Urban climate governance informed by behavioural insights: A commentary and research agenda

Abstract

Chinese

Policy and governance interventions often build on a rational choice perspective of human behaviour. Over the years, the behavioural sciences have highlighted how people sometimes deviate in predictable ways from this perspective. Building on a systematic analysis of 200 peer-reviewed publications published between 2009 and 2018, this article discusses the core cognitive biases and heuristics uncovered by the behavioural sciences, and gives insights into how these can be exploited to develop urban climate governance interventions to promote behaviours that help mitigate climate change at city level. The article concludes with a research agenda for this promising area of research for scholars of urban climate governance.

Keywords

behavioural insights built environment commentary environment governance sustainability

Introduction

Cities are significant point-sources of greenhouse gas emissions, resource consumption and other causes of global climate change (Rosenzweig et al., 2018; UN, 2018; van der Heijden et al., 2019). Individual behaviour plays a key role in this (Dodman, 2009). For example, modifiable behaviours such as energy consumption for domestic and commercial buildings and urban transport use account for 40–70% of the energy consumed at city level (Al-Mofleh et al., 2010; De Almeida et al., 2011). A considerable amount is wasted, however, because of poor behavioural choices: rather than switching appliances off completely, people opt for the default standby power option (Rusk et al., 2011); rather than choosing novel energy-efficient construction processes, developers stay with the status quo energy-intensive approaches they are used to (Martek et al., 2019); and rather than opting for ridesharing or carpooling, home-to-work commuters stick to the perceived convenience of using their own cars (Nneoh et al., 2017).

If people are motivated to make changes in individual behaviour this may, therefore, have a large overall impact on climate mitigation at city level. Conventional urban climate governance interventions such as direct regulation or economic incentives have not, to date, provided satisfactory policy solutions to the problems caused by harmful behaviour at the individual level (Luque-Ayala et al., 2018; van der Heijden, 2014). These interventions are often premised on a rational choice perspective, which assumes that individuals make a cost–benefit analysis when deciding whether to comply with direct regulation (Lehmann Nielsen and Parker, 2012), and make rational self-interested decisions when responding to economic incentives (McMahon, 2015). However, people do not always behave as predicted by the rational choice perspective. People often lack the time, information and mental capacity to make ‘rational’ choices, and instead fall back on heuristics (mental shortcuts) and cognitive biases (including those relating to habits and social norms) when making decisions (Ariely, 2008; Kahneman, 2011; Simon, 1945).

Recent advances in the behavioural sciences (including psychology, cognitive science and behavioural economics) could help to develop urban climate governance interventions that are more responsive to the heuristics and biases that shape people’s behaviours (OECD, 2017b; Seo, 2017). Urban climate governance is understood here to mean the processes undertaken by governments and others to steer the actions and behaviours of individuals and organisations to achieve desired climate mitigation and climate adaptation goals at a city level (Luque-Ayala et al., 2018; Romero-Lankao et al., 2018; van der Heijden, 2013). To understand the potential of insights from the behavioural sciences for urban climate governance, this article takes stock of the behavioural science literature as it has engaged with broader questions of public governance over the last ten years. It builds on a systematic review of 200 peer-reviewed publications. Appendix A, available online, gives a full overview of these 200 publications and the approach to sourcing and analysing them.

The discussion that follows first addresses the core heuristics and biases that the current behavioural science literature identifies as leading to harmful individual choices and contributing to high greenhouse gas emissions, excessive levels of resource consumption, and other causes of global climate change at city level. After exploring these biases and heuristics, the article shows how they can be exploited in interventions to promote behaviours that help mitigate climate change at city level. The article concludes with a research agenda for further exploring this promising area of research for scholars of urban climate governance.

Insights from the behavioural sciences

The making and implementation of policy have for a long time been built on rational choice theory, and this is often still the case. Rational choice theory is an analytical framework from neoclassical economics for understanding and modelling the social and economic behaviour of groups of people – for example, the population of a city or country. A central aspect of this theory is that people are rational beings and are thought of as having ‘stable, coherent and well-defined preferences rooted in self-interest and utility maximisation that are revealed through their choices’ (McMahon, 2015: 141). When people can choose from a variety of alternatives, they are expected to choose the alternative that has the highest worth or value to them – ‘utility maximisation’ (Read, 2007). It should be noted here that even if policymakers do not specifically choose policy interventions on the basis of traditional rational choice theory, path dependency may explain why the types of interventions that build on rational choice theory are still dominant (Duit, 2007; Rixen and Viola, 2015).

These understandings of choice behaviour, utility and the related ‘homo economicus’ stereotype have received considerable criticism, however (Pinto-Prades and Abellan-Perpiñan, 2012). Scholars of the behavioural sciences point out that people may desire one thing (such as living an environmentally sustainable lifestyle) but choose to do something else (eat organic food from disposable plastic containers, fly to international climate action conferences, and so on). In part, this has to do with our personal and ever-changing understanding of the utility we get from a specific decision (Kahneman and Tversky, 1979). This utility includes, but is not limited to, the utility we expected to get at the time of choosing a behaviour or action (decision utility), the utility we experience at the time of engaging in the behaviour or action (experienced utility), and the utility we remember after having engaged in that behaviour or action (remembered utility). These understandings of utility may coincide, as neoclassical economics assumes, but often they will not (Friedman et al., 2014). For example, our remembered utility of a past harmful behaviour may be considerably more positive than the experienced utility at the time of engaging in that behaviour (e.g. the guilt we experienced when flying to the international climate conference may fade over time), leading us to choose that harmful behaviour again in the future (Kahneman et al., 1997).

Also, it is reported that people routinely overestimate the positive utility (pleasure, joy, opportunity) and underestimate the negative utility (pain, regret, risk) they expect to get from a choice (Kahneman, 2011). Besides these deviations from the utility function, scholars have pointed out that people are less rational in making choices under uncertainty than is predicted by neoclassical economics. In the mid-1940s the American economist and political scientist Herbert Simon (1916–2001) was one of the first to show that people find it difficult to obtain a full understanding of many of the decision problems they face. It is also often impossible for us to acquire all the relevant information to make a rational decision and, even if we could get all this information, we would probably lack the mental capacity or the time to process it. In other words, when making decisions humans possess only ‘bounded rationality’ and must make decisions by ‘satisficing’ – we choose what makes us happy enough (Simon, 1945).

Building on these insights, scholars from the behavioural sciences have identified several patterns of behaviour that characterise the way people make decisions and how people deviate in predictable ways from neoclassical assumptions of rationality. Their work indicates that we rely on cognitive biases and heuristics (‘mental shortcuts’) when making choices and shows that this sometimes results in suboptimal outcomes. There are a variety of explanations of why we make these ‘irrational’ choices. A widely acknowledged explanation is dual process theory – often referred to as system 1 (or automated) and system 2 (or reflexive) behaviour (Kahneman, 2011; but, for a critique, see Glimcher, 2011). The argument is that the brain capacities that we have inherited from our ancestors are well developed for making the kind of automated life-or-death choices (system 1) that are needed to survive in the African savannah, but are ill-suited to making the reflexive and complex choices (system 2) that give the greatest benefit in modern market economies (Bissonnette, 2016). In sum, biases and heuristics help with ‘reduc[ing] complex tasks of assessing probabilities and predicting values to simpler judgmental operations’ (Tversky and Kahneman, 1974: 1124).

A selected overview of heuristics and biases

While the full set of biases and heuristics uncovered over the last decades is too vast to discuss here (but, for an easily digestible overview, see McRaney, 2012, 2014), it is helpful to touch on those that were identified in the systematic review as among the most persistent, and those that it is particularly relevant to address through urban climate governance interventions informed by behavioural insights.

A first relevant insight is that people have a strong loss aversion (Kahneman et al., 1991). Research has indicated that when making decisions, losses loom larger than improvements or gain and, consequently, people prefer to avoid losses than to acquire gains. It is sometimes argued that, for small or moderate amounts of money, losses loom twice as large as gains (Tversky and Kahneman, 1991). What is relevant to note here is that people define losses and gains relative to a reference point, which is often their status quo at the point of making the decision. Thus, when people are offered solar panels on their house for an installation cost of US$1000, and a possible yield of US$1500 over the lifetime of the panels, they may choose not to install the panels because the possible loss of the investment looms larger than its gain (i.e., a US$2000 yield is required to make up for the US$1000 investment; see further, Greene, 2011).

People are also likely to stick with routines, habits and customary and default options, even when better options are available and even when there is no cost associated with switching. This is referred to as status quo bias (Samuelson and Zeckhauser, 1998) – for example, contractors tend to continue with known construction processes, rather than to switch to more environmentally sustainable ones (van der Heijden, 2015). The classic example is a field study on preferences regarding service reliability and rates of electric power among consumers in California (Hartman et al., 1991). In the study, one group was told that they were currently experiencing high reliability and relatively high rates, and the other group that they were currently experiencing low reliability but rates that were 30% below the high-reliability rate. When asked whether they would be keen to switch plan, 60% of the ‘high reliability’ group desired to stay with their status quo (high reliability at a higher cost) and only 6% opted to switch to the lower reliability at the reduced cost (the remaining 34% opted for another plan). Strikingly, 58% of the ‘low reliability’ group also wished to stay with their status quo (low reliability at the reduced rate), and only 6% opted to switch to the high reliability at the higher cost (again, the remaining 36% opted for another plan). In sum, irrespective of the status quo, most respondents chose to stay with it.

People also give stronger weight to a payoff that is received closer to the present time, when they are faced with the choice of getting a payoff at an earlier or a later moment. This is known as present bias and is often explained by the psychological desire to have certainty and resolve events immediately (O’Donoghue and Rabin, 2015). The flipside is that people discount the future consequences of their current actions, and postpone losses or dealing with losses until later, a tendency known as hyperbolic discounting (Hardisty et al., 2012). The rate of discounting changes with the time horizon faced; that is, people give high discount rates for short time horizons, but low discount rates for long time horizons. For example, when given a choice, people would prefer to get US$50 now rather than US$60 tomorrow, but would prefer US$60 one year and one day from now rather than US$50 in a year’s time (Benhabib et al., 2010). Insights into hyperbolic discounting may also explain why people procrastinate about making choices that do not come with immediate and significant gains, such as changing their energy plans, installing solar panels or switching from travelling by car to travelling by bus (Pollitt and Shaorshadze, 2013).

Finally, when making decisions, people are heavily affected by the anchoring effect (Furnham and Boo, 2011) and the framing effect (Borah, 2011) of the information provided. If people are given a cue or signal (an ‘anchor’) and are then asked to make a choice, they are likely to be heavily influenced by the cue or signal even when it is not related to the object of choice. For example, if people are first asked to recall the last three digits of their social security number and are then asked to estimate the number of cities in the world with a population of over one million inhabitants, those with a low digit-value are likely to underestimate the number of cities, whereas those with a high digit-value are likely to overestimate it (Tversky and Kahneman, 1974). The higher the ambiguity, the lower the familiarity with the problem, or the more trustworthy the source of information, the stronger the anchoring effect (van Exel et al., 2006). Also, seemingly inconsequential changes in the formulation of a choice problem (‘framing’) affect people’s preferences. In other words, framing an outcome as a marginal monetary loss or a huge environmental gain may make all the difference in seeking to encourage environmentally sustainable behaviour (Tversky and Kahneman, 1981).

Some critical reflections

Some critical reflections are warranted here. While the behavioural sciences present strong arguments against the traditional ‘homo economicus’ stereotype, and back these arguments with sound research, it should be kept in mind that they seek to nuance rather than replace dominant theories that describe why and how people make particular choices under uncertainty (Kahneman, 2011; Kosters and van der Heijden, 2015). As well as thinking along the lines of the insights presented above, our research is also open to include a range of other, perhaps less biological or economic, origins of ‘irrational’ behaviour, such as learned, social and perhaps even institutional ones (Wolfram et al., 2019).

Also, the choice of policy and governance interventions that build on the traditional rational choice perspective may very well be influenced by path dependency rather than by a conscious decision to follow the ‘choice model’ underpinning this theory: existing institutions may ‘logically’ result in a continuation of interventions informed by rational choice, rather than in a change of those interventions in favour of interventions that build on different choice theories. Notably, notions of path dependency as discussed in the broader institutional literature (e.g. Duit, 2007; Rixen and Viola, 2015) strongly resonate with notions of status quo bias discussed in the behavioural science literature (discussed above). Also, discursive institutionalism recognises the relational aspect and the ideological embeddedness of behavioural transformation (e.g. Carstensen and Schmidt, 2016; Schmidt, 2011); this topic resonates strongly with the social proof heuristic discussed in the behavioural science literature (and also discussed below).

In addition, the behavioural sciences help us to explain why similar facts and knowledge may be experienced and interpreted differently by different people and organisations. This relates in part to the way in which for some time the media have referred to human-made climate change as an unsettled question in the broader academic community, as well as to a tendency towards ‘cognitive dissonance’ when people realise the impact of climate change and their role in the required solutions (e.g. Bonneuil and Fressoz, 2016; Hoffman, 2015). As highlighted before, people may respond fundamentally differently to a solution presented as a win and one presented as a loss: around the globe, carbon pricing is often framed by the dominant media and populist politicians as a carbon tax that is costly for common people, rather than as an opportunity to share the cost in the here and now before it is too late or before the cost is passed on to future generations (Andrew, 2008; Rootes, 2014).

Finally, people’s differing ontologies and epistemologies also affect how they process, trust and treat information. For example, those holding a realist ontology may consider climate change as something that is too complex for humans to understand fully but that nevertheless requires action, whereas those holding a constructivist ontology may argue that climate change only exists in human experience, and that no (specific) action should be taken unless all human errors are removed from the existing knowledge of climate change (e.g. Aven et al., 2011; Marsh and Stoker, 2010).

Interventions informed by behavioural insights

Scholars have begun to call for policy and governance interventions that are more sensitive to behavioural insights. They argue that the biases and heuristics that normally result in harmful behaviour can be targeted to achieve desired behaviour. A body of literature has grown across the policy and administrative sciences that seeks to understand how and with what effects behavioural insights can shed light on governance interventions to achieve desired outcomes (for reviews of this literature, see Grimmelikhuijsen et al., 2017; Kosters and van der Heijden, 2015; van der Heijden, 2019). A second body of literature has emerged that is questioning the ethical and epistemic challenges of policy and governance interventions informed by behavioural insights (e.g. Abdukadirov, 2016; Wright and Ginsburg, 2012). This section explores examples of the instrumental application of behavioural insights in urban climate governance. In the next section, the ethical and epistemic challenges are given attention.

Throughout the behavioural science literature, changing of defaults and forced choice are argued to be the strongest interventions for overcoming status quo bias and choice procrastination (Alemanno and Spina, 2014; Baldwin, 2014). A default is the pre-set condition that comes into force when an individual decides not to choose among alternatives. Studies indicate that defaults are ‘sticky’, meaning that people tend not to switch from one alternative to the next unless they are explicitly given an incentive to do so (Johnson and Goldstein, 2003). Thus, if people can choose between being supplied with ‘conventional’ energy or being supplied with ‘green’ energy from renewable sources, the default option they are presented with will matter. For example, studying the behaviour of people in Germany who were given a choice to switch from the default ‘green’ energy or water supply to a cheaper but less sustainable alternative, Pichert and Katsikopoulos (2008) found that over 90% of people tended not to switch. This finding is reflected in similar findings across a range of choice options ranging from organ donation to retirement savings and, indeed, the supply of energy from renewable sources (Hedlin and Sunstein, 2016).

Defaults work as passive choices, but people can also be given an ‘active choice’. Here, another illustrative example comes from Germany (Liebig and Rommel, 2014). In a field study, more than 900 households in Berlin were provided with a ‘no junk mail’ sticker to reduce junk mail and the related waste of resources. People can order this sticker themselves at no cost, but they often delay doing so. When the householders were presented with the sticker at their home address, they had to make an active choice as to whether to use it or not. It was observed that 16% of people decided to put the sticker on their mailbox. The researchers pushed their research further, however, since they were interested in whether a ‘forced choice’ would yield even better results. One group of households were given the sticker in their mailbox (providing them with an ‘active choice’), and for another group, the sticker was attached halfway onto the mailbox, forcing the owners either to remove the sticker or to attach it fully (‘forced choice’). The forced choice option was found to be more effective than the active choice option: 21% of people stuck the half-attached sticker to their mailbox. The argument here is that in situations of active or forced choice, people are required to reflect on their choices and are pushed out of automated or habitual behaviour (Hedlin and Sunstein, 2016).

Seeking to overcome procrastination in a different way, various field studies have been carried out on the effect of pre-commitment strategies. The idea is to let people commit to a future goal, ideally in public. Pre-commitment strategies seek to align future behaviour with what people think, in the present, of themselves and their future behaviour. Besides targeting procrastination, these strategies may also be helpful for overcoming loss aversion and impulsive harmful behaviour (Benartzi, 2012; Elster, 2000). An illustrative example is a series of field studies on anti-littering interventions carried out in various Dutch municipalities between 2007 and 2009 (van Baaren et al., 2010). In one of the studies, researchers went door-to-door in a neighbourhood to ask whether residents would be willing to put a sticker on their door, car or another visible place with the text ‘keep our neighbourhood tidy’. For the 89% of those who committed to doing so, their address was marked on a list in their presence, to underline their public commitment to the goal. In all the intervention studies, signs with the text ‘keep our neighbourhood tidy’ were also placed at designated waste containers. The pre-commitment strategy was found to be among the most effective interventions across the studies and reduced littering by 17% in the intervention scenario compared with a 2% reduction in the non-intervention scenario.

Another promising intervention is to expose people to social norms and address their ‘social proof heuristic’. It is argued that social norms indicate what is acceptable in a social group and what is not, and that people conform to social norms to find a sense of belonging among their peers (Sunstein, 2015; White et al., 2011). The classic example is a study carried out in Sacramento, USA, between 2008 and 2011, involving an intervention group of 35,000 households and a control group of 50,000 households (Ayres et al., 2013; Cooney, 2011). The intervention group received information on their energy consumption compared with the average consumption of their neighbours (their social peers). Both descriptive information on comparative energy consumption and injunctive messages (emoticons: a smiley for those with below-average consumption and a frowning face for those with above-average consumption) were provided, as well as tips on how to save energy. The intervention had a considerable impact on high energy users, who reduced their consumption by nearly 7% relative to high energy users who did not receive reports. The study has been repeated in various forms around the world. For example, a comparable social norm intervention resulted in a 4–5% reduction in water consumption in Belen, Costa Rica, in 2014 (Datta et al., 2015).

A fifth and, for this article, final promising intervention is goal framing (Bizer et al., 2011; Lindenberg, 2008). Goal framing seeks to highlight the consequences of people’s behaviour and is expected to help to steer individuals towards desired behaviour (by highlighting its positive consequences) and to steer them away from undesired behaviour (by highlighting its negative consequences). For example, Avineri and Waygood (2013) illustrate how framing the difference in the carbon emissions of two modes of transport for a trip as a gain or framing it as a loss can have a substantial difference on the mode of transport people choose. That is, presenting the carbon emissions of transport option X as worse than those of transport Y is more likely to result in people choosing the desired option Y than presenting option Y as better than option X. Building on insights from the behavioural sciences, it is assumed that people respond more strongly to loss-frames than to gain-frames. In another study, Guo et al. (2017) show how the framing of information can help to mitigate bottleneck congestion in public transport systems, and thus make public transport a more attractive alternative. In a field study they explored whether extending the apparent length of overcrowded subway lines on the Washington DC subway map would help divert people to underutilised lines. They found that extending the length of an overcrowded line on the map by 20% can shift up to 7% of passengers to another line.

Some critical reflections

Again, some critical reflections are warranted. The above examples all give positive outcomes of studies on, and experiments using, insights from the behavioural sciences in urban climate governance. It goes without saying that less positive results have also been presented (OECD, 2017a, 2017b, 2017c). Of more relevance to note here is that undesired effects have also been identified. Scholars have pointed out rebound effects when actual behaviour offsets the beneficial effects of technological or other solutions (van der Heijden, 2014). For example, after purchasing an energy-efficient car people may decide to drive more, or after installing thermal insulation in their home they may decide to keep it warmer than before, because they feel justified in doing so after making the environmentally sustainable investment. Some scholars have even identified ‘prebound’ effects when people begin using more resources in anticipation of a future change in their own behaviour or in anticipation of technology they will obtain (Sunikka-Blank and Galvin, 2012).

Even more problematic are situations in which the behavioural interventions backfire and result in a situation where the people targeted by the intervention consume more resources or produce more waste after the intervention than they did before. This was indeed observed for a small group of households who obtained the smiley emoticon on their energy bills in the example discussed above. Now that they knew they were using less energy than their peers, this injunctive message provided a justification for using more energy (Ayres et al., 2013; Cooney, 2011).

In addition, from the above discussion readers may get the impression that the use of insights from the behavioural sciences in urban climate governance is widespread, or at least widely studied. However, the opposite appears to be true. Of the set of 129 publications initially identified for the literature review, only 5% (n = 6) were categorised in Web of Science as area studies, environmental studies or urban studies. Of the full set of 200 publications included in the review, only 7% (n = 13) had one or more words indicating an urban focus in their title, key words or abstract – see further, Appendix A, available online. Insights from the examples discussed here had to be triangulated with sources from an even broader, but unstructured, exploration of the literature carried out for the purpose of writing this article.

Applying insights from the behavioural sciences: Ethical and epistemic challenges

Applying behavioural insights in urban climate governance will inevitably generate controversy. Over the years, concerns have been raised about the legitimacy and ethics of governance interventions informed by behavioural insights (Abdukadirov, 2016; Alemanno and Spina, 2014). These concerns can largely be traced back to an influential book that has popularised the use of behavioural insights in public policy and governance, Nudge: Improving Decisions About Health, Wealth and Happiness (Thaler and Sunstein, 2009). Besides explaining the instrumental value of applying behavioural insights in public policy and governance in exceptionally clear language, its authors introduce a political philosophy that combines freedom of choice with choice guidance by the government or other authorities and is known as ‘libertarian paternalism’. It is particularly this political philosophy that has spurred an ever-growing, and sometimes vicious, normative rhetoric on the use of behavioural insights in public policy and governance (Bubb and Pildes, 2014; Sunstein, 2017). Unfortunately, the polemic has rather moved the academic debate away from questioning and exploring the instrumental value of behavioural insights and how they provide another arrow in the quiver of policymakers and practitioners who seek to achieve desirable societal outcomes by changing the behaviour of individuals and organisations. As a governance instrument, the use of behavioural insights may be better fitted to some political philosophies than others, but it is not married to a specific political philosophy. Whether or not it fits the political philosophy of a country or city is, ultimately, a question for local decision-makers to answer and to account for (Baldwin, 2014; Milne, 2012).

A second set of concerns that have been raised about the use of governance interventions informed by behavioural insights is epistemic in nature. While the starting point of the use of behavioural insights is that people do not behave in the way that rational choice theory predicts, the solutions provided to overcome people’s biases and heuristics still assume some objective rationality that is external to people. However, some scholars argue that rationality and irrationality are social constructs and qualifiers for behaviour. They are not facts, they have no distinct structural foundations in our brains, and they cannot be objectively proved to be right or wrong (Bissonnette, 2016; McMahon, 2015). Also, the call for a ‘rational’ application of behavioural insights to overcome ‘irrational’ behaviour in individuals and organisations appears paradoxical. Why would decision-makers not be influenced by the same heuristics and biases that they seek to address in others (Hallsworth et al., 2018; Vlaed et al., 2016)? They may, for example, be biased in their support for or opposition to the use of governance interventions informed by behavioural insights (or they may simply follow path dependency, as discussed before). When convinced that a specific solution will work, policymakers are likely to search for evidence that supports their earlier convictions and are unlikely to be swayed by arguments that go against them (‘confirmation bias’). Finally, as illustrated, academics find that interventions building on these insights sometimes have desirable effects and sometimes do not (Loewenstein et al., 2014; Osman et al., 2018); and evidence that a behavioural intervention has the desired effect in a specific policy area or geographic location is by no means a guarantee that the same intervention will have the same impact elsewhere (Agarwal et al., 2009; Bradbury et al., 2013).

While these concerns warn us to be careful when seeking to apply behavioural insights in urban climate governance, the growing knowledge base for behavioural insights points to promising avenues for shifting harmful individual behaviour at city level towards beneficial behaviour. However, building on the examples presented here, modest results should be expected from applying these insights. The literature review indicates that those interested in applying these insights should expect improvement percentages in single rather than double digits, and non-significant (or bounded) rather than significant patterns of change across populations. However, small behavioural changes achieved in large urban populations may ultimately deliver greater net improvements than large changes in a small proportion of urban populations (the Rose Hypothesis, cf. Milne, 2012). The types of interventions presented here can be implemented as relatively low-cost add-ons or as complements to existing urban governance interventions, rather than requiring sweeping changes in existing governance systems or costly technological solutions, and may have a substantial impact at city level (van der Heijden, 2017).

Conclusion: A research agenda for the use of behavioural insights in urban climate governance

Given the high rate of harmful individual behaviour at city level and the accumulated consequences of this behaviour for climate change, a further exploration, application and testing of urban climate governance informed by behavioural insights seems warranted. This article set out to review a large body of scholarship, published between 2009 and 2018, as it relates to the use of behavioural insights in urban climate governance. From this review, it has become clear that whilst the overall interest in behavioural sciences in public governance is substantial, we know little about how, where and with what effects these insights are applied in urban climate governance. Whilst this article has pointed at a number of promising interventions, readers should keep in mind that little academic research has been published on this exact topic. This leaves demanding research challenges for the next decade.

Obviously, it is essential to be critical of the potential gap between policy rhetoric and action and results on the ground. While there is ample talk about how behavioural science can inform public governance, we see very little discussion of this in the academic urban climate governance literature. It may be that scholars have not yet fully embraced this approach to urban climate governance, but it may also be that those involved in governing urban climate action have not embraced it. If the former is true, more research into existing interventions appears necessary, while if the latter is true, more experimentation with this approach to urban climate governance appears necessary. Here the current trend of urban living laboratories may be exceptionally suitable for experiments with various behavioural interventions at the city level.

It is also essential to scrutinise the explanatory reach of the accumulated knowledge base. Whilst there is scant research on the use of behavioural insights in urban climate governance, there is ample insight into their use in other areas of public governance. The set of factors that explain the success or failure of this approach to public governance may – and most probably will – be the starting point for studies that criticise this set for being too limited for a full understanding of real-world examples of urban climate governance that is informed by behavioural science, and its outcomes.

It is equally important to create a stronger connection between knowledge on public governance informed by behavioural science and the theoretical frameworks that are central to urban studies, institutional studies and climate governance studies. This article has illustrated how insights from the behavioural sciences complement insights from institutional analysis, and vice versa (e.g. status quo bias as complementary to path dependency, and social proof heuristics as complementary to discursive institutionalism). Complementary theories may, ultimately, give a more finely grained understanding of why some urban climate governance interventions informed by behavioural insights yield their desired outcomes in some contexts but not others.

Last but not least, a final set of core challenges is to understand whether, and how, promising examples of urban climate governance informed by behavioural science can be scaled up; whether and how synergies can be created between these interventions and other governance instruments such as direct regulation and market-based incentives, so that their impact as a whole is greater than the sum of the impacts of each of them; and how we can ensure that the progress (to be) made will not be reversed by future swings in political leadership. Future scholarship may wish to gain a deeper understanding of which design and implementation strategies are effective for achieving such synergies, as well as of the entrenchment of urban climate governance interventions informed by behavioural science that yield desirable outcomes.

To conclude, important advances have been made in the behavioural sciences in general and, albeit to a much smaller extent, in how behavioural sciences relate to urban climate governance. This scholarship is strongly supported by a sound foundation of experimental and observational research published over the last decade. We now have a strong base on which to continue and expand our research in this important area of growth, and we are faced with challenging research questions on the use of behavioural sciences in urban climate governance that will, no doubt, generate important insights in the critical decade that lies ahead of us.

Supplemental Material

USJ846002_supplemental_material – Supplemental material for Urban climate governance informed by behavioural insights: A commentary and research agenda

Supplemental material, USJ846002_supplemental_material for Urban climate governance informed by behavioural insights: A commentary and research agenda by Jeroen van der Heijden in Urban Studies

Footnotes

Acknowledgements

The author wishes to thank the anonymous reviewers and the editors of the journal for helpful suggestions to an earlier draft of this article.

Funding

The author wishes to acknowledge financial support from the Australian Research Council (grant number DE15100511), the Netherlands Organisation for Scientific Research (grant number 016165322) and the New Zealand Government Regulatory Practice Initiative (G-REG).

ORCID iD

Jeroen van der Heijden

References

Abdukadirov

(ed.) (2016) Nudge in Theory and Action: Behavioral Design in Policy and Markets. Cham: Springer.

Agarwal

Gabaix

Driscoll

, et al. (2009) The age of reason: Financial decisions over the life cycle and implications for regulation. Brookings Papers on Economic Activity 2: 51–117.

Al-Mofleh

Taib

Salah

(2010) Malaysian energy demand and emissions from the transport sector. Transport 25(4): 448–453.

Alemanno

Spina

(2014) Nudging legally: On the checks and balances of behavioral regulation. Icon – International Journal of Constitutional Law 12(2): 429–456.

Andrew

(2008) Market failure, government failure and externalities in climate change mitigation: The case for a carbon tax. Public Administration and Development 28(5): 393–401.

Ariely

(2008) Predictably Irrational: The Hidden Forces That Shape Our Decisions. New York: HarperCollins.

Aven

Renn

Rosa

(2011) On the ontological status of the concept of risk. Safety Science 49(8–9): 1074–1079.

Avineri

Waygood

(2013) Applying valence framing to enhance the effect of information on transport-related carbon dioxide emissions. Transportation Research Part A: Policy and Practice 48(February): 31–38.

Ayres

Raseman

Shih

(2013) Evidence from two large field experiments that peer comparison feedback can reduce residential energy usage. The Journal of Law, Economics, and Organization 29(5): 992–1022.

10.

Baldwin

(2014) From regulation to behaviour change: Giving nudge the third degree. Modern Law Review 77(6): 831–857.

11.

Benartzi

(2012) Save More Tomorrow. New York: Penguin Group.

12.

Benhabib

Bisin

Schotter

(2010) Present-bias, quasi-hyperbolic discounting, and fixed costs. Games and Economic Behavior 69(2): 205–223.

13.

Bissonnette

(2016) From the moral to the neural: Brain scans, decision-making, and the problematization of economic (ir)rationality. Journal of Cultural Economy 9(4): 364–381.

14.

Bizer

Larsen

Petty

(2011) Exploring the valence-framing effect: Negative framing enhances attitude strength. Political Psychology 32(1): 59–80.

15.

Bonneuil

Fressoz

J-B

(2016) The Shock of the Anthropocene: The Earth, History and Us. London: Verso Books.

16.

Borah

(2011) Conceptual issues in framing theory: A systematic examination of a decade’s literature. Journal of Communication 61(2): 246–263.

17.

Bradbury

McGimpsey

Santori

(2013) Revising rationality. Journal of Education Policy 28(2): 247–267.

18.

Bubb

Pildes

(2014) How behavioral economics trims its sails and why. Harvard Law Review 127(6): 1593–1678.

19.

Carstensen

Schmidt

(2016) Power through, over and in ideas: Conceptualizing ideational power in discursive institutionalism. Journal of European Public Policy 23: 318–337.

20.

Cooney

(2011) Evaluation Report: OPOWER SMUD Pilot, Year 2. Chicago, IL: Navigant Consulting.

21.

Datta

Miranda

Zoratto

, et al. (2015) A Behavioral Approach to Water Conservation: Evidence from Costa Rica. Washington, DC: World Bank Group.

22.

de Almeida

Fonseca

Schlomann

, et al. (2011) Characterization of the household electricity consumption in the EU, potential energy savings and specific policy recommendations. Energy and Buildings 43(8): 1884–1894.

23.

Dodman

(2009) Blaming cities for climate change? An analysis of urban greenhouse gas emissions inventories. Environment and Urbanization 21(1): 185–201.

24.

Duit

(2007) Path dependency and institutional change: The case of industrial emission control in Sweden. Public Administration 85(4): 1097–1118.

25.

Elster

(2000) Ulysses Unbound: Studies in Rationality, Precommitment, and Constraints. Cambridge: Cambridge University Press.

26.

Friedman

Isaac

James

, et al. (2014) Risky Curves: On the Empirical Failure of Expected Utility. London: Routledge.

27.

Furnham

Boo

(2011) A literature review of the anchoring effect. The Journal of Socio-Economics 40(1): 35–42.

28.

Glimcher

(2011) Foundations of Neuroeconomic Analysis. Oxford: Oxford University Press.

29.

Greene

(2011) Uncertainty, loss aversion, and markets for energy efficiency. Energy Economics 33(4): 608–616.

30.

Grimmelikhuijsen

Jilke

Olsen

, et al. (2017) Behavioral public administration: Combining insights from public administration and psychology. Public Administration Review 77(1): 45–56.

31.

Guo

Zhao

Whong

, et al. (2017) Redesigning subway map to mitigate bottleneck congestion: An experiment in Washington DC using mechanical turk. Transportation Research Part A: Policy and Practice 106(December): 158–169.

32.

Hallsworth

Egan

Rutter

McCrae

(2018) Behavioural Government: Using Behavioural Science to Improve How Governments Make Decisions. London: The Behavioural Insights Team.

33.

Hardisty

Appelt

Weber

(2012) Good or bad, we want it now: Fixed-cost present bias for gains and losses explains magnitude asymmetries in intertemporal choice. Journal of Behavioral Decision Making 21(4): 348–361.

34.

Hartman

Doane

Woo

C-K

(1991) Consumer rationality and the status quo. The Quarterly Journal of Economics 106(1): 141–162.

35.

Hedlin

Sunstein

(2016) Does active choosing promote green energy use: Experimental evidence. Ecology Law Quarterly 43(1): 107–142.

36.

Hoffman

(2015) How Culture Shapes the Climate Change Debate. Stanford, CA: Stanford University Press.

37.

Johnson

Goldstein

(2003) Do defaults save lives? Science 302(5649): 1338–1339.

38.

Kahneman

(2011) Thinking Fast and Slow. New York: Farrar, Straus and Giroux.

39.

Kahneman

Tversky

(1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2): 263–292.

40.

Kahneman

Knetsch

Thaler

(1991) Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives 5(1): 193–206.

41.

Kahneman

Wakker

Sarin

(1997) Back to Bentham? Explorations of experienced utility. Quarterly Journal of Economics 112(2): 375–405.

42.

Kosters

van der Heijden

(2015) From mechanism to virtue: Evaluating nudge theory. Evaluation 21(3): 276–291.

43.

Lehmann Nielsen

Parker

(2012) Mixed motives: Economic, social, and normative motivations in business compliance. Law & Policy 34(4): 428–462.

44.

Liebig

Rommel

(2014) Active and forced choice for overcoming status quo bias: A field experiment on the adoption of ‘no junk mail’ stickers in Berlin, Germany. Journal of Consumer Policy 37(3): 423–435.

45.

Lindenberg

(2008) Social rationality, semi-modularity and goal-framing: What is it all about? Analyse & Kritik 30(2): 669–687.

46.

Loewenstein

Sunstein

Golman

(2014) Disclosure: Psychology changes everything. Annual Review of Economics 6(6): 391–419.

47.

Luque-Ayala

Marvin

Bulkeley

(eds) (2018) Rethinking Urban Transitions. London: Routledge.

48.

McMahon

(2015) Behavioral economics as neoliberalism: Producing and governing homo economicus. Contemporary Political Theory 14(2): 137–158.

49.

McRaney

(2012) You Are Not So Smart. New York: Gotham Books.

50.

McRaney

(2014) You Are Now Less Dumb. New York: Gotham Books.

51.

Marsh

Stoker

(eds) (2010) Theory and Methods in Political Science. 3rd edition. New York: Palgrave.

52.

Martek

Hosseini

Shrestha

, et al. (2019) Barriers inhibiting the transition to sustainability within the Australian construction industry: An investigation of technical and social interactions. Journal of Cleaner Production 211(February): 281–292.

53.

Milne

EMG

(2012) A public health perspective on transport policy priorities. Journal of Transport Geography 21: 62–69.

54.

Nneoh

Chipilu

Marshall

(2017) What encourages people to carpool? An evaluation of factors with meta-analysis. Transportation 44(2): 423–447.

55.

O’Donoghue

Rabin

(2015) Present bias: Lessons learned and to be learned. American Economic Review 104(5): 273–279.

56.

OECD (2017a) Behavioural Insights and Public Policy: Lessons from around the World. Paris: OECD.

57.

OECD (2017b) Tackling Environmental Problems with the Help of Behavioural Insights. Paris: OECD.

58.

OECD (2017c) Use of Behavioural Insights in Consumer Policy. Paris: OECD.

59.

Osman

Radford

Lin

, et al. (2018) Learning lessons: How to practice nudging around the world. Journal of Risk Research 1–9. Epub ahead of print 26 September 2018. DOI: 10.1080/13669877.2018.1517127.

60.

Pichert

Katsikopoulos

(2008) Green defaults: Information presentation and pro-environmental behaviour. Journal of Environmental Psychology 28(1): 63–73.

61.

Pinto-Prades

Abellan-Perpiñan

(2012) When normative and descriptive diverge: How to bridge the difference. Social Choice and Welfare 38(4): 569–584.

62.

Pollitt

Shaorshadze

(2013) The role of behavioural economics in energy and climate policy. In: Fouquet

(ed.) Handbook on Energy and Climate Change. Cheltenham: Edward Elgar, pp. 523–546.

63.

Read

(2007) Experienced utility: Utility theory from Jeremy Bentham to Daniel Kahneman. Thinking & Reasoning 13(1): 45–61.

64.

Rixen

Viola

(2015) Putting path dependence in its place: Toward a taxonomy of institutional change. Journal of Theoretical Politics 27(2): 301–323.

65.

Romero-Lankao

Burch

Hughes

(2018) Governance and policy. In: Rosenzweig

Solecki

Romero-Lankao

, et al. (eds) Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network. Cambridge: Cambridge University Press, pp. 585–606.

66.

Rootes

(2014) A referendum on the carbon tax? The 2013 Australian election, the Greens, and the environment. Environmental Politics 23(1): 166–173.

67.

Rosenzweig

Solecki

Romero-Lankao

, et al. (2018) Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network. Cambridge: Cambridge University Press.

68.

Rusk

Mahfouz

Jones

(2011) Electricity’s ‘disappearing act’: Understanding energy consumption and phantom loads. Technological Directions 71(1): 22–25.

69.

Samuelson

Zeckhauser

(1998) Status quo bias in decision making. Journal of Risk and Uncertainty 1(1): 7–59.

70.

Schmidt

(2011) Speaking of change: Why discourse is key to the dynamics of policy transformation. Journal of Critical Policy Analysis 5(2): 106–126.

71.

Seo

(2017) The Behavioral Economics of Climate Change. Cambridge, MA: Academic Press.

72.

Simon

(1945) Administrative Behavior. A Study of Decision-Making Processes in Administrative Organization. New York: Free Press.

73.

Sunikka-Blank

Galvin

(2012) Introducing the prebound effect: The gap between energy performance and actual energy consumption. Building Research & Information 40(3): 260–273.

74.

Sunstein

(2015) The ethics of nudging. The Yale Journal on Regulation 32(1): 413–452.

75.

Sunstein

(2017) Human Agency and Behavioural Economics: Nudging Fast and Slow. Cham: Palgrave Macmillan.

76.

Thaler

Sunstein

(2009) Nudge: Improving Decisions about Health, Wealth and Happiness. Revised edition. London: Penguin.

77.

Tversky

Kahneman

(1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157): 1124–1131.

78.

Tversky

Kahneman

(1981) The framing of decisions and the psychology of choice. Science 211(4481): 453–458.

79.

Tversky

Kahneman

(1991) Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics 106(4): 1039–1061.

80.

UN (2018) Yearbook of Global Climate Action 2018. New York: United Nations Climate Change Secretariat/Marrakech Partnership.

81.

van Baaren

Horstman

Pol

, et al. (2010) Voorbij Bijplaatsingen: Gedragsinterventies voor het effectief terugdringen van afval afvalcontainers. The Hague: Nederland Schoon/Agentschap NL/NVRD.

82.

van der Heijden

(2013) Is new governance the silver bullet? Insights from the Australian Buildings Sector. Urban Policy and Research 31(4): 453–471.

83.

van der Heijden

(2014) Governance for Urban Sustainability and Resilience: Responding to Climate Change and the Relevance of the Built Environment. Cheltenham: Edward Elgar.

84.

van der Heijden

(2015) What ‘works’ in environmental policy-design? Lessons from experiments in the Australian and Dutch building sectors. Journal of Environmental Policy & Planning 17(1): 44–64.

85.

van der Heijden

(2017) Eco-financing for low-carbon buildings and cities: Value and limits. Urban Studies 54(12): 2894–2909.

86.

van der Heijden

(2019) Behavioural insights and regulatory practice: A review of the international academic literature. Wellington: Victoria University of Wellington/Government Regulatory Practice Initiative (G-REG).

87.

van der Heijden

Bulkeley

Certomá

(2019) Urban Climate Politics: Agency and Empowerment. Cambridge: Cambridge University Press.

88.

van Exel

NJA

Brouwer

WBF

van der Berg

, et al. (2006) With a little help from an anchor. Discussion and evidence of anchoring effects in contingent valuation. Journal of Socio-Economics 35(5): 836–853.

89.

Vlaed

King

Dolan

, et al. (2016) The theory and practice of ‘nudging’: Changing health behaviours. Public Administration Review 78(4): 550–561.

90.

White

Smith

Terry

, et al. (2011) Social influence in the theory of planned behaviour: The role of descriptive, injunctive, and in-group norms. British Journal of Social Psychology 48(1): 135–158.

91.

Wolfram

van der Heijden

Juhola

, et al. (2019) Learning in urban climate governance: Concepts, issues and challenges. Journal of Environmental Policy & Planning 21(1): 1–15.

92.

Wright

Ginsburg

(2012) Behavioral law and economics: Its origins, fatal flaws, and implications for liberty. Northwestern University Law Review 106(1): 1033–1092.

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