Potential for large-scale CO2 removal via enhanced rock weathering with croplands


Enhanced silicate rock weathering (ERW), deployable with croplands, has potential use for atmospheric carbon dioxide (CO2) removal (CDR), which is now necessary to mitigate anthropogenic climate change1. ERW also has possible co-benefits for improved food and soil security, and reduced ocean acidification2,3,4. Here we use an integrated performance modelling approach to make an initial techno-economic assessment for 2050, quantifying how CDR potential and costs vary among nations in relation to business-as-usual energy policies and policies consistent with limiting future warming to 2 degrees Celsius5. China, India, the USA and Brazil have great potential to help achieve average global CDR goals of 0.5 to 2 gigatonnes of carbon dioxide (CO2) per year with extraction costs of approximately US$80–180 per tonne of CO2. These goals and costs are robust, regardless of future energy policies. Deployment within existing croplands offers opportunities to align agriculture and climate policy. However, success will depend upon overcoming political and social inertia to develop regulatory and incentive frameworks. We discuss the challenges and opportunities of ERW deployment, including the potential for excess industrial silicate materials (basalt mine overburden, concrete, and iron and steel slag) to obviate the need for new mining, as well as uncertainties in soil weathering rates and land–ocean transfer of weathered products.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: CDR via ERW with croplands.
Fig. 2: Augmentation of pledged CO2 emissions reduction by ERW.
Fig. 3: Costs of carbon extraction via ERW with croplands.
Fig. 4: Forecast increases in national bulk silicate production over the next century.

Data availability

Datasets on global crop production and yield are available at http://www.earthstat.org/, accessed on 18 December 2019. Datasets on global crop irrigation are available at https://zenodo.org/record/1209296, accessed on 18 December 2019. Datasets on global precipitation are available at http://www.climatologylab.org/terraclimate.html, accessed on 18 December 2019. Datasets on global soil surface pH are available at https://daac.ornl.gov/SOILS/guides/HWSD.html, accessed on 18 December /2019. Datasets on global soil temperature are available at https://esgf-node.llnl.gov/search/cmip5/, accessed on 18 December 2019. Datasets on diesel prices are available at https://data.worldbank.org/indicator/EP.PMP.DESL.CD. Datasets on mining costs are available at http://www.infomine.com/. Datasets on gross national income per capita are available at https://data.worldbank.org/indicator/ny.gnp.pcap.pp.cd. Datasets for projections of future GDP linked to Shared Socioeconomic Pathways are available at https://tntcat.iiasa.ac.at/SspDbSource data are provided with this paper.

Code availability

The Matlab codes developed for this study belong to the Leverhulme Centre for Climate Change Mitigation. The authors will make them available upon reasonable request.


  1. 1.

    Intergovernmental Panel on Climate Change (IPCC). Global Warming Of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C Above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways (World Meteorological Organization, 2018).

  2. 2.

    Kantola, I. B. et al. Potential of global croplands and bioenergy crops for climate change mitigation through deployment for enhanced weathering. Biol. Lett. 13, 20160714 (2017).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Zhang, G., Kang, J., Wang, T. & Zhu, C. Review and outlook for agromineral research in agriculture and climate change mitigation. Soil Res. 56, 113–122 (2018).

    Google Scholar 

  4. 4.

    Beerling, D. J. et al. Farming with crops and rocks to address global climate, food and soil security. Nat. Plants 4, 138–147 (2018).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Mercure, J.-F. et al. Macroeconomic impact of stranded fossil fuel assests. Nat. Clim. Chang. 8, 588–593 (2018).

    ADS  Google Scholar 

  6. 6.

    Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data 10, 2141–2194 (2018).

    Google Scholar 

  7. 7.

    United Nations Environment Programme The Emissions Gap Report 2018 (United Nations Environment Programme, 2018).

  8. 8.

    Hagedorn, G. et al. Concerns of young protesters are justified. Science 364, 139–140 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Hansen, J. et al. Young people’s burden: requirement of negative CO2 emissions. Earth Syst. Dyn 8, 577–616 (2017).

    ADS  Google Scholar 

  10. 10.

    Rockström, J. et al. A roadmap for rapid decarbonisation. Science 355, 1269–1271 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    The Royal Society Greenhouse Gas Removal Technologies (The Royal Society, 2018).

  12. 12.

    Pacala, S. et al. Negative Emissions Technologies And Reliable Sequestration (National Academy of Sciences, 2018).

  13. 13.

    Seifritz, W. CO2 disposal by means of silicates. Nature 345, 486 (1990).

    ADS  Google Scholar 

  14. 14.

    Schuiling, R. D. & Krijgsman, P. Enhanced weathering: an effective and cheap tool to sequester CO2. Clim. Change 74, 349–354 (2006).

    ADS  CAS  Google Scholar 

  15. 15.

    Kohler, P., Hartmann, J. & Wolf-Gladrow, D. A. Geoengineering potential of artificially enhanced silicate weathering of olivine. Proc. Natl Acad. Sci. USA 107, 20228–20233 (2010).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Hartmann, J. et al. Enhanced chemical weathering as a geoengineering strategy to reduce atmospheric carbon dioxide, supply nutrients, and mitigate ocean acidification. Rev. Geophys. 51, 113–149 (2013).

    ADS  Google Scholar 

  17. 17.

    Taylor, L. L. et al. Enhanced weathering strategies for stabilizing climate and averting ocean acidification. Nat. Clim. Chang. 6, 402–406 (2016).

    ADS  CAS  Google Scholar 

  18. 18.

    Kelland, M. E. et al. Increased yield and CO2 sequestration potential with the C4 cereal crop Sorghum bicolor cultivated in basaltic rock dust amended agricultural soil. Glob. Change Biol. 26, 3658–3676 (2020).

    ADS  Google Scholar 

  19. 19.

    Renforth, P. & Henderson, G. Assessing ocean alkalinity for carbon sequestration. Rev. Geophys. 55, 636–674 (2017).

    ADS  Google Scholar 

  20. 20.

    Smith, P. et al. Land-based options for greenhouse gas removal and their impacts on ecosystem services and the sustainable development goals. Annu. Rev. Environ. Res. 44, 255–286 (2019).

    Google Scholar 

  21. 21.

    Renforth, P. The potential of enhanced weathering in the UK. Int. J. Greenhouse Gas Control 10, 229–243 (2012).

    CAS  Google Scholar 

  22. 22.

    Strefler, J. et al. Potential and costs of carbon dioxide removal by enhanced weathering of rocks. Environ. Res. Lett. 13, 034010 (2018).

    ADS  Google Scholar 

  23. 23.

    Fuss, S. et al. Negative emissions—Part 2: Costs, potentials and side effects. Environ. Res. Lett. 13, 063002 (2018).

    ADS  Google Scholar 

  24. 24.

    Baik, E. et al. Geospatial analysis of near-term potential for carbon-negative bioenergy in the United States. Proc. Natl Acad. Sci. USA 115, 3290–3295 (2018).

    CAS  Google Scholar 

  25. 25.

    Heck, V., Gerten, D., Lucht, W. & Popp, A. Biomass-based negative emissions difficult to reconcile with planetary boundaries. Nat. Clim. Chang. 8, 151–155 (2018).

    ADS  CAS  Google Scholar 

  26. 26.

    Amann, T. & Hartmann, J. Ideas and perspectives: synergies from co-deployment of negative emissions technologies. Biogeosciences 16, 2949–2960 (2019).

    ADS  CAS  Google Scholar 

  27. 27.

    Mayer, A. et al. The potential of agricultural land management to contribute to lower global surface temperature. Sci. Adv. 4, eaaq0932 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Groffman, P. M. et al. Calcium additions and microbial nitrogen cycle processes in a northern hardwood forest. Ecosystems 9, 1289–1305 (2006).

    CAS  Google Scholar 

  29. 29.

    Dietzen, C., Harrison, R. & Michelsen-Correa, S. Effectiveness of enhanced mineral weathering as a carbon sequestration tool and alternative to agricultural lime: an incubation experiment. Int. J. Greenhouse Gas Control 74, 251–258 (2018).

    CAS  Google Scholar 

  30. 30.

    Smith, P., Haszeldine, R. S. & Smith, S. M. Preliminary assessment of the potential for, and limitations to, terrestrial negative emissions technologies in the UK. Environ. Sci. Process. Impacts 18, 1400–1405 (2016).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    DeLucia, E., Kantola, I., Blanc-Betes, E., Bernacchi, C. & Beerling, D. J. Basalt application for carbon sequestration reduces nitrous oxide fluxes from cropland. Geophys. Res. Abstr. 21, EGU2019–EGU4500 (2019).

    Google Scholar 

  32. 32.

    Das, S. et al. Cropping with slag to address soil, environment, and food security. Front. Microbiol. 10, 1320 (2019).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).

    ADS  CAS  PubMed  Google Scholar 

  34. 34.

    Clark, P. U. et al. Consequences of twenty-first-century policy for multi-millennial climate and sea-level change. Nat. Clim. Chang. 6, 360–369 (2016).

    ADS  Google Scholar 

  35. 35.

    Crowder, D. W. & Reganold, J. P. Financial competitiveness of organic agriculture on a global scale. Proc. Natl Acad. Sci. USA 112, 7611–7616 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Bebbington, A. J. & Bury, J. T. Institutional challenges for mining and sustainability in Peru. Proc. Natl Acad. Sci. USA 106, 17296–17301 (2009).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Renforth, P. et al. Silicate production and availability for mineral carbonation. Environ. Sci. Technol. 45, 2035–2041 (2011).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Renforth, P. The negative emission potential of alkaline materials. Nat. Commun. 10, 1401 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Tubana, B. S., Babu, T. & Datnoff, L. E. A review of silicon in soils and plants and its role in US agriculture: history and future perspectives. Soil Sci. 181, 393–411 (2016).

    CAS  Google Scholar 

  40. 40.

    Washbourne, C.-L. et al. Rapid removal of atmospheric CO2 in urban soils. Environ. Sci. Technol. 49, 5434–5440 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Lekakh, S. N. et al. Kinetics of aqueous leaching and carbonization of steelmaking slag. Metallurg. Mater. Trans. B 39, 125–134 (2008).

    ADS  Google Scholar 

  42. 42.

    Haynes, R. J., Belyaeva, O. N. & Kingston, G. Evaluation of industrial waste sources of fertilizer silicon using chemical extractions and plant uptake. J. Plant Nutr. Soil Sci. 176, 238–248 (2013).

    CAS  Google Scholar 

  43. 43.

    Rodd, A. V. et al. Surface application of cement kiln dust and lime to forage land: effect on forage yield, tissue concentration and accumulation of nutrients. Can. J. Soil Sci. 90, 201–213 (2010).

    CAS  Google Scholar 

  44. 44.

    Ramos, C.G. et al. Evaluation of soil re-mineralizer from by-product of volcanic rock mining: experimental proof using black oats and maize crops. Nat. Res. Res. 10.1007/s11053–019–09529-x (2019).

  45. 45.

    Savant, N. K., Datnoff, L. E. & Snyder, G. H. Depletion of plant-available silicon in soils: a possible cause of declining rice yields. Commun. Soil Sci. Plant Anal. 28, 1245–1252 (1997).

    CAS  Google Scholar 

  46. 46.

    Ning, D. et al. Impacts of steel-slag-based fertilizer on soil acidity and silicon availability and metals-immobilization in a paddy soil. PLoS One 11, e0168163 (2016).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Chen, J. Rapid urbanization in China: a real challenge to soil protection and food security. Catena 69, 1–15 (2007).

    Google Scholar 

  48. 48.

    United Nations Global Land Outlook 1st edn (United Nations Convention to Combat Desertification, 2017).

  49. 49.

    Smith, M. R. & Myers, S. S. Impact of anthropogenic CO2 emissions on global human nutrition. Nat. Clim. Chang. 8, 834–839 (2018).

    ADS  CAS  Google Scholar 

  50. 50.

    Cui, Z. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363–366 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Pidgeon, N. F. & Spence, E. Perceptions of enhanced weathering as a biological negative emissions option. Biol. Lett. 13, 20170024 (2017).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Daval, D., Calvarusa, C., Guyut, F. & Turpault, M.-P. Time-dependent feldspar dissolution rates resulting from surface passivation: experimental evidence and geochemical implications. Earth Planet. Sci. Lett. 498, 226–236 (2018).

    ADS  CAS  Google Scholar 

  53. 53.

    Ricke, K., Drout, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Chang. 8, 895–900 (2018).

    ADS  CAS  Google Scholar 

  54. 54.

    Cox, E., Pidgeon, N. F., Spence, E. M. & Thomas, G. Blurred lines: the ethics and policy of greenhouse gas removal at scale. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2018.00038 (2018).

  55. 55.

    Berner, R. A. Rate control of mineral dissolution under Earth surface conditions. Am. J. Sci. 278, 1235–1252 (1978).

    ADS  CAS  Google Scholar 

  56. 56.

    Maher, K. The dependence of chemical weathering rates on fluid residence time. Earth Planet. Sci. Lett. 294, 101–110 (2010).

    ADS  CAS  Google Scholar 

  57. 57.

    Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci. Data 5, 170191 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Huang, Z. W. et al. Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns. Hydrol. Earth Syst. Sci. 22, 2117–2133 (2018).

    ADS  Google Scholar 

  59. 59.

    Siebert, S. & Doll, P. Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. J. Hydrol. 384, 198–217 (2010).

    ADS  Google Scholar 

  60. 60.

    Aagaard, P. & Helgeson, H. C. Thermodynamic and kinetic constraints on reaction-rates among minerals and aqueous-solutions. 1. Theoretical considerations. Am. J. Sci. 282, 237–285 (1982).

    CAS  Google Scholar 

  61. 61.

    Lasaga, A. C. Chemical-kinetics of water-rock interactions. J. Geophys. Res. 89, 4009–4025 (1984).

    ADS  CAS  Google Scholar 

  62. 62.

    Brantley, S. L., Kubicki, J. D. & White, A. F. Kinetics of Water–Rock Interaction (Springer, 2008).

  63. 63.

    Harley, A. D. & Gilkes, R. J. Factors influencing the release of plant nutrient elements from silicate rock powders: a geochemical overview. Nutr. Cycl. Agroecosyst. 56, 11–36 (2000).

    CAS  Google Scholar 

  64. 64.

    Taylor, L. L. et al. Biological evolution and the long-term carbon cycle: integrating mycorrhizal evolution and function into the current paradigm. Geobiology 7, 171–191 (2009).

    ADS  CAS  Google Scholar 

  65. 65.

    Nelson, P. N. & Su, N. Soil pH buffering capacity: a descriptive function and its application to some acidic tropical soils. Aust. J. Soil Sci. 48, 201–207 (2010).

    Google Scholar 

  66. 66.

    Cerling, T. Carbon dioxide in the atmosphere: evidence from Cenozoic and Mesozoic paleosols. Am. J. Sci. 291, 377–400 (1991).

    ADS  CAS  Google Scholar 

  67. 67.

    Taylor, L., Banwart, S. A., Leake, J. R. & Beerling, D. J. Modelling the evolutionary rise of ectomycorrhizal on sub-surface weathering environments and the geochemical carbon cycle. Am. J. Sci. 311, 369–403 (2011).

    ADS  CAS  Google Scholar 

  68. 68.

    Banwart, S. A., Berg, A. & Beerling, D. J. Process-based modeling of silicate mineral weathering responses to increasing atmospheric CO2 and climate change. Glob. Biogeochem. Cycles 23, GB4013 (2009).

    ADS  Google Scholar 

  69. 69.

    Petavratzi, E., Kingman, S. & Lowndes, I. Particulates from mining operations: a review of sources, effects and regulations. Miner. Eng. 18, 1183–1199 (2005).

    CAS  Google Scholar 

  70. 70.

    Cepuritis, R., Garboczi, E. J., Ferraris, C. F., Jacobsen, S. & Sorensen, B. E. Measurement of particle size distribution and specific surface area for crushed concrete aggregate fines. Adv. Powder Technol. 28, 706–720 (2017).

    CAS  Google Scholar 

  71. 71.

    Navarre-Sitchler, A. & Brantley, S. Basalt weathering across scales. Earth Planet. Sci. Lett. 261, 321–334 (2007).

    ADS  CAS  Google Scholar 

  72. 72.

    Brantley, S. L. & Mellott, N. P. Surface area and porosity of primary silicate minerals. Am. Mineral. 85, 1767–1783 (2000).

    ADS  CAS  Google Scholar 

  73. 73.

    Moosdorf, N., Renforth, P. & Hartmann, J. Carbon dioxide efficiency of terrestrial weathering. Environ. Sci. Technol. 48, 4809–4816 (2014).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Salisbury, J. E. et al. Seasonal observations of surface waters in two Gulf of Maine estuary-plume systems: relationships between watershed attributes, optical measurements and surface p CO2. Estuar. Coast. Shelf Sci. 77, 245–252 (2008).

    ADS  Google Scholar 

  75. 75.

    Darling, P. & Society for Mining, Metallurgy and Exploration (U.S.). SME Mining Engineering Handbook 3rd edn (Society for Mining, Metallurgy and Exploration, 2011).

  76. 76.

    InfoMine, Mining Cost Service http://www.infomine.com/ (Infomine, 2009).

  77. 77.

    Tromans, D. Mineral comminution: energy efficiency considerations. Min. Eng. 21, 613–620 (2008).

    CAS  Google Scholar 

  78. 78.

    Hartmann, J. & Moosdorf, N. The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochem. Geophys. Geosyst. 13, Q12004 (2012).

    ADS  Google Scholar 

  79. 79.

    Protected Planet: The World Database on Protected Areas (WDPA)/The Global Database on Protected Areas Management Effectiveness (GD-PAME) https://www.protectedplanet.net/ (UNEP-WCMC and IUCN, 2018).

  80. 80.

    ROTARU, A. S. et al. Modelling a logistic problem by creating an origin-destination cost matrix using GIS technology. Bull. UASVM Horticulture 71, https://doi.org/10.15835/buasvmcn-hort:9697 (2014).

  81. 81.

    Osorio, C. Dynamic origin-destination matrix calibration for large-scale network simulators. Transport. Res. C 98, 186–206 (2019).

    Google Scholar 

  82. 82.

    International Energy Agency The Future of Rail, Opportunities for Energy and the Environment (International Energy Agency, 2019).

  83. 83.

    Liimatainen, H., van Vliet, O. & Aplyn, D. The potential of electric trucks—an international commodity-level analysis. Appl. Energy 236, 804–814 (2019).

    Google Scholar 

  84. 84.

    GDP (current US$) https://data.worldbank.org/indicator/NY.GDP.MKTP.CD (The World Bank, 2016).

  85. 85.

    Bauer, N. et al. Shared socio-economic pathways of the energy sector – quantifying the narratives. Glob. Environ. Change 42, 316–330 (2017).

    Google Scholar 

  86. 86.

    Xi, F. et al. Substantial global carbon uptake by cement carbonation. Nat. Geosci. 9, 880–883 (2016).

    ADS  CAS  Google Scholar 

  87. 87.

    U.S. Geological Survey. Mineral Commodity Summaries 2006 (US Geological Survey, 2006).

Download references


We thank A. Azapagic and J. Shepherd for comments on an earlier draft, and acknowledge discussions with additional members of the Royal Society-Royal Academy of Engineering Greenhouse Gas Removal Working Group. We acknowledge funding of this research with a Leverhulme Research Centre Award (RC-2015-029) from the Leverhulme Trust. We thank L. Taylor for advice and discussions during model development and J. Quirk for data and analysis on plant weathering. P.R. acknowledges UKRI funding under the UK Greenhouse Gas Removal Programme (NE/P019943/1, NE/P019730/1); I.A.J. acknowledges financial support from the Research Council of the University of Antwerp. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling responsible for CMIP and thank the climate modelling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Author information




D.J.B., E.P.K., M.R.L., P.W., S.Q. and S.A.B. designed the study, E.P.K. and M.R.L. undertook model development and coding, with input from P.W., S.A.B., S.Q. and D.J.B. E.P.K. undertook data analysis and synthesis, R.M.E. and L.K. undertook the GIS transport analyses, P.R. did the silicate production modelling, and N.F.P. wrote sections on public perception. J.-F.M., H.P., N.R.E. and P.B.H. provided data on national energy production and sources, and CO2 emissions for both scenarios. M.G.A., R.H.J., C.R.P., M.K., B.S. and I.A.J. all provided input on sections and addition of appropriate references specific to their area of expertise. D.J.B. and S.A.B. wrote the manuscript, with input from J.H.

Corresponding author

Correspondence to David J. Beerling.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Johannes Lehmann, Keith Paustian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 Performance model schematic.

Detailed methods are provided in Methods sections ‘CDR simulation framework’ and ‘Model advances’. Spatially resolved key drivers are mapped in Extended Data Fig. 8; sources given in Supplementary Table 14.

Extended Data Fig. 2 Cumulative energy demand for rock grinding by nation.

Results are shown for the top seven nations of the world (ag), and the top five European nations (hl), as ranked by net CDR capacity, with increasing fractional cropland area of ERW deployment. Curves depict simulations for the BAU and 2 °C energy policy scenarios. The grey-shaded area for each nation represents the 90% confidence interval calculated for basalts with relatively slow- versus fast-weathering rates for the BAU scenario; short green dashed lines indicate the 90% confidence limits of the corresponding 2 °C scenario simulations.

Extended Data Fig. 3 Simulated net CDR with croplands via ERW.

Net rates of CO2 sequestration on croplands (annual and perennial combined) for the four targeted global CDR rates, 0.5 Gt CO2 yr–1, 1.0 Gt CO2 yr–1, 1.5 Gt CO2 yr–1 and 2.0 Gt CO2 yr–1 (Table 1) for the BAU (ad) and the 2 °C (eh) energy policy scenarios.

Extended Data Fig. 4 Schematic overview of the environmental economics model.

Interactions between the performance model, calculating net CDR, and the major components of the environmental economic model. Spatially resolved key drivers are mapped in Extended Data Fig. 9; sources given in Supplementary Table 14. Brown shading denotes inputs; blue shading denotes processes.

Extended Data Fig. 5 Cumulative silicate demand by nation.

Results are shown for the seven nations of the world (ag) and the five European nations (hl) with the highest CDR, as ranked by net CDR capacity, with increasing fractional cropland area deployment of ERW. Note the y-axis scale changes for European countries. Curves are the same irrespective of energy policy scenario.

Extended Data Fig. 6 Secondary CO2 emissions from logistical ERW operations in 2050.

a–d, Results are shown for the seven nations of the world (a, c) and the five European nations (b, d) with the highest CDR potential for the BAU scenario (a, b) and for the 2 °C energy policy scenario (c, d). For each country, from left to right, bars are for fractions of 0.25, 0.5, 0.75 and 1.0 of ERW deployment on croplands. Under the BAU scenario, CO2 emissions from grinding dominate secondary emissions associated with ERW, except for France, where low-carbon nuclear power dominates. Under the 2 °C energy policy scenario (c and d), secondary CO2 emissions generally drop for most nations as they transition to low-carbon energy sources in 2050 and implement negative emissions.

Extended Data Fig. 7 Multi-year performance model simulations of weathering.

Illustrative multi-year simulations of annual basalt application with the performance model showing the effects on soil pH, average efficiency of CDR (RCO2), and soil mineral masses over a 10-year time horizon. ac, pH, RCO2 and mineral mass results for the tholeiitic basalt, respectively. df, pH, RCO2 and mineral mass results for the alkali basalt (Supplementary Tables 13). All simulations used the same p80 particle size (100 µm) and were undertaken at 20 °C. Multi-year simulations capture the effect of basaltic minerals undergoing dissolution at different rates, with some minerals continuing to undergo dissolution and capture CO2 after the first year of application. Such simulations allow average rates of weathering and CDR from repeated basaltic rock dust applications to be computed. Our extended theory underpinning the simulation framework tracks cohorts of particles applied each year and their mineral composition over time to account for cumulative effects (Supplementary Methods).

Extended Data Fig. 8 Spatially resolved drivers of the performance model.

a, Soil temperature from the Hadley Centre coupled Earth System Model (HadGEM) RCP 8.5 simulation for 2050 (the worst-case scenario). b, The HYDE harmonized soil pH database. c, Annual cropland soil water infiltration (irrigation water + precipitation minus evapotranspiration). d, e, Net primary production index for perennial  and annual crops as derived from FAO datasets, respectively. Data sources and spatial resolution are specified in Supplementary Table 14.

Extended Data Fig. 9 Spatially resolved drivers for environmental economics modelling.

a, Industrial diesel prices. b, c, CO2 emissions intensity for the BAU scenario (b) and the 2 °C scenario (c). d, Gross national income per capita. e, Industrial electricity prices. Data sources and spatial resolution are specified in Supplementary Table 14.

Extended Data Fig. 10 Relationship between particle size, surface area and grinding energy.

a, Relationship between particle size and surface area. b, Relationship between surface area and grinding energy. c, Relationship between particle size and grinding energy. p80 is defined as 80% of the particles having a diameter less than or equal to the specified size. Derived from data in ref. 73.

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary Figures S1-S25, Supplementary Tables S1-S15, and Supplementary References.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Beerling, D.J., Kantzas, E.P., Lomas, M.R. et al. Potential for large-scale CO2 removal via enhanced rock weathering with croplands. Nature 583, 242–248 (2020). https://doi.org/10.1038/s41586-020-2448-9

Download citation


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.