【摘要】 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.更多还原

【Abstract】 For the problem that traditional evaluation of carbon efficiency pays more attention on the economic output, but neglects the factors of welfare and population, this paper has built up an evaluation system of "full carbon efficiency", which involves economy, welfare and population; also, according to the strict restriction that DEA model requires the number of evaluation samples should be 2 times more than the number of indicators, this paper has introduced the principal component analysis to streamline the evaluation index of "full carbon efficiency". Firstly,28 key factors of production are selected as input indicators, and the principal component analysis is used to reduce the dimension to 8 synthetic indexes; then, the output index of "full carbon efficiency" involved 3 factors of economic output, welfare promotion and population-carrying capacity, is built up; finally, DEA-VRS evaluation model is used to conduct evaluation of "full carbon efficiency" in China’s 30 provinces. The evaluation result shows, the combination of principal component analysis and DEA method is effective on evaluating the regional "full carbon efficiency"with more indicators; From 2011 to 2014, "full carbon efficiency" of Guangxi has been promoted from weak to valid, "full carbon efficiency"of Tianjin, Hebei, Hunan and Liaoning have been promoted from invalid to valid, "full carbon efficiency" of Shanxi, Zhejiang, Guangdong and Chongqing have fallen from valid to invalid,and "full carbon efficiency" of Hubei is unchanged.更多还原