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Target setting and allocation of carbon emissions abatement based on DEA and closest target: an application to 20 APEC economies

机译:基于DEA和最近目标的碳排放量分配:20 APEC经济体的申请

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Regarded as an effective method for treating the global warming problem, carbon emissions abatement (CEA) allocation has become a hot research topic and has drawn great attention recently. However, the traditional CEA allocation methods generally set efficient targets for the decision-making units (DMUs) using the farthest targets, which neglects the DMUs' unwillingness to maximize (minimize) some of their inputs (outputs). In addition, the total CEA level is usually subjectively determined without any consideration of the current carbon emission situations of the DMUs. To surmount these deficiencies, we incorporate data envelopment analysis and its closest target technique into the CEA allocation problem. Firstly, a two-stage approach is proposed for setting the optimal total CEA level for the DMUs. Then, another two-stage approach is given for allocating the identified optimal total CEA among the DMUs. Our approach provides more flexibility when setting new input and output targets for the DMUs in CEA allocation. Finally, the proposed approaches are applied for CEA target setting and allocation for 20 Asia-Pacific Economic Cooperation economies.
机译:被认为是治疗全球变暖问题的有效方法,碳排放减排(CEA)分配已成为一个热门的研究主题,最近引起了极大的关注。然而,传统的CEA分配方法通常使用最远的目标设定决策单元(DMU)的有效目标,这些目标忽略了DMUS不愿意最大化(最小化)其一些输入(输出)。此外,总CEA水平通常在没有考虑DMUS的当前碳排放情况的情况下主观确定。为了克服这些缺陷,我们将数据包络分析及其最近的目标技术纳入CEA分配问题。首先,提出了一种用于为DMUS设置最佳总CEA水平的两阶段方法。然后,给出另一种两阶段方法,用于在DMUS中分配识别的最佳总CEA。我们的方法在为CEA分配中设置DMU的新输入和输出目标时提供了更大的灵活性。最后,拟议的方法适用于CEA目标设定和20个亚太经济合作经济体的分配。

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