摘要
碳排放交易机制作为国家实现“双碳”目标的核心政策工具,其减污降碳的政策效应引起广泛关注。文章将二氧化碳减排与环境污染治理纳入同一研究框架,基于2010—2019年中国30省份面板数据,以碳排放交易机制为政策背景,采用改进熵权-TOPSIS模型计算环境污染指数测度当地生态环境质量和环境污染程度,以碳排放量与环境污染指数的交乘项表征减污降碳水平,运用双重差分模型与合成控制法实证检验碳排放交易机制的减污降碳效应。研究结果发现:①与非试点省份相比,从政策的平均处理效应来看,碳排放交易机制降低了试点省份二氧化碳和环境污染物的排放,在加入相关控制变量后,试点省份的减污降碳水平提升了16%。从试点个体的政策效应来看,北京、上海、天津、重庆表现较优即碳污排放呈显著下降趋势,广东表现次之,而湖北表现较差。②作用机制分析和异质性检验发现,碳排放交易机制通过能源消耗结构调整和技术创新实现减污降碳协同治理水平的提升,同时,减污降碳水平呈现区域异质性,东部和西部地区表现最优,中部地区表现不佳。③进一步,以灰色关联模型探究各省份碳排放与环境污染物的减污降碳水平协同减排潜力,计算结果发现,上海、广东、广西、贵州、新疆等5个省份的平均关联度值大于其他省份,呈现较高的减污降碳潜力。因此,要加快推进并完善中国碳排放交易市场建设,发挥市场激励型的碳排放交易政策对二氧化碳排放和环境污染物的协同减排作用,通过加大技术创新投入和能源消费结构的转型升级、制定区域差异化的减污降碳政策,加快实现碳排放交易政策的协同效应。
As a key policy tool for China’s achievement of the goals of reaching peak carbon emissions and carbon neutrality,the carbon emissions trading mechanism has attracted widespread attention for its effect on reducing pollution and carbon emissions.This study incorporated carbon dioxide reduction and environmental pollution control into the same research framework.Based on the panel data of 30 provinces in China from 2010 to 2019,it took the carbon emissions trading mechanism as the policy background and used the improved entropy weight TOPSIS model to calculate the environmental pollution index to measure the local environmental quality and environmental pollution degree.The intersection term of carbon emissions and environmental pollution index was used to characterize the level of pollution reduction and carbon reduction.The difference-in-differences model and the synthetic control method were used to empirically test the pollution and carbon emission reduction effects of the carbon emissions trading mechanism.The results showed that:①From the perspective of the average treatment effect of the policy,the carbon emissions trading mechanism reduced the emissions of carbon dioxide and environmental pollutants in the pilot provinces compared with non-pilot provinces.After adding relevant control variables,the level of pollution and carbon reduction in the pilot provinces increased by 16%.From the perspective of the policy effects of pilot individuals,Beijing,Shanghai,Tianjin,and Chongqing performed better;that is,their carbon emissions showed a significant downward trend,followed by Guangdong,while Hubei performed worse.②The influential mechanisms and heterogeneity test revealed that the carbon emissions trading system achieved an increase in the synergistic governance level of pollution and carbon reduction through energy consumption restructuring and technological innovation.③Meanwhile,the pollution and carbon reduction levels showed regional heterogeneity,with the eastern and western regions performing best and the central region performing poorly.Furthermore,a grey correlation model was used to investigate the relationship between the pollution and carbon reduction levels and the potential for synergistic emission reduction in each province.The results found that the average correlation degree of Shanghai,Guangdong,Guangxi,Guizhou,and Xinjiang was greater than other provinces,showing a higher level and potential.Therefore,it is necessary to accelerate and improve the construction of the national carbon emissions trading market;give full play to the synergistic emissions reduction effects of market-based carbon emissions trading policies on carbon dioxide emissions and environmental pollutants;and accelerate the synergy of carbon emissions trading policies by increasing investment in technological innovation and the transformation and upgrading of energy consumption structures and formulating regionally differentiated carbon emission reduction policies.
作者
陆敏
徐好
陈福兴
LU Min;XU Hao;CHEN Fuxing(School of Statistics and Data Science,Nanjing Audit University,Nanjing Jiangsu 211815,China)
出处
《中国人口·资源与环境》
CSSCI
CSCD
北大核心
2022年第11期121-133,共13页
China Population,Resources and Environment
基金
国家社会科学基金一般项目“基于深度学习的数据波动率预测及其应用研究”(批准号:19BTJ035)
江苏省社科应用研究精品工程课题“江苏生态环境与经济发展相互促进机制研究”(批准号:20SYC-137)
江苏省高校优势学科建设工程资助项目(PAPD)。
关键词
减污降碳
碳排放交易机制
双重差分模型
改进熵权-TOPSIS模型
灰色关联模型
pollution and carbon reduction
carbon emissions trading mechanism
difference‑in‑differences model
improved entropy weight TOPSIS model
grey correlation model
