Spatiotemporal patterns of global carbon intensities and their driving forces

Yabo Zhao; Ruiyang Chen; Peng Zang; Liuqian Huang; Shifa Ma; Shaojian Wang

doi:10.1016/j.scitotenv.2021.151690

Spatiotemporal patterns of global carbon intensities and their driving forces

Sci Total Environ. 2022 Apr 20:818:151690. doi: 10.1016/j.scitotenv.2021.151690. Epub 2021 Nov 16.

Authors

Yabo Zhao¹, Ruiyang Chen², Peng Zang², Liuqian Huang², Shifa Ma³, Shaojian Wang⁴

Affiliations

¹ School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China; Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China.
² School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China.
³ School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China. Electronic address: mashf@gdut.edu.cn.
⁴ School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China. Electronic address: 1987wangshaojian@163.com.

PMID: 34798095
DOI: 10.1016/j.scitotenv.2021.151690

Abstract

Carbon intensity (CI) is a valuable indicator of the balances struck by the world's governments between economic growth and environmental issues. This study investigates spatiotemporal variations in the CI levels related to energy consumption, as well as the spatial heterogeneity of its driving forces, in 208 countries globally during 2000-2018. To do this, we obtained data from the International Energy Agency (IEA) and the World Bank, employing methods of exploratory spatial data analysis (ESDA) and standard deviation ellipse (SDE) in order to analyze CI's spatiotemporal variations. We also performed a geographically weighted regression (GWR) analysis to determine the spatial heterogeneity of CI and the strength of its influencing factors. Our results reveal that: (1) Carbon emissions from energy consumption increased, while CI decreased globally, with the CI of most countries and regions declining significantly. (2) Global CI evidenced a heterogeneous spatial distribution, with higher-value areas concentrated in Asia and lower-value areas in Africa and Western Europe; obvious spatial agglomeration was also presented, especially with respect to High-High and Low-Low agglomerations, and the gravity center point moved from the northeast to the southwest. (3) The 8 influencing factors investigated in this study all had effective explanatory power in relation to CI globally. These factors showed significant spatial heterogeneity, and energy structure was the only factor to have a fully positive influence on CI, while foreign direct investment, foreign trade openness, industrial structure, total population, and energy intensity, mainly exerted a positive influence, and the urbanization rate and GDP per capita exerted a negative influence. By clarifying the spatiotemporal variations characteristics of global CI and the spatial heterogeneity of its influencing factors, this study provides a targeted reference for reducing CI and promoting sustainable development, globally.

Keywords: Carbon intensity; Geographically weighted regression; Global countries; Influencing factors; Spatial heterogeneity.

MeSH terms

Carbon Dioxide / analysis
Carbon* / analysis
China
Economic Development*
Spatial Regression
Urbanization

Substances

Carbon Dioxide
Carbon