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Volume 40, Issue 2 (2025)
GeoRes 2025, 40(2): 161-172 |
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Received: 2024/11/22 | Accepted: 2025/04/28 | Published: 2025/06/18
Received: 2024/11/22 | Accepted: 2025/04/28 | Published: 2025/06/18
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Behzadfar M, Shirani Z. Determinants of Behavioral Insights in Urban Design Policy-Making with an Emphasis on Reducing Greenhouse Gas Emissions. GeoRes 2025; 40 (2) :161-172
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Authors
M. Behzadfar *1, Z. Shirani1
1- Department of Urban Planning, Faculty of Architecture and Urban Planning, Iran University of Science and Technology, Tehran, Iran
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Background
Cities account for a significant share of energy consumption and greenhouse gas emissions, and mitigating climate change depends on both individual and collective behaviors. Traditional urban design policies have largely relied on deterministic and technical approaches, with limited attention to behavioral sciences. In contrast, behavioral insights can inform more effective policies for reducing greenhouse gas emissions and influencing citizen behavior by identifying the factors that shape decision-making and everyday habits.
Previous Studies
Several studies have examined behavior change aimed at reducing greenhouse gas emissions. Top-down approaches, relying on strict regulations, have imposed requirements for carbon reduction but have demonstrated limited sustainability [Ockwell et al., 2010]. In contrast, bottom-up approaches, which preserve citizens’ freedom of choice and provide incentives, have shown greater effectiveness in changing behavior [Perry et al., 2015]. The origins of behavioral insights trace back to psychology and behavioral economics research from the 1950s to the 1980s [Leonard, 2008; BIT, 2021], with a milestone being Daniel Kahneman’s Nobel Prize in 2002. Subsequently, governments and organizations such as the OECD and the European Commission established specialized units to apply this approach in policymaking [OECD, 2017a; BIN NL, 2017]. Research has shown that education alone does not produce sustainable behavior change, while other factors, such as habits, emotions, and social context, play a more significant role [Shove, 2010; Jaspal et al., 2014; Durrans et al., 2019]. These findings highlight the importance of integrating behavioral insights into urban design.
Aim(s)
This study examined the factors influencing these behavioral insights, with an emphasis on reducing greenhouse gas emissions in urban design policies.
Research Type
This article is a systematic review with a quantitative-analytical approach.
Research Society, Place and Time
This study is a systematic review with a quantitative-analytical approach. The research population comprised scholarly articles published in reputable international journals related to human behavior and behavioral insights in the context of environmental issues and urban design. Data were retrieved from Google Scholar, Science Direct, and Web of Science. The search period began in 2011, due to the growing attention to social dimensions in addressing climate issues, and continued up to the time of the study.
Sampling Method and Number
The sampling method in this study involved a systematic screening of articles based on PRISMA guidelines and predefined inclusion and exclusion criteria. Initially, 1,745 articles were identified from the databases. Following three stages of screening and careful review, articles that directly addressed factors influencing human behavior and behavioral insights in the context of environmental issues and urban design, and provided sufficient data for content analysis, were selected. Ultimately, 26 articles were included as the research sample for analysis.
Used Devices & Materials
In this study, the databases Google Scholar, Science Direct, and Web of Science were used to retrieve relevant articles. Data analysis was conducted using content analysis to identify key concepts and categories. Additionally, the Shannon entropy method was applied in Excel 2024 to prioritize the extracted indicators and sub-indicators.
Findings by Text
The findings of the study indicated that among the selected articles, a total of 52 sub-codes were initially identified, which were subsequently merged and redefined into 19 main codes and ultimately categorized into 7 key indicators (Table 1). Analysis of these indicators revealed that identity, influence from others, and choice architecture were the most significant and frequently cited factors shaping behavioral insights (Table 2). Within the choice architecture indicator, default rules were more influential than the physical environment and infrastructure. For the influence-from-others indicator, the behavior of peers had the greatest impact, whereas observing the actions of prominent individuals had the least significance. Regarding identity, social norms and collective lifestyle were the most critical, while individual identity ranked lower. Social capital was primarily affected by appropriate social interactions and trust, with message salience and reminders being less impactful. Capability largely depended on economic status and skills, receiving lower priority. Awareness was dominated by education and knowledge of past decision outcomes. Finally, in monitoring and control, feedback and rewarding positive behavior were most prominent. Overall, based on average rankings, choice architecture, identity, and influence from others held the highest priority, whereas monitoring and control and human emotions were least influential in shaping behavioral insights relevant to urban design policies and greenhouse gas reduction.
Table 1. Summary of Findings from the Reviewed Studies
Table 2) Priority Estimation of Indicators and Sub-Indicators Extracted from Selected Articles Based on Shannon Entropy Method
Main Comparisons to Similar Studies
The findings of this study are largely consistent with previous research, indicating that various behavioral factors play a crucial role in shaping citizens’ environmental choices and reducing greenhouse gas emissions. Choice architecture, including sub-indicators such as infrastructure and default rules, had a significant impact on sustainable behavior, aligning with the findings of Sovacool et al. [2020] and Muñoz et al. [2019], although budgetary and planning constraints can limit its effectiveness [Stern, 2008]. Nudge policies and default options also correspond with the studies of Thaler & Sunstein [2008] and Johnson et al. [2012], though cultural factors may reduce their impact [Newel & Siikamaki, 2014]. Findings related to individual and social identity and social norms align with Lu et al. [2023] and Fielding & Hornsey [2016], showing that identity and social pressure reinforce pro-environmental behaviors. The roles of social capital and citizen participation correspond with Putnam [2000] and Wüstenhagen et al. [2007], while monitoring and personalized feedback mechanisms, as noted by Abraham et al. [2007] and Bird & Legault [2018], facilitate behavioral change. Additionally, emotional factors and place attachment, supported by Ojala [2012] and Scannell & Gifford [2010], underscore the importance of interactive and aesthetically designed urban spaces in promoting environmental behaviors.
Suggestions
From a research perspective, future studies could examine the interactions among these factors and explore the role of mediating parameters, such as environmental awareness and income level, in the relationship between choice architecture and the reduction of greenhouse gas emissions. Additionally, investigating the impact of cultural differences on the effectiveness of incentive-based and regulatory policies across countries could provide a better understanding of the barriers and enablers of pro-environmental behavior change. Furthermore, exploring strategies to enhance social capital through urban design and assessing its influence on environmental behaviors could inform the development of more effective policy interventions.
From a practical perspective, it is recommended that urban policymakers facilitate pro-environmental behaviors by designing interactive public spaces, promoting sustainable public transportation, and providing personalized feedback on energy consumption. Implementing choice architecture policies, such as setting environmentally friendly default options, can help reduce resource use and optimize energy consumption. Additionally, employing participatory strategies, offering economic incentives, and developing community-based educational programs are effective approaches to mitigating greenhouse gas emissions through behavioral change in urban settings.
Conclusion
The seven key factors including choice architecture, identity, influence from others, social capital, rational justification, monitoring and control, and human emotions play a significant role in shaping behavioral insights relevant to urban design policies with an emphasis on reducing greenhouse gas emissions. Among these, choice architecture, identity, and influence from others hold the greatest importance, whereas human emotions and monitoring and control exhibit the least impact.
Acknowledgments: The authors express their gratitude to all individuals who contributed to the preparation and publication of this article.
Ethical Permission: Not reported by the authors.
Conflict of Interest: Not reported by the authors.
Author Contributions: Behzadfar M (first author) Introduction Writer/ Methodologist/Main Researcher/ Statistical Analyst/Discussion Writer (50%); Shirani Z (second author) Introduction Writer/ Methodologist/Main Researcher/ Statistical Analyst/Discussion Writer (50%)
Funding: Not reported by the authors.
Cities account for a significant share of energy consumption and greenhouse gas emissions, and mitigating climate change depends on both individual and collective behaviors. Traditional urban design policies have largely relied on deterministic and technical approaches, with limited attention to behavioral sciences. In contrast, behavioral insights can inform more effective policies for reducing greenhouse gas emissions and influencing citizen behavior by identifying the factors that shape decision-making and everyday habits.
Previous Studies
Several studies have examined behavior change aimed at reducing greenhouse gas emissions. Top-down approaches, relying on strict regulations, have imposed requirements for carbon reduction but have demonstrated limited sustainability [Ockwell et al., 2010]. In contrast, bottom-up approaches, which preserve citizens’ freedom of choice and provide incentives, have shown greater effectiveness in changing behavior [Perry et al., 2015]. The origins of behavioral insights trace back to psychology and behavioral economics research from the 1950s to the 1980s [Leonard, 2008; BIT, 2021], with a milestone being Daniel Kahneman’s Nobel Prize in 2002. Subsequently, governments and organizations such as the OECD and the European Commission established specialized units to apply this approach in policymaking [OECD, 2017a; BIN NL, 2017]. Research has shown that education alone does not produce sustainable behavior change, while other factors, such as habits, emotions, and social context, play a more significant role [Shove, 2010; Jaspal et al., 2014; Durrans et al., 2019]. These findings highlight the importance of integrating behavioral insights into urban design.
Aim(s)
This study examined the factors influencing these behavioral insights, with an emphasis on reducing greenhouse gas emissions in urban design policies.
Research Type
This article is a systematic review with a quantitative-analytical approach.
Research Society, Place and Time
This study is a systematic review with a quantitative-analytical approach. The research population comprised scholarly articles published in reputable international journals related to human behavior and behavioral insights in the context of environmental issues and urban design. Data were retrieved from Google Scholar, Science Direct, and Web of Science. The search period began in 2011, due to the growing attention to social dimensions in addressing climate issues, and continued up to the time of the study.
Sampling Method and Number
The sampling method in this study involved a systematic screening of articles based on PRISMA guidelines and predefined inclusion and exclusion criteria. Initially, 1,745 articles were identified from the databases. Following three stages of screening and careful review, articles that directly addressed factors influencing human behavior and behavioral insights in the context of environmental issues and urban design, and provided sufficient data for content analysis, were selected. Ultimately, 26 articles were included as the research sample for analysis.
Used Devices & Materials
In this study, the databases Google Scholar, Science Direct, and Web of Science were used to retrieve relevant articles. Data analysis was conducted using content analysis to identify key concepts and categories. Additionally, the Shannon entropy method was applied in Excel 2024 to prioritize the extracted indicators and sub-indicators.
Findings by Text
The findings of the study indicated that among the selected articles, a total of 52 sub-codes were initially identified, which were subsequently merged and redefined into 19 main codes and ultimately categorized into 7 key indicators (Table 1). Analysis of these indicators revealed that identity, influence from others, and choice architecture were the most significant and frequently cited factors shaping behavioral insights (Table 2). Within the choice architecture indicator, default rules were more influential than the physical environment and infrastructure. For the influence-from-others indicator, the behavior of peers had the greatest impact, whereas observing the actions of prominent individuals had the least significance. Regarding identity, social norms and collective lifestyle were the most critical, while individual identity ranked lower. Social capital was primarily affected by appropriate social interactions and trust, with message salience and reminders being less impactful. Capability largely depended on economic status and skills, receiving lower priority. Awareness was dominated by education and knowledge of past decision outcomes. Finally, in monitoring and control, feedback and rewarding positive behavior were most prominent. Overall, based on average rankings, choice architecture, identity, and influence from others held the highest priority, whereas monitoring and control and human emotions were least influential in shaping behavioral insights relevant to urban design policies and greenhouse gas reduction.
Table 1. Summary of Findings from the Reviewed Studies
Table 2) Priority Estimation of Indicators and Sub-Indicators Extracted from Selected Articles Based on Shannon Entropy Method
Main Comparisons to Similar Studies
The findings of this study are largely consistent with previous research, indicating that various behavioral factors play a crucial role in shaping citizens’ environmental choices and reducing greenhouse gas emissions. Choice architecture, including sub-indicators such as infrastructure and default rules, had a significant impact on sustainable behavior, aligning with the findings of Sovacool et al. [2020] and Muñoz et al. [2019], although budgetary and planning constraints can limit its effectiveness [Stern, 2008]. Nudge policies and default options also correspond with the studies of Thaler & Sunstein [2008] and Johnson et al. [2012], though cultural factors may reduce their impact [Newel & Siikamaki, 2014]. Findings related to individual and social identity and social norms align with Lu et al. [2023] and Fielding & Hornsey [2016], showing that identity and social pressure reinforce pro-environmental behaviors. The roles of social capital and citizen participation correspond with Putnam [2000] and Wüstenhagen et al. [2007], while monitoring and personalized feedback mechanisms, as noted by Abraham et al. [2007] and Bird & Legault [2018], facilitate behavioral change. Additionally, emotional factors and place attachment, supported by Ojala [2012] and Scannell & Gifford [2010], underscore the importance of interactive and aesthetically designed urban spaces in promoting environmental behaviors.
Suggestions
From a research perspective, future studies could examine the interactions among these factors and explore the role of mediating parameters, such as environmental awareness and income level, in the relationship between choice architecture and the reduction of greenhouse gas emissions. Additionally, investigating the impact of cultural differences on the effectiveness of incentive-based and regulatory policies across countries could provide a better understanding of the barriers and enablers of pro-environmental behavior change. Furthermore, exploring strategies to enhance social capital through urban design and assessing its influence on environmental behaviors could inform the development of more effective policy interventions.
From a practical perspective, it is recommended that urban policymakers facilitate pro-environmental behaviors by designing interactive public spaces, promoting sustainable public transportation, and providing personalized feedback on energy consumption. Implementing choice architecture policies, such as setting environmentally friendly default options, can help reduce resource use and optimize energy consumption. Additionally, employing participatory strategies, offering economic incentives, and developing community-based educational programs are effective approaches to mitigating greenhouse gas emissions through behavioral change in urban settings.
Conclusion
The seven key factors including choice architecture, identity, influence from others, social capital, rational justification, monitoring and control, and human emotions play a significant role in shaping behavioral insights relevant to urban design policies with an emphasis on reducing greenhouse gas emissions. Among these, choice architecture, identity, and influence from others hold the greatest importance, whereas human emotions and monitoring and control exhibit the least impact.
Acknowledgments: The authors express their gratitude to all individuals who contributed to the preparation and publication of this article.
Ethical Permission: Not reported by the authors.
Conflict of Interest: Not reported by the authors.
Author Contributions: Behzadfar M (first author) Introduction Writer/ Methodologist/Main Researcher/ Statistical Analyst/Discussion Writer (50%); Shirani Z (second author) Introduction Writer/ Methodologist/Main Researcher/ Statistical Analyst/Discussion Writer (50%)
Funding: Not reported by the authors.
References
1. Abrahamse W, Steg L, Vlek C, Rothengatter T (2007). The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. Journal of Environmental Psychology. 27(4):265-276. [Link] [DOI:10.1016/j.jenvp.2007.08.002]
2. Adger WN (2001). Social capital and climate change. Shanghai: Tyndall Centre for Climate Change Research. [Link] [DOI:10.1017/CBO9780511596667]
3. Alfasi N, Margalit T (2021). Toward the sustainable metropolis: The challenge of planning regulation. Sustainability. 13(15):8189. [Link] [DOI:10.3390/su13158189]
4. Amin R, Nath H (2023). Environmental justice and education: Bridging the gap between ecology, equity, and access. Journal of Advanced Zoology. 44(S3). [Link] [DOI:10.17762/jaz.v44iS-3.1061]
5. Armstrong AK, Krasny ME, Schuldt JP (2018). Communicating climate change: A guide for educators. Ithaca: Cornell University Press. [Link] [DOI:10.7591/9781501730801]
6. Bauer JM, Reisch LA (2019). Behavioural insights and (un) healthy dietary choices: A research agenda for better evidence. A research agenda for economic psychology. 104-122. [Link] [DOI:10.4337/9781788116060.00013]
7. Behavioural Insights Team (2013). Applying behavioural insights to charitable giving. London: Behavioural Insights Team report. [Link]
8. Berardi U (2017). A cross-country comparison of the building energy consumptions and their trends. Resources, Conservation and Recyclin. 123:230-241. [Link] [DOI:10.1016/j.resconrec.2016.03.014]
9. Bernauer T, Gampfer R (2015). How robust is public support for unilateral climate policy?. Environmental Science & Policy. 54:316-330. [Link] [DOI:10.1016/j.envsci.2015.07.010]
10. BIN NL (2017). A wealth of behavioural insights: 2017 edition. Netherlands: Behavioural Insights Network Netherlands. [Link]
11. BIT (Behavioural Insights Team). (2012). Applying behavioural insights to reduce fraud, error and debt. UK Cabinet Office. [Link]
12. BIT (Behavioural Insights Team). (2013). Applying behavioural insights to charitable giving [Charitable giving report]. [Link]
13. Bird S, Legault L (2018). Feedback and behavioral intervention in residential energy and resource use: A review. Current Sustainable/Renewable Energy Reports. 5(1):116-126. [Link] [DOI:10.1007/s40518-018-0106-8]
14. Cabinet Office (2012). Applying behavioural insights to reduce fraud, error and debt. London: The Behavioural Insights Team. [Link]
15. Centola D (2018). The truth about behavioral change. MIT Sloan Management Review, Massachusetts Institute of Technology. [Link]
16. Chan EY, Faria AA (2022). Political ideology and climate change-mitigating behaviors: Insights from fixed world beliefs. Global Environmental Change. 72:102440. [Link] [DOI:10.1016/j.gloenvcha.2021.102440]
17. Ciccone A, Fyhri A, Sundfør HB (2021). Using behavioral insights to incentivize cycling: Results from a field experiment. Journal of Economic Behavior & Organization. 188:1035-1058. [Link] [DOI:10.1016/j.jebo.2021.06.011]
18. Corner A, Randall A (2011). Selling climate change? The limitations of social marketing as a strategy for climate change public engagement. Global environmental change.21(3):1005-1014. [Link] [DOI:10.1016/j.gloenvcha.2011.05.002]
19. Haselswerdt J, & Bartels BL (2015). Public opinion, policy tools, and the status quo: Evidence from a survey experiment. Political Research Quarterly. 68(3), 607-621. [Link] [DOI:10.1177/1065912915591217]
20. Durrans S, Last A, Boiro H, Goncalves A, Mabey D, Greenland K (2019). Moving like birds: A qualitative study of population mobility and health implications in the Bijagós Islands, Guinea Bissau. Social Science & Medicine. 230:204-213. [Link] [DOI:10.1016/j.socscimed.2019.03.019]
21. Edmondson D, Conroy D, Romero-Canyas R, Tanenbaum M, Czajkowski S (2022). Climate change, behavior change and health: A multidisciplinary, translational and multilevel perspective. Translational Behavioral Medicine. 12(4):503-515. [Link] [DOI:10.1093/tbm/ibac030]
22. Fielding KS, Hornsey MJ (2016). A social identity analysis of climate change and environmental attitudes and behaviors: Insights and opportunities. Frontiers in Psychology. 7(298). [Link] [DOI:10.3389/fpsyg.2016.00121]
23. Frantz CM, Mayer FS (2009). The emergency of climate change: Why are we failing to take action? Analyses of Social Issues and Public Policy. 9(1): 205-222. [Link] [DOI:10.1111/j.1530-2415.2009.01180.x]
24. Giddens A (2009). Politics of climate change. London: Polity Press. [Link]
25. Gifford R (2007). Environmental psychology: Principles and practice. Vancouver: Optimal Books. [Link]
26. Golkar K (2018). Creating sustainable place: Reflections on urban design theory. Tehran: Shahid Beheshti University Press. [Persian] [Link]
27. Greenwood N, Warren P (2022). The role of climate finance beyond renewables: Behavioural insights. Journal of International Development. 34(8):1570-1586. [Link] [DOI:10.1002/jid.3656]
28. Griskevicius V, Tybur JM, Van den Bergh B (2010). Going green to be seen: status, reputation, and conspicuous conservation. Journal of personality and social psychology. 98(3):392. [Link] [DOI:10.1037/a0017346]
29. Hao S, Zhang L, Hou R, Lau SS, Lau SSY (2024). Research on the physiological and psychological impacts of extraordinary nature on emotions and restorative effects for young adults. Journal of Environmental Psychology. 97:102345. [Link] [DOI:10.1016/j.jenvp.2024.102345]
30. He Y, Sun Y, Zhao Z, Chen M, Waygood EOD, Shu Y (2024). Impact of social-psychological factors on low-carbon travel intention: Merging theory of planned behavior and value-belief-norm theory. Heliyon. 10(6):e28161. [Link] [DOI:10.1016/j.heliyon.2024.e28161]
31. Hochman G, Ariely D (2015). Behavioral economics. In: Emerging trends in the social and behavioral sciences. New Jersey: John Wiley & Sons. p. 1-14. [Link] [DOI:10.1002/9781118900772.etrds0022]
32. Jaspal R, Nerlich B, Cinnirella M (2014). Human responses to climate change: Social representation, identity and socio-psychological action. Environmental Communication. 8(1):110-130. [Link] [DOI:10.1080/17524032.2013.846270]
33. Jenny MA, Lehrer L, Eitze S, Sprengholz P, Korn L, Shamsrizi P, et al (2022). Accelerating climate protection by behavioural insights: The Planetary Health Action Survey (PACE). The Lancet Planetary Health. 6(S19). [Link] [DOI:10.1016/S2542-5196(22)00281-9]
34. John P (2014). Policy entrepreneurship in UK central government: The behavioural insights team and the use of randomized controlled trials. Public Policy and Administration. 29(3):257-267. [Link] [DOI:10.1177/0952076713509297]
35. Johnson EJ, Shu SB, Dellaert BG, Fox C, Goldstein DG, Häubl G, et al (2012). Beyond nudges: Tools of a choice architecture. Marketing Letters. 23(2):487-504. [Link] [DOI:10.1007/s11002-012-9186-1]
36. Judge M, Bouman T, Steg L, Bolderdijk JW (2024). Accelerating social tipping points in sustainable behaviors: Insights from a dynamic model of moralized social change. One Earth. 7(5):759-770. [Link] [DOI:10.1016/j.oneear.2024.04.004]
37. Kirchler B, Kollmann A, Sinea A, Volintiru C (2024). Behavioural insights into energy consumption in times of crisis. Energy Research & Social Science. 117:103744. [Link] [DOI:10.1016/j.erss.2024.103744]
38. Kuehnhanss CR (2019). The challenges of behavioural insights for effective policy design. Policy and Society. 38(1):14-40. [Link] [DOI:10.1080/14494035.2018.1511188]
39. Lee JH, Burns RC (2022). Older adults' preferences for natural environment and rural life: Intergenerational transmission of pro-environmental motivation. Journal of Outdoor Recreation and Tourism. 39:100556. [Link] [DOI:10.1016/j.jort.2022.100556]
40. Leonard TC (2008). Richard H. Thaler, Cass R. Sunstein, Nudge: Improving decisions about health, wealth, and happiness. Constitutional Political Economy. 19(4):356-360. [Link] [DOI:10.1007/s10602-008-9056-2]
41. Letcher T (2021). Climate change: Observed impacts on planet earth. New York: Elsevier. [Link]
42. Lu X, Lu Z, Mao J, Sun Z, Cui Z, Huang Y, et al (2023). Place attachment as an indicator of public participation in low-carbon community development: A case study of Beijing, China. Ecological Indicators. 154:110658. [Link] [DOI:10.1016/j.ecolind.2023.110658]
43. Luthra S, Kumar S, Garg D, Haleem A (2015). Barriers to renewable/sustainable energy technologies adoption: Indian perspective. Renewable and Sustainable Energy Reviews. 41:762-776. [Link] [DOI:10.1016/j.rser.2014.08.077]
44. Marteau TM, Hollands GJ, Fletcher PC (2012). Changing human behavior to prevent disease: The importance of targeting automatic processes. Science. 337(6101):1492-1495. [Link] [DOI:10.1126/science.1226918]
45. Mendes V (2022). Climate smart cities? Technologies of climate governance in Brazil. Urban Governance. 2(2):270-281. [Link] [DOI:10.1016/j.ugj.2022.08.002]
46. Mont O, Neuvonen A, Lähteenoja S (2014). Sustainable lifestyles 2050: Stakeholder visions, emerging practices and future research. Journal of cleaner production. 63:24-32. [Link] [DOI:10.1016/j.jclepro.2013.09.007]
47. Mortensen CR, Neel R, Cialdini RB, Jaeger CM, Jacobson RP, Ringel MM (2019). Trending norms: A lever for encouraging behaviors performed by the minority. Personality and Social Psychology Bulletin. 10(2):201-210. [Link] [DOI:10.1177/1948550617734615]
48. Mundaca L, Sonnenschein J, Steg L, Höhne N, Ürge-Vorsatz D (2019). The global expansion of climate mitigation policy interventions, the Talanoa Dialogue, and the role of behavioural insights. Environmental Research Communications. 1(6):061001. [Link] [DOI:10.1088/2515-7620/ab26d6]
49. Mundaca L, Zhu X, Hackenfort M (2022). Behavioural insights for sustainable energy use. Energy Policy. 171:113292. [Link] [DOI:10.1016/j.enpol.2022.113292]
50. Muñoz P, Zwick S, Mirzabaev A (2020). The impact of urbanization on Austria's carbon footprint. Journal of Cleaner Production. 263:121326. [Link] [DOI:10.1016/j.jclepro.2020.121326]
51. Newell RG, Siikamäki J (2014). Nudging energy efficiency behavior: The role of information labels. Journal of the Association of Environmental and Resource Economists. 1(4):555-598. [Link] [DOI:10.1086/679281]
52. Nolan JM, Schultz PW, Cialdini RB, Goldstein NJ, Griskevicius V (2008). Normative social influence is underdetected. Personality and Social Psychology Bulletin. 34(7):913-923. [Link] [DOI:10.1177/0146167208316691]
53. Ockwell D, O'Neill S, Whitmarsh L (2010). Behavioural insights: Motivating individual emissions cuts through communication. In: Routledge handbook of climate change and society. London: Routledge. p. 341-350. [Link]
54. OSU.EDU [Internet]. Behavior change tactics for urban challenges: Insights from practitioners; c2025 [Cited 2025,13 September]. Available from: https://u.osu.edu/cityextension/2020/02/26/behavior-change-tactics-for-urban-challenges/ [Link]
55. OECD (2017a). Tackling environmental problems with the help of behavioural insights. Paris: OECD Publishing. [Link]
56. OECD (2017b). Behavioural insights and public policy: Lessons from around the world. Paris: OECD Publishing. [Link]
57. Oh RRY, Fielding KS, Nghiem LTP, Chang CC, Carrasco LR, Fuller RA (2021). Connection to nature is predicted by family values, social norms and personal experiences of nature. Global Ecology and Conservation. 28:e01632. [Link] [DOI:10.1016/j.gecco.2021.e01632]
58. Ojala M (2012). Regulating worry, promoting hope: How do children, adolescents, and young adults cope with climate change?. International Journal of Environmental and Science Education. 7(4):537-561. [Link]
59. Perry C, Chhatralia K, Damesick D (2015). Behavioural insights in health care: Nudging to reduce inefficiency and waste. London: Health Foundation. [Link]
60. Putnam RD (2000). Bowling alone: The collapse and revival of American community. New York: Simon & Schuster. [Link] [DOI:10.1145/358916.361990]
61. Rogers T, Goldstein NJ, Fox CR (2018). Social mobilization. Annual review of psychology. 69:357-381. [Link] [DOI:10.1146/annurev-psych-122414-033718]
62. Scannell L, Gifford R (2010). The relations between natural and civic place attachment and pro-environmental behavior. Journal of Environmental Psychology. 30(3):289-297. [Link] [DOI:10.1016/j.jenvp.2010.01.010]
63. Schultz PW, Nolan JM, Cialdini RB, Goldstein NJ, Griskevicius V (2007). The constructive, destructive, and reconstructive power of social norms. Psychological Science. 18(5):429-434. [Link] [DOI:10.1111/j.1467-9280.2007.01917.x]
64. Senbel M, Ngo VD, Blair E (2014). Social mobilization of climate change: University students conserving energy through multiple pathways for peer engagement. Journal of Environmental Psychology. 38:84-93. [Link] [DOI:10.1016/j.jenvp.2014.01.001]
65. Shafir E, editor (2013). The behavioral foundations of public policy. Princeton: Princeton University Press. [Link] [DOI:10.1515/9781400845347]
66. Sheppard S, Iype DM, Cote S, Salter J (2015). Special report-A synthesis of PICS-Funded social. The Pacific Institute for Climate Solutions: Victoria, BC, Canada. [Link]
67. Shogren JF, Taylor LO (2008). On behavioral-environmental economics. Review of Environmental Economics and Policy. 2(1):26-44. [Link] [DOI:10.1093/reep/rem027]
68. Shove E (2010). Beyond the ABC: Climate change policy and theories of social change. Environment and Planning A: Economy and Space. 42(6):1273-1285. [Link] [DOI:10.1068/a42282]
69. Sovacool BK, Hook A, Martiskainen M, Brock A, Turnheim B (2020). The decarbonisation divide: Contextualizing landscapes of low-carbon exploitation and toxicity in Africa. Global Environmental Change. 60:102028. [Link] [DOI:10.1016/j.gloenvcha.2019.102028]
70. Stern N (2008). The economics of climate change. American economic review. 98(2):1-37. [Link] [DOI:10.1257/aer.98.2.1]
71. Steg L, Vlek C (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology. 29(3):309-317. [Link] [DOI:10.1016/j.jenvp.2008.10.004]
72. Sussman TJ, Jin J, Mohanty A (2016). Top-down and bottom-up factors in threat-related perception and attention in anxiety. Biological psychology. 121:160-172. [Link] [DOI:10.1016/j.biopsycho.2016.08.006]
73. Thaler RH, Sunstein CR (2008). Nudge. New Haven: Yale University Press. [Link]
74. Troussard X, Van Bavel R (2018). How can behavioural insights be used to improve EU policy?. Intereconomics. 53(1):8-12. [Link] [DOI:10.1007/s10272-018-0711-1]
75. Tsang S, Burnett M, Hills P, Welford R (2009). Trust, public participation and environmental go vernance in Hong Kong. Environmental Policy and Governance. 19(2):99-114. [Link] [DOI:10.1002/eet.502]
76. Ulrich RS (1984). View through a window may influence recovery from surgery. Science. 224(4647):420-421. [Link] [DOI:10.1126/science.6143402]
77. UNEP (2017). Consuming differently, consuming sustainably: Behavioural insights for policymaking. Nairobi: United Nations Environment Programme. [Link]
78. Venkateshwarlu B, Goutam CK (2024). COST-Benefit analysis of solar energy: A case study Dehra Region of Kangra District in Himachal Pradesh. OmniScience: A Multi-Disciplinary Journal. 14(1):60-72. [Link] [DOI:10.37591/osmj.v14i01.131502]
79. Wang T, Shen B, Ma T (2024). Individual low-carbon behaviors and influencing factors: Insights from a behavior survey study in China. Heliyon. 10(10):e31094. [Link] [DOI:10.1016/j.heliyon.2024.e31094]
80. Westerhoff L, Sheppard SR, Iype DM, Cote S, Salter J (2018). Social mobilization on climate change and energy: An evaluation of research projects in British Columbia, Canada. Energy Research & Social Science. 46: 368-380. [Link] [DOI:10.1016/j.erss.2018.07.022]
81. WMO (2007). Intergovernmental panel on climate change. Geneva: World Meteorological Organization. 52(1-43):1. [Link]
82. Wüstenhagen R, Wolsink M, Bürer MJ (2007). Social acceptance of renewable energy innovation: An introduction to the concept. Energy Policy. 35(5):2683-2691. [Link] [DOI:10.1016/j.enpol.2006.12.001]