From niches to norms: the promise of social tipping interventions to scale climate action

From niches to norms: the promise of social tipping interventions to scale climate action | npj Climate Action

Abstract

The net-zero transition poses unprecedented societal challenges that cannot be tackled with technology and markets alone. It requires complementary behavioral and social change on the demand side. Abandoning entrenched detrimental norms, including those that perpetuate the fossil-fueled lock-in, is notoriously difficult, preventing change and limiting policy efficacy. A nascent literature tackles social tipping interventions—STI, aiming at cost-effective disproportionate change by pushing behaviors past an adoption threshold beyond which further uptake is self-reinforcing. Intervening on target groups can greatly reduce the societal cost of a policy and thus holds promise for precipitating change. This article takes stock of the potential of STI to scale climate action by first reviewing the theoretical insights arising from behavioral public policy based on applications of threshold models from sociology and economics; then, it assesses the initial evidence on the effectiveness of STI, in light of the outcomes of laboratory and online experiments that were designed to study coordination on an emergent alternative to the initial status quo. Lastly, the article identifies potential conceptual limitations and proposes fruitful avenues for increasing the robustness of STI assessments beyond theory and small-scale experimentation.

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Introduction

One of the causes of inaction on climate change mitigation is the daunting scale of individual, societal, and systemic changes needed to decarbonize our economies. Yet, change is nonlinear 1, and seemingly stable social conventions such as fertility norms, expectations about gender roles in the workplace, and tolerance for smoking in public spaces are being quickly overturned by the efforts of committed minorities 2, 3, 4.

A fundamental question relating to social coordination on large-scale transformations such as global decarbonization efforts is under what conditions can climate action be rapidly scaled up. Addressing it is critical since the targets of the Paris Agreement are now beyond the reach of incrementalism. Only rapid, radical emission cuts can close the gap to avoid warming over 1.5 °C by the 2030s 5, a threshold above which dangerous tipping points may be triggered, such as the collapse of the West Antarctic ice sheet and the destabilization of the Greenland ice sheet 6.

Behavioral change is a key mitigation strategy since demand-side options have a high mitigation potential 7. Yet, it has only recently started being discussed in the literature, compared to traditionally studied supply-side solutions. While behavioral change needs to be supplemented by supply-side policies such as technology mandates and pricing of the external costs from fossil fuels’ use 8, achieving decarbonization within this century requires rapid and widespread citizen involvement. But how much difference can individuals make? According to ref. 9, a change towards climate-friendly behavior by citizens can reduce greenhouse gas (GHG) emissions substantially: up to one-third of the total EU mitigation target pledged for the Paris Agreement can be saved with behavioral change. A similar magnitude (about 20 pct savings) is found in ref. 10.

Yet, operationalizing systemic change at scale is difficult, given the well-known diversity of individual and group responses and the political costs of implementing paternalistic regulation. Furthermore, mass behavioral change might be undesirable as it shifts the burden from producers to consumers, reducing decarbonization efforts upstream. Targeting well-selected groups can circumvent this conundrum by promoting a gradual process that does not exempt fossil fuel producers from their responsibilities while maximizing the speed of change within politically feasible boundaries. Given the above, this article focuses on the promise of targeted demand-side interventions to accelerate change towards low-carbon choices.

To this end, we draw from established insights from behavioral economics and from a new and rapidly expanding literature that explores the concept of social tipping. According to Milkoreit “tipping points in general can be defined as the point or threshold at which small quantitative changes in the system trigger a non-linear change process that is driven by system-internal feedback mechanisms and inevitably leads to a qualitatively different state of the system, which is often irreversible” 11. This general definition applies both to natural and social phenomena. Here, we focus on the latter. Recent examples of (likely) social tipping in renewable energy technology adoption include solar energy 2, 12, 13, as well as electric vehicles and offshore wind 14. In the domain of individual choice, Copenhagen has been credited to have transitioned away from driving to cycling around 2018, with about as many cyclists as drivers crossing the city center 15.

Specifically, this perspective is concerned with social tipping interventions (STI), actions such as policies and nudges aimed at triggering self-reinforcing surges in support of decarbonization (henceforth referred to as climate actions). Figure 1 sketches how STI work: a targeted “kick” (i.e., the intervention) destabilizes the status quo and drives the system into an alternative stable equilibrium, leading to disproportionate and possibly irreversible changes. An important specificity of the work on social tipping is thus its focus on deliberate interventions aimed at desirable change 16. Successful STI intentionally promote contagion with targeted interventions that have the potential to not only instigate change among the target but also among the susceptible untreated population.

A targeted intervention (the “kick”) destabilizes the status quo (High CO2) and drives the system into an alternative stable equilibrium (Low CO2).

Such features make STI particularly appealing to stimulate climate action. This is likely to be an important contributor to the exponential growth in the use of the concept of social tipping in the social sciences 16. Furthermore, the appeal of STI is likely reinforced by their reliance on circumscribed, targeted interventions to achieve cost-effective and scalable climate action with significant potential 17, 18. Demand-side interventions, including those aiming at tipping, are also less contentious and less vulnerable to political opposition and watering down by vested interests than traditional price and quantity measures to curb supply-side emissions. For instance, taxation tends to be met with resistance when the sanctioned behavior is widespread and legitimized (normative). A well-known example is the outcry that followed the fuel tax increase in France in 2018, sparking the Yellow Vests Protests and leading to the tax cancellation. Such “carbon tax aversion” has been found even for tax and dividend policy proposals whose revenues are entirely redistributed 19. However, the COVID-19 pandemic has demonstrated that norms and conventions can be quickly destabilized by an unforeseen event, highlighting the malleability of behavior and the potential for swift overturning of unsustainable practices.

Importantly, attitudes and opinions are characterized by alternating periods of inertia and rapid transition 20, 21. Examples of recent dramatic shifts include attitudes towards same-sex marriage, climate change beliefs in the US, support for plastic bans in the EU, and the global rise of the Fridays for Future mobilization. On the other hand, examples of lock-in also abound, e.g., unsustainable built environment and extractive sourcing of non-renewable resources, as well as poverty traps. Both stagnation and rapid change can thus be seen as potential societal outcomes, with social norms and beliefs playing an important equilibrium selection role 22. A successful transition away from fossil fuels is thus likely to hinge as much on policies as on expectations (and on the interaction among the two).

To fix ideas, let us consider the effect of a subsidy to incentivize households’ uptake of renewable energy by making it more affordable, although still more expensive than a tariff based on a mix of fossil fuels. It will certainly increase the uptake of the green tariff, but the effect may be limited insofar as the tariff gap is still sizeable. However, an additional intervention might boost adoption by addressing two well-documented behavioral tendencies: pluralistic ignorance (“a situation in which the majority privately rejects a norm, but individuals go along with the norm because they believe incorrectly that most others accept it” 23,) and conditional cooperation (the common tendency “to contribute more to a public good the more others contribute” 24).

Their joint effect is likely to hinder a conditional cooperator from switching to renewable energy, as she will be inclined to think that others are not willing to act, thus reinforcing the status quo. A further impediment comes from unobservability: sourcing renewable electricity is a private choice that is invisible to others. Insofar as many people care about what choices signal about themselves 25 and are conditionally cooperative, lack of visibility will hinder the adoption of green tariffs by reducing both the share of image-concerned early adopters and the scope for imitation by conditionally cooperative followers 26, 27. Both channels (increased adoption and diffusion via imitation) are shut, compared to a visible behavior such as cycling or driving an electric vehicle.

The above issues apply to the many more climate-relevant decisions that are characterized by limited visibility, including offsetting CO2, avoiding short-haul flights, and switching to a vegetarian or vegan diet. The ensuing risk is being trapped in the high-carbon, low-adoption niche. Clearly, the government and other agents of change, such as educators, NGOs, and firms, have a prominent role in steering society away from high-carbon traps. Somewhat reassuringly, social forces working against pro-environmental change can switch to working in its favor 3. For instance, Andre and colleagues find that correcting misperceptions about fellow citizens’ willingness to act against climate change increases donations to a climate charity by a representative sample of US individuals 28.

One way to frame the issue of the ecological transition is thus in terms of shifting malleable normative views. Since the ecological transition can be viewed as a large-scale social coordination problem, success in overcoming it depends on expectations about future climate policies, technological development, and, more broadly, about the pace and trajectory of decarbonization by actors at micro, meso, and macro scales. Put differently, behavioral change does not take place in a vacuum. It hinges on interdependent beliefs about the state of the world and the actions of others 29.

This is where STI come into play. Whether due to technological, economic, or social reasons, tipping hinges on coordination since the value to one adopter increases non-linearly in the share of adopters 3. The concept that critical transitions can occur in complex systems was developed in ecology but illuminates many social phenomena in which one is more likely to adopt a behavior the more prevalent it is 20. Once the tipping point is reached, the actions of a minority group trigger a cascade of behavior change that rapidly increases the acceptance of a minority view 30, 31, 32.

In the next sections, we assess the theoretical implications and initial empirical evidence in support of the claim that STI can precipitate change and scale up climate action. We begin by positioning social tipping in the broader literature about tipping points (TP).

Tipping in the literature

TP have been used to describe various phenomena across diverse domains in the natural sciences, notably in earth system science, ecology, and climate modeling 12, 33, 34, 35, 36, 37. Figure 2 provides a qualitative representation of different TP concepts’ usages over two domains—in the spirit of refs. 11, 38—the natural sciences and the social sciences.

The different concepts of Tipping Points are represented over two domains: the natural sciences and the social sciences.

In the natural sciences, particularly in studies focusing on the climate, there has been a shift from traditional perspectives that assumed quasi-linear relationships (for instance, between GHG accumulation and global temperature rise) to considering non-linearities triggering abrupt changes, as in the case of the Antarctic ice sheet or the Amazon rainforest area 39, 40, 41, 42. Accordingly, the concept of ecological tipping points (ETP) is now widely recognized in climate science. In the social sciences, and particularly in economics, a longstanding tradition of experimental studies utilizes the concept of TP in its “ecological” sense, referring to it as “thresholds”. These works usually model ETP as the contribution thresholds to public goods—for instance, embodying low-carbon alternatives—that must be reached to allow for the production or enhancement of the collective benefits 43, 44, or to avoid potential disasters 45, 46, 47, 48, 49, 50, 51, 52, 53, 54.

Social scientists have also begun to explore the dynamics leading to crossing social tipping points (STP)—focusing on tipping behavior within social-ecological systems—whether in the presence or not of ecological thresholds. While the literature on ETP has experienced widespread popularity across multiple disciplines particularly after 2000, social tipping has been featured in a relatively more recent body of work 11. While ETP are predominantly discussed in the natural sciences, the distinction between the two is less pronounced in the social sciences, where many experiments often explore the interaction between social (strategic) behavior and the threat of disasters represented by ETP. Hence, these social science studies (e.g., ref. 55) belong to the intersection between ETP pertaining to climate change and STP related to human behavior. We sketch such features in Fig. 2.

Within the STP literature, increasing attention has been devoted to the prospects of deliberately triggering social tipping through targeted interventions 18, 56. STI are characterized by the introduction of an exogenous change, a “kick” to the system aimed at destabilizing the status quo (for instance, through a new policy). Such intentional shock to an initially stable equilibrium sets STI apart from endogenously-driven changes that happen spontaneously at the population level. Noteworthy studies cover examples of the latter phenomena on a variety of issues from species conservation to cultural practices 4, 57, 58, 59, 60. STI are studied in a narrower literature typically concerned with accelerating pro-environmental behavior change.

Social Norms

Much of the work on STI stems from recognizing that social norms can serve as a mechanism to alter behavior even at a large scale 3. This links the literature on STI to the literature on social norms. The impact of social norms on individuals’ behavior has been formalized in various recent works, for instance in 61, 62, 63, 64. For a review of the different formalizations, see ref. 65. While other types of norms—such as unconditional beliefs regarding what is right to do—have been found to also play a role in shaping collectively beneficial individuals’ decisions 66, 67, social norms—i.e., what individuals believe others do or consider the right thing to do 29, 68—are arguably more amenable to change 69, 70, 71. Thus, suggestions for operationalizing STI often rely upon the idea that social norms in support of climate action can be “exogenously” seeded through targeted interventions to create widespread change.

Norm-based interventions operationalize this idea. For instance, some studies explore the effectiveness of norm-based interventions in promoting sustainable behavior regarding fishermen’s management of natural resources 72, households’ energy consumption 73, 74, 75 and households’ residential water use 76. This literature points to the potential of promoting individual climate action by giving prominence to otherwise invisible green choices 23, 77. For a review of the different social norms interventions for increasing pro-environmental behavior, we refer the reader to 78.

While social norm interventions and STI both aim to promote behavior change through social influence, they differ in approach and scope. Norm nudges leverage social norms to encourage individuals to conform to the perceived norms of a community, providing information about others’ behavior or emphasizing social expectations. STI target tipping points within social systems to achieve larger-scale behavioral changes, by mobilizing targeted individuals with the aim to initiate a cascading effect that leads to widespread and persistent adoption of new behaviors or ways of life.

Threshold models of collective behavior

Threshold Models (TM) have a long history in social science and offer valuable insights into understanding collective behavior when individuals behave in ways contingent on one another, as is the case in many observed social dynamics, including riots, epidemics, migration, voting, and strikes 30, 31, 79, 80, 81, 82. They are particularly attractive due to their ability to explain complex phenomena straightforwardly when agents—who are generally assumed to maximize their utility—have to choose between two possible actions (typically, one representing the status quo and the other an alternative action), with the costs and benefits of each depending on how many other agents choose which action. Hence, each individual in a TM is characterized by a threshold value—ranging from 0 to 100—representing the proportion of others who must take a certain action before that individual is motivated to follow suit. The weaker an individual’s conviction in changing to the alternative, the higher the share of others needed to trigger the individual’s decision to switch. These models thus build in conformism and the ensuing inertia in abandoning an entrenched norm.

TM allow for high levels of heterogeneity with minimal informational requirements 17, 83. Accommodating threshold heterogeneity is a crucial advantage of these models. In fact, an important feature of TP for behavioral change is that they are likely to be idiosyncratic and vary substantially across individuals 28, 84. By considering the exact distribution of thresholds, TM enable the calculation of equilibrium outcomes in social dynamics 83, 85. Depending on the shape of the distribution of thresholds, different predictions of the equilibrium outcomes can emerge. Figure 3 depicts an example of thresholds bimodally and symmetrically distributed around 50%, in the left panel, and the respective cumulative distribution function (CDF), in the right panel. The 45-degree line shows the proportion of people whose threshold is met at any given point. When the CDF is above the 45-degree line, there are more individuals in the system choosing the alternative action than those that are needed. In the case depicted in Fig.

The distribution of thresholds is represented in panel (a) and the respective cumulative distribution function is represented in panel (b). The example is adapted from ref. 85.

More recent refinements offer a network-based microfoundation for understanding STP by incorporating features from network cascade models 32. Another fruitful avenue to extend this literature is to focus on the role of different “basins of attraction” in catalyzing cooperation and on analyzing the ensuing dynamics. Evolutionary Game Theory can be helpful, as it is inherently designed to study the evolution of cooperation, emphasizing off-equilibrium behavior 86. A step in this direction is represented by ref. 87.

Experimental evidence on social tipping interventions

Experimental research on STI is a growing study area. Early work in this field combined theoretical models with online experimentation involving a coordination game—a social interaction in which all agents face the incentive to match strategies 88—in a population with homogeneous preferences 89. They investigated the coordination dynamics in reaching a critical mass by introducing a “committed minority” of stubborn actors (bots) that always switched to the alternative choice, even when it was disadvantageous. The fraction of committed bots ranged from 15% to 35%. The main finding in ref.

A lab-in-the-field experiment was later conducted in university cafeterias, with an intervention based on promoting the use of reusable mugs instead of one-way cups 90. The results of this study indicated that real-world environmental dilemmas—albeit at a small-scale level—can exhibit tipping.

Other recent studies have challenged some of the findings from earlier research, emphasizing the heterogeneity of individuals in social dynamics and arguing that the assumption of homogeneity might have led to underestimating the critical mass required for tipping 17. They suggested that the critical mass needed for tipping may vary depending on factors such as the size of the intervention, the target population, and the distribution of preferences within the population.

Other experimental work has examined further aspects, highlighting the importance of group identity in the success of STI 91. Their online experiment, conducted around the 2020 US federal elections, demonstrated that group identities based on political labels, by adding value to the status quo and detracting value from alternative norms, can undermine social tipping after intervention. Hence, their work suggests that policymakers consider the potential resistance or backlash from certain political groups when designing and implementing STI.

Various factors can influence the efficacy of these interventions. The importance of an iterative design process, careful piloting, and adaptation before scaling up interventions to enhance their efficacy must be considered 23. This highlights the importance of considering the specific context and characteristics of the target population when designing and implementing STI.

The promise (and peril) of social tipping interventions to scale climate action

Practical applications

Is behavioral change an important driver in the transition away from fossil fuels? To address this consequential question, we examine specific instances of STI that have the potential to deliver large emission reductions.

In certain domains, we are already approaching tipping points, removing the need for intervention. Renewable power, especially solar and wind power, has already matured to the point of being competitive with fossil fuels 92. Notably, in terms of technological advancements, heat pumps are anticipated to become a significant breakthrough in the near future 93, while super-efficient solar cells are expected to emerge within the next three to 5 years 94. Similarly, in the transportation sector, there are promising indications of tipping points being reached, particularly with the growing adoption of electric vehicles in major markets like China and Europe 95.

In considering the potential for promising STI, interesting insights can be drawn from ref. 96. They examine companies’ measurement of their emissions using primary data rather than the current voluntary reporting based on the industry average. Emissions reporting is expected to lead to a two-stage change: if enough firms switch to voluntary reporting emissions, firms who will not report such emissions will have the reputation of “dirty firms.” And, once enough firms switch to reporting such emissions using primary data, firms that will continue to use industry averages as their emission estimates will be perceived as more-than-average polluters. Accordingly, one can expect that past a given adoption threshold, all remaining firms will follow suit and commence reporting their emissions and using primary data. An alternative STI, building on the findings of ref. 97, could involve the widespread implementation of environment-related food labels, providing information about the environmental impact associated with products to increase consumers’ awareness. This would lead to a shift in purchasing behavior towards products with a lower environmental footprint. In turn, a growing demand for low-carbon food would encourage firms to adjust their practices to meet this demand. This competition would drive a positive feedback loop, with firms striving to outdo each other in terms of sustainability practices and environmental performance. Ultimately, the widespread adoption of transparent labels to disclose the carbon content of food can be viewed as an STI that, if successful, would shift consumption past a tipping point where sustainable food choices become the norm.

The above example is not only illustrative of the potential of STI to drive transformative change, but also of their potential pitfalls. As shown in subsection 2, social contagion hinges on reaching an increasingly broader consensus on a new behavior or choice, to a point at which further adoption is likely even among the least susceptible to change. But obstacles, whether financial (e.g., higher cost of sustainably sourced and packaged food), behavioral (e.g., inertia), or reputational issues such as lack of trust in the labels due to greenwashing, may slow down and possibly hinder the spread of an emergent climate-friendly norm. More generally, while initial laboratory evidence suggests that a relatively small fraction of the population (about 25%) is sufficient for tipping, field experimental evidence is needed to assess the external validity of this finding and demonstrate whether STI can induce sizeable change towards climate neutrality. A recent field experiment in university cafeterias finds mixed results: different STI either promoted the diffusion or decay of sustainable consumption norms, and for positive tipping to happen a larger fraction of around 50% adopters was necessary 90.

Benefits and opportunities

STI are a promising tool for researchers and policymakers aiming to study and catalyze behavioral change toward more sustainable patterns. Their potential for cost-effectiveness is particularly advantageous compared to traditional policies, which tend to be either costly (e.g., subsidies) or unpopular (e.g., taxes), or both. In terms of cost-effectiveness, for instance, the Inflation Reduction Act is projected to require ~$369 billion in subsidies and tax credits over a decade for renewable energy, electric vehicles, and other initiatives 98, making it the largest climate spending package in American history. While behavioral STI are unlikely to induce comparably large societal changes, they typically cost significantly less, likely orders of magnitude less. Regarding acceptability, these interventions are anticipated to have higher levels of acceptance compared to other measures such as taxes. Additionally, it is important to note that acceptability is not a prerequisite for the effectiveness of STI, unlike a successful tax policy that relies on compliance. Even in societies where a significant portion of voters align with right-wing populist parties associated with higher levels of climate change denial 99, 100, STI for climate change do not require individuals to personally believe in or agree with the desired behavior, given that these interventions leverage social factors such as conformity, image concerns, and conditional cooperation. Interestingly, in a study focusing on a norm-based intervention to promote climate-friendly behavior in the US, a larger positive treatment effect was observed among climate change deniers compared to non-deniers, as well as among Republicans compared to Democrats 28.

Other factors contribute to the appeal of tipping interventions. First, while targeting only specific actors or groups, effective STI will push climate action past the tipping point, the critical threshold beyond which change spreads to the susceptible untreated via an indirect cascade effect, similar to contagion 84. Hence, in principle, outsized effects can be obtained with relatively inexpensive and circumscribed interventions. Secondly, promising STI entail seeding social norms to trigger self-reinforcing dynamics that amplify desired behaviors. This aspect potentially eliminates the need for continuous intervention, contributing to the efficiency of STI in terms of resource allocation 18. Third, by leveraging existing social networks, these interventions can effectively spread through interpersonal connections, accelerating adoption 32. For instance, network topology matters and can be used to promote the diffusion of ideas and behaviors, with recent evidence suggesting that complex contagion is more likely to spread from the network periphery 2. Furthermore, STI have the potential to generate multiple co-benefits, beyond the primary behavior change targeted (borrowing this idea from, e.g., refs. 101, 102), amplifying the interventions’ overall impact and contributing to a broader range of behavioral changes.

Challenges and limitations

While the above reasons suggest that targeting and cost-effectiveness are key advantages of STI, it is important to acknowledge that certain factors may influence their effectiveness, such as intervention design, implementation methods, and evaluation frameworks 23. Also, one must consider that there might be adverse effects of STI: What if the intervention has negative unintended consequences or even triggers a negative spillover or even a backlash?

It is also essential to address several critiques and limitations that have emerged in the literature. One critique that has been raised is the tendency to abuse the concept of STI, as highlighted by Milkoreit, who warns that “seeing the world through tipping-point glasses” may hinder both research and policy advice. Specifically, she suggests that overuse includes premature labeling of social change processes as tipping, as well as lack of evidence for tipping dynamics 16.

Reality checks and caution against excessive hope in the effectiveness of STI are warranted. Yet, it is worth noting that the youth of this field and scarcity of available data means that most of the literature is so far primarily conceptual, so it is to some extent inevitable that many of the papers reviewed in ref. 16(and here) are somewhat speculative and suffer from lack of robust empirical evidence. Furthermore, a significant limitation in the current state of STI theoretical research is the assumed homogeneity in thresholds in some of the pioneering models used, despite evidence that individuals differ in the degree of conformism and interdependence in beliefs 17. Such heterogeneity is likely to be especially relevant for climate preferences and actions. Accordingly, recent work has modeled threshold heterogeneity (as in refs. 83, 85), in contrast to earlier work 89—where the notion of a critical mass may not have a straightforward meaning if the populations considered were heterogeneous. By accounting for individual differences, such as diverse preferences, beliefs, and positions in the (social) network, TM can provide a more realistic representation of the behavioral patterns involved in STI.

It is also worth noting that, in our view, STI are an important complement to traditional policies, not a substitute. Many societal issues relating to decarbonization involve coordination. An obvious example is mobility choices such as switching away from ownership of combustion engine vehicles towards alternatives such as electric vehicles (EV), shared mobility, public transportation, or active commuting in cities. While to some extent idiosyncratic, the benefit-cost calculus of such choices is a function of three key aspects: monetary and non-monetary costs (e.g., cost, time spent, and hedonic well-being associated with commuting), others’ choices (as they affect, e.g., congestion and safety) and incentives (for instance investments to expand bike lane availability, preferential access to roads and subsidies for sustainable mobility). These intertwined levers tilt the balance between alternative choices, either paving the way or acting as roadblocks toward change.

That is, tipping often hinges on the combined effect of economic considerations, coordination, and expectations about the future, since, due to network effects, the value to one adopter of a green alternative (e.g., biking instead of driving, or driving an EV instead of a combustion engine one) typically increases in the (expected) share of adopters 3. In such a context, society faces an equilibrium selection problem and may be stuck in the “brown” status quo, even if the “green” alternative is preferable. As argued by Nyborg, “public policy might help move the economy from one equilibrium to another. [...] A potentially important task for environmental policy is thus to help economic agents coordinate in more environment-friendly ways” 103. This is arguably what happened in Norway, which deployed temporarily generous incentives such as purchase subsidies and access to bus lanes for EV. These incentives are no longer necessary, as the majority of vehicles are now electric, and have been reduced or removed, without concrete risks of a return to the previous equilibrium.

We end this section by mentioning a potential limitation related to social interventions regarding the expected small effect size, given that commonly observed average effect sizes in norm-nudge interventions are not large, with magnitudes such as the 1.4 percentage point effect documented in ref. 104. Despite the small magnitude, these effect sizes can be significant in achieving climate change objectives through STI, given that the ensuing costs are generally very low and relatively easy to scale up.

Future research

The study of STI is still in its nascent stage, offering numerous opportunities for further investigation that can enhance our understanding of social tipping and the interventions’ effectiveness. Figure 4 summarizes the most relevant open questions that we hope can be answered in future work. A crucial area for future research lies in developing an incentive-compatible method to measure individuals’ thresholds. Such a method would enable a more comprehensive understanding of the factors influencing tipping points and the degree of individual variation and, more specifically, individuals’ readiness to adopt and sustain low-carbon practices. Although the nascent experimental literature on STI has provided valuable insights into the dynamics of STI, little is yet known about the potential of STI at larger scales and in the field. Conducting large-scale randomized controlled trials in the field is to assess the intervention’s potential to have a meaningful impact in real-world settings. By implementing STI on a broader scale, researchers can gain insights into the scalability and effectiveness of STI across diverse populations and contexts for achieving climate goals. Moreover, building upon the insights from previous research (e.g., refs. 105, 106), future investigations can delve deeper into the role of group identities in STI. This may include studying systems with multiple group affiliations in hierarchical structures 107 or pushing further the study from ref. 91, investigating how political framing can contribute to developing tailored messaging and targeting strategies (e.g., framing climate actions as a global vs national challenge depending on individuals’ political orientation 108). Additionally, the identification of the most effective targeting strategy represents an ongoing inquiry within the STI research. Specifically, assessing whether it is more effective to target individuals who are amenable to change or those who are resistant 17, 85 can guide the design and implementation of interventions that maximize their impact on driving behavior change and accelerating global decarbonization efforts. Machine Learning approaches using Large Language Models can also help in targeting with the aim of reducing its cost 109. Also, the optimal number of interventions remains an open question. Investigating the welfare effects of interventions can shed light on the broader societal implications of STI. Furthermore, there is still a need to empirically estimate how cost-effective STI are, when compared to more traditional supply-side solutions. Lastly, the issue of measurability with regard to both the location of the social tipping point (detection and even forecasting via early warning signals) and the empirical distribution of individual thresholds are key challenges for future work. Some methods, such as the detection of tipping points in the earth systems, are now well-established, but the complexity of social systems poses additional diagnostic challenges 18. We may, for instance, fail to detect the onset of social tipping even if the process has already started due to the difficulty of measuring emergent social phenomena at the relevant time scale.

A summary of open questions within the research on Social Tipping Interventions for climate action.

Outlook

In this perspective article, we have addressed the following objectives: discussing the tool of social tipping intervention (section 1), reviewing some of the burgeoning literature that is concerned with social tipping, using the lens of behavioral economics of climate action (section 2), and conveying our excitement about STI while presenting the inherent challenges (section 3). We hope that this behavioral perspective will stimulate further discussion and research.

Data availability

We do not analyse or generate any datasets, because our work consists in a perspective piece based on a literature review.

1.
Gladwell, M. The Tipping Point: How Little Things Can Make a Big Difference Vol. 279 (Little, Brown, 2006).
2.
Centola, D. Change: How To Make Big Things Happen Vol. 352 (Hachette UK, 2021).
3.
Nyborg, K. et al. Social norms as solutions. Science 354, 42–43 (2016).
4.
Mackie, G. Ending footbinding and infibulation: a convention account. Am. Sociol. Rev. 61, 999–1017 (1996).
5.
Lee, J.-Y.et al. Future Global Climate: Scenario-Based Projections and Near-Term Information (Cambridge University Press, 2021).
6.
Armstrong McKay, D. I. et al. Exceeding 1.5 c global warming could trigger multiple climate tipping points. Science 377, eabn7950 (2022).
7.
Creutzig, F. et al. Demand-side solutions to climate change mitigation consistent with high levels of well-being. Nat. Clim. Chang. 12, 36–46 (2022).
8.
Mann, M. E.The New Climate War: The Fight to Take Back Our Planet Vol. 368 (PublicAffairs, 2021).
9.
Van de Ven, D.-J., González-Eguino, M. & Arto, I. The potential of behavioural change for climate change mitigation: a case study for the european union. Mitig. Adapt. Strateg. Glob. Chang. 23, 853–886 (2018).
10.
Dietz, T., Gardner, G. T., Gilligan, J., Stern, P. C. & Vandenbergh, M. P. Household actions can provide a behavioral wedge to rapidly reduce us carbon emissions. Proc. Natl. Acad. Sci. 106, 18452–18456 (2009).
11.
Milkoreit, M. et al. Defining tipping points for social-ecological systems scholarship-an interdisciplinary literature review. Environ. Res. Lett. 13, 033005 (2018).
12.
Nijsse, F. J. M. M. et al. The momentum of the solar energy transition. Nat. Commun.14, 6542 (2023).
13.
Guilbeault, D., Becker, J. & Centola, D. in Complex Spreading Phenomena in Social Systems: Influence and Contagion in Real-World Social Networks (eds. Lehmann, S., Ahn, Y., Y.) 3–25 (Springer, 2018).
14.
Geels, F. W. & Ayoub, M. A socio-technical transition perspective on positive tipping points in climate change mitigation: Analysing seven interacting feedback loops in offshore wind and electric vehicles acceleration. Technol. Forecast. Soc. Chang. 193, 122639 (2023).
15.
Kaaronen, R. O. & Strelkovskii, N. Cultural evolution of sustainable behaviors: pro-environmental tipping points in an agent-based model. One Earth 2, 85–97 (2020).
16.
Milkoreit, M. Social tipping points everywhere?-patterns and risks of overuse. Wiley Interdiscip. Rev. Clim. Chang. 14, e813 (2023).
17.
Efferson, C., Vogt, S. & Fehr, E. The promise and the peril of using social influence to reverse harmful traditions. Nat. Hum. Behav. 4, 55–68 (2020).
18.
Otto, I. M. et al. Social tipping dynamics for stabilizing earth’s climate by 2050. Proc. Natl. Acad. Sci. 117, 2354–2365 (2020).
19.
Douenne, T. & Fabre, A. Yellow vests, pessimistic beliefs, and carbon tax aversion. Am. Econ. J. Econ. Policy 14, 81–110 (2022).
20.
Scheffer, M. Critical Transitions in Nature and Society, 384 (Princeton University Press, 2020).
21.
Ye, M. et al. Collective patterns of social diffusion are shaped by individual inertia and trend-seeking. Nat. Commun. 12, 5698 (2021).
22.
Morsky, B. & Akçay, E. Evolution of social norms and correlated equilibria. Proc. Natl. Acad. Sci. 116, 8834–8839 (2019).
23.
Constantino, S. M. et al. Scaling up change: a critical review and practical guide to harnessing social norms for climate action. Psychol. Sci. Public Interest 23, 50–97 (2022).
24.
Fischbacher, U., Gächter, S. & Fehr, E. Are people conditionally cooperative? evidence from a public goods experiment. Econ. Lett. 71, 397–404 (2001).
25.
Bénabou, R. & Tirole, J. Identity, morals, and taboos: beliefs as assets. Quart. J. Econ. 126, 805–855 (2011).
26.
Bollinger, B. & Gillingham, K. Peer effects in the diffusion of solar photovoltaic panels. Mark. Sci. 31, 900–912 (2012).
27.
Sexton, S. E. & Sexton, A. L. Conspicuous conservation: the Prius halo and willingness to pay for environmental bona fides. J. Environ. Econ. Manag. 67, 303–317 (2014).
28.
Andre, P., Boneva, T., Chopra, F. & Falk, A. Misperceived Social Norms and Willingness to Act Against Climate Change.
https://www.econtribute.de/RePEc/ajk/ajkdps/ECONtribute_101_2021.pdf(20….
29.
Bicchieri, C. The Grammar of Society: The Nature and Dynamics of Social Norms, 278 (Cambridge University Press, 2005).
30.
Granovetter, M. Threshold models of collective behavior. Am. J. Sociol. 83, 1420–1443 (1978).
31.
Schelling, T. C. Dynamic models of segregation. J. Math. Sociol. 1, 143–186 (1971).
32.
Wiedermann, M., Smith, E. K., Heitzig, J. & Donges, J. F. A network-based microfoundation of granovetter’s threshold model for social tipping. Sci. Rep. 10, 11202 (2020).
33.
Lenton, T. M. et al. Tipping elements in the earth’s climate system. Proc. Natl. Acad. Sci. USA 105, 1786–1793 (2008).
34.
Lenton, T. M. Tipping positive change. Philos. Trans. R. Soc. B 375, 20190123 (2020).
35.
Lenton, T. M. Environmental tipping points. Annu. Rev. Environ. Resour. 38, 1–29 (2013).
36.
Moore, F. C. et al. Determinants of emissions pathways in the coupled climate–social system. Nature 603, 103–111 (2022).
37.
Richardson, K. et al. Earth beyond six of nine planetary boundaries. Sci. Adv. 9, eadh2458 (2023).
38.
Russill, C. & Nyssa, Z. The tipping point trend in climate change communication. Glob. Env. Chang. 19, 336–344 (2009).
39.
Pattyn, F. & Morlighem, M. The uncertain future of the antarctic ice sheet. Science 367, 1331–1335 (2020).
40.
Goldstein, A., Turner, W. R., Gladstone, J. & Hole, D. G. The private sector’s climate change risk and adaptation blind spots. Nat. Clim. Chang. 9, 18–25 (2019).
41.
IPCC. Impacts, Adaptation and Vulnerability.
https://www.ipcc.ch/report/ar6/wg2/(2014).
42.
Liu, T. et al. Teleconnections among tipping elements in the earth system. Nat. Clim. Chang. 13, 67–74 (2023).
43.
Sonnemans, J., Schram, A. & Offerman, T. Public good provision and public bad prevention: the effect of framing. J. Econ. Behav. Organ. 34, 143–161 (1998).
44.
Gueth, W., Levati, M. V. & Soraperra, I. Common and private signals in public goods games with a point of no return. Resour. Energy Econ. 41, 164–184 (2015).
45.
Milinski, M., Sommerfeld, R. D., Krambeck, H.-J., Reed, F. A. & Marotzke, J. The collective-risk social dilemma and the prevention of simulated dangerous climate change. Proc. Natl Acad. Sci. USA 105, 2291–2294 (2008).
46.
Milinski, M., Röhl, T. & Marotzke, J. Cooperative interaction of rich and poor can be catalyzed by intermediate climate targets: a letter. Clim. Chang. 109, 807–814 (2011).
47.
Tavoni, A., Dannenberg, A., Kallis, G. & Löschel, A. Inequality, communication, and the avoidance of disastrous climate change in a public goods game. Proc. Natl Acad. Sci. USA 108, 11825–11829 (2011).
48.
Dannenberg, A., Löschel, A., Paolacci, G., Reif, C. & Tavoni, A. On the provision of public goods with probabilistic and ambiguous thresholds. Environ. Resour. Econ. 61, 365–383 (2015).
49.
Brown, T. C. & Kroll, S. Avoiding an uncertain catastrophe: climate change mitigation under risk and wealth heterogeneity. Clim. Chang. 141, 155–166 (2017).
50.
İriş, D., Lee, J. & Tavoni, A. Delegation and public pressure in a threshold public goods game. Environ. Resour. Econ. 74, 1331–1353 (2019).
51.
Barrett, S. et al. Climate engineering reconsidered. Nat. Clim. Chang. 4, 527–529 (2014).
52.
Bosetti, V., Heugues, M. & Tavoni, A. Luring others into climate action: coalition formation games with threshold and spillover effects. Oxf. Econ. Pap. 69, 410–431 (2017).
53.
Lade, S. J., Tavoni, A., Levin, S. A. & Schlüter, M. Regime shifts in a social-ecological system. Theor. Ecol. 6, 359–372 (2013).
54.
Gosnell, G. & Tavoni, A. A bargaining experiment on heterogeneity and side deals in climate negotiations. Clim. Chang. 142, 575–586 (2017).
55.
Waichman, I., Requate, T., Karde, M. & Milinski, M. Challenging conventional wisdom: experimental evidence on heterogeneity and coordination in avoiding a collective catastrophic event. J. Environ. Econ. Manag. 109, 102502 (2021).
56.
Farmer, J. D. et al. Sensitive intervention points in the post-carbon transition. Science 364, 132–134 (2019).
57.
Travers, H., Walsh, J., Vogt, S., Clements, T. & Milner-Gulland, E. Delivering behavioural change at scale: What conservation can learn from other fields. Biol. Conserv. 257, 109092 (2021).
58.
Castilla-Rho, J. C., Rojas, R., Andersen, M. S., Holley, C. & Mariethoz, G. Social tipping points in global groundwater management. Nat. Hum. Behav. 1, 640–649 (2017).
59.
Schief, M., Vogt, S. & Efferson, C. Investigating the structure of son bias in Armenia with novel measures of individual preferences. Demography 58, 1737–1764 (2021).
60.
Efferson, C., Vogt, S., Elhadi, A., Ahmed, H. E. F. & Fehr, E. Female genital cutting is not a social coordination norm. Science 349, 1446–1447 (2015).
61.
Kimbrough, E. O. & Vostroknutov, A. Norms make preferences social. J. Eur. Econ. Assoc. 14, 608–638 (2016).
62.
Michaeli, M. & Spiro, D. From peer pressure to biased norms. Am. Econ. J. Microecon. 9, 152–216 (2017).
63.
Krupka, E. L. & Weber, R. A. Identifying social norms using coordination games: why does dictator game sharing vary? J. Eur. Econ. Assoc. 11, 495–524 (2013).
64.
Bašić, Z. & Verrina, E. Personal Norms—and Not Only Social Norms—Shape Economic Behavior
https://papers.ssrn.com/abstract=3720539(2023).
65.
Capraro, V., Halpern, J. Y. & Perc, M. From outcome-based to language-based preferences. J. Econ. Lit.
https://doi.org/10.1257/jel.20221613(2024).
66.
Catola, M., D’Alessandro, S., Guarnieri, P. & Pizziol, V. Personal norms in the online public good game. Econ. Lett. 207, 110024 (2021).
67.
Capraro, V. & Rand, D. G. Do the right thing: experimental evidence that preferences for moral behavior, rather than equity or efficiency per se, drive human prosociality. Judgm. Decis. Mak. 13, 99–111 (2018).
68.
Bicchieri, C. Norms in The Wild: How to Diagnose, Measure, and Change Social Norms Vol. 264 (Oxford University Press, 2016).
69.
Paluck, E. L. & Shepherd, H. The salience of social referents: a field experiment on collective norms and harassment behavior in a school social network. J. Pers. Soc. Psychol. 103, 899 (2012).
70.
Andrighetto, G. & Vriens, E. A research agenda for the study of social norm change. Philos. Trans. R. Soc. A 380, 20200411 (2022).
71.
Haaland, I., Roth, C. & Wohlfart, J. Designing information provision experiments. J. Econ. Lit. 61, 3–40 (2023).
72.
Diekert, F., Eymess, T., Luomba, J. & Waichman, I. The creation of social norms under weak institutions. J. Assoc. Environ. Resour. Econ. 9, 1127–1160 (2022).
73.
Allcott, H. Social norms and energy conservation. J. Public Econ. 95, 1082–1095 (2011).
74.
Allcott, H. & Rogers, T. The short-run and long-run effects of behavioral interventions: experimental evidence from energy conservation. Am. Econ. Rev. 104, 3003–3037 (2014).
75.
Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J. & Griskevicius, V. The constructive, destructive, and reconstructive power of social norms. Psychol. Sci. 18, 429–434 (2007).
76.
Landon, A. C., Woodward, R. T., Kyle, G. T. & Kaiser, R. A. Evaluating the efficacy of an information-based residential outdoor water conservation program. J. Clean. Prod. 195, 56–65 (2018).
77.
Carattini, S., Levin, S. & Tavoni, A. Cooperation in the climate commons. Rev. Environ. Econ. Policy
https://doi.org/10.2139/ssrn.4682577(2019).
78.
Farrow, K., Grolleau, G. & Ibanez, L. Social norms and pro-environmental behavior: a review of the evidence. Ecol. Econ. 140, 1–13 (2017).
79.
Macy, M. W. Chains of cooperation: threshold effects in collective action. Am. Sociol. Rev. 56, 730–747 (1991).
80.
Morsky, B., Magpantay, F., Day, T. & Akçay, E. The impact of threshold decision mechanisms of collective behavior on disease spread. Proc. Natl. Acad. Sci. 120, e2221479120 (2023).
81.
Granovetter, M. & Soong, R. Threshold models of diffusion and collective behavior. J. Math. Sociol. 9, 165–179 (1983).
82.
Macy, M. W. & Evtushenko, A. Threshold models of collective behavior ii: the predictability paradox and spontaneous instigation. Sociol. Sci. 7, 628–648 (2020).
83.
Andreoni, J., Nikiforakis, N. & Siegenthaler, S. Predicting social tipping and norm change in controlled experiments. Proc. Natl Acad. Sci. USA 118, e2014893118 (2021).
84.
Berger, J., Efferson, C. & Vogt, S. Tipping pro-environmental norm diffusion at scale: opportunities and limitations. Behav. Public Policy 7, 581–606 (2023).
85.
Efferson, C., Vogt, S. & von Flüe, L. in The Oxford Handbook of Cultural Evolution
https://doi.org/10.1093/oxfordhb/9780198869252.013.69(Oxford Academic, 2023).
86.
Young, H. P. The evolution of conventions. Econometrica 61, 57–84 (1993).
87.
De, S., Nau, D. S., Pan, X. & Gelfand, M. J. Tipping Points For Norm Change in Uman Cultures, 61–69 (Springer, Cham, 2018).
88.
Baronchelli, A., Felici, M., Loreto, V., Caglioti, E. & Steels, L. Sharp transition towards shared vocabularies in multi-agent systems. J. Stat. Mech. 2006, P06014 (2006).
89.
Centola, D., Becker, J., Brackbill, D. & Baronchelli, A. Experimental evidence for tipping points in social convention. Science 360, 1116–1119 (2018).
90.
Berger, J. Social tipping interventions can promote the diffusion or decay of sustainable consumption norms in the field. evidence from a quasi-experimental intervention study. Sustainability 13, 3529 (2021).
91.
Ehret, S., Constantino, S. M., Weber, E. U., Efferson, C. & Vogt, S. Group identities can undermine social tipping after intervention. Nat. Hum. Behav. 6, 1669–1679 (2022).
92.
IRENA, I. R. E. A. Renewable Power Generation Costs In 2020 (eBook Partnership, 2022).
93.
Crownhart, C. MIT Technology Review.
https://www.technologyreview.com/2024/01/08/1085105/heat-pumps-decarbon….
94.
Merchant, E. F. MIT Technology Review.
https://www.technologyreview.com/2024/01/08/1085124/super-efficient-sol….
95.
Spaiser, V. et al. Global Tipping Points Report.
https://global-tipping-points.org/(2023).
96.
Stenzel, A. & Waichman, I. Supply-chain data sharing for scope 3 emissions. npj Clim. Action 2, 7 (2023).
97.
Muller, L., Lacroix, A. & Ruffieux, B. Environmental labelling and consumption changes: a food choice experiment. Environ. Resour. Econ. 73, 871–897 (2019).
98.
Reynolds, P. The Economist Joe Biden’s Signature Legislation Passes the Senate, at Last.
https://www.economist.com/united-states/2022/08/09/joe-bidens-signature….
99.
Jylhä, K. M. & Hellmer, K. Right-wing populism and climate change denial: the roles of exclusionary and anti-egalitarian preferences, conservative ideology, and antiestablishment attitudes. Anal. Soc. Issues Public Policy 20, 315–335 (2020).
100.
Agius, C., Rosamond, A. B. & Kinnvall, C. Populism, ontological insecurity and gendered nationalism: Masculinity, climate denial and covid-19. Pol. Relig. Ideol. 21, 432–450 (2020).
101.
Elf, P., Gatersleben, B. & Christie, I. Facilitating positive spillover effects: new insights from a mixed-methods approach exploring factors enabling people to live more sustainable lifestyles. Front. Psychol. 9, 2699 (2019).
102.
Thøgersen, J. Spillover processes in the development of a sustainable consumption pattern. J. Econ. Psychol. 20, 53–81 (1999).
103.
Nyborg, K. No man is an island: social coordination and the environment. Environ. Resour. Econ. 76, 177–193 (2020).
104.
DellaVigna, S. & Linos, E. Rcts to scale: comprehensive evidence from two nudge units. Econometrica 90, 81–116 (2022).
105.
Chen, Y. & Li, S. X. Group identity and social preferences. Am. Econ. Rev. 99, 431–457 (2009).
106.
Bronchal, A. Better the devil you know: the effects of group identity uncertainty on coordination efficiency. J. Econ. Behav. Organ. 214, 634–656 (2023).
107.
Catola, M., D’Alessandro, S., Guarnieri, P. & Pizziol, V. Multilevel public goods game: levelling up, substitution and crowding-in effects. J. Econ. Psychol. 97, 102626 (2023).
108.
Pizziol, V., Demaj, X., Di Paolo, R. & Capraro, V. Political ideology and generosity around the globe. Proc. Natl Acad. Sci. USA 120, e2219676120 (2023).
109.
Debnath, R., Creutzig, F., Sovacool, B. K. & Shuckburgh, E. Harnessing human and machine intelligence for planetary-level climate action. npj Clim. Action 2, 20 (2023).

Acknowledgements

Alessandro Tavoni is supported by the European Union through the ERC Consolidator Grant “Green Tipping”, project number 101089107. We thank one of the Associate Editors, Israel Waichman, two anonymous referees, and Boris Wieczorek for their helpful comments and suggestions.

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Pizziol, V., Tavoni, A. From niches to norms: the promise of social tipping interventions to scale climate action. npj Clim. Action 3, 46 (2024). https://doi.org/10.1038/s44168-024-00131-3

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DOI: https://doi.org/10.1038/s44168-024-00131-3