- Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261-1264.
- Aydin, S., & Şenel, T. (2025). Evaluating energy efficiency in Turkish electric distribution using network DEA and GA models. Scientific Reports, 15(1), 21790.
- Azami, S., & Azhand, H. (2025). Total factor ecological energy efficiency and total factor carbon efficiency of Iranian provinces: An application of data envelopment analysis and Tobit model. Applied Theories of Economics, 12(3), 49–80 (In Persian).
- Azami, S., Pour-Karimi, L., & Sadri, S. (2018). Total factor CO₂ emission performance in Iranian manufacturing industries: Meta-frontier non-radial Malmquist index approach. Journal of Research in Economic Modeling, 8(31), 131–163 (In Persian).
- Balali, H., Farzanegan, M. R., Zamani, O., & Baniasadi, M. (2023). Air pollution and economic sanctions in Iran (No. 03-2023). MAGKS Joint Discussion Paper Series in Economics.
- Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
- Battese, G. E., Rao, D. S. P., & O'Donnell, C. J. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of productivity analysis, 21(1), 91-103.
- (2022). BP statistical review of world energy 2022 (71st ed.). bp p.l.c.
- Central Bank of the Islamic Republic of Iran. (n.d.). Net capital stock by type of asset at constant 1395 prices [Data set]. Tehran, Iran (In Persian).
- Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
- Chen, Y., Wang, M., Feng, C., Zhou, H., & Wang, K. (2021). Total factor energy efficiency in Chinese manufacturing industry under industry and regional heterogeneities. Resources, Conservation and Recycling, 168, 105255.
- Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1-4.
- Crippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M., Schaaf, E., ... & Pekar, F. (2025). GHG emissions of all world countries—2025 report. Publications Office of the European Union.
- Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of operational research, 132(2), 245-259.
- Eftekhary, M., Gholami, P., Safari, S., & Shojaee, M. (2012). Ranking normalization methods for improving the accuracy of SVM algorithm by DEA method. Modern applied science, 6(10), 26–36.
- Ervural, B. C., Ervural, B., & Zaim, S. (2016). Energy efficiency evaluation of provinces in Turkey using data envelopment analysis. Procedia-Social and Behavioral Sciences, 235, 139-148.
- Feng, C., Wang, M., Zhang, Y., & Liu, G. C. (2018). Decomposition of energy efficiency and energy-saving potential in China: A three-hierarchy meta-frontier approach. Journal of Cleaner Production, 176, 1054-1064.
- Hosseini, K., & Stefaniec, A. (2019). Efficiency assessment of Iran's petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure. Energy, 189, 116112.
- Hosseinzadeh-Bandbafha, H., Safarzadeh, D., Ahmadi, E., & Nabavi-Pelesaraei, A. (2018). Optimization of energy consumption of dairy farms using data envelopment analysis–A case study: Qazvin city of Iran. Journal of the Saudi Society of Agricultural Sciences, 17(3), 217-228.
- Huang, X., & Zhao, R. (2024). Regional differences and improvement potential of carbon emission efficiency in China’s power industry: insights from meta-frontier DEA model considering integer constraints. Frontiers in Energy Research, 12, 1339553.
- Intergovernmental Panel on Climate Change. (2006). 2006 IPCC guidelines for national greenhouse gas inventories (Vol. 2: Energy; Vol. 3: Industrial Processes and Product Use). Institute for Global Environmental Strategies.
- Intergovernmental Panel on Climate Change. (2019). 2019 refinement to the 2006 IPCC guidelines for national greenhouse gas inventories (E. Calvo Buendia, K. Tanabe, A. Kranjc, J. Baasansuren, M. Fukuda, S. Ngarize, A. Osako, Y. Pyrozhenko, P. Shermanau, & S. Federici, Eds.). Geneva, Switzerland: Author.
- Iqbal, M., Ma, J., Mushtaq, Z., Ahmad, N., Yousaf, M. Z., Tarawneh, B., ... & Zaitsev, I. (2025). Energy efficiency evaluation of construction projects using data envelopment analysis and Tobit regression. Scientific Reports, 15(1), 11444.
- Ministry of Energy of the Islamic Republic of Iran. (2022). Energy balance sheet of Iran, 1400 [2021/22]. Tehran, Iran (In Persian).
- Mirzaei, M., & Bekri, M. (2017). Energy consumption and CO2 emissions in Iran, 2025. Environmental research, 154, 345-351.
- Mohammadi, M., Fattahi, S., & Sohaili, K. (2021). Examination of relationship between energy consumption and Iran’s provinces gross product using wavelet analysis with panel data. Iranian Energy Economics, 10(40), 125–159 (In Persian).
- Mozayani, A. H. (2019). Estimating provincial capital stock in Iran economy. Quarterly Journal of Fiscal and Economic Policies, 7(26), 7-29.
- Nazemi, A., Karimi, F., Mamipoor, S., & Feshari, M. (2019). Energy efficiency in Iran provinces: DEA approach. Quarterly Journal of Energy Policy and Planning Research, 5(1), 103–142. (In Persian).
- O’Donnell, C. J., Rao, D. S. P, & Battese, G. E. (2008). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical economics, 34(2), 231-255.
- Pakkar, M. S. (2015). Using DEA and AHP for Multiplicative Aggregation of Hierarchical Indicators. American Journal of Operations Research, 5(05), 327–336.
- Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16-20.
- Seyef, A. M., & Hamidi Razi, D. (2017). Identification of factors that influence Iran’s provincial energy intensity index: A spatial dynamic panel data approach. Quarterly Energy Economics Review, 13(53), 61–103 (In Persian).
- Shafie-Pour, M., & Ardestani, M. (2007). Environmental damage costs in Iran by the energy sector. Energy Policy, 35(9), 4413-4423.
- Shahraki, A. S., Caloiero, T., & Bazrafshan, O. (2023). Influence of climatic factors on yields of pistachio, mango, and bananas in Iran. Sustainability, 15(11), 8962.
- Statistical Center of Iran. (2022a). Results of the labour force survey, 1400 [2021/22]. Tehran, Iran (In Persian).
- Statistical Center of Iran. (2022b). Statistical yearbook of Iran, 1400 [2021/22]. Tehran, Iran (In Persian).
- Statistical Center of Iran. (2023). Provincial accounts: Gross value added of economic activities by province at constant 1400 prices [Data set]. Tehran, Iran (In Persian).
- Tian, Z., Hu, G., & Mu, X. (2024). Urban green total factor energy efficiency and its decomposition under a multidimensional heterogeneity framework: a case study of the Yellow River Basin, China. Environmental Science and Pollution Research, 31(36), 48999-49025.
- Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498-509.
- Tone, K. (2002). A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143(1), 32–41.
- Tone, K. (2004). Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. Paper presented at the North American Productivity Workshop III, Toronto, Canada.
- S. Environmental Protection Agency. (2023). Report on the social cost of greenhouse gases: Estimates incorporating recent scientific advances. Washington, DC: National Center for Environmental Economics.
- Valadkhani, A., & Moradi-Motlagh, A. (2025). Examining inefficiency in countries with high energy consumption: A benchmarking approach. Energy Economics, 145, 108487.
- Wang, J., Xu, R., & Ye, S. (2025). Spatiotemporal evolution characteristics and influencing factors of China’s interprovincial industrial green efficiency based on the super-efficiency SBM model. Frontiers in Environmental Science, 13, 1625233.
- Wang, K., Wang, Y., & Zhang, J. (2025). Regional differences and convergence of land use efficiency towards carbon neutrality in China’s Yangtze river economic belt. Frontiers in Environmental Science, 13, 1577863.
- Wang, X., Lu, Y., Chen, C., Yi, X., & Cui, H. (2024). Total-factor energy efficiency of ten major global energy-consuming countries. Journal of Environmental Sciences, 137, 41-52.
- Ye, Y., Xu, Z., & Chen, W. (2023). The heterogeneous effect of financial technology on green total factor productivity in China. Journal of Innovation & Knowledge, 8(3), 100390.
- Yu, J., Zhou, K., & Yang, S. (2019). Regional heterogeneity of China's energy efficiency in “new normal”: A meta-frontier Super-SBM analysis. Energy Policy, 134, 110941.
- Zhang, N., & Choi, Y. (2013). Environmental energy efficiency of China's regional economies: A non-oriented slacks-based measure analysis. The social science journal, 50(2), 225-234.
- Zhao, P. J., Zeng, L. E., Lu, H. Y., Zhou, Y., Hu, H. Y., & Wei, X. Y. (2020). Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model. Science of the Total Environment, 741, 140026.
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