Global and regional drivers of land-use emissions in 1961–2017 (original) (raw)

Data availability

The agricultural emissions data are curated by the FAO and freely available from FAOSTAT at http://faostat.fao.org/. Land-use change emissions (BLUE) data and all of our results are available at https://sustsys.ess.uci.edu/CALUE.html and https://doi.org/10.6084/m9.figshare.12248735.

Code availability

Computer codes or algorithms used to generate results that are reported in the paper and central to the main claims are available at https://github.com/ChaopengHong/Land-use_Emissions.

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Acknowledgements

A. LoPresti helped with preliminary analysis; E. Hansis made important contributions to the development of the BLUE model. K. Hartung helped with additional BLUE simulations. We thank A. Jain and D. Goll for providing ISAM and ORCHIDEE-CNP data. We thank R. A. Houghton and A. A. Nassikas for providing data from ref. 29. We thank R. T. Conant for providing crop-specific fertilizer application rates. The manuscript also benefitted from discussions with R. Andrew, L. Chini, H. van Grinsven, K. Hartung, R. A. Houghton, S. Kloster, E. Lambin, D. Lobell, P. Meyfroidt, G. Peters, Y. Qin, T. Raddatz, J. Randerson, M. Raupach, D. Tong and T. West. C.H., J.A.B. and S.J.D. were supported by the US National Science Foundation and US Department of Agriculture (INFEWS grant EAR 1639318). J.P. and J.E.M.S.N. were supported by the German Research Foundation’s Emmy Noether Programme (PO1751/1-1). R.B.J. acknowledges support from the Gordon and Betty Moore Foundation (grant GBMF5439).

Author information

Authors and Affiliations

  1. Department of Earth System Science, University of California, Irvine, Irvine, CA, USA
    Chaopeng Hong & Steven J. Davis
  2. School of Global Policy and Strategy, University of California, San Diego, San Diego, CA, USA
    Jennifer A. Burney
  3. Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
    Julia Pongratz
  4. Department of Land in the Earth System, Max Planck Institute for Meteorology, Hamburg, Germany
    Julia Pongratz & Julia E. M. S. Nabel
  5. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO, USA
    Nathaniel D. Mueller
  6. Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA
    Nathaniel D. Mueller
  7. Department of Earth System Science, Stanford University, Stanford, CA, USA
    Robert B. Jackson
  8. Woods Institute for the Environment, Stanford University, Stanford, CA, USA
    Robert B. Jackson
  9. Precourt Institute for Energy, Stanford University, Stanford, CA, USA
    Robert B. Jackson
  10. Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA, USA
    Steven J. Davis

Authors

  1. Chaopeng Hong
  2. Jennifer A. Burney
  3. Julia Pongratz
  4. Julia E. M. S. Nabel
  5. Nathaniel D. Mueller
  6. Robert B. Jackson
  7. Steven J. Davis

Contributions

S.J.D., C.H., J.A.B. and J.P. conceived the study. C.H., S.J.D. and J.A.B. performed the analyses, with support from J.P. and J.E.M.S.N. on datasets, and from N.D.M and R.B.J. on analytical approaches. C.H. and S.J.D. led the writing with input from all co-authors. All co-authors reviewed and commented on the manuscript.

Corresponding authors

Correspondence toChaopeng Hong, Jennifer A. Burney or Steven J. Davis.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Jan Minx, David Reay, Stefan Wirsenius 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 Differences in global cumulative land-use emissions attributed to processes and products, obtained using different accounting methods.

a–r, Estimated global cumulative land-use emissions attributed to processes (a–c) and products (d–r) over 1961–2017, using different accounting methods to distribute land-use change emissions over time and to products. Our base results (a, d) reflect emissions occurring in each year owing to past changes in land use (legacy emissions; left), calculated using GWP100, and allocated among crops and livestock by land area used. Different methods to distribute land-use change emissions over time have also been evaluated, that is, all future emissions from a change in land use are assigned to the year of the change (committed emissions; middle) and committed emissions amortized uniformly over 20 years (uniformly distributed emissions; right). Different methods to distribute land-use change emissions to products have also been evaluated, that is, allocating among crops and livestock by change in land area (g–i), calories of production (j–l), mass of production (m–o) and change in mass of production (p–r) (see Methods). Error bars denote uncertainty ranges (68% intervals), determined by uncertainties in land-use change emissions and in agricultural emissions, as well as uncertainties in the GWP100 values. Carbon uptake from agriculture abandonment (negative emissions) is not shown.

Our base case allocates the feed crop emissions to the crops themselves (bars). The results of the other accounting—allocating the feed crop emissions to the livestock that consumed the feed—are shown by dots. Numbers are the emissions related to crops fed to livestock (negative values for crops and positive values for livestock) in units of Mt CO2-eq.

a–i, Curves show trends in CO2 (orange), CH4 (green), N2O (purple) and total GHG emissions (black) for the global total (a) and for each region (b–i). For our base case, we aggregate all GHG emissions (that is, CO2, CH4 and N2O) in units of CO2-eq using GWP100 values of CH4 and N2O. Solid curves show trends in CH4, N2O and total GHG emissions calculated using GWP100, with the shading reflecting the range of uncertainty in GWP100 values. To assess the sensitivity of the results to metric choices, we also estimate emissions using the GWP* method. Dashed curves show trends in CH4 and total GHG emissions calculated using GWP* (in units of CO2-e*). For CO2 and N2O, CO2-eq and CO2-e* emissions are identical. The length of CO2-e*emissions records is reduced because interannual variability is smoothed with a 20-year running average.

Extended Data Fig. 4 Comparison of global and regional agricultural emissions between this work, EDGAR and USEPA.

a–i, Curves show trends in agricultural emissions for the global total (a) and for each region (b–i), estimated in this work (green; based on FAO data), by EDGAR (orange) and by USEPA (blue). All estimated emissions are converted into CO2-equivalents, based on the same GWP100 values from the IPCC Fifth Assessment Report (34 for CH4 and 298 for N2O). The shaded areas reflect the range of uncertainty in agricultural emissions in this work, determined by Monte Carlo analysis (performed by varying activity data, parameter values and emission factors from those used in the FAO database).

Extended Data Fig. 5 Comparison of global and regional land-use change emissions between two bookkeeping models.

a–i, Curves show trends in land-use change emissions for the global total (a) and for each region (b–i), estimated by BLUE (black; used in this work) and H&N (orange; available until 2015 only). The average of the bookkeeping models (green line) is also shown. The range from the uncertainty simulations with BLUE is shown as the 68% uncertainty range of our estimates (grey areas), with the five additional simulations using different assumptions indicated by thin lines: different assumptions on land-use transitions (purple) and different assumptions on carbon values (green).

Extended Data Fig. 6 Comparison of global and regional land-use emissions, obtained using different land-use change emissions from two bookkeeping models.

a–i, Curves show trends in land-use emissions for the global total (a) and for each region (b–i), obtained using land-use change emissions from BLUE (black; used in this work) and H&N (orange; available until 2015 only). The average of the two bookkeeping models (green line) is also shown. In this work, we combined agricultural emissions from the FAO with land-use change emissions estimated by the BLUE model to calculate total land-use emissions. We performed sensitivity analyses by also combining the agricultural emissions from the FAO with the land-use change emissions estimated by another bookkeeping model (H&N) and with the average of two bookkeeping models (BLUE and H&N). Lighter grey areas represent uncertainty ranges (68% intervals) of our estimates, determined by uncertainties in land-use change emissions and in agricultural emissions, as well as uncertainties in the GWP100 values. Darker grey areas show uncertainties only related to land-use change emissions, determined from additional simulations with the BLUE model.

a–i, Curves show trends in land-use emissions (black), emissions intensity of land use (orange) and agricultural production (red) for the global total (a) and for each region (b–i), using land-use change emissions from BLUE (solid lines; used in this work) and the combination of two bookkeeping models (dashed lines; available until 2015 only). In this work, we combined agricultural emissions from the FAO with land-use change emissions estimated by the BLUE model to perform Pale analysis of land-use emissions. We also performed an additional Pale analysis by using agricultural emissions from the FAO combined with the average of land-use change emissions from two bookkeeping models (BLUE and H&N). Lighter grey areas represent uncertainty ranges (68% intervals) of our estimates, determined by uncertainties in land-use change emissions and in agricultural emissions, as well as uncertainties in the GWP100 values. Darker grey areas show uncertainties related only to land-use change emissions, determined from additional simulations with the BLUE model.

a–i, Curves show changes in the Pale factors contributing to land-use change (LUC, solid lines) emissions and agricultural (Ag, dashed lines) emissions over the period 1961–2017 for the global total (a) and for each region (b–i) relative to 1961. Results shown are for our base assumptions (see Extended Data Fig. 1), and different curves are labelled in a. Oceania is not shown.

Extended Data Fig. 9 Estimated land-use emissions over the period 1961–2017 for nine world regions.

a–i, Estimated land-use emissions for each region (a–i) by process, product group and GHG emitted. In each panel, net emissions are shown by the bold black line.

Extended Data Fig. 10 Changes in 2007–2017 in Pale factors of the 50 country–product sources with the largest annual emissions.

a–d, Bars show the per cent change in annual land-use emissions (a), per capita production (b), land intensity of production (c) and emissions intensity of land use (d) for each country–product combination.

a–c, Curves show trends in emissions intensity of processes including CH4 emissions from rice (a) and dairy cattle (b) production and N2O emissions from fertilizer use for rice production (c). d–i, Curves show trends in emissions intensity of major agricultural products including rice (d), maize (e), soybeans (f), oil palm (g), dairy cattle (h) and sheep and goats (i). Total emissions per calorie of agricultural production (d–i) in the six countries that produce the most of each product tend to decrease over time, but in all cases remain greater than zero.

Extended Data Table 1 Data sources used for the study

Full size table

Extended Data Table 2 169 agricultural products and their categorization

Full size table

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Hong, C., Burney, J.A., Pongratz, J. et al. Global and regional drivers of land-use emissions in 1961–2017.Nature 589, 554–561 (2021). https://doi.org/10.1038/s41586-020-03138-y

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