Why logistic models matter in climate risk planning
Physical climate risks are becoming a dominant concern for institutional investors, with their complex and often cascading effects threatening to disrupt financial markets at scale.
Ortec Finance, a provider of technology and solutions for risk and return management, recently explored the role of damage functions for assessing physical climate risks.
As the likelihood and severity of extreme climate events increase, investors are turning to scenario-based modelling frameworks, particularly those that incorporate physical risk damage functions, to quantify potential losses and understand how market dynamics might shift in response to climate change.
At its core, a physical risk damage function describes the relationship between climate factors—like rising temperatures—and the economic or physical harm they may cause. These functions help investors estimate losses and calibrate strategies across different warming scenarios. The structure of the function matters: linear models imply steady, incremental impacts, while exponential or non-linear models capture the accelerating nature of climate risk more accurately, it said.
Within the field of climate scenario analysis, quadratic and logistic damage functions are the most commonly employed. Quadratic models assume damages increase with the square of temperature rise, reflecting a gradual acceleration. In contrast, logistic models depict threshold-based impacts—limited damage up to a tipping point, followed by a rapid spike and eventual saturation.
The Network for Greening the Financial System (NGFS), a prominent body shaping climate scenarios used by central banks and regulators, currently relies on quadratic damage functions in its modelling. These models, updated in the NGFS Phase V iteration, now include additional climate stressors and delayed economic feedback loops. Despite these enhancements, the quadratic approach remains structurally limited.
This is a critical shortcoming. The World Meteorological Organization has confirmed that 2024 reached a record-high 1.55°C above pre-industrial levels, placing us near the upper bound where quadratic functions remain reliable. Under scenarios with low transition ambition, where warming exceeds 2°C, non-linear and abrupt damage patterns are increasingly likely.
Ortec Finance offers a sophisticated modelling framework via its ClimateMAPS solution, which separates chronic and acute physical risks. This enables a more nuanced assessment of how different risk categories affect macroeconomic indicators and asset classes. The system incorporates a 5°C logistic trajectory for high warming scenarios and provides direct insights into the separate contributions of each risk type.
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