What is a Damage Function in Climate Models?


The mathematical heart of climate-economy modelling—and a driver of risk perception and policy outcomes

Today In integrated assessment models (IAMs) and climate-economy forecasts, one term carries enormous weight: the damage function. This is the part of the model that quantifies how much economic loss is caused by each degree of warming.



At its core, a damage function translates rising temperatures into GDP loss, based on assumptions about physical impacts like sea level rise, extreme weather, productivity declines, and infrastructure damage.



Most commonly used structure: The classic Nordhaus DICE model uses a quadratic damage function: Economic loss (%) = α × (Temperature Increase)² Where α is a coefficient based on historical and empirical data.



Controversy and Complexity:


  1. Critics argue these functions underestimate catastrophic risks, especially tipping points and nonlinear impacts.
  2. Newer models incorporate regional heterogeneity, uncertainty, and non-market losses (e.g., biodiversity, cultural heritage).
  3. Alternatives include concave, sigmoidal, or empirically calibrated functions derived from econometric or catastrophe modelling.

Why It Matters: The damage function heavily influences policy recommendations, carbon pricing models, and net present value of transition investments. A conservative function may suggest gradual mitigation, while a steep one justifies urgent intervention.



Understanding and challenging the damage function is essential for anyone using IAM outputs in financial decision-making.



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