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Rebound effects could offset more than half of avoided food loss and waste

Nature Food volume 4, pages 585–595 (2023)Cite this article

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Abstract

Reducing food loss and waste (FLW) could lessen the environmental impacts of food systems and improve food security. However, rebound effects—whereby efficiency improvements cause price decreases and consumption increases—may offset some avoided FLW. Here we model rebounds in food consumption under a scenario of costless FLW reduction. We project that consumption rebound could offset 53–71% of avoided FLW. Such rebounds would imply similar percentage reductions in environmental benefits (carbon emissions, land use, water use) and improvements in food security benefits (increased calorie availability), highlighting a tension between these two objectives. Evidence from energy systems suggests that indirect effects not included in our analysis could further increase rebounds. However, costs of reducing FLW would reduce rebounds. Rebound effects are therefore important to consider in efforts aimed at reducing FLW.

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Fig. 1: Conceptual model of rebound effects from shifts in supply and demand.
Fig. 2: Modelled shifts in food price and consumption when waste and loss are avoided.
Fig. 3: Regional differences in change in waste avoided, change in loss avoided and change in the market quantity traded for three food types.
Fig. 4: Rebound effects and sensitivity to price elasticities.
Fig. 5: Environmental and food security impacts of rebound effects from avoided FLW.
Fig. 6: Additional robustness checks regarding the cost of avoided FLW and non-constant elasticities.

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Data availability

We used public data from FAOSTAT (https://www.fao.org/faostat/en/), the United Nations Environment Program Food Waste Index Report database (https://www.unep.org/resources/report/unep-food-waste-index-report-2021) and the 2019 State of Food and Agriculture Report (https://www.fao.org/documents/card/en?details=ca6030en). We also used data from relevant literature as cited in our study54,55. All data used in this study are included as supplementary information and are also publicly available at https://github.com/mhegwood/foodwaste. Source data are provided with this paper.

Code availability

Data analysis was conducted in MATLAB (version 9.11.0.1809720 (R2021b) Update 1) and Mathematica (version 11.3). The code used in this study is included as supplementary information and is also publicly available at https://github.com/mhegwood/foodwaste.

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Acknowledgements

We thank P. Newton, S. Dueñas-Ocampo, R. Benzeev, L. Frankel-Goldwater, W. Eichhorst, R. Langendorf and H. Brumberg for their feedback on earlier drafts of this paper and R. Langendorf for helpful feedback and discussion on the economic analysis. M.H. and M.G.B. acknowledge funding from the US Department of Agriculture National Institute of Food and Agriculture (award number 2020-38420-30727) and the University of Colorado Boulder Cooperative Institute for Research in Environmental Sciences (start-up grant to M.G.B.). S.J.D. was supported by the US National Science Foundation and US Department of Agriculture (INFEWS grant EAR 1639318) and by the ClimateWorks Foundation (grant 22-2100).

Author information

Authors and Affiliations

  1. Department of Environmental Studies, University of Colorado Boulder, Boulder, CO, USA

    Margaret Hegwood & Matthew G. Burgess

  2. Center for Social and Environmental Futures, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA

    Margaret Hegwood & Matthew G. Burgess

  3. Department of Economics, University of Colorado Boulder, Boulder, CO, USA

    Matthew G. Burgess

  4. Department of Economics, University of California, Irvine, CA, USA

    Erin M. Costigliolo

  5. Department of Earth System Science, University of California, Irvine, CA, USA

    Erin M. Costigliolo & Steven J. Davis

  6. Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK

    Pete Smith

  7. Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Bojana Bajželj

  8. Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA

    Harry Saunders

  9. Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA

    Steven J. Davis

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Contributions

S.J.D. conceived the study. M.H., M.G.B., E.M.C. and S.J.D. performed the analyses, with support and advice from H.S., P.S. and B.B. on analytical approaches. M.H., M.G.B. and S.J.D. led the writing with input from all co-authors. All co-authors reviewed and commented on the paper.

Corresponding authors

Correspondence to Margaret Hegwood, Matthew G. Burgess or Steven J. Davis.

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Nature Food thanks Marc Bellemare, Dan Blaustein-Rejto and Robert Heilmayr for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Note 1 and Tables 1–11.

Reporting Summary

Supplementary Data 1

Data compilation.

Supplementary Data 2

Food security calculations.

Supplementary Data 3

MATLAB input data pulled from Supplementary Data 1.

Supplementary Data 4

Model-generated results from Supplementary Software 1.

Supplementary Data 5

Input data for Fig. 4b.

Supplementary Software 1

MATLAB model code and Mathematica code for Fig. 4b.

Source data

Source Data Fig. 3

Raw data for Fig. 3.

Source Data Fig. 4

Raw data for Fig. 4a.

Source Data Fig. 5

Raw data for Fig. 5a,b.

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Hegwood, M., Burgess, M.G., Costigliolo, E.M. et al. Rebound effects could offset more than half of avoided food loss and waste. Nat Food 4, 585–595 (2023). https://doi.org/10.1038/s43016-023-00792-z

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