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Why Consequence Modeling Belongs at the Center of Asset Hardening Decisions
Simply knowing that an asset could ignite a wildfire is not enough to make a sound hardening decision. Ignition probability tells you where a fire might start. It does not tell you what happens next, or how bad it could get. That distinction matters significantly when hardening budgets are finite and prioritization decisions have real consequences for communities and infrastructure.
As Buckley outlined in a Utility Dive article, ignition is not risk. Risk is the possibility of damage, loss, and harm occurring from an ignition. Two assets with similar ignition probabilities can produce very different outcomes depending on what lies in the potential fire’s path. Understanding that difference is what makes consequence modeling a planning tool, not just an analytical one.
Why Not All Ignitions Are Created Equal
All utility assets carry some probability of failure and ignition. That probability varies based on asset condition, age, design, conductor type, surrounding vegetation, slope, and local weather patterns. But even assets with identical ignition probabilities can pose very different levels of risk depending on where they sit and what a fire starting there would affect.
Consequence modeling addresses this by integrating fire spread prediction with forecasted weather to determine where a fire would travel from a specific asset ignition point and what it would impact along the way. The outputs, structures threatened, population exposed, acreage burned, give planners a concrete basis for comparing assets that ignition probability alone cannot provide.
Not all ignitions are created equal. Not all fires are created equal. Consequence modeling is what makes those differences visible and actionable.
Using Consequence Data to Prioritize Hardening
Once consequence is understood at the asset level, it can be combined with ignition probability to calculate expected risk, the foundational metric for defensible hardening prioritization. But consequence modeling also supports a longer-term planning view that goes beyond a single season or event.
By leveraging weather and landscape data over ten or more years, utilities can identify which assets have been consistently associated with the highest potential consequences from ignition across a range of historical conditions. That longitudinal view separates assets that appear risky in one bad season from those that carry genuine long-term exposure. It is the latter that warrant priority hardening investment.
With consequence data grounding the analysis, any hardening decision can be assigned a quantifiable risk reduction. When that reduction is divided by the cost of the mitigation, the result is a risk spend efficiency (RSE) figure. RSE analysis allows utilities to compare hardening options across their asset portfolio and direct limited resources toward the investments that will reduce overall system risk most effectively per dollar spent.
Regulatory Context and Planning Implications
California’s Public Utility Commission regulations have brought consequence modeling and RSE analysis into formal wildfire mitigation planning requirements for utilities in that state. But the planning value of this approach extends well beyond California. Wildfire-related regulation is expanding in scope and complexity across other states, and the underlying logic, that utilities need to demonstrate not just what they did but why they prioritized it, applies regardless of the specific regulatory framework in place.
On the operational side, consequence data also informs Public Safety Power Shutoff (PSPS) decision-making. Knowing which assets pose the highest expected consequence under forecast conditions gives operators a more defensible basis for de-energization decisions than ignition probability alone.
A More Complete Basis for Investment Decisions
Consequence modeling does not replace engineering judgment or operational expertise. It gives planners and risk managers the information needed to make hardening decisions that are grounded in expected impact rather than visible hazard alone. For utilities building or refining their wildfire mitigation programs, integrating consequence modeling into the prioritization process is what moves asset hardening from reactive to systematic.
This article draws on analysis originally published in Utility Dive by David Buckley, former COO of Technosylva.
