中国碳排放强度预测及其影响因素动态效应建模
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摘要
全世界各国对全球气候变化日益关注，并采取一系列措施减少温室气体的排放，阻止全球气候变暖。作为全球最大的发展中国家，中国政府作出承诺，提出到2020年，单位GDP的碳排放量相比2005年下降40%-45%的目标。实现碳排放强度的降低需要考虑能源强度、能源结构与产业结构等众多影响因素。本文针对中国2020年碳排放强度进行预测，深入研究中国改革开放以来碳排放强度的变化规律，分析碳排放强度影响因素的动态效应和贡献度，为中国制定减排政策，实现碳排放强度的降低提供理论依据。主要创新点如下：
（1）基于离散二阶差分方法的中国碳排放强度预测与分析。依据1980-2009年的碳排放与GDP数据，采用离散二阶差分方法(Discrete Difference EquationPrediction Model，DDEPM)对我国2020年的碳排放量与GDP数值进行预测，通过计算得出我国2020年碳排放强度的预测值，从而进一步估算我国未来十年降低碳排放强度的潜力；应用ISM模型，对我国降低碳排放阻碍因素进行建模，理顺我国降低碳排放阻碍因素之间的关系，通过ISM模型中的聚类分析，找出现阶段我国降低碳排放的关键阻碍因素。
（2）碳排放强度影响因素分解及其预测值规律性检验。为使碳排放强度的影响因素研究具有系统性，本文利用Kaya恒等式的改进模型将我国碳排放强度分解为碳排放因子、能源强度、能源结构和产业结构。基于1980-2009年的我国碳排放强度等各项所需数据，将分解出的能源强度、能源结构与产业结构与碳排放强度进行数据定义，同时检验所需时间序列的平稳性，随后对三种影响因素和碳排放强度分别进行VAR模型构建。基于加入DDEPM预测数据的我国1980-2020年的各项整合数据，构建三种影响因素与碳排放的VAR模型，将其与实际数据模型进行对比，检验DDEPM预测数据规律性的准确度。（3）基于结构向量自回归模型(SVAR)的碳排放强度影响因素分析。根据SVAR模型构建的特点，对碳排放强度、能源强度、煤炭消耗比重与第三产业比重进行整体协整检验；检验通过后构建碳排放强度与三种影响因素整体SVAR模型,并利用SVAR模型中的脉冲响应函数与方差分解，分析能源强度、煤炭消耗比重与第三产业比重对碳排放强度的动态效应与贡献度。分析结果显示，第三产业比重对碳排放强度的冲击效应峰值为0.58%，贡献度最大值为41.43375%；能源强度冲击效应峰值为0.28%，贡献度最大值为9.794%；煤炭消耗比重冲击效应峰值为0.19%，贡献度最大值为6.696%。
The Global Climate Change has aroused general concern of various countries allover the world. Many countries take measures to reduce greenhouse gas emission, inorder to prevent the Global Climate Warming. As the biggest developing country inthe world, Chinese government promises that the carbon emission intensity in2020will be reduced by40%-45%compared with the carbon emission intensity in2005. Toachieve the reduction target, the Chinese government needs to consider manyinfluence factors such as energy intensity, energy structure, industrial structure and soon. The paper forecasts the Chinese carbon emission intensity of2020, and study onthe variation law of the carbon emission intensity, analyzing the dynamic effect andcontribution degree of influence factors, in order to provide the theoretical basis onthe emission reduction policy formulation of China, to establish the carbon emissionintensity reduction. The main innovative points as follows:
(1)The carbon emission intensity prediction and analysis of China based on theDiscrete Difference Equation Prediction Model(DDEPM). According to the carbonemission (annul data1980-2009) and the GDP (annul data1980-2009), forecast thecarbon emission data and GDP data of2020by using the DDEPM. Then calculate thecarbon emission intensity prediction data of2020, in order to estimate the carbonemission intensity reduction potential of China in the future ten years; Model theobstructive factors of carbon emission intensity by using the ISM method, straightenout the relationship of these obstructive factors. Find the key obstructive factorsaccording to the cluster analysis of ISM model.
(2)Influence factors decomposition of carbon emission intensity and regularityinspection of the prediction data. In order to systemic analysis the influence factors ofcarbon emission intensity, the paper decomposed the carbon emission intensity byusing the improved Kaya identity. The carbon emission divisor, energy intensity,energy structure, industry structure are decomposed form the improved Kaya identity.Define the energy intensity, energy structure, industry structure by the various data(annul data from1980-2009) that the study required. Test the stationarity of the threeinfluence factors, then separately construct the VAR model of the three factors andcarbon emission intensity. Integrate the various required prediction (annul data2010-2020) to the practical data, to construct the VAR model of the integration data.Then compare the integration data model to the practical data model, to check the accurate degree of the DDEPM.
(3)The carbon emission intensity influence factors analysis based on the StructuralVector Autoregression model. According to the characteristics of the SVAR modelconstruction, Systematically co-integration test the carbon emission intensity, energyintensity, coal consumption proportion and tertiary industry proportion. After the testpassed, construct the SVAR model of the carbon emission intensity and its threeinfluence factors. Analyze the relationship of the carbon emission intensity and energyintensity, coal consumption proportion, tertiary industry proportion with the ImpulseResponse Function; Analyze the contribution rates of the three influence factors bythe Variance Decomposition. The analysis results show that the shock effect of tertiaryindustry proportion could get the0.58%, and its highest contribution rates is41.434%;the shock effect of energy intensity could get the0.28%, the highest contribution ratesis9.794%; the maximum shock effect of coal consumption proportion is0.19%, itshighest contribute rates is6.696%.
（1）基于离散二阶差分方法的中国碳排放强度预测与分析。依据1980-2009年的碳排放与GDP数据，采用离散二阶差分方法(Discrete Difference EquationPrediction Model，DDEPM)对我国2020年的碳排放量与GDP数值进行预测，通过计算得出我国2020年碳排放强度的预测值，从而进一步估算我国未来十年降低碳排放强度的潜力；应用ISM模型，对我国降低碳排放阻碍因素进行建模，理顺我国降低碳排放阻碍因素之间的关系，通过ISM模型中的聚类分析，找出现阶段我国降低碳排放的关键阻碍因素。
（2）碳排放强度影响因素分解及其预测值规律性检验。为使碳排放强度的影响因素研究具有系统性，本文利用Kaya恒等式的改进模型将我国碳排放强度分解为碳排放因子、能源强度、能源结构和产业结构。基于1980-2009年的我国碳排放强度等各项所需数据，将分解出的能源强度、能源结构与产业结构与碳排放强度进行数据定义，同时检验所需时间序列的平稳性，随后对三种影响因素和碳排放强度分别进行VAR模型构建。基于加入DDEPM预测数据的我国1980-2020年的各项整合数据，构建三种影响因素与碳排放的VAR模型，将其与实际数据模型进行对比，检验DDEPM预测数据规律性的准确度。（3）基于结构向量自回归模型(SVAR)的碳排放强度影响因素分析。根据SVAR模型构建的特点，对碳排放强度、能源强度、煤炭消耗比重与第三产业比重进行整体协整检验；检验通过后构建碳排放强度与三种影响因素整体SVAR模型,并利用SVAR模型中的脉冲响应函数与方差分解，分析能源强度、煤炭消耗比重与第三产业比重对碳排放强度的动态效应与贡献度。分析结果显示，第三产业比重对碳排放强度的冲击效应峰值为0.58%，贡献度最大值为41.43375%；能源强度冲击效应峰值为0.28%，贡献度最大值为9.794%；煤炭消耗比重冲击效应峰值为0.19%，贡献度最大值为6.696%。
The Global Climate Change has aroused general concern of various countries allover the world. Many countries take measures to reduce greenhouse gas emission, inorder to prevent the Global Climate Warming. As the biggest developing country inthe world, Chinese government promises that the carbon emission intensity in2020will be reduced by40%-45%compared with the carbon emission intensity in2005. Toachieve the reduction target, the Chinese government needs to consider manyinfluence factors such as energy intensity, energy structure, industrial structure and soon. The paper forecasts the Chinese carbon emission intensity of2020, and study onthe variation law of the carbon emission intensity, analyzing the dynamic effect andcontribution degree of influence factors, in order to provide the theoretical basis onthe emission reduction policy formulation of China, to establish the carbon emissionintensity reduction. The main innovative points as follows:
(1)The carbon emission intensity prediction and analysis of China based on theDiscrete Difference Equation Prediction Model(DDEPM). According to the carbonemission (annul data1980-2009) and the GDP (annul data1980-2009), forecast thecarbon emission data and GDP data of2020by using the DDEPM. Then calculate thecarbon emission intensity prediction data of2020, in order to estimate the carbonemission intensity reduction potential of China in the future ten years; Model theobstructive factors of carbon emission intensity by using the ISM method, straightenout the relationship of these obstructive factors. Find the key obstructive factorsaccording to the cluster analysis of ISM model.
(2)Influence factors decomposition of carbon emission intensity and regularityinspection of the prediction data. In order to systemic analysis the influence factors ofcarbon emission intensity, the paper decomposed the carbon emission intensity byusing the improved Kaya identity. The carbon emission divisor, energy intensity,energy structure, industry structure are decomposed form the improved Kaya identity.Define the energy intensity, energy structure, industry structure by the various data(annul data from1980-2009) that the study required. Test the stationarity of the threeinfluence factors, then separately construct the VAR model of the three factors andcarbon emission intensity. Integrate the various required prediction (annul data2010-2020) to the practical data, to construct the VAR model of the integration data.Then compare the integration data model to the practical data model, to check the accurate degree of the DDEPM.
(3)The carbon emission intensity influence factors analysis based on the StructuralVector Autoregression model. According to the characteristics of the SVAR modelconstruction, Systematically co-integration test the carbon emission intensity, energyintensity, coal consumption proportion and tertiary industry proportion. After the testpassed, construct the SVAR model of the carbon emission intensity and its threeinfluence factors. Analyze the relationship of the carbon emission intensity and energyintensity, coal consumption proportion, tertiary industry proportion with the ImpulseResponse Function; Analyze the contribution rates of the three influence factors bythe Variance Decomposition. The analysis results show that the shock effect of tertiaryindustry proportion could get the0.58%, and its highest contribution rates is41.434%;the shock effect of energy intensity could get the0.28%, the highest contribution ratesis9.794%; the maximum shock effect of coal consumption proportion is0.19%, itshighest contribute rates is6.696%.
引文
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