{"title":"用于清洁交通能源效率评估的部分偏好交叉效率灵活模型","authors":"Meiling Li, Ying-Ming Wang, Jian Lin","doi":"10.1142/s021962202450010x","DOIUrl":null,"url":null,"abstract":"<p>The rapid development of the transportation industry benefits from the consumption of energy, but the excessive dependence on petroleum fuels makes it a major source of air pollution. In order to achieve green and high-quality development of the transportation industry, many countries are committed to scientifically evaluating the utilization efficiency of clean energy, which has attracted wide attention from the whole society. Significantly, without considering the diversity and complexity of pollutants, indicators used in previous studies were unable to cover all pollutants when establishing the evaluation index system. Meanwhile, as an efficient tool, data envelopment analysis (DEA) is extensively used when it comes to efficiency evaluation. However, the absolute preference of existing benevolent and aggressive cross-efficiency models limits its application scenarios. To address the challenges above, an improved flexible cross-efficiency DEA model is proposed considering both same and different benevolence coefficients of decision-making units (DMUs) on the basis of pointing out the inadequacy of the previous model. The concepts of consensus coefficient and group preference are introduced in the aggregation of cross-efficiency. Besides, based on the theory of undesirable output, the consumption of nonclean energy is taken into account as the input indicator to characterize the degree of pollution. The results show that the obtained cross-efficiency value and efficiency ranking of clean transportation energy change sensitively under various benevolent coefficients. There is an important practical significance to consider the independent preference information of DMUs for the evaluation and ranking of cross-efficiency.</p>","PeriodicalId":50315,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Flexible Cross-Efficiency Model with Partial Preference for Efficiency Evaluation of Clean Transportation Energy\",\"authors\":\"Meiling Li, Ying-Ming Wang, Jian Lin\",\"doi\":\"10.1142/s021962202450010x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The rapid development of the transportation industry benefits from the consumption of energy, but the excessive dependence on petroleum fuels makes it a major source of air pollution. In order to achieve green and high-quality development of the transportation industry, many countries are committed to scientifically evaluating the utilization efficiency of clean energy, which has attracted wide attention from the whole society. Significantly, without considering the diversity and complexity of pollutants, indicators used in previous studies were unable to cover all pollutants when establishing the evaluation index system. Meanwhile, as an efficient tool, data envelopment analysis (DEA) is extensively used when it comes to efficiency evaluation. However, the absolute preference of existing benevolent and aggressive cross-efficiency models limits its application scenarios. To address the challenges above, an improved flexible cross-efficiency DEA model is proposed considering both same and different benevolence coefficients of decision-making units (DMUs) on the basis of pointing out the inadequacy of the previous model. The concepts of consensus coefficient and group preference are introduced in the aggregation of cross-efficiency. Besides, based on the theory of undesirable output, the consumption of nonclean energy is taken into account as the input indicator to characterize the degree of pollution. The results show that the obtained cross-efficiency value and efficiency ranking of clean transportation energy change sensitively under various benevolent coefficients. There is an important practical significance to consider the independent preference information of DMUs for the evaluation and ranking of cross-efficiency.</p>\",\"PeriodicalId\":50315,\"journal\":{\"name\":\"International Journal of Information Technology & Decision Making\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Information Technology & Decision Making\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1142/s021962202450010x\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Technology & Decision Making","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1142/s021962202450010x","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
交通运输业的快速发展得益于能源的消耗,但对石油燃料的过度依赖使其成为大气污染的主要来源。为了实现交通运输业的绿色、高质量发展,许多国家都致力于科学评价清洁能源的利用效率,这引起了全社会的广泛关注。值得注意的是,在建立评价指标体系时,由于没有考虑污染物的多样性和复杂性,以往研究中使用的指标无法涵盖所有污染物。同时,数据包络分析法(DEA)作为一种高效的工具,在效率评价中被广泛应用。然而,现有的仁慈型和激进型交叉效率模型的绝对偏好限制了其应用场景。针对上述挑战,在指出以往模型不足的基础上,提出了一种改进的灵活交叉效率 DEA 模型,该模型考虑了决策单元(DMU)相同和不同的仁慈系数。在交叉效率的聚合中引入了共识系数和群体偏好的概念。此外,基于不良产出理论,将非清洁能源消耗作为表征污染程度的输入指标。结果表明,所得到的清洁交通能源交叉效率值和效率排序在不同的仁系数下会发生敏感变化。考虑 DMU 的独立偏好信息进行交叉效率的评价和排序具有重要的现实意义。
A Flexible Cross-Efficiency Model with Partial Preference for Efficiency Evaluation of Clean Transportation Energy
The rapid development of the transportation industry benefits from the consumption of energy, but the excessive dependence on petroleum fuels makes it a major source of air pollution. In order to achieve green and high-quality development of the transportation industry, many countries are committed to scientifically evaluating the utilization efficiency of clean energy, which has attracted wide attention from the whole society. Significantly, without considering the diversity and complexity of pollutants, indicators used in previous studies were unable to cover all pollutants when establishing the evaluation index system. Meanwhile, as an efficient tool, data envelopment analysis (DEA) is extensively used when it comes to efficiency evaluation. However, the absolute preference of existing benevolent and aggressive cross-efficiency models limits its application scenarios. To address the challenges above, an improved flexible cross-efficiency DEA model is proposed considering both same and different benevolence coefficients of decision-making units (DMUs) on the basis of pointing out the inadequacy of the previous model. The concepts of consensus coefficient and group preference are introduced in the aggregation of cross-efficiency. Besides, based on the theory of undesirable output, the consumption of nonclean energy is taken into account as the input indicator to characterize the degree of pollution. The results show that the obtained cross-efficiency value and efficiency ranking of clean transportation energy change sensitively under various benevolent coefficients. There is an important practical significance to consider the independent preference information of DMUs for the evaluation and ranking of cross-efficiency.
期刊介绍:
International Journal of Information Technology and Decision Making (IJITDM) provides a global forum for exchanging research findings and case studies which bridge the latest information technology and various decision-making techniques. It promotes how information technology improves decision techniques as well as how the development of decision-making tools affects the information technology era. The journal is peer-reviewed and publishes both high-quality academic (theoretical or empirical) and practical papers in the broad ranges of information technology related topics including, but not limited to the following:
• Artificial Intelligence and Decision Making
• Bio-informatics and Medical Decision Making
• Cluster Computing and Performance
• Data Mining and Web Mining
• Data Warehouse and Applications
• Database Performance Evaluation
• Decision Making and Distributed Systems
• Decision Making and Electronic Transaction and Payment
• Decision Making of Internet Companies
• Decision Making on Information Security
• Decision Models for Electronic Commerce
• Decision Models for Internet Based on Companies
• Decision Support Systems
• Decision Technologies in Information System Design
• Digital Library Designs
• Economic Decisions and Information Systems
• Enterprise Computing and Evaluation
• Fuzzy Logic and Internet
• Group Decision Making and Software
• Habitual Domain and Information Technology
• Human Computer Interaction
• Information Ethics and Legal Evaluations
• Information Overload
• Information Policy Making
• Information Retrieval Systems
• Information Technology and Organizational Behavior
• Intelligent Agents Technologies
• Intelligent and Fuzzy Information Processing
• Internet Service and Training
• Knowledge Representation Models
• Making Decision through Internet
• Multimedia and Decision Making
[...]