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针对传统碳效率评价较多关注经济产出而忽视了福利与人口因素的不足,构建了涉及经济、福利及人口的“全碳效率”评价体系;并针对DEA模型要求评价样本数大于指标数2倍的严格约束,引入主成分分析法进行“全碳效率”评价指标的精简.首先,选取28个关键生产要素作为投入指标,并应用主成分分析法降维为8个合成指标;然后,构建涉及经济产出、福利提升和人口承载三要素的“全碳效率”产出指标;最后,应用DEA-VRS评价模型对我国其中30个省域“全碳效率”进行评价.评价结果显示,主成分分析与DEA相结合的方法用于评价包含指标较多的区域“全碳效率”是有效的;2011年至2014年,广西的“全碳效率”从弱有效提升为有效,天津、河北、湖南和辽宁的“全碳效率”从无效提升为有效,山西、浙江、广东和重庆的“全碳效率”从有效降至无效,而湖北的“全碳效率”没有变化.
In view of the fact that the traditional carbon efficiency evaluation focuses more on the economic output but ignores the shortcomings of the welfare and population factors, a “complete carbon efficiency” evaluation system is built involving the economy, welfare and population. The DEA model requires that the number of evaluation samples is larger than the index The number of double the number of strict constraints, the introduction of the principal component analysis of the “carbon efficiency ” evaluation of the reduction.First, select 28 key production factors as input indicators, and application of principal component analysis to reduce the dimension of 8 composite indicators ; Then, it builds the output index of “full carbon efficiency” which involves three elements of economic output, welfare improvement and population carrying; Finally, applying the DEA-VRS evaluation model to evaluate the total carbon efficiency of 30 provinces in China, The results show that the combination of principal component analysis and DEA is effective in evaluating the area containing more indicators “total carbon efficiency ”; from 2011 to 2014, Guangxi’s “total carbon efficiency ”From weak to effective promotion, the“ total carbon efficiency ”of Tianjin, Hebei, Hunan and Liaoning increased from ineffective to effective, and the“ total carbon efficiency ”of Shanxi, Zhejiang, Guangdong and Chongqing decreased from effective to ineffective, Hubei’s “full carbon efficiency” has not changed.