基于偏正态混合效应模型的碳强度影响因素研究

发布时间：2018-03-03 23:17

本文选题：偏正态混合效应模型　切入点：碳强度　出处：《杭州电子科技大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着我国经济的高速增长,以及工业化和城镇化进程的不断推进,能源消费总量不断上升。巨大的能源消耗带来了二氧化碳的大量排放,环境问题日益凸显。而我国已在全球碳排放量排名中位居第一,一方面需要在国际社会承担相应的碳减排任务,另一方面需要维持经济的快速发展。因此,提高能源利用效率,降低碳强度,促进我国低碳、可持续发展已成为各方学者研究重点。然而,经过显著性检验,我国各地区碳强度纵向数据并不服从正态分布,若仍以传统计量经济方法进行建模研究,会使统计分析结果缺乏稳健性。鉴于此,立足于实际碳强度数据分布特征,对其构建偏正态分布模型进行统计推断,并据此研究碳强度影响因素,为完善我国碳减排政策提供对策与建议显得尤为重要。首先,本文基于EM算法给出偏正态混合效应模型中未知参数的极大似然估计。进而,应用参数Bootstrap构建偏正态单项分类模型感兴趣参数的精确检验方法。在此基础上,选取2000至2014年我国各省市自治区碳强度数据,验证其偏正态分布特征。继而,构建偏正态混合效应模型,研究我国各省市自治区碳强度变动的主要影响因素。最后,将上述模型参数估计结果与正态混合效应模型进行比较,以说明偏正态混合效应模型的优良性。研究结果表明,人均GDP、能耗强度、第二产业比重、对外贸易依存度等因素的变化,均会对我国各省份的碳强度产生显著影响。其中,加大科研投入并促进技术进步、降低第二产业比重以及促进对外开放水平等措施,均有利于降低我国各省份碳强度。而通过降低能耗强度来提高能源利用效率,从而降低碳强度是最为直接、有效的方式。同时,我国目前仍处于粗放型的经济发展中,需加快向集约型的经济发展方式转变。
[Abstract]:With the rapid economic growth of our country, as well as the continuous progress of industrialization and urbanization, the total amount of energy consumption has been rising. The huge energy consumption has brought a large amount of carbon dioxide emissions. Environmental problems are becoming increasingly prominent. China has already ranked first in the global ranking of carbon emissions. On the one hand, it needs to undertake the corresponding task of reducing carbon emissions in the international community, on the other hand, it needs to maintain rapid economic development. Reducing carbon intensity, promoting low carbon, sustainable development has become the focus of scholars. However, after significant test, the longitudinal data of carbon intensity in different regions of China are not suitable for normal distribution. If the traditional econometric method is still used to model the model, the results of statistical analysis will be lack of robustness. In view of this, based on the distribution characteristics of actual carbon intensity data, the statistical inference is made on the construction of partial normal distribution model. Based on this, it is very important to study the influencing factors of carbon intensity, and to provide countermeasures and suggestions for the improvement of China's carbon emission reduction policy. Firstly, based on EM algorithm, the maximum likelihood estimation of unknown parameters in the model of partial mixing effect is given, and then, the maximum likelihood estimation of the unknown parameters in the model is given based on the EM algorithm. On the basis of the accurate test method of the interested parameters of the biased individual classification model based on the parameter Bootstrap, the carbon intensity data of provinces, cities and autonomous regions of China from 2000 to 2014 are selected to verify the characteristics of partial normal distribution. A partial mixing effect model is constructed to study the main factors affecting carbon intensity change in China's provinces, cities and autonomous regions. Finally, the estimated results of the above model parameters are compared with the normal mixing effect model. The results show that the changes of per capita GDP, energy consumption intensity, secondary industry specific gravity and foreign trade dependence will have a significant impact on the carbon intensity of the provinces in China. Such measures as increasing the investment in scientific research and promoting technological progress, reducing the proportion of secondary industries and promoting the level of opening to the outside world are all conducive to reducing the carbon intensity in various provinces of China. Therefore, reducing carbon intensity is the most direct and effective way. At the same time, our country is still in the extensive economic development, so it is necessary to speed up the transformation to intensive economic development mode.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F124;X321

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