TY - JOUR
T1 - Assessing green performance of power plants by multiple hybrid returns to scale technologies
AU - Azadi, Majid
AU - Karimi, Balal
AU - Ho, William
AU - Saen, Reza Farzipoor
N1 - Funding Information:
Authors would like to appreciate the constructive comments of four Reviewers.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2022/4/30
Y1 - 2022/4/30
N2 - 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.
AB - 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.
KW - Data envelopment analysis (DEA)
KW - Electricity industry
KW - Joint inputs
KW - Joint outputs
KW - Multi-output settings
KW - Multiple hybrid returns-to-scale (MHRTS)
KW - Output-specific inputs
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U2 - 10.1007/s00291-022-00682-z
DO - 10.1007/s00291-022-00682-z
M3 - Article
AN - SCOPUS:85129230133
SN - 0171-6468
VL - 44
SP - 1177
EP - 1211
JO - OR Spectrum
JF - OR Spectrum
IS - 4
M1 - 4
ER -