摘要
【目的】工业生产碳排放是中国生产活动产生碳排放的最主要来源。本文旨在测度工业生产能源消费的碳排放量和碳排放强度,并对其区域差异及影响因素进行探究,为碳减排提供相应政策建议,助力实现碳达峰、碳中和。【方法】利用碳排放分类、Dagum基尼系数、核密度估计和空间计量模型对2005—2019年全国及区域碳排放的区域差异、动态演进和影响因素进行分析。【结果】(1)2005—2019年工业生产能源消费碳排放量呈现增长趋势,由东部>中部>西部地区演变为东部>西部>中部地区;碳排放强度呈现下降趋势,始终为西部>中部>东部地区。(2)碳排放量和碳排放强度的全国总体差异较大且呈现上升趋势,其中,碳排放量总体基尼系数由2005年的0.3793上升到2019年的0.3861,碳排放强度总体基尼系数由2005年的0.3160上升到2019年的0.3990。东部地区碳排放量区域内差异最大,区域内基尼系数均值为0.4119;西部地区碳排放强度区域内差异最大,区域内基尼系数均值为0.3175。(3)工业生产能源消费碳排放量主要受到产业结构和外商直接投资等因素的影响,碳排放强度主要受到外商直接投资和科技创新等因素的影响。此外,城镇化率在东部地区会显著增大碳排放,在西部地区会显著减少碳排放,表现出区域差异性。【结论】工业生产能源消费碳排放区域差异较大,区域碳排放的动态演进特征存在明显差异。使用空间计量模型能够较好地识别全国及三大地区碳排放的影响因素及其空间效应,对于因地制宜采取相应措施进行降碳减排具有重要意义。
[Objective]Carbon emissions from industrial production are the most important source of carbon emissions from production activities in China.The purpose of this study was to measure the carbon emission quantity and intensity of industrial production energy consumption,explore their regional differences and influencing factors,and provide corresponding policy recommendations for carbon emission reduction to help achieve carbon peak and carbon neutrality.[Methods]The regional differences,dynamic changes,and influencing factors of national and regional carbon emissions from 2005 to 2019 were analyzed using carbon emission classification,Dagum Gini coefficient,kernel density estimation,and spatial econometric models.[Results](1)The quantity of carbon emissions from industrial energy consumption showed an increasing trend from 2005 to 2019.Carbon emission quantity was eastern region>central region>western region,then changed to eastern region>western region>central region.Carbon emission intensity showed a downward trend.The order of carbon emission intensity from high to low was always the western region,the central region,and the eastern region.(2)The overall differences of carbon emission quantity and intensity across the country were large and showed an upward trend,among which,the overall Gini coefficient of carbon emission quantity increased from 0.3793 in 2005 to 0.3861 in 2019,and the overall Gini coefficient of carbon emission intensity increased from 0.3160 in 2005 to 0.3990 in 2019.The eastern region had the largest intra-regional difference in carbon emission quantity,and the average Gini coefficient was 0.4119.The western region had the largest intra-regional difference in carbon emission intensity,and the average Gini coefficient was 0.3175.(3)Carbon emission quantity of industrial production energy consumption was mainly affected by industrial structure and foreign direct investment,and carbon emission intensity was mainly affected by foreign direct investment and scientific and technological innovation.In addition,high urbanization rate significantly increased carbon emissions in the eastern region,while significantly reduced carbon emissions in the western region,showing regional differences.[Conclusion]There were significant regional differences in carbon emissions from industrial production energy consumption,and there were obvious differences in the dynamic change characteristics of regional carbon emissions.The use of spatial econometric model can better identify the influencing factors and spatial effects of carbon emissions in the country and the three regions,which is of great significance for taking corresponding measures to reduce carbon emissions according to local conditions.
作者
王青
傅莉媛
孙海添
WANG Qing;FU Liyuan;SUN Haitian(School of Economics,Faculty of Economics,Liaoning University,Shenyang 110036,China)
出处
《资源科学》
CSSCI
CSCD
北大核心
2023年第6期1239-1254,共16页
Resources Science
基金
国家社会科学基金重大项目(21ZDA053)。
关键词
能源消费碳排放
区域差异
核密度估计
碳排放分类
空间杜宾模型
影响因素
carbon emissions of energy consumption
regional differences
kernel density estimation
carbon emission classification
Spatial Durbin Model
influence factor
作者简介
王青,女,辽宁沈阳人,教授,研究方向为现代统计方法与宏观计量分析。E-mail:qingwang@lnu.edu.cn;通讯作者:傅莉媛,女,辽宁大连人,博士研究生,研究方向为能源经济与碳排放。E-mail:fuliyuan0123@163.com。
