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date: 23 July 2017

Mental Models and Risk Perceptions Related to Climate Change

Summary and Keywords

Mental models are the sets of causal beliefs we “run” in our minds to infer what will happen in a given event or situation. Mental models, like other models, are useful simplifications most of the time. They can, however, lead to mistaken or misleading inferences, for example, if the analogies that inform them are misleading in some regard. The coherence and consistency of mental models a person employs to solve a given problem are a function of that person’s expertise. The less familiar and central a problem is, the less coherent and consistent the mental models brought to bear on that problem are likely to be. For problems such as those posed by anthropogenic climate change, most people are likely to recruit multiple mental models to make judgments and decisions.

Common types of mental models of climate change and global warming include: (a) a carbon emissions model, in which global warming is a result of burning fossil fuels thereby emitting CO2, and of deforestation, which both releases sequestered CO2 and decreases the possible sinks that might take CO2 out of the atmosphere; (b) a stratospheric ozone depletion mental model, which conflates stratospheric ozone depletion with global warming; (c) an air pollution mental model, in which global warming is viewed as air pollution; and (d) a weather change model, in which weather and climate are conflated. As social discourse around global warming and climate change has increased, mental models of climate change have become more complex, although not always more coherent. One such complexity is the belief that climate changes according to natural cycles and due to factors beyond human control, in addition to changes resulting from human activities such as burning fossil fuels and releasing other greenhouse gases.

As our inference engines, mental models play a central role in problem solving and subjective projections and are hence at the heart of risk perceptions and risk decision-making. However, both perceiving and making decisions about climate change and the risks thereof are affective and social processes foremost.

Keywords: climate change, mental models, risk perception, climate change communication, causal thinking


Mental models are the sets of causal beliefs we “run” in our minds to infer what will happen in a given event or situation. If someone steps on the gas and envisions the engine combusting more fuel and spewing more greenhouse gases out the tailpipe, that thought process reflects the use of his or her mental model, which embodies causal beliefs. Introduced by Craik in 1943, mental models have been suggested as a unifying framework for how we reason (Craik, 1943).

Investigations of mental models have relied on a panopoly of methods, including historical investigation of scientific thinking by cognitive psychologists and others (e.g., Nersessian, 1992a, 1992b; Nerssessian & Chandrasekharan, 2009). To gain insight into mental models of climate change that existed before the psychology and ethnography of mental models of climate change emerged late in the last century requires such historical analysis. Investigations of nascent scientific theories of climate change and of anthropogenic global warming show that people have been arguing about climate change for a long time. Human causation of climate change is key in these arguments. Hume, for example, discussed human and natural influence on climate change, referencing longstanding philosophical—and even political—debates (Fleming, 1998; Hume, 1742). A century after Hume, Jefferson engaged climate change, calling for more data (Bergh, 1907). Jefferson argued that his (decades of meteorological and ecological) data would show “the effect of clearing and culture towards changes of climate” (Jefferson, 1824; see also Rebok, 2014, pp. 123–125). History suggests Jefferson’s thinking, and that of his contemporaries, flourished as a defense of the Americas from disparagement as a place that was wild and uncivilized (Calael, 2014; Fleming, 1998).

Current disagreements differ in context and details but continue to demonstrate the vigor with which causal thinking about climate change can be used strategically in political, philosophical, and value conflicts and fall victim to them. Thus, mental models of climate change can be seen as central in susceptibility to—or inoculation against—key arguments, (e.g., Cook & Lewandowsky, 2011), or as instrumental in fanning debate. As a consequence, in the United States where political debate about climate change has dampened some but not disappeared, less than 20% of the population realizes that over 90% of scientists (let alone the actual 97% of scientists) agree that humans are causing climate change (Cook et al., 2013, 2016; Leiserowitz, Maibach, Roser-Renouf, Feinberg, & Rosenthal, 2016).

Mental Models Research and Methods

In 1983 two key volumes titled Mental Models were published (Gentner & Stevens, 1983; Johnson-Laird, 1983). The two volumes comprise superficially unrelated approaches to and perspectives on mental models, although both rely on a common underlying premise: that mental models are the engines of thought, inference, and choice. Johnson-Laird’s sole-authored book focuses on logic and reasoning, illustrated with logical puzzles and card games, such as Wason selection tasks to examine deductive reasoning, and proposes an overarching theoretical approach to mental models. In contrast, the edited volume by Gentner and Stevens tackles reasoning about complex physical systems in the real world. Given that domain matters and influences how people reason (Markman & Gentner, 2001), it is perhaps not surprising that the development of research on mental models of climate change has been influenced much more by studies of mental models and naïve or folk theories of other physical systems than by that on deductive reasoning.

The edited volume by Gentner and Stevens (1983) contains studies of mental models of such physical processes as electricity (Gentner & Gentner, 1983) and motion (McCloskey, 1983). In the first chapter, Donald Norman identifies four elements of mental models research. Interpreted here to apply to studies of climate change and global warming, they include: (1) the target system, in this case global warming and climate change; (2) a conceptual model of the target system, of the type reflected in the expert decision models (mostly influence diagrams) used in some mental models studies (e.g., Bostrom, Atman, Fischhoff, & Morgan, 1994; Morgan, Fischoff, Bostrom, & Atman, 2002; Read, Bostrom Morgan, Fischhoff, & Smuts, 1994; Reynolds, Bostrom, Read, & Morgan, 2010); (3) the user’s mental model of the target system; and (4) “the scientist’s conceptualization of that mental model” (Norman, 1983). This article describes the third of these—the user’s mental model of the target system—based primarily on the fourth, social scientists’ conceptualization of users’ mental models.

Mental models of climate change have been investigated and represented comparatively, by reference to the conceptual model, and/or descriptively, as a list of causal beliefs and the relationships between those beliefs (e.g., Bostrom et al., 1994), and by some in terms of pictures or descriptions of the dynamics inferred from problem solving (e.g., Moxnes & Assuad, 2012; Moxnes & Saysel, 2009; Sterman & Booth Sweeney, 2002). By far the most common approach to representing mental models is as a set of survey responses. In some cases the surveys have been designed extremely carefully for this purpose (e.g., Tobler, Visschers, & Siegrist, 2012; also Read et al., 1994), while in others the rationale for item and survey design is less obvious. One perhaps unintended consequence of the survey response representation strategy is that the coherence and usefulness of current mental models is not always evident from the data. As Norman notes, asking people to describe their mental models is questionable, since they may not know, and may even change their thinking in the course of answering questions in reaction to the perceived demands of the survey or interviewer (Norman, 1983). These reactions might not necessarily represent the mental models that other circumstances would elicit.

Inductive approaches to researching what people think about global warming include thought-listing, feelings, or image association tasks (e.g., Leiserowitz, 2006; Lorenzoni, Leiserowitz, Doria, Poortinga, & Pidgeon, 2006; Smith & Joffe, 2013). In such studies, researchers often derive themes inductively from open-ended responses to questions about what first comes to mind in response to the term global warming, or climate change. Such content analyses may reference psychological theories—for example, theories of association and dual processing, which inform affective image analysis (Leiserowitz, 2006)—but many do not reference a conceptual model of climate change (the target system), sometimes explicitly because the authors see mental models as cognitive and they are interested in affective—not cognitive—responses (e.g., Leiserowitz, 2005, 2006). In two such studies (Lorenzoni et al., 2006, Smith & Joffe, 2013) over half of all free associations correspond to impacts of climate change, and many responses remain uncategorized. Nevertheless, response categories found across these studies resemble the most salient findings from other mental models research and include themes such as ice melting, weather, pollution, and ozone depletion, discussed in detail later in this article.

One way of assessing the usefulness of mental models is to research them with an explicit problem solving or decision-making approach. The formalities of decision analysis simplify assessing how causal knowledge about a hazardous process like climate change might be used or useful. The mental models research approach to risk communication developed by researchers at Carnegie Mellon (Bostrom, Fischhoff, & Morgan, 1992; de Bruin & Bostrom, 2013; Morgan et al., 2002; Morgan, Fischhoff, Bostrom, Lave, & Atman, 1992) exemplifies this type of strategy. This approach includes several distinct phases, three of which are entailed in researching mental models. First, the conceptual model of the target system in this approach takes the form of a decision model representing how the best science might inform policy and personal risk mitigation decisions, in this case about climate change. The conceptual model is thus focused on decisions about risks and what to do about them and is generally informed by several scientific disciplines and domains of professional expertise. It can be developed through expert elicitation or literature, for example, and does not aim to represent a specific or disciplinary scientific model (see Wood, Bostrom, Bridges, & Linkov, 2012, for additional discussion). Second, semi-structured interviews assessing mental models and related perceptions of the risks of climate change and how to decrease those risks are content-analyzed by comparison with the decision model. Inspired by think-aloud studies used in other mental models and human-centered design approaches (Ericsson & Fox, 2011; Ericsson & Simon, 1993; Fox, Ericsson, & Best, 2011), the interview protocols often include a think-aloud task but are constructed primarily around prompts asking participants to talk about the hazard, and how they or others might be exposed to, experience risk from, or reduce the risk of the hazard. This analysis is conceptually anchored in the decision model, but open-ended. Third, the interviews inform the design of survey instruments in order to survey larger samples, representative of those for whom risk communications are being developed. Together, the interviews and surveys inform the risk communication scientist’s conceptualization of mental models of climate change.

Several mental models studies of climate change have relied on coding schemes derived from decision analyses (e.g., Bostrom et al., 1992, 1994; Reynolds et al., 2010), such as those conducted by researchers using a mental models approach to risk communication (de Bruin & Bostrom, 2013; Wood et al., 2012). These decision analyses, conducted by decision analysts with input from an interdisciplinary set of scientists, were carried out for the purposes of integrated climate assessment modeling (Morgan et al., 1992, 2002) and hence intended to inform decisions to reduce, stop, or adapt to anthropogenic climate change and its consequences. Mental models of potential risk communication participants and/or decision-makers are assessed in these studies, through mental models interviews. The interview protocol is informed by inductive, ethnographic approaches and begins with open-ended questions, but becomes progressively more structured and reactive, to elicit causal thinking about all major phases of the underlying hazardous process (Böhm & Pfister, 2001; Hohenemser, Kasperson, & Kates, 1985) as well as climate change mitigation and adaptation decisions, and to collect think-aloud protocols while participants solve problems or do tasks such as sorting pictures by their relevance to climate change, or rank ordering climate change mitigation strategies by their effectiveness. Further, this research approach includes a confirmatory survey research step, following the mental models interviews (Bostrom et al., 1992; de Bruin & Bostrom, 2013; Morgan et al., 2002), and explicit guidance on evaluating climate change communications designed on the basis of the decision analysis, mental models interviews, and survey results.

Another approach to assessing mental models of climate change is based on systems modeling. This type of approach generally entails laboratory experiments asking participants to solve dynamic greenhouse gas emissions reductions problems (e.g., Moxnes & Assuad, 2012) or draw the dynamics of greenhouse gas emissions reductions for specific scenarios (e.g., Sterman & Booth Sweeney, 2002; see also Doyle & Ford, 1998). Consistently, participants of all ages and levels of education demonstrate misunderstanding of the stocks and flows in accumulation problems; they underestimate delays (Moxnes & Assuad, 2012; Sterman & Sweeney, 2007) and use simple heuristics that lead them to insufficient corrections (too late or too little) and wrong answers. One such heuristic is matching the shape of the output, for example, the net concentration of greenhouse gases in the atmosphere, to the shape of the input, for example, greenhouse gas emissions. This heuristic ignores the dynamics of stocks, flows, and delays, reflecting a lack of appreciation that even if emissions were to fall to zero, concentrations of greenhouse gases in the atmosphere would only fall slowly and mean global temperatures would continue to rise for a few decades (Sterman & Booth Sweeney, 2002). These misunderstandings and heuristics are described by the authors as mental models.

Norman (1983) notes that mental models are incomplete and unstable, that is, details are often neglected. They also have fuzzy boundaries and tend to get confused with other mental models of similar systems (Collins & Gentner, 1987; DiSessa, 1982; see also Gentner, 2002). Findings to date demonstrate that mental models of climate change tend to be incomplete and often have very fuzzy boundaries with other environmental problems, as described in the section on mental models and risk perception research. Further, mental models may be unscientific and involve what Norman calls “superstitious” behavior patterns that are seen as relatively effortless, both mentally and physically (Khemlani, Barbey, & Johnson-Laird, 2014). Mental models of climate change exhibit these traits, and self-reports of actions taken to address climate change suggest support for what Norman calls superstitious behavior patterns as well. However, there is more research demonstrating the first (incompleteness) and second (confusion with other systems) than there is on superstitious behavior patterns.

Mental Models and Risk Perceptions Research

Our understanding of risk perceptions of global warming and climate change has its roots in research from the early 1980s (Fischhoff & Furby, 1983), which also led to a line of research on mental models of climate change and global warming that emerged in the early 1990s, informed by Gentner and colleagues’ prior research in other domains (Bostrom et al., 1994; Morgan et al., 2002; Read et al., 1994). Other lines emerged in parallel in different fields, including cognitive anthropology (Kempton, 1991a, 1991b), science education (Boyes & Stanisstreet, 1992, 1993), and geography (Löfstedt, 1991). Commonalities between these lines of research include using open-ended, ethnographic approaches to interviewing research participants in order to explore their thinking about global warming, with the apparent aim of identifying the scientific sophistication and accuracy of mental models, as well as discerning different types of mental models. To this latter end, studies categorized mental models in terms of their focal causal beliefs, such as an air pollution model of global warming (Bostrom et al., 1994; Boyes & Stanisstreet, 1992; Kempton, 1991a, 1991b) and Kempton’s (1991a) photosynthesis model. Many of these interview studies served as the bases for developing subsequent survey instruments.

A number of findings from these early, primarily qualitative, mental models studies on global warming and climate change are recurrent and persistent and have proven consistent with larger-scale survey research. Among these are a handful of common types of mental models of climate change or the greenhouse effect: (a) a carbon emissions model, in which global warming is a result of burning fossil fuels thereby emitting CO2, and of deforestation, which decreases the possible sinks that might take CO2 out of the atmosphere; (b) a stratospheric ozone depletion mental model, which conflates stratospheric ozone depletion with global warming; (c) an air pollution mental model, in which global warming is viewed as air pollution; and (d) a weather change model, in which weather and climate are conflated. Each of these is discussed in more detail in subsequent sections.

Later research focused instead on the dynamics of climate change processes, as described in the section on mental models research and methods. A consistent finding from this research is that—as in other systems thinking—even the highly educated tend to have a weak grasp of the feedbacks and latencies in climate change processes, underestimating the rate of greenhouse gas accumulation and the rates of greenhouse gas emissions abatement required to return to anything like pre-industrial background atmospheric concentrations of greenhouse gases (Assuad & Moxnes, 2006; Booth Sweeney & Sterman, 2000, 2007).

Some researchers include mental models in their larger definitions of risk perceptions (e.g., Reynolds et al., 2010), whereas others might distinguish mental models from causal beliefs and attitudes and include risk perceptions as a subset of attitudes. Causal thinking about climate change encompasses concepts of exposure source, mechanisms and pathways, susceptibility or vulnerability to climate change, climate change mechanisms (processes) and manifestations, and consequences of climate change, as well as feedback loops and latencies in climate change processes. While mental models are deterministic, causal thinking about consequences can be deterministic or uncertain, and even probabilistic. The formal definition of risk often entails the magnitude and probability of harmful consequences (Aven & Renn, 2009; Bostrom, 1998; NRC, 1996). Risk perceptions include these dimensions, but also perceptions of familiarity and controllability, dread and catastrophic potential, and affective and emotional responses (Finucane, Alhakami, Slovic, & Johnson, 2000; Fischhoff et al., 1978; Slovic, 2000, 2016). However, perceived characteristics of risks vary depending on how they are presented, likely dependent on causal thinking; for example, a comparison of perceptions of global warming and some of its causes two decades ago showed that deforestation was viewed as riskier than global warming, and energy production was seen as less risky than either (McDaniels, Axelrod, & Slovic, 1996). As a later study confirmed (Böhm & Pfister, 2001), in a causal chain, human activities appear likely to be perceived as less risky than the resulting environmental emissions, pollutants, or consequences.

By analogy with integrated climate assessment modeling, mental models of climate change have the potential to be complex (see e.g., Morgan, 2011, p. 713ff) for a discussion of integrated climate assessment models). And in recent studies of climate change mental models and perceptions, there is evidence suggestive of increasing complexity. This includes for example attribution of climate change to both human and natural causes, along with greater consideration of multiple sources of various greenhouse gases, including methane and occasionally other greenhouse gases along with carbon dioxide (e.g., Reynolds et al., 2010). Reynolds et al. (2010) repeated the study carried out by Read et al. (1994), using the written questionnaire that was based on Bostrom et al. (1994) and designed to assess mental models of climate change. This study suggested that (a) many of the same misconceptions found in the early nineties persist, but some have decreased in prevalence, (b) awareness of the role of carbon dioxide in global warming has increased, (c) belief in scientific consensus about climate change and concern about it has not increased in the United States as much as one might expect since the early nineties, given the subsequent release of International Panel on Climate Change consensus reports, and (d) mental models of climate change may also be more complicated due to increased attention to natural climate variability. Failure to grasp the degree of scientific consensus and increased focus on natural climate variability could both well be the result of intentional misinformation campaigns, which have emphasized scientific conflict and uncertainty, as well as natural climate variability (Freudenburg, Gramling, & Davidson, 2008). In July 2010 a national survey (probability sample, N = 2030; Leiserowitz & Smith, 2010) replicated many of the findings in Reynolds et al. (2010), using many of the same questions. Subsequent research continues to show gross underestimation of scientific consensus (Ding, Maibach, Zhao, Roser-Renouf, & Leiserowitz, 2011; Leiserowitz et al., 2016; McCright, Dunlap, & Xiao, 2013; van der Linden, Leiserowitz, Feinberg, & Maibach, 2015), dual attribution of climate change to human and natural causes, but also greater appreciation of the role of anthropogenic emissions in global warming (Howe Mildenbergr, Marlon, & Leiserowitz, 2015; Leiserowitz & Smith, 2010).

Common Mental Models of Climate Change and Global Warming

CO2 and Deforestation: Sources and Sinks

Of perhaps most interest is the extent to which mental models include causal links from energy use to burning fossil fuels, and from fossil fuel usage to increasing carbon dioxide concentrations in the atmosphere, resulting in global warming. Among the most frequently cited causes of global warming after air pollution is driving cars and trucks, or traffic, followed by carbon dioxide emissions, and sometimes deforestation, corresponding to the views that the most effective ways to reduce global warming are to drive less, reduce CO2 emissions, and plant trees (e.g., Böhm & Pfister, 2001). In one mental models study of a convenience sample of adults in the United States two decades ago, CO2 was among the causes volunteered by interviewees in response to the initial open-ended question (a free association prompt), but was mentioned by only about a fifth of those interviewed (as was carbon monoxide), whereas about two-thirds mentioned automobile use, emissions or pollution, or industrial emissions as causes, a little under half mentioned deforestation, and about a third mentioned fossil fuels (Bostrom et al., 1994). In both 1992 and 2009, clearing tropical rainforests was the most endorsed cause of global warming, although by 2009 burning fossil fuels had tied with it (Reynolds et al., 2010).

The fact that carbon dioxide and other greenhouse gases trap longwave radiation—heat that is reradiating from the earth—was missing from most people’s mental models of global warming at the turn of the century (Aubrun & Grady, 2001). This gap has been identified as critical by some researchers (e.g., Ranney et al., 2012).

The sparsest and least well-specified portions of mental models are those pertaining to greenhouse gas emissions, including sources and accumulation, as is evident from alternative models of climate change, particularly the air pollution and ozone depletion mental models described in the following sections. Some research examining the relationship between mental models and policy support finds that holding a CO2 mental model of climate change is associated with greater support for carbon policies to address climate change (e.g., Bostrom et al., 2012).

Air Pollution

A majority of participants in studies of climate change mental models and perceptions spontaneously mention air pollution or endorse it as a cause of global warming and climate change, across socially and culturally diverse samples, and across time (e.g., Bord, Fisher, & O’Connor, 1998; Bostrom et al., 1994; Boyes & Stanisstreet, 1997; Dunlap, 1998; Huxster, Uribe-Zarain, & Kampton, 2015; Kempton, 1991a; Koulaidis & Christidou, 1999; Leiserowitz, 2007; Reynolds et al., 2010). In some instances this is expressed as air pollution from cars and trucks, in others as smog or pollution from industry. To distinguish this model from the “ozone hole” model, Kempton (1991) calls this the tropospheric air pollution model. In one study asking participants to identify which environmental risks they would classify as global, air pollution was selected by a plurality of study participants (40%), followed by ozone depletion (32.7%), much more frequently than climate change (named by 15.3%) (Böhm, Stahl, Henning, Mader, & Preuβ‎, 1998; see Böhm & Pfister, 2001).

Respondents who use an air pollution model call for stricter pollution controls on industry as a solution (e.g., Kempton, 1991a) and tend not to discriminate between poor environmental practices and specific sources of greenhouse gas emissions (Bostrom et al., 2012; Read et al., 1994; Reynolds et al., 2010).

In studies of air pollution perceptions among students in northwest England, 5th-grade students (10–11 years old) volunteered carbon monoxide and carbon dioxide as air pollutants in response to questions about “things in the air that should not be there” (Thornber, Stanisstreet, & Boyes, 1999), and the most likely pollutants to be mentioned by older students were CO, CO2, and chlorofluorocarbons (CFCs) (Myers, Boyes, & Stanisstreet, 1999). Similar findings in other countries have led researchers to conclude that it is a challenge for young students to understand that air is not synonymous with oxygen but is a mixture of gases and that students have difficulties distinguishing between these gases (Skamp, Boyes, & Stanisstreet, 2004). While a majority of students agree that carbon dioxide is also an element of unpolluted air, a fourth of younger students (6th graders) inappropriately agree that CFCs are an element of unpolluted air (Skamp et al., 2004).

In several of the U.K. student studies from the 1990s, students mentioned leaded gasoline as a cause of an increased greenhouse effect. The authors speculate that “the idea that global warming could be reduced if vehicles used lead-free gasoline, probably originated in a more general conflation of ideas, perhaps originating in an imprecise use of language—that all pollutants cause ‘pollution,’ and that ‘pollution’ causes a range of deleterious effects” (Boyes & Stanisstreet, 2001, p. 78; see also Boyes & Stanisstreet, 1996; Stanisstreet & Boyes, 1996). This is consistent with the finding that among adults air pollution is highly stigmatized and evokes a negative response (Bickerstaff, 2004). Further supporting this interpretation, participants in mental models studies sometimes reference littering and pollution more generally as things they do that might contribute to climate change and reducing these as ways of helping to slow or stop climate change (e.g., Truelove & Parks, 2012).

Stratospheric Ozone Depletion: The Ozone Hole Model

One of the most initially surprising yet consistent and persistent findings from mental models research on global warming, the greenhouse effect and climate change is the confusion or substitution of stratospheric ozone depletion for the actual mechanisms of the greenhouse effect. Even more widespread than the air pollution model for several decades (see e.g., Boyes & Stanisstreet, 1993, 1997; Koulaidis & Christidou, 1999; Leiserowitz, 2007), the ozone hole and stratospheric ozone depletion continue to show up in people’s mental models and explanations for global warming and climate change (Leiserowitz & Smith, 2010; Reynolds et al., 2010).

In a study examining how students explain environmental causes of skin cancer, Boyes and Stanisstreet (1998) show that many students confound the greenhouse effect and stratospheric ozone depletion to such an extent that they see them both as causing skin cancer by allowing more ultraviolet (UV) rays to reach the earth and do not distinguish between UV and heat rays. Although still widespread, the attribution of increases in the greenhouse effect to the ozone hole appears less prevalent among college students (48% think it true; Boyes & Stanisstreet, 1992) than among younger students (80% of 861 U.K. students ages 11–16; Boyes & Stanisstreet, 1993). Many studies suggest that pre-service and in-service primary science teachers also hold this type of mental model and so may be reinforcing it in the classroom (e.g., Arslan, Cigdemoglu, & Moseley, 2012; Boon, 2010; Papadimitrou, 2004; Plutzer et al., 2016).

Despite some evidence suggesting a decrease in adherence to an ozone hole or stratospheric ozone depletion mental model since the 1990s (Reynolds et al., 2010), many adults continue to confuse or conflate the two processes (Davis, 2015; Leiserowitz & Smith, 2010). Examples of these findings include study participants defining the greenhouse effect as the protective ozone layer, or seeing an increased greenhouse effect as the most likely consequence of the ozone hole. Several authors have suggested that the poorly understood dual role of chlorofluorocarbons as ozone depleters and greenhouse gases contributes to the persistence of this type of mental model (e.g., McNeal et al., 2014). As discussed with regard to air pollution models, a lack of distinction between gases and their actions and reactions in the atmosphere is also at play.

Weather Change

Both explicitly and implicitly, weather and climate can be confounded. Mental models studies show that some people treat weather and climate as synonymous for all practical purposes, including in their explicit definitions (Bostrom et al., 1994; Read et al., 1994; Reynolds et al., 2010; Leiserowitz & Smith, 2010).

In several studies, weather and temperature anomalies predict climate change beliefs (Capstick, Whitmarsh, Poortinga, Pidgeon, & Upham, 2015). Expressed belief in anthropogenic climate change and global warming can be influenced by transient temperatures, with those experiencing warmer temperatures more likely to express belief in global warming (Hamilton & Stampone, 2013; Howe, Markowitz, Lee, Ko, & Leiserowitz, 2012; Joireman, Barnes Truelove, & Duell, 2010; Li, Johnson, & Zaval, 2011; Risen & Critcher, 2011). Experimental evidence suggests, however, that this is not due to conceptual conflation of weather and climate, but rather to attribute substitution, by which evidence that readily comes to mind (“it’s hot”) is preferentially used over more diagnostic or pertinent evidence (Zaval et al., 2014). Although the Zaval study does not actually examine mental models, it does attempt to control for them by providing information on the non-equivalence of transient weather and climate. Despite the potential flaws of this approach (which assumes readily rectifiable knowledge deficits), the study is a caution to mental models researchers regarding the importance of considering judgment and decision-making processes as alternative explanations for what appear to be causal inferences.

Scientific controversies regarding the attribution of specific extreme weather events to climate change illustrate that it is challenging to distinguish between changes in the weather and changes in climate (Stone et al., 2009; Stott, Stone, & Allen, 2004). Scientists have been loath to attribute extreme drought and storm events to climate change, despite mounting evidence that decision-makers want information about how climate change impacts local weather events such as droughts and floods (Sippel et al., 2015). Further, even those making such attributions highlight the uncertainties that remain about making them (Stott et al., 2016). Neither were news stories about climate change in the United States in the 1990s pegged to extreme weather (Ungar, 1999). However, some attribute the rise of public consciousness about the greenhouse effect in the United States to the extreme heat and drought of the summer of 1988 (Ungar, 1992). While there is widespread belief that climate change will result in increases in storms (e.g., Bostrom & Lashoff, 2007; Krosnick, Holbrook, & Visser, 2000), and that it is happening now (e.g., Howe et al., 2015), mental models studies have not generally highlighted attribution of specific extreme weather events to climate change. However, in a recent mental models study of the hurricane forecast and warning system, global climate change was volunteered as a causal factor contributing to hurricane development by hurricane specialists, broadcasters, and emergency managers but not by weather forecasters (Bostrom et al., 2016). One recent study examining public opinion over the first decade of this millennium concluded that extreme weather events have no influence on concern over climate change (Brulle et al., 2012). However, that study employed four temporally and spatially aggregated measures of extreme weather, which may not have corresponded to experienced salience (and hence neither to attribute substitution), and the study did not investigate mental models or causal beliefs (see “Personal Experience, Extreme Weather Events, and Perceptions of Climate Change”).

Other Perceived Consequences of Climate Change

Sources and paths of greenhouse gas emissions and accumulation are sparse in most lay mental models, as illustrated, for example, by the common omission of residential heating and cooling and even electricity production as causes of global warming (Bostrom et al., 1994; Reynolds et al., 2010) and the omission of greenhouse gases other than carbon dioxide and CFCs (albeit for mistaken reasons, not because they are a greenhouse gas), although more recent studies suggest growing awareness of methane as a greenhouse gas, for example (Reynolds et al., 2010).

Consistent with air pollution, ozone depletion, and weather change mental models, study participants envision a plethora of consequences of climate change, ranging from warmer temperatures and melting glaciers to human health effects. Among the most widely found beliefs about environmental effects caused by climate change are hotter, steamier climates, and melting glaciers (e.g., Leiserowitz & Smith, 2010). Despite evidence that mental models do produce inferences of increased weather extremes, the degree and temporal and spatial extent of such changes inferred from mental models is less clear. Many mental models studies represent such findings solely qualitatively (Boyes & Stanisstreet, 2001; Huxster et al., 2015; Löfstedt, 1991). A few studies analyze participants’ drawings of climate change processes, but qualitatively (e.g., Degen et al., 2014; Libarkin, Thomas, & Ording, 2015). Other studies include incidental reference to participants’ quantitative estimates of climate change derived from their mental models, for example, of global temperature changes (e.g., Reynolds et al., 2010) or reactions to quantitative estimates of global climate change (e.g., Kempton, 1991a). Study participants who believe in anthropogenic climate change tend to overestimate average temperature increases relative to IPCC estimates. Systems dynamics studies have, however, demonstrated repeatedly that people severely underestimate accumulation and feedback processes and hence the degree to which current atmospheric concentrations have already committed the world to future environmental changes (Assuad & Moxnes, 2006; Sterman & Sweeney, 2007; see also Degen et al., 2014).

In one early mental models study, 59% of participants mentioned human health effects; 27% mentioned skin cancer; 27% mentioned respiratory problems due to the air breathed, or not enough air to breathe; 16% mentioned some unspecified type of cancer; and 14% mentioned sunburn (Bostrom & Fischhoff, 2001; Bostrom et al., 1994). Coeval studies of students found similarly that ozone hole or ozone depletion mental models of global warming included the belief that global warming would lead to increases in skin cancer (Boyes & Stanisstreet, 1992, 1993, 1998).

Mental Models and Simple Analogies

Thinking by analogy can drive and determine what mental models people recruit and represent mentally to address specific problems (Bostrom, 2008; Day & Gentner, 2007; Dunbar & Blanchette, 2001; Greca & Moreira, 2000; Vosniadou, 1989). Historical documentation of early science on the greenhouse effect reveals simple analogies that guided and reflected scientific thinking at the time. In a speech in 1896, Arrhenius states that “Fourier and Pouillet suggested that the atmosphere functioned like the glass in the frame of a hotbed” (see Rodhe, Charlson, & Crawford, 1997), letting the sun’s rays in, but keeping heat (infrared) radiation from going out. This has clearly been a productive analogy for over a century, but this simple mental model of the mechanism for anthropogenic global warming has also been blamed for widely observed shortcomings in lay mental models of climate change, and even for the “wait-and-see approach” to climate change (e.g., Chen, 2012). Common descriptions of global warming processes from lay interviews include elements such as holes in the ozone that let more UV light in (i.e., they describe a process akin to stratospheric ozone depletion) and a blanket of pollution that then traps UV light, heating up the atmosphere.

In a few recent studies, a description such as the following has been shown to improve study participants’ understanding of global warming and strengthen support for mitigation policies: “Earth transforms sunlight’s visible light energy into infrared light energy, which leaves Earth slowly because it is absorbed by greenhouse gases. When people produce greenhouse gases, energy leaves Earth even more slowly––raising Earth’s temperature” (Ranney, Clark, Reinholz, & Cohen, 2012). This short description avoids several potential pitfalls of familiar analogies, for example, by differentiating types of energy, referencing visible light energy instead of UV energy, which is associated with stratospheric ozone depletion, and by discussing energy leaving earth and being absorbed, rather than trapping.

Reinfried, Aeschbacherand, and Rottermann (2012) argue that understanding the greenhouse effect requires these two elements (radiation conversion at the earth’s surface and heat absorption by atmospheric CO2), and tackles the same problem with this explanation:

  • Radiant energy coming from the sun is for the most part absorbed by the earth’s surface and not reflected; this absorbed energy is re-radiated outward from the earth in an altered form—no longer as light, but as thermal radiation.

  • The CO2 molecules absorb the heat radiation and release it again in all directions. Thus they slow down the escape of the heat energy into space. Solar radiation on the other hand passes the CO2 molecules unhindered (Reinfried et al., 2012, p. 158).

These are examples of recent climate change studies that build on mental models research to probe whether people differentiate between ultraviolet and infrared light, energy, or radiation in their thinking about global warming and the greenhouse effect (Davis, 2015; Ranney et al., 2012; Reinfried et al., 2012). Of the conceptual elements in one mental models study, radiation-related knowledge alone was extremely low, showing floor effects (Davis, 2015). This suggests that further exploration of the broader effects of distinguishing between visible light energy and infrared energy is warranted. Further, radiation terminology is likely to present particular challenges. In a recent study of mental models and perceptions of radiation, half of 9th-grade students interviewed in Vienna stated UV rays comes from the sun, and most thought of UV and infrared (IR) as invisible, but half also found the statement “every object emits radiation” unbelievable, and thought radiation came only from artificial, electronic sources (Neumann & Hopf, 2012).

Climate Change Mental Models, Reasoning, and Risk Decisions

Controlling for other factors, mental models can be important predictors of preferences and decision-making (Bostrom et al., 2012; O’Connor, Bord, & Fisher, 1999) but may explain less variability than other factors, such as prior attitudes about available decision options (Bostrom et al., 2012) or other predispositions (e.g., Kahan et al., 2012). In the example cited above, where Ranney et al. (2012) demonstrate changes in beliefs about and acceptance of global warming with a very short statement characterizing causal processes, note that their statement does not reference other beliefs because their work assumes deficits in mechanistic understanding of global warming (i.e., knowledge gaps).

Nevertheless, evidence suggests that mental models of climate change influence judgment, inference, and preferences (Böhm & Pfister, 2001; Ranney et al., 2012; Reinfried et al., 2012; Reynolds et al., 2010). Educational research (e.g., Boyes & Stanisstreet, 1993, 2009, 2012) has been particularly informative with regard to the extent to which prior mental models influence reasoning about climate change. Mental models vary in general by level and type of expertise (e.g., Bostrom et al., 2016; Chi et al., 1981; Morss et al., 2015), though there is less research on this specifically with regard to climate change. Lay problem solving is more likely to be characterized as involving multiple, inconsistent mental models (corresponding to superficial characteristics of the problem, rather than the underlying structure of the problem), as contrasted with the highly (hierarchically) structured, consistent mental models of experts (those with extensive training and expertise in solving the type of problem at hand) (Chi et al., 1981; DiSessa, 1982; Gentner & Stevens, 1983; Markman & Gentner, 2001).

Robust evidence demonstrates inconsistencies and ambiguities in lay mental models of climate change, although there is little research comparing the mental models of adults with differing types of expertise or professional interest in climate change (but see expert elicitation studies, such as Morgan, 2014; Morgan & Keith, 1995; Zickfeld, Morgan, Frame, & Keith, 2010). Research in educational settings illustrates how mental models of climate change evolve as students advance through secondary school, with some misconceptions diminishing and scientific conceptions becoming more prevalent (e.g., Boyes & Stanisstreet, 1993; Fisher, 1998). As also noted, however, some studies suggest that undergraduate college students and high school students have similar mental models of climate change (Boyes & Stanisstreet, 1992), and misperceptions persist among pre-service and in-service teachers (Arslan et al., 2012; Boon, 2010), possibly hindering effective learning and intergenerational change.

Further, mental models influence interpretation and use of new information (e.g., Kempton, 1986; Neibert & Gropengeisser, 2014). Mental models are “sticky,” and people tend to retain their existing mental models of a process barring replacement of their priors (Moxnes & Assuad, 2012; Moxnes & Saysel, 2009; Neibert & Gropengeisser, 2014; Reinfried et al., 2012; Vosniadou & Brewer, 1992).

However, knowledge about risks alone is not sufficient to explain beliefs and behavior (Böhm & Pfister, 2000, 2001; Epstein, 1994; Finucane, Alhakami, Slovic, & Johnson, 2000; Lindenberg & Steg, 2007; Slovic, 1987, 2000, 2016; Wildavsky & Dake, 1990). Altogether, as environmental scientists and policymakers are increasingly aware, providing more information, while important, is not sufficient to generate appropriate public concern for some risks or to allay public fears about others (Leiserowitz, 2006; Slovic, 2010). Recent opinion polls suggest that concern about climate change may be rising in the United States again (Leiserowitz et al., 2016) but is far from topping lists of concerns.

Gaps and Opportunities

From this body of research it is evident that mental models of climate change matter, and how they can matter, but it is not clear how much they matter. Key insights include that a relatively small set of mental models are prevalent and commonly used to reason about climate change.

There remains a need for more systematic exploration of if, and if so how, mental models of climate change inform or influence energy use. More systematic experimental study of how mental models of climate change in general influence inferences and decision-making about climate change mitigation would help further elucidate the reliability and relevance of mental models findings to date for educational and communication purposes. Particularly important is developing a better understanding of the relative roles and mutual influences of the mental models and other known and perhaps stronger influences on attitudes and behaviors, such as trust in information sources (e.g., Attari, Krantz, & Weber, 2016); the visceral influence of temperature (Risen & Critcher, 2011; Zaval, Keenan, Johnson, & Weber, 2014), as discussed in the section on weather and climate; and political ideology, through partisan-motivated reasoning (Bolsen, Druckman, & Cook, 2014). Attempts to correct the consequences of motivating reasoning include introducing the concept of motivated reasoning (Cook, Bedford, & Mandia, 2014); systematic debunking of specific misconceptions (Cook & Lewandowsky, 2011; Lewandowsky, Ecker, Seifert, Schwarz, & Cook, 2012); reinforcing correct information to reduce biases (Lewandowsky, Gignac, & Vaughan, 2013; van der Linden et al., 2015); and programs of analogy and experience with simulations of dynamic systems (Sterman et al., 2015). Many of these appear promising.

Although it is tempting to believe that knowledge and mental models drive opinions and behavior, it is evident that affect and values drive judgments (Finucane, Alhakami, Slovic, & Johnson, 2000; Steg & deGroot, 2012), attention, and information acquisition (Feldman, Myers, Hmielowski, & Leiserowitz, 2014; see discussion in NASEM, 2017) and hence can selectively reinforce and shape mental models. To achieve lasting conceptual change requires considering social influences as well (Contractor & DeChurch, 2014).

Further Readings

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Reynolds, T. W., Bostrom, A., Read, D., & Morgan, M.G. (2010). Now what do people know about global climate change? Survey studies of educated laypeople. Risk Analysis, 30(10), 1520–1538.Find this resource:


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