Trumpism, climate and COVID: Social bases of the new science rejection

RESEARCH ARTICLE Trumpism, climate and COVID: Social bases of the new science rejection Lawrence C. HamiltonID* Department of Sociology, University of New Hampshire, Durham, New Hampshire, United States of America * lawrence.hamilton@unh.edu Abstract Although the hazards posed by greenhouse warming and COVID-19 are quite different, diagnosis and mitigation prospects for both depend heavily on science. Unfortunately, the reality of both threats has been subject to politicized science rejection in the US, making these deadly problems less tractable. There are substantial parallels between the two cases of science rejection, including common rhetoric and conservative political leadership. Sur- vey research has reached widely-replicated conclusions regarding the social bases of cli- mate-change perceptions. Corresponding studies of COVID-19 perceptions have found some political commonalities, but less agreement on other details. Here, we address this gap using generalized structural equation modeling (GSEM) and 2021 US survey data to make direct comparisons between the social bases of rejecting the reality of anthropogenic climate change (ACC) and rejecting COVID-19 vaccination. Trumpism, operationalized from approval of ex-president Trump, is viewed as an intervening variable that influences both types of science rejection. Trumpism itself is predicted by age, race, evangelical reli- gion, ideology, and receptivity to seemingly non-political conspiracy beliefs. Considering direct as well as indirect effects (through Trumpism), climate change and vaccine rejection

are similarly predicted by white and evangelical identity, conspiracism, and by education×i- deology and friends×party interactions. The finding that Trumpism exacerbates science rejection could also apply to other science- and expertise-related topics unrelated to climate and COVID. These results invite broader comparisons across topics, with analogous move- ments in other countries, and continued tracking as US Trumpism evolves beyond Trump. 1. Introduction Climate change has long been recognized by scientists as a major problem—potentially an existential threat to social and ecological systems [1,2]. The SARS-Cov2 virus with its variants emerged more recently but quickly attained major threat status as well, causing over one mil- lion known deaths in the US and six million worldwide in its first two years; the true totals are likely much higher [3,4]. Although the hazards posed by greenhouse warming and COVID-19 are quite different, diagnosis and mitigation prospects for both depend heavily on science. Unfortunately, scientific analysis, policy discussions, and even the basic reality of both threats PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 1 / 20 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Hamilton LC (2024) Trumpism, climate and COVID: Social bases of the new science rejection. PLoS ONE 19(1): e0293059. https://doi. org/10.1371/journal.pone.0293059 Editor: Aaron Ettinger, Carleton University Faculty of Public Affairs, CANADA Received: September 3, 2022 Accepted: September 15, 2023

Published: January 10, 2024 Copyright: © 2024 Lawrence C. Hamilton. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: Research supported in part by a grant to LH from the US National Science Foundation (OPP- 1748325). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

have become targets of politicized science rejection in the US, making these deadly problems less tractable. The arguments and tactics deployed against scientists and science-based mitigation for cli- mate change on one hand, and COVID-19 on the other, have some basic elements in common [5–7]. Science rejection in each case has been led by conservative media and political leaders, encouraging the wide partisan divisions seen on general-public surveys. Although these com- monalities are clear, they leave further hypotheses worth testing. In what other respects do the social bases of science rejection on COVID-19 resemble those of climate change, which have been established through more than 40 years of research on environmental concern [8,9]? While general correlations between political identity and views on climate change or COVID-19 are well known, we have less knowledge regarding common (or divergent) patterns with other individual characteristics including age, gender, education, religion, race or income. The more immediate personal impacts of COVID-19 compared with climate change, and its direct links to individual behavior, might give rise to differences in demographic patterns. Cer- tain interaction effects relating to information uptake that have been noted with climate change and environmental issues also remain untested regarding COVID-19. Finally, there are new elements in the US political landscape—notably the mainstreaming of conspiracy beliefs and the authoritarian, grievance-focused emergence of Trumpism.

The analysis that follows tests Trumpism and openness to conspiracy beliefs alongside more familiar demographic characteristics as predictors of rejecting the reality of human- caused climate change and COVID-19 vaccination. Data are from a nationally representative US survey conducted in mid-2021, when Trump was no longer in office. At that time vaccina- tions had been approved and made widely available, but employer mandates were not yet in effect. Because Trumpism emerged recently, growing out of traditional conservatism but dis- carding many of its tenets, it is modeled here as an intervening variable that might be predicted by some of the same demographic characteristics as climate and COVID responses, while con- tributing further direct effects of its own. 2. Background Conservative political identity has long been associated with lower concern about environ- mental problems [9–11], including the reality of anthropogenic climate change or ACC [8,12,13], and climate mitigation policies [14,15]. Politics also played a large role in shaping the evolution of US policy on COVID-19, affecting public perceptions of its seriousness as well as support for public-health countermeasures from social distancing to mask wearing and vac- cination. General-public surveys find strong associations between political identity and indi- vidual COVID-related behavior [16–21], while aggregate analyses show corresponding associations between county or state-level political indicators and public-health outcomes such as stay-at-home compliance, vaccination and death rates [22–26].

Survey researchers historically operationalized political identity in terms of ideology or party, two indicators that have become more overlapping in the US due to polarization and party sorting [27–30]. Another political indicator used in some recent studies has been support for president/ex-president Trump. When tested, Trumpism (operationalized as Trump voting or approval) exhibits additional effects beyond ideology or party, but comparable to them in strength [14,31–33]. While drawing from a traditional conservative base, Trumpism incorpo- rates stronger elements of personality cult, authoritarianism and scapegoating, with lower or even reversed emphasis on traditionally conservative values such as personal morality, anti- communism and limited government. Shao and Hao [19] present arguments and empirical PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 2 / 20

support for treating Trump support as an intervening variable; a similar approach is taken in this paper. Conspiratorial thinking has been a conspicuous feature of US politics at least since at least the 1950s [34] and appears to be growing in prominence across a wide range of topics [35,36]. Although some conspiracy beliefs focus on political leaders (e.g., Obama is Muslim, or Trump really won the 2020 election), many others have been directed against scientists [37], on topics including climate change [38,39], vaccinations [39] and COVID-19 [5,40–42]. There are con- spiracy beliefs discounting both anthropogenic climate change and the seriousness of COVID- 19. Showing that specifically climate or COVID-related beliefs predict other views on those same topics seems circular, however. The analysis that follows instead tests an indicator for receptivity to extreme conspiratorial beliefs that have not been much politicized and are unre- lated to climate or COVID. Multiple studies on the social bases of views on climate change and other environmental problems have replicated two interactions involving political identity. The most common are information×political-identity effects in which, for example, concerns about climate change increase with education among liberals and moderates, but do not increase and may even decrease with education among the most conservative. Variations include party, ideology or worldview used as political-identity indicators; and education [12,14,43–47], self-assessed

understanding [13,48], tested science literacy [49,50], or numerical literacy [51,52] as informa- tion indicators. Similar interactions have been found with non-climate dependent variables relating to vaccines [53], perceptions of local flooding or weather trends [54–56], renewable energy [57], and other environment-related issues [58,59]. In all these findings the interactions take a similar form, often graphed with a right-facing megaphone shape: the best-educated partisans stand the farthest apart (e.g., Fig 3A–3D in [12]). Such information×identity-type interactions reflect the influence of partisan cueing— absorbing cues from partisan elites, co-partisans, or (negatively) perceived antagonists, regard- ing which beliefs are appropriate for your identity—as a heuristic in “forming” your own views on difficult issues [60–64]. Better-educated or otherwise information-rich individuals tend to be more sensitive to partisan cues. The public-opinion impacts of messaging from political and partisan-media elites have been prominent regarding both climate change [27,61,64–67] and COVID-19 [18,68,69]. Information-processing behaviors such as biased assimilation [13,70] and motivated reasoning [71–75], to the extent these are stronger among information elites, likely contribute to the observed information×identity interactions as well. A second, less dramatic interaction effect follows from survey reports that Americans increasingly choose to associate with people of their own political persuasion, a pattern that

intensifies polarization [76–78]. Political identity×friends-type interactions have been con- firmed in several recent studies involving local weather trends [56], wildlife management [79], and local weather or climate [80]. Their common form is that political-identity effects are stronger, or partisan gradients steeper, when most of the respondent’s friends are reported as belonging to the same political party. This may be especially true among conservatives. 3. Research questions and hypotheses Sociopolitical identity—as indicated by political party, ideology, or most recently support for Donald Trump—has been consistently linked to rejection of both anthropogenic climate change (ACC) and COVID-19 vaccination. It is not established to what extent other basic respondent characteristics such as age, gender, race, income, religion or income also show sim- ilar or divergent patterns across these two important cases of science rejection. Exploring such comparisons provides the basic motivation for our analysis. Because the social bases of ACC PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 3 / 20

views have been well studied, we adapt those results into specific hypotheses, tested here using new data on both ACC and COVID-19 vaccine rejection. H1: Trump support will be greater among older respondents, and those who identify as white, evangelical Christian, conservative and Republican. H2: Rejecting the reality of ACC, and personal rejection of COVID-19 vaccination, will both be more common among ideologically conservative, Republican and Trump-supporting individuals. H2a: Support for ex-president Trump thus can be modeled as an intervening (mediating) vari- able, related to ideology, party and other background characteristics, with both direct and indirect effects on ACC and vaccine rejection. H3: Individuals who self-identify as white or as evangelical Christians (traits often linked to conservative identity) will be more likely to reject ACC and vaccination. H4: Education×ideology interaction effects, widely observed in ACC studies, will also affect vaccination responses in similar ways: ideological divisions on both topics widening with education. H5: Partisan divisions on Trump support, ACC and vaccine rejection will be wider among respondents who report having mostly same-party friends (friends×party interaction). H6: After controlling for the background, sociopolitical, interaction and identity effects noted above, Trump support, rejection of ACC and rejection of COVID-19 vaccination will also be more common among individuals who are receptive to unrelated conspiracy beliefs.

The next section describes a new survey dataset, and the analytical methods used to test these hypotheses in a unified multivariate model. 4. Data and methods 4.1 POLES 2021 survey Data analyzed here are from the Polar, Environment and Science survey, conducted in sum- mer and early fall 2021 (POLES 2021). This representative US survey is well suited to the research questions posed above because it carried both climate and COVID-19 items, along with a range of different indicators for sociopolitical identity and respondent background. The survey also carried a number of conspiracy-belief items [41], several of them on topics that have not been politicized and are unrelated to climate or COVID. Other POLES 2021 ques- tions on science-related topics replicated those asked five years earlier on the POLES 2016 sur- vey [14,81,82]. In addition, the 2021 survey carried new questions reflecting changes in the US political context. Finally, and necessitated by the pandemic-driven shift from telephone inter- viewing to online data collection, the newer survey carried attention-check items to screen out thoughtless respondents—such as those who answered all the questions too quickly, or “straightlined” their agreement or disagreement with blocks of incompatible statements. Research protocols were approved by the Institutional Review Board for the Protection of Human Subjects in Research at the University of New Hampshire (IRB-FY2021-38), and the online questionnaire distributed in two stages: June/July and September/October 2021. After

quality screening, we obtained 1,134 valid completions. Sampling for this survey, organized by Qualtrics, aimed for a nationally representative profile of US adults with respect to age, gender, race, education and political party. PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 4 / 20

Table 1 lists variables analyzed here, with response summaries and codes used for model- ing. The variables include background characteristics, political identification, and two conspir- acy questions, along with three endogenous (intervening or dependent) variables of interest: approval of former president Trump, views on climate change, and COVID-19 vaccination status. Experimentally, we calculated probability weights that made the sample marginally more representative. The weights had little effect on relationships or endogenous variable dis- tributions, however, and caused effect estimates to be less precise (inflated the standard errors). Consequently, weighting is not used in the main analyses that follow. Whether unweighted or weighted, the mean Trump approval (Trumpism) was 3.9 on a 1–7 scale; 29 or 30 percent rejected the idea of ACC (NoACC); and 22 percent said they were not planning to get vacci- nated against COVID-19 (NoVax). The fractions rejecting ACC and vaccination appear simi- lar, but these are not the same people: only 9 percent of the sample falls in both camps. The questions about flat Earth and Moon landings are unusual for such research, although topic-specific conspiracy beliefs are well known in public discussion of Trump support, cli- mate change and vaccination. In our survey 10 percent agreed the Earth is flat, and 12 percent agreed that NASA did not land on the Moon. A further 9 percent were unsure whether the Earth is flat, and 17 percent unsure about the Moon landings. These results parallel those of

other recent nationwide surveys that asked flat Earth or Moon landing questions [83,84]. See [41] for comparisons of flat Earth and Moon landing responses to other science-related Table 1. Variables in this analysis, with response percentages from 2021 survey (n = 1,134) and codes used for modeling (Table 2). Endogenous variables Trumpism—Strongly disapprove of former president Trump (1, 36%); somewhat disapprove (2, 5%); lean toward disapproving (3, 5%); neither approve nor disapprove (4, 7%); lean toward approving (5, 7%); somewhat approve (6, 16%); strongly approve (7, 24%). NoACC—Which of the following do you think is more accurate? Climate change is not happening now (1, 5%); climate change is happening now, caused mainly by natural forces (1, 25%); Climate change is happening now, caused mainly by human activities (0, 63%); don’t know (0, 7%). NoVax—Which of the following describes your own situation, regarding vaccination against COVID-19? I do not plan to be vaccinated (1, 22%); I plan to be vaccinated but have not done so yet (0, 13%); I have received first dose of a 2-shot vaccine (0, 9%); I am fully vaccinated (single dose of 1-shot, or both doses of 2-shot) (0, 56%). Exogenous variables Age—Range 18 to 94 years, mean 48 years. Gender (Female)—Female (1, 51%); Male (0, 47%). Missing values: fewer than 2% identified as non-binary or gave no answer. Race (White)—White non-Hispanic (1, 64%); Black or African American (0, 12%); Hispanic (0, 13%); Asian American (0, 6%); Native American (0, 1%); other or mixed (0, 3%); prefer not to say (0, <1%).

Income—Household income less than $15k (1, 12%); $15–30k (2, 16%); $30–70k (3, 36%); $70–120k (4, 20%); $120– 200k (5, 11%); over $200k (6, 5%). Education—High school or less (–1, 25%); technical school or some college (0, 25%); college graduate (1, 32%); postgraduate work (2, 17%). Religion (Evangelical)—Identify as Christian Evangelical (1, 19%); other Protestant (0, 12%); other Catholic (0, 22%); other Mormon (0, 1%); Jewish (0, 3%); Muslim (0, 3%); Buddhist (0, 2%); Hindu (0, <1%); atheist (0, 4%); agnostic (0, 4%); something else (0, 14%); nothing in particular (0, 16%). Ideology—Extremely liberal (–3, 11%); fairly liberal (–2, 16%); moderate, lean liberal (–1, 12%); moderate, lean neither (0, 19%); moderate, lean conservative (1, 10%); fairly conservative (2, 16%); extremely conservative (3, 16%). Party—Democrat (–1, 46%); Independent (0, 7%); Republican (1, 42%); other or don’t know (coded as missing, 5%). Friends—Most of my friends prefer the same party I do (1, 46%); most prefer different parties (0, 15%); about evenly divided (0, 25%); don’t know (0, 14%). Conspiracist—Sum of moonfake, NASA astronauts did not land on the Moon—disagree (1, 70%), unsure (2, 17%), agree (3, 12%); and earthflat, the Earth is flat, not round—disagree (1, 81%), unsure (2, 9%), agree (3, 10%). Conspiracist ranges from 2 (disagree with both conspiracies, 64%) to 6 (agree with both conspiracies, 4%). https://doi.org/10.1371/journal.pone.0293059.t001 PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 5 / 20

statements that are undoubtedly true, such as the Earth is billions of years old, or revolves around the Sun. Conspiracy responses in the June/July and September/October survey stages were nearly identical. There also were no significant shifts in Trump approval, nor in the proportions rejecting ACC or COVID-19. Vaccination eligibility in the US was rolled out by age group in 2021, so older respondents would have had more time to obtain vaccinations, especially in the survey’s June/July stage. To minimize age bias our coding contrasts those who, at the time of the survey, said they were either fully vaccinated, partly vaccinated, or planning to be vacci- nated—versus rejectionists who said they do not plan to be vaccinated. The rejectionist fraction showed only a slight, nonsignificant drop from summer to early fall. Because response patterns stayed nearly the same, we pool summer and fall responses for Table 1 and the analyses that follow. 4.2 Analytical methods Among the endogenous variables listed in Table 1, approval of former president Trump (Trumpism) ranges from 1 (strongly disapprove) to 7 (strongly approve). Most respondents leaned toward one extreme or the other, leaving few in the middle. An alternative dichotomous coding for this question was considered but proved to work less well, as described in section 5.3. Two other endogenous items, climate change and vaccination, offered non-ordinal response choices. Among these choices, rejecting the scientific consensus on ACC, and reject-

Trump really won the 2020 election, climate change is a hoax, or COVID-19 vaccinations con- tain tracking microchips. Obviously, such conspiracy beliefs correlate with other views on Trump, climate or vaccines. The conspiracist variable defined in Table 1 is a less tautological indicator based on receptivity to two extreme beliefs that, unlike Trump, climate or vaccines, have not been explicitly linked to conservative identity: that NASA faked the Moon landings, or that the Earth is flat rather than round. Few respondents in our survey agreed with both views, but more than a third agreed with or were open to at least one. Trumpism is a comparatively recent and still-evolving phenomenon, unlike party, ideology and other demographic characteristics that have been studied through five decades of “social bases” research [9,85,86]. At Trump rallies in particular, Trumpism manifests as a personality PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 6 / 20

cult flavored by grievances, authoritarianism and white nationalism. In the future, Trump’s political style and the devotion of his base might spread to other leaders. His Republican rivals have been described as trying to “out-Trump Trump” [87,88] or “claim the Trumpist mantle” [89]; retrospective accounts refer to his “historical dominance” and a “Trump era” that did not necessarily end with his presidency. Such descriptions foreshadow Trumpism as a phenome- non persisting beyond Trump. Currently, however, his personality cult appears central, while his targets and positions may shift—as in past reversals regarding COVID scientists and vac- cines [18]. It seems best at this point to operationalize Trumpism simply in terms of expressed Trump support, as previous researchers have done, while recognizing that more elaborate con- tent-based measures could be developed and validated in future research. As an emergent and currently important sociopolitical identity, Trumpism or Trump sup- port can be depicted as an intervening variable—partly descending from older identity mark- ers (such as ideology and party) and conspiracism, while also contributing direct effects of its own. Intervening-variable hypotheses invoke a structural equation modeling approach [90]. Fig 1 schematically depicts the model applied in this study. Because our endogenous variables (Trumpism, NoACC and NoVax) are ordinal or dichotomous, we use a variation called gener- alized structural equation modeling (GSEM). By incorporating generalized linear modeling

into the structural equation framework, GSEM permits multi-equation models with nonlinear link functions and non-Gaussian distribution families, such as ordered logistic regression (logistic link function, ordinal distribution family)—equivalent to binomial logit, if the depen- dent variables are dichotomies. Data management and calculation of GSEM models, with diag- nostic tests and graphing of interaction effects, were carried out using Stata 16.1. 5. Results 5.1 GSEM modeling Results from GSEM analysis modeling climate-change and vaccination rejection as functions of demographic and identity/belief characteristics, with Trump support as an intervening vari- able, appear in Table 2. GSEM applies generalized linear modeling (GLM) within a structural equation framework. In this case, the GLM aspect involves ordered logistic regression. Table 2 shows logit coefficients with their robust standard errors, the latter calculated by a generalized Fig 1. Schematic representation of the generalized structural equation model (GSEM). https://doi.org/10.1371/journal.pone.0293059.g001 PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 7 / 20

Huber/White/sandwich estimator. Stars summarize probabilities from z tests employing these standard errors. As expected, conservatives, Republicans, whites and evangelicals are all significantly more likely to approve of Trump (hypothesis H1). Trump support also is more common among peo- ple receptive to seemingly unpolitical conspiracy beliefs—that NASA faked the Moon landings, or the Earth is really flat (H6). Finally, as detailed in section 5.2 on interactions, Trump support is higher among Republicans who say that most of their friends belong to the same party (H5). Trump support in turn predicts both rejection of ACC and rejection of COVID-19 vaccina- tion. Although the positive signs of these direct Trumpism effects are not surprising, their sig- nificance is noteworthy because the analysis already controls for other characteristics including ideology, party, race and religion, as well as education×party and friends×party inter- actions. Trumpism, as hypothesized, exhibits a direct effect on science rejection for both issues, net of the effects from other identity indicators (H2). Identification as white or evangelical, on the other hand, does not directly affect ACC or vaccine rejection—contrary to hypothesis H3. Although Trumpism increases the odds of both climate and vaccine rejection (H1), its effects on climate are stronger. Several cross-cutting relationships may account for this differ- ence. Vaccine rejection is greatest among younger respondents (opposite to climate rejection),

presumably because older people have a sharper sense of their own mortality, are more accus- tomed to following medical advice, and face higher risks from COVID-19. Vaccine rejection also is greater among women. Finally, lower household income and openness to conspiracy beliefs increase the odds of rejecting vaccination, whereas neither income nor conspiracism directly affect climate views (supporting only the first part of H6). Trumpism plays a significant role as intervening variable in our analysis, consistent with the conclusions of Shao and Hao [19]. This means that background factors might exert indi- rect (mediated) effects through Trumpism, in addition to whatever direct effects they have (H2a). Such indirect effects are approximated as products of coefficients along a sequence of paths from exogenous to final endogenous variables. Where the sequence includes one positive Table 2. Logit coefficients (with robust standard errors), from GSEM using ordered logit regression. Estimation sample n = 1,061; variable definitions and coding are given in Table 1. Endogenous variables Exogenous variables Trumpism NoACC NoVax Age –0.022 (0.004)*** 0.018 (0.004)*** –0.024 (0.006)*** Female –0.226 (0.133) 0.091 (0.163) 0.556 (0.184)** White 0.611 (0.169)*** –0.136 (0.204) –0.068 (0.226) Income 0.006 (0.062) 0.006 (0.070) –0.214 (0.214)** Evangelical 0.542 (0.160)*** 0.207 (0.190) 0.029 (0.212) Education –0.111 (0.077) –0.321 (0.094)*** –0.428 (0.113)*** Ideology 0.304 (0.047)*** 0.213 (0.055)*** 0.305 (0.062)***

Education × ideology 0.006 (0.035) 0.119 (0.042)** 0.111 (0.048)* Friends 0.420 (0.125)*** –0.251 (0.168) 0.075 (0.180) Party 0.990 (0.096)*** 0.035 (0.128) 0.052 (0.150) Friends × party 0.678 (0.134)*** 0.576 (0.175)** 0.579 (0.193)** Conspiracist 0.427 (0.064)*** 0.060 (0.076) 0.239 (0.078)** Trumpism 0.181 (0.042)*** 0.096 (0.049)* *p < 0.05 **p < 0.01 ***p < 0.001 (two-tailed z tests). https://doi.org/10.1371/journal.pone.0293059.t002 PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 8 / 20

and one negative coefficient, the indirect effect is negative. Where the sequence includes either two positive or two negative coefficients, the indirect effect is positive. For ACC rejection, indi- rect effects through Trumpism significantly modify (strengthen or weaken) conclusions that might be drawn from the direct effects of most background characteristics (age, white, evangel- ical, ideology, party, same-party friends and conspiracist beliefs). For example, the total effect of evangelical identity on ACC rejection is significantly stronger if we consider not only its positive direct effect, but also its positive indirect effect through Trumpism: evangelicals are more likely to support Trump, and (even controlling for party, ideology, race etc.) Trump sup- porters are more likely to reject ACC. Both party and conspiricism show significant indirect effects through Trumpism on vaccine rejection as well. Indirect effects on vaccine rejection from age, identification as white or evangelical, ideology and same-party friends fall just short of significance (all 0.10 > p > 0.05). Because the predictors education, ideology, friends and party appear also in interaction terms, their main effects in Table 2 can be interpreted as the effect of that variable when its interacting counterpart equals zero. For example, the main effects shown for education on Trumpism, NoACC and NoVax represent the effects of education when ideology = 0; that is, the effects of education among ideological moderates. Among moderates, education has little

effect on Trump support. Even among moderates, however, education decreases the probabil- ity of rejecting ACC or vaccination—as visualized by the down-sloping “moderate” curves in Fig 2A and 2B. Similarly, the main effects of having same-party friends correspond to the friends effect when party = 0; that is, the effect of same-party friends among independents. Having mostly same-party friends increases the probability that self-identified independents will approve of Trump (Fig 3A) but has little effect on their ACC or vaccination rejection (Fig 3B and 3C). Fig 2. Education×ideology interaction effects on (a) probability respondent rejects reality of anthropogenic climate change, or (b) does not intend to get vaccinated for COVID-19. Marginal probabilities adjusted for other covariates, calculated from the GSEM model in Table 2. https://doi.org/10.1371/journal.pone.0293059.g002 PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 9 / 20

5.2 Interaction effects Views on climate change and vaccination are both predicted by education×ideology interac- tions in which education just slightly affects the probability of science rejection among extreme conservatives but lowers this probability among all other groups (H4). Fig 2 visualizes these two interactions as marginal probability plots, adjusted for other covariates in the GSEM model of Table 2. Fig 2A shows that, other things being equal, among extremely conservative respondents the probability of rejecting ACC rises slightly with education (from 0.41 to 0.43) but declines with education among all other groups. For example, the conditional probability of ACC rejection declines from 0.35 among moderates with high school education to 0.19 among moderates with college education. Fig 2B similarly shows that the conditional probabil- ity of rejecting COVID vaccination decreases slightly (from 0.36 to 0.30) with education among extreme conservatives but declines more steeply with education among all other groups (for example, from 0.25 to 0.10 among moderates). Similar interactions have been seen in many studies of climate change and other environ- mental topics, so Fig 2A is not surprising. Such interactions involving COVID vaccine rejec- tion (Fig 2B) have not previously been documented, however. A recent study by Shoots- Reinhard et al. [91] reports a complementary result: ideological differences in COVID-19 risk perceptions were most pronounced among individuals with higher verbal (but not numerical)

ability. Individuals with greater verbal ability, like those with higher education, may be more responsive to partisan cueing. Both climate-change and vaccination views exhibit greater partisan divisions among people with mostly same-party friends (friends×party interaction; hypothesis H5). Trumpism also is higher among people with mostly same-party friends. Fig 3 visualizes these statistically Fig 3. Friends×party interaction effects on probability respondent (a) strongly approves of ex-President Trump, (b) rejects reality of anthropogenic climate change, or (c) does not intend to get vaccinated for COVID-19. Marginal probabilities adjusted for other covariates, calculated from the GSEM model in Table 2. https://doi.org/10.1371/journal.pone.0293059.g003 PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 10 / 20

significant interactions. In each case, the gap between Republicans and Democrats widens when most of the respondent’s friends identify with the same party they do. Almost 90 percent of Republicans with mostly same-party friends approve of Trump, compared with fewer than 70 percent of Republicans with mixed or different-party friends (Fig 3A). Impacts on climate and vaccine rejection are smaller in absolute magnitude but have similar meaning. The Demo- crat–Republican gap in COVID vaccine or ACC rejection widens from about .01 (when they have mixed or different-party friends) to 0.17 or 0.20 (when they have mostly same-party friends). 5.3 Robustness of results How stable are the findings described here? This section reviews diagnostic and goodness-of- fit statistics and evaluates some alternative model specifications. By inflating standard errors (making estimates less precise), multicollinearity can raise problems in multivariate analyses. This is particularly a risk when interaction terms are involved. Testing for multicollinearity, we used ordinary least squares to approximate variance inflation factors (VIF) and tolerances (1/VIF) for independent variables in each equation of our GSEM analysis. Chatterjee and Hadi [92] write that VIF of 10.0 or higher indicate serious multicollinearity; other sources suggest that a VIF of 4.0 could be cause for concern. For the NoACC and NoVax equations in Table 2, VIF ranged from 1.04 to 2.85, with a mean of 1.61, well below thresholds for concern. VIF are even lower for the Trumpism equation: 1.02 to 2.44,

with a mean of 1.51. These results indicate that despite the interaction terms, multicollinearity is not problematic for our analysis. Goodness of fit is an ambiguous concept with logit and ordered-logit analyses, which lack a simple counterpart to linear regression’s explained-variance measure, R2. A variety of pseudo R2 statistics have been proposed, but these can give divergent results and should be interpreted alongside other goodness-of-fit statistics [93]. McFadden’s pseudo R2, the most widely used, equals 0.20 for the NoACC equation in Table 2. Correct classification rate for this NoACC equation is 77% with 87% specificity and 54% sensitivity. A Hosmer-Lemeshow goodness-of- fit χ2 test does not reject the fitted model, nor does a specification link test. For the NoVax equation in Table 2, McFadden’s pseudo R2 equals 0.19; correct classifica- tion rate is 81% with 96% specificity and 23% sensitivity. As with NoACC, a Hosmer-Leme- show goodness-of-fit χ2 test does not reject the fitted model, nor does a specification link test. Unlike the binomial NoACC and NoVax equations, the equation for ordinal Trump support employs ordered logit regression, for which correct classification rates and Hosmer-Lemeshow tests are less appropriate. Pseudo R2 for this equation equals 0.21. Specification link tests sug- gest that we could obtain a better fit by using a dichotomous version of this variable (support Trump or not) instead of the 7-category ordinal measure employed. But a dichotomous indica-

tor for Trumpism is substantively less appealing. It also works less well analytically: using dichotomous instead of ordinal Trump support worsens the fit of both NoACC and NoVax equations, as indicated by comparisons of their respective Bayesian information criteria (BIC). That is, ordinal Trump support predicts NoACC and NoVax better than dichotomous Trump support does. These results support keeping the ordinal version. Two exogenous variables in our analysis, education (4 categories) and party (3 categories) were modeled as ordinal predictors having approximately linear effects. Such treatment fol- lows many previous studies (for graphed examples involving climate change and vaccines, see Figs 2 and 3 in [53]) and yields parsimonious, interpretable results consistent with hypotheses. An alternative approach preferred by some authors would be to enter education and party as unordered categorical variables. Although using categorical education and party requires PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 11 / 20

substantially more complicated models (six additional parameters in each equation), it does not improve fit. The categorical-predictor models yield almost the same pseudo R2 values and worse BIC. 6. Discussion Trumpism’s impacts on two existentially consequential cases of science rejection, climate change and COVID vaccination, reinforce its importance as an emergent sociopolitical iden- tity. Although Trump’s personal future is uncertain, his deep effects on US society are unlikely to go away soon; under some scenarios they could intensify. Even if support for Trump himself narrows, for example, elements of conspiracism and science rejection might become more pronounced among his core believers, or attach to new grievances and leaders. The potentially global consequences of science rejection by a leading US political party underline the need for continued scrutiny. An intervening-variable framework along the lines of Fig 1, and proposi- tions supported by this analysis, could apply in studies of Trumpism beyond Trump. Lower trust in scientists (generically) among US conservatives is well known [94–96], although sometimes coexisting with abstract expressions of trust in science itself. Surveys find liberal/conservative divisions across many specific fields besides climate and COVID—includ- ing disbelief in central propositions of biology, geology and astronomy; and lower trust in sci- entists regarding nuclear power, genetically modified organisms, environmental protection, renewable energy, forest management, and vaccination long before COVID [53,97–99]. Con-

servative (and Trump-supporter) distrust of epidemiologists emerged quickly during the 2016 Zika pandemic [32,100]. Bias across these major fields runs consistently in the same direction —contrary to early hypotheses that there might be an opposite bias among liberals against vac- cines, nuclear power or genetically modified organisms. In contrast to most surveys, some experimental studies have reported items where liberals express greater skepticism—but that seems most common with counterfactual experimental prompts such as an invented scientific consensus that nuclear power is the most efficient form of alternative energy [101], or a fic- tional expert asserting that carrying concealed handguns decreases violent crime [102]. The breadth of real fields where science rejection occurs tends to undercut explanations based on scientific content in particular cases. Examples of content-based explanations include religious objections to science on evolution or the age of the Earth [103], or a liberal/conserva- tive split on impact science vs. production science (e.g., science pointing out environmental or occupational health risks, vs. science that advances manufacturing or commerce) [104–106]. Disproportionately conservative science resistance across many fields also argues against explanations based on real conduct by scientists themselves, who in most areas are little known to the public. Hamilton and Safford [18] considered alternative hypotheses as they might apply to one specific case, the rapid fall of trust in the US Centers for Disease Control (CDC) observed

among Republicans during early months of the COVID-19 pandemic. They concluded that only elite cues—specifically, changing pronouncements from Trump—could explain the abruptness and scale of partisan decline. Other studies have found significant Trump-sup- porter effects regarding climate, COVID and other topics [14,19,31–33]. Trumpism exacer- bates the broader trend toward conservative science rejection across many fields. Matt Motta and coauthors recently studied “canine vaccine hesitancy” (CVH), a rising dis- trust among dog owners regarding long-established vaccines against rabies [107]. Their 2023 survey of 2,200 US adults found surprisingly high levels of doubts and non-vaccination, with concerns borrowed from misinformation about human vaccines. For example, 37 percent believed that vaccination might cause pets to develop autism. Political dimensions of this PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 12 / 20

phenomenon were not the original paper’s focus, but their dataset, also published, includes a question on 2020 voting. CVH is almost twice as common among Trump voters compared with Biden voters (60 vs. 33 percent). In multivariate analysis (by the author of this paper, not Motta et al.), Trump vs. Biden voting dominates other background factors (political party, gen- der, age, education, religion and race) as the strongest predictor of CVH. This example illus- trates the ease with which politicized rejection in one area of science can generalize or spill over to other areas. It also highlights Trumpism as a key element in this process, extending to topics where Trump himself has said nothing. Although conservative distrust of science does not apply to every field, it is demonstrably widespread and fits with broad trends toward anti-intellectualism [108] or rejection of exper- tise [109], populist impulses that can be mobilized to support economic or political goals [110]. The scientific method itself, a system for critically testing ideas against reality, will seem objectionable when it fails to support identity-bound convictions. In their study of online dis- course among flat Earthers, Diaz Ruiz and Nilsson [111] note the importance of grievances and group identification, which make fact-checking irrelevant. Similar observations could apply more widely, including conspiratorial claims about climate and COVID. In the case of climate change and COVID, preexisting biases against scientists were reinforced by messaging

from economic and political elites serving interests such as fossil fuel use or Trump’s re- election. Trump approval correlates with other conspiracy or science-rejecting views. Trumpists indicate higher agreement not only with flat Earth and Moon landing conspiracies, but also that vaccinations implant tracking microchips, and COVID-19 dangers have been exaggerated by scientists. At the same time, they express lower agreement with scientific conclusions that the Earth is billions of years old, humans evolved from earlier forms of life, human activities are changing the climate, or vaccines are mostly beneficial [41]. Trump’s expressed positions have sometimes changed rapidly, including a reversal on COVID-19 vaccinations—from his administration’s initiative (Operation Warp Speed, intended to spur vaccine development) to post-administration opposition, when it seemed President Biden could get credit. Trump’s views on the Centers for Disease Control reversed even faster, in just a few months [18]. In both cases, Trump’s followers echoed his reversals. To some degree, the content of Trumpism hinges on whatever Trump says. Among supporters surveyed for a CBS News poll in August 2023, 71 percent reported feeling that what Trump tells them is true—much higher than the fractions expressing trust in their friends and family, conservative media, or religious leaders [112]. Noting that such attitudes insulate Trump sup- porters even from other conservative viewpoints, one observer wrote that “Trumpworld is a

bubble within a bubble” [113]; another remarked that “If reality contradicts Trump, reality is wrong” [114]. Science and expertise remain outside the control of this bubble, and often in opposition to it. Unfortunately, there are deadly consequences to having a large fraction of the US public reject expertise regarding anthropogenic climate change, pandemics and other hard realities. As with any study, the findings here depend on the data at hand—a US survey conducted in mid-2021. There is much scope for cross-national comparisons. Outside the US, climate change and COVID are salient issues with ideological divisions [115–117], although the reality of these problems may be less disputed by mainstream parties. US left/right party or ideologi- cal divisions do not necessarily map onto the politics of other countries. Trumpism might, however, have analogues elsewhere in populist leaders or movements that combine grievances and anti-immigrant sentiments with authoritarianism and rejection of mainstream liberal democratic values [118,119]. PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 13 / 20

The 2021 date of our US data also is an important limitation. The phenomena studied here —rejection of climate change, COVID-19 vaccination, and admiration for former president Trump—are potentially subject to change. Indeed, temporal caveats apply to any public-opin- ion research, and perhaps more so now than in the past. The stability of conclusions from ear- lier surveys must be an empirical question. On the other hand, similarities shown here between the social bases of ACC and COVID vaccination rejection, one older and one com- paratively recent phenomenon, suggest a degree of stability that could apply to future science- linked issues as well. 7. Conclusions GSEM analysis of a nationally representative US survey conducted in mid-2021 finds both commonalities and differences in the social bases of rejecting anthropogenic climate change and rejecting COVID-19 vaccination. Prominent similarities include: $ Conservative ideology and Trumpism directly increase the likelihood of science rejection in both cases. $ Identification as white or evangelical do not directly affect science rejection, but do so indi- rectly through Trumpism. $ Science rejection on both issues becomes less common with higher education. However, the effects of education are significantly stronger among moderates and liberals. $ Having mostly same-party friends increases the probability of science rejection among Republicans. Notable contrasts between ACC and COVID-19 vaccine rejection include: $ The probability of rejecting ACC increases with age, whereas the probability of vaccine rejec-

tion decreases with age. $ Women are more likely than men to reject vaccination, but the genders are roughly similar (other things being equal) with regard to climate change. $ Vaccine rejection occurs more often in low-income households. Income has no impact on climate-change views. $ Receptivity to conspiracy beliefs directly increases the likelihood of vaccine rejection, while having mainly an indirect effect (through Trumpism) on climate rejection. Indirect effects through Trumpism on both ACC rejection and COVID-19 vaccine rejec- tion add to the direct effects of white and evangelical identity, ideology, party, conspiracism, and the friends×party interaction—strengthening the positive total effects in each case. These conclusions are robust within our 2021 survey dataset but invite comparisons with other issues, other countries, and new data, as political alignments, media reporting, and public per- ceptions on these issues evolve. Supporting information S1 File. Stata-format POLES 2021 survey dataset. (DTA) S2 File. Do-file for GSEM analyses. (DO) PLOS ONE Trumpism, climate and COVID PLOS ONE | https://doi.org/10.1371/journal.pone.0293059 January 10, 2024 14 / 20

Author Contributions Conceptualization: Lawrence C. Hamilton. Data curation: Lawrence C. Hamilton. Formal analysis: Lawrence C. Hamilton. Funding acquisition: Lawrence C. Hamilton. Investigation: Lawrence C. Hamilton. Methodology: Lawrence C. Hamilton. Writing – original draft: Lawrence C. Hamilton. Writing – review & editing: Lawrence C. Hamilton. References 1. IPCC. 2021. Climate Change 60–2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Masson-Del- motte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pe´ an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekc¸i, R. Yu & B. Zhou (eds.). New York: Cambridge University Press. https://doi.org/10.1017/ 9781009157896 2. USGCRP. 2017. Climate Science Special Report: Fourth National Climate Assessment (NCA4), Vol- ume I. Washington, D.C.: U.S. Global Change Research Program. https://science2017. globalchange.gov/ 3. Adam D. 2022. COVID’s true death toll: Much higher than official figures. Nature 603:562. https://doi. org/10.1038/d41586-022-00708-0 4. WHO. 2022. “WHO coronovirus dashboard.” World Health Organization. https://covid19.who.int/ accessed 6/24/2022. 5. Lewandowsky S. & Cook J. 2020. “Coronavirus conspiracy theories are dangerous–here’s how to stop them spreading.” The Conversation April 20. https://theconversation.com/coronavirus-conspiracy- theories-are-dangerous-heres-how-to-stop-them-spreading-136564

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