The global food system is a major driver of most-pressing sustainability challenges such as climate change, unsustainable resource extraction, and altered biogeochemical cycles of N and P1-5. Food loss and waste is at the heart of the problem6,7. With 1/3 of food uneaten but lost/wasted, not only do resources consumed in producing the food ‘go down the drain’ but also the wasted food continues to brew health and pollution problems8,9. Here we show that food waste recovery and upcycling can effectively mitigate food’s unintended consequences. Using global primary data, we estimated that recovering and treating food waste via composting, anaerobic digestion, or feed-making technologies could reduce carbon footprints by 614–1041 Mt CO2-eq, compared to landfill baseline, potentially offsetting global food system’s climate burden by 4.5–7.6%. Up to 6.6 Mt nutrients could be re-captured for soil-crop-based upcycling. The feed-making option could generate up to 155.6 Mt novel feeds for animal-based upcycling, potentially replacing 41% and 9% of maize and soy in global livestock feeding. Feed grain replacement can bring an array of cascading benefits such as land and fertilizer spared and pollutions avoided, which are evaluated for the U.S. and China as case-study countries.
Biological Sciences - Article
Reducing food’s resource and climate footprints via food waste upcycling
https://doi.org/10.21203/rs.3.rs-1404610/v1
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posted 10 Mar, 2022
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The global food system imposes huge pressure on planet Earth, altering biogeochemical cycles of N and P, driving land use change and unsustainable resource extraction, and contributing to biodiversity loss and climate change1-5. Global food loss and waste (FLW) is at the heart of the problem. About 1/3 of food produced for humans is not eaten but lost/wasted10,11, undermining our ability to combat hunger. Production of the uneaten food accounts for 8-10% of anthropogenic emissions and uses 30% of arable land and 20% of freshwater withdraw6. Food wastage also has dramatic effect on depleting finite essential resources such as P12 and aggravating reactive N (Nr) problems13,14. Making matters worse, wasted food in landfills (developed economies) or open dump (low-income countries) can brew additional problems, such as transmitting diseases via breeding of vectors and releasing GHGs and other pollutants to the atmosphere and hydrosphere8,15.
With FLW amounting to >1.3-1.6 billion tons annually11,16 and potentially doubling by 205017, a fundamental shift is needed, from ‘waste disposal’ default to a ‘regenerative’ system through recovery-treatment-upcycling. Toward this end, food waste treatment technologies (FWTTs) such as aerobic composting (AC), anaerobic digestion (AD), and contemporary technologies that convert recovered food waste into feeds (Re-Feed) appear promising. A growing number of studies have demonstrated the utility of these FWTTs for handling large volume of food waste, documenting their capacities in recovering nutrients and reducing climate burdens (see SI for summary description of the FWTTs.) But rigorous analysis has not been done to distill key sustainability metrics from individual case-studies and to gain insight regarding broader implications at the global scale.
Here, we report findings from integrated and multi-tiered analyses to fill the knowledge gap and to move the field forward. Our conceptual framework (Fig. 1) comprises three interlinked processes: (i) systems-based operation metrics assembled via meta-analysis of literature data and coupled with statistical interrogation for AC, AD, and Re-Feed, with landfill (LF) as a baseline comparison; (ii) global significance of FWTTs in mitigating resource and carbon footprints under various food waste recovery-treatment scenarios; (iii) cascading effect of food-waste-derived-feeds (novel feeds) replacing maize and soy in animal diet through avoidance analysis, taking the U.S. and China as examples.
First, we built a foundational database to gather parameters of system input, output, and sustainability indices for AC, AD, Re-Feed, and LF (the FWTT database, Supplementary Table 1). Literature search and systemic review criteria are described in Methods. Notably, all studies selected were based on real-life food waste treatment operations, i.e., no experimental or modeling results were included; all conformed to the same technically-defined system boundaries (Fig. 1) that cover from food waste collection to transport to treatment and to product quantification. Upon completion, our FWTT database consisted of >470 data entries from 93 studies that are globally distributed (Fig. 2a).
All food waste management options, including LF, consume energy and water to operate (Extended Data Fig. 1a, b). All generate liquid effluent that needs further treatment or assimilation prior to discharge, although the treatment/assimilation is beyond system boundaries and thus not evaluated. Acidification potential and eutrophication potential derived via life-cycle assessment (LCA) were reported in a number of studies (Extended Data Fig. 1c, d).
We focused on system core parameters, including global warming potential (GWP) and yield of product (compost, biogas and digestate, and novel feeds), normalized for 1000 kg (1 metric ton) of raw food waste treated (the base unit). Presented in Fig. 2 are the means and 95% confidence intervals (computed using bootstrapping procedures, see Methods) for the core parameters. Compared to LF baseline, GWP decreased dramatically with all FWTTs. Mean GWP were 78 (6–150, 95% CI), -80 (-170–9), -62 (-110 – -14) kg CO2-eq per base unit treated via AC, AD, and wet-feed making (WF), comparing to 936 (640–1232) via LF. Note that GWP as reported in original studies accounted for direct GHG emissions during the operations plus LCA-based calculations for operating the system as well as for product-substitution effect, for example, novel feeds substituting feed grains therefore avoiding relevant carbon footprints associated with the feed grain production18. This explains the ‘carbon negative’ results for AD and WF. (GWP for dry-feed making was not included in the bootstrapping results due to limited number of observations.) FWTT product yields per base unit treated (Fig. 2c) included 291 (206–375, 95% CI) kg compost (AC), 125 (98–153) m3 biogas plus 250 (153–348) kg digestate (AD), or 152 (125–180) kg novel feed (normalized to 82% DM, see Methods). These core parameters, together with relevant nutrient profiles (Extended Data Table 1), provided the basis for subsequent global analysis.
For global-scale analysis, we first compiled a FLW database to augment food loss and waste parameters, expanding data from the landmark publication of Gustavsson et al.11 (2011) with additional and more recent reports (Methods). From our completed FLW database (including 117 studies; Supplementary Table 1), food loss and waste parameters (% loss/waste) were derived, distinctive for each of seven global-regions (Extended Data Table 2). The parameters represent in-field food loss (P1), post-harvest handling loss (P2), manufacturing loss (P3), distribution loss (P4), retail/wholesale waste (P5), consumer at-home waste (P6a), and consumer out-of-home waste (P6b). Subsequently, we calculated the amounts of food loss and waste using FAO food availability data (averaged over 2017-201919) and the FLW parameters (Extended Data Table 3). Aggregated global FLW are shown in Extended Data Fig. 2. See SI for cross-region and cross-study comparison.
Next, food waste recovery scenarios and treatment schemes were designed to accommodate a range of possibilities. Wasted food at consumer-level (P6a, P6b) is scattered in countless homes and foodservice places, resembling non-point sources; food discard materials in pre-consumer sectors P2-P5 generally concentrate in fewer places, resembling point sources. Presumably, food waste recovery is lower for the non-point sources than point sources. Excluding P1 (in-field loss to remain in the field), we designated three across-sector recovery scenarios as Low (60-35-50%, i.e., 60% recovery for P2 through P5, 35% recovery for P6a, and 50% for P6b), Medium (70-50-65%), and High (80-65-80%). Accordingly, food waste recovery amounts were calculated under the three scenarios per global-region (Extended Data Table 4). We then considered two treatment schemes for recovered food waste: (i) 100% treatment via AC or AD or Re-Feed (the individual treatment scheme), to assess the capacity in resource recovery and carbon mitigation of a given FWTT; (ii) 1/3 treatment each via AC and AD and Re-Feed (the combined scheme), for evaluating mixed deployment of FWTTs; this scheme accommodates differential food waste re-purposing and re-use strategies, for example, species-specific feeding strategies20 or higher quality food waste for Re-Feed and poorer quality materials for AC or AD (more discussion later).
Finally, global significance of applying FWTTs in lowering carbon footprints and recovering resources was evaluated (Methods). Per global-region results are in Extended Data Table 4-5. Aggregated global-total results (Table 1) indicate that reductions in GWP (compared to LF baseline) would amount to 614–878 Mt (AC), 727–1041 Mt (AD), or 714–1021 Mt (WF) under the individual treatment scheme, or 685–980 Mt CO2-eq under the combined scheme. Our findings are significant. The entire global food system’s carbon footprints amount to 13.7 billion tons CO2-eq21, up to nearly 8% of which could potentially be offset by changing from food waste disposal to AC, AD, and/or Re-Feed treatment for upcycling.
Resource recovery capacities are also remarkable (Table 1). Nutrients recovered (sum of N, P, K) would amount to 2.7–6.6 Mt for soil-crop-based upcycling. These nutrients are especially valuable in organic farming to replenish harvest removal of nutrients, or in regions such as the global south where nutrient deficiency is a major factor limiting soil productivity22. Estimated biogas generation amounted to 29.8–127.9 billion m3, equivalent to 656-2815 billion MJ energy23. Additionally, estimated novel feeds production ranged from 36.3 to 155.6 Mt (Table 1), depending on recovery scenarios and treatment schemes. These novel feeds would embody proteins equivalent to 61.3–263.0 Mt maize plus 5.5–23.7 Mt soy (Methods), or energy equivalent to 27.2–116.6 Mt maize plus 8.1–35.0 Mt soy (Extended Data Table 6). The equivalent amounts of maize and soy could offset 9-41% and 2–9% (protein-basis) or 4–18% and 3–14% (energy-basis) of maize and soy currently used in global livestock feeding (648 and 255 Mt19). As mentioned earlier, grain replacement could bring a host of cascading benefits in terms of resources spared and emissions avoided. Such ‘grain replacement dividends’ are assessed via avoidance analysis below.
We selected the U.S. and China as case-study countries for data availability. Our estimation for grain replacement dividends focused on land, fuel, water that would be spared (the natural resource domain); fertilizer and herbicides no longer needed (agricultural chemicals domain); Nr (N2O, NH3, NO3-) and GHGs emissions avoided (environment-climate domain). A comprehensive database was assembled (the Maize&Soy database, Supplementary Table 1) to document an array of data relevant to maize and soy production systems in the two countries (Methods), and to facilitate avoidance analysis. Briefly, yields of the two crops and application rates of agricultural chemicals were from national statistics (Extended Data Table 7). Fuel and water consumption data were based on systemic literature analysis (Supplementary Table 3). Nr loss factors were from meta-analysis of literature findings (Extended Data Table 7). GHG emissions and energy consumption (avoided) were calculated using LCA approach (Methods) with relevant parameters described in Supplementary Table 4-5. Final calculations were made focusing on the amounts of maize and soy to be replaced with novel feeds, on protein- vs. energy-basis (Methods), assuming 1/3 of food waste recoveries under the low, medium, and high scenarios is treated via Re-Feed.
Illustrated in Fig. 3 are quantitative ‘chain reactions’ from food waste recovery to novel feeds production to maize and soy substitution and to avoidance results, for 1/3 of the food waste recovered under the medium scenario with protein-based calculations. For the U.S., 19.1 Mt recovered food waste would generate 2.9 Mt novel feeds, which could replace 4.9 Mt maize plus 0.4 Mt soy (Fig. 3). Replacement of the feed grains could spare 0.7 million ha land, which can be re-allocated for producing human food or serving as conservation reserves or wildlife habitat. Also spared would include 0.1 Mt nutrients and 1.9 kt herbicides, along with agricultural fuel equivalent to 14.0 billion MJ energy, plus 1.7 billion tons of irrigation water. Estimated avoidance of Nr losses would total 26.1 kt (as N2O, NH3 and NO3-; Fig. 3). LCA-based GHG emissions avoided would amount to 3.5 Mt CO2-eq, which is like taking 1.4 million cars off the road24. Comparatively, energy-based calculations led to smaller dividends (Extended Data Table 8), owing to less amounts of maize being replaced (maize production is more resource-intensive with greater environmental footprints than soy; Extended Data Table 7).
For China, estimated grain replacement dividends exceeded that for the U.S. in all categories due to higher amounts of food waste materials and lower efficiency of the cropping systems (Extended Data Table 7). As shown in Fig. 3, land to be spared would total 3.3 million ha, equivalent to 6% of current maize and soy acreage in China25. Also spared include 0.8 Mt nutrients, 6.8 kt herbicides, along with agricultural fuel equating to 55.6 billion MJ. Of particular interest is irrigation water no longer needed, totaling 3.1 billion tons. This is noteworthy because much of China’s maize production regions have experienced severe water deficit, with groundwater over-withdraw threatening regional food and water security26. Avoided Nr losses would total 207.9 kt. GHG emissions avoided would be 16.8 Mt CO2-eq, potentially offsetting 6% of the carbon footprints of China’s agriculture sector27.
Biosafety of feed is critical for animal as well as public health. The EU currently bans food waste feeding to livestock, although its recent approval of processed animal proteins for feeding under certain conditions is considered a ‘milestone’ in making the EU food chain more sustainable28. Some other countries including the U.S., Korea, and Japan have laws and regulations in place to safeguard the biosafety of food-waste-derived-feeds (see description in SI). In principle, feeds made from food waste via adequate thermal treatments are safe for monogastric species; public health risks are minimal29. Notably, thermal treatment does not destroy prions, which are abnormal proteins associated with transmission of bovine spongiform encephalopathy from infected ruminant tissues29. Therefore, feeding post-consumer food waste to ruminants is prohibited. Importantly, food waste is not created equal. Species-specific feeding strategies would allow matching food waste types/sources with animal species for optimal benefit while minimizing health risks20,30. For instance, plant-based pre-consumer food discards would be best suited for ruminants, examples including unsalable fruit and vegetables that are generated in very large amounts9. Other food discards, such as post-consumer food waste, can be made into safe and nutritious feeds for monogastric animals. Japan and South Korea are global leaders in this regard31,32.
One of the limitations is that raw data on FWTT operation parameters are highly variable, and the quality of data from each original study cannot be evaluated. This is inherent because the original studies involved real-life operations, thus the advantage of classic experimentation with built-in estimates of the variability is non-existent. On the other hand, the very fact that study results are firmly grounded in actual instead of simulated or experimental settings makes our overall findings more applicable in real-world situations. With the bootstrapping procedures, our results are robust for the core parameters in supporting various analyses. Another limitation is that FWTT operations generate solid residues that need to be incinerated or landfilled, and liquid effluent that needs proper treatment prior to discharge. But relevant resource and environmental burdens associated with handling the residual wastes are not quantified. We presume that those burdens would be relatively small and insufficient to alter the main findings and conclusions.
To seize the tremendous opportunities implicated in our findings – recovering and treating food waste via FWTTs to potentially offset up to nearly 8% of food’s climate burdens as well as up to 41% and 9% of maize and soy in global livestock feeding, meanwhile re-capturing nutrients for crop production – requires transformative changes with actionable measures that are suitable to local, regional, or national conditions. Detailed discussion on specific measures is beyond the scope of this study. The central focus should be on expanding the adoption of FWTTs that are appropriate for specific waste streams with the goal of optimizing resource recovery, reducing N and P losses, and minimizing climate impact. It is imperative to create life-cycle-based resource and carbon credit framework in support of leveraging FWTTs to address long-term sustainability goals. Creative policies and entrepreneurial incentives that minimize potential adverse consequential outcomes will be essential to encourage investment and commercialization in higher-value food waste re-purposing and re-use practices, and to foster technological innovation and integration for greater synergy and less tradeoffs. The biosafety concerns of food-waste-derived-feeds must be addressed rigorously, for example, by re-assessing current thermal processing conditions to ensure compliance with the highest biosafety standards, by defining and differentiating low bio-hazard food waste types/sources for targeted upcycling, and by developing science-based hazard analysis and risk-based preventive controls for food waste processing facilities. Educational programs are necessary to change societal perceptions from thinking food waste as garbage-for-disposal to collecting and treating it as untapped resources in support of circular and regenerative agri-food systems.
For the first time, to our knowledge, the global significance of recovering and treating food waste to mitigate resource and climate burdens is described quantitatively, drawing from global primary data. As societies strive to tackle pressing sustainability challenges, recovering food waste for treatment and upcycling is vital and viable. Food waste treatment technologies offer flexible resource- and climate-smart tools, providing a path toward a more sustainable future.
Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at (https://….
1 Steffen, W., Richardson, K., Rockstrm, J., Cornell, S. E. & Srlin, S. Planetary Boundaries: Guiding Human Development on a Changing Planet. Science (2015).
2 Clark, M. A., Domingo, N., Colgan, K., Thakrar, S. K. & Hill, J. D. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science 370, 705-708 (2020).
3 Zhang, X. et al. Quantification of global and national nitrogen budgets for crop production. Nature Food 2, 529-540, doi:10.1038/s43016-021-00318-5 (2021).
4 Bajželj, B. et al. Importance of food-demand management for climate mitigation. Nature Climate Change 4, 924-929, doi:10.1038/nclimate2353 (2014).
5 Chen, M. & Graedel, T. E. A half-century of global phosphorus flows, stocks, production, consumption, recycling, and environmental impacts. Global Environmental Change 36, 139-152, doi:10.1016/j.gloenvcha.2015.12.005 (2016).
6 Food and Agriculture Organization of the United Nations (FAO). 2013. Food Wastage Footprint: Impacts on Natural Resources. Bio-intelligence Service, The United Nations.
7 zu Ermgassen, E. K., Phalan, B., Green, R. E. & Balmford, A. Reducing the land use of EU pork production: where there's swill, there's a way. Food policy 58, 35-48, doi:10.1016/j.foodpol.2015.11.001 (2016).
8 Kaza, S., Lisa Yao, Perinaz Bhada-Tata, and Frank Van Woerden. What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. Urban Development Series. Washington, DC: World Bank. doi:10.1596/978-1-4648-1329-0. License: Creative Commons Attribution CC BY 3.0 IGO. (2018).
9 Xue, L. et al. China’s food loss and waste embodies increasing environmental impacts. Nature Food, doi:10.1038/s43016-021-00317-6 (2021).
10 United Nations Environment Programme (UNEP). 2021. Food Waste Index Report 2021. Nairobi, Kenya.
11 Gustavsson, J., Cederberg, C., Sonesson, U., Van Otterdijk, R. & Meybeck, A. Global food losses and food waste: extent, causes and prevention. (2011).
12 Leinweber, P. et al. Handling the phosphorus paradox in agriculture and natural ecosystems: Scarcity, necessity, and burden of P. Ambio 47, 3-19, doi:10.1007/s13280-017-0968-9 (2018).
13 Reis, S. et al. Synthesis and review: Tackling the nitrogen management challenge: from global to local scales. Environ Res Lett 11, 120205, doi:10.1088/1748-9326/11/12/120205 (2016).
14 Sutton, M. A. et al. The nitrogen decade: mobilizing global action on nitrogen to 2030 and beyond. One Earth 4, 10-14, doi:10.1016/j.oneear.2020.12.016 (2021).
15 Uekert, T., Pichler, C. M., Schubert, T. & Reisner, E. Solar-driven reforming of solid waste for a sustainable future. Nature Sustainability 4 (2020).
16 Porter, S. D., Reay, D. S., Higgins, P. & Bomberg, E. A half-century of production-phase greenhouse gas emissions from food loss & waste in the global food supply chain. Science of the Total Environment 571, 721-729, doi:10.1016/j.scitotenv.2016.07.041 (2016).
17 Lopez Barrera, E. & Hertel, T. Global food waste across the income spectrum: Implications for food prices, production and resource use. Food policy 98, 101874, doi:10.1016/j.foodpol.2020.101874 (2021).
18 Kim, M. H. & Kim, J. W. Comparison through a LCA evaluation analysis of food waste disposal options from the perspective of global warming and resource recovery. Science of the Total Environment 408, 3998-4006, doi:10.1016/j.scitotenv.2010.04.049 (2010).
19 FAOSTAT Datebase (Food and Agriculture Organization of the United Nations, accessed 15 January 2021); http://www.fao.org/faostat/zh/#data.
20 Dou, Z., D. Galligan, and G. Shurson. “Food waste as untapped resources for climate mitigation." In: The Role of Agricultural Science and Technology in Climate 21 Project Implementation. Council for Agricultural Science and Technology. Ames, Iowa. https://www.cast-science.org/publication/the-role-of-agricultural-science-and-technology-in-climate-21-project-implementation/. (2021).
21 Poore, J. & Nemecek, T. Reducing food's environmental impacts through producers and consumers. Science 360, 987-992 (2018).
22 Food and Agriculture Organization of the United Nations (FAO), 2021. Soil Nutrient Budget. Global, regional and country trends, 1961–2018. FAOSTAT Analytical Brief Series No 20. Rome.
23 Kamopas, W. & Kiatsiriroat, T. Regeneration of Mono-ethanolamine Solution After Biogas Purification by Electrical Heating with Assisted Ultrasonic Wave. Waste Biomass Valori 10, 3879-3884, doi:10.1007/s12649-018-0326-6 (2018).
24 The Global Fuel Economy Initiative: Delivering Climate Action, GFEI, 2018; http://www.indiaenvironmentportal.org.in/files/file/gfei-climate-report-sprds.pdf.
25 National Bureau of Statistics of China National Agricultural Products Cost-beneft Data Compilation (NAPCDC) (China Statistics Press, 2018).
26 Yu, C. et al. Managing nitrogen to restore water quality in China. Nature 567, 516-520, doi:10.1038/s41586-019-1001-1 (2019).
27 Gao, B. et al. Chinese cropping systems are a net source of greenhouse gases despite soil carbon sequestration. Global change biology 24, 5590-5606, doi:10.1111/gcb.14425 (2018).
28 Peyraud, J. L. a. M. M. Future of EU livestock: How to contribute to a sustainable agricultural sector. European Commission, B-1049 Brussels. (2020).
29 Shurson, G. C., Urriola, P. E. & Ligt, J. L. G. Can we effectively manage parasites, prions, and pathogens in the global feed industry to achieve One Health? Transboundary and Emerging Diseases 69, 4-30, doi:10.1111/tbed.14205 (2021).
30 Van Zanten, H. H. E., Van Ittersum, M. K. & De Boer, I. J. M. The role of farm animals in a circular food system. Global Food Security 21, 18-22, doi:10.1016/j.gfs.2019.06.003 (2019).
31 Padeyanda, Y., Jang, Y.-C., Ko, Y. & Yi, S. Evaluation of environmental impacts of food waste management by material flow analysis (MFA) and life cycle assessment (LCA). Journal of Material Cycles and Waste Management 18, 493-508, doi:10.1007/s10163-016-0510-3 (2016).
32 Salemdeeb, R., Zu Ermgassen, E. K., Kim, M. H., Balmford, A. & Al-Tabbaa, A. Environmental and health impacts of using food waste as animal feed: a comparative analysis of food waste management options. J Clean Prod 140, 871-880, doi:10.1016/j.jclepro.2016.05.049 (2017).
Part I. Systems-based operation parameters of FWTTs
FWTT database. Literature search and selection criteria: The primary source of data was the Web of ScienceÒ Core Collection. Search keywords in various combinations [“food loss” OR “food waste”, AND “anaerobic digestion” OR “composting” OR “feed” OR “landfill”, AND “life cycle assessment (LCA)” OR “life cycle inventory (LCI)”] were used for the initial search. Additional references were identified by cross searching the lists of literature cited in some of the primary publications. English language publications were used for the most part, plus publications in Chinese language. The initial search results are screened for inclusion with subsequent data extraction based on the following criteria: (i) the reports must be for real-life actual food waste treatment operations, i.e. not laboratory experiments or simulation studies; (ii) the treatment must be for food waste materials solely, i.e. no co-digestion or co-composting; (iii) the study was conducted with system boundaries as defined in Fig. 1, covering from food waste collection to transport to treatment and to the generation of product and byproduct; (iv) at lease one parameter on system operation or sustainability is reported.
Data extraction and inclusion: From studies selected, an array of data was extracted and entered into the database, including two data categories. The first is system operation metrics, including system input (energy, water), output (product, byproduct), and LCA-based assessment of global warming potential (GWP), acidification potential (AP), and eutrophication potential (EP), as reported in original studies. These parameters were normalized for the base unit of 1000 kg (1 metric ton) ton raw food waste material treated. The second data category includes information on treatment type (AC, AD, DF, WF, etc.), treatment capacity or size of operation, where (in what country) the study was performed and when (year of publication). Once selected for inclusion in the database, little filtering (e.g. outlier identification and exclusion) was applied to the original data as extracted from individual studies. The final FWTT database consisted of >470 data entries from 93 studies with global distribution (Fig. 2).
Data analysis. Descriptive statistics (mean, standard error of the mean) was performed using Excel 2013. Note that as case studies, the original reports had little or no information regarding variance of the data. Aggregated results were graphically displayed in Extended Data Fig. 1, for system input, some of the environmental effect, per base unit treated.
Due to the lack of repeated measures and variance estimates in the studies included, meta-analysis was conducted using bootstrapping procedure. The bootstrapped mean and 95% confidence interval were estimated for core parameters, including GWP and product/byproduct yields of AC, AD, or Re-Feed per base unit treated. These parameters are used in various analyses subsequently. With the bootstrapping procedure, all estimation was conducted using STATA 17MP, StataCorp LLC, College Station TX. Estimation of the means and 95% confidence interval for the core parameters was performed using a bootstrapped simulation with 1000 replicates for linear regression model. To permit for small departures from normality, a robust estimation of the variance was used. All marginal (model adjusted) means were reported with their respective 95% confidence interval (95% CI).
Part II. Global significance of FWTTs, recovery-treatment scenario analysis
FLW database. Since the landmark publication in 2011 on global food loss and waste11, many studies have emerged with additional information on food loss/waste parameters, providing a more granular and broader coverage. We thus constructed a FLW database with literature search, study screening and selection, and data extraction. Briefly, “food waste” OR “food loss” are used as the keyword to search the peer-reviewed literatures from Web of Science and China National Knowledge Infrastructure up to October 2021. To further ensure the relevance of selected publications, we screen out articles that contained data on FLW for (i) at least one food commodity (e. g. cereals, oilseeds, fruits and vegetables, meat, fish, milk), one food supply stage, and one region or country; (ii) provided explicit, or calculable, loss factors for a food commodity at some stage along the supply chain.
Data extracted from selected studies were organized for food loss and waste factors along the food supply chain in seven sector/subsectors: P1 for at farm harvest loss, P2 for postharvest handling and storage loss, P3 for manufacturing loss, P4 for distribution loss, P5 for retailing waste, P6a for consumer waste at home, and P6b for consumer waste out-of-home. The seven sector/subsectors are in line with FAO33. Information on the origin of the country or region for which the data were derived was also entered into our database. The final FLW database consisted of 1,135 data point entries from 117 studies.
From the FLW database, we derived food loss/waste parameters specific for each of the seven global-regions (Fig. 2). Aggregated data are presented in Extended Data Table 2.
Global-region FLW amount. Employing the calculation methods of Gustavsson et al. (2011), global-region FLW amounts P1 through P6 were calculated based on food availability data (FAO-FBS19; averaged 2017-2019) multiplied by FLW parameters obtained above (Extended Data Table 2). Results are summarized in Extended Data Table 3, and graphically illustrated in Extended Data Fig. 2.
Food waste recovery scenarios and treatment schemes. Food wasted at consumer-level (P6a, P6b) is scattered in countless homes and foodservice places, resembling non-point sources; food lost in sectors P2-P5 generally concentrate in fewer places, resembling point sources. We presumed that food waste recovery would be lower for the non-point sources than point sources. Excluding food recovery for P1 (in-field food loss to remain in the field), we designated food loss/waste recovery rates to be 60, 70, and 80% recovery for P2-P5 (post-harvest through retail/wholesale loss and waste); 35, 50, 65% for P6a (consumer at-home waste), and 50, 65, 80% for P6b (consumer out-of-home waste). Consequently, across-sector recovery scenarios would be 60-35-50% (low), 70-50-65% (medium), and 80-65-80% (high).
Two treatment schemes were considered: (i) 100% of food waste recovered to be treated via AC or AD or Re-Feed (the individual treatment scheme), (ii) recovered food waste to be treated via AC and AD and Re-Feed, 1/3 each (the combined scheme). The individual scheme would allow us to estimate the resource and climate mitigation capacities of AC, AD, or Re-Feed solely. The combined scheme would be more flexible and adaptive, as food waste is not created equal20; high quality food waste would be suited for feed-making whereas poor quality food waste might be best treated via AC or AD.
Food waste recoveries are then calculated by multiplying FLW amounts (Extended Data Table 3) with the rates under low, medium, and high scenarios. Subsequently, GWP reduction and resource recovery capacities were calculated, described below.
Global warming potential. For each of the seven global-regions, GWP for different FWTTs is calculated as:
where i represents AC, or AD, Re-Feed or LF; 
represents the GWP of a given FWTT; 
represents the parameters of GWP for different FWTTs (Fig. 2b); 
denotes the amount of food waste recovered under the low, medium, high scenarios with two treatment schemes. Per global-region results are in Extended Data Table 5; Aggregated results as global sums are presented in Table 1.
Resource recovery capacity of FWTTs. The amounts of FWTT products (compost, biogas and digestate, novel feeds) per global-regions are calculated as follows:
where i represents a given FWTT product/byproduct (compost, biogas and digestate, novel feed); 
represents yield of products (compost, digestate, biogas, novel feeds) as shown in Fig. 2c; and represents the amount of food waste recovered under the low, medium, high scenarios and treated via the two schemes.
Macro-nutrients of total N, P and K contained in the product (compost or digestate obtained above) and the energy equivalence of biogas are then calculated as below:
where i stands for type of product (compost or digestate); j for N or P or K; 
for the concentration of the nutrient in the given product (Extended Data Table 1); and 
for water content of the product; 
for heating value of biogas with 65% volume of methane of 22 MJ m-3 23. Results on nutrient and biogas recoveries per global-region are in Extended Data Table 4. Aggregated global sums are in Table 1.
Estimation of feed grain replacement. Maize and soybeans are major feed grains used in modern-day livestock diets. Crude protein and gross energy are critical nutritional attributes in feedstuffs pertaining to animal nutrition and feeding. Marta et al. (2018), analyzing feed samples generated from restaurant food waste via contemporary Re-Feed technologies, reported crude proteins averaging 24% and ether extract 7.94%34. Using the equation of Son and Kim (2017)35 (below),
we calculated the gross energy to be 4888 kcal kg-1 DM or 20.5 MJ kg-1 DM for the feed samples of Marta et al. These values (crude protein 24% DM basis, gross energy 20.45 MJ kg-1 DM) are comparable to those of global primary data36 on raw consumer food waste (crude protein averaging 19.7% DM and gross energy 20.2 MJ kg-1 DM). Therefore, we proceeded to calculate quantities of maize and soy that could be replaced with novel feeds on the basis of matching the crude protein vs. matching gross energy content, respectively, as below:
where i represents crude protein or gross energy; 
represents feed production coefficient (i.e. product yield of Re-Feed; 152 kg feed per base unit; Fig. 2); 
represents novel feed dry matter content; 
represents novel feed nutritional (crude protein or gross energy) content; 
and 
represent the substation amount of maize and soybean, respectively; 
and 
represent the dry matter content of maize and soybean; 
and 
represent the nutritional (crude protein or gross energy) content of maize and soybean (Extended Data Table 1); the average ratio of global maize and soybean feed consumption in 2017-2019 is 0.72 : 0.2819.
The grain replacement amounts under the low, medium, high food waste recovery scenarios with the individual vs. combined treatment scheme are summarized for global total, U.S., and China in Extended Data Table 6.
Part III. Cascading effect through avoidance analysis
The cascading impact (i.e. grain replacement dividends) from novel feeds replacing maize and soy was evaluated for U.S. and China. The two countries were selected mainly for the availability of data needed in various calculations.
Land, fertilizer, and herbicide spared. Yields of maize and soybeans along with application rates of fertilizers (N, P, K) and herbicides for U.S. and China were obtained from national statistics25,37 (Extended Data Table 7). Acreage of land and amounts of fertilizer and herbicides spared (no longer needed) due to novel feeds replacing maize and soy are calculated as below:
Where 
, 
, 
stand for total, maize and soy acreage, respectively; 
and 
for maize and soy yield for the U.S. (or China), respectively; 
for total fertilizer amounts (including N, P and K) spared; 
and 
for relevant application rate of the fertilizer (Extended Data Table 7); 
for total herbicides amounts spared; 
and 
for relevant application rates of herbicide (Extended Data Table 7).
Fuel and water spared. A sub-database of fuel and water consumption was constructed for U.S. and China maize and soybean production systems from literature data-mining. Peer-reviewed relevant publications were identified via Web of Science databases and China National Knowledge Infrastructure. Search key words included “maize” OR “corn” OR “soybean” AND “yield” AND “irrigation” OR “fuel consumption” AND “US” OR “China” in the abstract and key words. Selection criteria included: (i) the data must be measured in field experiment; experiments under rain-proof shelter conditions and model simulations were excluded; (ii) crop yield and water productivity were reported; (iii) fuel use or energy consumption for agricultural inputs; (iv) experimental sites were described. From 841 published studies we collected, normalized and aggregated water and fuel consumption parameters were derived (Supplementary Table 3). Then, amounts of fuel and water spared are calculated as below:
where 
denotes energy consumption now spared; 
and 
represent energy consumption factors for maize and soybean, respectively, Supplementary Table 3; 
and 
represent irrigation water use (i.e. excluding rain-fed maize or soy production) for maize and soybean, respectively. We estimated irrigation water use by dividing the crop yield of maize, and soybean by the rate of irrigation water productivity (IWP). A meta-analysis was performed to quantify IWP of maize, and soybean in China and U.S.
Nr loss avoided. Another sub-database was constructed to document Nr losses in U.S. and China maize and soybean production systems. Literature search was based on Web of Science databases and China National Knowledge Infrastructure for the period of 1990 to 2017. Search keywords included different combinations of “N2O” OR “NO3-” OR “NH3” AND “maize” OR “corn” OR “soybean” AND “China” OR “US”. Selection criteria were: (i) the N applied was in the form of urea or ammonium or nitrate; studies using slow release or controlled release fertilizer or organic materials such as manure or compost were excluded; (ii) At least one form of Nr loss (N2O emission, NO3- leaching, or NH3 volatilization) was determined and reported; the measurement of N loss must be performed during field operations and throughout the crop growing season, (iii) N2O emission was measured using closed static chamber, or acetylene inhibition methods; NH3 volatilization were measured by continuous air flow chamber method, venting method, Drager-Tube method, or microclimate and wind tunnel method; NO3- leaching was determined using suction cup and lysimeters or from soil samples, or soil water hydrological modeling. The final sub-database consisted of 930 observations from 186 published studies. Normalized and aggregated results are presented in Extended Data Table 7. Subsequently, avoided Nr losses are calculated as below:
where the 
, 
and 
represent avoided N2O emission, NH3 volatilization and NO3- leaching, respectively; 
and 
represent the N input to maize and soybean production; EFs represent the ratios of N2O emission, NH3 volatilization and NO3- leaching to total N input for maize and soybean production (see Extended Data Table 7).
GHG emissions avoided. The novel feeds replace the maize and soybean demand, thus the GHG emissions associated with agricultural inputs (N, P and K fertilizers, seeds, herbicide, insecticide and irrigation water), direct Nr losses from cropland plus fuel consumption can be avoided. We established a GHG emissions database related to U.S. and China maize and soybean production systems based on literature data-mining. Search key words included “maize” OR “corn” OR “soybean” AND “GHG emission” AND “US” OR “China” in the abstract and key words. Normalized and aggregated GHG emission parameters are shown in Supplementary Table 4 and the avoided GHG emission is calculated as below:
where 
stand for avoided GHG emissions; i for the feed grains (maize and soy); j for the seven agricultural inputs (above); 
for parameters of agricultural inputs (Extended Data Table 7); 
for spared acreage of maize and soybean; and 
for GHG emission parameters of the various agricultural inputs in maize and soy production systems.
References
33 Food and Agriculture Organization of the United Nations (FAO), 2014. Definitional Framework of Food Loss; FAO: Rome, Italy.
34 Marta, C. et al. Pet Food as the Most Concrete Strategy for Using Food Waste as Feedstuff within the European Context: A Feasibility Study. Sustainability-Basel 10, 2035, doi:10.3390/su10062035 (2018).
35 Son, A. R. & Kim, B. G. Prediction equations for gross energy in feed ingredients. FASEB Journal (2017).
36 Dou, Z. X., Toth, J. D. Global primary data on consumer food waste: Rate and characteristics – A review. Resources, Conservation and Recycling 168, 105332, doi:10.1016/j.resconrec.2020.105332 (2020).
37 USDA Census of Agriculture (United States Department of Agriculture, accessed 5 January 2022); https://www.nass.usda.gov/Publications/AgCensus/2017/Full_Report/Volume_1,_Chapter_1_US/usv1.pdf.
38 Lee, S. H., Choi, K. I., Osako, M. & Dong, J. I. Evaluation of environmental burdens caused by changes of food waste management systems in Seoul, Korea. The Science of the total environment 387, 42-53, doi:10.1016/j.scitotenv.2007.06.037 (2007).
39 Aye, L. & Widjaya, E. R. Environmental and economic analyses of waste disposal options for traditional markets in Indonesia. Waste management 26, 1180-1191, doi:10.1016/j.wasman.2005.09.010 (2006).
40 Blengini, G. A. Using LCA to evaluate impacts and resources conservation potential of composting: A case study of the Asti District in Italy. Resources, Conservation and Recycling 52, 1373-1381, doi:10.1016/j.resconrec.2008.08.002 (2008).
41 Takata, M. et al. The effects of recycling loops in food waste management in Japan: based on the environmental and economic evaluation of food recycling. The Science of the total environment 432, 309-317, doi:10.1016/j.scitotenv.2012.05.049 (2012).
42 Boldrin, A., Andersen, J.K., Møller, J., Favoino, E. & Christensen, T.H. Composting and compost utilization: accounting of greenhouse gases and global warming contributions. Waste Management & Research 27, 800-812, doi:10.1177/0734242X09345275” (2009).
43 Martinez-Blanco, J. et al. The use of life cycle assessment for the comparison of biowaste composting at home and full scale. Waste management 30, 983-994, doi:10.1016/j.wasman.2010.02.023 (2010).
44 Oldfield, T. L., White, E. & Holden, N. M. The implications of stakeholder perspective for LCA of wasted food and green waste. Journal of Cleaner Production 170, 1554-1564, doi:10.1016/j.jclepro.2017.09.239 (2018).
45 Jensen, M. B., Moller, J. & Scheutz, C. Assessment of a combined dry anaerobic digestion and post-composting treatment facility for source-separated organic household waste, using material and substance flow analysis and life cycle inventory. Waste management 66, 23-35, doi:10.1016/j.wasman.2017.03.029 (2017).
46 Colón, J. et al. Environmental assessment of home composting. Resources, Conservation and Recycling 54, 893-904, doi:10.1016/j.resconrec.2010.01.008 (2010).
47 Quirós, R. et al. Environmental assessment of two home composts with high and low gaseous emissions of the composting process. Resources, Conservation and Recycling 90, 9-20, doi:10.1016/j.resconrec.2014.05.008 (2014).
48 Ma, H. M., Gao, B., Han, W., Chen, Z. X., Li, B. Investigation and analysis on current status of soil utilization of kitchen waste anaerobic digestate. China Biogas 39, 27-34 (2021).
49 Righi, S., Oliviero, L., Pedrini, M., Buscaroli, A. & Della Casa, C. Life Cycle Assessment of management systems for sewage sludge and food waste: centralized and decentralized approaches. Journal of Cleaner Production 44, 8-17, doi:10.1016/j.jclepro.2012.12.004 (2013).
50 Heuzé V., T. G., Lebas F. Maize grain. Feedipedia, a programme by INRAE, CIRAD, AFZ and FAO. https://feedipedia.org/node/556 Last updated on September 7, 2017, 14:29. (2017).
51 Heuzé V., T. G. Soybean (general). Feedipedia, a programme by INRAE, CIRAD, AFZ and FAO. https://www.feedipedia.org/node/753 Last updated on August 3, 2016, 10:37. (2016).
Data management
The various databases were created and updated in Excel. All data are maintained at the College of Resources and Environmental Sciences, China Agricultural University. Sigma Plot 12.5 and Excel 2013 software were used to draw graphics. Sankey diagram was drawn with Origin 2021.
Data availability
Data supporting the findings of this study are available within the paper and its Supplementary Information. The dataset used for the analyses is available from the corresponding author upon request.
Acknowledgements
The authors thank Dr. James Ferguson, Dr. David Galligan, and Dr. Linda Baker for pre-submission reviews of the manuscript with helpful feedback. This work is supported by a Penn Global Engagement grant, a Pennsylvania Department of Agriculture research grant, a USDA-NIFA IDEAS Program grant (#2022-68014-36664/project accession no. 1028184), the Taishan Scholarship Project of Shandong Province (No. TS201712082), and a China postdoctoral science foundation grant (2020M670539).
Author contributions Z.D. and Z.C. designed and directed the research; T.C. contributed to project conceptualization; X.T., H.Z., Y.L. and S.F. participated in literature data-mining; Y.W. and H.Y. performed data management and analyses; D.S. conducted bootstrapping procedures and reviewed statistical results and interpretation; Z.D., Y.W., H.Y., and Z.C. worked collectively on data organization and manuscript development; Z.D. wrote the manuscript; All authors reviewed the manuscript.
Competing interests The authors declare no competing interests.
Additional information
Supplementary information is available for this paper.
Correspondence and requests for materials should be addressed to Z.D. or Z.C.
Peer review information
Reprints and permissions information is available at www.nature.com/reprints.
Table 1. Global warming potential (GWP) reduction, nutrient recovery (sum of N, P, K), biogas and novel feed production capacities at the global scale. Estimation is for three food waste recovery scenarios of Low (60-35-50%), Medium (70-50-65%), and High (80-65-80%) with two treatment schemes of (i) 100% treatment via aerobic composting (AC) or anaerobic digestion (AD) or feed-making technologies (Re-Feed), (ii) a combined scheme (AC+AD+Re-Feed) with 1/3 of recovered food waste amounts for each treatment option.
|
|
GWP Reduction* |
Nutrient Recovery |
Biogas |
Novel Feeds |
|
|
Mt CO2-eq |
Mt |
Billion m3 |
Mt |
|
Low recovery scenario |
||||
|
AC |
613.8 |
3.6 |
- |
- |
|
AD |
727.5 |
4.6 |
89.4 |
- |
|
Re-Feed |
714.1 |
- |
- |
108.8 |
|
AC+AD+Re-Feed |
685.1 |
2.7 |
29.8 |
36.3 |
|
Medium recovery scenario |
||||
|
AC |
745.9 |
4.4 |
- |
- |
|
AD |
884.1 |
5.6 |
108.7 |
- |
|
Re-Feed |
867.8 |
- |
- |
132.2 |
|
AC+AD+Re-Feed |
832.6 |
3.3 |
36.2 |
44.1 |
|
High recovery scenario |
||||
|
AC |
878.0 |
5.2 |
- |
- |
|
AD |
1040.6 |
6.6 |
127.9 |
- |
|
Re-Feed |
1021.5 |
- |
- |
155.6 |
|
AC+AD+Re-Feed |
980.0 |
3.9 |
42.6 |
51.9 |
*As compared to landfill baseline.
There is NO Competing Interest.
- Dousupplementaryadditionaldata.pdf
Supplementary I
- Dousupplementarydatabasereferences.docx
Supplementary II
- DouExtendedFigsTables.docx
Extended Data Tables & Figs