# Joint-outcome prediction markets for climate risks

## TL;DR

This paper explores using prediction markets to forecast climate risks by integrating expert knowledge and addressing challenges like circularity and information asymmetry.

## Contribution

The novelty is proposing and testing prediction markets with joint-outcome spaces for forecasting climate-related variables like GHG concentrations and temperature.

## Key findings

- Prediction markets with joint-outcome spaces can aggregate expert judgments effectively.
- Experiments on UK rainfall and temperature show potential for credible climate forecasts.
- Such markets could support policy making and risk disclosure with longer-term forecasts.

## Abstract

Predicting future climate requires the integration of knowledge and expertise from a wide range of disciplines. Predictions must account for climate-change mitigation policies which may depend on climate predictions. This interdependency, or “circularity”, means that climate predictions must be conditioned on emissions of greenhouse gases (GHGs). Long-range forecasts also suffer from information asymmetry because users cannot use track records to judge the skill of providers. The problems of aggregation, circularity, and information asymmetry can be addressed using prediction markets with joint-outcome spaces, allowing simultaneous forecasts of GHG concentrations and temperature. The viability of prediction markets with highly granular, joint-outcome spaces was tested with markets for monthly UK rainfall and temperature. The experiments demonstrate these markets can aggregate the judgments of experts with relevant expertise, and suggest similarly structured markets, with longer horizons, could provide a mechanism to produce credible forecasts of climate-related risks for policy making, planning, and risk disclosure.