Monetizing Climate Forecasts

Better information about future climate-related risks is valuable to companies and other organizations, and a new sector of climate service providers has emerged to provide it. Better climate projections might have clear societal benefits but does that make climate analytics an attractive commercial investment?

Climate analytics, the provision of information about present and future climate risks, is a growing industry. In addition to an increasing number of start ups providing climate services, established consultancies and data vendors are developing their own capabilities, sometimes through acquisitions. Climate change is an important problem and better evaluations of climate risks are undoubtedly of benefit to society, but this does not necessarily make climate analytics an attractive investment. How might the economics of the burgeoning climate analytics sector play out? As with climate change itself, we can think about the future of the sector by considering some different scenarios.

In one scenario most long-range climate risk projections end up looking very similar. Many providers base their products on the same publicly available CMIP model runs and then add value by downscaling these simulations to higher resolutions and cross-referencing them with the location of assets and infrastructure. If they use similar downscaling methods, or methods that give similar results, climate risk information will become a commodity and suppliers’ margins will be squeezed.

Alternatively, the long-range predictions of different providers might be materially different. In this scenario users will be faced with the problem of choosing a provider when they have no way to judge the quality of the predictions. Short-range weather forecasters can be assessed on their track records but this is not practical when horizons are measured in years and decades. This is an example of “asymmetric information” between buyers and sellers. Markets suffering for information asymmetries are vulnerable to failure as buyers will not be prepared to pay for quality they can’t verify and sellers won’t invest in quality they can’t demonstrate.

Another possibility is that one or two providers rise to dominance by securing a large share of the market and become de facto standard setters. Their dominance might not be the result of accuracy, because accuracy cannot be ascertained, but because it is easier for people to justify using a product everyone else is using. Strong branding and positive feedback thus create a barrier to entry for other firms.

The first two outcomes are not great for investors as providers will struggle to monetize their forecasts. The third scenario, with a dominant provider, is potentially more attractive for investors who back this provider, if they can identify them.

The scenarios outlined above are not the only potential outcomes and climate analytic providers may find ways to avoid commodification and solve the information asymmetry problem. Tackling these issues should be an integral part of any climate service provider’s business plan.

The original version of this article appeared as Monetizing climate forecasts on LinkedIn.