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Informed investments in clean energy technologies

Informed investments in clean energy technologies
  • IEA Energy Technology Perspectives 2020 Special Report on Clean Energy Innovation (OECD Publishing, 2020).

  • Energy Technology RD&D Budgets (IEA) (accessed 31 May 2024); https://www.iea.org/data-and-statistics/data-product/energy-technology-rd-and-d-budget-database-2

  • OECD Data Explorer (OECD) (accessed 31 May 2024); https://stats.oecd.org/index.aspx?r=814876

  • Hourihan, M. A Primer on Federal R&D Budget Trends (AAAS, 2021).

  • Bertram, C. et al. Energy system developments and investments in the decisive decade for the Paris Agreement goals. Environ. Res. Lett. 16, 074020 (2021).

    Article 

    Google Scholar 

  • Kreibiehl, S. et al. in Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) 1547–1640 (Cambridge Univ. Press, 2022).

  • World Energy Investment 2024 (IEA, 2024).

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

    Article 

    Google Scholar 

  • Kurth, M. et al. A portfolio decision analysis approach to support energy research and development resource allocation. Energy Policy 105, 128–135 (2017).

    Article 

    Google Scholar 

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

    Article 

    Google Scholar 

  • Nagy, B., Farmer, J. D., Bui, Q. M. & Trancik, J. E. Statistical basis for predicting technological progress. PLoS ONE 8, e52669 (2013).

    Article 

    Google Scholar 

  • Kavlak, G., McNerney, J. & Trancik, J. E. Evaluating the causes of cost reduction in photovoltaics modules. Energy Policy 123, 700–710 (2018).

    Article 

    Google Scholar 

  • Gambhir, A., et al. How are future energy technology costs estimated? Can we do better? Int. Rev. Environ. Resour. Econ. 15, 271–318 (2021).

    Article 

    Google Scholar 

  • Verdolini, E., Anadón, L. D., Baker, E., Bosetti, V. & Aleluia Reis, L. Future prospects for energy technologies: insights from expert elicitations. Rev. Environ. Econ. Policy 12, 133–153 (2018).

    Article 

    Google Scholar 

  • Nemet, G. F. Improving the crystal ball. Nat. Energy 6, 860–861 (2021).

    Article 

    Google Scholar 

  • 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).

    Article 

    Google Scholar 

  • Gabriel, S. A., Conejo, A. J., Fuller, J. D., Hobbs, B. F. & Ruiz, C. Complementarity Modeling in Energy Markets (Springer, 2013).

  • Bosetti, V. et al. Sensitivity to energy technology costs: a multi-model comparison analysis. Energy Policy 80, 244–263 (2015).

    Article 

    Google Scholar 

  • Pless, J., Hepburn, C. & Farrell, N. Bringing rigour to energy innovation policy evaluation. Nat. Energy 5, 284–290 (2020).

    Article 

    Google Scholar 

  • 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).

    Article 

    Google Scholar 

  • Popper, S. W., et al. Natural Gas and Israel’s Energy Future: Near-Term Decisions from a Strategic Perspective (RAND Corporation, 2009).

  • Guivarch, C. et al. Using large ensembles of climate change mitigation scenarios for robust insights. Nat. Clim. Change 12, 428–435 (2022).

    Article 

    Google Scholar 

  • Baker, E., Bosetti, V. and Salo, A. Robust portfolio decision analysis: an application to the energy research and development portfolio problem. Eur. J. Oper. Res. 284, 1107–1120 (2020).

  • Sivaram, V., Cunliff, C., Hart, D., Friedmann, J.& Sandalow, D. Energizing America (Columbia University SIPA Center on Global Energy Policy, 2020).

  • Chong, H. Closing the Gap: Priorities for the U.S. Department of Energy’s Building RD&D Portfolio (Information Technology and Innovation Foundation Center for Clean Energy Innovation, 2022).

  • IEA Energy Innovation Forum 2024 (IEA, 2024).

  • Trancik J. E. et al. Technology Improvement and Emissions Reductions as Mutually Reinforcing Efforts: Observations from the Global Development of Solar and Wind Energy (Institute for Data, Systems and Society, Massachusetts Institute of Technology, 2015).

  • U.S. Leadership and the Historic Paris Agreement to Combat Climate Change (Obama White House Archives, 2015).

  • Multi-Year Program Plan (US Office of Clean Energy Demonstrations (OCED), 2023).

  • Innovation Fund Progress Report (European Union, 2023).

  • Clarke, L. & Baker, E. Workshop Report: RD&D Portfolio Analysis Tools and Methodologies (Joint Global Change Research Institute, 2011).

  • Allen, A. et al. End-to-end data-driven weather prediction. Nature 641, 1172–1179 (2025).

    Article 

    Google Scholar 

  • Shrader, J. G., Bakkensen, L. & Lemoine, D. Fatal Errors: the Mortality Value of Accurate Weather Forecasts Working Paper 31361 (National Bureau of Economic Research, 2023).

  • Eash-Gates, P. et al. Sources of cost overrun in nuclear power plant construction call for a new approach to engineering design. Joule 4, 2348–2373 (2020).

  • Wang, N., Akimoto, K. & Nemet, G. F. What went wrong? Learning from three decades of carbon capture, utilization and sequestration (CCUS) pilot and demonstration projects. Energy Policy 158, 112546 (2021).

    Article 

    Google Scholar 

  • Way, R., Ives, M. C., Mealy, P. & Farmer, J. D. Empirically grounded technology forecasts and the energy transition. Joule 6, 2057–2082 (2022).

    Article 

    Google Scholar 

  • Grant, N., Hawkes, A., Mittal, S. & Gambhir, A. The policy implications of an uncertain carbon dioxide removal potential. Joule 5, 2593–2605 (2021).

    Article 

    Google Scholar 

  • Klemun, M. M., Kavlak, G., McNerney, J. & Trancik, J. E. Mechanisms of hardware and soft technology evolution and the implications for solar energy cost trends. Nat. Energy 8, 827–838 (2023).

    Article 

    Google Scholar 

  • Lafond, F. et al. How well do experience curves predict technological progress? A method for making distributional forecasts. Technol. Forecast. Soc. Change 128, 104–117 (2018).

    Article 

    Google Scholar 

  • Shayegh, S., Sanchez, D. L. & Caldeira, K. Evaluating relative benefits of different types of R&D for clean energy technologies. Energy Policy 107, 532–538 (2017).

    Article 

    Google Scholar 

  • Santhakumar, S., Meerman, H. & Faaij, A. Improving the analytical framework for quantifying technological progress in energy technologies. Renew. Sustain. Energy Rev. 145, 111084 (2021).

    Article 

    Google Scholar 

  • Tsai, P. H. et al. Quantitative technology forecasting: a review of trend extrapolation methods. Int. J. Innov. Technol. Manag. 20, 2330002 (2023).

    Google Scholar 

  • Way, R., Lafond, F., Lillo, F., Panchenko, V. & Farmer, J. D. Wright meets Markowitz: how standard portfolio theory changes when assets are technologies following experience curves. J. Econ. Dyn. Control 101, 211–238 (2019).

    Article 
    MathSciNet 

    Google Scholar 

  • Nuñez-Jimenez, A., Knoeri, C., Hoppmann, J. & Hoffmann, V. H. Beyond innovation and deployment: modeling the impact of technology-push and demand-pull policies in Germany’s solar policy mix. Res. Policy 51, 104585 (2022).

    Article 

    Google Scholar 

  • McNerney, J., Farmer, J. D., Redner, S. & Trancik, J. E. Role of design complexity in technology improvement. Proc. Natl Acad. Sci. USA 108, 9008–9013 (2011).

    Article 

    Google Scholar 

  • Morgan, G. Use (and abuse) of expert elicitation in support of decision-making for public policy. Proc. Natl Acad. Sci. USA 111, 7176–7184 (2014).

    Article 

    Google Scholar 

  • Meng, J., Way, R., Verdolini, E. & Anadon, L. D. Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition. Proc. Natl Acad. Sci. USA 118, e1917165118 (2021).

  • Trancik, J. E. Testing and improving technology forecasts for better climate policy. Proc. Natl Acad. Sci. USA 118, e2109417118 (2021).

    Article 

    Google Scholar 

  • Albright, R. What can past technology forecasts tell us about the future? Technol. Forecast. Soc. Change 69, 443–464 (2002).

    Article 

    Google Scholar 

  • Apreda, R., Bonaccorsi, A., dell’Orletta, F. & Fantoni, G. Expert forecast and realized outcomes in technology foresight. Technol. Forecast. Soc. Change 141, 277–288 (2019).

    Article 

    Google Scholar 

  • Kott, A. & Perconti, P. Long-term forecasts of military technologies for a 20–30 year horizon: an empirical assessment of accuracy. Technol. Forecast. Soc. Change 137, 272–279 (2018).

    Article 

    Google Scholar 

  • Fye, S., Charbonneau, S., Hay, J. & Mullins, C. An examination of factors affecting accuracy in technology forecasts. Technol. Forecast. Soc. Change 80, 1222–1231 (2013).

    Article 

    Google Scholar 

  • Bonaccorsi, A., Apreda, R. & Fantoni, G. Expert biases in technology foresight. Why they are a problem and how to mitigate them. Technol. Forecast. Soc. Change 151, 119855 (2020).

    Article 

    Google Scholar 

  • O’Hagan, A., et al. Uncertain Judgements: Eliciting Experts’ Probabilities (Wiley, 2006).

  • Gonzalez, C., Sanchez-Segura, M. I., Dugarte-Peña, G. L. & Medina-Dominguez, F. Valence matters in judgments of stock accumulation in blood glucose control and other global problems. J. Dyn. Decis. Mak. 4, 3 (2018).

    Google Scholar 

  • Ziegler, M. S., Song, J. & Trancik, J. E. Determinants of lithium-ion battery technology cost decline. Energy Environ. Sci. 14, 6074–6098 (2021).

    Article 

    Google Scholar 

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

    Article 

    Google Scholar 

  • Shiraki, H. & Sugiyama, M. Back to the basic: toward improvement of technoeconomic representation in integrated assessment models. Clim. Change 162, 13–24 (2020).

  • Ruegg, R., O’Connor, A. C. & Loomis, R. J. Evaluating Realized Impacts of DOE/EERE R&D Programs. Standard Impact Evaluation Method Report DOE/EE-1117 (Lawrence Berkeley National Lab. (LBNL), 2014).

  • Transforming Our World: the 2030 Agenda for Sustainable Development A/RES/70/1 (UN General Assembly, 2015).

  • Barrage, L. & Nordhaus, W. Policies, projections, and the social cost of carbon: results from the DICE-2023 model. Proc. Natl Acad. Sci. USA 121, e2312030121 (2024).

    Article 

    Google Scholar 

  • Weitzman, A. Prices vs. quantities. Rev. Econ. Stud. 41, 477–491 (1974).

    Article 

    Google Scholar 

  • Krey, V. et al. MESSAGEix-GLOBIOM Documentation (International Institute for Applied Systems Analysis, 2020).

  • Sepulveda, N. A., Jenkins, J. D., de Sisternes, F. J. & Lester, R. K. The role of firm low-carbon electricity resources in deep decarbonization of power generation. Joule 2, 2403–2420 (2018).

    Article 

    Google Scholar 

  • Peñasco, C., Anadón, L. D. & Verdolini, E. Systematic review of the outcomes and trade-offs of ten types of decarbonization policy instruments. Nat. Clim. Change 11, 257–265 (2021).

  • Salo, A., Keisler, J & Morton, A. (eds) Portfolio Decision Analysis: Improved Methods for Resource Allocation International Series in Operations Research & Management Science Vol. 162 (Springer, 2011).

  • Anadon, L. D., Baker, E. & Bosetti, V. Integrating uncertainty into public energy research and development decisions. Nat. Energy 2, 17071 (2017).

    Article 

    Google Scholar 

  • Moore, F. C. et al. Mini-PAGE, an open-source implementation of the PAGE09 integrated assessment model. Sci. Data 5, 180187 (2018).

    Article 

    Google Scholar 

  • Marangoni, G., Lamontagne, J. R., Quinn, J. D., Reed, P. M. & Keller, K. Adaptive mitigation strategies hedge against extreme climate futures. Clim. Change 166, 37 (2021).

    Article 

    Google Scholar 

  • Strnad, F. M., Barfuss, W., Donges, J. F. & Heitzig, J. Deep reinforcement learning in World-Earth system models to discover sustainable management strategies. Chaos 29, 123122 (2019).

    Article 
    MathSciNet 

    Google Scholar 

  • Saltelli, A. A short comment on statistical versus mathematical modelling. Nat. Commun. 10, 3870 (2019).

    Article 

    Google Scholar 

  • Lempert, R. J., Popper, S. & Bankes, S. Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis (RAND Corporation, 2003).

  • National Research Council Understanding Risk: Informing Decisions in a Democratic Society (National Academies Press, 1996).

  • Herman, J. D., Reed, P. M., Zeff, H. B. & Characklis, G. W. How should robustness be defined for water systems planning under change? J. Water Resour. Plan. Manag. 141, 04015012 (2015).

    Article 

    Google Scholar 

  • Marchau, V. A., Walker, W. E., Bloemen, P. J. & Popper, S. W. Decision Making under Deep Uncertainty: from Theory to Practice (Springer, 2019).

  • Martin, C. J. in Negotiating Agreement in Politics (eds Mansbridge, J. & Martin, C. J.) (American Political Science Association, 2013).

  • Kasprzyk, J. R., Nataraj, S., Reed, P. M. & Lempert, R. J. Many objective robust decision-making for complex environmental systems undergoing change. Environ. Model. Softw. 42, 55–71 (2013).

    Article 

    Google Scholar 

  • Brunhart-Lupo, N., Bush B., Gruchalla, K., Potter, K. & Smith, S. Collaborative Exploration of Scientific Datasets Using Immersive and Statistical Visualization Report NREL/PR-2C00-79574 (National Renewable Energy Lab. (NREL), 2021).

  • Overpeck, J. T., Meehl, G. A., Bony, S. & Easterling, D. R. Climate data challenges in the 21st century. Science 331, 700–702 (2011).

    Article 

    Google Scholar 

  • Davis, S. J. et al. Net-zero emissions energy systems. Science 360, eaas9793 (2018).

    Article 

    Google Scholar 

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