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基于产业结构视角的能源富集区碳排放效应研究—以山西省为例
董洁芳
学位类型博士
导师王强 ; 张小雷
2017-05-01
学位授予单位中国科学院大学
学位授予地点新疆乌鲁木齐
学位专业理学博士
关键词产业结构 碳排放 影响因素 Stirpat模型 面板模型 山西
摘要中国政府承诺将于2030年左右使碳排放达到峰值并争取尽早实现。要实现碳减排的承诺,进行产业结构转型升级是中国绿色、低碳发展的必然选择。结合中国区域发展的实际,能源富集区由于“路径依赖”的存在,在产业结构调整方面将会面临更大挑战,因此也面临着更大的碳减排压力。同时,能源富集区域还面临着既要保障国家能源供应安全,又要实现自身经济增长与低碳转型的双重挑战。在此情况下,深入研究能源富集区域不同产业的碳排放效应以及产业结构变动对碳排放的作用机理具有十分重要的理论与现实意义。选择中部地区典型的能源富集及经济欠发达省份——山西省为研究对象。作为典型能源富集区域,山西产业结构及碳排放影响因素的演变具有较强代表性,研究获得的结论对能源富集区产业结构调整、低碳发展具有一定的借鉴意义。本文综合利用经济地理学、能源经济学、区域经济学、产业经济学以及计量经济学等相关理论与方法,将理论基础阐释与实证分析相融合,深入研究了山西产业结构变动与碳排放的动态演进关系以及工业行业碳排放等问题。并以山西为例,探索欠发达的能源富集区产业结构对碳排放的影响机制,从产业结构优化的视角为资源型区域低碳发展提供基础理论支撑。论文的主要内容包括:第一,对产业结构演进及低碳发展的有关理论进行梳理;第二,详细考察山西产业结构演进过程,从产值与就业两个方面分析产业结构变动趋势;第三,核算山西总体及分三次产业的碳排放,并对其能源结构、能源强度、碳排放强度以及产值与碳排放的相关性进行定量分析;第四,综合运用LMDI指数分解与STIRPAT模型,对山西总体碳排放的影响因素进行深入分析;第五,利用多层次指数分解法对各产业碳排放的影响因素进行辨识,并比较各因素在产业间存在的差异。同时,利用SBM-Undesirable模型分别测算碳排放约束下三次产业的全要素能源生产效率,从而判断各产业能源效率低下的主要原因;第六,对工业内部22个行业的碳排放量进行计算,并结合各行业产值数据,利用聚类分析方法探究山西工业内部行业结构及碳生产率的变动情况;第七,基于绿色索洛模型和IPAT概念方程构建面板回归模型,对产业结构变动影响碳排放的内在机理进行研究;第八,根据重要结论提出相关政策建议,分析论文不足,提出进一步研究的展望。论文的主要结论和创新点有:(1)结合能源结构变化,分析了山西总体碳排放以及各产业碳排放的演化趋势。发现山西碳排放总量的波动与经济增长速度相关性显著。尽管山西碳排放强度和能源强度不断降低,但以煤炭为主的能源消费结构未得到有效改善。三次产业层面的分析表明产业间在能源消费结构和碳排放量上存在明显差异。第一产业和第二产业能源消费以原煤和电力为主,第三产业能源消费以石油制品和电力为主。从碳排放总量来看,第二产业碳排放比重最大,且第二产业中工业产值与碳排放之间的相关系数最高。(2)将非线性STIRPAT模型与LMDI方法融合起来,从存量和流量的角度构建了对能源富集区碳排放研究具有普适性的分析模型。基于此模型,揭示了山西能源消费碳排放变动的内在机制。LMDI分解结果表明,经济规模效应是促使山西碳排放增加的主要因素。相反地,能源强度效应是抑制碳排放增加的关键因素。能源结构效应和产业结构效应均对碳排放起到微弱促进作用。基于岭回归的STIRPAT多变量非线性回归结果显示,经济水平、产业结构以及城镇化均促进碳排放。这一结论验证了LMDI分析所获的结果。除了这三个因素之外,岭回归结果还表明能源强度的降低和能源结构的改善可以有效减少碳排放。(3)拓展了多变量、非线性LMDI模型的多层次指数分解方法,并将其运用于产业层面的分析,揭示了山西不同产业碳排放驱动因素间存在的具体差异。研究结果显示,劳动生产率是导致各产业碳排放增加的主要因素。其中,第二产业的劳动生产率对碳排放的影响最大。相反地,能源强度被证明是抑制三次产业碳排放的关键因素。与第一、第二产业相比,该效应对第三产业碳排放的抑制作用更为明显。能源结构与就业人口对碳排放的影响分产业有所不同。就能源结构而言,其累积效应促进第一产业碳排放的增长。相反地,该效应抑制第三产业碳排放的上升。(4)将CO2作为非期望产出,对SBM-Undesirable模型进行扩展并利用此模型对各产业全要素能源生产效率低下的原因进行深入研究。结果显示,山西第一产业全要素能源效率低下的主要原因为二氧化碳的过度排放、其次为能源、劳动力以及资本的冗余投入。与第一产业相比,第二产业的全要素能源效率较高,这说明在考虑非期望产出的情况下,第二产业消耗单位能源可以获得更高产出。同时,导致第二产业全要素能源生产效率低下的原因包括能源消费的冗余投入、劳动力的过度投入。第三产业投入与产出松弛变量分析显示,其投入要素中,70.44万吨标煤的能源,14.52万人的劳动力以及6.28亿元的资本属于冗余投入。(5)对工业行业内部产值与碳排放的研究揭示了山西各行业碳生产率演化规律。研究发现,煤炭开采和洗选业,石油加工、炼焦和核燃料加工业,化学原料和化学制品制造业,黑色金属冶炼和压延加工业,有色金属冶炼和压延加工业以及电力、热力生产和供应业是山西工业产值比重最大的6个行业。对各行业产值比重增长率的分析显示山西工业重型化趋势明显,山西经济增长对能源矿产等资源的依赖在逐步加强。电力、热力生产和供应业,石油加工、炼焦和核燃料加工业、黑色金属冶炼和压延加工业碳排放总额最大。而烟草制品业,仪器仪表制造业以及纺织服装、服饰业碳排放量较小。同时碳排放占比较大的行业碳排放量增长速度较快,表明山西省碳排放越来越集中于特定行业。进一步,结合碳生产率,将细分的22个行业通过系统聚类分为5种类型:低产值、低排放;高产值、高排放;低产值、高排放;中产值、中排放和中产值、高排放。(6)发展了适于能源富集区碳排放研究的绿色索洛模型,揭示了产业结构变动对碳排放影响的作用机理。利用相关经济数据,选取工业产值在总产值中比重,第三产业产值在总产值中比重以及第三产业产值与第二产业产值之比来表征产业结构,并建立静态面板回归模型。面板单位根检验显示所选变量均平稳,且面板Pedroni协整检验显示变量之间存在长期均衡关系。面板回归结果表明,山西产业结构调整对碳排放影响显著。通过对表征碳排放的三个变量在不同模型中系数变动的区间进行对比可知,第二产业比重变动对碳排放总量影响最大。产业结构从以第二产业为主向以第三产业为主的转型可以逐渐降低碳排放。
其他摘要China has pledged to make carbon emissions to peak around 2030 and try to achieve as soon as possible. Moreover, carbon emissions per unit of GDP in 2030 will by 60% to 65% lower than that in 2005. To achieve carbon reduction commitment and green low-carbon development, it is the inevitable choice for China to upgrading its industrial structure. From the actual situation of China's regional development, energy enrichment region which due to the existence of "path dependence", will face greater pressure, and thus face greater pressure to reduce emissions in the process of industrial structure adjustment. At the same time, energy enrichment regions are also facing the dual challenges of not only ensuring the security of national energy supply, but also realizing their own economic growth and low carbon transformation. In this case, it is of great theoretical and practical significance to conduct in-depth study the effect of carbon emissions and the mechanism of industrial structure change on carbon emissions in these energy enrichment regions.Choose Shanxi Province which is a typical energy enrichment and less-developed region in central China as the research object to conduct research. Owning to its representativeness, the conclusions that obstained from the research of shanxi's industrial structure and the evolution of carbon emissions influence factors has a certain reference significance to other energy enrichment regions. Through the comprehensive use of multidisciplinary approach, such as economic geography, energy economics, regional economics, industry economics and econometrics, this paper had studied deeply on the industrial structure transformation and its dynamic ralationship with carbon emission’s variation in Shanxi Province. Furthermore, this paper had explored the inner mechanism between industrial structure and carbon emissions in less developed energy enrichment regions and the conclusions will offer theoretical support for these regions’ low carbon development.The main contents of this paper include: First, review the evolution of industrial structure and the related theory of low-carbon development. Second, from the aspects of output and employment, a detailed investigation of shanxi industrial structure evolution process had been carried out. Third, calculated carbon emissions for Shanxi overall and each industry and analyzed the correlation between carbon emissions and the output. Fourth, by using LMDI and STIRPAT model, the influence factors of Shanxi's overall carbon emissions had been studied in-depth. Fifth, the driving forces of each industy’s carbon emssions had been identified by appling multi-level index decomposition. Moreover, in order to determine the main reason for the low energy efficiency of each industry, the SBM-Undesirable model was ultilized to calculate the total factor energy efficiency of each industy. Sixth, the 22 indistrial sectors’s carbon productivity had been calculated and by clustering analysis method, these sectors were divided into five typies. Seventh, based on the Green Solow Model and IPAT identity, a panel regression model had constructed to study the inner mechanism of the change of industrial structure to the carbon emissions. Eighth, according to the main conclusion, relevant policy recommendations were put forward. In addition, the shortage of this paper had been analysized and further research prospect was put forward.The main conclusions and innovations of this paper are as follows.(1) Combined with the change of energy structure, the paper analyzed the overall carbon emissions in shanxi and the evolution trend of the industrial carbon emissions. It is found that the fluctuation of carbon emissions in Shanxi is significantly related to the economic growth. Although the carbon emission intensity and energy intensity of Shanxi have been decreasing, the energy consumption structure dominated by coal has not been effectively improved. The three industry level analysis indicated that there are significant differences in energy consumption structure and carbon emissions between industries. Both the primary industry and secondary industry’s energy consumption is dominated by coal and electricity. However, the energy consumption of the tertiary industry is dominated by petroleum products and electricity. Furthermore, the total carbon emissions from secondary industry is the largest, and the correlation coefficient between the industrial output and carbon emissions is the highest.(2) By combined the nonlinear STIRPAT model with the LMDI method, a universal analysis model for the study of carbon emissions in the energy enrichment region had been constructed from the perspective of the stock and the flow. Based on this model, this paper reveals the inherent mechanism of carbon emissions change in Shanxi. The results of LMDI decomposition show that economic scale effect is the main factor to increase carbon emissions in Shanxi. On the contrary, the energy intensity effect is the key factor to restrain the increase of carbon emissions. Both energy structure effect and industrial structure effect play a marginal role in the increase of carbon emissions. The results of STIRPAT regression based on ridge regression show that economic level, industrial structure and urbanization rate promote carbon emissions. This conclusion verifies the results obtained by LMDI analysis. In addition to these three factors, ridge regression results also indicate that the reduction of energy intensity and the improvement of energy structure can effectively reduce carbon emissions.(3) Through expand the multi-variable, nonlinear LMDI model and apply it to the analysis of the industrial level’s carbon emissions, this paper reavels the influencing factors varied from primary industry to teriary industry. Specifically speaking, the labor productivity is the main factor that leads to carbon emissions increase. Moreover, the labor productivity has the greatest impact on secondary industry carbon emissions. In contrast to labour productivity, energy intensity has been proved to be a key factor in inhibiting three industrial carbon emissions. Compared with the primary and secondary industry, the inhibitory effect of energy intensity effect of the tertiary industry is more obvious. The impact of energy structure and employment population on carbon emissions varies from industry to industry. In terms of energy structure, its cumulative effect promotes the growth of carbon emissions in the primary industry. On the contrary, its cumulative effect curb carbon emissions rise in tertiary industry.(4) Using CO2 as an undesirable output, the SBM-Undesirable model is extended and this model is used to study the causes of low efficiency of total factor energy production in each industry. The results show that the main reason for the total energy efficiency of the primary industry in Shanxi is mainly due to the excessive emission of carbon dioxide, followed by the energy, labor and capital investment. Compared with the primary industry, the total energy efficiency of the secondary industry is higher, which means that in the case of non-expected output, the energy consumption per unit of the second industry can have higher output. At the same time, the reasons for the low efficiency of total factor energy production in the secondary industry include the redundant investment in energy consumption and the excessive investment of labor force. Analysis of the input and output slumps of the tertiary industry shows that the energy input of 704,400 tons of standard coal, the labor force of 145,200 people and the capital of 628 million yuan are redundant inputs.(5) The study of industrial output and carbon emissions in the industrial sector reveals the evolution of carbon productivity in various industries in Shanxi. The results indicate the following six industrial sectors have the highest output value: Coal Mining and Dressing, Petroleum Processing, Coking and Nuclear Fuel Processing, Manufacture Raw Chemical Materials and Chemical Products, Smelting and Pressing of Ferrous Metals, Smelting and Pressing of Nonferrous Metals and Production and Supply of Electricity and Heat. The analysis of the growth rate of output value of various industries shows the proportion of heavy industry is growing, which indicate the dependence of Shanxi's economic growth on energy and mineral resources is gradually strengthened. The three sectors with the highest carbon emissions are Production and Supply of Electricity and Heat, Petroleum Processing, Coking and Nuclear Fuel Processing and Smelting and Pressing of Ferrous Metals, respectively. While the sectors of Tobacoo Manufaturing, Manufacture of Measuring Instrument and Machinery and Manufacture of Garments and Accessories have the least carbon emissions. At the same time, carbon emissions accounted for a large proportion of carbon emissions grew faster, indicating that carbon emissions in Shanxi Province are increasingly focused on specific industrial sectors. In addition, combined with carbon productivity, 22 industries that focus on inspection are divided into five types through system clustering: Low output value, Low emission; High output value, High emission; Low output value, High emission; Medium production, Medium emission and Medium output, High emissions.(6) Developed a green Solow model for carbon emissions research in energy enrichment areas, revealing the mechanism of the impact of industrial structure change on carbon emissions. Using the relevant economic data, select the proportion of industrial added value in the total output value, the proportion of the tertiary industry added value in the total output value and the ratio of the tertiary industry added value and the secondary industry added value to characterize the industrial structure and establish the static panel regression model. The panel unit root test shows that the selected variables are stable and the panel Pedroni cointegration test shows that there is a long-term equilibrium relationship between these variables. The results of panel regression show that Shanxi industrial structure adjustment has a significant impact on carbon emissions. By comparing the three variables that characterize the carbon emissions in different models, the change of the proportion of the second industry has the greatest influence on the total carbon emission. Industrial structure from the secondary industry to the tertiary industry-based transformation can gradually reduce carbon emissions.
学科领域自然地理学
语种中文
文献类型学位论文
条目标识符http://ir.xjlas.org/handle/365004/14777
专题研究系统_荒漠环境研究室
作者单位中国科学院新疆生态与地理研究所
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董洁芳. 基于产业结构视角的能源富集区碳排放效应研究—以山西省为例[D]. 新疆乌鲁木齐. 中国科学院大学,2017.
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