Skip to main content

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

  • Article
  • Published:

A meta-analysis assessing the effectiveness of demand-side interventions for sustainable food consumption and food waste reduction

Nature Food volume 7, pages 88–99 (2026)Cite this article

Subjects

Abstract

Shifting consumers towards more sustainable food consumption and avoiding food waste have been identified as key levers in mitigating food systems-related climate change impacts. Here we conducted a machine-learning-assisted systematic review and meta-analysis of 306 effect sizes from 110 articles, covering over 2.4 million observations, to assess the effectiveness of demand-side interventions targeting actual or incentivized behaviours. On average, we find small effect sizes across both food consumption and food waste interventions. Effect sizes vary substantially across intervention types, with certain choice architecture interventions, such as availability and defaults, driving much of the overall effect in both domains, while incentives also show promise in reducing food waste. These effects remain robust even after accounting for severe publication bias, which notably reduces average estimates for other intervention types. Sensitivity analyses further underscore the need for future research to systematically identify when, how and why interventions are effective.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Descriptive statistics for intervention and outcome categories.
Fig. 2: Average effect sizes by intervention category for food consumption and food waste outcomes.
Fig. 3: Predicted effect sizes by outcome, setting and population.
Fig. 4: Predicted effect sizes by study design, experimental method and risk of bias.

Similar content being viewed by others

Data availability

Data used in the analysis can be accessed via the OSF repository at https://osf.io/f75y8.

Code availability

The code to replicate the analysis can be accessed via the OSF repository at https://osf.io/f75y8.

References

  1. Crippa, M. et al. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat. Food 2, 198–209 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Xu, X. et al. Global greenhouse gas emissions from animal-based foods are twice those of plant-based foods. Nat. Food 2, 724–732 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Poore, J. & Nemecek, T. Reducing food’ s environmental impacts through producers and consumers. Science 992, 987–992 (2018).

    Article  ADS  Google Scholar 

  4. Benton, T., Bieg, C., Harwatt, H., Pudassaini, R. & Wellesley, L. Food System Impacts on Biodiversity Loss. Three Levers for Food System Transformation in Support of Nature (Chatham House, 2021).

  5. Creutzig, F. et al. Towards demand-side solutions for mitigating climate change. Nat. Clim. Change 8, 260–263 (2018).

    Article  ADS  MathSciNet  Google Scholar 

  6. IPCC: Summary for Policymakers. in Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) (Cambridge Univ. Press, 2022).

  7. Bajželj, B. et al. Importance of food-demand management for climate mitigation. Nat. Clim. Change 4, 924–929 (2014).

    Article  ADS  Google Scholar 

  8. Creutzig, F. et al. Demand-side solutions to climate change mitigation consistent with high levels of well-being. Nat. Clim. Change 12, 36–46 (2022).

    Article  ADS  Google Scholar 

  9. Dubois, G. et al. It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures. Energy Res. Soc. Sci. 52, 144–158 (2019).

    Article  Google Scholar 

  10. Reisch, L. A. Shaping healthy and sustainable food systems with behavioural food policy. Eur. Rev. Agric. Econ. 48, 665–693 (2021).

    Google Scholar 

  11. Reisch, L. A. et al. Mitigating climate change via food consumption and food waste: a systematic map of behavioral interventions. J. Clean. Prod. 279, 123717 (2021).

    Article  Google Scholar 

  12. Ammann, J., Arbenz, A., Mack, G., Nemecek, T. & El Benni, N. A review on policy instruments for sustainable food consumption. Sustain. Prod. Consum. 36, 338–353 (2023).

    Article  Google Scholar 

  13. Ran, Y. et al. Effects of public policy interventions for environmentally sustainable food consumption: a systematic map of available evidence. Environ. Evid. 13, 10 (2024).

  14. Pollicino, D., Blondin, S. & Attwood, S. The Food Service Playbook for Promoting Sustainable Food Choices (World Resources Institute, 2024).

  15. Reynolds, C. et al. Review: Consumption-stage food waste reduction interventions—what works and how to design better interventions. Food Policy 83, 7–27 (2019).

    Article  Google Scholar 

  16. Chang, K. B., Wooden, A., Rosman, L., Altema-Johnson, D. & Ramsing, R. Strategies for reducing meat consumption within college and university settings: a systematic review and meta-analysis. Front. Sustain. Food Syst. 7, 1103060 (2023).

  17. Greene, D., Nguyen, M. & Dolnicar, S. How to entice restaurant patrons to order low-emissions meals? A meta-analysis and research agenda. Appetite 188, 106612 (2023).

    Article  PubMed  Google Scholar 

  18. Grundy, E. A. C. et al. Interventions that influence animal-product consumption: a meta-review. Futur. Foods 5, 100111 (2022).

    Article  CAS  Google Scholar 

  19. Kwasny, T., Dobernig, K. & Riefler, P. Towards reduced meat consumption: a systematic literature review of intervention effectiveness, 2001–2019. Appetite 168, 105739 (2021).

    Article  PubMed  Google Scholar 

  20. Harguess, J. M., Crespo, N. C. & Hong, M. Y. Strategies to reduce meat consumption: a systematic literature review of experimental studies. Appetite 144, 104478 (2020).

    Article  PubMed  Google Scholar 

  21. Bianchi, F., Garnett, E., Dorsel, C., Aveyard, P. & Jebb, S. A. Restructuring physical micro-environments to reduce the demand for meat: a systematic review and qualitative comparative analysis. Lancet Planet. Health 2, e384–e397 (2018).

  22. Meier, J., Andor, M. A., Doebbe, F. C., Haddaway, N. R. & Reisch, A. Review: Do green defaults reduce meat consumption?. Food Policy 110, 102298 (2022).

    Article  Google Scholar 

  23. Taufik, D., Verain, M. C. D., Bouwman, E. P. & Reinders, M. J. Determinants of real-life behavioural interventions to stimulate more plant-based and less animal-based diets: a systematic review. Trends Food Sci. Technol. 93, 281–303 (2019).

    Article  CAS  Google Scholar 

  24. Barker, H., Shaw, P. J., Richards, B., Clegg, Z. & Smith, D. What nudge techniques work for food waste behaviour change at the consumer level? A systematic review. Sustainability 13, 11099 (2021).

    Article  ADS  Google Scholar 

  25. Tian, X. et al. A meta-analytical review of intervention experiments to reduce food waste. Environ. Res. Lett. 17, 064041 (2022).

  26. Zhang, J., Huang, Y., Zhu, J. & Zhao, L. A meta-analysis on the effectiveness of food-waste reducing nudges. Food Policy 120, 102480 (2023).

  27. Szaszi, B. et al. No reason to expect large and consistent effects of nudge interventions. Proc. Natl Acad. Sci. USA 119, e2200732119 (2022).

  28. Vevea, J. L. & Hedges, L. V. A general linear model for estimating effect size in the presence of publication bias. Psychometrika 60, 419–435 (1995).

    Article  Google Scholar 

  29. Duval, S. & Tweedie, R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56, 455–463 (2000).

    Article  ADS  CAS  PubMed  Google Scholar 

  30. Stanley, T. D. & Doucouliagos, H. Meta-regression approximations to reduce publication selection bias. Res. Synth. Methods 5, 60–78 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. Tipton, E. et al. Why meta-analyses of growth mindset and other interventions should follow best practices for examining heterogeneity: Commentary on Macnamara and Burgoyne (2023) and Burnette et al. (2023). Psychol. Bull. 149, 229–241 (2023).

  32. Bryan, C. J., Tipton, E. & Yeager, D. S. Behavioural science is unlikely to change the world without a heterogeneity revolution. Nat. Hum. Behav. 5, 980–989 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Imbens, G. W. et al. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47, 5–86 (2009).

    Article  Google Scholar 

  34. Bouma, J. Evaluating environmental policy: the use and usefulness of experiments. J. Environ. Econ. Policy 10, 468–480 (2021).

    Article  Google Scholar 

  35. Roe, B. E. & Just, D. R. Internal and external validity in economics research: tradeoffs between experiments, field experiments, natural experiments, and field data. Am. J. Agric. Econ. 91, 1266–1271 (2009).

    Article  Google Scholar 

  36. Harrison, G. W. & List, J. A. Field experiments. J. Econ. Lit. 42, 1009–1055 (2004).

    Article  Google Scholar 

  37. Creutzig, F. et al. in Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) Ch. 5 (Cambridge Univ. Press, 2022)

  38. Mertens, S., Herberz, M., Hahnel, U. J. J. & Brosch, T. The effectiveness of nudging: a meta-analysis of choice architecture interventions across behavioral domains. Proc. Natl Acad. Sci. USA 119, e2107346118 (2022).

  39. Reisch, L. A. & Sunstein, C. R. Plant-based by default. One Earth 4, 1205–1208 (2021).

    Article  ADS  Google Scholar 

  40. Khanna, T. M. et al. A multi-country meta-analysis on the role of behavioural change in reducing energy consumption and CO2 emissions in residential buildings. Nat. Energy 6, 925–932 (2021).

    Article  ADS  CAS  Google Scholar 

  41. Espinosa, R. & Nassar, A. The acceptability of food policies. Nutrients 13, 1483 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Reisch, L. A., Sunstein, C. R. & Gwozdz, W. Beyond carrots and sticks: Europeans support health nudges. Food Policy 69, 1–10 (2017).

    Article  Google Scholar 

  43. Lohmann, P. M., Gsottbauer, E., Farrington, J., Human, S. & Reisch, L. A. Choice architecture promotes sustainable choices in online food-delivery apps. PNAS Nexus 3, pgae422 (2024).

  44. Bauer, J. M., Nielsen, K. S., Hofmann, W. & Reisch, L. A. Healthy eating in the wild: an experience-sampling study of how food environments and situational factors shape out-of-home dietary success. Soc. Sci. Med. 299, 114869 (2022).

  45. Krefeld-Schwalb, A., Sugerman, E. R. & Johnson, E. J. Exposing omitted moderators: explaining why effect sizes differ in the social sciences. Proc. Natl Acad. Sci. USA. 121, e2306281121 (2024).

  46. Lohmann, P. M. Long-lasting impact of information on meat consumption. Nat. Food 4, 205–206 (2023).

  47. Ferraro, P. J. Counterfactual thinking and impact evaluation in environmental policy. New Dir. Eval. 2009, 75–84 (2009).

    Article  Google Scholar 

  48. Laffan, K. Context counts: an exploration of the situational correlates of meat consumption in three Western European countries. Behav. Public Policy 8, 685–700 (2024).

  49. Butera, L. & List, J. A. An Economic Approach to Alleviate the Crises of Confidence in Science: with an Application to the Public Goods Game (National Bureau of Economic Research, 2017)

  50. Dreber, A. et al. Using prediction markets to estimate the reproducibility of scientific research. Proc. Natl Acad. Sci. USA 112, 15343–15347 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  51. Al-Ubaydli, O., Lee, M. S., List, J. A., Mackevicius, C. L. & Suskind, D. How can experiments play a greater role in public policy? Twelve proposals from an economic model of scaling. Behav. Public Policy 5, 2–49 (2021).

    Article  Google Scholar 

  52. Milkman, K. L. et al. Megastudies improve the impact of applied behavioural science. Nature 600, 478–483 (2021).

  53. Lohmann, P. M., Gsottbauer, E., Gravert, C. & Reisch, L. A. Nudging, fast and slow: experimental evidence from food choices under time pressure. Environ. Resour. Econ, 88, 2595–2627 (2025).

  54. Simonsohn, U., Simmons, J. & Nelson, L. D. Above averaging in literature reviews. Nat. Rev. Psychol. 1, 551–552 (2022).

    Article  Google Scholar 

  55. Hallsworth, M. A manifesto for applying behavioural science. Nat. Hum. Behav. 7, 310–322 (2023).

    Article  PubMed  Google Scholar 

  56. Szaszi, B., Goldstein, D. G., Soman, D. & Michie, S. Generalizability of choice architecture interventions. Nat. Rev. Psychol. 4, 518–529 (2025).

    Article  Google Scholar 

  57. Savani, M. M. Growing the role of qualitative methods in the behavioural public policy toolkit. Behav. Public Policy https://doi.org/10.1017/bpp.2025.10026 (2025).

  58. Duluins, O. & Baret, P. V. The paradoxes of the protein transition maintain existing animal production and consumption systems. Nat. Food 5, 725–730 (2024).

  59. Aschemann-Witzel, J. et al. Defining upcycled food: the dual role of upcycling in reducing food loss and waste. Trends Food Sci. Technol. 132, 132–137 (2023).

    Article  CAS  Google Scholar 

  60. Barone, A. M. & Aschemann-Witzel, J. Food handling practices and expiration dates: consumers’ perception of smart labels. Food Control 133, 108615 (2022).

    Article  Google Scholar 

  61. Nielsen, K. S. et al. Realizing the full potential of behavioural science for climate change mitigation. Nat. Clim. Change 14, 322–330 (2024).

    Article  ADS  Google Scholar 

  62. Stern, P. C. A reexamination on how behavioral interventions can promote household action to limit climate change. Nat. Commun. 11, 918 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  63. Gravert, C. & Shreedhar, G. Effective carbon taxes need green nudges. Nat. Clim. Change 12, 1073–1074 (2022).

    Article  ADS  Google Scholar 

  64. Alt, M., Bruns, H., DellaValle, N. & Murauskaite-Bull, I. Synergies of interventions to promote pro-environmental behaviors—a meta-analysis of experimental studies. Glob. Environ. Change 84, 102776 (2024).

  65. Fesenfeld, L. P., Wicki, M., Sun, Y. & Bernauer, T. Policy packaging can make food system transformation feasible. Nat. Food 1, 173–182 (2020).

    Article  Google Scholar 

  66. Rueda, O., Mogollón, J. M., Stenzel, F., Tukker, A. & Scherer, L. A protein transition can free up land to tap vast energy and negative emission potentials. One Earth 7, 293–310 (2024).

    Article  Google Scholar 

  67. Duluins, O. & Baret, P. V. A systematic review of the definitions, narratives and paths forwards for a protein transition in high-income countries. Nat. Food 5, 28–36 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Kim, B. F. et al. Country-specific dietary shifts to mitigate climate and water crises. Glob. Environ. Change 62, 101926 (2020).

    Article  Google Scholar 

  69. Ball, T. S. et al. Food impacts on species extinction risks can vary by three orders of magnitude. Nat. Food 6, 848–856 (2025).

  70. Haddaway, N. R. & Macura, B. The role of reporting standards in producing robust literature reviews. Nat. Clim. Change 8, 444–447 (2018).

    Article  ADS  Google Scholar 

  71. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & Group*, P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151, 264–269 (2009).

    Article  PubMed  Google Scholar 

  72. Ronto, R. et al. Identifying effective interventions to promote consumption of protein-rich foods from lower ecological footprint sources: a systematic literature review. PLoS Glob. Public Health 2, e0000209 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Gandhi, L., Kiyawat, A., Camerer, C. & Watts, D. J. Hypothetical Nudges Provide Misleading Estimates of Real Behavior Change (Academy of Management Proceedings, 2024)

  74. Callaghan, M., Müller-Hansen, F., Hilaire, J. & Lee, Y. T. NACSOS: NLP Assisted Classification, Synthesis and Online Screening (v0.1.0). Zenodo https://doi.org/10.5281/ZENODO.4121526 (2020).

  75. van de Schoot, R. et al. An open source machine learning framework for efficient and transparent systematic reviews. Nat. Mach. Intell. 3, 125–133 (2021).

    Article  Google Scholar 

  76. Callaghan, M. W. & Müller-Hansen, F. Statistical stopping criteria for automated screening in systematic reviews. Syst. Rev. 9, 273 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).

    Article  ADS  Google Scholar 

  78. Münscher, R., Vetter, M. & Scheuerle, T. A review and taxonomy of choice architecture techniques. J. Behav. Decis. Mak. 29, 511–524 (2016).

    Article  Google Scholar 

  79. Ringquist, E. Meta-Analysis for Public Management and Policy (John Wiley & Sons, 2013).

  80. Chinn, S. A simple method for converting an odds ratio to effect size for use in meta-analysis. Stat. Med. 19, 3127–3131 (2000).

    Article  CAS  PubMed  Google Scholar 

  81. Higgins, J. P. Cochrane Handbook for Systematic Reviews of Interventions (Cochrane Collaboration and John Wiley & Sons, 2008).

  82. Wilson, D. B. Practical meta-analysis effect size calculator (version 2023.11.27). Campbell Collaboration https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php (2023).

  83. IntHout, J., Ioannidis, J. P. A., Rovers, M. M. & Goeman, J. J. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open 6, e010247 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Rubio-Aparicio, M., Sánchez-Meca, J., López-López, J. A., Botella, J. & Marín-Martínez, F. Analysis of categorical moderators in mixed-effects meta-analysis: consequences of using pooled versus separate estimates of the residual between-studies variances. Br. J. Math. Stat. Psychol. 70, 439–456 (2017).

    Article  PubMed  Google Scholar 

  85. Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. Introduction to Meta-Analysis Vol. 16 (Wiley, 2009).

  86. Egger, M., Smith, G. D., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629–634 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Maier, M., VanderWeele, T. J. & Mathur, M. B. Using selection models to assess sensitivity to publication bias: a tutorial and call for more routine use. Campbell Syst. Rev. 18, e1256 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Vevea, J. L. & Woods, C. M. Publication bias in research synthesis: sensitivity analysis using a priori weight functions. Psychol. Methods 10, 428–443 (2005).

    Article  PubMed  Google Scholar 

  89. Burnette, J. L. et al. A systematic review and meta-analysis of growth mindset interventions: for whom, how, and why might such interventions work? Psychol. Bull. 149, 174–205 (2022).

  90. Vevea, J. L., Coburn, K. & Sutton, A. in The Handbook of Research Synthesis and Meta-Analysis Vol. 2, 383–430 (Russell Sage Foundation, 2019).

  91. Coburn, K. M., Vevea, J. L. & Coburn, M. K. M. weightr: Estimating weight-function models for publication bias. R package version 2.0.2 (CRAN, 2019).

  92. McShane, B. B., Böckenholt, U. & Hansen, K. T. Adjusting for publication bias in meta-analysis: an evaluation of selection methods and some cautionary notes. Perspect. Psychol. Sci. 11, 730–749 (2016).

    Article  PubMed  Google Scholar 

  93. Frampton, G. et al. Principles and framework for assessing the risk of bias for studies included in comparative quantitative environmental systematic reviews. Environ. Evid. 11, 12 (2022).

  94. Sterne, J. A. et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355, 4–10 (2016).

    Google Scholar 

  95. Sterne, J. A. C. et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 366, l4898 (2019).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank our research assistants C. Rutherford, A. Sunil, C. Ma, L. Greening, B. Carvalho and S. Thode for their invaluable contributions. We are also grateful to M. Dewies for his guidance and support. L.A.R., A.P. and J.M.B. acknowledge research funding from Novo Nordisk Fonden grant number NNF21SA0069203. S.L.F. acknowledges financial support from the Economic and Social Research Council’s grant to Behavioural Research UK (grant reference: ES/Y001044/1). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Authors and Affiliations

  1. El-Erian Institute of Behavioural Economics and Policy, Judge Business School, University of Cambridge, Cambridge, UK

    Paul M. Lohmann & Lucia A. Reisch

  2. Department of Management, Society and Communication, Copenhagen Business School, Copenhagen, Denmark

    Alice Pizzo, Jan M. Bauer & Lucia A. Reisch

  3. School of Public Policy and Global Affairs, University of British Columbia, Vancouver, British Columbia, Canada

    Tarun M. Khanna

  4. Department of Experimental Psychology, University College London (UCL), London, UK

    Sarah L. Flecke

  5. Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany

    Max Callaghan & Jan C. Minx

Authors
  1. Paul M. Lohmann
    View author publications

    Search author on:PubMed Google Scholar

  2. Alice Pizzo
    View author publications

    Search author on:PubMed Google Scholar

  3. Jan M. Bauer
    View author publications

    Search author on:PubMed Google Scholar

  4. Tarun M. Khanna
    View author publications

    Search author on:PubMed Google Scholar

  5. Sarah L. Flecke
    View author publications

    Search author on:PubMed Google Scholar

  6. Max Callaghan
    View author publications

    Search author on:PubMed Google Scholar

  7. Jan C. Minx
    View author publications

    Search author on:PubMed Google Scholar

  8. Lucia A. Reisch
    View author publications

    Search author on:PubMed Google Scholar

Contributions

P.M.L., A.P., J.M.B., T.M.K., J.C.M. and L.A.R. conceptualized the research. P.M.L., A.P., J.M.B. and T.M.K. developed the literature search and screening strategy. P.M.L., A.P., J.M.B. and S.L.F. manually screened the literature and collected the data. M.C. performed the machine learning-assisted screening. P.M.L. performed the formal analysis, validation and visualization. P.M.L., A.P., J.M.B., S.L.F., T.M.K. and L.A.R. analysed and interpreted the results. P.M.L. and A.P. wrote the paper with contributions from all authors.

Corresponding author

Correspondence to Paul M. Lohmann.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Food thanks Lenny Koh, Jens-Peter Loy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Supplementary Information (download PDF )

Supplementary Tables 1–16, Figs. 1–10, Literature Overview, Analyses, Methods and Risk-of-Bias Classification Tool.

Reporting Summary (download PDF )

Supplementary Data 1 (download XLSX )

Data table of key study characteristics at the effect level.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lohmann, P.M., Pizzo, A., Bauer, J.M. et al. A meta-analysis assessing the effectiveness of demand-side interventions for sustainable food consumption and food waste reduction. Nat Food 7, 88–99 (2026). https://doi.org/10.1038/s43016-025-01279-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s43016-025-01279-9

Search

Advanced search

Quick links

Nature Briefing

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

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing