【作者】 林涛；

【导师】 赵涛；

【作者基本信息】 天津大学 ， 管理科学与工程， 2013， 博士

【摘要】 在2009年的哥本哈根全球气候变化大会上，中国政府作出承诺：到2020年，单位GDP的碳排放量相比2005年下降40%-45%。这体现了我国作为一个大国的责任感。但是，目前我国处于城市化、工业化迅速发展的阶段，完成这样的减排目标有很大的挑战性。这就要求全国每个省区都积极行动，为实现碳减排目标而努力。天津市是中国的经济重镇，也是碳排放大户。天津碳减排的效果对于我国能否实现碳减排的目标具有重要的影响。本文针对天津市碳足迹的影响因素进行深入研究，为天津市制定减排政策，实现碳足迹的降低提供理论依据。主要研究内容如下：（1）基于LMDI法的天津市能源消耗碳足迹影响因素分解。首先，核算出天津市2001—2009年的能源消耗碳足迹，进而计算出天津市碳足迹生态压力，并对碳足迹生态压力进行分析。然后建立LMDI模型对天津市三大产业以及34个工业部门进行因素分解，将碳足迹的变化分解为碳足迹因子、能源强度、能源结构、经济结构、经济发展五个因素，通过对因素分解结果的分析确定各个因素对碳足迹变化的作用，结果表明经济发展是天津市碳足迹增加最主要的促进因素，能源强度是最主要的抑制因素。（2）基于CDM和GFI法的天津市能源强度因素分解。由前一章的结论可知，能源强度是天津市碳足迹增加最主要的抑制因素。为了实现天津市的碳足迹的降低，就要充分发挥能源强度对碳足迹增长的抑制作用，这就需要充分了解能源强度的影响因素。本文首先分析了CDM法和GFI法的特点，然后利用CDM法将2001—2009年天津市三大产业能源强度的变化分解为结构份额和效率份额，利用GFI法将天津市工业能源强度分解为技术进步、能源结构和产业结构，结合两种方法的计算结果可知：对于三大产业，技术进步是能源强度下降的最主要的促进因素，而产业结构是最主要的抑制因素；对于工业，对能源强度变动的影响作用由大到小依次是技术进步、产业结构、能源结构。（3）基于VAR模型的碳足迹影响因素的动态作用分析。根据VAR模型构建的要求，对碳足迹以及因素分解方法分解出的能源强度、能源结构、经济结构进行平稳性检验和协整检验；检验通过后构建碳足迹与这三种影响因素的VAR模型,并利用脉冲响应函数与方差分解方法分析能源强度、能源结构、经济结构对碳足迹的长期动态作用。分析结果显示，能源强度、能源结构、经济结构三种因素对碳足迹的长期解释性较强，存在长期的影响作用。更多还原

【Abstract】 On the UN Framework Convention on Climate Change in Copenhagen in2009,Chinese government promised that the carbon emission intensity of China in2020would be reduced by40%-45%compared with the carbon emission intensity in2005.This reflects our sense of responsibility as a big country. However, China is in thestage of rapid development of urbanization, industrialization. So the emissionreduction target would be very challenging. Every province has to carry out positiveactions to achieve the carbon reduction targets. Tianjin is the economic center ofChina, making large carbon emissions. Tianjin carbon emission reduction effect hasimportant implications for the goal of carbon reduction. In this paper, the impacts ofthe carbon footprint of Tianjin factors were intensively analyzed to provide atheoretical basis for Tianjin to establish emission reduction policies. The maincontents are as follows:(1) Influencing factor analysis on Tianjin carbon footprint based on the LMDImethod. First, the accounting of the carbon footprint of Tianjin in2001-2009wasgiven, and then the ecological pressure was calculated and analyzed. Then with LMDImethod, changes of carbon footprint of Tianjin as a whole and34industrial sectorswas decomposed into carbon footprint factor, energy intensity, energy structure,economic structure and economic development as the five factors. According to thefactor decomposition results, the various factors for the changes in the role of thecarbon footprint were determined. The results showed that the economic developmentof Tianjin increased carbon footprint as the most important contributing factor, andenergy intensity was the main inhibiting factor.(2) Factors decomposition of Tianjin Industry energy intensity based on completedecomposition and GFI. According to the conclusion of the previous chapter, theenergy intensity is the main inhibiting factor of carbon footprint in Tianjin. In order toreduce carbon footprint in Tianjin, we should make full use of the inhibition of energyintensity, which needs fully understanding on the impact of influencing factors onenergy intensity. This paper first analyzed the characteristics of CDM and GFI. Andthen the change of energy intensity in Tianjin from the year of2001-2009wasdecomposed into structure share and efficiency share based on CDM, and then thechange of energy intensity was decomposed into technological progress, energy and industrial structures by using the method of GFI. Considering the results of CDM andGFI, for the three major industries, the role of technological progress is the mostimportant contributing factor to the decline in energy intensity, and industrial structureis the main inhibiting factors; and for industry, the effects of the factors on the changein energy intensity in descending order are technical progress, industrial structure andenergy structure.(3) Based on VAR model, dynamic effects of influencing factors of carbonfootprint were analyzed. According to the requirements of the VAR model, thestationary test and cointegration test were used to the carbon footprint as well as thefactors of energy intensity, energy structure and economic structure; after tests werepassed, VAR model of carbon footprint and three influencing factors was set up. Andimpulse response function and variance decomposition method were used to analyzethe long-term dynamic effects of energy intensity, energy structure, the economicstructure to the carbon footprint. The result of the analysis showed that the factors ofenergy intensity, energy structure and economic structure had the strong explanatoryto carbon footprint, with a long-term effect.更多还原