【作者】 赵爱文；

【导师】 李东；

【作者基本信息】 南京航空航天大学 ， 管理科学与工程， 2012， 博士

【摘要】 中国碳排放和能源消费问题成为世界关注的焦点问题。中国目前的经济结构以高能耗和高碳排放产业为主，产业结构重型化造成高能源消费倾向和碳偏好。中国经济增长正在受到越来越多的制约，如气候条件、环境损耗和能源约束，其中最关键的瓶颈是能源问题。能源对中国经济的制约越来越严重，由能源消费导致的碳排放也在急剧增加。中国人均二氧化碳排放处于全球的73位，但排放总量已居全球前列。为了应对经济社会发展中日益凸显的能源约束以及日益严重的环境问题，中国政府在2009年9月的联合国气候变化峰会上，提出到2020年单位GDP碳排放强度要在2005年的基础上下降40%-45%的目标，在中国能源会议上首次提出在“十二五”期间一次能源消费总量控制在41亿吨标准煤，在《十二五规划纲要》中提出在“十二五”期间单位GDP能源强度降低16%和单位GDP碳排放强度降低17%的目标，并作为约束性指标纳入国民经济发展长期规划。碳排放、能源消费与经济增长挂钩，说明碳排放、能源消费与经济增长受到同样重视。如何在保证经济增长的条件下降低能源消费和碳排放是中国政府必须面对的严峻问题。本文通过理论模型和实证研究对中国碳排放、能源消费与经济增长的关系进行深入分析和研究。本文对中国碳排放与经济增长、能源消费与经济增长之间的关系进行灰色关联分析，结果表明中国碳排放与经济增长、能源消费与经济增长之间存在正相关关系，同时结合“十二五”规划和2020年减排目标，建立了无约束规划和约束规划下的中国能源消费总量和能源消费结构预测模型，并进一步预测两种不同条件下的碳排放总量，并进行比较。本文通过LMDI因素分解法分析了中国碳排放、能源消费的影响因素。结果表明，经济增长是碳排放量和能源消费量增加的驱动因素，能源强度下降是抑制碳排放量和能源消费增加的主要因素，并对降低碳排放和能源消费的关键因素——能源强度进行因素分解，建立了基于能源强度因素分解模型的能源强度扩展模型，通过建立随机模型来分析各变量对能源强度的非比例影响，通过最佳子集回归建立了能源强度主要影响因素的回归方程，回归结果表明，对能源强度影响最大的是工业能源强度，其次是工业结构，然后是第三产业能源强度。本文通过协整理论、误差修正模型及Granger因果关系分析了中国碳排放与经济增长、能源消费与经济增长的长期均衡关系和短期动态关系，Granger因果关系表明，中国碳排放与经济增长、能源消费与经济增长之间互为双向因果关系。通过建立VAR和VEC模型，把碳排放、能源消费与经济增长纳入统一框架内进行研究，通过脉冲函数与方差分解进一步分析了模型的动态特征。脉冲函数表明中国经济增长对碳排放和能源消费的影响路径几乎相同，呈现“镜对称”现象。本文通过EKC曲线研究中国碳排放与经济增长、能源消费与经济增长之间的耦合关系以及曲线是否存在拐点。模拟结果表明碳排放总量、人均碳排放量与人均GDP之间存在“N”型EKC曲线，且不存在拐点。碳排放强度与人均GDP之间存在“N”型EKC曲线，存在拐点。能源消费总量、人均能源消费与人均GDP之间存在“N”型EKC曲线，且不存在拐点。能源强度与人均GDP之间存在“N”型EKC曲线，存在拐点。本文通过Tapio脱钩指标及其扩展模型，研究中国碳排放与经济增长、能源消费与经济增长之间的脱钩弹性，并对脱钩弹性进行因素分解，结果表明碳排放与经济增长之间、能源消费与经济增长之间呈现弱脱钩的关系；同时引入具有逻辑关系的中间变量，进一步深入研究碳排放、能源消费与经济增长之间的脱钩关系，结果表明碳排放和经济增长的脱钩弹性与能源消费和经济增长的脱钩弹性变化趋势非常相似，两者弹性值近似相等。说明中国碳排放与经济增长的脱钩弹性是由能源消费和经济增长的脱钩弹性造成的。最后根据研究结果，提出了控制能源总量、降低能源强度、优化产业结构及大力开发新能源等对策。更多还原

【Abstract】 The issue regarding carbon emissions and energy consumption in China has drawn a lot ofworld-wide attention. Currently, China’s economic structure is mainly characterized by high energyconsumption and high carbon emission, which is recognized as a heavy industrial structure and resultsin emergence of high-energy-consumption tendency and carbon preference accordingly. As wellknown, China’s economic development has been suffering from a variety of constraints, such asclimatic conditions, environmental depletion and energy constraints, of which the most criticalbottleneck is the energy-related problem. On the one hand, the unreasonable energy consumption hasbeen generating more and more negative impact on China’s economic health growth. On the otherhand, the nation has also witnessed a sharp increase in carbon emissions caused by energyconsumption. According to certain published statistics, China’s per capita carbon dioxide emissionsare No.73in the global listing while the total emissions volume has been ranked the forefront.In response to the increasingly serious problems of energy constraints and environmental pollutionduring China’s economic process, the Chinese government proposed at the United Nations Summit onClimate Change held in September2009that, by2020, the carbon emission intensity per unit of GDPwould be declined by40%-45%in comparison with that in2005. Meanwhile, the nation firstly raisedin the "12th Five-Year Plan Outline" that during the "12th Five-Year" period, the total one-time energyconsumption would be controlled within4.1billion tons of standard coal, and that the energy intensityand the carbon emission intensity per unit of GDP would decrease by16%and17%respectively, bothinvolved in the long-term planning of national economic development as binding targets. The fact thatthe Carbon emissions and energy consumption are linked to the economic growth indicates that thesethree aspects are the same emphasis. It is a grim problem faced by the Chinese government as to howto reduce energy consumption and carbon emissions while ensuring its economic growth. In this paper,the relationship between carbon emissions, energy consumption and economic growth is analyzed indepth using theoretical models and empirical research.The grey relational analysis is conducted both between the carbon emissions with economicgrowth energy consumption and between the energy consumption with economic growth followed bythe result showing that the carbon emissions and energy consumption are positively correlated witheconomic development. Considering the five-year plan for2011to2015as well as emission reductiontarget by2020, the author further develops the China’s total energy consumption and energyconsumption structure prediction models with unconstrained programming and constrainedprogramming, and then predicts the national total carbon emissions with these two different conditions, respectively.The LMDI factor decomposition analysis is applied to explore the influencing factors related withcarbon emissions and energy consumption, which turns out that the factor of economic growth is thedriver contributed to increase in carbon emissions and energy consumption while energy decline is themain reason why carbon emissions and energy consumption are prevented. To begin with, factordecomposition analysis is carried out on energy intensity-a critical element for reducing carbonemission and energy consumption, and an energy intensity extension model is built in accordancewith the energy intensity factor decomposition. Next, analysis is made as to how every variabledisproportionately affects the energy intensity via the stochastic model. Finally, a regression equationconcerning the main influence factors of energy intensity is built based on optimum subset regression,which leads to the conclusion that the main factor affecting energy intensity is industrial energyintensity, followed by the industrial structure and energy intensity of the tertiary industry in sequentialorder.Besides, this paper discusses the long-run equilibrium relationship and the short-term dynamicrelationship between carbon emissions and economic growth and between energy consumption andeconomic growth respectively using Co-integration theory, error correction model and Grangercausality analysis. The Granger causality analysis indicates a mutual cause-and-effect relationshipbetween the carbon emissions and energy consumption with economic growth. Additionally, carbonemissions, energy consumption and economic growth are integrated into a unified framework throughthe establishment of the VAR and VEC model, and the dynamic characteristics of the model is furtherdefined through the impulse function and variance decomposition analysis. Note that, the impulsefunction presents a mirror symmetry phenomenon, implying that China’s economic development hasalmost the same influence path of carbon emissions and as that of energy consumption.Furthermore, the EKC curve study is employed to focus on the aforementioned relations and toexplore whether there exists any inflection point in the curve. As the simulation results show, thereexist: the N-type EKC curve without any inflection point among that carbon emissions, carbonemissions per capita and GDP per capita; the N-type EKC curve with inflection points between thecarbon emission intensity and GDP per capita; the N-type EKC curve without any inflection pointbetween the total energy consumption, energy consumption per capita and GDP per capita; the N-typeEKC curve with inflection points between the energy intensity and GDP per capita.Then the relationship of carbon emissions and economic growth and of energy consumption andeconomic development are investigated deeply using the Tapio decoupling indicators and theirextended models together with factors decomposition of decoupling elasticity. The result shows weak decoupling relationship between the two pairs mentioned above. At the same time the introduction ofintermediate variables of the logical relationship further explains the decoupling relationship betweencarbon emissions, energy consumption and economic growth, verifying very similar change trend ofdecoupling elasticity with approximately equal elasticity values between the two pairs’ relationship. Inconclusion, the decoupling relationship elasticity between China’s carbon emissions and economicgrowth is directly caused by that between energy consumption and economic growth.According to the study, some countermeasures are suggested as to how to control the total energy,to effectively reduce energy intensity, optimize the industrial structure and to vigorously develop newenergies.更多还原