资源科学 ›› 2020, Vol. 42 ›› Issue (11): 2170-2183.doi: 10.18402/resci.2020.11.10

• 碳排放 • 上一篇    下一篇

基于Context-dependent DEA方法的碳排放减额分配策略

朱卫未1,2,3(), 缪子阳1,2(), 淦贵生4   

  1. 1.南京邮电大学高质量发展评价研究院,南京 210003
    2 南京邮电大学信息产业融合创新与应急管理研究中心,南京 210003
    3.江苏科技大学经济管理学院,镇江 212003
    4.江苏永鼎股份有限公司,苏州 215211
  • 收稿日期:2019-11-25 修回日期:2020-06-04 出版日期:2020-11-25 发布日期:2021-01-25
  • 通讯作者: 缪子阳
  • 作者简介:朱卫未,男,江苏盐城人,教授,江苏省社科优青(2019),江苏省“青蓝工程”中青年学术带头人(2018),研究方向为数据包络分析、系统评价与决策。E-mail: kirbyzhu@vip.163.com
  • 基金资助:
    国家自然科学基金项目(71771126);江苏省社会科学基金项目(17GLB013);江苏省普通高校研究生科研创新计划项目(KYCX19_0997)

Carbon emission reduction allocation strategy based on the context-dependent DEA method

ZHU Weiwei1,2,3(), MIAO Ziyang1,2(), GAN Guisheng4   

  1. 1. Institute of High-Quality Development Evaluation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
    2. Research Center of Information Industry Integrated Innovation and Emergency Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
    3. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212003, China
    4. Jiangsu Etern Company Limited, Suzhou 215211, China
  • Received:2019-11-25 Revised:2020-06-04 Online:2020-11-25 Published:2021-01-25
  • Contact: MIAO Ziyang

摘要:

碳排放控制是新形势下全球应对气候变化的重要内容,建立一套兼顾效率和公平的碳排放减额分配方案迫在眉睫。本文以效率评价结果为基础,采用具有公平性质的Context-dependent DEA方法和引入保证公平性的减排能力系数构建了基于Context-dependent DEA方法的改进型中心资源分配模型,选取了2016年中国30个省(市、区)数据为算例进行碳排放的减额分配,最后通过基尼系数衡量分配方案的公平性。结果表明:①东部地区的碳排放效率显著优于中西部地区,同时需要承担的减排额度也高于中西部地区;②部分传统能源大省(区)的实际碳排放额与目标碳排放额差距较大,处于超排严重且碳排放效率低下的状态,需加快经济转型步伐并贯彻落实减排政策;③减额分配方案较为合理地提升了省(市、区)的碳排放效率,并且一定程度地降低了基尼系数。研究结果不仅为如何有效、公平的分配减排额提供了新视角,也为国家层面的减排目标分解及省域层面减排政策的制定提供了参考。

关键词: Context-dependent DEA, 碳排放, 资源分配, 基尼系数, 中国

Abstract:

Carbon emission control is an important part of the global response to climate change under the new situation of development. It is urgent to establish a set of carbon emission reduction distribution plans that takes both efficiency and fairness into account. Based on the efficiency evaluation results, this study adopted the context-dependent data envelopment analysis (DEA) method that is fair in nature and introduced the emission reduction capacity coefficient that can ensure fairness in constructing an improved central resource allocation model. It used the data of 30 provinces (municipalities, autonomous regions) of China’s mainland in 2016 as an example to carry out the carbon emission reduction allocation, and finally measured the fairness of the allocation scheme by Gini coefficient. The results show that: (1) The carbon emission efficiency of the eastern region is significantly better than that of the central and the western regions, and the amount of emission reduction required is also higher than that of the central and the western regions. (2) The actual carbon emissions of some large traditional energy provinces (autonomous regions) show a large gap from the target carbon emissions, which are in a state of serious over emission and low carbon emission efficiency, and it is necessary to accelerate the pace of economic transformation and implement emission policies strictly in these provinces (autonomous regions). (3) The carbon emission reduction distribution plan reasonably improves the carbon emission efficiency of the provinces (municipalities, autonomous regions) and reduces the Gini coefficient to a certain extent. The research results not only provides a new perspective on how to allocate emission reductions effectively and fairly, but also provides a reference for the decomposition of emission reduction targets at the national level and the formulation of emission reduction policies at the provincial level.

Key words: Context-dependent DEA, carbon emissions, resource allocation, Gini coefficient, China