以环境评估为基础的公平减少和重新分配排放许可证的方法

Huaqing Wu , Shaofu Du , Liang Liang , Yafang Zhou
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引用次数: 70

摘要

如何分配初始排放许可对于总量控制和交易机制的运行效率至关重要。数据包络分析(data envelopment analysis, DEA)被证明更适合公平地解决这一问题,因为这里涉及到基于绩效的分配。在本文中,我们提出了一种新的DEA方法来公平地减少和重新分配管理区域的排放许可。本文提出的DEA模型基于以下思路:各决策单元(dmu)根据各自的生态效率和当前排放水平,通过引入讨价还价博弈,相互竞争以获得最小的减排量;另一方面,中央政府通过重新分配剩余的排放许可来提高整体效率。该方法应用于15个欧盟成员国(EU15)的农业温室气体排放数据集。应用结果表明,削减和再分配机制是公平的,有利于在最优规模上运行的国家,而不利于在最优规模上运行的国家。此外,它有效地提高了系统的整体效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A DEA-based approach for fair reduction and reallocation of emission permits

How to allocate initial emission permits is vital for the cap-and-trade system in controlling the total emission level and improving the operation efficiency of the trade mechanism. And data envelopment analysis (DEA) has been proved to be more suitable to fairly resolve the problem as a performance-based allocation is involved here. In this paper, we present a novel DEA approach for fair reduction and reallocation of emission permits for areas under administration. The presented DEA models are based on the ideas, where, according to their eco-efficiency and current emission levels, all decision making units (DMUs) compete with each other for the least reduction amount by introducing a bargaining game, on the other hand, the central authority can improve the overall efficiency by reallocating the remaining emission permits. The approach is applied on the dataset of agricultural greenhouse gas emissions from 15 European Union members (EU15). The results of the application show that the reduction and reallocation mechanism is fair, which benefits the countries that work at the optimal scale operation while punishes those that fail to operate on the optimal scale. Furthermore, it is effective in improving the overall efficiency of the system.

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来源期刊
Mathematical and Computer Modelling
Mathematical and Computer Modelling 数学-计算机:跨学科应用
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