期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 706, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.scitotenv.2019.135942
关键词
Energy price; CO2 emissions; Spatial econometric; STIRPAT model; Assessment; China
资金
- National Natural Science Foundation of China [71703025, 71673048]
- MOE (Ministry of Education of China) Project of Humanities and Social Sciences [17YJC910004, 19YJC910002]
- Fujian Agriculture and Forestry University Fund Project for Distinguished Young Scientific Research Talents Program [xjq201821]
Excessive greenhouse gas emissions pose a global environmental concern. This paper examines the impact of energy price on China's CO2 emissions based on an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) structural human ecology model. We utilize Chinese provincial data from 2002 to 2016 and apply the spatial panel data techniques to explicitly consider spatial correlations and spillover effects between observations. Our results show that energy price has a significant negative impact on China's CO2 emissions after controlling for other economic and energy market factors and spatial correlations of these variables. We identify a significant direct impact of the focal province's price change on CO2 emissions and an indirect effect exerted by energy price changes in adjacent provinces. Such spatial spillover effects are also observed in other determinants identified in the extended STIRPAT model. We also document a path dependent pattern in China's provincial CO2 emissions with last period CO2 emissions influencing the current CO2 emission levels. Our results show that the negative impact of energy price on CO2 emissions remains qualitatively the same after incorporating the influence of previous period CO2 emissions. Our results are also robust to the inclusion of lagged energy price measures, alternative spatial economic models, and alternative spatial weight matrices. Overall, our paper highlights the role of energy market in curbing CO2 emissions and promoting sustainable economic development. (C) 2019 Elsevier B.V. All rights reserved.
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