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Measuring the relative performance of forest management units: A chance-constrained DEA model in the presence of the non-discretionary factor
University of Guilan, Sowmeh Sara, Iran.
University of Guilan, Sowmeh Sara, Iran.
Islamic Azad University, Rasht Branch, Rasht, Iran.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (RCR)
2019 (English)In: Canadian Journal of Forest Research, ISSN 0045-5067, E-ISSN 1208-6037, Vol. 49, no 7, p. 788-801Article in journal (Refereed) Published
Abstract [en]

In this study, we develop a marginal chance-constrained data envelopment analysis model in the presence of non-discretionary inputs and hybrid outputs for the first time. We call it a stochastic non-discretionary DEA model (SND-DEA), and it is developed to measure and compare the relative efficiency of forest management units under different environmental management systems. Furthermore, we apply an output-oriented DEA technology to both deterministic and stochastic scenarios. The required data are collected from 24 forest management plans (as decision-making units and included four inputs and equal amount of outputs. The findings of this practical research show that the modified SND-DEA model in different probability levels give us apparently different results compared to the output from pure deterministic models. However, when we calculate the correlation measure, the probability levels give us a strong positive correlation between stochastic and deterministic models. Therefore, approximately 40% of the forest management plans based on the applied SND-DEA model should substantially increase their average efficiency score. As the major conclusion, our developed SND-DEA model is a suitable improvement over previous developed models to discriminate the efficiency and/or the inefficiency of decision-making units to hedge against risk and uncertainty in this type of forest management problems.

Place, publisher, year, edition, pages
2019. Vol. 49, no 7, p. 788-801
Keywords [en]
data envelopment analysis (DEA), discretionary and nondiscretionary factors, hybrid outputs, risk management in the presence of stochastic data, forest management
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-35629DOI: 10.1139/cjfr-2018-0229Scopus ID: 2-s2.0-85067554856OAI: oai:DiVA.org:miun-35629DiVA, id: diva2:1288336
Available from: 2019-02-13 Created: 2019-02-13 Last updated: 2019-07-09Bibliographically approved

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Olsson, Leif

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