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基于产业链分析的长三角地区CO2与大气污染物排放研究
引用本文:李蓥,黄成,邢贞成,刘逸凡,王海鲲.基于产业链分析的长三角地区CO2与大气污染物排放研究[J].中国环境管理,2021,13(6):50-60.
作者姓名:李蓥  黄成  邢贞成  刘逸凡  王海鲲
作者单位:污染控制与资源化研究国家重点实验室, 南京大学环境学院, 江苏南京 210023;国家环境保护城市大气复合污染成因与防治重点实验室, 上海市环境科学研究院, 上海 200233;大气与地球系统科学国际合作联合实验室, 南京大学大气科学学院, 江苏南京 210023;污染控制与资源化研究国家重点实验室, 南京大学环境学院, 江苏南京 210023;大气与地球系统科学国际合作联合实验室, 南京大学大气科学学院, 江苏南京 210023
摘    要:长三角地区作为我国大气污染较为严重区域之一,如何在保持经济增长的同时减少CO2与大气污染物的排放已成为一个重要挑战。本研究基于2007年与2012年长三角区域间投入产出表,定量分析了长三角地区省市间贸易引致的二氧化碳和大气污染物排放转移特征和变化趋势。同时,运用产业关联系数法,从前向关联与后向关联双重视角分析了长三角地区减缓CO2和大气污染物排放的关键行业。研究结果表明,长三角的SO2、PM2.5排放总量表现为消费端大于生产端,CO2、NOx排放总量表现为生产端大于消费端。安徽省总体呈现为长三角地区贸易的SO2、NOx与PM2.5排放净调出地,而上海与浙江表现为多数污染物排放净调入地。CO2与大气污染物协同前向减排的关键行业为江苏省、浙江省和安徽省的电力、热力的生产和供应业,安徽省的煤炭开采和洗选业等,可以通过生产端技术革新和能源结构优化来促进减排;CO2与大气污染物后向协同减排的关键行业为江苏省、浙江省和安徽省的建筑业等,对于这些行业,调整消费结构是有效的减排措施。为更好地制定长三角地区减排与污染防治政策,应当综合考虑行业减排、协同减排等,以确保经济持续增长的同时达到减排目标。

关 键 词:区域间投入产出模型  产业关联  排放转移  二氧化碳  大气污染物

Study on CO2 and Air Pollutant Emissions in the Yangtze River Delta Based on Industry Chain Analysis
LI Ying,HUANG Cheng,XING Zhencheng,LIU Yifan,WANG Haikun.Study on CO2 and Air Pollutant Emissions in the Yangtze River Delta Based on Industry Chain Analysis[J].Chinese Journal of Environmental Management,2021,13(6):50-60.
Authors:LI Ying  HUANG Cheng  XING Zhencheng  LIU Yifan  WANG Haikun
Institution:State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China;National Key Laboratory for the Causes and Prevention of Compound Air Pollution in Environmental Protection Cities, Shanghai Academy of Environmental Sciences, Shanghai 200233, China;Joint Laboratory for International Cooperation in Atmospheric and Earth System Science, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China; State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China;Joint Laboratory for International Cooperation in Atmospheric and Earth System Science, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Abstract:As a severely polluted area in China, Yangtze River Delta (YRD) has been faced with an important challenge to maintain economic growth while reducing carbon dioxide and air pollutant emissions. Based on the multi-regional input-output tables of the YRD in 2007 and 2012, this study quantitatively analyzed the embodied emission transfer characteristics and trends of carbon dioxide and air pollutants induced by inter-provincial trade. Then, the key industries for mitigating CO2 and air pollutant emissions from both forward and backward correlation perspectives were identified by the industrial correlation coefficient method. The results showed that the total SO2 and PM2.5 emissions in the YRD in the consumption side were larger than those in the production side, and the total CO2 and NOx emissions in the production side were larger than those in the consumption side. Anhui Province was a net exporter of SO2, NOx and PM2.5 emissions from trade in the YRD, while Shanghai and Zhejiang showed a net inflow of most pollutants. The key industries for the forward emission reduction of CO2 and air pollutants were the production and supply of electricity and heat in Jiangsu, Zhejiang and Anhui, and coal mining and washing industry in Anhui. Measures like technological innovation and optimization of energy structure can be implemented to these industries to promote emission reduction. The key industries for backward synergistic emission reduction were the construction industry in Jiangsu, Zhejiang and Anhui. For these industries, adjusting the consumption structure is an effective emission reduction measure. In order to better formulate emission reduction and pollution prevention policies in the YRD, comprehensive consideration should be given to emission reductions from industry and collaboration perspectives to achieve economic growth and emission reduction targets simultaneously.
Keywords:multiregional input-output model  industrial correlation  emissions transportation  CO2  air pollutant
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