Measuring Long-Term Recovery from Wildfires: Lessons from the 2018 Camp Fire

Josie Zenger

Our new data analyst Josie Zenger recently completed her masters dissertation, a joint winner of Best Dissertation, in the Msc Social Research Methods at the London School of Economics. We publish a summary here.

Wildfires are becoming one of the most destructive manifestations of the climate crisis. As hotter, drier conditions intensify across the world, communities face increasingly severe fire seasons that threaten lives, infrastructure, and local economies. In the United States, the 2018 Camp Fire in Butte County, California, stands as the most destructive wildfire in state history—destroying over 18,800 structures, displacing thousands of residents, and claiming 85 lives.

Yet despite its scale, tracking the rate of recovery for communities in Butte County in the years after the Camp Fire remains surprisingly difficult. Traditional post-disaster data such as local GDP, building recovery timelines, and infrastructure assessments are often incomplete, inconsistent, or unavailable. These gaps make it harder for policymakers to design effective recovery and resilience strategies.

To address these challenges, my research investigated a data-driven method for assessing wildfire recovery: nighttime light (NTL) satellite data, which can serve as a proxy for local economic activity. By tracking changes in light intensity within the burn region carved by the Camp Fire, I examined how the disaster affected economic activity and what the trajectory of recovery looked like over two years.

Key Challenges in Understanding Post-Wildfire Recovery

1. Sparse and coarse economic data

Natural disaster damage does not conform to regional or district geographic boundaries. But local economic indicators—such as township-level GDP—are rarely available at the resolutions needed for area-specific disaster analysis. This makes it difficult to measure the Camp Fire’s impact across affected communities or to understand what recovery looked like at the neighborhood scale.

2. Incomplete Reporting

While CAL FIRE reports when a fire is “contained,” there is no official public record of when residual burning ends. This uncertainty produces noisy satellite data around the time of the fire and complicates attempts to analytically isolate the start of the recovery period. Furthermore,

despite initial commitments, Butte County has not released a long-term recovery report for the Camp Fire, creating significant gaps in public data that remote sensing can help fill.

What Nighttime Lights Reveal About Recovery After the Camp Fire

My research applied daily NASA Black Marble NTL data and a difference-in-differences event-study design comparing light intensity inside the burn area to a nearby, unaffected 10-kilometer buffer region.

Three major findings emerged:

1. A clear, measurable drop in light intensity after the fire
   
   Before the Camp Fire, the burn area and the buffer region followed stable, parallel trends in nighttime light levels. Immediately after the fire, NTL in the burn area fell—confirming significant disruption to normal residential and commercial activity.

2. Recovery was slow and incomplete, even two years later

   Across 100 weeks of post-fire data, nighttime light levels in the burn area never returned to their pre-fire baseline. This persisted despite rebuilding efforts, community-led resilience initiatives, and the return of some displaced residents.

3. The Camp Fire had a substantial impact on local economic activity
   
   Using a range of elasticity values to translate changes in NTL to changes in GDP, I estimated that economic activity in the burn area remained 5–25% below pre-fire levels two years after the disaster. Even under conservative assumptions, the economic shock was both tangible and prolonged.

Policy Actions Needed to Strengthen Post-Wildfire Recovery

To ensure effective, equitable, and evidence-based recovery from future wildfires, governments and disaster-response institutions should take three key actions:

1. Invest in open-access, high-resolution recovery monitoring

Remote sensing tools like NTL data provide low-cost, spatially targeted insights into recovery patterns and can help identify communities at risk of long-term economic stagnation. Integrating these tools into state-level disaster databases would offer continuous, objective monitoring.

2. Strengthen long-term recovery planning and reporting

Wildfire recovery extends far beyond the date of containment. Agencies like CAL FIRE and county disaster offices should:

· publish detailed post-fire reports,

· record the true end date of burning (not just containment), and

· maintain accessible administrative datasets on rebuilding, permits, and population return.

Even minor procedural changes—such as reporting the final extinguishment date—can dramatically improve the accuracy of quantitative recovery models.

3. Target recovery funds to communities with the slowest rebound

By identifying areas where light intensity remains low long after a fire, policymakers can better direct grants, housing aid, mental health services, and infrastructure investments to the neighborhoods most in need. This is particularly crucial in rural, older, or lower-income communities like Paradise and Magalia in Butte County, where vulnerabilities compound and recovery lags.

Notes:

Josephine Zenger’s masters dissertation, “Measuring Long-Term Recovery from Wildfires: The Case of the 2018 Camp Fire,” was a joint winner for Best Dissertation in the Msc Social Research Methods at the London School of Economics. You can read the full brief here. After completing her degree, Josephine was recruited by Chronos Sustainability Ltd to work as a Sustainability Data Analyst.

Image credit: NASA, Joshua Stevens