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

Nature volume 583, pages 242–248 (2020)Cite this article

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Abstract

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.

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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.

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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/SspDb. Source 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.

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Acknowledgements

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.

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Authors and Affiliations

  1. Leverhulme Centre for Climate Change Mitigation, Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK

    David J. Beerling, Euripides P. Kantzas, Mark R. Lomas & Peter Wade

  2. Advanced Resource Efficiency Centre, Management School, University of Sheffield, Sheffield, UK

    Rafael M. Eufrasio & Lenny Koh

  3. School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK

    Phil Renforth

  4. Lancaster Environment Centre, Lancaster University, Lancaster, UK

    Binoy Sarkar

  5. School of Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UK

    M. Grace Andrews & Rachael H. James

  6. National Oceanography Centre, Southampton, UK

    Christopher R. Pearce

  7. Global Systems Institute, Department of Geography, University of Exeter, Exeter, UK

    Jean-Francois Mercure

  8. Cambridge Centre for Energy, Environment and Natural Resource Governance, University of Cambridge, Cambridge, UK

    Jean-Francois Mercure, Hector Pollitt & Neil R. Edwards

  9. Cambridge Econometrics, Cambridge, UK

    Hector Pollitt

  10. Environment, Earth and Ecosystems, The Open University, Milton Keynes, UK

    Philip B. Holden & Neil R. Edwards

  11. Department of Agricultural and Consumer Economics, Institute for Sustainability, Energy, and Environment, University of Illinois, Urbana, IL, USA

    Madhu Khanna

  12. School of Mathematics and Statistics, Hicks Building, University of Sheffield, Sheffield, UK

    Shaun Quegan

  13. Understanding Risk Research Group, School of Psychology, Cardiff University, Cardiff, UK

    Nick F. Pidgeon

  14. Research Group Plants and Ecosystems, University of Antwerp, Antwerp, Belgium

    Ivan A. Janssens

  15. Earth Institute, Columbia University, New York, NY, USA

    James Hansen

  16. Global Food and Environment Institute, University of Leeds, Leeds, UK

    Steven A. Banwart

  17. School of Earth and Environment, University of Leeds, Leeds, UK

    Steven A. Banwart

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  1. David J. Beerling
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Contributions

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.

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The authors declare no competing interests.

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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.

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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 (a–g), and the top five European nations (h–l), 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 (a–d) and the 2 °C (e–h) 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 (a–g) and the five European nations (h–l) 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. a–c, pH, RCO2 and mineral mass results for the tholeiitic basalt, respectively. d–f, pH, RCO2 and mineral mass results for the alkali basalt (Supplementary Tables 1–3). 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.

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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

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