systematic literature review on behavioral barriers of climate change mitigation in households

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Systematic literature review on behavioral barriers of climate change mitigation in households.

1. Introduction

2. research background, 3.1. research approach, 3.2. collecting, preparing, and analyzing data, 4.1. behavior change models, 4.2. barriers of households behavior change in climate change mitigation, 4.3. the newest interventions to deal with behavioral barriers: boost and nudge, 5. discussion.

• Demonstrate if and under what circumstances a larger effect of informing about health co-benefits can be achieved. Potential approaches may entail (a) changing the format or (b) the context in which the health information is presented [ 42 ].
• Estimate the impact of providing information on direct health co-benefits versus public health co-benefits on citizens’ willingness to implement mitigation actions. This could be done by providing one group of households with information on direct health co-benefits, and a second group with information on public health co-benefits of the same mitigation actions [ 42 ].
• Include actions of personal preferences or beliefs regarding health. It could be the case, for example, that the present results were driven mainly by participants who have comparatively high preferences for healthy life choices, particularly since a positive relationship between health behaviors and climate mitigation behavior is appreciated. Such research could further elucidate the motivational factors that drive citizens’ willingness to implement mitigation actions [ 42 ].
• Link up climate policies with direct health effects, which can support GHG mitigation efforts at two levels: Firstly, by accruing to the individual citizen, this can lead to small but tangible results on households’ willingness to adopt suggested climate-friendly consumption changes. Secondly, potential health co-benefits may increase public acceptance of regulation of private consumption to reduce the household carbon footprint [ 42 ].

6. Conclusions and Future Research Areas

• Provision of information;
• Economic instruments;
• Regulative instruments;
• Communication;
• Direct governmental expenditures;
• Procedural instruments.

Author Contributions

Conflicts of interest.

• Osberghaus, D. Prospect theory, mitigation and adaptation to climate change. J. Risk Res. 2017 , 20 , 909–930. [ Google Scholar ] [ CrossRef ]
• van den Bergh, J.C.J.M.; Ferrer-I-Carbonell, A.; Munda, G. Alternative models of individual behaviour and implications for environmental policy. Ecol. Econ. 2000 , 32 , 43–61. [ Google Scholar ] [ CrossRef ]
• Gowdy, J.M. Behavioral economics and climate change policy. J. Econ. Behav. Organ. 2008 , 68 , 632–644. [ Google Scholar ] [ CrossRef ]
• Dubois, G.; Sovacool, B.; Aall, C.; Nilsson, M.; Barbier, C.; Herrmann, A.; Bruyère, S.; Andersson, C.; Sköld, B.; Nadaud, F.; et al. It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures. Energy Res. Soc. Sci. 2019 , 52 , 144–158. [ Google Scholar ] [ CrossRef ]
• Pollitt, M.G.; Shaorshadze, I.; Fouquet, R. The role of behavioural economics in energy and climate policy. In Handbook on Energy and Climate Change ; Edward Elgar Publishing: Cheltenham, UK, 2013; pp. 523–546. [ Google Scholar ]
• Poortinga, W.; Steg, L.; Vlek, C.; Wiersma, G. Household preferences for energy-saving measures: A conjoint analysis. J. Econ. Psychol. 2003 , 24 , 49–64. [ Google Scholar ] [ CrossRef ]
• Ramos, A.; Gago, A.; Labandeira, X.; Llamas, P.L. The Role of Information for Energy Efficiency in the Residential Sector. Electron. J. 2015 , 52 , 17–29. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Tranfield, D.; Denyer, D.; Smart, P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag. 2003 , 14 , 207–222. [ Google Scholar ] [ CrossRef ]
• Creutzig, F.; Fernandez, B.; Haberl, H.; Khosla, R.; Mulugetta, Y.; Seto, K.C. Beyond Technology: Demand-Side Solutions for Climate Change Mitigation. Annu. Rev. Environ. Resour. 2016 , 41 , 173–198. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Vringer, K.; Blok, K. The direct and indirect energy requirements of households in The Netherlands. Energy Policy 1995 , 23 , 893–910. [ Google Scholar ] [ CrossRef ]
• Jones, R.V.; Lomas, K.J. Determinants of high electrical energy demand in UK homes: Appliance ownership and use. Energy Build. 2015 , 101 , 24–34. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ivanova, D.; Stadler, K.; Steen-Olsen, K.; Wood, R.; Vita, G.; Tukker, A.; Hertwich, E. Environmental Impact Assessment of Household Consumption. J. Ind. Ecol. 2015 , 20 , 526–536. [ Google Scholar ] [ CrossRef ]
• Gintis, H. A framework for the unification of the behavioral sciences. Behav. Brain Sci. 2007 , 30 , 1–16. [ Google Scholar ] [ CrossRef ]
• Camerer, C.F.; Loewenstein, G.; Rabin, M. Advances in Behavioral Economics ; Princeton University Press: Princeton, NJ, USA, 2004; ISBN 9780691116822. [ Google Scholar ]
• Brekke, K.A.; Johansson-Stenman, O. The behavioural economics of climate change. Oxf. Rev. Econ. Policy 2008 , 24 , 280–297. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Evensky, J. Adam Smith’s theory of moral sentiments: On morals and why they matter to a liberal society of free people and free markets. J. Econ. Perspect. 2005 , 19 , 109–130. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Jakučionytė-Skodienė, M.; Dagiliūtė, R.; Liobikienė, G. Do general pro-environmental behaviour, attitude, and knowledge contribute to energy savings and climate change mitigation in the residential sector? Energy 2020 , 193 , 116784. [ Google Scholar ] [ CrossRef ]
• Lacroix, K. Comparing the relative mitigation potential of individual pro-environmental behaviors. J. Clean. Prod. 2018 , 195 , 1398–1407. [ Google Scholar ] [ CrossRef ]
• Nahar, D.; Verma, P. Shaping public behavior and green consciousness in India through the ‘Yo!Green’ Carbon Footprint Calculator. Carbon Manag. 2018 , 9 , 127–144. [ Google Scholar ] [ CrossRef ]
• Okaka, F.O.; Odhiambo, B.D.O. Urban residents’ awareness of climate change and their autonomous adaptive behaviour and mitigation measures in the coastal city of Mombasa, Kenya. South. Afr. Geogr. J. 2018 , 100 , 1–16. [ Google Scholar ] [ CrossRef ]
• Volochovic, A.; Simanaviciene, Z.; Štreimikienė, D. GHG Emission Reduction by Behavioral Changes in Lithuanian Households. Eng. Econ. 2012 , 23 , 242–249. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Camerer, C.; Issacharoff, S.; Loewenstein, G.; O’Donoghue, T.; Rabin, M. Regulation for Conservatives: Behavioral Economics and the Case for “Asymmetric Paternalism”. Univ. Pa. Law Rev. 2003 , 151 , 1211. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Van De Ven, D.-J.; Gonzalez-Eguino, M.; Arto, I. The potential of behavioural change for climate change mitigation: A case study for the European Union. Mitig. Adapt. Strat. Glob. Chang. 2017 , 23 , 853–886. [ Google Scholar ] [ CrossRef ]
• Williamson, K.; Satre-Meloy, A.; Velasco, K.; Green, K. Climate Change Needs Behavior Change: Making the Case for Behavioral Solutions to Reduce Global Warming ; Rare: Arlington, VA, USA, 2018. [ Google Scholar ]
• Adger, W.N.; Arnell, N.W.; Tompkins, E.L. Successful adaptation to climate change across scales. Glob. Environ. Chang. 2005 , 15 , 77–86. [ Google Scholar ] [ CrossRef ]
• Jaeger, C.; Kasemir, B.; Stoll-Kleemann, S.; Schibli, D.; Dahinden, U. Climate change and the voice of the public. Integr. Assess. 2000 , 1 , 339–349. [ Google Scholar ] [ CrossRef ]
• Van Sluisveld, M.A.; Martínez, S.H.; Daioglou, V.; Van Vuuren, D. Exploring the implications of lifestyle change in 2 °C mitigation scenarios using the IMAGE integrated assessment model. Technol. Soc. Chang. 2016 , 102 , 309–319. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Faber, J.; Schroten, A.; Bles, M.; Sevenster, M.; Markowska, A.; Smit, M.; Rohde, C.; Dutschke, E.; Kohler, J.; Gigli, M.; et al. Behavioural Climate Change Mitigation Options and their Appropriate Inclusion in Quantitative Longer Term Policy Scenarios ; European Commission, DG Climate Action, contract number 070307/2010/576075/SER/A4, Main Report; CE Delft: Delft, The Netherlands, 2012. [ Google Scholar ]
• Mohammad, A.; Shrestha, P.; Kumar, S. Urban residential energy use in Kandahar, Afghanistan. Cities 2013 , 32 , 135–142. [ Google Scholar ] [ CrossRef ]
• Sivak, M. Where to live in the United States: Combined energy demand for heating and cooling in the 50 largest metropolitan areas. Cities 2008 , 25 , 396–398. [ Google Scholar ] [ CrossRef ]
• Abrahamse, W.; Steg, L.; Vlek, C.; Rothengatter, T. A review of intervention studies aimed at household energy conservation. J. Environ. Psychol. 2005 , 25 , 273–291. [ Google Scholar ] [ CrossRef ]
• Dietz, T.; Gardner, G.T.; Gilligan, J.M.; Stern, P.C.; Vandenbergh, M.P. Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions. Proc. Natl. Acad. Sci. USA 2009 , 106 , 18452–18456. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Grottera, C.; La Rovere, E.L.; Wills, W.; Pereira, O.A. The role of lifestyle changes in low-emissions development strategies: An economy-wide assessment for Brazil. Clim. Policy 2020 , 20 , 217–233. [ Google Scholar ] [ CrossRef ]
• Allcott, H.; Mullainathan, S. Behavior and Energy Policy. Science 2010 , 327 , 1204–1205. [ Google Scholar ] [ CrossRef ]
• Berg, N.J.V.D.; Hof, A.F.; Akenji, L.; Edelenbosch, O.Y.; Van Sluisveld, M.A.; Timmer, V.J.; Van Vuuren, D. Improved modelling of lifestyle changes in Integrated Assessment Models: Cross-disciplinary insights from methodologies and theories. Energy Strat. Rev. 2019 , 26 , 100420. [ Google Scholar ] [ CrossRef ]
• Nauges, C.; Wheeler, S. The Complex Relationship Between Households’ Climate Change Concerns and Their Water and Energy Mitigation Behaviour. Ecol. Econ. 2017 , 141 , 87–94. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Michie, S.; Van Stralen, M.M.; West, R. The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implement. Sci. 2011 , 6 , 42. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Costello, A.; Abbas, M.; Allen, A.; Ball, S.; Bellamy, R.; Friel, S.; Groce, N.; Johnson, A.; Kett, M.; Lee, M.; et al. Managing the health effects of climate change. Lancet 2009 , 373 , 1693–1733. [ Google Scholar ] [ CrossRef ]
• Myers, T.; Nisbet, M.C.; Maibach, E.W.; Leiserowitz, A.A. A public health frame arouses hopeful emotions about climate change. Clim. Chang. 2012 , 113 , 1105–1112. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Sauerborn, R.; Kjellstrom, T.; Nilsson, M. Invited Editorial: Health as a crucial driver for climate policy. Glob. Heal. Action 2009 , 2 , 2. [ Google Scholar ] [ CrossRef ]
• Nisbet, E.K.L.; Gick, M.L. Can health psychology help the planet? Applying theory and models of health behaviour to environmental actions. Can. Psychol. Psychol. Can. 2008 , 49 , 296–303. [ Google Scholar ] [ CrossRef ]
• Amelung, D.; Fischer, H.; Herrmann, A.; Aall, C.; Louis, V.R.; Becher, H.; Wilkinson, P.; Sauerborn, R. Human health as a motivator for climate change mitigation: Results from four European high-income countries. Glob. Environ. Chang. 2019 , 57 , 101918. [ Google Scholar ] [ CrossRef ]
• Stephenson, J.; Barton, B.; Carrington, G.; Gnoth, D.; Lawson, R.; Thorsnes, P. Energy cultures: A framework for understanding energy behaviours. Energy Policy 2010 , 38 , 6120–6129. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ornaghi, C.; Costanza, E.; Kittley-Davies, J.; Bourikas, L.; Aragon, V.; James, P. The effect of behavioural interventions on energy conservation in naturally ventilated offices. Energy Econ. 2018 , 74 , 582–591. [ Google Scholar ] [ CrossRef ]
• Stoll-Kleemann, S.; O’Riordan, T.; Jaeger, C.C. The psychology of denial concerning climate mitigation measures: Evidence from Swiss focus groups. Glob. Environ. Chang. 2001 , 11 , 107–117. [ Google Scholar ] [ CrossRef ]
• González-Hernández, D.L.; Meijles, E.; Vanclay, F. Household Barriers to Climate Change Action: Perspectives from Nuevo Leon, Mexico. Sustainability 2019 , 11 , 4178. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Berman, H.; Shwom, R.; Cuite, C. Becoming FEW Conscious: A Conceptual Typology of Household Behavior Change Interventions Targeting the Food-Energy-Water (FEW) Nexus. Sustainability 2019 , 11 , 5034. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Werfel, S. Household behaviour crowds out support for climate change policy when sufficient progress is perceived. Nat. Clim. Chang. 2017 , 7 , 512–515. [ Google Scholar ] [ CrossRef ]
• Fishbein, M.; Ajzen, I. Predicting and Changing Behaviour: The Reasoned Action Approach ; Psychology Press: East Sussex, UK, 2010; ISBN 0-203-83802-5. [ Google Scholar ]
• Bryan, E.; Deressa, T.T.; Gbetibouo, G.A.; Ringler, C. Adaptation to climate change in Ethiopia and South Africa: Options and constraints. Environ. Sci. Policy 2009 , 12 , 413–426. [ Google Scholar ] [ CrossRef ]
• Schleich, J.; Gassmann, X.; Faure, C.; Meissner, T. Making the implicit explicit: A look inside the implicit discount rate. Energy Policy 2016 , 97 , 321–331. [ Google Scholar ] [ CrossRef ]
• Marteau, T.M. Towards environmentally sustainable human behaviour: Targeting non-conscious and conscious processes for effective and acceptable policies. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2017 , 375 , 20160371. [ Google Scholar ] [ CrossRef ]
• Cheung, L.T.O.; Chow, A.S.; Fok, L.; Yu, K.-M.; Chou, K.L. The effect of self-determined motivation on household energy consumption behaviour in a metropolitan area in southern China. Energy Effic. 2016 , 10 , 549–561. [ Google Scholar ] [ CrossRef ]
• Thaler, R.H.; Bernartzi, S. Save More Tomorrow: Using Behavorial Economics in Increase Employee Savings. J. Political Econ. 2004 , 112 , 164–187. [ Google Scholar ] [ CrossRef ]
• Agarwal, S.; Sing, T.F.; Yang, Y. The impact of transboundary haze pollution on household utilities consumption. Energy Econ. 2020 , 85 , 104591. [ Google Scholar ] [ CrossRef ]
• Ponce, P.; Alvarado, R.; Ponce, K.; Alvarado, R.; Granda, D.; Yaguana, K. Green returns of labor income and human capital: Empirical evidence of the environmental behavior of households in developing countries. Ecol. Econ. 2019 , 160 , 105–113. [ Google Scholar ] [ CrossRef ]
• Mee, K.J.; Instone, L.; Williams, M.; Palmer, J.; Vaughan, N. Renting Over Troubled Waters: An Urban Political Ecology of Rental Housing. Geogr. Res. 2014 , 52 , 365–376. [ Google Scholar ] [ CrossRef ]
• Instone, L.; Mee, K.J.; Palmer, J.; Williams, M.; Vaughan, N. Climate change adaptation in the rental sector. In National Climate Change Adaptation Research Facility ; Gold Coast: Boca Raton, FL, USA, 2013; ISBN 978-1-925039-11-5. [ Google Scholar ]
• Elrick-Barr, C.; Smith, T.; Preston, B.; Thomsen, D.C.; Baum, S. How are coastal households responding to climate change? Environ. Sci. Policy 2016 , 63 , 177–186. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hackett, B.; Lutzenhiser, L. Social structures and economic conduct: Interpreting variations in household energy consumption. Sociol. Forum 1991 , 6 , 449–470. [ Google Scholar ] [ CrossRef ]
• Malama, A.; Makashini, L.; Abanda, F.; Ng’Ombe, A.; Mudenda, P. A Comparative Analysis of Energy Usage and Energy Efficiency Behavior in Low- and High-Income Households: The Case of Kitwe, Zambia. Resources 2015 , 4 , 871–902. [ Google Scholar ] [ CrossRef ]
• Cattaneo, C. Internal and external barriers to energy efficiency: Which role for policy interventions? Energy Effic. 2019 , 12 , 1293–1311. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wiedenhofer, D.; Smetschka, B.; Akenji, L.; Jalas, M.; Haberl, H. Household time use, carbon footprints, and urban form: A review of the potential contributions of everyday living to the 1.5 °C climate target. Curr. Opin. Environ. Sustain. 2018 , 30 , 7–17. [ Google Scholar ] [ CrossRef ]
• Bertoldi, P.; Rezessy, S.; Oikonomou, V. Rewarding energy savings rather than energy efficiency: Exploring the concept of a feed-in tariff for energy savings. Energy Policy 2013 , 56 , 526–535. [ Google Scholar ] [ CrossRef ]
• Austin, D.; Dinan, T. Clearing the air: The costs and consequences of higher CAFE standards and increased gasoline taxes. J. Environ. Econ. Manag. 2005 , 50 , 562–582. [ Google Scholar ] [ CrossRef ]
• Sorrell, S.; Dimitropoulos, J.; Sommerville, M. Empirical estimates of the direct rebound effect: A review. Energy Policy 2009 , 37 , 1356–1371. [ Google Scholar ] [ CrossRef ]
• Alberini, A.; Gans, W.; Towe, C. Free riding, upsizing, and energy efficiency incentives in Maryland homes. Energy J. 2016 , 37 , 259–290. [ Google Scholar ] [ CrossRef ]
• Bertoldi, P. Are current policies. In ECEEE Summer Study Proceeding ; European Council for an Energy Efficient Economy: Stockholm, Sweeden, 2017. [ Google Scholar ]
• Wagner, K. Environmental preferences and consumer behavior. Econ. Lett. 2016 , 149 , 1–4. [ Google Scholar ] [ CrossRef ]
• Boomhower, J.; Davis, L.W. A credible approach for measuring inframarginal participation in energy efficiency programs. J. Public Econ. 2014 , 113 , 67–79. [ Google Scholar ] [ CrossRef ]
• Valkila, N.; Saari, A. Attitude–behaviour gap in energy issues: Case study of three different Finnish residential areas. Energy Sustain. Dev. 2013 , 17 , 24–34. [ Google Scholar ] [ CrossRef ]
• Steg, L. Promoting household energy conservation. Energy Policy 2008 , 36 , 4449–4453. [ Google Scholar ] [ CrossRef ]
• Kasemir, B.; Dahinden, U.; Åsa, G.S.; Schüle, R.; Tabara, D.; Jaeger, C.C. Citizens’ perspectives on climate change and energy use. Glob. Environ. Chang. 2000 , 10 , 169–184. [ Google Scholar ] [ CrossRef ]
• Lo, A.Y. The role of social norms in climate adaptation: Mediating risk perception and flood insurance purchase. Glob. Environ. Chang. 2013 , 23 , 1249–1257. [ Google Scholar ] [ CrossRef ]
• Nolan, J.M.; Schultz, P.W.; Cialdini, R.B.; Goldstein, N.J.; Griskevicius, V. Normative Social Influence is Underdetected. Pers. Soc. Psychol. Bull. 2008 , 34 , 913–923. [ Google Scholar ] [ CrossRef ]
• Schultz, P.W.; Nolan, J.M.; Cialdini, R.B.; Goldstein, N.J.; Griskevicius, V. The Constructive, Destructive, and Reconstructive Power of Social Norms. Psychol. Sci. 2007 , 18 , 429–434. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Girod, B.; Van Vuuren, D.; Hertwich, E. Climate policy through changing consumption choices: Options and obstacles for reducing greenhouse gas emissions. Glob. Environ. Chang. 2014 , 25 , 5–15. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Boudet, H.; Ardoin, N.M.; Flora, J.; Armel, K.C.; Desai, M.; Robinson, T.N. Energy behaviours of northern California Girl Scouts and their families. Energy Policy 2014 , 73 , 439–449. [ Google Scholar ] [ CrossRef ]
• Thaler, R.; Sunstein, C. Nudge: Improving Decisions about Health, Wealth, and Happiness ; Yale University Press: London, UK, 2008; ISBN 978-0-300-12223-7. [ Google Scholar ]
• Grüne-Yanoff, T.; Hertwig, R. Nudge Versus Boost: How Coherent are Policy and Theory? Minds Mach. 2015 , 26 , 149–183. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hertwig, R.; Grüne-Yanoff, T. Nudging and Boosting: Steering or Empowering Good Decisions. Perspect. Psychol. Sci. 2017 , 12 , 973–986. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Ehrig, T.; Katsikopoulos, K.V.; Kuorikoski, J.; Pöyhönen, S.; Sunder, S. Limitations of Behaviorally Informed Policy Under Social Interaction. SSRN Electron. J. 2015 , 2 , 99–105. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mongin, P.; Cozic, M. Rethinking nudge: Not one but three concepts. Behav. Public Policy 2018 , 2 , 107–124. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Dellavalle, N.; Sareen, S. Nudging and boosting for equity? Towards a behavioural economics of energy justice. Energy Res. Soc. Sci. 2020 , 68 , 101589. [ Google Scholar ] [ CrossRef ]
• Raue, M.; Scholl, S.G. The Use of Heuristics in Decision-Making under Risk and Uncertainty. In Psychological Perspectives on Risk and Risk Analysis—Theory, Models and Applications ; Raue, M., Lermer, E., Streicher, B., Eds.; Springer: New York, NY, USA, 2018; ISBN 978-3-319-92478-6. [ Google Scholar ]
• Kolovos, K.G.; Kyriakopoulos, G.; Chalikias, M.S. Co-evaluation of basic woodfuel types used as alternative heating sources to existing energy network. J. Environ. Prot. Ecol. 2011 , 12 , 733–742. [ Google Scholar ]
• Zamparas, M.; Kyriakopoulos, G.L.; Kapsalis, V.C.; Drosos, M.; Kalavrouziotis, I.K. Application of novel composite materials as sediment capping agents: Column experiments and modelling. Desalin. Water Treat. 2019 , 170 , 111–118. [ Google Scholar ] [ CrossRef ]
• Grigoropoulos, C.; Doulos, L.T.; Zerefos, S.; Tsangrassoulis, A.; Bhusal, P. Estimating the benefits of increasing the recycling rate of lamps from the domestic sector: Methodology, opportunities and case study. Waste Manag. 2020 , 101 , 188–199. [ Google Scholar ] [ CrossRef ]
• Ardavani, O.; Zerefos, S.; Doulos, L.T. Redesigning the exterior lighting as part of the urban landscape: The role of transgenic bioluminescent plants in mediterranean urban and suburban lighting environments. J. Clean. Prod. 2020 , 242 , 118477. [ Google Scholar ] [ CrossRef ]
• Papalambrou, A.; Doulos, L.T. Identifying, Examining, and Planning Areas Protected from Light Pollution. The Case Study of Planning the First National Dark Sky Park in Greece. Sustainability 2019 , 11 , 5963. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Doulos, L.; Sioutis, I.; Kontaxis, P.; Zissis, G.; Faidas, K. A decision support system for assessment of street lighting tenders based on energy performance indicators and environmental criteria: Overview, methodology and case study. Sustain. Cities Soc. 2019 , 51 , 101759. [ Google Scholar ] [ CrossRef ]
• Doulos, L.T.; Sioutis, I.; Tsangrassoulis, A.; Canale, L.; Faidas, K. Revision of Threshold Luminance Levels in Tunnels Aiming to Minimize Energy Consumption at No Cost: Methodology and Case Studies†. Energies 2020 , 13 , 1707. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mavridou, T.; Doulos, L.T. Evaluation of Different Roof Types Concerning Daylight in Industrial Buildings during the Initial Design Phase: Methodology and Case Study. Buildings 2019 , 9 , 170. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Arabatzis, G.; Myronidis, D.; Myronidis, D. Contribution of SHP Stations to the development of an area and their social acceptance. Renew. Sustain. Energy Rev. 2011 , 15 , 3909–3917. [ Google Scholar ] [ CrossRef ]
• Ntanos, S.; Ntanos, A.; Salmon, I.; Ziatas, T. Public awareness on Renewable Energy Sources: A case study for the Piraeus University of Applied Sciences. In Proceedings of the 5th International Symposium and 27th National Conference on Operational Research, Athens, Greece, 9–11 June 2016; EEEE: Piscataway, NJ, USA, 2016; pp. 18–23. [ Google Scholar ]
• Drosos, D.; Kyriakopoulos, G.L.; Arabatzis, G.; Tsotsolas, N. Evaluating Customer Satisfaction in Energy Markets Using a Multicriteria Method: The Case of Electricity Market in Greece. Sustainability 2020 , 12 , 3862. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Stankuniene, G.; Streimikiene, D.; Kyriakopoulos, G.L. Systematic Literature Review on Behavioral Barriers of Climate Change Mitigation in Households. Sustainability 2020 , 12 , 7369. https://doi.org/10.3390/su12187369

Stankuniene G, Streimikiene D, Kyriakopoulos GL. Systematic Literature Review on Behavioral Barriers of Climate Change Mitigation in Households. Sustainability . 2020; 12(18):7369. https://doi.org/10.3390/su12187369

Stankuniene, Gintare, Dalia Streimikiene, and Grigorios L. Kyriakopoulos. 2020. "Systematic Literature Review on Behavioral Barriers of Climate Change Mitigation in Households" Sustainability 12, no. 18: 7369. https://doi.org/10.3390/su12187369

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Browse Econ Literature

• Working papers
• Software components
• Book chapters
• JEL classification

More features

• Subscribe to new research

RePEc Biblio

Author registration.

• Economics Virtual Seminar Calendar NEW!

Some searches may not work properly. We apologize for the inconvenience.

Systematic Literature Review on Behavioral Barriers of Climate Change Mitigation in Households

• Author & abstract
• 37 References
• 17 Citations
• Most related
• Related works & more

Corrections

(Laboratory of Energy Systems Research, Lithuanian Energy Institute, Breslaujos str. 3, 44403 Kaunas, Lithuania)

(School of Electrical and Computer Engineering, Electric Power Division, Photometry Laboratory, National Technical University of Athens, 9 Heroon Polytechniou Street, 15780 Athens, Greece)

Suggested Citation

Download full text from publisher, references listed on ideas.

Follow serials, authors, keywords & more

Public profiles for Economics researchers

Various research rankings in Economics

RePEc Genealogy

Who was a student of whom, using RePEc

Curated articles & papers on economics topics

Upload your paper to be listed on RePEc and IDEAS

New papers by email

Subscribe to new additions to RePEc

EconAcademics

Blog aggregator for economics research

Cases of plagiarism in Economics

About RePEc

Initiative for open bibliographies in Economics

News about RePEc

Questions about IDEAS and RePEc

RePEc volunteers

Participating archives

Publishers indexing in RePEc

Privacy statement

Found an error or omission?

Opportunities to help RePEc

Get papers listed

Have your research listed on RePEc

Open a RePEc archive

Have your institution's/publisher's output listed on RePEc

Get RePEc data

Use data assembled by RePEc

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Psychological barriers moderate the attitude-behavior gap for climate change

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft

Affiliation Center for Psychology, Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal

Roles Conceptualization, Funding acquisition, Project administration, Resources, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – review & editing

* E-mail: [email protected]

• João Vieira, 
• São Luís Castro, 
• Alessandra S. Souza

• Published: July 5, 2023
• https://doi.org/10.1371/journal.pone.0287404
• Reader Comments

Behavioral change has been increasingly recognized as a means for combating climate change. However, being concerned about climate problems and knowing the importance of individual actions in mitigating them is not enough for greater adherence to a more sustainable lifestyle. Psychological barriers such as (1) finding change unnecessary; (2) conflicting goals; (3) interpersonal relationships; (4) lack of knowledge; and (5) tokenism have been proposed as an explanation for the gap between environmental attitudes and actions. Yet, so far, this hypothesis has remained untested. This study aimed to assess if psychological barriers moderate the association between environmental attitudes and climate action. A sample of Portuguese individuals (N = 937) responded to a survey measuring climate change beliefs and environmental concerns as an index of environmental attitudes, a scale of self-reported frequency of environmental action, and finally, the dragons of inaction psychological barrier scale. Our participants revealed generally elevated positive environmental attitudes. These attitudes were positively and moderately related to greater self-reported frequency of environmental action in areas such as reusing materials, reduced consumption of animal products, water and energy saving, and airplane use, but not driving less. Critically, the association between attitudes and behavior was negatively moderated by psychological barriers for the reuse, food, and saving domains, but not for driving or flying. In conclusion, our results corroborate the assumption that psychological barriers can partly explain the attitude-behavior gap in the climate action domain.

Citation: Vieira J, Castro SL, Souza AS (2023) Psychological barriers moderate the attitude-behavior gap for climate change. PLoS ONE 18(7): e0287404. https://doi.org/10.1371/journal.pone.0287404

Editor: Juan Antonio García, University of Castilla-La Mancha: Universidad de Castilla-La Mancha, SPAIN

Received: February 6, 2023; Accepted: June 5, 2023; Published: July 5, 2023

Copyright: © 2023 Vieira et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The relevant materials, data, and analysis scripts are publicly available at the Open Science Framework: https://osf.io/j9th2/ ( https://doi.org/10.17605/OSF.IO/J9TH2 ).

Funding: This work was supported by national funding from the Portuguese Foundation for Science and Technology (UIDB/00050/2020). A. S. Souza was supported by a grant (CEECINST/00159/2018) awarded to the Center for Psychology of the University of Porto (FCT/UIDB/00050/2020) by Fundação para a Ciência e a Tecnologia (FCT, Portugal). This research was supported by a grant (NORTE-01-0145-FEDER-000071) from the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Global surface temperatures are rising at unprecedented levels due to an increase in the concentration of greenhouse gases produced by human activity [ 1 , 2 ]. This warming trend has propelled an increased frequency of extreme weather events, which has and will continue to have a significant impact on human physical [ 3 , 4 ] and mental health [ 5 , 6 ]. Despite the irreversibility of some of the consequences of climate change, drastically reducing anthropogenic greenhouse gas emissions is imperative to prevent global warming from reaching the 1.5°C mark [ 7 ]. Reducing these emissions will require major changes in human behavior toward a more sustainable lifestyle [ 8 – 11 ]. The frequency with which people engage in pro-environmental behaviors is assumed to be related to their environmental attitudes [ 12 – 15 ], yet the link between environmental attitudes and behavior has been proposed to be weakened by several psychological barriers [ 16 – 19 ]. To the best of our knowledge, no previous studies directly evaluated whether psychological barriers moderate the association between attitudes and pro-environmental behavior. The goal of the present study is to fill this gap by measuring environmental attitudes, self-reported frequency of environmental behaviors, and psychological barriers to assess whether psychological barriers partly explain the relationship between attitudes and behavior.

Environmental behaviors and attitudes

Household consumption is a country’s leading contributor to greenhouse gas emissions (ca. 65% to 75% of the emissions), especially in the high-income countries of Europe and North America [ 10 , 11 ]. Household carbon emissions are defined as the intensity of carbon emissions resulting from individuals’ and groups’ behavioral activities that support their lifestyles [ 9 , 20 ]. In Western countries, the main contributors to these emissions are transportation, housing, and food consumption [ 9 – 11 , 21 ]. Therefore, changes in demand and consumption patterns of households have tremendous climate change mitigative potential [ 8 , 11 ]. Shifting transportation and dietary habits, as well as encouraging energy-saving at home, is a fundamental challenge requiring a deep understanding of the psychological factors involved in lifestyle choices, attitudes toward the environment, and opportunity costs for ecological behaviors [ 22 , 23 ]. In the present study, we measured the self-reported behavior frequency in the most relevant domains of ecological behavior, namely, transportation, food choices, and household energy and water consumption to serve as a first step toward better understanding ecological behavior.

Psychological models have been put forth to explain how psychosocial variables function together to stimulate the motivation to act [ 12 , 15 ]. Most of these models agree that positive attitudes towards the environment, which are related to feelings of personal moral responsibility to act in favor of the environment (i.e., environmental personal norms), are necessary for pro-environmental behavior. The widely used value-belief-norm theory [ 24 , 25 ] and the more recent comprehensive action-determination model [ 12 , 26 ] postulate that our personal norms are activated when we feel that something valuable is being threatened. Hence, activating each individual’s personal norms related to the environment requires the interaction between environmental beliefs and values.

To believe in environmental problems, an individual needs to possess enough knowledge to recognize the existence of environmental issues [ 27 – 29 ]. Beliefs can be about recognizing the presence of an unnatural climate change, knowing its negative impacts, and the human responsibility for it. Additionally, it also includes believing in personal responsibility. Beliefs are sometimes directly associated with pro-environmental behavior [ 22 , 30 ], but they mainly impact intention and willingness through interaction with other constructs such as environmental values, social norms, and knowledge [ 27 , 31 , 32 ].

Values guide individuals through the selection and evaluation of behaviors [ 33 ] and dictate what individuals are concerned about when making decisions [ 34 , 35 ]. In the environmental context, egoistic, altruistic, and biospheric concerns are particularly relevant [ 35 – 38 ]. While egoistic concerns guide individuals toward self-enhancement, altruistic concerns are associated with the benefit of others, and biospheric concerns fall on valorizing nature and the environment. Biospheric concerns appear to have the strongest association with pro-environmental action, followed by altruistic and, finally, egoistic ones [ 22 , 32 , 35 , 38 – 40 ], but all three types of concerns are usually correlated [ 35 , 41 – 43 ]. Thus, when an individual expresses concern for environmental problems, their worries are oriented to the following three possible valued objects: the self, other people, and all living beings. Believing that something valued is being threatened is an important aspect of developing favorable environmental attitudes, even when those concerns are at a personal level (egoistic) [ 41 ]. The present study assessed environmental attitudes by measuring climate change-related beliefs and environmental concerns. We predicted that believing in the negative impact of climate change paired with displaying environmental concerns for either the self, others, or nature should lead to favorable attitudes regarding climate change.

The attitude-behavior gap

Studies often report a moderate relationship between attitudes and pro-environmental behaviors [e.g., r = 0.36 in 12, r = 0.482 in 22]. Despite this relationship, a growing number of studies reports that being concerned or feeling responsible for acting in favor of the environment is not enough to increase pro-environmental behavior [ 12 , 14 , 18 , 44 – 47 ]. This gap has been named the attitude-behavior gap or the value-action gap . Terms such as belief-action gap , knowledge-action gap , and attitude-behavior inconsistency have also been employed [ 18 ]. Critically, not all pro-environmental actions are similarly affected by attitudes [ 14 ]. The higher the perceived costs of an action, the lesser the effect attitudes have on actual behaviors [ 48 ]. This could be explained by environmentally concerned individuals feeling dissatisfied when their actions do not correspond to their values and attitudes, yet they also do not want to bear significant personal costs or discomfort in pursuing them. Accordingly, the low-cost hypothesis states that the lower the personal costs in a situation, the easier it is for actors to transform their attitudes into corresponding behavior [ 48 , 49 ]. The cost of an action usually depends on the efforts necessary to execute it; for example, recycling can increase in cost if no nearby structures allow for the correct waste disposal and people would need to walk or drive more to dispose of their waste [ 50 ]. Decreasing meat consumption [ 51 – 53 ] and reducing personal vehicle use [ 54 – 56 ], two of the behaviors with the highest carbon emissions, are usually perceived as highly costly behaviors because sustainable alternatives are seen as less convenient (e.g., unappealing vegetarian meal alternatives or uncomfortable public transportation).

The weak association between attitudes and behavior could be partially explained by measurement inadequacies such as social desirability biases, temporal gaps, and faulty instruments [ 18 ]. The literature has also increasingly pointed to the relevance of assessing structural barriers (i.e., external systemic or infrastructure barriers) such as the accessibility and condition of sustainable alternatives, which can also hinder action-taking by aware and concerned individuals. For example, in the case of household carbon emissions, the cost of more sustainable items like organic and vegan food [ 57 , 58 ], a perceived lack of available time [ 45 , 59 , 60 ], and difficulty in accessing structures or products in some areas [ 61 , 62 ] have been pointed as barriers preventing concerned individuals from acting in accordance with their attitudes. However, even in situations where contextual and external factors do not heavily constrain individuals, the frequency of environmental action may still be low [ 14 , 16 ].

Psychological barriers

Psychological barriers have been put forth to explain why concerned individuals do not act according to their attitudes even when not restricted by structural barriers [ 16 ]. For example, a mixed method analysis by Lorenzoni et al. [ 63 ] showed that, at the individual level, uncertainty, distrust in information sources, externalizing responsibility, conflicting priorities, and fatalism were used as justifications for individual inaction in the climate context. Gifford [ 16 ] reviewed The Dragons of Inaction , an enumeration and explanation of 29 different psychological factors that impede adherence to climate change mitigation behaviors. These psychological barriers were categorized into seven groups: Limited Cognition, Ideologies, Comparison with others, Sunk costs, Discredence, Perceived Risks, and Limited Behavior.

Gifford and Chen [ 17 ] evaluated the impact of some of these psychological barriers on intentions to engage in mitigative food choices (e.g., purchase of organically grown food). They observed that the dismissal of anthropogenic climate change evidence was the strongest perceived barrier to mitigation, the second being incompatible financial and time investments, followed by satisfaction with current mitigation behaviors. Gifford’s psychological barriers were also negatively associated with self-reported energy conservation: the existence of other conflicting goals and aspirations had the strongest negative correlation with energy conservation behaviors, and interpersonal influences the weakest [ 64 ].

Each of these studies pointed to a different structure of psychological barriers. More recently, Lacroix et al. [ 19 ] developed a short scale to measure psychological barriers to pro-environmental behavior that could be applied across multiple domains of environmental behavior, the Dragons of Inaction Psychological Barriers (DIPB) scale. After a set of three studies, the final scale measured five barriers: (a) Change Unnecessary , that is, the denial of environmental problems and of the necessity to act; (b) Conflicting Goals and Aspirations , comprising limited time, sunk costs, and difficulty in changing habits; (c) Interpersonal Relationships , covering the fear of social disapproval or criticism; (d) Lacking Knowledge , representing not knowing how to change; and (e) Tokenism , the belief that no more personal investment is needed to change. The five barriers model was invariant for a wide range of behaviors that the authors considered as high-difficulty (e.g., eating less meat and driving less) and low-difficulty (e.g., recycling, saving water, saving energy, buying green); nonetheless, the impact of each barrier varied across behaviors. The interpersonal relationships barrier was the strongest for eating less meat, the conflicting goals and aspirations barrier was more substantial for driving less, and the lacking knowledge barrier was the strongest for buying green. Tokenism did not differ across behaviors. The DIPB scale was also adapted to the Colombian population, with a similar five-factor structure adequately describing the data [ 65 ]. Wang et al. [ 66 ] showed that the DIPB was associated with inaction and mediated the relation between positive emotions towards nature (awe) and climate change inaction. Here we adapted the DIPB scale for the Portuguese population to measure the five psychological barriers identified in these previous studies.

The current study

Psychological barriers have been pointed out as a possible explanation of inaction in individuals concerned about climate change but not effectively acting on their beliefs [ 16 ]. The DIPB scale was created to measure the effect of psychological barriers on concerned individuals under the assumption that they may help explain the attitude-behavior gap [ 19 ]. However, to the best of our knowledge, no study has assessed whether psychological barriers help explain this gap.

The current study aimed to test whether psychological barriers, measured by the DIPB scale, partially explain the gap between environmental attitudes and pro-environmental behaviors. Our goal was to test the model illustrated in Fig 1 . Attitudes were measured by climate change beliefs and environmental values [ 12 ]. To include a wide range of pro-climatic behavior from more to less impactful, we measured the self-reported frequency of pro-environmental behaviors in the following domains: transportation, food, energy and water saving, waste management, and sustainable purchase. We expected to find that behaviors related to transportation and food would be less affected by attitudes and would be hindered more by psychological barriers than the lower-investment counterparts (waste management, purchase, and conservation).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0287404.g001

We collected data in Portugal. Previous surveys have shown that the Portuguese are aware and concerned about climate change and the environment [ 30 , 67 , 68 ]. Nevertheless, the country has a low carbon-emission reduction performance [ 69 ], and the citizens report engaging mainly in low-impact behaviors [ 67 ]. As a member of the Organization for Economic Cooperation and Development (OECD) and the European Union, Portugal shares characteristics with similar countries [ 10 ], where citizens living in urban contexts have higher levels of education, more accessible public transportation systems and a better supply of low carbon products. These characteristics are usually associated with a reduction of structural barriers for environmental action, thus making Portugal a suitable context to study the attitude-behavior gap and the role of psychological barriers.

Materials and methods

Participants.

The study was distributed online between November 2021 and March 2022. We recruited 937 Portuguese-speaking individuals residing in Portugal via the University of Porto’s mailing list, various online groups, and the promotion ad feature of Facebook and Instagram. Overall, 650 participants identified as women (69%), 280 as men (30%), and seven as other/no response (1%). Ages ranged from 18 to 80, with the average being 36 years ( SD = 16). Most participants reported having some form of higher education degree (67%), 290 concluded high school (31%), and only 18 did not complete secondary education (2%). More than half (56.7%) resided in Portugal’s two most populated and urbanized cities (Porto and Lisbon). Participants completed a written online informed consent form at the beginning of the study (i.e., they read and clicked on consent statements before moving on to the main study) and were debriefed at the end. If participants did not consent to the terms of participation, the study was ended before completion of the questionnaire. The Faculty of Psychology and Education Sciences ethics committee approved the research protocol (Ref.ª 2021/09-08).

Instruments

Environmental concerns..

We used the Environmental Concern Items from Schultz [ 35 ], which has been referred to as a robust measurement of environmental concern [ 70 ]. It comprises 12 items, divided into four altruistic (e.g., "All people"), four egoistic (e.g., "My lifestyle”), and four biospheric (e.g., "Animals") domains of environmental concern. Participants rated the 12 items using a scale from 1 (not important) to 7 (supreme importance) in response to the question: "I am concerned about the environmental problems because of the consequences for… . " . The instrument has good reliability and correlates with other measures of environmental attitude, self-reported pro-environmental behavior, and social value orientation [ 35 , 41 , 70 ]. Items are shown in Table 1 .

https://doi.org/10.1371/journal.pone.0287404.t001

Climate change beliefs.

We measured climate change beliefs by adapting items from The Climate Change Attitude Survey scale [ 71 ]. The belief sub-scale comprises nine items rated on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The used items are presented in Table 1 .

Frequency of pro-environmental behaviors.

We built a questionnaire to measure the self-reported frequency of the most relevant pro-environmental behaviors in the following domains: transportation, food, energy and water saving, waste management, and sustainable purchase [ 11 , 67 , 72 , 73 ]. The set of 21 items (see Table 2 ) was rated regarding their frequency on a Likert scale from 1 (rarely) to 5 (very often). We asked participants to rate the frequency of each action at the moment of the scale completion (during the COVID-19 pandemic) and what they believe was the frequency of their behavior before the onset of the COVID-19 pandemic. We intended to assess if behaviors changed because of the ongoing pandemic situation (which could particularly affect transportation behaviors). Responses were similar for both questions; hence, we will only present the results regarding the current self-reported frequency of action.

https://doi.org/10.1371/journal.pone.0287404.t002

Psychological barriers.

We used the 22-item DIPB scale [ 19 ] and two additional items that were removed from the original final scale (items 23 and 24) but were recommended to be assessed in contexts where the government plays an important role. We included these items to assess if they could bring relevant information to measuring psychological barriers in Portugal since the Portuguese tend to attribute to local and national government a high (larger than the European Union average) responsibility in fighting climate change [ 74 ]. Each item was answered on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). All items are presented in Table 3 . The DIPB scale assesses five psychological barriers: (a) Change Unnecessary, (b) Conflicting Goals and Aspirations, (c) Interpersonal Relationships, (d) Lacking knowledge, and (e) Tokenism.

https://doi.org/10.1371/journal.pone.0287404.t003

Before answering the scale, participants first selected one behavior from a list of five pro-environmental behaviors to serve as a reference while rating each item. The selected behavior should be one that participants believed was necessary to help the environment but which they were not currently doing or were not doing often enough. The behaviors were: (a) eating less meat; (b) taking public transport, cycling, or walking rather than driving; (c) reducing water use (e.g., taking shorter showers, repairing leaks); (d) making more eco-friendly purchases, and (e) recycling all items permitted by their local programs. Due to a programming error, one domain of environmental behavior formerly tested in the DIPB scale (i.e., wearing a sweater rather than turning up the heat in the winter) was not included in the list.

We requested permission from the original authors of the scales to use and adapt them in the present study. Our first step was to translate all items into Portuguese. We focused our translation on capturing relevant concepts to measure our research questions [ 75 , 76 ]. All three authors translated all scales independently and then discussed the three translations to settle on a final version of each item. Afterward, an environmental science major assessed the translation and deemed the terminology suitable. The translated items used in the present study are available on our OSF page: https://osf.io/j9th2/

Completion of the questionnaire took, on average, 15-min. Participants completed the scales in the following order: (1 st ) environmental concern, (2 nd ) climate change beliefs, (3 rd ) frequency of ecological behaviors, (4 th ) the psychological barriers scale, and (5 th ) demographics questions.

Data analysis

We performed confirmatory factor analysis to evaluate the structure of the responses to the environmental concerns, climate change beliefs, and psychological barriers scales. For the frequency of pro-environmental behavior, we first ran an exploratory factor analysis to determine the structure of the measurement of these behaviors, followed by a confirmatory factor analysis with the final identified model. All analyses were performed using R. 4.1.3 [ 77 ]. We used the lavaan package [ 78 ] for the confirmatory factor analysis, and the package psych [ 79 ] for the exploratory factor analyses and the reliability of our measurements. Finally, we used the base R Stats package to compute the moderation analysis using the lm function [ 77 ].

We first tested the multivariate normality of our data using the Energy test and Mardia’s multivariate skewness and kurtosis tests. Given that our data was ordinal, non-normal, and the sample size was large, we employed a diagonally weighted least squares (DWLS) estimation based on a polychoric correlation matrix. This method produces more accurate factor loadings estimates than the more commonly used maximum likelihood estimation method [ 80 , 81 ].

To assess model adequacy, we used the following model fit indices: chi-square goodness-of-fit statistic (χ2), the root-mean-square error of approximation (RMSEA), Bentler’s comparative fit index (CFI), and the standardized root-mean-square residual (SRMR). For χ2, a small, non-significant value indicates a good fit. However, χ2 is known to be affected by sample size. RSMEA smaller than 0.06 indicates a good fit, and of 0.08 or less, a reasonable fit. For the CFI, values higher than 0.95 indicate a good fit, and values between 0.90 and 0.95 indicate an adequate fit. Finally, for the SRMR, a value below 0.08 indicates an acceptable fit. The cutoff criteria were based on Hu and Bertler [ 82 ]. Note that these cutoff criteria are not static and should be weighted considering the data since the index values can be influenced by sample size, the number of variables analyzed, and missing data [ 83 ].

Finally, we calculated the reliability of each of our measurements. Since our data is ordinal and non-normal, we report ordinal alpha (α), ordinal omega total (ωt), and Guttman’s greatest lower bound (GLB) [ 84 ].

All the materials, data, and analysis scripts used are publicly available at: https://osf.io/j9th2/

Table 1 presents descriptive statistics and reliability of the Environmental Concerns and Climate Change Beliefs items. Table 2 shows these values for the Frequency of Pro-Environmental Behavior, and Table 3 for the Psychological Barrier scales. The item numbering represents the order in which items were presented in the scale for rating. For the Psychological Barrier scale, we present the item order used in this study and the scale’s original numbering. We described each item’s mean (M) and standard deviation (SD). Ordinal alpha (α) for each initial factor (before item removal) is presented after the factor’s name and a column with the alpha value after removing different items inside that factor. For the factors with only two items, we presented the item’s correlation.

Environmental attitudes

Our goal was to create an environmental attitude second-order latent factor consisting of a factor of environmental concerns and a factor of climate change beliefs. We first tested each of these constructs independently, with one model for responses in the environmental concerns scale, and another for the climate change beliefs scale. Regarding environmental concerns, participants reported higher levels of biospheric concerns ( M = 6.33, SD = 1.52), followed by altruistic ( M = 6.26, SD = 1.11), and lastly egoistic concerns ( M = 5.56, SD = 1.15), replicating prior results [ 35 ]. CFA revealed that the 3-factor solution proposed by Schultz [ 35 ] had an adequate fit, χ2 (51) = 90.119, CFI = 0.991, RMSEA = 0.029 [0.019, 0.038], SRMR = 0.060. Concerning the Climate Change Belief scale, overall, participants reported high levels of agreement with the items ( M = 4.71, SD = 0.64), indicating that they believed in climate change. Following prior research [ 71 ], we tested whether these items formed a single factor, yet this model had a SRMR value above the cut-off, χ2 (27) = 60.429, CFI = 0.968, RMSEA = 0.036 [0.024, 0.049], SRMR = 0.081. The inclusion of a residual covariation between items 6 . The actions of individuals can make a positive difference in global climate change and 9 . I can do my part to make the world a better place for future generations , was necessary to obtain an acceptable and very good fit value, χ 2 (26) = 11.744, CFI = 1, RMSEA = 0.00 [0.00, 0.00], SRMR = 0.042. These items are highly correlated (b = 0.58) and they are the only ones covering the belief in individual contributions to fighting climate change. This change aligns with previous proposals dividing beliefs into the dimensions of awareness of consequences and ascription of responsibility [ 12 ]. Reliability results for our final factors were as follows: Egoistic Concerns (α = .91, ωt = .93, GLB = .91); Altruistic Concerns (α = .91, ωt = .94, GLB = .95); Biospheric Concerns (α = .97, ωt = .91, GLB = .98); Beliefs (α = .94, ωt = .97, GLB = .98).

Next, we created a second-order latent variable of Attitudes on which the three factors of environmental concerns (Egoistic, Altruistic, Biospheric) and the Beliefs factor loaded. The model had good fit: χ 2 (184) = 226.295, CFI = 0.995, RMSEA = 0.016 [0.007, 0.022], SRMR = 0.064. Despite some research pointing at the weaker relationship between egoistic concerns and environmental-related attitudes and behaviors [ 70 ], we retained this factor in our model since it positively correlated with the other concern factors and loaded on the second-order latent variable of Attitudes [ 35 , 41 ]. Fig 2 presents the full model and the standardized loadings.

Note . Loadings and factor correlations are given as standardized parameters.

https://doi.org/10.1371/journal.pone.0287404.g002

Environmental behaviors

We assessed the self-reported frequency of environmental behaviors in six domains: transportation, food, energy and water saving, waste management, and sustainable purchase. Behaviors related to transportation ( M = 3.34, SD = 1.35), food ( M = 3.33, SD = 1.28), and sustainable purchases ( M = 3.51, SD = 0.96) were reported to occur less frequently than behaviors related to energy ( M = 4.25, SD = 0.98) and water saving ( M = 4.45, SD = 0.88), and waste management ( M = 4.20, SD = 0.96). To describe the relationship between these behaviors, we performed an exploratory factor analysis. We used principal axis factor analysis to extract factors for its relative tolerance of nonnormality and a Promax rotation since the factors are assumed to be correlated [ 85 ]. A scree plot indicated that five factors should be retained [ 86 ]. Items were assigned to the factors where they had the strongest loadings. Items with loadings below 0.4 were removed, which led to the removal of the following items: 3, 6, 8, 9, 19, and 20. After removal of these items, no loadings were salient on more than one factor.

Fig 3 presents the five-factor solution obtained. Factor 1 was named Reuse since items loading on it were related to sustainable waste management and investment in local products. Factor 2 was called Driving because items loading on this factor refer to reducing driving habits. Factor 3 was labeled Flying because items loaded on it mentioned reducing flying or choosing other transport instead of flying. Factor 4 was called Saving due to the items loading on it being related to actions that save water or electricity. Finally, Factor 5 was named Food because it captured items about changes in food choices. We ran a CFA with robust estimators on this solution. Model fit was acceptable, χ2 (70) = 141.201, CFI = 0.976, RMSEA = 0.033 [0.025,0.041], SRMR = 0.038. In general, factors were moderately correlated, except for the relation between Driving and Saving. Reliability results for our final factors were as follows: Driving ( r = 0.38); Flying ( r = 0.53); Food (α = .83, ωt = .83, GLB = .83); Saving (α = .63, ωt = .65, GLB = .65); Reuse (α = .71, ωt = .75, GLB = .72). Note that for factors with only two items, the reliability estimates represent the correlation between items.

Note . Loadings, residual variances, and factor correlations are given as standardized parameters. Dotted lines indicate loadings that were constrained to be equal to allow for local factor identifiability.

https://doi.org/10.1371/journal.pone.0287404.g003

We tested if we could replicate in our sample the five-factor structure found in Lacroix et al. [ 19 ]. The model with all 24 items included had a poor fit, χ2 (242) = 1223.564, CFI = 0.889, RMSEA = 0.066 [0.062,0.070], SRMR = 0.082. The analysis of the loadings and factor reliabilities indicated that the two additional items that were not part of the final DIPB scale (items 23 and 24) loaded poorly (<0.3) on their respective factors (standardized loading of 0.099 and 0.221, respectively). Removal of these items improved the reliability of the Tokenism (α = 0.82) and Lacking Knowledge (α = 0.85) latent factors, and produced a good fitting model, χ2 (199) = 493.105, CFI = 0.963, RMSEA = 0.040 [0.035,0.044], SRMR = 0.066. Yet, there was still an item (item 21) that showed a standardized loading of 0.265 which is below the criterion of 0.3. Since we are still left with four items in the tokenism factor, we decided to remove this low loading item, and its removal further improved the Tokenism factor’s reliability (α = 0.85). All other loadings were above 0.47. The final model with the removal of items 21, 23 and 24 had an acceptable fit: χ2 (179) = 464.670, CFI = 0.963, RMSEA = 0.041 [0.037, 0.046], SRMR = 0.069. Fig 4 displays the psychological barriers model with standardized loadings. As for the factor covariances, no covariance was negative, and the strongest relationship was found between Change Unnecessary and Tokenism (.92) and the lowest between Change Unnecessary and Lacking Knowledge (.35). Reliability results for our final factors were as follows: Change Unnecessary (α = .91, ωt = .93, GLB = .93); Conflicting Goals and Aspirations (α = .84, ωt = .88, GLB = .88); Interpersonal Relationships (α = .88, ωt = .92, GLB = .88); Lacking Knowledge (α = .85, ωt = .87, GLB = .86); Tokenism (α = .85, ωt = .68, GLB = .87).

Note . Loadings, residual variances, and factor correlations are given as standardized parameters.

https://doi.org/10.1371/journal.pone.0287404.g004

We also tested a second-order factor model where each of the five barriers loaded on a general psychological barrier factor. The model slightly misfitted the data, as indicated by the larger SRMR index (> 0.08), χ2(184) = 659.171, CFI = 0.928, RMSEA = 0.053 [0.067, 0.77], SRMR = 0.085. The modification indices indicated that model fit could be vastly improved if we considered the covariance between the Change Unnecessary and the Tokenism factors. The high correlation values between these two factors suggest a strong association. The DIPB scale measurement of Tokenism implies that current efforts are sufficient, and change is not necessary, which overlaps with the content of the Change Unnecessary factor and may explain some specific variance related to these factors. By including the covariance between Change Unnecessary and Tokenism, model fit improved, χ2(183) = 516.954, CFI = 0.956, RMSEA = 0.044 [0.040, 0.49], SRMR = 0.072. Fig 5 illustrates the second-factor model for the psychological barriers.

Note . Loadings are given as standardized parameters.

https://doi.org/10.1371/journal.pone.0287404.g005

Moderation analysis

Finally, our last aim was to assess if the psychological barriers moderated the influence of attitudes on the frequency of pro-environmental behaviors, thereby partly explaining the attitude-behavior gap. We performed a latent moderation analysis using a factor score procedure [ 87 ]. Although this approach neglects measurement error, given our sample size ( N = 937) estimation error for the correlations in our sample should be low [ 88 ]. Simulations also show that this method performs relatively well compared to other methods even under suboptimal conditions, such as with lower reliabilities, null to medium correlations between the predictor and the moderator, and unequal factor loadings [ 87 ].

The latent moderation analysis involved three steps. First, we independently estimated the individual latent scores for each pro-environmental behavior factor separately using DWLS estimation. The models were saturated, and hence the fit was perfect. These latent scores served as our latent dependent variable in the moderation analysis. Second, we estimated individual factor scores for our latent predictors, namely Attitudes and Psychological Barriers, in a single model that allowed them to correlate. When estimating the model of Attitudes and Psychological Barriers together, the model did not converge if we included the correlation between the Change Unnecessary and Tokenism factors. Since the model without this correlation only showed a small misfit on its own, we proceeded with the estimation of the model without it. Model fit was acceptable, χ2(808) = 2456.02, CFI = 0.920, RMSEA = 0.047 [0.045, 0.49], SRMR = 0.078. The correlation estimated between attitudes and psychological barriers was r = – 0.46.

Table 4 contains the statistics for each regression model. The confidence intervals of 95% (CI) for each of the coefficients was obtained via bootstrapping with 1000 replications. F -statistic and adjusted R 2 for each of the regression models was as follows, Reuse: F (3,933) = 48.95, p < .001, R 2 = 0.13; Food: F (3,933) = 42.71, p < .001, R 2 = 0.12; Saving: F (3,933) = 14.62, p < .001, R 2 = 0.04; Flying: F (3,933) = 24.58, p < .001, R 2 = 0.07; and Driving: F (3,933) = 4.62, p = .003, R 2 = 0.01. Attitudes were a significant positive predictor of the frequency of all environmental behaviors except for Driving. Psychological Barriers were found to be a significant moderator for the relationship between Attitudes and the frequency of behaviors related to Reuse, Food, and Saving. Psychological Barriers did not significantly moderate the relationship between attitudes and Flying and Driving.

https://doi.org/10.1371/journal.pone.0287404.t004

We graphicly illustrated the moderation effect of Psychological Barriers for each Pro-Environmental Behavior in Fig 6 . Linear predictions between Attitudes (independent variable) and Pro-Environmental Behavior (dependent variable) were computed for those with low, average, and high levels of Psychological Barriers (i.e., one SD below the mean, the mean, and one SD above the mean, respectively). For Reuse and Food, the effect of psychological barriers mattered most at high levels of environmental attitudes. For Saving, the pattern with the effects of psychological barriers reversed for high and low levels of environmental attitudes.

Note . The vertical lines represent the 95% confidence intervals for the standardized behavior frequency across different levels of psychological barriers.

https://doi.org/10.1371/journal.pone.0287404.g006

In this study, we evaluated whether psychological barriers could partly explain the gap between environmental attitudes and engagement in different domains of environmental action. We assessed environmental attitudes as the latent factor representing the covariation of environmental concerns and climate change beliefs. We used this model to assess if favorable environmental attitudes relate to a higher frequency of climate action. Finally, we examined if psychological barriers, as assessed by the DIPB scale, moderated the relation between attitudes and behavior frequency in each domain of action. Next, we will first discuss our findings regarding the relationship between attitudes and behavior, followed by how psychological barriers may moderate it.

Environmental attitudes and behaviors

Our sample of Portuguese individuals was highly concerned about climate change and believed in the human impact on this phenomenon. Climate change beliefs were very high on all levels, and environmental concerns were higher in the biospheric and altruistic domains than the egoistic one. Previous studies indicated that biospheric and altruistic values tend to be the most correlated with the intention to act [ 32 , 40 ], yet all concerns are usually correlated to each other [ 35 , 41 – 43 ]. Accordingly, we were able to extract a single factor reflecting pro-environmental attitudes from the measures of climate change beliefs and environmental concerns.

The frequency of pro-environmental behaviors showed some variability. Behaviors with lower carbon impact, such as reducing water use, conserving energy, and recycling or using less plastic, were done more frequently than reducing car use, airplane traveling, and eating less meat or animal products. These results align with the idea that high carbon impact behaviors require more investment to change [ 10 , 48 ].

Positive environmental attitudes predicted a higher frequency of environmental behavior across all our tested domains except for behaviors related to driving less. However, it is worth pointing out that the association between attitudes and self-reported frequency was not very high (estimates ranged between .025 and .381), which is consistent with previous reports of a modest association between environmental attitudes and behavior [ 12 ].

The DIPB scale was created to evaluate psychological barriers across different environmental behavior domains and was originally developed and tested in a Canadian sample [ 19 ]. We were able to replicate the original 5-factor structure in our Portuguese sample with some minor model adaptations. This pattern is similar to the one obtained in a Colombian sample [ 65 ]. The short format of the DIPB scale and the generality of the 5-factor solution extracted from it suggests that the scale is useful to, briefly and efficiently, measure psychological barriers to pro-environmental action in diverse cultural contexts.

In our sample, change unnecessary and interpersonal relationships were overall the lowest-rated barriers, while tokenism, lacking knowledge, and conflicting goals were the strongest barriers. It is worth noting that the items concerning the perceived government’s duty in facilitating action (items 23 and 24) were removed due to low model adequacy, but these were the highest-rated barriers to pro-environmental behavior in our sample. We believe the DIPB scale did not capture this phenomenon well, but the dismissal of personal responsibility might be a promising psychological barrier to be further explored in the future. An Australian study [ 89 ] showed that attributing greater responsibility to the government for environmental protection was related to more negative environmental intentions and behavior than attributing responsibility to the community. Although the measurement of this barrier through items 23 and 24 did not work well within the context of the DIPB scale, it does not mean that this barrier is not a relevant determinant of environmental action. Future studies should consider how to measure this psychological barrier effectively.

In our study, the psychological barriers moderated the effect of attitudes on behavior domains like waste management (reuse factor), eating less meat (food factor), and conservation behaviors (saving factor). Psychological barriers had a stronger influence on individuals with a higher environmental positive attitude, indicating that they may help explain the attitude-behavior gap that prevents concerned individuals from effective action. This relation was clear for the reuse and food factors but less so for the saving factor. Critically, this moderation was not significant in the transportation domains (car usage and plane traveling). The moderation effects observed here align with our predictions that psychological barriers may partly explain the attitude-behavior gap in some behaviors but not others. The transportation and food domains are considered of high difficulty to change [ 19 ]. Nevertheless, against our expectations, we only observed a moderation effect for food choices, but not driving or flying. This may suggest that although both behaviors are of high difficulty and have a high impact on the carbon footprint, they cannot be explained in the same way.

On the one hand, in our analysis, food habits were explained by attitudes with psychological barriers moderating that relationship. In the literature, meat-eating behavior interventions appear often associated with values and attitudes [ 90 ]. For example, self-transcending values effectively increase negative attitudes toward meat consumption [ 91 ], and attitudes about animals are important in choosing vegetarianism [ 92 ]. Furthermore, lack of knowledge (which is one of the assessed psychological barriers in our study) usually acts as a barrier to decreasing meat intake; and interventions focused on increasing awareness coupled with knowledge on how to replace meat with less impactful food choices increase positive attitudes and intentions towards meat reduction [ 90 , 93 ].

On the other hand, our analysis indicates that transportation behaviors (i.e., flying and driving) were less explained by attitudes, and no moderation effect was observed. The willingness to reduce flying was positively associated with attitudes, yet this behavior was not moderated by psychological barriers. Unfortunately, we recorded no information about the flying habits of our sample. It has been observed that habitual plane traveling is associated with a higher resistance to reducing flying in the future compared to those who air travel only occasionally [ 94 ] or prefer other means of transportation [ 95 ]. Flight reduction is also associated with how accessible and efficient the alternatives are (e.g., train) [ 94 ]. Hence it is possible that our results could be explained by the air travel habits of our sample. Future studies should control the frequency of air travel to assess if psychological barriers play different roles depending on how much people use this mode of transportation. Driving was the only domain not predicted by climate change attitudes. This result might point to other stronger motives behind driving habits that are not covered by our scales. For example, Innocenti et al. [ 96 ] found that people are biased against public transportation, preferring to travel by car even when the costs of this option are higher. A possible explanation is that cars and driving symbolize freedom, independence, status, and a pleasurable activity [ 97 ]. Other factors, such as travel time, which is longer for public transportation, are also crucial when choosing a traveling mode [ 98 ]. As such, people with complex activity patterns (e.g., taking children to school before going to work) might prefer to avoid the rigid and unreliable public transportation system, preferring to travel by personal transportation, which can be either bike or car. Note that bike traveling is also limited by the existence of safe and adequate bike lanes [ 99 ]. As for flying, we have not assessed the preferred transportation modes of our sample. Hence, we could not test whether psychological barriers vary depending on the driving habits of our participants. Future studies should consider measuring this variable.

A meta-analysis on behavioral interventions to promote household action on climate change pointed out that interventions on reducing meat eating tend to be more effective than reducing private car usage [ 100 ]. Interventions on meat reduction usually center around removing external barriers/facilitating mitigation behavior, while transportation interventions revolve mostly around information-based interventions, increasing the appeal of not using cars. These studies indicate that while food mitigation behaviors can be mostly linked with the variables assessed by our study, driving and flying habits might be associated with social status, previous behaviors, and practicality over alternative methods (i.e., public transport, walking, riding a bike), which were not evaluated here and may well explain why we could not effectively capture the motives behind driving and flying habits.

Limitations and future directions

Collecting data via a survey has its limitations. Self-reported measures of pro-environmental behavior might not entirely reflect authentic environmental action as it may depend on the participant’s interpretation of the question and recollection of their activity [ 101 ] or be influenced by social desirability [ 102 ]. It remains, therefore, a challenge to objectively monitor the frequency of pro-environmental action. Nonetheless, we forward some promising alternative methods. Laboratory observations through experimental tasks do not rely as much on interpretation and offer experimenters higher degrees of control [ 101 ]. Laboratory tasks usually rely on assessing participants’ decisions in different manipulated scenarios. For example, the Pro-Environmental Behavior Task [ 103 ] requires participants to make several trips. On each trip, they can choose between an environmentally friendly or unfriendly mode of transportation with manipulated waiting periods. Another way to curb recollection and interpretation restrictions is through momentary ecological assessment using mobile devices to register behaviors while they are happening [ 104 ].

There may be some limitations of generalizability related to our sample demographics. We collected data in Portugal from relatively young, mostly female, urban, and digitally active individuals (since this study was advertised via email and social media). Our results therefore may not generalize to individuals with different demographic characteristics. We note, however, that we replicated with our sample the main results observed in other studies with North-American and Latin-American samples [ 19 , 65 ]; hence our findings are likely to generalize across cultures.

This study showed that the DIPB scale has some degree of explanatory power toward the existence of a gap between environmental attitudes and (self-reported) ecological behavior. However, future studies could integrate these barriers into a broader model framework, such as the comprehensive action determination model [ 26 ]. The effects of habits and perceived behavioral control were shown to have a direct relationship with pro-environmental behavior [ 12 ], and their inclusion in this model framework might add explanatory power to the attitude-behavior gap.

Furthermore, we lacked a measure of structural barriers. Future studies should consider including a measure of structural and external barriers to better separate the influence of psychological barriers from other factors that may prevent people from taking more action. A lack of accessibility to infrastructures might be increasing behavior costs. For example, enhanced accessibility to recycling facilities encourages people to recycle [ 105 ]. Low availability of public transportation can be responsible for increased car use in rural areas [ 61 ], while product prices impact what people eat [ 57 ]. Hence, the accessibility of some structures or products may help explain the lack of action from concerned individuals beyond intrinsic barriers.

A final note regarding the measurement of psychological barriers falls upon the necessity to develop more robust ways to study people’s tendency to place responsibility for action on the government and industry, which was not well assessed by the DIPB scale but had a strong presence in our sample.

Conclusions

The present study enhanced our understanding of the relationship between attitudes, psychological barriers, and pro-climate behaviors. We demonstrated that not all types of behaviors are equally influenced by attitudes and the perception of psychological barriers. The DIPB scale could, to some degree, be a helpful tool to explain the attitude-behavior gap. This knowledge will help to plan more focused and efficient interventions which focus on breaking psychological barriers. Behaviors related to food choices, waste management, or water and energy saving can benefit from an intervention related to increasing attitudes while not ignoring the potential effects of psychological barriers. Finally, driving seems to be more resistant to change, and merely stimulating positive environmental attitudes or dismantling psychological barriers may not be enough.

• 1. Eyring V, Gillett NP, Achutarao KM, et al. Human influence on the climate system. In: Masson-Delmotte V, Zhai P, Pirani A, et al. (eds) Climate Change 2021 : The Physical Science Basis . Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 2021, pp. 423–552.
• 2. Masson-Delmotte V, Zhai P, Pirani A, et al. (eds). Summary for policymakers. In: Climate Change 2021 : The Physical Science Basis . Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 2021, pp. 3–32.
• View Article
• PubMed/NCBI
• Google Scholar
• 7. Marotzke J, Govindasamy Bala, Long Cao, et al. Future global climate: scenario-based projections and near-term information. In: Masson-Delmotte V, Zhai P, Pirani A, et al. (eds) Climate Change 2021 : The Physical Science Basis . Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 2021, pp. 553–672.
• 9. Druckman A, Jackson T. Understanding Households as Drivers of Carbon Emissions. In: Clift R, Druckman A (eds) Taking Stock of Industrial Ecology . Cham: Springer International Publishing, pp. 181–203.
• 20. Abdel-Aziz A, Meza MJF, Hubacek K, et al. Emissions Trends and Drivers. In: Sixth Assessment Report , Climate Change 2022 : Mitigation of Climate Change , the Working Group III contribution . Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_Chapter_02.pdf (2021, accessed 26 August 2022).
• 33. Clayton SD (ed). The Oxford Handbook of Environmental and Conservation Psychology . 1st ed. Oxford University Press. Epub ahead of print 28 September 2012. https://doi.org/10.1093/oxfordhb/9780199733026.001.0001
• 67. Kantar. Special Eurobarometer 490 . EB91.3, Belgium: Kantar Belgium, https://data.europa.eu/data/datasets/s2212_91_3_490_eng?locale=en (2019, accessed 26 June 2021).
• 68. Pohjolainen P, Kukkonen I, Jokinen P. Public Perceptions on Climate Change and Energy in Europe and Russia . European Social Service, 2018.
• 69. Burck J, Uhlich T, Bals C, et al. Climate Change Performance Index 2022 . Germany, https://ccpi.org/wp-content/uploads/CCPI-2022-Results-1.pdf#page=8&zoom=100,0,0 (2021, accessed 13 October 2022).
• 77. R Core Team. R: A Language and Environment for Statistical Computing. The R Project for Statistical Computing , https://www.R-project.org/ (2022).
• 79. Revelle W. psych: Procedures for Psychological, Psychometric, and Personality Research, https://CRAN.R-project.org/package=psych (2022).
• 92. Rothgerber H. 2—Attitudes Toward Meat and Plants in Vegetarians. In: Mariotti F (ed) Vegetarian and Plant-Based Diets in Health and Disease Prevention . Academic Press, pp. 11–35.

Systematic Literature Review on Behavioral Barriers of Climate Change Mitigation in Households

• Gintare Stankuniene , D. Štreimikienė , Grigorios L. Kyriakopoulos
• Published in Sustainability 8 September 2020
• Environmental Science, Economics

Ask This Paper

By using this feature, you agree to AI2's terms and conditions and that you will not submit any sensitive or confidential info.

AI2 may include your prompts and inputs in a public dataset for future AI research and development. Please check the box to opt-out.

Ask a question about " "

Supporting statements, figures and tables from this paper.

43 Citations

Contribution of individual behavioural change on household carbon footprint, nudges for enhancing sustainable energy consumption in households, prioritising climate change mitigation behaviours and exploring public health co-benefits: a delphi study, survey results on using nudges for choice of green-energy supplier, areas of individual consumption reduction: a focus on implemented restrictions and willingness for further cut-backs, enhancing climate neutrality and resilience through coordinated climate action: review of the synergies between mitigation and adaptation actions, to select effective interventions for pro-environmental behaviour change, we need to consider determinants of behaviour, how to support sustainable energy consumption in households, mortality management and climate action: a review and reference for using terror management theory methods in interdisciplinary environmental research, the contribution of changes in climate-friendly behaviour, climate change concern and personal responsibility to household greenhouse gas emissions: heating/cooling and transport activities in the european union, 97 references, the complex relationship between households’ climate change concerns and their water and energy mitigation behaviour, the potential of behavioural change for climate change mitigation: a case study for the european union, household barriers to climate change action: perspectives from nuevo leon, mexico, household actions can provide a behavioral wedge to rapidly reduce us carbon emissions.

• Highly Influential

How are coastal households responding to climate change

The role of lifestyle changes in low-emissions development strategies: an economy-wide assessment for brazil, comparing the relative mitigation potential of individual pro-environmental behaviors, climate policy through changing consumption choices: options and obstacles for reducing greenhouse gas emissions, household behaviour crowds out support for climate change policy when sufficient progress is perceived, ghg emission reduction by behavioral changes in lithuanian households, related papers.

Showing 1 through 3 of 0 Related Papers

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Systematic Literature Review on Behavioral Barriers of Climate Change Mitigation in Households

2020, Sustainability

Achieving climate change mitigation goals requires the mobilization of all levels of society. The potential for reducing greenhouse gas (GHG) emissions from households has not yet been fully realized. Given the complex climate change situation around the world, the importance of behavioral economic insights is already understood. Changing household behavior in mitigating climate change is seen as an inexpensive and rapid intervention measure. In this paper, we review barriers of changing household behavior and systematize policies and measures that could help to overcome these barriers. A systematic literature review provided in this paper allows to define future research pathways and could be important for policy-makers to develop measures to help households contribute to climate change mitigation.

Related Papers

Sustainability: The Journal of Record

Michael Dekay

Proceedings of the …

Thomas Dietz

Benjamin Sovacool

Through their consumption behavior, households are responsible for 72% of global greenhouse gas emissions. Thus, they are key actors in reaching the 1.5 °C goal under the Paris Agreement. However, the possible contribution and position of households in climate policies is neither well understood, nor do households receive sufficiently high priority in current climate policy strategies. This paper investigates how behavioral change can achieve a substantial reduction in greenhouse gas emissions in European high-income countries. It uses theoretical thinking and some core results from the HOPE research project, which investigated household preferences for reducing emissions in four European cities in France, Germany, Norway and Sweden. The paper makes five major points: First, car and plane mobility, meat and dairy consumption, as well as heating are the most dominant components of household footprints. Second, household living situations (demographics, size of home) greatly influence the household potential to reduce their footprint, even more than country or city location. Third, household decisions can be sequential and temporally dynamic, shifting through different phases such as childhood, adulthood, and illness. Fourth, short term voluntary efforts will not be sufficient by themselves to reach the drastic reductions needed to achieve the 1.5 °C goal; instead, households need a regulatory framework supporting their behavioral changes. Fifth, there is a mismatch between the roles and responsibilities conveyed by current climate policies and household perceptions of responsibility. We then conclude with further recommendations for research and policy.

Chris Riedy

The question of how to successfully facilitate the changes needed to avoid dangerous climate change has become a critical one for researchers and practitioners. In this article, I use Integral Theory to guide a scan of one important element of climate change response-household behavior change. The Integral scan is the first step in a long-term research project to develop an integral approach to household behavior change. It comprises an initial exploration of the perspectives needed to design behavior change strategies and suggests areas that will require further research as a more comprehensive map of initiatives emerges. I sketch out the process and principles of an integral approach to household behavior change initiatives, and on designing translative and transformative approaches.

Ecological Economics

Sarah Ann Wheeler

Nature Communications

Jocelyn Bélanger

No consensus exists regarding which are the most effective mechanisms to promote household action on climate change. We present a meta-analysis of randomised controlled trials comprising 3,092,678 observations, which estimates the effects of behavioural interventions holding other factors constant. Here we show that behavioural interventions promote climate change mitigation to a very small degree while the intervention lasts (d = −0.093 95% CI −0.160, −0.055), with no evidence of sustained positive effects once the intervention ends. With the exception of recycling, most household mitigation behaviours show a low behavioural plasticity. The intervention with the highest average effect size is choice architecture (nudges) but this strategy has been tested in a limited number of behaviours. Our results do not imply behavioural interventions are less effective than alternative strategies such as financial incentives or regulations, nor exclude the possibility that behavioural interventions could have stronger effects when used in combination with alternative strategies.

Sustainability

Erik Meijles

It is necessary to mobilize households so that they make changes to their everyday activities to address climate change. However, in the academic literature, there has been little focus on the perceived barriers to climate change action at the household level. Previous research has also highlighted a need for more studies in Latin America. This study contributes to the literature by filling these gaps. In a face-to-face and online survey administered in Nuevo Leon, Mexico, we asked participants what barriers impede their household from taking action to address climate change. Using thematic analysis, seven main barriers were identified: (i) everyday life; (ii) awareness of climate change; (iii) lack of perceived locus of control; (iv) physical limitations of the dwelling; (v) social, (vi) regulatory; and (vii) economic. Given the significant potential effects of climate change in the Nuevo Leon region, a better understanding of the barriers that prevent households from addressing cl...

Environment, Development and Sustainability

victor saenz corral

Journal of Energy & Environmental Law

Abstract: The individual and household sector accounts for roughly 40 percent of United States energy use and carbon dioxide emissions, yet the laws and policies directed at reductions from this sector often reflect a remarkably simplistic model of behavior. This Essay addresses one of the obstacles to achieving a “behavioral wedge ” of individual and household emissions reductions: the lack of an accessible, brief summary for policymakers of the key findings of behavioral and social science studies on household ...

Arief Azizy

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Climatic Change

Carrie Armel

E3S Web of Conferences

Cristina Visconti

international scholarly and scientific research & innovation

Mohammad Tamim

Tatiana Filatova

Susanne Moser

Nature Climate Change

Erin Bohensky

Caroline Oates

Wiley Interdisciplinary Reviews: Climate Change

Robert Gifford

Anne Marike Lokhorst

Anke Blöbaum

Center for Climate Change Communication

Analyses of Social Issues and Public Policy

Susan Clayton

Journal of Development and Agricultural Economics Vol. 10(9), pp. 279-291,2018

Cynthia Onunka , Eberechukwu Ihemezie

BMC public health

Dorian Litvine

Sustainable Communities Research Group

Jeff Bennett

Sarah Royston (formerly Hards)

Smart and Sustainable Built Environment

Carolyn S Hayles

Renewable and Sustainable Energy Reviews

Raquel Antolín-lópez

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

SYSTEMATIC REVIEW article

Promising behavior change techniques for climate-friendly behavior change – a systematic review.

• 1 Department of Sustainable Environmental Health Sciences, Medical School OWL, Bielefeld University, Bielefeld, Germany
• 2 Department of Environment and Health, Bielefeld School of Public Health, Bielefeld University, Bielefeld, Germany

Introduction: Besides societal and governmental actions to mitigate greenhouse gases, individual behavioral changes are also urgently needed to limit global temperature rise. However, these individual changes have proven to be difficult to achieve in the general population.

Methods: We conducted a systematic review in five electronic databases with the aim of systematically depicting the content of interventions that promote climate-friendly behavior in individuals and households in high- and upper-middle-income countries.

Results: We included 25 studies. The analyses included identification of the used Behavior Change Techniques (BCTs) and comparison of their promise ratio. Across our three outcome categories energy consumption, water consumption, and mobility the most frequently used BCT categories are not the ones that are most promising in terms of behavior change.

Discussion: Based on these results, our recommendation for climate change mitigation interventions is to include components that provide concrete instructions on how to perform the desired behavior (shaping knowledge) , setting goals and commitments (goals and planning) , substituting undesired behavior, and practicing desired behavior (repetition and substitution) . Other reviews with similar aims use different wordings, definitions, or degrees of detail in their intervention component labelling which makes it difficult to compare the results. We recommend to use a standardized classification system, like the BCT taxonomy in combination with the promise ratio, which this study has shown to be a suitable tool to classify applied intervention techniques and give an indication of successful techniques.

1 Introduction

The anthropogenic climate change is a global crisis with serious implications for public health and therefore requires appropriate action strategies, otherwise the impact on public health and the health of the planet will escalate in the near future ( 1 ). Climate change implications, like long periods of heat and drought, or heightened regional frequency and magnitude of precipitation and storms will have a lasting impact on land use, food security, and food production systems ( 1 – 4 ). The associated multiple health risks to the world’s population are already measurable today, in both low- and high-income countries ( 5 ).

Climate change mitigation and adaptation are the most important strategies to address current and future climate change implications. The primary goal of mitigation interventions is to reduce anthropogenic emissions or enhance the decrease of greenhouse gases, either on a societal or governmental level ( 1 ). Those actions to reduce carbon-emissions are a particularly effective approach to curbing greenhouse gases. However, far-reaching individual behavioral changes are urgently needed to achieve the goal of limiting global temperature rise as well. Lifestyle changes of individuals or communities can be implemented to curb carbon emissions, such as reducing daily car use, switching to green energy sources, or reducing energy use, meat and overall consumption. There are indications that households are responsible for about 72% of the global greenhouse gas emissions ( 6 ). The ability to take effective action to mitigate climate change is unevenly distributed among populations ( 7 ). In particular households of high-income countries like the United States and most Western European countries are major contributors to the emission of greenhouse gases and therefore an important target group for interventions ( 6 , 8 ). Analyses show that more than 60% of global greenhouse gas emissions stem from high income countries in North America and Western Europe ( 9 , 10 ). It is suggested that practice, policy and research should focus on behaviors associated with the greatest greenhouse gas emissions, i.e., mobility, housing and food ( 10 ).

Despite the knowledge in the general population about the causes and risks of climate change, sustainable behavioral changes seem difficult to achieve due to various barriers like strong habits and convenience ( 11 ). To date, a large number of intervention studies have been published that address different target groups and deal with changing of various aspects of climate-relevant behavior. Not only do the intervention concepts differ considerably in terms of their objectives and strategies but they also vary greatly in terms of their effectiveness ( 12 ). Particularly promising and reproducible intervention approaches that lead to a sustainable, climate-friendly behavioral change have not yet been identified. The majority of published interventions use a variety of stimuli which leads to difficulties in identifying the main factor for effectiveness ( 13 ). The content and techniques of successful interventions hence needs to be examined more closely and systematically ( 13 ). Therefore, one aim was to identify effective household interventions for climate-friendly behavior and their components (i.e., applied behavior change techniques).

The conceptualization of human behavior has been part of research over decades. There are many models that present influencing factors and therefore how to possibly change behavior, i.e., Theory of Planned Behavior, Social Cognitive Theory, Transtheoretical Model among others. The influencing factors presented and validated in those models can help to determine the various social and psychological components that affect behavior and accordingly develop or identify effective intervention strategies. Behavioral theories are seen as the crucial starting points of every intervention. Strategies developed to tackle behavioral patterns are addressing the so-called mechanism of action which are constructs from behavioral theories ( 14 ). One approach to systematize intervention strategies is the Behavior Change Technique Taxonomy (BCTTv1) developed by Michie et al. ( 15 , 16 ). This Behavior Change Techniques (BCTs) classification system was established to ensure a universally valid nomenclature and standardized language in the description of intervention methods that allows to specify, implement, evaluate and replicate complex behavior change interventions. Complex interventions are defined by one or more of the following characteristics: a high number of and interactions between intervention components, a high number and difficulty of behaviors required by those delivering or receiving the intervention, a high number of groups or organizational levels targeted by the intervention, and a high number and variability of outcomes ( 17 ). The theoretical framework of the BCTTv1 is the Behavior Change Wheel by Michie et al. ( 18 ) which is based on the assumption that a person’s behavior is based on the factors capability, opportunity, and motivation. One or more of them has to change in order to change a behavior. Michie et al. ( 18 ) developed nine intervention functions for this to choose from depending on the nature of the targeted behavior. These functions can be translated into the specific BCT for changing behavior ( 19 ). The BCTTv1 has already been applied in many review studies that have focused primarily on interventions to promote health-related behavioral changes, e.g., to reduce sedentary behavior or promote physical activity in the context of various diseases ( 20 – 22 ). But, to our knowledge none of the previous systematic reviews with a focus on intervention strategies to promote climate-friendly behaviors ( 12 , 23 – 26 ) has systematically classified the components of interventions based on the BCTTv1. Therefore, the second aim of this review was to analyze whether certain BCTs or combinations of different BCTs are more frequently used. To get an indication of the relative effectiveness of each BCT, a promise ratio was calculated as a quotient of a BCT’s frequency in promising and non-promising interventions ( 20 ).

The aim of any intervention that promotes climate-friendly behavior is to change current behaviors, and it is thus important that the BCTs that lead to behavioral changes are identified. A systematic recording of techniques and concepts can help to better understand how interventions can lead to climate-friendly behavior. Moreover, it may ensure that successful measures are reproducible. In addition, needs-based measures can be developed for specific population groups in order to achieve a sustainable impact.

2 Materials and methods

2.1 identifying and selecting studies.

We searched five databases in the fields of Medicine, Psychology, Geography, Public Health, and Ecology, namely MEDLINE (PubMed), PsycINFO, CINAHL, Embase, and Web of Science. Searches were limited to the languages English and German and the time period of the last 15 years (2007 to 2022). The search was conducted between 10/20/2022 and 10/21/2022 by LM using the search terms presented in Table 1 . The reporting follows the PRISMA statement for reporting systematic reviews and meta-analyses ( 28 ).

Table 1 . Search terms used in the electronic search using PICO ( 27 , 28 ).

Table 2 presents the inclusion and exclusion criteria. We included interventions that aim at behavior change in climate change mitigation or adaptation. Eligible studies measure quantifiable effects and focus on households or individual adults over the age of 18 years in upper middle- or high-income countries. Due to comparability, we excluded studies that reported on a category that was not reported in other eligible studies and studies that reported aggregated scores across different categories. Further, due to transferability, recycling behavior was excluded because of its country-specific framework conditions that would make transferability difficult.

Table 2 . Inclusion and exclusion criteria.

The screening process ( Figure 1 ) was conducted in teams of two authors. It started with title screening and disagreement led to inclusion of the study for the following abstract and full-text screening.

Figure 1 . PRISMA flow diagram of study selection process [layout following Page et al. ( 28 )].

2.2 Data extraction

From the included studies we extracted information regarding study characteristics (authors, year of publication, location of study (country and continent), sample size, mean age, sex/gender, and socio-economic status), as well as information regarding intervention characteristics (BCTs, BCT categories, outcome category, primary outcomes, results for primary outcomes, and theory basis) [all authors]. For each study two authors independently extracted information from the papers into an Excel sheet (LM, SLL, MR, MLG, TMC). The information was later merged by one of the authors.

2.3 Quality assessment

The methodological quality of the included studies was assessed using the Effective Public Health Practice Projects (EPHPP) quality assessment tool for quantitative studies ( 30 ). Based on the framework, two authors, TMC and MR, independently assessed each of the studies in the domains 1: selection bias, 2: study design, 3: confounders, 4: blinding, 5: data collection methods, and 6: withdrawals and drop-outs. Discrepancies were discussed and resolved. The overall study quality ratings were determined based on the individual domain ratings in consultation with the author SLL. Depending on the number of domains that were rated as “weak”, the overall study rating was either “weak” (two or more “weak” domain ratings), “moderate” (one “weak” domain rating), or “strong” (no “weak” domain ratings). The description and assessment scheme of the individual domains and the overall rating are publicly available on the EPHPP website.

2.4 Behavior change technique taxonomy and promise ratio

To identify and compare the used techniques to achieve behavior change in the interventions we used the Behavior Change Technique Taxonomy v1 (BCTTv1). It provides standardized definitions for intervention components aimed at changing study participants’ behavior ( 15 , 31 ). Coding was conducted independently by two authors, LM and SLL ( 32 ). All text sections describing the interventions in the included studies were scanned. The relevant text sections were then imported into a spreadsheet and assigned to BCT. After coding the first five publications, rules, definitions, and ambiguities were discussed and clarified. Finally, the codes were compared and final agreements were made.

The frequency of the used BCTs in each included intervention was counted [LM]. The relationship between the number of BCTs used and the promise rating of interventions was analyzed with a one-way ANOVA and the (very and quite) promising interventions were contrasted with the non-promising interventions by means of planned comparisons [MR]. As Levene’s test indicated homogeneity of variances, no adjustments were made to the results. A promise ratio was calculated to give an idea of the respective contribution of a specific BCT to the effectiveness of the intervention [SLL, MLG]. The promise ratio is an indicator that has been used before for this purpose ( 21 , 22 , 33 ) and results from the quotient of the frequency of a BCT in the (very and quite) promising interventions and the frequency in non-promising interventions ( 20 ). According to its developers Gardner et al. ( 20 ) we classified interventions as “very promising” if there was a significant effect on at least one indicator in the pre-post comparison of the intervention group. This effect also had to be greater than in a comparison group. Interventions were classified as “quite promising” if either a significant pre-post effect or a significant effect compared to a comparison group was found. Interventions were classified as “non-promising” if there was neither a significant pre-post change within the intervention group nor differences compared to a comparison group ( 20 ). To avoid over-interpretation of sparse data, BCTs were classified as promising if they were used in at least twice as many promising interventions as non-promising interventions (i.e., promise ratio ≥ 2), and in at least two interventions in total.

3.1 Screening process

In total, 13,617 publications were retrieved via database search [LM]. Of those 4,045 duplicates and 1,062 publications with ineligible document types, like reviews or anthologies were removed [LM]. The remaining 8,510 titles were screened (LM, MLG, MR, SLL, TMC), of which 7,948 were excluded. This resulted in an abstract screening of 562 studies (LM, MLG, MR, SLL, TMC), of which 464 studies were excluded. We then screened 98 studies by full text (LM, MLG, MR, SLL, TMC). Of these, 68 studies were excluded. An additional five studies were excluded after the screening process. Four of them reported on a category (e. g. flood adaptation or clothing sufficiency) no other study reported on, so that comparability was not possible. One was a follow-up study that we merged with the origin study. In the end, 25 studies were included in the review. The screening process is depicted in Figure 1 .

3.2 Study characteristics

The 25 studies included data from 14 countries on six continents. Fourteen studies were conducted in Europe ( 34 – 47 ), five in Asia ( 48 – 52 ), three in Australia ( 53 – 55 ) and one each in North America ( 56 ), South America ( 57 ) and Africa ( 58 ).

The different outcomes of the included studies can be assigned to the categories energy consumption [ n  = 17, ( 34 , 36 – 40 , 42 , 46 – 53 , 56 , 58 )], mobility [ n  = 8, ( 35 , 36 , 38 , 41 , 43 , 54 , 56 , 57 )], and water consumption [ n  = 3, ( 44 , 45 , 55 )]. Three of the studies included two categories and are therefore assigned twice ( 36 , 38 , 56 ).

Sample sizes varied between 16 and 4,358 participants. Most studies had less than 100 [ n  = 8, ( 35 , 39 , 43 , 47 , 48 , 53 , 55 , 56 )] or between 100 and 499 participants [ n  = 12, ( 34 , 36 , 38 , 42 , 45 , 46 , 49 – 52 , 54 , 58 )]. Three studies had 500 to 999 participants ( 41 , 44 , 57 ), two studies had more than 1,000 ( 37 , 40 ). Recruitment happened either via advertising ( n  = 11) or via invitation ( n  = 15). One study used both methods ( 43 ).

Male and female participants were included in 14 studies ( 34 , 37 , 38 , 41 – 46 , 52 – 54 , 56 , 57 ). Eleven studies did not report on sex or gender.

Mean age of participants was reported by nine out of 25 studies and ranged from 35.4 years ( 43 , 57 ) to 51.07 years ( 54 ). In five studies it was between 40 and 50 years ( 34 , 38 , 44 , 46 , 54 ), in three studies between 35 and 40 years ( 45 , 56 , 57 ) and one study had intervention groups of both age categories ( 43 ). Five studies included only adults ( 41 , 46 , 53 , 56 , 57 ). Two studies explicitly stated to include adults as well as people under 18 years as part of the household that took part in the intervention ( 38 , 39 ). Nine studies did not report on age at all ( 35 , 36 , 40 , 47 – 49 , 51 , 55 , 58 ).

Regarding socioeconomic status (SES) of the participants ten studies gave no information, and 15 studies reported on income and/or education level ( 34 , 36 – 38 , 41 , 43 , 44 , 46 , 50 – 53 , 56 – 58 ).

Study characteristics are presented in Table 3 .

Table 3 . Details of included studies.

3.3 Frequency of used behavior change techniques

We identified 29 out of 93 BCTs across all interventions. On average, an intervention employed 4.4 BCTs (median = 4; min. = 2; max. = 9). A detailed list about the frequency of application of individual BCTs in each category and the applied BCTs per intervention is provided in Supplementary Tables S1 , S2 .

These BCTs cover 13 out of 16 BCT categories. Of the total amount of assigned codes ( n  = 110), 16% ( n  = 18) belong to the BCT category natural consequences , 16% ( n  = 18) to feedback and monitoring , 15% ( n  = 17) to comparison of behavior , 12% ( n  = 13) to goals and planning , 10% ( n  = 11) to shaping knowledge , 9% ( n  = 10) to reward and threat , 6% ( n  = 7) to repetition and substitution , 5% ( n  = 5) to associations , 3% ( n  = 3) to social support , 3% ( n  = 3) to comparison of outcomes , 3% ( n  = 3) to antecedents , 1% ( n  = 1) to identity , and 1% ( n  = 1) to self-belief (see Figure 2 ).

Figure 2 . Frequenc y ( n ) of behavior change technique categories.

The most used individual BCTs were Feedback on outcome(s) of behavior ( n  = 14), Information about social and environmental consequences ( n  = 12), Instruction on how to perform a behavior ( n  = 11) and Social comparison ( n  = 11). Other codes were used six times or less.

3.4 Promise ratio

In very promising interventions the number of BCTs ranged from 1 to 6, in quite promising interventions from 2 to 8 and in the five non-promising interventions from 2 to 4 (see Table 4 ). There was no significant relationship between the number of BCTs used and the promise rating of interventions (F [2,24] = 1.09, p  = 0.35). Although (very and quite) promising interventions used more BCTs (very promising: m  = 3.90, SD = 1.37; quite promising: m  = 4.00, SD = 1.94) than did non-promising interventions ( m  = 2.80, SD = 0.84), the difference was not significant ( t [22] = 1.47, p  = 0.155).

Table 4 . Frequency of BCT category stratified by very, quite, and non-promising interventions and promise ratios.

The techniques associations, reward and threat, identity and self-belief were unique in the very and quite promising interventions. The promise ratio (PR), which gives an indication of the effectiveness of the contribution of a specific BCT to an intervention was highest for the BCTs shaping knowledge (PR = 9) and goals and planning (PR = 8) followed by repetition and substitution (PR = 6) (see Table 4 ). For the BCTs feedback and monitoring and comparison of behavior a PR of 4 was calculated, and a PR of 3 for natural consequences. The BCTs social support , comparison of outcome and antecedents were only used for three interventions each, resulting in a PR = 2.

3.5 Outcomes and effectiveness of interventions differentiated by category

Each intervention study was assigned to a category (i.e., energy consumption, water consumption, and mobility). The behavior change outcomes as well as the intervention techniques, categorized with the BCTTv1, will be presented in this section according to the assigned category. The classification of whether the intervention was very, quite or non-promising based on the definition by Gardner et al. ( 20 ) is also presented. Table 3 gives additional context to the studies.

3.5.1 Water consumption

This category includes three studies ( 44 , 45 , 55 ). The particular observed behavior is shower frequency ( 45 ), shower duration, water use or flow rate ( 44 , 55 ).

Two of the studies implemented a very promising ( 44 , 45 ) and one study a quite promising ( 55 ) intervention. As per our BCT-coding, the intervention strategy by Willis et al. ( 55 ) is based on feedback and monitoring as well as antecedents (i.e., digital shower meter) to reduce shower duration, water volume and flow rate in the study group. The pre-post-comparison showed a significant reduction in mean shower time (mean difference: 1.34 min, p  < 0.05), water volume used (mean difference: 15.40 L, p  < 0.05) as well as flow rate (mean difference 1.00 L/min, p  < 0.05). The authors state that most of the participants who already had short shower durations (i.e., below 5 min) further reduced their time while long shower durations could not be reduced ( 55 ). The second study compared duration and shower frequency in a pre-post-design using the intervention techniques goals and planning, natural consequences (economic vs. financial consequences), associations, and identity but without significant effects. Only a subgroup analysis revealed that one condition (environmental consequences) had a significant impact on reducing shower frequency in a four-day period [Mean: 2.98 times (pre); Mean: 2.67 times (post); p  < 0.01] ( 45 ). The third study compared the intervention with a control group. We found that feedback and monitoring, natural consequences, comparison of behavior and antecedents regarding shower behavior (i.e., shower time, flow rate average temperature) were applied. As a result, in all domains studied significant reductions were found (shower time: −51 s, p  < 0.01; flow rate: −0.2 L/min, p  < 0.05; average temperature: −0.32°C, p  < 0.05) ( 44 ).

3.5.2 Mobility

The category mobility was addressed in eight intervention studies ( 35 , 36 , 38 , 41 , 43 , 54 , 56 , 57 ) with the aim to change the mode or frequency of mobility and to reduce greenhouse gas emissions. The interventions of the category mobility are predominated by the BCT categories natural consequences, comparison of behavior and reward and threat.

There were four very promising interventions ( 36 , 41 , 43 , 57 ) which promoted a significant shift to alternative mobility compared to the baseline and a comparator. Two of the studies promoted bicycle use for commuting ( 41 , 57 ). In the first intervention study with the techniques reward and threat the shift from car use to e-cycling was significantly fostered (baseline of 100% car users to 68% e-cycling six months after start) ( 41 ). The second intervention study ( 57 ) on bicycle-use was based on natural consequences, comparison of behavior and repetition and substitution , as per our coding, and showed no significant difference between intervention and control group after the intervention (bike use: Intervention: n  = 208 (47.5%), Control: n  = 184 (42.0%), p  = 0.10). The other three studies aimed to increase the use of various alternative means of transport for everyday routes. One analyzed if reward and threat , feedback and monitoring and comparison of behaviors may lead to more frequent bike use in sports club teams ( 43 ). The effect of a more frequent bicycle use in the teams was significant during the intervention in comparison to baseline ( F (2) = 3.62, p  < 0.05). However, the effect was only temporary and car use increased again after the intervention ( 43 ). In the fourth very promising study we identified the techniques antecedents , shaping knowledge , natural consequences and comparison of behavior ( 36 ). The intervention did not show any difference between the intervention and control group, yet revealed in both groups significant pre-post differences (vehicle use in intervention group at baseline: Mean: 21,966 kWh (SE: 3,171) and in year 3: Mean: 14,907 kWh (SE: 2,014); p  = 0.02) ( 36 ).

Two quite promising interventions ( 35 , 54 ) revealed a significant pre-post effect on mobility. The first study ( 54 ) examined changes in transportation choice behavior, i.e., reduced car use, and increased bus and walking trips between an intervention and control group. The intervention techniques we identified were goals and planning, self-belief, shaping knowledge and repetition and substitution . As a main result the intervention significantly increased walking trip time (Mean increase: 3.18 min, SD: 7.70 min, p  < 0.05) as well as walking distance (Mean increase: 0.39 km, SD: 0.71 km, p  < 0.01) ( 54 ). The second study focused on influencing the mode of transportation quantity using goals and planning, feedback and monitoring, natural consequences and comparison of behavior as techniques ( 35 ). The pre-post comparison in the intervention group showed significant differences for aspects of individual travel behavior, i.e., decreased car dependency (Cohen’s d = 0.28) and increased active mobility such as walking and cycling (Cohen’s d = 0.45).

Two non-promising interventions also intended to reduce car usage and increase active mobility but could not reveal significant effects ( 38 , 56 ). The intervention techniques we identified were social support , natural consequences and comparison of behavior ( 38 ) and feedback and monitoring , natural consequences , repetition and substitution and comparison of outcomes ( 56 ).

3.5.3 Energy consumption

More than half of the included studies ( n  = 17) assessed interventions regarding behavior change in energy consumption, i.e., gas and electricity use ( 34 , 36 – 40 , 42 , 46 – 53 , 56 , 58 ). The predominantly used intervention techniques in this category were feedback and monitoring ( n  = 11), comparison of behavior ( n  = 11), shaping knowledge ( n  = 10) and natural consequences ( n  = 10).

Five of the interventions were rated as very promising ( 34 , 36 , 39 , 47 , 58 ). The first study applied the BCT categories feedback and monitoring, shaping knowledge, comparison of behavior as well as repetition and substitution , which resulted in total energy reduction in the intervention group (gas and electricity was measured, no proof of significance given). The difference between intervention and control group was also significant (Mean difference of energy savings: 7.9%, t (16) = −1.83, p  < 0.05) ( 39 ). The second intervention applied goals and planning , reward and threat , feedback and monitoring , comparison of behavior , shaping knowledge and social support ( 47 ). This led to a significant energy reduction effect compared to the control condition ( F (2, 85) = 5.02, p  = 0.009) ( ibid. ). The third study used the BCT categories goals and planning, feedback and monitoring, natural consequences and comparison of behavior. Households exposed to the interventions saved significantly more energy compared to the control group ( F (2,186) = 9.02, p  < 001) ( 34 ). The fourth study implemented the BCT categories feedback and monitoring , shaping knowledge , natural consequences , associations and repetition and substitution ( 58 ). This led to significant energy reduction in the intervention group (Mean reduction: −24.50 kWh, p  < 0.05). The reduction was higher than in the control group, however no effect size was reported in the study ( 58 ). The fifth very promising intervention in this category used the intervention techniques antecedents, shaping knowledge, natural consequences and comparison of behavio r ( 36 ). The study revealed differences in the pre-post comparison between the intervention and control group, respectively (electricity use in intervention group at baseline: Mean: 15.0 kWh (SE: 1.29) and in year 3: Mean: 12.0 kWh (SE: 0.95); p  < 0.01), yet there were no significant differences between intervention and control group ( 36 ).

Seven interventions were classified as quite promising and promoted significant energy savings either compared to baseline or a comparator ( 40 , 42 , 49 – 53 ). In the first study ( 52 ) we found seven techniques that were applied in their intervention (i.e., feedback and monitoring , natural consequences , comparison of behavior , associations , repetition and substitution , comparison of outcome , reward and threat ). They stated that a significant reduction of energy consumption was achieved within the different intervention groups as well as between the control and intervention group (no effect estimates in study) ( ibid. ). The second ( 42 ) and third study ( 50 ) each applied two strategies: goals and planning and monitoring and feedback and shaping knowledge and associations , respectively. The second study showed a significant effect between the two conditions ( F (1, 116) = 15.93, p  < 0.001, ⴄ 2  = 0.12) ( 42 ). The third study revealed significant pre-post reductions (mean energy consumption reduction: 225.63 kWh, p  < 0.001) ( 50 ). The fourth quite promising intervention study ( 51 ) used feedback and monitoring, natural consequences and comparison of behavior and showed a significant effect in the pre-post comparison of the information and environmental contribution feedback (mean percentage of electricity saved: 29–49%, p  < 0.05). In the fifth study ( 40 ) we coded the intervention strategies goals and planning, feedback and monitoring, shaping knowledge, natural consequences and reward and threat . Their analyses revealed a significant influence of the intervention on energy consumption (Constant: 10.48, Beta (intervention arm 1): −3.5, p  < 0.05; Beta (intervention arm 2): −0.47, p  < 0.01; R-squared = 0.93). The sixth quite promising intervention ( 53 ) used goals and planning , social support , shaping knowledge , comparison of behavior , comparison of outcomes and reward and threat and showed an increase in energy saving behaviors in the intervention group (Mean number of activities at baseline: 9.49 (SD = 3.90), Mean number of activities post intervention: 14.04 (SD = 0.52), p  < 0.001). The seventh intervention applied the techniques shaping knowledge , natural consequences , comparison of behavior and associations ( 49 ). Electricity consumption decreased significantly in the intervention group from baseline to post-intervention (Mean decrease: 2.21 kWh per capita per day, p  < 0.01) ( 49 ).

Five interventions were rated as non-promising, because they did not foster any significant energy savings, neither in the intervention groups in a pre-post comparison nor compared to a control group ( 37 , 38 , 46 , 48 , 56 ). One study ( 48 ) used the three most frequently used techniques in the energy category: feedback and monitoring , shaping knowledge and comparison of behavior. The second ( 37 ) and third study ( 46 ) used only two techniques each, feedback and monitoring and comparison of behavior and goals and planning and natural consequences, respectively. The fourth study ( 38 ), using social support , natural consequences and comparison of behavior, did not reveal any significant changes either and was therefore one of the non-promising interventions. The fifth study ( 56 ) used the techniques feedback and monitoring , natural consequences , repetition and substitution and comparison of outcomes. It could not reveal any significant behavior change effect of the intervention.

3.6 Methodological quality

All 25 studies were assessed regarding the six EPHPP quality domains, as described in the methods section (see chapter 2.3). A total of 14 studies (56%) received a “weak” overall quality rating ( 34 , 35 , 38 , 40 , 42 , 43 , 45 – 47 , 49 , 50 , 53 , 55 , 58 ). Nine studies (36%) received a “moderate” overall quality rating ( 37 , 39 , 41 , 48 , 51 , 52 , 54 , 56 , 57 ) and only two studies (8%) a “strong” overall quality rating ( 36 , 44 ). Selection bias was mostly rated ‘moderate’ if not ‘weak’ because of widespread unclarity concerning the response rates, although almost all samples were judged to be reasonably to fully representative of the respective target population. Most authors provided adequate information on study design and randomization, but the randomization procedure was rarely described. Three of the studies had a randomized controlled trial or controlled clinical trial design, while all others had either cohort analytic (two group pre-post) or interrupted time series designs. Lack of information about potential confounders between groups and whether and how they were controlled, and also the validity and reliability of data collection methods, lead to ‘weak’ ratings for about half of the studies in these categories. Only “weak” and “moderate” ratings were given for blinding, as hardly any information was provided on whether the outcome assessors or the study participants themselves were aware of participants’ intervention status. In slightly more than a quarter of the studies, the authors did not report on withdrawal and drop-out rates, resulting in a ‘weak’ rating. When withdrawal and drop-out rates were reported, however, they were mostly strong. The results of the quality assessment are presented in the Supplementary Table S3 .

4 Discussion

The aim of this review was to investigate which intervention techniques, represented through Behavior Change Techniques (BCTs), have been used and proved promising in interventions to promote climate-friendly behavior of individuals and households. To our knowledge, this is the first review on climate mitigation and adaptation behavior change using the Behavior Change Technique Taxonomy (BCTTv1).

Our results show that several intervention strategies to promote climate-friendly behavioral changes in the categories energy, water and mobility were effective. The BCT categories feedback and monitoring , shaping knowledge , natural consequences , and comparison of behavior are part of more than a third of the 25 included studies, however, the most effective BCTs according to their frequency in promising studies, as indicated by the promise ratio, seem to be goals and planning , shaping knowledge and repetition and substitution .

The setting or agreement on a goal for a behavior or an outcome, commitment, as well as the prediction of barriers or facilitators are part of goals and planning ( 31 ). Consistent with our results, reviews on energy consumption behavior as well as a review study by Nisa et al. ( 10 ), who analyzed RCTs of climate-friendly behavior interventions for households, found goal setting ( 18 , 55 ) and commitment ( 17 , 55 ) to be effective. However, they state that the effect of commitment is to be considered with caution as giving up commitment leads to exclusion of the study in RCT studies. Homburg et al. ( 59 ) found that if they randomly selected a person who received instruction, commitment or goal setting as an intervention technique, they are 54–75% more likely to exhibit climate-friendly behavior during the study period than a randomly selected person who did not receive this intervention. They labelled one effective technique instruction . The BCT equivalent is instructions on how to perform the behavior in the category shaping knowledge ( 31 ) that we found to be particularly effective. Wynes et al. ( 60 ) also found instructions to be effective. They used the categorization by Osbaldiston and Schott ( 61 ) who combined different wordings of reviews (e.g., information, knowledge, persuasion) under the label instructions. Nevertheless, they found a rather small effect of instructions in their meta-analysis ( 61 ). Rau et al. ( 12 ) recommend a combination of techniques involving education, training and feedback. The word training seems to be similar to instructions on how to perform the behavior . Repetition and substitution was mainly labelled for one of its defining techniques in this review, i.e., behavioral practice/rehearsal ( 31 ). Particularly this technique has proven to be part of habit formation and therefore a sustained behavior change ( 62 ). Surprisingly, there are only few studies supporting our findings ( 63 , 64 ). Possibly, the identification of such studies is more difficult due to different wording.

There is the above-mentioned evidence which supports our findings, yet there are studies that highlight other techniques to be most effective. The strongest effects in the review of Nisa et al. ( 13 ) were found for nudges/choice architecture and social comparison . The former as such are not described in the BCTTv1 but could be attributed to the BCT restructuring the physical environment ( 31 ). Other studies found social influence techniques ( 26 , 65 ), feedback ( 24 , 25 , 60 ), gamification , community-based techniques ( 25 ), and appliance labeling ( 65 ) to be effective techniques. It becomes clear that category systems or labelling of intervention techniques in intervention studies have some overlaps (e.g., goal setting, commitment, feedback ), but also have differences in wording, definition, or degree of detail. Nonetheless, it seems that goals and planning , shaping knowledge and repetition and substitution might be recommendable basic components for behavior change interventions.

4.1 Characteristics of included studies

In regard to the characteristics of the identified studies, there are several points worth noting. First, the continents from which the studies originate show a fairly uneven distribution. As reported, most of the identified studies were conducted in Europe, a few in Asia and Australia, and only one in North America, South America, and Africa, respectively. Although the historically low per capita CO 2 emissions in South America and Africa may partly explain the low number of studies, North America has, and historically has had, comparatively high per capita CO 2 emissions ( 66 ) and is still only represented by a single study. The results are hence only directly generalizable to interventions targeting individuals and households in high- and upper-middle-income countries, primarily from Europe.

The behavioral outcome categories targeted by the included intervention studies were also very unevenly distributed. Most studies targeted energy consumption, about a third targeted mobility, and a few targeted water consumption. This is in line with the findings of Wynes et al. ( 60 ) and Rau et al. ( 12 ), who also identified more interventions targeting energy consumption than interventions targeting other outcome categories combined. This phenomenon might be due to high feasibility and easy outcome monitoring. Household energy conservation measures are described as straight-forward and easy to perform ( 60 , 67 ). Saving energy by lowering shower water or air temperature by a few degrees might be associated with lower cost or less effort for adaptation and maintenance than trading car rides for bike rides, for example, for both the participant and the one measuring the changes ( 67 ).

Lastly, the goal of this study was to review mitigation and adaptation behavior change interventions. However, we only found one study with an intervention targeting climate change adaptation behavior using our search criteria, which ultimately had to be excluded as well. Given that climate change is now inevitable ( 1 ), more research focusing on resilient adaptation measures would likely prove helpful. Generally speaking, more research in regard to climate-friendly behaviors, preferably with a focus on behaviors with the most impact, such as mobility and energy behaviors ( 8 , 68 – 70 ), will be needed.

4.2 Quality of included studies

The quality of the studies we identified in this review leaves room for improvement. The quality ratings are mostly the result of poor overall reporting in the studies, which is common in intervention research ( 16 , 71 – 76 ) and often prevents accurate rating. Underreported aspects include among others response rates, randomization procedures, potential confounders and whether and how they were controlled, validity and reliability of data collection methods, blinding, and withdrawal as well as dropout rates. In addition, a lot of the studies do not meet basic reporting standards in regard to the study population which affects comparability.

The vast majority of the intervention studies employed cohort analytic (two group pre-post) or interrupted time series designs. The remaining three studies used randomized controlled trial or controlled clinical trial designs, which have been found to be lacking in climate-friendly intervention research ( 60 ). These are generally appropriate study designs to test mitigation and adaptation interventions, however there are some design aspects that could be improved. For one, more randomization in regard to the group allocation would be preferable for more robust study results. Furthermore, about one third of the studies used control groups that received different interventions or did not use a control group at all. To truly determine intervention components that reliably change climate-friendly behavior more studies with control groups that did not receive the intervention are essential. Lastly, the implementation of longitudinal designs with follow-up measures is needed for interventions targeting climate-friendly behavior, as potentially promising effects do not necessarily persist beyond the intervention period ( 13 ). However, a lack of follow-up measures in intervention studies targeting climate-friendly behavior has been noted ( 60 ). Of the studies reviewed here, seven used longitudinal designs (longer than 12 months), and only five studies collected follow-up data after the end of the intervention period. Consistent with the findings of Nisa et al. ( 13 ), positive intervention effects were partially sustained at follow-up in only one of these five studies ( 54 ), whereas the others reported non-significant or non-sustained effects ( 38 , 43 , 47 , 56 ).

4.3 Strengths and limitations of this review

The strengths of this systematic review lie in a number of different aspects. For one, its focus on areas of daily life where climate-friendly changes are particularly difficult to realize is to be emphasized, as these are relevant levers and targets for research and practice. In addition, this review mainly includes households as intervention participants, which gives the interventions a realistic setting, as most significant changes in climate-friendly behavior affect the entire household or require its participation and hence underlines its relevance for practice. To ensure that our review covers a substantial amount of existing research, we used the five largest and most relevant databases in our literature search, spanning different key disciplines. The intervention components were described using a proven standardized instrument, the Behavior Change Taxonomy, and additionally analyzed by calculating promise ratios for each BCT to determine their success.

Even though the BCTTv1 was developed primarily for intervention design, reporting, and replication, the authors were well aware of its potential use in systematic reviews ( 15 , 77 ) as “a reliable method for extracting information about intervention content, thus identifying and synthesizing discrete, replicable, potentially active ingredients (or combinations of ingredients) associated with effectiveness” ( 15 ). Besides the other advantages, using the BCTTv1 allowed us to compare studies targeting the same behavior change domains, which would otherwise be difficult to compare, by focusing on the BCTs that were used in the interventions rather than the varying outcome measures employed.

Although the BCTTv1 has its strengths, such as easier classification and greater comparability, the use of the system in this study and its consequences for the interpretation of the results need to be discussed. For one, as already mentioned, the studies found in this review and interventions in general are often described in insufficient detail ( 16 , 71 – 76 ). This makes it more difficult to discern specific BCTs ( 78 , 79 ). This means, on the one hand, that it is likely that not all intervention techniques that have actually been delivered by the researchers were identified with the BCTTv1 and, on the other hand, that it is possible that the identified BCTs might not entirely match the actual interventions. The lack of detail in intervention descriptions, however, is a problem that other reviews using other standardized or non-standardized classification systems will inevitably also encounter and do not have a standardized way to deal with, thus adding to the problem.

Another important aspect to consider when evaluating BCTs is that it is difficult to single out the effects of individual BCTs. One reason for this is, that each of studies reviewed used at least two BCTs in their intervention programs. The use of multiple interacting components in intervention research is common ( 16 , 71 , 80 ) and using such complex interventions, i.e., using more than one BCT to target different barriers and facilitators of the behavior, has been recommended to change target behaviors ( 12 ). Such multicomponent interventions using multiple BCTs are not necessarily the more promising interventions ( 81 ). However, when combinations of techniques are based on theory, interventions may be more effective ( 76 , 81 – 83 ). In our review, the number of BCTs per study was not significantly correlated with the promise rating of the included studies either. Therefore, although the promise ratio of the individual BCT is reported here, it is important to keep in mind that BCTs are typically used in combination and may be effective only in that specific combination. In addition, BCTs judged to be less effective here could possibly be effective in combination with other BCTs. Like Andor et al. ( 65 ) and Nisa et al. ( 13 ), we also encourage researchers to use fewer intervention components simultaneously in future studies to better differentiate the potential of individual techniques or specific combinations of techniques. If researchers wish to use combinations of intervention components, they should preferably be theory based.

Lastly, we evaluated the BCTs used in the studies outside of the broader intervention context. Even though the evaluation of the entire intervention process is important for future replication of successful interventions ( 80 ), incoherent and incomplete intervention reporting makes evaluation difficult. Assessing the context and mode of delivery of the interventions and the theory base of the combination of BCTs was beyond the scope of the present systematic review. In the future, researchers should describe interventions, their theory base, and their context and mode of delivery in more detail and consider labelling the used intervention techniques according to the BCTTv1 ( 15 ) to standardize reporting and enhance comparability, thereby minimizing research waste and improving replicability and synthesis. Researchers need to keep in mind, however, that the feasibility and effectiveness of intervention strategies also depend on the kind of behavior, intervention design, the frequency of the behavior, behavior costs, and factors influencing the maintenance ( 67 ). This means that while BCTs help with classification, transparency, and reproducibility, they may not necessarily help with applicability across populations and behaviors. Fit-for-purpose tailoring is always needed to an extent, considering factors like the nature of the target behavior and the determinants of the behavior.

Concerning the promise ratio, one aspect to bear in mind when interpreting the results is that it cannot depict the isolated effect of individual BCTs. Other BCTs, different target behaviors, populations, settings and the diverse designs and possible embedded biases cannot be factored out with this method alone. The promise ratio only indicates the ratio of BCTs in promising versus non-promising interventions and does not take into account the effect sizes. This means that the actual behavior change achieved in interventions that are labelled promising could be negligible, but still affect the promise ratio of the respective BCTs. However, this is only a concern in regard to studies with very high sample sizes. Meta-analyses and mega-analyses (which do estimate effect sizes) could offer complementary information about which interventions are most effective. Furthermore, the publication bias in research favors an overestimation of the promise ratio of the BCTs, highlighting the importance of publishing non-significant intervention results. Lastly, following Gardner et al. ( 20 ), we assessed the promise ratio of the BCTs rather conservatively, that is, only when “they were used in two or more interventions, and at least twice as many promising as non-promising interventions” ( 20 ). As a consequence, BCTs that were rarely employed in the surveyed interventions were not assessed, but could nevertheless show potential and might warrant further investigation. Despite its drawbacks, calculating the promise ratio enabled us to easily identify intervention techniques that show promise and thus warrant further and more robust investigation, as well as, to highlight research gaps.

5 Conclusion

A wide range of intervention techniques have been used in climate mitigation or adaptation behavior change interventions for individuals and households in upper-middle and high-income countries, but certain techniques are more frequently used within and across the intervention categories. The three most frequently used intervention technique categories, however, are not the technique categories that are most promising in terms of behavior change. Based on the currently available evidence, our recommendation for individuals, communities, municipalities, or other entities planning to implement climate change mitigation interventions is to include components that include providing concrete instructions on how to perform the desired behavior ( shaping knowledge ), setting goals and commitments ( goals and planning ), substituting undesired behavior, and practicing desired behavior ( repetition and substitution ), as interventions with these components show the most promise.

Other reviews with similar aims use different wordings, definitions, or degrees of detail in their intervention component labelling which makes comparison of results difficult. We recommend to use a standardized classification system, like the BCT taxonomy in combination with the promise ratio, which this study has shown to be a suitable tool to classify applied intervention techniques and present an indication of successful techniques. In our experience, their combined strengths clearly outweigh their limitations. However, the limitations of the included studies, concerning intervention methods and reporting standards, still severely inhibit the potential results of reviews like this one. Going forward, intervention studies targeting climate-friendly behavior should consider designing and reporting their intervention components based on the BCTTv1 definitions, to facilitate replication and synthesis.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author.

Author contributions

LM: Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing, Formal analysis. SLL: Writing – original draft, Writing – review & editing, Conceptualization, Data curation, Formal analysis, Investigation, Methodology. MR: Writing – original draft, Writing – review & editing, Conceptualization, Data curation, Formal analysis, Investigation, Methodology. CH: Writing – review & editing. ML-G: Writing – original draft, Writing – review & editing, Conceptualization, Data curation, Formal analysis, Investigation, Methodology. TMC: Conceptualization, Data curation, Formal analysis, Funding acquisition Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. We acknowledge the financial support of the German Research Foundation (DFG) and the Open Access Publication Fund of Bielefeld University for the article processing charge.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2024.1396958/full#supplementary-material

1. Intergovernmental Panel on Climate Change. Climate change 2023. Synthesis Report In: Summary for Policymakers . Geneva, Switzerland: Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change Core Writing Team H. Lee and J. Romero (eds) (2023) doi: 10.59327/IPCC/AR6-9789291691647.001

Crossref Full Text | Google Scholar

2. European Parliamentary Research Service. EU climate action policy: Responding to the global emergency. European Union; (2021) Available at: https://www.europarl.europa.eu/RegData/etudes/STUD/2021/689378/EPRS_STU(2021)689378_EN.pdf

Google Scholar

3. Hoegh-Guldberg, O, Jacob, D, Taylor, M, Bindi, M, Brown, S, Camilloni, I, et al. Impacts of 1.5°C global warming on natural and human systems: an IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty; 2018 [cited 2024 Jan 4]. Available at: https://www.ipcc.ch/site/assets/uploads/sites/2/2019/02/SR15_Chapter3_Low_Res.pdf

4. Battisti, DS, and Naylor, RL. Historical warnings of future food insecurity with unprecedented seasonal heat. Science . (2009) 323:240–4. doi: 10.1126/science.1164363

PubMed Abstract | Crossref Full Text | Google Scholar

5. Watts, N, Amann, M, Arnell, N, Ayeb-Karlsson, S, Beagley, J, Belesova, K, et al. The 2020 report of the lancet countdown on health and climate change: responding to converging crises. Lancet . (2021) 397:129–70. doi: 10.1016/S0140-6736(20)32290-X

6. Dubois, G, Sovacool, B, Aall, C, Nilsson, M, Barbier, C, Herrmann, A, et al. It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures. Energy Res Soc Sci . (2019) 52:144–58. doi: 10.1016/j.erss.2019.02.001

7. Hampton, S, and Whitmarsh, L. Choices for climate action: A review of the multiple roles individuals play. One Earth . (2023) 6:1157–72. doi: 10.1016/j.oneear.2023.08.006

8. Lacroix, K. Comparing the relative mitigation potential of individual pro-environmental behaviors. J Clean Prod . (2018) 195:1398–407. doi: 10.1016/j.jclepro.2018.05.068

9. Andrew, RM, and Peters, GP. The Global Carbon Project's fossil CO2 emissions dataset: Version 2021v34; 2021 [cited 2024 May 22]. Available at: https://zenodo.org/records/5569235 .

10. Papies, EK, Nielsen, KS, and Soares, VA. Health psychology and climate change: time to address humanity's most existential crisis. Health Psychol Rev . (2024):1–31. doi: 10.1080/17437199.2024.2309242

11. Stankuniene, G, Streimikiene, D, and Kyriakopoulos, GL. Systematic literature review on behavioral barriers of climate change mitigation in households. Sustain For . (2020) 12:7369. doi: 10.3390/su12187369

12. Rau, H, Nicolai, S, and Stoll-Kleemann, S. A systematic review to assess the evidence-based effectiveness, content, and success factors of behavior change interventions for enhancing pro-environmental behavior in individuals. Front Psychol . (2022) 13:901927. doi: 10.3389/fpsyg.2022.901927

13. Nisa, CF, Bélanger, JJ, Schumpe, BM, and Faller, DG. Meta-analysis of randomised controlled trials testing behavioural interventions to promote household action on climate change. Nat Commun . (2019) 10:4545. doi: 10.1038/s41467-019-12457-2

14. Carey, RN, Connell, LE, Johnston, M, Rothman, AJ, Bruin, M, Kelly, MP, et al. Behavior change techniques and their mechanisms of action: A synthesis of links described in published intervention literature. Ann Behav Med . (2019) 53:693–707. doi: 10.1093/abm/kay078

15. Michie, S, Richardson, M, Johnston, M, Abraham, C, Francis, J, Hardeman, W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med . (2013) 46:81–95. doi: 10.1007/s12160-013-9486-6

16. Michie, S, Wood, CE, Johnston, M, Abraham, C, Francis, JJ, and Hardeman, W. Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data). Health Technol Assess . (2015) 19:1–188. doi: 10.3310/hta19990

17. Craig, P, Dieppe, P, Macintyre, S, Michie, S, Nazareth, I, and Petticrew, M. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ . (2008) 337:a1655. doi: 10.1136/bmj.a1655

18. Michie, S, van Stralen, MM, and West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci . (2011) 6:42. doi: 10.1186/1748-5908-6-42

19. Michie, S, Atkins, L, and West, R. The behaviour change wheel. A guide to designing interventions; o. J. Available at: https://www.behaviourchangewheel.com/about-wheel

20. Gardner, B, Smith, L, Lorencatto, F, Hamer, M, and Biddle, SJH. How to reduce sitting time? A review of behaviour change strategies used in sedentary behaviour reduction interventions among adults. Health Psychol Rev . (2016) 10:89–112. doi: 10.1080/17437199.2015.1082146

21. Hailey, V, Rojas-Garcia, A, and Kassianos, AP. A systematic review of behaviour change techniques used in interventions to increase physical activity among breast cancer survivors. Breast Cancer . (2022) 29:193–208. doi: 10.1007/s12282-021-01323-z

22. Brierley, ML, Chater, AM, Smith, LR, and Bailey, DP. The effectiveness of sedentary behaviour reduction workplace interventions on Cardiometabolic risk markers: A systematic review. Sports Med . (2019) 49:1739–67. doi: 10.1007/s40279-019-01168-9

23. McAndrew, R, Mulcahy, R, Gordon, R, and Russell-Bennett, R. Household energy efficiency interventions: A systematic literature review. Energy Policy . (2021) 150:112136. doi: 10.1016/j.enpol.2021.112136

24. Abrahamse, W, Steg, L, Vlek, C, and Rothengatter, T. A review of intervention studies aimed at household energy conservation. J Environ Psychol . (2005) 25:273–91. doi: 10.1016/j.jenvp.2005.08.002

25. Iweka, O, Liu, S, Shukla, A, and Yan, D. Energy and behaviour at home: A review of intervention methods and practices. Energy Res Soc Sci . (2019) 57:101238. doi: 10.1016/j.erss.2019.101238

26. Mi, L, Gan, X, Sun, Y, Lv, T, Qiao, L, and Xu, T. Effects of monetary and nonmonetary interventions on energy conservation: A meta-analysis of experimental studies. Renew Sust Energ Rev . (2021) 149:111342. doi: 10.1016/j.rser.2021.111342

27. Liberati, A, Altman, DG, Tetzlaff, J, Mulrow, C, Gotzsche, PC, Ioannidis, JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol . (2009) 62:e1–e34. doi: 10.1016/j.jclinepi.2009.06.006

28. Page, MJ, McKenzie, JE, Bossuyt, PM, Boutron, I, Hoffmann, TC, Mulrow, CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ . (2021) 372:n71. doi: 10.1136/bmj.n71

29. The World Bank Group. The World by Income and Region: World Development Indicators (2023). Available at: https://datatopics.worldbank.org/world-development-indicators/the-world-by-income-and-region.html

30. Ciliska, D, Miccuci, S, Dobbins, M, and Thomas, BH. Quality assessment tool for quantitative studies 2010. Available at: https://www.ephpp.ca/quality-assessment-tool-for-quantitative-studies/

31. BCT Taxonomy. BCT Taxonomy v1. Online Training [cited 2023 Dec 12]. Available at: https://www.bct-taxonomy.com/

32. Finne, E, Glausch, M, Exner, A-K, Sauzet, O, Stölzel, F, and Seidel, N. Behavior change techniques for increasing physical activity in cancer survivors: a systematic review and meta-analysis of randomized controlled trials. Cancer Manag Res . (2018) 10:5125–43. doi: 10.2147/CMAR.S170064

33. Howlett, N, García-Iglesias, J, Bontoft, C, Breslin, G, Bartington, S, Freethy, I, et al. A systematic review and behaviour change technique analysis of remotely delivered alcohol and/or substance misuse interventions for adults. Drug Alcohol Depend . (2022) 239:109597. doi: 10.1016/j.drugalcdep.2022.109597

34. Abrahamse, W, Steg, L, Vlek, C, and Rothengatter, T. The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. J Environ Psychol . (2007) 27:265–76. doi: 10.1016/j.jenvp.2007.08.002

35. Ahmed, S, Adnan, M, Janssens, D, and Wets, G. A personalized mobility based intervention to promote pro-environmental travel behavior. Sustain Cities Soc . (2020) 62:102397. doi: 10.1016/j.scs.2020.102397

36. Bardsley, N, Büchs, M, James, P, Papafragkou, A, Rushby, T, Saunders, C, et al. Domestic thermal upgrades, community action and energy saving: A three-year experimental study of prosperous households. Energy Policy . (2019) 127:475–85. doi: 10.1016/j.enpol.2018.11.036

37. Bonan, J, Cattaneo, C, d’Adda, G, and Tavoni, M. Can social information programs be more effective? The role of environmental identity for energy conservation. J Environ Econ Manag . (2021) 108:102467. doi: 10.1016/j.jeem.2021.102467

38. Büchs, M, Bahaj, AS, Blunden, L, Bourikas, L, Falkingham, J, James, P, et al. Promoting low carbon behaviours through personalised information? Long-term evaluation of a carbon calculator interview. Energy Policy . (2018) 120:284–93. doi: 10.1016/j.enpol.2018.05.030

39. Fijnheer, JD, van Oostendorp, H, Giezeman, G-J, and Veltkamp, RC. Competition in a household energy conservation game. Sustain For . (2021) 13:11991. doi: 10.3390/su132111991

40. Ghesla, C, Grieder, M, Schmitz, J, and Stadelmann, M. Pro-Environmental Incentives and Loss Aversion: a Field Experiment on Electricity Saving Behavior. Energy Policy . (2019) 137:111131. doi: 10.2139/ssrn.3186068

41. De Kruijf, J, Ettema, D, Kamphuis, CB, and Dijst, M. Evaluation of an incentive program to stimulate the shift from car commuting to e-cycling in the Netherlands. J Transp Health . (2018) 10:74–83. doi: 10.1016/j.jth.2018.06.003

42. McCalley, LT, Vries, PW, and Midden, CJH. Consumer response to product-integrated energy feedback: behavior, goal level shifts, and energy conservation. Environ Behav . (2011) 43:525–45. doi: 10.1177/0013916510371053

43. Moser, C, Frick, V, Seidl, R, and Blumer, YB. Teaming up for sustainability: promoting sustainable mobility behaviour through sports clubs in Switzerland. Energy Res Soc Sci . (2019) 53:89–97. doi: 10.1016/j.erss.2019.02.016

44. Tiefenbeck, V, Goette, L, Degen, K, Tasic, V, Fleisch, E, Lalive, R, et al. Overcoming salience Bias: how real-time feedback fosters resource conservation. Manag Sci . (2018) 64:1458–76. doi: 10.1287/mnsc.2016.2646

45. Tijs, MS, Karremans, JC, Veling, H, Lange, MA, van Meegeren, P, and Lion, R. Saving water to save the environment: contrasting the effectiveness of environmental and monetary appeals in a residential water saving intervention. Soc Influ . (2017) 12:69–79. doi: 10.1080/15534510.2017.1333967

46. van der Werff, E, Taufik, D, and Venhoeven, L. Pull the plug: how private commitment strategies can strengthen personal norms and promote energy-saving in the Netherlands. Energy Res Soc Sci . (2019) 54:26–33. doi: 10.1016/j.erss.2019.03.002

47. Wemyss, D, Castri, R, Cellina, F, Luca, V, Lobsiger-Kägi, E, and Carabias, V. Examining community-level collaborative vs. competitive approaches to enhance household electricity-saving behavior. Energ Effic . (2018) 11:2057–75. doi: 10.1007/s12053-018-9691-z

48. Erell, E, Portnov, BA, and Assif, M. Modifying behaviour to save energy at home is harder than we think…. Energ Buildings . (2018) 179:384–98. doi: 10.1016/j.enbuild.2018.09.010

49. He, HZ, and Kua, HW. Lessons for integrated household energy conservation policy from Singapore’s southwest eco-living program. Energy Policy . (2013) 55:105–16. doi: 10.1016/j.enpol.2012.10.067

50. Lu, Y, Kua, HW, Yu, M, and Ruan, T. Paper or screen? Examining the effectiveness of messaging delivery means in promoting household energy conservation in China. Resour Conserv Recycl . (2018) 139:27–39. doi: 10.1016/j.resconrec.2018.07.002

51. Mi, L, Qiao, L, Du, S, Xu, T, Gan, X, Wang, W, et al. Evaluating the effect of eight customized information strategies on urban households’ electricity saving: A field experiment in China. Sustain Cities Soc . (2020) 62:102344. doi: 10.1016/j.scs.2020.102344

52. Shen, M, Lu, Y, Kua, HW, and Cui, Q. Eco-feedback delivering methods and psychological attributes shaping household energy consumption: evidence from intervention program in Hangzhou, China. J Clean Prod . (2020) 265:121755. doi: 10.1016/j.jclepro.2020.121755

53. Hall, N, Romanach, L, Cook, S, and Meikle, S. Increasing energy-saving actions in low income households to achieve sustainability. Sustainability . (2013) 5:4561–77. doi: 10.3390/su5114561

54. Ma, L, Mulley, C, and Liu, W. Social marketing and the built environment: what matters for travel behaviour change? Transportation . (2017) 44:1147–67. doi: 10.1007/s11116-016-9698-2

55. Willis, RM, Stewart, RA, Panuwatwanich, K, Jones, S, and Kyriakides, A. Alarming visual display monitors affecting shower end use water and energy conservation in Australian residential households. Resour Conserv Recycl . (2010) 54:1117–27. doi: 10.1016/j.resconrec.2010.03.004

56. Grabow, M, Bryan, T, Checovich, M, Converse, A, Middlecamp, C, Mooney, M, et al. Mindfulness and climate change action: a feasibility study. Sustain For . (2018) 10:51508. doi: 10.3390/su10051508

57. Diniz, IMS, Duarte, MFS, Peres, KG, ESA, O, and Berndt, A. Active commuting by bicycle: results of an educational intervention study. J Phys Act Health . (2015) 12:801–7. doi: 10.1123/jpah.2013-0215

58. Thondhlana, G, and Kua, HW. Promoting household energy conservation in low-income households through tailored interventions in Grahamstown, South Africa. J Clean Prod . (2016) 131:327–40. doi: 10.1016/j.jclepro.2016.05.026

59. Homburg, A, and Lange, F. Klimaschonendes Verhalten fördern – Beiträge der Umweltpsychologie; (2020) [Cited 2024 Jan 8]. Available at: https://www.umweltpsychologie.de/wissen/klimaschonendes-verhalten/

60. Wynes, S, Nicholas, KA, Zhao, J, and Donner, SD. Measuring what works: quantifying greenhouse gas emission reductions of behavioural interventions to reduce driving, meat consumption, and household energy use. Environ Res Lett . (2018) 13:113002. doi: 10.1088/1748-9326/aae5d7

61. Osbaldiston, R, and Schott, JP. Environmental sustainability and behavioral science. Environ Behav . (2012) 44:257–99. doi: 10.1177/0013916511402673

62. Linder, N, Giusti, M, Samuelsson, K, and Barthel, S. Pro-environmental habits: an underexplored research agenda in sustainability science. Ambio . (2022) 51:546–56. doi: 10.1007/s13280-021-01619-6

63. Cinderby, S, Roberts, J, and Bruin, A. Promoting the transfer of pro-environmental behaviours between home and workplaces. Curr Res Ecol Soc Psychol . (2023) 5:100143. doi: 10.1016/j.cresp.2023.100143

64. Quested, TE, Marsh, E, Stunell, D, and Parry, AD. Spaghetti soup: the complex world of food waste behaviours. Resour Conserv Recycl . (2013) 79:43–51. doi: 10.1016/j.resconrec.2013.04.011

65. Andor, MA, and Fels, KM. Behavioral economics and energy conservation – A systematic review of non-price interventions and their causal effects. Ecol Econ . (2018) 148:178–210. doi: 10.1016/j.ecolecon.2018.01.018

66. Our World in Data. Per capita CO₂ emissions: Our World in Data; 2023 [cited 2023 Dec 21]. Available at: https://ourworldindata.org/grapher/co-emissions-per-capita

67. Moore, HE, and Boldero, J. Designing interventions that last: A classification of environmental behaviors in relation to the activities, costs, and effort involved for adoption and maintenance. Front Psychol . (2017) 8:1874. doi: 10.3389/fpsyg.2017.01874

68. Marchi, L, Vodola, V, Visconti, C, Gaspari, J, and Antonini, E. Contribution of individual behavioural change on household carbon footprint. E3S Web Conf . (2021) 263:5024. doi: 10.1051/e3sconf/202126305024

69. Wynes, S, and Nicholas, KA. The climate mitigation gap: education and government recommendations miss the most effective individual actions. Environ Res Lett . (2017) 12:74024. doi: 10.1088/1748-9326/aa7541

70. Edmondson, D, Conroy, D, Romero-Canyas, R, Tanenbaum, M, and Czajkowski, S. Climate change, behavior change and health: a multidisciplinary, translational and multilevel perspective. Transl Behav Med . (2022) 12:503–15. doi: 10.1093/tbm/ibac030

71. Bohlen, LC, Michie, S, Bruin, M, Rothman, AJ, Kelly, MP, Groarke, HNK, et al. Do combinations of behavior change techniques that occur frequently in interventions reflect underlying theory? Ann Behav Med . (2020) 54:827–42. doi: 10.1093/abm/kaaa078

72. Grant, SP, Mayo-Wilson, E, Melendez-Torres, GJ, and Montgomery, P. Reporting quality of social and psychological intervention trials: a systematic review of reporting guidelines and trial publications. PLoS One . (2013) 8:e65442. doi: 10.1371/journal.pone.0065442

73. Candy, B, Vickerstaff, V, Jones, L, and King, M. Description of complex interventions: analysis of changes in reporting in randomised trials since 2002. Trials . (2018) 19:110. doi: 10.1186/s13063-018-2503-0

74. Hoffmann, TC, Glasziou, PP, Boutron, I, Milne, R, Perera, R, Moher, D, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ . (2014) 348:g1687. doi: 10.1136/bmj.g1687

75. Lorencatto, F, West, R, Stavri, Z, and Michie, S. How well is intervention content described in published reports of smoking cessation interventions? Nicotine Tob Res . (2013) 15:1273–82. doi: 10.1093/ntr/nts266

76. Prestwich, A, Sniehotta, FF, Whittington, C, Dombrowski, SU, Rogers, L, and Michie, S. Does theory influence the effectiveness of health behavior interventions? Meta Anal Health Psychol . (2014) 33:465–74. doi: 10.1037/a0032853

77. Michie, S, Johnston, M, and Carey, R. Behavior change techniques. In: Gellman MD, editor. Encyclopedia of behavioral medicine. 2nd ed. (2020). Cham: Springer International Publishing; Imprint Springer; 2020. p. 206–13 Available at: https://link.springer.com/referenceworkentry/10.1007/978-3-030-39903-0_1661

78. Wood, CE, Hardeman, W, Johnston, M, Francis, J, Abraham, C, and Michie, S. Reporting behaviour change interventions: do the behaviour change technique taxonomy v1, and training in its use, improve the quality of intervention descriptions? Implement Sci . (2016) 11:84. doi: 10.1186/s13012-016-0448-9

79. Johnston, M, Johnston, D, Wood, CE, Hardeman, W, Francis, J, and Michie, S. Communication of behaviour change interventions: can they be recognised from written descriptions? Psychol Health . (2018) 33:713–23. doi: 10.1080/08870446.2017.1385784

80. Moore, GF, Audrey, S, Barker, M, Bond, L, Bonell, C, Hardeman, W, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ . (2015) 350:h1258. doi: 10.1136/bmj.h1258

81. Dombrowski, SU, Sniehotta, FF, Avenell, A, Johnston, M, MacLennan, G, and Araújo-Soares, V. Identifying active ingredients in complex behavioural interventions for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities: a systematic review. Health Psychol Rev . (2012) 6:7–32. doi: 10.1080/17437199.2010.513298

82. Prestwich, A, Conner, M, Hurling, R, Ayres, K, and Morris, B. An experimental test of control theory-based interventions for physical activity. Br J Health Psychol . (2016) 21:812–26. doi: 10.1111/bjhp.12198

83. Abrahamse, W. Encouraging pro-environmental behaviour: What works, what doesn't, and why . London, San Diego: Academic Press (2019).

Keywords: sustainability, mitigation, intervention, household, energy use, water use, mobility, BCT

Citation: Masciangelo L, Lopez Lumbi S, Rinderhagen M, Hornberg C, Liebig-Gonglach M and Mc Call T (2024) Promising behavior change techniques for climate-friendly behavior change – a systematic review. Front. Public Health . 12:1396958. doi: 10.3389/fpubh.2024.1396958

Received: 06 March 2024; Accepted: 29 July 2024; Published: 12 August 2024.

Reviewed by:

Copyright © 2024 Masciangelo, Lopez Lumbi, Rinderhagen, Hornberg, Liebig-Gonglach and Mc Call. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Timothy Mc Call, [email protected]

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Factors Influencing Farmers’ Climate Change Mitigation and Adaptation Behavior: A Systematic Literature Review

• Open Access
• First Online: 23 August 2022

Cite this chapter

You have full access to this open access chapter

• Bernadette Kropf 3 &
• Hermine Mitter 3  

2197 Accesses

1 Citations

Researchers increasingly explore farmers’ climate change behavior and the respective influencing factors. This has resulted in extensive, but hitherto unstructured knowledge. We analyze 50 peer-reviewed scientific studies and identify behavioral factors and their influence on farmers’ mitigation and adaptation behavior. Our results show a broad variety of behavioral factors, including cognitive factors which refer to perceptions of a specific risk or behavior, social factors which are influenced by farmers’ interactions with their social peers, and factors which depend on farmers’ personal disposition. Depending on the characteristics of the respective behavioral factor, the implementation of mitigation and adaptation measures is facilitated or impeded.

You have full access to this open access chapter,  Download chapter PDF

Similar content being viewed by others

Climate change awareness, perceived impacts, and adaptation from farmers’ experience and behavior: a triple-loop review

Farmers’ behaviors and attitudes toward climate change adaptation: evidence from Vietnamese smallholder farmers

How do farmers perceive climate change? A systematic review

• Farmers’ behavior
• Climate change
• Behavioral theories
• Agriculture

1 Introduction

Agriculture offers specific potential to reduce greenhouse gas (GHG) emissions, for instance, by substituting fossil fuels with renewable energy sources, by applying energy-saving technologies, and by reducing inorganic fertilizer use and soil degradation (Moerkerken et al. 2020 ). At the same time, agriculture is one of the sectors most vulnerable to climate change. For instance, projected changes in climate, such as an increase in the frequency and severity of droughts, spring frosts, or heavy precipitation events, may adversely affect agricultural yields and farm income and may threaten food security (Arbuckle et al. 2013 ; Niles et al. 2016 ). Hence, farmers’ climate change behavior, i.e., implementing mitigation and adaptation measures, is decisive to cut GHG emissions, to reduce or avoid adverse climate change impacts and to grasp emerging opportunities.

Human behavior results from complex relationships between individual behavioral factors and is specific to the cultural and geographic context. The characteristics of behavioral factors and their relationships facilitate or impede the implementation of climate change behavior (Grothmann and Patt 2005 ). Scientists develop and apply behavioral theories in order to identify behavioral factors, structure their relationships, and explain and analyze their influence on behavior. Behavioral theories vary in scope and may include not only behavioral but also context factors (e.g., regional, farm, and sociodemographic farmer characteristics) that also influence behavior (Grothmann and Patt 2005 ; West et al. 2019 ).

A detailed understanding of behavioral factors is essential to explain the antecedents of individuals’ mitigative and adaptive behavior in different contexts. It also facilitates the development of empirically informed public measures, and thereby increases their adoption by farmers and their effectiveness (Dessart et al. 2019 ; Grothmann and Patt 2005 ; van Valkengoed and Steg 2019 ). Behavioral factors, their relationships, and influence on farmers’ climate change behavior have been the subject of various scientific studies in recent years. This has resulted in comprehensive, though unstructured scientific knowledge. We analyze peer-reviewed scientific studies that analyze factors influencing farmers’ climate change behavior in order to organize and structure empirically investigated behavioral factors and make the scientific knowledge more accessible to a wider audience. We focus exclusively on behavioral factors and do not include context factors. In particular, we aim to i) summarize mitigation and adaptation measures relevant to agriculture, ii) elicit applied behavioral theories, and iii) identify behavioral factors and their influence on farmers’ mitigation and adaptation behavior.

2 Data and Method

We apply a systematic multistep literature review to identify relevant peer-reviewed studies. Consecutive review steps as well as the defined criteria are summarized in Table  1 . A total of 50 studies met the defined criteria. The complete list of reviewed studies is available upon request.

We analyze the selected 50 studies using a qualitative content analysis, computer-assisted with the Atlas.ti text analysis software, and deploy a deductive-inductive coding approach (Friese 2020 ; Mayring 2015 ). Deductive codes are mainly derived from behavioral theories on climate change behavior (Grothmann and Patt 2005 ; van Valkengoed and Steg 2019 ). They are refined by inductive codes derived from behavioral factors identified in the reviewed studies.

The definition of investigated behavioral factors varies substantially between the reviewed studies, which hampers their comparison. For instance, Niles et al. ( 2016 ) define social norms as favorable perception of environmental regulations, but do not explicitly refer to farmers’ interactions with peers or other important social contacts. Another example is perceived outcome efficacy (a behavioral factor of Protection Motivation Theory (PMT) and the Model of Private Proactive Adaptation to Climate Change (MPPACC)) and attitude toward behavior (a behavioral factor of the Theory of Planned Behavior (TPB)), both relate to evaluating the perceived and expected outcomes of a particular measure. We address this challenge by structuring and summarizing relevant text passages, such as definitions of behavioral factors and merging similar or nearly identical behavioral factors.

3 Results of the Systematic Literature Review

3.1 sample description.

The reviewed studies are almost evenly distributed across the considered regions (i.e., 16 studies from Australia and New Zealand, 18 from North America, and 16 from Europe, with a focus on northern and western European countries). It is salient that all reviewed studies have been conducted since 2005, with a peak in data collection in 2011 and 2012 (12 each). Some datasets are used to investigate several aspects and are referenced in more than one of the reviewed studies (e.g., Arbuckle et al. 2013 ; Mase et al. 2017 ). Most studies were published in 2017 (8), followed by 2016 and 2019 (7 each). With regard to applied data collection methods, quantitative methods (such as standardized online, personal, postal, or telephone surveys) are dominant, in 31 of the reviewed studies. Qualitative methods (such as workshops and/or semi-structured or unstructured interviews) are used in 11 studies. A combination of quantitative and qualitative methods is applied in 8 studies.

3.2 Applied Theories

More than half (28 of 50) of the reviewed studies refer to behavioral and other sociopsychological theories or models. The theories are either used as originally developed or are adjusted to the respective research objectives, cultural or geographic contexts. For this reason, selected theories are combined or specific behavioral factors are extracted to guide the qualitative or quantitative analysis. Adjustments were made in most studies, and few refer to more than one behavioral theory.

The most frequently cited theory is the TPB (8) (e.g., Roesch-McNally et al. 2017 ; Wheeler et al. 2013 ), followed by the MPPACC (6) (e.g., Eakin et al. 2016 ; Mitter et al. 2019 ), the Value-Beliefs-Norm Theory (VBN, 4) (e.g., Davidson et al. 2019 ; Sanderson and Curtis 2016 ), the PMT, (3) (e.g., Käyhkö 2019 ; van Duinen et al. 2015 ), and the Five Capitals Model (3) (e.g., Seidl et al. 2021 ; Wheeler et al. 2013 ). Other theories or models, such as the Construal Level Theory (Niles et al. 2015 ; van Haden et al. 2012 ), the Identity Control Model (Morton et al. 2017 ), and the Model of Adaptive Capacity (e.g., Marshall et al. 2012 ) are applied in only one or two studies each.

3.3 Farmers’ Actual and Intended Climate Change Behavior

The reviewed studies address a wide range of mitigation and adaptation measures on farms. The examples given in Table  2 are structured along the categories defined by IPCC ( 2014 ) and Wheeler et al. ( 2013 ). Behavioral intentions are considered the most proximal antecedent of behavior (Ajzen 1985 , 1991 ; Grothmann and Patt 2005 ). Despite a likely discrepancy between farmers’ intended and actual climate change behavior (Niles et al. 2016 ), for simplicity, we do not differentiate between intended and implemented mitigation and adaptation measures.

The vast majority of studies (40 of 50) investigate farmers’ adaptation behavior, six examine farmers’ mitigation behavior, and four analyze both. We note that some measures could serve both mitigation and adaptation purposes. These measures are assigned to the categories analyzed in the reviewed studies.

3.4 Behavioral Factors

We categorize the identified behavioral factors into cognitive, social, and dispositional factors, following Dessart et al. ( 2019 ). Cognitive factors refer to the perception of a specific risk or behavior and the associated thought processes, such as learning and reasoning. We further differentiate between three subcategories of cognitive factors: risk-specific, behavior-specific, and avoidance factors. Social factors refer to relationships with other individuals or groups of individuals. Dispositional factors reflect farmers’ personalities. They are relatively permanent and do not relate to a specific risk or behavior (Dessart et al. 2019 ).

3.4.1 Cognitive Factors

Risk-specific factors.

refer to climate change beliefs, perceptions, and evaluations of climate change risks including their impacts on one’s farm or region.

Climate Change Beliefs refer to farmers’ beliefs in anthropogenic climate change and its causes which are frequently measured, resulting in diverging types of climate change believers (Arbuckle et al. 2013 ; Davidson et al. 2019 ; Hyland et al. 2016 ; Kuehne 2014 ; van Haden et al. 2012 ). However, climate change belief has shown to be an imprecise antecedent of farmers’ climate change behavior. While some studies find a significant positive correlation between farmers’ climate change beliefs and mitigation and adaptation measures (e.g. Hamilton-Webb et al. 2017 ; van Haden et al. 2012 ; Woods et al. 2017 ), others did not (e.g. Arbuckle et al. 2013 ; Davidson et al. 2019 ; Mase et al. 2017 ). Interestingly, Niles et al. ( 2016 ) and Rogers et al. ( 2012 ) identify climate change belief as an antecedent of adaptation intentions, but not of farmers’ actual adaptation behavior. Doll et al. ( 2017 ), Kuehne ( 2014 ), and Merloni et al. ( 2018 ) point out that farmers adapt to climate change in order to respond to immediate risks and ensure the viability of their farms, irrespective of their climate change belief.

Risk Perception is indicated by farmers’ perceived and expected changes in climate and induced adverse and beneficial impacts on agricultural production and marketing (Mitter et al. 2019 ; van Valkengoed and Steg 2019 ). Farmers most frequently mention rising temperatures and increasingly severe extreme weather events such as droughts or intense rainfall, hail or storm events. When asked about adverse climate change impacts (i.e., risks), they often refer to declining water availability and crop yields (Nicholas and Durham 2012 ; van Haden et al. 2012 ). Furthermore, they mention aggravated working conditions (Doll et al. 2017 ; Yoder et al. 2021 ), increasingly severe soil erosion (Roesch-McNally et al. 2017 ), and lower farm incomes (Barnes and Toma 2012 ). Perceived beneficial impacts (i.e., opportunities) include an extended vegetation period and yield increases (Hyland et al. 2016 ; Mitter et al. 2019 ). Perceived changes in climate and induced impacts have been found to significantly facilitate the implementation of adaptation measures (Li et al. 2017 ; Morton et al. 2017 ; van Duinen et al. 2015 ). Morton et al. ( 2017 ) even reveal that farmers who have experienced two extreme events in the past five years are more likely to implement contractive measures. Wheeler et al. ( 2021 ) point to feedback loops between farmers’ risk perceptions and their adaptation behavior. I.e., farmers who were already facing high risk and therefore implemented structural and contractive measures showed decreasing risk perceptions, while others who initially perceived less adverse climate change impacts and therefore took structural or expansive measures showed increasing risk perceptions.

Behavior-Specific Factors

refer to the perception and evaluation of climate change mitigation and adaptation measures.

Perception of Outcome Efficacy refers to farmers’ individual experiences and expectations about the effectiveness of mitigation and adaptation measures and has been identified as an important antecedent of climate change behavior (Kragt et al. 2017 ; Moerkerken et al. 2020 ; van Duinen et al. 2015 ). For example, the implementation of mitigation and adaptation measures is more likely if farmers believe that these measures effectively reduce GHG emissions (Kragt et al. 2017 ), increase the resilience of farms to climate change (Kragt et al. 2017 ), or provide synergies with other desirable farming goals, such as improving soil quality (Roesch-McNally et al. 2018 ). Although farmers are positive about the effectiveness of some measures, perceived tradeoffs impede the implementation, such as increased use of pesticides or additional costs associated with direct sowing or frost protection measures in vineyards (Käyhkö 2019 ; Nicholas and Durham 2012 ). Some farmers disagree with the effectiveness of financial adaptation measures, such as insurance against drought or hail damage. They argue that these measures may create a financial dependence, instead of stimulating more climate-friendly or adaptive farming practices (Wheeler and Lobley 2021 ). Perceived low outcome efficacy of incremental adaptation measures facilitates the implementation of contractive measures that are assumed to be more effective in reducing economic risks resulting from climate change (Käyhkö 2019 ). However, the perceived outcome efficacy of measures already implemented on one’s own farm land significantly influences farmer’s willingness to implement expansive measures (Morton et al. 2017 ).

Perception of Costs refers to money, time, or effort spent on climate change behavior. The implementation of mitigation and adaption measures is impeded when investment costs are perceived to be high and benefits in the immediate future are perceived to be low (van Duinen et al. 2015 ; van Haden et al. 2012 ; Wheeler and Lobley 2021 ). The review results also indicate that climate change mitigation and adaptation measures that allow farmers to harness synergies with other desirable farming goals, such as efficiency improvement in fuel, electricity or nitrogen use, and hence increase farm incomes, are more likely to be implemented (Mitter et al. 2019 ; Tzemi and Breen 2019 ; van Haden et al. 2012 ).

Perception of Self-Efficacy refers to farmers’ individual evaluations of their own capabilities and confidence in effectively implementing mitigation and adaptation measures (Roesch-McNally et al. 2017 ). Numerous studies identify perceived self-efficacy as a significant positive antecedent of farmers’ climate change mitigation and adaptation behavior (e.g. Arbuckle et al. 2013 ; Niles et al. 2016 ; Raymond and Spoehr 2013 ). In contrast, some studies show a significant negative influence of perceived self-efficacy on farmers’ climate change behavior. They conclude that farmers with a high confidence in already implemented measures may (consciously or unconsciously) disregard the implementation of additional incremental or transformational adaptation measures (Roesch-McNally et al. 2017 ; Rogers et al. 2012 ) or may favor contractive adaptation measures (Morton et al. 2017 ). Van Duinen et al. ( 2015 ) reveal a non-significant correlation between farmers’ perceived self-efficacy and the implementation of incremental adaptation measures and explain this finding with the widespread implementation of these measures.

Avoidance Factors

are emotional responses to perceived climate change risks. They do not reduce monetary or physical harm, but rather avert negative emotional impacts of the perceived risk and act as a barrier to successful long-term climate change adaptation (Grothmann and Patt 2005 ).

Denial of climate change impacts means that the risks of climate change are underestimated which impedes the implementation of adaptation measures. Farmers doubt to be adversely affected by climate change in the future although they have already experienced adverse impacts (Mitter et al. 2019 ). They do not have strong opinions about climate change and its impacts, or perceive other risks, such as policy changes and public pressure more pressing (Barnes and Toma 2012 ; Wheeler and Lobley 2021 ).

Wishful Thinking is about downplaying adverse climate change impacts and believing that one’s own farm may not be affected. Therefore, farmers do not see the need to adapt their behavior to climate change (Barnes and Toma 2012 ; Mitter et al. 2019 ).

Religious Faith refers to the belief that adverse climate change impacts are an act of God and that perceived risks can be reduced through spiritual actions (Mitter et al. 2019 ), such as praying instead of implementing frost protection measures (Nicholas and Durham 2012 ).

Fatalism is related to the perception and expectation of adverse climate change impacts, while neglecting one’s own possibilities to implement adaptation measures. For example, farmers have not implemented measures due to conflicting information about climate change (Kuehne 2014 ) or their lacking knowledge about potential adaptation measures (Mitter et al. 2019 ), leading them into fatalism and resignation.

3.4.2 Social Factors

Descriptive social norms,.

i.e., perceptions of how other people behave, significantly influence farmers’ climate change behavior. For example, knowing and learning from peers (Hamilton-Webb et al. 2017 ; Kragt et al. 2017 ; Lu et al. 2021 ), belonging to a professional agricultural network, and visiting other farmers (Marshall et al. 2012 ; Niles et al. 2016 ; Roesch-McNally et al. 2017 ) have a significant positive influence on the farmer’s behavior to mirror mitigation and adaptation measures. Moreover, how often and with whom an individual farmer interacts is critical (Niles et al. 2016 ).

Injunctive Social Norms

refer to perceptions about what ought to be done and are not identified as a significant antecedent for farmers’ climate change behavior (Lu et al. 2021 ). They may even impede the implementation of innovative mitigation and adaptation measures that deviate from traditional practices and may cause problems or failures (Käyhkö 2019 ; Yoder et al. 2021 ).

Trust in Advice and Media

such as from natural resource managers, significantly influences farmers’ adaptation behavior (Raymond and Spoehr 2013 ). Wheeler and Lobley ( 2021 ) find that supporting farmers to identify reliable information is important in implementing adaptation measures.

3.4.3 Dispositional Factors

General risk attitude.

as indicated by farmer self-assessment (Wheeler et al. 2013 ) or number of insurance products purchased (Seidl et al. 2021 ), is identified as a significant antecedent for incremental and transformational adaptation measures.

Place Attachment

refers to farmers’ connectedness with their physical and social environment, including their social and professional network, home region, farm, and other entities (Marshall et al. 2012 ). Place attachment significantly strengthens the implementation of incremental, but hampers the introduction of structural adaptation measures such as farm relocation (Eakin et al. 2016 ; Marshall et al. 2012 ; Rogers et al. 2012 ).

Personal Responsibility

which translates into a perceived moral obligation to implement measures, significantly facilitates farmers’ mitigation (Davidson et al. 2019 ; Kragt et al. 2017 ) and adaptation behavior (Roesch-McNally et al. 2017 ). For instance, Sanderson and Curtis ( 2016 ) identify perceived personal responsibility to mitigate GHG emissions and to protect groundwater as significant antecedents of adaptation measures.

Value Systems

reflect solid and deeply engrained ideas of desirable and undesirable behavior (Morton et al. 2017 ; Sanderson and Curtis 2016 ). Farmers, who value openness, innovation, and technology prefer to implement innovative mitigation and adaptation measures (Davidson et al. 2019 ; Lu et al. 2021 ; Moerkerken et al. 2020 ; Rogers et al. 2012 ; Tzemi and Breen 2019 ; Wheeler et al. 2013 ) but do not intend to implement contractive adaptation measures (Mase et al. 2017 ). Farmers, who endorse environmental protection and conservation values, significantly prioritize the implementation of agronomic mitigation and adaptation measures (Davidson et al. 2019 ; Roesch-McNally et al. 2017 ; Wheeler et al. 2013 ). However, farmers holding these values hesitate to implement contractive measures, suggesting that these farmers value their land for more than short-term profitability (Morton et al. 2017 ) and do not implement these measures solely in response to perceived climate change (Mitter et al. 2019 ). In contrast, farmers valuing profit and resource maximization significantly prioritize expansive (Morton et al. 2017 ) or contractive measures (Wheeler et al. 2013 ). Käyhkö ( 2019 ) notes that farmers with a strong profit orientation may favor financial measures to deal with economic risks. Farmers with dominant traditional and conservative values prefer to postpone adaptation measures and are significantly less likely to implement contractive measures, indicating that they want to preserve their farm endowments for the next generation (Wheeler et al. 2013 ). Conversely, Eggers et al. ( 2015 ) find that traditionalists are more skeptical of adaptation measures and more prone to abandon their farms. However, it remains an open question whether this result is rather driven by farmers’ values or by farm characteristics, i.e., small farm size and a lower competitiveness relative to other farms.

4 Discussion and Conclusions

The systematic literature review provides a comprehensive and structured summary of behavioral factors and their influence on farmers’ climate change behavior in developed countries. We find that farmers across regions implement mitigation as well as incremental and transformational adaptation measures, which is influenced by a combination of cognitive, social and dispositional behavioral factors.

It is salient that some factors, such as risk perception and outcome efficacy, have been investigated in more regional and cultural contexts with similar results in terms of direction of influence. In contrast, avoidance factors, which impede the implementation of mitigation and adaptation measures and thus are highly relevant for the development of public measures, have rarely been investigated. These results are in line with the meta-analysis of van Valkengoed and Steg ( 2019 ) on factors influencing climate change adaptation behavior in the general public. They – inter alia – identify descriptive norms, perceived self-efficacy and outcome efficacy as the strongest antecedents of adaptation behavior and argue for putting a greater research emphasize on these and other understudied behavioral factors. For instance, farmers’ emotional states due to climate change impacts have been sparsely investigated yet.

The behavioral theories applied in the analyzed studies synthesize empirically tested sets of behavioral factors and their influence on climate change behavior. Frequently applied modifications suggest that behavioral theories allow for adjustments to the particular research interest and context. At the same time, they offer transparent and clear guidance for research processes and build an adequate basis for understanding farmers’ climate change behavior more properly. Nevertheless, definitions of behavioral factors partly overlap and their boundaries are blurred, which points to the importance of concretizing commonalities and differences. Despite effortful, this could facilitate comparing and upscaling of results, as well as knowledge sharing across contexts. It may also support the development of public measures that aim to encourage farmers’ behavior change (West et al. 2019 ).

It is evident that the reviewed studies deal to a larger share with adaptation than with mitigation measures. This may be due to the fact that adaptation is mainly considered a private and mitigation mainly a public endeavor. However, results indicate that the implementation of mitigation and adaptation measures underlie similar behavioral factors, such as perceived outcome efficacy, perceived costs, social norms, and values toward innovation or the environment. With regard to perceived outcome efficacy and perceived costs, it is apparent that perceived synergies with other desirable farming goals facilitate the implementation of climate change measures. This is of particular relevance for mitigation measures which primarily benefit the general public through reduced GHG emissions and only secondarily provide private benefits to farmers. However, recently adopted public strategies, such as specified targets for the European agricultural sector based on the Paris Agreement (LULUCF Regulation 2018 ) or the European Green Deal (EU COM 2019 ) increasingly force the agricultural sector to reduce GHG emissions which makes farmers’ climate change mitigation behavior more relevant. Future public measures aiming to encourage on-farm mitigation should thus emphasize private benefits to facilitate their implementation.

Investigations on the mutual influence of behavioral factors, as well as a complementary review of the influence of regional, farm and sociodemographic farmer characteristics on climate change behavior, may further deepen the understanding for farmers’ climate change behavior. The behavioral factors identified may form the basis for further research. For instance, they may inform the design of behavior change interventions or the elicitation and development of empirically based farmer types that diverge in their mitigation and adaptation behavior. In addition, previously understudied behavioral factors such as farmers’ social norms, avoidance factors, or their emotional state due to climate change impacts point to a future research agenda in the agricultural context.

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In P. D. J. Kuhl, & D. J. Beckmann (Eds.), Action Control. From Cognition to Behavior (pp. 11–39). SSSP Springer Series in Social Psychology. Berlin & Heidelberg, Deutschland: Springer. https://doi.org/10.1007/978-3-642-69746-3_2 .

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes , 50 (2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T .

Arbuckle, J. G., Morton, L. W., & Hobbs, J. (2013). Farmer beliefs and concerns about climate change and attitudes toward adaptation and mitigation. Evidence from Iowa. Climatic Change, 118 , 551–563. https://doi.org/10.1007/s10584-013-0700-0 .

Barnes, A. P., & Toma, L. (2012). A typology of dairy farmer perceptions towards climate change. Climatic Change , 112 , 507–522. https://doi.org/10.1007/s10584-011-0226-2 .

Davidson, D. J., Rollins, C., Lefsrud, L., Anders, S., & Hamann, A. (2019). Just don’t call it climate change: Climate-skeptic farmer adoption of climate-mitigative practices. Environmental Research Letters , 14 (3), 034015. https://doi.org/10.1088/1748-9326/aafa30 .

Dessart, F. J., Barreiro-Hurlé, J., & van Bavel, R. (2019). Behavioural factors affecting the adoption of sustainable farming practices: A policy-oriented review. European Review of Agricultural Economics 46 (3), 417–471. https://doi.org/10.1093/erae/jbz019 .

Doll, J. E., Petersen, B., & Bode, C. (2017). Skeptical but adapting: What midwestern farmers say about climate change. Weather, Climate, and Society , 9 (4), 739–751. https://doi.org/10.1175/WCAS-D-16-0110.1 .

Eakin, H., York, A., Aggarwal, R., Waters, S., Welch, J., Rubiños, C., Smith-Heisters, S., Bausch, C., & Anderies, J. M. (2016). Cognitive and institutional influences on farmers’ adaptive capacity: Insights into barriers and opportunities for transformative change in central Arizona. Regional Environmental Change , 16 , 801–814. https://doi.org/10.1007/s10113-015-0789-y .

Eggers, M., Kayser, M., & Isselstein, J. (2015). Grassland farmers’ attitudes toward climate change in the North German Plain. Regional Environmental Change, 15 , 607–617. https://doi.org/10.1007/s10113-014-0672-2 .

EU COM (European Commission) (2019). Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Commitee and the Commitee of the Regions. The European Green Deal . COM/2019/640 final. Brussel, Belgium. Retrieved November 8, 2021, from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52019DC0640 .

Friese, S. (2020). ATLAS.ti 8 Windows. User Manual . Berlin, Germany: ATLAS.ti Scientific Software Development GmbH. Retrieved November 8, 2021, from https://atlasti.com/2017/07/25/atlas-ti-8-windows-user-manual/ .

Grothmann, T., & Patt, A. (2005). Adaptive capacity and human cognition: The process of individual adaptation to climate change. Global Environmental Change , 15 (3), 199–213. https://doi.org/10.1016/j.gloenvcha.2005.01.002 .

Hamilton-Webb, A., Manning, L., Naylor, R., & Conway, J. (2017). The relationship between risk experience and risk response: A study of farmers and climate change. Journal of Risk Research , 20 (11), 1379–1393. https://doi.org/10.1080/13669877.2016.1153506 .

Hyland, J. J., Jones, D. L., Parkhill, K. A., Barnes, A. P., & Williams, A. P. (2016). Farmers’ perceptions of climate change: Identifying types. Agriculture and Human Values , 33 , 323–339. https://doi.org/10.1007/s10460-015-9608-9 .

IPCC (Intergovernmental Panel on Climate Change) (2014). Annex II: Glossary [Agard, J., Schipper, E. L. F., Birkmann, J., et al. (Eds.)]. In V. R. Barros, C. B. Field, D. J Dokken, et al. (Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1757–1776). Cambridge, UK & New York, NY, US: Cambridge, University Press. Retrieved November 8, 2021, from https://www.ipcc.ch/report/ar5/wg2/ .

Käyhkö, J. (2019). Climate risk perceptions and adaptation decision-making at Nordic farm scale – A typology of risk responses. International Journal of Agricultural Sustainability , 17 (6), 431–444. https://doi.org/10.1080/14735903.2019.1689062 .

Kragt, M. E., Dumbrell, N. P., & Blackmore, L. (2017). Motivations and barriers for Western Australian broad-acre farmers to adopt carbon farming. Environmental Science & Policy , 73 , 115–123. https://doi.org/10.1016/j.envsci.2017.04.009 .

Kuehne, G. (2014). How do farmers’ climate change beliefs affect adaptation to climate change? Society & Natural Resources , 27 (5), 492–506. https://doi.org/10.1080/08941920.2013.861565 .

Li, S., Juhász-Horváth, L., Harrison, P. A., Pintér, L., & Rounsevell, M. D. A. (2017). Relating farmer’s perceptions of climate change risk to adaptation behaviour in Hungary. Journal of Environmental Management , 185 , 21–30. https://doi.org/10.1016/j.jenvman.2016.10.051 .

Lu, J., Singh, A. S., Koundinya, V., Ranjan, P., Haigh, T., Getson, J. M., Klink, J., & Prokopy, L. S. (2021). Explaining the use of online agricultural decision support tools with weather or climate information in the Midwestern United States. Journal of Environmental Management , 279 , 111758. https://doi.org/10.1016/j.jenvman.2020.111758 .

LULUCF Regulation (2018). Regulation (EU) 2018/841 of the European Parliament and of the Council of 30 May 2018 on the inclusion of greenhouse gas emissions and removals from land use, land use change and forestry in the 2030 climate and energy framework, and amending Regulation (EU) No 525/2013 and Decision No 529/2013/EU . Retrieved November 8, 2021, from http://data.europa.eu/eli/reg/2018/841/oj .

Marshall, N. A., Park, S. E., Adger, W. N., Brown, K., & Howden, S. M. (2012). Transformational capacity and the influence of place and identity. Environmental Research Letters , 7 , 034022. https://doi.org/10.1088/1748-9326/7/3/034022 .

Mase, A. S., Gramig, B. M., & Prokopy, L. S. (2017). Climate change beliefs, risk perceptions, and adaptation behavior among Midwestern U.S. crop farmers. Climate Risk Management, 15 , 8–17. https://doi.org/10.1016/j.crm.2016.11.004 .

Mayring, P. (2015). Qualitative Inhaltsanalyse – Grundlagen und Techniken (12., überarb. Aufl.). Weinheim, Deutschland & Basel, Schweiz: Beltz.

Google Scholar  

Merloni, E., Camanzi, L., Mulazzani, L., & Malorgio, G. (2018). Adaptive capacity to climate change in the wine industry: A Bayesian Network approach. Wine Economics and Policy , 7 (2), 165–177. https://doi.org/10.1016/j.wep.2018.11.002 .

Mitter, H., Larcher, M., Schönhart, M., Stöttinger, M., & Schmid, E. (2019). Exploring farmers’ climate change perceptions and adaptation Intentions: Empirical evidence from Austria. Environmental Management , 63 , 804–821. https://doi.org/10.1007/s00267-019-01158-7 .

Moerkerken, A., Blasch, J., van Beukering, P., & van Well, E. (2020). A new approach to explain farmers’ adoption of climate change mitigation measures. Climatic Change , 159 , 141–161. https://doi.org/10.1007/s10584-019-02595-3 .

Morton, L. W., McGuire, J. M., & Cast, A. D. (2017). A good farmer pays attention to the weather. Climate Risk Management , 15 , 18–31. https://doi.org/10.1016/j.crm.2016.09.002 .

Nicholas, K. A., & Durham, W. H. (2012). Farm-scale adaptation and vulnerability to environmental stresses: Insights from winegrowing in Northern California. Global Environmental Change , 22 (2), 483–494. https://doi.org/10.1016/j.gloenvcha.2012.01.001 .

Niles, M. T., Lubell, M., & Brown, M. (2015). How limiting factors drive agricultural adaptation to climate change. Agriculture, Ecosystems & Environment , 200 , 178–185. https://doi.org/10.1016/j.agee.2014.11.010 .

Niles, M. T., Brown, M., & Dynes, R. (2016). Farmer’s intended and actual adoption of climate change mitigation and adaptation strategies. Climatic Change , 135 , 277–295. https://doi.org/10.1007/s10584-015-1558-0 .

Raymond, C. M., & Spoehr, J. (2013). The acceptability of climate change in agricultural communities: Comparing responses across variability and change. Journal of Environmental Management, 115 , 69–77. https://doi.org/10.1016/j.jenvman.2012.11.003 .

Roesch-McNally, G. E., Gordon Arbuckle, J., & Tyndall, J. C. (2017). What would farmers do? Adaptation intentions under a Corn Belt climate change scenario. Agriculture and Human Values , 34 , 333–346. https://doi.org/10.1007/s10460-016-9719-y .

Roesch-McNally, G. E., Arbuckle, J. G., & Tyndall, J. C. (2018). Barriers to implementing climate resilient agricultural strategies: The case of crop diversification in the U.S. Corn Belt. Global Environmental Change , 48 , 206–215. https://doi.org/10.1016/j.gloenvcha.2017.12.002 .

Rogers, M., Curtis, A., & Mazur, N. (2012). The influence of cognitive processes on rural landholder responses to climate change. Journal of Environmental Management , 111 , 258–266. https://doi.org/10.1016/j.jenvman.2012.07.015 .

Sanderson, M. R., & Curtis, A. L. (2016). Culture, climate change and farm-level groundwater management: An Australian case study. Journal of Hydrology , 536 , 284–292. https://doi.org/10.1016/j.jhydrol.2016.02.032 .

Seidl, C., Wheeler, S. A., & Zuo, A. (2021). The drivers associated with Murray-Darling Basin irrigators’ future farm adaptation strategies. Journal of Rural Studies, 83 , 187–200. https://doi.org/10.1016/j.jrurstud.2020.10.048 .

Tzemi, D., & Breen, J. (2019). Climate change and the agricultural sector in Ireland: Examining farmer awareness and willingness to adopt new advisory mitigation tools. Climate Policy, 19 (5), 611–622. https://doi.org/10.1080/14693062.2018.1546163 .

van Duinen, R., Filatova, T., Geurts, P., & van der Veen, A. (2015). Coping with drought risk: Empirical analysis of farmers’ drought adaptation in the south-west Netherlands. Regional Environmental Change , 15 , 1081–1093. https://doi.org/10.1007/s10113-014-0692-y .

van Haden, R., Niles, M. T., Lubell, M., Perlman, J., & Jackson, L. E. (2012). Global and local concerns: What attitudes and beliefs motivate farmers to mitigate and adapt to climate change? PLOS ONE , 7 (12) , e52882. https://doi.org/10.1371/journal.pone.0052882 .

van Valkengoed, A. M., & Steg, L. (2019). Meta-analyses of factors motivating climate change adaptation behaviour. Nature Climate Change , 9, 158–163 . https://doi.org/10.1038/s41558-018-0371-y .

West, R., Godinho, C. A., Bohlen, L. C., Carey, R. N., Hastings, J., Lefevre, C. E., & Michie, S. (2019). Development of a formal system for representing behaviour-change theories. Nature Human Behaviour , 3 , 526–536. https://doi.org/10.1038/s41562-019-0561-2 .

Wheeler, R., & Lobley, M. (2021). Managing extreme weather and climate change in UK agriculture: Impacts, attitudes and action among farmers and stakeholders. Climate Risk Management , 32 , 100313. https://doi.org/10.1016/j.crm.2021.100313 .

Wheeler, S., Zuo, A., & Bjornlund, H. (2013). Farmers’ climate change beliefs and adaptation strategies for a water scarce future in Australia. Global Environmental Change , 23 (2), 537–547. https://doi.org/10.1016/j.gloenvcha.2012.11.008 .

Wheeler, S. A., Nauges, C., & Zuo, A. (2021). How stable are Australian farmers’ climate change risk perceptions? New evidence of the feedback loop between risk perceptions and behaviour. Global Environmental Change , 68 , 102274. https://doi.org/10.1016/j.gloenvcha.2021.102274 .

Woods, B. A., Nielsen, H. Ø., Pedersen, A. B., & Kristofersson, D. (2017). Farmers’ perceptions of climate change and their likely responses in Danish agriculture. Land Use Policy , 65 , 109–120. https://doi.org/10.1016/j.landusepol.2017.04.007 .

Yoder, L., Houser, M., Bruce, A., Sullivan, A., & Farmer, J. (2021). Are climate risks encouraging cover crop adoption among farmers in the southern Wabash River Basin? Land Use Policy , 102 , 105268. https://doi.org/10.1016/j.landusepol.2020.105268 .

Download references

Acknowledgements:

This work was supported by the Austrian Climate and Energy Fund within the Austrian Climate Research Program, research project FARMERengage (grant number KR18AC0K14641).

Author information

Authors and affiliations.

Department of Economics and Social Sciences, Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria

Bernadette Kropf & Hermine Mitter

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Bernadette Kropf .

Editor information

Editors and affiliations.

Institut für Nachhaltige Wirtschaftsentwicklung, Universität für Bodenkultur Wien, Wien, Österreich

Manuela Larcher

Erwin Schmid

Rights and permissions

Open Access Dieses Kapitel wird unter der Creative Commons Namensnennung 4.0 International Lizenz ( http://creativecommons.org/licenses/by/4.0/deed.de ) veröffentlicht, welche die Nutzung, Vervielfältigung, Bearbeitung, Verbreitung und Wiedergabe in jeglichem Medium und Format erlaubt, sofern Sie den/die ursprünglichen Autor(en) und die Quelle ordnungsgemäß nennen, einen Link zur Creative Commons Lizenz beifügen und angeben, ob Änderungen vorgenommen wurden.

Die in diesem Kapitel enthaltenen Bilder und sonstiges Drittmaterial unterliegen ebenfalls der genannten Creative Commons Lizenz, sofern sich aus der Abbildungslegende nichts anderes ergibt. Sofern das betreffende Material nicht unter der genannten Creative Commons Lizenz steht und die betreffende Handlung nicht nach gesetzlichen Vorschriften erlaubt ist, ist für die oben aufgeführten Weiterverwendungen des Materials die Einwilligung des jeweiligen Rechteinhabers einzuholen.

Reprints and permissions

Copyright information

© 2022 Der/die Autor(en)

About this chapter

Kropf, B., Mitter, H. (2022). Factors Influencing Farmers’ Climate Change Mitigation and Adaptation Behavior: A Systematic Literature Review. In: Larcher, M., Schmid, E. (eds) Alpine Landgesellschaften zwischen Urbanisierung und Globalisierung. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-36562-2_14

Download citation

DOI : https://doi.org/10.1007/978-3-658-36562-2_14

Published : 23 August 2022

Publisher Name : Springer VS, Wiesbaden

Print ISBN : 978-3-658-36561-5

Online ISBN : 978-3-658-36562-2

eBook Packages : Social Science and Law (German Language)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Systematic literature review on behavioral barriers of climate change mitigation in households

• Stankuniene G
• Streimikiene D
• Kyriakopoulos G

Achieving climate change mitigation goals requires the mobilization of all levels of society. The potential for reducing greenhouse gas (GHG) emissions from households has not yet been fully realized. Given the complex climate change situation around the world, the importance of behavioral economic insights is already understood. Changing household behavior in mitigating climate change is seen as an inexpensive and rapid intervention measure. In this paper, we review barriers of changing household behavior and systematize policies and measures that could help to overcome these barriers. A systematic literature review provided in this paper allows to define future research pathways and could be important for policy-makers to develop measures to help households contribute to climate change mitigation.

Author supplied keywords

• Behavior change
• Behavior change barrier
• Climate change mitigation
• Energy consumption
• Nudge and Boost intervention

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

CITATION STYLE

Stankuniene, G., Streimikiene, D., & Kyriakopoulos, G. L. (2020, September 1). Systematic literature review on behavioral barriers of climate change mitigation in households. Sustainability (Switzerland) . MDPI. https://doi.org/10.3390/SU12187369

Readers' Seniority

PhD / Post grad / Masters / Doc 35

Researcher 16

Lecturer / Post doc 6

Professor / Associate Prof. 5

Readers' Discipline

Social Sciences 13

Economics, Econometrics and Finance 13

Environmental Science 10

Engineering 7

Save time finding and organizing research with Mendeley

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 26 August 2024

Progress and gaps in climate change adaptation in coastal cities across the globe

• Mia Wannewitz   ORCID: orcid.org/0000-0003-1769-9877 1 ,
• Idowu Ajibade 2 ,
• Katharine J. Mach   ORCID: orcid.org/0000-0002-5591-8148 3 , 4 ,
• Alexandre Magnan 5 , 6 , 7 ,
• Jan Petzold   ORCID: orcid.org/0000-0003-0508-3362 1 ,
• Diana Reckien   ORCID: orcid.org/0000-0002-1145-9509 8 ,
• Nicola Ulibarri   ORCID: orcid.org/0000-0001-6238-9056 9 ,
• Armen Agopian 3 , 4 ,
• Vasiliki I. Chalastani 10 ,
• Tom Hawxwell   ORCID: orcid.org/0000-0003-1073-983X 11 ,
• Lam T. M. Huynh   ORCID: orcid.org/0000-0002-2801-8240 12 ,
• Christine J. Kirchhoff   ORCID: orcid.org/0000-0002-2686-6764 13 ,
• Rebecca Miller 14 , 15 ,
• Justice Issah Musah-Surugu 16 ,
• Gabriela Nagle Alverio   ORCID: orcid.org/0000-0001-7050-3381 17 ,
• Miriam Nielsen   ORCID: orcid.org/0000-0003-0037-294X 18 ,
• Abraham Marshall Nunbogu 19 ,
• Brian Pentz 20 ,
• Andrea Reimuth   ORCID: orcid.org/0000-0001-9347-849X 1 ,
• Giulia Scarpa 21 ,
• Nadia Seeteram   ORCID: orcid.org/0000-0002-2266-7573 22 ,
• Ivan Villaverde Canosa   ORCID: orcid.org/0000-0002-9344-6452 23 ,
• Jingyao Zhou   ORCID: orcid.org/0009-0004-6882-6797 1 ,
• The Global Adaptation Mapping Initiative Team &
• Matthias Garschagen   ORCID: orcid.org/0000-0001-9492-4463 1  

Nature Cities volume  1 ,  pages 610–619 ( 2024 ) Cite this article

161 Altmetric

Metrics details

• Climate-change adaptation

Coastal cities are at the frontlines of climate change impacts, resulting in an urgent need for substantial adaptation. To understand whether, and to what extent, cities are on track to prepare for climate risks, this paper systematically assesses the academic literature to evaluate evidence on climate change adaptation in 199 coastal cities worldwide. Results show that adaptation in coastal cities is rather slow, of narrow scope and not transformative. Adaptation measures are predominantly designed based on past and current—rather than future—patterns in hazards, exposure and vulnerability. City governments, particularly in high-income countries, are more likely to implement institutional and infrastructural responses, whereas coastal cities in lower-middle-income countries often rely on households to implement behavioral adaptation. There is comparatively little published knowledge on coastal urban adaptation in low- and middle-income countries, and regarding particular adaptation types such as ecosystem-based adaptation. These insights make an important contribution for tracking adaptation progress globally and help to identify entry points for improving adaptation of coastal cities in the future.

Similar content being viewed by others

Status of global coastal adaptation

Developing countries can adapt to climate change effectively using nature-based solutions

Quality of urban climate adaptation plans over time

Coastal cities are engines of economic growth and innovation, yet they are also hotspots of disasters and climate risk 1 , 2 , 3 . These cities face increasing environmental changes such as record-breaking sea-surface temperatures 4 and in turn an increase in hazards such as tropical cyclones, floods, storms, erosion and heatwaves 5 , 6 , 7 . Such changes dynamically interact with urban vulnerabilities driven by, for example, inequality, poverty and inadequate infrastructure 8 . Yet, coastal urban risk is not uniform, as climate change impacts and risks vary across coastal cities depending on geomorphological conditions, climatic and human drivers of coastal change, urban development, and other factors 6 , 9 , 10 . In the face of future increases in urbanization and climate change impacts, coastal cities are under pressure to adapt to, and reduce, current and future risks to ensure sustainable and equitable urban development 11 , 12 . As centers of economic activities and key players in the global political economy with substantial capacities, coastal cities have the potential to shape and advance the future of climate adaptation in meaningful and innovative ways 13 . Although the need for transformative adaptation in coastal cities—that is, adaptation that changes the fundamental attributes of a social-ecological system in anticipation of climate change and its impacts 14 —has been stressed in principle 2 , 15 , little is known about the actual progress of adaptation in coastal cities across the globe.

Given the unique challenges and opportunities in coastal cities as hotspots of risk and centers of economic activity, we argue that assessing their current state of adaptation is important, not least as a knowledge base for tracking countries’ progress in climate action within the Global Stocktake under the Paris Agreement 16 . Understanding how coastal cities are responding to climate impacts is crucial for identifying successes and gaps, and for advancing adaptation efforts at large. Studies have assessed different types of urban adaptation, for example, institutional 17 or ecosystem-based 18 , certain actor types involved in urban adaptation (for example, ref. 20 ), urban adaptation in particular regions (for example, refs. 19 , 21 , 22 , 23 ) or coastal adaptation planning 24 , 25 . However, a systematic global assessment of the literature on empirical evidence for implemented coastal urban adaptation—including its response types, actors and level of transformation—does not yet exist. Such an assessment is particularly relevant in the face of the latest Intergovernmental Panel on Climate Change’s (IPCC) report’s finding that coastal cities tend to implement adaptation interventions reactively in response to high-impact events such as floods and storms 26 , and that many gaps remain in urban adaptation to climate change induced hazards across regions 13 .

This study therefore aims to provide a global analysis of empirical evidence of adaptation in coastal cities, including gaps and shortcomings. It also aims to inform policy and practice to advance effective adaptation strategies in response to current and projected climate impacts. To address these objectives, the study is guided by four questions that also serve to structure the results section: (1) How is evidence for coastal urban adaptation spread across the globe? (2) Which hazards and trends of exposure and vulnerability are reported? (3) Which actors are reported to be involved in which types of responses? And (4) what is the speed, scope and depth of reported coastal urban adaptation?

By answering these four questions, this study extends earlier assessments of the state of adaptation more generally 27 by systematically analyzing the empirical evidence of coastal urban responses to climate change, as published in the peer-reviewed academic literature. We assessed the state of adaptation in coastal cities as reported between 2013 and 2020, and examine major patterns in relation to average income levels and city size. Coastal cities here are defined as urban areas with central functions such as markets, medical services and schools; they are of relative importance to the surrounding area, regardless of population size; and are located entirely or partly on the coastline or within the low-elevation coastal zone (LECZ), or within the influence of coastal or tidal hydrology. Our sample covers adaptation activities in 199 cities reported in 683 articles, of which 183 were qualitatively coded using a questionnaire composed of 30 questions (see Methods for details). Our analysis is hence limited to what is being reported in the scientific literature and might include some hard-to-quantify biases that need to be addressed through additional datasets in the future, for example, by covering documents published by civil society actors on adaptation in coastal cities in the Global South where, according to our analysis, fewer studies are available than for higher-income countries. However, we argue that our approach and analysis nevertheless can provide highly relevant insights not only on urban adaptation research but also on the patterns of actual adaptation activities as adaptation research has been expanding massively, now capturing a wide spectrum of activities on the ground. Studies such as these therefore provide an increasingly important knowledge base for tracking adaptation activities 27 .

Evidence for coastal urban adaptation across the globe

The considered literature covers adaptation evidence from coastal cities in all regions and income groups, yet with some considerable differences (Fig. 1 ; see Supplementary Data 1.1 for a detailed list of countries covered in the sample). Most publications present evidence for adaptation from coastal cities in Asia (30%), followed by North America (23%), Europe (16%) and Africa (13%). Compared with the global share of inhabitants living in the LECZ between 0 m and 10 m above sea level 28 , 29 , some regions are overrepresented. This is most evident for North America, Australasia and small island states, which are home to 5%, 0.6% and 0.5% of the global population in the LECZ, respectively, yet, in our sample of coastal urban adaptation evidence, they represent 23%, 11% and 3% of assessed coastal cities. Other regions are underrepresented, which is most evident for Asia given its high number of inhabitants in the LECZ. Although inhabiting 75% of the global population in the LECZ, only 31% of our assessed urban coastal adaptation evidence stems from this region.

Green shading represents the country’s income classification according to the World Bank 82 ; the size and color of the dots visualizes the location of the covered coastal cities and their population size (Supplementary Data 3 ); the most covered coastal cities are listed according to frequency at the bottom right. Map source: Natural Earth 85 .

Source data

The majority of adaptation in coastal cities is reported in high-income economies (56%), which is in stark contrast to the fact that only 16% of the population located in the LECZ live in such economies. Of the reported coastal cities, 19% and 24% of the population are in upper- and lower-middle-income economies, respectively. Given that upper- and lower-middle-income countries account for 34% and 43% of the global population in the LECZ 28 , 29 , respectively, the coastal cities in these income groups are substantially underrepresented in our sample, meaning in the academic literature. Only 1% of the reported activities represent coastal cities in low-income economies (for example, Maputo, Beira and Inhambane in Mozambique). Given that the global population share of people who live in the low-income LECZ is about 8%, they are also underrepresented in our sample.

In terms of the coverage of different sizes of coastal cities (Supplementary Data 1.2 ), the assessed literature mostly presents evidence for adaptation in coastal cities with fewer than 250,000 inhabitants (48% of the reported cases). This pattern can partly be explained by our definition of coastal cities on the basis of their central functions, rather than population thresholds. Evidence for adaptation from mid-sized coastal cities with 250,000–1,000,000 inhabitants is less well-covered in our sample (the examples are mainly in North America and Europe). Thirty-five percent of the reported adaptation happens in coastal cities with >1,000,000 inhabitants, with a majority of cases in Africa and Asia. Some megacities (that is, cities with more than ten million inhabitants) such as New York, Jakarta, Manila and Lagos are covered by multiple studies (see Fig. 1 ). Most empirical evidence for adaptation in coastal megacities stems from Asia (57%), which aligns with the fact that 15 out of 20 coastal megacities are located in Asia 30 , and also with Asia's high overall population share in the LECZ (75%) 28 , 29 .

Hazards and trends of exposure and vulnerability

In terms of hazards, the adaptation activities reported in the sample predominantly address sea-level rise, different types of flooding and, to a lesser extent, storm surges, cyclones and erosion (see Fig. 2 ). A majority of the assessed cases (65%) considers more than one hazard. Such consideration of multiple hazards is most evident for the combination of sea-level rise with storm surges, coastal and pluvial flooding, as well as coastal erosion. This finding suggests that multi-hazard considerations nowadays play a strong role in urban climate risk assessments, in line with what the conceptual literature would be calling for 6 , 10 .

Risk emerges from the interplay of hazards, exposure and vulnerability 14 . The figure displays the number of cities considering past and current patterns (orange bars), and future trends (blue and green bars) for different hazards (top), as well as the exposure and vulnerability of people and businesses, buildings and infrastructure, and environmental assets (bottom).

Studies predominantly consider past and current events with regards to hazard timescales and scenarios (Fig. 2 ). Studies often consider future hazard trends in principle but not in a quantified manner. Although modeled trends and scenarios are quite frequently used as a basis for adaptation to sea-level rise, flooding and storm surges, they are much less common for other hazards.

The picture is even more striking regarding how other risk factors—notably the exposure and vulnerability of people and assets in coastal cities—are considered. In the vast majority of coastal cities, reported adaptation considers only past and current patterns, with the population being the most important element considered, followed by particularly vulnerable groups, residential buildings and the coastline (Fig. 2 ). In scenarios in which future trends in exposed and vulnerable assets are considered, they are accounted for in a general or conceptual way, but not in terms of quantified scenarios. Across our sample, the consideration of the presented elements at risk correlates weakly with a country’s income level. The higher the income group, the more likely that exposure and vulnerability aspects are considered (Supplementary Data 1.3 ).

Responses and actors

Most of the reported adaptation in coastal cities can be categorized as technological/infrastructural and behavioral/cultural adaptation (Fig. 3 ). But combinations of these two, as well as of technological and institutional responses, were also frequently reported. Ecosystem-based responses are the least reported across all world regions, particularly in low-, lower-middle and upper-middle-income countries.

Response types are grouped (on the basis of work by Berrang-Ford et al. 27 ) into technological (that is, enabling, implementing or undertaking technological innovation or infrastructural development), behavioral or cultural (enabling, implementing or undertaking lifestyle and/or behavioral change), institutional (enhancing multi-level governance or institutional capabilities) or ecosystem-based (enhancing, protecting or promoting ecosystem services for adaptation) categories.

The prominence of different response and actor types varies across country and income groups (Fig. 3 ), as well as city size. Most cases reporting technological or infrastructural responses are from coastal cities in high-income countries. The coverage of institutional responses shows a similar pattern. A correlation analysis confirms that the higher the gross national income (GNI) per capita, the more likely that institutional adaptation (Spearman’s ρ  = 0.23, P  < 0.01) and less likely that behavioral adaptation (Spearman’s ρ  = −0.35, P  < 0.01) is mentioned (Supplementary Data 1.4 ). Institutional responses are mostly reported to be implemented by state actors, especially city governments (Supplementary Data 1.5 ), which are the most commonly mentioned actor type across our sample. Correlation analysis reveals that the higher the GNI per capita, the more likely that the city government is assessed as an actor in adaptation (Spearman’s ρ  = 0.30, P  < 0.01), and the less likely that individuals/households are mentioned (Spearman’s ρ  = −0.23, P  < 0.01) (Supplementary Data 1.6 ). Our analyses also reveal that the bigger a city, the less likely that individual/household adaptation is mentioned (Spearman’s ρ  = −0.30, P  < 0.01) and the more likely that a city government is assessed as an actor involved in adaptation (Spearman’s ρ  = 0.20, P  < 0.01) (Supplementary Data 1.6 ).

Reported behavioral or cultural responses are most likely to be assessed together with individuals or households as implementing actors (Supplementary Data 1.7 ). This response type dominates the reported adaptation evidence in coastal cities in lower-middle-income countries. Accordingly, individuals/households are mostly reported as adaptation actors here, whereas state actors such as city and sub-city governments are less frequently assessed as implementers. In contrast to this, we find a low involvement of individuals in low-income economies; however, the very small number of cases in the low-income category needs to be considered here.

Although the assessed literature mostly presents adaptation evidence implemented by one type of actor (in our sample, mostly city governments followed by individuals/households), there is also reported evidence for multiple actors involved in urban adaptation. In many cases, individuals/households and city governments are mentioned together. Furthermore, combinations of city and national governments, or a combination of the two with the sub-city local government, are reported more frequently than other combinations (Supplementary Data 1.8 ).

Looking at adaptation types across regions (Fig. 3 and Supplementary Data 1.7 ), behavioral adaptation is less likely to be reported in North American coastal cities ( ϕ coefficient = −0.21, P  < 0.01) and coastal cities in Central and South America, but more likely to be reported in coastal cities in Africa and Asia. For the last two, we find less evidence for institutional and ecosystem-based adaptation; these adaptation categories are more likely to be assessed in European and North American coastal cities. Evidence for technological adaptation is most likely to be assessed in European coastal cities; research on institutional adaptation evidence features most highly in North and South America.

Speed, scope and depth of adaptation

Transformative adaptation can be assessed along the dimensions of depth (how deep institutional, and other changes, are), speed (how fast adaptation is planned and implemented) and scope (with which geographical and sectoral breadth adaptation happens) 27 , 31 . Overall, we find that reported adaptation remains at rather low depth, scope and speed in coastal cities, across all income groups and regions, with little evidence of reduced risks due to adaptation (Fig. 4 ). Neither income level nor population size predicts more or less transformative adaptation (Supplementary Data 1.9 ).

The depth, speed and scope of adaptation are dimensions of transformative adaptation 27 , 31 . Displayed numbers represent the share of studies evaluated to report low, medium or high levels of depth, speed and scope of adaptation within different country groups in terms of average income according to the World Bank 82 .

Few examples of urban adaptation with deeper changes (that is, entirely new practices that involve deep structural reform, a fundamental change in mindset, major shifts in perceptions or values, and/or changing institutional or behavioral norms) stem from cities in high-income economies or small island states. Given the small number of cases featuring such fundamental forms of adaptation, we provide an aggregated overview of specific studies below.

Some cases reported self- or state-led resettlement 32 , 33 to adapt to climate change impacts in coastal cities. In cities such as Singapore and Hong Kong 34 , and several Swedish cities 35 , existing infrastructural measures are complemented by preparedness and recovery measures, as well as ecosystem-based approaches. Progress in the institutionalization and mainstreaming of basin-wide planning, the integration of adaptation into mitigation and development planning, and the establishment of legislation to reinforce adaptation in sectors such as construction, are considered as evidence of more transformative adaptation in coastal cities. We also identified evidence for medium adaptation depth across countries with different income levels, where the assessed responses reflect a shift away from existing practices, norms or structures to some extent. In coastal cities located in high-income countries in Europe, such as Rotterdam, Dordrecht and Helsinki, medium-depth adaptation is linked to the testing of innovative, design-oriented adaptation approaches, the development of collaborative governance approaches, and public–private partnerships for improving funding and innovation 36 , 37 , 38 , 39 , 40 . In smaller US coastal cities such as Dunedin and Fernandina Beach, changes towards cross-sectoral, comprehensive and more integrative risk management plans 41 , 42 were described. Bigger US cities such as New York and Miami Beach are implementing both large-scale infrastructure investments for flood protection 43 , 44 , 45 and planning, and/or complementary adaptation measures such as ecosystem-based and soft adaptation approaches 43 , 46 .

In Asian coastal cities in lower- and upper-middle-income countries, medium-depth adaptation includes changes in adaptive behavior of individuals and households (for example, changes in livelihoods or migration 33 , 47 , 48 , 49 , 50 ), as well as institutional-scale adaptations (for example, the establishment of new institutions responsible for adaptive planning, disaster risk reduction planning at various scales, or mainstreaming climate change policies in other sectors 51 , 52 , 53 ). The only case with evidence of medium-depth adaptation in a low-income country is Maputo, Mozambique, which has mainstreamed climate change adaptation into its development plans, attributed clear responsibilities for addressing climate change impacts, and started participatory urban planning processes 54 .

For the majority of coastal cities covered in our sample, adaptation remains at low depth across income groups and regions, meaning that evidence for adaptation largely represents expansions of existing practices, with minimal change in underlying values, assumptions or norms. Examples are a continuous focus on traditional infrastructural measures to avoid flooding 55 , 56 , continued uptake of flood insurance 57 , or incremental adaptation in the form of reactive coping due to limited capacities 58 , 59 .

The scope of responses in our sample is mostly narrow, across both income groups and regions, meaning that evidence for coastal urban adaptation measures is largely localized and fragmented, with limited evidence of coordination or mainstreaming across sectors, jurisdictions or levels of governance.

The speed of coastal urban adaptation is mostly considered slow—especially in high-, upper-middle- and lower-middle-income countries, and a majority of regions. This means that adaptations are incremental, consisting of small steps and slow implementation.

Given that depth, scope and speed of adaptation were evaluated as rather low across our sample, it is not surprising that there is little evidence for risk being reduced through these measures. Although we identified some cases that present evidence for risks being overcome through, for example, ecosystem-based 60 , 61 and technological/infrastructural adaptation 45 , 62 , some are linked to negative side-effects or lacking long-term perspectives 63 or even represent maladaptation 56 , 64 , 65 .

Based on the analysis of adaptation in coastal cities reported in the academic literature, we highlight five key findings and close by discussing their implications for research and policy-making in the field of coastal urban adaptation to climate change.

First, our assessment shows that the knowledge and coverage of adaptation in coastal cities is highly uneven, with some coastal cities receiving a lot of scientific attention, and large gaps remaining. For example, small and mid-sized coastal cities in Africa, Asia and Central and South America are currently not part of the global scientific debate, despite the fact that more adaptation might be happening on the ground, reported in other types of documents such as white papers or NGO reports. In our assessment based on the peer-reviewed and mostly English-language academic literature, coastal cities in low-, lower-middle and upper-middle-income countries are underrepresented. Given that cities in Africa, Asia, and Central and South America are expected to experience a highly dynamic interplay of urbanization, highly vulnerable informal settlements and future climate change impacts (see page 7 of ref. 66 ), this is a considerable gap in research that needs to be addressed urgently. Researchers and funding agencies should therefore make a dedicated push towards increasing the evidence-base, specifically in this segment of cities. Furthermore, other data sources such as non-peer-reviewed reports and other grey literature need to be assessed in the future to complement the evidence provided in the peer-reviewed scientific literature.

Second, we generally found that hazards, exposure and vulnerability are considered on the basis of past and current events and conditions. The use of future climate scenarios or other quantitative assessments taking into account future hazard trends remains scarce, and the picture is even more troublesome in terms of the future trends of exposure and vulnerability. Most reported adaptation is not based on a thorough consideration—let alone quantified scenarios—of future developments in the exposure and vulnerability of at-risk people, infrastructure, ecosystems and other assets. This leads to skewed assumptions on future risk, jeopardizing the relevance and validity of knowledge for adaptation planning. Although this finding confirms earlier observations with respect to the low consideration of future exposure and vulnerability trends in National Adaptation Plans 67 and cities 24 , it is nevertheless striking given the high importance of dynamic changes in these domains for changing future risk in coastal cities, for example, through further coastal urbanization or ongoing socio-economic marginalization 6 , 8 .

Third, we find that the lower the income group of the country the coastal cities are located in, the more likely individuals/households are reported as prime adaptation actors. At the same time, government responses and planned adaptation are more often reported in coastal cities in wealthier countries. This suggests that residents with limited resources in poorer coastal cities have to carry most of the adaptation burden 68 , which is often met with behavioral changes due to the lack of institutional and/or technological support. These results corroborate other studies regarding the inequality in the urban adaptation gap (see pages 34 of ref. 66 and page 941 of ref. 26 ), which is most pronounced among the poor.

Fourth, the bigger a city, the more likely that technological responses and protection are assessed. This relationship was also found in other studies 69 . At the same time, there is a lack of reported empirical evidence on ecosystem-based adaptation. Technology-based measures such as flood-barriers or pumping installations are essential protective mechanisms in the short- and mid-term, for example, for storm water management. However, they can lead to a lock-in and maladaptive path dependency in the long-term if coastal hazards continue to rise and hard protection fails or reaches limits of financial and technical feasibility as well as cultural acceptance 70 , 71 . More research on alternative and complementary adaptation measures is therefore needed to guide mixed approaches in the future.

Fifth, our findings suggest urgent needs for transformative adaptation in coastal cities. Across all regions and income groups, scientifically reported adaptation in coastal cities remains at rather low depth, scope and speed. Neither income level nor population size predicted more or less progressive adaptation behavior. Given the high exposure and vulnerability of many coastal cities already today, this finding is alarming as adaptation to future climate change will require many cities to go beyond business as usual risk management to effectively manage and reduce the accelerating risks and vulnerabilities 2 , 15 , 72 . This finding affirms other assessments of urban adaptation 26 and stresses the persistent need for transformative adaptation in coastal cities. It is possible that the cumulative effects of incremental responses could, over time, lead to meaningful and even transformative adaptation; however, the speed and amount of change needed to mitigate current and future risks, could mean that incremental adaptation is tantamount to playing 'catch-up' as climate impacts accelerate.

The extreme changes in the oceans and coasts seen in the recent past, with, for example, new temperature records 4 , 73 , 74 and low sea-ice extent 75 , highlights the scale and speed of adaptation that will be needed. Yet, taking the scientifically reported adaptation evidence as a proxy for the state of adaptation in coastal cities, our findings suggest that adaptation in coastal cities is rather slow, narrow, and fragmented (in other words, non-transformative) in an environment that is transforming rapidly. At the same time, our findings point towards an increasing range of adaptation activities in coastal cities. This evidence mapping can help to point researchers to blind spots in adaptation research in coastal cities and it provides entry points for improving urban adaptation planning.

We followed the ROSES protocol 76 to produce a systematic map of evidence for climate change adaptation in coastal cities (Supplementary Table 1 ). We base our findings on the combination of a systematic review of scientific literature on coastal urban adaptation to climate change across three reference databases (see Extended Data Fig. 1 , which follows the ROSES flow diagram for systematic reviews 77 ) with a content analysis based on a coding protocol, following the Global Adaptation Mapping Initiative (GAMI) process.

Relevant peer-reviewed, scientific, English-language literature on the topic of coastal urban adaptation was identified in a four-tiered search process.

Literature search and data extraction

Publications of the category 'cities and settlements by the sea' were extracted from the GAMI database—a systematic dataset comprising over 1,600 articles on climate adaptation. After a preliminary overview of the 361 resulting publications, further searches through the reference databases Web of Science and Scopus, and discussions among the co-authors (most of whom are well-acquainted with the literature in this particular field), it was decided that the GAMI selection did not adequately represent the large pool of existing literature on coastal urban adaptation. Hence, in a second step, a search string (in English) based on boolean search terms was used to systematically search Web of Science (Core Collection) and Scopus for relevant peer-reviewed, scientific literature over the years 2013 to 2020. The period stretches from the end of the IPCC’s fifth assessment cycle to the cut-off date for considering scientific literature of the sixth assessment cycle. With this we extended the original GAMI search by one year; we did not include 2021 and 2022 due to the coding time-frame. Although the basis of the search string was adopted from the GAMI process 78 , 79 , it was extended by tailored search terms to yield more topic-relevant publications. The search strings and respective hits can be found in Supplementary Information 1 . In a final step, the results of all three searches were combined and duplicates were removed.

We are aware that systematic searches such as this are subject to limitations. Our approach neither considered grey literature such as reports, nor did it use non-English search strings, and thus it is predominately built on English-language publications, which might have led to biases in the results. We nonetheless decided to use this approach to take steps towards a global stocktake of adaptation in coastal cities on the basis of scientific, peer-reviewed literature, using it as a first indication for the state of knowledge on coastal urban adaptation, and as a proxy for understanding where coastal cities currently stand in adapting to climate change. From the perspective of the authors, the added value in these respects outweigh the limitations of the study.

A total of 683 scientific publications entered the screening process, in which the coders assessed whether a publication should be included in the analysis. Overall, only peer-reviewed publications were considered, which excludes conference contributions (further inclusion/exclusion criteria are listed in Supplementary Information 1 ). A total of 501 publications were excluded because they did not meet the inclusion criteria. Six publications were not available in English language, and two were either not accessible or not found. Requests to the authors for access were unanswered. See Supplementary Table 2 for an overview of all included, excluded and not found or accessible publications.

The included publications were analyzed via a systematic content analysis. The publications were distributed among coders considering their interests and capacities, ensuring that no coder analyzed their own publications. Using the online survey platform SoSci Survey Version 3.5.01, coders completed one coding questionnaire per city covered in the manuscript. This means that for one publication, several questionnaires could have been completed in the case that it dealt with two or more cities. In total, 183 publications (Supplementary Table 2 ) covering 284 cases from 199 cities and/or settlements with central functions such as schools, supermarkets and medical services were included in the coding and statistical analysis, as well as four unspecified urban areas. The literature database (Supplementary Table 2 ) and the coding database (Supplementary Data 2 ) can be found as supplements.

Data quality

We ensured coder consistency and reliability through an introduction to the commonly developed questionnaire; a code book/protocol with detailed definitions of all codes (Supplementary Information 1 ); a pre-coding period with interim meetings to discuss issues and confusions; and multiple other meetings with all of the coders involved. The coding included, among others, the following categories: hazard type; exposure and vulnerability; actor type; response type; and, as indicators for transformational adaptation, the depth, speed and scope of adaptation (see Supplementary Information 1 for the full list of codes and variables). About 10% of the entire dataset (that is, 72 publications) was double-coded to check inter-coder reliability. Conflicts regarding inclusion/exclusion arose to 12.8%. Of the 16 fully double-coded publications, inter-coder variability rose to a maximum of 22.2%, meaning a convergence in roughly 80% of provided answers, which was accepted as sufficient to consider the dataset as robust. The data, in the form of codes, were extracted from the ScoSci Survey platform, cleaned and statistically analyzed in IBM SPSS Statistics 23, following the original GAMI approach 78 , 80 , 81 . Coders provided their level of confidence (low, medium, high) to evaluate the depth, speed and scope of adaptation; the final analysis only considered medium- and high-confidence judgements to increase the robustness of the findings.

Data analysis

To obtain an overview of the dataset, descriptive statistical analyses were performed to assess the frequency and proportion of all variables. To identify potential patterns, frequencies were assessed across the World Bank income economies categories (hereafter income groups) 82 and also across regions following the classification used in ref. 27 . Moreover, we used different correlation tests to explore potential relationships that two variables, GNI per capita 83 and city size (in terms of population, Supplementary Data 3 ), have with patterns of actor involvement, adaptation type and depth, and the speed and scope of adaptation. We are aware that income indicators and the urban population size are by far not the only factors influencing adaptation in complex socio-ecological systems 84 ; however, they provide valuable, globally available and comparable starting points for not only describing, but also explaining, emerging patterns of urban coastal adaptation. Hence, our objective was to evaluate the existence of any relationship between these two variables (GNI per capita and city size) with our assessed variables. The Spearman’s rank correlation was employed to ascertain the relationship between GNI per capita and city size with actor involvement. The correlation coefficient ranges between −1 and 1, indicating negative and positive correlations, respectively. The significance of the correlation coefficient is examined by the t -test, which assesses the null hypothesis that there is no monotonic relationship between the two variables. The null hypothesis is rejected if the P -value is less than 0.05. The relationship between adaptation actors and response categories was determined using the χ 2 test, which is a common statistical method for measuring the association between binary variables. The strength and direction of the association are represented by the ϕ coefficient. This coefficient, like the Spearman correlation, ranges from −1 to 1, with values close to −1 indicating a strong negative association, values close to 1 indicating a strong positive association, and values close to 0 indicating a weak or no association. The significance of the ϕ coefficient is also examined using a P -value.

To conduct a cross-sectional comparison of population data in the LECZ across different regions, we utilized “The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3” dataset 28 . Within this dataset, we specifically selected the population data from “Gridded Population of the World, Version 4 (GPWv4), Revision 11” and the elevation data from 'CoastalDEM90' as core datasets, due to their particular applicability in global-scale and coastal analyses. The analysis provides data about the share of residents living in the LECZ globally in the considered income economies and regions, which is used to understand the relative coverage of adaptation evidence reported in our sample.

The assessment of transformational adaptation in coastal cities builds on the coders’ qualitative evaluation of the three dimensions of transformation 31 ; that is, depth, speed and scope (definitions of the categories can be found in Supplementary Information 1 ) of the reported adaptation evidence. In addition, the confidence in their respective responses was assessed and only high- and medium-confidence evaluations were taken into account in the final assessment of speed, scope and depth.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data and analyses used for this study are available in the Supplementary Information , Supplementary Data , Supplementary Tables and Source Data . The Supplementary Information describes the searches (and their combinations) used to generate the literature sample, the inclusion and exclusion criteria for the literature, and a code book providing descriptions of all of the codes. Supplementary Data allows access to all correlation tables, the full coding database, and the list of sources for the city populations used in Fig. 1 . The base layer 85 for Fig. 1 is publicly available, as are the LECZ population data 28 , the country groupings according to average income levels by the World Bank 82 , and the GNI per capita 83 used for the analyses. Supplementary Table 1 displays the full ROSES map report for the study; Supplementary Table 2 provides the full list of the included and excluded literature, including the author(s), title, journal, year and doi. Source Data are provided with this paper.

Hallegatte, S. Future flood losses in major coastal cities. Nat. Clim. Change 3 , 802–806 (2013).

Google Scholar  

Kuhl, L. et al. Transformational adaptation in the context of coastal cities. Ann. Rev. Environ. Resour. 46 , 449–479 (2021).

Pelling, M. The Vulnerability of Cities: Natural Disasters and Social Resilience (Routledge, 2003); https://doi.org/10.4324/9781849773379 .

Jones, N. The ocean is hotter than ever: what happens next? Nature 617 , 450–450 (2023).

Becker, M., Karpytchev, M. & Hu, A. Increased exposure of coastal cities to sea-level rise due to internal climate variability. Nat. Clim. Change 13 , 367–374 (2023).

Glavovic, B. et al. in Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds. Pörtner, H.-O. et al.) 2163–2194 (Cambridge Univ. Press, 2022); https://doi.org/10.1017/9781009325844.019

Laino, E. & Iglesias, G. Extreme climate change hazards and impacts on European coastal cities: a review. Renew. Sustain. Energy Rev. 184 , 113587 (2023).

Garschagen, M. & Romero-Lankao, P. Exploring the relationships between urbanization trends and climate change vulnerability. Clim. Change 133 , 37–52 (2015).

Magnan, A. K. et al. Sea level rise risks and societal adaptation benefits in low-lying coastal areas. Sci. Rep. 12 , 10677 (2022).

Magnan, A. K. et al. Status of global coastal adaptation. Nat. Clim. Change 13 , 1213–1221 (2023).

Rosenzweig, C. et al. in Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network (eds. Rosenzweig, C. et al.) 17–42 (Cambridge Univ. Press, 2018); https://doi.org/10.1017/9781316563878.007

Wolff, C., Nikoletopoulos, T., Hinkel, J. & Vafeidis, A. T. Future urban development exacerbates coastal exposure in the Mediterranean. Sci. Rep. 10 , 14420 (2020).

Adelekan, I. et al. What the Latest Science on Impacts, Adaptation and Vulnerability Means for Cities and Urban Areas (Indian Institute for Human Settlements, 2022); https://doi.org/10.24943/SUPSV209.2022

IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds. Möller, V. et al.) Annex II: Glossary, 1757–1776 (Cambridge Univ. Press, 2022).

Solecki, W., Pelling, M. & Garschagen, M. Transitions between risk management regimes in cities. Ecol. Soc. 22 , 38 (2017).

Paris Agreement (UNFCCC, 2016).

Patterson, J. J. More than planning: diversity and drivers of institutional adaptation under climate change in 96 major cities. Glob. Environ. Change 68 , 102279 (2021).

Frantzeskaki, N. Seven lessons for planning nature-based solutions in cities. Environ. Sci. Policy 93 , 101–111 (2019).

Aguiar, F. et al. Adaptation to climate change at local level in Europe: an overview Environ. Sci. Policy 86 , 38–63 (2018).

Klein, J., Juhola, S. & Landauer, M. Local authorities and the engagement of private actors in climate change adaptation. Environ. Plann. C 35 , 1055–1074 (2017).

Dilling, L., Pizzi, E., Berggren, J., Ravikumar, A. & Andersson, K. Drivers of adaptation: responses to weather- and climate-related hazards in 60 local governments in the Intermountain Western U.S. Environ. Plann. A 49 , 2628–2648 (2017).

Filho, W. L. et al. Strengthening climate change adaptation capacity in Africa—case studies from six major African cities and policy implications. Environ. Sci. Policy 86 , 29–37 (2018).

Reckien, D. et al. Quality of urban climate adaptation plans over time. npj Urban Sustain. 3 , 1–14 (2023).

Olazabal, M., de Gopegui, M. R., Tompkins, E. L., Venner, K. & Smith, R. A cross-scale worldwide analysis of coastal adaptation planning. Environ. Res. Lett. 14 , 124056 (2019).

Le, T. D. N. Theoretical frameworks in climate change adaptation planning: a comparative study in coastal cities of developing countries. J. Environ. Plann. Manag. 66 , 424-444 (2023).

Dodman, D. et al. in Climate Change 2022: Impacts, Adaptation and Vulnerability (eds. Pörtner, H.-O. et al.) 907–1040 (Cambridge Univ. Press, 2022); https://doi.org/10.1017/9781009325844.008

Berrang-Ford, L. et al. A systematic global stocktake of evidence on human adaptation to climate change. Nat. Clim. Change 11 , 989–1000 (2021).

Urban-Rural Population and Land Area Estimates, v3 (1990, 2000, 2015) (NASA, 2021).

MacManus, K., Balk, D., Engin, H., McGranahan, G. & Inman, R. Estimating population and urban areas at risk of coastal hazards, 1990–2015: how data choices matter. Earth Syst. Sci. Data 13 , 5747–5801 (2021).

World Urbanization Prospects: The 2018 Revision (United Nations, 2019); https://population.un.org/wup/publications/Files/WUP2018-Report.pdf

Termeer, C. J. A. M., Dewulf, A. & Biesbroek, R. Transformational change: governance interventions for climate change adaptation from a continuous change perspective. J. Environ. Plann. Manage. 60 , 558–576 (2016).

Albert, S. et al. Heading for the hills: climate-driven community relocations in the Solomon Islands and Alaska provide insight for a 1.5 °C future. Reg. Environ. Change 18 , 2261–2272 (2018).

Islam, Md. M., Sallu, S., Hubacek, K. & Paavola, J. Migrating to tackle climate variability and change? Insights from coastal fishing communities in Bangladesh. Clim. Change 124 , 733–746 (2014).

Chan, F. K. S., Chuah, C. J., Ziegler, A. D., Dąbrowski, M. & Varis, O. Towards resilient flood risk management for Asian coastal cities: lessons learned from Hong Kong and Singapore. J. Clean. Prod. 187 , 576–589 (2018).

Wamsler, C. et al. Operationalizing ecosystem-based adaptation: harnessing ecosystem services to buffer communities against climate change. Ecol. Soc. 21 , 31 (2016).

Blok, A. Climate riskscapes in world port cities: situating urban-cosmopolitan risk communities via Ulrich Beck’s comparative tactics. Glob. Netw. 20 , 500–521 (2020).

Dircke, P. & Molenaar, A. Climate change adaptation; innovative tools and strategies in Delta City Rotterdam. Water Pract. Technol. 10 , 674–680 (2015).

Francesch-Huidobro, M. Collaborative governance and environmental authority for adaptive flood risk: recreating sustainable coastal cities: Theme 3: pathways towards urban modes that support regenerative sustainability. J. Clean. Prod. 107 , 568–580 (2015).

Gersonius, B., van Buuren, A., Zethof, M. & Kelder, E. Resilient flood risk strategies: institutional preconditions for implementation. Ecol. Soc. 21 , 26270012 (2016).

Mees, H. L. P., Driessen, P. P. J. & Runhaar, H. A. C. Legitimate adaptive flood risk governance beyond the dikes: the cases of Hamburg, Helsinki and Rotterdam. Reg Environ Change 14 , 671–682 (2014).

Díaz, P., Stanek, P., Frantzeskaki, N. & Yeh, D. H. Shifting paradigms, changing waters: transitioning to integrated urban water management in the coastal city of Dunedin, USA. Sustain. Cities Soc. 26 , 555–567 (2016).

Butler, W. H., Deyle, R. E. & Mutnansky, C. Low-regrets Incrementalism: land use planning adaptation to accelerating sea level rise in Florida’s coastal communities. J. Plann. Educ. Res. 36 , 319–332 (2016).

Jeuken, A., Haasnoot, M., Reeder, T. & Ward, P. Lessons learnt from adaptation planning in four deltas and coastal cities. J. Water Clim. Change 6 , 711–728 (2015).

Molinaroli, E., Guerzoni, S. & Suman, D. Do the adaptations of Venice and Miami to sea level rise offer lessons for other vulnerable coastal cities? Environ. Manag. 64 , 391–415 (2019).

Wakefield, S. Miami Beach forever? Urbanism in the back loop. Geoforum 107 , 34–44 (2019).

Pinto, P. J., Kondolf, G. M. & Wong, P. L. R. Adapting to sea level rise: emerging governance issues in the San Francisco Bay region. Environ. Sci. Policy 90 , 28–37 (2018).

Alam, A. & Miller, F. Slow, small and shared voluntary relocations: learning from the experience of migrants living on the urban fringes of Khulna, Bangladesh. Asia Pac. Viewp. 60 , 325–338 (2019).

Buchori, I. et al. Adaptation to coastal flooding and inundation: mitigations and migration pattern in Semarang City, Indonesia. Ocean Coast. Manag. 163 , 445–455 (2018).

Rahman, M. K., Paul, B. K., Curtis, A. & Schmidlin, T. W. Linking coastal disasters and migration: a case study of Kutubdia island, Bangladesh. Prof. Geogr. 67 , 218–228 (2015).

See, J. & Wilmsen, B. Just adaptation? Generating new vulnerabilities and shaping adaptive capacities through the politics of climate-related resettlement in a Philippine coastal city. Global Environ. Change 65 , 102188 (2020).

Porio, E. Climate change vulnerability and adaptation in metro Manila: challenging governance and human security needs of urban poor communities. Asian J. Soc. Sci. 42 , 75–102 (2014).

Walch, C. Adaptive governance in the developing world: disaster risk reduction in the State of Odisha, India. Clim. Dev. 11 , 238–252 (2019).

Wong, E. et al. Policy environment for the tourism sector’s adaptation to climate change in the South Pacific—the case of Samoa. Asia Pac. J. Tour. Res. 18 , 52–71 (2013).

Broto, V. C., Boyd, E. & Ensor, J. Participatory urban planning for climate change adaptation in coastal cities: lessons from a pilot experience in Maputo, Mozambique. Curr. Opin. Environ. Sustain. 13 , 11–18 (2015).

Malott, D., Robertson, L., Hiei, K. & Werner, H. Next Tokyo 2045: a mile-high tower rooted in intersecting ecologies. CTBUH J. 2 , 30–35 (2015).

Neise, T. & Revilla Diez, J. Adapt, move or surrender? Manufacturing firms’ routines and dynamic capabilities on flood risk reduction in coastal cities of Indonesia. Int. J. Disaster Risk Reduct. 33 , 332–342 (2019).

Cannon, C., Gotham, K. F., Lauve-Moon, K. & Powers, B. The climate change double whammy: flood damage and the determinants of flood insurance coverage, the case of post-Katrina New Orleans. Clim. Risk Manag. 27 , 100210 (2020).

Ajibade, I., McBean, G. & Bezner-Kerr, R. Urban flooding in Lagos, Nigeria: patterns of vulnerability and resilience among women. Glob. Environ. Change 23 , 1714–1725 (2013).

Okaka, F. O. & Odhiambo, B. D. O. Households’ perception of flood risk and health impact of exposure to flooding in flood-prone informal settlements in the coastal city of Mombasa. IJCCSM 11 , 592–606 (2019).

Lu, F. Research on the Performance and Enlightenment of New York Storm Surge Adaptive Landscape Infrastructure. E3S Web Conf. 118 , 03026 (2019).

Moyles, C. & Craul, T. Scenic hudson’s long dock park cultivating resilience: transforming a post-industrial brownfield into a functional ecosystem. J. Green Building 11 , 55–77 (2016).

Spekker, H. & Heskamp, J. Flood protection for the city of Beira. Bautechnik 94 , 872–874 (2017).

Chuang, H.-W., Yang, S.-R., Ou, T. & Lin, T.-P. Climatic adaptation of coastal communities on the southwest of Taiwan. J. Mar. Sci. Technol. 24 , 1063 (2016).

Fenton, A., Paavola, J. & Tallontire, A. The role of microfinance in household livelihood adaptation in Satkhira district, southwest Bangladesh. World Dev. 92 , 192–202 (2017).

Fischer, A. P. Pathways of adaptation to external stressors in coastal natural-resource-dependent communities: Implications for climate change. World Dev. 108 , 235–248 (2018).

Revi, A. et al. The Summary for Urban Policymakers of the IPCC’s Sixth Assessment Report (Cambridge Univ. Press, 2022); https://doi.org/10.24943/SUPSV511.2022

Garschagen, M., Doshi, D., Moure, M., James, H. & Shekhar, H. The consideration of future risk trends in national adaptation planning: conceptual gaps and empirical lessons. Clim. Risk Manag. 34 , 100357 (2021).

Johnson, L. et al. Intervention: The Invisible Labor of Climate Change Adaptation (SSRN, 2023); https://doi.org/10.2139/ssrn.4416499

Filho, W. L. et al. Assessing the impacts of climate change in cities and their adaptive capacity: towards transformative approaches to climate change adaptation and poverty reduction in urban areas in a set of developing countries. Sci. Total Environ. 692 , 1175–1190 (2019).

Aerts, J., Botzen, W., Bowman, M., Dircke, P. & Ward, P. Climate Adaptation and Flood Risk in Coastal Cities (Routledge, 2014); https://doi.org/10.4324/9781849776899

Haasnoot, M. et al. Defining the solution space to accelerate climate change adaptation. Reg Environ. Change 20 , 37 (2020).

Revi, A. et al. Towards transformative adaptation in cities: the IPCC’s Fifth Assessment. Environ. Urban. 26 , 11–28 (2014).

Hobday, A. J. et al. With the arrival of El Niño, prepare for stronger marine heatwaves. Nature 621 , 38–41 (2023).

Turton, S. Global average sea and air temperatures are spiking in 2023, before El Niño has fully arrived. We should be very concerned. The Conversation (20 June 2023).

Thompson, T. Arctic sea ice hits 2021 minimum. Nature https://doi.org/10.1038/d41586-021-02649-6 (2021).

Article   Google Scholar  

Haddaway, N. R., Macura, B., Whaley, P. & Pullin, A. S. ROSES for Systematic Map Reports. Version 1.0. figshare https://doi.org/10.6084/m9.figshare.5897389 (2018).

Haddaway, N. R., Macura, B., Whaley, P. & Pullin, A. S. R OSES Flow Diagram for Systematic Reviews. Version 1.0. figshare https://doi.org/10.6084/m9.figshare.5897389 (2018).

Berrang-Ford, L. et al. The Global Adaptation Mapping Initiative (GAMI): Part 1 — Introduction and Overview of Methods (Protocol Exchange, 2021); https://doi.org/10.21203/rs.3.pex-1240/v1

Lesnikowski, A. et al. The Global Adaptation Mapping Initiative (GAMI): Part 3 — Coding Protocol (Protocol Exchange, 2021); https://doi.org/10.21203/rs.3.pex-1242/v1

Thomas, A. et al. Global evidence of constraints and limits to human adaptation. Reg. Environ. Change 21 , 85 (2021).

Ulibarri, N. et al. A global assessment of policy tools to support climate adaptation. Clim. Pol. 22 , 77–96 (2022).

World Bank Country and Lending Groups (World Bank, 2019); https://datahelpdesk.worldbank.org/knowledgebase/articles/906519

World Bank Data Team. New Country Classifications by Income Level: 2019 – 2020 (World Bank, 2022); https://blogs.worldbank.org/en/opendata/new-country-classifications-income-level-2019-2020?source=post_page

Dakey, S., Deshkar, S., Joshi, S. & Sukhwani, V. Enhancing resilience in coastal regions from a socio-ecological perspective: a case study of Andhra Pradesh, India. Sustainability 15 , 9565 (2023).

Natural Earth: 1:10 m Physical Vectors — Free vector and Raster Map Data at 1:10 m, 1:50 m, and 1:110 m Scales (Natural Earth, 2024); https://www.naturalearthdata.com/

Download references

Acknowledgements

This work was supported by the following grants: The German Federal Ministry of Education and Research, via the TRANSCEND project (grant no. 01LN1710A1 to J.P., J.Z., M.G. and M.W.), the FloodAdaptVN project (grant no. 01LE1905F1 to A.R.) and the LIRLAP project (grant no. 01LE1906B1 to A.R. and M.G.); NSF CMMI CAREER (grant no. 1944664 to C.J.K.); the Japan Society for the Promotion of Science through the Grant-in-Aid Research Fellowship (grant no. 23KJ0544 to L.T.M.H.); the European Union’s Horizon 2020 research and innovation programme, via the LOCALISED project (grant agreement no. 101036458 to D.R.), the RiskPACC project (grant agreement no. 101019707 to D.R.), and the NWO (JPI Urban Europe Grant, agreement no. 438.21.445 to D.R.). We thank A. Alegria for extensive graphic design support.

Author information

A list of authors and their affiliations appears at the end of the paper.

A full list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany

Mia Wannewitz, Jan Petzold, Andrea Reimuth, Jingyao Zhou & Matthias Garschagen

Department of Environmental Sciences, Emory University, Atlanta, GA, USA

Idowu Ajibade

Department of Environmental Science and Policy, Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, FL, USA

Katharine J. Mach & Armen Agopian

Leonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, FL, USA

UMR LIENSs 7266, La Rochelle University-CNRS, La Rochelle, France

Alexandre Magnan

World Adaptation Science Programme, United Nations Environment Programme (Secretariat), Nairobi, Kenya

Cawthron Institute, Nelson, New Zealand

Department of Urban and Regional Planning and Geo-Information Management, Faculty ITC, University of Twente, Enschede, the Netherlands

Diana Reckien

Department of Urban Planning & Public Policy, University of California Irvine, Irvine, CA, USA

Nicola Ulibarri

Laboratory of Harbour Works, Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens (NTUA), Zografou, Greece

Vasiliki I. Chalastani

HafenCity University, Hamburg, Germany

Tom Hawxwell

Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa City, Japan

Lam T. M. Huynh

School of Engineering Design and Innovation and Department of Civil & Environmental Engineering, Penn State University, University Park, PA, USA

Christine J. Kirchhoff

Huntington-USC Institute on California and the West, University of Southern California, Los Angeles, CA, USA

Rebecca Miller

Bill Lane Center for the American West, Stanford University, Stanford, CA, USA

University of Ghana Business School, Department of Public Administration and Health Service Management, Accra, Ghana

Justice Issah Musah-Surugu

Nicholas School of the Environment at Duke University, Sanford School of Public Policy at Duke University, Duke University School of Law, Durham, NC, USA

Gabriela Nagle Alverio

Department of Earth and Environmental Sciences, Columbia University, New York, NY, USA

Miriam Nielsen

Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada

Abraham Marshall Nunbogu

Global Science Team, The Nature Conservancy, Arlington, VA, USA

Brian Pentz

School of Earth and Environment, University of Leeds, Leeds, UK

Giulia Scarpa

Columbia Climate School, Columbia University, New York, NY, USA

Nadia Seeteram

School of Geography, University of Leeds, Leeds, UK

Ivan Villaverde Canosa

You can also search for this author in PubMed   Google Scholar

The Global Adaptation Mapping Initiative Team

• Mia Wannewitz
• , Idowu Ajibade
• , Katharine J. Mach
• , Alexandre Magnan
• , Jan Petzold
• , Diana Reckien
• , Nicola Ulibarri
• , Vasiliki I. Chalastani
• , Tom Hawxwell
• , Lam T. M. Huynh
• , Christine J. Kirchhoff
• , Justice Issah Musah-Surugu
• , Gabriela Nagle Alverio
• , Miriam Nielsen
• , Abraham Marshall Nunbogu
• , Brian Pentz
• , Giulia Scarpa
• , Ivan Villaverde Canosa
•  & Matthias Garschagen

Contributions

M.W., M.G., I.A., K.J.M., A.M., J.P., D.R. and N.U. conceived and designed the experiments. M.W., I.A., K.J.M., A.M., J.P., D.R., N.U., A.A., V.I.C., T.H., L.T.M.H., C.J.K., R.M., J.I.M.-S., G.N.A., M.N., A.M.N., B.P., A.R., G.S., N.S., I.V.C. and J.Z. performed the experiments. M.W., M.G., J.P. and J.Z. analyzed the data. The Global Adaptation Mapping Initiative Team contributed the materials and analysis tools. M.W. and M.G. wrote the paper.

Corresponding author

Correspondence to Matthias Garschagen .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Cities thanks Shruthi Dakey, Gregorio Iglesias and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended data fig. 1 roses flowchart for systematic maps..

RepOrting standards for Systematic Evidence Syntheses (ROSES) were used to follow a standardized and transparent approach to searching and screening scientific literature. For each step in the process, numbers of publications are disclosed.

Supplementary information

Supplementary information.

Supplementary information on GAMI authors, literature searches, inclusion and exclusion criteria and the code book.

Reporting Summary

Supplementary table 1.

The ROSES map report.

Supplementary Table 2

A list of the literature included and excluded.

Supplementary Data 1

Correlation Tables 1.1–1.9.

Supplementary Data 2

Coding database.

Supplementary Data 3

Data sources for city populations.

Source Data Fig. 1

Unprocessed geospatial urban population and income data.

Source Data Fig. 2

Raw data: the considered risk factors in the assessed coastal cities.

Source Data Fig. 3

Raw data: the number of cities per response and actor type.

Source Data Fig. 4

Raw data: the speed, scope and depth of reported adaptation.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Wannewitz, M., Ajibade, I., Mach, K.J. et al. Progress and gaps in climate change adaptation in coastal cities across the globe. Nat Cities 1 , 610–619 (2024). https://doi.org/10.1038/s44284-024-00106-9

Download citation

Received : 20 November 2023

Accepted : 15 July 2024

Published : 26 August 2024

Issue Date : September 2024

DOI : https://doi.org/10.1038/s44284-024-00106-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.