中国碳排放强度影响因素、收敛性及溢出性研究

发布时间：2018-03-25 02:40

本文选题：碳排放强度　切入点：空间集聚　出处：《重庆大学》2016年博士论文

【摘要】：根据国际能源署(IEA,2009)统计数据,2007年中国二氧化碳排放量已超过美国,成为全球第一大二氧化碳排放国。在未来较长时期内,中国的经济仍将以较快速度增长,加之城市化、工业化进程的推动会进一步加剧经济增长与能源环境之间的矛盾,使我国面临更大的减排压力。在此背景下,中国政府于2009年首次提出具体温室气体减排目标,即到2020年,我国单位国内生产总值(GDP)CO2排放量(碳强度)比2005年下降40%~45%,并将约束性指标纳入国民经济和社会发展中长期规划中。由于区域经济发展和资源禀赋的不均衡性,我国碳排放存在显著的省际和区域差异。为实现减排目标,中国必须充分考虑碳排放的空间分布特征,针对性地出台相应的产业和能源政策,才能公平有效地以较低的社会经济成本实现减排目标。鉴于此,本文就我国碳排放强度的影响因素、收敛性及其空间溢出性等关键问题进行讨论,以期为政府制定和实施节能减排政策提供理论支持和政策建议。本文根据IPCC的参考方法,估算了我国及各省市的碳排放及碳排放强度数据。首先,从时间序列数据的角度,对我国碳排放强度的影响因素进行协整分析。然后,基于省域面板数据分析了我国碳排放强度的演变趋势和区域差异,并运用空间统计分析方法从空间集聚效应和辐射效应两个方面讨论了中国碳排放强度的空间分布特征。再后,通过构建纳入空间效应的绝对?收敛模型、条件?收敛模型以及俱乐部收敛模型,考察了碳排放强度的收敛特征。最后,利用空间滞后模型(SLM)、空间误差模型(SEM)以及空间杜宾模型(SDM)考察引起区域碳减排差异的深层次原因,并对各因素的空间效应进行分解,进一步了解不同变量对碳排放强度的直接效应、间接效应和总体效应的影响及冲击,以此检验不同变量在碳排放强度的空间交互过程中是否具有显著空间溢出性。基于我国碳排放强度表现出的数据特点进一步划分为1997-2005年和2006-2013年两个阶段,考察不同时间阶段的解释变量的贡献程度以及空间溢出效应的演变趋势。通过以上理论研究和实证检验,得到以下结论:(1)能源结构、城市化和人均GDP与我国碳排放强度之间存在长期稳定的协整关系,三个变量对碳排放强度的弹性系数分别为1.98、0.97和-0.65,过分依赖煤炭的能源结构是我国节能减排的最大障碍。(2)省域碳排放强度具有显著的空间相关性,说明我国省域碳排放强度空间分布并非是完全随机的状态,而是具有显著的空间依赖特征,相似碳排放强度水平的省域倾向于集聚分布。(3)我国碳排放强度的空间集聚呈现出逐渐优化的态势。观察期内的碳排放强度散点图显示,“低-低”集聚型的规模不断扩大,“高-高”集聚型的规模在逐渐减少。(4)不同省份在碳排放强度在空间集聚中所扮演的角色和贡献具有明显差异。“高-高”集聚中的内蒙古、宁夏、山西和甘肃等地对推高周边地区碳排放强度的带动作用强烈,“低-低”集聚中的广东、福建和浙江等省对碳排放强度的负向带动作用较强。(5)我国碳排放强度存在显著绝对?收敛、条件?收敛以及俱乐部收敛特征,俱乐部收敛模型显示东部收敛速度最快,中部次之,西部最慢。(6)碳排放强度的空间滞后项显著为负,表明周边地区碳排放强度水平会对本地区的碳减排产生重要影响,因此利用空间面板模型来研究碳排放强度问题更为合理。(7)经济发展、所有权制度、对外开放水平和外商直接投资的提高可以降低区域碳排放强度,能源结构、能源禀赋、产业结构和城市化水平的提高会增加区域碳排放强度。(8)技术进步、外商直接投资、对外开放水平、城市化、能源结构和能源禀赋具有显著的空间溢出效应,表明以上变量的变动不仅会对本地区的碳排放强度产生影响,还会通过空间传导机制辐射至周边地区。(9)优化外商直接投资结构、提高经济增长水平是目前实现碳减排目标的有效途径。最后,依据所得研究结论,提出了碳减排的政策建议,同时指出了本研究存在的不足以及后续研究方向。
[Abstract]:According to the International Energy Agency (IEA, 2009) statistics, in 2007 the amount of carbon dioxide emissions China has overtaken the us to become the world's largest emitter of carbon dioxide. In the future for a long period, Chinese economy will continue to grow at a faster speed, coupled with the city, to promote the industrialization process will further aggravate the contradiction between economic growth and energy environment that China is facing greater pressure to reduce emissions. Under this background, China government first proposed specific greenhouse gas emission reduction targets in 2009, to 2020, China's unit of gross domestic product (GDP) CO2 emissions (carbon intensity) than in 2005 decreased by 40%~45%, and the constraint index into the national economic and social development long term planning. Because of the unbalanced development of regional economy and resources, China's carbon emissions are inter provincial and regional difference. To achieve emission reduction targets, China must fully consider carbon The spatial distribution characteristics of emissions, targeted to the introduction of the corresponding industrial and energy policies to fair and effective with the social economic low cost to achieve emission reduction targets. In view of this, the factors influencing the strength of China's carbon emissions, and convergence of the spatial spillover of such key problems are discussed, in order to provide theoretical support and policy suggestions for the government to formulate and implement energy-saving emission reduction policies. Based on the IPCC method, to estimate the carbon emission and carbon emission intensity data of our country and various provinces. First of all, from the time series data, the cointegration analysis of the influencing factors of China's carbon intensity. Then, the provincial panel based on the data analysis of the evolution trend of carbon emission intensity in China and regional differences, and using spatial statistics analysis method of two aspects from the space agglomeration effect and radiation effect is discussed. The carbon emission intensity China space The distribution characteristics. Then, by building into the space effect of the absolute convergence model?? convergence model and club convergence model, investigated the convergence characteristics of carbon emission intensity. Finally, using the spatial lag model and spatial error model (SLM) (SEM) and spatial Durbin model (SDM) study by deep-seated reasons regional differences in carbon emission reduction, decomposition and the factors of the space effect, to further understand the different variables on the direct effect of carbon emission intensity, the indirect effect and the overall effect of the influence and impact, in order to test whether different variables have significant spatial spillover in spatial interaction of carbon emission intensity. The data characteristics of carbon emission intensity I the show is based on further divided into 1997-2005 and 2006-2013 years in two phases, the evolution and spatial spillover effects on the contribution of different time periods of the explanatory variables Trend. Through the above theoretical analysis and empirical test, we get the following conclusions: (1) the energy structure, there is a long-run cointegration relationship between carbon emission intensity and per capita GDP city of China, the coefficient of elasticity of the three variables on the carbon emission intensity were 1.98,0.97 and -0.65, over reliance on coal energy structure is the biggest barriers to energy conservation and emission reduction in China. (2) with the provincial carbon emission intensity shows significant spatial correlation, spatial distribution is not a provincial carbon emission intensity in China is a state of complete random, but has significant spatial dependence, similar to the provincial level on the tendency of carbon emission intensity distribution. (3) cluster agglomeration the situation has been improved the strength of China's carbon emission space. The carbon emission intensity in the observation period of the scatter plot showed that the "low - low" cluster continues to expand the scale of high high agglomeration type in size gradually reduced. (4) Played in different provinces of carbon emission intensity in the spatial agglomeration of the role and contribution has obvious difference. High high agglomeration in Inner Mongolia, Ningxia, Shanxi and Gansu, the leading role of carbon high strength surrounding areas strongly, "low - low" agglomeration in Guangdong, Fujian and Zhejiang etc. on the carbon emission intensity of the negative strong leading role. (5) the intensity of carbon emissions in China have significant absolute convergence, convergence conditions?? and the club convergence characteristics, club convergence model shows that the East has the fastest convergence rate, the slowest in the West. Nakabe Jinno, (6) the lag of carbon emission intensity space is significantly negative. The level of carbon emission intensity shows that the surrounding area will have a significant impact on carbon emissions in the region, so using the spatial panel model to study the carbon emission intensity is more reasonable. (7) economic development, ownership system, opening to the outside world and the level of foreign Direct investment can reduce the regional carbon emissions intensity, energy structure, energy resources, industrial structure and the city improvement will increase regional carbon emission intensity. (8) technological progress, foreign direct investment, the level of opening up, the city, the energy structure and energy has significant spatial spillover effect, show that the above variables the changes will not only affect the intensity of carbon emissions in the region, but also through the conduction mechanism of space radiation to the surrounding area. (9) to optimize the structure of foreign direct investment, improve the level of economic growth is an effective way to achieve the emission reduction targets at present. Finally, according to the research conclusion, put forward policy suggestions to reduce carbon emissions, and points out that the direction of the research limitations and future research.

【学位授予单位】：重庆大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：X321;F224

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