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Author: katamartin

articles/climate-risk-explainer/index.md

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 How likely is it that wildfire will destroy a home? That a storm surge will flood a community? Or that a heat wave will overwhelm an emergency room? When faced with questions like these, high-quality climate risk assessments help us make better decisions. But who has access to risk data, and how do we know it’s high quality?
 
-The private sector understands the need for climate risk data. Recently, the Boston Consulting Group estimated private equity investment opportunities in the climate and resilience adaptation market will grow from $0.5 trillion to $1.3 trillion per year by 2030, and identified climate intelligence solutions as the subsector expected to grow the most quickly.<Cite id='oehling.2025' /> The public is also starting to draw connections between the estimationprediction of risk and its consequences for everyday people, most clearly when it comes to insurance availability and cost. In states that elect insurance commissioners, and where climate change is most obvious, some of these elections have become proxies for [public frustration over rising premiums](https://grist.org/elections/climate-impacts-put-insurance-commissioner-races-in-the-spotlight).
+The private sector understands the need for climate risk data. Recently, the Boston Consulting Group estimated private equity investment opportunities in the climate and resilience adaptation market will grow from $0.5 trillion to $1.3 trillion per year by 2030, and identified climate intelligence solutions as the subsector expected to grow the most quickly.<Cite id='oehling.2025' /> The public is also starting to draw connections between the estimation of risk and its consequences for everyday people, most clearly when it comes to insurance availability and cost. In states that elect insurance commissioners, and where climate change is most obvious, some of these elections have become proxies for [public frustration over rising premiums](https://grist.org/elections/climate-impacts-put-insurance-commissioner-races-in-the-spotlight).
 
 Yet despite the consequence of climate risk estimates, and the number of analytics firms producing them, almost none of this data is available to the public.<Cite id='condon.2023' /> You can type an address into Redfin and get a handful of climate risk scores, but you can’t see how those scores were calculated, or download data for a county. Your tax dollars may have funded the creation of the base datasets that an analytics company uses in its wildfire risk model, but for your local government to use the model to protect your neighborhood, they likely have to sign a restrictive contract with a company, and pay a significant fee. As legal scholar Madison Condon summarizes, “The climate risk information available to individual citizens and municipalities &mldr; is limited and expensive to access.”<Cite id='dawkins.2023' />
 
 Access isn’t the only problem. Closed-door risk assessments are also difficult to evaluate or trust. A consistent finding in the few published comparisons of risk providers, including our own [work](https://carbonplan.org/research/climate-risk-comparison), is that different providers frequently assign significantly different levels of risk to the same location.<Cite ids={['hain.2021', 'schubert.2024']} /> The Global Association of Risk Providers (GARP) pointed to the problem clearly when they found that, across 13 companies, “leading vendors can deliver strikingly different results.”<Cite id='paisley.2025' /> Without knowing a risk product’s underlying methods, and how it compares to others, it’s hard to know whether it is appropriate for any given use. And without open model intercomparison, the science driving risk estimation is slow to advance.
 
-Open Climate Risk is the first public option for building-level climate risk estimates in the United States, starting with wildfire risk. It is intended to be a usable dataset, a model for intercomparison, and a demonstration of the kind of transparency that could enable rapid, important improvements in climate risk estimation across providers. To create our open source wildfire model, we began with publicly available base datasets, then developed and applied a scientific model for projectingpredicting risk of loss from wildfire. Anyone can inspect the dataset and methods that underpin our estimates. We’ve also created a fully open map tool that allows users to explore, download, and analyze the dataset. We’ve released all of this under open software licenses, which apply in perpetuity: this means the data and code, once accessed, can be used freely forever.
+Open Climate Risk is the first fully open option for building-level climate risk estimates in the United States, starting with wildfire risk. It is intended to be a usable dataset, a model for intercomparison, and a demonstration of the kind of transparency that could enable rapid, important improvements in climate risk estimation across providers. To create our open source wildfire model, we began with publicly available base datasets, then developed and applied a scientific model for projecting risk of loss from wildfire. Anyone can inspect the dataset and methods that underpin our estimates. We’ve also created a fully open map tool that allows users to explore, download, and analyze the dataset. We’ve released all of this under open software licenses, which apply in perpetuity: this means the data and code, once accessed, can be used freely forever.
 
 In this explainer, we describe what Open Climate Risk does, how we built it, and how it compares to other efforts. We share how to access all of the underlying data and modeling, and explain why this transparency matters. We also discuss our next steps for the platform, and gesture towards a broader vision for public access to risk data. But first, we need to introduce climate risk and risk modeling.
 
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 This first analysis evaluated only historically burned areas, but we provide data for the entire country. So to evaluate our wildfire risk estimates more holistically, we compared them to two other public fire risk datasets in order to understand where they align and diverge: (1) the Scott et al. (2024) dataset that our work builds on; and (2) the CAL FIRE Fire Hazard Severity Zones dataset.<Cite id='calfire.2024' hide /> Scott et al. (2024) follows methods very similar to ours, with the biggest exception being how risk is spread to developed lands. CAL FIRE’s methods differ substantially from ours, but do include a representation of wind-driven spread. This comparison assesses risk values at the level of individual buildings — which intentionally focuses the analysis on developed lands, where our methods differ most significantly from Scott et al. (2024).
 
-We find generally good agreement with the Scott et al. (2024) dataset, with a few important differences. Across the entire country, the average census-tract level correlation at individual buildings was 0.79, with an absolute bias of 0.0002% RPS, indicating general agreement. Compared to Scott et al. (2024), our approach projectspredicts higher risk in a few notable regions — the east slopes of the Cascades in Washington and Oregon, the mountain foothills in southern California, and the Texas panhandle. These analyses have practical significance for users: areas with lower correlation are places where the choice of a risk dataset has a greater consequence.
+We find generally good agreement with the Scott et al. (2024) dataset, with a few important differences. Across the entire country, the average census-tract level correlation at individual buildings was 0.79, with an absolute bias of 0.0002% RPS, indicating general agreement. Compared to Scott et al. (2024), our approach projects higher risk in a few notable regions — the east slopes of the Cascades in Washington and Oregon, the mountain foothills in southern California, and the Texas panhandle. These analyses have practical significance for users: areas with lower correlation are places where the choice of a risk dataset has a greater consequence.
 
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 If you use, or want to use, climate risk assessments in your community, we hope that Open Climate Risk will be helpful to you in multiple ways. First, you can readily access data for the buildings in your county, census tract, or census block. These estimates can also provide you with a point of comparison, and basis for asking questions about evaluation, if you purchase a dataset from a private company. Second, you can join detailed datasets about your community to Open Climate Risk’s dataset, to support tailored analyses. We are excited to hear about efforts like these, how our platform could improve, and how we might partner with local groups in developing customized datasets to answer specific questions.
 
-If you are a researcher, policymaker, or advocate, we hope Open Climate Risk can be both a direct source of data and a demonstration of why open data matters. By building a radically open climate risk platform, we’ve shone light on the many small, yet consequential, decisions that are made along the way in developing building-level risk estimates. In the U.S., the quality of climate risk models is an increasingly grave and urgent matter. Whether at a national, state, or local level, risk estimates guide decisions on how to adapt to climate change, and where to allocate resources. These decisions are limited by the quality of the predictive models available to the public. As we launch Open Climate Risk, we hope that its free, transparent data will help communities and individuals better understand, and plan for, the risks they face.
+If you are a researcher, policymaker, or advocate, we hope Open Climate Risk can be both a direct source of data and a demonstration of why open data matters. By building a radically open climate risk platform, we’ve shone light on the many small, yet consequential, decisions that are made along the way in developing building-level risk estimates. In the U.S., the quality of climate risk models is an increasingly grave and urgent matter. Whether at a national, state, or local level, risk estimates guide decisions on how to adapt to climate change, and where to allocate resources. These decisions are limited by the quality of the models available to the public. As we launch Open Climate Risk, we hope that its free, transparent data will help communities and individuals better understand, and plan for, the risks they face.
 
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