metadata: authors: 0: author: Lokshin,Michael M.
1: author: Torre,Ivan
2: author: Hannon,Michael Jonathan
3: author: Purroy Vitola,Miguel Eduardo
count: World
docna: 0: docna: Do More Informed Citizens Make Better
            Climate Policy Decisions ?
docty: Policy Research Working Paper
abstracts: cdata!: This study explores the relationship
            between perceptions of catastrophic events and beliefs about
            climate change. Using data from the 2023 Life in Transition
            Survey, the study finds that contrary to conventional
            wisdom, more accurate knowledge about past catastrophes is
            associated with lower concern about climate change. The
            paper proposes that heightened threat sensitivity may
            underlie both the tendency to overestimate disaster impacts
            and increased concern about climate change. The findings
            challenge the assumption that a more informed citizenry
            necessarily leads to better climate policy decisions.
            Instead, they suggest that psychological factors, like
            anxiety and risk perception, play crucial roles in shaping
            climate attitudes. Illuminating these dynamics can help
            societies to foster a more nuanced and constructive public
            dialogue about the urgent challenges facing our planet and
            our species.
display_title: Do More Informed Citizens Make
            Better Climate Policy Decisions ?
listing_relative_url: /research/2024/09/34393450/more-informed-citizens-make-better-climate-policy-decisions
topicv3: Climate Change m1326829 567,Climate Change and Vulnerable Groups
            m1327305 1770
publishtoextweb_dt: 2024-09-20T00:00:00Z
dois: 10.1596/1813-9450-10921
no_of_pages: 33
author_keys: 0: author_key: 144523
1: author_key: 1521219
2: author_key: 2431724
3: author_key: 2343073
model_extractions: Policy Research Working Paper 10921 Do More Informed Citizens Make Better Climate Policy Decisions?

# _Michael M. Lokshin_

_Iván Torre Michael Hannon Miguel E. Purroy_ Europe and Central Asia Region Office of the Chief Economist September 2024
Policy Research Working Paper 10921

# **Abstract**

This study explores the relationship between perceptions of catastrophic events and beliefs about climate change. Using data from the 2023 Life in Transition Survey, the study finds that contrary to conventional wisdom, more accurate knowledge about past catastrophes is associated with lower concern about climate change. The paper proposes that heightened threat sensitivity may underlie both the tendency to overestimate disaster impacts and increased concern about climate change. The findings challenge the assumption that a more informed citizenry necessarily leads to better climate policy decisions. Instead, they suggest that psychological factors, like anxiety and risk perception, play crucial roles in shaping climate attitudes. Illuminating these dynamics can help societies to foster a more nuanced and constructive public dialogue about the urgent challenges facing our planet and our species.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mlokshin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Do More Informed Citizens Make Better Climate Policy Decisions?

_Michael M. Lokshin, Iván Torre, Michael Hannon, and Miguel E. Purroy_<sup>_1_</sup>

**JEL:** Q54, D83, Q58

**Keywords:** climate change; catastrophes; threat sensitivity; public opinion; risk

perceptions; science communication; anxiety

> 1 Michael Lokshin is Lead Economist and Ivan Torre Senior Economist in the Chief Economist Office of the Europe and Central Asia Region of the World Bank. Michael Hannon is an Associate Professor of Philosophy at the University of Nottingham. Miguel E. Purroy is a consultant to the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
# **1. Introduction**

On March 11, 2011, a 9.0-magnitude earthquake off Japan’s Pacific coast triggered a tsunami that swept over Honshu Island in Japan, killing more than 19,500 people. The tsunami damaged the power supply to the cooling system of three reactors of the Fukushima Daiichi nuclear power plant, causing them to melt. This nuclear accident was rated the highest, level 7, on the International and Radiological Event Scale (World Nuclear Association 2023).

Although no deaths or cases of radiation sickness have been reported, the response to the Fukushima catastrophe was transformative at a global scale (Latre et al. 2017).<sup>2</sup> Japan suspended 21 of its 54 nuclear energy units (Mounfield 2017). Germany decided to shut down all its nuclear power plants by 2022, and Taiwan, China, decided to do so by 2025. Italy reversed its decision to resume its nuclear energy program. France, where nuclear power accounts for about 70 percent of power generation, decided to reduce nuclear dependence to 50 percent of its energy mix by 2025. The Swiss parliament decided to phase out nuclear energy altogether by 2034, and Spain confirmed its plans to close all nuclear stations by 2035. Public opinion was instrumental in supporting these often-costly reforms, which altered the global energy landscape.<sup>3</sup> This paper examines the relationship between how well-informed people are about major global events and their attitudes toward climate change. We assess respondents’ awareness using responses to questions about how many people died in four disasters: the 1984 Bhopal chemical plant accident, the 1986 Chernobyl nuclear power plant accident, the 2010 Haiti earthquake, and the 2011 Fukushima nuclear power plant accident.<sup>4</sup> The question asks, “Could you estimate how many people died from these disasters?” and presents respondents with six response choices regarding the number of deaths in each accident – less than 100, less than 1,000, 10,000, 100,000, Million or more, and Do not know.

<u>Figure 1</u> shows the distribution of answers to that question. The majority of LiTS respondents underestimated the fatalities in the Bhopal chemical leak accident of 1984 (almost 70 percent of respondents) and the Haiti earthquake of 2010 (about 61 percent). At the same time, the

> 2 In 2018, the Fukushima nuclear plant worker died from lung cancer believed to be the result of radiation exposure (BBC 2018).

> 3 For example, Jarvis et al. (2022) estimate that the total costs of phase-out of the nuclear energy for Germany could be up to €160 billion.

> 4 Background information on these events is included in Appendix A.

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respondents grossly overestimated the number of deaths from both nuclear accidents. More than 75 percent of respondents think the number of deaths caused by the Chernobyl nuclear plant accident exceeded 10,000, with almost 17 percent believing that the accident caused a million or more deaths, but the actual estimates range from 50 deaths directly related to the accident to 4,000 total deaths from the radiation exposure over the lifetime.<sup>5</sup> About 92 percent of respondents think that the Fukushima accident resulted in fatalities much exceeding the actual estimates of no radiation-related deaths. Fifteen percent of respondents estimate the total number of fatalities in millions and almost 30 percent in hundreds of thousands.

We estimate a model that relates respondents’ perceptions of climate change and their willingness to pay for climate-change-mitigating policies with the aggregate index of their knowledge about fatalities in four global catastrophes. We use the instrumental variable (IV) approach to mitigate the effect of systematic non-response bias. Sensitivity analysis confirms the stability of our results to a wide range of alternative model specifications, aggregation methods, and variable definitions.

This paper presents evidence that challenges the assumption that more knowledgeable citizens invariably make better political decisions, particularly regarding climate change. It is widely believed that policies rooted in ignorance may lead to unjustified risks; our findings suggest that the opposite can be true in certain contexts. We show that individuals with _less accurate_ perceptions of catastrophe-related deaths tend to hold _more accurate_ beliefs about the societal risks of climate change and that people with more accurate beliefs about fatalities from catastrophes tend not to take climate change as seriously as scientists believe they should.

These findings challenge the conventional wisdom that better-informed citizens will make decisions that are in society’s best interests. In some cases, an inflated sense of risk from catastrophes may be more effective at instilling the urgency needed to address critical global challenges. Our findings suggest that a more nuanced understanding of the relationship between

> 5 These numbers correspond surprisingly well with the result of the 2012 All Russia Omnibus survey, which asked respondents to evaluate the number of deaths from the Chernobyl and Fukushima nuclear disasters using similar scales (Levada Center 2012). Thirty-three percent of Russian respondents thought that at least 100,000 people died in Chernobyl and 25 percent thought that as many people died in Fukushima; 10 percent of the respondents believed that millions for people died in Chernobyl, and 9 percent reported that millions died in Fukushima. A quarter of respondents responded “do not know” about Chernobyl and a third reporting not knowing about Fukushima.

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knowledge, risk perception, and policy preferences is required to effectively address complex societal challenges like climate change.

This paper is organized as follows. Section 2 discusses the literature related to the study and the theoretical background. Section 3 describes the data used for the empirical analysis, which is presented in Section 4. Section 5 presents the empirical results, and Section 6 the sensitivity analysis. Section 7 provides a general discussion of our findings. Section 8 concludes the paper with key insights and implications of the paper’s findings.

# **2. Relevant literature and theoretical background**

A commonly held belief is that policies rooted in ignorance may lead to unjustified risks, potentially harming the common good. This perspective can be traced back to _Republic_ , in which Plato criticizes democracies for relying on the opinions of uninformed citizens while disregarding the informed counsel of experts (Plato 1997). The view aligns with the modern “information deficit model” of science communication, which posits that a lack of sufficient information is the primary obstacle to public understanding and engagement with scientific or technical issues, such as climate change or vaccine hesitancy (Suldovsky 2017).

Contemporary scholars have echoed this perspective, arguing that public ignorance poses a significant threat to effective governance (Brennan 2016, <u>Caplan 2007, Somin 2016). False beliefs</u> about COVID-19, for example, may have prevented actions that could save lives (Roozenbeek et <u>al. 2020, Loomba et al 2021). However, recent research challenges this view (Kellstedt et al. 2008).</u> For example, <u>Kahan et al. (2012)</u> demonstrate that “ _members of the public with the highest degrees of science literacy and technical reasoning capacity were not the most concerned about climate change. Rather, they were the ones among whom cultural polarization was greatest._ ” Motivated reasoning, cultural cognition, and ideological predispositions play a more significant role than knowledge in shaping individuals’ perceptions of risk and policy preferences (Kahan et <u>al. 2011, Kahan 2013, Drummond and Fischhoff 2017). In addition, cognitive reflection,</u> numeracy, education, and knowledge of basic scientific facts can all _magnify_ partisan bias and political polarization (Hannon 2022).

This suggests that public disagreements about global warming stem not from a lack of information but from fundamental conflicts of interests and worldviews. Generalizing this point, <u>Hannon</u> 4
<u>(2022)</u> argues that “ _a smarter, better-educated citizenry would not necessarily diminish polarization, lead to better policy decisions, or improve democracy._ ” Overall, there is little evidence to suggest that public apathy towards climate change is driven primarily by a lack of knowledge (Hornsey et al. 2016; <u>Kahan et al. 2012; Kellstedt et al. 2008; Malka et al. 2009).</u>

# **3. Data and definitions of variables**

Our data come from the 2023 round of the Life in Transition Survey (LiTS) conducted by the European Bank for Reconstruction and Development (EBRD) and the World Bank and covering the “transition” economies of Europe and Central Asia and several comparator countries in Western Europe, the Middle East, and North Africa (EBRD 2023). The 2023 round of the LiTS was conducted in 37 countries in Europe, Central Asia, and Northern Africa and includes nationally representative samples of around 1,000 households per country.<sup>6</sup>

# _3.1 Perceptions of climate change_

To understand the beliefs and preferences regarding climate change, we examine responses to the question, _“How convinced are you personally that climate change is real?”_ for which we construct a binary indicator that takes the value of 1 if the respondent answers _“quite convinced”_ or _“entirely convinced,”_ and 0 otherwise. We coded the question _“How convinced are you personally that climate change is manmade?”_ in the same way. To capture respondents’ awareness of the potential impacts of climate change, we examine responses to two questions: _“Do you think climate change seriously affects or will seriously affect you during your lifetime?”_ and _“Do you think climate change seriously affects or will seriously affect the children of today during their lifetime?”_ Two dummy variables take the value of 1 if the respondent answers _“Yes”_ and 0 if “No” to each question.

To understand whether respondents’ policy preferences translate into actions, we examine the question, _“To what extent do you agree or disagree with each of the following statements: I would be willing to pay more in taxes if the extra money were used to...”_ with options including _“...reduce/prevent pollution such as improving the quality of air or water, or dealing with_

> 6 The 37  countries surveyed in the 2023 round of LiTS are Albania, Algeria, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Estonia, Georgia, Germany, Greece, Hungary, Jordan, Kazakhstan, Kosovo, the Kyrgyz Republic, Latvia, Lebanon, Lithuania, Moldova, Mongolia, Montenegro, Morocco, North Macedonia, Poland, Romania, the Russian Federation, Serbia, the Slovak Republic, Slovenia, Tajikistan, Tunisia, Türkiye,  Uzbekistan, and West Bank and Gaza.

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_waste/sewage,” “...fight global warming or the greenhouse effect,”_ and _“...prevent the loss of plant or animal species or biodiversity.”_ We created three binary indicators for each category that take the value of 1 when respondents answered _“Agree”_ or _“Strongly Agree”_ and 0 otherwise.

# _3.2 Perceptions of the death toll from catastrophes_

We use the responses about the number of fatalities in each disaster to construct an index measuring the degree of overestimation of the fatalities. For each disaster, we compute the difference between the respondent’s estimate and the actual number of deaths by order of magnitude (for example, if a respondent estimates that the number of fatalities was about 100,000 and the actual number was about 1,000, the difference would be an overestimation of two orders of magnitude). We aggregate responses across the four disasters as a weighted sum, with the weights of each response inversely proportional to the number of non-responses to the question.

Because only 39 percent of respondents provided valid responses for all four disasters, we impute missing responses for the 15 percent of respondents who answered at least two disaster-related questions using an Ordered Random Forest Machine Learning technique <u>(Lechner and Okasa 2019).</u><sup>7</sup> We estimate four catastrophe-specific models to predict the probabilities that a respondent gives an answer for each casualty category conditional on individual covariates (age, gender, education level, marital status, religion, mental health indicators, main sources of information, urban/rural location, household composition, household income, and distance to the location of the

> 7 Of the 35,572 respondents who were asked this question, 36.8 percent responded “do not know” to all four disasters. 9.9 percent provided a valid answer for three disasters, 7.7 percent provided a valid response for two disasters, and 7.3 percent provided a valid answer for one disaster. The share of respondents who responded “Do not know” was 56 percent for Bhopal, 49 percent in Fukushima, 48 percent in Haiti, and 41 percent in Chernobyl.

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disaster). We include non-linear combinations of these variables to enhance model fit. We then impute the value corresponding to the category with the highest probability.<sup>8</sup>

# _3.3 Other variables_

We use data from the World Development Indicators (WDI) for country-level indicators such as GDP per capita and population size (World Bank 2024). We derive the distance to catastrophe variable based on the centroid of the event (locations of the plants or the epicenter of the earthquake). We construct the distance to nuclear plants from the respondent’s primary sampling unit (PSU) indicator was constructed based on the coordinates of each nuclear plant in operation compiled by the <u>International Atomic Energy Agency (2023).</u><sup>9</sup> <u>Table 1 provides descriptive</u> statistics of all the variables in the analysis.

# **4. Empirical strategy**

We examine the effects of factors determining the respondent’s evaluation of the number of deaths by estimating the following equation for each disaster _d_ : i i i i i , (2) dd dd dd dd dd ii where, i is a set of individual characteristics such as the respondent’s age, gender, highest level OO = �RRFF −AAFF �= αα XX + γγ GG + θθ + εε , jj= 1, ... ,4 of education, marital status, religion, mental health, and main source of information; i is a vector XX of regional characteristics, such as distance to the disaster site, the distance to a nuclear station GG index, and the type of location (urban or rural); is the time-invariant country _c_ fixed effect, and i is an innovation term. ii θθ εε

> 8 In each estimation, we use 80 percent of the observations with no missing responses for a particular catastrophe question to train the model and evaluate the model’s performance on the remaining 20 percent of observations. We then impute missing responses based on the model out-of-sample prediction. We opted for bootstrapping as our sampling method, constructing 1,000 trees in the forest (bootstrap replications) to optimize prediction performance, as in Lechner and Okasa (2019). Our model consistently outperformed a model in which the most frequent category of each outcome was used as a predictor. The accuracy rate of our model exceeds that of the “most frequent category” model by 8.6 percent for Bhopal, 11.5 percent for Chernobyl, 21.4 percent for Haiti, and 9.9 percent for Fukushima. McNemar’s test (McNemar 1947) rejects the null hypothesis that the “most frequent category” model outperforms our model with 99 percent significance in all four scenarios.

> 9 To assess the closeness of a PSU to nuclear plants, we calculated an index of distances from that PSU to all nuclear plants in the world as a sum of inverted distances: = ∑NN p 1 �� ~~p~~ , where is a distance indicator for PSU _i_ , p pp nnppiinn are coordinates of PSU _i_ and DDnuclear plantii jj=1 ��CC _j_ ii , pp _p_ −CC is distance penalizing parameter, and N is the number of nuclear stations in the world.    jjnnppn DDii CCii and CCjj

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The probability that i is equal to _dh_ is given by the probability that i i i dd dd dd falls between cutoff points _kh-1_ and _kh_ , such that _k0_ is assumed to be equal to -∞ and _kH_ to +∞: ii OO αα XX + γγ GG + θθ + εε i h h−1 i i i h) (3) dd dd dd ii Assuming i is normally distributed, we can use an ordered probit to estimate the parameters of Pr�OO = dd �= Pr (kk < αα XX + γγ GG + θθ + εε ≤kk (3). εε Questions used to construct i have a large number of “ _do not know_ ” responses. We assume there dd are two reasons why respondents would choose “ _do not know_ ”. They may be genuinely unaware OO of the number of fatalities in a disaster, or they may simply want to finish the interview sooner and give that response to speed up the process. If “ _do not know_ ” responses are non-random, for example, if respondents who are more likely to overestimate fatalities are also more likely to be impatient about the interview process, the estimates in equation (3) could be biased.

To address this potential source of bias, we model the non-response process by estimating the probability of giving a response (other than “ _do not know_ ”) as a function of the respondent characteristics jointly with estimating the ordered probit in equation (3). We use the number of “ _do not know_ ” responses to other questions in the section of the LiTS that contains the disaster question (module 4) as an instrument for the selection equation. The exclusion restriction here is based on the assumption that the desire to finish the interview sooner is not related to factual knowledge about disaster fatalities but is correlated with the probability of giving “do not know” answers to the disaster questions. The selection equation could be then expressed as follows: answers to the disaster questions. The selection equation could be then expressed as follows: i i i i i > 0), (4) dd dd dd ii where, i equals to 1 if a meaningful response is given to the question about disaster ss = Ι(ττ XX + ππ GG + ηηNN + θθ + εε _d_ and 0 dd otherwise, I(.) denotes an indicator function, i is the number of “do not know” answers in the ss questionnaire, and i is an error term. Assuming that i i ) have a bivariate normal distribution NN with mean 0 and correlation _ρ_ , the system of equations (3) and (4) could be estimated by the εε (εε , εε maximum likelihood estimator (De Luca and Perotti 2011).

To assess the relationship between perceptions of catastrophes of the one hand and beliefs and preferences about climate change on the other, we estimate the following equation: 8
where, B i stands for the variable _j_ capturing some aspect of the beliefs and preferences about jj climate change of individual _i_ in country _c_ , _Oic_ is the index of overestimation of disaster fatalities. are time-invariant country fixed effects, and i is an innovation term. Given that the dependent variables ii _BP_<sup>_j_</sup> are binary, equation (5) is estimated by a probit model. ωω νν

# **5. Results**

In this section, we investigate which characteristics explain the variation in the overestimation of fatalities across respondents. We then analyze whether the overestimation of fatalities affects people’s beliefs and preferences toward climate change.

# _5.1 Factors explaining variations in perceptions of fatalities_

<u>Table 2</u> presents the marginal effects of our ordered probit estimation of equation (3). These marginal effects show changes in the probability of selecting the correct category when a control variable changes by one unit. Women are less likely than men to choose the correct category of fatalities for the Bhopal, Chernobyl, and Fukushima catastrophes; they are more precise regarding the number of deaths caused by the Haiti earthquake. The estimations show no significant educational gradient for the Bhopal and Haiti catastrophes, but respondents with master’s degrees or PhDs are more likely than others to select the correct fatality category.

Marital status does not affect their perceptions of fatalities, but religion does: on average, people affiliated with religious groups are less accurate in the estimates of the catastrophes’ death tolls. Respondents who felt anxious or depressed during the week before the survey also provided less accurate responses. Respondents whose primary source of information is social media seem to be better informed about fatalities caused by the Bhopal catastrophe but less informed about deaths in Chernobyl and Fukushima. Living in a large family appears to positively affect the precision of the responses for Bhopal and Fukushima (but not the other disasters), and urban residents seem less certain than rural residents about the number of deaths in the Bhopal and Fukushima catastrophes. The proximity of a nuclear plant increases the likelihood of selecting the correct category, except for Fukushima. Finally, distance to a particular catastrophe site does not significantly affect the probability of selecting a correct category.

The large number of “do not know” could bias the results shown in <u>Table 2. We address this</u> problem by implementing a Heckman-style selection bias correction using the number of “do not 9
know” responses to other questions in the section of the LiTS questionnaire as an instrument for the selection equation (4).

# _5.2 Addressing the non-response bias_

<u>Table 3</u> shows the results of the maximum likelihood estimation of ordered probit with the Heckman correction model <u>(De Luca and Perotti 2011).</u><sup>10</sup> The model’s correction factor – indicative of the correlation between a respondent’s likelihood to provide a response and the nature of that response itself – shows significant coefficients for Haiti, Chernobyl, and Fukushima. This result suggests that the decision to respond is not random but is correlated with the respondents’ knowledge or perceptions about each catastrophe. Ignoring this bias could distort the representation of public sentiment in our findings. In addition, the _p-_ values from the likelihood ratio tests in Table 3 suggest the capacity of the model correction to account for the bias present in the Haiti, Chernobyl, and Fukushima catastrophe questions. However, the selection-corrected coefficients in <u>Table 3</u> are qualitatively and even quantitatively similar to those shown in <u>Table 2,</u> suggesting that, despite the presence of a sample selection bias, it does not appear to significantly affect the main pattern of results.

# _5.3 Beliefs about climate change_

<u>Table 4</u> presents the results of estimating equation (5) with a binary probit model for four dependent variables capturing beliefs that: climate change is real, climate change is manmade, climate change will affect the respondent personally, and climate change will have an impact on the children of today during their lifetime.<sup>11</sup> The estimations show strong qualitative similarities. Except for the question about whether climate change is real, respondents who overestimate catastrophe deaths are more likely to think that climate change is manmade and will affect them and current children. A one standard deviation increase in the overestimation index is associated with a 1.5-percentage point increase in the probability of responding that climate change is manmade, a 2.5-percentage point increase in the probability of believing that climate change will affect the respondent during his/her lifetime, and

> 10 Inclusion of the Heckman adjustment helps tackle the nuanced issue of selection bias, particularly bias stemming from non-responses. By accounting for the link between respondents’ inclination to respond and the content of their responses we obtain insights that better reflect perceptions and knowledge across the population.

> 11 Distances to a catastrophe site are insignificant in the regular and IV ordered probit estimations. For that reason, we dropped these variables from the estimations shown in <u>Tables 4, 5, and 6</u> for conciseness. We show the linear regression estimates of the models shown in Tables 4 and 5 in Tables B1 and B2 in Appendix B.

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a 1.1-percentage points increase in the probability of believing that climate change will affect the children of today during their lifetime.<sup>12</sup> The magnitudes of these effects are equal to or greater than the effects of a one standard deviation increase in household income.<sup>13</sup> Regarding other individual characteristics, women and older respondents appear more concerned about all four belief dimensions. There is also a pronounced education gradient. Less educated respondents are less likely to believe climate change is real, manmade, or will affect them and children. Single people appear more concerned about climate change than other respondents. Psychological health seems to be an important determinant of attitudes about climate change: Respondents who felt anxiety the week before the survey are significantly more likely to respond positively to all four climate change concern questions. That relation is reversed for respondents who reported depression. Respondents who indicated social media as their primary source of information and those residing in large families are also more likely to answer these questions positively.

_5.4 Willingness to pay higher taxes to mitigate the consequences of climate change_ Are the factors determining whether people are ready to pay higher taxes to mitigate the consequences of climate change consistent with those that explain their attitudes toward climate change? <u>Table 5</u> displays the marginal effects of probit estimations of equation (5) with dependent variables reflecting the willingness to finance a reduction in pollution, combat climate change, and prevent biological loss through higher taxes. The overestimation of fatalities from the four catastrophes has a positive and significant impact on the readiness of respondents to finance activities that would offset the anthropogenic impact on nature and climate. The effect of a one standard deviation increase in the overestimation of fatalities on the probability of being willing to pay taxes is 2.7 percentage points for reducing or preventing pollution, 2.3 percentage points for combating climate change, and 2.9 percentage points for reducing or preventing biological loss. These effects exceed those associated with a one standard deviation increase in household income.

> 12 The average value of the overestimation index is 0.870, which multiplied by the coefficient of 0.018 in column 3 of <u>Table 4</u> (the marginal effect at the mean) results in 1.5 percentage points.

> 13 A one standard deviation increase in household income is associated with a 1.1 percentage point increase in the belief that climate change being manmade, and no statistically significant effect for the belief that climate change has an impact on the respondent or on the children of today.

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In contrast to the strong and statistically significant gender effect observed in the estimations of the attitudes towards climate change issues, gender does not affect willingness to pay (Table 4). Age appears to affect the willingness to pay taxes to reduce or prevent pollution and combat climate change (these results are consistent with those of Cojocaru et al. (2024)); it does not have a statistically significant effect on the willingness to pay to prevent or reduce biological loss.

Similarly to the results on beliefs, the willingness to pay results reveal a strong education gradient. Respondents with a master’s degree and PhDs are significantly more likely to agree to pay higher taxes to finance climate change actions than respondents with lower levels of education. In line with the findings on beliefs, we observe a significant increase in willingness to pay among respondents who experienced anxiety the week before the survey and those who relied on social media as their primary source of information; depression has no impact on the willingness to pay for any of the three outcomes. Respondents in households with higher income per capita are more likely to agree on raising taxes to fund planet-saving activities, consistent with the “affluence hypothesis” (Franzen 2003).

# **6. Sensitivity analysis**

To test the robustness of our results, we re-estimate our models using different assumptions about the construction of our index (Table 6). The first column of Table 6 shows the marginal effect of the index using our baseline specification. These results are identical to the results shown in <u>Tables 4</u> and <u>5. These estimations are based on the sample of respondents who provided at least two</u> “valid” (other than “do not know”) responses to the questions about the death tolls from the four catastrophes, with imputed responses for those who did not provide four valid responses (a total of 19,896 observations). The second column shows the marginal effects of the index using a sample restricted to 13,732 observations with valid responses to all four catastrophes. The results are qualitatively very similar to those of the baseline specification, with the magnitudes of the point estimates slightly smaller for the set of variables on beliefs about climate change and slightly larger for the variables on the willingness to pay taxes to mitigate climate change.

The third column presents the results based on the sample of 17,142 observations with at least three valid responses for which the remaining “do not know” response was imputed. The results are qualitatively similar to the baseline results, except for the effect on the belief that climate 12
change affects the children of today, which is no longer statistically significant. In columns 4 and 5, the index is replaced by a dummy indicating whether the index is positive (overestimation dummy) or negative (underestimation dummy), respectively. The results are consistent with the baseline specification: Overestimating (underestimating) the fatalities is associated with a higher (lower) probability of believing that climate change is manmade, will affect the respondent, and will affect the children of today.

Similarly, the overestimation (underestimation) of fatalities is associated with a higher (lower) willingness to pay taxes to finance climate change mitigation. In column 6, we reformulate our index of overestimating fatalities as a weighted average of the absolute difference between the actual and reported order of magnitude of the catastrophes’ death toll. By using the absolute rather than the simple difference, this index represents the degree of inaccuracy (rather than the degree of overestimation) of the respondent’s perceptions of the death toll. The degree of inaccuracy has a similar pattern of correlations with the beliefs and preferences about climate change as the degree of overestimation.

To formally validate the stability of our results, we test the joint equality of marginal effects across all specifications, including the baseline. The test fails to reject the equality of marginal effects hypothesis in all but two cases – for the estimation based on the question about the impact of climate change on the respondent and children alive today. However, the signs of the effects are consistent with those in the baseline specification, and although the differences in magnitudes are statistically significant, they are not significantly different in the economic sense.

# **7. Discussion**

Our findings demonstrate that individuals who overestimate catastrophe deaths are more likely to acknowledge the anthropogenic nature of climate change and recognize its significant personal and societal impacts. They also tend to support policies aimed at mitigating the effects of climate change. Conversely, those with more accurate perceptions of past catastrophes show less willingness to act against climate change’s damaging effects. These findings appear paradoxical, given that misinformation is typically associated with climate change skepticism rather than heightened concern (Treen et al. 2020). It highlights a potential trade-off between accuracy and motivation in the context of climate action.

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It is unlikely that awareness of catastrophe deaths is causally linked to attitudes toward climate change. A more plausible explanation is that both the tendency to overestimate fatalities and attitudes about climate change stem from a common underlying factor or disposition. We hypothesize that a personality trait, such as heightened threat sensitivity, may drive both tendencies. If this hypothesis is true, correcting factual misconceptions about past disasters would not reduce concern for climate action because it would not address the fundamental psychological factors driving heightened concern.

Research in personality psychology suggests that individuals differ in their threat sensitivity and risk tolerance (Zuckerman & Kuhlman 2000, <u>Nicholson et al. 2005). Some people have a more</u> anxious or cautious temperament than others and are quicker to detect and react to potential dangers. Studies have also linked threat sensitivity to increased perceptions of risk concerning environmental hazards (Sjöberg 2000). People with high threat sensitivity may overestimate the impacts of past disasters because they focus on potential risks and worst-case scenarios (Butler <u>and Mathews 1987). This same heightened vigilance towards threats could shape their perceptions</u> of climate change, making them both quicker to see it as a severe and imminent danger and more emotionally affected by dire predictions. If it does, it could lead to a stronger belief in the reality and risks of climate change and greater motivation to take preventive action. In essence, threat sensitivity could be a psychological mechanism driving both the overestimation of the impact of past disasters and the perception of climate change as a severe threat requiring urgent action.

Our data support this hypothesis. Respondents who experienced anxiety in the week before the surveys were significantly more likely to overestimate catastrophe deaths, think that climate change is real, manmade, and to express willingness to take action. These findings align with research showing that threat sensitivity is associated with various mental health conditions, including anxiety. Our results suggest that anxiety and heightened threat sensitivity may explain both misperceptions about catastrophes and beliefs and attitudes toward climate change. However, the causal relationship might run in the opposite direction: People who believe that climate change poses severe threat and hold exaggerated beliefs about the impacts of past disasters may experience elevated anxiety levels.

Confounding variables, such as media exposure and the social environment, may affect the results <u>(Brulle et al. 2012). People exposed to content that emphasizes worst-case scenarios and alarming</u> 14
impacts from climate change and catastrophes may develop exaggerated perceptions of associated risks and harms. Similarly, being embedded in a social environment characterized by elevated levels of concern about climate change or frequent discussions of catastrophic risks could simultaneously influence people’s beliefs and fuel their anxiety. This hypothesis suggests that providing accurate information may be insufficient to change beliefs and attitudes, as deeper psychological dispositions also shape individuals’ perceptions (Drummond and Fischhoff 2017, <u>Hannon 2022, Kahan et al. 2011, Kahan 2013). Instead, appealing to threat sensitivity and anxiety</u> may be more effective in fostering appreciation of the risks posed by climate change.<sup>14</sup> Leveraging threat sensitivity might be effective in the short term, but it risks undermining the foundations of informed democratic decision-making and scientific credibility. Is it morally permissible to induce anxiety to align people’s perceptions with discomforting facts or to inflate their sense of risk to mobilize action? Such an approach could be viewed as manipulative and potentially harmful to mental health. Moreover, if the public discovers that risks were intentionally exaggerated, it could lose trust in scientific institutions and the government, potentially undermining future efforts to address threats.

The question of authority is also crucial: Who should be charged with managing public risk perception? How can one ensure that such entities are benevolent and accountable? Inflated risk perceptions might drive immediate action, but they could hinder the development of a nuanced, fact-based understanding of complex issues like climate change. It could also lead to excessive resource allocation, potentially at the cost of funding efforts to address other critical societal issues <u>(Sunstein 2002). These concerns highlight the complexity of science communication in high-</u> stakes scenarios like climate change or pandemics.

Our approach contrasts with theories of cultural cognition, which state that individuals are motivated to interpret scientific evidence in ways that conform to their cultural worldviews (Kahan <u>et al. 2011; Kahan et al. 2012). We hypothesize that a common underlying disposition – heightened</u> threat sensitivity – may explain both misperceptions of catastrophes and attitudes about climate change. Our analysis also diverges from accounts of “ _motivated reasoning”_ – the general tendency

> 14 This dynamic between threat perception and belief formation is not unique to climate change; during the COVID19 pandemic, some argued that emphasizing worst-case scenarios was necessary to ensure public compliance with preventive measures, while others (e.g., Bradley and Roussos 2021) contended that this approach could lead to panic and erosion of trust in institutions if the dire predictions did not materialize.

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to seek out, interpret, evaluate, and weigh evidence and arguments in ways that are systematically biased toward desired conclusions (Taber and Lodge 2006, Dietz 2013, <u>Hart and Nisbet 2012). We</u> find that individuals who overestimate the risks of catastrophes tend to have more accurate beliefs about the risks of climate change, even though their specific beliefs about catastrophes are less precise. This relationship cannot be fully explained by motivated reasoning because it is unclear why individuals would be motivated to hold accurate beliefs about climate change while simultaneously holding inaccurate beliefs about catastrophes.

# **8. Conclusions**

Our findings suggest a counterintuitive relationship between perceptions of catastrophe death tolls and beliefs and preferences regarding climate change: people who hold inaccurate beliefs about the magnitude of fatalities from past catastrophes are more likely to acknowledge human activities as the primary cause of climate change, recognize its significant personal and societal ramifications, and support policies designed to mitigate the negative anthropogenic impact on the climate. These results challenge the common assumption that a more knowledgeable citizenry will necessarily make better decisions about pressing issues like climate change.

We suggest that the tendency to both overestimate catastrophe deaths and the propensity to view climate change as a serious threat requiring action may stem from a common underlying disposition, such as heightened threat sensitivity or anxiety. If this is the case, simply providing accurate information about past disasters may not dampen concern about climate change, as the fundamental psychological drivers of risk perception would remain unchanged.

Our findings have important implications for understanding the complex factors shaping public opinion on climate change and other societal risks. They suggest that effective communication strategies may need to go beyond simply conveying facts and figures and also engage with the emotional and cognitive dispositions that influence how people process and respond to information. This effort could involve framing messages in ways that connect with people’s values and experiences, fostering a sense of efficacy and agency, and acknowledging the role of anxiety and threat perception in shaping beliefs and attitudes.

Our research also highlights the need for further investigation into the psychological mechanisms underlying risk perceptions and policy preferences. Future studies could directly measure threat 16
sensitivity and examine its relationship to beliefs about climate change and other hazards. Experimental designs could test whether interventions aimed at reducing anxiety or modifying cognitive biases influence responses to information about climate risks and catastrophes. More research is also needed to disentangle the complex interplay between individual psychological factors, media exposure, social norms, and political contexts in shaping public opinion on these issues.

As society grapples with the challenges posed by climate change and other global threats, understanding the factors that drive public perceptions and behaviors will be crucial for developing effective strategies for communication, engagement, and policy design. In keeping with recent literature on the topic, our findings underscore the importance of moving beyond a narrow focus on knowledge deficits and instead exploring the rich tapestry of psychological, social, and cultural influences that shape how people make sense of and respond to complex risks. Illuminating these dynamics can help societies foster a more nuanced and constructive public dialogue about the urgent challenges facing our planet and our species.

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