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Uncertainty in forecasts of long-run economic growth

Author

Listed:
  • P. Christensen

    (Department of Agricultural and Consumer Economics, University of Illinois at Urbana–Champaign, Urbana, IL 61801)

  • K. Gillingham

    (School of Forestry & Environmental Studies, Yale University, New Haven, CT 06511; Department of Economics, Yale University, New Haven, CT 06511; School of Management, Yale University, New Haven, CT 06511)

  • W. Nordhaus

    (Department of Economics, Yale University, New Haven, CT 06511)

Abstract

Forecasts of long-run economic growth are critical inputs into policy decisions being made today on the economy and the environment. Despite its importance, there is a sparse literature on long-run forecasts of economic growth and the uncertainty in such forecasts. This study presents comprehensive probabilistic long-run projections of global and regional per-capita economic growth rates, comparing estimates from an expert survey and a low-frequency econometric approach. Our primary results suggest a median 2010–2100 global growth rate in per-capita gross domestic product of 2.1% per year, with a standard deviation (SD) of 1.1 percentage points, indicating substantially higher uncertainty than is implied in existing forecasts. The larger range of growth rates implies a greater likelihood of extreme climate change outcomes than is currently assumed and has important implications for social insurance programs in the United States.

Suggested Citation

  • P. Christensen & K. Gillingham & W. Nordhaus, 2018. "Uncertainty in forecasts of long-run economic growth," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(21), pages 5409-5414, May.
    • Handle: RePEc:nas:journl:v:115:y:2018:p:5409-5414
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    Citations

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    Cited by:

    1. Vadim Faruarovich Islamutdinov & Evgeniy Igorevich Kushnikov, 2020. "Long-term Forecast of the Dependence of the Economy of the Khanty-Mansi Autonomous Okrug-Ugra (Russia) on the Sectors of the Fuel and Energy Complex," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 382-389.
    2. Huang, Rui & Lv, Guonian, 2021. "The climate economic effect of technology spillover," Energy Policy, Elsevier, vol. 159(C).
    3. Moritz A. Drupp & Martin C. Hänsel, 2021. "Relative Prices and Climate Policy: How the Scarcity of Nonmarket Goods Drives Policy Evaluation," American Economic Journal: Economic Policy, American Economic Association, vol. 13(1), pages 168-201, February.
    4. Ar'anzazu de Juan & Pilar Poncela & Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2022. "Economic activity and climate change," Papers 2206.03187, arXiv.org, revised Jun 2022.
    5. Moritz A. Drupp & Frikk Nesje & Robert C. Schmidt, 2022. "Pricing Carbon," CESifo Working Paper Series 9608, CESifo.
    6. Simon Dietz & Bruno Lanz, 2019. "Growth and Adaptation to Climate Change in the Long Run," CESifo Working Paper Series 7986, CESifo.
    7. Halvard Buhaug & Jonas Vestby, 2019. "On Growth Projections in the Shared SocioeconomicPathways," Global Environmental Politics, MIT Press, vol. 19(4), pages 118-132, November.
    8. Burgess, Matthew G. & Ritchie, Justin & Shapland, John & Pielke, Roger Jr, 2020. "IPCC baseline scenarios over-project CO2 emissions and economic growth," SocArXiv ahsxw, Center for Open Science.
    9. Baker, Justin S. & Van Houtven, George & Phelan, Jennifer & Latta, Gregory & Clark, Christopher M. & Austin, Kemen G. & Sodiya, Olakunle E. & Ohrel, Sara B. & Buckley, John & Gentile, Lauren E. & Mart, 2023. "Projecting U.S. forest management, market, and carbon sequestration responses to a high-impact climate scenario," Forest Policy and Economics, Elsevier, vol. 147(C).
    10. Magalhães Filho, L.N.L. & Roebeling, P.C. & Costa, L.F.C. & de Lima, L.T., 2022. "Ecosystem services values at risk in the Atlantic coastal zone due to sea-level rise and socioeconomic development," Ecosystem Services, Elsevier, vol. 58(C).
    11. Lomborg, Bjorn, 2020. "Welfare in the 21st century: Increasing development, reducing inequality, the impact of climate change, and the cost of climate policies," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    12. Li, Mingquan & Shan, Rui & Hernandez, Mauricio & Mallampalli, Varun & Patiño-Echeverri, Dalia, 2019. "Effects of population, urbanization, household size, and income on electric appliance adoption in the Chinese residential sector towards 2050," Applied Energy, Elsevier, vol. 236(C), pages 293-306.
    13. Vivek Srikrishnan & Yawen Guan & Richard S. J. Tol & Klaus Keller, 2022. "Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model," Climatic Change, Springer, vol. 170(3), pages 1-20, February.
    14. Burgess, Matthew G. & Langendorf, Ryan E. & Ippolito, Tara & Pielke, Roger Jr, 2020. "Optimistically biased economic growth forecasts and negatively skewed annual variation," SocArXiv vndqr, Center for Open Science.
    15. Anton A. Gerunov, 2022. "Performance of 109 Machine Learning Algorithms across Five Forecasting Tasks: Employee Behavior Modeling, Online Communication, House Pricing, IT Support and Demand Planning," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 15-43.
    16. Christian L. E. Franzke & Marcin Czupryna, 2020. "Probabilistic assessment and projections of US weather and climate risks and economic damages," Climatic Change, Springer, vol. 158(3), pages 503-515, February.
    17. Liu, Yinshan & Wang, Yuanfeng & Shi, Chengcheng & Zhang, Weijun & Luo, Wei & Wang, Jingjing & Li, Keping & Yeung, Ngai & Kite, Steve, 2022. "Assessing the CO2 reduction target gap and sustainability for bridges in China by 2040," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).

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