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Transient and Persistent Energy Efficiency in the Wastewater Sector based on Economic Foundations

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  • Stefano Longo
  • Mona Chitnis
  • Miguel Mauricio-Iglesias
  • Almudena Hospido

Abstract

Given the increasing importance of the wastewater sector in terms of energy usage, the understanding of the level of energy efficiency of wastewater treatment plants (WWTPs) is useful to both the industry itself as well as policy makers. Here, based on economic foundations, we apply a Stochastic Frontier Analysis (SFA) approach for energy demand modelling to estimate energy efficiency in the wastewater sector. Using specific SFA models and panel data from 183 Swiss WWTPs over the period 2001 to 2015, the paper illustrates that distinguishing between persistent and transient inefficiency is essential to deduce appropriate energy efficiency diagnosis in WWTPs. In this respect, persistent energy inefficiency is found to be more severe than transient energy inefficiency. Furthermore, it is shown that the age of the equipment influences the demand for energy and the energy savings due to technological innovation are quantified. Finally, economies of output density and scale are estimated demonstrating that for plants operating below optimal scale significant energy savings can be achieved if plants would be operated at higher size. Moreover, our analysis reveals also that for plants larger than 100,000 Population Equivalent, at least from an energy efficiency point of view, it would be no more beneficial to increase their scale.

Suggested Citation

  • Stefano Longo & Mona Chitnis & Miguel Mauricio-Iglesias & Almudena Hospido, 2020. "Transient and Persistent Energy Efficiency in the Wastewater Sector based on Economic Foundations," The Energy Journal, , vol. 41(6), pages 233-254, November.
    • Handle: RePEc:sae:enejou:v:41:y:2020:i:6:p:233-254
    DOI: 10.5547/01956574.41.6.slon
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    References listed on IDEAS

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    1. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    2. Hillard Huntington & Eric Smith, 2011. "Mitigating Climate Change Through Energy Efficiency: An Introduction and Overview," The Energy Journal, , vol. 32(1_suppl), pages 1-6, June.
    3. Gale A. Boyd, 2008. "Estimating Plant Level Energy Efficiency with a Stochastic Frontier," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 23-44.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    5. Longo, Stefano & d’Antoni, Benedetto Mirko & Bongards, Michael & Chaparro, Antonio & Cronrath, Andreas & Fatone, Francesco & Lema, Juan M. & Mauricio-Iglesias, Miguel & Soares, Ana & Hospido, Almudena, 2016. "Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement," Applied Energy, Elsevier, vol. 179(C), pages 1251-1268.
    6. Massimo Filippini & Thomas Geissmann & William H. Greene, 2018. "Persistent and transient cost efficiency—an application to the Swiss hydropower sector," Journal of Productivity Analysis, Springer, vol. 49(1), pages 65-77, February.
    7. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    8. 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.
    9. Soren Leth-Petersen, 2002. "Micro Econometric Modelling of Household Energy Use: Testing for Dependence between Demand for Electricity and Natural Gas," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 57-84.
    10. Longo, S. & Mauricio-Iglesias, M. & Soares, A. & Campo, P. & Fatone, F. & Eusebi, A.L. & Akkersdijk, E. & Stefani, L. & Hospido, A., 2019. "ENERWATER – A standard method for assessing and improving the energy efficiency of wastewater treatment plants," Applied Energy, Elsevier, vol. 242(C), pages 897-910.
    11. Yangseon Kim & Peter Schmidt, 2000. "A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data," Journal of Productivity Analysis, Springer, vol. 14(2), pages 91-118, September.
    12. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107029514, January.
    13. Caves, Douglas W & Christensen, Laurits R & Swanson, Joseph A, 1981. "Productivity Growth, Scale Economies, and Capacity Utilization in U.S. Railroads, 1955-74," American Economic Review, American Economic Association, vol. 71(5), pages 994-1002, December.
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