基于LMDI分解和STIRPAT模型的哈萨克斯坦能源碳排放影响因素的定量分析（英文）

编号 zgly0001634851

文献类型 期刊论文

文献题名 基于LMDI分解和STIRPAT模型的哈萨克斯坦能源碳排放影响因素的定量分析（英文）

作者 李佳秀  陈亚宁  李稚  刘志辉 

作者单位 StateKeyLaboratoryofDesertandOasisEcology  XinjiangInstituteofEcologyandGeography  CAS  CollegeofResourceandEnvironmentScience  XinjiangUniversity  CollegeofResourcesandEnvironment  UniversityofChineseAcademyofSciences 

母体文献 Journal of Geographical Sciences 

年卷期 2018年07期

年份 2018 

分类号 X11  X32 

关键词 quantitativeanalysis  energy-relatedCO2emissions  STIRPATmodel  Kazakhstan 

文摘内容 Quantitative analysis of the impact factors in energy-related CO2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO2 emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO2 emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO2 emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO2 emissions, which produced 110.86 Mt CO2 emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO2 emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused –110.90 Mt CO2 emissions with a contribution rate of –43.94%, followed by the energy carbon structure effect resulting in –18.76 Mt CO2 emissions with a contribution rate of –7.43%. In order to provide an in-depth examination of the change response between energy-related CO2 emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO2 emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.