Influencing mechanisms of renewable energy development on carbon emission intensity in China

被引:11
作者
Wang, Yiqi [1 ]
Lei, Ting [1 ]
机构
[1] Changan Univ, Sch Econ & Management, Xian 710064, Shaanxi, Peoples R China
关键词
Renewable energy development; Carbon emission intensity; Dynamic spatial Durbin model; Heterogeneity; Moderating effect; ECONOMIC-GROWTH; CO2; EMISSIONS; DIOXIDE EMISSION; CONSUMPTION; IMPACT;
D O I
10.1016/j.jenvman.2024.123402
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Faced with increasingly severe climate and energy crises, promoting renewable energy development has become an inevitable choice for realizing carbon reduction strategy and energy structure transformation. This paper selects the panel data of 30 provinces in China from 2001 to 2021 and constructs a dynamic spatial Durbin model to study the effect of renewable energy development on carbon emission intensity. The study shows a significant positive spatial correlation between renewable energy development and carbon emission intensity in Chinese provinces. Renewable energy development can significantly reduce carbon emission intensity, and affect the carbon emission intensity of neighboring regions through spatial spillover effects in both the short and long term. In the central-western and southern regions, energy-rich regions, regions with high levels of environmental pollution, high levels of fiscal decentralization, and high levels of green finance, the inhibitory effect of renewable energy development on carbon emission intensity is more significant, suggests that its impact effects are characterized by heterogeneity. Enhancing energy use efficiency, promoting industrial structure optimization, and strengthening green technology innovation capability can significantly improve the inhibition effect of renewable energy development on carbon emission intensity.
引用
收藏
页数:14
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共 69 条
[11]   Influencing mechanisms and decoupling effects of embodied carbon emissions: An analysis based on China's industrial sector [J].
Cui, Shengnan ;
Xu, Ping ;
Wang, Yanqiu ;
Shi, Yingjian ;
Liu, Chuang .
SUSTAINABLE PRODUCTION AND CONSUMPTION, 2023, 41 :320-333
[12]   A slacks-based measure of super-efficiency in data envelopment analysis: A comment [J].
Du, Juan ;
Liang, Liang ;
Zhu, Joe .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2010, 204 (03) :694-697
[13]   Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data [J].
Du, Kerui ;
Li, Pengzhen ;
Yan, Zheming .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2019, 146 :297-303
[14]   Matlab Software for Spatial Panels [J].
Elhorst, J. Paul .
INTERNATIONAL REGIONAL SCIENCE REVIEW, 2014, 37 (03) :389-405
[15]   Dynamic spatial panels: models, methods, and inferences [J].
Elhorst, J. Paul .
JOURNAL OF GEOGRAPHICAL SYSTEMS, 2012, 14 (01) :5-28
[16]   Does Fiscal Decentralization Promote or Inhibit the Improvement of Carbon Productivity? Empirical Analysis Based on China's Data [J].
Feng, Guo ;
Xue, Shulian ;
Sun, Renjin .
FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 10
[17]   Global trading of renewable electricity-based fuels and chemicals to enhance the energy transition across all sectors towards sustainability [J].
Galimova, Tansu ;
Ram, Manish ;
Bogdanov, Dmitrii ;
Fasihi, Mahdi ;
Gulagi, Ashish ;
Khalili, Siavash ;
Breyer, Christian .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2023, 183
[18]   Green technology innovation and carbon emissions nexus in China: Does industrial structure upgrading matter? [J].
Gao, Pengfei ;
Wang, Yadong ;
Zou, Yi ;
Su, Xufeng ;
Che, Xinghui ;
Yang, Xiaodong .
FRONTIERS IN PSYCHOLOGY, 2022, 13
[19]   Spillover effect of energy intensity targets on renewable energy consumption in China: A spatial econometric approach [J].
Ge, Tao ;
Ding, Ziqi ;
Lu, Xiaoya ;
Yang, Keling .
RENEWABLE ENERGY, 2023, 217
[20]   Renewable Energy Consumption: Does It Matter for China's Sustainable Development? [J].
He, Yugang ;
Wei, Wei .
ENERGIES, 2023, 16 (03)