Assessing green performance of power plants by multiple hybrid returns to scale technologies

Majid Azadi, Balal Karimi, William Ho, Reza Farzipoor Saen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Efficiency measurement is a key and strategic factor in improving an organization’s performance and increasing their competitive advantage. Nevertheless, measuring efficiency in settings with multicomponent production technologies is a major issue with the existing approaches in the literature. The main contribution of the current paper is to develop a novel nonparametric approach to evaluate efficiency and obviate some of the theoretical barriers in multi-output settings. To this end, for the first time, new technologies assuming multiple hybrid returns-to-scale (MHRTS) with output-specific inputs, joint inputs, and outputs are developed. The new technologies are based on some of the axiomatic principles in data envelopment analysis (DEA) for forming a new production possibility set (PPS) to measure the efficiency of decision-making units (DMUs). By implementing the MHRTS technologies with output-specific inputs, joint inputs, and outputs, the proposed models can deal with undesirable outputs. Compared with the existing technologies in the DEA literature, the new technologies not only can incorporate output-specific inputs, joint inputs, and outputs for the performance evaluation of DMUs but also obviate existing theoretical barriers in the MHRTS technology. The applicability and usefulness of the proposed method are validated using a case study in the energy sector.

Original languageEnglish
Article number4
Pages (from-to)1177-1211
Number of pages35
JournalOR Spectrum
Issue number4
Publication statusPublished - Apr 30 2022


  • Data envelopment analysis (DEA)
  • Electricity industry
  • Joint inputs
  • Joint outputs
  • Multi-output settings
  • Multiple hybrid returns-to-scale (MHRTS)
  • Output-specific inputs

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Management Science and Operations Research

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