Study on the Spatial–Temporal Characteristics and Influencing Factors of the Synergistic Effect of Pollution and Carbon Reduction: A Case Study of the Chengdu–Chongqing Region, China

Ting Zhang, Zeyu Zhang, Xiling Zhang, Li Zhou () and Jian Yao ()
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Ting Zhang: College of Architecture and Environment, Sichuan University, Chengdu 610065, China
Zeyu Zhang: College of Architecture and Environment, Sichuan University, Chengdu 610065, China
Xiling Zhang: College of Architecture and Environment, Sichuan University, Chengdu 610065, China
Li Zhou: College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China
Jian Yao: College of Architecture and Environment, Sichuan University, Chengdu 610065, China

Sustainability, 2025, vol. 17, issue 18, 1-24

Abstract: In the context of China’s “double carbon” goals, examining the spatial–temporal characteristics and influencing factors of the synergistic effect of pollution control and carbon reduction (SEPCR) in the Chengdu–Chongqing region (CCR) is crucial for advancing both air pollution (AP) control and carbon emissions (CE) mitigation. This study uses data on AP and CE from 2007 to 2022 and employs the coupling coordination degree (CCD) model, spatial autocorrelation analysis, and kernel density estimation to investigate the spatial–temporal distribution and dynamic evolution of the CCD between AP and CE in the CCR. Additionally, the Tobit regression model is applied to identify the key factors influencing this synergy. The results indicate that (1) during the study period, the air pollutant equivalents (APE) in the CCR showed a declining trend, while CE continued to increase; (2) the overall level of coupling coordination remained low, exhibiting an evolutionary pattern of initial increase, subsequent decrease, and then recovery, with synergistic effects showing slight improvement but significant fluctuations; (3) the SEPCR in the CCR was generally dispersed, exhibiting no significant spatial autocorrelation. A “core–periphery” structure emerged, with Chongqing and Chengdu as the core and peripheral cities forming low-value zones. Low–low clusters indicative of a “synergy poverty trap” also appeared; (4) economic development (PGDP), openness level (OP), and environmental regulation intensity (ER) are significant positive drivers, while urbanization rate (UR), industrial structure upgrading (IS), and energy consumption intensity (EI) exert significant negative impacts.

Keywords: pollution and carbon reduction; Chengdu-Chongqing region; spatial-temporal characteristics; coupling coordination degree model; Tobit regression model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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