IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v210y2026ics030142152500518x.html

Institutional reform over technical fixes: The energy waste severity index for tackling persistent inefficiency and advancing SDG 7

Author

Listed:
  • Andrews, Antony
  • Kimpton, Sean

Abstract

Why do some advanced economies remain persistently energy productivity-inefficient despite technological progress? This study argues that the constraint is institutional, not merely technical. Using a dynamic Bayesian stochastic frontier model for 37 OECD countries (2000–2021), this study develops the Energy Waste Severity Index (EWSI), a frontier-based metric that captures both the level and persistence of energy productivity inefficiency. The EWSI reveals a clear divide: countries such as Poland and Ireland face high, entrenched waste consistent with structural and governance frictions, whereas Japan, Switzerland, New Zealand, and the United States pair low inefficiency with weaker persistence. Because persistence signals institutional drag, technical fixes alone are insufficient; coordinated policy and governance reforms are required. By pairing benchmarking with persistence, the EWSI provides policymakers with a practical diagnostic tool to identify bottlenecks, align energy planning with skills, investment, and regulation, and monitor progress toward achieving SDG 7.

Suggested Citation

  • Andrews, Antony & Kimpton, Sean, 2026. "Institutional reform over technical fixes: The energy waste severity index for tackling persistent inefficiency and advancing SDG 7," Energy Policy, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:enepol:v:210:y:2026:i:c:s030142152500518x
    DOI: 10.1016/j.enpol.2025.115011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030142152500518X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2025.115011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. David I. Stern & Astrid Kander, 2012. "The Role of Energy in the Industrial Revolution and Modern Economic Growth," The Energy Journal, , vol. 33(3), pages 125-152, July.
    2. Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2017. "A meta-analysis on the price elasticity of energy demand," Energy Policy, Elsevier, vol. 102(C), pages 549-568.
    3. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676, July.
    4. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    5. repec:aen:journl:2011v32-02-a03 is not listed on IDEAS
    6. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    7. Subal C. Kumbhakar & Almas Heshmati, 1995. "Efficiency Measurement in Swedish Dairy Farms: An Application of Rotating Panel Data, 1976–88," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(3), pages 660-674.
    8. Sun, Huaping & Edziah, Bless Kofi & Sun, Chuanwang & Kporsu, Anthony Kwaku, 2022. "Institutional quality and its spatial spillover effects on energy efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    9. Grigorios Emvalomatis, 2012. "Adjustment and unobserved heterogeneity in dynamic stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 37(1), pages 7-16, February.
    10. Chaoyi Chen & Mehmet Pinar & Rocío Román-Collado, 2024. "Green Innovation and Energy Efficiency: Moderating Effect of Institutional Quality Based on the Threshold Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(12), pages 3063-3094, December.
    11. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, , vol. 32(2), pages 59-80, April.
    12. Daron Acemoglu & Philippe Aghion & Leonardo Bursztyn & David Hemous, 2012. "The Environment and Directed Technical Change," American Economic Review, American Economic Association, vol. 102(1), pages 131-166, February.
    13. Unruh, Gregory C., 2000. "Understanding carbon lock-in," Energy Policy, Elsevier, vol. 28(12), pages 817-830, October.
    14. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633.
    15. Erol Terzi & Serpil Gumustekin Aydin & Mehmet Ali Cengiz & Ali Sajid, 2023. "Stochastic Energy Performance Evaluation Using a Bayesian Approach," Journal of Mathematics, Hindawi, vol. 2023, pages 1-7, October.
    16. Filip Fidanoski & Kiril Simeonovski & Violeta Cvetkoska, 2021. "Energy Efficiency in OECD Countries: A DEA Approach," Energies, MDPI, vol. 14(4), pages 1-21, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jean Joseph Minviel & Timo Sipiläinen, 2018. "Dynamic stochastic analysis of the farm subsidy-efficiency link: evidence from France," Journal of Productivity Analysis, Springer, vol. 50(1), pages 41-54, October.
    2. Andrews Antony & Emvalomatis Grigorios, 2024. "Efficiency Measurement in Healthcare: The Foundations, Variables, and Models – A Narrative Literature Review," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 18(1), pages 1-24.
    3. Gralka, Sabine, 2018. "Stochastic frontier analysis in higher education: A systematic review," CEPIE Working Papers 05/18, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    4. Michel Moreaux & Jean-Pierre Amigues & Manh-Hung Nguyen, 2026. "Capital Sunk, Emissions Locked: The Economics of Energy Transitions under Carbon Constraints," Working Papers hal-05522961, HAL.
    5. Jean Joseph Minviel & Timo Sipiläinen, 2021. "A dynamic stochastic frontier approach with persistent and transient inefficiency and unobserved heterogeneity," Agricultural Economics, International Association of Agricultural Economists, vol. 52(4), pages 575-589, July.
    6. Sedat Alataş & Burcu Hiçyılmaz & Etem Karakaya, 2024. "Material demand and material efficiency for sustainable development in the European Union countries: A stochastic frontier analysis," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(1), pages 166-183, February.
    7. Rishan Adha & Cheng‐Yih Hong & Su‐Fen Yang & Syamsiyatul Muzayyanah, 2024. "Re‐Unveiling the energy efficiency impact: Paving the way for sustainable growth in ASEAN countries," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(5), pages 5812-5824, October.
    8. Iordanis Parikoglou & Grigorios Emvalomatis & Fiona Thorne, 2022. "Precision livestock agriculture and productive efficiency: The case of milk recording in Ireland," Agricultural Economics, International Association of Agricultural Economists, vol. 53(S1), pages 109-120, November.
    9. Ajayi, Victor & Weyman-Jones, Tom, 2021. "State-level electricity generation efficiency: Do restructuring and regulatory institutions matter in the US?," Energy Economics, Elsevier, vol. 104(C).
    10. Yakubu Abdul-Salam & Euan Phimister, 2017. "Efficiency Effects of Access to Information on Small-scale Agriculture: Empirical Evidence from Uganda using Stochastic Frontier and IRT Models," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(2), pages 494-517, June.
    11. Badunenko, Oleg & Galeotti, Marzio & Hunt, Lester C., "undated". "Better to grow or better to improve? Measuring environmental efficiency in OECD countries with a Stochastic Environmental Kuznets Frontier," FEEM Working Papers 316226, Fondazione Eni Enrico Mattei (FEEM).
    12. Lachaud, Michée A. & Bravo-Ureta, Boris E., 2022. "A Bayesian statistical analysis of return to agricultural R&D investment in Latin America: Implications for food security," Technology in Society, Elsevier, vol. 70(C).
    13. Deng, Yaguo & Veiga, Helena & Wiper, Michael Peter, 2016. "Efficiency evaluation of Spanish hotel chains," DES - Working Papers. Statistics and Econometrics. WS 23897, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Roberto Colombi & Subal Kumbhakar & Gianmaria Martini & Giorgio Vittadini, 2014. "Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency," Journal of Productivity Analysis, Springer, vol. 42(2), pages 123-136, October.
    15. Baños-Pino, José F. & Boto-García, David & Zapico, Emma, 2021. "Persistence and dynamics in the efficiency of toll motorways: The Spanish case," Efficiency Series Papers 2021/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    16. Adom, Philip Kofi & Adams, Samuel, 2020. "Decomposition of technical efficiency in agricultural production in Africa into transient and persistent technical efficiency under heterogeneous technologies," World Development, Elsevier, vol. 129(C).
    17. Yu, Chenyang & Tan, Yuanfang & Zhou, Yu & Zang, Chuanxiang & Tu, Chenglin, 2022. "Can functional urban specialization improve industrial energy efficiency? Empirical evidence from China," Energy, Elsevier, vol. 261(PA).
    18. Galán Camacho, Jorge Eduardo & Veiga, Helena & Wiper, Michael Peter, 2013. "Bayesian analysis of dynamic effects in inefficiency : evidence from the Colombian banking sector," DES - Working Papers. Statistics and Econometrics. WS ws131918, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Zhang, Lin, 2017. "Correcting the uneven burden sharing of emission reduction across provinces in China," Energy Economics, Elsevier, vol. 64(C), pages 335-345.
    20. Aalbers, Rob & Shestalova, Victoria & Kocsis, Viktória, 2013. "Innovation policy for directing technical change in the power sector," Energy Policy, Elsevier, vol. 63(C), pages 1240-1250.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:210:y:2026:i:c:s030142152500518x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.