Climate models need to extend beyond academic institutions and become critical tools for everyday business use—similar to credit and profit forecasting models—according to executives at a recent Standard & Poor’s event. As the scope of climate risks increases, private market players are using these models to mitigate risks more effectively.
“We do not understand how correlated and cascading events will affect economies and financial institutions in any systematic way. The situation is even more challenging for tipping points, like potential changes in the Amazonian rainforests or shifts in major ocean circulation components,” said Dr. Terence Thompson, Chief Science Officer at S&P Global Climate Center of Excellence. “We are just beginning to understand how and when these changes might occur, as well as their possible impacts.”
For businesses, this means leveraging models to prepare for various future scenarios, including shifts in temperature, precipitation, and extreme weather events that could disrupt supply chains, asset values, and overall market stability. These insights were shared during a presentation titled Linking Climate Change to Economic and Financial Impacts.
With a growing need for precision in climate-related financial forecasting, the dialogue emphasized both the potential and the challenges of integrating complex models into private market strategies.
A key challenge for private sector players managing climate risks is accessing reliable, long-term data. Amanda McCarty, Director of the Climate Ready Nation program, noted, “Many people across the spectrum are seeking higher-quality information for the six-month to 10-year time scales.”
This demand for more granular and accurate data has driven significant investment in climate models that offer actionable insights for businesses seeking to adapt their operations and investment strategies, McCarty explained.
Yet, developing robust climate-economic models is far from straightforward. Lars Peter Hansen, an economist from the University of Chicago, highlighted the complexities involved in integrating economic and physical climate models. He pointed out the challenge for private market participants lies in understanding how economic variables, such as investment returns or insurance premiums, might shift in response to changing climate patterns.
“We have to think about these as simultaneous systems that interact with each other,” Hansen said. “As an economist, I cannot provide credible socioeconomic pathways without considering the interaction between climate change and uncertainties, because economies will respond.”
Hybrid models, which blend traditional climate models with artificial intelligence and machine learning, may offer a promising path to broader adoption, according to S&P’s Carl. He noted that these new models represent a significant investment opportunity for the private sector.
“We now have hybrid models that combine both machine learning, artificial intelligence, and dynamical models,” Carl explained. These hybrid approaches can help businesses achieve more accurate projections, especially when handling complex datasets across various regions and time scales.
However, while advanced modeling holds significant promise, Carl also highlighted the inherent trade-offs and uncertainties. He emphasized that “understanding what each dataset offers is critically important,” particularly when trade-offs between time span and spatial resolution can lead to differing outcomes.
All participants agreed on the need for greater investment in climate modeling to support private sector adaptation efforts.
McCarty stressed that “continuing to work in that space to fill the many gaps we have” is crucial for providing businesses with the localized, high-resolution data they need. The private sector’s ability to adapt to changing climate conditions often hinges on access to the most accurate and up-to-date models, she noted.
Ultimately, there is a close relationship between advancements in climate modeling and the needs of private market participants. As Lars Peter Hansen concluded, the goal is to “use models to make the best guesses” while remaining prepared for “potentially adverse outcomes.”
