A reduction in global meat consumption can significantly reduce the adverse environmental effects of the food system, but it would require widespread dietary changes. Such shifts to sustainable diets depend on several behavioural factors that have not yet been addressed in relation to the food system. This study links a behavioural diet shift model to an integrated assessment model to identify the main drivers of global diet change and its implications for the food system. The results show that the social norm effect (for instance, the extent of vegetarianism in the population that accelerates a further switch to a vegetarian diet) and self-efficacy are the main drivers of widespread dietary changes. These findings stress the importance of value-driven actions motivated either by intrinsic identity or by group dynamics over health and climate risk perceptions in steering diet change dynamics.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Pathways to achieving nature-positive and carbon–neutral land use and food systems in Wales
Regional Environmental Change Open Access 09 February 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
The input data of the model presented in this study may be seen in Supplementary Table 3. The input data are obtained from calibration according to historical data of agro-economic variables as described in Methods. The historical data used in calibration are obtained from the statistics of the United Nations Food and Agriculture Organization, available at http://www.fao.org/faostat/en/#data. This study also made use of online Global Burden of Disease datasets (http://ghdx.healthdata.org) provided by the Institute for Health Metrics and Evaluation, University of Washington.
The model used in this study (the FeliX) and its input data are available at https://github.com/iiasa/Felix-Model/tree/master/Current%20Version. The custom computer code written in Python (IPython Notebooks) and used to analyze simulation results may be accessed on https://github.com/sibeleker/FeliX_DietChange.
Creutzig, F. et al. Towards demand-side solutions for mitigating climate change. Nat. Clim. Change 8, 260–263 (2018).
Steg, L. Limiting climate change requires research on climate action. Nat. Clim. Change 8, 759–761 (2018).
Rogelj J. et al. Global warming of 1.5 °C. in 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 (eds Masson-Delmotte, V. et al.) Ch. 2 (IPCC, 2018).
Grubler, A. et al. A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies. Nat. Energy 3, 515–527 (2018).
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).
Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).
Stehfest, E. et al. Climate benefits of changing diet. Clim. Change 95, 83–102 (2009).
Popp, A., Lotze-Campen, H. & Bodirsky, B. Food consumption, diet shifts and associated non-CO2 greenhouse gases from agricultural production. Glob. Environ. Change 20, 451–462 (2010).
Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).
Obersteiner, M. et al. Assessing the land resource–food price nexus of the sustainable development goals. Sci. Adv. 2, e1501499 (2016).
Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).
Food Balance Sheets (FAO, 2018).
Food and Drink Report: the Era of the Mindful Consumer (Waitrose, 2018); https://www.waitrose.com/home/about_waitrose/the-waitrose-fooddrinkreport.html
De Boer, J., Schösler, H. & Boersema, J. J. Climate change and meat eating: an inconvenient couple? J. Environ. Psychol. 33, 1–8 (2013).
Macdiarmid, J. I., Douglas, F. & Campbell, J. Eating like there’s no tomorrow: Public awareness of the environmental impact of food and reluctance to eat less meat as part of a sustainable diet. Appetite 96, 487–493 (2016).
Dhont, K. & Hodson, G. Why do right-wing adherents engage in more animal exploitation and meat consumption? Personal. Individ. Differ. 64, 12–17 (2014).
Dhont, K., Hodson, G. & Leite, A. C. Common ideological roots of speciesism and generalized ethnic prejudice: the social dominance human–animal relations model (SD‐HARM). Eur. J. Personality 30, 507–522 (2016).
Hodson, G. & Earle, M. Conservatism predicts lapses from vegetarian/vegan diets to meat consumption (through lower social justice concerns and social support. Appetite 120, 75–81 (2018).
Rydzak, F., Obersteiner, M., Kraxner, F., Fritz, S. & McCallum, I. FeliX3—Impact Assessment Model: Systemic View Across Societal Benefit Areas Beyond Global Earth Observation (International Institute for Applied Systems Analysis, 2013).
Walsh, B. J. et al. New feed sources key to ambitious climate targets. Carbon Balance Manag. 10, 26 (2015).
Walsh, B. et al. Pathways for balancing CO2 emissions and sinks. Nat. Commun. 8, 14856 (2017).
Beckage, Brian. Linking models of human behaviour and climate alters projected climate change. Nat. Clim. Change 8, 79–84 (2018).
Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Process. 50, 179–211 (1991).
Boer, H. & Seydel, E. in Predicting Health Behaviour: Research and Practice with Social Cognition Models (eds Conner, M. & Norman, P.) Ch. 4 (Open University Press, 1996).
Slovic, P. Perception of risk. Science 236, 280–285 (1987).
Fox, N. & Ward, K. Health, ethics and environment: a qualitative study of vegetarian motivations. Appetite 50, 422–429 (2008).
Stehfest, E. Food choices for health and planet. Nature 515, 501–502 (2014).
Westhoek, H. et al. Food choices, health and environment: effects of cutting Europe’s meat and dairy intake. Glob. Environ. Change 26, 196–205 (2014).
Springmann, M., Godfray, H. C. J., Rayner, M. & Scarborough, P. Analysis and valuation of the health and climate change cobenefits of dietary change. Proc. Natl Acad. Sci. USA 113, 4146–4151 (2016).
Willet, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).
Rogers, E. M. Diffusion of Innovations 4th edn (Simon and Schuster, 2010).
Bass, F. M. A new product growth for model consumer durables. Manage. Sci. 15, 215–227 (1969).
Vranken, L., Avermaete, T., Petalios, D. & Mathijs, E. Curbing global meat consumption: emerging evidence of a second nutrition transition. Environ. Sci. Pol. 39, 95–106 (2014).
Bryant, B. P. & Lempert, R. J. Thinking inside the box: a participatory, computer-assisted approach to scenario discovery. Technol. Forecast. Soc. 77, 34–49 (2010).
Kwakkel, J. H. & Jaxa-Rozen, M. Improving scenario discovery for handling heterogeneous uncertainties and multinomial classified outcomes. Environ. Modell. Softw. 79, 311–321 (2016).
Hayhoe, K. When facts are not enough. Science 360, 943–943 (2018).
Kahan, D. Fixing the communications failure. Nature 463, 296 (2010).
Kahan, D. M. et al. The polarizing impact of science literacy and numeracy on perceived climate change risks. Nat. Clim. Change 2, 732–735 (2012).
Fielding, K. S., McDonald, R. & Louis, W. R. Theory of planned behaviour, identity and intentions to engage in environmental activism. J. Environ. Psychol. 28, 318–326 (2008).
Nigbur, D., Lyons, E. & Uzzell, D. Attitudes, norms, identity and environmental behaviour: using an expanded theory of planned behaviour to predict participation in a kerbside recycling programme. Brit. J. Soc. Psychol. 49, 259–284 (2010).
Gatersleben, B., Murtagh, N. & Abrahamse, W. Values, identity and pro-environmental behaviour. Contemp. Soc. Sci. 9, 374–392 (2014).
Fanghella, V., d’Adda, G. & Tavoni, M. On the use of nudges to affect spillovers in environmental behaviors. Front. Psychol. 10, 61 (2019).
Reese, G. & Junge, E. Keep on rockin’in a (plastic-) free world: collective efficacy and pro-environmental intentions as a function of task difficulty. Sustainability 9, 200 (2017).
Jugert, P. et al. Collective efficacy increases pro-environmental intentions through increasing self-efficacy. J. Environ. Psychol. 48, 12–23 (2016).
Pohjolainen, P., Vinnari, M. & Jokinen, P. Consumers’ perceived barriers to following a plant-based diet. Brit. Food J. 117, 1150–1167 (2015).
McCollum, D. L. et al. Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices. Transport Res. D–Tr. E. 55, 322–342 (2017).
Pettifor, H., Wilson, C., McCollum, D. & Edelenbosch, O. Y. Modelling social influence and cultural variation in global low-carbon vehicle transitions. Glob. Environ. Change 47, 76–87 (2017).
Rydzak, F., Obersteiner, M. & Kraxner, F. Impact of global earth observation-systemic view across GEOSS societal benefit area. Int. J. Spat. Data Infrastruct. Res. 5, 216–243 (2010).
Obersteiner M., Rydzak F., Fritz S., & McCallum I. in The Value of Information (eds Laxminarayan, R. & Macauley, M. K.) 67–90 (Springer, 2012).
Rogers, R. W. A protection motivation theory of fear appeals and attitude change1. J. Psychol. 91, 93–114 (1975).
Povey, R., Conner, M., Sparks, P., James, R. & Shepherd, R. The theory of planned behaviour and healthy eating: examining additive and moderating effects of social influence variables. Psychol. Health 14, 991–1006 (2000).
de Ridder, D., Kroese, F., Evers, C., Adriaanse, M. & Gillebaart, M. Healthy diet: health impact, prevalence, correlates, and interventions. Psychol. Health 32, 907–941 (2017).
Tikir, A. & Lehmann, B. Climate change, theory of planned behavior and values: a structural equation model with mediation analysis. Clim. Change 104, 389–402 (2011).
Bockarjova, M. & Steg, L. Can protection motivation theory predict pro-environmental behavior? Explaining the adoption of electric vehicles in the Netherlands. Glob. Environ. Change 28, 276–288 (2014).
JCJH, Aerts et al. Integrating human behaviour dynamics into flood disaster risk assessment. Nat. Clim. Change 8, 193–199 (2018).
Renger, D. & Reese, G. From equality‐based respect to environmental activism: antecedents and consequences of global identity. Polit. Psychol. 38, 867–879 (2017).
Moser, S. & Kleinhückelkotten, S. Good intents, but low impacts: diverging importance of motivational and socioeconomic determinants explaining pro-environmental behavior, energy use, and carbon footprint. Environ. Behavior 50, 626–656 (2017).
Bamberg, S. & Möser, G. Twenty years after Hines, Hungerford, and Tomera: a new meta-analysis of psycho-social determinants of pro-environmental behaviour. J. Environ. Psychol. 27, 14–25 (2007).
Robinson, E., Thomas, J., Aveyard, P. & Higgs, S. What everyone else is eating: a systematic review and meta-analysis of the effect of informational eating norms on eating behavior. J. Acad. Nutr. Diet. 114, 414–429 (2014).
Fritsche, I., Barth, M., Jugert, P., Masson, T. & Reese, G. A social identity model of pro-environmental action (SIMPEA). Psychol. Rev. 125, 245–269 (2018).
Hunter, E. & Röös, E. Fear of climate change consequences and predictors of intentions to alter meat consumption. Food Policy 62, 151–160 (2016).
Alló, M. & Loureiro, M. L. The role of social norms on preferences towards climate change policies: a meta-analysis. Energ. Policy 73, 563–574 (2014).
Steinberg, L. & Monahan, K. C. Age differences in resistance to peer influence. Dev. Psychol. 43, 1531–1543 (2007).
Knoll, L. J., Leung, J. T., Foulkes, L. & Blakemore, S.-J. Age-related differences in social influence on risk perception depend on the direction of influence. J. Adolescence 60, 53–63 (2017).
Sterman, J. D. Business Dynamics: Systems Thinking and Modeling for a Complex World (Irwin/McGraw-Hill, 2000).
Renner, B. & Schwarzer, R. The motivation to eat a healthy diet: how intenders and nonintenders differ in terms of risk perception, outcome expectancies, self-efficacy, and nutrition behavior. Pol. Psychol. Bull. 36, 7–15 (2005).
Global Health Data Exchange. Global Burden of Disease Study 2017 (Institute for Health Metrics, 2019); http://ghdx.healthdata.org/gbd-2017 .
Leahy, E., Lyons, S., & Tol, R. S. An Estimate of the Number of Vegetarians in the World Working Paper 340 (ESRI, 2010).
Eker, S., Rovenskaya, E., Obersteiner, M. & Langan, S. Practice and perspectives in the validation of resource management models. Nat. Commun. 9, 5359 (2018).
Barlas, Y. Formal aspects of model validity and validation in system dynamics. Syst. Dynam. Rev. 12, 183–210 (1996).
Valin, H. et al. Description of the GLOBIOM (IIASA) model and comparison with the MIRAGE-BioF (IFPRI) model. Crops 8, 3.1 (2013).
Saltelli, A. et al. Global Sensitivity Analysis: The Primer (John Wiley & Sons, 2008).
Saltelli, A. et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput. Phys. Commun. 181, 259–270 (2010).
Sobol, I. M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simulat. 55, 271–280 (2001).
Jaxa-Rozen, M. & Kwakkel, J. Tree-based ensemble methods for sensitivity analysis of environmental models: a performance comparison with Sobol and Morris techniques. Environ. Modell. Softw. 107, 245–266 (2018).
Herman, J. & Usher, W. SALib: an open-source Python library for sensitivity analysis. J. Open Source Softw. 2, 97 (2017).
Saltelli, A. Making best use of model evaluations to compute sensitivity indices. Comput. Phys. Commun. 145, 280–297 (2002).
Lamontagne, J. R., Reed, P. M., Marangoni, G., Keller, K. & Garner, G. G. Robust abatement pathways to tolerable climate futures require immediate global action. Nat. Clim. Change 9, 290–294 (2019).
Lempert, R. J., Groves, D. G., Popper, S. W. & Bankes, S. C. A general, analytic method for generating robust strategies and narrative scenarios. Manag. Sci. 52, 514–528 (2006).
Lempert, R. J., Bryant, B. P. & Bankes, S. C. Comparing Algorithms for Scenario Discovery (RAND, 2008).
Lamontagne, J. R. et al. Large ensemble analytic framework for consequence‐driven discovery of climate change scenarios. Earths Future 6, 488–504 (2018).
Rozenberg, J., Guivarch, C., Lempert, R. & Hallegatte, S. Building SSPs for climate policy analysis: a scenario elicitation methodology to map the space of possible future challenges to mitigation and adaptation. Clim. Change 122, 509–522 (2014).
Guivarch, C., Rozenberg, J. & Schweizer, V. The diversity of socio-economic pathways and CO2 emissions scenarios: insights from the investigation of a scenarios database. Environ. Modell. Softw. 80, 336–353 (2016).
Eker, S. & van Daalen, E. A model-based analysis of biomethane production in the Netherlands and the effectiveness of the subsidization policy under uncertainty. Energ. Policy 82, 178–196 (2015).
Kwakkel, J. H. The exploratory modeling workbench: an open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making. Environ. Modell. Softw. 96, 239–250 (2017).
This research was supported by the International Institute for Applied Systems Analysis (IIASA) and by the European Research Council Synergy grant ERC-2013-SyG. 610028-IMBALANCE-P. We thank Hugo Valin for his suggestion to use the Global Burden of Disease datasets for modelling the health risk of red meat consumption. We thank Ansa Heyl for a language check.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Methods, Supplementary Figs. 1–18, Supplementary Tables 1–3, Supplementary References 1–11
Rights and permissions
About this article
Cite this article
Eker, S., Reese, G. & Obersteiner, M. Modelling the drivers of a widespread shift to sustainable diets. Nat Sustain 2, 725–735 (2019). https://doi.org/10.1038/s41893-019-0331-1
This article is cited by
Pathways to sustainable land use and food systems in Canada
Sustainability Science (2023)
Pathways to achieving nature-positive and carbon–neutral land use and food systems in Wales
Regional Environmental Change (2023)
Incorporating human behaviour into Earth system modelling
Nature Human Behaviour (2022)
Dietary change in high-income nations alone can lead to substantial double climate dividend
Nature Food (2022)
From Ampesie to French fries: systematising the characteristics, drivers and impacts of diet change in rapidly urbanising Accra
Sustainability Science (2022)