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The Affect Heuristic-Cultural Cognition Theory (AH-CCT) model and the Solution Aversion-based (SA) model both suggest affect, meaning feelings or discrete emotions about a target, mediates associations between ‘culture,’ such as political ideology or cultural biases, and risk responses, such as risk perceptions, protective behaviours, and supportive attitudes towards protective policy. However, the models differ respectively by defining negative affect as directed towards the hazard (‘hazard affect’) or a specific behaviour or policy response (‘solution aversion,’ negative affect about a proposed risk reduction method). We compare these models with longitudinal mediation analysis of U.S. COVID-19 survey data (n = 866 in smallest-sample wave). Solution aversion accounted for more associations of culture with risk perceptions, such as personal risk, collective risk, and risk severity; behaviour and behavioural intentions, regarding mask wearing, avoiding large public gatherings, and vaccination; and support for risk mitigation policies, regarding mask mandates, public gathering bans, and vaccination mandates. Statistically significant direct effects were rare and were mainly for egalitarian cultural bias; indirect effects occurred for egalitarians, political conservatives, and individualists. Implications for further research on risk responses are discussed relative to limited previous work on these affect-mediation models. 

How do cultural biases, trust in government, and perceptions of risk and protective actions influence compliance with regulation of COVID-19? Analyzing Chinese (n = 646) and American public opinion samples (n = 1,325) from spring 2020, we use Grid–Group Cultural Theory and the Protective Action Decision Model to specify, respectively, cultural influences on public risk perceptions and decision-making regarding protective actions. We find that cultural biases mostly affect protective actions indirectly through public perceptions. Regardless of country, hierarchical cultural biases increase protective behaviors via positive perceptions of protective actions. However, other indirect effects of cultural bias via public perceptions vary across both protective actions and countries. Moreover, trust in government only mediates the effect of cultural bias in China and risk perception only mediates the effect of cultural bias in the United States. Our findings suggest that regulators in both countries should craft regulations that are congenial to culturally diverse populations. 

Although early concepts of risk perception measures distinguished cognitive from affective items, until recently multi-dimensional taxonomies were absent from risk perception studies, and even more from tests of their association with behavior or policy support. Six longitudinal panel surveys on U.S. COVID-19 views (n = 2004 February 2020, ending April 2021) allowed testing of these relationships among ≤ 10 risk perception items measured in each wave. Confirmatory factor analyses revealed consistent distinctions between personal (conditioning perceived risk on taking further or no further protective action), collective (U.S., global), affective (concern, dread), and severity (estimates of eventual total U.S. infections and deaths) measures, while affect (good-bad feelings) and duration (how long people expect the outbreak to last) did not fit with their assumed affective and severity (respectively) parallels. Collective and affective/affect risk perceptions most strongly predicted both behavioral intentions and policy support for mask wearing, avoidance of large public gatherings, and vaccination, controlling for personal risk perception (which might be partly reflected in the affective/affect effects) and other measures. These findings underline the importance of multi-dimensionality (e.g. not just asking about personal risk perceptions) in designing risk perception research, even when trying to explain personal protective actions. 

Identifying and understanding risk perceptions—“how bad are the harms” to humans or to what they value that people see as potentially or actually arising from entities or events—has been critical for risk analysis, both for its own sake, and for expected associations between risk perceptions and subsequent outcomes, such as risky or protective behavior, or support for hazard management policies. Cross-sectional surveys have been the dominant method for identifying and understanding risk perceptions, yielding valuable data. However, cross-sectional surveys are unable to probe the dynamics of risk perceptions over time, which is critical to do while living in a dynamically hazardous world and to build causal understandings. Building upon earlier longitudinal panel studies of Americans’ Ebola and Zika risk perceptions using multi-level modeling to assess temporal changes in these views and inter-individual factors affecting them, we examined patterns in Americans’ COVID-19 risk perceptions in six waves across 14 months. The findings suggest that, in general, risk perceptions increased from February 2020 to April 2021, but with varying trends across different risk perception measures (personal, collective, affective, affect, severity, and duration). Factors in baseline risk perceptions (Wave 1) and inter-individual differences across waves differed even more: baseline ratings were associated with how immediate the threat is (temporal distance) and how likely the threat would affect people like oneself (social distance), and following the United States news about the pandemic. Inter-individual trend differences were shaped most by temporal distance, whether local coronavirus infections were accelerating their upward trend, and subjective knowledge about viral transmission. Associations of subjective knowledge and risk trend with risk perceptions could change signs (e.g. from positive to negative) over time. These findings hold theoretical implications for risk perception dynamics and taxonomies, and research design implications for studying risk perception dynamics and their comparison across hazards. 

Two decades ago a research team clarified that cross-sectional associations of risk perceptions and protective behavior can only test an “accuracy” hypothesis: e.g., people with higher risk perceptions at Ti should also exhibit low protective behavior and/or high risky behavior at Ti. They argued that these associations are too often interpreted wrongly as testing two other hypotheses, only testable longitudinally: the “behavioral motivation” hypothesis, that high risk perception at Ti increases protective behavior at Ti+1, and the “risk reappraisal” hypothesis, that protective behavior at Ti reduces risk perception at Ti+1. Further, this team argued that risk perception measures should be conditional (e.g., personal risk perception if one’s behavior does not change). Yet these theses have garnered relatively little empirical testing. An online longitudinal panel study of U.S. residents’ COVID-19 views across six survey waves over 14 months in 2020–2021 tested these hypotheses for six behaviors (hand washing, mask wearing, avoiding travel to infected areas, avoiding large public gatherings, vaccination, and [for five waves] social isolation at home). Accuracy and behavioral motivation hypotheses were supported for both behaviors and intentions, excluding a few waves (particularly in February–April 2020, when the pandemic was new in the U.S.) and behaviors. The risk reappraisal hypothesis was contradicted—protective behavior at one wave increased risk perception later—perhaps reflecting continuing uncertainty about efficacy of COVID-19 protective behaviors and/or that dynamic infectious diseases may yield different patterns than chronic diseases dominating such hypothesis-testing. These findings raise intriguing questions for both perception- behavior theory and behavior change practice. 

Abstract Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthesize research on the trust and trustworthiness of AI in the environmental sciences and propose a research agenda. Evidential and conceptual histories of research on trust and trustworthiness reveal persisting ambiguities and measurement shortcomings related to inconsistent attention to the contextual and social dependencies and dynamics of trust. Potentially underappreciated in the development of trustworthy AI for environmental sciences is the importance of engaging AI users and other stakeholders, which human–AI teaming perspectives on AI development similarly underscore. Co‐development strategies may also help reconcile efforts to develop performance‐based trustworthiness standards with dynamic and contextual notions of trust. We illustrate the importance of these themes with applied examples and show how insights from research on trust and the communication of risk and uncertainty can help advance the understanding of trust and trustworthiness of AI in the environmental sciences. 

Understanding human responses to pandemics can improve public health. A survey of US residents (n = 2004) February 28, 2020, very early in the coronavirus pandemic, tested predictors of five “protective” actions: washing hands, wearing masks, avoiding travel, avoiding large public gatherings, and avoiding Asians (given COVID-19’s first appearance in China). We added to the Protective Action Decision Model—positing threat, protective action, and stakeholder perceptions as immediate predictors of intentions—objective and subjective coronavirus knowledge as predictors of these perceptions, and psychological distance to predict threat perceptions. We presumed objective and subjective knowledge were affected by following US and China news about COVID-19. Structural equation modeling indicated adequate fit for this parsimonious model; variance explained in behavioral intentions ranged from .12 (handwashing) to .33 (Asians). Behavioral intentions rose with higher threat, action, and stakeholder (trust) perceptions, psychological distance reduced threat perceptions, objective knowledge reduced threat and action perceptions but increased trust, and subjective knowledge did the opposite. Coronavirus-news following increased both objective and subjective knowledge, but subjective knowledge exhibited stronger associations and US news dominated China news. Moderate model fit and variance explained might reflect model parsimony and/or data collection when US cases were in the low double digits. 

Cross-sectional surveys, despite their value, are unable to probe dynamics of risk perceptions over time. An earlier longitudinal panel study of Americans’ views on Ebola risk inspired this partial replication on Americans’ views of Zika risks, using multilevel modeling to assess temporal changes in these views and inter-individual factors affecting them, and to determine if similar factors were influential for both non-epidemics in the USA. Baseline Zika risk scores – as in the Ebola study – were influenced by dread of the Zika virus, perceptions of a near-miss outbreak, and perceived likelihood of an outbreak. Judgments of both personal risk and national risk from Zika declined significantly, and individual rates of news following predicted slower decline of perceived national risk in both cases. However, few other factors affected changes in Zika risk judgments, which did not replicate in a validation half-sample, whereas several factors slowed or increased the rate of decline in Ebola judgments of the U.S. risk. These differences might reflect differences in the diseases caused by these two viruses – e.g., Ebola’s much greater lethality – but more longitudinal studies across multiple diseases will be needed to test that speculation. Benefits of such studies to health risk analysis outweigh the difficulties they pose.