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A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator

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  • Wang, H.

Abstract

This paper presents a generalized framework to construct composite indicator, which can be used in static and dynamic analysis. By grouping DMUs (decision making units) first, the proposed approach is more flexible to derive weights for entities featuring diverse characteristics. A more neutral set of weights can be obtained through investigating the lower and upper bound of possible weights. Subsequently, we introduce a slack-based composite indicator from the perspective of distance function, which facilitates studying entities' improvement potential in sub-indicators. Furthermore, the slacks-based composite indicator is combined with the Malmquist index to conduct dynamic assessment, aiming to quantify the evolvement of composite indicator over time and the underlying driving forces. To illustrate the usefulness of the proposed approach, it is applied to construct the Sustainable Energy Index for 109 countries worldwide in 2005–2010. Our results show that the high-income country group has the best sustainable energy performance among all the three country groups in 2010. The dynamic assessment indicates the worldwide sustainable energy development level declined during 2005–2010, and the efficiency change was the main negative driving force. More discussions and implications are presented in the paper.

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  • Wang, H., 2015. "A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator," Energy, Elsevier, vol. 80(C), pages 114-122.
    • Handle: RePEc:eee:energy:v:80:y:2015:i:c:p:114-122
    DOI: 10.1016/j.energy.2014.11.051
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    18. Iftikhar, Yaser & Wang, Zhaohua & Zhang, Bin & Wang, Bo, 2018. "Energy and CO2 emissions efficiency of major economies: A network DEA approach," Energy, Elsevier, vol. 147(C), pages 197-207.
    19. Wang, H. & Zhou, P., 2018. "Multi-country comparisons of CO2 emission intensity: The production-theoretical decomposition analysis approach," Energy Economics, Elsevier, vol. 74(C), pages 310-320.
    20. Tianjiao Wang & Yelin Fu, 2020. "Constructing Composite Indicators with Individual Judgements and Best–Worst Method: An Illustration of Value Measure," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(1), pages 1-14, May.
    21. Gunnarsdottir, I. & Davidsdottir, B. & Worrell, E. & Sigurgeirsdottir, S., 2020. "Review of indicators for sustainable energy development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    22. Andrea De Montis & Vittorio Serra & Amedeo Ganciu & Antonio Ledda, 2020. "Assessing Landscape Fragmentation: A Composite Indicator," Sustainability, MDPI, vol. 12(22), pages 1-23, November.
    23. Wang, H. & Zhou, P. & Wang, Qunwei, 2016. "Constructing slacks-based composite indicator of sustainable energy development for China: A meta-frontier nonparametric approach," Energy, Elsevier, vol. 101(C), pages 218-228.

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