Experience-driven perceptions misalign with assessed heat risk in the United States

Domain: neuroscience
Published: 2025-12-24
Editorial outcome: minor_revision

Abstract
Extreme heat poses a risk to public health, yet illness and death from heat exposure are largely preventable through behavioral adaptation. However, individuals’ perceptions of local heat risks often diverge from expert assessments, limiting protective behaviors. Here we introduce the Risk Analysis–Perception Framework, which quantifies the alignment between assessed risk, based on a standardized public health metric from the Centers for Disease Control and Prevention Heat and Health Index, and perceived risk, modeled using a multi-year national survey (2018–2022). Applying the framework across US counties, we find substantial misalignments between assessed and perceived risk, particularly in the Pacific Northwest and in Appalachia. These gaps highlight how personal experience, contextual cues, and mental shortcuts shape risk perceptions that often diverge from expert assessments of vulnerability. Through systematic risk alignment mapping, the Risk Analysis–Perception Framework provides an empirical approach to target climate risk communication, adaptation strategies, and policy interventions.

Introduction
Extreme heat is one of the most direct and widespread threats to human health from climate change. Recent heat waves, intensified by global warming, have killed thousands of people, and the risks associated with heat exposure are increasing. Heat-related deaths are preventable with appropriate preparation, but many members of the public do not adequately understand the risks associated with extreme heat and many are unprepared, underscoring the characterization of heat waves as “silent killers”. Moreover, while the acute effects of heat waves are being intensively studied, chronic impacts have received much less attention. This disconnect between the scientific understanding of heat risks and public awareness creates a fundamental challenge for climate adaptation: how can communities prepare for threats they don’t fully perceive, and how can risk communication bridge the gap between expert assessments and lived experience?

Addressing this disconnect between assessed hazards and public understanding requires frameworks that carefully integrate how climate risks are both scientifically assessed and subjectively perceived. The Risk Analysis–Perception (RAP) framework provides a systematic approach for explicitly quantifying gaps between expert risk assessments and public perceptions across diverse populations. Drawing on dual-process theories of cognition, the RAP framework recognizes that people’s responses to climate hazards often rely more heavily on experiential, affect-driven processes rather than the deliberative analysis that characterizes scientific risk assessment. This mismatch between experiential and analytic risk evaluations may create systematic patterns of public misalignment with assessed risks that vary across communities and geographic regions.

Risk perceptions are crucial for motivating responses to warnings and promoting adaptation behaviors for diverse threats. The catastrophic damages from Hurricanes Katrina and Sandy, for example, motivated major investments of over US$10 bn in flood risk-reduction measures. Similarly, as Americans’ experiences with extreme heat increase, improvements in public awareness, outreach, and policymaking can help stimulate protective measures. Unlike hurricanes, extreme heat can occur in virtually all regions of the U.S., necessitating comprehensive, nationwide geographic public health metrics on heat vulnerability and perceived risk to support preparedness and culturally informed risk communication efforts.

Worry represents the affective dimension of risk perceptions, reflecting a person’s engagement with potential future hazards. Often rooted in underlying beliefs, values, and lived experiences, high levels of worry about threats can drive adaptive behaviors and policy support, fostering mitigation efforts and promoting community resilience. Conversely, low levels of worry may hinder proactive measures, leaving communities more vulnerable to extreme heat impacts and reducing the effectiveness of risk communication efforts. However, the relationship between worry and action is not always direct or causal. As noted in prior work on the risk perception paradox, individuals may perceive high risk but remain unable or unwilling to act due to structural, social, or personal barriers—such as limited financial resources, physical ability, or trust in institutions. These dynamics are particularly relevant for vulnerable populations, such as older or low-income adults, who may experience high perceived risk but lack the capacity to respond effectively. Therefore, understanding assessment-perceptionalignment is necessary, but not sufficient for effective preparedness; it must inform targeted interventions that address both psychological and structural barriers to action.

A heat risk perception ranking of census tracts developed for the summer of 2015 found that residents in northern areas of the U.S. had lower risk perceptions than those in the south; it also identified the western Mountain region as having high exposure to extreme heat yet relatively low heat risk perceptions. The index included a measure of worry, but the study only produced relative rankings for one summer. Similarly, Allan et al. mapped variations in risk perceptions of eight hazards, including extreme heat, across 115 National Weather Service County Warning Areas (CWA). Yet, CWAs are not widely used by risk managers and communicators as practical units of analysis. Furthermore, neither study systematically tested predictors of community-level risk perceptions nor examined alignment between perceived and assessed risks–key components of the RAP framework that we advance here.

Understanding the spatial variations in worry about extreme heat, and how these perceptions align with assessed hazards, is crucial for developing psychologically informed communication strategies that enhance community preparedness, resilience, and adaptive capacity. To support these efforts, we advance methods in multilevel regression with poststratification (MRP) to model the percentage of people worried about heat in each state and county across the contiguous U.S. (CONUS), producing maps showing the geographic variation in perceived heat risk. We compare these estimates with the national Heat and Health Index (HHI), a comprehensive measure of heat-related health outcomes and social vulnerability developed by the Centers for Disease Control and Prevention (CDC). We refer to the HHI as assessed heat risk because heat risk ultimately manifests through its impact on health. We calculate the difference between assessed and perceived heat risks to identify alignment gaps in each state and county, providing a concrete application of the RAP framework. Finally, through regression analyses, we examine the correlates of alignment gaps to aid interpretation of misalignments between assessed and perceived heat risks, supporting the development of targeted, culturally responsive risk communication strategies.

Prior research has shown that sociodemographic data (gender, age, race, income, disability status) and contextual environmental factors (temperature, land cover) are predictive of heat-related risk perceptions. Drawing on this literature and the RAP framework’s emphasis on integrating assessed and perceived risk factors, we employ recent advancements in MRP that leverage machine learning model ensembling to develop estimates of the percentage of residents in each state and county who are worried about extreme heat (see “Methods”). Our model employs five years of time-aggregated cross-sectional surveys (N=11,113) where each respondent is geolocated and matched with local sociodemographic and physical data. We build five alternative models to predict perceived heat risk, ensemble estimates from the three best performing models, and map the results to identify spatial variations in perceived heat risk and their alignment with expert hazard assessments across the CONUS.

Results
Geographic patterns of perceived and assessed risk
Our model estimates that 35.3% of Americans in the CONUS are worried about extreme heat, which is 2.8 percentage points lower than the 38.1% raw survey data before modeling adjustments. The state-level worry estimates varied from 20% in Wisconsin to 57% in Texas. Only five states (AZ, TX, OK, CA, NV) have majority populations who are worried about extreme heat. At the county level, values range from 17% in Iron County, Wisconsin to 65% in Hidalgo County, Texas. Only 4% (115 of 3109) of CONUS counties have majorities who are at least moderately worried about extreme heat affecting their local area. Residents of southern areas are more worried about extreme heat than those to the north. In general, counties in interior northern areas, especially in northern Michigan and Wisconsin, have the lowest levels of worry. In contrast, inland counties along the southwestern U.S. border and in the central valley of California have some of the highest levels of worry about extreme heat.

States with the highest assessed heat risk from the CDC’s Heat and Health Index (HHI) are concentrated in the west and south. Several densely-populated counties in the east, including in Appalachia, in the southern Atlantic coastal plains, and in Florida also face high health risks from heat. Differencing the assessed and perceived risk maps reveals distinct spatial patterns in alignments and misalignments across the CONUS. Most areas nationwide show higher assessed than perceived risk, with some counties in the Pacific Northwest and in Michigan, in Appalachia, and in Maine showing particularly strong misalignments. Areas where assessed risk is lower than perceived risk are generally located in the upper Midwest and in the mid-Atlantic region. These geographic patterns provide the foundation for applying the RAP framework’s approach to understanding exposure-perception gaps.

Alignment and misalignment between assessed and perceived heat risk
The areas with the highest misalignments between assessed and perceived heat risk are highlighted when areas with relatively small differences (alignment gaps within ±40) are removed from the maps. Six states (Washington, Oregon, Nevada, Arizona, Oklahoma, and Kentucky) have substantially higher assessed than perceived heat risk. In contrast, no states have such large misalignments in the opposite direction (i.e., with substantially higher perceived than assessed risk). County-level maps reveal that mismatches between assessed and perceived heat risks are nationwide. Many rural counties across the West (especially in the Pacific Northwest), in Michigan and Georgia, and in parts of Appalachia show low perceived heat risk despite high assessed heat risk.

Regional correlations between assessed and perceived heat risk vary substantially. The West and South show weak but positive correlations (R²=0.21 and 0.19, respectively), indicating modest alignment. The Midwest shows weaker alignment (R²=0.09) while the Northeast shows no correlation (R²=0).

County quadrant analysis reveals distinct geographic patterns. Most counties fall into Quadrant I (high assessed/low perceived risk; 1442 counties) or Quadrant IV (low assessed/low perceived risk; 1552 counties), reflecting generally low perceived risk nationwide. Quadrant II (high assessed/high perceived risk) contains 113 counties concentrated in the South and Midwest, particularly in Texas and California. Only two counties fall in Quadrant III (high perceived/low assessed risk): Bossier Parish, Louisiana and Tom Green County, Texas.

Urban-rural differences are pronounced: urban counties (>1 million residents) are over 10 times more likely than rural counties (<10,000 residents) to have majority heat worry. Geographically, Quadrant I counties are widespread, Quadrant II counties cluster along the southwestern border, and Quadrant IV counties concentrate in the upper Midwest and Northeast. These patterns provide the foundation for targeted risk communication and adaptation planning through the RAP framework.

Demographic and socioeconomic influences on risk perception
Regression analysis reveals several significant predictors of the alignment gap between assessed and perceived heat risk, with an adjusted R2 value of 0.21. Counties with higher proportions of residents over 65 (e.g., in the rural upper Midwest) tend to underestimate heat risk relative to assessments (coefficient 8.523, p<0.01), as do counties with higher proportions of Hispanic residents (coefficient 6.076, p<0.01). However, the interaction between Hispanic population and poverty rates (e.g., counties in southern Texas) shows an inverse relationship (−7.045, p<0.05). Counties with higher proportions of Black residents tend to overestimate heat risk relative to assessments (−5.553, p<0.01). Higher educational attainment (proportion with a bachelor’s degree) is also associated with overestimating heat risk (−11.542, p<0.01), in the coastal metropolitan regions of the Northeast and Mid-Atlantic, for example. Conversely, higher poverty rates (e.g., in Appalachia) are associated with underestimating heat risk (10.260, p<0.01). Additional three-way interaction effects reveal that counties with the highest proportions of older and lower-income Americans, whether predominantly White (−8.634, p<0.01) or non-White (−5.371, p<0.05 for non-White), tend to overestimate heat risk relative to assessments. This pattern likely includes many southern counties where heat concern is widespread (e.g., in New Mexico and Texas).

Model performance and robustness
The heat risk perception estimates from the five models all produced Area Under the Curve (AUC) values of approximately 0.66 when evaluated on held-out splits of the survey data. These AUC values reflect the inherent challenges of predicting complex social attitudes at small geographic scales, where individual and contextual factors create substantial heterogeneity. AUC values here serve primarily as a diagnostic check for model learning, with the close match between cross-validated and final test/holdout AUCs indicating that all models captured meaningful signal from the survey data rather than overfitting. For small area estimation applications, predictive performance is best evaluated by comparing modeled estimates to the observed survey data. We measured performance using the Mean Absolute Error (MAE), calculated as the difference between the weighted mean percentage of worried residents in the five most surveyed counties (n≥112) and the modeled estimates. We compared five individual models and two ensemble approaches: one combining all five models (MAE=2.01) and another using only the machine learning models (MAE=1.57). The machine learning ensemble performed best and was selected for final analysis. This MAE of 1.57 indicates improved performance compared to prior MRP studies of climate and hazard-related opinions. The top-performing individual models consistently identified temperature, latitude, and CDC social vulnerability measure of racial and ethnic minority status as the predictors with the greatest influence on heat risk perception.

Methods
Data
We used three types of data to model perceived heat risk—survey data, poststratification data, and aggregate (geographic-level) predictors.

Survey data
The survey is based on 10 nationally-representative “waves” collected each spring and fall from 2018–2022 via the IPSOS KnowledgePanel (Table 2). Despite the “KnowledgePanel” name, we do not employ panel data, but rather aggregate 10 cross-sectional surveys using this representative online panel of U.S. adults ages 18 and older who are recruited and paid to complete self-administered questionnaires in a web-based environment. Through the survey, we collected demographic information (sex, age, race), location (latitude, longitude), and risk perceptions of various climate and weather-related hazards and their impacts. To protect respondent anonymity, individual locations were randomly displaced between 100 and 2000 feet depending on the local population density, ensuring privacy while preserving spatial accuracy for analysis. Respondent locations were used to match individuals with county, state, and region information as well as geographic-level predictors.

To measure extreme heat risk perceptions, survey respondents were asked, “How worried are you that extreme heat might harm your local area?” Prior research demonstrates that worry as a measure of perceived heat risk is highly correlated with perceived health harms from heat waves to one’s community, family, and self. Responses to the worry question were dichotomized for modeling, with respondents who answered “Very worried” or “Moderately worried” coded as 1, indicating perceived heat risk. Respondents who answered “A little worried,” “Not at all worried,” or “Refused” were coded as 0, indicating little or no perceived heat risk. Excluding “A little worried” from the “positive” response category was a deliberate choice. We argue that this threshold represents a minimal level of perceived risk and is unlikely to drive the adaptive behaviors needed to effectively address the escalating public health risks of extreme heat. By focusing on stronger expressions of worry, our approach ensures that the resulting estimates are both meaningful and actionable for understanding public concern and guiding policy responses. In addition, by dichotomizing the response variable, we can estimate population percentages for every state and county in the CONUS (rather than a relative risk index) thereby providing a direct measure of where majorities or minorities of people are worried about heat—an actionable metric that can easily be used to support decision making.

Poststratification data
We used the 2022 5-year American Community Survey (ACS) data from the U.S. Census to construct a poststratification dataframe for modeling across all survey years from 2018 to 2022. Custom race by education by sex population crosstabs were prepared for all U.S. states and all CONUS counties and county-equivalents. Demographic categories in the poststratification frame align exactly with those collected in the survey. The ACS does not directly provide race by education by sex cross-tabulations because of non-mutually exclusive relationships between race and ethnicity membership. We were able to use the ACS data to construct count crosstabs for ‘Hispanic or Latino’, ‘White, non-Hispanic or Latino’, ‘African American’, ‘Other, non-Hispanic or Latino’ racial categories. This approach generates some error since Americans who identify as ‘African American, Hispanic or Latino’ will be double counted in both the ‘African American’ and the ‘Hispanic or Latino’ categories; in practice, however, this error is minimal since this group is extremely small. Perceived heat risk is modeled at both the state and county levels using separate poststratification frames for each level. The state-level frame includes state and Census Region 9 information, while the county-level frame additionally includes county information. Thus, each row in the poststratification frame contains the population counts of a specific demographic subgroup within a defined geographic area.

Model predictors
Predictors used in modeling are described in Supplementary Table 1. Individual-level predictors included race, age, sex, and state of residence, consistent with prior MRP studies. Year and U.S. census Region 9 were also included at the individual level to account for increasing perceived heat risk over time and spatial coherence in extreme heat events, respectively.

Geographic-level (aggregate) predictors, including climatological, sociodemographic, and partisanship indicators were selected based on the literature and were further refined through exploratory data analysis and variable selection algorithms. Heat-specific measures were obtained from the PRISM Climate Group and the Federal Emergency Management Agency (FEMA). Socioeconomic, housing, and additional demographic data were obtained from the CDC and the ACS. Historical state- and county-level voting behavior data are from the MIT Election Data and Science Lab given the partisan nature of climate change beliefs in the U.S. Additional contextual predictors, including the mean area of a state or county covered by impervious surfaces and the latitude centroid for each state or county, were obtained using ArcGIS Pro (ESRI, v3.4). The former provides information about urbanicity and urban heat island effects and the latter capture temperature gradients.

Modeling perceived heat risk
We use a multilevel regression with poststratification (MRP) framework to estimate the percentage of residents worried about extreme heat in each county. MRP proceeds in two phases: hierarchical modeling to generate predictions for demographic-geographic subgroups, followed by poststratification using Census population weights. MRP is considered the gold standard for small area estimation of public opinions. To enhance traditional MRP approaches, we tested five models implementing systematic predictor selection and alternative algorithms, following Broniecki et al.

Three models employed machine learning algorithms with hyperparameter optimization: extreme gradient boosting (XGBoost), Random Forest (RF), and Elastic Net. Two hierarchical (multilevel) models incorporated systematic feature selection using Principal Component Analysis (PCA) or best subsets selection. PCA reduced dimensionality by converting correlated predictors into uncorrelated components, while best subsets regression evaluated all predictor combinations within a set.

Implementation
We refined predictors by removing variables with >95% identical observations and pairwise correlations >0.8, reducing multicollinearity and enhancing model performance. All models included demographic variables (state, region, race, age, sex) as random effects and year as a fixed effect. Machine learning models utilized all 16 predictors, while hierarchical models employed variable selection to identify optimal subsets. The baseline model was hierarchical and only included the demographic and year variables.

Data were split 70–30 into training and test sets, stratified by state. Numerical predictors were Yeo-Johnson transformed to reduce skewness and then normalized. Hyperparameter tuning employed Latin hypercube sampling (500–1000 combinations) with 10-fold cross-validation using an area under the curve (AUC) metric. For hierarchical models, we tested predictor combinations using identical cross-validation procedures. The tuned models were then trained on the full training set and evaluated on the test set. Similar cross-validated and test AUCs indicate good model fit without over- or underfitting, as observed in our results.

Model selection and validation
Final models were trained on all survey data and applied to poststratification frames, with results weighted by Census demographics and averaged across 2018–2022 to minimize seasonal effects. Model validation compared predictions against survey data from the five most populous states and counties using mean absolute error (MAE).

The ensemble of machine learning models (XGBoost, Random Forest, Elastic Net) achieved the lowest error compared with a baseline model that included only individual-level and geographic predictors. Predictions were ensembled as weighted averages of individual model predictions, with weights determined by MAE rank such that lower-MAE (higher-ranked) models had greater influence. Weights were then normalized to sum to 1 and applied to predictions before calculating the ensemble MAEs.

Alignment gap analysis
Assessed heat risk was defined using the Heat-Health Index (HHI), which integrates data from four modules: (1) Historical Heat and Health Burden (past heat wave occurrences and heat-related mortality); (2) Sensitivity (pre-existing health conditions increasing heat susceptibility); (3) Sociodemographic Characteristics (poverty, unemployment, social isolation affecting adaptive capacity); and (4) Natural and Built Environment (vegetation, land cover, urban infrastructure, and air pollution). The HHI was generated at the ZIP code level and aggregated to county and state levels using HUD crosswalk data, weighted by residential address proportions.

Calculating misalignments
Misalignments between assessed and perceived heat risk were calculated as the difference between rescaled HHI and the percentage of residents worried about extreme heat in each geographic area:

Alignment Gap_i = HHI_i - Worry_i

Where: HHI_i is the Heat-Health Index rescaled to 0-100 for county i, and Worry_i is the estimated percentage of residents worried about extreme heat from ensemble model estimates (2018–2022). The HHI was rescaled to correspond with the possible percentage range of Worry_i.

County-level data were plotted to examine distribution across four quadrants (High/Low assessed risk x High/Low perceived risk). Regression analysis using Census data explored how sociodemographic factors influence alignment gaps while controlling for interactions between variables.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Misalignments between assessed and perceived 1 risks exacerbate vulnerability to extreme heat\nThis study looks to advance methods in Multilevel regression with Poststratification (MrP) to model the percentage of people\nworried about heat within the US, looking at states and counties. The goal is to provide maps about heat “worry” across the\nCONUS. Such work can better understand the role of risk perception in driving vulnerability to heat risk, and to enable its\nsystematic incorporation into new vulnerability indices and communication strategies.\nThe authors compare their estimates of heat risk perception (“perceived risk”)—which is based on 11,000+ survey responses\nfrom 2019-2022--with the National Heat and Health Index from the CDC (assessed risk). The difference between the two risk\ntypes is termed the “aligned gap”.\nThe methods to look at perceived risk use advancements for a unique model (AutoMrP) that leverages machine learning and\nmodel ensembles.\nThe revision of the paper is much improved from the first version; much more understandable with methods more easily\nfollowed and results that make more sense and are interpreted well.\nI only have a few minor comments for clarity.\nIntroduction\nOverall very good and outlines the literature and justification well. A lot of methods are presented in the introduction though,\nwhich shouldn’t be.\nLine 231 – should this be low perceived risk versus high assessed risk? (in the following sentences it gives high perceived\nrisk versus low assessed risk within the statement)\nLine 357 – attribute heat-related deaths to what? To climate change? Or just be able to say if they are heat caused or heat\nassociated?\nLine 375 – Those 65 and older, while considered “older adults,” does not mean they are not able to do move. This sentence\nis a bit of a hasty generalization of those >65 age. I think that the statement can be geared more toward people who do lack\nmobility, whether older or not, and that people who are older are more likely to lack mobility or not be able to get to places.\nBut there are important physiological concerns for older people (>65) too that could be mentioned that would be a factor of\ntheir assessed risk. E.g., lower ability to sweat, taking more medications. (see: Ebi, K. L., Capon, A., Berry, P., Broderick, C.,\nde Dear, R., Havenith, G., ... & Jay, O. (2021). Hot weather and heat extremes: health risks. The lancet, 398(10301), 698-\n708. Or see Vanos, J., Guzman-Echavarria, G., Baldwin, J. W., Bongers, C., Ebi, K. L., & Jay, O. (2023). A physiological\napproach for assessing human survivability and liveability to heat in a changing climate. Nature communications, 14(1),\n7653.)\nLine 378 – I think a more appropriate term here is systematic racism, including structural racism and institutional racism.\nLine 379 – “Such communities may need additional support and resources” – for what? Complete the thought/sentence for\nclarity.", "reviewer_2": "This is an interesting study that examines an important topic, namely the gap between expert risk assessment and public\nrisk perception of climate impacts, as well as the the demographic and geographic predictors of these. While the paper\nfocusses on a specific country (the contiguous United State) I believe that the work has broader relevance for other contexts\nand climate related risks. Overall, I believe that the authors have addressed the previous reviewers’ comments raised in the\nlast round of review. However, I do have some queries that I feel could be addressed to make the findings of the paper as\nclear as possible, as well as some points regarding the implied link between risk perception and risk preparedness that\nshould be considered.\n[NOTE: My comments are from the perspective of a social scientist with expertise in risk perception rather than a specialist in\nclimate science or impact indices.]\nCOMMENT 1\nThis is a general comment, but I felt that it should be acknowledged that individual level risk perception (which we might\nexpect to see reflected in measures of worry) and individual level risk preparedness are not always perfectly associated with\none another (e.g. as Wachinger et al. 2013 discuss in their paper on the Risk Perception Paradox). While, this paper deals\nwith risk perception rather than reported willingness to undertake some form of protective response, I do feel that it is good to\nacknowledge that there may be barriers to increased risk perception facilitating risk preparedness (e.g. older, poorer adults\nmight feel at risk but lacking the resources and/or physical ability to undertake protective responses), as there seems to be\nan implicit assumption that increasing perceived risk will lead to greater risk reduction behaviour.\nCOMMENT 2\nIn the paper the alignment gap is characterized as being HHI (a normalized measure of the Heat and Health Index) –\nWorry (normalised), with the alignment gap being displayed in 3c and 3f. It was not clear to me from the methodology and\nsupplementary material how HHI and worry were normalised to ensure comparability. I feel that it would be good to signpost\nthis more clearly as a reader may be confused as to how deducting estimated percentage of those worried from the HHI\ngives a ‘true’ measure of (mis)alignment (i.e. as % worried has a ‘true’ 0-1 point of nobody and everybody, HHI is a\ncomposite indicator)\nCOMMENT 3\nIn characterising participants as being worried vs not worried is it specified that those who refused to answer the question\nwere classified as not worried. Was any comparison done of what the % worried vs not worried looked like when the\nrefusing participants were removed?\nCOMMENT 4\nA minor point, but I feel that Figure 3 would benefit from the maps on the bottom row having the same key as the one directly\nabove. I realise that the difference was due to some values not being present in one version of the map but having a key as\nto what the full range of values could be for each map can assist comparability and interpretation.\nCOMMENT 5\nOn Lines 238-242 the three-way age*poverty*ethnicity interaction reported in the regression table is interpreted as “…high\nperceived versus assessed heat risk is particularly pronounced in socioeconomically disadvantaged white-majority areas,\nlikely in southern counties where high perceived risk of extreme heat is widespread”. As an international reader it was not\nclear to me from this how age fits into this explanation. Is it because southern counties tend to have older residents?\nCOMMENT 6\nIn the discussion of the Low-Low quadrant, the point: “Indeed, many areas with some of the largest underestimation of heat\nrisks also have some of the highest national per-capita heat-related mortality rates” seems to be a little confusing, as this has\nbeen identified as a lower assessed risk quadrant. I believe that the authors may be pointing out that as people in these\nareas do not perceive themselves to be at a high risk from heat health impacts, they may underestimate future risks under a\nwarmer climate and thus be less well prepared. However, this could perhaps be clearer.\nREFERENCE\nWachinger, G., Renn, O., Begg, C., & Kuhlicke, C. (2013). The risk perception paradox—implications for governance and\ncommunication of natural hazards. Risk analysis, 33(6), 1049-1065.", "reviewer_3": "Summary\nThis manuscript presents a nationwide analysis of the misalignment between assessed and perceived risks of extreme heat\nin the United States. The authors use Multilevel Regression with Poststratification (MrP), enhanced by ensemble learning\ntechniques, to estimate the proportion of residents worried about extreme heat at the county level using national survey data\n(N=11,113). These estimates are then compared against the CDC’s Heat and Health Index (HHI) to compute an “alignment\ngap” metric, representing the difference between assessed and perceived risk. The results are presented as spatial maps\nand analyzed through regression to understand sociodemographic correlates of misalignment.\nComment\n1. Limited Novelty and Conceptual Contribution\nThe primary conclusion—that public perception of extreme heat risk does not always match objective health vulnerability—is\nnot new. Numerous studies have previously explored the disconnect between risk perception and environmental hazard\nexposure, particularly in the context of climate change and public health (e.g., Van Valkengoed & Steg, 2019; Liu et al.,\n2013). Additionally, geographically resolved perception data (e.g., Howe et al., 2019) have already mapped spatial\nvariability in climate concern across the U.S. As a result, the manuscript offers little theoretical, empirical, or methodological\ninnovation beyond confirming well-known patterns. Its central message—“improving public awareness can reduce\nvulnerability”—is intuitive and broadly applicable across many environmental risks (flooding, wildfire, pandemics), thus\nweakening its specificity and contribution.\n- Van Valkengoed & Steg (2019): https://doi.org/10.1038/s41558-018-0371-y\n- Liu et al. (2013): https://doi.org/10.1186/1471-2458-13-913\n- Howe et al. (2019): https://doi.org/10.1073/pnas.1813145116\n2. Conceptual Ambiguity and Misuse of “Alignment Gap”\nThe construction of the alignment gap metric—defined as the difference between normalized perceived risk (survey-based\nworry) and normalized assessed risk (CDC HHI)—raises several conceptual concerns. These two variables differ\nfundamentally in their source, scale, and meaning. One is an affective, self-reported perception; the other is a composite\npublic health indicator.No theoretical justification is provided for subtracting these indicators or interpreting the result as a\nmeaningful measure of misalignment. This operation risks producing misleading patterns, as perceived and assessed risks\nare shaped by distinct cognitive and sociocultural processes (Slovic, 2000; Bostrom et al., 1994). Moreover, the paper\nimplicitly treats misalignment (especially underestimation) as problematic, without establishing a normative or behavioral\nfoundation for what constitutes “accurate” perception.\n- Slovic, P. (2000). The Perception of Risk.\n- Bostrom, A., Morgan, M. G., Fischhoff, B., & Read, D. (1994). What do people know about global climate change? 1. Mental\nmodels. Risk Analysis, 14(6), 959–970. https://doi.org/10.1111/j.1539-6924.1994.tb00065.x\n3. Modest Model Performance and Lack of Transparency\nThe use of MrP and ensemble learning is appropriate, but the model results are overstated. The reported AUC of 0.67 and\nMAE of 2.57% indicate modest predictive performance at best. In applied social science and epidemiology, AUC values\nbelow 0.70 are generally considered weak and unreliable for classification or prediction tasks(Hosmer 2013).\n- Hosmer et al. (2013): Applied Logistic Regression.\nDespite these limitations, the manuscript describes the model as showing “strong performance,” which is misleading.\nMoreover, model diagnostics beyond AUC and MAE (e.g., cross-validation accuracy, uncertainty intervals) are not reported.\nImportant details regarding variable selection, model weights, and the ensemble strategy are relegated to supplementary\nmaterials and are insufficient for evaluation or replication. Additionally, no external validation or behavioral ground truth is\nprovided to confirm that modeled “worry” is linked to actual preparedness or health outcomes."}, "author_response": "1\nResponses from authors are in blue.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nMisalignments between assessed and perceived 1 risks exacerbate vulnerability to extreme heat\nThis study looks to advance methods in Multilevel regression with Poststratification (MrP) to model the\npercentage of people worried about heat within the US, looking at states and counties. The goal is to\nprovide maps about heat “worry” across the CONUS. Such work can better understand the role of risk\nperception in driving vulnerability to heat risk, and to enable its systematic incorporation into new\nvulnerability indices and communication strategies.\nThe authors compare their estimates of heat risk perception (“perceived risk”)—which is based on\n11,000+ survey responses from 2019-2022--with the National Heat and Health Index from the CDC\n(assessed risk). The difference between the two risk types is termed the “aligned gap”.\nThe methods to look at perceived risk use advancements for a unique model (AutoMrP) that leverages\nmachine learning and model ensembles.\nThe revision of the paper is much improved from the first version; much more understandable with\nmethods more easily followed and results that make more sense and are interpreted well.\nI only have a few minor comments for clarity.\nIntroduction\nOverall very good and outlines the literature and justification well. A lot of methods are presented in the\nintroduction though, which shouldn’t be.\nThank you for pointing this out - two paragraphs and an equation describing our methods have been\nmoved from the introduction section to the Methods section, which is the appropriate place for them. We\nalso redesigned the methods section quite a bit to flow more naturally from survey data to modeling to the\nfinal “gap analysis” and comparison with assessed risk (described in detail below).\nLine 231 – should this be low perceived risk versus high assessed risk? (in the following sentences it\ngives high perceived risk versus low assessed risk within the statement)\nYes, thank you for identifying this – we have largely re-written this section for improved clarity.\ncaused or heat associated?\nWe clarified this (now on Line 304) by removing “attribute” and adding “caused by heat” which allows\nus to focus simply on the need to identify heat-related deaths as caused by heat (rather than on pre-\nexisting illnesses for example).\nLine 375 – Those 65 and older, while considered “older adults,” does not mean they are not able to do\nmove. This sentence is a bit of a hasty generalization of those >65 age. I think that the statement can be\ngeared more toward people who do lack mobility, whether older or not, and that people who are older are\nmore likely to lack mobility or not be able to get to places. But there are important physiological concerns\nfor older people (>65) too that could be mentioned that would be a factor of their assessed risk. E.g.,\nlower ability to sweat, taking more medications. (see: Ebi, K. L., Capon, A., Berry, P., Broderick, C., de\nDear, R., Havenith, G., ... & Jay, O. (2021). Hot weather and heat extremes: health risks. The lancet,\n398(10301), 698-708. Or see Vanos, J., Guzman-Echavarria, G., Baldwin, J. W., Bongers, C., Ebi, K. L.,\n& Jay, O. (2023). A physiological approach for assessing human survivability and liveability to heat in a\nchanging climate. Nature communications, 14(1), 7653.)\nThese points and citations are very helpful - we have incorporated them directly into the text (Lines 297-\n300)\nLine 378 – I think a more appropriate term here is systematic racism, including structural racism and\ninstitutional racism.\nWe agree and improved the text with these suggestions (Line 289 now).\nLine 379 – “Such communities may need additional support and resources” – for what? Complete the\nthought/sentence for clarity.\nThank you for flagging this incomplete sentence - we complete the thought now and explain that\nadditional resources are necessary to prepare for and mitigate the effects of extreme heat. (Line 289).\nReviewer #2 (Remarks to the Author):\nThis is an interesting study that examines an important topic, namely the gap between expert risk\nassessment and public risk perception of climate impacts, as well as the the demographic and geographic\npredictors of these. While the paper focusses on a specific country (the contiguous United State) I believe\nthat the work has broader relevance for other contexts and climate related risks. Overall, I believe that the\nauthors have addressed the previous reviewers’ comments raised in the last round of review. However, I\ndo have some queries that I feel could be addressed to make the findings of the paper as clear as possible,\n3\nas well as some points regarding the implied link between risk perception and risk preparedness that\nshould be considered.\n[NOTE: My comments are from the perspective of a social scientist with expertise in risk perception\nrather than a specialist in climate science or impact indices.]\nCOMMENT 1\nThis is a general comment, but I felt that it should be acknowledged that individual level risk perception\n(which we might expect to see reflected in measures of worry) and individual level risk preparedness are\nnot always perfectly associated with one another (e.g. as Wachinger et al. 2013 discuss in their paper on\nthe Risk Perception Paradox). While, this paper deals with risk perception rather than reported willingness\nto undertake some form of protective response, I do feel that it is good to acknowledge that there may be\nbarriers to increased risk perception facilitating risk preparedness (e.g. older, poorer adults might feel at\nrisk but lacking the resources and/or physical ability to undertake protective responses), as there seems to\nbe an implicit assumption that increasing perceived risk will lead to greater risk reduction behaviour.\nThis is an insightful and valid critique that has been central to our revisions and motivated us to develop\nour Perception–Exposure Alignment (RAP) framework. The reviewer correctly identifies that the\nassumption that higher perceived risk will naturally lead to protective action is not always supported.\nWachinger et al. (2013) demonstrate that intervening factors like trust, perceived responsibility, and\npersonal capacity often block the translation of risk perception into action—what they term the \"risk\nperception paradox.\"\nThe RAP framework explicitly addresses this concern by recognizing that perception-exposure alignment\nis necessary but not sufficient for effective climate adaptation. Our framework integrates insights from\ndual-process theories of cognition (System 1 vs System 2 thinking) with structural barriers to action,\nacknowledging that even when communities achieve better alignment between perceived and assessed\nrisks through improved System 2 analytical processing, System 1 emotional responses, and cultural\nknowledge systems, structural inequalities and resource constraints may still prevent protective responses.\nTo address this directly, we have substantially modified our entire introduction and discussion sections to\ncite Kahneman, Slovic, Wachinger and Renn, and the risk perception paradox. We now explain how\nbarriers can prevent action even when risk perception is appropriate. We also now explicitly state that\n\"Therefore, understanding perception-exposure alignment is necessary, but not sufficient for effective\npreparedness; it must inform targeted interventions that address both psychological and structural barriers\nto action.\"\nIn addition, we enhance our four-quadrant classification system by labeling the Danger Zone and\n4\nidentifying areas not only where perception-exposure gaps exist, but also where different types of\ninterventions are needed. For example, where both perceived and assessed risk are relatively high,\nexisting risk awareness can be leveraged, but resources must focus on removing barriers to protective\naction.\nThe RAP framework thus provides a more careful and nuanced approach than simply assuming that\ncorrecting perception-exposure misalignments will automatically lead to better outcomes. Instead, it\noffers a systematic way to identify where different combinations of perception-based and capacity-\nbuilding interventions are most needed. Our detailed spatial analysis enables targeting of resources to\naddress both cognitive-cultural factors and structural barriers simultaneously, particularly for vulnerable\npopulations like older, poorer adults who may understand risks but lack the resources or physical ability\nto respond effectively.\nWe are grateful for the reviewer’s support here.\nCOMMENT 2\nIn the paper the alignment gap is characterized as being HHI (a normalized measure of the Heat and\nHealth Index) – Worry (normalised), with the alignment gap being displayed in 3c and 3f. It was not clear\nto me from the methodology and supplementary material how HHI and worry were normalised to ensure\ncomparability. I feel that it would be good to signpost this more clearly as a reader may be confused as to\nhow deducting estimated percentage of those worried from the HHI gives a ‘true’ measure of\n(mis)alignment (i.e. as % worried has a ‘true’ 0-1 point of nobody and everybody, HHI is a\ncomposite indicator)\nThe reviewer raises an important methodological point about the comparability of our alignment gap\nmeasure. We have clarified our approach and strengthened the theoretical justification through our\nPerception–Exposure Alignment (RAP) framework.\nRegarding the methodological clarification, we now rescale the HHI to between 0 and 100 to match the\nactual bounds of our worry estimates (0 to 100%) and use the unscaled estimated percent worried in each\ncounty directly (rather than normalizing both measures). The alignment gap is calculated as: Rescaled\nHHI – Percent Worried. We have added detailed explanation of this rescaling procedure in our methods\nsection and supplementary materials to ensure full transparency.\nRegarding the theoretical framework, the reviewer correctly notes an important limitation: our alignment\ngap is not a \"true\" measure of misperception in an absolute sense. The RAP framework explicitly\naddresses this concern by positioning the alignment gap as a comparative heuristic that reveals systematic\npatterns of mismatch between expert-assessed and public-perceived risk rather than claiming to measure\n“objective” misperception. We intentionally avoid using the words “objective” versus “subjective” risk\nfor this reason. The RAP framework recognizes that both expert risk assessments (like the HHI) and\n5\npublic risk perceptions are constructed through different but valid knowledge systems. Expert\nassessments emerge from deliberative, analytical and data-driven methods using epidemiological\ninformation and vulnerability indicators, while public perceptions are derived more from lived\nexperience, cultural values, and local knowledge networks. The alignment gap thus reveals where these\ndifferent knowledge systems diverge, not which one is \"correct.\"\nInterpretation and Limitations: We have substantially revised our discussion to reinforce that the\nalignment gap should not be overinterpreted as objective ground truth. Instead, it serves as a diagnostic\ntool for identifying where targeted, culturally-informed risk communication interventions may be most\nneeded. Areas with large alignment gaps—whether positive or negative—signal opportunities for\ndialogue between expert knowledge and community understanding rather than suggesting that one\nperspective needs to be \"corrected.\"\nOur four-quadrant classification system is designed to move beyond simple \"correct vs. incorrect\"\nframings toward understanding different patterns of knowledge integration that require different\ncommunication approaches. This addresses the reviewer's underlying concern about treating composite\nindicators as directly comparable measures.\nWe hope this approach, grounded in the RAP framework, provides a more theoretically sophisticated and\nmethodologically transparent way to understand perception-exposure relationships while avoiding\noverinterpretation of our alignment gap metric.\nCOMMENT 3\nIn characterising participants as being worried vs not worried is it specified that those who refused to\nanswer the question were classified as not worried. Was any comparison done of what the % worried vs\nnot worried looked like when the refusing participants were removed?\nWe examined this issue more closely. Only 63 people refused the question out of a sample of 11,113\n(approximately 0.6% of the sample). Therefore we did not conduct further analyses with those\nrespondents recoded as “worried”. However, given that there were less than 1% of “Refused”\nrespondents, we are confident that our results would not differ enough to warrant detailed comparisons as\nthe model performance or mapped patterns could not change significantly with such a minor adjustment.\nCOMMENT 4\nA minor point, but I feel that Figure 3 would benefit from the maps on the bottom row having the same\nkey as the one directly above. I realise that the difference was due to some values not being present in one\n6\nversion of the map but having a key as to what the full range of values could be for each map can assist\ncomparability and interpretation.\nThank you for highlighting this. We updated the top row of Figure 3 to ensure all legends are identical.\nWe appreciate the reviewer’s attention to this detail–the maps are indeed easier to compare when the\nlegends are the same.\nCOMMENT 5\nOn Lines 238-242 the three-way age*poverty*ethnicity interaction reported in the regression table is\ninterpreted as “…high perceived versus assessed heat risk is particularly pronounced in\nsocioeconomically disadvantaged white-majority areas, likely in southern counties where high perceived\nrisk of extreme heat is widespread”. As an international reader it was not clear to me from this how age\nfits into this explanation. Is it because southern counties tend to have older residents?\nWe agree - our interpretation of the three-way interaction wasn’t clear; we’ve updated our models and\nshow the best-performing model now. We have revised this section to be more clear.\nClarified interpretation on lines 213-217: The three-way interaction (age x poverty x white/non-white)\nindicates that counties with the highest proportions of older and poorer Americans—whether\npredominantly white or non-white—tend to perceive high risks from extreme heat relative to assessments,\nlikely including many southern counties where heat concern is widespread and resources for staying cool\nare limited.\nCOMMENT 6\nIn the discussion of the Low-Low quadrant, the point: “Indeed, many areas with some of the largest\nunderestimation of heat risks also have some of the highest national per-capita heat-related mortality\nrates” seems to be a little confusing, as this has been identified as a lower assessed risk quadrant. I believe\nthat the authors may be pointing out that as people in these areas do not perceive themselves to be at a\nhigh risk from heat health impacts, they may underestimate future risks under a warmer climate and thus\nbe less well prepared. However, this could perhaps be clearer.\nWe appreciate the reviewer’s careful reading and agree that the sentence in question was confusing\nbecause it did not distinguish between past (historical) versus projected future risk. We clarify this on\nlines 279-283 now: “Limited cooling infrastructure and preparedness create vulnerability to climate\nsurprises, with some states projected to have high future per-capita heat mortality rates despite current\nlow risk classifications.”\nWachinger, G., Renn, O., Begg, C., & Kuhlicke, C. (2013). The risk perception paradox—implications\nfor governance and communication of natural hazards. Risk analysis, 33(6), 1049-1065.\nReviewer #3 (Remarks to the Author):\nSummary\nThis manuscript presents a nationwide analysis of the misalignment between assessed and perceived risks\nof extreme heat in the United States. The authors use Multilevel Regression with Poststratification (MrP),\nenhanced by ensemble learning techniques, to estimate the proportion of residents worried about extreme\nheat at the county level using national survey data (N=11,113). These estimates are then compared against\nthe CDC’s Heat and Health Index (HHI) to compute an “alignment gap” metric, representing the\ndifference between assessed and perceived risk. The results are presented as spatial maps and analyzed\nthrough regression to understand sociodemographic correlates of misalignment.\nComment\n1. Limited Novelty and Conceptual Contribution\nThe primary conclusion—that public perception of extreme heat risk does not always match objective\nhealth vulnerability—is not new. Numerous studies have previously explored the disconnect between risk\nperception and environmental hazard exposure, particularly in the context of climate change and public\nhealth (e.g., Van Valkengoed & Steg, 2019; Liu et al., 2013). Additionally, geographically resolved\nperception data (e.g., Howe et al., 2019) have already mapped spatial variability in climate concern across\nthe U.S. As a result, the manuscript offers little theoretical, empirical, or methodological innovation\nbeyond confirming well-known patterns. Its central message—“improving public awareness can reduce\nvulnerability”—is intuitive and broadly applicable across many environmental risks (flooding, wildfire,\npandemics), thus weakening its specificity and contribution.\n- Van Valkengoed & Steg (2019): https://doi.org/10.1038/s41558-018-0371-y\n- Liu et al. (2013): https://doi.org/10.1186/1471-2458-13-913\n- Howe et al. (2019): https://doi.org/10.1073/pnas.1813145116\nperceived risk often diverges from objective vulnerability—is supported by much prior work across\nenvironmental domains. We now engage this literature more directly. We would like to point out,\nhowever, that this critique overlooks several key theoretical, empirical, and methodological contributions\nunique to our study that provide novel insights relevant to both research and practice.\nFirst, in terms of novelty and scope, while two prior studies (Howe et al. 2019; Allan et al. 2020) have\nmapped extreme weather risk perceptions, our work makes significant advances through 1) our use of\ninterpretable proportions of populations rather than relative indices and county-level estimates, both of\nwhich make our results far more actionable than the results from previous studies; 2) the temporal breadth\nand generalizability (our analysis spans five years of survey data (2018–2022) unlike earlier work based\non a single season or event; 3) our focus on heat risk perceptions at a comprehensive, national scale,\nunlike Van Valkengoed & Steg (2019) who explore adaptation psychology and climate-health perception\nbroadly and Liu et al. (2013) who examine heat risk perceptions in a single province in China. Heat,\nwhich in Europe killed over 70,000 people in 2003, over 60,000 in 2022, and over 47,000 in 2023; it is\nalso the deadliest climate-related hazard in the U.S., yet remains under-studied in scientific research and\nseriously under-addressed in public policy.\nSecond, our study makes a major methodological contributions by operationalizing the “alignment gap”\nwhich no prior study has attempted. We introduce a novel, spatially explicit method to quantify the\nmismatch between assessed (CDC Heat and Health Index) and perceived risk across the entire CONUS.\nWhile the idea of perception misalignment is not new, the execution at national scale with a directly\ninterpretable “gap” metric (percentage point difference between actual concern and objective risk) is both\ninnovative and practically valuable for resource targeting. While this may seem obvious and simple with\nhindsight, it took substantial experimentation and testing to create an interpretable and rigorously-defined\nmetric. In addition, by employing a quadrant typology for policy relevance that categorizes counties into\nfour alignment quadrants (High/High, High/Low, etc.), we present a simple yet powerful diagnostic tool\nfor local policymakers that will go well beyond a broad statement that “improving public awareness can\nreduce vulnerability” because it yields quantitative assessments of every county in the US. Such\ninformation enables targeted intervention strategies tailored to local alignment profiles—an advancement\nfrom more theoretical treatments of risk perception.\nThird, while we agree that the statement “improving public awareness can reduce vulnerability” is\nintuitive, we argue that the geographic variation in misalignment—and the sociodemographic correlates\nwe uncover—provides new theoretical grounding for why such disconnects persist and where\ninterventions are most urgently needed. For instance, our finding that white-majority, high-poverty\ncounties exhibit underestimation of risk challenges prevailing assumptions about vulnerability and calls\nattention to overlooked at-risk populations in historically cooler regions. We also show that high\nperceived risk in relatively low-assessed-risk counties (e.g., in the mid-Atlantic) may reflect anticipatory\n9\nadaptation or localized political factors, underscoring the complex interplay between media, governance,\nand lived experience—an area deserving deeper theoretical exploration.\nFourth, we believe that innovation in environmental risk literature can occur in multiple ways, and that\nour study does this by extending theory to underexplored hazards (here: heat vs. flood or wildfire), by\nimproving methodological precision (our county-level MRP estimates with demographic correlates), and\nby enhancing translational utility (the alignment-gap framework tied to public health planning). The\nimplication that confirmation of “well-known patterns” limits value underestimates the importance of\nprecision, geographic targeting, and timely application to a hazard that is under-recognized in both policy\nand public discourse.\nThus, while the general notion of risk-perception misalignment is established in case studies, this analysis\nmakes concrete contributions by delivering actionable, fine-scale, comprehensive estimates of perceived\nand assessed heat risk; by quantifying spatial misalignments to inform geographically-targeted\ninterventions; by uncovering demographic correlates of perception gaps with implications for equity and\njustice; and by advancing methodological rigor in risk perception modeling.\nWe argue that the manuscript’s insights are urgently needed given the accelerating public health threat\nfrom extreme heat and the continued under-recognition of this hazard in federal and local preparedness\nframeworks. The work may not reinvent risk perception theory—but it materially advances its application\nwhere lives are at stake.\n2. Conceptual Ambiguity and Misuse of “Alignment Gap”\nThe construction of the alignment gap metric—defined as the difference between normalized perceived\nrisk (survey-based worry) and normalized assessed risk (CDC HHI)—raises several conceptual concerns.\nThese two variables differ fundamentally in their source, scale, and meaning. One is an affective, self-\nreported perception; the other is a composite public health indicator.No theoretical justification is\nprovided for subtracting these indicators or interpreting the result as a meaningful measure of\nmisalignment. This operation risks producing misleading patterns, as perceived and assessed risks are\nshaped by distinct cognitive and sociocultural processes (Slovic, 2000; Bostrom et al., 1994). Moreover,\nthe paper implicitly treats misalignment (especially underestimation) as problematic, without establishing\na normative or behavioral foundation for what constitutes “accurate” perception.\n- Slovic, P. (2000). The Perception of Risk.\n- Bostrom, A., Morgan, M. G., Fischhoff, B., & Read, D. (1994). What do people know about global\nclimate change? 1. Mental models. Risk Analysis, 14(6), 959–970. https://doi.org/10.1111/j.1539-\n6924.1994.tb00065.x\ndistinction between affective perceptions and epidemiological risk indicators. These are important\nconcerns, and we welcome the opportunity to clarify the motivation, theoretical basis, and interpretive\nlimits of our approach.\nOn the question of different constructs, it is correct that perceived heat risk (as worry) and assessed heat\nrisk (as measured by the CDC’s Heat and Health Index, HHI) are fundamentally different constructs. This\nis central to our analysis—not a limitation, but the premise of our inquiry. As articulated by Slovic (2000)\nand Bostrom et al. (1994), risk perception and expert risk assessment often diverge, precisely because\nthey emerge from different cognitive, experiential, and social processes. Our study does not conflate these\nconstructs; rather, it quantifies their divergence to explore where and for whom such gaps emerge.\nWhile the critique questions the theoretical basis for subtracting these variables, the logic of our approach\nis rooted in the same framework used by numerous studies in environmental psychology and risk\ncommunication that contrast lay and expert judgments (e.g., Morgan et al., 2002). Subtracting normalized\nvalues of perceived and assessed risks serves as a practical, interpretable heuristic for identifying\nalignment or misalignment—not a claim of objective error per se, but a starting point for asking: Are\npeople in areas of objectively higher heat-health vulnerability aware of their elevated risk?\nWe are explicit that the alignment gap is not a measure of “error” in an epistemological sense, but a\ncomparative diagnostic tool that supports targeting of outreach and adaptation efforts. This is consistent\nwith public health practice, where communication strategies often seek to narrow the perception gap to\nimprove behavioral responsiveness.\nIn terms of viewing a misalignment as important, the reviewer is right to note that we treat\nunderestimation (relative to expert assessments) as particularly problematic. This position is not merely\nnormative—it is grounded in behavioral science and health risk literature showing that low perceived risk\nreduces adaptive behavior uptake (Weinstein, 1989; Leiserowitz, 2006). For hazards like extreme heat—\nwhere individual and institutional responses can mitigate harm—perception drives action. If a county has\nhigh assessed risk (e.g., due to age, poverty, poor housing, and health vulnerability), but low public\nconcern, it is reasonable and evidence-based to identify this misalignment as a public health concern.\nIn terms of bounding our study and transparency, we agree that combining indicators from different\nsources and scales requires caution. To address this we do not assign normative “accuracy” thresholds to\nindividual counties but instead examine broad spatial patterns, using a 50% threshold for worry and the\nmedian HHI, and sociodemographic correlates. We are transparent about the limitations and assumptions\nof this metric in the Discussion section and call for further research into how alignment relates to actual\nbehavior and outcomes.\nrather a comparative indicator useful for identifying communities where public concern may lag behind—\nor exceed—heat-related health vulnerability. This framework is grounded in established theories of risk\nperception and communication and supports practical applications in public health and climate adaptation.\nWe clarify these points in the manuscript to better distinguish the heuristic nature of the alignment gap\nand to acknowledge the epistemological complexity of defining “accurate” risk perception in this context.\n3. Modest Model Performance and Lack of Transparency\nThe use of MrP and ensemble learning is appropriate, but the model results are overstated. The reported\nAUC of 0.67 and MAE of 2.57% indicate modest predictive performance at best. In applied social science\nand epidemiology, AUC values below 0.70 are generally considered weak and unreliable for\nclassification or prediction tasks (Hosmer 2013).\n- Hosmer et al. (2013): Applied Logistic Regression.\nDespite these limitations, the manuscript describes the model as showing “strong performance,” which is\nmisleading. Moreover, model diagnostics beyond AUC and MAE (e.g., cross-validation accuracy,\nuncertainty intervals) are not reported. Important details regarding variable selection, model weights, and\nthe ensemble strategy are relegated to supplementary materials and are insufficient for evaluation or\nreplication. Additionally, no external validation or behavioral ground truth is provided to confirm that\nmodeled “worry” is linked to actual preparedness or health outcomes.\nWe appreciate the reviewer’s attention to model evaluation and transparency, and agree that responsible\nmodeling requires careful qualification of performance metrics, as well as clear documentation of\nmethods. However, we respectfully disagree with the characterization of the model as \"unreliable\" based\non the reported AUC and MAE, and we offer the following clarifications and revisions to address these\nconcerns.\nThe critique frames an AUC of 0.67 as “unreliable,” citing standards from applied logistic regression\n(Hosmer et al., 2013). While AUC thresholds around 0.70 are a conventional benchmark for binary\nclassifiers, they are less directly applicable in the context of small-area estimation of public opinion using\nMRP—where the objective is not individual-level classification, but population-level inference.\nIndeed, in prior published work in climate opinion modeling using MRP (e.g., Howe et al., 2015;\nMildenberger et al., 2017), similar or slightly higher AUCs (0.68–0.72) have been treated as acceptable.\nWe agree that 0.66 (our updated AUC) reflects fair, not strong, predictive performance—but it does not\nimply unreliability, especially in the context of estimating proportions across more than 3,000 diverse\nU.S. counties with limited direct observations in many of them. Moreover, the new MAE of 1.57\npercentage points is well within the range of typical error in small-area perception models and provides a\n12\nmore directly interpretable measure of model precision.\nTo address this concern, we have revised the manuscript language to avoid the phrase “strong\nperformance,” and instead explain that MAE is a better measure of estimate confidence across the entire\nCONUS.\nWe agree that key model diagnostics should be more prominently reported and that the methods section\ncan be strengthened. In response, we have:\n- Redesigned the Methods section to clearly define our approach first, and then provide detailed\nexplanations of the implementation of that framework, including new information about\npoststratification data and methods that was missing previously\n- Report additional cross-validation metrics (e.g., county-level R², coverage of 95% uncertainty\nintervals, RMSE) in the main text and supplementary materials.\n- Report cross-validation error when building the MRP model on the survey-level (individual-\nlevel) dataset\n- Describe variable selection procedures more clearly in the main text, including which predictors\nwere retained across ensemble models and their relative influence.\n- Expand explanation of ensemble strategy, including weighting approach, to ensure replicability.\nThese additions substantially enhance transparency and allow for better evaluation of the model’s\nreliability and generalizability.\nWe recognize that the study does not include direct behavioral or health outcome validation of perceived\nheat risk. This is a limitation we explicitly acknowledge in the manuscript. That said, the variable of\n“worry about heat” has been validated in prior work as a meaningful predictor of both climate concern\nand risk-related behavior (e.g., Leiserowitz et al., 2006; van der Linden, 2015, Lee et al., 2025), and our\nmodel captures county-level variation in this established risk perception construct.\nWe agree that future research should link modeled perception estimates to direct behavioral or\npreparedness indicators (e.g., air conditioning access, heat action plan adoption, or morbidity rates),\nand we have added this direction explicitly to the “Future Research” section.\nWhile we acknowledge that the model is not high-performing by strict classification standards, its\npredictive accuracy is within the expected range for spatial perception modeling using survey-based\nMRP, especially when estimating proportions at fine geographic scales. The inclusion of ensemble\ntechniques, cross-validation, and uncertainty quantification further bolsters the robustness of our\napproach. We have updated the manuscript throughout to improve transparency and to more accurately\ncharacterize model performance."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I appreciate the author's considered and detailed response to the points raised, in particular regarding methodological\nclarification on how the alignment gap was measured and the conflation of risk perception and preparedness. I feel that my\nprior concerns have been addressed.", "reviewer_3": "Thanks for the response and revision. The revision has addressed all of my concerns in my comments."}, "author_response": ""}]

Decision letter