江苏省设区市尺度的碳排放核算及影响因素研究  

Carbon Emission Accounting and Influencing Factors in the Prefecture-level Cities of Jiangsu Province

作　　者：费杰 杨孟 张惠玉 杨轩一 徐亢 刁一伟 吴丹 FEI Jie;YANG Meng;ZHANG Huiyu;YANG Xuanyi;XU Kang;DIAO Yiwei;WU Dan(School of Environmental Engineering,Wuxi University,Wuxi 214105,China;School of Atmospheric and Remote Sensing,Wuxi University,Wuxi 214105,China)

机构地区：[1]无锡学院环境工程学院,江苏无锡214105 [2]无锡学院大气与遥感学院,江苏无锡214105

出　　处：《三峡生态环境监测》2023年第2期26-35,共10页Ecology and Environmental Monitoring of Three Gorges

基　　金：江苏省环境监测科研基金项目(NO2110)。

摘　　要：本文利用联合国政府间气候变化专门委员会(intergovernmental panelonclimatechange,IPCC)清单法核算了江苏省13个设区市1999—2020年的碳排放量,并利用可拓展随机性环境影响评估模型(stochastic impactsby regression onpopulation,affluence,andtechnology;STIRPAT)分析经济、人口、能源强度和能源结构对碳排放的影响。各设区市的碳排放存在较大差异,淮安的年均碳排放量(3.00×10^(7)t CO_(2))处于中游水平,苏州具有最大的年均碳排放量,约为淮安的5倍。2020年,宿迁、常州和盐城的碳排放量同比增加,其他城市的碳排放量同比下降或者零增长,其中,淮安、扬州、泰州、南通和徐州的碳排放量持续下降。STIRPAT模型拟合结果表明:(1)碳排放与人口的关系具有地区差异,苏南城市的人口与碳排放均为显著正相关,南通、徐州和泰州则为显著负相关;(2)人均GDP、第二产业占比、能源强度(单位GDP能耗)以及能源结构(原煤占一次能源消费比重)与碳排放总量具有显著的正相关关系,可通过优化产业结构、降低能源强度和优化能源结构降低江苏省的碳排放量。In this paper,the IPCC inventory method was used to account for the carbon emissions of 13 cities in Jiangsu Province from 1999 to 2020,and the STIRPAT(stochastic impacts by regression on population,affluence,and technology)model was used to analyze the effects of economy,population,energy intensity(energy consumption per unit of GDP)and energy structure(the pro⁃portion of primary energy consumption accounted for by raw coal)on carbon emissions.There are large differences in carbon emis⁃sions among the cities.The average annual carbon emission(3.00×107 t CO2)in Huai’an is at the middle level,but that in Suzhou is the largest,about five times larger than that of Huai’an.In 2020,the carbon emissions increased year-on-year in Suqian,Chang⁃zhou and Yancheng,but decreased in the other cities or had zero growth,especially continuously declined in Huai’an,Yangzhou,Taizhou,Nantong and Xuzhou.The results of the STIRPAT model show that:(i)the relationship between carbon emission and popu⁃lation has regional differences,with a significantly positive correlation in southern Jiangsu cities and substantially negative in Nan⁃tong,Xuzhou and Taizhou;(ii)the GDP per capita,the proportion of secondary industry,energy intensity and energy structure have a significantly positive correlation with total carbon emissions.Carbon emission in Jiangsu province can be reduced optimising the industrial structure,reducing energy intensity and optimising the energy structure.

关 键 词：STIRPAT模型 人口 人均GDP 能源强度与结构 

分 类 号：X831[环境科学与工程—环境工程]