Evaluating Urban Efficiency in Air Pollution Management: A DEA Study of 20 Iranian Cities

Authors

  • Reza Kargar * Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.48314/ijorai.v1i3.74

Abstract

Data Envelopment Analysis (DEA) is a widely recognized quantitative tool for evaluating the efficiency of cities in managing resources and mitigating air pollution. This study considers controllable inputs, such as green space and investment in urban infrastructure, and incorporates undesirable outputs, including CO, NO₂, and PM2.5 concentrations, to reflect the negative impact of pollution. Considering these outputs allows for a more realistic and practical assessment of urban performance. The efficiency of 20 major Iranian cities is evaluated using classical DEA models with Variable Returns to Scale (VRS). Including undesirable outputs ensures that higher pollution levels reduce efficiency scores, reflecting environmental degradation. Efficiency targets for inefficient cities are computed, indicating practical adjustments in controllable inputs without expecting immediate reductions in pollution, which are influenced by multiple uncontrollable factors [1]. These results provide actionable insights for urban managers and policymakers. By comparing actual inputs with DEA-based targets, cities can benchmark performance, prioritize interventions, and adopt best practices from more efficient cities. The combination of DEA with undesirable outputs, alongside managerial judgment, creates a practical framework for improving resource allocation and environmental performance in Iranian cities [2], [3].

Keywords:

Data envelopment analysis, Air pollution, Undesirable outputs

References

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Published

2025-09-16

Issue

Vol. 1 No. 3 (2025): International Journal of Operations Research and Artificial Intelligence (IJORAI)

Section

Articles

How to Cite

Kargar, R. . (2025). Evaluating Urban Efficiency in Air Pollution Management: A DEA Study of 20 Iranian Cities. International Journal of Operations Research and Artificial Intelligence , 1(3), 131-138. https://doi.org/10.48314/ijorai.v1i3.74