清洁能源发展、二氧化碳减排与区域经济增长
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摘要
中国现在是世界上最大的石油进口国和二氧化碳排放国,积极发展清洁能源对于保障能源安全、控制二氧化碳排放和实现绿色经济增长具有重要现实意义。而清洁能源发展对二氧化碳减排的作用到底有多大?需要大量资金投入的清洁能源发展能否促进经济增长?这是各级政府管理部门和相关学者关注的焦点。为了回答这两个关键问题,本文基于中国30个省区市1979—2016年的真实数据和2017—2030年的预测数据构成的面板数据,运用非参数可加回归模型深入探究清洁能源发展对区域经济增长和二氧化碳排放的线性和非线性影响。研究结果显示:单纯从线性角度来看,清洁能源发展没有起到显著减少二氧化碳排放和促进经济增长的作用。但是,这并不代表清洁能源在不同发展阶段对二氧化碳减排和经济增长的积极影响也是有限的。非线性结果表明:在不同发展阶段,清洁能源发展对东中西三大区域二氧化碳排放和经济增长的影响差异明显。因此,中央和各地方政府应该根据清洁能源在不同发展阶段发挥的不同作用、因时施策,以充分发挥清洁能源发展在二氧化碳减排和经济增长中的促进作用。
The huge emission of carbon dioxide has made China the focus of attention of the international community, and the Chinese government is facing increasing pressure to reduce carbon dioxide emissions. To vigorously develop clean energy is not only an important measure to ensure energy security and control carbon dioxide emissions, but also an important role in upgrading industrial structure and achieving green economic growth. Based on the panel sample data of 30 provinces in China mainland from 1979 to 2016 and the forecast data from 2017 to 2030, this paper investigates the linear and non-linear effects of clean energy development on regional economic growth and carbon dioxide emissions by using non-parametric additive regression model. The results show that from a linear point of view, the development of clean energy has not played a significant role in reducing carbon dioxide emissions and promoting economic growth. However, this does not mean that the positive impact of clean energy on carbon dioxide emission reduction and economic growth at different stages of development is limited. The non-linear results show that at different stages of development, the impact of clean energy development on carbon dioxide emissions and economic growth in the three regions of East, West and Central China is significantly different.Five major conclusions are obtained. First, the development of clean energy has an "M-shaped" non-linear impact on carbon dioxide emissions in the eastern region. In the early stage, the development of clean energy has not played a role in reducing carbon dioxide emissions, due to its small size of clean energy comparing with large emissions from industrial development. Second, the development of clean energy has a smooth and positive "U-shaped" non-linear impact on carbon dioxide emissions in the central region. The third, clean energy also exerts a positive "U-shaped" non-linear impact on carbon dioxide emissions in the western region, indicating that in the later stage, the role of clean energy in reducing carbon dioxide emissions cannot be reflected. Fourth, the development of clean energy displays a gentle "W-shaped" non-linear impact on economic growth in the eastern region, indicating that in the early stage, the development of clean energy has not played an important part in promoting economic growth. Fifth, the non-linear impact of clean energy development on economic growth in the central region shows a smooth "W-shaped" model. This means that in the early stage, clean energy development did not contribute to stimulating local economic growth. In addition, financial investment could increase the burden of local economic growth to a certain extent. To alleviate the burden of fiscal inputs, the government could support the development of clean energy enterprises by means of free use of land for factory construction and exemption of import tariffs on technology and equipment. Comparing with previous studies, this paper makes two innovative contributions. First, most of the existing literatures assume that the relationship between economic variables is a linear form, and use the traditional linear model to study the subject. In fact, economic phenomena are complex and dynamic, which leads to non-linear relationships between economic variables. Ignoring a large number of non-linear relationships among economic variables could lead to large errors in the estimated results. Second, most of the existing literature studies the impact of clean energy development on economic growth and carbon dioxide emissions at the national level. But there are significant differences in economic development, industrial structure and energy consumption among regions. If ignoring these objective regional differences, it is difficult to apply the conclusions obtained from the overall national level studies to the development of clean energy needs in different regions. In order to make up for the deficiencies of existing research, this paper uses the newly developed nonparametric additive regression model with data-driven characteristics to study the impact of clean energy development on regional economic growth and carbon dioxide emissions.
The huge emission of carbon dioxide has made China the focus of attention of the international community, and the Chinese government is facing increasing pressure to reduce carbon dioxide emissions. To vigorously develop clean energy is not only an important measure to ensure energy security and control carbon dioxide emissions, but also an important role in upgrading industrial structure and achieving green economic growth. Based on the panel sample data of 30 provinces in China mainland from 1979 to 2016 and the forecast data from 2017 to 2030, this paper investigates the linear and non-linear effects of clean energy development on regional economic growth and carbon dioxide emissions by using non-parametric additive regression model. The results show that from a linear point of view, the development of clean energy has not played a significant role in reducing carbon dioxide emissions and promoting economic growth. However, this does not mean that the positive impact of clean energy on carbon dioxide emission reduction and economic growth at different stages of development is limited. The non-linear results show that at different stages of development, the impact of clean energy development on carbon dioxide emissions and economic growth in the three regions of East, West and Central China is significantly different.Five major conclusions are obtained. First, the development of clean energy has an "M-shaped" non-linear impact on carbon dioxide emissions in the eastern region. In the early stage, the development of clean energy has not played a role in reducing carbon dioxide emissions, due to its small size of clean energy comparing with large emissions from industrial development. Second, the development of clean energy has a smooth and positive "U-shaped" non-linear impact on carbon dioxide emissions in the central region. The third, clean energy also exerts a positive "U-shaped" non-linear impact on carbon dioxide emissions in the western region, indicating that in the later stage, the role of clean energy in reducing carbon dioxide emissions cannot be reflected. Fourth, the development of clean energy displays a gentle "W-shaped" non-linear impact on economic growth in the eastern region, indicating that in the early stage, the development of clean energy has not played an important part in promoting economic growth. Fifth, the non-linear impact of clean energy development on economic growth in the central region shows a smooth "W-shaped" model. This means that in the early stage, clean energy development did not contribute to stimulating local economic growth. In addition, financial investment could increase the burden of local economic growth to a certain extent. To alleviate the burden of fiscal inputs, the government could support the development of clean energy enterprises by means of free use of land for factory construction and exemption of import tariffs on technology and equipment. Comparing with previous studies, this paper makes two innovative contributions. First, most of the existing literatures assume that the relationship between economic variables is a linear form, and use the traditional linear model to study the subject. In fact, economic phenomena are complex and dynamic, which leads to non-linear relationships between economic variables. Ignoring a large number of non-linear relationships among economic variables could lead to large errors in the estimated results. Second, most of the existing literature studies the impact of clean energy development on economic growth and carbon dioxide emissions at the national level. But there are significant differences in economic development, industrial structure and energy consumption among regions. If ignoring these objective regional differences, it is difficult to apply the conclusions obtained from the overall national level studies to the development of clean energy needs in different regions. In order to make up for the deficiencies of existing research, this paper uses the newly developed nonparametric additive regression model with data-driven characteristics to study the impact of clean energy development on regional economic growth and carbon dioxide emissions.
引文
曹静,2009:《走低碳发展之路:中国碳税政策的设计及CGE模型分析》,《金融研究》第12期。
陈诗一,2009:《能源消耗、二氧化碳排放与中国工业的可持续发展》,《经济研究》第4期。
董直庆、王辉,2019:《环境规制的“本地—邻地”绿色技术进步效应》,《中国工业经济》第1期。
林伯强、孙传旺,2011:《如何在保障中国经济增长前提下完成碳减排目标》,《中国社会科学》第1期。
林伯强、李江龙,2014:《基于随机动态递归的中国可再生能源政策量化评价》,《经济研究》第4期。
林伯强、李江龙,2015:《环境治理约束下的中国能源结构转变——基于煤炭和二氧化碳峰值的分析》,《中国社会科学》第9期。
鲁万波、仇婷婷、杜磊,2013:《中国不同经济增长阶段碳排放影响因素研究》,《经济研究》第4期。
彭水军、张文城,2013:《中国居民消费的碳排放趋势及其影响因素的经验分析》,《世界经济》第3期。
邵帅、李欣、曹建华,2019:《中国的城市化推进与雾霾治理》,《经济研究》第2期。
邵帅、张曦、赵兴荣,2017:《中国制造业碳排放的经验分解与达峰路径——广义迪氏指数分解和动态情景分析》,《中国工业经济》第3期。
史贝贝、冯晨、张妍、杨菲,2017:《环境规制红利的边际递增效应》,《中国工业经济》第12期。
魏浩,2016:《知识产权保护强度与中国的高新技术产品进口》,《数量经济技术经济研究》第12期。
张健华、王鹏,2010:《中国银行业广义malmquist生产率指数研究》,《经济研究》第8期。
张晓娣、刘学悦,2015:《征收碳税和发展可再生能源研究——基于OLG-CGE模型的增长及福利效应分析》,《中国工业经济》第3期。
张友国,2015:《碳排放视角下的区域间贸易模式:污染避难所与要素禀赋》,《中国工业经济》第8期。
Bhattacharya,M.,S.R.Paramati,I.Ozturk,and S.Bhattacharya,2016,“The Effect of Renewable Energy Consumption on Economic Growth:Evidence from Top 38 Countries”,Applied Energy,162,733—741.
Bravo-ortega,C.,and A.Marin,2011,“R&D and Productivity:A Two Way Avenue?”,World Development,39(7),1090—1107.
Buja,T.Hastie,and R.Tibshirani,1989,“Linear Smoothers and Additive-models”,Annals of Statistics,17(2),453—510.
Destek,M.A.,and A.Aslan,2017,“Renewable and Non-renewable Energy Consumption and Economic Growth in Emerging Economies:Evidence from Bootstrap Panel Causality”,Renewable Energy,111,757—763.
Dogan,E.,and F.Seker,2016,“Determinants of CO2 Emissions in the European Union:The Role of Renewable and Non-renewable energy”,Renewable Energy,94,429—439.
Engle,R.,D.Hendry,and J.Richard,1983,“The Exogeneity”,Econometrica,51(2),277—304.
Inglesi-Lotz,R.,2016,“The Impact of Renewable Energy Consumption to Economic Growth:A Panel Data Application”,Energy Economics,53,58—63.
Granger,C.W.J.,1988,“Some Recent Developments in a Concept of Causality”,Journal of Econometrics,139(1/2),199—211.
Hausman,J.A.,1978,“Specification Tests in Econometrics”,Econometrica,46(6),1251—1271.
Kahia,M.,M.Kadria,M.S.B.A?ssa,2016,“What Impacts of Renewable Energy Consumption on CO2 Emissions and the Economic and financial development?A Panel Data Vector Autoregressive (PVAR) Approach”,In Renewable Energy Congress (IREC),7th International,IEEE.
Kahia,M.,M.B.Aissa,and C.Lanouar,2017,“Renewable and Non-renewable Energy Use-economic Growth Nexus:The case of MENA Net Oil Importing Countries”,Renewable and Sustainable Energy Reviews,71,127—140.
Ko?ak,E.,and A.?arkgüne?i,2017,“The Renewable Energy and Economic Growth Nexus in Black Sea and Balkan Countries”,Energy Policy,100,51—57.
Stone,C.J.,1985,“Additive Regression and Other Nonparametric Models”,Annals of Statistics,689—705.
Wu,D.M.,1974,“Alternative Tests of Independence between Stochastic Regressors and Disturbances:Finite Sample Results”,Econometrica:Journal of the Econometric Society,529—546.
Zoundi,Z.,2017,“CO2 Emissions,Renewable Energy and the Environmental Kuznets Curve,A Panel Cointegration Approach”,Renewable and Sustainable Energy Reviews,72,1067—1075.
(1)因为从2009年开始,《中国财政年鉴》才单独列出中央和地方财政的可再生能源投资额,所以我们仅对2008—2016年中央和地方财政对可再生能源投入进行数据分析。
(2)数据来源:《四川省水电站名录2017最新版》、《云南省水电站名录2017年最新版》和《湖北省水电站名录2017年最新版》。
(3)根据国家统计局的划分标准,西部地区包括甘肃、宁夏、青海、新疆、陕西、四川、重庆、贵州、云南、内蒙古、广西和西藏,共12个省份;中部地区包括安徽、江西、河南、湖北、湖南、吉林、山西和黑龙江,共8个省份;东部地区包括江苏、浙江、山东、北京、上海、天津、广东、辽宁、海南、福建和河北,共11个省份。由于存在数据残缺问题,本文样本不包括西藏自治区。
(4)此图是基于30个省份面板数据绘制得出,其中图(a)中每条曲线表示一个省份清洁能源发展与经济增长之间关系形式,图(b)中的每条曲线表示一个省份清洁能源发展与二氧化碳排放之间的关系形式。
陈诗一,2009:《能源消耗、二氧化碳排放与中国工业的可持续发展》,《经济研究》第4期。
董直庆、王辉,2019:《环境规制的“本地—邻地”绿色技术进步效应》,《中国工业经济》第1期。
林伯强、孙传旺,2011:《如何在保障中国经济增长前提下完成碳减排目标》,《中国社会科学》第1期。
林伯强、李江龙,2014:《基于随机动态递归的中国可再生能源政策量化评价》,《经济研究》第4期。
林伯强、李江龙,2015:《环境治理约束下的中国能源结构转变——基于煤炭和二氧化碳峰值的分析》,《中国社会科学》第9期。
鲁万波、仇婷婷、杜磊,2013:《中国不同经济增长阶段碳排放影响因素研究》,《经济研究》第4期。
彭水军、张文城,2013:《中国居民消费的碳排放趋势及其影响因素的经验分析》,《世界经济》第3期。
邵帅、李欣、曹建华,2019:《中国的城市化推进与雾霾治理》,《经济研究》第2期。
邵帅、张曦、赵兴荣,2017:《中国制造业碳排放的经验分解与达峰路径——广义迪氏指数分解和动态情景分析》,《中国工业经济》第3期。
史贝贝、冯晨、张妍、杨菲,2017:《环境规制红利的边际递增效应》,《中国工业经济》第12期。
魏浩,2016:《知识产权保护强度与中国的高新技术产品进口》,《数量经济技术经济研究》第12期。
张健华、王鹏,2010:《中国银行业广义malmquist生产率指数研究》,《经济研究》第8期。
张晓娣、刘学悦,2015:《征收碳税和发展可再生能源研究——基于OLG-CGE模型的增长及福利效应分析》,《中国工业经济》第3期。
张友国,2015:《碳排放视角下的区域间贸易模式:污染避难所与要素禀赋》,《中国工业经济》第8期。
Bhattacharya,M.,S.R.Paramati,I.Ozturk,and S.Bhattacharya,2016,“The Effect of Renewable Energy Consumption on Economic Growth:Evidence from Top 38 Countries”,Applied Energy,162,733—741.
Bravo-ortega,C.,and A.Marin,2011,“R&D and Productivity:A Two Way Avenue?”,World Development,39(7),1090—1107.
Buja,T.Hastie,and R.Tibshirani,1989,“Linear Smoothers and Additive-models”,Annals of Statistics,17(2),453—510.
Destek,M.A.,and A.Aslan,2017,“Renewable and Non-renewable Energy Consumption and Economic Growth in Emerging Economies:Evidence from Bootstrap Panel Causality”,Renewable Energy,111,757—763.
Dogan,E.,and F.Seker,2016,“Determinants of CO2 Emissions in the European Union:The Role of Renewable and Non-renewable energy”,Renewable Energy,94,429—439.
Engle,R.,D.Hendry,and J.Richard,1983,“The Exogeneity”,Econometrica,51(2),277—304.
Inglesi-Lotz,R.,2016,“The Impact of Renewable Energy Consumption to Economic Growth:A Panel Data Application”,Energy Economics,53,58—63.
Granger,C.W.J.,1988,“Some Recent Developments in a Concept of Causality”,Journal of Econometrics,139(1/2),199—211.
Hausman,J.A.,1978,“Specification Tests in Econometrics”,Econometrica,46(6),1251—1271.
Kahia,M.,M.Kadria,M.S.B.A?ssa,2016,“What Impacts of Renewable Energy Consumption on CO2 Emissions and the Economic and financial development?A Panel Data Vector Autoregressive (PVAR) Approach”,In Renewable Energy Congress (IREC),7th International,IEEE.
Kahia,M.,M.B.Aissa,and C.Lanouar,2017,“Renewable and Non-renewable Energy Use-economic Growth Nexus:The case of MENA Net Oil Importing Countries”,Renewable and Sustainable Energy Reviews,71,127—140.
Ko?ak,E.,and A.?arkgüne?i,2017,“The Renewable Energy and Economic Growth Nexus in Black Sea and Balkan Countries”,Energy Policy,100,51—57.
Stone,C.J.,1985,“Additive Regression and Other Nonparametric Models”,Annals of Statistics,689—705.
Wu,D.M.,1974,“Alternative Tests of Independence between Stochastic Regressors and Disturbances:Finite Sample Results”,Econometrica:Journal of the Econometric Society,529—546.
Zoundi,Z.,2017,“CO2 Emissions,Renewable Energy and the Environmental Kuznets Curve,A Panel Cointegration Approach”,Renewable and Sustainable Energy Reviews,72,1067—1075.
(1)因为从2009年开始,《中国财政年鉴》才单独列出中央和地方财政的可再生能源投资额,所以我们仅对2008—2016年中央和地方财政对可再生能源投入进行数据分析。
(2)数据来源:《四川省水电站名录2017最新版》、《云南省水电站名录2017年最新版》和《湖北省水电站名录2017年最新版》。
(3)根据国家统计局的划分标准,西部地区包括甘肃、宁夏、青海、新疆、陕西、四川、重庆、贵州、云南、内蒙古、广西和西藏,共12个省份;中部地区包括安徽、江西、河南、湖北、湖南、吉林、山西和黑龙江,共8个省份;东部地区包括江苏、浙江、山东、北京、上海、天津、广东、辽宁、海南、福建和河北,共11个省份。由于存在数据残缺问题,本文样本不包括西藏自治区。
(4)此图是基于30个省份面板数据绘制得出,其中图(a)中每条曲线表示一个省份清洁能源发展与经济增长之间关系形式,图(b)中的每条曲线表示一个省份清洁能源发展与二氧化碳排放之间的关系形式。
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