Economic Growth and Environmental Quality in a Post-Pandemic World: New Directions in the Econometrics of the Environmental Kuznets Curve 9781032373508, 9781032373515, 9781003336563

Table of contents :
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
List of Contributors
Chapter 1 Examination of the Environmental Kuznets Curve Hypothesis with Quadratic and Cubic Functional Models: An Econometric Analysis of European Countries
Chapter 2 The Effect of Trade, Renewable Energy, and Economic Growth on CO2 Emissions in Central and Eastern Europe
Chapter 3 Air Pollution and COVID-19 Nexus: Insights from Wavelet Approach for Selected Groups of Countries
Chapter 4 The Impact of Economic Growth, International Trade, and Carbon Dioxide Emissions on Portuguese Energy Consumption
Chapter 5 Renewable Energy, Carbon Emissions, and Economic Growth: The Comparison between EKC and RKC
Chapter 6 COVID-19 and Energy Transition: A Review
Chapter 7 EKC Modelling in a Post-pandemic Era: A Policy Note on Socio-ecological Trade-offs
Chapter 8 Sustainable Development through Carbon Neutrality: A Policy Insight from Foreign Direct Investment and Service Policy
Chapter 9 Reset the Industry Redux through Corporate Social Responsibility: The COVID-19 Tourism Impact on Hospitality Firms through Business Model Innovation
Chapter 10 Addressing the Nexus between Economic Growth and Environmental Pollution in a Small Petroleum-Exporting Transition Economy
Chapter 11 Revising the Environmental Kuznets Curve in the Post-COVID-19 Era from an SDGs Perspective
Chapter 12 Revisiting the Environmental Kuznets Curve (EKC): An Analysis Using the Sectoral Output and Ecological Footprint in India
Chapter 13 The Contribution of Transport Modes to Carbon Emissions in Turkey
Chapter 14 The Roles of Education and Export Diversification in the Improvement of Environmental Quality: A Comparison between China and India
Chapter 15 Are Economic Advancements Catalysts for Carbon Emissions? Depicting the Indian Experience

Citation preview

Economic Growth and Environmental Quality in a Post-pandemic World

In response to the damage caused by a growth-led global economy, researchers across the world started investigating the association between environmental pollution and its possible determinants using different models and techniques. Most famously, the environmental Kuznets curve hypothesizes an inverted U-shaped association between environmental quality and gross domestic product (GDP). This book explores the latest literature on the environmental Kuznets curve, including developments in the methodology, the impacts of the pandemic, and other recent findings. Researchers have recently broadened the range of the list of drivers of environmental pollution under consideration, which now includes variables such as foreign direct investment, trade expansion, financial development, human activities, population growth, and renewable and nonrenewable energy resources, all of which vary across different countries and times. And in addition to CO2 emissions, other proxies for environmental quality – such as water, land, and ecological footprints – have been used in recent studies. This book also incorporates analysis of the relationship between economic growth and the environment during the COVID-19 crisis, presenting new empirical work on the impact of the pandemic on energy use, the financial sector, trade, and tourism. Collectively, these developments have improved the direction and extent of the environmental Kuznets curve hypothesis and broadened the basket of dependent and independent variables which may be incorporated. This book will be invaluable reading for researchers in environmental economics and econometrics. Muhammad Shahbaz, School of Management and Economics, Beijing Institute of Technology, China. Daniel Balsalobre Lorente, Department of Political Economy and Public Finance, Economic and Business Statistics and Economic Policy, University of Castilla La Mancha, Spain. Rajesh Sharma, SCMS, Nagpur, and Constituent of Symbiosis International University, Pune, India.

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Economic Growth and Environmental Quality in a Post-pandemic World New Directions in the Econometrics of the Environmental Kuznets Curve Edited by Muhammad Shahbaz, Daniel Balsalobre Lorente, and Rajesh Sharma

First published 2023 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2023 selection and editorial matter, Muhammad Shahbaz, Daniel Balsalobre Lorente and Rajesh Sharma; individual chapters, the contributors The right of Muhammad Shahbaz, Daniel Balsalobre Lorente and Rajesh Sharma to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted 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 identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-032-37350-8 (hbk) ISBN: 978-1-032-37351-5 (pbk) ISBN: 978-1-003-33656-3 (ebk) DOI: 10.4324/9781003336563 Typeset in Bembo by Deanta Global Publishing Services, Chennai, India Access the Support Material: www​.routledge​.com​/9781032373508

Contents

List of Contributors 1 Examination of the Environmental Kuznets Curve Hypothesis with Quadratic and Cubic Functional Models: An Econometric Analysis of European Countries

viii

1

MUHAMMAD SHAHBAZ, KENAN İLARSLAN, AND MÜNEVVERE YILDIZ

2 The Effect of Trade, Renewable Energy, and Economic Growth on CO2 Emissions in Central and Eastern Europe

34

NUNO CARLOS LEITÃO, DANIEL BALSALOBRE-LORENTE, AND MUHAMMAD SHAHBAZ

3 Air Pollution and COVID-19 Nexus: Insights from Wavelet Approach for Selected Groups of Countries

49

MUHAMMAD IBRAHIM SHAH, AVIK SINHA, ARSHIAN SHARIF, AND SOLOMON PRINCE NATHANIEL

4 The Impact of Economic Growth, International Trade, and Carbon Dioxide Emissions on Portuguese Energy Consumption

61

NUNO CARLOS LEITÃO, CLARA CONTENTE DOS SANTOS PARENTE, AND DANIEL BALSALOBRE-LORENTE

5 Renewable Energy, Carbon Emissions, and Economic Growth: The Comparison between EKC and RKC

81

CONGYU ZHAO, XIUCHENG DONG, AND KANGYIN DONG



vi  Contents

6 COVID-19 and Energy Transition: A Review

107

DANIEL BALSALOBRE-LORENTE, WALTER FERRARESE, ISMAEL GÁLVEZ-INIESTA, MONICA A. GIOVANNIELLO, AND ELPINIKI BAKAOUKA

7 EKC Modelling in a Post-pandemic Era: A Policy Note on Socio-ecological Trade-offs

121

AVIK SINHA AND NICOLAS SCHNEIDER

8 Sustainable Development through Carbon Neutrality: A Policy Insight from Foreign Direct Investment and Service Policy 149 EDMUND NTOM UDEMBA

9 Reset the Industry Redux through Corporate Social Responsibility: The COVID-19 Tourism Impact on Hospitality Firms through Business Model Innovation

177

JAFFAR ABBAS, KHALID AL-SULAITI, DANIEL BALSALOBRE LORENTE, SYED ALE RAZA SHAH, AND UMER SHAHZAD

10 Addressing the Nexus between Economic Growth and Environmental Pollution in a Small PetroleumExporting Transition Economy

202

ELKHAN RICHARD SADIK-ZADA, ANDREA GATTO, AND MUBARIZ MAMMADLI

11 Revising the Environmental Kuznets Curve in the Post-COVID-19 Era from an SDGs Perspective

217

MUHAMMAD AZAM, AHMED IMRAN HUNJRA, MAHNOOR HANIF, AND QASIM ZUREIGAT

12 Revisiting the Environmental Kuznets Curve (EKC): An Analysis Using the Sectoral Output and Ecological Footprint in India

233

MUHAMMED ASHIQ VILLANTHENKODATH

13 The Contribution of Transport Modes to Carbon Emissions in Turkey MUHAMMAD SHAHBAZ, TUĞRUL BAYAT, AND MEHMET TANYAŞ

251

Contents vii

14 The Roles of Education and Export Diversification in the Improvement of Environmental Quality: A Comparison between China and India

275

MUHAMMAD SHAHBAZ, MANTU KUMAR MAHALIK, SHUJAAT MUBARAK, AND SHAWKAT HAMMOUDEH

15 Are Economic Advancements Catalysts for Carbon Emissions? Depicting the Indian Experience NIKUNJ PATEL, YASWANTH KAREDLA, ROHIT MISHRA, AND PRADEEP KAUTISH

301

List of Contributors

Khalid Al-Sulaiti Al Rayyan International University College in partnership with University of Derby UK - Doha, Qatar Muhammad Azam Department of Economics, Ghazi University, Dera Ghazi Khan, Pakistan Elpinik Bakaouka Department of Business Economics, Universitat de les Illes Balears (UIB), Spain Daniel Balsalobre Lorente Department of Applied Economics I University of Castilla-La Mancha, Cuenca, Spain Department of Applied Economics University of Alicante, Spain Tuğrul Bayat Bolvadin Faculty of Applied Sciences Afyon Kocatepe University, Turkey Kangyin Dong School of International Trade and Economics, University of International Business and Economics, Beijing, China UIBE Belt & Road Energy Trade and Development Center, University of International Business and Economics, Beijing, China Xiucheng Dong School of International Trade and Economics, University of International Business and Economics, Beijing, China UIBE Belt & Road Energy Trade and Development Center, University of International Business and Economics, Beijing, China Walter Ferrarese Department of Applied Economics, Universitat de les Illes Balears, Palma de Mallorca, Spain 

List of Contributors  ix

Ismael Gálvez-Iniesta Department of Applied Economics, Universitat de les Illes Balears, Palma de Mallorca, Spain Andrea Gatto Center for Economic Development and Social Change, Napoli, Italy Wenzhou-Kean University, CBPM, Wenzhou, Zhejiang Province, China Natural Resources Institute, University of Greenwich, Chatham Maritime, UK Monica A. Giovanniello Department of Applied Economics, Universitat de les Illes Balears, Palma de Mallorca, Spain Shawkat Hammoudeh Drexel University, United States Mahnoor Hanif University Institute of Management Sciences – PMAS – Arid Agriculture University Rawalpindi, Pakistan Ahmed Imran Hunjra Rabat Business School, International University of Rabat, Morocco Kenan İlarslan Bolvadin Faculty of Applied Sciences Afyon Kocatepe University, Turkey Yaswanth Karedla Student, Institute of Management, Nirma University, S.G. Highway, Ahmedabad, India Pradeep Kautish Associate Professor, Institute of Management, Nirma University, S.G. Highway, Ahmedabad, India Nuno Carlos Leitão Polytechnic Institute of Santarém, Center for Advanced Studies in Management and Economics, Évora University, and Center for African and Development Studies, Lisbon University, Portugal Mantu Kumar Mahalik Department of Humanities and Social Sciences Indian Institute of Technology, Kharagpur West Bengal, India Mubariz Mammadli Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan

x List of Contributors

Rohit Mishra Student, Institute of Management, Nirma University, S.G. Highway, Ahmedabad, India Shujaat Mubarak Muhammad Ali Jinnah University, Karachi, Pakistan Solomon Prince Nathaniel Department of Economics, University of Lagos, Akoka, Nigeria Nikunj Patel Associate Professor, Institute of Management, Nirma University, S.G. Highway, Ahmedabad, India Elkhan Richard Sadik-Zada Institute of Development Research and Development Policy, Ruhr-University, Bochum, Germany Centre for Environment, Resources and Energy, Faculty of Management and Economics, Bochum, Germany Clara Contente dos Santos Parente University of castilla-La Mancha Nicolas Schneider The London School of Economics and Political Science (LSE), Department of Geography and Environment, Houghton Street, London, UK Muhammad Ibrahim Shah Independent researcher, Edmonton, Canada Alma mater Department of Economics, University of Dhaka, Bangladesh Syed Ale Raza Shah School of Economics and Finance, Xi’an Jiaotong University, Xian, China Muhammad Shahbaz School of Management and Economics Beijing Institute of Technology, China Umer Shahzad Research Institute of the University of Bucharest, Social Sciences Division, University of Bucharest, Romania Arshian Sharif Department of Economics and Finance Sunway University, Malaysia

List of Contributors  xi

Avik Sinha Centre for Excellence in Sustainable Development, Goa Institute of Management, Goa, India Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon Mehmet Tanyaş Maltepe University, Faculty of Business and Management Sciences, Turkey Edmund Ntom Udemba Faculty of Economics Administrative and Social sciences Istanbul Gelisim University, Istanbul, Turkey Muhammed Ashiq Villanthenkodath School of Social Sciences and Humanities B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai, India Münevvere Yıldız Bolvadin Faculty of Applied Sciences Afyon Kocatepe University, Turkey Congyu Zhao School of International Trade and Economics, University of International Business and Economics, Beijing, China UIBE Belt & Road Energy Trade and Development Center, University of International Business and Economics, Beijing, China Qasim Zureigat School of Business, Sulaiman AlRajhi University, Saudi Arabia

1

Examination of the Environmental Kuznets Curve Hypothesis with Quadratic and Cubic Functional Models An Econometric Analysis of European Countries  uhammad Shahbaz, Kenan İlarslan, and M Münevvere Yıldız

1.1 Introduction According to a report published by the World Health Organization in 2020,1 preventable environmental risks cause about a quarter of all deaths and disease burden worldwide, and at least 13 million deaths occur yearly. Air pollution, one of the most significant risks posed to health, causes 7 million preventable deaths annually; worse, more than 90% of people breathe polluted air. Industrialization, population growth, urbanization, hazardous wastes, fossil fuel consumption, and technological developments can be listed as the main factors that increase global carbon emissions. The carbon emission caused by fossil fuels used in energy production, which is necessary for activities carried out to enhance and improve human comfort and quality of life, also leads to major problems. According to Heydari et al. (2021), greenhouse gases in the atmosphere have increased significantly due to the use of fossil fuels incidental to the Industrial Revolution. Consequently, global warming and climate change have been the main concerns, especially in the past two decades. According to assessments made by Dettner and Blohm (2021) and Gul et al. (2015), one of the prime movers and propellants of global warming is the use of fossil fuels, which add significant amounts of greenhouse gases to the atmosphere. Therefore, the common and extensive use of these resources contributes to greenhouse gas emissions, especially carbon dioxide emissions, which are shown as the main factor in global warming and ozone layer depletion. The increase in carbon emissions brings along several diversified problems. The most important of these problems is global warming due to the greenhouse gas effect caused by increased carbon emissions (Ustaoglu et al., 2021). As a consequence of global warming, profound climate changes (Macrina and Zurbenko, 2021) and natural disasters due to climate change (Rossati, 2017) create critical uncertainties regarding the world being a liveable place in the future. The climate change caused by greenhouse gas emissions causes DOI: 10.4324/9781003336563-1

2  Examination of the Environmental Kuznets

reductions in agricultural production (Kalakoti, 2021), health problems concerning well-being of humans, especially chronic obstructive pulmonary disease (COPD), asthma, shortness of breath, and cardiovascular diseases (Manisalidis et al., 2020) and has potential adverse effects such as constraints on access to water resources (Demirhan, 2020), impairments on biodiversity, and forest ecosystem (Velepucha et al., 2016; Shahbaz et al., 2013). Another consequence of carbon emissions is air pollution. As Tahir et al., (2021) stated, air pollution raises the temperature of the earth, and this situation adversely affects human life. Toxic chemicals and toxic compounds such as sulphur oxide, nitrous oxide, carbon dioxide and carbon monoxide are added to the air we breathe, reducing air quality. These toxic compounds threaten life by reducing air quality and causing harmful environmental changes on a global scale. The destruction of the ecosystem as a consequence of the biological and chemical deterioration of the atmosphere caused by air pollution threatens human health and is also a global problem with serious economic and financial implications throughout the world. In this context, air pollution has a negative effect on the sustainable economic growth of countries (Dong et al., 2021), creates a significant burden on the budget by increasing health expenditures (Shen et al., 2021a), negatively affects stock returns and investor behaviour (Wu et al., 2020; Lepori, 2016), causes strong anomalies in the stock market (Nguyen and Pham, 2021), reduces agricultural productivity (Koondhar et al., 2020; Wang et al., 2020c; Giannadaki et al., 2018), negatively affects the employment of qualified workers (Wang et al., 2021; Li et al., 2020; Liu and Yu, 2020), poses a threat to tourism activities (Zhang et al., 2020; Churchill et al., 2020; Lapko et al., 2020), increases internal migration (Cui et al., 2019), and adversely affects employee productivity (Chen and Zhang, 2021; Yang and Xu, 2020; Neidell, 2017). Although various strategies have been implemented worldwide to reduce carbon emissions in line with the Kyoto Protocol and the Paris Agreement, the use of fossil fuels to promote economic development continues to contribute significantly to carbon dioxide (CO2) emissions. Therefore, the relationship between economic growth and environmental quality, as captured by the Environmental Kuznets Curve (EKC), has been the subject of ongoing high-pitched debate (Churchill et al., 2018). In other words, most of the studies between per capita income and carbon emissions are handled in the context of the EKC hypothesis. EKC is the theory that deals with the relationship between per capita income and environmental degradation. Accordingly, there is an “inverted U” relationship between environmental pollution and per capita income. In other words, when economic growth increases, environmental pollution initially increases. After a certain threshold value, the trend reverses, and, as the level of economic development increases, environmental pollution decreases with the increase in environmental awareness (Tenaw and Beyene, 2021; Adebayo, 2021; Leal and Marques, 2020; Shahbaz, 2019; Wang et al., 2015). The theoretical background of the “inverted U”-shaped EKC describing a changing relationship between economic growth and environmental stress

Examination of the Environmental Kuznets  3

(Churchill et al., 2018; Canas et al., 2003) is justified as follows. Environmental quality behaves like any other economic good that people are willing to pay more for as income increases – increasing levels of welfare place environmental concerns higher on the political agenda. As income rises, the economic structure changes as the economy shifts toward services and light manufacturing industries, and higher income levels and technological eco-efficiency caused by more or less voluntary changes in consumption patterns pollute the environment less and, thus, improve environmental quality. However, the empirical studies show that the EKC curve has “U” shaped or other formations (Minlah and Zhang, 2021; Akadırı et al., 2021; Alola and Donve, 2021; Işık et al., 2021; Yilanci and Pata, 2020), suggesting that researchers contextualize and address the issue under different scenarios. Therefore, the controversial results about traditional second-order EKC have led researchers to explore different third-order (cubic) EKC patterns. Considering the cubic effect, an N-shaped relationship is expected for EKC if the coefficients of gross domestic product (GDP) or per capita income, which are β1 > 0, β2 < 0, and β3 > 0, and an inverse N-shaped relationship if β1 < 0, β2 > 0, and β3 < 0 are expected. An N-shaped relationship was found in most studies dealing with EKC in cubic form. These include studies conducted by Hasanov et al. (2021) He et al. (2021), Moriwaki and Shimizu (2021), Xu et al. (2020), Shahani and Raghuvansi (2020), Ahmad et al. (2019), Pala (2018), Allard et al. (2018), Murthy and Gambhir (2018), Sinha and Bhatt (2017), Moutinho et al. (2017), Balsalobre and Herranz (2016), Balın and Akan (2015), and Balibey (2015), which can be cited as examples. According to Liu and Song (2020), there are three mechanisms by which financial development can affect carbon emissions: the consumption effect, the production expansion effect, and the technological innovation effect. The consumption effect occurs when a sound financial sector can provide consumers with sufficient credit to purchase enough consumables such as houses, cars, and air conditioners. Thus, the increase in financial resources improves citizens’ living standards and human activities, ultimately increasing energy consumption and carbon emissions (Neog and Yadava, 2020; Acheampong et al., 2019). Instead of the individual and/or family perspective, the production expansion effect and the technological impact occur in line with the perspective of the firm. When a sound financial system can channel sufficient funds to firms’ production processes, firms can expand their production scales as a result of sufficient credit required for production (Gök, 2020; Samour et al., 2019; Li and Ouyang, 2019; Acheampong et al., 2019). When more products are produced, more energy is consumed and more carbon is released (Shen et al., 2021b). This is the production expansion effect. Finally, technological innovations and firms’ research and development (R&D) require a large number of financial resources that can be provided and guaranteed by an advanced financial system. Thus, an advanced financial system can promote new technologies to conserve non-recyclable resources and reduce emissions (Shahbaz et al., 2020; Acheampong, 2019; Abbasi and Riaz, 2016). This reduction in

4  Examination of the Environmental Kuznets

emissions is a result of the innovation effect. Moreover, the well-developed financial sector provides access to low-cost capital or creates incentives for firms and governments to invest in environmentally friendly projects (Lv and Li, 2021; Lahiani, 2020; Tsaurai, 2019). Another factor affecting carbon emissions is renewable energy production. Renewable energy has low or even zero carbon emissions compared to fossil fuels. Using renewable energy instead of fossil fuels effectively reduces carbon emissions (Yao et al., 2019). Stating that renewable energy consumption is an important driving force for a quality environment (Bekun et al., 2021), they emphasized the role of renewable energy consumption in reducing carbon emissions in their studies. There is a clear consensus in the empirical literature that renewable energy production and use have a reducing effect on carbon emissions, and studies in this direction may be exemplified as follows: Balsalobre et al. (2021), Azam et al. (2021), Radmehr et al. (2021), Yuping et al. (2021), Kirikkaleli and Adebayo (2021), Vo et al. (2020), Saidi and Omri (2020), Akram et al. (2020), Mert et al. (2019), Charfeddine and Kahia (2019), Pata (2018), and Mert and Bölük (2016). Numerous studies in the literature are trying to reveal the factors affecting carbon emissions in different countries. At this point, the study aims to comprehensively reveal the relationship between per capita income, financial development level, renewable energy production, and carbon emissions and the aspects of this relationship. In the study, in which the 1990–2019 period data regarding 19 European countries2 were used, the EKC hypothesis was examined for quadratic and cubic structures by using the fixed-effect panel quantile regression method. Moreover, the obtained results were confirmed by panel cointegration regressions. The period of the study, the method used, and the countries included distinguish the study from other studies in the literature and add originality. According to the main result obtained from the study, we have reached strong empirical evidence that EKC has an “inverted U” image in the quadratic model and an “N shape” in the cubic model. The study is comprised of four parts. In the introduction (Section 1.1), the subject’s importance and the problem’s introduction within the context of the theoretical background are addressed. In Section 1.2, the research hypotheses are developed in the light of the theoretical discussions on the subject, and in Section 1.3, the practical econometric methodology is introduced, and the findings are presented. In the fourth and last section (Section 1.4), the findings obtained from the research, results, and suggestions for decision-makers are shared.

1.2 Literature Review and Hypothesis Development The rise in carbon emissions has been threatening our world from past to present, and human effort to reduce emissions has not yet been successful. For this reason, the scientific world constantly examines the factors that affect carbon emissions in different dimensions by separating them at the periodic and

Examination of the Environmental Kuznets  5

national levels. In this regard, many studies in the field have taken their place in the literature. The place of the variables used in this part of the study in the literature and the studies on the results obtained are summarized below. 1.2.1 Studies Examining the Relationship between Carbon Emissions and Economic Growth

Most of the studies examining the relationship between carbon emission and GDP have focused on the EKC hypothesis. Based on the EKC hypothesis, it is stated that, in the early times, when the economic growth in the countries increased, the carbon emission level increased in parallel with the increase in the amount of production and energy consumption. However, with the increase in the welfare level of the country, policies for environmental factors came to the fore, and they gravitated toward clean energy sources. Studies by Grossman and Krueger (1991), Shafik and Bandyopadhyay (1992), and Panayotou (2003) were the first studies in the literature to examine the EKC hypothesis. In studies based on the EKC hypothesis, it is seen that the effect of economic growth on carbon emissions is negative in some studies and positive in others, depending on the level of economic development of the countries. As it is understood from the studies in the literature, different results emerge on whether economic growth and financial development have a positive or negative effect on the environmental factors of the countries. Most of the studies have investigated the validity of the EKC hypothesis by considering different countries. Table 1.1 lists some of the studies examining the relationship between economic growth and carbon emissions in Panel A, and the results obtained in these studies are summarized. Following the study’s aims and in line with the theoretical expectations obtained from the literature review, the hypotheses developed to examine the effect of national income per capita on carbon emissions are specified below. H1a: According to EKC in quadratic form, the coefficients of per capita income have the values of β1 > 0, β2 < 0. H1b: According to EKC in cubic form, the coefficients of per capita income have the values of β 1> 0, β2 < 0, β3 > 0. 1.2.2 Studies Examining the Relationship between Carbon Emissions and Financial Development Level

Financial development is generally evaluated in two dimensions. The first dimension is the expansion of the financial institutions in the country, and the other dimension is the increase in the share of financial assets in income (Aslan and Korap, 2006). When both dimensions of financial development are considered together, it can be said that it is an essential indicator of economic growth. Expansion and growth in the economy inevitably lead to an increase in production opportunities; thus, the level of carbon emissions will

Period

Countries

Method

Findings

Panel A: Certain studies examining the relationship between GDP/per capita income and carbon emissions Cole et al. (1997) 1970–1992 OECD countries Generalized least They demonstrated the existence of the EKC hypothesis squares for local pollutants. However, they stated that indicators with global or indirect effects show a monotonous increase in income, and they predict turning points in per capita income levels with high standard errors. Song et al. (2008) 1985–2005 China Panel cointegration test They revealed the existence of a long-term relationship between GDP and waste gas, wastewater and solid waste. In addition, the results showed that all three pollutants exhibit an inverted U shape. Wang et al. (2011) 1995–2007 China Panel cointegration There is a cointegration relationship between carbon and panel vector emissions, energy consumption, and economic error correction growth. Energy consumption and economic growth modelling are the cause of carbon emissions in the long run. It has been stated that it is difficult to reduce the carbon emission level in China in the long term and that the policies to be implemented in the direction of reduction may cause a certain level of inhibition of economic growth in China. Jaunky (2011) 1980–2005 36 high-Income countries Generalised methods of A one-way relationship from GDP to CO2 emissions in moments the short and long term has been demonstrated. The increase in GPD increases the carbon emission level in the short and long term. However, it has also been revealed that there are stabilizations in economically rich countries.

Study

Table 1.1 Empirical Literature Summary

6  Examination of the Environmental Kuznets

1980–2025 Venezuela

1970–2009 Malaysia

1990–2011 14 Asian countries

1965–2008 South Africa

Robalino-López et al. (2015)

Begum et al. (2015)

Apergis and Öztürk (2015)

Shahbaz et al. (2013)

It revealed the validity of the U-shaped EKC hypothesis for five countries, and an inverted U curve was defined in three countries. In the short run, a unidirectional causality running from economic growth to energy consumption was found. In the long run, there is a unidirectional causality relationship from energy consumption and economic growth to carbon emissions. Seemingly unrelated The study tried to reveal the forecasts for the future regression years within the framework of different economic scenarios by using the past data. The results show that Venezuela does not provide the EKC hypothesis, but it can stabilize its environmental effects in the medium term by turning to renewable energy with economic growth. ARDL bounds test, They revealed that the EKC hypothesis was not valid in dynamic ordinary Malaysia during the study period. While the increase least squared in energy consumption and GDP has a positive effect on CO2 in the long run, no significant effect of population growth has been found. Generalized method of The relationship between emissions and per capita moments (GMM) income emerged in an inverted U shape, which provided evidence for the existence of the EKC hypothesis for countries. ARDL bounds test, The results of the study reveal that economic growth Granger causality increases energy emissions, while financial development decreases it. This indicates the validity of the EKC hypothesis for South African countries. (Continued)

1990–2008 12 Middle Eastern countries Panel cointegration, FMOLS, panel causality

Ozcan (2013)

Examination of the Environmental Kuznets  7

Alshehry and Belloumi (2017)

1971–2011 Saudi Arabia

1982–2011 Mexico, Indonesia, South Korea, Turkey and Australia (MIKTA) countries Can and Gozgor (2017) 1964–2014 France

1990–2012 58 countries

Saidi and Hammami (2016)

Bakirtas and Cetin (2017)

1972–2013 Next 11 countries

Shahbaz et al. (2016)

Countries

Period

Study

Table 1.1 Continued

They determined that economic growth is the cause of carbon emissions for Bangladesh and Egypt. In addition, there is a unidirectional causality relationship from economic growth to carbon emissions for Indonesia and Turkey, which ultimately proves the validity of the EKC hypothesis. For all panel data, it shows that energy use increases carbon emissions, and the increase in GDP has a positive significant effect both in the whole panel and in Europe, demonstrating the validity of the EKC hypothesis. It has been determined that the EKC hypothesis is not valid for MIKTA countries.

Findings

Dynamic ordinary least In France, the EKC hypothesis is valid. Energy squares consumption has a positive effect on carbon emissions. High economic complexity suppresses the carbon emission level in the long run. ARDL, Granger The EKC hypothesis is not valid for Saudi Arabia. causality In the long run, there is a unidirectional causal relationship from economic growth to transport carbon emissions and road transport energy consumption.

Panel vector autoregression (VAR)

GMM

Granger causality

Method

8  Examination of the Environmental Kuznets

1985–2018 China

ARDL bounds test, FMOLS, canonical cointegration regression (CCR) ARDL bounds test, Granger causality

The validity of the EKC hypothesis in China has been demonstrated with the relationship in the form of an inverted U curve obtained in the short and long term based on electricity production and consumption. Ozturk and Acaravci 1960–2007 Turkey They showed that the carbon emission level increased in (2013) line with the expansion in income in the beginning in Turkey but showed a decreasing trend when it reached the stationary point, and the validity of the EKC hypothesis was revealed. Panel B: Certain studies examining the relationship between financial development and carbon emissions Shahbaz et al. (2021) 1870–2017 United Kingdom Bootstrapping ARDL Financial development and energy consumption lead to bounds test environmental degradation. It has been stated that there is a U-shaped relationship between financial development and CO2 emissions. Gozbası et al. (2021) 1995–2017 American, Asian, and Panel quantile According to the results obtained for the whole European continent (34 regression sample, financial development, fossil fuel energy countries) consumption, and tourism revenues increase pollution. This effect of financial development continues up to high quantile levels. Guo (2021) 1988–2018 China Bayer–Hanck and Maki According to the results obtained from the analyses, cointegration tests, the level of financial development in China has a FMOLS, DOLS reducing effect on carbon emissions. Bui (2020) 1990–2012 100 countries Two-stage least squares The empirical results confirm the positive direct (2SLS) and threeimpact of financial development on environmental stage least squares degradation. The development of the financial (3SLS) system also leads to more energy demand and, consequently, more pollutant emissions. Liu ve Song (2020) 2007–2016 China Spatial econometric It is stated that the overall effect is that financial method development will reduce carbon emissions. (Continued)

Jiang et al. (2021)

Examination of the Environmental Kuznets  9

1971–2008 India

Boutabba (2014)

Ehigiamusoe and Lean (2019)

1990–2014 122 countries

Cetin and Ecevit (2017) 1960–2011 Turkey

1980–2015 83 countries

Acheampong et al. (2020)

Countries

Period

Study

Table 1.1 Continued Findings

Generalised methods of Financial development has had a reducing effect on moments the emission intensity in developed and developing countries. It has been revealed that the non-linear and regulatory effects of the development in financial markets differ in terms of countries. ARDL bounds test, It has been seen that financial development has a Granger causality positive effect on carbon emissions in the long run and is the cause of environmental degradation in India. With the Granger causality test, a unidirectional causal relationship from financial development to carbon emissions was determined. ARDL bounds test, Financial development, economic growth, and trade Granger causality openness have a positive long-term impact on carbon emissions in Turkey. The study revealed the validity of the EKC hypothesis for Turkey and that economic and financial development affect environmental degradation. FMOLS, DOLS In analyses that consider all countries, energy consumption, economic growth, and financial development have harmful effects on carbon emissions. In the analyses made by disaggregating the countries according to their income level, it was observed that economic growth and financial development reduced CO2 emissions in highincome countries, while the opposite effect was observed in low- and middle-income countries.

Method

10  Examination of the Environmental Kuznets

1990–2014 155 countries

Generalized method of Globally, financial development has an increasing effect moments on carbon emissions. On the other hand, although this situation is similar in emerging markets and countries, the effect of financial development on carbon emissions is not significant in developed countries. Tsaurai (2019) 2003–2014 W. African countries Classic Panel regression In the study, it was seen that only local bank loans provided to the financial sector had a significant effect on increasing carbon emissions. Wang et al. (2020b) 1990–2017 N-11 countries Westerlund It has been stated that economic growth and cointegration test financial development have a positive effect on carbon emissions in the relevant countries, while technological innovation and renewable energy consumption have a negative effect. Zaidi et al. (2019) 1990–2016 APEC countries Westerlund The results show that globalization and financial cointegration test, development have a decreasing effect on carbon FMOLS emissions for Asia-Pacific Economic Cooperation (APEC) countries, while economic growth and energy density have an increasing effect. Zhang (2011) 1980–2009 China Cointegration test, It has been determined that financial development is the Granger causality driving force behind carbon emissions in China. Panel C: Certain studies examining the relationship between renewable energy and carbon emissions Acheampong et al. 1980–2015 46 sub-Saharan African Fixed and random Renewable energy reduces carbon emissions. Financial (2019) countries effect Panel development and population growth cause an regression increase in emissions. (Continued)

Jiang and Ma (2019)

Examination of the Environmental Kuznets  11

Period

Countries

1990–2014 66 developing countries

1990–2015 25 Asian countries

1990–2012 24 Asian countries

Hanif et al. (2019)

Lu (2017)

Dogan and Seker (2016) 1985–2011 40 top renewable energy countries

Akram et al. (2020)

Adams and Acheampong 1980-2015 46 sub-Saharan African (2019) countries

Study

Table 1.1 Continued Findings

Democracy and renewable energy have revealed effects that reduce carbon emission. Although foreign direct investments, trade openness, population, and economic growth are the driving forces behind carbon emissions for these countries, it is also noteworthy that economic growth reduces carbon emissions at the point where democracy emerges. Classic Panel Renewable energy has a statistically significant and regression, fixednegative effect on carbon emissions at all quantile effect panel quantile levels. The increase in renewable energy in regression developing countries will be effective in reducing carbon emissions. FMOLS, DOLS According to FMOLS and DOLS results, the increase in renewable energy consumption, trade openness, and financial development cause a decrease in carbon emissions. Two-step system The use of renewable energy in Asian countries avails GMM the control of carbon emissions, while the use of non-renewable energy increases carbon emissions as expected. Panel cointegration, In some of the countries examined, it has been Granger causality determined that carbon emission has a positive effect on renewable energy consumption, and there is a causal relationship between carbon emission and renewable energy consumption.

GMM

Method

12  Examination of the Environmental Kuznets

1990–2014 15 major renewable energy- FMOLS, Granger consuming countries causality

1980–2014 8 sub-Saharan African countries

1970–2018 Argentina

Saidi and Omri (2020)

Vural (2020)

Yuping et al. (2021)

ARDL, Gradual Shift Causality

Panel cointegration, DOLS

Panel FMOLS, Panel DOLS, System GMM

1995–2014 28 European Union countries

Leitao and Balsalobre (2020)

Panel cointegration, FMOLS, DOLS

1990–2013 107 countries

Nguyen and Kakinaka (2019)

The study examined the relationship between renewable energy consumption and carbon emissions, taking into account the development level of countries. It is seen that renewable energy has positive effects with carbon emissions for low-income countries, while the relationship is negative in high-income countries. Econometric results prove that trade openness and renewable energy reduce climate change and environmental degradation. Empirical study has found that economic growth also has an increasing effect on carbon dioxide emissions. The efficiency of renewable energy reduces carbon emissions. There is no causal relationship between renewable energy and carbon emissions in the long run, and there is a bidirectional relationship in the short run. While non-renewable energy and trade have a significant impact on carbon emissions, renewable energy emissions have a mitigating effect. Renewable energy consumption and globalization reduce emissions in the short and long term.

Examination of the Environmental Kuznets  13

14  Examination of the Environmental Kuznets

increase due to the increase in energy demand. However, on the other hand, the increasing financial development of countries can bring forth the use of clean energy technologies in production processes, and in such cases, financial development can also reduce carbon emissions. In this regard, some of the studies in the literature found the relationship positive, while others found it negative. Studies in the literature examining the effects of financial development on carbon emissions can be seen in Panel B in Table 1.1. The hypotheses developed to examine the effect of financial development level on carbon emissions under the study’s aims and in line with the theoretical expectations obtained from the literature review are given below. H2: There is a significant relationship between the level of financial development and carbon emissions. 1.2.3 Studies Examining the Relationship between Carbon Emissions and Renewable Energy

Energy resources have an important place in the turning of the economic wheels of countries. Especially the increase in the amount of energy used in production is associated with economic growth. However, the important point here is the environmental pollution that occurs as a result of the use of energy while the countries are growing. A significant portion of the world’s energy needs is met by using fossil fuels. However, the need for clean energy sources is increasing day by day due to the scarcity of these fuels and their negative effects on the environment. The duty of the countries is to gravitate toward clean energy sources that will take environmental factors into account while establishing the balance between energy consumption and economic welfare. Efforts of economically developed countries, unions, and organizations with environmental policies to increase the use of renewable energy sources increase social awareness daily. A large number of funding sources are offered by the European Union, especially for developing countries, in order to encourage the use of renewable energy sources. As stated by Shahbaz et al. (2021), because of the environmental damage and limited availability of fossil fuels, countries are increasingly striving to find and expand renewable energy sources and are becoming less dependent on non-renewable fuels. Resources such as solar energy, the kinetic energy of streams and waves, wind energy, geothermal and biomass energy, and the power of sea waves are called renewable energy sources. They are considered difficult to run out in terms of ease of manufacture, low costs, and energy production after a short investment period. It is expected that the increase in the production of renewable energy sources will reduce carbon emissions. Most of the studies examining the effect of renewable energy on carbon emissions in the literature have focused on the consumption of renewable energy sources. A few studies have examined the relationship between renewable energy production and carbon emissions. However, all countries are faced with the following reality: almost all of

Examination of the Environmental Kuznets  15

the renewable energy sources produced are consumed. All studies examining renewable energy and carbon emissions are expected to produce similar results in this context. The results of some studies examining renewable energy and carbon emissions are given in Panel C in Table 1.1. The hypotheses developed to examine the effect of renewable energy production on carbon emissions in accordance with the aims of the study and in line with the theoretical expectations obtained from the literature review are given below. H3: There is a negative relationship between renewable energy production and carbon emissions.

1.3 Econometric Methodology 1.3.1 Data

The data used in the study are annually based and cover the period 1990–2019. In the study, the dependent variable is carbon emission, and the independent variables are per capita income, renewable energy production, and financial development level. All data were included in the analysis over their natural logarithms. A data set consisting of a total of 570 observations for the 30-year period (T = 30) of European countries (N = 19) was used. However, the data set shows unbalanced panel data characteristics due to the missing data in some years. Eviews 11 SE and Stata 16.1 programs were used for statistical and econometric analysis. Abbreviations, definitions, and sources related to the variables are presented in Table 1.2. 1.3.2 Econometric Method

In the study, the fixed-effect panel quantile regression analysis method will be used in order to measure the effect of financial development level, renewable energy production, per capita income, and the quadratic and cubic form of GDP on carbon emissions.3 Using panel quantile regression methodology, we can examine the determinants of carbon emissions across European countries across the conditional distribution. The use of traditional regression methodology may lead to over- or underestimation of relevant coefficients, or these techniques may not be successful in detecting a significant relationship, as they

Table 1.2 Definition of Variables Variable

Definition

Source

lnco2 lnpgdp lnfindev lnrenew

Carbon emission amount GDP per capita Financial development index Renewable energy production

www​.globalcarbonatlas​.org www​.worldbank​.org www​.imf​.com www​.ourworldindata​.org

16  Examination of the Environmental Kuznets

focus on average effects (Khan et al., 2020). Therefore, in this study, a panel quantile method with fixed effects will be used, which makes it possible to estimate the conditional heterogeneous covariance effects of carbon emission causes and thus to control for unobserved individual heterogeneity. The panel econometrics specification used in this study is the Method of Moments Quantile Regression (MM-QR) developed by Machado and Silva (2019). The advantages of this method, which has become a central study subject and widely used in recent years (Akram et al., 2021; Cheng et al., 2021; Halliru et al., 2020; Wang et al.,, 2020a; Salehnia et al., 2020; Huang et al., 2020), are highlighted and specified as follows. In this respect, quantile regression is more reliable because the classical ordinary least squares (OLS) assumptions of error terms with zero mean, constant variance, and normal distribution are difficult to meet. Therefore, OLS can provide robust results even when classical econometric assumptions fail. Compared to OLS regression, quantile regression can select any quantile point for parameter estimation. Because it does not make any specific assumptions about the distribution of error terms, its sensitivity to outliers is much less than mean regression, so it can provide more accurate and robust regression results. This method is preferred, as it captures all significant variation between predicted and observed variables and, thus, avoids erroneous regression coefficients. The pantile quantile regression method does not follow any distribution assumptions. While classical regression methods do not consider differential heterogeneity, this method deals with differential heterogeneity of panel data along with distribution heterogeneity. The panel quantile regression method also looks for unobserved heterogeneity for each crosssection and measures various parameters at different quantiles. In short, panel quantile regression analysis was used in the study because it provides more informative data, greater variability, and degrees of freedom, thus, increasing the efficiency of parameter estimations. Machado and Silva (2019) expressed the quantile regression for the X variable belonging to the position-scale family in estimating the conditional quantiles as follows: Yit = a i + X it’ b + (d i + Z it’ g )U it



{

}

(

)

’

P d i + Z it’ g > 0 = 1. a , b ’, d , g ’ is the probability of the predicted parameters here. Moreover, as shown by (a i , d i ) , i = 1,..., n individual fixed effects i, and Z

is a k-vector expressed by the l element of differentiable transformations of the components of X.

Z l = Z l ( X ), l = a,..., k

X it is independent and uniformly distributed within the framework of each constant i and the independent time (t) element. On the other hand, U it is independent and identically distributed over individual i and time and orthogonal

Examination of the Environmental Kuznets  17

with respect to X it . Based on this information, the quantile regression of moments is expressed as follows:

QY (t | X ) = (a i + d iq(t )) + X it’ b + Z it’ g q(t )

QY (t | X ) shows the quantile distribution of the dependent variable Y, whereas (ai + d iq(t )) shows scalar influence. q(t ) as being the t -th quantile, the estima-

tion is solved by optimization of the following problem:

minq

å å r (R t

i

it

)

- (d i + Z it’ g )q

t

rt ( A ) = (t - 1)AI {A £ 0} + TAI {A > 0} denotes the check function. 1.3.3 Empirical Analysis and Findings 1.3.3.1 Descriptive Statistics

The results of the basic statistical tests performed to obtain preliminary information about the variables used in the study and to understand the relationship between them are presented in Table 1.3. An a priori clue as to whether the data has a normal distribution is that the skewness and kurtosis values are close to 0 and 3 (You et al., 2017). However, as seen in Table 1.3, the skewness and kurtosis values of all the variables are far from these values. In addition, according to the results of the Jarque-Bera test, not all variables are normally distributed at the 1% significance level. The fact that the data do not satisfy the assumption of normality is an obstacle to the use of least squares regression. For this reason, the quantile regression approach, which stretches this assumption, was used in the study.

Table 1.3 Basic Statistical Tests

 Mean  Median  Maximum  Minimum  Std. Dev.  Skewness  Kurtosis  Jarque-Bera  Probability

lnco2

lnpgdp

lnpgdp2

lnpgdp3

lnfindev

lnrenew

 4.746  4.528  6.958  2.040  1.132 −0.027  2.272  12.628 (0.001)***

 10.265  10.333  11.685  7.456  0.738 −1.077  4.737  181.951  (0.000)***

 105.930  106.782  136.548  55.600  14.598 −0.793  4.063  86.621  (0.000)***

 1098.195  1103.445  1595.623  414.589  218.769 −0.526  3.601  34.952  (0.000)***

−0.457 −0.393  0.000 −1.970  0.275 −1.181  5.227  250.542  (0.000)***

 7.505  8.835  11.172 −1.532  3.599 −1.426  3.630  202.688  (0.000)***

Notes: Significance: ***1%.

18  Examination of the Environmental Kuznets 1.3.3.2 Unit Root Test Results

When panel data is used to test for the existence of a unit root, cross-section dependency needs to be tested. If the cross-sectional dependency is rejected in the panel data set, first generation unit root tests can be used. If there is a crosssection dependency, using the second generation unit root tests can enable us to make more consistent, efficient, and powerful estimations (Bojnec and Fertö, 2020; Cai and Menegaki, 2019). According to the results of the cross-sectional dependency tests presented in Table 1.4, cross-section dependency exists for all variables, as all probabilities obtained under the p-value are less than 0.01. According to this result, it is appropriate to apply second generation panel unit root tests to test the stationarity of the variables. In the study, the unit root test statistics (CADF) of each cross-section (country) were averaged, and the Cross-Sectionally Augmented Im, Pesaran and Shin (CIPS) test, which is the unit root test statistic for the entire panel, was used. The CIPS statistic can be expressed as follows: n



CIPS = N -1

åCADF i

t =1

The results of the CIPS unit root test (see Table 1.5) show that the null hypothesis of all variables, except for the lnco2 variable, is rejected at first-degree differences in the trend-free model. Therefore, the CIPS unit root test results indicate that the lnco2 variable is stationary at the level, whereas the other variables are unstable at the level and are stationary at the first-degree difference. 1.3.3.3 Panel Cointegration Analysis Results

At this stage of the study, whether there is a long-term equilibrium relationship between the variables was investigated with the Westerlund panel cointegration test, which is one of the second-generation tests. This cointegration test was preferred in analyses because it can be used for unbalanced panel data, and

Table 1.4 Cross-section Dependency Test Results Models

Breusch-Pagan LM Test

Pesaran CD Test

Variables

Statistic

p-Value

Statistic

p-Value

lnco2 lnpgdp lnpgdp2 lnpgdp3 lnfindev lnrenew

1883.437 4622.685 4625.255 4625.482 3343.990 354.149

(0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***

24.684 67.944 67.961 67.960 56.288 6.962

(0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***

Notes: Significance: ***1%.

Examination of the Environmental Kuznets  19 Table 1.5 CIPS Panel Unit Root Test Results Level Model Without trend

With trend

Variable lnco2 lnpgdp lnpgdp2 lnpgdp3 lnfindev lnrenew lnco2 lnpgdp lnpgdp2 lnpgdp3 lnfindev lnrenew

First Difference

Test statistics −2.171 −1.400 −1.126 −0.893 −5.597 −12.438 −2.701 1.045 1.198 1.312 −5.021 −10.594

Probability 0.015** 0.081* 0.130 0.186 0.000*** 0.000*** 0.003*** 0.852 0.885 0.905 0.000*** 0.000***

Variable −0.150 −3.449 −3.432 −3.404 −3.960 −6.897 −2.952 −1.103 −1.226 −1.331 −2.509 −4.293

Test statistics 0.441 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.002*** 0.135 0.110 0.092* 0.006*** 0.000***

Notes: Significance: *** 1%, ** 5%, * %10.

they want the time dimension (T) to be larger than the unit size (N) (Tatoğlu, 2018). Table 1.6 shows the cointegration test results of the models. According to the results of the Westerlund panel cointegration test, the H0 hypothesis stating that there is no cointegration was rejected, and it was accepted that there was a cointegration relationship between the variables. Accordingly, in both models, it has been observed that there is a cointegration relationship between carbon emissions and per capita income, financial development level, and renewable energy production. Greene (2019) and Gujarati (2011) stated that, when there is a cointegrated relationship between the variables, it would be inefficient to take the differences in the data because it would hide the longterm relationship between the variables. For these reasons, the variables were included in the panel quantile regression analysis with their level values. 1.3.3.4 Panel Quantile Regression Analysis Results

Quantitative regression is frequently used, especially when the assumptions required for least squares regression are not met. In the study, the panel quantile regression models were established to investigate the per capita income, financial development level, and the effect of renewable energy production on carbon emissions and to show the fixed effects of bi and mt , country, and time are as follows: Table 1.6 Westerlund Panel Cointegration Test Results

Quadratic function Cubic function

Test Statistics

p-Value

1.885 3.021

0.029** 0.001***

Notes: Significance: *** 1%, ** 5%.

20  Examination of the Environmental Kuznets Model 1 : Qln co2 (t |.) = a1,t ln pgdpi ,t + a 2,t ln pgdp 2i ,t + a 3,t ln findevi ,t +a 4,t ln renewi ,t + bi + mt Model 2 : Qln co2 (t |.) = a1,t ln pgdpi ,t + a 2,t ln pgdp 2i ,t + a 3,t ln pgdp 3i ,t +a 4,t ln findevi ,t + a 5,t ln renewi ,t + bi + mt

Table 1.7 shows the results of the fixed-effect panel quantile approach presented by Machado and Silva (2019), and the results are discussed below. Accordingly, it has been seen in both quadratic and cubic form models that the level of financial development has a significant effect on increasing carbon emissions in European countries. This effect has an increasing effect at different quantile levels. Therefore, it can be interpreted that the financial ecosystem in Europe allocates resources to economic units in a way that increases carbon emissions. The carbon emission reduction effect of renewable energy production has been demonstrated in both models. However, this reducing effect acts within very small limits. In the quadratic model, per capita income has a positive and significant effect on carbon emissions, but this effect is gradually decreasing. After a certain level, this effect was found to be negative and significant. This effect also demonstrates a decreasing trend. Therefore, the coefficients of national income per capita in quadratic form EKC have values corresponding to lnpgdp > 0, lnpgdp2 < 0 and are statistically significant. Accordingly, it has been empirically proven that the EKC is valid as an inverted U shape in quadratic form. On the other hand, in the cubic form, an increasing, then decreasing, and then increasing trend in per capita carbon emissions is displayed. According to this result, it has been empirically revealed that the Circumferential Kuznets Curve tends to be N shape in cubic form. The results in cubic form are statistically significant from the 1st to the 5th quantile level but not after the median quantile level. These results show that EKC has an N-shaped appearance at low and medium quantile levels. The tests were performed to understand whether the coefficient values obtained at different quantile levels differ, and their results are presented in Table 1.8. According to the information presented in Table 1.8, the Delta test and heteroscedasticity and autocorrelation consistent (HAC) robust test statistics are statistically significant at the 1% level. Therefore, the H0 hypothesis, which is expressed as equal slopes along the quantiles, is rejected. This finding can be interpreted as evidence that the relationship between explained and explanatory variables varies across different quantiles. 1.3.3.5 Robust Check and Determination of Turning Points

At this stage of the study, the validity of the findings obtained from the panel quantile regression analysis was tested with the Panel-FMOLS and PanelDOLS models. In addition, in the context of the findings obtained from these

−0.082 (0.000)*** −0.075 (0.000)*** −0.069 (0.000)*** −0.060 (0.000)*** −0.055 (0.000)*** −0.055 (0.000)*** −0.052 (0.000)*** −0.047 (0.001)*** −0.043 (0.010)***

1.641 (0.000)*** 1.510 (0.000)*** 1.318 (0.000)*** 1.285 (0.000)*** 1.189 (0.000)*** 1.099 (0.000)*** 1.026 (0.000)*** 0.939 (0.001)*** 0.849 (0.015)**

0.114 (0.162) 0.131 (0.047)** 0.148 (0.004)*** 0.161 (0.000)*** 0.174 (0.000)*** 0.186 (0.000)*** 0.196 (0.000)*** 0.208 (0.000)*** 0.220 (0.001)***

lnfindev −0.011 (0.006)*** −0.011 (0.001)*** −0.010 (0.000)*** −0.010 (0.000)*** −0.010 (0.000)*** -0.010 (0.000)*** −0.009 (0.000)*** −0.009 (0.001)*** −0.009 (0.005)***

lnrenew

90

80

70

60

50

40

30

20

10

Quantile Levels

Notes: Significance: *** 1%, ** 5%, * 10%. Figures in parentheses are p-values.

lnpgdp

lnpgdp

2

Model 1: Quadratic Function 18.224 (0.003)*** 14.574 (0.002)*** 11.758 (0.002)*** 9.110 (0.005)*** 7.077 (0.019)** 4.806 (0.124) 2.680 (0.448) 0.469 (0.910) −2.165 (0.670)

lnpgdp −1.812 (0.004)*** −1.440 (0.003)*** −1.155 (0.003)*** −0.882 (0.007)*** −0.674 (0.028)** −0.442 (0.164) −0.225 (0.530) 0.000 (1.000) 0.269 (0.603)

lnpgdp2

Model 2: Cubic Function

Table 1.7 Panel Quantile Regression with Fixed Effects Estimation Results

0.059 (0.005)*** 0.046 (0.004)*** 0.037 (0.005)*** 0.028 (0.011)** 0.021 (0.040)** 0.013 (0.214) 0.005 (0.622) −0.001 (0.908) −0.010 (0.538)

lnpgdp3

0.196 (0.037)** 0.199 (0.006)*** 0.202 (0.000)*** 0.205 (0.000)*** 0.206 (0.000)*** 0.209 (0.000)*** 0.211 (0.000)*** 0.213 (0.001)*** 0.215 (0.006)***

lnfindev

−0.011 (0.010)*** −0.010 (0.001)*** −0.010 (0.000)*** −0.010 (0.000)*** −0.010 (0.000)*** −0.010 (0.000)*** −0.009 (0.000)*** −0.009 (0.000)*** −0.009 (0.008)***

lnrenew

Examination of the Environmental Kuznets  21

22  Examination of the Environmental Kuznets Table 1.8 Results of Quantile Slope Equality Tests Model 1: Quadratic Function Test Statistic Adj. Test Statistic

Model 2: Cubic Function

Delta Test

HAC Robust Test Delta Test

HAC Robust Test

18.315 (0.000)*** 20.476 (0.000)***

17.256 (0.000)*** 19.293 (0.000)***

9.618 (0.000)*** 10.985 (0.000)***

16.640 (0.000)*** 19.004 (0.000)***

Notes: Significance: *** 1%, ** 5%, * 10%. Figures in parentheses are p-values.

models, the first and second turning points of the inverted U and N-shaped EKC were calculated. Accordingly, the panel cointegration regression analysis results for quadratic and cubic forms are given in Table 1.9. The results of the panel cointegration regression analysis shown in Table 1.9 show that the EKC hypothesis is valid in the quadratic form in the European countries as of the examined period in an inverted U shape. As a matter of fact, lnpgdp > 0, lnpgdp2 < 0 is verified in both panel cointegration regression models, and it is statistically significant. The turning point in quadratic form averages at $25,222. The fact that 15 European countries, except for Greece, Poland, Portugal, and Turkey, have a per capita income higher than the turning point suggested by the results in Table 1.9 increases the probability that the EKC curve may display an N-shaped image. Accordingly, the coefficients calculated for the cubic form are statistically significant in both models, and values of lnpgdp > 0, lnpgdp2 < 0, and lnpgdp3 > 0 indicate that the EKC has an N shape. In cubic form, EKC’s first turning point averages at $10,351, and Table 1.9 Prediction Results of Panel Cointegration Regressions

lnpgdp lnpgdp2 lnpgdp3 lnfindev lnrenew Turning Points4

Model 1: Quadratic Function

Model 2: Cubic Function

Panel FMOLS

Panel DOLS

Panel FMOLS

Panel DOLS

1.207 (0.000)*** −0.187 (0.000)***

1.338 (0.000)*** −0.066 (0.000)***

0.146 (0.000)*** −0.072 (0.000)*** X1: 25.208 $

0.282 (0.000)*** −0.018 (0.000)*** X1: 25.236 $

12.307 (0.001)*** −1.206 (0.002)*** 0.039 (0.004)*** 0.230 (0.001)*** −0.013 (0.000)*** X1:10.688 $ X2: 83.810 $

23.245 (0.000)*** −2.298 (0.000)*** 0.075 (0.000)*** 0.206 (0.049)** −0.014 (0.013)** X1: 10.015 $ X2: 74.065 $

Notes: Significance: *** 1%, ** 5%.

Examination of the Environmental Kuznets  23

the second turning point averages at $78,937. In addition, it was observed that the level of financial development had an increasing effect on carbon emissions and a decreasing effect on renewable energy production, and these findings are in agreement with the panel quantile regression analysis findings. Therefore, according to the results obtained from panel quantile regression analysis and panel cointegration regression analysis, we have reached strong empirical evidence for the effects of per capita income, financial development level, and renewable energy production on carbon emission behaviour in European countries.

1.4 Conclusions and Suggestions Carbon emissions have been increasing significantly since the Industrial Revolution and have confronted our world with a serious climate crisis. The Kyoto Protocol in 1997 aimed to control the situation by limiting greenhouse gas emissions, especially in industrialized countries. However, despite the passing of years, the level of carbon emissions did not remain stagnant, and the increase continued. The Paris Agreement, which aims to keep the global temperature increase in the range of 1.5–2 degrees Celsius in 2016, was signed by 191 countries in the first place. The main purpose of both agreements is to reduce carbon emissions and raise public awareness about the climate crisis. While individual contributions can be made to reduce carbon emissions, it is important to control it mainly through regulations to be put forward by policymakers. Considering that a significant part of the emission is caused by industry, economic indicators and impacts come to the fore. In this study, the factors affecting carbon emissions were investigated using the panel quantile regression method for a sample consisting of 19 European countries, using the 1990–2019 period data. With the help of descriptive statistics, the general conditions of the variables were revealed, and it was observed that the assumption of normal distribution was not met. Therefore, the panel quantile regression model was preferred as the basic method of the study, and in the application of the model, predictions were made with the MM-QR method proposed by Machado and Silva (2019). Attempts to confirm the results obtained by panel cointegration regressions were made. The contribution of this study to the literature is to use the panel quantile regression method in the research of this relationship, and it aims to reach empirical evidence on the extent to which per capita income, renewable energy production, and financial development level directly affect carbon emissions from the perspective of European countries. There are three important results obtained from the study. According to this, (1) the financial ecosystem in 19 European countries within the scope of the research allocates resources to economic units in a way that increases carbon emissions. Furthermore, it was determined that this contribution showed an increasing trend, especially as seen from the panel quantile regression results. Therefore, in European countries, the financial system is seen as a reason for

24  Examination of the Environmental Kuznets

the increase in carbon emissions with the mechanisms it acquires. Financial development leads to better, higher living standards. Well-developed financial markets can provide more consumer loans. These loans will help individuals consume more durable goods such as automobiles, electronic devices, and real estate. Consumption will continue to increase as financial markets provide more credit, which will further deteriorate the environment. (2) Renewable energy production, on the other hand, has a net effect on reducing carbon emissions. However, this effect seems to have been very limited in the past 30 years. According to this finding, we can say that more investments should be made in renewable energy production in the fight against carbon emissions. (3) The effect of per capita income on carbon emissions has been discussed in the context of the EKC hypothesis, and rather explicit empirical evidence has been obtained that the quadratic model has an inverted U-shaped view and the cubic model has an N-shaped image. Within this framework, we contemplate that the N-shaped EKC, which appears in the cubic model, needs explanation in the context of the theoretical background. Accordingly, the N-shaped EKC, whose first turning point was at $10,351 on average and the second turning point was at $78,937 on average, can be said to indicate that environmental awareness and education levels are very high in Europe, so it can be concluded that they make consumption choices that will contribute to environmental protection at the income level between these two turning points. Furthermore, it can be said that beyond the level of $10,351, individuals make consumption preferences for technological and service sector products. The increase in carbon emissions beyond the second turning point of $78,937 may be due to individuals’ preferences for luxurious and substitute goods.

Notes 1 WHO (2020). Global strategy on health, environment and climate change: The transformation needed to improve lives and well-being sustainably through healthy environments. Geneva: World Health Organization. https://apps​.who​.int​/iris​/bitstream​/handle​ /10665​/331959​/9789240000377​-eng​.pdf​?sequence​=1​&isAllowed=y 2 The sample of the study includes the following countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, Turkey, and the United Kingdom. 3 In order to determine the most suitable econometric specification, the Hausman test was performed and fixed effects model was preferred according to chi-square statistics (12.84), p-value = 0.012 for quadratic form, chi-square statistics (10.35), p-value = 0.035 for cubic form. a 4 Turning points were calculated for quadratic function as X 1 = - 1 and as 2a 2 -a 2 - a 22 - 3a1a 3 -a 2 + a 22 - 3a1a 3 X1 = , X2 = for cubic function. 3a 3 3a 3

Examination of the Environmental Kuznets  25

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2

The Effect of Trade, Renewable Energy, and Economic Growth on CO2 Emissions in Central and Eastern Europe Nuno Carlos Leitão, Daniel Balsalobre-Lorente, and Muhammad Shahbaz

2.1 Introduction The study of energy economics, particularly the impact of renewable or nonrenewable energy consumption on economic growth or climate change, with a specific focus on the effect on carbon dioxide emissions, has drawn the attention of economists and various disciplines that study biodiversity and deforestation. From this perspective, applied economics has sought to test the concept of sustainability in the most diverse adjacent areas. Nevertheless, conferences on environmental issues and such treaties (Kyoto agreement, Paris convention) and pioneering studies (Grossman and Krueger 1995; Holtz-Eakin and Selden 1995) have allowed economic science to awaken a collective consciousness in respect to climate change and global warming. In this context, some studies are interconnected and correlate with each other, which assesses the issues of the impact of climate change, where studies proliferate based on the Kuznets environmental curve (EKC) assumptions. Typically, studies seek to determine whether an economy is in a pre-industrial or post-industrial phase. Thus, it appears that the more developed economies have a different awareness of environmental issues, with more concerns about environmental issues and using cleaner technologies. In this context, our research assesses the relationship between renewable energy consumption, economic growth, international trade, and carbon dioxide emissions applied to countries in Central and Eastern Europe from 1995– 2015. The data are organized in panels (autoregressive distributed lag (ARDL) model and generalised method of moments system (GMM–SYS)). Recent studies on this regional bloc demonstrate that EKC’s hypotheses are inconclusive. Furthermore, our analysis does not directly follow the assumptions of the EKC because these countries have economic divergence and regional asymmetries between them and the European Union (EU). The contributions of this chapter are related to the fact that it presents a theoretical survey on the recent literature review, both in terms of this regional bloc and other geographic spaces. Second, we consider the short and long-term effects of the behaviour of economic growth, renewable energies, and international trade on carbon dioxide emissions, using different types of estimators in panel data. DOI: 10.4324/9781003336563-2

The Effect of Trade, Renewable Energy, & Economic Growth  35

Finally, the study also seeks to contribute with some recommendations for economic policymakers.

2.2 Literature Review In the last years, we have observed substantial research on the EKC (Shaari et al. 2020; Farhani and Balsalobre-Lorente 2020; Beyene and Kotosz 2020; Balsalobre-Lorente et al. 2021a, 2021b; Leitão et al. 2021; Oikonomou et al. 2021; Liu 2021; Akadiri et al. 2021). In this context, we can also refer to an increment of EKC applied to Central and Eastern Europe (Józwik et al. 2021; Simionescu et al. 2021; Simionescu 2021; Kułyk and Augustowski 2020; Florea et al. 2020). The empirical study of Shaari et al. (2020) applied a panel ARDL model (autoregressive distributed lag). Their econometric results showed that renewable energy decreases carbon dioxide (CO2) emissions in the long run. Moreover, the economic growth and population positively impact CO2 emissions, showing an increase in pollution emissions. Table 2.1 presents selected studies of EKC assumptions. Considering time series (fully modified least squares (FMOLS), canonical cointegration regression (CCR), dynamic ordinary least squares (DOLS)), the study of Farhani and Balsalobre-Lorente (2020) selected three countries (China, the United States, and India) to test EKC hypotheses. The authors used economic growth and coal, gas, and oil consumption as explanatory variables. Using all explanatory variables, the case of the United States found an inverted U curve. Nevertheless, China supported the EKC – when the authors used only gas consumption. In contrast, EKC expectations were found in the India case, including coal and oil consumption. Balsalobre-Lorente et al. (2021b) use panel data in EU–5 countries for the period 1990–2015. The panel fully modified least squares (FMOLS) estimator results in a non-linear relationship between economic growth and CO2 emissions. The same tendency is valid for the association between air transport. Furthermore, the variables of renewable energy and the public budget used in energy innovation are negatively correlated with pollution emissions. According to these results, the authors consider that the variables contribute to environmental improvement. However, the link between foreign direct investment and CO2 emissions shows a positive relationship, demonstrating that the results are due to climate change and greenhouse gases. The effects of economic complexity and renewable energy were considered by Leitão et al. (2021) for the Brazil, Russia, India, China and South Africa (BRICS) experience using panel data. This research concluded that renewable energy and economic complexity support the improvements in the environment. Furthermore, the study also confirmed an inverted U curve between economic growth and CO2 emissions. Oikonomou et al. (2021) considered the linkages between energy consumption, economic growth, renewable energy, and carbon dioxide emissions

36  The Effect of Trade, Renewable Energy, & Economic Growth Table 2.1 Selected Studies of EKC Hypothesis Studies

Period

Balsalobre-Lorente et al. (2021) Leitão et al. (2021)

1990–2015 Panel data – FMOLS

Liu (2021)

1965–2016

Beyene and Kotosz (2020

1990–2013

1990–2015

Akadiri et al. (2021) 1995 –2016 Józwik et al. (2021)

1995–2016

Simionescu (2021)

1990–2019

Simionescu et al. (2021)

1996–2019

Florea et al. (2020)

2000–2017

Methodology

EKC assumptions

EKC confirmed by EU–5 countries Panel data – FMOLS; EKC confirmed by BRICS DOLS; FE; Panel countries quantile regression time series ARDL Invalid EKC by China in the model long run Panel data – ARDL Invalid EKC by China in the model (PGM) long run and East African countries Panel data – ARDL EKC confirmed by 16 island model (PGM) states Time series ARDL EKC was confirmed only by model Poland Panel threshold EKC confirmed by Central models and Eastern European countries Panel data – ARDL EKC confirmed by Baltic model (PGM) countries and the group of Visegrád countries Panel data – ARDL Invalid EKC by Central and model (PGM) East European agriculture sector

Source: Authors elaboration considering the literature review.

in 11 large polluting economies from 1996–2019. Using a panel ARDL model, the estimates indicated that renewable energy and economic growth decreased emissions. Energy consumption positively correlates with CO2 emissions, and renewable energy aims to reduce carbon emissions. When the authors employed a dynamic panel, the results demonstrated that CO2 emissions positively impact the long run. The linkage of renewable energy, trade, economic growth, and CO2 emissions was studied by Liu (2021) using a time series ARDL model. The econometric results showed that the relationship between economic growth and CO2 emissions presents invalid EKC assumptions in the long run. This study also confirmed that renewable energy intensifies sustainable environmental practices. The study also demonstrated that the variables of trade and nonrenewable energy consumption are positively correlated with CO2 emissions. Another experience studied in East African countries was considered by Beyene and Kotosz (2020) from 1990 to 2013. The authors used an ARDL model (pooled mean group (PMG)), and the empirical results demonstrated that globalization is positively associated with climate changes in the long

The Effect of Trade, Renewable Energy, & Economic Growth  37

run. Though, political stability facilitates improvements in the environment. Besides, this study does not find the EKC hypothesis in the long run. Akadiri et al. (2021) considered 16 island states to test the role of the EKC with a PMG of the ARDL model. The conclusions of this study showed that, in the long run, there is an inverted U-shaped curve between economic growth and CO2 emissions. The variable of trade decreases pollution emissions, and globalization increases pollution levels. The case of Central Europe was investigated by Józwik et al. (2021), employing an ARDL model with time series for the period of 1995–2016. The results of this research found the EKC in Poland. However, the hypothesis of EKC is not valid for other countries. The authors concluded that non-renewable energy consumption accelerates CO2 emissions. Simionescu’s (2021) investigation obtained different results, i.e., the EKC is valid in Central and Eastern European countries, using the panel threshold models and considering economic growth, renewable energy, and pollution emissions. In the same line, Simionescu et al. (2021) reflected the EKC for Baltic countries and the group of Visegrad countries. The authors used a panel ARDL model to find a non-linear relationship between renewable energy and CO2 emissions. Furthermore, the relationship between economic growth and pollution emission reveals an inverse N curve. The results also found that renewable energy consumption helps obtain sustainable development and environmental improvements. The studies of Kułyk and Augustowski (2020) and Florea et al. (2020) applied the EKC approach to the Central and East European agriculture sectors. Using a panel ARDL model, Florea et al.’s (2020) research did not find an inverted U curve between economic growth and CO2 emissions. Nevertheless, Florea et al. (2020) also found that agricultural activity increases climatic change and renewable energies decrease CO2 emissions.

2.3 Data and Methodology This research reflects the relationship between trade, renewable energy, economic growth, and CO2 emissions. Our sample covers 1995–2015 in Central and Eastern European countries (Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia). Croatia was not included in this sample because this country had only just joined the European Union in 2013; therefore, the levels of economic convergence were not standardized compared to other countries considering the analysis period. We start the econometric strategy with panel units root tests such as Levin, Lin, and Chu (2002), ADF–Fisher chi-square (Dickey and Fuller 1979), and Phillips–Perron (Phillips and Perron 1988), Im et al. (2003) to observe whether the variables used in this research are stationary in level or integrated at their first order. In the next step, we test the multicollinearity between the variables used in the study and the presence of cross-sectional independence. The causality

38  The Effect of Trade, Renewable Energy, & Economic Growth

among the variables was considered the criterion of Dumitrescu Hurlin’s (2012) pairwise causality. Finally, the model was applied to the panel ARDL model suggested by Pesaran and Shin (1999) and Pesaran et al. (2001). The panel ARDL model, namely PMG, allows for evaluating the long and short run. This research also considers a dynamic panel data – GMM-System – suggested by Arellano and Bover (1995) and Blundell and Bond (1998). Considering the literature, the estimator of GMM-System is valid if the output has no serial correlation of second order (AR2), and the instruments used in specification should be correct according to the Sargan test. Based on previous studies by Shaari et al. (2020), Behera and Mishra (2020), Akadri et al. (2021), and Simionescu et al. (2021), we present the next function considered:

CO2=f (GDP, RE, TRADE)

(1)

In Equation 1, CO2 represents the carbon dioxide emissions, GDP is the income per capita, RE (renewable energy), and TRADE signifies trade openness. The expected signs are GDP > 0; RE < 0; TRADE > 0, or TRADE < 0. Regarding the recent empirical studies (Behera and Mishra 2020; Busu 2020; Akadri et al. 2021; Simionescu et al. 2021), we formulate the following panel ARDL equation: ∆LCO2it = α0it + α1itLCO2i(t-1) + α2itLGDPi(t-1) + α3itLREi(t-1) + α4itLTRADEi(t-1) + ∑pj=0 γ1LCO2i(t-j) + ∑pj=0 γ2LGDPi(t-j) + ∑pj=0 γ3 LREi (t-j) + ∑pj=0 γ4LTRADEi(t-j) + ψECTi(i-t) + µit (2) In Equation 2, all variables use the logarithm form, and the white noise is represented by µit; the first difference is represented by ∆, and ψECT is the error correction. The dependent variable is carbon dioxide emissions (CO2) from the Carbon Dioxide Information Analysis Center and World Development Indicators of the World Bank. The independent variables selected are income per capita (GDP), renewable energy (RE), and trade openness (TRADE). The source for GDP and TRADE are taken from Development Indicators of World Bank and Eurostat–National Accounts. Lastly, the variable of renewable energy is from the Development Indicators of the World Bank and International Energy Agency. The system generalized moments method – GMM-SYS – is expressed by: LCO2it = α0it + α1itLCO2i(t-1) + α2itLGDPit + α3itLREit + α4itLTRADEit + vt + ni + eit (3) Where vt is the trend, ni is the unobserved time, and eit corresponds to random distribution.​ Considering the literature review and the empirical studies, we formulate the following hypothesis: H1: Economic growth causes climate change and a higher level of environmental damage.

The Effect of Trade, Renewable Energy, & Economic Growth  39

The empirical studies of the EKC demonstrate that economic growth is directly correlated with greenhouse gas. Balsalobre-Lorente et al. (2021a) and Simionescu (2021) support our hypothesis. H2: Using renewable energies makes it possible to reduce pollution emissions. The most recent empirical studies demonstrate that sustainable practices and renewable energies allow for a better quality of life and air. In this context, the studies by Leitão and Balsalobre-Lorente (2020), Balsalobre-Lorente et al. (2021b), and Leitão et al. (2021) found a negative impact of renewable energy and CO2 emissions. H3a: International trade can promote environmental improvements. H3b: International trade accentuates climate change. Literature states two currents regarding the relationship between trade and pollution emissions. Thus, the dominant paradigm points to a negative relationship between international trade and CO2 emissions. The differentiation of products and innovation factors reduce CO2 emissions (Leitão 2021; Leitão et al. 2021; and Doran and Jianu 2020). However, there is an alternative current explained by the theories of comparative advantages, in which there is a positive correlation between international trade and CO2emissions. Moreover, most studies in CEE countries find a positive association (e.g., Józwik et al. 2021).

2.4 Estimation and Discussion Table 2.2 presents the values for descriptive statistics for each of the variables used in this investigation. In this context, CO2 emissions (LCO2) and

TRADE [+, –]

CO2 ECONOMIC GROWTH [+]

RENEWABLE ENERGY [–]

Figure 2.1 Summary of the excepted signs seeing the literature review.

40  The Effect of Trade, Renewable Energy, & Economic Growth Table 2.2  Descriptive Statistics Variables

Mean

Std. Dev.

Minimum

Maximum

LCO2 LGDP LRE LTRADE Observations

4,765 3.930 1.104 2.020 209

0.579 0.331 0.267 0.140

3.738 3.133 0.549 1.641

5.586 4.439 1.606 2.259

Source: Authors elaboration considering the EViews software.

economic growth (LGDP) should be highlighted, as they present the highest values for maximum values. Table 2.3 describes the unit root test results considering a summary battery of tests. Regarding the results, it is possible to refer that the variables of CO2 emissions, economic growth, renewable energy, and trade are integrated into the first difference. Besides, the variables of economic growth and trade are stationary at the levels using the Phillips–Perron test. The next step is testing the multicollinearity problems (see Table 2.4). Following the contributions of Leitão et al. (2021), Leitão (2021), and Koengkan and Fuinhas (2020), we observe that the variables of renewable energy, trade, and economic growth have no multicollinearity because the test of variance inflation factor (VIF) is less than five. Thus, the literature considers this rule to pass this test. After checking the unit root and multicollinearity tests, it is necessary to test the cross-section independence using the estimation procedure. Based on the empirical works of Koengkan et al. (2021), Fuinhas et al. (2017), and Balsalobre-Lorente et al. (2021b), we can see that the variables used in this research have cross-section independence between them. According to Koengkan and Fuinhas (2020), Fuinhas et al. (2017) mean that the countries used in this sample have similar attributes.​ The Johansen Fischer panel cointegration test is presented in Table 2.6. The trace tests and the maximum Eigen show that the variables are cointegrated in the long run. Table 2.7 shows the causality results among the variables using the criterion of Dumitrescu Hurlin pairwise causality. The test shows a unidirectional causality between economic growth (LGDP) and renewable energy (LRE). The linkages between economic growth (LGDP) and trade (LTRADE) also present a unidirectional causality. Furthermore, we see a bidirectional causality between renewable energy (LRE) and carbon dioxide emissions (LCO2). In this line, we also observe bidirectional causality between trade openness (LTRADE) and renewable energy (LRE).

The Effect of Trade, Renewable Energy, & Economic Growth  41 Table 2.3 Panel Unit Root Test Variables

Level

Carbon dioxide emissions Method Levin, Lin, & Chu t Im, Pesaran and Shin W-stat  ADF - Fisher Chi-square PP - Fisher Chi-square

LCO2 Statistic −0105 1.278 21.834 17.607 Level LGDP Statistic −3.120*** 0.536 11.029 7.904 Level LRE Statistic 0.131 2.121 11.178 19.629 Level LTRADE Statistic −2.055** 1.089 12.414 16.489

Economic growth Method Levin, Lin & Chu t Im, Pesaran and Shin W-stat  ADF - Fisher Chi-square PP - Fisher Chi-square Renewable energy Method Levin, Lin, and Chu Im, Pesaran, and Shin W-stat  ADF - Fisher chi-square PP - Fisher chi-square Trade Method Levin, Lin & Chu t Im, Pesaran and Shin W-stat  ADF - Fisher Chi-square PP - Fisher Chi-square

First difference P-value (0.458) (0.899) (0.349) (0.613) P-value (0.000) (0.704) (0.945) (0.992) P-value (0.552) (0.983) (0.942) (0.481) P-value (0.019) (0.862) (0.901) (0.685)

DLCO2 Statistic −2.905*** −3.842*** 62.727 126.593 First difference DLGDP Statistic −3.705*** −2.770*** 36.910** 45.836*** First difference DLRE Statistic −5.144*** −6.252*** 77.4730*** 1790.303*** First difference DLTRADE Statistic −7.989*** −6.602*** 80.003*** 135.584***

Source: Authors elaboration considering the EViews software. p-values are in parenthesis; ***p < 0.01, ** p < 0.05.

Table 2.4 Test of Variation Inflation Factor Variables

Variance Inflation Factor (VIF)

1/VIF

LGDP LRE LTRADE Mean-variance inflation factor

1.29 1.28 1.02 1.20

0.774 0.78 0.980

Source: Authors elaboration considering the EViews software.

P-value (0.000) (0.000) (0.000) (0.000) P-value (0.000) (0.002) (0.012) (0.000) P-value (0.000) (0.000) (0.000) (0.000) P-value (0.000) (0.000) (0.000) (0.000)

42  The Effect of Trade, Renewable Energy, & Economic Growth Table 2.5 Test of Cross-section Independence Variables

CD test – Pesaran cross-section

Prob.

LGDP LRE LTRADE

16.083*** 16.525*** 11.548***

(0.000) (0.000) (0.000)

Source: Authors elaboration considering the EViews software. p-values are in parenthesis; ***p < 0.01.

Table 2.6 Cointegration Rank Test: Trace and Maximum Eigen Value Hypothesized

Fisher Stat.

No. of CE(s)

(from Trace test)

Prob.

Fisher Stat. (from Max Eigen test)

Prob.

None At most 1 At most 2 At most 3

126.1*** 51.45*** 29.10* 44.82***

 (0.000)  (0.000)  (0.085)  (0.001)

 94.40***  39.01***  18.85  44.82***

 (0.000)  (0.006)  (0.531)  (0.001)

Source: Authors elaboration considering the EViews software. p-values are in parenthesis; ***p < 0.01, * p < 0.1.

Table 2.7 Dumitrescu Hurlin Pairwise Causality Test  Null Hypothesis:  LGDP does not homogeneously cause LCO2  LCO2 does not homogeneously cause LGDP

W-Stat. 1.871 1.467

Zbar-Stat. 1.304 0.589

Prob.  (0.192) (0.556)

 LRE does not homogeneously cause LCO2  LCO2 does not homogeneously cause LRE

2.865*** 2.325**

3.067 2.108

(0.002) (0.035)

 LTRADE does not homogeneously cause LCO2  LCO2 does not homogeneously cause LTRADE

2.229* 1.088

1.938 −0.084

(0.053) (0.933)

 LRE does not homogeneously cause LGDP  LGDP does not homogeneously cause LRE

0.519 4.776***

−1.091 6.467

(0.275) (0.000)

 LTRADE does not homogeneously cause LGDP  LGDP does not homogeneously cause LTRADE

0.816 2.325**

-0.564 2.114

(0.573) (0.035)

 LTRADE does not homogeneously cause LRE  LRE does not homogeneously cause LTRADE

3.046*** 2.798***

3.395 2.955

(0.000) (0.003)

Source: Authors elaboration considering the EViews software. p-values are in parenthesis; ***p < 0.01, **p < 0.05, * p < 0.1.

The Effect of Trade, Renewable Energy, & Economic Growth  43

Table 2.8 reports the results using PMG of ARDL model. In the longrun effects, the variables of economic growth (LGDP) and renewable energy (LRE) have statistical significance at 5% and 1%, respectively. Previous studies (Akadri et al. 2021; Leitão et al. 2021; Simionescu et al. 2021) show that economic activity accentuates greenhouse effects and climate change. However, the result obtained from renewable energy (LRE) demonstrates their aim to improve the environment. The empirical studies of Oikonomou et al. (2021), Balsalobre-Lorente et al. (2021b), and Leitão et al. (2021) also found this relationship between the variables. We observe that economic growth and renewable energies on CO2 emissions are similar considering the short-run effects. Furthermore, the coefficient of international trade (LTRADE) presents a positive impact on CO2 emissions, showing that trade accentuates pollution emissions. According to this result, we can conclude that the revealed comparative advantages theory explains this result. Table 2.9 presents the results by country in the short run. The speed of adjustment, or error correction term (ECT), gives a negative sign and is statistically significant at 1% level, showing convergence between Bulgaria, the Czech Republic, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia in the relationship to pollution emissions. Regarding the coefficient of renewable energy (LRE), we observe that Bulgaria, the Czech Republic, Poland, Romania, and Slovenia negatively impact CO2 emissions. The variable is statistically significant at a 1% level for these countries. The relationship between trade (LTRADE) and CO2 emissions presents a positive effect and is statistically significant at a 1% level, except in the Czech Republic. According to trade theory, we can refer to the low quality of products and explain this type of trade by the comparative advantages.

Table 2.8 Pooled Mean Group of ARDL Model (1,1) Variables

Coefficient

Std. Error

Long Run Equation LGDP 0.044** 0.021 LRE −0.210*** 0.032 LTRADE −0.0048 0.064 Short Run Equation ECT 0.002 0.374 D(LGDP) 0.191*** 0.067 D(LRE) −0.119* 0.071 D(LTRADE) 0.141*** 0.052 C −0.300 2.087 Log-likelihood 502.473 Hausman test 0.382 (0.944) Observations 198 Source: Authors elaboration considering the EViews software. p-values are in parenthesis; ***p < 0.01, *p < 0.1.

t-Statistic

P-value  

2.094 −6.569 −0.075

(0.038) (0.000) (0.941)

0.006 2.818 −1.669 2.671 −0.144

(0.995) (0.005) (0.097) (0.008) (0.886)

44  The Effect of Trade, Renewable Energy, & Economic Growth Table 2.9 Pooled Mean Group of ARDL Model (1,1) by Countries in Short Run Countries

ECT

D(LGDP)

D(LRE)

D(LTRADE) Constant

Bulgaria

−0.707*** (0.002) −0.1422*** (0.000) 3.306 (0.118) −0.399*** (0.000) −0.534*** (0.000) −0.298*** (0.000) −0.365*** (0.000) −0.002 (0.129) −0.645*** (0.001) −0.168*** (0.009)

0.166*** (0.001) 0.216*** (0.000) 0.766* (0.098) 0.113*** (0.000) 0.087*** (0.000) 0.216*** (0.000) −0.001 (0.857) 0.126*** (0.000) 0.183*** (0.000) 0.087*** (0.001)

−0.189*** (0.000) −0.624*** (0.000) 0.112 (0.732) 0.008 (0.196) 0.125** (0.013) -0.037 (0.355) −0.167*** (0.005) −0.283*** (0.000) 0.057*** (0.000) −0.198*** (0.000)

0.104*** (0.001) −0.277*** (0.000) 0.245 (0.712) 0.179*** (0.001) 0.148*** (0.000) 0.328*** (0.000) 0.129*** (0.003) 0.306*** (0.000) 0.114*** (0.004) 0.139*** (0.002)

Czech Republic Estonia Hungary Latvia Lithuania Poland Romania Slovakia Slovenia

3.345*** (0.001) 0.565*** (0.004) −18.819 (0.727) 1.895* (0.060) 2.984* (0.063) 1.266** (0.019) 2.006* (0.077) 0.107 (0.738) 2.940 (0.104) 0.713 (0.249)

Source: Authors elaboration considering the EViews software. p-values are in parenthesis; ***p < 0.01, *p < 0.1.

Finally, we can conclude that economic activity via economic growth is directly associated with a higher pollution level. Table 2.10 displays the econometric results using GMM-SYS. Our results show that the coefficients are similar between the first and second-step estimators. The model does present serial correlation and specification problems so that we can read the econometric results. The lagged variable of CO2 emissions (LCO2t-1) is statistically significant at 1%. Besides, we observe that, in the long run, greenhouse effects increase (0.83%; 0.567%). This result is according to previous studies such as Leitão and Balogh (2020) and Leitão and Balsalobre-Lorente (2020). Renewable energy (LRE) aims to decrease carbon emissions in both steps estimator. Oikonomou et al. (2021) and Balsalobre-Lorente et al. (2021b) also observed this relationship between the variables. The variable of international trade (LTRADE) showed that it is according to pollution haven hypothesis (PHH) theory (see Table 2.10).

The Effect of Trade, Renewable Energy, & Economic Growth  45 Table 2.10 GMM-SYS with the First and Second Steps One-step results Variables LCO2t-1 LGDP LRE LTRADE C Observation Two-step results Variables LCO2t-1 LGDP LRE LTRADE C Observation Serial correlation test (second order) Specification test (Sargan)

Coef. 0.823*** −0.016 −0.134*** 0.132* 0.779 189

Std. Err. 0.102 0.226 0.044 0.077 0.513

Z 8.10 −0.71 −3.05 1.72 1.52

P-value (0.000) 0.478 (0.002) (0.085) (0.129)

Coef. 0.567*** −0.024 −0.272*** 0.159** 2.117** 189 0.359 1.000

Std. Err. 0.196 0.034 0.091 0.062 1.055

Z 2.90 −0.70 −2.99 2.57 2.01

P-value (0.004) (0.484) (0.003) (0.010) (0.045)

Source: Authors elaboration considering the STATA software. p-values are in parenthesis; *** p < 0.01, ** p < 0.05, * p < 0.1.

2.5 Conclusion This research considers the role of renewable energy, international trade, and economic growth on CO2 emissions in Central and Eastern Europe from 1995 to 2015. Before applying the econometric model, we performed routines typically used in empirical studies in this area. So, we started with the unit root tests, where it was observed that the variables in this study were integrated into the first differences. However, beyond that, the tests carried out demonstrated that there are no multicollinearity problems. Furthermore, the cross-section independence and cointegration tests showed that the variables under study are cointegrated in the long term. Finally, the Dumitrescu and Hurlin tests were applied regarding the causality between the variables. It was concluded that there is bidirectional causality between renewable energies and CO2 emissions and between international trade and renewable energies. The PMG model allows observing the short- and long-run effects of all countries used in this research. Moreover, this methodology makes available the short-run effects of each country. The results reveal that all countries are similar in the short and long run. We stressed that renewable energy improves the environment, and climate change decreases in this context. Nevertheless, economic growth and international trade positively affect CO2 emissions. The link between international trade and CO2 emissions is explained by comparative

46  The Effect of Trade, Renewable Energy, & Economic Growth

advantages and the PHH theory. The empirical results considering the analysis by each country showed convergence in the relationship between the pollution emissions of eight countries of this bloc, namely Bulgaria, the Czech Republic, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia. The economic growth confirms a positive effect on CO2 emissions, showing that sustainability practices should be implemented in Central and Eastern European countries. In this line, only the Czech Republic presents a negative impact of international trade on pollution emissions. Again, our results showed that comparative trade advantages explain all countries except the Czech Republic. The econometric results with dynamic panel data (GMM-System) complement the ARDL model results. In the long run, the lagged variable of carbon emissions presents a positive effect, demonstrating that climate change and greenhouse effects increase in this regional bloc. We also observed with GMM-SYS that international trade intensifies a higher pollution level, though, renewable energy showed an environmental improvement. Regarding the empirical analysis, it is possible to issue some recommendations in terms of economic policy. Thus, this group of economies appears to have regional asymmetries, in which international trade is based on the assumptions of comparative advantage theories. On the other hand, the panel data for the ARDL model revealed that economic growth is associated with more polluting practices at the level of each of the countries in a short-term analysis. That is, sustainability falls short of what is expected. However, renewable energies seem to show environmental concern in this group of studied countries. The post-COVID-19 period should be crucial for this group of countries. As mentioned, we are evaluating a group of countries with regional asymmetries. It is observed that economic growth and economic activity are associated with climate change and greenhouse effects. In this context, Central and Eastern European countries must follow the measures implemented by the EU and the digitization of the economy and public health indicators. Furthermore, the digitalization and the vaccination rate against COVID-19 will reduce CO2 emissions with fewer means of transport. In addition, we believe that society after COVID-19 is more aware of climate change, the need to preserve the environment, and more sustainable alternative practices, especially in large urban centres, such as electric vehicles, electric scooters, and electric bicycles. Considering future works, we think it will be interesting to evaluate each of the countries using times series such as the ARDL model, cointegration using the FMOLS, DOLS, CCR estimator, the vector error-corrected model (VECM), or even the quantile regression are some suggestions. This context allows us to extend the time series and to verify whether there is efficiency in the demand for renewable energy and the impact of this variable on economic growth and its greenhouse effects, as well as the introduction of some variables such as the vaccination rate against COVID-19 and the use of alternative vehicles and evaluating the impact of these variables on economic growth and polluting emissions.

The Effect of Trade, Renewable Energy, & Economic Growth  47

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Air Pollution and COVID-19 Nexus Insights from Wavelet Approach for Selected Groups of Countries Muhammad Ibrahim Shah, Avik Sinha, Arshian Sharif, and Solomon Prince Nathaniel

3.1 Introduction Almost all the countries where COVID-19 has been reported have taken preventive measures such as lockdowns, quarantine, social distancing, or staying at home to prevent the virus’s spread. This particular virus necessitates these responses because of its highly contagious nature. Not all viruses are spread the same way, and the ability of asymptomatic carriers to transmit and the ability of the virus to live on certain surfaces longer than others is what makes it hard to combat without drastic measures. Since the isolation and shutdown were enacted because of the pandemic, pollution levels worldwide have fallen drastically, mainly because of the shutdowns of many manufacturing industries. With the social distancing, fewer vehicles on the road, factories being closed, and no planes flying, this planet has come to a standstill, and the world has reported great environmental improvements. However, despite the more significant positive impact on the environment brought about by the pandemic, scientists have reported another more pressing threat: the death toll due to COVID-19 is likely to be higher in places where pollution in the air is normally bad (Ogen, 2020). Air pollution, although not an immediate threat like COVID-19, is one of the highest mortality risk factors, and World Health Organization (WHO) reports that about 7 million people die each year from this type of pollution only (Isaifan, 2020). The people who die prematurely from air pollution are almost the same demographic who are dying from COVID-19: people with lung or heart diseases and respiratory problems and those who are immunecompromised. It is not surprising, as air pollution favours respiratory diseases and generally worsens the clinical course of any respiratory infection. High pollution leads to lungs being more vulnerable; therefore, it makes sense that pollution will significantly impact lung functionality in someone with the disease. Of course, air pollution does not directly cause COVID-19, but this indicates that the degree of respiratory air pollution is highly relevant to the question of how susceptible people are to this disease and how severe its course is. Regardless of the exact mechanisms of action, it is evident that the more polluted air one breathes, the more often one gets this respiratory disease, and DOI: 10.4324/9781003336563-3

50  Air Pollution and COVID-19 Nexus

the more severe the course becomes. While Wuhan, China, for example, has one of the highest PM2.5 (Particulate Matter 2.5) concentrations in the world, the Milan, Italy region has the highest burden in Europe, and these two regions were epicentres of COVID-19. Given that these pollution particles improve the transmissibility of respiratory viruses such as COVID-19, it is essential to investigate the relationship between air pollution and COVID-19. The study seeks to address the following issues. First, air pollution is one of the most significant health emergencies of our time, yet it is not treated as one because of the people’s inability to directly feel the impact of air pollution. However, this pandemic has made the case in spades. Therefore, our study aims to investigate the concrete relationships and mechanisms of action of air pollution concerning COVID-19 by looking at the ten most polluted countries and the ten least polluted countries. Our initial hypothesis is that regions with more air pollution have more severe outbreaks of COVID-19, but those with low pollution suffer less from this disease outbreak. Second, although few studies have investigated how air pollution is linked to the severity of COVID-19, to the best of our knowledge, this is the first study which links air pollution with COVID-19 using the wavelet methodology. This pandemic is a supply shock because it can affect the air quality at different time horizons. For example, it may have a significant positive impact on air quality in the short run, but people’s perspective toward air quality may change significantly due to this shock in the longer time horizon. Because this event is also a demand shock, it is more likely to push different governmental and non-governmental organizations for alternative energy resources (e.g., renewable ones), which will not pollute the environment and create any health-related hazards (Alzahrani et al., 2014). We apply the wavelet-based Granger causality test to capture both demand and supply shocks. Third, the study’s objective differs from those of Wu et al. (2020), who found a direct link between air pollution and COVID-19. Our study looks at the possible indirect relationship between COVID-19 and the air quality index (AQI) level of a country. By investigating the indirect relationship between these two parameters, our study offers a more in-depth understanding of how air pollution may affect COVID-19 cases or any respiratory virus. The implications are significantly high both for the short and long run. Our objective is to direct policymakers toward sustainable development goals and make them aware of how these will be affected due to COVID-19. A recent estimate shows that the goals are threatened by every possible angle (UN, 2020). By designing policies for achieving the goals of sustainable development or especially the goal of a sustainable city and good health, our study offers an understanding of the policies needed to mitigate the pollution level existing in these countries. The study does not just limit itself to the 20 countries in respect, but the implications are also highly global. Although several studies have explored the relationship between pollution and COVID-19, including Wu et al. (2020), Setti et al. (2020a, 2020b), Cole et al. (2020), and Liang et al. (2020), their findings are limited in scope. However, our study is concerned

Air Pollution and COVID-19 Nexus  51

with many different countries with diverse backgrounds, such as they differ in terms of socio-economic conditions, environmental qualities, health care systems, economic growth, and sustainable practices. Therefore, our study has the feature of attracting more policymakers to redefine sustainable development goals. By designing policies for both heavily polluted and least polluted regions, our study’s implications become global. This also offers an understanding of how future policy designs should be implemented and practices that should be initiated among the people to start working on mitigation strategies in air pollution levels and protect against any future respiratory viruses that may threaten the socio-economic and environmental conditions in a country. This study is organized as follows: the second section describes the data and method used, the third section provides the results, and the final section concludes.

3.2 Data and Methodology The study analyses the dependence and direction of causality between AQI and COVID-19. Overall, AQI is calculated by taking the maximum of all six individual AQI pollutants: particulate matter (PM2.5, PM10), ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2), and carbon monoxide (CO) emissions. We took daily AQI data from the World Air Quality Index (WAQI) project for three months, starting from February 1, 2020, to May 6, 2020, for 20 countries. Because countries have different monitoring stations, we use several monitoring stations and then get their average to calculate the overall AQI from individual pollutants. On the other hand, daily counts of COVID-19 cases per million comes from the European Centre for Disease Prevention and Control (ECDC). We selected ten countries from the top 15 most polluted and the ten countries from the top 15 least polluted countries in the 2019 World Air Quality report provided by IQAir. The list of 20 countries is provided in Table 3.1. Because this is a novel virus, many uncertainties still revolve around it and, therefore, formulating policy directions via usual methods may not be appropriate. So, to examine the dependence between our two variables, we rely on the wavelet coherence technique (WCT), which can uncover a potentially underlying lead-lag relationship between the increase in AQI and daily observations of confirmed cases of COVID-19 at different times as well as at different frequencies (Khalid and Habimana, 2021). We use the continuous wavelet transformation (CWT) as opposed to discrete wavelet transformation (DWT) because DWT is dependent on time series being dyadic (power of 2) in nature (Kumar and Kamaiah, 2017). Additionally, we apply the waveletbased Granger causality approach to determine causal effects between air quality and the dynamics of daily infected cases of COVID-19 at different time

52  Air Pollution and COVID-19 Nexus Table 3.1 List of Countries 10 Most Polluted Countries

10 Least Polluted Countries

Afghanistan Bangladesh China India Indonesia Mongolia Nepal United Arab Emirates Uzbekistan Vietnam

Canada Denmark Estonia Finland Germany New Zealand Norway Portugal Spain Sweden

Source: 2019 World Air Quality Report, IQAir (2020).

scales. Granger causality is particularly useful in this situation because COVID19 can affect air quality both as a demand shock and as a supply shock.

3.3 Empirical Results 3.3.1 Wavelet Transform Coherence

We utilized wavelet transform coherence (WTC) to ascertain the existence of a causal association between COVID-19 and the AQI in the most and least polluted countries. This method furnishes the common power (aspects) and comparative phase of various time series in existing time-frequency space. The cone of influence (COI) tests are also considered to examine the anti-cyclical association between COVID-19 and air quality in the selected sample. Figures 3.1 and 3.2 present the WTC between COVID-19 and air quality for the most and least polluted countries. Here, the pattern is followed with respect to the frequency, i.e., daily dynamics from zero to 4 days are considered as short-run, 4 to 8 days are the medium run, 8 to 16 days are the long run, and 32 and more days are the very long run. See Figures 3.1 and 3.2 on the book’s website at: www​.routledge​.com​ /978103237350. In Figure 3.1, the overall causal association between COVID-19 and AQI in the least polluted countries show strong but mixed co-movement in short, medium, and long runs across the whole time frame, i.e., February 2020 to June 2020. In Canada’s case, the arrows point up to the left side, showing that COVID-19 and air quality are out-phase, depicting an anti-cyclic effect with COVID-19 leading throughout the period, i.e., COVID-19 has a negative causal influence on the air quality in the short-, medium-, and long-run period. On the other hand, the results show mixed findings for Denmark. During the short-run period, the arrows are pointing down to the left side, suggesting an out-phase relationship between COVID-19 and air quality,

Air Pollution and COVID-19 Nexus  53

in which air quality is leading in May and June 2020. Moreover, the arrows are pointing down to the right side, confirming a cyclic relationship between COVID-19 and air quality, which means both variables have a positive comovement in which COVID-19 is leading in the long run. In the case of Estonia, Finland, Portugal, and Spain, the findings are homogenous in that the arrows are pointing down to the left side, suggesting that both are variables have an out-phase relationship in which AQI is leading (AQI has a negative causal influence on COVID-19 during the short-, medium-, and long-run period). On the other hand, the findings for Norway are exciting; during the short run period, the arrows are upward on the right side (downward), suggesting a positive co-movement between COVID-19 and air quality from February to March (April to May) where air quality (COVID-19) is leading. The results confirm that both variables have a causal influence on each other with a lead-lag effect in the short run. However, during the long run period, the arrows are heading down toward the left side, confirming both variables having an out-phase relationship with AQI is leading (means AQI has a negative causal influence on COVID-19) in the long-run period. For the case of Germany and New Zealand, in the short-, medium-, and long-run periods, most of the arrows point upward, confirming that COVID19 and air quality have a positive connection. The findings affirmed that both variables have an in-phase relationship with each other in which AQI is leading (AQI has a causal influence on COVID-19) in the short, medium, and long run. Moreover, in the case of Sweden, all the arrows point down to the left side, suggesting both variables have an anti-cyclic effect in which AQI is leading (AQI has a negative causal influence on COVID-19) throughout the period. Technically speaking, the findings are very mixed in the relationship between COVID-19 and AQI. The effect of AQI is found to be negative on COVID-19 in Estonia, Finland, Portugal, Spain, and Norway. However, the findings confirm a negative influence of COVID-19 on AQI in Canada, Denmark, Germany, New Zealand, and Sweden. In Figure 3.2, the overall causal association between COVID-19 and AQI in the most polluted countries show negative co-movement in short, medium, and long runs across the whole time-frequency domain. The relationship between COVID-19 and air quality is very similar in Afghanistan, Bangladesh, India, Mongolia, and Uzbekistan. In these cases, the majority of the arrows point up to the left side, confirming that COVID-19 and AQI have an antiphase relationship, and COVID-19 is leading (in simple terms, COVID-19 has a negative causal influence on AQI) during the short-, medium-, and longrun period. The findings are very logical concerning polluted countries, and the COVID-19 pandemic is worsening the air quality, mainly in the polluted countries. Moreover, the findings are very interesting for the case of China and Indonesia. During the short-, medium-, and long-run period, all the arrows point down to the right side, suggesting that COVID-19 and air quality have

54  Air Pollution and COVID-19 Nexus

an in-phase relationship, and COVID-19 is leading (the COVID-19 has a positive causal influence on air quality) in China and Indonesia. For the case of Vietnam, the relationship between COVID-19 and air quality is negative because all the arrows point down toward the left side, suggesting that both variables have an out-phase relationship, and air quality is leading (air quality has a negative causal influence on COVID-19 during the short-, medium-, and long-run period). Moreover, for the case of the United Arab Emirates (UAE), the findings are mixed. In the short run period, the arrows are pointing both down to the right side and up to the left side, suggesting that COVID-19 and air quality have both cyclic and anti-cyclic effects, suggesting that COVID-19 has a positive (negative) influence on air quality during the period of February to March (May to June). Moreover, during the long run period, the arrows point down to the left side, confirming the in-phase relationship (air quality negatively influences COVID-19). On the other hand, for the cause of Nepal, most of the arrows are on the left side but on the x-axis, suggesting both variables have an anti-cyclic effect with no lead and lag effect (COVID-19 and air quality have a negative connection with each other). In general, the findings of WTC confirm that COVID-19 has a negative influence on the AQI in the case of Afghanistan, Bangladesh, India, Mongolia, and Uzbekistan. Moreover, COVID-19 has a positive influence on air quality in the case of China and Indonesia. However, the relationship between these variables is negative in the case of Vietnam, where COVID-19 is leading. For the UAE, air quality is leading, and for the case of Nepal, both variables are negative with no lead and lag effect. 3.3.2 Wavelet Granger Causality

Table 3.2 reports the results of the wavelet-based Granger causality for the ten least polluted countries. The test is used for six frequency domains (D1 to D6). The result shows no directional causal linkage between AQI and COVID-19 in all the economies for domains D1 and D2, except in Portugal. The absence of directional causal linkage between AQI and COVID-19 implies that there is no significant influence of air quality on the prevalence of COVID-19 in these countries because pollution is still low. At pollution stages, the spread of COVID-19 is minimal, but the same may not be true when air quality worsens. In the third domain (D3), wavelet causality runs from COVID-19 to AQI in Canada and Germany, but the reverse causality existed in Portugal. From the fourth (D4) to the sixth domain (D6), AQI strongly influences COVID-19 in the economy. This finding is akin to previous studies as regards the horrendous impact of air quality on COVID-19 diffusion (see Frontera et al., 2020; Bontempi, 2020; Coccia, 2020; Fattorini and Regoli, 2020; Brandt et al., 2020; Bashir et al., 2020; Muhammad et al., 2020; Frontera et al., 2020; Sicard et al., 2020; Otmani et al., 2020).

Air Pollution and COVID-19 Nexus  55

Unlike in Table 3.2, where there is no directional causal linkage between AQI and COVID-19 in all the economies for domains D1 and D2, except in Portugal, Table 3.3 reveals different directions of causality across all the country’s domains (D1–D2). At D3, AQI significantly influences COVID19 in most of the most polluted countries, except in Bangladesh and Nepal. However, in the domains D4–D6, the influence of AQI on the spread of COVID-19 became stronger. In China, for instance, the causality between AQI to COVID-19 has been significant from D1–D6. This confirms the strong link between both variables and complements the outcome of previous studies (Yongjian et al., 2020; Chen et al., 2020; Li et al., 2020; Wang et al., 2020) on the influence of air quality/pollution on the spread of COVID-19 in China.

3.4 Conclusion The visual impacts observed during the COVID-19 pandemic are only temporary. As soon as the economies open, pollution and emissions will rise. Therefore, the COVID-19 pandemic cannot possibly show a silver lining for the environment. Instead, scientists have discovered that air quality can make a person more susceptible to COVID-19 infection. To this end, we have utilized the wavelet approach to understand the causal relationship between COVID-19 and the AQI. We have compared the top ten polluted countries to the top ten least polluted countries to ascertain possible future directions these countries can take to tackle their pollution problem. Our results can be summarized as follows. First, for Estonia, Finland, Portugal, Spain, and Norway, our results suggest that if AQI decreases, COVID-19 transmission will increase. In the case of Canada, Denmark, Germany, New Zealand, and Sweden, COVID-19 negatively influences AQI. So here, if COVID-19 increases, the AQI will decrease. These are the least polluted economies. Second, in the case of the most polluted countries, five countries (Afghanistan, Bangladesh, India, Mongolia, and Uzbekistan) show that COVID-19 negatively influences the AQI. For China and Indonesia, however, COVID-19 increases air pollution. Third, the findings of wavelet-based Granger causality confirmed that the causal connection is solid and significant in the most polluted countries during the short and medium run period, whereas, during the long run period, the causal connection is significant for both groups of sample countries, which are least and most polluted countries. Therefore, in the most polluted countries, the causality is very strong in the long, short, and middle run. We have found that increased AQI decreases COVID-19 infection in the five least polluted countries. This is probably because there are other factors at play here. The AQI in these countries is so low that it does not affect COVID-19 transmission. For the other five countries, such as Canada, Denmark, Germany, New Zealand, and Sweden, however, fear of COVID-19 infection does lower the AQI. This happens because people follow government restrictions in these countries, which ultimately results

1.4349 1.1716 2.5490 1.1785 1.8386 2.4841 4.8791 2.3351 2.3377 1.2179 4.4185 1.4878 0.1514 2.3418 1.8292 10.0771a 1.4976 5.9156 1.5801 2.2815

COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19

Canada

Note: a significance level 1%, b significance level 5%.

Sweden

Spain

Portugal

Norway

New Zealand

Germany

Finland

Estonia

Denmark

D1

Associations

Countries 0.9566 1.4719 5.9711 2.0126 3.5191 1.3501 1.5024 1.9794 5.6330 4.4041 1.2309 3.0224 1.8940 6.4138 3.5063 11.3531a 2.0385 1.4481 2.1609 2.4826

D2 18.1776a 3.6105 2.6856 3.0695 0.3453 1.9834 2.3343 1.1491 6.9270a 5.4919 4.5997 1.7551 1.1304 7.2181 2.0954 11.5487a 2.9402 0.6071 1.2641 1.3437

D3

Table 3.2 Results of Wavelet-based Granger Causality Test for Least Polluted Countries

12.6445a 0.5836 9.9496a 5.2832 5.5708 3.1098 1.2891 2.0628 20.6785b 5.7833b 9.6660a 7.1605b 23.0005a 1.1212 2.6102 17.1478a 3.7004 2.4174 4.8864 2.2329

D4 19.2290a 10.1742a 27.5557a 14.2882a 6.4521b 17.0088a 16.3777a 21.7985a 9.7108a 15.9087a 14.0794a 15.0227a 16.9209a 9.6794a 1.2288 38.7319a 15.0833a 17.4087a 13.0613a 13.2884b

D5

18.8755a 20.6841a 12.1665a 13.1517a 13.6683a 18.4139a 28.7350a 18.5486a 15.6387a 9.3430a 26.5006a 25.2497a 14.7622a 32.9409a 14.3392a 9.3041a 22.2910a 15.7216a 16.6201a 10.4131a

D6

56  Air Pollution and COVID-19 Nexus

Note: a significance level 1%, b significance level 5%.

Vietnam

Uzbekistan

The UAE

Nepal

Mongolia

Indonesia

India

China

Bangladesh

11.7664 11.9675b 21.9581a 4.6808 7.1959a 9.5753a 4.8179a 7.4436b 5.9060 2.7702a 3.2806a 6.4863a 2.5684b 4.7573 3.3721 1.8915b 2.1162b 7.8151b 7.8217b 13.0697b

15.3433 6.5467b 4.2678b 1.5301 7.2473a 6.1258b 9.8891a 6.3896b 1.6625 2.1226b 4.5372b 4.0922b 29.0870a 2.5318 3.8514 2.2879b 7.6622a 4.7507b 6.1198 4.9777

COVID-19 → AQI AQI → COVID-19 COVID → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19 COVID-19 → AQI AQI → COVID-19

Afghanistan a

D2 a

D1

Associations

Countries a

13.8271 16.3798b 10.6026b 1.8458 8.9808a 6.6398a 12.3741b 11.8408a 4.1214 3.7608a 18.9575b 5.3869a 9.7229a 9.6655 5.1754a 2.2944b 7.4897b 2.6198a 6.0321b 6.2717b

D3

Table 3.3 Results of Wavelet-Based Granger Causality Test for Most Polluted Countries

b

5.9927 12.1949a 4.4107b 5.8642b 6.4738b 3.5608a 4.2744a 10.6392b 2.3533 2.8648a 3.1569b 6.7044a 4.6317b 13.7005b 2.8082b 2.4971a 5.5522b 12.3014a 12.4598b 19.5749b

D4 a

13.6598 14.0612a 9.3106a 9.5381b 13.0457a 5.6333b 10.4222a 4.1073b 1.5938 3.0592a 4.8333a 10.3240a 12.2712a 9.6307b 10.1621b 7.8897a 8.6106b 7.2655a 11.7276a 20.5113b

D5

14.9355a 14.7128a 8.0472a 10.2142a 8.3743a 15.8940a 4.8303a 21.4162a 12.3842a 6.5473a 12.6379a 6.7765a 10.2590a 16.8445a 26.5109a 24.4269a 12.3246b 26.5374a 11.6582b 30.3396a

D6

Air Pollution and COVID-19 Nexus  57

58  Air Pollution and COVID-19 Nexus

in low movement and AQI. Fear of COVID-19 infection can cause AQI to drop, which is also evident in Afghanistan, Bangladesh, India, Mongolia, and Uzbekistan. Here, the capital cities of India and Bangladesh (Delhi and Dhaka, respectively) are two of the most polluted cities in terms of air pollution. Therefore, it can be suggested that the governments should act on providing long-term, sustainable, systemic measures to counteract the problem of AQI. Firecrackers are one of the largest contributors to PM2.5. Therefore, the sale and usage of firecrackers should be banned with immediate effect for health and safety reasons. The bulk of the air pollution is vehicular in these cities, so while moves to curtail firecrackers are laudable and should definitely be encouraged, unless the surge in vehicles on the roads is stemmed, these countries will not see any real difference in air quality. Hence, any vehicles not conforming to basic emission norms should be removed. For China and Indonesia, we have found that COVID-19 increases AQI, and this can be attributed to the existing fossil fuels in these countries. For example, the Centre for Research on Energy and Clean Air (CREA) report has shown that Indonesia did not improve air quality despite the lockdown and restrictions because of coal plants (Myllyvirta et al., 2020). So, these two countries should invest further in renewable energy sources and stop subsiding the fossil fuel industry. This will positively affect the sustainable development goals because renewable energy sources are key to mitigating climate change. Future research can utilize other approaches to understand the problem of the most polluted countries. Specifically, the cases of China, India, and Bangladesh should be investigated further in order to derive policy directions for these countries, as they are the most polluted countries in the world.

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60  Air Pollution and COVID-19 Nexus http://www​.simaonlus​.it​/wpsima​/wpcontent​/uploads​/2020​/03​/COVID​_19​_position​ -paper​_ENG​.pdf (accessed on 22 May 2020). Sicard, P., De Marco, A., Agathokleous, E., Feng, Z., Xu, X., Paoletti, E., … Calatayud, V. (2020). Amplified ozone pollution in cities during the COVID-19 lockdown. Science of the Total Environment, 735, 139542. United Nations (UN). (2020). UN report finds COVID-19 is reversing decades of progress on poverty. Healthcare and Education. Available at: https://www​.un​.org​/development​/ desa​/en​/news​/sustainable​/sustainable​-development​-goals​-report​-2020​.html Wang, P., Chen, K., Zhu, S., Wang, P., & Zhang, H. (2020). Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resources, Conservation and Recycling, 158, 104814. Wu, X., Nethery, R. C., Sabath, M. B., Braun, D., & Dominici, F. (2020). Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis. Science Advances, 6(45), eabd4049. Yongjian, Z., Jingu, X., Fengming, H., & Liqing, C. (2020). Association between shortterm exposure to air pollution and COVID-19 infection: Evidence from China. Science of the Total Environment, 727, 138704.

4

The Impact of Economic Growth, International Trade, and Carbon Dioxide Emissions on Portuguese Energy Consumption Nuno Carlos Leitão, Clara Contente dos Santos Parente, and Daniel Balsalobre-Lorente

4.1 Introduction Over the past few years, energy and environmental economics have gained a dynamic in international economics. Climate change and energy efficiency have become the dominant issues in international affairs. In the 1990s, the environmental Kuznets curve (EKC) (Grossman and Krueger 1995) made it possible for economists worldwide to study the association between economic growth and polluting emissions. Although this context has been highly debated in the literature, economists have formulated and continue to develop econometric models to understand the relationship between climate change and economic growth. Most studies demonstrate that developed economies tend to be concerned with environmental issues and improvements. Indeed, the various international ecological conferences also allowed economists and policymakers to address this issue. In this context, Brown (2007) and Morris (2021) demonstrate that human environmental intervention cause climate change. For instance, environmental degradation affects society and human rights, emphasizing regions where desertification is observed or rising sea levels, which affects the human species, forcing the weakest populations to migrate. Another issue introduced by the EKC is the relationship between international trade, climate change, and energy efficiency. In this association, three schools support the relationship between international trade, energy demand, and carbon dioxide emissions. In this context, it is possible to highlight the neoclassical theory of Heckscher-Ohlin that allows us to verify that there are countries abundant in capital and others relatively abundant in the labour factor. As can be seen, countries that are abundant in capital tend to export differentiated products and, consequently, high quality. However, countries that export high-quality products are supported by intra-industry trade and monopolistic competition models (Leitão 2021; Leitão and Balogh 2020). Thus, differentiated trade will negatively correlate with climate change and greenhouse effects. However, the strong dependence of energy resources on nonrenewable energy consumption may explain the positive correlation of international trade with carbon dioxide DOI: 10.4324/9781003336563-4

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emissions or nonrenewable energy consumption. In this context, in addition to the Heckscher-Ohlin model, it is essential to highlight the pollution haven hypothesis (PHH) theory. More recently, we have seen in the literature some studies that have debated the issues of international trade with climate issues and energy consumption in the Portuguese economy (Leitão and Balsalobre-Lorente 2020; Leitão 2021; Balsalobre-Lorente et al. 2021). As can be seen, the supply of energy consumption must be improved, which is correlated with energy efficiency. However, energy production is fundamentally associated with nonrenewable energies, with particular emphasis on fossil energies, which entail higher costs of structural adjustment on the environment, namely via the intensity of energy consumption and, consequently, an increase in the carbon dioxide emissions and greenhouse effects (Liu et al. 2021; Fuinhas et al. 2017; Filipvic et al. 2015). In this context, we observe that the linkage between carbon dioxide emissions and energy consumption can be examined in the literature as a causal relationship between both variables. Furthermore, several empirical studies mention that economic growth involves the excessive use of nonrenewable energies and promotes climate change (Ozturk et al. 2010; Leitão 2015; Fuinhas et al. 2017; Leitão and Balogh 2020). In this line, Wen et al. (2021) considered the relationship between globalization, energy consumption, economic growth, and carbon dioxide emissions in South Asia. The authors demonstrated that globalization, economic development, and energy consumption encourage pollution emissions. Yang et al. (2021) evaluated the effects of green growth, renewable energy, and globalization on pollution emissions in the United States. The authors showed a causality between the explanatory variables and carbon dioxide emissions in the long run. Similar conclusions confirmed Leitão and Shahbaz’s (2013) empirical study, when the authors applied dynamic panel data for 18 countries with different development, showing that globalization and energy consumption are positively correlated with carbon dioxide emissions. Having presented the chapter’s underlying questions, this investigation seeks to contribute to the literature in three distinct ways: (1) to show recent literature; (2) to assess the impact of economic growth, international trade, and carbon dioxide emissions on energy consumption and respective energy efficiency for the Portuguese case; and (3) present a contribution to political decisionmakers in the field of economic and energy policy. The chapter is structured as follows: the literature review appears in Section 4.2; in Section 4.3, we give the methodology used, the data and the respective statistical sources, and the formulated hypotheses. Next, in Sections 4.4 and 4.5, the econometric results and the discussion of the results are demonstrated. Finally, in Section 4.6, conclusions and recommendations for economic policy emerge.

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4.2 Literature Review The efficiency of energy has been discussed in empirical studies. This section presents the linkage between international trade, economic growth, carbon dioxide emissions, and energy use. Economic activity and international trade need higher energy consumption levels in this context. Besides, increased energy consumption causes environmental damage and climate change, namely growing carbon dioxide emissions. 4.2.1 Economic Growth and Energy Use

Many studies found a positive bidirectional causality between economic growth and energy use (Tang and Tan 2012; Dritsaki and Dritsaki 2014; Mukhtarov et al. 2017; Ayinde et al. 2019; Yusoff et al. 2020; Krkosková 2021). Tang and Tan (2012) explored the linkage between electric consumption and growth, considering the Portuguese experience for the period 1974–2008. As a result, Tang and Tan (2012) confirmed the bidirectional relationship between both variables and concluded that Portugal presented higher dependence on energy consumption. The research of Dritsaki and Dritsaki (2014) considered the relationship between energy use, carbon dioxide emissions, and economic growth for three southern European countries (Greece, Spain, and Portugal) from 1960 to 2009. The authors used the cointegration panel data (fully modified least squares and dynamic least squares). The econometric results showed that economic growth and carbon dioxide emissions positively affect energy use. Thus, economic activity needs higher energy demand levels, and climate change is stimulated by nonrenewable energy use. Leitão and Balsalobre-Lorente (2020) considered the cointegration between energy use, economic growth, urban population, and carbon dioxide emissions for the Portuguese economy using the autoregressive distributed model (ARDL) and vector autoregressive (VAR) model for the period 1960–2015. The results revealed that carbon dioxide emissions, economic growth, and exports are positively correlated with energy use in the long run. The carbon dioxide emissions and economic growth of oilproducing countries in the Middle East between 1995 and 2017 were investigated by Zanjani et al. (2021). The study demonstrated that governments should implement standard policies to improve environmental and air quality. For instance, the experience of Azerbaijan was studied by Mukhtarov et al. (2017), considering VAR model for the period 1990–2015, and they found bidirectional causality between economic growth and energy consumption use. Ayinde et al. (2019) used the vector error correction model (VECM) to evaluate the case of Nigeria, and the result found a bidirectional relationship between foreign direct investment and energy consumption. However, the study does not observe a causality between trade, energy consumption, urbanization, and energy use. From a different perspective, Yusoff et al. (2020), testing the link between energy use, economic growth, and carbon

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dioxide emissions in Afghanistan, observed bidirectional causality between economic growth and energy use. The experience of EU-28 countries was considered by Filipvic et al. (2015). The authors concluded that energy prices, taxes, and economic growth aim to decrease the dependence on energy intensity. Besides, energy consumption has a positive effect on energy intensity. The Visegrad countries, namely Slovakia, Hungary, the Czech Republic, and Poland, were investigated by Krkosková (2021). The results demonstrate causality between economic growth and energy use in Slovakia, Hungary, and the Czech Republic. The correlation between financial development and energy use was considered by Lefatsa and Garidzirai (2021). They used Granger causality as an econometric strategy and an ARDL model. The empirical results demonstrated that financial development and industrialization have a unidirectional relationship with energy consumption. Furthermore, the authors found a bidirectional nexus between urbanization and energy consumption. Nevertheless, the relationship between economic growth and energy use does not present any causality between the variables. The empirical study of Vo et al. (2019) tested the role of non-renewable energy use, renewable energy, economic growth, and population on carbon dioxide emissions in countries is the Association of Southeast Asian Nations (ASEAN). Regarding the results for Indonesia, Myanmar, and Malaysia, it is possible to conclude that the relationship between economic growth and pollution emissions is inconclusive. Furthermore, energy use and population are positively associated with carbon dioxide emissions. Koengkan’s (2018) research evaluates the effects of international trade, economic growth, and financial openness on energy use by Bolivia, Columbia, Ecuador, and Peru, applying dynamic panel data. International trade and economic growth present a positive association with energy use. However, financial openness demonstrated a negative impact on energy use. The nexus of carbon dioxide emissions, energy consumption, international trade, and economic growth was considered by Wasti and Zaidi (2020) who applied Kuwait to an ARDL model. The article demonstrated that carbon dioxide emissions and energy consumption stimulate economic growth. Besides, the causality of Granger found a bidirectional causality between pollution and energy consumption and one unidirectional correlation between energy consumption and international trade. Lastly, the experience of Middle East/North African (MENA) countries was considered by Saqib (2021). The study found a unidirectional association between economic growth and energy consumption for six economies (Algeria, Kuwait, Morocco, Qatar, Saudi Arabia, and Turkey). 4.2.2 International Trade, Carbon Dioxide Emissions, and Energy Use

The issue of international trade and the energy demand, especially nonrenewable energy, can be explained through the Heckscher-Ohlin theorem and the PHH theory. Both theories explain that exports and international trade

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are associated with high levels of energy demand and, consequently, carbon dioxide emissions and global warming increase. In this context, the study by Alkhateeb and Mahmood (2019) applied to the case of Egypt demonstrates that international trade and economic growth depend on energy demand, indicating a positive impact with statistical significance between trade and energy consumption. The same is valid for economic activity and energy consumption. The association between trade, economic growth, energy consumption, and pollution emissions in the Turkish experience was investigated by Cetin et al. (2020). Using an ARDL model, they observed that economic growth and energy consumption are directly related to carbon dioxide emissions. A different perspective presents the study by Leitão (2021) when considering the effect of renewable energies and international trade on carbon dioxide emissions for the Portuguese case. Leitão’s (2021) results show that international trade via product differentiation is negatively correlated with climate change and pollution emissions. In this case, the economic theory supporting these econometric results is based on monopolistic competition, trade intensity, or intra-industry trade (Leitão and Balogh 2020). The link between international trade and energy consumption for Asian economies (Pakistan, India, China, and Bangladesh) was considered by Arif et al. (2017). Regarding the panel ARDL model results, this study demonstrates that international trade contributes to increased energy use demand. However, the authors also found that international trade encourages economic growth. Nevertheless, recent articles such as Odhiambo (2021) and Leitão and Balsalobre-Lorente (2020) defend trade openness aimed to decrease energy consumption. The Brazilian experience was investigated by Hdom and Fuinhas (2020) from 1975 to 2016, using cointegration regressions and Granger causality. The empirical results reveal that trade has a bidirectional causality with electric production, and natural gas presents a bidirectional causality with economic growth. Litavcová and Chovancová (2021) considered the investigation applied to 14 countries of the Danube regions between 1990 and 2019. The authors used the ARDL model and causality relationship between the variable’s economic growth, energy use, and carbon dioxide emissions for each country separately. The results showed a cointegration between carbon dioxide emissions, energy use, and income per capita in four countries: Austria, the Czech Republic, Slovakia, and Slovenia. Furthermore, the causality results demonstrated a bidirectional relationship in the long run between the variables used in this research for Bosnia and Herzegovina (Litavcová and Chovancová 2021:13). The correlation between trade, energy use, economic growth, and pollution emissions was reflected by Majeed and Asghar (2021) to evaluate developing countries – D8 and G7 (developed economies). These authors used a panel cointegration and causality, and the results confirmed that pollution emissions positively impact economic growth, energy use, and trade openness in D8 economies. Nevertheless, economic development and international trade promote air quality in G7 economies. In this context, Malik (2021) examined

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the association between income per capita, energy consumption, and carbon dioxide emissions in Turkey using a generalised method of moments (GMM) estimator with time series. The author formulated three equations: (1) economic growth assumes a dependent variable, (2) energy use was considered the dependent variable, and (3) carbon dioxide emissions are the dependent variable. Considering the results of the second equation, we observe that carbon dioxide emissions and economic growth positively affect energy consumption. The results demonstrate that economic growth and gas emissions are associated with high energy consumption. The Southeast Asian countries were studied by Nosheen et al. (2019) using time series (ARDL model). They found energy consumption, international trade, and economic growth affect air quality. Though, the financial development aims to decrease pollution emissions and improve the improvement of the environment. Ozturk et al. (2021) investigated the linkage between pilgrimage tourism, economic growth, and energy consumption in Saudi Arabia. The authors used cointegration regression (dynamic ordinary least squares (DOLS) and full modified ordinary least squares (FMOLS)). Considering the econometric results with DOLS, they found that energy consumption and pilgrimage tourism positively impact climate change. Besides, there is bidirectional causality between pilgrimage and economic growth and unidirectional causality between tourism and air quality. Subsequently, Hongxing et al. (2021) tested the relationship between energy use and economic growth for African blocs. They found that foreign aid, international trade, foreign direct investment, and pollution emissions positively impact economic growth.

4.3 Methodology This chapter assesses the effects of economic growth (GrossDPmp), international trade (TR), and carbon dioxide emissions per capita (CP) on energy consumption (ENC) for the Portuguese economy for the period 1970–2018, using times series, namely ARDL model and cointegration models (FMOLS, canonical cointegration regression, and DOLS model). The ARDL model and the cointegration diagnostic tests were obtained using the STATA software (Kripfganz and Schneider 2018). Cointegration models were determined using EViews software. For a better understanding of time series cointegration models, see, for example, the developments of Phillips and Hansen (1990), Park (1992), and Hamilton (1994). Regarding methodology, we started by testing the stationarity of the variables under study using the Augmented DickeyFuller test statistic (ADF). Dicker and Fuller’s (1979) argument can be presented as follows, as it is an equation of the Ar (1) type: Yt = pyt-1 + µit (1) Note that yt is the variable under study, namely energy consumption (LnENC), carbon dioxide emissions per capita (LnCP), gross domestic

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product (GDP) at market price (LnGrossDPmp), and trade openness (LnTR). Furthermore, p represents the regressors that describe the unit root. Finally, µit means the error term. In this context, when p = 1, the study variable presents unit root, i.e., the variable is not stationary. Subsequently, the following equation was estimated: LnENC = β0+β1LnCP + β2LnGrossDPmp + β3 LnTR + µit (2) Where the dependent variable is energy consumption (LnENC), and the explanatory variables are carbon dioxide emissions per capita (LnCP), GDP at market price (LnGrossDPmp), and international trade (LnTR). The random residual term assumes µit. All variables are in logarithmic form. The dependent variable is expressed in the logarithm form of energy use per capita and was collected from IEA Statistics and the World Bank data set. Following the contributions of Leitão (2021), Sukhadolets et al. (2021), Elfaki et al. (2021), and Saleem et al. (2021), Equation 2 can take the following form for the ARDL model: ∆LnENC = α0 + α1∆ LnENCt-1 + α2 ∆LnCPt-1 + α3∆ LnGrossDPmpt-1 + α4 Ln∆TRt-1 + Σnt = 1α1∆ LnENCt-1 + Σnt = 0α2∆ LnCPt-1+ Σnt = 0α3∆LnGrossDPmt-1+ Σnt = 0α4∆ LogTRt-1 + γECMt-1 + e (3) Where ∆ symbolized the operator change, the corrected error term is called ECMt-1, and the short- and long-term adjustment by γ (Leitão 2021). As analyzed by Leitão (2021), Leitão and Balogh (2020), Pearson et al. (2001), and Sukhadolets et al. (2021), the ARDL model is based on two conditions: Hypothesis zero: α0 = α1LnENCt-1 = α2LnCPt-1 = α3LnGrossDPmpt-1 = α4Ln∆TRt-1,

signifies no relationship in the long run. Hypothesis alternative: α0 ≠ α1 LnENCt-1 ≠ α2 LnCPt-1 ≠ α3LnGrossDPmpt-1 ≠ α4Ln∆TRt-1,

denotes the relationship in the long run. Finally, we present Granger causality to understand the causality between the variables under study. This methodology aims to test unidirectional causality between two variables; on the contrary, there is bidirectional causality between the variables. According to Engle and Granger (1987), to obtain the causality between the variables, an intermediate step is necessary; it is required to determine VAR and then apply the Granger causality. This investigation used this procedure through the STATA software. The hypotheses presented below accounted for the literature review realized in Section 4.2. H1: (a) There exists a positive correlation between carbon dioxide emissions and energy consumption. (b) Is there a bidirectional relationship between carbon dioxide emissions and energy consumption?

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Hypothesis 1b considers that Granger’s causality will be applied, and we are interested in understanding whether there is a linkage between the variables. LnCP – logarithm of carbon dioxide emissions per capita. Data for this variable were collected from the Carbon Dioxide Information Analysis Center and the World Bank databases. The studies by Leitão and Balsalobre-Lorente (2020), Lefatsa and Garidzirai (2021), and Litavcová and Chovancová (2021) demonstrate that excessive energy use translates into an increase in carbon dioxide emissions or vice versa. For instance, in this context, Kongkuah et al. (2021) and Amasyali and Gohary (2018) also defended this relationship between the variables. H2: Economic growth requires high levels of energy consumption. In Hypothesis 2, we admit a strong dependence on energy consumption for economic growth to occur. Numerous studies (Mukhtarov et al. 2017; Ayinde et al. 2019; Yusoff et al. 2020; Krkosková 2021; Szymczyk et al. 2021) find a positive relationship between growth and energy consumption and a causal relationship between both variables. For example, for the Portuguese experience, the studies of Shahbaz et al. (2015) and Moutinho et al. (2017) also found this assumption between the variables. LnGrossDPmp – logarithm of GDP at market price from the World Bank national accounts data and Organization for Economic Cooperation and Development (OECD) national accounts. H3: International trade is heavily dependent on energy consumption. In this hypothesis, we consider that the relationship between international trade and energy consumption is based on the logic of the Heckscher-Ohlin model and the PHH theory. Arif et al. (2017), Hdom and Fuinhas (2020), and Nosheen et al. (2019) find a positive effect of international trade on energy consumption. LnTR – logarithm of goods exports in the GDP percentage from the World Bank national accounts data and OECD national accounts.

4.4 Results In this section, we consider the empirical study. The objective will be to analyze the properties of the variables used and the impact of economic growth, international trade, and carbon dioxide emissions on energy consumption in Portugal. Furthermore, we are interested in knowing whether the explanatory variables introduced in the equation contribute to a smooth adjustment of energy consumption – that is, to a decrease in energy consumption or, on the contrary, to an increase in energy consumption. We begin the interpretation and empirical analysis of carbon dioxide emissions, economic growth, and international trade effects on energy consumption through descriptive statistics

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and correlations between variables. Subsequently, we look at unit roots using the ADF statistic. Next, the econometric model is presented with the cointegration regressions (FMOLS, canonical cointegration regression, and DOLS) and the ARDL model (Kripfganz and Schneider 2018). Both estimation methods allow the cointegration between the variables being studied to be assessed in the long term (Leitão 2021). Descriptive statistics are presented in Table 4.1. In a first analysis, the variables GDP at market price (LnGrossDPmp) and energy consumption (LnENC) are those that present the highest values for the maximum values (maximum statistic). The values skewness and kurtosis allow us to evaluate the standard distribution statistics. The results demonstrate that all the variables in studies have negative values in skewness, which reveals that the skewed has values on the left. On the other hand, it is still observed that kurtosis is positive, indicating a “heavy tail” distribution. The correlations between the variables considered in this study are shown in Table 4.2, which are based on the association between explanatory variables (carbon dioxide emissions per capita (LnCP); economic growth (LnGrossDPmp), and trade openness (LnTR)) and the dependent variable energy consumption (LnENC); we note that all explanatory variables are positively correlated with energy consumption.

Table 4.1  Descriptive Statistics Statistics

LnENC

LnCP

LnGrossDPmp

LnTR

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Probability

3.184 3.255 3.401 2.822 0.186 −0.443 1.681 0.089

0.591 0.661 0.799 0.245 0.160 −0.501 1.905 0.105

10.871 11.032 11.419 9.908 0.453 −0.488 1.921 0.115

1.799 1.789 1.937 1.577 0.086 −0.445 2.854 0.436

Note: The descriptive statistics were determined by EViews software. The variables are expressed in logarithm form.

Table 4.2 Correlations between the Variables Used in This Research Statistics

LnENC

LnCP

LnGrossDPmp

LnTR

LnENC LnCP LnGrossDPmp LnTR

1.000 0.991 0.967 0.744

1.000 0.938 0.748

1.000 0.820

1.000

Note: The correlations were determined by EViews software. The variables are expressed in logarithm form.

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The ADF test to observe whether the variables of energy consumption (LnENC), carbon dioxide emissions per capita (LnCP), GDP at market price (LnGrossDPmp), and trade openness (LnTR) have a unit root or are stationary are presented in Table 4.3. Considering the equation in levels, we can conclude that the variables of energy use (LnENC) and carbon dioxide emission per capita (LnCP) are stationary. Besides, according to the ADF test, all variables used in this investigation are integrated into the first differences. Table 4.4 gives information about lag order selection criteria. According to the results, we observe, using the Akaike information criterion (AIC), that the VAR model should be estimated at order 2. Table 4.5 also gives information about the statistics of LR (sequential modified; final prediction error (FPE); Schwarz information criterion (SC); Hannan-Quinn information criterion (HQ)). Table 4.5 presents the results with cointegration regressions to estimate the long-run effects of carbon dioxide emissions, economic growth, and international trade on energy use. The coefficient of carbon dioxide emissions per capita (LnCP) positively affects energy consumption. The variable is statistically significant at a 1% level, showing that pollution emissions are associated with energy consumption. The studies of Mukhtarov et al. (2017) and Koengkan (2018) support our result. In addition, previous studies carried out for the Portuguese economy, such as Shahbaz et al. (2015), Leitão (2015), Moutinho (2015), Leitão and Balsalobre-Lorente (2020), found a bidirectional relationship between carbon dioxide emissions and energy consumption. The economic growth (LnGrossDPmp) is positively correlated with energy use, and the variable is statistically significant at a 1% level. Therefore, according to the literature review (Ayinde et al. 2019; Yusoff et al. 2020; Krkosková 2021), economic activity needs higher energy use. The coefficient of international trade (LnTR) positively impacts energy use, showing that this result is described by the Heckscher-Ohlin model and the PHH theory. In addition, the previous studies of Alkhateeb and Mahmood (2019) and Hdom and Fuinhas (2020) found a positive association between international trade and energy use. The results with the ARDL model are presented in Table 4.6. The coefficients are similar to the short and long-run effects: the carbon dioxide emissions Table 4.3 Unit Root Test: ADF Criteria ADF Variables

Level t-Statistic and Prob.

First Difference t-Statistic and Prob.

LnENC LnCP LnGrossDPmp LnTR

−2.639* (0.093) −2.601* (0.099) −2.062 (0.2601) −1.248 (0.646)

−5.729***(0.000) −6.222*** (0.000) −4.361*** (0.001) −6.641***(0.000)

Note: p-value is in parentheses; *** (1%), and * (10%). The unit roots test was determined by EViews software. The variables are expressed in logarithm form.

247.3952 403.9427 420.3288 429.9804

0 1 2 3

NA 276.6887* 25.91294 13.46735

LR 1.42e-10 2.07e-13* 2.08e-13 2.94e-13

FPE

Note: The lag length criteria were determined by EViews software.

LogL

Lag

Table 4.4 Lag Length Criteria

-11.32071 -17.85780 -17.87576* -17.58049

AIC -11.15687 -17.03864* -16.40127 -15.45066

SC

-11.26029 -17.55572* -17.33201 -16.79507

HQ

The Impact of Economic Growth  71

72  The Impact of Economic Growth Table 4.5 The Impact of Economic Growth, International Trade, Carbon Dioxide Emissions on Portuguese Energy Use with Cointegration Regressions Variables

Fully Modified Least Squares Model

Canonical Cointegration Regression

Dynamic Least Squares

LnCP LnGrossDPmp LnTR Constant

0.101*** (0.000) 0.830*** (0.000) 0.146*** (0.001) 1.354*** (0.000)

0.100*** (0.000) 0.829*** (0.000) 0.149*** (0.002) 1.350*** (0.000)

0.101*** (0.000) 0.835*** (0.000) 0.161*** (0.000) 1.318*** (0.000)

Note: p-value is in parentheses; *** (1%). The cointegration regressions were determined by EViews software. The variables are expressed in logarithm form.

Table 4.6 The Impact of Economic Growth, International Trade, and Carbon Dioxide Emissions on Portuguese Energy Use with ARDL Model Variables

ADJ

LnENCt-1 LnCP LnGrossDPmp LnTR Variables LLP D1 LnGrossDPmp D1 LD Constant Observations Adj. R2

−0.625***(0.000)

Long Run 0.823*** (0.000) 0.097* (0.051) 0.108*** (0.000) Short Run 0.206* (0.092) 0.065** (0.049) −0.988*** (0.004) 0.854*** (0.000)

42 0.84

Note: p-value is in parentheses; *** (1%), and * (10%). The ARDL model was determined by STATA software. The variables are expressed in logarithm form.

(LnCP) variable is directly associated with energy demand, and pollution emissions correlate with energy use intensity. For instance, the empirical studies of Vo et al. (2019) and Leitão and Balsalobre-Lorente (2020) support this relationship between carbon dioxide emissions and energy demand. We observe that the adjustment coefficient (ADJ) negatively affects energy use, and the lagged variable is statistically significant at a 1% level. Furthermore, the empirical studies of Lefatsa and Garidzirai (2021), Odhiambo (2021), and Leitão and Balsalobre-Lorente (2020) also found a negative adjustment for energy consumption. In this context, we reflect on the adjustment in energy demand and observe a smooth adjustment of energy use. The income per capita is directly correlated with energy consumption and carbon dioxide emissions based on EKC postulations. For example, Shahbaz et al. (2015) and Moutinho et al. (2017) support the result.

The Impact of Economic Growth  73

Regarding the relationship between international trade (LnTR) and energy demand, we observe a positive effect, and the international trade is statically significant at a 10% level in the short run. This result is according to the studies of Koengkan (2018), Nosheen et al. (2019), and Hdom and Fuinhas (2020). Table 4.7 demonstrates a long-run association between the variables in this study when we consider Kripfganz and Schneider’s (2018) arguments and the ARDL bounds test (Leitão 2021; Lefatsa and Garidzirai 2021; Leitão and Balsalobre-Lorente 2020). The following tables allow us to analyze the diagnostics of autoregressive distributed lag. Thus, in Tables 4.8 and, 4.9 it is observed that there are no serial correlation problems. The application of the white test allows us to determine that the obtained value (0.450) is accepted for homoscedasticity (Table 4.10). The VAR model determined Granger causality statistics. Table 4.11 demonstrates a unidirectional relationship between energy consumption (LnENC) and carbon dioxide emissions per capita (LnCP). It also verifies that there is a unidirectional relationship between international trade (LnTR) and carbon dioxide emissions per capita (LnCP).

Table 4.7 The Impact of Economic Growth, International Trade, Carbon Dioxide Emissions on Portuguese Energy Use with ARDL and Bound Test Tests

10% I (0)

10% I (1)

5% I (0)

5% I (1)

1% I (0)

1% I (1)

P-value I (0)

P-value I (1)

F T

2.880 −2.55

4.051 −3.44

3.507 −2.89

4.826 −3.83

4.984 −3.59

6.631 −4.60

0.005 0.001

0.025 0.018

Note: The diagnostic ARDL bounds test was determined by STATA software.

Table 4.8 The Diagnostic ARDL: The Durbin-Watson Durbin-Watson d-statistic (8.42) = 2.184 Note: The diagnostic ARDL was determined by STATA software.

Table 4.9 The Diagnostic ARDL: Autocorrelation Test Bresch-Godfrey LM Prob > Chi2 = 0.336 Note: The diagnostic ARDL was determined by STATA software.

74  The Impact of Economic Growth Table 4.10 The Diagnostic ARDL: Homoskedasticity Versus Heteroskedasticity White Test Prob > Chi2 = 0.4501 Note: The diagnostic ARDL was determined by STATA software.

Table 4.11 Granger Causality Wald Test Equation

Excluded

Chi2

Prob

LnENC LnEC LnENC LnENC LnCP LnCP LnCP LnCP LnGrossDPmp LnGrossDPmp LnGrossDPmp LnGrossDPmp LnTR LnTR LnTR LnTR

LnCP LnGrossDPmp LTR All LnENC LnGrossDPmp LnTR All LnENC LnCP LTR All LnENC LnCP LnGrossDPmp All

223.16*** 5.8085* 7.759** 1131.4*** 0.132 0.335 0.719 92.785*** 83.613*** 258.84*** 6.7391*** 708.66*** 19.434*** 48.393*** 7.9836** 117.45***

(0.000) (0.055) (0.021) (0.000) (0.936) (0.845) (0.698) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.018) 0.000)

Note: p-value is in parentheses; *** (1%), and * (10%). The Granger causality was determined by STATA software. The variables are expressed in logarithm form.

Regarding the bidirectional relationship, we can refer to the link between energy consumption (LnENC) and the GDP market price (LnGrossDPmp). The empirical studies of Dritsaki and Dritsaki (2014), Mukhtarov et al. (2017), and Krkosková (2021) also found a bidirectional relationship between the variables demonstrating that economic growth needs energy sources. Moreover, numerous studies have pointed to a similar conclusion over the past few years. So then, our results corroborate with the formulated hypothesis and numerous previous studies. We also observe the bidirectional association between energy consumption (LnENC) and international trade (LnTR). Odhiambo (2021), Wang et al. (2021), and Marques et al. (2017) give support to our result. Once again, our results are supported by the Heckscher-Ohlin model and demonstrate that export capacity is associated with the theory of advantages. In addition, the bidirectional relationships between economic growth (LnGrossDPmp) and international trade (LnTR). The empirical studies of Khan et al. (2018) and Intisar et al. (2020) also reflected a bidirectional causality between the variables. Thus, theories of economic growth demonstrate that international trade can promote economic growth. Literature evidence that

The Impact of Economic Growth  75

numerous scientific articles validate this result. For instance, Elfaki et al. 2021 and Nguyen and Bui (2021) are in line with this permission.

4.5 Conclusions In this section, we reflect on the results of the empirical study. Then, we refer to recommendations for energy and economic policymakers and present clues for future work. The results obtained in the empirical study demonstrate that the variables used are integrated into the first differences when applying the unit root tests. Besides, it has also been observed that the variable energy used (LnENC) and carbon dioxide emissions (LnCP) are stationary at levels with the application of the ADF. The various tests used in this research show that the variable energy consumption, economic growth, international trade, and carbon dioxide emissions are cointegrated in the long term. Regarding the model estimated with cointegration regressions and the ARDL model, it is observed that carbon dioxide emissions are associated with energy use, i.e., carbon dioxide emissions promote high levels of nonrenewable energy consumption. Besides, international trade and economic growth cause high levels of energy consumption. However, the ARDL model allows us to verify that the adjustment costs via energy consumption tend to decrease. This result showed that Portuguese energy consumption is inclined to be a smooth adjustment cost. The empirical studies of Lefatsa and Garidzirai (2021), Odhiambo (2021), and Leitão and Balsalobre-Lorente (2020) also found a similar result. The results obtained by the Granger methodology demonstrate a bidirectional relationship between energy consumption variables and economic growth. Furthermore, there is a bidirectional causality between energy consumption and international trade. Lastly, there is a bidirectional association between economic growth and international trade. The results presented are supported by recent reports and energy balances prepared by the General Directorate of Energy and Geology of the Portuguese Economy (DGEG). DGEG demonstrates that primary energy consumption in the Portuguese economy is fundamentally derived from oil and derivatives, natural gas, and biomass. According to the reports, the past few years have seen a decline in coal. These energy sources are highly harmful to public health and put environmental health into question, particularly with greenhouse effects. The same source demonstrates that Portugal’s energy dependence from 2010 onward is above 75%, a situation that remains until 2018. Furthermore, from 2005 to 2013, energy imports decreased; however, from 2013 to 2018, there was a tendency for energy imports to increase. Several empirical studies (Busu 2020; Leitão and Balsalobre-Lorente 2020; Hdom and Fuinhas 2020; Leitão 2021) demonstrate that renewable energies contribute to economic growth and, consequently, reduce the greenhouse effect. Additionally, as referred to in the literature, it should be noted that the Kyoto Protocol (1997), Paris Agreement (2015), and the Directive

76  The Impact of Economic Growth

2009/28/EC contributed to the development and implementation of renewable energies in the economy and society to achieve sustainable development. Therefore, the Portuguese economy should reduce its dependence on the external energy supply. In this context, the Portuguese energy policy should continue implementing renewable energies. Parallel to this issue, it will be necessary to create infrastructure and public policies that cooperate closely with the private sector, as it involves large public and private capital investments. In terms of clues for future work, it is essential to mention that it would be interesting to introduce other independent variables in the estimated model, such as globalization, energy intensity, and human development index, to understand how these variables allow for better energy demand. Following the dominant hypothesis of international trade theories, the liberalization and increased transactions in goods and services allow for better energy demand. Still, carbon dioxide emissions and greenhouse effects tend to increase. Another issue that seems interesting to us to investigate is the transition to the digital economy and its relationship with decarbonization – these two axes being associated with sustainable finance. Furthermore, the pandemic (COVID-19) accelerated the digitalization process in the world economy, and society became aware of the importance of decarbonization. In this context, the European economy has done some work, but it is still a recent matter regarding the digital transition with the introduction of the digital program. Regarding the economic policy recommendations and policymakers, we can affirm that continuing less polluting energy resources and cleaner practices and replacing these more polluting resources and their transition to the digital economy will allow sustainable economic development. However, as we have already mentioned, it is still necessary for cooperation between public policies and the business fabric with the objective of cleaner innovation in terms of energy policy.

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5

Renewable Energy, Carbon Emissions, and Economic Growth The Comparison between EKC and RKC Congyu Zhao, Xiucheng Dong, and Kangyin Dong

5.1 Introduction The past two decades have witnessed fast-paced economic development, with its gross domestic product (GDP) rising from $33,650 billion in 2000 to $84,710 billion in 2020 (World Bank Database). Simultaneously, the total energy consumption has soared, leading to great global carbon dioxide (CO2) emissions. Specifically, global CO2 emissions reached more than 32,284.1 million tonnes in 2020, 1.35 times the level in 2000 (BP, 2021). Afterward, the global warming problem gets people’s attention due to large CO2 emissions. Many international cooperation frameworks have been formed, including “the Kyoto Protocol” and “the Paris Agreement on Climate Change”. More importantly, the carbon neutral and carbon peaks have also been put forward by many countries in the world. Nevertheless, how to fulfil the goal of dual carbon targets still needs further exploration. The world’s primary energy consumption in 2000 was 394.47 exajoules, more than 150 times the consumption of renewable energy (BP, 2021). Even in 2020, fossil fuel still plays a dominant role in energy supply and consumption (Yao et al., 2019). Renewable energy has low or even zero carbon emissions compared with fossil fuels, and using renewable energy to replace fossil fuels effectively reduces carbon emissions (EIA, 2019). Therefore, the specific global warming mitigation effect of energy structure transformation and renewable energy consumption (REC) is worth exploring. In addition, some developing countries may not advance renewable energy power generation and storage technology. As a result, the relationship between REC and CO2 may differ in different countries and, thus, need further verification. The economy’s development may have a positive relationship with energy consumption and, thus, contribute to the increasing CO2 emissions. However, Grossman and Krueger (1991, 1995) pointed out that the economic development–CO2 emissions nexus may be non-linear, the so-called environmental Kuznets curve (EKC) hypothesis. In addition, three effects, namely scale, structure, and technology, were proposed accordingly (Panayotou, 1993). Similarly, rising traditional energy consumption (i.e., coal, petroleum, and gas) is a direct result of economic growth but is it true that the economy will always DOI: 10.4324/9781003336563-5

82  Renewable Energy, Carbon Emissions, and Economic Growth

lead to more primary energy consumption? Due to the technological advancement and energy transition, is it possible that economic development brought about an initial phase of REC reduction followed by a subsequent phase of improvement? In other words, is the economic development–REC nexus also non-linear, and does the renewable Kuznets curve (RKC) hypothesis exist? Apart from this, the difference between turning points in EKC and RKC is uncertain and, thus, requires empirical analyses. Therefore, several questions have ignited our interest: (1) as a kind of clean energy, can the consumption of renewable energy achieve a win-win situation of energy structure transformation and CO2 emissions reduction? (2) Does economic development have an impact on renewable energy? Is their relationship linear or non-linear? (3) Does economic development have the same effect on CO2 and REC? (4) Do heterogeneous effects exist in different regions? To answer this question, we tend to study the impact of REC on CO2 emissions within the framework using a dynamic panel model. Furthermore, we also reveal the economic development–REC nexus following EKC analysis based on the data of all countries and regions worldwide. We also explore the difference between EKC and RKC and discuss heterogeneous effects. This study, therefore, contributes to the emerging literature in the following three aspects. First, current literature on EKC mainly focuses on the curve’s shape, while the role of REC is often ignored. A few papers have considered the REC–CO2 nexus, but only this study considers all countries and regions worldwide. Therefore, we help expand the scope of EKC research. Second, we reveal the non-linear relationship between REC and economic development and compare the turning points in EKC and RKC. These analyses help to understand how the energy consumption structure transforms and provide a reference for the government to mitigate climate warming further. Third, this chapters analyzes the asymmetry and heterogeneity of EKC and RKC in different countries and regions, which helps analyze the specific environmental pollution and energy consumption structure from the degree of economic development in developing and developed countries. The remainder of this study is organized as follows. Section 5.2 reviews the related literature. Section 5.3 presents the econometric model and data. Section 5.4 reports the estimation procedures and empirical results. Section 5.5 further compares the turning points in EKC and RKC and discusses the asymmetry and heterogeneity. Section 5.6 concludes this study and provides several policy recommendations.

5.2 Literature Review 5.2.1 Studies on the REC–CO2 Emissions Nexus

In recent years, a growing body of scholars has shed light on the impact of REC on CO2 emissions. Using a panel data set for China covering 1993–2016, Dong et al. (2018) investigated the dynamic causal links among CO2, gross domestic product (GDP), and REC and found that nuclear energy and renewable energy

Renewable Energy, Carbon Emissions, and Economic Growth  83

play important roles in mitigating CO2 emissions. Additionally, according to Ridzuan et al. (2020), renewable energy, urbanization, and agricultural subsectors were presented as important factors affecting CO2 emissions in Malaysia for the period 1978–2016. Based on the autoregressive distributed lag (ARDL) approach, Sinha and Shahbaz (2018) have explored the influencing factors of CO2 emissions in India from 1971 to 2015 and concluded that renewable energy and trade have a significant negative impact on CO2 emissions. The same result was reported by Jebli and Youssef (2015). In addition, Bölük and Mert (2015) paid attention to the case of Turkey, while Sugiawan and Managi (2016) studied the situation in Indonesia. More studies have focused on a particular region, such as Europe or Africa (Al-Mulali et al., 2016; Jebli et al., 2016; Bilgili et al., 2016). To be more specific, Yao et al. (2019) explored the role of renewable energy in reducing global CO2 emissions in the case of 17 major developing countries as well as six regions of the world from 1990 to 2014 and concluded that a 10% rise in REC rate would lead to a 1.6% CO2 reduction. When it comes to developing countries, Zoundi (2017) utilized the approach of panel cointegration to investigate the impact of renewable energy on CO2 emissions for 25 selected African countries, and their empirical results showed that renewable energy has a significant negative effect on CO2 emissions both in the short and long run. Similarly, Liu et al. (2017) also considered organizations with developing countries (e.g., the Association of Southeast Asian Nations). This assertion was supported by many other scholars who have also conducted an analysis based on the European Union. López-Menéndez et al. (2014) also examined the impact of renewable energy on CO2 emissions based on the data for 27 countries of the European Union during the period 1996–2010, and the results implied that an extended EKC exists. Their findings were consistent with the conclusions of Bölük and Mert (2014) and Ma et al. (2021). 5.2.2 Studies on the RKC Hypothesis

The impact of economic development on REC has attracted the attention of numerous researchers in the past few decades. Many scholars believe economic development is conducive to the consumption of renewable energy. Specifically, based on the panel data from 1997–2017 at the national and regional levels of China, Wang et al. (2021) revealed that economic growth stimulates REC, while financial development impacts it negatively. The same result was reported by Eren et al. (2019), whose conclusion suggested statistically significant and positive impacts of economic growth and financial development on REC in the case of India. Their findings were supported by Alam and Murad (2020). However, other researchers reached an opposite conclusion based on empirical results, which indicate that economic development does not reduce energy consumption from fossil sources (Alvarado et al., 2021). In addition, according to Sharma et al. (2021), the results showed a two-way positive relationship

84  Renewable Energy, Carbon Emissions, and Economic Growth

between economic growth and non-renewable energy and a two-way negative relationship between economic growth and renewable energy. Moreover, many scholars claimed that the nexus between economic development and REC was non-linear, thus detecting a U-shape relationship (Destek and Sinha, 2020). For example, Simionescu (2021) confirmed the U-shaped RKC at the national level and for agriculture in the sample countries. Ahmad et al. (2021) detected a similar finding that economic development mitigated the non-renewable energy use intensity (inverted U-shaped curve) in the national data set and the eastern economic region of China. 5.2.3 Literature Gaps

Based on the above analysis (Sections 5.2.1 and 5.2.2), there are still some research gaps. First, there has not been a consensus among scholars on whether the economic development–REC nexus is linear or non-linear; however, evaluating the impact of economic development on REC is meaningful and can provide some reference for energy structure transformation. Second, the comparison between GDP–CO2 and GDP–REC has not been involved in existing research; understanding the difference between the impact of economic development on environmental pollution and REC is of great significance. Third, scant attention has been given to the asymmetric and heterogeneous impact in different regions and countries.

5.3 Econometric Model and Data 5.3.1 Econometric Model

We first explore the impact of REC on CO2 emissions based on the EKC hypothesis. Thus, we introduce CO2 emissions as the explained variable and REC, and the economic development and its quadratic term are core explanatory variables. We also take the population and the industry development into consideration. Furthermore, we adopt a dynamic panel model. The empirical equation in the EKC model can be represented as follows:

CO2it = f (CO2i ,t -1, Pgdpit , Pgdpit 2 , REC it , Popit , Indit ) (1)

In addition to the EKC hypothesis, we also pay attention to the RKC hypothesis. In this estimation, the REC is used as an explained variable, while economic growth and its quadratic term are core explanatory variables. Moreover, the control variables remain unchanged (i.e., population and industrial development). The dynamic panel model is also used to evaluate the impact of economic development on REC. The empirical equation in the RKC model can be represented as follows:

Renewable Energy, Carbon Emissions, and Economic Growth  85



REC it = f ( RECi ,t -1, Pgdpit , Pgdpit 2 , Popit , Indit ) (2)

where i shows the countries and regions in our sample (i = 1, 2,¼, 266) and t represents the time span (2000–2020). CO2 represents CO2 emissions, Pgdp presents the per capita GDP in each country and region; REC represents the portion of REC in the total amount of energy consumption, Ind indicates the percentage of value added in the industry (including construction) on total GDP, and Pop represents the population in each country and region. To eliminate possible heterogeneity in the empirical study, logarithms of all the variables are adopted to improve the estimation accuracy. The estimation models in the EKC hypothesis and RKC hypothesis can be rewritten as follows: ìln CO2it = a 0 + a1 ln CO2i ,t -1 + a 2 ln Pgdpit + a 3 (ln Pgdpit )2 + a 4 ln REC it ï 6 ï ï + a i ln X it + e it ïï i =5 (3) í 2 ïln REC it = b0 + b1 ln RECi ,t -1 + b 2 ln Pgdpit + b3 (ln Pgdpit ) ï 5 ï + bi ln X it + V it ï ïî i =4

å

å

where a 0 and e it represent the intercept term and random disturbance term in the EKC model, respectively. While b0 and V it represent the intercept term and random disturbance term in the RKC model, respectively. The parameters a i (i=2,...,6) denote the estimated coefficients in the EKC model and the estimated coefficients in the RKC model. CO2 is our dependent variable in EKC and REC indicates our dependent variable in RKC. The control variables are shown in vector X. 5.3.2 Measurements of the Variables and Data Sources

To assess the EKC hypothesis and RKC hypothesis in the whole world for the period 2000–2020, we find our data from the World Bank (World Bank Database). The CO2 and REC are the main objects that we study, in which CO2 means CO2 emissions in metric tons per capita, and REC denotes the percentage of REC of total final energy consumption. We include the gross domestic product per capita (current US$) of each country and region in the world, which is denoted by Pgdp . Moreover, to study whether there is a non-linear relationship between economic development and environmental pollution, this chapter also considers the quadratic term of Pgdp after logarithm. In addition, Ind is the percentage of value added in the industry (including construction) to the total GDP. And Pop refers to

86  Renewable Energy, Carbon Emissions, and Economic Growth

the total population in each sample country and region. Table 5.1 shows the descriptive statistical information of all the variables.

5.4 Estimation Procedures and Empirical Results 5.4.1 Cross-sectional Dependence Tests

Cross-sectional dependence tests can help improve the reliability and consistency of the estimation results (Grossman and Krueger, 1995). Therefore, conducting cross-sectional dependence tests before the impact analysis is necessary. Thus, the Breusch-Pagan LM is conducted in our study (Breusch and Pagan, 1980). The results of the three tests are shown in Table 5.2. The result of the EKC hypothesis is 15,380.04, and this coefficient is significant at the 1% significance level. So, the null hypothesis is rejected. Because the null hypothesis is that there is no cross-sectional dependence in the data, this result means there is cross-sectional dependence. Similarly, in the cross-sectional dependence test in the RKC model, the result is 24,344.07, and this coefficient is also significant at the 1% significance level. Therefore, we should be more cautious when selecting an estimation model to evaluate the hypothesis of EKC and RKC. 5.4.2 Baseline Regression of EKC

To explore the impact of REC on CO2 emissions based on the EKC hypothesis, it is necessary to adopt an appropriate estimation model to improve the accuracy of the estimation. Endogeneity may exist in this study because we aim to evaluate the impact of REC on CO2 emissions, and the two-way causality can be a problem. To be more specific, CO2 emissions may also exert an effect on the REC. Because of climate change and increased greenhouse gas, more and more countries are raising their concerns about dealing with a large amount of CO2 emissions and are, therefore, increasing the proportion of renewable energy they consume. In this situation, traditional panel methods such as ordinary least square (OLS), fixed effect model (FE), and random effect Table 5.1 Descriptive Statistics of the Variables (after the Logarithm) Variable

Obs.

Mean

Std. dev.

Min

Max

lnCO2 lnrecon lnPgdp lnPgdp2 lnInd lnPop

4522 4569 5286 5286 4887 5556

0.6615 2.6645 3.8646 17.2884 3.2011 −0.0730

1.4887 1.7522 1.5343 12.2326 0.4546 3.0491

−4.1158 −7.6009 −0.0995 0.0000 1.1475 −6.9705

3.8649 4.5885 7.5523 57.0373 4.4750 6.6532

Notes: Obs. stands for the observations of the variables, Mean refers to the average value of the variables, Std. Dev. represents standard deviation, Min and Max indicate the minimum and maximum values of the variables, respectively.

Renewable Energy, Carbon Emissions, and Economic Growth  87 Table 5.2 Results of the Cross-sectional Dependence Tests Test

Statistics

Prob.

Breusch-Pagan LM test for EKC hypotheses Breusch-Pagan LM test for RKC hypothesis

15380.04*** 24344.07***

0.000 0.000

Note: *** represents significance at the 1% level.

model (RE) may not be accurate enough to estimate the nexus between REC and CO2 emissions. In addition, because our data have cross-sectional correlations, more advanced estimation methods are required. For the dynamic panel, the differential generalized method of moments (DIF-GMM), proposed by Arellano and Bond (1991), as well as the system generalized method of moments (SYS-GMM), proposed by Arellano and Bover (1995) and Blundell and Bond (1998), are suitable methods. Therefore, the DIF-GMM and the SYS-GMM methods are used to benchmark our estimation in the EKC and RKC hypotheses. The Arellano-Bond (A-B) and Sargan tests are indispensable (Che et al., 2013; Roodman, 2009) in the model of SYS-GMM. More specifically, the A-B test mainly addresses the correlation of different disturbance terms and is used to test the existence of weak instrumental variables in the estimation. The results are shown in Table 5.3. As Table 5.3 shows, the results of AR (1) are significant, while the results of AR (2) are insignificant, and the results of the Sargan test are also insignificant, which means there is no weak instructor problem. In addition, we also consider the OLS, FE, and RE to test the robustness of the estimation results. In addition, the estimated coefficients and significance levels of different models are generally consistent, so the results of this chapter are reliable. In terms of the independent variable, namely economic development and its quadratic term, as well as the REC, the coefficients of the above three variables are significant at the 1% level, which means that economic development has a non-linear effect on CO2 emissions, and the nexus of REC and CO2 emissions also exists. More specifically, in the results estimated by the DIF-GMM method, the coefficient of the primary term of Pgdp is positive. In contrast, the coefficient of the quadratic term of Pgdp is negative, indicating a significant inverted U-shaped relationship between the level of economic development and CO2 emissions. In other words, when economic development is in its initial stage, CO2 emissions increase with the economy’s growth. However, after economic development passes its turning points, a negative correlation occurs between the above two variables, and economic development can significantly inhibit CO2 emissions. Another thing worth mentioning is that there is a turning point at which CO2 emissions reach their maximum. Regarding the REC–CO2 emissions nexus, when REC increases by 1%, CO2 emissions will decrease by 0.2125%, meaning that enhancing REC and total energy consumption can effectively reduce CO2 emissions. This may be

88  Renewable Energy, Carbon Emissions, and Economic Growth Table 5.3 Results of EKC in the World Variable

Static panel estimation OLS

lnCO2i,t -1 – lnrecon lnPgdp lnPgdp2 lnInd lnPop _ Cons AR(1) AR(2) Sargan

−0.2401*** (−37.5641) 1.3789*** (45.9599) −0.0885*** (−23.1477) 0.2958*** (11.8500) 0.0507*** (13.2285) −3.3443*** (−35.5745) – – –

Dynamic panel estimation

RE

FGLS

DIF-GMM

SYS-GMM

–

–

−0.1825*** (−23.6638) 0.4608*** (23.9528) −0.0386*** (−14.6630) 0.1230*** (6.2622) 0.0511 (1.5117) −0.3747*** (−5.2140) – – –

−0.2401*** (−37.5641) 0.4451*** (24.1254) −0.0295*** (−10.9254) 0.1600*** (7.7413) 0.0187* (1.6522) −0.4523*** (−5.4290) – – –

0.2398*** (8.3521) −0.2125*** (−8.1570) 0.2139*** (6.0427) −0.0183*** (−4.3584) 0.0563*** (2.9858) 0.1123 (1.4629) 0.2535* (1.8944) 0.0033 0.8205 0.9808

0.8100*** (47.5143) −0.1213*** (−14.4055) 0.0633*** (3.2341) −0.0017 (−0.7679) 0.0443*** (2.7782) 0.0339*** (6.2107) 0.0832 (1.2112) 0.0004 0.9286 0.9803

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

because, compared with fossil energy, renewable energy has the advantage of zero emissions. Dong et al. (2018) also reached a similar conclusion: increased renewable energy intensity reduces CO2 emissions. As for the control variables (i.e., Ind and Pop ), both the impact of industrial development and population growth on CO2 emissions are positive. To be more specific, an increase of Ind by 1% can increase CO2 emissions by approximately 0.0443%. Meanwhile, an increase of Pop by 1% can increase CO2 emissions by approximately 0.0339%. Thus, the acceleration impact of the industry is relatively larger than that of the population, implying that the development of secondary and tertiary industries plays a more important role in increasing CO2 emissions worldwide. The possible reason is that industrial production consumes a large amount of energy and releases polluting gases and wastewater. In addition, the increase in population will lead to increased production and living activities, leading to increased CO2 emissions. This result is consistent with Abokyi et al. (2019) and Wu et al. (2021). 5.4.3 Baseline Regression of RKC

Similar methods are used to test the RKC hypothesis; the results are shown in Table 5.4. In terms of the independent variable, both the coefficients of the

Renewable Energy, Carbon Emissions, and Economic Growth  89 Table 5.4 Results of RKC in the World Variable

Static panel estimation OLS

lnreconi,t -1 – lnPgdp lnPgdp2 lnInd lnPop _ Cons AR(1) AR(2) Sargan

−0.8657*** (−11.5032) 0.0425*** (4.4168) −0.5863*** (−9.8871) 0.1268*** (13.3988) 7.0779*** (36.9090) – – –

Dynamic panel estimation

RE

FGLS

DIF-GMM

SYS-GMM

–

–

−0.4771*** (−12.1525) 0.0691*** (13.0510) −0.2254*** (−5.6218) 0.1036 (1.4685) 4.0491*** (30.6562) – – –

−0.4940*** (−13.8558) 0.0663*** (12.8641) −0.2385*** (−6.0508) 0.2166*** (6.9627) 4.0322*** (24.1483) – – –

0.6119*** (12.7265) −0.1059*** (−3.3639) 0.0118** (2.3690) −0.0193 (−0.6538) 0.0547 (1.2748) 1.3232*** (7.6324) 0.0922 0.1795 0.9938

0.9683*** (146.9629) −0.0431*** (-3.2694) 0.0069*** (3.1453) −0.0050 (−0.2163) 0.0268*** (3.4826) 0.1455* (1.6610) 0.0815 0.1886 0.9926

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

economic development and its quadratic term are significant at the 1% level, which means that the nexus between economic development and REC is non-linear. To be more specific, in the results estimated by the DIF-GMM method, the coefficient of the primary term of Pgdp is negative, while the coefficient of the quadratic term of Pgdp is positive, indicating a U-shaped relationship between the level of economic development and REC. In other words, when economic development is in its initial stage, REC decreases with the growth of the economy. However, after economic development passes its turning points, a positive correlation occurs between the above two variables, and economic development can significantly enhance the utilization of renewable energy. Another thing worth mentioning is that there is a turning point at which REC reach its minimum value. The possible reason is that in the initial stage of economic development, the production cost of new renewable energy is higher than that of fossil energy, and technical bottlenecks also inhibit renewable energy utilization. Meanwhile, the production capacity of traditional renewable energy is very limited, resulting in the increasing proportion of fossil energy in economic development. As industrialization accelerates, the utilization rate of renewable energy remains stable in the long run. Finally, with the improvement of economic development and technological bottleneck breakthrough, the renewable energy utilization rate shows a rising trend. Sharma et al. (2021) and Wang et al. (2021) also reach a similar conclusion.

90  Renewable Energy, Carbon Emissions, and Economic Growth

As for the control variables (i.e., Ind and Pop ), the population can exert a positive effect on REC. More specifically, an increase of Pop by 1% can increase REC by approximately 0.0268%. However, industry development shows no significant impact on REC. These results are consistent with the findings of Gargallo et al. (2020).

5.5 Further Discussion 5.5.1 The Comparison between EKC and RKC

The previous results show that the PGDP–CO2 emissions nexus in EKC and the PGDP–REC nexus in RKC are non-linear. Thus, it is important to investigate the difference and the relationship between the curve of EKC and RKC. Because both EKC and RKC have their turning point, do they have the same turning point? Do the two turning points have a sequence relationship regarding the level of economic development? To answer these questions, we calculate their turning point respectively according to Eq. (3). To be more specific, the turning point in the EKC model is a2 = - ( 0.2139 / ( -0.0183*2 ) ) = 5.84426, while the turning point in the 2 * a3 b RKC model is - 2 = - ( -0.1059 / ( 0.0118*2 ) ) = 4.487288. In addition, the 2 * b3 comparison of EKC and RKC in the world is shown in Figure 5.1. By comparing the above turning points, it is easy to find that the turning point in RKC is prior to that in EKC. In other words, only by adjusting the energy structure and development mode of a country’s economy can EKC cross the turning point as soon as possible and achieve low-pollution

Figure 5.1 The comparison of EKC and RKC in the world.

Renewable Energy, Carbon Emissions, and Economic Growth  91

growth. When the economy develops to a certain stage, the energy consumption structure can be adjusted and upgraded first, and then the CO2 emissions can be further reduced based on the advanced economy and intensive industry development mode. Therefore, we believe that economic growth and the transformation of energy consumption structure have provided the foundation for reducing environmental pollution. In addition, it should be noted that the difference between the two turning points is small, indicating that when RKC reaches the turning point, the economic development level of a country has entered the structural effect stage in the EKC curve, and, thus, the turning point in the EKC curve is coming soon. This conclusion is of great policy significance: to cross the turning point of CO2 emissions as soon as possible, we should first actively adjust the energy structure and promote REC. In the meantime, we may as well promote REC according to our level of the economy. If we only focus on upgrading energy consumption while neglecting our current economic development, the result may backfire. 5.5.2 Asymmetric Analysis 5.5.2.1 Asymmetric Analysis of EKC

Economic development, REC, industry development, and population significantly impact CO2 emissions; however, the degree of the impact may vary, and such impact may be related to the level of CO2 emissions. In other words, an asymmetric situation may exist. Thus, we divide the sample into five quantiles based on the levels of CO2 emissions and rerun the regression for the subsamples using the quantile regression approach developed by Koenker (2004). The formula can be expressed as follows:

Qq (CO2 x ) = a q0 +

åa X qi

qi

(4)

where X qi refers to all the independent variables; q represents the quantiles, namely 10th, 25th, 50th, 75th, and 90th; and a q0 and a qi are parameters to be estimated. The figure of quantiles is presented in Figure 5.2. According to the estimated coefficients shown in Table 5.5, the impacts of economics and REC on CO2 emissions are significant across all quantiles. Specifically, the coefficient of economic development is significantly positive, and the quadratic term of economic development is significantly negative, indicating that the economic development–CO2 emissions nexus is non-linear while verifying EKC again and further showing that the results in the baseline regression are robust. In addition, REC has a negative relationship with CO2 emissions, and the value of the coefficient of REC decreases remarkably, from −0.1889 in the 10th quantile to −0.3974 in the 90th quantile. This implies that the changing pattern of the inhabitation impact on CO2 emissions is not always simply linear; conversely, it may also be non-linear. In other words, when the REC is quite low, the marginal impact of REC on CO2 emissions is relatively

Figure 5.2 Change in panel quantile regression coefficients in EKC in the world.

92  Renewable Energy, Carbon Emissions, and Economic Growth

Renewable Energy, Carbon Emissions, and Economic Growth  93 Table 5.5 Results of Panel Quantile Regression of EKC in the World Dependent variable: lnCO2 Variable

Quantiles 10th

lnrecon lnPgdp lnPgdp2 lnInd lnPop _ Cons

25th

50th

75th

90th

−0.1889*** −0.2060*** −0.2214*** −0.2957*** −0.3974*** (−21.9923) (−26.6625) (−30.6960) (−29.8967) (−30.2967) 1.7930*** 1.6714*** 1.4386*** 0.9200*** 0.8129*** (44.4816) (46.0817) (42.4844) (19.8187) (13.2038) −0.1243*** −0.1154*** −0.0941*** −0.0398*** −0.0342*** (−24.1968) (−24.9654) (−21.8079) (−6.7270) (−4.3569) 0.1044*** 0.2130*** 0.3375*** 0.4737*** 0.4603*** (3.1135) (7.0608) (11.9807) (12.2668) (8.9870) 0.0669*** 0.0585*** 0.0500*** 0.0330*** 0.0149* (12.9893) (12.6344) (11.5712) (5.5648) (1.8950) −4.5016*** −4.1919*** −3.6930*** −2.4773*** −1.4721*** (−35.6424) (−36.8861) (−34.8066) (−17.0329) (−7.6312)

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

small, while when the REC is improved, the marginal impact of REC on CO2 emissions will gradually increase. That is to say, the mitigated impact of REC on CO2 emission is weak but then exerts a comparatively more inhibited effect later. Thus, it is suggested that we need to accelerate renewable energy development and increase the proportion of REC in total energy consumption. The faster and more advanced the development of renewable energy, the more significant the effect of REC on CO2 emissions will be. 5.5.2.2 Asymmetric Analysis of RKC

Based on the above research on the asymmetry of EKC, this chapter then conducts a similar analysis of the RKC hypothesis. Quantile regression is still used to test the impact of economic development on REC under different quantiles. The formula can be expressed as follows:

Qq ( REC x ) = bq0 +

åb X qi

qi

(5)

where X qi refers to all the independent variables; q represents the quantiles, namely 10th, 25th, 50th, 75th, and 90th; and bq0 and bqi are parameters to be estimated. The figure of quantiles is presented in Figure 5.3. According to the estimated coefficients shown in Table 5.6, economic impacts on REC are significant in 50th, 75th, and 90th quantiles, which means

Figure 5.3 Change in panel quantile regression coefficients in RKC in the world.

94  Renewable Energy, Carbon Emissions, and Economic Growth

Renewable Energy, Carbon Emissions, and Economic Growth  95 Table 5.6 Results of Panel Quantile Regression of RKC in the World Dependent variable: lnCO2 Variable

Quantiles 10th

lnPgdp lnPgdp2 lnInd lnPop _ Cons

25th

50th

75th

−0.6821*** −0.5571*** −0.6312*** −0.5414*** (−3.3310) (−3.9668) (−10.6440) (−10.5215) 0.0058 −0.0071 0.0153** 0.0190*** (0.2206) (−0.3955) (2.0163) (2.8915) −1.7443*** −1.1145*** −0.1967*** 0.0979** (−10.8103) (−10.0711) (−4.2099) (2.4150) 0.4223*** 0.1978*** 0.0257*** −0.0070 (16.4013) (11.1995) (3.4520) (−1.0763) 8.7135*** 7.8832*** 5.7932*** 5.0056*** (16.6987) (22.0286) (38.3428) (38.1768)

90th −0.7148*** (−14.2803) 0.0566*** (8.8327) 0.2607*** (6.6105) −0.0235*** (-3.7421) 4.9318*** (38.6705)

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

that the economic development–REC nexus is non-linear when the REC is high level, and their nexus is simply linear when REC is underdeveloped. When the proportion of REC on total energy consumption is very low – that is when most countries are in the early stage of industrialization – economic development may rely on traditional energy. So, economic growth would increase traditional energy consumption (i.e., petroleum and coal); thus, economic development inhibits REC. However, with the development of REC, the proportion of REC on all energy consumption is gradually enhanced, and the non-linear relationship between economic development and REC would appear. After the economic development passes its turning point, it can significantly positively impact REC. Therefore, this finding is in line with the conclusion of baseline regression. To realize the goal of dual carbon targets, it is necessary to promote the development of the economy on account that only after entering a more developed stage can economic development play a positive role in the consumption of renewable energy. Then environmental problems and climate change can be alleviated. 5.5.3 Heterogeneous Analysis

Different regions and countries have different geographical locations with various levels of resource endowment. Apart from this, their economic development and energy utilization are also different. Thus, it is of great significance to analyze EKC and RKC in a subsample of regions and countries. To be more specific, by referring to BP (2021), we divided the world into seven

96  Renewable Energy, Carbon Emissions, and Economic Growth

regions, namely North America, South and Central America EKC, Europe, Commonwealth of Independent States (CIS), Middle East, Africa, and Asian Pacific. In addition, we also pay attention to several representative countries with comparatively large GDPs, for example, the developed countries such as the United States, Canada, the United Kingdom, Australia, and Japan and developing countries such as China, India, and South Africa. Afterward, we conduct FE and RE regression in each sample, and the results of regions are shown in Tables 5.7–5.13. More importantly, based on each regression result, we summarize the turning points in EKC and RKC, respectively, and list them in Table 5.14. In addition, we also draw two graphs based on the above regression results, which are shown in Figures 5.4 and 5.5. As shown in Table 5.7, we can see that, in North America, REC can significantly reduce CO2 emissions. In addition, both EKC and RKC hypotheses are verified in this region. More specifically, in the economic development process, environmental pollution shows an upward trend and then decreases gradually. While as for REC, due to the comparatively early industrialization stage, industrialized mass production requires traditional energy use, so REC in North America reduces when the economy grows at an initial stage. After RKC passes its turning point, economic development can significantly increase REC by raising environmental protection awareness and technology advancement. Furthermore, we calculate the turning point in EKC and RKC, and the results show that the turning point in EKC is 6.42, while in RKC, it is 5.36; therefore, we reckon that, in North America, the turning point in RKC is prior to that in EKC, which is consistent with the finding in the baseline regression.

Table 5.7 Results of EKC and RKC in North America EKC

lnrecon lnPgdp lnPgdp2 lnInd lnPop _ Cons

RKC

FE

RE

FE

RE

−0.2873*** (−11.2935) 0.2294 (0.9750) −0.0185 (−0.9376) 0.4183*** (4.7076) −0.2297* (−1.8348) 1.5268*** (2.7655)

−0.6038*** (−3.3792) 3.9986*** (3.8709) −0.3114*** (−3.2236) −1.1399*** (−2.8795) −0.4667*** (−3.6733) −3.7798 (-0.9562)

– – −3.6390*** (−2.9972) 0.3360*** (3.3600) −0.6844 (−1.3988) 0.0230 (0.0327) 14.2547*** (6.0993)

– – −3.6908*** (−5.9233) 0.3440*** (5.8912) −0.8008*** (−2.7688) −0.6545*** (−16.8372) 16.2741*** (7.7547)

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

Renewable Energy, Carbon Emissions, and Economic Growth  97 Table 5.8 Results of EKC and RKC in South and Central America EKC

lnrecon lnPgdp lnPgdp2 lnInd lnPop _ Cons

RKC

FE

RE

FE

RE

−0.5606*** (−10.1270) −0.0196 (−0.1340) 0.0197 (1.1277) −0.0207 (−0.3062) 0.4585*** (2.6415) 1.8097*** (4.8500)

−0.2812*** (−5.3225) −0.2539 (−0.4533) 0.0923 (1.3247) −0.5499** (−2.0543) −0.1748*** (−2.8107) 3.2548*** (3.2395)

– – −0.6290** (−2.4971) 0.0650** (2.1410) 0.0592 (0.4958) 0.0607 (0.1976) 4.3559*** (8.6358)

– – −0.7001*** (−2.8671) 0.0712** (2.3446) 0.0912 (0.7649) 0.2502 (1.5527) 4.1853*** (8.4873)

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

Table 5.9 Results of EKC and RKC in Europe EKC

lnrecon lnPgdp lnPgdp2 lnInd lnPop _ Cons

RKC

FE

RE

FE

RE

−0.2209*** (−19.7603) 0.1448*** (3.0121) −0.0066 (−1.2791) 0.2968*** (7.0484) −0.3283*** (−3.6804) 0.9176*** (5.2316)

−0.2309*** (−21.6724) 0.1934*** (4.5916) −0.0110** (−2.4444) 0.3062*** (7.4646) −0.1365*** (−4.2566) 0.8190*** (4.7163)

– – 0.5222*** (2.9273) −0.0171 (−0.8890) −1.6612*** (−11.7546) 1.8796*** (5.7981) 5.8866*** (9.6817)

– – 0.0332 (0.2030) 0.0350** (2.0099) −1.7545*** (−12.4898) 0.0473 (0.5188) 6.9379*** (11.7255)

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

Table 5.8 shows the EKC and RKC in South and Central America. In this subsample, a 1% increase in REC can lead to approximately 0.56% CO2 emissions. Consequently, South and Central American countries should pay attention to their REC when they aim to reduce their CO2 emissions. Moreover, the results also show that the relationship between economic development and

98  Renewable Energy, Carbon Emissions, and Economic Growth Table 5.10 Results of EKC and RKC in CIS EKC

lnrecon lnPgdp lnPgdp2 lnInd lnPop _ Cons

RKC

FE

RE

FE

RE

−0.3952*** (−7.8656) −0.2649*** (−2.6951) 0.0452*** (3.3160) 0.1259 (1.5120) −0.4182* (−1.8825) 2.2031*** (7.7973)

−0.1911*** (−7.2690) −0.0834 (−0.3061) 0.0540 (1.3362) −0.1801 (−1.0905) 0.1077** (2.3304) 2.1739*** (3.5291)

– – 0.7082*** (3.9600) −0.1052*** (−4.2914) −0.0053 (−0.0327) −0.9140** (−2.1544) −0.1986 (−0.3604)

– – 0.6617*** (3.8032) −0.1001*** (−4.1685) 0.0022 (0.0135) −0.6986* (−1.7911) −0.2495 (−0.2018)

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

Table 5.11 Results of EKC and RKC in Middle East EKC

lnrecon lnPgdp lnPgdp2 lnInd lnPop _ Cons

RKC

FE

RE

FE

RE

−0.0297 (−1.1733) 0.5164*** (3.5329) −0.0318* (−1.7795) −0.2576** (−2.2882) −0.2749*** (−4.2550) 1.5970*** (2.6708)

−0.0796*** (−5.5334) 0.5360** (2.3198) −0.0005 (−0.0196) 0.3076*** (3.0846) −0.1104*** (−2.8848) −1.6269** (−2.1250)

– – 2.1128*** (3.4780) −0.2767*** (−3.7709) −0.9818** (−2.0020) 0.8053*** (2.9417) −1.6550 (−0.6211)

– – −1.0269 (−0.9925) 0.0551 (0.4692) −4.2623*** (−7.6728) 0.7526*** (2.8653) 17.6201*** (4.9611)

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

CO2 emissions is insignificant, while the economic development–REC nexus is non-linear. That is to say, the EKC hypothesis does not exist in South and Central America, but the RKC hypothesis exists. More specifically, the turning point in RKC in South and Central America is 4.84, which is smaller than that in EKC.

Renewable Energy, Carbon Emissions, and Economic Growth  99 Table 5.12 Results of EKC and RKC in Africa EKC

lnrecon lnPgdp lnPgdp2 lnInd lnPop _ Cons

RKC

FE

RE

FE

RE

−0.0835*** (−3.0453) 1.3719*** (6.7673) −0.1710*** (−6.0121) −0.0537 (−0.4782) 0.1988 (1.2773) −1.4578*** (−2.9583)

0.0582*** (2.7133) −0.2215 (−0.3573) 0.0144 (0.1610) 0.8819*** (6.4423) 3.4352*** (18.2783) −5.9794*** (−4.8662)

– – −0.4938 (−0.4736) 0.0127 (0.0868) 2.6404*** (5.9742) −0.5939 (−0.7442) −5.5566** (−2.3002)

– – −3.0896 (−0.7753) 0.2833 (0.4906) −5.3302*** (−10.9431) 1.4302 (1.1927) 25.1581*** (3.5238)

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

Table 5.13 Results of EKC and RKC in the Asia Pacific EKC

lnrecon lnPgdp lnPgdp2 lnInd lnPop _ Cons

RKC

FE

RE

FE

RE

−0.2640*** (−6.3109) 0.7495*** (16.4274) −0.0662*** (−11.3140) −0.0709 (−0.7228) −0.3562*** (−2.7444) 0.9220** (2.0049)

−0.2889*** (−7.0457) 0.7115*** (15.7625) −0.0636*** (−10.7496) 0.0126 (0.1277) −0.1561** (−2.4551) 0.4069 (0.9594)

– – −0.6566*** (−11.8715) 0.0577*** (7.1312) −0.0364 (−0.2443) 0.5407*** (2.7865) 3.3840*** (5.0943)

– – −0.6377*** (−11.6740) 0.0572*** (7.1019) −0.1050 (−0.7174) 0.3707*** (3.2101) 3.8966*** (6.2601)

Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively; the values in parentheses represent t-statistics.

Although the RKC hypothesis cannot be verified in Europe in Table 5.9, the economic development–CO2 emissions nexus is significant and non-linear, and thus, the EKC hypothesis can be verified. Moreover, the turning point in EKC in Europe is 8.79, the largest among all these regions. Therefore, it is believed that only when economic developments reach a high level for European countries can economic growth lead to a reduction in environmental

100  Renewable Energy, Carbon Emissions, and Economic Growth Table 5.14 Turning points of EKC and RKC of Selective Regions Region

EKC

RKC

North America South and Central America Europe CIS Middle East Africa Asia Pacific

6.42 – 8.79 – 8.12 4.01 5.66

5.36 4.84 – – – – 5.69

pollution. In Table 5.10, both EKC and RKC in CIS are not significant. Consequently, economic development only has a linear relationship with CO2 emissions and REC in CIS. This may be related to the region’s own characteristics, such as the stage of economic development. From Tables 5.11–5.12 5.13, we can see that the EKC hypothesis exists in the Middle East, Africa, and the Asian Pacific. The turning point in EKC in these three regions are 8.12, 4.01, and 5.66, respectively. Unfortunately, RKC only exists in the Asian Pacific. In addition, in the Asian Pacific, the turning points in EKC and RKC are similar, which means that when economic development reaches a certain level, the inhibition effect of the economy on the environment and the promotion effect of the economy on REC may appear at the same time. There is no obvious difference between the inhibition and promotion effect regarding the level of economic development. In sum, the EKC hypothesis has different characteristics in different regions. More specifically, EKC turning points in North America and Europe are 6.42 and 8.79, while in Africa and the Asian Pacific, they are 4.01 and 5.66, indicating that EKC turning points in developing countries and regions appear at a relatively low level of economic development. The reason is that globalization helps developing countries acquire advanced energy conservation and emission reduction technologies and management experience from developed countries so that they can get rid of high pollution in the early stage of development and arrive at the turning point when their GDP per capita is relatively low. Yao et al. (2019) also draw a similar conclusion. As for the EKC and RKC hypotheses in some developing and developed countries, the results are shown in Figures 5.4 and 5.5. It can be seen that EKC and RKC hypotheses significantly pass tests in the United Kingdom, Australia, and China. Nevertheless, the relationship between economic development, environmental pollution, and REC is not a significant quadratic term in the United States, Canada, Japan, India, and South Africa. This may be due to the comparatively small number of data samples from different countries and the large diversity of each country; thus, the results displayed are not as clear as the results in subsamples of each region. The RKC curve of three developing countries (e.g., China, India, and South Africa) shows a downward trend,

Renewable Energy, Carbon Emissions, and Economic Growth  101

Figure 5.4 The comparison of EKC and RKC of selective regions.

102  Renewable Energy, Carbon Emissions, and Economic Growth

Figure 5.5 The comparison of EKC and RKC of selective countries.

Renewable Energy, Carbon Emissions, and Economic Growth  103

indicating that the current economic development still reduces their REC. It may be because the production cost of new renewable energy, for example, hydropower, wind power, and photovoltaic power stations, is higher than that of fossil fuels. Moreover, technical bottlenecks may exist, and traditional renewable energy production capacity is very limited, which leads to the increasing proportion of fossil energy in developing countries. Specifically, India lacks effective modern energy infrastructure, so it must use renewable energy. China’s results may be caused economic growth occurring too quickly and too much demand for fossil energy during the sample period.

5.6 Conclusions and Policy Implications Based on the data of 266 countries and regions in the world from 2000 to 2020, we utilize OLS, FE, RE, DIF-GMM, and SYS-GMM methods to explore the EKC hypothesis from a new perspective of REC. Further, the RKC hypothesis is proposed and verified in this chapter. In addition, the differences between the EKC curve’s turning points and the RKC curve are compared and explained. And asymmetric as well as heterogeneous effects are also analyzed. The main conclusions are as follows: (1) REC effectively mitigates CO2 emissions within the framework of EKC. To be more specific, a 1% increase in the share of REC in total energy consumption leads to a 21% reduction in CO2 emissions. Both economic development and REC exert significant impacts on environmental pollution, while economic development has a non-linear relationship with CO2 emissions. (2) We confirm the existence of RKC for REC in the world, meaning that the non-linear economic development–REC nexus can be found worldwide. REC decreases obviously with the economy’s growth and then gradually increases as the economy develops. (3) Both EKC and RKC have their turning points regarding economic development. We compare the turning points in EKC and RKC and reckon that the RKC turning point of the entire sample takes place before the turning point in EKC, which suggests that economic development first leads to an increase in the REC and then inhibits the CO2 emissions afterward. (4) Asymmetry exists in both EKC and RKC. As for EKC, with the increase in CO2 emissions, the mitigation effect of REC on CO2 emissions is significantly strengthening. In addition, for the RKC hypothesis, when REC is relatively small, the economic development–REC nexus is linear, which then becomes non-linear as REC increases. (5) EKC and RKC curves of different regions and countries show different characteristics. The turning point in EKC in developing countries is smaller than that in developed countries. Because of international trade and other communication, developing countries can acquire more efficient energy-use technologies from developed countries, namely the “learning effect”, which can help developing countries pass their turning point at a relatively lower level of economic development.

104  Renewable Energy, Carbon Emissions, and Economic Growth

Therefore, this chapter puts forward the following three policy recommendations. First, because renewable energy development can effectively reduce CO2 emissions, countries must vigorously strengthen the development and utilization of renewable energy. Meanwhile, they also need to develop renewable energy generation and storage technology. Developing countries should attract investment from developed countries to develop the renewable sector and learn advanced energy development and utilization technologies to promote industrial upgrading. At the same time, to achieve the global CO2 emission reduction target as soon as possible, developed countries should actively transfer advanced energy technology to developing countries to achieve a win-win situation. Second, there is a non-linear relationship between economic development and renewable energy, which indicates that continuously improving the economic level is necessary. We believe that governments can provide subsidies for renewable energy development. The development of renewable energy and technological upgrading requires vast investment initially, but it has obvious positive externalities to society. Reasonable government subsidies are in line with economic efficiency. Specifically, China, as a middle-income country, has shown a trend of energy structure transformation despite an insignificant EKC curve during the sample period, meaning that the effect of economic adjustment has initially emerged. In order to achieve the ultimate goal of energy conservation and ecological environment development, we should intensify our efforts to achieve economic transformation and strive to develop and utilize renewable energy. Third, the correlation between the turning points of the EKC and RKC curve is identified in this chapter. Thus, a unique strategy is needed according to the country’s renewable energy utilization. To be more specific, the relationship between economic development and the REC is still negative in some countries. In this case, forcing energy structure transformation may negatively affect the country’s economic development. Therefore, it is more beneficial to improve renewable energy with comparative advantages and gradually increase the proportion of REC. Moreover, they should also pay attention to their resource endowment and technology level. Thus, upgrading the energy consumption structure can be realized step by step.

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6

COVID-19 and Energy Transition A Review Daniel Balsalobre-Lorente, Walter Ferrarese, Ismael Gálvez-Iniesta, Monica A. Giovanniello, and Elpiniki Bakaouka

6.1 Introduction The COVID-19 pandemic has been a historical global economic shock. As the virus transmitted across countries, governments all around the world implemented extraordinary lockdown measures, which affected the whole economy. Due to the fast development and implementation of vaccination in many countries, the medical emergency is now less dramatic, and governments have started to ease the extraordinary precautionary measures. Still, the pandemic is not fully under control, and, importantly, its effects have not yet evaporated, mainly because of both the intensity and the persistence of the economic shock. Among all the sectors, the global energy sector emerges as one of the most affected ones. The relationship between COVID-19 and energy has been widely studied from different perspectives, mainly: (1) as a matter of energy crisis, which affects both energy demand and energy prices, asking questions such as to what extent the COVID-19 outbreak affected both of these dimensions? Or how does this impact depend on containment measures, and does it differ across countries? And (2) as a matter of potential transitions: what opportunities and challenges does the COVID-19 crisis bring for the transitions toward renewable energy sources? (Kanda and Kivimaa, 2020). The main objective of this review is to provide a broad summary of the impact of the COVID-19 pandemic on the energy sector. We will focus on the two most relevant energy sources: the oil market and the renewable sector. In particular, this work reviews recent studies on the effect of the recent pandemic on the oil market and on the renewable energy sector, highlighting both the challenges and the opportunities that are arising for the renewable energy sector. We will also emphasize the main implications of the global shock for the energy transition. The chapter is structured as follows. Section 6.2 presents an overview of the impact of COVID-19 on the global energy sector. Section 6.3 reviews its effects on the oil sector, focusing on both prices and volatility. Section 6.4 examines its effects on renewable energy sources and the implications for the energy transition. Finally, Section 6.5 concludes.

DOI: 10.4324/9781003336563-6

108  COVID-19 and Energy Transition

6.2 COVID-19 and the Global Energy Sector The energy sector plays a key role in the global economy. Its importance in most countries worldwide goes far beyond its share in gross domestic product (GDP) (except for oil and gas producers), as it constitutes an essential input to nearly all of the goods and services of modern economies (Szczygielski et al., 2021). The close relationship between energy use, output, and economic growth has already been proven: an economic slowdown reduces energy demand which, in turn, reduces energy consumption, further reducing output and economic growth (Gozgor et al., 2018; Ozturk and Acaravci, 2010; Shahbaz et al., 2013), validating the so-called “growth hypothesis”. Regarding its impact on energy demand, the literature has extensively documented large reductions globally and across countries. As governments implemented confinement measures unprecedented in modern societies, hundreds of thousands of workers lost or interrupted their jobs, causing a strong shock to the economy, which experienced a dramatic reduction in both production and consumption (Chen et al., 2020) and herein affecting prices. According to the International Energy Agency (IEA), the global energy demand dropped by 3.8% during the first quarter of 2020 compared to the same period in 2019 (Hoang et al., 2021). And by the end of 2020, the decline was around 6%, a sevenfold greater fall than the 2009 financial crisis. During the more severe phase of the lockdown, in advanced economies such as Germany, Italy, France, or the United Kingdom (UK), the drop in electricity consumption was more than 10% compared to the same period in the previous year (Jiang et al., 2021). The high connectedness in the world energy market is behind the fast transmission of the pandemic effects across countries, and similarly, it has also played an important role in reinforcing its initial impacts. Using a panel-data analysis, Akyildirim et al. (2022) evaluate the spillover effects of the COVID-19 shock by constructing, for 29 developed and developing countries, a measure of how much each country transmits a given shock to other countries and how much a given shock is received from other countries. They find that oil-exporting countries are the ones responsible for the diffusion of shocks. Moreover, they show that, during the COVID-19 pandemic, the degree of connectedness across countries has intensified, as it tends to increase in periods of high uncertainty. Hauser et al. (2020) analyze the impact of COVID-19 on the rising prices of the five main energy commodities, oil and coal globally, gas, and CO2 certificates in Europe. They find that the COVID-19 pandemic’s impact on energy prices has largely differed across sectors. In particular, they find that the impact was unambiguously high on the oil and electricity markets. In contrast, they show that the effect of COVID-19 on coal and gas prices was very small and mainly indirect. Because energy commodities are directly connected with economic activity, the economic downturn triggered by the pandemic has negatively affected these hard commodities (Ozili and Arun, 2020). In contrast, the lockdown

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measures implemented by the governments to slow down the spread of the virus have positively affected other prices of the economy, such as food and agricultural commodities, pushed up by the increase in household consumption (Hobbs, 2020; Prentice et al., 2020). The COVID-19 crisis has also meant a significant shift in the traditional transmission of return spillovers across commodity prices. In particular, Farid et al. (2022) show that during the COVID19 pandemic, there is evidence of strong transmission of return shock between energy, metals, and agriculture commodities. The authors show that before the COVID-19 outbreak, such shocks only propagated within the energy commodity group. Depending on the severity and scale of containment measures, the overall magnitude of the COVID-19 outbreak in the energy sector has greatly differed across countries. On the one hand, in countries such as South Korea or Japan, where lockdowns were less severe, the drop in energy demand was relatively mild, around 10% with respect to the previous year (Hoang et al., 2021; Kang et al., 2021). On the other hand, in countries with highly controlled lockdowns, such as China or Europe, energy demand decreased 15% (Hoang et al., 2021). In Europe, during the weeks of extreme lockdown measures, regional weekly energy demand reduction reached more than 17%. Within Europe itself, we also see great heterogeneity. Figure 6.1 displays the average growth rate in final energy consumption from 2019 to 2020 for a subset of European countries. On average, in the EU-27, the final energy consumption dropped by 4.1%. In Spain or Italy, the reduction was similar to the average, while in Germany or France, the drop was larger – 6.8 and 5.7,

Netherlands Finland Portugal Denmark Sweden Spain EU-27 Italy Belgium Germany France –40

–20 Total

0

%

20

Oil and Pretoleum

Figure 6.1 Yearly growth rate of energy consumption in 2020.

40 Renewables

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respectively. In contrast, in the Netherlands, Finland, and Portugal the final consumption of energy was almost constant with respect to the previous year. Figure 6.1 also shows the average growth rate in energy for oil and petroleum and renewable energies. As we discussed before and will be emphasized in the next sections, we see that the drop in energy consumption was heterogeneous across subsectors, with large reductions in oil and petroleum consumption and important increases in renewable energies. In this regard, Bahmanyar et al. (2020) compare the impact of the COVID19 pandemic on the consumption of electricity in several European countries. They construct an index that measures the average demand reduction compared to a reference period. They find that in countries with more stringent restrictions, such as Spain, Italy, Belgium, or the UK, the reductions were more noticeable than in those with less restrictive measures, such as the Netherlands and Sweden. More surprisingly, they also show that in countries that experienced a more severe outbreak, the electricity consumption could not return to its normal trajectory in the short run.

6.3 COVID-19 and the Oil Market As pointed out, despite a strong consensus for a transition toward the adoption of clean and renewable energy sources, today, oil is still the primary source of energy power. In particular, as shown in Figure 6.2, fossil fuel consumption

100

Bio Fuel & Waste Hydro Nuclear

80 Natural Gas 60

Oil

40

20 Coal 0 1970

1980

1990

Year

2000

2010

2020

Figure 6.2 Share world total energy supply, by sources: 1970–2018.

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(coal, oil, and natural gas) represents more than 80% of the world’s total energy supply. Also, the aforementioned transition, just like the one that occurred in the mid-nineteenth century from solid to fossil fuels, seems to be rather slow (see for example, Smil (2008, 2014)). This implies that a proper understanding of the oil market is still crucial for a better comprehension of the economic system in general.1 For example, in 2001, the United States (US) economy saw an eight-month recession: through a multivariate analysis in which oil prices were a key factor, Muellbauer and Nunziata (2001) were able to predict such an event. Also, by using the reduction in oil supply caused by five major conflicts in the Middle East as a proxy for oil price movements, Hamilton (2003, 2011) shows that oil price increases are a reliable predictor of the US real GDP growth, whereas oil price decreases are not as powerful and that such a relationship is likely to be non-linear. Furthermore, most recently, Charfeddine et al. (2020), although admitting that the oil price–GDP bond is weaker than in the past, confirmed Hamilton’s (2003) qualitative result through an extended data set up to the fourth quarter of 2019. Finally, apart from a possible benefit of an oil price increase for the stock markets of oil-exporting countries, the fact that high oil prices negatively affect the stock market is well established.2 The recent event of the COVID-19 outbreak poses the question of whether other types of shocks, such as pandemics, have a causal relation with the oil market fluctuations and, if yes, how strong and persistent these effects may be. Typically, oil price reductions are linked to pandemic outbreaks, mainly due to the slowing in the global economy (Qin et al., 2020).3 For example, Dutta et al. (2020) find that these negative effects, although heterogeneously, spread across all international crude oil markets. Oil-exporting countries have probably felt the most dramatic consequences of the pandemic. If, on the one hand, some countries have been and will be able to counter the blow because of, for instance, the levels of public debt, other countries such as Algeria or Iraq are too dependent on this resource. The lack of exporting revenues will hit their entire economy hard, and crucial sectors, such as health care, will feel the consequences. As pointed out, two key themes are whether and how the pandemic affected (i) oil prices and (ii) oil price volatility. In this regard, Narayan (2020) discusses the relative importance of the COVID-19 outbreak and the spreading of negative oil price news on oil prices and the oil market volatility. Concerning oil price variations, the author shows that there is, indeed, a causal relation with the COVID-19 outbreak and that the magnitude of such effect significantly depends on the number of infections. In particular, it has been estimated that such an effect is stronger beyond the threshold of 84.479 infections. Regarding oil price volatility, it turns out that oil price news plays a relatively more important role when such volatility is already high. Needless to say, this does not exclude COVID-19 itself as a source of oil price volatility. After all, in the COVID-19 era, oil price volatility experienced approximately a sixfold

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increase, and after April 2020, it reached one of its historical peaks (Devpura and Narayan, 2020). Thus, if, causal relation may be rather intuitive, estimating its magnitude is important in terms, for instance, of diversification choices of the investment portfolio. Devpura and Narayan (2020) estimate an increase in daily oil price volatility between 8.56% and 17.07% and between 11.41% and 22.02% as a consequence of a one standard deviation increase in deaths and cases, respectively. The same qualitative result is obtained by Zhang (2021) for the China National Petroleum share price index and by Salisu and Adediran (2020) with their analysis of the Disease-Equity Market Volatility (EMV-ID). Finally, another key question is how long-lasting these effects may be. GilAlana and Monge (2020) provide evidence that shows that the crude oil price time series exhibit an order of integration close, but strictly lower than unity. Thus, if, on the one hand, the series is mean reverting, namely, the effects of shocks are temporary, on the other hand, the effect of COVID-19, in particular, is going to affect crude oil prices in a lasting way. More specifically, if oil demand is expected to go back and beyond its prepandemic level rather quickly, oil supply will have a much slower recovery. Oil demand is expected to be mainly driven by the petrochemical industry, which accounts for approximately 14% of the global oil demand.4 It is also expected to support the demand in the medium/long run, in which, for instance, the demand for the transportation sector is forecasted to reduce gradually. Oil supply, as said, will not grow as fast. The pandemic induced an investment drop in new machinery, which, in turn, limited the production pace. For example, US oil producers reduced investments by 35% between the first and the second quarter of 2020. This lack of technological advancement will hinder a progressive drop in the minimum price per barrel to generate positive revenues. An important aspect of the market is that countries such as Saudi Arabia can survive with a price per barrel of around $30 because of the relatively low production costs, whereas countries such as Venezuela need a price of $50 per barrel. Also, some companies have been forced to stop production completely due to a binding capacity storage restriction.

6.4 COVID-19 and Clean Energy Transition: Challenges and Opportunities As already stated, the COVID-19 pandemic, by disrupting business activities, had a large impact on global energy demand (Cozzi et al., 2020), and it seems quite likely that it is going to affect global energy consumption for the next few decades (Shan et al., 2021). Like the fossil fuels market, the renewable and sustainable energies sector, which in the past decade witnessed rapid growth, is now facing serious challenges due to the COVID-19 pandemic (Madurai Elavarasan et al., 2021). Before the pandemic, many countries were rapidly transitioning to renewable and sustainable energy. Recent innovations and public policies considerably

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lowered the cost of clean energy. Consequently, for instance, solar and wind power were predicted to outpace fossil fuels soon (Hosseini and Wahid, 2016). The growth in the renewable energy sector was so strong that even fossil fuelbased industries started investing in mitigation strategies as the oil price kept rising. However, the investments in the renewable energy sector decreased because of the COVID-19 pandemic and the subsequent fall in oil prices, which made fossil fuels relatively cheaper. The rapid decrease in the volume of private and public investments in the renewable sector, which have been redirected toward fossil fuel-intensive sectors, has been one of the main consequences of the recent pandemic events. Thus, in the post-pandemic period, we may witness an extreme slowdown in the global renewable energy capacity and the development of renewable energy itself. Nonetheless, the International Renewable Energy Agency (IRENA) reports that the global renewable energy capacity in 2020, despite the economic slowdown of the previous year, has increased by 421 gigawatts (GW), whereas it has witnessed a modest increase of only 265 GW in 2021. In the post-COVID-19 period, the renewable energy sector faces many challenges. Three main aspects to consider are the effect of oil prices in the energy sector, the reduction of public funds for renewable energy projects, and the changes in the consumers’ behaviour. As mentioned, one of the biggest consequences of the pandemic has been the sudden drop in oil prices driven by a decline in fuel demand due to mandatory lockdowns and quarantines, which, in turn, had a huge negative impact on the transportation sector. The effects of the COVID-19 outbreak on the oil and gas industry can be split into short-run and long-run effects. In the short-run, the crisis resulted in a drop of nearly 25% of the production in the industry, which caused a reduction in the number of oil exploitation projects from more than 800 in 2019 to only 265 in 2021 (Norouzi, 2021). In the long run, it has been estimated that this enormous fall in fossil fuels consumption may drive a further decrease in the CAPEX (capital expenses) and research and development (R&D) investments in the oil and gas market of about 30–40% in the US alone. However, the overall investment decline in the energy sector – and the renewable sector in particular – in developed countries is only the tip of the iceberg. The combination of the pandemic and the fall of oil prices has exacerbated the vulnerability of (not just oil-exporting) developing countries. Developing countries are very sensitive to energy costs and prefer using cheaper energy sources such as oil instead of investing in renewable energy. These two crises may also trigger a long-term economic world crisis (Scott R. Baker et al., 2020), which is already threatening the world’s transition to “clean” energies by provoking a global decrease in both public and private investments in renewable energy sector. In the attempt to speed up the economic recovery, many countries are prioritizing investment strategies dominated by fossil fuels

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and bidding on the oil-free cost (Malamud and Núñez, 2020), hurting the sustainability and the progress of a “green” transition (Jiang et al., 2021). However, as Hosseini (2020) and Norouzi (2021) pointed out, the recent oil price crisis may not only have negative effects. As prices in the fossil sector go down and investments reduce, the sector’s competitiveness would diminish. This, in turn, would increase the relative competitiveness of the renewable energies sector. Moreover, the fossil fuels markets crisis may threaten the oil and gas contracts, at least in the far post-COVID-19 era, as those effects will not be immediately evident. In the period of extreme crisis, the returns and volatility of oil prices did not have any causal effects on energy stock indices (Hammoudeh et al., 2021). By exploring all returns series over ten period years, from 2010 to 2020, Hammoudeh et al. (2021) find that, while oil returns do impact the renewable stock index returns during normal market conditions (Paiva, Rivera-Castro, and Andrade, 2018; Zhao, 2020), this is not true in the case of the extreme market conditions. Thus, if oil prices remain structurally low, also as a result of the breakup of OPEC+ due to the oil war between Saudi Arabia and Russia, the investment in renewable energy may grow (Steffen et al., 2020). By affecting supply chains, manufacturing facilities, and private investments, the COVID-19 pandemic hurt most of the economy’s businesses, including the renewable energy sector. However, this sector was especially affected because of the withdrawal of public funds from renewable energy projects in favour of the most urgent health expenditures (Jiang et al., 2021) as a direct consequence of the pandemic. The crisis followed by the outbreak of COVID-19 exposed one of the fundamental problems for the development of an economy relying on renewable energies: it heavily relies on government funds. Renewable energy projects do not only require large initial investments, but they also have high technical requirements and high operation and maintenance expenses (Haar, 2020; Levine and Steele, 2021), which makes renewable energy investments very challenging for developing countries (Nguyen and Kakinaka, 2019). The IEA reported a decrease of 10% in 2020 in the number of final investment decisions (FID) for public renewable energy projects concerning 2019 (Cozzi et al., 2020). Moreover, the supply chain disruptions have even delayed even renewable energy projects already under construction, and, overall, there has been a drop of almost 600,000 clean energy jobs by the end of April 2020. In the post-COVID-19 era, increasing public funds to finance renewable energy development projects may be a viable solution to the economic crisis. The industry is labour-intensive, which makes it an excellent public investment choice for increasing jobs and reviving the economy. In 2018, about 11 million people were employed in the sector, and they could rise to up to 84 million by 2050 (Environmental and Energy Study Institute, 2015). The post-pandemic period is a crucial moment for the energy transition. Policymakers could seize the opportunity provided by low interest rates and, throughout the expected stimulus packages, put in place policies to stimulate

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private investments in clean energy technologies. In this way, governments can respond to two major issues of the near future: the economic crisis and the energetic one. While the electricity demand of the industrial and the commercial sectors fell because of the COVID-19 restriction measures, the residential energy load increased. In some European countries, this increase was almost 40% (IEA, 2020). The pandemic and confinement measures boosted private energy consumption by modifying consumers’ behaviour and social practices. In fact, although all restrictive measures dropped the global energy demand with the closure of many business and manufacturing activities and the limitation of mobility, it also increased consumption (heating, cooling, cooking, etc.) in the private sector (Bahmanyar et al., 2020).5 The pandemic has directly affected users’ habits. The peak time for electricity demand changed; not only did the peak days move from Wednesday–Friday pre-pandemic to Monday–Tuesday during the pandemic (Abu-rayash and Dincer, 2020), but the peak times were delayed as well (Chen et al., 2020; van Fan et al., 2020). As people were forced to carry out their activities at home, they were starting their day a few hours later than usual, saving commuting time. At the same time, people started enjoying longer days. Thus, COVID19 has provoked big structural changes in energy demand and consumption. Bahmanyar et al. (2020) compare countries that adopted severe restrictions measures, such as Spain, Italy, Belgium, and the UK, with countries such as the Netherlands and Sweden that implemented less severe restrictions. They find that the consumption profiles in the two groups of countries reflect the differences in peoples’ activities at the time of the restrictions. For example, in Sweden, the energy demand remained almost unaffected (rising slightly) concerning the same period of 2019. In contrast, significant reductions in energy demand were experienced in Spain (25%), Italy (17.7%), Belgium (15.6%), the UK (14.2%), and even the Netherlands (11.6%), where more restrictive measures were undertaken. Stabilizing the energy demand is key when dealing with renewable energies because it indicates their economic sustainability. During the pandemic, not only has the energy demand changed, but also its geographical distribution. Lots of consumers migrated from urban to rural areas (Carmon et al., 2020), as they were required to telework or study from home (Bick et al., 2020). The question of whether telework, although decreasing energy consumption in offices, will decrease the overall energy consumption is still open, as the answer will strongly depend on the users’ behaviour and their energy consumption at home. Given the lack of studies in this direction, it is still impossible, for example, to establish whether teleworking and e-learning are better than centralized offices and schools to stabilize demand and push renewables development (Hook et al., 2020; O’Brien and Yazdani Aliabadi, 2020). However, some of the new social practices that arose with the pandemic (teleworking, studying from home, e-commerce, etc.) may persist and deeply change the energy consumption pattern in cities and rural areas and provide an

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incentive to plan interconnected power grid systems which tend to be more stable. An important requirement of power grids is to maintain stable operations, i.e., low fluctuations in voltage and demand. Some renewable energy sources, such as wind and solar power, are intermittent. For example, solar energy has poor grid stability because of unreliable sunlight hours and clouds, representing investors’ main concern (Schmietendorf et al., 2017). The migration of the workforce to the rural area and, more generally, lower demand in big cities may advance the development of interconnected grids. Energy storage is essential to balance grids powered by renewables, but it is also costly. Thus, more interconnected grids can be a cheaper and more efficient solution. Different regions have different resources/climate profiles, so by interconnecting the grids between different regions, it is possible to redirect energy where it is needed without storing it. This, in turn, would also lower the use of fossils when the renewable supply from a given region is low.

6.4 Conclusions The COVID-19 pandemic has triggered a historical global economic shock. As the virus transmitted across countries, governments worldwide implemented extraordinary lockdown measures which affected the whole economy. Among all the sectors, the global energy sector emerges as one of the most affected ones. This unprecedented crisis has posed many challenges but has also created unique opportunities for the transition to renewable energies. First, the fall in the oil price, on the one hand, made fossil fuels cheaper and relatively more competitive with respect to sustainable energies. On the other hand, such a drop has increased the risk of investing in fossil fuels. As a result, oil companies may cut their investments, creating new room for investment in renewable energies. Second, as governments are facing an economic downturn with weak industries, high unemployment, and low demand for goods and services, we expect central banks to keep the interest rate low. This may boost investments in the (labour-intense) renewable energy sector under the right stimulus packages. Finally, the changes in the private demand for energy may drive private energy efficiency and renewable energy technologies usage. This will also lead to lower carbon emissions, as working from home allows consumers to reduce commuting with both private and public transportation. Furthermore, the change in consumption patterns promotes R&D in smart grids, which integrate different energy sources, stabilize the power demand and supply of energies, and promote consumers’ adoption of self-consumption energy facilities and environmentally responsible practices.

Notes 1 For a detailed description of the impact of oil price volatility on firms’ profitability, see, for example, Alaali (2020).

COVID-19 and Energy Transition  117 2 Mixed evidence has instead been reported for oil-importing companies (see, for example, Silvapulle et al. 2017). 3 Interestingly enough, a negative relationship between pandemic outbreaks and oil prices has not been generally reported. Examples are the 2016 bird flu pandemic in which oil prices actually increased, or the 2009/2010 swine flu pandemic, in which such effect has been almost negligible. 4 This corresponds to 13 million barrels per day. It is also interesting to notice that, despite the large demand for oil, the industry ranks third in terms of CO2 emissions. This is because a large share of the demand does not act as a fuel but, rather, as feedstock. 5 It is worth noticing that this resulted in an increase in the use of renewable energy, but this comes as no surprise, as renewable energy sources are often prioritized with respect to conventional power plants (Carmon et al. 2020).

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COVID-19 and Energy Transition  119 Jiang, Peng, Yee Van Fan, and Jiří Jaromír Klemeš. 2021. “Impacts of COVID-19 on Energy Demand and Consumption: Challenges, Lessons and Emerging Opportunities.” Applied Energy 285(January). doi: 10.1016/j.apenergy.2021.116441. Kanda, Wisdom, and Paula Kivimaa. 2020. “What Opportunities Could the COVID-19 Outbreak Offer for Sustainability Transitions Research on Electricity and Mobility?” Energy Research and Social Science 68(June):101666. doi: 10.1016/j.erss.2020.101666. Kang, Hyuna, Jongbaek An, Hakpyeong Kim, Changyoon Ji, Taehoon Hong, and Seunghye Lee. 2021. “Changes in Energy Consumption According to Building Use Type under COVID-19 Pandemic in South Korea.” Renewable and Sustainable Energy Reviews 148(May):111294. doi: 10.1016/j.rser.2021.111294. Levine, Mark D., and Robert V. Steele. 2021. “Climate Change: What We Know and What Is to Be Done.” Wiley Interdisciplinary Reviews: Energy and Environment 10(1):1–12. doi: 10.1002/wene.388. Madurai Elavarasan, Rajvikram, Rishi Pugazhendhi, Taskin Jamal, Joanna Dyduch, M. T. Arif, Nallapaneni Manoj Kumar, G. M. Shafiullah, Shauhrat S. Chopra, and Mithulananthan Nadarajah. 2021. “Envisioning the un Sustainable Development Goals (SDGs) through the Lens of Energy Sustainability (SDG 7) in the Post-COVID-19 World.” Applied Energy 292(April). doi: 10.1016/j.apenergy.2021.116665. Malamud, Carlos, and Rogelio Núñez. 2020. “El COVID-19 en América Latina: Desafíos Políticos, Retos Para los Sistemas Sanitarios e Incertidumbre Económica.” 1–12. Muellbauer, John, and Luca Nunziata. 2001. “Credit, the Stock Market and Oil: Forecasting U.S. GDP.” CEPR Discussion Paper 2906:1–47. Narayan, Paresh Kumar. 2020. “Oil Price News and COVID-19—Is There Any Connection?” Energy Research Letters 1(1):1–5. doi: 10.46557/001c.13176. Nguyen, Kim Hanh, and Makoto Kakinaka. 2019. “Renewable Energy Consumption, Carbon Emissions, and Development Stages: Some Evidence from Panel Cointegration Analysis.” Renewable Energy 132:1049–1057. doi: 10.1016/j.renene.2018.08.069. Norouzi, Nima. 2021. “Post-COVID-19 and Globalization of Oil and Natural Gas Trade: Challenges, Opportunities, Lessons, Regulations, and Strategies.” International Journal of Energy Research 45(10):14338–14356. doi: 10.1002/er.6762. O’Brien, William, and Fereshteh Yazdani Aliabadi. 2020. “Does Telecommuting Save Energy? A Critical Review of Quantitative Studies and Their Research Methods.” Energy and Buildings 225:110298. doi: 10.1016/j.enbuild.2020.110298. Ozili, Peterson K., and Thankom Arun. 2020. “Spillover of COVID-19: Impact on the Global Economy.” SSRN Electronic Journal 99317. doi: 10.2139/ssrn.3562570. Ozturk, Ilhan, and Ali Acaravci. 2010. “The Causal Relationship between Energy Consumption and GDP in Albania, Bulgaria, Hungary and Romania: Evidence from ARDL Bound Testing Approach.” Applied Energy 87(6):1938–1943. doi: 10.1016/j. apenergy.2009.10.010. Paiva, Aureliano Sancho Souza, Miguel Angel Rivera-Castro, and Roberto Fernandes Silva Andrade. 2018. “DCCA Analysis of Renewable and Conventional Energy Prices.” Physica. Part A: Statistical Mechanics and its Applications 490:1408–1414. doi: 10.1016/j. physa.2017.08.052. Prentice, Catherine, Jinyan Chen, and Bela Stantic. 2020. “Timed Intervention in COVID19 and Panic Buying.” Journal of Retailing and Consumer Services 57(January). doi: 10.1016/j.jretconser.2020.102203. Qin, Meng, Yu-Chen Zhang, and Chi-Wei Su. 2020. “The Essential Role of Pandemics: A Fresh Insight Into the Oil Market.” Energy Research Letters 1(1):1–6. doi: 10.46557/001c.13166.

120  COVID-19 and Energy Transition Salisu, Afees, and Idris Adediran. 2020. “Uncertainty Due to Infectious Diseases and Energy Market Volatility.” Energy Research Letters 1(2):1–6. doi: 10.46557/001c.14185. Schmietendorf, Katrin, Joachim Peinke, and Oliver Kamps. 2017. “The Impact of Turbulent Renewable Energy Production on Power Grid Stability and Quality.” European Physical Journal B 90(11). doi: 10.1140/epjb/e2017-80352-8. Shahbaz, Muhammad, Saleheen Khan, and Mohammad Iqbal Tahir. 2013. “The Dynamic Links between Energy Consumption, Economic Growth, Financial Development and Trade in China: Fresh Evidence from Multivariate Framework Analysis.” Energy Economics 40:8–21. doi: 10.1016/j.eneco.2013.06.006. Shan, Yuli, Jiamin Ou, Daoping Wang, Zhao Zeng, Shaohui Zhang, Dabo Guan, and Klaus Hubacek. 2021. “Impacts of COVID-19 and Fiscal Stimuli on Global Emissions and the Paris Agreement.” Nature Climate Change 11(3):200–206. doi: 10.1038/ s41558-020-00977-5. Silvapulle, Param, Russell Smyth, Xibin Zhang, and Jean Pierre Fenech. 2017. “Nonparametric Panel Data Model for Crude Oil and Stock Market Prices in Net Oil Importing Countries.” Energy Economics 67:255–267. doi: 10.1016/j.eneco.2017.08.017. Smil, Vaclav. 2008. “Moore’s Curse and the Great Energy Delusion.” The American 2(6):1–6. Smil, Vaclav. 2014. “The Long Slow Rise of Solar and Wind.” Scientific American 310(1):52– 57. doi: 10.1038/scientificamerican0114-52. Steffen, Bjarne, Florian Egli, Michael Pahle, and Tobias S. Schmidt. 2020. “Navigating the Clean Energy Transition in the COVID-19 Crisis.” Joule 4(6):1137–1141. doi: 10.1016/j.joule.2020.04.011. Szczygielski, Jan Jakub, Janusz Brzeszczyński, Ailie Charteris, and Princess Rutendo Bwanya. 2021. “The COVID-19 Storm and the Energy Sector: The Impact and Role of Uncertainty.” Energy Economics (March):105258. doi: 10.1016/j.eneco.2021.105258. Zhang, Yue. 2021. “The COVID-19 Outbreak and Oil Stock Price Fluctuations: Evidence From China.” Energy Research Letters 2(3):2–6. doi: 10.46557/001c.27019. Zhao, Xiaohui. 2020. “Do the Stock Returns of Clean Energy Corporations Respond to Oil Price Shocks and Policy Uncertainty?” Journal of Economic Structures 9(1). doi: 10.1186/s40008-020-00229-x.

7

EKC Modelling in a Post-pandemic Era A Policy Note on Socio-ecological Trade-offs Avik Sinha and Nicolas Schneider

7.1 Introduction Global warming has emerged as a major concern to be addressed. Yet, it is largely attributed to the anthropogenic release of greenhouse gas (GHG) emissions from fossil fuel combustion (Schneider, 2022). Early, the Meadows Report (Donella et al., 1972) highlighted the necessity to reshape the nature of our development path and planted the seed of what should be our long-term vision of desirable progress. In theory, the concept of sustainable development (SD) emerged as one that “meets the needs of the present without compromising the ability of future generations to meet their own” and is based on three pillars (economic prosperity, social equity, and environmental protection) (WCED, 1987). Empirically, such a relationship has been extensively inspected through the influential environmental Kuznets curve (EKC) framework, by reference to the pioneer theory of Simon Kuznets (1967) on inequalities. Traced back to Grossman and Krueger (1991), who examined the North American Free Trade Agreement’s (NAFTA) effect on a set of pollutant concentrations. This theory reframed the nature of the environmental debate by positing the existence of an inverted U-shaped relationship between aggregate income and environmental degradation. Although environmental degradation is expected to first rise with industrial development, a turning point may be reached, after which the trend would reverse, and the basic conflict between economic and climate objectives would become solved. This concept was revolutionary at the time because it reshaped the well-established human activities–environmental degradation relationship and highlighted that economic and environmental targets might be simultaneously achievable. Nonetheless, innovations stand as the key mechanism driving this dynamic shift in the carbon structure of any development process, and much less is known regarding its nature and particular features (Holdren and Ehrlich, 1974). The emergence of the EKC concept can be traced back to an old debate on how to elaborate a development framework deemed suitable to balance the costs and benefits associated with anthropogenic activities (Holtz-Eakin and Selden, 1995). While the literature on this topic is extensive, the existence of the EKC and its practical policy implications have failed to generate a DOI: 10.4324/9781003336563-7

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consensus so far (Jaunky, 2011). Besides, existing studies have mostly relied on a reduced-bivariate-EKC-form, using gross domestic product (GDP) to proxy “economic activity”, thus, hiding other fundamental components within a “black box” featuring an undefined share of technological innovations, diffusions, and value-added creation (Panayotou, 1993). However, during the recent break from the pandemic, the world has seen a surge in technological innovations. To an extent, those adaptative strategies rooted around digital channels prevented productivity from declining (especially in the services sector), enabling individuals to reduce their physical contacts and interactions and, thus, slow down the virus diffusion across cities. Information and communication technologies (ICTs)1 are yet known to require important energy and resource needs with mixed environmental effects (Magazzino et al., 2021). Indeed, the historical impact of ICT on the manufacturing and energy sector has long been ambiguous because of both direct and indirect effects (Van Ark et al., 2003). On the one hand, by lowering transaction costs and boosting knowledge creation, ICT would improve the efficiency gains and induce spillovers and network effects, thus, translating into higher multi-factor productivity growth (Berndt and Morrison, 1995; Pilat and Wölfl, 2004; Schreyer, 2000; Steinmueller, 2001). While ICT is correlated with the production of equipment and the running of infrastructure (server parks and data centres) (Røpke and Christensen, 2012), it may also improve the reliability and efficiency of the transmission of the energy grid,2 as well as enhance the storage and distribution of power (Amin and Rahman, 2019; Susam and Hudaverdi Ucer, 2019). Through finer monitoring and grid control systems, expanding ICT may reduce the technological gap (i.e., “missing middle”) across countries, which is tightly linked to environmental performance (Houghton, 2010). This is in line with the Earth Institute of Columbia University (2016), which listed several relevant ICT-based innovations in the energy sector, making the Sustainable Development Goals (SDGs) more achievable (i.e., untitled “ICT & SDGs”). Among these innovations, plant information models (PIM), knowledge organization systems and semantic technology (KOSST), and cyber learning platform for network education and training (CLP4NET) are underlined. Thus, by offering the possibility of much faster technology upgrading, smart motor systems, as well as providing services at low cost, ICT systems are becoming a prominent energy-sector enabler. On the other hand, ICTs also induce an important amount of technological waste whose material recovery remains uncertain (Elliot, 2007). Also, under the scenario that the ICT power-related need is higher than its energy efficiency gains, those technologies may exhibit negative environmental externalities because of the cumulative power demand they trigger and associated carbon emissions they release (in the case of a non-decarbonized national electricity supply). Finally, being majorly automation-driven, these energy innovation initiatives might create negative social externalities because they replace a share of the labour force and, thus, influence unemployment trends. One relevant

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example is that the decline of coal mining led to a lower carbon footprint due to a gradual shift from highly polluting to low carbon resources but drove down employment in the manual, labour-intensive industry. As the example shows, technological innovations may lead to more sustainable development, but it is not always conducive to generating employment (Sinha et al., 2022c). Consequently, the new technological wave induced by COVID-19 adaptative policies and strategies is generating mixed environmental and social externalities. And beyond the COVID-19 pandemic, this conflicting and ambiguous dynamic is likely to persist. Therefore, there is a need to further understand under which reasonable circumstances one may prevail over the other reciprocally and enable policymakers to handle this trade-off optimally. Undoubtedly, this research can be traced back to the seminal work of Arrow et al. (1961), which introduced the concept of capital-labour substitution. As the economic activities around the world now have been mostly digitally transformed, it is highly likely that the COVID-19 outbreak strengthened this dynamic along each stage of the supply chain, with heterogeneous effects across countries, regions, and sectors. For the reasons mentioned above, there is a point in investigating whether the technological shock induced by COVID-19-related adaptative strategies and policies would emerge as an environmental panacea or open Pandora’s box of social imbalances in the post-pandemic era. In this chapter, we attempt to create a policy framework accounting for present and future environment– social policy trade-offs. We formulate the following hypothesis: is there an optimum policy that enables decision-makers to reconcile social and ecological impacts of technological innovation under a post-COVID-19 era constrained by SDG achievements? Accordingly, this chapter seeks to contribute to the literature in three ways. •

•

Above all, over the past three decades, a long line of researchers has examined how economic and environmental indicators interact. Despite its early focus on the ecological impacts of anthropogenic development, conclusions associated with this literature differ and often conflict. Thus, this research draws researchers’ attention to current methodological issues and supplies a state-of-the-art survey of the EKC topic, starting by outlining the main features of past empirical analyses and drawing an analytical review of the field, with conclusions thought to enlarge the research field and suggest new alternatives. Second, we then investigate whether the technological shock induced by COVID-19-related adaptative strategies and policies would emerge as an environmental panacea (i.e., lower energy inefficiency and storage losses but larger power needs and technological waste) or open Pandora’s box of social imbalances (i.e., productivity gains through spillover effects but capital-to-labour substitution mechanisms and unemployment due to automation). In the wake of fourth Industrial (digital) Revolution, it appears crucial to examine the extent to which an optimum policy reconciles those

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•

ambiguous effects in a post-pandemic era. Hence, a theoretical mathematical baseline is developed to model the evolutionary impacts of a set of policy instruments, including CO2 emissions, total factor productivity, and employment, with non-linear patterns characterizing their relationships within a multi-variate EKC framework. Third and finally, a set of implications for theory and policy reforms are presented, along with policy caveats and assumptions and prospects for future fruitful research directions.

Besides the introduction, this chapter is organized as follows. Section 7.2 presents a state-of-the-art review of the empirical literature and a methodological discussion. Section 7.3 introduces the EKC baseline and theoretical model setup, whereas Section 7.4 shows the modelling outcomes and extensions, as well as a discussion. Finally, in Section 7.5, concluding remarks are provided with prospects for future research.

7.2 Survey of the Empirical EKC Literature First introduced by Grossman and Krueger (1991), the EKC theory has been extensively examined in various case studies and panels. In this section, a stateof-the-art review of the topic is presented. First, we outline the EKC baseline (i.e., GDP-environmental degradation nexus), stratifying by multi- (2.1) and single-country approaches (2.2). Then, we offer a discussion based on the insights drawn from this state-of-the-art review (2.3). Overall, we underline the methodological issues that previous studies have attempted to address so far, along with prospects for technical improvements. However, for an exhaustive review of the energy–GDP–CO2 emissions nexus, see Kaika and Zervas (2013), Sarkodie and Stresov (2019), Shahbaz and Sinha (2019), Schneider (2020), and Magazzino and Schneider (2020). 7.2.1 Multi-country EKC Studies

A first strand of the literature investigated the non-linear nature of the growth– pollution relationship through multi-country approaches. The authors compiled time-series data for a set of countries sharing a broad homogeneity and conducted an EKC testing methodology for the entire sample. This sub-section presents the most relevant and recent multi-country studies of the EKC between GDP and environmental degradation. Above all, a share of this literature validated the existence of the EKC while dealing with wide samples of economies. Upon the most relevant ones, we find Shafik and Bandhopadhyay (1992) for 149 economies; Panayotou (1993) for 68 countries; Selden and Song (1994) for 30 economies; Dinda et al. (2000) for 33 economies; Stern and Common (2001) for 73 countries; York et al. (2003a) for 142 countries; Cole (2004) for Organisation for Economic Co-operation and Development (OECD) countries; Dijkgraaf and Vollebergh

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(2005) for 24 countries; Apergis and Payne (2009) for 6 Central American countries; Lean and Smyth (2010) for 5 Association of Southeast Asian Nations (ASEAN) countries; Leitão (2010) for 94 countries; Castiglione et al. (2012) for 28 countries; Iwata et al. (2012) for 11 OECD countries; Ben Jebli et al. (2013) for 25 OECD economies; Bilgili et al. (2016) for 17 OECD economies; Kais and Sami (2016) for 58 economies; Zaman and Moemen (2017) for 90 countries; Haseeb et al. (2018) for Brazil, Russia, India, China, and South Africa (BRICS); Sarkodie (2018) for 17 African economies; Alshubiri and Elheddad (2020) for 32 OECD countries; Dogan and Inglesi-Lotz (2020) for 7 European Union (EU) countries; Kong and Khan (2019) for 14 developed and 15 developing countries; Le (2019) for 10 ASEAN countries; Adeel-Farooq et al. (2020) for6 ASEAN economies; Leal and Marques (2020) for 20 OECD economies; Murshed et al. (2021) for 6 Asian countries. Conversely, other multi-country studies failed to validate this theory. Among them, one finds Moomaw and Unruh (1997) for 16 transition economies; Agras and Chapman (1999) for 34 economies; Gangadharan and Valenzuala (2001) for 51 countries; Acaravci and Ozturk (2010) for 19 EU economies; Narayan and Narayan (2010) for 43 developing economies; Arouri et al. (2012) for 12 Middle Eastern/North African (MENA) economies; Baek (2015a) for 12 major nuclear energy-consuming economies; Baek (2015b) for Arctic countries; Heidari et al. (2015) for 5 ASEAN countries; Antonakakis et al. (2017) for 106 countries; Aye and Edoja (2017) for 31 developing countries; Cai et al. (2018) for Group of 7 (G-7) economies; Hu et al. (2018) for 25 developing countries; Moutinho et al. (2020) for 12 of the Organization of the Petroleum Exporting Countries (OPEC); Pata and Aydin (2020) for 6 hydropower generating nations. Finally, Lee et al. (2010) reported mixed evidence, as results indicated an inverted U-shaped relation between growth and pollution in America and Europe but not in Africa, Asia, and Oceania. In the same vein, Akadırı et al. (2021) only confirmed the EKC’s presence for BRICS countries in the long run. Besides, some new empirical approaches have attempted to test this theory using more elaborate and inclusive techniques and metrics. For instance, Al-Mulali et al. (2015), Ozturk et al. (2016), and Ulucak and Bilgili (2018) utilized ecological footprint as the dependent variable and a proxy for environmental degradation; Luzzati and Orsini (2009) inspected whether energy acts as a cofounder in the growth–environment nexus; Katz (2015) relied on the EKC framework to study the water use–GDP link, whereas Bimonte and Stabile (2017) assessed the land consumption–income relationship through the EKC lens; Mehmood and Tariq (2020) extended this analysis to institutional settings and confirmed the existence of a U-shaped curve between globalization and CO2 emissions for eight South Asian countries, while Dogan and Inglesi-Lotz (2020) brought a specific focus on the role of economic structure; Fang et al. (2021) tested the validity of the EKC between urbanization and atmospheric pollution; Magazzino et al. (2020) examined the municipal solid waste (MSW) production–income per capita nexus through the EKC. Table 7.1 outlines the main information in this literature.

Countries

42 economies 149 economies 68 economies 30 economies 16 transition economies 34 economies 33 economies 51 economies 73 economies 142 economies OECD economies 24 economies 19 EU economies 6 central American economies 5 ASEAN economies 94 economies 97 economies 43 developing economies 36 high-income economies 12 MENA economies 28 economies 11 OECD economies 25 OECD economies 12 nuclear energy-consuming economies Arctic economies 5 ASEAN economies 17 OECD economies 58 economies

Author(s)

Grossman and Krueger (1991) Shafik and Bandhopadhyay (1992) Panayotou (1993) Selden and Song (1994) Moomaw and Unruh (1997) Agras and Chapman (1999) Dinda et al. (2000) Gangadharan and Valenzuala (2001) Stern and Common (2001) York et al. (2003a) Cole (2004) Dijkgraaf and Vollebergh (2005) Acaravci and Ozturk (2010) Apergis and Payne (2009) Lean and Smyth (2010) Leitão (2010) Lee et al. (2010) Narayan and Narayan (2010) Jaunky (2011) Arouri et al. (2012) Castiglione et al. (2012) Iwata et al. (2012) Ben Jebli et al. (2013) Baek (2015a) Baek (2015b) Heidari et al. (2015) Bilgili et al. (2016) Kais and Sami (2016)

1977–1988 1960–1990 1988 1979–1987 1950–1992 1971–1991 1979–1990 1998 1960–1990 1996 1980–1997 1960–1997 1960–2005 1971–2004 1980–2006 1981–2000 1980–2001 1980–2004 1980–2005 1981–2005 1996–2008 1960–2003 1980–2009 1980–2009 1960–2010 1980–2008 1977–2010 1990–2012

Period

Table 7.1 Summary of EKC Studies on the Growth–Pollution Nexus: Multi-country Approach

FE FE OLS RE, FE OLS, FE FE OLS OLS and 2SLS FE, RE STIRPAT FE and RE OLS ARDL VECM DOLS FE, RE GMM Panel cointegration GMM and VECM CCE OLS ARDL FMOLS, DOLS FMOLS, DOLS ARDL PSTR FMOLS, DOLS GMM

Methodology Ø Ø T Ø Ø T Ø T Ø Ø Ø Ø T T T T Ø Ø Ø T T NE R NE T T R T

Energy data

Yes Yes Yes Yes No No Yes No Yes Yes Yes Yes No Yes Yes Yes Mixed No Yes No Yes Yes Yes No No No Yes Yes

EKC

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5 African economies BRICS countries 106 economies 31 developing economies Next 11 economies 90 economies G-7 economies 17 African economies BRICS economies 25 developing economies 32 OECD economies 14 developed and 15 developing economies 10 ASEAN economies MENA countries BRICS / Next 11 economies APEC economies 24 OECD economies 7 EU economies 6 ASEAN economies 20 OECD economies 12 OPEC economies 6 hydropower consuming economies Next 11 economies 35 Asia Pacific economies 27 OECD countries BRICS countries 6 South Asian countries 8 Asian countries

1980–2011 1980–2013 1971–2011 1971–2013 1990–2014 1975–2015 1965–2015 1971–2013 1993–2013 1996–2012 1990–2015 1977–2014 1993–2014 1990–2015 1990–2017 1990–2015 1980–2014 1980–2014 1985–2012 1990–2016 1992–2015 1965–2016 1990–2017 1990–2017 1990–2015 1995–2018 1980–2016 1990–2015

STIRPAT GMM VAR, IRF DHC, DPTR GMM GMM ARDL RE, FE, WC, ECM WC, DHC OLS, DOLS GMM, FE VECM, GMM FE, RE GMM GMM GMM CCE PPC, FMOLS MG, PMG ARDL PCSE ARDL Quantile regression Quantile regression Cup-FM, FMOLS PMG-ARDL AMG, CCE-MG CS-ARDL

T T T T F/R T RE T T R Ø Ø Ø R F/R T F T Ø F/R T R R R T F F R

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

Notes: T, F, and R refer to total energy consumption, fossil fuel energy consumption, and renewable energy consumption, respectively. Ø indicates that no energy consumption data were included in the estimation model. “Yes” indicates that the EKC hypothesis is supported, while “No” refers to its empirical rejection.

Lin et al. (2016) Sinha and Sen (2016) Antonakakis et al. (2017) Aye and Edoja (2017) Sinha et al. (2017) Zaman and Moemen (2017) Cai et al. (2018) Sarkodie (2018) Haseeb et al. (2018) Hu et al. (2018) Alshubiri and Elheddad (2020) Kong and Khan (2019) Le (2019) Shahbaz et al. (2019) Sinha et al. (2019) Sinha and Sengupta (2019) Destek and Sinha (2020) Dogan and Inglesi-Lotz (2020) Adeel-Farooq et al. (2020) Leal and Marques (2020) Moutinho et al. (2020) Pata and Aydin (2020) Sinha et al. (2020a) Sinha et al. (2020b) Zafar et al. (2020) Akadırı et al. (2021) Murshed et al. (2021) Sharma et al. (2021)

EKC Modelling in a Post-Pandemic Era  127

128  EKC Modelling in a Post-Pandemic Era 7.2.2 Single-Country EKC Studies

More recently, another strand of the literature emerged by performing investigations at the country level. Authors conducted time-series examinations to avoid generalizing results over heterogeneous countries and, thus, offer more reliable country-specific policy implications. Studies confirming the EKC hypothesis can be found in Jalil and Mahmud (2009), and Sun et al. (2021) for China; Fodha and Zaghdoud (2010), and Shahbaz et al. (2014) for Tunisia; Iwata et al. (2010) for France; Baek and Kim (2013) for Korea; Shahbaz et al. (2013) for Romania; Saboori et al. (2016) for Malaysia; Pata (2018) for Turkey; Işık et al. (2019a, 2019b) for the United States (US) (50 and ten states, respectively); Rana and Sharma (2019) for India; Sarkodie and Ozturk (2020) for Kenya; and Ongan et al. (2021) for the US. Nonetheless, this hypothesis failed to be empirically supported by Soytas et al. (2007) for the US; Fei et al. (2011), Wang et al. (2016), and Pata and Caglar (2021) for China; Alam et al. (2011), Yang and Zhao (2014), and Ahmad et al. (2016) for India; Balibey (2015) for Turkey; Mikayilov et al. (2018) for Azerbaijan; Iskandar (2019) for Indonesia; Hasanov et al. (2019) for Kazakhstan; Koc and Bulus (2020) for Korea; Minlah and Zhang (2021) for Ghana; and Shikwambana et al. (2021) for South Africa. Table 7.2 underlines key information from this literature. 7.2.3 Methodological Challenges and Discussion

This state-of-the-art review highlights three important points. Above all, while the EKC literature is rich and extensive, results differ and sometimes conflict. Thus, as concluded by Kaika and Zervas (2013), no clear conclusion has been drawn so far, perhaps because the income–CO2 emissions nexus remains highly sensitive to the carbon content of the type of energy consumed along the supply chain, which, in turn, is a non-avoidable industrial input. Also, the ways authors have attempted to address the complex economic–environment relationship diverge in several ways: the diversity of methodologies employed, the heterogeneity of samples of countries considered, and the variety of model specifications adopted. First, existing studies have demonstrated a strong capacity to apply various quantitative tools to a single research question. On the one hand, statistical assumptions may differ across tests and procedures, making the results less reliable and comparable across studies. On the other hand, using more powerful data techniques is thought to bring more consistent findings, especially when applied to complement a standard and less complex model. While early papers relied on standard panel estimators (i.e., fixed effects, random effects panel estimators), we noticed that others employed modified panel versions accounting for lagged values and, thus, reducing the endogeneity bias induced by reverse causality (i.e., dynamic ordinary least squares, fully modified ordinary least squares) and even innovative estimators with advantageous

EKC Modelling in a Post-Pandemic Era  129 Table 7.2 Summary of EKC Studies on the Growth–Pollution Nexus: Single-Country Approach Author(s)

Countries

Period

Soytas et al. (2007) Jalil and Mahmud (2009) Fodha and Zaghdoud (2010) Iwata et al. (2010) Fei et al. (2011) Alam et al. (2011) Baek and Kim (2013) Shahbaz et al. (2013) Shahbaz et al. (2014) Yang and Zhao (2014) Balibey (2015) Ahmad et al. (2016) Saboori et al. (2016) Sinha (2016) Wang et al. (2016) Sinha and Bhattacharya (2016) Sinha and Bhattacharya (2017) Mikayilov et al. (2018) Pata (2018) Sinha and Shahbaz (2018) Hasanov et al. (2019) Işık et al. (2019a) Işık et al. (2019b) Iskandar (2019) Rana and Sharma (2019) Koc and Bulus (2020) Minlah and Zhang (2021) Pata and Caglar (2021) Sarkodie and Ozturk (2020)

US China Tunisia

Sharif et al. (2020) Sun et al. (2021) Ongan et al. (2021) Shikwambana et al. (2021)

Methodology

Energy data

EKC

1960–2004 GC 1975–2005 ARDL 1961–2004 VECM

T T Ø

No Yes Yes

France China India Korea Romania Tunisia India Turkey India Malaysia India China India

1970–2003 1985–2007 1971–2016 1971–2007 1980–2010 1971–2010 1970–2008 1974–2011 1971–2014 1980–2009 2001–2013 1990–2012 2001–2013

NE T T F/R/N T Ø T Ø F Ø T T T

Yes No No Yes Yes Yes No No No Yes Yes No Yes

India

2001–2013 FE, RE

T

Yes

Azerbaijan Turkey India

1992–2013 DOLS, FMOLS 1974–2014 ARDL, FMOLS 1971–2015 ARDL

T R F

No Yes Yes

Kazakhstan US (50 States) US (10 States) Indonesia India Korea Ghana

1992–2013 1980–2015 1980–2015 1981–2016 1982–2013 1971–2017 1960–2014

Ø F R Ø Ø T/R Ø

No Yes Yes No Yes No No

China Kenya

1980–2016 ARDL 1971–2013 ARDL, ECM

R T/R

No Yes

China Turkey China US South Africa

1961–2016 1965–2017 1990–2017 1991–2019 1994–2019

F/R F/R T F/R Ø

Yes Yes Yes Yes No

ARDL OLS GC, TYC, IRF ARDL ARDL, VECM, GC VECM GC VAR, IRF ARDL, GC ARDL GMM VECM, IRF, GC FE, RE

FMOLS AMG FE, CCE ARDL TYC ARDL RWGC

ARDL QARDL VAR, GC DA SQMK

Notes: T, F, NE, and R refer to total energy consumption, fossil fuel energy consumption, nuclear energy consumption, and renewable energy consumption, respectively. Ø indicates that no energy consumption data were included in the estimation model. “Yes” indicates that the EKC hypothesis is supported, while “No” refers to its empirical rejection.

130  EKC Modelling in a Post-Pandemic Era

standard-error clustering features (i.e., generalized method of moments, panel quantile regression, Driscoll-Kraay standard errors), sometimes combined with a stochastic regression on population, affluence, and technology framework. More recently, we observed the novel utilization of advanced panel procedures allowing for heterogeneity (mean group estimator, augmented mean group estimator) and cross-sectional dependence among the series (i.e., common correlated effects mean group estimator), which fills an important gap in the methodological literature. Often supplemented by cointegration techniques (Johansen cointegration test; Pedroni panel cointegration), various macro-econometric tools have been utilized to test the dynamic short- and long-run interactions among economic and environmental indicators (autoregressive distributed lag bounds, vector error correction model). Finally, one must not avoid the non-negligible role accorded to causality tests along with modern EKC testing procedures. Starting with the standard one (i.e., Granger causality test), researchers have employed more innovative versions (TodaYamamoto causality test, Dumitrescu-Hurlin causality test, rolling window Granger causality). Overall, a prominent place is now dedicated to variance analyses (i.e., forecast error variance decomposition, impulse response function), widely used to check the validity of the causal inference ahead of the sample period using Cholesky decomposition. Second, an important insight drawn from the EKC literature is that most of the multi-country studies referenced here have conducted their empirical analyses based on large and heterogeneous samples of countries. This is laudable if the study aims at performing a global (or regional) scale assessment but less intuitive in the case of a panel elaborated with data from countries differing in their stages of development. Naturally, we acknowledge the data availability constraint (and the trade-off between N / T sizes) facing most researchers, which led to the over-reliance on panel settings to assess the presence of the EKC. However, we argue that the literature lacks single-country examinations, which might limit the design and supply of local-scaled policy recommendations. Third, another diverging point stands in the various approaches that researchers have adopted so far. To investigate the empirical relevance of the EKC hypothesis between economic growth and environmental pollution, previous papers frequently relied on bivariate or trivariate models3 (i.e., they added energy data as a single continuous explanatory factor). Similarly, export diversification–CO2 emissions frameworks have often been enriched with aggregate income data, thus, avoiding the inclusion of other fundamental determinants. Although they are easier to implement, we hereby consider that relying on such a model is limited because including a unique additional factor cannot sufficiently control for omitted variable bias. To understand this reasoning, one should refer to the theoretical architecture of the stochastic regression on population, affluence, and technology model, which extends the conventional IPAT (Impact of Population, Affluence, and Technology) framework and isolates the respective effect of each anthropogenic driver on global environmental change

EKC Modelling in a Post-Pandemic Era  131

(York et al., 2003b). As a matter of fact, IPAT and ImPACT (Modified Impact of Population, Affluence, and Technology) models remain accounting equations which fail to allow hypothesis testing, as the known values of some terms determine the value of the missing term. Also, they assume proportionality in the functional relationship between factors, which conflicts with the development of socio-ecological theory requiring more flexibility when empirically testing the anthropogenic factors–impacts relationship instead of simply assuming it within the structure of the model. Hence, this is of high interest to assess which other sub-factors may lead to an EKC relationship, as incorporating multiple confounding factors is thought to substantially limit statistical interferences, biased estimations, and misleading interpretations. All in all, we acknowledge that no clear picture has emerged for policymakers regarding the effective existence of the EKC, as associated findings seem to have diverged rather than converge over time. Another reason stands in the chronology of these investigations and the development of new econometric tools across decades. •

•

•

From the 1990s to the 2000s, the first wave of empirical papers relied on the conventional approach: large sample of countries, data corresponding to multi-pollutants, bivariate4 GDP/environmental pollution models, and standard econometric regression methodologies (i.e., ordinary least squares, fixed effects, and random effects) with often, a relatively small sample period (i.e., see Shafic and Bandyopadhyay (1992), Grossman and Krueger (1991), Selden and Song (1994), Stern and Common (2001)). Until the 2010s, the second wave of EKC studies has been characterized by the narrower focus on a single pollutant (i.e., typically using CO2 emissions as a proxy for environmental degradation) but using series covering large groups of countries and more developed panel estimations methodologies (generalized method of moments; panel quantile regression, dynamic ordinary least square; fully modified ordinary least squares). However, time periods remained relatively limited due to data availability constraints (i.e., see Acaravci and Ozturk (2010), Lee et al. (2010), Leitão (2010), Narayan and Narayan (2010)). Over the recent 2010–2020 period, studies have shifted from multi- to single-country approaches, notably because of newly available time-series data over relatively long periods allowing a sufficient number of consecutive observations for causality testing methodologies (i.e., see Alam et al. (2011), Shahbaz et al. (2014), Işık et al. (2019a, 2019b)). To fill the gap led by simple growth–pollution frameworks, this third wave of papers introduced new multi-variate model specifications incorporating production factors (i.e., gross fixed capital formation, labour, or population), energy inputs (i.e., total energy use, low-carbon energy use, or fossil energy use), trade indicators (i.e., trade openness or trade volumes), but also financial (i.e., foreign direct investments) and institutional indicators (i.e., government quality, corruption index). Also, accurate methodologies

132  EKC Modelling in a Post-Pandemic Era

(cointegration and causality tests, variance analysis) have emerged as a complement to standard regressions and, thus, revisit the tested relationships ahead of the selected sample period. Besides, this is under this recent context of advanced panel procedures, accounting for heterogeneity within the sample and cross-sectional dependence among the series, that have emerged as relevant EKC testing tools.

7.3 EKC Model Set-Up and Theoretical Baseline This section outlines the conventional theory under the EKC hypothesis and the commonly used econometric specifications. The EKC hypothesis, which can be traced back to Grossman and Krueger (1991), posits the existence of an inverted U-shaped relationship between aggregate income and environmental degradation. While environmental degradation is expected to first rise with economic development, a turning point would be reached, after which the trend would reverse, and the basic conflict between economic and environmental objectives could become solved through innovation dynamics. In doing so, the EKC theory stresses that the income–environment nexus may share non-linear interactions. As a matter of fact, in the earliest phases of the country’s development, primary production trends are limited by the incipient dynamic of economic activity. In contrast, natural resources are abundantly available, and the absolute amount of MSW generated is controlled. However, as industries grow, a substantial reduction in natural resource availability is observed. On the other hand, the cumulative extraction, combustion, and export of resources generally drive the atmospheric concentration of pollutants and environmental degradation. Throughout this period, the association between the per capita income or economic activity and environmental pollution is positive and linear (Kaika and Zervas, 2013; Haseeb et al., 2018). However, after an identified turning point, further economic (and notably per capita income) improvements are accompanied by a depletion of environmental degradation. This results from the internalization of environmental externalities, energy efficiency measures, renewable energy reforms, and a global ecological awareness of consumers. According to Al-Mulali et al. (2015), the upper-middle and high-income economies might be the most likely countries to follow the EKC, whereas the low and lower-middle income countries seem far from this development pattern. Indeed, such a structural shift is conditioned by low-carbon technology availability and diffusion speed, making it less accessible to low and lowermiddle income countries. In Figure 7.1, the graphical representation of the EKC is provided. The plotted inverted U-shaped structure indicates the association between per capita environmental deterioration and per capita income as projected by Grossman and Krueger (1991). Under some (yet partially identified) conditions, further economic growth contributes to establishing a sustainable path. Inversely, this theory postulates that no comprehensive reforms can be achieved without a

EKC Modelling in a Post-Pandemic Era  133

Figure 7.1 An environmental Kuznets curve (EKC).

sufficient level of economic development. This is in line with the basic philosophy of the EKC hypothesis as outlined in Beckerman’s (1992, p.482) vision: “although economic growth usually leads to environmental deterioration in the early stages of the process, in the end, the best and probably the only way to attain a decent environment in most countries is to become rich”. Despite the various approaches used in assessing the EKC, most of them follow a common framework specification, with the associated reduced panel functional form, as employed in Işık et al. (2019a, 2019b) and Hasanov et al. (2019):

Yit = a it + b1X it + b2X 2it + b3Zit + mit (1)

Where the dependent variable Y represents the following:

ì Negative Environmental Externalities Y= í (2) îNegative Social Exterrnalities

And α refers to the intercept (constant), X captures the level of income (i.e., a proxy for economic growth), Z denotes a matrix of other explanatory variables, and μ is the error term derived throughout the cross-sections i and time t.

134  EKC Modelling in a Post-Pandemic Era

For the sake of analysis, unemployment is assumed to be a negative social externality. Now, while moving along the growth trajectory, it is the mandate of the nations to have control over the environmental degradation issues. The countries rely more on environmental technologies to improve energy efficiency and reduce environmental degradation. These technologies are recognized as the major drivers of economic growth in recent times. However, while these technologies are helping the countries to achieve economic growth, these technologies might have a darker side, which is still unexplored. This dark side of environmental innovation might be experienced in terms of its social impacts (Saha et al., 2022). The rise in environmental innovation might not be readily acceptable to the industry; hence, the environmental degradation might start increasing at a decreasing rate. But at the same time, developing and deploying these innovations will require a constant labour force supply. However, with time’s graduation, the labour demand will start shrinking. Therefore, unemployment will start decreasing at a decreasing rate. Now, if both these trajectories are analyzed, it might be said that environmental degradation will reach a maximum while unemployment will reach a minimum. So, at some point, the environmental innovations will achieve its desired purpose, both ecologically and socially. However, in the wake of COVID-19, both these impacts were intensified. This intensification can be experienced in two ways. Just before the outbreak of COVID-19, the world envisaged the emergence of Industry 4.0, which was aimed at digitizing technological innovations. The impact of these innovations can be experienced in reduced environmental degradation, and the outbreak of COVID-19 has helped intensify this impact. The slowdown and the closure of the manufacturing and transportation sectors have allowed these innovations to transform the business scenario through automation and digitization. This has helped reduce the overall ecological footprint of these sectors. On the other hand, the economic slowdown and the digitization of businesses during the COVID-19 period have shrunk the labour market. It would not be ambitious to say that these innovations have substituted human labour to a great extent. Under these circumstances, the respective minimum and maximum points achieved in the already explained curvilinear associations might not hold anymore. For the environmental degradation, the curve will start coming down, as the environmental degradation has shown a sign of reduction. However, the curve will start going up for unemployment, as unemployment has shown a sign of increase. Going by this explanation, it might be said that the innovation–environmental degradation association might take an inverted U-shaped form, whereas the innovation–unemployment association might take a U-shaped form. Now, both of these associations will coexist, resulting in a policy trade-off scenario, i.e., both these aspects will follow opposing trajectories with a gradual rise in innovation. From this perspective, Eq. (1) might give different outcomes

EKC Modelling in a Post-Pandemic Era  135

regarding its turnaround point. The turnaround point achieved from Eq. (1) is given by the following:

¶Yit = b1 + 2b2X it ¶X it

Or,

¶ 2 Yit = 2b2 ¶X it 2

Now, 2b2 can denote either minimum or maximum value of the Eq. (1). As X it is absent in the second derivative, Eq. (1) will have a global minimum or maximum. Given this situation, the following two situations can arise:

¶ 2Yit ì 2b2 > 0, Equation has global minimum =í ¶X it 2 î2b2 < 0, Equation has global maximum

The global maximum is achieved where the slope of the association turns negative after achieving zero, whereas the global minimum is achieved where the slope of the association turns positive after achieving zero. The first scenario can arise in the case of environmental degradation, which is expected to fall after reaching a high, while the second scenario can arise in the case of unemployment, which is expected to rise after reaching a low. The opposite signs of 2b2 denotes the presence of a possible trade-off between these two aspects. In light of this discussion, Eq. (1) can be written as:

EDit = a itED + b1EDX it - b2ED2X 2it + b3EDZit + mitED (3)



UN it = a UN - b1UN X it + b2UN 2X 2it + b3UN Zit + mUN it it (4)

Here, ED and UN are environmental degradation and unemployment, respectively. Now, the coexistence of these two associations will be analyzed in the following section.

7.4 Extensions and Discussion As the environmental degradation and unemployment issues are arising because of ongoing innovational endeavours, and these two issues are coexisting in opposing directions, both associations may have certain overlap(s). These possible overlapping domains can be achieved by solving Equations (3) and (4). Assuming the overlap happens at the innovation to be at X *it , the following can be stated:

136  EKC Modelling in a Post-Pandemic Era a itED + b1EDX *it - b2ED2X *it2 + b3EDZit + m itED = a UN - b1UN X *it + bU2 N 2X it*2 + b3UN Zit + mitUN it

Or, 2 X *it ( b1ED + b1UN ) - X it*2 ( b2ED2 + bUN ) 2

{(

)

(

) (

(

)

(

)

+ a itED - a UN + Zit b3ED - b3UN + m itED - m UN it it

)} = 0

X *it2 b2ED2 + b2UN 2 - X *it b1ED + b1UN - g it = 0



Or, (5) [Assuming

{( a

ED it

)

(

) (

- a UN + Zit b3ED - b3UN + m itED - m UN it it

)} = g

it

³ 0]

From Eq. (5), it is evident that two solutions exist for X *it . This shows that these two associations will overlap twice. Solving Eq. (5) gives the following:

(

) (b

X *it = b1ED + b1UN ±



ED 1

+ b1UN

)

2

(

) (

)

2 + 4 g it b2ED2 + bUN / 2 b2ED2 + b2UN 2 (6) 2

For the 2 solutions of X *it to lie on the cartesian plane, 2 (b1ED + b1UN ) + 4g it (b2ED2 + bUN ) > 0. This condition is realistic to assume, as 2 the innovations will not take any imaginary form. Assuming these two solutions appear at two different times, i.e., t = {t1, t 2} , then the following can be written: ì é ED UN ï ê †1 + †1 ïë X *it = í ï é † ED + † UN + 1 ï êë 1 î

(

) (†

ED 1

+ †1UN

)

(

) (†

ED 1

+ †1UN

)

2

2

2 ù ED 2 UN 2 + 4 ‡it † 2ED2 + † UN ,t = t1 2 ú / 2 †2 + †2 û

(

)

(

)

2 ù ED 2 UN 2 + 4 ‡it † 2ED2 + † UN ,t = t 2 2 ú / 2 †2 + †2 û

(

)

(

)

In empirical pursuit, the value of X *it is generally considered in logarithmic terms. Hence, the two intersection points between the two associations at t = {t1, t 2 } can be expressed as: e

é ED UN ê b1 + b1 êë

2

é ED UN + ê b1 + b1 êë

2

( (

ù

2 2 ) (b1ED +b1UN ) +4 git (b2ED2 +bUN ) úúû /2(b2ED2 +bUN ) 2 2

, at t = t1

ù

2 2 ) (b1ED +b1UN ) +4 git (b2ED2 +bUN ) úúû /2(b2ED2 +bUN ) 2 2

e , at t = t 2 These two points can be called as PIA and PIB, i.e., the two respective policy intervention points. In Figure 7.2, these two points are denoted. The negative environmental externality–economic growth driver and negative social externality–economic growth driver associations intersect at these points. Now, it is worthwhile noting that, while both these associations are coexisting, they are

EKC Modelling in a Post-Pandemic Era  137

Figure 7.2 Diagrammatic representation of policy intervention points.

moving in opposite directions. Now, in such a situation, none of the associations will be able to achieve the respective maximum or minimum values, as it might reinforce the trade-off. Hence, an optimum policy needs to be discovered, and this point might exist at the intersection of these two associations. The development of the innovations started at the beginning of Industry 4.0. This was before the outbreak of COVID-19. Hence, it can be anticipated that the PIA is already crossed. As it is not possible to revert the innovations, the policymakers now have to wait for PIB, which will definitely occur in the post-pandemic period. Given the nature of the economic growth trajectory of the nations, it can be expected that the presence of the turnaround points outside the sample range will ensure that the trade-off will appear in the post-pandemic regime. Along these trajectories, the associations are assumed to reach a point t. At this point, the values of negative environmental and social externalities are assumed to take the values TPEE and TPSE. If these points are achieved before the turnaround points of these associations, then the policymakers have to wait for the trade-off to occur. Otherwise, the policymakers have to wait for the next policy intervention point. This trade-off is inevitable, as the development and deployment of the innovations cannot be ceased. This can be determined by determining the maximum distance between the two negative externalities. If the distance Dit between two externalities occur at the level of innovation at X it** , then the following is possible:

max Dit = max ( EDit - UN it ) (7)

138  EKC Modelling in a Post-Pandemic Era

This association can be expressed as the following:

max Dit :

dDit dX it

= 0 X it = X it**

(

)

ED 2 **2 ED ED ì a ED + b1ED X** it - b 2 X it + b3 Zit + m it dDit d ï it Or, = í dX it dX it ï - a UN - bUN X** + bUN 2X**2 + bUN Z + m UN it 1 it 2 it 3 it it î

(

(

) (

)

ü ï ý=0 ï þ

)

dDit UN = b1ED - 2b2ED 2X** + 2b2UN 2X** it - -b1 it = 0 dX it



Or,



ED UN Or, X** / 2 b2ED 2 + b2UN 2 (8) it = b1 + b1



\

(

) (

)

d 2Dit 2 = -2 b2ED2 + bUN < 0 2 dX it 2

(

)

The presence of X ** it to be within or outside the sample range will decide the time of the trade-off. It is evident that PIA < X ** it < PI B . So, it might be difficult for the policymakers to simultaneously attain the objectives of SDG 8 and 13, as decent work and economic growth will entail a social balance, while the innovations will reduce environmental degradation. Retaining the maximum distance between these two associations will require X ** it to be a constant, which is not practicable. Hence, policymakers must wait for PIB to appear in the post-pandemic regime. In order to stabilize the economic system by having control over this trade-off, the policymakers need to put efforts to delay the occurrence of this point by flattening both curves. This brief discussion gives an idea of the general notion of the EKC hypothesis in the post-COVID-19 situation. By far, the EKC hypothesis has been predominantly used as an ecological impact assessment tool. However, in the wake of Industry 4.0 and the outbreak of COVID-19, its role can be recognized as a policy determining tool, which can capture the trade-off between the policy impacts (Sinha et al., 2022a, 2022b). The curvilinear association divulged through the empirical schema of the EKC hypothesis gives it the flexibility to go beyond the traditional environmental impact assessment and capture the evolutionary impacts of economic growth and its drivers on the structural factors of an economy. As the economic growth scenario across the world is transforming the post-COVID-19 regime, the role of the EKC hypothesis as a policy trade-off tool should be recognized.

7.5 Concluding Remarks Achieving SDGs 8 and 13 is now widely recognized as crucial. Over the past three decades, many researchers have examined how economic and environmental indicators interact. Despite its early focus on the ecological impacts of

EKC Modelling in a Post-Pandemic Era  139

anthropogenic development, conclusions associated with this literature differ and often conflict. In this chapter, we first supplied a state-of-the-art review of the EKC topic and shed light on the methodological challenges the empirical literature has attempted to overcome. Then, we investigated whether the technological shock induced by COVID-19-related adaptative strategies and policies would emerge as an environmental panacea (i.e., lower energy inefficiency and storage losses but larger power needs and technological waste) or open Pandora’s box of social imbalances (i.e., productivity gains through spillover effects but capital-to-labour substitution mechanisms and unemployment due to automation). In the wake of fourth Industrial (digital 4.0) Revolution, it appears crucial to examine the extent to which an optimum policy reconciles those ambiguous effects in a post-pandemic era, with a rising pressure to meet SDGs 8 and 13. Hence, a theoretical mathematical baseline is developed to model the evolutionary impacts of a set of policy instruments, including CO2 emissions, total factor productivity, and employment, with non-linear patterns characterizing their relationships within a multi-variate EKC framework. Modelling findings show that economic growth drivers do reduce the negative environmental externalities while increasing the negative social externalities, following thresholds of the respective associations and identified conditions, with an optimum solution between these two competing policy impacts. In this pursuit, the role of the EKC hypothesis should be recognized as a major policy designing tool capable of capturing the socio-ecological tradeoffs arising, especially in the post-pandemic period. Nomenclature AMG: ARDL: CCE-MG: CLP4NET: DA: DHC: DOLS: ECM: EKC: FE: FMOLS: GC: GMM: IRF: KOSST: MG: OLS: PCSR: PIM:

augmented mean group autoregressive distributed lag bounds Common Correlated Effects Mean Group estimator cyber learning platform for network education and training decomposition analysis Dumitrescu-Hurlin causality test dynamic ordinary least squares error correction model environmental Kuznets curve fixed effects fully modified ordinary least squares Granger causality test generalized method of moments impulse response function knowledge organization systems and semantic technology mean group ordinary least squares panel corrected standard errors plant information models

140  EKC Modelling in a Post-Pandemic Era

PMG-ARDL: PPC: PSTR: RE: RWGC: SDGs: SQMK: STIRPAT: TYC: VAR: VECM: WC:

pooled mean group-autoregressive distributed lag model Pedroni panel cointegration panel smooth transition regression random effects rolling window Granger causality Sustainable Development Goals sequential Mann-Kendall test stochastic regression on population, affluence, and technology Toda-Yamamoto causality test vector autoregressive vector error correction model Westerlund cointegration

Acknowledgements: Comments from the editor and the anonymous referees are gratefully acknowledged. However, the usual disclaimer applies. Ethics approval and consent to participate: Not applicable Consent to publish: Not applicable Availability of data and materials: Not applicable Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Notes 1 This term refers to the role of unified communications (computers and telephone networks) allowing for the storage and the transmission of information in a digital form. It also includes the integration of telecommunications and computers, enterprise software, storage, and audio-visual systems (Murray, 2011). But the concept of ICT is broad and evolving, thus encompassing a multi-disciplinary field of research. This notably covers information systems (IS) (Walsham et al., 2007), human computer interaction (HCI) (Dearden, 2008), and communication studies (CS) (Mansell, 2002). 2 This is in line with Laitner and Martinez (2008) when argued that “for every kilowatthour consumed by ICT systems, a savings of 10 kilowatt-hours were enabled”. 3 Trivariate models contrast to bivariate frameworks, as they incorporate an energy determinant within their econometric specification. With three variables, a model is considered multi-variate. 4 Bivariate models contain an economic variable and an environmental variable capturing only economic growth and environmental degradation, respectively.

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8

Sustainable Development through Carbon Neutrality A Policy Insight from Foreign Direct Investment and Service Policy Edmund Ntom Udemba

8.1 Introduction The effort toward reducing carbon emissions can never be put off. Climate change and global warming remain global issues that call for emergency action. Climate change impacted by excessive human industrial activities after the preindustrial era has been among the major global challenges (Elum and Momodu, 2017). These activities have given rise to global warming, causing changes and adverse effects on the natural environment and obstructing and altering the functioning of ecosystems, human society, and lives. Global warming has caused so much damage and is still causing damages such as alteration of the earthly features, e.g., rising sea levels, melting of the Antarctica region, shortages of food and water, and extinction of many species of wildlife. The climate change that emanates from human activities is associated with excessive heat waves generated from the excessive utilization of energy sources, mostly fossil fuels (Elum and Momodu, 2017). The excessive utilization of energy sources is due to accelerated industrial changes geared toward progressive economic growth and performance, which cut across different sectors of the economy. The workings of economic growth involve the interactions of different sectors (both private and public sector) of the economy as patterned by the officials of any country, which ranges from the agricultural, construction, industrial, financial, energy/power and petroleum, and trade and service sectors (Udemba, 2020b). Each sector works toward the promotion and achievement of a set goal which enhances the life of the masses via development and entire economic growth. The activities prevalent in most of the mentioned sectors involve using fossil fuels to power the mechanical and electrical systems of the sectors. The system involves mechanical and electrical inputs in the production and distributive functions of the sectors. Products depending on the industrial specifications and specializations, such as automobiles, textiles, footwear, foods, chemicals, weapons, and others, are produced with mechanical and electrical technologies that utilize a great amount of fossil fuel energy sources. The production process permits emission of carbon into the atmosphere, which impacts the environment in adverse ways. The distribution process also involves the emission of pollutants into the environment through DOI: 10.4324/9781003336563-8

150  Sustainable Development through Carbon Neutrality

motor exhaust. Shifting from the industrial sectors, the agricultural sector (both subsistence and mechanized farming) permits pollution into the environment (Stapleton, 2014). This happens through land and forest recovery and ploughing of the exposed land, employing heavy machines that utilize fossil fuels in farming, drainage and irrigation process, and applying chemicals such as fertilizers and pesticides. Farming activities affect both the air and water of the environment. When trees are cut down, it exposes the atmosphere to excessive untapped carbon dioxide (CO2) meant to be absorbed by the trees. The escalation and drifting of chemicals into bodies of water via runoff, such as erosions, irrigation, and drainage, make the bodies of water toxic to both aquatic and human life (Stapleton, 2014). All human activities toward economic advancement revolve around practices that employ energy utilization and are proven to generate global warming if diversification into other greener energy sources is not feasible (Elum and Momodu, 2017). The impact of foreign direct investments (FDI) on economic and environmental operations toward growth and development cannot be neglected when considering the activities in some sectors, especially the industrial sectors. FDI is the act of transferring productive activities by foreign investors into the economy of a foreign country because of their perceived gains in doing business in such a country (Tan and Meyer, 2011). Many factors attract foreign investors, such as friendly policies that will foster the growth of foreign investment, availability of both human and primary resources at a low cost, access to the market, and the prospect of economies of scale (Anyanwu, 2012). No doubt, the positive and negative impact of FDI on the economy have been questioned by many scholars with varying research results on FDI but with non-homogeneous results (Sbia et al., 2014; Shahbaz et al., 2015, 2018; Abdouli and Hammami, 2017, 2020; Agha and Khan, 2015; Hakimi and Hamid, 2016; Udemba et al., 2019; Udemba, 2020a). Many support the positive sides by citing the spillover effects of FDI on the economy, which tend to help solve unemployment problems, skill transfer, and technological advancement in the country. In contrast, others doubt its positivity regarding capital flight and the environmental impact of FDI. The critics of FDI believe that most foreign investors are opportunists who exploit the less rigid policies of the sited countries (e.g., developing and emerging economies) by developing products in a manner that produces pollution in the country. Most of their productive activities involve the excessive use of fossil fuels, which tends to produce harmful emissions. The critics’ view of FDI is equally challenged on the premise of innovative actions of the foreign investors, which come in the form of technological advancement and development, which can foster clean production and pave the way for a low carbon economy. Another point worth considering is the static nature of society and the activities prevailing in society. So far, society’s productive and economic activities are progressive and not static. The innovative mindset of investors likely will prevail in positively advancing both the economic and environmental performance by introducing new technologies capable of using renewable energy sources. FDI in the

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United Arab Emirates (UAE) has been increasing despite the demarketing of the region’s accommodation of FDI because of the instability of the countries within the region (Ahmad, 2018). Prospective investors fleeing the neighbouring countries consider the UAE as their choice of transferring their products and business ventures because of its economic and political stability. Between 2018 and 2019, the UAE recorded FDI inflows of 32%, with a figure amounting to US$13.8 billion. According to United Nations Conference on Trade and Development’s (UNCTAD’s) World Investment Report 2020, the UAE was the largest FDI recipient within the sub-region of West Asia in 2019. Most investments are concentrated in the oil and gas sectors, tourism and trade, real estate, finance and insurance, and manufacturing and construction. The breakdown of FDI inflow in the UAE is represented in Table 8.1. However, as the UAE economy is progressing in innovative ways, with investment and industrial activities, it will tend to expand and intertwine with other sectors such as service. It has been argued that the progressiveness of economic performance comes in different stages according to the environmental Kuznets curve hypothesis (EKC), which impacts the environment in each stage (Omer, 2008). Among the stages are the structural and technological effect stage, the second and final stages with structural effects on the economy. These structural and technical effects come with innovation and greater awareness on the side of the masses that tends toward embracing service sectors for a cleaner environment. The final stage of the EKC hypothesis depicts the developed economy, while the first stage (scale effect stage) depicts the developing economies in their starting stage. Service sectors in most developed nations are among the key drivers of their economies, both in economic and environmental performance. The UAE is among the countries with stable growth in its service sector and is on par with developed economies. This is because of its policies and capacity to diversify its economical operation from a hydrocarbon economy to a service-oriented economy. It has been proven that the UAE is one of the most prosperous countries in the Gulf Cooperation Council (GCC) region because of its diversification strategy and ability to maintain stability (Johnson and Babu, 2020). According to HSBC Trade Forecast, the UAE service sector is trending positively on the right path and is expected to be a factor in helping the country’s economy compared to other sectors that are subject to global macroeconomic volatility and continuous low oil prices. The service sector’s contribution to total exports in Table 8.1 2017–2019 FDI Inflow in the UAE Foreign Direct Investment

2017

2018

2019

FDI Inward Flow (million USD) FDI Stock (million USD) Number of Greenfield Investments*** Value of Greenfield Investments (million USD)

10354 129934 333 8791

10385 140319 382 11895

13788 154107 445 13557

Source: Nachum, 2001.

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the UAE increased from 16% in 2000 to about 24% in 2015 (Kynaston and Roberts, 2015). The five key service sectors in the UAE are real estate, retail, maritime transport, tourism, and financial services. Despite the positivity of the service sector to the development of the overall economy, the sector is considered one of the hidden inducers of CO2 emissions in any economy due to its pooling and pull effects. All service sector units are potential emission promoters regarding the production cycle, which starts with assembling resources for production to the final stage of distribution. Transportation, as part of the service sector, is coordinated throughout the production cycle. These transporting services are considered the highest inducer of direct emissions in the sector’s production (Alcantara and Padilla, 2009). Therefore, as part of the service sector, the transportation sector generates strong emissions within its production and by servicing other sectors in a pull effect. The breakdown of sectorial contribution to the UAE GDP in terms of employment and value added reflects the service sector’s sensitivity to the country’s economic performance (see Table 8.2). This study considers a country-specific investigation of the economic and environmental performance of the UAE, as it is among the countries that always stands out in achieving its progressive and economic development target. In 2021, the country set a goal to achieve sustainable development. The twin agendas embedded in this vision of sustainable development are good environmental and economic performance combined with social development. In the pursuit of good environmental performance engender strategies to curtail high pollutant emissions and improve air quality. To effectively execute the plans to balance the environmental performance and economic performance, the country established the Ministry of Climate Change and Environment, an institution saddled with the responsibility of mitigating climate and environmental changes. The major part of the UAE’s effort in addressing climate change and environmental issues is the reduction of carbon emissions. This is a vital policy when considering the economy’s foundation as a resource-based economy. The UAE is considered a hydrocarbon economy because of its oil and gas reserves and production and consumption richness. In the resource mix of the country, oil production is greater than gas production, but in consumption, the reverse is the case. This is capable of enhancing carbon emission. The energy production mix of UAE is shown in Figure 8.1.

Table 8.2 Sectorial Breakdown of Economic Activity Sector

Agriculture

Industry

Service

Employed by Sector (in % of Total Employment) Value Added (in % of GDP) Value Added (Annual % change)

3.7 0.7 7.1

23.3 46.8 2.0

73.0 46.9 1.9

Source: World Bank.

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Figure 8.1 Graphical illustration of energy production (oil and natural gas) in the UAE.

Initiatives such as monitoring and tracking global greenhouse gas (GHG) emissions to improve policies aimed at their reduction, establishing energy plans to reduce carbon emissions to 70% that extend to 2050, and adopting low carbon technologies through alternative energy sources to curb carbon emission. On this premise, the present study seeks to investigate the environmental performance of the UAE with service and FDI as indicators. The author thought it novel to investigate the country’s environmental performance by employing FDI and service as among the determinants of economic growth and the environment. CO2 emissions are taken as a proxy for the environment. Employing service in the environment modelling for a better exposition of environmental performances is unique to this study. The novel aspect of this present study is seen with the application of service and FDI as among the indicators in testing the environmental performance of the UAE. The two indicators (FDI and services) are considered sensitive or key sectors that positively impact the UAE economy and have a pulling effect on the environment. With the help of the EKC hypothesis, it is perceived that, at some point in economic growth, a developing economy will tend toward awareness of environmental performance and become more inclined to services. At this stage, the economy will experience quality environmental performance. To the author, no study has adopted this insight of testing the environmental performance with services. Hence, the author wishes to test: (a) the impact of service on environmental performance, (b) whether the trending and positive impact of FDI on the economy is practically acknowledged or just abstract, and (c) whether

154  Sustainable Development through Carbon Neutrality

the positivity of FDI on the economy has a spillover effect to environmental performance. In the remaining parts of this study, Section 8.2 provides a literature review and theoretical background. Section 8.3 details the study’s methodology and modelling. Section 8.4 presents the empirical results and discussion, and Section 8.5 gives a summary of the study and offers policy recommendations.

8.2 Literature Review and Theoretical Background 8.2.1 Literature Review

There have been many studies on carbon emissions targeting the effective means of reducing them in the environment. Many scholars have used different variables and methodologies in researching this topic with varying findings which allow further research to proffer solutions to minimize global warming. Using diverse methods resulting in varying findings, several studies (Alola et al., 2019; Zafar et al., 2019; Alola, 2019; Sharif et al., 2020; Liu et al., 2020; Charfeddine and Khediri, 2016; Ozatac et al., 2017; Basarir and Arman, 2014; Shahbaz et al., 2013, 2014; Ozcan, 2013; Sbia et al., 2017; Sarkodie and Ozturk, 2020; Shahbaz et al., 2015; Merican et al., 2007; Acharyya, 2009; Blanco et al., 2013; Zhang and Zhou, 2016; Sapkota and Bastola, 2017; Udemba, 2019; Al-mulali and Tang, 2013; Neequaye, 2015; Tang and Tan, 2015; Udemba, 2019; Udemba et al., 2020) have explored the case of China, while Udemba (2020a) studied India and Nigeria. Hence, Alola et al. (2019) studied 16 European countries and confirmed that non-renewable energy consumption negatively impacts the environment, while renewable energy sources positively impact the environment. In their study of emerging economies, Zafar et al. (2016) confirmed the negative effect of non-renewable energy use on the environment. Also, they confirmed the adverse effect of trade openness on the environment. Alola (2019) used quarterly data from the United States (US) to do an autoregressive distributed lag (ARDL) analysis of the US environmental performance and found that trade policy, monetary policy, and the migration index exert a positive and significant impact on CO2 emissions in the long run. Sharif et al., 2020, in the case of Turkey, applied a quantile autoregressive lagged approach and found a reverse effect from the selected variables on the ecological footprint (EFP). Liu et al., 2020, applied a semi-parametric panel fixed effects model and found a relationship between globalization and CO2 emissions for G-7 countries. Charfeddine and Khediri (2016) worked for the UAE with multiple structural breaks and regime switching and found EKC for the case of the UAE. Also, they found that electricity consumption, trade openness, and urbanization improved the environment. Ozatac et al. (2017) applied a bound test and error correction model to study Turkey’s emissions and found EKC for Turkey. Basarir and Arman, 2014 researched the UAE emissions and found EKC. Shahbaz et al. (2013) explored the case of the UAE with the ARDL-bound method and found cointegration among the variables. Also, they found that exporting improved

Sustainable Development through Carbon Neutrality  155

the environment. They also applied Granger causality and found that GDP and urbanization caused CO2 emissions. Ozcan (2013) found evidence of the EKC hypothesis for the Middle East countries. Sbia et al. (2017) applied an ARDLbound testing approach to study the UAE and found a positive relationship between carbon emission and GDP, electricity, and urbanization. Sarkodie and Ozturk (2020) studied Kenya’s economy with the ARDL-bound test regarding environmental performance and found EKC for Kenya. They also found that energy use, importing, and urbanization had an unfavourable impact on CO2 emissions. Shahbaz et al. (2015) applied the ARDL method to study the Pakistan economy and found cointegration among the variables of energy consumption and economic growth. Merican et al. (2007) applied ARDL to study South Asia and found FDI added to the pollution in Malaysia and Thailand but improved the environment in Indonesia. Acharyya (2009) applied ARDL to study India and found that FDI adds to the Indian economy and environmental pollution. Blanco et al. (2013) studied 18 Latin American countries with panel Granger causality and found that FDI causes carbon emissions. Zhang and Zhou (2016) applied the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to study China’s environment and found that FDI contributes to CO2 emission reductions. Sapkota and Bastola (2017) applied panel fixed and  random effects models  to study 14 Latin American countries and found the pollution haven hypothesis and EKC. Udemba (2019) applied ARDL to study Indonesian economic and environmental performance and found pollution halo hypothesis. Al-mulali and Tang (2013) applied the Pedroni cointegration  test fully modified ordinary least square (OLS) test to study the GCC countries and found variables are cointegrated; energy consumption and GDP growth increase CO2 emissions while FDI inflows have a long-run negative relationship with CO2 emissions. Neequaye (2015) applied a fixed effects model to study 27 developing countries and found EKC hypothesis and total GHG emissions from the energy and industrial sectors. Tang and Tan (2015) applied cointegration and Granger causality to study pollution in Vietnam and found the existence of long-run equilibrium among the variables and EKC. Udemba et al. (2019, 2020) applied ARDL-bound testing to research China’s pollution and found a positive relationship between pollutant emissions with all other variables. Udemba (2020a) researched India’s pollution with structural break and ARDL and found a positive and significant link between EFP and agriculture, energy use, and population, with a negative link between EFP and FDI. Udemba (2020a) applied ARDL to study Nigeria’s pollution and found that both the economic growth and EFP are increasing at the same pace, and there is a positive relationship between income (GDP per capita) and the selected independent variables (EFP, agriculture, FDI, energy use). 8.2.2 Theoretical Background

The theoretical background of this study is based on pollution haven hypothesis (PHH), which is a hypothesis that is centred on the impact of FDI on

156  Sustainable Development through Carbon Neutrality

environmental performance. The hypothesis is that foreign investors and their productive activities are detrimental to the environmental performance of the host countries. Also, proponents of PHH reference foreign investors’ activities in developing and emerging economies. It is assumed that most developing economies operate with less stringent environmental regulations, which promote economic growth and performance at the expense of environmental performance (Copeland, 2013; Zarsky, 1999; Bommer, 1999). Most civilized or developed nations are strict in maintaining environmental performance, making it inconducive for investors to practice their productive functions. The policies are stringent enough to limit the excesses of the investors who ordinarily want to produce pollution-inclined products. Because most developing countries are considered safe exploring grounds for these investors, they utilize the opportunity to foster their activities. Aside from the less stringent laws in developing countries, other attractive factors draw investors to the developing economies. These factors range from closeness to human and raw materials (Anyawu, 2012), closeness to market, less competition, and the prospect for economies of scale in the new environment. Most developing economies are resource-based economies which attract investors because of the availability of the resources needed in their productive activities. Access to less expensive human labour and markets for their products also adds to the advantage of outsourcing some of their productive activities to the developing economies. Because there is less competition from the local producers, the foreign investors will have the advantage of economies of scale, thereby expanding and transforming their enterprises into large-scale ventures. The PHH comes into play when the activities of the foreign investors are perceived as detrimental to the environmental performance of the host countries. Supporters of the PHH believe that foreign investors are opportunists who explore the weak aspects of the regulative laws of the developing countries to impact unfavourably on the environment through the production of pollutioninclined materials or products. They argue that most of these investors are still practising dirty production with old technologies that rely wholly on fossil fuels, and this process emits excess pollution into the environment. Some scholars support their arguments with scientific research, such as Sbia et al. (2014), Shahbaz et al. (2015, 2018), Abdouli and Hammami (2017, 2020), Agha and Khan (2015), Hakimi and Hamid (2016), Udemba et al. (2019), and Udemba (2020a). As noted, this hypothesis can be challenged with a different view. A divergent opinion arises to counter this hypothesis concerning the economic advantages of foreign investors and investments. Economic advantages, such as the spillover effect, of the foreign investment into the host economies are vital to the divergent view of the PHH. The spreading effects, which range from employment opportunities, the introduction of new technologies into the host economies, skills and knowledge transfers within the industry, and economies of scale, are all supporting forces to the argument of

Sustainable Development through Carbon Neutrality  157

those in support of FDI. They further support their argument based on the innovative nature and process involved in the operation of the foreign investors who will quickly adapt new technologies in their productive activities. This opinion opposing the PHH is called the pollution halo hypothesis. Some previous studies that support the pollution halo hypothesis include Cole and Elliot (2005), Acharyya (2009), Zhang and Zhou (2016), Blanco et al. (2013), and Udemba et al. (2019).

8.3 Methodology 8.3.1 Model Specification, Variables, and Data

This present study investigates the possibility of achieving sustainable development through carbon neutrality by undertaking a policy insight from FDI and service. This study explores the effectiveness of this assertion by linking the chosen variables and empirically analyzing them in a linear framework, using CO2 as a proxy for the environment as the variable of interest (dependent) and GDP per capita as economic growth or performance, FDI, service, and energy use as independent variables. The present study is specified and modelled according to ARDL approaches by Pesaran and Shin (1998) and Pesaran and Weeks (2001) for linear and cointegration estimations. The model specification displays both the linear dynamic and cointegration models as follows:

DLCO2t = A0 + A1LGDP + A2FDI + A3SERV + A4LEU + e t (1) LCO2t = A0 + A1LGDPt -1 + A2 FDI t -1 + A3SERVt -1 + A4 LEU t -1 r -1

+

d -1

å

å

d -1

d -1

b1DLCO2t -i +

i =0

i =0



+

d -1

b 2DLGDPt -i +

å

b 4DSERVt -i +

i =0

åb DFDI 3

t -i

i =0

åb DLEU 5

t -i

+ ECM t -i + e t

(2)

i =0

LGDP2t = A0 + A1LGDPt -1 + A2 FDI t -1 + A3SERVt -1 + A4 LEU t -1 r -1

+

å

d -1

d -1

i =0



+

d -1

å

b1DL LGDPt -i +

å

b 4DSERVt -i +

i =0

d -1

b 2DLCO2t -i +

i =0

3

t -i

i =0

åb DLEU 5

i =0

åb DFDI

t -i

+ ECM t -i + e t

(3)

158  Sustainable Development through Carbon Neutrality FDI t = A0 + A1LGDPt -1 + A2 FDI t -1 + A3SERVt -1 + A4 LEU t -1 r -1

+

d -1

å

b1DFDI t -i +

å i =0

i =0

d -1



+

d -1

b 2DLCO2t -i +

åb DLGDP

t -i

3

i =0

d -1

å

b 4DSERVt -i +

i =0

åb DLEU 5

+ ECM t -i + e t

t -i

(4)

i =0

SERVt = A0 + A1LGDPt -1 + A2 FDI t -1 + A3SERVt -1 + A4 LEU t -1 r -1

+

d -1

å

b1DSERVt -i +

i =0

+

å i =0

d -1



d -1

b 2DLCO2t -i +

åb DLGDP

t -i

3

i =0

d -1

å

b 4DFDI t -i +

åb DLEU 5

+ ECM t -i + e t

t -i

(5)

i =0

i =0

LEU t = A0 + A1LGDPt -1 + A2 FDI t -1 + A3SERVt -1 + A4 LEU t -1 r -1

+

d -1

åb DLEU + åb DLCO 1

t -i

d -1



+

2

2 t -i

i =0

i =0

+

åb DLGDP 3

t -i

i =0

d -1

åb vFDI + åb DSERV 4

i =0

d -1

t -i

5

t -i

+ ECM t -i + e t

(6)

i =0

From Eqs (1)–(6), the variables employed in this study are expressed as CO2, GDP, FDI, SERV, and EU, which depict carbon emission (million tonnes of CO2) as a proxy for the environment, GDP per capita (constant 2010 US$), foreign direct investment, net inflows (% of GDP), services, value added (% of GDP) (NB: as defined by the data source, World Bank indicators). Services correspond to International Standard Industrial Classification (ISIC) divisions 50–99, and they include value added in wholesale and retail trade (including hotels and restaurants) transport, and government, financial, professional, and personal services, such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the ISIC, revision 3 or 4) and energy use (million tonnes of oil equivalent). The energy use comprises two key energy sources in the UAE economy: oil and natural gas. Data for these two energy sources and carbon emissions are gathered from

Sustainable Development through Carbon Neutrality  159

the British Petroleum (BP) Statistical Review of World Energy (2019). Data for the GDP per capita, FDI, and service are sourced from the World Bank Development Indicators. Apart from FDI and service, which are expressed as GDP percentages, all other variables are expressed in logarithm form. Debates on the rightful indicator to measure environmental performance have always arisen among energy and environmental research scholars. The contention has been between CO2, GHG, methane, and nitrous oxide. Many scholars choose one of the indicators based on the study’s objective. Mostly, scholars consider CO2 the right indicator when narrowing down the studies to emissions from industrial and manufacturing perspectives of economic performance, where the majority of pollutant emissions are CO2. Apart from carbon emissions, there are other emissions that affect the atmosphere and are capable of inducing climate change. From the perspective of the Intergovernmental Panel on Climate Change (IPCC, 2014), classifications of the gases that are capable of causing a change in the environment are as follows: CO2 (76%), methane (16%), nitrous oxide (6.2%). Looking at the composition according to the IPCC (2014), CO2 seems to be higher than any other indicator in emission. The author adopts CO2 as the indicator to measure the environment’s quality with respect to this study’s objective, which borders on carbon neutrality. Also, with the focus on the industrial aspect of the economic performance as it revolves around FDI and service, CO2 is considered the right indicator for effectively investigating the environment. The UAE 1980–2018 annual data is employed in this present study. A summary of the data and the variables is presented in Table 8.3. The author’s expectation as regards the relationships that exist among the variables are as follows: because the UAE economy is a developing economy with more emphasis on economic growth, GDP per capita is expected to have a positive (+) relationship with CO2, meaning that as the economy tends toward upward growth, carbon emissions will increase as well, thereby affecting the environmental performance negatively. FDI is expected to have either a Table 8.3 Definition of Variables, Measurements, and Sources No Variable

Short Form Definition/Measurements

1

Carbon emission

CO2

2

GDP Per capita

GDP

3

Foreign direct investment Services Energy use

FDI

4 5

Serv EU

Source: Author’s compilation.

Carbon emission (million tonnes of CO2) as a proxy for environment (sourced from BP statistics review, 2019) Gross domestic product per capita (measured in constant 2010 USD) Foreign direct investment, inflow/percentage of GDP Services, value added (% of GDP) Energy use (summation of oil and natural gas, all measured in million tonnes and oil equivalent for gas) (sourced from BP stats review, 2019)

160  Sustainable Development through Carbon Neutrality

positive (+) or negative (−) relationship with CO2 depending on foreign investors’ operations and policy regulating foreign investors’ activities. Considering the nature of the service sector with the increasing awareness of the environmental performance and the innovative mindset of the operators in this sector, a negative relationship between CO2 and services is expected. However, the relationship between carbon emissions and services could be positive when considering the pulling effect from the transport services among the service units. The composition of the energy used as oil and natural gas, which are more fossil fuels, shows that the relationship between carbon emissions and energy use will be positive. The preliminary assessment of the relationships that exist between the explanatory variables of interest with the dependent variable (CO2) are as follows.​ Equation (1) represents the modelling of dynamic linear ARDL, while Equations (2)–(6) depict the modelling of the cointegration relationship among the variables according to ARDL-bound testing by Pesaran et al. (2001). From Equation (1), Ai = (i = 1, 2, 3, 4, and 5) and e t represent the coefficients of the estimated variables, which reflect the scale of each variable in the relationship between dependent and independent variables and the error term or the residual of the model. Equations (2)–(6) depict the modelling of the cointegration (for the long- and short- run relationship) among the selected variable. The error correction term and the differenced form of the variables to express the short-run relationship among the variables are depicted as e t and ∆. The ability to converge and establish equilibrium in the long run, which will enhance long-term relationships among the variables, is represented by ECM t -i . The long- and short-run coefficients are depicted by Ai & βi =1, 2, 3, 4, and 5, respectively. The estimation of the cointegration is based on ARDL-bound testing. The test is expressed in a hypothetical statement before estimation, and the remark and conclusion are based on the estimation’s findings. Thus, the null hypothesis against cointegration is expressed as H0 = A1 = 0, while the alternative hypothesis in support of the presence is expressed as H0 = A1 ≠ 0. The analysis is done by comparing F-statistics (F-stats) with the critical values of the two bounds (upper and lower). If the F-stats are greater than the values of the two bounds, the null hypothesis will not be accepted, meaning cointegration is present in the analysis, but if the F-stats are less than the values of critical bounds, the null hypothesis will be accepted, meaning there is no cointegration in the analysis. If the F-stats are in between the two bounds, it is considered inconclusive. The approaches adopted in this study are descriptive statistics, unit root estimate, maximum lag selection, ARDL-bound testing with linear regression, and vector error correction model (VECM) Granger causality estimate. The stability and normal distribution of the data applied in this study are tested with a summary of statistics. Because of the tendencies of non-stationarity of time series, the unit root was tested with both conventional (Augmented DickeyFuller, 1979; Philip-Perron, 1990; Kwiatkowski Philips-Schmidt-Shin, 1992) unit root methods and the structural break analysis to ascertain the stationarity

Sustainable Development through Carbon Neutrality  161 R/ship between CO2 & service 500.0

300.0 250.0 200.0 150.0 100.0 50.0 -

2012

2016

2004

CO2

service

1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016

AXIS TITLE

CO2

2008

1996

2000

1980 1984 1988 1992

-

AXIS TITLE CO2

FDI

300 250 200 150 100 50

EU

2016

2012

CO2

Energy consumption mix

80.0

Axis Title

2008

2004

2000

1996

1992

1988

1984

1980

0

60.0

Gas, 65.8

40.0

oil, 45.1

20.0 2016

2013

2010

2007

2004

2001

1998

1995

1992

1989

1986

1983

1980

-

Figure 8.2 Graphical presentation of preliminary assessment of the relationships that exist between the explanatory variables of interest with the dependent variable (CO2).

of the variables. Akaike information criterion (AIC) was applied to determine the maximum lag. Diagnostic estimates were adopted equally for the

162  Sustainable Development through Carbon Neutrality

confirmation of the stability and fitness of the variables. Tests such as serial correlation, heteroscedasticity, and cumulative sum square (CUSUM square) were performed.

8.4 Empirical Results and Discussion 8.4.1 Descriptive Statistics

Summary of the statistics is among the methods applied in this study to ascertain the data’s stability and normality. The output of the descriptive statistics is shown in Table 8.4. From the output, some level of stability and normality is established. The Jarque-Bera output revealed a stable and normal distribution of the data with insignificant statistics of almost all the variables except FDI. Even the output from the kurtosis determines the data’s normality, with the variables’ figures falling below 3 except for FDI and service. The descriptive statistics findings impact the author’s decision to undertake symmetric analysis. 8.4.2 Unit Root Tests

Unit root tests are performed with the conventional methods, as noted in the methodology section, to ascertain the stationarity of the variables in the study. As stated before, time series data have a high tendency to not be stationary because of some structural effect that can leave a permanent shock and effect on the economy, which will affect the movement of the data while researching the economy. Structural effects could be in the form of economic recession brought by financial or oil price shocks or even natural disasters. The recent

Table 8.4 Summary of Statistics Variables

LCO2

GDP_CON FDI

LEU

SERVICE

 Mean  Median  Maximum  Minimum  Std. Dev.  Skewness  Kurtosis

 139.7284  124.8219  276.9802  25.91987  77.12010  0.316799  1.988541

 2.11E+11  1.79E+11  3.93E+11  9.40E+10  9.77E+10  0.484917  1.834898

 1.443549  0.447367  6.767152 −1.166836  1.950997  1.249102  3.778164

 53.22140  46.33458  110.9372  9.310338  31.01522  0.408722  2.026466

 45.98889  46.65629  57.79558  26.84579  7.095479 -0.456285  3.103214

 Jarque-Bera  Probability

 2.314806  0.314301

 3.734317  0.154562

 11.12567  0.003838

 2.625976  0.269015

 1.370587  0.503942

 Sum  Sum Sq. Dev.

 5449.409  226005.4

 8.23E+12  3.63E+23

 56.29842  144.6428

 2075.634  36553.88

 1793.567  1913.141

 Observations

 39

 39

 39

 39

 39

Source: Performed by author.

Sustainable Development through Carbon Neutrality  163

outbreak of COVID-19 has negatively impacted several economies around the globe. Several oil price shocks from the 1970s to the present oil shock and the 2008–2009 global financial meltdown can affect the workings or the movement of time series data, thereby making the data non-stationary. These shocks, if not accounted for, will definitely mislead the outcome of the analysis. The unit root test also helps in identifying the order (i.e., at level l (0), at first diff l (1), or mixed order) of integration among the variables, that is, in what order the variable affirms stationarity. From the unit root tests with the conventional methods such as Augmented Dickey-Fuller (1979), PhilipPerron (1990), and Kwiatkowski Philips-Schmidt-Shin (1992), the author found mixed order of integration among the variables. Aside from the estimation with the conventional methods, structural break tests with Chow tests were performed to ascertain whether there is a break capable of obstructing the movement of the variables. This is vital in stationarity analysis because of the inability of the traditional methods to account for shocks in an economy. Sometimes, the conventional methods of testing the unit root account for the structural break as stationary. This is the reason for adopting the structural break to avoid misleading results. Before the author performed the structural break test, a CUSUM square test was performed to ascertain the stability of the data, and it was found unstable. For this, the author performed the Chow test to confirm whether there is a structural break, and the output showed the presence of a break, with all the outputs (F-stats, likelihood ratio, and Wald test) showing highly significant. The graphical presentation of variables was inspected to verify the likely years of break, and 2003 was chosen based on the movement on the graph of FDI. Aside from FDI and service, all other variables were seen trending upward. It was noticed that the break in FDI actually took place between 2000–2010. Notable shocks such as the terrorist attacks on September 11, 2001, with its effect on oil price, the oil market shock of 2003 because of the threat of invasion of Iraq by US President George W. Bush, and the 2008–2009 global financial meltdown took place within those periods. These shocks are capable of impacting the times series data. The graphical presentation of the variables and results of both CUSUM square and the Chow tests are shown in Figure 8.4 and Table 8.5.​​ 8.4.3 Linear and ARDL-Bound Estimate

The author performed ARDL-bound testing and linear regression; the outputs are displayed in Table 8.7. The strength of the relationship between the model of this study and the dependent variable is depicted with R2 and Adjusted R2 with values of 0.999, respectively. This shows that 99.9% of the change in the dependent (CO2) variable is explained by independent (GDP, FDI, SERV, and EU) variables, and the error term explains the remaining changes. The autocorrelation was confirmed from the model with the value of Durbin Watson at 2.05, which falls within the acceptable range. Also, the LM test of serial correlation and heteroscedasticity was performed, and the outputs showed the

164  Sustainable Development through Carbon Neutrality

300

LCO2

250 200 150 100 50 0 1980 1985 1990 1995 2000 2005 2010 2015 8

FDI

6

60

service

50

4

40

2

30

0 –2 1980 1985 1990 1995 2000 2005 2010 2015 120

gdp con 4.0E+11 3.6E+11 3.2E+11 2.8E+11 2.4E+11 2.0E+11 1.6E+11 1.2E+11 8.0E+10 1980 1985 1990 1995 2000 2005 2010 2015

20 1980 1985 1990 1995 2000 2005 2010 2015

LEU

100 80 60 40 20 0 1980 1985 1990 1995 2000 2005 2010 2015

Figure 8.3 Graphical presentation of variables’ trends.

absence of serial correlation and heteroscedasticity problems with the insignificant nature of the output. Cumulative and CUSUM square tests were performed equally to determine the model’s stability, and the result confirmed that the model is stable and reliable, with the blue lines in both tests well situated inside the two red lines. The output is shown in the figures immediately after the linear regression table. Cointegration is confirmed with ARDLbound testing even at a 1% significant level. The result is shown in Table 8.7. The maximum lag selection was estimated with AIC, and 3 is confirmed as the maximum lag. The error correction shows a negative coefficient (−0.369826), which is highly significant at 1%. This shows that there will be convergence in the long run at a 37% speed of adjustment. This shows the likelihood of establishing equilibrium and relationships among the variables in the long run. The result of the linear estimation is as follows: economic growth (GDP) has a positive relationship with CO2 both in the short run and long run, which portrays poor environmental performance. This means that, as the economy is recording positive growth and performance, the quality of the environment and its performance is degenerating through excess emission of CO2. This could be possible in an economy dominated by industrial and productive activities that carry on fossil fuels capable of emitting excessive CO2. It is worth noting that a

Sustainable Development through Carbon Neutrality  165 12 8 4 0 –4 –8 –12 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 CUSUM

5% Significance

1.6 1.2 0.8 0.4 0.0 –0.4 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 CUSUM of Squares

5% Significance

Figure 8.4 The unstable cumulative sum square in confirmation of the stability of the data.

Table 8.5 Structural Break Test (Chow Break Point Test) Chow Break Point Test: 2003  Null Hypothesis: No breaks at specified break points F-statistic 8.390929 Prob. F(5,29) Log likelihood ratio 34.89506 Prob. chi-square(5) Wald Statistic  41.95465 Prob. chi-square(5)

0.0001 0.0000 0.0000

Source: Computed by author. Note: The null hypothesis states that there is no structural break, but with the significant level of the result at 1%, the null hypothesis is rejected.

166  Sustainable Development through Carbon Neutrality Table 8.6 Conventional (ADF, PP, and KPSS) Unit Root Test Variables

Level

1st Diff

Intercept

Intercept & Trend

LCO2 LGDP LEU FDI LSERV

0.6432 2.0365 1.2335 −2.0178 −3.0375**

−1.7245 −2.4972 −1.3850 −3.7332** −2.9514

LCO2 LGDP LEU FDI LSERV

0.8336 1.7662 1.3640 −2.1665 −3.0259**

−1.7172 −2.7513 −1.2704 −2.7663 −2.9199

LCO2 LGDP LEU FDI LSERV

0.7448*** 0.7224** 0.7434** 0.4996** 0.3761*

0.1480** 0.2061** 0.1694** 0.0700 0.1453*

Intercept ADF −6.8035*** −4.1305*** −6.3638*** −3.6972*** −6.9942*** PP −6.8246*** −4.1239*** −6.3786 −5.8475*** −7.1620*** KPSS 0.2086 0.4940** 0.3630* 0.0513 0.2040

Intercept & Trend

Order

−6.8805*** −4.8462*** −6.6977*** −3.6328** −6.9886***

I (1) I (1) I (1) MIXED MIXED

−6.9218*** −4.8110*** −6.6977*** −5.7588*** −7.1550***

I (1) I (1) I (1) I (1) MIXED

0.0770 0.0943 0.0652 0.0511 0.1026

Note: Null hypothesis is non-stationary for ADF & PP and stationary for KPSS. The signs depict (*) significant at 10%, (**) significant at 5%, (***) significant at 1%, and (no) not significant. *MacKinnon (1996) one-sided p-values.

percentage point change in GDP will cause a 3.96E-12 percentage increase on CO2 emission (i.e., decrease in the quality of the environment). The impact of GDP on the environment is quite small, considering the size of the coefficient of GDP (3.96E-12) and the insignificant level of the result. This supports the author’s expected positive sign in the relationship between CO2 emission and GDP. This finding is in support of the findings by Al-mulali et al. (2018) for the UAE, Shahbaz et al. (2013, 2014) for the UAE, Sbia et al. (2017) for the UAE, and Balsalobre-Lorente et al. (2019). Also, a positive relationship is initiated between FDI and CO2 emission in both the short and long run, which means that, as FDI increases, CO2 emissions increase, thereby causing damage to the environmental performance of the UAE. This follows the author’s expectation coupled with the graphical representation of the relationship between FDI and CO2 as portrayed in Figure 8.2. Quantitatively, a percentage change in FDI will cause a 0.735216% increase on CO2 emission at a 1% significant level both in the short and long run, respectively. This finding supports the findings by Merican et al. (2007) for Malaysia and Thailand, Blanco et al. (2013) for 18 Latin American countries, and Acharyya (2009) for India. Again, a positive relationship is exposed between energy use and CO2 emission at a 1% significant level. It shows that as fossil fuel energy sources are at an increasing mode,

Sustainable Development through Carbon Neutrality  167 Table 8.7 Linear Estimate with ARDL-Bound Testing (CO2 Equation) Variables D(LGDP) D(FDI) D(SERV) D( LEU) D(DUMMY) CointEq(-1)* LGDP FDI SERV LEU DUMMY C R2 Adj.R2 D. Watson Bound test(Long Path) F-stats Wald test(Short Path) F-stats P-val Serial Correlation test F-stats R2 P-val Heteroskedasticity estimate F-stats R2 Prob

Coefficients 3.96E-12 0.735216 −0.102540 2.735342 −1.266074 −0.369826 8.67E-11 0.735216 −0.102540 2.735342 −1.266074 9.991120 0.999 0.999 2.052 5.530681***

SE Short Path 2.25E-11 0.163595 0.063861 0.069366 2.056744 0.052933 Long-path 3.71E-11 0.316854 0.063861 0.102734 2.056744 2.719533

K=5,@ 1%

t-statistics

P-value

0.175762 4.494127 −1.605666 39.43364 −0.615572 −6.986721 0.125585

0.8620 0.0002*** 0.1220 0.0000*** 0.5442 0.0000***

2.320364 −1.605666 26.62551 −0.615572 3.673837

0.9012 0.0295*** 0.1220 0.0000*** 0.5442 0.0013***

I(0)=3.4

I(1)=4.1

9740.380*** 0.000000 0.327077 1.087531 0.5806 1.055346 12.78341 0.8998

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

the carbon emission generated in the process is equally increasing, which is an accurate picture of a developing economy like the UAE. This finding did not surprise the author when considering the energy mix of the UAE, which largely comprises natural gas and oil, which are rated high in the emission of pollutant gases. This equally corresponds with the author’s expected sign of the relationship between energy use and carbon emission, which is also plotted graphically in Figure 8.2. Moreover, a negative relationship between service and CO2 emission is not significant. This is a good story for the UAE economy, which is dominated by the service sector ranging from real estate, retail, maritime transport, tourism, and financial services. This means as the performance of the service sector is increasing, the CO2 emission decreases, which positively impacts the environment’s quality. Though this finding is not significant, it is a signal that a good

168  Sustainable Development through Carbon Neutrality

policy could be framed in consolidating the sector to enhance economic and environmental performance. These findings are displayed in Table 8.7.​ 8.4.4 Diagnostic Tests: Residual Stability and Reliability (Cumulative Sum and Cumulative Sum Square)

After the first estimation of the CUSUM square to ascertain the stability of the data and model in this empirical study, the author re-estimates the CUSUM and CUSUM square to ensure that the data’s stability is assured. The final output shows that the unstable nature of the data and the residual has been corrected. The stability and reliability of the model are displayed in Figure 8.5 and Figure 8.6. 8.4.5 Granger Causality Tests (VECM)

The author applied the Granger causality test as one of the methods of analyzing this scientific research. The Granger causality test was performed as a robust check to other estimations such as ARDL linear regression to confirm whether the findings from the two analyses will point toward the same conclusion. This will always support the author’s claim if the findings from the different estimates correspond to the author’s objective. The Granger causality test is a scientific hypothesis to determine whether a series’ can forecast another. Granger causality gives direct and deep inference on the relationship between two series by determining which of the two series is impacting the other. It passes the level of establishing the sign of a relationship between two series and exposes where the force that necessitates the relationship comes from. This is the ability to forecast the series. The forecast could either be one way (uni-directional) or two ways (bi-directional). Because of the non-stationarity of the series, the author adopts VECM Granger causality for easy identification of both longand short-run inferences in the causality estimate. The findings from the Block exogenous/VECM Granger causality estimate, which accommodate both the short run and long run, are shown in Table 8.8. From the Granger causality output, the author makes the following remarks: in the short run, a uni-directional causal inference is established between CO2 and energy use, between FDI and energy use, and between FDI and service. Bi-directional causal inference is observed between FDI and GDP. Moreover, in the long run, a uni-directional causal transmission is established between CO2 and other series (i.e., FDI and service) except GDP. Also, a uni-directional causal transmission is observed between energy use and other series (i.e., FDI and service) except GDP. Bi-directional causal transmission is observed between CO2 and energy use.

Sustainable Development through Carbon Neutrality  169 12 8 4 0 -4 -8 -12 04

05

06

07

08

09

10

11

CUSUM

12

13

14

15

16

17

18

5% Significance

Figure 8.5 Plot of cumulative sum of recursive residuals.

1.6

1.2

0.8

0.4

0.0

-0.4

04

05

06

07

08

09

10

CUSUM of Squares

11

12

13

14

15

5% Significance

Figure 8.6 Plot of cumulative sum square of recursive residuals.

16

17

18

170  Sustainable Development through Carbon Neutrality

8.5 Summary and Policy Insight The present study focuses on testing the UAE’s economic and environmental performance to suggest achievable ways of reducing carbon emissions in the country, which will assist in curbing global warming. Following the author’s objective, which centres on the study of carbon emission reduction, indicators and series were chosen in line with the objective and expectations of the author. Because the UAE’s economic performance is perceived as a smooth one, which is anchored on industrial and manufacturing activities and capable of emitting carbon emissions because of excess utilization of fossil fuels, the author adopts CO2 emissions, as a proxy for environment, FDI, economic growth proxy with GDP per capita, energy use, and service as the indicators and series in this study. Preliminary studies of the series were done to check their relationship with the dependent variable, which is CO2 emission. This study is unique in its application of service policy as one of the series to test the environmental performance of the UAE. This idea is based on the performance of the service sector and the domineering presence of the sector in the UAE economic operation. The theoretical background of this study is based on PHH. The author employed scientific approaches such as ARDL-bound testing and linear estimations, Granger causality (VECM), and structural break tests for in-depth empirical study and analyses of this topic. Findings from this study are as follows: from the linear ARDL regression, economic growth (GDP) has a positive relationship with CO2 both in the short and long run, which portrays poor environmental performance. This means that as the economy is recording positive growth and performance, the quality of the environment and its performance is degenerating through excess emission of CO2. Also, a positive relationship is indicated between FDI and CO2 emissions in both the short and long run, which means that as FDI increases, CO2 emissions increase, thereby causing damage to the environmental performance of the UAE. Again, a positive relationship is exposed between energy use and CO2 emissions at a 1% significant level. It shows that as fossil fuel energy sources are in an increasing mode, the carbon emission generated in the process is equally increasing, which is a true picture of a developing economy like the UAE. Moreover, a negative relationship between service and CO2 emissions is not significant. This is good for the UAE economy, which is dominated by the service sector, ranging from real estate, retail, maritime transport, tourism and financial services. This means that as the performance of the service sector increases, the CO2 emissions decrease, which positively impacts the environment’s quality. The finding from the Granger causality perspective are as follows: in the short run, a uni-directional causal inference is established between CO2 and energy use, between FDI and energy use, and between FDI and service. Bi-directional causal inference is observed between FDI and GDP. Moreover, in the long run, a uni-directional causal transmission is established between CO2 and other series (i.e., FDI and service) except GDP. Also, a

∆LCO2

[0.694] √√ [0.760] [0.135] [ 0.553]

LGDP 0.731 √√ 0.548 4.011 1.186

√√ [0.377] [0.078] [0.035] [0.011]

LEU 6.829** 0.568 √√ 6.737** 8.136**

5.000* 0.435 √√ 3.755 2.993

∆LEU

[0.033] [0.753] √√ [0.034] [0.017]

[ 0.082] [0.805] √√ [0.153] [0.224] FDI 0.823 1.333 0.324 √√  0.568

4.202 8.584*** 4.777* √√ 7.193***

∆FDI

[0.662] [0.514] [0.852] √√ [0.752]

[0.122] [0.014] [0.092] √√ [ 0.027]

SERV 0.784 1.731 0.403 1.096 √√

0.125 3.034 0.125 1.653 √√

∆SERV

[0.675] [0.420] [0.817] [0.585] √√

[0.939] [0.219] [0.939] [0.437] √√

Note: *, **, *** represent 10%, 5%, and 1% significance, respectively. Figures in [ ] are probabilities while the figures before the brackets are the chi-squares (χ2) = chisquares (χ2) [p-values].

[0.967] √√ [0.970] [0.040] [0.632]

0.067 √√ 0.061 6.440** 0.918

∆LGDP

√√ [0.815] [0.223] [0.346] [0.102]

Short-Run Path

∆LCO2 √√ ∆LGDP  0.410 ∆LEU 3.005 ∆FDI 2.121 ∆SERV 4.566 Long-Run Path LCO2 LCO2 √√ LGDP 1.952 LEU 5.095* FDI 6.683** SERV 8.971***

Variables

Table 8.8 Short- and Long-Run VECM Granger Causality Analysis/Block Exogeneity Wald Tests

Sustainable Development through Carbon Neutrality  171

172  Sustainable Development through Carbon Neutrality

uni-directional causal transmission is observed between energy use and other series (i.e., FDI and service) except GDP. Bi-directional causal transmission is observed between CO2 and energy use. From the findings, we can deduce the nexus between FDI, energy use, GDP, service, and CO2 in determining the UAE’s economic and environmental performance. Also, the PHH is established. There is a synergy between the findings from ARDL-bound tests, linear approach, and Granger in confirmation of the impact of the selected variables on the dependent variable (CO2 emissions), which fall into the author’s expectations. This study and its findings will be of great importance to the UAE confederates and other resource-rich countries within the Arabian states that border the Persian Gulf. The policy inference from this study will be beneficial to them in maintaining their environmental performance. Having seen the findings from this study, the author wishes to draw policy framing based on the findings. First, from the findings, service negatively impacts carbon emissions. This indicates that the country’s service sector could effectively curb the country’s CO2 emission rate. It is vital for the UAE authorities to utilize the service sector to moderate the negative impact of economic performance on the country’s environmental performance. This can be done by charging the service industries to adopt technological innovation, thereby shifting to a greener and sustainable economy. There could be sensitization and encouragement from the side of the authorities, such as honouring any unit of the service sector that is perceived as working toward sustaining the green economy either with nationwide broadcasting to impact the reputation of the firm or by subsidies in the energy utilization of such firms to encourage them to use renewable energy sources. Service sectors, such as banks, could be charged to offer green services such as green loans, green savings accounts, mobile banking, green credit cards, and online transactions. Applying green technology in various products and services of retail services supports the green economy. Transportation services should be encouraged to shift from fossil fuel-based automobiles to renewable fuel-based automobiles. Also, bidirectional causal transmissions are observed between FDI and GDP, between energy use and CO2. This emphasis on the importance of FDI to the economic performance of the UAE and FDI negatively impacts the environmental performance from the linear regression. It would be politically and economically wise to check the excess of the foreign investors and encourage them to go into low carbon productive ventures and maximize the adoption of renewable energy sources. The leadership of the UAE has adopted different initiatives to tackle the impact of CO2 emissions on the country, including monitoring and tracking of GHG emissions in order to develop better policies aimed at reducing them, establishing energy plans to reduce carbon emissions to 70% that extend to 2050, and adopting low carbon technologies through an alternative source of energy. Also, establishing the Ministry of Climate Change and Environment, an institution saddled with the responsibility of mitigating climate and environmental changes, is a noble policy that will go a long way in curtailing the devastating nature of CO2 emissions.

Sustainable Development through Carbon Neutrality  173

Conclusively, the UAE has good prospects of achieving carbon neutrality in the near future if it can harness the policy implications outlined in this study and make its existing policies more objective and subjective.

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Sustainable Development through Carbon Neutrality  175 Pesaran, M. H., & Weeks, M. (2001). Non-nested hypothesis testing: An overview. A Companion to Theoretical Econometrics, 279–309. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. Sapkota, P., & Bastola, U. (2017). Foreign direct investment, income, and environmental pollution in developing countries: Panel data analysis of Latin America. Energy Economics, 64, 206–212. Sarkodie, S. A., & Ozturk, I. (2020). Investigating the environmental Kuznets curve hypothesis in Kenya: A multivariate analysis. Renewable and Sustainable Energy Reviews, 117, 109481. Sbia, R., Shahbaz, M., & Hamdi, H. (2014). A contribution of foreign direct investment, clean energy, trade openness, carbon emissions and economic growth to energy demand in UAE. Economic Modeling, 36, 191–197. Sbia, R., Shahbaz, M., & Ozturk, I. (2017). Economic growth, financial development, urbanization and electricity consumption nexus in UAE. Economic Research-Ekonomska Istraživanja, 30(1), 527–549. Shahbaz, M., Khan, S., & Tahir, M. I. (2013). The dynamic links between energy consumption, economic growth, financial development and trade in China: Fresh evidence from multivariate framework analysis. Energy Economics, 40, 8–21. Shahbaz, M., Nasir, M. A., & Roubaud, D. (2018). Environmental degradation in France: The effects of FDI, financial development, and energy innovations. Energy Economics, 74, 843–857. Shahbaz, M., Nasreen, S., Abbas, F., & Anis, O. (2015). Does foreign direct investment impede environmental quality in high-, middle-, and low-income countries? Energy Economics, 51, 275–287. Shahbaz, M., Sbia, R., Hamdi, H., & Ozturk, I. (2014). Economic growth, electricity consumption, urbanization and environmental degradation relationship in United Arab Emirates. Ecological Indicators, 45, 622–631. Sharif, A., Baris-Tuzemen, O., Uzuner, G., Ozturk, I., & Sinha, A. (2020). Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from Quantile ARDL approach. Sustainable Cities and Society, 57, 102138. Stapleton, E. M. (2014). Agriculture as industry: The failure of environmental and agricultural policy to adapt to the modern agricultural landscape. Albany Government Law Review, 7, 321. Tan, D., & Meyer, K. E. (2011). Country-of-origin and industry FDI agglomeration of foreign investors in an emerging economy. Journal of International Business Studies, 42(4), 504–520. Tang, C. F., & Tan, B. W. (2015). The impact of energy consumption, income and foreign direct investment on carbon emissions in Vietnam. Energy, 79, 447–454. Udemba, E. N. (2019). Triangular nexus between foreign direct investment, international tourism, and energy consumption in the Chinese economy: Accounting for environmental quality. Environmental Science and Pollution Research International, 26(24), 24819–24830. Udemba, E. N. (2020a). A sustainable study of economic growth and development amidst ecological footprint: New insight from Nigerian Perspective. Science of the Total Environment, 732, 139270. Udemba, E. N. (2020b). Mediation of foreign direct investment and agriculture towards ecological footprint: A shift from single perspective to a more inclusive perspective for India. Environmental Science and Pollution Research International, 27(21), 26817–26834.

176  Sustainable Development through Carbon Neutrality Udemba, E. N., Güngör, H., & Bekun, F. V. (2019). Environmental implication of offshore economic activities in Indonesia: A dual analyses of cointegration and causality. Environmental Science and Pollution Research International, 26(31), 32460–32475. Udemba, E. N., Magazzino, C., & Bekun, F. V. (2020). Modeling the nexus between pollutant emission, energy consumption, foreign direct investment, and economic growth: New insights from China. Environmental Science and Pollution Research International, 27(15), 17831–17842. Zafar, M. W., Mirza, F. M., Zaidi, S. A. H., & Hou, F. (2019). The nexus of renewable and non-renewable energy consumption, trade openness, and CO2 emissions in the framework of EKC: Evidence from emerging economies. Environmental Science and Pollution Research International, 26(15), 15162–15173. Zafar, T., Zafar, K., Zafar, J., & Gibson, A. A. (2016). Integration of 750 MW renewable solar power to national grid of Pakistan–An economic and technical perspective. Renewable and Sustainable Energy Reviews, 59, 1209–1219. Zarsky, L. (1999). Havens, halos and spaghetti: Untangling the evidence about foreign direct investment and the environment. Foreign Direct Investment and the Environment, 13(8), 47–74. Zhang, C., & Zhou, X. (2016). Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China. Renewable and Sustainable Energy Reviews, 58, 943–951.

9

Reset the Industry Redux through Corporate Social Responsibility The COVID-19 Tourism Impact on Hospitality Firms through Business Model Innovation Jaffar Abbas, Khalid Al-Sulaiti, Daniel Balsalobre Lorente, Syed Ale Raza Shah, and Umer Shahzad

9.1 Introduction The advent of the pandemic (COVID-19) has affected each segment of society and posed numerous adverse effects on social, environmental, and economic factors; however, this global crisis has affected the travel, tourism, and hospitality sectors at large (Hui, Yupeng, Chenglong, Haiqin, & Daomeng, 2021; Irshad et al., 2021; N. A. Khan, Khan, Moin, & Pitafi, 2020; Wu, Pitafi, Pitafi, & Ren, 2021). The COVID-19 pandemic hit the business world hard, which poses a financial recession worldwide (Alexandru et al., 2020). Many countries implemented strict policies and lockdowns to contain the rapid transmission of the fatal virus (Akhtar, Sun, Akhtar, & Chen, 2019; Bratianu, 2020). Because of these tight restrictions on social gatherings, running business and economic activities as usual is not possible (J. Aman, Abbas, Lela, & Shi, 2021; Mubeen, Han, Abbas, Álvarez-Otero, & Sial, 2021; Mubeen, Han, Abbas, Raza, & Bodian, 2021). The pandemic has developed many challenges for business firms (J. Aman et al., 2021; Z. Li, Wang, Abbas, Hassan, & Mubeen, 2022; Paulson et al., 2021; Rao, Saleem, Saeed, & Ul Haq, 2021; Yoosefi Lebni et al., 2021). Regardless of their size, business firms need to think outside the box by utilizing their in-hand employees’ skills and available resources, innovation, and resilience to become proactive and dynamic in adapting business model innovation (BMI) to develop new business models to survive (Bratianu & Bejinaru, 2020; Fasan, Soerger Zaro, Soerger Zaro, Porco, & Tiscini, 2021; Popkova, DeLo, & Sergi, 2021; C. Wang, Wang, Abbas, Duan, & Mubeen, 2021). In the advent of the pandemic, business firms have followed the ethics of the corporate social responsibility (CSR) guidelines and offered relief packages for their employees and society (Islm et al., 2021; Lai, Pitafi, Hasany, & Islam, 2021; Pitafi & Ren, 2021). It permits firms to act flexibly even amid uncertainty to continue their business activities (Akhtar, Nadeem Akhtar, Usman, Ali, & DOI: 10.4324/9781003336563-9

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Iqbal Siddiqi, 2020; Mubeen, Han, Abbas, & Hussain, 2020; Siddiqi, Sun, & Akhtar, 2020). Firms follow innovative business strategies to gain short- and long-term benefits (Al Halbusi, Al-Sulaiti, Abbas, & Al-Sulaiti, 2022; X. Li, Abbas, Dongling, Baig, & Zhang, 2022; Yan Li, Al-Sulaiti, Dongling, Abbas, & Al-Sulaiti, 2022). Business organizations can attain advantages by increasing their business volume (Ge et al., 2022; Yu, Abbas, Draghici, Negulescu, & Ain, 2022; Zhang et al., 2022). Firms can increase employees’ productivity even in a crisis (Liu, Qu, Wang, Abbas, & Mubeen, 2022; Shi, Ullah, Zhu, Dou, & Siddiqui, 2021). Businesses in the most affected countries can encounter the severity of the pandemic through corporate-level innovative strategies that seek support from different firms worldwide (Akhtar, Sun, Chen, & Akhtar, 2019; Younis et al., 2020). The global health crisis demands safety measures to curb this disease (Riaz, Akhtar, Jinghong, & Gul, 2021). There is an urgent need to prioritize the health needs for people worldwide (Fattahi et al., 2020; Pouresmaeil, Abbas, Solhi, Ziapour, & Fattahi, 2019). The pandemic crisis has affected nurses’ quality of life (Lebni et al., 2021). These measures include social distancing, mandatory quarantine campaigns, and border closures. It has halted the hospitality and tourism industry worldwide. Travel, tourism, and hotels are the most affected sectors and have become resilient to revitalize to a next normal from different epidemics, viruses, and pandemics, including earthquakes, severe acute respiratory syndrome (SARS), Ebola, Zika, and Middle East respiratory syndrome (MERS) (Novelli, Gussing Burgess, Jones, & Ritchie, 2018). The unparalleled environmental, natural, and COVID-19 pandemic effects show that the crisis is evident. It has posed long-term structural and transformative changes in the tourism, leisure, and hospitality sectors (Škare, Soriano, & Porada-Rochoń, 2021). The current pandemic has had significant adverse impacts. It has resulted in global travel restrictions and a steep decline in hotels, restaurants, and tourist demand (Verma & Gustafsson, 2020). The vast majority of tourism, travel, and hospitality business firms have experienced the worst effects of the COVID-19 pandemic, and globally, domestic tourism revenues declined by 51%, which amounted to $2.86 trillion. In addition, experts predict that the tourism market will recover rapidly and reach the next average level in 2019 or 2023 (Aleta, Hu, Ye, Ji, & Moreno, 2020). COVID-19 is a fatal virus that has drastically affected global economies and health care systems by posing health and economic crises (Anderson, Heesterbeek, Klinkenberg, & Hollingsworth, 2020; Brewer, 2016; Mayhew & Anand, 2020; McKenna & Bargh, 1998). The most dreadful outbreak resulted in a sharp decline in tourism, hotel, and hospitality firms’ businesses, which are prominent contributors to the global service industry (E. Avery, 2010; C. L. Jones et al., 2015). COVID-19 has halted hospitality firms’ operations (Jaffar Abbas, Dake, Su, & Arash, 2021) and deleteriously affected tourism and workers’ behaviour and mental well-being (Jaffar Aman, Abbas, Mahmood, Nurunnabi, & Bano, 2019; Bauer et al., 2021; Park, Boatwright, & Johnson Avery, 2019). Hospitality businesses and corporations recorded a steep decline

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in business activities, as tourists cancelled their already planned tours in fear of the COVID-19 contagion during their travels (E. J. Avery, 2017; Mamirkulova et al., 2020; Meadows, Meadows, Tang, & Liu, 2019). Travelling triggers virus transmission risks to many air passengers because of insufficient effective vaccine availability (Hu & Zhang, 2014; Reynolds & M, 2005; Shin, Sharma, Nicolau, & Kang, 2021; Su, McDonnell, Wen, et al., 2021; Tonsaker, Bartlett, & Trpkov, 2014). Tourists can become the leading carriers of the lethal virus, and they might transfer COVID-19 to the local communities of the destinations (Jaffar Abbas, Mubeen, Iorember, Raza, & Mamirkulova, 2021; Abbott, 2021; Hollingsworth, Ferguson, & Anderson, 2006; Yibai Li, Wang, Lin, & Hajli, 2018; Zhong, Huang, & Liu, 2021). Because of the restrictions due to this pandemic, many hotel, tourism, and hospitality business firms are proactively involved in CSR practices to support the frontline workforce and other personnel engaged in curbing the COVID-19 disease (Islam, Islam, et al., 2021; Pitafi, Rasheed, Kanwal, & Ren, 2020). Many business firms have initiated CSR engagement to fight against the COVID-19 pandemic, and scholars view it as strategic philanthropy (Latif, Malik, Pitafi, Kanwal, & Latif, 2020). Strategic philanthropy signifies the strategic benefits of CSR for the local community and larger society. Strategic philanthropy CSR refers to the industry’s exclusive resources, and hotels’ and hospitality businesses’ philanthropy CSR includes other endeavours such as giving unoccupied rooms and leftover food items as a charity (Singal, 2015). During the COVID-19 pandemic, many leading corporations, such as Marriot International, InterContinental Hotels Group, and Hilton Hotels & Resorts, have actively started CSR practices for strategic philanthropy and donated food items and rooms for the workers of the health care systems. The need for electricity consumption and renewable energy amid the COVID-19 crisis has become vital (Abbasi, Abbas, & Tufail, 2021). Renewable energy helps reduce the challenges of environmental problems for business firms (Abbasi, Adedoyin, Abbas, & Hussain, 2021).The existing literature has sufficiently examined hospitality research; however, very few studies have focused on hotel and hospitality companies’ CSR in terms of strategic philanthropy during the COVID-19 pandemic (Rhou & Singal, 2020). This study has identified this literature gap and aims to explore the effects of CSR practices in hotel and hospitality business firms based on strategic philanthropy at the advent of the COVID-19 pandemic.

9.2 Global Travel, Tourism, and Regional Contributions Business firms operating from the European region have played a significant role in the travel, tourism, and hospitality sectors. It is important to note that European Union (EU) tourism contributes 50% to the global tourism business (Naslund, Aschbrenner, Marsch, & Bartels, 2016; Siddiqi, Sun, & Akhtar, 2019). Tourism and travel activities generated from European nations typically make a significant business contribution of 48% to outbound global tourism and

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travel (Akhtar, Siddiqi, et al., 2021; Boluk, Cavaliere, & Higgins-Desbiolles, 2019; Islam, Pitafi, Arya, et al., 2021). The hospitality and tourism firms dominantly contribute to the service industry worldwide (Akhtar, Chen, Siddiqi, Zeng, & Islam, 2021; Anser, Shafique, Usman, Akhtar, & Ali, 2020; P. T. Iorember, Jelilov, Usman, Isik, & Celik, 2021; Kanwal, Rasheed, Pitafi, Pitafi, & Ren, 2020). Small- and medium-sized enterprises (SMEs) firms are known as the economic engines and are essential drivers of gross domestic product (GDP) for destination countries (Ali, Usman, Pham, Agyemang-Mintah, & Akhtar, 2020; Islam, Pitafi, Akhtar, & Xiaobei, 2021; Wondirad, Kebete, & Li, 2021). Hospitality companies started CSR practices and contributed a social good to human society (Jaffar Abbas et al., 2019; Akhtar, Nadeem Akhtar, et al., 2020; Akhtar, Siddiqi, et al., 2020; Ali et al., 2020; Anser et al., 2020)​ According to 2018–2020 data related to DACH (the DACH region refers to the three Central European countries of  Germany (D), Austria (A), and Switzerland (CH)) travel, hotels, and tourism activities, destinations contributed $5.1 trillion to their national GDPs. In 2019, the GDP of Austria received $446.31, Germany $3861.55, and Switzerland $704.83 billion, respectively. Likewise, during 2020, Austria’s GDP received $432.89, Germany $3780.55, and Switzerland $707.87 billion, respectively (UNWTO, 2019). DACH states received $3.86 trillion to their GDP (see Figure 9.1). The EU region welcomes almost 600 million tourists, contributing significantly to hospitality firms’ businesses and local countries’ GDP each year (Daye, Charman, Wang, & Suzhikova, 2019; Neuburger & Egger, 2020). Travel, tourism, and hospitality activities significantly contributed to the service industry and global economies. This sector made a notable contribution ($9.3 trillion) to the economies worldwide and directly made a considerable contribution ($2.9 trillion) during the fiscal year 6000

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2019. Conversely, hospitality firms experienced the hardest hit of the COVID19 pandemic, which resulted in a steep decline of 98% until May 2020, as a result of COVID-19 transmission fears and preventive measures, such as the ban on travel, border closures, and global mobility restrictions. In 2020, a decline of 56% in international tourists and a shortfall of 300 million arrivals was recorded (January-May 2020) compared to 2019 records for the same time. The revenue loss of $320 billion to global tourism receipts, including export revenue, was a 300% greater loss than the 2009 economic crisis. The pandemic resulted in a financial operations crisis, posed a widespread international health challenge, and caused unprecedented disruption for each sector of the worldwide economy. Figure 9.2 shows international tourism destinations and tourist arrivals. Tourism and hospitality sectors contribute to local and global socio-economic developments and increase energy consumption. A rise in tourist arrivals increases energy consumption demands to support tourism activities (Paul Terhemba Iorember, Goshit, & Dabwor, 2020). Any rise in travel and tourism has caused effects on residents and environmental sustainability (P. Iorember, Usman, & Jelilov, 2019; Usman, Iorember, & Olanipekun, 2019). The COVID-19 outbreak has affected global economic operations and posed a greater shock to the hospitality industry in the sub-region (Jelilov, Iorember, Usman, & Yua, 2020). It needs better policies to handle international tourist arrivals and clean and green energy use to reduce economic expenditure and ensure environmental sustainability (Usman et al., 2019). The global pandemic crisis has changed energy consumption demands and influenced globalization and the service industry by rethinking innovation to gain recovery strategies (Usman, Olanipekun, Iorember, & Abu-Goodman, 2020). The COVID19 outbreak has also massively affected financial markets and increased the inflation rate (Dabwor, Iorember, & Yusuf Danjuma, 2020; Goshit, Jelilov, Iorember, Celik, & Davd-Wayas, 2020). Besides, it has affected the budgets of many governments and expenditure patterns of agriculture plans and the welfare of households (Paul Terhemba Iorember & Jelilov, 2018). EU travel and tourism firms experienced the most burdensome influences, and the hospitality industry in Europe became the second hardest-hit industry, facing a 58% decline in international tourist arrivals. The Middle East reported a 51% decline, and the United States (US) and Africa both faced a steep 47% decline in arrivals of world tourists during 2020. Scholars of the hospitality industry consider cultural and perceived socio-economic factors as the leading contributors to local communities of the tourist destinations (Joo, Xu, Lee, Lee, & Woosnam, 2021; Lindberg & Johnson, 1997; Mamirkulova et al., 2020).

9.3 Global Health Challenges and Economic Crisis Caused Decline in the Service Industry The advent of fatal diseases, epidemics, and global pandemics results in global health challenges and economic disasters. Globally, pandemics and transmittable diseases develop various mental health issues and adversely influence

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the hospitality industry and tourists’ behaviour and, ultimately, their mental well-being. The hospitality firms and various corporations experienced a sharp decline in their business operations due to travellers’ cancellations because of the COVID-19 (E. J. Avery, 2017; Mamirkulova et al., 2020; Meadows et al., 2019). The World Health Organization (WHO) announced that COVID-19 was a global public health-related emergency that required international attention 30 January 2020. They further declared it a global pandemic on 11 March 2020. Table 9.1 shows confirmed cases, deaths, case-mortality rate, and death toll per 100,000 population. As of December 10, 2021, the US government has reported 49,538,947 COVID-19 cases and 793,228 deaths, with a case-mortality ratio of 1.60%. Indian officials have declared it the second-most affected country, reporting 34,666,241 confirmed cases, a death toll of 474,111, and a case-mortality rate of 1.40%. Brazil remains the third most affected nation with 22,167,781 cases, a death toll of 616,251, and a case-mortality ratio of 2.80%, respectively. The United Kingdom announced 10,671,538 cases, a death toll of 146,444, and a case-mortality ratio of 1.40%. Figure 9.3 states that the US succeeded in minimizing the spread of the COVID-19 virus to some extent. The US has shown a decline in the daily new cases on a 7-day average as of 3 May 2021. The ongoing second wave of COVID-19 has massively spread in Indian cities, and the government has Table 9.1 COVID-19: Confirmed Cases and Mortality in the Most Affected Nations as of 10 December 2021 Country

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49,538,947 34,666,241 22,167,781 10,671,538 9,722,639 8,966,681 8,153,025 6,339,828 6,144,644 5,348,123 5,246,766 5,152,264 5,086,381 4,258,340 3,905,319 3,732,589 3,692,939 3,071,064 2,850,604 1,288,053

793,228 474,111 616,251 146,444 279,280 78,407 121,017 104,201 130,446 116,708 88,237 134,472 128,874 143,909 295,602 86,796 94,979 90,038 20,295 28,803

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241.66 34.7 291.99 219.11 193.44 93.98 180.46 125.34 157.33 259.7 187.43 223.01 256.01 53.18 231.71 228.59 213.99 153.76 117.09 13.30

Source: John Hopkins University CSSE COVID-19 Data https://coronavirus​.jhu​.edu​/data​/mortality

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recently reported more than 400,000 new cases. Thus, India has recently faced a severe and fatal infection. Russia has declared a rising trend, and there are more new daily cases recorded as of 3 May 2021. Brazil has reported a rise in new cases recently. Similarly, many countries are facing the second and third waves of the pandemic. India has become a new COVID-19 epicentre. There are 19,557,457 cases of COVID-19 in India. The hardest hit countries experienced widespread disturbance to the service industry, including hospitality, tourism, and travel, which make up a major contributor to global economies’ GDP. As of 3 May 2021, COVID-19 had infected more than 153.565 million individuals, causing a death toll of more than 3.218 million people; however, more than 130.919 million patients successfully recovered from this fatal infectious disease worldwide (Lange, 2021). COVID-19 is a lethal virus, and WHO experts declared it one of the deadliest diseases and the worst pandemic of modern human history ( J. Abbas, 2020; Su, McDonnell, Cheshmehzangi, et al., 2021). The symptoms of COVID-19 vary widely, from none to life threatening (Jaffar Abbas, Dake, et al., 2021). Influxes of air passengers and international tourist increase the virus transmission risk among destination communities. Tourists fear infection, as there is no 100% effective treatment for COVID-19 (Su, McDonnell, Wen, et al., 2021). The pandemic has affected about 200 territories and countries and caused health and economic problems worldwide (Acter et al., 2020; Agarwal et al., 2021; Lange, 2021). See Table 9.1 for detailed information on cases and mortality ratios. 9.3.1 CSR Practices in Tourism and Hospitality Firms during the COVID-19 Pandemic

The emergence of COVID-19 has changed the ways of doing business, and many organizations have paid more attention to social responsibilities in business activities and engaged in CSR practices. The fundamental notion of CSR is that either businesses should make a positive contribution to benefit society or internal advantages beyond stakeholders’ self-interests (Jaffar Abbas, Dake, et al., 2021; Coles, Fenclova, & Dinan, 2013; Jones, Comfort, & Hillier, 2006; Yoosefi Lebni et al., 2021). Carroll (1999) described the pyramid model of CSR and explained that it is the combination of four fundamental responsibilities, including philanthropic, legal, economic, and ethical duties. He debated that business firms should not only focus on achieving financial goals within their legal limits, but also define ethical standards by implementing philanthropic activities (Carroll, 1999). Lantos (2002) offered three CSR archetypes, including strategic, ethical, and philanthropic CSR, and further attempted to build on the pyramid model of CSR (Lantos, 2002). Practising ethical CSR shows that business firms follow at least the environmental and social contribution of their business practices. At the same time, philanthropic CSR describes organizations’ charitable activities by sacrificing firms’ business profits and contributing to society and other social stakeholders. On the other hand, strategic

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CSR elucidates business firms’ engagement in CSR activities, focusing on growing social welfare and firms’ benefits, such as cost management, minimizing energy expenditure, brand management, etc. Correspondingly, Rhou and Singal (2020) proposed a basic idea of CSR for strategic philanthropy in the hotel, tourism, and hospitality sectors and described how socially responsible firms should follow the business practices based on CSR activities during the global crisis (Rhou & Singal, 2020). The existing literature evidenced that numerous researchers adopted the stakeholder theory to view CSR practices by considering broader social (community support and disaster relief), economic (saving operational expenses and efficient management system), and environmental characteristics (resource management and water and energy conservation). Besides, firms’ socially responsible practices for internal and external stakeholders, such as local communities, society, customers, suppliers, and employees, gain business benefits (Coles et al., 2013; Colvin, Witt, & Lacey, 2020; Rhou & Singal, 2020). The global crisis posed by COVID-19 provided an opportunity to examine business firms’ genuine commitment to practice authentic CSR activities, as economic strains and disasters possibly push companies to proceed with short-term benefits to minimize their long-term expenditure, such as CSR investments. Business firms and corporations involved in CSR practices during crisis times earn “reputational capital” that enhances firm value (Muller & Kräussl, 2011; Tarakci, Ateş, Floyd, Ahn, & Wooldridge, 2018). Another study put corporations’ CSR practices into three categories during the COVID-19 outbreak, including companies’ commercial activities to safeguard stakeholders interests, altruistic CSR activities for the protection of vulnerable people and society, and different activities to meet the standards of philanthropy and protection of commercial interests (García-Sánchez & García-Sánchez, 2020). With the pandemic’s appearance, many business organizations and corporations, including local and global manufacturers, retailing, and telecommunication firms, started practising CSR activities. Companies and corporations started donations in terms of financial contribution, donated products, protective equipment for staff, unlimited internet data for stakeholders and clients, lowered insurance rates, and adjusted work schedules for health care workers (He & Harris, 2020; Manuel & Herron, 2020). Accordingly, ties between local communities and the hospitality industry encourage firms to initiate CSR activities, and it has, perhaps, motivated hospitality companies to provide relief during the COVID-19 global crisis. The literature evidenced that hospitality firms have risen to serve local communities during epidemics, pandemics, and disasters. After the 2004 tsunami in the Indian Ocean, local hospitality firms and hotels working in Phuket introduced CSR initiatives and provided foodstuff and rooms to relief and health care workers, released hotel employees to help rescue and recovery processes, and delivered food to needy persons in the time of crisis (Henderson, 2007). The COVID-19 pandemic is an unprecedented crisis to tourism, hotels, and hospitality firms. Many hospitality firms and corporations have started CSR

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activities to combat the economic crisis. Some prominent CEOs and business leaders of companies such as United Airlines, Marriott hotels, Lyft, and Disney have taken pay cuts to help people in need during the pandemic crisis (Manuel & Herron, 2020). Fiji hotels, restaurants, and tourism and travel associations offered virtual training courses and programme certifications for tourism, hotels, and other hospitality workforces impacted by COVID-19 (Shin et al., 2021). Rhou and Singal (2015) debated that strategic philanthropy CSR activities are vital for hospitality companies and corporations as local communities appreciate them in difficult times and crises, which helps businesses gain “reputational capital” to cultivate their business profits (Rhou & Singal, 2020). Hospitality firms offering unoccupied rooms and foodstuff to health workers, volunteering for local communities, and being involved in various activities to improve local communities’ well-being, is the actual practice of strategic philanthropy CSR by hospitality firms during a crisis (Singal, 2015).

9.4 The 2030 Agenda of New Economic, Social, and Environmental Strategies Human beings now live in a new economic and social environment, and we need to redefine public and private strategies, decisions, and actions to deal with global threats that may affect freedom of movement and the welfare state. These situations are common in developing countries, and there is a solution, namely the 2030 Agenda. This route requires different actors’ cooperation, commitment, and responsible approaches, which should guide their actions to seek common interests that can correct the problems identified in the United Nations’ Sustainable Development Goals. The integrated, consistent, and comprehensive framework for business contributions in the field of strategic organization provides a lasting competitive advantage for companies to leverage their strengths to implement strategies to neutralize external threats (Bose & Luo, 2011; Iorember, Iormom, Jato, & Abbas, 2022; Jawad et al., 2023; Micah et al., 2023). Based on the assumption that companies in the industry may be heterogeneous in terms of their strategic resources, previous research suggests that their resources are a source of lasting competitive advantage (Song, Yeon, & Lee, 2021). Corporate resources mean all the assets, functions, characteristics, information, and knowledge that enable a company to implement strategies that improve its efficiency and effectiveness (Maqsood, Abbas, Rehman, & Mubeen, 2021; Mubeen et al., 2020). Among the various types of corporate resources, this study has the potential to make companies more resilient to negative shocks, such as the COVID-19 pandemic, particularly as they devise value-creating strategies (Fahlenbrach, Rageth, & Stulz, 2021). Specifically, shareholders and potential investors are more likely to pay attention to a company’s business risk in the event of a sudden external change (Azizi, Atlasi, Ziapour, Abbas, & Naemi, 2021; Hussain, Abbas, Wei, & Nurunnabi, 2019; NeJhaddadgar et al., 2020; Su, McDonnell, Li, et al., 2021). Thus, this study found three corporate-level

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aspects: innovation, firm, and CSR strategy. In suffering from loss of income, businesses’ proficiencies in managing the fears of COVID-19 matter, resulting in different market assessments (N. Khan et al., 2017; Mahmood, Han, Ali, Mubeen, & Shahzad, 2019). In this regard, business characteristics such as financial environments, innovation, and CSR are expected to comprehensively represent the endurance and resilience of firms reacting to COVID-19 shock, as detailed further below.

9.5 Firms’ Environmental, Social, and Governance (ESG) Performance during COVID-19 The study explores the organization’s environmental, social, and governance (ESG) performance in the market-wide economic crisis caused by the COVID19 pandemic. The unique situation creates an unparalleled opportunity for investors to question ESG performance as a signal of future equity performance and risk mitigation (Broadstock, Chan, Cheng, & Wang, 2021). According to a survey using China’s new data set, this study documented that ESG performance generally outperforms low ESG portfolios. Still, ESG performance mitigates financial risk during economic crises and weakens the role of ESG performance during “normal” periods. The literature has confirmed that ESG performance has become more vital during the COVID-19 pandemic crisis. This study examined the consequences of ESG investment practices. A previous study conducted on the COVID-19 shock and its unprecedented economic impact has brought many effects, such as uncertainty about the future of climate change measures. A survey of equity returns during the COVID-19 shock to investigate investor views and expectations on environmental issues showed that companies with responsible strategies on environmental issues experienced better equity returns (Garel & Petit-Romec, 2021). Climate change initiatives drive the effects (e.g., reducing environmental emissions and energy use). They are more pronounced in companies with high ownership and long-term investors. Past literature has not observed it before. Overall, the results showed that the COVID-19 shock did not distract investors from environmental issues, but on the contrary, led them to pay great rewards for climate responsibility. The study highlights the health, economic and social impacts of the COVID19 virus and emphasizes the need for collaboration between all agents to face scenarios never seen before. The study analyzes the pandemic effects on the hospitality industry during the pandemic. The result showed that some companies have demonstrated significant commitment to contribute a social good and have developed strategies to mitigate the outcome of COVID-19, just as others have developed some strategies for different purposes (Garel & PetitRomec, 2021). A study examined the impact of COVID-19 on corporate performance using financial data from listed Chinese companies. The study showed that COVID-19 is adversely affecting the business performance of the company. The negative impact of COVID-19 on a company’s performance

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becomes more noticeable as the company’s investment scale and sales showed a steep decline (Shen, Fu, Pan, Yu, & Chen, 2020). Besides, the literature analysis showed that the pandemic has adversely affected firms’ corporate performance and made it more vulnerable, as it posed significant adverse effects on business growth. The findings showed one of the first pieces of empirical evidence and indicated a linkage between the COVID-19 pandemic and corporate performance.

9.6 The COVID-19 Pandemic Impact on the Restaurant Industry The COVID-19 pandemic has caused a significant decline in stock markets around the world, and hospitality companies are facing serious financial problems. Protecting and maintaining corporate value is essential for companies to survive a crisis. Scholars have extensively examined the influence of corporate social responsibility (CSR) on corporate value (Akhtar, Nadeem Akhtar, et al., 2020; Akhtar, Siddiqi, et al., 2020; Ali et al., 2020; Anser et al., 2020). Event and finite difference surveys have shown that engaging in CSR increases corporate equity returns and stakeholder attention during a pandemic (Qiu, Jiang, Liu, Chen, & Yuan, 2021). Community-related CSR has a stronger and faster impact on stock returns than customer- and employee-related CSR. The study showed that companies seeking to improve equity market performance during a pandemic could invest in CSR to protect communities, customers, and employees and attract more stakeholder attention. The study examined the effect of COVID-19 on US restaurant firms’ stock returns, which varied according to the firms’ pre-pandemic characteristics by employing three firm-level dimensions (financial conditions, corporate strategies, and ownership structure). This study employed 795 firm-year observations obtained from annual reports (Song et al., 2021). According to a survey, restaurant companies with large past characteristics, high advantage, high cash flow, low return on assets (ROA), and advanced internationalization were more likely to respond to COVID-19 stock prices than similar companies. On the other hand, dividends, franchises, institutional ownership, and management ownership did not have a significant easing effect on the relationship between COVID-19 and equity returns. This study sheds light on research topics by providing insights into the drivers of restaurant company equity returns during the COVID-19 shock. In future studies, the variables and methods used in the current study can be used to better understand the problem.

9.7 The Crisis of COVID-19 and Business Model Innovation (BMI) Firms The hospitality industry has experienced the hardest hits posed by the COVID-19 lockdowns and restrictions to combat the pandemic worldwide. The first theoretical and practical observations in the hospitality industry

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exhibited that business model innovation (BMI) is the solution to recovering from the COVID-19 crisis and coping well with the new normal of doing businesses (Breier et al., 2021). Interestingly, some companies in the hospitality industry have already begun to adapt their business models successfully. A study explored the reasons and methods of these successful recovery attempts through BMI by conducting multiple case studies of six Austrian hospitality companies. The literature specified that BMI is suitable during and after the crisis to generate new income sources to ensure higher liquidity margins. As a global disaster, the COVID-19 crisis has serious implications for the development of the global economy and threatens the survival of businesses around the world (Mamirkulova et al., 2020). It seems inevitable that this natural turmoil has hit the global economy and caused a major crisis for businesses. The study investigated how Chinese companies innovate their marketing strategies by critically identifying the type of marketing innovation of a company, using two aspects: innovation motivation and collaborative innovation level (Y. Wang, Hong, Li, & Gao, 2020). The study also explored the impact of external environments, internal benefits (such as dynamic capabilities and resource reliance), and corporate characteristics on the choice and implementation of marketing innovation strategies for Chinese companies. This provides valuable insights for businesses for better responding to similar crisis events in the future. The COVID-19 pandemic is changing the environment, raising many challenges that require innovative solutions, and changing the outlook for innovation. Studies have investigated innovation response times for specific organizational actors by analyzing data from commercial innovation databases. Scholars have argued that innovation response time is highly dependent on how organizations perceive time, and innovation start-ups are expected to be the fastest and universities to be the slowest in responding to crises. Controlling a set of external factors of structural change supports the hypothesis about start-ups. Contrary to our expectations, universities do not differ significantly in innovation response times compared to existing companies (Ebersberger & Kuckertz, 2021). The authors provide specification curve analysis to support the robustness of the findings. The study showed the importance of start-up– corporate collaboration and open innovation, especially in the aftermath of the crisis.

9.8 Discussion and Conclusion The emerging world has not implemented strict actions to follow CSR guidelines in business. There are deficient business marketing tactics, consumer awareness is not high, and responsible business culture is not practised. However, firms following CSR activities gain loyal clients with positive business growth even in times of crisis, such as the COVID-19 pandemic. COVID-19 brought the most unanticipated external shock and global crisis for business and modern human society, which has slowed down the process of globalization. From the first month of 2020, the COVID-19 pandemic

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posed a downturn and restrictions on mobility, bans on air travel, and the closure of many tourist and leisure activities worldwide (Henderson, 2007; Manuel & Herron, 2020; Puaschunder, Gelter, & Sharma, 2020; Shin et al., 2021). By April 2020, more than 50% of the global population faced restrictions, as many countries imposed lockdowns and air travel bans, which disrupted economic activities, public lives, and international mobility (Jaffar Abbas, Mubeen, et al., 2021; Pham, Dwyer, Su, & Ngo, 2021; Su, McDonnell, Cheshmehzangi, et al., 2021; Su, McDonnell, Wen, et al., 2021). Lockdowns and restrictions caused a steep decline in global consumption and reduced global trade. The first half of 2020 reported a 50% decline in international foreign direct investments, and the developed world experienced a 75% decrease in world trade and investments (Abodunrin, Oloye, & Adesola, 2020; Fu, Hereward, MacFeely, Me, & Wilmoth, 2020; Mayhew & Anand, 2020). The pandemic has suppressed human social interaction patterns in global economies and spilt over into an economic crisis comparable to the beginning of the global Great Depression (Fu et al., 2020). Experts forecasted that the COVID-19 economic crisis would leave the most profound economic, social, and health consequences since World War II and that world economies would report per capita declines not seen since 1870 (Bank, 2020). Because of protective measures, such as social distancing, the financial fallout reported a sharp decline of 38% in consumption in China, 78% in Great Britain, 50% in the US, and 49% in Germany (Abodunrin et al., 2020; Mayhew & Anand, 2020). All these adverse consequences massively hit the performance of the hotel, tourism, and hospitality companies worldwide. The mid-career personnel faced a drastic 70% decline in unemployment, and companies changed 40 million European workers’ status to short-term work. These challenges have massively affected the hotel, tourism, and hospitality business organizations’ operations. The global health care crisis has adversely influenced the mental well-being of hospitality firms’ employees. Business companies and corporations have initiated CSR activities to minimize the adverse effects on employees’ health, which is helpful to enhance employees’ safety. These positive steps increase employees’ productivity for businesses, and organizations can better combat the virus consequences on business activities (Kraus et al., 2020; Mayhew & Anand, 2020).

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200  Resetting Industry through Corporate Responsibility Singal, M. (2015). How is the hospitality and tourism industry different? An empirical test of some structural characteristics. International Journal of Hospitality Management, 47, 116–119. doi:10.1016/j.ijhm.2015.03.006. Škare, M., Soriano, D. R., & Porada-Rochoń, M. (2021). Impact of COVID-19 on the travel and tourism industry. Technological Forecasting and Social Change, 163, 120469. doi:10.1016/j.techfore.2020.120469. Song, H. J., Yeon, J., & Lee, S. (2021). Impact of the COVID-19 pandemic: Evidence from the US restaurant industry. International Journal of Hospitality Management, 92, 102702. Su, Z., McDonnell, D., Cheshmehzangi, A., Abbas, J., Li, X., & Cai, Y. (2021). The promise and perils of unit 731 data to advance COVID-19 research. BMJ Global Health, 6(5), e004772. doi:10.1136/bmjgh-2020-004772. Su, Z., McDonnell, D., Li, X., Bennett, B., Segalo, S., Abbas, J., … Xiang, Y. T. (2021). COVID-19 vaccine donations-vaccine empathy or vaccine diplomacy? A narrative literature review. Vaccines (Basel), 9(9), 1024. doi:10.3390/vaccines9091024. Su, Z., McDonnell, D., Wen, J., Kozak, M., Abbas, J., Šegalo, S., … Xiang, Y.-T. (2021). Mental health consequences of COVID-19 media coverage: The need for effective crisis communication practices. Globalization and Health, 17(1), 4. doi:10.1186/ s12992-020-00654-4. Tarakci, M., Ateş, N. Y., Floyd, S. W., Ahn, Y., & Wooldridge, B. (2018). Performance feedback and middle managers’ divergent strategic behavior: The roles of social comparisons and organizational identification. Strategic Management Journal, 39(4), 1139– 1162. doi:10.1002/smj.2745. Tonsaker, T., Bartlett, G., & Trpkov, C. (2014). Health information on the Internet: Gold mine or minefield? Canadian Family Physician Medecin de Famille Canadien, 60(5), 407–408. UNWTO, W. (2019). International tourism highlights, 2019 edition. In UNWTO Madrid, Spain. Usman, O., Iorember, P. T., & Olanipekun, I. O. (2019). Revisiting the environmental Kuznets curve (EKC) hypothesis in India: The effects of energy consumption and democracy. Environmental Science and Pollution Research International, 26(13), 13390– 13400. doi:10.1007/s11356-019-04696-z. Usman, O., Olanipekun, I. O., Iorember, P. T., & Abu-Goodman, M. (2020). Modelling environmental degradation in South Africa: The effects of energy consumption, democracy, and globalization using innovation accounting tests. Environmental Science and Pollution Research International, 27(8), 8334–8349. doi:10.1007/s11356-019-06687-6. Verma, S., & Gustafsson, A. (2020). Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach. Journal of Business Research, 118, 253–261. doi:10.1016/j.jbusres.2020.06.057. Wang, C., Wang, D., Abbas, J., Duan, K., & Mubeen, R. (2021). Global financial crisis, smart lockdown strategies, and the COVID-19 spillover impacts: A global perspective implications from Southeast Asia. Frontiers in Psychiatry, 12(1099), 643783. doi:10.3389/ fpsyt.2021.643783. Wang, Y., Hong, A., Li, X., & Gao, J. (2020). Marketing innovations during a global crisis: A study of China firms’ response to COVID-19. Journal of Business Research, 116, 214–220. Wondirad, A., Kebete, Y., & Li, Y. (2021). Culinary tourism as a driver of regional economic development and socio-cultural revitalization: Evidence from Amhara National Regional State, Ethiopia. Journal of Destination Marketing and Management, 19, 100482. doi:10.1016/j.jdmm.2020.100482.

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10 Addressing the Nexus between Economic Growth and Environmental Pollution in a Small Petroleum-Exporting Transition Economy Elkhan Richard Sadik-Zada, Andrea Gatto, and Mubariz Mammadli 10.1 Introduction Approximately one-quarter of the global carbon dioxide (CO2) emissions emanate from the economic activities of the economies that belong to the group of petroleum-abundant and exporting countries (Pearce, 2020). With Saudi Arabia on the top, Russia, Iran, Mexico, and Brazil belong to the list of the top 20 countries with the greatest CO2 emissions. The lion’s share of these emissions can be attributed to the carbon footprint of the petroleum value chain (Sadik-Zada and Loewenstein, 2020). About 15% to 40% of the greenhouse gases (GHG) of transport fuels worldwide stems from producing, transporting, and refining crude oil into transport fuels such as gasoline or diesel (Masnadi et al., 2018; Masnadi et al., 2021). Because of geological and economic reasons, most emission-intensive stages of the petroleum value chain are located in the oil and natural gas producing countries themselves (Masnadi et al., 2021). Furthermore, because of the abundance of oil and natural gas, the electricity mixes of these countries are dominated by fossil fuels (Ike et al., 2020). Hence, there is a growing interest in the income–environment relationship in fossil fuel-abundant settings (Danmaraya et al., 2021). The current war in Ukraine and the related energy crisis in Europe have led to a substantial increase in petroleum prices. As a result, oil exporters are experiencing a surge in export revenues and relatively high economic growth rates. Should we not expect greater emissions in these countries due to the increasing economic activity? Or would this oil and natural gas-driven revenue increase lead to the employment of advanced carbon-saving technologies? The literature on the income–emissions nexus in petroleum-producing countries is rather scarce. The existing literature is confined to panel analyses and is rather inconclusive (Sadik-Zada and Gatto, 2021). Except for the Russian Federation, in the work of Mihalischev and Raskina (2015), to the best of our knowledge, no systematic analysis on the drivers of GHG emission dynamics DOI: 10.4324/9781003336563-10

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in the post-communist transition economies has been put forward – both on a cross-country and case study level. No single study addresses the dynamics of atmospheric pollution in a small transition economy. Hence, an assessment of this nexus for Azerbaijan would contribute to the literature on environmental upgrading in transition economies and provide applied recommendations regarding the environmental policies in Azerbaijan. Displaying a per capita GDP of more than 14,300 USD and relatively high competitiveness indicators – number 35 according to the World Bank – Azerbaijan could be a benchmark case study both for transition and petroleum-exporting countries with a very high level of human capital. By focusing on the Azerbaijani economy, which has shown impressive progress in ecological laws, ecological projects, and economic growth, this study aims to assess the environmental Kuznets curve (EKC) hypothesis, i.e. the inverted U-shaped average income–environment relationship, for the case of Azerbaijan. Exploring a single country case instead of a cross-country panel has some advantages because such estimations account for the respective country context, are free of aggregation bias, and can be used for concrete environmental policy formulations (Rao et al., 2009). Against the backdrop of the nascent critique of the EKC-conjecture, the study could contribute to the ongoing debate on the validity of it in the context of transition economies (Sadik-Zada and Gatto, 2023). Income growth and environmental protection are often seen as a tradeoff. Nonetheless, the United Nations’ Sustainable Development Goals (SDGs) propose a policy agenda based on the triangulation of results between economy, environment, and society, whereby governance plays a pivotal role (UN, 2015). The Agenda 2030 portrays energy as a milestone for economic growth and sustained socio-economic and environmental objectives. In achieving the balance within these goals, energy can be a determinant dimension of this multifaceted mosaic (Gatto and Drago, 2021; Aldieri et al., 2021). On 18 July 2022, the European Union (EU) signed a new agreement with Azerbaijan aiming at a greater volume of natural gas supplies to Europe, and environmental upgrading in Azerbaijan is in line with this progressive dualism, i.e., alignment of economic growth with climate policies. Baku-Absheron of Azerbaijan has long been one of the most contaminated regions worldwide. In contrast to most oil-exporting developing countries, industrialization in Azerbaijan commenced in the late 1860s. Environmental pollution has led to severe toxigenic mutations in some industrial cities of the Absheron peninsula, especially in the city of Sumgait (Rinner et al., 2011). Almost half of the population in these parts of the peninsula have experienced severe health problems due to air pollution and soil contamination (Eigenmann, 2013). Ecological degradation concerns and environmental protection policies have been on the rise in the local political agenda since the mid-2000s (ADB, 2005). Furthermore, Azerbaijan is not a typical developing country because of its high level of human capital development. All the mentioned features

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indicate the uniqueness of the case of Azerbaijan and, at the same time, a pleading for taking the case study of Azerbaijan under a magnifying glass. This chapter proceeds as follows: Section 10.2 elaborates on the major sources of GHGs in Azerbaijan and scrutinizes the policies toward protecting the natural environment. Section 10.3 elaborates on the empirical methodology and presents empirical results. Section 10.4 concludes.

10.2 Income and Greenhouse Gas Emissions in Azerbaijan 10.2.1 Profile of the Azerbaijani Economy

After years of negative economic growth in the early 1990s, the Azerbaijani economy started to grow again after the stabilization of the political situation in July 1993. The signature of the Contract of the Century between the Azerbaijani government and 36 Western, Saudi and Japanese oil and gas companies on 20 Sept. 1994 led to a massive attraction of foreign direct investments into the Azerbaijani economy. Between 1996 and 2016, the annual growth rate of per capita GDP was 8,1%. 2013 total GDP reached 50 Billion USD. With a per capita GDP of 14500 USD in purchasing power parity terms in 2020, Azerbaijan belongs to the group of upper-middle-income countries (WDI 2020). Interestingly, despite the inexorably growing economy and per capita income, in the time interval between 1990 and 2018 the per capita carbon emissions have been decreasing. The growth of mineral extraction has not been accompanied by the proportional growth of the manufacturing sector. The dominance of the petroleum sector deepened during the last two decades. The oil sector generates more than 90% of total exports. The share of manufacturing in GDP was on the decline from 1990 to 2012. The share of the services sector in GDP has grown up to 42%. This sector engages 49% of all employees in Azerbaijan. Some researchers explain the weakness of the Azerbaijani manufacturing sector over the high exchange rate of the local currency because of oil exports. Such a development is known as Dutch disease. Due to this negative impact of the oil sector on the growth rate of the nonoil industry petroleum sector could have a negative impact on the GHG emissions over shrinking industrial output. This is so because of the relative carbon intensity of heavy industry and machinery (Sadik-Zada and Gatto, 2021). 10.2.2 Carbon Footprint of the Petroleum Sector of Azerbaijan

Despite being driven by the carbon-intensive extractive industries, carbon emissions per capita have declined in Azerbaijan for three decades. In addition, most of the electricity produced in Azerbaijan is powered by coal, oil, and natural gas-based power stations. As for many other developing and transition economies, fossil fuel-based energy generation causes most of the harmful air pollution from stationary sources in Azerbaijan (Sadik-Zada et al., 2022). Mustafaev and Yuzbashov (2001) assessed the data from 60 industrial objects in the country and measured the emissions based on their own measurements.

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The oil industry directly caused 70.5% of air pollution from stationary sources. In this regard, Azerbaijan is not much different from other petroleumreliant economies. Purification techniques inherited from old Soviet technology could filter out 29.7% of the harmful substances . Mustafaev and Yuzbashov (2001) compiled data on the pollution impact of the oil value chain. Their estimations are based on data collected from different institutions, especially the Azerbaijan National Academy of Sciences (ANAS), State Oil Company of Azerbaijan (SOCAR), and Azerbaijan International Operating Company (AIOC). Their assessment results are summarized in Table 10.1. As Table 10.2 indicates, the assessment of the GHG emissions at the different stages of the petroleum value chain showed that CO2 is the major atmospheric pollutant at the extraction and refining stage. Based on these findings, we might assert that the GHG emission problem in Azerbaijan, like in many other petroleum-producing and processing countries, is a CO2 problem. This is why we explicitly focus on CO2 as a proxy for emissions and environmental degradation in assessing the growth-emission nexus. 10.2.3 GHG Emissions and Environmental Policies and Regulation

Between 1990 and 2010, the energy sector caused 76% of the cumulative GHG emissions in Azerbaijan, whereas the agriculture sector absorbed 15%, and the rest of the economy the remaining 9%. The decrease in emissions

Table 10.1 Emissions and Their Structure at Different Stages of Oil Value Chain Emissions, g/ton Extraction Separation Transfer Refining Total

NO2 8.5 5.7 0.45 1.5 16.5

SO x 190 4.9 10.2 60.7 265.8

CO 70 684 3.7 230 987.7

CH 3.2 93.2 0.2 168 264.6

C 20 H12 0.3 0.5 0.015 3.56 4.4

Source: Mustafaev and Yuzbashov (2001).

Table 10.2 Annual Emissions in Tons from Chirag-Platform and Sangachal Refinery

1 2 3 4 5 6 7

Pollutants

Chiraq-1

Sangachal terminal

Carbon dioxide Nitrogen oxides Carbon monoxide Sulphur dioxide Solid particles Non-saturated VOC Methane Total

216735.0 697.9 477.3 93.9 18.3 818.5 862.2 219703.1

415821.0 469.8 1089.0 0.3 12.9 4592.0 120.3 422105.3

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in the energy sector is mostly related to the vanishing of the local petroleum extraction technologies. The technological and engineering basis of the postSoviet oil sector of Azerbaijan depends on imported technology, and the local petroleum machinery vanished during the early 1990s. Since the dissolution of the Union of Soviet Socialist Republics (USSR), mobile sources have been the major drivers of GHG emissions. The number of cars increased from 260,000 in 1990 to 1.4 million in 2017. The trucks also increased by more than five times in the respective time frame. The number of buses, nevertheless, increased by only 75%. Despite the pleadings of the National Environmental Action Plan (NEAP) and Canadian International Development Agency (CIDA) representatives to tax mobile transport to promote more environmentally friendly means of transport, there has been no tax reform in this direction GHG emissions have been steadily decreasing over the past three decades. In contrast to post-Soviet countries, this decrease was not a result of environmental policies or technological development. As mentioned above, the structural crisis and the collapse of the central State Planning Agency (GOSPLAN) led to this positive environmental development. The per capita CO2 emission has decreased 1.7 times in comparison to that in 1990. Mostly industrialized urban areas of Azerbaijan, especially the city of Sumgait, which, for a very long time, used to be one of the centres of the chemical industry and metallurgy of the former Soviet Union (FSU), belong to the most ecologically contaminated areas worldwide. This is the reason why the Sumgait area is still a textbook case for modern ecotoxicology with intimidating cases of genetic mutations in human beings, fish, and animals (Rinner et al., 2011). Even after the drastic demise of the chemical industry in Sumgait, more than half of the population of Sumgait had environmental pollutionrelated illnesses (Eigenmann, 2013). Asian Development Bank (2005) identifies five major environmental concerns for the Republic of Azerbaijan, including: 1. Pollution from industrial and energy production, transport, and other sources. 2. Water and wastewater management. 3. Land degradation vulnerability to natural risks and disaster management. 4. Threats to environmentally protected areas and elements of ecosystems. 5. Regional environmental concerns. Despite environmental pollution problems, there are several terrains in which environmental rehabilitation projects brought ground-breaking improvements, especially in soil decontamination, which commenced over 100 years ago on the Absheron peninsula (Asian Development Bank, 2005). One and half centuries later, in June 2007, the president of Azerbaijan, Ilham Aliyev, signed the “Comprehensive action plan for improving the ecological conditions in the Azerbaijan Republic during 2006-2010”, and one

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of the elements of this plan was the transformation of the Black City into a White City, free from environmental pollution. Increasing financial leeway, political commitment, and economic stimulus were the major drivers in the total renewal of the large areas which were unprecedentedly polluted. In 2003, Azerbaijan adopted the “National Action Plan on Environmentally Sustainable Socio-Economic Development”. According to Vidadili et al. (2017), in its first line, this plan targets the reduction of GHG emissions and has been serving as the major driver of renewable energy generation in Azerbaijan since its ratification. In 2004, the government of Azerbaijan started to enhance projects which were supposed to increase Azerbaijan’s renewable energy generation capacity. That year, the government drafted the State Agency on the Use of the Alternative and Renewable Energy Sources (SAARES). Since 2009, renewable energy companies have been exempt from customs duties and taxes. With Decrees No. 112 and 113 of the Cabinet of Ministers, which came into force in April 2014, the companies importing technology for the generation of renewable energy are free from customs duties and VAT until 2024 (Vidadili et al., 2017). The decree of the president on the plan for the improvement of the ecological situation from 2006–2010 and finally the establishment of the SAARES in 2009 played an important role in the creation of the incentives for the reduction of GHGs in Azerbaijan. On 2 May 2014, the president of Azerbaijan officially joined the aims and targets of the International Renewable Energy Agency (IRENA) by the Law for Endorsement of the Rules of IRENA. In 2017, the Azerbaijani Parliament ratified the Paris Agreement and the nationally determined pledges within the Paris Agreement, whereby most of these pledges correspond with the surge of renewable energy sources in the energy mix and the adoption of the carbon-saving technologies within the petroleum value chains.

10.3 Empirical Methodology and Results 10.3.1 Quantitative Analysis

To analyze the income–environment relationship in Azerbaijan, we tested the EKC conjecture with per capita carbon emissions as dependent variable and per capita income as the independent variable. EKC is a stylized fact that indicates an inverted U-shaped relationship between the level of per capita income and environmental pollution. The test is predicated on the conventional black-box procedure (Sadik-Zada and Gatto, 2021; Sadik-Zada and Loewenstein, 2020). Within the framework of this chapter, we test the following estimation equations:

Per Capita Emissionsit = a + b1 gPCI t + b 2 gPCI t2 + e t (1)



Gross Emissionsit = a + b1Yt + b 2 gYt 2 + e t (2)

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The dependent variable in Equation (1) is environmental pollution per capita; gPCI t indicates an average income per capita; e it is the error term. Equation (2) assesses the relationship between the growth rate of gross GDP, gYt , and gross emissions. Equation (1) tests the relationship between the growth of per capita income and per capita emissions. This procedure is different from the standard econometric tests of the EKC hypothesis. This divergence is because the usual specification has not yielded any statistical significance. Equation (2) is supposed to test the relationship between gross GDP and gross emissions. 10.3.2 Autoregressive Distributed Lag Model (ARDL)

Like most economical time series, the income and environmental pollution series might be non-stationary. Hence, the series has to be tested for the existence of the unit roots. We employ the autoregressive distributed lag (ARDL) bounds testing approach to cointegration proposed by Pesaran and Sin (1998) and Pesaran et al. (2001), which is a widely applied method in the assessment of environmental issues (Jalil and Mahmud, 2009; Shahbaz et al., 2015; Iwata et al., 2010; Jalil and Feridun, 2011). This approach is applicable for the mixed stationary time series, i.e., I ( 0 ) and I (1) series, but not I ( 2 ) and above (Giles, 2013; Al-Mulali and Ozturk, 2015). ARDL assesses the short- and long-run effects of the explaining variables simultaneously. This enables distinguishing between welfare variables’ short- and long-run effects on the environment. Another advantage of ARDL is its consistency and relatively high effectiveness in analyzing relatively small sample sizes (Al-Mulali and Ozturk, 2015; Niklas and Sadik-Zada, 2019). Probably, the most important advantage of the method is the exclusion of the endogeneity problems and allowance of the feedback effects (Al-Mulali and Ozturk, 2015; SadikZada and Niklas, 2021). Another important feature of the ARDL/bounds testing methodology is that it enables the assignment of different lag lengths as they enter the model. In the following, the estimation follows the Akaike information criterium. The first step in the ARDL is the estimation of the equation using a simple ordinary least squares approach. The bounds testing tests the null hypothesis of no cointegration against the alternative hypothesis. The data relating to GHG emissions for the period 1990–2018 is provided by both the State Statistical Committee of Azerbaijan and the World Bank Development Indicators (WDI). Total GHG emissions per capita and CO2 emissions per capita serve as dependent variables. GDP per capita in constant 2010 USD is supposed to serve as an independent variable. Total GHG emissions encompass not only CO2 but all emissions.

Addressing the Nexus between Economic Growth  209 10.3.3 Estimation Results 10.3.3.1 Economic Growth and Per Capita GHG Emissions

Following Equation (1), we first tested the estimation equation with the squared value of emissions. Estimation indicates that the squared value of CO2 emissions has n statistically significant impact on emissions. This clearly negates the inverted U-shaped relationship between per capita income and per capita GHG emissions. To assess the short- and long-run relationship, we run the bi-variate ARDL estimator, with per capita income as the only independent variable. The ARDL estimator indicates that economic growth has statistically significant short- and long-run effects on the level of per capita carbon emissions (Table 10.3). The model explains 43.7% of the variation of per capita CO2 emissions. An increase in economic growth by one unit leads to a 1.1% decrease in emissions. In the short run, the relationship is positive: one unit increase of economic growth corresponds to a 0.6% increase in emissions. In addition, the Pesaran-Shin-Smith ARDL-bonds test also confirms that the economic growth rate and the level of per capita emissions are cointegrated. As indicated in Figure 10.1, cumulative sum control chart (CUSUM) and CUSUMSQ confirm that the model is stable (Brown et al., 1975). Furthermore, the study establishes a positive long-term relationship between gross GDP and total GHGs: a 1% increase in gross GDP leads to a 0.21% increase in total emissions (Table 10.4). The Pesaran-Shin-Smith ARDL-bonds test confirms that the economic growth rate and the level of per capita emissions are cointegrated. CUSUM and CUSUMSQ are presented in Figure 10.2 and confirm that the model is stable. Table 10.3 ARDL-Test of the Economic Growth-Emissions Nexus in Azerbaijan, 1990–2018 Variables

Adjustment Term

L.ln_CO2_Per_Capita

−0.239*** (0.0665)

gY D.gY

Long-Run

−0.0110* (0.00601)

Constant Observations R-squared

27 0.437

27 0.437

Note: Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

Short-Run

0.00671** (0.00248) 0.316*** (0.100) 27 0.437

210  Addressing the Nexus between Economic Growth

CUSUM squared

CUSUM squared

1

0

1993

2018

Time

Figure 10.1  CUSUM squared.

Table 10.4 ARDL-Test of the Gross GDP–Total GHGs Nexus in Azerbaijan, 1990–2018 Variables

Adjustment Term

ln_GDP GDP-squared L.ln_total GHGs D.ln_GDP

−0.835*** (0.221)

Long-Run 0.210** (0.0818) −0.001 (0.0000)

−0.212 (0.184) −0.001 (0.0000) 5.140** (2.160)

D.GDP_squared Constant Observations R-squared

28 0.506

Short-Run

28 0.506

Note: Standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1.

28 0.506

Addressing the Nexus between Economic Growth  211

CUSUM squared

CUSUM squared

1

0

2018

1993 Time

Figure 10.2  CUSUM squared.

10.4 Conclusion In this chapter, we have shown that because of the unique features of Azerbaijan, generalizations of the recent panel studies on the income–environment relationship in petroleum-exporting countries could give very confined inferences in the respective context. Hence, we have made the first step in the rigorous analysis of the income–environment relationship in the Republic of Azerbaijan. We scrutinize the case study based on assessing the existing legal documents and country-specific time-series analyses. Despite the economy’s strong recovery, significantly since the signature of the Contract of the Century on 20 September 1996, the per capita CO2 emissions have been inexorably decreasing. The study decisively rejects the inverted U-shaped income–pollution relationship. Using the alternative specifications and application of the ARDL model, we find that Azerbaijan experienced a rather carbon-saving economic growth over the past two decades. A 1% increase in GDP growth leads to a 0.01 decrease in the per capita CO2 emissions in the long run. There is no short-term effect of economic growth on the level of average per capita emissions. Furthermore, the study establishes a positive long-term relationship between the level of gross GDP and the gross GHG emissions. Contradicting at first glance, the results could be reconciled as follows: a greater level of income leads to greater emissions. We conclude that there is a positive longterm gross GDP and total GHGs nexus. However, an increase in income leads to decreasing marginal increases in emissions. These results emanate from the

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negative GDP growth and per capita emissions nexus. Hence, the inferential statistical findings also indicate that the growing Azeri economy has become more climate friendly. Within the empirical analysis, we followed the black-box strategy. In the follow-up studies, this black box, i.e., the study with just one independent variable, has to be turned into a white box. This implied assessment of the role of the structural change and especially the role of the diminishing share of manufacturing and the surge of the tertiary sectors in GDP. In addition, the decreasing carbon intensity of the economy implies that applying the non-parametric regression approaches with time-invariant carbon-elasticities of growth would yield more reliable empirical results.

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11 Revising the Environmental Kuznets Curve in the PostCOVID-19 Era from an SDGs Perspective Muhammad Azam, Ahmed Imran Hunjra, Mahnoor Hanif, and Qasim Zureigat 11.1 Introduction An important topic of discussion at the Climate Change Conference (COP24) in Poland was the role motivational dialogue may play in furthering various objectives (Duan et al., 2016). Despite this, the fundamental goal is to achieve a long-term balance between environmental and economic development, although the world is focused on lowering COVID-19 and promoting economic growth. According to the Intergovernmental Panel on Climate Change’s (IPCC) 2018 report, global warming has increased by 1.5 degrees Celsius above pre-industrial levels, and an estimated annual investment of $1.5 trillion is required to achieve the goals of the Paris Climate Agreement (Nations, 2015; UNFCCC, 2020). The fact that governments will take the lead in funding projects to combat climate change is undeniable, and as a result, macroeconomic policies that will play a significant role in the economic development of green technology will be required. The recent global pandemic has profoundly impacted economic growth and human well-being, particularly in the health sector. Reducing COVID-19, on the other hand, may have an impact on specific economic sectors. As a result, some studies have used conventional approaches to measure the effects of economic reductions from COVID-19 by incorporating interactions between different sectors. Furthermore, sustainable development should lead the economy to adopt cleaner production, as demonstrated by the environmental Kuznets curve (EKC) hypothesis’s consistent validation. Although many studies have attempted to validate this myth, they cannot demonstrate or guarantee the long-term viability of development and growth for the environment. We can, therefore, establish a strong link between environmental quality and gross domestic product (GDP) by utilizing the EKC. This chapter focuses on the relationship between environmental degradation, macroeconomics, and the COVID-19 situation. Key ideas based on the literature are discussed in this chapter about the interplay between environmental degradation, climate change, macroeconomic factors, and the COVID19 situation, and some important insights into basic theories, strong evidence, and methods used in previous studies to identify and discuss organization are DOI: 10.4324/9781003336563-11

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also discussed. Environmental pollution emissions, economic growth size, and regulatory magnitude are all discussed in this chapter, as are other relevant relationships, in the hopes of fostering growth in the financial economy and overall system performance at the macroeconomic level. The important debate in this chapter is enriched by some key considerations to understand the macroeconomic policy relationships. Discussing macroeconomic and microeconomic policies and addressing climate change in the current paradigm is critical. According to numerous viewpoints and strong evidence, climate change is influenced by economic and policy decisions (Hălbac-Cotoară-Zamfir et al., 2019; Orlove et al., 2020). In contrast, other studies from the literature also point to the unimportant or unrelated connections between major economic and environmental pollutant factors and economic growth (Koshlaf & Ball, 2017; N. Yang et al., 2018). However, there is some evidence that macroeconomic policies have a threshold impact on economic growth and development (Marsiglio & Privileggi, 2021; Sun et al., 2019). The chapter also discusses the broader perspectives of divergent views and strong evidence in the literature on environmental degradation factors, macroeconomic factors, and the COVID-19 situation. Other general concepts of macroeconomic factors and the relationship to the environment must be considered in more detail to include policy-binding features in the model. Some concepts such as the importance of economic growth and the environment have emerged from sustainable development models and gained great importance through the United Nations (UN) Conference on Sustainable Development. Different books have been written which have extensively explained theoretical and fundamental debates on environmental degradation and identified the importance of different environmental policies and channels at the global level. Because of the global COVID-19 epidemic, environmental degradation factors and economic growth are receiving considerable attention and are becoming increasingly popular (European Commission, 2020; Shukla, 2020) because of investments in numerous businesses and environmental projects. Many economies’ economic growth and environmental degradation might be greatly decreased if particular effects are not implemented. In many countries, macroeconomic policies and climate change could be greatly reduced or even cause problems if no extraordinary measures are taken. Various theories and empirical evidence recently emerged in the literature on studies focusing on the macroeconomic and environmental policies nexus are explored in detail in this chapter. More research is required on the traditional ideas of how macroeconomics interacts with climate change to include elements of policy coordination in simulations. New economic growth models have arisen, such as the concept of environment inclusion and the importance of the environmental pollutants through industrial sectors at the UN climate change conferences on sustainable development. Economic growth and environmental policies have been revived in the literature, both theoretically and empirically, and many regulations and channels just at the global level have been identified

Revising the Environmental Kuznets Curve  219

as important. The COVID-19 pandemic crises worldwide have heightened interest in macroeconomic growth, and environmental effects through the threshold are crucial to public policy. As a result of the lockdown, the loss of job climate projects’ has been raised. Most economies’ macroeconomic and financial policies and environmental issues will decline significantly without unique consequences, which could lead to a crisis. The chapter provides guidance on how to combat the negative effects of the COVID-19 pandemic by increasing the function of economic policy stabilization in balancing environmental crises. Here, the focus is on policymakers who can learn important lessons about how climate change affects macroeconomics and the other way around. Institutions and regional bodies that support climate change mitigation projects and other statutory institutions, such as lending institutions and governments, may find close relevance to this discussion in light of the upcoming crisis and recession, which are expected to have a severe impact on COVID-19-based climate change mitigation initiatives. A major focus of this chapter, however, is on the discussion of the importance of the environment to the economic situation around the world, particularly in developing economies, and how macroeconomic policy issues and their connection with climate change can provide beneficial outcomes. This chapter aims to add to the existing body of knowledge by giving an in-depth examination of the relationship between macroeconomics and the environment, with a particular focus on developing nations. By also analyzing the effects of an unexpected pandemic crisis on this paradigm, this chapter adds to the debate.

11.2 Environmental Kuznets Curve The EKC was created in 1991 with the study of the North American Free Trade Agreement (NAFTA) conducted by Grossman and Krueger (1991; Stern, 2004). The EKC hypothesis suggests an inverted U-shaped curve, which states that economic growth will first harm the environment and then improve it. In 1955, Simon Kuznets developed a model that showed an inverted U-shape link between per capita income and income inequality. Income inequality rises at first but then begins to drop after a specific threshold is reached. As a result, in the early phases of economic growth, income distribution is unequal, but as the economy continues to grow, the distribution becomes equal (Maneejuk et al., 2020). The Kuznets curve is the name given to this empirical phenomenon. As far back as the 1990s, the curve was given a new dimension. The EKC is intuitively appealing because it is based on sound reasoning. “The environmental Kuznets curve depicts a hypothetical relationship between various indicators of environmental degradation and per capita wealth” by Stern (2018). It is hypothesized that industry and urbanization significantly deplete natural resources and generate significant amounts of industrial and municipal waste in the early stages of economic development. Economic growth and pollution are linked in the sense that economic expansion contributes to an increase in environmental pollution. Similarly, pollution

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increases rapidly in the early phases of modernization, as the primary purpose is to expand material output, and individuals are more concerned with making a profit than maintaining clean air and water quality. Rapid economic growth entails higher consumption of resources as well as increasing pollution emissions, putting additional pressure on the environment’s quality and resulting in increased pollution (Hankey et al., 2012b, 2012a; S. et al., 2012). People at these stages of economic development are too impoverished to invest in enhancing the quality of the environment, and they are more likely than not to ignore the negative repercussions of economic expansion on the environment. Multidimensional poverty is reported to be particularly prevalent in the early phases of economic development, while environmental protection is said to be frequently neglected. There is, therefore, a positive linear relationship between economic expansion and environmental damage at this point in history. The advancement of the industrialization process, technical and technological advancements, and the expansion of the service sector all contribute to reducing pollution (Shih, 2019). As a result, environmental pressure grows faster than income in the early stages of development and slows down with GDP growth as income increases at higher income levels. The green economy, particularly renewable energy, are essential for alleviating poverty, a particularly serious problem in developing nations (Claar, 2020; Jiang et al., 2020).

11.3 Some Basic Facts and Empirical Evidence The literature on econometric approaches for evaluating the EKC hypothesis or identifying the turning moment is extremely diverse. Panel data is used in certain studies, whereas time series data is used in others. Some writers include a cubic term of income to provide additional flexibility (possible N-shaped form), while others pick alternative functional forms such as non-parameterized functions or no polynomial functional forms. A crucial consideration is an econometric procedure to use, as this can impact the findings (Aljadani et al., 2021; Barış-Tüzemen et al., 2020; Churchill et al., 2018). Grossman and Krueger (1991) observed an inverted U-shaped curve between SO2 and smoke levels and per capita income while researching the relationship between air quality and economic development (Aye & Edoja, 2017; Shahbaz et al., 2019). The EKC has received its first official confirmation of its validity. These findings show that the concentration of air contaminants rises with income at first, then decreases as income increases. According to the 1992 World Bank Development Report, as revenues rise, so will investment in environmental quality improvement and the resources available to fund it, popularizing the EKC theory even further. According to Di Falco (2005), economic expansion is the best and arguably the only means to improve the quality of the environment in most countries, even though it frequently leads to environmental damage in the early phases. Econometric examination of the relationship between environmental quality and economic growth has thus far been the subject of many empirical studies. The notion of the EKC has

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been and continues to be a major focus of this research. Empirical models that examine the EKC typically observe environmental deterioration as a dependent variable in connection to economic indicators and their squares as the independent variable. Numerous studies have examined the EKC concept for individual countries, as was indicated above. It is worth noting that the EKC theory has yet to be tested in several countries other than those mentioned above, including China, Turkey, Malaysia, and Pakistan (K. Ahmed & Long, 2012; Javid & Sharif, 2016; Khan, 2021). Researchers in China, Algeria, and Malaysia looked at the relationship between economic growth and carbon dioxide (CO2) emissions (Pata & Caglar, 2021; Suki et al., 2020). Alsamara et al. (2018) investigated the EKC hypothesis using CO2 emissions and the ecological footprint. If CO2 emissions are used instead of ecological footprint, then the EKC hypothesis does not hold. Autoregressive distributed lag model (ARDL),  vector error correction model (VECM) methodologies were used to evaluate the EKC hypothesis for Peru and India (Rana & Sharma, 2019; Sultan et al., 2021; Usman et al., 2019). According to the findings of some studies, the EKC hypothesis is valid in Malaysia. When it came to CO2 emissions and per capita real GDP in China, the same methodology was used to examine an EKC link between those emissions and GDP (Akadırı et al., 2021; Boamah et al., 2017; Du et al., 2018). The researchers found extensive evidence for the EKC hypothesis. EKC was not found to be the case in Cambodia, according to their findings (Tang & Chen, 2019). For example, the EKC analysis can be extended to include an entire region or a group of countries. A large number of studies have examined the connections mentioned above between different regions and countries, and they are all readily available. Many studies focus on the Middle East and North Africa (MENA). The results of the analysis of single MENA countries are extremely diverse. An investigation into the causality relationship between CO2 emissions, energy consumption, and real GDP for 9 MENA countries (Algeria, Bahrain, Egypt, Jordan, Kuwait, Lebanon, Morocco, Oman, and Qatar) was conducted by (Arouri et al., 2012). To test the EKC hypothesis, each country’s CO2 emissions are considered. The EKC hypothesis for the MENA region is supported at the regional level. On the other hand, there is little evidence to support the EKC hypothesis for the studied countries, apart from Jordan. The findings of Farhani et al. (2014) and Farhani and Balsalobre-Lorente (2020) support this conclusion. The authors examined the following ten MENA nations from 1990 to 2010: Algeria, Bahrain, Egypt, Iran, Jordan, Morocco, Oman, Saudi Arabia, Syria, and Tunisia. The authors tested the EKC hypothesis and considered modifying it to better understand the relationship between sustainability and human development. The EKC hypothesis, i.e., the relationship between environmental degradation and income, is an inverted U shape. Two previous studies used parametric approaches on MENA panel data sets and found results that supported the EKC hypothesis; however, research produced the opposite

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resultant findings. From 1980 to 2010, these researchers studied ten countries in the MENA region to see if the EKC hypothesis was true. The findings hypothesised an inverted U relationship between pollution and economic development but found it to be false (Li & Xu, 2021; Zeng et al., 2021). The EKC hypothesis is confirmed for the panel using panel data estimators that account for cross-sectional dependence and parameter heterogeneity (Isik et al., 2021; Maranzano et al., 2021). In addition, the Association of Southeast Asian Nations (ASEAN) countries has drawn the attention of many researchers. The EKC hypothesis was also validated in ASEAN nations (Adeel-Farooq et al., 2021; Phong, 2019). On the other hand, the EKC hypothesis is only weakly supported in Brazil, Russia, India, and China (BRIC countries), as economic growth and energy consumption influence the BRIC countries (Akadırı et al., 2021; Danish et al., 2020; Dong et al., 2017; Haseeb et al., 2018). However, income-based groupings demonstrate a high correlation between the economic growth stage and the existence of an inverted U-shaped EKC. The EKC hypothesis is only confirmed in developed countries, i.e., highincome countries, and not in underdeveloped countries. The EKC theory in a variety of countries is based on their varying levels of economic development and prosperity (Koondhar et al., 2021). They found that the EKC hypothesis was true for high-, middle-, and low-income nations investigated. A U-shape relationship is found in the EKC hypothesis based on empirical evidence (Alam, 2014; Arnaut & Lidman, 2021; Bölük & Mert, 2014; Cai et al., 2020; Koshta et al., 2021). Individual drivers’ effects on certain environmental processes are identified through EKC studies, which calculate coefficients using an income distribution that is suitably broad. Several scholars have also disputed the EKC assumption. In summary, the EKC hypothesis remains relevant for studying the wide range of global environmental–economic linkages. Despite these objections, recent studies continue to explore it, fuelling a normative discussion. To emphasize the potentially positive role of government policies, which are typically more ambitious in advanced countries (Gill et al., 2017; Piłatowska & Włodarczyk, 2018; Raza et al., 2020; B. Yang et al., 2021), these studies assume that the EKC directly reflects an “induced policy response”, which can outline how societies would demand more strict environmental standards as income rises. Due to government shortcomings, the COVID-19 epidemic has exposed some uncertainty in macroeconomic policy instruments. Macroeconomic strategies have not been clear until now on whether the collapse of economies based on severe health crises caused by environmental quality in the twentyfirst century will allow economies to find development solutions. Economic growth could lead to the biggest problems in the future if policy experts do not come up with effective answers. Economic stability following the COVID-19 pandemic necessitates immediate action on environmental quality mitigation (Sharifi & Khavarian-Garmsir, 2020; Wells et al., 2020). For this reason, a new macroeconomic policy perspective is needed that considers the impact

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of socio-economic policies and economic institutions on environment quality mitigation initiatives from a policy mix viewpoint. Macroeconomic policy strategies will be viable in the future only if they take into account welfare rather than capitalism, as socialist theories gain attention (Aduhene & OseiAssibey, 2021; Bambra et al., 2020; Prawoto et al., 2020; Shadat, 2020; Sharifi & Khavarian-Garmsir, 2020). Globally, economic development would decelerate sharply when countries are already fatigued by the COVID-19 pandemic approach. There are many economic issues facing the emerging and developing economies, including continuing COVID-19 outbreaks, growing inflation, record levels of debt, and increasing income inequality. Developing economies are particularly vulnerable to the outlook. Many emerging markets’ demand for products and services is expected to fall due to several factors. At the same time, governments in many of these economies are running out of policy options for dealing with new difficulties such as COVID-19 outbreaks, supply-chain bottlenecks, and inflationary pressures, as well as heightened financial vulnerabilities in wide swaths of the developing globe. Economic development would be more likely if all these dangers were combined (Al-Doori et al., 2021; Bellalah et al., 2020; Kubota, 2021; Singh et al., 2020; Souza & Silva, 2020; Susilawati et al., 2020). After considering development goals, some new dimensions are required for economic growth and environmental relationship. These development goals must include promoting growth, creating jobs, and improving infrastructure; increasing the productive capacity of developing countries is essential. It will take fundamental economic transformation and risk-informed investments to increase poor countries’ capacity to manufacture vaccines, combat poverty, and manage climate change in a well-directed manner. Countries with low productive capacity are relegated to providing unprocessed commodities because of their distance, low economies, or limited resources (Ahmed et al., 2021; Bamidele & Amole, 2021; Chen et al., 2021; Gupta et al., 2021; Krumer-Nevo & Refaeli, 2021; Tanvir et al., 2021). Global initiatives to improve productivity, infrastructure, diversification, and export development in the poorest countries should not be curtailed. The upgrading of industrial and technology infrastructure and the development of long-term infrastructure are all made possible by regional integration. There are greater economic challenges that must be considered in environmental challenges. Debt-burdened middleincome countries (Afanasiev & Shash, 2020; Becker et al., 2020; Ochinyabo, 2021; Xu et al., 2021), on the other hand, find it difficult to invest in their people and economies as part of recovery packages. They lack the resources to recover from the disaster and are even more caught in a trap that restricts their ability to grow further. The EKC hypothesis after the COVID-19 pandemic must be tested after considering economic development challenges in the form of reducing high-income inequality have been put on hold in some circumstances to focus on COVID-19. To increase production capacity, international development partners’ assistance must be coordinated and complementary. The integration processes play a significant role in modernizing production and

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technological infrastructure, intraregional investments, and building long-term infrastructure. An economic and environmental nexus must be tested after considering climate finance. According to the UN special report, a long-term recovery hinges on a global effort to address climate change head-on, invest in adaptation, and make climate finance accessible to developing countries. Developing countries and the global economy are degraded because of weak development, debt burdens, and decreased exports. The EKC hypothesis must consider that climate change and vaccination inequities are causing structural inadequacies. Climate finance and change in uneven trade, finance, and taxation institutions are critical factors to consider.

11.4 Critical Reflections and Some New Dimensions Prior studies on EKC at the global level have typically adopted economic laws regardless of the spatial dimension. As a result, conventional frameworks are used to analyze spatial agglomeration, territorial specialization, and the environmental quality consequences of regionally diverse outputs at dramatically different operating sizes (Espoir & Sunge, 2021; Fang, 2021; Kang et al., 2016; Karahasan & Pinar, 2021; Mosconi et al., 2020; Zhou & Wang, 2018). Since environmental economics has been studied extensively over the past few decades, few studies have incorporated economic geography and regional science views to address spatial challenges in the environment–economic nexus. Because of philosophical differences, more creative spatial EKC techniques have less effective knowledge of spatial variability as a significant driver of the environment–economic nexus. Regional economic performance and ecological degradation processes have occasionally been linked using simplified techniques, but only in a few cases have the intrinsic change drivers been identified as interacting with each other at the same spatial scale. The last time major contributions to EKC were based on cross-country data analysis was quite some time ago (Barua & Hubacek, 2008; Inglesi-Lotz & Bohlmann, 2014; Kim et al., 2018; Mazzanti et al., 2011). Most of the studies have increasingly faced the problem of regionally varied income-environment linkages using these hypotheses. In addition, several of these studies have questioned the relevance of previously recorded interactions by criticizing existing quantitative analytic procedures in favour of more complicated strategies for estimating equation parameters (Inglesi-Lotz & Bohlmann, 2014; Mazzanti et al., 2011). Recent research on the environment–economic nexus looks at sample cases in connection to a more explicit analysis of individual reactions and their impact on the environment in disaggregated geographical units. More reliable statistical data sets are required for multilevel analysis considering spatial aspects after COVID-19, ranging from small, homogeneous areas to entire ecological regions, than the findings of cross-country studies. COVID-19 has a diverse impact on developing economies at the city level. So, there is a crucial requirement to apply to have a low degree of data

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fluctuation, and their significance for policymaking is typically very high. With district-based qualitative approaches and local inquiries, it is possible to build a solid understanding of robust, spatially explicit EKCs, regardless of the process of environmental degradation being studied. Even if we assume that social development has varying environmental implications mediated by (more or less) adaptable regional settings, we can still see how geography plays a role in explaining regional differences in the distribution of natural resources. It is important to understand the impact of the country or region and microdimensions, including individual actors, local community, and cross-district dimensions, by verifying a spatially explicit environment–economy relationship at different operational scales are important to be evaluated. Additionally, risk management has taken centre stage around the world. In the past, many scholars have pointed out the COVID-19 pandemic’s resemblances and connections to environmental degradation. It is imperative to evaluate risk management in the EKC hypothesis. While the pandemic had a worldwide reach, its consequences were disproportionately felt by those most at risk, and its ambiguous character was akin to climate change. Difficulties need resolving systemic concerns through revolutionary socio-economic and political transformation, which raises resiliency to a wide range of shocks at all scales. Economic and political elites have used COVID-19 to achieve various aims, including limiting access to natural resources, decreasing environmental and social norms that regulate investment, and limiting civic space. Multiple environmental challenges must be additionally considered in the EKC hypothesis, which the world is facing today, including reactivation policies, for example, will continue to put pressure on natural resources and the lands and territories of people who have lived and worked in these areas for a long period (Cheng et al., 2020; Karimi et al., 2022; Rauf et al., 2018; Zhang et al., 2021). Only the most extreme forms of violence are employed against defenders, such as the death of people. The use of litigation to try to quiet environmental defenders from their defence work is also used to try to silence or otherwise divert environmental defenders from their defence work because of the time and resources they must spend defending themselves or in a courtroom. It is not just multinationals or businesses using intimidation to get their hands on land and natural resources. However, national and sub-national elites are also involved in land acquisition (Gerber, 2014; Montefrio, 2014).

11.5 Conclusions The EKC theory will mitigate economic and environmental issues in the world following the COVID-19 pandemic. Following the pandemic, this book chapter aims to explore new dimensions and challenges. Capital savings, improving financial capacity (financial regulation, credit channels, and liquidation channels), structural transformation in the macroeconomic, financial system play a vital role in environmental degradation. The link between these macroeconomic issues and environmental degradation is critical because of its

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wide-ranging consequences. It is difficult regarding macroeconomic policies, particularly for developing countries, as evidenced by COVID-19’s findings on spatial and district difficulties. Regarding green financial infrastructure projects, production capacity, and innovation in the economy, there are environmental degradation concerns of a variety of types. This chapter can assist policymakers, particularly those in developing countries, by providing practical guidelines for evaluating macroeconomic factors in the EKC hypothesis following the COVID-19 pandemic to reduce the effects of global warming. As a result of this chapter, a better understanding is developed regarding some new dimensions and influences that link macroeconomic growth with environmental degradation in developing economies around the globe. There are numerous ways macroeconomic development variables can be used for environmental degradation, particularly in poor economies. Policymakers may want to consider a wide EKC hypothesis testing. Economic issues and strategies that can improve environmental quality in a streamlined and effective manner should be included in an effective EKC hypothesis.

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230  Revising the Environmental Kuznets Curve Koshta, N., Bashir, H. A., & Samad, T. A. (2021). Foreign trade, financial development, agriculture, energy consumption and CO2 emission: Testing EKC among emerging economies. Indian Growth and Development Review. https://doi​.org​/10​.1108​/IGDR​-10​ -2019​-0117. Krumer-Nevo, M., & Refaeli, T. (2021). COVID-19: A poverty-aware perspective. American Journal of Orthopsychiatry. https://doi​.org​/10​.1037​/ort0000566. Kubota, S. (2021). The macroeconomics of COVID-19 exit strategy: The case of Japan. Japanese Economic Review. https://doi​.org​/10​.1007​/s42973​-021​-00091​-x. Li, X., & Xu, L. (2021). Human development associated with environmental quality in China. PLOS ONE. https://doi​.org​/10​.1371​/journal​.pone​.0246677. Maneejuk, N., Ratchakom, S., Maneejuk, P., & Yamaka, W. (2020). Does the Environmental Kuznets Curve exist? An international study. Sustainability (Switzerland). https://doi​.org​/10​.3390​/su12219117. Maranzano, P., Cerdeira Bento, J. P., & Manera, M. (2021). The role of education and income inequality on environmental quality: A panel data analysis of the EKC hypothesis on OECD countries. SSRN Electronic Journal. https://doi​.org​/10​.2139​/ssrn​ .3813082. Marsiglio, S., & Privileggi, F. (2021). On the economic growth and environmental tradeoff: A multi-objective analysis. Annals of Operations Research. https://doi​.org​/10​.1007​/ s10479​-019​-03217​-y. Mazzanti, M., Montini, A., & Zoboli, R. (2011). Municipal waste production, economic drivers, and “new” waste policies: EKC evidence from Italian regional and provincial panel data. SSRN Electronic Journal. https://doi​.org​/10​.2139​/ssrn​.952948. Montefrio, M. J. F. (2014). Growing low-carbon commodities in upland Philippines: Integration of smallholders in biofuels and rubber production regimes. In ProQuest Dissertations and Theses. Mosconi, E. M., Colantoni, A., Gambella, F., Cudlinová, E., Salvati, L., & RodrigoComino, J. (2020). Revisiting the Environmental Kuznets Curve: The spatial interaction between economy and territory. Economies. https://doi​.org​/10​.3390​/ economies8030074. Ochinyabo, S. (2021). The coronavirus pandemic and fiscal sustainability in Nigeria. International Journal of Advanced Research in Statistics, Management and Finance. https://doi​ .org​/10​.48028​/iiprds​/ijarsmf​.v8​.i1​.11. Orlove, B., Shwom, R., Markowitz, E., & Cheong, S. M. (2020). Climate decisionmaking. Annual Review of Environment and Resources. https://doi​.org​/10​.1146​/annurev​ -environ​-012320​-085130. Pata, U. K., & Caglar, A. E. (2021). Investigating the EKC hypothesis with renewable energy consumption, human capital, globalization and trade openness for China: Evidence from augmented ARDL approach with a structural break. Energy. https://doi​ .org​/10​.1016​/j​.energy​.2020​.119220. Phong, L. H. (2019). Globalization, financial development, and environmental degradation in the presence of Environmental Kuznets Curve: Evidence from ASEAN-5 countries. International Journal of Energy Economics and Policy. https://doi​.org​/10​.32479​/ijeep​.7290. Piłatowska, M., & Włodarczyk, A. (2018). Decoupling economic growth from carbon dioxide emissions in the EU countries. Montenegrin Journal of Economics. https://doi​.org​ /10​.14254​/1800​-5845​/2018​.14​-1​.1. Prawoto, N., Purnomo, E. P., & Zahra, A. A. (2020). The impacts of Covid-19 pandemic on socio-economic mobility in Indonesia. International Journal of Economics and Business Administration. https://doi​.org​/10​.35808​/ijeba​/486.

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12 Revisiting the Environmental Kuznets Curve (EKC) An Analysis Using the Sectoral Output and Ecological Footprint in India Muhammed Ashiq Villanthenkodath 12.1 Introduction Agriculture, industry, and service are the key sectors of any economy for catering outputs and inputs (Souza, 2014; Villanthenkodath et al., 2022). As a key player in reducing poverty, creating structural transformation, and boosting the economy, agriculture is considered the primary sector of an economy (Cervantes-Godoy & Dewbre, 2010). Unfortunately, the agriculture-driven gross domestic product (GDP) growth for improving biodiversity, reducing poverty, ensuring food security, etc., are in turmoil because of the adverse effect of climatic change (Aryal et al., 2020; Karimi et al., 2018). For instance, nearly 24% of global greenhouse gas (GHG) emissions come from the agriculture, forestry, and other land use (AFOLU) sector (Pachauri et al., 2014). Thus, it is possible to infer that India is not free from this menace, as climate change is a global phenomenon. Concurrently, the role of the industrial sector is much important, as it creates exports, employment, human capital, and a multiplier effect on GDP (DTI, 2017). Hence, this sector is considered the lifeline of any economy. However, the unrestricted use of non-renewable energy sources, such as fossil fuels, in the production process of different industries creates a significant threat to the natural environment. Hence, the industrial sector creates waste and emissions in the natural environment (Li, 2017). Simultaneously, although the service sector boosts the economy, it creates pollution as the extensive use of energy for ventilation, lighting, heating, water, cooling, and disposal of waste (Ehigiamusoe et al., 2022). The selection of India can be justified in various ways. First, India is the fourth-largest economy and the second most populated nation  (Ahmed & Wang, 2019). Second, according to the United Nations (UN) classification, it is the fastest-growing economy in the world and a developing country (Ali et al., 2022). Third, besides being the third-largest energy consumer, India is highly dependent on non-renewable energy in their total primary energy (Murshed & Tanha, 2021). Fourth, because of mounting demands for resources by its population, India suffers an ecological deficit of nearly 150% (Ali et al., 2022). Fifth, the changes adopted in human activities related to the pandemic epoch of COVID-19 have led the Indian economy to a conjunction phase DOI: 10.4324/9781003336563-12

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nearly similar to the recession period. For instance, India’s GDP contracted to 23.9%1 in the first quarter of 2020 as compared to the same quarter of the previous year. Finally, India’s carbon dioxide (CO2) emissions have seen a drastic fall of 15% in the first quarter of the pandemic period 2020. The reduction in demand for coal, oil, and gas consumption made CO2 emissions fall by 30%, witnessed for the first time in the past four decades. However, the intensity of CO2 may differ in the different sectors of the economy. Theoretically, the environmental effect of agricultural activities take place when methane is emitted during the production of rice; nitrous oxide is emitted when using fertilizer; emissions are released during food processing, packing, and transportation; and CO2 is emitted while preparing to replace forest with the agriculture (Ehigiamusoe et al., 2022). The link between environmental pollution and the industrial sector is portrayed in ecological modernization theory. It argues that modernization and social transformation are the outcomes of industrialization. In this line, environmental problems may surge as society progresses from low to middle stage development at the expense of the environment. However, movement to a service-based economy at a later stage may decrease the adverse environmental effect of industrial growth (Mol & Spaargaren, 2000). In the case of the service sector, various human needs, such as health, education, transport, trade, and many others, require more use of energy for light, water, ventilation, heating, disposal of waste, and cooling (Jebli & Kahia, 2020). The environmental effect of the service sector may take place through recreational activities, government services, and professional and personal services (Ehigiamusoe et al., 2022; Hashmi et al., 2020). Thus, this study finds the answer to the question of whether agricultural, industrial, and service sectors matter for environmental quality. Against this background, the underlying aim of this study is to assess the impacts of the agricultural, industrial, financial, and service sectors on environmental quality in India. Hence, the motive of this study is raised, as India has been facing a severe threat of climate change and the scarcity of literature assessing the environmental effects of sectoral growth. Although studies assess the role of agriculture growth on ecological footprint (EF) and carbon emissions in Brazil, Russia, India, China, and South Africa (BRICS) (Pata, 2021); the industrial growth and CO2 emissions nexus in Asian countries (Zafar et al., 2020); service sector and CO2 emissions in India (Villanthenkodath et al., 2022), the dearth of studies continues. To our knowledge, a thorough investigation of this issue is still pending in India. Thus, this study makes some contributions to the extant literature by deviating from the previous studies in different ways. First, although most of the previous studies use CO2 emissions to measure environmental quality (Mahalik et al., 2021; Villanthenkodath et al., 2022; Villanthenkodath & Mahalik, 2022), this study employs the EF as a measure of environmental quality in India while assessing the sectoral level growth on the environment. As an aggregate measure of environmental degradation, it argues that EF has more importance in the policy formulation related to clean energy subsidies, land use, and many others

Revisiting the Environmental Kuznets Curve  235

(Alvarado et al., 2021). Second, the non-linear relationship between sectoral growth (i.e., agricultural, industrial, and service sectors) and environmental pollution is not yet conducted in a single framework by using EF as a measure of environmental quality. Thus, it enables us to reveal the role of agricultural, industrial, and service sectors on EF in India. Further, it helps us to understand whether there is an inverted U-shaped or a U-shaped non-linear relationship between environmental pollution and sectoral growth. Hence, it helps to formulate the sectoral level policies for environmental protection. Third, this study controls for energy consumption, population, and urbanization to avoid the omitted variables in a multivariate framework with appropriate technique. The study proceeds in the following sections: related literature briefs in Section 12.2; model, data briefing, and econometric methodology are represented in Section 12.3; the empirical analysis portrayed is in Section 12.4; and conclusion and policy implications are delineated in Section 12.5.

12.2 Literature Review Most of the macroeconomic studies carried out in the literature on the environment are deterrent to the policy implication because of the misleading and inconclusive outcomes. Thus, this study outlines the literature in the following fashion. The first segment allotted is for the studies exploring the link between agricultural output and environmental pollution, then studies on industrial output and environmental pollution, and finally, studies assessing the service sector’s impact on environmental quality. Various challenges have been facing agriculture because of global climate change on the one hand (Uddin, 2020), and, on the other, the CO2 emissions coming from the agricultural sector due to fossil fuels consumption constitute nearly 22% of the total global share (Ben Jebli et al., 2015). Moreover, the excessive use of non-renewable energy has been identified as the cause of increasing pollution in the agriculture sector (Ben Jebli & Ben Youssef, 2017; Uddin, 2020). For instance, a Pakistan-based study by Gokmenoglu and Taspinar (2018) found evidence that the EKC hypothesis, as the linear (quadratic) term of GDP, is positive (negative) while inducing the model with agriculture. A study by BalsalobreLorente et al. (2019) in BRICS countries found that carbon emissions intensify because of agricultural output. A similar outcome was reached by Aziz et al. (2020) for Pakistan while exploring the EF effect of agricultural production. However, Zaman and Moemen (2017) have reached a contrasting finding, as agricultural output reduces carbon emissions in 90 countries. Further, the carbon emissions mitigation role of agricultural output has been reached by Liu et al. (2017) for four Association of Southeast Asian Nations (ASEAN) (Indonesia, Malaysia, the Philippines, and Thailand) and for Turkey (Dogan, 2016). Concurrently, the inverted U-shaped non-linear impact of agricultural output on pollution has been found by Mahmood et al. (2019) for Saudi Arabia and Zafeiriou and Azam (2017) for Spain, but it is not significant in France and Portugal.

236  Revisiting the Environmental Kuznets Curve

In some studies, the impact of industrial output on pollution is determinant. For instance, Liu and Bae (2018) found that industrialization spurs CO2 emissions in China; a similar conclusion has been reached by Shahbaz et al. (2014) for Bangladesh, Dong et al. (2019) for intermediate and low-income countries, and Mentel et al. (2022) for sub-Saharan Africa. Similarly, Li and Lin (2015) have identified the carbon emission-aggravating role of industrial output in 73 countries. In contrast, Ehigiamusoe (2020) found that the reduction of carbon emissions in ASEAN + China is due to industrial output while assessing the link between environmental degradation and industrial output. Further, the study advocates that the finding may be due to energy-saving technological progress, efficient production process, and the use of cleaner production methods introduced in the industrial sector to help the countries to curb pollution. Similarly, Wen et al. (2014) found a reduction in carbon emissions as an increase in the industrial output in the long run for China’s iron and steel industry. Besides, environmental pollution and industrial output are also assessed in a non-linear framework. For example, Shahbaz et al. (2014) found an inverted U-shaped relationship between carbon emissions  and industrial output in Bangladesh. A similar conclusion has been reached by Xu and Lin (2015) for China by employing non-parametric additive regression models. However, a U-shaped non-linear relationship between carbon emissions and industrial output was obtained by Ali et al. (2021) for Vietnam. Further, the environmental effect of the service sector has been exposed in few studies. For instance, applying the autoregressive distributed lags (ARDL) methodology, Samargandi (2017) for Saudi Arabia found that value addition in the service sector fosters CO2 emissions. For 99 countries, Poumanyvong and Kaneko (2010) found the environment degrading role of service sector outputs while examining the link between carbon emissions and service outputs. However, few studies observed that service sector output is vital in improving environmental quality. In this vein, Villanthenkodath et al. (2022) found that the service sector is capable of reducing the CO2 emissions in India. A similar outcome has been reached by Rafiq et al. (2016) for 53 countries. Unfortunately, these studies are conducted in a linear approach. Thus, few studies assess the role of the service sector in a non-linear framework. It consists of the work done by Murshed et al. (2020) for the Organization of Petroleum Exporting Countries (OPEC). The outcome of the study is that there is an inverted U-shape non-linear impact of the service sector on environmental degradation. The same finding has been reached by Hashmi et al. (2020) for Pakistan while examining the impact of service sector output on pollution.

12.3 Empirical Model, Data Description, and Econometric Methodology 12.3.1 Empirical Model and Data Description

Taking insights from the seminal work of Grossman and Krueger (1991) and Grossman and Krueger (1995) to confirm a non-linear relationship between

Revisiting the Environmental Kuznets Curve  237

economic growth and environmental degradation, this study hypothesized that there is an inverted U-shaped relationship between sectoral growth and environmental degradation. It means that the EKC hypothesis reveals that economic growth will bring welfare to the environment. The empirical model has been constructed by taking a cue from Dogan and Inglesi-Lotz (2020), Uddin (2020), and Villanthenkodath et al. (2021). It can be specified as follows in Equation 1: EFt = f ( AGRt , AGRSQt , INDt , INDSQt , SERt , SERSQt , ECt , POPt ,URBt (1)

Where EF is the ecological footprint and AGR and AGRSQ are the agriculture value-added and its square term, respectively. IND means the industrial valueadded, INDSQ analyses the square term of industrial value-added, POP represent the population, URB is urbanization, and EC stands for energy consumption. The econometric specification of the model with the natural logarithm for the empirical analysis is adopted by the following studies: Villanthenkodath and Arakkal (2020) and Villanthenkodath and Mahalik (2020). lnEFt = a 0 + a1lnAGRt + a 2lnAGRSQt + a 3lnINDt + a 4lnINDSQt + a 5lnSERt +a 6lnSERSQt + a 7lnECt + a 8lnPOPt + a 9lnURBt + mt

(2)

In Equation 2, the intercept is represented by a 0 . a 2 .... a 9 , which stands for the coefficients of the explanatory variables in the model. Based on the hypothetical EKC, the direction of a1,a 3 and a 5 are supposed to be positive. However, the direction of a 2 ,a 4 , and a 6 assumes as negative; only the conventional EKC holds. Moreover, the variables of the study are detailed in Table 12.1, which offers the definition, measurement, and source of each variable for the period 1984–2018. The selection of years was dictated by the availability of data for the dependent variable, i.e., EF. 12.3.2 Econometric Methodology

The first phase in the empirical analysis is determining the order of integration of the variables for choosing the appropriate econometric models for the analysis. We have employed the augmented Dickey-Fuller (ADF) test to attain this objective. The null hypothesis of the non-stationarity is examined in opposition to the alternative hypothesis of stationarity. The first difference stationary, or I (1) series, indicates that all the variables are non-stationary in the levels, but it becomes stationary at their first difference. If the variables are I (0), then such variables are level stationary. After confirming the integration order, the ARDL bounds testing approach of cointegration proposed by Pesaran (1999) and Pesaran et al. (2001) has been employed for establishing the long-run relationship between the variables. The ARDL bounds testing approach is superior to other cointegration methods that can be listed as follows. First, it can be applied in the case of a small sample size. Second, irrespective of the order of integration, i.e., I (0)/ I (1) or a mix of

238  Revisiting the Environmental Kuznets Curve Table 12.1 Definition of Variables Variable Definition

Measurement

Source

EF AGR

Per capita Constant of 2015 US$

Global Footprint Network World Development Indicators

Constant of 2015 US$ Constant of 2015 US$ Metric ton Percent Percent

World Development Indicators World Development Indicators BP Statistics World Development Indicators World Development Indicators

IND SER EC POP URB

Ecological footprint Agriculture, value-added Industry, value-added Service, value-added Energy consumption Population Urbanization

Source: Author’s compilations.

the order of the variables, this method can be employed. Third, the problem of endogeneity can be solved by using the optimal lag in the model specification. Last, it offers superior results over other conventional cointegration. The specified model was estimated using the ARDL bounds testing approach based unrestricted error correction model as follows: p

DlnEFt = l0 +

å i =1

p

+

p

l1i DlnEFt -i +

å i =1

å

å i =1

å i =1

ål DlnSER

t -i

6i

i =1

p

l7i DlnSERSQt -i +

t -i

3i

p

l5i DlnINDSQt -i +

p

ål lnAGRSQ i =1

p

l4i DlnINDt -i +

i =1

+

p

l2i DlnAGRt -i +

å

l8i DlnECt -i +

i =1

p

ål DlnPOP 9i

t -i

i =1

p

+

ål

10i

DlnURBt -i + j1DlnEFt -1 + j2DlnAGRt -1

(3)

i =1

+j3DlnAGRSQt -1 + j4 DlnINDt -1 + j5DlnINDSQt -1 + j6DlnSERt -1 +j7DlnSERSQt -1 + j8DlnECt -1 + j9DlnPOPt -1 + j10 DlnURBt -1 + mt

In Equation 3, D stands for the first difference operator, l0 represents the constant, and mt is the stochastic error terms. The process of the bounds testing approach for the long-run relationship using ARDL is based on the Wald test or F test. The null hypothesis of no cointegration, i.e., H0 : j1 = j2 = j3 = j4 = j5 = j6 = j7 = j8 = j9 = j10 = 0, is tested against the alternative hypothesis of cointegration, i.e., H0 :j1 ¹ j2 ¹ j3 ¹ j4 ¹ j5 ¹ j6 ¹ j7 ¹ j8 ¹ j3 ¹ j4 ¹ j5 ¹ j6 ¹ j7 ¹ j8 ¹ j 9 ¹ j10 ¹ 0 , in the long run. The decision related to the

Revisiting the Environmental Kuznets Curve  239

long-run relationship is based on the F-statistics. If the F-statistics surpass the critical value, then we conclude the existence of a long-run relationship and vice versa. If the estimated value falls between the critical value, we cannot conclude the cointegration. The long-run elasticities can also be estimated using Equation 3. However, the error correction model is represented in the following equation: p

DlnEFt = l0 +

p

å

l1i DlnEFt -i +

i =1

å

t -i

3i

p

å

l5i DlnINDSQt -i +

i =1

p

ål lnAGRSQ i =1

p

l4i DlnINDt -i +

i =1

+

å i =1

p

+

p

l2i DlnAGRt -i +

ål DlnSER

t -i

6i

i =1

p

(4)

p

ål DlnSERSQ + ål DlnEC + ål DlnPOP 7i

i =1

t -i

8i

t -i

i =1

9i

t -i

i =1

p

+

ål

10i

DlnURBt -i + j1DlnEFt -1 + j ECTt -1 + m1

i =1

In Equation 4, ECT stands for the error correction term, the coefficient of error correction term, i.e., j has to be negative and less than one, and it shows the time taken for the adjustment toward the long-run equilibrium.

12.4 Empirical Results and Discussion This section focuses on the empirical simulations carried out in this study. First, preliminary analysis in terms of summary statistics is followed by the visual plot and then correlation matrix analysis of all variables under consideration. Table 12.2 highlights the descriptive statistics, where service sector valueadded not only has the highest average but also has the highest minimum and maximum, while all the variables, except energy consumption and population, are positively skewed. Further, Figure 12.1 depicts the trend and pattern of the studied variables, which highlights the increasing trend for all the variables during 1984–2018. However, Table 12.3 represents the correlation matrix of the studied variables. The outcome shows the linear association between the variables. Moreover, a positive and significant relationship exists between EF and sectoral level value-added. Precisely, this result indicates that EF moves in the same direction as agriculture value-added, industrial value-added, service sector value-added, energy consumption, population, and urbanization. Hence, the outcomes of the correlation analysis need a substantial analysis by using inferential statistics. In time series modelling, the need for stationary analysis is important for circumventing spurious effects. The current study has implemented the ADF

240  Revisiting the Environmental Kuznets Curve Table 12.2  Summary Statistics  

LNEF

LNAGR LNIND

LNSER

LNEC

LNPOP LNURB

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability Sum Sum Sq. Dev. Observations

−0.128 −0.183 0.190 −0.352 0.165 0.496 1.988 2.931 0.231 −4.470 0.927 35

26.165 26.137 26.708 25.667 0.306 0.028 1.973 1.542 0.463 915.766 3.187 35

26.586 26.578 27.848 25.369 0.748 0.056 1.793 2.143 0.342 930.511 19.009 35

2.629 2.597 3.501 1.680 0.534 -0.054 1.918 1.724 0.422 92.025 9.689 35

20.777 20.796 21.025 20.458 0.173 −0.262 1.834 2.384 0.304 727.183 1.018 35

Source: Author’s estimation.

Figure.12.1  Visual plot of variables.

26.176 26.090 27.261 25.132 0.655 0.071 1.746 2.322 0.313 916.145 14.566 35

3.343 3.329 3.527 3.183 0.102 0.195 1.864 2.105 0.349 117.015 0.353 35

Revisiting the Environmental Kuznets Curve  241 Table 12.3  Correlation Matrix Probability LNEF

LNAGR

LNIND

LNSER

LNEC

LNPOP

LNURB

LNEF

− − − 1.0

− − − − − − 1.0

− − − − − − − − − 1.0

− − − − − − − − − − − − 1.0

− − − − − − − − − − − − − − − 1.0

− − − − − − − − − − − − − − − − − − 1.0

1.0

LNAGR  0.972 (23.977) [0.000] LNIND 0.973 (24.163) [0.000] LNSER 0.969 (22.375) [0.000] LNEC 0.971 (23.204) [0.000] LNPOP 0.942 (16.162) [0.000] LNURB 0.979 (27.752) [0.000]

0.992 (45.634) [0.000] 0.994 (50.478) [0.000] 0.994 (53.025) [0.000] 0.987 (35.982) [0.000] 0.992 (46.130) [0.000]

0.998 (98.262) [0.000] 0.997 (69.138) [0.000] 0.991 (41.503) [0.000] 0.998 (82.924) [0.000]

0.997 (70.648) [0.000] 0.992 (46.376) [0.000] 0.998 (87.871) [0.000]

0.994 (53.339) [0.000] 0.995 (56.686) [0.000]

0.985 (32.434) [0.000]

Source: Author’s estimation. Note: Values inside [] and () are probability and t-Statistic, respectively.

unit root test to assess the stationarity properties of the variables, as seen in Table 12.4. The outcomes of the unit root test reveal the order of integration is one, i.e., the first difference stationary among the variables vector. Subsequently, the study has established the long-run relationship between the variables with the help of Pesaran’s ARDL bounds test. The result shows the clear existence of a long-run relationship among the series that has been explored in the study. Thus, the 1% level significant upper bound of Narayan (2005) is below the calculated F-statistics (= 10.903) and the outcome reported in Table 12.5, in which the optimum parsimonious lag has been chosen by the Akaike information criterion (AIC). After that, the results of the conducted diagnostic tests are portrayed in the last segment of Table 12.5, which shows that the model is free from heteroscedasticity, serial correlation, and autoregressive conditional heteroskedasticity (ARCH) problems. Moreover, the ARDL model is well specified, as the Ramsey reset test offers the desired result. The cumulative sum of recursive residuals (CUSUM) and the CUSUM Square of recursive residuals (CUSUMsq) have been employed for the model as proposed by Brown et al. (1975). The plot of the same is depicted in Figure 12.2. The long-run and short-run results obtained from the estimated model are reported in Table 12.6, in which the coefficient of linear and quadratic terms

242  Revisiting the Environmental Kuznets Curve Table 12.4 ADF Test of Unit Root ADF Level

t-Statistic

First difference

t-Statistic

LNEF LNAGV LNIND LNSER LNEC LNPOP LNURB

−1.430 −2.883 −2.991 −1.901 −2.119 −2.263 −0.579

Δ LNEF ΔLNAGV Δ LNIND Δ LNSER Δ LNEC Δ LNPOP Δ LNURB

−6.259* −5.321* −4.111* −5.839* −4.369* −3.916** −3.714**

Source: Author’s estimation. Note: * and ** indicates 1% and 5% statistical significance, respectively.

Table 12.5 ARDL Bounds Test Model Test Statistic F-statistic – k Diagnostic test χ2 NORMAL χ2 SERIAL χ2 RAMSEY χ2 ARCH

æ lnAGRt , lnAGRSQt , lnINDt , nINDSQt , lnSERt , ö ÷ lnEFt = f ç ç ÷ lnSERSQt lnECt , lnPOPt , lnURBt è ø Value 10.903 – 9

Significance 10% 5% 1%

I(0) 1.8 2.04 2.5 – 0.101[0.951] 1.015[0.384] 0.064[0.804] 0.830[0.370]

I(1) 2.8 2.08 3.68 –

Source: Author’s estimation. Note: Author used critical value of Narayan (2005). The values inside [] are probability values.

Figure 12.2 CUSUM and CUSUMsq for Model.

Revisiting the Environmental Kuznets Curve  243 Table 12.6 ARDL Results Model Variable Long run lnAGRt lnAGRSQt lnINDt lnINDSQt lnSERt lnSERSQt lnECt lnPOPt lnURBt C Short run D(lnAGRt ) D(lnAGRSQt ) D(lnINDt ) D(lnINDSQt ) D(lnSERt ) D(lnSERSQt ) D(lnECt ) D(lnPOPt ) D(lnURB ) C ECT(t-1) Model Adequacy R2 Adjusted R2

Coefficient

Std. Error

t-Statistic

Prob.

−3.852 0.080 −18.071* 0.349* 27.269* −0.508* 0.674* −6.010* 4.038*** 23.184

4.075 0.078 5.086 0.098 9.337 0.172 0.149 2.154 2.404 57.778

−0.945 1.029 −3.553 3.572 2.921 −2.948 4.521 −2.790 1.680 0.401

0.356 0.316 0.002 0.002 0.009 0.008 0.000 0.012 0.109 0.693

−2.985 0.062 −14.000* 0.271* 21.126* −0.393* 0.522** −4.656* 3.129 17.962 −0.775

7.246 0.138 4.725 0.090 6.580 0.121 0.232 1.614 2.137 87.318 0.057

−0.412 0.449 −2.963 3.021 3.211 −3.246 2.250 −2.885 1.464 0.206 −13.530

0.685 0.659 0.008 0.007 0.005 0.004 0.037 0.010 0.160 0.839 0.000

Durbin-Watson stat F-statistic

2.327 425.270[0.000]

– –

0.83 0.81

Source: Author’s estimation. Note: * and ** indicate 1% and 5% level, respectively.

of value-added in agricultural sector shows a negative and positive sign, respectively, with EF, but it is insignificant both in the short and long run. Thus, there is no evidence of agricultural growth-based EKC presence in India. This is possible because India may replace conventional input-intensive agriculture with sustainable agriculture practices. Moreover, nutrient-producing plants may help to decrease the use of fertilizers. Our findings align with Zafeiriou and Azam (2017) for France and Portugal, as it is not significant. However, it deviates from Mahmood et al. (2019) for Saudi Arabia and Zafeiriou and Azam (2017) for Spain. For the industrial sector, it found that the linear term is negative and significant, but the quadratic term is positive and significant while influencing the

244  Revisiting the Environmental Kuznets Curve

EF in both the short and long run. Hence, the inverted U-shape hypothetical EKC does not hold for the industrial sector, as there is a positive sign in the square and a negative sign in the linear term of real industrial sector growth. This finding is possible because there is a need for large-scale fossil fuel consumption for industrial sector expansion in any county, which, in turn, hurts the ecology of any country. Thus, this finding matches with Ali et al. (2021) for Vietnam. However, it does not support the findings of Shahbaz et al. (2014) for Bangladesh and Xu and Lin (2015) for China. For the service sector, it was observed that there is a significantly positive (negative) impact of linear (quadratic) service sector growth on EF. Therefore, it indicates the holding of conventional EKC for service sector growth. Thus, initial pollution is obvious from this sector, but it is doubtful that it requires only three times less energy use per unit of value-added than the industrial sector-driven growth (IEA, 2015). Hence, this finding is on par with Murshed et al. (2020) for OPEC and Hashmi et al. (2020) for Pakistan. In line with the preconceived notion, if other things remain constant, then the energy consumption coefficient positively impacts EF in both the short and long run. It indicates that the total primary energy consumption comprises non-renewable energy sources. Thus, incorporating renewable energy technologies is important for efficient consumption from renewable sources such as photovoltaic, and wind, and biofuel sources. However, the finding supports the observation made by Wen et al. (2021) for selected South Asian economies and Katircioglu (2014) for Turkey, while the findings deviate from Mahalik et al. (2021) for India. At the same time, population growth improves the environmental quality by reducing the EF in both the short and long run. It may be possible when the existing people think about population control, as there is pressure on land, water scarcity, and many others. As a result, there is a possibility of improving the environmental quality. Thus, the finding is not corollary to Begum et al. (2015) for Malaysia and Regmi and Rehman (2021) for Nepal, and it deviates from Yang and Wang (2020) for China and Yang et al. (2021) for the Organization for Economic Cooperation and Development (OECD). Besides, urbanization harms the environment as it increases EF in both the short and long run. It may be due to the construction of residential buildings and infrastructure development as part of urbanization, which needs lots of energy, thereby fostering the EF. Similarly, the lack of treatment for pollutants in the country also boosts the EF. Hence, it is corollary with Cheng and Hu (2022) for China, Anwar et al. (2022) for 15 Asian countries, and Mehmood and Mansoor (2021) for East Asian and Pacific countries. However, it diverts from Li and Haneklaus (2022) for Group of 7 (G-7) countries. Finally, the incorporated error correction term in the model shows high speed in convergence to long-run equilibrium is about a speed of 77% per year. Table 12.7 delineates the outcome of the dynamic ordinary least square (DOLS) method. The finding emanating from DOLS is consistent with the long-run ARDL model. Thus, the finding is consistent and robust across the techniques.

Revisiting the Environmental Kuznets Curve  245 Table 12.7  DOLS Variable

Coefficient

Std. Error

t-Statistic

Prob.

Long run lnAGRt lnAGRSQt lnINDt lnINDSQt lnSERt lnSERSQt lnECt lnPOPt lnURBt C R2 Adjusted R2

0.179 0.005 −16.972 0.328 15.141 −0.286 0.296 −2.207 0.593 54.837 0.99 0.99

4.519 0.087 2.838 0.054 2.597 0.048 0.146 0.585 0.987 61.625 – –

0.040 0.054 −5.981 6.113 5.831 −5.922 2.028 −3.774 0.601 0.890 – –

0.969 0.958 0.000 0.000 0.000 0.000 0.061 0.002 0.557 0.388 – –

Source: Author’s estimation.

12.5 Conclusion and Policy Implications The study examines the non-linear environmental impacts of India’s agricultural, industrial, and service sectors, spanning from 1984 to 2018. This study is conducted within the EKC framework by using EF as a proxy for environmental quality by controlling the energy consumption, population, and urbanization. We have applied ARDL bounds testing approach along with DOLS to analyze the long run and short run. The study attained the following conclusion: although it is not significant, there is a U-shaped impact of the agriculture sector on EF; the industrial sector exhibited a significant U-shaped impact on EF; an inverted U-shaped link was found for the service sector for influencing the EF; energy consumption and urbanization increase the EF, but population reduces the it. Because the agriculture sector was found to be not harmful to the environment, policymakers can rely on expanding the agriculture sector by using cleaner production technology and reducing pollution-intensive production methods. Therefore, India can ensure its food security along with environmental quality. However, the U-shaped relationship found for the industrial sector and the environment indicates that the mitigation of pollution is not possible once the country becomes well industrialized. Hence, the innovations of green technology and its diffusion are highly needed for the industrial sector to mitigate pollution. To this end, the country can collaborate with other nations to enhance the potential of industries by incorporating sustainable technology. Conversely, an inverted U-shaped environmental effect from the service sector was observed, which underlines the urgency for India to boost

246  Revisiting the Environmental Kuznets Curve

the service sector without damaging the natural environment. Thus, policies should focus on less energy use in service sector activities. Further, renewable energy consumption enhancement programmes can be introduced by providing subsidies and imposing taxes on non-renewable energy consumption. Moreover, systematic urban planning is necessary for the mitigation of climate change, and the population needs to be controlled to further improve the natural environment.

Note 1 As reported by industry body FICCI’s Economic Outlook Survey (http://www​.ficci​.in​ /ficci​-surveys​.asp).

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13 The Contribution of Transport Modes to Carbon Emissions in Turkey Muhammad Shahbaz, Tuğrul Bayat, and Mehmet Tanyaş

13.1 Introduction Climate change caused by greenhouse gas emissions and its effects on global warming is among the dilemmas facing the international community regarding environmental quality. This dilemma is an important problem that needs to be resolved urgently. This is mainly because greenhouse gas emissions are responsible for ozone depletion and increases in average global temperature. Besides that, environmental degradation is also responsible for stroke, lung cancer, respiratory and heart diseases, increase in death rates, deterioration of natural resources, and infrastructure damage in agricultural lands (Shahbaz et al., 2013). Moreover, air pollution harms the economy in several ways: it costs human life, reduces people’s capability to work, affects vital products such as food, damages cultural and historical monuments, reduces the capability of ecosystems to perform functions that societies need, and costs money in remediation or restoration (UNECE, 2021). In 2015, the World Health Organization (WHO) and the Organization for Economic Cooperation and Development (OECD) estimated that the economic cost of premature death and similar health problems caused by air pollution in Europe was approximately US$1.6 trillion. Also, this situation leads to important discussions both at the national and international level every passing day. The problem of “Growing up (economic development) ignoring the environment? Grow in a way that protects the environment?” summarizes the situation that caused the dilemma. Because economic development requires production, raw materials and final products must be transported to the desired point (customer or other dimensions/stakeholders in the supply chain), and energy consumption is needed for this. Unfortunately, approximately 95% of the energy consumption in the transportation sector is met by petroleum, which is one of the fossil fuels (Ozkaya, 2004). Transportation is defined as a service that provides the displacement of people (passengers) and goods (freight), creating time and place utility through the various modes of transportation in order to meet customer requests and needs (Barda, 1970: 16; Bowersox et al., 2013: 187). Road, rail, water, aviation, and pipelines are the main transport modes of freight. DOI: 10.4324/9781003336563-13

252  Transport Modes to Carbon Emissions in Turkey

Freight transportation driven by economic activities is an important source of energy consumption and greenhouse gas (GHG) emissions (Hao et al., 2015: 94). Accordingly, GHG reduction is becoming the most important agenda item in the global freight transportation industry. When carbon emission figures related to transportation (excluding sea transport) in Turkey are analysed, the emission amount, which was 36.46 metric tons of carbon dioxide equivalent (MtCO2) in 2000, increased 2.26 times and reached 82.43 MtCO2 in 2019. Based on this information, the ratio of transportation-related carbon emissions to general carbon emissions increased from 16% in 2000 to 20% in 2019. This shows that carbon emissions from transportation should be controlled (UNFCC, 2021). Although the geographical location of Turkey provides an opportunity for the use of all modes of transportation, the different geographical structures of the regions reveal a heterogeneous structure in terms of transport supply. For example, factors such as terrestrial climatic conditions and land structures in the interior of the country are decisive in the development of transportation systems. Besides that, Turkey’s high and rugged topography is one of the leading factors that make road and rail transportation difficult and increase the difficulty of construction, maintenance, and repair of roads (Bayraktutan and Ozbilgin, 2014: 70). Table 13.1 shows the total amount of carbon dioxide (CO2) emissions, and the amount of cargo carried in transport modes between 1980 and 2019 in Turkey. When the table is analysed in terms of carbon emissions, it is seen that emissions have increased over the years. Emissions, which were 75 MtCO2 in 1980, reached 405 MtCO2 in 2019. This shows that carbon emissions increased 4.39 times in 2019 compared to 1980, and annual average carbon emissions increased by 11.25%. Moreover, there were decreases in carbon emissions in Table 13.1 The Amount of Carbon Emissions and the Amount of Freight Carried in Transport Modes (1980–2019) Year Carbon Emission Road Freight Rail Freight Air Freight Sea (Coastal) Freight MtCO2 Million Ton-Km Million Ton-Km Million Ton-km Million Ton-Km 1980 1990 2000 2005 2010 2015 2016 2017 2018 2019

75 152 230 264 314 381 401 425 419 405

37.507 65.710 161.552 166.831 190.365 244.329 253.139 262.739 266.502 267.579

5.167 8.031 9.895 9.152 11.462 10.474 11.661 12.869 14.478 14.707

10 101 385 383 1.149 2.882 3.494 4.800 5.949 6.816

0.47 3.17 10.65 35.00 61.18 87.03 94.93 107.92 114.23 118.77

Sources: TÜİK 2021a (Road and rail freight transport), World Bank, 2021a (air freight), OECD, 2021a (sea), TÜİK, 2021c (The amount of carbon emission).

Transport Modes to Carbon Emissions in Turkey  253

1994, 1999, 2001, 2008, 2010, 2013, 2018, and 2019 due to the effects of economic and financial crises on sectors (Akcay and Güngen, 2019). It is also seen in the table that the freight carried in transport modes increases every year. In addition, the highest increase occurred in air freight transportation. When the amount and rate of freight carried in transport modes between 1980 and 2019 are compared, it is seen that the freight carried on the road increased from 37.507 million tons-km to 267.579 million tons-km (6 times), rail increased from 5.167 million tons-km to 14.708 million tons-km (1.18 times), air increased from 10 million tons-km to 6.816 billion tons-km (660 times), and sea coastal container transportation increased from 0.47 million tons-km to 118.77 million tons-km (253 times) (TUIK, 2021a). According to the average annual increase amounts, it is seen that the increase in road transport is 16%, railroad 5%, air freight transport 1600% times, and maritime container freight transport 650% times. These increased rates require more efficient use of transportation vehicles, which are the main fossil fuel consumers. Despite the increase in the freight carried in transport modes in 2018 and 2019 compared to previous years, the reduction in emissions indicates an increased sensitivity to environmental pollution in Turkey. This situation gives hope for environmental protection. Driven by rapid economic development, Turkey’s freight transport volume has experienced rapid growth in recent years. Besides that, the number of trucks and vans, locomotives, and aircraft increased by 4–1.19 and 2.76 times, respectively, in the period 2000–2019 (TUIK, 2021b). Such growth in freight transport and means of transport leads to increases in energy consumption and GHG emissions (Hao et al., 2015, 2011c; Guo et al., 2014). The rapid growth in the volume of freight transport in Turkey causes great concerns about environmental impacts. This situation makes it impossible to implement the policies created to protect the environment. It is seen that the increasing trade volume in the East–West and North–South directions makes Turkey a potential transportation centre between Europe, the Balkans, the Middle East, the Caucasus, and Eastern Mediterranean countries (EDAM, 2007). In order to become a transportation and logistics centre, it will require Turkey to come to the fore in freight transportation and storage. However, freight flow and storage will cause environmental pollution due to fossil fuel consumption. For this reason, the study reveals the impact of the transportation carried out in the current transport modes on the environment and proposes various policies in order to prevent negative impacts. Freight transportation is expected to continue its growth trend in the coming years. In such a case, it is critical to anticipate the future growth pattern of Turkey’s freight transport and to prepare appropriate policies to address GHG emissions. In this context, the energy and environmental effects of the transportation sector have been extensively studied in recent years (Hao et al., 2015, 2014, 2011b; Geng et al., 2013). Table 13.2 shows the distribution of Turkey’s export and import value through transportation modes between 2017–2020. The main mode of

254  Transport Modes to Carbon Emissions in Turkey Table 13.2 Distribution of the Value of Turkey’s Export and Import by Transport Modes Transport Modes

Export %

Import %

2017

2018

2019

2020

2017

2018

2019

2020

Sea Road Air Rail Other*

58.2 29.2 10.8 0.4 1.4

62.8 27.8 8.2 0.4 0.8

60.3 30.1 8.2 0.5 0.8

59.5 31.3 7.5 0.8 0.9

58.5 16.2 14.7 0.5 10.2

65.8 15.9 12.8 0.6 5.0

62.0 17.7 13.9 0.7 5.7

57.3 19.1 17.9 1.0 4.7

Source: Republic of Turkey Ministry of Trade (2021a). * Includes pipeline, postal shipments, electric power transmission, and self-propelled vehicles.

transportation has been sea transportation for both exports and imports. In 2020, the rate of utilization of sea transport decreased in both exports and imports compared to 2019. The container crisis during the COVID-19 pandemic can be seen as the main reason for the decrease. The roadway was the second most used mode of transportation. It is observed that the rate of utilization of road transport in both exports and imports increased in 2020 compared to 2019. A significant increase is observed in imports despite the decrease in exports in air transport. Although there is an increase in both exports and imports in 2020 compared to 2019, it shows that rail transportation has not been used effectively in foreign trade yet. While maritime and road transport are at the forefront in terms of transportation for foreign trade, air transport also has an important place when transportation percentages are considered. Considering the contribution of the transportation sector to CO2 emissions, it is very important to understand how the transported freight and transportation infrastructure affect CO2 emissions (Awaworyi Churchill et al., 2021: 2). Around a quarter of CO2 emissions globally came from transport in 2018 (IEA, 2019). In addition, International Transport Forum’s (ITF) estimates show that, in the absence of effective mitigation measures, global emissions from the transport sector will increase by 60% by 2050 (ITF, 2019). Especially in recent years, it has been observed that climate changes have a strong impact on the human system and environmental quality. Therefore, great emphasis is attached the importance of reducing energy consumption and limiting pollutant emissions in order to promote environmental protection. In this context, researchers focus on these issues, and various policy recommendations are developed. Most of the studies have investigated the impact of economic development and energy demand on environmental degradation. Other studies reveal that energy consumption and economic development may not explain environmental degradation alone (Saidi and Hammami, 2017; Zhang and Lin, 2012; Oztürk and Acaravci, 2013). In addition, when studies on transportation are examined, it is seen that freight transportation, infrastructure data belonging to one or two transportation types, total transported load, or total infrastructure are discussed. However, this situation prevents obtaining

Transport Modes to Carbon Emissions in Turkey  255

specific information about transport modes. No study has been found that evaluates all modes of transport separately and evaluates them specifically to transport mode. In this study, the freight carried in each mode of transportation (except pipelines) is represented by itself. In this context, the potential future effects of Turkey’s freight transportation on CO2 emissions used to represent environmental degradation are analysed depending on the transport modes. Accordingly, the study is critical to preparing appropriate policies for future modes of transport. The transportation sector, which is thought to have an impact on carbon emissions in the study, is represented by the amount of cargo carried by road, rail, sea, and air. Transportation, which is one of the most important components of logistics, reflects general economic activity through personal transportation preferences based on income levels and cultural influences, as well as physical movements resulting from transactions related to services and goods (Geng et al., 2013). However, due to the common use of fossil fuels, freight transportation and transportation infrastructure cause environmental degradation, especially due to fuel/energy consumption (Isik et al., 2020; Saidi and Hammami, 2017; Xie et al., 2017; Benali and Feki, 2020; Rasool et al., 2019). On the other hand, transportation service carbon emission intensity, energy intensity, energy structure, and transportation mode intensity also seem to reduce environmental degradation (Isik et al., 2020). The number of studies on the relationship between transportation and environmental degradation is increasing day by day. However, there is no study on environmental degradation with all modes of transport. The contribution of the study to the literature can be summarized in three points: i) Whether there is a significant relationship between each mode of transport (road, air, sea, rail) and environmental degradation. ii) The direction and degree of any significant relationship, if any, between modes of transport and environmental degradation. iii) Suggestions are presented to policymakers and practitioners in order to change the direction of the positive relationship between transport modes and environmental degradation and to maintain the negative relationship if any. In this study, the causal relationship is investigated between freight transportation in different transport modes (excluding pipelines) and CO2 emissions, which is considered the most basic indicator of environmental degradation. In this context, the amount of freight carried on the road, sea, rail, and air in Turkey is considered the independent variable, and the amount of CO2 emissions is the dependent variable. The autoregressive distributed lag (ARDL) bounds testing method is used to reveal the relationship between them. The study consists of five sections. After the introduction, the concepts of transportation and environmental pollution are explained in the second section. The third section includes the literature on transportation and environmental

256  Transport Modes to Carbon Emissions in Turkey

degradation. The fourth section provides information about the methodology, then the findings are discussed, and the last section includes conclusions and recommendations.

13.2 Literature Review and Hypothesis Development Transportation plays a vital role in daily life and the development of countries. Besides that, it helps link between different places, promoting trade and development. In contrast, transportation activities are an important source of fossil fuel energy consumption, which has a detrimental impact on the environment and has an enormous and growing share in global carbon emissions. Therefore, the transportation sector is becoming one of the most important sectors for the reduction of CO2 emissions worldwide. In addition, freight transport is expected to increase in the future, which will lead to an increase in energy requirements (UNFCC, 2021; ITF, 2019; Işık et al., 2020; Shafique et al., 2020; Timilsina and Shrestha, 2009; IEA, 2017; Linton et al., 2015). In this section, the findings obtained from the studies between transportation and environmental pollution and CO2 emissions are presented as a summary. Empirical findings from previous studies on the subject are not clear and insufficient. Habib et al. (2021) tried to determine the dynamic links between road transport density, road passenger transport, road freight transport, and road carbon emissions in Group of 20 (G-20) countries in the presence of economic growth, urbanization, crude oil price, and trade openness for the period 1990– 2016. The variables are analysed with the Kao and Westerlund cointegration tests. According to the findings, it is revealed that road transport intensity, road passenger transport, and road freight transport have a positive and significant effect on road transport CO2 emissions. Economic growth and urbanization are major contributors to road transport CO2 emissions; trade openness and the price of crude oil significantly reduce road transport CO2 emissions. Dumitrescu and Hurlin panel causality test results reveal unidirectional causality from road transport intensity and road transport load to road transport CO2 emissions. However, the causality between road passenger transport and road transport CO2 emissions is bidirectional. Alcántara and Padilla (2006) investigate the productive sectors for CO2 emissions by examining the Spanish economy. The effect of the increase in the added value of the considered sectors on CO2 emissions is investigated. As a result of the study, it is seen that road transport is at the forefront of the sectors that stand out for carbon emissions. Kharbach and Chfadi (2017) analyse the relationship between CO2 emissions, energy consumption, and economic growth in the road transport sector in Morocco. They use cointegration analysis to check whether the environmental Kuznets curve (EKC) hypothesis is valid. According to the results obtained, although it is shown that economic growth will lead to a decrease in emissions in the transportation sector, it supports the EKC hypothesis. Ahmed et al. (2020) researched the CO2 emissions of the transport sector in India from 1980

Transport Modes to Carbon Emissions in Turkey  257

to 2015. Their results show that economic growth and road transport sector energy consumption increase emissions. They also concluded that road-related infrastructure increases transport emissions while urbanization reduces emissions. Afaq et al. (2021) investigate the symmetrical and asymmetrical effects of physical infrastructure on energy consumption, economic growth, and air pollution in Pakistan. The energy consumption model results show that aircraft carriers and road infrastructure increase energy consumption in the short run. Road infrastructure has a positive impact on CO2 emissions, while aircraft carriers have a negative long-term impact. According to the non-linear ARDL results, aircraft carriers, roads, and trade also show asymmetric effects. Finally, the positive shock to the highway (increase) seems to have a significantly positive effect on pollution, but the negative shock (decrease) has no effect. Erdogan et al. (2020) examine the effects of air and rail transport on environmental pollution, using annual time series data for a panel of the top eight air traveller countries (China, Germany, India, Ireland, Japan, Turkey, the United Kingdom (UK), and the United States (US)) over the period 1995–2014. The estimation results show that air transport contributes to emissions, while rail transport and urban growth reduce emissions during the study period. Saleem et al. (2018) investigated the air–rail transport and environmental pollution link for the Next-11 countries from 1975 to 2015. While air transport has a negative relationship with natural resource rents (the difference between the price of a commodity and the average cost of producing it), air passengers have an increasing effect on emissions. Shouket et al. (2019) analyse the environmental impacts of air and rail transport over the period 1975–2016 in the context of Pakistan. Findings show that rail passengers transported have a positive effect on emissions, while air–rail transport and travel services degrade environmental quality by depleting natural resources. Sohail et al. (2021) investigate the asymmetric effect of air–rail transport on environmental pollution in Pakistan. They apply the non-linear ARDL method and note that the positive shock (increase) on the air passenger carried and the rail passenger transported increases carbon emissions. This reveals that a 1% increase in the number of air passengers (rail passengers) in Pakistan increases environmental pollution by 0.21% (0.32%). While the positive shock (increase) in the transported rail passengers increases environmental pollution, it is seen that the negative shock (decrease) in the transported rail passengers reduces the environmental pollution in the short term. Kalayci and Ozden (2021) examined the link between maritime transport, trade liberalization, industrial development, and CO2 emissions in China. Fully modified least square, dynamic ordinary least square, canonical cointegrated regression, autoregressive distributed lag-limit test and generalized moments method were used with the annual data ranging from 1960 to 2019. According to the results of the study, there is an important long-term relationship between maritime transport, trade liberalization, industrial development, and CO2 emissions. On the other hand, the short-term autoregressive distributed lag-limit test estimation results reveal that industrial development and maritime

258  Transport Modes to Carbon Emissions in Turkey

transport are the main determinants of CO2 emissions in the short run. Again, Kalayci and Ozden (2020) also examined the link between CO2 emissions and maritime transport, trade liberalization, and industrial development in their study in China. Phillips-Perron (PP) and Zivot-Andrews unit root tests, fully modified least squares (FMOLS), dynamic ordinary least squares (DOLS), canonical cointegration regression (CCR), ARDL and generalized moments method (GMM) methods were used data from 1980–2013. According to the results obtained, there is an empirically proven long-term consistent relationship between maritime transport, trade liberalization, industrial development, and CO2 emissions. Similarly, the short-term ARDL estimation results reveal that the main determinants of CO2 in the short-term change at 1% significance level in industrial development and maritime transport. In the short term, about 78% of the shocks in industrial development, maritime transport, and trade liberalization are compensated within a certain period of time, and the system is re-established in the long term. Ben Jebli and Belloumi (2017) examine the dynamic causal links between CO2 emissions, real gross domestic product (GDP), renewable energy sources, waste consumption, and maritime and rail transport, covering the period 1980–2011 in Tunisia. ARDL approach and Granger causality tests are used to examine the short- and long-run relationships between the variables. The empirical results show that there is bidirectional short-run causality between CO2 emissions and maritime transport and a unidirectional causality running from real GDP, combustible renewable energy sources, waste consumption, and rail transport to CO2 emissions. Long-term estimates reveal that real GDP contributes to the reduction of CO2 emissions, and the consumption of combustible renewable energy sources and waste and sea and rail transport have a positive impact on emissions. In addition, they state that maritime transport, waste use, and combustible renewable resources significantly affect CO2 emissions, while any increase in maritime transport reduces flammable renewable energy consumption and waste use. Besides that, maritime transport is significantly associated with CO2 leakage, which shows that Tunisian transport is highly polluted because of excessive consumption of non-renewable energy. In conclusion, they state that maritime transport is the mode that contributes the most to air pollution, causing increased CO2 leakage in Tunisia. Saidi and Hammami (2017) investigated the causal relationships between freight transportation, economic growth, and environmental degradation in 75 countries through the generalized moments method for the period 2000– 2014. The results of the study found that freight transportation, economic growth, energy use, and trade increase environmental degradation. Timilsina and Shrestha (2009) investigated CO2 emission factors for Asian economies. Decomposition analysis is used to analyse data from 1980 to 2005. GDP per capita, population growth, and transport energy intensity have been shown to be the main drivers of growth in transport sector CO2 emissions. Andres and Padilla (2018) investigated the drivers of GHG emissions for the transport sector using panel data forecasting approaches for European economies for the

Transport Modes to Carbon Emissions in Turkey  259

period 1980–2014. Their results confirm that there is a positive relationship between transportation indicators and GHG emissions. Both transport energy intensity and transport volume have been shown to contribute significantly to GHG emissions. Aircraft flights are also responsible for global warming, as they release hydrocarbons, carbon monoxide, and nitrogen oxides into the atmosphere. Chandran and Tang (2013) investigated the effects of economic growth, transport sector, and foreign direct investment on CO2 emissions using a panel of five Association of Southeast Asian Nations (ASEAN) countries. Empirical results confirm that the positive effects of the transport sector and economic growth contribute to CO2 emissions, while foreign direct investment appears to have an insignificant impact on emissions. Saboori et al., (2014) investigate the long-term relationships between the transportation sector and carbon emissions for the OECD countries for the period 1960–2008. Their findings confirm bidirectional causality between transportation sector energy consumption and CO2 emissions. Danish and Baloch (2018) explore Pakistan’s dynamic long-term relationship between economic growth, transport-related energy consumption, and environmental quality. Environmental quality is measured by sulphur dioxide (SO2) emissions. Their results confirm that there is a positive relationship between energy use in the transportation sector and environmental quality. Kumbaroglu (2011) tries to determine the factors that increase or decrease CO2 emissions in Turkey in terms of scale effect, composition effect, energy density, and carbon intensity. The refined Laspeyres method is used in the analysis of disaggregated data at the sectorial level (electricity, manufacturing, transportation, household, and agriculture) for the period 1990–2007. According to the results obtained, it is seen that the scale effect is an important source of increase in emissions in the electricity, manufacturing, and transportation sectors. It is also observed that emissions from the manufacturing and transportation sectors have decreased because of energy intensity improvements. Akbostancı et al. (2017) divided the CO2 emission factors of the Turkish economy into five sectors (agriculture, forestry and fisheries sector, manufacturing and construction sector, public electricity and heat generation sector, transportation and housing sector) for the period 1990–2013. The log mean divisia index (LMDI) method is used for decomposition analysis. Their results show that energy intensity is one of the determining factors behind economic activity alongside the variation in CO2 emissions. Especially for the manufacturing and construction sector, the fuel mixture component reduces CO2 emissions in times of crisis when economic activity is low. In addition, it is seen that the contributions of the housing and transportation sectors have gained importance in recent years. Existing studies to reveal the relationship between freight transport and the environment are generally estimated through intermediary variables such as energy consumption, gross national product, population, urbanization, transportation infrastructure, financial development, openness (export and import), and bank loans. In this regard, the study offers a roadmap to both policymakers

260  Transport Modes to Carbon Emissions in Turkey

and practitioners by focusing on the freight carried in different types of transportation modes.

13.3 Econometric Methodology 13.3.1 Data

In this study, Turkey’s transportation data covering the period 1970–2019 were analysed. Because of Turkey’s potential to become a logistics and transportation centre, it is expected to be in an important position in this field in the future. Therefore, the study focuses on modes of transport. The dependent variable, carbon emission (lnco2), covers the period 1970–2019 and was compiled from the Turkish Statistical Institute (TUIK) website. Among the independent variables, the amount of freight transported by road (lnroadton), sea (lnseaton), rail (lnrailton), and air (lnairton) cover the period 1970–2019; road and rail transport data were collected from TUIK (2021a), air transport data from World Bank (2021a), and maritime transport data from the OECD (2021a). All the variables were used in the analysis over their natural logarithms. 13.3.2 Econometric Method

In this section, the ARDL model and its mathematical infrastructures are summarized in the study. 13.3.2.1 Stationarity Analysis

Stationarity means that the statistical properties (mean, variance, and covariance) of a process that produces a time series do not change over time and remain the same (Hor, 2015: 110). If the time series variables are not stationary, the estimation may be biased and insufficient in the regression analyses to be made. Therefore, it is necessary to test whether the data are stationary or not. In this context, unit root tests are one of the most used methods to determine whether the series are stationary (İlarslan, 2021: 149). 13.3.2.2 ARDL Bounds Testing

The ARDL bounds testing method will be used to test the relationship between the freight carried in transport modes and carbon emissions in the study. The ARDL approach has become popular recently because it has several advantages compared to other cointegration analyses. The main reason for this is the ability of the model to predict the long and short-term parameters simultaneously to avoid the problems caused by non-stationary time series data. In other words, the advantage of the method is that it can be used to describe both long-term relationships and short-term dynamic interactions (Akel and Gazel, 2015: 30–31). The method also eliminates the omitted-variable and autocorrelation problems by estimating the short- and long-term components of the

Transport Modes to Carbon Emissions in Turkey  261

model at the same time (Haug and Ucal, 2019: 299; Srinivasan et al., 2012: 215; Hamuda et al., 2013; 62). Unlike other cointegration approaches, ARDL analysis does not require that all the variables in the model be stationary at the same level, but it should be taken into account and verified that none of the variables should be stationary at greater than one degree (Jalil and Feridun, 2011: 286; Alam and Adil, 2019: 286). In other words, it is a requirement that the variables are not stationary at I(2) or higher in the ARDL bounds test application. In the ARDL method, the unconstrained error correction model is determined first within the framework of mathematical notation in the model numbered (1) below. m

D ln co2 = a0 +

p

n

å

b1i D ln co2t - i +

i =1

å

b2i D ln airton t - i +

i =0

r

+

å

b5i D ln rail +

i =0

åb D ln seaton 3i

t -i

i =0

s

åb D ln road + 6i

(1)

i =0

+d1 ln co2t -1 + d2airton t -1 + d3 ln seaton t -1 + d4 ln rail t -1 + d5 ln road t -1 + ei

In the model α – constant term. Δ – difference operator. ε – error term. m, n, p, r, and s – lag lengths. After the model is estimated, the “boundary test” is performed to investigate the existence of a long-term equilibrium relationship between the variables. Intended for that, the hypotheses developed in the context of the Wald test (F statistic) can be written as follows: H0: δ1 = δ2 = δ3 = δ4 = δ5 = 0 There is no cointegration H1: δ1 ≠ δ2 ≠ δ3 ≠ δ4 ≠ δ5 ≠ 0 There is a cointegration If the calculated F statistic exceeds the upper critical limit (large), it is concluded that there is a cointegration (long-run relationship) between variables. In other words, the basic hypothesis that there is no long-term relationship between the variables in the model is rejected. If the F statistic falls between the limits, the test is inconclusive, or, in other words, the test evidence is insufficient. If the test statistic is smaller than the lower limit, a long-term relationship cannot be found (Pan and Mishra, 2017; Jalil and Mahmud, 2009), or, in other words, the basic hypothesis that there is no long-term relationship is accepted (Yavuz, 2014: 419). If a cointegration relationship is detected between the variables as a result of the boundary test, the next step is to determine the long-term coefficients. Equation (2) is used to determine the long-term coefficients.

262  Transport Modes to Carbon Emissions in Turkey m

p

n

åa lnco +åa lnairton +åa lnpseaton

lnco2 =a0 +

1i

2t-i

2i

i=1



t-i

3i

i=0

r

i=0

(2)

s

åa lnrailton+åa lnroadton+

+

4i

5i

i=0

t-i

i

i=0

After determining the long-term relationship between the variables in the ARDL method, it is necessary to determine the short-term relationships between the variables. The error correction model is applied to determine the short-term relationship. The created error correction model (3) can be defined mathematically as follows: m

D ln co2 = a0 +

n

å

l1i D ln co2 t - i +

i =1

p



+

ål D ln airton 2i

r

å

l 3i D ln pseaton t - i +

i =0

t -i

i =0

s

å

l 4 i D ln railton t - i +

i =0

ål D ln roadton 5i

t -i

(3)

i =0

+l 7 ECM t -1 + ei 13.3.3 Empirical Analysis and Findings 13.3.3.1 Descriptive Statistics

Descriptive statistical information provides a general view and information about the data used in the study. Descriptive statistics about the data created for this purpose are given in Table 13.3. According to Table 13.3, because the Jarque-Bera test probability values are p > 0.05, it can be said that the variables show a normal distribution. It is seen that the variable with the highest standard deviation in the variables considered within the scope of the study is the amount of cargo carried via air. This situation also shows the variable with the highest risk, as well as showing the biggest difference between the maximum and minimum values. Table 13.3 Basic Statistical Tests

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability

lnco2

lnair

lnrail

lnroad

lnsea

 4.783503  4.972389  6.052863  2.821290  0.923551 −0.462582  2.127033  4.044997  0.132324

 4.981189  5.339684  8.826956  1.526056  2.068394  0.102822  1.941601  2.421871  0.297918

 9.053409  9.031326  9.596079  8.550113  0.237875  0.273313  2.631515  0.905378  0.635916

 11.37965  11.56098  12.49717  9.766923  0.837399 −0.353757  1.823465  3.926687  0.140388

 2.404983  2.287669  4.777172 −0.763570  1.766429 −0.244797  1.711707  3.165668  0.205392

Transport Modes to Carbon Emissions in Turkey  263 13.3.3.2 Unit Root Test Results

In econometric analysis, the stationarity of the variables is very important to avoid spurious regression results. Therefore, augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests were applied in the study to see the stationarity results of the series. The results are shown in Table 13.4. In the analysis phase, it was seen that carbon emission, used as the independent variable, and the cargo series transported by air, rail, road, and sea, which are the independent variables, were not stationary in their levels according to the results of the ADF and PP unit root tests; therefore, the first differences were taken. In the first difference, it was seen that all variables were stationary in all three models according to the ADF and PP tests. Therefore, all variables show I(1) property. 13.3.3.3 ARDL Cointegration Analysis Results 13.3.3.3.1 BOUND TEST RESULTS

After demonstrating that the series used in the study were stationary at first differences according to both ADF and PP tests, efforts were made to determine whether there was a cointegration (long-term) relationship between the variables with the ARDL bound test. In the ARDL bound test method, the appropriate lag length must be determined in the first step. Therefore, it is necessary to know the maximum lag length. In this context, as stated by many researchers, because annual data was being used, the maximum lag length was chosen as four, and the most suitable model was determined automatically (Karagöl et al., 2007; İpek and Esener, 2014; Tatlı, 2015; Terzi and Bekar, 2019; Çıraklı, 2019). Hereunder, the most suitable model providing the smallest akaike information criterion (AIC) critical value was determined as the ARDL (1, 0, 0, 0, 1) model. The estimation results obtained within the framework of the model created are given in Table 13.5. According to the test results stated in Panel B in Table 13.5, it was seen that the model error term had a normal distribution. In addition, it has been determined that there is no autocorrelation and changing variance problem in the model, and there is no modelling error. Thereafter, the existence of a cointegration relationship between the variables was investigated by means of the boundary test. If the test statistic exceeds the upper limit, it is concluded that there is a cointegration relationship. If the F statistic falls between the limits, the test is inconclusive; that is, the test evidence is insufficient. If the test statistic is smaller than the lower limit, a long-term relationship cannot be found (Pan and Mishra, 2017; Jalil and Mahmud, 2009). Boundary test results are presented in Table 13.6. According to the boundary test results, when the calculated F statistics value (9.351626) is compared with the critical values, it is seen that it has a greater value than all the upper limit values at all significance levels (1%, 2.5%, 5%, and 10%). Therefore, the hypothesis of H0 “There is no cointegration

lnco2 lnair lnrail lnroad lnsea Δlnco2 Δlnair Δlnrail Δlnroad Δlnsea

Model 3 6.278 4.903 1.538 4.770 2.570 −4.076*** −2.097** −7.052*** −4.204*** −2.753***

Model 1 −5.743*** 0.204 −0.736 −1.910 2.073 −6.779*** −7.950*** −7.362*** −5.743*** −6.782***

Model 2 −1.945 −2.285 −2.865 −1.537 −1.766 −8.298*** −7.934*** −6.597*** −6.033*** −6.781***

Model 1 −3.748*** 0.204 −0.736 −1.910 −1.489 −6.773*** −7.950*** −6.494*** −5.732*** −6.759***

Model 2 −1.964 −2.313 −2.978 −1.655 −1.892 −8.309*** −7.934*** −7.360*** −6.031*** −6.951***

Model 3 4.624 5.242 1.623 4.179 2.018 −3.919*** −5.465*** −7.054*** −4.330*** −4.806***

Note: Model 1 – constant; Model 2 – constant and trend; Model 3 – none. The optimal delay length was selected automatically according to Schwarz information criterion in ADF and Newey-West Bandwidth in PP. Significance level: *: %10; **: %5; ***: %1.

First Difference

Level

PP

ADF

Table 13.4 Results of the ADF and PP Unit Root Test

264  Transport Modes to Carbon Emissions in Turkey

Transport Modes to Carbon Emissions in Turkey  265 Table 13.5 ARDL Prediction Model and Diagnostic Tests Panel A: ARDL (1,0,0,0,1) Model Estimate Results Variable

Coefficient

DLNCO2(-1) −0.054636 DLNAIR −0.005021 DLNRAIL 0.143342 DLNROAD 0.199337 DLNSEA −0.025646 DLNSEA(-1) 0.094599 C 0.022504 Panel B: Diagnostic Tests Test Jarque-Bera Normal test Breusch-Godfrey LM test Breusch-Pagan-Godfrey test Ramsey Reset test

Std. Error

t-Statistic

Prob.* 

0.168246 0.045749 0.083141 0.097795 0.043624 0.038568 0.016674

−0.324740 −0.109746 1.724079 2.038315 −0.587881 2.452774 1.349685

0.7476 0.9133 0.0947 0.0501 0.5609 0.0200 0.1869

Statistic 2.295 1.002 0.354 1.866

Probability (0.317) (0.423) (0.901) (0.158)

relationship between the variables” is rejected, and it is accepted that there is a cointegration relationship between the variables. According to the test results, it is revealed that there is a long-term relationship between carbon emissions and the variables of freight transported by road, air, rail, and sea. After that, long- and short-term estimation results are made through the ARDL model. 13.3.3.3.2 LONG- AND SHORT-TERM FORECAST RESULTS

After determining the cointegration relationship between the variables with the bounds test, the long-term relationship between the variables should be estimated. The generated model (4) can be mathematically defined as follows: m

å

lnco2 =a0 +

i=1



r

p

n

å

a1i lnco2t-i +

åa lnpseaton

a 2i lnairton t-i +

i=0

s

åa lnrailton+åa lnroadton+e

+

4i

i=0

3i

i=0

5i

t-i

(4)

i

i=0

The functional pattern of the ARDL error correction model (5) is given below, which was developed to investigate the short-term dynamics between the variables in the next stage of the study.

266  Transport Modes to Carbon Emissions in Turkey m

D ln co2 = a0 +

ål D ln co 1i

i =1

2 t -i

+

ål D ln airton + ål D ln pseaton 2i

å

3i

t -i

i =0

s

l 4 i D ln railton t-i +

i =0

t -i

i =0

r

+

p

n

ål D ln roadton 5i

t -i

(5)

i =0

+l 7 ECM t-1 + ei

Here ECM refers to the error correction term, and its coefficient (λ) should have a negative sign and be statistically significant (Paul, 2014: 3). The coefficient shows the rate of return to long-term equilibrium after a short-term shock (Folarin and Asongu, 2019: 969). Table 13.7 shows the short- and long-term coefficient estimates and diagnostic test results obtained with the ARDL model. According to Table 13.7, the effect of the increase in the amount of freight transported by road is positive (increasing) and statistically significant on carbon emissions. There is broad consensus that the amount of freight transported on the road has a positive effect on carbon emissions. Accordingly, this finding is similar to the literature (Habib et al., 2021; Afaq et al., 2021; Ahmed et al., 2020; Alcántara and Padilla, 2006). The increase in the amount of freight transported by rail has an increasing effect on carbon emissions, and this effect is statistically insignificant. The main reason for the meaninglessness may be due to the low share of the rail in freight transportation. As a matter of fact, when evaluated in terms of the amount of freight transported within the country in 2019, it is seen that only 5.1% was transported by rail transport (OECD; World Bank). Moreover, when the amount of freight transported in transport modes in 2019 is compared to 2000, the share of rail transportation in Turkey decreased from 5.8% to 5.1%. Also, the main reason rail transport increases carbon emissions is that most of the energy consumed in rail transportation (about 89% diesel, 11% electric) is diesel fuel (Turkish State Railways, 2011). In addition, the ineffectiveness of rail transportation is another reason. The increase in the amount of freight carried by sea has an increasing effect on carbon emissions, and this effect is statistically insignificant. The reason for this situation may be that the variable used belongs to coastal transportation (cabotage), and coastal transportation has low share (0.04% in 2019) in total freight transportation (OECD, 2021a). The root cause for the increasing effect of maritime transport on carbon emissions is thought to be due to the consumption of petroleum products, such as fuel oil, diesel fuel, etc. The error correction coefficient is negative and significant in line with theoretical expectations. All of the short-term deviations in the system are eliminated within a year and come back to balance in the long term. After the long- and short-term coefficient estimations, cumulative sum quality control charts (CUSUM) and the cumulative sum square (CUSUMQ)

Transport Modes to Carbon Emissions in Turkey  267 Table 13.6 Boundary Test Results Test Statistic

Value

Sig.

Lower Bound I(0)

Upper Bound I(1)

F-statistic k

 9.351626 4

10%  5%  2.5%  1% 

2.2 2.56 2.88 3.29

3.09 3.49 3.87 4.37

tests are performed in order to see the stability of the long-term coefficients. Test results are shown in Figure 13.1. From the CUSUM and CUSUMSQ test results shown in Figure 13.1, it is seen that the consecutive residuals do not go beyond the 5% critical value limits. According to this situation, it can be said that the model is consistent in the long term, and there is no structural break or change in the examined period.

13.4 Conclusions and Suggestions Production and consumption are the main driving forces behind the environmental impact. In order to produce and consume, the products must be transported from the point of production to the point of consumption. Although transport modes are mainly divided into three main groups – land, air, and water – transportation networks cover six types of transportation, namely road, sea, rail, air, inland waterway, and pipeline. At the basic level, transport infrastructure, defined as roads, railways, airports, and ports, is often an important determinant of productivity and economic growth. This study aims to determine the effect of transport modes on carbon emissions in Turkey and covers the period 1970–2019. It was desired to reach empirical evidence on the subject by making analyses within the framework of the ARDL boundary test. This study reveals the effect of the cargo carried by road, air, sea, and rail on carbon emissions in Turkey. In this way, it contributes to filling the existing research gap in this field. According to the results obtained from the econometric analyses made as of the relevant period, (1) especially in recent years, the amount of carbon emissions tends to decrease despite the increase in the amount of cargo carried in Turkey. This result shows that the sensitivity to environmental pollution has increased in Turkey and is promising for environmental protection. (2) It has been determined that there is a cointegration relationship between the freight carried in transport modes and carbon emissions. (3) It has been observed that the effect of the cargo transported on the road on carbon emissions is statistically significant, and the sign is positive. The main reason for this is thought to be due to the still high cost of emission reduction technologies and applications in road transport. (4) The effect of maritime transport on carbon emissions is insignificant but has a positive sign, which is thought to be mostly due to the consumption

268  Transport Modes to Carbon Emissions in Turkey Table 13.7 Long- and Short-Term Forecast Results Variable

Coefficient

Panel A: Long Run DLNAIR −0.004761 DLNRAIL 0.135916 DLNROAD 0.189011 DLNSEA 0.065381 C 0.021338 Panel B: Short Run D(DLNSEA) −0.025646 CointEq(-1)* −1.054636

Std. Error

t-Statistic

Prob.

0.043156 0.081669 0.094376 0.059137 0.013930

−0.110312 1.664226 2.002735 1.105589 1.531804

0.9129 0.1061 0.0540 0.2774 0.1357

0.024711 0.130651

−1.037819 −8.072157

0.3074 0.0000

of non-renewable energy resources on the vessel. (5) The impact of rail transport on carbon emissions is insignificant, but the sign is positive. It is thought that the main reason for this is the widespread use of diesel locomotives and the inability to make effective use of rail transportation. (6) The effect of the air cargo on carbon emissions was insignificant, but the sign is negative. The reason for this is thought to be that air cargo grows faster than other modes of transport, and, thus, the emission per cargo is reduced by carrying more cargo. In addition, increasing the flight distance in airline transportation further reduces CO2 emissions compared to short-haul flights. (7) It is seen that the deviations that may arise between the variables, in the long run, are completely eliminated within one year. It is thought to be that the results obtained from this study are important for the protection of the environment because, depending on the results obtained, it carries various suggestions for both decision-making authorities, researchers, and practitioners. In this context, the fact that rail, sea, and road transport have a positive effect on carbon emissions shows that various measures should be taken in terms of public authority. This will contribute to the reduction of carbon emissions. For example, instead of fossil fuels, more hydropower should be encouraged through the production and use of renewable energy sources such as wind, solar, and biofuels. Also, it is possible to encourage the use of logistics companies (outsourcing) to create a scale effect in order to reduce emissions, expand the use of vehicles with new generation engines, train drivers on efficient energy use, and increase the awareness of employees in the road transport sector about the environment. On the other hand, the negative effect of air cargo gives hope for the future. In this context, environmental pollution will be prevented by ensuring more effective use of airlines and shifting the cargo in other transport modes them. Besides, it is necessary to continuously develop and encourage the use of new technologies that contribute to the reduction of emissions. In this context, various support projects can be created. Because, from both a macro and micro perspective, reducing emissions is a

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Figure 13.1 CUSUM and CUSUMSQ test results.

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Transport Modes to Carbon Emissions in Turkey  269

270  Transport Modes to Carbon Emissions in Turkey

wise long-term investment that contributes to various development goals and will ultimately yield significant benefits. Moreover, because of limited petroleum resources, alternative fuels should be used. In this context, in order to increase fuel efficiency, innovations and improvements in vehicles, improving the behaviour of drivers, various regulations to be made in transportation infrastructures, and regulating the purchase and use of vehicles through taxation are among the various duties of the state and municipalities, as well as automobile manufacturers. After all, when discussing the “National Transportation Master Plan” and “Logistics Master Plan” for Turkey, the relationship between transportation and the environment should also be considered.

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272  Transport Modes to Carbon Emissions in Turkey Hao, H., Wang, H.W., Yi, R. (2011c). Hybrid modeling of China’s vehicle ownership and projection through 2050. Energy 36(2), 1351–1361. Haug, A., Ucal, M. (2019). The role of trade and FDI for CO2 emissions in Turkey: Nonlinear relationships. Energy Economics 81. https://doi​.org​/10​.1016​/j​.eneco​.2019​.04​ .006. Hor, C. (2015). Modeling international tourism demand in Cambodia: ARDL model. Review of Integrative Business and Economics Research 4(4), 106–120. IEA (International Energy Agency). (2017). CO2 emissions from fuel combustion: Highlight. www​.iea​.org​/publications​/freepublications​/publication​/CO2​Emis​sion​sfro​ mFue​lCom​bust​ionH​ighl​ights2017​.pdf. Accessed on 11 April 2022. IEA. (2019). Tracking Transport 2019. Paris: International Energy Agency. İlarslan, K. (2021). Uluslararası Fosil Yakıt Fiyatlarının Finansal Piyasalar Üzerindeki Etkisinin ARDL Sınır Testi ile İncelenmesi: 1986–2019 Dönemi Türkiye Örneği. Finansal Araştırmalar ve Çalışmalar Dergisi 13(24), 143–158. https://doi​.org​/10​.14784​/ marufacd​.879206. İpek, E., Esener, S.Ç. (2014). Borçlanmayı savunmak: Dış borcun bir belirleyicisi olarak savunma harcamaları. Eskişehir Osmangazi Üniversitesi İİBF Dergisi 9(3), 69–94. Isik, M., Sarica, K., Ari, I. (2020). Driving forces of Turkey’s transportation sector CO2 emissions: An LMDI approach. Transport Policy 97, 201–219. ITF. (2019). ITF Transport Outlook 2019. Paris: OECD Publishing. Jalil, A., Feridun, M. (2011). The impact of growth, energy and financial development on the environment in China: A cointegration analysis. Energy Economics 33, 284–291. https://doi​.org​/10​.1016​/j​.eneco​.2010​.10​.003. Jalil, A., Mahmud, S. (2009). Environment Kuznets Curve for CO2 emissions: A cointegration analysis for China. Energy Policy 37, 5167–5172. https://doi​.org​/10​.1016​ /j​.enpol​.2009​.07​.044. Kalayci, S., Özden, C. (2020). The linkage among sea transportation, trade liberalization and industrial development in the context of carbon dioxide emissions: An empirical investigation from China. https://doi​.org​/10​.1101​/2020​.07​.13​.200386. Kalayci, S., Özden, C. (2021). The linkage among sea transport, trade liberalization and industrial development in the context of CO2: An empirical investigation from China. Frontiers in Environmental Science 9, 633875. https://doi​.org​/10​.3389​/fenvs​.2021​.633875. Karaagöl, E., Erbaykal, E., Ertuğrul, H.M. (2007). Türkiye’de ekonomik büyüme ile elektrik tüketimi ilişkisi: Sınır testi yaklaşımı. Doğuş Üniversitesi Dergisi 8(1), 72–80. https://doi​ .org​/10​.31671​/dogus​.2019​.243. Kharbach, M., Chfadi, T. (2017). CO2 emissions in Moroccan road transport sector: Divisia, cointegration, and EKC analyses. Sustainable Cities and Society 35. https://doi​ .org​/10​.1016​/j​.scs​.2017​.08​.016. Kumbaroglu, G. (2011). A sectoral decomposition analysis of Turkish CO2 emissions over 1990–2007. Fuel and Energy Abstracts 36, 2419–2433. https://doi​.org​/10​.1016​/j​.energy​ .2011​.01​.027. Linton, C., Grant-Muller, S., Gale, W. (2015). Approaches and techniques for modelling CO2 emissions from road transport. Transport Reviews 35, 1–2. https://doi​.org​/10​.1080​ /01441647​.2015​.1030004. OECD. (2021a). Denizyolunda taşınan yük miktarları. https://data​.oecd​.org​/transport​/ container​-transport​.htm​#indicator​-chart. Ozkaya, S.Y. (2004). Yenilenebilir Enerji Kaynakları, Uluslararası Ekonomik Sorunlar (Ağustos 2004) Sayı XIV, Turkey Ministry of Foreign Affairs (MFA), 2021. https:// www​.mfa​.gov​.tr​/yenilenebilir​-enerji​-kaynaklari​.tr​.mfa.

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14 The Roles of Education and Export Diversification in the Improvement of Environmental Quality A Comparison between China and India Muhammad Shahbaz, Mantu Kumar Mahalik, Shujaat Mubarak, and Shawkat Hammoudeh 14.1 Introduction Energy consumption has increased worldwide due to growing urbanization and rising economic growth. The excessive use of energy has resulted in rising carbon emissions in the atmosphere in the whole world, especially in the fastgrowing, developing and less developed countries (LDCs). Therefore, global warming is increasingly garnering the attention of policymakers, governments, and researchers across the globe. In this context, the carbon emissions–economic growth nexus has captivated the attention of scholars and policymakers interested in decreasing carbon emissions without compromising economic growth. The emissions–growth link is heavily reliant on the way a nation produces and consumes its energy (Mirza and Kanwal, 2017), depicting energy consumption as the profound contributor in the emissions–growth nexus. Along with energy consumption, several studies (Begum et al., 2015; Ahmed et al., 2016) have highlighted many other factors (e.g., technological innovations, financial and trade liberalization) in the emissions–growth nexus. Given the fact that education performs a momentous role in reducing carbon emissions by promoting energy efficiency, some studies have considered this factor in energy, carbon dioxide (CO2) emissions, and economic growth framework (Broadstock et al., 2016), but they did not trace any evidence on the effect of education on CO2 emissions to be consistent (Démurger and Fournier, 2011). For example, Hill and Magnani (2002) and Gangadharan and Valenzuela (2001) asserted that education aggravates CO2 emissions and influences environment negatively, while Zovak and Periša (2013) found that education is helpful in promoting energy efficiency and spurring pollution-reducing technology. Additionally, education adds skills, knowledge, and training into the framework of human capital (Kwon, 2009). Thus, an improvement in human capital framework will help economies to opt for alternative sources of energy in production processes. In a similar direction, Kwon (2009) claimed that human capital (particularly education) can be instrumental in the mitigation of carbon DOI: 10.4324/9781003336563-14

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emissions which not only enhances the ability of human beings to increase energy efficiency but also enables them to understand the value of energy security and the consequence of environmental pollution. Contrarily, Williamson (2017) indicated that education does not influence carbon emissions. The mixed results call for a robust and comprehensive study to clarify the impact of education on CO2 emissions. These ambivalent results can be rooted in the assertion of researchers such as Broadstock et al. (2016) who claimed that education produces two types of opposing outcomes. First, once education is expanded by increasing the enrolments rates in a society in which enrolment rates are very low (Gangadharan and Valenzuela, 2001), it may increase the consumption of energy, primarily via thermal sources, and, thus, increases carbon emissions. Further, the knowledge, skills, and abilities attained through education can lead to increases in CO2 emitters’ activities (Hill and Magnani, 2002). Second, researchers (e.g., Dasgupta et al., 2002) also acknowledged that increasing education can improve environmental quality. Export diversification is critical for structural changes and economic development. Owing to the small market size and a few exportable products, LDCs need to diversify their exports. Interestingly, there exists a lack of consensus regarding the impact of export diversification on CO2 emissions. Some argue that export diversification profoundly adds to carbon emissions, as every newly introduced product increases CO2 emissions significantly. However, Lange and Ziegler (2017) and Shahbaz et al. (2018) argued that export diversification improves production efficiency and reduces carbon emissions. The data over the period 1971–2017 is taken for empirical estimation of carbon emissions function in India and China. The reason for choosing to empirically model the determinants of environmental quality in carbon emissions function is that those most globally populous countries are emerging in nature and continue to grow at almost equally high space. These economies are also an ideal example of empirical analysis because they are the members of the Brazil, Russia, India, China, and South Africa (BRICS) group, which accounted for 41% of global CO2 emissions in 2015. The individual shares of the Chinese and Indian economies in global carbon emissions are 28% and 6%, respectively, compared to 1% for Brazil, 5% for Russia, and 1% for South Africa. This shows that Chinese and Indian economies among the BRICS economies are the major contributors in global CO2 emissions. In addition, existing work on energy and environmental economics used the sum of exports and imports as the proxy of international trade in carbon emissions function for developed countries (DCs) and LDCs (e.g., Halicioglu, 2009; Bento and Moutinho, 2016). Moreover, the more recent empirical literature applies export diversification as a proxy of international trade, especially in modelling of emissions and energy demand functions (Gozgor and Can (2016) for Turkey, Gozgor and Can (2017) for China, Apergis et al. (2018) for DCs, and Shahbaz et al. (2019) for the United States. The possible reason for considering export diversification to represent international trade and to add it as one of the main elements of carbon emissions function is that diversity of

The Roles of Education and Export Diversification  277

export quality has profound implications on carbon emissions. Thus, Gozgor and Can (2016, 2017) argue that any attempt to add a new product into the export basket not only consumes greater energy but also emits carbon into the atmosphere. In light of the significance of education and export diversification as indicated, it is important to consider such significant drivers of carbon emissions function for China and India. In doing so, this study contributes to existing energy economics literature in the following directions. (i) It examines as to how education and diversification (export) are linked with environment quality in India and China by using the EKC framework. (ii) The Kim and Perron (2009) unit root test, which takes care of structural beak, is applied for testing the integrating properties of the variables. (iii) The bootstrapping autoregressive-distributed lag (B-ARDL) approach is applied to analyze the cointegration relationship between carbon emissions and its determinants. Our findings reveal the presence of integration between CO2 emissions and its influencers. Economic growth adds to CO2 emissions in the early phase of growth in Chinese and Indian economies; however, it reduces emissions in the second phase. In China, export diversification deteriorates environment quality by emitting carbon emissions, but Indian export diversification lowers carbon emissions. Energy consumption is a prime influencer of emissions. In China and India, the environmental Kuznets curve (EKC) is validated. The rest of the study is structured as follows. Section 14.2 reviews the existing literature on the linkage between education, export diversification, and carbon emissions. Section 14.3 describes the data collection, empirical modelling, and methodology. Section 11.4 presents the results and discussion. Section 14.5 provides conclusions and policy implications.

14.2 Literature Review We divide the literature review section into two sections: (i) education–carbon emissions nexus and (ii) the nexus between export diversification and carbon emissions. 14.2.1 Education and Carbon Emissions Nexus

Although researchers started focusing on the environmental impact of growth long before, Grossman and Krueger (1991, 1995) revived the interest of researchers and policymakers to further study the environmental issues in the context of economic growth and the EKC. This became the basis for studying the environment–growth paradox. The EKC posits that an inverted U-shaped association exists between economic growth and environmental degradation. This implies that it is possible to improve environment in the long run as economic growth continues to grow beyond a certain threshold level. This beneficial evidence is confirmed by Shahbaz et al. (2013), Zaman et al. (2016),

278  The Roles of Education and Export Diversification

and Sapkota and Bastola (2017). The environmental degradation is primarily linked with CO2 emissions being emitted from human’s economic activities (Wang et al., 2015). Whereas one of the key dimensions of human development is education (Pablo and Sánchez, 2015; Mubarik et al., 2018), this study considers education as one of the major drivers in the EKC model for emerging economies. While considering the role of education in the EKC model, this variable is anticipated to have two effects on environmental quality (Balaguer and Cantavella, 2018). The opponents argue that people with a low level of education lack awareness about protecting environmental quality and continue to consume nonrenewable energy. The heavy consumption of non-renewable energy degrades environmental quality via increasing carbon emissions to natural environment (Gangadharn and Valenzuela, 2001; Hill and Magnani, 2002). The proponents’ view is that people with higher levels of education can safeguard environmental quality because they are aware of the danger of pollution for the environment. For instance, the knowledge derived from education not only creates awareness in the minds of educated people but also enables them to use cleaner technologies for the protection of the natural environment (Lange and Ziegler, 2017). The point to ponder is that developing human capital with the help of education may help countries to reach a level in which economic development contributes to environmental quality. Lee and Chang (2008) show a significant impact of education (secondary) on reducing poverty and increasing economic development. In this context, Mehrara et al. (2015) portrays education an essential factor to reduce carbon emissions. They argue that education increases the exploitation of alternative energy resources by creating awareness and knowledge about energy sufficiency. Likewise, Pablo and Sánchez (2015) also found education as a significant factor to decrease carbon emissions. Likewise, Cole et al. (2008) confirm the results of Dasgupta et al. (2000) in the case of China. They took 15 industries from various regions of China and concluded that the regions with higher levels of education had higher environmental regulations’ compliance as compared to the regions with lower levels of education. Using the ARDL procedure on Australian data from 1950 to 2014, Balaguer and Cantavella (2018) investigated the influence of education on environmental quality. Their findings confirmed the presence of EKC hypothesis in Australia. They recommended higher levels of education for promoting environmental quality in Australia. Despite having numerous studies on education, economic growth, and carbon emissions paradox, these studies do not account for any standard proxy of education. Most of the researchers take average schooling years as a proxy of education, while studying the role of education in affecting environment. Moreover, few studies, such as Cole et al. (2008) and Pablo and Sánchez (2015), assert that the most appropriate proxy for measuring education to study its impacts in the carbon emissions–growth nexus is the “secondary enrolment rate”.

The Roles of Education and Export Diversification  279 14.2.2 Export Diversification and CO2 Emissions Nexus

Since Krueger and Grossman (1991), international trade has captivated the attention of scholars across the globe. Researchers have contrasting views on the effect of international trade and export diversification on carbon emissions. One group of economists and policymakers agree that international trade and export diversification improve efficient production. Others, however, claim that trade globalization has just transferred carbon emissions from developed economies to developing economies. Hence, the debate on the juxtaposition of environment and trade lacks consensus and requires further scientific inquiry. It is obvious from the complex shape of the EKC hypothesis, the growth– environment paradox is complex and subtle (Copeland and Taylor, 2013). According to Kruger and Grossman (1991), this growth–environment paradox can be expounded by three essential elements, namely scale effect, technique effect, and composition effect. Trade liberalization first leads to a scale effect by raising the productivity of a nation. Further, it stimulates different kinds of economic activities leading toward composition effect, and last, it can entice the government to impose stringent environmental regulations leading toward technique effect (Grossman and Kruger, 1991, 1995, 1996). In this context, export diversification can play an instrumental role. The scholastic work on export diversification argues that the negative impact of international trade on environment can be controlled via export diversification. For example, Cadot et al. (2011) demonstrate that the negative influence of international trade on environment in LDCs can be attributed to undiversified exportable produce. DCs first increase the levels of export diversification once they attain a specific level of diversity, and then they tend to reconcentrate on exports (Cadot et al., 2013). Precisely, the process of economic development follows a dynamic pattern that can be elaborated as an “export diversification Kuznets curve”. Dutt et al. (2008) demonstrated that export diversification had a profound impact on both economic development and environmental quality. However, lesser scholastic works have been steered to analyze its impacts on environment. The few studies conducted on this issue show mixed results for the effect of export diversification on environment. For example, Gozgor and Can (2016) examined the association of export diversification and pollution in Turkey. He found that increasing export diversification leads to increased CO2 emissions. Further, he argued that higher diversification may lead to decreased CO2 emissions. Gozgor and Can (2017) examined the juxtaposition between carbon emissions, export quality matter, and energy consumption for the Chinese economy covering the annual data period of 1971–2010 within a time series modelling. Their findings underscore a special role for export product quality, as it decreases carbon emissions in China. It is worth mentioning that export products diversification occurs to a particular income level. After the country tends to focus on export concentration, it employs an inverted U-shaped association of export diversification with income levels (Cadot et al., 2011). Further, Klinger and Lederman (2006) calculated $22,500 as the

280  The Roles of Education and Export Diversification

turning point, and Cadot et al. (2011) calculated $2,500 as the turning point. The diversification phase may increase CO2 emissions. Because export diversification may require more consumption of energy, it can further increase carbon emissions.

14.3 The Data, Modelling, and Methodology 14.3.1 The Data and Modelling

We apply EKC framework to study the juxtaposition between education, export diversification, and emissions by taking two major Asian economies (China and India) as the case. Data from 1971 to 2017 were obtained for analysis. The data on real gross domestic product (GDP) per capita (in constant 2010 US$), carbon emissions (in metric tons per capita), energy consumption (in kg of oil equivalent per capita), and secondary enrolment is collected from the World Development Indicators (CD-ROM, 2019). Export diversification index data is acquired from the International Monetary Fund database. We have also used total population to convert real GDP, energy consumption, secondary enrolment, and carbon emissions into per capita units. The basic equation of the CO2 emissions function is modelled below:

Ct = f (Yt ,Yt 2 , EEt ) (1)

where Ct is CO2 emissions per capita, Y is income per capita and EEt is energy consumption. We include education as a prime variable in the empirical model of augmented carbon emissions function. It is argued by Balaguer and Cantavella (2018) that a society’s education determines what energy sources to use as well as to what extent the sources should be utilized to maintain an adequate living standard. Further, Dasgupta et al. (2002) acknowledge that a rise in education may positively affect environmental quality due to the importance and awareness of environment. Educated societies can force the government to execute strict environmental regulations and encourage investors to adopt cleaner and better environment technologies for producing economic output. This social awareness helps in improving environmental standards as well as living standards. The “export product quality” means an upsurge in the value of exporting products. The commencement of this procedure stems from the beginning step of development. Its progression still happens until one country can achieve the high-income level for its economy (Shahbaz et al., 2019). To fulfil the world’s high demand for product quality, developing economies are interested in producing quality products which are also added into their export basket. It is necessary, at this phase, for LDCs to develop the quality human capital and for institutions to keep the pace of product and process innovation (Apergis et al., 2018). Therefore, the process of improving export product quality might cause this phenomenon of environmental degradation affecting carbon emissions. We transform the linear specification into log-linear specification for attaining empirical efficient results. This transformation reduces

The Roles of Education and Export Diversification  281

sharpness of longitudinal data. In doing so, the equation of augmented carbon emissions function is modelled below in Equation 2:

ln Ct = a1 + a 2 ln Yt + a 3 ln Yt 2 + a 4 ln Et + a 5 ln Dt + a 6 ln EEt + mi (2)

where, ln , Ct , Yt , Et , Dt and EEt show natural-log, carbon emissions, economic growth, education, export diversification, and energy consumption, respectively. We expect a validation of the EKC hypothesis if a 2 > 0 and a 3 < 0, implying an inverted U-shaped association between CO2 emissions and growth; otherwise, the relationship is U-shaped if a 2 < 0 and a 3 > 0 . Education improves the environment by suppressing the carbon emissions if a 4 < 0; otherwise, the opposite holds if a 4 > 0 . Moreover, if a 5 < 0 , it shows that export diversification lowers carbon emissions and elevates the quality of environment; however, export diversification pollutes the environment by surging emissions if a 5 > 0. Energy consumption elevates emissions if a 6 > 0 . The term mi is the residual term and based on the assumption of normal distribution. Hill and Magnani (2002) note that the increasing levels of education in situations of low enrolment rates may lead to increased usage of non-renewable energy resources, further raising the emission levels. The knowledge and skills acquired through process of education can increase energy-intensive activities. On the other hand, education brings improvement to environmental quality via enhancing clean and green energy demand. An increased environmental quality through better human capital will also enable economies to achieve more sustainable economic growth. In addition, human capital is not all about educational attainment and but also creates environmental awareness in the mind of people to use more renewable energy than non-renewable energy. Thus, better environmental health associated with human capital is possible. We may predict that a low rate of enrolment is accompanied by CO2 emissions, but after a certain level, further education attainment can decrease carbon emissions and improves environmental quality. Two stages are vital in the context of changing the export products basket. (i) The first stage is diversifying the export basket. Particularly, the export basket of consumers of emerging and developing economies is comprised of traditional and limited products (Gozgor and Can, 2017). However, the export basket of the countries is diversifiable because of the growing demand from other countries toward new quality products (Dennis and Shepherd, 2011). Similarly, Klinger and Lederman (2006) calculated the turning point of export basket diversification as $22,500, but Cadot et al. (2011) indicated it as $25,000. (ii) After this point, countries move forward to the next stage (i.e., Stage 2): export concentration. In contrast, high-income countries emphasize knowledge-intensive and complicated products instead of manufacturing each and every product (Can and Gozgor, 2017). Meanwhile, countries try to reduce the production of those products which may create a harmful effect on the environment during the concentration stage. Consequently, it can be expected that during the concentration stage carbon emissions will fall. This also suggests

282  The Roles of Education and Export Diversification

a quadratic (inverted U) association between export diversification and carbon emissions. Carbon emissions are accompanied by the export basket diversification (first stage), and after reaching a threshold level of export diversification, emissions decline with the second stage of export diversification (i.e., export concentration). Following the above economic discussion, we further augment the carbon emissions function by including squared terms of education and export diversification to test whether association between education (export diversification) and carbon emissions is an inverted U shape or U-shaped. The equation of augmented emissions function is modelled with squared terms as following: ln Ct = b1 + b 2 ln Yt + b3 ln Yt 2 + b4 ln Et + b5 ln Et2 + b6 ln Dt + b 7 ln Dt2 + b 8 ln EEt + mi

(3)

We expect that b4 > 0 and b5 < 0 which will ascertain the inverted U-shaped association between education and carbon emissions (Balaguer and Cantavella, 2018); otherwise the U-shaped association exists. The association between export diversification and carbon emissions is an inverted U shape if b6 > 0 and b 7 < 0 (Apergis et al., 2018), and a U-shaped relationship exists if b6 < 0 and b 7 > 0 . 14.3.2 The Bootstrapping ARDL (B-ARDL) Approach

We applied the B-ARDL technique (McNown et al., 2018) for estimating the modelled relationships. The novelty of the B-ARDL technique is its ability to overcome weak size and power properties. The traditional ARDL approach of Pesaran and Shin (1999) and Pesaran et al. (2001) cannot take care of these issues. The B-ARDL model performs better than the conventional ARDL approach, as it generates a new test to boost the power of the F-test in the case of a small sample (50). Additionally, it conducts three tests. First, it conducts the F-test on all the lagged level variables; second, it conducts t-tests for the dependent variables at the lagged level; and third, it conducts t-tests for the independent variables at the lagged level. Additionally, the B-ARDL approach is insensitive to the level of integration of the variable. This makes B-ARDL relevant for modelling the time series data with small sample sizes (Goh et al., 2017). This indicates that this test can be used even though all the variables are found to be integrated of order one, i.e., I(1), and covers the unknown single structural break while testing the long-run association between the variables. Moreover, the critical values produced in the B-ARDL testing process eliminate the probability of indecision cases, which is the case in the conventional ARDL approach. Another advantage of using the B-ARDL approach is its applicability in a situation in which the estimated model entails more than one explanatory variable. Likewise, in contrast to the conventional ARDL, the B-ARDL rejects the assumption of stern exogeneity

The Roles of Education and Export Diversification  283

of the regressors on the basis of the argument that the strict exogeneity condition may not prevail in the real world while dealing with the macroeconomic relationships. Following Goh et al. (2017), we specify B-ARDL mathematically, where the ARDL (p, q, r), model with a three variable framework is as follows: p



yt =

q

å

ay

’ i t -i

+

i =1

r

å

s

å

b ’J xt - j +

g k’ zt -k +

j =0

k =0

åt D ’ l

t ,l

+ mt (4)

i =1

where i, j, k, and l denote the lags (i = 1, 2… p; j = 0, 1, 2, …, q; k = 0, 1, 2,…r; l = 0, 1, 2,…s) and t represents time, yt is the response variable, and xt and zt are the explanatory variables. Additionally, Dt ,l is the dummy variable, b and g represent the coefficients of the lagged explanatory variables, and t ’ is the coefficient of the dummy variable. Finally, mt represents the error term with a zero average and a finite variance. Equation 4 can be specified in an error-correction form as follows: p -1

Dyt = f yt -1 + g xt -1 + y zt -1 +

q -1

r -1

ål Dy + åd Dx + åp Dz ’ i

t -i

i =1



’ J

’ k

t- j

j =1

k =1

t -k

(5)

s

+

åw D ’ l

t ,l

+ et

i =1

p

In Equation 5, f =

q

å

ai , g =

i =1

r

å

b J , and y =

j =0

åg . At this point, l , d , p , k

i

J

k

k =0

and wl justify the associated functions in Equation 4, which is computed by using the constant term (c ) . Equation 5 is obtained by converting the vector auto-regression in the levels into its error-correction form. Equation 6 is developed by applying the constant term (c ) in the unconditional model: Dyt = c + f yt -1 + gxt -1 + y zt -1 +

p -1

å i =1



r -1

+

q -1

åd Dx ’ J

j =1

t- j

(6)

s

åp Dz + åw D ’ k

k =1

li’Dyt -i +

’ l

t -k

t ,l

+ et

i =1

For confirming the existence of integration among the variables, yt ,xt , and zt , all three null hypotheses should be rejected. The hypotheses can be written as follows: The F1 test that relies on all the related error-correction terms (ECT) (H0: f = g = y = 0 against H1: any of f , g ,y are different from zero). The F2 test that relies on all regressor(s) terms (H0: g = y = 0 against H1: either g or y is different from zero).

284  The Roles of Education and Export Diversification

The t-test that relies on the lagged dependent variable (H0: f = 0 against H1: f is different from zero). Finally, critical values of the F1 and t-tests are produced in the conventional ARDL approach. This is also another limitation for which it does not take into account the test statistic for the F2 test on the lagged independent variables. Nevertheless, this limitation can be rectified by using the B-ARDL approach.

14.4 Empirical Results and Their Discussion We applied the structural break ADF unit root test (Kim and Perron, 2009) to find the level of integration of variables, keeping in view the structural break in the longitudinal data series. The traditional tests, such as ADF (Dickey and Fuller, 1981), PP (Philip and Perron, 1988) and Ng-Perron (2001), can give ambivalent results because of their inability to take into account the structural breaks in the data. Table 14.1 depicts the results of the Kim and Perron (2009) test. All the variables appear to be stationary at level with intercept and trend for India and China. Series has structural breaks. In China, structural breaks occurring in 2001, 2004, 2003, and 2002 are found in carbon emissions, economic growth, education, export diversity, and energy consumption, respectively. These break years for the Chinese variables are due to the passing of the Air Pollution Control Law in 2000, which affected carbon emissions in 2001, the tightening of monetary policy in 2004, the strengthening of external policy in 2003, and the following overall economic policy changes in 2002. For the Indian economy, structural breaks are visible in emissions, growth, education, export diversity, and energy consumption for the years 2002, 1996, Table 14.1 Unit Root Analysis with Structural Breaks Variable

T-Statistic

Prob.Value

Break Year

T-Statistic

China

Prob. Value

Break Year

India

lnCt

−3.8908

0.4417

2001

−3.6385

0.7444

2002

lnYt

−3.6442

0.6010

2004

−3.5219

0.8036

1996

ln Et

−3.4567

0.4781

2004

−3.8646

0.6090

1998

ln Dt

−3.2848

0.8070

2003

−4.2864

0.3443

2005

ln EEt

−2.9545

0.6751

2002

−2.4979

0.3367

1999

D lnCt

−4.9578

0.0410**

2001

−7.4896

0.0100*

1998

D lnYt

−4.9567

0.0415**

2012

−8.9646

0.0100*

2002

D ln Et

−6.4989

0.0100*

1998

−8.5032

0.0100*

1993

D ln Dt

−7.1095

0.0100*

1998

−10.7497

0.0100*

1998

D ln EEt

−4.6981

0.0701***

2001

−7.8386

0.0100*

2006

Note: * and ** show significance at the 1% and 5% levels, respectively.

The Roles of Education and Export Diversification  285

1998, 2005, and 1999, respectively. The year 2002 is found as one of the structural break years for the Indian economy, which is due to the implementation of the extended version of the Air Act in 2002. The government of India liberalized the foreign direct investment (FDI) driven industrial policy in 1996, which was followed by a trade policy review in 1998, and both the national jute policy and electricity act and the new telecom policy occurred in 1999. These policy shocks to the economy have some direct and indirect effects on the dynamics of carbon emissions. Given that, it is always important to capture the role of structural breaks in the EKC modelling. In a nutshell, we found carbon emissions, growth, education, export diversity, and energy consumption stationary at first difference with structural breaks in both China and India. This confirms the unique order of integration of the variables. This unique order of the integrating properties of the variables motivates us to apply the cointegration approach. The empirical results of the B-ARDL cointegration are noted in Table 14.2. These empirical results depict presence of cointegration for China and India when we use the carbon emissions as the dependent variable in the B-ARDL model for the period of 1971–2017. After examining the cointegrating association between carbon emissions and its constituents, the long-run effect of economic growth, education, exports diversification, and energy consumption on carbon emissions is reported in Table 14.3. 14.4.2.1 Results on the Nexus between Education and Carbon Emissions 14.4.2.1.1 CHINA

Findings show that education has an inverse and significant influence on emissions at the 1% significance level. This shows that education contributes to the quality of environment by reducing emissions. Deng (1992) and Becker (1993) argue that education performs an instrumental role in the “hard truth development” of any economy. These results concur with those of Tsai (2017). They highlighted that an improvement in human capital framework could assist nations to opt for alternative sources of energy in the production process. Furthering their suggestion, Tsai (2017) explained that raising the levels of education not only expand the social awareness about the protection of environmental quality but also enable economies to mitigate carbon emissions. Mcbeath et al. (2014), in their recent book on “environmental education”, also argue that because China’s environment was in a crisis for many decades and, following the implementation of “environmental education” in the United Kingdom (UK) (mid-1960s) and the United States (US) (1970), the Chinese government has not only added “environmental education” in the national educational policy of primary and secondary education levels during 1983, but it also recognized it as a “field of study”. In China, a quadratic association exists between education and carbon emissions. It implies that, in the first phase, increasing education aggravates the

2004 2004 2003 2002 2002 1996 1998 2005 1999

2, 2, 2, 2, 2

2, 2, 1, 1, 2

2, 1, 2, 2, 2

2, 2, 2, 2, 2

2, 2, 1, 2, 2

2, 2, 2, 2, 2

2, 2, 1, 1, 2

2, 1, 2, 2, 2

2, 2, 2, 2, 2

Yt = f (Ct , Et , Dt , EEt )

Et = f (Ct ,Yt , Dt , EEt )

Dt = f (Ct ,Yt , Et , EEt )

EEt = f (Ct ,Yt , Et , Dt )

India Ct = f (Yt , Et , Dt , EEt )

Yt = f (Ct , Et , Dt , EEt )

Et = f (Ct ,Yt , Dt , EEt )

Dt = f (Ct ,Yt , Et , EEt )

EEt = f (Ct ,Yt , Et , Dt )

12.0818*

2.1081

10.8089*

3.1801

9.0800*

11.069*

12.8017*

2.1819

2.0890

10.9801*

FPSS

Note: * and ** show significance at the 1% and 5% levels, respectively.

2001

Break Year

2, 2, 1, 2, 2

Lag Length

The Bounds Testing Approach to Cointegration

China Ct = f (Yt , Et , Dt , EEt )

Estimated Models

Table 14.2 The Bootstrapped ARDL Cointegration Analysis

-3.4003*

-1.1022

−2.1203*

−1.1212

−4.0202*

−4.5905*

−4.4007*

−0.4607

−0.4567

−3.2209**

TDV

-4.1808*

−2.0801

−5.0818*

−2.1808

−4.0808*

−3.2322**

−3.2109**

−0.2001

−0.2311

−3.7807**

TIV

0.7686

0.4805

0.7080

0.4585

0.7688

0.8507

0.8104

0.4164

0.4567

0.6978

R2

3.1214

3.3402

5.0204

3.3240

5.0204

5.0291

5.1990

3.0907

7.0987

5.3245

Q-stat

Diagnostic Analysis

1.0101

2.1060

1.2106

2.1161

2.1006

2.1908

2.0981

1.0908

1.0987

2.0098

LM(2)

0.9101

0.6263

0.8151

0.7253

0.8050

0.7116

0.7052

0.4350

0.5430

0.8657

JB

286  The Roles of Education and Export Diversification

The Roles of Education and Export Diversification  287

environment by escalating emissions. After a certain threshold, the increase in education improves the environment. However, coefficient values show that a 1% rise in education causes carbon emissions to rise by 1.5347%, and the negative sign of the squared term confirms the existence of a delinking between emissions and education at higher levels of education. This association between education and carbon emissions is termed as the EKC. Similarly, Balaguer and Cantavella (2018) validated the EKC hypothesis (between education and carbon emissions) for China. However, the relationship between export diversification and emissions is U-shaped, confirming the invalidation of the EKC. 14.4.2.1.2 INDIA

The association between education and carbon emissions in India is positive and significant at 1%, which demonstrates that education negatively contributes to the quality of the environment by increasing the activities which are the source of carbon emissions. Ceteris paribus, a 1% increase in education raises carbon emissions by 0.5453%. Given the positive effect of education on carbon emissions, it may be further argued that the majority of people with a low level of education (primary schooling) lack sufficient awareness about the quality of environment. On the basis of the Education Commission (1964–1966), the National Council of Education Research and Training (NCERT, 1961)1 has added environmental education as a meaningful learning subject in the curriculum of primary school education in India. Though the National Policy on Education (NPE, 1986) has, subsequently, given the special role to environmental education in India, its implementation has been far from satisfactory as far as the environmental quality awareness and protection are concerned.2 The linear and squared terms of education have an inverse but insignificant effect on carbon emissions. This validates the argument that, though environmental degradation is negatively linked with education in both the short and long run, the insignificant effect on carbon emissions shows that the role of education in improving environmental quality is ineffective in India. The linear and squared terms of export diversification also shows an inverted U-shaped association between both variables. This also validates the argument that carbon emissions are directly linked with export diversification initially, and after reaching a threshold level of export diversification, carbon emissions start to decline because of the adoption of export concentration, i.e., the second stage of export diversification. 14.4.2.2 Results on the Nexus between Export Diversification and Carbon Emissions 14.4.2.2.1 CHINA

The results show that export diversification has a direct impact on CO2 emissions at the 1% level. This illustrates that export diversification in China is

−6.2029

−3.1330

–

2.7229

–

1.6707

−2.9232

–

–

Prob. Value 0.5819

0.4567

0.8934

0.7689

0.7980

– –

−0.0746*

−0.1114*

–

0.0260*

–

0.8166*

−0.0484*

0.9268

1.5231

F-Statistic 1.3171

0.3078

0.1209

0.3456

0.3034

Stable Stable

lnYt 2

ln Et

ln EEt

BYear

Stable Stable

2.0091

2.0190

0.1945

0.8184

F-Statistic 0.9003

1.6278

0.9288

−0.0444**

– –

0.1011

0.0918

0.6014

0.4492

Prob. Value 0.6094

–

–

−2.4218

15.4601

2.9509

1.4106*

−1.2479

0.0164*

−2.5277

−0.2047** −0.0218**

2.5821

−6.4555

−9.3160 7.1023

1.5347**

−0.1041*

−10.1006* 1.3627*

Note: * and ** show significance at the 1% and 5% levels, respectively.

2 c Re msay CUSUM CUSUMsq

2 c Hetero

2 c ARCH

2 c serial

R Durbin-Watson Stability Analysis Test 2 c Normal

2

ln D

2 t

ln Dt

ln E

−27.9662 7.9578

−5.8003* 1.0095*

2 t

T-Statistic

Stable Stable

2.1132

2.0010

1.9790

0.2319

F-Statistic 0.2555

1.6578

0.9875

−0.0721**

0.9345*

–

−0.3470*

–

0.5453*

−0.2617*

−10.3449* 3.5883*

Coefficient

Coefficient

Coefficient

T-Statistic

India

China

Constant lnYt

Variables

Dependent Variable = lnCt

Table 14.3  Long-Run Analysis

– –

0.0911

0.1001

0.1320

0.4013

Prob. Value 0.8800

–

–

−2.2505

4.6851

–

−2.6049

–

4.0950

−6.0058

−9.9017 5.8903

T-Statistic

Stable Stable

1.8971

2.0101

0.1310

1.9876

F-Statistic 1.4124

1.9267

0.9841

−0.0726**

1.7479*

−1.5935**

2.4131**

-0.0031

−0.0019

−0.2960*

−24.5280* 3.9283*

Coefficient

– –

0.1203

0.1101

0.7191

0.1236

Prob. Value 0.4935

–

–

−2.1681

5.3087

−2.0775

2.0459

−0.0135

-0.0011

−3.3207

−13.827 −.4439

T-Statistic

288  The Roles of Education and Export Diversification

The Roles of Education and Export Diversification  289

not environment friendly. A 1% rise in export diversification causes carbon emissions to rise by 0.0260%, keeping other variables constant. On the policy front, upgrading export quality is a key policy issue because of the structural transformation that happened in the Chinese economy from an agriculturebased to manufacturing-oriented economy during 1990s. In such a direction, the export product quality has been upgraded by the Chinese government by using various implemented policies to mitigate the growing domestic and external demands for the products (Gozgor and Can, 2017). Similarly, Wang and Wei (2008) indicated that exports share has increased from 6% in 1995 to 25% in 2005. This is not congruent with Gozgor and Can (2017) who noted that export quality diversification is negatively linked with environmental degradation by lowering carbon emissions. 14.4.2.2.2 INDIA

Export diversification in India has significant inverse impact on carbon emissions. Findings reveal that a 1% rise in export diversification decreases carbon emissions by 0.2370%, all else is the same. Though the Indian economy is one of the fastest growing market economies in the world, export growth was negative in 2015–2016. Although the major concern was to revive India’s export volume growth, it is, at the same time, projected that India’s exports of goods should reach US$882 billion by 2020, which, in turn, signifies India’s greater export position in the world’s goods export market. In doing so, the Indian government implemented a demand-based export market diversification from the supply-based export market. These results are congruent with Gozgor and Can (2017) who noted the negative relationship between export diversification and environmental degradation. Contrarily, Gozgor and Can (2016), and Liu et al. (2019) reported that export diversification deteriorates the environment by escalating the levels of carbon emissions in the case of 125 DCs and LDCs. 14.4.2.3 Results on the Nexus between Economic Growth and Carbon Emissions 14.4.2.3.1 CHINA

For China, the linear error term of real GDP has a significant direct impact on emissions, whereas squared terms of real GDP per capita has an inverse and significant impact on carbon emissions. Our results also demonstrate that a 1% rise in real GDP per capita escalates carbon emissions by 1.0095%, and the negative sign of the squared term shows the validation of delinking carbon emissions and real GDP per capita at higher income levels in China. This relationship confirms the presence of the EKC for the Chinese economy. In such a case, Fan et al. (2018), in their recent study, argue that developing renewable energy not only helps the Chinese economy in reducing non-renewable energy consumption (e.g., coal, oil, and natural gas) in the long run but also reduces emissions levels of atmospheric pollutants. Moreover, China’s renewable energy

290  The Roles of Education and Export Diversification

sector is an example of carbon emissions reduction linked with growth in the long run. In this context, the recent report based on Global Commission on the Geopolitics of Energy Transformation (2019) indicates, “no country has put itself in a better position to become the world’s energy superpower than China”. This is indicative of Chinese sustainable green environment power due to renewable energy. This empirical evidence is similar to the available studies of Li et al. (2016), Shahbaz et al. (2017), Xu (2018), and Zhang et al. (2019) who supported the validation of the EKC in China. On the contrary, Kim et al. (2017) argued that the existence of the EKC varies with the selection of the sample period and the specification of the empirical models. 14.4.2.3.2 INDIA

The results exhibited in Table 14.4 portray that, in the long run, the linear terms of real GDP per capita exerts direct impact on CO2 emissions. At the same time, the negative and significant sign of the squared term of real GDP per capita shows an inverse effect of GDP per capita on CO2 emissions. Specifically, we find that a 1% rise in real GDP per capita surges carbon emissions by 3.5883%, and the negative sign of the squared term underscores the validation of the delinking between emissions and real GDP per capita at the higher income level for the Indian economy. The results are in line with the conception of the EKC. In terms of justification, it is true that the environmental quality in India has benefitted from rising economic growth in the long-run rather than the short-run. This is possible, which may be due to the fact that the presence of scale effect increases energy consumption because of an increased demand of economic activities for energy demand. In short run, the rising energy consumption not only increases economic growth but also degrades the quality of the environment by emitting large amounts of carbon and other greenhouse gases. In the long run, economic growth may be beneficial for the Indian economy, as it may improve the environment in the long run by bringing structural transformation from the industrial sector to the service sector. But the service sector continues to grow without hurting the health of the natural environment for the simple reason that it reduces energy intensity or encourages energy conservation by importing energy saving technology into production activities. The economic reforms in the 1990s are evidence of the higher growth in India. Increased economic growth also allows the government of India to implement both the “Clean Ganga Mission” under the Environmental Protection Act (EPA, 1986) and the recent “Swacch Bharat Abhiyan” in 2014 as a clean environmental quality mission. These clean environmental policies may be one of the reasons for the negative effect of economic growth on carbon emissions in India. This evidence of the EKC concurs with Kanjilal and Ghosh (2013), Shahbaz et al. (2015), and Usman et al. (2019) who validated the presence of the EKC in India.

T-Statistic −0.7844 1.3855 −0.2364

1.6889 –

−0.8245 -

6.9124 −2.2275

−2.9578 –

–

Prob. Value 0.3387

0.3110

0.1224

0.1589

0.7667

– –

China Coefficient −0.0082 0.1964 −0.4672

0.0691*** –

−0.0068 –

1.2045* −0.0193**

−0.5905* 0.4919

1.6829

F-Statistic 2.1615

1.2067

2.4856

1.5806

0.2988

Stable Stable

Stable Stable

0.4210

1.5591

1.5061

0.9698

F-Statistic 2.5750

1.7602

−0.8196* 0.5278

1.2291* −0.0175*

−0.0122 0.0033

0.0434 1.2692**

Coefficient −0.0243*** 0.4276 −1.3425

Note: * and ** show significance at the 1% and 5% levels, respectively.

2 c Re msay CUSUM CUSUMsq

2 c Hetero

c

2 ARCH

2 c serial

R Durbin-Watson Stability Analysis Test 2 c Normal

2

ECM t -1

D ln Dt2 D ln EEt DBreak Year

2 t

D ln E D ln Dt

D lnYt 2 D ln Et

Constant D lnYt

Variables

Dependent Variable = D lnCt

Table 14.4  Short-Run Analysis

– –

0.6764

0.1490

0.2266

0.3897

Prob. Value 0.2759

–

−4.1396 –

9.5230 −2.0384

−1.2010 0.2469

0.8292 2.5032

T-Statistic −1.9297 1.4721 −0.6173

Stable Stable

0.7130

0.5487

0.2063

2.1190

F-Statistic 0.9026

2.0863

−0.4995* 0.3772

0.7857* −0.0117

−0.0625 -

0.2255** –

India Coefficient 0.0237* −0.0234 0.2847

– –

0.4802

0.9227

0.6519

0.1256

Prob. Value 0.6367

–

−4.8851 –

3.2566 −1.2164

−0.7406 –

2.4111 –

T-Statistic 4.4282 −0.1816 0.1475

Stable Stable

0.7232

0.5769

0.0766

0.1261

F-Statistic 0.2606

2.0024

−0.5039* 0.3191

0.7134* −0.0118

−0.0493 0.6328

0.1971*** −0.1436

Coefficient 0.0244* −0.2533 1.5621

– –

0.4742

0.9132

0.7833

0.8819

Prob. Value 0.8778

–

−5.4580 –

2.9335 −1.0398

−0.5971 0.9663

1.7639 -0.2408

T-Statistic 3.1025 −1.9403 0.7235

The Roles of Education and Export Diversification  291

292  The Roles of Education and Export Diversification 14.4.2.4Results on the Nexus between CO2 Emissions and Energy Consumption 14.2.2.4.1 CHINA

Findings clearly reveal a direct association of energy consumption with CO2 emissions in China. It is noted that a 1% increase in energy consumption increases carbon emissions by 0.8166%–1.4106%, all else is the same. The impact of the dummy variable is negative and significant at the 1% level. This confirms that the implementation of the Air Pollution Control Law in 2000 has contributed significantly to reducing pollutants, which has improved environmental quality in China. This further enhances the argument that the pollution regulatory framework implemented in China not only improved environmental quality but also reduced the carbon emissions via reducing energy consumption. It signals people not to use old vehicles or not to use vehicles without having a certificate from the pollution board. This has created fear in the minds of people of possibility receiving penalties if they create larger pollution by using old vehicles or not having the certificate of their used vehicles from pollution control board. The stability analysis also confirms the normal distribution of the error term. We do not find any evidence of autoregressive heteroscedasticity, white heteroscedasticity, and serial correlation. The empirical form of carbon emissions function is well specified as confirmed by the Ramsey reset test statistics. 14.2.2.4.1 INDIA

Energy consumption in India adds to carbon emissions significantly. The impact of the dummy variable on carbon emissions is negative and statistically significant at the 5% level. This shows that the implementation of the extended version of the Air Act in 2002 has had a momentous effect on promoting environmental quality by decreasing carbon emissions. This further validates the argument that improving environmental quality in India is possible when the pollution control act is effectively implemented. This is true when the Indian economy has benefitted on the environmental quality-enhancing front by charging penalties on excessive discharge of air pollution generated by the old two and four wheeler vehicles or by using the vehicles without having the certificate from the pollution control board. The stability analysis indicates that the error term is normally distributed. Existence of autocorrelation, autoregressive heteroscedasticity, and white heteroscedasticity is not confirmed. The empirical form of the carbon emissions function is well designed. 14.4.3 Analysis of the Short-Run Findings for China and India

Table 14.5 exhibits the results of the short-run analysis. In the case of China, we could not validate the EKC (between growth and emissions) in the short run. This shows that the existence of the EKC hypothesis is a long-run

The Roles of Education and Export Diversification  293

phenomenon in China. Further, education shows a direct and significant effect on environmental degradation. Likewise, export diversification is negatively but insignificantly linked with environmental quality. Energy consumption had a direct and significant impact on emissions. Similarly, for the Indian economy, the EKC hypothesis is invalid in the short run. Education has a direct and significant influence on emissions. Export diversification reduces carbon emissions but insignificantly. Energy consumption adds to carbon emissions significantly. The dummy has a negative and significant impact on carbon emissions. Further, the non-linear relationship between education and carbon emissions is positive but insignificant for China; however, the phenomenon is an inverted U shape and insignificant. Finally, the linear and squared terms of export diversification have a negative and positive effect but are insignificant for China and India. The sign of ECM t -1 is negative and significant and authenticates a long-run association between the variables, through the adjustment of the short-run disequilibrium. The significance of ECM t -1 with a negative sign underscores the speed of adjustment from the short-run to long-run equilibrium. Again, the significant ECM t -1coefficient with a negative sign verifies the convergence of variables to their long-run equilibriums. In the case of China (and India), the coefficients of ECM t -1 are −0.5905 and −0.8196 (and −0.4995 and −0.5039) for the linear and non-linear models, respectively. This shows that the short run variations are corrected by 59.05–81.95% and 59.05–-49.95% of each year toward the long-run paths of the variables in China and India, respectively. The variance of carbon emissions in the empirical model is explained by its determinants, which are also reflected in the overall values of R2, i.e., 0.4919 for China and 0.3772 for India. In both countries, the autocorrelation of the residual term of carbon emissions is not confirmed, whereas normal distribution of the error term for both economies is present. The absence of white heteroscedasticity, serial correlation, and autoconditional heteroscedasticity is also validated. The specification of the short-run empirical model for China and India is well specified by the Ramsey reset. Finally, the study uses the cumulative sum of recursive residuals (CUSUM) and the CUSUM of square (CUSUMsq) suggested by Brown et al. (1975) in order to check the stability of the B-ARDL carbon emissions model. The model stability is also found by employing both the CUSUM and the CUSUMsq tests in the short-run estimation, as both the plots for the tests remain within the critical limits of the 5% level of significance (see Figure 14.1).

14.5 Conclusion and Policy Implications This study has investigated how education and export diversification affect carbon emissions in China and India over the period 1971–2017. Further, to tackle the issue of structural breaks in the data series, we employ the unit root

294  The Roles of Education and Export Diversification China 12

1.6

8

1.2

4

0.8

0 0.4

–4

0.0

–8

5% Significance

CUSUM of Squares

India 12

1.6

8

1.2

4

2016

2014

2012

2010

2008

2006

2004

2016

2014

2012

2010

2008

2006

2004

2002

CUSUM

2002

–0.4

–12

5% Significance

0.8

0

CUSUM

5% Significance

CUSUM of Squares

2016

2014

2012

2010

2008

2006

2016

2014

2012

2010

–0.4 2008

–12 2006

0.0

2004

–8

2004

0.4

–4

5% Significance

Figure 14.1 CUSUM and CUSUMsq.

test developed by Kim and Perron (2009). This test takes into account the single unidentified structural break in the data series. The novel B-ARDL is employed to test the presence of cointegration, in the long run, between carbon emissions and their determinants. The empirical analysis also reveals the presence of cointegration between the variables. Economic growth increases and then decreases carbon emissions in both China and India. In China, education is positively linked with environmental quality, but in India, education adds to environmental degradation, revealing different awareness levels about the environment in both countries. Exports diversification also increases and then decreases carbon emissions in China and India. Energy consumption is positively linked with CO2 emissions. Moreover, our results confirm the existence of the EKC in China and India. The juxtaposition between education and carbon emissions has an inverted U shape in China. Furthermore, the inverted U-shaped association between export diversification and carbon emissions is also noticed for India, but it is U-shaped for the Chinese economy. On the policy front, our findings bear some vital implications for major emerging countries such as India and China, as they may face the environmental

The Roles of Education and Export Diversification  295

consequences of climate change and global change. We also conclude that economic growth escalates emissions in China, however, it suppresses emissions in case of India. These results challenge the conventional myth and appear to be surprising. It reveals that where economic growth is strengthening China’s economy, it is also becoming a major source of environmental degradation. In short, inferring from our findings, China is attaining growth at the expense of environmental degradation. On the contrary, the growth in India appears to be responsible in the sense that Indian growth is suppressing the carbon and other greenhouse gas emissions. The prime reason, among others, may be the rise of the service sector in India. On the basis of these findings, it can be suggested that Indian government maintain the pace of growth by sticking to the existing policies, as they are more environmentally conducive. Our findings suggest that the Chinese government relook at policies developed to promote growth and the environment hand in hand. Interestingly, the findings on the impact of education on emissions are exactly contrary to the findings on the impact of growth on emissions. We conclude, on the basis of our estimations, that education in China is a source of improving the quality of the environment, whereas education in India is a source of emissions. The results in China show that the Chinese economy has already reached the level where education’s positive effects on the environment have surpassed its negative effects. However, in India, a large number of the population is uneducated, and education is in an expansion phase. In other words, it shows that increased educational capacity is one of the ways that can help Chinese economies to raise environmental awareness among households, and thereby it does not only reduce energy intensity but also improves environmental quality by suppressing carbon emissions. On the other hand, low educational capacity may be one of the reasons that India should care about enhancing the awareness of the energy efficiency of households. From a policy perspective, the Chinese government should expand the level of higher education for the sake of further environmental quality, but the Indian government should create more social awareness about the consequences of climate change and global warming. We also conclude that export diversification is a source of emissions in India. However, it reduces emissions in the case of China. This shows that the Indian economy does not capitalize on export diversification to promote environmental quality, whereas the Chinese economy, to a greater extent, is benefitting from this impact. It implies that exports promote environmental quality owing to gained energy efficiency, when adding export product quality to the export commodity basket. But this is not the case for the Indian economy, where when adding export products into the export basket the consumers use more of energy, which results in degradation of environmental quality through increasing carbon emissions. Overall, the findings argue that export diversification is good for China but is not good for India. Therefore, on the policy front, the Chinese government should promote export diversification,

296  The Roles of Education and Export Diversification

as it is conducive to environmental quality, whereas the Indian government should be careful while embarking on export diversification. Finally, we find energy consumption a source of emissions (carbon and greenhouse gases) for both emerging economies. This implies that the degradation of the environmental quality of Indian and Chinese economies comes from the use of thermal or other non-renewable energy sources. The governments of both economies should focus on substituting the non-renewable energy sources with other alternative, environmentally friendly sources of energy in order to keep the pace of growth uninterrupted while improving the environment. If this is ignored, then the climate policy on reducing energy consumption will hamper the process of economic growth. If this happens, then these developing nations will face more challenges in addressing poverty, unemployment, and income inequality. However, these challenges may not come for India and China as our findings validated the inverted EKC hypothesis, which not only indicates the productive effect of higher economic growth on the quality of the environment but also enables these economies to reduce unemployment and poverty and improve income distribution.

Notes 1 http://ncert​.nic​.in/ 2 http://www​.yourarticlelibrary​.com​/education​/environmental​-education​-in​-india​-concept​-and​-role​/45202

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15 Are Economic Advancements Catalysts for Carbon Emissions? Depicting the Indian Experience Nikunj Patel, Yaswanth Karedla, Rohit Mishra, and Pradeep Kautish

15.1 Introduction Asserting India’s commitment and resolve toward the cause of climate change, its political leadership has been vocal about arresting and reversing environmental degradation by creating new clean energy capacities and performing as an agent for the global good. By 2030, it aims to surpass its Paris Agreement target by saving 32 million tonnes of carbon dioxide (CO2) emissions annually.1 The country has joined several bilateral and multilateral partnerships on the diplomatic front to attract investment, propagate clean technology, and facilitate green collaborations. As the human civilization is progressing toward its economic development pursuits, the entropy of the planet is also advancing in tandem. Consumption-led growth has been the primary driver of economic development in modern society (Saito, 2007; Georgiou, 2012). The early 1900s witnessed an emerging manufacturing powerhouse in the West, whilst the mid-twentieth century gave momentum to the proposition of globalization and international cooperation. During the latter part of the century, the efforts toward environmental conservation shaped up in the form of various accords and treaties at a time when the Asian economies were catching up in terms of industrialization. In due course of these events, the economic well-being of the society improved. Post-independence, India experienced the so-called Hindu growth rate, wherein the annual growth rate stagnated at around 3.5%, while per capita income growth averaged around 1.3% (Virmani, 1997). Economic growth in India was propelled by the government’s liberalization policies in the early 1990s, which led to the emergence of a strong private sector (Desai, 1999). As per capita gross domestic product (GDP) and purchasing power grew, the nation’s growth engine was fuelled through consumption, demand, and public expenditure. The dawn of the twenty-first century established India as an emerging services-based economy (Joshi and Mudigonda, 2008). There has been an ever-increasing sense of consciousness amongst the global community in environmental conservation. Ideas of reducing global warming and greenhouse emissions soon gained critical mass. This led to a series of policy formulations and initiatives at various international forums in the form of the Kyoto DOI: 10.4324/9781003336563-15

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Protocol, Montreal Protocol of the Vienna Convention, the recent Paris Agreement etc. There have been ample studies to establish the environmental Kuznets curve (EKC) hypothesis in context with India (Al-Mulali et al., 2016; Adebola Solarin et al., 2017; Pal and Mitra, 2017; Rana and Sharma, 2019; Usman et al., 2019). However, there is still a dearth of studies delving into many realms such as carbon emissions, ecological footprint consumption, and globalization in a specific context to India. Our study explores the impact of consumption and development-related metrics on the environment by establishing empirical relationships through statistical methodology, namely, the autoregressive Distributed lag (ARDL) bounds test approach. The essence of this chapter is structured according to the following sections. Section 15.2 discusses related research, Section 15.3 describes the data and methodology, Section 15.4 deals with the empirical results, and Section 15.5 addresses the study’s conclusion and policy implications.

15.2 Literature Review Many researchers have studied the effect of macro-economic variables on the environment. The most popular theory that links the effect of economic growth on the environment is the EKC hypothesis; it was the first such empirical study (Grossman and Krueger, 1995). The theory suggests an inverted U-shaped relationship between economic growth and pollution. According to the EKC hypothesis, pollution rises at the early stage of economic development, but the pattern reverses as high-income levels are achieved. This is primarily because, to achieve economic growth in the initial development period, the economies have to compromise with environmental degradation to emphasize the economic output. However, after reaching a certain level, the country starts investing in technological advancement, which leads to reducing the pollution level. Recently, Shahbaz and Sinha (2019) and Purcel (2020) provided extensive literature on the EKC hypothesis. They highlighted the studies that validate the EKC hypothesis; however, there was no consensus on the acceptance of the EKC hypothesis across the world. We have divided the literature based on the relationship of each independent variable concerning CO2. Existing studies on the relationship between pollution and financial development for various countries reveal that they could be either positive or negatively correlated. CO2 emissions have been observed to increase with greater financial development (Menyah and Wolde-Rufael, 2010; Tamazian and Bhaskara Rao 2010; Zhang, 2011; Sehrawat et al., 2015; Zhao et al., 2021). Notwithstanding, comprehensive studies also indicate a negative relationship between pollution levels and financial development in an economy (Talukdar and Meisner, 2001; Tamazian et al., 2009; Jalil and Feridun, 2011; Yuxiang and Chen, 2011; Ozturk and Acaravci, 2013). As electricity requirements for most countries are predominantly met through conventional sources, there exists a positive correlation between energy consumption and pollution, as evident from numerous studies

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(Danish et al., 2018; Khan et al., 2019; Munir and Riaz, 2019; Abumunshar et al., 2020; Ali et al., 2020; Shaari et al., 2020; Yilanci et al., 2020). Similarly, manufacturing contributes to pollution levels in industrialized economies (Mahmood and Chaudhary, 2012; Rahman and Kashem, 2017; Rauf et al., 2018). Moreover, pollution levels are found to be positively correlated with economic development across the various countries studied (Attari et al., 2016; Ali et al., 2017; Rahman and Kashem, 2017; Sun et al., 2017; Pata, 2018; Zaidi and Saidi, 2018; Akadiri et al., 2019; Saidu Musa and Maijama’a, 2020; U. and Mitra, 2020). Studies pertaining to the relationship between pollution and globalization indicate diverse findings. Many countries are shown to follow the EKC hypothesis (Ghosh, 2018; Cerdeira Bento and Moreira, 2019; Farooq et al., 2020; Liu et al., 2020). Several studies indicate a positive relationship between pollution and globalization (Leitão and Shahbaz, 2013; Koengkan et al., 2020; Sharma et al., 2020; Pata and Caglar, 2021). However, abundant studies indicate a negative relationship between pollution and globalization (Shahbaz et al., 2017a; Salahuddin et al., 2019; Chen and Lee, 2020; Ibrahiem and Hanafy, 2020). 15.2.1 Pollution and Financial Development

Financial development provides efficient and effective utilization of savings into investments. Many researchers have established the relationship between financial development and environmental degradation in the literature. On the one hand, financial development strengthens the financial system, lowering the cost of capital and increasing investment in industries. At the same time, the financial intermediaries also provide funds to the household to buy durable consumer products that build confidence among industries to manufacture more products such as automobiles, refrigerators, air conditioners etc., which lead to environmental degradation (Sadorsky, 2010; Zhang, 2011; Sehrawat et al., 2015). Further, financial development leads to foreign direct investment inflows into the countries, which fosters economic growth and increases CO2 emissions (Frankel and Romer, 1999). Few researchers have also argued that an increase in foreign direct investment (FDI) promotes the advancement and adoption of new technology in the country, which are energy efficient and lead to lower intensity CO2 emissions (Tamazian et al., 2009; Yuxiang and Chen, 2011). The literature has no consensus on the relationship between financial development and pollution. Few researchers support the notion that financial development leads to a reduction in CO2 emissions (Talukdar and Meisner, 2001; Tamazian et al., 2009; Jalil and Feridun, 2011; Yuxiang and Chen, 2011; Lee et al., 2015); whereas few also identified environmental degradation due to financial development (Tamazian and Bhaskara Rao, 2010; Zhang, 2011; Sehrawat et al., 2015; Zhao et al., 2021). Yuxiang and Chen (2011) examined the nexus between financial development and the environmental performance of China during the 1999–2006

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period. They claimed that financial development improves environmental quality by increasing income and capitalization, exploiting new technology, and implementing regulations that lead to improvements in the environment. Jalil and Feridun (2011) studied the impact of growth, energy, and financial development on the environment in China and observed similar results during the 1953–2006 period. However, Zhang (2011) examined the impact of financial development on CO2 emissions during the 1980–2009 period using the Johansen cointegration test and found that financial development is one of the main drivers of increased CO2 emissions. This is consistent with the recent study of Zhao et al. (2021), who found that environmental pollution is positively related to energy consumption, negatively related to financial depth, and positively related to financial efficiency. Sehrawat et al. (2015) examined the Indian economy during the 1971–2011 period and found economic growth, energy consumption, financial development, and urbanization as the main causes of environmental degradation. Similarly, Tamazian and Bhaskara Rao (2010), in a study based on 24 transition countries, concluded that financial liberalization might harm environmental quality if it is not accomplished within a strong institutional framework. Talukdar and Meisner (2001) analyzed the private sector involvement in environmental degradation in 44 developing countries during the 1987–1995 period. They found a negative sense of the involvement of the private sector in environmental degradation in developing countries. Further, they also found that, in the presence of a well-functioning domestic capital market and through increased participation by developed economies, there is a reduction in environmental degradation. However, financial development makes it possible for industries to invest in expanding their production capacity, which may increase emissions of environmental pollutants (Tamazian and Bhaskara Rao, 2010; Zhang, 2011). Tamazian et al. (2009) examined the impact of economic and financial development on the environment during the 1992–2004 period in Brazil, Russia, India, and China (BRIC nations) using the standard reducedform modelling approach. They observed that a higher degree of economic and financial development reduces environmental degradation. Similarly, Lee et al. (2015) examined the relationship between CO2 and financial development in Organization for Economic Cooperation and Development (OECD) countries from 1971 to 2007. They also found a negative relationship between financial development and CO2 emissions. Nonetheless, Ozturk and Acaravci (2013) found an insignificant impact of financial development on CO2 emissions for the Turkish economy during the 1960–2007 period. 15.2.2 Pollution and Fossil Fuels

Fossil fuels have also mediated the nexus between growth and pollution. In order to achieve higher economic growth, industries heavily rely on fossil fuels, thereby contributing to pollution. Economic growth and coal, oil,

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and natural gas consumption influence the environment negatively (Khan et al., 2019). In other words, more energy use results in more CO2 emissions (Ang, 2007). The use of fossil fuels to generate energy has become challenging to environmental quality. Energy consumption from renewable sources has a negative impact on CO2 emissions, while energy consumption from non-renewable sources has a positive impact (Salim et al., 2014; Balogh and Jámbor, 2017; de Souza et al., 2018; Munir and Riaz, 2019; Abumunshar et al., 2020; Ali et al., 2020). As a result, to cope with the increasing energy demand, many countries have started shifting from conventional energy sources to renewable energy to improve environmental quality (Kuriqi et al., 2019). Gas and oil consumption can have harmful effects on the environment. However, the effect of oil consumption is higher than the effect of the consumption of gas on the environment (Shaari et al., 2020). Transport energy consumption significantly affects CO2 emissions (Danish et al., 2018). The majority of researchers have documented a positive relationship between the use of fossil fuels and pollution (Ang, 2007; Lotfalipour et al., 2010; Balogh and Jámbor, 2017; Lin, 2017; Danish et al., 2018; de Souza et al., 2018; Khan et al., 2019; Munir and Riaz, 2019; Abumunshar et al., 2020; Ali et al., 2020; Shaari et al., 2020; Sharma and Kautish, 2020; Yilanci et al., 2020). The recent study by Sharma and Kautish (2020) examined the non-linear impact of coal and oil-based electricity production on CO2 emissions in India during the 1976–2016 period using the nonlinear autoregressive distributed lag (NARDL) approach. They found that the upside of coal-fired electricity shocks contributes significantly to the long-term increase in pollution. Shaari et al. (2020) documented deleterious effects of gas and oil consumption in 20 Organization of Islamic Cooperation (OIC) countries. However, as compared to gas consumption, oil consumption affects the environment to a greater extent. Balogh and Jámbor (2017) examined the determinants of CO2 for 168 countries during the 1990–2013 period using generalized method of moments (GMM) models. They found a positive role of nuclear energy and renewable energy production in reducing CO2 emissions, while coal energy has increased environmental pollution. Further, Munir and Riaz (2019) observed that an increase in fossil fuel consumption leads to increased CO2 emissions in South Asian countries. Abumunshar et al. (2020), in Turkey during the 1985–2015 period, and Ali et al. (2020), in Pakistan during the 1975–2014 period, also observed similar results. Lotfalipour et al. (2010) observed a unidirectional relationship between GDP, petroleum products, and natural gas consumption to CO2 emissions in Iran. Lin (2017) revisited the coal consumption, CO2 emissions, and economic growth in China and India during the 1969–2015 period. They found a reduction in coal consumption due to increased growth in both countries, thereby reducing CO2 emissions in China. However, economic growth further increases CO2 emissions in India. Further, Yilanci et al. (2020) examined pollution havens in Brazil, Russia, India, China, and

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South Africa (BRICS countries) during the 1982–2014 period. They have found that the long-term effect of energy consumption is mainly polluting the BRICS countries. 15.2.3 Pollution and Manufacturing

Fossil fuels are one of the primary sources of energy consumption worldwide (Yıldız, 2018). Industrial production and energy consumption have significant positive impacts on CO2 emissions (Rahman and Kashem, 2017). Manufacturing activities require higher energy consumption to achieve higher economic growth, and in the absence of abundant renewable energy sources, these activities rely more on non-renewable sources. Hence, the use of non-renewable sources leads to environmental degradation. Industrial production is the driving force of CO2 emissions (Hocaoglu and Karanfil, 2011). The majority of researchers have observed a positive relationship between manufacturing activities and pollution (Hocaoglu and Karanfil, 2011; Banerjee and Rahman, 2012; Mahmood and Chaudhary, 2012; Acar and Tekce, 2014; Lin et al., 2014; Asane-Otoo, 2015; Attari et al., 2016; Rahman and Kashem, 2017; Rauf et al., 2018; Canh et al., 2019; Wu et al., 2020; Zafar et al., 2020). Rauf et al. (2018) observed a positive impact of industry, agriculture, services, energy consumption, and trade openness on CO2 emission in China. This is consistent with Banerjee and Rahman (2012) and Rahman and Kashem (2017), who also observed a positive influence of industrial production and energy consumption on CO2 emissions in Bangladesh. Acar and Tekce (2014) observed the share of industry, per capita energy use, population density, and urbanization as determinants of industrial emissions. Lin et al. (2014) also witnessed a unidirectional causality running from industrial growth to CO2 emissions in China during the 1980–2012 period. This is consistent with the recent study of Canh et al. (2019) in 106 countries during the 1995–2012 period and Zafar et al. (2020) in 46 countries during the 1991–2017. Attari et al. (2016) examined CO2 emissions and industrial growth in Pakistan during the 1971-2009 period. They found a unidirectional causality between per capita industrial incomes to per capita CO2 emissions. Asane-Otoo (2015) examined the carbon footprint and emission determinants in Africa using a panel of 45 African countries during the 1980–2009 period. The researcher observed that industrialization drives emissions in middle-income countries. This may be due to diverting FDIs into the lower-middle or middle-income countries. Mahmood and Chaudhary (2012) noted foreign direct investment, manufacturing valueadded, and population density have positive impacts on CO2 emissions. Wu et al. (2020) analyzed the driving forces for high-speed economic development in China during the 2005–2011 period. They observed increases in the proportion of secondary industry, low road density, and GDP per capita, which contributed significantly to the increase in particulate matter (PM)

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pollution. Karedla et al. (2021) observed a positive relationship between manufacturing as well as GDP on carbon emissions. 15.2.4 Pollution and Economic Development

A large number of studies have examined the linkages between economic growth and the environment after the most popular EKC theory (Grossman and Krueger, 1991). The majority of the studies found a positive relationship between economic growth and pollution (Menyah and Wolde-Rufael, 2010; Pao and Tsai, 2011; Park and Hong, 2013; Alshehry and Belloumi, 2015; Farhani and Ozturk, 2015; Ali et al., 2017, 2020; Zaidi and Saidi, 2018; Pata, 2018; Saidu Musa and Maijama’a, 2020; Sharma et al., 2020; Song, 2021). Menyah and Wolde-Rufael (2010) examined energy consumption, pollutant emissions, and economic growth in South Africa during the 1965–2006 period. They found a positive and significant relationship between pollutant emissions and economic growth. Pao and Tsai (2011) examined the causality between GDP and CO2 for BRIC countries during the 1980–2007 period and found a positive causal relationship. Alshehry and Belloumi (2015) also observed a bidirectional causality between CO2 emissions and economic growth in Saudi Arabia during the 1971–2010 period. This was similar to Farhani and Ozturk’s (2015) results for Tunisia. However, Park and Hong (2013) found a coincidental relationship between economic growth and CO2 emissions, whereas fossil fuels emit CO2. Ali et al. (2017) revisited the EKC hypothesis for Malaysia during the 1971–2012 period. They found a positive association between economic growth and emissions. Similarly, Zaidi and Saidi (2018) also observed a positive relationship in Sub-Saharan African countries from 1990 to 2015. Saidu Musa and Maijama’a (2020) found that economic growth and energy consumption have significant positive effects on environmental pollution. Further, the crude oil price was negatively related to environmental pollution in Nigeria. Pata (2018) observed increased CO2 emissions due to increased per capita GDP, per capita energy consumption, financial development, urbanization, and industrialization in Turkey during the 1974–2013 period. Further, Song’s (2021) recent study observed a positive association between GDP per capita and CO2 emissions per capita in China. Sharma et al. (2020) also observed a positive relationship between GDP and CO2; they also found that globalization significantly intensifies CO2 emissions in South Asia. U. and Mitra (2020) also found an indirect relationship between GDP and pollution. They found that FDI positively and significantly impacts pollution, and GDP attracts FDI. Akadiri et al. (2019) also refuted the direct impact of GDP on CO2 emissions in Iraq. Ali et al. (2020) observed an inverted U-shaped relationship between economic development and CO2 emissions in Pakistan. However, they found that fossil fuel energy consumption positively impacts CO2 emission. This is consistent with the results of Sun et al. (2017) for China during the 1980–2012 period.

308  Are Economic Advancements Catalysts for Carbon Emissions? 15.2.5 Pollution and Globalization

Globalization provides developing countries an unprecedented opportunity to achieve accelerated economic growth through trade and investment (Osano and Koine, 2016). Rapid globalization has led to significant developments in the manufacturing sector in many emerging economies through technological advancement or technology transfer (Uddin, 2020). In addition, multinational corporations (MNCs) prefer markets in which production costs are relatively low. The results of the relationship between pollution and globalization are mixed. Few identified a positive relationship (Leitão and Shahbaz, 2013; Koengkan et al., 2020; Sharma et al., 2020; Pata and Caglar, 2021). Leitão and Shahbaz (2013) investigated the relationship between CO2 emissions, urbanization, and globalization during the 1990–2010 period in 18 countries. They found that globalization has a positive and significant impact on CO2 because it is the primary driver of increased production in the country through the substantial use of domestic resources. This is consistent with Koengkan et al. (2020) for 18 Latin American and Caribbean countries from 1990 to 2014. Sharma et al. (2020) examined international economic endeavours that affected CO2 emissions in South Asian countries during the 1980–2015 period. They found that, because of globalization, FDI inflows and energy consumption increased significantly, leading to increased CO2 levels. This is consistent with the results of Pata and Caglar (2021), who also observed globalization, income, and trade openness as driving factors of CO2 in China during the 1980–2016 period. Few studies observed a negative relationship between pollution and globalization (Shahbaz et al., 2017b; Salahuddin et al., 2019; Chen and Lee, 2020; Ibrahiem and Hanafy, 2020; Liu et al., 2020). Shahbaz et al. (2017b) examined the impact of globalization on CO2 emissions in China during the 1970–2012 period using the Bayer and Hanck cointegration approach. They found a negative relationship between globalization and CO2 emissions; however, economic growth and coal consumption significantly drive CO2 emissions. Salahuddin et al. (2019) also observed similar results in South Africa. With the help of technological innovation, globalization reduces pollution levels (Chen and Lee, 2020). Liu et al. (2020) examined the role of globalization in CO2 emissions in Group of 7 (G-7) countries. They found an inverted U-shaped relationship between globalization and CO2; however, increased economic output led to increased CO2. This is consistent with the study of Ibrahiem and Hanafy (2020) in Egypt. The relationship between globalization and CO2 differs with the income level of countries. Globalization is increasing the level of emissions for low-income countries, which has been attributed to the actions of rich countries that have disposed of their pollution to developing countries (Uddin, 2020). Cerdeira Bento and Moreira (2019) examined the environmental impact of FDI in 67 developing and 34 developed countries. They found a positive relationship for developing countries while

Are Economic Advancements Catalysts for Carbon Emissions?  309

a negative relationship for developed countries. Farooq et al. (2020) analyzed the relationship between globalization and FDI in 57 Organisation of Islamic Cooperation (OIC) countries from 1991 to 2017. They found a negative relationship between high-income countries and a positive relationship between OIC and low-income countries. However, Ghosh (2018) examined globalization and the environment in Asian countries during the 1974–2014 period. The researcher did not find any relationship for high and upper middle-income countries; nevertheless, it was positive for lowincome countries. Our study is the most recent and is closely related to the literature on capturing various consumption and development-related environmental metrics in India. None of these studies examines the interplay between these and other environmental indicators in India. As a result, our study is the first to look into this link, contributing to the body of knowledge on consumption, production, and the environment.

15.3 Data Description and Methodology 15.3.1 Data Description

The present research investigates the long-term and short-term relationship of CO2 emissions with financial development, electricity production from fossil fuels, manufacturing value-added GDP, and globalization. Hence, Eq. (1) represents the CO2 as a function of all the variables being studied. To deemphasize outliers and improve model fit, all the series are transformed into natural logarithm values.

lnCO2t = f ( lnFDt , lnFFt , lnINDGDPt , lnGDPt , lnGLOBt ) (1)

The time series data of the variables from 1971 to 2019 is considered for the study with data sources and descriptions as mentioned in Table 15.1. 15.3.2 Methodology

For the study, we have used the ARDL approach proposed by Pesaran et al. (1996), which was later modified by adding bounds testing methods by Pesaran et al. (2001). We chose the ARDL approach over other cointegration techniques because it has two main inherent advantages. First, the variables under study can be stationary only at level; I (0) or level 1; I (1) or a mix of both. Second, the approach is effective with smaller sample sizes. In order to examine cointegration among the variables specified in Equation 1, the error correction model (ECM) representation of ARDL is formulated with reference to CO2 emissions in Equation 2:

310  Are Economic Advancements Catalysts for Carbon Emissions? Table 15.1 Data Description and Sources Are Economic Advancements Catalysts for Carbon Emissions: Depicting the Indian Experience Variable

Variable Representation

Description

Source

CO2 emissions (metric tonnes per capita)

CO2

Financial development (IMF)

FD

This shows the per capita emission of carbon dioxide in metric tonnes for India This represents the quality, efficiency, and performance of the financial institutions and markets of India This shows the percent of electricity generated using fossil fuels for consumption in India The share of the manufacturing output out of the total economy of India is represented by this variable This represents India’s total economic output per individual belonging to the country This shows the degree of globalization of India in economic, social, and political aspects

World Development Indicators (WDI), World Bank International Monetary Fund (IMF)

Electricity FF production from oil, gas, and coal sources (% of total) Manufacturing value-added (% of GDP)

INDGDP

GDP per capita (constant 2010 US$)

GDP

KOF overall globalization

GLOB

WDI, World Bank

WDI, World Bank

WDI, World Bank

KOF Swiss Economic Institute

Are Economic Advancements Catalysts for Carbon Emissions?  311 DlnCO2 = a 0 + a1 lnCO2t -1 + a 2lnFDt -1 + a 3 lnFFt -1 + a 4 lnINDGDPt -1 + n

a 5lnGDPt -1 + a 6lnGLOBt -1 +

n

åb DlnCE + åb DlnFD 1i

t -i

2i

i =1

n

n

å

b3i DlnFFt -i +

i =1

n

å

b4i DlnINDGDPt -i +

i =1

t -i

+

i =1

å

b5i DlnGDPt -i +

(2)

i =1

n

+

åb DlnGLOB 6i

t -i

+ et

i =1

Here, Δ represents the first difference operator; a1 ¼¼a 7 and b1 ¼..b 7 represent coefficients of the ARDL model in the long run and short run, respectively; i and n represent optimal and threshold lag, respectively; and e t represents the white noise terms. The existence of cointegration is tested using the estimated long-run coefficients as mentioned in Equation 2. To test the same, the null hypothesis is that there exists no long-term relationship among the variables a1 = a 2 = a 3 = a 4 = a 5 = a 6 = a 7 = 0, and the alternate hypothesis is that there exists cointegration among the variables a1 ¹ a 2 ¹ a 3 ¹ a 4 ¹ a 5 ¹ a 6 ¹ a 7 ¹ 0 . In order to check the same, the Wald F-test is conducted, and F statistics will be obtained along with the upper and lower bound critical values. The null hypothesis is rejected if the F statistics obtained are above the upper bound values, and the null hypothesis is not rejected if the F statistics are below the lower bound critical values. In case the F statistics are between the upper and lower bound values, the existence of a long-term relationship is considered as inconclusive.

15.4 Results and Discussion Figure 15.1 depicts the plots of the variables under study. The plots show the annual movement of the variables from the year 1971 to 2019. CO2 emissions, fossil fuels, GDP per capita, and GLOB are showing an upward trend. High volatility is seen in the INDGDP. FD saw a rise during the 1995–2000 period. At the same time, fossil fuels saw a rise from the 1980 period. GLOB saw a significant rise after 1990, owing to the economic reforms taken in the Indian economy during 1991. Table 15.2 depicts the descriptive statistics of the variables selected for the CO2 emission model. Among the variables under study, GDP has the highest volatility, whereas INDGDP has the lowest volatility. As the initial part of the study, Ng and Perron’s (2001) unit root test with GLS-detrended as spectral estimation method and Akaike information criteria (AIC) as information criteria was performed to ensure that none of the variables is integrated at order (2). Unlike other unit root tests (such as augmented

Figure 15.1 Plots of the variables under study.

312  Are Economic Advancements Catalysts for Carbon Emissions?

Are Economic Advancements Catalysts for Carbon Emissions?  313 Table 15.2  Descriptive Statistics Variable

CO2

FD

FF

INDGDP GDP

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability

−0.2546 −0.2629 0.6471 −1.0146 0.4975 0.1838 1.8901 2.7907 0.2477

−1.2160 4.2862 2.7777 −0.9254 4.3482 2.7789 −0.7017 4.4399 2.8829 −2.1058 4.0010 2.5898 0.4677 0.1428 0.0555 −0.6652 −0.7305 −0.5560 1.8242 1.9266 4.1452 5.2546 6.1629 5.2022 0.072*** 0.0459** 0.074***

6.6190 6.5141 7.6743 5.9442 0.5392 0.4597 1.9369 4.0330 0.1331

GLOB 3.7260 3.6560 4.1309 3.3956 0.2870 0.2950 1.3718 5.9978 0.051***

Source: Authors’ calculations from Eviews. Note: *** indicates 10% statistical significance level.

Dickey-Fuller, Phillip-Perron, and Kwiatkowski–Phillips–Schmidt–Shin tests), the Ng-Perron test overcomes the problem of size distortions when there is a large negative root in the moving average of the first different series (Schwert, 2002). The ARDL model is built with AIC in the next step. Later, the bound test was performed to learn about the long-term relationship between the predicted and the explanatory variables. Table 15.3 shows the unit root test results of the variables under study. The unit root test results infer that no variables are stationary at level, but at first difference, all the variables are stationary, rejecting the null hypothesis of having a Table 15.3 Ng Peron Unit Root Test Results At level Variable

MZa

CE −5.09711 FD −0.59566 FF −4.11705 INDGDP −7.58300 GDP −0.21544 GLOB −0.36147 At first difference Variable MZa ΔCE −22.7794** ΔFD −14.5812* ΔFF −20.2152** ΔINDGDP −23.4687** ΔGDP −22.8287** ΔGLOB −9.68792**

MZt

MSB

MPT

−1.46520 −0.36166 −1.30611 −1.57133 −0.12087 −0.18728

0.28746 0.60716 0.31724 0.20722 0.56102 0.51811

17.2987 21.8911 20.7708 12.7798 69.6403 18.6773

MZt −3.36356 −2.67900 −3.16614 −3.28107 −3.31558 −2.19717

MSB 0.14766 0.18373 0.15662 0.13981 0.14524 0.22680

MPT 4.06851 1.76030 4.58722 4.73459 4.36835 2.54355

Source: Authors’ calculations from Eviews. Note: Δ represents the first difference. ** and *** indicate 5% and 10 % statistical significance levels, respectively.

314  Are Economic Advancements Catalysts for Carbon Emissions? Table 15.4 ARDL Cointegration Results Model

99% Critical Values

F-stat

Lower Bound Upper Bound lnCEt = f (lnFDt , lnFFt , lnINDGDPt , lnGDPt , lnGLOBt ) 3.06

4.15

9.193*

Source: Authors’ calculations from Eviews. Note: * indicates 1% statistical significance level.

unit root in the time series. This ensures that ARDL can be performed because all the variables are integrated of order 1. In order to find the existence of a long-term relationship among the variables, a bounds test is conducted. The results of the same are presented in Table  15.4. The results reveal that the F-statistic is greater than the critical values of the upper bound at 1% significance level. So, we can reject the null hypothesis of no cointegration among the variables. This concludes that there exists a cointegration among the CO2 emissions, economic development, manufacturing output, fossil fuels, and globalization. Table 15.5 presents the long-term elastic estimates of the CO2 model. In the long-run, financial development has no significant impact on CO2 emissions, and fossil energy consumption shows a positive and significant impact on CO2 emissions. Our results are consistent with that of Khan et al. (2019) Munir and Riaz (2019), Abumunshar et al. (2020), Ali et al. (2020), and Sharma and Kautish (2020). The coefficient of 0.843 for the FF variable indicates that a 1% increase in fossil energy consumption results in an increase in CO2 emissions by 0.843%. The larger dependency on fossil fuels for energy consumption is responsible for the higher CO2 emissions in India. Manufacturing valueadded as a percent of GDP significantly and positively impacts CO2 emissions. Our study supports the claims of Hocaoglu and Karanfil (2011), Banerjee and Rahman (2012), Mahmood and Chaudhary (2012), Acar and Tekce (2014), Table 15.5 Long-run Coefficient Estimates Independent Variables FD FF INDGDP GDP GLOB C

CO2 Coefficient [Std. Error]

T-Stat [Prob.]

−0.065 [0.0759] 0.843 [0.299] 0.932 [0.259] 1.006 [0.093] −0.342 [0.168] −11.972 [1.872]

−0.862 [0.402] 2.821 [0.012**] 3.586 [0.003*] 10.821 [0.000*] −2.035 [0.059***] -6.396 [0.000*]

Source: Authors’ calculations from Eviews. Note: *, **, and *** indicate 1%, 5%, and 10% statistical significance, respectively.

Are Economic Advancements Catalysts for Carbon Emissions?  315

Lin et al. (2014), Asane-Otoo (2015), Attari et al. (2016), Rahman and Kashem (2017), Rauf et al. (2018), Canh et al. (2019), Wu et al. (2020), and Zafar et al. (2020). The coefficient of 0.932 for the INDGDP variable indicates that a 1% increase in INDGDP variable will increase CO2 emissions by 0.932%. This implies that industrialization positively contributes to the CO2 emissions, as the industrial sector is heavily dependent on energy input derived from fossil fuels that are responsible for CO2 emissions. The GDP per capita shows a significant and positive impact on CO2 emissions in India. Our study supports the claim of Menyah and Wolde-Rufael (2010), Pao and Tsai (2011), Park and Hong (2013), Alshehry and Belloumi (2015), Farhani and Ozturk (2015), Ali et al. (2017, 2020), Zaidi and Saidi (2018), Pata (2018), Saidu Musa and Maijama’a (2020), Sharma et al. (2020), and Song (2021). The coefficient of 1.006 for GDP indicates that a 1% increase in GDP will increase CO2 emissions by 1.006%. Because India is a developing country, the increasing economic activity needs a higher energy requirement, which is catered to by using fossil fuels and absolute technologies responsible for higher emissions of CO2 into the atmosphere. GLOB has a significant and negative impact on CO2 emissions. Our study is consistent with that of Shahbaz et al. (2017b), Salahuddin et al. (2019), Chen and Lee (2020), Ibrahiem and Hanafy (2020), and Liu et al. (2020). The coefficient of −0.342 for GLOB indicates that a 1% increase in globalization will decrease CO2 emissions by 0.342%. With an increase in globalization, there will be a faster and greater inflow of knowledge, capital, and green technologies that helps in reducing the burden on the environment. Globalization also encourages global trade and increases financial liberalizations in the form of more FDI inflows from developed nations to India for investments in cleaner technologies. This is consistent with Shahbaz et al. (2021), who observed environmental quality through trade openness. Table 15.6 shows the short-run estimates of the CO2 model. INDGDP shows a positive and significant relationship with the CO2 emissions in India. For every 1% increase in the INDGDP there will be a decrease of 0.244% in CO2. Economic growth shows a positive and significant impact on CO2 emissions. For every 1% increase in economic growth, there will be a 1.212% increase in CO2 emissions. The error correction coefficient ECM is negative and significant, which is expected. The magnitude of the error correction coefficient shows the speed of adjustment of the dependent variable to the long-run equilibrium point. Any disequilibrium in the dependent variable is adjusted back to the equilibrium with a speed of 63.3% in one period. Different diagnostic tests such as normality, heteroskedasticity, and serial correlation are performed to ascertain the stability of the model. Table 15.6 also shows the diagnostic results of the model. To check the normality assumption of the model, the Jaque-Bera test is performed. The null hypothesis that residuals are normally distributed is accepted. The Breusch-Pagan test is performed to check the null hypothesis of homoscedastic disturbance terms is accepted. The Ramsey reset test suggests the CO2 model is a good fit. The Breusch-Godfrey

316  Are Economic Advancements Catalysts for Carbon Emissions? Table 15.6 Short-Run Coefficient Estimates Independent Variables

ΔFD ΔFF(-1) ΔFF(-2) ΔINDGDP ΔINDGDP(-1) ΔGDP ΔGDP(-1) ΔGLOB ΔGLOB(-1) ΔGLOB(-2) ECM Diagnostic tests R-squared Adjusted R-squared Durbin-Watson stat Normality [Jarque-Bera (p-value)] Serial correlation [LM Test F-statistic (p-value)] Heteroscedasticity [Breusch-Pagan-Godfrey (p-value)] Ramsey RESET Test [F-statistic (p-value)]

CO2 Coefficient [Std. Error]

T-Stat [Prob.]

0.063 [0.039] −0.083 [0.041] −0.187 [0.043] −0.244 [0.076] −0.567 [0.086] 1.212 [0.112] 0.318 [0.099] −0.129 [0.099] 0.373 [0.109] −0.592 [0.115] −0.633 [0.067]

1.61 [0.127] −2.005 [0.062***] −4.313 [0.001*] −3.208 [0.006*] −6.618 [0.000*] 10.777 [0.000*] 3.223 [0.005*] −1.307 [0.210] 3.427 [0.004*] −5.16 [0.000*] −9.406 [0.000*]

0.882 0.829 2.141 0.293 (0.864) 1.611 (0.235) 1.167 (0.381) 0.358 (0.725)

Note: * indicates 1% statistical significance. Source: Authors’ calculations from Eviews.

test also suggests no serial correlation in the residuals. Figure 15.2 reports the structural stability of the model using the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) plots, which suggest a stable model without any break.

15.5 Conclusion and Policy Implication Our study attempts to investigate the long- and short-run relationship between India’s CO2 emissions with the KOF globalization index, financial development, electricity production from fossil fuels, manufacturing value-added, and GDP per capita using annual time series data from 1971 to 2019 using ARDL bounds test. The bounds test validates the presence of a significant long-run cointegration between CO2 and other variables. Empirical results reveal that CO2 emissions have a significant and positive long-run relation with electricity production from fossil fuels, manufacturing

Are Economic Advancements Catalysts for Carbon Emissions?  317

Figure 15.2 Plots of CUSUM and CUSUM of squares.

value-added, and GDP per capita; a negative relation with the KOF globalization index; and an insignificant relation with financial development. The larger dependency on fossil fuels for energy consumption is responsible for the higher CO2 emissions in India. Industrialization positively contributes to the CO2 emissions, as the industrial sector is heavily dependent on energy input derived from fossil fuels responsible for CO2 emissions. Because India is a developing country, the increasing economic activity needs higher energy requirements, which are catered to through the overutilization of fossil fuels and the use of obsolete technologies responsible for higher emissions of CO2. With increased globalization, there will be a faster and greater inflow of knowledge, capital, and green technologies, reducing the environmental burden. The results about the relationship of the variables under study provide valuable insights from a policy perspective. This is because the trade-off between economy and ecology needs to be optimized while designing any public policy with far-reaching implications. The government of India has a long-standing framework for the country’s development and climate change-related objectives through the National Action Plan on Climate Change (NAPCC), which encompasses a range of measures such as the National Solar Mission and the National Mission for Enhanced Energy Efficiency etc. Similar schemes and policies related to renewable energy must be promoted, thus incentivizing the use of non-polluting or renewable energy sources. Moreover, along the lines of a green tax levied on automobiles, a pollution tax or carbon tax must be proposed on manufacturing industries, which cause environmental degradation. At the same time, there should be incentives for the adoption of green technologies by industries. Policies promoting globalization and consequent FDI inflow must be encouraged because FDI introduces cleaner technologies and world-class management best practices that boost operational productivity and mitigates pollution. India has been championing the cause of environmental conservation on various international platforms. As a developing nation, which has been witnessing rapid industrialization and urbanization in the recent decades – balancing its

318  Are Economic Advancements Catalysts for Carbon Emissions?

commitment toward economic growth on one hand and environmental conservation on the other – demands policymakers to be as poised as a funambulist. The findings from this study may prove indispensable for making prudent policy-related decisions in such a scenario.

Note 1 Extracted from the Indian Prime Minister’s address at Climate Adaptation Summit 2021.

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