Will carbon emission trading policy improve the synergistic reduction efficiency of pollution and carbon? Evidence from 216 Chinese cities

Abstract

Based on the panel data of 216 Chinese cities from 2008 to 2018, the synergistic reduction efficiency of pollution and carbon (SREPC) was calculated using the super‐efficiency slack‐based measure model. Differences‐in‐differences (DID), propensity score matching (PSM)–DID, and spatial‐DID were adopted to study the impact of the carbon emission trading (CET) policy on SREPC. The study results show that SREPC was not improved overall, with an unbalanced development characteristic of high efficiency in the east and low efficiency in the west. The Meta‐frontier results show that there were indeed significant differences between pilot and non‐pilot cities before and after the implementation of the CET policy. Mechanism tests reveal that CET promoted SREPC by improving energy efficiency and optimizing industrial structures. Spatial analysis shows that CET had spatial spillover effects and decreased SREPC in neighboring cities. These reduction effects occurred in neighboring non‐pilot cities and showed an inverted U‐shaped relationship with increasing geographical distance, particularly significant within 500–600 km. Inverse data envelopment analysis shows that CET can utilize the synergistic reduction potential of pollution and carbon.

Regional and Urban-Rural Differences in Carbon Emissions of Chinese Residents: Dynamic Evolution and Structural Decomposition

“…Shen et al [52] found that the development of digital finance can improve the efficiency of land circulation in rural areas, thereby enhancing the productivity and sustainable development of the agricultural sector. Existing literature using panel data from 216 Chinese cities found that the establishment of a carbon trading market mechanism can enhance the synergistic effects of environmental regulation and low-carbon policies [53].…”

Agricultural Carbon Emissions in China: Estimation, Influencing Factors, and Projection of Peak Emissions

“…The input-output approach, focusing on energy demand and emission factors, was employed by Yuan et al to assess the carbon emission dynamics in various Chinese industries [8]. Additionally, models such as Agri-LCI, SPAC, and DNDC have been instrumental in estimating carbon emissions across diverse regions and sectors [9].…”