Iranian Journal of Economic Research

Energy Efficiency Assessment in 11 Emerging Economies: A Three-Stage DEA-SBM Approach with Undesirable Outputs

Document Type : Research Paper

Authors

Akram Akbari 1
Parviz Mohamadzadeh 2

1 Assistant Professor, Department of Economic Development and Planning, University of Tabriz, Tabriz, Iran

2 Full Professor, Department of Economic Development and Planning, University of Tabriz, Tabriz, Iran

https://doi.org/10.22054/ijer.2026.92371.1474
Abstract
This study evaluated the energy and environmental efficiency of 11 selected emerging economies using a three-stage model of data envelopment analysis–slacks-based measure (DEA-SBM) framework with undesirable outputs. Capital formation, labor, and energy consumption were used as input variables, while real gross domestic product (GDP) was treated as the desirable output and energy-related CO₂ emissions as the undesirable output. In the first stage, initial efficiency scores were estimated through a DEA-SBM model under the assumption of variable returns to scale (VRS). In the second stage, a stochastic frontier analysis (SFA) was employed to separate the effects of external environmental factors (e.g., government effectiveness, GDP per capita, urbanization, and the share of renewable energy) from managerial inefficiency and statistical noise. In the third stage, the input variables were adjusted to account for these external influences, and the efficiency scores were recalculated. The findings revealed substantial heterogeneity in energy and environmental performance across the selected countries. After adjustment, Turkey, Egypt, and Brazil achieved the highest efficiency scores, whereas Iran, Vietnam, and South Africa remained the farthest from the efficiency frontier. The results further indicated that a higher share of renewable energy was associated with lower excess energy consumption. Overall, enhancing energy–environmental performance in emerging economies requires more efficient input allocation, greater deployment of renewable energy, and policies that reduce energy intensity while supporting low-carbon economic growth.
Introduction
Energy efficiency has become a central issue at the intersection of economic growth, environmental sustainability, and energy security. In emerging economies, energy is a necessary input for industrialization, infrastructure expansion, urban development, and technological advancement. However, inefficient energy use increases production costs, heightens dependence on fossil fuels, and exacerbates environmental degradation through higher CO₂ emissions. Consequently, energy efficiency should not be viewed merely as a technical indicator; rather, it should be evaluated as a measure of energy–environmental performance that reflects how effectively countries transform capital, labor, and energy into desirable economic outputs while minimizing undesirable environmental outcomes.
The significance of this issue is particularly pronounced in emerging economies, where policymakers face the dual challenge of sustaining economic growth while mitigating the environmental consequences of energy-intensive development. Conventional single-stage efficiency models may produce biased estimates because they fail to distinguish internal managerial inefficiency from the effects of external environmental conditions and statistical noise. This limitation is especially important, as variations in institutional quality, income levels, urbanization, and structures of renewable energy can influence observed efficiency scores independently of the actual performance of decision-making units (DMU).
To address this issue, the present study employed a three-stage data envelopment analysis–slacks-based measure (DEA-SBM) framework with undesirable outputs to provide a more accurate and robust assessment of the energy–environmental efficiency of selected emerging economies. The study aimed to address three main research questions: (1) What is the initial technical efficiency of the selected emerging economies in transforming capital, labor, and energy into economic output while minimizing CO₂ emissions? (2) How do external environmental factors—specifically government effectiveness, GDP per capita, urbanization, and the share of renewable energy—affect input slacks? And (3) how do adjusted efficiency scores and country rankings change after the effects of external environmental conditions and statistical noise are removed from the initial efficiency estimates?
Materials and Methods
This study evaluated the energy–environmental efficiency of 11 emerging economies over the period 2000–2023. The selected emerging economies included Brazil, China, Egypt, India, Iran, Mexico, Morocco, the Philippines, South Africa, Turkey, and Vietnam. The data was obtained from the World Bank’s World Development Indicators and governance-related databases. The analysis employed a three-stage DEA-SBM model with undesirable outputs. This model was particularly suitable because it could account for input excesses and undesirable environmental outputs within a non-radial framework.
In the first stage, a DEA-SBM model under the assumption of variable returns to scale (VRS) was used to estimate the initial efficiency scores. Capital formation, labor, and energy consumption were specified as inputs, while real gross domestic product (GDP) served as the desirable output and energy-related CO₂ emissions as the undesirable output. This specification enabled the assessment of whether countries could reduce input use and undesirable emissions while maintaining or increasing desirable economic outputs.
In the second stage, a stochastic frontier analysis (SFA) was applied to decompose input slacks into three components: the effects of external environmental conditions, managerial inefficiency, and statistical noise. The environmental variables included GDP per capita, government effectiveness, urbanization, and the share of renewable energy. The share of renewable energy was included only in the energy-slack equation because of its direct influence on the structure of energy consumption. In the third stage, the original input variables were adjusted based on the SFA results, and the DEA-SBM model was re-estimated to obtain efficiency scores that more accurately reflected the intrinsic energy–environmental performance of the selected economies.
Results and Discussion
The first-stage results revealed substantial cross-country variation in initial technical efficiency. India, Turkey, and Brazil achieved the highest average efficiency scores, indicating superior performance in transforming capital, labor, and energy into economic output while minimizing CO₂ emissions. In contrast, Iran and South Africa recorded the lowest efficiency levels, reflecting a considerable distance from the efficiency frontier. These findings suggest that the selected emerging economies exhibit marked differences in their energy–efficiency patterns, with variations in input use, energy intensity, and environmental performance playing an important role in shaping their initial efficiency rankings.
The second-stage results showed that external environmental factors significantly influenced input slacks. GDP per capita had a positive and statistically significant effect on capital slack, indicating that higher income levels were not necessarily associated with more efficient capital use. Urbanization reduced both capital and labor slacks, suggesting that agglomeration economies, improved infrastructure, and more efficient labor–market matching contributed to better resource utilization. However, urbanization increased energy slack, implying that urban expansion may place additional pressure on energy systems when not accompanied by energy-efficient infrastructure and technologies. The share of renewable energy had a negative and statistically significant effect on energy slack, indicating that a cleaner energy mix was associated with lower excess energy consumption.
After adjusting the input variables in the third stage, the efficiency scores and country rankings changed substantially. Turkey, Egypt, and Brazil emerged as the most efficient economies after accounting for the effects of external environmental conditions and statistical noise. Egypt’s improved ranking suggested that its initial efficiency had been partly constrained by unfavorable external conditions. By contrast, India’s decline in the adjusted ranking indicated that part of its initial performance might have reflected favorable environmental conditions rather than superior managerial efficiency. Iran, Vietnam, and South Africa remained the least efficient economies after adjustment, confirming that their poor performance could not be attributed solely to external conditions or random shocks. Instead, their persistent inefficiencies appear to stem from structural challenges, including input allocation, high energy intensity, and a limited capacity to convert energy inputs into economic output while minimizing environmental impacts.
Conclusion
According to the findings, ignoring undesirable outputs and external environmental conditions can lead to biased assessments of energy–environmental efficiency in emerging economies. By separating the effects of external environmental factors and statistical noise from managerial inefficiency, the three-stage DEA-SBM model provides a more accurate measure of intrinsic efficiency than conventional single-stage approaches. The results suggested that countries with relatively low efficiency should not rely solely on expanding input use or pursuing conventional growth-oriented policies. Instead, they must prioritize more efficient input allocation, reducing energy intensity, expanding the deployment of renewable energy, and strengthening institutional capacity to support efficient, low-carbon production. Overall, the findings underscore that improving energy–environmental efficiency should be regarded as a strategic policy priority for emerging economies, enhancing economic competitiveness, promoting environmental sustainability, and supporting long-term development.

Keywords

Energy Efficiency
Three-stage DEA
SBM
Undesirable Outputs
Emerging Economies
Subjects
Addis, A.K. (2025). Sustainability and efficiency analysis of 42 countries: Super SBM-DEA model and the GML productivity index with undesirable outputs. Ecological Indicators, 177, 113767. https://doi.org/10.1016/j.ecolind.2025.113767
Ang, B.W. (2006). Monitoring changes in economy-wide energy efficiency: From energy–GDP ratio to composite efficiency index. Energy Policy, 34(5), 574–582. https://doi.org/10.1016/j.enpol.2005.11.011
Apergis, N., Aye, G.C., Barros, C.P., Gupta, R. & Wanke, P. (2015). Energy efficiency of selected OECD countries: A slacks based model with undesirable outputs. Energy Economics, 51, 45–53.
DOI: 10.1016/j.eneco.2015.05.022
Arabi, B., Munisamy, S. & Emrouznejad, A. (2015). A new slacks-based measure of Malmquist–Luenberger index in the presence of undesirable outputs. Omega, 51, 29–37.
https://doi.org/10.1016/j.omega.2014.08.006
Behboodi, D., Akbari, A. & Ahmadzadeh Deljavan, F. (2015). Evaluation of carbon dioxide emission efficiency based on energy consumption. In Proceedings of the First National Conference on Industrial Economics of Iran, Tabriz, Iran. [In Persian].
Charnes, A. & Cooper, W.W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3–4), 181–186. https://doi.org/10.1002/nav.3800090303
Chaubey, V., Mohanta, K.K. & Verma, R. (2026). Measuring performance and productivity of G20 nations in the industrial sector: Enhancing environmental energy efficiency using the global Malmquist–Luenberger DEA approaches. Sustainable Futures, 11, 101647. https://doi.org/10.1016/j.sftr.2026.101647
Chen, X., Chen, Y., Huang, W. & Zhang, X. (2023). A new Malmquist-type green total factor productivity measure: An application to China. Energy Economics, 117, 106408.
https://doi.org/10.1016/j.eneco.2022.106408
Chung, Y.H., Färe, R. & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240.
https://doi.org/10.1006/jema.1997.0146
Cooper, W.W., Seiford, L.M. & Tone, K. (2006). Introduction to data envelopment analysis and its uses: With DEA-Solver software and references. Springer. https://doi.org/10.1007/0-387-29122-9
Cornillie, J. & Fankhauser, S. (2004). The energy intensity of transition countries. Energy Economics, 26(3), 283–295.
https://doi.org/10.1016/j.eneco.2004.04.015
Ebrahimi Salari, T., Gorjipour, M.J. & Osmani, F. (2020). Evaluating industrial environmental efficiency in natural gas consumption: Application of DEA and directional distance function approaches at the provincial level. Journal of Industrial Economics Researches, 4(12), 11–24. https://doi.org/10.30473/indeco.2021.7415 [In Persian].
Edziah, B.K. & Opoku, E.E.O. (2024). Enhancing energy efficiency in Asia-Pacific: Comprehensive energy policy analysis. Energy Economics, 138, 107831. https://doi.org/10.1016/j.eneco.2024.107831
Emrouznejad, A. & Yang, G.L. (2016). A framework for measuring global Malmquist–Luenberger productivity index with CO₂ emissions on Chinese manufacturing industries. Energy, 115, 840–856.
https://doi.org/10.1016/j.energy.2016.09.032
Energy Institute. (2024). Statistical Review of World Energy 2024. Energy Institute. https://www.energyinst.org/statistical-review
Fried, H.O., Lovell, C.A.K., Schmidt, S.S. & Yaisawarng, S. (2002). Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis, 17(1–2), 157–174. https://doi.org/10.1023/A:1013548723393
He, J., Lau, W.Y. & Ng, K.H. (2025). A machine learning-enhanced three-stage SBM-DEA model with undesirable outputs for measuring green total factor productivity on a global scale. Finance Research Letters, 86, 108611. https://doi.org/10.1016/j.frl.2025.108611
Hu, J.L. & Wang, S.C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34(17), 3206–3217.
https://doi.org/10.1016/j.enpol.2005.06.015
International Energy Agency, International Renewable Energy Agency, United Nations Statistics Division, World Bank, & World Health Organization. (2020). Tracking SDG 7: The energy progress report 2020. World Bank.
https://documents1.worldbank.org/curated/en/344121590675372111/pdf/Tracking-SDG-7-The-Energy-Progress-Report-2020.pdf
International Energy Agency. (2024). Energy efficiency 2024. International Energy Agency. https://www.iea.org/reports/energy-efficiency-2024
Li, S., Wu, M. & Song, M. (2023). Analysis of regional differences in green energy efficiency in countries along “the Belt and Road” Initiative zone: Based on super efficiency DEA model and Malmquist index method. Frontiers in Energy Research, 11, 1109045.
https://doi.org/10.3389/fenrg.2023.1109045
Lu, M., Yu, J., Li, F., Zhang, Y. & Jia, Q. (2026). A three-stage evaluation of airline low-carbon competitiveness. Transportation Research Part D: Transport and Environment, 152, 105173.
https://doi.org/10.1016/j.trd.2025.105173
Mardani, A., Zavadskas, E.K., Streimikiene, D., Jusoh, A. & Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis approach in energy efficiency. Renewable and Sustainable Energy Reviews, 70, 1298–1322. https://doi.org/10.1016/j.rser.2016.12.030
Mohamadzadeh, P. & Najjar Bazogh Gharechopogh, H. (2014). The ICT effect on productive efficiency in the selected developing countries. Journal of Economic Research, 49(3), 677–698.
https://doi.org/10.22059/jte.2014.52447 [In Persian].
Mohamadzadeh, P., Akbari, A. & Akbari, A. (2014). Measuring the efficiency of knowledge-based economy in selected MENA countries: Data envelopment analysis. In Proceedings of the National Conference on New Approaches in Business Management, Tabriz, Iran. [In Persian].
Mohamed, E.F., Abdullah, A. & Jaaffar, A.H. (2024). Energy efficiency in developing countries: A systematic review of current findings and directions towards a net zero economy. International Journal of Energy Economics and Policy, 14(5), 495–508.
https://doi.org/10.32479/ijeep.15719
Oh, D.H. (2010). A global Malmquist–Luenberger productivity index. Journal of Productivity Analysis, 34(3), 183–197.
https://doi.org/10.1007/s11123-010-0178-y
Pastor, J.T. & Lovell, C.A.K. (2005). A global Malmquist productivity index. Economics Letters, 88(2), 266–271.
https://doi.org/10.1016/j.econlet.2005.02.013
Patterson, M.G. (1996). What is energy efficiency? Concepts, indicators and methodological issues. Energy Policy, 24(5), 377–390.
https://doi.org/10.1016/0301-4215(96)00017-1
Rasekhi, S. & Ghanbartabar, S. (2023). Decoupling dynamism of energy consumption, economic growth, and pollution in Iran: New evidence from factor analysis at triple levels of energy. Iranian Journal of Economic Research, 28(97), 6–43.
https://doi.org/10.22054/ijer.2024.76778.1233 [In Persian].
Ren, X., Zhang, Z. & Palma, A. (2025). Energy efficiency trends in developing countries: Are we seeing convergence or polarization? Energy Economics, 148, 108695.
https://doi.org/10.1016/j.eneco.2025.108695
Sadeghi, S.K., Akbari, A. & Mamipour, S. (2012). A study of Kuznets relationship in selected Islamic countries emphasizing environmental efficiency: A data envelopment analysis approach. Iranian Energy Economics, 1(2), 127–148. [In Persian].
Saidi, K. & Omri, A. (2020). The impact of renewable energy on carbon emissions and economic growth in 15 major renewable energy-consuming countries. Environmental Research, 186, 109567. https://doi.org/10.1016/j.envres.2020.109567
Shah, W.U.H., Zhu, N., Hao, G., Yan, H. & Yasmeen, R. (2024). Energy efficiency evaluation, technology gap ratio, and determinants of energy productivity change in developed and developing G20 economies: DEA super-SBM and MLI approaches. Gondwana Research, 125, 70–81. https://doi.org/10.1016/j.gr.2023.07.017
Shu, T., Liao, X., Yang, S. & Yu, T. (2024). Towards sustainability: Evaluating energy efficiency with a super-efficiency SBM-DEA model across 168 economies. Applied Energy, 376, 124254.
https://doi.org/10.1016/j.apenergy.2024.124254
Stern, D.I. (2012). Modeling international trends in energy efficiency. Energy Economics, 34(6), 2200–2208.
https://doi.org/10.1016/j.eneco.2012.03.009
Su, W. (2023). The drivers and barriers of energy efficiency. Energy Policy, 178, 113598. https://doi.org/10.1016/j.enpol.2023.113598
Suzuki, S. & Nijkamp, P. (2016). An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries: Design of a target-oriented DFM model with fixed factors in data envelopment analysis. Energy Policy, 88, 100–112.
https://doi.org/10.1016/j.enpol.2015.10.007
Tang, Y., Ding, H., Shan, X. & Wang, X. (2023). Application of the novel three-stage DEA model to evaluate total-factor energy efficiency: A case study based on 30 provinces and 8 comprehensive economic zones of China. Results in Engineering, 20, 101417.
https://doi.org/10.1016/j.rineng.2023.101417
Taylor, P.G., d’Ortigue, O.L., Francoeur, M. & Trudeau, N. (2010). Final energy use in IEA countries: The role of energy efficiency. Energy Policy, 38(11), 6463–6474.
https://doi.org/10.1016/j.enpol.2009.05.009
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509. https://doi.org/10.1016/S0377-2217(99)00407-5
Trotta, G. (2020). Assessing energy efficiency improvements and related energy security and climate benefits in Finland: An ex post multi-sectoral decomposition analysis. Energy Economics, 86, 104640. https://doi.org/10.1016/j.eneco.2019.104640
Wang, C.N., Hsu, H.P., Wang, Y.H. & Nguyen, T.T. (2020). Eco-efficiency assessment for some European countries using slacks-based measure data envelopment analysis. Applied Sciences, 10(5), 1760. https://doi.org/10.3390/app10051760
Wang, C.N., Nguyen, T.T.T., Dang, T.T. & Hsu, H.P. (2023). Exploring economic and environmental efficiency in renewable energy utilization: A case study in the organization for economic cooperation and development countries. Environmental Science and Pollution Research, 30(28), 72949–72965. https://doi.org/10.1007/s11356-023-27408-0
Wang, C.N., Nguyen, T.T.V., Chiang, C.C. & Le, H.D. (2024). Evaluating renewable energy consumption efficiency and impact factors in Asia-Pacific economic cooperation countries: A new approach of DEA with undesirable output model. Renewable Energy, 227, 120586. https://doi.org/10.1016/j.renene.2024.120586
Wang, L.W., Le, K.D. & Nguyen, T.D. (2019). Assessment of the energy efficiency improvement of twenty-five countries: A DEA approach. Energies, 12(8), 1535. https://doi.org/10.3390/en12081535
Xu, G., Feng, S., Guo, S. & Ye, X. (2023). The spatial-temporal evolution analysis of carbon emission of China’s thermal power industry based on the three-stage SBM-DEA model. International Journal of Climate Change Strategies and Management, 15(2), 247–263. https://doi.org/10.1108/IJCCSM-08-2022-0115
Yan, Y., Chen, Y., Han, M. & Zhen, H. (2022). Research on energy efficiency evaluation of provinces along the Belt and Road under carbon emission constraints: Based on super-efficient SBM and Malmquist index model. Sustainability, 14(14), 8453. https://doi.org/10.3390/su14148453
Yu, D. & He, X. (2020). A bibliometric study for DEA applied to energy efficiency: Trends and future challenges. Applied Energy, 268, 115048. https://doi.org/10.1016/j.apenergy.2020.115048
Zaman, A.U. & Valiyattoor, V. (2025). Evaluating the green total factor productivity and convergence among emerging and resource intensive economies. Discover Sustainability, 6, 1053.
https://doi.org/10.1007/s43621-025-01614-x
Zhang, C. & Chen, P. (2022). Applying the three-stage SBM-DEA model to evaluate energy efficiency and impact factors in RCEP countries. Energy, 241, 122917. https://doi.org/10.1016/j.energy.2021.122917
Zhao, C., Zhang, H., Zeng, Y., Li, F., Liu, Y., Qin, C. & Yuan, J. (2018). Total-factor energy efficiency in BRI countries: An estimation based on three-stage DEA model. Sustainability, 10(1), 278.
https://doi.org/10.3390/su10010278
Zheng, L., Zhan, F. & Ren, F. (2025). Carbon dioxide emission-reduction efficiency in China’s new energy vehicle sector toward sustainable development: Evidence from a three-stage super-slacks based-measure data envelopment analysis model. Sustainability, 17(16), 7440. https://doi.org/10.3390/su17167440
Zhou, S. & Xu, Z. (2022). Energy efficiency assessment of RCEP member states: A three-stage slack based measurement DEA with undesirable outputs. Energy, 253, 124170.        https://doi.org/10.1016/j.energy.2022.124170

  • Receive Date 25 April 2026
  • Revise Date 01 July 2026
  • Accept Date 15 June 2026
  • First Publish Date 15 June 2026

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