Joint-outcome prediction markets for climate risks

range climate, and lies at the heart of the information asymmetry discussed in the introduc- tion. Therefore, the accountability provided by incentive-compatible compensation is a desir- able feature for all forward-looking information. The markets described in this paper have since been included in a wider verification study of climate-related prediction market forecasts with horizons of up to one year [30]. However, the only way to test whether prediction markets can be effective for forecasting problems on horizons of years to decades is to establish markets on these time scales. These time scales are longer than the horizons of prediction markets that have been run up until now, so aspects of their design would require careful consideration: participants would need confidence in the stability and longevity of the institution operating the market funds backing the credits would have to be segregated, and the monetary value of credits should appreciate to reflect interest accrued by the segregated funds. This study has demonstrated the ability of carefully designed prediction markets to effi- ciently aggregate expertise relevant to climate. Extending the concept to longer horizons could produce more credible forecasts of greater use for policy making, strategic planning, and cli- mate-related risk disclosures. Acknowledgments The authors would like to thank Winton Group, as well as participants in a 2016 workshop hosted by Winton Group which elaborated the initial market design. They would also like to

thank the university teams who participated in the 2018 temperature-rainfall markets. Author Contributions Conceptualization: Mark S. Roulston, Kim Kaivanto. Formal analysis: Mark S. Roulston. Methodology: Mark S. Roulston. Writing – original draft: Mark S. Roulston, Kim Kaivanto. References 1. Andrews MB, Ridley JK, Wood RA, Andrews T, Blockley EW, Booth B, et al. Historical simulations with HadGEM3-GC3. 1 for CMIP6. Journal of Advances in Modeling Earth Systems. 2020; 12(6): e2019MS001995. 2. O’Neill BC, Tebaldi C, Van Vuuren DP, Eyring V, Friedlingstein P, Hurtt G, et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development. 2016; 9 (9):3461–3482. 3. Katzav J, Thompson EL, Risbey J, Stainforth DA, Bradley S, Frisch M. On the appropriate and inappro- priate uses of probability distributions in climate projections and some alternatives. Climatic Change. 2021; 169:1–20. 4. Bernanke BS, Woodford M. Inflation forecasts and monetary policy. Journal of Money Credit and Bank- ing. 1997; 29: 653–684. 5. Jackson AL, Sumner S. Velocity futures markets: Does the Fed need a structural model? Economic Inquiry. 2006; 44:716–28. 6. Sumner S, Jackson AL. Using prediction markets to guide global warming policy. In 63rd 153 Interna- tional Atlantic Economic Conference 2008 Dec 9 (pp. 14–18). 7. Schwalm CR, Glendon S, Duffy PB. RCP8. 5 tracks cumulative CO2 emissions. Proceedings of the National Academy of Sciences. 2020; 117:19656–7. 8. Hausfather Z, Peters GP. RCP8. 5 is a problematic scenario for near-term emissions. Proceedings of

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