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陈红蕾,聂文丽.中国碳排放影子价格的度量及空间计量分析.生态学报,2018,(14).http://dx.doi.org/10.5846/stxb201708021386  
中国碳排放影子价格的度量及空间计量分析
Slacks-based efficiency measures and spatial analysis for measuring the shadow price of Chinese carbon emissions
投稿时间:2017-08-02  修订日期:2018-02-08
DOI: 10.5846/stxb201708021386
关键词非期望产出  碳排放效率  影子价格  空间计量
Key Wordsundesired output  carbon emission efficiency  shadow price  spatial measurement
基金项目国家自科基金重点项目(71333007);国家自然科学基金面上项目(71273115);国家社会科学基金重大项目(15ZDA054)
作者单位E-mail
陈红蕾 暨南大学经济学院 gzchl@163.com 
聂文丽 暨南大学经济学院 niewenli99@163.com 
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摘要:
在中国即将实行全国碳交易市场的背景下,围绕碳排放的影子价格展开研究,首先将二氧化碳作为非期望产出,采用数据包络分析中的超效率SBM模型(Super-SBM),对中国30个省份的碳排放影子价格进行度量,为中国推行全国性的碳交易市场提供定价参考;其次,将碳排放影子价格与碳排放效率两者相结合,对比分析中国碳排放的省域间差异及其原因,探讨中国推进碳减排的区域方向;在此基础上,通过 指数分析中国各省份间碳减排成本的空间相关性,并据此建立时间空间双向固定的空间杜宾模型,分析碳减排成本的影响因素。研究结果显示:对于碳排放效率较高的省份,如广东、福建、上海、江苏、浙江等东部地区,其CO2实际排放量接近目标排放量,碳减排成本亦达到了较高的水平,难以直接实现进一步的碳减排;对于碳排放效率较低和碳排放影子价格较低的省份,如河南、河北、内蒙古、新疆、云南、甘肃、陕西、山西、贵州等区域,实现碳减排相对较易,应成为中国全面实施碳减排重点关注的区域;根据碳排放影子价格的空间相关性分析,中国各省份之间的碳减排成本呈现区域的集中性,在减排过程中应该注重省份间的合作,互惠互利;当碳减排进入攻坚期时,碳减排政策应从降低碳减排成本入手,并在不降低碳排放效率的基础上,加大对外开放程度,优化能源消费结构。
Abstract:
In the context of China’s forthcoming national carbon trading market, this study focuses on the shadow price of carbon emissions. Firstly, we defined carbon dioxide as undesirable output, and using the super-efficient slack based model (SBM) based on data envelopment analysis (DEA), the shadow prices of carbon emissions for 30 provinces were measured, which provide suggestions on how to price carbon for China’s forthcoming national carbon trading market. Secondly, we combine the shadow price of carbon emission efficiencies, analyzing the inter-provincial differences in carbon emissions and the regional direction of carbon abatement in China. On this basis, the spatial correlation in carbon abatement costs among provinces in China was analyzed exponentially, and a time–space two-way fixed SDM was established to analyze the factors affecting carbon abatement costs. The innovation of this study lies in the definition of carbon emission efficiency; most existing studies cannot give a definition of carbon emission efficiency, whereas here, we define it as the ratio of the target emissions to actual emissions. The target emissions can be obtained through the process of solving the shadow price of carbon emissions.Through the super-SBM and SDM, and by using Max-DEA and Matlab, we can draw the results as follows: first, for provinces with high carbon emission efficiencies, such as Guangdong, Fujian, Shanghai, Jiangsu, Zhejiang, and other eastern regions, whose actual degrees of CO2 emissions are close to the target emissions, higher carbon abatement costs make it more difficult to achieve further carbon efficiency; for provinces with low carbon emission efficiencies, such as Henan, Hebei, Inner Mongolia, Xinjiang, Yunnan, Gansu, Shaanxi, Shanxi, Guizhou, and other developing regions, which show higher carbon emissions and lower carbon abatement costs, it is easier to increase carbon emission efficiency. Second, according to the spatial correlation analysis of the shadow price of carbon emissions, the carbon abatement costs between provinces are regionally concentrated; the areas with lower emissions are concentrated in the eastern areas and the areas with higher emissions are concentrated in the western areas, which indicates that China should pay more attention to the cooperation between provinces. Third, on the basis of spatial correlation, we establish the SDM to explore how to reduce the carbon abatement costs. Through the model equation, we can establish the factors affecting the shadow price of carbon emissions, which can provide a way to reduce carbon emissions deeply. In the results, China’s carbon emission reduction is in a crucial period, and the policy for reducing carbon emissions should not only start by achieving common development between the west and east areas but also by reducing the carbon abatement costs, which can be achieved by increasing the degree of openness to the outside world and optimizing the structure of energy consumption.
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