【作者】 金欢欢；

【导师】 陈钰芬；

【作者基本信息】 浙江工商大学 ， 统计学， 2022， 博士

【摘要】 2020年9月,我国提出“二氧化碳排放力争于2030年前达到峰值,努力争取2060年前实现碳中和”。本文在“碳达峰”战略目标下,以碳足迹为研究对象,分别从区域和产业视角对碳足迹进行了多维测算,分析了碳足迹的影响因素,并构建了以“碳足迹”总量约束为目标的优化分析框架,旨在为推进“碳达峰”战略目标提供参考或依据。本文的主要内容有:第一,从区域视角测算了我国碳足迹的水平。首先,基于排放因子法测算了2010-2019年各地区的碳足迹,并利用Moran‘s I指数分析了区域碳足迹的空间相关性,发现区域碳足迹的集聚状态较为稳定,经济落后地区以L-L型集聚模式为主,经济最为发达的地区一般呈现为L-H型或L-L型;其次,将碳汇纳入碳足迹测算框架,发现上海、天津、江苏、山东和北京等地因经济增长产生了碳赤字,而内蒙古、黑龙江、云南等地区则在碳生态平衡上具有明显优势;最后,构建了区域投入产出模型,从价值链角度开展了区域碳足迹分析,发现区域碳足迹呈现出“凸型”特征和“同向”特征。另外,各地区完全排放强度高于直接排放强度,故按直接排放的碳足迹核算碳责任有失偏颇。第二,从产业视角测算了我国碳足迹的水平。首先,构建产业碳足迹投入产出模型,发现碳足迹产业分布较为集中,前五大产业的碳足迹水平占总量的79.05%。重工业部门的碳吸附能力最强,但不同部门的碳足迹感应力系数差异明显,核算碳足迹时需根据中间产品供需进行核减;其次,考虑产业转移关系以及产品生产生命周期,提出了EIO-LCA模型,发现产业部门可分为“碳结果”社区、“多重碳作用”社区、“碳原因”社区和“碳中介”社区。不同社区消耗碳的模式不同,在碳排放中承担的责任有明显差异;最后,构建了多区域投入产出表,并利用QAP回归分析了碳足迹转移的影响因素,发现工业部门是碳足迹的主要来源部门,占全行业碳足迹总量的76.54%。欠发达地区为发达地区长期提供中间产品。地区间的价值流动是碳足迹转移的最主要媒介,能源强度和环境规制强度也是导致碳足迹转移的主要因素。第三,对碳足迹的影响因素进行了分解。首先,对Kaya恒等式进行了改进,发现经济发展是碳足迹水平提升的最主要原因,高能耗产业是影响全社会碳足迹水平的核心产业。其次,提出了基于碳足迹生态压力的分解框架,发现不同产业之间的影响因素存在差异。但从整体上看,经济发展是造成碳足迹生态压力的最主要原因,而产业结构与人口规模对生态压力的作用尚未显现;最后,基于前述结论,提出了IDA-PDA分解模型,发现规模效率是经济发展效应的主要来源,但不同地区因经济发展状态呈现不同的碳足迹状态。第四,研究了碳足迹总量约束下的生产效率优化问题。首先,构建了基于投入产出导向的能源发电效率测度模型,发现投入产出的变化幅度差异很大,呈现出“经济越发达、燃料减少幅度越大”的特点。发电量的增幅呈现出“东中西”依次递减的特征,且不同地区的发展潜力差异显著;其次,利用ZSG-DEA模型,以“三新”制造业的企业为对象分析电力优化配置问题,发现各产业的中游企业投入产出效率都相对较低,而上游企业效率较高,由此导致更多的将用电量分配至上游企业。下游企业的用电量分配依产业差别显著。最后,将非合作博弈理论引入到电力优化配置模型,发现不同产业的电量分配可归纳为三种类型。另外,不同产业的上游、中游和下游企业的用电效率差别较大。最后,对本文研究中存在的不足进行了讨论,并提出了下一步研究的方向。更多还原

【Abstract】 In September 2020,China proposed that "carbon dioxide emissions should peak by 2030 and achieve carbon neutrality by 2060".In this paper,under the "carbon peak" strategic objectives,with carbon footprint as the research object,we measure the carbon footprint from the perspectives of regional and industry,analyzes the factors that influence the carbon footprint and build the optimization analysis framework aimed at "carbon footprint" total constraint.The main contents of this paper are as follows:(1)The level of China’s carbon footprint is estimated from a regional perspective.Firstly,the carbon footprint of each region from 2010 to 2019 is estimated based on the emission factor method,and the spatial correlation of regional carbon footprint is analyzed by Moran’s I index.We find that the agglomeration of regional carbon footprint is relatively stable,and the economically backward regions are mainly L-L agglomeration pattern,while the most economically developed regions are L-H or L-L agglomeration pattern.Secondly,it is found that Shanghai,Tianjin,Jiangsu,Shandong and Beijing have carbon deficit due to economic growth,while Inner Mongolia,Heilongjiang and Yunnan have obvious advantages in carbon ecological balance.Finally,a regional input-output model is constructed to analyze the regional carbon footprint from the perspective of value chain.It is found that the regional carbon footprint presents the characteristics of "convex shape" and "co-direction".In addition,the total emission intensity of each region is higher than the direct emission intensity,so it is biased to calculate the carbon liability according to the carbon footprint of direct emissions.(2)The level of China’s carbon footprint is estimated from the perspective of industry.Firstly,the input-output model of industrial carbon footprint is constructed,and it is found that the carbon footprint of the top five industries is concentrated,accounting for 79.05% of the total carbon footprint.The heavy industry sector has the strongest carbon adsorption capacity,but the carbon footprint sensing force coefficient of different sectors is obviously different,so the carbon footprint should be deducted according to the supply and demand of intermediate products.Secondly,considering the industrial transfer relationship and product production life cycle,the EIO-LCA model is proposed,and it is found that industrial sectors can be divided into "carbon result" community,"multiple carbon effects" community,"carbon cause" community and "carbon intermediary" community.Different communities have different patterns of carbon consumption and different responsibilities in carbon emissions.Finally,a multi-regional input-output table is constructed,and the influencing factors of carbon footprint are analyzed by QAP regression.It is found that industrial sector is the main source of carbon footprint,accounting for 76.54% of the total carbon footprint of the whole industry.Less developed regions have long provided intermediate goods to developed regions.Regional value flow is the most important medium of carbon footprint transfer,and energy intensity and environmental regulation intensity are also the main factors leading to carbon footprint transfer.(3)The influencing factors of carbon footprint are decomposed.Firstly,the Kaya identity is improved,and it is found that economic development is the main reason for the improvement of carbon footprint level,and high-energy industries are the core industries that affect the carbon footprint level of the whole society.Secondly,a decomposition framework based on the ecological pressure of carbon footprint is proposed,and it is found that the influencing factors are different among different industries.But on the whole,economic development is the most important reason for the ecological pressure of carbon footprint,while the effect of industrial structure and population size on the ecological pressure is not obvious.Finally,based on the above conclusions,the IDA-PDA decomposition model is proposed,and it is found that scale efficiency is the main source of economic development effect,but different regions show different carbon footprint states due to economic development state.(4)The optimization of production efficiency under the constraint of total carbon footprint is studied.Firstly,an input-output oriented measurement model of energy generation efficiency is constructed.It is found that the input-output variation varies greatly,showing that "the more developed the economy is,the greater the reduction of fuel".The growth rate of power generation is decreasing from east to West,and the development potential of different regions is significantly different.Secondly,the ZSG-DEA model is used to analyze the optimal allocation of electricity in the "three New industries" manufacturing enterprises.It is found that the input-output efficiency of the midstream enterprises in each industry is relatively low,while the efficiency of the upstream enterprises is high,which leads to more electricity distribution to the upstream enterprises.The electricity consumption distribution of downstream enterprises varies significantly by industry.Finally,the non-cooperative game theory is introduced into the power optimal allocation model,and it is found that the electricity distribution of different industries can be classified into three types.In addition,the power efficiency of upstream,midstream and downstream enterprises in different industries varies greatly.Finally,the shortcomings of this research are discussed,and the direction of further research is put forward.更多还原