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Forest carbon offset projects face a significant and growing wildfire threat: The study projects a 55% increase in fire exposure to global forest carbon projects by 2080, meaning the damaging wildfires that have already affected these projects in recent years are likely to become more common as climate change makes forests hotter and drier.
Current wildfire prediction tools are inadequate for protecting carbon investments: Existing models used to forecast long-term fire risk have limited accuracy, which is problematic because carbon offset programs depend on forests remaining intact for decades. This research developed a more accurate AI model trained on 7 million satellite observations of global wildfires to better predict these risks.
The new AI model provides transparency about wildfire dangers: By using explainable AI (rather than a "black box" approach), the researchers created a tool that not only makes better predictions but can also show why it's making those predictions—helping policymakers and carbon market managers understand which specific factors drive wildfire risk in different regions.
Immediate action is needed to protect carbon offset credibility: With wildfire risk projected to increase substantially, carbon offset programs need to proactively assess and mitigate these threats now. Without intervention, the carbon credits these projects generate may become unreliable if the forests burn and release their stored carbon back into the atmosphere.

Abstract

Carbon offset programs are critical in the fight against climate change. One emerging threat to the long-term stability and viability of forest carbon offset projects is wildfires, which can release large amounts of carbon and limit the efficacy of associated offsetting credits. However, analysis of wildfire risk to forest carbon projects is challenging because existing models for forecasting long-term fire risk are limited in predictive accuracy. Therefore, we propose an explainable artificial intelligence (XAI) model trained on 7 million global satellite wildfire observations. Validation results suggest substantial potential for high resolution, enhanced accuracy projections of global wildfire risk, and the model outperforms the U.S. National Center for Atmospheric Research's leading fire model. Applied to a collection of 190 global forest carbon projects, we find that fire exposure is projected to increase 55% [37-76%] by 2080 under a mid-range scenario (SSP2-4.5). Our results indicate the large wildfire carbon project damages seen in the past decade are likely to become more frequent as forests become hotter and drier. In response, we hope the model can support wildfire managers, policymakers, and carbon market analysts to preemptively quantify and mitigate long-term permanence risks to forest carbon projects.

6 pages, 5 figures. Published in ICLR 2023 Workshop: Tackling Climate Change with Machine Learning

Key Takeaways

Forest carbon offset projects face a significant and growing wildfire threat: The study projects a 55% increase in fire exposure to global forest carbon projects by 2080, meaning the damaging wildfires that have already affected these projects in recent years are likely to become more common as climate change makes forests hotter and drier.
Current wildfire prediction tools are inadequate for protecting carbon investments: Existing models used to forecast long-term fire risk have limited accuracy, which is problematic because carbon offset programs depend on forests remaining intact for decades. This research developed a more accurate AI model trained on 7 million satellite observations of global wildfires to better predict these risks.
The new AI model provides transparency about wildfire dangers: By using explainable AI (rather than a "black box" approach), the researchers created a tool that not only makes better predictions but can also show why it's making those predictions—helping policymakers and carbon market managers understand which specific factors drive wildfire risk in different regions.
Immediate action is needed to protect carbon offset credibility: With wildfire risk projected to increase substantially, carbon offset programs need to proactively assess and mitigate these threats now. Without intervention, the carbon credits these projects generate may become unreliable if the forests burn and release their stored carbon back into the atmosphere.

Abstract

6 pages, 5 figures. Published in ICLR 2023 Workshop: Tackling Climate Change with Machine Learning

Widespread Increases in Future Wildfire Risk to Global Forest Carbon Offset Projects Revealed by Explainable AI

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Abstract

Paper Info

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Widespread Increases in Future Wildfire Risk to Global Forest Carbon Offset Projects Revealed by Explainable AI

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links