中国化工产业碳排放效率时空演变及影响因素

丁树芳; 王远; 董战峰; 钟小剑

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (6) : 3508-3521.

碳排放控制

中国化工产业碳排放效率时空演变及影响因素

丁树芳^1,2,3, 王远^1,2,3, 董战峰⁴, 钟小剑^1,2,3

作者信息 +

Spatiotemporal evolution and influencing factors of carbon emission efficiency in China's chemical industry

DING Shu-fang^1,2,3, WANG Yuan^1,2,3, DONG Zhan-feng⁴, ZHONG Xiao-jian^1,2,3

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摘要

本文在采用超效率SBM模型测算中国30个省市2007~2021年化工产业碳排放效率的基础上,运用空间分析方法及核密度估计刻画了整体及不同地区化工产业碳排放效率的时空演变规律,进一步借助Tobit模型对其影响因素进行了识别.结果显示:研究期间中国化工产业碳排放效率虽呈现波动上升趋势但整体水平较低,均值为0.629,且多数时间具有“东部(0.750)>西部(0.584)>中部(0.530)”的特征.化工产业碳排放效率最终呈现“西南—东北”向分布,其空间格局变动较大并逐渐形成“三足鼎立”局面,且多数地区以低效率为主.此外,其基尼系数均值为0.322,总体上存在较大的空间差异,东部地区化工产业碳排放效率高于中西部,超变密度是产生区域差异的主要来源.化工产业碳排放效率整体演进趋势良好,总体上的省域差距在逐渐缩小,东中西地区趋势良好但需注意西部地区分化趋势的进一步加大.化工产业集聚及能源结构、经济发展水平对其碳排放效率具有明显的推动作用,其中化工产业能源结构影响系数最大为0.9942.因此,政府应将优化化工产业能源结构置于首位,并充分考虑化工产业集聚以及地区经济发展的正向作用,同时加强区域协作,制定地区差异化政策,进而促进化工产业碳排放效率的提升.

Abstract

Based on the super-efficiency SBM model to measure the carbon emission efficiency (CEE) of China's chemical industry across 30 provinces from 2007 to 2021, this study employs spatial analysis methods and kernel density estimation to characterize the spatiotemporal evolution patterns of CEE at both the national and regional levels. Furthermore, a Tobit regression model is applied to identify its influencing factors. Although the CEE of China's chemical industry exhibited a fluctuating upward trend during the study period, the overall level remained relatively low, with a mean value of 0.629. Moreover, a persistent regional disparity was observed, following the order of eastern China (0.750) > western China (0.584) > central China (0.530). The spatial distribution of CEE ultimately displayed a "southwest-northeast" orientation, with significant shifts in spatial patterns gradually forming a "tripartite balance" structure, though most regions remained at low efficiency levels. Additionally, the mean Gini coefficient was 0.322, indicating substantial spatial heterogeneity overall. The CEE in eastern China surpassed that of the central and western regions, with hypervariable density identified as the primary source of regional disparities. The overall evolutionary trend of CEE in the chemical industry was positive, with interprovincial gaps gradually narrowing. While the trends in eastern, central, and western China were generally favorable, attention should be paid to the increasing divergence in the western region. Industrial agglomeration, energy structure, and economic development level significantly promoted CEE, with the energy structure of the chemical industry having the strongest impact which coefficient is 0.9942. Therefore, the government should prioritize optimizing the energy structure of the chemical industry while fully leveraging the positive effects of industrial agglomeration and regional economic development. Additionally, enhancing interregional collaboration and formulating region-specific policies are crucial for further improving the CEE of the chemical industry.

导出引用

丁树芳, 王远, 董战峰, 钟小剑. 中国化工产业碳排放效率时空演变及影响因素[J]. 中国环境科学. 2025, 45(6): 3508-3521

DING Shu-fang, WANG Yuan, DONG Zhan-feng, ZHONG Xiao-jian. Spatiotemporal evolution and influencing factors of carbon emission efficiency in China's chemical industry[J]. China Environmental Science. 2025, 45(6): 3508-3521

中图分类号： X22

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