UC Berkeley

In recent years, the risk of catastrophic wildfire has escalated rapidly in the Western U.S. and globally. Earlier spring snowmelt and increasing moisture deficits have caused a dramatic rise in burned area. Climate warming does not act alone, however, in the explanation of rising wildfire risk. Population migration to high-risk areas and historical fire suppression policies have also contributed to the uptick in risk. These factors, in combination with aging electric utility infrastructure, have left the electric-power sector acutely vulnerable to catastrophic wildfire risk. In California, electric utilities are investing significant resources to buy down their exposure to wildfire and adapt to a future with more climate extremes. The research described in the following chapters dives into the costs and risk implications of such high-stakes electric-power sector adaptation investments. Probabilistic machine-learning models are trained and evaluated alongside econometric methods to address the low-probability, high-consequence nature of wildfire outcomes. Spatially-granular, rigorously-estimated measures of risk are shown to be fundamental ingredients to effective analysis of wildfire risk, management, and policy. One of the overarching findings is that cost-effective adaptation in the electric-power sector hinges crucially on the level of adaptation outside the electric-power sector. Overall, cross-sector collaboration across all levels of wildfire risk management is necessary to ensure electric-power sector adaptation investments deliver their intended risk reduction benefits and mitigate the threat of catastrophic wildfire damages.