Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-23T00:17:18.232Z Has data issue: false hasContentIssue false

Sticky brown sludge everywhere: can sludge explain barriers to green behaviour?

Published online by Cambridge University Press:  22 February 2024

Ganga Shreedhar*
Affiliation:
Department of Psychological and Behavioural Science, London School of Economics and Political Science, London, UK
Cahal Moran
Affiliation:
Department of Psychological and Behavioural Science, London School of Economics and Political Science, London, UK
Stuart Mills
Affiliation:
Economics Department, Leeds University, Leeds, UK
*
Corresponding author: Ganga Shreedhar, Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Behavioural science has sought to promote pro-environmental behaviours including climate-friendly dietary change, and to reduce travel emissions and excessive wastes. Nevertheless, there is a debate about how effective behavioural interventions are, and in turn, about the real barriers to enduring pro-environmental behaviour change. In this context, we conceptualise brown sludge as multi-level impediment to pro-environmental behaviour change, which results in higher environmental costs shared by the broader society, rather than solely by the individual actor. We propose that brown sludge comprises an array of additional transaction costs, encompassing, but not restricted to, psychological, temporal, and uncertainty costs. Brown sludge can occur at the individual, social, institutional, and societal levels. Examples include confusing eco-information, delay and disinformation campaigns, and complicated systems and infrastructure leading to carbon lock-in.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Introduction

Behavioural science has long sought to promote pro-environmental behaviours such as changing one's diet and reducing one's travel and household waste. Some (Fischhoff, Reference Fischhoff2021; van der Linden et al., Reference Van Der Linden, Pearson and Van Boven2021) are positive about using behavioural science for pro-environmental purposes. Others (Nisa et al., Reference Nisa, Bélanger, Schumpe and Faller2019; Shreedhar, Reference Shreedhar2023) are sceptical about solely relying on behavioural science tools like nudging. Others still (Gravert and Shreedhar, Reference Gravert and Shreedhar2022; Mills and Whittle, Reference Mills and Whittle2023) emphasise the importance of a policy mix which includes nudging and more interventionist approaches like taxation and mandates. This perspective reflects the position of the Intergovernmental Panel on Climate Change (IPCC), whose recent report on ‘Mitigation’ includes ‘choice architecture’ as one of several tools for building an environmentally sustainable future (IPCC, 2022).

What prevents people from pursuing pro-environmental behaviours? In this article, we argue the behavioural science concept of ‘sludge’ is useful for understanding why individuals and communities may struggle to pursue these ends, even when they want to undertake green behaviours. While sludge is subject to some definitional debate (Sunstein, Reference Sunstein2018; Soman, Reference Soman2020; Sunstein and Gosset, Reference Sunstein and Gosset2020; Newall, Reference Newall2022; Mills, Reference Mills2023), it is generally understood as frictions or burdens which impede individual behaviours (Thaler, Reference Thaler2018). All these perspectives draw on the complementary, and often overlapping, literature on administrative burden (e.g., Herd et al., Reference Herd, DeLeire, Harvey and Moynihan2013; Moynihan et al., Reference Moynihan, Herd and Harvey2015; Christensen et al., Reference Christensen, Aarøe, Baekgaard, Herd and Moynihan2019; Madsen et al., Reference Madsen, Mikkelsen and Moynihan2020; Baekgaard and Tankink, Reference Baekgaard and Tankink2022), though this literature focuses less on choice architecture compared to the sludge literature (Sunstein, Reference Sunstein2022).

Newall's (Reference Newall2022, p. 6) comprehensive review of sludge defines it as, ‘many different techniques … that make [people] worse off, as judged by themselves,’ a perspective shared by others (e.g., Thaler and Sunstein, Reference Thaler and Sunstein2021; Sunstein, Reference Sunstein2021, Reference Sunstein2022; Hortal and Contreras, Reference Hortal and Contreras2023). Shahab and Lades (Reference Shahab and Lades2021) relate sludge to the transaction cost literature in economics. They argue sludge is a type of transaction cost induced through choice architecture. For instance, poor information disclosure may make valuable information harder to find. This creates a search cost, which is understood as a kind of sludge. Broadly, in this article, we follow the transaction cost approach to sludge as presented by Shahab and Lades (Reference Shahab and Lades2021). We extend their framework to include a broader variety of costs, such as psychological, time, and uncertainty costs; and to include those techniques that increase environmental costs which are borne by society at large, apart from possibly (but not necessarily always) the individual themselves.

We use the concept of sludge to examine several examples of barriers to pro-environmental behaviour. In doing so, we develop the concept of brown sludge, a form of sludge that specifically impedes pro-environmental behaviours. Much like regular sludge, brown sludge emerges from different places. Some is due to poor design: for instance, where a green policy is preferable but requires excessive paperwork relative to carbon-intensive alternatives, which may impose an administrative burden on people and thereby lead them to stick with the status quo. Some may be ‘legacy sludge’: for instance, green alternatives, which are likely newer choices, might be poorly ‘tacked onto’ existing systems (such as government websites). Additionally, some may be intentional. For instance, we argue greenwashing through unverifiable eco-labels, as well as disinformation about environmental harms from fossil fuels, can be understood as purposeful attempts to misdirect individuals and obscure important information, thereby creating additional uncertainty about the costs and benefits of alternatives in the choice environment – this is to say, sludge (Shahab and Lades, Reference Shahab and Lades2021).

In dissecting various examples through the lens of brown sludge, we contribute an explicitly behavioural perspective to the question of pro-environmental barriers and the transition to green alternatives. We also highlight the limits of sludge as a conceptual tool for explaining these barriers. In doing so, we reveal some of the limits of behavioural public policy. Some barriers to pro-environmental behaviour can be understood as brown sludge and lead one to speculate on behavioural science solutions (e.g., green nudges). However, others firmly stretch the explanatory power of brown sludge and, in turn, lead one to conclude that traditional economic and public policy changes are likely needed to affect pro-environmental behaviour. We hope these contributions represent a constructive development within the emerging ‘critical’ behavioural policy debate, of which environmental policy has been a key focus (Nisa et al., Reference Nisa, Bélanger, Schumpe and Faller2019; Chater and Loewenstein, Reference Chater and Loewenstein2022; De Ridder et al., Reference De Ridder, Kroese and van Gestel2022; Gravert and Shreedhar, Reference Gravert and Shreedhar2022; Mills and Whittle, Reference Mills and Whittle2023).

The structure of this article is as follows. In the section ‘Brown sludge’, we review several examples of barriers to green behaviours and relate them to brown sludge. We loosely organise these examples into categories of individual, social, and institutional levels, though there is some overlap between these categories, which are used illustratively rather than definitively. Individual-level examples include confusing eco-labels and some examples of greenwashing and carbon-washing. Here, confusing information creates search and time costs which can be understood as brown sludge. Social-level examples include climate disinformation and distraction campaigns. Here, confusing information combines with some social elements and framing strategies to create search and uncertainty costs which can be understood as brown sludge. Institutional-level examples include poorly designed green investment schemes, delays, and the complex provision of green services. Here, institutional myopia and administrative burdens create time and uncertainty costs which can be understood as brown sludge.

Each ‘level’ reveals the applicability, but also the limits, of brown sludge as an explanation of barriers to green behaviours. In the section ‘Brown infrastructure’, we contrast brown sludge with the concept of brown infrastructure to reveal the conceptual limits of brown sludge as an explanation of barriers and thus the limits of behavioural interventions as policy solutions to some barriers to environmental barriers. The section ‘Conclusion’ offers some discussion of brown sludge (and brown infrastructure).

Brown sludge

As above, we loosely organise the examples of barriers to green behaviours into three levels such as individual, social, and institutional. These are illustrative levels for the purposes of discussion and are summarised in Table 1. Generally, individual-level barriers focus on individual understanding and decision-making. Social-level barriers focus on interpersonal and community behaviours. Institutional-level barriers focus on interactions between individuals and institutions, such as government. These barriers can increase sludge by increasing time, uncertainty, search, evaluation, and psychological costs. They can further interact with brown infrastructure, which physically and spatially structures the choice architecture at all three previous levels.

Table 1. Brown sludge: mechanisms, behavioural effects, and mitigation measures

Eco-labels, greenwashing, and carbonwashing

Individually, brown sludge can take the form of confusing eco-information. Many products have different eco-labels, or similarly vague environmental–social–governance (ESG) disclosures. Eco-information is not in itself brown sludge. There are several benefits of eco-information and environmental disclosure. Conventional economic theory suggests that disclosing eco-information allows firms to signal their green credentials. Consumers can, in turn, identify and choose brands that they perceive to be more eco-friendly. Behavioural theories suggest green consumers are motivated by personal ‘warm glow’ effects when they choose eco-labelled alternatives (Delmas and Lessem, Reference Delmas and Lessem2017). Thus, eco-information creates mutual benefit for brands and consumers (van't Veld, Reference Van't Veld2020). Disclosures such as individual carbon footprints may also educate and nudge people towards pro-environmental choices (Taufique et al., Reference Taufique, Nielsen, Dietz, Shwom, Stern and Vandenbergh2022).

Eco-information is also popular across the ideological spectrum (Clark and Russell, Reference Clark and Russell2005). Liberals see it as a cost-effective and voluntary way of promoting pro-environmental choices. Rights-advocates see it as a way of keeping polluters publicly accountable. Policymakers may use it as a mechanism for regulation. Finally, conservatives support the freedom-of-choice approach which comes from disclosures rather than mandates.

Yet, eco-information becomes brown sludge when it is vague or false, hard to understand, and costly (or impossible) to verify. The sheer amount of eco-information alone may create search costs for consumers. The ‘Ecolabel Index’ tracks 456 eco-labels in 25 industry sectors across several countries. In just the UK, there are 87 eco-labels. Some, like Fairtrade and the Marine Stewardship Council (MSC), are from non-profit certifiers. Others, like Organic Farmers and Growers certification, come from for-profit organisations, though organisations which are still compliant with UK regulations. People find the meaning of eco-labels confusing, creating uncertainty costs, and limiting the benefits of transparency (Brécard, Reference Brécard2014). Uncertainty, evaluation, and search costs may also give ‘moral wiggle room’ (Dana et al., Reference Dana, Weber and Kuang2007), by providing situational excuses to avoid confusing eco-information and remain ignorant of environmental impacts (Momsen and Ohndorf, Reference Momsen and Ohndorf2020).

Further still, for-profit firms may create labels themselves. These do not necessarily correspond to specific regulations and are not verified by third-party certifiers, for instance, Procter and Gamble's ‘Future Friendly’ label. In some instances, firms may simply use visual information, like green-coloured packaging or pictures of ‘happy’ animals, to trigger pro-environmental associations (Seo and Scammon, Reference Seo and Scammon2017). Where labels come from third-parties, procedural transparency can vary. Where firms create their own labels, harmful practices can emerge. For instance, firms can adopt eco-labels to pass off brown products as green. This is known as greenwashing. Greenwashing practices mislead consumers about the environmental credentials of products and services (Delmas and Burbano, Reference Delmas and Burbano2011), creating further uncertainty, mistrust, and related psychological costs (Chen and Chang, Reference Chen and Chang2013; Szabo and Webster, Reference Szabo and Webster2021). Consumers may further avoid efforts to deliberately debunk misinformation, given the evaluation costs from verifying labels and the added moral wiggle room afforded by greenwashing (Momsen and Ohndorf, Reference Momsen and Ohndorf2022).

In addition to greenwashing, In and Schumacher (Reference In and Schumacher2021) escribe what they call ‘carbonwashing’. This is when firms selectively communicate carbon information which does not match their environmental impact and unsubstantiated promises about future ambitions. Firms engage in carbonwashing by taking advantage of a lack of standardisation in Environmental Social and Governance (ESG) indicators and carbon reporting. For instance, they launch unverified carbon reduction plans and ‘Net Zero’ targets, or they emphasise (marginal) carbon reduction efforts undertaken by the firm. In terms of costs, and thus sludge, the effects of carbonwashing are likely similar to those of greenwashing.

Disinformation and distraction campaigns

Climate disinformation is a form of brown sludge, misdirecting people by misrepresenting or misstating climate information. This creates uncertainty costs, leading policymakers and the public to question anthropogenic global warming, and search costs, as fact must be disassociated from fiction. Lobbying campaigns that cast doubt on climate science are perpetrators of this sludge. Oreskes and Conway (Reference Oreskes and Conway2010b) show how these ‘merchants of doubt’ funnel resources to contrarian scientists and think tanks in order to sow climate change doubts. Climate doubt can delay policy support and action (van der Linden et al., Reference Van Der Linden, Leiserowitz, Feinberg and Maibach2015; Shreedhar and Mourato, Reference Shreedhar and Mourato2020). It also causes legitimate perspectives to be questioned, discouraging individuals and communities from responding to environmental threats (Oreskes and Conway, Reference Oreskes and Conway2010b).

For instance, since the 1980s, climate change has become a contested, politicised issue in the USA. This is a result of lobbying and disinformation campaigns (Oreskes and Conway, Reference Oreskes and Conway2010b; McCright and Dunlap, Reference McCright and Dunlap2011). These strategies are not new and have been used to influence individuals and institutions in the past. For instance, to cast doubt on links between cigarettes and cancer, or links between man-made pollution and acid rain (Oreskes and Conway, Reference Oreskes and Conway2010b). Politicising the scientific consensus redirects social discourse away from discussing solutions to a problem and towards debating the existence of the problem in the first place (McCright and Dunlap, Reference McCright and Dunlap2011). Disinformation creates ambiguity and uncertainty costs, which can entrench the status quo (Sunstein, Reference Sunstein2018).

Recent disinformation campaigns have focused on distracting or delaying action, rather than outright denial of anthropogenic climate change. Fossil fuel firms have hired public relation firms to emphasise the benefits of fossil fuels. For instance, in 2020, several social media influencers participated in the #CookingWithGas campaign, which claimed that food tasted better when cooked with natural gas. This campaign was funded by the American Gas Association and the American Public Gas Administration, two trade groups (Lever, Reference Lever2020). Another example concerns the wildfires which afflicted Australia in 2020. News Corp – an Australian media organisation – promoted claims that arsonists were responsible for the fires, rather than climate change causing overly-dry conditions. Rumours of arsonist involvement could be traced back to a bot-induced social media disinformation campaign (Readfearn, Reference Readfearn2020). These distraction campaigns come to dominate narratives, creating search costs.

Climate disinformation qualifies as brown sludge in two ways. Firstly, it impedes individuals through misdirecting individual efforts, creating time costs. This could be misdirection towards behaviours which have marginal effects. Furthermore, fostering misunderstanding could lead to deleterious behaviours, even when performed in good faith. Secondly¸ it impedes individuals through misdirecting social discourse. Delaying discourses that impede institutions and communities in supporting pro-environmental behaviours create impediments for those same groups and sow uncertainty and distrust, resulting in significant costs. For example, misinformation may delay political action on climate change, as recently occurred with the Republican Party in the USA voting down the climate health law.

One response to disinformation and social misinformation is direct public engagement in science and politics. Oreskes and Conway (Reference Oreskes and Conway2010a) note that scientists have traditionally focused on research, believing that truth will triumph provided the research is credible – a viewpoint which has not been borne out by the facts. Additionally, involving citizens in deliberative assembles over economic, social, and environmental issues has been shown to reduce polarisation and increase engagement with climate evidence (Devaney et al., Reference Devaney, Torney, Brereton and Coleman2020). More active collaboration between experts and citizens could be the basis for tackling some brown sludge at the social level.

Unclear instructions and complicated systems

Brown sludge can also exist at the institutional level. Often, this is in the form of complex processes; confusing language and instructions; burdensome paperwork; and long waiting times (Herd et al., Reference Herd, DeLeire, Harvey and Moynihan2013; Moynihan et al., Reference Moynihan, Herd and Harvey2015). All create costs. Considering recycling is managed by local authority councils in the UK. For many, recycling is difficult because there is uncertainty about which materials can be recycled. Likewise, instructions can be confusing and collection schedules are subject to change. The variability of recycling regimes in the UK means it is difficult to get consistent information. Housing circumstances (e.g., house type and ownership) also impact access to recycling resources. All these factors create barriers between generally pro-recycling intentions and inconsistent recycling behaviours (Geiger et al., Reference Geiger, Steg, van der Werff and Ünal2019; Roy et al., Reference Roy, Berry and Dempster2022).

Poor instructions and high costs of accessing recycling infrastructure are examples of brown sludge. One solution might be simpler recycling instructions on packaging. However, this subsection emphasises that some brown sludge also emerges from institutional design. For instance, tedious paperwork which deters the adoption of environmental policies is brown sludge. The UK's Green Home Grants scheme is considered. This scheme allows homeowners in UK to apply for a cash voucher to undertake energy-efficient home improvements: installing double-glazing, insulation, and heat pumps.

Preliminary evidence suggests that brown sludge pervades this scheme. Launching in September 2020, the scheme is closed in March 2021. During this 6 months, 8,557 applicants had work completed and vouchers paid. Another 54,500 received approval for payment pending work. Another 23,500 needed to provide more information before approval. Reasons included incorrect paperwork, or applications being for work not covered by the scheme. Thus, approximately 27% of applicants struggled with the scheme when it was available. While there is no published processing timescale, some applicants reported waiting months for approval (Ingrams, Reference Ingrams2022) – quite the feat of sludge, given the brevity of the policy itself.

De Vries et al. (Reference De Vries, Rietkerk and Kooger2020) discuss the administrative burden involved in greening one's home through a series of ‘stages’. At the ‘awareness’ stage, homeowners must navigate complex and technical energy efficiency information. At the ‘consideration’ stage, homeowners must identify reliable and trustworthy contractors. At the ‘decision’ stage, homeowners must navigate institutional processes for grants, subsidiaries, or tax exemptions. Each stage places new frictions on the homeowner. Some stages, such as the ‘decision’ stage, also create uncertainty – one does not know if they will receive approval. Both friction and uncertainty may deter any action at all (Sunstein, Reference Sunstein2018).

Misaligned incentives and regulations across groups may exacerbate brown sludge. For instance, renters often have limited incentives to invest in greening their home because it is a large, upfront cost in an asset they do not own. Equally, landlords have limited incentives to invest in their assets beyond meeting relatively low legal energy standards (in the UK, landlords must comply with a poor energy standard of E, rising to D in 2024), because they often do not pay the cost of the energy bills. These are economic impediments, not sludge, yet where such impediments already exist, institutional brown sludge is likely to be especially potent.

This is because brown sludge can reduce take-up of green subsidies and programmes. Lades et al. (Reference Lades, Clinch and Kelly2021) discuss how administrative burden can reduce pro-environmental investments in the case of heat pumps and show that these burdens can exacerbate tendencies to procrastinate. Johnson et al. (Reference Johnson, Ndebele and Newburn2022) suggest that transaction costs may be so large that they eliminate incentives for households to implement landscape conservation programmes. Simplification of processes to apply for and access programs can help. Grieder et al. (Reference Grieder, Kistler and Schmitz2022) find that small and medium enterprises (SMEs) are more likely to adopt energy-efficiency measures when the benefits of doing so are simplified. As above, simplification could be an individual-level approach such as clearer recycling instructions. Equally, simplification could be at an institutional level, such as removing unnecessary paperwork, reducing uncertainty in processes, and conducting sludge audits to ensure policy incentives align with desired behaviours (Sunstein, Reference Sunstein2022).

Brown infrastructure

The above discussion presents various opportunities for using behavioural science interventions to remove barriers to green behaviour. Simplification, standardisation, and other ‘sludge-busting’ techniques all respond to transaction costs, which arise from choice architecture. Yet, some examples discussed begin to touch on policy solutions which go beyond choice architecture. We call barriers to green behaviour that arise due to factors beyond choice architecture – such as economic barriers – brown infrastructure.

Brown infrastructure can be understood as barriers which effectively exclude preferable options from individuals’ choice sets. For instance, imagine one wishes to cycle to work, rather than drive. Lacking a cycle lane, one could still cycle, but the risks and discomfort of doing so will remain high. Likewise, any benefits are also reduced. The best policy response is unlikely to be nudging, as the issue is not low motivation to cycle or a lack of knowledge that cycling exists. Instead, the solution is likely to be expanding the choice set, so that cycling becomes a viable, beneficial – and indeed attractive – option.

Further examples abound: poorly connected (or no) public transport alternatives leading to car dependency; a lack of green spaces due to urban development policies; no access to rural green spaces due to poor or inhibitive ‘right to roam’ policies; and so on. When choices such as greening homes, changing commuting behaviours, or buying greener products are outside of the choice set, they will logically not be chosen. If the current set of choices presents no way of realising one's goals, no change in choice architecture will remove this barrier.

It is worth reflecting on the links between brown sludge and brown infrastructure. Recent debates within behavioural public policy have begun to encourage this (Chater and Loewenstein, Reference Chater and Loewenstein2022), and within the green nudging space, there is increasing recognition of the partnership between behavioural science and more traditional policy mechanisms (Nisa et al., Reference Nisa, Bélanger, Schumpe and Faller2019; Gravert and Shreedhar, Reference Gravert and Shreedhar2022). Within the administrative burden literature, it is common to acknowledge the importance of a variety of interventions to resolve challenges (Herd et al., Reference Herd, DeLeire, Harvey and Moynihan2013; Moynihan et al., Reference Moynihan, Herd and Harvey2015; Christensen et al., Reference Christensen, Aarøe, Baekgaard, Herd and Moynihan2019; Madsen et al., Reference Madsen, Mikkelsen and Moynihan2020; Baekgaard and Tankink, Reference Baekgaard and Tankink2022).

We have argued that brown sludge may exacerbate pre-existing economic barriers. For instance, where a person wishes to pursue an option but not because of economic barriers, any sludge which surrounds the options they can pursue is likely to be especially burdensome. Furthermore, uncertainty and search costs may be so substantial that individuals fail to perceive all their available options, experiencing brown sludge as if it were brown infrastructure. As previously discussed, emerging evidence shows that the propensity to avoid eco-information increases with the introduction of merely nominal information costs due to the tendency to exploit any moral wiggle room (Momsen and Ohndorf, Reference Momsen and Ohndorf2020; Reference Momsen and Ohndorf2022). When moral wiggle room interacts with additional costs constituting sludge from brown infrastructure, such as time costs, it may not be surprising that we fail to see enduring pro-environmental behaviour change. The relationship is just nuanced; and examining the barriers to green behaviours necessitates consideration of both.

As with brown sludge, brown infrastructure emerges from various places. Often, brown infrastructure is the default policy mindset. Because green policies are typically newer perspectives, they may come with less supporting evidence and induce less institutional confidence owing to a lack of experience (Mills and Whittle, Reference Mills and Whittle2023). e-Waste recycling in the UK is considered. UK local authorities provide waste collection and recycling to only a few types of waste – typically paper and plastic. Waste from electronics (e-waste) receives little or no public provision. This is despite the UK being the second largest producer of e-waste (Environmental Audit Committee, 2020). Recycling e-waste would induce typical recycling costs, such as collection costs. But e-waste requires different recycling processes compared to paper and plastic, inducing additional costs. Furthermore, common e-waste, such as batteries, smartphones, computer accessories, and computers themselves, are (relatively) new consumer goods and may not yet be cognizant to policymakers. The result is recycling provision that is more brown than green.

Brown infrastructure also comes from legacy decisions, a problem which also leads to sludge when administrative processes are just carried on, rather than scrutinised (Sunstein, Reference Sunstein2022). For instance, an initial decision to prioritise driving over cycling adds to the infrastructural costs of prioritising cycling later. Norton (Reference Norton2011) offers an interesting study in this area. They argue that the demise of electrified streetcars – once popular public transport systems in American cities – emerged from poor city and regulatory design. Cars were allowed on streetcar lines, causing traffic jams. Yet only streetcar providers had to bear the cost of road and track maintenance. As such, fares rose to cover these costs, while the service was declining in quality. This elevated the apparent benefits of car-ownership, causing people to adopt cars instead and shifting city planning priorities for decades towards individual vehicle ownership. Legacy decisions can make green policies look costlier and less convenient than brown infrastructure, further compounding the default mindset.

This is similar to what Rosenbloom et al. (Reference Rosenbloom, Markard, Geels and Fuenfschilling2020) and others (e.g., Hickel and Kallis, Reference Hickel and Kallis2020) argue in relation to the so-called carbon lock-in problem, where existing institutions and cultural patterns leave limited space for households to switch to alternatives. As they put it, carbon lock-in comes from ‘interconnected technologies, infrastructures, regulations, business models, and lifestyles’. As above, physical infrastructure may be dominated by roads. Taking the carbon lock-in perspective, one comes to see that even significant behavioural economic incentives may not be a solution to some problems of provision. For instance, carbon taxes may raise the cost of running a car, but pro-environmental alternatives will not be pursued if those alternatives still do not exist.

Societal impediments are important because they might prevent individuals from pursuing options they would like to. But availability itself is important in shaping individuals’ attitudes, beliefs, and preferences (Galbraith, Reference Galbraith1977), which in turn feeds back into discussions of brown sludge and behavioural science (Fuller, Reference Fuller2020). Several studies show that the availability and proximity of green space determine green space usage (Maat and de Vries, Reference Maat and de Vries2006; Neuvonen et al., Reference Neuvonen, Sievänen, Tönnes and Koskela2007). The lack of access to nature can be profound for both pro-environmental motivation and behaviour. Soga et al. (Reference Soga, Evans, Yamanoi, Fukano, Tsuchiya, Koyanagi and Kanai2020) note that a lack of experiences in green space (and nature broadly) can lead to ‘biophobia’. This fear or avoidance of nature, in turn, leads to a lower willingness to undertake pro-environmental behaviours. Public transport is another worthwhile area to consider. Segregated cycle lanes and clearly painted cycle paths are an important determinant of bicycle usage (Doğru et al., Reference Doğru, Webb and Norman2021). Adequate provision, rather than just information, is crucial for encouraging habitual alternative transport usage (Neoh et al., Reference Neoh, Chipulu and Marshall2017; Kristal and Whillians, Reference Kristal and Whillians2020; Gravert and Collentine, Reference Gravert and Collentine2021).

While not brown sludge, brown infrastructure is important to consider. From an environmental policy perspective, brown infrastructure cannot be overlooked, even when important choice-architectural solutions could also be pursued. From a behavioural policy perspective, brown infrastructure shapes human preferences, attitudes, and beliefs, and we could exacerbate some effects of brown sludge. Tackling brown infrastructure is not a matter of choice architecture (though behavioural science may play a role), but more often can be about assessing and expanding an individual's choice set so they can pursue their own goals.

Conclusion

Brown sludge contributes to the literature on behavioural climate policy by extending explanations for why people fail to pursue green behaviours, despite wanting to (Carlsson et al., Reference Carlsson, Gravert, Johansson-Stenman and Kurz2021; van der Linden et al., Reference Van Der Linden, Pearson and Van Boven2021; Gravert and Shreedhar, Reference Gravert and Shreedhar2022). We argue several aspects of common barriers to green behaviour can be explained through brown sludge in terms of added transaction costs, such as search costs, time costs, and uncertainty costs.

This article also contributes to a growing body of literature which is critical in questioning the limits of behavioural science to affect substantial behavioural change (Loewenstein and Chater, Reference Loewenstein and Chater2017; Nisa et al., Reference Nisa, Bélanger, Schumpe and Faller2019; Chater and Loewenstein, Reference Chater and Loewenstein2022; Mills and Whittle, Reference Mills and Whittle2023). While brown sludge can explain some barriers to green behaviour and points to some behavioural interventions to encourage pro-environmental behaviours, brown sludge is also limited as an explanation of some barriers. To account for this limitation, we also reflect on brown infrastructure, which describes barriers to green behaviours that do not arise from choice architecture.

References

Ambec, S. and Ehlers, L. (2016), ‘Regulating via the polluter-pays principle’, The Economic Journal, 126: 884906.CrossRefGoogle Scholar
Andersson, J. J. (2019), ‘Carbon taxes and CO2 emissions: Sweden as a case study’, American Economic Journal: Economic Policy, 11(4): 130.Google Scholar
Attari, S. Z., DeKay, M. L., Davidson, C. I. and De Bruin, W. B. (2010), ‘Public perceptions of energy consumption and savings’, Proceedings of the National Academy of Sciences, 107: 1605416059.CrossRefGoogle ScholarPubMed
Baekgaard, M. and Tankink, T. (2022), ‘Administrative burden: untangling a bowl of conceptual spaghetti’, Perspectives on Public Management and Governance, 5(1): 1621.CrossRefGoogle Scholar
Barr, S. (2006), ‘Environmental action in the home: investigating the ‘value-action’ gap’, Geography, 91(1): 4354.CrossRefGoogle Scholar
Berry, P., Boons, F., Doherty, B., Green, B., Hill, A., MacInnes, N., McGonigle, D., McQuatters-Gollop, A., Moller, S., Munoz, M. and Oliver, T. (2022), ‘Integrating a systems approach into Defra’. https://www.gov.uk/government/publications/integrating-a-systems-approach-into-defra/integrating-a-systems-approach-into-defra [02 June 2023].Google Scholar
Blake, J. (1999), ‘Overcoming the ‘value-action gap’ in environmental policy: tensions between national policy and local experience’, Local Environment, 4: 257278.CrossRefGoogle Scholar
Blanken, I., van de Ven, N. and Zeelenberg, M. (2015), ‘A meta-analytic review of moral licensing’, Personality and Social Psychology Bulletin, 41: 540558.CrossRefGoogle ScholarPubMed
Brécard, D. (2014), ‘Consumer confusion over the profusion of eco-labels: lessons from a double differentiation model’, Resource and Energy Economics, 37: 6484.CrossRefGoogle Scholar
Carattini, S., Carvalho, M. and Fankhauser, S. (2018), ‘Overcoming public resistance to carbon taxes’, Wiley Interdisciplinary Reviews: Climate Change, 9: e.531.Google ScholarPubMed
Carlsson, F., Gravert, C., Johansson-Stenman, O. and Kurz, V. (2021), ‘The use of green nudges as an environmental policy instrument’, Review of Environmental Economics and Policy, 15: 216237.CrossRefGoogle Scholar
Chater, N. and Loewenstein, G. (2022), ‘The i-Frame and the s-Frame: how focusing on the individual-level solutions has led behavioural public policy astray’, Behavioral and Brain Sciences. doi:10.1017/S0140525(22002023.Google ScholarPubMed
Chen, Y. and Chang, C. (2013), ‘Greenwash and green trust: the mediation effects of green consumer confusion and green perceived risk’, Journal of Business Ethics, 114: 489500.CrossRefGoogle Scholar
Christensen, J., Aarøe, L., Baekgaard, M., Herd, P. and Moynihan, D. P. (2019), ‘Human capital and administrative burden: the role of cognitive resources in citizen-state interactions’, Public Administration Review, 80(1): 127136.CrossRefGoogle Scholar
Clark, C. D. and Russell, C. S. (2005), ‘Public information provision as a tool’, Environment, Information and Consumer Behaviour, 6: 111.Google Scholar
Dana, J., Weber, R. A. and Kuang, J. X. (2007), ‘Exploiting moral wiggle room: experiments demonstrating an illusory preference for fairness’, Economic Theory, 33: 6780.CrossRefGoogle Scholar
De Ridder, D., Kroese, F. and van Gestel, L. (2022), ‘Nudgeability: mapping conditions of susceptibility to nudge influence’, Perspectives on Psychological Science, 17(2): 346459.CrossRefGoogle ScholarPubMed
De Vries, G., Rietkerk, M. and Kooger, R. (2020), ‘The hassle factor as a psychological barrier to a green home’, Journal of Consumer Policy, 43: 345352.CrossRefGoogle Scholar
Delmas, M. A. and Burbano, V. C. (2011), ‘The drivers of greenwashing’, California Management Review, 54: 6487.CrossRefGoogle Scholar
Delmas, M. A. and Lessem, N. (2017), ‘Eco-premium or eco-penalty? Eco-labels and quality in the organic wine market’, Business and Society, 56: 318356.CrossRefGoogle Scholar
Devaney, L., Torney, D., Brereton, P. and Coleman, M. (2020), ‘Ireland's citizens’ assembly on climate change: lessons for deliberative public engagement and communication’, Environmental Communication, 14(2): 141146.CrossRefGoogle Scholar
Doğru, O. C., Webb, T. L. and Norman, P. (2021), ‘What is the best way to promote cycling? A systematic review and meta-analysis’, Transportation Research Part F: Traffic Psychology and Behaviour, 81: 144157.CrossRefGoogle Scholar
Environmental Audit Committee (2020), Electronic Waste and the Circular Economy. House of Commons Environmental Audit Committee.Google Scholar
Fischhoff, B. (2021), ‘Making behavioural science integral to climate science and action’, Behavioural Public Policy, 5(4): 439453.CrossRefGoogle Scholar
Fuller, C. G. (2020), ‘Uncertainty, insecurity, individual relative autonomy and the emancipatory potential of Galbraithian economics’, Cambridge Journal of Economics, 44: 229246.Google Scholar
Galbraith, J. K. (1977), The Affluent Society. UK: Pelican Penguin.Google Scholar
Geiger, J. L., Steg, L., van der Werff, E. and Ünal, A. B. (2019), ‘A meta-analysis of factors related to recycling’, Journal of Environmental Psychology, 64: 7897.CrossRefGoogle Scholar
Global Witness (2022), Enemies of the State? Global Witness. https://www.globalwitness.org/en/campaigns/environmental-activists/enemies-state/ [04 November 2022].Google Scholar
Goldberg, M. H., van der Linden, S., Ballew, M. T., Rosenthal, S. A., Gustafson, A. and Leiserowitz, A. (2019), ‘The experience of consensus: video as an effective medium to communicate scientific agreement on climate change’, Science Communication, 41: 659673.CrossRefGoogle Scholar
Gravert, C. and Collentine, L. O. (2021), ‘When nudges aren't enough: norms, incentives and habit formation in public transport usage’, Journal of Economic Behavior and Organization, 190: 114.CrossRefGoogle Scholar
Gravert, C. and Shreedhar, G. (2022), ‘Effective carbon taxes need green nudges’, Nature Climate Change, 12. doi:10.1038/s41558-022-01515-1.Google Scholar
Grieder, M., Kistler, D. and Schmitz, J. (2022), ‘How Sludge Impairs the Effectiveness of Policy Programmes: A Field Experiment with SMEs’. National Research Programme Working Paper No. 10/2023. https://nfp73.ch/download/77/230511_SNF_NFP73_PB_Schmitz_EN.pdf?inline=true [02 June 2023].Google Scholar
Grimmer, M. and Miles, M. P. (2017), ‘With the best of intentions: a large sample test of the intention-behaviour gap in pro-environmental consumer behaviour’, International Journal of Consumer Studies, 41: 210.CrossRefGoogle Scholar
Herd, P., DeLeire, T., Harvey, H. and Moynihan, D. P. (2013), ‘Shifting administrative burden to the state: the case of medicaid take-up’, Public Administration Review, 73(1): 6981.CrossRefGoogle Scholar
Hickel, J. and Kallis, G. (2020), ‘Is green growth possible?’, New Political Economy, 25: 469486.CrossRefGoogle Scholar
Hortal, A. and Contreras, L. E. S. (2023), ‘Behavioral public policy and well-being: towards a normative demarcation of nudges and sludges’, Review of Behavioural Economics, 10(2). doi:10.1561/105/00000168.CrossRefGoogle Scholar
In, S. Y. and Schumacher, K. (2021), Carbonwashing: A New Type of Carbon Data-Related ESG Greenwashng. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3901278 [02 June 2023].Google Scholar
Ingrams, S. (2022), Green Homes Grant Explained. Which? https://www.which.co.uk/reviews/home-grants/article/green-homes-grant-aX6Py8n2pzQB [01 April 2023].Google Scholar
IPCC (2022), Climate Change 2022: Impacts, Adaption and Vulnerability. IPCC. https://www.ipcc.ch/report/ar6/wg2/ [23 November 2022].Google Scholar
Johnson, R. J., Ndebele, T. and Newburn, D. A. (2022), ‘Modelling transaction costs in household adoption of landscape conservation practices’, American Journal of Agricultural Economics, 105(1): 341367.CrossRefGoogle Scholar
Kollmuss, A. and Agyeman, J. (2022), ‘Mind the gap: why do people act environmentally and what are the barriers to pro-environmental behavior?’, Environmental Education Research, 8: 239260.CrossRefGoogle Scholar
Kristal, A. S. and Whillians, A. V. (2020), ‘What we can learn from five naturalistic field experiments that failed to shift commuter behavior’, Nature Human Behaviour, 4: 169176.CrossRefGoogle Scholar
Lades, L. K., Clinch, J. P. and Kelly, J. A. (2021), ‘Maybe tomorrow: how burdens and biases impede energy-efficiency investments’, Energy Research and Social Science, 78: 102154.CrossRefGoogle Scholar
Lambe, F., Ran, Y., Kwamboka, E., Holmlid, S., Lycke, K., Ringström, S., Annebäck, J., Ghosh, E., O'Conner, M. and Bailis, R. (2020), ‘Opening the black pot: A service design-driven approach to understanding the use of cleaner cookstoves in peri-urban Kenya’, Energy Research and Social Science, 70: 101754.CrossRefGoogle Scholar
Langer, A. and Eisend, M. (2007), ‘The impact of eco-labels on consumers: less information, more confusion?’, European Advances in Consumer Research, 8: 338339.Google Scholar
Larcom, S., Rauch, F. and Willems, T. (2017), ‘The benefits of forced experimentation: Striking evidence from the London underground network’, The Quarterly Journal of Economics, 132(4): 20192055.CrossRefGoogle Scholar
Lever, R. (2020), The Gas Industry is Paying Instagram Influencers to Gush Over Gas Stoves. Mother Jones. https://www.motherjones.com/environment/2020/06/gas-industry-influencers-stoves/ [28 December 2022].Google Scholar
Loewenstein, G. and Chater, N. (2017), ‘Putting nudges in perspective’, Behavioural Public Policy, 1(1): 2653.CrossRefGoogle Scholar
Maat, K. and de Vries, P. (2006), ‘The influence of the residential environment on green space travel: testing the compensation hypothesis’, Environmental Planning A, 38: 21112127.CrossRefGoogle Scholar
Madsen, J. K., Mikkelsen, K. S. and Moynihan, D. P. (2020), ‘Burdens, sludge, ordeals, red tape, oh my! A user's guide to the study of frictions’, Public Administration, 100(2): 375393.CrossRefGoogle Scholar
Mazzucato, M. and Penna, C. C. R. (2016), ‘Beyond market failures: the market creating and shaping roles of state investment banks’, Journal of Economic Policy Reform, 19: 305326.CrossRefGoogle Scholar
McCright, A. M. and Dunlap, R. E. (2011), ‘The politicization of climate change and polarization in the American public's views of global warming, 2001–2010’, The Sociological Quarterly, 52: 155194.CrossRefGoogle Scholar
Mercue, J. F., Pollitt, H., Bassi, A. M., Viñuales, J. E. and Edwards, N. R. (2016), ‘Modelling complex systems of heterogeneous agents to better design sustainability transitions policy’, Global Environmental Change, 37: 102115.CrossRefGoogle Scholar
Middleton, J. (2011), ‘“I'm on autopilot, I just follow the route”: exploring the habits, routines, and decision-making practices of everyday urban mobilities’, Environment and Planning A, 43(12): 28572877.CrossRefGoogle Scholar
Mills, S. (2023), ‘Nudge/sludge symmetry: on the relationship between nudge and sludge and the resulting ontological, normative and transparency implications’, Behavioural Public Policy, 7(2): 309332.CrossRefGoogle Scholar
Mills, S. and Whittle, R. (2023), ‘Seeing the nudge from the trees: the 4S framework for evaluating nudges’, Public Administration. doi: 10.1111/padm.12941.Google Scholar
Momsen, K. and Ohndorf, M. (2020), ‘When do people exploit moral wiggle room? An experimental analysis of information avoidance in a market setup’, Ecological Economics, 169: 106479.CrossRefGoogle Scholar
Momsen, K. and Ohndorf, M. (2022), ‘Information avoidance, selective exposure, and fake (?) news: Theory and experimental evidence on green consumption’, Journal of Economic Psychology, 88: 102457.CrossRefGoogle Scholar
Moynihan, D., Herd, P. and Harvey, H. (2015), ‘Administrative burden: learning, psychological, and compliance costs in citizen-state interactions’, Journal of Public Administration Research and Theory, 25(1): 4369.CrossRefGoogle Scholar
Muehlegger, E. J. and Rapson, D. S. (2023), ‘Correcting estimates of electric vehicle emissions abatement: implications for climate policy’, Journal of the Association of Environmental and Resource Economists, 10: 263282.CrossRefGoogle Scholar
Neoh, J. G., Chipulu, M. and Marshall, A. (2017), ‘What encourages people to carpool? An evaluation of factors with meta-analysis’, Transportation, 44: 423447. https://doi.org/10.1007/s11116-015-9661-7.CrossRefGoogle Scholar
Neuvonen, M., Sievänen, T., Tönnes, S. and Koskela, T. (2007), ‘Access to green areas and the frequency of visits – a case study in Helsinki’, Urban Forestry & Urban Greening, 6: 235247.CrossRefGoogle Scholar
Newall, P. W. S. (2022), ‘What is sludge? Comparing Sunstein's definition to others’, Behavioural Public Policy. doi:10.1017/bpp.2022.12.Google Scholar
Nielsen, K. S., Nicholas, K. A., Creutzig, F., Dietz, T. and Stern, P. C. (2021), ‘The role of high-socioeconomic-status people in locking in or rapidly reducing energy-driven greenhouse gas emissions’, Nature Energy, 6: 10111016.CrossRefGoogle Scholar
Nisa, C. F., Bélanger, J. J., Schumpe, B. M. and Faller, D. G. (2019), ‘Meta-analysis of randomised controlled trials testing behavioural interventions to promote household action on climate change’, Nature Communications, 10: 113.CrossRefGoogle ScholarPubMed
Norton, P. D. (2011), Fighting Traffic: The Dawn of the Motor Age in the American City. USA: MIT Press.Google Scholar
Oreskes, N. and Conway, E. M. (2010a), Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues From Tobacco Smoke to Global Warming. UK: Bloomsbury Press.Google Scholar
Oreskes, N. and Conway, E. M. (2010b), ‘Defeating the merchants of doubt’, Nature, 465: 686687.CrossRefGoogle ScholarPubMed
Pennycook, G. and Rand, D. G. (2019), ‘Lazy, not biased: susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning’, Cognition, 188: 3950.CrossRefGoogle Scholar
Pettifor, A. (2020), The Case for the Green New Deal. UK: Verso Books.Google Scholar
Potts, J. (2000), The New Evolutionary Microeconomics. UK: Edgar Elgar.Google Scholar
Povitkina, M., Jagers, S. C., Matti, S. and Martinsson, J. (2021), ‘Why are carbon taxes unfair? Disentangling public perceptions of fairness’, Global Environmental Change, 70: 102356.CrossRefGoogle Scholar
Ramos, G., Hynes, W., Müller, J. M. and Lees, M. (2019), Systemic Thinking for Policymaking: the Potential of Systems Analysis for Addressing Global Policy Challenges in the 21st Century. OECD. https://www.oecd.org/naec/averting-systemic-collapse/SG-NAEC [06 November 2020].Google Scholar
Readfearn, G. (2020), The Australian Says it Accepts Climate Science, so Why Does it Give a Platform to “Outright Falsehoods”? The Guardian. https://www.theguardian.com/media/2020/jan/15/the-australian-says-it-accepts-climate-science-so-why-does-it-give-a-platform-to-outright-falsehoods [02 June 2023].Google Scholar
Robbins, T. W. and Costa, R. M. (2017), ‘Habits’, Current Biology, 27(22): 12001206.CrossRefGoogle ScholarPubMed
Rosenbloom, D., Markard, J., Geels, F. W. and Fuenfschilling, L. (2020), ‘Why carbon pricing is not sufficient to mitigate climate change – and how “sustainability transition policy” can help’, Proceedings of the National Academy of Sciences, 117: 86648668.CrossRefGoogle Scholar
Roy, D., Berry, E. and Dempster, M. (2022), ‘“If it is not made easy for me, I will just not bother”. A qualitative exploration of the barriers and facilitators to recycling plastics’, PLoS One, 17: 0267284.CrossRefGoogle Scholar
Seo, J. Y. and Scammon, D. L. (2017), ‘Do green packages lead to misperceptions? The influence of package colors on consumers’ perceptions of brands with environmental claims’, Marketing Letters, 28: 357369.CrossRefGoogle Scholar
Setzer, J. and Vanhala, L. C. (2019), ‘Climate change litigation: a review of research on courts and litigants in climate governance’, Wiley Interdisciplinary Reviews: Climate Change, 10: 580.Google Scholar
Shahab, S. and Lades, L. K. (2021), ‘Sludge and transaction costs’, Behavioural Public Policy. doi:10.1017/bpp.2021.12.Google Scholar
Shreedhar, G. (2023), When Green Nudges (don't) Work’ in ‘Behavioural Economics and the Environment. UK: Routledge.Google Scholar
Shreedhar, G. and Mourato, S. (2020), ‘Linking human destruction of nature to COVID-19 increases support for wildlife conservation policies’, Environmental and Resource Economics, 76(4): 963999.CrossRefGoogle ScholarPubMed
Shue, H. (2022), ‘Unseen urgency: delay as the new denial’, WIREs Climate Change, 809.Google Scholar
Soga, M., Evans, M. J., Yamanoi, T., Fukano, Y., Tsuchiya, K., Koyanagi, T. F. and Kanai, T. (2020), ‘How can we mitigate against increasing biophobia among children during the extinction of experience?’, Biological Conservation, 242: 108420.CrossRefGoogle Scholar
Sunstein, C. R. (2018), ‘Sludge and ordeals’, Duke Law Journal, 68: 18431883.Google Scholar
Sunstein, C. R. (2021), ‘Sludge: What Stops us From Getting Things Done and What to do About it. USA: MIT Press.CrossRefGoogle Scholar
Sunstein, C. R. (2022), ‘Sludge audits’, Behavioural Public Policy, 6(4): 654673.CrossRefGoogle Scholar
Sunstein, C. R. and Gosset, J. L. (2020), ‘Optimal sludge? The price of program integrity’, Duke Law Journal Online, 70: 74.Google Scholar
Szabo, S. and Webster, J. (2021), ‘Perceived greenwashing: the effects of green marketing on environmental and product perceptions’, Journal of Business Ethics, 171: 719739.CrossRefGoogle Scholar
Taufique, K. M. R., Nielsen, K. S., Dietz, T., Shwom, R., Stern, P. C. and Vandenbergh, M. P. (2022), ‘Revisiting the promise of carbon labelling’, Nature Climate Change, 12: 132140.CrossRefGoogle Scholar
Thaler, R. H. (2018), ‘Nudge, not sludge’, Science, 361: 431.CrossRefGoogle Scholar
Thaler, R. H. and Sunstein, C. R. (2008), Nudge: Improving Decisions About Health, Wealth, and Happiness. UK: Penguin Books.Google Scholar
Thaler, R. H. and Sunstein, C. R. (2021), ‘Nudge: The Final Edition’. UK: Penguin Books.Google Scholar
Treen, K. M. d., Williams, H. T. and O'Neill, S. J. (2020), ‘Online misinformation about climate change’, Wiley Interdisciplinary Reviews: Climate Change, 11: 665.Google Scholar
Tvinnereim, E. and Mehling, M. (2018), ‘Carbon pricing and deep decarbonisation’, Energy Policy, 121: 185189.CrossRefGoogle Scholar
Van Der Linden, S. (2017), ‘Beating the hell out of fake news’, Ethical Record: Proceedings of the Conway Hall Ethical Society, 122(6): 47.Google Scholar
Van Der Linden, S., Leiserowitz, A. A., Feinberg, G. D. and Maibach, E. W. (2015), ‘The scientific consensus on climate change as a gateway belief: experimental evidence’, PLoS One, 10: 0118489.CrossRefGoogle ScholarPubMed
Van Der Linden, S., Pearson, A. R. and Van Boven, L. (2021), ‘Behavioural climate policy’, Behavioural Public Policy, 5: 430438.CrossRefGoogle Scholar
Van't Veld, K. (2020), ‘Eco-labels: modeling the consumer side’, Annual Review of Resource Economics, 12: 187207.CrossRefGoogle Scholar
Verplanken, B. and Whitmarsh, L. (2021), ‘Habit and climate change’, Current Opinion in Behavioral Sciences, 42: 4246.CrossRefGoogle Scholar
Wells, R., Howarth, C. and Brand-Correa, L. I. (2021), ‘Are citizen juries and assemblies on climate change driving democratic climate policymaking? An exploration of two case studies in the UK’, Climatic Change, 168: 122.CrossRefGoogle ScholarPubMed
Wood, W. and Rünger, D. (2016), ‘Psychology of habit’, Annual Review of Psychology, 67(1): 289314.CrossRefGoogle ScholarPubMed
Wood, W., Tam, L. and Witt, M. G. (2005), ‘Changing circumstances, disrupting habits’, Journal of Personality and Social Psychology, 88(6): 918933.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Brown sludge: mechanisms, behavioural effects, and mitigation measures