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中国管理科学 ›› 2019, Vol. 27 ›› Issue (4): 48-55.doi: 10.16381/j.cnki.issn1003-207x.2019.04.005

• 论文 • 上一篇    下一篇

多目标条件下企业碳配额分配和政府公平——基于(p,α)比例公平的视角

夏晖1, 王思逸1, 蔡强2   

  1. 1. 电子科技大学经济与管理学院, 四川 成都 611731;
    2. 四川广播电视大学宏观经济与金融研究中心, 四川 成都 610073
  • 收稿日期:2017-07-29 修回日期:2018-01-10 出版日期:2019-04-20 发布日期:2019-06-12
  • 通讯作者: 夏晖(1969-),男(汉族),四川成都人,电子科技大学经济与管理学院副教授,管理学博士,研究方向:碳减排机制、公司金融,E-mail:xiahui@uestc.edu.cn. E-mail:xiahui@uestc.edu.cn
  • 基金资助:

    国家自然科学基金面上项目(71473031);教育部人文社会科学研究项目(14YJA790062)

Optimal Allocation of Corporate Carbon Quotas and Government's Fairness under Multi-objective Decisions -From the Perspective of (p,α) Proportional Fairness

XIA Hui1, WANG Si-yi1, CAI Qiang2   

  1. 1. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China;
    2. Macro Economy and Finance Research Center, Sichuan Radio and TV University, Chengdu 610073, China
  • Received:2017-07-29 Revised:2018-01-10 Online:2019-04-20 Published:2019-06-12

摘要: 本文在企业和政府主从博弈框架下,研究政府兼顾社会福利和减排成本的多目标条件下企业最优碳配额分配方式,并采用(p,α)比例公平建立了最优分配与政府公平态度之间的对应关系,进一步分析企业间减排效率差异对碳配额最优分配以及政府所持公平态度的影响。采用遗传算法模拟结果显示,在政府最大化社会福利和最小化减排总成本的多目标下,政府始终给予高效率减排企业更多的配额,在企业间减排效率差异较小和较大的行业,减排效率对最优配额分配和政府所持公平态度的影响是完全不同的。当行业内企业减排效率差异较小时,随减排效率差异的增加,低效率企业所得配额逐渐减少,政府公平性逐渐降低;当企业减排效率差异较大时,情况刚好相反,低效率企业的配额逐渐增加,政府变得越来越注重公平。除此之外,政府公平性并不意味着低效率企业得到更多的碳配额,当企业间减排效率差异增加到某一区间,随减排效率差异的增加,低效率企业所得配额逐渐减小,而政府公平逐渐增加。我们的结论为相关部门多目标下的最优碳配额分配决策以及分析政府的公平性提供了有益参考。

关键词: (p, &alpha, )比例公平, 主从博弈, 多目标决策, 碳配额分配, 遗传算法

Abstract: For government, there are several certain goals considered when distributing carbon quota. For instance, to maximize the total welfare of the society and to minimize the reduction cost. Meanwhile, a certain blue print of distribution reflects government's attitude towards fairness, and it also influences enterprises' production decision, especially between enterprises with different efficiency in reduction. In this paper, under the frame of leader-followers game, the optimal carbon quota allocation strategy when the government realizes multi-objective optimization under taking account of both social welfare and emission reduction costs is studied. We use (p, α)-proportional fairness is used to establish the corresponding relationship between the optimal allocation and the government's attitude towards fairness. And then how enterprises' differences on reduction efficiency influences government's attitude towards fairness is analyzed. Genetic algorithm simulation results show that the government always gives greater quota to high efficiency enterprise when government wants to minimize the reduction cost and to maximize the social welfare. The results also show that how the differences in the efficiency of emission reduction influence the optimal allocation and government's attitude towards fairness will change completely differently within industries with differences between the efficiency of enterprises. When there is a small difference in the efficiency of emission reduction, with the increase of the difference of emission reduction efficiency, the quota of inefficient enterprises gradually decreased, and the government attitude towards fairness is getting smaller and smaller. When the difference in efficiency is larger, the situation is just the opposite. The government paid more attention to fairness, while low-efficiency enterprises get more carbon. Meanwhile, government's attitude towards fairness doesn't mean the low-efficiency enterprise obtain greater quota. As the difference in efficiency in a certain range, the increase in the difference will lead to smaller quota to the low-efficiency enterprise and a higher fairness in allocation. The above-mentioned results provide a useful reference and uncover the superiority of base line method in multi-objective optimization within industry with difference in reduction efficiency. And industries with different reduction efficiency apply to different distribution policy.

Key words: (p, α) proportional fairness, leader-followers game, multi-objective decisions, carbon quota allocation, genetic algorithm

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