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Tropical forest loss enhanced by large-scale land acquisitions

Nature Geoscience volume 13, pages 482–488 (2020)Cite this article

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Abstract

Tropical forests are vital for global biodiversity, carbon storage and local livelihoods, yet they are increasingly under threat from human activities. Large-scale land acquisitions have emerged as an important mechanism linking global resource demands to forests in the Global South, yet their influence on tropical deforestation remains unclear. Here we perform a multicountry assessment of the links between large-scale land acquisitions and tropical forest loss by combining a new georeferenced database of 82,403 individual land deals—covering 15 countries in Latin America, sub-Saharan Africa and Southeast Asia—with data on annual forest cover and loss between 2000 and 2018. We find that land acquisitions cover between 6% and 59% of study-country land area and between 2% and 79% of their forests. Compared with non-investment areas, large-scale land acquisitions were granted in areas of higher forest cover in 11 countries and had higher forest loss in 52% of cases. Oil palm, wood fibre and tree plantations were consistently linked with enhanced forest loss while logging and mining concessions showed a mix of outcomes. Our findings demonstrate that large-scale land acquisitions can lead to elevated deforestation of tropical forests, highlighting the role of local policies in the sustainable management of these ecosystems.

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Fig. 1: Distribution of publicly available LSLAs across Latin America, sub-Saharan Africa and Southeast Asia.
Fig. 2: Share of land, forests and forest loss within investment areas.
Fig. 3: Distribution of LSLAs and influence of rates of forest loss.
Fig. 4: Forest loss in oil palm plantations in Liberia.

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Data availability

The datasets generated and analysed during the current study are publicly available or are available from the corresponding author on reasonable request.

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Acknowledgements

K.F.D. was supported in part by Columbia University’s Data Science Institute. K.F.D, J.D., P.D., M.C.R. and M.T. were partially supported by the National Socio-Environmental Synthesis Center (SESYNC) through NSF grant DBI-1052875. J.D., M.C.R. and P.D. are part of the Marie Skłodowska-Curie Action (MSCA) Innovative Training Network (ITN) grant agreement no. 861509 – NEWAVE.

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Authors and Affiliations

  1. Department of Geography and Spatial Sciences, University of Delaware, Newark, DE, USA

    Kyle Frankel Davis

  2. Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA

    Kyle Frankel Davis

  3. Data Science Institute, Columbia University, New York, NY, USA

    Kyle Frankel Davis

  4. The Earth Institute, Columbia University, New York, NY, USA

    Heejin Irene Koo & Milad Kharratzadeh

  5. Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, Netherlands

    Jampel Dell’Angelo

  6. Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, CA, USA

    Paolo D’Odorico & Mokganedi Tatlhego

  7. Graduate School of Geography, Clark University, Worcester, MA, USA

    Lyndon Estes

  8. Oxford Martin School, University of Oxford, Oxford, UK

    Laura J. Kehoe

  9. The Nature Conservancy, London, UK

    Laura J. Kehoe

  10. Humboldt-University Berlin, Geography Department and Integrative Research Institute for Transformations in Human-Environment Systems (IRI THESys), Berlin, Germany

    Tobias Kuemmerle

  11. Faculty of Agronomy and Forest Engineering, Eduardo Mondlane University, Maputo, Mozambique

    Domingos Machava & Natasha Ribeiro

  12. Universidade Zambeze, Beira, Mozambique

    Aurélio de Jesus Rodrigues Pais

  13. Department of Civil and Environmental Engineering, Politecnico di Milano, Milano, Italy

    Maria Cristina Rulli

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Contributions

K.F.D., J.D., P.D., L.J.K., T.K., N.R., M.C.R. and L.E. designed the research; K.F.D., H.I.K., M.K., D.M. and A.d.J.R.P. performed the research; K.F.D., H.I.K., M.K., M.C.R. and M.T. analysed the data; and K.F.D., J.D., P.D., L.J.K., M.K., T.K., N.R., M.C.R., M.T. and L.E. wrote the paper.

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Correspondence to Kyle Frankel Davis.

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Peer review information Primary Handling Editors: Clare Davis; Xujia Jiang.

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Extended data

Extended Data Fig. 1 Map of study countries.

Land area under contract in these 15 countries currently makes up 51% of the world’s LSLA area for all intended uses8.

Extended Data Fig. 2 Annual rates of forest loss.

Forest loss plots are separated by region between Latin America (a), sub-Saharan Africa (b), and Southeast Asia (c).

Extended Data Fig. 3 Annual rates of deforestation for random pixels within oil palm concessions and ‘matched’ non-investment pixels.

Significant enhancements of forest loss within oil palm concessions were observed in (a) Cameroon, (b) Republic of Congo, (c) Indonesia, (d) Liberia, and (e) Malaysia. Data on contract year (blue histograms) came from the Land Matrix19. For Cameroon, Indonesia, and Malaysia, 1, 1, and 21 oil palm deals, respectively, had reported contract years before 2000 (not shown). Insufficient data on contract year were available for oil palm concessions in Republic of Congo. Y-axes scales vary between panels.

Extended Data Fig. 4 Annual rates of deforestation for random pixels within investments and ‘matched’ non-investment pixels.

Significant enhancements of forest loss within investments were observed in: wood fiber concessions in (a) Republic of Congo, (b) Indonesia, and (c) Malaysia; tree plantations in (d) Indonesia, (e) Liberia, and (f) Malaysia; mining concessions in (g) Brazil, (h) Colombia, and (i) Peru; logging concessions in (j) Central African Republic; and economic land concessions (ELCs) in (k) Cambodia. Data on contract year (blue histograms) came from the Land Matrix19. Insufficient data on contract year were available for panels (a), (c), (e), (h), and (j). Y-axes scales vary between panels.

Extended Data Fig. 5 Contract year of LSLAs in study countries.

Frequency distribution of contract year reported by Land Matrix19 in all countries for (a) all investment types, (b) logging investments, (c) mining investments, (d) oil palm investments, (e) tree plantation investments, and (f) wood fiber investments.

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Davis, K.F., Koo, H.I., Dell’Angelo, J. et al. Tropical forest loss enhanced by large-scale land acquisitions. Nat. Geosci. 13, 482–488 (2020). https://doi.org/10.1038/s41561-020-0592-3

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