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1、城镇化进程中山西省碳排放量影响因素城镇化进程中山西省碳排放量影响因素 分析及预测研究分析及预测研究Reaearch on Influence Factors Analysis and Prediction of Shanxis Carbon Emissons in the Process of Urbanization摘要在中国的快速城镇化进程中，大量农村人口涌入城市，人均能源消费迅速增加，城市人口增长也将引起交通、住房、基础设施等化石能源消费的增加，化石能源引起的碳排放量增加使全球气候变化日趋加剧，城镇化高速发展阶段的中国面临着巨大的碳减排压力。为了实现到 2020 年，碳排放强度比 200
2、5 年下降 40%-45%碳减排承诺，中国政府对节能目标进行省级分解，要想实现碳排放分解目标，需要各省份根据自己的经济发展情况，找到影响碳排放的驱动因素以及预测未来的碳排放趋势，继而据此制定合理的减排政策。本论文选取山西省作为研究对象，深入研究了城镇化进程中山西省的碳排放影响因素，预测了山西省 2012-2020 年的碳排放情况，主要研究内容为：（1）利用 PATH-STIRPAT 模型分析人口、财富、城镇化、产业结构、能源效率 5 个变量对碳排放量的驱动作用及解释变量间的交互作用，通过计算路径系数结合偏最小二乘回归分析，将 5 个自变量的弹性系数分解为直接弹性系数和间接弹性系数，分析结果显示
3、：直接弹性系数中能源效率的值最大，其后依次为人口、财富、产业结构、城镇化；间接弹性系数中，能源效率对其他自变量的间接弹性系数均为负值，其他变量对能源效率的间接弹性系数也均为负值，能源效率是降低碳排放的主要途径；人口、财富、产业结构、能源效率、城镇化率每增长 1%，碳排放量相应增长 1.121%、0.434%、1.576%、-0.998%、1.021%； （2）利用结构向量自回归（SVAR）模型分析影响因素间的动态冲击效应。根据模型的构建要求，对产业结构、城镇化、碳排放量 3 变量进行平稳性检验，检验通过后构建 SVAR 模型，并进行模型识别，继而进行格兰杰因果分析、脉冲响应分析和方差分解分析，
4、分析结果显示第二产业比重的增加、城镇化进程的推进增加了碳排放，城镇化率的波动最容易传递到其他变量上。（3）基于时间序列角度，构建人口、价格、经济发展水平、经济结构、城镇化 4 变量与山西省能源消费量的协整方程，利用协整方程预测低速、基准、高速三种经济增长情景下山西省的能源消费量；基于灰色模型预测模型结合成分数据球坐标变换预测能源消费结构；根据能源消费量和能源消费结构预测结果估算 2012-2020 年低速、基准、高速经济情景下山西省的碳排放量，预测结果表明在任一种经济增长方式下，碳排放量都呈现出持续增长态势，但是增长速度有差异，三种情景下的碳排放年均增长率均低于 1990-2011 年碳排放的
5、年均增长率。关键词：山西省；城镇化；碳排放量； PATH-STIRPAT 模型；SVAR 模型 ABSTRACTUrbanization level works as an important indicator of a countrys modernization. Chinas urbanization is pushing at full steam. Any city, to a large extent, is consumer-oriented with rural populations influx, urban residents energy consumption be
6、havior is changing and the increase of per capita energy expenditure is unavoidable. In addition, the growth of urban population will contribute to more energy consumption in sectors such as traffic, housing, infrastructure and so on, which not only results in more fossil fuel consumption in cities,
7、 but also deteriorates the situation of global warming. In the rapid development path of urbanization, China is facing with great burden of reducing carbon emission. In order to realize the commitment of reducing carbon emission intensity in 2005 by 40%-45% up to 2020, Chinese government makes a dec
8、omposition of the energy- saving target at provincial level. Thus, each province should take its economic development into consideration and find out the main driving factors behind carbon emission to formulate its own mitigation policy, which may help it to achieve decomposition goal and to contrib
9、ute to the realization of the whole countrys carbon emission target. Based on the points above, this paper chooses Shanxi as the study object, and then investigates the carbon emission drivers of Shanxi under the process urbanization. Moreover, this paper also makes a prediction of the carbon emissi
10、on of Shanxi province in the period of 2012-2020. The main conclusions drawn in this study are listed as follows:(1) The driving effects of variables: population, per capita wealth, urbanization, industrial structure and energy efficiency in carbon emission are analyzed by using the PATH-STIRPAT mod
11、el. The results indicate that: In terms of direct path coefficient, the energy efficiency wealths is the largest, followed by population, per capita, urbanization, industrial structure. In terms of indirect coefficient, energy structure and urbanization have smaller impacts on other variables. As to
12、 the value of total path coefficient, the variable ranks in this order: per capita wealth, urbanization, population, energy efficiency and industrial structure. With 1% increase in population, per capita wealth, industrial structure, energy efficiency and urbanization , the growth of carbon emission
13、 will reach 1.121%, 0.434%, 1.576%、-0.998% and 1.021%, respectively. (2) The SVAR model is used to investigate the dynamic effect among the influencing factors. This paper conducts a stationary test of three variables: industrial structure, urbanization and carbon emission, due to the requirements o
14、f building this model. After getting through these tests, the SVAR model is built to develop Granger causality analysis, SVAR model recognition, impulse-response analysis and variance decomposition. And the results show that the rise in the proportion of the second industry and the advance of urbani
15、zation will lead to more carbon emission, and the fluctuation in the rate of urbanization is easy to pass to other variables while it is insusceptible to others.(3) From time series perspective, the future energy consumption in Shanxi is predicted with co-integration analysis, selecting population,
16、price, level of economic development, economic structure, urbanization as variables based on three scenarios: low-speed economic growth ,benchmark, high-speed economic growth.The energy structure is also predicted based on grey model combining spherical data transformation. Upon the predicted energy consumption and energy structure, the carbon emission of Shanxi in period of 2012-2020 is estimated. And the analysis shows that carbon emissions will increase in all the scenarios, but th
