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Evaluating the risks of alternative energy policies: a case study of industrial energy efficiency

Abstract

Numerous studies have shown the potential for US manufacturing to cut its energy costs by installing more efficient equipment that offers competitive payback periods, but the realization of this potential is hindered by numerous obstacles. This paper evaluates seven federal policy options aimed at revitalizing US manufacturing by improving its energy economics while also achieving environmental and energy reliability goals. Traditionally, policy analysts have examined the cost-effectiveness of energy policies using deterministic assumptions. When risk factors are introduced, they are typically examined using sensitivity analysis to focus on alternative assumptions about budgets, policy design, energy prices, and other such variables. In this paper, we also explicitly model the stochastic nature of several key risk factors including future energy prices, damages from climate change, and the cost of criteria pollutants. Using these two approaches, each policy is "stress tested" to evaluate the likely range of private and social returns on investment. Overall, we conclude that the societal cost-effectiveness of policies is generally more sensitive to alternative assumptions about damages from criteria pollutants and climate change compared with energy prices; however, risks also vary across policies based partly on the technologies they target. Future research needs to examine the macroeconomic consequences of the choice between a lethargic approach to energy waste and modernization in manufacturing versus a vigorous commitment to industrial energy productivity and innovation as characterized by the suite of policies described in this paper.

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Notes

  1. http://www.superiorenergyperformance.net/

  2. http://www.cleanenergyministerial.org/our_work/buildings_and_industry/participants.html

  3. http://www.dsireusa.org/documents/summarymaps/PACE_Financing_Map.pdf

  4. https://www.gov.uk/green-deal-energy-saving-measures

  5. Indeed, the very premise of Monte Carlo modeling is that, for complex models that can only be run thousands rather than millions of times, the distribution of a particular set of runs is a sample of the “distribution of distributions” based on the random number sequence that is realized and may itself have a substantial variance of the mean (and of the other moments of the distribution).

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Acknowledgments

Support for this research was provided by Oak Ridge National Laboratory and the Department of Energy’s Office of Policy and International Affairs. Assistance with the analysis of the Superior Energy Performance and Implementation Support Services policies was provided by Roderick Jackson of Oak Ridge National Laboratory. Assistance with the Small Firm Energy Management program was provided by Rodrigo Cortes, and assistance with the analysis of Industrial Motor Rebates was provided by Ben Deitchman, both from the Georgia Institute of Technology. We also wish to thank two reviewers for the Energy Efficiency journal for their constructive comments. Any remaining errors in this paper are the responsibility of the authors alone.

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Correspondence to Marilyn A. Brown.

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Brown, M.A., Baer, P., Cox, M. et al. Evaluating the risks of alternative energy policies: a case study of industrial energy efficiency. Energy Efficiency 7, 1–22 (2014). https://doi.org/10.1007/s12053-013-9196-8

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Keywords

  • Industrial energy efficiency
  • Energy policy
  • Risk analysis
  • Monte Carlo simulation