Letter | Published:

Assessing the efficiency of changes in land use for mitigating climate change

Naturevolume 564pages249253 (2018) | Download Citation


Land-use changes are critical for climate policy because native vegetation and soils store abundant carbon and their losses from agricultural expansion, together with emissions from agricultural production, contribute about 20 to 25 per cent of greenhouse gas emissions1,2. Most climate strategies require maintaining or increasing land-based carbon3 while meeting food demands, which are expected to grow by more than 50 per cent by 20501,2,3,,2,4. A finite global land area implies that fulfilling these strategies requires increasing global land-use efficiency of both storing carbon and producing food. Yet measuring the efficiency of land-use changes from the perspective of greenhouse gas emissions is challenging, particularly when land outputs change, for example, from one food to another or from food to carbon storage in forests. Intuitively, if a hectare of land produces maize well and forest poorly, maize should be the more efficient use of land, and vice versa. However, quantifying this difference and the yields at which the balance changes requires a common metric that factors in different outputs, emissions from different agricultural inputs (such as fertilizer) and the different productive potentials of land due to physical factors such as rainfall or soils. Here we propose a carbon benefits index that measures how changes in the output types, output quantities and production processes of a hectare of land contribute to the global capacity to store carbon and to reduce total greenhouse gas emissions. This index does not evaluate biodiversity or other ecosystem values, which must be analysed separately. We apply the index to a range of land-use and consumption choices relevant to climate policy, such as reforesting pastures, biofuel production and diet changes. We find that these choices can have much greater implications for the climate than previously understood because standard methods for evaluating the effects of land use4,5,6,7,8,9,10,11 on greenhouse gas emissions systematically underestimate the opportunity of land to store carbon if it is not used for agriculture.

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Data availability

LPJmL modelling results, in the form of global carbon and native net primary productivity maps, are available at https://doi.org/10.1594/PANGAEA.893761. The different datasets used to run LPJmL for this study are publicly available and described in Supplementary Information along with links. Any other materials generated for this study are available from the corresponding author on reasonable request.

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Change history

  • 10 January 2019

    In this Letter, the PANGAEA repository was referred to incorrectly in the ‘Code availability’ and ‘Data availability’ sections of Methods: the link should be https://doi.org/10.1594/PANGAEA.893761 instead of https://doi.org/10.1594/PANGAEA.877266. In addition, the sentence, “However, the more commonly used system 2 (75 kg ha−1 yr−1) generates roughly the same benefits as system 1…” should read, “However, the more commonly used system 2 (75 kg ha−1 yr−1) generates roughly the same benefits as sugarcane ethanol…” These errors have been corrected in the online versions of the Letter.


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We thank the David and Lucille Packard Foundation and the Norwegian Agency for Development Cooperation for financial support. We thank L. Germer for work on programming the Carbon Benefits Calculator, and J. Moretti and C. Klirs for help with graphics. We thank R. Boddey and A. Cardoso for additional data, advice and citations necessary for the Brazil examples. We thank R. Conant, K. Erb and H. Haberl for contributions to early thinking for this project, and C. Malins, S. Yeh and M. O’Hare for comments on early drafts.

Reviewer information

Nature thanks L. Firbank and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information


  1. Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA

    • Timothy D. Searchinger
  2. World Resources Institute, Washington, DC, USA

    • Timothy D. Searchinger
  3. Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden

    • Stefan Wirsenius
  4. Integrative Research Institute on Transformations of Human Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Berlin, Germany

    • Tim Beringer
  5. Centre International de Recherche sur l’Environnement et le Développement (CIRED), Montpelier, France

    • Patrice Dumas
  6. Centre de coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Montpelier, France

    • Patrice Dumas


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All authors contributed to all features of the paper. In addition, T.D.S. conceived the idea for the research and wrote the paper. S.W. programmed our spreadsheet model. T.B. performed global vegetation modelling and spatial analysis. P.D. estimated product-specific PEMs.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Timothy D. Searchinger.

Extended data figures and tables

  1. Extended Data Fig. 1 Carbon benefits of different crop production systems based on the carbon benefits index.

    Error bars reflect the range of literature estimates of vegetation and soil carbon stocks. Source data

  2. Extended Data Fig. 2 Carbon benefits of different potential Iowa cropland uses based on the carbon benefits index.

    Error bars reflect the range of literature estimates of vegetation and soil carbon stocks. Source data

  3. Extended Data Fig. 3 Above- and below-ground carbon stocks of potential natural vegetation under current climate, used to derive COCs with the carbon loss method.

    Data simulated with the LPJmL model and adjusted at the biome level according to reference values from the literature (see Supplementary Information).

  4. Extended Data Fig. 4 Soil carbon stocks of potential natural vegetation under current climate used to derive COCs with carbon loss method.

    Data simulated with LPJmL and adjusted at the biome level according to reference values from the literature (see Supplementary Information).

  5. Extended Data Fig. 5 Annual net primary productivity of potential native vegetation under current climate used to derive COCs with carbon gain method.

    Data simulated with LPJmL.

  6. Extended Data Table 1 Global-average COCs, PEMS and GHGs for a selection of food, feed and fibre items, calculated using the carbon loss method and 4% time discounting
  7. Extended Data Table 2 Global-average COCs, PEMS and GHGs for a selection of food, feed and fibre items, calculated using the carbon loss method and 4% time discounting (continued from Extended Data Table 1)
  8. Extended Data Table 3 Consumption GHG costs for a selection of biofuels

Supplementary information

  1. Supplementary Information

    This document explains the basic concepts behind the carbon benefits index and its components, and includes Supplementary Fig. 1 and Supplementary Tables 1–9. It provides sensitivity calculations and describes the sources for information presented in the examples in the main manuscript.

  2. Supplementary Data

    This file contains the Carbon Benefits Calculator, which allows users to calculate the carbon benefits of a farm or hectare of land under existing and proposed new uses or management. Users may use various default values or provide site-specific information.

  3. Supplementary Data

    Zipped file containing ESRI ASCII files in geographic coordinates (WGS-84) for the maps shown in Extended Data Fig. 3, which shows estimated carbon stocks of native vegetation under current climate simulated with LPJmL and adjusted at the biome level according to literature reference values.

  4. Supplementary Data

    Zipped file containing ESRI ASCII files in geographic coordinates (WGS-84) for the map shown in Extended Data Fig. 4, which shows soil carbon stocks of native vegetation under current climate simulated with LPJmL.

  5. Supplementary Data

    Source Data for Supplementary Fig. 1.

Source data

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