Xiao-Hong Chen, Fang-Yi Zhou, Ji-Xin Cheng, Dong-Bin Hu
{"title":"[Spatiotemporal Evolution and Inequality of Marginal Cost of Carbon Emission Reduction in Chinese Cities].","authors":"Xiao-Hong Chen, Fang-Yi Zhou, Ji-Xin Cheng, Dong-Bin Hu","doi":"10.13227/j.hjkx.202408127","DOIUrl":null,"url":null,"abstract":"<p><p>Studying the spatiotemporal evolution and inequality characteristics of the marginal cost of carbon reduction at the city level is crucial for formulating effective and fair carbon reduction policies. Using panel data from 236 prefecture-level cities in China during the period 2006-2019, this study employed a directional distance function parameter estimation method to measure the marginal carbon dioxide reduction costs in cities in China. Building on this result, the study analyzed the temporal and spatial evolution, classification, inequality, and spatial convergence characteristics of these marginal reduction costs using models such as kernel density functions, the Dagum Gini coefficient, and spatial convergence. The key findings are as follows: ①From 2006 to 2019, China's marginal carbon reduction cost (measured in CO<sub>2</sub>e) showed a trend of first declining and then rising. The average value first decreased from 7.45 thousand yuan per ton to 5.58 thousand yuan per ton and then increased to 20.36 thousand yuan per ton. ②The curve of China's marginal carbon reduction cost showed a U-shaped trend over the period 2006-2019, with the majority of cities positioned to the left of the lowest point. ③The inequality in China's marginal carbon reduction costs followed a pattern of initial increase followed by decrease. The overall Gini coefficient declined from 0.220 in 2006 to 0.151 in 2019. ④From 2006 to 2019, China's city-level marginal reduction costs demonstrated <i>σ</i>-convergence characteristics and supported the <i>β</i>-spatial convergence mechanism. There was significant heterogeneity in reduction costs across different economic regions, and the classification of city-level marginal reduction costs showed a trend toward polarization. Although China has achieved some results in carbon reduction, the task difficulty and cost have gradually increased as carbon reduction action has increased. Therefore, to facilitate the coordinated reduction of carbon reduction costs across cities in China, the government should establish inter-city collaborative mechanisms for emissions reduction, promote the coordinated development of green and low-carbon industries among cities, enhance the collaborative application of energy efficiency and energy-saving technologies across urban areas, deepen the collaborative advancement of environmental education and public participation, and innovate the design of inter-city financing models and incentive mechanisms. This would not only help to reduce emission reduction costs but also promote the balanced development of the regional low-carbon economy, helping to achieve the Twin Carbon Targets.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 9","pages":"5428-5440"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"环境科学","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.13227/j.hjkx.202408127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0
Abstract
Studying the spatiotemporal evolution and inequality characteristics of the marginal cost of carbon reduction at the city level is crucial for formulating effective and fair carbon reduction policies. Using panel data from 236 prefecture-level cities in China during the period 2006-2019, this study employed a directional distance function parameter estimation method to measure the marginal carbon dioxide reduction costs in cities in China. Building on this result, the study analyzed the temporal and spatial evolution, classification, inequality, and spatial convergence characteristics of these marginal reduction costs using models such as kernel density functions, the Dagum Gini coefficient, and spatial convergence. The key findings are as follows: ①From 2006 to 2019, China's marginal carbon reduction cost (measured in CO2e) showed a trend of first declining and then rising. The average value first decreased from 7.45 thousand yuan per ton to 5.58 thousand yuan per ton and then increased to 20.36 thousand yuan per ton. ②The curve of China's marginal carbon reduction cost showed a U-shaped trend over the period 2006-2019, with the majority of cities positioned to the left of the lowest point. ③The inequality in China's marginal carbon reduction costs followed a pattern of initial increase followed by decrease. The overall Gini coefficient declined from 0.220 in 2006 to 0.151 in 2019. ④From 2006 to 2019, China's city-level marginal reduction costs demonstrated σ-convergence characteristics and supported the β-spatial convergence mechanism. There was significant heterogeneity in reduction costs across different economic regions, and the classification of city-level marginal reduction costs showed a trend toward polarization. Although China has achieved some results in carbon reduction, the task difficulty and cost have gradually increased as carbon reduction action has increased. Therefore, to facilitate the coordinated reduction of carbon reduction costs across cities in China, the government should establish inter-city collaborative mechanisms for emissions reduction, promote the coordinated development of green and low-carbon industries among cities, enhance the collaborative application of energy efficiency and energy-saving technologies across urban areas, deepen the collaborative advancement of environmental education and public participation, and innovate the design of inter-city financing models and incentive mechanisms. This would not only help to reduce emission reduction costs but also promote the balanced development of the regional low-carbon economy, helping to achieve the Twin Carbon Targets.