EVALUATION OF SUSTAINABLE ENERGY GENERATION EFFICIENCY BASED ON DATA ENVELOPMENT ANALYSIS USING PORTFOLIO THEORY: A LITERATURE REVIEW
Keywords:
Aggregation Methods, Data Envelopment Analyses (DEA), Energy Generation Sources, Generation Cost/Risk, Portfolio Model, Sustainable Energy MixAbstract
Sustainable energy generation has become the solution approach to environmental issues associated with energy generation due to the ever-increasing global energy consumption and demand for sustainable development goals. The generation of secure and inexpensive clean energy is dependent upon the critical assessment of the associated risks and costs of diverse energy generation sources. To spread the associated risks with energy technologies, a portfolio optimization model is popularly used. Recently, policies on energy started including the environmental and social impacts of energy generation sources. As a result, sustainability has become paramount in energy mix design decisions. A sustainability goal in energy mix design aims to improve resource use and reduce environmental harm. Incorporating sustainability requires adding environmental and social cost indicators to total generation costs in portfolio optimization models. However, conventional methods often prioritize economic factors over environmental and social aspects. This review examines aggregation models and their impact on energy source preferences and optimized portfolios. It recommends multiplicative, pairwise interaction, and multi-linear aggregation models as more effective than additive models in integrating all sustainability dimensions for better energy mix decisions. Effective aggregation models helped include more renewable and clean energy sources in optimized energy portfolios. This study reviewed recent literature (2022–2025) on sustainable energy generation efficiency using data envelopment analysis (DEA). Among methods reviewed, the portfolio approach was found most effective due to its ability to diversify cost and risk across sustainability dimensions, aiding in technology assessment and optimal energy mix development.
References
Ahmadi, S. A., Mirlohi, S. M., Ahmadi, M. H., & Ameri, M. (2020). Portfolio optimization of power plants by using renewable energy in Iran. International Journal of Low-Carbon Technologies, 16(2), 463–475. https://doi.org/10.1093/ijlct/ctaa079
Allan, G., Eromenko, I., McGregor, P., & Swales, K. (2011). The regional electricity generation mix in Scotland: A portfolio selection approach incorporating marine technologies. Energy Policy, 39(1), 6–22. https://doi.org/10.1016/j.enpol.2010.08.028
Amin, G. R., & Hajjami, M. (2021). Improving DEA cross-efficiency optimization in portfolio selection. Expert Systems with Applications, 168, 114280. https://doi.org/10.1016/j.eswa.2020.114280
An, Q., Tao, X., Dai, B., & Xiong, B. (2020). Bounded-change target-setting approach: Selection of a realistic benchmarking path. Journal of the Operational Research Society, 72(3), 663–677. https://doi.org/10.1080/01605682.2019.1700185
Arnesano, M., Carlucci, A. P., & Laforgia, D. (2012). Extension of portfolio theory application to energy planning problem: The Italian case. Energy, 39(1), 112–124. https://doi.org/10.1016/j.energy.2011.06.053
Awerbuch, S., & Berger, M. (2003). Applying portfolio theory to EU electricity planning and policy making. IEA/EET Working Paper, EET/2003/03. https://scispace.com
Awerbuch, S., & Yang, S. (2007). Efficient electricity generating portfolios for Europe: Maximising energy security and climate change mitigation. EIB Papers, 12(2), 8–37. https://www.econstor.eu
Bar-Lev, D., & Katz, S. (1976). A portfolio approach to fossil fuel procurement in the electricity utility industry. The Journal of Finance, 31(3), 933–947. https://doi.org/10.2307/2326437
Basso, A., & Funari, S. (2001). A data envelopment analysis approach to measure the mutual fund performance. European Journal of Operational Research, 135(3), 477–492. https://doi.org/10.1016/S0377-2217(00)00311-8
Bhattacharya, A., & Kojima, S. (2012). Power sector investment risk and renewable energy: A Japanese case study using portfolio risk optimization method. Energy Policy, 40, 69–80. https://doi.org/10.1016/j.enpol.2010.09.031
Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299–312. https://doi.org/10.1016/j.ejor.2013.09.032
Cabello, J. M., Navarro-Jurado, E., Rodríguez, B., Thiel-Ellul, D., & Ruiz, F. (2019). Dual weak–strong sustainability synthetic indicators using a double reference point scheme: The case of Andalucía, Spain. Operational Research, 19(3), 757–782. https://doi.org/10.1007/s12351-018-0390-5
Cinelli, M., Coles, S. R., & Kirwan, K. (2014). Analysis of the potentials of multi-criteria decision analysis methods to conduct sustainability assessment. Ecological Indicators, 46, 138–148. https://doi.org/10.1016/j.ecolind.2014.06.011
Delarue, E., De Jonghe, C., Belmans, R., & D’Haeseleer, W. (2011). Applying portfolio theory to the electricity sector: Energy versus power. Energy Economics, 33(1), 12–23. https://doi.org/10.1016/j.eneco.2010.05.003
deLlano-Paz, F., Calvo-Silvosa, A., Antelo, S. I., & Soares, I. (2017). Energy planning and modern portfolio theory: A review. Renewable and Sustainable Energy Reviews, 77, 636–651. https://doi.org/10.1016/j.rser.2017.04.045
Doukas, H., Karakosta, C., & Psarras, J. (2010). Computing with words to assess the sustainability of renewable energy options. Expert Systems with Applications, 37(7), 5491–5497. https://doi.org/10.1016/j.eswa.2010.02.061
Dyckhoff, H., & Allen, K. (2001). Measuring ecological efficiency with data envelopment analysis (DEA). European Journal of Operational Research, 132(2), 312–325. https://doi.org/10.1016/S0377-2217(00)00154-5
Dyer Maut, J. S. (2016). Multiattribute utility theory. In J. Figueira, S. Greco, & M. Ehrogott (Eds.), Multiple criteria decision analysis (pp. 285–314). Springer. https://doi.org/10.1007/978-1-4939-3094-4_8
Edirisinghe, N. C. P., & Zhang, X. (2007). Generalized DEA model of fundamental analysis and its application to portfolio optimization. Journal of Banking & Finance, 31(11), 3311–3335. https://doi.org/10.1016/j.jbankfin.2007.04.008
Elkington, J. (1998). Cannibals with forks: The triple bottom line of 21st century business. New Society Publishers.
Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4–8. https://doi.org/10.1016/j.seps.2017.01.008
Feng, C., & Wang, M. (2017). Analysis of energy efficiency and energy savings potential in China’s provincial industrial sectors. Journal of Cleaner Production, 164, 1531–1541. https://doi.org/10.1016/j.jclepro.2017.07.081
Gan, G., Lee, H. S., Lee, L., Wang, X., & Wang, Q. (2019). Network hierarchical DEA with an application to international shipping industry in Taiwan. Journal of the Operational Research Society, 71(6), 991–1002. https://doi.org/10.1080/01605682.2019.1603792
Hacatoglu, K., Dincer, I., & Rosen, M. A. (2015). A new model to assess the environmental impact and sustainability of energy systems. Journal of Cleaner Production, 103, 211–218. https://doi.org/10.1016/j.jclepro.2014.06.050
Hacatoglu, K., Dincer, I., & Rosen, M. A. (2015). Sustainability assessment of a hybrid energy system with hydrogen-based storage. International Journal of Hydrogen Energy, 40(3), 1559–1568. https://doi.org/10.1016/j.ijhydene.2014.11.079
Hampf, B. (2014). Separating environmental efficiency into production and abatement efficiency: A nonparametric model with application to US power plants. Journal of Productivity Analysis, 41(3), 457–473.
Haugen, M., Blaisdell-Pijuan, P. L., Botterud, A., Levin, T., Zhou, Z., & Belsnes, M. (2024). Power market models for the clean energy transition: State of the art and future research needs. Applied Energy, 357, 122495. https://doi.org/10.1016/j.apenergy.2023.122495
Honma, S., & Hu, J. L. (2008). Total-factor energy efficiency of regions in Japan. Energy Policy, 36(2), 821–833. https://doi.org/10.1016/j.enpol.2007.10.026
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
Ioannou, A., Angus, A., & Brennan, F. (2017). Risk-based methods for sustainable energy system planning: A review. Renewable and Sustainable Energy Reviews, 74, 602–615. https://doi.org/10.1016/j.rser.2017.02.082
Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value tradeoffs. Cambridge University Press.
Kim, Y. J., Cho, S. H., & Sharma, B. P. (2021). Constructing efficient portfolios of low-carbon technologies. Renewable and Sustainable Energy Reviews, 150, 111515. https://doi.org/10.1016/j.rser.2021.111515
Li, J., Gallego-Schmid, A., & Stamford, L. (2024). Integrated sustainability assessment of repurposing onshore abandoned wells for geothermal power generation. Applied Energy, 359. https://doi.org/10.1016/j.apenergy.2024.122670
Li, K., & Lin, B. Q. (2015). Metafrontier energy efficiency with CO₂ emissions and its convergence analysis for China. Energy Economics, 48, 230–241. https://doi.org/10.1016/j.eneco.2015.01.006
Lim, S., Oh, K. W., & Zhu, J. (2014). Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market. European Journal of Operational Research, 236(1), 361–368. https://doi.org/10.1016/j.ejor.2013.12.002
Liu, W., Zhang, D. Q., Meng, W., Li, X. X., & Xu, F. (2011). A study of DEA models without explicit inputs. Omega, 39(5), 472–480. https://doi.org/10.1016/j.omega.2010.10.005
Lo-Iacono-Ferreira, V. G., Garcia-Bernabeu, A., Hilario-Caballero, A., & Torregrosa-López, J. (2022). Measuring urban sustainability performance through composite indicators for Spanish cities. Journal of Cleaner Production, 359, 131982. https://doi.org/10.1016/j.jclepro.2022.131982
Makridou, G., Andriosopoulos, K., Doumpos, M., & Zopounidis, C. (2016). Measuring the efficiency of energy-intensive industries across European countries. Energy Policy, 88, 573–583. https://doi.org/10.1016/j.enpol.2015.06.042
Malala, O. N., & Adachi, T. (2020). Portfolio optimization of electricity generating resources in Kenya. The Electricity Journal, 33(4), 106733. https://doi.org/10.1016/j.tej.2020.106733
Mari, C. (2014). Hedging electricity price volatility using nuclear power. Applied Energy, 113, 615–621. https://doi.org/10.1016/j.apenergy.2013.08.016
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974
Marrero, G. A., Puch, L. A., & Ramos-Real, F. J. (2015). Mean-variance portfolio methods for energy policy risk management. International Review of Economics & Finance, 40, 246–264. https://doi.org/10.1016/j.iref.2015.02.013
Mukherjee, K. (2008). Energy use efficiency in US manufacturing: A nonparametric analysis. Energy Economics, 30(1), 76–96. https://doi.org/10.1016/j.eneco.2006.11.004
Mukherjee, K. (2010). Measuring energy efficiency in the context of an emerging economy: The case of Indian manufacturing. European Journal of Operational Research, 201(3), 933–941. https://doi.org/10.1016/j.ejor.2009.04.012
Novacheck, J., & Johnson, J. X. (2017). Diversifying wind power in real power systems. Renewable Energy, 106, 177–185. https://doi.org/10.1016/j.renene.2016.12.100
Park, S. Y., Yun, B.-Y., Yun, C. Y., Lee, D. H., & Choi, D. G. (2016). An analysis of the optimum renewable energy portfolio using the bottom–up model: Focusing on the electricity generation sector in South Korea. Renewable and Sustainable Energy Reviews, 53, 319–329. https://doi.org/10.1016/j.rser.2015.08.029
Pomerol, J. C., & Barba-Romero, S. (2000). Multicriterion decision in management: Principles and practice. Springer.
López Prol, J., de Llano Paz, F., Calvo Silvosa, A., Pfenninger, S., & Staffell, I. (2024). Wind-solar technological, spatial and temporal complementarities in Europe: A portfolio approach. Energy, 292, 130348. https://doi.org/10.1016/j.energy.2024.130348
Ramanathan, R. (2006). Evaluating the comparative performance of countries of the Middle East and North Africa: A DEA application. Socio-Economic Planning Sciences, 40(2), 156–167. https://doi.org/10.1016/j.seps.2004.10.002
Rombauts, Y., Delarue, E., & D’haeseleer, W. (2011). Optimal portfolio-theory-based allocation of wind power: Taking into account cross-border transmission-capacity constraints. Renewable Energy, 36(9), 2374–2387. https://doi.org/10.1016/j.renene.2011.02.010
Roques, F., Hiroux, C., & Saguan, M. (2010). Optimal wind power deployment in Europe—a portfolio approach. Energy Policy, 38(7), 3245–3256. https://doi.org/10.1016/j.enpol.2009.07.048
Rowley, H. V., Peters, G. M., Lundie, S., & Moore, S. J. (2012). Aggregating sustainability indicators: Beyond the weighted sum. Journal of Environmental Management, 111, 24–33. https://doi.org/10.1016/j.jenvman.2012.05.004
Santos-Alamillos, F. J., Thomaidis, N. S., Usaola-García, J., Ruiz-Arias, J. A., & Pozo-Vázquez, D. (2017). Exploring the mean-variance portfolio optimization approach for planning wind repowering actions in Spain. Renewable Energy, 106, 335–342. https://doi.org/10.1016/j.renene.2017.01.041
Shakouri, M., Lee, H. W., & Choi, K. (2015). PACPIM: New decision-support model of optimized portfolio analysis for community-based photovoltaic investment. Applied Energy, 156, 607–617. https://doi.org/10.1016/j.apenergy.2015.07.060
Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61. https://doi.org/10.1287/mnsc.44.1.49
Simar, L., & Wilson, P. W. (2000). A general methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics, 27(6), 779–802. https://doi.org/10.1080/02664760050081951
Simoglou, C. K., Biskas, P. N., Marneris, I. G., & Roumkos, C. G. (2022). Long-term electricity procurement portfolio optimization. Electric Power Systems Research, 202, 107582. https://doi.org/10.1016/j.epsr.2021.107582
Stempien, J. P., & Chan, S. H. (2017). Addressing energy trilemma via the modified Markowitz mean-variance portfolio optimization theory. Applied Energy, 202, 228–237. https://doi.org/10.1016/j.apenergy.2017.05.145
Stewart, T. J. (2005). Dealing with uncertainties in MCDA. In J. Figueira, S. Greco, & M. Ehrogott (Eds.), Multiple criteria decision analysis: State of the art surveys (pp. 445–466). Springer. https://doi.org/10.1007/0-387-23081-5_11
Sykes, J. P., & Trench, A. (2014). Finding the copper mine of the 21st century: Conceptual exploration targeting for hypothetical copper reserves. Society of Economic Geologists, Inc. Special Publication, 18, 273–300. https://doi.org/10.5382/SP.18.14
Tao, et al. (2020). Energy efficiency evaluation based on data envelopment analysis: A literature review. Energies, 13(14), 3548. https://doi.org/10.3390/en13143548
Tarnaud, A. C., & Leleu, H. (2018). Portfolio analysis with DEA: Prior to choosing a model. Omega, 75, 57–76. https://doi.org/10.1016/j.omega.2017.02.003
Thies, C., Kieckhäfer, K., Spengler, T. S., & Sodhi, M. S. (2019). Operations research for sustainability assessment of products: A review. European Journal of Operational Research, 274(1), 1–21. https://doi.org/10.1016/j.ejor.2018.04.039
Turkson, C., Acquaye, A., Liu, W., & Papadopoulos, T. (2020). Sustainability assessment of energy production: A critical review of methods, measures and issues. Journal of Environmental Management, 264, 110464. https://doi.org/10.1016/j.jenvman.2020.110464
United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development (A/RES/70/1). https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf
Von Neumann, J., & Morgenstern, O. (1947). Theory of games and economic behavior (2nd ed.). Princeton University Press.
Wang, J. J., Jing, Y. Y., Zhang, C. F., & Zhao, J. H. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13(9), 2263–2278. https://doi.org/10.1016/j.rser.2009.06.021
Wang, Z. H., Zeng, H. L., Wei, Y. M., & Zhang, Y. X. (2012). Regional total factor energy efficiency: An empirical analysis of industrial sector in China. Applied Energy, 97, 115–123. https://doi.org/10.1016/j.apenergy.2011.12.071
Wang, K., Wei, Y. M., & Huang, Z. (2018). Environmental efficiency and abatement efficiency measurements of China’s thermal power industry: A data envelopment analysis based materials balance approach. European Journal of Operational Research, 269(1), 35–50. https://doi.org/10.1016/j.ejor.2017.04.053
Welch, E., & Barnum, D. (2009). Joint environmental and cost efficiency analysis of electricity generation. Ecological Economics, 68(8–9), 2336–2343. https://doi.org/10.1016/j.ecolecon.2009.03.004
Westner, G., & Madlener, R. (2010). The benefit of regional diversification of cogeneration investments in Europe: A mean-variance portfolio analysis. Energy Policy, 38(12), 7911–7920. https://doi.org/10.1016/j.enpol.2010.09.011
Westner, G., & Madlener, R. (2011). Development of cogeneration in Germany: A mean-variance portfolio analysis of individual technology’s prospects in view of the new regulatory framework. Energy, 36(8), 5301–5313. https://doi.org/10.1016/j.energy.2011.06.038
Wu, J., Yin, P. Z., Sun, J. S., Chu, J. F., & Liang, L. (2016). Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspective. European Journal of Operational Research, 254(3), 1047–1062. https://doi.org/10.1016/j.ejor.2016.04.034
Wu, J., Xiong, B., An, Q., Sun, J., & Wu, H. (2017). Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs. Annals of Operations Research, 255(1–2), 257–276. https://link.springer.com
Wu, R., & Sansavini, G. (2021). Energy trilemma in active distribution network design: Balancing affordability, sustainability and security in optimization-based decision-making. Applied Energy, 304, 117891. https://doi.org/10.1016/j.apenergy.2021.117891
Yang, G., Shen, W., Zhang, D., & Liu, W. (2014). Extended utility and DEA models without explicit input. Journal of the Operational Research Society, 65(8), 1212–1220. https://doi.org/10.1057/jors.2013.68
Zhang, N., Kong, F., & Yu, Y. (2015). Measuring ecological total-factor energy efficiency incorporating regional heterogeneities in China. Ecological Indicators, 51, 165–172. https://doi.org/10.1016/j.ecolind.2014.07.041
Zhang, Y. J., Sun, Y. F., & Huang, J. (2018). Energy efficiency, carbon emission performance, and technology gaps: Evidence from CDM project investment. Energy Policy, 115, 119–130. https://doi.org/10.1016/j.enpol.2017.12.056
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. https://doi.org/10.1016/j.soscij.2013.01.003
Zhang, Z. (2003). Why did the energy intensity fall in China’s industrial sector in the 1990s? The relative importance of structural change and intensity change. Energy Economics, 25(6), 625–638. https://doi.org/10.1016/S0140-9883(03)00042-2
Zhao, H., Guo, S., & Zhao, H. (2019). Provincial energy efficiency of China quantified by three-stage data envelopment analysis. Energy, 166, 96–107. https://doi.org/10.1016/j.energy.2018.10.063
Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189(1), 1–18. https://doi.org/10.1016/j.ejor.2007.04.042
Zhou, Z., Xiao, H., Jin, Q., & Liu, W. (2018). DEA frontier improvement and portfolio rebalancing: An application of China mutual funds on considering sustainability information disclosure. European Journal of Operational Research, 269(1), 111–131. https://doi.org/10.1016/j.ejor.2017.07.010
Zhou, H., Yang, Y., Chen, Y., & Zhu, J. (2018). Data envelopment analysis application in sustainability: The origins, development and future directions. European Journal of Operational Research, 264(1), 1–16. https://doi.org/10.1016/j.ejor.2017.06.023
Zhou, P., & Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8), 2911–2916. https://doi.org/10.1016/j.enpol.2008.03.041
Zhu, Z., Wang, K., & Zhang, B. (2014). Applying a network data envelopment analysis model to quantify the eco-efficiency of products: A case study of pesticides. Journal of Cleaner Production, 69, 67–73. https://doi.org/10.1016/j.jclepro.2014.01.064
Zhu, L., & Fan, Y. (2010). Optimization of China's generating portfolio and policy implications based on portfolio theory. Energy, 35(3), 1391–1402. https://doi.org/10.1016/j.energy.2009.11.024
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