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Metafrontier frameworks for estimating solar power efficiency in the United States using stochastic nonparametric envelopment of data (StoNED)

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  • Delnava, Haleh
  • Khosravi, Ali
  • El Haj Assad, Mamdouh

Abstract

Solar energy is one of the most promising energy sources as it its significantly reduce greenhouse gas (GHG) emissions compared to fossil fuels. In this study, we employ the meta frontier framework to estimate US solar energy performance in 2019 using stochastic non-parametric envelopment of data (StoNED) under the convex and non-convex frameworks. This estimation allows us to monitor operating inefficiencies and technological gaps in each observation. In addition, we investigate the potential impact of the specification of a convex production technology in relation to the use of a nonconvex technology in the comparative analysis. This methodological reflection is mainly supported by the recent engineering literature that provides evidence of the non-convex hypothesis. The results indicate that a multifaceted approach must be taken to ensure the supply of energy. Given that sunny states have the potential to transmit energy to other states, the drawbacks, such as environmental concerns and high investment expenses, drive policymakers to look for other alternatives, such as adapting panels that are suitable for specific conditions.

Suggested Citation

  • Delnava, Haleh & Khosravi, Ali & El Haj Assad, Mamdouh, 2023. "Metafrontier frameworks for estimating solar power efficiency in the United States using stochastic nonparametric envelopment of data (StoNED)," Renewable Energy, Elsevier, vol. 213(C), pages 195-204.
    • Handle: RePEc:eee:renene:v:213:y:2023:i:c:p:195-204
    DOI: 10.1016/j.renene.2023.06.007
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    1. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
    2. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-598, October.
    3. Cliff Huang & Tai-Hsin Huang & Nan-Hung Liu, 2014. "A new approach to estimating the metafrontier production function based on a stochastic frontier framework," Journal of Productivity Analysis, Springer, vol. 42(3), pages 241-254, December.
    4. Henry Tulkens, 2006. "On FDH Efficiency Analysis: Some Methodological Issues and Applications to Retail Banking, Courts and Urban Transit," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 311-342, Springer.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    7. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    8. Mekhilef, S. & Saidur, R. & Safari, A., 2011. "A review on solar energy use in industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1777-1790, May.
    9. Kuosmanen, Timo, 2006. "Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework," Discussion Papers 11864, MTT Agrifood Research Finland.
    10. Worrell, Ernst & Laitner, John A & Ruth, Michael & Finman, Hodayah, 2003. "Productivity benefits of industrial energy efficiency measures," Energy, Elsevier, vol. 28(11), pages 1081-1098.
    11. Mark Andor & Frederik Hesse, 2014. "The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)," Journal of Productivity Analysis, Springer, vol. 41(1), pages 85-109, February.
    12. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    13. Kumbhakar, Subal C. & Park, Byeong U. & Simar, Leopold & Tsionas, Efthymios G., 2007. "Nonparametric stochastic frontiers: A local maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 137(1), pages 1-27, March.
    14. 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, Elsevier, vol. 132(2), pages 245-259, July.
    15. Sarıca, Kemal & Or, Ilhan, 2007. "Efficiency assessment of Turkish power plants using data envelopment analysis," Energy, Elsevier, vol. 32(8), pages 1484-1499.
    16. Emili GRIFELL‐TATJÉ & Kristiaan KERSTENS, 2008. "Incentive Regulation And The Role Of Convexity In Benchmarking Electricity Distribution: Economists Versus Engineers," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 79(2), pages 227-248, June.
    17. Golany, B & Roll, Y, 1989. "An application procedure for DEA," Omega, Elsevier, vol. 17(3), pages 237-250.
    18. Timo Kuosmanen & Andrew Johnson & Antti Saastamoinen, 2015. "Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 7, pages 191-244, Springer.
    19. Oleg Badunenko & Daniel J. Henderson & Subal C. Kumbhakar, 2012. "When, where and how to perform efficiency estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(4), pages 863-892, October.
    20. Bauer, Paul W. & Berger, Allen N. & Ferrier, Gary D. & Humphrey, David B., 1998. "Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods," Journal of Economics and Business, Elsevier, vol. 50(2), pages 85-114, March.
    21. Alhuyi Nazari, Mohammad & Ahmadi, Mohammad H. & Ghasempour, Roghayeh & Shafii, Mohammad Behshad & Mahian, Omid & Kalogirou, Soteris & Wongwises, Somchai, 2018. "A review on pulsating heat pipes: From solar to cryogenic applications," Applied Energy, Elsevier, vol. 222(C), pages 475-484.
    22. Cesaroni, Giovanni & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2017. "Global and local scale characteristics in convex and nonconvex nonparametric technologies: A first empirical exploration," European Journal of Operational Research, Elsevier, vol. 259(2), pages 576-586.
    23. Hayami, Yujiro & Ruttan, Vernon W, 1970. "Agricultural Productivity Differences Among Countries," American Economic Review, American Economic Association, vol. 60(5), pages 895-911, December.
    24. Lin, Boqiang & Du, Kerui, 2014. "Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: An application to Chinese energy economy," Energy, Elsevier, vol. 76(C), pages 884-890.
    25. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    26. Timo Kuosmanen & Andrew L. Johnson, 2010. "Data Envelopment Analysis as Nonparametric Least-Squares Regression," Operations Research, INFORMS, vol. 58(1), pages 149-160, February.
    27. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    28. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    29. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
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