Nicolas Schneider: "For investors and industries, more granular information on physical risk impacts means a better adaptability to future shocks"

Could you explain the specificity of your approach and how it changes our understanding of climate risks?

Over the last couple of years, EDHEC has focused its research on extending integrated climate economics modeling to incorporate the advances of climate science and make them suitable for financial economics and asset pricing applications. Our modeling has focused on the global macroeconomic effects of climate change.

The granular approach we now advocate represents a significant shift by examining economic impacts at a finer level. Indeed, global and high-resolution climate-economic models are deeply interconnected. Early Integrated Assessment Models (IAMs), like Nordhaus's Dynamic Integrated Climate-Economy (DICE) model, pioneered estimating climate damages in economic terms using global aggregate damage functions. However, these models smoothed out regional differences and underestimated extreme warming impacts, relying heavily on data from advanced economies.

Subsequent research, such as Schlenker and Roberts (1) and Burke et al. (2), revealed threshold-driven, non-linear impacts - e.g., crop yield collapses and disproportionate GDP losses with rising temperatures. High-resolution studies, like Kotz et al. (3), further exposed sharp regional disparities by analyzing subnational data and extreme weather effects, significantly raising global damage estimates.

In sum, this evolution - from early IAMs to high-resolution empirical studies - marked a major shift in our understanding of climate risks. Moving from aggregate, global models (a generalization of all areas that fails to precisely forecast the future damages that any of them will individually experience) to more granular approaches has uncovered threshold effects and regional vulnerabilities that were previously overlooked.

Our perspective is that the global average is a limited statistic, as climate change impacts vary widely across regions and sectors. This divergence creates a mix of net ‘winners’ and ‘losers’ (in absolute terms), with their distribution across the economy remaining uncertain. We exploit the granular origins of climate-change-related damages and offer finer-scaled predictions.

You have just finalized a major paper (4) that models the economic impact of climate change impacts at an unprecedented granular level in terms of geography. Could you describe the key points of your method and your findings?

This study comes on the heels of the paper by Kotz et al. (3), adopted into the latest scenarios of the Network for Greening the Financial System (NGFS). It advanced our understanding of climate-driven economic risks by demonstrating the importance of regional detail and extreme weather events. Our study builds on this foundation by expanding its scope and ensuring greater consistency across methods and data (4).

We contribute to this important body of work in three key areas:

1. High(er) Spatial Resolution and Coverage:

We extrapolated the analysis to additional subnational provinces, covering regions responsible for 95% of global economic production. This finer resolution reveals localized climatic exposure and economic heterogeneity with greater precision, uncovering impacts that are smoothed over at coarser scales. As a result, we observe a more severe global damage function due to the heightened sensitivity of certain regions. The amplification of global damages reflects the greater heterogeneity revealed at finer scales, which coarser models inherently smooth over.  Importantly, when analyzed at Kotz’s resolution, our results align with theirs, confirming the consistency of our approach.

2. Validation Across Data and Methods:

To strengthen confidence in our findings, we tested both Kotz et al.’s results and our extensions using alternative climate datasets and multiple statistical methods, including parametric, non-parametric, and semi-parametric approaches. This validation demonstrates the robustness of our extensions, while aligning with Kotz et al.’s core findings on regional disparities and climate-driven economic damages.

3. Refining Climate Model Inputs:

We addressed known biases in a small subset of the climate models underpinning the work of the Intergovernmental Panel on Climate Change (IPCC). A subset of CMIP6 GCMs may be “too hot”, with representations of cloud feedback in some models associated with higher-than-consensus global surface temperature response to doubled atmospheric CO2 concentrations - equilibrium climate sensitivity (ECS) and global warming after 70 years of a 1% per annum increase in CO2 - transient climate response (TCR). To mitigate the threat of bias potentially introduced by this phenomenon, we follow Hausfather et al.'s (5) recommended procedure of excluding models with temperature increases derived from TCR and ECS that fall outside the “likely” ranges (TCR: 1.4-2.2 degree C, 66% likelihood, and ECS: 2.5-4 degree C, 90% likelihood). That leaves us with 15 “likely” GCMs that form the basis of our macroeconomic projections.

This analysis highlights two critical insights. First, the fact that localized risks matter. Small-scale variations in climate exposure and economic activity can produce disproportionately large impacts, particularly in vulnerable regions and sectors. Second, the benefits for the global implications are key. Indeed, accurately capturing these local effects increases aggregate global damage estimates, underscoring the urgency of both adaptation and mitigation strategies.

In short, we complement and extend Kotz et al.’s work by offering greater spatial detail and ensuring methodological robustness, providing policymakers, central banks, and investors with reliable insights into climate risks.

What do the mid-century scenarios you propose reveal about regional economic risks, and how can they inform near-term strategies for investors and policymakers?

While much attention often centers on end-of-century outcomes, my research includes projections for mid-century scenarios which reflect risks that are largely built-in due to current trajectories, regardless of whether emissions are curbed.

To be more precise: mid-century projections (2040–2050) provide a practical near-term window for understanding climate risks, reflecting damages that are largely locked-in due to historical and current emissions. Unlike end-century simulations, mid-century results carry higher reliability because they involve shorter extrapolation horizons and are underpinned by a vast ensemble of simulations - spanning 30 global climate models, multiple emission pathways, and time horizons. This ensemble-based approach improves the robustness of our findings and allows us to identify regional vulnerabilities with greater precision.

For a long time, the climate space has largely focused on mitigation targets, particularly reducing fossil fuel use in the energy supply mix. While mitigation remains crucial for long-term stability, the near-certainty of extreme weather events and climate shocks by mid-century underscores the need to place at least as much emphasis on adaptation.

Our work aims to support this shift toward adaptation by answering three key questions that are particularly relevant for investors seeking regional climate risk solutions:

1. What is the size of future climate shocks at the local level, and how do they compare to the global average?

High-resolution projections reveal significant heterogeneity in climate risks, with tropical regions and low-lying coastal areas expected to experience above-average damages. For instance, mid-century results indicate severe heat stress and crop failures in parts of West Africa, South Asia, and Central America, while rising sea levels and storm surges threaten coastal infrastructure and habitability.

2. In a warmer future, how much will it cost to produce the same output as today?

As physical risks intensify, maintaining economic productivity in exposed regions will require significant additional input factors - such as irrigation, fertiliser, and mechanisation in agriculture, or energy-intensive cooling systems in infrastructure. These adaptations come at a rising cost, particularly in regions with limited economic capacity to respond.

3. Who will pay these costs, and what are the asset pricing implications?

The adjustments needed to sustain productivity will not be distributed evenly. Regions and sectors that are heavily exposed to mid-century risks will face mounting climate premiums in asset pricing. Investors and regulators will need to assess how these premiums reflect localized risks and who ultimately bears the economic burden of adaptation.

While adaptation strategies - like irrigation, mechanization, and changes to crop varieties -offer pathways to limit economic losses, geography imposes a fixed constraint on their effectiveness (6). Land is immobile, unlike other production factors, meaning highly exposed regions - particularly those near the equator - will continue to face declining productivity even with adaptation. This raises critical questions about how to shift land usage to more climate-resilient areas to balance global food supply and economic stability. Achieving this will require large-scale processing of high-resolution physical climate data and remote-sensing information to quantify land productivity shocks and their broader macroeconomic implications.

In summary, mid-century projections, grounded in robust ensemble-based approaches, reveal inevitable physical risks that demand immediate attention. While mitigation remains essential to avoid catastrophic end-century outcomes, mid-century results underscore the immediate need to prioritize adaptation strategies and assess their regional costs and financial impacts.

Your work employs high-resolution climate simulations and advanced econometric modelling. Looking ahead, what advancements in data or methods do you think could further improve our understanding of the economic impacts of climate change, particularly at a regional level?

We must acknowledge that recent developments in both large-scale spatial econometric regressions and the elaboration of global climate databases have brought about a significant shift in the climate-economics literature. Increased level of spatial granularity, constrained persistence, and widened scope of potential climatic drivers of macroeconomic productivity have gone beyond standard globally averaged predictions.

However, the implications of these new-generation studies for regional economic outputs remained unknown until quite recently. The reason is partially technological: the most recent high-frequency raw climate data, either observations or simulations, produced by the National Aeronautics and Space Administration (NASA) and other organizations (National Oceanic and Atmospheric Administration (NOAA), European Centre for Medium-Range Weather Forecasts (ECMWF), etc) are provided in the form of vectorized products containing time (up to days; hours under certain conditions) and spatially-downscaled (up to 1 km x 1km) measurements.

Billion-level dimensions and global coverage make these data products computationally technical to handle (because very heavy: Tb-size), besides their idiosyncratic structure.
Processing this scale of data therefore requires advanced computational tools (such as High-Performance Computing (HPC) systems and Shared Computing Clusters (SCC)) enabling users to extract and force millions of climate simulations into global economic models linking macro-financial outputs to plausibly exogenous year-to-year fluctuations in climate exposure.

When correctly conducted, these innovative approaches substantially improve the precision of regional risk assessments by offering projections of future impacts on production systems and asset performance with an outstanding spatial and temporal resolution; besides the advantage of providing predictions structured from ‘real’ empirical data. The combination of these factors gives a strong competitive advantage to the kind of granular modelling that is at the core of our research.

Looking ahead, while current models provide valuable insights, gaps remain. Existing projections still face trade-offs between temporal detail (e.g., hourly heatwaves or extreme rainfall) and spatial precision. Achieving both requires significant computational resources and data processing capabilities, which current tools cannot yet fully support. Addressing these gaps would allow us to better capture short-lived but intense shocks - like floods and storms - that drive significant localized damages.

Moving forward with investors: the spatial momentum must be caught. In the future, the creation of a “best-in-class” data product combining global coverage at higher spatial precision and detailed, high-frequency variability will significantly improve our ability to quantify risks at the local (including physical asset) level. Such advancements will refine localized risk assessments, enhance sector- and industry-specific projections, and provide the granularity needed for investors to price risks more effectively.

One final direction involves improving the performance of non-parametric statistical methods to handle research in large dimensional contexts.

By enhancing the precision of climate-economic insights, these advancements will enable investors to stress-test portfolios, refine asset valuations, and better anticipate how physical risks will affect their holdings.

References

(1) Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proceedings of the National Academy of sciences, 106(37), 15594-15598 - https://doi.org/10.1073/pnas.0906865106

(2) Burke, M., Hsiang, S. M., & Miguel, E. (2015). Global non-linear effect of temperature on economic production. Nature, 527(7577), 235-239 - https://doi.org/10.1038/nature15725

(3) Kotz, M., Levermann, A., & Wenz, L. (2024). The economic commitment of climate change. Nature, 628(8008), 551-557 - https://doi.org/10.1038/s41586-024-07219-0

(4) Forthcoming working paper - Rebonato, R & Schneider, N. In the meantime, we invite you to browse through the main papers by the researcher interviewed here:

a) Schneider, N. (2024). Endogeneity and other problems in curvilinear income-waste response function estimations. Stochastic Environmental Research and Risk Assessment, 38(2), 357-382 - https://doi.org/10.1007/s00477-023-02598-8

b) Schneider, N. (2023). Testing for integrated electricity series–A formalized synthesis of known problems. The Electricity Journal, 36(6), 107289 - https://doi.org/10.1016/j.tej.2023.107289

c) Schneider, N. (2023). Climate policy, resource owners’ anticipations and the green paradox: model set-up and empirical considerations. Journal of Environmental Economics and Policy, 12(1), 33-43 - https://doi.org/10.1080/21606544.2022.2071344

d) Schneider, N. (2022). Unveiling the anthropogenic dynamics of environmental change with the stochastic IRPAT model: A review of baselines and extensions. Environmental Impact Assessment Review, 96, 106854 - https://doi.org/10.1016/j.eiar.2022.106854

(5) Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W., & Zelinka, M. (2022). Climate simulations: recognize the ‘hot model’ problem. Nature, 605(7908), 26-29 - https://doi.org/10.1038/d41586-022-01192-2

(6) Burke, M., & Emerick, K. (2016). Adaptation to climate change: Evidence from US agriculture. American Economic Journal: Economic Policy, 8(3), 106-140 - https://doi.org/10.1257/pol.20130025