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Analyzing single-action bias in dynamic climate change environments: insights from feedback and probability | Humanities and Social Sciences Communications

Abstract

“Single action bias” (SAB) characterizes individuals’ inclination to undertake only one preventive measure against climate change, disregarding potentially more effective alternatives. This bias poses a significant obstacle to comprehensive responses to climate change. While dynamic climate simulators have been developed to raise awareness of climate change and encourage pro-environmental behaviors, the prevalence of SAB within these tools remains unexplored. This study introduces the “Single Action Bias-Interactive Climate Change Simulator” (SAB-ICCS) to investigate SAB’s manifestation in dynamic scenarios. Utilizing the framework of the Interactive Climate Change Simulator (ICCS), known for its efficacy in fostering pro-environmental actions, the SAB-ICCS explores how feedback, probability, and their interplay influence SAB prevalence during climate mitigation and adaptation decision-making. A total of 160 participants were randomly assigned to four conditions in the SAB-ICCS, varying feedback presence and climate change probability. Participants engaged in climate mitigation and adaptation actions, simulating the repercussions of climate change through investment choices in climate mitigation and adaptation (consisting of three insurance plans). The study’s dependent variables were the participants’ actions towards climate mitigation and adaptation. Results revealed a substantial prevalence of single action proportion (42%) compared to other action proportions. Furthermore, the total monetary investment was significantly higher when taking optimal actions than when exhibiting SAB. Moreover, a higher probability of climate change resulted in a higher prevalence of SAB (49%) than a lower probability (35%). Interestingly, feedback availability did not significantly impact SAB prevalence. Though both feedback and the probability of climate change influenced how participants exhibited SAB, and the absolute monetary investment was also significantly affected. This research enhances our comprehension of SAB within educational climate simulations, which is vital for informing climate education and policymaking. It offers insights for policymakers and educators to develop interventions addressing SAB, enhancing climate action strategies by understanding probability and feedback influences.

Introduction

Climate change is a major global concern (Cook et al., 2016). Despite heightened awareness and the urgency for action, many individuals and societies struggle to enact effective measures (Bergquist et al., 2019). One potential obstacle to pro-active action is the phenomenon of “single action bias” (SAB). SAB refers to the tendency of people to focus on and take action on a single problem or issue while disregarding the potential impact of other related problems or issues (Weber, 2020). Within the context of climate change, SAB may lead individuals to focus on isolated actions like energy conservation or recycling, overlooking the necessity for comprehensive systemic changes (Zhao and Luo, 2021).

Though, taking multiple actions against climate change does not necessitate a better outcome against climate change. As noted in the literature, the nature of the action, the timing of the said action, and its importance in the system (within which the action is taken, e.g., an ecosystem or climate system) can play a crucial role in determining the effectiveness of the action taken (Lenton et al., 2022). Moreover, the impact of an action on the system is also influenced by the number of stakeholders involved. If most of the population converges on a single action instead of dispersing efforts across multiple actions, the system may reach a “critical number” and lead to systemic changes (Lenton et al., 1997).

Nevertheless, climate scientists widely recognize the importance of the synergy between climate mitigation (reducing future climate change) and adaptation (reducing losses from climatic disasters) strategies to effectively counter the adverse effects of climate change (Choudhary and Dutt, 2021; VijayaV.R. et al., 2012). Primarily because the rate of climate change has reached levels where mitigation alone is insufficient; rather, a combined approach that allows adaptation to changing climatic conditions while mitigating future risks is necessary (VijayaV.R. et al.,

SAB can lead to “negative spillover effects,” where successfully engaging in one pro-environmental behavior decreases the likelihood of undertaking additional actions (Truelove et al., 2014; Zhao and Luo, 2021). Studies have shown that taking action against climate change gives people an illusory sense of security and reduces concern and fear (Weber, 2006). Additionally, taking action against climate change makes people feel morally superior (Truelove et al., 2014). These reduced concerns and heightened moral perceptions could exacerbate the negative spillover effects of SAB, potentially diminishing subsequent actions against climate change (Truelove et al., 2021).

Furthermore, the influence of negative spillover effects extends beyond individuals to encompass large organizations, which may exhibit behavior akin to SAB while maintaining a moral high ground (De Freitas Netto et al., 2020). Practices such as greenwashing, where organizations falsely portray themselves as climate-conscious without implementing substantive changes, exemplify this phenomenon (De Freitas Netto et al., 2024). Such actions stand as barriers towards pro-environmental action and might also affect it negatively in the long run. The prevalence of SAB has been documented in previous research (Truelove et al., 2014; Weber, 2006, 2020; Zhao and Luo, 2021), and attempts have been made to reduce it through education and debiasing tools (Sparkman et al.,

Simulation tools play a pivotal role in enhancing our understanding of climate change and facilitating pro-environmental decision-making (Choudhary et al., 2023; Choudhary and Dutt, 2021; Dutt and Gonzalez, 2012; Kumar and Dutt, 2018). For example, Dutt and Gonzalez ( 2012) used a Dynamic Climate Change Simulator (DCCS) to correct misconceptions about Earth’s climate. Similarly, the Interactive Climate Change Simulator (ICCS) was created to increase investment in climate mitigation and adaptation (Choudhary and Dutt, 2021). The ICCS allows participants to mitigate and adapt to climate change and provides feedback on the outcomes of their decisions. This feedback helps participants understand the impact of their actions on climate change and form strategies to minimize climatic damage (Choudhary and Dutt, 2006). Finally, the interaction between probability and feedback also increases total monetary investment in climate change (Choudhary and Dutt, 2021).

However, there is a gap in the literature regarding the prevalence of SAB in dynamic scenarios, particularly in the context of emissions and damage reduction over time within monetary investment tasks. More precisely, little attention has been given to studying the prevalence of SAB in monetary decisions related to climate mitigation and adaptation, especially in dynamic climate scenarios. Moreover, there is a notable absence of research on the prevalence of SAB in climate mitigation and adaptation decisions when considering the effects of feedback and varying probabilities of climate change.

Furthermore, the lack of literature on SAB in educational climatic microworlds is significant, given their increasing importance in climate education and policymaking. Therefore, it is imperative to investigate whether individuals exhibit SAB tendencies in such tools and, if such tendencies exist, to examine potential factors within climate simulation tools that impact the prevalence of SAB within them. Consequently, it is necessary to develop a tool that provides an effective platform for individuals to showcase their climate investment strategies, offering options for mitigation and adaptation against climate change. Simultaneously, such a tool should enable researchers to accurately capture participants’ investment strategies and actions to counter climate change and its calamities over time.

For this purpose, we developed a “Single Action Bias-Interactive Climate Change Simulator” (SAB-ICCS) based on the Interactive Climate Change Simulator (ICCS) proposed by Choudhary and Dutt ( 2021) to analyze SAB in mitigation and adaptation decisions. The SAB-ICCS enabled participants to take up to four monetary actions against climate change in every trial in the form of mitigation and adaptation (three insurance schemes: life, health, and property insurance) using an imaginary currency provided in the SAB-ICCS tool. The SAB-ICCS also examined how feedback (available/unavailable) and the probability of climate change (high/low) affect people’s mitigation and adaptation decisions regarding climate change (Choudhary and Dutt,

Monte Carlo simulations were used to determine the optimal course of action in the SAB-ICCS tool that maximizes monetary assets. Unlike deterministic forecasting methods, Monte Carlo simulations utilize a pool of randomly generated data to generate a wide range of potential outcomes (Bonate, Bonate 2001). In the context of the SAB-ICCS, this approach facilitated the exploration of various investment strategies with differing monetary values, determining on average which strategy yields the most optimal outcome. Consequently, the SAB-ICCS is a tool for gauging the prevalence of SAB in individuals’ monetary decisions, comparing single actions with optimal strategies, and evaluating the impacts of feedback and climate change probability on singular actions. We subsequently discuss specific literature that helps formulate our hypotheses.

Literature review

Behavioral Biases in climate change: the prevalence and impact of SAB

The literature on climate change shows that people are biased when making related decisions (Choudhary and Dutt, 2021; Cronin et al., 2009; Dutt, 2011; Kumar and Dutt, 2018; J. D. Sterman, 2011). Specifically, the prevalence of SAB has been well documented in climate literature (Truelove et al., 2014; Weber, 2020; Werfel, 2017; Zhao and Luo, 2021). For example, people often prefer visible, immediate policies (e.g., cleaning polluted rivers) to more effective long-term policies (e.g., investing in renewable energy research) (Patt and Zeckhauser, 2000). Although people are willing to act on climate change, they limit themselves to the first, most convenient action and do not take further action, exhibiting SAB (Patt and Zeckhauser, 2021).

For example, when Japanese citizens are reminded of their past energy-saving actions, they are less likely to support carbon tax policies (Raimi, 2017; Werfel, 2021). Thus, people are more likely to take a single, more convenient action against climate change than optimal or multiple actions. Thus, we hypothesize the following:

H1: Participants interacting with the SAB-ICCS are likely to exhibit a bias in taking only a single action against climate change compared to multiple actions.

H2: Participants interacting with the SAB-ICCS are likelier to exhibit SAB than to take optimal actions against climate change.

Influence of feedback on SAB

Experiencing a climate disaster has a major impact on people’s awareness and future pro-environmental behavior (Bergquist et al., 2019; Choudhary and Dutt, 2021; Kumar and Dutt, 2018; Weber, 2006). Those who have encountered such disasters tend to allocate greater resources toward climate change mitigation and adaptation efforts (Bergquist et al., 2019; Spence et al., 2011; Van Valkengoed and Steg, 2019). Based on the affect theory (Scherer and Ekman, 2014), the emotions aroused by experiencing climatic calamities can influence people’s actions and promote pro-environmental actions toward climate change. For instance, a UK flood increased the sample’s investment in climate mitigation (Spence et al., 2011), and a meta-analysis showed that experience was a predictor of adaptation behavior (Van Valkengoed and Steg, 2019). Similarly, studies on climate microworlds have shown that feedback increases investment in climate change (Chaturvedi et al., 2018; Choudhary and Dutt, 2021). Moreover, based on the concept of bounded rationality, people’s cognitive limitations may affect their ability to analyze and respond to situations and use a heuristic approach (Simon, 1990). Thus, SAB may reduce people’s climate investment, and feedback may lead people to invest in the first option that appeals to them. Thus, we hypothesize as follows:

H3: The prevalence of SAB is higher when feedback is present than when feedback is absent.

Influence of climate change probability on SAB

Research on climate change probability suggests that a higher probability of climate change increases people’s concerns and willingness to take pro-environmental action, leading to increased investment in climate change. This is because a higher probability increases the likelihood of facing the adverse effects of climate change (Milinski et al., 2008; Weber, 2006). Based on affect theory, the high probability of climate change may also emotionally overwhelm people’s ability to thoroughly analyze the situation and follow a heuristic approach while investing in climate change (Scherer and Ekman, 2014). SAB may lead people to invest in only one of the available mitigation and adaptation options. People may prefer mitigation or adaptation, depending on the chance of future climate catastrophes. However, based on past research (Weber, 2006), we speculate that if the probability of climate change is high, people may take multiple actions to reduce its negative effects.

Additionally, feedback and probability interact (Hertwig, 2012; Hertwig and Erev, 2009; Kahneman and Tversky’, 1979). With feedback, people tend to overestimate high-probability events and underestimate low-probability ones. High-probability events are underestimated without feedback, and low-probability events are overestimated (Hertwig, 2012; Hertwig and Erev, 2009; Kahneman and Tversky’, 1979). Climate microworld studies suggest that a higher probability of climate change with feedback leads to greater investment in climate change than a lower probability with feedback (Choudhary and Dutt, 2021). Thus, we hypothesize as follows:

H4: The prevalence of SAB is lower when the probability of climate change is higher.

H5: Feedback with a high probability of climate change will lead to fewer instances of SAB for mitigation and adaptation decisions than feedback with a low probability of climate change.

Thus, the objective of the present study is to examine the prevalence of SAB in dynamic climate scenarios using the SAB-ICCS tool.

SAB-ICCS tool

The SAB-ICCS tool was adapted from the original ICCS tool developed by Choudhary and Dutt ( 2021) to study the influence of feedback and the probability of climate change on individuals’ monetary decisions against climate change. The SAB-ICCS tool incorporates an anthropogenic climate change model and allows participants to invest against climate change by making monetary investments in climate mitigation and adaptation. Within SAB-ICCS and in tune with the real world, investment in climate mitigation reduces the future likelihood of climate change occurrence, and investment in climate adaptation reduces the losses that occur due to climatic calamities (Hasson et al., 2010). Participants in SAB-ICCS could invest in climate mitigation and adaptation using an imaginary currency (EC); an initial starting amount of 8760 EC in the form of annual income was provided to participants to make their investments. Additionally, each participant had a starting property wealth of 50,000,00 EC. The participants’ annual income and property wealth were affected in case of climatic calamity occurrence. Participants were free to invest their entire yearly available income in any trial towards climate mitigation and adaptation, or they could decide not to invest anything towards climate change. Investment in climate mitigation could vary between zero and the annual income available to invest. Investment in climate adaptation was made by purchasing insurance schemes. Three insurance plans were available in the SAB-ICCS tool and could be purchased by spending a fixed corresponding amount, i.e., health (1173 EC/year), life (488 EC/year), and property insurance (1884 EC/year). If participants did not have enough EC remaining to the corresponding insurance scheme amount, they could not purchase that plan.

The SAB-ICCS tool was 36 trials long, and each trial was represented as a year. Participants were supposed to make investments against climate change in each given trial using the EC available to them with the goal of minimizing the losses that occurred due to climate change. In the SAB-ICCS tool, annual income and property wealth losses occurred due to climatic calamities. Three climatic calamities were incorporated within the SAB-ICCS tool, i.e., floods, cyclones, and droughts, and they inflicted damages in the form of injury, fatality, and property damages. Occurrence of injury and fatality due to climatic calamities reduced the participant’s annual income in the subsequent year/trial. In comparison, the occurrence of property damage reduced the participant’s property wealth. The goal of participants was to increase their total wealth in SAB-ICCS. The total wealth in SAB-ICCS was defined as the sum of annual income not invested against climate change and the remaining property wealth at the end of the 36th trial.

The SAB-ICCS interface mainly consisted of two screens, i.e., the investment screen and the feedback screen. As shown in Fig. 1, the investment screen provided participants with information regarding the game parameters, like the annual income available to invest against climate change, property wealth, current year, anthropogenic probability of climate change occurrence, and total wealth, among other game parameters. Crucial information like the probability of climate change, total income not invested, and property wealth were also available in a visual format for better comprehension. Furthermore, as the name suggests, the investment screen allowed participants to invest in climate change. The SAB-ICCS provided participants with four investment options, i.e., mitigate against climate change and the three insurance schemes, which served as adaptation methods against climate change. Thus, SAB-ICCS allowed participants to make up to four investment actions against climate change in any trial. Using the investment screen, participants could decide how many actions they wanted to take against climate change and click the “Invest” button (below the mitigation option) to finalize their investment. Here, the tool allows one not to take any climate mitigation or adaptation actions in a trial. In the feedback present condition, participants were informed about the outcome of their monetary decisions using the feedback screen (Fig. 2) after each trial; however, no such feedback was provided in the feedback absent condition.

The screen allows participants to make monetary investments against climate change and informs them about the various game parameters. The arrows have been used to highlight the mitigation and adaptation options available to participants.

The screen presents participants with the outcome of their monetary actions.

The feedback screen provided participants with information regarding the outcome of their monetary decisions. As shown in Fig. 2., it first informed participants if a climatic calamity had occurred, and if yes, which climatic calamity occurred (for example, Cyclone). Next, it mentioned the investments made by the participants towards climate change, followed by the details of damages incurred due to climatic calamity, if any. Finally, it mentioned the total wealth. The feedback screen also used visual presentations to showcase the damages incurred due to climate change. Participants could click the “Return To Game” button to return to the investment screen for the subsequent round (for a detailed description of the functioning and interface of the ICCS tool, kindly refer to Choudhary and Dutt ( 2021)). In the next section, we highlight how the ability of the ICCS tool to allow participants to take multiple pro-environmental actions against climate change in any trial makes it an optimal tool for studying the prevalence of SAB in climate simulations.

SAB-ICCS: optimal investment and single action bias

As mentioned earlier, the SAB-ICCS tool was developed to study the prevalence of SAB. The SAB-ICCS tool allowed participants to take as many as four preventive actions against climate change in any trial (Fig. 1). Participants could decide to buy all three insurance schemes while investing their remaining money in mitigation if they wanted to do so. Inversely, they also had the option of not investing in any given trial by entering the amount zero in the mitigation box and not selecting any of the insurance schemes, thus taking zero preventive actions against climate change. In the SAB-ICCS tool, an action was considered a single action (i.e., exhibiting SAB) when a participant decided to invest in only one preventive action (whether mitigation or adaptation, i.e., any of three insurance schemes) against climate change. If a participant in any given trial decided to invest in two or more options or did not invest against climate change (i.e., zero action), then such actions were considered not to be exhibiting SAB.

In the SAB-ICCS tool, each preventive action served a unique purpose to counter the cataclysmic effects of climate change. While mitigating climate change reduced the future probability of climate change, investment in each insurance scheme uniquely safeguarded the participants’ monetary assets (annual income and property wealth) (Choudhary and Dutt, 2021). Thus, within the SAB-ICCS environment, taking multiple actions against climate change was essential for safeguarding one’s assets while reducing the future likelihood of climate change.

More precisely, under the SAB-ICCS conditions, the optimal thing (the best course of action to increase the total wealth) was to take two actions, i.e., always buy property insurance and use the remaining capital to invest in climate mitigation. The most optimal course of action for the SAB-ICCS tool was determined by simulating different investment strategies over a fixed number of times using Monte Carlo Simulations. The experiment conducted by Choudhary and Dutt ( 2021) indicated that the participants who invested in climate mitigation and adaptation simultaneously had a higher total wealth remaining at the end of the task than participants who only invested in climate mitigation or adaptation, but not both. Thus, in a dynamic environment, we created different investment scenarios ranging from investing partially in mitigation and purchasing an insurance plan to buying all three insurances and investing the remaining capital in mitigation. Overall, twenty different strategies were simulated. Each investment strategy was simulated ten thousand times using Monter Carlo Simulations, and the average total wealth remaining was obtained for each investment strategy. Like the SAB-ICCS tool with the simulated scenarios, the occurrence of climate change was kept as a probabilistic factor.

Additionally, parameters like the anthropogenic probability of climate change and the ability to buy insurance only when required monetary assets are available were also accounted for in the simulated environment, similar to the SAB-ICCS tool. Thus, within SAB-ICCS, the best course of action was to take two actions in each trial, i.e., always purchase property insurance with a 100% chance and invest the remaining capital into mitigating climate change. Therefore, in the SAB-ICCS tool, if a participant bought property insurance and invested in mitigating climate change in the same trial (not necessarily his entire remaining capital after buying property insurance), then such an action was considered optimal in the SAB-ICCS tool.

In its nature, the SAB-ICCS tool resembles real-world settings, allowing participants to mitigate and adapt against climate change or take no action against it. Other climate simulation tools primarily try to create awareness about climate change by informing participants about the implications of different pro-environmental actions on carbon concentrations or the adverse effects of high carbon concentrations (Dutt and Gonzalez, 2012; Kumar and Dutt, 2018; Sterman et al., 2012) However, the SAB-ICCS tool’s ability to make monetary investments in climate mitigation and adaptations makes it an optimal tool for studying the prevalence of SAB in climatic microworlds. Additionally, its resemblance to real-world settings allows us to compare our results to studies conducted in real-world settings to draw comparative and crucial inferences.

Methodology

Experimental design

The data for the experiment was collected from 160 participants randomly assigned to one of the four between-subject conditions based on two independent variables. The conditions differed based on the probability of climate change occurrence and the availability of the feedback, i.e., cubic feedback (n = 40), cubic no-feedback (n = 40), linear feedback (n = 40), and linear no-feedback (n = 40). Each condition of the ICCS tool was 36 trials long. Participants within each condition of the ICCS tool were asked to make monetary investments against climate change using an imaginary currency (EC) introduced in each ICCS condition. In the feedback conditions (i.e., cubic and linear feedback), participants were provided with the outcomes of their monetary actions against climate change. After every round of investment, participants were informed whether a climatic calamity occurred or not, and in the case of a climatic calamity, they were also informed about the corresponding damages caused by the calamity. Inversely, the no-feedback conditions (i.e., cubic no-feedback and linear no-feedback) did not provide participants with the outcome of their monetary decisions against climate change. The cubic conditions (i.e., cubic feedback and cubic no-feedback) resulted in a higher probability of climate change occurrence than the linear conditions (i.e., linear feedback and linear no-feedback). The following equation was used to determine the future probability of climate change based on participants investment in climate mitigation:

where, \({p}\) is the probability of a climate change disaster; \({m}\) is the return on the mitigation amount; the \(\mathop{\sum }\nolimits_{{\rm{t}}=1}^{{\rm{n}}}{{\rm{investment}}}_{{\rm{t}}}\) is the sum of investments made in mitigating climate change between the first year and the current (\({{\rm{n}}}^{{\rm{th}}}\)) year; the\(\,\mathop{\sum }\nolimits_{{\rm{t}}=1}^{{\rm{n}}}{{\rm{income}}}_{{\rm{t}}}\) is the sum of income available for making investments between the first year and the current (\({{\rm{n}}}^{{\rm{th}}}\)) year; and, \(k\) is the exponent parameter.

The exponent parameter \((k)\) was taken to be one in the linear condition, though it was taken to be three in the cubic condition. Thus, even if the amount invested in climate mitigation remained the same for the participants in the cubic and linear conditions, the participants who were subjected to cubic probability models faced a higher likelihood of facing climatic calamity than those subjected to linear probability models (for more details, please see Choudhary and Dutt, 2021).

Dependent variable

The study’s dependent variable was the “action count” (AC) taken against climate change, and it was determined by computing whether an investment was made in climate mitigation and the three insurance schemes in each trial. For each trial, the AC was zero (minimum; 0AC) if participants decided not to invest against climate change, and AC was four (maximum; 4AC) if an investment was made in all three insurance schemes and climate mitigation. Thus, the value of AC varied between zero (0AC) and four (4AC) for each trial based on the number of investments made across different insurance schemes and climate mitigation in that given trial. As previously mentioned, there were a total of 36 trials in each condition of the SAB-ICCS tool; thus, for each participant, we obtained 36 values that would be distributed across 0AC, 1AC, 2AC, 3AC, and 4AC. Each participant’s NAC (where N could be 0, 1, 2, 3, or 4) across the 36 trials was converted to the NAC proportion for obtaining their action metrics. The NAC proportions for each participant for a given action count were obtained by the following formula:

Where, the \({{\rm{NAC}}}_{i}\) will be 0 if the specific NAC was not done in the \(i\)th trial, and it will be 1 if the specific NAC was executed in the \(i\)th trial. For example, if a participant across 36 trials did not take any zero actions (0AC) (0/36), took single action (1AC) seven times (7/36), double action (2AC) sixteen times (16/36), triple action (3AC) nine times (9/36), and four actions (4AC) four times (4/36). Then, the participants’ action proportions were obtained to be 0.0, 0.19, 0.44, 0.25, and 0.11, respectively, for each action count. The proportion of NAC was averaged across all the participants in a condition to yield the average proportion of NAC for the condition.

We also computed the AC proportion for the optimal action (i.e., buying property insurance and investing in mitigation in the same trial) by following the same principle. The optimal AC was obtained by determining the number of optimal actions taken and averaging it over 36 trials as per the following formula:

Where, Optimal ACi will be 0 if buying property insurance and investing in mitigation was not done in the \(i\)th trial, and it will be 1 if buying property insurance and investing in mitigation was executed in the \(i\)th trial. For example, if the participant invested optimally six times, i.e., bought property insurance and invested in mitigation six times (6/36), then the participant’s Optimal AC was obtained to be 0.17.

The effect of feedback and the probability of climate change on people’s investment preferences towards mitigation or adaptation when taking a single action was also studied. People’s investments in mitigation and the three insurance plans, i.e., life, health, and property insurance, were tabulated for each trial in which they took a single action against climate change within the ICCS tool. Next, an action proportion for single actions was formed as per the following formula:

Where, \({{\rm{NST}}}_{i}\) is the specific type of investment, i.e., mitigation and the three insurance plans (Life, health, and property insurance) where a specific NAC was executed. For example, if a participant took a single action in 7 of the 36 trials and invested three times in mitigation (3/7), one time in life insurance (1/7), one time in health insurance (1/7), and two times in property insurance (2/7). Then, the participant’s action proportion for a single action was obtained to be 0.43 for mitigation, 0.14 for life insurance, 0.14 for health insurance, and 0.29 for property insurance, respectively.

The obtained action proportion helped us determine whether people exhibit the “single-action bias” when making decisions about climate change. Furthermore, the obtained action proportion for the single action helped us determine that under the effect of feedback and the probability of climate change, which action is preferred while taking a single action against climate change.

Moreover, we also computed the actual monetary investment made (in EC) under different conditions of feedback and probability when exhibiting SAB or taking optimal actions.

The average monetary investment made under different conditions of feedback and probability when exhibiting SAB was obtained as per the following formula:

Where, “\({\rm{Monetary\; investment\; made\; in\; X\; during}}{{\rm{SAB}}}_{i}\)“ is the amount invested in “X” action (where X could be mitigation, or three insurance actions, i.e., health, life, and property insurance) during SAB, and “\({{\rm{T}}}_{i}\)“ has a value of one for the trial \(i\) when X action was executed and has a value of zero for trial \(i\) when X was not executed.

Similarly, the average monetary investment made when taking optimal action against climate change was obtained as per the following formula:

Where, “\({\rm{Monetary\; investment\; made\; during}}{{\rm{Optimal}}}_{i}\)“ is the amount invested in optimal action (investment in mitigation and purchasing property insurance), and “\({{\rm{TR}}}_{i}\)“ has a value of one for trial \(i\) when optimal action was executed and has a value of zero for trial \(i\) when optimal action was not executed. For example, if the participant invested optimally six times and invested 1000 EC, 2000 EC, 1000 EC, 2000 EC, 3000 EC, and 3000 EC across the six trials, then his average monetary investment when taking optimal AC was 12000/6 EC, i.e., 2000 EC.

Participants

The Ethics Committee of the host institute approved the study, and signed written consent was collected from all the participants. Amazon M-Turk, a crowdsourcing platform, was used to recruit participants from India randomly. The participants’ ages ranged from 18 to 52 years (mean = 30.09 years, SD = 5.72 years). Males constituted 72% of the participants, while the remaining were female. 71% of participants had a STEM background, while the remaining had a non-STEM background, and 32% had a master’s degree while the remaining had completed their undergraduate degree. The average time to complete the study was 27 min, and each participant was paid INR 30 as participation compensation.

Procedure

The participants were given instructions before the experiment started and their interaction with the ICCS tool (kindly see the annexure section for the instruction text). The ICCS tool comprised a total of 36 trials, with participants instructed to make climate change investment decisions in each trial. Within the ICCS tool, participants had the flexibility to invest in climate mitigation or adaptation measures. Their range of actions spanned from refraining from investment (i.e., not purchasing any insurance and allocating 0EC towards climate mitigation) to investing in all four available actions (i.e., purchasing all three insurances and allocating resources towards climate mitigation), contingent upon their monetary resources. Participants across all conditions were required to complete all trials. Upon completing the final trial, participants were thanked for their participation and duly compensated for their efforts.

Data analysis

We used analysis of variance (ANOVA) to examine whether a significant difference existed between two or more means. A one-way ANOVA was applied to test whether higher reliance on single action bias existed on the individual’s monetary investment decisions when making mitigation and adaptation decisions against climate change. Thus, we performed a one-way ANOVA between the action proportion of single actions and other possible courses of action. Next, we performed a one-way ANOVA to study the prevalence of SAB under the absence or presence of feedback and the high and low probabilities of climate change. Similarly, we performed one one-way ANOVA to test the influence of feedback on an individual’s preference for a single action toward mitigation and adaptation decisions against climate change. Also, we performed a one-way ANOVA to test the influence of the probability of climate change on an individual’s preference for single action towards mitigation and adaptation decisions against climate change. A one-way ANOVA was also performed to determine the difference between the average monetary investment made under different conditions. Finally, we performed a mixed-factorial ANOVA to test the influence of feedback and the probability of climate change interaction on an individual’s preference for a single action toward mitigation and adaptation decisions against climate change. The alpha level for analysis was set at 0.05 (or 5%).

Results

Prevalence of single-action bias

We first investigated the prevalence of SAB, and we found that it was very prevalent. As shown in Fig. 3, the proportion of 1AC was significantly more than other AC’s across the four conditions of the SAB-ICCS tool. The proportion of 1AC (0.42) was significantly higher than 0AC (0.02). [F (1, 318) = 146.83, p < 0.01, ηp2 = 0.32] (Fig. 3a). The proportion of 1AC (0.42) was significantly higher than that of 2AC (0.33) [F (1, 318) = 3.82, p = 0.05, ηp2 = 0.01] (Fig. 3b). The proportion of 1AC (0.42) was significantly higher than that of 3AC (0.13) [F (1, 318) = 58.15, p < 0.01, ηp2 = 0.16] (Fig. 3c). Finally, the proportion of 1AC (0.42) was significantly higher than that of 4AC (0.10) [F (1, 318) = 77.61, p < 0.01, ηp2 = 0.20] (Fig. 3d). The results support H1.

a The proportion of action in single action and zero action. b Proportion of single action and two actions. c The proportion of actions in a single action and three actions. d proportion of action in a single action and four actions. Error bars indicate 95% CI around the point estimate.

Next, we investigated the prevalence of SAB and optimal AC and found that the prevalence of SAB was greater than that of optimal AC. As shown in Fig. 4, the proportion of 1AC (0.42) was significantly higher than the proportion of optimal actions taken (0.08) [F (1, 318) = 96.61, p < 0.01, ηp2 = 0.23] (Fig. 4a). The results support H2. Moreover, the average monetary investment made (through EC) when taking optimal AC (4124.60 EC) was significantly higher than the average monetary investment made when exhibiting SAB (2067.92 EC) [F (1, 2907) = 320.95, p < 0.01, ηp2 = 0.09] (Fig. 4b).

a Proportion of 1AC and optimal AC across different conditions of feedback and climate probability. b The average total investment is made in 1AC and optimal AC. Error bars indicate 95% CI around the point estimate.

Next, we studied the effect of feedback and the probability of climate change on the proportion of 1AC. We observed that feedback did not significantly affect the proportion of SAB. However, a higher probability of climate change was associated with a significantly higher proportion of SAB compared to scenarios with low probability. The proportion of 1AC in the presence of feedback (.44) was not significantly different than the proportion of 1AC in the absence of feedback (0.40) [F (1, 158) = 0.33, p = 0.57, ηp2 = 0.00]. These results do not support H3. Moreover, the average total monetary investment made (through EC) when exhibiting SAB in the presence of feedback (2082.28 EC) was not significantly different than the average total monetary investment in the absence of feedback (2052.24 EC) [F (1, 2425) = 0.09, p = 0.75, ηp2 = 0.00].

As shown in Fig. 5, the proportion of 1AC in the high probability of climate change (.49) was significantly higher than the proportion of 1AC in the low probability of climate change (0.35) [F (1, 158) = 4.27, p < 0.05, ηp2 = 0.03]. The results do not support H4. Moreover, the average total monetary investment made (through EC) when exhibiting SAB in the high probability of climate change (1704.66 EC) was significantly lower than the average total monetary investment made in the low probability of climate change (2567.31 EC) [F (1, 2907) = 320.95, p < 0.01, ηp2 = 0.09] (Fig. 4b).

a Proportion of 1AC in high and low probability of climate change when exhibiting SAB. b The average total investment made under high and low probability of climate change when exhibiting SAB. Error bars indicate 95% CI around the point estimate.

Next, we analyzed the preference of the single action taken (1AC), i.e., when taking a single action against climate change, which mode of investment was preferred amongst mitigation, health, life, and property insurance under different feedback and probability of climate change conditions. Figure 6 displays the 1AC proportion towards mitigation, health, life, and property insurance under the feedback and the probability of climate change conditions.

a Division of single-action proportions in the feedback condition. b Division of single-action proportions in the probability of climate change.

Influence of feedback on the preference of single action

We statistically compared the preference for 1AC exhibited and found that feedback significantly affected mitigation and life insurance. Similarly, the probability of climate change significantly affected mitigation and property insurance. As shown in Fig. 7, the proportion of 1AC exhibited towards climate mitigation was found to be significantly higher in the presence of feedback (0.57) than in the absence of feedback (0.33) [F (1, 111) = 11.34, p < 0.01, ηp2 = 0.09] (Fig. 7a). The proportion of 1AC exhibited towards life insurance was found to be significantly less in the presence of feedback (.16) than in the absence of feedback (0.36) [F (1, 111) = 14.31, p < 0.01, ηp2 = 0.11] (Fig. 7b). The proportion of 1AC exhibited towards health insurance was not significantly different in the presence of feedback (0.12) than in the absence of feedback (0.19) [F (1, 111) = 2.78, p = 0.1, ηp2 = 0.02]. The proportion of 1AC exhibited towards property insurance was not significantly different in the presence of feedback (0.15) than in the absence of feedback (0.12) [F (1, 111) = 0.56, p = 0.46, ηp2 = 0.01].

a Proportion of 1 AC for mitigation in the presence and absence of feedback. b Proportion of 1 AC for life insurance in the presence and absence of feedback. Error bars indicate 95% CI around the point estimate.

As shown in Fig. 8, the average monetary investment made (through EC) when exhibiting SAB in climate mitigation in the presence of feedback (2691.26 EC) was significantly lower than the average monetary investment made in the presence of feedback (4029.11 EC) [F (1, 1175) = 56.38, p < 0.01, ηp2 = 0.05]. The average monetary investment in three adaptive insurance schemes remained the same across the feedback present and absent conditions, as the amount needed to invest in an insurance scheme was constant across conditions.

The average investment made in different investment actions in the presence and absence of feedback when exhibiting SAB.

Influence of climate change probability on preference of single action

We investigated the influence of climate change probability on preference of single action. As shown in Fig. 9, the proportion of 1AC exhibited towards climate mitigation was found to be significantly higher in the low probability of climate change (0.58) than in the high probability of climate change (0.38) [F (1, 111) = 7.47, p < 0.01, ηp2 = 0.06] (Fig. 9a). The proportion of 1AC exhibited towards property insurance was found to be significantly lower in the low probability of climate change (0.08) than in the high probability of climate change (0.19) [F (1, 111) = 8.69, p < 0.01, ηp2 = 0.07] (Fig. 9b). The proportion of 1AC exhibited towards health insurance was not significantly different in the low probability of climate change (0.12) than in the high probability of climate change (0.18) [F (1, 111) = 1.72, p = 0.19, ηp2 = 0.02]. The proportion of 1AC exhibited towards life insurance was not significantly different in the low probability of climate change (0.23) than in the high probability of climate change (0.26) [F (1, 111) = 0.34, p = 0.56, ηp2 = 0.00].

a Proportion of 1 AC for mitigation in the high and low probability of climate change. b Proportion of 1 AC for property insurance in the high and low probability of climate change. Error bars indicate 95% CI around the point estimate.

As shown in Fig. 10, the average monetary investment made (through EC) when exhibiting SAB in the high probability of climate change (2768.14 EC) was significantly lower than the average monetary investment made in the low probability of climate change (3472.48 EC) [F (1, 1175) = 16.37, p < 0.01, ηp2 = 0.01]. The average monetary investment in three adaptive insurance schemes remained the same across the feedback present and absent conditions, as the amount needed to invest in an insurance scheme was constant across conditions.

The average investment made in different investment actions in the high and low probability of climate change when exhibiting SAB.

Influence of interaction between feedback and climate change probability on preference of single action

Next, we statistically analyzed the effect of feedback and probability interaction on the preference for 1AC exhibited and found no significant effect. The interaction between feedback and the probability of climate change did not significantly affect people’s action preference(s) when 1AC was taken against climate change. No significant difference was observed for 1AC taken towards mitigation owing to the interaction between feedback and the probability of climate change [F (1, 109) = 0.61, p = 0.44, ηp2 = 0.01]. No significant difference was observed for a 1AC taken towards life insurance due to the interaction between feedback and the probability of climate change [F (1, 109) = 0.81, p = 0.37, ηp2 = 0.01]. Similarly, no significant difference was observed for 1AC taken towards health insurance due to the interaction between feedback and the probability of climate change [F (1, 109) = 0.001, p = 0.98, ηp2 = 0.00]. Lastly, no significant difference was observed for 1AC taken towards property insurance due to the interaction between feedback and the probability of climate change [F (1, 109) = 0.04, p = 0.84, ηp2 = 0.00]. These results do not support H5.

Discussion and conclusions

This study examines the prevalence of SAB when making monetary decisions in dynamic climate scenarios under the influence of feedback and the probability of climate change. Our findings reveal that participants predominantly opted for a single preventive action against climate change despite the availability of multiple measures. Feedback did not significantly affect the SAB when making decisions regarding climate change. However, climate change probability notably influenced participants’ inclination towards SAB. Feedback and the probability of climate change influenced the preference for the single action taken. The interaction between feedback and the probability of climate change did not affect single-action preferences.

In accordance with H1 and H2, participants exhibited SAB against climate change rather than taking multiple or optimal actions, respectively. These results support previous research highlighting the prevalence of biases (Choudhary et al., 2023; Cronin et al., 2009; Sterman, 2011) and specifically SAB (Truelove et al., 2014; Weber, 2006; Zhao and Luo, 2021) when people make decisions against climate change. The SAB-ICCS tool gave participants multiple options to invest against climate change through climate mitigation and adaptation using a fictitious currency that was only useful in the SAB-ICCS tool. Despite that, participants took only one preventive action against climate change rather than multiple preventive actions. The behavior of participants can be explained based on Weber’s ( 2006) work; the author argues that the fear of climate change is a “flag” in people’s minds that some preventive action must be taken to counter the cataclysmic effects of climate change but remains in place only until one such action is taken. Thus, our results regarding H1 and H2 suggest a similar inclination towards single-action decision-making within a simulated dynamic climate change environment. Moreover, we found that the average monetary investment towards climate change when taking optimal action was significantly more than when exhibiting SAB. While SAB alone may not denote inefficient climate change investment (Lenton et al., 2022), notably diminished monetary allocations under SAB raise concerns. Thus, our findings reiterate the importance of overcoming SAB when making decisions against climate change.

In contrast to H3, feedback did not significantly affect the overall proportion of SAB. Moreover, the average total monetary investment when exhibiting SAB remained statistically non-significant regardless of feedback’s presence or absence. These results are consistent with prior research on feedback in environmental decisions (Raimi, 2017; Weber, 2006). For example, Weber (

Although the feedback did not affect the proportion of SAB, feedback did affect the proportions of mitigation and life insurance actions investments in those trials when SAB was exhibited. The mitigation proportions were higher when the feedback was present than when it was absent. Conversely, instances of investment in life insurance were lower in the presence of feedback than in its absence. It is possible that participants experiencing calamities in the feedback-present condition were influenced by strong emotions, as suggested by the affect theory (Scherer and Ekman, 2014), leading them to exhibit a greater proportion of mitigation and a smaller proportion of life insurance actions. Furthermore, the average investment in mitigation was much smaller in SAB trials when feedback was present than when it was absent. Thus, when emotions run high, people seem to care less about their lives and their mitigation together. These results are also consistent with Herbert Simon’s theory of bounded rationality (Simon, 1990), where the single-action biased decisions in the presence of feedback about calamities led people to make bounded rational decisions about mitigation and life insurance compared to conditions without feedback.

In contrast to H4, the proportion of SAB was significantly higher when the probability of climate change was high than low. According to the affect theory (Scherer and Ekman, 2014), the increased probability of climate change may have overwhelmed individuals’ ability to analyze the situation properly, potentially leading them to exhibit SAB. Furthermore, the average total monetary investment when exhibiting SAB was significantly less in the high probability condition than in the low probability condition of climate change. The heightened probability may have emotionally overwhelmed participants, preventing them from taking an optimal approach and rather bounded rational decisions (Simon, 1990). Conversely, participants in the low probability condition may have analyzed the game parameters better, resulting in significantly higher and less-bounded rational investments against climate change, similar to when taking optimal action against climate change.

The climate change probability also affected participants’ actions when exhibiting SAB. Particularly, participants showed higher mitigation proportions and higher investments in mitigation while exhibiting SAB in scenarios with low probability compared to high probability. This behavior may have arisen from smaller bounded rational decisions (Simon, 1990) in conditions that showed smaller damages due to the low probability of climate change. Perhaps, in low probability conditions, due to smaller bounded rational decisions, participants likely analyzed the game parameters more thoroughly. Conversely, participants exhibited a higher proportion towards property insurance in scenarios with a high probability of climate change compared to those with low probability. This shift in behavior suggests that participants recognized property damage as the most significant threat to their monetary assets among all the potential cataclysmic effects of climate change. Moreover, in conditions of high probability of climate change, participants may have encountered property damage more frequently. According to the affect theory, this heightened exposure could have increased emotional arousal. Consequently, to safeguard their total monetary assets (the game’s primary goal), participants in the high probability condition may have prioritized protecting their most valuable asset, namely property, over other climate change mitigation measures (Choudhary and Dutt, 2021).

In contrast to H5, the interaction between feedback and the probability of climate change did not significantly affect the proportions of SAB. A likely reason for this could be the non-significant effect of feedback in the SAB-ICCS experiment. Additionally, the probability of climate change affected the tendency to exhibit SAB: the proportion of SAB was significantly higher when the probability of climate change was high than low. A high probability of climate change propagated SAB towards property insurance (adaptive technique), whereas a low probability of climate change propagated SAB towards mitigation. Overall, these factors led to a non-significant effect of feedback and the impact of the probability of climate change to affect SAB.

The influence of feedback and the probability of climate change on our tendency to exhibit Single Action Bias (SAB) offers valuable insights for climate literature. Despite the ideal of adopting a holistic approach encompassing both mitigation and adaptation strategies, our study reveals the persistent challenge of SAB, consistent with prior research (Hansen et al., 2004; Truelove et al., 2014; Weber, 1997; Zhao and Luo, 2021). Therefore, this research emphasizes the need for further investigation into the adverse effects of SAB and similar biases on climate change. It also offers a roadmap for researchers to understand the factors influencing the prevalence of SAB and sheds light on the types of actions taken when exhibiting SAB under different feedback and probability conditions. This comprehensive understanding can inform the development of targeted interventions to mitigate SAB and promote more informed decision-making in climate-related contexts.

While our research focused on studying the prevalence of SAB in a simulated dynamic climate change environment, we acknowledge its limitations. Firstly, our tool, SAB-ICCS, was not specifically designed to counter SAB. Instead, it was developed to study the prevalence of SAB, utilizing game parameters inspired by similar environmental simulation tools (Choudhary and Dutt, 2021). Secondly, participants in the SAB-ICCS tool acted as standalone decision-makers for the anthropogenic probability of climate change. In reality, addressing climate change often requires collective action by families or communities. Therefore, exploring the influence of investment partners or groups on the prevalence of SAB would be an interesting and important avenue for future research. Thirdly, the SAB-ICCS tool only considered the anthropogenic factors of climate change. Despite heavy investment in climate mitigation, unexpected cataclysmic events can still occur due to stochastic elements of climate change. These unforeseen outcomes may significantly influence people’s investment behavior against climate change. Finally, the tool may address the issue of no climate-friendly actions, but it does not currently incorporate actions that may harm the climate. Here, abstaining from harmful climate actions may also be crucial in combating climate change. These limitations highlight areas for future research and tool development to enhance further our understanding of SAB and its implications in climate decision-making.

Thus, we plan to develop various updated versions of the SAB-ICCS tool in the future, which can account for the current shortcomings. Also, we aim to integrate principles of suggestibility into climate simulation tools to reduce the prevalence of SAB. Through real-time feedback and guidance, participants will receive information on the most effective actions to combat climate change, thereby encouraging more informed decision-making. Overall, these enhancements aim to mitigate SAB and contribute to more effective climate change mitigation and adaptation strategies.

Though focusing on a single action may seem to lead to a systematic change (Lenton et al., 2022), the current climatic situation demands investment in climate mitigation and adaptation (VijayaV.R. et al., 2012). Therefore, there is a need to develop simulation tools that counter SAB and promote bias-free decision-making against climate change while educating and promoting pro-environmental behavior. The present research presents insightful findings that carry significant implications for climate policymakers and educators, shedding light on the widespread prevalence and underlying determinants of SAB in climate decision-making. By acknowledging these insights, policymakers can develop targeted interventions to encourage comprehensive action against climate change. Additionally, educators can leverage the study’s valuable insights to enhance decision-making processes within educational programs, highlighting the necessity for ongoing research endeavors to refine methodologies and devise innovative tools or pedagogical approaches aimed at mitigating biases in climate decision-making. The nuanced understanding of how probability and feedback influence SAB, as demonstrated in this study, is essential for crafting more effective, behaviorally informed climate action strategies. These findings serve as a cornerstone for refining educational and decision-making tools tailored to contemporary contexts to mitigate SAB and foster a comprehensive approach to climate change mitigation and adaptation. Recognizing and mitigating biases such as SAB is imperative for bolstering our capacity to combat climate change and advocate for pro-environmental practices.

In conclusion, the present research contributes to the existing literature by confirming the prevalence of SAB in climatic decisions and underscores its persistence even within a simulated dynamic climate change environment, where individuals make climate change investments using only imaginary currency. This highlights the severity of SAB in shaping our approach to addressing climate-related challenges. Moreover, our findings reveal the influence of feedback and the probability of climate change on individuals’ propensity to exhibit SAB, offering valuable insights for guiding decision-makers towards more advantageous choices across diverse scenarios. These insights lay the groundwork for refining educational and decision-making tools tailored to contemporary contexts, aiming to mitigate SAB and foster a holistic approach to climate change mitigation and adaptation. Recognizing and addressing biases such as SAB is paramount for bolstering our capacity to combat climate change effectively and promoting pro-environmental practices.

Data availability

The raw data supporting the conclusions of this article can be accessed, the dataset contains raw data files for the four of the between-subject conditions detailed in the manuscript. For accessing the raw data, please visit https://doi.org/10.7910/DVN/Z2SC9J.

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The authors declare no competing interests.

Ethical approval

The studies involving human participants were reviewed and approved by the Indian Institute of Technology Mandi ethics committee. The participants had provided their written informed consent to participate in this study.

Informed consent

Informed written consent was collected from all the participants who participated in the study at the start of the experiment using a consent form integrated in the SAB-ICCS tool. Only participants who provided their informed consent were allowed to advance in the experiment.

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Choudhary, G., Dutt, V. Analyzing single-action bias in dynamic climate change environments: insights from feedback and probability. Humanit Soc Sci Commun 11, 749 (2024). https://doi.org/10.1057/s41599-024-03268-y

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DOI: https://doi.org/10.1057/s41599-024-03268-y