Published in the journal Nature Climate Change, the paper from academics at the Universities of Lancaster and Exeter details how expert ‘prediction markets’ could improve the climate-risk forecasts that guide key business and regulatory decisions.

Organisations now appreciate that they have to consider climate risks within their strategic plans — whether that relates to physical risks to buildings and sites, or risks associated with transitioning to achieve net zero.

However, the forward-looking information needed to inform these strategic decisions is limited, the researchers say.

Dr Kim Kaivanto, a co-author from Lancaster University’s Department of Economics, said: “The institutional arrangements under which climate-risk information is currently provided mirrors the incentive problems and conflicts of interest that prevailed in the credit-rating industry prior to the 2007/8 financial crisis.

“In order to make sense of emissions scenarios and to support planning and decision-making, organisations have a pressing need for this type of forward-looking expert risk information.

“Understanding climate risks requires diverse and complementary expertise from political science, economics and policy, as well as country-specific knowledge on the major emitters. Prediction markets incentivise and reward participants with distinct expertise and information to come forward — and they offer a level playing field for experts from these complementary fields of expertise.”

Mark Roulston, one of the Exeter University co-authors said, “If providers of climate forecasts are paid upfront irrespective of accuracy, you don’t need to be an economist to spot the problem with that arrangement.”

In their paper, ‘Prediction-market innovations can improve climate-risk forecasts’ the authors detail how expert ‘prediction markets’ can help overcome the structural problems and shortfalls in the provision of forward-looking climate-risk information — something that will become more vital as the demand for long-range climate information increases.

Prediction markets are designed to incentivise those with important information to come forward, and facilitate the aggregation of information through the buying and selling of contracts that yield a fixed payoff if the specified event occurs. An outcome of interest — such as average CO2 concentration in the year 2040, for example — is partitioned into intervals. Expert participants compare the results of their own modelling with the prices of these intervals, and purchase or sell claims on these intervals if their model suggests the price is too low or too high.

With a well-designed market such as Lancaster University’s AGORA prediction-market platform, the price of a contract can be interpreted as the market-based probability of the event happening.

These kinds of long-range markets have not been established to date due, in part, to regulatory obstacles. However, the researchers believe the markets can be designed to overcome these obstacles by avoiding the ‘pay-to-play’ aspect of existing prediction markets in which the losses of less-well-informed individuals fund the winnings of better-informed individuals. Instead, markets can be structured as vehicles for distributing research funding to experts and modellers in a manner that is consistent with the principles of effective altruism: an initial stake provided by a sponsor is distributed to participants in accordance with the quality and quantity of information they bring into the market through their trading activity.

They add that access to participation in the markets would need to have selection criteria to ensure diversity of views and a range of expertise to ensure they are able to aggregate diverse sources of information.

The paper’s authors are Kim Kaivanto of Lancaster University, and Mark Roulston, Todd Kaplan and Brett Day of the University of Exeter.

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Journal Reference:

1. Mark Roulston, Todd Kaplan, Brett Day, Kim Kaivanto. Prediction-market innovations can improve climate-risk forecasts. Nature Climate Change, 2022; DOI: 10.1038/s41558-022-01467-6

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