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Published in final edited form as: Psychol Health Med. 2023 Mar 13;28(7):1938–1949. doi: 10.1080/13548506.2023.2185644

Climate Change Views Examined Through a Behavioral Medicine Frame: Are There Potential Target Mechanisms for Change Beyond Political Ideology?

Rebecca E Lubin a, Donald Edmondson b, Michael W Otto a
PMCID: PMC10497712  NIHMSID: NIHMS1912489  PMID: 36912581

Abstract

The threat of climate change is associated with both profound health consequences and failures by many individuals to take preventive actions. Behavioral science research on health behavior engagement may serve as a lens through which to better understand attitudes associated with the threat of climate change. This study was designed to examine individual differences in attitudinal responses to climate change, understanding the degree to which these responses can be predicted by both political beliefs and more readily modified psychological factors commonly associated with health behavior engagement: locus of control, anxiety sensitivity, delay discounting, and intolerance of uncertainty. Participants (N=234) were US adults (62% male; 57% Non-Hispanic White; 44% Democrat) who completed an online survey. Stepwise multiple linear regressions examined which variables provided non-redundant prediction in models of climate change beliefs and concerns. In addition to providing support for the role of political affiliation and related ideology in climate change views (9–23% variance), this study underscores the importance of a behavioral health frame in understanding climate change concerns and beliefs. Known risk factors for negative health behaviors—prominently, locus of control, anxiety sensitivity, and delay discounting—contributed strongly to the understanding of these views, accounting for 4–28% of variance. Our findings encourage greater attention to health behavior-related constructs for understanding attitudes relevant to climate change action.

Keywords: climate change, political ideology, locus of control, anxiety sensitivity, delay discounting, intolerance of uncertainty

Introduction

The Intergovernmental Panel on Climate Change (IPCC) 6th assessment report (IPCC, 2022) communicated the global consensus that human activity has caused global warming, and rapid individual, corporate, and governmental action can mitigate some of climate change’s negative consequences. Despite this scientific consensus and calls for action, a significant number of individuals within the United States (US) continue to deny the reality and causes of climate change or the importance of rapid engagement in preventative actions. In recent polls, approximately 40% of Americans express little-to-no concern that climate change will harm them personally (Bell et al., 2021), and a similar proportion believe that climate change is neither human-caused (Leiserowitz et al., 2020) nor a major threat to the nation’s wellbeing (Pew Research Center, 2020).

Climate change has potential for devastating health consequences; environmental factors related to climate change are associated with 13M deaths yearly (World Health Organization, 2016) and climate change is predicted to worsen morbidity and mortality through changes in air and water quality, infectious disease vectors, and extreme weather, with up to 83M excess deaths predicted by 2100 (Bressler, 2021; Patz et al., 2005; Weinberger et al., 2017). Importantly, the US is one of the largest emitters of greenhouse gases (GHG; Boden et al., 2017); the impact of US policies and action on GHG emissions means that opposition of this single country – and its constituents – to climate action can derail global efforts to avert devastating worldwide consequences.

As such, it is important to increase scientific understanding of factors which may underly beliefs and actions related to climate change mitigation in order provide researchers and policymakers tools with which to better encourage pro-environmental attitudes and behaviors (PEABs) among US citizens. We previously proposed an expansive role for health psychology and related fields in simultaneously addressing both health behaviors and PEABs: the “co-benefits” model of health and climate behavior suggests overlapping causes and benefits of both sets of core behaviors (Chevance et al., 2022; Edmondson et al., 2022). The health and climate benefits of some behavior changes are well understood, but whether the two share mechanisms of behavior change avoidance is not.

Research has identified a number of factors that carry significant weight in predicting climate change attitudes, beliefs, and behaviors. Particularly noteworthy is the degree to which political party affiliation has been associated with attitudinal responses to climate change, with meta-analysis documenting a moderate effect size for the lower levels of climate change belief found among those endorsing a Republican affiliation (Hornsey et al., 2016). The ideologies of social dominance orientation (SDO) and right-wing authoritarianism (RWA) account for much of the effect of Republican party affiliation on climate change views (Stanley & Wilson, 2019). Addditionally, religiosity, which has been shown to be closely related to both political affiliation and ideology, predicts climate change views and policy preferences on an individual and country-specific level (Mostafa, 2016; Sharma et al., 2021). These findings are important in that they help characterize individuals who may be least likely to adopt recommended PEABs; however, political ideology and religiosity represent relatively stable and rigid constructs (Brace et al., 2004; van Prooijen & Krouwel, 2019; Zmigrod, 2020), offering little leverage for behavioral or climate scientists seeking mechanistic targets for intervention.

An alternative frame for engaging beliefs about climate change and PEAB is offered by studies targeting health behavior, particularly those concerning the rejection of empirically-supported behavior change recommendations (e.g., smoking, drinking, overeating, under-exercise) to prevent deleterious future health consequences (Bavel et al., 2020; Nielsen et al., 2021). These studies have investigated psychological factors that affect beliefs and actions associated with health behaviors. For example, elevated anxiety sensitivity (AS) and greater delay discounting (DD) (i.e., preference for smaller rewards now vs. greater future rewards) are strongly linked with a range of negative health behaviors (Amlung et al., 2016; Bickel et al., 2019; Horenstein et al., 2018; Otto et al., 2016) as well as other politically-linked health behaviors (i.e., COVID-19 protective behaviors and attitudes; Leventhal et al., 2021; Milligan et al., 2021; Neelon et al., 2021). Preliminary studies have shown DD to be relevant to climate change views (Jacquet et al., 2013; Weber et al., 2007). Internal locus of control (LOC) has been identified as a correlate of greater engagement in protective health behaviors such as healthy eating and regular exercise (Cobb-Clark et al., 2014; Kesavayuth et al., 2020; Steptoe & Wardle, 2001) and is also correlated with PEABs and greater concern about climate change (Engqvist Jonsson & Nilsson, 2014; Fielding & Head, 2012; Mostafa, 2016). Lastly, there is additional evidence that intolerance of uncertainty (IoU) is associated with a bias toward more immediate but less valuable rewards (Luhmann et al., 2011), potentially reducing inherently future-oriented PEABs. Importantly, each of these factors—LOC, AS, DD, and IoU—are potential mechanistic targets underlying negative health behaviors that have been influenced by interventions (Fitzgerald et al., 2021; Oglesby et al., 2017; Robichaud, 2013; Rung & Madden, 2018), although evidence on LOC modification needs updating (Baker, 1979; Coughlin et al., 2000). That is, relative to more rigid factors such as political ideology, these psychological factors may be useful intervention targets for influencing PEABs.

Thus, the purpose of the current cross-sectional study is to examine whether these malleable psychological predictors of health behavior change are also reliably and uniquely associated with beliefs and attitudes towards climate change. This research purpose is consistent with a Science of Behavior Change (SOBC) approach to mechanistic intervention development, emphasizing the importance of documenting an association between a putative mechanism of behavior change and a behavior change outcome (Nielsen et al., 2018). As such, this study represents a first step in elucidating the intervention potential associated with these targets for influencing PEABs. Further, we will evaluate whether ideological beliefs associated with party affiliation – namely conservatism, RWA, SDO, and religiosity (Malka et al., 2012; Stanley & Wilson, 2019) – better account for predictive variance in climate change views than political party affiliation. The result will be a statistical accounting of relatively modifiable and relatively immutable correlates of climate change beliefs and attitudes.

Methods

Participants and Procedures

Participants (N=234) were recruited through Amazon Mechanical Turk (MTurk), a crowdsourcing recruitment tool, for a study which examined the impact of COVID-19 on a range of psychiatric symptoms, transdiagnostic psychological factors, and health behaviors. Study inclusion criteria were self-reported age of 18 years or older, current residency in the US, and ability to read and respond in English and provide informed consent. After providing electronic consent, participants completed questionnaires requiring approximately 30 minutes of time and were compensated for this effort. Research shows that MTurk data is comparable to data collected via more traditional means if best practice recommendations are followed (Aguinis et al., 2021; Chmielewski & Kucker, 2020); in accordance with these recommendations, participants were excluded if they did not pass data quality assessments such as incorrect responses to quality assurance questions or insufficient survey-taking duration (e.g., 31 participants excluded). This study was approved by the Boston University Institutional Review Board.

Measures

Participants were asked to report on demographics, which included age, gender (female=0, male=1), racial/ethnic minority status (NH White=0, Others=1), and education. Political party affiliation was assessed with a single question: ‘What political party are you registered under?’ Possible responses included: ‘I am not registered under a political party,’ ‘Democrat,’ ‘Republican,’ ‘Independent,’ and ‘Other (write-in) and were coded (Republican=1, Other=2). Religiosity was assessed with the Religious Commitment Inventory (RCI; Worthington et al., 2003), a 10-item self-report measure; Cronbach’s α=.941. Authoritarianism was assessed with the Very Short Authoritarianism Scale (VSA; Bizumic & Duckitt, 2018), a 6-item self-report measure assessing dimensions of conservatism and authoritarian aggression and submissions, with higher scores indicating greater authoritarianism. Cronbach’s α=.485. Social dominance was measured with the Social Dominance Orientation-7 (SDO7) Scale, a 16-item measure of individual differences in two dimensions of social dominance orientation (SDO): dominance (i.e., support for overt oppressive and aggressive intergroup behaviors that maintain the subordination of other groups) and anti-egalitarianism (i.e., preference for intergroup inequalities maintained by more subtle ideologies and social policies that enhance hierarchical systems; Ho et al., 2015). The mean score for each subdimension was used. Cronbach’s α for social dominance = .643, for anti-egalitarianism = .737. Conservatism was assessed with the Social and Economic Conservatism Scale (SECS; Everett, 2013), a 12-item scale used to assess political conservativism in the areas of social and economic conservativism, e.g., abortion, gun ownership, and fiscal responsibility. Participants rate the extent to which they feel positive or negative towards each; the mean of these items was used, with higher scores indicating greater conservatism. Cronbach’s α=.767.

Psychological Factors

The Intolerance of Uncertainty - Short Form is a 12-item self-report measure assessing responses to unpredictable situations, the future, and the general unknown (Carleton et al., 2007). Higher scores reflect increased IoU for the composite sum of the 12 items. Cronbach’s α=.913. Locus of control was assessed with the Levenson Multidimensional Locus of Control Scales (MLCS; Levenson, 1973). The MLCS is 24-item measure composed of three subscales (Internal Locus of Control, Powerful Others, and Chance). In the present study, only the internal LOC subscale was utilized; a higher rating indicates stronger internal LOC. Cronbach’s α=.809. Delay discounting was assessed with a computer-based monetary discounting task which presented participants with choices between specific monetary amounts and time frames for receiving that amount (Koffarnus & Bickel, 2014). This computerized task used an adjusting amount procedure to derive indifference points; the discount rates (k) were used as the primary outcome measure for this task, with all k values natural-log transformed to decrease any potential positive skew (D. C. Lee et al., 2015). A higher k value indicates a steeper discounting of delayed rewards. Anxiety sensitivity was measured with the total score of the Anxiety Sensitivity Index-3 (ASI-3), an 18-item self-report measure of an individual’s tendency to fear and perceive anxiety related sensations as harmful (Taylor et al., 2007). This scale has been found to be internally consistent, reliable, and valid among clinical and nonclinical samples (Taylor et al., 2007). Cronbach’s α=.957.

Climate Change

Climate change beliefs and concerns (two key dimensions of pro-climate views commonly explored in this literature; McCright et al., 2016; van der Linden, 2015) were assessed with 5 items rated on a scale from 0 (‘fully disagree’) to 6 (‘fully agree’). Components of climate change beliefs include belief in its reality, its causation through human activities, and its impact (Leviston & Walker, 2012) as indexed by the mean score for two items (“The Earth’s climate is getting warmer” and “Global warming is caused by human activities;” Cronbach’s α=.665). Components of climate change concerns include perceived seriousness, dangerousness, or risk of climate change (Kvaløy et al., 2012), as indexed by the mean score for three items (“The seriousness of global warming is exaggerated in the news media” (reverse scored), “Global warming is bad for human health,” and “Global warming will be a serious problem in my local community;” Cronbach’s α=.285).

Statistical Analyses

All analyses were conducted using Statistical Package for Social Sciences (SPSS) version 27. Preliminary analyses assessed zero-order associations among predictor variables and outcome variables using Pearson’s or point-biserial correlations. Subsequently, stepwise multiple regression analyses were used to examine which of the significant bivariate predictors provided non-redundant prediction of climate change beliefs and concerns.

Results

Of the 234 participants included in these analyses, 62.0% identified as male. The mean age was 36.0 years (SD = 12.3). The sample was both racially/ethnically and politically diverse; 56.8% of the sample identified as Non-Hispanic Whites, and 44.0% of participants endorsed Democratic party affiliation, 39.7% endorsed a Republican party affiliation, and 10.7% were registered as Independents. Sample descriptive statistics are presented in Table 1.

Table 1.

Descriptive Statistics of Variables

Variable M (SD) or %
Age* 35.96 (12.34)
Gender (% male) 61.97%
Race/Ethnicity (% NH White) 56.84%
Education (% Bachelor’s degree or above) 79.91%
Political Party (% Republican) 39.74%
Religiosity 32.36 (10.21)
Right Wing Authoritarianism* 0.01 (1.23)
SDO - Dominance 3.63 (0.90)
SDO - Anti-Egalitarianism 3.59 (1.00)
Social & Economic Conservatism 62.68 (12.17)
Internal LOC 33.26 (7.94)
Intolerance of Uncertainty 35.85 (9.01)
Anxiety Sensitivity 37.72 (17.01)
Delay Discounting, ln(k) −2.55 (3.47)
Beliefs in Global Warming 4.42 (1.15)
Concern About Global Warming 3.68 (0.97)
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Note.

*

1 participant value missing.

Table 2 provides the bivariate relationships amongst climate change views and the continuous and dichotomous variables examined.

Table 2.

Bivariate Correlations Between Predictor Variables and Climate Change Variables

Predictors Climate Change Beliefs (r or rpb) Climate Change Concerns (r or rpb)
Demographic    
Age .091 .150*
Education −.017 .015
Gender −.095 −.109
Racial/Ethnic Group −.121 −.053
Political/Ideological
Political Party .082 .161*
Religiosity .057 −.181**
Conservatism −.030 −.267***
Authoritarianism −.160* −.303***
Social Dominance −.269*** −.414***
Anti-Egalitarianism −.299*** −.443***
Psychological
Locus of Control .508*** .132*
Delay Discounting −.175** −.165**
Anxiety Sensitivity .088 −.221***
Intolerance of Uncertainty .192** −.161*
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Note.

*

p < .05.

**

p < .01.

***

p<.001.

Age was significantly positively associated with the level of climate change concerns, but not beliefs.1 Education was not significantly associated with either climate change beliefs or concerns. Climate change belief was significantly negatively associated with several variables related to political views and ideology (authoritarianism and both subdimensions of SDO, dominance and anti-egalitarianism), but not all (religiosity or social/economic conservatism). However, climate change concern was significantly negatively correlated with all variables related to political views and ideology.

With regard to psychological variables, greater climate change belief was significantly correlated with greater internal LOC, greater IoU, and lower DD, but not AS. Similarly, greater climate change concern was significantly correlated with greater internal LOC and lower IoU, DD, and AS.

Stepwise Regression Models

Climate Change Beliefs.

Only significant bivariate predictors were included in this stepwise linear regression: anti-egalitarianism, social dominance, authoritarianism, IoU, LOC, and DD. Of ideological predictors, only anti-egalitarianism was statistically significant (β=−.288, t(230)=−5.364, p<.001), accounting for 8.6% of the variance in climate change beliefs. Of psychological predictors, internal LOC (β=.518, t(230)=9.884, p<.001) and DD (β=−.117, t(230)=−2.183, p=.030) were statistically significant, together accounting for an additional 27.6% of the variance. The full model accounted for 36.2% of the variance in climate change beliefs, F(3,230)=45.002, p< .001.

Climate Change Concern.

Significant bivariate predictors were included in this stepwise linear regression: age, political party, religiosity, and all ideological and psychological predictors. Age was not significant. Of political/ideological predictors, only anti-egalitarianism (β=−.331, t(229)=−5.263, p<.001), and authoritarianism (β=−.241, t(229)=−4.158, p<.001) were significant predictors, accounting for 23.2% of the variance; political party was non-significant when they were included in the model. Of psychological predictors, only internal LOC (β=.240, t(228)=3.848, p<.001) and AS (β=−.164, t(229)=−2.451, p=.015) accounted for an additional 4.3% of the variance. The full model accounted for 27.5% of the variance in climate change concern, F(4,229)=23.100, p<.001.

Discussion

Individual behaviors can simultaneously benefit both health and climate change, but whether similar psychological mechanisms that undermine behavior change for health may also explain failure to adopt PEABs has not been determined. This study replicated past research showing that political and ideological factors provide a frame for understanding individual differences in climate change beliefs and concerns, and extended that research to provide initial evidence that psychological variables associated with health behaviors also provide a strong frame for understanding these differences.

The predictive value of psychological variables was evident after statistical adjustment for political and ideological determinants of climate change beliefs and concerns. Political orientation was significantly related to climate change beliefs and concerns, but ideological factors provided better statistical prediction than political orientation. Specifically, higher anti-egalitarianism emerged as uniquely associated with lower belief ratings for climate change, and both higher anti-egalitarian and authoritarianism emerged as significantly associated with lower climate change concern. These ideological factors explained 8.6% in climate change beliefs and 23.2% of the variance in climate change concerns.

Compared to political ideology, psychological factors that have historically been associated with health behaviors explained a greater proportion of unique variance in climate change beliefs (internal LOC and DD accounted for 27.6% of variance, vs. 9% for ideological factors). However, ideology was more important for climate change concerns, explaining 23% of the variance vs. 4% for psychological factors (LOC and AS). Climate change consequences harm low SES and minoritized populations first and worst (EPA, 2021), and authoritarian political responses to climate change have accelerated, so the alignment of anti-egalitarian and pro-authoritarian ideology with low climate change concerns is chilling, but not surprising.

The direction of psychological findings was consistent with previous studies of health behavior. Greater LOC is linked to better health behaviors (e.g., Steptoe & Wardle, 2001) as is lower DD (Bickel et al., 2019) and lower AS (see Otto et al., 2016). The pattern and magnitude of associations between psychological mechanisms of adaptive health behavior change and climate beliefs/concerns encourage optimism about behavioral medicine’s role in understanding and intervening on climate change denial and inaction. Psychological variables complement the established role of political orientation/ideological factors; but psychological factors, particularly DD and AS, may be more modifiable mechanistic targets for co-beneficial climate/health interventions (Baker, 1979; Fitzgerald et al., 2021; Rung & Madden, 2018). This study represents a first step in elucidating the intervention potential associated with these targets for influencing health behaviors, climate views, and perhaps pro-environmental behaviors. The most powerful individual behaviors for reducing GHG emissions are also important health behaviors (e.g., plant-based diet, active transport; Chevance et al., 2022; Edmondson et al., 2022).

There are several limitations to the current study. First, it focused on beliefs and concerns about climate change – using questions based on important concepts within climate change literature – not behaviors; the field is still working to identify effective measures of pro-environmental behaviors and climate change views (Lange & Dewitte, 2019), and further research examining psychological factors associated with these constructs would benefit from the use of nuanced, validated measures. Second, data collection and recruitment methods represented a moderate sample size of adults in the young-to-middle adult age range, potentially limiting our ability to detect age effects and the generalizability of our findings. Poor internal consistency in some predictor measures (authoritarianism and dominance) encourage additional research examining the relationship of these constructs with climate change views, perhaps using alternate measures. Lastly, cross-sectional associations should not be interpreted as causal, and weaknesses of self-report should be considered.

Nonetheless, this research shows promising support for the application of behavioral medicine expertise and methods to address climate climate change, and suggests potential intervention targets to influence PEABs. Further, our findings are consistent with a co-benefits model of health behavior, as well as the mechanistic, health behavior focus on climate change recommended by the SOBC program: https://scienceofbehaviorchange.org/climate-change/. Indeed, related preliminary work has already begun examining the efficacy of PEAB interventions using techniques known to target mechanistic factors examined here to increase PEABs (Lee et al., 2020).

Funding:

Effort on this manuscript for Ms. Lubin and Drs. Edmondson and Otto was supported, in part, by the NIH Columbia University Science of Behavior Change Resource and Coordinating Center (U24AG052175). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Declaration of Interest Statement

The authors would like to acknowledge the following relationships: Dr. Otto receives compensation as a consultant for Big Health and receives book royalties from multiple publishers. No other authors have relevant financial or non-financial interests to report.

1

This finding is inconsistent with some reviews which have indicated an association of younger age with greater climate change risk perception (e.g., Hornsey et al., 2016; Milfont et al., 2021), and adds to the perspective noted elsewhere that findings on this relationship are inconsistent and may be country-dependent (Arıkan & Günay, 2021). It is worth noting that this finding may be attributable to the relatively narrow age range of our sample; the majority of participants were middle age or younger.

Data Availability Statement

The de-identified that support the findings of this study are available (as allowable according to institutional IRB standards) by emailing the corresponding author, RL, upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The de-identified that support the findings of this study are available (as allowable according to institutional IRB standards) by emailing the corresponding author, RL, upon reasonable request.

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