中部六省碳排放因素分析和趋势预测——基于STIRPAT和灰色GM(1,1)模型的实证研究
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摘要
利用STIRPAT面板模型分析中部六省2000-2012年碳排放情况,发现:六省的碳排放量递增,碳排放强度递减,地区差异显著。人均收入水平的二次项系数显著为负,环境库兹涅茨假说成立。城镇化水平、技术水平与碳排放量、排放强度呈负相关;人口规模、能源结构与碳排放量、排放强度呈正相关。运用灰色GM(1,1)模型,预测中部六省2015年、2020年碳排放量分别达到12万吨、18万吨,碳排放强度分别降至6.7吨/万元、4.7吨/万元。最后提出了节能减排的措施。
The STIRPAT panel model is used to analyze the carbon emissions of the six central provinces from 2000 to 2012, and it is found that the carbon emissions of the six provinces are increasing, the carbon emission intensity is decreasing, and the regional differences are significant. The quadratic coefficient of per capita income level is significantly negative, and the environmental Kuznets hypothesis is established. Urbanization level and technical level are negatively correlated with carbon emissions and emission intensity; population size and energy structure are positively correlated with the carbon emissions and emission intensity. Using the gray GM(1,1) model, it is predicted that the carbon emissions in the six central provinces in 2015 and 2020 will reach 120,000 tons and 180,000 tons respectively, and the carbon emission intensity will be reduced to 6.7 tons/10,000 yuan and 4.7 tons/10,000 yuan respectively. Finally, measures for energy conservation and emission reduction are proposed.
The STIRPAT panel model is used to analyze the carbon emissions of the six central provinces from 2000 to 2012, and it is found that the carbon emissions of the six provinces are increasing, the carbon emission intensity is decreasing, and the regional differences are significant. The quadratic coefficient of per capita income level is significantly negative, and the environmental Kuznets hypothesis is established. Urbanization level and technical level are negatively correlated with carbon emissions and emission intensity; population size and energy structure are positively correlated with the carbon emissions and emission intensity. Using the gray GM(1,1) model, it is predicted that the carbon emissions in the six central provinces in 2015 and 2020 will reach 120,000 tons and 180,000 tons respectively, and the carbon emission intensity will be reduced to 6.7 tons/10,000 yuan and 4.7 tons/10,000 yuan respectively. Finally, measures for energy conservation and emission reduction are proposed.
引文
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(1)资料来源:国家发展和改革委员会能源研究所(国家发展和改革委员会能源研究所.中国可持续发展能源暨碳排放情景分析综合报告[R].2003:37):王刚、冯霄(汪刚,冯霄.基于能量集成的CO2减排量的确定[J].化工进展,2006,25(12):1467-1470)。
[2]Ang,B.W.,Zhang F.Q.,Choi K.Factoring Changes in Energy and Environmental Indicators though Decomposition[J].Energy,1998,23(6).
[3]Peters,G.,C.L.Weber,D.Guan,K.Hubacek.2007.“China’s Growing CO2Emissions:A Race between Lifestyle Changes and Efficiency Gains.”Environmental Science and Technology,41(17):5939-5944.
[4]陈诗一.中国碳排放强度的波动下降模式及经济解释[J].世界经济,2011(4).
[5]李小平,卢现祥.国际贸易、污染产业转移和中国工业CO2排放[J].经济研究,2010(1).
[6]何小钢,张耀辉.行业特征,环境规制与工业CO2排放-基于中国工业36个行业的实证考察[J].经济管理,2011(11).
[7]何小钢,张耀辉.中国工业碳排放影响因素与CKC重组效应-基于STIRPAT模型的分行业动态面板数据实证研究[J].中国工业经济,2012(1).
[8]齐绍洲,林屾,王班班.中部六省经济增长方式对区域碳排放的影响---基于Tapio脱钩模型、面板数据的滞后期工具变量法的研究[J].中国人口·资源与环境,2015(5):59-66.
[9]Ehrlich,P.R.,Holdren,J.p..Impact of Population Growth[J].Science,1971(171):1212-1217
[10]Dietz.T,and Rosa.E.A.Rethinking the Environmental Impact of Population,Affluence and Technology[J].Human Ecology Review,1994:277-300.
[11]刘思峰,党耀国.灰色系统理论及其应用[M].三版.北京:科学出版社,2004.
[12]徐明德.灰色GM(1,1)模型及其在能源预测中的应用[J].应用能源技术,1995(2):6-7.
(1)资料来源:国家发展和改革委员会能源研究所(国家发展和改革委员会能源研究所.中国可持续发展能源暨碳排放情景分析综合报告[R].2003:37):王刚、冯霄(汪刚,冯霄.基于能量集成的CO2减排量的确定[J].化工进展,2006,25(12):1467-1470)。