Why Economic Forecasting Tools May Be Misleading on Climate Risk

New research from University of New South Wales suggests that widely-used economic models may be significantly underreporting the potential financial consequences of climate change. By factoring in global—not just local—weather patterns, economists have found that projected GDP losses increase sharply under high-emission scenarios.

This finding raises key concerns for corporate strategists and policymakers relying on economic forecasting tools that may not fully capture the systemic risks posed by a warming planet.

Why Global Weather Patterns Matter in Forecasting

Traditional economic models typically assess how regional climate events influence national productivity. However, these frameworks often overlook how global weather dynamics can impact interconnected markets, trade flows, and international supply chains.

The study shows that when global temperature and climate volatility are taken into account, economic models forecast far more severe GDP declines by the end of the century. In one widely-used model, projected GDP losses jumped from 11% to 40%. Another model predicted losses rising from 28% to as high as 86% under continued high-emission scenarios.

Supply Chain Vulnerabilities and Global Spillover Effects

Global economic interdependence means that extreme weather in one region can trigger disruptions far beyond local borders. For example:

• Heatwaves or droughts across multiple regions can simultaneously disrupt global commodity markets.
• High global temperatures increase the chance of concurrent extreme weather events, weakening resilience strategies based on geographic diversification.
• International trade routes, critical for buffering local disruptions, become less reliable when multiple regions face climate-related stress simultaneously.

These dynamics can lead to cost-push inflation, reduced trade volumes, and higher input volatility—factors with direct consequences for businesses operating across borders.

Policy and Pricing Implications for Carbon and Emissions

The research team applied their findings to the DICE 2023 model—one of the standard integrated assessment tools used in climate policy—and found that the optimal level of warming shifted downward significantly. The model's welfare-optimal temperature target dropped from 2.7°C to 1.7°C when global climate effects were included.

This change also led to a recommendation for significantly higher carbon pricing, especially after 2030, aligning more closely with the emissions reduction pathways advocated by the Intergovernmental Panel on Climate Change (IPCC).

A Call for Integrated Climate and Economic Modeling

While the study acknowledges challenges in projecting future scenarios based on historical data, it underscores the need for more integrated approaches in economic modeling. Excluding global climate effects may be leading to systemic underestimation of risk in both national and international economic planning.

The message is clear: long-term strategies—whether related to investments, supply chain design, or sustainability planning—must account for broader climate-related vulnerabilities, not just local disruptions.