中国城市CO2排放和空气质量协同变化特征及驱动因素研究

The Spatio-Temporal Impacts and Driving Factors of the Synergistic Effects of Reducing Pollution and Carbon Emissions in Cities of China

中文关键词:  减污降碳  城市  时空异质化影响  政策分析

英文关键词:reduction of pollution and carbon emissions  cities  spatio-temporal heterogeneity  policy analysis

中文摘要:

      城市既是受到气候变化和空气污染影响的重点区域，又是落实应对气候变化和大气污染防控政策的关键主体。在城市尺度上研究减污降碳政策的协同效应及其时空异质化的影响规律，将为因地制宜地制定减污降碳政策提供依据。本研究分析了2012—2019年我国284个地级市减污降碳协同效应指数的动态时空变化特征和规律；而后通过构建STIRPAT模型和地理时空加权回归（GTWR）模型，探讨了低碳政策、大气污染物防控政策、产业结构等驱动因素对减污降碳协同效应的时空异质化影响机制。结果表明：全国主要地级市耦合协调度指数平均值由2012年的0.79增加至2019年的0.85，环京津冀区域、汾渭平原等京津冀大气污染传输通道城市区域耦合协调度指数显著提高。以低碳试点城市为代表的区域，其降碳政策、减污政策、产业结构、人口规模、城镇化水平以及技术投入对减污降碳协同效应的影响存在显著的空间异质性。华北平原城市群作为大气污染防治的重点区域，主要通过减污政策提高协同效益；低碳政策主要在京津冀城市群、长江中游以及东南沿海地区城市发挥作用；中西部城市则主要通过产业结构调整、加大技术投入实现协同效益。最后，基于此提出促进城市减污降碳协同效益的对策建议。

英文摘要:

      Cities are not only hot spots affected by climate change and air pollution, but also key entities in implementing policies to address climate change and control air pollution. It is essential to study the spatio-temporal synergistic effects of carbon and pollution reduction policies and their influential mechanisms on the urban scale to provide a basis for formulating carbon and pollution reduction policies according to local conditions. This study analyzed the dynamic spatio-temporal change characteristics of the synergistic effect index of carbon and pollution reduction in 284 prefecture level cities in China from 2012 to 2019. Then the STIRPAT model and Geographic spatio-temporal Weighted Regression (GTWR) model were built to analysis the spatio-temporal heterogeneity impact mechanisms of low-carbon policy, air pollution control policy, industrial structure and other driving factors. The results showed that the average coupling coordination degree index of major prefecture-level cities increased from 0.79 in 2012 to 0.85 in 2019, the coupling coordination index of cities in the Beijing-Tianjin-Hebei region, the Fenwei Plain, and other Beijing-Tianjin-Hebei air pollution transmission channels significantly improved. In the region represented by low-carbon pilot cities, the impacts of carbon reduction policies, pollution reduction policies, industrial structure, population size, ²urbanization level and technology inputs on the synergistic effects had significant spatial heterogeneity. As a key region of air pollution control, the urban agglomeration in the North China Plain mainly improved the synergy through pollution reduction policies; while low carbon policies mainly played roles in the Beijing-TianjinHebei urban agglomeration, the middle reaches of the Yangtze River and cities in the southeast coastal areas; while industrial structure adjustment and technology improvements were the key driving factors in cities located in the central and western regions. According to the above-mentioned results, policy implications and suggestions were provided for promoting the synergistic co-benefits of carbon emission reduction and air pollution control.