Climate Change and Industry Structure in China, 2-Volume Set 9781003004455

Table of contents :
Cover
Volume 1
Cover
Half Title
Series
Title
Copyright
Contents
List of figures
List of tables
Preface
Part I Emission reduction analysis
1 Literature review of contaminant disposal modeling under the productivity framework
2 Interprovincial CO2 shadow price research based on the parametric model
3 Regional decomposition of CO2 emission reduction potential and emission reduction targets
4 Research on CO2 marginal abatement cost in Chinese cities
Part II Strategic response
5 Industrial restructuring strategy to mitigate and control CO2 emissions
References
Index
Volume 2
Cover
Half Title
Series
Title
Copyright
Contents
List of figures
List of tables
Preface
Part I Theories and practice
1 Industrial structure in China and emission of greenhouse gases
2 Interconnections between industrial structure and climate change
3 Practical implications of China’s response to climate change
Part II Emission features
4 Research on the relationship between interprovincial industrial structure, income level and CO2 emissions
5 Quantitative assessment of CO2 emissions from China’s production sector
6 Quantitative evaluation and analysis of CO2 emissions in China’s industrial sector
7 Analysis of industrial CO2 emissions and influencing factors A case study of Zhejiang Province
References
Index

Citation preview

i

Climate Change and Industry Structure in China

In order to effectively address global warming, many countries have signifcantly reduced the amount of carbon dioxide emissions that are put into the atmosphere. From the perspective of industrial structure, this volume examines the emission reduction potentials and abatement costs in China. By making an empirical analysis of the emission reduction, the author proposes some practical strategies. The book comprehensively summarizes related theories and research of contaminant disposal modeling, and estimates the shadow price of interprovincial CO2 emissions, the emission reduction potential of different regions, and the marginal emission reduction cost based on the parametric model. It fnally puts forward the strategy to adjust the industrial structure in China. The book hence provides solid evidence for policy-makers to help mitigate CO2 emissions through industrial restructuring strategy. Wei Chu is a professor at Renmin University of China. His research focuses on the analysis of energy effciency, evaluation of abatement cost of pollutants, and the residential energy demand.

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China Perspectives

The China Perspectives series focuses on translating and publishing works by leading Chinese scholars, writing about both global topics and Chinarelated themes. It covers Humanities & Social Sciences, Education, Media and Psychology, as well as many interdisciplinary themes. This is the frst time any of these books have been published in English for international readers. The series aims to put forward a Chinese perspective, give insights into cutting-edge academic thinking in China, and inspire researchers globally. Titles in economics partly include: The Chinese Path to Economic Dual Transformation Li Yining Hyperinfation A World History Liping He Game Theory and Society Weiying Zhang China’s Fiscal Policy Theoretical and Situation Analysis Gao Peiyong Trade Openness and China’s Economic Development Miaojie YU Perceiving Truth and Ceasing Doubts What Can We Learn from 40 Years of China’s Reform and Opening-Up? Cai Fang For more information, please visit https://www.routledge.com/series/CPH

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Climate Change and Industry Structure in China Mitigation Strategy Wei Chu

iv

First published in English 2020 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 52 Vanderbilt Avenue, New York, NY 10017 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2020 Wei Chu The right of Wei Chu to be identifed as author of this work has been asserted by him in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identifcation and explanation without intent to infringe. English Version by permission of China Renmin University Press. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record has been requested for this book ISBN: 978- 0- 367- 43577- 6 (hbk) ISBN: 978- 1- 003- 00445- 5 (ebk) DOI:10.4324/9781003004455 Typeset in Times New Roman by Newgen Publishing UK

v

Contents

List of fgures List of tables Preface

vi vii ix

PART I

Emission reduction analysis 1 2 3 4

Literature review of contaminant disposal modeling under the productivity framework

1 3

Interprovincial CO2 shadow price research based on the parametric model

30

Regional decomposition of CO2 emission reduction potential and emission reduction targets

42

Research on CO2 marginal abatement cost in Chinese cities

69

PART II

Strategic response 5

117

Industrial restructuring strategy to mitigate and control CO2 emissions

119

References Index

142 154

vi

Figures

1.1 3.1 3.2 3.3 3.4 3.5 3.6 4.1 4.2 4.3 4.4 4.5 4.6 5.1

Schematic diagram of directional distance function and output distance function CO2 emission reductions in the eastern, central, and western regions (1995–2007) CO2 emission reduction potential in the eastern, central, and western regions (1995–2007) CO2 marginal abatement costs in the eastern, central, and western regions (1995–2007) Distribution of Equity Index and Effciency Index of emission reduction in each province (1995–2007) Emission reduction priority Preferred emission reduction effciency Directional distance function and shadow price Trends of input and output variables (2001–2008) Shadow price comparison in the eastern, middle, and western regions (2001–2008) Provincial shadow price ranking (2001–2008) Fifteen cities with the highest marginal abatement costs and the lowest 15 cities (2001–2008) City shadow price coeffcient of variation (2001–2008) China’s GDP, fscal revenue and expenditure and local energy conservation protection expenditure trends (2000–2012)

7 53 53 56 59 60 61 78 88 92 93 93 94 125

vii

Tables

1.1

Summary of evidence-based literature on studies about environmentally sensitive productivity 16 2.1 Descriptive statistics of various variables 37 2.2 Average productivity and average CO2 shadow price of provinces (cities, districts) in China (1995–2007) 37 3.1 Descriptive statistics of various variables (1995–2007) 50 3.2 CO2 emission reductions by province, accounting for the national proportion and emission reduction potential (1995–2007) 51 3.3 CO2 shadow price estimates by province (1995–2007) 55 3.4 Fairness Index, Effciency Index, and Capacity Index (average between 1995 and 2007) 58 3.5 CO2 emission reduction capacity of major provinces under different principles (1995–2007) 62 3.6 Regression analysis of emission reduction potential (1995–2007) 64 3.7 EKC test of CO2 shadow price 64 4.1 Results of China’s 2010 marginal carbon abatement costs 75 4.2 Comparison of literature based on the parametric model for directional distance function at home and abroad 82 4.3 Various energy standard coal conversion factors and carbon emission factors 86 4.4 Descriptive statistics of input–output variables (2001–2008) 87 4.5 Comparison of output and input in terms of regions (2001–2008) 87 4.6 Comparison of sample representativeness (2008) 89 4.7 Direction distance function parameter estimates 90 4.8 Descriptive statistics of eight direction distance functions and shadow prices 90 4.9 Comparison of the results of CO2 shadow price calculation in different literatures 91 4.10 Summary of factors affecting marginal abatement costs 97 4.11 Descriptive statistics for explanatory variables 98 4.12 Initial regression analysis results 100

viii

viii 4.13 4.14 4.15 4.16 4.17 4.18

Tables

Further study of pollutants Comparison table of sample cities Individual cities’ effect parameters Provincial marginal emission reduction cost (2001–2008) Marginal emission reduction cost of cities (2001–2008) Relative coeffcients of variables (relative coeffcient and signifcance of Spearman) 4.19 Multicollinearity of explanatory variables

101 104 108 109 110 114 115

ix

Preface

The low-carbon transformation of the industrial structure is not only an inevitable choice for dealing with climate change, but also an important embodiment of the transformation of the economic development mode. This book starts from the perspective of industrial structure and quantifes the main characteristics of China’s carbon dioxide emissions, and proposes corresponding emission reduction strategies and countermeasures based on the analysis results. The book consists of two volumes. In volume one, this book reviews the current status of greenhouse gas (GHG) emissions and challenges that China faces; sorts out the theoretical literature on the mechanism of industrial structure CO2 emissions, and the conductive effects based on the perspective of industrial structure and systematically summarizes the corresponding analysis model; estimates and predicts regional CO2 emissions in China at the interprovincial level and uses the econometric model to identify the infuencing factors of per capita CO2 emissions; measures and disintegrates CO2 emissions from industrial sectors in China on industrial basis; taking Zhejiang industry as the research object, investigates and compares the differences of industrial CO2 emissions between Zhejiang and other developed provinces. In volume two, interdisciplinary/urban CO2 reduction potentials and costs are assessed by further examining the regions, using a combination of econometric analysis, linear programming, and scenario analysis and looking into the preference problem in the decomposition of emission constraint target; fnally, based on the perspective of industrial structure, key areas and links for controlling and slowing carbon emissions are identifed and four strategic concepts of “increasing productivity,” “reducing emissions,” “proposing solutions,” and “transforming production” are proposed. This volume has the following main fndings. First, this volume quantitatively evaluates China’s CO2 emission reduction potential and marginal abatement cost, and further examines the relationship between industrial structure and CO2 emission reduction. The average emission reduction potential of China’s CO2 is about 40%, and it shows the regional differences in the eastern < central < western; the level

x

x

Preface

of economic development and proportion of the tertiary industry is negatively correlated with the relative emission reduction potential, while the energy intensity, coal weight, and technological progress as well as capital deepening are positively correlated with emission reduction potential. The industrial structure, energy intensity, and energy consumption structure have a greater impact on emission reduction potential. The interprovincial level of CO2 marginal abatement costs in China is 94.4–139.5 yuan/ton, and the average marginal abatement cost at the urban level is 967 yuan/ton, both of which are characterized by the eastern > central > western, and the heterogeneity between the regions/cities is still expanding. There is a U-shaped relationship between marginal abatement costs and income, and the level of urbanization is positively related to marginal abatement costs, while the proportion of secondary industry, degree of openness, and per-capita transportation infrastructure are signifcantly negatively correlated with marginal abatement costs. Second, based on the principles of fairness and effciency, this volume simulates the regional emission quota allocation scheme and proposes two market instruments that can be adopted in the future. If only the principle of fairness is considered, then regions with higher per-capita CO2 emissions and higher economic development levels should bear more emission reduction obligations; if only effciency principles are considered, then they have greater emission reduction potential and lower marginal abatement costs. The provinces should undertake more emission reduction tasks; if both dimensions of equity and effciency are considered, the provinces that need to be focused on, including Inner Mongolia, Shanxi, and Hebei, can be identifed. In the future, the cost of mandatory emission reduction will become higher and higher. The characteristics of emission reduction between provinces/urban cities should be fully considered, and the total cost of emission reduction should be reduced through market mechanisms. One possible solution is the emission trading system, which reduces the cost of abatement through transactions between regions with higher marginal abatement costs and those with lower marginal abatement costs; or a tax system with unilateral payments, i.e., the central government imposes a tax on carbon emissions in areas with higher marginal abatement costs, and transfers some of the tax payments to areas with lower marginal abatement costs to compensate for their emission reductions. There are three major innovations in this book: frst, the theoretical model is used to defne the mechanism of the industrial structure’s contribution to GHG emissions; second, a large number of quantitative analyses are used to identify key areas and essential drivers of GHG emissions in different industries/regions in China. The specifc direction and size of the industrial structure for GHG emissions were examined. Third, based on the dimensions of fairness and effciency, China’s GHG emission reduction potential and decomposition goals were evaluated and simulated. In addition, based on

xi

Preface

xi

the aforementioned theoretical and conclusions of empirical research, four industrial structure adjustment strategies of “increasing productivity,” “reducing emissions,” “proposing solutions,” and “transforming production” were proposed. This book also has many defciencies. First, limited by the availability of data and relatively rough selection of industry, no microscopic analysis was conducted to further subdivide industry categories to the four-digit level; second, the calculation of carbon emissions of different sectors is mainly based on the burning of terminal fossil fuels without considering the release of GHGs during the production process. Due to the lack of more detailed data, carbon sequestration of special sectors (such as forestry) is not considered. Besides, in terms of the calculation of GHG emission, existing studies on GHG emission accounting are based on the coeffcients given in the IPCC’s emission accounting checklist. Actually, differences in the quality of energy products in various regions and industries and degree of oxidation in combustion process will result in differences between the calculated data and actual emissions, which can only be improved through further refnement of data and development of accounting methods. Finally, the adjustment of industrial structure may affect the situation as a whole, and changes in the structure of a certain sector will be transmitted to other related departments, therefore further infuencing the development of entire national economy. The analysis of the impact of different industrial structure adjustment policies on macroeconomic or sectoral economy should be based on the input–output table for the Computable General Equilibrium (CGE) analysis. Constrained by time and effort, this book is less involved in this direction, which needs to be further studied. The relevant research of this book comes from the National Social Science Fund Project I hosted, “Climate Change and the Strategic Study of China’s Industrial Structure Adjustment during the Twelfth Five-Year Plan Period” (10CJY002), and the National Natural Science Foundation Project “Regarding Regional Carbon Equity with Equity and Effciency.” The research results in the Design and Comparison of the Right Allocation Plan (41201582), and the research also received the “Beijing Municipality’s Household Energy Consumption Model and Energy-saving Approaches” (9152011) and the Mingde Young Scholar Program of Renmin University of China Support for Carbon Dioxide Abatement Cost Curve (13XNJ016). Some of the chapter content has been published in journals, and comments are given at the beginning of each chapter. During the study, President Shen Manhong of Ningbo University, Associate Professor Ni Jinlan from the University of Nebraska, Associate Professor Du Limin from Zhejiang University, Associate Professor Cai Shenghua from the Chinese Academy of Sciences, Dr.  Yu Dongzheng, Student Huang Wenruo, and Student Su Xiaolong participated in some of the collaborative research, or participated in the writing and revision of some chapters, or provided a large number of research assistants, and I express my sincere gratitude to them for their contributions. In addition, we would also

newgenprepdf

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Preface

like to sincerely thank the people’s publishing house Zhai Yanhong for his efforts in publishing the book. Due to the limited research energy and the lack of experience of the authors, the errors or defects in this book sincerely welcome criticism and correction from experts and readers.

1

Part I

Emission reduction analysis

3

1

Literature review of contaminant disposal modeling under the productivity framework1

In order to quantitatively study carbon dioxide emission reduction, it is necessary to model the pollutant disposal. Therefore, it is necessary to frst review the relevant models in the form of a literature review and select the corresponding modeling tools. This book will be modeled in the subsequent quantitative analysis based on environmental production techniques under the productivity framework. Traditional productivity analysis tends to focus on the ratio of companies using various valuable input factors to valuable saleable products. The effciency boundary means that the output is unchanged and the input factors are the least, or the output is fxed under the condition of fxed input factors. This method of measurement ignores the “undesirable output”2 of pollution, thereby underestimating the true productivity of frms under stronger environmental controls. Because the company needs to invest extra cost to reduce pollutants under the stronger environmental control, or reduce the output correspondingly to reduce pollution emissions, the cost (or reduced output) used to reduce pollution is included in the calculation. The input (or output) of the production of the enterprise are not included in their productivity calculations but not the positive social effects of reducing pollutants, thus underestimating the true productivity of these enterprises, which will further affect the decision-makers’ environmental regulation policy formulation.3 The “undesirable output” of pollutants is not included in the classical production theory and productivity measurement. The main reason for this is that the market price of pollutants cannot be determined. Traditional accounting methods and production theories cannot directly deal with them, so they cannot be weighted.4 To measure its true productivity, the latest theoretical research has integrated environmental pollution as an “undesirable output” into the production framework to measure “environmental sensitive productivity.”5 This chapter is a review of the development of this theory. The structure of this chapter is organized as follows. The frst part introduces the existing main research ideas and theoretical models; the second part introduces the specifc estimation methods of each model; the third section summarizes and compares the existing empirical research, and the existing research defciency comment; the fnal part is the conclusion. DOI: 10.4324/9781003004455-2

4

4

Emission reduction analysis

1.1 Research ideas and theoretical development In the existing environmental sensitivity productivity analysis, there are three main ideas: index method, distance function, and directional distance function. 1.1.1 Index method Traditional productivity indices include Fisher, Tornqvist, and Malmquist. These productivity indices are built by weighting different input or output factors6 to construct a multifactor effciency measurement index. If “nonconsensus output” is considered, the price of undesirable output needs to be set by means of polluting emissions trading price or estimated shadow price, and is added to each productivity index based on the same weighting method. Pittman (1983) frst explored this issue.7 He pointed out: “The biggest diffculty and challenge is how to allocate shadow prices for undesired outputs,” although the shadow price of pollutants can be estimated based on survey data on manufacturers’ abatement costs (Pittman, 1983), or by evaluating unintended production. The external damage price (Repetto et al., 1996)8 is used for calculation, but in practice, it is often impossible to pass research because it is diffcult to distinguish between capital and other inputs used for production and for pollutant emission reduction (de Boo, 1993). Obtaining the real emission reduction amount and emission reduction expenditure of the manufacturer and the external damage of pollutants to society cannot be accurately calculated due to the transfer of time and space.9 In addition, the accuracy of existing evaluation methods is also controversial (Hailu & Veeman, 2000). The Malmquist index does not require price information on input and output factors, but as Chung et  al. (1997) pointed out, the traditional Malmquist index cannot be calculated when including undesired outputs, and then they moved to the directional distance function (directional distance). Based on the function, the Malmquist–Luenberger Productivity Index (hereinafter referred to as the ML Index) is proposed, which can measure the total factor productivity when there is unintended output, and at the same time consider the increase of desirable output. The reduction in unsatisfactory output has the good nature of the Malmquist index. Therefore, the ML index has been widely used in subsequent studies. 1.1.2 Distance function Beginning in the 1990s, theorists began to use distance functions to include undesired outputs and to derive shadow prices for environmentally sensitive productivity and undesired output (Färe et  al., 1993; Ball et  al., 1994;

5

Literature review

5

Yaisawarng & Klein, 1994; Coggin & Swinton, 1996; Hetemäki, 1996; Hailu & Veeman, 2000). The distance function is essentially an application of the frontier production function. The biggest difference between the frontier production function and the traditional production function is that the former considers the ineffciency term of the decision-making unit (DMU), that is, in the actual economic operation, the basic under the given input conditions, the unit is affected by external uncontrollable factors, and there will be a certain effciency loss, so the potential maximum output may not be achieved. This is more in line with the actual situation, because production ineffciency is ubiquitous, and fully effective economic operations are rare (Yue Shujing et al., 2009). The distance function actually depicts the effciency of the previous edge effciency, the distance from each unit in the production set to the production front. Färe et al. (1993) used the distance function to conduct environmental sensitivity productivity research earlier. The basic theoretical model of the distance function based on output is as follows. Suppose the input vector x∈R_+^N, the output vector u∈R_+^M, the production technique P(x)={u: x can produce u}, allowing the output for weak disposability instead of strong one,10 according to Shepard and Chipman (1970), the output distance function is defned as: Do (x, u) = inf {θ: (u/θ)∈P(x)}

(1.1)

Under this defnition, a minimum threshold is required to achieve the goal of expanding output to the frontier. θ ≤ 1, when and only θ = 1, the unit effciency is on the leading edge. In the same way, the input distance function is to fx the output and minimize the input. Let the input vector x∈R_+^N, the output vector u∈R_+^M, the production technique L(u)={x: x can produce u}, then the input distance function is defned as: DI (x, u) = sup {ρ: (x/ρ)∈L(u)}

(1.2)

The maximum value of ρ is required here to minimize the input under fxed output. When ρ = 1, the effciency of the point is at the leading edge. In addition, based on the distance function of the input or output direction, the shadow price of the undesired output can be further derived, and the output price r = (r1, …, rM), assuming r ≠ 0,11 the income function can be defned as R (x, r) = sup {ru: Do (x, u) ≤1}

(1.3)

For the convex output set P(x), the relationship between R(x, r) and Do(x, u) (Shepard & Chipman, 1970; Färe, 1988), constructing the Lagrange function

6

6

Emission reduction analysis

and fnding the frst order of output guide, you can get the shadow price of undesired output relative to the desired output: ˆ ˙D ( x,u) * o ˙Do ( x,u) / ˙um’ rm’ = R ˜ ± rm  ( x,u ) = R ˜ ˘ o  = rm ˜ ˙Do ((x,u ) / ˙um ˇ ˙um’ 

(1.4)

o

Here, the observed price of the desired output rm is used as the standardized price, because the desired output has an observable and market-oriented price, and rm ’ is the absolute shadow price of the undesired output, so the output/ input distance.12 The function can also be used to calculate the contaminant shadow price. 1.1.3 Directional distance function Chung et al. (1997) frst proposed a directional distance function for environmental sensitivity productivity analysis. The difference between the directional distance function and the ordinary distance function is that the assumptions for the joint production of desirable and undesired outputs are different. The distance function only considers the maximum expansion of the desired output, while the directional distance function examines the decrease in the undesired output while examining the increase in desirable output, only if the desired output cannot continue to expand and is unsatisfactory. When the output cannot continue to decrease, the observation point is at the forefront of effciency. Its model is as follows. N M Suppose the input vector x ∈ R + , the desirable output vector y ∈ R + , the J undesired output b ∈ R + , and the production technique is defned as P(x) = {(y, b): x can produce (y, b)}, which has two characteristics: (i) Consensus output is freely dispositioned, undesired output is weakly disposed of (y, b)∈P(x),

if y’≤ y, (y’, b) ∈P(x)

(1.5)

(y, b)∈P(x),

if 0 ≤ θ ≤ 1, (θy, θb)∈P(x)

(1.6)

(ii) Joint production: (y, b)∈P(x),

if b = 0, then y = 0

(1.7)

The directional distance function frst needs to construct a direction vector of M J g=(gy, –gb) and g∈R ×R , which is used to constrain the direction of change of the desired output and the undesired output. The size of the change, that is, the increase (decrease) of the desired (unintended) output on the path

7

Literature review

7

specifed by the direction vector, the specifc choice of the direction vector depends on factors such as research needs or policy orientation preferences. The directional output distance function can be defned as:

{ (

}

˜ Do x, y, b; g y , gb = sup ˜ : y + ˜ g y , b − ˜ gb ˆ P ( x )

(

)

)

(1.8)

β represents the degree to which a given unit’s desirable output (unsatisfactory output) can be expanded (reduced) compared to the most effcient unit on the leading edge production surface. If β  = 0, it means that this decision unit is on the leading-edge production side, which is the most effcient. The larger the value of β, the greater the potential for the desired output of the decisionmaking unit to continue to increase, and the smaller the space for the reduction of undesired output, so the lower the effciency. The directional distance function is a general form of Shephard’s output distance function (Chung et al., 1997). When the direction vector g  = (1, 0), the Shephard yield distance function is a special case of the directional distance function. The main relationships and differences between them can be illustrated by Figure 1.1. In Figure 1.1, P(xt) is the production possible set, and the output distance function is extended along the ray defned by the origin and the observation point A, and the desired output yt is expanded to the front edge in the same proportion as the undesired output bt. The point C of the directional output function is: the path of the given direction vector g = (gy, – gb), the expansion of the desired output yt and the reduction of the undesired output bt, thereby go to point B at the front of the output. Obviously, for the distance function, moving from the invalid point A  to the C point on the leading edge, there is either an excessive undesired output, or a desirable output insuffciency, while the directional distance function is not only considered. The expansion of desirable output, and the minimization of undesired output, can more

y C B g=(gy,-gb)

A(yt,bt)

P(xt )

b

Figure 1.1 Schematic diagram of directional distance function and output distance function

8

8

Emission reduction analysis

accurately describe its real productivity. In recent years, the use of directional distance function model to measure environmental sensitivity productivity has been increasing.

1.2 Model solving method Whether it is the distance function or the directional distance function developed in the later stage, the production boundary they use to construct is to use the multiple sets of input–output data to derive the production front, and to make the decision-making units in the sample and the production frontier. The best advantages are compared to solve the relative effciency values of each decision unit. At present, the solution to the model can be generally divided into two types: parameterized and non-parametric. The parametric solution mainly includes:  Parametric Line Program (PLP) and Stochastic Frontier Analysis (SFA). Here, translog, quadratic, and hyperbolic functions are adopted for the parameterized distance function form; nonparametric solutions mainly refer to Data Envelopment Analysis (DEA). The following mainly introduces four widely used solving methods. 1.2.1 Parametric distance function solution based on translog function The parameterized output/input distance function method can overcome the shortcomings of the exponential method. Färe et al. (1993) frst used the parameterized distance function to study environmental sensitivity productivity. The idea is to select the super-logarithm function to parameterize the output distance function Do(x,u)13 and to minimize the distance between all samples and the production front by linear programming constraints, and the value of the output distance function is environmentally sensitive productivity. Its super-logarithmic function is set to: N

M

n=1 M M

m=1

lnDo ( x,u ) = ˜ 0 + ˙ ° n lnxn + ˙ ˜ m lnum + +

( )

( )

1 N N ˙˙ ° ’ (lnxn ) ln xn’ 2 n=1 n=11 nn

N M 1 ˜ ’ (lnum ) ln u ’ + ˙˙˛ nm (lnxn ) ln (um ) ˙˙ m 2 m=1m’ =1 mm n=1 m=1

(1.9)

Assuming that the function of equation (1.9) has general symmetry and homogeneous constraints, the method of Aigner and Chu (1968) is used to minimize the deviation of the sample from the leading edge, that is, to solve the following linear programming problem:

(

Max  k =1 ˝lnDo x ,u ˙ K

k

k

) − ln1ˆˇ

(1.10)

9

Literature review

9

s.t. (i)

(ii)

(iii) (iv)

(

k

lnDo x ,u

(

) ˛ 0,

k

k

  lnDo x ,u

k

k

  lnum

(

k

lnDo x ,u k

˛lnum

°

M m=1

k

) ˝ 0,

) ˝ 0,

k = 1,… , K

m = 1,… , i, k = 1,… , K

m = i + 1,… , M , k = 1,… , K

˜ m = 1, n = 1,… , N

M

M

m ˜=1

m=1

° ˜ mm˜ = ° rnm = 0, m = 1,… ,M, n = 1,… ,N

(v) ˜ mm’ = ˜ m’m , m = 1,… ,M, m’ = 1,… ,M ° nn’ =° n’n ,

n = 1,… ,N,

n ˜ = 1,… , N

where k = 1, …, K represents different observational samples, the frst i outputs are desirable outputs, and the later (m – i) outputs are undesired outputs. The objective function in equation (1.10) is to minimize the deviation of all samples from the optimal leading edge. Constraints (i) ensure that each sample is at the leading edge or below the leading edge; constraint (ii) guarantees that the shadow price of the desired output is non-negative; constraint (iii) guarantees that the non-conforming output shadow price is not positive; constraint (iv) pairs the output, which is applied once in a row to ensure that the production technology meets the output weak disposal assumption; constraint (v) is a symmetry constraint. Once the values of the parameters in the distance function are solved using (1.10), the environmental sensitivity productivity of the sample and the shadow price of the undesired output can be calculated. 1.2.2 Solution of directional distance function based on quadratic function If the directional distance function is set instead of the distance function, the super-logarithmic function is generally not used, and the quadratic function is used, because the quadratic function satisfes the constraints required for the directional distance function characteristic (Färe & Grosskopf, 2006). Generally, the direction vector g = (1, –1)14 can be set. Assuming k = 1, …, K represents a different observation sample, the quadratic direction distance function can be expressed as:

10

10

Emission reduction analysis

˜ N M J D0 ( x,g,b;1, −1) = ˜ 0 +ˆ n=1 ˜ n xn + ˆ m=1 ° m g m + ˆ j =1 ˛ j b j N 1 N ˆ ˆ ˜ ( x )( x ) 2 n=1 n ˇ=1 nnˇ n nˇ M 1 M + ˆ m=1 ˆ m ˇ=1 ° mm ˇ (um )(um ˇ ) 2 J 1 J + ˆ j =1 ˆ j ˇ=1 ˛ mmˇ b j b jˇˇ 2 N J m N ˆ n=1 ˆ m=1 ˝ nm ( xn )( gm ) +ˆ n=1 ˆ j=1 ˙nj ( xn ) b j

+

( )( )

+ ˆ m=1 ˆ j =1 µ nm ( g n ) (bm ) m

J

(1.11)

( )

The parameter solution is also based on the idea of linear programming, which minimizes the sum of the distances of the observations to the boundary. ˜ K Min k =1 ˝˙ Do ( xk ,g k ,bk ;1,−1) − ln1ˆˇ s.t. ˜ Do ( xk ,g k ,bk ; 1,−1) ˙ 0, k = 1,…,K ˜ ˜Do ( xk ,g k ,bk ; 1,−1) ˝ 0, m = 1,…,i, k = 1,…, K (ii) k ˜g m (i)

(iii)

(iv) (v)

˜ ˜Do ( xk ,g k ,bk ; 1,−1) k

˜b j

˜ ˜Do ( xk ,g k ,bk ; 1,−1) k

˜xn

°

M

m

mm’

m=1

− ˛ µ mj = 0 , m = 1,… ,M j =1

M

jj˜

M

°˜

j

J

J

j ˜=1

˙ 0, n = 1,… ,N, k = 1,… ,K

° − ° j =1 ˛ = −1,

M

˛°

˛˛

j = 1,… ,J, k = 1,…,K

J

m=1

m ˜=1

ˆ 0,

− ˛ µ mj = 0 , j = 1,… ,J m=1 J

nm

− ° °nj = 0 , n = 1,… ,N j=1

(1.12)

11

Literature review

11

(vi) ˜ mm ’ = ˜ m ’m , m = 1,… , M , m ’ = 1,… , M

˜ nn ’ = ˜ n ’n , n = 1,… , N , n° = 1,… , N = γ jj ’ γ= , j 1,… , N , j’j

j ′ = 1,… , N

Constraints (i)  ensure that each sample is at the leading edge or below the leading edge; constraints (ii) and (iii) ensure monotonicity of desirable and undesired outputs, respectively, while constraints (iv) also apply to inputs The monotonic constraint, constraint (v), satisfes the transformation property of the direction distance function, and the constraint (vi) is the symmetry constraint. Using (1.12) to solve the values of each parameter in the direction distance function, the environmental sensitivity productivity of different samples can be obtained, and the shadow price of the undesired output can be calculated. 1.2.3 Stochastic Frontier Analysis (SFA) The stochastic frontier production function was frst proposed by Aigner et al. (1977). In the study of environmental sensitivity productivity, it is also used as a parameter estimation method. Compared with the deterministic parameter estimation method, it will be caused by uncertain factors. The impact is taken into consideration, from the aspects of technical ineffciency or random error, to fnd out why the sample production is ineffcient and deviating from the production boundary. More importantly, the SFA method can give the statistics of the variables to be estimated. Its conclusions are more robust in terms of other parameter estimation methods. Murty and Kumar (2003) used SFA and output distance functions to evaluate production effciency. The stochastic output distance function is defned as follows: Do = F ( X ,Y , ˜, ° ) + ˛,

(1.13)

Do is the distance function value, F(.) represents the production technique, X and Y are the input and output vectors, α and β are the parameters to be estimated, and ε is the error term. Because the data of the dependent variable Do cannot be directly obtained,15 in order to solve this problem, Ferrier and Lovell (1990), Grosskopf and Hayes (1993), Coelli and Perelman (1996), and Kumar (1999) use the output distance function once. The subfeatures are transformed, and in the case of ignoring the perturbation term, equation (1.13) is transformed into: D0 ( X , ˜Y ) = ˜ D0 ( X ,Y ) ,

˜ > 0.

(1.14)

12

12

Emission reduction analysis

Generally, a scaling variable can be arbitrarily selected. For example, if the 1 Mth output is selected, let λ = , then (1.14) becomes: YM D ( X ,Y / Y ) = D0 ( x,Y ) / YM 0

(1.15)

M

Take the logarithm of the above formula (1.15) and become ˜ Y ˝ ln(D0 / YM ) = f ˛ X, , ˜ , °ˆ , ° YM ˙

(1.16)

f can be expressed as a logarithmic form of a function expression, further transformed into: ˝ Y ˇ −ln (YM ) = f ˆ X, , ˜ , ° − ln ( D0 ) ˙ YM ˘

(1.17)

After adding the random error v and the production ineffciency error u (i.e., the –ln(Do) term), the stochastic boundary yield distance function is expressed as: ˇ ˝ Y −ln (YM ) = f ˆ X, ,˜, ° + ˇ + µ ˙ YM ˘

(1.18)

Estimating the (1.18) formula gives the parameter values to be estimated and their statistics. In addition, the directional distance function can also be estimated using a random frontier function method. Färe et  al. (2005) used Kumbhakar and Lovell (2000) to set the random frontier function. Using the directional distance function to calculate, the defnition of production technology is: T=

{(x, y, b) R

N + M +J +

}

, ( y, b ) P ( x ) , xL ( y, b ) , and its function is as follows16:

)

(

˜ k k k k 0 = Do x , y , b ,1, −1 +˘ , k = 1, 2…K k

(1.19) k

2

k

where εk = vk – uk, v is a random statistical error, ˇ ˜ N (0, ˜ v and u is due k k to technology. The error caused by ineffciency, u k ˜ N (0, ˜ 2u ), v and u are

13

Literature review

13

independent and identical, and independent of each other, and then use g The conversion of the directional distance function at g = (1, –1) is:

(

)

(

)

˜ ˜ k k k k k k k k k Do x , y + α ,b − α ,1, −1 + α = Do x , y , b ,1, −1

(1.20)

If we put the fgure into (1.19):

)

(

˜ k k k k k k k − ˜ = Do x , y + ˜ , b − ˜ , 1, −1 + ° k

(1.21)

k

Generally, α =b is used, and then the equation (1.21) is estimated by OLS or maximum likelihood methods to calculate the environmental sensitivity productivity, and the method can also estimate the statistic of each coeffcient. 1.2.4 Solution based on non-parametric data envelope analysis (DEA) method The DEA method has a large number of applications in the production environment frontier function. In recent years, with the deepening of the distance function and directional distance function research, research on measuring the environmental sensitivity productivity by DEA method has emerged (Färe et al., 1989; Ball et al., 1994; Yaisawarng & Klein, 1994; Chung et al., 1997; Tyteca, 1997; Lee et al., 2002; Kumar, 2006; Hu Angang et al., 2008; Tu Zhengge, 2009). Suppose there are input and output data for k samples (yk, bk, xk), k = 1, …, K. When production activities are subject to environmental regulations, the environmental production equation for the kth sample is expressed as follows:

(

k˜

F x ;b

k˜

) = max˝

K

z yk

k=1 k

(1.22)

s.t.

˜

k=1 k kj

˛

k=1 k

˛

k=1 k

K

K

K

z b = bk’j , j = 1,…,J z xkn ˜ xk’n , n = 1,… , N z ˜ 0,

k = 1,…,K

where zk (k  = 1, …, K) is the intensity variable, the purpose is to give each observation sample point weight when establishing the production boundary.

14

14

Emission reduction analysis

If the zk is not accumulated and limited, the model is fxed-scale compensation, and vice versa remuneration for scale. On the basis of the establishment of the boundary, the objective equation maximizes the desired output, and the constraint on the undesired output refects its weak disposition, that is, the reduction of undesired output will inevitably lead to the reduction of desirable output. The right side of the second constraint inequality represents the input in actual production, and the left side represents the most effcient production input in theory. The inequality indicates that the theoretical input must be less than or equal to the actual production input, and also indicates the free disposal of the input. However, the shortcoming of the above method is that it does not take into account the reduction of undesired output, but only the pursuit of the maximization of desirable output. Chung (1997) considers the development of the directional distance function and uses the DEA method while considering research on productivity issues under the premise of an increase in desirable output and a decrease in undesired output. t t t If producer 〖k ′( xk ’, yk ’, bk ’) is defned, the directional environment t

( ) can be

production frontier function under the reference technique P x expressed as:

(

)

˜ t t t t t Do xk ′ , yk ′ , bk ′ ; yk ′ , −bk ′ = max β

t

(1.23)

s.t. z yk,m ˜ (1+° ) yk ˙,m , m = 1,… ,M

ˇ

k =1 k

ˆ

k =1 k k, j

˝

K

K

K

t

t

t

z b = (1 − ° ) bk ˝, j , j = 1,… ,J t

t

t

t

t

t

z xk,n ˜ xk °,n , n = 1,… N

k =1 k

t

zk ˜ 0 , k = 1,…,K Compared with model (1.22), model (1.23) imposes constraints on the desired output, thereby increasing the desired output while minimizing the unintended output. It is possible to refect the connotation of environmentally sensitive productivity by considering the expansion and reduction of output in two different dimensions. Parametric estimation and non-parametric estimation have their own advantages. In general, the parametric method needs to preset the distance function as a certain function expression. The advantage is that the parameter

15

Literature review

15

expression can be differentiated and algebraic (Hailu & Veeman, 2000). By means of linear programming, stochastic frontier analysis, etc., the parameter values in the distance function can be estimated, and the environmental sensitivity productivity values of each decision unit and the shadow price of the undesired output are calculated. However, if linear programming is used to solve the parameters, the relevant statistics are often not available (Hailu & Veeman, 2000)17; if the random frontier rule is used, the parameter values and corresponding statistics can be calculated, and the ineffciency can be further decomposed into techniques. Ineffciency, allocation ineffciency and random error, but the method also requires a preset function form, and the distribution of error terms is assumed to be strong. When non-parametric DEA is used to estimate the production front, as there is no need to make a priori assumptions on the production function structure, no parameters need to be estimated, no ineffcient behavior is allowed, and total factor productivity (TFP changes) can be decomposed, so there is more attention and application (Färe et  al., 1998). In addition, the non-parametric DEA approach avoids residual autocorrelation when using time series or panel data (Färe et al., 1989; Yaisawarng & Klein, 1994). However, the non-parametric DEA method is sensitive to the sample data. The error of the abnormal sample value will affect the position of the production frontier, and then affect the value of the environmental sensitivity productivity. Therefore, the accuracy of the sample data is high. In addition, the non-parametric DEA method can generally only be used for productivity measures and is rarely used to estimate shadow prices for undesired outputs (Färe et al., 1998).

1.3 Review of existing empirical studies The development of theoretical research is inseparable from the continuous verifcation of empirical research. Table  1.1 lists some important empirical documents. Through combining and summarizing these documents, the following conclusions can be drawn. 1.3.1 In terms of research objects, foreign countries focus on micro-scale research, and domestic focus on macro-level analysis The research objects of most foreign literatures are mainly based on the production activities of micro-enterprises. In the selection of micro-enterprises, it is important to consider industrial production-oriented enterprises that are heavily invested or dependent on certain polluting raw materials in the production process, such as thermal power stations, which use fossil energy to burn and emit a lot of pollution. Gases, such as SO2, not only cause greater pollution to the atmosphere, but also affect human health. Therefore, in the existing literature, research on environmentally sensitive productivity studies using SO2 emitted from power stations as undesired outputs is dominant

16

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Table 1.1 Summary of evidence-based literature on studies about environmentally sensitive productivity Authors

Evidence-based methods Model

Data

Functions Assessment Minimum cost method

Variables Input

56 power plants Labor/capital/ in the US low-sulfur from 1973 to fuels/high1979 sulfur fuels 30 paperPaper pulp/ making energy/ factories in capital/ the US in labor 1976

Main results Desirable Output

Undesirable Output

Productivity

Shadow price

Electricity generated

SO2

_

SO2: 0.195 (US$/pounds, 1979 price)

Paper

BOD/TSS/PART/ SOx

Effciency: 0.9182

BOD: 1,043.4, TSS: 0, PART: 25,270, SOx: 3,696 (US$/ton, 1976 price) SO2: 305 (1990), 251.6 (1991), 322.9 (1992), Average 292.7 (US$/ton, 1992 price) SO2: 1,703 (US$/ton, 1973 price)

Gallop and Roberts (1985)

Cost _ function

Färe et al. (1993)

DF(O)

Translog Parameter method

Coggins and Swinton (1996)

DF(O)

Translog Parameter method

14 heating and Sulfde/energy/ Electricity power plants labor/ generated in Wisconsin capital in 1990–1992

SO2

Effciency: 0.946

Boyd et al. (1996)

DDF

_

DEA

SO2

Average effciency: 0.933

Chung et al. (1997)

DDF

_

DEA

Coal-fueled Fuel/labor/ Net power plants capital electricity in the US (fxed Yaisawarng and input)/sulfur Klein 1994 (undesirable data output) 30 Swedish Labor/wood Pulp paperfber/energy/ making capital factories in 1986–1990

BOD/COD/TSS

M index:0.997 (improved effciency:0.977; technological progress:1.02)

_

17

Kolstad and _ Turnovsky (1998)

Quadric form

_

Swinton (1998)

DF(O)

Translog Parameter method

Murty and Kumar (2003)

DF(O)

Translog Parameter method SFA

Hailu and Veeman (2000)

DF(I)

Translog Parameter method

51 coalgenerated power plants in eastern America in 1970–1979 Coal-fueled power plants in 1990–1998 Florida Industries with water pollution issues in India (samples from 60 companies) Aggregated data of Canadian papermaking sector for 36 years between 1959 and 1994

Sulfur/ash/ capital/ thermal energy

Electricity generated

SO2

ML index: 1.039 (improved effciency: 0.955; technological progress:1.088) _

Energy/labor/ capital/ sulfur

Electricity generated

SO2

Effciency: 0.978

SO2: 157.10 (US$/ton, 1996 price)

Capital/labor/ energy/ materials

Turnover

BOD/COD/TSS

Effciency: 0.899

BOD: 0.246, COD: 0.0775 (million Rupee/ ton, 1994/95 price)

Energy/wood residue/ wood pulp/ other raw materials/ production labor/ managerial labor/ capital

Wood pulp/ BOD/TSS newsprint/ paper board/ other types of paper

TE: 0.996, M Index: 0.878, ML Index: 1.044

BOD: 123, TSS: 286 (US$/million ton, 1986 price)

SO2: 0.071; Ash: 0.121 (US$/pounds, price in 1976)

(continued)

18

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Table 1.1 (Cont.) Authors

ReigMartinez et al. (2001) Lee et al. (2002)

Evidence-based methods

Data

Variables

Model

Functions Assessment

DF(O)

Translog Parameter method

18 pottery factories in Spain

DDF

_

43 Korean Installed power plants capacity/ of 1990–1995 fuels/labor

DEA

Input

Main results Desirable Output

Raw materials/ Ceramic capital/ pavement labor

Productivity

Shadow price

Cement/waste oil

Average effciency: 0.927

Cement: 336.6 (euro/ton), waste oil: 125.5 (euro/kg) SOx: 3,107, NOx: 17,393, TSP: 51,093 (US$/ton) CO2: 331.89, SO2: 59,997.95 NOx: 154,583.63 (US$/ton) 1980 SO2: 395.3; 1985 SO2: 1,871.7; 1990 SO2: 556.8; 1995 SO2: 486.7 (US$/ton) SO2: 167.4, Ash: 127.7 (US$/pounds, 1976 price)

Power SOx/NOx/TSP generation

Salnykov and DDF Zelenyuk (2005)

Translog Parameter method

50 countries

Labor/arable GNP land/energy/ capital/

Atkinson and Dorfman (2005)

DF(I)

Translog Parameter method

43 US private for-proft power plants in 1980, 1985, 1990, 1995

Energy/labor/ capital

Lee (2005)

DF(I)

Translog Parameter method

Färe et al. (2005)

DDF

Quadric form

51 US thermal Capital/heat/ power sulfde/ash generating units in 1977–1986 209 US thermal Labor/ power plants installed in 1993/1997 capacity/ fuel

Deterministic parameters

Undesirable Output

CO2/SO2/NOx

_

Effciency: 0.8433

Power SO2 generation

Classical effciency: 0.564277 LIBS effciency: 0.553187

Power Sulfur/ash generation

TE: 0.945

Power SO2 generation

1993: 0.814, 1997: 0.785

1993 SO2: 1,117; 1997 SO2: 1,974 (US$/ton)

19

SFA

1993: 0.798, 1997:0.804

Kumar (2006)

DDF

_

DEA

41 countries in 1971–1992

Labor /capital/ GDP energy

Färe et al. (2007)

DDF

_

DEA

Ke et al. (2008)

DF(O)

Translog Parameters method

92 thermal power plants in the US in 1995 30 provinces in China from 1996 to 2002

Capital/labor/ fuel heat (coal, oil and gas) Capital/labor

Van Ha et al. DF(O) (2008)

Translog Parameter/ 63 papermaking Capital/labor/ measurement workshops in energy/ assessment Vietnam in waste 2003 paper/other materials/ social capital

CO2

M index: 0.9998 (Improved effciency: 1.0019; technological progress:0.9981) ML index: 1.0002 (Improved effciency: 0.9997; Technological progress: 1.0006)

1993 SO2: 76, 1997 SO2: 142 (US$/ ton,1982–1984 price) _

Power SO2 generation NOx

_

_

GDP

SO2

East: 0.831, Middle: 0.706, West: 0.682

Paper

BOD/COD/TSS

Effciency: 0.72

East: 0.516, Middle: 0.508, West: 0.529 (hundred million yuan/ ton, 1996 price) BOD: 575.2, COD: 1,429.7, TSS: 3,354.8 (US$/ton, 2003 price)

(continued)

20

Table 1.1 (Cont.) Authors

Evidence-based methods

Data

Model

Functions Assessment

Ghorbani and Motallebi (2009)

DF(O)

Translog Parameters method

Hu et al. (2008)

DDF

Tu (2008)

DDF

Wang et al. (2008)

DDF

_

Variables

Main results

Input

Desirable Output

Undesirable Output

Productivity

Shadow price

85 dairy farms in Iran in 2006

Farm area/ energy/ labor/feed

milk

CH4/CO2/N2O

_

CH4: 0.61, CO2: 0.058, N2O: 0.59 (price ratio against milk)

DEA

30 provinces in China from 1999 to 2005

Capital/labor

GDP

CO2/COD/SO2/ waste water/ solid waste

DEA

Industrial enterprises above a designated size in 30 provinces from 1998 to 2005

Capital/ energy/ labor

Industrial added value

SO2

DEA

1980–2004 Capital/labor APEC 17 countries and regions

GDP

CO2

Highest in the east and lowest in the west (specifc value depends on the type of undesirable output) East: relatively harmonious relationship between industry and environment Middle and west: imbalances between environmental protection and industrial growth ML: 1.0056 (Technological progress:0.76%)

_

21

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Tu (2009)

DDF

_

DEA

Wu (2009)

DDF

_

DEA

Yue & Liu (2009)

Inversed _ output Reciprocal method DDF

DEA

Yang & Shao DDF (2009)

_

DEA

Zhou & Gu (2009)

_

DEA

DDF

Industrial Capital/ enterprises energy/ above a labor designated size in 30 provinces from 1998 to 2005 1998–2007 Capital/labor China’s 31 provinces industrial sector 36 industrial Capital/labor sectors in China between 2001 and 2006

Industrial added value

SO2

_

SO: 2.09 (RMB, hundred million yuan/ ten thousand tons, 1998 price unchanged)

Industrial added value

COD/ SO2

_

Industrial added value

SO2

30 provincial industrial sectors from 1998 to 2007 Industry data of large and mediumsized industrial enterprises in Shanghai from 1997 to 2004

Capital/labor

Industrial added value

SO2

National average ML: 1.085 (contribution of technological progress 95.29%) Effciency value: inverse algorithm: 0.55; reciprocal approach: 0.49; directional distance function: 0.68 East: 0.886, Middle: 0.703, West: 0.686

Capital/labor/ energy

Total industry

SO2

2006 technological effciency index: 0.6437 (heavy industry); 0.7396 (light industry)

_

_

_

(continued)

22

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Table 1.1 (Cont.) Authors

Evidence-based methods

Data

Model

Functions Assessment

Chen et al. (2010)

DDF

_

DEA

Wang et al. (2010)

DDF

_

DEA

Dong et al. (2010)

_

_

DEA

Wu et al. (2010)

DDF

_

DEA

Industrial enterprises above a designated size in 11 provinces in the east of China from 2000 to 2007 30 provinces in China from 1998 to 2007 29 provinces, 1995–2006 East/Central/ Western region, 2000–2007

Variables

Main results

Input

Desirable Output

Capital/labor

Capital/labor/ energy Capital/labor/ sown area/ energy Capital/labor/ human capital

Undesirable Output

Productivity

Shadow price

Output value SO2

ML: 0.902 (2007)

_

Industrial added value Actual regional GDP GDP

COD/ SO2

National average:0.712 _ (VRS); 0.657 (CRS)

Reciprocal of environmental pollution index COD/ SO2

ML: 1.008

_

_

_

Note: price shown is the price of the year unless there is special explanation. DF(O):  Output distance function; DF(I):  input distance function; DDF:  directional distance function; DEA:  Data Envelope Analysis method; SFA:  stochastic frontier approach; M index: Malmquist productivity index; ML index: Malmquist–Luenberger productivity index; VRS: variable scale; CRS: invariant scale; BOD: biochemical oxygen demand; COD: chemical oxygen demand; TSS: total suspended solids; PART: particles; SOx sulfur oxides:

23

Literature review

23

(Gallop & Roberts, 1985; Coggins & Swinton, 1996; Lee et  al., 2002; Färe et  al., 2005, 2007); in addition, some industrial producers whose emissions are easily metered, such as BOD/COD emissions from paper mills, water pollutants (Färe et al., 1993; Chung et al., 1997; Hailu & Veeman, 2000; Van Ha et  al., 2008), waste oils discharged from ceramic plants (Reig-Martinez et al., 2001) are also used in the environment. As countries continue to pay attention to greenhouse gas (GHG) emissions issues, some scholars have begun to shift their perspectives to macro-level research. They usually measure and compare the environmental sensitivity productivity of major GHG emissions such as CO2 and NOx, which are equivalent in economic development level (such as OECD, transition economies) or geographically similar countries (such as APEC, EU) (Salnykov & Zelenyuk, 2005; Kumar, 2006; Wang Bing et al., 2008). Limited by the lack of data at the domestic enterprise level, especially the pollutant data is diffcult to obtain. Most of the research on China is based on the macro-level, mainly to measure the environmental sensitivity productivity and the marginal abatement cost of pollutants in different provinces or industries. For example, Ke et  al. (2008) used the output distance function and the super-logarithmic function form to measure the environmental sensitivity productivity of 30 provinces in mainland China from 1996 to 2002, and estimated the shadow of SO2 pollutants. Hu Angang et al. (2008) used the directional distance function earlier, using CO2, COD, SO2, total wastewater discharge and total solid waste discharge as indicators of undesired output, measuring 30 provinces in mainland China environmentally sensitive productivity during 1999–2005. At the industry level, there are mainly Tu Zhengge (2008, 2009), Wu Jun (2009), Yue Shujing and Liu Fuhua (2009), Yang Jun and Shao Hanhua (2009), Zhou Jian and Gu Liuliu (2009), Chen Ru et al. (2010). Based on the SO2 data of China’s provincial industrial sector, the scholars used the directional distance function to measure the environmental sensitivity productivity of the industrial sectors in various provinces; at the regional level, Wang Bing et  al. (2010), Dong Feng et  al. (2010), Wu Jun (2010) adopts provincial input–output data. In addition to capital and labor, the input includes energy consumption, human capital and other factors. The output end includes two Eleventh Five-Year Plan of COD and SO2. The pollutants required to be forced to reduce emissions use the directional distance function to calculate the environmentally sensitive productivity at the provincial level. In addition, in the study of Tu Zhenge (2008), Wang Bing et al. (2010) scholars, the factors affecting environmentally sensitive productivity were further analyzed and the environmental Kuznets curve and pollution paradise hypothesis have been empirically tested. These studies have important implications for understanding the differences in environmentally sensitive productivity between industries and regions, but they are limited by data factors, and their microscopic mechanisms are often not revealed.

24

24

Emission reduction analysis

1.3.2 In the theoretical hypothesis, the original part of the hypothesis needs to be relaxed, but the diffculty of the model is increased This is mainly refected in two aspects. The frst is the assumption of the shadow price symbol. In the existing theoretical model, in order to ensure that the solution of the model has economic signifcance, the shadow price of the unsatisfactory output is generally set to be non-positive, especially in the process of solving the parameterized function. The monotonicity of the undesired output to the distance function equation is specifed. There are also some literatures that use the DEA method to solve the problem. After the shadow price is obtained, the shadow prices of different positive and negative symbols are interpreted or rejected. However, as discussed by Van Ha et  al. (2008), some pollutants, such as suspended particles (mostly wood residue) in wastewater in the paper industry process, although appearing to be “unwanted” pollutants, It can be recycled as raw materials through different processes, thus turning “sub-output” into “positive output,” and its shadow price becomes positive. Therefore, it is necessary to relax the existing shadow price of undesired output. Second is the assumption of “complete effciency” and “no redundancy.” As pointed out by Lee et  al. (2002), previous literature have assumed that the production front is completely effcient, but under the premise of certain technology, the unit inputs, outputs, or unit-desired outputs of each decisionmaking unit are not desirable. Outputs are all different. If imperfect effciency is considered, the results of environmentally sensitive productivity will necessarily differ. Therefore, the shadow price of pollutants calculated based on the assumption of full effciency will be different from the result of incomplete effciency when other conditions are the same. As pointed out by Boyd et al. (1996), there is a gap between the theoretically estimated shadow price of pollutants and the actual observed price of pollutant emission trading in the trading market, possibly due to the imperfect effciency.18 In addition, Fukuyama and Weber (2009) further pointed out that most of the existing directional distance function studies do not take into account the possible redundancy (slacks), and redundancy will also lead to imperfect effciency, resulting in biased environmental sensitive productivity. 1.3.3 In the algorithm implementation, the function form setting is quite different, and the calculation process is still complicated First of all, the function form and estimation method are quite different, and each has its own advantages and disadvantages. Although there are two kinds of distance function and directional distance function in the theoretical model, the setting and solving methods of the function form are very different. As has been summarized above, the parametric method solution includes two main forms: deterministic function analysis and SFA. The deterministic function can be set to super-logarithm, quadratic or hyperbolic; DEA

25

Literature review

25

requires the use of a set of linear programming equations (inequalities) to fnd the optimal solution. The commonly used method in the empirical estimation of the distance function is the method of deterministic linear programming, which needs to set the function form, and only a small number of methods using measurement estimation (Hetemäki, 1996).19 The advantage of linear programming is that it is relatively simple to use without any distribution assumptions, even in the case of small samples, a large number of parameters can be calculated; the disadvantage is that the parameters are calculated rather than estimated (Kumbhakar & Lovell, 2000), so provide statistical criteria for consistency of conclusions, which may lead to bias in evaluations, as outputs may be affected by random disturbances. Some documents adopt a two-step analysis method to solve this problem; that is, frst use the linear programming method to calculate the distance function, and then use the distance function value as the explanatory variable, and use the parameter random distance function to estimate the parameters.20 Although the non-parametric method has the advantage of not having to set the function form, when calculating the shadow price of the contaminant, the shadow price cannot be obtained by differential calculation, and the statistic cannot be provided. In addition, the production frontier boundary is easily interfered by the error points, causing the result to deviate signifcantly from the actual situation. In addition, when the directional distance function is applied, the selection of the directional vector is relatively simple. In the general theoretical research, a relatively neutral attitude is adopted, which is determined as (1,  –1); that is, the ratio of expansion and contraction of desirable output and undesired output is 1. However, not all governments have a neutral preference. According to different research needs and policy preferences, the specifc choice of directional vector should not be fxed as (1,  –1), but so far no scholars have conducted research on the selection of non-neutral vectors, so the theoretical results that are more focused on the expansion of desirable output or more biased towards the unsatisfactory output remain to be explored. Calculation process is more complicated and generally requires programming. Because the number of selected research objects is often large, more than 100 decision-making units, plus the model itself has several constraints on each unit, the calculation process is more complicated. At the same time, because the directional distance function is still a relatively new research feld, there is no relevant solving software and program at present, which generally requires the researcher to implement it by himself,21 which also hinders the popularization of related research to some extent. 1.3.4 In the conclusion of the study, there is a certain gap with the reality, and it is still necessary to strengthen its policy signifcance Because the models, data, and calculation methods used by many research institutes are different, the differences in their research conclusions are also

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Emission reduction analysis

large, and there is a certain gap between theoretical expectations and actual observations. For example, in the study of SO2 emissions from power plants, the average environmental sensitivity productivity is about 0.9, and the variance is small, indicating that although these power plants may have different production equipment and technical levels, they are “close to the best frontier of production.” This is in contrast to the intuition in practice; in addition, the SO2 shadow price estimated from environmental sensitivity productivity ranges from $167/ton to $1,703/ton, while the market price of US SO2 license transactions is $64–200/ton (Ellerman et  al., 2000). The large differences between these research fndings and reality indicate that the existing model settings may require further revision and improvement, as the full effciency assumptions mentioned above may need to be further relaxed. The practical application and policy signifcance of the conclusions of environmental sensitivity productivity research can be summarized into three aspects. First, quantitative evaluation of environmental performance and environmental productivity of economic units producing “unintended outputs” to verify whether “environmental regulation” it affects the productivity of enterprises and the competitiveness of enterprises. Second, with the analysis of environmental sensitivity productivity, the marginal abatement cost of pollution of different enterprises and departments can be measured, so as to set the initial price and environment of the pollutant trading market. Taxes and fees are provided as a basis; in addition, the fndings of environmentally sensitive productivity can be further extended to the estimation of environmental control costs, thus providing a reference for the formulation of pollutant control policies. The investigation of environmental performance in different industries and different regions can guide low-effciency units to promote high-effciency production. The measurement of environmental control costs will guide policy-makers to make appropriate environmental control policies under the predetermined policy objectives. The determination of the shadow price can help the regulator to set the penalty for the discharge of different pollutants, and the manufacturer can also use this information to determine whether it is cost-effective to purchase the emission right, so as to carry out the most effcient production activities. Of course, all of this is based on the results of theoretical research with reference and reproducibility.

1.4 Conclusion Through the introduction and evaluation of the above-mentioned environmental sensitivity productivity research, it can be seen that with attention to environmental issues, more and more scholars are investigating other factors such as pollutants when examining the productivity of enterprises (regions and countries). Based on the original productivity theory, theoretical models such as distance function and directional distance function are developed, and the parametric and non-parametric methods are used to solve and simulate the possible effects of desirability on real productivity. Although great

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progress has been made in theory, there is still a certain gap between the reality and policy guidance. In the future, more in-depth and detailed research is needed. First of all, it is necessary to further improve and relax the existing assumptions in theory, especially considering how to determine the optimal production frontier under the conditions of incomplete effciency and redundancy, and to develop appropriate criteria to select the appropriate directionality vector, which determines the path that each decision unit approaches toward the production front. Secondly, the model itself and its implementation need to be further simplifed. The current research on environmental sensitivity productivity requires the integration of economics, management operations research, mathematics and other related knowledge, and also needs to be realized by computer programming, which hinders the degree of integration to some extent. If you develop the corresponding software package, or the general program code, I believe it will attract more researchers. In addition, in the empirical application, more open microdata support is needed. On the one hand, microdata are helpful to reveal the real behavior preference and technology of the enterprise, which can better reveal its mechanism of action. At the same time, it is easy to open the data and repeat the experiment. The mutual testing of different models makes the theoretical results closer to real problems and more effectively applied to policy formulation and production practices.

Notes 1 This chapter is based on the revision of Research Summary of Environment Sensitive Productivity, World Economy, 2011, vol. 5, co-published by Wei Chu, Huang Ruowen, and Shen Manhong. 2 It is referred to as “Bad Output” in the published journal by Färe et al. Here, it is translated into “Undesirable Output” from the study by Hu et al. (2008). 3 This is also where another argument that “environmental regulations will reduce the productivity (competitiveness) of frms” emerges. 4 The direct approach is mostly adopted at the beginning to address this issue, or shifting undesirable output through monotone decreasing function so that the resulting data can be included in the normal production formula when technical production conditions remain unchanged. Detailed methods include changing undesirable output into input elements (Liu & Sharp, 1999), or apply additive inversion to undesirable output (Berg et al., 1992; Ali & Seliford, 1990) or multiplication inversion (Golany & Roll, 1989; Lovell et al., 1995). Refer to Scheel (2001) for detailed illustration. 5 Environmentally sensitive productivity, environmental productivity, environmental performance, environmental effciency are mostly adopted expressions for productivity measurement scale of “undesirable output.” Here, we use the term employed by Hailu and Veeman (2000) and Kumar (2006) as environmentally sensitive productivity.

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Emission reduction analysis

6 The weight of input or output is generally confrmed by the proportion of input in cost or output in profts. 7 Pittman (1983) extended the multiproductivity index proposed by Caves, Christensen and Diewert (1982). By taking 30 paper-making factories in Wisconsin and Michigan as the research target, he used his measurement assessment of marginal cost and assessment of pollution control cost in 1977 by the EPA as well as the census data retrieved from the reduction in pollution control cost by companies between 1976 and 1977 by US Census, respectively, to add the pollution reduction cost as the shadow price of undesirable output, constructing a multiproductivity index of undesirable output and comparing it with traditional productivity index. 8 Repetto et  al. (1996) used market assessment of adjusted marginal pollution damage cost to calculate adjusted productivity index and calculated the productivity of three industries in the US, including the paper-making industry. 9 A typical example is air pollution. Apart from the infuence of pollution on nearby residents, it also affects the health of residents in other areas. Likewise, some of the health impact cannot be revealed immediately, thus making it diffcult to evaluate the damage cost of a certain region or in a specifc year exactly. 10 Weak disposability: if u∈P(x), θ∈[0,1], then θu∈P(x); free/strong disposability: if v ≤ u ∈P(x), then v ∈P(x). When it is weakly disposed, reducing undesirable output will only proportionately reduce desirable output, or reducing undesirable output means that it is imperative to abandon valuable desirable output. This is also means the negative shadow price of undesirable output. When it is strongly disposed, undesirable output can be freely dealt with to maintain desirable output unchanged. 11 It is not assumed here that r is non-negative, but partial prices are allowed to be non-positive. 12 It is not assumed here that r is non-negative, but partial prices are allowed to be non-positive. 13 The advantage of the output distance function: frst, it can fully express the technique, it is a scalar value (combined with the multi-output feature compared to the scalar production function); second, it satisfes the output weak deal; fnally, the output of the distance function and the income function are dual relationships, from which the shadow price can be found. Because the output distance function is less than or equal to 1, its natural logarithm is less than or equal to 0, so the value is “maximum.” 14 This setting is in line with the regulative intentions, or proportionately increase desirable output and reduce undesirable output. 15 If Do is set to the boundary effciency point 1, the left side of the equation is non-variable, the intercept cannot be calculated, and the parameter estimation is biased. It is useless to take the logarithm to the left of the equation to make it zero. 16 When considering the productivity of undesired output factors, because the directional distance function is a vector, when the manufacturer reaches the production boundary, the technical ineffciency value is 0, so the dependent variable on the left side is set to zero. 17 Bootstrapping is employed by Grosskopf, Hayes and Hirschberg (1995) to overcome this defciency. 18 Based on Lee et al. (2002)’s measurement, shadow price of the pollutant assessed with imperfect effciency may lower than that with the perfect effciency by 10%.

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19 Vardanyan and Noh (2006) argued about the possible dependence of environmentresulted shadow price on the choice of the form of distance function and parameterization which is found better than any other methods. 20 General random parameter distance functions can refer to Aigner et al. (1977) and Meenusen and Broeck (1977). 21 Popular programming software includes Mathmatics, GAMS, LINDO/LINGO, MatLab, etc. In addition, Excel can be used for general linear planning problems. However, due to the limit to its solver, professional software like Solver should be used.

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2

Interprovincial CO2 shadow price research based on the parametric model1

The global warming caused by greenhouse gas (GHG) emissions has become an international consensus. Without mandatory carbon emissions interventions, climate change will worsen the Earth’s ecological environment and affect human survival and development. As the largest country in terms of total carbon emissions, China is under heavy international pressure. Western developed countries, led by Europe and the USA, have put forward the requirement of “incorporating China into the world carbon emission reduction indicator system.” If this requirement is implemented, the impact on China’s future economic development will be extremely large. Therefore, in the special period of China’s vigorous construction of an environmentfriendly society, it is necessary to actively incorporate carbon emissions into the development indicators of the economic environment. Before the international community’s emission reduction targets have been imposed on China, it is urgent to propose carbon in line with China’s national policy recommendations for emissions reductions. After implementing carbon emission control, enterprises will gradually assume GHG control and abatement costs, and may even reduce industrial output, which will lead to a reduction in economic profts and an impact on the economic growth of the entire society. If the carbon tax imposes a decline in carbon emissions and energy consumption, it will also cause different degrees of decline in economic growth rate, employment rate, consumption and investment levels (He Juhuang et  al., 2002; Wei Taoyuan & Romslod, 2002; Gao Pengfei & Chen Wenying, 2002; Cao Jing, 2009; Su Ming et al., 2009; Zhang Mingxi, 2010), which caused suppression of economic growth, and the higher the carbon tax level, the higher the GDP loss. Therefore, measuring the economic cost of reducing the marginal output caused by carbon emissions is a very important practical issue. This has important reference value for determining the carbon tax rate level and will provide a reliable theoretical basis for policy-makers. The economic cost of carbon emission reduction is often described by “shadow price.” However, because the carbon dioxide emitted in the production process does not have the nature of market transactions, it is diffcult to obtain its price information directly from the market. Therefore, this chapter DOI: 10.4324/9781003004455-3

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Interprovincial CO2 shadow price

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adopts an international comparison. The general environmental directional distance function method is used to estimate the potential price of carbon dioxide, thereby obtaining the economic cost of unit carbon emission reduction. Due to the imbalance of economic development level in various regions of China, the environmental cost for economic growth is not the same. Therefore, the estimation of CO2 shadow price for each province (city, district) in China will help to fully understand China’s actual situation. The structure of this chapter is as follows. The frst part will explain the concept of shadow price and literature review; the second section gives the research method of this chapter  – the environmental directional distance function; the third and fourth parts are the data variable description and the empirical result analysis; the fnal part is the conclusions of this chapter.

2.1 Shadow price concept and literature review Under the pure market economy conditions, enterprises discharge waste in the production process, and there is an inconsistency between the private cost and social cost of the enterprise, thus generating negative externalities. Once the government regulates and restricts the discharge behavior of enterprises, the enterprise will assume the “external cost” of pollution control, which will reduce its output value and profts. If there is no environmental control, enterprises will not have to consider the external cost of pollution control, and will produce more products, increasing production value. The difference in output between the two can be called the cost of pollutants. Therefore, the pollutant emissions and economic output of the production activities of the enterprise are taken as two kinds of outputs: the former is the undesired output, and the latter is the desirable output, also called the “bad” output and the “good” output, respectively. Then the shadow price of the pollutant is the output increased by abandoning one unit of pollution, or the output reduced by one unit of pollution (taking into account the cost of treatment caused by the production of unit pollutants). In other words, the shadow price of pollutants is the marginal cost of reducing unit pollution. By using the shadow price of pollutants, the marginal effect of pollutant emissions changes on economic output can be measured, thus providing a basis for formulating appropriate environmental control policies and guiding enterprises to carry out low-pollution production activities. Because there is no real market price for contaminants, measuring the shadow price requires special methods. There are two main methods commonly used in the existing literature to solve the shadow price of pollutants. One is based on the parametric model: it can estimate the specifc parameter form of the environmental production function including the pollution factor and then the environmental output. The function seeks partial derivatives to obtain the marginal effect of pollution (Färe et  al., 1993, 2006); or uses the duality between the output distance function and the income function to derive the shadow of pollutant emissions in the form of parameter price

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(Coggins & Swinton, 1996). The other is to use a non-parametric method, which relies less on the assumption of the function form, but calculates the environmental production frontier function through mathematical linear programming techniques (such as DEA), and further based on the intertemporal environment production frontier function. To measure the marginal effect of pollution emissions on cutting-edge output (Boyd et al., 1996; Lee et al., 2002; Tu Zhengge, 2009, etc.). Chen Shiyi (2010) uses both parametric and nonparametric methods to measure the shadow price of carbon dioxide. The two methods of measurement yield similar results. The development of the model of the parametric approach has gone through two phases. Beginning in the 1990s, the academic community began to use distance functions to contain environmental output and derive shadow prices for undesired outputs (Färe et al., 1993; Ball et al., 1994; Yaisawarng & Klein, 1994; Coggin & Swinton, 1996; Hetemäki, 1996; Hailu & Veeman, 2000). Based on the parameterized output distance function model, Färe et  al. (1993) used Pittman’s (1983) data to evaluate the effciency of 30 paper mills in Wisconsin and Michigan in 1976, and to produce biochemical oxygen demand in production. The shadow prices of undesired outputs such as (BOD) and total suspended particulate matter (TSS) were measured. Coggins and Swinton (1996) also used the output distance function to calculate the technical effciency and SO2 shadow price of 14 thermal power plants in Wisconsin. Hailu and Veeman (2000) used a parametric input distance function including desirable and undesired outputs to build productivity including environmentally sensitive factors, and built a Malmquist index based on the input angle, in addition to building a shadow price model and empirical studies using time-series data from the 1959–1994 paper industry in Canada. Shadow price estimates indicate that the marginal cost of vendor pollution control continues to rise over time. The directional distance function developed since then has deepened the research in this feld. Chung et  al. (1997) specifed the determination of the optimal boundary when the directional distance function reduces the undesired output while increasing the desired output and built the Malmquist–Luenberger index to provide new ideas for productivity research under environmental control. Many scholars have followed this new approach. Lee et al. (2002) used the panel data, directional distance function, and DEA method of 43 power plants in Korea in 1990–1995 to deal with various pollution caused by power generation: sulfde (SOx), nitrogen oxides (NOx). The shadow price of total suspended particulate matter (TSP) is measured. Färe et al. (2005) used the directional distance function to calculate the SO2 shadow price of 207 thermal power plants in the USA in 1993 and 1997 using the deterministic parameter method and the stochastic frontier method, respectively. The parameter estimation method has many advantages in model estimation and interpretation, especially the environmental production function in the form of directional distance function. By solving the unknown parameters

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Interprovincial CO2 shadow price

in the preset function, the environmental production function can be obtained intuitively and the function can be utilized. The mathematical method calculates the shadow price information and is widely used in the calculation of shadow prices and the determination of environmental productivity. Therefore, the use of directional distance function is of great signifcance for building environmental production frontier functions.

2.2 Derivation of directional distance function and shadow price The theoretical model of the directional distance function was proposed by Chung et al. (1997), who elaborated on how to use the directional distance function to study productivity when studying Swedish pulp mills. The difference between the directional distance function and the ordinary distance function is that the assumptions for the joint production of good and bad outputs are different. The ordinary distance function only considers the expansion of output, while the directional environmental distance function considers the increase in output while reducing the bad output. By deriving the partial production of the environmental production function based on the directional distance function, the shadow price of the pollutant (i.e., bad output) can be solved. Assume the input vector of each production department, good and bad output vector, under certain production technology conditions: P(x) = {(y, b): x can produce (y, b)}, has the following two characteristics: (i) Weak disposition of bad output: when (y, b) ∈ P(x), 0 ≤ ∈ ≤ 1, (∈y, ∈b) ∈ P(x); Free disposition of good output: when (y, b) ∈ P(x), y′ ≤ y, (y′, b) ∈ P(x); (ii) Co-production (good-output null-joint): when (y, b) ∈ P(x), if b = 0, then y = 0. The directional distance function needs to construct a direction vector of g = (gy, gb), and this vector will be used to constrain the direction and variation of the M good output and the J bad output, that is, increase the constraintspecifed path good output and reduce the bad output. Thus, the directional output distance function can be defned as: ˜˜˜° DO ( x, y, b; g y , gb ) = sup °: (y +° g y ,b −° gb ) ˛ P( x)

{

}

(2.1)

where β indicates the degree to which a given good product (bad product) can be enlarged (reduced) compared to the most effcient unit on the leading edge production surface. If β is equal to 0, it means that this decision unit is on the leading edge production side, which is the most effcient. The larger the β value, the greater the potential for the decision-making unit to continue to

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increase in output, and the greater the room for further reduction of the bad output, indicating that the effciency is lower. Regarding the method of solving the directional distance function, a parameter or non-parametric method can be employed. Here we use the parameterized hyperlog function (translog) method proposed by Chung (1996) to select the direction vector g = (1, –1). This is in line with the intention of complying with neutral policy control, that is, scaling up the desired output and reducing the number of undesired outputs. Assuming k = 1, …, K represents a different observation sample, so the parameter form of the directional distance function is:

( )

N M J ˜ 1 N N ln[1 + Do ( x, y, b;1,1)] = ˜ 0 + ˙ ˜ n x n + ˙ ° m y m + ˙ ˛ j b j + ˙ ˙ ˜ ’ ( x n ) x ’ n 2 n=1 nˆ=1 nnn n=1 m=1 j =1 M M J J 1 1 + ˙ ˙ ° ’ ( ym ) y ’ + ˙ ˙ ˛ ’ b j b ’ m j 2 m=1 mˆ=1 nn 2 j =1 jˆ=1 mm N M N J 1 1 + ˙˙ ˝ ( x n ) y + ˙˙ ˙ ( x n ) b j nm m nj 2 n=1 m=1 2 n=1 j=1 j M J 1 + ˙ ˙ µ nm ( y n )( b m ) 2 m=1 j =1 (2.2)

( ) ( )

( )(

)

( )

The parametric equation solving is based on the idea of linear programming, which minimizes the distance between each observation and the boundary: ˜ K min ˘ k =1 ln[1+ Do ( x , y, b;1,1)] − ln(1+ 0)

{

}

s.t. (i) (ii)

(iii)

(iv) (v)

˜˜° ln[1+ Do (x, y,b,1,−1)] ° 0, k = 1,..., K

˜{ln ˜°1+ Do (x k , y k , b k ;1,1˛˝} k

˜lng m ˜{ln ˜°1+ Do (x k , y k , b k ;1,1˛˝} k

˜lnb j

˜{ln °˜1+ Do (x k , y k , b k ;1,1˛˝} k

˜lnx n

˛

M m=1

° m − ˛ j =1 ˛ = −1, J

j

˙, m = 1, …,i, k = 1,…, K ˙ 0, j = 1,…, J, k = 1,…, K

˙ 0, n = 1,…, N, k = 1, …, K

(2.3)

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Interprovincial CO2 shadow price

˛

M m ˜=1

˛

J

°

M

j˜ =1

m=1

°

35

− ˛ j =1 µ mj = 0, m = 1,…, M J

mm˜

˜ − ˛ m=1 µ = 0, M

j = 1,…, J

mj

jj˜

˜ nm − ° j=1 ˜nj = 0, n = 1,…, N J

(vi) ° mm˜ =° m ˜m , m = 1,…, M, m ˜ = 1, …, M ˜ nn˜ =˜ n ˜n , n = 1,…, N, n˜ = 1, …, N

˜ jj˜ = ˜ j˜ j ,

j = 1,…, N,

j˜ = 1, …, N

The objective function is to minimize the deviation of all sample points to the boundary line. Constraints (i)  ensure that each sample is on the leading edge or under the leading edge; constraints (ii) and (iii) ensure the diminishing and incremental monotony of the desired output and the undesired output, respectively, while constraint (iv) the input also has an increasing monotonic constraint; constraint (v) satisfes the transformation property of the direction distance function, and constraint (vi) is the symmetry constraint. After estimating the parameters by the above linear programming, the frstorder partial derivative of the directional distance function can be obtained, and the shadow price of the undesired output relative to the desired output can be obtained:

rb = ry

˜Do (x, y, b;1,1) ˜b ˜Do (x, y, b;1,1) ˜y

(2.4)

Among them, the observed price of the desired output ry is used as the standardized price, because the desired output has an observable and marketoriented price, and rb is the absolute shadow price of the undesired output.

2.3 Variable description and estimation results This section uses the time series data from 1995 to 2007 to estimate the carbon dioxide shadow price in China by province (city, district) through the “two inputs, two outputs” environmental production function. Among them, the two inputs are capital and labor, the good output is GDP, and the bad output is CO2 emissions. Among the 31 provinces (cities, districts) in China, due to the lack of data in Tibet, it was not included, while Chongqing was added

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Emission reduction analysis

to Sichuan Province for estimation. Therefore, 29 provinces (cities, districts) were the research objects. Capital stock: generally use the “permanent inventory method” to estimate the actual capital stock of each year. Here, we mainly refer to the existing research results of Zhang Jun et  al. (2004) and extend the capital stock sequence to 2007 according to the published method. Calculated at constant price in 2005, the unit is 100 million yuan. Labor: foreign countries generally use working hours as a labor input variable, but limited by the availability of data. Here, the number of employed people in the current year published in the China Statistical Yearbook is 10,000. GDP output data:  from the China Statistical Yearbook from previous years, in order to facilitate comparison with the indicators published by the National Bureau of Statistics, calculated at constant prices in 2005, the unit is 100 million yuan. CO2 emission data:  the existing research institutions do not have CO2 emission data by province (city, district), but because CO2 emissions are mainly from fossil energy consumption, conversion and cement production, for the sake of accuracy, energy consumption is fne here. It is divided into coal consumption, oil consumption (further subdivided into gasoline, kerosene, diesel, fuel oil) and natural gas consumption. All energy consumption and conversion data are taken from the regional energy balance sheet in the China Energy Statistics Yearbook. The cement production data comes from the Guotaian Financial Database. The specifc calculation formula for carbon dioxide emissions from fossil energy consumption is as follows: CO2 = ˛ i =1CO2i = ˛ i =1 Ei × CFi × CCi × COFi × (44 / 12 ) 6

6

(2.5)

Here, CO2 represents the estimated total amount of carbon dioxide emissions from various types of energy consumption; i represents various energy sources, including coal, gasoline, kerosene, diesel, fuel oil, and natural gas; Ei is the consumption of various energy sources in various provinces and cities. Total CFi is the conversion factor, which is the average calorifc value of various fuels; CCi is the carbon content, which is the carbon content of the unit heat; COFi is the carbon oxidation factor, which refects the oxidation rate of the energy. Level 44/12 indicates the conversion coeffcient of carbon atom mass to carbon dioxide molecular mass; the CO2 emission coeffcient of various emission sources mainly refers to IPCC (2006) and the National Climate Change Coordination Group Offce and the Energy Research Institute of the National Development and Reform Commission (2007). Descriptive statistics for each of the above variables are shown in Table 2.1. The estimated results of the average productivity of environmental factors and the average price of CO2 shadows in China’s provinces (cities, districts) from 1995 to 2007 are shown in Table 2.2. It can be seen that among the 29 provinces (cities, districts), the fve provinces with the highest environmental

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Interprovincial CO2 shadow price

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Table 2.1 Descriptive statistics of various variables Variables

K (100 million yuan)

L (10,000 person)

GDP (100 million yuan)

CO2 (10,000 tons)

Average Maximum Minimum Standard deviation

9,194.59 50,421.50 434.80 8,443.63

2,238.83 6,568.20 226.00 1,570.28

4,804.88 29,400.00 201.20 4,523.79

12,373.68 59,383.50 627.67 8,941.19

Table 2.2 Average productivity and average CO2 shadow price of provinces (cities, districts) in China (1995–2007) Provinces

Effciency

Shadow price (yuan/ton)

Rank

Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0.9978 0.9257 0.7667 0.9436 0.8906 0.4948 0.8724 0.7987 0.8699 0.7610 0.8807 0.7461 0.9992 0.9430 0.7634 0.8432 0.8256 0.8714 1.0000 0.9954 0.9977 0.6766 0.5892 0.2179 0.5644 0.9785 0.8485 0.7670 0.6022

1,611 759 468 187 218 647 323 473 1,713 1,169 1,064 355 1,008 452 843 486 430 488 1,426 378 627 598 86 666 404 136 165 129 418

2 8 16 25 24 10 23 15 1 4 5 22 6 17 7 14 18 13 3 21 11 12 29 9 20 27 26 28 19

productivity ranks are: Guangdong (1.00), Fujian (0.9992), Beijing (0.9978), Hainan (0.9977), and Guangxi (0.9954). The last fve provinces listed are: Xinjiang (0.6022), Guizhou (0.5892), Shaanxi (0.5644), Liaoning (0.4948), and Yunnan (0.2179).

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Emission reduction analysis

If we refer to the CO2 shadow price of the province (city, district), the fve provinces with the most expensive shadow prices are: Shanghai (¥1,713), Beijing (¥1,611), Guangdong (¥1,426), Jiangsu (¥1,169), and Zhejiang. (¥1,064), which is basically the most developed province in China, and the fve provinces with the lowest shadow prices are Shanxi (¥187), Qinghai (¥165), Gansu (¥136), Ningxia (¥129), and Guizhou (¥86); these are also economically underdeveloped areas. From the annual estimation results, it can be found that Guangdong has been at the production boundary during the period of 1995–2007. Under certain technical conditions, Guangdong’s material input, economic output, and pollution level are always at the frontier effciency, and there is no redundancy2; Beijing Fujian has also been in production boundary for 11  years and 12  years, respectively, and the CO2 shadow price averages of these three provinces and cities are ranked very high, respectively, ranking third, second, and sixth, indicating the level of production effciency and CO2 shadow. There may be a positive correlation between price sizes. In addition, Shanghai, Jiangsu, Zhejiang, and other provinces and cities with strong economic strength have higher CO2 shadow price averages, ranking frst, fourth and ffth, respectively, while CO2 in economically underdeveloped areas such as Qinghai, Gansu, and Ningxia average price of shadows is generally low, ranking only 26th, 27th, and 28th. From this, the level of economic development has a certain relationship with the shadow price of CO2. As indicated earlier, the CO2 shadow price is a straightforward refection of carbon abatement costs, i.e., higher CO2 shadow prices indicate higher carbon abatement costs in the region, and vice versa. Therefore, the economic costs of reducing carbon emissions in Shanghai, Beijing, Guangdong, Jiangsu, Zhejiang, Fujian, and other places are much higher than those in Qinghai, Gansu, and Ningxia. Excessive carbon emission reduction quotas will inevitably lead to the economics of these regions. The output has been greatly reduced. On the other hand, these industrially developed regions are also the main sources of carbon emissions in China. If they are strictly controlled, China’s carbon emission reduction effect will be more obvious. Based on the analysis of the mean time sample, the book will compare the trend and characteristics of the CO2 shadow price of 10 provinces, cities, and districts in China. In order to make the results of the comparison more signifcant, the top fve provinces (Shanghai, Beijing, Guangdong, Jiangsu, and Zhejaing) and last fve provinces (Guizhou, Ningxia, Gansu, Qinghai, Shanxi) were selected. The CO2 shadow price trend in Shanghai and Beijing are quite consistent. In addition to the different starting points in 1995, they experienced a sharp decline in 1995–1997, a small decline in 1997–1999, a steady increase in 1999– 2006, and a fall in 2006–2007. The CO2 shadow price trend in Guangdong, Jiangsu, and Zhejiang is signifcantly different from that in Shanghai and Beijing. In 1997–2000, the CO2 shadow price gradually increased, and 2000– 2004 was basically at a stable state. After 2004, the CO2 shadow price was

39

Interprovincial CO2 shadow price

39

changed by a comparison. The low level jumped to a level similar to that of Shanghai and Beijing, especially Guangdong, surpassing the shadow price of CO2 in Shanghai and Beijing, ranking frst in the country. The provinces (cities, districts) with low CO2 shadow prices have relatively stable fuctuations and small changes. CO2 shadow prices have been below the high shadow price provinces (cities, districts) and lower than the high shadow price provinces (the city). The lowest historical value of the district, which experienced a small arc of rising and falling cycles in 1998–2003, and began a slow upward trend from 2004. Therefore, as far as the country is concerned, the shadow price change of carbon dioxide is mainly the result of changes in economically developed provinces and cities. The reasons for the changes in CO2 shadow price trends in different regions are, in the fnal analysis, related to the high energy-consuming industries and service-oriented industries that the regions face in their own economic development, in the process of complying with the national macro-control, and in the continuous adjustment of the industrial structure. The problem of choice, the layout of high-tech, low-pollution industries and low-tech, low-effciency and high-pollution industries is not fully covered in this chapter, so there is no discussion here. Because the environmental production estimation method is carried out step by year, it may not fully refect the increase in economic output caused by the improvement of production effciency caused by technological progress during the cross-year comparison, so the actual CO2 shadow price may be exaggerated. The level of growth needs to be viewed by the audience. The comparison results of the regions in the same year are closer to the real situation. They examine the CO2 shadow price refected by different levels of productivity; that is, the cost of carbon emission reduction.

2.4 Problem discussion and research conclusions Because the estimation result of shadow price may not be comparable in a real sense to the actual price level, it refects the high and low levels of CO2 shadow price among different provinces (cities, districts), but is still worthy of attention. The order of magnitude of the CO2 shadow price has a certain reference signifcance for the determination of the carbon tax policy. According to the preliminary plan recently released by the Ministry of Finance, the carbon tax in China is taxed at 10 yuan per ton of carbon dioxide. Compared with the estimated results, the proposed carbon tax is far less expensive than the theoretical estimate. In most of the literature on carbon tax, the carbon tax rate is often set at the level of 50–100 yuan/ton, and is much higher than the proposed price in China. Of course, the low tax rate on carbon tax also has its rationality. The carbon tax has a role in reducing carbon emissions and improving environmental quality, and it also has a signifcant inhibitory effect on the economy. Its social impact will spread to employment, consumption, investment, etc. as the economic effects change, and the more impact of the high tax rate carbon

40

40

Emission reduction analysis

tax scheme on the economy is also greater. As China has just begun to try to tax carbon emissions, the low threshold is more suitable for the current situation and can mitigate the adverse impact of policy changes on the economy. With the gradual accumulation of China’s carbon tax collection experience and the continuous maturity of the collection system, the future carbon tax rate will be adjusted to the optimal carbon tax level. According to the estimation results of CO2 shadow price in Table 2.2 and its characteristics, the shadow prices of provinces (cities, districts) are not only different but also quite different; from the highest price Shanghai (¥1,713) to the lowest price Guizhou (¥86), the difference is nearly 20 times. Such a result indicates that higher requirements for the determination of the optimal carbon tax for fairness and effciency will be imposed in the future. From the development trend of CO2 shadow price in the country over the years, the shadow price of CO2 will show an increasing trend, and it can be speculated that the future carbon tax rate will have a similar growth trend. In the past, the lack of effective market price information for environmental pollutants such as carbon dioxide has caused many studies related to environmental factors to stagnate, and the government cannot accurately grasp the policy tools (such as carbon) to achieve its policy objectives, the carbon tax policy under emission control, and now the shadow price of pollutants can be estimated to make the research in the feld of environmental economics more in-depth, and this chapter is a practical application of this frontier feld. The article focuses on the empirical study of the shadow price of CO2. Using the directional distance function, the environmental production theory and the parametric solution method, the CO2 shadow price of different provinces (cities, districts) in China was measured from 1995 to 2007. We came to the following conclusions. First, from the perspective of regional cross-section, for Guangdong, Beijing, Shanghai, Jiangsu, and Zhejiang provinces with strong economic strength, the average price of CO2 shadows is higher than that of economically underdeveloped areas such as Qinghai, Gansu, and Ningxia. These provinces and cities have higher carbon emission reduction costs. The policy suggestion refected in this is that the government should formulate corresponding emission reduction measures according to different policy intentions:  when targeting carbon emission reduction, stricter restrictions should be imposed on high-shadow price areas, such as higher carbon. For tax level, while aiming at minimizing the economic losses caused by carbon emission reduction, low-shadow price areas should be allowed to bear more emission reduction targets, while subsidies provided by developed regions are a short-term approach. Secondly, from the longitudinal point of view of time, the general historical trend of the CO2 shadow price is the frst to fall frst, and the CO2 shadow price is lower. Therefore, as far as the whole country is concerned, the shadow price of CO2 is mainly driven by economically developed regions, which also indicates an increasing trend of carbon tax levels.

41

Interprovincial CO2 shadow price

41

Thirdly, from the perspective of the actual effect of the estimation results, the estimated CO2 shadow price and the actual price level may not be directly equated, but have important reference signifcance for determining the level of carbon tax; and the estimation results refect the phenomenon of different levels of CO2 shadow price between different economic development levels, which can be used as a reference standard for the graded carbon tax rate. It also implies that the “one size fts all” carbon tax rate will not fully realize its policy intentions.

Notes 1 This chapter is the improvement based on the Study of CO2 Shadow Price in Different Provinces in China, Academic Journal of Panyang Lake, 2012, 3, copublished by Wei Chu and Huang Wenruo. 2 Frontier effciency refers to the maximum economic output and minimum pollution emission with certain input; the existence of redundancy means that actual economic output is fewer than frontier production or actual pollution is greater than frontier pollution with certain amount of input.

42

3

Regional decomposition of CO2 emission reduction potential and emission reduction targets1

3.1 Background of emission abatement and quota allocation Although the Copenhagen conference failed to reach any effective emission reduction agreement, it is a solid frst step for humanity to cope with climate change in the twenty-frst century. The basic four countries composed of China and other large developing countries2 successfully resisted the mandatory emission reduction targets demanded by developed countries at the meeting, and strengthened future climate change negotiations should follow the Kyoto Protocol and “Bali route” under the leadership of the UN. As China’s infuence in the global political, economic, and environmental felds is growing, as a rising “responsible power,” China proposes an alternative target of 40–45% reduction in CO2 emission intensity by 2020. After the Copenhagen conference, the Renewable Energy Law Amendment passed by the National People’s Congress and the Climate Response Offce in the vicinity of the National People’s Congress can be regarded as concrete measures to deal with international challenges.3 In the government’s Twelfth Five-Year Plan, the goal of reducing carbon intensity will be integrated into various plans and policies, as well as previous energy conservation and emission reduction targets (Sustainable Development Strategy Research Group of the Chinese Academy of Sciences, 2009). The ensuing question is:  How to regionally decompose the national CO2 intensity constraint target?4 As many scholars have disputed, due to the imbalance of economic development in China, there are great differences in the carrying capacity and acceptability of reforms between different regions and different departments (Liu Shucheng, 2008), its own level of industrial development, and energy saving. The energy structure is also inconsistent. Therefore, in the area of the CO2 emission intensity target and the progress of the decomposition is the “one size fts all” and “step-by-step” policy, or the “differentiated” and “divide and conquer” step-by-step approach, and the basis and principle of decomposition are worthy of further discussion (Chang Xinghua, 2007; Wei Chu et al., 2010). Previously, international research and policy recommendations for climate change were mostly dominated by the West (IPCC, 2007; Stern, 2008; UNEP,

DOI: 10.4324/9781003004455-4

43

Regional decomposition

43

2008), but they were often opposed by developing countries, and the focus of their debate was on how to refect the principle of common and differentiated responsibilities, and how to ensure the fairness of the development of countries while achieving climate change mitigation. At present, the three representative programs abroad include: the Constriction and Convergence program proposed by the Global Commons Institute in 1990. The program starts from the current per-capita emission level and envisages the per-capita emission targets of different countries. After convergence to a certain level in the future, all countries will reduce emissions together and stabilize GHG concentrations to an acceptable level (Gao, 2006); the climate change framework launched by Brazil in the 1997 Kyoto Protocol negotiations. In the Summary of the Protocols of the Protocol (the “Brazil Text” program), the concept of effective GHG emissions is proposed, and the relative emission reduction obligations are set for Annex I countries. If they cannot be completed within the commitment period, they will be exceeded. Emissions penalties set up a Clean Development Fund to support adaptation and mitigation of climate change projects (Qi Yue & Xie Gaodi, 2009); in the Greenhouse Development Rights Framework proposed by the Stockholm Environment Institute (SEI) in Sweden, only rich people have the responsibility and ability to reduce emissions by setting development thresholds, The development needs of the poor with a barrier below the development threshold, the allocation of global emission reductions based on the total population capacity (purchasing GDP reduction) and total liability (cumulative historical emissions) exceeding the development threshold (Shen Gang, 2009). In the above schemes, although historical emissions are considered, most are based on national emission indicators, neglecting the principle of per-capita equity. In addition, they do not consider the development needs of countries at different stages, neglecting the distribution of demand for future emissions, and still biased from a fair perspective (Pan Jiahua & Zheng Yan, 2009). Chinese scholars have also conducted a lot of research on global GHG distribution. The Research Group of the Development Research Center of the State Council (2009) proposed a “national emission account” program based on property rights theory and externality theory, and clearly defned the historical emission rights and future emission rights of countries; establish national emission accounts for countries, and allocate emission rights to countries according to the principle of equal per capita, so that “common but differentiated responsibilities” can be clearly defned. Jiahua and Ying (2009) proposed a “carbon budget plan” based on the theory of human development. From the basic needs of people, the corresponding carbon budget rights between 1900 and 2050 were initially allocated to countries according to the per-capita mode. Dealing with self-overdraft or surplus status not only ensures a dual goal of fairness and sustainability, but also designs a carbon budget balancing mechanism and funding mechanism. Ding Zhongli et al. (2009), also based on the “per-capita cumulative emission index” idea, calculated the per-capita cumulative emissions, deserved emission allowances,

44

44

Emission reduction analysis

and emission allowances for 2006–2050 in countries from 1900 to 2005, and calculated the defcits of countries. China is currently committed to the goal of reducing CO2 emission intensity by 2020. This goal can be expected to be completed in theory.5 In the medium and long term, future CO2 emission reduction is imperative. If more stringent energy-saving and emission reduction technologies are adopted, with effective international technology transfer and fnancial support, China’s carbon emissions may peak in 2030–2040 and then enter a period of stability and decline (He Jiankun, 2011; He Jiankun et al., 2008; Jiang Kezhen et al., 2009; Ding Zhongli et al., 2009). Therefore, it is more meaningful to analyze the CO2 emission reduction potential and emission reduction space of each province, and provide some reference for the future allocation of emission reduction targets. This chapter attempts to answer the following questions. What is the potential and space for CO2 reduction in each region? How high is the marginal cost of reducing emissions? Which provinces need to be focused on when considering the fairness and effciency of CO2 emission reduction targets? This chapter considers several parameters. The fairness dimension includes the responsibility and ability to reduce climate change in different regions. The effciency dimension includes the emission reduction potential, marginal abatement cost, emission ratio, emission reduction ratio, and emission intensity of different regions. The quantitative emission estimation and ranking of each province’s emission reduction obligations have been carried out from the perspectives of emission reduction fairness and effciency. The results show that there may be some conficts between regional distribution fairness and effciency of CO2 emission reduction, and the fnal allocation priority and priority will depend on decision-makers’ consideration of fairness and effciency. In addition, this chapter explains the differences in provincial CO2 emission reduction potential and fnds that industrial structure, energy intensity, and energy structure have a greater impact on emission reduction potential. The structure of this chapter is organized as follows. The frst part introduces the basic ideas and models and data; the second section evaluates the regional CO2 emission reduction potential based on China’s provincial data, and estimates the marginal cost of regional CO2 emission reduction; the third section is from fairness and effciency, respectively. From this perspective, the province’s emission reduction capacity is evaluated and ranked; the fourth section is the explanation of the difference in regional CO2 emission reduction potential; the last is related discussion and policy implications.

3.2 Models, methods, and data Traditional production theories cannot directly deal with undesired outputs. Indirect methods can be used to convert undesired outputs so that the transformed data can be included in the normal output function under

45

Regional decomposition

45

technically unchanging conditions, including:  converting undesired outputs into input factors, or performing additive inverse or multiplicative inverse transformation, as detailed in Scheel (2001); in addition, the directional distance function was developed by Färe et al. (1989), Chung et al. (1997), etc. By building environmental production technology to continue productivity and shadow price measurement, the specifc application of the measurement of interprovincial technical effciency in China can be seen in the studies of Hu Angang et  al. (2008); Fu Jiafeng et  al. (2010), and Tu Zhengge (2009), who examined industrial productivity and industrial SO2 based on the measurement of shadow price; Wang Bing et al. (2010) adopted the measurement of interprovincial Malmquist–Luenberger productivity under environmental control. However, as pointed out by Fukuyama and Weber (2009), the existing direction distance function does not consider the possible redundancy, which leads to a certain bias in the fnal effciency evaluation. This chapter uses an extended Scaks-Based Measure (SBM) for estimation. The basic idea of the SBM model is to consider the effciency of the redundancy at the input and output ends. There is no excessive investment in the optimal performance point at the leading edge, and there is no shortage of output. On this basis, Cooper et al. (2007) proposed an extended SBM model that considers undesired outputs.6 The basic expression is as follows. There are n decision-making units, the input vector x∈Rm, the production of the desired output yb∈Rs2, and the undesired output yb∈Rs2, defning the corresponding matrix as X=[x1,…,xn]∈Rmxn, Yg=[y1g,…,yng]∈Rs1xn, Yb=[y1b,…,ynb] ∈Rs2xn, and suppose X,Yg,Yb>0. The production possible set P is defned as: g

b

g

g

b

b

P = {(x, y , y ) | x ° X ˜ , y ˛ Y ˜ , y ° Y ˜ , ˜ ° 0}

(3.1)

where λ∈Rn is the intensity vector, and the production possible set P in (3.1) is equivalent to the effciency, which is expressed as: −

1− *

˜ = min

g b s 2 sr ˇ 1 ˛ s1 sr + 1+ ˆ˙ ˙ r=1 yb  s1 + s2 ˝ r=1 y g ro ro ˘

s.t. xo = X ˜ + s g

g

m si 1 ˙ i=1 m xi0

−1

yo = Y ˜ − s

g

(3.2a)

46

46

Emission reduction analysis b

b

−

g

yo = Y ˜ + s

b

b

s °, s ° 0,s ° , ˜ ° o where s-∈Rm, sb∈Rs2 are excessive inputs and excessive undesired outputs, respectively, and sg∈Rs1 represents the desired output of the shortage, that is, the redundancy of inputs, unconsumed outputs, and desirable outputs. The formula (3.2a) is a strict decreasing function of si-, srg and srb, and satisfes 0 < ρ * ≤ 1. The sample point is at the leading edge if and only if ρ* = 1, that is, it is effcient, and the redundancy value of the input, the desired output, and the undesired output is 0. In the actual calculation, the weighting factor is often applied according to the relative importance of input and the desired output and the undesired output. Based on the objective function in (3.2a), the weighted effciency is expressed as: − −

1− *

˜ = min

m w s 1 ˝ i i m i=1 xi0

g g b b s 2 wr sr ˘ 1 ˙ s1 wr sr 1+ + ˝ r=1 b  ˇ˝ s1 + s2 ˆ r=1 yrog yro 

(3.2b)

where wi, wrg and wrb are the weights of input i, desirable output r, and undesired m s1 s2 − g b − output r, respectively, and ˛ i=1 wi = m , w ≥ 0, ˜ r=1 wr +˜ r=1 wr = s1 + s2 , g

i

b

wr ≥ 0, wr ≥ 0. 3.2.1 Emission reduction potential model For ineffcient sample points, the non-conforming output corresponding to the feasible target on the leading edge is ˜y b = y b − s b* o o

(3.3)

It can be solved by (3.2b), and the actual undesired output of the sample points can be observed, so the optimal target undesired output of each sample point can be calculated, if the undesired output is defned as CO2 emissions, which can defne the feasible abatement and the abatement potential at the time t of the sample point i. b*

FAi,t = si,t

(3.4a)

47

Regional decomposition b*

b

APi,t = si,t

yi,t

47

(3.5a)

where FAi,t is the excess CO2 emissions of sample point i at time t, indicating a reduction in CO2 emissions compared to the leading edge effective point. APi,t represents the emission reduction potential of the sample point, and its value is between 0 and 1. The higher the value of APi,t, the more the CO2 emission of the sample point is excessive, and the greater the emission reduction potential of the region. If you increase the total amount of feasible emission reductions in each region, you can get the regional (national) aggregate feasible abatement and calculate the regional (national) aggregate abatement potential. AFAt = ˜ i=1 FAi,t = ˜ i=1 si,t n

n

AAPt = ˜ i=1 FAi,t

˜

n

n i=1

b*

(3.4b)

yi,t = ˜ i=1 si,t b

n

b*

˜

n i=1

b

yi,t

(3.5b)

3.2.2 Shadow price model In addition, with the method of Charnes and Cooper (1962), the dual linear programming of (3.2a) can be expressed as: g

g

b

max u yo − vxo − ub yo g

g

b

(3.6)

b

s.t. u Y − vX − u Y ° 0 v˜

1 °1 x ˝ m ˛ o˙ g

g

u ˜

b

b

b

b

s g

b

u ˜

g

1+u yo − vxo − u yo

g

1+u yo − vxo − u yo s

˛1 y g ˙ ˝ oˆ ˛1 y b ˙ ˝ oˆ

where s = s1 + s2, [1/xo] represents the row vector (1/x1o,…,1/xmo), the dual vector v∈Rm, ub∈Rs2, ug∈Rs1 can be interpreted as input, non-consensus output and virtual price of desirable output, respectively.

48

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Emission reduction analysis

According to the method of Färe et al. (1993) and Lee et al. (2002), the ratio of the shadow price of inconsistent output to the desired output is equal to its marginal conversion rate. In the form of parameterized distance function, it can be expressed as the distance function is not satisfactory. The ratio of the output to the frst derivative of the desired output, in the non-parametric form, is the dual value of the unconstrained output and the desired output constraint in the dual linear programming, that is, the following relationship: p

b

p

g

=

u

b

u

g

(3.7a)

Assuming that the price pg of the desired output is a market-oriented standardized price, the shadow price of the undesired output can be derived as: b

g

p = p °

u

b

u

g

(3.7b)

The shadow price in (3.7b) can be regarded as the marginal cost of CO2 emissions, and the more severe the pollution emissions, the lower the shadow price (Coggins & Swinton, 1996), so for regions with lower pollutant shadow prices, the risk of opportunity costs that may result from environmental controls or the imposition of emission constraints is relatively small. For the country, it is necessary to consider the possible economic impacts of environmental policies while meeting the emission reduction targets. Therefore, the CO2 marginal abatement costs in different regions can be used as one of the effciency indicators for prioritizing regional emission reductions. 3.2.3 Variables and data This chapter analyzes the capital, labor, and energy of 29 provinces in China from 1995 to 2007 as the input factors, and analyzes the GDP of each province as the desired output and the CO2 emissions of each province as unproductive output. Capital stock: the “permanent inventory method” is widely used to estimate the actual capital stock of each year. Here, we mainly refer to the existing research results of Zhang Jun et al. (2004) and extend its sequence to 2007 according to its published method. Calculated at constant prices in 2005, the unit is 100 million yuan. Labor: foreign countries generally use working hours as a labor input variable, but this is limited by the availability of data. Here, the number of employed people in the current year published in the China Statistical Yearbook is 10,000. Energy: the data come from the China Energy Statistics Yearbook over the years. Tibet lacks energy data and is not included in the sample. The unit is 10,000 tons of standard coal.

49

Regional decomposition

49

GDP output data:  from the China Statistical Yearbook from previous years, in order to facilitate comparison with the indicators published by the National Bureau of Statistics, the unit is calculated at the constant price of 2005, the unit is 100 million yuan. CO2 data:  existing research institutions have not yet had provincial CO2 emission data. As CO2 emissions are mainly derived from fossil energy consumption, conversion, and cement production, for the sake of accuracy, this chapter breaks down energy consumption into coal consumption and oil consumption (further subdivided into gasoline, kerosene, diesel, fuel oil) and natural gas consumption.7 A large part of the primary energy consumption process is used to generate electricity and heat. Although the energy and heat generated by this part of energy consumption may not be used in the province, the resulting CO2 does remain in the province, so this chapter, when calculating energy consumption in addition to the terminal energy consumption, also includes energy for power generation and heating. All energy consumption and conversion data in this chapter are taken from the regional energy balance sheet in the China Energy Statistics Yearbook. The cement production data come from the Guotaian Financial Database. The specifc calculation formula for carbon dioxide emissions from fossil energy consumption activities is as follows: 6

6

i=1

i=1

CO2 = ˛ CO2i = ˛ Ei × CFi × CCi × COFi × (44 / 12)

(3.8)

Here, CO2 represents the estimated total carbon dioxide emissions of various types of energy consumption; i represents a variety of energy consumption, including coal, gasoline, kerosene, diesel, fuel oil and natural gas; Ei is the province’s various energy consumption Total; CFi is the conversion factor, which is the average calorifc value of various fuels; CCi is the carbon content, which is the carbon content of the unit heat; COFi is the carbon oxidation factor, which refects the oxidation rate of the energy. Level: 44/12 indicates the conversion coeffcient of carbon atom mass to carbon dioxide molecular mass; the CO2 emission coeffcient of various emission sources mainly refers to IPCC (2006) and the National Climate Change Coordination Group Offce and the Energy Research Institute of the National Development and Reform Commission (2007). Descriptive statistics for each of the above variables can be found in Table 3.1.

3.3 Empirical research 3.3.1 Interprovincial emission reduction potential Calculate the CO2 redundancy of each province according to (3.2b), and obtain the CO2 feasible emission reduction and emission reduction potential

50

50

Emission reduction analysis

Table 3.1 Descriptive statistics of various variables (1995–2007) Variables

Capital (100 million yuan)

Labor (10,000 people)

Energy (10,000 tons standard coal)

GDP (100 million yuan)

CO2 emission (10,000 tons)

Average Standard deviation Minimum Maximum

9,194.594 8,443.632

2,238.833 1,570.283

6,525.56 4,676.368

4,804.882 4,523.785

12,373.68 8,941.185

434.8 50,421.5

226 6,568.2

303 28,552

201.2 29,400

627.6658 59,383.5

of each province according to (3.4a) and (3.5a), as shown in Table 3.2. The true meaning of “CO2 emission reduction” in column (I)  means that if the input and output of the region are operated in accordance with the most advantageous mode of the frontier, while maintaining the input and the  desired output unchanged, the amount of CO2 emission reduction that can be achieved is actually the excessive CO2 emissions in the region. According to the province’s CO2 emission reductions, the total amount of emission reductions in the country can be obtained in the same year, and the proportion of CO2 emission reductions in each province can be calculated as a percentage of the total amount of emission reductions in the country. See column (II). A proportional measure of the impact of the region’s emission reductions on the country, the higher the proportion, indicating that the region’s overall impact on the overall reduction of emissions, it should also be the area of concern for emission reduction; column (III), the “emission reduction potential,” refers to the proportion of “excess CO2 emissions” in the region to actual CO2 emissions, which is the level of ineffciency of CO2 emissions in the region. If the value is higher, it indicates that there is greater ineffciency, but it also shows that the region has greater potential for emission reduction through technological advancement and effciency improvements. In the effective areas on the cutting-edge curve, such as Beijing, Shanghai, and Guangdong, the CO2 emission reduction and emission reduction potential are both 0, which does not mean that the area does not need environmental treatment or no CO2 emission reduction. Space refers to the fact that compared with other ineffcient provinces; these regions cannot achieve further reduction of CO2 under the conditions of maintaining current technical conditions, input levels and desired output, that is, the region is currently in Pareto Excellent state. If you want to cut CO2, its desirable output will also decline.8 It can be seen from Table  3.2 that the emission reduction potentials of different provinces are very different. In 1995–2007, Beijing, Shanghai, and Guangdong have been at the forefront of production, and their relative emission reduction potential is 0; Fujian, Guangxi, and Hainan and other provinces are also at the forefront in some years; while Guizhou, Ningxia,

51

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Table 3.2 CO2 emission reductions by province, accounting for the national proportion and emission reduction potential (1995–2007) Provinces

(I) CO2 emission reduction

(II) Proportion of national emission reduction (%)

(III) Reduction potential (%)

1995–1999 2000–2004 2005–2007 1995–2007 1995–1999 2000–2004 2005–2007 1995–2007 1995–1999 2000–2004 2005–2007 1995–2007 Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0 1,978.9 10,336.7 3,027.7 5,309.8 9,197.0 5,359.4 6,729.3 0 4,426.5 1,372.0 5,164.9 0 1,470.0 6,456.4 6,643.3 3,676.9 3,933.9 0 525.1 36.6 8,133.8 5,132.0 1,602.9 3,445.6 3,160.2 629.1 1,334.3 3,474.8

0 2,421.4 12,728.5 10,555.1 9,008.5 9,778.9 4,804.5 5,653.2 0 4,226.9 3,410.1 6,597.0 381.6 1,698.3 8,471.8 8,606.3 6,574.4 2,158.4 0 1,698.4 507.1 8,243.9 7,345.2 1,998.4 3,703.3 3,636.0 918.8 2,194.6 4,465.0

0 1,902.7 18,380.4 16,679.1 19,600.8 11,871.3 8,544.3 7,010.9 0 11,675.8 9,267.2 8,302.8 3,200.7 3,376.2 27,285.7 17,954.6 10,108.0 8,624.4 0 3,621.6 866.2 8,634.6 1,2416.2 6,836.6 7,111.2 4,637.4 1,385.4 4,965.6 6,105.8

0 2,131.5 13,112.9 9,073.2 10,030.3 10,037.9 5,880.9 6,380.4 0 6,022.6 3,977.9 6,439.8 885.4 1,997.7 12,038.3 10,008.6 6,275.4 4,333.4 0 1,690.9 409.0 8,291.7 7,664.2 2,962.8 4,390.6 3,684.1 915.0 2,503.2 4,462.8

0 1.93 10.08 2.95 5.18 8.97 5.23 6.56 0 4.32 1.34 5.04 0.00 1.43 6.30 6.48 3.59 3.84 0 0.51 0.04 7.93 5.00 1.56 3.36 3.08 0.61 1.30 3.39

0 1.84 9.66 8.01 6.84 7.42 3.65 4.29 0 3.21 2.59 5.01 0.29 1.29 6.43 6.53 4.99 1.64 0 1.29 0.38 6.26 5.57 1.52 2.81 2.76 0.70 1.67 3.39

0 0.79 7.65 6.94 8.15 4.94 3.55 2.92 0 4.86 3.86 3.45 1.33 1.40 11.35 7.47 4.21 3.59 0 1.51 0.36 3.59 5.17 2.84 2.96 1.93 0.58 2.07 2.54

0 1.46 9.01 6.23 6.89 6.89 4.04 4.38 0 4.14 2.73 4.42 0.61 1.37 8.27 6.87 4.31 2.98 0 1.16 0.28 5.69 5.26 2.03 3.02 2.53 0.63 1.72 3.07

0 42.8 56.1 25.9 67.6 57.0 63.3 58.4 0 24.8 12.2 52.8 0 30.3 32.3 43.5 31.1 39.8 0 10.6 4.0 47.8 74.5 32.0 52.7 65.5 58.6 72.7 59.4

0 42.8 56.1 25.9 67.6 57.0 63.3 58.4 0 24.8 12.2 52.8 0 30.3 32.3 43.5 31.1 39.8 0 10.6 4.0 47.8 74.5 32.0 52.7 65.5 58.6 72.7 59.4

0 24.5 53.7 71.8 75.2 48.4 59.7 44.9 0 30.8 32.4 49.4 25.3 34.6 49.4 51.1 49.3 45.6 0 35.6 41.1 34.0 80.0 55.6 55.0 60.6 62.0 84.0 60.2

0 38.1 54.6 53.6 70.7 54.5 59.1 51.3 0 23.6 19.6 51.8 7.5 30.0 35.1 44.8 41.5 34.1 0 22.7 22.4 42.3 76.5 37.6 51.1 63.3 60.2 75.8 60.9

52

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Emission reduction analysis

Inner Mongolia, Gansu, Xinjiang, Qinghai, Jilin, Hebei, Liaoning, Shanxi, Anhui, Heilongjiang, Shaanxi, and other provinces have more than 50% reduction potential, which means that compared with the frontier areas, these provinces have large differences in factor allocation, technical level, and management effciency, resulting in more than half of the CO2 emissions from economic production being excessive emissions. In terms of the scale of CO2 emission reduction, in 1995–2007, the proportion of CO2 emission reductions in Hebei, Shandong, Inner Mongolia, Liaoning, Henan, Shanxi, Sichuan, and Guizhou accounted for more than 5% of the total national emission reduction. The total amount of emission reductions in the above eight provinces accounts for 55% of the total national emission reduction. In the near term, in 2005–2007, the emission reductions of Shandong, Inner Mongolia, Hebei, Henan, Shanxi, and Guizhou accounted for more than 5% of the total national emission reductions. The proportion of the volume is 46.7%. In particular, Shandong and Inner Mongolia need to pay attention to the fact that the amount of CO2 that can reduce emissions is not only higher, but also in an increasing trend. According to (3.4b) and (3.5b), the CO2 emission reductions and emission reduction potentials in the eastern, central, and western regions can be calculated, as shown in Figure 3.1 and Figure 3.2. It can be seen from Figure 3.1 that the CO2 emission reductions that can be achieved in the eastern, central, and western regions in 1995 were about 1 billion tons, and in 2007 they climbed to about 2.5 billion tons. To be specifc, the proportion of emission reduction of the total national emission reduction by the eastern, central, and western regions was 33.4%, 34.6%, and 32%, respectively, between 1995 and 2007, indicating that the eastern, central, and western regions are more average in terms of the scale and proportion of emission reductions. In addition, as can be seen from Figure  3.2, the emission reduction potentials of the eastern, central, and western regions are different. The average emission reduction potential in the eastern region is around 28%, and the central average is 48%. The western region saw the greatest emission reduction potential of 55.7%, and the emission reduction potential of the central and western regions began to increase from 2000, indicating that the excessive emissions due to ineffciency in production are increasing. The national average emission reduction potential is about 40%. Take 2007 as an example. In the same year, the national total CO2 emissions were 5.92 billion tons. If all regions have effective areas on the frontier, such as Beijing, Shanghai, and Guangdong, goal-setting and producing through effciency improvements and catching up with the frontier can save nearly 40% of CO2 emissions while maintaining existing inputs and GDP. Wei Chu et al. (2010) have conducted a similar analysis on the potential of China’s energy conservation and emission reduction. The conclusion is that the national energy-saving potential in 2006 and 2007 is about 39%, while the SO2 emission reduction potential is about

53

10 9 Hundred million tons

8 7 6 5 4 3 2 1 0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 East

Middle

West

Figure 3.1 CO2 emission reductions in the eastern, central, and western regions (1995–2007)

70

East

Middle

West

Nationwide

60 50 40 30 20 10 0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Figure 3.2 CO2 emission reduction potential in the eastern, central, and western regions (1995–2007)

54

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Emission reduction analysis

33–34% due to CO2. Emissions are closely related to energy consumption. The CO2 emission reduction potential obtained in this chapter is 40% and is consistent with its conclusion. 3.3.2 Regional marginal cost of emission reduction According to formula (3.7), the shadow price of CO2 emission reduction in each province can be calculated, which can be regarded as the marginal cost of reducing CO2 in each region. Generally, if the shadow price is higher, the marginal cost of reducing emissions is greater; if the shadow price is higher, it indicates that the economic cost of reducing emissions is smaller. If environmental targets are required to be regulated in different regions and industries, from the perspective of economic costs, regions and industries with lower marginal abatement costs can be selected frst. The calculation results of the average shadow price of CO2 in each province are shown in Table 3.3. It can be seen from Table 3.3 that between 1995 and 2007, Shanxi, Guizhou, Inner Mongolia, Ningxia, Hebei and other places have the lowest CO2 marginal abatement costs, while marginal abatement costs in Beijing, Fujian, Guangdong, Hainan and Zhejiang. The highest, the marginal abatement cost of Beijing (266.5 yuan/ton) is nearly nine times that of Shanxi (31.1 yuan/ ton), which also indicates the pollution caused by many factors such as industrial structure, energy structure, environmental control and other factors in different provinces. The cost of abatement varies widely. In general, economically effcient and economically developed regions have relatively high shadow prices for pollutants, while regions with more severe pollution emissions have lower shadow prices (Coggins & Swinton, 1996). In the near term, in 2005–2007, the marginal abatement cost of CO2 in Hebei, Shanxi, Inner Mongolia, Shandong, Henan, Sichuan, Guizhou and other places was even zero, indicating that CO2 in the above provinces has serious excessive emissions and CO2 implementation. The economic costs of emission reductions are small and can be noted in the above provinces. In addition, based on the data from the provinces in Table 3.3, the time trend of CO2 marginal cost reduction in the eastern, central, and western regions is calculated, as shown in Figure  3.3. It can be found that, from a national perspective, the marginal abatement cost of CO2 between 1995 and 2007 is 94.4–139.5 yuan/ton (constant price in 2005), if it is calculated in US dollars, it is US$11.5–17/ton (2005 price), which is consistent with the existing estimate of the marginal cost of China’s CO2 emission reduction, such as He Juhuang et al. (2002) and Wang Can et al. (2005) using the CGE model to calculate China’s 2010 CO2 marginal emission reduction. The cost is $23/ton, $11–26.5/ton, and $12.5–32/ton, respectively. From a regional perspective, CO2 abatement costs in the central and western regions showed an upward trend before 2002, declined after 2002, and rebounded in 2005. This indicates that before 2002, the GHG emissions in various regions did not surge, and the environmental quality improved.

55

Regional decomposition

55

Table 3.3 CO2 shadow price estimates by province (1995–2007) Provinces

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

Average CO2 shadow price (yuan/ton, unchanged in 2005)

Rank (1995–2007)

1995–1999

2000–2004

2005–2007

1995–2007

305.9 103.6 80.8 15.9 59.0

214.9 137.1 60.1 64.9 63.2

286.7 182.6 0.0 0.0 0.0

266.5 134.7 54.2 31.1 47.0

1 9 25 29 27

78.1 67.8 76.1 122.1 138.3 160.7 86.6 282.2 128.9 124.2 103.6 61.6 111.9 183.1 66.2 262.8 95.2 46.4 124.0 87.2 63.5 75.1 50.0 73.8

107.2 100.1 114.7 164.2 170.0 182.8 104.9 246.8 160.2 99.7 103.1 116.1 171.8 210.9 160.4 153.8 76.7 42.4 148.5 116.1 82.0 86.5 56.7 91.0

81.0 97.6 127.8 214.1 49.2 156.1 121.0 183.1 156.5 0.0 0.0 121.2 130.2 217.1 154.0 159.5 0.0 0.0 106.5 107.8 94.5 91.5 38.7 96.1

90.0 87.1 102.9 159.5 129.9 168.1 101.5 245.7 147.3 86.1 79.5 96.3 139.2 201.7 122.7 197.0 66.1 34.1 129.4 103.1 77.8 83.3 50.0 85.6

17 18 14 6 10 5 15 2 7 19 22 16 8 3 12 4 24 28 11 13 23 21 26 20

Therefore, the cost of CO2 abatement increased, and there was a certain environmental deterioration during 2003–2005. It was noted that in 2003, China experiences a high rapid expansion of energy-consuming industries and investment which have led to a rebound in energy intensity (Liao et al., 2007), which may be one of the reasons for the “richer” CO2 emissions, which will reduce the marginal cost reduction. After 2005, the rebound in CO2 shadow prices is due to the implementation of the energy-saving emission reduction strategy leading to further reductions in the marginal cost of CO2. It is worth noting that there is a certain difference between the eastern region and the central and western regions. Before 1998, the marginal abatement cost increased, and then it was in a downward channel, but it rebounded in 2002 and has since declined moderately. However, in general, the marginal

56

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Emission reduction analysis

200

150

100

50

0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 East

Middle

West

Nationwide

Figure 3.3 CO2 marginal abatement costs in the eastern, central, and western regions (1995–2007)

abatement cost of CO2 in the eastern region is always higher than that in the central and western regions, indicating that there may be a certain inverse relationship between the marginal abatement cost of CO2 and the level of economic development.

3.4 Interprovincial emission reduction and emission reduction effciency Setting the regional emission reduction target decomposition needs to consider the two dimensions of fairness and effciency of regional emission reduction (Qi Yue & Xie Gaodi, 2009), in which fairness should frst guarantee the basic rights that everyone should enjoy (refer to Winkler et al., 2002). It can be measured by two parameters. One is to reduce the responsibility of climate change, which can be measured by per-capita CO2 emissions,9 and the other is the ability to pay, measured by per-capita GDP. The area with higher percapita CO2 emissions and per-capita GDP, should have greater responsibility for reducing emissions. The principle of effciency is also the principle of optimal resource allocation. Obviously, CO2 emissions per unit of GDP output is an important indicator of effciency (Winkler et al., 2002; Qi Yue & Xie Gaodi, 2009). In addition, according to Table 3.1 and Table 3.2, we can calculate the province’s own emission reduction potential, marginal abatement cost, actual emissions and the proportion of emission reductions in the country, etc. If a region has high CO2 emission intensity, high emission reduction potential, and a lower marginal abatement cost, while CO2 emissions and emission reductions

57

Regional decomposition

57

account for a higher proportion of the country, which indicates that the ineffcient emissions in production in the region are large, the economic cost of achieving emission reduction is low, and the contribution to the country is high from the perspective of effciency in terms of high emission reduction targets. When setting the CO2 emission reduction targets of each province, policymakers need to comprehensively consider the principles of emission reduction equity and emission reduction effciency. Equity reduction is to ensure fair and reasonable development of all regions and appropriate emission space, and emission reduction effciency is more focused. The real emission reduction potential, opportunity cost and impact on the whole country in different regions, in order to effectively carry out regional comparison, the CO2 Abatement Capacity Index can be quantitatively constructed. The calculation method is as follows: ACIi,t = ω × Equalityi,t + (1 – ω) × Effciencyi,t

(3.9)

Equalityi,t is the CO2 emission reduction fairness index, which is a standardized synthesis index based on the per-capita CO2 emission level and the percapita GDP level. Equalityi,t is the CO2 emission reduction effciency index, according to the CO2 emission intensity and CO2 emission in the region. The index that combines the national weight, CO2 emission reduction potential, CO2 emission reduction and national CO2 emission reduction, and the CO2 emission reduction cost are standardized.10 The parameter ω is the policymaker’s preference for emission reduction and emission reduction effciency. The principle of “equal importance of fairness and effciency”, if ω = 0.5 is taken to calculate the fnal CO2 emission reduction capacity index. The results are shown in Table 3.4. The indicators and indices in Table  3.4 are both between 0 and 1.  The higher the value, the more the emission reduction tasks should be undertaken. It can be seen from column (III) of Table 3.4 that if only the interprovincial emission reduction equity is considered, then regions with higher per-capita CO2 emissions and higher economic development levels, including Shanghai, Beijing, Tianjin, Inner Mongolia, Ningxia Zhejiang, Liaoning, Shanxi and other provinces should undertake more emission reduction obligations; from the perspective of column (IV), if only considering the effciency of emission reduction and the impact on national emission reduction, then there is higher emission reduction potential and CO2 emissions. Intensity, lower marginal abatement costs, and provinces with greater impacts on CO2 emissions and emissions reductions, such as Guizhou, Shanxi, Inner Mongolia, Hebei, Shandong, Ningxia, Liaoning, etc., should undertake more emission reduction tasks. At the same time, considering the two dimensions of fairness and effciency, and giving the same weight as shown in the ranking of CO2 emission reduction capacity index in column (V), it can be seen that Inner Mongolia, Shanxi, Shanghai, Ningxia, and Hebei provinces also ranked high, thus should be paid special attention to in the process of decomposing the

58

newgenrtpdf

Table 3.4 Fairness Index, Effciency Index, and Capacity Index (average between 1995 and 2007) Provinces

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

(I) Fairness indicators

(II) CO2 Effciency Index

Per-ca+pita CO2 emission

Per- capita GDP

Emission Emission Emission reduction Reduction intensity ratio potential proportion

0.70 0.93 0.46 0.71 0.93 0.64 0.49 0.40 1.00 0.40 0.48 0.13 0.16 0.02 0.42 0.19 0.22 0.08 0.32 0.00 0.07 0.07 0.26 0.05 0.19 0.18 0.31 0.87 0.52

0.86 0.59 0.19 0.14 0.19 0.28 0.17 0.19 1.00 0.38 0.45 0.07 0.28 0.08 0.29 0.12 0.12 0.10 0.43 0.07 0.13 0.08 0.00 0.06 0.09 0.05 0.10 0.11 0.18

0.00 0.20 0.38 0.75 0.73 0.37 0.46 0.35 0.03 0.10 0.08 0.34 0.02 0.15 0.18 0.26 0.31 0.20 0.02 0.15 0.09 0.25 0.98 0.21 0.34 0.54 0.46 1.00 0.45

0.18 0.15 0.77 0.48 0.42 0.58 0.29 0.38 0.30 0.78 0.54 0.38 0.21 0.17 1.00 0.69 0.44 0.35 0.75 0.18 0.00 0.63 0.29 0.19 0.24 0.15 0.00 0.06 0.20

0.00 0.50 0.71 0.70 0.92 0.71 0.77 0.67 0.00 0.31 0.26 0.68 0.10 0.39 0.46 0.59 0.54 0.45 0.00 0.30 0.29 0.55 1.00 0.49 0.67 0.83 0.79 0.99 0.80

0.00 0.16 1.00 0.69 0.76 0.77 0.45 0.49 0.00 0.46 0.30 0.49 0.07 0.15 0.92 0.76 0.48 0.33 0.00 0.13 0.03 0.63 0.58 0.23 0.33 0.28 0.07 0.19 0.34

(III) Fairness Index

(IV) Effciency (V) Capability Index Index

Reduction cost

Scores

Rank

Scores

Rank

Score

Rank

0.00 0.13 0.52 1.00 0.62 0.26 0.27 0.21 0.09 0.14 0.08 0.21 0.01 0.11 0.28 0.31 0.23 0.12 0.04 0.15 0.05 0.40 0.90 0.14 0.21 0.32 0.29 0.57 0.28

0.783 0.759 0.326 0.429 0.564 0.462 0.328 0.298 1.000 0.388 0.466 0.099 0.219 0.053 0.354 0.153 0.173 0.092 0.371 0.035 0.097 0.075 0.132 0.055 0.143 0.115 0.202 0.487 0.349

(2) (3) (14) (8) (4) (7) (13) (15) (1) (9) (6) (23) (16) (28) (11) (19) (18) (25) (10) (29) (24) (26) (21) (27) (20) (22) (17) (5) (12)

0.036 0.229 0.676 0.726 0.692 0.538 0.449 0.420 0.083 0.357 0.251 0.420 0.080 0.195 0.566 0.522 0.402 0.289 0.164 0.181 0.093 0.492 0.750 0.252 0.358 0.424 0.323 0.563 0.414

(29) (22) (4) (2) (3) (7) (10) (13) (27) (17) (21) (12) (28) (23) (5) (8) (15) (19) (25) (24) (26) (9) (1) (20) (16) (11) (18) (6) (14)

0.409 0.494 0.501 0.578 0.628 0.500 0.389 0.359 0.542 0.373 0.359 0.260 0.150 0.124 0.460 0.338 0.287 0.190 0.267 0.108 0.095 0.284 0.441 0.154 0.250 0.269 0.263 0.525 0.381

(10) (7) (5) (2) (1) (6) (11) (14) (3) (13) (15) (22) (26) (27) (8) (16) (17) (24) (20) (28) (29) (18) (9) (25) (23) (19) (21) (4) (12)

59

Regional decomposition

59

Efficiency 1.00

Guizhou

Shanxi Shanxi

Inner Mongolia

Hebei Henan 0.50

Shandong

Ningxia

Liaoning

Tianjin Shanghai 0.00

Hainan

0.00

Beijing 0.50

Fairness 1.00

Figure 3.4 Distribution of Equity Index and Effciency Index of emission reduction in each province (1995–2007)

targets of emission reduction areas. Before, that is, the provinces that need to be considered in the process of decomposing the targets of emission reduction areas. Figure 3.4 shows a scatter plot of the average fairness and effciency scores for the provinces between 1995 and 2007, and plots the 45-degree line, with the horizontal axis representing the fairness index for emissions reduction and the vertical axis representing the effciency index for emission reductions. The metric line indicates that the effciency weight of the point on the line is equal to the fair weight, which is the CO2 emission reduction capacity index calculated in column (V) of Table 3.4. If the distance from the origin is further, it indicates that the emission reduction capability is higher; that is, it is needed. The provinces that focus on reducing emissions, in addition, the farther the sample is from the 45-degree moving average, the greater the gap between the effciency index and the fairness index of the province’s emission reduction, and the sample points above the 45-degree line belong to the emission reduction effciency is higher than the reduction. The provinces with fairness and the sample points below the 45-degree line belong to the provinces with emission reduction fairness higher than emission reduction effciency. It can be seen from the fgure that Shanghai, Beijing and Tianjin are in the lower right corner, which is far from the 45-degree line. That is to say, from the perspective of emission reduction, these three regions should bear greater emission reduction obligations, but in terms of emission reduction effciency, the marginal abatement cost may be higher than other regions; correspondingly, the provinces in the upper left corner, such as Guizhou, Shanxi, Hebei, Henan, Shandong, Ningxia, Liaoning and other provinces are reducing emissions and are more effcient provinces, but it is worth noting that

60

60

Emission reduction analysis Efficiency 1.00

Guizhou

Shanxi

Hebei Shandong

Inner Mongolia

Ningxia

Henan Liaoning

0.50

Tianjin

0.00

Beijing

Hainan

0.00

0.50

Shanghai Fairness 1.00

Figure 3.5 Emission reduction priority

Guizhou and Henan provinces are far from the 45-degree line, indicating that from a fair perspective, these provinces have relatively low emission reduction obligations; in addition, the only ones are high emission reductions. The provinces of the region with fairness and high emission reduction effciency are Inner Mongolia, indicating that the province has the responsibility and obligation to undertake more emission reduction targets, both in terms of emission reduction and effciency. Correspondingly, Hainan Province, which is in the lower left corner and close to the origin, can appropriately relax its emission reduction tasks from the perspective of fairness and effciency, and adopt differentiated emission reduction targets. For provinces where the fairness and effciency rankings confict, such as Beijing, Tianjin, Shanghai, Guizhou, etc., it is to set a reduction target or a different target equivalent to the national average, depending on the decisionmakers’ equity in CO2 emission reduction. The relative weight of effciency, if the decision-maker considers the principle of equality of emission reduction priority, it may be desirable to set the equity index weight ω = 1/3. At this time, as shown in Figure 3.5, the original 45-degree line will tilt to the lower right. The key area that needs attention at this time is the sample point that is far from the origin and near the shadow range. Conversely, if the decisionmakers pay more attention to energy reduction effciency, it may be worthwhile

61

Regional decomposition

61

Efficiency 1.00

Guizhou

Shanxi

Inner Mongolia

Hebei Shandong

Ningxia

Henan 0.50

Liaoning

Tianjin

0.00

Beijing

Hainan 0.00

0.50

Shanghai Fairness 1.00

Figure 3.6 Preferred emission reduction effciency

to set the equity index weight ω = 1/3. As shown in Figure 3.6, the original 45-degree line will be closer to the effciency axis, and the focus needs to be reduced. The row area is also a sample point that is far from the far point and is near the shadow range. Once the relative weighting factors are determined, the provinces can be ranked to identify key areas for CO2 reduction. According to the above discussion, according to the different weight distribution of emission reduction fairness and emission reduction effciency, the corresponding CO2 emission reduction capability index and ranking can be calculated. Table  3.5 lists the top and bottom fve provinces, although the different weights for the allocation of emission reduction fairness and emission reduction effciency will affect the relative ranking of the provinces (such as Beijing, Shanghai, etc.), but Table 3.5 reveals that for some provinces, whether in the principle of fairness or effciency, the rankings of emission reduction capacity indexes are relatively consistent. For example, Inner Mongolia and Shanxi provinces are ranked high, indicating that these provinces should and can undertake larger emission reduction tasks, while Hainan, Guangxi, Jiangxi, Yunnan and other provinces rank lower. It indicates that the emission reduction tasks of these provinces should be appropriately relaxed to ensure their necessary development needs.

62

62

Emission reduction analysis

Table 3.5 CO2 emission reduction capacity of major provinces under different principles (1995–2007) Equity is as important Fairness principle is as effciency (ω = 1/2) prioritized (ω = 2/3) Top 6 provinces Last 5 provinces

Effciency principle is prioritized (ω = 1/3)

Neimenggu, Shanxi, Shanghai, Neimenggu, Neimenggu, Shanxi, Hebei, Guizhou, Tianjin, Beijing, Shanghai, Ningxia, Ningxia, Liaoning Shanxi, Ningxia Hebei, Liaoning Hainan, Fujian, Guangxi, Hainan, Hainan, Guangxi, Guangxi, Jiangxi, Jiangxi, Yunnan, Jiangxi, Fujian, Yunnan Hunan Yunnan

3.5 Further discussion on emission reduction potential and marginal cost of emission reduction According to the above analysis, the interprovincial CO2 emission reduction potential varies greatly, and the infuencing factors are discussed in more depth here. Let’s set up a generalized model: yi,t = α + Zi,t β + ηi + εi,t

(3.10)

where yi,t is the potential reduction of the tth year of the ith province; α is a constant term, ηi and εi,t are individual effects and disturbance terms, respectively, β is a regression coeffcient, Zi,t is an exogenous explanatory variable, due to subtraction, the potential of the discharge is the CO2 redundancy generated by the provinces relative to the optimal production status of the frontier. Therefore, it refects more ineffciencies in the production process due to technical level and management effciency. According to the relevant literature and previous discussions, the following explanatory variables are as follows. Level of economic development:  as previously observed, regions with higher levels of economic development tend to be at the forefront or close to the frontier, and their production processes are more effcient. Excessive CO2 emissions due to ineffciency are relatively small, and per-capita GDP is used here. Logarithmically it represents the level of economic development in each region and is expected to be negatively correlated with the emission reduction potential. Energy intensity: the energy intensity and energy productivity are mutually reciprocal. The higher the energy intensity, the lower the energy effciency level, indicating that the more energy is wasted in production, and the more excessive CO2 emissions are generated. Therefore, the potential for emission reduction is also large, and it is expected to be positively related to the emission reduction potential.

63

Regional decomposition

63

Energy consumption structure:  the difference in energy consumption structure in different regions is of great signifcance to the impact of actual CO2 emissions.11 According to Auffhammer and Carson (2008), the proportion of coal consumption in total energy consumption in each province is taken as the energy structure variable, and the higher proportion of coal consumption, the greater emission reduction potential, the higher the expected symbol is. Industrial structure:  the regional industrial structure level is measured by the proportion of the tertiary industry in each province. Because the tertiary industry is a low-energy and low-emission industry, the third industry accounts for the higher proportion of the national economy, and it “can save” CO2 emission potential. The less, the expected sign is negative. Factor endowment structure:  according to common sense, capital deepening will promote the improvement of total factor productivity (Yang Wenju, 2006), which will reduce the ineffciency in production and reduce excessive CO2 emissions; but at the same time, as Wei Chu, Shen Manhong (2008) discovered that excessive capital deepening may be due to the deviation from China’s “resource endowment” advantage, and it is more biased towards energy-intensive industries and has a certain negative impact on economic effciency, resulting in more ineffective CO2 emissions, so we will use the average logarithmic term of capital to characterize and test the factor endowment structure employed by each province. Technological progress:  the development and application of low-carbon technologies is obviously an important factor affecting the potential of emission reduction. With the application of new technologies and new equipment, new emission reduction potentials will be continuously explored. Here, the logarithm of time trends is used to characterize the marginal effect of technological advancement factors on CO2 emission reduction is characterized by a positive sign. The above variables are all based on the data from 1995 to 2007 from the China Statistical Yearbook. Because the emission reduction potential is between 0 and 1, the limit Tobit model needs to be used for estimation. In addition, random effects and the fxed effects are estimated12 and the results are shown in Table 3.6. From the regression results in Table  3.6, the symbols of each variable are basically consistent with expectations, except that the industrial structure variables in the fxed effect model are not signifcant, other regression results are more signifcant, among which: the higher the level of economic development, the higher the proportion of the tertiary industry, the lower the emission reduction potential, while the energy intensity, coal consumption proportion, technological progress, and capital deepening are positively related to the emission reduction potential. Here, the industrial structure, energy intensity and energy consumption structure are interprovincial. The potential for emission reduction has a greater impact.

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Table 3.6 Regression analysis of emission reduction potential (1995–2007) Interpret variables

Tobit estimates

Economic development level (per-capita GDP) Energy consumption intensity

–0.260 *** (0.053) 0.372 *** (0.035) 0.304 *** (0.087) 0.137 *** (0.014) –0.787 *** (0.211) 0.171 *** (0.044) –0.171 *** (0.121) 243.8

Proportion of coal consumption Technological improvement Proportion of the tertiary industry Capital deepening Constant term Log likelihood R2 Rho Obs

0.43 319

Random effect –0.181 *** (0.043) 0.352 *** (0.028) 0.221 *** (0.074) 0.110 *** (0.011) –0.485 *** (0.153) 0.130 *** (0.037) –0.092 (0.098) 0.80 0.37 377

Fixed effect –0.118 * (0.062) 0.382 *** (0.045) 0.173 * (0.095) 0.071 *** (0.02) 0.019 (0.228) 0.133 *** (0.047) –0.204 (0.132) 0.71 0.58 377

Note: *, **, *** represent signifcance at 10%, 5% ,and 1%, respectively.

Table 3.7 EKC test of CO2 shadow price13 Interpret variables

Random effect

Fixed effect

Per-capita GDP

0.514 *** (0.072) –0.133 ** (0.064) 4.760 *** (0.036) 0.224 0.515 345

0.516 *** (0.065) –0.126 ** (0.061) 4.749*** (0.089) 0.224 0.513 345

Per-capita GDP Squared term Constant term R2 Rho Obs

Note: *, **, *** represent signifcant at 10%, 5%, and 1%, respectively.

In addition, according to Ankarhem’s (2005) research on industrial pollutants in Sweden, the relative size of shadow prices refects the cost of reducing emissions in different regions. The more CO2 emissions are cut, the higher the marginal cost reduction, and the higher the shadow price. Therefore, the CO2 marginal cost reduction can be regarded as an environmental degradation indicator for the environmental Kuznets curve test. For this we have also carried out related tests, and the results are shown in Table 3.7.

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It can be seen from Table 3.7 that the primary GDP per capita is signifcantly positive and the secondary term is signifcantly negative, indicating that the CO2 shadow price and the per-capita GDP shows a U-shaped curve relationship with the existing environmental Kuznets curve. The hypothesis is the same, that is, in the early stage of economic development, with the increase of income and CO2 emissions, the marginal cost of CO2 reduction is decreasing, but with the further increase of income, the emission of pollutants shows a downward trend. At this time, the marginal cost reduction will rise, according to Table 3.6, which calculates that the per-capita GDP at the lowest shadow price (that is, the peak CO2 emission) is 6.8–77  million yuan. At this time, the minimum marginal cost of CO2 is 163.5–165 yuan/ton. According to the available data, some provinces have reached this turning point.

3.6 Conclusion This chapter conducts a preliminary study on the regional decomposition target of CO2 emission reduction. Based on the extended SBM model, the CO2 emission reduction potential and CO2 marginal abatement cost of 29 provinces across the country were measured during 1995–2007, considering fairness and effciency. Based on this principle, the key provinces that need to be paid attention to when decomposing the target of emission reduction areas are proposed, and the differences between the interprovincial emission reduction potential and the marginal abatement cost are measured and interpreted. The main conclusions of this chapter include: 1. After considering the unsatisfactory output of CO2, Beijing, Shanghai, and Guangdong are at the forefront of production; compared to the above three provinces, Guizhou, Ningxia, Inner Mongolia, Gansu, Xinjiang, Qinghai, Jilin, Hebei, Liaoning, Shanxi, Anhui, Heilongjiang, and other provinces have an emission reduction potential of more than 50%, which means that more than half of the CO2 emissions from production in these provinces are ineffcient “excessive” emissions compared to the frontier. 2. Considering the impact of CO2 emission reductions across the province on the country, between 1995 and 2007, CO2 emission reductions in Hebei, Shandong, Inner Mongolia, Liaoning, Henan, Shanxi, Sichuan, and Guizhou accounted for the total amount of national emission reductions. The proportions are all over 5%, and the total amount of emission reductions in the above eight provinces accounts for 55% of the total national emission reduction. In 2005–2007, the emission reductions of Shandong, Inner Mongolia, Hebei, Henan, Shanxi, and Guizhou accounted for more than 5% of the total national emission reductions. The above six provinces accounted for 46.7% of the total national emission reductions. In particular, Shandong and Inner Mongolia need to pay attention to the fact that the amount of CO2 that can reduce emissions has a higher impact on the country, and it is on an increasing trend.

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3. From the marginal cost of CO2 emission reduction, Shanxi, Guizhou, Inner Mongolia, Ningxia, Hebei and other places have the lowest CO2 marginal abatement costs, while Beijing, Fujian, Guangdong, Hainan, Zhejiang and other places have the highest marginal abatement costs. In the near term, in 2005–2007, the marginal abatement cost of CO2 in Hebei, Shanxi, Inner Mongolia, Shandong, Henan, Sichuan, Guizhou and other places was even zero, indicating that CO2 in the above provinces has serious excessive emissions and CO2 implementation. The economic costs of emission reductions are small. 4. The national average emission reduction potential between 1995 and 2007 is about 40%, of which the emission reduction potentials of the eastern, central and western regions are 28%, 48%, and 55.7%, respectively, refecting the ineffciency in production in different regions. There is a big difference in the level of excessive emissions; the scale of CO2 that can be reduced in the country has increased from about 1 billion tons in 1995 to 2.5 billion tons in 2007, with the eastern, central, and western regions accounting for an average of 33.4%. 34.6%, and 32%; the national average CO2 marginal abatement cost is 94.4–139.5 yuan/ton, of which the eastern marginal abatement cost is the highest, with an average of 157.6 yuan/ton, the central region is 98 yuan/ton, and the western region is the lowest at 79.9 yuan/ton. 5. When setting regional emission reduction targets, we need to consider the two dimensions of fairness and effciency of emission reduction. If only the principle of fairness is considered, then regions with higher per-capita CO2 emissions and higher economic development levels, such as Shanghai and Beijing, Tianjin, Inner Mongolia, Ningxia and other provinces, should undertake more emission reduction obligations; if they consider emission reduction effciency and impact on national emission reduction, then they have greater emission reduction potential, lower marginal abatement costs, and CO2 emissions. Provinces with major impacts on emissions reduction, such as Hebei, Shanxi, Guizhou, Inner Mongolia, Shandong and other provinces, should undertake more emission reduction tasks; if both dimensions of equity and effciency are considered, it is necessary to focus on the decomposition of targets in emission reduction areas. The areas to be considered include Inner Mongolia, Shanxi, Hebei, Shandong, Liaoning and other provinces. 6. Analysis of the differences in interprovincial emission reduction potentials shows that the higher the level of economic development and the tertiary industry, the smaller the relative emission reduction potential, while the intensity of energy consumption, coal consumption accounts for the proportion of primary energy consumption, technological progress and capital deepening. Other factors are positively related to the emission reduction potential, and the industrial structure, energy intensity and energy consumption structure have a greater impact on the interprovincial emission reduction potential. The environmental Kuznets test on the

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marginal abatement cost of CO2 shows that with the increase of income, the marginal abatement cost of CO2 shows a U-shaped curve with a decrease frst and then an increase, and the lowest per-capita income level at the infection point is 68,000–78,000 yuan. The policy implications of this chapter are very clear. In the future, when decomposing regional targets for designing CO2 emission reduction, we need to focus on the actual emissions and economic development level of the region from the perspectives of fairness and effciency, combined with regional emission reduction potential, marginal abatement costs, and the degree of impact of emissions and emission reductions on the whole country is designed to meet the actual development of the provinces, with targeted and practical emission reduction targets. For provinces with greater emission reduction potential and affordability, they can bear more responsibility for emission reduction obligations, and for provinces with higher marginal abatement costs and lower emission reduction capacity, the emission reduction targets can be appropriately reduced. For each province, it is necessary to continue to deepen the energy conservation and emission reduction strategy in the Eleventh Five-Year Plan, seize the favorable opportunity of economic structure adjustment and optimization, and grasp the key points for the overall low level of industrial production and terminal energy effciency. Use energy units and departments to eliminate backward production capacity, strengthen energy effciency supervision of new projects, and vigorously improve energy effciency; continuously optimize industrial structure, encourage the development of low-energy, low-emission service industries, and strengthen the transformation and upgrading of manufacturing industries; In the current and future fundamental position of China’s energy, we strive to reach the international leading level in the feld of clean coal utilization, accelerate the construction and service of renewable energy, and optimize the energy consumption structure.

Notes 1 This chapter is based on Regional Allocation of Carbon Dioxide Abatement in China, China Economic Review, 2012, 23(2) co-published by Wei Chu, Ni Jinlan and Du Limin. 2 Abbreviation for Brazil, South Africa, India, and China. 3 See Amendment to the Law of Renewable Energies taken into effect from April 1, 2010. Available online at:  http://fnance.sina.com.cn/roll/20100101/08167183891. shtml 4 In terms of the regional decomposition of energy reduction goals, NDRC has adopted the approach of self-declared emission reduction goals by different regions. Most of the provinces are in line with the goals of China, while some of them have set their goals lower or higher than energy conservation goal by 20% and emission reduction goal by 10%.

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5 CO2 emission intensity is impacted by energy consumption intensity and the energy emission coeffcient. If energy can be conserved by 20%, the goal set in the Eleventh Five-Year Plan can be realized as expected with energy consumption intensity decline by 20% between 2010 and 2020, the goal of reducing CO2 emission intensity by 40% can be achieved when energy consumption structure remains unchanged. If more renewable energies and clean energies can be adopted, CO2 emission intensity can be further reduced. 6 This model is in fact consistent with directional distance function, which aims to ensure that desirable output can remain stable while reducing input redundancy and undesirable output redundancy, thus equivalent to (–gx, 0, –gb) in directional distance function. 7 CO2 emitted from fossil fuel energy consumption, transfer and cement production accounts for over 97% of the total CO2 emissions. CO2 is also emitted by lime, calcium carbide, and steel and iron production are not considered due to the diffculty in obtaining data and the very slight proportion. 8 If more effective sample points are included in comparison, such as Hong Kong, which is relatively more effective than Beijing, Shanghai and Guangdong, the optimal frontier will change and emission reduction potential of existing samples will also differ. 9 The original author adopted the accumulative emissions between 1915 and 1999 for consideration. Given the relative short development phase of industrialization and its lower level, per-capita emission is used for measurement. 10 The reciprocal of CO2 emission reduction cost is reversed and all the data are conversed with the standard method of minimum – maximum, or zi=(xi – MinX)/ (MaxX – MinX) and the method of simple weighted mean is used when the value is combined into an exponent. 11 CO2 emitted by burning of coal is 1.6 times that of natural gas, 1.2 times of oil while nuclear power, hydro power and solar power are clean energies with zero CO2 release. 12 The Hausman test is used to determine fxed effect model and random effects model cannot reject random effects model (Prob>chi2(6)=0.11, Chi2(6)=10.36). 13 The Hausman test is used to examine the fxed model and the random model cannot reject zero hypotheses (Prob>chi2(2)=0.93, Chi2(2)=0.14).

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4

Research on CO2 marginal abatement cost in Chinese cities1

4.1 Urban greenhouse gas emissions Climate change is a common challenge facing humanity. In response to climate change, China has proposed a relative emission reduction target of 40–45% reduction in CO2 emission intensity by 2020. However, when decomposing regional carbon reduction targets, fairness and effciency issues are faced. China has a vast territory, and there are large differences in resource endowments, industrial development, and energy structure in various regions. Regional economic development is extremely uneven (Liu Minglei et  al., 2011; Liu Shucheng, 2008). In the previous provincial-level decomposition of China’s “Eleventh Five-Year Plan” energy conservation and emission reduction targets, most provinces selected regional energy conservation and emission reduction targets similar to national targets, due to the lack of “bottom-up” regional emission reduction costs and emission reduction potential. Analysis by Price et al. (2011) shows that this one-size-fts-all allocation scheme is essentially contrary to the principles of fairness and effciency (World Bank, 2009) and has led to consequences not considered by policy designers, such as individual provinces for industrial enterprises and even residents cutting power and falsifed statistical data. At the heart of the climate change issue is the allocation of carbon credits (Metz et al., 2007). A cost-effective allocation must ensure that the marginal costs of the last unit of each party’s emissions are consistent, and the EU’s carbon dioxide license trading market (EU-ETS) and the carbon tax system implemented in countries such as northern Europe are the two main market instruments. Under long-term equilibrium conditions, the license price in the carbon emission trading market is equal to the marginal abatement cost of the enterprise, or the government’s carbon tax is equal to the marginal abatement cost of the enterprise. At this time, the total cost of emission reduction is minimized (Baumol & Oates, 1988). When designing a regional decomposition plan for carbon emission reduction targets, our government must not only consider the issue of equity between regions, but also consider the costeffectiveness of the program; that is, the lower cost of marginal abatement

DOI: 10.4324/9781003004455-5

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and greater emission reduction. Potential areas should be given more emission reduction tasks to minimize the overall cost of abatement. How to reveal the true potential and cost of carbon emission reduction in various regions, thus avoiding the perfunctory attitude of local governments on emission reduction issues (Kousky & Schneider, 2003), is a realistic issue worthy of further discussion. Marginal abatement cost (MAC) can directly refect the potential space and implementation cost of emission reduction in different countries and regions. In the past two decades, carbon dioxide, the World Bank, the United Nations, and other international organizations have widely used carbon dioxide marginal abatement cost information to economically assess different climate change mitigation policy sets (Kesicki & Strachan, 2011). This information is also applicable to the decomposition of carbon emission reduction targets in different regions of the country; that is, the CO2 marginal abatement cost of each region can directly refect the potential effciency of regional carbon emission reduction, and at the same time, it can also be used for the carbon rights transaction to be established. The initial transaction price in the market provides a reference basis and provides a realistic basis for the government to formulate carbon taxes and other fscal instruments (Wei et al., 2013). Because of its great practical signifcance, the research on the marginal abatement cost of carbon dioxide has become the focus and hotspot of theoretical research. In general, because carbon dioxide is not a normal commodity that can be traded, it cannot be refected in market transactions to refect its scarcity, that is, the lack of price signals for the special output of carbon dioxide; Like pollutants, there is a negative externality, and its damage to the economy, ecology, and human health is diffcult to measure and aggregate through market prices, which will lead to an overestimation of the current level of economic output, and the true damage to carbon dioxide cannot be quantitatively evaluated. Therefore, the research on the marginal abatement cost of carbon dioxide has become the basis of much theoretical research and practical work such as green national economic accounting, environmental governance cost–beneft analysis, environmental policy formulation and evaluation. Cities, as the most important gathering place for production activities, are also the concentrated source of fossil energy consumption and carbon emissions (Zhang Jinping et al., 2010). According to estimates by the World Bank, energy-related GHGs generated by Chinese cities account for 70% of total emissions. With the continuous advancement of urbanization and modernization, the Chinese population will increase to 350 million urban residents in the next 20  years (World Bank, 2012). The IEA predicts that by 2030, China’s urban energy consumption will account for 83% of the country’s total, and this will generate a considerable proportion of carbon emissions (IEA, 2007). Therefore, the city will become the main battlefeld for controlling GHGs in China in the future. Previous studies generally used provincial administrative divisions as the basic unit for investigation (Liu Minglei et  al., 2011; Wang Qunwei et  al.,

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2011), and some documents based on national and industrial levels (Chen, 2005; Chen Shiyi, 2010c), but there are few studies on the city level. In fact, research by the OECD and the World Bank on China all believe that the use of cities as basic geographic units not only enables more effective implementation of environmental policies, but also contributes to the country’s overall reduction in energy intensity and carbon intensity per unit of GDP (Hallegatte et al., 2011; World Bank, 2012). For urban managers, there are many specifc measures that can be used to control GHG emissions: land-use decisions, residential business rulemaking, traffc control, and waste disposal; once emission reduction measures are implemented at the city level, Level 1 government learning and implementing similar policies can even affect smaller business and residential activities (Kousky & Schneider, 2003). Therefore, the consideration of carbon reduction in cities as the basic geographical unit is of great signifcance, and the marginal cost research of urban carbon emission reduction is a preliminary basic research, which is not only conducive to identifying the “highland” of carbon emission reduction in existing cities in China. With “squatting,” it can also help to understand the drivers behind the differences in MACs in different cities. This chapter is a study on the above background, which aims to answer the following three scientifc questions. First, how high is the level of CO2 emissions at the urban level in China? Second, what is the marginal cost of CO2 emission reduction in China’s cities? Third, what are the factors that affect the cost of CO2 marginal abatement in cities? The structure of this chapter is as follows:  the frst section summarizes the previous research literature; the second constructs the CO2 MAC model and sets the corresponding functional formula; the third section is based on the statistical yearbook and related information on the urban level, input and output data for accounting and estimation; the fourth part calculates, compares, and analyzes the urban CO2 MAC; the ffth section quantitatively analyzes the difference of urban CO2 marginal abatement cost, and identifes the impact of urban carbon emission reduction and the main infuencing factors of cost; fnally, the relevant research conclusions are summarized, and policy implications and feasible countermeasures discussed.

4.2 Previous literature review 4.2.1 Research on urban carbon dioxide emissions Foreign studies on urban CO2 have been carried out previously. In addition to scientifc accounting of CO2 emissions at the urban level, different methods have been developed to identify and quantify the infuencing factors. For example, Glaeser and Kahn (2010) used the 66 metropolitan cities in the USA as research samples, using the 2001 National Resident Survey and the 2000 Population and Housing Census data, according to the four energy types of gasoline, fuel oil, natural gas, and electricity consumption estimates of CO2

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emissions from households in transportation, heating, and daily energy consumption. The results show that San Diego has the lowest CO2 emissions per household at 9 tons/year, while Memphis has the highest, reaching 32 tons/year. The CO2 emission level is negatively correlated with population density, central aggregation degree, and winter temperature, and is positively correlated with the summer temperature of the region and the proportion of coal in the fuel of the regional power plant. China’s urban CO2 emissions have also attracted the attention of a large number of scholars. Due to the insuffciency of urban construction and urban carbon inventory methodology in China, many scholars frst calculate and estimate urban CO2 emissions, and are committed to developing urban GHG inventory preparation methods that are consistent with China’s national conditions and data characteristics. For example, Dhakal (2009) estimated and analyzed China’s urban energy consumption and CO2 emissions, and found that the city consumed 84% of the country’s commercial energy, of which the largest 35 cities accommodate 18% of the population, consuming 40% of the country’s energy and contribution of 40% of CO2 emissions. In the four municipalities, per-capita energy consumption and per-capita CO2 emissions have increased sevenfold since the 1990s, and further policy measures are urgently needed to alleviate further GHG emissions. Xie Shichen et al. (2009), based on the 2007 Shanghai Energy Balance Data and IPCC accounting method, estimated the CO2 emissions from the burning of fossil energy in Shanghai, and plotted the CO2 circulation map. The results show that in 1995–2007, Shanghai’s energy-related CO2 emissions were growing at an average annual rate of 5%. Among all sources in 2007, the power sector contributed the most at 35.4%, followed by the secondary industry (34.4%) and the transportation industry (23.8%), commercial and residential. Contributions to the agricultural sector are relatively small, at 4%, 2%, and 0.4%, respectively. Cai Bofeng (2011) defnes the urban boundary and urban carbon emission range, and based on the GIS model, estimates the CO2 emissions of prefecture-level cities in China in 2005, of which direct emissions reached 1.77 billion tons, while total emissions were 2.734 billion tons, accounting for 48.9% of the country’s total emissions that year. Xu Cong et  al. (2011) revised the CO2 emission method caused by traditional energy consumption based on the NICE model developed by the Japan Industrial Technology Research Institute, and comprehensively examined agriculture, industry, construction, tertiary industry, and transportation, and the carbon dioxide emissions of the six major sectors of the residents’ lives, and statistical data were given to estimate the city’s CO2 emissions in 2005–2008. Cai Bofeng (2012a) has a detailed comparison of the characteristics and differences between the international urban GHG inventory and the national GHG production. The former often adopts the consumption model, while the latter mainly adopts the production model. In view of the shortcomings of China’s urban GHG inventory research, the paper can put forward the model of China’s urban GHG inventory preparation, and use the data of Beijing and New York to make a practical comparison.

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The results show that the total emissions of New York City is slightly lower than that of Beijing, and per-capita CO2 emissions are slightly higher than those in Beijing. Based on the scientifc estimation of urban CO2 emissions, scholars have further quantitatively analyzed the characteristics and driving factors of CO2 emissions in China’s cities. For example, Zhang Jinping et  al. (2010) used the back propagation (BP) neural network method to select four municipalities of Beijing, Tianjin, Shanghai, and Chongqing as research samples, and measured and forecasted CO2 historical emissions, emission structure and low carbon levels from 1995 to 2008. The analysis found that the CO2 emissions of these four municipalities are increasing year by year, and the emission trend depends on the city’s CO2 emission structure. The optimization and upgrading of industrial structure have a signifcant effect on mitigating carbon emissions. Cai Bofeng (2012b), based on China’s 0.1° large-scale CO2 emission grid data, shows that the global Moran index is 0.27 and signifcant, indicating that there is a positive autocorrelation of CO2 emissions in space, while the local Moran index reveals the key cities are the core areas of CO2 emissions, which have signifcant positive spillover effects on the surrounding areas. These key cities directly determine the spatial pattern of CO2 emissions in China. In addition, based on the analysis of 349 cities, it is found that there is a signifcant U-type EKC curve between economic and CO2 emissions, that is, with the increase of per-capita GDP, the per-capita CO2 shows a trend of rising frst and then decreasing. 4.2.2 Carbon dioxide marginal abatement cost research According to the method of deriving the MAC of carbon dioxide, the current research can be divided into three categories. 4.2.2.1 Based on expert carbon dioxide abatement costs The basic idea is to use the most advanced available technical solutions as the reference line to conduct technical evaluations of various emission reduction measures in different countries and different industries. After summing up, calculate the emission reduction potential and abatement costs. Then, according to the order of their cost from low to high, they form a carbon dioxide MAC curve. This type of thinking is mainly based on engineering schemes for evaluation and summing up, so it is a kind of “bottom-up” research ideas. The most typical case is McKinsey’s global carbon dioxide MAC curve (Mckinsey Company, 2009). Different emission reduction measures, such as nuclear power generation technology and waste water recycling technology, are ranked from low to high in terms of their emission reduction costs (CO2 equivalent per ton). For measures with negative emission reduction costs, they are generally considered as priority measures or “regretless choices.” In addition to different research institutions (such as the World Bank) and scholars

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using carbon dioxide marginal abatement cost curve for Poland, Mexico, Ireland, etc., the country’s carbon dioxide emission reduction potential and abatement costs are evaluated and analyzed (Johnson et al., 2009; Motherway & Walker, 2009; Poswiata & Bogdan, 2009). Although the expert-based MAC curve is easy to understand and provides policy-makers with a rich set of tools and their respective priorities, the theoretical community is very controversial and believes that it has many shortcomings (Kesicki & Strachan, 2011). For example, there are differences in the boundaries and connotations of cost and beneft defnitions that cause them to ignore other potential costs and benefts (Ekins et al., 2011); they do not take into account the interaction between emission reduction measures; rebound effect (Greening et al., 2000); does not evaluate the institutional barriers and transaction costs associated with implementing emission reduction measures, resulting in “negative” abatement costs (Bréchet & Jouvet, 2009), in addition, the MAC curve is mostly based on static technical characteristics and does not take into account the intertemporal dynamics and inertia characteristics of different abatement measures (Adrien & Stephane, 2011). 4.2.2.2 Carbon dioxide abatement costs based on economic-energy models Such methods generally frst construct a partial equilibrium or general equilibrium model, and then change the constraints; such as increasing the emission reductions to obtain the corresponding shadow price, you can get the MAC information at different emission reduction levels (Kesicki & Strachan, 2011). According to the model setting, it can be further divided into two types: one is to use a bottom-up energy system model, such as MARKAL, POLES model, etc. (Criqui et al., 1999; Gao Pengfei et al., 2004). Paying more attention to the energy sector, use non-aggregated data, and achieve optimal technology set through linear programming and set certain constraints. Most energy system models are used to analyze the situation of one country, and some can be used for international emissions trading analysis. The model uses a top-down computable general equilibrium analysis, such as EPPA, GEM-E3, and GREEN models (Ellerman & Decaux, 1998), using aggregated data from all sectors, and is subjected to simulation economic systems. The new equilibrium state after external disturbances (such as carbon taxes) derives the marginal abatement costs. The MAC based on the economic-energy model can show the emission reduction potential of different sectors, but it is limited by the characteristics of the derived model itself, and there are some inherent defects. When using energy system models to derive marginal abatement costs, energy demand is exogenous and limited to the energy sector itself, ignoring linkages with other economic sectors; when using CGE models to derive marginal abatement costs, the impact of energy policy on other sectors and international trade can be captured, but CGE cannot accurately provide its adjustment path when calculating the new equilibrium after disturbance, and therefore may

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underestimate marginal abatement costs (Springer, 2003). In addition, the MACs estimated by different economic-energy models vary widely, mainly due to such economic-energy models themselves, such as setting a higher Aminton trade elasticity coeffcient, or assuming alternative elasticity between elements. Higher will make the carbon dioxide marginal abatement cost lower, and the division of regions and sectors will lead to an overestimation of the carbon dioxide MAC (Fischer & Morgenstern, 2006); therefore, the assumptions imposed on the economic-energy model and the setting of the parameters will infuence the fnal derivation of the carbon dioxide MAC distribution (Marklund & Samakovlis, 2007). Taking different models at home and abroad as examples of China’s marginal carbon abatement cost in 2010 (see Table 4.1), it can be seen that due to differences in parameter assumptions, model structure settings, and data sources of different models, the conclusions of model evaluation are often inconsistent (Gao Pengfei et al., 2004); from the current research progress of this type of model, it is not enough to provide reliable and suffcient information for decision-makers, and the theoretical model still needs to be improved. 4.2.2.3 Carbon dioxide abatement cost curve based on micro supply side This type of model is based primarily on the micro level, defning the set of production possibilities by setting detailed production techniques and Table 4.1 Results of China’s 2010 marginal carbon abatement costs Researchers (Institutes)

Models

Carbon emission reduction (Mt)

Marginal abatement costs (US$/t)

Massachusetts Institute of Technology Australian Bureau of Agriculture and Resource Economics He Juhuang et al. (2002)a Gao Pengfei et al (2004) Wang Can et al. (2005)a

EPPA

100

4

10

9

GTEM

100

8

10

18

10.5

11

10

35

10

12.5

CGE MARKALMACRO TED-CGE

100

18

Emission reduction rate (%)

Marginal emission reduction costs (US$/t)

Note:  aThe original text is expressed in RMB, and is converted at the current exchange rate for comparison purposes.

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economic constraints. The derived carbon dioxide MACs can be interpreted as: carbon dioxide emissions under given market, technical conditions, and opportunity costs (De Cara & Jayet, 2011). Most of these models use production functions to quantitatively characterize the relationship between carbon dioxide MACs and emission reductions. Typical is the linear carbon dioxide MAC function defned by Nordhaus: this function can be used for research at the national level, MC is the marginal cost, r is the emission reduction rate, and the unknown parameters α and β are observed through the engineering data, such as cost-to-ft estimates (Nordhaus, 1991). Although the model can describe the trend of marginal abatement costs, it is diffcult to obtain real data for countries. This type of model has recently emerged as a new branch and development. With the expansion of production theory and the intersection with environmental economics, researchers have incorporated pollutants including carbon dioxide into production models based on the production theory framework. By constructing environmental production techniques to estimate the shadow price of carbon dioxide (Färe et  al., 1993), due to the lesser theoretical assumptions of applied and realistic observations, such models have been used in a large number of carbon dioxide shadow prices at different levels. For example, Rezek and Campbell (2007) used the generalized maximum entropy to estimate the MACs of atmospheric pollutants such as carbon dioxide and sulfur dioxide in thermal power plants in the USA, and the feasibility of constructing an emissions trading market for different pollutants were discussed (Marklund & Samakovlis, 2007). The directional distance function is used to estimate the carbon dioxide abatement costs of EU member states. On this basis, the fairness and effciency of EU carbon emission reduction target  allocation are discussed in Park and Lim (2009) and are based on transcendental logarithmic form. The distance function estimates the carbon dioxide MACs of thermal power plants in Korea and discusses the costs of different abatement options; Choi et al. (2012) use nonradial redundancy-based data envelopment analysis for China’s interprovincial carbon dioxide MACs. Domestic scholars have also begun to use this idea to evaluate industrial MACs. For example, Chen Shiyi (2010c, 2011) evaluated the marginal abatement cost of carbon dioxide in different sectors of China’s industry, and initially discussed the issue of environmental tax; Tu Zhengge (2012) also examined the carbon dioxide abatement costs of China’s eight major industrial sectors and discussed the choice of emission reduction strategies. In summary, the above three research methods and perspectives have their scope and shortcomings. The expert-based marginal abatement cost curve is simple and easy to read, but its bottom-up analysis based on static individuals makes it diffcult to dynamically evaluate the combined effects of abatement measures; the MAC results from the economic-energy model estimates. It is relatively stable, but the model construction is complex, and it is sensitive

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to assumptions and parameters. The conclusion is lack of consistency. The MAC based on the production supply side is simple and intuitive, but the current research is still in a discrete “point” shape. This chapter will focus on the third method, which uses the pollutant price model derived from the production function to estimate the marginal cost of CO2 reduction.

4.3 Marginal abatement cost model 4.3.1 Directional distance function based on output This chapter will use the Directional Distance Function (DDF) to derive the marginal abatement cost model for pollutants. The model is proposed by Chung et al. (1997). The basic idea is to examine the decrease in undesired output while examining the increase in desirable output, when the desired output cannot continue to expand or the undesired output fails to continue decreasing, the observation point is at the forefront of effciency. The specifc application to China can be seen in Hu Angang et al. (2008) for the measurement of China’s interprovincial total factor productivity with different pollutants; Tu Zhengge (2008, 2009) and Tu Zhengge and Liu Leizhen (2011) on industrial productivity and industrial SO2 shadow price measurement. Wang Bing et  al. (2010, 2011) measure the interprovincial Malmquist– Luenberger productivity under environmental control, and Chen Shiyi (2010a, b, 2011) China’s industrial CO2 abatement costs and green productivity measurement and other literature. The model is basically expressed as follows. Assume the input vector, the desirable output vector, the undesired output, and the production technique is defned as P(x) = {(y, b):  x can produce (y, b)}, which has two characteristics: (i) Consensus output is freely disposed of, and undesired output is weakly disposed. This means when y, b) ∈ P(x), y′ ≤ y, (y′, b) ∈ P(x); when (y, b) ∈ P(x), 0 ≤ θ ≤ 1, (θy, θb) ∈ P(x). (ii) Consensus and undesired output are jointly produced. Its mathematical expression is (y, b) ∈ P(x), if b = 0, then y = 0. It shows that if you want zero pollution, you can only stop production, otherwise as long as production, it will produce undesired output. The directional distance function frst needs to construct a direction vector M J of g = (gy, –gb), and g ∈ R × R , which is used to constrain the direction of change of the desired output and the undesired output. The size of the change, that is, the increase (decrease) of the desired (unintended) output on the path specifed by the direction vector, the specifc choice of the direction vector

78

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depends on factors such as research needs or policy orientation preferences. The directional output distance function can be defned as:

(

)

{ (

}

)

D x, y, b= ; g y , −gb sup β : y + β g y ,b − β gb ∈ P( x)

(4.1)

β represents the degree to which a given unit’s desirable output (unsatisfactory output) can be expanded (reduced) compared to the most effcient unit on the leading-edge production surface. If β  = 0, it means that this decision unit is on the leading-edge production side, which is the most effcient. The larger the value of β, the greater the potential for the desired output of the decisionmaking unit to continue to increase, and the smaller the space for the reduction of undesired output, so the lower the effciency. The directional distance function inherits the basic properties of the distance function (Färe et  al., 2005), including a monotonous decrease in the desired output, a monotonous increase in the undesired output, and, in addition, a conversion attribute, namely:

(

)

(

D x, y + α ,b − α ; g y , −gb + α = D x, y, b; g y , −gb

)

(4.2)

The DDF is a general form of Shephard’s output distance function (Chung et  al., 1997). When the direction vector g  =  (1, 0), the Shephard yield distance function is a special case of the directional distance function. Figure 4.1 depicts the relationship between the two: P(x) is the set of possible production, the output distance function is along the ray determined by the origin and observation point A, and the desired output y is the same as the undesired output b. The ratio is extended to point C on the frontier surface; and the idea of the directional output distance function is: the path of the given direction vector g = (gy, –gb), the expansion of the desired output y, while reducing the

y

slope=-q/p Slope

C )

B(

g=(gy,-gb)

A(y,b) P(x) b

Figure 4.1 Directional distance function and shadow price

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Marginal abatement cost

79

undesired production exit b to reach point B of the frontier of the output. Obviously, for the distance function, moving from the invalid point A to the C point on the leading edge, there is either an “excessive” undesired output, or a “insuffcient” desirable output, while the directional distance function is not considered. 4.3.2 Shadow price model of pollutants As pointed out by Chamber et al. (1998) and Färe et al. (2001), there is a dual relationship between the direction distance function and the income function based on the output radial direction, so if Shepard’s Lemma is applied to its duality, you can get the shadow price of the output. The income function can be expressed as: R(= x, p, q ) max{py − qb : D( x, y, b;1, −1) ≥ 0} M

(4.3) J

where = p ( p1 , ⊃ , pM ) ∈ R + and = q (q1 , ⊃ , qJ ) ∈ R +, respectively, are the shadow prices of the desired output y and the undesired output b, the function R(x, p, q) represents the producer, given the desired output price p and the undesired output price q, the biggest gain that can be obtained. Because the direction distance function is non-negative (Chambers et al., 1998), namely: D( x, y, b; g ) ≥ 0 → ( y, b) ∈ P ( x)

(4.4)

therefore, the income function (4.3) can be expressed as a direction distance function as ˜ R (= x, p, q ) max py + qb : D( x, y, b : g ) ≥ 0

{

}

(4.5)

Because the expansion of output cannot exceed the non-parametric frontier production surface, the relationship between the boundary beneft function and the income function can be obtained and expressed as the direction distance function in (4.5):

{

D( x, y, b : g ) ˜ { R( x, p, q ) − ( py + qb)} / pg y − qgb

}

(4.6)

Once the extreme points are obtained, the Shepard lemma can be applied to obtain the shadow price relationship between the output and the pollutants. Finally, the shadow price ratio of the undesired output and the desired output can be equal to the marginal conversion rate (Färe et al., 1993). In the form of parameterized distance function, it can be expressed as the ratio of the distance function to the frst derivative of the undesired output and the desired

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output. In the non-parametric form, it is the undesired output and the desired output in the dual linear programming. The dual value of the constraint is: q ∂D( x, y, b : g ) / ∂b ∂y = = = MRTTy ,b p ∂D( x, y, b : g ) / ∂y ∂b

(4.7)

In (4.7), the relative price of the output to the pollutant is equal to the relative change in the output caused by the change of one unit of pollutant, that is, the output reduced by abandoning one unit of pollution (or increasing the output of one unit of pollution) so that the shadow price model of the pollutant can be obtained. If we further assume that the shadow price of the desired output is equal to its market price (or normalized to 1), then the price q of the undesired output can be expressed as the product of the explicit price p and the marginal conversion rate of the output contaminant, which is: q= p ×

∂D / ∂b ∂D / ∂y

(4.8)

In Figure 4.1, the shadow price expressed by (4.8) is the tangent slope of the projection point of any point on the leading-edge production surface, which refects the trade-off between the desired output y and the undesired output b. That is, the value of the output that is abandoned when reducing pollutants, and therefore can be used as the opportunity cost of the pollutant or the marginal abatement cost (Färe et al., 1993; Murty et al., 2007). 4.3.3 Empirical model setting and solving According to the specifc expression form of the directional distance function, it is mainly divided into two types: parametric and non-parametric. Among them, the parametric model mainly includes superlogarithm, quadratic and stochastic frontier models; in the environmentally sensitive productivity model with parameterized effciency frontiers, the DEA model and the SBM model are mainly used (Wei Chu et al., 2011). Parametric models have their own strengths compared to non-parametric models. In general, non-parametric DEA does not require a-priori assumptions on the production function structure, but is sensitive to sample data. The abnormal sample value error affects the position of the production front; in addition, the non-parametric DEA method is mostly used for productivity measurement because it is diffcult to obtain. The frst derivative is therefore rarely used to estimate the shadow price of undesired output (Färe & Grosskopf, 1998). In comparison, the parametric method needs to preset the production frontier as a certain function expression. The advantage is that the parameter expression can be differentiated and algebraic (Hailu & Veeman, 2000), and the

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non-conformity of each decision unit can be calculated. The shadow price is produced, so the parametric solution method is widely used in empirical research. In addition, in the selection of the parametric model form, the superlog (translog) and quadratic functions can generally be selected for ftting. Table 4.2 compares the similarities and differences between the model forms and assumptions of related literatures using the directional distance function to solve the parametric model at home and abroad. It can be seen that the measurement of carbon shadow price by the parametric model of directional distance function is very common in the world. The preset function forms mainly include superlogarithm and quadratic forms. Domestic research on carbon abatement costs is still relatively rare. In the existing shadow price literature based on DDF, most of them use a non-parametric distance function method (Chen Shiyi, 2010; Tu Zhengge, 2008, 2009; Liu Minglei et al., 2011); in the application of the parameter method, only Chen Shiyi (2010) measured the shadow price of CO2 in 38 industries in China through the translog function form. In the theoretical study of functional form selection, Färe et al. (2010) and Vardanyan and Noh (2006) used the Monte Carlo method to compare the performance of these two types of functions and found that regardless of the type of production technology, the type functions are superior to the superlogarithmic function, and the super-logarithmic function often violates the relevant assumptions required by the DDF. The result is often much lower than the quadratic function, and there is a certain deviation. Therefore, based on the comprehensive research conclusions, this chapter fnally uses the parameterized quadratic function to express the DDF. In this chapter, the direction vector g = (1, –1) is set. At this time, the choice of direction vector satisfes the general environmental regulation requirements, that is, the expansion of desirable output and the reduction of undesired output are symmetrical. In the selection of input–output variables, inputs mainly include:  capital (x1), labor (x2) and energy (x3), the desired output is the economic output of each region (y), and the undesired output is CO2 emissions (b), taking into account individual differences (k) and time trends (t) in cities. The specifc direction distance function is set to: 3

D ( x, y, b; g ) = ˜ 0 + ˙˜ n xn + °1 y + ˛ 1b + n=1

1 3 3 1 2 ˙˙˜ ’ x x ’ + ° y 2 n=1 n’ =1 nn n n 2 2

3 3 1 2 + ˛ 2 b + ˙˝ n xn y + ˙˙n xn b + µ yb + ˇk + ˘t 2 n=1 n=1

(4.9)

In order to solve the unknown parameters in the empirical model (4.9), a linear programming method is used for estimation (Färe et al., 1993, 2005; Hailu &

82

newgenrtpdf

Table 4.2 Comparison of literature based on the parametric model for directional distance function at home and abroad Authors

Samples

Variables

Function

Model assumption D^0

Salnykov and Zelenyuk (2005)

50 countries

Färe et al. (2005)

209 US power plants 1993/ 1997

Färe et al. (2006)

36 US states 1960–1996

Marklund and Samakovlis (2007)

15 EU countries 1990–2000 Five Indian power plants 1996–2004

Murty et al. (2007)

Chen Shiyi (2010)

38 industrial sectors in China 1980–2008

Input: labor/capital/ energy/land; consensus output: GNP; unsatisfactory output: CO2/SO2/NOx Input: labor/capital/energy; desirable output: power generation; undesired output: SO2 Input: labor/capital/energy/land; desirable output: livestock/ crop; undesired output: leaching/runoff Input: labor/capital/energy; desirable output: GDP; undesired output: CO2 Input: labor/capital/energy; desirable output: power generation; undesired output:SO2/NOx Input: labor/capital/energy/ intermediate inputs; desirable output: gross industrial output; undesired output: CO2

D0%0

dD/dy%0

dD/dB^0

dD/dx^0

Translog_LP

√

Quadratic_LP Quadratic_COLS

√ √

√

√

Quadratic_LP

√

√

√

√

Quadratic_LP Quadratic_COLS

√ √

√

√

√

Quadratic_ML

√

Translog_LP

√

√

√

√

Note: Translog and Quadratic are two preset function forms of directional distance function parameters respectively; LP, COLS, and ML refer to linear program, corrected ordinary least square, and maximum likelihood, respectively.

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Veeman, 2000), specifcally including the following objective functions and constraints: K

min ° ˆˇ D ( xk , yk , bk ;1, −1) − 0˘ k =1

s.t. (i ) D ( xk , yk , bk ; g )  0, k = 1,...., K

(ii ) D ( xk , yk , bk ; g ) / b  0, k = 1,..., K

(iii ) D ( xk , yk , bk ; g ) / y  0, k = 1,..., K

(

(4.10)

)

(iv) D xn , k , yk , bk ; g / xn , k  0, n = 1,..., N ; k = 1,, ..., K (v) ˜1 − ° 1 = −1, ˜ 2 = µ = ° 2 , ˝ n = ˙n , n = 1, 2, 3 (vi ) ˆ nn ’ = ˆ n ’n , n, n ’ = 1, 2, 3 In (4.10), the objective function formula minimizes the dispersion of all samples with the leading edge (Aigner & Chu, 1968), and constraint (i) ensures that all observation points are feasible; that is, they satisfy the directional distance, the non-negative feature of the function; constraint (ii) imposes a monotonous increase in the undesired output b; i.e., if the undesired output b increases, the direction distance function value D does not decrease; (iii) is the monotonic decline of the desired output y; i.e., other conditions remain unchanged, if the desired output y increases, the ineffciency D will not increase; constraint (iv) is the monotonic constraint imposed on each input element, That is, when other conditions are constant, if the input x increases, the direction distance function does not decrease (Marklund & Samakovlis, 2007); constraints (v)  and (vi) correspond to the transformation properties and symmetry of the direction distance function, respectively.

4.4 Input–output data at the city level 4.4.1 Data sources and processing This chapter uses the “three inputs–two outputs” data for model demonstration. Among them, capital, labor, and energy are the input factors, and GDP and CO2 emissions are taken as the desired and undesired outputs, respectively. The selection of urban samples in China is based on the statistics of the main 354 prefecture-level cities and above published in the China Urban Statistical Yearbook. In order to prevent the changes in the statistical caliber caused by the change of administrative divisions, the administrative divisions have been screened over the calendar year. As the China Urban Statistical Yearbook does not publish energy consumption information of local cities, the only publicly available data source is the urban energy consumption data in the China Environmental Yearbook, but the key points in the China

84

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Emission reduction analysis

Environmental Yearbook are selected. The city is not the same as the city covered by the China Urban Statistical Yearbook. Therefore, according to the availability of data, the urban sample matching the fnal variable is reduced to 113 prefecture-level cities. In addition, due to the lack of historical fxed capital investment data in nine cities including Sanya City, Haikou City, Lhasa City of Tibet Autonomous Region, and Xining City of Qinghai Province, the number of urban samples selected was 104 cities. The time period selection of the sample also varies due to differences in different data sources. In estimating the capital stock, the longest possible time series is needed to eliminate the impact of the initial capital stock bias on the subsequent sequence, so the investment data of the fxed assets in the China Urban Statistical Yearbook goes back to 1994; The China Environmental Yearbook did not begin to publish more complete energy consumption data for key cities nationwide until 2001. In addition, China’s prefecture-level cities have undergone many adjustments, which has led to the continuous change in the number of prefecture-level cities in the China Urban Statistical Yearbook, and the key cities announced in the China Environmental Yearbook remain basically unchanged, so in order to ensure data consistency, the fnal data starting and ending year sequence is determined as 2001–2008. For a list of specifc cities, see Table 4.14. 4.4.2 Main variables Labor data (L):  foreign countries generally use working hours as the input variable of labor, but limited by the availability of data, using the number of employees in the unit at the end of the year published in the China Urban Statistical Yearbook, the unit is 10,000. GDP data (Y): the economic output data of each city are taken from the China Urban Statistical Yearbook over the years, calculated at the constant price in 2001, and the unit is 100 million yuan. Capital stock (K): the “permanent inventory method” can be used to estimate the actual annual capital stock, as follows: K= I i ,t + (1− δ i )K i,t −1 i ,t

(4.11)

Here, Ki,t is the capital stock of the city i at year t, Ii,t is the investment of the city i at year t, and δi is the fxed capital depreciation rate. When the base period year is selected, the above equation can be converted to: t

K t = K 0 1− ˜ + ˆI k (1− ˜ ) t

k =1

t −k

(4.12)

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Marginal abatement cost

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To calculate the capital stock for each year, three important parameters need to be determined. First, the capital depreciation rate δi, with reference to relevant literature, can be assumed that China’s fxed capital depreciation rate is 9.7% each year (Zhang Jun et al., 2004). The second parameter is the base period initial capital stock K0, estimated according to the method of King and Levine (1994). Assuming that the capitaloutput ratio is constant under steady state conditions, it can be expressed as: = ki ii / (δ + λ gi + (1 − λ )g w )

(4.13)

Here, ii is the investment rate of urban i in steady state, which can be expressed by the average investment rate of the city, λ gi + (1 − λ )g w, which is the economic growth rate at steady state. It is obtained by weighting the growth rate of the city and the average growth rate of the national city, where λ is a measure of the mean value of growth, and according to the literature, the value is generally 0.25 (Easterly et al., 1993); gi is the average growth rate of the city; gw is the average growth rate of the national city, with 1994 as the initial year. Then the initial capital stock of the year can be expressed as K i,94 = ki ×Yi,94 , where Y is the real GDP of city i in 1994. The third parameter is the fxed asset investment amount Ii,t of each year, which can be obtained through the fxed asset investment and urban fxed asset investment price index of each city over the years, but as the China Urban Statistical Yearbook has not announced the investment price index, the urban GDP defator is replaced. Using the above method, the complete capital stock sequence can be calculated. The data used are all derived from the data published in the China Urban Statistical Yearbook and calculated at the constant price in 2001. The unit is 100 million yuan. Energy consumption data (E):  because the China Urban Statistical Yearbook has not published energy consumption data at the city level over the years, when selecting energy data, reference and use of relevant energy data from different data sources: frst, the three major fossil energy consumption data of fuel coal, raw coal, and fuel oil (in 10,000 tons) in industrial energy consumption in key cities; second, the China City Statistical Yearbook published the household gas consumption in each city and household LPG consumption; in addition to the historical data of the cities in China’s Energy Statistics Yearbook (in 10,000 kWh). Therefore, the city’s energy consumption includes three parts:  industrial energy, domestic energy, and electricity consumption. For traffc energy information, due to the inability to obtain the number of cars, fuel consumption standards and travel frequencies at various city levels, accurate accounting cannot be performed. This may also be the place to be improved in the future after having the car travel data.

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Table 4.3 Various energy standard coal conversion factors and carbon emission factors Energy products Unit

Standard coal conversion factor

Default value of carbon content (kgC/GJ)

Average low calorifc value (kcal/kg, kcal/m3)

CO2 emission coeffcient (kg CO2/kg, kg CO2/m3)

Fuel coal Raw coal Fuel oil Gas

0.7143 0.9 1.4286 5.714

25.8 25.8 21.1 12.1

5,000 6,300 10,000 4,000

1.980 2.495 3.239 0.743

1.7143

17.2

12,000

3.169

0.1229

–

860

–

Ton Ton Ton Ten cubic meters Ton

Liquefed petroleum gas Energy used Kilowatt hour

The calculation formula for energy consumption is: Ei ,t = E

industry

+E

household

= ˝ Ei ,t × coef i ,t

(4.14)

In formula (4.14), Ei is the total consumption of various fossil energy sources in city i, and coef is the folding coeffcient of different energy products. Refer to the standard coal conversion coeffcient published in the China Energy Statistical Yearbook to convert various energy consumption values into uniform units of tons of standard coal according to the combustion heat value of different energy sources (see Table 4.3 for the energy discount coeffcient). CO2 emission data (b): there are no research institutions publishing data on CO2 emissions at the urban level. However, because CO2 emissions are mainly derived from the consumption and conversion of fossil energy, the consumption of different fossil energy products and their carbon emissions are converted according to the above factor to estimate urban CO2 emissions. The specifc accounting formula is as follows: CO2 = ˛ Ei × CFi × CCi × COFi ×

44 12

(4.15)

Here, CO2 represents the estimated total amount of carbon dioxide emissions from various fossil energy consumption; i represents the energy consumed by various sources, Ei is the total consumption of various fossil energy in urban i, and CFi is the conversion factor, that is, the average of various fuels. Calorifc value CCi is the carbon content, which means the carbon content of the unit heat, COFi is the carbon oxidation factor, which refects the

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oxidation rate of the energy, and 44/12 means the carbon mass is converted to carbon dioxide, the conversion factor of molecular mass. Among them, CFi × CCi × COFi is called the carbon emission coeffcient, and CFi × CCi × COFi × 44/12 is the carbon dioxide emission coeffcient. The carbon emission factors of various emission sources are mainly based on the emission list coeffcient of IPCC (2006) and are adjusted in conjunction with the low-calorifc value of various energy products in China published in the China Energy Statistical Yearbook. The conversion factors and emission factors of the fnal urban energy products are shown in Table 4.3. 4.4.3 Data feature statistics According to the above data accounting, the descriptive statistics of each input–output variable are shown in Table 4.4. In order to better refect the regional differences, the input–output variables of the eastern, central, and western regions were compared, as shown in Table 4.5. It can be seen that they show signifcant differences. On the input factors, the characteristics of the eastern cities are higher than Table 4.4 Descriptive statistics of input–output variables (2001–2008) Variables

Unit

Samples Average

Labor (L)

10,000 people 100 million yuan 10,000 tons of standard coal 100 million yuan 10,000 tons

832

Capital stock (K) Energy consumption (E) GDP (Y) CO2 (b)

832

47.46

Standard Minimum Maximum deviation 68.74

2.32

696.25

1,148.80 1,902.63 44.31

17,784.69

832

920.66

816.89 16.61

5,694.50

832

825.74 1,358.56 19.29

13,560.44

832

2,219.49 1,952.49 31.58

12,825.78

Table 4.5 Comparison of output and input in terms of regions (2001–2008) Unit Labor Capital stock Energy consumption GDP CO2

Eastern

Central

Western

62.6 (92.0) 1,678.1 (2,542.4) 1,169.2 (962.2) 1,030.5 (1,436.8) 2,757.1 (2,238.7)

35.8 (33.1) 723.8 (850.0) 800.5 (628.4) 453.3 (514.7) 2,004.9 (1,644.4)

33.2 (35.1) 664.3 (877.2) 607.8 (530.3) 353.5 (395.7) 1,494.2 (1,344.2)

Note: The mean value is reported and the standard deviation is shown in parentheses.

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3.5 3 2.5 2 1.5 1 0.5 0

2001 Labor

2002

2003 Capital Stock

2004

2005

2006

Energy Consumption

2007 GDP

2008 CO2

Figure 4.2 Trends of input and output variables (2001–2008)

those of the central cities, and the central cities are higher than those of the western cities. In terms of output and pollution emissions, there is also a signifcant decline in the east and west. Some analysis based on the provincial level is consistent. In addition, in order to understand the time-varying trend of each input– output variable, the variables in 2001 were processed  =  1, and the trend is shown in Figure 4.2. It can be seen that the labor force changes are relatively fat and the changes are small. Under the high-speed capital stock, GDP has achieved rapid growth. If it was 1 in 2001, it will increase to 2.5 in 2008, with an average annual growth rate of 14%. Energy consumption is highly consistent with the trend of CO2 emissions, with an average annual growth rate of around 10%, and both are lower than GDP growth rate, which also indicates the energy consumption intensity (= energy consumption/GDP) and CO2 emission intensity of these cities (= the two indicators of CO2 emissions/ GDP are declining year by year). Table  4.6 compares the characteristics of the 2008 sample cities with all 354 prefecture-level cities. It can be seen that the selected 104 sample cities are generally better representative of China’s urban entities, with a land area of 41.8% of all cities, 60% of all urban population, and labor accounts for 74% of all urban labor, creating 78% of GDP in all cities. In addition, these 104 sample cities are also the driving force for China’s rapid economic growth. They are less than 3% of the country’s land area. These cities have a 17% national population and 7% of the labor force, but the economic value created has reached 46%. Of course, while supporting the rapid development of the national economy, these cities also consume 44% of energy and emit 46% of CO2. Therefore, the 104 cities selected by the Institute are not only the support points for China’s economic development, but also energy-intensive consumption and pollution-intensive areas.

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Table 4.6 Comparison of sample representativeness (2008) Indicators Land coverage (square kilometers) Population (10,000 people) Employed people (10,000 people) GDP (100 million yuan) Energy consumption (10,000 tons standard coal) CO2 emission (10,000 tons)

Sample cities

All prefecture-level cities(a)

Nationwide(b)

261,923

626,361 (41.8%)

9,600,000 (2.7%)

22,773.05

37,619.34 (60.5%)

132,802 (17.1%)

5,343.48

7,186.3 (74.4%)

145,001.5

186,189.7 (77.9%)

314,045.4 (46.2%)

128,231

–

291,448 (44%)

31,0092.9

–

668,465.1(c) (46.4%)

75,564 (7.1%)

Note: The proportion of 104 sample cities is reported in brackets. (a) The data comes from the annual database of the China Economic and Trade Network, including 354 cities in all prefecture-level cities, subprovincial cities, and municipalities directly under the Central Government. (b) The data are from the China Economic Database, which is the national aggregate data; (c) Data from the US Energy Agency’s International Energy Statistics, the carbon footprint of fossil energy (www.eia.gov).

4.5 Empirical results 4.5.1 Parameter estimation Based on the above theoretical model and data, the MINOS 5 solver of the General Algebraic Modeling System (GAMS 22.0) software is used to frst solve the unknown parameters in the model (4.9)–(4.10). To overcome the convergence problem in linear programming, all variables were normalized using the mean of input and output (Färe et al., 2005). The standardized data mean that the input–output set (x, y, b) = (1, 1, 1); that is, for a representative city, it uses the average input to obtain the average output. The results of all the solved parameters are shown in Table 4.7. Due to the large number of urban samples, the individual effect estimates are shown in Table 4.15. According to the estimated parameter values in Table  4.7, the ineffciency values and shadow prices of different cities in different years can be estimated. As the direction distance function also needs to satisfy the nulljointness hypothesis, if the contaminant b = 0 and y > 0, the DDF should not be feasible; i.e., DDFthe central city>the western city are presented. In terms of output and pollution emissions, there is also a signifcant trend of decreasing east, middle, and west. Second, there is an average of 7.67% ineffcient production and emissions in all sample cities, and the average MAC is 967 yuan/ton. From the regional perspective, the central region (604 yuan/ton) and the west (559 yuan/ton) are closer and slower, while the eastern region is higher (1,418 yuan/ton), rising in fuctuations; Shanghai’s highest is 22,990 yuan/ton, followed by Tianjin and Chongqing, and the lowest, Ningxia, is only 420 yuan/ton; in all cities, the four municipalities and the larger provincial capitals have higher MACs; the lowest for Zhangjiajie City is only 324 yuan/ton, the highest cost of abatement (Shanghai) and the lowest city (Zhangjiajie) marginal abatement cost ratio of up to 70:1, there is a huge heterogeneity; except 2006, the rest of the years have shown a signifcant increase trend, indicating that the MAC of the city is becoming more and more differentiated, and the relative deviation between regional cities is much more moderate. The huge difference in MACs between cities means that there is a large market trading space (Newell & Stavins, 2003); that is, under static conditions, if the city’s abatement cost is higher, the more costs the market mechanism

103

Marginal abatement cost

103

reduces, the greater the potential for cost reduction. Conversely, if the cities are more homogeneous, the advantages of market instruments will decline. Given the huge difference of 70:1 between Chinese cities, the overall cost of abatement can be reduced by means such as the emissions trading market. For example, economically developed cities with higher MACs can purchase their development from the trading market quotas required – as long as the price required to purchase an emission permit is lower than the marginal cost of its own emission reduction, and the less economically disadvantaged city benefts from the sale of emission permits due to its lower MACs. Of course, if you are in an environment where there is no set total emission reduction, you can also consider it through tax transfer payment, but at this time, in addition to the two sides of the transaction, a strong central government participation is required, namely the central government taxes are imposed on economically developed cities to allow them to increase their emissions, and then transferred to less-developed cities through fscal transfer payments to make up for their losses, as long as the tax rate is lower than the MAC of developed cities and the transfer payment is not lower than the marginal cost of reducing emissions in less-developed cities is a welfare improvement for the whole society, and this unilateral payment method – in the absence of effciency losses – is equivalent to the carbon trading market mechanism. The second conclusion is to identify possible reasons for the difference in the cost of urban marginal abatement. There is a signifcant positive linear relationship between the level of urban economic development and the MAC. The inverted U-shaped curve hypothesis of EKC has not been verifed in the sample of cities selected in this book; the level of urbanization also affects the cost of abatement. If the proportion of non-agricultural population in the urban population is higher, the cost of emission reduction will increase. Given these two main drivers, plus China’s continued high-speed urbanization in the coming period, and the background of “multiple percapita income of urban and rural residents by 2020,” it can be expected that the margin of the city will be increasing for a long time to come. The cost of abatement will continue to show a growth trend. If the cities (regions) with more developed economies are forced to reduce emissions, the economic costs and costs may be increased. Therefore, the potential and cost of emission reduction between cities can be fully considered difference factors, as far as possible through the market mechanism to achieve the realization of the overall emission reduction targets, rather than imposing restrictions on individuals or a certain region. In addition, from the analysis of infuencing factors, the proportion of secondary production, openness and per-capita transportation infrastructure are signifcantly negatively correlated with MACs. This may become a viable feld and awkward place for cities to implement emission reductions, that is, to follow the principle of “frst easy and then diffcult,” frst to implement emission reductions for industries and sectors with lower emission reduction

104

Table 4.14 Comparison table of sample cities EAST Beijing

Prefecturelevel city Sample city

Beijing

Tianjin

Prefecturelevel city Sample city

Tianjin

Hebei

Prefecturelevel city Sample city

Shijiazhuang

Tangshan

Shijiazhuang

Tangshan

Liaoning Prefecturelevel city Sample city

Shenyang Shenyang

Shanghai Prefecturelevel city Sample city

Shanghai

Jiangsu

East

Tianjin Xingtai

Dalian

Qinhuang- Handan dao Qinhuang- Handan dao Anshan Fushun

Dalian

Anshan

Fushun

Benxi

Nanjing

Wuxi

Xuzhou

Changzhou Suzhou

Nantong

Nanjing

Wuxi

Xuzhou

Changzhou Suzhou

Nantong

Zhejiang Prefecturelevel city Sample city

Hangzhou

Ningbo

Wenzhou

Jiaxing

Huzhou

Shaoxing

Hangzhou

Ningbo

Wenzhou

Jiaxing

Huzhou

Shaoxing

Fujian

Fuzhou

Xiame

Putian

Sanming

Quanzhou

Zhangzhou

Nanping

Longyan

Fuzhou

Xiamen

Shandong Prefecturelevel city Sample city

Jinan

Qingdao

Zibo

Zaozhuang Dongying

Yantai

Weifang

Jining

Jinan

Qingdao

Zibo

Zaozhuang

Yantai

Weifang

Liaoning

Guang- Prefecturedong level city Sample city

Guangzhou

Shaoguan

Shenzhen

Zhuhai

Shantou

Foshan

Jiangmen Zhanjiang

Guangzhou

Shaoguan

Shenzhen

Zhuhai

Shantou

Foshan

Zhanjiang

Hainan

Prefecturelevel city Sample city

Haikou

Sanya

Prefecturelevel city Sample city Jilin Prefecturelevel city Sample city Heilong- Prefecturejiang level city Sample city

Taiyuan

Datong

Yangquan

Changzhi

Jincheng

Shuozhou

Jinzhong Yuncheng

Taiyuan Changchun

Datong Jilin

Yangquan Siping

Changzhi Liaoyuan

Tonghua

Baishan

Songyuan Baicheng

Changchun Harbin

Jilin Qiqihar

Jixi

Hegang

Shuangyashan

Daqing

Yichun

Jiamusi

Harbin

Qiqihar

Anhui

Hefei

Wuhu

Bangbu

Huainan

Maanshan

Huaibei

Tongling

Anqing

Hefei Nanchang

Wuhu Jingdezhen

Pingxiang

Jiujiang

Maanshan Xinyu

Yingtan

Ganzhou Ji’an

Nanchang Zhengzhou

Kaifeng

Luoyang

Anyang

Hebi

Xinxiang Jiaozuo

Zhengzhou

Kaifeng

Luoyang

Wuhan

Prefecturelevel city Sample city

Prefecturelevel city Sample city

Shan’xi

Jiangxi Henan

Hubei

West

Beijing

Prefecturelevel city Sample city Prefecturelevel city Sample city Prefecturelevel city Sample city

Prefecturelevel city Sample city Hunan Prefecturelevel city Sample city Neime- Prefecturenggu level city Sample city Guangxi Prefecturelevel city Sample city Sichuan Prefecturelevel city Sample city

Baoding

Zhangjiakou

Chengde

Jinzhou

Yingkou

Baoding Benxi

Dandong

Jinzhou

Shanghai

Quanzhou

Daqing

Yellowstone Shiyan

Jiujiang Pingdingshan Pingdingshan Yichang

Wuhan Changsha

Zhuzhou

Xiangtan

Yichang Hengyang

Changsha Hohhot

Zhuzhou Baotou

Xiangtan Wuhai

Chifeng

Hohhot Nanning

Baotou Liuzhou

Guilin

Chifeng Zhangzhou Beihai

Nanning Chengdu

Liuzhou Zigong

Guilin Panzhihua Luzhou

Chengdu

Lianyun- Huaian gang Lianyungang Jinhua Quzhou

Panzhihua Luzhou

Anyang Xiangfan

Jiaozuo Ezhou

Jingmen

Xiaogan

Shaoyang

Yueyan

Changde

Zhangjiajie

Tongliao

Yueyang Erdos

Changde Hulun Buir

Zhangjiajie Bayannur

Fangchenggang

Qinzhou

Guigang

Mianyang

Guangyuan

Suining

Beihai Deyang

Mianyang

105

Cangzhou Langfang Hengshui

Fuxin

Liaoyang Panjin

Tieling

Yancheng Yangzhou Zhenjiang Taizhou

Zhaoyang Huludao

Suqian

Yangzhou Zhoushan Taizhou

Lishui

Taizhou Ningde

Taian

Weihai

Taian

Weihai

Rizhao

Maoming Zhaoqing Huizhou

Laiwu

Linyi

Dezhou

Liaocheng Binzhou

Heze

Meizhou

Shanwei

Heyuan

Yangjiang

Qingyuan

DongZhong- Chao- Jieyang Yunfu guan shan zhou

Chaohu

Liuan

Haozhou

Chizhou

Xuancheng

Zhoukou

Zhumadian

Ya’ab

Bazhou

Xizhou

Linfen

Lvliang

Qitaihe

Mudanjiang Mudanjiang Chuzhou

Heihe

Ruihua

Fuyang

Suzhou

Yichun

Fuzhou

Shangrao

Puyang

Xuchang

Luohe

Jingzhou

Huanggang

Xianning Suizhou

Yiyang

Binzhou

Yongzhou

Huaihua

Loudi

Yulin

Baise

Hezhou

Hechi

Laibin

Chongzuo

Neijiang

Leshan

Nanchong Meishan

Yibin

Guangan Dazhou

Huangshan

Sanmenxia Nanyang

Shangqiu Xinyang

Ulanqab

Ziyang

106

Table 4.14 (Cont.) ChongqingPrefecturelevel city Sample city Guizhou Prefecturelevel city Sample city Yunnan Prefecturelevel city Sample city Shaanxi Prefecturelevel city Sample city Gansu Prefecturelevel city Sample city Qinghai Prefecturelevel city Sample city Ningxia Prefecturelevel city Sample city Xinjiang Prefecturelevel city Sample city

Chongqing Chongqing Guiyang

Liupanshui

Zunyi

Anshun

Guiyang Kunming

Qujing

Zunyi Yuxi

Baoshan

Shaotong

Lijiang

Kunming Xi’an

Qujing Tongchuan

Baoji

Xianyang

Weinan

Yan’an

Xi’an Lanzhou

Tongchuan Jiayuguan

Baoji Jinchang

Xianyang silver

Tianshui

Yan’an Wuwei

Lanzhou Xining

Jinchang

Yinchuan

Shizuishan

Yinchuan Urumqi

Shizuishan Karamay

Urumqi

Karamay

Wu Zhong Guyuan

Zhongwei

Simao

Lincang Yulin

Zhangye

Pingliang

107

Ankang

Shangluo

Jiuquan

Qingyang Dingxi

Longnan

108

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Table 4.15 Individual cities’ effect parameters City code

Cities

Coeffcient City code

Cities

Coeffcient City code

Cities

c0101 c0201 c0301 c0302 c0303 c0304 c0306 c0401 c0402 c0403 c0404 c0501 c0502 c0504 c0601 c0602 c0603 c0604 c0605 c0607 c0701 c0702 c0801 c0802 c0806 c0810

Beijing Tianjin Shijiazhuang Tangshan Qinhuangdao Handan Baoding Taiyuan Datong Yangquan Changzhi Hohhot Baotou Chifeng Shenyang Dalian Anshan Fushun Benxi Jinzhou Changchun Jilin Harbin Qiqihar Daqing Mudanjiang

0 0.3907 0.53426 0.48135 0.70679 0.35466 0.61357 0.26179 0.54568 0.57003 0.5974 0.68878 0.64256 0.58892 1.28187 1.23958 0.99806 0.65054 0.59309 0.67877 1.64336 0.58292 0.8403 0.62836 1.9518 0.6424

Shanghai Nanjing Wuxi Xuzhou Changzhou Suzhou Nantong Lianyungang Yangzhou Hangzhou Ningbo Wenzhou Jiaxing Huzhou Shaoxing Taizhou Hefei Wuhu Ma Anshan Fuzhou Xiamen Quanzhou Nanchang Jiujiang Jinan Qingdao

–2.02221 0.77753 1.48579 0.75819 0.66525 0.95044 0.62124 0.58684 0.80706 1.68934 0.55053 0.8999 0.54651 0.70465 0.63682 0.88326 0.72409 0.66978 0.60986 0.82782 1.06117 0.8142 0.98668 0.64682 1.19891 1.12994

Zibo Zaozhuang Yantai Weifang Jining Taian Weihai Zhengzhou Kaifeng Luoyang Pingdingshan Anyang Jiaozuo Wuhan Yichang Changsha Zhuzhou Xiangtan Yue Yang Changde Zhangjiajie Guangzhou Shaoguan Shenzhen Zhuhai Shantou

c0901 c1001 c1002 c1003 c1004 c1005 c1006 c1007 c1010 c1101 c1102 c1103 c1104 c1105 c1106 c1110 c1201 c1202 c1205 c1301 c1302 c1305 c1401 c1404 c1501 c1502

c1503 c1504 c1506 c1507 c1508 c1509 c1510 c1601 c1602 c1603 c1604 c1605 c1608 c1701 c1704 c1801 c1802 c1803 c1806 c1807 c1808 c1901 c1902 c1903 c1904 c1905

Coeffcient City code 1.13827 0.63243 0.73598 0.70125 0.56476 0.74562 0.72554 0.59689 0.62702 0.61916 0.53869 0.56828 0.53136 0.85296 0.60126 0.83302 0.69862 0.67409 0.80214 0.93969 0.62659 1.95591 0.62449 2.99697 0.98615 0.76707

c1906 c1908 c2001 c2002 c2003 c2005 c2201 c2301 c2303 c2304 c2306 c2401 c2403 c2501 c2502 c2701 c2702 c2703 c2704 c2706 c2801 c2803 c3001 c3002 c3101 c3102

Cities Foshan Zhanjiang Nanning Liuzhou Guilin North Sea Chongqing Chengdu Panzhihua Luzhou Mianyang Guiyang Zunyi Kunming Qujing Xi’an Tongchuan Baoji Xianyang Yan’an Lanzhou Jinchang Yinchuan Shizuishan Urumqi Karamay

Coeffcient 0.61833 0.85005 0.72123 0.69044 0.69127 0.69205 0.47202 0.94475 0.52054 0.67017 0.84332 0.54869 0.68014 0.76549 0.5978 0.90677 0.60781 0.60846 0.59409 0.61781 0.62504 0.62283 0.58968 0.56872 0.73596 0.69322

109

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Table 4.16 Provincial marginal emission reduction cost (2001–2008)

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Ningxia Xinjiang

2001

2002

2003

2004

2005

2006

2007

2008

Provincial average

Rank

5,223 1,754 579 577 500 638 509 640 – 577 514 445 511 458 545 499 883 420 651 447 1,137 514 465 544 468 461 391 482

6,960 1,813 595 578 473 634 512 687 8,312 593 528 447 554 458 558 510 901 428 701 423 1,199 532 477 554 478 467 393 490

8,182 1,968 618 602 487 643 646 700 9,406 618 561 458 579 471 573 520 960 431 763 433 1,275 540 481 582 493 474 404 492

1,0381 2,157 652 613 516 672 671 677 11,526 656 594 472 618 493 603 537 1,021 444 826 444 1,399 556 497 617 502 485 421 500

12,141 2,433 711 640 546 714 822 689 32,989 718 639 488 655 513 661 559 1,108 458 888 483 1,463 579 513 618 516 491 425 514

14,799 2,612 746 676 591 766 741 713 19,908 774 715 515 709 543 704 570 1,107 470 1,159 476 1,781 614 542 674 531 497 431 529

23,507 3,018 800 757 632 839 774 754 55,799 834 788 546 782 550 759 597 1,207 524 1,296 480 2,063 641 558 733 556 505 448 553

39,241 3,548 813 760 681 917 802 777 – 859 851 581 823 565 803 615 1,332 546 1,416 497 2,330 682 569 754 583 518 448 565

15,054 2,413 689 650 553 728 685 705 22,990 704 649 494 654 506 651 551 1,065 465 962 460 1,581 582 513 634 516 487 420 516

2 3 10 14 18 7 11 8 1 9 15 24 12 23 13 19 5 26 6 27 4 17 22 16 21 25 28 20

110

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Table 4.17 Marginal emission reduction cost of cities (2001–2008) Cities

2001

2002

2003

2004

2005

2006

2007

2008

Average

Rank

Beijing Tianjin Shijiazhuang City Tangshan City Qinhuangdao City Handan Baoding City Taiyuan City Datong City Yangquan City Changzhi City Hohhot Baotou City Chifeng City Shenyang city Dalian Anshan City Fushun City Benxi City Jinzhou City Changchun City Jilin City Harbin City Qiqihar City Daqing City Mudanjiang City Shanghai

5,223 1,754 721

6,960 1,813 728

8,182 1,968 786

10,381 2,157 829

12,141 2,433 863

14,799 2,612 909

23,507 3,018 970

39,241 3,548 986

15,054 2,413 849

2 5 22

669 443

718 446

758 453

832 464

998 469

1,087 479

1,195 502

1,237 510

937 471

18 72

621 439 866 551 429 461 455 533 512 977 821 625 502 473 431

643 441 833 553 438 487 461 544 414 976 834 612 488 463 429

509 1,028 488 642 402

512 1,197 490 655 405 8,312

648 444 883 560 440 527 471 569 421 980 882 617 491 465 423 773 520 1,231 488 678 403 9,406

673 462 891 584 447 529 516 603 430 1,038 941 642 506 473 433 803 539 1,121 475 713 399 11,526

730 496 952 573 467 568 549 648 441 1,134 1,009 661 516 529 439 1,095 549 1,117 495 746 400 32,989

748 506 997 586 472 650 589 732 452 1,300 1,086 690 529 551 441 912 571 1,158 505 790 400 19,908

812 521 1,143 609 485 792 609 807 480 1,491 1,249 733 543 570 450 954 593 1,238 523 845 410 55,799

796 539 1,168 623 489 761 656 895 492 1,707 1,447 768 552 569 458 994 610 1,281 523 892 412

709 481 966 580 458 597 538 666 455 1,200 1,033 669 516 511 438 922 550 1,171 498 745 404 22,990

29 66 15 44 75 43 52 34 76 9 13 33 55 56 83 19 49 11 61 28 95 1

111

Nanjing Wuxi Xuzhou Changzhou Suzhou Nantong Lianyungang Yangzhou Hangzhou Ningbo Wenzhou Jiaxing Huzhou Shaoxing Taizhou Hefei Wuhu Maanshan Fuzhou Xiamen Quanzhou Nanchang Jiujiang Jinan Qingdao Zibo Zaozhuang Yantai Weifang Jining Tai’an Weihai

951 643 596 458 655 459 400 454 835 671 475 407 392 397 421 497 409 428 589 433 537 379 755 735 635 488 519 466 472 428 408

988 662 604 484 678 465 404 459 868 683 497 418 398 402 427 499 411 431 594 626 442 534 381 781 758 653 490 547 473 469 431 416

1,031 705 621 493 742 477 407 465 924 746 542 432 409 424 447 515 418 441 607 668 461 549 394 814 770 673 494 573 484 484 436 427

1,103 773 648 507 836 495 412 473 989 794 567 455 439 448 466 533 430 452 650 718 487 573 413 855 808 709 514 614 521 508 456 442

1,210 847 728 534 995 506 430 494 1,131 832 580 506 456 481 486 557 440 465 679 782 505 600 426 983 878 807 529 705 566 536 483 464

1,325 935 730 560 1,169 521 446 507 1,358 974 626 543 491 512 501 603 461 480 725 866 535 633 453 1,052 947 879 557 760 599 563 506 476

1,449 1,054 750 594 1,289 542 451 547 1,573 1,121 658 565 513 525 562 643 479 515 811 963 572 669 430 1,181 1,011 942 589 836 639 634 519 486

1,580 1,147 645 608 1,315 561 468 546 1,825 1,202 671 572 529 537 623 688 507 549 879 994 596 698 431 1,263 1,080 995 604 885 669 704 530 501

1,204 846 665 530 960 503 427 493 1,188 878 577 487 453 466 491 567 444 470 692 802 504 599 413 960 873 787 533 680 552 546 474 453

8 23 35 54 17 59 89 63 10 20 45 65 77 74 64 46 81 73 30 24 58 40 91 16 21 26 53 32 48 50 71 78 (continued)

112

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Table 4.17 (Cont.) Cities

2001

2002

2003

2004

2005

2006

2007

2008

Average

Rank

Zhengzhou Kaifeng Luoyang Pingdingshan Anyang Jiaozuo Wuhan Yichang Changsha Zhuzhou Xiangtan Yueyang Changde Zhangjiajie Guangzhou Shaoguan Shenzhen Zhuhai Shan Tou Foshan Zhangjiang NanNing Liuzhou Guilin Beihai Chongqing Chengdu Panzhihua

668 393 537 489 465 445 1,309 457 586 422 412 394 380 324 1,734 400

700 391 547 496 470 457 1,337 464 610 421 414 411 390 323 1,887 401

726 392 547 507 483 467 1,455 465 618 429 416 408 392 322 2,159 402

772 392 562 518 499 482 1,567 475 651 448 425 417 397 326 2,431 418

824 397 610 538 498 485 1,727 490 687 460 439 429 408 326 2,721 423

912 399 652 582 542 494 1,921 492 773 474 464 462 445

922 395 697 617 554 506 2,162 503 844 478 484 471 454

483 431 445 414 476 477 390

507 430 566 416 490 469 396 338 1,199 890 450

539 469 579 432 498 493 400 341 1,275 909 456

574 480 608 444 527 498 406 346 1,399 954 466

607 485 636 453 647 513 412 361 1,463 1,008 488

826 402 635 563 509 487 1,719 495 727 463 450 439 420 324 3,015 443 2,373 646 505 673 459 588 536 416 365 1,781 1,104 502

3,550 467 2,671 698 511 707 468 609 517 422 370 2,063 1,224 486

4,064 460 2,957 734 521 701 474 639 546 421 382 2,330 1,369 493

794 395 598 539 502 478 1,650 480 687 449 438 429 410 324 2,695 427 2,667 599 479 614 445 559 506 408 358 1,581 1,035 474

25 97 42 51 60 69 6 67 31 79 84 87 93 104 3 90 4 41 68 39 80 47 57 94 100 7 12 70

1,137 822 449

113

Quzhou Mianyang Guiyang Zunyi Kunming Qujing Xi’an Tongchuan Baoji Xianyang Yan’an Lan’Zhou Jinchang Yinchuan Shizuishan Urumqi Karamay

371 416 567 362 686 402 841 346 408 414 328 578 343 398 384 576 388

375 412 581 372 692 417 882 352 410 414 329 590 343 400 385 593 387

378 418 594 367 725 439 916 352 442 424 330 604 345 425 383 596 389

383 421 621 372 720 513 953 351 446 428 331 619 350 430 411 606 394

389 433 645 380 749 487 1,022 352 448 424 335 633 349 433 416 w625 404

403 446 662 422 831 516 1,082 352 449 432 338 643 351 441 422 648 410

408 445 701 415 882 585 1,183 356 459 443 339 657 354 445 452 686 421

421 445 721 418 892 615 1,264 376 472 459 342 674 361 453 444 704 426

391 429 636 389 772 497 1,018 355 442 429 334 625 349 428 412 629 402

98 86 36 99 27 62 14 101 82 85 103 38 102 88 92 37 96

114

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Table 4.18 Relative coeffcients of variables (relative coeffcient and signifcance of Spearman) y_mac y_mac

x_2c

x_3c

x_fdi

x_urban

x_popden x_CO2int x_rCO2

x_CO2den x_rroad

x_rbus

x_grecov x_rnet

1

0.5378 0 x_2c –0.1128 0.0038 x_3c 0.2633 0 x_fdi 0.3662 0 x_urban 0.2366 0 x_popden 0.247 0 x_CO2int –0.3579 0 –0.0555 x_rCO2 0.1557 x_CO2den 0.0915 0.0189 x_rroad 0.2597 0 x_rbus 0.3307 0 x_grecov 0.0973 0.0125 x_rnet 0.5128 0 x_rgdp

x_rgdp

1 0.0424 0.278 0.1186 0.0023 0.5102 0 0.3273 0 0.1791 0 –0.4759 0 0.1143 0.0034 0.2165 0 0.6502 0 0.3568 0 0.3436 0 0.6789 0

1 –0.9362 0 –0.0747 0.0558 0.04 0.3065 0.02 0.6097 0.3499 0 0.4476 0 0.3169 0 0.0856 0.0283 –0.195 0 0.0915 0.019 –0.1392 0.0003

1 0.1641 0 0.1398 0.0003 0.1253 0.0013 –0.3833 0 –0.3774 0 –0.1649 0 0.0361 0.3557 0.3548 0 –0.0514 0.1884 0.3075 0

1 0.1082 0.0055 0.2578 0 –0.4103 0 –0.1565 0.0001 0.102 0.0089 0.449 0 0.0778 0.0462 0.2961 0 0.4469 0

1 0.3834 0 –0.0466 0.2331 0.164 0 0.3312 0 0.3025 0 0.5302 0 0.2006 0 0.3611 0

1 –0.0172 0.6605 0.0909 0.0197 0.687 0 0.1812 0 0.1296 0.0009 0.0933 0.0168 0.3488 0

1 0.7903 0 0.5229 0 –0.2162 0 –0.161 0 –0.0745 0.0562 –0.367 0

1 0.7418 0 0.1987 0 0.0501 0.1992 0.1539 0.0001 0.0209 0.5927

1 0.2785 0 0.0975 0.0124 0.1759 0 0.2442 0

1 0.2516 0 0.3834 0 0.4994 0

1 0.0116 0.7661 0.3712 0

1 0.2654 0

1

115

Marginal abatement cost

115

Table 4.19 Multicollinearity of explanatory variables Variable

VIF

VIF

VIF

VIF

x_rgdp x_2c x_3c x_fdi x_urban x_popden x_CO2int x_rCO2 x_CO2den x_rroad x_rbus x_grecov x_rnet Mean VIF

4.95E+13 19.95 20.17 1.4 1.97 4752.33 1.16E+14 9.26E+13 11,340.83 2.16 1.98 1.25 2.33 1.99E+13

4.95E+13 1.99E+01 20.14 1.38 1.97 1.49 1.16E+14 9.26E+13

3.96 19.94 20.14 1.38 1.97 1.49 1.78

3.84 1.44 1.38 1.78 1.34 1.75

2.15 1.97 1.25 2.33 2.15E+13

2.15 1.97 1.25 2.33 5.3

2.15 1.84 1.22 2.29 1.9

costs. For example, for cities with a higher proportion of the secondary industry, priority can be given to reducing emissions in this area. Similarly, public transportation is also a viable and worthy sector. In addition, the negative correlation between the degree of openness and the MAC may be due to the higher degree of openness, the more available and available abatement technology options and means, so that the cost of abatement will be relatively lower, this also provides other feasible and economical ways to reduce emissions for city managers. This chapter is limited to time and energy. There are defciencies in the following aspects: frst, in terms of data, because the sources of urban datarelated variables are more scattered, only 104 representative cities are included, and the data are updated to 2008; given more information sources, such as fossil energy consumption, urban private car numbers, and urban average temperature, we will be able to more accurately estimate the amount of energy and carbon emissions, and identify the impact of natural conditions such as temperature on MACs. In the research method, the linear programming method is used to solve the model. Although the relevant constraints can be directly applied, the statistic cannot be obtained for solving the parameters. In the future, other methods such as maximum likelihood estimation or maximum entropy estimation could be considered to get a more robust estimate of the parameters.

Note 1 This chapter is based on Marginal CO2 Emission Reduction Cost and its Infuential Factors in Chinese Cities, World Economy, 2014 (7)  published by Wei Chu with some of the content edited, revised or deleted.

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Industrial restructuring strategy to mitigate and control CO2 emissions

5.1 Major research fndings Based on the perspective of industrial structure, this book quantitatively analyzes the characteristics of CO2 emissions, CO2 emission reduction potential and abatement costs in China from the macro and meso levels, and examines the direction and strength of industrial structure on CO2 emissions and emission reduction, and thus provides theoretical support and scientifc basis for China to control and slow down greenhouse gases (GHGs) through industrial restructuring strategies. Overall, the book has the following four main fndings. First, the book theoretically clarifes the relationship between industrial structure changes and CO2 emissions, and answers the basic theoretical questions of whether industrial structure can affect CO2 emissions. Theoretical model analysis shows that different industrial sectors have signifcant differences in carbon emission levels due to differences in energy use and structure, that is, different “carbon productivity” among industrial sectors. Therefore, the change in the relative proportion between different industrial sectors – that is, the change in industrial structure – will affect the overall carbon emission quantity and emission scale through the change of carbon productivity. Therefore, it is theoretically confrmed that the industrial structure change is the change of GHG emissions. One of the important reasons is that it provides realistic and feasible means and methods for mitigating GHG emissions, and also provides theoretical evidence for industrial restructuring. In addition, from the relevant domestic and international practical experience and the actual situation in China, the coal-based energy structure in China cannot be adjusted in the short term, the alternative energy cannot be applied in a short period of time, and the space for continued improvement of energy effciency is shrinking. It should be considered to reduce GHG emissions by adjusting the industrial structure of high-energy consumption and high emissions, and this adjustment is not limited to the adjustment between the three industries, but more should be refected in the adjustment and upgrading of the secondary and tertiary industries. However, it should

DOI: 10.4324/9781003004455-7

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be noted that the structural adjustment of low-carbon industrialization that has occurred in certain periods has also increased economic ineffciency. Therefore, when industrial restructuring is needed to address climate change, it should conform to the basic laws of industrial development and fully consider the stability of economic growth. Even if there are signifcant differences in carbon productivity between different industries, it does not mean that industries with low carbon productivity need to be the target of industrial restructuring, but need to consider the carbon emissions impact and industrial infuence of each industry. Second, this book systematically analyzes and summarizes the emission characteristics of CO2 in China, identifes the main infuencing factors of CO2 emissions in China, key industries and regions, and examines the role of industrial structure in CO2 emissions. From the regional perspective, the total carbon dioxide emissions of Shandong, Hebei, Guangdong, Jiangsu, Sichuan, Henan, and Liaoning provinces accounted for 46% of the national total. From the perspective of per-capita CO2 emissions, the non-equilibrium characteristics of the eastern>central>western regions are presented. Coal, oil, and cement production is the main source of emissions; by 2015, per-capita emissions may exceed 7 tons, and in 2020 it will reach 9 tons, but still far below the 2007 US per-capita emissions (19.4 tons), and the EU level was basically fat in 2007 (8.6 tons); from the total emissions, China’s total CO2 emissions in 2015 and 2020 reached 10 billion tons and 12 billion tons, respectively. The proportion of heavy industry is signifcantly positively correlated with the per-capita carbon dioxide emissions. In addition, the level of economic development, energy consumption structure, urbanization level, and technological progress are also the main factors affecting China’s carbon dioxide emissions. In terms of industries and sectors, the CO2 generated by fossil energy consumption and conversion in China’s six major production industries of agriculture, industry, construction, transportation, commerce and energy increased from 2.813 billion tons in 1996 to 7.303 billion tons in 2011. The average annual growth rate is 6.5%. Especially since entering the accelerated phase of heavy chemical industry in 2002, CO2 emissions have also accelerated. In terms of emission scale, CO2 emissions from six production industries in China accounted for 91% of the national fossil energyrelated CO2 emissions in that year, accounting for 23% of global stone energy-related CO2 emissions. The main sources of CO2 in China are energy, industry, and transportation. In 2011, these three sectors accounted for 97% of CO2 emissions from all industries. In the industrial sector, metal products, non-metal products and chemical industries are the main sources of industrial CO2 emissions, accounting for 71% of the total industrial CO2 emissions. China’s CO2 emissions from different industries and different periods have similar patterns, namely: the expansion of output scale is the main reason for the increase of CO2 emissions. The adjustment of industrial structure

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and the improvement of energy effciency of the sector are the main ways to inhibit CO2 emissions, but the inhibitory effect is still not enough to offset the growth effect of the scale of output, the energy structure and carbon emissions of energy products have also slowed down CO2 emissions, but the impact is small. Third, this book quantitatively evaluates China’s CO2 emission reduction potential and marginal abatement cost, and further examines the relationship between industrial structure and CO2 emission reduction. The average emission reduction potential of CO2 in China is about 40%, and there are regional differences in the easternwestern. Shanxi, Guizhou, Inner Mongolia, Ningxia, Hebei and other places have the lowest CO2 marginal abatement costs. The marginal abatement costs of Beijing, Fujian, Guangdong, Hainan, and Zhejiang are the highest. The average marginal abatement cost (MAC) of China’s cities is 967 yuan/ton. From the regional point of view, it also shows the characteristics of the east>middle>west. In all cities, the ratio of the MAC of the highest abatement cost (Shanghai) to the lowest (Zhangjiajie) is as high as 70:1, and there is huge heterogeneity. And this difference shows a signifcant increase trend. The MAC of CO2 may have a U-shaped relationship with income. The level of urbanization is positively related to the MAC, while the proportion of the secondary industry, the degree of openness and the per-capita transportation infrastructure are signifcantly negatively correlated with the MAC. Finally, based on the principles of fairness and effciency, this book simulates the regional emission quota allocation scheme and proposes two market instruments that can be adopted in the future. When setting regional emission reduction targets, we need to consider the two dimensions of fairness and effciency of emission reduction. If only the principle of fairness is considered, then regions with higher percapita CO2 emissions and higher economic development levels, such as Shanghai, Beijing, Tianjin, Inner Mongolia, Ningxia, and other provinces

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should undertake more emission reduction obligations; if considering the effciency of emission reduction and the impact on national emission reduction, then have greater emission reduction potential, lower MACs, and CO2 emissions and emission reductions. For provinces with greater infuence in the country, such as Hebei, Shanxi, Guizhou, Inner Mongolia, Shandong, and other provinces, they should undertake more emission reduction tasks; if both dimensions of equity and effciency are considered, it is necessary to consider them when decomposing targets in emission reduction areas. The regions include Inner Mongolia, Shanxi, Hebei, Shandong, Liaoning, and other provinces. Considering the future of China’s rapid urbanization process and the background of “double the per-capita income of urban and rural residents by 2020,” it can be expected that the cost of slowing or controlling CO2 emissions in China’s interprovincial/urban areas is becoming more and more expensive, if the economy is forced to develop. Provincial/ urban emission reductions may result in greater economic costs and costs. Therefore, the interprovincial/intercity emission reduction potential and cost difference factors should be fully considered, and the market mechanism should be used to achieve emission reduction targets and reduce overall abatement costs. Two options can be considered. First, the emission trading system, which reduces the cost of abatement through mutual trading between regions with higher and lower MACs; second, taxation that can be considered for unilateral payments. The system is that the central government imposes a tax on carbon emissions in areas with higher MACs, and transfers part of the tax to the lower MAC to compensate for its emission reduction losses. In the complete case of information, the welfare of these two social systems is equivalent.

5.2 Basic ideas for industrial structure adjustment 5.2.1 Current domestic and international situation in controlling greenhouse gas emissions The formulation and introduction of any policy needs to fully consider the changes in the external environment. At present, the international and domestic situations facing China’s control of GHGs and implementation of energy conservation and emission reduction mainly include the following. From the perspective of the international situation, there are mainly three profound and major changes. First, the global economy is uncertain and energy prices are fuctuating. Since the 2008 fnancial crisis, the global economy has been full of uncertainty. Economic uncertainty is also transmitted to the international energy market. From 2000 to 2008, the world’s three major energy prices continued to rise; after the 2008 fnancial crisis, total energy demand fell sharply, energy prices fell sharply, but with the adjustment of macroeconomic policies and the

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recovery of emerging countries’ demand, energy prices entered a new round. Since 2011, affected by the US shale gas revolution, the global economic slowdown and the overcapacity of major oil producers, energy prices have once again entered a downward channel, and the fuctuations have increased, which has intensifed the uncertainty of energy investment, production and consumption. Second, the international climate agreement can be expected, and the pressure on GHG emission reduction will increase. The international community is fully aware of the importance of the coordinated control of GHG emissions and has conducted numerous international negotiations. In 2014, in the UN Lima Climate Agreement, countries achieved an international consensus for the frst time and promised to decide their own emission reduction plans. Overall, the international community has reached a broadly acceptable climate agreement. As a responsible big country and the largest emitter of GHGs, China will undoubtedly undertake more energy conservation and emission reduction tasks. Third, the low-carbon approach has become a trend, and low-carbon economy, low-carbon life, and low-carbon energy have become the development trend. This is frst manifested in the low carbonization of economic development. Developed countries such as the United Kingdom and Japan have taken the lead in achieving low-carbon economic transformation through breakthroughs in low-carbon energy-saving technologies and institutional innovation and industrial transformation. Second, the lifestyle is low-carbon. Through energy demand management and low-carbon education, more and more residents choose low-carbon lifestyles and try to reduce domestic energy and carbon emissions. Finally, it is refected in the low carbonization of the energy structure. Countries are committed to the development of renewable energy and clean energy to achieve the goal of low carbonization of the energy structure. Predicated on the domestic situation, there are three main changes and characteristics. First, the concept of governing the country according to law has won the consensus of the whole society. The Fourth Plenary Session of the 18th CPC Central Committee clearly stated that it is necessary to “govern the country according to law and govern by the constitution.” The broad recognition and implementation of the concept of governing the country according to law will further improve the laws and regulations for energy conservation and emission reduction and climate change management, and emphasize the implementation of the system at the same time; the implementation of the concept of governing the country according to law will form a good institutional environment, and further promote the operation of market mechanisms to promote GHG emission reduction and other energy conservation and emission reduction work. In addition, the popularization of the rule of law will form a good atmosphere for energy conservation and emission reduction in the whole society.

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Second, positive progress has been made in economic restructuring. China’s economic restructuring has made positive progress and the industrial structure has been optimized. After 2008, the proportion of the secondary industry has also declined due to the continuous decline in the proportion of the industry. The proportion of the tertiary industry has gradually increased. By 2013, the proportion of the tertiary industry in China has reached 46%, surpassing the proportion of the secondary industry (43%) for the frst time. However, the proportion of China’s tertiary industry is still lower than that of the United States (80%) and other developing countries (50%). Third, energy security, energy effciency, energy structure and environmental pollution are still outstanding. For example, the energy supply and demand gap continues to threaten national energy security. In 2012, China’s import dependence on crude oil and natural gas was as high as 58% and 29%, respectively. At the same time, China’s energy import method is relatively simple, and its source is relatively concentrated, mainly through sea transportation; energy security is extremely vulnerable to threats; energy effciency is still low, and between 1990 and 2011, China’s energy intensity fell by 52%. Although energy effciency has been greatly improved, there are still large differences compared with developed countries. In 2011, China’s energy consumption per unit of GDP was 2.5 times the world average, 5.4 times that of Japan, and 3.4 times that of the USA. In addition, coal-based energy structure and environmental pollution are diffcult to reverse in the short term. Oil, less gas, coal accounted for about 70% of the primary energy for a long time, and the coal-based energy structure is diffcult to fundamentally change in the short term. The coal-based energy structure has also brought about a large number of environmental problems, causing serious pollution and ecological damage to the atmosphere, water bodies and soils, and thus affecting the health of residents. 5.2.2 Main challenges in controlling current greenhouse gas emissions The main challenges facing China’s current energy conservation and emission reduction and control of GHG emissions are as follows. First, under the background of the new economic normal, investment funds are under pressure. At present, China has entered a new economic normality from the transition to a high-speed growth period. As shown in Figure 5.1, the economic growth rate and the fscal revenue and expenditure growth rate have gradually shifted from high-level to low-level operation, and the corresponding investment funds are also facing greater fnancial pressure. For example, in 2007, the total amount of local government fnance and energy conservation expenditures was 96.1 billion yuan, accounting for 0.36% of the GDP in the same period. By 2012, the proportion was only 0.56%. It can be expected that as China’s reform enters the deep water area and the macro economy enters the deceleration shift period, controlling GHG

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Growth rate of GDP Growth rate of fiscal revenue

40

30

Growth rate of fiscal expenditure Growth rate of energy conservation expenditure The ratio of energy conservation expenditure to GDP

125 0.7 0.6 0.5 0.4 0.3

20

0.2 10

0

0.1 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

Figure 5.1 China’s GDP, fscal revenue and expenditure and local energy conservation protection expenditure trends (2000–2012)

emissions and implementing energy conservation and emission reduction may face the following three challenges. First, the requirements of “adjusting structure and promoting transformation” are energy-saving. The platoon work puts forward higher standards and requirements. Second, under the new normal state, the fnancial funds available to the government may also decrease or even decrease the total amount. The fnancial expenditures required for energy conservation and emission reduction may face greater pressure; in the period of economic slowdown or even decline, local governments in order to stabilize growth, under the actual pressure of reducing the original local fscal revenue sources, the demand for investment is large, and it is inevitable that the energy conservation and emission reduction work will be placed in a secondary position. Therefore, the diffculty in controlling GHGs and implementing energy conservation and emission reduction will be greatly enhanced. Second, the potential for energy conservation and emission reduction will decrease in the future, and the cost will increase. During the Eleventh FiveYear Plan and the Twelfth Five-Year Plan period, China’s energy conservation and emission reduction has made great achievements. A large number of equipment and projects have been put into production successively. A considerable part of the backward production capacity with high energy consumption, high pollution, and low output has been eliminated. Starting from the Twelfth Five-Year Plan, energy conservation and emission reduction work has been carried out in terms of capacity replacement, mergers and acquisitions, environmental protection relocation, upgrading and transformation. During the Thirteenth Five-Year Plan period, the space for relying on new projects and large-scale elimination of backward production capacity has been small,

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and the potential for energy conservation and emission reduction will decline in the future. In addition, the cost of energy conservation and emission reduction is also rising. Taking thermal power generation as an example, during the Eleventh Five-Year Plan period, many small thermal power plants have been shut down, and a large number of ultra-supercritical units have been launched. The thermal effciency of power generation is close to the world advanced level. Under such conditions, the economic cost of thermal power effciency is further improved. It will be very high. Third, the goal of controlling GHG emissions conficts with local performance assessments and other short-term goals. In China, some local governments and offcials still regard economic growth as the top priority, lacking understanding of the urgency and arduousness of controlling GHG emissions, energy conservation and emission reduction, and paying insuffcient attention to transforming development methods and adjusting industrial structure. The promotion of emission reduction work failed to handle the relationship between the two. Moreover, the interests of the central and local governments are inconsistent, which also leads to the partial implementation of some policies. In addition, short-term policy objectives often contradict policy objectives and long-term policy objectives for responding to emergencies. For example, the government needs to recover the economy by guaranteeing GDP growth targets, but the recovery economy often increases the diffculty of achieving energy conservation and emission reduction targets. Fourth, the non-marketization distortion of energy prices is not conducive to the control of GHGs. The formation mechanism of China’s existing energy prices mainly includes government pricing and monopolistic prices formed by monopoly. Due to factors such as single pricing entity and information asymmetry, the market has not played its due role in the allocation of energy resources. The price of energy cannot refect the scarcity of its resources and the externalities generated during the consumption process. The overall price level is low, which not only causes huge waste, but also adjusts the economic structure of China, the development of energy conservation and emission reduction work and the ecological environment. The improvement has created a huge obstacle. Fifth, infrastructure and support capabilities are relatively weak. China’s GHG emission reduction management institutions and management systems are in the initial stage of development, and the level of supervision, coverage, and supervision are all constrained, and the capacity building of grassroots teams is weak. In addition, there are many shortcomings in the energy and GHG emissions statistical accounting system, monitoring and early warning, energy auditing, technology research and development and promotion and application. The relevant energy conservation and environmental protection industries have not been fully developed and cannot meet the current GHG emission reduction work. Sixth, China’s enterprises and citizens have weak awareness of energy conservation and need to be popularized. Enterprises generally pay insuffcient

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attention to GHG emission reduction work. Due to insuffcient policy coverage, low energy prices, lack of market mechanisms, and insuffcient government supervision, enterprises lack the inherent motivation and incentives for spontaneous energy conservation and emission reduction, which has caused enterprises to pay less attention to their work and internal management systems are not perfect. In addition, there is a gap between the awareness of energy conservation and environmental protection of Chinese citizens compared with developed countries. This is mainly due to the lack of public participation mechanism and low level of citizen participation. In addition, the level of education and publicity related to GHG emission reduction in China still needs to be improved, which also hinders the popularization of citizens’ awareness of energy conservation and environmental protection. 5.2.3 Basic ideas of industrial restructuring in the short and medium term In summary, in the short and medium term, maintaining a certain rate of economic growth will remain one of the main goals of China’s national economic development. The economic restructuring will also take a certain period of time to complete, so GHG emissions will increase further in the future. As a major driving force for curbing GHG emissions energy effciency, there is also a great improvement in resistance, because the Eleventh Five-Year Plan energy conservation and emission reduction binding target requires a 20% reduction in energy intensity. For this reason, all parts of the country have already been launched. A large number of more energy-effcient and economical infrastructures and various energy-saving policies have been applied adequately. Then, in the Twelfth Five-Year Plan and Thirteenth FiveYear Plan periods, the space for reducing energy intensity through equipment renewal and administrative control has been further limited, so industrial restructuring in the short to medium term will become another major way to control GHG emissions. The adjustment of industrial structure has its own evolutionary law. There are also interrelated relations between industries. It is impossible to force the transformation of a certain industry/industry due to its high level of GHG emissions. At the same time, it is necessary to maintain economic stability while controlling GHGs. Rapid growth, guaranteeing urbanization and industrialization processes, energy conservation and emission reduction strategies, and other strategic objectives are mutually connected and cannot be ignored. Therefore, adjusting the industrial structure to control GHG emissions requires the consideration of other strategies and planning from a global perspective. The basic ideas of China’s industrial restructuring in the short and medium term can be summarized as follows: comprehensively implement the scientifc development concept, accelerate the transformation of economic development mode as the main line, adhere to the basic national policy of conserving resources and protecting the environment, so as to

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slow down and control GHG emissions and enhance with the goal of industrial sustainable development, we will vigorously develop low-carbon industries that save energy, clean development and sustainable development, promote the optimization and upgrading of traditional highcarbon industries, vigorously develop circular economy, optimize energy structure, and strictly control high-energy consumption, high-emissions industry, accelerate the elimination of backward production capacity; promote the healthy and coordinated development of the primary, secondary and tertiary industries, and gradually form an industrial structure based on agriculture, high-tech industries as the forerunner, basic industries and manufacturing industries, and comprehensive development of the service industry, and further optimize the spatial layout of the industry, improve its ability to cope with climate change, and make new contributions to protecting the global climate. 5.2.5 Basic principles of industrial restructuring in the short to medium term The basic principles of industrial restructuring in China in the short to medium term include the following fve points. First, we must adhere to the principle of organically combining industrial policies, climate change policies and other relevant policies. Actively responding to and adapting to climate change is the direction and goal of industrial restructuring. Industrial restructuring is the main way and means to reduce and control GHG emissions. It is necessary to adjust the industrial restructuring strategy with China’s climate change policy and the successful implementation of energy-saving emission reduction policies, ecological protection and construction policies and other organic integration, so as to achieve the overall consideration and coordination between different policies. Second, we must adhere to the combination of market mechanisms and government guidance. It is necessary to give full play to the role of the main body of the enterprise, follow the characteristics and laws of the industry itself, give full play to the basic role of market  allocation of resources, strengthen market supervision to promote fair competition and effcient development; the government is fulflling its basic public service responsibilities and ensuring basic needs. At the same time, the operation of non-essential services will be given more to the market regulation, and comprehensive use of fscal, tax, price, fnancial and other policy measures will be used to guide the development of the industry and achieve optimal resource allocation. Third, we must adhere to the principle of relying on scientifc and technological progress and technological innovation to drive industrial upgrading. Scientifc and technological progress and technological innovation are effective ways to reduce GHG emissions and improve the adaptability of climate

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change. They are also a favorable support for upgrading industrial technology and realizing industrial transformation and upgrading. We must vigorously develop new energy, renewable energy technologies and new energy-saving technologies to promote the development of carbon absorption technology and various adaptive technologies will promote its application in the industry and enhance the overall technical level of the industry. Fourth, we must adhere to the principle of combining industrial restructuring with energy effciency and optimizing energy structure initiatives. Industrial restructuring is only one of the factors affecting climate change. In order to comprehensively and effectively control GHG emissions, it is necessary to adopt multiple approaches. While actively adjusting the industrial structure, it will vigorously promote the improvement of internal energy effciency, and develop and promote the use of renewable clean energy and optimize energy structure. Fifth, we must adhere to the principle of scientifc layout of industrial structure. The adjustment of industrial structure is not only the change of relative proportion between different industries, but also the adjustment of different geographical distribution. It is necessary to follow the division requirements of the main functional areas, optimize the industrial structure and layout between urban and rural areas, and optimize the domestic market and international market industrial structure layout.

5.3 Strategic conception of industrial structure adjustment In order to control the speed and scale of GHG emissions, China’s industrial restructuring can follow the four strategic ideas of “plus,” “subtract,” “lift,” and “transfer.” 5.1.1 Addition strategy: vigorously develop low-carbon and carbon-fxing industries The addition strategy consists of two levels. One is to develop low-carbon industries and promote the development of the overall industrial structure by promoting the development of low-carbon sectors with low energy consumption, low emissions and high output, while improving the overall industry carbon emissions productivity. The second level is the development of carbon-fxing industries, which promotes the development of industries with carbon sequestration properties, sequestering carbon dioxide from existing atmospheres, thereby forming carbon sinks and directly reducing carbon emissions. Its specifc strategic content includes: (1) Promote the scale and proportion of the development of service industries with low energy consumption and low emissions. Put the modern service industry in a priority position, expand the total scale of the

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(2)

(3)

(4)

(5)

Strategic response service industry, and increase the proportion of the service industry in the three industrial structures. Adhere to the equal emphasis on production service industry and life service industry, modern service industry and traditional service industry, and vigorously develop various service formats that support production, beneft the people and increase employment, and focus on cultivating modern logistics, design consulting, e-commerce, health services, etc. The new service format will promote the development of the service industry, increase its proportion, and increase its level, ensuring that the value added of the service industry will reach 47% of GDP at the end of the Twelfth Five-Year Plan. Cultivate and strengthen strategic emerging industries. To strengthen and expand the high-tech industry that plays a major role in economic and social development, accelerate the cultivation and development of emerging industries such as information, biology, new materials, new energy, aerospace and other industries that meet the requirements for energy conservation and emission reduction, and actively develop new materials industries to ensure that by 2015, the added value of strategic emerging industries will increase to about 8% of GDP. Develop ecologically effcient agriculture. Signifcantly reduce the use of chemical fertilizers and pesticides, promote the use of organic fertilizers, improve soil carbon sequestration capacity, make full use of agricultural and sideline residues as biomass energy and organic fertilizer raw materials, promote solar energy and biogas technology, improve farmers’ health and living environment, and protect food safety. Promote the development of forestry and related biological carbon sequestration industries. Continue to implement forestry key ecological construction projects such as afforestation, returning farmland to forests and grassland, natural forest resources protection, and shelter forest systems, curb the increasing trend of grassland desertifcation, restore grassland vegetation and grassland coverage, and build on existing forest and grassland carbon sequestration. Increase forestry carbon sinks. At the same time, cultivate other biological carbon sequestration industries, such as the development of economic algae and oil-producing energy algae farming. By 2015, we will strive to achieve a forest coverage rate of 21.66%. Explore the development of marine carbon sequestration industry. About 13% of the carbon dioxide produced by fossil fuels on the earth is absorbed by terrestrial vegetation, and 35% is absorbed by the ocean. At present, countries are in the starting stage in this feld. When implementing the plan for marine economic development, China should combine Hong Kong’s industrial characteristics, actively explore and develop marine low-carbon technologies, tap the potential of marine carbon sequestration, form relevant industrial chains such as research and development of marine carbon sequestration technologies, carbon-fxing equipment

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manufacturing, and carbon-fxing technology applications, and form an international community as soon as possible. (6) Increase research on carbon capture and storage technologies. CO2 capture and storage (CCS) technology has been listed as a frontier technology in China’s medium- and long-term science and technology development plan. Because the technology is still in the preliminary research stage, China should pay close attention to the progress of CCS technology and carry out related technology and project cooperation with developed countries. Carry out carbon-capture experimental projects in the thermal power, coal chemical, cement and steel industries, and build a demonstration project of carbon dioxide capture, oil displacement and storage for large-scale practical applications in the future. 5.3.2 Subtraction strategy: contain heavy chemical industry, shut down the backward industry The subtraction strategy also includes two levels. One is to curb the scale and speed of expansion of high-energy, high-emission, low-output, highcarbon sectors at a relative level, so that the proportion of these sectors has declined, or to maintain relative Stable state, thus relatively reducing the scale and speed of GHG emissions of the entire industry, to achieve the goal of relative emission reduction; second, in the absolute level, eliminate backward production capacity, thereby directly reducing the GHG emissions brought by this part of production activities. Specifcally includes the following: (1) To curb the scale and growth rate of heavy chemical industry with high energy consumption and high emissions. Strictly control new heavy chemical industry projects with high energy consumption, high emissions and overcapacity, further improve industry access standards for high energy consumption, high emissions and overcapacity, improve energy effciency and environmental protection barriers, and strengthen energy conservation, environmental protection, land and safety. Such as index constraints, establish and improve project approval, approval, and record responsibility system, and strictly control new projects. (2) Accelerate the elimination of backward production capacity. In accordance with the principle of “controlling the total amount, eliminating backwardness, mergers and acquisitions, and independent innovation,” we will coordinate industrial, environmental protection, land and fnancial policies, improve the exit mechanism of backward production capacity, strictly implement the “two high” industry elimination standards, and implement the state’s production capacity reduction in some industries’ excessive and redundant construction policies and measures to strictly control the new capacity of high-energy industries such as cement, steel, electrolytic aluminum, and coke.

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5.3.3 Upgrade strategy: upgrade traditional industries and improve resource effciency The addition strategy is mainly to increase low-carbon industries. The subtraction strategy is mainly to reduce high-carbon industries, and the improvement strategy is mainly for stocks, that is, to maintain the scale of existing industrial sectors, through industrial transformation and upgrading, resource recycling, and energy effciency. Improve the way to achieve the same input, increase output, or output to reduce energy and other factors, thereby improving the economic effciency of GHG emissions, reducing the GHG emission intensity per unit of output, and achieving indirect emission reduction. Specifcally include the following strategic content: (1) Promote the transformation and upgrading of traditional manufacturing. For China’s traditional equipment manufacturing sector, the focus is on improving the level of localization of technical equipment and improving the overall level of R&D, processing, manufacturing and system integration through independent innovation, introduction of technology, cooperative development, and joint manufacturing; the department should use industrialization to promote industrialization, use high-tech and advanced practical technologies to transform, promote the in-depth integration of information and industrialization, and increase the proportion of independent intellectual property rights, independent brands and high-end products. According to energy, resource conditions and environmental capacity, we will focus on adjusting the product structure, enterprise organization structure and industrial layout of the raw material industry to improve product quality and technical content. (2) Actively explore and develop circular economy. In accordance with the requirements of the new industrialization road, we will actively promote the reduction of clean production and resource utilization in the industrial sector, reuse and resource utilization, and form a more mature model of circular economy development within enterprises, enterprises and parks, and reduce cement as much as possible. The use of lime, steel, calcium carbide and other products to reduce GHG emissions from the source and production processes. At the same time, it researches and promotes advanced waste incineration and landfll gas recycling technologies, promotes the industrialization of waste treatment, and reduces GHG emissions such as methane during waste disposal. By 2015, the comprehensive utilization rate of industrial solid waste will reach over 72%. (3) Strive to improve energy effciency in key industries. Improve the concentration of key industries such as steel, cement, non-ferrous metals, machinery, and automobiles, reduce the energy consumption level of energy-consuming products in key industries, and improve energy effciency. Implement energy-saving key projects and strengthen energysaving management. Implement energy-saving renovation projects such as

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boiler kiln renovation, motor system energy conservation, energy system optimization, energy-saving technology industrialization demonstration project, contract energy management promotion project, etc., and promote energy conservation in industries, construction, transportation and other felds and industries. 5.3.4 Transfer strategy: transfer high-carbon product production and replace high-carbon energy The transfer strategy has three main meanings. The frst is to reduce the production of productive carbon sources through the transfer of industrial structure in spatial layout. Developed countries have transferred the production of a large number of high-carbon products industries to developing countries. At the same time, they have also constrained developing countries through international negotiations. Under the premise that they cannot temporarily change the basic framework of international climate negotiations, they can consider part of the developed regions in China in due course. The geographical transfer of carbon industry to other neighboring countries; the second is to reduce carbon through the carbon product trade strategy; that is, to form a trade defcit of high-carbon products through international trade, thereby indirectly reducing domestic energy consumption and carbon emissions. It is the transfer of energy between different carbon sources in the energy production conversion sector; that is, the replacement between clean energy and traditional fossil energy. Specifc strategic content includes: (1) Optimize industrial layout for domestic and international markets, and promote capacity transfer and overseas investment. In the process of industrial transformation and upgrading, the central and western regions will undertake a large number of industrial transfer in the east. The central and western regions should adhere to the requirements of the main functional zoning, adhere to high standards according to the resource and environmental carrying capacity and development potential, and ban high-energy consumption and high pollution industries and backwardness. Transfer of production capacity. Support qualifed enterprises to go abroad to invest overseas, and transfer some industries that do not have labor advantages, resource and energy consumption, carbon emissions and other serious pollution to other countries. (2) Strictly control the export of high-carbon products and increase the import of energy-intensive products to replace domestic production. We will adopt measures such as adjusting export tax rebates and tariffs to strictly control the export of high-energy, high-emission and resourcebased products, and increase the import scale of corresponding resourcebased raw materials, energy-intensive and carbon-intensive processed products. In addition, the use of fscal and taxation means to encourage the import of equipment, instruments and technical materials for clean

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production, and to ban the production technology, equipment and products that are explicitly eliminated by the state. (3) Develop renewable energy sources to replace traditional fossil energy sources. Under the premise of environmental protection and resettlement, we will develop hydropower in an orderly manner, strive to build a large-scale wind power industry and wind power base, promote the development and utilization of biomass energy, and actively support solar power, solar heat, geothermal energy, and ocean. We can develop and utilize such resources, actively and steadily promote the construction of nuclear power, and replace traditional fossil energy with clean renewable energy. By 2015, non-fossil energy accounts for 11.4% of total primary energy consumption, of which commercialized renewable energy accounts for more than 9.5% of total energy consumption.

5.4 Key felds and links of industrial structure adjustment According to previous empirical studies, the most important areas of concern for the impact of GHGs on various sectors are: the energy sector, the industrial sector, and the transportation sector. The three sectors account for the national economy. More than 97% of GHG emissions from fossil energy are consumed in the sector. From the perspective of the direction and size of the various factors affecting GHG emissions, the expansion of production scale is the main cause of the increase in GHG emissions, and the adjustment of industrial structure and the improvement of energy effciency have played a positive role in mitigating GHGs. The industry, the optimization of energy consumption structure and the carbon emission coeffcient effect also inhibited the growth of GHGs to some extent. 5.4.1 Energy sector The energy sector, especially the electric power and heat production industries, is one of the main sources of GHG emissions. The expansion of its output scale has led to an increase in GHG emissions, and its annual decline in the proportion of the economy has slowed down CO2 emissions. In addition, energy intensity effect and energy structure effects within the department are also greatly affected; therefore, the structural adjustment of the energy sector is mainly concentrated in three aspects. First, we must focus on controlling the scale and speed of power production expansion mitigating GHG emissions from energy production and conversion processes. Under the premise of ensuring economic production and energy use, rationally arrange new investment to avoid redundant construction. New energy projects must meet the relevant access standards for energy conservation and environmental protection, and small power plants and small coal mines that do not comply with national industrial policies. Wait for the shutdown and transfer to eliminate backward production capacity.

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The second is to vigorously optimize the energy structure. On the basis of protecting the ecology, we will develop hydropower in an orderly manner, actively promote nuclear power construction on the basis of ensuring safety, and appropriately develop small-scale distributed power sources using natural gas and coal-bed methane as fuel, and use biomass to generate electricity, biogas, biomass solid fuel and liquid fuel. Focusing on the development and utilization of biomass energy, we will develop renewable energy such as wind energy, solar energy, biomass energy and geothermal energy according to local conditions. The third is to continue to improve energy effciency. Vigorously develop high-effciency and clean power generation technologies such as supercritical units and large combined cycle units with a single machine of 600,000 kW and above, develop cogeneration and combined heat and power gas technologies to improve power generation effciency and strengthen the power grid construction, using advanced transmission, transformation, distribution technology and equipment to reduce energy consumption. 5.4.2 Industry The industrial sector is also the main source of GHG emissions, and its most infuential sectors include: metal products, non-metal products and petroleum processing, while equipment manufacturing has low emissions and high output; production scale expansion is the main factor leading to an increase in industrial CO2 emissions, and the improvement of energy effciency within the industrial sector and the adjustment of sectoral structure are the two main ways to reduce GHG emissions. The improvement of energy structure and the low carbonization of fuel carbon emission coeffcient have a relatively small contribution to industrial CO2 emissions. Therefore, the structural adjustments to the industrial sector are mainly concentrated in three areas. First, we must vigorously curb the excessive growth of heavy-duty industries with high energy consumption and high emissions. Especially for steel, non-ferrous metals, petrochemicals, building materials and other high-energyconsumption and high-emission departments, we must strengthen existing industrial policies, strict market access standards for high-energy-consuming industries, improve energy-saving and environmental protection thresholds, and adopt export tax rebates. Measures such as tariffs will curb the export of “two high and one capital” (high energy consumption, high emission, resource type) products, adjust the scale of high energy consumption and high pollution industries, and reduce the proportion of high energy consumption and high pollution industries; industrial policies, severely polluting ironmaking, steelmaking, cement and chemical production capacity. The second is to accelerate the development of equipment manufacturing and other high-tech industries. In particular, the development of advanced manufacturing industries with the focus on revitalizing the equipment manufacturing industry, encourage the use of high-tech and advanced applicable

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technologies to transform and upgrade the manufacturing industry, accelerate the development of high-tech industries and information industries, and increase the proportion of “low-carbon” industries in industrial development. In addition, it is necessary to promote cleaner production and circular economy models in the industrial sector to improve resource utilization. In accordance with the principles of reduction, reuse, and resource utilization, we will vigorously promote the construction of comprehensive recycling and reuse systems for resources, and focus on promoting technological transformation of energy conservation and consumption reduction in industries such as iron and steel, non-ferrous metals, electric power, petrochemicals, construction, coal, building materials, and papermaking. For metallurgy, building materials, chemical and other industries, strengthen measures such as nitrous oxide emission control to control GHG emissions from industrial production processes. 5.4.3 Transportation industry The expansion of the transportation industry is the main driver of the increase in GHGs, and the main factors that mitigate GHG emissions are energy intensity effects and energy structure effects. If we only focus on the transportation sector itself, its internal structural adjustment mainly focuses on three aspects. First, we must control the scale of the transportation industry infrastructure, optimize the transportation structure of railways, highways, waterways, civil aviation, and pipelines, and focus on urban and intercity development. The rapid transportation network will give priority to the development of urban public transportation, increase the proportion of rail transit in urban transportation, and properly control the growth rate of private transportation. The second is to improve the fuel effciency of vehicles. Accelerate the elimination of old vehicles with high energy consumption, promote the implementation of the national standard for the “fuel consumption limit of passenger cars,” control the development of highfuel-consuming vehicles from the source, adopt the fuel-saving model, and improve the load rate, passenger load factor and transportation. Turnover capacity, improve fuel effciency and reduce fuel consumption. The third is to accelerate the development of electrifed railways, develop high-effciency electric locomotives, and encourage enterprises to develop and produce hybrid vehicles, pure electric vehicles, and other vehicles that use renewable alternative energy sources. In addition, another factor in the surge of GHGs in the transportation industry is China’s accelerating urbanization process. Therefore, controlling the GHG emissions of the transportation sector requires not only the adjustment of the internal structure of the transportation sector, but also the multichannel solution of urban planning and other measures. For example, to control the scale of development of megacities, scientifcally formulate urban spatial planning, and adhere to the group-type urban pattern. On the one

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hand, intercity transportation is called connecting large and medium-sized cities, and on the other hand, within the city, it is necessary to break through the circulation between buildings. The passage unblocks the city’s microcirculation system. 5.4.4 Agriculture The agricultural GHGs are mainly methane and nitrous oxide. The GHG emissions of agricultural sources in China account for about 17% of the total GHG emissions in the country. In the agricultural production process, not only are GHGs released, but also the carbon sink function of agriculture can be effective. To offset the GHG emissions caused by production processes and energy consumption; in addition, the agricultural sector not only needs to slow down GHGs, but more importantly, it needs to adjust the ability of the agricultural sector to adapt to climate change through structural adjustment, thus promoting the structural adjustment of the agricultural industry. There is a need to consider the mitigation and adaptation of climate change capabilities in the agricultural sector from the two levels of GHG emissions and GHG absorption. The focus can be on the following. First, we must strengthen the construction of agricultural infrastructure and improve the ability of agriculture to adapt to climate change. Accelerate the implementation of large-scale irrigation districts with water-saving transformation as the center, focus on the construction of feld projects, update and rebuild aging mechanical and electrical equipment, improve irrigation and drainage systems; eliminate backward agricultural machinery, promote less tillage and no-tillage, joint operations, etc., mechanized agronomic technology, the use of electric motors in fxed work sites, the development of renewable energy such as water, wind and solar energy in agricultural machinery. Vigorously develop rural biogas, promote the application of rural renewable energy technologies such as solar energy, fuel-saving and coal-saving stoves. Second, we must strengthen the construction of ecological agriculture, optimize the structure of agricultural products, and slow down GHG emissions from agriculture. Implement agricultural non-point source pollution prevention and control projects, continue to promote low-emission high-yield rice varieties and semi-dry cultivation techniques, adopt scientifc irrigation and soil testing and formula fertilization techniques, scientifcally apply chemical fertilizers, guide the application of organic fertilizers, and reduce nitrous oxide emissions from farmland; promote conservation tillage with straw cover and no-tillage as the main content, develop straw to raise livestock, return to the abdomen and increase soil organic carbon content; at the same time expand the cultivation of economic crops and forage crops, cultivate high yield potential and good quality, comprehensive varieties of excellent animals and plants with outstanding resistance and wide adaptability, systematically cultivate and select drought-resistant, anti-caries, high-temperature resistant, pest-resistant

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and other resistant varieties, research and development of excellent ruminant breed technology and large-scale feeding management techniques, etc. Finally, we must actively develop forestry, increase carbon sinks, and reduce GHG emissions. Strictly control the reclamation of land in areas with fragile ecological environment, promote the further development of afforestation work, and continue to promote forestry key ecological construction projects such as natural forest resource protection, returning farmland to forests and grasslands, shelter forest systems, wildlife protection and nature reserve construction. We will do a good job in the construction of biomass energy forestry bases, and further increase the capacity of land carbon storage and carbon sinks to reduce GHG emissions on the basis of protecting existing forest carbon storage. 5.4.5 Construction industry Broad-based urban building energy consumption includes housing construction, water supply and drainage, heating, ventilation, lighting, air conditioning, household appliances, cooking and other felds. The construction industry’s own GHG emissions are small, but the building-related heating and other energy consumption and GHG emissions from the use of the building are considerable. According to statistics, the energy consumed in the construction and use of buildings worldwide accounts for about 50% of total energy consumption, and China is about 47%. However, the life expectancy of buildings in China is generally short, plus the average insulation level of building exterior walls. It is one-third of the European countries in the same latitude, thus resulting in a total lifetime comprehensive energy consumption per unit of building area is 2–3 times that of developed countries. In general, in the early planning, design and construction of China’s construction industry, energy conservation concepts and standards are lacking, and supervision is weak, and the effciency of the construction process is paid more attention, but the long-term use and energy-saving effciency of the use process are less considered. The structural adjustment of the construction industry needs to be carried out in three aspects. First, it is necessary to develop green buildings, promote green construction, and strengthen energy conservation and emission reduction throughout the construction of the project to achieve low-cost, environmentally friendly and effcient production. Second, for new buildings, strict implementation of building energy-saving standards, the use of advanced energy-saving emission reduction technology, vigorously promote the application of high-strength steel and high-performance concrete; promote the application of high-performance, low-material consumption, renewable recycling of building materials, and actively carry out recycling and utilization of construction waste and waste products; making full use of straw and other products to produce plant fberboard; promoting residential full-renovation

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and assembly construction, and promoting renewable energy, such as the application of solar water heaters in buildings. Third, on the existing stock buildings, on the one hand, we must promote the urban heating measurement reform in the northern regions, and on the other hand, increase the energysaving renovation of large public buildings and ordinary houses. 5.4.6 Service industry The scale of GHG emissions in the service industry is much lower than that in the industrial sector, but there is still a large potential for emission reduction and space. The main focus should be on increasing the proportion of the service industry, while optimizing the internal structure of the service industry and providing services to other productive sectors. The consulting services for energy conservation and emission reduction mainly focus on the following three aspects. First, we must promote the accelerated development of the service industry and increase the proportion of the service industry. We will vigorously develop modern service industries such as fnance, insurance, logistics, information and legal services, accounting, intellectual property, technology, design, and consulting services, and actively develop industries with high demand potential such as culture, tourism, and community services, and accelerate education, training, and aged care services reform and development in areas such as health care. Standardize and upgrade traditional service industries such as commerce, catering, accommodation, etc., and promote organizational forms and service methods such as chain operations, franchising, agency, multimodal transport, and e-commerce. The second is to vigorously optimize and improve the internal energy effciency of the service industry. Promote the implementation of energy standards and labels in public institutions such as hotels, restaurants, offce buildings, schools, hospitals, etc., promote the use of high-effciency energysaving appliances and electric lamps, and implement the transformation of systemic energy-saving projects such as lighting, air conditioning, water pumps, and top heat utilization. Strengthen the management of overpackaging of goods, reduce the use of disposable articles, strictly enforce the restrictions on plastics, rationally plan and construct effcient and convenient modern logistics networks, vigorously develop third-party logistics, implement urban common distribution and centralized distribution, and improve logistics distribution effciency. The third is to vigorously develop energy-saving service industries and energy-saving consulting services. Vigorously develop energy-saving service industries that use contract energy management and power demand side management mechanisms, establish a vibrant, distinctive and standardized energy-saving service market, and establish a relatively complete energysaving service system.

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5.5 Safeguard measures for the implementation of industrial structure adjustment strategy In order to ensure that the GHG emission binding targets can be achieved through effective industrial restructuring in the coming period, it is necessary to comprehensively utilize various government means and market-based means. To this end, it is recommended to ensure the adjustment of industrial structure through the following measures. 5.5.1 Strengthening the target responsibility assessment system The central government has decomposed the target of carbon dioxide emissions per unit of GDP and established a statistical monitoring and evaluation system. In order to achieve this strategic goal, by 2015, the value-added of service industry will increase to 47%, and the proportion of added value of strategic emerging industries will increase to 8%. This is an important way to achieve this. From the central to the provincial level, two industrial structure indicators, as well as other indicators (such as the proportion of the frst two or three industries are regularly monitored and evaluated the proportion of heavy chemical industry, etc.); at the provincial level, there is no strict industrial structure adjustment targets, but also according to the province’s own industrial structure characteristics and related industrial structure development plans, formulate corresponding industrial structure adjustment targets and measures to promote GHG emission reduction targets in various regions and the realization of national industrial restructuring targets. 5.5.2 Implementation of the existing industrial restructuring policies Strictly implement and implement the Guidance Catalogue for Industrial Structure Adjustment issued by the State, the Catalogue of Guidance for Foreign Investment Industries, and the Catalogue of Regional Industry Guidance; continuously improve the access threshold for industries with high energy consumption, high emissions and overcapacity, and improve project approval, approval and fling system, strictly control new and backward production capacity; resolutely eliminate and dismantle outdated production capacity, according to the list of “eliminating backward production capacity enterprises,” smelting and lead in iron making, steel making, coke, ferroalloy, calcium carbide, copper (recycled copper) (including recycled lead) smelting, zinc (including regenerative zinc) smelting, cement (clinker and mill), fat glass, paper, alcohol, leather, printing and dyeing, chemical fber, lead storage batteries and other high-energy consumption, high-pollution industries, backward production capacity resolutely eliminated.

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5.5.3 Accelerate the pace of market-oriented reforms and increase industrial concentration It is necessary to further increase the pace of market-oriented reforms, break the situation of regional administrative monopoly and market segmentation, and use assets as a link to achieve reintegration of resource elements through acquisitions, mergers, and reorganizations, to avoid duplication of construction and formation of backward production capacity between regions. Enterprises will become stronger and bigger, continuously increase industrial concentration and form economies of scale. 5.5.4 Form a price mechanism that refects the cost of resources and the environment It is necessary to rationalize the relationship between factor prices and price factors (such as coal, oil, natural gas, etc.) to form a price mechanism that refects resource costs and environmental costs, thereby guiding low carbonization and new carbon industry through price adjustment leverage lowcarbon production capacity. 5.5.5 Comprehensive use of fscal and taxation means to support the transformation and upgrading of traditional industries The fundamental driving force for the transformation and upgrading of traditional industries is to improve product quality through innovation, such as fscal interest subsidies, tax reductions, equipment loans, subsidies for conversion, accelerated equipment depreciation, high carbon product export tax increases, low carbon products export tax rebates, etc. Measures to support traditional enterprises to carry out technological transformation and equipment upgrades, thereby enhancing the competitiveness of traditional industries, improving the technical content and added value of products, and at the same time forming new industrial growth points. 5.5.6 Providing public welfare support for technology, management, and training for the elimination industry For declining industries, shutting down and transferring industries and other backward industries, it is necessary to provide corresponding technical and operational guidance, consulting and employee skills training and other public services, and incorporate them into the government’s public welfare social service system, thereby reducing social and economic shocks caused by production capacity.

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154

Index

Note: Page numbers in bold refer to tables and in italics to fgures. agriculture 72, 120, 128, 130, 137–138 Aigner, D. 8 Aigner, D. et al. (1977) 11 Ankarhem, M. 64 Atkinson, S. E. 18 Auffhammer, M. 63 Australian Bureau of Agriculture and Resource Economics 75 backward production capacity 67, 125, 131, 134, 140, 141 Bali roadmap 42 Beijing: carbon marginal abatement cost 54, 66, 93, 94, 109, 110; carbon shadow price 38–39, 40, 55; emission reduction potential 50, 121; emissions 51, 58, 59, 60, 61, 62, 65, 73 Bohm, P. 95–96 Boyd, G. et al. (1996) 16, 24 Cai Bofeng 72, 73 Campbell, R. C. 76, 91 “carbon budget plan” 43 carbon capture and storage see CCS carbon emissions, marginal abatement costs (MAC) 69–115: economicenergy models 74–75; expert-based 73–74; infuencing factors 95–102; micro supply side 75–77; provincial 109; urban 71–73, 110–113 carbon emissions, reduction potential 42–67: challenges to 125–127; emission reduction potential model 46–47; interprovincial 49–54, 56–62; reduction capacity index 57–59, 61; regional marginal cost 54–56; shadow price model 47–48

carbon emissions, shadow price: directional distance function 6–8, 33–35; distance function 4–6; evidence-based studies 16, 18, 20, 22; index method 4; provincial 30–41, 45, 47–48, 54–56, 64–65; theory 24–26 carbon sequestration 129–130 carbon sinks 129, 130, 137, 138 carbon tax 30, 39–41, 69, 70 Carson, R. T. 63 Caves, D. W. 28n7 CCS (carbon capture and storage) 131 CGE (Computable General Equilibrium) model 54, 74, 75 Chamber, R. et al. (1998) 79 Charnes, A. 47 Chen et al. (2010) 22 Chen Shiyi (2010) 32, 76, 77, 81, 82, 92 Cheng Shi 95 China Environmental Yearbook 83–84 China Statistical Yearbook 36, 48, 49 China Urban Statistical Yearbook 83–84, 85 Chipman, S. 5 Choi, Y. et al. (2012) 76 Christensen, L. R. 28n7 Chung (1996) 34 Chung (1997) 14 Chung, Y. H. et al. (1997) 4, 6, 16, 32, 33, 45, 77 circular economy 132, 136 Clean Development Fund 43 CO2 see carbon emissions coal consumption: industrial restructuring 119–120, 121, 124, 134–135; marginal abatement cost (MAC) 85–86, 87, 89; regional 48, 49, 50, 63, 64, 66, 72

155

Index Coelli, T. 11 Coggins, J. S. 16, 32 “complete effciency” 24 Computable General Equilibrium model see CGE construction industry 120, 138–139 contaminant disposal modeling 3–27: data envelope analysis (DEA) 13–15; parameterized distance function method 8–9; quadratic direction distance function 9–11; stochastic frontier analysis (SFA) 11–13 Cooper, W. W. 47 Cooper, W. W. et al. (2007) 45 Copenhagen conference 42 Coppel, J. 95 Criqui, P. et al. (1999) 95 Dasgupta, S. et al. (2001) 95 DDF (directional distance function) 6–8, 23–25, 31–35, 45, 68n6, 76–83, 89–91 DEA (Data Envelopment Analysis) model 8, 13–15, 24–25, 32, 80, 92, 95 deterministic linear programming 25 Dhakal, S. 72 Diewert, W. 28n7 Ding Zhongli et al. (2009) 43–44 directional distance function see DDF distance function 4–6, 8–15, 23–25, 32, 48, 76 Dong Feng et al. (2010) 22, 23 Dorfman, J. H. 18 Du, L.-M. et al. (2012) 96 economic restructuring 124–125, 127 effciency boundary 3 Effciency Index 58–59 Eleventh Five-Year Plan 23, 67, 69, 125–126, 127 emerging industries 130, 140 emission reduction effciency 56–62, 66 emission reduction potential 44, 46–47, 49–54, 56–58, 62–67, 73–74, 121–122 energy conservation 52, 67, 69, 122–127, 133, 134, 138 energy consumption per unit of GDP 124 energy effciency 62, 67, 119–120, 124, 129, 132–133, 135, 139 energy prices 122–123, 126 energy sector 74, 134–135 energy security 124 equipment manufacturing sector 132, 135–136

155

EU-ETS (greenhouse gas emissions trading) 69 external damage price 4 fairness 43–44, 56–62, 66, 69, 76, 121 Fairness Index 58–59 Färe, R. et al. (1989) 45 Färe, R. et al. (1993) 5, 8, 32, 48 Färe, R. et al. (2001) 79 Färe, R. et al. (2005) 12, 18, 32 Färe, R. et al. (2007) 19 Färe, R. et al. (2010) 81 Ferrier, G. D. 11 forestry 130, 138 fossil energy: carbon emissions 36, 49, 95–96, 120, 130; urban consumption 70, 72, 85–86 frontier production function 5 Fu Jiafeng et al. (2010) 45 Fujian: carbon marginal abatement cost (MAC) 54, 109; carbon shadow price 37–38, 55; emission reduction potential 50, 58, 66, 121; emissions 51 Fukuyama, H. 24, 45 Gallop, F. M. 16 Gansu: carbon marginal abatement cost (MAC) 93, 109; carbon shadow price 37–38, 55; emission reduction potential 52, 58, 65, 121; emissions 51 Gao Pengfei et al. (2004) 75 gas 85, 86, 124 see also natural gas GHG (greenhouse gas) emissions: industrial restructuring and 119, 122–129, 131–132, 134–140; regional 43, 54; urban 69–73 Ghorbani, M. 20 Glaeser, E. L. 71 Global Commons Institute 43 global economy 122–123 government role 31, 40, 71, 102, 122–128, 140 greenhouse gas emissions see GHG emissions Grosskopf, S. 11, 28n17 Gu Liuliu 21 Guangdong: carbon marginal abatement cost 54, 109; carbon shadow price 37, 38–39, 40, 55; emission reduction potential 50, 58, 66, 121; emissions 40, 51, 65, 120 Guangxi: carbon marginal abatement cost (MAC) 93, 109; carbon shadow

156

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Index

price 37, 55; emission reduction potential 50, 58, 61, 62; emissions 51 Guizhou: carbon marginal abatement cost (MAC) 54, 109; carbon shadow price 37–38, 40, 55; emission reduction potential 50, 58, 60, 61, 62, 65, 66, 121; emissions 51, 52, 65 Hailu, A. 17, 32 Hainan: carbon marginal abatement cost 54; carbon shadow price 37, 55; emission reduction potential 50, 58, 59, 60, 61, 62, 66, 121 Hayes, K. 11, 28n17 He Juhuang et al. (2002) 75 heavy chemical industry 120, 131 heavy industry 120, 135 high-carbon transfer 133–134 Hirschberg, J. 28n17 Hoeller, P. 95 Hu Angang et al. (2008) 20, 23, 45, 77 hydropower 68n11, 134, 135 IEA (International Energy Agency) 70 imports 124, 133 India 96 industrial restructuring 119–141: agriculture 137–138; carbon-fxing industries 129–131; construction industry 138–139; domestic and international situation 122–124; energy sector 134–135; existing policies 140; industrial concentration 141; industrial sector 135–136; low-carbon industries 129–131; market-oriented reforms 141; price mechanism 141; public welfare support 141; service industry 139; target responsibility assessment system 140; transportation industry 136–137; upgrading of traditional industries 141 industrial sector 23, 119, 76, 120, 132, 135–136: secondary 72, 97, 99, 124; tertiary 63, 66, 97, 99, 119, 121, 124 infrastructure 96, 97, 99, 103, 121, 126, 127 International Energy Agency see IEA Japan 124 Jiahua, P. 43 Jiangsu 37, 38, 40, 51, 55, 58, 109, 120 Kahn, M. E. 71 Ke, T. Y. et al. (2008) 19, 23 King, R. G. 85

Klepper, G. 95 Kolstad, C. D. 17 Kumar, S. 11, 17, 19 Kumbhakar, S. C. 12 Kyoto protocol 42, 43 Larsen, B. 95–96 Lee, J.-D. et al. (2002) 18, 24, 32, 48 Lee, M. 18 Levine, R. 85 Liaoning: carbon marginal abatement cost 109, 110; carbon shadow price 37, 55; emission reduction potential 121; emissions 51, 57, 58, 59, 60, 61, 62, 65, 120 Lim, J. 76 Liu Fuhua 21 Liu Leiyi 77 Liu Minglei et al. (2011) 91, 92, 96 Lovell, C. K. 11, 12 low-carbon approach 63, 120, 123, 129–131 Malmquist index of productivity 4, 32 Malmquist–Luenberger index of productivity 32, 45, 77 Maradan, D. 95 marine carbon sequestration 130–131 market mechanisms 122, 123, 127, 128 Marklund, P.-O. 82, 91 Massachusetts Institute of Technology 75 McKinsey & Company 73 methane 137 Monte Carlo method 81 Motallebi, M. 20 multiproductivity index 28n7 Murty, M. 11, 17 Murty, M. et al. (2007) 82, 96 national emission account 43 natural gas 36, 49, 68n11, 124, 135 New York City 72–73 Ningxia: carbon marginal abatement cost 93, 102, 109; carbon shadow price 37, 38, 55; emission reduction potential 50, 57, 58, 59, 60, 61, 62, 66, 121; emissions 51, 65 nitrogen oxides (NOx) 32 nitrous oxide 137 “no redundancy” 24, 38 Noh, D.-W. 29n19, 81, 91 non-fossil energy 134

157

Index non-parametric method 25, 32, 34 nuclear power 68, 134, 135 OECD (Organisation for Economic Co-operation and Development) 71, 95 oil consumption 36, 49, 85, 86, 120, 124 parameter estimation method 11, 32–33 Park, H. 76 Perelman, S. 11 Peterson, S. 95 petroleum gas 86 Pittman, R. W. 4, 28n7, 32 Price, L. et al. (2011) 69 private car ownership 85, 136 productivity of environmental factors, average 36–37 Qin Shaojun et al. (2011) 91, 96 Qinghai 37, 51, 55, 58, 65, 121 Reig-Martinez, F. et al. (2001) 18 renewable energy 68n5, 123, 129, 134, 135, 137 Renewable Energy Law Amendment 42 Repetto, R. et al. (1996) 28n8 resource effciency 132–133 resource utilization 132, 136 Rezek, J. P. 76, 91 Roberts, M. J. 16 Salnykov, M. 18, 82, 91 Samakovlis, E. 82, 91 SBM (Scaks-Based Measure) model 45, 80 Scheel, H. 45 scientifc development 127, 128–129, 131, 137 SEI (Stockholm Environment Institute), Greenhouse Development Rights Framework 43 service industries 129–130, 139, 140 SFA (stochastic frontier analysis) 11–13, 24 Shaanxi 37, 51, 55, 58, 109, 121 shale gas 122 Shanghai: carbon marginal abatement cost 91, 93, 94, 96, 102, 109, 110; carbon shadow price 37, 38–39, 40, 55, 91; emission reduction potential 50, 121; emissions 51, 57, 58, 59, 60, 61, 62, 65, 72 Shanghai Energy Balance 72 Shanxi: carbon marginal abatement cost 109; carbon shadow price 37, 38, 55;

157

emission reduction potential 52, 54, 121–122; emissions 51, 57, 58, 59, 60, 61, 62, 65–66, 72 Shao Hanhua 21 Shen Manhong 63 Shepard, R. N. 5 Shephard output distance function 7 SO2 see sulfur dioxide State Council Development Research Center 43 stochastic frontier analysis see SFA Stockholm Environment Institute see SEI subtraction strategy 131–132 sulfde (SOx) 32 sulfur dioxide (SO2) 15, 16–22, 23, 26, 32, 45, 52, 77, 95 sustainability 43, 128 Sweden 43, 64 Swinton, J. R. 16, 17, 32 target responsibility assessment system 140 TFP (total factor productivity) 15 Thirteenth Five-Year Plan 125–126, 127 total factor productivity see TFP total suspended particulate matter see TSP traditional industries, upgrading of 132–133 traditional productivity analysis 3–27: Data Envelopment Analysis (DEA) 8, 13–15, 24; directional distance function 4–14; index method 4; transportation industry 136–137 TSP (total suspended particulate matter) 32 Tu Zhenge 20, 21, 23, 45, 76, 77, 95 Turnovsky, M. H. 17 Twelfth Five-Year Plan 42, 125, 127, 130 UN Lima Climate Agreement 123 upgrading strategy 132–133 urban carbon dioxide emissions 70–73, 108, 110–113 urbanization 70, 99, 102, 103, 121, 122 USA 32, 71–72, 122, 124 Van Ha, N. et al. (2008) 19, 24 Vardanyan, M. 29n19, 81, 91 Vassiliev, A. 95 Veeman, T. S. 17, 32 Wang Bing et al. (2008) 20 Wang Bing et al. (2010) 22, 23, 45, 77

158

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Index

Wang Bing et al. (2011) 77 Wang Can et al. (2005) 75 Weber, W. L. 24, 45 Wei, C. et al. (2013) 91, 96 Wei Chu 63 Wei Chu et al. (2010) 52 World Bank 70, 71 Wu Jun 21, 23 Wu Jun et al. (2010) 22 Xie Shichen et al. (2009) 72 Xinjiang 37, 51, 55, 58, 65, 109, 121 Xu Cong et al. (2011) 72

Yang Jun 21 Ying, C. 43 Yuan Peng 95 Yue Shujing 21 Yunnan 37, 51, 55, 58, 62, 109 Zelenyun, V. 18, 82, 91 Zhang Jinping et al. (2010) 73 Zhang Jun et al. (2004) 36, 48 Zhejiang: carbon marginal abatement cost 91, 109; carbon shadow price 37, 38, 40, 55; emission reduction potential 121; emissions 51, 54, 58, 66 Zhou Jian 21

i

Climate Change and Industry Structure in China

As carbon dioxide is the most signifcant source of greenhouse gases today, its emission quantity has become a primary focus of governments, scholars and the general public. From the perspective of industrial structure, the book mainly explores the features of carbon emissions in China. The author thoroughly studies related theories and literature about industrial structure and climate change, and reviews the different developmental histories of developed countries and China. Based on historical data, this volume discusses the infuence of interprovincial industrial structure and income level on carbon emissions, and tries to estimate different industrial sectors’ carbon emissions. It especially studies the case of Zhejiang Province, and analyses several factors which affect its industrial CO2 emissions. The book provides international readers with rich information about the characteristics, patterns and drivers of China’s CO2 emissions, which will definitely help scholars and students better understand China’s economy. Wei Chu is a professor at Renmin University of China. His research focuses on the analysis of energy effciency, evaluation of abatement cost of pollutants and the residential energy demand.

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China Perspectives

The China Perspectives series focuses on translating and publishing works by leading Chinese scholars, writing about both global topics and Chinarelated themes. It covers Humanities & Social Sciences, Education, Media and Psychology, as well as many interdisciplinary themes. This is the frst time any of these books have been published in English for international readers. The series aims to put forward a Chinese perspective, give insights into cutting-edge academic thinking in China, and inspire researchers globally. Titles in economics partly include: The Chinese Path to Economic Dual Transformation Li Yining Hyperinfation A World History Liping He Game Theory and Society Weiying Zhang China’s Fiscal Policy Theoretical and Situation Analysis Gao Peiyong Trade Openness and China’s Economic Development Miaojie Yu Perceiving Truth and Ceasing Doubts What Can We Learn from 40 Years of China’s Reform and Opening-Up? Cai Fang For more information, please visit www.routledge.com/series/CPH

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Climate Change and Industry Structure in China CO2 Emission Features Wei Chu

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First published in English 2020 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 52 Vanderbilt Avenue, New York, NY 10017 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2020 Wei Chu The right of Wei Chu to be identifed as author of this work has been asserted by him in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identifcation and explanation without intent to infringe. English Version by permission of China Renmin University Press. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record has been requested for this book ISBN: 978- 1- 138- 33077- 1 (hbk) ISBN: 978- 0- 429- 44765- 5 (ebk)

Typeset in Times New Roman by Newgen Publishing UK

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Contents

List of fgures List of tables Preface

vii x xiii

PART I

Theories and practice 1 2 3

Industrial structure in China and emission of greenhouse gases

1 3

Interconnections between industrial structure and climate change

31

Practical implications of China’s response to climate change

53

PART II

Emission features 4 5 6

Research on the relationship between interprovincial industrial structure, income level and CO2 emissions

81 83

Quantitative assessment of CO2 emissions from China’s production sector

111

Quantitative evaluation and analysis of CO2 emissions in China’s industrial sector

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Contents

7 Analysis of industrial CO2 emissions and infuencing factors: a case study of Zhejiang Province References Index

159 190 196

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Figures

0.1 1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9

Research technology roadmap of this book Aggregate CO2 emissions in China (1971–2012, unit: million tons) Per-capita CO2 emissions and intensity trend in China (1971–2010, with 1971 designated as 1) Changes in the primary, secondary and tertiary industries in China (1978–2013, unit: %) Internal structural changes in China’s primary industry (1978–2013, unit: %) Internal industrial structure changes in China’s light and heavy industries (1994–2010, unit: %) Internal structural changes to the tertiary industry in China (1978–2010, unit: %) Industrial structure adjustment and energy structure optimization Environmental Kuznets curve Economic output scale of six industries (1996–2009, unit: RMB 100 million, 2005 price) Trend of fossil energy consumption in the six major industries (1996–2009, unit: 1 million tons of standard coal) CO2 emissions from the six major industrial fossil energy consumption industries (1996–2009, unit: million tons) Comparison of predictions of CO2 emissions in China by different agencies (unit: million tons) China’s GDP, energy and CO2 emissions (1996–2009) Absolute contribution of factors affecting greenhouse gas emissions (1996–2009, unit: million tons) Relative contribution rate of CO2 infuencing factors at 7-point period (unit: %) Effect of decomposition of CO2 changes in different sectors (1996–2009, unit: million tons of CO2) Decomposition of industrial structure effects in different sectors (1996–2009, unit: million tons of CO2)

xv 13 14 25 26 27 28 37 39 121 122 122 123 124 125 126 127 128

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viii 5.10 5.11 5.12 5.13 5.14 5.15 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 7.1 7.2 7.3 7.4

List of fgures Decomposition of the infuencing factors of GHG emission in agriculture (1996–2009, unit: million ton) Decomposition of factors affecting GHG emission in industry (1996–2009, unit: million ton) Decomposition of factors infuencing construction GHG emissions (1996–2009, unit: million ton) Decomposition of factors affecting transportation GHG emission (1996–2009, unit: million ton) Decomposition of factors affecting business GHG emission (1996–2009, unit: million ton) Decomposition of factors affecting GHG emissions in the energy sector (1996–2009, unit: millions of tons) Total output value of China’s industrial sector from 1996 to 2009 (unit: 100 million yuan, constant price in 2005) Industry distribution of China’s industrial output value from 1996 to 2009 Trends in fossil energy consumption in China’s industrial sector from 1996 to 2009 (unit: million tons of standard coal) Various fossil energy structures in China’s industrial sector (unit: %) Fossil energy consumption structure of China’s industrial sector from 1996 to 2009 (unit: %) CO2 emissions of China’s industrial sector from 1996 to 2009 (unit: million tons) Industry distribution of industrial CO2 emissions in China from 1996 to 2009 China’s total industrial output value, fossil energy consumption and CO2 emissions trend (1996–2009, 1996=1) Contribution of factors affecting industrial CO2 emissions in China (1996–2009, unit: million tons of CO2) Absolute contribution of industrial CO2 infuencing factors in different periods (unit: million tons of CO2) Contribution of various sectors in the effects of CO2 changes (1996, 2009, unit: million tons of CO2) Contribution of different industries in the industrial restructuring effect (unit: million tons of CO2) Distribution of industrial greenhouse gas emissions in Zhejiang Province (2008) Comparison of industrial greenhouse gas emissions scale of major cities and municipalities above scale (2008) Comparison of industrial greenhouse gas industry distribution in major provinces and cities (2008) Comparison of per-capita industrial greenhouse gas emissions in major provinces and cities (2008)

129 130 131 132 134 135 141 141 144 145 145 146 147 148 149 150 151 154 164 175 175 176

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List of fgures Comparison of CO2 emission intensity of key industrial sectors in major provinces and cities (2008) 7.6 Comparison of energy emission structures of key industrial sectors in major provinces and cities (2008) 7.7 Comparison of greenhouse gas emissions from industrial enterprises above a designated size in Zhejiang Province (2004, 2008) 7.8 Comparison of CO2 emission intensity of industrial enterprises above a designated size in Zhejiang Province (2004, 2008) 7.9 Comparison of energy emission structures in industrial enterprises above a designated size in Zhejiang Province (2004, 2008) 7.10 Relative contributions of factors affecting industrial greenhouse gas emissions from large-scale industries in Zhejiang Province (2008)

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7.5

177 179 182 183 184 186

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Tables

1.1 1.2 1.3 1.4 2.1 3.1 4.1 4.2 4.3 4.4 4.5 5.1 5.2 6.1 6.2 6.3 6.4 7.1 7.2 7.3

Ratio of greenhouse gas emissions in major regions of the world Comparison of greenhouse gases in major countries of the world Comparison of sector emissions of greenhouse gases in major countries (2011) Energy consumption structure of major countries (2012) Comparison between EKC model and factor decomposition model Emission reduction targets of some developed countries by 2020 and 2050 Empirical conclusion of EKC in China Coeffcients of CO2 emissions from various sources Measurement results and model selection Scenario setting of explanatory variables Forecast results of CO2 emissions Fossil energy product classifcation standard, folding coeffcient and CO2 emission coeffcient Economic output of six major industries, fossil energy consumption and CO2 emissions Classifcation comparison table of industrial sectors Total industrial output value, fossil energy consumption and CO2 emissions of the industrial sector during 1996–2009 Decomposition of changes in CO2 emission in the industrial sector (1996, 2009) Absolute contribution values of various sectors in the industrial structure effect between 1996 and 2009 Industry classifcation of major industrial sectors CO2 emission factors of major energy products GHG emissions, energy consumption and industrial output value of industrial enterprises above a designated size in Zhejiang Province in 2008

6 8 10 11 50 54 91 93 99 104 107 116 117 139 142 152 155 161 163 166

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List of tables 7.4 7.5 7.6 7.7

GHG emissions of industrial sectors above a designated size in fve provinces and cities in 2008 (10,000 tons of CO2 equivalent) Energy consumption of industrial sectors above a designated size in fve provinces and municipalities in 2008 (10,000 tons of standard coal) Gross industrial output value of industrial enterprises above a designated size in fve provinces and municipalities in 2008 (100 million yuan) GHG emissions, energy consumption and industrial output value of industrial enterprises above a designated size in Zhejiang Province in 2004

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169 171 173 180

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Preface

Climate change has become a huge challenge that the world needs to cope with. In the long-term future, China will still be at a stage of rapid urbanization and industrialization. Energy consumption and the resulting greenhouse gas (GHG) emissions will also continue to grow. In response to global climate change, the Chinese government has made tremendous efforts to promise energy intensity targets, CO2 intensity targets and CO2 emission peaks, respectively. From the perspective of research, the control of GHG emissions, especially carbon dioxide emissions, depends primarily on factors such as economic growth, industrial structure, energy consumption intensity and energy consumption structure. Among them, the industrial structure plays a very important role. Based on the perspective of industrial structure, this book analyzes China’s characteristics of carbon dioxide emissions, emission reduction potential and cost, and emission reduction strategies, and examines the important role that industrial structure plays in it so as to provide support and basis for GHGs abatement and control through industrial structure adjustment strategies. This book consists of two volumes and examines the characteristics of CO2 emissions in China based on the perspective of industrial structure, as well as emission reduction potentials and abatement costs. Specifcally, this book will answer the following two questions: First, what are the infuencing factors of CO2 emissions? What role does the industrial structure play? Second, what are the key sectors and industries, potential and cost of emission reduction for CO2 emissions in China? How does the industrial structure affect it? For this reason, this book will be divided into four sections for research. The frst section is a summary of theory and practice, including three chapters of volume one. Among them, Chapter 1 reviews the current status of GHG emissions and challenges that China faces. Chapter 2, based on the perspective of industrial structure, combs the theoretical literature on the mechanism and conduction effect of industrial structure on CO2 emissions, and systematically summarizes the corresponding analytical models. Chapter 3 is a summary of the current domestic and international experience in dealing with climate change.

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The second section explains the characteristics of CO2 emissions in four chapters of volume one. Among them, Chapter  4 estimates and predicts regional CO2 emissions in China at the interprovincial level and quantitatively examines the correlation between per-capita CO2 emissions and industrial structure with the measurement model to identity the per-capita CO2 emissions; Chapters  5 and 6 measure and disintegrate CO2 emissions from six major production industries and 33 industrial sectors in China separately on industrial basis and also compare the effect of industrial structure in a more concrete and profound manner. Differences in industrial CO2 emissions between Zhejiang and other developed provinces are investigated and compared in Chapter 7 with the industries in Zhejiang Province as the research target, while the impact of industrial structure, technology and other driving factors are also examined. The third section, which includes four chapters of volume two, is the analysis of CO2 emission. Chapter 1 frst reviews the theoretical model and empirical application of pollutant disposal, which laid the theoretical foundation for the subsequent CO2 marginal abatement cost analysis; Chapter 2 estimates the marginal abatement cost for CO2 emission reduction in various provinces in China based on the parametric model, which can be used to set initial price of emission rights, or as a carbon tax benchmark. In Chapter 3, a non-parametric model is used to evaluate the CO2 emission reduction potential and marginal abatement costs in each province in China. It also examines regional CO2 emission quota allocation schemes under different decision preferences; Chapter 4, taking China’s prefecture-level cities as the research object, performs parametric modeling and quantitative estimation of urban CO2 emissions and marginal abatement costs, and further analyzes the infuence of industrial structure and other factors on marginal abatement costs. The fourth section consists of one chapter of volume two to elaborate on countermeasures. Chapter  5 systematically summarizes the theoretical and empirical parts of the book, identifes important areas and links for controlling and mitigating CO2 emissions in China, and then proposes strategic ideas, concepts and supporting measures accordingly for China to control and mitigate CO2 emissions through industrial restructuring. The overall research roadmap and corresponding research methods of this book are shown in Figure 0.1. Following the basic principles of “from reality to reality,” this book frst explores the status of global GHG emissions in depth and puts forward corresponding scientifc issues. Second, in-depth literature research is carried out to determine research methods through theoretical analysis. The subsequent empirical studies use quantitative analysis tools such as decomposition and econometric models at different levels in different regions and industries to quantitatively analyze per-capita carbon emissions and carbon emission intensity indicators; thereafter, quantitative model, quantitative analysis, linear programming and scenario analysis methods are used in a comprehensive manner to quantitatively evaluate CO2 emission reduction potential and abatement costs of provinces and cities, and the impact of

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Preface Research Content

Research Procedure Step 1 Summary of Practices and Theories

Status Quo & Dilemma

Literature Review

xv

Methodology

Practical Implications

Study of regulations and literature and case studies

Influence of Industrial Structure on CO2 Emissions Step 2 CO2 Emissions

Trans-border

Total CO2

Provinces

Industries

Per-Capita CO2

Regions

CO2 Intensity

Relationship between Industrial Structure and CO2 Emissions Step 3 Analysis of CO2 Emissions

Province

Ci˜es

Step 4 Responsive Strategies

Reduc˜on Poten˜al

Quota alloca˜on

Quantitative model Linear programming Measurement Model Scenario Analysis

Reduc˜on cost

Strategic Response Key Sectors

Quantitative model and measurement model

Strategic Planning

Basic Guidelines

Regulation Studies

Figure 0.1 Research technology roadmap of this book

industrial structure on CO2 emission reduction is also investigated with further exploration of the decomposition of regional GHG emission reduction issues. Finally, based on the perspective of industrial structure, key areas and links for controlling and mitigating CO2 emissions are identifed and strategic ideas and countermeasures are proposed. Volume one conducts an in-depth analysis of global GHG emissions and proposes corresponding scientifc issues. Based on in-depth literature research, the decomposition model and measurement analysis method are used so as to identify the factors affecting carbon emissions in different regions/departments. Volume one has the following main fndings. First, the book theoretically confrms that industrial structure changes are an important reason for changes in GHG emissions. Due to the difference in energy consumption structure and effciency within the sector, there

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is different “carbon productivity” among different industries. Therefore, when the relative proportions between different industries change, the overall carbon emission scale will be affected by the change in carbon productivity. Taking into account China’s real industrial background and energy consumption structure characteristics, we should focus on reducing GHG emissions by adjusting the industrial structure of high-energy consumption and high emissions. This adjustment is not limited to the adjustment between the three industries, but should be refected by the adjustment and upgrading within the secondary and tertiary industries. It should be noted that the basic laws of industrial development should be followed, and the carbon emission infuence and industrial infuence of each industry should be comprehensively considered. Second, the book identifes the main infuencing factors of China’s CO2 emissions, key industries and regions, and examines the role of industrial structure in CO2 emissions. From the perspective of per-capita CO2 emissions, it shows the non-equilibrium characteristics of the eastern > central > western regions. It is estimated that by 2015 and 2020, the per-capita emissions may reach 7 tons and 9 tons, respectively. The proportion of heavy industry is signifcantly positively correlated with the per-capita carbon dioxide emissions. In addition, the level of economic development, energy consumption structure, urbanization level and technological progress are also the main factors affecting China’s carbon dioxide emissions. From the perspective of industry and sector, energy processing conversion, industry and transportation are the most important sources of emissions; in the industrial sector, metal products, non-metal products and chemical industries are the main sources of emissions. The expansion of production scale is the main factor leading to the increase of carbon emissions in the industry/sector, and the improvement of internal energy effciency and the adjustment of industry/sector structure are the two main ways to reduce GHG emissions. The contribution of energy structure improvement and low-carbon fuel carbon emission coeffcient is relatively small. The relevant research of this book comes from the National Social Science Fund Project I hosted, “Climate Change and the Strategic Study of China’s Industrial Structure Adjustment during the Twelfth Five-Year Plan Period” (10CJY002) and the National Natural Science Foundation Project “Regarding Regional Carbon Equity with Equity and Effciency”. The research results from the Design and Comparison of the Right Allocation Plan (41201582) and the research also received the “Beijing Municipality’s Household Energy Consumption Model and Energy-saving Approaches” (9152011) and the Mingde Young Scholar Program of Renmin University of China Support for Carbon Dioxide Abatement Cost Curve (13XNJ016). Some of the chapter content has been published in the journals, and comments are given at the beginning of each chapter. During the study, President Shen Manhong of Ningbo University, Associate Professor Ni Jinlan from the University of Nebraska, Associate Professor Du Limin from Zhejiang University, Associate

newgenprepdf

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Professor Cai Shenghua from the Chinese Academy of Sciences, Dr.  Yu Dongzheng, Student Huang Wenruo and Student Su Xiaolong participated in some of the collaborative research, or participated in the writing and revision of some chapters, or provided a large number of research assistants, and I express my sincere gratitude to them for their contributions. In addition, we would also like to sincerely thank the people’s publishing house Zhai Yanhong for his efforts in publishing the book. Due to the limited research energy and the lack of experience of the authors, the errors or defects in this book sincerely welcome criticism and correction from experts and readers.

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Part I

Theories and practice

3

1

Industrial structure in China and emission of greenhouse gases

Climate change has been a critical global issue of universal concern in the international community, which has triggered increasing attention and discussions. Climate change refers to the constant and signifcant changes in temperature, precipitation and air fow in a certain period of time in the climate system with a basic time span of over a decade. For nearly a hundred years, in particular in the past 30 years or so, the earth has experienced climate change fundamentally as gradual warming. The concept of global warming, used as the description of the phenomenon of constantly rising temperature in climate change, is actually more restricted than that of climate change. But in terms of the causal relationship, global warming is the culprit of other phenomena of climate change, whose outcomes are ominous for most of the regions.

1.1 Infuence of climate change and emission of greenhouse gases 1.1.1 Infuence of climate change on the globe As stated in the IPCC Forth Assessment Report, in the last 100 years (from 1906 to 2005), the average surface temperature has risen by 0.74°C, which may make the twentieth century the warmest 100 years in the past 1000 years and the latter part of the twentieth century the warmest 50 years in the past 1300 years (Soloman, 2007). There are both natural and anthropogenic factors leading to climate change. However, according to IPCC’s research and report, human activities since the Industrial Revolution, in particular the emission of greenhouse gases (GHGs) resulting from the exponential consumption of fossil fuels in industrialization by developed countries, are the primary causes of climate change. Climate change is a complex phenomenon featured with chronicity and uncertainties, as refected by diversity in its causes, far-reaching and profound impact, changes whose scale and extent are unavoidable and unquantifable in the short term and hindrance to human activities to put off climate change. Without the implementation of necessary measures, it is expected that by the end of the twenty-frst century, the average surface temperature could rise DOI: 10.4324/9780429447655-2

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Theories and practice

by 1.1–6.4°C. As a result, glacier melting will be accelerated, leading to sea level rise by 0.2–0.6 m, pronouncing changes in ecosystems and island countries and coastal regions suffering from severe natural disasters. The IPCC Assessment Report, bolstered by the latest and more supportive evidence, has further demonstrated that global warming has been an inarguable fact which may result primarily from human activities since the Industrial Revolution. The signifcant increase in the concentration of CO2 and methane in the air, which far exceeds that before industrialization thousands of years ago, is largely ascribed to these activities. With the growing concern of the international community on the growth of human-sourced GHG emissions and their contribution to global warming, promoted by the 1990 IPCC First Assessment Report, the United Nations Framework Convention on Climate Change (UNFCCC) was adopted in the United Nations Conference on Environment and Development in 1992, which focused on climate change and GHG emission reduction cooperation. In order to clarify the national emission reduction obligations, the Kyoto Protocol, themed on quantifed emission cuts, was adopted at the third meeting of the UNFCCC. Climate change, which is characterized by global warming, has evolved into a typical global environmental problem. It brings a dual effect to the physical and socio-economic aspects and generates a global natural ecosystem, water resources, coastal belts, agriculture and animal husbandry. The series has a signifcant impact and poses serious challenges to the survival and development of human society. According to Chinese scientists’ predictions, the average annual precipitation in China will increase in the future, and the possibility of extreme weather and climate events in the country will increase. The arid area may expand and the possibility of desertifcation will increase. The coastal sea level will continue to rise. The retreat of the Qinghai-Tibet Plateau and Tianshan glaciers will accelerate (National Development and Reform Commission, 2007). First of all, climate change will destroy the earth’s ecosystem. The continuous increase in temperature will have a great impact on the natural ecosystem, undermining the self-stability of the ecosystem, making the ecological environment suitable for animals and plants deteriorate and causing biodiversity to drastically decrease. At the same time, the problem of water resources is outstanding. Some rivers that are replenished with glaciers and melted water are affected by the increase in temperature. The run-off of rivers will increase and spring food peaks come earlier. It is expected that in the next 20 or 30 years, the melting of glaciers in the Himalayas will accelerate, increasing the probability of fooding and mudslides, causing serious impact on water resources, and the river run-off will gradually decrease, laying the foundation for future water shortages. Second, climate change will raise sea level. The warming of the sea increases the sea level gradually. As seawater will absorb more than 80% of the added heat of the climate system, the increase in seawater temperature will

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lead to the expansion of seawater. In addition, the increase in air temperature will accelerate the melting of glaciers. These two factors provide the impetus for sea-level elevation. Since 1978, the Arctic sea ice extent has decreased by 2.7% per decade, while it has decreased by 7.4% in summer. Sea-level rise will exacerbate foods, seawater erosion and other disasters, endangering the infrastructure of island cities, threatening the economic development of coastal areas and even submerging coastal cities with low elevation. The areas most affected by sea-level rise are the Indian subcontinent and Southeast Asia, including China’s Shanghai and the Yangtze River Delta region. Third, climate change will increase extreme weather. Increased climate change has triggered natural disasters such as droughts and foods, which has increased the probability of extreme weather. American scholars have found that the frequency of low-intensity hurricanes has not changed much in the past 30 or 40 years, but the frequency of high-intensity hurricanes has doubled. In 2005 alone, there were two serious meteorological disasters in the world. One was Hurricane Katrina in the USA and the other was Typhoon “Matsa” in China. In most parts of Africa and the Asian continent, more drought and fooding will be experienced. In addition, in the global warming process, abnormal weather with unusually cold weather has also appeared in some areas. Finally, climate change will have a great impact on human survival. Climate anomalies have increased instability factors in production activities and raised the issue of rising investment costs. Changes in crop sowing time and planting structure will have an impact on agricultural production. By 2050, crop yields in East and Southeast Asia are expected to increase by 20%, while Central and South Asia will decrease production by 30%. The coastal areas have become areas of high incidence of foods and extreme weather, posing danger to human life and survival activities. In the United Kingdom, once the global average temperature rises by 3–4°C, the annual loss due to food increases from 0.1% of GDP to 0.2–0.4% of GDP.

1.1.2 Composition of global greenhouse gases With the occurrence of climate warming, regardless of natural factors, the burning of fossil fuels in human activities is an absolute precipitating factor, and the gases that can result in global warming emitted through the combustion of fossil fuels are collectively referred to as greenhouse gases (GHGs). In the 1920s, French scientists discovered the greenhouse effect of nature, that is, some gases in the nighttime atmosphere can absorb and refect infrared light to the ground, slowing the decline in the surface temperature of the earth at night, and the greenhouse effect reduces the temperature difference between day and night, making the earth more suitable for the survival and development of life. Since the Industrial Revolution, more and more fossil fuels have been burnt and the concentration of GHGs in the atmosphere has

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continuously increased. The balance of the natural global warming effect has been broken and the global climate has become overheated. There are many kinds of GHGs, among which the six major gases referred to in the International Convention on Climate Change are CO2, CH4, N2O, PFC, SF6 and HFC, generated mainly from energy activities, industrial production processes, agricultural activities, land-use change, forestry and urban waste treatment. Among them, CO2 and N2O are mainly emitted from the combustion of fossil fuels in energy activities, CH4 from coal mining and post-mining activities, fugitive methane emissions are produced by oil and natural gas systems and CH4 is released from biomass fuels, CO2 from the production of cement, lime, steel and calcium carbide in industrial production processes and N2O from adipic acid production are the most important sources of GHG emissions. From the perspective of GHG, the increase of CO2 concentration is mainly caused by the use of fossil energy and landuse changes, while that of CH4 and N2O concentrations is mainly caused by agriculture. Table 1.1 shows the proportion of GHGs in major regions of the world in 2011. It can be seen that on a global basis, CO2 accounts for 74% of GHG emissions, followed by CH4 and N2O gases. In terms of specifc countries and regions, Japan and Brazil are special. Japan’s CO2 emissions account for 93% of the total GHG emissions, which is much higher than the world average. Brazil’s CO2 emissions account for a relatively small proportion, representing only 39% of the country’s GHG emissions. In addition, the proportion of methane is higher than that of other countries, reaching 35%, which requires additional attention in reducing GHG emissions. China’s GHG emissions are similar to those of the USA, the European Union and South Africa. Among them, CO2 accounts for 86%, followed by CH4 and N2O, and fuorine-containing gases.

Table 1.1 Ratio of greenhouse gas emissions in major regions of the world Economies World Developed economies BRICs

USA EU Japan Brazil Russia India China South Africa

CO2

CH4

N2O

PFC+HFC+SF6

74% 81% 81% 93% 39% 72% 75% 86% 82%

17% 10% 9% 2% 35% 21% 20% 9% 12%

8% 5% 8% 2% 24% 5% 4% 4% 4%

2% 3% 2% 4% 1% 1% 2% 2% 2%

Note: The EU consists of 28 member states. If there is no special remark, the same defnition applies. Source: World Resources Institute, CAIT 2.0.

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According to relevant data, the global CO2 concentration increased from 280 ppm before the Industrial Revolution to 379 ppm in 2005. The CH4 concentration increased from 715 ppb (1 ppb  =  10–9) before the Industrial Revolution to 1774 ppb in 2005, and the N2O concentration increased from 270 ppb before the Industrial Revolution to 319 ppb in 2005. It is mentioned in the Stern Review that even if the annual discharge rate remains unchanged, by 2050, the concentration of GHGs in the atmosphere will be double what they were before the Industrial Revolution, reaching an equivalent of 550 ppm of CO2. However, with investment in the construction of high-carbon infrastructure in different countries and increasing demand for energy and transportation, the rate of GHG emissions will increase rather than remaining unchanged. It is expected that by 2035, it will reach a level of an equivalent weight of 550 ppm of CO2. According to this development and gas emission levels, it is inferred that there is at least 75%, or even 99% of probability that the average global temperature will increase by over 2°C. In summary, as the most important representative of GHGs, CO2 emission quantity should have and already has become one of the targets of regular publicity by governments, scholars and the public. In view of the timescale required to remove CO2 in the atmosphere, past and future artifcial CO2 emissions will cause the earth’s warming and sea-level rise to last more than a thousand years (Qin, 2008). Therefore, it is necessary to use CO2 emissions as the primary control target to formulate strong and effective emission reduction policies and measures. If necessary measures to prevent and reduce emissions cannot be adopted as soon as possible, sustained climate warming will cause incalculable damage to social production and human life. 1.1.3 Carbon emission of major countries of the world According to the estimates of the World Resources Institute, from the beginning of the Industrial Revolution in 1850 to 2011, most of the current GHGs in the atmosphere are a result of emissions from developed countries. Table 1.2 shows the comparison of GHG emissions in major countries of the world. The USA and the European Union ranked frst and second, respectively, in cumulative emissions from 1850 to 2011. In retrospect of the development history of Western developed countries represented by the USA and the European Union, it can be clearly seen that in the early days of the Industrial Revolution, developed countries took advantage of their frst-mover advantage to take the lead in the period of rapid urbanization and modernization, and realized domestic construction of infrastructure, accumulation of social wealth and high degree of modernization of the social economy, thus entering the era of post-industrialization. At the same time, however, it also consumes a large amount of fossil energy and emits a large amount of GHGs. According to Table  1.2, China ranks third in cumulative CO2 emissions between 1850 and 2011, which is approximately 10.8% of global cumulative emissions, far less than the 27.7% and 25% of the USA and the European

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Table 1.2 Comparison of greenhouse gases in major countries of the world

China USA EU Japan India World

Cumulative emissions between 1850 and 2011

Year 2011

Emissions (million metric tons)

Rank

Proportion

Emissions (million metric tons)

Rank

Proportion

Per-capita emission (metric tons per person)

Rank of per-capita emission

140,860.3 361,300.0 325,545.1 49,858.1 35,581.3 1,304,687.3

China USA EU Japan India World

140,860.3 361,300.0 325,545.1 49,858.1 35,581.3 1,304,687.3

9,035.0 5,333.1 3,667.4 1,211.6 1,860.9 32,273.7

1 2 3 7 5

28.0% 16.5% 11.4% 3.8% 5.8% 100.0%

6.7 17.1 7.3 9.5 1.5 4.6

48 11 39 25 114

Source: World Resources Institute, CAIT 2.0

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Union. China’s modernization construction began in the late 1980s. Given that China is still in a stage of rapid socio-economic development, the rate of energy consumption and GHG emissions has increased. In 2011, China’s total GHG emissions reached 9 billion tons, positioning itself as the country with the largest total emissions, which accounts for 28% of the world’s total. China’s per-capita emissions also reached 6.7 metric tons per capita, exceeding the world average (4.6 metric tons per capita). It is expected that China’s GHG emissions will grow rapidly by 3% between 2012 and 2020. Under the requirements of eradicating climate change and controlling GHG emissions of the time, China is still confronted with the heavy task of reducing emissions. Because fossil fuel combustion is the main source of GHG production, the energy consumption sector is the central source of carbon emissions in various countries and regions. Therefore, in-depth investigation of GHG emissions at the energy consumption sector level is an intuitive and effective means to facilitate the clear direction of high-energy and high-emissions sectors and can help control the GHG emissions process and focus on the issues. Table 1.3 shows the levels and proportions of CO2 emitted by different sectors in major countries of the world in 2011. In particular, carbon emissions of the major sectors within the energy sector, including power heating, manufacturing and construction and transportation, are elaborated in detail. In terms of the total amount, China’s GHG emissions in 2011 are much higher than those of other major economies, which are 1.7, 2.5, 7.5, and 4.9 times the carbon emissions of the USA, the European Union, Japan and India, respectively. From the perspective of the sector structure of GHG emissions, the major source of emissions is the combustion of fossil fuels, accounting for more than 80% of the total. For instance, 81.4% of China’s GHGs are caused by the burning of energy. Japan has the highest proportion, which takes up as high as 99.6%. In addition, 90.3% of the US GHG comes from energy use. The proportion for the EU and India is 81.2% and 80.7%, respectively. Among the other sources of emissions, industrial production processes in China and Japan also contribute signifcantly to GHG emissions. The agricultural sector in the USA, the European Union and India is the second largest source of GHG emissions. Changes in land use and forestry can often reduce GHG emissions. For example, in Japan, its mitigation contribution has reached 11.4%. In the USA and the European Union, the fgure is also above 6%, and India’s forestry carbon sink also contributes 5.4%. China’s land use and forestry has comparatively less pronouncing effect on reducing GHG emissions, accounting for only 2.8% of total emissions. Further observation of the internal composition of energy utilization departments in various countries can also reveal the differences among countries. In China’s energy combustion and utilization, the main source of emissions comes from the electricity and heating sector, which contributes 41.4% of the country’s GHGs, while manufacturing and construction contributing 24% of the total national emission. Transportation, other fuels and

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Table 1.3 Comparison of sector emissions of greenhouse gases in major countries (2011) Emission sectors

Energy sector Electricity and heating sector Manufacturing and construction Transportation Combustion of other fossil fuels Fugitive emission Industrial production process Agriculture Wastes Land-use change and forestry Bunker fuel Total

China

USA

EU

Japan

India

Million tons CO2 equivalent

%

Million tons CO2 equivalent

%

Million tons CO2 equivalent

%

Million tons CO2 equivalent

%

Million tons CO2 equivalent

%

8,392.0 4,266.0 2,487.5 623.3 710.3 304.8 1,255.7 708.2 196.7 –292.3 47.6 10,307.9

81.4 41.4 24.1 6.0 6.9 3.0 12.2 6.9 1.9 –2.8 0.5 100

5,670.8 2,478.0 597.9 1,638.1 627.2 329.6 243.9 472.3 163.1 –415.1 148.2 6,283.2

90.3 39.4 9.5 26.1 10.0 5.2 3.9 7.5 2.6 –6.6 2.4 100

3,688.2 1,494.3 550.6 897.3 676.4 69.5 214.1 494.4 141.2 –277.8 284.5 4,544.7

81.2 32.9 12.1 19.7 14.9 1.5 4.7 10.9 3.1 –6.1 6.3 100

1,196.8 561.2 244.8 219.7 168.4 2.7 79.2 26.8 4.6 –137.1 31.4 1,201.7

99.6 46.7 20.4 18.3 14.0 0.2 6.6 2.2 0.4 –11.4 2.6 100

1,913.3 963.5 471.6 169.9 269.5 38.8 161.2 353.0 58.7 –128.1 12.6 2370.6

80.7 40.6 19.9 7.2 11.4 1.6 6.8 14.9 2.5 –5.4 0.5 100

Source: World Resources Institute, CAIT 2.0.

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fugitive emissions only take up a small proportion. The proportion of the largest emission-using sectors in other major countries is similar to that of China, with the largest contribution from the electricity and heat conversion sectors, but the emission composition of the manufacturing, construction and transportation sectors is quite different. For countries such as the USA and the European Union, the transport sector contributes more than that of the manufacturing and construction industries. Although emissions from the manufacturing and construction industries rank the second in energy-use sectors in Japan and India, emissions from transportation and other fuels cannot be taken for granted as well. 1.1.4 Comparison of energy structure of major countries It is pointed out in the IPCC report that fossil fuel combustion is the main cause of CO2 emissions from GHGs, while most non-fossil energy sources are clean-energy types, the use of which does not produce carbon emissions. Therefore, the use of energy in different regions will be instrumental in tracing the origin of carbon emissions. Table 1.4 shows the structure of energy consumption in major regions of the world in 2012, which distinguishes whether energy types are used as fossil energy and subdivides fossil energies into three categories: crude oil, natural gas and raw coal. The comparative results show that China has a prominent feature in energy utilization structure, with China’s energy consumption structure dominated by the use of raw coal. As the distribution of resources within the territory of China is characterized by “rich reserves of coal, limited oil and lack of natural gas,” resource endowment under the distribution of these natural resources directly leads to the fact that China’s energy use is strongly dependent on Table 1.4 Energy consumption structure of major countries (2012) Economies

World USA EU Japan Brazil Russia India China South Africa

Fossil fuels

Non-fossil Fuels

Coal

Oil

Natural gas

Nuclear energy

Water and electricity

Other renewable resources

29.9% 19.8% 17.6% 26.0% 4.9% 13.5% 52.9% 68.5% 72.5%

33.1% 37.1% 36.5% 45.6% 45.7% 21.2% 30.5% 17.7% 21.7%

23.9% 29.6% 23.9% 22.0% 9.6% 54.0% 8.7% 4.7% 2.7%

4.5% 8.3% 11.9% 0.9% 1.3% 5.8% 1.3% 0.8% 2.6%

6.7% 2.9% 4.4% 3.8% 34.4% 5.4% 4.6% 7.1% 0.4%

1.9% 2.3% 5.7% 1.7% 4.1% 0.0% 1.9% 1.2% 0.1%

Data sources: BP Statistical Review of World Energy 2013.

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the development and utilization of coal resources, whose dependence is much greater than in other parts of the world. The proportion of non-fossil energy used in China is less than 10%, which is quite different from those in Europe, America and Japan. India and South Africa have similar energy structures with China, which is dominated by the use of raw coal as the main energy with a relatively small proportion of non-fossil energy and a relatively high use of oil and natural gas. When the energy structure of the USA, Japan and the European Union is compared, it can be seen that energy structures of Europe and the USA appear to be more balanced. In particular, the utilization rate of non-fossil energy in the EU is higher and has exceeded the proportion of raw coal utilization, revealing that the economic development in the EU region is experiencing a relatively clean production model. The energy use structure dominated by the use of coal in China is bound to undergo changes so as to gradually eliminate high-pollution, high-emission pollution-type fossil energies while increasing the importance of greater use of clean energy. Differences in energy use structure are instrumental in understanding the possible role of various regions in GHG emissions. In particular, the proportion of non-fossil energy use will directly explain the efforts and progress made in cleaner production and sustainable development in the region. However, given the large differences in carbon emission factors of various energy sources that are burned, the internal use structure of fossil energy cannot directly relate to CO2 emissions, and further attention must be paid to the actual total carbon emission data based on the conversion and calculation of the carbon emission factors of various energy sources.

1.2 Greenhouse gases emissions and corresponding measures in China 1.2.1 Greenhouse gas emissions in China It can be seen from the historical trend of CO2 emission in China in Figure 1.1 that with the deepening of reform and opening up, China’s carbon emissions have shown a slight upward trend year by year from 1971 to 1996 with relatively stable growth rate. During the Asian fnancial crisis from 1997 to 2000, China’s carbon emissions stopped growing and fell slightly, but soon resumed growth momentum in 2001 and increased emissions at a faster rate than before, with a clear trend of continued upward growth. It can be seen that China’s carbon emissions are inseparable from the country’s own socioeconomic development and global economic environment. China is currently experiencing a period of rapid industrialization and urbanization. It is also facing the peak of energy infrastructure construction. Population growth, upgrading of consumption structure and construction of urban infrastructure will bring a huge demand for energy and continual growth of GHG emissions. Given the tremendous base in China, even the

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9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000

0

1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1,000

Figure 1.1 Aggregate CO2 emissions in China (1971–2012, unit: million tons) Source: IEA: CO2 Emissions from Fuel Combustion – HIGHLIGHTS (2014 edition)

absolute growth of small growth rates will be enormous. The annual urbanization rate of 1% in China means that tens of millions of rural people will food into cities, resulting in huge energy demand. Moreover, as China’s per-capita GDP, especially that in rural areas, is still at a relatively low level, improvement in living standards will inevitably lead to an increase in GHG emissions. The pull of these needs has created a rapid increase in energy and carbon emissions in China for a long time at present and in the future. If differences in the development of various regions in China are taken into consideration, emissions in the central and western regions will continue to grow for a longer period of time. It is pointed out in the World Energy Outlook 2007 issued by the International Energy Agency (IEA) that under the reference scenario and alternative policy scenario from 2005 to 2030, China’s primary energy demand increased at an average annual rate of 3.2% and 2.5%, and energyrelated CO2 emissions will increase by 3.3% and 2.2%, respectively, annually on average. Therefore, from a holistic perspective, the total amount of CO2 emissions will continue to rise in the coming period. Although the simple total carbon emissions can show China’s historical emissions visually, it is not enough to accurately describe the key issues in the carbon reduction process because the aggregate indicators do not consider the economic development situation and impact of the population. What kind of carbon emission indicators are adopted is also one of the major causes of disagreements in international climate negotiations. Per-capita carbon emissions and carbon intensity are considered to be more reasonable carbon emission assessment indicators because of economic output and population

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7 6 5 4 3 2

0

1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1

per-capita CO2

CO2 per GDP (exchange rate)

Figure 1.2 Per-capita CO2 emissions and intensity trend in China (1971–2010, with 1971 designated as 1) Source: IEA: CO2 Emissions from Fuel Combustion – HIGHLIGHTS (2014 edition)

factors. Figure 1.2 shows the trend of per-capita emission and carbon emission intensity (measured by the constant price of carbon dioxide per unit of GDP, calculated in 2005 dollars) during 1971–2012 in China. According to the data in Figure 1.2, the per-capita CO2 emission trend in China is similar to the total CO2 emission, which can be divided into three phases. The frst phase was the period of slow and steady growth from 1971 to 1996, the second phase was from 1997 to 2000 featuring temporary decline in per-capita emission. The third phase was from 2000 to the present, with rapid increase in per-capita CO2 emission. It can be inferred that in the post-2000 period in China, the socio-economic development and modernization are in an accelerated period, and the demand for energy resources tends to increase sharply, which is refected in the rapid rise in per-capita carbon emissions. The historical trend of China’s carbon intensity differs greatly, as it is characterized by an overall downward trend. However, the period of decline in carbon intensity in China was concentrated between 1978 and 2001, indicating that the amount of CO2 emitted per unit of GDP in China gradually decreased during this period of time. This shows that progress was made in China’s production technology and that China’s GDP growth rate was greater than the increase in CO2 emissions in these years. From 2002 to 2012, the fuctuation of carbon intensity began to be insignifcant, displaying a lack of motivation to reduce continually. Obviously due to the relatively backward production technology at an early stage of China there were few opportunities for the rapid upgrading of technology. However, as further technological upgrading required the investment of more capital and time, carbon intensity showed short-term fuctuations and stagnation in the short run.

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Despite the huge total amount of GHG emissions in China, per-capita emissions are still very low, far below that of developed countries. However, over a long period of time, the development mode of China’s economy has been characterized by extensive growth, relying primarily on the rapid and ineffcient consumption of energy and resources. Energy consumption per unit of GDP and energy of major industrial products are higher than average levels of other major energy consumers in the world. At the current stage of global industrial division of labor system, countries such as the USA, Japan and Europe have entered the post-industrial era, vigorously developing the knowledge and service economies and occupying a leading position in the global industrial value chain, while Chinese industry is still at a low end. There is a big gap between China and the developed countries in terms of industrial technology content, added value and competitiveness. The arduous task of reducing emissions and reducing consumption in China will still exist in the medium to long term. 1.2.2 International pressure of emission reduction The establishment of a fair and effective international climate governance mechanism has become one of the major issues in today’s world politics. A climate game centered on “development space” is in full swing. In September 2009, Premier Wen Jiabao, then Premier of the State Council, proposed at the UN climate change summit that China’s per-unit GDP carbon dioxide emissions in 2020 would be signifcantly lower than in 2005 (40% to 45%). Earlier, the White House announced that then-President Barack Obama would pledge at the Copenhagen Climate Change Conference that GHG emissions in 2020 would be reduced by 17% in 2005. Drawing on the historical experience of the industrialization process in developed countries and considering that China is still in the process of industrialization and urbanization, along with the advancement of China’s industrialization process and acceleration of urbanization, energy demand and consumption by various industries will continue to rise, which will inevitably lead to a continuous increase in atmospheric CO2 concentrations. If no effective measures are taken, China’s future per-capita carbon emissions and total carbon emissions will continue to increase, therefore exacerbating global climate change. According to the EU’s initiative for reducing emissions (stabilizing 550 ppm of atmospheric GHGs in 2050) and the maximum quota China can obtain in various distribution programs, China will face a gap of 4.7 billion tons of carbon emissions in the medium to long term (to 2020). In the long term (up to 2050), even if China’s economic growth slows down, the demand for carbon emissions is still rising, rendering the country a high carbon emission gap of 41.7 billion tons (Liu et al., 2008). It can be predicted that China’s total CO2 emissions and per-capita emissions are likely to continue to increase, an

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outcome detrimental to China’s participation in the international negotiations on the allocation of carbon emission reduction responsibilities. Although in the Kyoto Protocol China does not have to assume mandatory emission reduction responsibilities and the “Bali Roadmap” does not include emission reduction targets for developing countries in the negotiation agenda for the next two years, developing countries are still obliged to take actions to reduce emissions that are required to be “measured, reportable, verifable” as developed countries. Such clauses are tantamount to putting China at the forefront of the emission reduction framework. The emission reduction targets that developed countries are trying to impose on China may overlook China’s future development space and development needs, and are unreasonable regulations for they are at the expense of China’s economic development. Therefore, even though China’s historical accumulated CO2 emissions are far less than the historical accumulation of developed countries in Europe and America, limited by people’s more profound understanding of climate change currently, the depletion and arduousness of the current emission reduction potential in developed countries and the purpose of revitalizing the economy through the sale of existing advanced emission reduction technologies and environmental protection equipment to obtain new growth opportunities and to promote the real economy dragged by the global fnancial crisis and the European debt crisis, the developed countries will exert increasing pressure on China’s market with huge potential for carbon emission reduction, regardless of its current development environment and their own historical emission responsibilities. In the international arena, various conferences held to tackle climate change recently have exposed obvious political pressure from Western developed countries, which are constantly requesting the re-establishment of a new emission system that includes China and other major emitters of GHGs. In the long run, in order to establish the image of a “responsible” country and fulfll its due international obligations, China is bound to face the pressure of controlling the emission of GHGs. At the same time, it is also an inherent requirement to transform the current unsustainable economic growth model, ease the threat of energy security and safeguard the ecological environment (Liu et al., 2008). 1.2.3 Domestic emission reduction pressure in China At the same time, letting industrialization and urbanization push up China’s CO2 emission while neglecting the ecological environmental issues such as climate change caused by economic development obviously does not meet the basic requirements for China to adhere to sustainable development. Practice has proved that with the rapid development of China’s economy and the continuous increase of population, the development and utilization of energy has increased substantially, resulting in a large increase in CO2 emissions. As a result, climate change will exacerbate, which, although with no obvious

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short-term effect, will result in long-term destructive, adverse consequences for China’s ecosystem. This will impede China’s future economic stability and orderly development. First, climate change will reduce the effciency of China’s development and utilization of natural resources. As an agricultural country, China’s agricultural system will be the frst to be affected by climate change, especially those areas with restricted adaptability, adjustment capacity and extremely fragile production (Wang, 2006). Due to the effect of changes in climate warming and humidifcation, there are many uncertainties in agricultural production, such as decline in the output of agricultural products, reduction in the amount of wood harvested and scarcity of water resources. Specifc manifestations are abnormal phenomena such as early sowing of crops or prolonged growth of crops, the corresponding adjustment of some vegetation sensitive to climate change, severe drought, extremely high temperature or cold damage in certain regions and the possible expansion of the occurrence and scope of crop pests and diseases, resulting in direct economic loss and reduction in agricultural and animal husbandry production. Because agricultural development depends to a large extent on the local climate, the climate of an area determines its ecological environment. When the industrial structure, crop types and local climate are combined, rational use and effective functioning of natural resources can be achieved so that ecological balance can be maintained, and the ultimate goal of sustainable development can be further implemented. Second, climate change will lead to the disorder of China’s natural ecosystem, as refected by the changing natural landscapes and wilderness areas, shrinking distribution of rare species, decreased biodiversity, expanded scope of disease and insect pests, growing frequency of forest fres and affected areas, and grassland wetland destroyed by inland drought. As pointed out in China’s Policies and Actions in Response to Climate Change, climate change has caused changes in the distribution of water resources in China. In the past 20 years, the total amount of water resources in the Yellow River, Huaihe River, Haihe River and Liaohe River has been signifcantly reduced, inland lakes have further shrunk, wetland resources have decreased and functions have deteriorated, and the total amount of river water resources in the South has increased slightly. Flood and waterlogging disasters are more frequent, drought disasters are more serious and extreme weather phenomena have increased signifcantly. If the climate continues to warm, it may increase the trend of aridity in the northern regions, further aggravating the water shortage situation and the contradiction between water supply and demand. Furthermore, climate change will destroy the living environment in China. Due to the natural geographical location of the coastal cities, they are vulnerable to climate change. Coastal cities will face more risks of coastal fooding and will be affected by more precipitation, storms, heat, the “heat island effect” and other adverse climate change factors. Climate change will also cause some damage to the infrastructure of the city. Warming of the climate

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will intensify the decay of urban and rural infrastructure, thus putting forward more stringent requirements for the manufacture and processing of future industrial products. Under the premise that the progress of production technology requires a lot of time, this new requirement puts a lot of pressure on the industrial R&D departments that cannot meet the technical needs at the moment. At the same time, due to the speed of urbanization, be it the city layout, planning, or construction, consideration of the risks of climate change and meteorological disasters and serious shortage of infrastructure (domestic water supply, electricity supply and transportation capacity, etc.) is not compatible with the continuous expansion of the scale of cities, therefore resulting in the apparent lag in current city’s capacity for disaster mitigation and management and increasing the possibility of urban crises. In addition, climate change will also bring harm to people’s property in public life. Climate change will impact public health by increasing the chances of disease occurrence and transmission, threaten the safety of major projects by boosting the probability of geological and meteorological disasters and exert adverse impacts on cultural and natural tourism resources by damaging the natural environment and biodiversity of natural ecological parks. People may not be able to enjoy fresh vegetables that are produced locally, and daily living conditions will be affected due to the damage to basic living facilities in urban and rural areas. People’s travel may also be affected, as the increase in extreme weather will reduce travel opportunities. Many tourist destinations may decay due to meteorological disasters or reach the verge of extinction. All of these hazards are the results of long-term accumulation of GHG emissions. Although the negative effects of climate change often take decades or even longer to appear, the risks of unknown physical changes are intertwined with uncertain social and economic consequences. It will increase the degree of harm. In particular, at the current stage when China is in a critical period of economic restructuring and adjustment of industrial structure, changes in climate and environment will bring new problems and challenges to China’s industrial development. During the formulation of plans for industrial development and structural adjustment, early consideration of climate and environmental changes can avoid another major restructuring of economic structure and industrial layout, thus greatly reducing the possibility of future mistakes. 1.2.4 China’s response to climate change As a responsible developing country, China attaches great importance to addressing climate change. In 1990, a related organization dealing with climate change was established. In 1998, the National Climate Change Coordination Group was established. Since the United Nations Conference on Environment and Development in 1992, the Chinese government took the lead in organizing the formulation of China’s 21st Century Agenda  – China’s White Paper on Population, Environment, and Development in the 21st

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Century. Starting from its own national conditions, it has formulated and implemented a national plan to address climate change and adopted a series of policies and measures to address climate change. In 2004, China issued the Outline and Development Plan of Energy in the Medium and Long Term (2004–2020) (Draft) and the Special Energy Conservation Plan in the Medium and Long Term; in 2005, the National People’s Congress passed the Renewable Energy Law of the People’s Republic of China and the Notice on Doing a Good Job in Building a Conservation-Oriented Society in the Near Future, Several Opinions on Accelerating the Development of Circular Economy, Decision on Issuing and Implementing the Interim Provisions for Promoting Industrial Structure Adjustment and Decision on Implementing the Scientifc Outlook on Development Strengthening the Environmental Protection were issued by the State Council in the same year. Two binding targets were clearly pointed out in the Eleventh Five-Year Plan for National Economic and Social Development in 2006 that the two indicators of the unit GDP’s energy consumption and the total discharge of major pollutants should be reduced by 20% and 10% on the basis of 2005, respectively. To improve the management system and working mechanism, in order to further strengthen its leadership in tackling climate change, the National Leading Group for Addressing Climate Change was established in 2007, chaired by the Premier of the State Council. Through the institutional reforms in 2008, the number of members of the National Leading Group for Addressing Climate Change expanded from the original 18 to 20, responsible for organizing and coordinating the nationwide work on climate change, and have successively formulated a series of documents and plans, including China’s National Climate Change Program, energy-saving and emission reduction targets for the Eleventh Five-Year Plan, and medium- to long-term planning for renewable energy. In 2010, a coordination and liaison offce was set up within the framework of the National Climate Change Leading Group to strengthen interdepartmental coordination and improve the scientifc decision-making in responding to climate change. National Supporting Organizations for Strategic Research on Climate Change and International Cooperation Center and Climate Change Research Center have been set up successively by relevant departments of the State Council. Climate change research institutes have been established by some universities and research institutes. Leading groups and specialized agencies for dealing with climate change have also been set up in all provinces (autonomous regions and municipalities directly under the central government) in China. Through vigorous adjustment of economic structure, improvement of energy effciency, development of low-carbon energy and renewable energy, afforestation and ecological construction, and implementation of family planning policies, during the period of the Twelfth Five-Year Plan, China’s energy consumption per unit of GDP dropped by 19.1%, CO2 emissions decreased by 14.29%, chemical oxygen demand emissions decreased by 12.45% and carbon dioxide emissions were reduced by 1.46 billion tons through energy saving and consumption reduction, which contributed greatly to controlling

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GHG emissions and mitigating climate change (Xinhua Net, 2011). The related policies and implementation of energy-saving and emission-reduction in the Eleventh Five-Year Plan provide a strong foundation and policy guarantee for further strengthening China’s ability to cope with climate change. Afterwards, China’s National Climate Change Program was issued in 2007, which clarifed the specifc goals, basic principles, objectives and key areas of China’s response to climate change by 2010, deployed relevant policies and measures to address climate change, and also expounded the stance of China against climate change and willingness as well as demand to continuously promote international cooperation and exchanges. In 2008, China’s Policy and Action for Addressing Climate Change (White Paper) was released, which put forward China’s strategic goal of responding to climate change and summed up achievements made in enhancing overall social participation, international cooperation and institution building. Before the Copenhagen climate talks in 2009, China announced a target of reducing carbon dioxide emissions per unit of GDP by 40% to 45% compared to 2005 by 2020, and in the Twelfth Five-Year Plan for National Economic and Social Development released in 2011, such binding indicators as that China’s non-fossil energy accounts for 11.4% of primary energy consumption, energy consumption per unit of GDP be reduced by 16%, and carbon dioxide emission per unit of GDP be reduced by 17%, oxygen and sulfur dioxide emissions be reduced by 8%, ammonia nitrogen and nitrogen oxide emissions b reduced by 10% and the forest coverage rate increased to 21.66% were specifed for the period between 2010 and 2015. As of 2013, China’s GDP intensity of carbon dioxide emissions had decreased by 28.6% from 2005, which was equivalent to a reduction of 2.5 billion tons of carbon dioxide emissions. In addition, from a long-term perspective, China’s carbon emissions will peak in 2030–2040 (Ding et  al., 2009; The Chinese Academy of Sciences Sustainable Development Strategy Research Group of Chinese Academy of Sciences, 2009; He et al., 2008; Jiang et  al., 2009), and the China–US Joint Climate Change Statement was also approved in China as the promise to reach the peak of carbon emissions as soon as possible. Therefore, it is imperative to implement a total reduction in carbon emissions in the medium to long term. In order to cope with the urgent need for climate change and control of GHG emissions in the future, China has embarked on the overall strategic research into low-carbon development strategy and adaptation to climate change and organized the Preparation of the National Plan for Climate Change (2011–2020) as the guidance for China’s work on climate change in the next 10 years. In order to realize the unit GDP index set in the Twelfth Five-Year Plan and other related energy-saving and emission reduction targets, it is clearly stated in the Twelfth Five-Year Plan for National Economic and Social Development that “integrated application and adjustment of industrial structure and energy structure, energy conservation and energy effciency, and increase of forest carbon sinks are required to signifcantly reduce the intensity of energy consumption and carbon dioxide emission intensity and effectively control

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greenhouse gas emissions.” In 2011, the Comprehensive Work Plan for Energy Conservation and Emission Reduction of the Twelfth Five-Year Plan issued by the State Council clearly stressed the overall requirements for the need to “adhere to optimizing the industrial structure, promoting technological progress, strengthening engineering measures, and strengthening management and guidance in order to signifcantly increase energy use and reduce pollutant emissions.” In the subsequent No. 41 Document Work Plan for Controlling Greenhouse Gas Emissions in the Twelfth Five-Year Plan issued by the State Council in 2011, specifc goals and means for China’s control of GHG emissions were clarifed, including the main objective of reducing production value of carbon dioxide by 17% compared with 2010, increasing the proportion of the value added by service industry and by strategic emerging industries of the total GDP by 47% and 8%, forming the energy-saving capacity of 300 million tons of standard coal, increasing new forest area by 12.5 million hectares and forest reserves by 600 million cubic meters. In 2014, the 2014–2015 Action Plan for Energy-saving and Emission-reducing Low Carbon Development issued by the State Council clearly stated the annual target for the unit GDP of CO2 in 2014 and 2015; the Planning of National Response to Climate Change (2014–2020) printed and issued in September 2014 clarifed the basic thinking and deployment of China’s climate change response by 2020. On November 12, 2014, the China–US Joint Climate Change Statement signed by China and the US announced the strengthening of cooperation in the feld of clean energy and environmental protection. The joint statement proposed that the United States planned to achieve a full-scale emission reduction target of 26–28% on the basis of 2005 and would strive to reduce emissions by 28% by 2025. China predicted that CO2 emissions would peak around 2030 and would strive to reach its peak as soon as possible and plans to increase the share of non-fossil energy in primary energy consumption to around 20% by 2030. Both parties agreed to continue and intensify their efforts over time. China’s policy on combating climate change has risen from the initial energy-saving and emission-reduction policy to the height of national strategy with its main policies focusing on actively promoting the structural adjustment of the economic industry, optimizing the energy structure, encouraging energy conservation and improving energy effciency, and investing in additional scientifc and technological research and development. The White Paper China’s Policies and Actions in Response to Climate Change, issued in 2011, also reiterated that optimizing industrial structure is an important measure to mitigate climate change during China’s Eleventh Five-Year Plan period and proposed to continue to adopt policy adjustment and institutional innovation, promote industrial optimization and upgrading, curb excessively rapid growth of high-energy-consuming and high-emission industries, increase the elimination of backward production capacity, vigorously develop modern service industries, actively foster strategic emerging industries, accelerate the development of low-carbon technologies and product promotion, and gradually foster low-carbon based energy, industry,

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transportation and construction systems during the period of the Twelfth FiveYear Plan. The academic community also believes that adjusting the industrial structure is the primary way to mitigate GHGs and respond to climate change, and proposes some suggestions for adjusting the industrial structure, including the development of knowledge-intensive and technology-intensive low-carbon industries, the development of modern service industries, and the development and strengthening the cycle economy (Feng, 2011; Liu et  al., 2008; State Council Development Research Center on Climate Change Task Force, 2009; Wang, 2009; Bao et al., 2008). However, such research is often based on qualitative analysis, therefore lacking in-depth quantitative analysis of the effect of industrial restructuring and focused industries. China’s international cooperation in the feld of climate change has also been deepened. In March 2010, the Interim Measures for Foreign Cooperation in the Field of Climate Change Response was promulgated to further standardize and promote international cooperation in addressing climate change. Based on the principle of “mutual beneft and win–win results, and being practical and effective,” China actively promotes and invests in various international cooperations in dealing with climate change. Whether it is multilateral or bilateral cooperation, efforts have been made to promote exchanges and reciprocal trust on all sides. China has become the offcial member of the Asia–Pacifc Partnership on Clean Development and Climate, and a participant in the G8 and fve major developing countries’ climate change dialogues and conferences on energy security and climate change in major economies; meanwhile, it has set up climate change dialogue and cooperation mechanisms with the European Union, India, Brazil, South Africa, Japan, the United States, Canada, the United Kingdom and Australia to fulfll their obligations and responsibilities for responding to global climate change through various international cooperation. At the 2007 UN Climate Change Negotiation Conference in Bali, Indonesia, China put forward three proposals, including the negotiation of the emission reduction targets of developed countries after 2012 and the tangible implementation of the provisions to provide fund and technological transfer for developing countries stipulated in the Convention and Protocol, which were recognized by all parties involved and were fnally adopted in the roadmap, making substantial contributions to the formation of the Bali roadmap. In the 2009 Copenhagen conference talks, the Chinese government announced the climate statement of the Delivery of the Bali Roadmap  – The Chinese government’s position on the Copenhagen climate change conference and proposed the principles and objectives of the Chinese conference on Copenhagen to further strengthen the comprehensive, effective and sustainable implementation of the Convention as well as the clarifcation of positions by developed countries in further quantifying emission reduction targets in the second commitment period of the Protocol, therefore playing a key role in breaking the stalemate in the negotiations and promoting consensus among all parties. In 2010, during the negotiations and consultations in Cancun, Mexico, China insisted on the openness, transparency, extensive

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participation and consensus reached during the negotiation process, and put forward constructive proposals for a number of negotiating issues. In particular, China has actively discussed and coordinated with various parties in a sincere and profound manner to exchange viewpoints during the negotiation of such issues with great disagreement as the long-term global goals, the second commitment period of the Kyoto Protocol, developing countries’ international consultations and analysis to mitigate actions and emission reduction commitments of the developed countries, facilitated mutual understanding and integrated political impetus, thus making important contributions to the Cancun meeting to achieve practical results and bring negotiations back on track. In addition, China has also actively participated in the project cooperation of the Clean Development Mechanism (CDM) for developing countries proposed in the Kyoto Protocol. The CDM is still the only effective channel to transfer emission reduction funds and reduction technologies from developed to developing countries. In order to roll out the clean development mechanism projects in China in an orderly manner, the Operation and Management Measures for Clean Development Mechanism was formulated and promulgated in 2005. To improve the effciency of the development and validation of the CDM project, the management approach was revised in 2010. According to the statistics in the white paper China’s Policies and Actions to Combat Climate Change (2011), as of July 2011, 3,154 CDM projects had been approved in China, with primary focus on new and renewable energy sources, energy conservation and effciency improvement as well as methane recycling. Of these, 1,560 projects have been successfully registered with the UN Executive Board of the CDM, accounting for 45.67% of the world’s total number of registered projects, and certifed emission reduction (CER) volume of the registered projects is expected to reach approximately 328 million tons of carbon dioxide emissions per year, representing 63.84% of the world total. China has also made achievements in building scientifc and technological research capacity for climate change. The frst and second National Assessment Report on Climate Change was organized to provide practical data and reporting basis for strengthening fundamental research in the feld of climate change. At the same time, several research topics related to climate change have been carried out, such as the correlations between climate change and environmental quality, coordinated control of GHGs and pollutants, response strategies for climate change and forestry, etc., which has served as strong theoretical support for China to formulate response plans for climate change mitigation and collaborative management of climate and environment. China actively sets up a data set of future climate change trends and regularly publishes projection data of climate change in Asia. In the research and development of climate-friendly technologies, the National High-Tech Research and Development Program (“863” Program) and the Science and Technology Support Plan, Clean Energy and Effcient Utilization Technologies, Key Industrial Energy Conservation Technologies

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and Equipment Development, Key Technologies and Material Development for Building Energy Conservation, and energy-saving technologies such as key technologies and equipment development for clean production and integration of low-carbon economic development models and key technologies are the pillars of the plan. With scientifc research and investment, a number of invention patents and major achievements with independent intellectual property rights have been achieved. Through the 863 Program and the Science and Technology Support Program, projects such as research and demonstration of carbon sequestration and emission reduction technologies in key agriculture and forestry ecosystems, research and demonstration of key technologies in forestry ecological construction, and research on monitoring, early warning and regulation of major agricultural climate disasters have been established. In 2010, the number of National Engineering Research (Technical) Center and the National Engineering Laboratory reached 288 and 91, respectively. In addition, China has actively promoted research and development in the felds of renewable energy and new energy development and utilization technologies, key technologies for smart grids, providing necessary support for technological R&D projects and has also obtained many excellent results. In 2009, a large share of investment of the Chinese government’s “4 trillion” economic stimulus plan was distributed to low-carbon projects, of which 210 billion was directly used for environmental protection and reduction of GHG emissions. In the coming period of the Twelfth Five-Year Plan, China will absorb the Eleventh Five-Year Plan and its excellent experience in energy conservation and emission reduction. It is stated in China’s Policy and Action for Addressing Climate Change (2011) that China would focus on the following issues to vigorously promote related work in response to climate change: frst, to strengthen legal construction and strategic planning and formulate corresponding emission reduction targets and emission reduction paths in conjunction with the current development situation, supplemented by relevant legal provisions. Second, to accelerate the adjustment of economic structure, curb the rapid growth of high-energy-consuming and high-emission industries, introduce new high-tech industries, promote the development of modern service industries, optimize and upgrade the industrial structure. Third, to optimize the energy structure and develop clean energy, increase investment in research and development of new energy technologies, promote the development and utilization of renewable new energy, mainly wind energy and solar energy, and increase the application and production of clean coal technology. Fourth, continue to implement key energy conservation projects, and focus on promoting energy conservation in key areas and key industries such as industry, construction, and transportation, and improve the effciency of energy use. Fifth, vigorously develop a circular economy and realize the full use of resources again to achieve a mode of economic development with fewer emissions. Sixth, a low-carbon pilot project will be facilitated in solid steps to establish an industrial system characterized by low carbon, adopt a consumption model, and actively carry out pilot projects for low-carbon industrial

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parks, low-carbon communities and low-carbon businesses. Seventh, gradually establish a carbon emissions trading market and, by standardizing voluntary emission reduction transactions and trials of emissions trading rights, give full play to the basic role of market mechanisms in optimizing resource allocation and achieve GHG emission control goals with minimum costs. In addition, there are measures to increase carbon sinks, improve the ability to adapt to climate change and continue to deepen international cooperation. With full recognition of the importance of responding to climate change, China has continuously adopted various approaches and methods to actively implement commitments to address climate change. This includes not only the introduction of a series of laws, normative documents and work programs, but also implementation of relevant standards and regulations, fscal taxation and industrial policies and other powerful tools in all aspects of economic life, which lay a solid and realistic foundation for China to grow clean development and reduce the amount of CO2 emissions.

1.3 Key features of changes in China’s industrial structure Since the reform and opening up, great changes have taken place in China’s industrial structure. Figure  1.3 depicts the changing trend of the proportion of China’s primary, secondary and tertiary industries in the national economy between 1978 and 2013. It can be seen that the proportion of the primary industry in GDP shows a declining trend, which fell to about 10% by 2013. The proportion of the secondary industry has experienced a process of constant fuctuations. Before 1990, the proportion of the primary industry

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1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

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Figure 1.3 Changes in the primary, secondary and tertiary industries in China (1978– 2013, unit: %)

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continued to decline, after which it began to maintain stable increase, which returned growth slightly after the 1998–2002 Asian fnancial turmoil and 2006, but remained stable between 40% and 50% for a long period of time; the proportion of the tertiary industry is continuously rising, and in 2013, the proportion of the tertiary industry exceeded the proportion of the secondary industry for the frst time, reaching 46.1%. The proportional relationship between the three industries has been signifcantly improved, with the industrial structure changing towards the direction of rationalization. According to the forecast of the Development Research Center of the State Council, by 2020, the proportion of the primary, secondary and tertiary industries will be 6%, 45%, and 49%, respectively. It can be seen that the future changes in China’s industrial structure will continue the previous evolutionary trend: the proportion of primary production continues to decline, the proportion of tertiary industries tends to increase, and the industrial structure of heavy industrialization will tend to improve. Figure  1.4 shows the structural changes in agriculture, forestry, animal husbandry, and fsheries within the primary industry between 1978 and 2013. The overall trend of change in various sectors of the primary industry in China is: the proportion of agriculture declines, which accounted for 80% of the primary industry in 1978 and fell to 55% in 2013; the proportion of forestry is relatively stable and has remained between 3% and 5%; the share of the primary industry in fshery and fsheries rose slightly, from 15% in 1978 and 1.6% in 2013 to 30% in 2013 and 10% in 2013.

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1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

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Figure 1.4 Internal structural changes in China’s primary industry (1978–2013, unit: %)

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In the process of industrialization in China, the growth of the secondary industry plays a major supporting role in overall economic growth, while the secondary industry has achieved greater development, with its contribution rate and pull rate to national economy ranking at the top among all three industries. According to the changes in the proportion of light and heavy industries, the evolution of the industrial structure can be roughly divided into four stages (Ma & Zhao, 2008):  the frst stage was from 1978 to the mid-1980s, during which time the strategy of supporting light industry while adjusting and reforming heavy industry was adopted to address the imbalance of light and heavy industrial structures; the second stage is the balance between light and heavy industries in the mid to late 1980s and early 1990s; the third stage is 1992–1998, with the proportion of light industry in the total industrial output value remaining stable while the industrial structure shows a clear trend of heavy industrialization; the fourth phase began in 1999, when heavy industry showed a rapid growth momentum, and the proportion of light industry in industrial economy dropped from 42.2% in 1994 to 28.6%. Heavy industry accounted for 71.4% of the total industrial economic output. The gap between heavy and light industries was signifcantly widened. The heavy industrialization trend was increasingly signifcant, and industrial growth once again took the form of a heavy industry-led pattern. Figure 1.5 shows the trend of structural changes in light and heavy industries since 1994.

80 Light Industry

Heavy Industry

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Figure 1.5 Internal industrial structure changes in China’s light and heavy industries (1994–2010, unit: %)

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45 Transport and Postal Wholesale and retail Accommodation and catering Finance Real estate Others

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Figure 1.6 Internal structural changes to the tertiary industry in China (1978–2010, unit: %)

The proportion of China’s tertiary industry in the national economy has been increasing year by year, and its internal structure has undergone tremendous changes. During the initial period of reform and opening up, China’s tertiary industry was mainly concentrated in traditional industries such as commerce, food, resident services, transportation, and post and telecommunications. After more than two decades of development, while the traditional service industry continues to grow, new industries such as tourism, information, consulting, technology services, community services, fnance, insurance, real estate, education and culture have also developed rapidly. Figure  1.6 shows the trend of changes in the output value of different sectors in China’s tertiary industry since 1978. It can be seen that the wholesale and retail industry fuctuated greatly before 1990 and then slowly declined. It did not start to recover until after 2006. Although its share of the tertiary industry declined from 27.8% in 1978 to 20.7% in 2010, it still remains the sector with the largest share; the proportion of traditional transportation, warehousing and postal services has fallen sharply, from 20.9% in 1978 to less than 11% in 2010; the development of the real-estate industry continues to accelerate, accounting for a steadily increasing proportion; the fnancial industry experienced a long-term decline before 2005 and has since accelerated growth; the accommodation and catering industry has remained relatively stable, accounting for a share of between 4% and 6%; in addition, it has been classifed as “other service industries”. The proportion of emerging service industries, which are categorized as “other service industries,” including “social services,” “scientifc research, and comprehensive technology services,” “education, culture, art and radio, flm, and television industries” and “health and social welfare,” has experienced a marked increase in the

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proportion of tertiary industries. In particular, starting from the early 1990s, its proportion increased from 25% in 1990 to 38.7% in 2010, an increase of nearly 14 percentage points. From the perspective of international experience and industrial upgrading theory, after the economic level has reached a certain level, the dominant position of the secondary industry should gradually transit to the tertiary industry. Compared with other countries in the world at similar stages of development, the proportion of the secondary industry in China is relatively high. The secondary industry, especially heavy industry, is still crucial to the economy. In terms of industrial structure, the proportion of light and heavy industries is out of balance, and heavy industrialization has not emerged. The transition will continue to show an upward trend; while during the same period, the development of the tertiary industry is relatively slow and lagging behind. The main reason behind this phenomenon is that for quite a long period of time, the assessment mechanism of the Chinese government has been based on GDP and encourages enterprises to export industrial products. At the same time, local governments prefer to invest in development under the existing arrangement of fscal decentralization, especially the industry and construction industry featuring large investment and quick outcomes that can stimulate rapid GDP growth in the short term (Zhou, 2003; Zhou, 2004). The infux of investment into the industry has caused excessive capital intensifcation, which has led to a rapid decline in the capital’s marginal pay and undermined the ability to absorb employment, as well as a squeeze out of labor in capital (Liu & Zhang, 2008). In a comprehensive view, China’s industrial structure is still confronted with the following issues. First, China’s agricultural infrastructure is weak with the need to optimize its internal. Since the reform and opening up, considerable progress has been made in China’s rural economy and a sound pattern has been formed with continuous adjustments to the agricultural industrial structure. However, constrained by natural resources on the one hand, current agricultural structure still fails to satisfy the needs of the rapidly growing industrialization process in terms of total volume. On the other hand, underdeveloped technological progress and inferior quality of the varieties of agricultural products make it diffcult to satisfy consumers’ increasing demand for quality agricultural products. In addition, the backwardness of the agricultural infrastructure and the severe shortage of public services have hindered the development of agriculture in depth, as the water conservancy facilities, deep processing industry chains for agricultural products, agricultural products preservation, storage and transportation and sales systems are still incompatible with the needs for economic and social development. Second, China’s industry is large, but not strong. Although the number of industries is expanding signifcantly, the quality of growth is not high. The proportion of China’s industrial added value to GDP has exceeded the highest value of industrialization in developed countries. On the one hand, China’s insuffcient investment in industrial research and development (R&D), lack

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of independent technological innovation of enterprises, combined with the imperfect independent innovation system, has resulted in the lack of continuous technological innovation capability of enterprises, which further reduces their core competitiveness (Jiang, 2005). On the other hand, although China has integrated into the world trading system, it is still at the low end of the global value chain, relying primarily on low labor prices to obtain the title of “world factory.” Mainly engaged in the processing and assembly stages of the smile curve, China fails to shape its competitive advantages in high-valueadded links such as upstream technology R&D, raw materials, downstream brands and service networks, therefore facing great diffculties in industrial upgrading (Ma Xiaohe & Zhao Shufang, 2008). In addition, China’s industrial development model has not yet fundamentally changed, with a highinput, high-consumption, high-emission and low-proft model still existing in large numbers. In exchange for orders of low value-added export, a large amount of energy resources has been consumed, causing an increasingly prominent issue of regional environmental pollution. According to related research, in 2004, CO2 produced by China’s net exports accounted for 23% of China’s total GHG emissions in the year, while in 2006 net energy exports accounted for 25.7% of China’s primary energy consumption, which also exacerbated climate change (Jin & Liu, 2009; Chen et al., 2008). Besides, the development of China’s service industry has lagged behind, with a small total scale and unreasonable internal structure. According to World Bank data, in recent years, the service industry in middle-income countries accounts for 53%, whereas its share in high-income countries is 72.5% and it is 46.1% in low-income countries. However, the value added by the tertiary industry to China’s GDP in 2010 is only 43.1%, whereas the development of the service industry lagged far behind. From an internal structure, developed countries mainly focus on emerging industries such as information, consulting, science and technology and fnance, while China’s tertiary industry mainly relies on traditional service industries such as catering and transportation. Although the development of new service industries is relatively fast, the proportion is still low. In addition, the innovation capability of China’s service industry is insuffcient. Compared with foreign service industries, its service quality and technical level are relatively low. There is a large gap between the organization scale, management level and marketing technology, which makes it diffcult to adapt to the needs of ferce international competition (Zhou, 1998; Jiang, 2005; Ma & Zhao, 2008).

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Interconnections between industrial structure and climate change

Climate change is affected by many factors, including natural conditions and human production activities. For this reason, international agencies have conducted extensive scientifc argumentation and research on this, such as the IPCC’s Fourth Assessment Report on Climate Change (AR4) (Metz et al., 2007) and the World Bank’s World Development Report (World Bank, 2010). These studies discuss such issues as the causes of climate change, the impact of human activities on climate change and how human society adapts and mitigates climate change. At present, a consensus has basically reached in the scientifc community that climate change, or a signifcant increase in global greenhouse gas (GHG) emissions, can be attributed to the massive consumption of fossil energy since the industrialization of mankind. Therefore, in the subsequent studies, the academic community mainly focused on such issues as the “construction of a greenhouse gas distribution framework,” “infuencing factors of greenhouse gas emissions” and “what measures are taken to mitigate and adapt to climate change,” with little examination of the relationship between humanity’s most frequent industrial activities, industrial structure, energy consumption and carbon emissions. Various practical experiences show that the occurrence of climate change is intricately entwined with a country’s industrial structure.

2.1 Research on the relationship between industrial development and climate change In terms of sources, carbon emissions mainly arise from the two aspects of production and consumption. Carbon emissions from Western developed countries are mostly generated in the consumption sector, and the ratio of carbon emissions between companies and residents is close to 3:7; whereas they are produced mainly in the production sector of developing countries, which is the opposite of the situation in their developed counterparts; that is, the ratio of carbon emissions of enterprises and residents is about 7:3. The large differences in the sources of carbon emissions between developed and developing countries indicate that in developing countries such as China, the major pressure for China to control carbon emissions will lie in the DOI: 10.4324/9780429447655-3

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production sector for a long time to come. Therefore, the development model of the national industry and the status of industrial structure will become one of the important parts of future actions to address climate change, which will face corresponding adjustments and supervision. 2.1.1 Industrial activities and carbon emissions With respect to the classifcation of industries, it is more common to follow the method of division of primary, secondary and tertiary industries. Due to the special nature of production activities in various industries, different carbon emission characteristics are exhibited. First is the discussion about the characteristics of the development and carbon emissions of modern agriculture that account for the largest share of the primary industry. From a holistic point of view, modern agriculture belongs to high-carbon agriculture, which consumes a lot of fossil energy in the production process. The specifc links related to the consumption of fossil fuels in agricultural production activities include the application of chemical fertilizers and pesticides, use of agricultural machinery, and processing, storage and transportation of agricultural products. Among them, as an important part of modern agriculture, the use of chemical fertilizers and pesticides has been used in large quantities in the production process, but there are potential disadvantages of high energy consumption and large pollution. Because China mainly uses coal for energy generation in the production of synthetic ammonia (made of urea), it is estimated that about 3.4 tons of CO2 will be emitted per ton of ammonia, which is the release of CO2 from the production process of fertilizers (Qi & Chen, 2010). In addition, improper application of chemical fertilizers and pesticides will also destroy the natural organic composition of soil and accelerate the rate of mineralization of organic carbon in farmland soil, thereby releasing more gases such as CO2 and methane into the atmosphere, and creating more pressure by increasing the concentration of GHGs in the air and accelerating the process of climate change. Industries and construction covered by the secondary industry are the largest sources of carbon emissions. The development of traditional industries is always closely related to the supply of fossil fuels. Compared with developed countries that have already entered the stage of post-industrialization and engaged in transferring heavy industrialization and high energy-consuming enterprises outward with constantly cleaning and premium industrial structure, China is still in the process of urbanization and modernization. In order to support national economic development and infrastructure construction, the industry is undergoing rapid development with great demand for energy supply. In particular, heavy chemical industries such as thermal power, metallurgy, non-ferrous metals, chemicals, petrochemicals, automobiles, shipbuilding and machinery manufacturing are typical industries with high energy consumption and severe pollution, whose development will result in huge carbon emissions. In addition, the energy consumption of construction

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industry is also very high, not only because of the high consumption of cement and steel used in construction industry, but also that of the building itself. According to the statistics of the UN Climate Change Special Committee, about 0.8 tons of CO2 are released per square meter of housing area built, while the energy consumed by buildings accounts for 20.7% of total energy consumption. Tertiary industry refers to the modern service industry that consists of fnance and insurance, news media, advertising consulting and tourism communications, most of which are basically clean industries with low energy consumption, low pollution, low- and even zero-carbon emissions, except for the transportation industry. The reason why the transportation industry is characterized by high consumption and high emission is the extensive use of automotive fuels, especially the extensive application of petroleum, kerosene and diesel fuel in transportation. A car emits 2.2 kilograms of CO2 on average for every liter of gasoline burned. Per-capita energy consumed for every 100 kilometers of a bus is 8.4% of a car, while that for an electric car is 3.4–4% of a car and the fgure for the subway is 5%. It can be inferred that at present, tens of millions of private cars constitute a large energy consumer in the transportation industry, and the growing number of private cars in the future will continue to push up the level of carbon emissions in the transportation sector. With the gradual promotion of the development of a low-carbon economy with carbon emission constraints as the main feature, a new category of “low-carbon industry” has been derived on the basis of traditional industries. The low-carbon industry integrates all low- and zero-carbon industries and includes the modern service industry and knowledge- and technologyintensive industrial and agricultural industries. Similar to the traditional classifcation of the three major industries, low-carbon industries can also be subdivided into low-carbon agriculture, low-carbon industries and lowcarbon services. Low-carbon agriculture includes organic agriculture, eco-agriculture, high-effciency agriculture and forestry in crop farming; lowcarbon industries mainly include high-tech industries in biomedicine, new materials, microelectronics, aerospace and marine technology, production and supply of new energy and renewable energy (solar, wind, hydro, biomass, biogas, nuclear and many other low- or non-carbon sources); low-carbon services are other traditional tertiary industries except transportation. It can be seen that in the process of transition from traditional industries to lowcarbon industries, the key point is to move high-consumption, high-emission industries toward innovative and clean production. 2.1.2 Industrial structure and climate change After the separate elaboration on the areas and characteristics of carbon emissions within the three industries, the fnal emission effects brought about by the different combinations of the three industries need to be

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comprehensively considered. Changes in industrial structure will affect climate change by changing the amount of GHGs released, whose mechanism of impact can be explained by the “structural dividend hypothesis” theory (Denison et  al., 1967; Maddison, 1987):  there are systematic differences in the productivity levels and growth rates across industries (sectors). If CO2 is viewed as an input element, the output of CO2 emissions per unit of different sectors, or CO2 productivity (or the reciprocal of CO2 emission intensity) is also different. When sectors with low productivity or low productivity growth shift to sectors with higher productivity or higher productivity growth, the total CO2 productivity of the economies composed of various sectors will increase. When total productivity growth rate exceeds the rate-weighted sum of productivity growth of each sector, the balance is the contribution of structural changes to productivity growth. For example, service industry tends to have a higher level of CO2 productivity (less CO2 per unit of output) relative to industry. Therefore, when other conditions remain unchanged, the increase in the proportion of service industry and the decline in the proportion of industry will lead to the increase in CO2 productivity and reduction in the amount of GHGs under the same output conditions of the overall macroeconomic situation. The indicator of carbon productivity refects the environmental (carbon emission) cost to obtain a certain amount of production and provides technical indicators for measuring carbon intensity in different industries. When dealing with the issue of climate change, human society mainly focuses on the two aspects of mitigation and adaptation. Mitigation refers to human’s increase in carbon sinks, reduction of GHG emissions and slowdown of the speed and scale of climate change by altering irrational production and lifestyles; adaptation to society’s response and behavior adjustment to climate change that has occurred or is expected to occur so as to resolve possible climate risks to maintain stable and sustainable continuation and development of human society. In the process of adapting to climate change, human production, life and consumption patterns will change and result in a series of industrial changes in economic development through direct and indirect effects. The most direct impact can be demonstrated by the changing agricultural planting structure and agricultural production layout as a result of the increase in temperature, while the industrial sector and service sectors using agricultural products as raw materials will also be indirectly affected; climate change will also change water resources and ecological systems; therefore, the industrial sectors system that originally relied on these resources/ecosystems will also change in production patterns and geographical distribution. In addition to changing existing industries, in order to mitigate the infuence of climate change, new sectors will be derived and developed, such as droughtresistant crops and water-saving industries. The passive change of industrial structure is an intuitive manifestation of climate change adaptation, but only when the initiative of industrial structure changes in the economic society is fully exerted; that is, the behavioral dynamics of climate change mitigation

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is effectively stimulated and strengthens a more solid foundation for better adaptability. The reason why industrial structure adjustment with stronger initiative can produce a progressive effect in response to climate change lies in the different energy consumption of different industrial units. Therefore, the proportion of development of different industries will directly affect the total demand for energy, which in turn can exert an indirect effect on CO2 emissions. The rapid development of high-energy-consuming industries will inevitably lead to a strong demand for energy supply, and the expansion of the proportion of tertiary industry in national economy will bring about a reduction in energy consumption, which will slow down the growth of carbon emissions and even reduce carbon emissions in the long run. Therefore, under the dual pressures of ecological environment and human society characterized by carbon emission constraints, the use of industrial structure adjustment as a key way to achieve the desired climate goals has a positive stimulating effect. By controlling the excessive development of heavy chemical industry and promoting orderly expansion of the tertiary industry, the economic development model will be transformed from an industrialized to an information-based economic structure, which will effectively promote the dual reduction of energy demand and carbon emissions. However, whether it is a country or a region, its industrial structure is determined by its economic development process at the current stage, inevitably adapted to the current state of economic development and is a development process of causality. When the development of primary industry does not play a role in ensuring food security, the development base of the secondary industry in the region cannot be guaranteed; and when the development scale of the secondary industry is small, and the per-capita income level is low, the development of the tertiary industry in the region will not be able to be settled. Therefore, the overall consideration of carbon emission reduction should not be viewed only from the reduction in energy consumption and emissions, regardless of the objective law in the process of economic development. Any overriding development that derails from the actual needs of development or objective track of growth may lead to disguised and distorted economic structure, thus undermining the long-term sustainable development of real economy. Therefore, cautious consideration of industrial structure adjustment plans is necessary. While developing industries with low energy consumption and high energy effciency, the effective method to reduce energy consumption and CO2 emissions is to standardize and guide the orderly development of high-energy and high-pollution industries and eliminate outdated production capacity incompatible with market needs. 2.1.3 Industrial structure and energy utilization Because energy consumption has a direct relationship with carbon emissions, when the relationship between industrial structure and carbon emissions

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is under discussion, it is possible to simultaneously examine the possible relationships between industrial structure adjustment and utilization of energies, which may offer more specifc approaches and methods for studying the role of industrial adjustment in emissions reduction. As different energy use structures and effciency will bring about different levels of carbon emission, the relationship between industrial structure and energy structure and energy effciency can be studied to help clarify the relationship between industrial structure and energy use, therefore providing more broad ideas and methods for solving the issue of climate change. Different types of industries will cause various CO2 emissions during the production process, which mainly depends on the specifc nature of production activities of different industries and their diverse needs for various types of energy. For example, some industries tend to consume coal resources, while others use less coal resources or tend to use other types of energy. With the same unit of energy consumption, industries that consume more coal or prefer to consume coal will generate more carbon dioxide. Therefore, the former is called high-carbon industry while the latter is called low-carbon industry.1 In this regard, changes in industrial structure will directly affect energy demand and change energy consumption. By increasing the structural ratio of the tertiary industry, reducing that of the primary industry and optimizing the internal structure of the secondary industry, in particular the layout of the high-energy-consuming industries in the industrial sector to enhance the industrial structure, balanced development of China’s energy consumption can be effectively promoted, getting rid of the overriding coal consumption ratio and forming a diversifed energy consumption structure system (Peng & Bao, 2006). The available model proves that the adjustment of industrial structure can optimize energy structure and reduce the total carbon emissions (Li et al., 2005). The model is assumed to be as follows. Assume that the energy consumption structure of high-carbon industries and low-carbon industries is fxed; that is, the proportion of consumption among various energy sources is fxed. Assume that the high-carbon industry has a high-carbon energy consumption of c1, a low-carbon energy consumption of d1, and an energy structure of c1/d1. The high-carbon energy consumption of the low-carbon industry is a1, the low-carbon energy consumption is b1, and its energy structure is a1/b1. Then c1/d1>a1/b1. The total energy consumption structure is x1/y1, where x1 = a1+c1 and y1 = b1+d1. As shown in Figure 2.1, the horizontal axis represents high-carbon energy consumption, while the vertical axis represents low-carbon energy consumption. When the scale of low-carbon industries is expanded by changing the consumption of high-carbon energy from a1 to a2 and the consumption of low-carbon energy from b1 to b2; at the same time, the scale of high-carbon industries is reduced by changing the consumption of high-carbon energy c1 to c2 and low-carbon energy consumption from d1 to d2, the total high-carbon energy consumption will change from x1 to x2, the low-carbon energy consumption from y1 to y2, and the energy structure from x1/y1 to x2/y2. From

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low-carbon energy consumption

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y2 y1 b2 b1 d1 d2 a1 a2 c2 x2 c1 x1 high-carbon energyconsumption

Figure 2.1 Industrial structure adjustment and energy structure optimization

Figure 2.1, it can be seen that x2/y2 1,k < 1, then gk > 0, so: ga1 + kc1

gb1 + kd1


I1, there is I∆ < I. This indicates that decline in the proportion of high-carbon industries and increase in the proportion of low-carbon industries has led to a decline in the national energy intensity. Although only the national economy of two industries is analyzed here, for a multisectoral national economy, the mechanism of adjustment of its industrial structure is essentially the same as that of the change in energy intensity. It can be seen that the optimization and adjustment of the industrial structure will positively promote and enhance the energy structure and effciency. The expansion of the overall share of clean and highly effcient industries will be very conducive to tackling the high pollution and ineffciency of energy use. Therefore, when issues of climate change, energy conservation and emission reduction are considered, from the perspective of energy structure and energy effciency, industrial upgrading and adjustment will be an effective way to solve the issue of climate change.

2.2 Relevant models and review of evidence-based empirical studies In the previous literature, there are generally three types of research that model industrial structure and CO2 emissions, and quantitatively analyze the relationship between them: estimation based on the environmental Kuznets curve, factor decomposition based on the Kaya equation and other related research. These three types of research will be introduced below, with the review of their respective advantages, disadvantages and characteristics at the end.

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2.2.1 Theoretical model based on the environmental Kuznets curve and empirical study The frst method is the environmental Kuznets curve (EKC) based on econometric analysis and the specifc curve is shown in Figure 2.2. EKC was frst proposed by Grossman and Krueger in 1991. The basic idea is that with the increase of per-capita income, environmental quality will begin to deteriorate. However, once a certain turning point is reached, the increase in per-capita income will in turn promote the continuous improvement of environmental quality. Afterwards, extensive volume of literature attempted to test the existence of the EKC from the perspective of empirical research and related predictions. In terms of the application of the method, the use of the EKC is often estimated by a regression model. By setting the level of per-capita income as the explanatory variable, the second or even multiple items of pollutants are set as explanatory variables for model ftting and calculation of the time at which the infection point appears. The initial research on EKC focused on environmental pollution such as sulfur dioxide, dust and water pollution. As people pay attention to climate change, a large number of CO2 analyses have emerged. Some studies, under the control of per-capita income, set industrial structure variables to quantitatively evaluate the relationship between changes in industrial structure and CO2 emissions. In Figure  2.2, the vertical axis represents the number of pollutants and the horizontal axis represents per-capita income. With the gradual increase in income, the number of pollutants is also increasing. When income grows to the turning point marked by the dotted line in the fgure, the amount of contamination at this time reaches the maximum value. The continued increase in income will no longer lead to increase of pollutants. Instead, it will start to show a downward trend, resulting in an inverted U-shape of the entire curve, or the shape high in the middle and low on both ends. Tucker (1995), based on 21  years of panel data from 137 countries, examined the relationship between per capita CO2 emissions and GDP per

Pollution

Turning point

Figure 2.2 Environmental Kuznets curve

Income/ Industrial Structure

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capita, fnding that the acceleration of economic growth could slow down the rate of CO2 emissions and that energy prices had a signifcant impact on CO2 emissions. Holtz-Eakin and Selden (1995), with panel data from more than 130 countries, examined the relationship between per-capita GDP and CO2 emissions and found that there is indeed an inverted U-shaped relationship between them, but the forecast results show that even until 2100, global CO2 emissions will continue to grow. The author believes this is mainly due to the fact that developing countries will maintain high growth rates of economy and population. By using random trend terms as indicators of technological progress in his research model that included explanatory variables such as structural changes, economic growth, fuel price changes and cement prices, Magnus (2002) tested the existence of the “inverted U-shaped” curve based on the CO2 data from Sweden since 1870. Friedl and Getzner (2003) studied the relationship between the level of economic development and CO2 in small, open and industrialized countries. Based on Austrian data, it was found that the cubic Kuznets model ftted best in the 1960–1999 period. That is, there is an “N-type” curve relationship. At the same time, due to the oil price crisis, structural breakpoints existed in the mid-1970s. In addition, the regression results also indicate that the import proportion and proportion of the tertiary industry are very signifcant. The former proves that there is a “pollution paradise.” The hypothesis is that the latter indicates that structural changes have a signifcant impact on CO2 emissions. Lantz and Feng (2006) studied CO2 emissions related to fossil energy in Canada and set three explanatory variables: per-capita GDP, population size and technological progress. The data include panel data for fve regions from 1970 to 2000. The conclusions are: per-capita GDP is not related to CO2, but there is an inverted U-shaped curve relationship with the population, and there is a U-shaped curve relationship with technological progress. Therefore, technological progress and population size are the main factors affecting CO2 emissions in Canada. Although there is relatively extensive research literature on EKC in China, more attention is paid to the effect of industrial waste water, sulfur dioxide, industrial dust and other pollutants. Empirical research on whether CO2 emissions in China comply with the EKC has only begun to emerge in recent years. Based on the EKC and derivative curves, Du et  al. (2007) conducted a statistical ftting of the time series data of CO2 emissions and per capita income of China, fnding that the cubic curve equation is more ftting than the traditional quadratic curve equation. It indicates the evolutionary characteristics of N-type instead of inverted U-shape curves between CO2 emissions and per-capita income, which means that China is still on the transitional stage promoting economic development and environmental protection simultaneously and is yet to reach a stage where the two will develop in coordination. Li and Jiang (2009) predicted CO2 emissions in China based on macro-level data from the national level. The forecast results based on the EKC method show that there should be a turning point in 2020. Tao and

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Song (2010) used the sample data from 1971 to 2008 in China to apply the ARDL model to China’s carbon dioxide emissions, energy consumption, gross national income (GNI) per capita, gross national product (GNP) per capita and foreign trade dependence. Quantitative studies have been carried out on the dynamic relationships between the two countries. The results show that there is a long-term equilibrium relationship between them with both a long-term and a short-term causal relationship between energy consumption and carbon dioxide emissions. The ARDL estimation results show that percapita energy consumption explains most of the carbon dioxide emissions followed by per-capita GNI and foreign trade. Du (2010) estimated the provincial CO2 emissions during 1995–2007 and examined the infuencing factors of CO2 emissions in China based on the EKC model. The results of the study indicate that there is an inverted U-shaped relationship between the level of economic development and per-capita CO2 emissions, thus confrming the existence of the EKC, and also fnding that the proportion of heavy industry, urbanization and coal consumption share a positive impact on CO2 emissions and that the level of per-capita CO2 emissions in the previous period had a signifcant positive impact on the current period’s emissions. Li and Chen (2011) used panel data to empirically examine the drivers of carbon emission in the country and the eastern, central and western regions, fnding that population size, income level, development of the secondary industry and energy intensity all contributed to carbon emissions while the level of urbanization and the development of the tertiary industry had no signifcant infuence on carbon emissions. In addition, the EKC model fails to apply to the relationship between carbon emission and economic development of China. Long and Chen (2011) measured CO2 emission from 1953 to 2007 in China, and used a simultaneous equations model to study the two-way causality between CO2 emission and per-capita GDP. The conclusions indicate that the inverted U-shape of the simple EKC model cannot explain the relationship between CO2 and per-capita GDP in China. As per-capita GDP increases, CO2 emissions increase while improvement in energy use effciency, energy consumption structure and acceleration of capital and equipment renewal will reduce CO2 emissions. By using the panel data of 29 provinces and autonomous in 1995–2007, Yu et  al. (2011) analyzed the relationship between the intensity of carbon dioxide emissions and level of economic development and industrial structure based on the environmental Kuznets theory. Through a number of metrological tests, the feasible generalized least square (FGLS) method was chosen for estimation. The conclusion shows that there is an N-shaped relationship between carbon intensity and per-capita GDP. The proportion of the secondary industry is positively correlated with carbon intensity, i.e. the higher the proportion of the secondary industry, the greater the intensity of carbon dioxide emission. In addition, they also conducted a scenario analysis of the relationship between economic development and carbon emission, concluding with no changes to industrial structure and implementation of additional policies, economic growth rate itself cannot

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lead to a large carbon emission intensity and the target of a 40–45% drop in carbon dioxide emission intensity by 2020 can hardly be achieved. 2.2.2 Factor decomposition model based on the Kaya equation and empirical research In terms of theories and policy analysis, in order to understand the relationship between GHG emissions and human economic life at the macro level, Kaya identity is often used for analysis. The basic expressions are as follows (Kaya, 1989): C=

C E Y × × ×P E Y P

(2.5)

Among them, C is the total amount of GHG emissions caused by various types of fossil energy consumption (which can be expressed as CO2, or other related emissions), E is the total consumption of various types of fossil energy, Y is total GDP and P total population. The above formula decomposes CO2 emissions into several components:  C/E represents the coeffcient of GHG emissions from energy products and E/Y measures the input–output effciency of energy products, or energy effciency. Y/P is used to measure the per-capita output level and P is used to indicate the impact of population changes on GHG emissions. Formula (2.5) shows that the amount of GHG emissions is affected by energy structure, energy effciency, per-capita income level and population size. If the proportion of renewable clean energy increases and energy effciency is improved, GHG emissions will be suppressed. If the level of per-capita income increases and the population size increases, GHG emissions will increase as well. The fnal total effect depends on the degree of change in different variables. If a medium-term perspective is adopted to examine the status of different industrial sectors and industries, formula (2.5) can be further extended to: C = ˛ i , j Ci , j = ˛ i , j Y ×

Yi Y

×

Ei Yi

×

Ei , j Ei

×

Ci , j Ei , j

(2.6)

Ci,j denotes CO2 emissions caused by the consumption of the jth fossil energy in the ith sector; Y is the sum of outputs of all sectors; Yi is the output level of the ith sector; Ei is The total amount of fossil energy consumed by the ith sector, Ei,j is the amount of jth energy consumed by the ith department; from the perspective of industry, the relative proportion of different industries in the economy (Yi/Y) can be examined, which also indicates the infuence of industrial on GHG emissions. Therefore, model (2.6) establishes a quantitative analysis relationship between GHG emissions and industrial structure.

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There are two methods of factor decomposition:  one is Structural Decomposition Analysis (SDA) using input–output technology, and the other is Index Decomposition Analysis (IDA) based on a non-summation technique. SDA is based on the input–output coeffcient and the fnal demand of the input–output table. Its advantages are that it can distinguish between direct and indirect energy demand and can identify the range of technical and structural effects; the IDA framework uses aggregated input and output data. Although it is not possible to identify direct/indirect energy demand and technical/structural effect, the advantage of this approach is that it can be applied to aggregate data or time series data at any level. In terms of specifc applications, the IDA method is more extensive than the SDA, including Laspeyres and Divisia indexes, such as Arithmetic Mean Divisia Index (AMDI), and Logarithmic Mean Divisia Index (LMDI). The pull factor decomposition method can be regarded as a differential-based method, which perfectly applies to the thinking of factor decomposition, i.e. suppose other factors remain unchanged, determine the impact of a factor change when the decomposition of variables is dealt with. Thanks to its simple calculation, it has been widely applied. The Di-factor factorization method does not differentiate the factors directly but differentiates the time. The main drawback of the traditional pull factor decomposition method is that the method often leaves massive unexplained residual. Although there is no research to prove that the method is optimal, Ang (2004), after extensive review and comparison, found that other decomposition methods may lead to large unexplained residuals, while LMDI has path independence, no residuals, zero-value processing, summation, etc., thus is better than other decomposition methods. The LMDI method chosen in this study is a complete, residue-free decomposition method (Ang et  al., 1998) with properties of time transposition and factor transposition, and the value of variables. When there are large fuctuations, it can also maintain a stable nature (Ang, 2004; Lee & Oh, 2006). Among the empirical application of foreign scholars, Torvanger (1991) used the Divisia index to empirically analyze CO2 emissions from the manufacturing sectors of nine OECD countries between 1973 and 1987, decomposing them into four factors of fuel carbon emission factors, departmental production structure, fuel proportion and sector energy intensity, with the result showing that decline in energy intensity during this period was the main factor of CO2 changes. Alcantara and Roca (1995) used the Laspeyres index decomposition method to decompose the fossil energy-related CO2 emissions in Spain. Ang and Pandiyan (1997) proposed two methods in their study for decomposing CO2 emissions based on the Divisia index and applied them to time series data in China, South Korea and Taiwan. The results show that the energy intensity effect is the most important factor affecting CO2 intensity. Ang et al. (1998) then compared the four decomposition methods of Laspeyres decomposition, Mean Divisia Index, Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index, and applied them to Singapore’s manufacturing electricity demand, Chinese industrial CO2 emissions and

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the decomposition of CO2 emissions from Korea’s power generation sector, fnding that the expansion of production scale and decline in energy intensity during the period from 1985 to 1990 in China’s industrial sector were the main factors affecting GHG emissions. Hatzigeorgiou et  al. (2008) decomposed energy-related CO2 emissions from Greece during the period 1990–2002 using the AMDI and LMDI methods, fnding that the income effect is the most important positive factor affecting GHG emissions, while energy intensity effect is the main factor controlling CO2 emissions. Lu et al. (2007) examined CO2 emissions from cars on highways in Germany, Japan, South Korea and Taiwan during the period 1990–2002 and decomposed them with the Divisia decomposition method into fuel emission factor, vehicle fuel intensity, automobile ownership, population intensity and economic growth. The conclusion shows that high economic growth and car ownership are the main factors for the increase of CO2 emissions, while population intensity signifcantly reduces GHG emissions. Sunil (2009) examined the power generation sector in seven countries in Asia–Pacifc and North America between 1990 and 2005 and used the LMDI method to decompose the CO2 emissions of the power sector. The conclusion is that the production scale effect is the primary reason for the increase in CO2 emissions during the period and that the power generation structure had a positive impact on CO2 emissions, while energy intensity partially mitigated CO2 emissions. Tunç et al. (2009) also used the LMDI decomposition method to study the factors affecting CO2 emissions in Turkey between 1970 and 2006. The results show that economic activity is the dominant factor infuencing CO2 emissions; the industrial adjustment effect of agriculture, industry and service industry was insignifcant; and that the energy intensity effect could slow down CO2 emissions. Predicated on Kaya identity, Lee and Oh (2006) used the Logarithmic Mean Divisia method to decompose CO2 emissions in 15 countries of the Asia–Pacifc Economic Cooperation (APEC) in the two time periods of 1980 and 1998, fnding that per-capita GDP and population growth was the dominant factor for the increase in CO2 emissions in most countries. The authors also believed that energy effciency and clean energy alternatives were the most promising areas of cooperation among APEC economies. Due to the substantial infuence of China’s CO2 emissions on global climate change, many foreign scholars have paid increasing attention to China’s CO2 emission trend and its infuential factors in recent years on national, industrial, sector and regional basis. Wang et al. (2005) adopted a Logarithmic Mean Divisia index to explore energy-related CO2 emissions in China between 1957 and 2000 based on Kaya identity, fnding that the accelerated CO2 emissions in China primarily resulted from the rapid increase in per-capita GDP while a substantial decline in energy consumption intensity contributed greatly to CO2 emission reduction. Wu et al. (2005) used the complete decomposition method to study the related CO2 emissions and infuencing factors of fossil energy in China from 1985 to 1999. The decline in energy intensity and the slowdown in the average labor productivity growth in industrial enterprises

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may explain the stable CO2 emissions during this period. Liu et al. (2007) also used the LMDI method to study the factors affecting China’s industrial sector CO2 emissions from 1998 to 2005. It was found that chemical industry, nonmetallic mineral products and ferrous metal smelting industry accounted for 59% of the increase in emissions during this period with industrial production scale and energy intensity effect as the most important drivers of industrial CO2 changes, while the effect of changes on fuel structure and industrial internal structure are smaller. Ma and Stern (2008) used the Logarithmic Divisia decomposition method to study the causes of CO2 changes in China during 1971–2003, fnding that the increase in CO2 emissions from the conversion of biomass energy to commercial energy is roughly equivalent to that caused by population increase. Moreover, the effect of technological advancement and that of scale was different before and after the reform and the positive effect of population growth on CO2 was gradually attenuated. The decrease in emissions in the late 1990s and the rise before 2000 may be due to errors in statistics. Zhang et al. (2009) disaggregated the CO2 emissions from 1991 to 2006 in different periods. The results show that economic activities are the most important positive factors affecting CO2 emissions, while the continuous improvement of energy effciency has reduced the amount of CO2 emissions. The effect of industrial restructuring is relatively small. Zha et  al. (2009) selected 36 industrial sectors as the research object and used an adaptive Divisia Decomposition method and an LMDI Decomposition method based on the statistics of industrial added value and end-use energy consumption between 1993 and 2003, fnding that the industrial structure effect gradually decreased before 1998 and remained stable after that. The energy intensity effect gradually attenuated throughout the study. The largest sectors in terms of industrial structure effect and energy intensity effect are electrical equipment manufacturing and chemical raw materials and products industry, while the sectors with the least impact are natural gas production and supply, petroleum processing and coking. Zha et  al. (2010) studied CO2 emissions caused by residential consumption in urban and rural areas of China during 1991–2004 and decomposed them using the Logarithmic Mean Divisia method. The conclusions show that the energy intensity effect is the most important factor in the reduction of CO2 emitted by urban and rural residents, while the income effect is the most important factor in the increase of emissions. In urban areas, the population effect has a positive driving effect on residents’ GHG emissions with incremental increase in its effect, while in rural areas, the population effect has started to decline since 1998. Steckel et al. (2011) decomposed China’s CO2 emissions based on the Laspeyres exponential decomposition method, fnding that the increase in emissions due to economic growth could be partially offset by the reduction in energy intensity during 1971–2000. However, great changes occurred between 2000 and 2007 which gave rise to the increase in carbon intensity of energy, which led to more rapid emission growth. In addition, by simulating China’s future energy intensity and CO2 emission intensity trend, they

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found that the corresponding emission reduction targets proposed by the Chinese government are consistent with their research conclusions. Zhang et al. (2011) examined provincial CO2 emissions in China from 1995 to 2009 and discussed potential strategies for future emissions reductions. The results showed that the continued increase in GDP led to increased CO2 emissions while the reduction in energy intensity signifcantly reduced emissions. Economic restructuring has become highly carbonized, increasing GHG emissions and reducing the effect of the optimization of energy structure on CO2 emissions. Based on the Input–Output Table of China from 1992 to 2005, Peng and Shi (2011) used the SDA to decompose the growth of carbon emissions into carbon intensity effect, technological effect, domestic demand effect and trade effect. The results of the study showed that there was an accelerated increase in carbon emissions, which is primarily the result of domestic demand rather than trade. The intensity of carbon emissions has a great impact on the control of CO2, which is mainly due to improvement in energy effciency rather than substitution between energy products and technological progress. This has led to an increase in CO2, refecting the fact that the technological structure is moving toward high energy consumption and high emissions. In the relevant literature of domestic scholars, Lin and Jiang (2009) analyzed the contribution rate of per-capita CO2 emissions between 1990 and 2007 based on the Kaya identity by using the LMDI method, fnding that the three factors of per-capita GDP, energy intensity of carbon structure and carbon structure had an important impact on China’s per-capita CO2 emissions, among which per-capita GDP and energy intensity had the greatest impact, while the impact of carbon structure was relatively small. Wei and Xia (2010) used the Logarithmic Divisia Mean method to decompose the per-capita CO2 emissions of 108 countries from 1980 to 2004 and summarized seven different emission models. The study on China revealed that economy growth effect and energy intensity effect were two major factors affecting China’s per-capita CO2 emissions, while the contribution of energy structure and carbon emission coeffcient effect was relatively small. Wang et al. (2010) also adopted the LMDI method to decompose China’s CO2 into 11 kinds of effect during 1995–2007. The main positive driving factors are per-capita GDP, number of vehicles, total population, economic structure and average household income, and negative driving factors consist of the energy intensity of the production sector, the average transport length of vehicles and the energy intensity of the residents’ lives; among them, growth of per-capita GDP is the largest driving force for CO2 emissions increase, and the decline in energy intensity of the production sector is the most important factor suppressing the growth of CO2 emissions. Lin and Sun (2011) used Kaya identity to decompose CO2 changes into six types of effect: income, urbanization factors, energy intensity, industrial structure, energy structure and population factors to decompose the historical CO2 emissions and predicted the scenario of CO2 emissions of 2020, fnding that income effect is the main factor

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leading to the increase of carbon emissions at this stage, followed by the role of urbanization factors. The energy intensity factor has the most obvious role in carbon emission reduction, and in terms of industrial structure factor, current economic development (i.e. urbanization and industrialization process) is not conducive to China’s emission reduction of carbon dioxide. In addition, combined with economic growth and carbon emission analysis, it is predicted that carbon dioxide emissions per unit of GDP could fall by 43.15% in comparison with that in 2005, a conclusion basically consistent with the target proposed by the Chinese government. 2.2.3 Other measurement and statistical research The third type of research is based on some statistical or quantitative regression methods. For example, based on the panel data from 110 countries, Galoeotti and Lanza (1999) conducted in-depth study of the relationship between percapita CO2 emissions and per-capita GDP, and based on this forecast, they found that CO2 and income levels were not common between a linear and log-linear function relationship, and the use of gamma function and Weibull function can better ft the relationship between the two. Garbaccio et al. (1999) established a dynamic computable general equilibrium model to examine the effect of carbon tax on CO2 emission reduction in China and carried out scenario simulation and analysis of different assumptions. Brannlund and Ghalwash (2008) used Swedish family-level cross-sectional data to study the relationship between income and pollution. The study found that the distribution of income had a certain impact on pollution emission. When the average income distribution remained the same, the more average the income distribution, the greater the pollution emissions. Based on non-parametric measures, Auffhammer and Carson (2008) added various explanatory variables such as capital adjustment rate, energy consumption structure and industrialization level to the model, and quantitatively evaluated the infuencing factors of CO2 emissions. In the study of China’s CO2 emissions and infuencing factors, besides the environmental Kuznets model and the factor decomposition method, Chinese scholars have also taken other methods to conduct related research, such as Tan et al. (2008) who measured the CO2 emissions of China’s industrial subsectors from 1991 to 2005, and used the gray correlation method to analyze the relationship between the industry’s carbon emissions and industrial development. The results show that the largest carbon emissions are in the heavy industry, such as ferrous metal smelting, non-metallic minerals manufacturing, chemical raw materials and electricity, while the leather industry, pharmaceutical industry and plastic products have relatively low emissions; there is a close link between the industrial output value and carbon emissions. And the different industrial structure has a great impact on carbon emissions. In the future policy design, industries with large carbon emissions per unit of GDP and slow carbon emissions reduction should be limited, such as

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petroleum processing and coking, electricity and extractive industries; Chen Shiyi (2009) used 38 double-digit industries of China’s industry from 1980 to 2006, capital, labor, energy and CO2 as input factors, total industrial output value as output, constructed a subindustry’s Translog production function and estimated Chinese industry. Total factor productivity based on energy consumption and emission constraints, analysis of energy consumption and carbon dioxide emissions through China’s green growth accounting impact of change and sustainable development. Duan Ying (2010) used the advanced level of industrial structure to quantitatively measure the industrial structure characteristics and used the data of Hubei Province from 1980 to 2008 to perform quantitative analysis. It was found that the change of industrial structure was the Granger cause of CO2 emission intensity. Liu Zaiqi and Chen Chun (2010) selected panel data from seven countries from 1990 to 2004 and used Seeming Unrelated Regression to perform a quantitative regression analysis of industrial structure adjustment and CO2 in each country. The results show that the impact of the frst, second and third industries in different countries is not the same. For China, the development of the tertiary industry will reduce CO2, so it is necessary to vigorously develop the tertiary industry, reasonably select leading industries and accelerate industrial upgrading. Xu Dafeng (2010) estimated the CO2 emissions of 24 industrial sectors in 22 industrial sectors and agriculture and service industries in Shanghai in 2007. Based on the input–output tables, the industry’s industrial infuence coeffcients and carbon impact coeffcient were calculated. It is suggested that the content of industrial restructuring should be those industries with small infuence coeffcients and carbon impact factors. Adjusting these industries will not only help reduce carbon emissions, but also will not much affect the economic growth and stability of the national economy. The conclusions show that gas production and supply, construction, metal smelting and rolling processing, industries with high industry infuence coeffcients and small carbon impact factors need to be cautious, including the textile industry, petroleum processing and the coking industry. Chen and Qi (2010) studied the relationship between agricultural GHG emissions and industrial structure, using the 1990–2008 rice planting area, the number of ruminants, the number of live pigs and the return of agricultural methane. The results showed that agricultural methane has the greatest impact. In addition, pig farming has a signifcant impact. Xu (2011) estimated the CO2 emissions of 27 industries (including agriculture and service industries) in 2007 in China and calculated the coeffcient of infuence of each industry and the carbon impact coeffcient of each industry according to the input–output table. The relative value is divided into four quadrants. It is proposed that industries with small industrial impact coeffcients and large carbon impact factors should be the focus of attention for industrial restructuring, which cannot only effectively reduce carbon emissions, but also affects associated industries and nationals. The impact of the economy is relatively small, including gas production and supply, non-metallic minerals and petroleum processing coking. In addition,

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the service industry’s carbon emission impact coeffcient is small and the industry impact coeffcient is large, so we need to vigorously develop the service industry. Chen (2011) measured the high-level change index of China’s industrial structure by adopting the Moore structural change index, and used the Granger causality test, impulse response function and variance decomposition method to propose that the advanced industrial structure in China is the cause of carbon emissions fuctuations. The industrial structure effect of higher levels on the volatility of carbon emissions is stable and there is a strong lag effect. Therefore, an advanced industrial structure is the only way for China to achieve a low-carbon economy.

2.3 Review of different methods The above three methods for studying GHG emissions and infuencing factors have their own advantages and disadvantages. When applied in practice, they often depend on the researcher’s research intentions, data characteristics and other conditions. The features and advantages and disadvantages of the EKC model and the factorization model are reviewed here. The correlation between the two is shown in Table 2.1. The EKC curve itself is an assumption based on empirical data observations, so its theory is often questioned and criticized. Some scholars believe that CO2 is not the same as other pollutants and does not have negative environmental external surroundings. Therefore, the EKC of CO2 may not exist; and some scholars believe that there are many faws in the econometric analysis that was used in EKC research before. The infection point and emissions are also questionable (Richmond & Kaufmann, 2006; Wagner, 2008). In the empirical application, the EKC model mainly selects per-capita income levels at the national or regional level to explain CO2 emissions. The model generally includes the per-capita income level and its quadratic or cubic terms, which can often increase the sense of other researchers in the measurement regression model. The explanatory variables of interest, such as industrial structure variables, population density, urbanization proportion, etc., but the conclusions are often quite different. Some research conclusions support the traditional environmental Kuznets’ inverted U-shaped curve hypothesis (Holtz-Eakin & Selden, 1995; Du Limin, 2010) and some indicate that there is a more complex N-type curve relationship between income levels and GHG emissions (Friedl & Getzner, 2003; Du Tingting et al., 2007; Yu Yihua et al., 2011). In addition, some studies have found that there is no intrinsic correlation between per-capita income level and CO2, i.e. there is no EKC (Lantz & Feng, 2006; Li & Chen, 2011). The difference in these conclusions is mainly due to the fact that the sample data and estimation methods chosen by the researchers are not the same, and whether controlling other variables in the regression model will also have an impact on the results. As the model setting in most of the studies has not been recognized, it may lead to errors (Auffhammer & Carson, 2008).

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Table 2.1 Comparison between EKC model and factor decomposition model Research target EKC model

Methods

Relationship Measured between regression income level and CO2 emission

Decomposition CO2, perof Kaya capita identity CO2, CO2 emission intensity

Factor decomposition

Application

Main conclusion

Infuence of industrial structure

State/ regional level data

Partially The proportion supports the of the tertiary “inverted industry U-shaped” signifcantly curve reduced hypothesis, the CO2 and emissions; the partially proportion of fnds the the secondary “N-type” industry curve and heavy industry had a signifcant positive impact State/region, There is The industrial industry/ generally structure sector positive is one of data effect on the factors output that slow scale, down CO2 negative emissions, effect on but the effect energy is less than intensity the energy and intensity industrial effect structure

Because the EKC model uses a measurement equation for estimation, it can increase the corresponding industrial structure variables to quantitatively examine the relationship between industrial structure and CO2. Most of the existing research results are in line with expectations, such as the tertiary industry signifcantly reducing CO2 emissions (Friedl & Getzner, 2003), while the development of the secondary industry and the rising proportion of heavy industries have a signifcant positive impact on CO2 emissions (Li & Chen, 2011; Du Limin, 2010). However, using the EKC model to analyze the industrial structure has a potential faw; that is, the model can only be used for the study of the macroscopic level, such as the structural adjustment of industries in January and February. If a more detailed examination of other industrial subsectors is required, it may be due to excessive settings. Subsector dummy variables consume the freedom of the model and reduce the explanatory power of the model. The factor decomposition method based on the Kaya equation is mathematically consistent. The biggest advantage is that it is simple and

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straightforward. It can accurately calculate the absolute contribution of different infuencing factors. However, because the decomposition process has a fxed formula, some important infuences are often ignored, so the analytical capacity is limited. If different decomposition methods are used, some decomposition residual items may be generated, thus affecting the accuracy of decomposition. The factor decomposition based on the Kaya equation can examine the total CO2 emissions and CO2 or CO2 emission intensity per capita. The data are mainly based on the aggregate data of countries (regions) or industries (departments). The infuencing factors generally include output scale effect and industrial structure. Effects, energy intensity effects, energy structure effects, etc., and some studies have extended Kaya equations to get more decomposition factors, such as urbanization effects, renewable energy effects, etc. There are more options for decomposition methods. The structure decomposition method or exponential decomposition method can be used, in which the exponential decomposition method mainly includes Lagrangian exponent decomposition and Dee index exponential decomposition, and the decomposition form can use additive decomposition or multiplicative decomposition, but at present, the logarithmic average Dickers’ decomposition method is the mainstream, which is characterized by the fact that the method has no residual error and can guarantee the accuracy of decomposition. In research conclusions, most studies found that income effects or production scale effects have a signifcant positive relationship with GHG emissions. Energy intensity effect is the main factor in slowing down CO2, while the impact of energy structure is relatively small (Ang et al., 1998; Wang et al., 2005). The research conclusions on industrial structure and CO2 emission are different because of the research of the country. In the research on China, most found that changes in the industrial structure can slow down the emission of CO2, but in terms of the relative energy intensity effect, the industrial structure GHG mitigation effect is relatively small (Liu et al., 2007; Zhang et al., 2009; Wei Chu & Xia Dong, 2010). Even at certain times, the industrial structure has a “high carbonization” trend, which makes the industry not conducive to reducing GHG emissions and further aggravates CO2 emissions (Zhang et al., 2011; Lin & Sun, 2011). From the comparison of the two types of models, the factorial decomposition method based on the Kaya equation is more widely used, and the logarithmic mean Dicks decomposition method is the most popular, which is due to the fact that the model itself has included the industrial structural factor term and can accurately capture the changes in the industrial structure caused by changes in CO2 emissions. As the purpose of this book is to examine the internal correlation between changes in industrial structure and CO2 emissions, subsequent empirical research will mainly use the factor decomposition method based on the Kaya equation. In the decomposition method, the logarithmic average decomposition method is mainly used.

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2.4 Conclusion Theoretical explanations and a large number of empirical studies have confrmed that changes in industrial structure are one of the key reasons for changes in CO2 emissions. Changes in industrial structure will affect the overall number and size of carbon emissions through changes in carbon productivity. Therefore, when the proportion of industries with lower carbon productivity increases, their energy use tends to be cleaner and more effcient, they can effectively reduce carbon emissions, and provide practical and feasible means and methods for mitigating climate change. At the same time, it was also found that the “high-carbon” industrial restructuring that occurred in certain periods did not promote the reduction of GHG emissions, or increase the economic ineffciency in the adjustment process of the industrial structure. Therefore, when carrying out industrial restructuring to cope with climate change, it is necessary to comply with the basic laws of industrial development and fully consider the fundamental requirements for the stability of economic growth. Even if there are signifcant differences in carbon productivity in different industries, it does not mean that industries with low carbon productivity need to become targets for industrial restructuring. It is necessary to comprehensively consider the carbon impact and industrial infuence of each industry in order to reduce carbon emissions and help mitigate climate change.

Note 1 Compared with the energy structure of industries in developed countries, industries in China mainly belong to high-carbon industries. However, in terms of domestic comparison, industries in China can be divided into high-carbon and low-carbon industries.

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Practical implications of China’s response to climate change

The issue of climate change is gradually evolving into another severe challenge encountered in the sustainable development of human society. Different climate policies have a structural impact on different industries in different regions and may bring serious political, economic and social problems. Heads of state, experts and scholars of various countries have paid close attention and are actively involved in various research and policy practices in response to climate change. According to statistics released by the International Energy Agency in 2009, the amount of CO2 emissions from fossil energy use in various countries shows that China’s total carbon emissions have surpassed that of the USA and have become the world’s No. 1 carbon emitter. This result indicates that China will face more public opinion and institutional pressure in international negotiations on climate change in the future, so it is imperative to practice low-carbon development. At home and abroad, different countries and regions have already tried to explore low-carbon development. Therefore, the development of low-carbon models at home and abroad should be summarized in a timely manner so that we can learn from their successful experiences and avoid repetitive detours.

3.1 Low-carbon development path of developed countries Although the developed countries also started to pay attention to and acted on climate change after completing their respective industrialization development, the attitudes and methods of countries in responding to and implementing climate change actions are quite different (Peng Shuijun & Zhang Wencheng, 2012):  the United Kingdom is one of the key leaders in climate change and was the frst country to advocate a low-carbon economy. The introduction of emission reduction measures is basically covered in all aspects; the EU mainly adopts market transactions to achieve greenhouse gas (GHG) emission reduction targets; in Japan, the low-carbon society is regarded as the future development direction and the long-term goal of the government; while the United States’ climate change policy is affected by the infuence of the group of its domestic interests.

DOI: 10.4324/9780429447655-4

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Table 3.1 Emission reduction targets of some developed countries by 2020 and 2050 Country/ region

Reduction target by 2020

Converted to 1990 base year target

2050 target

UK EU Japan USA

34% based on 1990 20–30% based on 1990 25% based on 1990 17% based on 2005

–34% –20% to –30% –25% –4%

80% 80–90% based on 1990 60–80% based on 2005 83% based on 2005 in comparison to 80% of 1990

Source: Wang & Zheng, Chief Editor, Report on Climate Change (2011) – The Diffcult Position of Durban and China’s Strategic Choices, Social Sciences Academic Press.

In view of their specifc national conditions, after the Copenhagen conference, 41 Annex I countries submitted medium-term quantifed emission reduction targets by 2020. The medium- and long-term emission reduction targets for the UK, EU, Japan, and the USA are shown in Table 3.1. It can be seen that in the comparison of the United Kingdom, Europe, Japan and the USA, the USA has made the least effort to implement carbon emission reductions, while the United Kingdom has most faithfully put forward rigorous carbon emission reduction targets that need to be achieved. The EU and Japan also actively perform their duties, putting forward more rational emission reduction targets. Based on the emission reduction targets put forward by various countries, there must be a feasible path to achieve them. China needs to systematically study the practical experience of these countries in responding to climate change and can only use reference and balance when formulating relevant policies. 3.1.1 Initiatives of the UK The United Kingdom, as the Western country that took the lead in guiding and participating in the Industrial Revolution, realized the scarcity and high emission of energy, and so began various studies in the feld of clean energy very early. When climate change received more and more international attention, the United Kingdom took the lead in establishing a good image of green energy conservation and implemented a series of legal measures and fscal policies to promote domestic GHG emission reduction activities, industrial structure energy-saving upgrades and low-carbon emissions from energy use. At the same time, the United Kingdom has also acted as a frm leader in the international response to climate change, and has led the organization and actively participated in various negotiations on carbon emissions issues and international negotiations.

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3.1.1.1 Early understanding In 2003, the UK put forward the concept of building a Low-Carbon Economy (LCE) for the frst time in the Energy White Paper “Our Future Energy  – Creating a Low-Carbon Economy”. The low-carbon economy describes such an economic system of less resource consumption and less environmental pollution to obtain more economic output, which is refected in minimizing the consumption of high-carbon energy sources such as coal and petroleum so as to achieve the result of low energy consumption and low emissions. The British government has also published various research reports and publications on climate change. The Stern Report:  Economics of Climate Change, Climate Change: Planning in the UK 2006 and some of the following annual climate publications have been infuential. The United Kingdom has made many “world frsts” in its response to climate change and carbon reduction: the UK is the frst country to levy a climate tax, and it is also the frst to meet GHG emission reduction targets and the “carbon budget.” 3.1.1.2 Form of taxation Britain began to collect climate tax in 2001 and was the frst country to introduce this tax. The specifc operation of the climate tax is as follows. In addition to residential electricity and transportation, each energy-consuming unit (industrial, commercial and public sectors that consume energy products for fuel purposes) must pay 15 pence of climate tax for each kilogram of electricity consumed. If you use clean energy such as renewable energy, you can get tax deductions. After the introduction of this tax, the United Kingdom can collect about 1 billion pounds of sterling tax revenue each year and use tax revenues to invest in and reduce GHG emissions. The UK’s Ministry of Finance has also introduced a climate tax reduction system that matches the climate tax: according to the principle of voluntariness, business owners and the Ministry of Finance have signed an agreement and approved annual pollutant reduction targets, which can be reduced by 80% as scheduled. The implementation of the climate tax and its related supporting measures has achieved very satisfactory results. Many companies in the UK have signed agreements with the Ministry of Finance, especially large-scale enterprises, and many companies have even fulflled their emission reduction missions for the entire country. These bring a huge positive effect. Obviously, the climate tax introduced by the United Kingdom is a wise move to reduce pollution, promote renewable energy and protect the ecological environment. 3.1.1.3 Form of taxation On November 26, 2008, the British Parliament passed the Climate Change Act, which stipulated in the form of laws the goals and specifc work of

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the British government in reducing energy consumption and reducing CO2 emissions, making the UK the frst in the world to establish a legally binding country for reducing GHG emissions and responding to climate change. The Climate Change Act consists of 101 articles and consists of six major parts. They have provisions concerning carbon emissions targets, carbon budgets, climate change committees, emissions trading systems, climate change adaptation and other supplementary provisions. To achieve emission reduction targets, the Climate Change Act requires the British government to establish a “carbon budget,” specifcally the carbon emissions in the corresponding cycle determined to maintain a balance between CO2 emissions and natural ecological capacity. At the upper limit, the economic sectors including energy, transportation and housing are all within the carbon budget. On December 1, 2008, the British Climate Change Committee created under the Climate Change Act offcially became a statutory committee and submitted its frst report:  “Creating a Low-Carbon Economy  – The UK’s Greenhouse Gas Mitigation Roadmap.” The report elaborated on the 2050 GHG emission reduction targets, methods and paths in the UK, and proposed a future emission reduction roadmap under the framework of three fve-year carbon budgets from 2008 to 2022. In April 2009, the UK Treasury Department announced that it would establish a “carbon emission budget” starting in 2009. It would arrange relevant fscal budgets according to the carbon budget so that it can be applied to all aspects of the economy and society, and fully support various aspects of GHG emission reductions. In July of the same year, the “Low Carbon Conversion Plan in the UK” and the “Renewable Energy Strategy in the UK” were issued, marking the UK becoming the frst country in the world to have a special carbon emission management plan within the framework of the government budget. 3.1.1.4 Form of technologies In addition, the British government promulgated supporting reform programs in various industry sectors, including the UK’s renewable energy strategy, the UK’s low-carbon industrial strategy and low-carbon transportation strategy. At the same time, the British government also actively supports green manufacturing, researches and develops new green technologies, leans towards lowcarbon industries from policies and funding and adds hundreds of millions of pounds of investment to industries related to low-carbon economy. New technologies such as capture and clean coal are in the leading position. In the UK’s 2009 budget, an additional 1.4 billion pounds of funds will be invested in low-carbon economy, including the following major aspects:  525  million pounds to support offshore wind power projects; 375 million pounds to enterprises, public buildings and families to increase the effciency of the use of energy and resources; 405 million pounds for the development of low-carbon supply chain industries such as wind and marine

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energy technologies and renewable energy technologies; 60 million pounds to support carbon capture projects; 70 million pounds to support the development of low-carbon economy in small-scale communities. It can be seen that the R&D and production of new energy are the core of the task of reducing emissions in the UK, and the use of wind energy is a major focus in the use of new energy in the UK. According to the plan, renewable energy sources will account for 15% of the energy supply by 2020. Of this, 30% of the electricity will come from renewable energy sources. The corresponding GHG emissions will be reduced by 20% and the oil demand will be reduced by 7%. In terms of housing, the UK government allocates funds:  3.2 billion pounds was used for energy-saving housing reforms, and for those who actively install clean energy devices in their homes. In terms of transportation, the carbon dioxide emission standards for new-production vehicles should be reduced by an average of 40% on a 2007 basis. 3.1.2 EU’s collective activities So far, the overall attitude of the European Union in dealing with climate change issues is relatively positive. Similar to the United Kingdom, has also taken the role of a leader in international climate negotiations and is one of the main forces in promoting climate negotiations. The industrialization of the European Union has meant it has become involved very early in the feld of energy conservation and environmental protection technology and has achieved many outstanding results and experiences. After the issue of environmental climate has risen as a hot topic of concern to the international community, the EU strongly hopes to use its frst-mover advantage in the feld of environmental technology to enrich its own strength through the teaching and selling of environmentally friendly products and technologies to grasp the dominance of global environmental governance. 3.1.2.1 Early understanding As early as June 2000, the European Union launched the EU Climate Change Program (ECCP), which aims to formulate the most cost-effective policies for the reduction of GHG emissions within the jurisdiction of the European Union. The plan is organized by the European Commission and launched. Various sectors, non-governmental organizations, multinational experts and other related parties participated in the project, involving energy, transportation, research, agriculture and the three fexible operating mechanisms stipulated in the Kyoto Protocol. In 2002, the EU’s 15 member states collectively adopted the Kyoto Protocol, which limits global GHG emissions. The EU promised that its emissions levels will be reduced by 8% between 2008 and 2012 relative to 1990 levels. Based on this, the EU has actively adopted a series of action plans and policy measures to develop emission reduction programs suitable for the economic

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development status and carbon emission levels of different member states and establish a GHG emission restriction system; and within each member state, a highly transparent and detailed national allocation plan has been formulated, specifc to different industries. In each of the companies involved, the company’s emission reduction activities are encouraged by various policies to achieve the ultimate goal of reducing GHG emissions. 3.1.2.2 Form of trades The carbon emissions trading scheme expanded from the EU is currently the world’s largest GHG quota trading market. The concept of “greenhouse gas emissions trading (ETS)” began in 1997 with the Kyoto Protocol and is one of the three mechanisms proposed in this book to reduce GHG emissions. This transaction mechanism was frst applied to the frst emission of carbon dioxide in GHG emissions, and the European Union has become a pioneer in this mechanism of transnational practice. The EU is trying to promote the establishment and operation of the carbon trading market, attempting to construct an EU emission trading system, building an EU-wide emissions trading market, and meeting the emission reduction targets in the most costeffective manner, making full use of market mechanisms. Its advantages lie in ensuring the normal functioning of market functions and preventing the negative impact of the fragmented domestic emissions trading scheme. The European Union has established a unifed ETS mechanism for its member states on the basis of the basic establishment of its carbon trading system. On January 1, 2005, the EU-ETS, the EU’s GHG emissions trade plan, was formally launched and then covered the EU’s 25 member states. The plan stipulates the countries’ mandatory emission reduction targets and conducts assessments in two phases of 2005–2007 and 2008–2012. If the task of emission reduction cannot be completed, the frst phase will be subject to a fne of 40 euro per ton of carbon dioxide. In the second phase, it will face a fne of 100 euros, and the fne will not offset the emission reduction obligations. In the second phase of the EU-ETS implementation, the member countries are allowed to participate in transactions through emission reduction credits obtained from emission reduction projects in developing countries and countries with economies in transition and are linked to Japan and the USA to form an open carbon emission system. The trading market has developed into the world’s largest GHG quota trading market. 3.1.2.3 Form of taxation In terms of taxation, some EU countries have adopted carbon taxes to control and reduce GHG emissions. The carbon tax is a new taxation product under the infuence of climate change in recent years. It is a tax type for carbon dioxide emissions. It has passed a ratio of carbon contained in fossil fuels such as coal, petroleum processed products (gasoline, jet fuel, etc.) and

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natural gas. The levy of carbon tax will increase the market price of fossil energy products, thereby promoting enterprises to economize on the use of resources, improve the effciency of energy use, increase the competitive advantage of non-fossil fuels in price to a certain extent and promote the use of non-fossil energy. Moreover, unlike the systems and measures required for emission reduction mechanisms such as total carbon emission control and carbon emissions trading, the collection of carbon taxes can only be achieved on the basis of the existing taxation system, and an additional small amount of management costs can be achieved. In the early 1990s, Finland, Sweden, Denmark, the Netherlands began to levy a carbon tax. The tax rate was levied on CO2 emissions or equivalent emissions basis and was collected through the fnal use of energy. In terms of tax rate setting, in 2008 Finland levied 20 euros per ton of CO2 emissions, Sweden 107.15 euros, Denmark’s standard CO2 tax rate was 12.10 euros, but different rates were applied to residents and businesses, and the residents’ tax rate was higher than the corporate tax rate. The proportion of carbon tax revenue in the four countries is about 0.4–0.7% of GDP and carbon tax accounts for about 1% of tax revenue. In terms of the use of carbon tax revenue, Finland’s carbon tax revenue is considered as general revenue, and in Denmark all the carbon taxes paid by residents are used to subsidize public natural gas and electricity heating systems. The carbon taxes paid by enterprises are used to reduce employers’ contributions to the labor market and subsidies for energy-saving investments. It can be seen that in the European countries that imposed carbon taxes earlier, the specifc implementation of the carbon tax was slightly different. However, the use of carbon tax revenue needs to be more focused on environmental protection and energy conservation. 3.1.2.4 Form of technologies In response to climate change issues, the EU has also made more strategic deployments on energy development and low-carbon technologies. In March 2007, the European Commission proposed the EU Strategic Energy Technology Plan, which aims to promote research and development of lowcarbon technologies in order to achieve the EU’s commitment to the climate change goals, which in turn will drive the EU’s economic development model toward high-effciency, low-emission transformation. The plan points out that by 2020, the proportion of renewable energy consumption in total consumption of energy in the EU will increase to 20%, and the consumption of primary energy such as coal, oil and natural gas will decrease by 20%, which will increase the share of biofuels in transportation energy consumption. There are several important energy use structural goals, such as proportions to 10%. In October 2007, the European Commission proposed that the EU increase investment support for low-carbon technologies and hopes to increase 50 billion euros in funds to develop various types of low-carbon technologies in the next 10 years. According to the proposal, the EU’s annual investment

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funds in the related research felds for the development of low-carbon technologies will increase from the current 3 billion euros to 8 billion euros and will jointly develop a low-carbon technology roadmap for EU development among researchers and corporate business communities. We will vigorously promote the development and use of low-carbon technologies in key areas with great potential for development such as wind energy, solar energy, biomass energy and carbon dioxide capture and storage. Until the end of 2007, the European Commission adopted the EU Energy Technology Strategic Plan, explicitly proposing to encourage research and actively promote lowcarbon energy technology. According to the development trend of the European Union in the new energy industry and low-carbon technology industry, the relevant parties expect that by 2020, the EU economy will increase by 2.8  million jobs due to the transition to a low-carbon economy, although the transition to a lowcarbon economy will also result in some jobs being lost, but the net increase in jobs is expected to reach 400,000. 3.1.3 Japan’s low-carbon society After signing the Kyoto Protocol, Japan has not been able to promote highprofle issues such as the United Kingdom and the European Union on climate change, but it has been active in recent years. Japan’s GHG emissions rank among the top in developed countries, mainly due to the rapid growth of energy consumption; in particular, Japan is the world’s third largest consumer of gasoline. Therefore, island countries like Japan, under the constraints of resources and environmental capacity, are the main entry point for GHG emission reductions. They reduce energy dependence by reducing the use of fossil fuels, increasing natural gas supply and building nuclear power plants, and will actively use the three emission reduction mechanisms stipulated in the Kyoto Protocol to create conditions for carbon reduction in the country. 3.1.3.1 Early understanding Japan frst launched the “Global Warming Measures Promotion Program” in 1998, and carried out carbon emission reduction actions in two stages: the frst stage was mainly industrial reduction, and the second stage in 2003 was housing and transportation. The department’s reductions mainly include initiatives to promote voluntary reduction of industries. Correspondingly, the “Global Warming Measures Promotion Act” has been adopted to establish the responsibility and basic measures for the central local government, enterprises and residents to cope with global warming. As for the legal protection of energy saving and emission reduction, Japan’s “Energy Conservation Law” plays a guiding role. In response to the grim situation after the frst world oil crisis in the 1970s, Japan enacted and implemented the Energy Use Rationalization Act (referred to as the Energy Conservation

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Act) as early as 1979. The law is for construction and machinery. The energyconsuming industry has made a series of energy-saving regulations. After signing the Kyoto Protocol, Japan has revised the Energy Conservation Law on several occasions in order to facilitate the economic reduction of carbon emissions through energy conservation. The revised Energy Conservation Law covers more areas of management, and will update the energy effciency assessment standards with the times to meet the new requirements for energy conservation in the new era. The law has greatly ensured the effciency of energy use, strengthened energy-saving and emission-reduction standards, developed universal energy-saving technologies and formed a new type of society that suppresses CO2 emissions. 3.1.3.2 Form of taxation In order to give full play to the role of the Energy Saving Act, Japan will give active assistance and cooperation in taxation policies, implement special accelerated depreciation and tax reduction policies for energy-saving technologies and equipment, and reduce or exempt taxes for enterprises that meet energy-saving targets on fossil fuels. Use and power users impose energy taxes and environmental taxes. The Ministry of Economy, Trade and Industry of Japan regularly publishes energy-saving product catalogues and provides special depreciation and tax-relief measures for energy-saving products included in the catalogues used by producers of enterprises. The reduction or exemption of taxes can be up to 20% of the equipment cost, and can be renewed on the basis of its normal depreciation, to extract nearly 30% of special depreciation. In terms of energy and transportation, taxes on automobile fuels and automobile purchase taxes are mainly included. Since 2003, coal has been taxed, and petroleum taxes have been adjusted to petroleum and coal taxes. Japan is also involved in the carbon tax. Since the Ministry of the Environment of Japan proposed the carbon tax scheme in 2004, after many modifcations, its tax rate has dropped from the earliest 1.83 yen/liter to 0.82 yen/liter, and the family burden has been from 3,000 days per year. The Yuan dropped to 2,000 yen. The lower carbon tax rate is one of the most important reasons for people’s widespread support. 3.1.3.3 Form of technologies On March 5, 2008, the Ministry of Economy, Trade and Industry of Japan announced the “Cool Earth Energy Technology Innovation Plan,” which has formulated a roadmap for the development of Japan’s energy innovation technology by 2050, and identifed 21 key innovations for innovative technologies, namely: high-effciency natural gas thermal power generation, high-effciency coal-fred power generation technologies, carbon dioxide capture and storage technologies, new solar power generation, advanced nuclear power generation technologies, superconducting and effcient transmission technologies,

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advanced road transportation systems, fuel cell vehicles, plug-in hybrid electric power automotive, biomass energy alternative fuels, innovative materials and production processing technology, innovative ironmaking processes, energyeffcient residential buildings, next-generation effcient lighting, stationary fuel cells, ultra-effcient heat pumps, energy-saving information equipment systems, electronics power technology, hydrogen generation, storage and transportation technology. In May 2008, the Japan General Scientifc and Technical Conference announced the “Low-carbon Technology Plan,” and proposed measures to achieve a low-carbon society’s technology strategy and environmental and energy technology innovations, involving rapid neutron breeder reactor cycle technology and ships with high energy effciency, smart transportation systems and many other innovative technologies. The fgures released in September 2008 show that in the science and technology-related budget, the development cost of environmental energy technologies that are only listed separately amounts to nearly 10 billion yen, of which the budget for innovative solar power generation technology is 5.5 billion yen. In April 2009, the Japanese government frst included the development of solar energy in the economic stimulus plan and reactivated the solar energy incentive policy as one of the core strategies for economic restructuring. The stimulus policy was innovative in this year’s 3.5 billion yen. Based on the solar power technical budget, an additional 1.6 trillion yen in environmental protection project expenditures will be added, mainly for the development and utilization of solar energy technologies. It is planned to reduce the price of solar power generation equipment to half of the current price in the next 3–5 years. The policy aimed to accelerate the construction of energy-effcient buildings and strove for 50% of homes to meet energy-saving requirements by 2019. At present, there are many energy and environmental technologies in Japan that are at the forefront of the world, such as the combined use of solar energy and thermal insulation materials, the reduction of energy-consuming cogeneration system technology for residential buildings, and waste-water treatment technologies and plastic recycling technologies. 3.1.3.4 Social patterns As early as April 2004, the Global Environment Research Fund, a subordinate of the Ministry of the Environment of Japan, formulated a research plan on “a low-carbon social scenario for Japan in 2050.” The research staff of this program is composed of nearly 60 researchers including universities, research institutions and companies. Starting from fve perspectives – development scenarios, long-term goals, urban structure, information and communication technologies and transportation – the study will focus on the development of a low-carbon society in Japan in 2050, scenarios and roadmaps, and propose specifc countermeasures in terms of technological innovation, institutional changes, and lifestyle changes. In February 2007, the project team pointed out in the research report that it is feasible to meet the energy demand for

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Japan’s social and economic development by 2050 while achieving the target of reducing emissions by 70% compared with the 1990 level, and it is feasible for the idea of a low-carbon society. In May the following year, the project team completed the “12 Actions for Low-Carbon Social Scenarios in Japan in 2050,” which covers the residential sector, industrial sector, transportation sector, energy conversion sector and related cross-sectors. The action includes three parts:  the future goal, obstacles to achieve the goal and its strategic countermeasures and implementation process and steps (Peng Shuijun & Liu Anping, 2010). In June 2008, the famous “Fukuda Blueprint” was born. The Japanese Prime Minister Yasuo Fukuda proposed a new countermeasure to prevent global warming in Japan. This marks the offcial formation of Japan’s lowcarbon strategy, which responds to Japan’s low-carbon development technology innovation. In July of the same year, the Japanese Cabinet of Ministers passed the “Action Plan for Realizing a Low-Carbon Society.” The Japanese government selects typical cities (larger cities with a population of more than 700,000 in Yokohama and Kyushu, local center cities with a population of 100,000–700,000), Okcheon City, Toyama City and a small-scale city-county village with a population of less than 100,000, Mizuno, Shimokawa-cho, Hokkaido, as an “environmental model city” that promotes the transition to a low-carbon society and leads the international trend, vigorously promotes the construction and production of wind energy and solar energy, establishes an environmentally friendly transportation system, and implements carbon reduction in these cities. There are plans to promote low-carbon development in society and build low-carbon cities. In April 2009, Japan announced the draft policy on “Green Economy and Social Transformation” and strengthened Japan’s low-carbon economy by implementing measures such as reducing GHG emissions. In addition to requesting environmental and energy measures to stimulate the economy, the draft policy also proposes mid- and long-term guidelines such as social capital, consumption, investment and technological innovation in order to achieve a low-carbon society and achieve a harmonious coexistence with nature. 3.1.4 Practice of the USA The USA has relatively weak political will to deal with global climate change issues. The decision to unilaterally withdraw from the Kyoto Protocol, particularly during the Bush administration, has created obstacles to international climate negotiations and cooperation. The Obama’s successor government has shown more positive attitudes and changed its consistent negative attitude toward climate issues. It can be seen that the USA is deeply affected by interest groups in terms of climate change, and there are many uncertainties in its political position. However, in recent years, especially after the fnancial crisis, mainstream American society has begun to change its attitude toward

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climate change issues and support the adoption of energy security to actively deal with climate change issues. 3.1.4.1 Early understanding As early as October 1993, the Clinton Administration announced a “Climate Change Action Plan,” clearly stating that by the year 2000, US GHG emissions will return to 1990 levels of emissions and promised to change the mode of economic development to promote economic development and provide more job opportunities. In February 2002, the Bush administration proposed a new environmental program called “New Approaches to Global Climate Change” that focuses on the voluntary GHG emission reduction plan, and states that by 2012, the USA will work to decrease greenhouse gases. The emission intensity was reduced by 18%, and the Climate Change Science Program (CCSP) and the Climate Change Technology Program (CCTP) were launched to explore cost-effective climate-environment technologies. Subsequently, the Bush administration launched the “Voluntary Corporate–Government Partnership Program” to provide tax incentives for those companies that voluntarily reduce emissions. The partnership includes cement, forestry, medicine, public utilities, information technology, retail, etc. throughout the country’s 50 states. On July 11, 2007, the US Senate proposed the Low Carbon Economy Act, which shows that the development path of a low-carbon economy is expected to become an important strategic choice for the USA in the future. 3.1.4.2 Legal form On March 31, 2009, the United States House Energy Committee proposed to the Congress the “The American Clean Energy and Security Act of 2009.” The bill consists of four parts: green energy, energy effciency, GHG emission reduction and transition to a low-carbon economy. The main contents of the Green Energy and Security Protection Act in the transition to a lowcarbon economy include:  ensuring the international competitiveness of US industries, green job opportunities and the transformation of laborers, exporting low-carbon technologies, and responding to climate change. The bill constitutes the legal framework for the transition of the USA to a lowcarbon economy. On June 26, 2009, the US House of Representatives passed the American Clean Energy and Security Act with a weak vote of 219:216. This comprehensive energy law is not a direct climate change law by its name. However, it contains important contents, such as total amount limit and transactionbased response to climate change. This is the frst US package to deal with climate change. It not only sets a timetable for the reduction of GHG emissions in the USA, it also designs emissions trading and attempts to achieve emission reduction targets at a minimum cost through market-based measures. The

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bill includes multisectoral planning in fve areas including plot energy, energy effciency, reduction of GHG emissions, transition to a clean energy economy and reduction of agro-forestry emissions. The specifc content concerns the development of renewable energy, carbon capture and storage technologies and low carbon. Transport fuels, clean electric vehicles and smart grids improve energy effciency in construction, electrical appliances, transportation and industrial sectors. The US Clean Energy Security Act stipulates the US carbon emission control limit. Compared with 2005, global warming pollution will be gradually reduced to 17% by 2020, to 42% in 2030, and to 83% in 2050. According to energy structure and housing construction, by 2012, renewable energy such as bioenergy, solar energy and wind energy will account for 10% of US electricity sources and will increase to 30% by 2020; the energy effciency of newly built buildings after 2012 will have to be 30%, after 2016, it needs to increase by 50%, basically achieving carbon-neutral or zero-carbon emissions. Once this law comes into force, it will cover 85% of the industry in the USA, basically covering all power companies and major industrial companies with CO2 equivalents exceeding 25,000 tons, and their coverage will be more extensive than the current EU climate change law. 3.1.4.3 Interstate forms The federal government of the USA has been slow to act on climate change issues, and local interstate governments have been active in dealing with climate change. This contrasts sharply, as some state governments have successively introduced emission reduction bills and measures. About 40 states have established GHG reporting systems. More than 30 states have established renewable energy development goals and formulated climate action plans, and more than 20 states have implemented emission trade policies. There have been various practical actions to deal with climate change. In December 2005, the North American and Mid-Atlantic states (later ten states) reached the Regional Greenhouse Gas Initiative (RGGI), which is a quota and trading system for CO2 emissions from power plants in the region and is the frst mandatory and market-based system in the USA to reduce GHG emissions. In order to facilitate the implementation of emission reduction targets, RGGI provides fexible mechanisms to allow the use of emission reduction credits outside the power sector. The state of California is among the state governments that are actively fulflling their responsibility for climate change mitigation. On July 31, 2006, the Governor of California, Arnold Schwarzenegger and the British Prime Minister Tony Blair announced an agreement to jointly explore the possibility of establishing a system for the emission of GHG emission rights for emitters, through the use of market forces and market incentives to control GHG emissions. In accordance with the agreement reached by both parties, they will establish a new transatlantic carbon dioxide trading market, and on

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August 31, 2006, California passed the Global Warming Solutions Act in the market trading mechanism. In the application, the purchase and sale of the allowable emissions index has a large incentive mechanism for reducing GHG emissions. 3.1.4.4 Form of trades Founded in 2003, the Chicago Climate Exchange (CCX) is the frst in the world and the only organization and trading platform in North America that voluntarily participates in GHG emission reduction transactions and legally binds emission reductions. You can accomplish your own emission reduction tasks through internal emission reduction, Joint Implementation methods, or emissions trading to conduct GHG emission reduction transactions. The emission reduction targets of CCX are divided into two phases. In the frst phase (2003–2006), all member units have an average reduction of more than 4% on the basis of the base year, and the second phase (2007–2010) is in the base year, based on an average reduction of more than 6% on the basis, and CCX’s emission reduction plan is legally binding. There are nearly 200 members of the Chicago Stock Exchange, from dozens of different industries such as aviation, automobiles, power, environment and transportation. Members are divided into two categories:  one is from entities, cities, and the other entities that emit GHGs, and they must comply with their promised emission reduction targets; the other is participants of the exchange. The exchange’s emissions trading program involves six GHGs, including carbon dioxide, methane, nitrous oxide, hydrofuorocarbons, perfuorinated compounds and sulfur hexafuoride. At present, the CCX is the second largest carbon sink trading market in the world, and the only six GHG emission reduction transactions in the world that simultaneously implement carbon dioxide, methane, nitrous oxide, hydrofuorocarbons, perfuorinated compounds and sulfur hexafuoride. As of June 16, 2006, its carbon trading volume reached 283 million metric tons, accounting for 80–90% of the EU’s total trading volume of the Kyoto Protocol’s climate trading system, making it the largest exchange in the EU system. 3.1.4.5 Form of technologies The USA attaches great importance to technological innovation in the effective use of energy in GHG emission reductions and is the country with the highest R&D investment in a low-carbon economy:  in the federal government’s budget, supporting energy conservation and new energy development is a policy priority, and the US Department of Energy’s Renewable Energy Agency is responsible for energy conservation and new energy development. The Bureau’s budget for 2009 is US$1.255 billion, which is mainly used for R&D and the promotion of renewable energy technologies, signifcantly increasing the production of clean energy and promoting the use of

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energy effciency technologies. It provides information services to promote the rapid transformation of energy systems. On February 15, 2009, the USA introduced the American Recovery Reinvestment Act, which has a total investment of US$787 billion. It has made the development of new energy an important part, including the development of high-effciency batteries and smart grids, carbon storage and carbon capture, renewable energy such as wind power and solar energy, plans to allocate US$50 billion to improve energy effciency and promote renewable energy production. In the new energy plan, it is expected to invest US$150 billion in 10  years. The R&D and promotion of solar energy, wind energy, biomass energy and other new energy projects have invested US$4 billion in government funds to support the reorganization, transformation and technological advancement of the automotive industry.

3.2 Industrial development and practices in China The developed countries started dealing with climate change issues earlier. They have invested a great deal of funds and policy support from the national level. They have formulated clear medium- and long-term goals and measures for climate change mitigation, mainly from improving energy effciency, innovative technologies and fscal and taxation policies. Market transactions and other aspects began, and then derived a new feld of low-carbon industry, a new growth point for the development of the national economy. In the process of developing a low-carbon economy, China has drawn lessons from the practices of developed countries, combined with specifc national conditions, starting from domestic high-energy-consuming, high-polluting industrial structures, and responding to global climate change issues by adjusting industrial structures. At present, some regions and cities have developed low-carbon cities by exploring industrial restructuring, developing low-polluting, highoutput tertiary industries and various emerging high-tech industries, which have signifcantly improved the local environmental conditions and achieved energy-saving and emission reduction effects. 3.2.1 Shanghai: rapidly developed modern service industry As China’s fnancial center, Shanghai has witnessed rapid economic growth in recent years, accompanied by rapid growth in energy consumption. Shanghai’s energy consumption has increased signifcantly since 2002, with an average annual growth rate of 8.9%. Shanghai is a city with a shortage of energy resources, and its dependence on foreign energy is relatively high. With the continued development of Shanghai’s economy, the total primary energy consumption will gradually approach the level of developed countries, and the resulting increase in carbon emissions will cause Shanghai to face more and more pressures to reduce emissions. Therefore, Shanghai has taken a series of actions in reducing energy consumption and reducing pollutant emissions,

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trying to establish an energy structure and industrial structure system that is compatible with it. In terms of energy structure adjustment, Shanghai will focus on promoting the use of low-carbon energy, increase the proportion of natural gas, actively promote the construction of renewable energy power projects such as wind power and photovoltaic power generation and increase the effciency of energy use. In the adjustment of industrial structure, priority will be given to the development of modern service industries and advanced manufacturing, supporting the development of high-tech enterprises and forming an industrial structure based on the service economy. The integration of modern service industry and advanced manufacturing industry is an important idea in the adjustment of Shanghai’s industrial structure. The development of the modern service industry in Shanghai is inseparable from the solid foundation for the development of advanced manufacturing industries. By adopting the cage-changing-bird strategy, enterprises with low output value and large pollution will be withdrawn and the vacated land will be transformed into a business center area; and the advanced development of the manufacturing industry is obviously also inseparable from the support of the modern service industry. The transformation and upgrading of high-end manufacturing, branding and information will also provide a fertile ground for the modern service industry. Shanghai actively transforms backward processes, equipment, products and enterprises with high-energy consumption and high pollution, and organizes the implementation of a mandatory phase-out system. In 2007 alone, Shanghai completed 571 industrial restructuring projects. Of the new Shanghai-based enterprises in 2011, the proportion of service-oriented companies reached 88.2%, and the registered capital of service companies accounted for 92.1% of the total registered capital of newly established enterprises. It can be seen that while reducing the reliance on heavy chemical industry, real estate and labor-intensive industries, the tertiary industry, especially modern service industry, has become an important force for the development of Shanghai’s economy and is a key means to cope with global climate change and reduce GHG emissions. Innovation is the core of the development of the modern service industry, and the innovative development of modern service industry is a wise decision that fts with the construction of Shanghai’s “two centers.” According to the deployment of the State Council, by 2020, Shanghai will basically establish an international fnancial center that is compatible with the international status of the renminbi and an international shipping center that has the ability to allocate global shipping resources. In light of these two major tasks, Shanghai has repeatedly innovated in the development of a modern service industry. In the construction of the international fnancial center, a number of innovative institutions such as the China Financial Futures Exchange and the Shanghai Clearing House opened one after the other; a number of important innovative businesses such as cross-border trade settlement of RMB and futures bonded delivery were launched in order; Shanghai equity trusteeship trading market

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offcially started. In the construction of the international shipping center, the frst batch of domestic international shipping brokerage companies was formally incorporated in Shanghai; the Shanghai Shipping Exchange offcially launched containerized freight price derivative transactions, and the semiannual transaction volume exceeded 80 billion yuan, and more and more banks and logistics companies began to pay attention to the shipping tariff derivatives transactions. A good institutional environment is created for the modern service industry and promotes the smooth and effective adjustment of industrial structure. Although Shanghai’s service economy has accounted for nearly 60% of the added value, it has achieved fairly good results, but it is still nearly 5  percentage points away from the Twelfth Five-Year Plan target. Therefore, the implementation of various security systems is a necessary condition for promoting the better development of Shanghai’s modern service industry, and the Shanghai government has also made greater efforts in this regard. In response to the obstacles encountered by modern service companies in terms of administrative examination and approval, talent introduction, etc., the Shanghai government has issued relevant tilt policies to ease the worries of the development of modern service industries. Through the reform measures, the Shanghai Pudong administrative examination and approval items and the average approval time both fell by 60%. Since January 1, 2012, the business tax that was formally piloted in Shanghai has changed the value-added tax initiative. This has actually led to a signifcant reduction in the burden on companies and a sharp increase in corporate vitality. Before the reform, the business tax of the service industry was about 5% and the overall tax burden of the service industry after the reform was reduced to about 3%. The pilot industry covers six modern service felds such as transportation, information technology and cultural creativity. The orderly development of the modern service industry has achieved remarkable results. 3.2.2 Beijing: rapidly emerging cultural and creative industry Beijing, as the capital of China, takes the Green Olympics as an opportunity. During the Eleventh Five-Year Plan period, it has carried out a series of explorations and practices of low-carbon development and has achieved good results: energy effciency has improved signifcantly. During the period from 2005 to 2010, the cumulative intensity of energy consumption per 10,000 yuan of GDP dropped by 26.6%. The cumulative intensity of energy consumption dropped by 26.6%, exceeding the Eleventh Five-Year Plan energy-saving and emission reduction target; the energy structure continued to be optimized, and the proportion of clean and high-quality energy reached 67% in 2009; the industrial structure was continuously upgraded, and the proportion of the tertiary industry reached 73% in 2008, which basically reached the industrial structure level of developed countries in the world. Given that the next decade will be a critical period for the rapid advancement of Beijing’s urbanization

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and internationalization, it will continue to experience an increase in GHG emissions but at a slower growth rate. As a high-energy-consumption city, the modern energy self-suffciency rate is basically zero. It is necessary for cities to cultivate new economic growth points and economic growth models in the process of developing a low-carbon economy. Rich historical and cultural resources not only allow Beijing to highlight the charm of the ancient capital, but also promote the rise of Beijing’s cultural and creative industries. In particular, in recent years, Beijing has taken cultural development as an important task in adjusting the industrial structure and transforming the economic development model. It has actively explored the “two-wheel drive” strategy of implementing technological innovation and cultural innovation, strengthened cultural innovation and promoted the development of cultural and creative industries. Cultural innovation and technological innovation together have become an important engine for promoting the transformation of the economic development mode in the capital, actively building cultural and creative platforms, planning and building cultural and creative industrial clusters and striving to build an advanced cultural capital with Chinese characteristics. According to the statistics from the Beijing Municipal Development and Reform Commission, Beijing’s cultural and creative industries have developed rapidly during the Eleventh Five-Year Plan period and have become the second pillar industry in the tertiary industry after the fnancial industry. From 2006 to 2009, the added value of Beijing’s cultural and creative industries grew at an average annual rate of 21.9%. In 2009, Beijing’s cultural and creative industries realized an added value of 148.99 billion yuan, accounting for 12.3% of the city’s regional GDP, and employed 1.149  million people. In 2010, Beijing’s cultural and creative industries realized an added value of 169.77 billion yuan, accounting for 12% of the city’s regional GDP. At present, Beijing’s cultural and creative industries have formed a sound foundation for development. The overall strength of the arts and performances, press and publication, radio, flm and television, and cultural and art works are all strong. The scale, quality and infuence of major cultural products and services are among the highest in the country. In press and publication, the book publishing unit in Beijing accounts for 41% of the country’s total. Newspapers and periodicals account for 30% of the country’s total. Audiovisual publishing units account for 43% of the country’s total. In radio and television, Beijing has more than 120 theaters and nearly 600 screens, ranking frst among cities in the country; Beijing produces movies that account for half of the country’s production; Beijing’s digital flm post-production capacity accounts for twothirds of the country’s. For the fourth consecutive year, the Beijing movie box offce ranks frst in the country. In terms of cultural artifacts, Beijing has become the world’s largest Chinese cultural relics art trading center. In 2010, the total amount of cultural relics in Beijing exceeded 50 billion yuan, accounting for about 80% of the country’s total. At present, there are more than 100 auction institutions for cultural relics and art works in Beijing, and

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more than 60 cultural relics business units, ranking frst in the nation. During the Eleventh Five-Year Plan period, Beijing’s auction sales of all kinds of cultural relics reached 39.4 billion yuan, ranking frst in the country. The vigorous development of Beijing’s cultural and creative industries cannot be separated from the help of the policy environment: provide policy support, promote the development of Beijing’s cultural industry with a relatively complete policy and regulatory system; provide institutional arrangements, improve the development of Beijing’s cultural industry with the improvement of institutional management system, coordinate environmental optimization, optimize the external environment for the development of cultural industries with the construction of service and factor markets and provide refned services, promote the integration of resources and integrate and allocate capital cultural industry resources with market methods. Beijing’s cultural and creative policy-related departments have made great efforts in the policy support system, planning and development layout, and improving and perfecting the industrial system. This has brought a strong force for Beijing’s cultural and creative industries, the development of the sunrise industry. At the same time, the cultural and creative industries are also inseparable from the fnancial industry’s strong support. Beijing Municipality has issued guidance on the “Financial Support for the Development of the Capital, Cultural and Creative Industries,” and conducted an early exploration of the policy of cultural and fnancial development in the national fnancial system. The People’s Bank of China issued the relevant policies to provide the basis for decision-making. In recent years, Beijing’s loans to support cultural and creative industries have maintained rapid growth. In 2010, the city’s loans to Chinese-funded banks reached 39.711 billion yuan, an increase of 67.7% year-on-year, far higher than the 13% growth rate of RMB loans during the same period. It refects the results of joint efforts of various banks and fnancial institutions. 3.2.3 Bishan, Chongqing: rapidly developed emerging industries Located in the western suburbs of Chongqing, Bishan was a typical agricultural county in the past. During the Eleventh Five-Year Plan period, according to the delineation of the industrial layout in the “One-hour Economic Circle of Chongqing” Economic and Social Development Plan in 2007, Chongqing will form an industrial structure of “one heart and four belts” and beneft from the Bishan Industrial Park, which passes along the high-speed industrial intensive belt along the high-speed line and the industrial dense belt along the high-speed highway, and has become a pioneering camp for the development of the two major industrial intensive belts. Machinery manufacturing, footwear manufacturing and new building materials industries have rapidly landed and become the three most important industry pillars in Bishan. Although the traditional “three pillar industries” have a good momentum of development, the disadvantages of high energy consumption, low technological content

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and low added value have made it diffcult for Bishan industries to grow and develop. In 2009, the county’s industrial output value was only 30 billion yuan. The emission of GHGs and pollutants from some small and mediumsized leather shoes and building materials companies is serious, resulting in the continuous deterioration of the natural ecological environment in Bishan, and the subsequent development is unsustainable and the industrial transformation is imminent. In 2009, Bishan took a “deep green ecological city” as its future development goal. With the transformation of the urban development model, it forced the transformation and upgrading of the industrial structure, rectifying and shutting down polluting industrial enterprises and thereby enhancing the deteriorating local climate and environment. Bishan has positioned its industrial park in an eco-industrial park. Based on the ecological environment construction, it has surpassed the construction concept of an ordinary city and has given priority to the construction of ecological environment, public rental housing, fnancial services and basic living facilities to attract investment. In order to speed up industrial transformation and upgrading, Bishan formulated a traditional industry investment of not less than 3 million yuan per mu, output of not less than 5 million yuan, tax revenue of not less than 100,000 yuan, polluting enterprises “one vote vetoed” investment promotion standard. The result of entry into the park of new industrial enterprises caused by the inherent advantages of the Bishan Industrial Park and high investment promotion standards is indeed unexpected. So far, Bishan has successfully introduced hundreds of domestic and foreign companies, including the world’s top companies, in the IT supporting industry projects. The total number of these companies accounted for about one-fourth of the city’s laptop business supporting companies, as well as small electric pen nuts and shafts. As large as the case and keyboard, they all began to produce in Bishan. It is estimated that by 2015, Bishan Book Supporting Base will create an output value of 120 billion yuan, accounting for 60% of the county’s industrial output value. Driven by the emerging industries such as the notebook industry, the Bishan economy has seen rapid development. In 2011, the total GDP of Bishan exceeded 20 billion yuan, and the economic growth rate reached 23.5%, ranking frst in the city; per-capita GDP reached US$5417, for the frst time surpassing the national and Chongqing average levels; fscal revenue doubled in two consecutive years, passing the threshold of 6 billion yuan to reach 6.15 billion yuan. This western county is achieving its own social value in the fast lane of economic growth. According to the plan, by 2013, Bishan will realize the goal of “100 billion industrial counties” and by 2016, the county will achieve an industrial output value of 200 billion yuan. By building a platform for the development of an eco-industrial park, the eco-environmental environment will be built frst, and ecological, livable and life-supporting facilities will be given top priority. This is the biggest success of gathering various types of investment resources. Based on the initial

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formation of China’s pen industry base, it will expand the three emerging industries of electronic information, equipment manufacturing and medicine and food, consolidate and upgrade traditional industries, and build a new type of industrial system with strong comparative advantages. For the reality that the notebook and notebook supporting industries are subordinate to the labor-intensive industries, Bishan has formulated a series of subsidy policies for the employees of notebook-supporting enterprises:  children of migrant workers can be enrolled in Bishan, and migrant workers under 35 can settle in Bishan and enjoyed the same treatment as Bishan citizens. In accordance with the 30-minute walking radius, life service areas, community parks, etc. were planned near the industrial park and 700,000 square meters of public rental housing were built, and priority was given to the employees of the notebook and laptop supporting enterprises. Only by forming a highland that attracts labor can we provide inexhaustible motive force for industrial development. In addition, the high cost of fnancing is also a key factor that restricts the development of matching small and medium-sized businesses. Bishan Industrial Park has specially introduced a Taiwan-based guarantee company with a registered capital of 1 billion yuan to provide fnancing guarantee services for these companies with service fees and margins that are lower than the industry average standards and provide enterprises with high-quality, convenient fnancial services. 3.2.4 Zibo, Shandong: upgraded and restructured new materials industry As a veteran industrial city, the industrial development of Zibo has a history of more than 100 years. The abundant mineral resources make it one of the few industrial and mining development areas in the earlier time. It is an important petrochemical and ceramics process and building material in China. Production area. The heavy industry in the secondary industry of Zibo City has played a signifcant role in stimulating economic development, but it has also laid the incentives for the irrational industrial structure. The concrete manifestations include low industrial structure, low industrial concentration and environmental pollution. The proportion of the primary industry in the three industries of Zibo City is relatively low, the proportion of the secondary industry is too large and is extremely heavy, and the tertiary industry is developing slowly and lagging behind. Most of the industrial enterprises are primary processing industries at the beginning of the industrial chain. The industries with high-end processing at the end account for a relatively small proportion; the industrial concentration is low, and the economies of scale are poor. From the perspective of energy consumption, in addition to electricity and a small amount of natural gas consumption, coal and other major energy sources basically rely on external adjustments with high external dependence. In recent years, the total energy consumption in Zibo City has grown year by year with economic development. In 2008, the total energy consumption in Zibo City reached 38.937 million tons of standard coal, an increase of 5.03%, and a net increase of 1.8651 million tons

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of standard coal. Zibo City’s industrial structure dominated by industries such as chemicals, building materials, textiles, and pharmaceuticals has led to a large amount of industrial waste and GHG emissions, causing serious pollution to the atmosphere and water environments. The defects of traditional industries have gradually become an obstacle to sustainable economic development in Zibo. The characteristics of resource depletion and high industrial energy consumption have brought tremendous pressure on economic development and economic transformation has become an urgent task. Zibo, a well-established old industrial base under the guidance of the new development ideas of the old industry, has made gratifying achievements. In the traditional feld, the development of the new materials industry has brought new development experience to Zibo. The high-tech new material industry is a new industrial layout after the elimination of old industries such as glass and ceramics and the upgrading of new industries. With the development of new material industries, in the 2010 Ninth New Materials Technology Forum, Zibo City was awarded the title of “New Material Name Capital” by the Chinese Material Research Society. At present, Zibo’s industrial structure has been signifcantly optimized to form seven new material industrial clusters based on advanced ceramic materials, new chemical materials and new types of refractories, among which green refrigerants, plasticizers and refractory fbers are among the largest in Asia. In the frst place, Zibo has become a new highlight. In the feld of high-tech ceramics alone, Zibo City has won more than 200 patents or achievements, including three state-level awards and 70 provincial and ministerial awards. According to statistics, during the Eleventh Five-Year Plan period, Zibo City supported an increase of 14.3% of GDP with an average annual energy consumption growth of 8.5%, ranking frst in the province for three consecutive years in the evaluation of energy conservation targets. In 2011, the total production value of Zibo City increased by 12% year-on-year, and the profts of industrial enterprises above the designated size reached 74.449 billion yuan, an increase of 36.42%, achieving a rapid increase in high effciency and environmental protection. This is the result of the refnement, intensifcation and connotative development of traditional industries. Zibo City has formulated and implemented a series of measures to build an energy-saving industrial system:  strictly defned market access standards, implementation of strict energy conservation assessment and review of fxed assets investment projects, implementation of responsibility target assessment and a one-vote veto system, mobilization of the enthusiasm of relevant departments and implementation of the transformation, upgrading of key industry equipment projects in the traditional industries and eliminating backward production capacity. In November 2007, the Zibo Municipal Government approved the establishment of the Total Pollutant Restriction Offce, which was the frst place in the province to set up the areas where total pollutant control agencies were set up, and implemented fscal incentives and emission reduction policies and measures, established

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emission reduction projects and set rigid emission reduction targets for emission reduction projects. In 2008, Zibo had a total of 34 SO2 emission reduction projects and eight CO2 emission reduction projects to beneft; the SO2 emission reduction rate was 12.7%, and the CO2 emission reduction rate was 4.74%, exceeding the preset 4.5% CO2 emission reduction and SO2 reduction of Zibo, 8% of the annual target mission. At present, seven enterprises in Zibo City have launched pilot projects for energy management system construction, 22 companies have carried out pilot projects for energy-saving voluntary agreements, 16 companies have conducted energy effciency benchmarking activities and 586 enterprises have conducted energy monitoring (Zhou & Jia, 2012). In order to encourage enterprises to upgrade, Zibo City has set up special guidance funds for the transfer of structural adjustments, allowing companies to see the government’s determination to encourage transformation and upgrading, and this has played an important guiding role. From 2006, Zibo City set up a special fund for energy conservation and consumption reduction to support relevant projects. In 2008 alone, the State and Shandong Province provided more than 90 million fnancial support for 29 energy-saving projects in Zibo. The synchronization of policies and funds has enabled traditional industries in a diffcult situation to see new development opportunities. Apart from investing in policies and investments to support the upgrading of traditional industries, Zibo City is inseparable from giving support to corporate talents and R&D. As of the end of 2011, the number of provincial-level engineering technology research centers and enterprise technology research centers in Zibo City had reached 193 (seven at a national level).

3.3 Experience and implications both in China and overseas 3.3.1 Summary of low-carbon development experience in foreign countries Responding to climate change and realizing low-carbon development are sensitive observations that developed countries have touched on earlier. Affected by the Industrial Revolution, developed countries completed the process of urbanization and modernization and saw earlier the problems of climate and environment left behind after the rapid development of the city. They have invested in a new social revolution at the end of the last century in order to slow down and reduce the adverse effects of the previous development. Among them, one of the most fundamental aspects of climate change mitigation and reduction of GHG emissions is energy use. Therefore, the research and development of energy-saving technologies and new energy are often the most practical and effective measures taken by developed countries. In addition, it assists in market incentives and administrative measures to clarify government responsibilities and use market mechanisms to control carbon emissions. To sum up, the successful experiences of developed countries can

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be summarized from the following aspects:  laws and regulations, fscal and taxation policies, low-carbon technologies, market instruments and lifestyles. 3.3.1.1 Formulate targeted planning and protective laws and regulations Outlines and plans can clearly defne emission reduction targets and paths and formulate development directions and approaches. In particular, it is economically feasible and in line with the cost–revenue rules of emission reduction. It can clearly defne the rights and interests of various parties and fully mobilize the active participation of governments, industries, and enterprises. On the basis of the green development plan, through institutional arrangements and institution building, create a stable political environment and guarantee mechanism for low-carbon development. For example, the carbon budget proposed in the Climate Change Act in the United Kingdom mandates the allocation of funds to the fscal budget for carbon emission reduction. This is a solid foundation for typical low-carbon development. 3.3.1.2 Active use of taxation policies and proper use of tax revenues A carbon tax on high-emissions and high-energy-consuming enterprises and subsidies on low-emissions and clean-energy technology companies can promote the optimization and upgrading of energy use structure and effciency. However, tax-neutral measures are more conducive to the promotion of lowcarbon development models. They can be used to return tax revenues back to the public to offset the negative impacts of tax revenues, or to reduce other distortionary taxes, and withdraw from some high-ranking companies mitigation and compensation, as well as funding and support for energy conservation and emission reductions. In order to reduce the cost of collection and guarantee effective collection, it can be expropriated from the production link based on the existing tax system. 3.3.1.3 Promotion of innovation in energy-saving technologies and R&D of low-carbon energies Actively invest funds and technical R&D forces in technology R&D, aiming at existing energy products to further improve energy production processes, especially technological breakthroughs based on desulfurization technology; develop new energy, and vigorously develop clean renewable energy such as wind energy and solar energy. Clean new energy development as an opportunity will vigorously promote the development of new energy industries and low-carbon industries, promoting energy-saving buildings and new energy vehicles and the promotion of new energy power plants and various carbon sequestration and carbon-capture technology industries. Low-carbon environmental protection industry upgrades and development.

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3.3.1.4 Establishment of carbon rights trading market and utilization of market incentives Through establishing the carbon rights trading market in the region, the company will provide relevant market incentives for active emission reduction, allowing companies to take excess emission reduction targets for trading on the market and at the same time allowing individuals who have not completed emission reduction targets to get their missing emission reductions from the market. In the long run, carbon trading will encourage various enterprises to carry out corresponding technological innovations, which is a better way to achieve total reduction of emissions in society as a whole. Today, the carbon trading platform is limited to only a few regions. In the future, it needs to establish a global carbon rights trading platform so that more countries and enterprises can participate in transactions. 3.3.1.5 Cultivation of low-carbon lifestyle and construction of low-carbon society A low-carbon lifestyle should be developed as a new trend in social norms by extensively advocating for the concept of low-carbon life, low-carbon transport, low-carbon consumption behavior, low-carbon construction and other low-carbon habits. We can start with simple things, like setting up a low-carbon community where the rational effect that integrates individual low-carbon practices into collective efforts can take form. In this way, the ultimate goal of a low-carbon society can be achieved where social norms of low-carbon production and low-carbon consumption will prevail. This will ensure greater defnition and predictability of industrial development and investment in a low-carbon economy, reducing the use of fossil fuels and emission reduction barriers for certain industries. 3.3.2 Experience and implications of Chinese industrial development The measures adopted by the developed countries to control and reduce carbon emissions will have profound implications for the future response to climate change in China. Its signifcance is not only refected in the abstract macro development ideas; how to achieve specifc effects through the implementation of various specifc measures is even more critical. China attaches great importance to the formulation of laws and regulations in response to climate change and has successively issued a series of guiding policy documents on energy conservation, emission reduction and climate change response. However, there is no single law or clause that can set carbon emission rules for companies or provide a budget for carbon emission reductions. If the relevant rules and regulations are only general proposals for future carbon and emission reduction approaches, they cannot be made clear on the key issues of low-carbon development. Whether it is for enterprises

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or local governments that do not provide good incentives, then low-carbon development will eventually fow into form. In terms of carbon-constrained fscal and taxation systems, they are constrained by China’s existing systems and basic national conditions and have not yet reached the effective basis for carbon taxation, although the current collection of resource tax and sewage charges can temporarily impose unrestricted fscal and tax constraints on carbon emissions. However, from the perspective of long-term carbon emission reductions, carbon taxation is an effective measure that can directly affect the effectiveness of emission reductions. China should gradually shift to a taxation system that uses carbon constraints as a direct target through resource tax reforms, and timely improve tax revenues, using channels, as far as possible, to invest these funds in various felds related to low carbon emission reduction to achieve a virtuous cycle of low-carbon development. The development of the new energy industry has become a new industry growth pole in China, and many regions are competing to build factories in order to be the frst benefciaries of the development of new energy industries. This excessively popular development has revealed some irrationality. In many places, there are problems of decentralized and redundant construction. Even if many new energy products are clean and pollution-free, it does not mean that their manufacturing processes are as harmless as their products. Ignoring this issue will also bring about new development issues for carbon emission reduction constraints. Therefore, R&D and investment funds for energy conservation and emission reduction should be used as far as possible in the most favorable areas, and strict monitoring of pollution emissions should be conducted from each stage of production to application. Carbon rights trading is another fnancial innovation under the market trading system. Although China has established carbon exchanges in Shanghai, Beijing and Tianjin, its trading mechanism and transaction types are far from being able to interface with international standards. The domestic transaction market is relatively closed and the transaction scale is limited. When participating in foreign carbon trading, domestic companies appear to be too passive, lacking the pricing power of carbon trading, and are mostly in a weak position in transactions. Therefore, we hope to achieve the ultimate goal of carbon emission reduction through carbon rights trading. Both domestic and foreign markets will face a series of practical problems. The construction of a low-carbon society has not been popularized in China, but the dissemination of low-carbon ideas has long since ceased to be new in our country, but it is a new trend that is close to the traditional “saving” ideas. The true cultivation of a low-carbon lifestyle is inseparable from all aspects of coordination and cooperation. The regulation of daily behaviors such as low-carbon transportation, low-carbon buildings and low-carbon consumption requires the emergence of appropriate means of transport, housing and consumer goods, and then needs and wishes can be successfully completed. The public only depends on the will, but lacking physical supplies

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that are actually available for selection means that the low-carbon lifestyle will become empty talk. It is precisely because China is in a different stage of development to the developed countries that there are obvious differences in the level of economic development, industrial development and energy structure, etc. The successful experience of foreign countries may not be replicated at home. However, there are already many cities in the country that are taking the lead in combating climate change. Their existing developments have more practical reference values for other cities in China. 3.3.2.1 Low-carbon development of the second industry As most cities in China are still in the process of industrialization, especially a group of old traditional industrial cities that have been in existence for a long time, their industrial development is based on the pattern of energyintensive industries as the center, and China’s future economic development continues. It is impossible to stop the industrialization process. Therefore, under the premise of not affecting the progress of modernization and industrialization, the transition to low carbonization and cleanliness of the internal production of secondary industries is particularly important. The original energy-consumption industries can be turned to high-tech industries such as new energy, new materials, advanced equipment manufacturing, bio-pharmaceuticals and IT industries. These emerging industries have high technological content, low emissions and strong competitiveness. The advantages will help realize the optimization and upgrading of the industry and the low-carbon development of the industry. 3.3.2.2 Innovative development of the tertiary industry In China, there are also cities where individual economic and social developments are close to the process of post-industrialization. Cities like Beijing and Shanghai have a higher proportion of tertiary production than that of secondary production, and they tend toward clean production within the secondary production. Next, the key issue is how to focus on realizing breakthrough development of the tertiary industry, achieving further optimization of the industrial structure and then feeding back the secondary production to provide more technical and fnancial support for the development. Therefore, the modern service industry, the modern fnancial industry and the creative culture industry in the tertiary industry have become the key development targets, replacing the original heavy chemical industry’s development share, saving the city a lot of energy consumption and GHG emissions. While the urban environment is being cleaned and defended, it has also achieved a highly effcient and stable socio-economic development. To sum up, combined with China’s basic national conditions, in dealing with climate change issues, more will start from the aspects of energy-saving

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emission reduction and fscal and taxation policies. Because there is a big difference between China and developed countries in terms of carbon trading, the ability to participate in international carbon trading is relatively limited. In the implementation process of energy-saving and emission-reduction, taking into account that China’s coal-based energy structure, alternative energy sources and energy effciency will not be signifcantly improved in the short term, it is highly relevant to China’s high energy consumption and high emissions. The industrial structure was mentioned on the surface and triggered a lot of attention. As a result, on the issue of China’s response to climate change, how to adjust the industrial structure with high energy consumption and high emissions has become an effective measure for realizing the purpose of mitigation, which not only ensures the restructuring of the three industries but is also refected in the internal adjustment and upgrade of the secondary and tertiary industries. The heterogeneity among different regions in China is relatively large. The adjustment of industrial structure and the transformation of regional economy will require a longer period of transition and power conversion. But as long as we grasp its own characteristics, seize the opportunities for development and fnd suitable regional development, the model and development industry, whether it is the adjustment of industrial structure as a means to achieve the ultimate goal of energy conservation and emission reduction, or the premise of climate change mitigation, and the adjustment of the industrial structure for the purpose of economic adjustment, will achieve effective harvest.

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4

Research on the relationship between interprovincial industrial structure, income level and CO2 emissions1

Global warming is an indisputable fact. A lot of the evidence shows that CO2based human-induced greenhouse gas (GHG) emissions are the main reasons. Global warming poses a serious threat to human production and life, and is related to the sustainable development of human society. Therefore, it has received extensive attention from the international community. The IPCC Fourth Assessment Report pointed out that climate change may cause irreversible effects. If the average global temperature increase exceeds 1.5–2.5°C before the Industrial Revolution, then 20–30% of species may become extinct, and over 3.5°C may lead to 40–70% of species going extinct. In the past 100 years, the global average temperature has risen by 0.74°C. It is expected that in the next 20 years, it will still warm at a rate of about 0.2°C per decade. Stability at the same level in 2000 is also estimated to be warming at a rate of 0.1°C per decade. Stern and Stern (2007) also warned that if humans do not take actions to reduce emissions, as early as 2035, the concentration of GHGs in the atmosphere will reach twice the amount before the Industrial Revolution, causing the average global temperature to rise by more than 2°C. The resulting losses will be equivalent to global GDP loses of at least 5% each year. The impact of climate change on China is also very signifcant. China’s National Climate Change Program indicates that the average annual temperature in China has increased by 0.5–0.8°C over the past 100 years. The warming in the last 50  years is particularly evident. The intensity and frequency of occurrence of extreme weather and climate events have increased signifcantly and have had a major impact on China’s social economy. The impact of climate change affects the whole world, and requires the joint efforts of the international community to achieve the emission reduction targets. In December 1997, the third meeting of the Parties to the United Nations Framework Convention on Climate Change, held in Kyoto, Japan, adopted the Kyoto Protocol, aimed at limiting the GHG emissions of developed countries. It is stipulated that by 2010, the emissions of six kinds of GHGs such as carbon dioxide in all developed countries will be reduced by 5.2% compared with 1990 emissions. China has signed and approved the Kyoto Protocol, but as a developing country, it has not assumed specifc emission reduction tasks. Therefore, it is considered to be a “free-rider” suspect. The DOI: 10.4324/9780429447655-6

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USA even used this as an excuse to withdraw from the Kyoto Protocol. On the other hand, with the advancement of industrialization and urbanization, China’s energy consumption has grown rapidly, resulting in a rapid increase in CO2 emissions. According to the report of the Netherlands Environmental Assessment Agency, China’s CO2 emissions totaled 6.2 billion tons in 2006, exceeding the USA as the world’s No. 1, and further rising to 6.72 billion tons in 2007, accounting for 24.3% of the world’s total emissions. The world’s total increase is 60%. To be sure, with the further increase of CO2 emissions, the international community will inevitably put forward higher requirements for China’s CO2 emission reduction. Especially after the arrival of the post-Kyoto era, our government will surely face increasing international CO2 emissions reduction pressure. For the Chinese government, there are two priorities:  frst, we should objectively and scientifcally assess the status of China’s CO2 and other GHG emissions and future trends in emissions for a period of time to provide a scientifc basis for a new round of international GHG emissions negotiations. What level will CO2 emissions in China reach in the next ten years? What is the maximum number of emission reduction tasks promised by our government? Only by conducting scientifc research on this issue can we strive for a fair emission reduction obligation for our country in a new round of international negotiations that will not hinder socio-economic development and will not harm the image of a responsible big country. Secondly, we should comprehensively and scientifcally analyze the major factors affecting China’s CO2 emissions and provide scientifc basis for implementing emission reduction strategies. Which factors have the most important impact on China’s CO2 emissions? How can we take effective measures to promote emission reduction strategies? Only by understanding this in depth can we take targeted measures to reduce emissions and make due contributions to curb global warming. After all, China has a vast territory and a large population, and global warming has a huge impact on China. Focusing on the above two issues, this chapter conducted a specifc study. For the frst time, this chapter estimated the CO2 emissions of various provinces in China from 1995 to 2007, built a provincial CO2 emissions panel data set, and used the corresponding panel data measurement methods to conduct in-depth analysis of CO2 emission infuencing factors, while passing the in-sample ftting standard. The models were selected based on the outof-sample forecasting criteria, and the optimal measurement model was determined. Then the CO2 emissions from China until 2020 were predicted through scenario simulation. The research in this chapter has important theoretical value and policy implications. Theoretically speaking, this chapter for the frst time evaluated the emissions of various provinces and established a provincial panel database, which is a signifcant improvement over previous studies of time-series data at the national level. At the same time, this chapter selects the model through the in-sample ftting standard and the outof-sample forecasting criteria to determine the optimal measurement model,

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which has certain advantages over the traditional environmental Kuznets curve model. In terms of policy implications, this chapter identifes the most important factors affecting CO2 emissions in China, and has important practical implications for CO2 emission reduction in China. At the same time, this chapter forecasts the CO2 emission trends in China over the next 10 years. This is for the Chinese government. For international CO2 emission reductions, negotiations have an important reference. The structure of this chapter is organized as follows. The frst part is a review of the relevant literature; the second part estimates the CO2 emissions of each province and carries out a corresponding analysis; the third part is the measurement model and estimation results; the fourth part is for China until 2020 annual forecasts of CO2 emissions and total emissions per capita; the fnal part is conclusions and policy recommendations.

4.1 Literature review Chapter 2 of this book has exhaustively introduced the types of research on the analysis of CO2 emission infuencing factors. This chapter will use the environmental Kuznets curve (EKC) model based on econometric analysis to quantitatively evaluate economic income levels and industries and the relationship between structure and GHG emissions. The concept of the Kuznets curve was proposed by the economist Kuznets in 1955 to describe the “inverted U-curve” relationship between income distribution and economic development. The EKC is a hypothesis frst proposed by Grossman and Krueger (1991), and assumes that there is also an “inverted U-shaped curve” relationship between environmental quality and economic development. That is, the environmental quality of a country will follow the per-capita income. The increase will gradually worsen and the pollution will intensify. However, when the level of economic development reaches a certain level, there will be an infection point. After that, the environmental quality will gradually improve as the per-capita income level increases. After this, a large number of studies on EKC emerged. Scholars tested this hypothesis from different aspects such as theoretical basis, infuence mechanism and empirical research. 4.1.1 Theoretical basis and formation mechanism of EKC Grossman and Krueger (1991) believe that economic development mainly affects environmental quality through three channels: scale effect, technical effect and structural effect. (1)  Scale effect:  economic growth often means more large-scale production activities and resource requirements. On the one hand, growth needs to rely on the use of resource inputs. On the other hand, more output levels also mean that pollution emissions increase. Therefore, the scale effect will have a negative impact on the environment. (2) Technological effects:  economic development is often accompanied by technological

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progress, and technological progress has two impacts on environmental quality. On the one hand, technological progress has improved production effciency, so the same level of output requires less input of resource elements, slowing the impact of production activities on nature and the environment. On the other hand, technological progress means that humans replace the original technology with cleaner, environmentally friendlier new technologies, processes and equipment. In order to effectively realize resource recycling and pollutant emission reduction, the technical effect will have a positive impact on environmental quality. (3)  Structural effects:  as income levels increase, people’s consumption structure will gradually change, and conducted to the production structure, the ultimate economic development has brought about the upgrading and optimization of the industrial structure. While reducing emissions and improving the quality of the environment, the structure also has a positive effect on the quality of the environment impact. Based on this theoretical basis, they believe that when the level of economic development is low, the scale effect of economic development far exceeds the technical and structural effects, and therefore presents a situation where the environmental quality gradually deteriorates as the level of per-capita income increases, but when the economy is at a higher level of development, the scale effect gradually declines, and the technical and structural effects continue to increase, eventually exhibiting a positive total effect. That is, environmental quality improves with increase in per-capita income level. From a trend point of view, it is also an inverted U-shaped curve relationship. In addition, there are other studies that have proposed the formation mechanism of EKC. For example, according to the market mechanism hypothesis, as the level of economic development increases, the externalities of natural resources and environmental pollution are gradually internalized by the market system and market prices will gradually refect the marginal social costs of resource and environmental pollution, which will increase resource prices. The increase in the cost of environmental pollution has further encouraged companies to adopt more advanced technologies and management to reduce costs (Unruh & Moomaw, 1998). The international trade hypothesis believes that the inverted U-shaped EKC curve actually refects the process of redistribution of environmental pollution between high-income countries and low-income countries. The product consumption structure of developed countries has not changed signifcantly but has been adopted internationally. The forms of trade and foreign direct investment have shifted pollution-intensive production sectors to developing countries. As a result, the environmental conditions in developed countries have improved and have entered the downward phase of the inverted U-shaped curve. The developing countries have adopted pollution-intensive production and the environment has worsened, being on the left-hand side of the inverted U-shaped curve (Muradian & Martinez-Alier, 2001; Lu Yang, 2012). The hypothesis of the elasticity of demand for quality of the environment believes that in the early stage of economic development, people mainly prefer the improvement and

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improvement of the economic level, while the demand for environmental quality is weaker. However, as the level of economic income continues to increase, people’s demands for environmental quality will also increase. With this increase, the income elasticity of the demand for environmental quality will increase. At this time, people will choose to sacrifce part of their income to obtain an improvement in the environment, and thus a fnal inverted Ushaped curve will be formed (Dinda, 2004). In addition, some scholars have elaborated on the formation mechanism of EKC from the perspective of changes in the intensity of environmental regulations and changes in government pollution investment (Li Yuwen et al., 2005; Zhong & Zhang, 2010). 4.1.2 Debate over the EKC model The EKC describes the relationship between income level and environmental quality, which can be simply set from the model: b=f(x,z)

(4.1)

where b represents the environmental quality index or environmental pollution, x is used to indicate economic development level and z is the other explanatory variable. Although the description of this model is very simple, and some studies on water and air pollution also support the existence of an inverted U-shaped EKC curve, there is still a lot of controversy in academic circles on the theoretical basis and empirical evidence of EKC, including the following. (1) The EKC only pays attention to the impact of economic development on the environment, but from an ecosystem perspective, the economic, social and environmental subsystems are interrelated, and economic development not only affects the environment, but also the economy. In addition, the economy is not the only factor that affects the environment (Zhongmao Zhang & Xuegang Zhang, 2010). From this point of view, model (4.1) only unilaterally describes the impact of the economic system on the environmental system; it does not systematically consider the two-way interaction between the two, and neglects the impact of social development; in addition, the EKC only refects generalization. The relationship between environment and income, even if there is an inverted U-shaped curve, also depicts the regional and short-term environmental impact, rather than the global long-term impact (Geng Qunzhi, 2008). (2) Selection and measurement of environmental quality or environmental pollution variable b. First of all, existing studies are mostly based on a certain pollutant, such as chemical oxygen demand (COD) in wastewater, or SO2 in exhaust gas. It is diffcult to fully refect the level of environmental damage and resource loss with these single-pollutant indicators. Second, the choice of different pollutants exists. There is controversy,

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such as how CO2 is considered to be a GHG rather than an atmospheric pollutant and there is still scientifc uncertainty about what kind of externality it will produce, and there are also signifcant differences in the characteristics of some regulated and unregulated pollutants. In addition, most studies are based on fow pollutants and have not yet refected inventory pollution studies; in the end, the measurement indicators for pollutants are different, such as being based on pollutant emission levels, being based on pollutant discharge concentration/strength and being based on per-capita pollutants and other indicators, which will lead to very different conclusions. (3) The choice of explanatory variable x is generally expressed in terms of per-capita income level or per-capita GDP, but there are also problems. First, the per-capita GDP measures only the average value of a country and fails to reveal the internal income distribution. The state, by comparison, may be more appropriate to choose the median; second, some researchers believe that countries in different stages fnd it diffcult to enter the next stage due to the inherent characteristics of their development stage, so their economic growth and the relationship with the environment is non-homogeneous. It also needs to consider factors such as the level of industrialization to carry out a phased analysis (Han Yujun & Lu Yang, 2009). Also, for other explanatory variables z, only the income level is included in the traditional EKC model. As research deepens, more and more studies examine the possible effects of other explanatory variables. However, these control variables are often more arbitrary and lack theoretical support. (4) For the setting and estimation of the functional relationship f, based on the traditional EKC theory, a quadratic equation needs to be established in order to confrm or falsify the existence of an inverted U-shaped curve, but in the empirical study, it has been found that some countries have some pollution. There is no inverted U-shape, but a signifcant linear, S-type or N-type relationship exists. Therefore, different functional forms need to be identifed and tested, which will lead to differences in conclusions; in addition, because the sample may be cross-section or panel data, the use of different measurement methods will also have an impact on the conclusion. 4.1.3 Empirical summary of EKC model in China The rapid growth of China’s economy and the increasingly severe environmental pollution situation has attracted the attention of a large number of scholars. For example, Peng Shuijun and Baoqun (2006) used the provincial panel data from 1996 to 2002 to empirically analyze six types of pollutants in the model. Controlling other infuencing factors, such as population size, technological progress, environmental protection policies, trade opening, industrial structure and other variables, they found that the inverted

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U-shaped curve relationship depends on the selection of pollutant indicators and model estimation methods, and on some pollutant indicators. In terms of industrial waste water discharge, sulfur dioxide emissions, etc., there may be a turning point where the relatively low level of per-capita income crosses the U-shaped curve of the environment. Fu Jiafeng et al. (2008) conducted an EKC simulation of the CO2 intensity per unit of GDP emissions from the perspective of production and consumption, and used the data from 44 countries from 1990 to 2004 to conduct unit root test and cointegration analysis of panel data. The research conclusions show that, for most developing countries, the CO2 emission intensity from the perspective of consumption is lower than the CO2 emission intensity from the perspective of production, indicating that most developing countries have a net export of carbon content in international trade, but whether or not it is based on the perspective of production or consumption, it is found that there is a signifcant inverted U-shaped relationship between CO2 emission intensity and GDP per capita. Han Yujun and Lu Yang (2009) questioned the hypothesis implied by the environmental Kuznets hypothesis, arguing that countries in different stages will be constrained by the inherent characteristics of their development stage. The traditional EKC assumes economic growth and environment. Relations depend not only on the level of income, but also on the level of industry. To this end, they selected 165 countries from 1980 to 2003 as samples. According to their income levels (per-capita GDP) and industrialization levels (the proportion of industrial added value and the proportion of manufacturing added value), the sample was divided into four groups. The relationship between per-capita CO2 emissions was investigated. The results showed that the “high-industry, high-income” group showed an inverted U-shaped trend and the “low-industry, low-income” group showed a weak inverted U-shaped relationship; the “low-industry, high income” group showed an N-shaped trend, and the “high-industry, low-income” group experienced simultaneous changes in environmental pollution and income growth. Xu Guangyue and Song Deyong (2010) conducted an EKC test on per-capita carbon emissions based on provincial panel data between 1990 and 2007, using panel unit root test, panel cointegration test method and panel-based hybrid least-squares method to estimate the results. There is an EKC curve in China and its eastern and central regions, but there is no inverted U-shaped relationship in the west. In addition, scenario analysis is conducted on the time when the national, eastern and central regions reach infection points. Du Limin (2010) frst estimated interprovincial CO2 emissions between 1995 and 2007 based on fossil energy consumption and cement production activities, and then constructed a model based on per-capita CO2 as an explanatory variable to examine the main infuencing factors of CO2 emissions in China, and using fxed-effect (FE), randomeffect (RE), feasible generalized least square (FGLS) and other methods to estimate the model, found that in the model estimation, the FGLS method is better than FE/RE methods, the dynamic model is better than static model

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and, in addition, the proportion of heavy industry, urbanization level and coal consumption proportions have a signifcant positive impact on China’s CO2 emissions. The per-capita CO2 emissions in the previous period will have a signifcant positive impact on current emissions. There is an inverted U-shape between the level of economic development and per-capita CO2 emissions. The relationship proves the hypothesis of the environmental Kuznets curve. Yu Yihua et al. (2011) studied the relationship between CO2 intensity, per-capita GDP and industrial structure based on provincial panel data. In order to correct the cross-correlation and autocorrelation problems brought by the panel data, an FGLS estimation method was selected after multiple econometric tests. It was found that there was no inverted U-shaped curve relationship, carbon intensity and economic development. There is an N-type relationship between levels, while the proportion of the secondary industry has a signifcant positive impact on carbon intensity. Yuan Peng and Cheng Shi (2011) chose a method that was different from the previous single environmental pollutant index. First, based on the environmental production technology, 284 urban industrial sectors from 2003 to 2008 were taken as samples to defne and measure the comprehensive environmental effciency index for wastewater, industrial SO2 and industrial smoke, and the EKC model test for the environmental quality variables. The conclusion shows that the average environmental effciency is between 0.934 and 0.951, the average annual output loss is 6.1% and there is a signifcant inverted Ushaped curve relationship between environmental effciency and economic growth. The infection point is per-capita income level reaching 30,800 yuan. In addition, the proportion of foreign investment, education income and population density are positively related to environmental effciency, and environmental governance, capital deepening and the proportion of secondary production have signifcantly reduced environmental effciency. According to the research object, variable setting, measurement model estimation method and main conclusions of different literatures, the above research on China EKC can be summarized and summarized as shown in Table 4.1. In summary, when different scholars researched and validated China’s EKC assumptions, there were signifcant differences in data selection, selection of pollutant indicators and setting of other control variables, as well as measurement estimation methods, except that some literatures had corresponding estimates for different estimation methods. In addition, there is a lack of robustness test for the setting of control variables, which leads to great differences in the conclusions, and this leads to policy uncertainty. Based on the above research literature, this chapter studied the infuencing factors of CO2 emissions in China and the emission trends in the next ten years. Its innovations and contributions lie in the following aspects:  frst, a more comprehensive and accurate estimation of the CO2 emissions of various provinces in China from 1995 to 2007, and the establishment of a provincial CO2 emissions panel database, which provides more information for

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Table 4.1 Empirical conclusion of EKC in China Literature

Samples

Pollutant variables

Other controlling variables

Model assessment technique

Conclusion

Peng & Bao (2006)

1996–2002 provincial panel data

Industrial waste water/CO/ dust/smog/ SO2/solid waste

FE, RE

1. Results depend on indicators and method selection. 2. There is an inverted U-shaped curve for industrial wastewater and SO2. 3. Effect of pollution control variables on EKC.

Yu et al. (2011)

1995–2007 panel data of 29 provinces in China

CO2 emission intensity

Population density, technological progress, environment al protection policies, trade liberalization, and industrial structure Industrial structure

FE, FGLS

Yuan & Cheng (2011)

2003–2008 industrial sectors in 284 cities

Industrial wastewater, industrial SO2 and industrial soot

Foreign investment, education investment, environmental governance, population density, capital deepening, industrial structure

FE

Xu and Song (2010) Du (2010)

1990–2007 provincial data 1995–2007 provincial data

Per-0capita CO2

1. N-type relationship between CO2 intensity and per-capita income. 2. The ratio of secondary production to carbon. Positive correlation of emission intensity. 3. If the industrial structure does not change, economic growth itself will hardly lead to a decrease in carbon intensity and achieve the 2020 target. 1. The average environmental effciency is 0.934–0.951, and the average potential output loss is 6.1%. 2. There is an inverted U-shaped relationship between percapita GDP and environmental effciency. The infection point is 30,800 yuan. 3. The proportion of foreign investment, education input and population density are positively correlated with environmental effciency. Environmental governance, capital deepening and the proportion of secondary production are negatively correlated with environmental effciency. An EKC curve exists nationwide and the eastern and middle parts of China while it does not exist in the west.

Energy consumption structure, industrial structure, industrial structure, urbanization level, time trend

FE, RE, FGLS

Per-capita CO2

1. Presenting an inverted U shape. 2. The proportion of heavy industry, urbanization level, coal structure, and emissions from the previous period are positively correlated.

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further quantitative analysis. In the past, the analysis of time series data at the national level was a big improvement. Second, unlike previous studies, this chapter did not set a specifc measurement model in advance. Instead, it used the in-sample ftting criteria and out-of-sample prediction criteria to select the model. The superior measurement model is more scientifc than the traditional EKC model. In addition, this chapter not only analyzes the infuencing factors of CO2 emissions in China, but also predicts the total CO2 emission and per-capita value for a period of time in the future. The negotiation of CO2 emission reduction and the implementation of domestic emission reduction strategies have important policy implications.

4.2 Estimates of CO2 emissions in different provinces Unlike other environmental pollution such as sulfur dioxide, dust and water pollution, China does not directly publish CO2 emission data. It must be estimated through fossil energy consumption, conversion activities and certain industrial production processes. This will be specifcally estimated through relevant calculation formulas. For provincial CO2 emissions from 1995 to 2007, the estimation basis is mainly the methods of the IPCC and the National Climate Change Coordinating Group Offce and the Energy Research Institute of the National Development and Reform Commission (Du et al., 2012). CO2 emissions mainly come from fossil energy consumption, conversion and cement production. For the sake of accuracy, energy consumption is further divided into coal consumption, oil consumption (including gasoline, kerosene, diesel, fuel oil) and natural gas consumption. A  large part of the coal consumption process is used to generate electricity and heat. Although some of the electricity and heat generated by this coal consumption may not be used in this province, the resulting CO2 does indeed remain in the province. Therefore, this chapter calculates energy. When it comes to consumption, in addition to terminal energy consumption, it also includes coal for power generation and heating. All of the energy consumption and conversion data in this chapter were taken from the regional energy balance sheet in the annual energy statistics yearbook and the cement production data were from the Guotai Security Financial Database. Due to the inaccessibility of data, this chapter does not estimate the CO2 emissions from the Tibet Autonomous Region. Because Chongqing was subordinated to Sichuan Province before 1997, for consistency of statistical calibers, this chapter will combine Chongqing and Sichuan provinces for calculation. The specifc calculation formula for carbon dioxide emissions from fossil energy consumption activities is as follows: EC = ˛ Ei × CFi × CCi × COFi ×

44 12

(4.2)

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Among them, CO2 represents the estimated total amount of carbon dioxide emissions from various energy consumption; i denotes a variety of energy consumption, including coal, gasoline, kerosene, diesel, fuel oil, and natural gas; and Ei is the province’s consumption of various energy sources. Total CFi is the conversion factor, which is the average calorifc power of various fuels, in units of trillion joules/ton, or trillion joules/million cubic meters; CCi is the carbon content, which represents the carbon content per unit of heat. The level is expressed in tons/trillion joules; COFi is a carbon oxidation factor, which refects the level of oxidation of energy. If it is equal to 1, it means complete oxidation, but it is usually less than 1, and often there is a part of carbon completely oxidized, but remaining in the residue or ash; because the relative mass of the oxygen atom is 16, and the relative mass of the carbon atom is 12, 44/12 represents the conversion factor that converts the mass of the carbon atom into the molecular mass of carbon dioxide. The difference between the two is about 3.67 times. Among them, CFi×CCi×COFi is called the carbon emission factor, and CFi×CCi×COFi×44/12 is the carbon dioxide emission factor. The calculation of carbon dioxide emissions from cement production is relatively simple. It is only necessary to multiply the cement output by the corresponding CO2 emission factor. Table 4.2 lists the CO2 emission factors for each source. After estimating the CO2 emissions from each province, we can further analyze the emission trends, emission structure and regional differences. In 1995, the per-capita CO2 emissions in the provinces were mainly concentrated in the range of 1–3 tons and the distribution was relatively concentrated. This shows that the difference between provinces is not very large. In the province’s per-capita emissions, the minimum is less than 1 ton (Hainan) and the maximum is about 5 tons (Tianjin). The 1999 kernel density function was basically

Table 4.2 Coeffcients of CO2 emissions from various sources Fuels

Coal

Carbon content 27.28 (t-C/TJ) Calorifc value 192.14 data (TJ/ten thousand tons or TJ/hundred million m3) Carbon 0.923 oxidation rate Carbon emission 0.484 coeffcient CO2 emission 1.776 coeffcient

Gasoline Kerosene Diesel

Fuel oil

Natural gas

Cement

18.90

19.60

20.17

21.09

15.32

–

448.00

447.50

433.30

401.90

3893.10

–

0.980

0.986

0.982

0.985

0.990 –

0.830

0.865

0.858

0.835

5.905 –

3.045

3.174

3.150

3.064

21.670 0.527

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similar to that of 1995, but the minimum and maximum values were slightly higher, 1.06 tons (Guangxi) and 5.39 tons (Shanghai). This refects that from 1995 to 1999, China’s energy consumption has not increased substantially, the energy consumption structure has not changed signifcantly, cement production has not signifcantly expanded; and on the other hand, it also refects the gaps and distribution of CO2 emissions among various provinces in China. There is no big change. In 2003, the distribution of CO2 emissions per capita in the provinces had major changes. Not only were the distributions even more dispersed, but per-capita emissions also increased signifcantly. Per-capita CO2 emissions are mainly distributed between 2 and 5 tons, of which 3 tons are the highest in the distribution density. The minimum is 1.35 tons (Guangxi) and the maximum is 6.33 tons (Ningxia). In 2007, the distribution of CO2 emissions in various provinces was further decentralized, mainly concentrated between 2 and 8 tons, of which about 5 tons was the highest density of emissions, but its density was still less than 0.2. The minimum is about 2 tons (Sichuan) and the maximum is more than 12 tons (Inner Mongolia). The gap between the two is further widened. The per-capita CO2 emission kernel density evolution trend refects the fact that since 2002, China’s economy has entered a new round of development channels, and industrialization and urbanization are advancing rapidly, leading to the fact that energy consumption and the output of cement, steel and other industrial products have increased substantially. It refects the fact that the development of the provinces is uneven and the gap is further widened. As we all know, the unbalanced level of economic development between regions in China, which inevitably makes the energy consumption and cement consumption between regions unbalanced, resulting in the imbalance of per-capita CO2 emissions among various regions. In addition, there are also signifcant differences in resource endowments between regions in China. The coal resources in the western region are relatively abundant and the proportion of coal consumption in the western provinces may be higher and the carbon emissions of coal are the highest, which may also lead to regional difference in CO2 emissions. From 1995 to 2000, the per-capita CO2 emissions in the eastern, central and western regions were maintained at relatively stable levels, the growth was not very large and the western region even fell, but after 2000, all three regions showed rapid growth. Judging from the absolute value of emissions in various regions, the per-capita CO2 emissions in the eastern region are signifcantly higher than those in the central and western regions, while the differences between the western region and the central region are not signifcant. This basic result is consistent with the level of economic development in various regions. It is consistent with resource endowments. The fgure also shows the national per-capita CO2 emission curve estimated in this chapter and the per-capita CO2 emission curve in 1995–2005 estimated by the World Bank. It can be seen from the fgure that the gap between the estimates in this chapter and World Bank estimates. It is very small, which, to a certain extent, corroborates the reliability of the

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estimation in this chapter, and also provides some support for the reliability of the following research conclusions. Fossil energy consumption and cement production are the main sources of CO2 emissions. In estimating per-capita CO2 emissions in each province, this chapter focuses on four sources of coal, oil, natural gas and cement, and calculates their respective emission proportions. Concerning the composition ratios of four different CO2 sources in the three regions of east, central and west, coal consumption is the most important source of CO2 emissions, and oil consumption as the second largest source of emissions has a much smaller proportion, and natural gas produces fewer CO2 emissions. The proportion of CO2 emissions from coal consumption in the central region and the western region exceeds 70%, and even the central region is close to 80%, which is obviously higher than about 70% of the eastern region. It is worth noting that the proportion of CO2 emissions from coal consumption in the western region dropped signifcantly before 2001, from nearly 80% in 1995 to about 70% in 2001, and has remained at this level since then. The proportion of CO2 emitted from oil consumption in the eastern region is close to 20%, which is higher than about 10% in the central and western regions, while the CO2 proportion of natural gas consumption in the eastern region and the central region is relatively small, less than 1%, but in the western region the proportion is much larger and is maintained at around 6%. This result is closely related to the endowment of energy resources and the level of economic development in each region. Cement is also an important CO2 emission source, accounting for about 10% of the total in three different regions, which is basically equal to the proportion of oil consumption in the central and western regions. From the above descriptive analysis, it can be seen that the per-capita CO2 emissions in various provinces in China have increased signifcantly since 1995, and the distribution of per-capita emissions between provinces has been decentralized year by year and regional differences have further widened. Due to the unbalanced economic development among regions, the per-capita CO2 emissions are uneven. With the highest per-capita CO2 emissions from the above descriptive analysis, it can be seen that the per-capita CO2 emissions in various provinces in China have increased signifcantly since 1995, and the distribution of per-capita emissions between provinces has been decentralized year by year, and regional differences have further widened. Due to the unbalanced economic development among regions, the per-capita CO2 emissions are uneven, with the highest per-capita CO2 emissions in the eastern region and much smaller in the central and western regions. From the perspective of emission structure, coal consumption is the main source of CO2 emissions in China, followed by oil consumption, while natural gas consumption varies greatly, and the western region is relatively high, while the eastern and central regions are basically negligible. This cannot be separated from China’s structure of energy consumption. At the same time, emissions from cement production cannot be ignored and their emissions are basically the same as those of oil consumption.

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4.3 Model, variables and statistics After the CO2 emissions panel data from provinces are constructed, a panel data model can be applied for a series of correlation analysis. Consider the following measurement model:

yit = ˜ + ° yi ,t −1 + Z it ˜+ ˛i + ˝ it

(4.3)

Among them, yit is the CO2 emission per capita in the t year of the i province, yit–1 is the frst-order lag term, α is the constant term, δ and β are the regression coeffcients, and ηi is the individual effect. Control the specifc nature of each province, εit is a disturbance item; Zit is an exogenous variable, including per-capita GDP, the proportion of gross industrial output value to the total industrial output value, urbanization level, coal consumption as a percentage of total energy consumption, private car ownership per capita, time trends and other factors. All variables are in logarithmic form. It is worth noting that this chapter did not set up an econometric model in advance, but instead estimated multiple models based on the difference in explanatory variables, and then selected the model through the in-sample ftting criteria and the out-of-sample forecasting criteria, which will be further described below. For the panel data model (4.3), if there is no lag item yit–1 in the model, it can be estimated by the Fixed Effect Model or the Random Effect Model. The difference between the two is that the random effects model is relatively more effective, but requires that the exogenous variable Zit is not related to the individual effect ηi, while the fxed effect model requires no more freedom between the external variable Zit and the individual effect ηi, but consumes more degrees of freedom. Each has advantages and disadvantages, and for this purpose, the Hausman test will choose between these two methods of estimation. Once the model is added with the lag item yit–1, it has a dynamic nature, and the Fixed Effect Model and the Random Effect Model are no longer applicable. In fact, in the dynamic model, because the lag variable yit–1 contains the individual effect ηi, the explanatory variable yit–1 and the individual effect ηi are related, so the random effect estimator is biased. For the fxed effect estimator, although the intra-conversion (within transformation) can eliminate T 1 the individual effect ηi, the transformed dynamic term yit −1 − ˛ y T −1 t = 2 it −1 T 1 and the disturbance term ˝ it − ˛ ˝ are still related, so there is still an T −1 t = 2 it endogeneity problem and thus a fxed effect. The estimate is also biased. For the dynamic panel model, Anderson and Hsiao (1981) proposed that the frst-order difference elimination of the individual effects of the model ηi, because of the difference between the explanatory variables and the

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disturbance items, they further suggest using an instrumental variable to estimate. Arellano and Bond (1991) further proposed to use all predetermined variables (yi1, yi2, …, yit–2) as explanatory variables and then use the Generalized Method of Moment (GMM) to estimate. As the latter uses more information and its estimation is more effective, in the following dynamic model, this chapter will use the GMM estimation method of Arellano and Bond (1991). The variables involved in the quantitative analysis of this chapter, except for the per-capita CO2 emissions of the provinces, have been explained and explained in detail above, other variables are constructed as follows. 1. Per-capita GDP (indicated by per_capita GDP). A  large number of studies have pointed out that there is a non-linear relationship between per-capita CO2 emissions and GDP per capita. The basic idea is that people’s requirements for the environment will change in different income phases. Therefore, the per-capita GDP index is added to this model. The provincial GDP and population data can be obtained from the provincial statistical yearbooks. To ensure comparability, this chapter converts the nominal GDP for each year to the actual value based on 1995. 2. The proportion of coal in total energy consumption (expressed by Ratio_ coal). CO2 emissions from various energy consumptions are not the same. Coal emissions are 1.6 times that of natural gas and 1.2 times that of oil. However, the consumption of nuclear power and renewable energy such as hydropower, wind power, and solar energy does not emit CO2. China’s energy consumption is dominated by coal, so it is of great signifcance to consider the impact of coal consumption on CO2 emissions. This indicator is expressed by the proportion of coal consumption in each province in the total primary energy consumption of each province (equivalent to the ratio after standard coal). The data needed to calculate the proportion of coal consumption in each province come from the “China Energy Statistical Yearbook” of the past years. 3. Heavy industry proportion (represented by Ratio_heavy). Use the proportion of gross industrial output to total industrial output. Relative to light industries, heavy industry energy consumption is much higher, so the emission of CO2 is also much higher. Since 1995, the proportion of heavy industries in various provinces in China has continued to rise, and there is a trend of further improvement. This will inevitably have an important impact on future CO2 emissions. It is worth pointing out that the statistical coverage of heavy industry shares in this chapter is all state-owned and non-state-owned industrial enterprises above a designated size. Nonstate-owned enterprises below the scale do not include statistics. This will, to a certain extent, overestimate the proportion of heavy industry, because it is often based on light industry. The data of gross industrial output value and light industrial output value come from the Statistical Data Collection of the New China 55 Years and the Statistical Yearbooks of each province.

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4. Urbanization level (represented by Ratio_urban). The advancement of urbanization requires not only the consumption of large amounts of iron and steel cement, but also changes in people’s living habits, leading to a signifcant increase in energy consumption. Therefore, the level of urbanization is an important factor affecting CO2. In general, the level of urbanization is expressed as the proportion of urban population to the total population, but we cannot obtain data on the statistical caliber of this indicator. When the Bureau of Statistics counts the urban population, the number of permanent residents of the city after 2004 is counted. Before 2004, only the household population was counted. There is a big difference between the two. The resident population not only includes the household registration population, but also includes migrants who have lived there for more than six months. Therefore, there is a big gap between the general and household registration data. The indicators of urbanization that are consistent in terms of statistical caliber in this chapter are the proportion of non-agricultural population according to household registration statistics in each province, which is lower than the level of urbanization that is usually calculated by the permanent population. The data come from the “China Demographic Yearbook” and the “China Population and Employment Statistics Yearbook”. 5. Per-capita private car ownership (represented by Per_car). With the development of the social economy, the number of privately owned vehicles in China has grown rapidly, which means the consumption of large amounts of fuel such as gasoline and the rapid increase in CO2 emissions. This chapter controls this. The private car ownership data in this chapter come from the “China Automotive Market Almanac” of the past years.

4.4 Measurement results and model selection 4.4.1 Measurement results and explanation Based on the econometric model (4.3), this chapter estimates seven regression models based on different explanatory variables. The results are shown in Table 4.3. Compared with setting a single econometric model in advance, the advantage is that it can determine the optimal econometric model through the specifcation search. Models 1–4 and model 6 do not involve the dynamic term of the interpreted variable. Therefore, the OLS-based fxed effect model or the FGLS-based random effect model can be used for estimation. Although both have advantages and disadvantages, the random effect model requires explanation. The variables and the individual effects ηi are irrelevant. For this purpose, the Hausman test is used to distinguish them. The test results show that all fve static models should be estimated using a fxed effect model. For dynamic models 5 and 7, this chapter estimates the GMM estimator developed by

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Table 4.3 Measurement results and model selection Explanatory variable

Model 1

Model 2

Model 3

Model 4

Model 5

Ln(per_GDP)

0.687*** (0.022) –0.017 (0.020)

0.622*** (0.034) –0.027 (0.019) 0.334** (0.113) 0.213*** (0.059)

1.033*** (0.044) –0.103*** (0.017) 0.543*** (0.096) 0.140*** (0.050) –0.262*** (0.065)

0.969*** (0.065) –0.118*** (0.021) 0.587*** (0.109) 0.124*** (0.050) –0.271*** (0.024) 0.032 (0.037)

0.743*** 0.962*** 0.689*** (0.049) (0.047) (0.021) –0.118*** –0.103*** –0.109*** (0.013) (0.017) (0.017) 0.407*** 0.402*** 0.308*** (0.082) (0.101) (0.100) 0.044*** 0.133*** 0.055*** (0.008) (0.049) (0.006) –0.242*** –0.258*** –0.243*** (0.016) (0.021) (0.049)

Ln(per_GDP)2 Ln(ratio_heavy) Ln(ratio_coal) Ln(time) Ln(per_cars) Ln(ratio_urban) Ln(per_CO2t-1) Constant Times of observation Number of items Measurement method Hausman test Arellano & Bond test R-squared AIC BIC RMSFE MAE

Model 6

Model 7

0.351*** 0.236*** (0.091) (0.104) 0.413*** 0.412*** (0.038) (0.021) 1.207*** 1.404*** 2.051*** 1.941*** 1.465*** 2.400*** 1.702*** (0.012) (0.045) (0.065) (0.132) (0.093) (0.111) (0.104) 377 377 377 348 319 377 319 29

29

29

29

29

29

29

Within

Within

Within

Within

GMM

Within

GMM

8.62** – 0.429 –251.61 –245.75 0.222 0.361

13.94*** 34.22*** 35.15*** – – – – –1.433

48.19*** – – –1.346

0.581 –286.36 –284.49 0.189 0.334

0.533 –402.59 –404.72 0.152 0.275

0.524 –395.62 –395.76 0.143 0.257

0.537 –373.94 –376.24 0.133 0.247

0.882 –760.20 –762.49 0.170 0.323

0.814 –756.31 –762.61 0.182 0.333

Note:  *** indicates that 1% level is signifcant, ** indicates that 5% level is signifcant, and * indicates that 10% level is signifcant.

Arellano and Bond (1991). The consistency of this estimator has an important premise; that is, there is no second-order sequence correlation in the perturbation terms after the frst difference. This can be tested using the test methods provided by Arellano and Bond (1991). The results are shown in Table 4.3. From the test results in the table, it can be seen that neither Model 5 nor Model 7 can reject the null hypothesis without the second-order sequence correlation. Therefore, the GMM estimation in this chapter is consistent. Model 1 fts the most basic quadratic EKC model. If there is indeed an inverted U-shaped curve between per-capita CO2 emissions and per-capita GDP, the coeffcient of the explanatory variable ln(per_GDP)2 should be signifcantly negative. However, from the regression results, it can be seen that in this simple regression, although the coeffcient of the explanatory variable

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ln(per_GDP)2 is negative, it is not signifcant, indicating that the simple EKC model is under study. China’s CO2 emissions do not apply. Model 2 further controls the proportion of heavy industry and the proportion of coal consumption on the basis of the basic regression model. The index of the proportion of heavy industry is the proportion of the total industrial output value of heavy industry to the total industrial output value, while the proportion of coal consumption refects the proportion of coal consumption in the total primary energy consumption. From the regression results, it can be seen that the coeffcient signs of these two explanatory variables are in accordance with common sense and expectations. The higher the proportion of heavy industry, the more CO2 emissions, and this relationship is signifcant at the 1% level. On average, for every 1 percentage point increase in heavy industry, CO2 emissions per capita will increase by about 0.334 percentage points. The increase in the proportion of coal consumption will also increase the per-capita CO2 emissions, and it will be signifcant at the 1% level. On average, the elasticity of per-capita CO2 emissions to coal consumption is about 0.213. It is noteworthy that, after controlling the proportion of heavy industry and the proportion of coal consumption, the coeffcient of the explanatory variable ln(per_GDP)2 is negative, but it is still not signifcant even at the level of 10%. Model 3 further increases the time trend explanatory variable ln(time) to control the effect of exogenous technological progress on CO2 emissions per capita in all provinces, which is also a commonly used method in related research. The time trend variable appears in logarithmic form, mainly to refect the fact that the marginal effect of technological progress on the reduction of CO2 emissions has become less and less with the passage of time. It is worth pointing out that, in the panel data model, the common effect of exogenous technological advances on provinces can also be achieved by adding the Time Specifc Effect λt, but in this chapter two problems emerge. First, in applications, when the fxed effect model is used to estimate, adding the time effect λt is equivalent to adding 13 parameters to be estimated, which will result in an additional 13 degrees of freedom loss. Second, when the regression model for out-of-sample prediction is used, the out-of-sample trend of time effect can be involved; although it can be artifcially set, additional assumptions will inevitably affect the reliability of predictions. However, the addition of the time trend variable ln(time) can help avoid the problem. Because one of the important purposes of this chapter is to make predictions, it is more appropriate to use time trend variables. From the regression results of Model 3, it can be seen that exogenous technological progress does have a signifcant impact on per-capita CO2 emissions. The sign of the coeffcient is negative, indicating that over time, exogenous technological progress tends to reduce CO2 emissions per capita. This result is understandable. Technological progress has improved the effciency of China’s energy use, resulting in a signifcant reduction in energy intensity (energy consumption per unit of GDP), which has led to a signifcant

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reduction in per-capita CO2 emissions. After controlling the factors of exogenous technological progress, the impact of heavy industry on per-capita CO2 emissions has been further enhanced, while the impact of coal consumption on CO2 emissions has been relatively reduced. The coeffcient of one-time term and quadratic item of per-capita GDP also changed. Although the coeffcient sign remains unchanged, the quadratic coeffcient becomes signifcant at the 1% level. With the development of society and economy, the ownership of percapita private cars in China is continuously increasing, and vehicle exhaust is considered to be an important source of CO2 emissions. For this reason, Model 4 further controls the impact of per-capita private car ownership on per-capita CO2 emissions. The regression results show that the regression coeffcient is not only very small, but is still not signifcant even at the level of 10%, which shows that per-capita private car ownership has a very small impact on China’s per-capita CO2 emissions, at least for the time being negligible. This result may be due to the fact that China’s per-capita private car ownership is still very low, and its CO2 emissions are basically negligible compared to industrial CO2 emissions. Models 5–8 also consider the impact of private car ownership per capita, but the results are not signifcant (and therefore are not reported in the table), which further illustrates the robustness of the results. Model 5 further considers the frst-order lags of per-capita CO2 emissions. The basic implication is that the amount of per-capita CO2 emissions in the previous period will have an impact on the per-capita CO2 emissions. As Auffhammer and Carson (2008) pointed out, the rationality of considering the lagged term of the explanatory variable lies in the lag of capital adjustment, and the depreciation of any capital equipment has a certain period, especially for large-scale machinery equipment, by adding lags. Items can better control this factor. Intuitively speaking, the per-capita CO2 emissions of the previous period should have a positive impact on current emissions. Due to the lag in the renewal of machinery and equipment, the more emissions in the previous period, the more emissions in this period there should be. The size of the regression coeffcient of the lagging term indicates the speed of capital adjustment. The smaller the coeffcient, the faster the capital adjustment, and the larger the coeffcient means the slower adjustment of capital, but the adjustment factor should obviously not be greater than 1. From the regression results of Model 5, it can be seen that the coeffcient of the lag item is 0.413, and it is signifcant at the 1% level, which is in line with our expectations. With the acceleration of China’s industrialization process, the level of urbanization in China has also continued to increase. Intuitively speaking, the increase in urbanization will generate more CO2 because urbanization requires a large amount of reinforced concrete, and the living habits of the urban population are also different from those of the rural population, and their energy consumption will multiply exponentially, thus the level of urbanization. It will be an explanatory variable that needs to be considered.

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Regrettably, we cannot get a consistent urbanization level indicator. The level of urbanization is generally represented by the urbanization rate, which is defned as the proportion of urban population in the total population. The population here refers to the permanent population. However, in 2004, China changed its population statistics. Before 2004, there were no statistics on the permanent population of cities in each province, but after 2004, the permanent population was counted. The “China Demographic Statistics Yearbook” provides the city population and rural population according to household registration statistics, so we can get the proportion of urban population according to household registration statistics. It must be pointed out that the level of urbanization calculated by household registration population is much lower than the actual rate of urbanization, especially in provinces with large population fows. As a comparison, we included the urbanization rate as an explanatory variable into the static model and the dynamic model, respectively. The regression results are shown in Models 6 and 7. From the regression results, we can see that, whether it is a static model or a dynamic model, the urbanization rate has a signifcant impact on CO2 emissions and the increase in urbanization levels does tend to increase CO2 emissions, but the coeffcients of the dynamic model are slightly smaller. 4.4.2 Model recognition and selection In general, the model setting is often based on R-squared and adjusted Rsquared, Akaike Information Criterion (AIC) and Schwarz Information Criterion (BIC). These criteria are only applicable to the goodness of ft in the model of the discriminant model, not for the out-of-sample prediction. For forecasting, the better discriminant index is to use the two indicators of Root Mean Squared Forecast Error (RMFSE) and Mean Absolute Error (MAE). Because an important purpose of this chapter is to predict China’s per-capita CO2 emissions and total emissions over a period of time in the future, the commonly used in-sample ft criteria may not be the optimal model selection criteria, and the out-of-sample forecasting criteria may be more practical. This chapter also reports on two model selection criteria. When the conclusions of the two are conficting, the out-of-sample forecasting criteria are the main criteria. The basic idea of the out-of-sample prediction criterion is that, in the case where the number of individuals is N, and the observation value of each individual is n+m times, the frst n observations of each individual are used to estimate the coeffcients of the model, and they are observed after m times. Values to examine the accuracy of the prediction. One-step-ahead forecast can be used to represent the true value, where i=1, 2, …, N, h=0, 1, …, m–1. For individual i, the difference between the true value and the predicted value is the prediction error, and a total of mN prediction errors are available. RMSFE squares out the square of this mN forecast error:

103

Industrial structure, income level, CO2 ˆ 1 RMFSE = ˘ ˇ mN

N

˛ i=1

2  ˛ e i,n + h +1  h=0 m−1

103

1/ 2

(4.4)

MAE is defned as: MAE =

1 mN

N

m−1

i=1

h=0

˛ ˛ e

i , n+ h +1

(4.5)

For different measurement models, the smaller the RMFSE and the MAE, the more accurate the prediction of the model. Therefore, the smaller the RMFSE and the MAE the better. This chapter uses the frst 9 years of the 13-year data to estimate regression model coeffcients, while the last 4 years of data are used to calculate RMFSE and MAE. It can be seen from the results in Table 4.3 that the RMFSE and MAE selection results of each model are consistent, and the results of Model 3 are relatively better. From the in-sample ftting criteria, the R2 of dynamic models 5 and 7 is signifcantly higher than that of the static model, and the AIC and BIC of the dynamic model are also relatively smaller, which means that the dynamic model has a higher degree of ft. However, for the prediction, the out-of-sample forecasting criteria are more important, so in the following forecasting, this chapter will mainly be based on the Model 3 forecasting.

4.5 Forecast The content of this section will be based on Model 3 estimated above to predict the per-capita CO2 emissions and total emissions in China. The forecast of the panel data model can be based on each province separately to make predictions, and then it can be used to obtain national values, or it can be directly used to predict the national data. Relatively speaking, subprovincial projections have to break down the interpretation variables of the national average GDP growth rate, population growth rate, and heavy industry proportion in the forecast period among the provinces. This not only greatly increases the workload, but in addition, the decomposition of indicators is diffcult to do. To be completely reasonable, it is likely to reduce the accuracy of predictions. For this purpose, this chapter will make predictions based on the national average. The 17th CPC National Congress clearly stated that by 2020, China’s per-capita GDP will be quadrupled by 2000, and the goal of building a well-off society in an all-round way will be achieved. By then, China’s per-capita GDP will exceed US$3,000, reaching the level of middleincome countries, and industrialization and urbanization will also be reported. In one paragraph, for this purpose, this chapter sets the forecast period from 2008 to 2020. Although we can predict a longer time span, we consider it appropriate to predict such a short period of 2008–2020, with accuracy and credibility of the forecast taken into consideration.

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4.5.1 Situation setting In terms of Model 3, because the explanatory variable and explained variable are of the same period, it is necessary to preset a specifc value of the explanatory variable to predict the explained variable. In this chapter, scenario simulation is used to set explanatory variables in advance, and then predict according to different scenario settings. This is also a commonly used method in forecasting. The different scenario settings in this chapter are shown in Table 4.4. Per-capita GDP growth rate: the 17th National Congress of the Communist Party of China explicitly stated that GDP per capita in 2020 quadrupled the number in 2000, and the goal of building a well-off society in an all-round way. In 2000, China’s per-capita GDP was 7,858 yuan, and as a result, it was estimated that it would reach 31,432 yuan in 2020, and the average annual growth rate of per-capita GDP in 20 years must reach 7.18%. In 2007, China’s per-capita real GDP has reached 14,866 yuan (calculated at 2000 prices), and the average annual growth rate of real GDP per capita reached 9.54% in 2001–2007. For this reason, the average annual growth rate of per-capita real GDP in 2008–2020 is not limited. Less than 5.93% will achieve the goal of quadrupling. Considering that the global economic crisis since 2008 will continue for some time, it is likely to enter a new round of economic growth after 2011. This chapter will set the growth rate of GDP per capita in 2008– 2010 to 7%, and set it to 8% in 2011–2015, 7% in 2016–2020, and increase and decrease one percentage point, respectively, as a high-growth and low-growth scenario based on the benchmark growth rate. The proportion of heavy industry:  at present, China’s economy is at the stage of heavy industrialization, and the proportion of heavy industry is continuously rising, from about 56% in 1995 to about 70% in 2007. When the current stage of development in China and the goal of ensuring economic Table 4.4 Scenario setting of explanatory variables Variables

2008–2010

2011-1015

2016–2020

Growth rate

Growth rate of per-capita GDP

8% 7% 6% 0%

9% 8% 7% –0.5%

8% 7% 6% –0.5%

High Medium Low –

0%

–1%

–1%

–

5%

5%

5%

–

Changing ratio of the proportion of heavy industry Changing ratio of the proportion of coal consumption Natural population growth rate

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Industrial structure, income level, CO2

105

growth by 8% as response to contemporary economic crisis are considered, the process of heavy industrialization may continue for a long period of time. For this reason, this chapter assumes that the proportion of heavy industry in 2008–2010 will remain at 70%, and will decline after 2011 by 0.5% annually and by 65% by 2020. Energy consumption structure:  China’s energy consumption is dominated by coal. Since 1995, the proportion of coal consumption in total energy consumption has remained at around 70%, the proportion of oil consumption has remained at around 20%, and the proportion of natural gas consumption is about 3%, while the proportion of clean energy such as nuclear power, hydropower and wind power is about 7%. It is worth pointing out that in recent years, the state encourages the development of renewable energy such as wind power, hydropower, and solar energy, as well as the development of nuclear power. The proportion will increase signifcantly in the next few years. This situation must be considered in scenario simulations. According to the objectives of the National Development and Reform Commission’s Medium and Long Term Development Plan for Renewable Energy, by 2010 and 2020, China’s renewable energy development and utilization will reach 300 million tons of standard coal and 600 million tons of standard coal, accounting for 10% and 16% of the total energy consumption, respectively. The goal of the National Development and Reform Commission’s Mid-term and long-term planning of nuclear power shows that by 2020, China’s installed nuclear power capacity will reach 40  million kilowatts and annual power generation will reach 280 billion kilowatt-hours, equivalent to more than 90 million tons of standard coal converted to standard coal. The proportion of total energy consumption will rise from less than 1% at present to about 3%. The increase in the proportion of renewable energy and nuclear power will replace some of the coal and oil consumption. In the light of these circumstances, this chapter assumes that the share of China’s coal consumption will remain unchanged at 70% from 2008 to 2010, and will be reduced by 1 percentage point between 2011 and 2020, and will drop to 65% by 2015 and further decrease by 2020 to 60%. Natural population growth rate:  according to the National Population Development Strategy Research Report released by the National Population Development Strategy Research Group, by 2010 China’s population will reach 1.36 billion, and by 2020 it will reach 1.45 billion. In 2007, China’s total population was 1.321 billion. According to the population forecast in the National Population Development Strategy Research Report, the average annual population growth rate in China in 2008–2010 should be 9.7%, and the average annual population growth in 2010–2020 is natural. The rate should be 6.4%. However, from the actual situation, since 2004, the natural population growth rate in our country has fallen below 6%, and there is still a trend of further decline. In 2007, it was 5.17% and in 2008 it was 5.08%. For this reason, this chapter simply assumes 2009–2020. The natural population growth rate in our country is 5%.2

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4.5.2 Forecast results and analysis Table  4.5 reports the forecast results of China’s per-capita CO2 emissions and total national CO2 emissions from 2008 to 2020. From the table, we can see that in the period 2008–2020, China’s per-capita CO2 emissions and total emissions will continue to rise and the results of different scenarios are quite similar. This is understandable. The rapid economic growth has promoted the process of industrialization and urbanization in our country. The increase in the level of industrialization and urbanization will inevitably increase the consumption of resources such as energy and cement, thus promoting the increase of CO2 emissions. In addition, from the perspective of energy consumption structure, the current energy consumption structure dominated by fossil energy cannot be substantially changed in the short term, which is obviously not conducive to CO2 emission reduction. From the perspective of per-capita CO2 emissions, even in the most pessimistic scenario, China’s per-capita CO2 emissions will still be only 9.67 tons by 2020, and in the most optimistic scenario, it will be only 8.09 tons. It is useful to make an international comparison. According to the report of the Netherlands Environmental Assessment Agency, in 2007, the average American CO2 emissions were 19.4 tons, Russia 11.8 tons, and EU 8.6 tons. Relatively speaking, China’s per-capita CO2 emissions are much lower than in developed countries, and even by 2020 it is only equivalent to the EU’s 2007 level. However, from the perspective of total CO2 emissions, total emissions in China will reach 7 billion tons in 2008. In the most pessimistic scenario, China’s total CO2 emissions will exceed 10 billion tons in 2014, and even in the most optimistic scenario, it will exceed 10 billion tons in 2017. The results of low per-capita emissions and high total emissions should be dialectically viewed. Relative to the USA and other developed countries, China’s per-capita CO2 emissions are still relatively low. If historical emissions are counted, the gap between the two will further expand, but due to the large number of people in China, the total amount of emissions will also be relatively large. Therefore, it is not objective for some countries to criticize that China’s total CO2 emissions are too large. However, the low per-capita emission does not mean that China cannot pay attention to emission reduction issues. After all, climate change is a global issue. China has a vast territory and a large population. Climate change has the greatest harm to China and must be taken seriously. What is certain is that in the post-Kyoto negotiations on international GHG emissions reductions, the international community’s demand for emission reductions in China will inevitably increase further. The Chinese government must be prepared for this. Although China’s CO2 emissions will continue to rise until 2020, this does not mean that China is incapable of reducing CO2 emissions. From the model results in this chapter, there are at least a few ways to control and reduce CO2 emissions. It is an effective way to change the current energy consumption structure, reduce the use of coal, and increase the proportion of renewable

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Table 4.5 Forecast results of CO2 emissions Year Per person Aggregation

High Medium Low High Medium Low

2008

2009

2010

2011

2012

2013

5.52 5.48 5.43 71.89 71.38 70.72

5.81 5.72 5.62 76.05 74.87 73.57

6.12 5.97 5.82 80.51 78.54 76.56

6.40 6.20 6.00 84.62 81.97 79.33

6.74 6.48 6.23 89.56 86.10 82.78

7.10 6.78 6.47 94.81 90.54 86.40

Note: per-capita emission units: tons; total emissions units: 100 million tons.

2014

2015

2016

2017

2018

2019

2020

7.47 7.86 8.25 8.59 8.95 9.30 9.67 7.09 7.41 7.73 8.01 8.29 8.57 8.86 6.71 6.97 7.23 7.44 7.65 7.87 8.09 100.25 106.01 111.83 117.02 122.53 127.96 133.72 95.15 99.94 104.78 109.12 113.50 117.92 122.52 90.05 94.01 98.00 101.35 104.74 108.29 111.87

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energy such as hydro power, wind power, solar energy, and nuclear power. At present, China’s coal consumption ratio is still close to 70%, while the use of renewable energy such as renewable energy and nuclear power is only about 7.5%, so there is much room for improvement. In fact, in recent years, the Chinese government has also vigorously advocated and supported the development of new energy. If there is a major breakthrough in new energy development technology and the development cost is signifcantly reduced, CO2 emissions will be better controlled. Second, reduce energy intensity (energy consumption per unit of GDP) through technological advancement, thereby reducing CO2 emissions. Although China’s energy effciency has been greatly improved, from 4.01 tons of standard coal per 10,000 yuan of GDP in 1995 to 1.16 tons in 2007, there is still much room for improvement compared with developed countries. This is undoubtedly another effective way to reduce CO2 emissions. Finally, optimize the industrial structure and reduce the proportion of heavy industries. Heavy industry is often a high-energy consumption industry. Under the current fossil energy-based energy consumption structure, the reorganization of industrial structure will inevitably increase CO2 emissions. At present, our country is in the middle and late stage of industrialization, and the proportion of heavy industry is still relatively high. However, with the further development of the economy and the further adjustment of the industrial structure, the issue of CO2 emissions will be alleviated to some extent.

4.6 Conclusions and policy suggestions With the increasing global warming problem, the issue of international emission reduction of carbon dioxide has attracted much attention. As one of the countries with the largest annual carbon dioxide emissions, China’s emission reduction policies have attracted particular attention. Scientifcally and objectively assessing the current status and future trends of CO2 emissions in China, in-depth analysis of major factors affecting CO2 emissions in China has important policy implications for the Chinese government to formulate relevant emission reduction policies. For the frst time, this chapter has relatively accurately estimated the CO2 emissions of various provinces in China from 1995 to 2007, built a provincial CO2 emissions panel database, and used panel data measurement methods to analyze in depth the infuencing factors of CO2 emissions in China, and adopted a combination of standard and out-of-sample forecasting criteria to select models, determine the optimal measurement model, and then predict the per-capita CO2 emissions and total emissions by 2020 through scenario simulation. The conclusions of this chapter are summarized as follows. (1) Since 1995, China’s per-capita CO2 emissions have increased signifcantly, from 2.45 tons in 1995 to 5.1 tons in 2007. There is a large difference in CO2 emissions per capita between provinces, and their distribution is

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decentralized year by year. Per-capita CO2 emissions are not balanced among different regions, with the highest per-capita CO2 emissions in the east and much smaller emissions in the central and western regions. From the perspective of emission structure, coal consumption is the main source of CO2 emissions in China, followed by oil consumption and cement production, while natural gas consumption varies greatly: the western region is relatively high while the eastern and central regions are basically negligible. (2) Whether it is a static model or a dynamic model, the measurement results show that the level of economic development, the proportion of heavy industry, the structure of energy consumption, the level of urbanization and technological progress are the most important factors affecting CO2 emissions in China. Reducing the proportion of coal consumption and the proportion of heavy industries will help reduce the per-capita CO2 emissions, while technological progress will reduce CO2 emissions by improving energy use effciency. (3) Scenario simulation forecast shows that by 2020, China’s per-capita CO2 emissions and total emissions will continue to rise. From the perspective of per-capita emissions, it is likely to exceed 7 tons in 2015 and reach 9 tons in 2020. However, relative to developed countries, China’s per-capita CO2 emissions are still very low. From the perspective of total emissions, total CO2 emissions in China after 2015 will likely exceed 10 billion tons, and in 2020 it may reach 12 billion tons or more. The fndings of this chapter have important policy implications: First, in the next decade or so, China’s per-capita CO2 emissions and total CO2 emissions will continue to rise. This is inseparable from China’s current economic development stage. Industrialization and urbanization have promoted the continuation of energy consumption. The growth will inevitably increase CO2 emissions. In the post-Kyoto International Emission Reduction Negotiations, the Chinese government must take this factor into account and strive for an emission reduction result in the international emissions reduction negotiations that will not hinder economic development and will not harm the image of a responsible big country. Second, although China’s total CO2 emissions are already quite high, percapita emissions are still relatively low. Even though China’s per-capita CO2 emissions will still be only about 9 tons by 2020, this is far lower than the US’s per-capita emission in 2007. The volume (19.4 tons) is basically the same as the EU level in 2007 (8.6 tons). If historical emissions are considered, the gap will further widen. The developed countries unilaterally emphasize that the total amount of CO2 emissions in China is objective and fair. China’s CO2 emissions are not only related to a large population number, but are also related to the stage of socio-economic development. Developed countries do not give China more support in terms of technology and funding to help

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reduce CO2 emission than simply and blindly accusing China of excessive total emissions. Finally, although China’s per-capita emissions are still very low, this does not mean that China has done nothing to reduce CO2 emissions. In fact, the Chinese government has made signifcant efforts to this end. In the further emission reduction, the Chinese government can start with energy consumption structure, industrial structure and technological progress, vigorously develop renewable energy to replace traditional fossil energy, limit the development of high-energy consumption heavy industry, strengthen scientifc research investment, improve energy use effciency, and thus control and reduce CO2 emissions.

Notes 1 This chapter is based on the paper “Economic development and carbon dioxide emissions in China: Provincial panel data analysis”, China Economic Review, 2012, 23(2), published by Du Limin, Wei Chu and Cai Shenghua. 2 Due to statistical reasons, the total number of people in each province is slightly different from the total number of people in the country. In 2007, the total population of the country was 1.321 billion, and the total population of each province was 1.296 billion (excluding Tibet). As the model in this chapter is the result of estimation based on provincial data, the cumulative number of provinces in 2007 is used as the base population in the forecast process.

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5

Quantitative assessment of CO2 emissions from China’s production sector1

According to the “National Climate Change Initial National Information Bulletin” of the People’s Republic of China, the CO2 emissions from fossil energy activities in China were 2.795 billion tons in 1994, of which the industrial sector accounted for 44.36%, the energy processing and conversion sector 34.42%, and the transportation sector 5.94%. For example, agriculture, residential life, and service industries account for 15.42% (National Development and Reform Commission, 2004). According to data from the World Resources Institute in 2007, of the 6.038 billion tons of CO2 emitted from all fossil energy activities, the power and heat sector contributed 48.5%, the manufacturing and construction industry accounted for 28.2%, and traffc accounted for 6.1% (WRI, 2011). It can be seen that China’s industry, especially the energy sector, has become China’s main source of greenhouse gas (GHG) emissions. In the future, in order to mitigate and adapt to the effects of climate change, China should actively adjust the industrial structure in the national economy. To this end, it is necessary to frst objectively evaluate the actual energy consumption and CO2 emission levels of various economic sectors and identify CO2 on this basis. The main infuencing factors of emissions provide the basis for scientifcally formulating corresponding climate policies. In this chapter, six main fossil energy consumption departments for agriculture, industry, energy, construction, transportation, and commerce were selected as research objects. The research period was 1996–2009, and the CO2 emissions of each sector will be estimated separately and based on logarithms. The average divergence factor decomposition (LMDI) factorizes the total CO2 emissions, and quantitatively calculates the relative contributions of factors such as industrial structure, output scale, sector energy intensity, and energy carbon emission structure. The structure of this chapter is as follows. The frst part introduces the Kaya identity decomposition model and calculation methods, the second section constructs related variables, the third section introduces the decomposition of the infuencing factors of the total GHG emissions, and the fourth section details the specifc industries. Analysis is given in the fnal part of the conclusion.

DOI: 10.4324/9780429447655-7

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5.1 Model and methods The Kaya identities were proposed by Japanese scholar Kaya at the IPCC workshop and are usually used to analyze the driving forces of CO2 emissions changes at the national level (Kaya, 1989). The expression is as follows: C=

C E Y ⋅ ⋅ ⋅P E Y P

(5.1)

Among them, C is the total amount of CO2 emissions caused by various types of fossil energy consumption, E total amount of various types of fossil energy consumption, Y total GDP, and P total population. According to our research purpose and data type, we can expand the above formula to get:

C = Si , j Ci , j = Si , j Y ×

Yi Ei Ei , j Ci , j × × × = Si , j Y × Si × I i × fi , j ×CC × i, j Y Yi Ei Ei , j

(5.2)

where: Ci,j represents the CO2 emissions caused by the consumption of the jth fossil energy stone in the ith sector Y is the sum of outputs of all departments, and Yi indicates the output level of the ith department Ei represents the total amount of fossil energy consumed by the ith department, Ei,j is the jth energy consumption of the ith department Si indicates the proportion of department i to total output used to measure the industrial structure, Si = Yi/Y Ii indicates the energy consumption intensity of department i. It is used to characterize the amount of energy consumed by departmental units. Ii = Ei/Yi Fi,j represents the consumption proportion of energy j in department i, used to characterize the energy structure of the department, fi,j = Ei,j/Ei CCi,j denotes the amount of CO2 emitted by the jth energy source in sector i. It is used to characterize the carbon emission structure of different energy sources. CCi,j = Ci,j/Ei,j Among them, changes in carbon emissions at the base period t = 0 and current t = T can be calculated as: Ctot = CT − C0 = YT  Si ,T  I i ,T  fi , j ,T  CCi , j ,T −Y0  Si,0  I i,0  fi , j ,0 CCi , j ,0 = C y + Cstr + Cint + C fuel + Ccoef

(5.3)

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113

That is, during the time period 0  – T, the change in CO2 emissions can be broken down into fve parts:  output scale effect ( C y ), industrial structure effciency ( Cstr ), sectoral energy intensity effect ( Cint ), energy structure effect ( C fuel ), and energy carbon emission coeffcient effect ( Ccoef ). Among them, ∅C y, ∅Cstr , ∅Cint, ∅C fuel, and ∅Ccoef can be calculated by the following formula: ˆ Yi ,T  C y =  i, j L(Ci, j ,T ,Ci , j ,0 ) × ln ˘  ˇ Yi,0 

(5.4)

ˆ Si ,T  Cstr =  i, j L(Ci, j ,T ,Ci , j ,0 ) × ln ˘  ˇ Si,0 

(5.5)

ˆ I i ,T  Cint =  i, j L(Ci, j ,T ,Ci , j ,0 ) × ln ˘  ˇ I i,0 

(5.6)

ˆ fi ,T  C fuel =  i , j L(Ci , j ,T ,Ci, j ,0 ) × ln ˘  ˇ fi,0 

(5.7)

ˆ CCi ,T  Ccoef =  i , j L(Ci , j ,T ,Ci, j ,0 ) × ln ˘  ˇ CCi,0 

(5.8)

It is defned as the logarithmic mean of CO2 emissions from the base period (t = 0) to the year T, that is:  Ci , j ,T L(Ci , j ,T ,Ci, j ,0 ) = (Ci, j ,T ,Ci , j ,0 ) / ln   Ci , j ,0 

   

(5.9)

5.2 Variable construction and statistics 5.2.1 Department choice, research period and source of data Because the statistics department did not publish the CO2 data of different industries, it is necessary to estimate the terminal fossil energy consumption of different departments. The existing China Energy Statistics Yearbook involves three types of statements for the physical energy consumption of the

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industrial sector. The frst is the energy balance sheet over the years, including the conversion of 20 different energy products (of which 17 are fossil energy sources): volume, loss, and terminal consumption in seven sectors; second, a balance sheet for 11 energy products (of which ten are fossil energy sources), also involving seven sectors; and third, end-use energy consumption in industrial subsectors, which announced 20 kinds of end-use data of energy products in 39 industries. It should be noted that primary energy, such as coal, converted by the power generation sector during the power generation process is not included in the end-use of the power generation department itself, but is refected in the “processing conversion” section of the energy balance sheet. The CO2 generated from energy activities such as processing and conversion of energy (such as thermal power generation and heating) is added to the electricity sector. In addition, in order to compare with the estimation data of foreign authoritative organizations, we will include electricity, gas, and water in the industrial sector. The production and supply industries are listed separately as the “energy conversion sector,” and their energy consumption and corresponding CO2 emissions are estimated based on the “end energy consumption of industrial sub-industries (physical quantity),” and will be the “China energy balance sheet over the years.” The amount of process conversion used for thermal power generation and heating in the table is adjusted to the energy consumed by the energy conversion sector. Correspondingly, the industrial sector has reduced the corresponding energy consumption of the electricity, gas and water production and supply industries. Based on the availability of data and the comparison of calculation results, the study period was fnally determined to be 14 years from 1996 to 2009. The department selected six major economic sectors that consume fossil energy: Agriculture:  the primary industry, or agriculture, forestry, animal husbandry, fshery and water conservancy Industry: with mining and manufacturing, excluding electricity, gas and water production and supply included Construction industry:  the construction industry in the secondary industry Transportation:  with transportation, warehousing and post and telecommunications included Business: wholesale and retail trade in the tertiary industry, accommodation and catering Energy sector: mainly referring to the electricity, gas and water production and supply industries that use fossil energy to generate electricity and produce heat. It does not include primary energy production departments such as coal mining. The economic output data of each department comes from the “industry added value” in the China Statistical Yearbook of the past years, but only includes the data after 2004. Only the aggregated data of the industry were

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115

released in 1996–2003, and the mining industry was missing industrial addedvalue data for manufacturing and electricity gas and water production and supply industries. Through the inquiry of the China Economic Net, we obtained the added value of all state-owned and non-state-owned industrial enterprises above a designated size during this period. As a result of several major changes in the statistical standards of the Chinese industrial sector, for example: statistical accounting for independent accounting companies before 1998, in 1998–2005, the caliber became all state-owned and non-state-owned with an annual main business income of more than 5 million yuan. Industrial enterprises included 2007–2010 caliber industrial enterprises with a business income of more than 5 million yuan. For data consistency, we adjusted the industrial sector’s share of the industry in the current year and multiplied by the industrial added value data for this period. Afterwards, according to the above-mentioned departmental setup, the industrial added-value of the production and supply of electricity, gas, and water was solely used as the energy sector output, and it was removed from the industry accordingly. The output defator for each sector is based on the GDP index and triple industrial valueadded index issued by the China Statistical Yearbook. The industrial addedvalue of the power sector is based on industry-based industrial products. The price index was defated. Finally, the economic output of all departments was converted to a constant price in 2005, with a unit of 100 million yuan. 5.2.2 Energy consumption and CO2 emission estimates According to the average calorifc value of the 17 fossil energy sources, they are converted and summed into standard coal, and divided into three categories according to their characteristics:  coal-based fuels, petroleum-based fuels, and natural gas fuels. For different energy product classifcation standards and discount coeffcients, see Table 5.1. GHG emissions are generally measured through fossil energy consumption. According to the preparation method of GHG inventories in the “Initial National Information on Climate Change in China”, the GHG emission coeffcients of different fossil energy consumption published by the IPCC, and the low calorifc values of different fossil energy sources in China published in China Energy Statistical Yearbook, CO2 emissions from fossil energy consumption activities can be calculated according to the following formula: 17

17

j =1

j =1

C = ˛ C j = ˛ E j × CC j × Di

(5.10)

Here, C denotes the total amount of carbon dioxide emissions; j denotes different fossil energy fuels; Ej is the total energy consumption of various industries; CCj denotes the default CO2 emission factor, the unit is kg CO2/ kilocal; Di denotes China energy, the average low calorifc value of the product,

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Table 5.1 Fossil energy product classifcation standard, folding coeffcient and CO2 emission coeffcient Type of energy products

Name

Unit

Converting standard energy factor (million tons of standard coal)

CO2 emission coeffcient (million tons of CO2)

Type of coals

Raw coal Washed coal Other coal washing Briquette Coke Coke oven gas

Ten thousand tons Ten thousand tons Ten thousand tons

0.00714 0.00900 0.00525

0.01980 0.02495 0.00792

Ten thousand tons Ten thousand tons One hundred million cubic meters One hundred million cubic meters Ten thousand tons

0.00600 0.00971 0.05930

0.02042 0.03048 0.07430

0.02880

0.02322

0.01107

0.02693

Ten thousand tons

0.01429

0.03070

Ten thousand tons Ten thousand tons Ten thousand tons Ten thousand tons Ten thousand tons

0.01471 0.01471 0.01457 0.01429 0.01714

0.02988 0.03083 0.03163 0.03239 0.03169

Ten thousand tons Ten thousand tons

0.01571 0.01310

0.02651 0.03070

One hundred million cubic meters

0.13300

0.21867

Other gases

Type of petroleum

Natural gas energy

Other coking products Crude oil Gasoline Kerosene Diesel Fuel oil Liquefed petroleum gas Refnery dry gas Other petroleum products Natural gas

in kcal/kg or kcal/m3. Among them, CCi×Di is the CO2 emission factor. The CO2 emission factors for various energy sources are shown in Table 5.1. 5.2.3 Industrial economic scale, energy consumption and CO2 emissions According to the above method, the CO2 quantity of each sector’s economic output, fossil energy consumption and emissions are calculated. For specifc data, see Table 5.2. Among them, the scale of the economic output of different industries is shown in Figure 5.1. It can be seen that in the six industries, the industrial sector is still the main component of the national economy

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Table 5.2 Economic output of six major industries, fossil energy consumption and CO2 emissions Industry

Year

GDP one hundred million yuan, 2005)

Fossil fuels (million tons of standard coal)

Coal

Petroleum Natural gas

CO2 emissions (million ton)

Coal

Petroleum

Natural gas

1. Agriculture

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

16,392.82 16,966.57 17,560.40 18,052.09 18,485.34 19,002.93 19,554.01 20,042.86 21,305.57 22,420.00 23,541.00 24,422.38 25,735.93 26,812.60

18.13 18.47 18.70 18.08 18.83 19.28 20.83 23.81 28.79 32.57 33.72 31.81 29.81 30.76

9.51 9.60 9.27 7.63 7.32 7.05 7.37 8.39 10.82 11.38 11.24 11.37 11.34 11.67

8.59 8.87 9.44 10.45 11.50 12.23 13.45 15.43 17.97 21.18 22.48 20.44 18.48 19.09

0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

44.95 45.80 46.15 43.78 45.22 46.02 49.59 56.65 68.84 77.25 79.66 75.57 71.19 73.36

26.40 26.69 25.83 21.28 20.44 19.66 20.59 23.41 30.12 31.61 31.23 31.59 31.43 32.28

18.50 19.11 20.32 22.50 24.78 26.36 29.00 33.25 38.72 45.64 48.42 43.98 39.76 41.08

0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2. Industry

1996 1997 1998 1999 2000 2001 2002 2003 2004

26,950.30 29,862.70 32,711.38 35,942.03 39,906.63 43,770.20 48,723.23 55,710.82 62,697.04

473.91 472.02 501.71 488.43 489.76 494.46 499.12 575.54 687.92

411.72 405.63 411.47 388.95 379.58 382.32 379.49 447.63 552.28

49.12 52.38 77.48 85.70 94.67 95.74 102.24 106.50 114.92

13.06 14.01 12.75 13.78 15.51 16.39 17.39 21.41 20.72

1257.78 1232.98 1314.02 1268.29 1262.51 1272.46 1280.52 1484.17 1795.48

1132.54 1099.95 1124.83 1060.38 1033.47 1039.86 1032.13 1219.40 1512.21

103.76 110.01 168.23 185.25 203.54 205.65 219.79 229.56 249.20

21.48 23.03 20.97 22.66 25.50 26.95 28.60 35.20 34.07 (continued)

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Table 5.2 (Cont.) Industry

GDP one hundred million yuan, 2005)

Fossil fuels (million tons of standard coal)

Coal

Petroleum Natural gas

CO2 emissions (million ton)

2005 2006 2007 2008 2009

70,436.22 80,190.28 93,031.66 102,850.45 112,297.33

777.80 837.80 900.62 944.29 981.15

644.00 690.03 744.05 771.43 813.28

110.66 117.06 120.54 132.12 128.07

23.14 30.72 36.03 40.75 39.80

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

5,148.28 5,282.14 5,757.53 6,005.10 6,347.39 6,779.02 7,375.57 8,268.01 8,937.72 10,367.31 12,153.38 14,120.51 15,462.24 18,331.31

13.75 14.14 14.76 15.21 15.68 16.74 18.30 20.29 23.47 25.25 27.62 29.71 25.36 31.23

4.76 4.46 4.54 4.03 4.00 3.83 3.88 3.95 4.24 4.49 4.83 4.56 4.40 4.58

8.79 9.68 10.20 11.10 11.57 12.82 14.33 16.25 19.04 20.57 22.57 24.87 20.83 26.52

4. Transportation 1996 1997 1998 1999 2000

4,604.92 5,028.63 5,561.11 6,238.01 6,773.24

86.15 86.88 88.48 92.71 97.96

11.37 10.63 8.63 6.71 6.39

74.62 76.09 79.56 85.50 91.03

3. Construction

Year

Coal

Petroleum

Natural gas

2044.76 2193.60 2311.19 2407.94 2505.27

1768.34 1890.67 1991.53 2056.00 2163.88

238.38 252.43 260.42 284.94 275.96

38.04 50.50 59.24 67.00 65.43

0.20 0.00 0.02 0.09 0.11 0.10 0.09 0.09 0.18 0.20 0.22 0.28 0.13 0.13

33.17 34.10 35.53 36.20 37.19 39.61 43.24 47.86 55.07 59.16 64.69 69.40 59.25 72.77

13.17 12.35 12.59 11.15 11.07 10.63 10.78 10.95 11.79 12.46 13.43 12.66 12.19 12.66

19.67 21.74 22.92 24.90 25.94 28.82 32.31 36.76 42.97 46.37 50.90 56.29 46.85 59.89

0.33 0.00 0.03 0.15 0.18 0.16 0.15 0.15 0.30 0.33 0.36 0.46 0.22 0.21

0.16 0.16 0.30 0.50 0.53

191.36 192.63 194.53 202.40 213.47

31.42 29.44 23.84 18.50 17.63

159.67 162.92 170.20 183.07 194.96

0.27 0.26 0.49 0.83 0.87

119

2001 2002 2003 2004 2005 2006 2007 2008 2009

7,369.79 7,894.95 8,378.62 9,591.56 10,666.16 11,729.37 13,113.60 14,074.11 14,600.52

100.80 108.12 122.36 146.16 164.32 181.09 196.48 204.64 210.95

6.11 6.19 6.96 5.94 5.81 5.55 5.28 4.76 4.58

93.86 100.40 113.58 137.56 154.42 170.48 185.83 191.48 195.53

0.82 1.54 1.83 2.66 4.09 5.06 5.38 8.40 10.84

219.24 234.50 265.17 315.01 353.46 388.97 422.26 436.18 448.90

16.88 17.10 19.27 16.44 16.09 15.29 14.53 13.09 12.57

201.01 214.88 242.90 294.18 330.64 365.37 398.90 409.27 418.51

1.36 2.53 3.01 4.38 6.73 8.31 8.84 13.81 17.82

5. Business

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

8,138.06 8,888.30 9,552.39 10,363.64 11,337.86 12,333.41 13,508.02 14,922.04 16,094.00 18,161.89 21,407.65 25,234.73 28,910.92 32,033.98

14.51 14.64 14.90 14.32 14.11 14.08 14.55 15.45 17.84 20.07 21.52 23.26 20.98 24.15

11.40 11.31 11.21 10.49 9.90 9.57 9.59 10.39 11.29 12.90 13.75 14.41 13.13 14.51

2.98 3.20 3.36 3.44 3.75 3.84 4.15 4.14 5.33 5.74 6.02 6.57 5.49 6.45

0.13 0.13 0.34 0.39 0.46 0.67 0.81 0.91 1.22 1.44 1.75 2.28 2.36 3.19

37.91 37.95 38.30 36.57 35.65 35.25 36.18 38.49 43.78 49.38 52.80 56.43 50.96 57.86

31.55 31.16 30.88 28.85 27.21 26.29 26.34 28.51 30.94 35.34 37.71 39.40 35.73 39.30

6.15 6.57 6.87 7.07 7.69 7.87 8.51 8.48 10.84 11.68 12.21 13.30 11.34 13.32

0.21 0.22 0.55 0.64 0.75 1.09 1.33 1.50 2.01 2.36 2.88 3.74 3.88 5.24

6. Energy sector

1996 1997 1998 1999 2000

4,925.13 5,614.65 5,923.45 5,976.76 6,120.21

462.91 456.31 456.49 486.66 514.43

434.60 420.53 425.05 456.79 483.75

27.24 32.68 28.42 26.72 26.51

1.07 3.10 3.02 3.15 4.17

1248.25 1226.39 1225.52 1312.54 1387.21

1186.35 1149.34 1157.87 1248.47 1322.15

60.14 71.96 62.68 58.89 58.20

1.76 5.09 4.97 5.18 6.86 (continued)

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Table 5.2 (Cont.) Industry

Year

2001 2002 2003 2004 2005 2006 2007 2008 2009

GDP one hundred million yuan, 2005) 6,260.97 6,311.06 6,367.86 6,520.69 6,794.56 6,984.80 7,138.47 7,266.96 7,434.10

Fossil fuels (million tons of standard coal)

Coal

Petroleum Natural gas

CO2 emissions (million ton)

542.39 605.10 719.43 804.42 900.12 1016.24 1102.80 1132.33 1202.24

511.13 573.29 683.88 762.00 859.99 978.70 1066.40 1098.85 1164.89

27.06 27.79 31.37 36.46 32.18 28.93 22.13 18.90 15.70

1463.88 1637.01 1949.12 2172.15 2436.14 2749.27 2984.04 3069.63 3251.79

4.20 4.02 4.19 5.97 7.95 8.62 14.27 14.57 21.65

Coal

Petroleum

Natural gas

1397.46 1569.31 1873.18 2082.37 2352.37 2672.16 2912.83 3005.69 3183.35

59.51 61.08 69.06 79.96 70.70 62.95 47.74 39.98 32.85

6.91 6.61 6.88 9.81 13.07 14.17 23.47 23.96 35.59

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Quantitative assessment 120,000

1.Agriculture 4.Transportation

2.Industry 5.Commerce

121

3.Construction 6. Energy Sector

100,000 80,000 60,000 40,000 20,000 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 5.1 Economic output scale of six industries (1996–2009, unit: RMB 100 million, 2005 price)

and has the fastest growth rate. According to 2005 constant prices, China’s GDP for the year was 28,484.48 billion yuan in 2009, and the total output of the above six sectors was 21,150.98 billion yuan, accounting for 74% of the country’s total output. Figure 5.2 depicts the fossil energy consumption of the six major industries from 1996 to 2009. It can be seen that the energy sector surpassed the industrial sector after 1999 and became a large consumer of fossil energy, and the energy sector and the industrial sector in 2002. After that, it showed an accelerated growth trend. According to the data published in the China Energy Statistical Yearbook, the total energy consumption in China was 2,829.75 million tons of standard coal in 2009, and the total energy consumption of the above six major industries was 2,480.5  million tons of standard coal, accounting for 87.7% of the country’s total energy consumption. Figure 5.3 shows the CO2 emissions from the consumption of fossil fuels in the six major industrial sectors from 1996 to 2009. Obviously, Figure 5.3 and Figure 5.2 show strong correlation. The energy sector surpassed the industrial sector after 1999. It has become the largest contributor to GHG emissions, accounting for more than 50% of its emissions. CO2 emissions from the industrial sector and the energy sector have been accelerating after 2002, and the GHG emissions of the transportation sector have also increased signifcantly. Because the statistics department has not released China’s recent GHG emission data, we frst compare the total amount of CO2 emitted by the six major industries estimated in this chapter with international authoritative

122

1,400

1.Agriculture

2.Industry

3.Construction

4.Transportation

5.Commerce

6. Energy Sector

1,200 1,000 800 600 400 200 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 5.2 Trend of fossil energy consumption in the six major industries (1996–2009, unit: 1 million tons of standard coal)

3,500

1.Agriculture

2.Industry

3.Construction

4.Transportation

5.Commerce

6. Energy Sector

3,000 2,500 2,000 1,500 1,000 500 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 5.3 CO2 emissions from the six major industrial fossil energy consumption industries (1996–2009, unit: million tons)

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Quantitative assessment 8,000

estimated

IEA

WRI

EIA

123

CDIAC

7,000 6,000 5,000 4,000 3,000 2,000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 5.4 Comparison of predictions of CO2 emissions in China by different agencies (unit: million tons) Note: The IEA, WRI, and EIA data are the amount of CO2 emitted from fossil fuel combustion (EIA, 2011; IEA, 2009, 2010; WRI, 2011). The CDIAC data include CO2 generated by cement production, which is calculated after deducting it. Fossil energyrelated CO2 emissions data (Boden et al., 2011).

institutions. See Figure 5.4. As only the economic sector is selected for estimation here, and other studies include CO2 emissions from household consumption, the total amount estimated in this chapter is slightly lower than other conclusions, but it is close to other research conclusions, and the trend is consistent. According to the estimates of the Energy Information Administration (EIA) of the United States, China’s fossil-energy-related CO2 in 2009 was 7,706.8 million tons, with a total global emission of 30,398.42 million tons (EIA, 2011). The total estimated six major economic sectors in the book are 6,410  million tons of CO2, accounting for 83% of the country’s fossil energy-related CO2 emissions, and 21.1% of global fossil energy-related CO2 emissions.

5.3 Analysis of the infuencing factors of adding greenhouse gas emissions Figure 5.5 frst describes the trend of CO2 emissions, economic output, energy consumption, total energy consumption intensity, and total energy product carbon emission coeffcient after allocation. From 1996 to 2009, for ease of understanding, all variables are shown in 1996. The year value is unitized to 1. It can be seen from Figure 5.5 that in addition to the temporary decline of CO2 in China, CO2 has been on an upward trend in other years. Especially since 2002, CO2 has signifcantly started to increase rapidly. By 2009, the total CO2 emission was 2.27 times the annual aggregate of the year 1996;

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Emission features

800 600 400 200 0 -200 -400

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Scale Effect (Y) Energy Consumption Intensity Effect (I) Carbon Emission Coefficient Effect (CC)

Industrial Structure Effect (S) Energy Structure Effect (f)

Figure 5.5 China’s GDP, energy and CO2 emissions (1996–2009)

energy consumption and CO2 emission trends are very consistent, while GDP increased from 661.6 billion yuan to 21,111 billion yuan, an increase of 3.2 times. The change in energy carbon emission coeffcient is relatively minimal. In 1996, the discharge of 2.63 million tons of CO2 per million tons of standard coal fell to 2.58 million tons of CO2 in 2009, a decrease of only 1.8% compared to 1996, indicating low carbon energy in energy consumption. The proportion is still low and no signifcant improvement has occurred. The magnitude of change in energy intensity is also relatively large. From 1996’s 0.016 (million tons of standard coal/100  million yuan) to 0.012 (million tons of standard coal/100 million yuan) in 2009, the cumulative decline is 27%, indicating that the effciency of energy use of the macro economy has improved signifcantly, but rebounded in 2002–2005. Based on models (5)–(11), China’s CO2 emissions change from 1996 to 2009 is decomposed year by year into: economic output scale effect, industrial structure effect, sectoral energy intensity effect, energy consumption structure effect and energy product carbon emission coeffcient effect. The absolute contribution of various factors to changes in CO2 is shown in Figure 5.6. From the decomposed impact factors, output scale effect (Y) is the most important positive infuence. The increase of national economic output has been driving the increase of CO2, and it has gradually increased in recent years; the industrial structure The contribution of effect (S) to carbon emissions is negative in most years, indicating that changes in the industrial structure are the main factors that inhibit the growth of CO2, and that the industrial structure effect

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Quantitative assessment 600%

400%

125

Scale Effect (Y)

Industrial Structure Effect (S)

Energy Consumption Intensity Effect (I)

Energy Structure Effect (f)

Carbon Emission Coefficient Effect (CC)

200%

0%

-200%

-400%

1996–2000

2000–2002

2002–2005

2005–2009

Figure 5.6 Absolute contribution of factors affecting greenhouse gas emissions (1996– 2009, unit: million tons)

is also gradually increasing; energy intensity effect (I) on carbon emissions The contribution was negative in most years, but its effect turned negative from 2002 to 2006, indicating that since 2002, energy effciency has deteriorated and has further exacerbated the increase in CO2. This trend continued until 2006. Only afterwards did the situation turn for the better; in addition, the energy consumption structure effect (f) and the carbon emissions structure effect (CC) of energy products can also mitigate CO2 emissions to a certain extent, but its impact is small during the study period, indicating that there has been no signifcant improvement in the energy consumption structure and low carbonization of energy products in China. In order to further understand the relative contribution of various infuencing factors in different periods, we divided the research period into four periods:  1996–2000, 2000–2002, 2002–2005, and 2005–2009, of which 2002 and 2005 are two important points. Since 2002, China’s CO2 has accelerated its upward trend, and its energy intensity has also increased. In 2005, it was time for China’s government to implement energy-saving and emissionreduction policies. If the government’s macro-control policies are effective, the drivers of CO2 during this period will be different from other periods. The relative contribution of each factor resulting from the fnal decomposition is shown in Figure 5.7. It can be seen that before the year 2000, the positive effect of the scale effect was very large, and the energy effciency improvement rate during this

126

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Emission features

600%

400%

Scale Effect (Y)

Industrial Structure Effect (S)

Energy Consumption Intensity Effect (I)

Energy Structure Effect (f)

Carbon Emission Coefficient Effect (CC)

200%

0%

-200%

-400%

1996–2000

2000–2002

2002–2005

2005–2009

Figure 5.7 Relative contribution rate of CO2 infuencing factors at 7-point period (unit: %)

period was also quite signifcant, which was suffcient to offset the increase in CO2 emission caused by the scale effect, and the industrial structure effect was positive. It shows that the adjustment of the industrial structure shows a trend of “high carbonization,” which has contributed to the increase of CO2 to a certain extent. In addition, the energy structure effect has also been signifcant, which has slowed the emission of greenhouse gases. During 2000–2002, the industrial structure effect was negative, as the main factor in slowing CO2 emissions, but the relative contribution of the scale effect exceeds all other effects, so the greenhouse gas continues to increase; from 2002 to 2005, the energy intensity effect has reversed, from the previous negative effect into a positive effect. In other words, energy effciency has not only failed to improve, but has caused degradation, which has intensifed CO2 emissions. After 2005, the deterioration of energy effciency has only improved, but its relative contribution is very small. The deterrence of CO2 mainly depends on industrial structure effect, but still not enough to make up for the increase in CO2 due to scale expansion. In addition, in order to understand the relative impacts of different industries in different infuencing factors, we used 1996 and 2009 as starting points for comparison to calculate the changes in CO2 emissions of the six major industries, as well as the effects of scale, industrial structure, and energy consumption. The absolute contribution of the intensity effect, energy structure effect, and carbon emission coeffcient effect is shown in Figure  5.8. It can be seen that in 2009, compared with 1996, an increase of 3596 million tons of CO2 emissions, of which the energy sector and industry contributed more

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127

6,000 5,000

1.Agriculture

2.Industry

3.Construction

4,000

4.Transportation

5.Commerce

6.Energy Sector

3,000 2,000 1,000 0 -1,000 -2,000

Figure 5.8 Effect of decomposition of CO2 changes in different sectors (1996–2009, unit: million tons of CO2)

than 90% of incremental CO2 emissions, the transport sector contributed 7.2%; in terms of the decomposition factor, the scale effect is the main engine for the increase of CO2 emissions. The above six major industries have added a total of 5,037 million tons of CO2 due to the expansion of production scale. Among them, the impact of the energy sector, industry and transportation industry is the largest, resulting in 2,417 million tons of CO2. The industrial structure effect has slowed CO2 emissions to a certain extent. The decline in the relative proportion of the energy sector in the national economy is the main reason for the negative effect of the industrial structure. The increase in the proportion of industrial economy has weakened the adjustment of the industrial structure to GHGs. The intensity of emission reduction; energy intensity effect is mainly affected by industry, energy sector and transportation industry, among which the energy intensity effect of industry and transportation industry is negative, indicating that the energy effciency of the two industries has been improved and will help In response to the reduction of GHG emissions, the energy intensity effect of the energy sector is a positive effect, indicating that during energy processing and conversion, there is a certain backwardness in energy effciency and this has led to new GHG emissions. In addition, industrial energy structure effects and carbon emission coeffcient effects are positive, indicating that the level of renewable energy and low-carbon energy utilization in industrial energy consumption during this period is not high, and the energy structure effect of the energy sector is the only signifcant negative effect. It shows that the energy structure of the

128

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Emission features

200 100 0 -100 -200 -300 -400

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Agriculture

Industry

Construction

Transportation

Commerce

Energy Sector

Figure 5.9 Decomposition of industrial structure effects in different sectors (1996– 2009, unit: million tons of CO2)

power generation sector has been improved, and more and more non-coal fossil fuels have been used. Further, in order to fully understand the effects of different industries in different periods in the industrial structure effect, we have carried out yearby-year decomposition of the absolute amount of CO2 caused by changes in the industrial structure of all six major industries, as shown in Figure 5.9. It can be seen that the industrial structure effect is always positive, indicating that the increasing proportion of industrial economy has led to an increase in CO2, while the decline in the proportion of agriculture and the energy sector has slowed down the emission of GHGs. The former is basically a positive effect. In 2006–2009, the mitigation effect was due to the decline in the relative proportion. The construction industry and commerce have a positive effect in most years. From the perspective of absolute CO2 changes caused by changes in industrial structure, changes in the proportions of the energy sector and industry have the greatest impact on GHG emissions. From the perspective of the impact of changes in CO2 emissions in various industries, the adjustment of industrial economic structure should become the focus. Overall, China’s rapid expansion of economic output during 1996–2009 was the main factor in the increase of CO2 emissions during this period. Adjustment of industrial structure and improvement of energy effciency of the sector slowed down the increase of CO2. In addition, changes in energy consumption structure and energy carbon emission factor also have a certain impact on the GHG, but the degree of impact is small. In the six major industries, the expansion of the industrial scale of the industrial, energy sector, and transportation industries, the increase in the proportion of industrial economies, and the increase in the energy intensity of the power generation sector

129

Quantitative assessment

129

are important factors leading to the increase in CO2 emissions, and the relative share of the energy sector in the economy. The decline, the improvement of energy effciency in the industrial and transportation industries, and the optimization of the fossil fuel structure in the energy sector have, to a certain extent, slowed CO2 emissions.

5.4 Further investigation into different industries The following six industries will be examined separately to identify the main infuencing factors and time trends of GHG emissions from the different industries. The specifc decomposition data can be found in Table 5.2. 5.4.1 Factors infuencing agricultural greenhouse gas emissions From the perspective of agricultural fossil energy-related GHG emissions (see Figure 5.10), the expansion of agricultural production scale is the main factor driving CO2 emissions, while industrial structure and energy intensity are the two most important emission reduction channels. Among them, the emission reductions caused by the continuous decline in the proportion of agriculture in the national economy have been increasing year by year, while the intensity of energy consumption has fuctuated greatly. During the period of heavy chemical industry in 2002–2005, the energy effciency has declined, but it has been boosted. After the GHG emissions, the energy intensity effect has gradually exerted a large effect due to the implementation of the energy

15 10 5 0 -5 -10

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Scale Effect (Y)

Industrial Structure Effect (S)

Energy Structure Effect (f) Energy Consumption Intensity Effect (I)

Carbon Emission Coefficient (CC)

Figure 5.10 Decomposition of the infuencing factors of GHG emission in agriculture (1996–2009, unit: million ton)

130

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Emission features

conservation and emission reduction strategy, and has surpassed the industrial structure effect and become the most important way to reduce emissions. The energy structure effect has also manifested differently in different periods. Before 2002, the optimization of agricultural energy structure slowed down some CO2 emissions, but the impact was small. In 2007 and 2008, there was even a positive effect on CO2 emissions. It is worth noting that CO2 caused by fossil energy consumption is only a part of agricultural GHG emissions, and a larger part of the source of emissions comes from methane and nitrous oxide from agricultural production, according to China’s submission to the United Nations. According to the data of the initial national climate change report, methane and nitrous oxide produced by agricultural activities account for 50.15% and 92.47% of the national methane and nitrous oxide emissions, respectively, and agricultural GHG emissions account for 17% of the total national GHG emissions. Therefore, the analysis of agricultural GHG emissions should not be limited to the consumption of fossil fuels, but also should include the production of agricultural products and livestock products. 5.4.2 Factors infuencing industrial greenhouse gas emissions The factors affecting industrial GHG emissions mainly include output scale effect, energy intensity effect and industrial structure effect. Figure  5.11 describes the absolute contribution of each infuencing factor. Among them,

400 300 200 100 0 -100 -200

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Scale Effect (Y)

Industrial Structure Effect (S)

Energy Structure Effect (f) Energy Consumption Intensity Effect (I)

Carbon Emission Coefficient Effect (CC)

Figure 5.11 Decomposition of factors affecting GHG emission in industry (1996– 2009, unit: million ton)

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Quantitative assessment

131

the continuous expansion of the industrial economy has led to a large number of GHG emissions, which is the main reason for the increase of industrial GHGs. At the same time, the proportion of the industrial economy in the national economy has further increased the CO2 emissions from the internal energy effciency of the industrial sector. The continuous improvement has played a positive role in slowing down GHG emissions. However, in 2003– 2005, due to the large investment in heavy chemical industry, the intensity of energy consumption did not fall. In addition, due to the special application of industrial technology, it is not possible for short-term internal conversion of fossil energy types, so the impact of energy structure effects is small, but the improvement of energy quality has contributed to the positive effects of carbon emission coeffcient effects in some periods, such as the use of lower carbon content energy products in 2007. The effect of the carbon emission coeffcient is greatly reduced to reduce CO2 emissions. On the whole, in order to control industrial GHG emissions, the main approaches rely on the control of current industrial production scale, adjustment of the proportion of industrial GDP and further improvement in energy effciency. In addition, low carbonization of energy consumption in industrial production should be improved. 5.4.3 Factors infuencing construction greenhouse gases emissions As shown in Figure 5.12, among the factors affecting the GHG emissions of the construction industry, the scale effect is also one of the main drivers of

10 5 0 -5 -10 -15 -20

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Scale Effect (Y)

Industrial Structure Effect (S)

Energy Structure Effect (f) Energy Consumption Intensity Effect (I)

Carbon Emission Coefficient Effect (CC)

Figure 5.12 Decomposition of factors infuencing construction GHG emissions (1996–2009, unit: million ton)

132

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Emission features

the increase in CO2 emissions, while the industrial structure effect fuctuates, showing a positive effect after 2005. It shows that due to the booming development of the real estate industry, the proportion of the construction industry has increased and the CO2 emissions have been promoted. In addition to some years, the energy intensity effect mainly shows strong GHG mitigation effects, energy structure effects and carbon emissions. The coeffcient effect also helps to control CO2 emissions, but the impact is small. It should be noted that in the consumption of residents, part of the GHG emissions are also related to the construction industry, such as building heating, etc. Buildings using energy-saving and environmentally friendly technologies cannot only effectively reduce GHG emissions during the construction process, but also in the building itself, so the construction industry needs to be analyzed globally from the perspective of product life cycle. From the perspective of the direction and size of the sector’s own GHG emissions, it is necessary to control the scale and proportion of the industry on the one hand, and actively exert energy intensity effciency and improve energy effciency on the other hand. 5.4.4 Factors infuencing greenhouse emissions in transportation Figure 5.13 depicts the distribution of major infuencing factors for GHG emissions from the transport sector during 1997–2009. It can be seen that the expansion of the scale of the transportation industry is the most

60 50 40 30 20 10 0 -10 -20 -30

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Scale Effect (Y)

Industrial Structure Effect (S)

Energy Structure Effect (f) Energy Consumption Intensity Effect (I)

Carbon Emission Coefficient Effect (CC)

Figure 5.13 Decomposition of factors affecting transportation GHG emission (1996– 2009, unit: million ton)

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133

important cause of the increase of GHG emissions. Among the factors controlling CO2 emissions, the energy intensity effect and the industrial structure effect have played a positive role. In particular, energy consumption intensity rebounded between 2002 and 2004, which boosted GHG emissions, illustrating a relatively signifcant negative effect in other periods. In comparison, the industrial structure effect fuctuated, and the proportion of transportation in GDP increased before 2002, after which 2004 and 2005 were excluded. Decline in the proportion of the transportation industry has effectively reduced carbon emissions. In addition, improvements in fossil fuels have had negative effects at all times, but they have less impact than other factors. It should be noted that in China’s existing energy statistics, there is a certain difference between the statistics of the transportation sector and the definition of the IPCC. The transportation industry in China’s energy balance table only examines the consumption of transportation enterprises and does not include private transportation or the transportation tools of various departments, while the IPCC includes the consumption of the transportation sector including the whole society transport vehicles, and separates the aviation, road, railway, water transport and pipeline transportation industries to carry out mobile source GHG emissions. It is estimated that there may be some errors in the factor decomposition, but in general, the main ways to control the GHG emissions of the transportation industry should include: controlling the scale and proportion of the industry, improving fuel effciency and clean energy. 5.4.5 Factors infuencing greenhouse gas emissions in business The breakdown of the factors affecting commercial GHG emissions is shown in Figure  5.14. It can be seen that the scale effect is also the main factor driving the increase of GHG emissions. In addition, in most periods, the corresponding proportion of CO2 emissions is also caused by the increase in the proportion of economy scale, but its effect is lower than the size effect; from the perspective of mitigating CO2, the energy intensity effect is the main factor controlling GHGs, except for 2004 and 2009, all of which have negative effects and their effects. It is very strong. In some years, such as 2008, it even surpassed the sum of scale effect and industrial structure effect, and caused the total amount of emissions in that year to decline. As most of the business uses secondary energy or natural gas, its energy structure changes and effects are not signifcant. In general, in the future, China’s industrial structure will gradually become excessive to the tertiary industry. Therefore, the scale of business development and the proportion of industry will continue to rise. These two effects will promote the further increase of greenhouse gases, thus reducing GHG emissions depends primarily on the improvement of energy effciency and further optimization of energy structure.

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10 5 0 -5 -10 -15

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Scale Effect (Y)

Industrial Structure Effect (S)

Carbon Emission Coefficient Effect (CC) Energy Structure Effect (f) Energy Consumption Intensity Effect (I)

Figure 5.14 Decomposition of factors affecting business GHG emission (1996–2009, unit: million ton)

5.4.6 Factors infuencing greenhouse gas emissions in the energy sector The pattern of GHG emissions in the energy sector is quite different from that of other industries. It can be seen from the decomposition of Figure 5.15 that the expansion of energy production and conversion industry scale is one of the reasons for promoting CO2 emissions. Another major factor is the energy intensity effect, which is equivalent in size and scale effect, even in some years. The size effect of the scale shows that in the energy sector, energy effciency has not only improved, but also tends to deteriorate, and further accelerates CO2 emissions. Among the factors affecting the control of GHGs, industrial restructuring has played an active and more signifcant effect, due to the decline in the proportion of GDP in the energy sector, has greatly slowed down CO2 emissions due to scale expansion and effciency degradation. In addition, energy structure effects and carbon emission coeffcient effects have been presented in most years as positive effects, which indicates that in the energy sector the proportion of high-carbon traditional fossil energy is increasing, which has intensifed GHG emissions to some extent. As the energy sector, especially the power production sector, is the main source of GHG emissions in China, it is necessary to pay close attention to the industry, focusing on controlling the production scale of China’s electricity,

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135

400 300 200 100 0 -100 -200 -300 -400

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Scale Effect (Y)

Industrial Structure Effect (S)

Energy Structure Effect (f) Energy Consumption Intensity Effect (I)

Carbon Emission Coefficient Effect (CC)

Figure 5.15 Decomposition of factors affecting GHG emissions in the energy sector (1996–2009, unit: millions of tons)

and working to improve the utilization effciency of primary energy to secondary energy conversion, reducing losses during grid transmission and distribution, and optimizing the proportion of clean, low-carbon energy in the power generation structure.

5.5 Conclusion Based on the extended Kaya equation, this chapter decomposes CO2 changes into economies of scale, industrial structure effects, sectoral energy intensity effects, energy consumption structure effects, and energy carbon emission coeffcient effects. The six major industrial sectors of agriculture, industry, construction, transportation, commerce and energy sectors in China were selected for research purposes in 1996–2009, and the corresponding CO2 panel data series related to economic output, energy consumption and fossil energy were constructed. On this basis, the Logarithmic Mean Divisia Index (LMDI) is used to decompose the total CO2 emissions. The main research conclusions include the following. 1. In 2009, the six major economic sectors collectively emitted 6,410 million tons of CO2, of which the energy sector accounted for the largest emissions, accounting for 51% of all emissions, and the energy sector, industry and transportation sectors accounted for 97% of all emissions. 2. In addition, 3,596  million tons of CO2 emissions were added between 1996 and 2006. Among them, the increase in economic output is the

136

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main reason for the increase in CO2 emissions, with a contribution rate of 140%; industrial restructuring and improvement of departmental energy effciency. To a certain extent, CO2 emissions are suppressed, and their relative contribution rates are –31% and –7.6%, respectively, but not enough to offset the scale effect of output; energy structure and energy carbon emission effects also slow down GHG emissions, but the impact is slight, and the contribution rate is only –0.31% and –1.4%, respectively. 3. In the future, in order to reduce GHG emissions, it is necessary not only to understand the infuencing factors, but also to grasp the relative infuence of various industries. It is necessary to focus on the speed of economic expansion of the industrial, energy and transportation industries, adjust the proportion of the industrial economy to the national economy, optimize the proportion of other industries, and further promote the energy sector and industrial energy effciency, and improve the use of clean energy.

Note 1 This chapter is the revision of Wei, C. & Yu, D. J. (2013) Effect study of the industrial structure of GHG emissions in production industries, Industrial Economy Research, 1.

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6

Quantitative evaluation and analysis of CO2 emissions in China’s industrial sector1

Chapter 5 mainly quantitatively evaluates the impact of six major productive industries on greenhouse gas (GHG) emissions from a macro perspective. The conclusions show that industry is an important source of GHG emissions, and the expansion of the industrial sector and the proportion of industry’s economic growth continue to rise. CO2 emissions have a positive feedback effect. In addition, the improvement of energy effciency within the industrial sector has partially mitigated GHG emissions. This chapter will focus on the industrial sector. Specifcally, it is based on 33 sectors of the mining and manufacturing industries. Based on its fossil energy terminal consumption, it estimates the CO2 emissions of various sectors between 1996 and 2009, based on the logarithm. LMDI is used to decompose the changes in CO2, thereby identifying the laws of emissions within various sectors of the industry, the infuencing factors of CO2 emissions, and the sectors that need to be focused. Because the model and method used in this chapter are the same as in the previous chapter, they are not introduced separately. The structure is as follows:  the frst section constructs the corresponding variables and introduction data, the second section describes the economic growth, energy consumption and CO2 emission characteristics of the industrial sector, the third section discusses the decomposition results, and fnally there is the conclusion.

6.1 Variable structure and data Estimates of CO2 emissions from China’s industrial sector are mainly based on the terminal fossil energy consumption of various sectors. Some sectors also have non-fossil energy-related GHG emissions in industrial production projects, such as cement, lime, steel, and calcium carbide production processes. In addition, some energy activities can also produce non-combustible GHGs, such as methane emissions from coal mining and post-mining activities, and methane emissions from oil and gas systems. Due to the lack of detailed data in the existing statistical system, it is impossible to accurately estimate this part of the emissions. The estimated GHGs here are mainly CO2 generated during the burning and consumption of fossil energy. The fossil energy terminal consumption data of various departments are derived from the Report DOI: 10.4324/9780429447655-8

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Emission features

on Industrial Sub-industry Terminal Energy Consumption in the China Energy Statistics Yearbook, which covers the end-use consumption of 20 kinds of energy products in 39 sectors of industry. This chapter focuses on the mining industry (fve sectors) and the manufacturing sector (28 sectors). There are several reasons for not choosing “electricity, gas and water production and supply industries”:  frst, the GHGs generated by the electricity and heat sectors have been divided into two parts, one is to convert primary energy such as coal and oil into GHGs emitted from secondary energy, and the other is to consume GHGs emitted by fossil energy during the production and operation process. Logically, energy should be converted. Some of the GHGs are included in the sector. Second, other international research institutes use the sectoral method to estimate GHG emissions, and they are also calculated separately as a source of emissions. We have separately used the energy sector as an industry in the last chapter. Research has been carried out, so this chapter excludes it from the industrial sector, focusing on the links between other nonenergy production industries and GHGs. In addition, due to the more detailed use of the industrial sector, in order to carefully analyze the consumption and emission characteristics of different energy products, this chapter divides all fossil energy into four categories: coal, coke, petroleum and natural gas, and according to Chapter 4, the calculation method introduced is to convert different energy products into standard energy sources and estimate the CO2 emissions related to fossil energy. The economic output data of all industrial sectors refer to the China Statistical Yearbook and the China Economic Network database in the past years. The statistical caliber is “the total industrial output value of all state-owned and non-state-owned industrial enterprises above a designated size,” and the missing data in 2004 can be obtained in an economic census yearbook. In the statistical yearbook, the detailed output defator of the subsector is not published. We refer to the “Industrial Price Index of Industrial Products by Sub-industry,” which includes major categories such as metallurgical industry, coal industry, and chemical industry. The industrial ex-factory price index, based on this information, averaging the total industrial output over the years, fnally obtained the output value of all industrial sectors based on the 2005 constant price. It should be noted that since the National Bureau of Statistics adopted the new industrial industry classifcation standard in 2003, for some industrial sectors such as “Other Mining Industry,” “Arts and Other Manufacturing,” and “Abandoned Resources and Waste Material Recovery Price Industry,” the data before 2003 are missing. Considering that the above three sectors accounted for less than 0.9% of the industrial economy and CO2 emissions in 2003, they have less impact on the overall industrial output, energy consumption and emissions. In total, fve extractive industry departments and 28 manufacturing departments were fnalized. According to Fisher-Vanden et al. (2004), the division of China’s industrial sector was organized into ten major categories, and the fnal selected 33 industrial sectors and large subsidiaries. The classifcation criteria can be found in Table 6.1. The study period is 14 years from 1996 to 2009.

139

Table 6.1 Classifcation comparison table of industrial sectors Major industries

Department code

Sectors

Mining sector

6 7 8 9

Coal mining and washing industry Oil and gas extraction industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass products industry Furniture manufacturing Paper and paper products industry Copying of the printing industry and recording media Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing

Food and beverage sector

Textile and leather products sector Wood furniture manufacturing

10 13 14 15 16 17 18 19 20 21 22 23 24

Petroleum processing industry Petrochemical industry Rubber and plastics products industry Non-metallic products industry Metal products industry

Equipment machinery and instrument industry

25 26 27 28 29 30 31 32 33 34 35 36 37 39 40 41

Chemical raw materials and chemical products manufacturing Pharmaceutical manufacturing Chemical fber manufacturing Rubber products industry Plastic products industry Non-metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal products industry General equipment manufacturing Special equipment manufacturing Transportation equipment manufacturing Electrical machinery and equipment manufacturing Communications equipment, computers and other electronic equipment manufacturing Instrumentation and culture, offce machinery manufacturing

140

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Emission features

6.2 Main features of the industrial sector in China 6.2.1 Features of industrial production structure in China In 1996, the total industrial output value of the 33 industrial sectors covered by the sample was 602.25 billion yuan. In 2009, it increased to 479.495 billion yuan, with an average annual growth rate of 17.3%. We frst mapped the industrial output value of various industrial sectors between 1996 and 2009 based on ten industrial categories, as shown in Figure 6.1. It can be seen that most industries have entered an accelerated growth period since 2002, especially in the “mechanical equipment industry” and “metal products industry.” In addition, “chemical industry,” “textile industry,” and “food industry” has also grown rapidly. Figure 6.2 depicts the relative weight and distribution of different sectors in the industrial economy between 1996 and 2009. Heavy machinery industries such as machinery and equipment manufacturing, metal products, and chemical industries account for a relatively high proportion of the industrial economy. These three industries account for more than 58% of the industrial economy, while light industries such as food and textiles are relatively large. Specifc to 33 departments, between 1996 and 2009, its top fve industrial cumulative GDP were:  “communication equipment, computers and other electronic equipment manufacturing” (9.96%), “ferrous metal smelting and rolling processing industry” (8.39%), “transport equipment manufacturing” (7.33%), “chemical raw materials and chemical products manufacturing” (7.32%) and “electric machinery and equipment manufacturing” (6.22%). The total industrial production value and the proportion of other sectors can be found in Table 6.2. 6.2.2 Characteristics of the energy consumption in the industrial sector of China In 1996, China’s industrial sector consumed a total of 534.5  million tons of standard coal fossil fuel. In 2009, it increased to 1086.8  million tons of standard coal. The annual growth rate of fossil energy consumption was 5.6%, much lower than the growth rate of industrial output. Figure 6.3 shows the fossil energy consumption of ten industrial sectors. From the time trend, most industrial sectors have relatively stable energy consumption before 2002. Some industries, such as the chemical industry, even experienced a slight decline during this period. However, after 2002, the energy consumption of some industries has risen sharply. It is also the most prominent in the metal products industry, non-metal products industry and chemical industry. From the perspective of departmental distribution, the “metal products industry” has the highest proportion of fossil energy consumption, from 27% in 1996 to 40% of the total fossil energy consumption in 2009. From the perspective of cumulative fossil energy consumption in the whole period, it ranks in the top

141

200,000

150,000

100,000

50,000

0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Mining Industry Petroleum Processing Metal Products Textile Rubber & Plastic Products

Food Petrochemical Industry Machinery Equipments Wood Furniture Non-metallic Products

Figure 6.1 Total output value of China’s industrial sector from 1996 to 2009 (unit: 100 million yuan, constant price in 2005)

Mining Industry Food

Machinery Equipments

Texle Wood Furniture Petroleum Processing

Metal Products Non-metallic Products Rubber & Plasc Products

Petrochemical Industry

Figure 6.2 Industry distribution of China’s industrial output value from 1996 to 2009

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newgenrtpdf

Table 6.2 Total industrial output value, fossil energy consumption and CO2 emissions of the industrial sector during 1996–2009 Departments

Aggregate Coal mining and washing industry Oil and gas extraction industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass products industry Furniture manufacturing Paper and paper products industry Copying of the printing industry and recording media

Cumulative absolute volume between 1996 and 2009

Proportion of departments (%)

Gross industrial output value (100 million yuan, price in 2005)

Fossil energy consumption (million tons of standard coal)

CO2 emissions (million tons)

Total industrial output value

2,664,955.9 66,984.5 73,781.4 13,497.8 14,624.2 10,691.0 12,7099.3

10,109.5 436.7 291.5 28.3 17.7 52.4 144.6

26,004.0 1,168.8 583.7 76.4 47.3 139.2 381.4

100 2.51 2.77 0.51 0.55 0.40 4.77

100 4.32 2.88 0.28 0.17 0.52 1.43

100 4.49 2.24 0.29 0.18 0.54 1.47

44,370.4 40,626.3 33,524.8 139,822.4 60,382.6

90.8 81.9 20.4 209.0 26.3

243.3 222.0 52.8 560.1 67.6

1.66 1.52 1.26 5.25 2.27

0.90 0.81 0.20 2.07 0.26

0.94 0.85 0.20 2.15 0.26

38,892.7 24,157.5

14.9 38.3

38.1 103.9

1.46 0.91

0.15 0.38

0.15 0.40

16,047.6 46,949.0 17,129.1

7.0 192.3 10.2

17.9 520.0 25.1

0.60 1.76 0.64

0.07 1.90 0.10

0.07 2.00 0.10

Fossil fuel consumption

CO2 emissions

143

Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing Chemical raw materials and chemical products manufacturing Pharmaceutical manufacturing Chemical fber manufacturing Rubber products industry Plastic products industry Non-metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal products industry General equipment manufacturing Special equipment manufacturing Transportation equipment manufacturing Electrical machinery and equipment manufacturing Communications equipment, computers and other electronic equipment manufacturing Instrumentation and culture, offce machinery manufacturing

16,411.4

6.7

16.1

0.62

0.07

0.06

135,680.1

900.1

2,007.6

5.09

8.90

7.72

195,050.7

1,661.2

4,168.4

7.32

16.43

16.03

510,76.0 28,311.6 26,528.6 60,459.2 116,105.4 223,498.2

65.1 77.1 45.9 39.0 1,826.1 3,153.9

173.9 190.5 122.6 98.5 4,679.3 8,568.2

1.92 1.06 1.00 2.27 4.36 8.39

0.64 0.76 0.45 0.39 18.06 31.20

0.67 0.73 0.47 0.38 17.99 32.95

100,426.3

213.5

551.3

3.77

2.11

2.12

81,397.8 127,544.5 78,002.3 195,448.8 165,800.4

69.3 119.8 77.9 105.2 43.6

182.8 328.7 195.7 269.7 106.1

3.05 4.79 2.93 7.33 6.22

0.69 1.18 0.77 1.04 0.43

0.70 1.26 0.75 1.04 0.41

265,433.6

35.1

78.1

9.96

0.35

0.30

29,200.7

7.5

18.8

1.10

0.07

0.07

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Emission features

500 400 300 200 100 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Mining Wood Furniture Rubber & Plastic Machinery Equipments

Food Petroleum Processing Non-metallic Products

Textile Petrochemical Industry Metal Products

Figure 6.3 Trends in fossil energy consumption in China’s industrial sector from 1996 to 2009 (unit: million tons of standard coal)

fve. The departments include: “black metal smelting and rolling processing industry” (31.2%), “non-metallic mineral products industry” (18.1%), “chemical raw materials and chemical products manufacturing” (16.4%), “oil processing, coking and nuclear fuel processing industry (8.9%) and “coal mining and washing industry” (4.3%). Accumulated energy consumption and the proportion of other sectors can be found in Table 6.2. Figure 6.4 is the relative proportion of different fuel consumption in the cumulative fossil energy consumption of different industrial sectors (excluding secondary energy, such as electricity and heat). It can be seen that most industries still focus on coal-based energy, such as food and textile. The proportion of coal consumption in the wood furniture industry and non-metal products industry exceeds 80%; the metal products industry mainly consumes coke fuels, and petroleum fuels are mainly used in petroleum processing industry, chemical industry, rubber plastic products industry and machinery industry. In addition, natural gas consumption in the chemical, mining and machinery manufacturing industries also accounts for a certain proportion. Figure 6.5 is the trend of consumption structure in China’s industrial fossil energy consumption. It can be seen that the proportion of coal-based fuel in all fossil energy consumption is gradually decreasing, from 55% of all fossil fuels in 1996 to 41% in 2009, but the proportion of coal has rebounded in 2002– 2005; the proportion of coke-based fuel consumption is gradually increasing, mainly because the production scale of metal products mainly based on coke consumption has been expanding; the proportion of petroleum-based fuel

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Quantitative evaluation and analysis

145

100% 80% 60% 40% 20% Machinery Equipments

Metallic Products

Oil

Non-metallic Products

Coke

Rubber & Plastic Products

Coal

Petrochemical Industry

Petroleum Processing

Wood Furniture

Textile

Food

Mining

0%

Natural Gas

Figure 6.4 Various fossil energy structures in China’s industrial sector (unit: %)

100% 80% 60% 40% 20% 0% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Coal

Coke

Oil

Natural Gas

Figure 6.5 Fossil energy consumption structure of China’s industrial sector from 1996 to 2009 (unit: %)

consumption increased before 2002, but it gradually shrank between 2002 and 2005. It remained stable until after 2005; the proportion of natural gas consumption changed little, staying between 3% and 5%.

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Emission features

6.2.3 Characteristics of GHG emission in the industrial sector of China In 1996, China’s industrial sector generated 1,405  million tons of CO2 emissions due to fossil energy consumption. By 2009, emissions increased to 2,760  million tons, an average annual increase of 5.3%. Figure  6.6 frst describes the trend of CO2 emissions from ten industrial sectors in China. It can be seen that the trend of GHG emissions in various industries is very consistent with the trend of fossil energy consumption. In 2002, the emissions of most industrial sectors began to rise rapidly, among which the metal products industry was the most prominent. The GHG emissions of the chemical industry, non-metal products industry, mining industry and petroleum processing industry also increased rapidly. Figure  6.7 shows the industry distribution of industrial GHG emissions in China. It can be found that the metal products industry, the non-metal products industry and the chemical industry are the main sources of industrial GHG emissions. The CO2 emissions of these three industries account for 71% of the total industrial GHG emissions. In addition, the proportion of emissions from the petroleum processing industry and mining industry proportion is also high. By department, the top fve highest proportions are:  ferrous metal smelting and rolling processing (32.9%), non-metallic

1,400 1,200

Mining

Food

Textile

Wood Furniture

Petroleum Processing

Petrochemical Industry

Rubber & Plastic Products

Non-metallic Products

Metallic Products

Machinery Equipments

1,000 800 600 400 200 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 6.6 CO2 emissions of China’s industrial sector from 1996 to 2009 (unit: million tons)

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Quantitative evaluation and analysis

Machinery Equipments

147

Mining Food Texle Wood Furniture

Metallic Products

Petroleum Processing

Petrochemical Industry

Non-metallic Products

Rubber & Plasc Products

Figure 6.7 Industry distribution of industrial CO2 emissions in China from 1996 to 2009

mineral products (18%), chemical raw materials and chemical manufacturing (16%), petroleum processing, coking and nuclear fuel processing industry (7.7%) and coal mining and washing industry (4.5%). The cumulative CO2 emissions and the proportion of other sectors can be seen.

6.3 Decomposition of China’s industrial CO2 emissions In the research sector, the output of China’s industrial sector showed a continuous growth trend. Compared with 1996, the total industrial output value in 2009 increased by nearly 7 times, while the fossil energy consumption and CO2 emissions only increased by about 1 time. Among them, the phenomenon of emissions in 1997, 1999 and 2000 decreased compared with the previous year, and there was a certain increase in other times. The total emissions in 2009 was 2,760 million tons, as shown in Figure 6.8. China’s industrial economy has achieved high-speed output with less energy consumption and CO2 emissions. What is its driving force? In order to identify the impact mechanism behind it, the CO2 emissions of 33 industrial sectors in China from 1996 to 2009 are decomposed into years: sector output scale effect, sector structure adjustment effect, sector energy intensity effect, energy consumption structure effect and energy products. The carbon emission coeffcient effect, the absolute contribution of various factors to CO2 changes is shown in Figure 6.9.

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Emission features

8

6

4

2 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 0

Total Industrial Output

Energy Consumption

CO2 Emissions

Figure 6.8 China’s total industrial output value, fossil energy consumption and CO2 emissions trend (1996–2009, 1996=1)

From the perspective of various infuencing factors after decomposition, the output scale effect (Y) is the most important positive infuencing factor, that is, the expansion of output scale leads to an increase in CO2 emissions. The contribution of industrial structure effect (S) to carbon emissions is negative in most years, indicating that the change of industrial structure is opposite to the direction of CO2 change, that is, industrial structure is one of the driving forces to curb the growth of GHG emissions. The energy intensity effect (I) is also negatively correlated with the change of carbon emissions, and the reduction of CO2 caused by the energy intensity effect is a major factor in mitigating CO2 emissions, but it is necessary to pay attention to the energy intensity in 1998. The effect is positive, which indicates that the energy effciency has deteriorated in the past, which in turn boosted CO2 emissions. In addition, the energy consumption structure effect (f) and the carbon emission structure effect (CC) of each energy product can also reduce CO2 emissions to a certain extent, but its absolute contribution is small. If only the comparison between the frst two years of 1996 and 2009 is considered, the relative contribution rate of the output scale effect of the entire industry’s increased CO2 emissions is 299%, which is the only positive factor leading to the increase of industrial CO2 emissions, while the industrial interior structural adjustments, declining energy intensity, improved energy structure, and use of low-carbon energy have all slowed CO2 emissions, with relative contribution rates of –8.97%, –186.3%, –0.7%, and –3.1%. From the perspective

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Quantitative evaluation and analysis

149

600

400

200

0

-200

-400 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Scale Effect (Y)

Industrial Structure Effect (S)

Energy Structure Effect (f) Energy Consumption Intensity Effect (I)

Carbon Emission Coefficient Effect (CC)

Figure 6.9 Contribution of factors affecting industrial CO2 emissions in China (1996– 2009, unit: million tons of CO2)

of relative contribution rate, the improvement of energy effciency and the adjustment of industrial structure are the two main ways to control industrial CO2 emissions. According to previous analysis, in 2003, most of the industrial sectors saw a signifcant increase in fossil energy consumption and CO2 emissions. In 2005, the country began to implement energy conservation and emission reduction strategies in order to understand the differences in emissions from various sectors of industry in different periods, and test whether there is a signifcant change in the emission pattern in key years, we have divided the study period into four periods: 1996–2000, 2000–2003, 2003–2005, and 2005–2009. The results of decomposition at different times are shown in Figure 6.10. As can be seen from the above fgure, the output scale effect is gradually increasing, which continues to promote the increase of CO2. The industrial structure effect is more signifcant before 2000 and after 2005, but it plays a small role between 2000 and 2005. It shows that the structural adjustment between industrial sectors during this period has not developed in the

150

150

Emission features

2,000 1,500 Scale Effect (Y)

1,000

Industrial Structure Effect (S)

500

Energy Consumption Intensity Effect (I)

0

Energy Structure Effect (f) Carbon Emission Coefficient Effect (CC)

-500 -1,000 -1,500

1996–2000

2000–2003

2003–2005

2005–2009

Figure 6.10 Absolute contribution of industrial CO2 infuencing factors in different periods (unit: million tons of CO2)

direction of “low carbonization.” Even in 2003–2005, the absolute contribution of industrial structure effect is positive, indicating that the industrial sector structure has heavy and high emission characteristics during this period. Not only did not slow down the GHG emission, but instead developed more high-emission sectors, which contributed to the increase of CO2; the energy intensity effect effectively reduced GHGs in different periods, but in the 2003–2005 heavy chemical industry the GHG mitigation effect has been reduced, but after 2005, with the introduction of national energy conservation and emission reduction policies, the energy intensity effect began to play a more active role, and the degree of CO2 mitigation is also increasing; Changes in energy structure have always contributed less in all periods, mainly because the industrial sector has its own technological development characteristics and cannot be realized in the short term. Energy transformation input factors cannot make use of clean renewable energy in the short term. Figure  6.11 separately examines the relative size of each sector in the factors affecting industrial CO2 changes in 1996 and 2009. It can be seen that in the total CO2 change, the metal products industry, the non-metal products industry and the petroleum processing industry contribute 84% of CO2 incremental emissions. In terms of infuencing factors, scale effect is the main cause of the increase of greenhouse gases. Among them, the increase of GHG output caused by the expansion of output of metal products, non-metal products and petroleum processing industry accounts for 74% of the total effect. In the industrial structure effect, the metal products industry, machinery and equipment manufacturing industry, chemical industry and wood furniture industry increased by 168 million tons of CO2 due to the relative economic

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Quantitative evaluation and analysis

151

4,000 3,000 2,000 1,000 0 -1,000 -2,000 -3,000

Overall CO2 Changes Mining

Scale Effect

Food

Petrochemical Industry Metallic Products

Industrial Structure Effect

Textile

Energy Consumption Intensity Effect

Wood Furniture

Rubber & Plastic Products

Energy Structure Effect

Carbon Emission Coefficient Effect

Petroleum Processing Non-metallic Products

Machinery Equipments

Figure 6.11 Contribution of various sectors in the effects of CO2 changes (1996, 2009, unit: million tons of CO2)

proportion, while other industrial sectors slowed down due to the relative economic decline. Millions of tons of CO2 emissions, and fnally 122 million tons of CO2 emissions due to industrial internal industrial structure changes, in all sectors, the expansion of metal products, machinery and equipment industry has signifcantly increased CO2, while mining, oil processing, the relative shrinkage of the economic share of the non-metallic products sector has contributed to CO2 emissions reductions. In the energy intensity effect, the energy effciency improvement of the metal products industry, the chemical industry and the non-metal products industry effectively controlled the reduction of CO2, but the petroleum processing industry experienced a regression in energy effciency, and additionally generated some CO2 increments, energy structure effect and energy carbon emission coeffcient effect. Although the overall impact is limited, the internal improvement of the department is very different. For example, the non-metal products industry, machinery and equipment manufacturing industry and mining industry energy structure show an optimization trend and are effective. Certain CO2 emissions have been curbed, but the metal products industry and the petroleum processing industry are more dependent on traditional high-carbon fossil energy based on the decomposition data of 33 industrial sectors (see Table 6.3). Finally, we examine separately the relative contributions of various industrial sectors in the industrial restructuring effects over the years, as shown

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Table 6.3 Decomposition of changes in CO2 emission in the industrial sector (1996, 2009) Departments

Total CO2 changes Ctotal

Output scale effect ∆CY

Industrial structure effect ∆Cstr

Consumption intensity effect ∆Cint

Energy structure effect ∆Cf

Carbon emission coeffcient effect ∆CCC

Aggregate Coal mining and washing industry Oil and gas extraction industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass products industry Furniture manufacturing Paper and paper products industry Copying of the printing industry and recording media

1,354.97 78.26

4,051.81 176.81

–121.55 –44.81

–2,524.01 –55.86

–9.19 0.07

–42.09 2.05

23.30 3.01

71.70 12.04

–56.45 5.66

10.07 –14.56

–1.88 –0.22

–0.14 0.10

–1.65

8.04

–0.76

–8.60

–0.32

–0.01

3.83

20.61

–5.24

–11.08

–0.46

0.00

3.58

57.87

–3.61

–49.82

–0.33

–0.53

–0.49 –0.66 –2.31 –1.48 2.64

40.37 34.22 5.96 84.00 9.25

–2.68 –9.21 –2.32 –10.61 –1.46

–38.23 –25.24 –5.81 –75.30 –4.95

–0.65 –0.42 –0.11 0.56 –0.19

0.70 –0.01 –0.03 –0.12 –0.02

0.52

5.05

–0.83

–3.54

–0.17

0.01

3.38

15.48

2.31

–14.32

–0.08

–0.01

0.07 15.34

2.47 81.33

0.47 –0.10

–2.69 –65.39

–0.18 –0.27

0.00 –0.24

–0.04

3.39

–0.32

–3.00

–0.17

0.06

153

Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing Chemical raw materials and chemical products manufacturing Pharmaceutical manufacturing Chemical fber manufacturing Rubber products industry Plastic products industry Non-metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal products industry General equipment manufacturing Special equipment manufacturing Transportation equipment manufacturing Electrical machinery and equipment manufacturing Communications equipment, computers and other electronic equipment manufacturing Instrumentation and culture, offce machinery manufacturing

0.23

2.27

–0.09

–1.87

–0.09

0.01

180.91

211.49

–89.97

50.53

0.14

8.73

64.08

749.55

16.23

–696.20

–1.32

–4.19

–0.42 –3.10 0.16 2.41 198.66

25.92 17.78 18.97 15.42 739.12

0.48 –4.31 –1.97 0.28 –66.24

–26.47 –17.17 –16.55 –13.17 –452.46

–0.24 0.36 –0.23 –0.11 –4.09

–0.12 0.24 –0.05 –0.01 –17.66

729.46

1,362.23

95.20

–707.17

6.26

–27.05

33.24

86.46

15.95

–65.96

–0.57

–2.64

0.11 5.30

28.56 61.58

3.70 14.22

–30.70 –69.69

–1.36 –0.13

–0.10 –0.67

2.61 9.20

32.63 40.53

4.55 10.86

–33.40 –41.25

–0.51 –1.15

–0.67 0.21

0.78

17.17

4.61

–20.13

–0.89

0.02

4.10

10.80

4.36

–10.84

–0.28

0.05

–0.06

2.73

0.55

–3.18

–0.16

0.00

154

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Emission features

100

0

-100

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Mining Textile Petroleum Processing Rubber & Plastic Products

Food Wood Furniture Petrochemical Industry Non-metallic Products

Figure 6.12 Contribution of different industries in the industrial restructuring effect (unit: million tons of CO2)

in Figure  6.12. It can be seen that before 2004, the relative increase in the proportion of the metal products industry in the industrial economy led to the industry’s signifcant contribution to GHG emissions, during which non-metallic products, petroleum processing, mining and chemical industries accounted for the gradual shrinkage of the industrial proportion has slowed down GHG emissions; after 2004, the internal structure of the industry has undergone signifcant changes, and the industrial structure effect of the metal products industry has shown a negative value, which means that the industrial share of the industry has appeared. The decline has led to a considerable reduction in GHG emissions. The petroleum processing industry and the mining industry have also produced negative effects of CO2 emissions due to the decline in the relative proportion of the sector, but the non-metallic products industry and the chemical industry have reversed. The proportion of the industry in its sector has increased signifcantly, and this has resulted in a larger scale of CO2 incremental emissions. In addition, the position of the mechanical equipment manufacturing industry in the economy has been on the rise, so its industrial structure effect has remained positive for most of the period, but its impact is small compared with the industrial restructuring effects of other sectors. The breakdown data for the more detailed 33 departments can be found in Table 6.4.

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Table 6.4 Absolute contribution values of various sectors in the industrial structure effect between 1996 and 2009 Industrial structure effect ∆Cstr

1997

1998

Aggregate Coal mining and washing industry Oil and gas extraction industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass products industry Furniture manufacturing Paper and paper products industry

–16.6 –4.8 –0.8 0.3

–38.8 –9.0 0.2 –0.2

0.2

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

–5.5 –5.0 –1.1 –0.3

–33.1 –5.0 –4.1 –0.2

1.3 –0.1 –10.0 0.1

–31.1 –1.2 –5.9 0.1

6.6 –5.8 –7.5 0.6

29.5 6.7 –5.5 1.6

–24.8 –1.3 –3.0 0.4

–78.8 –3.7 –7.6 0.7

–16.3 –3.6 –8.0 0.9

0.5 2.8 –4.6 2.4

–14.0 –0.5 –15.2 0.2

–0.3

0.1

–0.2

–0.2

–0.1

–0.3

–0.3

0.3

0.5

0.0

–0.3

0.2

0.4

–3.8

–0.2

–0.8

–0.6

–0.3

–0.9

–1.2

0.6

1.0

0.6

0.9

0.8

0.1

–2.6

–1.5

–1.0

–0.7

–0.3

–0.1

–0.8

1.2

–0.3

0.1

1.6

1.2

0.7 0.7 0.0 –2.5 –0.3

–1.4 –0.6 0.2 –3.0 0.3

–0.3 –0.1 –0.2 –0.9 –0.2

0.6 –0.7 –0.3 0.5 –0.1

–0.1 –1.2 0.1 0.0 0.1

0.4 –1.1 0.1 –0.2 –0.2

–1.6 –2.1 –0.8 –0.8 –0.4

–1.5 –3.8 –0.7 1.0 –0.6

1.1 0.7 –0.3 1.0 0.2

0.2 0.3 –0.3 –1.0 0.0

–0.7 –0.5 –0.4 –1.1 –0.1

–0.4 –0.9 –0.2 –2.1 0.2

1.2 1.0 –0.1 –1.5 –0.2

0.0

0.0

–0.1

–0.1

0.1

–0.1

0.0

–0.3

0.0

–0.1

–0.1

–0.2

–0.1

0.7

–1.3

0.3

0.1

0.0

–0.3

–0.4

0.3

0.6

0.5

1.1

1.0

0.5

0.1 –0.3

–0.1 1.1

0.0 0.6

0.0 1.2

0.1 0.3

0.0 –0.2

0.1 –1.3

0.2 0.0

0.0 0.4

0.1 –1.1

0.0 –0.1

0.0 –0.3

0.0 –1.3 (continued)

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Table 6.4 (Cont.) Industrial structure effect ∆Cstr Copying of the printing industry and recording media Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing Chemical raw materials and chemical products manufacturing Pharmaceutical manufacturing

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

0.1

0.0

0.0

–0.2

0.1

0.0

0.0

–0.2

0.0

–0.1

0.0

0.0

0.0

0.0

0.2

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

–0.1

–0.7

–5.3

–3.4

–1.7

–8.8

–9.3

–16.8

–7.9

–16.7

–29.0

–17.2

–19.8

–0.1

3.5

7.0

2.0

0.8

0.3

–2.2

–5.5

–3.4

–6.0

2.7

6.6

–1.6

12.8

0.6 0.4 0.0

1.6 0.1 0.2

0.4 2.2 –0.3

0.3 1.8 –0.9

0.5 –5.7 0.0

0.1 –1.1 0.2

–0.9 –0.2 –0.5

–2.9 –0.5 –0.2

0.2 0.3 –0.6

–0.6 –0.1 0.0

–0.2 0.0 –0.2

–0.3 –2.4 –0.4

1.7 –0.7 0.6

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Quantitative evaluation and analysis

157

6.4 Summary This chapter conducts a detailed survey of 33 sectors in ten industries in the industry from 1996 to 2009. It conducts statistical analysis of economic output, energy consumption and GHG emissions of various sectors, and industrial CO2 based on LMDI method. Decomposed into different effects the main conclusions include the following. 1. Between 1996 and 2009, China’s total industrial output value maintained an average annual growth rate of 17.3%. Among them, machinery and equipment manufacturing, metal products, and chemical industries accounted for a higher proportion of the industrial economy; fossil energy consumption grew at an average annual rate of 5.6%. After 2002, the energy consumption of some industries has risen sharply. Among them, the fossil energy consumption of metal products industry has the highest proportion. In 2009, it accounted for 40% of the total industrial fossil energy consumption. From the perspective of fossil energy consumption structure, the proportion of coal is gradually increasing. It experienced decline, but rebounded in 2002–2005; the proportion of coke consumption increased due to the expansion of the production scale of metal products; the proportion of petroleum fuel consumption increased before 2002, but in 2002–2005 it gradually shrank until it is stable after 2005; the proportion of natural gas consumption has changed little, staying between 3% and 5%. The GHG emissions of the industrial sector increased by an annual average of 5.3%. In 2002, emissions from most industrial sectors began to rise rapidly. The metal products industry, nonmetal products industry and chemical industry are the main sources of industrial GHG emissions. CO2 accounts for 71% of the total industrial GHG emissions. 2. In 2009, compared with 1996, industrial CO2 increased by 1355 million tons, of which the relative contribution rate of output scale effect was 299%. Industrial structure effect, energy intensity effect, energy structure effect and carbon emission coeffcient effect, respectively, are –8.97%, – 186.3%, –0.7%, and –3.1%. Among the factors affecting industrial CO2 emissions, the expansion of the production scale of the sector is the main factor leading to the increase of industrial CO2 emissions, and the improvement of energy effciency within the industrial sector and the adjustment of the sector structure are the two main ways to reduce greenhouse gas emissions. Because industrial production technology cannot replace traditional fossil energy with clean energy in the short term, the improvement of energy structure and the low carbonization of fuel carbon emission coeffcient have relatively little contribution to industrial CO2 emissions. 3. The output scale effect gradually increased during the inspection period and continued to drive the increase in CO2. In all industries, the expansion

158

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Emission features

of the output of metal products, non-metal products and petroleum processing industries led to an increase in GHG emissions of 74% of the total effect of output. 4. The industrial structure effect was more signifcant before 2000 and after 2005, but it played a small role between 2003 and 2005, and the absolute contribution was positive, indicating that the industrial sector structure during this period was characterized by heavy and high emissions. Instead of slowing down GHGs, more high-emission sectors have been developed, contributing to the increase in CO2. In all industries, the expansion of the metal products industry and machinery and equipment industry has signifcantly increased CO2, while the relative shrinkage of the economic sector in the mining, petroleum processing, and non-metal products sectors has contributed to CO2 emission reduction. 5. The energy intensity effect has effectively reduced GHGs in different periods, but it has been curbed in the upward stage of heavy chemical industry in 2003–2005, and its GHG mitigation effect has declined. After 2005, the energy intensity effect began to play a more active role. The role of it is also slowing down the degree of CO2. In all industries, the energy effciency improvements in the metal products, chemical and non-metallic industries have effectively controlled the reduction of CO2, but the petroleum processing industry has experienced a decline in energy effciency and led to some CO2 increments. 6. The energy structure and fuel carbon emission coeffcient effects contribute less in each period, but there are certain differences between departments. The energy structure of the non-metal products industry, machinery and equipment manufacturing industry and the mining industry has shown an optimization trend and effectively curbed some CO2 emissions, but the metal products industry and the petroleum processing industry are more dependent on traditional high-carbon fossil energy sources.

Note 1 This chapter is predicated on the “Study of the Features and Infuential Factors of Industrial GHG Emissions in China,” written by Wei Chu and Yu Dongjun, which is recorded in Environmental Economy and Policies, edited by Li Shantong, Economy and Science Press, 2012. Some of the content is revised or deleted.

159

7

Analysis of industrial CO2 emissions and infuencing factors A case study of Zhejiang Province

In recent years, the issue of global warming has attracted international attention. As a responsible developing country, China has taken active measures to deal with climate change, and all regions have also responded positively. As a developed coastal province in the east, Zhejiang Province attaches great importance to climate change work. Under the unifed guidance of the state, in 2007, the Zhejiang Provincial Climate Change Leading Group was set up, and the leading group set up an offce to undertake the daily work of tackling climate change. In 2010, Zhejiang Province announced the Zhejiang Province Climate Change Program, which clarifed the goals, principles and policy measures for Zhejiang Province to respond to climate change in 2012. According to the Zhejiang Province Climate Change Program, energy consumption in the industrial sector is the main source of greenhouse gas (GHG) emissions. Among the 385 million tons of carbon dioxide equivalent emitted by Zhejiang Province in 2007, the proportion of energy activities and industrial production sectors was as high as 93.55%. Therefore, systematic accounting and statistical analysis of carbon emissions from different sectors of the industrial sector is the basis for understanding the status, development trends and levels of GHG emissions in the industrial sector in Zhejiang Province. This chapter takes the industrial sector of Zhejiang Province as the research object, based on the second economic census data of Zhejiang Province, measures and counts the GHG emissions of 37 industrial sectors in Zhejiang Province in 2008; to understand the location of Zhejiang Province in the same provinces and countries. For the horizontal comparison of relevant indicators with Beijing, Shanghai, Jiangsu, Guangdong and the whole country; for the understanding of the development trend of greenhouse gases in the industrial sector of Zhejiang Province, the vertical comparison with the frst economic census data of Zhejiang Province in 2004. In addition, the factor decomposition method is used to quantitatively measure the relative contribution of different factors such as economic output, energy intensity, industrial structure, industrial scale and energy structure to the growth of industrial GHGs in Zhejiang Province.

DOI: 10.4324/9780429447655-9

160

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Emission features

The structure of this chapter is arranged as follows. The frst section defnes and explains the main concepts, calculation methods and data sources of the research. The second section accounts for the GHG emissions of the industrial sector in Zhejiang Province, and compares its basic distribution and characteristics horizontally and vertically. The third section identifes and decomposes the factors affecting industrial GHG emissions, and fnally the conclusions and countermeasures are presented.

7.1 Main concepts, calculation methods and data sources 7.1.1 Concept and scope of research According to the scope of preparation of GHG inventories in the Initial National Information Circular on Climate Change, the GHGs calculated in this chapter mainly include the following three types: carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), and emissions are converted to the corresponding CO2 equivalents. In view of the small proportion of GHG emissions of industrial enterprises below the scale, the scope of research is defned as industrial enterprises above designated size. In addition, for ease of analysis and reading, this chapter divides the 37 industrial sectors in the economic census into 12 major categories: mining, food and beverage, textile and leather products, wood furniture and paper, petroleum processing, chemical industry, rubber and plastic products industry, non-metal products industry, metal products industry, equipment machinery and instrument industry, power industry, and other industries. The specifc classifcation criteria are detailed in Table 7.1. 7.1.2 Measurement GHG emissions are generally measured by fossil energy consumption according to the preparation method of GHG inventories in the Initial National Information Circular on Climate Change, the GHG emission coeffcient of different fossil energy consumption published by the Intergovernmental Panel on Climate Change (IPCC), and announced in the China Energy Statistics Yearbook. The low calorifc value of different fossil energy sources and the CO2 emissions caused by fossil energy consumption activities can be calculated according to the following formula: 19

19

i=1

i=1

CO2 = ˛ CO2i = ˛ Ei × Ci × Di

(7.1)

Here, CO2 represents the total amount of carbon dioxide emissions; subscript I represents the 19 fossil energy products involved in the Zhejiang Economic Census; Ei is the total energy consumption of the subsector; Ci represents

161

Table 7.1 Industry classifcation of major industrial sectors 12 major industries Mining industry

Food & beverage industry

Textile & leather products industry

Sector code 6 7 8 9 10 11 13 14 15 16 17 18 19

Wood furniture manufacturing industry

20 21 22 23 24

Petroleum processing industry Petrochemical industry

Rubber & plastics industry Non-metallic industry Metal products industry

Equipment machinery and instrument industry

25 26 27 28 29 30 31 32 33 34 35 36 37 39 40

Sectors Coal mining and washing industry Oil and gas extraction industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Other mining industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass products industry Furniture manufacturing Paper and paper products industry Copying of the printing industry and recording media Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing Chemical raw materials and chemical products manufacturing Pharmaceutical manufacturing Chemical fber manufacturing Rubber products industry Plastic products industry Non-metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal products industry General equipment manufacturing Special equipment manufacturing Transportation equipment manufacturing Electrical machinery and equipment manufacturing Communications equipment, computers and other electronic equipment manufacturing (continued)

162

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Emission features

Table 7.1 (Cont.) 12 major industries

Electricity Other sectors

Sector code

Sectors

41

Instrumentation and culture, offce machinery manufacturing Crafts and other manufacturing Electricity and heat production and supply Waste resources and waste materials recycling industry Gas production and supply industry Water production and supply industry

42 44 43 45 46

Note: Please refer to the division in Fisher-Vanden et al. (2004) for the division criteria for the 12 major categories.

the default CO2 emission factor, the unit is kilogram CO2/kcal; Di represents the average low calorifc value of Chinese energy products, in kilocalories/kg or kilocalories/cubic meter. Among them, Ci×Di is called the carbon dioxide emission coeffcient. The emission of other GHGs is calculated by the same formula (7.1), and is not listed here. The CO2 emission factors of various energy products are shown in Table 7.2. 7.1.3 Data sources The energy consumption of industrial enterprises above designated size and the industrial output value of industrial enterprises above designated size are from the frst economic census data of Zhejiang Province in 2004 and the second economic census data of Zhejiang Province in 2008. Among them, the total industrial output value in 2004 was reduced to the constant price in 2008 using the “price index of industrial enterprises above designated size of Zhejiang Province,” and the price defator was from the 2009 Zhejiang Statistical Yearbook. The energy consumption of industrial enterprises above designated size in Beijing, Shanghai, Guangdong, Jiangsu and the whole country, and the industrial output value of industrial enterprises above a designated size are from the main data bulletin of the second national economic census in 2008, Beijing, Shanghai, Guangdong, Jiangsu, Zhejiang, and the country’s total population at the end of the year, from the 2009 China Statistical Yearbook.

7.2 Industrial greenhouse gas emissions in Zhejiang Province 7.2.1 Total amount and distribution of industrial greenhouse gas emissions Using the calculation method of formula (7.1) and the second economic census data of Zhejiang Province in 2008, the carbon dioxide (CO2) emissions

163

Case study of Zhejiang Province

163

Table 7.2 CO2 emission factors of major energy products Energy products

Unit

Reference index coeffcient

Default value of carbon content (kgC/GJ)

Raw coal Washed coal Other coal washing Briquette Coke Other coking products Coke oven gas

Ton Ton Ton

0.7143 0.9 0.2–0.7

25.8 25.8 25.8

5,000 6,300 2,000

1.980 2.495 0.792

Ton Ton Ton

0.5–0.7 0.9714 1.1–1.5

26.6 29.2 25.8

5,000 6,800 6,800

2.042 3.048 2.693

5.714–6.143

12.1

4,000

0.743

1.286

70.8

900

1.359

1.7–12.1

12.1

1,250

0.232

11-13,3

15.3

9,310

2.187

1.7572

17.5

12,000

3.224

1.4286 1.4714 1.4714 1.4571 1.4286 1.7143

20.0 18.9 19.5 20.2 21.1 17.2

10,000 10,300 10,300 10,200 10,000 12,000

3.070 2.988 3.083 3.163 3.239 3.169

1.5714 1–1.4

15.7 20.0

11,000 10,000

2.651 3.070

Ten cubic meters Blast furnace gas Ten cubic meters Other gas Ten cubic meters Natural gas Ten cubic meters Liquefed natural Ton gas Crude Ton Gasoline Ton Kerosene Ton Diesel Ton Fuel oil Ton Liquefed Ton petroleum gas Refnery dry gas Ton Other petroleum Ton products

Average low calorifc value (kcal/kg), (kcal/m3)

CO2 emission coeffcient (kg CO2/ kg), (kg CO2/m3)

Note: Data in the third column “Reference index coeffcient” are derived from the China Energy Statistical Yearbook; the fourth column, “Carbon content default value,” is derived from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 2 Energy, tables 2.2 and 2.3; the ffth column of “average low calorifc value” comes from the China Energy Statistical Yearbook and the National Development and Reform Commission’s Response to Climate Change “Announcement on Announcement of 2009 China’s Regional Power Grid Default Emission Factors” (July 2, 2009).

of industrial enterprises above a designated size in Zhejiang Province were about 354 million tons, methane (CH4) emissions are 0.64 million tons and nitrous oxide (N2O) is about 0.47 million tons. The total amount of GHGs is about 356  million tons of carbon dioxide equivalent. The proportion of carbon dioxide is 99.57%, which indicates that carbon dioxide is the main source of GHG emissions in the industrial sector of Zhejiang Province.

164

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Emission features

Electricity Industry 46%

Others 0%

Mining 0% Food & Beverage 1% Rubber & Plastics 1% Equipments & Mechanical Instruments 2% Wood Furniture & Paper-making 2% Textile and Leather 4% Petrochemical Industry 6%

Petroleum Processing 22%

Non-metallic Products 7% Metallic Products 9%

Figure 7.1 Distribution of industrial greenhouse gas emissions in Zhejiang Province (2008)

From the distribution of industrial GHG emissions (see Figure 7.1), the top fve industries are: power industry emissions (164.248 million tons), petroleum processing industry (794.24  million tons), metal products industry (304.62 million tons), non-metallic products industry (24.416 million tons) and chemical industry (20.725 million tons); the above fve industries accounted for 90% of the province’s above-scale industrial emissions, of which the power industry accounted for the highest proportion, reaching 46%. Other industry sectors, such as rubber and plastic products, food and beverage, mining, and other industries, account for a small proportion. Specifc to the 37 industrial sectors, the top fve sectors of emissions are:  electricity and heat production and supply (164.248  million tons), petroleum processing, coking and nuclear fuel processing (794.24 million tons), ferrous metal smelting and the rolling processing industry (274.97  million tons), the non-metallic mineral products industry (24.416 million tons) and the chemical raw materials and chemical products manufacturing industry (171.43 million tons); the above fve departments accounted for 88% of the province’s industrial sector above a designated size. The main GHG emission data of 37 industrial sectors can be found in Table 7.3.

165

Case study of Zhejiang Province

165

7.2.2 Level of industrial greenhouse gas emissions Based on the 2008 National Economic Census Data Bulletin, this chapter selects the average data of several major developed provinces in China, including Beijing, Shanghai, Jiangsu, Guangdong and the whole country, and compares them with relevant indicators in Zhejiang Province (see Table 7.4, Table 7.5, and Table 7.6 for relevant data). 7.2.2.1 Comparison of total industrial greenhouse gas emissions In 2008, the GHG emissions of industrial enterprises above a designated size were 10.054 billion tons of CO2 equivalent. Among them, the GHG emissions of industrial enterprises above a designated size in Beijing, Shanghai, Zhejiang, Jiangsu and Guangdong provinces, ranked from high to low, were: Jiangsu (657  million tons), Guangdong (469  million tons), Zhejiang (356  million tons), Shanghai (249 million tons), and Beijing (115 million tons). The above fve provinces and municipalities accounted for 18.36% of the total industrial emissions above a designated size. From the perspective of relative scale (Figure  7.2), the carbon dioxide emissions from fossil fuel combustion in industrial sectors above a designated size account for about 3.54% of the national total, although this proportion is higher than Beijing (1.14%) and Shanghai (2.48%). However, it is lower than Guangdong (4.66%) and Jiangsu (6.54%). 7.2.2.2 Comparison of industry distribution of industrial GHG Figure  7.3 shows the industry distribution of industrial GHG emissions above a designated size in 2008 in fve provinces and cities. It can be seen that due to the differences in industrial structure in various regions, the relative contributions of various sectors to regional industrial GHG emissions are also different. For example, the petroleum processing industry is the sector with the largest contribution to industrial GHG emissions in Beijing and Shanghai, accounting for 45.4% and 35.9%, respectively. For Zhejiang, Jiangsu and Guangdong provinces, the power industry accounts for industrial GHG emissions. The highest proportions were 46.2%, 36% and 40%, respectively. Although the contribution of different departments is different, in general, the GHG emissions of the fve sectors of electricity, petroleum processing, metal products, chemicals, and non-metal products account for a large proportion. For example, the fve sectors of Beijing accounted for 95.5% of industrial GHG emissions, 95.8% in Shanghai, 89.9% in Zhejiang, 89% in Jiangsu, and 87.3% in Guangdong. The share of other sectors is relatively small.

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Table 7.3 GHG emissions, energy consumption and industrial output value of industrial enterprises above a designated size in Zhejiang Province in 2008 Sector code

6 7 8 9 10 11 13 14 15 16 17 18 19 20

Sectors

Coal mining and washing industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass Furniture manufacturing Paper and paper products industry

CO2 emission (10,000 tons)

CH4 emission (10,000 tons)

N2O emission (10,000 tons)

CO2 equivalent (10,000 tons)

Energy consumption (10,000 tons standard coal)

Gross industrial output (100 million)

107.10

0.0011

0.0017

107.63

40.44

7.71

7.77

0.0001

0.0001

7.80

3.89

25.17

1.96

0.0000

0.0000

1.97

2.41

29.61

36.00

0.0008

0.0005

36.16

21.23

81.94

58.61

0.0009

0.0009

58.88

42.27

637.35

58.68 82.23 2.26 1,361.26 84.12

0.0008 0.0010 0.0001 0.0157 0.0013

0.0009 0.0013 0.0000 0.0212 0.0012

58.95 82.63 2.26 1,367.90 84.51

38.28 55.42 3.04 1,147.93 68.95

298.72 377.73 216.08 4482.06 1,445.71

62.09

0.0010

0.0009

62.38

47.77

1,090.28

22.45

0.0004

0.0003

22.56

40.62

372.15

15.31 689.02

0.0004 0.0077

0.0002 0.0108

15.37 692.40

16.02 498.62

449.91 865.27

167

21 22 23 24 25 26 27 28 29 30 31 32 33 34

Copying of the printing industry and recording media Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing Chemical raw materials and chemical products manufacturing Pharmaceutical manufacturing Chemical fber manufacturing Rubber products industry Plastic products industry Non-metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal products industry General equipment manufacturing Special equipment manufacturing

13.84

0.0003

0.0002

13.90

14.15

256.19

11.47

0.0003

0.0001

11.52

17.08

386.93

7,915.91

0.3108

0.0653

7,942.38

3695.76

1,104.41

1,706.60

0.0292

0.0237

1,714.29

972.40

2,644.82

114.98

0.0014

0.0018

115.53

97.63

615.49

241.62

0.0041

0.0033

242.69

234.90

1,546.34

100.92 258.13 2,469.98

0.0015 0.0035 0.0324

0.0015 0.0038 0.0366

101.39 259.34 2481.56

68.82 181.94 1,114.24

398.18 1,498.65 1,123.68

2,739.80

0.0247

0.0317

2749.74

1,030.93

1,643.93

151.62

0.0028

0.0019

152.26

108.25

1,399.08

143.60 238.73

0.0029 0.0045

0.0018 0.0031

144.20 239.75

115.90 186.51

1,768.23 2,974.16

55.68

0.0011

0.0007

55.93

43.07

941.98 (continued)

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Table 7.3 (Cont.) Sector code

Sectors

35

Transportation equipment manufacturing Electrical machinery and equipment manufacturing Communication equipment, computers and other electronic equipment Instrumentation and culture, offce machinery manufacturing Crafts and other manufacturing Waste resources and waste materials recycling industry Electricity and heat production and supply Gas production and supply industry Water production and supply industry

36 37 39 40 41 42 44 43 Total

CO2 emission (10,000 tons)

CH4 emission (10,000 tons)

N2O emission (10,000 tons)

CO2 equivalent (10,000 tons)

Energy consumption (10,000 tons standard coal)

Gross industrial output (100 million)

138.55

0.0036

0.0015

139.09

111.90

2,624.49

87.45

0.0022

0.0008

87.74

106.05

3,668.19

42.48

0.0009

0.0005

42.66

55.74

1,705.58

9.72

0.0003

0.0001

9.76

15.45

513.21

31.43

0.0006

0.0004

31.56

28.69

678.02

5.17

0.0001

0.0001

5.19

4.21

228.11

16,345.17

0.1769

0.2554

16,424.83

6,313.25

2,732.75

1.50

0.0000

0.0000

1.50

1.11

87.37

0.63

0.0000

0.0000

0.64

14.84

84.01

35414.4

0.6355

0.4744

35,568.9

16,559.7

41,003.5

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Table 7.4 GHG emissions of industrial sectors above a designated size in fve provinces and cities in 2008 (10,000 tons of CO2 equivalent) Sector code

Sectors

6 7 8 9 10 11 13 14 15 16

Coal mining and washing industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing

17 18

Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass Furniture manufacturing Paper and paper products industry Copying of the printing industry and recording media Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing Chemical raw materials and chemical products manufacturing Pharmaceutical manufacturing Chemical fber manufacturing Rubber products industry Plastic products industry

19 20 21 22 23 24 25 26 27 28

National

Beijing

Shanghai

Zhejiang

Jiangsu

Guangdong

14,2782 1,880 564 1,795 6,702 4,294 3,588 250 7,530 999

4 5 0 6 33 29 76 1 16 22

0 0 0 0 30 34 23 0 92 28

108 8 2 36 59 59 83 2 1,368 85

2386 21 0 151 150 100 247 3 1,077 183

0 20 5 27 272 159 145 6 1,229 266

584 1,479

1 1

2 8

62 23

20 228

79 46

268 9,894 278 250 208,279

4 23 11 4 5,222

4 67 8 10 8,934

15 692 14 12 7,942

5 1,080 9 24 6,826

41 1,573 76 77 12,170

66,435

265

1,495

1,714

9,102

1,501

2,801 2,452 1,579 1,545

29 1 10 12

37 6 48 44

116 243 101 259

135 496 110 126

137 55 93 288 (continued)

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Table 7.4 (Cont.) Sector code

Sectors

29 30 31

Non-metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal products industry General equipment manufacturing Special equipment manufacturing Transportation equipment manufacturing Electrical machinery and equipment manufacturing Communication equipment, computers and other electronic equipment Instrumentation and culture, offce machinery manufacturing Crafts and other manufacturing Waste resources and waste materials recycling industry Electricity and heat production and supply Gas production and supply industry Water production and supply industry

32 33 34 35 36 37 39 40 41 42 44 43 Total

National

Beijing

Shanghai

Zhejiang

Jiangsu

Guangdong

53,150 175,559 17,557

456 1,895 5

231 7,387 42

2,482 2,750 152

3,177 14,519 210

4,807 2,875 388

2,163 4,364 1,897 3,327 2,291 975

15 27 44 85 10 11

51 87 38 72 31 16

144 240 56 139 88 43

318 604 91 160 217 92

346 126 71 113 327 397

187

3

2

10

20

44

1279 109 27,3085 3,093 132 10,05398

57 2 3,108 12 2 11,509

4 3 5,682 399 0 24,918

32 5 16,425 2 1 35,569

20 7 23,689 117 1 65,719

256 15 18,665 172 10 46,877

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Table 7.5 Energy consumption of industrial sectors above a designated size in fve provinces and municipalities in 2008 (10,000 tons of standard coal) Sector code 6 7 8 9 10 11 13 14 15 16 17 18 19 20 21 22 23

Sectors Coal mining and washing industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass Furniture manufacturing Paper and paper products industry Copying of the printing industry and recording media Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing

National

Beijing

Shanghai

Zhejiang

Jiangsu

Guangdong

53,072 1,044 427

3 5 0

0 0 0

40 4 2

898 12 1

0 13 6

877 3,741

3 17

0 16

21 42

88 105

19 214

2,221 1,639 140 5,035 626

19 34 1 10 10

24 14 1 50 18

38 55 3 1,148 69

69 113 5 923 125

100 80 4 588 166

385

0

4

48

15

81

905

2

6

41

116

44

191 5,304 213

3 11 12

5 38 10

16 499 14

5 580 11

47 727 62

186

2

8

17

16

78

89,528

2,587

4,266

3,696

3,149

5,689 (continued)

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Table 7.5 (Cont.) Sector code

Sectors

24

Chemical raw materials and chemical products manufacturing Pharmaceutical manufacturing Chemical fber manufacturing Rubber products industry Plastic products industry Non-metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal products industry General equipment manufacturing Special equipment manufacturing Transportation equipment manufacturing Electrical machinery and equipment manufacturing Communication equipment, computers and other electronic equipment Instrumentation and culture, offce machinery manufacturing Crafts and other manufacturing Waste resources and waste materials recycling industry Electricity and heat production and supply Gas production and supply industry Water production and supply industry

25 26 27 28 29 30 31 32 33 34 35 36 37 39 40 41 42 44 43 Total

National

Beijing

Shanghai

Zhejiang

Jiangsu

Guangdong

32,998

164

860

972

4,754

796

1,571 1,503 895 1,162 22,871 63,314

17 1 6 10 211 702

27 8 26 48 125 2,724

98 235 69 182 1,114 1,031

95 364 87 118 1,379 5,260

70 38 58 276 2,123 1,057

10,210

4

30

108

150

224

1,404 2,388 1,156 2,080 1,559

12 20 25 53 10

48 78 30 81 39

116 187 43 112 106

235 368 84 154 193

276 100 74 110 323

1,062

30

71

56

291

506

167

4

5

15

25

47

601 61

23 1

4 3

29 4

16 6

126 10

10,4147 1,295 257 416,235

1,249 8 8 5,278

2,193 181 9 11,048

6,313 1 15 16,560

9,136 44 17 29,005

7,289 94 48 21,563

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Table 7.6 Gross industrial output value of industrial enterprises above a designated size in fve provinces and municipalities in 2008 (100 million yuan) Sector code 6 7 8 9 10 11 13 14 15 16 17 18 19 20 21 22 23 24

Sectors Coal mining and washing industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass Furniture manufacturing Paper and paper products industry Copying of the printing industry and recording media Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing Chemical raw materials and chemical products manufacturing

National

Beijing

Shanghai

Zhejiang

Jiangsu

Guangdong

14,626 3,761 2,728

257 29 0

0 0 0

8 25 30

241 70 6

0 100 93

1,869 23,917

4 232

0 292

82 637

142 1,536

150 1,494

7,717 6,250 4,489 21,393 9,436

148 136 30 64 98

333 175 330 347 472

299 378 216 4,482 1,446

303 408 275 4,880 2,159

746 489 256 1,747 1,725

5,871 4,804

8 15

129 84

1,090 372

369 747

1174 382

3,073 7,874 2,685

48 71 120

198 213 189

450 865 256

158 964 218

831 1,324 653

2,498

17

166

387

398

873

22,629

753

1,203

1,104

1,057

1,898

33,955

307

1,862

2,645

6,582

3,124 (continued)

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Table 7.6 (Cont.) Sector code

Sectors

25 26 27 28 29 30

Pharmaceutical manufacturing Chemical fber manufacturing Rubber products industry Plastic products industry Non-metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal products industry General equipment manufacturing Special equipment manufacturing Transportation equipment manufacturing Electrical machinery and equipment manufacturing Communication equipment, computers and other electronic equipment Instrumentation and culture, offce machinery manufacturing Crafts and other manufacturing Waste resources and waste materials recycling industry Electricity and heat production and supply Gas production and supply industry Water production and supply industry

31 32 33 34 35 36 37 39 40 41 42 44 43 Total

National

Beijing

Shanghai

Zhejiang

Jiangsu

7,875 3,970 4,229 9,897 20,943 44,728

Guangdong

264 4 23 77 297 597

278 45 171 546 477 1,639

615 1546 398 1,499 1,124 1,644

873 1,257 575 1,196 1,800 6,420

499 154 324 2,423 2,221 1,493

20,949

68

414

1,399

2,191

1,817

15,030 24,688 14,521 33,395 30,429

211 409 434 1153 387

974 2,216 854 2,572 1,741

1,768 2,974 942 2,624 3,668

2,722 4,559 2,099 3,617 5,765

3,095 1,530 1,151 3,453 7,145

43,903

2,386

5,267

1,706

9,927

15,374

4,984

211

352

513

1,145

1,352

4,089 1,138

82 0

172 0

678 228

243 0

1,067 0

32,316 1,507 913 499,078

1,242 111 26 10,320

1,206 114 35 25,065

2,733 87 84 41,003

2,470 140 71 67,582

3,653 292 213 64,317

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Case study of Zhejiang Province

175

Beijing 1.14% Shanghai 2.48% Other Provinces 81.64%

Zhejiang 3.54% Jiangsu 6.54% Guangdong 4.66%

Figure 7.2 Comparison of industrial greenhouse gas emissions scale of major cities and municipalities above scale (2008)

100% 80% 60% 40% 20% 0%

Beijing Others Mining

Shanghai

Zhejiang Rubber & Plastics Wood Furniture

Jiangsu

Guangdong

Food & Beverage Machinery Equipments

Figure 7.3 Comparison of industrial greenhouse gas industry distribution in major provinces and cities (2008)

7.2.2.3 Comparison of per-capita industrial GHG emissions Per-capita CO2 is an important indicator to measure the level of CO2 emissions in a country or region. Due to the lack of energy consumption and carbon sink data from other sectors, the comparison here is the industrial GHG emissions above the per-capita size, rather than the total per-capita GHG emissions in the region.

176

176

Emission features

15 Per-Capita CO2 Emissions

Ton CO2 per capita 10

5

0

Nationwide

Beijing

Shanghai

Zhejiang

Jiangsu

Guangdong

Figure 7.4 Comparison of per-capita industrial greenhouse gas emissions in major provinces and cities (2008)

Figure 7.4 refects the per-capita industrial GHG emissions of the country and major provinces and cities in 2008. Among them, the per-capita industrial GHG emission level of the country is 7.57 ton of CO2 equivalent/person; Shanghai and Jiangsu are higher than the national average, respectively, 13.20 and 8.56 (ton of CO2 equivalent/person); Beijing, Zhejiang and Guangdong are below the national average at 6.79, 6.95 and 4.91 (ton of CO2 equivalent/ person). Among them, Zhejiang’s per-capita industrial GHG emissions are 91.8% of the national average. 7.2.2.4 Industrial carbon dioxide emission intensity comparison Carbon dioxide emission intensity is an indicator used to measure the relationship between the economy and carbon emissions, and represents the carbon dioxide emissions per unit of output. The Twelfth Five-Year Plan has clearly defned carbon dioxide intensity as a constraint indicator. Therefore, it is important to evaluate the carbon dioxide emission intensity level and trend of various industrial sectors for future carbon dioxide intensity target decomposition. Overall, in 2008, the industrial output value of industrial enterprises a above designated size produced an average of 20,100 tons of CO2. The industrial carbon dioxide intensity of Beijing, Shanghai, Zhejiang, Jiangsu and Guangdong was generally lower than the national average:  the lowest in Guangdong was 0.729 (10,000 tons CO2 equivalent/100  million yuan), the highest is Beijing 1.115 (10,000 tons CO2 equivalent/100  million yuan), Zhejiang is relatively low compared to several other provinces and cities at 0.867 (10,000 tons CO2 equivalent/100  million yuan), the equivalent of national average is 43.1%.

177

Case study of Zhejiang Province 12

177

Thousand tons CO2 Equivalent/ hundred million yuan

8

4

0

Electricity Industry Nationwide

Petroleum Processing Beijing

Metallic Products Shanghai

Petrochemical Industry

Zhejiang

Jiangsu

Non-metallic Products Industry Guangdong

Figure 7.5 Comparison of CO2 emission intensity of key industrial sectors in major provinces and cities (2008)

Previously, it has been pointed out in the analysis of Figure 7.3 that the fve industries of electricity, petroleum processing, metal products, chemicals, and non-metal products have a great impact on industrial GHG emissions. Therefore, Figure 7.5 focuses on industrial carbon dioxide emissions for these fve major industries. It can be seen that the carbon dioxide emission intensity of the power industry and the petroleum processing industry is signifcantly higher than that of other sectors. For the power industry, the national average carbon dioxide emission intensity is 8.45 (10,000 tons CO2 equivalent/100  million yuan), except for Jiangsu Province, which is higher than the national average; the other four provinces and cities are generally lower than the national average, of which the carbon dioxide intensity of Beijing is signifcantly lower than that of other provinces and cities at only 2.5 (10,000 tons of CO2 equivalent/100  million yuan). The carbon dioxide emission intensity of the power industry in Zhejiang Province is 6.412 (10,000 tons of CO2 equivalent/100  million yuan), which is 2.56 of the carbon dioxide emission intensity of the Beijing power industry, indicating that there is still a lot of room for decline. In the petroleum processing industry, the national average carbon dioxide emission intensity is 9.2 (10,000 tons CO2 equivalent/100  million yuan). The difference in carbon dioxide emission intensity between the fve provinces and cities is not large:  the highest is Shanghai, the lowest is Guangdong, and the carbon dioxide emission intensity of the Zhejiang oil processing industry is 7.192 (10,000 tons CO2 equivalent/100 million yuan). In addition, Figure 7.5 also reveals that in the metal products industry and

178

178

Emission features

chemical industry, Zhejiang’s carbon dioxide emission intensity is lower than the national average, and lowest in the fve provinces and cities, indicating that its carbon dioxide production effciency is higher. In the non-metallic products industry, Shanghai’s carbon dioxide emission intensity is much lower than other provinces and cities, and Zhejiang’s carbon emission intensity is close to the national average. In general, Zhejiang’s industrial carbon dioxide emission intensity is lower than the national average, especially in the metal products industry and chemical industry has certain effciency advantages, but there is still a certain gap between the power industry and non-metal products industry and other provinces and cities. 7.2.2.5 Comparison of energy product emissions structure The energy emission structure is used to measure the amount of carbon dioxide emissions from the burning of fossil fuels. This indicator can be used to measure the energy consumption structure of a region or industry. According to the CO2 emission coeffcient of different fossil energy products, the energy products are divided into three groups: high carbon energy (emission coeffcient greater than 3), medium carbon energy (emission coeffcient greater than 2 but less than 3) and low carbon energy (emission coeffcient less than 2). If the energy product emission structure index becomes larger, it means that the energy consumption structure of the industry or region increases the proportion of high-carbon energy use, and vice versa, the proportion of low-carbon energy use increases. Generally speaking, in 2008, the industrial sector above a designated size will need to emit 24.15 million tons of CO2 per 10,000 tons of standard coal. In the fve provinces and cities in comparison, the highest carbon dioxide emissions of fossil energy combustion in Jiangsu’s industrial units above a designated size is 2.27 ton of CO2 equivalent/ton of standard coal, while Zhejiang is the lowest at 2.148 ton of CO2 equivalent/ton of standard coal, which indicates that low-carbon energy accounts for the energy structure consumed by industrial enterprises above a designated size in Zhejiang Province. The ratio is higher than other provinces and cities. Figure  7.6 compares the energy emissions structure of fve key industries. In the power industry, Zhejiang’s energy emission structure is “highly carbonized,” and its unit energy consumption emits the highest amount of carbon dioxide, which is 2.601 (tons of CO2 equivalent/ton of standard coal), but still lower than the national average. In the petroleum processing industry, Zhejiang Province is only lower than Jiangsu Province. In the metal products industry, Zhejiang Province is signifcantly lower than the national and other three provinces and cities, only slightly higher than Guangdong; the chemical industry is the lowest in Zhejiang, indicating that in the metal products industry and chemical industry, Zhejiang’s energy structure is the same as

179

Case study of Zhejiang Province 4

179

Ton CO2 Equivalent/ton standard coal

3

2

1

0

Electricity Industry

Nationwide

Petroleum Processing

Beijing

Metallic Products

Shanghai

Zhejiang

Petrochemical Industry

Jiangsu

Non-metallic Products

Guangdong

Figure 7.6 Comparison of energy emission structures of key industrial sectors in major provinces and cities (2008)

other provinces. Compared with the city, it is more “low carbonized”; in addition, in the non-metal products industry, Zhejiang’s energy structure is close to the national average, but signifcantly higher than Shanghai. In general, Zhejiang’s industrial energy structure has a certain “low carbonization” characteristics, especially in the metal products industry and chemical industry, the unit energy GHG emission level is low, but there is still a certain gap between the non-metal products industry and other provinces and cities. 7.2.3 Trend of industrial greenhouse gas emissions Based on the frst economic census data of Zhejiang Province in 2004 and the second economic census data of Zhejiang Province in 2008, the key points are to compare the total GHG emissions, industry distribution, carbon dioxide emission intensity, energy emission structure and other indicators of industrial enterprises above a designated size. To understand its dynamic trend, the main data of Zhejiang’s above-scale industries in 2004 are shown in Table 7.7. 7.2.3.1 Trends in total emissions and industry distribution In 2004, GHG emissions from industrial enterprises above a designated size in Zhejiang Province were 244 million tons; in 2008, GHG emissions from industrial enterprises above a designated size in Zhejiang Province were 356 million tons. It increased by 45.6% on the basis of 2004.

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Table 7.7 GHG emissions, energy consumption and industrial output value of industrial enterprises above a designated size in Zhejiang Province in 2004 Sector code

Sectors

6 7 8 9 10 11 13 14 15 16 17 18

Coal mining and washing industry Ferrous metal mining and dressing industry Non-ferrous metal mining and dressing industry Non-metallic mining and dressing industry Agricultural and sideline food processing industry Food manufacturing Beverage manufacturing Tobacco industry Textile industry Textile and garment, shoes and hat manufacturing Leather, fur, feather (velvet) and its products Wood processing and wood, bamboo, rattan, brown, grass Furniture manufacturing Paper and paper products industry Copying of the printing industry and recording media Culture and education sporting goods manufacturing Petroleum processing, coking and nuclear fuel processing Chemical raw materials and chemical products manufacturing Pharmaceutical manufacturing

19 20 21 22 23 24 25

CO2 emission (10,000 tons)

CH4 emission (10,000 tons)

N2O emission (10,000 tons)

CO2 equivalent (10,000 tons)

Energy consumption (10,000 tons standard coal)

Gross industrial outpour (100 million)

13.48 5.96 0.50 38.49 59.46 61.90 66.64 2.24 1,296.00 72.81 65.34 52.45

0.0001 0.0001 0.0000 0.0012 0.0011 0.0008 0.0009 0.0001 0.0164 0.0015 0.0013 0.0008

0.0002 0.0001 0.0000 0.0004 0.0008 0.0009 0.0010 0.0000 0.0198 0.0010 0.0009 0.0008

13.54 5.99 0.50 38.64 59.72 62.19 66.95 2.25 1,302.25 73.13 65.63 52.69

5.96 3.22 1.20 22.47 35.84 33.65 40.99 2.59 794.26 47.70 37.30 35.19

3.59 18.24 19.40 168.45 394.73 169.45 195.17 156.59 2519.52 753.42 716.15 193.74

11.43 607.79 16.09

0.0004 0.0069 0.0004

0.0001 0.0095 0.0002

11.48 610.76 16.16

8.55 322.04 10.94

202.33 414.07 143.37

10.84

0.0003

0.0001

10.88

10.51

197.43

6,587.88

0.2619

0.0539

6,609.86

3,235.19

555.91

922.75

0.0124

0.0139

927.14

479.27

1,881.31

84.12

0.0010

0.0013

84.53

59.68

386.44

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26 27 28 29 30 31 32 33 34 35 36 37 39 40 41 42 44 43 Total

Chemical fber manufacturing Rubber products industry Plastic products industry Non-metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal products industry General equipment manufacturing Special equipment manufacturing Transportation equipment manufacturing Electrical machinery and equipment manufacturing Communication equipment, computers and other electronic equipment Instrumentation and culture, offce machinery manufacturing Crafts and other manufacturing Waste resources and waste materials recycling industry Electricity and heat production and supply Gas production and supply industry Water production and supply industry

171.95 91.05 206.83 2,508.92 774.14

0.0035 0.0015 0.0035 0.0307 0.0084

0.0022 0.0013 0.0029 0.0382 0.0111

172.70 91.47 207.78 2,520.95 777.62

150.48 51.19 125.43 1,036.78 375.53

700.76 205.92 822.01 652.89 565.59

116.77

0.0025

0.0014

117.26

80.34

750.39

113.62 219.83 26.26 77.68 85.25 45.16

0.0024 0.0044 0.0007 0.0024 0.0024 0.0011

0.0014 0.0028 0.0003 0.0008 0.0008 0.0006

114.11 220.77 26.36 77.97 85.54 45.35

72.06 132.41 19.97 58.71 71.60 39.58

1,066.23 1,631.86 475.49 971.41 1,521.27 1,110.02

9.97

0.0004

0.0001

10.01

10.00

170.90

33.45 9.07

0.0008 0.0002

0.0004 0.0001

33.58 9.11

20.47 4.46

327.31 83.08

9,812.01 49.48 1.97 24,329.58

0.1136 0.0007 0.0000 0.4869

0.1527 0.0004 0.0000 0.3226

9,859.81 49.62 1.98 24,436.27

3,807.96 23.85 11.16 11,278.54

1,925.88 22.30 58.88 22,151.48

182

182

Emission features

18,000

ten thousand tons

2004

2008

12,000

6,000

0 Metallic Products

Mining

Petrochemical Industry

Electricity Industry

Equipments & Mechanical Instruments

Rubber & Plastics

Petroleum Processing

Wood Furniture

Food & Beverage

Textile and Leather

Non-metallic Products

Others

Figure 7.7 Comparison of greenhouse gas emissions from industrial enterprises above a designated size in Zhejiang Province (2004, 2008)

Judging from the trend of GHG emissions in the sector (see Figure 7.7), in addition to the reduction of GHG emissions in the non-metal products industry and other industries, GHG emissions in other industries have increased to varying degrees. According to the absolute amount of changes in GHGH emissions, the top fve are:  power industry (65.65  million tons), metal products industry (20.37 million tons), petroleum processing industry (133.33 million tons), chemical industry (8.88 million tons), and equipment machinery and instrument industry (10.7  million tons). In terms of relative changes, the metal products industry saw the largest increase in GHG, increasing by 202% from 2004 to 2008, followed by mining (+162%), chemical industry (+75%), and power (+67%); the smallest increase is in the textile and leather products industry. Compared with 2004, the GHG increased by 5.1% in 2008, while the non-metal products industry and other industries decreased by 1.6% and 8.8%, respectively, on the basis of 2004. 7.2.3.2 Trends in carbon dioxide emission intensity In 2004, the carbon dioxide emission intensity of industrial enterprises above a designated size in Zhejiang Province was 1.103 (ton of CO2/100  million yuan), and in 2008 it was decreased to 0.867 (ton of CO2/100 million yuan), a decrease of 21.4%.

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2004

2008

Mining

Ten thousand tons CO2/ hundred million yuan

Metallic Products

15

183

10

5

Electricity Industry

Petrochemical Industry

Rubber & Plastics

Food and Beverage

Petroleum Processing

Textile and Leather

Equipment and Machinery

Non-metallic Products

Wood Furniture

Others

0

Figure 7.8 Comparison of CO2 emission intensity of industrial enterprises above a designated size in Zhejiang Province (2004, 2008)

Figure  7.8 compares the carbon dioxide emission intensity of industrial enterprises above a designated size in Zhejiang Province in 2004 and 2008. From an absolute level, the petroleum processing industry has the highest carbon dioxide emission intensity, which was 11.89 (10,000 tons CO2/ 100 million yuan) in 2004 and 7.19 tons (ton CO2/100 million yuan) in 2008; followed by the power industry (2004) 10,000 tons of CO2/100 million yuan, 61,000 tons of CO2/billion yuan in 2008), non-metallic products industry (38,600 tons of CO2/100  million yuan in 2004, and 22,100 tons of CO2/ 100 million yuan in 2008). In addition, as can be seen from Figure 7.8, the carbon dioxide emission intensity of most industries has decreased compared to 2004. From the perspective of relative change, the carbon dioxide emission intensity of other industries declined the most, down by 95% from 2004, followed by the wood furniture paper industry (–46.8%) and the non-metal products industry (– 42.8%). There are also some industries that have rebounded in carbon dioxide emission intensity, with the mining industry having the largest growth rate and its carbon dioxide emission intensity increasing by 280%, followed by the metal products industry (+49.5%) and the power industry (+17.4%). 7.2.4 Trends in energy emissions structure In 2004, the carbon dioxide emissions per unit of fossil energy combustion in industrial enterprises above designated size in Zhejiang Province was 2.167 million tons (CO2/10,000 tons of standard coal). In 2008, this index fell

184

184 3

Emission features

Equivalent ton of CO2/ton of standard coal

2

1

Metal Products

Mining

Petroleum Processing

Electricity Industry

Petrochemical Industry

2004

Non-metallic Products

Food & Beverage

Rubber & Plastics

Equipment Machinery and Instrument

Textile & Leather

Wood Furniture

Others

0

2008

Figure 7.9 Comparison of energy emission structures in industrial enterprises above a designated size in Zhejiang Province (2004, 2008)

to 2.148 (10,000 tons CO2/10,000 tons of standard coal), with a decrease of 0.88% indicates that the energy structure of the industrial sector is optimized and improved. Figure  7.9 compares the energy product emissions structure of industrial enterprises above a designated size in Zhejiang Province in 2004 and 2008. It can be seen that the energy emission structure of most industries has shown a downward trend, indicating that its energy input structure is gradually showing “low carbonization.” From the perspective of decline, other industries produced 15,400 tons of standard coal per ton in 2004. CO2 decreased to 0.36 million tons of CO2 in 2008, a drop of 76.4%. Others were wood furniture papermaking (–28.9%) and textile leather products (–26.9%). In addition, some industries’ energy emission structure show a trend of “high carbonization.” For example, the metal products industry has increased from 1.91 (ton of CO2/ton of standard coal) in 2004 to 2.43 (ton of CO2/ton of standard coal) in 2008, an increase of 27%. This is followed by mining (+26.5%) and petroleum processing (+5.2%).

7.3 Factor decomposition of industrial greenhouse gas emissions in Zhejiang Province 7.3.1 Introduction of method Changes in GHGs can be decomposed by KAYA identities, so that the relative contribution of factors such as output size, industrial structure, energy

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185

structure, and energy effciency to GHG emissions can be calculated to identify the most important factors affecting GHGs. An extended Kaya identity can be expressed as: C = ˛˛ Cij = ˛ i ˛ × i

j

i

Ei Yi

×

Yi Y

×

Y ×P P

(7.2)

Here, C, E, and P represent CO2 emissions, energy consumption, industrial production and population, respectively; Cij is the CO2 emission of the jth energy of the ith industry; Eij is the jth energy of the ith industry consumption; Ei is the energy consumption of the ith industry; Yi is the output of the ith industry; and Y is the sum of all industry output. Formula (7.2) decomposes the carbon emission changes caused by energy Cij consumption into the following six effects: emission factor effect ( ), indusEij trial energy structure effect (

Eij Ei

), industrial energy intensity effect (

Ei Yi

), indus-

Y Y trial structure effect ( i ), output scale effect ( ) and the population size Y P effect (P). 7.3.2 Results analysis Based on formula (7.2), the relative contributions of the six effects of GHG emissions changes in industrial sectors above designated size in Zhejiang Province in 2004 and 2008 were calculated. The results are shown in Figure 7.10. From the perspective of various infuencing factors, the energy structure, output scale and population size have positive effects on the increase of GHG emissions, while emission factors, energy intensity and industrial structure show negative effects. From the contribution rate of various factors to the change of GHG emissions, the contribution rate of output scale effect is 155.75%, and then the order of absolute value is: energy intensity effect – 30.66%, industrial structure effect –28.35%, population size effect is 6.28%, the emission factor effect is –4.19%, and the energy structure effect is 1.16%. This indicates that the increase in the total industrial output value of the industrial sector above a designated size in Zhejiang Province is the main driving engine for the increase of GHGs in the industrial sector in Zhejiang Province, while the change in the internal energy intensity of the sector and the optimization of the internal structure of the industrial sector in Zhejiang Province have slowed down the GHG increases. In addition, population growth, energy structure changes, and energy product emissions factors also have a certain

186

186

Emission features

200% 150%

Relative Contribution (%)

100% 50% 0% -50%

Emission Factor Effect

Energy Structure Effect

Energy Consumption Intensity Effect

Industrial Structure Effect

Output Scale Effect

Population Scale Effect

Figure 7.10 Relative contributions of factors affecting industrial greenhouse gas emissions from large-scale industries in Zhejiang Province (2008)

impact on GHG changes. A detailed analysis of the relevant factors will be conducted below. 7.3.2.1 Output scale effect The rapid development of the industrial economy is the main reason for the growth of greenhouse gas emissions in the industrial sector in Zhejiang Province. According to the constant price in 2008, the total industrial output value of industrial enterprises above a designated size rose from 221,154.48 billion yuan in 2004 to 4,103,038 million yuan in 2008, an increase of 85%; while the energy consumption in the same period was from 11,278,540 tons in 2004. Coal increased to 165.597 million tons of standard coal in 2008, and energy consumption increased by only 46.8%. The corresponding increase in GHGs is close to energy consumption, only 45.6%. This shows that the industrial economy is growing faster than the growth of energy consumption and GHG emissions. 7.3.2.2 Energy intensity effect The reduction in energy intensity within the industrial sector is an important way to reduce GHG emissions. Industrial energy intensity indicators are generally expressed in terms of the amount of energy consumed per unit of industrial production, which can be used to refect the energy effciency of economic activities. On the whole, the energy intensity of industrial enterprises above a designated size in Zhejiang Province has dropped from 5,100 tons of standard coal/100  million yuan in 2004 to 4,000 tons of standard coal/

187

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187

100  million yuan in 2008. To be specifc, the energy consumption intensity of other industries, petroleum processing industries, and non-metal products. The energy intensity of the metal products industry has dropped by as much as 79%, 43% and 38%, respectively, indicating that the energy effciency of these industries has been greatly improved through the implementation of energy-saving and emission reduction measures, and has played a key role in reducing GHG emissions in the industry. At the same time, however, the energy intensity of some industries will not fall, such as the energy intensity of the mining industry increased by 200%, and the sectors that have a greater impact on industrial GHG emissions, such as the metal products industry, the chemical industry and the power industry. The consumption intensity increased by 17.7%, 16.9% and 16.8%, respectively, in 2004. As the reduction of energy intensity is an important way to reduce GHG emissions, it indicates that the future control of GHGs needs to focus on the energy intensity of sectors of “high-level operation.” 7.2.3.3 Industrial structure effect Industrial restructuring is also an important means of mitigating GHG emissions. The effect of industrial restructuring is refected in two aspects: on the one hand, the reduction in the economic share of the highcarbon sector will promote carbon emission reduction. For example, in the power industry, its GHG emissions account for about 40%, and its share of the industrial economy has dropped from 8.7% in 2004 to 6.7% in 2008, thus slowing down the absolute level of overall industrial GHG emissions; On the other hand, if the proportion of the low-carbon sector in the industrial economy increases, it will also promote GHG emission reduction. For example, the equipment machinery and equipment industry, its GHG emissions only account for about 2% of the total emissions, but its share of the industrial economy rose from 28% in 2004 to 32% in 2008, so it also slowed down to some extent. Therefore, vigorously implementing industrial restructuring and optimizing industrial structure has great practical signifcance and policy implications. 7.2.3.4 Energy structure effect Optimization of energy structure and improvement of energy quality are also important factors affecting GHG emissions. The energy structure effect shows a weak positive effect on the growth of GHG emissions, while the emission factor effect is a weak negative effect, and the sum of the two effects is a negative effect. For different energy products, the carbon dioxide emissions per unit of energy consumption are different. The potential policy implication is that, while ensuring the same amount of energy input, it is possible to adjust and optimize the energy structure to develop clean low-carbon type and even “carbon-free” energy to reduce GHG emissions.

188

188

Emission features

7.2.3.5 Population scale effect The population scale effect is also an important factor affecting GHG emissions. From 2004 to 2008, the population of Zhejiang Province increased from 457 million to 0.469 billion. The increase in population will inevitably lead to the expansion of energy consumption. Therefore, the increase in population size has positively promoted industrial GHG emissions in Zhejiang Province. From the perspective of reducing GHG emissions, Zhejiang Province must still frmly implement the family planning policy.

7.4 Main conclusions and countermeasures Considering that in the future, the industrial economy of Zhejiang Province will continue to grow, and the process of urbanization and industrialization will be further deepened, in order to control and slow down the GHG emissions and growth rate of the industrial sector in Zhejiang Province, the following suggestions are proposed. First of all, we must increase research and development efforts to improve energy effciency. From an industry perspective, the power sector not only accounts for a high proportion of emissions, but its energy intensity is more than twice that of Beijing and other regions. There is a lot of room for effciency improvement, so it needs to be given special attention. Departments that are slowing down, such as metal products and mining industries, should strengthen the management of energy intensity. For sectors with high absolute energy intensity, such as the petroleum processing industry and the non-metal products industry, energy conservation should be continued. Discharge policy, speed up the elimination of outdated equipment, introduce advanced technology and equipment, reduce energy consumption per unit of output value, and promote and improve energy effciency. Second, we must optimize the industrial structure and accelerate industrial transformation and upgrading. The optimization of industrial structure is very effective in reducing carbon dioxide emissions. It is necessary to focus on those sectors that have a greater impact on GHG emissions and a higher economic proportion, and vigorously implement industrial transformation and upgrading, such as: power industry, metal products industry, petroleum processing industry, chemical industry and other departments. This proportion of the economy does not increase signifcantly or decrease to a certain extent; on the other hand, it is necessary to accelerate the development of low-carbon industries with low energy consumption, low emissions and high added value, such as equipment machinery and instrument industry, waste resources and waste materials recycling industry, etc. Let the proportion of these industries increase gradually. Low-carbon development of the industrial economy in Zhejiang Province should be promoted by “subtracting” and “adding” the industrial structure.

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In addition, we must optimize the energy structure and actively develop low-carbon energy. To curb the trend of high carbonization of energy consumption in the metal products industry, mining industry and petroleum processing industry, we must actively improve the energy utilization structure of the power industry, oil processing industry, textile industry, non-metallic mineral products industry and other sectors with large energy consumption. The utilization of high-quality energy products and clean energy products should be promoted, development and utilization of new energy and renewable energy be accelerated, and emission rate of industrial GHGs in Zhejiang Province be slowed down through low-carbon energy.

Note This chapter is predicated on the research report “Research into the GHG emissions in Zhejiang Province – analysis of the materials about the second Economic Census” written by Wei Chu. This report was enrolled in the Selected Research Topics of the 2nd Economic Census in Zhejiang Province by the Leading Offce of the 2nd economic consensus in Zhejiang Province, Zhejiang University Press, 2011.

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Note: Page numbers in bold refer to tables and in italics to diagrams. adaptation 34–35 agriculture: adaptation and 34; carbon emissions 32, 117, 122; crop yields 5; economic output scale 121; fossil energy 114, 122; GHGs (greenhouse gases) 6, 9, 48, 129–130; impact of climate change 5, 17; industrial structure 26, 29; low-carbon 33 Alcantara, V. 43 ammonia 20, 32 Anderson, T. W. 96 Ang, B. W. 43 Ang, B. W. et al. (1998) 43 animal husbandry 17, 26 APEC (Asia–Pacifc Economic Cooperation) 44 Arctic sea ice decrease 5 Arellano, M. 97, 99 Asia–Pacifc Economic Cooperation see APEC Asia–Pacifc Partnership on Clean Development and Climate 22 Auffhammer, M. 47, 101 Bali Roadmap 16, 22 Bao Qun 88 Beijing 69–71, 162, 165, 169–174, 175, 176–177, 179 biodiversity 4, 17, 18 biomass fuels 6, 45 Bishan, Chongqing 71–73 Blair, T. 65 Bond, S. 97, 99 Brännlund, R. 47 Brazil 6, 11, 22 BRICS countries 6

business sector: carbon emissions 119, 122, 127, 128; economic output scale 121; fossil energy 114, 122; GHGs (greenhouse gases) 133–134 California 65–66 capital intensifcation 29 carbon capture 57, 65, 76 carbon emissions: “carbon emission budget” 56; carbon emission coeffcient effect 46, 113, 124, 125, 126, 127, 130, 131, 132, 134–135, 149, 150, 151, 152, 157, 158; forecast 103, 107, 123; fossil energy and 40, 42, 43, 44, 53, 89, 92–93, 95, 106, 111–136, 137–158, 165, 183–184; housing construction 33, 65; industrial economic scale 116–123; industrial sector 111, 117–118, 122, 137–189; infuencing factors 123–129; intensity 13, 14, 34, 41, 45, 46, 90; major countries 7–11; new-production vehicles 57; per-capita 13–15, 46, 51, 89–90, 92, 93–97, 99–103, 106, 108–110, 175–176; power sector 44, 111, 114–115; production value target 21; provinces 92–108; service industries 33–34, 48–49, 111 carbon sinks 9, 20, 25, 34 carbon tax 47, 58–59, 61, 76, 78 carbon trading 65, 66, 77, 78 cars, private ownership 33, 96, 98, 101 Carson, R. T. 47, 101 CCX (Chicago Climate Exchange) 66 CDM (Clean Development Mechanism) 23 CH4 see methane

197

Index chemical industry 45, 140, 146, 150–151, 154, 164, 178–179, 182; heavy 32, 35, 68, 79, 129, 131, 150 Chen Chun 48 Chen Shiyi 48 Chen Si 41 Chen Weihong 48 Chen Zhaorong 41, 49 Cheng Shi 90 Chicago Climate Exchange see CCX China: CPC National Congress 103, 104; Development Research Center of the State Council 26; domestic pressure 16–18; Eleventh Five-Year Plan 19–21, 24, 69–71, 74; energy consumption 84; energy structure 11–12; GHGs (greenhouse gases) 6, 8–9, 10, 11, 12–15; Input-Output Table 46; “National Climate Change Initial National Information Bulletin” 111; National Climate Change Program 20, 83; policy response 18–25; Twelfth Five-Year Plan 19–22, 24, 69, 176 China Economic Network 138 China Energy Statistical Yearbook 113, 114, 115, 121, 138, 162 China–US Joint Climate Change Statement 20, 21 Chongqing province 71–73, 92 Clean Development Mechanism see CDM coal: carbon emissions 32, 36, 41, 90, 92, 93, 94–95, 97, 100–101, 109, 116, 117–120, 124, 147, 152, 163, 168, 169, 178, 179, 183–184; clean 24; energy structure and 11–12, 104, 105, 108, 115, 121, 122, 140, 142, 144, 145, 157, 166, 171; industrial structure effect 155; output scale effect 186; output value 173, 180; taxation 61; total industrial consumption 121; Zibo 73–74 coal mining: carbon emissions 142, 152; energy consumption 142, 144, 171; GHGs (greenhouse gases) 6, 147, 166, 169, 180; industrial output value 142, 173; industrial structure effect 155 coastal areas 4–5, 17 cold weather, abnormal 5, 17 commerce see business sector construction industry: carbon emissions 111, 118, 122, 128; economic output scale 121; energy consumption

197

32–33; fossil energy 114, 122; GHGs (greenhouse gases) 11, 131–132; investment in 29 Copenhagen Climate Change Conference 15, 20, 22, 54 Denmark 59 desertifcation 4 developed countries: carbon emissions 31, 106, 109; emerging industries 30; fossil energy 3; GHGs (greenhouses gases) 6, 7, 16, 31; Kyoto protocol 83; low-carbon development 53–67, 75–76, 77; post-industrialization 32; product consumption structure 86; reduction targets 16, 22–23 developing countries: carbon emissions 31–32, 40, 89; China’s lead and 22–23; development and climate change 17; international trade hypothesis 86; reduction targets and 16, 58 diesel fuel 33, 93, 116, 163 droughts 5, 17 Du Limin 41, 89–90 Du Tingting et al. (2007) 40 Duan Ying 48 economic output 13, 27, 55, 114–117, 121, 124, 128, 135, 138 ecosystem, destruction of 4, 17, 24, 87 elasticity of demand for quality of environment hypothesis 86–87 electricity sector 9, 10, 11, 162: carbon emissions 92, 114–115, 177, 179, 184; energy consumption 11, 172; GHGs (greenhouse gases) 138, 164, 165, 168, 170, 181, 182; industrial output value 174; production scale 135; renewable sources 57, 65; taxation 55 energy consumption per unit of GDP 15, 19, 20, 69, 100, 108 energy effciency: Beijing 69; China 108, 124–129, 131–132, 134–136, 148–149, 151, 157, 158; Energy Conservation Law 61; impact of 36, 37–38, 42, 44, 45, 46; technologies 61–62; USA 64–65, 67; Zhejiang province 185, 186–188; Zibo 75 energy infrastructure construction 12 energy intensity effect: agricultural sector GHGs (greenhouse gases) 129–130; all sector GHGs (greenhouse gases) 158; and carbon emissions 43–46, 51, 113, 124–127, 147–148,

198

198

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150–151, 157; construction sector GHGs (greenhouse gases) 131–132; energy sector GHGs (greenhouse gases) 10, 111, 134–135; transportation sector GHGs (greenhouse gases) 7, 9–11, 132–133; Zhejiang province 185, 186–187 energy processing and conversion sector 111, 127 energy sector: carbon emissions 119–120, 121, 122, 126–129; economic output 119–120, 121; fossil energy 114, 121–122; GHGs (greenhouse gases) 10, 111, 134–135 energy structure: major countries 11–12; optimization of Chinese 24, 36–38, 46, 69, 133, 184 energy structure effect: China 124, 125, 126–127, 129, 130–132, 134, 135, 149, 150, 151, 152, 157; Zhejiang Province 185, 186, 187 ETS (greenhouse gas emissions trading) 58 EU (European Union): debt crisis 16; ECCP (EU Climate Change Program) 57; emission reduction measures 15, 53–54; energy consumption structure 11, 12; forms of technologies 59–60; GHG trading 58; GHGs (greenhouse gases) 6, 7, 8, 9, 10, 106; initiatives 57–60; Kyoto Protocol 66; lowcarbon technologies 59–60; per-capita carbon emissions 109; Strategic Energy Technology Plan 59–60; taxation 58–59 exports 29, 30 extinction 83 extreme weather events 4, 5, 17, 18, 83 Feng, Q. 40 fertilizers 32 fnancial industry 28, 79 Finland 59 Fisher-Vanden, K. et al. (2004) 138 fsheries 26, 114 fooding 4–5, 17 forestry 6, 9, 10, 20–21, 24, 26, 114 fossil energy: agricultural production 32; carbon emissions 40, 42, 43, 44, 53, 89, 92, 106, 111–136, 137–158, 165, 183–184; by country 10, 11–12; energy sector 40, 42, 43, 44, 53, 89, 92, 106, 113–114, 128, 129, 133, 140, 142, 144;

GHGs (greenhouse gases) and 5–6, 7, 9, 160; industrial economic output 117–120, 121; by industrial sector 122; industrialization 3, 5, 31, 32; island countries 60; taxation and 58–59, 61 Friedl, B. 40 Fu Jiafeng et al. (2008) 89 Fukuda, Y. 63 G8 (Group of Eight) 22 Galeotti, M. 47 Garbaccio, R. F. et al. (1999) 47 gas extraction industry 142, 152, 155 gasoline 33, 60, 93, 116, 163 GDP per-capita: Bishan 72; and carbon emissions 39–41, 44, 46, 47, 88–90, 97, 99–101; China 13, 103, 104 Getzner, M. 40 Ghalwash, T. 47 GHGs (greenhouse gases): agriculture 9, 129–130; business sector 133–134; China 6, 8–9, 10, 11, 12–15; and climate change 3–4; composition of 5–7; construction industry 11, 131–132; developed countries 6, 7, 16, 31; EU 6, 7, 8, 9, 10, 106; fossil energy and 5–6, 7, 9, 160; heating sector 138, 164, 168, 170, 181; India 6, 8, 10; industrial sector 130–131, 146–147; industrial structure 22, 33–35; international pressure to reduce 15–16; Japan 6, 8, 10; manufacturing industry 9, 11, 166–170, 180–181; measurement of 160–162; metal products industry 146, 151, 154, 157, 164; national emission reduction 4; net exports 30; non-metal products 146, 158, 164; petroleum processing industry 164; power sector 134–135, 164; predicted global increase 7; regional 13; Russia 6; service industries 21–22; South Africa 6; trading of 58; transportation sector 7, 9–11, 132–133; USA 6, 7, 8, 10 glacial melting 4–5 global fnancial crisis 16 greenhouse gases see GHGs Grossman, G. M. 39, 85 Guangdong 162, 165, 169–174, 175, 176–177, 179 Haihe River 17 Han Yujun 89 Hatzigeorgiou, E. et al. (2008) 44

199

Index

199

heating sector: carbon emissions 9, 10, 92–93, 111, 114; energy consumption 172; GHGs (greenhouse gases) 138, 164, 168, 170, 181; industrial output value 174 heavy chemical industries 32, 35, 68, 79, 129, 131, 150, 158 heavy industry 27, 29: carbon emissions 41, 47, 90, 97, 100–101, 103–105, 108, 109, 110; Zibo 73 heavy machinery industries 140 high-carbon industries 7, 32, 36–38 Himalayas 4 Hokkaido, Japan 63 Holtz-Eakin, D. 40 housing 33, 57, 65 Hsiao, C. 96 Huaihe River 17 human survival 5 Hurricane Katrina 5

(greenhouse gases) 129–130, 131–132; manufacturing industries 155–156; transportation sector GHGs (greenhouse gases) 132–133; Zhejiang Province 185, 186, 187 “Initial National Information on Climate Change in China” (IPCC) 115 Inner Mongolia 94 International Convention on Climate Change 6 international cooperation 19, 22–23 international trade hypothesis 86 IPCC Assessment Reports 3, 4, 83

income effect 44, 45, 46, 51 income per-capita 35, 39–42, 49, 85–86, 88–90 India 5, 6, 8, 9, 10, 11, 12 industrial development 12, 15, 18, 27, 29, 30–38, 67–75, 77–80 Industrial Revolution 3, 4, 5, 7, 54, 75, 83 industrial sector: carbon emissions 45, 48, 111, 117–118, 122, 137–189; economic output scale 121; energy consumption and 140, 142–145; fossil energy 113–115, 122, 144–145, 165; GHGs (greenhouse gases) 44, 129–130, 131–132, 146–147; impact of climate change on 34; industrial production structure 140, 141; primary 25–26, 32, 35, 36, 73; secondary 25–27, 29, 32, 35, 36, 41, 50, 73, 79, 90; tertiary 25–26, 28–29, 30, 33, 35–36, 40, 48, 50, 69, 70, 73, 79–80, 133 industrial structure effect 155–156: agricultural sector GHGs (greenhouse gases) 129; business sector GHGs (greenhouse gases) 133–134; carbon emissions and 45, 49, 124–128, 148–150, 151, 152, 154, 157–158; construction sector GHGs (greenhouse gases) 131–132; energy sector GHGs (greenhouse gases) 135; industrial sector GHGs

kerosene 33, 93, 116, 163 Krueger, A. B. 39, 85 Kyoto Protocol 4, 16, 23, 57–58, 60, 63, 66, 83–84

Japan 6, 8, 9, 10, 11–12, 53–54, 58, 60–63 Jiang Yujun 40, 46 Jiangsu 162, 165, 169–174, 175, 176–178, 179

Lantz, V. 40 Lanza, A. 47 Lee, K. 44 Li Boqiang 40 Li Weibing 41 Liaohe River 17 light industry 27, 97 Lin Boqiang 46 Liu, L.-C. et al. (2007) 45 Liu Zaiqi 48 living standards 13, 18 Long and Chen (2011) 41 Lu, I. et al. (2007) 44 Lu Yang 89 Ma, C. 45 Magnus, L. 40 manufacturing industries: carbon emissions 43, 111, 137–140, 142–143, 152–153, 154; energy consumption 171–172; GHGs (greenhouse gases) 9, 11, 166–170, 180–181; industrial output value 173–174; industrial structure effect 155–156; Shanghai 68 market mechanism hypothesis 86 metal products industry: carbon emissions 150–151, 153, 158; energy

200

200

Index

consumption 140, 144, 157; GHG (greenhouse gases) 146, 151, 154, 157, 164; output value 141, 143; Zhejiang province 177–179, 182–184, 187–189 methane (CH4) 4, 6–7, 130, 160, 163, 166, 168, 180 mining industry: carbon emissions 137–138, 139, 141, 142, 144–147, 151, 152, 154, 155, 158; Zhejiang province 161, 166, 169, 171, 173, 175, 180, 182–184 mitigation: California 65; carbon emissions structure effect 125; energy intensity effect 44, 132, 133, 148, 150, 158; energy use 75; Five-Year Plans 20, 21–23; industrial structure effect 51, 80, 128, 187; land use and forestry 9; taxation 76; UK 56 N2O see nitrous oxide National Engineering Laboratory 24 National Engineering Research (Technical) Center 24 natural disasters 4, 5 natural gas: carbon emissions 93, 95, 116, 117–120, 163; energy consumption 105, 109, 144–145, 157; energy structure and 11, 12; GHGs (greenhouse gases) 6; Japan 60, 61; taxation 59 Netherlands 59, 84 nitrogen oxide 20, 30 nitrous oxide (N2O) 6–7, 66, 130, 160, 163 non-fossil fuels 11, 12, 20, 21, 59, 137 non-metal products industry: carbon emissions 150–151, 158; energy consumption 140; fossil energy 144, 145; GHGs (greenhouse gases) 146, 158, 164; Zhejiang Province 164, 177–179, 182–183, 187 nuclear energy 11, 97, 105, 108, 144, 147, 164 Obama, B. 15 Oh, W. 44 oil: carbon emissions 92, 93, 95; energy consumption 11–12, 105, 109; GHGs (greenhouse gases) 6; UK 57; Zhejiang Province 163, 177 oil crisis 40, 60 oil extraction industry 142, 152, 155 output scale effect 113, 124, 147–150, 152, 157–158, 185–186 oxygen 20

Pandiyan, G. 43 Peng Shuijun 88 petrochemical industry: carbon emissions 146, 147, 151, 154, 177, 179, 182, 183, 184; fossil energy 144, 145; GHGs (greenhouse gases) 164; output value 141; Zibo 73 petroleum 116, 117–120: processing industry 45, 48, 139, 141, 143– 147, 150–151, 153–154, 156, 158; transportation 33; Zhejiang Province 164–165, 167, 169, 171, 173, 177, 178, 179, 180, 182–184, 188–189 population growth 12, 44, 45, 104, 105 population scale effect 186, 188 power sector: carbon emissions 44, 111, 114–115; energy intensity 128; GHGs (greenhouse gases) 134–135, 164; technologies 61–62, 76; USA 65; Zhejiang province 164, 165, 177–178, 182–183, 187–189 production scale effect 44, 51 production sector 31–32, 111–136: energy consumption 115–123; energy intensity 46; GHGs (greenhouse gases) 123–135 productivity 34, 44, 48, 52 public health 17, 18 public services 29 Qi Yanbin 48 Qinghai-Tibet Plateau 4 Roca, J. 43 Russia 6, 11, 106 Schwarzenegger, A. 65 sea level rise 4–5, 7 seawater temperature 4–5 Selden, T. 40 service industries: carbon emissions 33–34, 48–49, 111; development of 28–29, 30, 79; GHGs (greenhouse gases) 21–22; Shanghai 67–69 Shanghai 48, 67–69, 94, 162, 165, 169–179 Sichuan province 92, 94 solar power 24, 62, 63, 67, 97 Song Deyong 89 Song Xingda 40–41 South Africa 6, 11, 12 Steckel, J. C. et al. (2011) 45

201

Index Stern, D. I. 45 Stern, N. 83 “structural dividend hypothesis” theory 34 sulfur dioxide 20, 89 Sun Chuanwang 46 Sunil, M. 44 Sweden 40, 59 Tan Dan et al. (2008) 47 Tao Changqi 40–41 taxation 55–56, 58–59, 61, 64, 69, 76, 78 temperature, global 3–4, 5, 7, 83 Tianshan 4 Torvanger, A. 43 tourism 18 traffc 111 transportation sector: biofuels 59; carbon emissions 33, 57, 111, 118–119, 122, 127, 129; decline of 28; economic output scale 121; fossil energy 114, 122; GHGs (greenhouse gases) 7, 9–11, 132–133; industrial structure effect 128; social patterns 62–63; taxation 61; technology 62 Tunç, G. I. et al. (2009) 44 Typhoon “Matsa” 5 UK (United Kingdom) 5, 53–57, 76 UNFCCC (United Nations Framework Convention on Climate Change) 4 United Nations Climate Change Negotiation Conference 22 United Nations Climate Change Special Committee 33 United Nations Conference on Environment and Development 4, 18 United Nations Framework Convention on Climate Change 83 urbanization: Beijing 49; carbon emissions 7, 32, 41, 46–47, 84, 90, 94, 98, 101–102, 103, 106, 109; domestic pressure 16–18; GHG (greenhouse

201

gases) 12–13; industrialization and 15; Zhejiang Province 188 USA (United States of America) 6–12, 20–21, 53–54, 58, 63–67, 84, 123, 109 Wang, C. et al. (2005) 44 Wang Feng et al. (2010) 46 wastewater 90, 91 water pollution 74, 87 water resources 4, 17, 34 water sector 11, 114–115, 168, 170, 172, 174, 181 Wei Chu 46 Wen Jiabao 15 World Bank 30, 31, 94 World Energy Outlook 2007 (International Energy Agency) (IEA) 13 World Resources Institute 7, 111 Wu, L. et al. (2005) 44 Xia Dong 46 Xu Dafeng 48 Xu Guangyue 89 Yellow River 17 Yu Yihua et al. (2011) 41, 90 Yuan Peng 90 Zha, D. L. et al. (2009) 45 Zhang, M. et al. (2009) 45 Zhang, Y. et al. (2011) 46 Zhejiang Province: data sources 162; energy emissions structure 178–179, 183–184; GHGs (greenhouse gases) 159, 162–184; industrial sector and carbon emissions 159–189 Zhejiang Province Climate Change Program 159 Zhejiang Provincial Climate Change Leading Group 159 Zibo, Shandong 73–75

202