  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.
