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本文首先对国内外碳排放强度影响因素研究动态进行系统论述,随后对碳排放强度的预测研究现状做出整体概述。将DDEPM与现有的碳排放预测方法进行比较,说明其优越性。应用DDEPM,用Matlab编程,基于1980年-2009年的碳排放数据和GDP数据,对2020年碳排放和GDP进行预测,通过计算得出降低中国碳排放强度的潜力巨大。基于中国能源以煤炭为主的现状,应用向量自回归模型(VAR),从煤炭能源消耗比重的角度,分析其对中国碳排放强度的影响。随后整合碳排放强度、煤炭消耗比重的预测数据和实际数据,将其整合数据进行向量自回归处理,其结果与碳排放强度与煤炭消耗比重实际数据的向量自回归进行比较,得出了两组模型结论的一致性,从变化规律的角度检验DDEPM预测的准确度;最后应用脉冲响应函数,分析碳排放相度与煤炭消耗比重的相关性。
This paper first systematically discusses the dynamic research on the influencing factors of carbon emission intensity at home and abroad, and then gives a general overview of the research status of carbon emission intensity prediction. Compare DDEPM with existing carbon emission forecasting methods to illustrate their superiority. Using DDEPM and Matlab programming, based on the carbon emission data and GDP data from 1980 to 2009, we can predict the carbon emissions and GDP in 2020. The potential for reducing carbon intensity in China is huge. Based on the present status of China’s energy-based coal, the VAR model is used to analyze the impact of China’s carbon intensity on the basis of coal’s energy consumption. Then the forecast data and the actual data of the intensity of carbon emission and the proportion of coal consumption are integrated and the integrated data are processed by vector autoregressive. The results are compared with the vector autoregression of actual data of carbon emission intensity and coal consumption proportion, and two sets of data are obtained The consistency of the model conclusion, the accuracy of DDEPM prediction from the perspective of the change rule; Finally, the impulse response function is used to analyze the correlation between the carbon emission phase and the proportion of coal consumption.