https://orcid.org/0000-0001-5794-7076
Figures
Abstract
This paper presents the findings of a global survey involving 1,510 researchers regarding climate change, focusing on behavioral patterns perceived as relevant to adaptation and mitigation efforts. Similar behavioral tendencies can be observed across both areas: inequity aversion, guilt aversion, and climate anxiety are seen as primary drivers (positive effects), while status quo bias, information avoidance, and mass numbing are regarded as considerable barriers (negative effects). Additionally, the paper finds that researchers’ diverse backgrounds partly explain differing perceptions, indicating the potential for intersectional collaboration in academia to address climate change.
Citation: Grüner S, Mußhoff O (2026) The relevance of behavioral patterns in dealing with human-induced climate change: Results from a survey with 1,510 researchers. PLOS Clim 5(8): e0000967. https://doi.org/10.1371/journal.pclm.0000967
Editor: Kenshi Baba, Waseda University: Waseda Daigaku, JAPAN
Received: February 14, 2026; Accepted: July 13, 2026; Published: August 25, 2026
Copyright: © 2026 Grüner, Mußhoff. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data can be found in the manuscript and supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
[…] solving climate change means mitigating quickly enough to slow it down to a pace at which we can adapt with reasonable comfort. (Hale 2024: 149)
1 Introduction
Global warming is one of the biggest challenges of our time. It has been linked to widespread impacts on economies, societies, and ecosystems, including an increase in the frequency and intensity of natural disasters and heatwaves, heat-related illnesses, water scarcity, migration of humans and non-human species, and shifts in farming conditions [1, 2].
Addressing climate change is difficult for at least four reasons. First, climate change can be understood as a “long problem” in which emissions and consequences span more than one generation [3]. Consequently, future interests are insufficiently considered by today’s generation [shadow interests; 3]. Second, curbing greenhouse gas emissions represents a global public good challenge, where the benefits are nonrival and nonexcludable [4]. Thus, free-riding is a dominant strategy that limits cooperation. Third, mistrust among nations due to geopolitical tensions complicates collective action [5]. Fourth, although the vast majority of people attribute climate change to human activities, populist views that disregard facts still exert a considerable political influence. An example is the U.S. Department of Energy (DOE), which claimed in 2025: “CO2-induced warming might be less damaging economically than commonly believed, and excessively aggressive mitigation policies could prove more detrimental than beneficial.”
Since climate change hinges on human behavior, understanding perception and action is essential for effective interventions and negotiations. An extensive body of literature examines the nexus between human behavior and climate change. Large-scale studies include, for example, the study of Bergquist et al. [6], which investigated the potential for behavior change in climate change mitigation within a second-order meta-analysis of 430 primary studies. They found that social comparison and financial approaches were more effective than information and feedback. Stechemesser et al. [7] analyzed 1,500 climate policies for emission reduction across 41 countries. In the buildings, electricity, industry, and transport sectors, measures with a major impact were identified, such as carbon taxes and fossil fuel subsidies. The effectiveness of various instruments was sector-dependent, reflecting the distinct behavioral drivers at play. In a multi-country study involving 40,000 respondents, Dechezleprêtre et al. [8] find that attitudes toward climate policies are primarily shaped by three determinants: the impact on respondents’ households, the effect on low-income households, and the subjective effectiveness in reducing emissions. A strong link between adaptation behavior and negative affect, descriptive norms, perceived self-efficacy, and outcome efficacy of adaptive actions has been found in a meta-study containing 106 studies in 23 countries by Van Valkengoed and Steg [9]. The authors also mention that knowledge and experience were less relevant for adaptation. According to a systematic review by Brullo et al. [10], enablers of adaptation are resources (money), leadership, awareness of climate risks and responses, support from higher-level institutions, and bridging and bonding social capital.
Starting with Kahneman and Tversky, behavioral scientists have increasingly investigated behavioral patterns that systematically deviate from rational choice theory (so-called behavioral biases or heuristics; e.g., [11]). Plenty of studies have analyzed behavioral biases within the fields of climate change mitigation and adaptation. For example, present bias – the tendency to prefer immediate rewards over future payoffs – is a prominent phenomenon cited in climate change literature [3]. Present bias can explain why individuals do not act proactively and instead stick to environmentally harmful energy sources such as fossil fuels. Another example is inequity aversion, the resistance to inequitable outcomes [12]. Inequity aversion in the field of climate change is important since it is linked to the perceived fairness and justice of taking on burdens. However, the sheer volume of behavioral biases in the literature makes it difficult to isolate the most relevant ones. In a similar vein, Gigerenzer [13] speaks of a “bias bias in behavioral economics.” Without identifying core mechanisms, policy-makers might design ill-directed measures addressing symptoms rather than actual drivers.
While most people care about climate change in principle, active engagement remains limited [e.g., 14]. This highlights the importance of comprehensively understanding the enabling conditions for effective adaptation and mitigation strategies. Against this background, the objective of this paper is threefold. First, it aims to provide a comprehensive overview of the extent to which behavioral patterns function as drivers of or barriers to climate change. Second, it examines potential differences in behavioral patterns between climate change mitigation and adaptation. Third, the paper explores determinants of behavioral biases. To this end, we present findings from a survey of 1,510 researchers from more than 60 countries, evaluating the relevance of 21 behavioral patterns for climate change adaptation and mitigation. The procedure helps overcome limitations linked to purely theoretical approaches, as human behavior not only systematically deviates from rational choice theory but is also contingent upon context and the current scientific and societal zeitgeist. In addition, as researchers frequently serve as intermediaries in policy advice, their insights are relevant for the design and implementation of interventions. Moreover, simultaneously examining multiple behavioral patterns through a uniform methodology avoids potential inconsistencies resulting from different measurement procedures. The study’s findings also complement large-scale research by uncovering underlying mechanisms that enrich aggregate results and provide insights for policy-making.
2 Materials and methods
2.1 Ethics statement
Prior to data collection conducted between 7 April 2025 and 24 July 2025, IRB approval was obtained from Martin Luther University Halle-Wittenberg (Ref. No. 25035GB). All participants provided formal, written consent (for details, see study design in Section A in S1 Appendix). Furthermore, the study population consisted exclusively of adults, as no minors were included in the research.
2.2 Study design
The first part of the survey gathered information on participants’ main field of research and current academic position. Afterward, participants were randomly assigned to one of the two groups: adaptation or mitigation. All participants were presented with a short description of 21 often-cited behavioral patterns (Table 1). The behavioral patterns can be classified into four categories: (i) Social preferences & reciprocity, addressing the challenges of collective action and cooperation (inequity aversion, guilt aversion, altruistic punishment), (ii) Intertemporal choice & decision inertia, covering resistance to change in long problems and time-inconsistency (present bias, status quo bias, sunk cost fallacy, regret aversion, hindsight bias), (iii) Bounded rationality & heuristic judgment, reflecting systematic errors in processing complex information and mental shortcuts (confirmation bias, overconfidence effect, underconfidence effect, availability heuristic, endowment effect, loss aversion, self-serving bias, stereotypical bias), and (iv) Affective processing & risk perception, focusing on emotions and defense against threats (climate anxiety, information avoidance, mass numbing, narrative fallacy, affect heuristic). This approach ensures that key areas relevant to climate change are examined. However, it is neither possible nor the goal of this study to provide an exhaustive analysis of all potentially relevant patterns in the context of climate change.
Participants were asked to evaluate the extent to which each pattern influences either adaptation to or mitigation of climate change. We refrained from communicating the names of behavioral patterns and instead relied on descriptions based on formulations derived from original or highly ranked publications. Responses were measured on a 5-point scale ranging from strong negative effect (i.e., reducing/suppressing influence) (=1) to strong positive effect (i.e., promoting/strengthening influence) (=5). Participants could also classify the relationship as “contradictory” if it exhibited both positive and negative effects, rendering the overall impact unclear (e.g., shift over time). Following the main task, participants answered a series of general questions concerning themselves and their perception of climate change. They were asked to estimate the extent to which climate change is attributable to human activities and to self-assess their expertise in climate change. Moreover, participants indicated whether they had published scientific work on climate change, reported their overall work experience in academia, and specified whether their research primarily focused on low- or middle-income countries. Finally, demographic data were collected, including age, gender, and main place of residence. The translated study design can be found in Section A in S1 Appendix.
2.3 Participants and recruitment
The target population comprised researchers from multiple disciplines and world regions. To achieve disciplinary and geographical diversity, we used the QS World University Rankings 2025 as a sample frame. The objective was not to focus exclusively on top-ranked universities but to generate a heterogeneous sample of universities across five global regions (the ranking distinguished between Africa, the Americas, Asia, Europe, and Oceania). Within each region, universities were randomly selected, and academic staff working in the following fields were identified through institutional websites: Biology, Business & Management, Economics, Language Sciences & Linguistics, Law & Legal Studies, Medicine & Health Sciences, Political Sciences & International Relations, Psychology, and Religion & Theology. To minimize potential temporal distortions, sampling was conducted by systematically rotating data collection across regions (region 1 → region 2 → region 3 → region 4 → region 5 → region 1). More details on the selection procedure can be found in Section B in S1 Appendix.
2.4 Approach to data analysis
- Step 1: Behavioral drivers of climate change – Graphical representation
The first step of the analysis consists of a graphical representation of how researchers perceive the relevance of behavioral patterns for climate change adaptation and mitigation. Participants evaluated the behavioral patterns on a five-point scale ranging from strong negative effect (=1) to strong positive effect (=5), with the option to classify effects as contradictory (=10). Particular attention was paid to behavioral patterns for which the combined proportion of “strong” and “slight” effects (either positive or negative) exceeded 50%. These patterns are referred to as “most relevant behavioral patterns” and are examined in more detail in step 2.
- Step 2: Determinants of the most relevant behavioral patterns – Regression analysis
Step 2 aims to address potential determinants of behavioral patterns. For this purpose, regressions were estimated. The regressions were not pre-registered; they are intended to exploratively identify characteristics associated with the perceived relevance of the behavioral patterns (e.g., the residence of researchers may affect their perception of behavioral patterns due to specific local conditions, requirements, and challenges). Ordered logit regressions were estimated, reporting odds ratios. In the survey, we also collected data on the number of scientific publications and the age of the researchers. However, pairwise correlations showed that these variables were highly correlated with academic position (r > 0.5). Consequently, we excluded them from data analysis.
Explanatory variables include (a) climate change-related determinants (perceived link between human activity and climate change, self-assessed expertise, publication record on climate change); (b) researcher-related determinants (academic position, academic discipline, and focus on low- or middle-income countries); and (c) socio-demographics (main region of residence, gender). The response category “contradictory” was excluded from data analysis due to a lack of logical order; the frequency of this category was relatively low. Further details can be found in Table 2.
3 Description of the sample
Data collection took place between April 7 and July 24, 2025, resulting in a total sample of N = 1,510 participants (for details, see Section C in S1 Appendix). Of these, 58.01% identified as male, 38.54% as female, and the remainder as non-binary/other or preferred not to disclose their gender. The average age was 47.98 years (SD = 13.05), ranging from 21 to 93 years. More than half of the participants held the position of Professor or Associate Professor. The most common disciplines were Economics (16.65%), Psychology (14.64%), and Biology (14.57%). Participants represented 68 countries, with the largest proportions from the United States (16.62%), the United Kingdom (9.21%), and Australia (7.02%). The sample also included participants from South America (e.g., Brazil 1.52%; Mexico 1.46%), Africa (e.g., South Africa 2.65%; Ethiopia 2.52%), and Asia (e.g., India 2.38%; Pakistan 1.52%). The average work experience in academia was 18.69 years (SD = 11.99), and nearly one-third reported a research focus on low- or middle-income countries.
Regarding climate change expertise, 92.32% of participants indicated at least a basic level of knowledge, while 10.86% reported high expertise or expert-level understanding. Approximately one-third had already published on climate change. The vast majority attributed climate change predominantly to human activities: over 90% stated that it is caused by human activities to at least a large extent, whereas slightly more than 3% believed it to be human-caused only to a small extent or not at all.
In this paper, the term “researchers” is used to describe the study participants. With over half the sample holding the rank of Professor or Associate Professor and averaging 18.69 years of scientific work experience, the participants are not laypersons; rather, they represent a highly educated demographic relative to the general public. While behavior-oriented fields like Economics (16.65%) and Psychology (14.64%) are well-represented, the sample is not primarily made up of experts, as only 10.86% reported a high or expert-level understanding of climate change.
It should be noted that the sample may be affected by selection bias, as people who deny human-induced climate change are likely to be underrepresented in such a study. For instance, one non-participating researcher sent us an angry email, saying that climate change is a “fancy topic” and likely to attract a lot of funding, and that he/she does not want to be involved in something like that. Another researcher claimed that there are “four forms of climate change: spring, summer, fall, and winter.”
4 Findings and discussion
4.1 Behavioral patterns of climate change
- (I). Adaptation to climate change
As shown in Fig 1, the behavioral patterns inequity aversion (62.58%), guilt aversion (59.68%), climate anxiety (59.03%), and narrative fallacy (51.25%) have the strongest positive effects on adaptation to climate change according to the participants. Conversely, status quo bias (70.49%), mass numbing (69.31%), information avoidance (68.64%), present bias (64.95%), confirmation bias (54.42%), and sunk cost fallacy (50.60%) are perceived as exerting the strongest negative effects.
- (II). Mitigation of climate change
According to Fig 2, inequity aversion (60.85%), guilt aversion (58.19%), and climate anxiety (57.25%) have the strongest positive effects on climate change mitigation. Behavioral patterns with the strongest negative effects are information avoidance (79.36%), status quo bias (76.17%), mass numbing (75.76%), present bias (67.25%), confirmation bias (59.52%), sunk cost fallacy (55.12%), and overconfidence (50.33%).
- (III). Comparison of adaptation and mitigation
Similar behavioral tendencies emerged for both adaptation and mitigation, with two exceptions that narrowly missed the 50% threshold: narrative fallacy (positive effect on mitigation, 49.27%) and overconfidence (negative effect on adaptation, 49.41%). Apart from minor differences in ranking, no systematic divergence between the two domains was observed.
- (IV). Core principles
Table 3 synthesizes the positive and negative consequences of behavioral patterns. The perceived positive effects of certain patterns may be interpreted through the lens of cooperation/ group fitness and affective defense against threats. For example, guilt aversion is known to explain cooperative behavior and prosociality [56, 57]. Conversely, negatively associated behavioral patterns may reflect tendencies to save cognitive resources and protect against psychological stress. Psychological stress may arise through framing effects [cf. 58, 59]. While gain framing tends to elicit hope and optimism, loss framing is more likely to evoke fear or threat. The relevance of framing for emotional changes is also reflected in brain activity, particularly in the amygdala [60].
- (V). Details on the “most relevant” behavioral patterns
Inequity aversion can be defined as the tendency of people to resist inequitable outcomes; i.e., they are willing to give up some material payoff to move in the direction of more equitable outcomes [33]. Inequity is a crucial topic in the field of climate change, as exposure to climate risks varies, and inaction leads to future generations suffering disproportionately [12]. There are potential incentives for free-riding behavior if burden-sharing is perceived as unfair. However, Fehr and Schmidt [33] show in their model that inequity aversion improves the prospects for voluntary cooperation. In addition, there is ample evidence of support for redistribution (e.g., taxing the rich to support the poor; [61].
Emotions are a key driver of human behavior. Loewenstein [62] argues that a lack of emotions causes serious problems (like global climate change). The framework of classical game theory reaches its limit when studying emotions since beliefs are not considered adequately, requiring approaches such as psychological game theory [63]. In our study, researchers indicated guilt aversion and climate anxiety to be central emotions in the field of climate change, both of which have the potential to spread socially [64, 65]. Guilt aversion can be understood as the tendency of people to experience guilt when they believe they let others down [25,66]. Guilt is an important topic in climate change since climate change is human-induced, and there is a gap between the main contributors and those bearing the brunt of its consequences. The unpleasant feeling of guilt can foster behavioral change (e.g., reduction in air travel or increased sustainable decision-making; [67, 68]). Climate anxiety is the tendency of people to experience heightened emotional, mental or somatic distress in response to changes in the climate system [18–20]. Climate change anxiety is widespread among children and young people [20]. In their meta-analysis, Kühner et al. [69] found evidence for a positive correlation between climate change anxiety and climate action.
Narratives, thinking in terms of stories [15], are crucial in the field of climate change. According to transportation theory, stories can influence real-world beliefs and make events more tangible, such as empathy for future generations [70]. Narratives also help reduce complexity – which has been shown to be effective in the field of nudges [cf. 71]. Relatedly, narratives can transform climate change into a question of social identity – which is theoretically grounded in identity economics [71,72].
Information avoidance refers to the tendency of people to avoid information, even when it is freely available [34, 35]. People’s ability to process information (especially in the information age) is restricted by limited rationality [73]. The concept of information avoidance reflects a fundamental trade-off: while more information can lead to better decision-making, it is often linked to unpleasant (expected) emotions. This phenomenon spans multiple domains of life, such as financial decision-making and medicine, but also extends to environmental and climate-related information (e.g., projected average global temperature). Rejecting climate facts is consistent with patterns of information avoidance, for example, when people aim to avoid cognitive dissonance arising from contradictions between personal beliefs and scientific evidence. The rejection of climate-related information can also be observed at the political level, most notably in populist regimes. A striking example is the presidency of Donald Trump in the United States [74].
Status quo bias can be understood as the tendency of people to stick with the status quo – that is, doing nothing or maintaining one’s current or previous decision – independent of their economic consequences [43]. A business-as-usual approach is a direct implication of loss aversion, according to which changes may create a perception of losses, but also require cognitive effort for adjustment. Status quo bias helps explain why breaking away from habitual patterns of behavior (e.g., consumption or production) is challenging, even when more environmentally or climate-friendly alternatives are available [75].
Mass numbing, that is, the tendency of people to become insensitive as numbers get larger and larger (“If I look at the mass I will never act”) [26, 27]. Thus, greater affectedness is associated with weaker emotional responses and a reduced willingness to act [76]. Insensitivity to large-scale losses, such as those associated with climate change, can partly be explained by psychic numbing [77, 78].
Present bias refers to the tendency of people to be susceptible to the over-pursuit of immediate gratification [31, 32]. The issue of time horizons lies at the heart of climate change challenges, sometimes also referred to as short-termism, the tragedy of the horizon, or the tyranny of the present [3]. The focus on short-termism is widespread, including electoral cycles and regular business reporting requirements. This creates the risk of underestimating the value of climate investments, whose benefits typically materialize only after considerable delays. Present bias also raises ethical issues, since future generations bear the consequences of today’s inaction [3].
The tendency of people to interpret subsequent evidence so as to maintain their initial beliefs can be referred to as confirmation bias [39]. To put it differently, information is perceived as more credible when it aligns with one’s own prior beliefs (i.e., selective information processing). This is relevant to climate change because confirmation bias may serve as a mechanism to explain the persistence of misinformation, as shown, for example, in the field of climate change [79, 80].
Sunk cost fallacy can be referred to as the tendency of people to consider past investments of money, time, or effort when making decisions, even though these costs have already been incurred and cannot be recovered [46]. In other words, after investing a considerable amount of money in fossil fuel infrastructure, it becomes difficult to exit these systems (a path-dependent process referred to as carbon lock-in; [81]). The sunk cost fallacy helps explain boundaries of transformation processes toward sustainable systems.
Overconfidence is the tendency of people to think some aspects of themselves, usually performance or information, are better than they actually are [50–53]. Unrealistic optimism in existing technologies or institutions can lead to passive human behavior; uncertainty and risks may also be underestimated. The overestimation of one’s knowledge (e.g., climate knowledge; [82]) can have various consequences, such as reduced demand for expert input or increased polarization in debates.
4.2 Determinants of the most relevant behavioral patterns
Regression analysis (Sections D and E in S1 Appendix) reveals two overarching tendencies. First, academic discipline – especially languages/linguistics and psychology – plays a considerable role in explaining behavioral patterns (e.g., overconfidence, status quo bias, information avoidance). Climate change constitutes inherently complex, interdisciplinary challenges encompassing health (e.g., vulnerable groups and mental health), economic transformation, and biodiversity. The findings underscore the importance of integrating multiple disciplinary perspectives [cf. 83, 84]. However, the success of interventions also depends on public acceptance [85].
Second, researchers’ regional background emerged as a relevant explanatory factor, for example, for present bias, overconfidence, and confirmation bias. The inclusion of scholars from Asia and Africa appears to be especially important. The climate change literature has long criticized the underrepresentation of the Global South (Global North Bias) and indigenous knowledge systems [86, 87]. The predominance of research focusing on WEIRD (Western, Educated, Industrialized, Rich, and Democratic) societies has been identified as an overly narrow lens [88, 89]—a critique equally applicable to climate research, as behavioral patterns vary substantially in relevance across regions in our study. Regional variation in behavioral relevance may reflect underlying socio-economic disparities; Finkelstein et al. [90], for example, find constraints and stress to be more pronounced among low-income populations when making purchase decisions. Moreover, regional diversity is of ethical importance: the regions most affected by climate change have often contributed relatively little to its causes [91].
Furthermore, the study suggests that researchers’ diversity regarding regional background and academic discipline can be a valuable resource for tailored strategies to address climate change. Since climate-related challenges vary across regions, researcher diversity is essential to prioritize behavioral patterns that are most relevant in a specific local context. Increased diversity in research has the potential to more effectively harness local indigenous knowledge systems in climate change adaptation and mitigation strategies [cf. 92]. From a theoretical point of view, diversity in climate research might be a crucial (production) factor for both adaptation and mitigation, echoing Tandon’s [93] critique of the limited diversity in climate-science research. This extends Loewenstein and Wojtowicz [73], who recently proposed to consider attention as an additional production factor (beyond the traditional ones, such as land, labor, technology, human capital, and information).
5 Concluding remarks
Based on a survey of 1,510 researchers, this paper examined the perceived relevance of behavioral patterns for addressing climate change. The results are relevant because human behavior is not only a major cause of climate change but also holds the potential to counteract it. Understanding behavioral patterns is therefore essential. We identified positive drivers (e.g., inequity aversion, guilt aversion, climate anxiety) and negative drivers (information avoidance, status quo bias, mass numbing) for both adaptation and mitigation. The results indicate a psychological duality: cooperative patterns act as positive drivers, while mechanisms for conserving cognitive resources function as systematic barriers.
Provided the results are robust, a wide range of policy implications emerge, focusing on either overcoming barriers or leveraging drivers. The emphasis on nudging to counteract the status quo bias could be strengthened, and more robust approaches to addressing carbon lock-ins could be designed to alleviate the effects of the sunk cost fallacy. Furthermore, social preferences and emotions, such as inequity aversion and the narrative fallacy, could be utilized to ensure that climate communication and policy measures are perceived as fair and tangible. Our findings also demonstrate that capturing different disciplinary and regional perspectives is crucial for understanding climate-related behavior, indicating the need for broad, interdisciplinary collaboration in climate research. Building on this, our study could also be understood as a call to work in larger groups to address big societal challenges. Promising paths include crowd science or megastudies [cf. 94].
As a caveat, it should be noted that researchers from the United States, the United Kingdom, and Australia account for nearly one-third of the sample. This indicates a concentration on Western, English-speaking industrialized nations. In other words, although many countries were included, the sample exhibits a bias toward WEIRD societies. Replication studies involving more non-Western societies would be valuable to verify the robustness of these results. For instance, collectivist cultures or regions with high uncertainty might evaluate behavioral patterns such as loss aversion or status quo bias differently. Further research is required in this regard. Additionally, the results of this study are limited to correlations; more research into causal effects is necessary.
Supporting information
S1 Appendix. Section A: Study design.
Section B: Details on the selection of the behavioral patterns. Section C: Descriptive statistics. Section D: Regression results. Section E: Full regression tables.
https://doi.org/10.1371/journal.pclm.0000967.s001
(DOCX)
Conflict of interest statement
The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.
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