Expanding the Reach of Operant Behavioral Economics in Climate Action


In our most recent edition of Green Behavior Analysis, we delved into the potential of operant behavioral economics as a framework for scrutinizing climate-relevant decision-making. Today’s post aims to delve deeper, exploring how we can harness these extensive behavioral insights to enhance our understanding of human choices, particularly those that have traditionally escaped focused research.

Recap: The Intersection of Behavioral Economics and Sustainable Thinking

In short, operant behavioral economics employs microeconomic principles (think individual decisions) to unravel the complexities of decision-making through the lens of varying reinforcer values and behavioral costs.

Consider the behavior change principles inherent in these frameworks, as in the importance of timely reinforcer delivery. Delay discounting describes an organism’s tendency to undervalue outcomes that are not immediately accessible. The more prolonged the wait for a reward, such as receiving a paycheck or facing the consequences of a poor choice, the less influence it will have on future actions. This concept can be leveraged to better understand an individual’s unique sensitivity to delay and, as we have discussed, to explore how environmental adjustments can influence the preference reversal commonly observed when dealing in delayed rewards.

Similarly, operant demand involves the principle of reinforcer magnitude—that the size of the outcome, in relation to the initial behavioral cost, dictates its capacity to modify behavior. In a research setting, operant demand offers insights into valuation; that is, how much effort an individual is willing to exert to contact a particular outcome. Researchers quantify behavioral cost in terms of conventional work (e.g., plunger pulls, lever presses), monetary expenditure, or even time “costs” (aligning well with the delay discounting framework). This approach provides an intriguing framework to explore routes by which the perceive value of commodities might be influenced—whether pharmacological substances, calorie-dense foods, or reinforcers particularly relevant to the climate change agenda.

Flexibility of Behavioral Economic Tasks

Initially, behavioral economic tasks, stemming from traditional roots, were designed as research tools in basic operant animal laboratories. Discounting, a function of research on delayed schedules of reinforcement. Operant demand, a tool for developing pre-clinical animal models of substance use and substance-use intervention. Typically, non-human participants would interact with operant mechanisms until meeting predefined criteria, then directly contact the targeted commodity.

Adapting these methods for human decision-making research presented ethical concerns. Such approaches could be time-consuming and resource-intensive, requiring significant commitment from participants. Moreover, the exposure to certain consequences (e.g., cocaine infusions) raised ethical questions. To address these issues, researchers devised a model for gauging behavioral economic decision-making that circumvents rigid laboratory procedures and the problematic exposure to commodities.

In place of traditional methods, some researchers now utilize simulated choice tasks. These structured tasks immerse respondents in an imagined scenario, constraining choices in a manner that mirrors real-life decisions. Researchers then collect data on discounting or demand patterns, for instance, “How many units of XX would you purchase at $4 each?” Past research supports the correlation between choices observed in natural settings and those obtained in controlled simulated choice tasks.

Sustainable Behavioral Economics

This adaptable research method holds vast potential. Numerous researchers have leveraged these simulated choice tasks to evaluate decision-making in areas crucial to sustainability, which have historically been challenging to study and align poorly with traditional behavior analytic research methods. This section illustrates two applications of these tasks within delay discounting and operant demand frameworks.

Optimizing Public Transportation through Operant Demand

The private transportation sector is a major emitter of carbon dioxide and is a prime candidate for optimization. Popularizing public transportation, especially in regions with supportive infrastructure (like urban areas), can be a key strategy to reduce over-reliance on personal vehicles. For public transit to be widely adopted, potential users need to perceive it as both accessible and valuable. From an operant demand perspective, making transit more efficient—such as modifying routes, optimizing fleet staging, and increasing vehicle availability—can be seen as reducing the ‘behavioral cost’ of choosing public transportation.

Photo by Riffat Muntaz (Unsplash)

Hack et al. (2023) applied this innovative take on operant demand to study public transportation. They designed a simulated task where potential riders reported their willingness to use local transit based on increasing travel times. Notably, they asked some participants to contemplate the climate and health implications of personal vehicle use. Their findings indicate that acute exposure to the impacts of private transportation might enhance willingness to opt for public alternatives, even if it entails longer travel times.

This application of operant demand to transportation choices embodies the potency of these flexible models. Through simulated tasks, researchers can empirically address questions that were previously beyond the scope of traditional research methodologies, paving the way for novel environmental interventions with significant implications for sustainability-driven behaviors.

Delay Discounting as a Measure of Extreme Weather Alert Responsiveness

The global climate shift is causing more erratic and severe weather patterns. In the United States, a significant portion of the population lives in areas prone to hurricanes, tornadoes, wildfires, and extreme drought or flooding.

The National Weather Service regularly issues weather alerts, especially during severe conditions. For these alerts to effect behavioral change—ideally, prompt preparation or evacuation—they need to be heeded swiftly. A quicker response generally allows more preparation time, as is the desired outcome. However, as studies show, excessive lead time can lead to procrastination in disaster preparedness, a potentially perilous behavior.

Gelino and Reed (2020) utilized the delay discounting framework to assess how novel tornado weather alerts could prompt more immediate responses. They varied the alert language and lead times, then measured the likelihood of respondents to cease their current activities and seek shelter. The study found that “lead time discounting” was less pronounced with the novel, impact-based warnings compared to standard alerts. This suggests that more effective language in alerts can give individuals more time to secure their safety, highlighting the potential of delay discounting as a tool for improving responses to emergency situations.

Although this demonstration is limited to tornado alerts, the logic holds as applicable to the greater array of disastrous weather. Delay discounting may prove invaluable as a research lens for studying evacuation procedures, infrastructural needs, and continued emergency alert deliveries.


The behavioral economic simulated choice task is a valuable addition to the toolkit of climate-focused social scientists. This research framework offers the flexibility needed to explore innovative interventions on a broader scale. When combined with traditional, tightly controlled behavior analytic research, these tasks hold promise for generating fresh insights and advancing research crucial for understanding and mitigating our collective carbon footprint.

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