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Integrating uncertainty into public energy research and development decisions

Nature Energy volume 2, Article number: 17071 (2017) Cite this article

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A Publisher Correction to this article was published on 22 November 2017

Abstract

Public energy research and development (R&D) is recognized as a key policy tool for transforming the world’s energy system in a cost-effective way. However, managing the uncertainty surrounding technological change is a critical challenge for designing robust and cost-effective energy policies. The design of such policies is particularly important if countries are going to both meet the ambitious greenhouse-gas emissions reductions goals set by the Paris Agreement and achieve the required harmonization with the broader set of objectives dictated by the Sustainable Development Goals. The complexity of informing energy technology policy requires, and is producing, a growing collaboration between different academic disciplines and practitioners. Three analytical components have emerged to support the integration of technological uncertainty into energy policy: expert elicitations, integrated assessment models, and decision frameworks. Here we review efforts to incorporate all three approaches to facilitate public energy R&D decision-making under uncertainty. We highlight emerging insights that are robust across elicitations, models, and frameworks, relating to the allocation of public R&D investments, and identify gaps and challenges that remain.

Predicting the future is extremely difficult1, yet it is nonsensical to ignore the knowledge and understanding we can acquire from experts when making decisions. It is well understood that using experts, and using them wisely, is a key component of evidence-based policymaking2.

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Figure 1: Analytic approach to supporting energy R&D decisions using expert elicitations.
Figure 2: Optimal R&D portfolios under different climate stabilization levels.
Figure 3: Optimal R&D portfolios under different total energy R&D budget constraints.
Figure 4: Public investment in energy R&D as a percentage of the total investment in energy technologies.

References

  1. Vaclav, S. Energy At a Crossroads: Global Perspectives and Uncertainties (MIT Press, 2005).

    Google Scholar 

  2. Sutherland, W. J. & Burgman, M. Policy advice: Use experts wisely. Nature 526, 317–318 (2015).

    Google Scholar 

  3. Anadon, L. D., Baker, E., Bosetti, V. & Reis, L. A. Expert views — and disagreements — about the potential of energy technology R&D. Climatic Change 136, 677–691 (2016). This paper brings together three sets of expert elicitations covering five important energy technologies and presents insights that include aggregated and disaggregated results and the impact of R&D on expected costs.

    Google Scholar 

  4. Baker, E., Bosetti, V. & Anadon, L. D. Special issue on defining robust energy R&D portfolios. Energy Policy 80, 215–218 (2015).

    Google Scholar 

  5. Nemet, G. F., Anadon, L. D. & Verdolini, E. Quantifying the effects of expert selection and elicitation design on experts’ confidence in their judgments about future energy technologies. Risk Anal. 37, 315–330 (2016).

    Google Scholar 

  6. Weyant, J. P., Knopf, B., De Cian, E. D., Keppo, I. & Van Vuuren, D. P. Introduction to the EMF28 study on scenarios for transforming the european energy system. Clim. Change Econ. 4, 1302001 (2013).

    Google Scholar 

  7. Morgan, M. G. Use (and abuse) of expert elicitation in support of decision making for public policy. Proc. Natl Acad. Sci. USA 111, 7176–7184 (2014). This paper provides an overview of the key practices and issues in the use of expert elicitations to inform policy, with examples that include environment and energy applications.

    Google Scholar 

  8. Baker, E., Olaleye, O. & Aleluia Reis, L. Decision frameworks and the investment in R&D. Energy Policy 80, 275–285 (2015). This paper isolates the impact of decision frameworks on R&D decisions using distributions over future technology costs from a combined set of expert elicitations.

    Google Scholar 

  9. Manne, A. S. Hedging Strategies for Global Carbon Dioxide Abatement: A Summary of Poll Results — EMF 14 Subgroup Analysis for Decisions under Uncertainty (Energy Modelling Forum, 1996).

    Google Scholar 

  10. Manne, A. S. & Richels, R. G. Buying Greenhouse Insurance: the Economic Costs of Carbon Dioxide Emission Limits (MIT press, 1992).

    Google Scholar 

  11. Varian, H. Micoreconomic Analsysis (Norton, 1992).

    Google Scholar 

  12. Dixit, A. K. & Pindyck, R. S. R. Investment Under Uncertainty (Princeton Univ. Press, 1994).

    Google Scholar 

  13. Pizer, W. A. et al. Optimal Choice of Policy Instrument and Stringency Under Uncertainty: the Case of Climate Change (Citeseer, 1997).

    Google Scholar 

  14. Lorenz, A., Schmidt, M. G. W., Kriegler, E. & Held, H. Anticipating climate threshold damages. Environ. Model. Assess. 17, 163–175 (2012).

    Google Scholar 

  15. Heal, G. & Kriström, B. Uncertainty and climate change. Environ. Resour. Econ. 22, 3–39 (2002).

    Google Scholar 

  16. Usher, W. & Strachan, N. An expert elicitation of climate, energy and economic uncertainties. Energy Policy 61, 811–821 (2013).

    Google Scholar 

  17. Santen, N. R. & Anadon, L. D. Balancing solar PV deployment and RD&D: a comprehensive framework for managing innovation uncertainty in electricity technology investment planning. Renew. Sustain. Energy Rev. 60, 560–569 (2016). This paper explores the role of the uncertainty framework (including approximate dynamic programming) on the balance between investments in solar R&D versus solar deployment given uncertainty around the future costs of solar PV.

    Google Scholar 

  18. Labriet, M., Kanudia, A. & Loulou, R. Climate mitigation under an uncertain technology future: a TIAM-WORLD analysis. Energy Econ. 34, S366–S377 (2012).

    Google Scholar 

  19. Verdolini, E., Bosetti, V., Anadón, L. D., Baker, E. & Aleluia Reis, L. The Future Prospects of Energy Technologies: Insights from Expert Elicitations Working Paper 47 2016 (FEEM, 2016).

    Google Scholar 

  20. Nemet, G. F. Beyond the learning curve: factors influencing cost reductions in photovoltaics. Energy Policy 34, 3218–3232 (2006).

    Google Scholar 

  21. Wirth, H. Recent Facts about Photovoltaics in Germany (Fraunhofer ISE, 2016).

    Google Scholar 

  22. Anadon, L. D., Chan, G. & Lee, A. in Transforming US Energy Innovation (eds Anadon, L. D., Bunn, M. & Narayanamurti, V. ) Ch. 2, 36–75 (Cambridge Univ. Press, 2014).

    Google Scholar 

  23. Verdolini, E., Anadon, L. D., Lu, J. & Nemet, G. F. The effects of expert selection, elicitation design, and R&D assumptions on experts’ estimates of the future costs of photovoltaics. Energy Policy 80, 233–243 (2015).

    Google Scholar 

  24. Gritsevskyi, A. & Nakićenovi, N. Modeling uncertainty of induced technological change. Energy Policy 28, 907–921 (2000).

    Google Scholar 

  25. Webster, M., Santen, N. & Parpas, P. An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty. Comput. Manag. Sci. 9, 339–362 (2012).

    MathSciNet  MATH  Google Scholar 

  26. Clarke, L. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Ch. 6, 413–510 (Cambridge Univ. Press, 2014). This section of the IPCC report covers the wide range of literature considering the impact of technology costs on mitigation without focusing on the role of public energy R&D.

    Google Scholar 

  27. Bellman, R. Dynamic programming and Lagrange multipliers. Proc. Natl Acad. Sci. USA 42, 767–769 (1956).

    MathSciNet  MATH  Google Scholar 

  28. Powell, W. B. Approximate Dynamic Programming: Solving the Curses of Dimensionality (John Wiley & Sons, 2007).

    MATH  Google Scholar 

  29. Baker, E. Increasing risk and increasing informativeness: equivalence theorems. Operations Res. 54, 26–36 (2006).

    MATH  Google Scholar 

  30. Pugh, G. et al. Energy R&D portfolio analysis based on climate change mitigation. Energy Econ. 33, 634–643 (2011).

    Google Scholar 

  31. Ricci, E. C., Bosetti, V., Baker, E. & Jenni, K. E. From Expert Elicitations to Integrated Assessment: Future Prospects of Carbon Capture Technologies (FEEM, 2014).

    Google Scholar 

  32. Iyer, G., Hultman, N., Fetter, S. & Kim, S. H. Implications of small modular reactors for climate change mitigation. Energy Econ. 45, 144–154 (2014).

    Google Scholar 

  33. Barron, R. & McJeon, H. The differential impact of low-carbon technologies on climate change mitigation cost under a range of socioeconomic and climate policy scenarios. Energy Policy 80, 264–274 (2015).

    Google Scholar 

  34. Olaleye, O. & Baker, E. Large scale scenario analysis of future low carbon energy options. Energy Economics 49, 203–216 (2015).

    Google Scholar 

  35. Anderson, B., Borgonovo, E., Galeotti, M. & Roson, R. Uncertainty in climate change modeling: can global sensitivity analysis be of help? Risk Anal. 34, 271–293 (2014).

    Google Scholar 

  36. Bosetti, V. et al. Sensitivity to energy technology costs: a multi-model comparison analysis. Energy Policy 80, 244–263 (2015). This paper conducts a structured sensitivity analysis informed by expert elicitations over energy technology costs across three important energy–economic models, shedding light on the impact of model structure on mitigation costs.

    Google Scholar 

  37. Lehtveer, M. & Hedenus, F. How much can nuclear power reduce climate mitigation cost? — Critical parameters and sensitivity. Energy Strategy Rev. 6, 12–19 (2015).

    Google Scholar 

  38. Morgan, M. G. Our knowledge of the world is often not simple: policymakers should not duck that fact, but should deal with it. Risk Anal. 35, 19–20 (2015).

    Google Scholar 

  39. Gillenwater, M. Probabilistic decision model of wind power investment and influence of green power market. Energy Policy 63, 1111–1125 (2013).

    Google Scholar 

  40. Iman, R. L. & Conover, W. J. A distribution-free approach to inducing rank correlation among input variables. Commun. Stat. Simulation Comput. 11, 311–334 (1982).

    MATH  Google Scholar 

  41. Chan, G. & Anadon, L. D. Improving decision making for public R&D investment in energy: utilizing expert elicitation in parametric models. Preprint at https://doi.org/10.17863/CAM.7842 (2016). This paper combines expert elicitations and LHS to optimize public R&D investments in six energy technology areas affecting costs in a total of 25 technologies, identifying areas with the largest returns.

  42. Olaleye, O. P. Role of Low Carbon Energy Technologies in Near Term Energy Policy. PhD thesis, Univ. Mass. Amherst (2016).

  43. Lemoine, D. & McJeon, H. C. Trapped between two tails: trading off scientific uncertainties via climate targets. Environ. Res. Lett. 8, 34019 (2013).

    Google Scholar 

  44. Baker, E., Chon, H. & Keisler, J. Carbon capture and storage: combining economic analysis with expert elicitations to inform climate policy. Climatic Change 96, 379–408 (2009).

    Google Scholar 

  45. Baker, E., Chon, H. & Keisler, J. Advanced solar R&D: combining economic analysis with expert elicitations to inform climate policy. Energy Econ. 31, S37–S49 (2009).

  46. Baker, E., Chon, H. & Keisler, J. Battery technology for electric and hybrid vehicles: expert views about prospects for advancement. Technol. Forecast. Soc. Change 77, 1139–1146 (2010).

    Google Scholar 

  47. Nemet, G. F. & Baker, E. Demand subsidies versus R&D: comparing the uncertain impacts of policy on a pre-commercial low-carbon energy technology. Energy J. 30, 49–80 (2009).

    Google Scholar 

  48. Jenni, K. E., Baker, E. D. & Nemet, G. F. Expert elicitations of energy penalties for carbon capture technologies. Int. J. Greenhouse Gas Control 12, 136–145 (2013).

    Google Scholar 

  49. Nemet, G. F., Baker, E. & Jenni, K. E. Modeling the future costs of carbon capture using experts’ elicited probabilities under policy scenarios. Energy 56, 218–228 (2013).

    Google Scholar 

  50. Nemet, G. F., Baker, E., Barron, B. & Harms, S. Characterizing the effects of policy instruments on the future costs of carbon capture for coal power plants. Climatic Change 133, 155–168 (2015).

    Google Scholar 

  51. Kahneman, D. Thinking, Fast and Slow (Macmillan, 2011).

    Google Scholar 

  52. Baker, E. & Solak, S. Management of energy technology for sustainability: how to fund energy technology research and development. Products Operat. Manag. 23, 348–365 (2014).

    Google Scholar 

  53. McJeon, H. Energy Technology Development And Climate Change Mitigation. PhD Thesis, Univ. Maryland (2012).

  54. Barron, R., Djimadoumbaye, N. & Baker, E. How grid integration costs impact the optimal R&D portfolio into electricity supply technologies in the face of climate change. Sustain. Energy Technol. Assess. 7, 22–29 (2014).

    Google Scholar 

  55. Baker, E. & Solak, S. Climate change and optimal energy technology R&D policy. Eur. J. Oper. Res. 213, 442–454 (2011).

    MathSciNet  MATH  Google Scholar 

  56. Marangoni, G., de Maere d’Aertrycke, G. & Bosetti, V. Optimal Clean Energy R&D Investments Under Uncertainty. FEEM Working Paper no. 2017.016 (2017).

  57. Barron, R. Analysis of the Impact of Technological Change on the Cost of Achieving Climate Change Mitigation Targets. PhD Thesis, Univ. Mass. Amherst (2015).

  58. Bistline, J. E. Energy technology R&D portfolio management: modeling uncertain returns and market diffusion. Appl. Energy 183, 1181–1196 (2016).

    Google Scholar 

  59. Baker, E., Bosetti, V., Anadon, L. D., Henrion, M. & Aleluia Reis, L. Future costs of key low-carbon energy technologies: harmonization and aggregation of energy technology expert elicitation data. Energy Policy 80, 219–232 (2015).

    Google Scholar 

  60. Baker, E., Clarke, L. & Shittu, E. Technical change and the marginal cost of abatement. Energy Econ. 30, 2799–2816 (2008).

    Google Scholar 

  61. Bistline, J. E. Electric sector capacity planning under uncertainty: climate policy and natural gas in the US. Energy Econ. 51, 236–251 (2015).

    Google Scholar 

  62. The Power of Change: Innovation for Development and Deployment of Increasingly Clean Electric Power Technologies (US National Academies Press, 2016).

  63. Mowery, D. & Rosenberg, N. The influence of market demand upon innovation: a critical review of some recent empirical studies. Res. Policy 8, 102–153 (1979).

    Google Scholar 

  64. Jaffe, A. B., Newell, R. G. & Stavins, R. N. A tale of two market failures: technology and environmental policy. Ecol. Econ. 54, 164–174 (2005). This paper discusses some of the key reasons why public investment in energy R&D is very important.

    Google Scholar 

  65. Gallagher, K. S. & Anadon, L. D. DOE Budget Authority for Energy Research, Development & Demonstration Database (Belfer Center for Science and International Affairs, 2016); https://www.belfercenter.org/publication/doe-budget-authority-energy-research-development-demonstration-database-2

    Google Scholar 

  66. Munsell, M. US solar market sets new record, installing 7.3 GW of solar PV in 2015. Solar Energy Industries Association (22 February 2016).

  67. Weiner, J. Median installed price of solar in the United States fell by 5–12% in 2015. Berkeley Lab News Center (24 August 2016).

  68. Ziolkowska, J. R. Evaluating sustainability of biofuels feedstocks: a multi-objective framework for supporting decision making. Biomass Bioenergy 59, 425–440 (2013).

    Google Scholar 

  69. Pietzcker, R. C. et al. System integration of wind and solar power in Integrated Assessment Models: A cross-model evaluation of new approaches. Energy Econ. http://doi.org/10.1016/j.eneco.2016.11.018(2016).

  70. Ackoff, R. L. Some unsolved problems in problem solving. Operat. Res. Q. 13, 1–11 (1962).

    Google Scholar 

  71. Anadón, L. D. Missions-oriented RD&D institutions in energy between 2000 and 2010: a comparative analysis of China, the United Kingdom, and the United States. Res. Policy 41, 1742–1756 (2012).

    Google Scholar 

  72. Rittel, H. W. & Webber, M. M. Dilemmas in a general theory of planning. Policy Sci. 4, 155–169 (1973).

    Google Scholar 

  73. Kriegler, E. et al. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change 123, 353–367 (2014). This paper presents a large effort to estimate the value of investing in R&D to improve energy technologies without a direct focus on uncertainty or on optimizing R&D investments across a range of areas.

    Google Scholar 

  74. Weyant, J. P. Accelerating the development and diffusion of new energy technologies: beyond the ‘valley of death’. Energy Econ. 33, 674–682 (2011).

    Google Scholar 

  75. Nemet, G. Inter-technology knowledge spillovers for energy technologies. Energy Econ. 34, 1259–1270 (2012).

    Google Scholar 

  76. Wiesenthal, T., Leduc, G., Haegeman, K. & Schwarz, H.-G. Bottom-up estimation of industrial and public R&D investment by technology in support of policy-making: the case of selected low-carbon energy technologies. Res. Policy 41, 116–131 (2012).

    Google Scholar 

  77. Mission (Department of Energy, 2015); http://energy.gov/technologytransitions/mission

  78. Office of Technology Transitions — Overview (US Department of Energy, accessed 10 April 2017); https://energy.gov/technologytransitions/downloads/office-technology-transitions-overview

  79. Carbon Valuation in UK Policy Appraisal: a Revised Approach (Department of Energy and Climate Change, 2009).

  80. Quality Assurance Tools and Guidance in BEIS (Department for Business, Energy and Industrial Strategy, 2016).

  81. Baker, E., Bosetti, V. & Anadon, L. D. Special issue on defining robust energy R&D portfolios. Energy Policy 80, 215–218 (2015).

    Google Scholar 

  82. Fiorese, G., Catenacci, M., Bosetti, V. & Verdolini, E. The power of biomass: experts disclose the potential for success of bioenergy technologies. Energy Policy 65, 94–114 (2014).

    Google Scholar 

  83. Fiorese, G., Catenacci, M., Verdolini, E. & Bosetti, V. Advanced biofuels: future perspectives from an expert elicitation survey. Energy Policy 56, 293–311 (2013).

    Google Scholar 

  84. Baker, E. & Keisler, J. M. Cellulosic biofuels: expert views on prospects for advancement. Energy 36, 595–605 (2011).

    Google Scholar 

  85. Rao, A. B., Rubin, E. S., Keith, D. W. & Granger Morgan, M. Evaluation of potential cost reductions from improved amine-based CO2 capture systems. Energy Policy 34, 3765–3772 (2006).

    Google Scholar 

  86. Chung, T. S., Patiño-Echeverri, D. & Johnson, T. L. Expert assessments of retrofitting coal-fired power plants with carbon dioxide capture technologies. Energy Policy 39, 5609–5620 (2011).

    Google Scholar 

  87. Chan, G., Anadon, L. D., Chan, M. & Lee, A. Expert elicitation of cost, performance, and RD&D budgets for coal power with CCS. Energy Procedia 4, 2685–2692 (2011).

    Google Scholar 

  88. Prospective Evaluation of Applied Energy Research and Development at DOE (Phase Two) (NRC, 2007). This report by the US National Research Council makes the case that public energy R&D decisions need to be informed by methods that account for the inherent uncertainty in the results of such investments.

  89. Abdulla, A., Azevedo, I. L. & Morgan, M. G. Expert assessments of the cost of light water small modular reactors. Proc. Natl Acad. Sci. USA 110, 9686–9691 (2013).

    Google Scholar 

  90. Anadón, L. D., Bosetti, V., Bunn, M., Catenacci, M. & Lee, A. Expert judgments about RD&D and the future of nuclear energy. Environ. Sci. Technol. 46, 11497–11504 (2012).

    Google Scholar 

  91. Baker, E., Chon, H. & Keisler, J. M. Advanced nuclear power: combining economic analysis with expert elicitations to inform climate policy. Preprint at http://dx.doi.org/10.2139/ssrn.1407048 (2008).

  92. Curtright, A. E., Morgan, M. G. & Keith, D. W. Expert assessments of future photovoltaic technologies. Environ. Sci. Technol. 42, 9031–9038 (2008).

    Google Scholar 

  93. Bosetti, V., Catenacci, M., Fiorese, G. & Verdolini, E. The future prospect of PV and CSP solar technologies: an expert elicitation survey. Energy Policy 49, 308–317 (2012).

    Google Scholar 

  94. Mason, I. How Low Will Photovoltaic Prices Go? An Expert Discussion (Near Zero, 2012).

    Google Scholar 

  95. Catenacci, M., Verdolini, E., Bosetti, V. & Fiorese, G. Going electric: expert survey on the future of battery technologies for electric vehicles. Energy Policy 61, 403–413 (2013).

    Google Scholar 

  96. Bistline, J. E. Energy technology expert elicitations: an application to natural gas turbine efficiencies. Technol. Forecast. Soc. Change 86, 177–187 (2014).

    Google Scholar 

  97. Gillenwater, M. Probabilistic decision model of wind power investment and influence of green power market. Energy Policy 63, 1111–1125 (2013).

    Google Scholar 

  98. Wiser, R. et al. Expert elicitation survey on future wind energy costs. Nat. Energy 1, 16135 (2016).

    Google Scholar 

  99. Nemet, G. F., Baker, E., Barron, B. & Harms, S. Characterizing the effects of policy instruments on the future costs of carbon capture for coal power plants. Clim. Change 133, 155–168 (2015).

    Google Scholar 

  100. Nemet, G. F. & Baker, E. Demand subsidies versus R&D: comparing the uncertain impacts of policy on a pre-commercial low-carbon energy technology. Energy J. 30, 49–80 (2009).

    Google Scholar 

  101. Bistline, J. E. & Weyant, J. P. Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach. Clim. Change 121, 143–160 (2013). This work uses a stochastic dynamic programming approach without relying on expert elicitations, and thus gives a sense of the literature that does not include such elicitations for parameterizing uncertainty.

    Google Scholar 

  102. McJeon, H. C. et al. Technology interactions among low-carbon energy technologies: what can we learn from a large number of scenarios? Energy Econ. 33, 619–631 (2011).

    Google Scholar 

  103. Baker, E. D., Bosetti, V. & Salo, A. Finding common ground when experts disagree: belief dominance over portfolios of alternatives Working Paper 46.2016 (FEEM, 2016).

    Google Scholar 

  104. Webster, M., Donohoo, P. & Palmintier, B. Water–CO2 trade-offs in electricity generation planning. Nat. Clim. Change 3, 1029–1032 (2013).

    Google Scholar 

  105. Anadon, L. D., Bunn, M. & Narayanamurti, V. (eds) Transforming US Energy Innovation (Cambridge Univ. Press, 2014).

    Google Scholar 

  106. Anadon, L., Bosetti, V., Bunn, M., Catenacci, M. & Lee, A. Expert judgments about RD&D and the future of nuclear energy. Environ. Sci. Technol. 46, 11497–11504 (2012).

    Google Scholar 

  107. Fishbone, L. G. & Abilock, H. Markal, a linear-programming model for energy systems analysis: technical description of the bnl version. Int. J. Energy Res. 5, 353–375 (1981).

    Google Scholar 

  108. Weyant, J. et al. in Climate Change 1995: Economic and Social Dimensions of Climate Change (eds Bruce, J. P., Lee, H. & Haites, E. F. ) Ch. 10, 367–396 (IPCC, Cambridge Univ. Press, 1996).

    Google Scholar 

  109. Pindyck, R. S. Climate change policy: what do the models tell us? J. Econ. Lit. 51, 860–872 (2013).

    Google Scholar 

  110. Nordhaus, W. D. A Question of Balance: Weighing the Options on Global Warming Policies. (Yale Univ. Press, 2014).

    Google Scholar 

  111. Edmonds, J. & Reilly, J. Global energy and CO2 to the year 2050. Energy J. 4, 21–47 (1983).

    Google Scholar 

  112. Bosetti, V., Carraro, C., Galeotti, M., Massetti, E. & Tavoni, M. WITCH A world induced technical change hybrid model. Energy J. 27, 13–37 (2006).

    Google Scholar 

  113. Tversky, A. & Kahneman, D. Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974).

    Google Scholar 

  114. Barbose, G. L. et al. Tracking the Sun VIII: the Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States (Lawrence Berkeley National Laboratory, 2015).

    Google Scholar 

  115. Cooke, R. M. The aggregation of expert judgment: do good things come to those who weight? Risk Anal. 35, 12–15 (2015).

    Google Scholar 

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Acknowledgements

We are grateful to Nidhi Santen for useful insights and feedback on the description on ADP, to Franklyn Kanyako for assistance with the figures, and to Patricia McLaughlin for her work proofreading it. L.D.A. is grateful to the Climate Change Initiative of the Doris Duke Charitable Foundation and the Science, Technology and Public Policy programme at the Harvard Kennedy School for supporting early work on this paper and to the EU H2020 research and innovation programme under the Grant Agreement No 730403 (INNOPATHS). V.B. is grateful to funding from the European Research Council under the European Community’s Programme ‘Ideas’ — Call identifier: ERC-2013-StG / ERC grant agreement number 336703 — project RISICO ‘RISk and uncertainty in developing and Implementing Climate change pOlicies’. We also acknowledge financial support from the United Nations Environment Program through the Green Growth Knowledge Platform (GGKP) Research Committee on Technology and Innovation for the preparation of GGKP Working paper 01-2016, a report that helped us identify the need for this Review. E.B. acknowledges the NSF-sponsored IGERT: Offshore Wind Energy Engineering, Environmental Science, and Policy (NSF DGE 1068864).

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  1. Laura Díaz Anadón, Erin Baker and Valentina Bosetti: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Politics and International Studies, University of Cambridge, 7 West Road, Cambridge CB3 9DT, UK

    Laura Díaz Anadón

  2. John F. Kennedy School of Government, Harvard University, 79 JFK Street, Cambridge, Massachusetts, 02138, USA

    Laura Díaz Anadón

  3. Department of Mechanical and Industrial Engineering, 120 Governors Drive, University of Massachusetts Amherst, Amherst, Massachusetts, 01002, USA

    Erin Baker

  4. Euro-Mediterranean Center on Climate Change and Fondazione Eni Enrico Mattei, Corso Magenta 63, Milano, 20123, Italy

    Valentina Bosetti

  5. Department of Economics, Bocconi University, via Sarfatti 25, Milano, 20136, Italy

    Valentina Bosetti

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Correspondence to Laura Díaz Anadón.

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Anadón, L., Baker, E. & Bosetti, V. Integrating uncertainty into public energy research and development decisions. Nat Energy 2, 17071 (2017). https://doi.org/10.1038/nenergy.2017.71

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