4.7 Article

The network energy and environment efficiency analysis of 27 OECD countries: A multiplicative network DEA model

期刊

ENERGY
卷 197, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.117161

关键词

Networked DEA; Multiplicative function; Energy efficiency

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This research enhances the ability of the data envelopment analysis (DEA) model to analyse regional energy and environmental efficiency in three aspects. The first is a networked efficiency analysis of the regional economy-energy-environment model, which allows us to analyse internal conflicts between different departments in the region. Secondly, we use a multiplicative function instead of a linear function for frontier efficiency analysis. Multiplicative function frontier efficiency analysis model has two advantages: (1) The multiplicative function can allow both increasing marginal productivity and diminishing marginal productivity, while linear functions can only deal with diminishing marginal productivity. (2) The variables involved in regional production activities in the multiplicative function are no longer independent of each other, but have synergistic effects. (3) There is no inconsistency between internal evaluation and external evaluation when using the multiplicative model to measure the efficiency of the network structure. Thirdly, we found friction efficiency and conflict efficiency by comparing the network model with the traditional single model. Friction efficiency represents the DMU internal conflicts impact and conflict represents the degree of efficiency of the process is affected by the internal conflict. They can help decision makers understand more clearly the reasons for the inefficiency within DMU. Finally, we use 27 OECD countries as examples to conduct case studies. The results show that the multiplicative model is more reasonable in calculating regional energy and environmental efficiency than the traditional DEA model. On the other hand, the networked analytical structure can give policymakers more detailed analysis results than single process method. (C) 2020 Elsevier Ltd. All rights reserved.

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