What is our assessment?
Our analysis concludes that, despite its limited global impact for reducing emissions, Reduce Overfishing is a “Worthwhile” climate solution that has other important benefits for ecosystem health and long-term food security.
Reduce Overfishing refers to the use of management actions that decrease fishing effort and therefore cut CO₂ emissions from fishing vessel fuel use on overfished stocks. Advantages include the potential to replenish depleted fish stocks, support ecosystem health, and enhance long-term food and job security. Disadvantages include the short-term reductions in fishing effort needed to allow systems to recover, which could impact local livelihoods and economies. While these interventions are not expected to reach globally meaningful levels of emissions reductions (>0.1 Gt CO₂‑eq/yr ), we conclude that Reduce Overfishing is “Worthwhile” with important ecosystem and social benefits.
Our analysis concludes that, despite its limited global impact for reducing emissions, Reduce Overfishing is a “Worthwhile” climate solution that has other important benefits for ecosystem health and long-term food security.
| Plausible | Could it work? | Yes |
|---|---|---|
| Ready | Is it ready? | Yes |
| Evidence | Are there data to evaluate it? | Yes |
| Effective | Does it consistently work? | Yes |
| Impact | Is it big enough to matter? | No |
| Risk | Is it risky or harmful? | No |
| Cost | Is it cheap? | ? |
Reducing overfishing lowers fuel use and CO₂ emissions from wild capture fishing vessels by reducing fishing effort on overfished stocks. This is typically achieved through management actions, such as seasonal closures, gear restrictions, and catch limits. Fishing effort, whether measured as the hours spent fishing or distance traveled, is generally proportional to fuel use. In addition to immediate reductions in emissions, reducing overfishing can allow overfished stocks to recover, which can lead to reduced future emissions since fuel use is lowered when fish are easier to catch and harvested sustainably.
Reducing fishing effort in locations with depleted and overfished wild fish stocks is expected to reduce emissions from fishing vessels. When stocks are overfished, fishers must exert additional effort, traveling further and/or searching longer to make the same catch, which increases fuel use and CO₂ emissions. Reducing overfishing through management actions, such as harvest control rules, gear restrictions, seasonal closures, stronger enforcement of existing regulations, and establishment of marine protected areas, can help fish stocks recover. Other policy tools, such as reducing harmful fuel subsidies that currently enable many otherwise unprofitable fishing fleets, are also likely to result in lower fuel use and CO₂ emissions. Healthy fish stocks can be caught with lower fishing effort, translating to future fuel savings and reduced CO₂ emissions. Global estimates suggest that reductions in overfishing could avoid up to 0.08 Gt CO₂‑eq/yr, representing almost half of the entire capture fisheries sector's annual emissions (0.18 Gt CO₂‑eq/yr ).
Currently, overfishing affects more than 35% of global wild marine fish stocks, increasing by 1%, on average, every year. Reducing overfishing not only lowers fuel use and emissions but also allows overfished stocks to recover. Healthy fish stocks strengthen marine food webs and contribute to ecosystem resilience and biodiversity. Overfishing has widespread consequences for diverse marine ecosystems, such as kelp forests, where declines in fish have led to overgrazing of the kelp by sea urchins. Over time, management interventions will also likely improve the sustainability and long-term reliability of coastal livelihoods and food security by supporting sustainable fisheries.
Policy and management tools for reducing overfishing and, by extension, fishing-related emissions come with some challenges. For instance, management measures or legal protections may not be fully effective if implementation or enforcement is weak. Management and enforcement can be particularly challenging on the high seas, where jurisdiction is limited or shared across many nations, and where illegal, unreported, and unregulated fishing can be widespread. Even when effective, fish stock recovery can take years to decades, and the costs and trade-offs are unlikely to be evenly distributed across fishing fleets. In the short term, efforts to reduce overfishing could create economic challenges for small-scale fishers who may have fewer resources and less capacity to adapt to management restrictions.
Andersen, N. F., Cavan, E. L., Cheung, W. W., Martin, A. H., Saba, G. K., & Sumaila, U. R. (2024). Good fisheries management is good carbon management. npj Ocean Sustainability, 3(1), 17. Link to source: https://doi.org/10.1038/s44183-024-00053-x
Bastardie, F., Hornborg, S., Ziegler, F., Gislason, H., & Eigaard, O. R. (2022). Reducing the fuel use intensity of fisheries: through efficient fishing techniques and recovered fish stocks. Frontiers in Marine Science, 9, 817335. Link to source: https://doi.org/10.3389/fmars.2022.817335
Food and Agriculture Organization of the United Nations. (2018). The state of world fisheries and aquaculture. Food and Agriculture Organization of the United Nations. Link to source: https://openknowledge.fao.org/handle/20.500.14283/i9540en
Food and Agriculture Organization of the United Nations. (2024). The State of World Fisheries and Aquaculture 2024 – Blue Transformation in action. Food and Agriculture Organization of the United Nations. Link to source: https://openknowledge.fao.org/handle/20.500.14283/cd0683en
Gaines, S. D., Costello, C., Owashi, B., Mangin, T., Bone, J., Molinos, J. G., ... & Ovando, D. (2018). Improved fisheries management could offset many negative effects of climate change. Science Advances, 4(8), eaao1378. Link to source: https://doi.org/10.1126/sciadv.aao1378
Gephart, J. A., Henriksson, P. J., Parker, R. W., Shepon, A., Gorospe, K. D., Bergman, K., ... & Troell, M. (2021). Environmental performance of blue foods. Nature, 597(7876), 360-365. Link to source: https://doi.org/10.1038/s41586-021-03889-2
Gulbrandsen, O. (2012). Fuel savings for small fishing vessels. Food and Agriculture Organization of the United Nations. Link to source: https://www.fao.org/4/i2461e/i2461e.pdf
Hilborn, R., Amoroso, R., Collie, J., Hiddink, J. G., Kaiser, M. J., Mazor, T., ... & Suuronen, P. (2023). Evaluating the sustainability and environmental impacts of trawling compared to other food production systems. ICES Journal of Marine Science, 80(6), 1567–1579. Link to source: https://doi.org/10.1093/icesjms/fsad115
Hoegh-Guldberg, O., Caldeira, K., Chopin, T., Gaines, S., Haugan, P., Hemer, M., ... & Tyedmers, P. (2023). The ocean as a solution to climate change: five opportunities for action. In The blue compendium: From knowledge to action for a sustainable ocean economy (pp. 619–680). Cham: Springer International Publishing. Link to source: https://oceanpanel.org/wp-content/uploads/2023/09/Full-Report_Ocean-Climate-Solutions-Update-1.pdf
Johnson, T. (2009). Fuel-Saving Measures for Fishing Industry Vessels. University of Alaska Fairbanks, Alaska Sea Grant Marine Advisory Program. Link to source: https://alaskaseagrant.org/wp-content/uploads/2022/03/ASG-57PDF-Fuel-Saving-Measures-for.pdf
Ling, S. D., Johnson, C. R., Frusher, S. D., & Ridgway, K. (2009). Overfishing reduces resilience of kelp beds to climate-driven catastrophic phase shift. Proceedings of the National Academy of Sciences, 106(52), 22341–22345. Link to source: https://doi.org/10.1073/pnas.0907529106
Machado, F. L. V., Halmenschlager, V., Abdallah, P. R., da Silva Teixeira, G., & Sumaila, U. R. (2021). The relation between fishing subsidies and CO2 emissions in the fisheries sector. Ecological Economics, 185, 107057. Link to source: https://doi.org/10.1016/j.ecolecon.2021.107057
Parker, R. W., Blanchard, J. L., Gardner, C., Green, B. S., Hartmann, K., Tyedmers, P. H., & Watson, R. A. (2018). Fuel use and greenhouse gas emissions of world fisheries. Nature Climate Change, 8(4), 333–337. Link to source: https://doi.org/10.1038/s41558-018-0117-x
Pauly, D., Christensen, V., Dalsgaard, J., Froese, R., & Torres Jr, F. (1998). Fishing down marine food webs. Science, 279(5352), 860–863. Link to source: https://doi.org/10.1126/science.279.5352.860
Ritchie, H., & Roser, M. (2021). Fish and overfishing. Our World in Data. Link to source: https://ourworldindata.org/fish-and-overfishing
Sharma, R., Barange, M., Agostini, V., Barros, P., Gutierrez, N.L., Vasconcellos, M., Fernandez Reguera, D., Tiffay, C., & Levontin, P., (Eds.). (2025). Review of the state of world marine fishery resources – 2025. FAO Fisheries and Aquaculture Technical Paper, No. 721. Rome. FAO. Link to source: https://doi.org/10.4060/cd5538en
Sumaila, U. R., Ebrahim, N., Schuhbauer, A., Skerritt, D., Li, Y., Kim, H. S., ... & Pauly, D. (2019). Updated estimates and analysis of global fisheries subsidies. Marine Policy, 109, 103695. Link to source: https://doi.org/10.1016/j.marpol.2019.103695
Sumaila, U. R., & Tai, T. C. (2020). End overfishing and increase the resilience of the ocean to climate change. Frontiers in Marine Science, 7, 523. Link to source: https://doi.org/10.3389/fmars.2020.00523
United Nations Global Compact & World Wildlife Fund. (2022). Setting science-based targets in the seafood sector: Best practices to date. Link to source: https://unglobalcompact.org/library/6050
World Bank. (2017). The sunken billions revisited: Progress and challenges in global marine fisheries. World Bank Publications. Link to source: http://hdl.handle.net/10986/24056
Improved manure management refers to the use of impermeable covers and physical or chemical treatments applied during the storage and processing of wet manure. These techniques can reduce methane emissions under anaerobic storage conditions and nitrous oxide emissions under aerobic conditions. They offer multiple environmental benefits, including reduced air pollution, reduced nutrient leaching and eutrophication of downstream aquatic systems, and reduced demand for energy-intensive synthetic fertilizers. Disadvantages include a relatively small climate impact and, except for covers, high costs. Even at an optimistic level of adoption, the climate impact is unlikely to be globally meaningful (<0.1 Gt CO₂‑eq/yr ). Despite this modest climate impact, we conclude that Improve Manure Management is a “Worthwhile” solution.
Based on our analysis, improved manure management using impermeable covers and physical or chemical treatments will reduce emissions, although not by a globally meaningful amount. However, because these manure management techniques are broadly available, we conclude this climate solution is “Worthwhile.”
| Plausible | Could it work? | Yes |
|---|---|---|
| Ready | Is it ready? | Yes |
| Evidence | Are there data to evaluate it? | Yes |
| Effective | Does it consistently work? | Yes |
| Impact | Is it big enough to matter? | No |
| Risk | Is it risky or harmful? | No |
| Cost | Is it cheap? | ? |
Manure generated from industrial livestock production contains significant quantities of organic carbon and nitrogen. Under low-oxygen conditions, bacteria convert organic material in manure to methane through anaerobic decomposition. Liquid manure, particularly from pigs and cows, produces significant quantities of methane. In oxygen-rich conditions, organic nitrogen in manure undergoes chemical reactions to produce nitrous oxide. Once produced, these GHGs diffuse towards the surface of the manure storage tank, where they are emitted into the atmosphere.
Improved manure management interrupts the production or release of methane and nitrous oxide through a structural barrier, or physical or chemical treatment processes. Manure storage covers made from impermeable synthetic materials effectively prevent the release of GHGs, and can be utilized in conjunction with biogas systems for energy generation. Chemical treatments, such as acidification and the addition of additives, suppress microbial activity, thereby inhibiting methane and nitrous oxide production. Physical processes, such as aeration and temperature reduction, similarly limit optimal conditions for microbial growth. Separating the solids and liquids from manure can also reduce the potential for methane production, enabling more effective solutions such as composting and anaerobic digestion.
Available technologies for manure management are mature and market-ready. However, empirical evidence of their effectiveness for reducing methane emissions is limited. Pilot studies indicate high effectiveness of manure acidification, moderate effectiveness of impermeable synthetic covers, and low effectiveness of manure additives. Except for the use of natural and synthetic impermeable covers, the overall adoption of these techniques is low.
Improved manure management can provide environmental benefits by reducing air pollution, preventing nutrient leaching from organic solids that settle into sludge, mitigating eutrophication in downstream aquatic ecosystems, and preventing soil acidification. In the food system, manure management allows for better alignment between crop needs and natural fertilizer characteristics. Since hauling liquid manure is expensive, manure storage and treatment methods promote efficient nutrient cycling and reduce the need for energy-intensive synthetic fertilizers. Abated methane in manure also limits ground-level ozone production upon application, thereby improving crop yields.
At the farm scale, the wide range of treatment options allows for a high level of customization in the manure management process to achieve joint goals of nutrient management, revenue generation, and emission reductions. Covers also directly mitigate risks to farmworker health and safety from manure handling, and manure treatment can further limit exposure to irritants and noxious gases, improving the health of surrounding communities.
Compared to no treatment and other manure-related solutions, such as composting and anaerobic digesters, evidence for the effectiveness of impermeable covers and manure treatment technologies is limited. At realistic levels of adoption, improving manure management is unlikely to have a globally meaningful climate impact (<0.1 Gt CO₂‑eq/yr ). High costs are also a key barrier to wider adoption, ranging from US$110–145/t CO₂‑eq for synthetic covers to US$500–3,000/t CO₂‑eq for other treatments.
Ambikapathi, R., Periyasamy, D., Ramesh, P., Avudainayagam, S., Makoto, W., & Evgenios, A. (2023). Effect of ozone stress on crop productivity: A threat to food security. Environmental Research, 236, 116816. Link to source: https://doi.org/10.1016/j.envres.2023.116816
Ambrose, H. W., Dalby, F. R., Feilberg, A., & Kofoed, M. V. W. (2023). Additives and methods for the mitigation of methane emission from stored liquid manure. Biosystems Engineering, 229, 209–245. Link to source: https://doi.org/10.1016/j.biosystemseng.2023.03.015
Bijay, S., & Craswell, E. (2021). Fertilizers and nitrate pollution of surface and ground water: an increasingly pervasive global problem. SN Applied Sciences, 3(4). Link to source: https://www.doi.org/10.1007/s42452-021-04521-8
Fangueiro, D., Hjorth, M., & Gioelli, F. (2015). Acidification of animal slurry--a review. J Environ Manage, 149, 46–56. Link to source: https://www.doi.org/10.1016/j.jenvman.2014.10.001
FAO. (2023a). Methane emissions in livestock and rice systems – Sources, quantification, mitigation and metrics. Rome. Link to source: https://doi.org/10.4060/cc7607en
FAO. (2023b). Pathways towards lower emissions – A global assessment of the greenhouse gas emissions and mitigation options from livestock agrifood systems. Link to source: https://doi.org/10.4060/cc9029en
Grossi, G., Goglio, P., Vitali, A., & Williams, A. G. (2019). Livestock and climate change: Impact of livestock on climate and mitigation strategies. Anim Front, 9(1), 69-76. Link to source: https://doi.org/10.1093/af/vfy034
Harrison, M. T., Cullen, B. R., Mayberry, D. E., Cowie, A. L., Bilotto, F., Badgery, W. B., Liu, K., Davison, T., Christie, K. M., Muleke, A., & Eckard, R. J. (2021). Carbon myopia: The urgent need for integrated social, economic and environmental action in the livestock sector. Glob Chang Biol, 27(22), 5726–5761. Link to source: https://doi.org/10.1111/gcb.15816
Hegde, S., Searchinger, T., & Díaz, M. J. (2025). Opportunities for Methane Mitigation in Agriculture: Technological, Economic and Regulatory Considerations. World Resources Institute: Washington DC. Link to source: https://www.wri.org/research/opportunities-methane-mitigation-agriculture-technological-economic-regulatory
Hou, Y., Velthof, G. L., & Oenema, O. (2015). Mitigation of ammonia, nitrous oxide and methane emissions from manure management chains: a meta-analysis and integrated assessment. Glob Chang Biol, 21(3), 1293–1312. Link to source: https://doi.org/10.1111/gcb.12767
Kanter, D. R., & Brownlie, W. J. (2019). Joint nitrogen and phosphorus management for sustainable development and climate goals. Environmental Science & Policy, 92, 1–8. Link to source: https://doi.org/10.1016/j.envsci.2018.10.020
Kupper, T., Häni, C., Neftel, A., Kincaid, C., Bühler, M., Amon, B., & VanderZaag, A. (2020). Ammonia and greenhouse gas emissions from slurry storage - A review. Agriculture, Ecosystems and Environment, 300(106963). Link to source: https://doi.org/10.1016/j.agee.2020.106963
Mohankumar Sajeev, E. P., Winiwarter, W., & Amon, B. (2018). Greenhouse Gas and Ammonia Emissions from Different Stages of Liquid Manure Management Chains: Abatement Options and Emission Interactions. J Environ Qual, 47(1), 30–41. Link to source: https://doi.org/10.2134/jeq2017.05.0199
Montes, F., Meinen, R., Dell, C., Rotz, A., Hristov, A. N., Oh, J., . . . Dijkstra, J. (2013). SPECIAL TOPICS—Mitigation of methane and nitrous oxide emissions from animal operations: II. A review of manure management mitigation options. J. Anim. Sci, 91, 5070–5094. Link to source: https://doi.org/10.2527/jas.2013-6584
Mukherji, A., Arndt, C., Arango, J., Flintan, F., Derera, J., Francesconi, W., Jones, S. Loboguerrero, A. M., Merrey, D., Mockshell, J., Quintero, M., Mulat, D. G., Ringler, C., Ronchi, L., Sanchez, M. E. N., Sapkota, T., & Thilsted, S. (2023). Achieving agricultural breakthrough: A deep dive into seven technological areas. Montpellier, France. Retrieved from: Link to source: https://hdl.handle.net/10568/131852.
Niles, M. T., Wiltshire, S., Lombard, J., Branan, M., Vuolo, M., Chintala, R., & Tricarico, J. (2022). Manure management strategies are interconnected with complexity across U.S. dairy farms. PLoS One, 17(6), e0267731. Link to source: https://doi.org/10.1371/journal.pone.0267731
Nour, M. M., Field, W. E., Ni, J.-Q., & Cheng, Y.-H. (2021). Farm-Related Injuries and Fatalities Involving Children, Youth, and Young Workers during Manure Storage, Handling, and Transport. Journal of Agromedicine, 26(3), 323–333. Link to source: https://doi.org/10.1080/1059924X.2020.1795034
Overmeyer, V., Trimborn, M., Clemens, J., Holscher, R., & Buscher, W. (2023). Acidification of slurry to reduce ammonia and methane emissions: Deployment of a retrofittable system in fattening pig barns. J Environ Manage, 331, 117263. Link to source: https://doi.org/10.1016/j.jenvman.2023.117263
Park, J., Kang, T., Heo, Y., Lee, K., Kim, K., Lee, K., & Yoon, C. (2020). Evaluation of Short-Term Exposure Levels on Ammonia and Hydrogen Sulfide During Manure-Handling Processes at Livestock Farms. Saf Health Work, 11(1), 109–117. Link to source: https://doi.org/10.1016/j.shaw.2019.12.007
Sokolov, V., VanderZaag, A., Habtewold, J., Dunfield, K., Wagner-Riddle, C., Venkiteswaran, J. J., & Gordon, R. (2019). Greenhouse Gas Mitigation through Dairy Manure Acidification. J Environ Qual, 48(5), 1435–1443. Link to source: https://doi.org/10.2134/jeq2018.10.0355
VanderZaag, A., Amon, B., Bittman, S., & Kuczyński, T. (2015). Ammonia Abatement with Manure Storage and Processing Techniques. In Costs of Ammonia Abatement and the Climate Co-Benefits (pp. 75–112). Link to source: https://doi.org/10.1007/978-94-017-9722-1
Wang, Y., Dong, H., Zhu, Z., Gerber, P. J., Xin, H., Smith, P., Opio, C., Steinfeld, H., & Chadwick, D. (2017). Mitigating Greenhouse Gas and Ammonia Emissions from Swine Manure Management: A System Analysis. Environ Sci Technol, 51(8), 4503–4511. Link to source: https://doi.org/10.1021/acs.est.6b06430
Wyer, K. E., Kelleghan, D. B., Blanes-Vidal, V., Schauberger, G., & Curran, T. P. (2022). Ammonia emissions from agriculture and their contribution to fine particulate matter: A review of implications for human health. J Environ Manage, 323, 116285. Link to source: https://doi.org/10.1016/j.jenvman.2022.116285
Rice production is a significant source of methane emissions and a minor source of nitrous oxide emissions. Most rice production occurs in flooded fields called paddies, where anaerobic conditions trigger high levels of methane production. This solution includes two related practices that each reduce emissions from paddy rice production: noncontinuous flooding and nutrient management. Noncontinuous flooding is a water management technique that reduces the amount of time rice paddy soils spend fully saturated, thereby reducing methane. Unfortunately, noncontinuous flooding increases nitrous oxide emissions. Nutrient management helps to address this challenge by controlling the timing, amount, and type of fertilization to maximize plant uptake and minimize nitrous oxide emissions.
Rice is a staple crop of critical importance, occupying 11% of global cropland (FAOstat 2025). Rice production has higher GHG emissions than most crop production, accounting for 9% of all anthropogenic methane and 10% of cropland nitrous oxide (Wang et al., 2020). Nabuurs et al. (2022) found methane emissions from global rice production to be 0.8–1.0 Gt CO₂‑eq/yr and growing 0.4% annually.
Rice paddy systems are fields with berms and plumbing to permit the flooding of rice for the production periods, which helps with weed and pest control (rice thrives in flooded conditions, though it does not require them). Paddy rice is the main source of methane from rice production. Upland rice is grown outside of paddies and does not produce significant methane emissions, so we excluded it from this analysis. Irrigated paddies are provided with irrigation water, while rain-fed paddies are only filled by rainfall and runoff (Raffa, 2021). For this analysis, we considered both irrigated and rain-fed paddies.
Flooded rice paddies encourage the production of methane by microbes. Conventional paddy rice production uses continuous flooding, in which the paddy is flooded for the full rice production period. Several approaches can reduce methane, with the most widespread being noncontinuous flooding. This is a collection of practices (such as alternate wetting and drying) that drain the fields one or more times during the rice production period. As a result, the paddy spends less time in its methane-producing state. This can be done without reducing rice yields in many, but not all, cases, and also significantly reduces irrigation water use (Bo et al., 2022). Impacts on yields depend on soils, climate, and other variables (Cheng et al., 2022).
A major drawback to noncontinuous flooding is that it increases nitrous oxide emissions from fertilizer compared to continuous flooding. High nitrogen levels in flooded paddies encourage the growth of bacteria that produce methane, reduce the natural breakdown of methane, and facilitate emissions of nitrous oxide to the atmosphere (Li et al., 2024). The effect is small compared to the mitigated emissions from methane reduction (Jiang et al., 2019), but remains serious. Use of nutrient management techniques, such as controlling fertilizer amount, type (e.g., controlled-release urea), timing, and application techniques (e.g., deep fertilization), can reduce these emissions. This is in part because nitrogen fertilizers are often overapplied, leaving room to increase efficiency without reducing rice yields (Hergoualc’h et al., 2019; Li et al., 2024).
Other practices also show potential but were not included in our analysis. These include the application of biochar to rice paddies and the use of rice cultivars that produce fewer emissions (Qian et al., 2023). Other approaches include saturated soil culture, System of Rice Intensification (“SRI”), ground-cover systems, raised beds, and improved irrigation and paddy infrastructure (Surendran et al., 2021).
Note that some practices, such as incorporating rice straw or the use of compost or manure, can increase nitrous oxide emissions (Li et al., 2024).
There is also evidence that, under some circumstances, noncontinuous flooding can sequester soil organic carbon by increasing soil organic matter. However, there are not enough data available to quantify this (Qian et al., 2023). Indeed, one meta-analysis found that noncontinuous flooding can actually lead to a decrease in soil organic carbon (Livsey et al., 2019). One complication is that many production areas plant rice two or even three times per year, and data are typically presented on a per-harvest or even per-flooded day basis. To overcome this challenge, we use data on the percentage of global irrigated rice land in single, double, and triple cropping from Carlson et al. (2016) to create weighted average values as appropriate.
Adalibieke, W., Cui, X., Cai, H., You, L., & Zhou, F. (2023). Global crop-specific nitrogen fertilization dataset in 1961–2020. Scientific Data, 10(1), Article 617. Link to source: https://doi.org/10.1038/s41597-023-02526-z
Alauddin, M., Rashid Sarker, Md. A., Islam, Z., & Tisdell, C. (2020). Adoption of alternate wetting and drying (AWD) irrigation as a water-saving technology in Bangladesh: Economic and environmental considerations. Land Use Policy, 91, Article 104430. Link to source: https://doi.org/10.1016/j.landusepol.2019.104430
Bijay-Singh, & Craswell, E. (2021). Fertilizers and nitrate pollution of surface and ground water: An increasingly pervasive global problem. SN Applied Sciences, 3(4), Article 518. Link to source: https://doi.org/10.1007/s42452-021-04521-8
Bo, Y., Jägermeyr, J., Yin, Z., Jiang, Y., Xu, J., Liang, H., & Zhou, F. (2022). Global benefits of non‐continuous flooding to reduce greenhouse gases and irrigation water use without rice yield penalty. Global Change Biology, 28(11), 3636–3650. Link to source: https://doi.org/10.1111/gcb.16132
Carlson, K. M., Gerber, J. S., Mueller, N. D., Herrero, M., MacDonald, G. K., Brauman, K. A., Havlik, P., O’Connell, C.S., Johnson, J.A., Saatchi, S., & West, P.C. (2017). Greenhouse gas emissions intensity of global croplands. Nature Climate Change, 7(1), 63–68. Link to source: https://doi.org/10.1038/nclimate3158
Carrijo, D. R., Lundy, M. E., & Linquist, B. A. (2017). Rice yields and water use under alternate wetting and drying irrigation: A meta-analysis. Field Crops Research, 203, 173–180. Link to source: https://doi.org/10.1016/j.fcr.2016.12.002
Cheng, H., Shu, K., Zhu, T., Wang, L., Liu, X., Cai, W., Qi, Z., & Feng, S. (2022). Effects of alternate wetting and drying irrigation on yield, water and nitrogen use, and greenhouse gas emissions in rice paddy fields. Journal of Cleaner Production, 349, Article 131487. Link to source: https://doi.org/10.1016/j.jclepro.2022.131487
Cui, X., Zhou, F., Ciais, P., Davidson, E. A., Tubiello, F. N., Niu, X., Ju, X., Canadell, J.P., Bouwman, A.F., Jackson, R.B., Mueller, N.D., Zheng, X., Kanter, D.R., Tian, H., Adalibieke, W., Bo, Y., Wang, Q., Zhan, X., & Zhu, D. (2021). Global mapping of crop-specific emission factors highlights hotspots of nitrous oxide mitigation. Nature Food, 2(11), 886–893. Link to source: https://doi.org/10.1038/s43016-021-00384-9
Damania, R., Polasky, S., Ruckelshaus, M., Russ, J., Chaplin-Kramer, R., Gerber, J., Hawthorne, P., Heger, M.P., Mamun, S., Amann, M., Ruta, G., & Wagner, F. (2023). Nature's Frontiers: Achieving Sustainability, Efficiency, and Prosperity with Natural Capital. World Bank Publications. Link to source: https://openknowledge.worldbank.org/entities/publication/855c2e15-c88b-4c04-a2e5-2d98c25b8eca
Enriquez, Y., Yadav, S., Evangelista, G. K., Villanueva, D., Burac, M. A., & Pede, V. (2021). Disentangling challenges to scaling alternate wetting and drying technology for rice cultivation: Distilling lessons from 20 years of experience in the Philippines. Frontiers in Sustainable Food Systems, 5, 1-16. Link to source: https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2021.675818/full
Food and Agriculture Organization of the United Nations. (2025). FAOSTAT Statistical Database, [Rome]: FAO, 1997. Link to source: https://www.fao.org/faostat/en/
Gerber, J. S., Ray, D. K., Makowski, D., Butler, E. E., Mueller, N. D., West, P. C., Johnson, J. A., Polasky, S., Samberg, L. H., & Siebert, S. (2024). Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation. Nature Food, 5(2), 125–135. Link to source: https://doi.org/10.1038/s43016-023-00913-8
Gu, B., Zhang, X., Lam, S. K., Yu, Y., Van Grinsven, H. J., Zhang, S., Wang, X., Bodirsky, B.L., Wang, S., Duan, J., Ren, C., Bouwman, L., de Vries, W., Xu, J., & Chen, D. (2023). Cost-effective mitigation of nitrogen pollution from global croplands. Nature, 613(7942), 77–84. Link to source: https://doi.org/10.1038/s41586-022-05481-8
Hergoualc’h, K., Akiyama, H., Bernoux, M., Chirinda, N., del Prado, A., Kasimir, A., MacDonald, J.D., Ogle, S.M., Regina, K., van der Weerden, T.J. (2019) 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 11: N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application. Cambridge University Press. Link to source: https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O%26CO2.pdf
Ishfaq, M., Farooq, M., Zulfiqar, U., Hussain, S., Akbar, N., Nawaz, A., & Anjum, S. A. (2020). Alternate wetting and drying: A water-saving and ecofriendly rice production system. Agricultural Water Management, 241, Article 106363. Link to source: https://doi.org/10.1016/j.agwat.2020.106363
Jameel, Y., Mozumder, M. R. H., Van Geen, A., & Harvey, C. F. (2021). Well‐Switching to Reduce Arsenic Exposure in Bangladesh: Making the Most of Inaccurate Field Kit Measurements. GeoHealth, 5(12), Article e2021GH000464. Link to source: https://doi.org/10.1029/2021GH000464
Jiang, Y., Carrijo, D., Huang, S., Chen, J., Balaine, N., Zhang, W., Van Groenigen, K.J. & Linquist, B. (2019). Water management to mitigate the global warming potential of rice systems: A global meta-analysis. Field Crops Research, 234, 47–54. Link to source: https://doi.org/10.1016/j.fcr.2019.02.101
Lampayan, R. M., Rejesus, R. M., Singleton, G. R., & Bouman, B. A. (2015). Adoption and economics of alternate wetting and drying water management for irrigated lowland rice. Field Crops Research, 170, 95–108. Link to source: https://doi.org/10.1016/j.fcr.2014.10.013
Li, L., Huang, Z., Mu, Y., Song, S., Zhang, Y., Tao, Y., & Nie, L. (2024). Alternate wetting and drying maintains rice yield and reduces global warming potential: A global meta-analysis. Field Crops Research, 318, Article 109603. Link to source: https://doi.org/10.1016/j.fcr.2024.109603
Liang, K., Zhong, X., Fu, Y., Hu, X., Li, M., Pan, J., Liu, Y., Hu, R., & Ye, Q. (2023). Mitigation of environmental N pollution and greenhouse gas emission from double rice cropping system with a new alternate wetting and drying irrigation regime coupled with optimized N fertilization in South China. Agricultural Water Management, 282, Article 108282. Link to source: https://doi.org/10.1016/j.agwat.2023.108282
Linquist, B. A., Adviento-Borbe, M. A., Pittelkow, C. M., van Kessel, C., & van Groenigen, K. J. (2012). Fertilizer management practices and greenhouse gas emissions from rice systems: a quantitative review and analysis. Field Crops Research, 135, 10–21. Link to source: https://doi.org/10.1016/j.fcr.2012.06.007
Liang, X. Q., Chen, Y. X., Nie, Z. Y., Ye, Y. S., Liu, J., Tian, G. M., Wang, G. H., & Tuong, T. P. (2013). Mitigation of nutrient losses via surface runoff from rice cropping systems with alternate wetting and drying irrigation and site-specific nutrient management practices. Environmental Science and Pollution Research, 20(10), 6980–6991. Link to source: https://doi.org/10.1007/s11356-012-1391-1
Livsey, J., Kätterer, T., Vico, G., Lyon, S. W., Lindborg, R., Scaini, A., Da, C.T,. & Manzoni, S. (2019). Do alternative irrigation strategies for rice cultivation decrease water footprints at the cost of long-term soil health? Environmental Research Letters, 14(7), 074011. Link to source: https://doi.org/10.1088/1748-9326/ab2108
Ludemann, C. I., Gruere, A., Heffer, P., & Dobermann, A. (2022). Global data on fertilizer use by crop and by country. Scientific data, 9(1), 1–8. Link to source: https://doi.org/10.1038/s41597-022-01592-z
Nabuurs, G-J., R. Mrabet, A. Abu Hatab, M. Bustamante, H. Clark, P. Havl.k, J. House, C. Mbow, K.N. Ninan, A. Popp, S. Roe, B. Sohngen, S. Towprayoon, 2022: Agriculture, Forestry and Other Land Uses (AFOLU). In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.009
Ogle, S. M., Wakelin, S. J., Buendia, L., McConkey, B., Baldock, J., Akiyama, H., ... & Zheng, X. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 4: Cropland. Cambridge University Press. Link to source: https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/
Qian, H., Zhu, X., Huang, S., Linquist, B., Kuzyakov, Y., Wassmann, R., ... & Jiang, Y. (2023). Greenhouse gas emissions and mitigation in rice agriculture. Nature Reviews Earth & Environment, 4(10), 716–732. Link to source: https://doi.org/10.1038/s43017-023-00482-1
Raffa, D.W. & Morales-Abubakar, A. L. (2021) Soil Health for Paddy Rice. Food and Agriculture Organization of the United Nations. Link to source: https://openknowledge.fao.org/server/api/core/bitstreams/fcd04aae-0389-411b-8a47-a622b23d642f/content
Roe, S., Streck, C., Beach, R., Busch, J., Chapman, M., Daioglou, V., Deppermann, A., Doelman, J., Emmet-Booth, J., Engelmann, J., Fricko, O., Frischmann, C., Funk, J., Grassi, G., Griscom, B., Havlik, P., Hanssen, S., Humpenöder, F., Landholm, D., LOmax, G., Lehmann, J., Mesnildrey, L., Nabuurrs, G., Popp, A., Rivard, C., Sanderman, J., Sohngen, B., Smith, P., Stehfest, E., Woolf, D., & Lawrence, D. (2021). Land‐based measures to mitigate climate change: Potential and feasibility by country. Global Change Biology, 27(23), 6025–6058. Link to source: https://doi.org/10.1111/gcb.15873
Salmon, J. M., Friedl, M. A., Frolking, S., Wisser, D., & Douglas, E. M. (2015). Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data. International Journal of Applied Earth Observation and Geoinformation, 38, 321–334. Link to source: https://doi.org/10.1016/j.jag.2015.01.014
Surendran, U., Raja, P., Jayakumar, M., & Subramoniam, S. R. (2021). Use of efficient water saving techniques for production of rice in India under climate change scenario: A critical review. Journal of Cleaner Production, 309. Link to source: https://doi.org/10.1016/j.jclepro.2021.127272
Suwanmaneepong, S., Kultawanich, K., Khurnpoon, L., Sabaijai, P. E., Cavite, H. J., Llones, C., Lepcha, N., & Kerdsriserm, C. (2023). Alternate Wetting and Drying as Water-Saving Technology: An Adoption Intention in the Perspective of Good Agricultural Practices (GAP) Suburban Rice Farmers in Thailand. Water, 15(3), Article 402. Link to source: https://doi.org/10.3390/w15030402
Xia, L., Lam, S. K., Chen, D., Wang, J., Tang, Q., & Yan, X. (2017). Can knowledge‐based N management produce more staple grain with lower greenhouse gas emission and reactive nitrogen pollution? A meta‐analysis. Global change biology, 23(5), 1917–1925. Link to source: https://doi.org/10.1111/gcb.13455
Zhang, W., Yu, J., Xu, Y., Wang, Z., Liu, L., Zhang, H., Gu, J., Zhang, J., & Yang, J. (2021). Alternate wetting and drying irrigation combined with the proportion of polymer-coated urea and conventional urea rates increases grain yield, water and nitrogen use efficiencies in rice. Field Crops Research, 268, Article 108165. Link to source: https://doi.org/10.1016/j.fcr.2021.108165
Zhang, Y., Wang, W., Li, S., Zhu, K., Hua, X., Harrison, M.T., Liu, K., Yang, J., Liu, L, & Chan, Y. (2023). Integrated management approaches enabling sustainable rice production under alternate wetting and drying irrigation. Agricultural Water Management, 281. Link to source: https://doi.org/10/1016/j.agwat.2023.108265
Eric Toensmeier
Ruthie Burrows, Ph.D.
James Gerber, Ph.D.
Yusuf Jameel, Ph.D.
Daniel Jasper
Alex Sweeney
Aiyana Bodi
James Gerber, Ph.D.
Hannah Henkin
Zoltan Nagy, Ph.D.
Ted Otte
Paul C. West, Ph.D.
Methane Reduction
We calculated per-hectare methane emissions using Intergovernmental Panel on Climate Change (IPCC) methodology (Ogle et. al, 2019). To develop regional emissions per rice harvest, we multiplied standard regional daily baseline emissions by standard cultivation period lengths, then multiplied by the mean scaling factor for noncontinuous flooding systems. However, the total number of rice harvests per year ranged from one to three. Carlson et al. (2016) reported a global figure of harvests on rice fields: 42% were harvested once, 50% were harvested twice, and 8% were harvested three times. We used this to develop a weighted average methane emissions figure for each region. National effectiveness ranged from 1.55 to 3.29 t CO₂‑eq /ha/yr (Table 1a).
Nitrous Oxide Reduction
Using data from Adalibieke et al. (2024) and Gerber et al. (2024), we calculated the current country-level rate of nitrogen application per hectare and a target rate reflecting improved efficiency through nutrient management. For a full methodology, see the Appendix.
In noncontinuously flooded systems, nitrous oxide emissions are 1.66 times higher per t of nitrogen applied (Hergoualc’h et al., 2019). Using the different emissions factors, we calculated total nitrous oxide emissions for 1) flooded rice with current nitrogen application rates, and 2) noncontinuously flooded rice with target nitrogen application rates.
The effectiveness of nutrient management for each country with over 100,000 ha of rice production ranged from –0.48 to 0.11 t CO₂‑eq /ha/yr (Table 1).
Combined Reduction
Combined effectiveness of methane and nitrous oxide reduction was 1.49–3.39 t CO₂‑eq /ha/yr (Table 1).
Table 1a. Combined effectiveness at reducing emissions, by country, for noncontinuous flooding with nutrient management.
Unit: t CO₂‑eq /ha/yr
| Afghanistan | 1.63 |
| Argentina | 2.70 |
| Bangladesh | 1.63 |
| Benin | 2.30 |
| Bolivia (Plurinational State of) | 2.70 |
| Brazil | 2.70 |
| Burkina Faso | 2.30 |
| Cambodia | 2.13 |
| Cameroon | 2.30 |
| Chad | 2.30 |
| China | 2.48 |
| Colombia | 2.70 |
| Côte d'Ivoire | 2.30 |
| Democratic People's Republic of Korea | 2.48 |
| Democratic Republic of the Congo | 2.30 |
| Dominican Republic | 2.70 |
| Ecuador | 2.70 |
| Egypt | 2.30 |
| Ghana | 2.30 |
| Guinea | 2.30 |
| Guinea-Bissau | 2.30 |
| Guyana | 2.70 |
| India | 1.63 |
| Indonesia | 2.13 |
| Iran (Islamic Republic of) | 3.29 |
| Italy | 3.29 |
| Japan | 2.48 |
| Lao People's Democratic Republic | 2.13 |
| Liberia | 2.30 |
| Madagascar | 2.30 |
| Malaysia | 2.13 |
| Mali | 2.30 |
| Mozambique | 2.30 |
| Myanmar | 2.13 |
| Nepal | 1.63 |
| Nigeria | 2.30 |
| Pakistan | 1.63 |
| Paraguay | 2.70 |
| Peru | 2.70 |
| Philippines | 2.13 |
| Republic of Korea | 2.48 |
| Russian Federation | 3.29 |
| Senegal | 2.30 |
| Sierra Leone | 2.30 |
| Sri Lanka | 1.63 |
| Thailand | 2.13 |
| Turkey | 3.29 |
| Uganda | 2.70 |
| United Republic of Tanzania | 2.30 |
| United States of America | 1.55 |
| Uruguay | 2.70 |
| Venezuela (Bolivarian Republic of) | 2.70 |
| Vietnam | 2.13 |
Unit: t CO₂‑eq /ha/yr
| Afghanistan | 0.03 |
| Argentina | 0.07 |
| Bangladesh | 0.06 |
| Benin | 0.03 |
| Bolivia (Plurinational State of) | 0.00 |
| Brazil | 0.00 |
| Burkina Faso | –0.02 |
| Cambodia | 0.01 |
| Cameroon | 0.00 |
| Chad | 0.01 |
| China | 0.01 |
| Colombia | –0.07 |
| Côte d'Ivoire | 0.02 |
| Democratic People's Republic of Korea | 0.02 |
| Democratic Republic of the Congo | 0.01 |
| Dominican Republic | –0.16 |
| Ecuador | –0.08 |
| Egypt | –0.15 |
| Ghana | 0.05 |
| Guinea | 0.01 |
| Guinea-Bissau | 0.01 |
| Guyana | –0.06 |
| India | –0.02 |
| Indonesia | 0.11 |
| Iran (Islamic Republic of) | –0.05 |
| Italy | 0.00 |
| Japan | 0.07 |
| Lao People's Democratic Republic | 0.02 |
| Liberia | 0.02 |
| Madagascar | 0.00 |
| Malaysia | –0.01 |
| Mali | –0.03 |
| Mozambique | 0.01 |
| Myanmar | 0.04 |
| Nepal | 0.04 |
| Nigeria | 0.01 |
| Pakistan | –0.04 |
| Paraguay | 0.01 |
| Peru | 0.09 |
| Philippines | 0.00 |
| Republic of Korea | 0.00 |
| Russian Federation | 0.04 |
| Senegal | –0.04 |
| Sierra Leone | 0.02 |
| Sri Lanka | 0.02 |
| Thailand | –0.03 |
| Turkey | 0.10 |
| Uganda | 0.00 |
| United Republic of Tanzania | 0.04 |
| United States of America | –0.05 |
| Uruguay | 0.03 |
| Venezuela (Bolivarian Republic of) | –0.48 |
| Vietnam | 0.00 |
Unit: t CO₂‑eq /ha rice paddies/yr
| Afghanistan | 1.67 |
| Argentina | 2.77 |
| Bangladesh | 1.69 |
| Benin | 2.34 |
| Bolivia (Plurinational State of) | 2.70 |
| Brazil | 2.70 |
| Burkina Faso | 2.28 |
| Cambodia | 2.15 |
| Cameroon | 2.30 |
| Chad | 2.32 |
| China | 2.48 |
| Colombia | 2.63 |
| Côte d'Ivoire | 2.32 |
| Democratic People's Republic of Korea | 2.50 |
| Democratic Republic of the Congo | 2.31 |
| Dominican Republic | 2.54 |
| Ecuador | 2.62 |
| Egypt | 2.16 |
| Ghana | 2.35 |
| Guinea | 2.32 |
| Guinea-Bissau | 2.32 |
| Guyana | 2.63 |
| India | 1.61 |
| Indonesia | 2.24 |
| Iran (Islamic Republic of) | 3.24 |
| Italy | 3.29 |
| Japan | 2.54 |
| Lao People's Democratic Republic | 2.15 |
| Liberia | 2.32 |
| Madagascar | 2.31 |
| Malaysia | 2.13 |
| Mali | 2.28 |
| Mozambique | 2.32 |
| Myanmar | 2.17 |
| Nepal | 1.67 |
| Nigeria | 2.32 |
| Pakistan | 1.59 |
| Paraguay | 2.71 |
| Peru | 2.79 |
| Philippines | 2.14 |
| Republic of Korea | 2.47 |
| Russian Federation | 3.33 |
| Senegal | 2.27 |
| Sierra Leone | 2.32 |
| Sri Lanka | 1.65 |
| Thailand | 2.10 |
| Turkey | 3.39 |
| Uganda | 2.31 |
| United Republic of Tanzania | 2.35 |
| United States of America | 1.49 |
| Uruguay | 2.72 |
| Venezuela (Bolivarian Republic of) | 2.22 |
| Vietnam | 2.13 |
Table 1b. Combined effectiveness at reducing emissions, by country, for noncontinuous flooding with nutrient management.
Unit: t CO₂‑eq /ha rice paddies/yr
| Afghanistan | 4.75 |
| Argentina | 7.85 |
| Bangladesh | 4.75 |
| Benin | 6.71 |
| Bolivia (Plurinational State of) | 7.85 |
| Brazil | 7.85 |
| Burkina Faso | 6.71 |
| Cambodia | 6.21 |
| Cameroon | 6.71 |
| Chad | 6.71 |
| China | 7.20 |
| Colombia | 7.85 |
| Côte d'Ivoire | 6.71 |
| Democratic People's Republic of Korea | 7.20 |
| Democratic Republic of the Congo | 6.71 |
| Dominican Republic | 7.85 |
| Ecuador | 7.85 |
| Egypt | 6.71 |
| Ghana | 6.71 |
| Guinea | 6.71 |
| Guinea-Bissau | 6.71 |
| Guyana | 7.85 |
| India | 4.75 |
| Indonesia | 6.21 |
| Iran (Islamic Republic of) | 9.57 |
| Italy | 9.57 |
| Japan | 7.20 |
| Lao People's Democratic Republic | 6.21 |
| Liberia | 6.71 |
| Madagascar | 6.71 |
| Malaysia | 6.21 |
| Mali | 6.71 |
| Mozambique | 6.71 |
| Myanmar | 6.21 |
| Nepal | 4.75 |
| Nigeria | 6.71 |
| Pakistan | 4.75 |
| Paraguay | 7.85 |
| Peru | 7.85 |
| Philippines | 6.21 |
| Republic of Korea | 7.20 |
| Russian Federation | 9.57 |
| Senegal | 6.71 |
| Sierra Leone | 6.71 |
| Sri Lanka | 4.75 |
| Thailand | 6.21 |
| Turkey | 9.57 |
| Uganda | 6.71 |
| United Republic of Tanzania | 6.71 |
| United States of America | 4.51 |
| Uruguay | 7.85 |
| Venezuela (Bolivarian Republic of) | 7.85 |
| Vietnam | 6.21 |
Unit: t CO₂‑eq /ha rice paddies/yr
| Afghanistan | 0.03 |
| Argentina | 0.07 |
| Bangladesh | 0.06 |
| Benin | 0.03 |
| Bolivia (Plurinational State of) | 0.00 |
| Brazil | 0.00 |
| Burkina Faso | 0.02 |
| Cambodia | 0.01 |
| Cameroon | 0.00 |
| Chad | 0.01 |
| China | 0.01 |
| Colombia | –0.07 |
| Côte d'Ivoire | 0.02 |
| Democratic People's Republic of Korea | 0.02 |
| Democratic Republic of the Congo | 0.01 |
| Dominican Republic | 0.16 |
| Ecuador | –0.08 |
| Egypt | –0.15 |
| Ghana | 0.05 |
| Guinea | 0.01 |
| Guinea-Bissau | 0.01 |
| Guyana | –0.06 |
| India | –0.02 |
| Indonesia | 0.11 |
| Iran (Islamic Republic of) | –0.05 |
| Italy | 0.00 |
| Japan | 0.07 |
| Lao People's Democratic Republic | 0.02 |
| Liberia | 0.02 |
| Madagascar | 0.00 |
| Malaysia | –0.01 |
| Mali | –0.03 |
| Mozambique | 0.01 |
| Myanmar | 0.04 |
| Nepal | 0.04 |
| Nigeria | 0.01 |
| Pakistan | –0.04 |
| Paraguay | 0.01 |
| Peru | 0.09 |
| Philippines | 0.00 |
| Republic of Korea | 0.00 |
| Russian Federation | 0.04 |
| Senegal | –0.04 |
| Sierra Leone | 0.02 |
| Sri Lanka | 0.02 |
| Thailand | –0.03 |
| Turkey | 0.10 |
| Uganda | 0.00 |
| United Republic of Tanzania | 0.04 |
| United States of America | –0.05 |
| Uruguay | 0.03 |
| Venezuela (Bolivarian Republic of) | –0.48 |
| Vietnam | 0.00 |
Unit: t CO₂‑eq /ha rice paddies/yr
| Afghanistan | 4.78 |
| Argentina | 7.93 |
| Bangladesh | 4.81 |
| Benin | 6.74 |
| Bolivia (Plurinational State of) | 7.85 |
| Brazil | 7.85 |
| Burkina Faso | 6.68 |
| Cambodia | 6.22 |
| Cameroon | 6.71 |
| Chad | 6.72 |
| China | 7.21 |
| Colombia | 7.21 |
| Côte d'Ivoire | 6.73 |
| Democratic People's Republic of Korea | 7.23 |
| Democratic Republic of the Congo | 6.71 |
| Dominican Republic | 7.69 |
| Ecuador | 7.77 |
| Egypt | 6.56 |
| Ghana | 6.76 |
| Guinea | 6.72 |
| Guinea-Bissau | 6.72 |
| Guyana | 7.79 |
| India | 4.73 |
| Indonesia | 6.31 |
| Iran (Islamic Republic of) | 9.52 |
| Italy | 9.57 |
| Japan | 7.27 |
| Lao People's Democratic Republic | 6.23 |
| Liberia | 6.72 |
| Madagascar | 6.71 |
| Malaysia | 6.20 |
| Mali | 6.20 |
| Mozambique | 6.72 |
| Myanmar | 6.25 |
| Nepal | 4.79 |
| Nigeria | 6.72 |
| Pakistan | 4.71 |
| Paraguay | 7.86 |
| Peru | 7.95 |
| Philippines | 6.21 |
| Republic of Korea | 7.20 |
| Russian Federation | 9.61 |
| Senegal | 6.67 |
| Sierra Leone | 6.73 |
| Sri Lanka | 4.77 |
| Thailand | 6.18 |
| Turkey | 9.67 |
| Uganda | 6.71 |
| United Republic of Tanzania | 6.75 |
| United States of America | 4.45 |
| Uruguay | 7.88 |
| Venezuela (Bolivarian Republic of) | 7.38 |
| Vietnam | 6.20 |
For conventional paddy rice, we assumed an initial cost of US$0 because many millions of hectares of paddies are already in place (Table 2). We used regional per-hectare average profits from Damania et al. (2024) as the source for net profit per year. Because the initial cost per hectare is US$0, the net cost per hectare is the negative of the per-hectare annual profit.
Table 2. Net cost and profit of conventional paddy rice by region in 2023.
Unit: US$/ha rice paddies
| Africa | 0.00 |
| East Asia | 0.00 |
| Europe | 0.00 |
| North America | 0.00 |
| South America | 0.00 |
| South Asia | 0.00 |
| Southeast Asia | 0.00 |
Unit: US$/ha rice paddies/yr
| Africa | 457.34 |
| East Asia | 543.67 |
| Europe | 585.43 |
| North America | 356.27 |
| South America | 285.69 |
| South Asia | 488.85 |
| Southeast Asia | 322.13 |
Unit: US$/ha rice paddies/yr
| Africa | -457.34 |
| East Asia | -543.67 |
| Europe | -585.43 |
| North America | -356.27 |
| South America | -285.69 |
| South Asia | -488.85 |
| Southeast Asia | -322.13 |
For noncontinuous flooding, we assumed an initial cost of US$0 because no new inputs or changes to paddy infrastructure are required in most cases. Median impact on net profit was an increase of 17% based on nine data points from seven sources. National results are shown in Table 3.
We assumed nutrient management has an initial cost of US$0 because in many cases, nutrient management begins with reducing the overapplication of fertilizer. Here we used the mean value from Gu et al. (2023), a savings of US$507.8/t nitrogen. We used our national-level data on overapplication of nitrogen to calculate savings per hectare. National results are shown in Table 3.
Combined Net Profit per Hectare
Net profit per hectare varies by country due to regional and some country-specific variables. Country-by-country results are shown in Table 3.
Net Net Cost Compared to Conventional Paddy Rice
Net net cost varies by country. Country-by-country results are shown in Table 3.
Table 3. Net cost and profit of noncontinuous flooding with nutrient management by region.
Unit: US$/ha rice paddies
| Afghanistan | 0.00 |
| Argentina | 0.00 |
| Bangladesh | 0.00 |
| Benin | 0.00 |
| Bolivia (Plurinational State of) | 0.00 |
| Brazil | 0.00 |
| Burkina Faso | 0.00 |
| Cambodia | 0.00 |
| Cameroon | 0.00 |
| Chad | 0.00 |
| China | 0.00 |
| Colombia | 0.00 |
| Cote d'Ivoire | 0.00 |
| Democratic People's Republic of Korea | 0.00 |
| Democratic Republic of the Congo | 0.00 |
| Dominican Republic | 0.00 |
| Ecuador | 0.00 |
| Egypt | 0.00 |
| Ghana | 0.00 |
| Guinea | 0.00 |
| Guinea–Bissau | 0.00 |
| Guyana | 0.00 |
| India | 0.00 |
| Indonesia | 0.00 |
| Iran (Islamic Republic of) | 0.00 |
| Italy | 0.00 |
| Japan | 0.00 |
| Lao People's Democratic Republic | 0.00 |
| Liberia | 0.00 |
| Madagascar | 0.00 |
| Malaysia | 0.00 |
| Mali | 0.00 |
| Mozambique | 0.00 |
| Myanmar | 0.00 |
| Nepal | 0.00 |
| Nigeria | 0.00 |
| Pakistan | 0.00 |
| Paraguay | 0.00 |
| Peru | 0.00 |
| Philippines | 0.00 |
| Republic of Korea | 0.00 |
| Russian Federation | 0.00 |
| Senegal | 0.00 |
| Sierra Leone | 0.00 |
| Sri Lanka | 0.00 |
| Thailand | 0.00 |
| Turkey | 0.00 |
| Uganda | 0.00 |
| United Republic of Tanzania | 0.00 |
| United States of America | 0.00 |
| Uruguay | 0.00 |
| Venezuela (Bolivarian Republic of) | 0.00 |
| Vietnam | 0.00 |
Non-continuous flooding and nutrient management.
Unit: US$/ha rice paddies/yr
| Afghanistan | 573.4 |
| Argentina | 354.8 |
| Bangladesh | 576.7 |
| Benin | 535.1 |
| Bolivia (Plurinational State of) | 354.1 |
| Brazil | 363.4 |
| Burkina Faso | 553.3 |
| Cambodia | 377.8 |
| Cameroon | 543.7 |
| Chad | 535.1 |
| China | 675.1 |
| Colombia | 397.7 |
| Cote d'Ivoire | 535.8 |
| Democratic People's Republic of Korea | 654.6 |
| Democratic Republic of the Congo | 535.6 |
| Dominican Republic | 428.4 |
| Ecuador | 390.3 |
| Egypt | 802.2 |
| Ghana | 535.5 |
| Guinea | 538.5 |
| Guinea–Bissau | 539.2 |
| Guyana | 382.0 |
| India | 607.9 |
| Indonesia | 382.3 |
| Iran (Islamic Republic of) | 726.7 |
| Italy | 567.9 |
| Japan | 636.0 |
| Lao People's Democratic Republic | 377.0 |
| Liberia | 535.3 |
| Madagascar | 535.0 |
| Malaysia | 401.2 |
| Mali | 561.0 |
| Mozambique | 535.5 |
| Myanmar | 380.7 |
| Nepal | 575.2 |
| Nigeria | 537.1 |
| Pakistan | 610.0 |
| Paraguay | 385.9 |
| Peru | 351.7 |
| Philippines | 399.5 |
| Republic of Korea | 678.2 |
| Russian Federation | 475.2 |
| Senegal | 569.9 |
| Sierra Leone | 535.1 |
| Sri Lanka | 591.1 |
| Thailand | 407.7 |
| Turkey | 694.5 |
| Uganda | 543.3 |
| United Republic of Tanzania | 537.4 |
| United States of America | 490.4 |
| Uruguay | 377.6 |
| Venezuela (Bolivarian Republic of) | 546.2 |
| Vietnam | 416.6 |
Non-continuous flooding and nutrient management.
Unit: US$/ha rice paddies/yr
| Afghanistan | -573.4 |
| Argentina | -354.8 |
| Bangladesh | -576.7 |
| Benin | -535.1 |
| Bolivia (Plurinational State of) | -354.1 |
| Brazil | -363.4 |
| Burkina Faso | -553.3 |
| Cambodia | -377.8 |
| Cameroon | -543.7 |
| Chad | -535.1 |
| China | -675.1 |
| Colombia | -397.7 |
| Cote d'Ivoire | -535.8 |
| Democratic People's Republic of Korea | -654.6 |
| Democratic Republic of the Congo | -535.6 |
| Dominican Republic | -428.4 |
| Ecuador | -390.3 |
| Egypt | -802.2 |
| Ghana | -535.5 |
| Guinea | -538.5 |
| Guinea–Bissau | -539.2 |
| Guyana | -382.0 |
| India | -607.9 |
| Indonesia | -382.3 |
| Iran (Islamic Republic of) | -726.7 |
| Italy | -567.9 |
| Japan | -636.0 |
| Lao People's Democratic Republic | -377.0 |
| Liberia | -535.3 |
| Madagascar | -535.0 |
| Malaysia | -401.2 |
| Mali | -561.0 |
| Mozambique | -535.5 |
| Myanmar | -380.7 |
| Nepal | -575.2 |
| Nigeria | -537.1 |
| Pakistan | -610.0 |
| Paraguay | -385.9 |
| Peru | -351.7 |
| Philippines | -399.5 |
| Republic of Korea | -678.2 |
| Russian Federation | -475.2 |
| Senegal | -569.9 |
| Sierra Leone | -535.1 |
| Sri Lanka | -591.1 |
| Thailand | -407.7 |
| Turkey | -694.5 |
| Uganda | -543.3 |
| United Republic of Tanzania | -537.4 |
| United States of America | -490.4 |
| Uruguay | -377.6 |
| Venezuela (Bolivarian Republic of) | -546.2 |
| Vietnam | -416.6 |
Non-continuous flooding and nutrient management.
Unit: US$/ha rice paddies/yr
| Afghanistan | -1,062 |
| Argentina | -640.5 |
| Bangladesh | -1,065 |
| Benin | -992.4 |
| Bolivia (Plurinational State of) | -639.8 |
| Brazil | -649.0 |
| Burkina Faso | -1,010 |
| Cambodia | -699.9 |
| Cameroon | -1,001 |
| Chad | -992.5 |
| China | -1,219 |
| Colombia | -683.4 |
| Cote d'Ivoire | -993.2 |
| Democratic People's Republic of Korea | -1,198 |
| Democratic Republic of the Congo | -992.9 |
| Dominican Republic | -714.1 |
| Ecuador | -676.0 |
| Egypt | -1,387 |
| Ghana | -992.8 |
| Guinea | -995.8 |
| Guinea–Bissau | -996.5 |
| Guyana | -667.7 |
| India | -1,096 |
| Indonesia | -704.5 |
| Iran (Islamic Republic of) | -1,312 |
| Italy | -1,053 |
| Japan | -1,179 |
| Lao People's Democratic Republic | -699.1 |
| Liberia | -992.6 |
| Madagascar | -992.4 |
| Malaysia | -723.3 |
| Mali | -1,018 |
| Mozambique | -992.8 |
| Myanmar | -702.8 |
| Nepal | -1,064 |
| Nigeria | -994.5 |
| Pakistan | -1,098 |
| Paraguay | -671.6 |
| Peru | -637.4 |
| Philippines | -721.6 |
| Republic of Korea | -1,221 |
| Russian Federation | -865.9 |
| Senegal | -1,027 |
| Sierra Leone | -992.4 |
| Sri Lanka | -1,080 |
| Thailand | -729.8 |
| Turkey | -1,279 |
| Uganda | -1,000 |
| United Republic of Tanzania | -994.7 |
| United States of America | -846.7 |
| Uruguay | -663.3 |
| Venezuela (Bolivarian Republic of) | -831.9 |
| Vietnam | -738.8 |
Non-continuous flooding and nutrient management.
Unit: US$/t CO₂‑eq
| Afghanistan | -222.1 |
| Argentina | -80.82 |
| Bangladesh | -221.5 |
| Benin | -147.2 |
| Bolivia (Plurinational State of) | -81.49 |
| Brazil | -82.60 |
| Burkina Faso | -151.2 |
| Cambodia | -112.5 |
| Cameroon | -149.3 |
| Chad | -147.7 |
| China | -168.9 |
| Colombia | -87.77 |
| Cote d'Ivoire | -147.6 |
| Democratic People's Republic of Korea | -165.8 |
| Democratic Republic of the Congo | -147.9 |
| Dominican Republic | -92.82 |
| Ecuador | -86.99 |
| Egypt | -211.5 |
| Ghana | -146.9 |
| Guinea | -148.1 |
| Guinea–Bissau | -148.2 |
| Guyana | -85.72 |
| India | -232.1 |
| Indonesia | -111.5 |
| Iran (Islamic Republic of) | -137.8 |
| Italy | -110.0 |
| Japan | -162.2 |
| Lao People's Democratic Republic | -112.2 |
| Liberia | -147.6 |
| Madagascar | -147.9 |
| Malaysia | -116.6 |
| Mali | -152.2 |
| Mozambique | -147.7 |
| Myanmar | -112.4 |
| Nepal | -222.2 |
| Nigeria | -148.0 |
| Pakistan | -233.3 |
| Paraguay | -85.41 |
| Peru | -80.22 |
| Philippines | -116.1 |
| Republic of Korea | -169.7 |
| Russian Federation | -90.08 |
| Senegal | -154.0 |
| Sierra Leone | -147.5 |
| Sri Lanka | -226.3 |
| Thailand | -118.1 |
| Turkey | -132.3 |
| Uganda | -149.1 |
| United Republic of Tanzania | -147.3 |
| United States of America | -190.1 |
| Uruguay | -84.18 |
| Venezuela (Bolivarian Republic of) | -112.7 |
| Vietnam | -119.1 |
Non-continuous flooding and nutrient management.
Cost per unit climate impact
The cost per t CO₂‑eq varies by country. Country-by-country results are shown in Table 3. The global weighted average is a savings of US$175.0/t CO₂‑eq (Table 4). Note that this is the same for both 100- and 20-yr results.
Table 4. Weighted average cost per unit climate impact.
Unit: US$/t CO₂‑eq
| Weighted average | -175.0 |
Learning curve data are not available for improved rice cultivation.
Speed of action refers to how quickly a climate solution physically affects the atmosphere after it is deployed. This is different from speed of deployment, which is the pace at which solutions are adopted.
At Project Drawdown, we define the speed of action for each climate solution as gradual, emergency brake, or delayed.
The noncontinuous flooding component of Improve Rice Production is an EMERGENCY BRAKE climate solution. It has a disproportionately fast impact after implementation because it reduces the short-lived climate pollutant methane.
The nutrient management component is a GRADUAL climate solution. It has a steady, linear impact on the atmosphere. The cumulative effect over time builds as a straight line.
Caveats like additionality and permanence do not apply to improve rice production as described here. If its carbon sequestration component were included, those caveats would apply.
Noncontinuous Flooding
Rigorous, up-to-date country-level data about the extent of noncontinuous flooding in rice production are in short supply. We found five sources reporting adoption in seven major rice-producing countries. We used these to create regional averages and applied them to all countries that produce more than 100,000 ha of rice (paddy and upland). The total estimated current adoption is 48.65 Mha, or 47% of global rice paddy area (Table 5). This should be considered an extremely rough estimate.
The available sources encompass different forms of noncontinuous flooding, including alternate wetting and drying (Philippines, Vietnam, Bangladesh), mid-season drainage (Japan), or both (China).
Table 5. Current adoption level (2025).
Unit: Mha
| Mean | 48.65 |
Noncontinuous flooding, ha installed.
Nutrient Management
We based nutrient management adoption on our analysis of the overapplication of nitrogen fertilizer on a national basis. Rather than calculate adoption in a parallel way to noncontinuous flooding, this approach provided a national average overapplication rate (the amount of nitrogen fertilizer which is applied that is not needed for crop growth and ends up as nitrous oxide emissions). We assume that every hectare of noncontinuous flooding is also using nutrient management.
We assume the adoption of both noncontinuous flooding and nutrient management for each hectare.
Adoption trend information here takes the form of annual growth rate (%), with a median of 3.76% (Table 6). Adoption rate data are somewhat scarce.
Table 6. Adoption trend.
Unit: %
| 25th percentile | 3.00 |
| Median (50th percentile) | 3.76 |
| 75th percentile | 4.25 |
Percent annual growth rate.
There are barriers to adoption of these techniques and practices. Not all paddy rice is suitable for improved water management, and under certain conditions, undesirable yield reductions are possible (Bo et al., 2022). Other challenges include water access, coordinating water usage between multiple users, and ownership of water pumps (Nabuurs et al., 2022).
There are many challenges in estimating paddy rice land. Food and Agriculture Organization (FAO) statistics can overcount because land that produces more than one crop is double or triple counted. Satellite imagery is often blocked by clouds in the tropical humid areas where rice paddies are concentrated.
A comprehensive effort to calculate total world rice paddy land reported 66.00 Mha of irrigated paddy and 63.00 Mha of rain-fed paddy (Salmon et al., 2015). Our own calculation of the combined paddy rice area of countries producing over 100,000 ha of rice found 104.1 Mha of paddy rice.
We summed high-resolution maps of paddy rice area appropriate for noncontinuous flooding (Bo et al., 2022) over maps of irrigated and rain-fed rice areas (Salmon et al., 2015) to determine a maximum adoption ceiling for each country. Several countries have already exceeded this threshold, and we included their higher adoption in our calculation. The sum of these, and therefore, the median adoption ceiling, is 77.53 Mha (Table 7).
Table 7. Adoption ceiling: upper limit for adoption level.
Unit: Mha
| Median | 77.53 |
Mha of improved rice production installed.
Table 8. Range of achievable adoption levels.
Unit: Mha
| Current adoption | 48.65 |
| Achievable – low | 49.56 |
| Achievable – high | 77.53 |
| Adoption ceiling | 77.53 |
Mha of improved rice production installed.
Given that both China and Japan have already attained adoption rates above our adoption ceiling (Bo et al., 2022; Zhang et al., 2019), we selected for our adoption ceiling our Achievable – High adoption level, which is 77.53 Mha (Table 8).
In contrast, the countries with the lowest adoption rates had rates under 3%. In the absence of a modest adoption example, we chose to use current adoption plus 10% as our Achievable – Low adoption level. This provides an adoption of 49.56 Mha.
As described under Adoption Ceiling above, adoption of nutrient management is already weighted based on regional or national adoption and should not be overcounted in the achievable range calculations.
We calculated the potential impact of improved rice, on a 100-yr basis, at 0.10 Gt CO₂‑eq/yr from current adoption, and 0.10, 0.16, and 0.16 from Achievable – Low, Achievable – High, and Adoption Ceiling, respectively (Table 9). On a 20-yr basis, the totals are 0.29, 0.29, 0.46, and 0.46, respectively.
Table 9. Climate impact at different levels of adoption.
Unit: Gt CO₂‑eq/yr
| Current adoption | 0.10 |
| Achievable – low | 0.10 |
| Achievable – high | 0.16 |
| Adoption ceiling | 0.16 |
Unit: Gt CO₂‑eq/yr
| Current adoption | 0.29 |
| Achievable – low | 0.29 |
| Achievable – high | 0.46 |
| Adoption ceiling | 0.46 |
The IPCC estimated a technical potential at 0.3 Gt CO₂‑eq/yr, with 0.2 Gt CO₂‑eq/yr as economically achievable at US$100/t CO₂ (100-yr basis; Nabuurs et al., 2022). Achieving the adoption ceiling of 76% of global flooded rice production could reduce rice paddy methane by 47% (Bo et al., 2022). Applying this percentage to the IPCC reported total paddy methane emissions of 0.49–0.73 Gt CO₂‑eq/yr yields a reduction of 0.23–0.34 Gt CO₂‑eq/yr (Nabuurs et al., 2022). Roe et al. (2021) calculated 0.19 Gt CO₂‑eq/yr. Note that these benchmarks only calculate methane from paddy rice, while we also addressed nitrous oxide from nutrient management.
The additional benefits of improved rice production arise from both practices (noncontinuous flooding and improved nutrient management) that form this solution.
Noncontinuous flooding can reduce the accumulation of arsenic in rice grains (Ishfaq et al., 2020). Arsenic is a carcinogen that is responsible for thousands of premature deaths in South and Southeast Asia (Jameel et al., 2021). The amount of arsenic reduced can vary by 0–90% depending upon the timing of the wetting and drying periods (Ishfaq et al., 2020).
Better nutrient management improves soil fertility and health, increasing resilience to extreme heat and droughts. Noncontinuous flooding also slows down the rate of soil salinization, protecting soil from degradation (Carrijo et al., 2017).
Rice irrigation is responsible for 40% of all freshwater use in Asia, and rice requires two to three times more water per metric ton of grain than other cereals (Surendran et al., 2021). Field studies across South and Southeast Asia have shown that noncontinuous flooding can typically reduce irrigation requirements 20–30% compared to conventional flooded systems (Suwanmaneepong et al., 2023; Carrijo et al., 2017) without adversely affecting rice yield or grain quality. This reduction in water usage alleviates pressure on water resources in drought-prone areas (Alauddin et al., 2020).
Adoption of noncontinuous flooding up to the adoption ceiling of 76% would reduce rice irrigation needs by 25%.
Both noncontinuous flooding and improved nutrient management reduce water pollution. Nitrogen utilization is generally poor using existing growing techniques, with two-thirds of the nitrogen fertilizer being lost through surface runoff and denitrification (Zhang et al., 2021). While noncontinuous flooding is primarily a water-efficiency and methane reduction technique, it can improve nitrogen use efficiency and reduce nitrogen runoff into water bodies (Liang et al., 2017; Liang et al., 2023). Improved nutrient management also reduces the excess fertilizers that could end up in local water bodies. Both mechanisms can mitigate eutrophication and harmful algal blooms, protect aquatic ecosystems, and ensure safer drinking water supplies (Bijay-Sing and Craswell, 2021).
Not all paddies are suitable, with variables including soil type, irrigation infrastructure and ownership, community partitioning and scheduling of water resources, field size, and more (Nabuurs et al., 2022; Enriquez et al., 2021).
Many rice farmers in Asia do not directly control irrigation access, but instead use a municipal system, which is not always available when needed for noncontinuous flooding production. In addition, they may not actually experience cost savings, as pricing may be based on area rather than amount of water. An additional change is that multiple plots owned or rented by multiple farmers may be irrigated by a single irrigation gate, meaning that all must agree to an irrigation strategy. Generally speaking, pump-based irrigation areas see the best adoption, with poor adoption in gravity-based irrigation system areas. Improved irrigation infrastructure is necessary to increase adoption of noncontinuous flooding (Enriquez et al., 2021).
Continuously flooded paddies have lower weed pressure than noncontinuous paddies, so noncontinuous flooding can raise labor costs or increase herbicide use. Not all rice varieties grow well in noncontinuous flooding (Li et al., 2024). In addition, it is difficult for farmers, especially smallholders, to monitor soil moisture level, which makes determining the timing of the next irrigation difficult (Livsey et al., 2019).
We did not identify any aligned or competing interactions with other solutions.
ha rice paddies
CH₄ , N₂O
In some cases, rice yields are reduced (Nabuurs et al., 2022). However, this has been excluded from our calculations because we worked from the adoption ceiling of Bo et al. (2022), which explicitly addresses the question of maximum adoption without reducing yields.
Long-term impacts on soil health of water-saving irrigation strategies have not been widely studied, but a meta-analysis by Livsey et al. (2019) indicates a risk of decreases in soil carbon and fertility.
Rice is the third most widely grown crop in terms of cultivated area and provides more calories directly to people than any other crop. It also is an important source of methane emissions. Here we show pixels in which at least 1% of the area is devoted to paddy (flooded) rice. Upland (unflooded) rice is included in the Improve Nutrient Management solution.
Cao, P., Bilotto, F., Gonzalez Fischer, C., Mueller, N. D., Carlson, K. M., Gerber, J.S., Smith, P., Tubiello, F. N., West, P. C., You, L., & Herrero, M. (2025). Mapping greenhouse gas emissions from global cropland circa 2020 [Data set, PREPRINT Version 1]. In review at Nature Climate Change. Link to source: https://doi.org/10.21203/rs.3.rs-6622054/v1
Tang, F. H. M., Nguyen, T. H., Conchedda, G., Casse, L., Tubiello, F. N., & Maggi, F. (2024). CROPGRIDS: A global geo-referenced dataset of 173 crops [Data set]. Scientific Data, 11(1), 413. Link to source: https://doi.org/10.1038/s41597-024-03247-7
Rice is the third most widely grown crop in terms of cultivated area and provides more calories directly to people than any other crop. It also is an important source of methane emissions. Here we show pixels in which at least 1% of the area is devoted to paddy (flooded) rice. Upland (unflooded) rice is included in the Improve Nutrient Management solution.
Cao, P., Bilotto, F., Gonzalez Fischer, C., Mueller, N. D., Carlson, K. M., Gerber, J.S., Smith, P., Tubiello, F. N., West, P. C., You, L., & Herrero, M. (2025). Mapping greenhouse gas emissions from global cropland circa 2020 [Data set, PREPRINT Version 1]. In review at Nature Climate Change. Link to source: https://doi.org/10.21203/rs.3.rs-6622054/v1
Tang, F. H. M., Nguyen, T. H., Conchedda, G., Casse, L., Tubiello, F. N., & Maggi, F. (2024). CROPGRIDS: A global geo-referenced dataset of 173 crops [Data set]. Scientific Data, 11(1), 413. Link to source: https://doi.org/10.1038/s41597-024-03247-7
Improved rice production has its greatest potential in regions where there is substantial paddy rice production and adequate water availability to allow farmers to implement drain/flood cycles throughout the growing season (noncontinuous flooding). Rice production is dominated by Asia, so the greatest potential for solution uptake is there. Brazil and the United States rank 9th and 11th for rice production, and each has regions where this solution would have multiple benefits. Because improved rice production solution may not decrease yields, not all paddy rice-growing areas are suitable. There are regions of great potential throughout Southeast Asia, particularly in Vietnam and Thailand.
Other factors besides biophysical factors govern the suitability of noncontinuous flooding. For example, farmers are more likely to release water in their fields if they are confident that water will be available for subsequent irrigation, which often depends on community structures.
There is very scarce information on adoption of noncontinuous flooding, although Bangladesh, China, Japan, and South Korea have relatively high uptake.
There is high consensus on the effectiveness and potential of noncontinuous flooding and nutrient management (Jiang et al., 2019; Zhang et al., 2023; Nabuurs et al., 2022; Qian et al., 2023).
Hergoualc’h et al. (2019) describe methane reduction and associated nitrous oxide increase from noncontinuous flooding in detail(2019). Bo et al. (2022) calculate that 76% of global rice paddy area is suitable to switch to noncontinuous flooding without reducing yields. Carlson et al. (2016) provide emissions intensities for croplands, including rice production. Ludemann et al. (2024) provide country-by-country and crop-by-crop fertilizer use data. Qian et al. (2023) review methane emissions production and reduction potential.
The results presented in this document summarize findings from 12 reviews and meta-analyses and 26 original studies reflecting current evidence from countries across the Asian rice production region. We recognize this limited geographic scope creates bias, and hope this work inspires research and data sharing on this topic in underrepresented regions.
In this analysis, we calculated the potential for reducing crop nitrogen inputs and associated nitrous oxide emissions by integrating spatially explicit, crop-specific data on nitrogen inputs, crop yields, attainable yields, irrigated extent, and climate. Broadly, we calculated a “target” yield-scaled nitrogen input rate based on pixels with low yield gaps and calculated the difference between nitrous oxide emissions under the current rate and under the hypothetical target emissions rate, using nitrous oxide emissions factors disaggregated by fertilizer type and climate.
We used Tier 1 emissions factors from the IPCC 2019 Refinement to the 2006 Guidelines for National Greenhouse Gas Inventories, including direct emissions factors as well as indirect emissions from volatilization and leaching pathways. Direct emissions factors represent the proportion of applied nitrogen emitted as nitrous oxide, while we calculated volatilization and leaching emissions factors by multiplying the proportion of applied nitrogen lost through these pathways by the proportion of volatilized or leached nitrogen ultimately emitted as nitrous oxide. Including both direct and indirect emissions, organic and synthetic fertilizers emit 4.97 kg CO₂‑eq/kg nitrogen and 8.66 kg CO₂‑eq/kg nitrogen, respectively, in wet climates, and 2.59 kg CO₂‑eq/kg nitrogen and 2.38 kg CO₂‑eq/kg nitrogen in dry climates. We included uncertainty bounds (2.5th and 97.5th percentiles) for all emissions factors.
We classified each pixel as “wet” or “dry” using an aridity index (AI) threshold of 0.65, calculated as the ratio of annual precipitation to potential evapotranspiration (PET) from TerraClimate data (1991–2020), based on a threshold of 0.65. For pixels in dry climates that contained irrigation, we took the weighted average of wet and dry emissions factors based on the fraction of cropland that was irrigated (Mehta et al., 2024). We excluded irrigated rice from this analysis due to large differences in nitrous oxide dynamics in flooded rice systems.
Using highly disaggregated data on nitrogen inputs from Adalibieke et al. (2024) for 21 crop groups, we calculated total crop-specific inputs of synthetic and organic nitrogen. We then averaged over 2016–2020 to reduce the influence of interannual variability in factors like fertilizer prices. These values are subsequently referred to as “current” nitrogen inputs. We calculated nitrous oxide emissions under current nitrogen inputs as the sum of the products of nitrogen inputs and the climatically relevant emissions factors for each fertilizer type.
Next, we calculated target nitrogen application rates in terms of kg nitrogen per ton of crop yield using data on actual and attainable yields for 17 crops from Gerber et al., 2024. For each crop, we first identified pixels in which the ratio of actual to attainable yields was above the 80th percentile globally. The target nitrogen application rate was then calculated as the 20th percentile of nitrogen application rates across low-yield-gap pixels. Finally, we calculated total target nitrogen inputs as the product of actual yields and target nitrogen input rates. We calculated hypothetical nitrous oxide emissions from target nitrogen inputs as the product of nitrogen inputs and the climatically relevant emissions factor for each fertilizer type.
The difference between current and target nitrogen inputs represents the amount by which nitrogen inputs could hypothetically be reduced without compromising crop productivity (i.e., “avoidable” nitrogen inputs). We calculated avoidable nitrous oxide emissions as the difference between nitrous oxide emissions with current nitrogen inputs and those with target nitrogen inputs. For crops for which no yield or attainable yield data were available, we applied the average percent reduction in nitrogen inputs under the target scenario from available crops to the nitrogen input data for missing crops to calculate the avoidable nitrogen inputs and emissions.
This simple and empirically driven method aimed to identify realistically low but nutritionally adequate nitrogen application rates by including only pixels with low yield gaps, which are unlikely to be substantially nutrient-constrained. We did not control for other factors affecting nitrogen availability, such as historical nutrient application rates or depletion, rotation with nitrogen fixing crops, or tillage and residue retention practices.
Irrigation water use efficiency involves reducing water use without compromising crop productivity by improving irrigation scheduling and/or equipment. Irrigation produces GHG emissions by altering biogeochemical cycling of carbon and nitrogen cycles in water and soils, and through energy use for pumping. Reducing the duration of soil saturation, the amount of groundwater extracted, and the total volume of water pumped can help reduce associated emissions. However, data on the effectiveness of improved water use efficiency in reducing emissions remain very limited. We will "Keep Watching" this solution as additional data become available.
Improving irrigation water use efficiency is a promising strategy for reducing emissions. However, additional data are needed to evaluate the magnitude of its impact and its effectiveness, especially under different environmental and management conditions. Therefore, this solution is classified as "Keep Watching."
| Plausible | Could it work? | Yes |
|---|---|---|
| Ready | Is it ready? | Yes |
| Evidence | Are there data to evaluate it? | Limited |
| Effective | Does it consistently work? | ? |
| Impact | Is it big enough to matter? | ? |
| Risk | Is it risky or harmful? | No |
| Cost | Is it cheap? | Yes |
Improving irrigation water use efficiency involves optimizing the timing, volume, and method of irrigation to reduce water use while still meeting crop water demand, thereby reducing emissions from soils, extracted groundwater, and pumping. Irrigation is the practice of adding water to croplands or pastures to reduce crop water stress and increase productivity. However, irrigation also creates wet soil conditions that promote nitrous oxide emissions, releases greenhouse gases that had been dissolved in groundwater, and, in some cases, uses energy to pump water. Increasing water use efficiency will reduce the duration of near-saturated soil conditions, potentially reducing nitrous oxide emissions from soils. For the ~40% of global irrigated croplands that rely on groundwater, increasing water use efficiency will reduce emissions from groundwater. For irrigation systems that use pumps powered by fossil fuels or non-renewable electricity, improving water use efficiency can also reduce pumping-related emissions. Of note, energy use for pumping is also addressed by Deploy Electric Irrigation Pumps.
Although the mechanisms by which improved irrigation water use efficiency can reduce emissions from soils, groundwater, and pumping are scientifically sound, the effectiveness of this solution is context-dependent, and data on effectiveness and potential impact are very limited.
Irrigation contributes to nitrous oxide emissions by stimulating denitrification, a microbial process that produces nitrous oxide emissions and tends to occur when soils are nearly saturated with water. Reducing the frequency and duration of near-saturated conditions through improved irrigation water use efficiency will likely reduce associated pulses of nitrous oxide emissions. One recent study reported that irrigation increased nitrous oxide emissions from U.S. croplands by 2.9 Mt CO₂‑eq/yr. However, data on nitrous oxide emissions under different types of irrigation management, including improved water use efficiency, are not yet available.
For croplands irrigated with groundwater, reducing water use will directly reduce emissions from groundwater degassing. Groundwater is often supersaturated in CO₂, meaning that it contains more dissolved CO₂ gas than the atmosphere. The excess CO₂ in groundwater accumulates from two sources: 1) the air space in soils tends to have high CO₂ concentrations from microbial respiration, and groundwater absorbs some of the CO₂ as it percolates through the soil profile; and 2) groundwater reacts with carbonate-containing minerals in aquifers. Similarly, dissolved nitrous oxide can also accumulate in groundwater, particularly in regions with heavy fertilizer use. However, the concentration of these GHGs in groundwater remains uncertain as it varies substantially between aquifers. Recent studies have estimated that degassing of CO₂ from groundwater produces 1.7–3.6 Mt CO₂‑eq/yr in the U.S., and one global study reported 6 Mt CO₂‑eq/yr ; however, many uncertainties remain in these studies.
For croplands that already rely on pumps for irrigation, improving irrigation scheduling to reduce water use will reduce emissions from energy use. However, other croplands rely on surface water and gravity irrigation methods and do not require pumps. For these croplands, switching to sprinklers or drip irrigation will increase water use efficiency but will also require the addition of pumps and associated energy use emissions.
Irrigation has a tremendous impact on the planet, accounting for nearly 90% of human-caused consumptive water use. Globally, around 23% of croplands are irrigated. Therefore, opportunities to increase water use efficiency abound, and improvements in irrigation water management can have widespread impacts. Many places are facing surface water shortages and groundwater depletion, and improving irrigation practices is a critical part of sustainable water management as resource availability changes. Increases in irrigation water use efficiency have the potential to help alleviate water scarcity when coupled with appropriate policy reforms. Moreover, reducing water use can also reduce energy and water costs for producers, and reductions in runoff can improve water quality and slow erosion, benefitting biodiversity and soil health.
Due to limited data, the effects of irrigation on emissions from groundwater and soils remain poorly understood. Additional data, including direct field measurements, are needed before we can confidently assess the effectiveness of improved irrigation water use efficiency in reducing emissions. The effectiveness of this solution depends on environmental and management conditions, the extent to which water use is reduced, and the method used to improve irrigation water use efficiency.
It is important that improvements in irrigation water use efficiency do not compromise crop yields. Efforts to improve irrigation water use efficiency that impose water stress and reduce yields can lead to the expansion of agricultural land, resulting in the loss of carbon-rich ecosystems.
Anand, S. K., Rosa, L., Mohanty, B. P., Rajan, N., & Calabrese, S. (2025). Balancing productivity and climate impact: A framework to assess climate-smart irrigation. Earth’s Future, 13(11), Article e2025EF006116. Link to source: https://doi.org/10.1029/2025EF006116
Bateman, E. J., & Baggs, E. M. (2005). Contributions of nitrification and denitrification to N2O emissions from soils at different water-filled pore space. Biology and Fertility of Soils, 41(6), 379–388. Link to source: https://doi.org/10.1007/s00374-005-0858-3
Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R., & Zechmeister-Boltenstern, S. (2013). Nitrous oxide emissions from soils: How well do we understand the processes and their controls? Philosophical Transactions of the Royal Society B: Biological Sciences, 368(1621), Article 20130122. Link to source: https://doi.org/10.1098/rstb.2013.0122
Driscoll, A. W., Marston, L. T., Ogle, S. M., Planavsky, N. J., Siddik, M. A. B., Spencer, S., Zhang, S., & Mueller, N. D. (2024). Hotspots of irrigation-related US greenhouse gas emissions from multiple sources. Nature Water, 2(9), 837–847. Link to source: https://doi.org/10.1038/s44221-024-00283-w
Elberling, B. B., Kovács, G. M., Hansen, H. F. E., Fensholt, R., Ambus, P., Tong, X., Gominski, D., Mueller, C. W., Poultney, D. M. N., & Oehmcke, S. (2023). High nitrous oxide emissions from temporary flooded depressions within croplands. Communications Earth & Environment, 4(1), Article 1. Link to source: https://doi.org/10.1038/s43247-023-01095-8
Flint, E. M., Ascott, M. J., Gooddy, D. C., Stahl, M. O., & Surridge, B. W. J. (2025). Anthropogenic water withdrawals modify freshwater inorganic carbon fluxes across the United States. Environmental Science & Technology, 59(8), 3949–3960. Link to source: https://doi.org/10.1021/acs.est.4c09426
Huo, P., & Gao, P. (2024). Degassing of greenhouse gases from groundwater under different irrigation methods: A neglected carbon source in agriculture. Agricultural Water Management, 301, 108941. Link to source: https://doi.org/10.1016/j.agwat.2024.108941
Huo, P., Li, H., Huang, X., Ma, X., Liu, L., Ji, W., Liu, Y., & Gao, P. (2022). Dissolved greenhouse gas emissions from agricultural groundwater irrigation in the Guanzhong Basin of China. Environmental Pollution, 309, Article 119714. Link to source: https://doi.org/10.1016/j.envpol.2022.119714
Kebede, E. A., Oluoch, K. O., Siebert, S., Mehta, P., Hartman, S., Jägermeyr, J., Ray, D., Ali, T., Brauman, K. A., Deng, Q., Xie, W., & Davis, K. F. (2025). A global open-source dataset of monthly irrigated and rainfed cropped areas (MIRCA-OS) for the 21st century. Scientific Data, 12(1), 208. Link to source: https://doi.org/10.1038/s41597-024-04313-w
McDermid, S., Mahmood, R., Hayes, M. J., Bell, J. E., & Lieberman, Z. (2021). Minimizing trade-offs for sustainable irrigation. Nature Geoscience, 14(10), 706–709. Link to source: https://doi.org/10.1038/s41561-021-00830-0
McDermid, S., Nocco, M., Lawston-Parker, P., Keune, J., Pokhrel, Y., Jain, M., Jägermeyr, J., Brocca, L., Massari, C., Jones, A. D., Vahmani, P., Thiery, W., Yao, Y., Bell, A., Chen, L., Dorigo, W., Hanasaki, N., Jasechko, S., Lo, M.-H., … Yokohata, T. (2023). Irrigation in the Earth system. Nature Reviews Earth & Environment, 4, 435–453. Link to source: https://doi.org/10.1038/s43017-023-00438-5
McGill, B. M., Hamilton, S. K., Millar, N., & Robertson, G. P. (2018). The greenhouse gas cost of agricultural intensification with groundwater irrigation in a Midwest U.S. row cropping system. Global Change Biology, 24(12), 5948–5960. Link to source: https://doi.org/10.1111/gcb.14472
Qin, J., Duan, W., Zou, S., Chen, Y., Huang, W., & Rosa, L. (2024). Global energy use and carbon emissions from irrigated agriculture. Nature Communications, 15(1), Article 3084. Link to source: https://doi.org/10.1038/s41467-024-47383-5
Rosa, L., Chiarelli, D. D., Sangiorgio, M., Beltran-Peña, A. A., Rulli, M. C., D’Odorico, P., & Fung, I. (2020). Potential for sustainable irrigation expansion in a 3 °C warmer climate. Proceedings of the National Academy of Sciences, 117(47), 29526–29534. Link to source: https://doi.org/10.1073/pnas.2017796117
Wood, W. W., & Hyndman, D. W. (2017). Groundwater depletion: A significant unreported source of atmospheric carbon dioxide. Earth’s Future, 5(11), 1133–1135. Link to source: https://doi.org/10.1002/2017EF000586
Yang, Y., Jin, Z., Mueller, N. D., Driscoll, A. W., Hernandez, R. R., Grodsky, S. M., Sloat, L. L., Chester, M. V., Zhu, Y.-G., & Lobell, D. B. (2023). Sustainable irrigation and climate feedbacks. Nature Food, 4(8), Article 8. Link to source: https://doi.org/10.1038/s43016-023-00821-x
Avery Driscoll, Ph.D.
Christina Swanson, Ph.D.
Heather McDiarmid, Ph.D.
James Gerber, Ph.D.
We define the Improve Nutrient Management solution as reducing excessive nitrogen use on croplands. Nitrogen is critical for crop production and is added to croplands as synthetic or organic fertilizers and through microbial activity. However, farmers often add more nitrogen to croplands than crops can use. Some of that excess nitrogen is emitted to the atmosphere as nitrous oxide, a potent GHG.
Agriculture is the dominant source of human-caused emissions of nitrous oxide (Tian et al., 2020). Nitrogen is critical for plant growth and is added to croplands in synthetic forms, such as urea, ammonium nitrate, or anhydrous ammonia; in organic forms, such as manure or compost; and by growing legume crops, which host microbes that capture nitrogen from the air and add it to the soil (Adalibieke et al., 2023; Ludemann et al., 2024). If more nitrogen is added than crops can use, the excess can be converted to other forms, including nitrous oxide, through microbial processes called denitrification and nitrification (Figure 1; Reay et al., 2012).
Figure 1. The agricultural nitrogen cycle represents the key pathways by which nitrogen is added to croplands and lost to the environment, including as nitrous oxide. The “4R” nutrient management principles – right source, right rate, right time, right place – increase the proportion of nitrogen taken up by the plant, therefore reducing nitrogen losses to the environment.
Illustrations: BioRender CC-BY 4.0
Farmers can reduce nitrous oxide emissions from croplands by using the right amount and the right type of fertilizer at the right time and in the right place (Fixen, 2020; Gao & Cabrera Serrenho, 2023). Together, these four “rights” increase nitrogen use efficiency – the proportion of applied nitrogen that the crop uses (Congreves et al., 2021). Improved nutrient management is often a win-win for the farmer and the environment, reducing fertilizer costs while also lowering nitrous oxide emissions (Gu et al., 2023).
Improving nutrient management involves reducing the amount of nitrogen applied to match the crop’s requirements in areas where nitrogen is currently overapplied. A farmer can implement the other three principles – type, time, and place – in a number of ways. For example, fertilizing just before planting instead of after the previous season’s harvest better matches the timing of nitrogen addition to that of plant uptake, reducing nitrous oxide emissions before the crop is planted. Certain types of fertilizers are better suited for maximizing plant uptake, such as extended-release fertilizers, which allow the crop to steadily absorb nutrients over time. Techniques such as banding, in which farmers apply fertilizers in concentrated bands close to the plant roots instead of spreading them evenly across the soil surface, also reduce nitrous oxide emissions. Each of these practices can increase nitrogen use efficiency and decrease the amount of excess nitrogen lost as nitrous oxide (Gao & Cabrera Serrenho, 2023; Gu et al., 2023; Wang et al., 2024; You et al., 2023).
For this solution, we estimated a target rate of nitrogen application for major crops as the 20th percentile of the current rate of nitrogen application (in t N/t crop) in areas where yields are near a realistic ceiling. Excess nitrogen was defined as the amount of nitrogen applied beyond the target rate (see Adoption and Appendix for more details). Our emissions estimates include nitrous oxide from croplands, fertilizer runoff, and fertilizer volatilization. They do not include emissions from fertilizer manufacturing, which are addressed in the Deploy Low-Emission Industrial Feedstocks and Boost Industrial Efficiency solutions. We excluded nutrient management on pastures from this solution due to data limitations and address nutrient management in paddy rice systems in the Improve Rice Production solution instead.
Adalibieke, W., Cui, X., Cai, H., You, L., & Zhou, F. (2023). Global crop-specific nitrogen fertilization dataset in 1961–2020. Scientific Data, 10(1), 617. Link to source: https://doi.org/10.1038/s41597-023-02526-z
Almaraz, M., Bai, E., Wang, C., Trousdell, J., Conley, S., Faloona, I., & Houlton, B. Z. (2018). Agriculture is a major source of NOx pollution in California. Science Advances, 4(1), eaao3477. Link to source: https://doi.org/10.1126/sciadv.aao3477
Antil, R. S., & Raj, D. (2020). Integrated nutrient management for sustainable crop production and improving soil health. In R. S. Meena (Ed.), Nutrient Dynamics for Sustainable Crop Production (pp. 67–101). Springer. Link to source: https://doi.org/10.1007/978-981-13-8660-2_3
Bijay-Singh, & Craswell, E. (2021). Fertilizers and nitrate pollution of surface and ground water: An increasingly pervasive global problem. SN Applied Sciences, 3(4), 518. Link to source: https://doi.org/10.1007/s42452-021-04521-8
Chivenge, P., Saito, K., Bunquin, M. A., Sharma, S., & Dobermann, A. (2021). Co-benefits of nutrient management tailored to smallholder agriculture. Global Food Security, 30, 100570. Link to source: https://doi.org/10.1016/j.gfs.2021.100570
Deng, J., Guo, L., Salas, W., Ingraham, P., Charrier-Klobas, J. G., Frolking, S., & Li, C. (2018). Changes in irrigation practices likely mitigate nitrous oxide emissions from California cropland. Global Biogeochemical Cycles, 32(10), 1514–1527. Link to source: https://doi.org/10.1029/2018GB005961
Domingo, N. G. G., Balasubramanian, S., Thakrar, S. K., Clark, M. A., Adams, P. J., Marshall, J. D., Muller, N. Z., Pandis, S. N., Polasky, S., Robinson, A. L., Tessum, C. W., Tilman, D., Tschofen, P., & Hill, J. D. (2021). Air quality–related health damages of food. Proceedings of the National Academy of Sciences, 118(20), e2013637118. Link to source: https://doi.org/10.1073/pnas.2013637118
Elberling, B. B., Kovács, G. M., Hansen, H. F. E., Fensholt, R., Ambus, P., Tong, X., Gominski, D., Mueller, C. W., Poultney, D. M. N., & Oehmcke, S. (2023). High nitrous oxide emissions from temporary flooded depressions within croplands. Communications Earth & Environment, 4(1), 1–9. Link to source: https://doi.org/10.1038/s43247-023-01095-8
Fixen, P. E. (2020). A brief account of the genesis of 4R nutrient stewardship. Agronomy Journal, 112(5), 4511–4518. Link to source: https://doi.org/10.1002/agj2.20315
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O’Connell, C., Ray, D. K., West, P. C., Balzer, C., Bennett, E. M., Carpenter, S. R., Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., … Zaks, D. P. M. (2011). Solutions for a cultivated planet. Nature, 478(7369), 337–342. Link to source: https://doi.org/10.1038/nature10452
Gao, Y., & Cabrera Serrenho, A. (2023). Greenhouse gas emissions from nitrogen fertilizers could be reduced by up to one-fifth of current levels by 2050 with combined interventions. Nature Food, 4(2), 170–178. Link to source: https://doi.org/10.1038/s43016-023-00698-w
Gerber, J. S., Carlson, K. M., Makowski, D., Mueller, N. D., Garcia de Cortazar-Atauri, I., Havlík, P., Herrero, M., Launay, M., O’Connell, C. S., Smith, P., & West, P. C. (2016). Spatially explicit estimates of nitrous oxide emissions from croplands suggest climate mitigation opportunities from improved fertilizer management. Global Change Biology, 22(10), 3383–3394. Link to source: https://doi.org/10.1111/gcb.13341
Gerber, J. S., Ray, D. K., Makowski, D., Butler, E. E., Mueller, N. D., West, P. C., Johnson, J. A., Polasky, S., Samberg, L. H., & Siebert, S. (2024). Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation. Nature Food, 5(2), 125–135. Link to source: https://doi.org/10.1038/s43016-023-00913-8
Gong, C., Tian, H., Liao, H., Pan, N., Pan, S., Ito, A., Jain, A. K., Kou-Giesbrecht, S., Joos, F., Sun, Q., Shi, H., Vuichard, N., Zhu, Q., Peng, C., Maggi, F., Tang, F. H. M., & Zaehle, S. (2024). Global net climate effects of anthropogenic reactive nitrogen. Nature, 632(8025), 557–563. Link to source: https://doi.org/10.1038/s41586-024-07714-4
Gu, B., Zhang, X., Lam, S. K., Yu, Y., van Grinsven, H. J. M., Zhang, S., Wang, X., Bodirsky, B. L., Wang, S., Duan, J., Ren, C., Bouwman, L., de Vries, W., Xu, J., Sutton, M. A., & Chen, D. (2023). Cost-effective mitigation of nitrogen pollution from global croplands. Nature, 613(7942), 77–84. Link to source: https://doi.org/10.1038/s41586-022-05481-8
Hergoualc’h, K., Akiyama, H., Bernoux, M., Chirinda, N., del Prado, A., Kasimir, Å., MacDonald, J. D., Ogle, S. M., Regina, K., & van der Weerden, T. J. (2019). Chapter 11: nitrous oxide Emissions from managed soils, and CO2 emissions from lime and urea application (2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories). Intergovernmental Panel on Climate Change. https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch11_Soils_nitrous oxide_CO2.pdf
Hergoualc’h, K., Mueller, N., Bernoux, M., Kasimir, Ä., van der Weerden, T. J., & Ogle, S. M. (2021). Improved accuracy and reduced uncertainty in greenhouse gas inventories by refining the IPCC emission factor for direct nitrous oxide emissions from nitrogen inputs to managed soils. Global Change Biology, 27(24), 6536–6550. Link to source: https://doi.org/10.1111/gcb.15884
IPCC, 2019. Summary for Policymakers. In: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, J. Malley, (eds.)].
Lam, S. K., Suter, H., Mosier, A. R., & Chen, D. (2017). Using nitrification inhibitors to mitigate agricultural nitrous oxide emission: A double-edged sword? Global Change Biology, 23(2), 485–489. Link to source: https://doi.org/10.1111/gcb.13338
Lawrence, N. C., Tenesaca, C. G., VanLoocke, A., & Hall, S. J. (2021). Nitrous oxide emissions from agricultural soils challenge climate sustainability in the US Corn Belt. Proceedings of the National Academy of Sciences, 118(46), e2112108118. Link to source: https://doi.org/10.1073/pnas.2112108118
Ludemann, C. I., Wanner, N., Chivenge, P., Dobermann, A., Einarsson, R., Grassini, P., Gruere, A., Jackson, K., Lassaletta, L., Maggi, F., Obli-Laryea, G., van Ittersum, M. K., Vishwakarma, S., Zhang, X., & Tubiello, F. N. (2024). A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): Nitrogen, phosphorus and potassium. Earth System Science Data, 16(1), 525–541. Link to source: https://doi.org/10.5194/essd-16-525-2024
Menegat, S., Ledo, A., & Tirado, R. (2022). Greenhouse gas emissions from global production and use of nitrogen synthetic fertilisers in agriculture. Scientific Reports, 12(1), 14490. Link to source: https://doi.org/10.1038/s41598-022-18773-w
Michaelowa, A., Hermwille, L., Obergassel, W., & Butzengeiger, S. (2019). Additionality revisited: Guarding the integrity of market mechanisms under the Paris Agreement. Climate Policy, 19(10), 1211–1224. Link to source: https://doi.org/10.1080/14693062.2019.1628695
Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature, 490(7419), Article 7419. Link to source: https://doi.org/10.1038/nature11420
Patel, N., Srivastav, A. L., Patel, A., Singh, A., Singh, S. K., Chaudhary, V. K., Singh, P. K., & Bhunia, B. (2022). Nitrate contamination in water resources, human health risks and its remediation through adsorption: A focused review. Environmental Science and Pollution Research, 29(46), 69137–69152. Link to source: https://doi.org/10.1007/s11356-022-22377-2
Pinder, R. W., Davidson, E. A., Goodale, C. L., Greaver, T. L., Herrick, J. D., & Liu, L. (2012). Climate change impacts of US reactive nitrogen. Proceedings of the National Academy of Sciences, 109(20), 7671–7675. Link to source: https://doi.org/10.1073/pnas.1114243109
Porter, E. M., Bowman, W. D., Clark, C. M., Compton, J. E., Pardo, L. H., & Soong, J. L. (2013). Interactive effects of anthropogenic nitrogen enrichment and climate change on terrestrial and aquatic biodiversity. Biogeochemistry, 114(1), 93–120. Link to source: https://doi.org/10.1007/s10533-012-9803-3
Qiao, C., Liu, L., Hu, S., Compton, J. E., Greaver, T. L., & Li, Q. (2015). How inhibiting nitrification affects nitrogen cycle and reduces environmental impacts of anthropogenic nitrogen input. Global Change Biology, 21(3), 1249–1257. Link to source: https://doi.org/10.1111/gcb.12802
Qin, Z., Deng, S., Dunn, J., Smith, P., & Sun, W. (2021). Animal waste use and implications to agricultural greenhouse gas emissions in the United States. Environmental Research Letters, 16(6), 064079. Link to source: https://doi.org/10.1088/1748-9326/ac04d7
Reay, D. S., Davidson, E. A., Smith, K. A., Smith, P., Melillo, J. M., Dentener, F., & Crutzen, P. J. (2012). Global agriculture and nitrous oxide emissions. Nature Climate Change, 2(6), 410–416. Link to source: https://doi.org/10.1038/nclimate1458
Rockström, J., Williams, J., Daily, G., Noble, A., Matthews, N., Gordon, L., Wetterstrand, H., DeClerck, F., Shah, M., Steduto, P., de Fraiture, C., Hatibu, N., Unver, O., Bird, J., Sibanda, L., & Smith, J. (2017). Sustainable intensification of agriculture for human prosperity and global sustainability. Ambio, 46(1), 4–17. Link to source: https://doi.org/10.1007/s13280-016-0793-6
Rurinda, J., Zingore, S., Jibrin, J. M., Balemi, T., Masuki, K., Andersson, J. A., Pampolino, M. F., Mohammed, I., Mutegi, J., Kamara, A. Y., Vanlauwe, B., & Craufurd, P. Q. (2020). Science-based decision support for formulating crop fertilizer recommendations in sub-Saharan Africa. Agricultural Systems, 180, 102790. Link to source: https://doi.org/10.1016/j.agsy.2020.102790
Scavia, D., David Allan, J., Arend, K. K., Bartell, S., Beletsky, D., Bosch, N. S., Brandt, S. B., Briland, R. D., Daloğlu, I., DePinto, J. V., Dolan, D. M., Evans, M. A., Farmer, T. M., Goto, D., Han, H., Höök, T. O., Knight, R., Ludsin, S. A., Mason, D., … Zhou, Y. (2014). Assessing and addressing the re-eutrophication of Lake Erie: Central basin hypoxia. Journal of Great Lakes Research, 40(2), 226–246. Link to source: https://doi.org/10.1016/j.jglr.2014.02.004
Selim, M. M. (2020). Introduction to the integrated nutrient management strategies and their contribution to yield and soil properties. International Journal of Agronomy, 2020(1), 2821678. https://doi.org/10.1155/2020/2821678
Shcherbak, I., Millar, N., & Robertson, G. P. (2014). Global metaanalysis of the nonlinear response of soil nitrous oxide (nitrous oxide) emissions to fertilizer nitrogen. Proceedings of the National Academy of Sciences, 111(25), 9199–9204. Link to source: https://doi.org/10.1073/pnas.1322434111
Shindell, D. T., Faluvegi, G., Koch, D. M., Schmidt, G. A., Unger, N., & Bauer, S. E. (2009). Improved attribution of climate forcing to emissions. Science, 326(5953), 716–718. Link to source: https://doi.org/10.1126/science.1174760
Sobota, D. J., Compton, J. E., McCrackin, M. L., & Singh, S. (2015). Cost of reactive nitrogen release from human activities to the environment in the United States. Environmental Research Letters, 10(2), 025006. Link to source: https://doi.org/10.1088/1748-9326/10/2/025006
Tian, H., Xu, R., Canadell, J. G., Thompson, R. L., Winiwarter, W., Suntharalingam, P., Davidson, E. A., Ciais, P., Jackson, R. B., Janssens-Maenhout, G., Prather, M. J., Regnier, P., Pan, N., Pan, S., Peters, G. P., Shi, H., Tubiello, F. N., Zaehle, S., Zhou, F., … Yao, Y. (2020). A comprehensive quantification of global nitrous oxide sources and sinks. Nature, 586(7828), 248–256. Link to source: https://doi.org/10.1038/s41586-020-2780-0
van Grinsven, H. J. M., Bouwman, L., Cassman, K. G., van Es, H. M., McCrackin, M. L., & Beusen, A. H. W. (2015). Losses of ammonia and nitrate from agriculture and their effect on nitrogen recovery in the European Union and the United States between 1900 and 2050. Journal of Environmental Quality, 44(2), 356–367. Link to source: https://doi.org/10.2134/jeq2014.03.0102
Vanlauwe, B., Descheemaeker, K., Giller, K. E., Huising, J., Merckx, R., Nziguheba, G., Wendt, J., & Zingore, S. (2015). Integrated soil fertility management in sub-Saharan Africa: Unravelling local adaptation. SOIL, 1(1), 491–508. Link to source: https://doi.org/10.5194/soil-1-491-2015
Wang, C., Shen, Y., Fang, X., Xiao, S., Liu, G., Wang, L., Gu, B., Zhou, F., Chen, D., Tian, H., Ciais, P., Zou, J., & Liu, S. (2024). Reducing soil nitrogen losses from fertilizer use in global maize and wheat production. Nature Geoscience, 17(10), 1008–1015. Link to source: https://doi.org/10.1038/s41561-024-01542-x
Wang, Y., Li, C., Li, Y., Zhu, L., Liu, S., Yan, L., Feng, G., & Gao, Q. (2020). Agronomic and environmental benefits of Nutrient Expert on maize and rice in Northeast China. Environmental Science and Pollution Research, 27(22), 28053–28065. Link to source: https://doi.org/10.1007/s11356-020-09153-w
Ward, M. H., Jones, R. R., Brender, J. D., de Kok, T. M., Weyer, P. J., Nolan, B. T., Villanueva, C. M., & van Breda, S. G. (2018). Drinking water nitrate and human health: an updated review. International Journal of Environmental Research and Public Health, 15(7), 1557. Link to source: https://doi.org/10.3390/ijerph15071557
Withers, P. J. A., Neal, C., Jarvie, H. P., & Doody, D. G. (2014). Agriculture and eutrophication: where do we go from here? Sustainability, 6(9), Article 9. Link to source: https://doi.org/10.3390/su6095853
You, L., Ros, G. H., Chen, Y., Shao, Q., Young, M. D., Zhang, F., & de Vries, W. (2023). Global mean nitrogen recovery efficiency in croplands can be enhanced by optimal nutrient, crop and soil management practices. Nature Communications, 14(1), 5747. Link to source: https://doi.org/10.1038/s41467-023-41504-2
Zaehle, S., Ciais, P., Friend, A. D., & Prieur, V. (2011). Carbon benefits of anthropogenic reactive nitrogen offset by nitrous oxide emissions. Nature Geoscience, 4(9), 601–605. Link to source: https://doi.org/10.1038/ngeo1207
Zhang, X., Fang, Q., Zhang, T., Ma, W., Velthof, G. L., Hou, Y., Oenema, O., & Zhang, F. (2020). Benefits and trade-offs of replacing synthetic fertilizers by animal manures in crop production in China: A meta-analysis. Global Change Biology, 26(2), 888–900. Link to source: https://doi.org/10.1111/gcb.14826
Avery Driscoll
Ruthie Burrows, Ph.D.
James Gerber, Ph.D.
Yusuf Jameel, Ph.D.
Daniel Jasper
Alex Sweeney
Eric Toensmeier
Aiyana Bodi
Hannah Henkin
Ted Otte
We relied on the 2019 Intergovernmental Panel on Climate Change (IPCC) emissions factors to calculate the emissions impacts of improved nutrient management. These are disaggregated by climate zone (“wet” vs. “dry”) and by fertilizer type (“organic” vs. “synthetic”). Nitrogen use reductions in wet climates, which include ~65% of the cropland area represented in this analysis (see Appendix for details), have the largest impact. In these areas, a 1 t reduction in nitrogen use reduces emissions by 8.7 t CO₂‑eq on average for synthetic fertilizers and by 5.0 t CO₂‑eq for organic fertilizers. Emissions savings are lower in dry climates, where a 1 t reduction in nitrogen use reduces emissions by 2.4 t CO₂‑eq for synthetic fertilizers and by 2.6 t CO₂‑eq for organic fertilizers. While these values reflect the median emissions reduction for each climate zone and fertilizer type, they are associated with large uncertainties because emissions are highly variable depending on climate, soil, and management conditions.
Based on our analysis of the adoption ceiling for each climate zone and fertilizer type (see Appendix), we estimated that a 1 t reduction in nitrogen use reduces emissions by 6.0 t CO₂‑eq at the global median (Table 1). This suggests that ~1.4% of the applied nitrogen is emitted as nitrous oxide at the global average, which is consistent with existing estimates (IPCC, 2019).
Table 1. Effectiveness at reducing emissions.
Unit: t CO₂‑eq /t nitrogen, 100-yr basis
| 25th percentile | 4.2 |
| Median (50th percentile) | 6.0 |
| 75th percentile | 7.7 |
Improving nutrient management typically reduces fertilizer costs while maintaining or increasing yields, resulting in a net financial benefit to the producer. Gu et al. (2023) found that a 21% reduction in global nitrogen use would be economically beneficial, notably after accounting for increased fertilizer use in places that do not currently have adequate access. Using data from their study, we evaluated the average cost of reduced nitrogen application considering the following nutrient management practices: increased use of high-efficiency fertilizers, organic fertilizers, and/or legumes; optimizing fertilizer rates; altering the timing and/or placement of fertilizer applications; and use of buffer zones. Implementation costs depend on the strategy used to improve nutrient management. For example, optimizing fertilizer rates requires soil testing and the ability to apply different fertilizer rates to different parts of a field. Improving timing can involve applying fertilizers at two different times during the season, increasing labor and equipment operation costs. Furthermore, planting legumes incurs seed purchase and planting costs.
Gu et al. (2023) estimated that annual reductions of 42 Mt of nitrogen were achievable globally using these practices, providing total fertilizer savings of US$37.2 billion and requiring implementation costs of US$15.9 billion, adjusted for inflation to 2023. A 1 t reduction in excess nitrogen application, therefore, was estimated to provide an average of US$507.80 of net cost savings, corresponding to a savings of US$85.21 per t CO₂‑eq of emissions reductions (Table 2).
Table 2. Cost per unit of climate impact, 100-yr basis.
Unit: 2023 US$/t CO₂‑eq
| Mean | -85.21 |
The improved nutrient management strategies considered for this solution are already well established and widely deployed (Fixen, 2020). Large nitrogen excesses are relatively easy to mitigate through simple management changes with low implementation costs. As nitrogen use efficiency increases, further reductions may require increasingly complex mitigation practices and increasing marginal costs. Therefore, a learning curve was not quantified for this solution.
Speed of action refers to how quickly a climate solution physically affects the atmosphere after it is deployed. This is different from speed of deployment, which is the pace at which solutions are adopted.
At Project Drawdown, we define the speed of action for each climate solution as emergency brake, gradual, or delayed.
Improve Nutrient Management is a GRADUAL climate solution. It has a steady, linear impact on the atmosphere. The cumulative effect over time builds as a straight line.
Emissions reductions from improved nutrient management are permanent, though they may not be additional in all cases.
As this solution reduces emissions rather than enhancing sequestration, permanence is not applicable.
Additionality requires that the emissions benefits of the practice are attributable to climate-related incentives and would not have occurred in the absence of incentives (Michaelowa et al., 2019). If they are not contingent on external incentives, fertilizer use reductions implemented solely to maximize profits do not meet the threshold for additionality. However, fertilizer reductions may be additional if incentives are required to provide access to the technical knowledge and soil testing required to identify optimal rates. Other forms of nutrient management (e.g., applying nitrification inhibitors, using extended-release or organic fertilizers, or splitting applications between two time points) may involve additional costs, substantial practice change, and technical expertise. Thus, these practices are likely to be additional.
Given that improved nutrient management takes a variety of forms and data on the adoption of individual practices are very limited, we leveraged several global datasets related to nitrogen use and yields to directly assess improvements in nitrogen use efficiency (see Appendix for details).
First, we calculated nitrogen use per metric ton of crop produced using global maps of nitrogen fertilizer use (Adalibieke et al., 2023) and global maps of crop yields (Gerber et al., 2024) for 17 major crops (see Appendix). Next, we determined a target nitrogen use rate (t nitrogen/t crop) for each crop, corresponding to the 20th percentile of nitrogen use rates observed in croplands with yield gaps at or below the 20th percentile, meaning that actual yields were close to an attainable yield ceiling (Gerber et al., 2024). Areas with large yield gaps were excluded from the calculation of target nutrient use efficiency because insufficient nitrogen supply may be compromising yields (Mueller et al., 2012). Yield data were not available for a small number of crops; for these, we assumed reductions in nitrogen use to be proportional to those of other crops.
We considered croplands that had achieved the target rate and had yield gaps lower than the global median to have adopted the solution. We calculated the amount of excess nitrogen use avoided from these croplands as the difference in total nitrogen use under current fertilization rates relative to median fertilizer application rates. As of 2020, croplands that had achieved the adoption threshold for improved nutrient management avoided 10.45 Mt of nitrogen annually relative to the median nitrogen use rate (Table 3), equivalent to 11% of the adoption ceiling.
Table 3. Current (2020) adoption level.
Unit: t nitrogen/yr
| Estimate | 10,450,000 |
Global average nitrogen use efficiency increased from 47.7% to 54.6% between 2000 and 2020, a rate of approximately 0.43%/yr (Ludemann et al., 2024). This increase accelerated somewhat in the latter decade, from an average rate of 0.38%/yr to 0.53%/yr. Underlying this increase were increases in both the amount of nitrogen used and the amount of excess nitrogen. Total nitrogen additions increased by approximately 2.64 Mt/yr, with the amount of nitrogen used increasing more rapidly (1.99 Mt/yr) than the amount of excess nitrogen (0.65 Mt/yr) between 2000 and 2020 (Ludemann et al., 2024). Although nitrogen use increased between 2000 and 2020 as yields increased, the increase in nitrogen use efficiency suggests uptake of this solution.
We estimated the adoption ceiling of improved nutrient management to be 95.13 Mt avoided excess nitrogen use/year, not including current adoption (Table 4). This value reflects our estimate of the maximum potential reduction in nitrogen application while avoiding large yield losses and consists of the potential to avoid 62.25 Mt of synthetic nitrogen use and 32.88 Mt of manure and other organic nitrogen use, in addition to current adoption. In total, this is equivalent to an additional 68% reduction in global nitrogen use. The adoption ceiling was calculated as the difference between total nitrogen use at the current rate and total nitrogen use at the target rate (as described in Current Adoption), assuming no change in crop yields. For nitrogen applied to crops for which yield data were not available, the potential reduction in nitrogen use was assumed to be proportional to that of crops for which full data were available.
Table 4. Adoption ceiling.
Unit: t nitrogen/yr
| Estimate | 105,580,000 |
We estimated that fertilizer use reductions of 69.85–91.06 Mt of nitrogen are achievable, reflecting current adoption plus nitrogen savings due to the achievement of nitrogen application rates equal to the median and 30th percentile of nitrogen application rates occurring in locations where yield gaps are small (Table 5).
This range is more ambitious than a comparable recent estimate by Gu et al. (2023), who found that reductions of approximately 42 Mt of nitrogen are avoidable via cost-effective implementation of similar practices. Differences in target nitrogen use efficiencies underlie differences between our estimates and those of Gu et al., whose findings correspond to an increase in global average cropland nitrogen use efficiency from 42% to 52%. Our estimates reflect higher target nitrogen use efficiencies. Nitrogen use efficiencies greater than 52% have been widely achieved through basic practice modification without compromising yields or requiring prohibitively expensive additional inputs. For instance, You et al. (2023) estimated that the global average nitrogen use efficiency could be increased to 78%. Similarly, cropland nitrogen use efficiency in the United States in 2020 was estimated to be 71%, and substantial opportunities for improved nitrogen use efficiency are still available within the United States (Ludemann et al., 2024), though Lu et al. (2019) and Swaney et al. (2018) report slightly lower estimates. These findings support our slightly more ambitious range of achievable nitrogen use reductions for this solution.
Table 5. Range of achievable adoption levels.
Unit: t nitrogen/yr
| Current adoption | 10,450,000 |
| Achievable – low | 69,850,000 |
| Achievable – high | 91,060,000 |
| Adoption ceiling | 105,580,000 |
We estimated that improved nutrient management has the potential to reduce emissions by 0.63 Gt CO₂‑eq/yr, with achievable emissions reductions of 0.42–0.54 Gt CO₂‑eq/yr (Table 6). This is equivalent to an additional 56–76% reduction in total nitrous oxide emissions from fertilizer use, based on the croplands represented in our analysis.
We estimated avoidable emissions by multiplying our estimates of adoption ceiling and achievable adoption by the relevant IPCC 2019 emissions factors, disaggregated by climate zone and fertilizer type. Under the adoption ceiling scenario, approximately 70% of emissions reductions occurred in wet climates, where emissions per t of applied fertilizer are higher. Reductions in synthetic fertilizer use, which are larger than reductions in organic fertilizer use, contributed about 76% of the potential avoidable emissions. We estimated that the current implementation of improved nutrient management was associated with 0.06 Gt CO₂‑eq/yr of avoided emissions.
Our estimates are slightly more optimistic but well within the range of the IPCC 2021 estimates, which found that improved nutrient management could reduce nitrous oxide emissions by 0.06–0.7 Gt CO₂‑eq/yr.
Table 6. Climate impact at different levels of adoption.
Unit: Gt CO₂-eq/yr, 100-yr basis
| Current adoption | 0.06 |
| Achievable – low | 0.42 |
| Achievable – high | 0.54 |
| Adoption ceiling | 0.63 |
Balanced nutrient concentration contributes to long-term soil fertility and improved soil health by enhancing organic matter content, microbial diversity, and nutrient cycling (Antil & Raj, 2020; Selim, 2020). Healthy soil experiences reduced erosion and has higher water content, which increases its resilience to droughts and extreme heat (Rockström et al., 2017).
Better nutrient management reduces farmers' input costs and increases profitability (Rurinda et al., 2020; Wang et al., 2020). It is especially beneficial to smallholder farmers in sub-Saharan Africa, where site-specific nutrient management programs have demonstrated a significant increase in yield (Chivenge et al., 2021). A review of 61 studies across 11 countries showed that site-specific nutrient management resulted in an average increase in yield by 12% and increased farmer’s’ income by 15% while improving nitrogen use efficiency (Chivenge et al., 2021).
While excessive nutrients cause environmental problems in some parts of the world, insufficient nutrients are a significant problem in others, resulting in lower agricultural yields (Foley et al., 2011). Targeted, site-specific, efficient use of fertilizers can improve crop productivity (Mueller et al., 2012; Vanlauwe et al., 2015), improving food security globally.
Domingo et al. (2021) estimated about 16,000 premature deaths annually in the United States are due to air pollution from the food sector and found that more than 3,500 premature deaths per year could be avoided through reduced use of ammonia fertilizer, a secondary particulate matter precursor. Better agriculture practices overall can reduce particulate matter-related premature deaths from the agriculture sector by 50% (Domingo et al., 2021). Nitrogen oxides from fertilized croplands are another source of agriculture-based air pollution, and improved management can lead to decreased respiratory and cardiovascular disease (Almarez et al., 2018; Sobota et al., 2015).
Nitrate contamination of drinking water due to excessive runoff from agriculture fields has been linked to several health issues, including blood disorders and cancer (Patel et al., 2022; Ward et al., 2018). Reducing nutrient runoff through better management is critical to minimize these risks (Ward et al., 2018).
Nutrient runoff from agricultural systems is a major driver of water pollution globally, leading to eutrophication and hypoxic zones in aquatic ecosystems (Bijay-Singh & Craswell, 2021). Nitrogen pollution also harms terrestrial biodiversity through soil acidification and increases productivity of fast-growing species, including invasives, which can outcompete native species (Porter et al., 2013). Improved nutrient management is necessary to reduce nitrogen and phosphorus loads to water bodies (Withers et al., 2014; van Grinsven et al., 2019) and terrestrial ecosystems (Porter et al., 2013). These practices have been effective in reducing harmful algal blooms and preserving biodiversity in sensitive water systems (Scavia et al., 2014).
Although substantial reductions in nitrogen use can be achieved in many places with no or minimal impacts on yields, reducing nitrogen application by too much can lead to yield declines, which in turn can boost demand for cropland, causing GHG-producing land use change. Reductions in only excess nitrogen application will prevent substantial yield losses.
Some nutrient management practices are associated with additional emissions. For example, nitrification inhibitors reduce direct nitrous oxide emissions (Qiao et al., 2014) but can increase ammonia volatilization and subsequent indirect nitrous oxide emissions (Lam et al., 2016). Additionally, in wet climates, nitrous oxide emissions may be reduced through the use of manure instead of synthetic fertilizers (Hergoualc’h et al., 2019), though impacts vary across sites and studies (Zhang et al., 2020). Increased demand for manure could increase livestock production, which has high associated GHG emissions. Emissions also arise from transporting manure to the site of use (Qin et al., 2021).
Although nitrous oxide has a strong direct climate-warming effect, fertilizer use can cool the climate through emissions of other reactive nitrogen-containing compounds (Gong et al., 2024). First, aerosols from fertilizers scatter heat from the sun and cool the climate (Shindell et al., 2009; Gong et al., 2024). Moreover, other reactive nitrogen compounds from fertilizers shorten the lifespan of methane in the atmosphere, reducing its warming effects (Pinder et al., 2012). Finally, nitrogen fertilizers that leave farm fields through volatilization or runoff are ultimately deposited elsewhere, enhancing photosynthesis and storing more carbon in plants and soils (Zaehle et al., 2011; Gong et al., 2024). Improved nutrient management would reduce these cooling effects.
Improved nutrient management will reduce emissions from the production phase of biomass crops, increasing their benefit.
(mixed) Improving nutrient management can reduce nutrient pollution in nearby and downstream ecosystems, aiding in their protection or restoration. However, this interaction can be mixed as fertilizer can also enhance terrestrial primary productivity and carbon sequestration in some landscapes.
Improved nutrient management will reduce the GHG production associated with each calorie and, therefore, the impacts of the Improve Diets and Reduce Food Loss and Waste solutions will be reduced.
Each of these solutions could decrease emissions associated with fertilizer production, but improved nutrient management will reduce total demand for fertilizers.
t avoided excess nitrogen application
N₂O
The world’s agricultural lands can emit high levels of nitrous oxide, the third most powerful greenhouse gas. These emissions stem from overusing nitrogen-based fertilizers, especially in regions in China, India, Western Europe, and central North America (in red). While crops absorb some of the nitrogen fertilizer we apply, much of what remains is lost to the atmosphere as nitrous oxide pollution or to local waterways as nitrate pollution. Using fertilizers more wisely can dramatically reduce greenhouse gas emissions and water pollution while maintaining high levels of crop production.
Project Drawdown
The world’s agricultural lands can emit high levels of nitrous oxide, the third most powerful greenhouse gas. These emissions stem from overusing nitrogen-based fertilizers, especially in regions in China, India, Western Europe, and central North America (in red). While crops absorb some of the nitrogen fertilizer we apply, much of what remains is lost to the atmosphere as nitrous oxide pollution or to local waterways as nitrate pollution. Using fertilizers more wisely can dramatically reduce greenhouse gas emissions and water pollution while maintaining high levels of crop production.
Project Drawdown
Improved nutrient management will have the greatest emissions reduction if it is targeted at areas with the largest excesses of nitrogen fertilizer use. In 2020, China, India, and the United States alone accounted for 52% of global excess nitrogen application (Ludemann et al., 2024). Improved nutrient management could be particularly beneficial in China and India, where nutrient use efficiency is currently lower than average (Ludemann et al., 2024). You et al. (2023) also found potential for large increases in nitrogen use efficiency in parts of China, India, Australia, Northern Europe, the United States Midwest, Mexico, and Brazil under standard best management practices. Gu et al. (2024) found that nitrogen input reductions are economically feasible in most of Southern Asia, Northern and Western Europe, parts of the Middle East, North America, and Oceania.
In addition to regional patterns in the adoption ceiling, greater nitrous oxide emissions reductions are possible in wet climates or on irrigated croplands compared to dry climates. Nitrous oxide emissions tend to peak when nitrogen availability is high and soil moisture is in the ~70–90% range (Betterbach-Bahl et al., 2013; Elberling et al., 2023; Hao et al., 2025; Lawrence et al., 2021), though untangling the drivers of nitrous oxide emissions is complex (Lawrence et al., 2021). Water management to avoid prolonged periods of soil moisture in this range is an important complement to nutrient management in wet climates and on irrigated croplands (Deng et al., 2018).
Importantly, improved nutrient management, as defined here, is not appropriate for implementation in areas with nitrogen deficits or negligible nitrogen surpluses, including much of Africa. In these areas, crop yields are constrained by nitrogen availability, and an increase in nutrient inputs may be needed to achieve target yields. Additionally, nutrient management in paddy (flooded) rice systems is not included in this solution but rather in the Improve Rice Production solution.
There is high scientific consensus that reducing nitrogen surpluses through improved nutrient management reduces nitrous oxide emissions from croplands.
Nutrient additions to croplands produce an estimated 0.9 Gt CO₂‑eq/yr (range 0.7–1.1 Gt CO₂‑eq/yr ) of direct nitrous oxide emissions from fields, plus approximately 0.3 Gt CO₂‑eq/yr of emissions from fertilizers that runoff into waterways or erode (Tian et al., 2020). Nitrous oxide emissions from croplands are directly linked to the amount of nitrogen applied. Furthermore, the amount of nitrous oxide emitted per unit of applied nitrogen is well quantified for a range of different nitrogen sources and field conditions (Reay et al., 2012; Shcherbak et al., 2014; Gerber et al., 2016; Intergovernmental Panel on Climate Change [IPCC], 2019; Hergoualc’h et al., 2021). Tools to improve nutrient management have been extensively studied, and practices that improve nitrogen use efficiency through right rate, time, place, and type principles have been implemented in some places for several decades (Fixen, 2020; Ludemann et al., 2024).
Recently, Gao & Cabrera Serrenho (2023) estimated that fertilizer-related emissions could be reduced up to 80% by 2050 relative to current levels using a combination of nutrient management and new fertilizer production methods. You et al. (2023) found that adopting improved nutrient management practices would increase nitrogen use efficiency from a global average of 48% to 78%, substantially reducing excess nitrogen. Wang et al. (2024) estimated that the use of enhanced-efficiency fertilizers could reduce nitrogen losses to the environment 70–75% for maize and wheat systems. Chivenge et al. (2021) found comparable results in smallholder systems in Africa and Asia.
The results presented in this document were produced through analysis of global datasets. We recognize that geographic biases can influence the development of global datasets and hope this work inspires research and data sharing on this topic in underrepresented regions.
In this analysis, we calculated the potential for reducing crop nitrogen inputs and associated nitrous oxide emissions by integrating spatially explicit, crop-specific data on nitrogen inputs, crop yields, attainable yields, irrigated extent, and climate. Broadly, we calculated a “target” yield-scaled nitrogen input rate based on pixels with low yield gaps and calculated the difference between nitrous oxide emissions under the current rate and under the hypothetical target emissions rate, using nitrous oxide emissions factors disaggregated by fertilizer type and climate.
We used Tier 1 emissions factors from the IPCC 2019 Refinement to the 2006 Guidelines for National Greenhouse Gas Inventories, including direct emissions factors as well as indirect emissions from volatilization and leaching pathways. Direct emissions factors represent the proportion of applied nitrogen emitted as nitrous oxide, while we calculated volatilization and leaching emissions factors by multiplying the proportion of applied nitrogen lost through these pathways by the proportion of volatilized or leached nitrogen ultimately emitted as nitrous oxide. Including both direct and indirect emissions, organic and synthetic fertilizers emit 4.97 kg CO₂‑eq/kg nitrogen and 8.66 kg CO₂‑eq/kg nitrogen, respectively, in wet climates, and 2.59 kg CO₂‑eq/kg nitrogen and 2.38 kg CO₂‑eq/kg nitrogen in dry climates. We included uncertainty bounds (2.5th and 97.5th percentiles) for all emissions factors.
We classified each pixel as “wet” or “dry” using an aridity index (AI) threshold of 0.65, calculated as the ratio of annual precipitation to potential evapotranspiration (PET) from TerraClimate data (1991–2020), based on a threshold of 0.65. For pixels in dry climates that contained irrigation, we took the weighted average of wet and dry emissions factors based on the fraction of cropland that was irrigated (Mehta et al., 2024). We excluded irrigated rice from this analysis due to large differences in nitrous oxide dynamics in flooded rice systems.
Using highly disaggregated data on nitrogen inputs from Adalibieke et al. (2024) for 21 crop groups (Table S1), we calculated total crop-specific inputs of synthetic and organic nitrogen. We then averaged over 2016–2020 to reduce the influence of interannual variability in factors like fertilizer prices. These values are subsequently referred to as “current” nitrogen inputs. We calculated nitrous oxide emissions under current nitrogen inputs as the sum of the products of nitrogen inputs and the climatically relevant emissions factors for each fertilizer type.
Next, we calculated target nitrogen application rates in terms of kg nitrogen per ton of crop yield using data on actual and attainable yields for 17 crops from Gerber et al., 2024 (Table S1). For each crop, we first identified pixels in which the ratio of actual to attainable yields was above the 80th percentile globally. The target nitrogen application rate was then calculated as the 20th percentile of nitrogen application rates across low-yield-gap pixels. Finally, we calculated total target nitrogen inputs as the product of actual yields and target nitrogen input rates. We calculated hypothetical nitrous oxide emissions from target nitrogen inputs as the product of nitrogen inputs and the climatically relevant emissions factor for each fertilizer type.
The difference between current and target nitrogen inputs represents the amount by which nitrogen inputs could hypothetically be reduced without compromising crop productivity (i.e., “avoidable” nitrogen inputs). We calculated avoidable nitrous oxide emissions as the difference between nitrous oxide emissions with current nitrogen inputs and those with target nitrogen inputs. For crops for which no yield or attainable yield data were available, we applied the average percent reduction in nitrogen inputs under the target scenario from available crops to the nitrogen input data for missing crops to calculate the avoidable nitrogen inputs and emissions.
This simple and empirically driven method aimed to identify realistically low but nutritionally adequate nitrogen application rates by including only pixels with low yield gaps, which are unlikely to be substantially nutrient-constrained. We did not control for other factors affecting nitrogen availability, such as historical nutrient application rates or depletion, rotation with nitrogen fixing crops, or tillage and residue retention practices.
Table S1. Crops represented by the source data on nitrogen inputs (Adalibieke et al., 2024) and estimated and attainable yields (Gerber et al., 2024). Crop groups included consistently in both datasets are marked as “both,” and crop groups represented in the nitrogen input data but not in the yield datasets are marked as “nitrogen only.”
| Crop | Dataset(s) |
|---|---|
| Barley | Both |
| Cassava | Both |
| Cotton | Both |
| Maize | Both |
| Millet | Both |
| Oil palm | Both |
| Potato | Both |
| Rice | Both |
| Rye | Both |
| Rapeseed | Both |
| Sorghum | Both |
| Soybean | Both |
| Sugarbeet | Both |
| Sugarcane | Both |
| Sunflower | Both |
| Sweet potato | Both |
| Wheat | Both |
| Groundnut | Nitrogen only |
| Fruits | Nitrogen only |
| Vegetables | Nitrogen only |
| Other | Nitrogen only |
Protect Seafloors is the long-term protection of the seafloor from degradation, which helps preserve existing sediment carbon stocks and avoid CO₂ emissions. Advantages of seafloor protection include the conservation of biodiversity and marine ecosystems, potentially low costs, and the ability for immediate implementation. Disadvantages include uncertainties in the effectiveness of legal protection at preventing degradation and in the amount of CO₂ emissions avoided, as well as the risk of displacement of degradation to non-protected areas and/or an increase in other types of degradation. Given these limitations, we conclude that Seafloor Protection is a climate solution to “Keep Watching” until more research can clearly show the carbon benefits of protection.
Based on our analysis, seafloor protection could avoid some CO₂ emissions while preserving critical marine ecosystems from degradation. However, the effectiveness of protection and the magnitude of avoided CO₂ emissions associated with protection are understudied and currently unclear. Therefore, we will “Keep Watching” this potential climate solution.
| Plausible | Could it work? | Yes |
|---|---|---|
| Ready | Is it ready? | No |
| Evidence | Are there data to evaluate it? | Limited |
| Effective | Does it consistently work? | No |
| Impact | Is it big enough to matter? | Yes |
| Risk | Is it risky or harmful? | No |
| Cost | Is it cheap? | Yes |
Protect Seafloors aims to reduce human impacts that can degrade sediment carbon stocks and increase CO₂ emissions. Protection is conferred through legal mechanisms, such as Marine Protected Areas (MPAs), which are managed with the primary goal of conserving nature. The seafloor stores over 2,300 Gt of carbon (roughly 8,400 Gt CO₂‑eq) in the top one meter of sediment. This marine carbon can be stable and remain sequestered for millennia. However, degradation of the seafloor from a range of human activities can disturb bottom sediments, resuspending the carbon and increasing its microbial conversion into CO₂. Currently, degradation of the seafloor primarily results from fishing practices, such as trawling and dredging, which are estimated to occur across 1.3% of the global ocean. Additional sources of degradation include undersea mining, infrastructure development (for offshore wind farms, oil, and gas), and laying telecommunications cables. Estimates of seafloor degradation are highly uncertain due to data limitations and the unpredictable nature of how these activities may expand in the future.
More evidence is needed to confirm whether legal seafloor protection is effective at reducing degradation and the extent to which degradation results in increased CO₂ emissions. While ~8% of the seafloor is currently protected through MPAs, there is mixed evidence that legal protection reduces degradation and CO₂ emissions. For instance, in a review of 49 studies examining the impacts of bottom trawling and dredging on sediment organic carbon stocks, most (61%) showed no change, while nearly a third (29%) showed carbon loss. More recent work suggests that trawling intensity might drive these mixed results, with more heavily trawled areas showing clear reductions in sediment organic carbon. Additionally, the few existing global estimates of CO₂ emissions from trawling and dredging range from 0.03 to 0.58 Gt CO₂/yr, highlighting the need for further research. The effectiveness of MPAs at preventing seafloor degradation is also mixed. In strictly protected areas with enforcement of no-take policies that prevent bottom fishing, MPAs could help minimize degradation and retain seafloor carbon. However, implementation can be challenging, as over half of existing MPAs generally allow high-impact activities. For instance, trawling and dredging occur more frequently in MPAs than in non-protected areas in the territorial waters of Europe.
Advantages of seafloor protection include its potential low cost and its ability to conserve often understudied biodiversity and ecosystems. Human activities, such as trawling and dredging, impact marine organisms on the seafloor, and ecosystem recovery can take years to occur. In the case of undersea mining, ecosystems may never fully recover. Increases in CO₂ emissions along the seafloor from degradation can also enhance local acidification and reduce the ocean's buffering capacity, both of which can affect marine organisms and the carbon sequestration capacity of seawater. Protection can also increase fisheries yields in neighboring waters and reduce other negative impacts of seafloor disturbances. While costs are somewhat uncertain, MPA expenses have been estimated to be an order of magnitude less than the often unseen ecosystem service benefits gained with protection, suggesting MPA expansion could provide cost savings.
Disadvantages of seafloor protection include uncertainties surrounding the effectiveness of preventing degradation and avoiding CO₂ emissions, as well as the potential increased risk of disturbance to other ocean areas. The amount and fate of CO₂ generated due to the degradation of seafloor carbon is complex and understudied. It can take months or even centuries for CO₂ produced at depth to reach the sea surface and atmosphere. Current estimates of CO₂ emissions due to dredging and trawling are widely debated and highly variable due to differing methods and assumptions. Large amounts of organic carbon will inevitably re-settle after seafloor disturbances, with no impact on CO₂, but estimates of just how much remain uncertain. The risk of protection-induced leakage, where a reduction in disturbances, such as trawling and dredging in MPAs, leads to increased fishing effort in other ocean areas, is also potentially high.
Amoroso, R. O., Pitcher, C. R., Rijnsdorp, A. D., McConnaughey, R. A., Parma, A. M., Suuronen, P., ... & Jennings, S. (2018). Bottom trawl fishing footprints on the world’s continental shelves. Proceedings of the National Academy of Sciences, 115(43), E10275-E10282. Link to source: https://doi.org/10.1073/pnas.1802379115
Atwood, T. B., Witt, A., Mayorga, J., Hammill, E., & Sala, E. (2020). Global patterns in marine sediment carbon stocks. Frontiers in Marine Science, 7, 165. Link to source: https://doi.org/10.3389/fmars.2020.00165
Atwood, T.B., Sala, E., Mayorga, J. et al. Reply to: Quantifying the carbon benefits of ending bottom trawling. Nature, 617, E3–E5 (2023). Link to source: https://doi.org/10.1038/s41586-023-06015-6
Atwood, T. B., Romanou, A., DeVries, T., Lerner, P. E., Mayorga, J. S., Bradley, D., ... & Sala, E. (2024). Atmospheric CO2 emissions and ocean acidification from bottom-trawling. Frontiers in Marine Science, 10, 1125137. Link to source: https://doi.org/10.3389/fmars.2023.1125137
Balmford, A., Gravestock, P., Hockley, N., McClean, C.J. and Roberts, C.M. (2004). The worldwide costs of marine protected areas. Proceedings of the National Academy of Sciences, 101(26), pp.9694-9697. Link to source: https://doi.org/10.1073/pnas.0403239101
Burdige, D. J. (2005). Burial of terrestrial organic matter in marine sediments: a re-assessment. Global Biogeochem. Cycles, 19:GB4011. Link to source: https://doi.org/10.1029/2004GB002368
Burdige, D. J. (2007). Preservation of organic matter in marine sediments: controls, mechanisms, and an imbalance in sediment organic carbon budgets? Chem. Rev., 107, 467–485. Link to source: https://doi.org/10.1021/cr050347q
Carr, M. E., Friedrichs, M. A. M., Schmeltz, M., Aita, M. N., Antoine, D., Arrigo, K., et al. (2006). A comparison of global estimates of marine primary production from ocean color. Deep-sea Res. II, Top. Stud. Oceanogr., 53, 741–770. Link to source: https://doi.org/10.1016/j.dsr2.2006.01.028
Clare, M. A., Lichtschlag, A., Paradis, S., & Barlow, N. L. M. (2023). Assessing the impact of the global subsea telecommunications network on sedimentary organic carbon stocks. Nature Communications, 14(1), 2080. Link to source: https://doi.org/10.1038/s41467-023-37854-6
Dureuil, M., Boerder, K., Burnett, K. A., Froese, R., & Worm, B. (2018). Elevated trawling inside protected areas undermines conservation outcomes in a global fishing hot spot. Science, 362(6421), 1403-1407. Link to source: https://doi.org/10.1126/science.aau0561
Epstein, G., Middelburg, J. J., Hawkins, J. P., Norris, C. R., & Roberts, C. M. (2022). The impact of mobile demersal fishing on carbon storage in seabed sediments. Global Change Biology, 28(9), 2875-2894. Link to source: https://doi.org/10.1111/gcb.16105
Estes, E. R., Pockalny, R., D’Hondt, S., Inagaki, F., Morono, Y., Murray, R. W., ... & Hansel, C. M. (2019). Persistent organic matter in oxic subseafloor sediment. Nature Geoscience, 12(2), 126-131. Link to source: https://doi.org/10.1038/s41561-018-0291-5
Kandasamy, S., & Nagender Nath, B. (2016). Perspectives on the terrestrial organic matter transport and burial along the land-deep sea continuum: caveats in our understanding of biogeochemical processes and future needs. Frontiers in Marine Science, 3, 259. Link to source: https://doi.org/10.3389/fmars.2016.00259
Muller-Karger, F. E., Varela, R., Thunell, R., Luerssen, R., Hu, C., and Walsh, J. J. (2005). The importance of continental margins in the global carbon cycle. Geophys. Res. Lett., 32:L01602. Link to source: https://doi.org/10.1029/2004gl021346
Putuhena, H., White, D., Gourvenec, S., & Sturt, F. (2023). Finding space for offshore wind to support net zero: A methodology to assess spatial constraints and future scenarios, illustrated by a UK case study. Renewable and Sustainable Energy Reviews, 182, 113358. Link to source: https://doi.org/10.1016/j.rser.2023.113358
Sala, E., Mayorga, J., Bradley, D., Cabral, R. B., Atwood, T. B., Auber, A., ... & Lubchenco, J. (2021). Protecting the global ocean for biodiversity, food and climate. Nature, 592(7854), 397-402. Link to source: https://doi.org/10.1038/s41586-021-03371-z
Sala, E., & Giakoumi, S. (2018). No-take marine reserves are the most effective protected areas in the ocean. ICES Journal of Marine Science, 75(3), 1166-1168. Link to source: https://doi.org/10.1093/icesjms/fsx059
Siegel, D. A., DeVries, T., Doney, S. C., & Bell, T. (2021). Assessing the sequestration time scales of some ocean-based carbon dioxide reduction strategies. Environmental Research Letters, 16(10), 104003. Link to source: https://doi.org/10.1088/1748-9326/ac0be0
(TMC, 2022) The Metals Company. (2022). How much seafloor will the nodule collection industry impact? Retrieved April 17, 2025, from Link to source: https://metals.co/how-much-seafloor-will-the-nodule-collection-industry-impact/
UNEP-WCMC and IUCN (2024). Protected Planet Report 2024. UNEP-WCMC and IUCN: Cambridge, United Kingdom; Gland, Switzerland. Link to source: https://digitalreport.protectedplanet.net/
Zhang, W., Porz, L., Yilmaz, R., Wallmann, K., Spiegel, T., Neumann, A., ... & Schrum, C. (2024). Long-term carbon storage in shelf sea sediments reduced by intensive bottom trawling. Nature Geoscience, 1-9. Link to source: https://doi.org/10.1038/s41561-024-01581-4
van de Velde, S. J., Hylén, A., & Meysman, F. J. (2025). Ocean alkalinity destruction by anthropogenic seafloor disturbances generates a hidden CO2 emission. Science Advances, 11(13).Link to source: https://doi.org/10.1126/sciadv.adp9112
Watson, S. C., Somerfield, P. J., Lemasson, A. J., Knights, A. M., Edwards-Jones, A., Nunes, J., ... & Beaumont, N. J. (2024). The global impact of offshore wind farms on ecosystem services. Ocean & Coastal Management, 249, 107023. Link to source: https://doi.org/10.1016/j.ocecoaman.2024.107023
More than one-third of all food produced for human consumption is lost or wasted before it can be eaten. This means that the GHGs emitted during the production and distribution of that particular food – including emissions from agriculture-related deforestation and soil management, methane emissions from livestock and rice production, and nitrous oxide emissions from fertilizer management – are also wasted. This solution reduces emissions by lowering the amount of food and its associated emissions that are lost or wasted across the supply chain, from production through consumption.
The global food system, including land use, production, storage, and distribution, generates more than 25% of global GHG emissions (Poore and Nemecek, 2018). More than one-third of this food is lost or wasted before it can be eaten, with estimated associated emissions being recorded at 4.9 Gt CO₂‑eq/yr (our own calculation). FLW emissions arise from supply chain embodied emissions (i.e., the emissions generated from producing food and delivering to consumers). Reducing food loss and waste avoids the embodied emissions while simultaneously increasing food supply and reducing pressure to expand agricultural land use and intensity.
FLW occurs at each stage of the food supply chain (Figure 1). Food loss refers to the stages of production, handling, storage, and processing within the supply chain. Food waste occurs at the distribution, retail, and consumer stages of the supply chain.
Food loss can be reduced through improved post-harvest management practices, such as increasing the number and storage capacity of warehouses, optimizing processes and equipment, and improving packaging to increase shelf life. Retailers can reduce food waste by improving inventory management, forecasting demand, donating unsold food to food banks, and standardizing date labeling. Consumers can reduce food waste by educating themselves, making informed purchasing decisions, and effectively planning meals. The type of interventions to reduce FLW will depend on the type(s) of food product, the supply chain stage(s), and the location(s).
When FLW cannot be prevented, organic waste can be managed in ways that limit its GHG emissions. Waste management is not included in this solution but is addressed in other Drawdown Explorer solutions (see Deploy Methane Digesters, Improve Landfill Management, and Increase Centralized Composting).
Almaraz, M., Houlton, B. Z., Clark, M., Holzer, I., Zhou, Y., Rasmussen, L., Moberg, E., Manaigo, E., Halpern, B. S., Scarborough, C., Lei, X. G., Ho, M., Allison, E., Sibanda, L., & Salter, A. (2023). Model-based scenarios for achieving net negative emissions in the food system. PLOS Climate 2(9), Article e0000181. Link to source: https://doi.org/10.1371/journal.pclm.0000181
Amicarelli, V., Lagioia, G., & Bux, C. (2021). Global warming potential of food waste through the life cycle assessment: An analytical review. Environmental Impact Assessment Review, 91, Article 106677. Link to source: https://doi.org/10.1016/j.eiar.2021.106677
Anríquez, G., Foster, W., Santos Rocha, J., Ortega, J., Smolak, J., & Jansen, S. (2023). Reducing food loss and waste in the Near East and North Africa – Producers, intermediaries and consumers as key decision-makers. Food and Agriculture Organization of the United Nations. Link to source: https://doi.org/10.4060/cc3409en
Babiker, M., Berndes, G., Blok, K., Cohen, B., Cowie, A., Geden, O., Ginzburg, V., Leip, A., Smith, P., Sugiyama, M., & Yamba, F. (2022). Cross-sectoral perspectives. In P. R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, & J. Malley (Eds.), Climate change 2022: Mitigation of climate change. Contribution of working group III to the sixth assessment report of the intergovernmental panel on climate change (pp. 1245–1354). Cambridge University Press. Link to source: https://doi.org/10.1017/9781009157926.014
Byrne, F., Medina, M. K., Mosqueda, E., Salinas, E., Suarez Peña, A. C., Suarez, J. D., Raimondi, G., & Molina, M. (2024). Sustainability impacts of food recovery & redistribution organizations. The Global FoodBanking Network. Link to source: https://www.foodbanking.org/wp-content/uploads/2024/08/FRAME-Methodology_Food-Recovery-to-Avoid-Methane-Emissions_GFN.pdf
Cattaneo, A., Federighi, G., & Vaz, S. (2021). The environmental impact of reducing food loss and waste: A critical assessment. Food Policy, 98, Article 101890. Link to source: https://doi.org/10.1016/j.foodpol.2020.101890
Cattaneo, A., Sánchez, M. V., Torero, M., & Vos, R. (2021). Reducing food loss and waste: Five challenges for policy and research. Food Policy, 98, Article 101974. Link to source: https://doi.org/10.1016/j.foodpol.2020.101974
Chen, C., Chaudhary, A., & Mathys, A. (2020). Nutritional and environmental losses embedded in global food waste. Resources, Conservation and Recycling, 160, Article 104912. Link to source: https://doi.org/10.1016/j.resconrec.2020.104912
Creutzig, F., Niamir, L., Bai, X., Callaghan, M., Cullen, J., Díaz-José, J, Figueroa, M., Grubler, A., Lamb, W.F., Leip, A., Masanet, E., Mata, É., Mattauch, L., Minx, J., Mirasgedis, S., Mulugetta, Y., Nugroho, S.B., Pathak, M., Perkins, P., Roy, J., de la Rue du Can, S., Saheb, Y., Some, S., Steg, L., Steinberger, J., & Ürge-Vorsatz, D. (2021). Demand-side solutions to climate change mitigation consistent with high levels of well-being. Nature Climate Change, 12(1), 36-46. Link to source: https://doi.org/10.1038/s41558-021-01219-y
Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F. N., & Leip, A. (2021). Food systems are responsible for a third of global anthropogenic GHG emissions. Nature Food, 2(3), 198-209. Link to source: https://doi.org/10.1038/s43016-021-00225-9
Davidenko, V., & Sweitzer, M. (2024, November 19). U.S. households that earn less spend a higher share of income on food. USDA Economic Research Service. Link to source: https://www.ers.usda.gov/data-products/charts-of-note/chart-detail?chartId=110391#:~:text=U.S.%20households%20were%20divided%20into,32.6%20percent%20of%20their%20income
de Gorter, H., Drabik, D., Just, D. R., Reynolds, C., & Sethi, G. (2021). Analyzing the economics of food loss and waste reductions in a food supply chain. Food Policy, 98, Article 101953. Link to source: https://doi.org/10.1016/j.foodpol.2020.101953
Delgado, L., Schuster, M., & Torero, M. (2021). Quantity and quality food losses across the value chain: A comparative analysis. Food Policy, 98, Article 101958. Link to source: https://doi.org/10.1016/j.foodpol.2020.101958
Eurostat (2024). Food waste and food waste prevention by NACE Rev. 2 activity [Dataset]. Link to source: https://ec.europa.eu/eurostat/databrowser/view/env_wasfw/default/table?lang=en&category=env.env_was.env_wasst
European Commission Knowledge Center for Bioeconomy (2024). EU Bioeconomy Monitoring System [Dataset]. Link to source: https://knowledge4policy.ec.europa.eu/bioeconomy/monitoring_en
Fabi, C., Cachia, F., Conforti, P., English, A., & Rosero Moncayo, J. (2021). Improving data on food losses and waste: From theory to practice. Food Policy, 98, Article 101934. Link to source: https://doi.org/10.1016/j.foodpol.2020.101934
Food and Agriculture Organization of the United Nations. (2014). Food wastage footprint: Full-cost accounting. Link to source: https://openknowledge.fao.org/server/api/core/bitstreams/6a266c4f-8493-471c-ab49-30f2e51eec8c/content
Food and Agriculture Organization of the United Nations. (2019). The state of food and agriculture 2019: Moving forward on food loss and waste reduction. Link to source: https://openknowledge.fao.org/server/api/core/bitstreams/11f9288f-dc78-4171-8d02-92235b8d7dc7/content
Food and Agriculture Organization of the United Nations. (2023). Tracking progress on food and agriculture-related SDG indicators 2023. Link to source: https://doi.org/10.4060/cc7088en
Food Waste Coalition of Action. (2024). Driving emissions down and profit up by reducing food waste. The Consumer Goods Forum and AlixPartners. Link to source: https://www.theconsumergoodsforum.com/wp-content/uploads/2024/06/Driving-Emissions-Down-Profit-Up-By-Reducing-Food-Waste-FWReport2024-1.pdf
Gatto, A., & Chepeliev, M. (2024). Reducing global food loss and waste could improve air quality and lower the risk of premature mortality. Environmental Research Letters, 19, Article 014080. Link to source: https://doi.org/10.1088/1748-9326/ad19ee
Goossens, Y., Wegner, A., & Schmidt, T. (2019). Sustainability assessment of food waste prevention measures: Review of existing evaluation practices. Frontiers in Sustainable Food Systems, 3(90). Link to source: https://doi.org/10.3389/fsufs.2019.00090
Guo, X., Broeze, J., Groot, J. J., Axmann, H., & Vollebregt, M. (2020). A worldwide hotspot analysis on food loss and waste, associated greenhouse gas emissions, and protein losses. Sustainability, 12(18), Article 7488. Link to source: https://doi.org/10.3390/su12187488
Hanson, C., & Mitchell, P. (2017). The Business Case for Reducing Food Loss and Waste. Link to source: https://champions123.org/sites/default/files/2020-08/business-case-for-reducing-food-loss-and-waste.pdf
Hegnsholt, E., Unnikrishnan, S., Pollmann-Larsen, M., Askelsdottir, B., & Gerard, M. (2018). Tackling the 1.6-billion-ton food loss and waste crisis. The Boston Consulting Group, Food Nation, State of Green. Link to source: https://web-assets.bcg.com/img-src/BCG-Tackling-the-1.6-Billion-Ton-Food-Waste-Crisis-Aug-2018%20%281%29_tcm9-200324.pdf
Hegwood, M., Burgess, M. G., Costigliolo, E. M., Smith, P., Bajzelj, B., Saunders, H., & Davis, S. J. (2023). Rebound effects could offset more than half of avoided food loss and waste. Nature Food, 4(7), 585-595. Link to source: https://doi.org/10.1038/s43016-023-00792-z
Jaglo, K., Kelly, S., & Stephenson, J. (2021). From farm to kitchen: The environmental impacts of U.S. food waste (Report No. EPA 600-R21 171). U.S. Environmental Protection Agency. Link to source: https://www.epa.gov/land-research/farm-kitchen-environmental-impacts-us-food-waste
Karl, K., Tubiello, F. N., Crippa, M., Poore, J., Hayek, M. N., Benoit, P., Chen, M., Corbeels, M., Flammini, A., Garland, S., Leip, A., McClelland, S., Mencos Contreras, E., Sandalow, D., Quadrelli, R., Sapkota, T., and Rosenzweig, C. (2024). Harmonizing food systems emissions accounting for more effective climate action. Environmental Research: Food Systems, 2(1), Article 015001. Link to source: https://doi.org/10.1088/2976-601X/ad8fb3
Kaza, Silpa, Lisa Yao, Perinaz Bhada-Tata, and Frank Van Woerden (2018). What a waste 2.0: A global snapshot of solid waste management to 2050. Urban Development Series. World Bank. Link to source: http://hdl.handle.net/10986/30317
Kenny, S. (2025). Estimating the Cost of Food Waste to American Consumers. (No. EPA/600/R25-048). U.S. Environmental Protection Agency Office of Research and Development. Link to source: https://www.epa.gov/system/files/documents/2025-04/costoffoodwastereport_508.pdf
Kenny, S., Stephenson, J., Stern, A., Beecher, J., Morelli, B., Henderson, A., Chiang, E., Beck, A., Cashman, S., Wexler, E., McGaughy, K., & Martell, A. (2023). From Field to Bin: The Environmental Impact of U.S. Food Waste Management Pathways (No. EPA/600/R-23/065). U.S. Environmental Protection Agency Office of Research and Development. Link to source: https://www.epa.gov/land-research/field-bin-environmental-impacts-us-food-waste-management-pathways
Kummu, M., De Moel, H., Porkka, M., Siebert, S., Varis, O., & Ward, P. J. (2012). Lost food, wasted resources: Global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use. Science of The Total Environment, 438, 447-489. Link to source: https://doi.org/10.1016/j.scitotenv.2012.08.092
Lipinski, B. (2024). SDG target 12.3 on food loss and waste: 2024 progress report. Champions 12.3. Link to source: https://champions123.org/sites/default/files/2024-09/champions-12-3-2024-progress-report.pdf
Mbow, C., Rosenzweig, C., Barioni, L. G., Benton, T. G., Herrero, M., Krishnapillai, M., Liwenga, E., Pradhan, P., Rivera-Ferre, M. G., Sapkota, T., Tubiello, F. N., & Xu, Y. (2019). Food security. In P. R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, & J. Malley (Eds.), Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (pp. 437–550). Cambridge University Press. Link to source: https://doi.org/10.1017/9781009157988.007
Marston, L. T., Read, Q. D., Brown, S. P., & Muth, M. K. (2021). Reducing water scarcity by reducing food loss and waste. Frontiers in Sustainable Food Systems, 5. Link to source: https://doi.org/10.3389/fsufs.2021.651476
Moraes, N. V., Lermen, F. H., & Echeveste, M. E. S. (2021). A systematic literature review on food waste/loss prevention and minimization methods. Journal of Environmental Management, 286. Link to source: https://doi.org/10.1016/j.jenvman.2021.112268
Nabuurs, G.-J., Mrabet, R., Hatab, A. A., Bustamante, M., Clark, H., Havlík, P., House, J. I., Mbow, C., Ninan, K. N., Popp, A., Roe, S., Sohngen, B., & Towprayoon, S. (2022). Agriculture, forestry and other land uses (AFOLU). In P. R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, & J. Malley (Eds.), Climate change 2022: Mitigation of climate change. Contribution of working group III to the sixth assessment report of the intergovernmental panel on climate change (pp. 747–860). Cambridge University Press. Link to source: https://doi.org/10.1017/9781009157926.009
Neff, R. A., Kanter, R., & Vandevijvere, S. (2015). Reducing food loss and waste while improving the public’s health. Health Affairs, 34(11), 1821-1829. Link to source: https://doi.org/10.1377/hlthaff.2015.0647
Nutrition Connect. (2023). Reducing waste from farm to plate: A multi-stakeholder recipe to reduce food loss and waste. Global Alliance for Improved Nutrition (GAIN). Link to source: https://nutritionconnect.org/news-events/reducing-food-loss-waste-farm-plate-stakeholder-recipe-compendium
Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987-992. Link to source: https://doi.org/10.1126/science.aaq0216
Porter, S. D., Reay, D. S., Higgins, P., & Bomberg, E. (2016). 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. Link to source: https://doi.org/10.1016/j.scitotenv.2016.07.041
Read, Q. D., Brown, S., Cuellar, A. D., Finn, S. M., Gephart, J. A., Marston, L. T., Meyer, E., Weitz, K.A., & Muth, M. K. (2020). Assessing the environmental impacts of halving food loss and waste along the food supply chain. Science of the Total Environment, 712, Article 136255. Link to source: https://doi.org/10.1016/j.scitotenv.2019.136255
Read, Q. D., & Muth, M. K. (2021). Cost-effectiveness of four food waste interventions: Is food waste reduction a “win–win?”. Resources, Conservation and Recycling, 168. Link to source: https://doi.org/10.1016/j.resconrec.2021.105448
ReFED. (2024). The methane impact of food loss and waste in the United States. Link to source: https://refed.org/uploads/refed-methane-report-final.pdf
Reynolds, C., Goucher, L., Quested, T., Bromley, S., Gillick, S., Wells, V. K., Evans, D., Koh, L., Carlsson Kanyama, A., Katzeff, C., Svenfelt, A., & Jackson, P. (2019). Review: Consumption-stage food waste reduction interventions – What works and how to design better interventions. Food Policy, 83, 7-27. Link to source: https://doi.org/10.1016/j.foodpol.2019.01.009
Rolker, H., Eisler, M., Cardenas, L., Deeney, M., & Takahashi, T. (2022). Food waste interventions in low-and-middle-income countries: A systematic literature review. Resources, Conservation and Recycling, 186. Link to source: https://doi.org/10.1016/j.resconrec.2022.106534
Searchinger, T., Waite, R., Hanson, C., & Ranganathan, J. (2019). Creating a sustainable food future. World Resources Institute. Link to source: https://research.wri.org/sites/default/files/2019-07/WRR_Food_Full_Report_0.pdf
Sheahan, M., & Barrett, C. B. (2017). Review: Food loss and waste in Sub-Saharan Africa. Food Policy, 70, 1-12. Link to source: https://doi.rog/10.1016/j.foodpol.2017.03.012
Swannell, R., Falconer Hall, M., Tay, R., & Quested, T. (2019). The food waste atlas: An important tool to track food loss and waste and support the creation of a sustainable global food system. Resources, Conservation and Recycling, 146, 534-545. Link to source: https://doi.org/10.1016/j.resconrec.2019.02.006
Thi, N. B. D., Kumar, G., & Lin, C.-Y. (2015). An overview of food waste management in developing countries: Current status and future perspective. Journal of Environmental Management, 157, 220-229. Link to source: https://doi.org/10.1016/j.jenvman.2015.04.022
Tubiello, F. N., Karl, K., Flammini, A., Gütschow, J., Obli-Laryea, G., Conchedda, G., Pan, X., Qi, S. Y., Halldórudóttir Heiðarsdóttir, H., Wanner, N., Quadrelli, R., Rocha Souza, L., Benoit, P., Hayek, M., Sandalow, D., Mencos Contreras, E., Rosenzweig, C., Rosero Moncayo, J., Conforti, P., & Torero, M. (2022). Pre- and post-production processes increasingly dominate greenhouse gas emissions from agri-food systems. Earth System Science Data, 14(4), 1795-1809. Link to source: https://doi.org/10.5194/essd-14-1795-2022
United Nations Environment Programme. (2024). Food waste index report 2024. Think eat save: Tracking progress to halve global food waste. Link to source: https://wedocs.unep.org/xmlui/handle/20.500.11822/45230
U.S. Food and Drug Administration. (2019). Food facts: How to cut food waste and maintain food safety. Link to source: https://www.fda.gov/food/consumers/how-cut-food-waste-and-maintain-food-safety
Wilson, N. L. W., Rickard, B. J., Saputo, R., & Ho, S.-T. (2017). Food waste: The role of date labels, package size, and product category. Food Quality and Preference, 55, 35-44. Link to source: https://doi.org/10.1016/j.foodqual.2016.08.004
World Bank. (2020). Addressing food loss and waste: A global problem with local solutions. Link to source: https://openknowledge.worldbank.org/entities/publication/1564bf5c-ed24-5224-b5d8-93cd62aa3611
WRAP (2023). UK Food System Greenhouse Gas Emissions: Progress towards the Courtauld 2030 target. Link to source: https://www.wrap.ngo/sites/default/files/2024-05/WRAP-MIANZW-Annual-Progress-Summary-report-22-23-Variation-1-2024-04-30.pdf
WRAP (2024). UK food system greenhouse gas emissions: Progress towards the Courtauld 2030 target. Link to source: https://www.wrap.ngo/sites/default/files/2024-12/WRAP-Courtauld-2030-GHG-2324.pdf
WWF-UK. (2021). Driven to waste: The global impact of food loss and waste on farms. :Link to source: https://files.worldwildlife.org/wwfcmsprod/files/Publication/file/5p58sxloyr_technical_report_wwf_farm_stage_food_loss_and_waste.pdf
WWF-WRAP. (2020). Halving food loss and waste in the EU by 2030: The major steps needed to accelerate progress. Link to source: https://www.wrap.ngo/resources/report/halving-food-loss-and-waste-eu-2030-major-steps-needed-accelerate-progress
Xue, L., Liu, G., Parfitt, J., Liu, X., Herpen, E. V., O’Connor, C., Östergren, K., & Cheng, S. 2017. Missing food, missing data? A critical review of global food losses and food waste data. Env Sci Technol. 51, 6618-6633. Link to source: https://doi.org/10.1021/acs.est.7b00401
Ziervogel, G., & Ericksen, P. J. (2010). Adapting to climate change to sustain food security. WIREs Climate Change, 1(4), 525-540. Link to source: https://doi.org/10.1002/wcc.56
Zhu, J., Luo, Z., Sun, T., Li, W., Zhou, W., Wang, X., Fei, X., Tong, H., & Yin, K. (2023). Cradle-to-grave emissions from food loss and waste represent half of total greenhouse gas emissions from food systems. Nature Food, 4(3), 247-256. Link to source: https://doi.org/10.1038/s43016-023-00710-3
Erika Luna
Aishwarya Venkat, Ph.D.
Ruthie Burrows, Ph.D.
Emily Cassidy, Ph.D.
James Gerber, Ph.D.
Yusuf Jameel, Ph.D.
Daniel Jasper
Alex Sweeney
Eric Toensmeier
Paul C. West, Ph.D.
Aiyana Bodi
Hannah Henkin
Megan Matthews, Ph.D.
Heather McDiarmid, Ph.D.
Ted Otte
Christina Swanson, Ph.D.
Paul C. West, Ph.D.
Our analysis estimates that reducing FLW reduces emissions 2.82 t CO₂‑eq (100-yr basis) for every metric ton of food saved (Table 1). This estimate is based on selected country and global assessments from nongovernmental organizations (NGOs), public agencies, and development banks (ReFED, 2024; World Bank, 2020; WRAP, 2024). All studies included in this estimate reported a reduction in both volumes of FLW and GHG emissions. However, it is important to recognize that the range of embodied emissions varies widely across foods (Poore & Nemecek, 2018). For example, reducing meat waste can be more effective than reducing fruit waste because the embodied emissions are much higher.
Effectiveness is only reported on a 100-yr time frame here because our data sources did not include enough information to separate out the contribution of different GHGs and calculate the effectiveness on a 20-yr time frame.
Table 1. Effectiveness at reducing emissions.
Unit: t CO₂‑eq /t reduced FLW, 100-yr basis
| 25th percentile | 2.75 |
| Mean | 3.11 |
| Median (50th percentile) | 2.82 |
| 75th percentile | 3.30 |
The net cost of baseline FLW is US$932.56/t waste, based on values from the Food and Agriculture Organization of the United Nations (FAO, 2014) and Hegensholt et al. (2018). The median net cost of implementing strategies and practices that reduce FLW is US$385.5/t waste reduced, based on values from ReFED (2024) and Hanson and Mitchell (2017). These costs include, but are not limited to, improvements to inventory tracking, storage, and diversion to food banks. Therefore, the net cost of the solution compared to baseline is a total savings of US$547.0/t waste reduced.
Therefore, reducing emissions for FLW is cost-effective, saving US$194.0/t avoided CO₂‑eq on a 100-yr basis (Table 2).
Table 2. Net cost per unit climate impact.
Unit: US$/t CO₂‑eq , 2023
| Median (100-yr basis) | -194.0 |
Learning curve data were not yet available for this solution.
Speed of action refers to how quickly a climate solution physically affects the atmosphere after it is deployed. This is different from speed of deployment, which is the pace at which solutions are adopted.
At Project Drawdown, we define the speed of action for each climate solution as emergency brake, gradual, or delayed.
Reduce Food Loss and Waste is an EMERGENCY BRAKE climate solution. It has the potential to deliver a more rapid impact than nominal and delayed solutions. Because emergency brake solutions can deliver their climate benefits quickly, they can help accelerate our efforts to address dangerous levels of climate change. For this reason, they are a high priority.
Reducing FLW through consumer behavior, supply chain efficiencies, or other means can lead to lower food prices, creating a rebound effect that leads to increased consumption and GHG emissions (Hegwood et al., 2023). This rebound effect could offset around 53–71% of the mitigation benefits (Hegwood et al., 2023). Population and economic growth also increase FLW. The question remains however, who should bear the cost of implementing FLW solutions. A combination of value chain investments by governments and waste taxes for consumers may be required for optimal FLW reduction (Gatto, 2023; Hegwood, 2023; The World Bank, 2020).
Strategies for managing post-consumer waste through composting and landfills are captured in other Project Drawdown solutions (see Improve Landfill Management, Increase Centralized Composting, and Deploy Methane Digesters).
Due to a lack of data we were not able to quantify current adoption for this solution.
Data on adoption trends were not available.
We assumed an adoption ceiling of 1.75 Gt of FLW reduction in 2023, which reflects a 100% reduction in FLW (Table 3). While reducing FLW by 100% is unrealistic because some losses and waste are inevitable (e.g., trimmings, fruit pits and peels) and some surplus food is needed to ensure a stable food supply (HLPE, 2014), we kept that simple assumption because there wasn’t sufficient information on the amount of inevitable waste, and it is consistent with other research used in this assessment.
Table 3. Adoption ceiling.
Unit: t reduced FLW/yr
| Median | 1,750,000,000 |
Studies consider that halving the reduction in FLW by 2050 is extremely ambitious and would require “breakthrough technologies,” whereas a 25% reduction is classified as highly ambitious, and a 10% reduction is more realistic based on coordinated efforts (Searchinger, 2019; Springmann et al., 2018). With our estimate of 1.75 Gt of FLW per year, a 25% reduction equals 0.48 Gt, while a 50% reduction would represent 0.95 Gt of reduced FLW.
It is important to acknowledge that, 10 years after the 50% reduction target was set in the Sustainable Development Goals (SDGs, Goal 12.3), the world has not made sufficient progress. The challenge has therefore become larger as the amounts of FLW keep increasing at a rate of 2.2%/yr (Gatto & Chepeliev, 2023; Hegnsholt, et al. 2018; Porter et al., 2016).
As a result of these outcomes, we have selected a 25% reduction in FLW as our Achievable – Low and 50% as our Achievable – High. Reductions in FLW are 437.5, 875.0, and 1,750 Mt FLW/year for Achievable – Low, Achievable – High, and Adoption Ceiling, respectively (Table 4).
Table 4. Adoption levels.
Unit: t reduced FLW/yr
| Current adoption (baseline) | Not determined |
| Achievable – low (25% of total FLW) | 437,500,000 |
| Achievable – high (50% of total FLW) | 875,000,000 |
| Adoption ceiling (100% of total FLW) | 1,750,000,000 |
An Achievable – Low (25% FLW reduction) could represent 1.23 Gt CO₂‑eq/yr (100-yr basis) of reduced emissions, whereas an Achievable – High (50% FLW reduction) could represent up to 2.47 Gt CO₂‑eq/yr. The adoption potential (100% FLW reduction) would result in 4.94 Gt CO₂‑eq/yr (Table 5). We only report emissions outcomes on a 100-yr basis here because most data sources did not separate the percentage of type of food wasted or disaggregate their associated emissions factors by GHG type. Estimated impacts would be higher on a 20-yr basis due to the higher GWP of methane associated with meat and rice production.
Table 5. Climate impact at different levels of adoption.
Unit: Gt CO₂‑eq/yr, 100-yr basis
| Current adoption (1.5% of total FLW) | Not determined |
| Achievable – low (25% of total FLW) | 1.23 |
| Achievable – high (50% of total FLW) | 2.47 |
| Adoption ceiling (100% of total FLW) | 4.94 |
We also compiled studies that have modeled the climate impacts of different FLW reduction scenarios, from 10% to 75%. For an achievable 25% reduction, Scheringer (2019) estimated a climate impact of 1.6 Gt CO₂‑eq/yr. Studies that modeled the climate impact of a 50% reduction by 2050 estimated between 0.5 Gt CO₂‑eq/yr (excluding emissions from agricultural production and land use change; Roe at al., 2021) to 3.1–4.5 Gt CO₂‑eq/yr (including emissions from agricultural production and land use change; Roe at al., 2021; Searchinger et al., 2019).
Multiple studies stated that climate impacts from FLW reduction would be greater when combined with the implementation of dietary changes (see the Improve Diets solution; Almaraz et al., 2023; Babiker et al.; 2022; Roe et al., 2021; Springmann et al., 2018; Zhu et al., 2023).
Households and communities can strengthen adaptation to climate change by improving food storage, which helps reduce food loss (Ziervogel & Ericksen, 2010). Better food storage infrastructure improves food security from extreme weather events such as drought or floods which make it more difficult to grow food and can disrupt food distribution (Mbow et al., 2019).
FLW accounts for a loss of about US$1 trillion annually (World Bank, 2020). In the United States, a four-person household spends about US$2,913 on food that is wasted (Kenny, 2025). These household-level savings are particularly important for low-income families because they commonly spend a higher proportion of their income on food (Davidenko & Sweitzer, 2024). Reducing FLW can improve economic efficiency (Jaglo et al., 2021). In fact, a report by Champions 12.3 found efforts to reduce food waste produced positive returns on investments in cities, businesses, and households in the United Kingdom (Hanson & Mitchell, 2017). FLW in low- and middle-income countries mostly occurs during the pre-consumer stages, such as storage, processing, and transport (Kaza et al., 2018). Preventive measures to reduce these losses have been linked to improved incomes and profits (Rolker et al., 2022).
Reducing FLW increases the amount of available food, thereby improving food security without requiring increased production (Neff et al., 2015). The World Resources Institute estimated that halving the rate of FLW could reduce the projected global need for food approximately 20% by 2050 (Searchinger et al., 2019). In the United States, about 30–40% of food is wasted (U.S. Food and Drug Administration [U.S. FDA], 2019) with this uneaten food accounting for enough calories to feed more than 150 million people annually (Jaglo et al., 2021). These studies demonstrate that reducing FLW can simultaneously decrease the demand for food production while improving food security.
Policies that reduce food waste at the consumer level, such as those that improve food packaging and require clearer information on shelf life and date labels, can reduce the number of foodborne illnesses (Neff et al., 2015; U.S. FDA, 2019). Additionally, efforts to improve food storage and food handling can further reduce illnesses and improve working conditions for food-supply-chain workers (Neff et al., 2015). Reducing FLW can lower air pollution from food production, processing, and transportation and from disposal of wasted food (Nutrition Connect, 2023). Gatto and Chepeliev (2024) found that reducing FLW can improve air quality (primarily through reductions in carbon monoxide, ammonia, nitrogen oxides, and particulate matter), which lowers premature mortality from respiratory infections. These benefits were primarily observed in China, India, and Indonesia, where high FLW-embedded air pollution is prevalent across all stages of the food supply chain (Gatto & Chepeliev, 2024).
For a description of the land resources benefits, please refer to the “water resources” subsection below.
Reducing FLW can conserve resources and improve biodiversity (Cattaneo, Federighi, & Vaz, 2021). A reduction in FLW reflects improvements in resource efficiency of freshwater, synthetic fertilizers, and cropland used for agriculture (Kummu et al., 2012). Reducing the strain on freshwater resources is particularly relevant in water-scarce areas such as North Africa and West-Central Asia (Kummu et al., 2012). In the United States, halving the amount of FLW could reduce approximately 290,000 metric tons of nitrogen from fertilizers, thereby reducing runoff, improving water quality, and decreasing algal blooms (Jaglo et al., 2021).
Interventions to address FLW risk ignoring economic factors such as price transmission mechanisms and cascading effects, both upstream and downstream in the supply chain. The results of a FLW reduction policy or program depend greatly on the commodity, initial FLW rates, and market integration (Cattaneo, 2021; de Gorter, 2021).
On the consumer side, there is a risk of a rebound effect: Avoiding FLW can lower food prices, leading to increased consumption and net increase in GHG emissions (Hegwood et al., 2023). Available evidence is highly contextual and often difficult to scale, so relevant dynamics must be studied with care (Goossens, 2019).
The production site is a critical loss point, and farm incomes, scale of operations, and expected returns to investment affect loss reduction interventions (Anriquez, 2021; Fabi, 2021; Sheahan and Barrett, 2017).
Reducing FLW can lower new demand for high-emissions foods, like ruminant meat.
Reducing FLW can lower demand for new production of livestock and crops, reducing the use of fertilizers or manure and therefore associated emissions.
Reducing FLW can reduce the demand to expand agriculture, support land conservation and restoration, and benefit water quality.
Reducing food loss and waste can reduce the demand for wild-harvested macroalgae.
(mixed) Reducing FLW can increase demand for cold storage, more efficient appliances, and optimized transport, which could reinforce the adoption of solutions targeting these improvements. However, reducing FLW could compete with other solutions if loss reductions are achieved mainly from producing less food, which could lead to lower refrigeration demand.
Food waste is used as raw material for methane digestors and composting. Reducing FLW may reduce the impact of those solutions as a result of decreased feedstock availability.
t reduced FLW
CO₂ , CH₄ , N₂O
Some FLW reduction strategies have trade-offs for emission reductions (Cattaneo, 2021; de Gorter et al., 2021). For example, improved cold storage and packaging are important interventions for reducing food loss, yet they require additional electricity and refrigerants, which can increase GHG emissions (Babiker et al., 2022; FAO, 2019).
A large volume of scientific research exists regarding reducing emissions of FLW effectively. The IPCC Sixth Assessment Report (AR6) estimates the mitigation potential of FLW reduction (through multiple reduction strategies) to be 2.1 Gt CO₂‑eq/yr (with a range of 0.1–5.8 Gt CO₂‑eq/yr ) (Nabuurs et al., 2022). This accounts for savings along the whole value chain.
Following the 2011 FAO report – which estimated that around one-third (1.3 Gt) of food is lost and wasted worldwide per year – global coordination has prioritized the measurement of the FLW problem. This statistic has served as a baseline for multiple FLW reduction strategies. However, more recent studies suggest that the percentage of FLW may be closer to 40% (WWF, 2021). The median of the studies included in our analysis is 1.75 Gt/yr of FLW (FAO, 2024; Gatto & Chepeliev, 2024; Guo et al., 2020; Porter et al., 2016; UNEP, 2024; WWF, 2021; Zhu et al., 2023), with an annual increasing trend of 2.2%.
Only one study included in our analysis calculated food embodied emissions from all stages of the supply chain, while the rest focused on the primary production stages. Zhu et al. (2023) estimated 6.5 Gt CO₂‑eq/yr arising from the supply chain side, representing 35% of total food system emissions.
When referring to food types, meat and animal products were estimated to emit 3.5 Gt CO₂‑eq/yr compared to 0.12 Gt CO₂‑eq/yr from fruits and vegetables (Zhu et al., 2023). Although meat is emissions-intensive, fruits and vegetables are the most wasted types of food by volume, making up 37% of total FLW by mass (Chen et al., 2020). The consumer stage is associated with the highest share of global emissions at 36% of total supply-embodied emissions from FLW, compared to 10.9% and 11.5% at the retail and wholesale levels, respectively (Zhu et al., 2023).
While efforts to measure the FLW problem are invaluable, critical gaps exist regarding evidence of the effectiveness of different reduction strategies across supply chain stages ( Cattaneo, 2021; Goossens, 2019; Karl et al., 2025). To facilitate impact assessments and cost-effectiveness, standardized metrics are required to report actual quantities of FLW reduced as well as resulting GHG emissions savings (Food Loss and Waste Protocol, 2024).
The results presented in this document summarize findings across 22 studies. These studies are made up of eight academic reviews and original studies, eight reports from NGOs, and six reports from public and multilateral organizations. This reflects current evidence from five countries, primarily the United States and the United Kingdom. We recognize this limited geographic scope creates bias, and hope this work inspires research for meta-analyses and data sharing on this topic in underrepresented regions and stages of the supply chain.
Agriculture produces about 12 Gt CO₂‑eq/yr, or 21% of total human-caused GHG emissions (Intergovernmental Panel on Climate Change [IPCC], 2023). Animal agriculture contributes more than half of these emissions (Halpern et al., 2022; Poore and Nemecek, 2018).
Ruminant animals, such as cattle, sheep, and goats produce methane – a GHG with 80 times the warming potential of CO₂ in the near term – in their digestive system (Jackson et al., 2024). Since agriculture is the leading driver of tropical deforestation, particularly for cattle and animal feed production, reducing ruminant meat consumption can avoid additional forest loss and associated GHG emissions.
We define improved diets as a reduction in ruminant meat consumption and a replacement with other protein-rich foods. Such a diet shift can be adopted incrementally through small behavioral changes that together lead to globally significant reductions in GHG emissions.
Reducing ruminant meat consumption, especially in high-consuming regions, has a globally significant potential for climate change mitigation. Ruminants contribute 30% of food-related emissions but generate only 5% of global dietary calories (Li et al., 2024).
Ruminant animals have digestive systems with multiple chambers that allow them to ferment grass and leaves. However, this digestion generates methane emissions through a process called enteric fermentation. In addition, clearing forests and grasslands for pastures and cropland to feed livestock emits CO₂, and livestock manure emits methane and nitrous oxide.
In 2019, an international team of scientists called the EAT-Lancet Commission developed benchmarks for a healthy, sustainable diet based on peer-reviewed information on human health and environmental sustainability (Willett et al., 2019). The commission estimated that red meat (beef, lamb, and pork) should be limited to 14 grams (30 calories) per day per person, or 5.1 kg/person/yr. Although the EAT-Lancet diet includes pork, our analysis looked specifically at limiting ruminant meat to 5.1 kg/person/yr because it has much higher GHG emissions than pork (Figure 1).
Figure 1. Greenhouse gas emissions associated with the production of protein-rich foods. Beef has the highest emissions per kilogram. These emissions data are from Poore & Nemecek (2018), with the exception of "Ruminant meat," which was calculated based on the amount of beef and lamb consumed in 2022.
Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987–992.
In this solution, we explored reducing ruminant meat consumption in middle- and high-income countries in which consumption exceeds 5.1 kg/person/yr. Furthermore, our analysis assumed ruminant meat is replaced with approximately the same amount of protein-rich plant- or animal-based foods, which are estimated to be about 20% protein by weight (Poore and Nemecek, 2018).
Bai, Y., Alemu, R., Block, S. A., Headey, D., & Masters, W. A. (2021). Cost and affordability of nutritious diets at retail prices: Evidence from 177 countries. Food policy, 99, Article 101983. Link to source: https://doi.org/10.1016/j.foodpol.2020.101983
Bouvard, V., Loomis, D., Guyton, K. Z., Grosse, Y., Ghissassi, F. E., Benbrahim-Tallaa, L., Guha, N., Mattock, H., & Straif, K. (2015). Carcinogenicity of consumption of red and processed meat. The Lancet Oncology, 16(16), 1599–1600. https://doi.org/10.1016/S1470-2045(15)00444-1
Bradbury, K. E., Murphy, N., & Key, T. J. (2020). Diet and colorectal cancer in UK Biobank: A prospective study. International Journal of Epidemiology, 49(1), 246–258. Link to source: https://doi.org/10.1093/ije/dyz064
Casey, J. A., Curriero, F. C., Cosgrove, S. E., Nachman, K. E., & Schwartz, B. S. (2013). High-density livestock operations, crop field application of manure, and risk of community-associated methicillin-resistant Staphylococcus aureus infection in Pennsylvania. JAMA Internal Medicine, 173(21), 1980–1990. Link to source: https://doi.org/10.1001/jamainternmed.2013.10408
Domingo, N. G. G., Balasubramanian, S., Thakrar, S. K., Clark, M. A., Adams, P. J., Marshall, J. D., Muller, N. Z., Pandis, S. N., Polasky, S., Robinson, A. L., Tessum, C. W., & Hill, J. D. (2021). Air quality–related health damages of food. Proceedings of the National Academy of Sciences, 118(20), Article e2013637118. Link to source: https://doi.org/10.1073/pnas.2013637118
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O’Connell, C., Ray, D. K., West, P. C., Balzer, C., Bennett, E. M., Carpenter, S. R., Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., ... Zaks, D. P. M. (2011). Solutions for a cultivated planet. Nature, 478, 337–342. Link to source: https://doi.org/10.1038/nature10452
Food and Agriculture Organization of the United Nations (FAO). (2025). FAO‑FAOSTAT: Food balances (2010-) [Data set]. Food balances for individual countries for the year 2022 (most recent year available). Retrieved March 25, 2025, from Link to source: https://www.fao.org/faostat/en/#data/FBS
Food and Agriculture Organization of the United Nations (FAO). (2023). Low-Income Food-Deficit Countries (LIFDCs) - List updated June 2023. Retrieved March 25, 2025, from Link to source: https://www.fao.org/member-countries/lifdc/en
Food and Agriculture Organization of the United Nations (FAO). (2017). Livestock solutions for climate change [Technical paper]. Link to source: https://www.fao.org/family-farming/detail/en/c/1634679/
Gerber, P. J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A., & Tempio, G. (2013). Tackling climate change through livestock: A global assessment of emissions and mitigation opportunities [Report]. Food and Agriculture Organization of the United Nations. Link to source: https://www.fao.org/4/i3437e/i3437e00.htm
Godfray, H. C. J., Aveyard, P., Garnett, T., Hall, J. W., Key, T. J., Lorimer, J., Pierrehumbert, R. T., Scarborough, P., Springmann, M., & Jebb, S. A. (2018). Meat consumption, health, and the environment. Science, 361(6399), Article eaam5324. Link to source: https://doi.org/10.1126/science.aam5324
Gupta, S., Vemireddy, V., Singh, D. K., & Pingali, P. (2021). Ground truthing the cost of achieving the EAT lancet recommended diets: Evidence from rural India. Global Food Security, 28, Article 100498. Link to source: https://doi.org/10.1016/j.gfs.2021.100498
Halpern, B. S., Frazier, M., Verstaen, J., Rayner, P.-E., Clawson, G., Blanchard, J. L., Cottrell, R. S., Froehlich, H. E., Gephart, J. A., Jacobsen, N. S., Kuempel, C. D., McIntyre, P. B., Metian, M., Moran, D., Nash, K. L., Többen, J., & Williams, D. R. (2022). The environmental footprint of global food production. Nature Sustainability, 5, 1027–1039. Link to source: https://doi.org/10.1038/s41893-022-00965-x
Harter, T., Lund, J. R., Darby, J., Fogg, G. E., Howitt, R., Jessoe, K. K., Pettygrove, G. S., Quinn, J. F., Viers, J. H., Boyle, D. B., Canada, H. E., De La Mora, N., Dzurella, K. N., Fryjoff-Hung, A., Hollander, A. D., Honeycutt, K. L., Jenkins, M. W., Jensen, V. B., King, A. M., ... Rosenstock, T. S. (2012). Addressing nitrate in California’s drinking water with a focus on Tulare Lake Basin and Salinas Valley groundwater [Report]. Center for Watershed Sciences, University of California. Link to source: https://ucanr.edu/sites/default/files/2012-03/138956.pdf
Heederik, D., Sigsgaard, T., Thorne, P. S., Kline, J. N., Avery, R., Bønløkke, J. H., Chrischilles, E. A., Dosman, J. A., Duchaine, C., Kirkhorn, S. R., Kulhanková, K., & Merchant, J. A. (2007). Health effects of airborne exposures from concentrated animal feeding operations. Environmental Health Perspectives, 115(2), 298–302. Link to source: https://doi.org/10.1289/ehp.8835
Herrero, M., Henderson, B., Havlík, P., Thornton, P. K., Conant, R. T., Smith, P., Wirsenius, S., Hristov, A. N., Gerber, P., Gill, M., Butterbach-Bahl, K., Valin, H., Garnett, T., & Stehfest, E. (2016). Greenhouse gas mitigation potentials in the livestock sector. Nature Climate Change, 6(5), 452–461. Link to source: https://doi.org/10.1038/nclimate2925
Hirvonen, K., Bai, Y., Headey, D., & Masters, W. A. (2020). Affordability of the EAT–Lancet reference diet: A global analysis. The Lancet Global Health, 8(1), e59–e66. Link to source: https://doi.org/10.1016/S2214-109X(19)30447-4
Intergovernmental Panel on Climate Change. (2023). Climate change 2023: Synthesis report. Contribution of working groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change [Core Writing Team, H. Lee, & J. Romero (Eds.)]. Link to source: https://doi.org/10.59327/IPCC/AR6-9789291691647
Jackson, R. B., Saunois, M., Martinez, A., Canadell, J. G., Yu, X., Li, M., Poulter, B., Raymond, P. A., Regnier, P., Ciais, P., Davis, S. J., & Patra, P. K. (2024). Human activities now fuel two-thirds of global methane emissions. Environmental Research Letters, 19(10), Article 101002. Link to source: https://doi.org/10.1088/1748-9326/ad6463
Kaluza, J., Wolk, A., & Larsson, S. C. (2012). Red meat consumption and risk of stroke: A meta-analysis of prospective studies. Stroke, 43(10), 2556–2560. Link to source: https://doi.org/10.1161/STROKEAHA.112.663286
Katare, B., Wang, H. H., Lawing, J., Hao, N., Park, T., & Wetzstein, M. (2020). Toward optimal meat consumption. American Journal of Agricultural Economics, 102(2), 662–680. Link to source: https://doi.org/10.1002/ajae.12016
Kim, B. F., Santo, R. E., Scatterday, A. P., Fry, J. P., Synk, C. M., Cebron, S. R., Mekonnen, M. M., Hoekstra, A. Y., de Pee, S., Bloem, M. W., Neff, R. A., & Nachman, K. E. (2020). Country-specific dietary shifts to mitigate climate and water crises. Global Environmental Change, 62, Article 101926. Link to source: https://doi.org/10.1016/j.gloenvcha.2019.05.010
Li, M., Wang, Y., Zhao, S., Chen, W., Liu, Y., Zheng, H., Sun, Z., He, P., Li, R., Zhang, S., Xing, P., & Li., Q. (2024). Improving the affordability and reducing greenhouse gas emissions of the EAT-Lancet diet in China. Sustainable Production and Consumption, 52, 445–457. Link to source: https://doi.org/10.1016/j.spc.2024.11.014
Li, Y., He, P., Shan, Y., Li, Y., Hang, Y., Shao, S., Ruzzenenti, F., & Hubacek, K. (2024). Reducing climate change impacts from the global food system through diet shifts. Nature Climate Change, 14(9), 943–953. Link to source: https://doi.org/10.1038/s41558-024-02084-1
Mariotti, F., & Gardner, C. D. (2019). Dietary protein and amino acids in vegetarian diets—A review. Nutrients, 11(11), Article 2661. Link to source: https://doi.org/10.3390/nu11112661
Mbow, C., Rosenzweig, C., Barioni, L. G., Benton, T. G., Herrero, M., Krishnapillai, M., Liwenga, E., Pradhan, P., Rivera-Ferre, M. G., Sapkota, T., Tubiello, F. N., & Xu, Y. (2019). Food security. In P. R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, & J. Malley (Eds.), Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (pp. 437–550). Cambridge University Press. Link to source: https://doi.org/10.1017/9781009157988.007
Meier, T., & Christen, O. (2013). Environmental impacts of dietary recommendations and dietary styles: Germany as an example. Environmental Science & Technology, 47(2), 877–888. Link to source: https://doi.org/10.1021/es302152v
Nelson, M. E., Hamm, M. W., Hu, F. B., Abrams, S. A., & Griffin, T. S. (2016). Alignment of healthy dietary patterns and environmental sustainability: A systematic review. Advances in Nutrition, 7(6), 1005–1025. Link to source: https://doi.org/10.3945/an.116.012567
Nijdam, D., Rood, T., & Westhoek, H. (2012). The price of protein: Review of land use and carbon footprints from life cycle assessments of animal food products and their substitutes. Food Policy, 37(6), 760–770. Link to source: https://doi.org/10.1016/j.foodpol.2012.08.002
Norwood, F. B., & Lusk, J. L. (2011). Compassion, by the pound: The economics of farm animal welfare. Oxford University Press. Link to source: https://global.oup.com/academic/product/compassion-by-the-pound-9780199551163?cc=ca&lang=en&
Pan, A., Sun, Q., Bernstein, A. M., Schulze, M. B., Manson, J. E., Willett, W. C., & Hu, F. B. (2011). Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. The American Journal of Clinical Nutrition, 94(4), 1088–1096. Link to source: https://doi.org/10.3945/ajcn.111.018978
Pan, A., Sun, Q., Bernstein, A. M., Schulze, M. B., Manson, J. E., Stampher, M. J., Willett, W. C., & Hu, F. B. (2012). Red meat consumption and mortality: Results from 2 prospective cohort studies. Archives of Internal Medicine, 172(7), 555–563. Link to source: https://doi.org/10.1001/archinternmed.2011.2287
Pimentel, D., & Pimentel, M. (2003). Sustainability of meat-based and plant-based diets and the environment. The American Journal of Clinical Nutrition, 78(3), 660S–663S. Link to source: https://doi.org/10.1093/ajcn/78.3.660S
Poore, J., & Nemecek, T. (2018) Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987–992. Link to source: https://doi.org/10.1126/science.aaq0216
Porter, S., & Cox, C. (2020, May 28). Manure overload: Manure plus fertilizer overwhelms Minnesota’s land and water. Environmental Working Group. Link to source: https://www.ewg.org/interactive-maps/2020-manure-overload/
Ripple, W. J., Smith, P., Haberl, H., Montzka, S. A., McAlpine, C., & Boucher, D. H. (2014a). Ruminants, climate change and climate policy. Nature Climate Change, 4(1), 2–5. Link to source: https://doi.org/10.1038/nclimate2081
Ripple, W. J., Estes, J. A., Beschta, R. L., Wilmers, C. C., Ritchie, E. G., Hebblewhite, M., Berger, J., Elmhagen, B., Letnic, M., Nelson, M. P., Schmitz, O. J., Smith, D. W., Wallach, A. D., & Wirsing, A. J. (2014b). Status and ecological effects of the world’s largest carnivores. Science, 343(6167), Article 1241484. Link to source: https://doi.org/10.1126/science.1241484
Ripple, W. J., Newsome, T. M., Wolf, C., Dirzo, R., Everatt, K. T., Galetti, M., Hayward, M. W., Kerley, G. I. H., Levi, T., Lindsey, P. A., Macdonald, D. W., Malhi, Y., Painter, L. E., Sandom, C. J., Terborgh, J., & Van Valkenburgh, B. (2015). Collapse of the world’s largest herbivores. Science Advances, 1(4), Article e1400103. Link to source: https://doi.org/10.1126/sciadv.1400103
Searchinger, T., Waite, R., Hanson, C., Ranganathan, J., Dumas, P., Matthews, E., & Klirs, C. (2019). Creating a sustainable food future: A menu of solutions to feed nearly 10 billion people by 2050 [Report]. World Resources Institute. Link to source: https://research.wri.org/wrr-food
Sinha, R., Cross, A. J., Graubard, B. I., Leitzmann, M. F., & Schatzkin, A. (2009). Meat intake and mortality: A prospective study of over half a million people. Archives of Internal Medicine, 169(6), 562–571. Link to source: https://doi.org/10.1001/archinternmed.2009.6
Springmann, M., Clark, M. A., Rayner, M., Scarborough, P., & Webb, P. (2021). The global and regional costs of healthy and sustainable dietary patterns: A modelling study. The Lancet Planetary Health, 5(11), e797–e807. Link to source: https://doi.org/10.1016/S2542-5196(21)00251-5
Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, M., & de Haan, C. (2006). Livestock’s long shadow: Environmental issues and options [Report]. Food and Agriculture Organization of the United Nations. Link to source: https://www.fao.org/4/a0701e/a0701e00.htm
Sun, J., Liao, X.-P., D’Souza, A. W., Boolchandani, M., Li, S.-H., Cheng, K., Luis Martínez, J., Li, L., Feng, Y.-J., Fang, L.-X., Huang, T., Xia, J., Yu, Y., Zhou, Y.-F., Sun, Y.-X., Deng, X.-B., Zeng, Z.-L., Jiang, H.-X., Fang, B.-H., … Liu, Y.-H. (2020). Environmental remodeling of human gut microbiota and antibiotic resistome in livestock farms. Nature Communications, 11(1), Article 1427. Link to source: https://doi.org/10.1038/s41467-020-15222-y
Tang, K. L., Caffrey, N. P., Nóbrega, D. B., Cork, S. C., Ronksley, P. E., Barkema, H. W., Polachek, A. J., Ganshorn, H., Sharma, N., Kellner, J. D., & Ghali, W. A. (2017). Restricting the use of antibiotics in food-producing animals and its associations with antibiotic resistance in food-producing animals and human beings: A systematic review and meta-analysis. The Lancet Planetary Health, 1(8), e316–e327. Link to source: https://doi.org/10.1016/S2542-5196(17)30141-9
Toumpanakis, A., Turnbull, T., & Alba-Barba, I. (2018). Effectiveness of plant-based diets in promoting well-being in the management of type 2 diabetes: A systematic review. BMJ Open Diabetes Research & Care, 6(1), Article e000534. Link to source: https://doi.org/10.1136/bmjdrc-2018-000534
Van Boeckel, T. P., Brower, C., Gilbert, M., Grenfell, B. T., Levin, S. A., Robinson, T. P., Teillant, A., & Laxminarayan, R. (2015). Global trends in antimicrobial use in food animals. Proceedings of the National Academy of Sciences, 112(18), 5649–5654. Link to source: https://doi.org/10.1073/pnas.1503141112
Vergnaud, A.-C., Norat, T., Romaguera, D., Mouw, T., May, A. M., Travier, N., Luan, J., Wareham, N., Slimani, N., Rinaldi, S., Couto, E., Clavel-Chapelon, F., Boutron-Ruault, M.-C., Cottet, V., Palli, D., Agnoli, C., Panico, S., Tumino, R., Vineis, P., … Peeters, P. H. M. (2010). Meat consumption and prospective weight change in participants of the EPIC-PANACEA study. The American Journal of Clinical Nutrition, 92(2), 398–407. Link to source: https://doi.org/10.3945/ajcn.2009.28713
Westhoek, H., Lesschen, J. P., Rood, T., Wagner, S., De Marco, A., Murphy-Bokern, D., Leip, A., van Grinsven, H., Sutton, M. A., & Oenema, O. (2014). Food choices, health and environment: Effects of cutting Europe’s meat and dairy intake. Global Environmental Change, 26, 196–205. Link to source: https://doi.org/10.1016/j.gloenvcha.2014.02.004
Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., Garnett, T., Tilman, D., DeClerck, F., Wood, A., Jonell, M., Clark, M., Gordon, L. J., Fanzo, J., Hawkes, C., Zurayk, R., Rivera, J. A., De Vries, W., Majele Sibanda, L., ... Murray, C. J. L. (2019). Food in the Anthropocene: The EAT–Lancet Commission on healthy diets from sustainable food systems. The Lancet, 393(10170), 447–492. Link to source: https://doi.org/10.1016/s0140-6736(18)31788-4
Willits-Smith, A., Odinga, H., O’Malley, K., & Rose, D. (2023). Demographic and socioeconomic correlates of disproportionate beef consumption among US adults in an age of global warming. Nutrients, 15(17), Article 3795. Link to source: https://doi.org/10.3390/nu15173795
We estimated that replacing 1 kg of ruminant meat with the same weight of other meat or protein-rich food reduces emissions by about 0.065 t CO₂‑eq (100-yr basis).
We derived GHG emissions from 1 kg of ruminant meat, 0.075 t CO₂‑eq (100-yr basis), from Poore and Nemecek’s (2018) database and modeling from Kim et al. (2020). Our calculation was based on the GHG footprint of a kg of meat from beef cattle, dairy cattle, and sheep. We weighted the average GHG footprint based on the fact that beef makes up the majority (83%) of ruminant meat consumption, with sheep meat making up a smaller proportion (17%), according to data from the United Nations’ Food and Agriculture Organization (FAO) Food Balances (FAO, 2025).
From Poore and Nemecek’s database, we also derived the average GHG emissions from consuming 1 kg of other protein-rich foods in place of ruminant meat. These foods were: pig meat (pork), poultry meat, eggs, fish (farmed), crustaceans (farmed), peas, other pulses, groundnuts, nuts, and tofu, which are all around 20% protein by weight. Using FAO data on food availability in 2022 as a proxy for consumption, we calculated that the weighted average of these substitutes is 0.01 t CO₂‑eq /kg.
We subtracted the weighted average emissions of these protein-rich foods (0.01 t CO₂‑eq /kg) from the weighted average emissions from ruminant meat production (0.075 t CO₂‑eq /kg) to calculate the emissions savings (0.065 t CO₂‑eq /kg) (Table 1). Our analysis assumed that substituting a serving of plant- or animal-based protein for ruminant meat reduces the production of that meat (see Caveats).
Kim et al. (2020) did not provide species-specific emissions, but we assumed that for ruminant meat, the breakdown of CO₂, nitrous oxide, and methane was the same as in Poore and Nemecek (2018) – 43% methane and 57% CO₂ and nitrous oxide.
Table 1. Effectiveness at reducing emissions.
Unit: t CO₂‑eq /kg avoided ruminant meat
| Mean (weighted average) | 0.065 |
Unit: t CO₂‑eq /kg avoided ruminant meat
| Mean (weighted average) | 0.13 |
Based on our analysis, the average cost of 1 kg of ruminant meat was US$21.29 compared with the weighted average US$20.73 for other protein-rich foods. This resulted in a savings of US$0.56/kg of food. This translates to an estimated savings of US$8.54/t CO₂ eq (Table 2).
Since the publication of the EAT-Lancet Commission's dietary benchmarks, several studies have been published on the affordability of shifting to the diet (Gupta et al., 2021; Hirvonen et al., 2020; Li et al., 2024; Springmann et al., 2021). Research findings have been mixed on whether this diet shift reduces costs for consumers. One modeling study found that while the diet may cost less in upper-middle-income to high-income countries, on average, it may be more expensive in lower-middle-income to low-income countries (Springmann et al., 2021).
As opposed to the EAT-Lancet commission, our analysis focused solely on the shift from ruminant meat toward other protein-rich foods, which doesn’t include other dietary shifts, such as reducing other kinds of meat, reducing dairy, or increasing fruits and vegetables. We found no published evidence on the economic impacts of the shift away from ruminant meat alone. However, we used data from Bai et al. (2020), which used food price data from the World Bank’s International Comparison Program (ICP) (2011), to estimate cost differences between ruminant meat and substitutes.
We converted these prices into 2023 US$ and calculated a weighted average cost of food substitutes, based on food availability from the FAO Food Balances (2025).
The limited information used for this estimate can create bias, and we hope this work inspires research and data sharing on the economic impact of reduced ruminant consumption.
Table 2. Cost per unit climate impact. Negative values reflect cost savings.
Unit: 2023 US$/t CO₂‑eq , 100-year basis
| Mean | -8.54 |
Improve Diets does not have a learning curve associated with falling costs of adoption. This solution does not address synthetically derived animal products, such as lab-grown meat, which could serve as replacements for ruminant meat. See Advance Cultivated Meat for more information
Speed of action refers to how quickly a climate solution physically affects the atmosphere after it is deployed. This is different from speed of deployment, which is the pace at which solutions are adopted.
At Project Drawdown, we define the speed of action for each climate solution as emergency brake, gradual, or delayed.
Improve Diets is an EMERGENCY BRAKE climate solution. It has the potential to deliver a more rapid impact than nominal and delayed solutions. The impact of this solution is two-fold: first, it reduces methane from enteric fermentation and manure management. Second, the solution reduces pressure on natural ecosystems, reducing deforestation and other land use changes, which create a large, sudden “pulse” of CO₂ emissions.
Because emergency brake solutions can deliver their climate benefits quickly, they can help accelerate our efforts to address dangerous levels of climate change. For this reason, they are a high priority.
We did not include Low-Income Food-Deficit countries (FAO, 2023) in this analysis because the solution does not apply to people who do not have access to affordable and healthy alternatives to ruminant meat or those with micronutrient deficiencies.
Although some amino acids, which are building blocks of protein, are present in lower-than-optimal proportions for human needs in some plant-based foods, mixing plant protein sources, as is typically done in vegetarian diets, can address deficiencies (Mariotti & Gardner, 2019).
Additionality is a concern for this solution. While ruminant meat consumption in middle- to high-income countries remained fairly stable between 2010 and 2022, some high-income countries have recently started reducing their ruminant consumption (see Adoption Trends). However, it’s difficult to determine current adoption and trends from national-level statistics, which average out low and high consumers within a country.
Another consideration is that the decision to eat less ruminant meat will ultimately lead farmers to produce fewer ruminant animals, but the substitution may not be one-to-one. For example, one modeling study found that cutting beef consumption by 1 kg may only reduce beef production by 0.7 kg (Norwood & Lusk, 2011).
Humans use more land for animal agriculture than for any other activity. However, the potential to remove and store carbon from the atmosphere by freeing up the land used in food production, as estimated by Mbow et al. (2019), was not included in this analysis.
Household-level data on food consumption are limited and not often comparable. In this analysis, we summarized current levels of food consumption on a national level, based on data on food availability from FAO Food Balances (2025). Because the data are averaged at a country level, we couldn’t estimate the current level of adoption for individuals of reduced ruminant meat consumption or the EAT-Lancet diet.
The EAT-Lancet recommended threshold of 5.1 kg of ruminant meat per person per year is in edible, retail weight. However, available data on per capita food availability from the FAO Food Balances is measured in carcass weight, which, for beef cattle, is about 1.4 times larger than a retail cut of meat. Therefore, in this analysis, we set the threshold of excess consumption in the Food Balances as greater than 7.2 kg carcass weight per person per year, which is 5.1 kg of retail ruminant meat per person per year.
In 110 of the 146 countries tracked by FAO, average annual consumption was more than 5.1 kg of ruminant meat per person per year. Some of the highest consuming nations include Mongolia (70.1 kg/person/yr), Argentina (33.3 kg/person/yr), the United States (27.5 kg/person/yr), Australia (25.3 kg/person/yr), and Brazil (25 kg/person/yr).
The 36 high- and middle-income countries with low (<5.1 kg/person/year) ruminant meat consumption include India (2 kg/person/yr), Peru (3.6 kg/person/yr), Poland (0.2 kg/person/yr), Vietnam (3.9 kg/person/yr), and Indonesia (2.4 kg/person/yr).
Ruminant meat consumption in high- and middle-income countries remained fairly stable between 2010 and 2022, according to data from FAO’s Food Balances, increasing only 3% overall from 8.2 to 8.5 kg/person/yr.
However, per capita ruminant meat consumption across high-consuming regions (the Americas, Europe, and Oceania) decreased. Consumption in South America and North America declined by 13% and 2%, respectively. Europe and Oceania saw the greatest declines, at 18% and 38%, respectively.
The adoption ceiling for this solution is the amount of total ruminant meat consumption across all 146 high- and middle-income countries tracked by the FAO. In 2022, the consumption of ruminant meat totaled 81.2 billion kg (Table 3).
Table 3. Adoption ceiling.
Unit: kg avoided ruminant meat/yr
| Estimate | 81,200,000,000 |
If all of the 110 countries consuming more than the EAT-Lancet recommendation cut consumption to 5.1 kg/person/yr (which is about an 85 g serving of ruminant meat every six days), that would lower annual global ruminant meat consumption by about half (53%), or 42.9 billion kg/yr. We used this as the estimated high achievable adoption value. The low achievable adoption value we estimated to be half of this reduction (26%), or 21.4 billion kg/yr (Table 4).
Table 4. Range of achievable adoption levels.
Unit: kg avoided ruminant meat/yr
| Current adoption | Not Determined |
| Achievable – low | 21,400,000,000 |
| Achievable – high | 42,900,000,000 |
| Adoption ceiling | 81,200,000,000 |
Improving diets by reducing ruminant meat consumption globally could mitigate emissions by 1.4–5.3 Gt CO₂‑eq/yr (Table 5).
Therefore, reducing ruminant meat consumption and replacing it with any other form of plant or animal protein can have a substantial impact on GHG emissions. Such a diet shift can be adopted incrementally with small behavioral changes that together lead to globally significant reductions in GHG emissions.
Table 5. Climate impact at different levels of adoption.
Unit: Gt CO₂‑eq/yr
| Current adoption | Not Determined |
| Achievable – low | 1.40 |
| Achievable – high | 2.80 |
| Adoption ceiling | 5.30 |
Unit: Gt CO₂‑eq/yr
| Current adoption | Not Determined |
| Achievable – low | 2.88 |
| Achievable – high | 5.76 |
| Adoption ceiling | 10.90 |
Reducing ruminant meat in diets of high-income countries can improve food security (Searchinger et al., 2019). Productive cropland that is used to grow animal feed could instead be used to produce food for human consumption (Ripple et al., 2014a).
Reducing ruminant meat consumption has multiple health benefits. Diets high in red meat have been linked to increased risk of overall mortality and mortality from cancer (Pan et al., 2012; Sinha et al., 2009). Excess red meat consumption is also associated with increased risk of cardiovascular disease, stroke, type 2 diabetes, colorectal cancer, and weight gain (Bouvard et al., 2015; Bradbury et al., 2020; Kaluza et al., 2012; Pan et al., 2011; Vergnaud et al., 2010). Diets that incorporate other sources of protein such as fish, poultry, nuts, legumes, low-fat dairy, and whole grains are associated with a lower risk of mortality and a reduction in dietary saturated fat, and can improve the management of diabetes (Pan et al., 2012; Nelson et al., 2016; Toumpanakis et al., 2018).
Reducing demand for meat also has implications for health outcomes associated with livestock production. Animal agriculture, especially industrial and confined feeding operations, commonly uses antibiotics to prevent and treat infections in livestock (Casey et al., 2013). Consistent direct contact with livestock exposes people, especially farmworkers, to antibiotic-resistant bacteria, which can lead to antibiotic-resistant health outcomes (Sun et al., 2020; Tang et al., 2017). Moreover, these exposures are not limited to farmworkers. In fact, a study in Pennsylvania found that people living near dairy/veal and swine industrial agriculture had a higher risk of developing methicillin-resistant Staphylococcus aureus (MRSA) infections (Casey et al., 2013).
A lower demand for ruminant meat could promote environmental justice by reducing the amount of industrial animal agriculture operations. This may benefit communities near these operations by reducing exposure to air and water pollution, pathogens, and odors (Casey et al., 2013; Heederik et al., 2007; Steinfeld et al., 2006).
Agricultural expansion for livestock production is a major driver of deforestation (Ripple et al., 2014b). Deforestation is associated with biodiversity loss through habitat degradation and destruction, as well as forest fragmentation (Steinfeld et al., 2006). Livestock farming can reduce the diversity of landscapes and can contribute to the loss of large carnivore, herbivore, and bird species (Ripple et al., 2015; Steinfeld et al., 2006). The clearing of forests for animal agriculture is especially prevalent in the tropics, and a lower demand for meat, particularly ruminant meat, could reduce tropical deforestation (Ripple et al., 2014b).
Animal agriculture, especially ruminants such as cattle, requires a lot of land (Nijdam et al., 2012). Life-cycle analyses have found that beef consistently requires the most land use among animal-based proteins (Nijdam et al., 2012; Meier & Christen, 2013; Searchinger et al., 2019). This high land use is mostly due to the amount of land needed to grow crops that eventually feed livestock (Ripple et al., 2014a). In the European Union, Westhoek et al. (2014) estimated that halving consumption of meat, dairy, and eggs would result in a 23% reduction in per capita cropland use.
While livestock is directly responsible for a small proportion of global water usage, a significant amount of water is required to produce forage and grain for animal feed (Steinfeld et al., 2006). In the United States, livestock production is the largest source of freshwater consumption, and producing 1 kg of animal protein uses 100 times more water than 1 kg of grain protein (Pimentel & Pimentel, 2003). Ruminant meats have some of the highest water usage rates of all animal protein sources (Kim et al., 2020; Searchinger et al., 2019; Steinfed et al., 2006).
Livestock production can contribute to water pollution directly and indirectly through feed production and processing (Steinfeld et al., 2006). Manure contains nutrients such as nitrogen and phosphorus, as well as drug residues, heavy metals, and pathogens (Steinfeld et al., 2006). Manure can pollute water directly from feedlots and can also leach into water sources when used as a fertilizer on croplands (Porter & Cox, 2020). For example, animal agriculture is one of the top polluters of water basins in central California (Harter et al., 2012)
In addition to CO₂, ruminant agriculture is a source of air pollutants such as methane, nitrous oxides, ammonia, and volatile organic compounds (Gerber et al., 2013). Fertilization of feed crops and deposition of manure on crops are the primary sources of nitrogen emissions from ruminant agriculture (Steinfeld et al., 2006). Air pollution in nearby communities can lead to poor odors and respiratory issues, which may affect stress levels and quality of life (Domingo et al., 2021; Heederik et al., 2007).
A total replacement of ruminant meat with other food may reduce food availability in arid climates, where ruminants graze on land not suitable for crop production.
While the shift from ruminant meat consumption to chicken and pork would curtail some of the demand for animal feed, it would not be reduced as much as a shift from ruminants to plant-based foods.
Pastures for grazing ruminants occupy 3400 million ha of land, more than any other human activity (Foley et al., 2011). Curtailing ruminant consumption can significantly reduce demand for land and facilitate the protection of carbon-rich ecosystems. If the adoption of this solution is aggressive, it could open up opportunities for the restoration of land-based ecosystems and some coastal wetlands.
This solution increases the supply of food. This makes more raw material available to increase the adoption potential of the following solutions:
(mixed) Reducing ruminant consumption could lead to less manure production and, therefore, nutrient pollution in proximal and downstream receiving ecosystems. However, if ruminant meat is replaced with food sources that generate more manure or require more fertilizer/pesticides, pollution could increase in proximal or downgradient receiving ecosystems.
Reducing ruminant meat consumption can reduce the amount of nutrients and manure available to manage, depending on whether it is substituted with plant-based foods or other meat.
kg avoided ruminant meat
CO₂, CH₄ , N₂O
There are climate and environmental trade-offs associated with the production of different kinds of protein. Producing ruminant meat is land-intensive and contributes to the conversion of natural ecosystems to pasture and animal feed. However, ruminants can live on land that is too dry for crop production and graze on plants not suitable for human consumption. In some low-income food-insecure countries (not included in this analysis), grazing animals may be an important source of protein.
Substituting ruminant meat with chicken, fish, or other meat can substantially reduce methane emissions, but comes with some environmental and animal welfare trade-offs.
Per capita ruminant meat consumption varies greatly around the world. According to the Food and Agriculture Organization of the United Nations (FAO), Mongolia had the highest per-person ruminant meat consumption (99 kg/person/yr) in 2022, followed by Argentina (47 kg/person/yr) and Turkmenistan (46 kg/person/yr).
Food and Agriculture Organization of the United Nations (FAO). (2025). FAO‑FAOSTAT: Food balances (2010–) [Data set, food balances for individual countries for the year 2022]. Retrieved March 25, 2025, from Link to source: https://www.fao.org/faostat/en/#data/FBS
Per capita ruminant meat consumption varies greatly around the world. According to the Food and Agriculture Organization of the United Nations (FAO), Mongolia had the highest per-person ruminant meat consumption (99 kg/person/yr) in 2022, followed by Argentina (47 kg/person/yr) and Turkmenistan (46 kg/person/yr).
Food and Agriculture Organization of the United Nations (FAO). (2025). FAO‑FAOSTAT: Food balances (2010–) [Data set, food balances for individual countries for the year 2022]. Retrieved March 25, 2025, from Link to source: https://www.fao.org/faostat/en/#data/FBS
The emissions intensity of beef production varies considerably between countries, due to the contribution of regional deforestation and other land changes (Kim et al. 2020; Poore and Nemecek, 2018) and the intensity of different cattle raising systems, with extensive, pasture-based systems relatively less efficient (in terms of land and CO₂‑eq /kg beef) (Herrero et al. 2016). For example, GHG emissions per kilogram of bovine meat from Brazil and Paraguay were five and 17 times higher, respectively, than those of Danish bovine meat (Kim et al. 2020). These differences were attributable to higher deforestation for grazing lands and methane emissions from enteric fermentation.
Emissions from beef production are skewed by producers with particularly high impacts. About a quarter of beef producers contribute more than 56% (an estimated 1.3 Gt CO₂‑eq ) of all GHGs attributable to beef cattle production.
Beef consumption per person in Mongolia and North and South America is especially high, and reducing it can benefit human health (see Benefits to People & Nature). According to the Food and Agriculture Organization of the United Nations (FAO), Mongolia had the highest per-person ruminant meat consumption (99 kg/person/yr) in 2022, followed by Argentina (47 kg/person/yr) and Turkmenistan (46 kg/person/yr).
For this analysis, we examined high- and middle-income countries that consume more than 5.1 kg/person/yr of ruminant meat (what we define as “excess consumption”). The United States has more excess ruminant meat consumption than any other country. A 2023 assessment of health survey data found that in the United States, about 12% of the population ate about half of all beef supplies (Willits-Smith et al., 2023).
Maps are based on global average emissions per kg of ruminant meat, which keeps the focus on consumption.
Consensus of effectiveness in reducing ruminant meat: High
There is a high level of consensus in the scientific literature that shifting diets away from ruminant meat mitigates GHG emissions. An IPCC special report on land found “broad agreement” that meat – particularly ruminant meat – was the single food with the greatest impact on the environment on a global basis, especially in terms of GHG emissions and land use (Mbow et al., 2019). The IPCC found that the range of cumulative emissions mitigation from diet shifts by 2050, depending on the type of shift, was as much as 2.7–6.4 Gt CO₂‑eq/yr. This estimate included shifts away from all meat, whereas our analysis focused on shifting away from ruminant meat alone.
The emissions associated with the production of different food products in this solution came from Poore and Nemecek (2018) and Kim et al. (2020). Poore and Nemecek developed a database of emissions footprints for different foods based on a meta-analysis of 570 studies with a median reference year of 2010 (Figure 1). It covers ~38,700 commercially viable farms in 119 countries and 40 products representing ~90% of global protein and calorie consumption.
According to Poore and Nemecek (2018), producing 1 kg of beef emits 33 times the GHGs emitted by producing protein-rich plant-based foods, such as beans, nuts, and lentils. But beef can also be replaced with any other non-ruminant meat (poultry, pork, or fish) to cut emissions. Substituting ruminant meat with any other kind of meat reduces average emissions by roughly 85%.
A 2024 study on dietary emissions from 140 food products in 139 countries found that shifting consumption toward the EAT-Lancet guidelines could reduce emissions from the food system 17%, or about 1.94 Gt CO₂‑eq/yr (Li, Y. et al., 2024).
The results presented in this document summarize findings from 42 studies (34 academic reviews and original studies, three reports from NGOs, and five reports from public and multilateral organizations). The results reflect current evidence from 119 countries, but observations are concentrated in Europe, North America, Oceania, Brazil, and China, and limited in Africa and parts of Asia. We recognize this limited geographic scope creates bias, and hope this work inspires research and data sharing on this topic in underrepresented regions.
Join the 80,000+ subscribers discovering how to drive meaningful climate action around the world! Every other week, you'll get expert insights, cutting-edge research, and inspiring stories.