Social Learning and Climate Action: How Communities Can Drive Environmental Policy Through Collective Behaviour

Syed Sibghatullah Shah
Quaid-i-Azam University, Islamabad, Pakistan
Correspondence to: s.sibghats@eco.qau.edu.pk

DOI: https://doi.org/10.70389/PJES.100012

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Syed Sibghatullah Shah – Conceptualization, Writing – original draft, review and editing
• Guarantor: Syed Sibghatullah Shah
• Provenance and peer-review:
  Commissioned and externally peer-reviewed
• Data availability statement: N/a

Keywords: Social trust, Social learning, Collective action theory, Community-based climate action, Policy incentives.

Peer Review
Received: 6 November 2024
Revised: 1 January 2025
Accepted: 2 January 2025
Published: 15 January 2025

Introduction

Scientists, governments, and communities all over the world agree that we need to act quickly on climate change.¹ To prevent the environment from deteriorating further, they also believe that sustainable practices are necessary. International groups and governments have put in place policies to reduce emissions, encourage the use of renewable energy, and support efforts to protect the environment.^2,3 However, these top-down regulations cannot, on their own, bring about the desired environmental impact. People must be involved for change to be meaningful and lasting, especially through localized community efforts.⁴ People are becoming more aware of climate problems, but their actions to help the environment are still not consistent and are often not enough to meet global goals. Environmental progress is disjointed and slow because many individuals are not motivated to work together, or they believe their efforts will not make much of a difference.

Communities, on the other hand, have untapped power to change the environment by working together.⁵ They are social structures that can support group behaviour by making people feel like they are part of a bigger, more important movement. People in a community can spread environmentally friendly ­habits to more people by emulating the actions of others who also care about the environment.⁶ This can start a chain reaction that affects more people because trusting other people in a community can make it seem less difficult to join in. Trust and social learning are two important parts of community life that can help people do things that are good for the environment.

The collective action theory by James Coleman is used in this article to explain these changes.^7,8 This theory helps us understand how people decide what to do in social situations. When people fail to recognize a big difference between the two kinds of expenses, cooperation is more likely to happen, says Coleman, because of the social factors that influence this, such as trust and community involvement. We argue that social learning and social trust are the two most critical factors in climate action for getting people to perform environmentally friendly actions, even if they are costly. The idea is that communities with strong social ties and clear actions on climate change can make it so that people always act in a way that is good for the environment. Projects in the community make people feel like they have power and a common goal when they watch and work on them. However, studies investigating the effects of social learning and trust on environmentally conscious actions are scarce. This is an issue since more people are turning to community-led environmental initiatives; this study aims to satisfy that need by developing a model that considers the impact of these factors on community-led climate action.

The primary objective of this study is to investigate how social factors such as trust and social learning, alongside policy incentives, influence individual participation in community-based climate action. This research aims to deepen understanding of the mechanisms by which communities can foster collective behaviour towards sustainability, particularly through trust-building and observational learning. By adapting Coleman’s collective action theory to a climate action context, this study seeks to create a utility model that accounts for individual cost-benefit assessments modulated by social trust, peer influence, and external incentives.

The study addresses the following research questions:

1. How does social trust within a community impact individual motivation to participate in climate-friendly behaviours?
2. How do external incentives, such as policy-driven rewards or benefits, influence participation rates in community climate action efforts?
3. What combination of social trust, social learning, and incentives yields the highest levels of community engagement in climate action?

Through these questions, the study aims to provide actionable insights into the social and structural ­factors that promote sustained, community-wide participation in climate initiatives, supporting policy development for enhanced collective climate ­resilience.

Literature Review

A global response is required to combat climate change, which is beyond the capabilities of any one individual. We need to find ways to work together and use the power of community dynamics. To solve global environmental problems, we need both official policies and community-led projects that can help people use sustainable methods.^9,10 Social learning, social trust, and collective action theory, among others, are significant frameworks for considering how communities might encourage members to engage in environmentally friendly behaviours. In terms of taking action on climate change, this literature review examines in great depth each idea: social trust, social learning, and collective action.

Collective Action and Climate Change

The idea of collective action helps us understand why people do things that are good for everyone.¹¹ It is especially helpful when trying to protect the environment because everyone benefits from things like clean air, and a variety of plants and animals. Collective action theory by Coleman¹² says that people weigh the benefits they think they will get from joining a group action against the costs they will have to pay. When it comes to climate action, the model shows how people choose to make choices that are better for the community and the environment. Some people will join a group action if they believe that the benefits to the group are greater than the costs to them. This is based on how involved the person is in society. However, social factors, such as how obvious it is that peers are participating and how well individual and group goals are aligned, can have a big impact on this ­process.

The idea of collective action further extended by Ostrom et al.¹³ adds that for collective action to work, everyone must believe that what they do is valued and appreciated by others in the community. This feeling of helping each other out can be strengthened by shared norms, trust, and being able to see how other people are acting in ways that are good for the environment. In turn, this lowers the “free-rider” problem, which happens when some people use shared resources without giving anything in return.¹⁴ When people see other people doing eco-friendly things, they are more likely to do them too. This makes people feel like they are all working towards the same sustainability goals. Ostrom¹⁵ and Shah et al.¹⁶ found that social influences can engage more people to protect the environment. Sharing goals and being active in the community make each person feel like their work is valued, which makes it more likely that everyone will join in.¹⁷ This view of things says that collective action theory not only explains what each person does but also focuses on the social structures that help groups work together on environmental problems.

Social Learning in Climate Action

Communities’ environmental behaviour is largely shaped by social learning, especially when people watch and copy the actions of others that are good for the environment.18 Bandura19 defined social learning as the process of options for behaviours through observation, imitation, and reinforcement. Observing the actions of others, considering the consequences, and, if they see those actions to be beneficial, mimicking them is how people acquire social skills. In the context of climate action, this could mean that if someone sees a neighbour reducing waste or installing solar panels, they are more likely to do the same if they can see the benefits. These benefits can be material, like saving energy, or social, like getting more respect from others. Thus, making climate-friendly actions known in a community works like social learning, and teaching people to value and use eco-friendly methods.

By giving positive feedback and support for behaviours that are seen, social reinforcement makes social learning more powerful.²⁰ Muro and ­Jeffrey²¹ assert that social reinforcement, like praise or approval from others, helps people see that being climate-positive is a good thing. Workshops or recycling programmes run by the community, for instance, can be used as social learning spaces. People who take part can learn habits that are good for the environment and get positive feedback from their peers, which makes them even more likely to keep these habits. Because they are rewarded for doing good things, this positive feedback makes people more likely to work on projects that are good for the environment. Social learning can help communities make it a habit to care about the environment.^22,23 Roy et al.,²⁴ for example, looked at how people recycle and found that more people did it in communities where recycling was already common. People felt good about recycling when they saw their neighbours do it. When communities build a shared identity based on sustainability, everyone feels responsible for helping the group reach its environmental goals due to which social learning is a powerful tool for fighting climate change.

Social Trust and Environmental Engagement

People who trust each other are more likely to work together to protect the environment because they believe that their contributions will be valued and returned by others in the community.²⁵ When people in a community trust each other, they see the risks of working together as lower. This makes it easier for people to justify putting themselves at risk to get benefits for everyone. Pretty and Smith²⁶ defined social trust as the belief that others will act in ways that benefit all. People are more inclined to help one another out and work together when they live in a community where they trust each other.²⁷

Studies show that trusting others is a strong sign of working with others to protect the environment. Smith et al.²⁸ found that people who trust other people in their community are more likely to make eco-friendly choices. People are less afraid to act as a group when they trust each other. Projects that help the environment that everyone can join can help people trust each other in their community. Being open, accepting, and helping each other out in community programmes make people more likely to trust them because they know their contributions are seen and valued.²⁹ Participatory environmental workshops or public forums on policies for sustainability, for instance, allow people to share their problems. When individuals have an opinion on the matter, they are more inclined to act in an eco-friendly manner because they have more trust in the collective actions.

The free-rider problem is a reason for people not wanting to work together to stop climate change.³⁰ This is when some people enjoy the benefits of collective environmental goods without doing anything to help. This problem can be lessened by building social trust, which makes people more accountable in the community. People are less worried about others taking advantage of their work when they believe their peers will contribute. A society with a lot of social trust allows people to feel like everyone is doing their part, which leads to more people taking part in community-led initiatives. Because individuals believe that cooperating is equitable and makes a difference, they are more inclined to maintain sustainable practices when accountability and trust function together.³¹

Methodology

The impact of social trust and social learning on community members’ climate change action is quantitatively investigated in this study. We have extended Coleman’s theory of collective action by adding two more factors that are important for climate action: social trust and social learning. This method examines different scenarios where community dynamics lead to actions that are good for the environment, by using theoretical modelling, simulations, and sensitivity analysis.

Theoretical Model Adaptation

The core of this study is an adapted utility model based on Coleman’s collective action theory, modified to include Social Trust (T) and Social Learning (L) as key influences on individual decisions to participate in climate action. We formulated a utility function, Ui, which represents the net benefit or “utility” that an individual i gains from engaging in climate-positive actions. This utility function combines perceived benefits, costs, and social influences that either enhance or diminish an individual’s willingness to participate in environmental practices.

U_i = T_i . (B_i – C_i) + L.E_i

Where, Ti is a social trust factor for individual i, which increases perceived benefits due to trust in community participation. Bi is the perceived benefit of climate action for individual iii (e.g., cleaner environment, better community health). Ci is the perceived cost of participation for individual i (e.g., cost of eco-friendly products, time, and effort). L is the social learning coefficient, representing the extent to which observed behaviours influence individual iii to adopt similar actions. Ei is the environmental impact of individual i’s actions, which is visible to others and reinforces collective behaviour.

The social trust (T) measure shows how much people believe in their community’s efforts to work together, which can change how they see the benefits of making choices that are good for the environment. A higher value means that people are more likely to trust others to participate in the community, which encourages them to do so. The impact of observing the actions of others on an individual’s motivation to combat climate change is quantified by the concept of social learning (L). A high value means that social learning is strong and that people are motivated to adopt climate-friendly behaviours when they see their peers doing them. Based on the effects of social trust, social learning, and each person’s cost-benefit analysis, the utility function allows one to guess the likelihood that a person will take actions that are good for the environment.

Simulation Design

We used simulations of a variety of community settings to look at how differences in social trust and social learning affect people’s willingness to take action on climate change. To see how each one affects group behaviour, the simulations tried out different mixes of social trust and social learning. Each scenario was made to show a community with different levels of trust and learning so that we could see how these things affected the number of people who took action on climate change (Table 1).

Table 1: community scenarios based on levels of trust and learning in climate-positive behaviour.
Scenario	Description
High Trust, High Learning	High levels of trust and learning indicate that community members rely heavily on one another and are profoundly impacted by eco-friendly practices.
Low Trust, Low Learning	Weak social linkages and a lack of visibility about climate-friendly behaviours are indicated by the community’s low-trust and low-learning values.
Mixed Scenarios	To examine the effects of each element independently, it would be helpful to conduct more simulations with either low trust and high learning or high trust and low learning.

There were 100 simulations of each situation, and each one showed a different way that the group could work with that set of trust and learning values. During these rounds, we were able to record different kinds of behaviour, which helped us figure out how ­participation rates change in response to different social factors. Random starting conditions were used for each simulation to find the individual prices (Ci) and benefits (Bi). These conditions were based on average community-level parameters, with some variation to account for differences between people. Based on the situation, values for social trust (T) and learning (L) were given that went from 0 to 1, with 1 being the most influential on behaviour. Python was used for simulations to make sure that the calculations were accurate and could be repeated. We did a sensitivity study to see how changes in perceived (Ci) and benefits (Bi) affect the number of people who take action on climate change when social trust and social learning are different. This analysis helped find key thresholds where small changes in costs or benefits had big effects on participation outcomes. This gave us a better understanding of the factors that people consider most important when making decisions.

By systematically increasing or decreasing the value of (Ci) in each scenario, we assessed how cost fluctuations affect participation. For example, we increased (Ci) by 10%, 20%, and 30% increments and wrote down what happened to the participation rates. We were able to figure out when higher costs stop people from participating, especially in places where people do not trust or value learning. In the same way, (Bi) was changed gradually to see how perceived benefit affects engagement. We tested benefit levels that were above and below the average for the community. This helped us figure out the lowest level of benefits needed to keep people participating in each case. The T ^ L parameters were also put through sensitivity tests. We found out how sensitive participation rates are to changes in social trust and social learning by slowly raising and lowering these values. This study found that trust or learning has a bigger effect on participation in different types of community settings. The results of the sensitivity analysis were saved as changes in participation rates, which were expressed as a share of the community taking action on climate change.

Data Analysis Techniques

After each simulation, statistics were used to search for the average number of participants, the standard deviations, and the importance of differences between situations. Theories, simulations, and sensitivity analysis are used together in this way to study how learning and social trust affect community-driven climate action. It looks at a lot of different community situations and the outcomes of different cost-benefit scenarios to get a full picture of the social factors that motivate people to act in environmentally friendly ways. The results can help lawmakers and community leaders who want to use social dynamics to get people in their communities to act in ways that are good for the environment.

Results

The complete analysis is given in the results section. It is based on mathematical deduction, simulation results, and sensitivity analyses.

Theoretical framework

In this study, we use Coleman’s theory of collective action to show how trust and social learning affect people’s preferences about climate change. This way of thinking sees tackling climate change as a public good that needs everybody’s help to make things better for everyone, like making ecosystems safer, air cleaner, and pollution lower. Our model builds on Coleman’s original one by adding social trust and social learning. Key elements in the model are,

1. We should all do something about climate change because it is a public good that benefits everyone, not just some people.
2. When people trust each other and have a lot of social capital, they are more likely to work together. Trust lowers the risks of participation by making people certain that other people will also contribute.
3. The term “social learning” is used here to describe the phenomenon wherein individuals’ behaviour is influenced by their peers’ actions in the community. This creates a positive loop of environmental engagement.

To incorporate these social dynamics, we define the utility function for each i as follows,

U_i = T_i . (B_i – C_i) + L . E_i

Where, Ui is the utility of individual i from participating in climate action. Ti is a social trust factor for individual i, which amplifies the perceived benefit. Bi is the personal benefit derived from collective climate action. Ci is a personal cost associated with adopting climate-friendly actions. L is the social learning ­coefficient, reflecting the degree of influence from observing others’ actions. Ei is the environmental impact of an individual’s actions, observable by the community. If an individual were to act in isolation, without considering any community influence, their utility from participating in climate action would simply be the difference between the benefit Bi and the cost Ci.

U_i^alone = B_i – C_i

This equation assumes that the individual only values the direct benefits and costs of their actions without any added effect from trust or learning factors. The basic utility is positive if the perceived benefit Biexceeds the cost Ci. In a collective action setting, social trust Ti plays a crucial role in amplifying the perceived benefit of contributing to climate action. Higher trust suggests that the individual believes their actions will align with others’ efforts, increasing the attractiveness of participation. Thus, we modify the basic utility by multiplying the benefit term by Ti.

U_i^trust = T_i . (B_i – C_i)

1. If Ti = 1, the individual has full trust in the community, so the net benefit is entirely realized.
2. If Ti < 1, trust is partial, meaning the individual perceives the benefit to a lesser extent.
3. If Ti = 0, there is no trust, and the individual sees no additional benefit from collective action.

Social learning captures the idea that observing others’ actions can influence an individual’s likelihood of participating. The observed environmental impact Ei, combined with the learning coefficient L, represents the added utility of social reinforcement,

U_i^learning = L . E_i

L reflects the extent to which the individual is influenced by others. If L = 1, social learning has a maximum influence; if L = 0, the individual is unaffected by observing others’ actions. Ei quantifies the environmental impact visible to the community, reinforcing collective behaviour as individuals observe tangible results. Finally, by integrating the trust-adjusted net benefit and the social learning influence, we derive the total utility function for individual iii:

U_i = T_i . (B_i – C_i) + L . E_i

This function suggests that individuals are more likely to participate in climate action if:

1. They have high social trust in their community’s efforts.
2. They can observe positive environmental impacts from others’ actions.

The term Ti · (Bi−Ci) shows that with higher social trust, individuals perceive greater benefits from climate action. Trust effectively “discounts” the personal costs, which makes it more appealing to take part. People who do not trust the community may not be willing to pay for things when trust is low. People are more likely to participate enthusiastically in activities where they have a high level of social trust, meaning they believe others are genuinely committed to recycling and protecting the environment. For example, there is a strong culture of recycling in Scandinavian countries, where people trust each other a lot. People are willing to sort and recycle their trash because they know that others are also doing their part. As a result, people feel like they are part of a larger, more trustworthy effort, which “discounts” their work and costs. In places with less social trust, on the other hand, people may not believe that others are acting honestly or consistently. In cities where people may not trust the government or their neighbours as much, for example, they may be less likely to recycle or make other environmentally friendly choices because they do not think their efforts will make a difference.

The L · EI term highlights the role of social learning. If L is high, it means that other people’s actions that make the environment better have a big effect on how people act. For example, if numerous people in a community start recycling, other people will likely follow as they see how helpful it is for everyone and are encouraged to join in. Sweden is the best at recycling and dealing with trash. The Swedish government has set up a lot of recycling programmes that give people clear information and reasons to recycle.³² Recycling has become very popular because of educational campaigns and community recycling programmes that are easy to see. Because of this, more than 99% of residents recycle, which helps create a culture where caring about the environment is normal.³³ Recycling has become an important part of the community’s identity because it is praised by others.

San Francisco, California, USA, has big plans to have no trash left over by 2030.³⁴ Public campaigns, education, and success stories from local businesses and households that have cut their waste by a lot are some of the ways the city encourages people to help reduce waste. Because of this project, more people in the community are recycling and composting, and the city is now keeping over 80% of its trash out of landfills. Residents’ actions become more sustainable as a result of these efforts’ success, which leads to a shift in culture. Some of the best things about Costa Rica are its biodiversity, its efforts to protect it, and its ecotourism.³⁵ When residents realize the benefits of preserving natural resources for ecotourism, they are more likely to become involved in community-led conservation efforts. Because of educational programmes that teach about the economic and environmental benefits of conservation, the country was able to successfully grow its protected areas to cover more than 25% of its land. A strong national identity based on caring for the environment and eco-friendly tourism has grown due to social learning.

Threshold for Collective Success

In many cases, a minimum participation threshold k is required for effective climate action. High average values of TI and L across a community can help achieve this threshold, as individuals are both motivated by trust and influenced by observed actions. Intending to cut down on pollution and traffic, Copenhagen has successfully added bike-sharing programmes to its public transportation system.³⁶ For the programme to work, there needs to be a certain number of participants (the “threshold”) to make sure that bikes are always available and that the infrastructure can continue to make money. The bike-sharing programme needs to be used by enough locals and tourists to make it financially and operationally stable. People are more likely to use bike-sharing services when they have trust in their safety and availability and see other people regularly using these services. Also, the city’s visible cycling culture creates a positive feedback loop where each participant encourages others to join, which builds the necessary critical mass.

Germany’s strong recycling culture is supported by both high trust and social learning.³⁷ Municipalities have established systems that make recycling efforts highly visible (Ei), and strong community trust (high Ti) ensures that individuals believe their efforts are part of a collective goal. This environment aligns with the High Trust, High Learning scenario, encouraging widespread participation. During Cape Town’s severe drought, the government encouraged people to save water by making people’s water usage public and giving prizes to those who saved water the most.³⁸ People saw those efforts to save water were being made (high Ei), which led to strong social learning (L), even in places where trust was moderate (Ti). This method fits the Low Trust, High Learning scenario, in which people are motivated by clear actions even though they have different levels of trust. Community gardening projects have worked well in New York, where people trust each other enough to take part. This is the same as the High Trust, Low Learning scenario, in which people are confident enough in their actions to help the climate because they trust others.

Threshold Contribution and Free-Rider Effect

P(N, k) denote the probability of effective climate action based on community size N and a threshold k. The threshold condition is defined as:

P (N, k) ´ (Ti . (Bi – Ci ) + L . Ei) ³ (1– P (N, k)) . Si^{free rider}

Where, S^{free rider} represents the free-rider effect, where individuals benefit without contributing. This inequality suggests that when the trust and learning factors are high enough to outweigh the free-rider effect, community participation in climate action will likely succeed. Consider a neighbourhood where 70% participation is required to meet the threshold for effective climate action (e.g., recycling), with P(N, k) = 0.7. If Sfree rider a rider is low (i.e., limited benefits from free- riding), then high values of Ti and L can easily meet the threshold, driving widespread engagement.

Policy and External Incentives

To further encourage participation in climate action, governments or organizations may introduce external incentives. G represents these incentives, which could include tax breaks, subsidies, or public recognition for participating in climate-positive behaviours. We extend the utility function to include G as follows,

U_i^total = T_i . (B_i – C_i) + L . E_i + G

G represents a fixed external incentive provided by the policy, independent of individual trust or social learning factors. When G is high, strong incentives can tip the balance in favour of participation, especially when Ti  or L is low. This addition can counterbalance lower levels of trust or social learning by providing direct benefits that make participation more appealing. Threshold Contribution with G: A sufficiently high G can help achieve a participation threshold k, where collective action becomes effective. For example, if a community requires 70% participation to see a ­meaningful recycling impact, an attractive incentive G can help surpass this threshold by encouraging otherwise reluctant individuals to participate.

Threshold Model with Policy Incentives

P(N, k) denote the probability of achieving effective climate action based on community size N and a threshold k of required contributors. We establish a threshold condition to ensure participation levels meet collective action goals:

P (N, k) ´ (Ti . (Bi – Ci ) + L . E_i + G) ³ (1– P (N, k)) . Si^{free rider}

This inequality suggests: If the left side (utility from participation) outweighs the right side (utility of free-riding), participation becomes the dominant strategy. High Ti, L,vG values make the left side more likely to exceed the free-riding benefit, thus increasing participation rates. Communities with an elevated level of trust and social learning naturally get more people to join, even if they only offer small incentives. High levels of G help reach important participation thresholds k, which are needed to get the most out of group climate actions. To encourage long-lasting, community-led climate action, this model stresses the importance of matching community trust, clear environmental impact, and strategic incentives.

Simulation Analysis

The simulation study examined how different mixes of social trust and social learning affect the number of people who take part in community-led climate action. Each simulation scenario went through more than 100 runs, which took into account the fact that people’s reactions to changes in costs and benefits can be different and that random factors can also affect participation. These rounds of testing made sure that the results were solid averages that took into account the different ways that communities work, rather than being skewed by extreme cases or one-time events. The results show that communities with different levels of trust and learning structures can get people to act in ways that are better for the environment in different ways. The average number of people who participated and the standard deviation for each situation can be seen below in Table 2. This shows how these social factors affect each other.

Table 2: Simulation results for different trust and learning scenarios.
Scenario	Avg. Participation Rate (%)	Standard Deviation (%)
High Trust, High Learning	83	4
Low Trust, Low Learning	35	6
High Trust, Low Learning	61	5
Low Trust, High Learning	57	5

High Trust, High Learning (83% Participation, SD = 4%)

In communities in which social trust and social learning are high, 83% of people who live there are involved in taking action on climate change. People who trust their community are more likely to think that their contributions will be returned, and people who learn from others are more likely to copy actions that are good for the climate. This mix makes a positive feedback loop where people feel supported in their choices to act and see immediate social and environmental effects that make them even more committed. This small ­difference shows that things stay the same from one iteration to the next. This suggests that communities with a lot of trust and learning can keep their high levels of participation even when people’s ideas about the costs and benefits change slightly.

Low Trust, Low Learning (35% Participation, SD = 6%)

When trust and social learning are not strong, participation drops to an average of 35%. People who do not trust collective efforts are more likely to be concerned that their actions will not be supported, which makes them less likely to adopt practices that are good for the environment. The low social learning factor also means that people do not see many actions that are good for the climate that they can copy. In communities with weak social structures, like the one in this scenario, people feel alone in their efforts, which makes them less likely to work towards common environmental goals. The larger deviation here suggests that people’s involvement in these communities is less stable and could change due to outside factors, such as temporary policies or incentives.

High Trust, Low Learning (61% Participation, SD = 5%)

The average participation rate in communities with high trust but low social learning is 61%. People who trust each other are more likely to take action on climate change because they are sure that their efforts will complement those of others. The low social learning factor, on the other hand, makes climate-positive behaviours less visible. This means that people may not see enough examples to make their actions more consistent. Trust is what makes people want to participate, but if there are not many examples of good behaviour, participation might not reach its full potential.

Low Trust, High Learning (57% Participation, SD = 5%)

In places where social trust is low but social learning is high, 57% of people participate, which is a little less than in the high-trust case. Individuals are motivated to act in ways that are good for the climate when they see others doing so, even if there is not a lot of trust in the community. But if people do not trust each other, they might not believe that these actions will last or that everyone is committed to them, which could make them less engaged. The high-learning factor partially compensates for low trust by creating a visible standard of behaviour, though individuals may lack ­confidence in the community’s support over time. Like in the high-trust, low-learning case, the participation rate stays pretty steady. The differences may be caused by how different people react to observed behaviours and the community’s inconsistent support. The results show several ways to get people in communities to take action on climate change. Building trust through community meetings or open decision-making processes can make people feel more confident in the work that is being done to protect the climate. Making climate-positive behaviours visible in the community can help people learn how to get along with others. Targeted strategies that deal with specific social problems, like building trust or making actions more visible, can help communities with low trust or learning gradually get more people involved.

Sensitivity Analysis

At various degrees of social trust and social learning, the sensitivity analysis found that changes in the amount that people believe something costs and how much they think it helps them impact the number of individuals that engage. This study finds important levels at which changes in costs or benefits have a big effect on participation. According to Table 3, in places where trust and learning are high, a 30% rise in costs only made people 10% less likely to participate, showing that those places are resilient. In Low Trust, Low Learning communities, on the other hand, participation dropped by 22% when costs went up. This shows how vulnerable these communities are to changes in costs (Table 4).

Table 3: Sensitivity analysis results – Cost variations.
Scenario	Cost Increase (%)	Change in Participation (%)
High Trust, High Learning	+10	−3
High Trust, High Learning	+20	−7
High Trust, High Learning	+30	−10
Low Trust, Low Learning	+10	−8
Low Trust, Low Learning	+20	−15
Low Trust, Low Learning	+30	−22

For benefit increases, Low Trust, Low Learning communities showed higher sensitivity, with a 30% benefit increase raising participation by 15%. This implies that even in low-trust settings, higher perceived benefits can encourage engagement.

Table 4: Sensitivity analysis, benefit variation.
Scenario	Benefit Increase (%)	Change in Participation (%)
High Trust, High Learning	+10	+4
High Trust, High Learning	+20	+9
High Trust, High Learning	+30	+13
Low Trust, Low Learning	+10	+5
Low Trust, Low Learning	+20	+11
Low Trust, Low Learning	+30	+15

The results show that social learning and trust are important factors in getting people in communities to take action on climate change. People are more likely to participate when they trust the situation, and social learning reinforces behaviour by showing them how to achieve good results. Communities with high levels of trust and clear actions that help the environment have the highest rates of participation. This suggests that local campaigns that focus on building trust and making actions clear can increase environmental involvement. Strategies that make climate actions more open, accessible, and noticeable are likely to get more people involved, especially in places where trust levels are moderate to high. Also, making climate action successes visible (for example, through community projects or public workshops) can boost social learning and encourage more people to get involved, even in places where trust is low. Policies that lower costs or boost perceived benefits (for example, tax breaks for eco-friendly products or reward programmes) could make it easier for people to participate.

Discussion

Through modelling climate action as a group behaviour affected by both community dynamics and individual cost-benefit analyses, this study highlights the complicated but important role that social factors play in getting people involved in the environment. According to the results, trust and learning are very important in communities, and outside incentives are very important for encouraging people to make choices that are good for the environment. Higher community trust makes people more likely to see the benefits of working together, as shown in the High Trust, High Learning scenario, where 83% of people participated, compared to only 35% in the Low Trust, Low Learning communities.

Cvitanovic^25,39 has shown that communities with high levels of trust are more likely to succeed in their environmental efforts as a whole. Finding that trust alone, even without a lot of social learning, can get a lot of people to participate (61% in High Trust, Low Learning) supports the idea that trust is the basis for long-lasting group actions.⁴⁰ With this new knowledge, policymakers can make better decisions, attempting to increase community trust might be a cheap way to encourage people to take action on climate change, even in places where social learning is not very ­common. The role of social learning in driving climate action was clear in all of the scenarios. For example, 57% of people in the Low Trust, High Learning setting took action, showing that clear environmental actions can raise awareness and get more people involved, even in places where trust is low. When people see others doing things that are good for the environment, the learning coefficient L increases the perceived environmental impact Ei, which makes environmentally friendly actions more appealing. Findings stress the role of social reinforcement in adopting environmentally friendly behaviours and are supported by this result.^41,42

According to the cascading effect of social learning, making climate actions more visible to the public is very important. This means that community-led campaigns that show off people’s contributions, like local recycling or energy conservation efforts, could increase participation by making the effects of each action more factual.^43,44 Adding policy incentives (G) to the model proved that incentives can still attract many participants even in settings where individuals lack trust and knowledge about one another. For example, the utility function shows that higher incentives can make up for weak social dynamics in Low Trust and Low Learning situations by giving people direct benefits that are greater than the cost of participating. As shown in the literature on behavioural economics and environmental policy, offering financial or social rewards for sustainable behaviours has been shown to increase their participation, especially when people do not have a lot of social drivers.^45,46 In regions where people lack social connections or opportunities to learn from others’ examples, this result has obvious implications for policymakers as it implies that targeted incentives can effectively motivate individuals to address climate change. Together, we can combat climate change, and policymakers can make it happen by carefully incentivizing people to reject the temptation to “free-ride.”

Policy Applications with Concrete Examples

Take Germany’s recycling culture as an example of how these findings might be translated into practical measures. With its Green Dot system, the German government has established a two-pronged approach to building public confidence in waste management and recycling by providing transparent information on recycling results and set standards for participants.⁴⁷ These programmes show how incentives and trust-building strategies can bring about long-term participation in environmental activities, which is in line with the High Trust, High Learning approach. Other regions’ policymakers might take a page out of this playbook by incorporating trust-building strategies into their own sustainability and recycling initiatives, such as providing more localized feedback or more transparent reporting.

The PES programme in Costa Rica, which pays farmers and ranchers to keep their land forest cover intact, is another good example.9 Benefits to the environment, such as less deforestation and more biodiversity, and public faith in the government are the driving forces behind this programme’s success.⁴⁸ Members of the community witness the concrete results of environmental initiatives, which enhances the impact of social learning. To progressively increase trust and engagement, governments in nations with moderate to low levels of trust may implement comparable initiatives, but they would likely need to supplement them with robust awareness efforts and small incentives.

Cultural Adaptability of the Model

Although the model shows promising predictive skills, it is worth investigating further into its adaptation to different cultures. For example, in collectivist Asian societies like South Korea and Japan, the value of community is frequently prioritized over personal gain.⁴⁹ While there may be an inherent increase in social trust in certain contexts, actions must be visible (social learning) to bring individual behaviours into line with group standards. Similar to Japan’s Eco-Town programme, which incorporates resource recovery and trash management into local identities, policies in these settings should centre on public recognition initiatives that honour community accomplishments.⁵⁰ In contrast, personal benefit incentives (such as cost savings or tax rebates) may be more effective in motivating participation in more individualistic societies, such as the United States, even when trust levels are initially low.

Cultural differences in trust-building and trust-maintenance practices might also impact the model’s practicality. Public support for top-down measures, such as carbon taxes or mandatory recycling, is often high in Scandinavian nations due to the high level of trust in government institutions.⁵¹ On the other hand, community-led projects may do better than government-led programmes in areas where faith in institutions has always been poor, like in parts of South America or sub-Saharan Africa. Making adjustments to the model to reflect these cultural subtleties guarantees its applicability in a wide range of global settings.

Maximizing the Effect of Rewards

The model’s incorporation of incentives (G) emphasizes their critical function in boosting engagement in low-trust, low-learning settings. During its water crisis, Cape Town’s Day Zero campaign, for example, used tiered pricing systems to incentivize conservation and advertised water-saving accomplishments.⁵² Even in a moderate-trust setting, this method was able to motivate behavioural change by combining visibility (high L) with financial incentives (G). By combining well-publicized successes with scalable incentives, similar approaches could be implemented in areas dealing with resource scarcity or drought. Policies about urban transportation, like Copenhagen’s bike-sharing systems, also show promise. The apparent obstacles to access were removed and mass adoption was promoted by incentives like subsidized memberships or free introductory rides. As a result, trust and social learning were fostered through a feedback loop that involved visible engagement. Integrating bike-sharing with public transportation networks or offering incentives for regular usage are two examples of how policymakers might tailor incentive structures to local preferences and improve such programmes in culturally diverse environments.

Policy Frameworks and Their Consequences

The model’s flexibility to accommodate cross-cultural and multi-level governance issues becomes crucial when this conversation is extended to encompass global climate action. For instance, the confidence and engagement of both the public and governments are crucial to the success of the Paris Agreement’s nationally determined contributions. People are more likely to commit at the national level if they have faith in global institutions and see clear climate initiatives led by their peers. Sharing examples of countries’ emission reduction achievements, like Bhutan’s carbon-negative status, could encourage international collaboration and serve as a model for other nations to follow. Social trust-building methods embedded into community-level forest conservation efforts could also assist international programmes like the United Nations’ REDD+ initiative. To ensure scalable and sustainable climate action internationally, policymakers must address the complex interplay of trust, social learning, and incentives. By doing so, they may create personalized plans to fit the unique demands of their communities.

Limitations

There are different social and cultural factors in each community that could change how trust and learning in climate action work. More studies in a range of community types would help to confirm these results. For ease of use, the model takes fixed values for trust and learning. In reality, trust and learning are fluid and can change because of things outside of people’s control, like bad government or environmental problems. Longitudinal studies that record these changing social dynamics could give us a better understanding of how people’s responses to climate change over time. Policy incentives are a key part of the model, but they might not work in the long term. Concerns have been raised in the literature about extrinsic versus intrinsic motivation that relying too much on incentives could make people less motivated to take action on climate change. To make things last longer, future research could look into hybrid models that use both short-term incentives and long-term community-building efforts.

Future Research

Field studies in different types of communities—rural, urban, and suburban—could validate and improve the model, allowing changes to be made based on the unique social and economic situations in each community. In the future, researchers might explore how trust and social learning change when policies are changed or when the environment changes. These kinds of studies could give us useful information about how to keep working on climate change for a long time. Researchers examining hybrid models that combine financial rewards with community-based rewards (like public praise) might find ways to keep people taking action on climate change without relying on external motivations alone. The issue that incentives may discourage environmentally beneficial actions could be resolved in this way. As social media grows, it might be interesting to study how digital tools teach people to work together to fix environmental issues in the future. Community- based apps or online forums may make it easier for more people to see and do things that are good for the environment, which may boost the effects of social learning. The results show that two important ways to get everyone to protect the environment are to build trust in communities and make climate actions public. In areas where social ties are weak, government incentives can be particularly useful in motivating enough individuals to take action on climate change to make a significant impact.

Conclusion

The study’s objective was to discover how policy incentives, social trust, and social learning affect people in communities who work together to safeguard the environment. People participate in environmental group projects for the same reasons: to learn from each other and establish trust, according to the results. Furthermore, they demonstrate the power of external incentives to motivate individuals, even in socially awkward settings. When people in a community have a high level of trust for one another, they are more motivated to take action against climate change because they feel their efforts will have a positive impact on others and themselves. When individuals observe the consequences of their actions on others, a process known as social learning occurs, amplifying this effect. More people are enticed to participate, leading to a positive feedback loop. With the addition of legislative incentives, the model demonstrates how external assistance, such as subsidies or recognition programmes, can increase participation and bring communities to the critical mass required for effective climate action.

As technology becomes more integrated into community engagement, future studies could look at how social media and digital platforms increase social learning and the visibility of climate actions, which could lead to more people getting involved. While incentives can get people involved, it is important to look into how they affect trust and motivation in the long term. Finding out whether these kinds of programmes encourage long-term participation or reliance on rewards could help people come up with more effective solutions. To encourage long-lasting climate action by communities, we suggest that policymakers and community groups use programmes that bring people together, like neighbourhood meetings or local environmental workshops, which can help build the trust that is needed for everyone to work together to solve climate change. To enhance the social learning benefits, a platform needs to be provided for community members to publicly recognise and celebrate their efforts to improve the environment. A great way to get more people interested is to write about local environmental protection activities or organise events that highlight positive deeds. Reimbursements for purchasing environmentally friendly products or rewards for participating in local environmental projects are examples of targeted policy incentives that can help compensate for weak social structures and achieve the necessary levels of engagement in areas where social learning and community trust are lacking.

References

1 Keohane RO. The global politics of climate change: Challenge for political science. Polit Sci Polit. 2015;48(1):19-26.
https://doi.org/10.1017/S1049096514001541
 
2 Abdmouleh Z, Alammari RA, Gastli A. Review of policies encouraging renewable energy integration & best practices. Renew Sustain Energy Rev. 2015;45:249-62.
https://doi.org/10.1016/j.rser.2015.01.035
 
3 Sen S, Ganguly S. Opportunities, barriers and issues with renewable energy development-A discussion. Renew Sustain Energy Rev. 2017;69:1170-81.
https://doi.org/10.1016/j.rser.2016.09.137
 
4 Hoicka CE, Savic K, Campney A. Reconciliation through renewable energy? A survey of indigenous communities, involvement, and peoples in Canada. Energy Res Soc Sci. 2021;74:101897.
https://doi.org/10.1016/j.erss.2020.101897
 
5 Ostarek M, Simpson B, Rogers C, Ozden J. Radical climate protests linked to increases in public support for moderate organizations. Nat Sustain. 2024;7:1626-32.
https://doi.org/10.1038/s41893-024-01444-1
 
6 Piscitelli A, D’Uggento AM. Do young people really engage in sustainable behaviors in their lifestyles?. Soc Indic Res. 2022;163(3):1467-85.
https://doi.org/10.1007/s11205-022-02955-0
 
7 Marsden PV. James Coleman, social capital, and economic sociology. In Handbook of Economic Sociology for the 21st Century: New Theoretical Approaches, Empirical Studies and Developments 2021;(pp. 33-45). Springer.
https://doi.org/10.1007/978-3-030-61619-9_3
 
8 Coleman JS. Foundations for a theory of collective decisions. Am J Soc. 1966;71(6):615-27.
https://doi.org/10.1086/224219
 
9 Fischer A, Holstead K, Hendrickson CY, Virkkula O, Prampolini A. Community-led initiatives’ everyday politics for sustainability-Conflicting rationalities and aspirations for change?. Environ Plan A. 2017;49(9):1986-2006.
https://doi.org/10.1177/0308518X17713994
 
10 Shah SS, Asghar Z. Dynamics of social influence on consumption choices: A social network representation. Heliyon. 2023;9(6):e17146.
https://doi.org/10.1016/j.heliyon.2023.e17146
 
11 Etter M, Albu OB. Activists in the dark: Social media algorithms and collective action in two social movement organizations. Organization. 2021;28(1):68-91.
https://doi.org/10.1177/1350508420961532
 
12 Coleman JS. Commentary: Social institutions and social theory. Am Soc Rev. 1990;55(3):333-9.
https://doi.org/10.2307/2095759
 
13 Ostrom E, Stern PC, Dietz T. Water rights in the commons. Water Resour Impact. 2003;5(2):9-12.
 
14 Panchanathan K, Boyd R. Indirect reciprocity can stabilize cooperation without the second-order free rider problem. Nature. 2004;432(7016):499-502.
https://doi.org/10.1038/nature02978
 
15 Ostrom E. A behavioral approach to the rational choice theory of collective action: Presidential address, American Political Science Association, 1997. Am Polit Sci Rev. 1998;92(1):1-22.
https://doi.org/10.2307/2585925
 
16 Shah SS, Serna RJ, Delgado OS. Modelling the influence of social learning on responsible consumption through directed graphs. Electron Res Arch. 2023;31(9):5161-206.
https://doi.org/10.3934/era.2023264
 
17 Hamari J, Sjöklint M, Ukkonen A. The sharing economy: Why people participate in collaborative consumption. J Assoc Inform Sci Technol. 2016;67(9):2047-59.
https://doi.org/10.1002/asi.23552
 
18 Deffner D, Kleinow V, McElreath R. Dynamic social learning in temporally and spatially variable environments. R Soc Open Sci. 2020;7(12):200734.
https://doi.org/10.1098/rsos.200734
 
19 Bandura A. Social Learning Theory. Englewood Cliffs; 1977.
 
20 Bandura A. Social-learning theory of identificatory processes. In Handbook of Socialization Theory and Research 1969;(Vol. 213, p. 262). Rand McNally & Company.
 
21 Muro M, Jeffrey P. A critical review of the theory and application of social learning in participatory natural resource management processes. J Environ Plan Manag. 2008;51(3):325-44.
https://doi.org/10.1080/09640560801977190
 
22 Robelia BA, Greenhow C, Burton L. Environmental learning in online social networks: Adopting environmentally responsible behaviors. Environ Educ Res. 2011;17(4):553-75.
https://doi.org/10.1080/13504622.2011.565118
 
23 Boyd R, Richerson PJ, Henrich J. The cultural niche: Why social learning is essential for human adaptation. Proc Natl Acad Sci. 2011;108(Suppl_2):10918-25.
https://doi.org/10.1073/pnas.1100290108
 
24 Roy D, Berry E, Dempster M. “If it is not made easy for me, I will just not bother”. A qualitative exploration of the barriers and facilitators to recycling plastics. PLoS One. 2022;17(5):e0267284.
https://doi.org/10.1371/journal.pone.0267284
 
25 Cvitanovic C, Shellock RJ, Mackay M, Van Putten EI, Karcher DB, Dickey-Collas M, et al. Strategies for building and managing ‘trust’ to enable knowledge exchange at the interface of environmental science and policy. Environ Sci Policy. 2021;123:179-89.
https://doi.org/10.1016/j.envsci.2021.05.020
 
26 Pretty J, Smith D. Social capital in biodiversity conservation and management. Conserv Biol. 2004;18(3):631-8.
https://doi.org/10.1111/j.1523-1739.2004.00126.x
 
27 Cook KS, Levi M, Hardin R, editors. Whom can we trust?: How groups, networks, and institutions make trust possible. Russell Sage Foundation; 2009.
 
28 Smith JW, Leahy JE, Anderson DH, Davenport MA. Community/agency trust and public involvement in resource planning. Soc Nat Res. 2013;26(4):452-71.
https://doi.org/10.1080/08941920.2012.678465
 
29 Bovaird T. Beyond engagement and participation: user and community coproduction of public services. Pub Admin Rev. 2007;67(5):846-60.
https://doi.org/10.1111/j.1540-6210.2007.00773.x
 
30 Roy NM. Climate change’s free rider problem: Why we must relinquish freedom to become free. Wm & Mary Env’t L & Pol’y Rev. 2020;45:821.
 
31 Jones GR, George JM. The experience and evolution of trust: implications for cooperation and teamwork. Acad Manag Rev. 1998;23(3):531-46.
https://doi.org/10.2307/259293
 
32 Gibovic D, Bikfalvi A. Incentives for plastic recycling: how to engage citizens in active collection. Empirical evidence from Spain. Recycling. 2021;6(2):29.
https://doi.org/10.3390/recycling6020029
 
33 Wallgren P, Rexfelt O, Nikitas A. Comparing the bad media-fuelled reputation of e-scooters with real-life user and non-user perceptions: evidence from Sweden. Transp Res Part F Traffic Psychol Behav. 2023;99:189-203.
https://doi.org/10.1016/j.trf.2023.10.005
 
34 Zaman A. Zero-waste: A new sustainability paradigm for addressing the global waste problem. In The Vision Zero Handbook: Theory, Technology and Management for a Zero Casualty Policy 2022;(pp. 1195-218). Springer International Publishing.
https://doi.org/10.1007/978-3-030-23176-7_46-1
 
35 Dimitrijevic GO, Silic D, Silic I. Can ecotourism offer sustainable development: Why Costa Rica succeeded. Glob J Bus Integral Secur. 2022.
 
36 Nikolaeva A. Smart cities and (smart) cycling: Exploring the synergies in Copenhagen and Amsterdam. J Urban Technol. 2024;31:29-49.
https://doi.org/10.1080/10630732.2024.2322007
 
37 Knickmeyer D. Social factors influencing household waste separation: a literature review on good practices to improve the recycling performance of urban areas. J Clean Product. 2020;245:118605.
https://doi.org/10.1016/j.jclepro.2019.118605
 
38 Calverley CM, Walther SC. Drought, water management, and social equity: Analyzing Cape Town, South Africa’s water crisis. Front Water. 2022;4:910149.
https://doi.org/10.3389/frwa.2022.910149
 
39 Danielsen F, Eicken H, Funder M, Johnson N, Lee O, Theilade I, et al. Community monitoring of natural resource systems and the environment. Annu Rev Environ Res. 2022;47(1):637-70.
https://doi.org/10.1146/annurev-environ-012220-022325
 
40 Glaveli N. Corporate social responsibility toward stakeholders and customer loyalty: Investigating the roles of trust and customer identification with the company. Soc Responsib J. 2021;17(3):367-83.
https://doi.org/10.1108/SRJ-07-2019-0257
 
41 Usman M, Rofcanin Y, Ali M, Ogbonnaya C, Babalola MT. Toward a more sustainable environment: understanding why and when green training promotes employees’ eco-friendly behaviors outside of work. Hum Res Manag. 2023;62(3):355-71.
https://doi.org/10.1002/hrm.22148
 
42 Pan C, Abbas J, Álvarez-Otero S, Khan H, Cai C. Interplay between corporate social responsibility and organizational green culture and their role in employees’ responsible behavior towards the environment and society. J Clean Product. 2022;366:132878.
https://doi.org/10.1016/j.jclepro.2022.132878
 
43 Chikowore N. “Every small action helps towards the greater cause”: online communities scaling up online community-led citizen science in addressing litter challenges in Scotland. Citiz Sci Theory Pract. 2023.
https://doi.org/10.5334/cstp.579
 
44 Fischer A, Holstead K, Hendrickson CY, Virkkula O, Prampolini A. Community-led initiatives’ everyday politics for sustainability-Conflicting rationalities and aspirations for change?. Environ Plan A. 2017;49(9):1986-2006.
https://doi.org/10.1177/0308518X17713994
 
45 Sunstein CR, Reisch LA. Automatically green: behavioral economics and environmental protection. Harv Envtl L Rev. 2014;38:127.
https://doi.org/10.2139/ssrn.2245657
 
46 Croson R, Treich N. Behavioral environmental economics: Promises and challenges. Environ Res Econ. 2014;58:335-51.
https://doi.org/10.1007/s10640-014-9783-y
 
47 Rousso AS, Shah SP. Packaging taxes and recycling incentives: The German green dot program. Natl Tax J. 1994;47(3):689-701.
https://doi.org/10.1086/NTJ41789100
 
48 Rudel TK, Defries R, Asner GP, Laurance WF. Changing drivers of deforestation and new opportunities for conservation. Conserv Biol. 2009;23(6):1396-405.
https://doi.org/10.1111/j.1523-1739.2009.01332.x
 
49 Hamamura T. Are cultures becoming individualistic? A cross-temporal comparison of individualism-collectivism in the United States and Japan. Pers Soc Psychol Rev. 2012;16(1):3-24.
https://doi.org/10.1177/1088868311411587
 
50 Sarkar AN. Eco-innovations in designing ecocity, ecotown and aerotropolis. J Arch Eng Technol. 2016;30:1-5.
 
51 Khastar M, Aslani A, Nejati M. How does carbon tax affect social welfare and emission reduction in Finland?. Energy Rep. 2020;6:736-44.
https://doi.org/10.1016/j.egyr.2020.03.001
 
52 Brühl J, Visser M. The Cape Town drought: a study of the combined effectiveness of measures implemented to prevent “Day Zero”. Water Res Econ. 2021;34:100177.
https://doi.org/10.1016/j.wre.2021.100177

Cite this article as:
Shah SS. Social Learning and Climate Action: How Communities Can Drive Environmental Policy Through Collective Behaviour. Premier Journal of Environmental Science 2025;2:100012

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