中国人均CO_2排放水平地区差异的成因分析——基于GINI系数和回归的Shapley Value分解方法  

Regional Disparities of Per Capita Carbon Emissions in China Based on the GINI Coeffi cient and Decomposition Method of Shapley Value

作　　者：柳亚琴[1] 赵国浩[2] 

机构地区：[1]山西财经大学经济学院,山西太原030006 [2]山西财经大学管理科学与工程学院,山西太原030031

出　　处：《生态经济》2015年第4期47-50,106,共5页Ecological Economy

基　　金：国家自然科学基金项目(70873079;71173141);山西省软科学研究项目(2013041015-04);山西省哲学社会科学"十二五"规划2011年度课题(晋规办字[2011]8号)

摘　　要：根据2000-2011年中国省际面板数据,以GINI系数为衡量指标,测算了人均CO2排放水平的地区差异,分析了人均CO2排放水平随时间的演变趋势,在此基础上采用基于回归的Shapley Value分解方法,对中国人均CO2排放水平地区差异形成原因进行了考察,探究各影响因素对差异程度的影响水平,从而为有效缩小地区差异和制定CO2减排政策提供有益的参考。结果表明：人均能源消费量因素的影响程度最大,平均贡献率为72.80%,各省份的经济发展水平和产业结构是人均CO2排放水平省际差异的第二大贡献因素,其平均贡献率依次为8.01%与10.20%,且在不同的时间段其结果有所不同。排在第四位和第五位的因素分别是城镇化水平和对外开放水平,二者都是影响人均CO2排放水平省际差异的重要因素,平均贡献率分别达到5.17%与3.81%。We measured regional disparities of per capita carbon emissions in China based on the GINI co-efficiency and analyzed the time evolution trend of per capita carbon emission levels using provincial panel data in China from 2000 to 2011. On the basis of regression Shapley Value decomposition method, we identified the regional disparities cause of per carbon emission levels and studied the effect of the various factors on regional disparities, which can provide the beneficial reference for reducing effectively regional disparities and developing CO2 emission reduction policies. The results show that per capita energy consumption was the most important affecting factor, which contributes the average rate of 72.80%. In addition, the economic development and industrial structure was the second largest contributor to regional disparities of per carbon emission levels, and contributed an average rate of 8.01% and 10.20% respectively which were different in difference periods. The urbanization and openness were also important reasons for the provincial differences and the average contribution rate accounted for 5.17% and 3.81%.

关 键 词：人均CO2排放水平 GINI系数 差异分解 Shapley Value方法 

分 类 号：F206[经济管理—国民经济]