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. 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.
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 Increase 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 Management 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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 /tN, 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 |
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1), 170191. https://doi.org/10.1038/sdata.2017.191
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. https://doi.org/10.1038/s41597-023-02526-z
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. https://doi.org/10.1038/s43016-023-00913-8
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.)].
Mehta, P., Siebert, S., Kummu, M., Deng, Q., Ali, T., Marston, L., Xie, W., & Davis, K. F. (2024). Half of twenty-first century global irrigation expansion has been in water-stressed regions. Nature Water, 2(3), 254–261. https://doi.org/10.1038/s44221-024-00206-9
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 t 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 N/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: tN/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: tN/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: tN/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.
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 (N2O), 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 (N2O), 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
Coastal wetland protection is the long-term protection of mangrove, salt marsh, and seagrass ecosystems from degradation by human activities. This solution focuses on legal mechanisms of coastal wetland protection, including the establishment of Protected Areas (PAs) and Marine Protected Areas (MPAs), which are managed with the primary goal of conserving nature. These legal protections reduce a range of human impacts, helping to preserve existing carbon stocks and avoid CO₂ emissions.
Coastal wetlands (defined as mangrove, salt marsh, and seagrass ecosystems, see Figure 1) are highly productive ecosystems that sequester carbon via photosynthesis, storing it primarily below ground in sediments where waterlogged, low-oxygen conditions help preserve it (Adame et al., 2024; Lovelock et al., 2017).
Figure 1. Types of coastal wetlands, from left to right: a salt marsh in Westhampton Beach (United States), a mangrove forest near Staniel Cay (Bahamas), and a seagrass meadow off Notojima Island (Japan).
Adobe Stock | istock; Maria T Hoffman | Adobe Stock; James White and Danita Delimont | AdobeStock
These ecosystems are also efficient at trapping carbon suspended in water, which can comprise up to 50% of the carbon sequestered in these settings (McLeod et al., 2011; Temmink et al., 2022). Coastal wetlands operate as large carbon sinks (Figure 2), with long-term carbon accumulation rates averaging 5.1–8.3 t CO₂‑eq /ha/yr (McLeod et al., 2011).
Figure 2. Overview of carbon storage in coastal wetlands. Salt marshes, mangroves, and seagrasses, commonly referred to as blue carbon ecosystems, store carbon in plant biomass and sediment.
Source: Macreadie, P. I., Costa, M. D., Atwood, T. B., Friess, D. A., Kelleway, J. J., Kennedy, H., ... & Duarte, C. M. (2021). Blue carbon as a natural climate solution. Nature Reviews Earth & Environment, 2(12), 826-839. Link to source: https://doi.org/10.1038/s43017-021-00224-1
Protection of coastal wetlands preserves carbon stocks and avoids emissions associated with degradation, which can increase CO₂, methane, and nitrous oxide effluxes. Nearly 50% of the total global area of coastal wetlands has been lost since 1900 and up to 87% since the 18th century (Davidson, 2014). With current loss rates, an additional 30–40% of remaining seagrasses, salt marshes, and nearly all mangroves could be lost by 2100 without protection (Pendleton et al., 2012). Protection of existing coastal wetlands is especially important because restoration is challenging, costly, and not yet fully optimized. For example, seagrass restoration has generally been unsuccessful (Macreadie et al., 2021), and restored seagrass systems can have higher GHG fluxes than natural systems (Mason et al., 2023).
On land, degradation often arises from aquaculture, reclamation and drainage, deforestation, diking, and urbanization (Mcleod et al., 2011). In the ocean, impacts often occur due to dredging, mooring, pollution, and sediment disturbance (Mcleod et al., 2011). For instance, deforestation of mangroves for agriculture removes biomass and oxidizes sediment carbon stocks, leading to high CO₂ effluxes and, potentially, methane and nitrous oxide emissions (Chauhan et al., 2017, Kauffman et al., 2016, Sasmito et al., 2019). Likewise, high CO₂ or methane effluxes from salt marshes commonly result from drainage, which can oxygenate the subsurface and fuel carbon loss, or from infrastructure such as dikes, which can reduce saltwater exchange and increase methane production (Kroeger et al., 2017). In another example, dredging in seagrass meadows drives high rates of ecosystem degradation due to reduced light availability, leading to die-offs that can increase erosion and reduce sediment carbon stocks by 21–47% (Trevathan-Tackett et al., 2018).
Our analysis focused on the avoided CO₂ emissions and retained carbon sequestration capacity conferred by avoiding degradation of coastal wetlands via legal protection. While degradation can substantially alter emissions of other GHGs, such as methane and nitrous oxide, we focus on CO₂ due to the limited availability of global spatial data on degradation types and extent and associated effluxes of all GHGs across coastal wetlands. Ignoring methane and nitrous oxide benefits with protection is the most conservative approach because limited data exist on emission profiles from both functional and degraded global coastal wetlands, and even PAs/MPAs can be degraded (Holmquist et al., 2023). This solution considered wetlands to be protected if they are formally designated as PAs or MPAs under International Union for Conservation of Nature (IUCN) protection categories I–IV (UNEP-WCMC &IUCN, 2024; see Appendix for more information).
Adame, M. F., Kelleway, J., Krauss, K. W., Lovelock, C. E., Adams, J. B., Trevathan-Tackett, S. M., Noe, G., Jeffrey, L., Ronan, M., Zann, M., Carnell, P. E., Iram, N., Maher, D. T., Murdiyarso, D., Sasmito, S., Tran, D. B., Dargusch, P., Kauffman, J. B., & Brophy, L. (2024). All tidal wetlands are blue carbon ecosystems. BioScience, 74(4), 253–268. Link to source: https://doi.org/10.1093/biosci/biae007
Balmford, A., Gravestock, P., Hockley, N., McClean, C. J., & Roberts, C. M. (2004). The worldwide costs of marine protected areas. Proceedings of the National Academy of Sciences, 101(26), 9694–9697. Link to source: https://doi.org/10.1073/pnas.0403239101
Baniewicz, T. (2020, September 2). Coastal Louisiana tribes team up with biologist to protect sacred sites from rising seas. Southerly. Link to source: https://southerlymag.org/2020/09/02/coastal-louisiana-tribes-team-up-with-biologist-to-protect-sacred-sites-from-rising-seas/
Barbier, E. B., Georgiou, I. Y., Enchelmeyer, B., & Reed, D. J. (2013). The value of wetlands in protecting southeast Louisiana from hurricane storm surges. PLoS ONE, 8(3), Article e58715. Link to source: https://doi.org/10.1371/journal.pone.0058715
Blanchard, L., Haya, B. K., Anderson, C., Badgley, G., Cullenward, D., Gao, P., Goulden, M. L., Holm, J. A., Novick, K. A., Trugman, A. T., Wang, J. A., Williams, C. A., Wu, C., Yang, L., & Anderegg, W. R. L. (2024). Funding forests’ climate potential without carbon offsets. One Earth, 7(7), 1147–1150. Link to source: https://doi.org/10.1016/j.oneear.2024.06.006
Borchert, S. M., Osland, M. J., Enwright, N. M., & Griffith, K. T. (2018). Coastal wetland adaptation to sea level rise: Quantifying potential for landward migration and coastal squeeze. Journal of Applied Ecology, 55(6), 2876–2887. Link to source: https://doi.org/10.1111/1365-2664.13169
Bruner, A. G., Gullison, R. E., & Balmford, A. (2004). Financial costs and shortfalls of managing and expanding protected-area systems in developing countries. BioScience, 54(12), 1119–1126. Link to source: https://doi.org/10.1641/0006-3568(2004)054[1119:FCASOM]2.0.CO;2
Chauhan, R., Datta, A., Ramanathan, A. L., & Adhya, T. K. (2017). Whether conversion of mangrove forest to rice cropland is environmentally and economically viable? Agriculture, Ecosystems & Environment, 246, 38–47. Link to source: https://doi.org/10.1016/j.agee.2017.05.010
Cullen-Unsworth, L. C., & Unsworth, R. (2018). A call for seagrass protection. Science, 361(6401), 446–448. Link to source: https://doi.org/10.1126/science.aat7318
Department of Climate Change, Energy, the Environment and Water. (2016). Wetlands and Indigenous values [Fact sheet]. Commonwealth of Australia. Link to source: https://www.dcceew.gov.au/sites/default/files/documents/factsheet-wetlands-indigenous-values.pdf
Dabalà, A., Dahdouh-Guebas, F., Dunn, D. C., Everett, J. D., Lovelock, C. E., Hanson, J. O., Buenafe, K. C. V., Neubert, S., & Richardson, A. J. (2023). Priority areas to protect mangroves and maximise ecosystem services. Nature Communications, 14(1), Article 5863. Link to source: https://doi.org/10.1038/s41467-023-41333-3
Davidson, N. C. (2014). How much wetland has the world lost? Long-term and recent trends in global wetland area. Marine and Freshwater Research, 65(10), 934–941. Link to source: https://doi.org/10.1071/MF14173
Di Minin, E., & Toivonen, T. (2015). Global protected area expansion: Creating more than paper parks. BioScience, 65(7), 637–638. Link to source: https://doi.org/10.1093/biosci/biv064
Dinerstein, E., Joshi, A. R., Hahn, N. R., Lee, A. T. L., Vynne, C., Burkart, K., Asner, G. P., Beckham, C., Ceballos, G., Cuthbert, R., Dirzo, R., Fankem, O., Hertel, S., Li, B. V., Mellin, H., Pharand‑Deschênes, F., Olson, D., Pandav, B., Peres, C. A., … Zolli, A. (2024). Conservation Imperatives: Securing the last unprotected terrestrial sites harboring irreplaceable biodiversity. Frontiers in Science, 2, Article 1349350. Link to source: https://doi.org/10.3389/fsci.2024.1349350
Donato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., & Kanninen, M. (2011). Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience, 4(5), 293–297. Link to source: https://doi.org/10.1038/ngeo1123
Eyre, B. D., Camillini, N., Glud, R. N., & Rosentreter, J. A. (2023). The climate benefit of seagrass blue carbon is reduced by methane fluxes and enhanced by nitrous oxide fluxes. Communications Earth & Environment, 4(1), Article 374. Link to source: https://doi.org/10.1038/s43247-023-01022-x
Feng, Y., Song, Y., Zhu, M., Li, M., Gong, C., Luo, S., Mei, W., Feng, H., Tan, W., & Song, C. (2025). Microbes drive more carbon dioxide and nitrous oxide emissions from wetland under long-term nitrogen enrichment. Water Research, 272, Article 122942. Link to source: https://doi.org/10.1016/j.watres.2024.122942
Fletcher, M.-S., Hamilton, R., Dressler, W., & Palmer, L. (2021). Indigenous knowledge and the shackles of wilderness. Proceedings of the National Academy of Sciences, 118(40), Article e2022218118. Link to source: https://doi.org/10.1073/pnas.2022218118
Garnett, S. T., Burgess, N. D., Fa, J. E., Fernández-Llamazares, Á., Molnár, Z., Robinson, C. J., Watson, J. E. M., Zander, K. K., Austin, B., Brondizio, E. S., Collier, N. F., Duncan, T., Ellis, E., Geyle, H., Jackson, M. V., Jonas, H., Malmer, P., McGowan, B., Sivongxay, A., & Leiper, I. (2018). A spatial overview of the global importance of Indigenous lands for conservation. Nature Sustainability, 1(7), 369–374. https://doi.org/10.1038/s41893-018-0100-6
Giakoumi, S., McGowan, J., Mills, M., Beger, M., Bustamante, R. H., Charles, A., Christie, P., Fox, M., Garcia‑Borboroglu, P., Gelcich, S., Guidetti, P., Mackelworth, P., Maina, J. M., McCook, L., Micheli, F., Morgan, L. E., Mumby, P. J., Reyes, L. M., White, A., … Possingham, H. P. (2018). Revisiting “success” and “failure” of marine protected areas: A conservation scientist perspective. Frontiers in Marine Science, 5, Article 223. Link to source: https://doi.org/10.3389/fmars.2018.00223
Guannel, G., Arkema, K., Ruggiero, P., & Verutes, G. (2016). The power of three: Coral reefs, seagrasses and mangroves protect coastal regions and increase their resilience. PLoS ONE, 11(7), Article e0158094. Link to source: https://doi.org/10.1371/journal.pone.0158094
Green, E. P., & Short, F. T. (Eds.). (2003). World Atlas of Seagrasses. University of California Press. Link to source: https://environmentalunit.com/Documentation/04%20Resources%20at%20Risk/World%20Seagrass%20atlas.pdf
Heck, N., Goldberg, L., Andradi‐Brown, D. A., Campbell, A., Narayan, S., Ahmadia, G. N., & Lagomasino, D. (2024). Global drivers of mangrove loss in protected areas. Conservation Biology, 38(6), Article e14293. Link to source: https://doi.org/10.1111/cobi.14293
Hochard, J. P., Barbier, E. B., & Hamilton, S. E. (2021). Mangroves and coastal topography create economic “safe havens” from tropical storms. Scientific Reports, 11(1), Article 15359. Link to source: https://doi.org/10.1038/s41598-021-94207-3
Holmquist, J. R., Eagle, M., Molinari, R. L., Nick, S. K., Stachowicz, L. C., & Kroeger, K. D. (2023). Mapping methane reduction potential of tidal wetland restoration in the United States. Communications Earth & Environment, 4(1), Article 353. Link to source: https://doi.org/10.1038/s43247-023-00988-y
Hutchinson, M. (2022, September 2). How coastal erosion is affecting the sacred lands of Indigenous Louisianians. Chênière: The Nicholls Undergraduate Humanities Review. Link to source: https://www.nicholls.edu/cheniere/2022/09/02/how-coastal-erosion-is-affecting-the-sacred-lands-of-indigenous-louisianians
Ickowitz, A., Lo, M. G. Y., Nurhasan, M., Maulana, A. M., & Brown, B. M. (2023). Quantifying the contribution of mangroves to local fish consumption in Indonesia: A cross-sectional spatial analysis. The Lancet Planetary Health, 7(10), e819–e830. Link to source: https://doi.org/10.1016/S2542-5196(23)00196-1
Jensen, K. (2022, July 6). Climate benefits of coastal wetlands and coral reefs show why they merit protection now. The Pew Charitable Trusts. Link to source: https://www.pewtrusts.org/en/research-and-analysis/articles/2022/07/06/climate-benefits-of-coastal-wetlands-and-coral-reefs-show-why-they-merit-protection-now
Kroeger, K. D., Crooks, S., Moseman-Valtierra, S., & Tang, J. (2017). Restoring tides to reduce methane emissions in impounded wetlands: A new and potent Blue Carbon climate change intervention. Scientific Reports, 7(1), Article 11914. Link to source: https://doi.org/10.1038/s41598-017-12138-4
Lamb, J. B., Van De Water, J. A., Bourne, D. G., Altier, C., Hein, M. Y., Fiorenza, E. A., Abu, N., Jompa, J., & Harvell, C. D. (2017). Seagrass ecosystems reduce exposure to bacterial pathogens of humans, fishes, and invertebrates. Science, 355(6326), 731–733. Link to source: https://doi.org/10.1126/science.aal1956
Leal, M., & Spalding, M. D. (Eds.). (2022, September 21). The state of the world’s mangroves 2022. Global Mangrove Alliance. Link to source: https://www.wetlands.org/publication/the-state-of-the-worlds-mangroves-2022/
Leal, M., & Spalding, M. D. (Eds.). (2024). The state of the world’s mangroves 2024. Global Mangrove Alliance. Link to source: https://www.mangrovealliance.org/mangrove-forests/
Leverington, F., Costa, K. L., Pavese, H., Lisle, A., & Hockings, M. (2010). A global analysis of protected area management effectiveness. Environmental Management, 46(5), 685–698. Link to source: https://doi.org/10.1007/s00267-010-9564-5
Lovelock, C. E., Fourqurean, J. W., & Morris, J. T. (2017). Modeled CO2 emissions from coastal wetland transitions to other land uses: Tidal marshes, mangrove forests, and seagrass beds. Frontiers in Marine Science, 4, Article 143. Link to source: https://doi.org/10.3389/fmars.2017.00143
Lu, C., Wang, Z., Li, L., Wu, P., Mao, D., Jia, M., & Dong, Z. (2016). Assessing the conservation effectiveness of wetland protected areas in Northeast China. Wetlands Ecology and Management, 24(4), 381–398. Link to source: https://doi.org/10.1007/s11273-015-9462-y
Macreadie, P. I., Costa, M. D., Atwood, T. B., Friess, D. A., Kelleway, J. J., Kennedy, H., Lovelock, C. E., Serrano, O., & Duarte, C. M. (2021). Blue carbon as a natural climate solution. Nature Reviews Earth & Environment, 2(12), 826–839. Link to source: https://doi.org/10.1038/s43017-021-00224-1
Macreadie, P. I., Robertson, A. I., Spinks, B., Adams, M. P., Atchison, J. M., Bell‑James, J., Bryan, B. A., Chu, L., Filbee‑Dexter, K., Drake, L., Duarte, C. M., Friess, D. A., Gonzalez, F., Grafton, R. Q., Helmstedt, K. J., Kaebernick, M., Kelleway, J., Kendrick, G. A., Kennedy, H., … Rogers, K. (2022). Operationalizing marketable blue carbon. One Earth, 5(5), 485–492. Link to source: https://doi.org/10.1016/j.oneear.2022.04.005
Mason, V. G., Burden, A., Epstein, G., Jupe, L. L., Wood, K. A., & Skov, M. W. (2023). Blue carbon benefits from global saltmarsh restoration. Global Change Biology, 29(23), 6517–6545. Link to source: https://doi.org/10.1111/gcb.16943
Mathews, D. L., & Turner, N. J. (2017). Ocean cultures: Northwest Coast ecosystems and Indigenous management systems. In P. S. Levin & M. R. Poe (Eds.), Conservation for the Anthropocene ocean: Interdisciplinary science in support of nature and people (pp.169–206). Academic Press. Link to source: https://doi.org/10.1016/B978-0-12-805375-1.00009-X
McCrea-Strub, A., Zeller, D., Sumaila, U. R., Nelson, J., Balmford, A., & Pauly, D. (2011). Understanding the cost of establishing marine protected areas. Marine Policy, 35(1), 1–9. Link to source: https://doi.org/10.1016/j.marpol.2010.07.001
Mcleod, E., Chmura, G. L., Bouillon, S., Salm, R., Björk, M., Duarte, C. M., Lovelock, C. E., Schlesinger, W. H., & Silliman, B. R. (2011). A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment, 9(10), 552–560. Link to source: https://doi.org/10.1890/110004
McIvor, A. L., Spencer, T., Möller, I., & Spalding, M. (2012). Storm surge reduction by mangroves (Natural Coastal Protection Series: Report No. 2). The Nature Conservancy and Wetlands International. Link to source: https://www.mangrovealliance.org/wp-content/uploads/2018/05/storm-surge-reduction-by-mangroves-1.pdf
McNally, C. G., Uchida, E. and Gold, A. J. (2011). The effect of a protected area on the tradeoffs between short-run and long-run benefits from mangrove ecosystems. Proceedings of the National Academy of Sciences, 108(34), 13945–13950. Link to source: https://doi.org/10.1073/pnas.1101825108
Menéndez, P., Losada, I. J., Torres-Ortega, S., Narayan, S., & Beck, M. W. (2020). The Global Flood Protection Benefits of Mangroves. Scientific Reports, 10(1), 4404. Link to source: https://doi.org/10.1038/s41598-020-61136-6
Noyce, G. L., Smith, A. J., Kirwan, M. L., Rich, R. L., & Megonigal, J. P. (2023). Oxygen priming induced by elevated CO2 reduces carbon accumulation and methane emissions in coastal wetlands. Nature Geoscience, 16(1), 63–68. Link to source: https://doi.org/10.1038/s41561-022-01070-6
Mcowen, C. J., Weatherdon, L. V., Van Bochove, J.-W., Sullivan, E., Blyth, S., Zockler, C., Stanwell-Smith, D., Kingston, N., Martin, C. S., Spalding, M., & Fletcher, S. (2017). A global map of saltmarshes. Biodiversity Data Journal, 5, Article e11764. Link to source: https://doi.org/10.3897/BDJ.5.e11764
Narayan, S., Beck, M. W., Wilson, P., Thomas, C. J., Guerrero, A., Shepard, C. C., Reguero, B. G., Franco, G., Ingram, J. C., & Trespalacios, D. (2017). The value of coastal wetlands for flood damage reduction in the Northeastern USA. Scientific Reports, 7(1), Article 9463. Link to source: https://doi.org/10.1038/s41598-017-09269-z
Pendleton, L., Donato, D. C., Murray, B. C., Crooks, S., Jenkins, W. A., Sifleet, S., Craft, C., Fourqurean, J. W., Kauffman, J. B., Marbà, N., Megonigal, J. P., Pidgeon, E., Herr, D., Gordon, D., & Baldera, A. (2012). Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS ONE, 7(9), Article e43542. Link to source: https://doi.org/10.1371/journal.pone.0043542
Renwick, A. R., Bode, M., & Venter, O. (2015). Reserves in context: Planning for leakage from protected areas. PLoS ONE, 10(6), Article e0129441. Link to source: https://doi.org/10.1371/journal.pone.0129441
Roberts, C. M., O'Leary, B. C., & Hawkins, J. P. (2020). Climate change mitigation and nature conservation both require higher protected area targets. Philosophical Transactions of the Royal Society B, 375(1794), Article 20190121. Link to source: https://doi.org/10.1098/rstb.2019.0121
Rodríguez-Rodríguez, D., & Martínez-Vega, J. (2022). Ecological effectiveness of marine protected areas across the globe in the scientific literature. In C. Sheppard (Ed.), Advances in marine biology (Vol. 92, pp. 129–153). Elsevier. Link to source: https://doi.org/10.1016/bs.amb.2022.07.002
Rosentreter, J. A., Maher, D. T., Erler, D. V., Murray, R. H., & Eyre, B. D. (2018). Methane emissions partially offset “blue carbon” burial in mangroves. Science Advances, 4(6), Article eaao4985. Link to source: https://doi.org/10.1126/sciadv.aao4985
Sasmito, S. D., Taillardat, P., Clendenning, J. N., Cameron, C., Friess, D. A., Murdiyarso, D., & Hutley, L. B. (2019). Effect of land‐use and land‐cover change on mangrove blue carbon: A systematic review. Global Change Biology, 25(12), 4291–4302. Link to source: https://doi.org/10.1111/gcb.14774
Schuerch, M., Spencer, T., Temmerman, S., Kirwan, M. L., Wolff, C., Lincke, D., McOwen, C. J., Pickering, M. D., Reef, R., Vafeidis, A. T., Hinkel, J., Nicholls, R. J., & Brown, S. (2018). Future response of global coastal wetlands to sea-level rise. Nature, 561(7722), 231–234. Link to source: https://doi.org/10.1038/s41586-018-0476-5
Sheng, P., Y., Paramygin, V. A., Rivera-Nieves, A. A., Zou, R., Fernald, S., Hall, T., & Jacob, K. (2022). Coastal marshes provide valuable protection for coastal communities from storm-induced wave, flood, and structural loss in a changing climate. Scientific Reports, 12(1), Article 3051. Link to source: https://doi.org/10.1038/s41598-022-06850-z
Temmink, R. J. M., Lamers, L. P. M., Angelini, C., Bouma, T. J., Fritz, C., van de Koppel, J., Lexmond, R., Rietkerk, M., Silliman, B. R., Joosten, H., & van der Heide, T. (2022). Recovering wetland biogeomorphic feedbacks to restore the world’s biotic carbon hotspots. Science, 376(6593), Article eabn1479. Link to source: https://doi.org/10.1126/science.abn1479
Thampanya, U., Vermaat, J. E., Sinsakul, S., & Panapitukkul, N. (2006). Coastal erosion and mangrove progradation of Southern Thailand. Estuarine, Coastal and Shelf Science, 68(1–2), 75–85. Link to source: https://doi.org/10.1016/j.ecss.2006.01.011
Trevathan‐Tackett, S. M., Wessel, C., Cebrián, J., Ralph, P. J., Masqué, P., & Macreadie, P. I. (2018). Effects of small‐scale, shading‐induced seagrass loss on blue carbon storage: Implications for management of degraded seagrass ecosystems. Journal of Applied Ecology, 55(3), 1351–1359. Link to source: https://doi.org/10.1111/1365-2664.13081
Unsworth, R. K. F., Cullen-Unsworth, L. C., Jones, B. L. H., & Lilley, R. J. (2022). The planetary role of seagrass conservation. Science, 377(6606), 609–613. Link to source: https://doi.org/10.1126/science.abq6923
UNEP-WCMC, & IUCN. (2024). Protected planet: The world database on protected areas (WDPA) and world database on other effective area-based conservation measures (WD-OECM) [Data set]. Retrieved November 2024, from https://www.protectedplanet.net
United Nations Environment Programme. (2014). The importance of mangroves to people: A call to action (J. van Bochove, E. Sullivan, & T. Nakamura, Eds.). United Nations Environment Programme World Conservation Monitoring Centre. Link to source: https://www.unep.org/resources/report/importance-mangroves-people-call-action
United Nations Environment Programme. (2020). Out of the blue: The value of seagrasses to the environment and to people. Link to source: https://www.unep.org/resources/report/out-blue-value-seagrasses-environment-and-people
U.S. Environmental Protection Agency. (2025a). Why are wetlands important? Link to source: https://www.epa.gov/wetlands/why-are-wetlands-important
U.S. Environmental Protection Agency. (2025b). About coastal wetlands. Link to source: https://www.epa.gov/wetlands/about-coastal-wetlands
Waldron, A., Adams, V., Allan, J., Arnell, A., Asner, G., Atkinson, S., Baccini, A., Baillie, J. E. M., Balmford, A., Beau, J. A., Brander, L., Brondizio, E., Bruner, A., Burgess, N., Burkart, K., Butchart, S., Button, R., Carrasco, R., Cheung, W., … Zhang, Y. P. (2020). Protecting 30% of the planet for nature: Costs, benefits and economic implications [Working paper]. Campaign for Nature. Link to source: https://pure.iiasa.ac.at/id/eprint/16560/1/Waldron_Report_FINAL_sml.pdf
Wang, F., Sanders, C. J., Santos, I. R., Tang, J., Schuerch, M., Kirwan, M. L., Kopp, R. E., Zhu, K., Li, X., Yuan, J., Liu, W., & Li, Z. (2021). Global blue carbon accumulation in tidal wetlands increases with climate change. National Science Review, 8(9), Article nwaa296. Link to source: https://doi.org/10.1093/nsr/nwaa296
West, T. A. P., Wunder, S., Sills, E. O., Börner, J., Rifai, S. W., Neidermeier, A. N., Frey, G. P., & Kontoleon, A. (2023). Action needed to make carbon offsets from forest conservation work for climate change mitigation. Science, 381(6660), 873–877. Link to source: https://doi.org/10.1126/science.ade3535
Worthington, T. A., Spalding, M., Landis, E., Maxwell, T. L., Navarro, A., Smart, L. S., & Murray, N. J. (2024). The distribution of global tidal marshes from Earth observation data. Global Ecology and Biogeography, 33(8), Article e13852. Link to source: https://doi.org/10.1111/geb.13852
Christina Richardson, Ph.D.
Ruthie Burrows, Ph.D.
Avery Driscoll
James Gerber, Ph.D.
Daniel Jasper
Christina Swanson, Ph.D.
Alex Sweeney
Paul West, Ph.D.
Aiyana Bodi
Avery Driscoll
James Gerber, Ph.D.
Hannah Henkin
Ted Otte
Christina Swanson, Ph.D.
We estimated that coastal wetland protection avoids emissions of 2.33–5.74 t CO₂‑eq /ha/yr, while also sequestering an additional 1.22–2.14 t CO₂‑eq /ha/yr depending on the ecosystem (Tables 1a–c; see the Appendix for more information). We estimated effectiveness as the avoided CO₂ emissions and the retained carbon sequestration capacity attributable to the reduction in wetland loss conferred by protection, as detailed in Equation 1. First, we calculated the difference between the rate of wetland loss outside PAs and MPAs (Wetland lossbaseline) versus inside PAs and MPAs, since protection does not entirely prevent degradation. Loss rates were primarily driven by anthropogenic habitat conversion. The effectiveness of protection was 53–59% (Reduction in loss). We then multiplied the avoided wetland loss by the sum of the avoided CO₂ emissions associated with the loss of carbon stored in sediment and biomass in one ha of wetland each year over a 30-yr timeframe (Carbonavoided emissions) and the amount of carbon sequestered via long-term storage in sediment carbon by one ha of protected wetland each year over a 30-yr timeframe (Carbonsequestration).
Equation 1.
We did this calculation separately for mangrove, salt marsh, and seagrass ecosystems, because many of these factors, such as carbon emission and sequestration rates, protection effectiveness, and loss rates, vary across ecosystem types. The rationale for increasing protection varies between coastal wetland ecosystem types, but in all cases, protection is an important tool for retaining and building long-lived carbon stocks. Additionally, climate impacts associated with this solution could be much greater than estimated if protection efficacy improves or is higher than our estimates of 53–59%.
Table 1a. Effectiveness at avoiding emissions and sequestering carbon in mangrove ecosystems.
Unit: t CO₂‑eq /ha protected/yr, 100-yr basis
| 25th percentile | 5.64 |
| mean | 6.80 |
| median (50th percentile) | 5.74 |
| 75th percentile | 7.42 |
Unit: t CO₂‑eq /ha protected/yr, 100-yr basis
| 25th percentile | 2.00 |
| mean | 2.14 |
| median (50th percentile) | 2.14 |
| 75th percentile | 2.38 |
Unit: t CO₂‑eq /ha protected/yr, 100-yr basis
| 25th percentile | 7.64 |
| mean | 8.94 |
| median (50th percentile) | 7.88 |
| 75th percentile | 9.81 |
Table 1b. Effectiveness at avoiding emissions and sequestering carbon in salt marsh ecosystems.
Unit: t CO₂‑eq /ha protected/yr, 100-yr basis
| 25th percentile | 2.79 |
| mean | 2.90 |
| median (50th percentile) | 2.90 |
| 75th percentile | 3.01 |
Unit: t CO₂‑eq /ha protected/yr, 100-yr basis
| 25th percentile | 1.59 |
| mean | 1.90 |
| median (50th percentile) | 1.88 |
| 75th percentile | 2.19 |
Unit: t CO₂‑eq /ha protected/yr, 100-yr basis
| 25th percentile | 4.38 |
| mean | 4.80 |
| median (50th percentile) | 4.78 |
| 75th percentile | 5.20 |
Table 1c. Effectiveness at avoiding emissions and sequestering carbon in seagrass ecosystems.
Unit: t CO₂‑eq /ha protected/yr, 100-yr basis
| 25th percentile | 2.11 |
| mean | 2.33 |
| median (50th percentile) | 2.33 |
| 75th percentile | 2.56 |
Unit: t CO₂‑eq /ha protected/yr, 100-yr basis
| 25th percentile | 1.04 |
| mean | 1.53 |
| median (50th percentile) | 1.22 |
| 75th percentile | 1.71 |
Unit: t CO₂‑eq /ha protected/yr, 100-yr basis
| 25th percentile | 3.15 |
| mean | 3.86 |
| median (50th percentile) | 3.56 |
| 75th percentile | 4.27 |
We estimate that coastal wetland protection costs approximately US$1–2/t CO₂‑eq for mangrove and salt marsh ecosystems and seagrass ecosystem protection saves US$6/t CO₂‑eq (Tables 2a–c). This is based on protection costs of roughly US$11/ha and revenue of US$23/ha compared with the baseline for mangrove/salt marsh and seagrass ecosystems, respectively. However, data related to the costs of coastal wetland protection are extremely limited, and these estimates are uncertain. These estimates likely underestimate the potentially high costs of coastal land acquisition, for instance.
The costs of coastal wetland protection include up-front costs of land acquisition (for salt marshes and mangroves) and other one-time expenditures as well as ongoing operational costs. Protecting coastal wetlands also generates revenue, primarily through increased tourism. For consistency across solutions, we did not include revenue associated with benefits other than climate change mitigation.
Due to data limitations, we estimated the cost of land acquisition for ecosystem protection for mangroves and salt marshes by extracting coastal forest land purchase costs reported by Dinerstein et al. (2024), who found a median cost of US$1,115/ha (range: US$78–5,910/ha), which we amortized over 30 years. For seagrass ecosystems, which do not generally require land acquisition, we based initial costs were on McCrea-Strub et al.’s (2011) findings that reported a median MPA start-up cost of US$208/ha (range: US$55–434/ha) to cover expenses associated with infrastructure, planning, and site research, which we amortized over 30 years.
Costs of PA maintenance were estimated as US$17/ha/yr (Waldron et al., 2020). While these estimates reflect the costs of effective enforcement and management, many PAs lack sufficient funding for effective management (Bruner et al., 2004). Costs of MPA maintenance were estimated at US$14/ha/yr, though only 16% of the MPAs surveyed in this study reported their current funding as sufficient (Balmford et al., 2004). Tourism revenues directly attributable to protection were estimated to be US$43/ha/yr (Waldron et al., 2020) based on estimates for all PAs and MPAs and excluding downstream revenues. For consistency across solutions, we did not include revenues associated with ecosystem services, which would increase projected revenue.
We also excluded carbon credits as a revenue source due to the challenges inherent in accurate carbon accounting in these ecosystems and their frequently intended use to offset carbon emissions, similar to reported concerns with low-quality carbon credits in forest conservation projects (West et al., 2023). Future actions could explore policies that increase market financing for coastal wetland protection in more holistic ways, such as contributions-based approaches as suggested for forests (Blanchard et al., 2024). Financial support will be critical for backing conservation implementation (Macreadie et al., 2022), particularly in the face of existing political and economic challenges that have historically limited expansion.
Table 2. Cost per unit climate impact.
Unit: 2023 US$/t CO₂‑eq , 100-yr basis
| estimate | 1 |
Unit: 2023 US$/t CO₂‑eq , 100-yr basis
| estimate | 2 |
Unit: 2023 US$/t CO₂‑eq , 100-yr basis
| estimate | -6 |
Negative value indicates cost savings.
We define a learning curve as falling costs with increased adoption. The costs of coastal wetland protection do not fall with increasing adoption, so there is no learning curve 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.
Protect Coastal Wetlands is an EMERGENCY BRAKE climate solution. It has the potential to deliver a more rapid impact than gradual 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.
Additionality in this solution refers to whether the ecosystem would have been degraded without protection. In this analysis, we assumed protection confers additional carbon benefits as it reduces degradation and associated emissions. Another aspect of additionality, though not directly relevant to our analysis, is whether coastal wetlands would have been protected in the absence of carbon financing. This could become increasingly important if protection efforts seek carbon credits, since many wetlands are protected for other benefits, such as flood resilience and biodiversity.
The permanence of stored carbon in coastal wetlands is another critical issue as climate change impacts unfold. For instance, with sea-level rise, the ability of salt marshes to expand both vertically and laterally can determine resiliency, suggesting that protection of wetlands might also need to include adjacent areas for expansion (Schuerch et al., 2018). On a global scale, recent research suggests that global carbon accumulation might actually increase by 2100 from climate change impacts on tidal wetlands (Wang et al., 2021), though more work is needed as other work suggests the opposite (Noyce et al., 2023). There is also substantial risk of reversal of carbon benefits if protections are reversed or unenforced, which can require long-term financial investments, community engagement, and management/enforcement commitments (Giakoumi et al., 2018), particularly if the land is leased.
Finally, there are significant uncertainties associated with the available data on coastal wetland areas and distributions, loss rates, drivers of loss, extent and boundaries of PAs/MPAs, and efficacy of PAs/MPAs at reducing coastal wetland disturbance. For example, the geospatial datasets we used to identify the adoption ceiling for this solution could include partially degraded systems, such as drained wetlands, where protection alone would not stop emissions or restore function without restoration – yet we lack enough data to distinguish these current differences at a global scale. Similarly, legal protection of coastal wetlands does not always prevent degradation (Heck et al., 2024). The emissions dynamics of both intact and degraded coastal wetlands are also uncertain. Even less is known about the impacts of different types of degradation on coastal wetland carbon dynamics and how they vary spatially and temporally around the world.
We estimated that approximately 8.04 million ha of coastal wetlands are currently protected, with 5.13 million ha recognized as PAs and MPAs in strict (I–II) protection categories and 2.90 million ha in non-strict protection categories (III–IV) (Tables 3a–c; Garnett et al., 2018; UNEP-WCMC & IUCN, 2024, see Appendix). Indigenous People’s Lands (IPLs) cover an additional 3.44 million ha; we did not include these in our analysis due to limited data, but we recognize that these sites might currently deliver conservation benefits. In total, we estimate that roughly 15% of all coastal wetlands have some protection (as MPAs or PAs in IUCN categories I–IV), though only about 9% are under strict protection (IUCN categories I or II). Across individual ecosystem types, strict protection categories (IUCN I–II) are highest for mangroves (~15%) and lowest for seagrasses (~7%).
Our estimates of PA and MPA protection (12–19%) were lower than previously reported estimates for mangroves (40–43%, Dabalà et al., 2023; Leal and Spalding, 2024), tidal marshes (45%, Worthington et al., 2024), and seagrasses (26%, United Nations Environment Programme [UNEP], 2020). This is likely because our calculations excluded IUCN categories (“not assigned,” “not applicable,” and “not reported”) that contain large areal estimates for each ecosystem type – 4.3 million ha (mangrove), 1.9 million ha (salt marsh), and 5.4 million ha (seagrasses) – because their protection category was unclear as well as IUCN protection categories V–VI, which permit sustainable use and where extractive activities that could degrade these ecosystems are less formally restricted. Our spatial analysis also differed (see Appendix).
Table 3a. Current extent of mangrove ecosystems under legal protection by ecosystem type (circa 2023). “Strict Protection” includes land within IUCN Categories I–II PAs or MPAs. “Nonstrict Protection” includes land within IUCN Categories III–IV PAs or MPAs. “Other” includes land within all remaining IUCN PA or MPA categories (Million ha protected).
Table 3. Current extent of ecosystems under legal protection by ecosystem type (circa 2023). “Strict Protection” includes land within IUCN Categories I–II PAs or MPAs. “Nonstrict Protection” includes land within IUCN Categories III–IV PAs or MPAs. “Other” includes land within all remaining IUCN PA or MPA categories.
Unit: million ha protected
| Strict Protection | 2.35 |
| Nonstrict Protection | 0.59 |
| Total (Strict + Nonstrict) | 2.94 |
| IPL | 1.86 |
| Other | 7.52 |
Unit: million ha protected
| Strict Protection | 0.62 |
| Nonstrict Protection | 0.62 |
| Total (Strict + Nonstrict) | 1.24 |
| IPL | 1.09 |
| Other | 3.14 |
Unit: million ha protected
| Strict Protection | 2.17 |
| Nonstrict Protection | 1.69 |
| Total (Strict + Nonstrict) | 3.86 |
| IPL | 0.49 |
| Other | 9.00 |
We calculated the rate of PA and MPA expansion based on their recorded year of establishment. Protection expanded by an average of 59,600, 19,700, and 98,500 ha/yr in mangrove, salt marsh, and seagrass ecosystems, respectively (Tables 4a–c; Figure 3a). Salt marsh ecosystems have the lowest absolute rate of coastal wetland protection expansion (Figure 3b), while seagrasses have the lowest expansion of PAs relative to their adoption ceiling (Figure 3, right). The median total annual adoption trend across the three ecosystems is roughly 123,100 ha/yr (roughly 0.12 million ha/yr).
Table 4. 2000–2020 adoption trend for legal protection of ecosystems.
Unit: ha/yr protected
| 25th percentile | 23,500 |
| mean | 59,600 |
| median (50th percentile) | 40,700 |
| 75th percentile | 76,600 |
Unit: ha/yr protected
| 25th percentile | 8,400 |
| mean | 19,700 |
| median (50th percentile) | 18,500 |
| 75th percentile | 23,300 |
Unit: ha/yr protected
| 25th percentile | 12,800 |
| mean | 98,500 |
| median (50th percentile) | 37,800 |
| 75th percentile | 142,900 |
Figure 3. (a) Areal trend in coastal wetland protection by ecosystem type (2000–2020). These values reflect only the area located within IUCN Class I–IV PAs or MPAs; (ha/yr protected). (b) Trend in coastal wetland protection by ecosystem type as a percent of the adoption ceiling. These values reflect only the area located within IUCN Class I–IV PAs or MPAs; (Percent). Source: Project Drawdown original analysis.
Credit: Project Drawdown
We estimate an adoption ceiling of 54.6 million ha of coastal wetlands globally, which includes 15.7 million ha of mangroves, 7.50 million ha of salt marshes, and 31.4 million ha of seagrasses (Tables 5a–c). This estimate is in line with recent existing global estimates of coastal wetlands (36–185 million ha), which have large ranges due to uncertainties surrounding seagrass and salt marsh distributions (Macreadie et al., 2021, Krause et al., 2025). The adoption ceiling of our solution is therefore a conservative estimate of potential climate impact if global areas are indeed larger than calculated. While the protection of all existing coastal wetlands is highly unlikely, these values are used to represent the technical limits of adoption of this solution.
Table 5. Adoption ceiling: upper limit for adoption of legal protection of ecosystems.
Unit: million ha protected
| estimate | 15.7 |
Unit: million ha protected
| estimate | 7.50 |
Unit: million ha protected
| estimate | 31.4 |
We defined the lower end of the achievable range for coastal wetland protection (under IUCN categories I–IV) as 50% of the adoption ceiling and the higher end of the achievable range as 70% of the adoption ceiling for each ecosystem (Tables 6a–c). These numbers are ambitious but precedent exists to support them. For instance, roughly 11 countries already protect over 70% of their mangroves (Dabalà et al., 2023), and the global “30 by 30” target aims to protect 30% of ecosystems on land and in the ocean by 2030 (Roberts et al., 2020). Further, a significant extent of existing global coastal wetland areas already fall under non-strict protection categories not included in our analysis (V–VI and “Other”). These are prime candidates for conversion to stricter protection categories, so long as the designation confers real conservation benefits; recent work suggests that stricter protection can coincide with increased degradation in some mangroves (Heck et al., 2024).
Current adoption of PAs and MPAs in many countries with the highest land areas of coastal wetlands is low. For example, protection levels (IUCN I–IV) in countries with the top 10 greatest mangrove areas ranges between less than 1% (India, Myanmar, Nigeria, and Papua New Guinea) to 8.8–21.2% (Australia, Bangladesh, Brazil, Indonesia, Malaysia, and Mexico;Dabalà et al., 2023). Expansion of PAs, particularly under IUCN I–IV categories, is a significant challenge with real implementation barriers due to competing land uses and local reliance on these areas for livelihoods. Further, protection does not guarantee conservation benefits, and significant funding is required to maintain/enforce these areas or they run the risk of becoming “paper parks” (Di Minin & Toivonen, 2015). Strong policy and financial incentives for conservation will be necessary to achieve these ambitious goals. Pathways for operationalizing protection could include finance, governance, and stakeholder alignment and will likely require a combination of these tactics around the world.
Table 6. Range of achievable adoption levels for ecosystems.
Unit: million ha protected
| Current Adoption | 2.94 |
| Achievable – Low | 7.85 |
| Achievable – High | 11.0 |
| Adoption Ceiling | 15.7 |
Unit: million ha protected
| Current Adoption | 1.24 |
| Achievable – Low | 3.75 |
| Achievable – High | 5.25 |
| Adoption Ceiling | 7.50 |
Unit: million ha protected
| Current Adoption | 3.86 |
| Achievable – Low | 15.7 |
| Achievable – High | 22.0 |
| Adoption Ceiling | 31.4 |
We estimated that coastal wetland protection currently avoids approximately 0.04 Gt CO₂‑eq/yr, with potential impacts of 0.27 Gt CO₂‑eq/yr at the adoption ceiling (Table 7a–c, see Appendix for more information on the calculations). The lower-end achievable scenario (50% protection) would avoid 0.14 Gt CO₂‑eq/yr, and the upper-end achievable scenario (70% protection) would avoid 0.20 Gt CO₂‑eq/yr (Tables 7a–c). These values are in line with Macreadie et al. (2021), who estimated a maximum mitigation potential from avoided emissions due to degradation (land conversion) of 0.30 (range: 0.14–0.47) Gt CO₂‑eq/yr for mangrove, salt marsh, and seagrass ecosystems. Our estimate was slightly lower, but within their range, and differed in a few key ways. We accounted for the effectiveness of protection at reducing degradation (53–59%, instead of assuming 100%), included retained carbon sequestration with each hectare protected, and used slightly different loss rates and ecosystem areas.
Table 7. Climate impact at different levels of adoption for ecosystems.
Unit: Gt CO₂‑eq/yr, 100-yr basis
| Current Adoption | 0.02 |
| Achievable – Low | 0.06 |
| Achievable – High | 0.09 |
| Adoption Ceiling | 0.12 |
Unit: Gt CO₂‑eq/yr, 100-yr basis
| Current Adoption | 0.01 |
| Achievable – Low | 0.02 |
| Achievable – High | 0.03 |
| Adoption Ceiling | 0.04 |
Unit: Gt CO₂‑eq/yr, 100-yr basis
| Current Adoption | 0.01 |
| Achievable – Low | 0.06 |
| Achievable – High | 0.08 |
| Adoption Ceiling | 0.11 |
Wetlands buffer coastal communities from waves and storm surge due to extreme weather and have important roles in disaster risk mitigation (Sheng et al., 2022; Guannel et al., 2016). Mangroves slow the flow of water and reduce surface waves to protect more than 60 million people in low-lying coastal areas, mainly in low- and middle-income countries (McIvor et al., 2012; Hochard et al., 2021). Wetlands also protect structures against damage during storms and lead to savings in insurance claims (Barbier et al., 2013; Sheng et al., 2022). Mangroves provide an estimated US$65 billion in flood protection globally (Menéndez et al., 2020). A study of the damages of Hurricane Sandy found that wetlands in the northeastern United States avoided US$625 million in direct flood damages (Narayan et al., 2017).
Wetlands are a contributor to local livelihoods, providing employment for coastal populations via the fisheries and tourism that they support. Coastal ecosystems, such as mangroves, are crucial for subsistence fisheries as they sustain approximately 4.1 million small-scale fishers (Leal and Spalding, 2022). Wetlands provide sources of income for low-income coastal communities as they make small-scale fishing accessible, requiring limited gear and materials to fish (Cullen-Unsworth & Unsworth, 2018). The economic value of mangrove ecosystem services is estimated at US$33,000–57,000/ha/yr and is a major contributor to the national economies of low- and middle-income countries with mangroves (UNEP, 2014).
Mangroves support the development of numerous commercially important fish species and strengthen overall fishery productivity. For example, research conducted across 6,000 villages in Indonesia found that rural coastal households near high and medium-density mangroves consumed more fish and aquatic animals than households without mangroves nearby (Ickowitz et al., 2023). Seagrasses also support fisheries as 20% of the world’s largest fisheries rely on seagrasses for habitats (Jensen, 2022). The amount and diversity of species within seagrasses also provide important nutrition for fishery species (Cullen-Unsworth & Unsworth, 2018).
Coastal wetlands are significant in cultural heritages and identities for nearby people. They can be associated with historical, religious, and spiritual values for communities and especially for Indigenous communities (UNEP, 2014). For example, a combination of sea-level rise and oil and gas drilling have contributed to the decline of coastal wetlands in Louisiana, which threatens livelihoods and deep spiritual ties of local Indigenous tribes (Baniewicz, 2020; Hutchinson, 2022). Indigenous people have a long history of managing and protecting coastal wetlands (Mathews & Turner, 2017). Efforts to protect these areas must include legal recognition of Indigenous ownership to support a just and sustainable conservation process (Fletcher et al., 2021).
Coastal wetlands are integral in supporting the biodiversity of surrounding watersheds. High species diversity of mangroves and seagrasses provide a unique habitat for marine life, birds, insects, and mammals, and contain numerous threatened or endangered species (Green and Short, 2003; U.S. EPA, 2025a). A variety of species rely on wetlands for food and shelter, and they can provide temporary habitats for species during critical times in their life cycles, such as migration and breeding (Unsworth et al., 2022). Wetlands can improve water quality, making the surrounding ecosystem more favorable to supporting marine life (Cullen-Unsworth & Unsworth, 2018). Seagrasses can improve coral health by filtering water and reducing pathogens that could cause disease (Cullen-Unsworth & Unsworth, 2018).
Wetlands reduce coastal erosion which can benefit local communities during strong storms (Jensen, 2022). Wetlands mitigate erosion impacts by absorbing wave energy that would degrade sand and other marine sediments (U.S. EPA, 2025b). Specifically, mangroves reduce erosion through their aerial root structure that retain sediments that would otherwise degrade the shoreline (Thampanya et al., 2006).
Coastal wetlands improve the water quality of watersheds by filtering chemicals, particles (including microplastics), sediment, and cycling nutrients (Unsworth et al. 2022). There is even evidence that wetlands can remove viruses and bacteria from water, leading to better sanitation and health for marine wildlife and humans (Lamb et al., 2017).
There are several risks associated with coastal wetland protection. Leakage, wherein protection in one region could prompt degradation of another, could reduce climate benefits (Renwick et al., 2015). Strict conservation of coastal wetlands could impact local economies, creating “poverty traps” if protection threatens livelihoods (McNally et al., 2011). Conservation projects also risk unequal distribution of benefits (Lang et al., 2023). In places where habitats are fragmented or existing infrastructure limits landward migration, even protected coastal wetlands are at risk of being lost with climate change (commonly known as “the coastal squeeze”; Borchert et al., 2018). Funding gaps risk reversal of climate benefits despite initial conservation efforts; most MPAs and PAs report a lack of funding (Balmford et al., 2004; Bruner et al., 2004). If coastal wetlands are subjected to human impacts that protection cannot prevent, such as upgradient nutrient pollution, there could also be a risk of increased GHG emissions (Feng et al., 2025) and ecosystem degradation.
Other ecosystems often occur adjacent to areas of coastal wetlands, and the health of nearby ecosystems can be improved by the services provided by intact coastal wetlands (and vice versa).
Reducing food loss and waste and improving diets reduce demand for agricultural land. These solutions reduce pressure to convert coastal wetlands to agricultural use, easing expansion of PAs.
Mangrove deforestation can occur for fuel wood needs. Fuel wood sourced from mangroves could be replaced with wood sourced from other forested ecosystems.
ha protected
CO₂
ha protected
CO₂
ha protected
CO₂
Trade-offs associated with protection of coastal wetlands include emission of other GHGs not quantified in this solution that have higher global warming potentials (GWP) than CO₂. Methane and nitrous oxide emissions can be measurable in coastal wetland ecosystems, though it is important to recognize that degradation can significantly impact the magnitude and types of effluxes, too. In mangroves, methane evasion can offset carbon burial by almost 20% based on a 20-yr GWP (Rosentreter et al., 2018). In seagrasses, methane and nitrous oxide effluxes can offset burial on average, globally, by 33.4% based on a 20-yr GWP and 7.0% based on a 100-yr GWP (Eyre et al., 2023). Finally, conservation of coastal land can also restrict development of desirable coastal property for other uses.
Mangrove ecosystems cover approximately 15.7 million ha globally; just five countries (Australia, Brazil, Indonesia, Mexico, and Nigeria) contain nearly 50% of the world’s mangrove ecosystem area (FAO, 2020). Green shaded areas indicate the general location of mangrove ecosystems; zoom in for details.
Liu, L., Zhang, X., & Zhao, T. (2022). GWL_FCS30: global 30 m wetland map with fine classification system using multi-sourced and time-series remote sensing imagery in 2020 [Data set, Version 1]. Link to source: https://doi.org/10.5281/zenodo.7340516
Mangrove ecosystems cover approximately 15.7 million ha globally; just five countries (Australia, Brazil, Indonesia, Mexico, and Nigeria) contain nearly 50% of the world’s mangrove ecosystem area (FAO, 2020). Green shaded areas indicate the general location of mangrove ecosystems; zoom in for details.
Liu, L., Zhang, X., & Zhao, T. (2022). GWL_FCS30: global 30 m wetland map with fine classification system using multi-sourced and time-series remote sensing imagery in 2020 [Data set, Version 1]. Link to source: https://doi.org/10.5281/zenodo.7340516
The current adoption, potential adoption, and effectiveness of coastal wetland protection is ecosystem-dependent (mangroves, salt marshes, seagrasses) and geographically variable. While coastal wetland protection can help avoid GHG emissions anywhere they occur, ecosystems with high rates of loss from human activity, and large unprotected areas have the greatest potential for avoiding emissions via protection.
For instance, seagrass ecosystems have the lowest current adoption of protection, ~12%, and highest adoption ceiling (31.4 Mha) (Tables 3 and 6). Protecting seagrasses also potentially can save money (–US$23/ha, Table 2) because they do not generally require land purchase (McCrea-Strub et al., 2011). Protection of seagrasses could therefore provide meaningful climate impact as well as substantial economic and ecologic benefits (Unsworth et al., 2022).
For seagrasses, countries like Australia (~10 Mha), Indonesia (~3 Mha), the United States (~0.5 Mha), and regions such as the Gulf of Mexico (~2 Mha) and the Western Mediterranean (~0.4 Mha), could be good initial targets for protection due to their significant seagrass extents (Green and Short, 2003). Countries that contain the top 10 largest areas of mangroves (Australia, Bangladesh, Brazil, India, Indonesia, Malaysia, Mexico, Myanmar, Nigeria, Papua New Guinea) might have the greatest potential to significantly expand adoption and scale climate impact (Dabalà et al., 2023). Likewise, salt marsh protection might be most beneficial in countries with the greatest extent, such as the United States (~1.7 Mha), Australia (~1.3 Mha), Russia (~0.7 Mha), and China (~0.6 Mha) (Mcowen et al., 2017).
There is high scientific consensus that coastal wetland protection is an important strategy for reducing wetland loss due to degradation and that degradation results in carbon stock loss from coastal wetlands. Rates of wetland loss are generally lower inside PAs than outside them. An analysis of over 4,000 PAs (wetland and non-wetland area) showed 59% of sites are in “sound management,” which generally reflects PAs with strong enforcement, management implementation, and conservation outcome indicators (Leverington et al., 2010). Here we used a conservative effectiveness of 59% for salt marshes and mangroves that are under legal protection, consistent with the value from Leverington et al. (2010). Other regional studies show similar PA effectiveness values, with 25–50% of wetland PAs in China exhibiting moderate to very high conservation effectiveness (Lu et al., 2016).
Seagrasses differ from mangroves and salt marshes in that they fall under MPA designation because they are subtidal, or submerged. In an analysis of effectiveness of 66 MPAs in 18 countries, nearly 53% of MPAs reported positive or slightly positive ecosystem outcomes (Rodríguez-Rodríguez & Martínez-Vega, 2022). Less is known about MPA effectiveness for seagrass meadows specifically; we assumed an effectiveness of 53% – similar to other MPAs.
Prevention of degradation via legal coastal wetlands protection avoids emissions by preserving carbon stocks while also retaining carbon sequestration capacity. Degradation of coastal wetlands results in measurable loss of short- and long-lived carbon stocks, with emissions that vary based on ecosystem and degradation type (Donato et al., 2011, Holmquist et al., 2023, Lovelock et al., 2017, Mcleod et al., 2011, Pendleton et al., 2012). Estimates of existing carbon stocks in coastal wetlands are substantial, ranging between 8.97–32.7 Gt of carbon (32.9–120 Gt CO₂‑eq ), most of which is likely susceptible to degradation (Macreadie et al., 2021).
The results presented in this document synthesize findings from 14 global datasets. We recognize that geographic bias in the information underlying global data products creates bias and hope this work inspires research and data sharing on this topic in underrepresented regions and understudied ecosystems.
In this analysis, we integrated global land cover data; shapefiles of PAs, MPAs, and IPLs; and ecosystem type (mangroves, salt marshes, seagrasses) data on carbon emissions and sequestration rates to calculate currently protected coastal wetland area, total global coastal wetland area, and avoided emissions and additional sequestration from coastal wetland protection by ecosystem type (mangroves, salt marshes, and seagrasses).
We used two land cover data products to estimate coastal wetland extent by ecosystem type (mangroves, salt marshes, seagrasses) inside and outside of PAs, MPAs, and IPLs: 1) a global 30 m wetland map, GWL_FCS30, for mangroves and salt marshes (Zhang et al., 2023), and 2) the global distribution of seagrasses map from UN Environment World Conservation Monitoring Centre (UNEP-WCMC & Short, 2021).
The IUCN defines PAs, including MPAs, as geographically distinct areas managed primarily for the long-term conservation of nature and ecosystem services. They are further disaggregated into six levels of protection, ranging from strict wilderness preserves to sustainable use areas that allow for some natural resource extraction (including logging). We calculated all levels of protection but only considered protection categories I–IV in our analysis of adoption. We recognized that other protection categories might provide conservation benefits. We excluded categories labeled as “Not Applicable (NAP),” “Not Reported (NR),” “Not Assigned (NAS),” as well as categories VI and VII. We also estimated IPL area based on available data, but emphasized that much of their extent has not been fully mapped nor recognized for its conservation benefits (Garnett et al., 2018). Additionally, the IPL dataset only covered land and therefore did not include seagrass ecosystems explicitly beyond the extent that ecosystems bordering terrestrial IPL areas were captured within the 1 km pixels of analysis. Coastal wetlands also lack data on the effectiveness of protection with IPLs, so we did not include IPL data as currently protected in our estimates.
We identified protected coastal wetland areas using the World Database on PAs (UNEP-WCMC & IUCN, 2024), which contains boundaries for each PA or MPA and additional information, including their establishment year and IUCN management category (Ia to VI, NAP, NR, and NAS). For each PA or MPA polygon, we extracted the coastal wetland area based on the datasets in the Land Cover Data section. Our spatial analysis required the center point of the pixel of each individual ecosystem under consideration to be covered by the PA or MPA polygon in order to be classified as protected, which is a relatively strict spatial extraction technique that likely leads to lower estimates of conservation compared to previous work with differing techniques (Dabalà et al., 2023).
We used the maps of IPLs from Garnett et al. (2018) to identify IPLs that were not inside of established PAs. We calculated the total coastal wetland area within IPLs (excluding PAs and MPAs) using the same coastal wetland data sources.
We aggregated coastal wetland loss rates by ecosystem type (mangroves, salt marshes, seagrasses). We used data on PA and MPA effectiveness to calculate the difference in coastal wetland loss rates attributable to protection (Equation A1). We compiled baseline estimates of current rates of coastal wetland degradation from all causes (%/yr) from existing literature as shown in the “Detailed coastal wetland loss data” tab of the Supporting Data spreadsheet and used in conjunction with estimates of reductions in loss, 53–59%, associated with protection.
Equation A1.
We then used the ratio of coastal wetland loss in unprotected areas versus PAs to calculate avoided CO₂ emissions and additional carbon sequestration for each adoption unit. Specifically, we estimated the carbon benefits of avoided coastal wetland loss by multiplying avoided coastal wetland loss by avoided CO₂ emissions (30-yr time horizon; Equation A2) and carbon sequestration rates (30-yr time horizon; Equation A3) for each ecosystem type. Importantly, the emissions factors we used account for carbon in above- and below-ground biomass and generally do not assume 100% loss of carbon stocks because many land use impacts may retain some stored carbon, some of which is likely resistant to degradation (see the “2. current state effectiveness tab” in the spreadsheet for more information). We derived our estimates of retained carbon sequestration from global databases on sediment organic carbon burial rates in each ecosystem (see the “2. current state effectiveness tab” in the spreadsheet for more information).
Equation A2.
Equation A3.
We then estimated effectiveness (Equation A4) as the avoided CO₂ emissions and the retained carbon sequestration capacity attributable to the reduction in wetland loss conferred by protection estimated in Equations S1–S3.
Equation A4.
Finally, we calculated climate impact (Equation A5) by multiplying the adoption area under consideration by the estimated effectiveness from Equation A4.
Equation A5.
Garnett, S. T., Burgess, N. D., Fa, J. E., Fernández-Llamazares, Á., Molnár, Z., Robinson, C. J., Watson, J. E. M., Zander, K. K., Austin, B., Brondizio, E. S., Collier, N. F., Duncan, T., Ellis, E., Geyle, H., Jackson, M. V., Jonas, H., Malmer, P., McGowan, B., Sivongxay, A., & Leiper, I. (2018). A spatial overview of the global importance of Indigenous lands for conservation. Nature Sustainability, 1(7), 369–374. https://doi.org/10.1038/s41893-018-0100-6
UNEP-WCMC, & Short, F. T. (2021). Global distribution of seagrasses (version 7.1) [Data set]. UN Environment World Conservation Monitoring Centre. https://doi.org/10.34892/x6r3-d211
UNEP-WCMC, & IUCN. (2024). Protected planet: The world database on protected areas (WDPA) and world database on other effective area-based conservation measures (WD-OECM) [Data set]. Retrieved November 2024, from https://www.protectedplanet.net
Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., & Liu, W. (2023). GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020. Earth System Science Data, 15(1), 265–293. https://doi.org/10.5194/essd-15-265-2023
The Protect Peatlands solution is defined as legally protecting peatland ecosystems through establishment of protected areas (PAs), which preserves stored carbon and ensures continued carbon sequestration by reducing degradation of the natural hydrology, soils, and/or vegetation. This solution focuses on non-coastal peatlands that have not yet been drained or otherwise severely degraded. Reducing emissions from degraded peatlands is addressed in the Restore Peatlands solution, and mangroves located on peat soils are addressed in the Protect Coastal Wetlands solution.
Peatlands are diverse ecosystems characterized by waterlogged, carbon-rich peat soils consisting of partially decomposed dead plant material (Figure 1). They are degraded or destroyed through clearing of vegetation and drainage for agriculture, forestry, peat extraction, or other development. An estimated 600 Gt carbon (~2,200 Gt CO₂‑eq ) is stored in peatlands, twice as much as the carbon stock in all forest biomass (Yu et al., 2010; Pan et al., 2024). Because decomposition occurs very slowly under waterlogged conditions, large amounts of plant material have accumulated in a partially decomposed state over millennia. These carbon-rich ecosystems occupy only 3–4% of land area (Xu et al., 2018b; United Nations Environment Programme [UNEP], 2022). Their protection is both feasible due to their small area and highly impactful due to their carbon density.
Figure 1. These photos show the diversity of peatlands that occur in different places, including a fen peatland and meadow complex in California (top left), a peat swamp in Indonesia (top right), a peat fen and forest in Canada (bottom left), and a peat bog in New Hampshire (bottom right).
Photo credits: Catie and Jim Bishop | U.S. Department of Agriculture; Rhett A. Butler; Garth Lenz; Linnea Hanson | U.S. Department of Agriculture
When peatlands are drained or disturbed, the rate of carbon loss increases sharply as the accumulated organic matter begins decomposing (Figure 2). Removal of overlying vegetation produces additional GHG emissions while also slowing or stopping carbon uptake. Whereas emissions from vegetation removal occur rapidly following disturbance, peat decomposition and associated emissions can continue for centuries depending on environmental conditions and peat thickness. Peat decomposition after disturbance occurs faster in warmer climates because cold temperatures slow microbial activity. In this analysis, we evaluated tropical, subtropical, temperate, and boreal regions separately.
Figure 2. Greenhouse gas emissions and sequestration in intact peatlands (left) and a drained peatland (right). Intact peatlands are a net greenhouse gas sink, sequestering carbon in peat through photosynthesis but also emitting methane due to waterlogged soils. Drained peatlands are a greenhouse gas source, producing emissions from peat decomposition and drainage canals. Modified from IUCN UK Peatland Programme (2024).
Source: IUCN UK Peatland Programme. (2024, July 10). New briefing addresses the peatlands and methane debate.
In addition to peat decomposition, biomass removal, and lost carbon sequestration, peatland disturbance impacts methane and nitrous oxide emissions and carbon loss through waterways (Figure 2; Intergovernmental Panel on Climate Change [IPCC] Task Force on National Greenhouse Gas Inventories, 2014; UNEP, 2022). Intact peatlands are a methane source because of methane-producing microbes, which thrive under waterlogged conditions. However, carbon uptake typically outweighs methane emissions. Leifield et al. (2019) found that intact peatlands are a net carbon sink of 0.77 ± 0.15 t CO₂‑eq /ha/yr in temperate and boreal regions and 1.65 ± 0.51 t CO₂‑eq /ha/yr in tropical regions after accounting for methane emissions. Peatland drainage reduces methane emissions from the peatland itself, but the drainage ditches can become potent methane sources (Evans et al., 2015; Peacock et al., 2021). Dissolved and particulate organic carbon also run off through drainage ditches, increasing CO₂ emissions in waterways from microbial activity and abiotic processes. Finally, rates of nitrous oxide emissions increase following drainage as the nitrogen stored in the peat becomes available to microbes.
Patterns of ongoing peatland drainage are poorly understood at the global scale, but rates of ecosystem disturbance are generally lower in PAs and on Indigenous peoples’ lands than outside of them (Li et al., 2024b; Wolf et al., 2021; Sze et al., 2021). The International Union for Conservation of Nature (IUCN) defines six levels of PAs that vary in their allowed uses, ranging from strict wilderness preserves to sustainable use areas that allow for some extraction of natural resources. All PA levels were included in this analysis (UNEP World Conservation Monitoring Center [UNEP-WCMC] and IUCN, 2024). Due to compounding uncertainties in the distributions of peatlands and Indigenous peoples’ lands, which have not yet been comprehensively mapped, and unknown rates of peatland degradation within Indigenous people’s lands, peatlands within Indigenous peoples’ lands were excluded from the tables but are discussed in the text (Garnett et al., 2018; UNEP-WCMC and IUCN, 2024).
Adams, V. M., Iacona, G. D., & Possingham, H. P. (2019). Weighing the benefits of expanding protected areas versus managing existing ones. Nature Sustainability, 2(5), 404–411. Link to source: https://doi.org/10.1038/s41893-019-0275-5
Atkinson, C. L., & Alibašić, H. (2023). Prospects for governance and climate change resilience in peatland management in Indonesia. Sustainability, 15(3), Article 3. Link to source: https://doi.org/10.3390/su15031839
Austin, K. G., Elsen, P. R., Coronado, E. N. H., DeGemmis, A., Gallego-Sala, A. V., Harris, L., Kretser, H. E., Melton, J. R., Murdiyarso, D., Sasmito, S. D., Swails, E., Wijaya, A., Winton, R. S., & Zarin, D. (2025). Mismatch between global importance of peatlands and the extent of their protection. Conservation Letters, 18(1), e13080. Link to source: https://doi.org/10.1111/conl.13080
Barnes, M. D., Glew, L., Wyborn, C., & Craigie, I. D. (2018). Prevent perverse outcomes from global protected area policy. Nature Ecology & Evolution, 2(5), 759–762. Link to source: https://doi.org/10.1038/s41559-018-0501-y
Bruner, A. G., Gullison, R. E., & Balmford, A. (2004). Financial costs and shortfalls of managing and expanding protected-area systems in developing countries. BioScience, 54(12), 1119–1126. Link to source: https://doi.org/10.1641/0006-3568(2004)054[1119:FCASOM]2.0.CO;2
Conchedda, G., & Tubiello, F. N. (2020). Drainage of organic soils and GHG emissions: Validation with country data. Earth System Science Data, 12(4), 3113–3137. Link to source: https://doi.org/10.5194/essd-12-3113-2020
Davidson, N. C. (2014). How much wetland has the world lost? Long-term and recent trends in global wetland area. Marine and Freshwater Research, 65(10), 934. Link to source: https://doi.org/10.1071/MF14173
Deshmukh, C. S., Julius, D., Desai, A. R., Asyhari, A., Page, S. E., Nardi, N., Susanto, A. P., Nurholis, N., Hendrizal, M., Kurnianto, S., Suardiwerianto, Y., Salam, Y. W., Agus, F., Astiani, D., Sabiham, S., Gauci, V., & Evans, C. D. (2021). Conservation slows down emission increase from a tropical peatland in Indonesia. Nature Geoscience, 14(7), Article 7. Link to source: https://doi.org/10.1038/s41561-021-00785-2
Dietrich, O., & Behrendt, A. (2022). Wet grassland sites with shallow groundwater conditions: Effects on local meteorological characteristics. Water, 14(21), Article 21. Link to source: https://doi.org/10.3390/w14213560
Dinerstein, E., Joshi, A. R., Hahn, N. R., Lee, A. T. L., Vynne, C., Burkart, K., Asner, G. P., Beckham, C., Ceballos, G., Cuthbert, R., Dirzo, R., Fankem, O., Hertel, S., Li, B. V., Mellin, H., Pharand-Deschênes, F., Olson, D., Pandav, B., Peres, C. A., … Zolli, A. (2024). Conservation imperatives: Securing the last unprotected terrestrial sites harboring irreplaceable biodiversity. Frontiers in Science, 2. Link to source: https://doi.org/10.3389/fsci.2024.1349350
Evers, S., Yule, C. M., Padfield, R., O’Reilly, P., & Varkkey, H. (2017). Keep wetlands wet: The myth of sustainable development of tropical peatlands – implications for policies and management. Global Change Biology, 23(2), 534–549. Link to source: https://doi.org/10.1111/gcb.13422
Felipe Cadillo, M. M., & Bennett, A. (2024). Navigating socio-political threats to Amazonian peatland conservation: Insights from the Imiria Region, Peru. Sustainability, 16(16), Article 16. Link to source: https://doi.org/10.3390/su16166967
Fluet-Chouinard, E., Stocker, B. D., Zhang, Z., Malhotra, A., Melton, J. R., Poulter, B., Kaplan, J. O., Goldewijk, K. K., Siebert, S., Minayeva, T., Hugelius, G., Joosten, H., Barthelmes, A., Prigent, C., Aires, F., Hoyt, A. M., Davidson, N., Finlayson, C. M., Lehner, B., … McIntyre, P. B. (2023). Extensive global wetland loss over the past three centuries. Nature, 614(7947), 281–286. Link to source: https://doi.org/10.1038/s41586-022-05572-6
Fuller, C., Ondei, S., Brook, B. W., & Buettel, J. C. (2020). Protected-area planning in the Brazilian Amazon should prioritize additionality and permanence, not leakage mitigation. Biological Conservation, 248, 108673. Link to source: https://doi.org/10.1016/j.biocon.2020.108673
Garnett, S. T., Burgess, N. D., Fa, J. E., Fernández-Llamazares, Á., Molnár, Z., Robinson, C. J., Watson, J. E. M., Zander, K. K., Austin, B., Brondizio, E. S., Collier, N. F., Duncan, T., Ellis, E., Geyle, H., Jackson, M. V., Jonas, H., Malmer, P., McGowan, B., Sivongxay, A., & Leiper, I. (2018). A spatial overview of the global importance of Indigenous lands for conservation. Nature Sustainability, 1(7), 369–374. Link to source: https://doi.org/10.1038/s41893-018-0100-6
Girkin, N. T., & Davidson, S. J. (2024). Protect peatlands to achieve climate goals. Science, 383(6682), 490–490. Link to source: https://doi.org/10.1126/science.adn4001
Girkin, N. T., Burgess, P. J., Cole, L., Cooper, H. V., Honorio Coronado, E., Davidson, S. J., Hannam, J., Harris, J., Holman, I., McCloskey, C. S., McKeown, M. M., Milner, A. M., Page, S., Smith, J., & Young, D. (2023). The three-peat challenge: Business as usual, responsible agriculture, and conservation and restoration as management trajectories in global peatlands. Carbon Management, 14(1), 2275578. Link to source: https://doi.org/10.1080/17583004.2023.2275578
Goib, B. K., Fitriani, N., Wicaksono, S., & Chitra, J. (2018). Restoring peat, improving welfare, and empowering women: Can we have it all? Link to source: https://wri-indonesia.org/en/insights/restoring-peat-improving-welfare-and-empowering-women-can-we-have-it-all
Goldstein, A., Turner, W. R., Spawn, S. A., Anderson-Teixeira, K. J., Cook-Patton, S., Fargione, J., Gibbs, H. K., Griscom, B., Hewson, J. H., Howard, J. F., Ledezma, J. C., Page, S., Koh, L. P., Rockström, J., Sanderman, J., & Hole, D. G. (2020). Protecting irrecoverable carbon in Earth’s ecosystems. Nature Climate Change, 10(4), 287–295. Link to source: https://doi.org/10.1038/s41558-020-0738-8
Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamäki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., … Fargione, J. (2017). Natural climate solutions. Proceedings of the National Academy of Sciences, 114(44), 11645–11650. Link to source: https://doi.org/10.1073/pnas.1710465114
Harris, L. I., Richardson, K., Bona, K. A., Davidson, S. J., Finkelstein, S. A., Garneau, M., McLaughlin, J., Nwaishi, F., Olefeldt, D., Packalen, M., Roulet, N. T., Southee, F. M., Strack, M., Webster, K. L., Wilkinson, S. L., & Ray, J. C. (2022). The essential carbon service provided by northern peatlands. Frontiers in Ecology and the Environment, 20(4), 222–230. Link to source: https://doi.org/10.1002/fee.2437
Harrison, M. E., & Paoli, G. D. (2012). Managing the risk of biodiversity leakage from prioritising REDD+ in the most carbon-rich forests: The case study of peat-swamp forests in Kalimantan, Indonesia. Tropical Conservation Science, 5(4), 426–433. Link to source: https://doi.org/10.1177/194008291200500402
Hein, L., Spadaro, J. V., Ostro, B., Hammer, M., Sumarga, E., Salmayenti, R., Boer, R., Tata, H., Atmoko, D., & Castañeda, J.-P. (2022). The health impacts of Indonesian peatland fires. Environmental Health, 21(1), 62. Link to source: https://doi.org/10.1186/s12940-022-00872-w
Helbig, M., Waddington, J. M., Alekseychik, P., Amiro, B., Aurela, M., Barr, A. G., Black, T. A., Carey, S. K., Chen, J., Chi, J., Desai, A. R., Dunn, A., Euskirchen, E. S., Flanagan, L. B., Friborg, T., Garneau, M., Grelle, A., Harder, S., Heliasz, M., … Schulze, C. (2020). The biophysical climate mitigation potential of boreal peatlands during the growing season. Environmental Research Letters, 15(10), 104004. Link to source: https://doi.org/10.1088/1748-9326/abab34
Hugelius, G., Loisel, J., Chadburn, S., Jackson, R. B., Jones, M., MacDonald, G., Marushchak, M., Olefeldt, D., Packalen, M., Siewert, M. B., Treat, C., Turetsky, M., Voigt, C., & Yu, Z. (2020). Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proceedings of the National Academy of Sciences, 117(34), 20438–20446. Link to source: https://doi.org/10.1073/pnas.1916387117
Humpenöder, F., Karstens, K., Lotze-Campen, H., Leifeld, J., Menichetti, L., Barthelmes, A., & Popp, A. (2020). Peatland protection and restoration are key for climate change mitigation. Environmental Research Letters, 15(10), 104093. Link to source: https://doi.org/10.1088/1748-9326/abae2a
IPCC Task Force on National Greenhouse Gas Inventories. (2014). 2013 supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: Wetlands (T. Hiraishi, T. Krug, K. Tanabe, N. Srivastava, J. Baasansuren, M. Fukuda, & T. G. Troxler, Eds.). Intergovernmental Panel on Climate Change. Link to source: https://www.ipcc.ch/site/assets/uploads/2018/03/Wetlands_Supplement_Entire_Report.pdf
IUCN. (2021). Peatlands and climate change (IUCN Issues Briefs). Link to source: https://iucn.org/sites/default/files/2022-04/iucn_issues_brief_peatlands_and_climate_change_final_nov21.pdf
IUCN UK Peatland Programme. (2024, July 10). New briefing addresses the peatlands and methane debate. Link to source: https://www.iucn-uk-peatlandprogramme.org/news/new-briefing-addresses-peatlands-and-methane-debate
Jalilov, S.-M., Rochmayanto, Y., Hidayat, D. C., Raharjo, J. T., Mendham, D., & Langston, J. D. (2025). Unveiling economic dimensions of peatland restoration in Indonesia: A systematic literature review. Ecosystem Services, 71, 101693. Link to source: https://doi.org/10.1016/j.ecoser.2024.101693
Jones, M. C., Harden, J., O’Donnell, J., Manies, K., Jorgenson, T., Treat, C., & Ewing, S. (2017). Rapid carbon loss and slow recovery following permafrost thaw in boreal peatlands. Global Change Biology, 23(3), 1109–1127. Link to source: https://doi.org/10.1111/gcb.13403
Kiely, L., Spracklen, D. V., Arnold, S. R., Papargyropoulou, E., Conibear, L., Wiedinmyer, C., Knote, C., & Adrianto, H. A. (2021). Assessing costs of Indonesian fires and the benefits of restoring peatland. Nature Communications, 12(1), 7044. Link to source: https://doi.org/10.1038/s41467-021-27353-x
Konecny, K., Ballhorn, U., Navratil, P., Jubanski, J., Page, S. E., Tansey, K., Hooijer, A., Vernimmen, R., & Siegert, F. (2016). Variable carbon losses from recurrent fires in drained tropical peatlands. Global Change Biology, 22(4), 1469–1480. Link to source: https://doi.org/10.1111/gcb.13186
Leifeld, J., & Menichetti, L. (2018). The underappreciated potential of peatlands in global climate change mitigation strategies. Nature Communications, 9(1), 1071. Link to source: https://doi.org/10.1038/s41467-018-03406-6
Leifeld, J., Wüst-Galley, C., & Page, S. (2019). Intact and managed peatland soils as a source and sink of GHGs from 1850 to 2100. Nature Climate Change, 9(12), 945–947. Link to source: https://doi.org/10.1038/s41558-019-0615-5
Li, B. V., Wu, S., Pimm, S. L., & Cui, J. (2024a). The synergy between protected area effectiveness and economic growth. Current Biology, 34(13), 2907-2920.e5. Link to source: https://doi.org/10.1016/j.cub.2024.05.044
Li, G., Fang, C., Watson, J. E. M., Sun, S., Qi, W., Wang, Z., & Liu, J. (2024b). Mixed effectiveness of global protected areas in resisting habitat loss. Nature Communications, 15(1), 8389. Link to source: https://doi.org/10.1038/s41467-024-52693-9
Loisel, J., Gallego-Sala, A. V., Amesbury, M. J., Magnan, G., Anshari, G., Beilman, D. W., Benavides, J. C., Blewett, J., Camill, P., Charman, D. J., Chawchai, S., Hedgpeth, A., Kleinen, T., Korhola, A., Large, D., Mansilla, C. A., Müller, J., van Bellen, S., West, J. B., … Wu, J. (2021). Expert assessment of future vulnerability of the global peatland carbon sink. Nature Climate Change, 11(1), 70–77. Link to source: https://doi.org/10.1038/s41558-020-00944-0
Marlier, M. E., Liu, T., Yu, K., Buonocore, J. J., Koplitz, S. N., DeFries, R. S., Mickley, L. J., Jacob, D. J., Schwartz, J., Wardhana, B. S., & Myers, S. S. (2019). Fires, smoke exposure, and public health: An integrative framework to maximize health benefits from peatland restoration. GeoHealth, 3(7), 178–189. Link to source: https://doi.org/10.1029/2019GH000191
Melton, J. R., Chan, E., Millard, K., Fortier, M., Winton, R. S., Martín-López, J. M., Cadillo-Quiroz, H., Kidd, D., & Verchot, L. V. (2022). A map of global peatland extent created using machine learning (Peat-ML). Geoscientific Model Development, 15(12), 4709–4738. https://doi.org/10.5194/gmd-15-4709-2022
Miettinen, J., Shi, C., & Liew, S. C. (2011). Deforestation rates in insular Southeast Asia between 2000 and 2010. Global Change Biology, 17(7), 2261–2270. https://doi.org/10.1111/j.1365-2486.2011.02398.x
Miettinen, J., Shi, C., & Liew, S. C. (2016). Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Global Ecology and Conservation, 6, 67–78. https://doi.org/10.1016/j.gecco.2016.02.004
Minasny, B., Adetsu, D. V., Aitkenhead, M., Artz, R. R. E., Baggaley, N., Barthelmes, A., Beucher, A., Caron, J., Conchedda, G., Connolly, J., Deragon, R., Evans, C., Fadnes, K., Fiantis, D., Gagkas, Z., Gilet, L., Gimona, A., Glatzel, S., Greve, M. H., … Zak, D. (2024). Mapping and monitoring peatland conditions from global to field scale. Biogeochemistry, 167(4), 383–425. https://doi.org/10.1007/s10533-023-01084-1
Minayeva, T. Yu., Bragg, O. M., & Sirin, A. A. (2017). Towards ecosystem-based restoration of peatland biodiversity. Mires and Peat, 19, 1–36. https://doi.org/10.19189/MaP.2013.OMB.150
Müller, J., & Joos, F. (2021). Committed and projected future changes in global peatlands – continued transient model simulations since the Last Glacial Maximum. Biogeosciences, 18(12), 3657–3687. https://doi.org/10.5194/bg-18-3657-2021
Nelson, K., Thompson, D., Hopkinson, C., Petrone, R., & Chasmer, L. (2021). Peatland-fire interactions: A review of wildland fire feedbacks and interactions in Canadian boreal peatlands. Science of The Total Environment, 769, 145212. Link to source: https://doi.org/10.1016/j.scitotenv.2021.145212
Noon, M. L., Goldstein, A., Ledezma, J. C., Roehrdanz, P. R., Cook-Patton, S. C., Spawn-Lee, S. A., Wright, T. M., Gonzalez-Roglich, M., Hole, D. G., Rockström, J., & Turner, W. R. (2022). Mapping the irrecoverable carbon in Earth’s ecosystems. Nature Sustainability, 5(1), 37–46. Link to source: https://doi.org/10.1038/s41893-021-00803-6
Pan, Y., Birdsey, R. A., Phillips, O. L., Houghton, R. A., Fang, J., Kauppi, P. E., Keith, H., Kurz, W. A., Ito, A., Lewis, S. L., Nabuurs, G.-J., Shvidenko, A., Hashimoto, S., Lerink, B., Schepaschenko, D., Castanho, A., & Murdiyarso, D. (2024). The enduring world forest carbon sink. Nature, 631(8021), 563–569. Link to source: https://doi.org/10.1038/s41586-024-07602-x
Peacock, M., Audet, J., Bastviken, D., Futter, M. N., Gauci, V., Grinham, A., Harrison, J. A., Kent, M. S., Kosten, S., Lovelock, C. E., Veraart, A. J., & Evans, C. D. (2021). Global importance of methane emissions from drainage ditches and canals. Environmental Research Letters, 16(4), 044010. https://doi.org/10.1088/1748-9326/abeb36
Posa, M. R. C., Wijedasa, L. S., & Corlett, R. T. (2011). Biodiversity and conservation of tropical peat swamp forests. BioScience, 61(1), 49–57. Link to source: https://doi.org/10.1525/bio.2011.61.1.10
Ritson, J. P., Bell, M., Brazier, R. E., Grand-Clement, E., Graham, N. J. D., Freeman, C., Smith, D., Templeton, M. R., & Clark, J. M. (2016). Managing peatland vegetation for drinking water treatment. Scientific Reports, 6(1), 36751. Link to source: https://doi.org/10.1038/srep36751
Sasmito, S. D., Taillardat, P., Adinugroho, W. C., Krisnawati, H., Novita, N., Fatoyinbo, L., Friess, D. A., Page, S. E., Lovelock, C. E., Murdiyarso, D., Taylor, D., & Lupascu, M. (2025). Half of land use carbon emissions in Southeast Asia can be mitigated through peat swamp forest and mangrove conservation and restoration. Nature Communications, 16(1), 740. Link to source: https://doi.org/10.1038/s41467-025-55892-0
Schulz, C., Martín Brañas, M., Núñez Pérez, C., Del Aguila Villacorta, M., Laurie, N., Lawson, I. T., & Roucoux, K. H. (2019). Uses, cultural significance, and management of peatlands in the Peruvian Amazon: Implications for conservation. Biological Conservation, 235, 189–198. Link to source: https://doi.org/10.1016/j.biocon.2019.04.005
Spitzer, K., & Danks, H. V. (2006). Insect biodiversity of boreal peat bogs. Annual Review of Entomology, 51, 137–161. Link to source: https://doi.org/10.1146/annurev.ento.51.110104.151036
Strack, M., Davidson, S. J., Hirano, T., & Dunn, C. (2022). The potential of peatlands as nature-based climate solutions. Current Climate Change Reports, 8(3), 71–82. Link to source: https://doi.org/10.1007/s40641-022-00183-9
Suwarno, A., Hein, L., & Sumarga, E. (2016). Who benefits from ecosystem services? A case study for central Kalimantan, Indonesia. Environmental Management, 57(2), 331–344. Link to source: https://doi.org/10.1007/s00267-015-0623-9
Syahza, A., Suswondo, Bakce, D., Nasrul, B., Irianti, W., & Irianti, M. (2020). Peatland policy and management strategy to support sustainable development in Indonesia. Journal of Physics: Conference Series, 1655, 012151. Link to source: https://doi.org/10.1088/1742-6596/1655/1/012151
Sze, J. S., Carrasco, L. R., Childs, D., & Edwards, D. P. (2021). Reduced deforestation and degradation in Indigenous Lands pan-tropically. Nature Sustainability, 5(2), 123–130. Link to source: https://doi.org/10.1038/s41893-021-00815-2
Tan, Z. D., Lupascu, M., & Wijedasa, L. S. (2021). Paludiculture as a sustainable land use alternative for tropical peatlands: A review. Science of The Total Environment, 753, 142111. Link to source: https://doi.org/10.1016/j.scitotenv.2020.142111
Thorburn, C. C., & Kull, C. A. (2015). Peatlands and plantations in Sumatra, Indonesia: Complex realities for resource governance, rural development and climate change mitigation. Asia Pacific Viewpoint, 56(1), 153–168. Link to source: https://doi.org/10.1111/apv.12045
Thornton, S. A., Setiana, E., Yoyo, K., Dudin, Yulintine, Harrison, M. E., Page, S. E., & Upton, C. (2020). Towards biocultural approaches to peatland conservation: The case for fish and livelihoods in Indonesia. Environmental Science & Policy, 114, 341–351. Link to source: https://doi.org/10.1016/j.envsci.2020.08.018
Turetsky, M. R., Benscoter, B., Page, S., Rein, G., van der Werf, G. R., & Watts, A. (2015). Global vulnerability of peatlands to fire and carbon loss. Nature Geoscience, 8(1), 11–14. Link to source: https://doi.org/10.1038/ngeo2325
Uda, S. K., Hein, L., & Sumarga, E. (2017). Towards sustainable management of Indonesian tropical peatlands. Wetlands Ecology and Management, 25(6), 683–701. Link to source: https://doi.org/10.1007/s11273-017-9544-0
Uda, S. K., Hein, L., & Atmoko, D. (2019). Assessing the health impacts of peatland fires: A case study for Central Kalimantan, Indonesia. Environmental Science and Pollution Research, 26(30), 31315–31327. Link to source: https://doi.org/10.1007/s11356-019-06264-x
UNEP. (2022). Global peatlands assessment: The state of the world’s peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands. Link to source: https://www.unep.org/resources/global-peatlands-assessment-2022
UNEP-WCMC and IUCN. (2024). Protected planet report. Link to source: https://digitalreport.protectedplanet.net
Waldron, A., Adams, V., Allan, J., Arnell, A., Asner, G., Atkinson, S., Baccini, A., Baillie, J. E. M., Balmford, A., Beau, J. A., Brander, L., Brondizio, E., Bruner, A., Burgess, N., Burkart, K., Butchart, S., Button, R., Carrasco, R., Cheung, W., …Zhang, Y. P. (2020). Protecting 30% of the planet for nature: Costs, benefits and economic implications. Link to source: https://pure.iiasa.ac.at/id/eprint/16560/1/Waldron_Report_FINAL_sml.pdf
Williams, M., Reay, D., & Smith, P. (2023). Avoiding emissions versus creating sinks—Effectiveness and attractiveness to climate finance. Global Change Biology, 29(8), 2046–2049. Link to source: https://doi.org/10.1111/gcb.16598
Wolf, C., Levi, T., Ripple, W. J., Zárrate-Charry, D. A., & Betts, M. G. (2021). A forest loss report card for the world’s protected areas. Nature Ecology & Evolution, 5(4), 520–529. Link to source: https://doi.org/10.1038/s41559-021-01389-0
Worrall, F., Howden, N. J. K., Burt, T. P., Rico-Ramirez, M. A., & Kohler, T. (2022). Local climate impacts from ongoing restoration of a peatland. Hydrological Processes, 36(3), e14496. Link to source: https://doi.org/10.1002/hyp.14496
Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018a). Hotspots of peatland-derived potable water use identified by global analysis. Nature Sustainability, 1(5), 246–253. Link to source: https://doi.org/10.1038/s41893-018-0064-6
Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018b). PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. CATENA, 160, 134–140. Link to source: https://doi.org/10.1016/j.catena.2017.09.010
Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W., & Hunt, S. J. (2010). Global peatland dynamics since the Last Glacial Maximum. Geophysical Research Letters, 37(13), L13402. Link to source: https://doi.org/10.1029/2010GL043584
Avery Driscoll
Ruthie Burrows, Ph.D.
James Gerber, Ph.D.
Daniel Jasper
Alex Sweeney
Aiyana Bodi
Hannah Henkin
Megan Matthews, Ph.D.
Ted Otte
Christina Swanson, Ph.D.
Paul C. West, Ph.D.
We estimated that protecting a ha of peatland avoids 0.92–13.47 t CO₂‑eq /ha/yr, with substantially higher emissions reductions in subtropical and tropical regions and lower emissions reductions in boreal regions (100-yr GWP; Table 1a–d; Appendix).
We estimated effectiveness as the avoided emissions attributable to the reduction in peatland loss conferred by protection (Equation 1). First, we calculated the biome-specific difference between the annual rate of peatland loss outside PAs (Peatland lossbaseline) versus inside PAs (Peatland lossprotected) (Appendix; Conchedda & Tubellio, 2020; Davidson et al., 2014; Miettinen et al., 2011; Miettinen et al., 2016; Uda et al., 2017, Wolf et al., 2021). We then multiplied the avoided peatland loss by the total emissions from one ha of drained peatland over 30 years. This is the sum of the total biomass carbon stock (Carbonbiomass), which degrades relatively quickly; 30 years of annual emissions from peat itself (Carbonflux); and 30 years of lost carbon sequestration potential, reflecting the carbon that would have been taken up by one ha of intact peatland in the absence of degradation (Carbonuptake) (IPCC Task Force on National Greenhouse Gas Inventories, 2014; UNEP, 2022). The carbon flux includes CO₂‑eq emissions from: 1) peat oxidation, 2) dissolved organic carbon loss through drainage, 3) the net change in on-field methane between undrained and drained states, 4) methane emissions from drainage ditches, and 5) on-field nitrous oxide emissions.
Equation 1.
Without rewetting, peat loss typically persists beyond 30 years and can continue for centuries (Leifield & Menichetti, 2018). Thus, this is a conservative estimate of peatland protection effectiveness that captures near-term impacts, aligns with the 30-yr cost amortization time frame, and is roughly consistent with commonly used 2050 targets. Using a longer time frame produces larger estimates of emissions from degraded peatlands and therefore higher effectiveness of peatland protection.
The effectiveness of peatland protection as defined here reflects only a small percentage of the carbon stored in peatlands because we account for the likelihood that the peatland would be destroyed without protection. Peatland protection is particularly impactful for peatlands at high risk of drainage.
Table 1. Effectiveness of peatland protection at avoiding emissions and sequestering carbon. Regional differences in values are driven by variation in emissions factors and baseline rates of peatland drainage.
Unit: t CO₂‑eq , 100-yr basis/ha of peatland protected/yr
| Estimate | 0.92 |
Unit: t CO₂‑eq , 100-yr basis/ha of peatland protected/yr
| Estimate | 4.42 |
Unit: t CO₂‑eq , 100-yr basis/ha of peatland protected/yr
| Estimate | 13.47 |
Unit: t CO₂‑eq , 100-yr basis/ha of peatland protected/yr
| Estimate | 13.23 |
We estimated that the net cost of peatland protection is approximately US$1.5/ha/yr, or $0.25/t CO₂‑eq avoided (Table 2). Data related to the costs of peatland protection are very limited. These estimates reflect global averages rather than regionally specific values, and rarely include data specific to peatlands. The costs of peatland protection include up-front costs of land acquisition and ongoing costs of management and enforcement. The market price of land reflects the opportunity cost of not using the land for other purposes, such as agriculture, forestry, peat extraction, or urban development. Protecting peatlands can also generate revenue through increased tourism. Costs and revenues are highly variable across regions, depending on the costs of land and enforcement and potential for tourism.
Dienerstein et al. (2024) estimated the initial cost of establishing a protected area for 60 high-biodiversity ecoregions. Amongst the 33 regions that were likely to contain peatlands, the median acquisition cost was US$957/ha, which we amortized over 30 years. Costs of protected area maintenance were estimated at US$9–17/ha/yr (Bruner et al., 2004; Waldron et al., 2020), though these estimates were not specific to peatlands. Additionally, these estimates reflect the costs of effective enforcement and management, but many existing protected areas lack adequate funds for effective enforcement (Adams et al., 2019; Barnes et al., 2018; Burner et al., 2004). Waldron et al. (2020) estimated that, across all ecosystems, tourism revenues directly attributable to protected area establishment were US$43/ha/yr, not including downstream revenues from industries that benefit from increased tourism. Inclusion of a tourism multiplier would substantially increase the estimated economic benefits of peatland protection.
Table 2. Cost per unit climate impact for peatland protection.
Unit: 2023 US$/t CO₂‑eq , 100-yr basis
| median | 0.25 |
A learning curve is defined here as falling costs with increased adoption. The costs of peatland protection do not fall with increasing adoption, so there is no learning curve 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 gradual, emergency brake, or delayed.
Protect Peatlands is an EMERGENCY BRAKE climate solution. It has the potential to deliver a more rapid impact than gradual 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.
Permanence, or the durability of stored carbon, is a caveat for emissions avoidance through peatland protection that is not addressed in this analysis. Protected peatlands could be drained if legal protections are reversed or inadequately enforced, resulting in the loss of stored carbon. Additionally, fires on peatlands have become more frequent due to climate change (Turetsky et al., 2015; Loisel et al., 2021), and can produce very large emissions pulses (Konecny et al., 2016; Nelson et al., 2021). In boreal regions, permafrost thaw can trigger large, sustained carbon losses from previously frozen peat (Hugelius et al., 2020; Jones et al., 2017). In tropical regions, climate change-induced changes in precipitation can lower water tables in intact peatlands, increasing risks of peat loss and reducing sequestration potential (Deshmukh et al., 2021).
Additionality, or the degree to which emissions reductions are above and beyond a baseline, is another important caveat for emissions avoidance through ecosystem protection (Atkinson & Alibašić, 2023; Fuller et al., 2020; Williams et al., 2023). In this analysis, additionality was addressed by using baseline rates of peatland degradation in calculating effectiveness. Evaluating additionality is challenging and remains an active area of research.
Finally, there are substantial uncertainties in the available data on peatland areas and distributions, peatland loss rates, the drivers of peatland loss, the extent and boundaries of PAs, and the efficacy of PAs at reducing peatland disturbance. Emissions dynamics on both intact and cleared peatlands are also uncertain, particularly under different land management practices and in the context of climate change.
Because peatlands are characterized by their soils rather than by overlying vegetation, they are difficult to map at the global scale (Minasny et al., 2024). Mapping peatlands remains an active area of research, and the adoption values presented here are uncertain. We estimated that 22.6 Mha of peatlands are located within strictly protected PAs (IUCN classes I or II), and 82.3 Mha are within other or unknown PA classes (Table 3a–e; UNEP, 2022; UNEP-WCMC & IUCN, 2024), representing 22% of total global peatland area (482 Mha). Because of data limitations, we did not include Indigenous peoples’ lands in subsequent analyses despite their conservation benefits. There are an additional 186 Mha of peatlands within Indigenous peoples’ lands that are not classified as PAs, with a large majority (155 Mha) located in boreal regions (Table 3; Garnett et al., 2018; UNEP, 2022).
Given the uncertainty in the global extent of peatlands, estimates of peatland protection vary. The Global Peatlands Assessment estimated that 19% (90.7 Mha) of peatlands are protected (UNEP, 2022), with large regional variations ranging from 35% of peatlands protected in Africa to only 10% in Asia. Using a peatland map from Melton et al. (2012), Austin et al. (2025) estimated that 17% of global peatlands are within PAs, and an additional 27% are located in Indigenous peoples’ lands (excluding Indigenous peoples’ lands in Canada covering large peatland areas).
Table 3. Current peatland area under protection by biome (circa 2023). Estimates are provided for two different forms of protection: “strict” protection, including IUCN classes I and II, and “nonstrict” protection, including all other IUCN classes. Regional values may not sum to global totals due to rounding.
Unit: Mha protected
| Area within strict PAs | 12.4 |
| Area within non-strict PAs | 41.7 |
Unit: Mha protected
| Area within strict PAs | 3.0 |
| Area within non-strict PAs | 10.1 |
Unit: Mha protected
| Area within strict PAs | 1.1 |
| Area within non-strict PAs | 1.6 |
Unit: Mha protected
| Area within strict PAs | 6.1 |
| Area within non-strict PAs | 28.9 |
Unit: Mha protected
| Area within strict PAs | 22.6 |
| Area within non-strict PAs | 82.3 |
We calculated the annual rate of new peatland protection based on the year of PA establishment for areas established in 2000–2020. The median annual increase in peatland protection was 0.86 Mha (mean 2.0 Mha; Table 4a–d). This represents a roughly 0.8%/yr increase in peatlands within PAs, or protection of an additional 0.2%/yr of total global peatlands. This suggests that peatland protection is likely occurring at a somewhat slower rate than peatland degradation – which is estimated to be around 0.5% annually at the global scale – though this estimate is highly uncertain and spatially variable (Davidson et al., 2014).
There were large year-to-year differences in how much new peatland area was protected over this period, ranging from only 0.2 Mha in 2016 to 7.9 Mha in 2007. The rate at which peatland protection is increasing has been decreasing, with a median increase of 1.7 Mha/yr between 2000 and 2010 declining to 0.7 Mha/yr during 2010–2020. Recent median adoption of peatland protection by area is highest in boreal (0.5 Mha/yr, Table 4a) and tropical regions (0.2 Mha/yr, Table 4d), followed by temperate regions (0.1 Mha/yr, Table 4b) and subtropical regions (0.01 Mha/yr, Table 4c) (2010–2020). Scaled by total peatland area, however, recent rates of peatland protection are lowest in the subtropics (0.04%/yr), followed by the boreal (0.14%/yr), the tropics (0.16%/yr), and temperate regions (0.19%/yr).
Table 4. Adoption trend for peatland protection in PAs of any IUCN class (2000–2020). The 25th and 75th percentiles reflect only interannual variance.
Unit: Mha of peatland protected/yr
| 25th percentile | 0.24 |
| mean | 0.87 |
| median (50th percentile) | 0.50 |
| 75th percentile | 0.89 |
Unit: Mha of peatland protected/yr
| 25th percentile | 0.07 |
| mean | 0.23 |
| median (50th percentile) | 0.10 |
| 75th percentile | 0.28 |
Unit: Mha of peatland protected/yr
| 25th percentile | 0.00 |
| mean | 0.04 |
| median (50th percentile) | 0.01 |
| 75th percentile | 0.04 |
Unit: Mha of peatland protected/yr
| 25th percentile | 0.05 |
| mean | 0.84 |
| median (50th percentile) | 0.25 |
| 75th percentile | 0.83 |
We considered the adoption ceiling to include all undrained, non-coastal peatlands and estimated this to be 425 Mha, based on the Global Peatlands Database and Global Peatlands Map (UNEP, 2022; Table 5e; Appendix). We estimated that 284 Mha of undrained peatlands remain in boreal regions (Table 5a), 26 Mha in temperate regions (Table 5b), 12 Mha in the subtropics (Table 5c), and 103 Mha in the tropics (Table 5d). The adoption ceiling represents the technical upper limit to adoption of this solution.
There is substantial uncertainty in the global extent of peatlands, which is not quantified in these adoption ceiling values. Estimates of global peatland extent from recent literature include 404 Mha (Melton et al., 2022), 423 Mha (Xu et al., 2018b), 437 Mha (Müller & Joos, 2021), 463 Mha (Leifield & Menichetti, 2018), and 488 Mha (UNEP, 2022). Several studies suggest that the global peatland area may still be underestimated (Minasny et al., 2024; UNEP, 2022).
Table 5. Adoption ceiling: upper limit for adoption of legal protection of peatlands by biome. Values may not sum to global totals due to rounding.
Unit: Mha protected
| Peatland area (Mha) | 284 |
Unit: Mha protected
| Peatland area (Mha) | 26 |
Unit: Mha protected
| Peatland area (Mha) | 12 |
Unit: Mha protected
| Peatland area (Mha) | 103 |
Unit: Mha protected
| Peatland area (Mha) | 425 |
UNEP (2022) places a high priority on protecting a large majority of remaining peatlands for both climate and conservation objectives. We defined the achievable range for peatland protection as 70% (low achievable) to 90% (high achievable) of remaining undrained peatlands. Only ~19% of peatlands are currently under formal protection within PAs (UNEP, 2022; UNEP-WCMC and IUCN, 2024). However, approximately 60% of undrained peatlands are under some form of protection if peatlands within Indigenous peoples’ lands are considered (Garnett et al., 2018; UNEP, 2022; UNEP-WCMC and IUCN, 2024). While ambitious, this provides support for our selected achievable range of 70–90% (Table 6a-e).
Ensuring effective and durable protection of these peatlands from drainage and degradation, including secure land tenure for Indigenous peoples who steward peatlands and other critical ecosystems, is a critical first step. Research suggests that local community leadership, equitable stakeholder engagement, and cross-scalar governance are needed to achieve conservation goals while also balancing social and economic outcomes through sustainable use (Atkinson & Alibašić, 2023; Cadillo & Bennett, 2024; Girkin et al., 2023; Harrison et al., 2019; Suwarno et al., 2015). Sustainable uses of peatlands include some forms of paludiculture, which can involve peatland plant cultivation, fishing, or gathering without disturbance of the hydrology or peat layer (Tan et al., 2021).
Table 6. Range of achievable adoption of peatland protection by biome.
Unit: Mha protected
| Current Adoption | 54 |
| Achievable – Low | 199 |
| Achievable – High | 255 |
| Adoption Ceiling | 284 |
Unit: Mha protected
| Current Adoption | 13 |
| Achievable – Low | 18 |
| Achievable – High | 24 |
| Adoption Ceiling | 26 |
Unit: Mha protected
| Current Adoption | 3 |
| Achievable – Low | 9 |
| Achievable – High | 11 |
| Adoption Ceiling | 12 |
Unit: Mha protected
| Current Adoption | 35 |
| Achievable – Low | 72 |
| Achievable – High | 92 |
| Adoption Ceiling | 103 |
Unit: Mha protected
| Current Adoption | 105 |
| Achievable – Low | 297 |
| Achievable – High | 382 |
| Adoption Ceiling | 425 |
We estimated that PAs currently reduce emissions from peatland degradation by 0.6 Gt CO₂‑eq/yr (Table 7a-e). Achievable levels of peatland protection have the potential to reduce emissions 1.3–1.7 Gt CO₂‑eq/yr, with a technical upper bound of 1.9 Gt CO₂‑eq/yr. The estimate of climate impacts under current adoption does not include the large areas of peatlands protected by Indigenous peoples but not legally recognized as PAs. Inclusion of these areas would increase the current estimated impact of peatland protection to 0.9 Gt CO₂‑eq/yr.
Other published estimates of additional emissions reductions through peatland protection are somewhat lower, with confidence intervals of 0–1.2 Gt CO₂‑eq/yr (Griscom et al., 2017; Humpenöder et al., 2020; Loisel et al., 2021; Strack et al., 2022). These studies vary in their underlying methodology and data, including the extent of peatland, the baseline rate of peatland loss, the potential for protected area expansion, which GHGs are considered, the time frame over which emissions are calculated, and whether they account for vegetation carbon loss or just emissions from the peat itself.
Table 7. Climate impact at different levels of adoption.
Unit: Gt CO₂ ‑eq/yr, 100-yr basis
| Current Adoption | 0.05 |
| Achievable – Low | 0.18 |
| Achievable – High | 0.24 |
| Adoption Ceiling | 0.26 |
Unit: Gt CO₂ ‑eq/yr, 100-yr basis
| Current Adoption | 0.06 |
| Achievable – Low | 0.08 |
| Achievable – High | 0.11 |
| Adoption Ceiling | 0.12 |
Unit: Gt CO₂ ‑eq/yr, 100-yr basis
| Current Adoption | 0.04 |
| Achievable – Low | 0.12 |
| Achievable – High | 0.15 |
| Adoption Ceiling | 0.17 |
Unit: Gt CO₂ ‑eq/yr, 100-yr basis
| Current Adoption | 0.46 |
| Achievable – Low | 0.95 |
| Achievable – High | 1.22 |
| Adoption Ceiling | 1.36 |
Unit: Gt CO₂ ‑eq/yr, 100-yr basis
| Current Adoption | 0.61 |
| Achievable – Low | 1.33 |
| Achievable – High | 1.71 |
| Adoption Ceiling | 1.90 |
Peatland protection can help communities adapt to extreme weather. Because peatlands regulate water flows, they can reduce the risk of droughts and floods (IUCN, 2021; Ritson et al., 2016). Evidence suggests that peatlands can provide a cooling effect to the immediate environment, lowering daytime temperatures and reducing temperature extremes between day and night (Dietrich & Behrendt, 2022; Helbig et al., 2020; Worrall et al., 2022).
When peatlands are drained they are susceptible to fire. Peatland fires can significantly contribute to air pollution because of the way these fires smolder (Uda et al., 2019). Smoke and pollutants, particularly PM2.5, from peatland fires can harm respiratory health and lead to premature mortality (Marlier et al., 2019). A study of peatland fires in Indonesia estimated they contribute to the premature mortality of about 33,100 adults and about 2,900 infants annually (Hein et al., 2022). Researchers have linked exposure to PM2.5 from peatland fires to increased hospitalizations, asthma, and lost workdays (Hein et al., 2022). Peatland protection mitigates exposure to air pollution and can save money from reduced health-care expenditures (Kiely et al., 2021).
Peatlands support the livelihoods of nearby communities, especially those in low- and middle-income countries. In the peatlands of the Amazon and Congo basins, fishing livelihoods depend on aquatic wildlife (Thornton et al., 2020). Peatlands in the Peruvian Amazon provide important goods for trade, such as palm fruit and timber, and are used for hunting by nearby populations (Schulz et al., 2019). Peatlands can also support the livelihoods of women and contribute to gender equality. For example, raw materials – purun – from Indonesian peatlands are used by women to create and sell mats used in significant events such as births, weddings, and burials (Goib et al., 2018).
Peatlands are home to a wide range of species, supporting biodiversity of flora and an abundance of wildlife (UNEP, 2022; Minayeva et al., 2017; Posa et al., 2011). Because of their unique ecosystem, peatlands provide a habitat for many rare and threatened species (Posa et al., 2011). A study of Indonesian peat swamps found that the IUCN Red List classified approximately 45% of mammals and 33% of birds living in these ecosystems as threatened, vulnerable, or endangered (Posa et al., 2011). Peatlands also support a variety of insect species (Spitzer & Danks, 2006). Because of their sensitivity to environmental changes, some peatland insects can act as indicators of peatland health and play a role in conservation efforts (Spitzer & Danks, 2006).
Peatlands can filter water pollutants and improve water quality and are important sources of potable water for some populations (Minayeva et al., 2017). Xu et al. (2018a) estimated that peatlands store about 10% of freshwater globally, not including glacial water. Peatlands are a significant drinking water source for people in the United Kingdom and Ireland, where they provide potable water for about 71.4 million people (Xu et al., 2018a).
Water Quality
See Water Resources section above.
Leakage occurs when peatland drainage and clearing moves outside of protected area boundaries and is a risk of relying on peatland protection as an emissions reduction strategy (Harrison & Paoli, 2012; Strack et al., 2022). If the relocated clearing also occurs on peat soils, emissions from peatland drainage and degradation are relocated but not actually reduced. If disturbance is relocated to mineral soils, however, the disturbance-related emissions will typically be lower. Combining peatland protection with policies to reduce incentives for peatland clearing can help avoid leakage.
Peatland protection must be driven by or conducted in close collaboration with local communities, which often depend on peatlands for their livelihoods and economic advancement (Jalilov et al., 2025; Li et al., 2024a; Suwarno et al., 2016). Failure to include local communities in conservation efforts violates community sovereignty and can exacerbate existing socioeconomic inequities (Felipe Cadillo & Bennet, 2024; Thorburn & Kull, 2015). Effective peatland protection requires development of alternative income opportunities for communities currently dependent on peatland drainage, such as tourism; sustainable peatland use practices like paludiculture; or compensation for ecosystem service provisioning, including carbon storage (Evers et al., 2017; Girkin et al., 2023; Suwarno et al., 2016; Syahza et al., 2020; Tan et al., 2021; Uda et al., 2017).
Protected areas often include multiple ecosystems. Peatland protection will likely lead to protection of other ecosystems within the same areas, and the health of nearby ecosystems is improved by the services provided by intact peatlands.
Restored peatlands need protection to reduce the risk of future disturbance, and the health of protected peatlands can be improved through restoration of adjacent degraded peatlands.
Reducing food loss and waste and improving diets reduce demand for agricultural land. These solutions reduce pressure to convert peatlands to agriculture use, easing expansion of protected areas.
None
ha protected
CO₂ , CH₄, N₂O
ha protected
CO₂ , CH₄, N₂O
ha protected
CO₂ , CH₄, N₂O
ha protected
CO₂ , CH₄, N₂O
None
There is high scientific consensus that protecting peatland carbon stocks is a critical component of mitigating climate change (Girkin & Davidson, 2024; Harris et al., 2022; Leifield et al., 2019; Noon et al., 2022; Strack et al., 2022). Globally, an estimated 11–12% of peatlands have been drained for uses such as agriculture, forestry, and harvesting of peat for horticulture and fuel, with much more extensive degradation in temperate and tropical regions (~45%) than in boreal regions (~4%) (Fluet-Chouinard et al., 2023; Leifield & Menichetti, 2018; UNEP, 2022). Rates of peatland degradation are highly uncertain, and the effectiveness of PAs at reducing drainage remains unquantified. In lieu of peatland-specific data on the effectiveness of PAs at reducing drainage, we used estimates from Wolf et al. (2021), who found that PAs reduce forest loss by approximately 40.5% at the global average.
Carbon stored in peatlands has been characterized as “irrecoverable carbon” because it takes centuries to millennia to accumulate and could not be rapidly recovered if lost (Goldstein et al., 2020; Noon et al., 2021). Degraded peatlands currently emit an estimated 1.3–1.9 Gt CO₂‑eq/yr (excluding fires), equal to ~2–4% of total global emissions (Leifield and Menichetti., 2018; UNEP, 2022). Leifield et al. (2019) projected that without protection or restoration measures, emissions from drained peatlands could produce enough emissions to consume 10–41% of the remaining emissions budget for keeping warming below 1.5–2.0 °C. Peatland drainage had produced a cumulative 80 Gt CO₂‑eq by 2015, equal to nearly two years worth of total global emissions. In a modeling study, Humpenöder et al. (2020) projected that an additional 10.3 Mha of peatlands would be degraded by 2100 in the absence of new protection efforts, increasing annual emissions from degraded peatlands by ~25% (an additional 0.42 Gt CO₂‑eq/yr in their study).
The results presented in this document synthesize findings from 11 global datasets, supplemented by four regional studies on peatland loss rates in Southeast Asia. We recognize that geographic bias in the information underlying global data products creates bias, and hope this work inspires research and data sharing on this topic in underrepresented regions.
This analysis quantifies the emissions associated with peatland degradation and their potential reduction via establishment of Protected Areas (PAs). We leveraged multiple data products, including national-scale peatland area estimates, a peatland distribution map, shapefiles of PAs and Indigenous peoples’ lands, available data on rates of peatland degradation by driver, country-scale data on reductions in ecosystem degradation inside of PAs, maps of biomass carbon stocks, and biome-level emissions factors from disturbed peat soils. This appendix describes the source data products and how they were integrated.
The global extent and distribution of peatlands is highly uncertain, and all existing peatland maps have limitations. Importantly, there is no globally accepted definition of a peatland, and different countries and data products use variable thresholds for peat depth and carbon content to define peatlands. The Global Peatland Assessment was a recent comprehensive effort to compile and harmonize existing global peatland area estimates (UNEP, 2022). We rely heavily on two products resulting from this effort: a national-scale dataset of peatland area titled the Global Peatland Database (GPD) and a map of likely peatland areas titled the Global Peatlands Map (GPM; 1 km resolution).
The GPM represents a known overestimate of the global peatland area, so we scaled area estimates derived from spatially explicit analyses dependent on the GPM to match total areas from the GPD. To develop a map of country-level scaling factors, we first calculated the peatland area within each country from the GPM. We calculated the country-level scaling factors as the country-level GPD values divided by the associated GPM values and converted them to a global raster. Some countries had peatland areas represented in either the GPD or GPM, but not both. Four countries had peatland areas in the GPM that were not present in the GPD, which contained 0.51 Mha of peatlands per the GPM. These areas were left unscaled. There were 38 countries with peatland areas in the GPD that did not have areas in the GPM, containing a total 0.70 Mha of peatlands. These areas, which represented 0.14% of the total peatland area in the GPD, were excluded from the scaled maps. We then multiplied the pixel-level GPM values by the scalar raster. Because of the missing countries, this scaling step very slightly overestimated (by 0.4%) total peatlands relative to the GPD. To account for this, we multiplied this intermediate map by a final global scalar (calculated as the global GPM total divided by the GPD total). This process produced a map with the same peatland distribution as the GPM but a total area that summed to that reported in the GPD.
Many coastal wetlands have peat soils, though the extent of this overlap has not been well quantified. Coastal wetlands are handled in the Protect Coastal Wetlands solution, so we excluded them from this solution to avoid double-counting. Because of the large uncertainties in both the peatland maps and available maps of coastal wetlands, we were not confident that the overlap between the two sets of maps provided a reliable estimate of the proportion of coastal wetlands located on peat soils. Therefore, we took the conservative approach of excluding all peatland pixels that were touching or overlapping with the coastline. This reduced the total peatland area considered in this solution by 5.33 Mha (1.1%). We additionally excluded degraded peatlands from the adoption ceiling and achievable range using country-level data from the GPD. Degraded peatlands will continue to be emissions sources until they are restored, so protection alone will not confer an emissions benefit.
We conducted the analyses by latitude bands (tropical: –23.4° to 23.4°; subtropical: –35° to –23.4° and 23.4° to 35°; temperate: –35° to –50° and 35° to 50°; boreal: <–50° and >50°) in order to retain some spatial variability in emissions factors and degradation rates and drivers. We calculated the total peatland area within each latitude band based on both the scaled and unscaled peatland maps with coastal pixels excluded. We used these values as the adoption ceiling and for subsequent calculations of protected areas.
We identified protected peatland areas using the World Database on Protected Areas (WDPA, 2024), which contains boundaries for each PA and additional information, including their establishment year and IUCN management category (Ia to VI, not applicable, not reported, and not assigned). For each PA polygon, we extracted the peatland area from the unscaled version of the GPM with coastal pixels removed.
Each PA was classified into climate zones (described above) based on the midpoint between its minimum and maximum latitude. Then, protected peatland areas were summed to the IUCN class-climate zone level, and the proportion of peatlands protected within each was calculated by dividing the protected area by the unscaled total area in each climate zone. The proportion of area protected was then multiplied by the scaled total area for each zone to calculate adoption in hectares within each IUCN class and climate zone. To evaluate trends in adoption over time, we aggregated protected areas by establishment year as reported in the WDPA. We used the same procedure to calculate the proportion of area protected using the unscaled maps, and then scale for the total area by biome.
We used the maps of Indigenous people’s lands from Garnett et al. 2018 to identify Indigenous people’s lands that were not inside of established PAs. The total peatland area within Indigenous people’s lands process as above.
Broadly, we estimated annual, per-ha emissions savings from peatland protection as the difference between net carbon exchange in a protected peatland versus an unprotected peatland, accounting for all emissions pathways, the drivers of disturbance, the baseline rates of peatland disturbance, and the effectiveness of PAs at reducing ecosystem degradation. In brief, our calculation of the effectiveness of peatland protection followed Equation S1, in which the annual peatland loss avoided due to protection (%/yr) is multiplied by the 30-yr cumulative sum of emissions per ha of degraded peatland (CO₂‑eq /ha over a 30-yr period). These two terms are described in depth in the subsequent sections.
Equation A1.
We calculated the avoided rate of peatland loss (%/yr) as the difference between the baseline rate of peatland loss without protection and the estimated rate of peatland loss within PAs (Equation A2), since PAs do not confer complete protection from ecosystem degradation.
Equation A2.
We compiled baseline estimates of the current rates of peatland degradation from all causes (%/yr) from the existing literature (Table A1). Unfortunately, data on the rate of peatland loss within PAs are not available. However, satellite data have enabled in-depth, global-scale studies of the effectiveness of PAs at reducing tree cover loss. While not all peatlands are forested and degradation dynamics on peatlands can differ from those on forests writ large, these estimates are a reasonable approximation of the effectiveness of PAs at reducing peatland loss. We used the country-level estimates of the proportionate reduction in loss inside versus outside of PAs from Wolf et al. (2021), which we aggregated to latitude bands based on the median latitude of each country (Table A1).
Table A1. Biome-level annual baseline rate of peatland loss, the effectiveness of protection at reducing loss, and the annual avoided rate of peatland loss under protection.
| Climate Zone | Mean Annual Peatland Loss (%/yr) | Proportionate Reduction in Loss Under Protection | Avoided Loss Under Protection (%/yr) |
|---|---|---|---|
| Boreal | 0.3% | 0.44 | 0.13% |
| Subtropic | 1.2% | 0.60 | 0.73% |
| Temperate | 0.6% | 0.56 | 0.33% |
| Tropic | 1.5% | 0.41 | 0.63% |
Emissions Factors for Peatland Degradation
Equation S3 provides an overview of the calculation of emissions from degraded peatlands. In brief, we calculated cumulative emissions as the biomass carbon stock plus the 30-yr total of CO₂‑equivalent fluxes from peat oxidation (Pox), dissolved organic carbon losses (DOC), methane from drainage ditches (Mditch), on-field methane (Mfield), on-field nitrous oxide (N) and the lost net sequestration from an intact peatland, accounting for carbon sequestration in peat and methane emissions from intact peatlands (Seqloss).
Equation A3.
The IPCC Tier 1 emissions factors for peatland degradation are disaggregated by climate zone (tropical, temperate, and boreal), soil fertility status (nutrient-poor versus nutrient rich), and the driver of degradation (many subclasses of forestry, cropland, grassland, and peat extraction) (IPCC 2014; Tables 2.1–2.5). Table III.5 of Annex III of the Global Peatlands Assessment provides a summarized set of emissions factors based directly on the IPCC values but aggregated to the four coarser classes of degradation drivers listed above (UNEP, 2022), which we use for our analysis. They include the following pathways: CO₂ from peat oxidation, off-site emissions from lateral transport of dissolved organic carbon (DOC), methane emissions from the field and drainage ditches, and nitrous oxide emissions from the field. Particulate organic carbon (POC) losses may be substantial, but were not included in the IPCC methodology due to uncertainties about the fate of transported POC. These emissions factors are reported as annual rates per disturbed hectare, and emissions from these pathways continue over long periods of time.
Three additional pathways that are not included in the IPCC protocol are relevant to the emissions accounting for this analysis: the loss of carbon sequestration potential from leaving the peatland intact, the methane emissions that occur from intact peatlands, and the emissions from removal of the vegetation overlying peat soils. Leifield et al. (2019) reported the annual net carbon uptake per hectare of intact peatlands, including sequestration of carbon in peat minus naturally occurring methane emissions due to the anoxic conditions. If the peatland is not disturbed, these methane emissions and carbon sequestration will persist indefinitely on an annual basis.
We accounted for emissions from removal of biomass using a separate protocol than emissions occurring from the peat soil due to differences in the temporal dynamics of loss. While all other emissions from peat occur on an annual basis and continue for many decades or longer, emissions from biomass occur relatively quickly. Biomass clearing produces a rapid pulse of emissions from labile carbon pools followed by a declining, but persistent, rate of emissions as more recalcitrant carbon pools decay over subsequent years. The entire biomass carbon stock is likely to be lost within 30 years. Average biomass carbon stocks over the extent of the peatland distribution in the GPM were calculated by latitude band based on the above and below ground biomass carbon stock data from Spawn et al. (2020). We presumed 100% of the biomass carbon stock is lost from peatland degradation, though in many cases some amount of biomass remains following degradation, depending on the terminal land use.
Peatland Degradation Drivers
Emissions from peatland loss depend on the driver of degradation (e.g., forestry, cropland, peat extraction; IPCC 2014). The GPD contains national-scale estimates of historical peatland loss by driver, which we used to calculate weights for each driver, reflecting the proportion of peatland loss attributable to each driver by latitude band. We took the weighted average of the driver-specific peatland emissions factors, calculated as the sum of the products of the weights and the driver-specific emissions factors.
Appendix References
Garnett, S. T., Burgess, N. D., Fa, J. E., Fernández-Llamazares, Á., Molnár, Z., Robinson, C. J., Watson, J. E. M., Zander, K. K., Austin, B., Brondizio, E. S., Collier, N. F., Duncan, T., Ellis, E., Geyle, H., Jackson, M. V., Jonas, H., Malmer, P., McGowan, B., Sivongxay, A., & Leiper, I. (2018). A spatial overview of the global importance of Indigenous lands for conservation. Nature Sustainability, 1(7), 369–374. https://doi.org/10.1038/s41893-018-0100-6
IPCC Task Force on National Greenhouse Gas Inventories. (2014). 2013 supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: Wetlands (T. Hiraishi, T. Krug, K. Tanabe, N. Srivastava, J. Baasansuren, M. Fukuda, & T. G. Troxler, Eds.). Intergovernmental Panel on Climate Change. https://www.ipcc.ch/site/assets/uploads/2018/03/Wetlands_Supplement_Entire_Report.pdf
Leifeld, J., Wüst-Galley, C., & Page, S. (2019). Intact and managed peatland soils as a source and sink of GHGs from 1850 to 2100. Nature Climate Change, 9(12), 945–947. https://doi.org/10.1038/s41558-019-0615-5
Spawn, S. A., Sullivan, C. C., Lark, T. J., & Gibbs, H. K. (2020). Harmonized global maps of above and belowground biomass carbon density in the year 2010. Scientific Data, 7(1), 112. https://doi.org/10.1038/s41597-020-0444-4
UNEP. (2022). Global peatlands assessment: The state of the world’s peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands. https://www.unep.org/resources/global-peatlands-assessment-2022
UNEP-WCMC and IUCN (2024), Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) [Online], Accessed November 2024, Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net.
Wolf, C., Levi, T., Ripple, W. J., Zárrate-Charry, D. A., & Betts, M. G. (2021). A forest loss report card for the world’s protected areas. Nature Ecology & Evolution, 5(4), 520–529. https://doi.org/10.1038/s41559-021-01389-0
This solution focuses on the legal protection of grassland and savanna ecosystems through the establishment of protected areas (PAs), which are managed with the primary goal of conserving nature and land tenure for Indigenous peoples. These protections reduce grassland degradation, which preserves carbon stored in soils and vegetation and enables continued carbon sequestration by healthy grasslands.
This solution only includes non-coastal grasslands and savannas on mineral soils in areas that do not naturally support forests. Salt marshes are included in the Protect Coastal Wetlands solution, grasslands on peat soils are included in the Protect Peatlands solution, grasslands that are the product of deforestation are included in the Restore Forests solution, and grasslands that have been converted to other uses are included in the Restore Grasslands and Savannas solution.
Grasslands, also called steppes (Europe and Asia), pampas (South America), and prairies (North America), are ecosystems dominated by herbaceous plants that have relatively low tree or shrub cover. Savannas are ecosystems characterized by low-density tree cover that allows for a grass subcanopy (Bardgett et al., 2021; Parente et al., 2024). Grasslands and savannas span arid to mesic climates from the tropics to the tundra; many depend on periodic fires and grazing by large herbivores. The dataset used to define grassland extent for this analysis classifies areas with sparse vegetation, including some deserts and tundra, as grasslands (Parente et al., 2024), but excludes planted and intensively managed livestock pastures. Hereafter we refer to all of these ecosystems, including savannas, as “grasslands.”
Historically, grasslands covered up to 40% of global land area, depending on the definition used (Bardgett et al., 2021; Parente et al., 2024; Suttie et al., 2005). An estimated 46% of temperate grasslands and 24% of tropical grasslands have been converted to cropland or lost to afforestation or development (Hoekstra et al., 2004). Nearly half of remaining grasslands are estimated to be degraded due to over- or undergrazing, woody plant encroachment, climate change, invasive species, addition of fertilizers or legumes for forage production, and changing fire regimes (Bardgett et al., 2021; Briggs et al., 2005; Gang et al., 2014; Ratajczak et al., 2012).
Grasslands store carbon primarily in soils and below-ground biomass (Bai & Cotrufo, 2022). A large fraction of the carbon that grasses take up is allocated to root growth, which over time is incorporated into soil organic matter (Bai & Cotrufo, 2022). When native vegetation is removed and land is tilled to convert grasslands to croplands, carbon from biomass and soils is lost as CO₂.
Estimates of total carbon stocks in grasslands range from 388–1,257 Gt CO₂‑eq (Conant et al., 2017; Goldstein et al., 2020; Poeplau, 2021). Soil carbon generally persists over long timescales and takes decades to rebuild, with one study estimating that 132 Gt CO₂‑eq in grasslands is vulnerable to loss, and that 25 Gt CO₂‑eq of that would be irrecoverable over a 30-year timeframe (Goldstein et al., 2020). Our analysis did not quantify the impacts of grazing or woody plant encroachment on grassland carbon stocks, which can be mixed, though grazing is discussed further in the Improve Livestock Grazing solution (Barger et al., 2011; Conant et al., 2017; Jackson et al., 2002; Stanley et al., 2024).
Long-term legal protection of grasslands through PAs and Indigenous peoples’ land tenure reduces conversion and therefore avoids conversion-related pulses of GHG emissions from plowing soils and removing biomass. We consider grasslands to be protected if they are 1) formally designated as PAs (UNEP-WCMC and IUCN, 2024), or 2) mapped as Indigenous peoples’ lands (IPLs) by Garnett et al. (2018) (Appendix). PAs vary in their allowed uses, ranging from strict wilderness preserves to sustainable-use areas that allow for some natural resource extraction; all levels were included in this analysis (UNEP-WCMC and IUCN, 2024).
IPLs and PAs reduce, but do not eliminate, ecosystem loss (Baragwanath et al., 2020; Blackman & Viet 2018; Li et al., 2024; McNicol et al., 2023; Sze et al. 2022; Wolf et al., 2023; Wade et al., 2020). Improving management to further reduce land use change within PAs and ensure ecologically appropriate grazing and fire regimes is a critical component of grassland protection (Jones et al., 2018; Meng et al., 2023; Vijay et al., 2018; Visconti et al., 2019; Watson et al., 2014). Additionally, market-based strategies and other policies can complement legal protection by reducing incentives for grassland conversion (e.g., Garett et al., 2019; Golub et al., 2021; Heilmayr et al., 2020; Lambin et al., 2018; Levy et al., 2023; Macdonald et al., 2024; Marin et al., 2022; Villoria et al., 2022; West et al., 2023). Our analyses are based on legal protection because the effectiveness of market-based strategies is difficult to quantify, but these strategies will be further discussed in an additional appendix (coming soon).
Adams, V. M., Iacona, G. D., & Possingham, H. P. (2019). Weighing the benefits of expanding protected areas versus managing existing ones. Nature Sustainability, 2(5), 404–411. https://doi.org/10.1038/s41893-019-0275-5
Ahlering, M., Fargione, J., & Parton, W. (2016). Potential carbon dioxide emission reductions from avoided grassland conversion in the northern Great Plains. Ecosphere, 7(12), Article e01625. https://doi.org/10.1002/ecs2.1625
Asamoah, E. F., Beaumont, L. J., & Maina, J. M. (2021). Climate and land-use changes reduce the benefits of terrestrial protected areas. Nature Climate Change, 11(12), 1105–1110. https://doi.org/10.1038/s41558-021-01223-2
Bai, Y., & Cotrufo, M. F. (2022). Grassland soil carbon sequestration: Current understanding, challenges, and solutions. Science, 377(6606), 603–608. Link to source: https://doi.org/10.1126/science.abo2380
Baragwanath, K., & Bayi, E. (2020). Collective property rights reduce deforestation in the Brazilian Amazon. Proceedings of the National Academy of Sciences, 117(34), 20495–20502. https://doi.org/10.1073/pnas.1917874117
Bardgett, R. D., Bullock, J. M., Lavorel, S., Manning, P., Schaffner, U., Ostle, N., Chomel, M., Durigan, G., L. Fry, E., Johnson, D., Lavallee, J. M., Le Provost, G., Luo, S., Png, K., Sankaran, M., Hou, X., Zhou, H., Ma, L., Ren, W., … Shi, H. (2021). Combating global grassland degradation. Nature Reviews Earth & Environment, 2(10), 720–735. https://doi.org/10.1038/s43017-021-00207-2
Barger, N. N., Archer, S. R., Campbell, J. L., Huang, C., Morton, J. A., & Knapp, A. K. (2011). Woody plant proliferation in North American drylands: A synthesis of impacts on ecosystem carbon balance. Journal of Geophysical Research: Biogeosciences, 116(G4), Article G00K07. https://doi.org/10.1029/2010JG001506
Barnes, M. D., Glew, L., Wyborn, C., & Craigie, I. D. (2018). Prevent perverse outcomes from global protected area policy. Nature Ecology & Evolution, 2(5), 759–762. https://doi.org/10.1038/s41559-018-0501-y
Bengtsson, J., Bullock, J. M., Egoh, B., Everson, C., Everson, T., O’Connor, T., O’Farrell, P. J., Smith, H. G., & Lindborg, R. (2019). Grasslands—More important for ecosystem services than you might think. Ecosphere, 10(2), Article e02582. https://doi.org/10.1002/ecs2.2582
Berg, A., & McColl, K. A. (2021). No projected global drylands expansion under greenhouse warming. Nature Climate Change, 11(4), 331–337. https://doi.org/10.1038/s41558-021-01007-8
Blackman, A., & Veit, P. (2018). Titled Amazon Indigenous communities cut forest carbon emissions. Ecological Economics, 153, 56–67. https://doi.org/10.1016/j.ecolecon.2018.06.016
Briggs, J. M., Knapp, A. K., Blair, J. M., Heisler, J. L., Hoch, G. A., Lett, M. S., & McCarron, J. K. (2005). An ecosystem in transition: Causes and consequences of the conversion of mesic grassland to shrubland. BioScience, 55(3), 243–254. https://doi.org/10.1641/0006-3568(2005)055[0243:AEITCA]2.0.CO;2
Bruner, A. G., Gullison, R. E., & Balmford, A. (2004). Financial costs and shortfalls of managing and expanding Protected-Area systems in developing countries. BioScience, 54(12), 1119–1126. https://doi.org/10.1641/0006-3568(2004)054[1119:FCASOM]2.0.CO;2
Carbutt, C., Henwood, W. D., & Gilfedder, L. A. (2017). Global plight of native temperate grasslands: Going, going, gone? Biodiversity and Conservation, 26(12), 2911–2932. https://doi.org/10.1007/s10531-017-1398-5
Chang, J., Ciais, P., Gasser, T., Smith, P., Herrero, M., Havlík, P., Obersteiner, M., Guenet, B., Goll, D. S., Li, W., Naipal, V., Peng, S., Qiu, C., Tian, H., Viovy, N., Yue, C., & Zhu, D. (2021). Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands. Nature Communications, 12(1), Article 118. https://doi.org/10.1038/s41467-020-20406-7
Conant, R. T., Cerri, C. E. P., Osborne, B. B., & Paustian, K. (2017). Grassland management impacts on soil carbon stocks: A new synthesis. Ecological Applications, 27(2), 662–668. https://doi.org/10.1002/eap.1473
Craine, J. M., Ocheltree, T. W., Nippert, J. B., Towne, E. G., Skibbe, A. M., Kembel, S. W., & Fargione, J. E. (2013). Global diversity of drought tolerance and grassland climate-change resilience. Nature Climate Change, 3(1), 63–67. https://doi.org/10.1038/nclimate1634
Dinerstein, E., Joshi, A. R., Hahn, N. R., Lee, A. T. L., Vynne, C., Burkart, K., Asner, G. P., Beckham, C., Ceballos, G., Cuthbert, R., Dirzo, R., Fankem, O., Hertel, S., Li, B. V., Mellin, H., Pharand-Deschênes, F., Olson, D., Pandav, B., Peres, C. A., … Zolli, A. (2024). Conservation Imperatives: Securing the last unprotected terrestrial sites harboring irreplaceable biodiversity. Frontiers in Science, 2. https://doi.org/10.3389/fsci.2024.1349350
Feng, S., & Fu, Q. (2013). Expansion of global drylands under a warming climate. Atmospheric Chemistry and Physics, 13(19), 10081–10094. https://doi.org/10.5194/acp-13-10081-2013
Gang, C., Zhou, W., Chen, Y., Wang, Z., Sun, Z., Li, J., Qi, J., & Odeh, I. (2014). Quantitative assessment of the contributions of climate change and human activities on global grassland degradation. Environmental Earth Sciences, 72(11), 4273–4282. https://doi.org/10.1007/s12665-014-3322-6
Garnett, S. T., Burgess, N. D., Fa, J. E., Fernández-Llamazares, Á., Molnár, Z., Robinson, C. J., Watson, J. E. M., Zander, K. K., Austin, B., Brondizio, E. S., Collier, N. F., Duncan, T., Ellis, E., Geyle, H., Jackson, M. V., Jonas, H., Malmer, P., McGowan, B., Sivongxay, A., & Leiper, I. (2018). A spatial overview of the global importance of Indigenous lands for conservation. Nature Sustainability, 1(7), 369–374. https://doi.org/10.1038/s41893-018-0100-6
Garrett, R. D., Levy, S., Carlson, K. M., Gardner, T. A., Godar, J., Clapp, J., Dauvergne, P., Heilmayr, R., le Polain de Waroux, Y., Ayre, B., Barr, R., Døvre, B., Gibbs, H. K., Hall, S., Lake, S., Milder, J. C., Rausch, L. L., Rivero, R., Rueda, X., … Villoria, N. (2019). Criteria for effective zero-deforestation commitments. Global Environmental Change, 54, 135–147. https://doi.org/10.1016/j.gloenvcha.2018.11.003
Goldstein, A., Turner, W. R., Spawn, S. A., Anderson-Teixeira, K. J., Cook-Patton, S., Fargione, J., Gibbs, H. K., Griscom, B., Hewson, J. H., Howard, J. F., Ledezma, J. C., Page, S., Koh, L. P., Rockström, J., Sanderman, J., & Hole, D. G. (2020). Protecting irrecoverable carbon in Earth’s ecosystems. Nature Climate Change, 10(4), 287–295. https://doi.org/10.1038/s41558-020-0738-8
Golub, A., Herrera, D., Leslie, G., Pietracci, B., & Lubowski, R. (2021). A real options framework for reducing emissions from deforestation: Reconciling short-term incentives with long-term benefits from conservation and agricultural intensification. Ecosystem Services, 49, Article 101275. https://doi.org/10.1016/j.ecoser.2021.101275
Graham, V., Geldmann, J., Adams, V. M., Negret, P. J., Sinovas, P., & Chang, H.-C. (2021). Southeast Asian protected areas are effective in conserving forest cover and forest carbon stocks compared to unprotected areas. Scientific Reports, 11(1), Article 23760. https://doi.org/10.1038/s41598-021-03188-w
Grasslands, Rangelands, Savannahs and Shrublands (GRaSS) Alliance. (2023). Valuing Grasslands: Critical ecosystems for nature, climate, and people [Discussion paper]. Link to source: https://www.birdlife.org/wp-content/uploads/2023/12/Valuing-Grasslands-Report-Dec-2023.pdf
Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamäki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., … Fargione, J. (2017). Natural climate solutions. Proceedings of the National Academy of Sciences, 114(44), 11645–11650. https://doi.org/10.1073/pnas.1710465114
Heilmayr, R., Rausch, L. L., Munger, J., & Gibbs, H. K. (2020). Brazil’s Amazon Soy Moratorium reduced deforestation. Nature Food, 1(12), 801–810. https://doi.org/10.1038/s43016-020-00194-5
Hoekstra, J. M., Boucher, T. M., Ricketts, T. H., & Roberts, C. (2005). Confronting a biome crisis: Global disparities of habitat loss and protection. Ecology Letters, 8(1), 23–29. https://doi.org/10.1111/j.1461-0248.2004.00686.x
Huang, J., Yu, H., Guan, X., Wang, G., & Guo, R. (2016). Accelerated dryland expansion under climate change. Nature Climate Change, 6(2), 166–171. https://doi.org/10.1038/nclimate2837
Huang, X., Ibrahim, M. M., Luo, Y., Jiang, L., Chen, J., & Hou, E. (2024). Land use change alters soil organic carbon: Constrained global patterns and predictors. Earth’s Future, 12(5), Article e2023EF004254. https://doi.org/10.1029/2023EF004254
IPCC Task Force on National Greenhouse Gas Inventories. (2019). Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Calvo Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize S., Osako, A., Pyrozhenko, Y., Shermanau, P. and Federici, S., Eds.). Intergovernmental Panel on Climate Change. Link to source: https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/0_Overview/19R_V0_00_Cover_Foreword_Preface_Dedication.pdf
Isbell, F., Craven, D., Connolly, J., Loreau, M., Schmid, B., Beierkuhnlein, C., Bezemer, T. M., Bonin, C., Bruelheide, H., de Luca, E., Ebeling, A., Griffin, J. N., Guo, Q., Hautier, Y., Hector, A., Jentsch, A., Kreyling, J., Lanta, V., Manning, P., … Eisenhauer, N. (2015). Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature, 526(7574), 574–577. https://doi.org/10.1038/nature15374
Jackson, R. B., Banner, J. L., Jobbágy, E. G., Pockman, W. T., & Wall, D. H. (2002). Ecosystem carbon loss with woody plant invasion of grasslands. Nature, 418(6898), 623–626. https://doi.org/10.1038/nature00910
Jones, K. R., Venter, O., Fuller, R. A., Allan, J. R., Maxwell, S. L., Negret, P. J., & Watson, J. E. M. (2018). One-third of global protected land is under intense human pressure. Science, 360(6390), 788–791. https://doi.org/10.1126/science.aap9565
Kachler, J., Benra, F., Bolliger, R., Isaac, R., Bonn, A., & Felipe-Lucia, M. R. (2023). Can we have it all? The role of grassland conservation in supporting forage production and plant diversity. Landscape Ecology, 38(12), 4451–4465. https://doi.org/10.1007/s10980-023-01729-4
Kemp, D. R., Guodong, H., Xiangyang, H., Michalk, D. L., Fujiang, H., Jianping, W., & Yingjun, Z. (2013). Innovative grassland management systems for environmental and livelihood benefits. Proceedings of the National Academy of Sciences, 110(21), 8369–8374. https://doi.org/10.1073/pnas.1208063110
Kim, J. H., Jobbágy, E. G., & Jackson, R. B. (2016). Trade-offs in water and carbon ecosystem services with land-use changes in grasslands. Ecological Applications, 26(6), 1633–1644. https://doi.org/10.1890/15-0863.1
Knapp, A. K., Chen, A., Griffin-Nolan, R. J., Baur, L. E., Carroll, C. J. W., Gray, J. E., Hoffman, A. M., Li, X., Post, A. K., Slette, I. J., Collins, S. L., Luo, Y., & Smith, M. D. (2020). Resolving the Dust Bowl paradox of grassland responses to extreme drought. Proceedings of the National Academy of Sciences, 117(36), 22249–22255. https://doi.org/10.1073/pnas.1922030117
Lambin, E. F., Gibbs, H. K., Heilmayr, R., Carlson, K. M., Fleck, L. C., Garrett, R. D., le Polain de Waroux, Y., McDermott, C. L., McLaughlin, D., Newton, P., Nolte, C., Pacheco, P., Rausch, L. L., Streck, C., Thorlakson, T., & Walker, N. F. (2018). The role of supply-chain initiatives in reducing deforestation. Nature Climate Change, 8(2), 109–116. https://doi.org/10.1038/s41558-017-0061-1
Lefcheck, J. S., Byrnes, J. E. K., Isbell, F., Gamfeldt, L., Griffin, J. N., Eisenhauer, N., Hensel, M. J. S., Hector, A., Cardinale, B. J., & Duffy, J. E. (2015). Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nature Communications, 6(1), Article 6936. https://doi.org/10.1038/ncomms7936
Levy, S. A., Cammelli, F., Munger, J., Gibbs, H. K., & Garrett, R. D. (2023). Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments. Global Environmental Change, 80, Article 102671. https://doi.org/10.1016/j.gloenvcha.2023.102671
Li, G., Fang, C., Watson, J. E. M., Sun, S., Qi, W., Wang, Z., & Liu, J. (2024). Mixed effectiveness of global protected areas in resisting habitat loss. Nature Communications, 15(1), Article 8389. https://doi.org/10.1038/s41467-024-52693-9
Li, J., Huang, L., Cao, W., Wang, J., Fan, J., Xu, X., & Tian, H. (2023). Benefits, potential and risks of China’s grassland ecosystem conservation and restoration. Science of The Total Environment, 905, Article 167413. https://doi.org/10.1016/j.scitotenv.2023.167413
Liechti, K., & Biber, J.-P. (2016). Pastoralism in Europe: Characteristics and challenges of highland-lowland transhumance. Revue Scientifique Et Technique (International Office of Epizootics), 35(2), 561–575. https://doi.org/10.20506/rst.35.2.2541
Macdonald, K., Diprose, R., Grabs, J., Schleifer, P., Alger, J., Bahruddin, Brandao, J., Cashore, B., Chandra, A., Cisneros, P., Delgado, D., Garrett, R., & Hopkinson, W. (2024). Jurisdictional approaches to sustainable agro-commodity governance: The state of knowledge and future research directions. Earth System Governance, 22, Article 100227. https://doi.org/10.1016/j.esg.2024.100227
Marin, F. R., Zanon, A. J., Monzon, J. P., Andrade, J. F., Silva, E. H. F. M., Richter, G. L., Antolin, L. A. S., Ribeiro, B. S. M. R., Ribas, G. G., Battisti, R., Heinemann, A. B., & Grassini, P. (2022). Protecting the Amazon forest and reducing global warming via agricultural intensification. Nature Sustainability, 5, 1018–1026. https://doi.org/10.1038/s41893-022-00968-8
McNicol, I. M., Keane, A., Burgess, N. D., Bowers, S. J., Mitchard, E. T. A., & Ryan, C. M. (2023). Protected areas reduce deforestation and degradation and enhance woody growth across African woodlands. Communications Earth & Environment, 4(1), Article 392. https://doi.org/10.1038/s43247-023-01053-4
Meng, Z., Dong, J., Ellis, E. C., Metternicht, G., Qin, Y., Song, X.-P., Löfqvist, S., Garrett, R. D., Jia, X., & Xiao, X. (2023). Post-2020 biodiversity framework challenged by cropland expansion in protected areas. Nature Sustainability, 6(7), 758–768. https://doi.org/10.1038/s41893-023-01093-w
Michalk, D. L., Kemp, D. R., Badgery, W. B., Wu, J., Zhang, Y., & Thomassin, P. J. (2019). Sustainability and future food security—A global perspective for livestock production. Land Degradation & Development, 30(5), 561–573. https://doi.org/10.1002/ldr.3217
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
Nugent, D. T., Baker-Gabb, D. J., Green, P., Ostendorf, B., Dawlings, F., Clarke, R. H., & Morgan, J. W. (2022). Multi-scale habitat selection by a cryptic, critically endangered grassland bird—The Plains-wanderer (Pedionomus torquatus): Implications for habitat management and conservation. Austral Ecology, 47(3), 698–712. https://doi.org/10.1111/aec.13157
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D’amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., & Kassem, K. R. (2001). Terrestrial ecoregions of the world: A new map of life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience, 51(11), 933–938. https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2
Parente, L., Sloat, L., Mesquita, V., Consoli, D., Stanimirova, R., Hengl, T., Bonannella, C., Teles, N., Wheeler, I., Hunter, M., Ehrmann, S., Ferreira, L., Mattos, A. P., Oliveira, B., Meyer, C., Şahin, M., Witjes, M., Fritz, S., Malek, Z., & Stolle, F. (2024a). Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning. Scientific Data, 11(1), Article 1303. https://doi.org/10.1038/s41597-024-04139-6
Parente, L., Sloat, L., Mesquita, V., Consoli, D., Stanimirova, R., Hengl, T., Bonannella, C., Teles, N., Wheeler, I., Hunter, M., Ehrmann, S., Ferreira, L., Mattos, A. P., Oliveira, B., Meyer, C., Şahin, M., Witjes, M., Fritz, S., Malek, Ž., & Stolle, F. (2024b). Global Pasture Watch—Annual grassland class and extent maps at 30-m spatial resolution (2000—2022) (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13890417
Pelser, A., Redelinghuys, N., & Kernan, A.-L. (2015). Protected Areas and ecosystem services—Integrating grassland conservation with human well-being in South Africa. In Biodiversity in Ecosystems—Linking Structure and Function. IntechOpen. https://doi.org/10.5772/59015
Petermann, J. S., & Buzhdygan, O. Y. (2021). Grassland biodiversity. Current Biology, 31(19), R1195–R1201. https://doi.org/10.1016/j.cub.2021.06.060
Poeplau, C. (2021). Grassland soil organic carbon stocks along management intensity and warming gradients. Grass and Forage Science, 76(2), 186–195. https://doi.org/10.1111/gfs.12537
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021
Ratajczak, Z., Nippert, J. B., & Collins, S. L. (2012). Woody encroachment decreases diversity across North American grasslands and savannas. Ecology, 93(4), 697–703. https://doi.org/10.1890/11-1199.1
Resare Sahlin, K., Gordon, L. J., Lindborg, R., Piipponen, J., Van Rysselberge, P., Rouet-Leduc, J., & Röös, E. (2024). An exploration of biodiversity limits to grazing ruminant milk and meat production. Nature Sustainability, 7(9), 1160–1170. https://doi.org/10.1038/s41893-024-01398-4
Saura, S., Bertzky, B., Bastin, L., Battistella, L., Mandrici, A., & Dubois, G. (2019). Global trends in protected area connectivity from 2010 to 2018. Biological Conservation, 238, Article 108183. https://doi.org/10.1016/j.biocon.2019.07.028
Sloat, L., Balehegn, M., & Johnson, P. (2025, May 2). Grasslands Are Some of Earth’s Most Underrated Ecosystems. World Resources Institute. https://www.wri.org/insights/grassland-benefits
Smith, M. D., Wilkins, K. D., Holdrege, M. C., Wilfahrt, P., Collins, S. L., Knapp, A. K., Sala, O. E., Dukes, J. S., Phillips, R. P., Yahdjian, L., Gherardi, L. A., Ohlert, T., Beier, C., Fraser, L. H., Jentsch, A., Loik, M. E., Maestre, F. T., Power, S. A., Yu, Q., … Zuo, X. (2024). Extreme drought impacts have been underestimated in grasslands and shrublands globally. Proceedings of the National Academy of Sciences, 121(4), Article e2309881120. https://doi.org/10.1073/pnas.2309881120
Spawn, S. A., Sullivan, C. C., Lark, T. J., & Gibbs, H. K. (2020). Harmonized global maps of above and belowground biomass carbon density in the year 2010. Scientific Data, 7(1), Article 112. https://doi.org/10.1038/s41597-020-0444-4
Stanley, P. L., Wilson, C., Patterson, E., Machmuller, M. B., & Cotrufo, M. F. (2024). Ruminating on soil carbon: Applying current understanding to inform grazing management. Global Change Biology, 30(3), Article e17223. https://doi.org/10.1111/gcb.17223
Su, X., Han, W., Liu, G., Zhang, Y., & Lu, H. (2019). Substantial gaps between the protection of biodiversity hotspots in alpine grasslands and the effectiveness of protected areas on the Qinghai-Tibetan Plateau, China. Agriculture, Ecosystems & Environment, 278, 15–23. https://doi.org/10.1016/j.agee.2019.03.013
Suttie, J. M., Reynolds, S. G., & Batello, C. (Eds.). (2005). Grasslands of the world (Vol. 34). Food and Agriculture Organization of the United Nations. Link to source: https://www.fao.org/4/y8344e/y8344e00.htm
Sze, J. S., Carrasco, L. R., Childs, D., & Edwards, D. P. (2021). Reduced deforestation and degradation in Indigenous Lands pan-tropically. Nature Sustainability, 5(2), 123–130. https://doi.org/10.1038/s41893-021-00815-2
United Nations Environment Programme World Conservation Monitoring Centre, & International Union for Conservation of Nature. (2024). Protected planet: The world database on protected areas (WDPA) and world database on other effective area-based conservation measures (WD-OECM) [Data set]. Retrieved November 2024, from https://www.protectedplanet.net
Vijay, V., Fisher, J. R. B., & Armsworth, P. R. (2022). Co-benefits for terrestrial biodiversity and ecosystem services available from contrasting land protection policies in the contiguous United States. Conservation Letters, 15(5), Article e12907. https://doi.org/10.1111/conl.12907
Villoria, N., Garrett, R., Gollnow, F., & Carlson, K. (2022). Leakage does not fully offset soy supply-chain efforts to reduce deforestation in Brazil. Nature Communications, 13(1), Article 5476. https://doi.org/10.1038/s41467-022-33213-z
Visconti, P., Butchart, S. H. M., Brooks, T. M., Langhammer, P. F., Marnewick, D., Vergara, S., Yanosky, A., & Watson, J. E. M. (2019). Protected area targets post-2020. Science, 364(6437), 239–241. https://doi.org/10.1126/science.aav6886
Wade, C. M., Austin, K. G., Cajka, J., Lapidus, D., Everett, K. H., Galperin, D., Maynard, R., & Sobel, A. (2020). What is threatening forests in Protected Areas? A global assessment of deforestation in Protected Areas, 2001–2018. Forests, 11(5), Article 539. https://doi.org/10.3390/f11050539
Waldron, A., Adams, V., Allan, J., Arnell, A., Asner, G., Atkinson, S., Baccini, A., Baillie, J. E. M., Balmford, A., Beau, J. A., Brander, L., Brondizio, E., Bruner, A., Burgess, N., Burkart, K., Butchart, S., Button, R., Carrasco, R., Cheung, W., … Zhang, Y. P. (2020). Protecting 30% of the planet for nature: Costs, benefits and economic implications [Working paper]. International Institute for Applied Systems Analysis. Link to source: https://pure.iiasa.ac.at/id/eprint/16560/1/Waldron_Report_FINAL_sml.pdf
Ward, M., Saura, S., Williams, B., Ramírez-Delgado, J. P., Arafeh-Dalmau, N., Allan, J. R., Venter, O., Dubois, G., & Watson, J. E. M. (2020). Just ten percent of the global terrestrial protected area network is structurally connected via intact land. Nature Communications, 11(1), Article 4563. https://doi.org/10.1038/s41467-020-18457-x
Watson, J. E. M., Dudley, N., Segan, D. B., & Hockings, M. (2014). The performance and potential of protected areas. Nature, 515(7525), 67–73. https://doi.org/10.1038/nature13947
West, T. A. P., Wunder, S., Sills, E. O., Börner, J., Rifai, S. W., Neidermeier, A. N., Frey, G. P., & Kontoleon, A. (2023). Action needed to make carbon offsets from forest conservation work for climate change mitigation. Science, 381(6660), 873–877. https://doi.org/10.1126/science.ade3535
Williams, M., Reay, D., & Smith, P. (2023). Avoiding emissions versus creating sinks—Effectiveness and attractiveness to climate finance. Global Change Biology, 29(8), 2046–2049. https://doi.org/10.1111/gcb.16598
Wolf, C., Levi, T., Ripple, W. J., Zárrate-Charry, D. A., & Betts, M. G. (2021). A forest loss report card for the world’s protected areas. Nature Ecology & Evolution, 5(4), 520–529. https://doi.org/10.1038/s41559-021-01389-0
Yao, J., Liu, H., Huang, J., Gao, Z., Wang, G., Li, D., Yu, H., & Chen, X. (2020). Accelerated dryland expansion regulates future variability in dryland gross primary production. Nature Communications, 11(1), Article 1665. https://doi.org/10.1038/s41467-020-15515-2
Yu, Q., Xu, C., Wu, H., Ke, Y., Zuo, X., Luo, W., Ren, H., Gu, Q., Wang, H., Ma, W., Knapp, A. K., Collins, S. L., Rudgers, J. A., Luo, Y., Hautier, Y., Wang, C., Wang, Z., Jiang, Y., Han, G., … Han, X. (2025). Contrasting drought sensitivity of Eurasian and North American grasslands. Nature, 639(8053), 114–118. https://doi.org/10.1038/s41586-024-08478-7
Zhu, K., Chiariello, N. R., Tobeck, T., Fukami, T., & Field, C. B. (2016). Nonlinear, interacting responses to climate limit grassland production under global change. Proceedings of the National Academy of Sciences, 113(38), 10589–10594. https://doi.org/10.1073/pnas.1606734113
Zhu, K., Song, Y., Lesage, J. C., Luong, J. C., Bartolome, J. W., Chiariello, N. R., Dudney, J., Field, C. B., Hallett, L. M., Hammond, M., Harrison, S. P., Hayes, G. F., Hobbs, R. J., Holl, K. D., Hopkinson, P., Larios, L., Loik, M. E., & Prugh, L. R. (2024). Rapid shifts in grassland communities driven by climate change. Nature Ecology & Evolution, 8(12), 2252–2264. https://doi.org/10.1038/s41559-024-02552-z
Avery Driscoll
Ruthie Burrows, Ph.D.
James Gerber, Ph.D.
Daniel Jasper
Alex Sweeney
Aiyana Bodi
Hannah Henkin
Ted Otte
Christina Richardson, Ph.D.
Christina Swanson, Ph.D.
Paul C. West, Ph.D.
We estimated that protecting 1 ha of grasslands avoids 0.06–0.90 t CO₂‑eq/yr, with emissions reductions tending to be higher in boreal and temperate regions than tropical and subtropical regions (100-yr GWP; Table 1a–d; Appendix).
We estimated effectiveness as the avoided emissions attributable to the reduction in grassland conversion conferred by protection (Equation 1; Appendix), assuming that converted grasslands are used as croplands due to data constraints. Although some grasslands are converted to intensively managed pastures or urban development, we assumed that the total land area converted to infrastructure is relatively small and emissions associated with conversion to planted pastures are comparable to those from conversion to cropland.
We aggregated estimates of avoided grassland conversion attributable to PAs from Li et al. (2024) to the biome level (Grassland lossavoided), then multiplied the result by the total emissions over 30 years from 1 ha of grassland converted to cropland. These emissions include the change in biomass and soil carbon on conversion to cropland (Carbonemissions), 30 years of lost carbon sequestration potential (Carbonuptake), and nitrous oxide emissions associated with soil carbon loss, which is a small component of total emissions (see Appendix for details; Chang et al. 2021; Huang et al., 2024; Intergovernmental Panel on Climate Change 2019; Poggio et al., 2021; Spawn et al., 2020).
Equation 1.
The effectiveness of grassland protection as defined here reflects only a small percentage of the carbon stored in grassland because we accounted for the likelihood that the grassland would be converted without protection. Grassland protection is particularly impactful for areas at high risk of conversion.
Table 1a–d. Effectiveness of grassland protection at avoiding emissions and sequestering carbon. Regional differences in values are driven by variation in carbon stocks, baseline rates of grassland conversion, and the effectiveness of PAs at reducing conversion.
Unit: t CO₂‑eq (100-yr basis)/ha/yr
| Estimate | 0.90 |
Unit: t CO₂‑eq (100-yr basis)/ha/yr
| Estimate | 0.54 |
Unit: t CO₂‑eq (100-yr basis)/ha/yr
| Estimate | 0.13 |
Unit: t CO₂‑eq (100-yr basis)/ha/yr
| Estimate | 0.06 |
The costs of grassland protection include up-front costs of land acquisition and ongoing costs of management and enforcement. The market price of land reflects the opportunity cost of not using the land for other purposes, such as agriculture or urban development. Data related to the costs of grassland protection are very limited.
We estimated that grassland protection provides a net cost savings of approximately US$0.53/ha/yr, or US$1.58/t CO₂‑eq avoided (Table 2). This estimate reflects global averages rather than regionally specific values, and some data are not specific to grasslands. Costs and revenues are highly variable across regions, depending on the costs of land and enforcement and the potential for tourism.
Dienerstein et al. (2024) estimated the initial cost of establishing a PA for 60 high-biodiversity ecoregions. Amongst the 20 regions that contain grasslands, the median acquisition cost was US$897/ha, which we amortized over 30 years. Costs of PA maintenance were estimated at US$9–17/ha/yr (Bruner et al., 2004; Waldron et al., 2020), though these estimates were not specific to grasslands. Additionally, these estimates reflect the costs of effective enforcement and management, but many existing PAs lack adequate funds for effective enforcement (Adams et al., 2019; Barnes et al., 2018; Burner et al., 2004).
Protecting grasslands can generate revenue through increased tourism. Waldron et al. (2020) estimated that, across all ecosystems, tourism revenues directly attributable to PA establishment were US$43 ha/yr, not including downstream revenues from industries that benefit from increased tourism. Inclusion of a tourism multiplier would substantially increase the estimated economic benefits of grassland protection.
Table 2. Cost per unit of climate impact for grassland protection. Negative value indicates cost savings.
Unit: 2023 US$/t CO₂‑eq , 100-yr basis
| median | -1.58 |
A learning curve is defined here as falling costs with increased adoption. The costs of grassland protection do not fall with increasing adoption, so there is no learning curve for this solution.
The term speed of action refers to how quickly a climate solution physically affects the atmosphere after it is deployed. This is separate from the 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.
Protect Grasslands is an EMERGENCY BRAKE climate solution. It reduces pulses of emissions from the conversion of grasslands, offering the potential to deliver a more rapid impact than gradual 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.
Permanence, or the durability of stored carbon, is a caveat for emissions avoidance through grassland protection that is not addressed in this analysis. Protected grasslands could be converted to agricultural uses or other development if legal protections are reversed or inadequately enforced, resulting in the loss of stored carbon. Many PAs allow for some human uses, and PA management that is not tailored to grazing needs, fire dependency, or woody plant encroachment can reduce carbon stocks within PAs (Barger et al., 2011; Chang et al., 2021; Conant et al, 2017; Jackson et al., 2002; Kemp et al., 2013; Popleau et al., 2011). Climate change is also causing widespread degradation of grasslands, including reductions in vegetation productivity that may reduce carbon storage over the long term even in the absence of additional disturbance (Chang et al., 2021; Gang et al., 2014; Li et al., 2023; Zhu et al., 2016). Climate change and aridification may also cause expansion of grassland extent (Berg & McColl, 2021; Feng & Fu, 2014; Huang et al., 2016), with mixed but overall negative impacts on terrestrial carbon uptake (Yao et al., 2020).
Additionality, or the degree to which emissions reductions are above and beyond a baseline, is another important caveat for emissions avoidance through ecosystem protection (Ahlering et al., 2016; Williams et al., 2023). In this analysis, additionality was addressed by using baseline rates of grassland conversion in calculating effectiveness. Evaluating additionality is challenging and remains an active area of research.
A total of 555 Mha of grasslands (excluding grasslands on peat soils, grasslands that are also coastal wetlands, and grasslands created through deforestation) are currently located within PAs, and an additional 832 Mha are located on IPLs not classified as PAs (Table 3e). That means that ~48% of grasslands are under some form of protection globally, with 6% in strict PAs, 13% in non-strict PAs, and 29% on IPLs that are not also PAs. As of 2023, tropical regions had the largest extent of protected grasslands (583 Mha), followed by boreal regions (339 Mha), and subtropical regions (293 Mha). In temperate regions, only 24% of grasslands (172 Mha) were under any form of protection (Table 3a–d).
Table 3a–e. Grassland under protection by biome (circa 2023). Estimates are provided for three different forms of protection: “strict” protection, including IUCN classes I and II; “non-strict” protection, including all other IUCN categories; and IPLs outside of PAs. Regional values may not sum to global totals due to rounding.
Unit: ha protected
| Strict PAs | 52,564,000 |
| Non-strict PAs | 82,447,000 |
| IPLs | 203,579,000 |
Unit: ha protected
| Strict PAs | 30,242,000 |
| Non-strict PAs | 51,033,000 |
| IPLs | 90,973,000 |
Unit: ha protected
| Strict PAs | 31,949,000 |
| Non-strict PAs | 83,745,000 |
| IPLs | 177,301,000 |
Unit: ha protected
| Strict PAs | 56,233,000 |
| Non-strict PAs | 166,356,000 |
| IPLs | 359,997,000 |
Unit: ha protected
| Strict PAs | 170,988,000 |
| Non-strict PAs | 383,581,000 |
| IPLs | 831,850,000 |
We calculated the annual rate of new grassland protection based on the year of PA establishment for areas established in 2000–2020. The median annual increase in grassland protection was 8.1 Mha (mean 11.4 Mha; Table 4e). This represents a roughly 1.5%/yr increase in grasslands within PAs, or protection of an additional 0.3%/yr of total global grasslands.
Grassland protection has proceeded more quickly in tropical regions (median increase of 4.0 Mha/yr) than in other climate zones (median increases of 1.2–1.6 Mha/yr) (Table 4a–d). Although the global rate of grassland protection fluctuates from year to year, the median rate has remained stable in recent decades at 10.2 Mha/yr from 2000–2009 and 10.4 Mha/yr from 2010–2020.
Table 4a–e. Adoption trend for grassland protection in PAs of any IUCN class (2000–2020). The 25th and 75th percentiles reflect only interannual variance (ha grassland protected/yr). IPLs are not included in this analysis due to a lack of data.
Unit: ha grassland protected/yr
| 25th percentile | 659,000 |
| median (50th percentile) | 1,338,000 |
| mean | 2,152,000 |
| 75th percentile | 3,007,000 |
Unit: ha grassland protected/yr
| 25th percentile | 692,000 |
| median (50th percentile) | 1,178,000 |
| mean | 1,728,000 |
| 75th percentile | 1,715,000 |
Unit: ha grassland protected/yr
| 25th percentile | 940,000 |
| median (50th percentile) | 1,580,000 |
| mean | 2,791,000 |
| 75th percentile | 3,226,000 |
Unit: ha grassland protected/yr
| 25th percentile | 2,628,000 |
| median (50th percentile) | 4,044,000 |
| mean | 4,711,000 |
| 75th percentile | 5,774,000 |
Unit: ha grassland protected/yr
| 25th percentile | 4,919,000 |
| median (50th percentile) | 8,140,000 |
| mean | 11,382,000 |
| 75th percentile | 13,722,000 |
Including grasslands that are currently protected, we estimated that there are approximately 2,891 Mha of natural grasslands that are not counted in a different solution (Table 5e). This ceiling includes 1,505 Mha that are not currently under any form of protection. This includes 533 Mha of eligible grasslands in boreal regions, 723 Mha in temperate regions, 626 Mha in the subtropics, and 1,008 Mha in the tropics (Table 5a–d).
To develop these estimates, we relied on the global grassland map from Parente et al. (2024), excluded areas that were included in the Protect Forests, Protect Peatlands, and Protect Coastal Wetlands solutions, and excluded areas that were historically forested according to the Terrestrial Ecoregions of The World dataset (Olson et al., 2001; Appendix). While it is not socially, politically, or economically realistic that all remaining grasslands could be protected, these values represent the technical upper limit to adoption of this solution.
Table 5a–e. Adoption ceiling: upper limit for adoption of legal protection of grasslands by biome. Values may not sum to global totals due to rounding.
Unit: ha protected
| Estimate | 533,033,000 |
Unit: ha protected
| Estimate | 723,429,000 |
Unit: ha protected
| Estimate | 626,474,000 |
Unit: ha protected
| Estimate | 1,008,375,000 |
Unit: ha protected
| Estimate | 2,891,311,000 |
We assigned a low achievable level of a minimum of 50% of grasslands in each climate zone (Table 6a–e). For boreal and tropical regions, in which 64% and 58%, respectively, of grasslands are already protected, we assume no change in PA (Table 6a, d). For temperate areas, the low achievable target reflects an increase of 189 Mha, or more than a doubling of the current PA extent (Table 6b). In subtropical zones, this target reflects an additional 20 Mha under protection (Table 6c). We assigned a high achievable level of 70% of grasslands in each climate zone, reflecting an additional 637 Mha of protected grasslands globally, or a 46% increase in the current PA extent (Table 6a–e).
Table 6a–e. Range of achievable adoption of grassland protection by biome.
Unit: ha protected
| Current Adoption | 338,590,000 |
| Achievable – Low | 338,590,000 |
| Achievable – High | 373,123,000 |
| Adoption ceiling | 533,033,000 |
Unit: ha protected
| Current Adoption | 172,248,000 |
| Achievable – Low | 361,715,000 |
| Achievable – High | 506,400,000 |
| Adoption ceiling | 723,429,000 |
Unit: ha protected
| Current Adoption | 292,995,000 |
| Achievable – Low | 313,237,000 |
| Achievable – High | 438,532,000 |
| Adoption ceiling | 626,474,000 |
Unit: ha protected
| Current Adoption | 582,586,000 |
| Achievable – Low | 582,586,000 |
| Achievable – High | 705,863,000 |
| Adoption ceiling | 1,008,375,000 |
Unit: ha protected
| Current Adoption | 1,386,419,000 |
| Achievable – Low | 1,596,128,000 |
| Achievable – High | 2,023,918,000 |
| Adoption ceiling | 2,891,311,000 |
We estimated that PAs currently reduce GHG emissions from grassland conversion by 0.468 Gt CO₂‑eq/yr (Table 7a–e). Achievable levels of grassland protection have the potential to reduce emissions 0.572–0.704 Gt CO₂‑eq/yr, with a technical upper bound of 1.006 Gt CO₂‑eq/yr (Table 7a–e). This indicates that further emissions reductions of 0.105–0.237 Gt CO₂‑eq/yr are achievable. For these benefits to be realized, grazing, fire, and woody plant management must be responsive to local grassland needs and compatible with the maintenance of carbon stocks. The solutions Improve Livestock Grazing and Deploy Silvopasture address the climate impacts of some aspects of grassland management.
Few other sources explicitly quantify the climate impacts of grassland protection, but the available data are roughly aligned with our estimates of additional mitigation potential. The Intergovernmental Panel on Climate Change estimated that avoided conversion of grasslands to croplands could reduce emissions by 0.03–0.7 Gt CO₂‑eq/yr (Nabuurs et al., 2022). Griscom et al. (2017) estimated that avoided grassland conversion could save 0.12 Gt CO₂‑eq/yr emissions from soil carbon only (not counting loss of vegetation, sequestration potential, or nitrous oxide), though their analysis did not account for current protection and relied on older estimates of grassland conversion.
Table 7a–e. Climate impact at different levels of adoption.
Unit: GtCO₂‑eq/yr, 100-year basis
| Current Adoption | 0.305 |
| Achievable – Low | 0.305 |
| Achievable – High | 0.336 |
| Adoption Ceiling | 0.481 |
Unit: GtCO₂‑eq/yr, 100-year basis
| Current Adoption | 0.093 |
| Achievable – Low | 0.195 |
| Achievable – High | 0.273 |
| Adoption Ceiling | 0.390 |
Unit: GtCO₂‑eq/yr, 100-year basis
| Current Adoption | 0.037 |
| Achievable – Low | 0.039 |
| Achievable – High | 0.055 |
| Adoption Ceiling | 0.078 |
Unit: GtCO₂‑eq/yr, 100-year basis
| Current Adoption | 0.033 |
| Achievable – Low | 0.033 |
| Achievable – High | 0.040 |
| Adoption Ceiling | 0.057 |
Unit: GtCO₂‑eq/yr, 100-year basis
| Current Adoption | 0.468 |
| Achievable – Low | 0.572 |
| Achievable – High | 0.704 |
| Adoption Ceiling | 1.006 |
Grassland plants often have deep root systems, leading to high soil carbon stocks (Sloat et al., 2025). These roots can absorb water and reduce discharge into surrounding water bodies during periods of excessive rain (GRaSS, 2024).
Different grassland plant species respond differently to drought. Differences in precipitation seasonality due to drought may allow some grass species to dominate over others (Knapp et al., 2020). Evidence suggests that higher species diversity can enhance grassland resilience to drought (Smith et al., 2024; Yu et al., 2025). Additionally, the deep root systems of grassland plants contribute to the drought resilience of these ecosystems (Sloat et al., 2025). More resilient, biodiverse grasslands are associated with greater ecosystem stability and productivity, and can maintain ecosystem services during periods of extreme weather, such as drought (Isbell et al, 2015; Lefcheck et al., 2015).
Grasslands are an important source of income for surrounding communities through tourism and other ecosystem services (Bengtsson et al., 2019). Protecting grasslands sustains the long-term health of the ecosystem, which is especially important for subsistence livelihoods that depend on intact landscapes for incomes (Pelser, 2015). Sources of income that are directly generated from grasslands include: meat, milk, wool, and leather and thatching materials to make brooms, hats, and baskets (GRaSS, 2024; Pelser, 2015). People living near grasslands often rely on grazing livestock for food and income (GRaSS, 2024, Kemp 2013, Su et al., 2019). Grasslands in China support the livelihoods of about 16 million people, many of whom live in poverty (Kemp et al., 2013). The Qinghai-Tibetan Plateau is especially important for grazing livestock (Su et al., 2019). Evidence has shown that declines in grassland productivity are also linked to declines in income (Kemp et al., 2013).
Grasslands can contribute to food security by providing food for livestock and supporting pollinators for nearby agriculture (Sloat et al., 2025). Grassland-based grazing systems are important sources of food for populations in low and middle-income countries, particularly in Oceania, Latin America, the Caribbean, the Middle East, North Africa, and sub-Saharan Africa (Resare Sahlin et al., 2023). Grasslands can support the food security of smallholder farmers and pastoralists in these regions by providing meat and milk (GRaSS, 2024; Michalk, 2018).
Grasslands are central to many cultures, and grassland protection can support shared cultural and spiritual values for many populations. They can be sources of identity for people living in or near grassland ecosystems who have strong connections with the land (Bengtsson et al., 2019, GRaSS, 2024). For example, in Mongolia grasslands sustain horses, which are central to the cultural identities and livelihoods of communities, particularly nomadic populations (Kemp et al., 2014). Grasslands can also be an important source of shared identity for pastoralists who move herds to graze based on seasonal cycles during the year (Liechti & Biber, 2016).
Many grasslands are biodiversity hot spots (Petermann & Buzhdygan, 2021; Su et al., 2019). Numerous plant and animal species are endemic to grasslands, meaning they have limited habitat ranges and can easily become endangered with habitat degradation (Sloat et al., 2025). In Germany, grasslands in PAs were found to have higher plant diversity than in non-PAs (Kachler et al., 2023). Grasslands are important habitats for bird species that rely on them for breeding grounds (GRaSS, 2024; Nugent et al., 2022).
The unique, deep root structures of some grassland plants can improve soil stability and reduce soil erosion (Bengtsson et al., 2019; GRaSS, 2024; Kemp et al., 2013).
Grasslands can regulate water flows and water storage. The root systems can help rainwater reach deep underground, recharging groundwater stores (Bengtsson et al., 2019; GRaSS, 2024).
Relying on grassland protection as an emissions reduction strategy can be undermined if ecosystem conversion that is not allowed inside a PA simply takes place outside of it instead (Aherling et al., 2016; Asamoah et al., 2021). If such leakage leads to conversion of ecosystems that have higher carbon stocks, such as forests, peatlands, or coastal wetlands, total emissions may increase. Combining grassland protection with policies to reduce incentives for ecosystem conversion can help avoid leakage.
PAs often include multiple ecosystems. Grassland protection will likely lead to protection of other ecosystems within the same areas, and the health of nearby ecosystems is improved by the services provided by intact grasslands.
These solutions reduce pressure to convert grasslands to agricultural use, easing the expansion of PAs.
Restored grasslands need protection to reduce the risk of future disturbance, and the health of protected grasslands can be improved through the restoration of adjacent degraded grasslands.
Grazing by large herbivores is critical for the health of many grasslands, and healthy grasslands are needed to support restoration of large herbivores.
Additional crop deployment can increase demand for agricultural land, reducing the grassland area available for protection.
Grassland protection may reduce land availability for renewable energy infrastructure.
ha of grassland or savanna protected
CO₂, N₂O
ha of grassland or savanna protected
CO₂, N₂O
ha of grassland or savanna protected
CO₂, N₂O
ha of grassland or savanna protected
CO₂, N₂O
Establishment of PAs may limit local access to grasslands for grazing or other forms of income generation, although effective management plans should account for the grazing needs of the protected grassland. Second, allocation of budgetary resources to PA establishment may divert resources from maintenance and enforcement of existing PAs. Finally, protection of grasslands may reduce land availability for renewable energy infrastructure, such as solar and wind power.
Consensus of effectiveness in reducing emissions and maintaining carbon removal: High
There is high scientific consensus that grassland protection reduces emissions by reducing conversion of grasslands to croplands. Grasslands have been extensively converted globally because of their utility for agricultural use, and many extant grasslands are at high risk of conversion (Carbutt et al., 2017; Gang et al., 2014). Li et al. (2024) found that PAs prevent conversion of approximately 0.35% of global grasslands per year. Although grasslands remain understudied relative to some other ecosystems, there is robust evidence that PAs and IPLs reduce forest conversion, with estimates in different regions ranging from 17–75% reductions in forest loss relative to unprotected areas (Baragwanth & Bayi, 2020; Graham et al., 2021; McNichol et al., 2023; Sze et al., 2022; Wolf et al., 2022). Additional research specific to grasslands on the effectiveness of PAs and IPLs at preventing land use change would be valuable.
Conversion of grasslands to croplands produces emissions through the loss of soil carbon and biomass (IPCC, 2019). A recent meta-analysis based on 5,980 soil carbon measurements found that grassland conversion to croplands reduces soil carbon stocks by a global average of 23%, or almost 30 t CO₂ /ha (Huang et al., 2024), before accounting for nitrous oxide emissions (IPCC, 2019), loss of biomass carbon stocks (Spawn et al., 2020), and loss of sequestration potential (Chang et al., 2021).
Regional studies also find that grassland protection provides emissions savings. For instance, a study of grasslands in Argentina and the United States found that conversion to croplands reduced total carbon stocks, including soil and biomass, by 117 t CO₂‑eq /ha (Kim et al., 2016). Ahlering et al. (2016) conclude that protecting just 210,000 ha of unprotected grasslands in the U.S. Northern Great Plains would avoid 11.7 Mt CO₂‑eq over 20 years, with emissions savings of 51.6 t CO₂‑eq /ha protected, or 35.6 t CO₂‑eq /ha after accounting for leakage and uncertainty.
The quantitative results presented in this assessment synthesize findings from 13 global datasets supplemented by three meta-analyses with global scopes. We recognize that geographic bias in the information underlying global data products creates bias and hope this work inspires research and data sharing on this topic in underrepresented regions.
This analysis quantifies the emissions avoidable through legal protection of grasslands via establishment of PAs or land tenure for Indigenous peoples. We leveraged a global grassland distribution map alongside other ecosystem distribution maps, shapefiles of PAs and IPLs, available data on rates of avoided ecosystem loss attributable to PA establishment, maps of grassland carbon stocks in above- and below-ground biomass, and biome-level estimates of soil carbon loss for grasslands converted to croplands. This appendix describes the source data products and how they were integrated.
Grassland Extent
We relied on the 30-m resolution global map of grassland extent developed by Parente et al. (2024), which classifies both “natural and semi-natural grasslands” and “managed grasslands.” This solution considers only the “natural and semi-natural grasslands” class. We first resampled the data to 1 km resolution by calculating the percent of the pixel occupied by grasslands. To avoid double counting land considered in other ecosystem protection solutions (Protect Forests, Protect Peatlands, and Protect Coastal Wetlands), we then adjusted the grassland map so that no pixel contained a value greater than 100% after summing all ecosystem types. These other ecosystems can overlap with grasslands either because they are non-exclusive (e.g., peatland soils can have grassland vegetation), or because of variable definitions (e.g., the grassland map allows up to 50% tree cover, which could be classified as a forest by other land cover maps). After adjusting for other ecosystems, we used the Terrestrial Ecoregions of the World data (Olson et al., 2001) to exclude areas of natural forest, because these areas are eligible for other solutions.
The resultant raster of proportionate grassland coverage was converted to absolute areas, and used to calculate the total grassland area for each of four latitude bands (tropical: –23.4° to 23.4°; subtropical: –35° to –23.4° and 23.4° to 35°; temperate: –50° to –35° and 35° to 50°; boreal: <–50° and >50°). The analysis was conducted by latitude bands in order to retain some spatial variability in emissions factors and degradation rates.
Protected Grassland Areas
We identified protected grassland areas using the World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, 2024), which contains boundaries for each PA and additional information, including their establishment year and International Union for Conservation of Nature management category (Ia–VI, not applicable, not reported, and not assigned). The PA boundary data were converted to a raster and used to calculate the grassland area within PA boundaries for each latitude band and each PA category. To evaluate trends in adoption over time, we also aggregated protected areas by establishment year as reported in the WDPA.
We used the maps of IPLs from Garnett et al. (2018) to identify IPLs that were not inside of established PAs. The total grassland area within IPLs was calculated according to the same process as for PAs.
Avoided Grassland Conversion
Broadly, we estimated annual, per-hectare emissions savings from grassland protection as the difference between net carbon exchange in a protected grassland and an unprotected grassland. This calculation followed Equation A1, in which the annual grassland loss avoided due to protection (%/yr) is multiplied by the 30-yr cumulative sum of emissions per hectare of grassland converted to cropland (CO₂‑eq /ha over 30 yr).
Equation A1.
The avoided grassland loss attributable to PAs was calculated from the source data for Figure 7 of Li et al. (2024), which provides the difference in habitat loss between protected areas and unprotected control areas between 2003–2019 by ecoregion. These data were filtered to only include grasslands, aggregated to latitude bands, and used to calculate annual linear rates of avoided habitat loss. Tropical and subtropical regions were not clearly distinguished, so the same rate was used for both.
Grassland Conversion Emissions
The emissions associated with grassland conversion to cropland include loss of above- and below-ground biomass carbon stocks, loss of soil carbon stocks, and loss of carbon sequestration potential. We used data on above- and below-ground biomass carbon stocks from Spawn et al. (2020) to calculate the average carbon stocks by latitude band for grassland pixels and cropland pixels. We used the 2010 ESA CCI land cover dataset for this calculation because it was the base map used to generate the biomass carbon stock dataset. The per-hectare difference between biomass carbon stocks in grasslands and croplands represents the emissions from biomass carbon stocks following grassland conversion.
We aggregated soil carbon stocks from SoilGrids 2.0 (0–30 cm depth) to latitude bands for grassland pixels from the 2015 ESA CCI land cover dataset, which was the base map used for the SoilGrids dataset (Poggio et al., 2021). To avoid capturing peatlands, which have higher C stocks, we excluded pixels with soil carbon contents >15% by mass (a slightly conservative cutoff for organic soils) prior to aggregation. We took the percent loss of soil carbon following grassland-to-cropland conversion from Table S8 of the meta-analysis by Huang et al. (2024), who also conducted their analysis by latitude band. Soil carbon losses are also associated with nitrous oxide emissions, which were calculated per the IPCC Tier 1 equations as follows using the default C:N ratio of 15:1.
We calculated the loss of carbon sequestration potential based on estimates of grassland annual net CO₂ flux, extracted from Table S2 from Chang et al. (2021). These data include field- and model-based measurements of grassland net CO₂ flux and were used to calculate median values by latitude band.
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