Estimating the Economic Cost of Climate Change? Start with Data.

ESG data and analytics

England is roasting as London records its hottest day ever—not many homes there have air conditioning because until now, they rarely needed it. Spain is literally on fire. Triple-digit high temperatures are forecast across much of the U.S. (again), and power grids are under stress as a dangerous heatwave affects over 100 million people. This is not just a “normal fluctuation” in weather patterns—it will keep happening, and the potential costs are immense.

But just how immense? How can we estimate the economic cost climate change is likely to impose on the world? We need data and ways to analyze the data. Where to begin? A recent session of the International Institute of Finance’s (IIF’s) DataTalk, held in partnership with the Oliver Wyman Forum, focused on Data and ESG and discussed this question. As we are big on data, especially when it comes to ESG, we wanted to share highlights from the discussion and we thank the IIF and Oliver Wyman for helping to organize current thinking on this issue into four key topics:

  1. Distinguish between data that measures physical risks and data that measures risks associated with the transition to a low-carbon world. Physical risks include extreme weather events and rising sea levels (likely to hit insurers hard). They also include societal conflicts and mass migrations brought on by a failure to limit global warming to 2 degrees Centigrade. Transition risks include reduced profitability as companies invest in new clean energy technologies (including green hydrogen, carbon capture, and many more that are on the horizon but getting closer), more sustainable approaches to agriculture and construction, and so on. There is also the disproportionate economic impact on emerging markets of transitioning away from fossil fuels. There may also be legal liabilities for failing to mitigate these risks. The war in Ukraine and the resulting disruption in global energy markets reminds us that the risks of a disorderly transition are likely to include unforeseeable events.
  1. Quality data and technology are critical. More types of data are being produced from more sources every day. This data is often more current and more accurate than official government data sources—the IIF/Oliver Wyman piece gives the example of satellite images of city lights and tanker traffic in and around Shanghai that provide a more up-to-date picture of China’s exporting activity than official statistics. However, that data is “unstructured” and often requires sophisticated data analytics tools, including artificial intelligence, to use. The article mentions “open-source data initiatives and efforts by organizations like the Network of Central Banks and Supervisors for Greening the Financial System (NGFS), which is creating a climate data repository that can help firms price risks.” 

On the subject of data and technology, CEO Today recently discussed data visibility in the supply chain. This article notes that integrating a logistics company’s technology infrastructure with a manufacturer’s provides access to shipment information such as locations and conditions, so that manufacturers can measure delivery time and track emissions related to a given delivery. This data gives stakeholders (executives, boards, and investors) insights into how well vendors are performing relative to ESG guidelines and targets. In addition, data can help reduce waste in the delivery chain; for example, optimizing a route reduces waste caused by improper storage. 

  1. Better coordination is needed. The regulatory landscape for ESG is, to use the IIF/Oliver Wyman article’s term, “uneven” and are evolving at different rates. The article calls for closer alignment between initiatives such as the European Union’s Sustainable Finance Disclosure Regulation, the U.S. Securities and Exchange Commission’s recent climate disclosure proposal, and the International Sustainability Standards Board. We agree that globally consistent taxonomies and reporting standards would help investors, companies, and all stakeholders to obtain data that is comparable regardless of one’s geographic location.
  1. Keep both the short and long term in mind. It is easier to measure risk with greater specificity over shorter horizons, and regulators naturally tend to focus their efforts there. But the big climate risks, while getting closer and more obvious all the time (see our opening paragraph) are still beyond the typical forecasting horizon. In OWL’s view, the IIF/Oliver Wyman discussion makes a critical point by saying that “investors and regulators have to get comfortable with approximating those risks, even as they work to reduce the uncertainty surrounding them.” In other words, let’s not let the pursuit of perfectly accurate data analyses get in the way of doing what is good enough for now. 

Of course, the economic impact is not the only type of loss we have to consider as a result of the climate catastrophe. We came close to losing Yosemite’s 3,000-year old Giant Sequoias to a fire recently, and while the firefighters saved the trees this time…well, we don’t have to finish the sentence. Some areas of the planet where humans have lived for thousands of years are becoming unlivable, and people who make their homes, raise their families and earn their livelihoods there will have to leave, not because they did something wrong, but because we all, collectively, are failing to respond to what the data is telling us we must do.


OWL ESG’s data and analytics are used by corporations, fund managers, and financial advisors to help them understand how they, their competitors, and their stakeholders and clients are exposed to ESG risks. To learn more, contact us