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Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry
This paper aims to contribute to the contemporary and imperative research on the performance and productivity growth of the oil industry. Among cutting edge methods, frontier analysis is a successful approach that has been widely used to assess the efficiency and productivity of entities with multip...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9125358/ https://www.ncbi.nlm.nih.gov/pubmed/35645438 http://dx.doi.org/10.1007/s00291-022-00683-y |
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author | Hatami-Marbini, Adel Arabmaldar, Aliasghar Asu, John Otu |
author_facet | Hatami-Marbini, Adel Arabmaldar, Aliasghar Asu, John Otu |
author_sort | Hatami-Marbini, Adel |
collection | PubMed |
description | This paper aims to contribute to the contemporary and imperative research on the performance and productivity growth of the oil industry. Among cutting edge methods, frontier analysis is a successful approach that has been widely used to assess the efficiency and productivity of entities with multiple resources and multiple outputs. This study first develops a unique framework based upon data envelopment analysis (DEA) to measure efficiency and productivity in the way that it tackles the uncertainty in data and undesirable outputs and, in turn, provides useful information to decision-makers. An adaptive robust optimisation is utilised to combat uncertain data whose distributions are unknown and consider the nexus between the level of conservatism and decision-makers’ risk preference. The key advantage of the proposed robust DEA approach is that the results remain relatively unchanged when uncertain conditions exist in the problem. An empirical study on the oil refinery is presented in situations of data uncertainty along with considering CO(2) emissions as the undesirable output to conduct environmental efficiency and productivity analysis of the 25 countries over the period 2000–2018. The empirical results obtained from the proposed approach give some imperative implications. First, results show that the price of robustness does not affect identically for varying technologies when assessing productivity in a global oil market, and the USA oil industry is observed as the highest productivity growth in all cases confirming its efforts for the rapid rise in oil extraction and production at low costs. There may be practical lessons for other nations to learn from the USA oil industry to improve productivity. Findings also support a considerable regress during the 2008 Global Financial Crisis in the oil industry compared to the rest of the periods in question, and due to monetary and fiscal stimulus, there is a sharp productivity growth from 2009 to 2011. The other implication that can be drawn is that the GDP growth rate and technology innovation can more effectively improve the productivity of the oil industry across the globe. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00291-022-00683-y. |
format | Online Article Text |
id | pubmed-9125358 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-91253582022-05-23 Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry Hatami-Marbini, Adel Arabmaldar, Aliasghar Asu, John Otu OR Spectr Original Article This paper aims to contribute to the contemporary and imperative research on the performance and productivity growth of the oil industry. Among cutting edge methods, frontier analysis is a successful approach that has been widely used to assess the efficiency and productivity of entities with multiple resources and multiple outputs. This study first develops a unique framework based upon data envelopment analysis (DEA) to measure efficiency and productivity in the way that it tackles the uncertainty in data and undesirable outputs and, in turn, provides useful information to decision-makers. An adaptive robust optimisation is utilised to combat uncertain data whose distributions are unknown and consider the nexus between the level of conservatism and decision-makers’ risk preference. The key advantage of the proposed robust DEA approach is that the results remain relatively unchanged when uncertain conditions exist in the problem. An empirical study on the oil refinery is presented in situations of data uncertainty along with considering CO(2) emissions as the undesirable output to conduct environmental efficiency and productivity analysis of the 25 countries over the period 2000–2018. The empirical results obtained from the proposed approach give some imperative implications. First, results show that the price of robustness does not affect identically for varying technologies when assessing productivity in a global oil market, and the USA oil industry is observed as the highest productivity growth in all cases confirming its efforts for the rapid rise in oil extraction and production at low costs. There may be practical lessons for other nations to learn from the USA oil industry to improve productivity. Findings also support a considerable regress during the 2008 Global Financial Crisis in the oil industry compared to the rest of the periods in question, and due to monetary and fiscal stimulus, there is a sharp productivity growth from 2009 to 2011. The other implication that can be drawn is that the GDP growth rate and technology innovation can more effectively improve the productivity of the oil industry across the globe. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00291-022-00683-y. Springer Berlin Heidelberg 2022-05-23 2022 /pmc/articles/PMC9125358/ /pubmed/35645438 http://dx.doi.org/10.1007/s00291-022-00683-y Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Hatami-Marbini, Adel Arabmaldar, Aliasghar Asu, John Otu Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry |
title | Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry |
title_full | Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry |
title_fullStr | Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry |
title_full_unstemmed | Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry |
title_short | Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry |
title_sort | robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9125358/ https://www.ncbi.nlm.nih.gov/pubmed/35645438 http://dx.doi.org/10.1007/s00291-022-00683-y |
work_keys_str_mv | AT hatamimarbiniadel robustproductivitygrowthandefficiencymeasurementwithundesirableoutputsevidencefromtheoilindustry AT arabmaldaraliasghar robustproductivitygrowthandefficiencymeasurementwithundesirableoutputsevidencefromtheoilindustry AT asujohnotu robustproductivitygrowthandefficiencymeasurementwithundesirableoutputsevidencefromtheoilindustry |