Analysis of the Spatial and Temporal Evolution of China’s Energy Carbon Emissions, Driving Mechanisms, and Decoupling Levels

Jingyi Ji, Chao Li, Xinyi Ye, Yuelin Song and Jiehua Lv ()
Additional contact information
Jingyi Ji: College of Economics and Management, Northeast Forestry University, Harbin 150040, China
Chao Li: College of Economics and Management, Northeast Forestry University, Harbin 150040, China
Xinyi Ye: College of Economics and Management, Northeast Forestry University, Harbin 150040, China
Yuelin Song: College of Forestry, Northeast Forestry University, Harbin 150040, China
Jiehua Lv: College of Economics and Management, Northeast Forestry University, Harbin 150040, China

Sustainability, 2023, vol. 15, issue 22, 1-23

Abstract: Excessive carbon emissions will cause the greenhouse effect and global warming, which is not conducive to environmental protection and sustainable development. In order to realize the goal of “carbon peak and carbon neutrality” as soon as possible, this paper utilizes the methodology provided by the IPCC to measure the carbon emissions and carbon intensity of China’s energy consumption. The classification method of carbon emission and the kernel density function method are used to explore the spatial and temporal evolution of regional carbon emissions. Based on the Log Mean Divided Index (LMDI) method, the drivers of China’s energy carbon emissions are measured. Based on the Tapio index function and the catch-up decoupling model, the decoupling status of Chinese provinces and the development gap with the benchmark provinces are examined. The results show that (1) China’s total energy carbon emissions show a “rising-declining-rising” trend from 2005 to 2021, and reach the first peak in 2013, totaling 1,484,984.406 million metric tons. China’s Hebei, Shanxi, and Shandong provinces have the highest energy carbon emissions. (2) China’s energy carbon emissions are influenced by multiple factors, and the contribution of each factor to energy carbon emissions is in the following order: economic development effect > energy intensity effect > energy structure effect > population size effect. (3) China’s catch-up provinces develop their economies at the expense of the environment and energy consumption.

Keywords: sustainable development; carbon peak and carbon neutrality; carbon emissions; Log Mean Divided Index (LMDI); catching-up decoupling; Tapio decoupling index (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/22/15843/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/22/15843/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:22:p:15843-:d:1278087

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().