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


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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  • Ackerman, F., & Heinzerling, L. (2003). Priceless: On knowing the price of everything and the value of nothing. New York: The New Press.

    Google Scholar 

  • Bernhardt, E. S., & Palmer, M. A. (2011). The environmental costs of mountaintop mining valley fill operations for aquatic ecosystems of the Central Appalachians. Annals of the New York Academy of Sciences, 1223, 39–57. doi:10.1111/j.1749-6632.2011.05986.x.

    Article  Google Scholar 

  • Brown, M.A., Chandler, J., Lapsa, M.V., Ally, M. (2011a). Adding a behavioral dimension to residential construction and retrofit policies. In K. E. Martinez, S. Laitner (eds.), Energy smart behaviors, people centered policies, and public engagement.

  • Brown, M. A., Cortes, R., & Cox, M. (2010). Reinventing industrial energy use in a resource-constrained world. In F. Sioshansi (Ed.), Smart living in the coming age of scarcity. Maryland Heights: Elsevier Press. Chapter 8.

    Google Scholar 

  • Brown, M. A., Jackson, R., Cox, M., Cortes, R., Deitchman, B., Lapsa, M. V. (2011b). Making industry part of the climate solution: Policy options to promote energy efficiency. Oak Ridge National Laboratory, ORNL/TM-2010/78, May.

  • Brown, M. A., & Sovacool, B. K. (2011). Climate change and global energy: Technology and policy options. Cambridge: The MIT Press.

    Google Scholar 

  • Committee on Climate Change Science and Technology Integration (CCCSTI) (2009). Strategies for the commercialization and deployment of greenhouse gas intensity-reducing technologies and practices. Washington, DC: U.S. Department of Energy, DOE/PI-0007.

  • Cox, M., Brown, M. A., Jackson, R. (2011). Regulatory reform to promote clean energy: The potential of output-based emissions standards. 2011 ACEEE Summer Study on Energy Efficiency in Industry.

  • Cullenward, D., Schipper, L., Sudarshan, A., & Howarth, R. B. (2011). Psychohistory revisited: Fundamental issues in forecasting climate futures. Climatic Change, 104(3–4), 457–472. doi:10.1007/s10584-010-9995-2.

    Article  Google Scholar 

  • Dergiades, T., & Tsoulfidis, L. (2008). Estimating residential demand for electricity in the United States. Energy Economics, 30(5), 2722–2730.

    Article  Google Scholar 

  • Department of Energy, Industrial Technologies Program (DOE/ITP) (2010). MotorMaster+. Olympia, WA: Washington State University Energy Extension Program.

  • Dunn, W. N. (2011). Public policy analysis: An introduction, fifth edition. Pearson Prentice Hall.

  • Energy Information Administration (EIA) (2010). Annual energy outlook 2010. Retrieved from

  • Energy Information Administration (EIA) (2011a). Analysis of Impacts of a Clean Energy Standard, as requested by Chairman Bingaman. Retrieved from

  • Energy Information Administration (EIA) (2011b). Annual energy outlook 2011. Retrieved from

  • Environment and Development Division (2001). Energy efficiency—promotion of energy efficiency in industry and financing of investments. United Nations. Retrieved from

  • Ericsson, K. (2006). Evaluation of the Danish voluntary agreements on energy efficiency in trade and industry. Retrieved from

  • Espey, J., & Espey, M. (2004). Turning on the lights: A meta-analysis of residential electricity demand elasticities. Journal of Agricultural and Applied Economics, 36(1), 65–81.

    Google Scholar 

  • European Association for the Promotion of Cogeneration (2001). A guide to cogeneration. Belgium. Retrieved from

  • Fleiter, T., Gruber, E., Eichhammer, W., & Worrell, E. (2012). The German energy audit program for firms—A cost-effective way to improve energy efficiency? Energy Efficiency. doi:10.1007/s12053-012-9157-7.

  • Freedman, B. S., & Watson, S. (2003). Output-based emission standards: Advancing innovative energy technologies. Washington, D.C.

  • Fuller, M. (2010). PACE financing programs: Enabling investments in clean energy. Retrieved on May 30, 2012.

  • Granade, H., Choi, J., Creyts, A., Derkach, P., Farese, S. N., Ostrowski, K. (2009). Unlocking energy efficiency in the U.S. Economy. McKinsey & Company. Retrieved from

  • Industrial Assessment Center (IAC) (2010). Database. Retrieved from on January 19, 2010.

  • International Energy Agency (IEA) (2011). Are we entering a golden age of gas? World energy outlook special report.

  • International Energy Agency (IEA) (2012). Energy management programmes for industry. Retrieved from January 20, 2012.

  • Jackson, R. K., Brown, M. A., Cox, M. (2011). Policy analysis of incentives to encourage adoption of the superior energy performance program. 2011 ACEEE summer study on energy efficiency in industry. Retrieved from

  • Ministry of Climate and Energy (2012). The Danish voluntary agreement scheme. Retrieved from January 20, 2012.

  • Muller, N. Z. (2011). Linking policy to statistical uncertainty in air pollution damages. The B.E. Journal of Economic Analysis & Policy, 11(1).

  • National Research Council. (2009). Hidden costs of energy: Unpriced consequences of energy production and use. Washington, DC: The National Academies Press.

    Google Scholar 

  • Nadel, S., Elliott, R. N., Shephard, M., Greenberg, S., Katz, G., & de Almeida, A. T. (2002). Energy-efficient motor systems: A handbook on technology, program and policy opportunities (2nd ed.). Washington, DC: American Council for an Energy-Efficient Economy.

    Google Scholar 

  • National Electrical Manufacturers Association (NEMA) (2012). Energy efficiency coalition for industry. Retrieved from

  • Office of Management and Budget (OMB) (2002). Guidelines and discount rates for benefit–cost analysis of federal programs. Retrieved from

  • Office of Management and Budget (OMB) (2009). 2010 discount rates for OMB Circular No. A-94. Retrieved December 8, 2011, from

  • Owen, D. (2010). The Efficiency Dilemma. The New Yorker, 78–85.

  • Pond, G. J., Passmore, M. E., Borsuk, F., Reynolds, L., & Rose, C. J. (2008). Downstream effects of mountaintop coal mining: Comparing biological conditions using family- and genus-level macroinvertebrate bioassessment tools. Journal of the North American Benthological Society, 27(3), 717–737. doi:10.1899/08-015.1.

    Article  Google Scholar 

  • Prindle, B. (2010). From shop floor to top floor: Best business practices in energy efficiency. Washington, DC: Pew Center on Global Climate Change.

    Google Scholar 

  • Productivity Commission (2011). Carbon emission policies in key economies, research report. Canberra.

  • Richardson, J. W. (2008). Simulation for applied risk management with an introduction to SIMETAR. Department of Agricultural Economics, Texas A&M University.

  • Sagoff, M. (1988). The economy of the Earth: Philosophy, law and the environment. Cambridge: Cambridge University Press.

    Google Scholar 

  • Sovacool, B. K. (2008). The dirty energy dilemma: What’s blocking clean power in the United States. Westport: Praeger Publishers.

    Google Scholar 

  • Thiruchelvam, M., & Kumar, S. (2003). Policy options to promote energy efficient and environmentally sound technologies in small- and medium-scale industries. Energy Policy, 31(10), 977–987. doi:10.1016/S0301-4215(02)00140-4.

    Article  Google Scholar 

  • Tol, R. S. J. (2005). The marginal damage costs of carbon dioxide emissions: An assessment of the uncertainties. Energy Policy, 33(16), 2064–2074. doi:10.1016/j.enpol.2004.04.002.

    Article  Google Scholar 

  • U.K. Department of Energy and Climate Change (UKDECC) (2012). The green deal. Retrieved on May 30, 2012.

  • U.S. Environmental Protection Agency (USEPA) (2004). Output-based regulations: A handbook for air regulators. Retrieved from

  • U.S. Environmental Protection Agency (USEPA) (2007). Inventory of U.S. greenhouse gas emissions and sinks: 1990–2001. Annex B. Washington, DC: U.S. EPA. Retrieved from

  • U.S. Environmental Protection Agency (USEPA) (2009). Energy portfolio standards and the promotion of combined heat and power, April. Retrieved from

  • U.S. Environmental Protection Agency (USEPA) (2010). Technical support document: Social cost of carbon for regulatory impact analysis under Executive Order 12866. Retrieved from

  • Vithayasrichareon, P., & MacGill, I. F. (2012). A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries. Energy Policy, 41, 374-392. Elsevier. doi:10.1016/j.enpol.2011.10.060.

  • Weimer, D. L., & Vining, A. R. (2011). Policy analysis: Concepts and practice, fifth edition. Englewood Cliffs, NJ, Pearson Prentice Hall.

  • Weitzman, M. (2009). On modeling and interpreting the economics of catastrophic climate change. The Review of Economics and Statistics, 91(February), 1–19.

    Article  Google Scholar 

  • Worrell, E., Laitner, J. A., Ruth, M., & Finman, H. (2003). Productivity benefits of industrial energy efficiency measures. Energy, 28(11), 1081–1098.

    Article  Google Scholar 

  • Wright, A., Martin, M., Nimbalkar,S., Quinn, J., Glatt, S., Orthwein, B. (2010). Results from the DOE 2008 save energy now assessment initiative. Oak Ridge National Laboratory, ORNL/TM-2010/145. Retrieved from

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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).

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