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Nexus among energy consumption structure, energy intensity, population density, urbanization, and carbon intensity: a heterogeneous panel evidence considering differences in electrification rates.
Sun, Jingqi; Guo, Xiaohui; Wang, Yuan; Shi, Jing; Zhou, Yiquan; Shen, Boyang.
Affiliation
  • Sun J; School of Economics and Management, North China Electric Power University, Beijing, 102206, China.
  • Guo X; School of Economics and Management, North China Electric Power University, Beijing, 102206, China. 951910616@qq.com.
  • Wang Y; School of Economics and Management, North China Electric Power University, Beijing, 102206, China.
  • Shi J; Kunming Bureau Of China Southern Power Grid EHV Transmission Company, Kunming, 650000, China.
  • Zhou Y; School of Economics and Management, North China Electric Power University, Beijing, 102206, China.
  • Shen B; Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK.
Environ Sci Pollut Res Int ; 29(13): 19224-19243, 2022 Mar.
Article in En | MEDLINE | ID: mdl-34713407
ABSTRACT
The main purpose of this article is to link the environment, economy, electricity, and society and put forward a new point of view. The current research mainly explores the relationship between the environment, economy, and society and lacks a discussion on electricity. Using a new research framework, this article examines the relationship between energy intensity, energy consumption structure, population density, urbanization rate, and carbon intensity based on relevant data from 2000 to 2017 in China. In the empirical research, according to the cluster analysis, China's 30 provinces are divided into three regions according to the electrification rate standard. The cross-sectional dependence test method is used to verify the cross-sectional dependence of the data, and the second-generation panel unit root test method is used. Exploring the relationship between the variables, this article finally uses the convergence analysis method to explore the degree of influence of each variable on the carbon intensity. The empirical results show that there are both short-term effects and long-term relationships in various regions, and the influencing factors of each region are different. It further shows that the carbon intensity of the four panels shows convergence, ß absolute convergence, and ß conditional convergence, but the main influencing factors in different regions are different. Finally, based on the results of empirical research, policy recommendations for reducing carbon intensity in different regions are put forward.
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Full text: 1 Database: MEDLINE Main subject: Urbanization / Carbon Type of study: Observational_studies / Prevalence_studies / Risk_factors_studies Country/Region as subject: Asia Language: En Year: 2022 Type: Article

Full text: 1 Database: MEDLINE Main subject: Urbanization / Carbon Type of study: Observational_studies / Prevalence_studies / Risk_factors_studies Country/Region as subject: Asia Language: En Year: 2022 Type: Article