Evaluating Environmental Efficiency of Iranian Provinces Using Fuzzy Window Data Envelopment Analysis (FWDEA) with Undesirable Output

Document Type : Original Article

Authors

1 Associate Professor, Department of Industrial Management. Faculty of Humanities, Meybod University, Meybod, Iran.

2 Master’s student, Department of Industrial Management. Faculty of Humanities, Meybod University, Meybod, Iran.

Abstract

Introduction: Over the past three decades, environmental challenges have become a global concern due to growing worries about the irreversible consequences of environmental degradation. Consequently, addressing environmental issues has increasingly become a priority for policymakers. Today, all countries are striving to achieve a balance between environmental protection and socioeconomic stability through the development of effective policies.
Objective: This study aims to examine and evaluate the environmental efficiency of Iran's provinces over different time periods, considering the inherent uncertainty in the data. In the real world, data is not always precise and deterministic, and deviations in data can significantly alter the results of efficiency evaluations. Therefore, it is essential to employ suitable methods to address data uncertainty when assessing efficiency. In this research, a Fuzzy Window Data Envelopment Analysis (FWDEA) model is utilized to evaluate the environmental efficiency of Iran's provinces. This model effectively accounts for data uncertainty and provides more accurate and reliable results.
Methodology: The approach used in this study incorporates undesirable outputs and can be applied to various structures in fuzzy data envelopment analysis. Based on a literature review, consultations with experts in the fields of the environment and data envelopment analysis, and available data, the input variables of the study were determined to be per capita energy consumption and per capita vehicles, while the output variable was defined as per capita pollutant emissions. Given the uncertainty about whether all units operate at optimal scale, the BCC model was employed. Furthermore, since it is easier to control outputs compared to inputs, an output-oriented data envelopment analysis model with variable returns to scale was assumed. Finally, the proposed fuzzy window data envelopment analysis model was implemented for 29 provinces of Iran over four time periods from 2017 to 2020, and the results were analyzed.
Findings: Data analysis using the proposed model revealed that East Azerbaijan province had the best environmental performance with an efficiency score of 0.837407, while Hormozgan province had the worst performance with an efficiency score of 0.332543 during the four years of the study. Examining the annual average efficiency of the provinces indicated that the trend of efficiency improvement or decline varied across provinces over the years and was not stable. Additionally, the results of the fuzzy window data envelopment analysis model showed that the efficiency of provinces varied in each consecutive time window and did not follow a fixed pattern.
Conclusion: In this study, a fuzzy DEA approach was employed to evaluate efficiency considering the ambiguous, unavailable, and imprecise nature of the data. Overall, the use of fuzzy window DEA in assessing the environmental efficiency of Iran's provinces is one of the best methods due to its high accuracy, ability to model fuzzy data, and identification of complex patterns. This approach can assist policymakers in identifying the strengths and weaknesses of provinces in terms of environmental efficiency and formulating appropriate policies to improve environmental performance. To enhance the environmental efficiency of provinces, it is necessary to develop dynamic environmental policies tailored to the specific conditions of each province. Moreover, given that environmental challenges are global, the application of similar approaches to evaluate environmental efficiency in other countries can also contribute to improving global environmental performance.

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