{"title":"中国区域能源环境效率:基于DEA窗口分析的动态评价","authors":"Ke Wang , Shiwei Yu , Wei Zhang","doi":"10.1016/j.mcm.2011.11.067","DOIUrl":null,"url":null,"abstract":"<div><p>Data envelopment analysis (DEA) has recently become a popular approach in measuring the energy and environmental performance at the macro-economy level. A common limitation of several previous studies is that they ignored the undesirable outputs and did not consider the separation of inputs into energy resources and non-energy resources under the DEA framework. Thus, within a joint production framework of considering both desirable and undesirable outputs, as well as energy and non-energy inputs, this study analyzes China’s regional total-factor energy and environmental efficiency. This paper utilizes improved DEA models to measure the energy and environmental efficiency of 29 administrative regions of China during the period of 2000–2008. In addition, the DEA window analysis technique is applied to measure the efficiency in cross-sectional and time-varying data. The empirical results show that the east area of China has the highest energy and environmental efficiency, while the efficiency of the west area is worst. All three areas of China have similar trends in the variation of efficiency and in general the energy and environmental efficiency of China slightly increased from 2000 to 2008. The regions of the east area have a more balanced development than the regions of the central area and west area according to energy and environmental efficiency.</p></div>","PeriodicalId":49872,"journal":{"name":"Mathematical and Computer Modelling","volume":"58 5","pages":"Pages 1117-1127"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.mcm.2011.11.067","citationCount":"358","resultStr":"{\"title\":\"China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation\",\"authors\":\"Ke Wang , Shiwei Yu , Wei Zhang\",\"doi\":\"10.1016/j.mcm.2011.11.067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Data envelopment analysis (DEA) has recently become a popular approach in measuring the energy and environmental performance at the macro-economy level. A common limitation of several previous studies is that they ignored the undesirable outputs and did not consider the separation of inputs into energy resources and non-energy resources under the DEA framework. Thus, within a joint production framework of considering both desirable and undesirable outputs, as well as energy and non-energy inputs, this study analyzes China’s regional total-factor energy and environmental efficiency. This paper utilizes improved DEA models to measure the energy and environmental efficiency of 29 administrative regions of China during the period of 2000–2008. In addition, the DEA window analysis technique is applied to measure the efficiency in cross-sectional and time-varying data. The empirical results show that the east area of China has the highest energy and environmental efficiency, while the efficiency of the west area is worst. All three areas of China have similar trends in the variation of efficiency and in general the energy and environmental efficiency of China slightly increased from 2000 to 2008. The regions of the east area have a more balanced development than the regions of the central area and west area according to energy and environmental efficiency.</p></div>\",\"PeriodicalId\":49872,\"journal\":{\"name\":\"Mathematical and Computer Modelling\",\"volume\":\"58 5\",\"pages\":\"Pages 1117-1127\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.mcm.2011.11.067\",\"citationCount\":\"358\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical and Computer Modelling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0895717711007527\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical and Computer Modelling","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0895717711007527","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation
Data envelopment analysis (DEA) has recently become a popular approach in measuring the energy and environmental performance at the macro-economy level. A common limitation of several previous studies is that they ignored the undesirable outputs and did not consider the separation of inputs into energy resources and non-energy resources under the DEA framework. Thus, within a joint production framework of considering both desirable and undesirable outputs, as well as energy and non-energy inputs, this study analyzes China’s regional total-factor energy and environmental efficiency. This paper utilizes improved DEA models to measure the energy and environmental efficiency of 29 administrative regions of China during the period of 2000–2008. In addition, the DEA window analysis technique is applied to measure the efficiency in cross-sectional and time-varying data. The empirical results show that the east area of China has the highest energy and environmental efficiency, while the efficiency of the west area is worst. All three areas of China have similar trends in the variation of efficiency and in general the energy and environmental efficiency of China slightly increased from 2000 to 2008. The regions of the east area have a more balanced development than the regions of the central area and west area according to energy and environmental efficiency.