Regional Disparities and Driving Factors of Residential Carbon Emissions: An Empirical Analysis Based on Samples from 270 Cities in China

Xiangjie Xie, Jing Wang and Mohan Liu ()
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Xiangjie Xie: School of Urban and Regional Sciences, Shanghai University of Finance and Economics, 777 Guoding Road, Yangpu District, Shanghai 200433, China
Jing Wang: School of Urban and Regional Sciences, Shanghai University of Finance and Economics, 777 Guoding Road, Yangpu District, Shanghai 200433, China
Mohan Liu: School of Urban and Regional Sciences, Shanghai University of Finance and Economics, 777 Guoding Road, Yangpu District, Shanghai 200433, China

Land, 2025, vol. 14, issue 3, 1-29

Abstract: Residential carbon emissions (RCEs) have become a major contributor to China’s overall carbon emission growth. A comprehensive analysis of the evolution characteristics of regional disparities in RCEs at the urban level, along with a thorough examination of the driving factors behind RCEs and the convergence, is crucial for achieving carbon reduction goals within regions. This study calculates the RCEs of 270 cities in China from 2011 to 2019 based on multiregional input–output tables and explores the regional differences and spatiotemporal evolution characteristics of RCEs using the Dagum Gini coefficient decomposition method and kernel density estimation. On this basis, we examine the driving factors of RCEs using an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) econometric model and further analyze the convergence of RCEs by introducing a β-convergence model. The results are as follows: (1) The regional disparity of RCEs in China generally shows a wave-like declining trend, with the primary source of this disparity being the differences between city tiers. (2) Kernel density estimation shows that the greater the urban rank, the larger the regional disparity; the RCE distribution in third- and lower-tier cities is more concentrated. (3) Population density, population aging, and education level significantly exert a negative influence on RCEs, whereas economic development level, number of researchers, and number of private cars are positively correlated with RCEs. (4) Each urban agglomeration’s RCEs exhibits significant β-convergence, but the driving factors of RCEs and their convergence differ significantly across the urban agglomerations. This study provides targeted policy recommendations for China to achieve its emission reduction goals effectively. Each city cluster should tailor its approach to strengthen regional collaborative governance, optimize urban layouts, and promote low-carbon lifestyles in order to facilitate the convergence of RCEs and low-carbon transformation.

Keywords: residential carbon emissions; regional disparities; STIRPAT model; ? -convergence (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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