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Global and regional drivers of land-use emissions in 1961–2017

Abstract

Historically, human uses of land have transformed and fragmented ecosystems1,2, degraded biodiversity3,4, disrupted carbon and nitrogen cycles5,6 and added prodigious quantities of greenhouse gases (GHGs) to the atmosphere7,8. However, in contrast to fossil-fuel carbon dioxide (CO2) emissions, trends and drivers of GHG emissions from land management and land-use change (together referred to as ‘land-use emissions’) have not been as comprehensively and systematically assessed. Here we present country-, process-, GHG- and product-specific inventories of global land-use emissions from 1961 to 2017, we decompose key demographic, economic and technical drivers of emissions and we assess the uncertainties and the sensitivity of results to different accounting assumptions. Despite steady increases in population (+144 per cent) and agricultural production per capita (+58 per cent), as well as smaller increases in emissions per land area used (+8 per cent), decreases in land required per unit of agricultural production (–70 per cent) kept global annual land-use emissions relatively constant at about 11 gigatonnes CO2-equivalent until 2001. After 2001, driven by rising emissions per land area, emissions increased by 2.4 gigatonnes CO2-equivalent per decade to 14.6 gigatonnes CO2-equivalent in 2017 (about 25 per cent of total anthropogenic GHG emissions). Although emissions intensity decreased in all regions, large differences across regions persist over time. The three highest-emitting regions (Latin America, Southeast Asia and sub-Saharan Africa) dominate global emissions growth from 1961 to 2017, driven by rapid and extensive growth of agricultural production and related land-use change. In addition, disproportionate emissions are related to certain products: beef and a few other red meats supply only 1 per cent of calories worldwide, but account for 25 per cent of all land-use emissions. Even where land-use change emissions are negligible or negative, total per capita CO2-equivalent land-use emissions remain near 0.5 tonnes per capita, suggesting the current frontier of mitigation efforts. Our results are consistent with existing knowledge—for example, on the role of population and economic growth and dietary choice—but provide additional insight into regional and sectoral trends.

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Fig. 1: Estimated emissions from land-use change and land management (land-use emissions) over the period 1961–2017.
Fig. 2: Global and regional drivers of land-use emissions in 1961–2017.
Fig. 3: Global distribution of key Pale factors in 2017.
Fig. 4: Intensities of the key Pale factors by country and by product.
Fig. 5: Trends in per capita land-use emissions.

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).

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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.

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Correspondence to Chaopeng Hong or Jennifer A. Burney or Steven J. Davis.

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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.

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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.

ar, Estimated global cumulative land-use emissions attributed to processes (ac) and products (dr) 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 (gi), calories of production (jl), mass of production (mo) and change in mass of production (pr) (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.

Extended Data Fig. 2 Global land-use emissions by product group in 2017, with two accountings of the emissions related to feed crops.

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.

Extended Data Fig. 3 Uncertainties in global and regional land-use GHG emissions over the period 1961–2017 related to emission metrics.

ai, 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.

ai, Curves show trends in agricultural emissions for the global total (a) and for each region (bi), 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.

ai, Curves show trends in land-use change emissions for the global total (a) and for each region (bi), 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.

ai, Curves show trends in land-use emissions for the global total (a) and for each region (bi), 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.

Extended Data Fig. 7 Comparison of global and regional trends in land-use emissions and Pale factors, obtained using different land-use change emission inventories.

ai, 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 (bi), 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.

Extended Data Fig. 8 Global and regional trends in Pale factors contributing to land-use emissions during 1961–2017, decomposed into land-use change emissions and agricultural emissions.

ai, 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 (bi) 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.

ai, Estimated land-use emissions for each region (ai) 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.

ad, 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.

Extended Data Fig. 11 Trends in emissions intensity of major agricultural products.

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). di, 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 (di) 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
Extended Data Table 2 169 agricultural products and their categorization

Supplementary information

Supplementary Table

Supplementary Table 1: The uncertainty ranges for activity data, parameters and emission factors for agricultural processes.

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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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