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Climate risk: melting Arctic ice
How ‘prediction markets’ could improve climate risk policies and investment decisions
A pivotal new study has shown that a market-led approach could be key to ensuring governments and businesses are properly informed about their exposure to future climate risks.
The new research, conducted by experts from the University of Exeter and Lancaster University, 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 climate risk information currently available to decision-makers is limited, hard to access and subject to the same biases that drove the Global Financial Crisis of 2007-08, the researchers say.
The study, ‘Prediction-market innovations can improve climate-risk forecasts’, is published in the leading journal Nature Climate Change on Thursday, September 1st 2022.
Mark Roulston, from the University of Exeter and co-author of the study 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.”
The research team explains 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. The Platform is part of a joint initiative with the University of Exeter to use prediction markets for forecasting longer-range climate risks, and even as an efficient mechanism for performance-driven allocation of funding for applied climate research.
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.
Dr Kim Kaivanto, a co-author from Lancaster University’s Department of Economics, said: “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.”
Date: 1 September 2022