Regional Allocation of CO2 Intensity Reduction Targets Based on Cluster Analysis

To meet China's CO2 intensity target of 40%-45% reduction by 2020 based on the 2005 level, a regional allocation method based on cluster analysis is developed. Thirty Chinese provinces are classified into six groups based on economy, emissions, and reduction potential indicators. Under the equity pr...

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Veröffentlicht in:Advances in climate change research 2012-12, Vol.3 (4), p.220-228
Hauptverfasser: Yuan, Yang, Wen-Jia, Cai, Can, Wang, Si-Qiang, Wang
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creator Yuan, Yang
Wen-Jia, Cai
Can, Wang
Si-Qiang, Wang
description To meet China's CO2 intensity target of 40%-45% reduction by 2020 based on the 2005 level, a regional allocation method based on cluster analysis is developed. Thirty Chinese provinces are classified into six groups based on economy, emissions, and reduction potential indicators. Under the equity principle, the two most developed groups axe assigned the highest reduction targets (55% and 65%, respectively). However, their reduction potent!al is limited. Under the efficiency principle, the two groups with the highest reduction potential take the highest targets (48% and 61%, respectively), but their economy is relatively backward. When equity and efficiency are equally weighted, the 5th group with a prominent reduction potential takes the highest target (54%), and the 2nd and the 3rd groups with large industry scales take the second highest target (49%). However, under all the three allocation schemes, the targets are not greater than 40% for the 4th and the 6th groups, which have a relatively low economic ability, emissions, and reduction potential. Due to inconsistency between economic and reduction potential, corresponding market mechanisms and policy instruments should be established to ensure equity and efficiency of regional target allocation.
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subjects cluster analysis
CO2 intensity reduction targets allocation
regional difference
中国市场
二氧化碳
减排
分配方案
碳排放强度
经济能力
聚类分析
还原电位
title Regional Allocation of CO2 Intensity Reduction Targets Based on Cluster Analysis
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