Intergovernmental Panel on Climate Change, http://www.ipcc.ch/ • United Nations Framework Convention on Climate Change, http://unfccc.int/ 2860.php • United Nations Environment Programme, http://maps.grida.no/theme/ climatechange • World Meteorological Organization, http://www.wmo.int/pages/themes/climate/ index_en.php# • International Energy Agency, http://www.ieagreen.org.uk/ • United States Global Change Research Program, http://www.globalchange.gov/ • United States Climate Change Science Program, http://www.climatescience.gov/ default.php • United States Climate Change Technology Program, http://www. climatetechnology.gov/ • US Department of Energy, http://www.eia.gov/, http://www.energy.gov/environ ment/climatechange.htm, http://www.netl.doe.gov/technologies/carbon_seq/ • National Oceanic and Atmospheric Administration, http://www.noaa.gov/cli mate.html • US Environmental Protection Agency, http://www.epa.gov/climatechange/ • US National Aeronautics and Space Administration, http://climate.nasa.gov/ • US National Science Foundation, http://www.nsf.gov/news/special_reports/ climate/ • US Department of State, http://www.state.gov/g/oes/climate/ • National Academy of Science, http://www.koshland-science-museum.org/ exhibitgcc/index.jsp • Earth Policy Institute, http://www.earth-policy.org/

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Table 1 2009 lectures Session 1 2 3

Speakers Wei-Yin Chen Wei-Yin Chen Neil Manson

4 5 6 7 8

Nathan Hammer Charles Wax David Case David Case Yan Ding

9 10 11 12

Cris Surbeck David Reed Deb Gochfeld, Kristie Willett Scott Gold

13

Jeff Roux

14 15 16 17 18 19 20 21 22 23

Clint Williford Walter Cleland Waheed Uddin Elizabeth Ervin John Seiner Nathan Hammer Robert Holt Wei-Yin Chen Robert Dahlin Tom Gale

24 25 26 27

Amala Dass Steve Davis

Title of lecture Overview of the causes of climate change – causes Overview of the impacts of climate change – impacts Reasoning economically about potential environmental catastrophes Atmospheric physics and chemistry Climate change – evidences and contrarian viewpoints Climate change and US laws International protocols and climate change Assessment of impact of sea level rise on coastal and estuarine infrastructure using numerical simulation model: CCHE2Dcoast Effects of climate change on water resources Global climate change – impacts on biodiversity Ecological and health impacts of climate change Surface chemistry and nanotechnologies: an approach for green energy Energy conservation – use of foil radiant barriers to reduce residential/commercial energy usage in summer/winter for cooling/heating Biological conversion of biomass Chemistry of CO2 Mobile and aerial sources of CO2 and abatement strategies Nuclear energy: statistics Fuel efficiency in transportation systems Photocatalytic reduction of CO2 and water splitting Carbon sequestration Overview of the impacts of climate change – solutions Integrated gasification combined cycle (IGCC) Combustion on the horizon – oxy-fuel combustion and chemical looping Fuel cells Computational chemistry Student presentations Student presentations

Lectures Presented by Faculty and Scholars Table 1 lists the lectures and speakers of the course in spring 2009. Among the 25 lectures, sessions 1, 3, 4, 5, 6, and 7 cover subjects in the “causes” category of climate change, sessions 2, 8, 9, 10, and 11 the “impacts,” and the remaining 14 sessions the “solutions.” Two sessions at the end of the semester are reserved

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for students’ project presentations. The contents of these 25 lectures are summarized below. More information about these lectures can be found in the slides used in these lectures, which have been uploaded for public dissemination: http://home.olemiss. edu/~cmchengs/Global%20Warming/.

Introduction to Climate Change: Causes, by Wei-Yin Chen This is the first of three overview lectures given in this class that bore the same title of the course, one each on the causes, impacts, and solutions of climate change. The first two lectures covered the first two topics using one set of slides. The first introductory lecture on the causes covered the following topics: • The rationale of offering a climate change class • Impacts of increasing energy demand and lack of energy infrastructure on climate change challenges • United Nation’s efforts on climate change mitigation • UNFCCC and Kyoto Protocol • Total CO2 emissions by country and CO2 emission per capita • Developed and developing nations’ different views on CO2 emission limits • Paleoclimate archives and the scientific observations of greenhouse effects caused by human activities • Greenhouse gases and their geographic and sectoral sources • Radiative forcing of different greenhouse gases and their implication of human activities • Nature climate change (i.e., the Milankovitch cycles) and its causes • Thermohaline circulation and ice age • Anthropogenic causes of climate change • Natural carbon emission, carbon sinks, and carbon cycle and their relation with temperature rise and mitigation technologies Finally, IPCC’s comparison of economic development, population growth, and energy usage was used to illustrate the societal causes of global warming. The recent increase in CO2 emissions was fueled more by economic growth than growing populations. It is not the poor masses, but the new and old rich that fuel global warming. And while energy and emission intensities have steadily decreased since the oil crisis in the 1970s, carbon intensity (carbon emission to energy consumption) has not. One conclusion could be drawn is that fixing prices for greenhouse gas emissions can help achieve emissions reduction, just like rising oil prices helped reduce energy and emissions intensity in the last decades. Some of these slides were extracted from the IPCC’s AR4 reports. Reading materials were assigned. Students were asked to start searching a term research topic.

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Introduction to Climate Change: Impacts, by Wei-Yin Chen The second lecture was on the impacts of climate change. History has shown that civilizations rise and fall to the pulse beats of climate. Erik Thorvaldsson’s (known as Erik the Red) discovery of Greenland in 982 A.D. and the subsequent collapse of the Norse settlement less than 500 years later due to severe weather was one example. Flooding is generally believed to have caused the collapse of the Harappan civilization of India, 2500–1600 B.C. The outset of the lecture discussed how the analysis of climate predictability had actually promoted the evolution of modern chaos theory in the 1960s and 1970s. Water’s specific volume increases when the temperature rises. Sea level rise causes the most direct impact of temperature rises. Ice cap melting contributes a lesser degree of impact on sea level rise. Temperature rise also causes decline in biodiversity and natural disasters including droughts, heat waves, flooding, and cyclones. Sea level rise can contribute to disease spread and loss of traditional lifestyle. The natural disasters, in turn, can generate loss of traditional lifestyle, losses of water and food resources, biodiversity losses, economic losses, famines, casualties, and disease spread. The abovementioned impacts were then discussed in more detail. IPCC’s statistics and projections of these impacts are presented. The projected impacts on water resources, ecosystems, food productivity, coasts, and human health were presented as a function of warming from 1990 to 2100. As of 2007, there are concerns in all of these five areas. The projected sea level rise and its effects on land area, population, and GDP showed that Asia will have the most severe consequences. Records show an increasing trend of natural disasters, especially flooding and cyclones. The natural fluctuation of climate, El Nino and La Nina, was introduced. El Nino and La Nina originate in the Pacific Ocean but affect climate globally. The increase in El Nino frequency as the climate changes since 1970 has only recently been appreciated. The potential impact of sea level rise on the Nile Delta is presented. Its impacts on freshwater resources (ground- and surface water) of small islands and low-lying coastal area were explained. Freshwater, in turn, has profound influences on agriculture, ecosystems, and human health. Food production is affected by water, temperature, CO2 level and ultraviolet radiation, and pest and diseases. Developing countries are expected to be affected more than the developed countries. Effects on tea and coffee productions in Kenya and Uganda were illustrated. Finally, the projected biodiversity loss between 2000 and 2050 and spread of several diseases, including malaria, were presented.

Reasoning Economically About Potential Environmental Catastrophes, by Neil Mason At the outset of his lecture, Neil Mason, a philosophy professor, explained why environmental scientists should care about economics. It is known that economic evaluation mediates science and policy. The results of science never get translated

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into action without some form of economic evaluation, either by private industry or by governmental regulators. Less obvious about the importance of economics is its role in moving forward a debate such as climate change. Skepticism about or outright denial of the results of scientific investigation can seem baffling, if not downright evil. Oftentimes, however, what is really going on is that skeptics or deniers (e.g., “intelligent design” proponents) wish to avoid the perceived evaluative implications (ethical, philosophical, religious, or economic) of the results of scientific investigation. Sometimes, the best way to move the debate forward is not to reiterate the science, but to address directly the evaluative presuppositions of the skeptics or deniers (e.g., “Why even think evolution is incompatible with belief in God?”). The major portion of the lecture covered the two competing approaches in making environmental policy decisions: the cost-benefit analysis (CBA) that has been the ascendancy in the USA (Sunstein 2005) and the precautionary principle (PP) that has been commonly adopted by the European Union and many of its member nations, as well as by the United Nations (Manson 2002; Sunstein 2005). The basic idea of CBA (Sunstein 2005) is to identify all potential costs and all potential benefits of a given course of action, assign to each cost and benefit both a probability of occurrence and a dollar value (positive or negative) of occurrence, crunch the numbers, and see whether the result is overall positive or negative. Using CBA, courses of action can be compared to see which course of action has the highest overall positive rating (or least bad negative rating). Then, policymakers should approve the course of action that maximizes net benefits or at least give some presumption in favor of that course of action (Sunstein 2005). Posner’s CBA calculations (Posner 2003) regarding the building of the Brookhaven particle accelerator are illustrative (Sunstein 2005). With CBA, in order for a potential cost to factor into the decision, there is some estimate both of its dollar value and of its probability of occurrence that must be available; otherwise, that potential cost is a nonfactor. The PP is considerably more vague than CBA, with a host of competing formulations (Manson 2002), but the basic idea is “better safe than sorry.” Given that the results of our industrial/technological innovations cannot be foreseen, regulators are justified in restraining such innovations unless and until enough evidence comes in that the activity in question will not produce harmful results. The approach is “risk averse.” Standard objections to these approaches and responses are presented. Many examples are given. It is one of the lectures of this class that promotes critical thing about the basis in policymaking. As a homework exercise, the instructor asked the students to write an essay on what approach they would choose in determining the policies toward climate change.

Atmospheric Physics and Chemistry, by Nathan Hammer Chemistry professor Nathan Hammer presented a lecture on atmospheric physics and chemistry. At its outset, the dynamical evolution of the Earth’s atmospheric gas

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since the primordial solar nebula 4.6 billion years ago was presented: from a composition similar to the emissions of today’s volcanoes (rich in CO2, H2O, and H2) to oxidizing gas that is rich in N2 and O2. O2 has been stable in the atmosphere for 400 million years, as a result of photosynthesis and decay of organics on Earth. Trace gases are introduced. Among them, CO2, CH4, and N2O are considered greenhouse gases due to their capabilities of absorbing vibration bands of the reflected sunlight from Earth’s surface. Dr. Hammer presented the infrared absorption bands of these compounds and that result in trapping a fraction of reflected energy in the atmosphere. These trace greenhouse gases increase over time as the result of increasing use of fossil fuel combustion and activities of chemical industries. Human behaviors have caused increases in trace gas emissions that lead to the formation of photochemical smog from automobile tail gas and ozone depletion from chlorofluorocarbons (CFCs). Geostrophic flow is driven by uneven distribution of pressure and temperature of Earth and the rotation of Earth (i.e., the Coriolis Effect); it, in turn, transports energy pole wards and reduces equator-to-pole temperature contrast. Prolonged temperature rise in this atmospheric circulation across the Pacific Ocean is called El Nino, or Southern Oscillation; similarly, prolonged temperature decrease is called La Nina. El Nino and La Nina usually happen 2–7 years with 9-month to 2-year durations. They cause global weather effects including droughts, hurricanes, moisture, and temperature patterns. Their causes, including global warming, are under active investigation. The four layers of atmosphere were introduced: troposphere, stratosphere, mesosphere, and thermosphere. The chemical characteristics of their gas components and the temperature and pressure variations in these layers were discussed. In the troposphere, the main concerns are acidic gases, photochemical smog, and greenhouse gases. In the stratosphere, O2 photochemically dissociates; the main issues are ozone depletion by nitrogen oxide (NO) and CFCs. The mesosphere is characterized by photochemical reactions of small diatomic molecules and reactions of atoms and ions. The thermosphere is characterized by ultraviolet radiation and ionic reactions. Dr. Hammer then discussed the mechanisms of the following chemical reactions or processes: • Formation of photochemical smog starts with photochemical decomposition of NO2 followed by ozone formation and a series of reaction involving volatile hydrocarbons. • Ozone destruction by NO. • Ozone destruction by hydroxyl radicals. • Ozone destruction by CFCs. • Aerosol formation dynamics. • Ionic reactions. The students were asked to write an essay about their understandings of the climate change as homework. The textbook by Wayne (1985), Seinfeld and Pandis (1998), and Jacob (1999) was recommended for additional information.

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Global Climate Change: Scale and Complexities, by Charles Wax The State climatologist and Mississippi State University Geosciences professor Charles Wax gave this lecture. At its outset, it covered the nature of climate change including the causes of Milankovitch cycles, sunspot activity, and emission of volcanic debris, which results in glaciations every 100,000 years and interglacials every 10,000 years. The correlations between the observed historical data between these nature phenomena were then discussed. The temperature decline after the medieval maximum, or the “little ice age,” that took place between 1400 and 1900 AD, was at least partially due to the maunder minimum, or the prolonged sunspot minimum. It was suggested that the observed El Nino may be correlated with the Atlantic multidecadal oscillation documented in history. Coupled with changes in instrumental measurements and data interpretation methods, he mentioned the possibility that what have been observed may be part of a long story of ups and downs.

Climate Change and US Laws, by David Case Law professor David Case gave two time-sensitive lectures, one on climate change and US laws and the other on climate change and international protocols. These lectures were given only weeks after President Obama was inaugurated. The proposals for climate change legislation at that time were discussed in detail: to include cap and trade, carbon tax, and Clean Air Act Amendment for regulating greenhouse gas emissions by EPA. The lecture covered the details of the process and subsequent developments, including Dr. Case’s expectations on Obama administration’s policy, of the 2007 Massachusetts v. U.S. Environmental Protection Agency (EPA), 549 U.S. 497 (http://www.supremecourt.gov/opinions/06pdf/05-1120.pdf, 2007). Massachusetts v. EPA is a US Supreme Court case decided 5-4 in which 12 states and several cities of the USA brought suit against the EPA to force the agency to regulate carbon dioxide and other greenhouse gases as pollutants. Finally, the lecture covered the status of the National Environmental Policy Act (NEPA), Endangered Species Act (ESA), and common law litigation, such as Connecticut v. American Electric Power (S.D. N.Y. 2005) and California v. General Motors (N.D. Cal. 2007).

Climate Change and International Protocols, by David Case Dr. Case’s second lecture on international protocols focuses on the contributions of the multinational scientific body Intergovernmental Panel on Climate Change (IPCC). It covered IPCC’s publications of the periodic Assessment Reports (AR), establishment of United Nations Framework Convention on Climate Change (UNFCCC), the functions of the Conference of Parties (COP), the journey of Kyoto Protocol, negotiations on post-Kyoto commitments, and skepticism about post-Kyoto regime. In addition to the Massachusetts v. EPA link mentioned above,

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Dr. Case assigned three other reading assignments extracted from the book by Gerrard (2007).

Assessment of Impact of Sea Level Rise on Costal and Estuarine Infrastructure Using Numerical Simulation Model, by Yang Ding Prof. Yan Ding of the National Center for Computational Hydroscience and Engineering gave a lecture on the impacts of sea level rise on costal and estuarine infrastructure using numerical simulation model. The lecture started with an overview on costal hazards due to hurricane, storm, and tides, current instrumental records for climate change, impacts of sea level rise, coastal zone structure, and coastal flood hazard zones. Most of this lecture was devoted to detailed numerical analysis of coastal and estuarine hydrodynamic and morphodynamic processes. Special cases were presented with comparison with recorded data. A list of references (including the slides on the web) was given, and the following homework assignments were assigned: 1. Find tidal datums in the Bench Mark Sheets of Gulfport Harbor, MS on the web site of NOAA Observational Data Interactive Navigation at http:// tidesandcurrents.noaa.gov/gmap3/. Draw a figure to display the MSL, NGVD, and NAVD. Then, retrieve tidal datums in the Bench Mark Sheets of the USCG New Canal Station, Lake Pontchartrain, LA. Find the differences of the tidal datums between the two locations. Explain why they are different. 2. What are the impacts of sea level rise on coasts and coastal communities? 3. What are coastal zones? How are coastal flood hazard zones defined by Federal Emergency Management Agency? 4. What is the CCHE2D-Coast model? What are the model’s capabilities to simulate coastal processes related to assessment of impacts of sea level rise?

Effects of Climate Changes on Water Resources, by Cristiane Queiroz Surbeck Civil engineering professor Cristiane Queiroz Surbeck gave a lecture about the effects of climate changes on water resources. At its outset, the lecture covered the global ocean conveyor belt (thermohaline circulation), the effects of climate change on the circulation and therefore the distribution of Earth’s water resource and quality and the hydrologic cycle. Then, the effects of climate change on water resources in the different regions of the North America were discussed. Through this case study, a few concepts and methods were discussed: the method for the estimation of hydrologic water budget through rainfall analysis, intensity-duration-frequency curves, and return period. A numerical example was given, and a specific water budget problem was given as homework.

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Impacts of Global Climate Change on Biodiversity, by David Reed Biology professor David Reed gave a lecture on the impacts of global climate change on biodiversity. At its outset, the definition, importance, and measure of biodiversity were covered. Origination and extinction through time have been observed in fossil record. The huge end-Permian mass extinction about 30,000 year ago marked the beginning of the diversity decline. The present extinction rate is shown to be at least 50 times higher than that of the post-Permian period. Cases of human-caused extinction were discussed. These cases led to the systematic and focal presentation of this lecture on the functions and importance of ecosystem services to the environment on Earth. These environmental services include food production, raw materials, recreation and water supply, atmospheric gases, water recycling, erosion control, soil formation, nutrient cycling, and purification of wastes. Plants are not only the sources of food, they are valuable sources of genetic materials, natural pesticides, and medicines. Climate change has shifted the range of 1,700 species toward the poles at an average rate of 6.1 km per decade, spread of diseases to higher elevations and more northerly latitudes, and population declines of polar bears and penguins. Since species habitats are more fragmented than in the past and are smaller than in the past, the impact of future climate change is expected to cause a higher rate of extinctions. About 10–20 % of all species on Earth are expected to go extinct by 2050.

Ocean and Human Health Consequences, by Deborah Gochfeld and Kristie Willett Prof. Deborah Gochfeld of the National Center for Natural Products Research and Prof. Kristie Willett of Pharmacology made two related presentations in a joint seminar on ecological impacts of climate change. Dr. Gochfeld discussed the ocean health and Dr. Willett the human health consequences. These two lectures extend David Reed’s biodiversity lecture into a more detailed analysis. Dr. Gochfeld first introduced the ocean stressors imposed by humans: overfishing, pollution/sedimentation/eutrophication, and habitat modification, which reduce the resilience of species, communities, and ecosystems to climate change efforts. It was explained how local economies near major coral reefs benefit from an abundance of fish and other marine creatures as a food source and how drugs are produced from corals. The major portion of the lecture was devoted to the discussions about how climate change affects the seawater temperature, ocean chemistry (especially the decrease of carbonate ion, CO32
, with increasing atmospheric CO2 concentration), sea level rise, ocean circulation, solar and UV irradiance, and pathogen distribution and virulence. The results of these immediate effects were discussed in detail. For instance, changes in natural ocean circulation induce extreme weather, terrestrial climate, and disappearance of shallow water and intertidal habitats. Extreme weather, in turn, causes increased coastal erosion, especially beaches, increased river volume, flooding, drought, runoff (freshwater, sediment,

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pollutants, nutrients), destruction of salt marshes, seagrass beds, mangroves, and coral reefs. Moreover, harmful algal blooms result from runoff of sediment, and nutrients are enhanced by elevated temperatures and solar radiation. Decrease in carbonate ion reduces the abilities of corals and other calcifying organisms (clams, oysters, mussels, and other important fish species) to produce skeletons or shells. Seawater rise causes flooded coastlines, wetlands, estuaries, disappearance of shallow water and intertidal habitats, and loss of nursery grounds, nesting, and feeding habitats of many organisms. Dr. Willett’s lecture covered stress-related problems, increased infectious disease, extreme events, increased number of poor, and increased agricultural yields. World Health Organization’s (WHO) climate change site at http://www.who.int/ globalchange/en/index.html was introduced at its outset. The primary, secondary, and tertiary impacts of climate change on human health at the outset were discussed. Temperature rise causes ozone and photochemical smog concentrations that induce several respiratory diseases and alleges. Statistics also illustrate that temperature correlates the populations of mosquitoes, ticks, snails, and parasites in contaminated water, which, in turn, transmit dengue fever, malaria, Lyme disease, schistosomiasis, and cholera. Extreme weather leads to flooding and drought that cause food shortage and malnutrition. The correlations between the temperature rise and the health issues observed in different parts of the world between 1990 and 1999 (Intergovernmental Panel on Climate Change 2007b) were also discussed. In addition to the IPCC report, WHO report (World Health Organization 2003) and EPA’s climate change site, http://www.epa.gov/climatechange/ were assigned as the reading materials.

Surface Chemistry and Nanotechnology: An Approach to Green Energy, by Scott Gold Prof. Scott Gold of the Louisiana Tech University gave a lecture on the surface chemistry and nanotechnology: an approach to green energy. This lecture covered two major portions: the sciences and technologies of template wetting nanofabrication and their technological applications in fuel cells and electrochemical supercapacitors. At the outset, it introduced the template materials, methods of wetting porous materials, reactive ion etching, sputtering processes, and posttreatment reactions that convert nanotube precursor to desired materials. He then discussed the applications of nanostructures from template wetting that include: • Ceramics such as sulfated zirconia (superacid) for proton exchange membrane for fuel cells and acid catalysis • Nanotubes of metals such as platinum, palladium, and gold as catalysts in fuel cells, and Raman spectroscopy • Piezoelectric microelectromechanical systems (MEMS) • Conductive and semiconducting polymers as electrical supercapacitors, photovoltaics, LEDs, photodiodes, sensor, and hydrogen storage

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The second half of Dr. Gold’s lecture focuses on fuel cells. Different types of fuel cells were introduced: alkaline, proton exchange membrane, direct methanol and other liquid fuels, phosphoric acid, molten carbonate, solid oxide, and enzymatic biofuel. Their applications and operating conditions were discussed. Research in fuel cells has been growing rapidly due to its low emissions, high-energy efficiency, and higher energy density than batteries. Reaction efficiency, design (current collection, fuel transport to catalyst, proton transport to membrane, and waste removal), material issues, and cost remain as technological barriers and research areas. Dr. Gold then presented the works of his group on nanofabrication: gold and platinum nanotubes on grapheme oxide (GO) and semiconducting polymer such as poly (3-hexylthiophene) (P3HT) nanotubes. Nanotube preparation and their enhancement in electron transfer rate coefficients for biofuel cell and supercapacitors were discussed in detail.

Energy Conservation: Use of Foil Radiant Barriers to Reduce Residential/Commercial Energy Usage in Summer/Winter for Cooling/Heating, by Jeff Roux Prof. Jeff Roux of mechanical engineering gave this lecture on building design for energy conservation. At the outset, he presented the important role of energy conservation in climate change mitigation. IPCC AR4 concluded that the buildings sector will have the highest economical potential for global mitigation as a function of carbon price in 2030. Major currently available commercial technologies and practices for this sector include efficient lighting and daylighting, more efficient electrical appliances, heating and cooling devices, improved cook stoves, improved insulation, passive and active solar design for heating and cooling, alternative refrigeration fluids, and recovery and recycle of fluorinated gases. Key mitigation technologies and practices projected to be commercialized before 2030 include integrated design of commercial buildings including technologies such as intelligent meters that provide feedback and control and solar photovoltaic integrated in buildings. The research at the University of Mississippi on improved insulation for residential dwellings was then discussed. The lecture presented the characteristics and properties of various fibrous materials (fiberglass, rock wool, cellulose, and polystyrene and polyurethane foams) and their principles of operation. The large surface area of fibers inhibits the air within the insulation from moving. Air has a low thermal conductivity and is an excellent insulator if it can be made to remain stationary. The lecture also presented experimentally measured data (including summer and winter) at an occupied north Mississippi residence, which were transformed to various profiles such as time histories of temperature, heat flux, and water vapor concentrations. A mathematical model that incorporated conduction and radiation heat transfer and moisture transport was developed to predict the changes in total heat flux. Model predictions showed good correlations with the experimentally measured heat flux.

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Biological Conversion of Biofuels, by Clint Williford Dr. Clint Williford, professor of chemical engineering, gave a lecture entitled biological conversion of biofuels. The lecture started with an introduction on the historical correlations among oil price, supply, and demand. It then covered the greenhouse gas emissions from different sectors and fuel sources. Looking ahead, biomass conversion to fuels has been considered one of the seven wedges to maintain a constant CO2 emission level between 2005 and 2055. This reduction corresponds to one billion tons of CO2 per year, or an increase of bioethanol usage by 50 times. Nevertheless, the investment on biomass conversion grew rapidly in the last decade due to higher energy cost, concern for energy security, climate change, political supports, and technological maturity. The major portion of this lecture was on the current biofuel technologies: biodiesel, grain corn ethanol, cellulosic ethanol (cell EtOH), and biobutanol. In addition to the details of their conversion technologies, he explained the heat content of the products, pollutant emissions (unburned hydrocarbons, CO, particulate matter, and NOx), fossil fuel replacement ratio, economics, and other issues during their usages. He then discussed his research on alternative biomass pretreatments for improved lignocellulosic ethanol production. Celluloses are abundant and widely spread. Conversion of cellulose to ethanol can relieve food demand from corn and cane; it is virtually eternal. Moreover, its positive impacts on CO2 emission and nonrenewable replacement are both high (over 90 %) and have been spurring biofuel mandate. The technological difficulties of the conversion and its cost remain bottlenecks in its development. The separation of cellulose from lignin, however, requires innovations in the areas of the selections of enzymes, pretreatment process, and improved lignin utilizations. Dr. Williford also discussed the controversies of biofuels including the impact of deforestation, nitrous oxide emission from fertilizer, food price, and food export.

Chemistry of CO2, by Walter Cleland Professor of chemistry Walter E. Cleland gave a lecture on the chemistry of CO2. This lecture was selected to promote critical thinking for developing innovative technologies for CO2 capture and utilization. It started with an introduction of the various stable oxides of carbon, physical properties of CO2, phase diagram, and Walsh diagram. Industrial processes involving CO2 production and utilizations were then presented. CO2 utilizations based on the physical properties of CO2 include refrigeration fluid, cleaning solvent, solvent as a reaction media and extraction, and food and agrochemical applications such as beverage additives and fumigant. CO2 utilizations based on the chemical properties of CO2 include the production of urea, salicylic acid, inorganic carbonates and pigments, propylene carbonate, naturalization of caustic waste water, and CO2 capture by various liquid and solid sorbents. The majority of this lecture covered more detailed discussions of the CO2 reactions for its production and utilizations. Productions of CO2 mainly come from combustion and gasification of fossil fuels and their derivatives in stationary and

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mobile sources. Fermentation, lime-kiln operations sodium phosphate manufacture, and natural gas wells also produce fairly sizable quantities of CO2. CO2 in these gas streams can be captured by amines (Girbotol process) and sodium or potassium carbonate process. CO2, along with KMnO4 (permanganate process) or K2Cr2O7 (dichromate process), has been used to chemically reduce H2S in gaseous streams to elemental sulfur. The lecture then covered the reactions that involve CO2 either as a reactant or as a product in the following categories: CO2 reaction with H2O; reaction with O2, CO, and O2; CO2 with H2 (reversed water-shift reaction); CO2 with NH3 (urea formation); CO2 reactions with organics that have been used in carbon capture; coordination chemistry of CO2 and metals; reaction of M-CO2; reactions in biological systems; use of CO2 as a C1 feedstock; carboxylation; polymerization; CO2 reductions; and photochemical reduction of CO2. The reactions of CO2 with organics are of three major types: organic reaction with RO
to form ROCOO
, with RNH2 to form RNHCOO
or RNHCONHR, and with RMgX or RLi to form RCOO
. CO2 is a poor ligand, but it does form a number of complexes and bonding modes with metals, which are important for activation of CO2 in catalytic reduction reactions. For instance, M-CO2 reacts proton or electrophile, such as R+, and converts to M-CO or M-C(O)OR, respectively. CO2 reacts with hydride, M-H, and forms M-O2CH. Organophosphines, PR3, react with M-CO2 and O = PR3. Isocyanide, M(CNR)-(CO2), decomposes and forms RNCO and M-CO. Reactions of CO2 in biological systems include animal metabolism, photosynthesis, enzyme-catalyzed carbonic acid decomposition, carboxylation of ribose, and catalytic reduction of CO2 to CO. Using CO2 as a C1 feedstock leads to the formation of carboxylates, lactones (RCOOR0 ), carbonates (RR0 NCOOR00 , ROC(O)OR0 ), ureas (RR0 NCONRR0 ), and isocyanates (RNCO). Moreover, as a C1 feedstock, CO2 reduces to formats (HCOO
), oxalates (
O2C-CO2
), formaldehyde (H2CO), CO, methanol, and methane. Carboxylation results in the formation of COO
group on carbon, nitrogen, or oxygen atom in an organic compound such as direct carboxylation in ionic liquid from imidazolium carbonate. With metal-salen catalyst, polycarbonates form from epoxides and CO2. Through metal and enzymatic catalytic mechanisms, CO2 has been hydrogenated to formic acid and methanol. Finally, the fundamentals of photoelectrochemical reduction of CO2 to formic acid and methanol were briefly discussed. A recent review by Beckman (2004) would be a valuable reading beyond this class.

Mobile and Area Source of Greenhouse Gas (GHG) and Abatement Strategies, by Waheed Uddin Civil engineering professor Waheed Uddin gave a lecture on mobile and area source of greenhouse gas (GHG) and abatement strategies. This lecture introduced five topics: • Quantifying traffic and built environment impacts on mobility and traffic congestion

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• Evaluation of traffic and built environment impacts on GHG emissions and global warming • Assessment of traffic and built environment impacts on air quality and public health • Application of remote sensing and geospatial technologies and air pollution models for traffic visualization • Environmental assessment and evaluation of abatement strategies The transportation sector accounts for 28 % of total GHG emissions in the USA and 33 % of the nation’s energy-related CO2 emissions (EIA 2007). The USA in turn is responsible for 22 % of CO2 emissions worldwide and for close to a quarter of worldwide GHG emissions (Energy Information Agency (EIA) 2007). Report has shown that GNP has been linearly proportional to the density of paved road, and GHG emissions have increased with increasing use of fossil fuels for the economic growth. Although average mileage per gallon of gasoline has increased 40.60 % from 1970 to 2000, average total fuel consumption per vehicle has decreased only 13.25 % from 1970 to 2000. Percent increase in vehicle mile traveled showed a strong correlation with increase in GDP. In addition to passenger cars, long haul tracks, light-duty trucks, aviation, marines, locomotives, motorcycles, and space missions are major portions of carbon emissions from mobile sources. Public transportation is one of the most significant means to reduce household carbon footprint. Moreover, traffic congestion and gridlock have steadily grown, and commuters spend 46 h annually stuck in traffic and waste five billion gallons of gas annually. The lecture then covered carbon emission from buildings, whose carbon footprint can be extracted from high-resolution satellite imagery more cost effectively than traditional aerial photography. Large urban built-up areas (heat islands) demand more energy causing more carbon emissions and consequently more air quality degradation. As temperature rises, so does the likelihood that smog will exceed national standards of air quality; more power generation will produce more carbon emissions. As a result, sustainable transportation development must consider the following factors: land use, urbanization and social integration, built-up area effects on environment (air, water), built environment impacts on physical inactivity, traffic fatalities and injuries, traffic-related emissions and air pollution, traffic-related pavement noise impacts, construction process and material resources, energy demand, and diminishing natural resources. To improve the spatial management and urban transport, it will be necessary to structure urban development with adequate transport policies and systems, manage efficiently the urban/rural/regional interfaces, and develop and implement enhanced GIS-based transport infrastructure asset management systems. Using the policy recommended by California’s Climate Action Team (2007), Dr. Uddin stated the importance of “smart land use and intelligent transportation system (ITS)” to make the second-largest contribution toward meeting the state’s ambitious GHG reduction goals. These policies include conservation and compact urban growth – by all government levels and by public, efficiency in vehicles by

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vehicle manufacturer and consumers, efficiency in traffic flow by transport agency, better commercial truck fleet management by transporters, public mass transit, high-occupancy vehicle lanes, carpooling, flexible work hours, nonmotorized transport by government levels and by public, equity in road user charges for pollution and vehicle-mile traveled (VMT), and development in clean fuel and energy sources.

Nuclear Energy: Statistics, by Elizabeth Ervin Civil engineering professor Elizabeth Ervin gave a lecture on nuclear energy with emphasis on statistical data. It was postulated at the outset that there are three motivations for developing nuclear energy today: it produces no controlled pollutants such as sulfur dioxides, nitrogen oxides, particulates, and GHG; it creates jobs and capital; and its image has changed in peoples’ mind – 73 % of people approved its development in 2006. As a result, 36 new plants were under construction in 14 countries, and 223 had been proposed in 2006. A major human error at Chernobyl caused 56 deaths. However, the US civilian nuclear reactor program had resulted in zero fatality, compared to 33,134 coal miner and coal transporter deaths from 1938 to 1995 and 54,000 aviation deaths. Moreover, coal-fired power plant releases 100 times more radiations than equivalent nuclear reactor. At the presentation in 2008, there were 439 nuclear reactors operating in 31 countries; they provided 15.2 % of the world’s electricity production in 2006 (34 % of EU and 20 % of USA). There were 104 commercial nuclear power reactor plants in the 64 sites and in 31 states in the USA. For seven states in 2006, nuclear energy made up the largest percentage of their electricity generated. Major unit operations of a nuclear power plant were introduced. Nuclear power generates economically competitive electricity, 1.82 cents per kWh, as compared to coal at 2.13 cents per kWh and natural gas 3.69 cents per kWh. Their power plants require much smaller spaces than those for biomass conversion plants, coal-fired power plants, and solar power plants. Disposal methods of nuclear fuel waste and low-level radioactive waste (LLRW) that consist of items that have come in contact with radioactive materials such as personal protective clothes have been developed. Fund, with an average of about 3.76 metric tons of dried used fuel per million dollars in the USA, has been committed to the management of nuclear waste. As of 2004, more than 690 containers have been loaded at 30 nuclear sites. This number is expected to grow to about 712 by 2015. Water discharged from a nuclear power plant contains no harmful pollutants and even meets regulatory standards for temperature. Prof. Ervin gave some facts about fission energy that are not commonly known. Nuclear energy has been widely adopted in medical procedures and cosmic. Many fruits and vegetables possess natural radioactivities. Uranium is a relatively abundant element (about as abundant as tin) that occurs naturally in Earth’s crust. To resolve the shortage in nuclear workforce, the US Nuclear Regulatory Commission has created a curriculum education grant program as a multidisciplinary technical elective.

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Fuel Efficiency in Transportation Systems, by Jack Seiner The late Prof. Jack M. Seiner of mechanical engineering and National Center for Physical Acoustics gave a lecture entitled fuel efficiency in transportation systems. The lecture covered six topics: the motivation for transportation efficiency, carbon emissions by light-duty vehicles, alternative engine concepts, alternate fuels, alternate power sources, and roles of aerodynamic efficiency. The brief opening segment of his talk reiterated the major causes of climate change and the importance of improved energy conservation and enhanced fuel efficiency in transportation systems in climate change mitigation. The discussion of carbon emissions by light-duty vehicles (the second part of the lecture) covered the concept of passenger miles per gallon (PMPG) for various transportation vehicles, estimation of auto sector CO2 emissions, and global CO2 emissions by economic sectors. It then reviewed the thermodynamics and thermal efficiencies of the two conventional piston-based engine cycles: the gasoline engine cycles and diesel engine cycles. These fundamentals led to major conclusions about the features and recent emphasis on diesel engines: diesel fuel has higher heat content than gasoline, and diesel engine has a 30–35 % higher thermal efficiency than conventional engine. For instance, a diesel engine in a light-duty vehicle such as a 2,000 lb Volkswagen Jetta gets 50 miles per gallon on the highway. The discussion of alternate engines (the third part of the lecture) covered automotive gas turbines, rotary Wankel engine, Di Pietro rotary air engine, and other types of compressed-air cars. The history, design features, advantages, and technical issues of these technologies were discussed. The comparison of these engines considered the factors including pollutant emissions, efficiency, engine weight, fuel type, maintenance effort, life expectancy, number of parts, warm-up period, fuel flexibility, noise, and throttle lag. In the discussion of alternate fuels (the fourth part of the lecture), the characteristics of gasoline with various alternate energy sources were compared: diesel, liquefied natural gas (LNG), compressed natural gas (CNG), ethanol and blended ethanol, liquid hydrogen, hydrogen at 150 bar, lithium, nickel metal hydride, lead acid battery, and compressed air. The number of vehicles that use these alternate fuels is increasing. Hydrogen and diesel have energy density higher than that of gasoline. Hydrogen does not have emission problems, but its storage in an efficient, safe, and cost-effective system has emerged as a major research area. Energies required for compression of H2 to a gaseous state at high pressure and for liquefying H2 are cost concerns. Energy loss in delivery in pipeline is another factor. Independently, means to store hydrogen by other materials have been investigated. Most of these works focus on hydrides, i.e., chemically bond hydrogen in a solid metal or carbon materials. In addition to hydrogen-packing density, performance indices include reversibility of hydrogen uptake and release, weight of hydride, kinetics of uptake and release, and the temperature and pressure dependence of H2-hydride equilibrium. H2 generation from hydrolysis of complex hydrides was discussed. Due to the complexity of these requirements, the US Department of Energy has announced a set of specific technical targets for hydrogen storage. The second half

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of the discussion of alternate fuels was placed on biofuels. The historic development of “flexible fuel vehicles” was discussed first. He then discussed the feedstock, conversion technologies, emissions, policies, and challenges of various biofuels, with the emphases on the bioethanol and biodiesel. The serious challenges in the production of bioethanol include energy consumptions in the conversion of grain to ethanol (hydrolysis, distillation, drying, and emission control), water consumption, fertilizer use, and decrease in food supply. Biodiesel has a higher thermal efficiency than gasoline; the various sources of biodiesels, emissions prospective, and costs were then covered. The alternate power sources for vehicles include hydrogen fuel cells, battery, and hybrids. This portion of the lecture covered the basic principles, comparison of various hydrogen storage systems, hybrid features and their prospective and their efficiencies in each of the energy conversion steps. The last segment of this lecture covers the aerodynamic efficiency. It included the discussions of the factors influencing airplane ticket price per the nautical mile and nautical mile per gallon of gas. Recent development of blended wing body (BWB) for airplane design has shown advantages in weight of aircraft, fuel efficiency, and, therefore, the direct operating cost. The propulsion/airframe integration, aerostructure integration aerodynamics, and controls remain as major design challenges.

Photochemical Reduction of CO2 and Water Splitting, by Nathan I. Hammer Chemistry professor Nathan Hammer, a spectroscopist, gave a lecture on photochemical reduction of CO2 and splitting of water. Solar energy is not only renewable, but also abundant. Indeed, more energy from sunlight strikes the Earth in 1 h (4.3  1020 J) than all the energy consumed on the planet in a year (4.1  1020 J). There is a high incentive to efficiently convert and store solar energy to reduce our reliance on fossil fuel. Photo- and electrochemical conversion of CO2 and H2O have been identified as one of the top five research areas in catalysis that require urgent attentions by the DOE’s Basic Energy Science (Bell et al. 2007). Photocatalytic conversion of CO2 to formic acid, formaldehyde, methanol, Co, and methane on catalyst surface has been demonstrated, and it is one of these potentially attractive approaches that brings CO2 to a higher energy state by using solar energy. While CO2 and H2O react with photochemically excited electrons, water splitting takes place at electron holes. Water splitting can also be achieved by photolysis of water alone where O2 forms on the photoanode and H2 forms on cathode. The lecture covered the importance of these technologies, their fundamental reaction mechanisms, the role of band gap, and the characteristics and selection of catalysts in these photochemical reactions. It also includes several prominent works that demonstrate photochemical fixation of CO2 on organic compounds, including the catalysts, reaction mechanisms, products, and secondary reactions.

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Carbon Sequestration, by Robert Holt Geology and geological engineering professor Robert Holt gave a lecture about his experiences in geologic carbon sequestration. He first explained the six known geologic sequestration options: • • • • • •

Depleted oil and gas reservoirs Use of CO2 in enhanced oil recovery Deep unused saline water-saturated reservoir rocks Deep unmineable coal seams Use of CO2 in enhanced coal bed methane recovery Other suggested options including basalts, oil shales, and cavities

There are four CO2 trapping mechanisms: structure and stratigraphic trapping, residual saturation trapping in large pores, solubility trapping by in situ water, and mineral trapping through chemical reactions. Mineral trapping is the most attractive process since it could immobilize CO2 for a long time. However, it is comparatively slow because it depends on dissolution of silicate minerals. Security of trapped CO2 (immobility) increases with time since the solubility and mineral trapping are slow. The annual CO2 consumption by the global chemical industries is only about 115 million metric tons, or less than 1 % of its production. At the same time, both natural and industrial analogues have provided evidences that geologic sequestration could be successful before other massive CO2 utilization and storage technologies are developed. Distribution of natural analogues in the world, such as oil and gas reservoirs and CO2 accumulation sites, is presented. Similarly, the distribution of industrial analogues in the world including natural gas storage, liquid waste disposal, and CO2 injection for enhanced oil and gas recovery were presented. The lecture covered the CO2 injection technology for enhanced recovery of hydrocarbons, the worldwide geologic storage potential, potential release pathways after injection, storage cost estimates, and monitoring technologies. Deep saline formation and sequestration in oil and gas fields have the highest capacities. The last segment of his lecture covered the development of monitoring technologies at the University of Mississippi. The technology is based on a conceptual model for CO2 transport in a saturated zone that involves buoyancy-driven CO2 fingering with pulsation through coarse layers. It is visualized that pools of CO2 are trapped beneath aquifer-confining layers; fingering and breakthrough of the trapped CO2 occur when CO2 pressure exceeds the non-wetting phase entry pressure. For phreatic aquifers, CO2 moves into the unsaturated zone and pooling above the capillary fringe. Holt’s research team has set up bench-scale apparatus and has been conducting field tests to answer the following basic scientific questions: 1. 2. 3. 4.

What controls CO2 partitioning and dissolution into the aqueous phase? What chemical reactions will occur and what are their rates? What are the sizes of zones of detectable CO2 and by-products? What type of monitoring design will be required to insure detection?

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Introduction to Climate Change: Solutions, by Wei-Yin Chen This lecture was intended to give an overview on the solutions to climate change. It was originally arranged as session #12, the opening session on climate change mitigation, but scheduling issues left us few options except for this arrangement. The lecture included four major segments: energy conservation and efficiency, alternative energy sources, advanced combustion and gasification for efficient carbon utilization and enabling carbon capture and sequestration, and other advanced technologies. The first segment on energy conservation and efficiency started with the IPCC’s comparison (Intergovernmental Panel on Climate Change 2007c) of sectoral economical potential for global mitigation as a function of carbon price in 2030. The residential and commercial buildings sector leads the six other sectors in sectoral economical potential for global mitigation, which reflects the important role of energy conservation. Both building design and personal behaviors, such as the replacement of standard incandescent bulbs by compact fluorescent light bulb and shutting off lights and personal computers after work, could make notable contributions to carbon reduction. Energy supply sector ranks second in sectoral economical potential for global mitigation. Thus, the impact factors of various emerging technologies for power generation were discussed. According to IPCC’s AR4, nuclear power, natural gas combined cycle, wind power, integrated coal gasification combined cycle (IGCC), pulverized coal combustion with oxygen (oxy-combustion) and carbon capture and sequestration (CCS), and pulverized coal combustion CCS are leading technologies that have the highest impact factors. The segment on alternative energy sources covered the technical principles of nuclear energy, biofuels, wind and tide, geothermal energy, and solar energy. The rationale, scientific principles, and technologies are discussed. For the biofuel production and power generation, it covered biodiesel from vegetable oil and fat, ethanol from sugar cane and corn, ethanol from lignocellulosics, and thermal conversion of biomass. The segment on advanced combustion and gasification for efficient carbon utilization and enabling carbon capture and sequestration covered the need for carbon capture and sequestration, the present sequestration technologies, and the post- and precombustion carbon capture technologies. Topics also included in the sequent discussion are basic design principles of oxy-fuel combustion, IGCC, chemical looping combustion, and integrated oxygen transport membrane for combustion. The segment on other advanced technologies covered the fundamental principles of photocatalytic reduction of CO2, electrochemical splitting of H2O, and geoengineering approaches. The four geoengineering approaches include the use of stratospheric aerosols, cloud albedo enhancement, ocean iron fertilization, and sunshade geoengineering.

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Integrated Gasification Combined Cycle (IGCC), by Robert Dahlin Dr. Robert Dahlin, director of the Power Systems and Environmental Research of the Southern Research Institute, gave a lecture on IGCC. The lecture started with the presentation of the rationales behind the widespread use of coal in the USA: coal’s abundance, wide availability, and more than 250 years’ reserve (comparing to limited natural gas supply). IGCC flow sheets were used to discuss its major unit operations and the process features. These features include: • Higher thermal efficiency than pulverized coal combustion • Lower emissions • Higher feedstock flexibility (coal, oil, natural gas, biomass, petroleum coke, and waste) • Higher product versatility (chemicals, liquid fuels, power, and gas fuel) • More economic means for CO2 capture than pulverized-coal combustion (due to the higher CO2 concentration from a gasifier) Coal gasification was first used for streetlight in 1792. The USA had 1,200 gas plants in 1920s, but the discovery of natural gas led to demise of these plants. Increased energy demand, high natural gas prices, and stringent environmental regulations focused interests on IGCC. There are 117 operating plants and 385 gasifiers worldwide. The lecture then covered the scientific reasons of the IGCC advantages over pulverized coal combustion mentioned above. IGCC removes the pollutants from synthesis gas before they are burned. High pressure and low gas volume provide favorable economics of pollutant removal; these operating characteristics also allow flexibility in pollutant concentration in feedstock. IGCC also produces less waste and consumes less water. IGCC uses two power cycles in series: gas turbine where power is generated from burning the syngas and steam turbine where power is generated from steam expansion. Syngas can also be used to produce chemicals and liquid fuels. Hydrogen can be used as a transportation fuel and fuel cell for generating electricity. These unit operations reveal not only the versatility of the IGCC process, but also a higher IGCC efficiency higher than that of pulverized coal combustion. Some features of the 513 MW IGCC demonstration plant in Kemper County, Mississippi, were then discussed. Lignite will be its primary fuel and natural gas its backup. It has an air-blown rather than an O2-blown gasifier. This modification eliminates the need of an air separation unit and reduces the capital and operating costs and cost of electricity. Moreover, it significantly reduces the emissions of SO2, NO2, CO, volatile organic compounds, and particulates. It is anticipated that about 50 % of the CO2 will be captured. The last segment of his lecture covered some of the research activities of Southern Company’s IGCC Power Systems Development Facility (PSDF) at Wilsonville,

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Alabama. These activities include the particulate removal by hot-gas filtration, hightemperature high-pressure particulate sampling system, development of drag correlations for the feeding system, tar cracking, gas clean up, and CO2 capture. CO2 capture for the high-pressure syngas in an IGCC plant will be less costly than a conventional pulverized coal combustion process. Southern Research is testing a wide spectrum of solvents and additives for capturing CO2; representative data were presented.

Oxy-firing and Chemical Looping, by Thomas K. Gale Dr. Thomas Gale, Power Systems Research Manager of the Southern Research Institute, presented his efforts on oxy-firing and chemical looping. Both are emerging carbon-capture-enabling technologies. The objective of Southern Research’s oxygen-fired CO2 recycle combustion project is to investigate, develop, optimize, and model O2-fired utility boilers by: • Retrofitting the existing Southern Company/Southern Research’s 1 MW pilotscale test facility • Utilizing an advanced oxy-fired coal burner • Measuring the operating and output responses to adjustable parameters • Comparing these responses with CFD modeling results The project has multiple participants. Coal is burnt by O2 and recycled CO2 in an oxy-fired boiler so that the flue gas contains mainly CO2, not N2 as the conventional pulverized coal combustion process. About 75 % of the CO2 is recycled to avoid excessive flame temperature and maintain flow and heat transfer requirements. Thus, advanced burner for oxy-firing will be carefully signed to allow flame shape and heat release to be controlled and to provide a stable attached flame without natural gas assist. Since N2 is not in the flue gas, the flue gas from an oxy-fired boiler has a 25 % volume of that from a conventional air-fired boiler, purification and compression are much less expensive for carbon sequestration. Air separation prior to combustion, however, creates sizable (about 25 %) energy penalty and notable electricity cost. Additional energy penalty comes from purification of CO2 and compression and sequestration. These concepts were introduced in this lecture. Moreover, Southern Research’s unique efforts were discussed that include Maxon oxy-fired burner, oxygen skid and piping system, distributed control system, gas flow control system, recycle system, safety system, conversion of facilities, axial temperature distribution and NO emissions from tests of two coals, computational fluid dynamics modeling conducted by Reaction Engineering International, and plans for the future tests. Parameter study for the future will be devoted to coal type, amounts of O2 in primary flow, burner quarl tip, staging through the recycle-gas tip on the sides of the burner, and staging through the over-fired ports, and percentage of recycle. Flue gas composition, carbon burnout, inleakage, temperature profile, heat transfer, stability

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of the test, acid-gas buildup, and apparent corrosion will be monitored during the tests. In the second segment of the lecture, the basic concepts of the emerging chemical looping technology and Southern Research’s research activities were introduced. An oxygen carrier, usually an oxidized metal, shuttles between two vessels in a cyclic chemical looping process. The oxide oxidizes (or gasifies) the fuel, such as coal and natural gas, in one reactor, while the reduced oxygen carrier is oxidized by air in a second reactor. Since the oxidant for burning fuel does not have N2, the CO2 concentration is high in the flue gas. Separation of CO2 from the fuel oxidation reactor of chemical looping process is much less expensive than separating CO2 from the flue gas of a pulverized coal combustor. Chemical looping is versatile, as it can be applied to both combustion and gasification. Finally, Dr. Gale predicts the overall future of coal-fired power generation in the face of CO2 emission regulation. Existing plants will have three choices as a result of immediate regulations: adding CO2 scrubbers, retrofitting the boiler by installing oxy-firing with flue-gas recycle, or closing the plant. New plants may include oxy-fired furnace without much if any recycle and advanced thermodynamic cycles to offset the energy penalty or other advanced power systems such as oxy-fired IGCC. In the long term, chemical looping and IGCC look promising.

Fuel Cells, by Amala Dass The lecture of chemistry professor Amala Dass included five arguments: fuel cell basics, fuel cell stacks and bipolar plates, types of fuel cells, proton exchange membrane fuel cells, and current status. A schematic is used to illustrate the major components and their functions in a hydrogen fuel cell (proton exchange membrane or PEM cell) at the outset. Slow reaction rate and hydrogen availability remain as challenges. To gain high power output, multiple fuel cells are arranged in series, and bipolar plates are developed. PEM fuel cell uses relatively low temperature and can start quickly. It has been adopted in cars and buses. Five other major types of fuel cells have been developed: phosphoric acid, direct methanol (DMFC), alkaline (AFC), molten carbonate (MCFC), and solid oxide (SOFC). Hydrogen is the fuel for all of these fuel cells. The operating temperature ranges, electrolytes, catalysts, and operating characteristics of these fuel cells were discussed. Schematics were presented to illustrate the chemical structure and characteristics of the most popular PEM, Nafion. The strong C-F bond resists chemical attacks. Nafion’s unique ionic properties are a result of incorporating perfluorovinyl ether groups terminated with sulfonate groups onto a tetrafluoroethylene (i.e., Teflon) backbone. The polymer is hydrophobic; the sulfonate side chains, SO3
, however, are hydrophilic. This leads to the desirable hydrophilic/hydrophobic micro-phase separated morphology. Advancement in nanotechnology has significantly enhanced efficiency of carbon-supported platinum catalysts.

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Hydrogen is the common fuel for several types of the fuel cells mentioned above. The issues facing the infrastructure of hydrogen economy include H2 production, delivery, storage, safety, and end-use materials. For fuel cell, the emerging technology faces challenges in the development of PEM stock, ancillary devices, fuel processors, fuel storage, fuel supply, and electric components. The industry for H2-powered fuel cell (H2FC) vehicles will have to overcome several critical technological barriers that include the hydrogen cost, H2 storage capacity at reduced cost, and fuel cell cost with higher durability. It also has to overcome the economic and institutional barriers that include safety, codes and standard development, H2 delivery infrastructure, domestic manufacturing and supply base, and public awareness and acceptance.

Computational Chemistry, by Steven Davis Professor Steven Davis of the chemistry department gave an overview of computational chemistry techniques and their applications to reaction energetics and dynamics. The Hartree-Fock method was discussed along with its failure to include an accurate description of dynamic electron correlation. Characterization of chemical structures along potential energy surfaces was presented using the harmonic oscillator approximation to calculate vibrational frequencies to determine minima (stable structures) and maxima (transition states). Accurate potential energy surfaces were presented as determined using both single and multiconfigurational wave functions. Examples of highly strained structures with potential for solar energy storage were presented and their thermal isomerizations discussed. One such example, tricyclo[3.1.0.02,6]hexane (Davis et al. 2003), illustrated the necessity of using a multiconfigurational wave function plus Moller-Plesset perturbation theory to achieve accurate energies and correct electronic descriptions of transition states. The use of trans double bonds in small hydrocarbon rings as a way to store potential energy was discussed and the advantages of using computational chemistry to determine relevant reaction pathways and energetics presented. The activation barriers were shown to be somewhat tunable by the substitution of heteroatoms for carbon atoms in the ring moiety (Davis et al. 2009). Multireferenced second-order Moller-Plesset perturbation theory (MRMP2) and coupled-cluster singles, doubles, non-iterative triplets [CCSD(T)] accuracies were compared for systems in which a single determinant was valid to provide a basis for accepting the MRMP2 energies for wave functions with strong multiconfigurational character. A brief overview of how to choose the correct theoretical model to accurately determine chemical properties using computational chemistry was discussed. It was hoped that the students would gain an appreciation for the powerful tool computational chemistry has become as a companion to the experimentalist.

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Table 2 Student presentations in 2008 and 2009 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Student Damon Webster Crystal Warren Joey Parkerson Michael McClure Eddie Smith Archer Davis Josh Sage Leanna Smith Brett Vescovo Grady Cutrer Sarah Mixon Alison Kinnaman Eric Williams Shaolong Wan Benson Gathitu Guang Shi Michael Brandes Whitney Hauslein Katherine Osborne Jonathan Jones Ifejesu Eni-olorunda Elizabeth Spence Ray Nalty

Research title Nuclear energy Implementation of solar panels on commercial properties and the costbased incentives Atmospheric carbon dioxide capture technologies Solar energy Home energy efficiency Ice cores Hydroelectric energy Green roofs US vs. global policy changes Green community Algae-based biofuels Microremediation Biomass utilization Oxy-coal combustion Integrated gasification combined cycle Chemical looping combustion Renewable wind energy Ocean power Heat island infrastructure effects on climate change The hydrogen economy, hydrogen fuel cells, and implementation in Oxford, Mississippi The hydrogen economy – harnessing wind energy How to go green Weather control

Activities of the Students Students were asked to choose a research topic at the beginning of the semester. They were asked to give a short oral report about their research status, questions, and difficulties in the middle of the semester. They were asked to give a term paper and an oral presentation at the end of the semester. A list of the students’ presentations is given in Table 2. The topics range from offering tips in everyday life to modern technologies. Their presentations and papers were evaluated by a panel formed by a

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group of faculties. Two outstanding works selected for the two awards were “Implementation of Solar Panels on Commercial Properties and the Cost-Based Incentives” by Crystal Warren and “Atmospheric Carbon Dioxide Capture Technologies” by Joey Keith Parkerson. They were given the title Ole Miss Idols of Climate Change Mitigation. In addition, each one of them received a $250 cash award. Their slides have been uploaded to the course Web site at http://home.olemiss.edu/~cmchengs/ Global%20Warming/ along with the formal lectures. Joey introduced the technology of capturing atmospheric CO2 by using NaOH in a process that includes two chemical looping cycles: one involves NaOH/Na2CO3 and the other Ca(OH)2/CaCO3/CaO. In the sodium loop, NaOH is converted to Na2CO3 in an air/CO2 contactor, and Na2CO3 is converted back to NaOH by Ca (OH)2 in a causticizer. In addition to the causticizer, the calcium loop includes calciner and a slacker. CaCO3 decomposes to CaO in the calciner, and CaO is hydrolyzed to Ca(OH)2 in the slacker. Joey presented the capture technology as well as the cost evaluation, about $240 per ton of carbon. He argued that it would be competitive if compared to the cost for capturing carbon emitted from vehicles since no mobile device has to be carried. Crystal proposed the widespread use of solar panels for commercial properties, legislative incentives for such installation, and mandatory installation by law. She presented the principles of solar panels, the rationales, cost estimate, and recommendations. Success stories of Wal-Mart, FedEx, and Google were discussed. She interviewed local business owners, including her mother and Wal-Mart, during her course of research. German government pays solar panel users 20 cents per kWh received from grid and receives 50 cents per kWh for energy sent back to grid. Crystal, on the other hand, proposed that federal, state, and local government each pays one third of the installation cost. She also proposed fixed energy prices for 15 years at 25 cents per kWh purchased from the grid and 50 cents per kWh for energy sent back to grid. The Chancellor of the University of Mississippi launched the campus Green Initiative in the spring of 2008 when the course was offered for the first time. The students were excited about the initiatives; they felt that they can share their enthusiasms by transforming what they learned in the semester into actions. The Green Initiative, however, was in its infancy, and the administrators in charge had just started to organize a committee, which will eventually layout the tasks. The students decided to examine the current policies and facilities of the University and explore if there were rooms for improvement. They spent a few evenings together and deliberated their thoughts. The product of their discussions is a list of recommendations, which can be found at: http://home.olemiss.edu/~cmchengs/Global% 20Warming/. An oral presentation was also made to the University administrators. The newly inaugurated provost, the retiring provost, and the university architect were among the participants for the presentation and its subsequent discussions. The two parts of the recommendations, policy and facility, were presented by the two students who were selected to be the idols of the climate change mitigation only a

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couple of days earlier. In the last years, knowledge about climate change, sustainable energy, and environment has indeed been gradually incorporated into the curriculum. Research collaborations and outreach activities in these areas have increased. The University and City of Oxford, Mississippi, have established a public transportation. This course has notably induced public awareness.

Future Directions Climate change affects the life of every living species on Earth. Only contributions from all concerned citizens could generate a monumental impact. Thus, climate change literacy must reach grassroot level that has several unprecedentedly largescale characteristics. Disseminating climate change knowledge secures the immediate need of a strong workforce in the battle against climate change, which is gravely lacking. It raises public awareness and promotes actions of world citizens. More importantly, it is expected to catalyze innovations. In his State of the Union address of 2011 (Obama 2011), President Obama stated the philosophy of his science and technology: “This is our generation’s Sputnik moment.” The Russian Sputnik space satellite program in 1958 has stimulated the space race that, in turn, promoted innovations in many areas of science and technology. President Obama also characterized the nature of the new race in the same address: “We’ll invest in biomedical research, information technology, and especially clean energy technology - an investment that will strengthen our security, protect our planet, and create countless new jobs for our people.” The clean energy activities are expected to grow rapidly in the near future. Mitigating GHG concentrations is closely related to the growths in energy demand, economy, and population. The adaption, impacts, and mitigation of GHG require knowledge in many different (if not all) fields and actions related to different sectors (if not all) of the civilization. Most notably, as this Handbook is organized, collaborations of political, legislative, educational, scientific, technological, and news media sectors will be necessary. For completeness, climate change literacy must cover all of these areas. Science and technology will be in the core of these efforts. In the next few decades, there will be a wide range of research and development investments on the establishment of an alternative energy infrastructure, especially in the areas of biomass conversion, solar panel and concentrator, nuclear energy, wind turbine, hydropower, fuel cell, and geothermal energy. Fossil fuels, particularly coal, are abundant and widespread, and fossil fuel-based power generation is expected to remain a major driving force of the economy in the near future. CO2 production, however, is much higher than its current utilization. Therefore, carbon capture and sequestration and large-scale utilization of CO2 will be of great interests. Moreover, much attention will also be placed on technologies that enable carbon capture including chemical looping combustion, oxy-fuel combustion, and integrated

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oxygen-transport membrane. Integrated coal gasification combined cycle (IGCC) is versatile and efficient. A hydrogen economy will depend on the development of technologies in several industrial sectors. Energy conservation and energy efficiency will be two major approaches to optimize the limited sources for power generation, transportation, buildings, and industrial sectors. High-risk and high-impact innovations are not only required for clean energy research, but also desirable for science, technology, engineering, and mathematics (STEM) educations with emphasis on climate change literacy. Government investments on these topics are expected to increase worldwide. Efforts of the National Science Foundation (2011) and NASA (2011) are just two major sources for literacy funding in the USA. Synergistic collaborative alliances are welcome for generating projects with unique objectives and broad impacts. Creative use of information technologies, such as global seminars, could facilitate communication network. At the time of writing this chapter, the author has jointly offered a video, online global seminar class on sustainability with a colleague at the National Pingtung University of Science and Technology in Taiwan. The objectives of this course are to introduce modern issues to graduate students around the globe and to induce healthy and constructive debates representing the students in different regions of the world and different sectors of the society. In addition to the speed of the Internet, there is plenty of information available on the World Wide Web; therefore, Web-based teaching is expected to be more versatile and creative in the future.

Conclusions Climate literacy can be offered with a group of faculties from different fields in various fashions. The broad nature of the subject renders it possible to cover only selected topics for the organized lectures and other topics for students’ research. This course has generated a set of useful slides public dissemination. However, the Web sites mentioned in section “Lectures Presented by Faculty and Scholars” are also available. Alternatively, some of the organized lectures can be replaced by students’ deliberations on subjects such as “Should we promote the production of bioethanol as an alternative fuel?” or “Should we promote the growth of genetically altered plants for increased photosynthesis?” These discussions can certainly promote critical thinking about important issues and absorbing knowledge in related fields. Activities of climate change literacy are expected to increase in the next decade. There will be more formal and short courses, workshops, summer camps, and outreach projects in other formats. Sustainability will be a central theme of these activities. With the adoption of modern information technology, climate change literacy is likely to accelerate President Obama’s prediction that “This is our generation’s Sputnik moment” (Obama 2011).

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References Beckman EJ (2004) Supercritical and near-critical CO2 in green chemical synthesis and processing. J Supercrit Fluids 28:121–191 Bell AT, Gates BC, Ray D (2007) Basic research needs: catalysis for energy. Office of Basic Energy Science, US Department of Energy, Washington, DC California’s Climate Action Team (2007) Climate action team proposed early actions to mitigate climate change in California, California Protection Agency. http://www.climatechange.ca.gov/ climate_action_team/reports/2007-04-20_CAT_REPORT.PDF Davis SR, Nguyen KA, Lammertsma K, Mattern DL, Walker JE (2003) Ab initio study of the thermal isomerization of tricyclo[3.1.0.02,6]hexane to (Z, Z)-1,3-cyclohexadiene through the (E, Z)-1,3-cyclohexadiene intermediate. J Phys Chem A 107:198–203 Davis SR, Veals JD, Scardino DJ, Zhao Z (2009) Isomerization barriers and strain energies of selected dihydropyridines and pyrans with trans double bonds. J Phys Chem A 113:8724–8730 Dessler AE, Parson EA (2010) The science and politics of climate change: a guide to the debate, 2nd edn. Cambridge University Press, Cambridge Energy Information Administration (EIA) (2008) Emissions of greenhouse gases in the United States 2007. Office of Integrated Analysis and Forecasting, U.S. Department of Energy, Washington, DC. http://www.eia.doe.gov/oiaf/1605/archive/gg08rpt/pdf/0573(2007).pdf Energy Information Agency (EIA) (2007) Annual energy outlook, with projections to 2030. US Department of Energy, Washington, DC. http://ftp.eia.doe.gov/forecasting/0383(2007).pdf. Accessed 16 Sept 2011 Gerrard MB (2007) Global climate change and U.S. law. American Bar Association, Chicago, pp 17–25, 32–58, 61–85 Intergovernmental Panel on Climate Change (2007a) Climate change 2007 – the physical science basis: working group I contribution to the fourth assessment of the IPCC. Cambridge University Press, Cambridge. www.ipcc.ch Intergovernmental Panel on Climate Change (2007b) Climate change 2007 – impact, adaptation and vulnerability: working group II contribution to the fourth assessment of the IPCC. Cambridge University Press, Cambridge. www.ipcc.ch Intergovernmental Panel on Climate Change (2007c) Climate change 2007 – mitigation of climate change: working group III contribution to the fourth assessment of the IPCC. Cambridge University Press, Cambridge. www.ipcc.ch Jacob DJ (1999) Atmospheric chemistry. Princeton University Press, Princeton Manson N (2002) Formulating the precautionary principle. Environ Ethics 4(3):263–274 National Aeronautics and Space Administration (2011) Global climate change education project. Langley Research Center, Hampton. http://www.nasa.gov/offices/education/programs/descrip tions/Global_Climate_Change_Education_Project.html National Science Foundation (2011) FY 2012 budget request to congress, Washington, DC. http:// www.nsf.gov/about/budget/fy2012/pdf/fy2012_rollup.pdf Obama B (2011) State of unions address. US Capital, Washington, DC. http://www.whitehouse. gov/the-press-office/2011/01/25/remarks-president-state-union-address Posner R (2003) Economic analysis of law. Aspen, New York Seinfeld JH, Pandis SN (1998) Atmospheric chemistry and physics: from air pollution to climate change. Wiley, New York Sunstein CR (2005) Cost-benefit analysis and the environment. Ethics 115:351–385 Tester JW, Drake EM, Driscoll MJ, Golay MW, Peters WA (2005) Sustainable energy choosing among options. MIT Press, Cambridge, MA Vanek FM, Albright LD (2008) Energy systems engineering: evaluation and implementation. McGraw-Hill, New York Wayne RP (1985) Chemistry of atmospheres. Clarendon, Oxford World Health Organization (2003) Climate change and human health – risks and responses. Summary. World Health Organization, Geneva. ISBN 9241590815

Reducing Personal Mobility for Climate Change Mitigation Patrick Moriarty and Damon Honnery

Contents Introduction: Travel Reductions for Climate Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Surface Transport Patterns in Four Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Present Transport Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Predicted Future Transport Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Creating Environmentally Sustainable Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Principles for Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changing Travel Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Voluntary Travel Reductions: An Australian Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reducing Travel: Changing Vehicle Occupancy Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reducing Travel: Changing Urban Land Use Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reducing Travel: Raising the Overall Level of Motoring Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reducing Travel: Lowering the Convenience of Car Travel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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In the high-mobility countries of the Organisation for Economic Cooperation and Development (OECD), many governments are seeking to reduce personal mobility, particularly car travel, for a variety of reasons. Reductions can be justified in general by concerns about global climate change, oil depletion and supply security, and traffic casualties. In urban areas, additional concerns are air pollution, traffic congestion, take-up of land by transport infrastructure, and quality of P. Moriarty (*) Department of Design, Monash University, Melbourne, VIC, Australia e-mail: [email protected] D. Honnery Department of Mechanical and Aerospace Engineering, Monash University, Melbourne, VIC, Australia e-mail: [email protected] # Springer International Publishing Switzerland 2017 W.-Y. Chen et al. (eds.), Handbook of Climate Change Mitigation and Adaptation, DOI 10.1007/978-3-319-14409-2_51

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urban life. Similarly, a variety of technological approaches are possible for addressing these problems in the context of global warming mitigation. This chapter examines policies for mobility reduction, as these can have a significant impact on climate change mitigation. It mainly restricts itself to the high-mobility countries of the OECD and uses four such countries (Australia, Japan, the UK, and the US) as case studies. The approaches considered here include: • Using modern Information Technology (IT) advances to promote travel substitution • Car pooling, especially in urban areas • Land use planning, particularly increased urban densities • Encouraging the use of more environmentally friendly travel modes • Raising the overall level (and perhaps also changing the structure) of motoring costs • Reducing the convenience of car travel. It is found that the use of IT, car pooling, and land use planning, whether voluntary or legislated, cannot be expected to produce much reduction in either car passenger-km or vehicle-km. Nor will reliance on voluntary approaches for car travel reduction by encouraging more use of environmentally friendly travel modes. Only the last two approaches can produce large and sustained reductions in travel greenhouse gas emissions, but heavy reliance on market forces such as very large increases in motoring costs is inequitable in OECD countries. The only equitable approach is to reduce the convenience of car travel, for example, by large travel speed reductions and by a reversal of the usual present ranking of travel modes: car, public transport, and active modes. Abbreviations

ABS BITRE bp-k BTS DfT EFMs EIA GDP GHG HOV IEA IPCC IT OECD OPEC

Australian Bureau of Statistics Bureau of Infrastructure, Transport, and Regional Economics (Australia) billion passenger-km Bureau of Transportation Statistics (US) Department for Transport (UK) Environmentally friendly modes Energy Information Administration (US) Gross Domestic Product Greenhouse gas High occupancy vehicle International Energy Agency Intergovernmental Panel on Climate Change Information Technology Organisation for Economic Cooperation and Development Organization of the Petroleum Exporting Countries

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PEB SBJ TDM UN WBCSD

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Pro-environmental behavior Statistics Bureau Japan Travel demand management United Nations World Business Council for Sustainable Development

Introduction: Travel Reductions for Climate Mitigation In the high-mobility countries of the Organisation for Economic Cooperation and Development (OECD), many governments are seeking to reduce personal mobility, particularly car travel, for a variety of reasons. Reductions can be justified in general by concerns about global climate change, oil depletion and supply security, traffic casualties, and even personal fitness (Moriarty and Kennedy 2004; Sallis et al. 2004). In urban areas, additional concerns are air pollution, traffic congestion, take-up of land by transport infrastructure, and quality of urban life. A variety of technological approaches are possible for addressing the problems listed, including improving vehicular fuel efficiency and developing alternative fuels and power systems. This chapter examines policies for mobility reduction, as these can have a significant impact on climate change mitigation. Nevertheless, the other arguments given above for the desirability of travel reductions can help acceptance of such policies. Although this study is mainly restricted to examining travel in only four OECD countries (Australia, Japan, the UK, and the US), the conclusions should have more general application, at least to other high-mobility OECD countries. The approaches considered here include: • Using modern Information Technology (IT) advances to promote travel substitution • Car pooling, especially in urban areas • Land use planning, particularly increased urban densities • Encouraging the use of more environmentally friendly travel modes • Raising the overall level (and perhaps also changing the structure) of motoring costs • Reducing the convenience of car travel. What level of greenhouse gas (GHG) reduction in passenger transport might be needed to avoid serious anthropogenic climate change? It is here assumed that reductions in the transport sector, including surface passenger transport, will need to match those in the world economy overall. The 2013 Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway 2.6 (RCP2.6) has used models showing that CO2 emissions from fossil fuels may have to be cut by the year 2050 to as little as 30.5 % of the year 2013 values, and fall to zero by 2070, if global temperature rises are to be limited to 2  C (Stocker et al. 2013).

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(The European Union regards a rise of 2  C since the industrial revolution as representing a prudent limit for avoiding dangerous climatic change.) Others believe that CO2 atmospheric concentration levels are already too high, and emissions thus need to be cut to zero before 2050 (Moriarty and Honnery 2011). In 2010, travel by all modes accounted for seven billion tonnes of CO2-equivalent emissions, with about 72 % coming from road transport (Sims et al. 2014). For overall CO2 reductions, transport cannot be ignored. Assume that global energy-related CO2 emissions have to follow the RCP2.6 limits and that by 2050 need to be cut to 2.9 billion tonnes of carbon (2.9 GtC) annual levels (Stocker et al. 2013). By 2050, passenger transport emission levels would also have to fall proportionally, if emission reductions are shared equally across sectors. But emission levels in the OECD countries are far higher than the world average. For example in 2013, the US emissions of CO2 from fossil fuel combustion was 3.8 times the world average (BP 2014). If equal per capita emissions for the entire world’s population are assumed by 2050, then (provided US share of world population remains at its present level) US surface passenger transport emissions would need to fall to around 8 % of their 2013 value. What this simple calculation shows is that reduction of a few percent in transport emissions will not suffice: a drastic reduction is needed. Overall, this study finds that the use of IT, car pooling, and land use planning, whether voluntary or legislated, cannot be expected to produce much reduction in either car passenger-km or vehicle-km. Nor will reliance on voluntary approaches for car travel reduction by encouraging more use of environmentally friendly modes (EFMs) of travel. Only the last two approaches can produce large and sustained reductions in travel greenhouse gas emissions, but heavy reliance on market forces, such as very large increases in motoring costs, is inequitable, at least in highly motorized countries. The only equitable approach is to reduce the convenience of car travel, perhaps by large travel speed reductions and by a reversal of the usual present ranking of travel modes: car, public transport, and active modes.

Surface Transport Patterns in Four OECD Countries Before attempting to discuss any specific policies for personal mobility reductions in any country, it is necessary to look at both the present transport situation and the path surface transport took in that country to reach the present position. Four OECD countries (Australia, Japan, UK, US), representing all four continents from which the OECD has members, were chosen as case studies. Together these countries span the range of transport-relevant parameters (for example, public transport share, gasoline costs, urban densities, car occupancy rates) found in OECD countries overall (The one exception is for non-motorized transport, so the experience of other OECD countries will be briefly considered for these modes.). These four countries also have good travel statistics available in English. It is also important to look at possible future patterns of surface travel. Accordingly, both the forecasts of global and regional travel from the latest IPCC Report

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(Sims et al. 2014) and official travel energy forecasts for the US have been included. In addition, the findings of researchers who have used the existence of travel time and money budgets to project travel in each of the various regions of the globe have been discussed. Levels of surface travel both much higher and much lower than at present have been predicted by various authorities. Studying past, present, and projected future travel patterns are important for several reasons. Comparison of present travel in the four countries can reveal insights into where policy levers should be applied for climate mitigation. Understanding is needed, for instance, as to why surface travel per capita in Japan is so much lower than in the other countries studied. Discussion of projected travel by official organizations in, say, the year 2040 is also important. If travel reductions are going to occur anyhow because of over-riding changes in technology, the economy, or lifestyles, the remaining task would then be to merely guide these changes.

Present Transport Patterns Vehicular Travel Table 1 shows the composition of vehicular surface travel in passenger-km per capita in the four countries considered. It is evident from the table that in 1960, car travel was well-established as the dominant mode in the US, but was still negligible in Japan. Australia and the UK were in an intermediate position, with Australia closer to the US in car ownership and use. Both personal travel by motorcycles and light trucks have been included with car travel. Motorcycle travel is negligible today in each country (less than 1 %), except in Japan. Even in Japan, the share of motor cycle travel appears to have peaked in the 1980s and today is only a few percent of total passenger travel (Statistics Bureau Japan (SBJ) 2014). Table 1 also shows that combined public transport patronage is today lower in the UK and the US than it was in 1960, but has grown slightly in Australia, and strongly in Japan, with most of the growth on rail. In fact, bus transport per capita peaked in Japan in the early 1970s at around 1,000 passenger-km/capita and has steadily fallen since. In contrast, train travel is resuming the strong growth that temporarily reversed in the mid-1990s. Figure 1 demonstrates that per capita private car travel over the past 10–20 years appears to have leveled out in Australia, Japan, and the UK. Even in the US, car travel per capita has since 2004 flattened out as well, before the recent global financial crisis actually reduced per capita travel levels. In Great Britain, even total car, van and taxi travel, fell from 674 billion passenger-km (bp-k) in 2007 to 643 bp-k in 2012 (DfT 2013). It is evident that the very high levels attained in the US are unlikely to be reached by the other three countries – or by any other country, in or out of the OECD. And perhaps not even again in the US: the total number of vehicles registered per 1,000 population may have peaked in 2007 (Davis et al. 2013). Another feature of the graph is that the later the country began its push to mass car ownership, the lower the peak level of travel per capita.

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Table 1 Per capita surface passenger-km of travel by mode and country, 1960 and 2011 Country Australia Australia Japan Japan UKa UKa US US

Year 1960 2011 1960 2011 1960 2011 1960 2011

Bus/tramb 390 870 495 705d 1,550 685 650 1,510

Train 875 670 1,955 3,090 785 1,110 200 195

Carc 6,500 13,620 130 6,350d 2,950 10,530 11,100 20,310

Total 7,765 15,155 2,580 10,145 5,285 12,325 11,950 22,015

Car % 83.7 89.9 5.0 62.6 55.8 85.4 92.9 92.3

a

Averages are for Great Britain only, i.e., excluding Northern Ireland Includes a small amount of coastal sea transport c Includes all private road vehicle travel d 2009 values Sources: US Department of Transportation (DoT) (2011), Department for Transport (DfT) (2012), Bureau of Infrastructure, Transport, and Regional Economics (BITRE) (2013), Davis et al. (2013), DfT (2013), Bureau of Transportation Statistics (BTS) (2014), SBJ (2014) b

Fig. 1 Surface travel per capita versus year for Australia, Japan, the UK, and the US, 1960–2011 (Sources: As for Table 1)

In Australia, at least, males have been over-represented in car travel and underrepresented in public transport travel compared with females. Further, females have been traditionally over-represented as car passengers, not drivers. This situation is changing, and today, the differences, although persisting, are small (Moriarty and Mees 2006). In all four countries, young female car driver licence-holding rates are approaching those for males. In a number of US states, there are now more female than male licence holders.

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Non-Motorized Travel So far, only vehicular surface travel has been considered. But non-motorized travel – walking and cycling – will, it is argued, be an important component of vehicular travel reductions for mitigating climate change. Non-motorized travel is better recorded for the journey to work trip, so most of the data presented here is for this trip type. For Melbourne, Australia, the available data span more than 50 years from 1951 to 2006, the date of the last national census. In Melbourne in 1951, about 11 % of work trips were by walking, with another 10 % on bicycle. By 2011, their share had fallen to 3.6 % and 1.3 %, respectively. Most of the drop in cycling had occurred by the late 1960s, although the fall in the share for walking was more uniform over time (Moriarty and Mees 2006). For Australia overall in 2011, the share of these modes was similar, with 4.5 % walking and 1.3 % cycling for the journey to work (BITRE 2013). As with motorized travel, females in the past had higher rates of non-motorized travel overall than males, but much greater rates of walking and lower rates of cycling for the work trip. Today their rate is similar to that for males. For Great Britain overall, journey to work mode of travel data go back to 1890. In the 1890s decade, 59.4 % walked and 2.0 % cycled to work. Cycling peaked in the 1940s decade at 19.6 %, with a further 17.2 % walking (Pooley and Turnbull 2000). By 2008, the figures had fallen to 3 % for cycle and and 11 % on foot. In terms of travel distance, for all trip types cycling fell from 23 bp-k to about 4 bp-k between 1952 and 2007, although bicycle travel has been almost constant at between 4 and 5 bp-k for the past two decades (DfT 2012, 2013). It is difficult to get data on the early use of non-motorized travel in Japan, but given the negligible vehicle ownership until the 1970s (ownership was only 5 per 1,000 population in 1960 (SBJ 2014)), the use of non-motorized modes was presumably high. In Tokyo, non-motorized trips were 25.8 % of the total in 1970 and still 21.7 % in 1990 for the journey to work (Newman and Kenworthy 1989, 1999). In the US, these modes were even less used than in Australia, with 2.8 % walking for the work trip in the US overall in 2009 (BTS 2014). In 1983, the figure for walking was a little higher at 4.3 %. The highest level of walking to work was found in small cities and towns, with both large metropolitan areas and rural areas having somewhat lower levels (US Census Bureau 2012). Ausubel et al. (1998) presented a graph showing that daily walking per capita by Americans was around 4 km in 1880 and did not fall below 3 km until 1960, after which it fell rapidly to well below 1 km today. These low values for non-motorized travel in the four countries today can be contrasted with several other OECD countries in Europe. Pucher and Dijkstra (2003) examined bicycle and walking trip-making in the urban areas in a number of OECD countries. Table 2 shows the results for selected countries for the year 1995. The authors caution that the definition of a trip varies from country to country, but even so, the results are startling, and shows what is possible even today, given the right conditions. Climate seems to have a very little effect on the level of non-motorized trip-making. Sweden, part of which lies above the Arctic Circle, has far higher levels than several countries with more benign climates, such as

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Table 2 Proportion of all urban trips by walking and cycling, various OECD countries, 1995 Country US Canada England and Wales France Italy Germany Sweden Denmark Netherlands

All nonmotorized trips (%) 7 12 16 28 28 34 39 41 46

Walking (%) 6 10 12 24 24 22 29 21 18

Cycling (%) 1 2 4 4 4 12 10 20 28

Source: Pucher and Dijkstra (2003)

France and Italy. And as shown for 46 world cities by Newman and Kenworthy (1999) for 1990, the average for walking and cycling combined for the work trip was 5.1 % for six Australian cities, 4.6 % for 13 US cities, but 18.4 % for 11 European cities.

Predicted Future Transport Patterns Vehicular Travel Schafer and Victor (2000), following earlier work, argued that in all countries, both rich and poor, people have a fixed travel time budget per day. They therefore concluded that a shift from slower modes (public transport and non-motorized modes) to car travel is needed for people to travel further for a given daily time outlay. It is certainly the case that the observed decline in the share of these slower modes has been accompanied by a rise in car – and air – travel. They also postulated that in high-income countries, households spend a fixed share of their income on travel. Personal travel levels themselves (including air travel, both domestic and international) were not anticipated to decline – indeed they saw large increases even in the high-mobility OECD countries. But to keep within their postulated daily travel time budget, they projected absolute declines in the level of car travel for present car-oriented countries, particularly the US, and large increases in high-speed travel (air and very fast train travel). Continued economic growth was seen as the method by which households could pay for this increased travel, while continuing to spend a constant share of household personal disposal income on travel. A variant of this approach has seen magnetically levitated (maglev) trains traveling at very high speeds in evacuated tunnels (to lower air friction) displacing car travel, presumably mainly for longer-distance trips (Ausubel et al. 1998; Moriarty and Honnery 2005). Clearly, air travel within urban areas is not an option. But medium length trips are also unlikely to be made by high speed modes, even by high speed rail. Trips can only be at high speed if stops are very far apart, as there are definite limits to the rates

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of acceleration and deceleration that humans can comfortably tolerate, which reduces average travel speed. Waiting at stops further reduces overall speeds. Although the surface travel reductions of Schafer and Victor do not translate into overall travel reductions (because of rising high speed travel) they do imply a reduction in future surface travel levels, including travel by car, in OECD countries. But it is doubtful that people do in fact have constant travel time budgets, even when aggregated at the city-wide or even national level. And the empirical evidence also suggests that the share of disposable household income spent on travel has risen over time in the four countries studied here (Moriarty 2002b). Further, different sub-groups of the general population have very different average travel time outlays, as shown by the more than twofold travel time difference between female pensioners and full-time working males found in a 1986 Australian travel survey (Moriarty and Honnery 2005). Further, as U.K. researchers Lyons and Urry (2005) have stressed the increasing ability to use travel time for other activities, such as using a laptop on public transport trips, argues against individuals having fixed travel time budgets. Other researchers, impressed by advances in the new IT, have also seen future reductions in surface travel. They have argued that not only surface travel, but all travel, urban and non-urban, surface and air travel, will decline because of substitution by IT. This view has been argued in some detail by MIT planner William Mitchell (2003). He used the term “demobilization” as a general term for the substitution of work, shopping, and other trips by networked computers. Others who have similarly envisaged a controlling role for IT as a means of travel substitution are Frances Cairncross (2001) and Joseph Pelton (2004). As Tal (2008) has documented, the argument that IT will radically reduce travel has now been made for almost three decades. Actual results in the form of travel reductions that can be ascribed to teleworking or teleshopping so far have been disappointing (Moriarty and Kennedy 2000). There are two separate points that need to be examined when evaluating the “telework will reduce travel” argument: • What is the present extent of teleworking and what is its likely growth in future? • What impact does a given level of telework have on overall travel? The numbers who telework for some or all of the time have not risen to anywhere near the levels forecast. Although Raiborn and Butler (2009) reported that in 2008, 11 % of the US workforce teleworked at least 1 day per month, up from 8 % in 2006, the percentage of “full-time equivalent” teleworkers will evidently be low. Even more important, teleworking households may not even reduce their travel overall. First, most households in OECD countries have more vehicle licence-holders than available personal vehicles. So even if a household car is not used for work travel on any given day, it may be used for other purposes by other household members – or by the teleworker for non-work trips. Second, teleworking could affect the location decisions of households. If the commute trip by one or more household members is reduced through telework, the household may locate further away from the city center, where land prices are lower. At least for Australian cities, outer suburban (and non-urban) households travel

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much further per capita than inner urban households (Moriarty 2002a). And for travel overall, including air travel, Smith (2008) has remarked that it is now so easy to plan travel by sitting at one’s desk that IT could well have increased travel. Similarly, teleshopping, or e-commerce, has also not fulfilled its early predictions; after more than a decade, e-commerce still only accounts for a 4 % share of all retail sales (US Census Bureau 2012), although for the category of computer hardware and software the share is much higher. With such a low overall share (and even at a much higher share, say 20 %), the effect on shopping trip frequency will probably be negligible, since a large variety of purchases are usually made on each shopping trip, even if only one shop (for example a super-market) is visited. It may also be true that much on-line shopping is additional to traditional shopping, and not a substitute. None of these proposals for travel substitution is really new – all have low-tech precedents. A small proportion of the workforce has always worked from home. Mail order catalogues enabled remote shopping more than a century ago. Today, a telephone and a mailbox are still all that is needed for teleshopping. But whether a letter, telephone, or networked computer is used for ordering merchandise, it still needs to be physically delivered to the householder – with the exception of software. Any saving in private travel will thus be partly offset by a rise in freight vehicle travel. “Cyber universities” are less discussed today than they were a decade ago, but they are really just a new version of the old correspondence courses. In all, greater change probably came from the introduction of instantaneous communication with the telephone and the radio, than from the later introduction of modern IT. Even if reductions in surface travel will not naturally occur by either the operation of travel time budgets or IT substituting for travel, there are other, more recent, arguments for future travel reductions. William Rees (2009) is a researcher who has a very pesimistic view on the likelihood of anything approaching “business-as–usual” in future cities. But his doubts are based on the need for very large reductions in both greenhouse gas emissions and oil consumption, and the limited ability of technical solutions like alternative fuels or efficiency gains to deliver in the limited time frame available. All these views are in sharp contrast to the travel forecasts for OECD countries offered by various international and national authorities. The US Energy Information Administration (EIA) (2014), in its Annual Energy Outlook 2014, saw a gradual fall in US transport energy use, from 2012 to 2040, with transport energy in 2040 being 4.3 % below that in 2012 in the base case scenario. Nevertheless, vehicle-km by light vehicles was forecast to grow steadily from 2012 out to 2040 in the base case, even though it fell in the aftermath of the Global Financial Crisis. The IPCC (Sims et al. 2014) warns that “Without policy interventions, a continuation of current travel demand trends could lead to a more than doubling of transport-related CO2 emissions by 2050 and more than a tripling by 2100 in the highest scenario projections.” The Organization of the Petroleum Exporting Countries (OPEC) (2013) gave an estimate of 897 million private transport vehicles in the world in 2010 and projected that this figure would rise to 1875 million in 2035. Although about two-thirds of all cars are presently in the OECD, the future annual growth in private vehicles in the OECD was forecast to be under 1 %. For China,

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OPEC forecast car ownership to grow from 58 million in 2010 to a massive 442 million in 2035. This optimistic forecast assumes, of course, that neither oil availability nor concerns about global climate change impose limits.

Non-Motorized Travel Most official future travel projections simply ignore non-motorized travel. If discussed at all, it is in the context of future travel in low-income countries. Schafer and Victor (2000) assumed that non-motorized forms of travel would be too slow for their time-constrained, high mobility future. Both the reports of the World Business Council for Sustainable Development (WBCSD) (2004) and the earlier IPCC reports devoted little space to non-motorized travel, with the WBCSD report assuming that it will gradually disappear as low-mobility societies move up the “ladder of mobility improvement,” by which is meant that motorized travel will supplant it. Non-motorized travel was thus regarded as an inferior travel mode, and technical solutions (alternative fuels, vehicle efficiency improvements, etc.) were seen as the most important means of tackling climate change. The very few available projections are thus usually only from advocates of these travel modes. Geurs and van Wee (2000) used backcasting to examine what changes were needed for a reduction in Dutch transport GHG emissions of 80–90 %. They concluded that compared to their “business as usual” scenario for 2030 transport, bicycle use, already high in the Netherlands (as shown in section “Non-Motorized Travel”), would need to double in terms of passenger-km in their major emissions reduction scenario. Given that car travel and motorbike/moped travel would need to be reduced by 50 % and 75 %, respectively, it is clear that cycling would become a major travel mode. The present role of walking and cycling in the Netherlands demonstrates what is politically possible, even today. Income levels in the Netherlands are higher than most countries in the OECD, so that, unlike the situation in low-income countries, the popularity of these modes is not based on economic necessity. And at 51–54 N, the climate is less benign for walking/cycling that most other OECD countries. Nevertheless, its flat terrain and high population density are advantageous. In all OECD countries, however, there is increased concern about obesity and personal fitness (Pucher and Dijkstra 2003; Sallis et al. 2004). Non-motorized travel modes are well-placed to benefit from this concern.

Creating Environmentally Sustainable Behavior Changing behavior as a means of achieving environmentally sustainable ends such as household travel and energy reductions is attractive because in principle it can be introduced rapidly. In fact, during gasoline shortages, changes in travel behavior can happen overnight, as motorists car pool, walk or use public transport to get to work. (However, after the emergency is over, very little permanent change in travel patterns has been observed.) These measures can also be very cheap to implement, compared with most other alternatives, and if voluntary, carry few political costs. The

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important questions to ask of psychology are the following: How much change can be expected? Which approaches work best? Do voluntary approaches work better than more coercive approaches? Which demographic groups are most likely to change? In this section an attempt will be made to answer these questions, first for general environmental change and then for transport change.

General Principles for Change Linda Steg (2008) has discussed three factors she considered important in promoting more environmentally sustainable behavior at the individual or household level: knowledge, motivation, and ability to make the necessary changes. Below, these three points are discussed in turn, then the significance of the findings for promoting pro-environment behavior (PEB) in general is discussed. Knowledge is a necessary condition for adopting PEB. People have to be aware of the consequences of their consumption, such as their household domestic electricity use or private car use, on the physical environment. But consequences are not always easy to determine. For instance, the public’s knowledge of the facts on climate change, and the reasons why nearly all climate scientists regard climate change as a serious problem, is generally poor, even among the educated populations of the OECD countries. The immense complexity of the climate change problem, with its multiple feedbacks operating at varying time scales, is one cause. Others include the significant natural variability of climate, making unambiguous attribution of cause difficult, and the stress on this uncertainty by industry groups with a strong interest in continued inaction on climate change. Second, not only is knowledge important, but the public must be motivated to change their behavior in a more environmentally appropriate direction. When surveyed, most people in OECD countries profess to be concerned about the environment in general, but such concern does not always translate into action, as is shown by the continued rise in household energy consumption in many OECD countries, for example. Further, in the case of climate change mitigation, the link between individual reductions in GHG emissions and environmental benefit, particularly local, is tenuous. Other factors important for success or otherwise in achieving behavioral change include any extra costs and effort incurred, or any reduction in personal convenience. Policies will thus be more effective if they simply involve purchase of more energy-efficient equipment and do not restrict perceived freedom of choice (Steg 2008). Recycling involves no monetary costs and has been made convenient by the provision of recycling bins at households and businesses, and so has been widely adopted. Unfortunately, much household recycling may be only of marginal benefit in achieving ecological sustainability (Wikipedia 2014). De Groot and Steg (2009) have further argued that ethical arguments (altruistic and biospheric considerations) for change in household practices are more effective than other approaches, such as those that stress cost savings. As they put it, it is better to get people to “act green” than to “act mean.” Cost saving arguments can come undone when, for example, energy costs fall or household circumstances change. An

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illustration of the former is the steep decline in sales of more energy-efficient hybrid electric vehicles in the US, following the fall of gasoline prices after mid-2008. Nevertheless, countries with higher costs for gasoline and domestic electricity and natural gas, in general use less energy. Third, people must also be in a position to make the necessary changes. Money is often a barrier. Many households may not have the financial means to buy the new, more energy-efficient equipment, or to pay for retro-fitting their house to cut heating energy. They may find it easier to continue paying higher monthly or quarterly bills than to find the money for an energy efficiency improvement. It is not only physical or financial impossibility that matters; even cultural norms may make an action perceived as impossible. Given these conditions needed for change, some suggestions for general strategies to promote environmental changes follow. Promoting the purchase of more energy-efficient appliances (such as compact fluorescent globes in place of incandescent ones) can be a succesful strategy, especially if, as in this case, not only are the purchase costs low, but households suffer little inconvenience from the change. In the case of compact fluorescent globes, both costs and inconvenience were so minor that several governments have been able to mandate the phasing out of the less-efficient incandescent globes (Brown 2009). Changing the costs of energy, for example through a carbon tax, can also help reduce energy consumption, as can increasing the costs of travel. The road pricing schemes in London, and particularly Singapore, have had some success in reducing travel levels and marked success in changing travel patterns. Researchers have found that higher income groups respond best to interventions for improving PEB. Studies in the US has shown that such groups are more likely to participate in “green energy” programs. For Australian capital cities, high income and high education households were also found to be more likely to have a strong environment commitment (Moriarty and Kennedy 2004). Torgler and GarcíaValiñas (2007) also reported that both better-educated and higher income individuals usually had stronger pro-environmental attitudes. They reasoned that “Wealthier citizens may have a higher demand for a clean environment and less environmental damages.” On the other hand, in the US, sports utility vehicle ownership is much higher among the more affluent, and air travel in all countries is similarly concentrated among the well-off. Clearly, the issue of environmental support is a complicated one, where actions may not coincide with attitudes (Kennedy et al. 2009). Thus the relationship between PEB and socioeconomic status is much more complex than revealed by surveys. Lower income households in OECD countries can be regarded as “involuntary environmentalists,” because their per capita use of public transport is greater, while their use of domestic energy or water, is usually lower than that for higher income households. Similarly the higher levels of environmental concern usually (but not always) found for females (Torgler and GarcíaValiñas 2007) could have a similar basis. In any case, as shown in section “Present Transport Patterns,” female use of more environmentally friendly transport modes is today not much better that that for males. These comments on involuntary environmentalism apply with even greater force to low income households of the

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industrializing world. Nevertheless, higher income households, despite their greater resource use, are more likely to respond to campaigns for increased PEB (Gifford and Nilsson 2014).

Changing Travel Behavior It is widely agreed that changing travel behavior is far harder than changing behavior for recycling or even general domestic energy conservation (e.g., Garling and Schuitema 2007; Dietz et al. 2009; Gifford and Nilsson 2014). In general, external constraints, whether real or perceived, on changing travel mode are usually severe in the car-oriented countries of the OECD. An important constraint is that alternatives to the car for any given trip usually involve longer travel times. It follows that bringing about voluntary change can be expected to be much more difficult than promoting other forms of pro-environmental behavior such as recycling. In addition, there are strong psychological benefits of car travel which other modes cannot match. Clearly, transport GHG emission reductions would be easy if a carbon-neutral fuel was readily available at about the same price as existing petroleum-based transport fuels and could be used in existing vehicles. Proenvironment behavior would then be as easy as shifting to another brand of gasoline. But such a convenient solution to transport’s GHG emissions will not be available for decades, if ever. Other approaches, such as travel demand management (TDM), are needed. Dietz et al. (2009) have examined ways in which US households can voluntarily cut their carbon emissions. From behavioral research, they estimated that, nationally, the “reasonably achievable emissions reduction” potential in the household sector overall is 20 %. This reduction can be achieved in a decade, they argued, if “the most effective nonregulatory interventions are used.” Although about 45 % of the estimated savings came from reductions in private vehicle fuel use, only about 11 % of this transport total came from travel reduction measures, namely from car pooling and trip chaining. Overall then, only 1 % (20 %  0.45  0.11) of all household sector emissions cou