中国减污降碳协同效应的时空特征及其影响机制分析

我国环境污染防治面临减污降碳协同推进的新挑战，为实现减污降碳协同增效的总目标，深入打好大气污染防治攻坚战，研究减污降碳协同效应的时空特征及其影响机制，对因地制宜制定经济社会发展全面绿色转型的政策具有重要现实意义. 本文以2011—2019年我国30个省(自治区、直辖市)为研究对象，分析大气污染物和碳排放的时空特征(不包括港澳台及西藏自治区的数据，下同)；同时，运用耦合协调模型计算碳减排与大气污染物控制系统的耦合协调度，进一步分析减污降碳协同效应在全国、八大经济区和各省份的时空特征；并利用时空地理加权回归模型(GTWR)，揭示了减污降碳协同效应影响因素的空间演化规律和作用机制. 结果表明:①2011—2019年我国碳排放量缓慢增长，污染物排放当量自2014年开始明显下降；大气污染物和碳排放主要集中在东北、北部沿海和黄河中游经济区. ②2011—2019年我国减污降碳的协同效应整体上明显提高，不同经济区和省份的减污降碳协同效应存在明显的时空差异. ③能源消费总量、能源消费强度和能源消费结构是减污降碳协同效应的主要影响因素，能源消费总量、能源消费结构、能源消费强度、产业结构和进出口贸易总额等因素对减污降碳协同效应的影响存在显著的空间异质性. 鉴于此，本文从制定差异化的减污降碳协同策略、推动能源结构优化转型、加强地区和部门间的协同治理等方面提出了减污降碳协同增效的对策建议. 

Spatio-Temporal Characteristics and Influencing Mechanism of Synergistic Effect of Pollution and Carbon Emission Reduction in China

Environmental pollution prevention and control in China is facing new challenges of pollution reduction and carbon emission reduction synergies. In order to achieve the overall goal of reducing pollution and carbon emission, promoting synergies and increasing efficiency, and to further play tough battle of air pollution control, the study on the spatio-temporal characteristics of the synergistic effect of pollution and carbon emission reduction and its influencing mechanism is of great practical significance in formulating policies for comprehensive green transition of economic and social development according to local conditions. This study first takes 30 provinces in China (data from Hong Kong, Macao, Taiwan and Tibet Autonomous Region are excluded, the same below) as the research object to analyze the spatial agglomeration characteristics of regional pollutants and carbon emissions. Meanwhile, the coupling coordination model is used to calculate the coupling coordination degree of carbon emission reduction and air pollutant control from 2011 to 2019. On this basis, the spatio-temporal variation of the synergistic effect of pollution and carbon emission reduction in eight economic zones and each province in China are further analyzed. The geographically temporally weighted regression model (GTWR) is used to reveal the spatio-temporal evolution law and mechanism of different factors on the synergistic effect of pollution and carbon reduction. The results show that: (1) The carbon dioxide emissions in China increased slowly from 2011 to 2019, and the air pollutant equivalent decreased significantly since 2014. The emissions of air pollutants and carbon dioxide were higher in the northeast, the northern coastal and the middle reaches of the Yellow River Economic Zone. (2) From 2011 to 2019, the synergistic effect of pollution and carbon emission reduction in China improved significantly on the whole. The synergistic effect of different economic zones and provinces varied greatly in space and time. (3) Total energy consumption, energy consumption intensity and energy consumption structure were the main factors influencing the synergistic effect of pollution and carbon emission reduction, and factors such as total energy consumption, energy consumption structure, energy consumption intensity, industrial structure and total import and export trade have significant spatial heterogeneity on the synergistic effect of regional pollution and carbon emission reduction. In view of this, the study puts forward suggestions for enhancing the synergistic effect from the aspects of developing differentiated collaborative strategies for pollution and carbon emission reduction, promoting energy transition and renewable energy development, and strengthening regional and inter-departmental collaborative governance.