This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: When policymakers and city officials plan for climate change, they rely on economic studies showing how extreme heat affects violent crime, public health, energy grids and crop yields. However, a new study published in the Journal of the Association of Environmental and Resource Economists reveals a hidden problem in that research: The temperatures used in many climate-impact studies can contain substantial measurement error, leading researchers to misestimate the true effects of temperature and, on average, potentially understate climate impacts by at least 15%.
The study, co-authored by Wake Forest assistant professor of economics Alex Yu and distinguished professor of economics Richard Carson (UC San Diego), demonstrates that standard methods researchers use to construct temperature measures can introduce substantial measurement error that varies systematically across locations and geography. Because physical weather stations are miles apart, researchers use statistical estimates of temperatures in specific cities, neighborhoods or farm fields. The study reveals that these best guesses, or "proxies," carry substantial measurement errors that traditional statistical assumptions fail to capture: Using more than 20 million crime records, the researchers first estimated the relationship between extreme heat and violent crime using city-level crime and temperature data as a benchmark.
They then aggregated both crime and temperature to the county level and estimated the same relationship, finding that the estimated effect of extreme heat became smaller at this coarser geographic scale. "If climate-impact studies misestimate how severely extreme heat drives crime rates, crop loss or health emergencies, policymakers may also misestimate the scale of the responses needed to address those impacts," said Yu. The research doesn't just expose the problem; it provides practical guidance for researchers.
No single temperature proxy works best everywhere: In areas with dense weather-station networks, nearby station measurements can outperform more complex gridded products, while gridded data may perform better where monitoring is sparse. More broadly, the study highlights the value of better spatial monitoring of temperature. Expanding and maintaining dense weather-station networks—and more carefully matching temperature measurements to where people, farms, businesses and ecosystems are actually exposed—can reduce measurement error at its source.
Carson and Chu A. (Alex) Yu, Mismeasured and Misunderstood: Unmasking the Temperature Proxy Problem in Climate Impact Estimates, Journal of the Association of Environmental and Resource Economists (2026). www.journals.uchicago.edu/doi/full/10.1086/742106 BSc Life Sciences & Ecology. Microbiology lab background with pharmaceutical news experience in oil, gas, and renewable industries. Full profile → Master's in physics with research experience.
Long-time science news enthusiast. Plays key role in Science X's editorial success.
Extract — continue reading at the source.
