The Carbon Emission Intensity Forecast Of China And Its Influence Factors Dynamic Effect Model Construction

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%.