Skip to main content

The potential of behavioural change for climate change mitigation: a case study for the European Union


Mainstream literature on climate change concentrates overwhelmingly on technological solutions for this global long-term problem, while a change towards climate-friendly behaviour could play a role in emission reduction and has received little attention. This paper focuses on the potential climate mitigation by behavioural change in the European Union (EU) covering many behavioural options in food, mobility and housing demand which do not require any personal up-front investment. We use the Global Change Assessment Model (GCAM), capturing both their direct and indirect implications in terms of greenhouse gas emissions. Our results indicate that modest to rigorous behavioural change could reduce per capita footprint emissions by 6 to 16%, out of which one fourth will take place outside the EU, predominantly by reducing land use change. The domestic emission savings would contribute to reduce the costs of achieving the internationally agreed climate goal of the EU by 13.5 to 30%. Moreover, many of these options would also yield co-benefits such as monetary savings, positive health impacts or animal wellbeing. These results imply the need for policymakers to focus on climate education and awareness programs more seriously and strategically, making use of the multiple co-benefits related with adopting pro-environmental behaviour. Apart from that, the relevance of behavioural change in climate change mitigation implies that policy-informing models on climate change should include behavioural change as a complement or partial alternative to technological change.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

    Integrated model to assess the global environment, for details:

  2. 2.

    We focus on the EU-27, so excluding Croatia which joined the EU in mid-2013. The reason behind this is that the GCAM model does not yet include Croatia in the modelled EU-region. Croatia represented about 0.83% of total population and 0.33% of total GDP in the European Union in 2015 (source: EuroStat).

  3. 3.

    Factors like income and household size (Poortinga et al. 2004) and social influences (Staats et al. 2004) are of high importance, among other factors.

  4. 4.

    We used GCAM version 4.2. See for a detailed description.

  5. 5.

    EU-15: Germany, UK, France, Italy, Spain, Austria, Netherlands, Belgium, Portugal, Sweden, Denmark, Finland, Greece, Ireland and Luxembourg. EU-12: Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, Slovenia, Bulgaria and Romania.

  6. 6.

    Some behavioural options, such as public transport commuting, joining a car-sharing programme and waste recycling might require investment from public or private entities to meet the consumer’s demand.

  7. 7.

    In contrast with Wynes and Nicholas (2017), who estimate the impact of more rigorous behavioural changes such as having one fewer child or living completely car free.

  8. 8.

    We separate the diet in EU-15 and EU-12 due to their relevant dietary preferences. We use the estimations of Bajželj et al. (2014) for West-Europe as a proxy for EU-15 and the estimations for East-Europe as a proxy for EU-12.

  9. 9.

    Since the food categories in GCAM do not exactly match with the categories in Table 2, we made sure that we applied the absolute changes in kcal/person/day of the GCAM food category containing the relevant category of Table 2.

  10. 10.

    See Table A2 in Appendix A in Kyle et al. (2011) for the total list of products that are included in MiscCrop.

  11. 11.

    Net amount of calories after the subtraction of all producer and consumer food waste

  12. 12.

    See footnote 5

  13. 13.

    The combined effect of food waste reduction and organic waste recycling applies to two out of three behavioural profiles in Sect. 3.3.

  14. 14.

    Note that we did not model any changes in heating or cooling demand, assuming that the individual’s heating/cooling demand at home and at work will be equal.

  15. 15.

    This could be supported by the argument that due to their intensive usage, car-sharing vehicles need significantly more maintenance over its lifetime. GHG emissions related to maintenance are a lot lower than those related to production of vehicles (own elaboration based on the World Input-Output Database (WIOD)).

  16. 16.

  17. 17.

    See footnote 16.

  18. 18.

    Here, we are ignoring wooden pellets, which is a significant separated waste stream that usually ends up in waste incinerators for practical reasons.

  19. 19.

    With immediate reduction targets of 20% in 2020, 40% in 2030 and 60% in 2040 compared with 1990 emission levels.

  20. 20.

    FFI CO2 includes all CO2 emissions related to fossil fuel use but no CO2 emissions from land use change

  21. 21.

    Although the figure seems to give slightly higher percentages, it should not be forgotten to subtract the small increase in emissions due to less biomass use from the total emission reduction. See data inside parenthesis in Fig. 2 caption.

  22. 22.

    Since GCAM runs in 5-year periods and the base year is 2010, the closest modelling year to the publication of this project is 2015. We are aware that this is effectively in the past, but the idea behind this is that the behavioural option is applied immediately. The 1–2 years of difference have a negligible effect on the total impact of each option.

  23. 23.

    See Table 11 in Appendix 3 for a detailed table on the emission savings for all behavioural options depending on the adoption year. Note that in case of a radical land use change, as we see with behavioural change in the food sector, we accounted the full land use savings to the year in which the adaptation takes place, even if the new vegetation is not completely grown yet.

  24. 24.

    With terrestrial carbon leakage, we mean the relocation of agricultural production due to a land use tax in the policy region.

  25. 25.

    A vegan diet with too little protein consumption is for example rather unhealthy.

  26. 26.

  27. 27.

    The behavioural options from this study that appear to positively influence subjective well-being according to Schmitt et al. (2018) are: Vegetarian diet, Urban cycling, Car sharing / car club, Public transport and Carpool commuting (insignificant impact), Reduce heating / cooling, Organic waste recycling/composting, Paper waste recycling and Plastic/Metal/Glass waste recycling.

  28. 28.

    Unless, a wrong implementation of the option is applied, such as a vegan diet without protein consumption or a suicidal cycling style.

  29. 29.

    The average amount of people carried by one car.

  30. 30.

    This lifetime ratio is also applied to the assumed vehicle lifetime in GCAM (decreasing from 25 to 10 years), resulting in an increasing average fuel efficiency of cars.

  31. 31.

    Although some countries like Germany, France, Spain, Italy and the UK have large distances from one outer point to the other outer point, there are usually good train and bus connections available within the country borders.

  32. 32.

    That is, we consider a flight from Brussels to Paris avoidable but a flight from Brussels to Marseille unavoidable. By dividing the number of flights between Belgium and France by two, we hope to have a proper estimate of avoidable flights.

  33. 33.

    We used a load-factor of 2.8, which is the average load factor of trips with BlaBlaCar, one of Europe’s biggest carpooling platforms for long distance trips:

  34. 34.

  35. 35.

    With the other 11% being either exported or simply lost out of sight

  36. 36.

  37. 37.

    The reason that unmanaged landfills are assumed to yield less CH4 emissions is based on the assumption that these are less dense and more widespread (open garbage field) than managed landfills, such that there is less anaerobic degradation of biogenic sources. Obviously these unmanaged garbage fields have other negative side effects on landscapes and potentially health.

  38. 38.

    Since food waste is a renewable source of (potential) energy, CO2 emissions resulting from food waste management are not counted by the IPCC standards. CH4 emissions due to landfilling are counted, as these would not have been released in a natural situation where the food would degrade aerobically.


  1. Abadie LM, Galarraga I, Milford AB, Gustavsen GW (2016) Using food taxes and subsidies to achieve emission reduction targets in Norway. J Clean Prod 134:280–297

    Article  Google Scholar 

  2. AF&PA (2009) 2008 Statistical summary of paper, Paperboard and Wood Pulp. American Forest & Paper Association, Washington

    Google Scholar 

  3. Alexander P, Rounsevell MD, Dislich C, Dodson JR, Engström K, Moran D (2015) Drivers for global agricultural land use change: the nexus of diet, population, yield and bioenergy. Glob Environ Chang 35:138–147

    Article  Google Scholar 

  4. American Heart Association (2014) The American Heart Association's Diet and Lifestyle Recommendations

  5. Arto I, Capellán-Pérez I, Lago R, Bueno G, Bermejo R (2016) The energy requirements of a developed world. Energy Sustain Dev 33:1–13

    Article  Google Scholar 

  6. Arto I, Genty A, Rueda-Cantuche JM, Villanueva A, Andreoni V (2012) Global resources use and pollution: vol. I, production, consumption and trade (1995–2008), JRC scientific and policy reports. European Commission Joint Research Centre (IPTS), Luxembourg

  7. Autio M, Heiskanen E, Heinonen V (2009) Narratives of ‘green’ consumers—the antihero, the environmental hero and the anarchist. J Consum Behav 8:40–53

    Article  Google Scholar 

  8. Bajželj B, Richards KS, Allwood JM, Smith P, Dennis JS, Curmi E, Gilligan CA (2014) Importance of food-demand management for climate mitigation. Nat Clim Chang 4(10):924–929

    Article  Google Scholar 

  9. Bogner J, Abdelrafie Ahmed M, Diaz C, Faaij A, Gao Q, Hashimoto S, Mareckova K, Pipatti R, Zhang T (2007) Waste management, in climate change 2007: mitigation. Contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA (eds). Cambridge University Press, Cambridge

    Google Scholar 

  10. Boldrin A, Hartling K, Laugen M, Christensen T (2010) Environmental inventory modelling of the use of compost and peat in growth media preparation. Resour Conserv Recycl 54:1250–1260

    Article  Google Scholar 

  11. Calvin K, Clarke L, Edmonds J, Eom J, Hejazi M, Kim S, Kyle P, et al (2011) GCAM Wiki documentation. Pacific Northwest National Laboratory.

  12. Capellán-Pérez I, González-Eguino M, Arto I, Ansuategi A, Dhavala K, Patel P, Markandya A (2014) New climate scenario framework implementation in the GCAM integrated assessment model. BC3 Working paper series 2014–04. Basque Centre for Climate Change (BC3), Leioa

    Google Scholar 

  13. Caves DW, Christensen LR (1988) The importance of economies of scale, capacity utilization, and density in explaining interindustry differences in productivity growth. Log Transport Rev 2:3–32

    Google Scholar 

  14. Central Bureau for Statistics (2016) Statistics Netherlands: Personenmobiliteit in Nederland; reiskenmerken en vervoerwijzen, regio's [Data file]. Retrieved from,6&D4=0-8&D5=0&D6=0-4&VW=T

  15. Chen TD, Kockelman KM (2015) Carsharing’s life-cycle impacts on energy use and greenhouse gas emissions. Transport Res Part D Transport and Environ 47:276–284

    Article  Google Scholar 

  16. Corral-Verdugo V, Mireles-Acosta JF, Tapia-Fonllem C, Fraijo-Sing B (2011) Happiness as correlate of sustainable behavior: a study of pro-ecological, frugal, equitable and altruistic actions that promote subjective wellbeing. Human Ecology Rev:95–104

  17. Costanzo M, Archer D, Aronson E, Pettigrew T (1986) Energy conservation behaviour: the difficult path from information to action. Am Psychol 41(5):521

    Article  Google Scholar 

  18. De Hartog JJ, Boogaard H, Nijland H, Hoek G (2010) Do the health benefits of cycling outweigh the risks? Environ Health Perspect:1109–1116

  19. Derraik JG (2002) The pollution of the marine environment by plastic debris: a review. Mar Pollut Bull 44(9):842–852

    Article  Google Scholar 

  20. Dietz T, Gardner GT, Gilligan J, Stern PC, Vandenbergh MP (2009) Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions. Proc Natl Acad Sci 106(44):18452–18456

    Article  Google Scholar 

  21. Druckman A, Chitnis M, Sorrell S, Jackson T (2011) Missing carbon reductions? Exploring rebound and backfire effects in UK households. Energy Policy 39(6):3572–3581

    Article  Google Scholar 

  22. EIA (2006) Saving energy recycling paper & glass. Energy Information Administration. September 2006. Retrieved 20 October 2007

  23. EC (2010a) TREMOVE Model V3.3.2, European Commission

  24. EC (2010b) Being wise with waste: the EU’s approach to waste management. European Commission. Available at

  25. Edmonds J, Reilly J (1985) Global energy: assessing the future. Oxford University Press, New York

    Google Scholar 

  26. Edmonds J, Wise M, Pitcher H, Richels R, Wigley T, Maccracken C (1997) An integrated assessment of climate change and the accelerated introduction of advanced energy technologies. Mitig Adapt Strateg Glob Chang 1(4):311–339.

    Article  Google Scholar 

  27. EPA (2015) Greenhouse gas emissions from management of selected materials in municipal solid waste. Online Waste Reduction Model (Warm) version 12. EPA530-R-98-013.

  28. Zero Waste Europe (2015) The potential contribution of waste management to a low carbon economy. October 2015.

  29. European Declaration on Paper Recycling (EDPR) 2015 Monitoring Report 2014 (2015), 14–10-2015, Available at:

  30. Eurostat (2016) Generation of waste by waste category, hazardousness and NACE Rev. 2 activity [Datafile]. Retrieved from

  31. Faber J, Schroten A, Bles M, Sevenster M,Markowska A, Smit M, Rohde C, Dütschke E, Köhler J, Gigli M, Zimmermann K, Soboh R, van’t Riet J (2012) Behavioural climate change mitigation options and their appropriate inclusion in quantitative longer term policy scenarios—main report CE Delft. Available at

  32. FAO (2010) FAOSTAT. Food and agriculture Organization of the United Nations, Rome, Italy. Available at

  33. FAO (2011) Global food losses and food waste—extend, causes and prevention. Rome, Italy

  34. Fujii S (2006) Environmental concern, attitude toward frugality, and ease of behavior as determinants of pro-environmental behavior intentions. J Environ Psychol 26(4):262–268

    Article  Google Scholar 

  35. Gadenne D, Sharma B, Kerr D, Smith T (2011) The influence of consumers’ environmental beliefs and attitudes on energy saving behaviours. Energy Policy 39(12):7684–7694

    Article  Google Scholar 

  36. GAIA (2012) On the road to zero waste. June, 2012. Available at

  37. García-Muros X, Markandya A, Romero-Jordán D, González-Eguino M (2017) The distributional effects of carbon-based food taxes. J Clean Prod 140:996–1006

    Article  Google Scholar 

  38. Gifford R (2011) The dragons of inaction: psychological barriers that limit climate change mitigation and adaptation. Am Psychol 66:290–302.

    Article  Google Scholar 

  39. Gifford R, Kormos C, McIntyre A (2011) Behavioral dimensions of climate change: drivers, responses, barriers, and interventions. WIREs Clim Change.

  40. González-Eguino M, Capellán-Pérez I, Arto I, Ansuategi A, Markandya A (2016) Industrial and terrestrial carbon leakage under climate policy fragmentation. Clim Pol.

  41. Grabs J (2015) The rebound effects of switching to vegetarianism. A microeconomic analysis of Swedish consumption behavior. Ecol Econ 116(2015):270–279

    Article  Google Scholar 

  42. Gustavsen GW, Rickertsen K (2013) Adjusting VAT rates to promote healthier diets in Norway: a censored quantile regression approach. Food Policy 42:88–95

    Article  Google Scholar 

  43. Hallström E, Carlsson-Kanyama A, Börjesson P (2015) Environmental impact of dietary change: a systematic review. J Clean Prod 91:1–11.

    Article  Google Scholar 

  44. Hards S (2011) Social practice and the evolution of personal environmental values. Environ Values, 23–42

  45. Howell R (2013) It’s not (just) “the environment, stupid!” Values, motivations, and routes to engagement of people adopting lower-carbon lifestyles. Glob Environ Chang 23(1):281–290.

  46. Intergovernmental Panel on Climate Change (IPCC) (2006) 2006 IPCC guidelines for national greenhouse gas inventories. Prepared by the National Greenhouse Gas Inventories Programme. Eggleston HS, Buendia L, Miwa K, Ngara T and Tanabe K (eds). IGES, Japan

  47. Intergovernmental Panel on Climate Change (IPCC) (2014) Climate change 2014–impacts, adaptation and vulnerability: regional aspects. Cambridge University Press, Cambridge

    Google Scholar 

  48. IPCC (2007) Summary for Policymakers, in Climate Change 2007: mitigation. Contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA (eds) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

  49. Jacob J, Jovic E, Brinkerhoff MB (2009) Personal and planetary well-being: mindfulness meditation, pro-environmental behavior and personal quality of life in a survey from the social justice and ecological sustainability movement. Soc Indic Res 93(2):275–294

    Article  Google Scholar 

  50. Kaida N, Kaida K (2016) Pro-environmental behavior correlates with present and future subjective well-being. Environ Dev Sustain 18(1):111–127

    Article  Google Scholar 

  51. Kummu M, De Moel H, Porkka M, Siebert S, Varis O, Ward PJ (2012) Lost food, wasted resources: global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use. Sci Total Environ 438:477–489

    Article  Google Scholar 

  52. Kyle GP, Luckow P, Calvin KV, Emanuel B, Nathan M, Zhou Y (2011) GCAM 3.0 Agriculture and land use: data sources and methods. PNNL-21025. Pacific Northwest National Laboratory, Richland

    Book  Google Scholar 

  53. Kyle P, Clarke L, Rong F, Smith S (2010) Climate policy and the long-term evolution of the U.S. building sector. Energy J 31(3):131–158

    Google Scholar 

  54. Laestadius LI, Neff RA, Barry CL, Frattaroli S (2014) “We don’t tell people what to do”: an examination of the factors influencing NGO decisions to campaign for reduced meat consumption in light of climate change. Glob Environ Chang 29:32–40

    Article  Google Scholar 

  55. Lange H, Meier L (2009) The new middle classes. Globalizing lifestyles, consumerism and environmental concern. Springer, Dordrecht

    Google Scholar 

  56. Lin SP (2013) The gap between global issues and personal behaviors: pro-environmental behaviors of citizens toward climate change in Kaohsiung, Taiwan. Mitig Adapt Strateg Glob Chang 18(6):773–783

    Article  Google Scholar 

  57. Lorenzoni I, Nicholson-Cole S, Whitmarsh L (2007) Barriers perceived to engaging with climate change among the UK public and their policy implications. Glob Environ Chang 17(3):445–459

    Article  Google Scholar 

  58. Lusk JL, Schroeter C (2012) When do fat taxes increase consumer welfare? Health Econ 21(11):1367–1374

    Article  Google Scholar 

  59. Masud MM, Akhtar R, Afroz R, Al-Amin AQ, Kari FB (2015) Pro-environmental behavior and public understanding of climate change. Mitig Adapt Strateg Glob Chang 20(4):591–600

    Article  Google Scholar 

  60. Mishra GS, Kyle P, Teter J, Morrison G, Yeh S, and Kim S (2013) Transportation module of global change assessment module (GCAM): model documentation version 1.0. Institute of Transportation Studies, University of California at Davis; and Pacific Northwest National Laboratory. Report UCD-ITS-RR-13-05. June 2013.

  61. Mont O (2004) Institutionalisation of sustainable consumption patterns based on shared use. Ecol Econ 50(1-2):135–153

  62. Moser SC (2010) Communicating climate change: history, challenges, process and future directions. Wiley Interdiscip Rev Clim Chang 1(1):31–53

    Article  Google Scholar 

  63. O’Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K, Rothman DS, van Ruijven BJ, van Vuuren DP, Birkmann J, Kok K, Levy M, Solecki W (2017) The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob Environ Chang 42:169–180

    Article  Google Scholar 

  64. O’Neill BC, Kriegler E, Riahi K, Ebi KL, Hallegatte S, Carter TR, Mathur R, van Vuuren DP (2014) A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim Chang 122(3):387–400

    Article  Google Scholar 

  65. O’Neill S, Nicholson-Cole S (2009) “Fear won’t do it” promoting positive engagement with climate change through visual and iconic representations. Sci Commun 30(3):355–379

    Article  Google Scholar 

  66. Ohe M, Ikeda S (2005) Global warming: risk perception and risk-mitigating behavior in Japan. Mitig Adapt Strateg Glob Chang 10(2):221–236

    Article  Google Scholar 

  67. Ohtomo S, Hirose Y (2007) The dual-process of reactive and intentional decision-making involved in eco-friendly behavior. J Environ Psychol 27(2):117–125

    Article  Google Scholar 

  68. Ozaki R (2011) Adopting sustainable innovation: what makes consumers sign up to green electricity? Bus Strateg Environ 20(1):1–17

    Article  Google Scholar 

  69. Pacala S, Socolow R (2004) Stabilization wedges: solving the climate problem for the next 50 years with current technologies. Science 305(5686):968–972

    Article  Google Scholar 

  70. Poortinga W, Steg L, Vlek C (2004) Values, environmental concern, and environmental behavior a study into household energy use. Environ Behav 36(1):70–93

    Article  Google Scholar 

  71. Quimby CC, Angelique H (2011) Identifying barriers and catalysts to fostering pro-environmental behavior: opportunities and challenges for community psychology. Am J Community Psychol 47(3–4):388–396

    Article  Google Scholar 

  72. Roy J (2012) Lifestyles, well-being and energy. Glob Energy Assess 1527–1548

  73. Samadi S, Gröne MC, Schneidewind U, Luhmann HJ, Venjakob J, Best B (2017) Sufficiency in energy scenario studies: taking the potential benefits of lifestyle changes into account. Technol Forecast Soc Chang (in press)

  74. Schäfer M, Jaeger-Erben M, dos Santos A (2011) Leapfrogging to sustainable consumption? An explorative survey of consumption habits and orientation in Southern Brazil. J Consumer Policy.

  75. Schmitt MT, Aknin LB, Axsen J, Shwom RL (2018) Unpacking the relationships between pro-environmental behavior, life satisfaction, and perceived ecological threat. Ecol Econ 143:130–140

    Article  Google Scholar 

  76. Semenza JC, Hall DE, Wilson DJ, Bontempo BD, Sailor DJ, George LA (2008) Public perception of climate change: voluntary mitigation and barriers to behavior change. Am J Prev Med 35(5):479–487

    Article  Google Scholar 

  77. Sheppard SR (2005) Landscape visualisation and climate change: the potential for influencing perceptions and behaviour. Environ Sci Pol 8(6):637–654

    Article  Google Scholar 

  78. Shwom R, Lorenzen JA (2012) Changing household consumption to address climate change: social scientific insights and challenges. Wiley Interdiscip Rev Clim Chang 3(5):379–395

    Article  Google Scholar 

  79. Staats H, Harland P, Wilke HA (2004) Effecting durable change a team approach to improve environmental behavior in the household. Environ Behav 36(3):341–367

    Article  Google Scholar 

  80. Stehfest E, Bouwman L, van Vuuren DP, Den Elzen MG, Eickhout B, Kabat P (2009) Climate benefits of changing diet. Clim Chang 95(1–2):83–102

    Article  Google Scholar 

  81. Suárez-Varela M, Guardiola J, González-Gómez F (2016) Do pro-environmental behaviors and awareness contribute to improve subjective well-being? Appl Res Quality Life 11(2):429–444

    Article  Google Scholar 

  82. Sullivan AB, Wang M (2010) Energy-consumption and carbon-emission analysis of vehicle and component manufacturing. Argonne National Laboratory, Lemont

    Book  Google Scholar 

  83. The Economist (2007) The price of virtue. June 7, 2007. Available at

  84. Thomson AM, Calvin KV, Smith SJ, Kyle GP, Volke A, Patel P, Edmonds JA (2011) RCP4. 5: a pathway for stabilization of radiative forcing by 2100. Climatic Change 109(1–2):77

    Article  Google Scholar 

  85. Thow AM, Downs S, Jan S (2014) A systematic review of the effectiveness of food taxes and subsidies to improve diets: understanding the recent evidence. Nutr Rev 72(9):551–565

    Article  Google Scholar 

  86. Transportation Research Board (2005) Transit Cooperative Research Program (TCRP). Report 108. Adam Millard-Ball, Gail Murray, Jessica ter Schure, Christine Fox, Nelson Nygaard Consulting Assoc., and Jon Burkhardt, Westat

  87. van Sluisveld MA, Martinez SH, Daioglou V, van Vuuren DP (2016) Exploring the implications of lifestyle change in 2 C mitigation scenarios using the IMAGE integrated assessment model. Technol Forecast Soc Chang 102:309–319

    Article  Google Scholar 

  88. van Westerhoven M (2013) Bepaling voedselverliezen in huishoudelijk afval in Nederland, Vervolgmeting 2013, CREM Amsterdam in opdracht van het ministerie van Infrastructuur en Milieu

  89. Ventour L (2008) The food we waste. WRAP food waste report v2. Available at Accessed 31 May 2016

  90. Welsch H, Kühling J (2011) Are pro-environmental consumption choices utility-maximizing? Evidence from subjective well-being data. Ecol Econ 72:75–87

    Article  Google Scholar 

  91. Whitmarsh L (2009) Behavioural responses to climate change: Asymmetry of intentions and impacts. J Environ Psychol 29(1):13–23

  92. WHO (2003) WHO & FAO joint WHO/FAO expert consultation on diet. Nutrition and the prevention of chronic diseases

  93. Willett W (2011) Eat, drink, and be healthy: the Harvard Medical School guide to healthy eating. Simon and Schuster, New York

    Google Scholar 

  94. Wynes S, Nicholas KA (2017) The climate mitigation gap: education and government recommendations miss the most effective individual actions. Environ Res Lett, 12(7)

  95. Xiao JJ, Li H (2011) Sustainable consumption and life satisfaction. Soc Indic Res 104(2):323–329

    Article  Google Scholar 

Download references


The authors thank Hector Pollitt and Francis Johnson for valuable comments and Ed Dearnley for a language check. This study received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 642260 (TRANSrisk project). Mikel González-Eguino and Iñaki Arto acknowledge financial support from the Ministry of Economy and Competitiveness of Spain (ECO2015-68023) and the Basque Government (IT-799-13). All data and model outputs of this article are available upon request to Dirk-Jan van de Ven (

Author information



Corresponding author

Correspondence to Dirk-Jan van de Ven.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.


Appendix 1: Background modelling of mobility and housing options

Public transport commuting

In the base year (2010), around 20.7 and 17.9% of total passenger kilometres in respectively EU-15 and EU-12 were due to commuting between home and work. Of this commuting transport demand, only 15.1% in EU-15 and 29.3% in EU-12 was met by public transport services (EC 2010a).

Carpool commuting

In the base year, car trips yielded around 76.2% of the total commuting passenger kilometres in EU-15, while around 58% in EU-12. Car load factorsFootnote 29 for commuting transport were 1.19 in EU-15 and 1.87 in EU-12, whereas car load factors for total car transport were 1.65 and 2, respectively (EC 2010a). Assuming a car load factor for commuting transport of 2 while respecting the share of commuting kilometres in total passenger kilometres (20.7 and 17.9% in EU-15 and EU-12 respectively), we increased the overall load factor for all four-wheel driven transport modes to 1.85 in EU-15 and 2.05 in EU-12 to model this behavioural option. Finally, in order to model only the emission savings as a result of this behavioural change, we cancelled out any kind of price elastic behaviour in favour of car transport following to this adjustment.


Since the commuting share of passenger transport is 20.7 and 17.9% in the EU-15 and EU-12, respectively (see previous options), we reduced total passenger transport demand by 4.14% in EU-15 and 3.6% in EU-12.

Urban cycling

According to EC (2010a), slow mode transport (walking and cycling) accounts for 18.9% of total urban passenger transport in the Netherlands in 2010, while total urban passenger transport accounts for about 29% of all passenger transport. Assuming the same percentage of slow mode transport in urban areas for the whole of EU-27, this comes down to an average share of 5.4% of total passenger transport that would be met by walking and cycling together. GCAM reports the share of walking to account for 1.9% of passenger transport in EU-27 in 2010, so we assume that the potential share of bicycles in total EU-27 passenger demand will be around 3.5%. Note that while we keep the cycling share to 3.5% during all periods for this behavioural option, the walking share is subject to market competition (and decreases rapidly due to an increasing cost of travel time, see also, Mishra et al. 2013).

Car sharing/car clubs: methods and assumptions

The calculation used to make assumptions for both emission-saving effects is as following. Based on a ratio of 27 members per shared car in the USA, the Trasportation Research Board (2005) reports an amount of 14.9 cars to be taken off the road for every car-club vehicle. Applying the ratio of 20 members per shared car in Europe, the estimate for Europe would be 11 cars per car-club vehicle. Correcting this estimate by the 40% reduction in vehicle kilometres of car-share members compared with private vehicle owners, this ratio comes down to 11 × 0.6 = 6.62. Finally, Chen and Kockelman (2015) state that the average privately owned new vehicle is replaced after approximately 6 years, whereas commercial car-club operations replace cars every 2 to 3 years due to more vehicle kilometres and faster wear and tear (Mont 2004). Assuming that the wear and tear to the car and the remaining life time is the same for privately sold second hand cars and those sold by car-sharing companies, we can state that a privately owned vehicle has two to three times the lifetime of a car-club vehicle. Applying a lifetime ratio of 2.5,Footnote 30 this means that every car-sharing vehicle takes 6.62/2.5 = 2.65 vehicles off the production line when assuming that there is no reduction in car use between car owners and car sharers. Furthermore, we assume an energy consumption related to car manufacture of 30 GJ vehicle−1 (Sullivan and Wang 2010) and a growing demand for cars proportionally to the growing demand for passenger kilometres in both EU-15 and EU-12. See Table 9 for a summary on the assumptions made for modelling the impacts of car sharing.

Table 9 Assumptions made to model car-sharing impact

Avoid short flights

We summed all the passenger kilometres on national flights within EU-27 member statesFootnote 31 and all flights to neighbouring countries (multiplied by half if at least one of the partner countries is a large country such as Germany, France, UK, Italy or SpainFootnote 32) to have a rough estimate of the potentially avoidable flights. We found that about 25% of all passenger kilometres on intra-EU flights are avoidable by these standards, and implicitly assume that it remains 25% until 2050.

As an alternative to flying for medium distance trips, we modelled a new category with four possible travel alternatives: coach, train, high-speed-rail and carpooling. Although we copied these transport modes from the original GCAM model, we assume significantly higher speeds for long distance bus, train and car transport (80, 100 and 100, respectively) and a higher load factor for cars.Footnote 33 Initially, each of these alternatives takes an equal share of the passenger kilometres to be replaced, but the mix between technologies is subject to mode competition as in other GCAM sectors.

Closer holidays

A rough analysis of Eurostat data on intercontinental passenger kilometres from EU-15 and EU-12 shows that respectively 85 and 91.5% of passenger kilometres are for leisure purposes and that the average intercontinental leisure trip by EU-15 and EU-12 consumers is respectively about 5900 and 2680 km long. We implicitly assume that these estimates will not change until 2050.


According to the ecoDriver project website, the EU initiative that started in 2010 to promote this fuel-efficient driving style, the long-term fuel reduction due to eco-driving is estimated to be 5%.Footnote 34 Following this number, we modelled this behavioural option by increasing the efficiency of all four-wheel light duty vehicles by 5% from 2015 onwards.

Reduce heating and cooling

To model the reduced usage of heating, we simply modified the residential HDD input (heating degree days) from 4920 to 4625 in EU-15 and from 6311 to 5930 in EU-12, a change that reduces the need for heating in winter by about 1 °C. Similarly, we changed the residential cooling degree days (CDD) input from 373 to 328 in EU-15 and from 343 to 302 in EU-12 to model a reduced use of air-conditioning in summer.

Appendix 2: Modelling and assumptions of GCAM waste module

To model the impacts of waste recycling by consumers, we focus on the three main streams of consumer waste: organic waste, paper/carton waste and non-paper packaging waste (consisting of mainly plastics, metals and glass). In most EU member states, it is possible for households to effectively recycle these types of waste by separating them. For modelling simplicity, we will assume from now that 100% of separated waste actually will be recycled (8% actually ended up between mixed waste in 2010, predominantly separated organic waste in landfills) and that 0% of mixed waste will be recycled (8% of mixed waste was actually recycled in 2010). See Fig. 6 for an overview of all waste and recycling streams in EU-27 in 2010.

Since 66% of household waste ended up between mixed waste in 2010, it is hard to determine the contents of these waste streams. Since we need to know the contents to model the potential emission reductions, we have to make an estimation of these contents. To do so, we looked at the best practice example of waste separation in Europe to gain information about the average household waste streams. According to GAIA (2012), European best example is a door-to-door waste collection program in Usurbil, Hernani and Oiartzun in the province of Gipuzkoa, Basque Country, Spain. The three towns together represented 33,628 citizens with a GDP per capita level close to the EU-27 average. Except for the 20% of waste that was collected from street bins and local street cleaning services, all household waste in these villages was separately collected. The household waste in these villages consisted of 46.8% organic waste (of which 33.8% food and 13% garden waste), 18.3% paper/carton waste, 32.3% industrial packaging waste (including 14.1% glass and 15.2% plastic and metal) and 2.6% other waste, such as chemicals or minerals

Since all EU-27 member states have a different waste collection scheme with regionally different priorities, we have multiplied the household waste composition as assumed above to the waste totals in every member state and have deducted the separated waste streams from these assumed waste streams. The remaining waste (i.e. the composition after deducting the separated waste streams per member state) is assumed to be the composition of waste within the mixed waste stream. On an EU-27 level, we find 45.6% of all mixed household waste to be organic, 13.6% to be paper/carton, 33% to be non-paper packaging waste and a remainder of 7.8% to be mineral or chemical waste (which we leave out of the model).

Fig. 6

EU-27 waste and recycling streams in 2010 in million tonnes (based on EuroStat data)

For the services and industrial sector (accounting for nearly one third of all mixed waste), waste has traditionally been much better separated. We therefore assume the same mixture of separated waste to hold for the limited amount of mixed waste streams from these sectors. Finally, we also find that about one fifth of the mixed waste in the waste collection industry. This is intentionally separated waste that has a degree of mixture too high to be recycled. Here we simply assume the average assumed waste composition as in the other 80% of mixed waste. The final assumed mixed waste contents in EU-27 are assumed to be 34.3% organic waste, 15.4% paper/carton waste, 31.2% non-paper packaging waste and 19.1% other waste (mainly mineral). Note that we only modelled the non-household sectors to have a full picture on all waste streams. All the behavioural options do apply to household waste only.

In total, 89% of all mixed waste in EU-27 was treated within the area,Footnote 35 with the majority being landfilled (in some cases with methane recovery for biogas production). However, there is an important trend going on in Germany, the Benelux and Scandinavia to incinerate mixed waste, either with or without energy recovery. In the baseline estimates, we assume that open burning and unmanaged landfilling of waste will be phased out linearly until 2050 and also managed landfilling will be phased out linearly until 2100, following Directive 2008/98/EC on waste management.

For the total emissions from landfilling, we used data from the European Environment Agency (EEA)Footnote 36 on landfill emissions on managed and unmanaged landfill sites. Following the IPCC guidelines (IPCC 2006), unmanaged landfill sites have on average 40% less emissions per unit of waste compared with managed landfills.Footnote 37 For modelling simplicity, we assume that all landfill emissions in one period are coming from waste that is landfilled in the same period. To fit our modelled waste streams (stemming from EuroStat data) with the EEA landfill emissions data, we use the methane yields per type of waste stream from EPA (2015). Following the IPCC guidelines, we do not model CO2 emissions from municipal waste management.

Organic waste

Organic waste consists of both food waste and garden waste. Since we have modelled the assumed amount of food waste in EU-27, we assume that all other organic household waste consists of garden waste (the relative share of garden waste is in line with the distribution in our case example as explained in the start of this Appendix). From 2010 onwards, we assume per capita garden waste to remain constant over time. Food waste consists of unavoidable food waste (which is a by-product of food consumption, predominantly skins and peels of fruits and vegetables, carcasses of pork and chicken, coffee and tea disposals) and avoidable food waste from the production, distribution and consumption of food. We estimated the unavoidable waste stream by GCAM food category by connecting the share of unavoidable waste compared with avoidable waste as reported by Ventour (2008) with our FAO’s’s (2011) estimates of avoidable food waste by food category. Estimates for unavoidable coffee and tea waste streams (NonFood-MiscCrop) come from van Westerhoven (2013a). See the assumed estimates in Table 10.

Table 10 Assumed unavoidable waste streams from different food categories (% of total weight)

Landfilling of organic waste results in large amounts of methane due to the anaerobic decomposition of organic materials. These are responsible for 2.75% of total GHG emissions in EU-27 in 2010. When incinerated, there will be no methane emissions from organic waste but there will be CO2 emissions, which have a significantly lower warming potential.Footnote 38 The energy density of organic waste, however, is very low, so energy recovery from incineration is not very productive. Finally, the preferred treatment for organic waste is to compost it using anaerobic digestion, creating both biogas and a valuable organic fertiliser replacing mineral fertilisers and returning about 15% of the organic carbon contents back into the soil. This is a form of carbon sequestration (IPCC 2006). Some methane emissions are released in the composting process, but these are limited compared with the methane released with landfilling. In the same way as landfill emissions, we linked data from EEA on composting emissions with Eurostat data on total tonnes composted to estimate the methane and nitrous oxide emissions per unit of food and garden waste composted. Finally, we used estimates from Boldrin et al. (2010) and Zero Waste Europe (2015) to estimate the total carbon and nitrogen content of both food and garden waste.

Paper/carton waste

We have separated paper waste, since nearly every EU member state offers the possibility to recycle paper and carton waste. Since paper products are made from pulp, which is obtained from forest products, the GCAM model will be helpful in calculating the emissions related to paper waste recycling. Like food and garden waste, paper waste is organic and therefore leads to methane emissions when landfilled. However, the rate in which one ton of paper waste produces methane is only about one fourth compared with that of food waste (EPA 2015). When incinerated, paper products can yield significant energy recovery due to an energy density that is more than twice that of food and garden waste. Finally, recycling of paper waste leads to significant GHG savings: producing new paper out of recycled paper reduces the amount of energy needed for paper production by 40% (EIA 2006). However, since about four fifth of this saved energy comes from biomass (black liquor) due to the high amount of wood waste in these production locations (Table 17 in AF&PA 2009), paper production from pulp consumes the majority of the biomass energy in the EU-27 energy mix for industrial products.

Plastic/metal/glass waste

Although industrial products such as plastic, metal and glass do not emit GHG emissions when landfilled, they do emit other pollutants, which are currently not modelled within GCAM. These pollutants are also emitted when incinerated, along with CO2. Glass and metal waste might also lead to health damages or complicate the whole waste collection procedure by cutting into garbage bags due to their sharp edges. Incineration with energy recovery from predominantly plastic waste is interesting due to its high energy density: around 50% higher than paper waste and four times higher than food and garden waste. Plastic, metal and glass waste however is most valuable when recycled: compared with producing new products, using recycled plastic, metal or glass reduces industrial energy use by 70, 60–95 and 5–30%, respectively (the Economist 2007). Given the average mixed waste composition in the EU-27, we have estimated that the average tonne of recycled industrial products saves about 30% of industrial energy compared with making the same final industrial products from virgin material (Zero Waste Europe 2015). It is important to note is that the majority of savings comes from recycling metal waste, which saves 60 to 95% (for aluminium) compared with making these products from virgin materials.

Appendix 3: Sensivity analysis based on starting year of behavioural change

Table 11 Avoided GHG emissions per behavioural option or profile dependent on starting year of behavioural change

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

van de Ven, DJ., González-Eguino, M. & Arto, I. The potential of behavioural change for climate change mitigation: a case study for the European Union. Mitig Adapt Strateg Glob Change 23, 853–886 (2018).

Download citation


  • Climate change
  • Mitigation
  • Behavioural change
  • Diet change
  • Mobility
  • Land-use change
  • Waste recycling
  • Policy costs
  • Footprint emissions