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  • Perspective
  • Published:

Differences and uncertainties in land-use CO2 flux estimates

Nature Reviews Earth & Environment volume 6, pages 747–766 (2025)Cite this article

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Abstract

Accurately estimating carbon dioxide (CO2) fluxes from land use and land-use change (FLUC) is critical to assessing nationally determined contributions and progress towards climate targets. In this Perspective, we compare five FLUC estimation approaches, discuss the origins of large uncertainties and discrepancies in estimates and consider how to improve estimate accuracy and better align individual estimates. Global FLUC estimates between 2000 and 2023 range from net emissions of 1.9 ± 0.6 PgC yr−1 (based on dynamic global vegetation models) to net removals of −1.0 PgC yr−1 (based on Earth observations), with other estimates from bookkeeping models, country reports and atmospheric inversions falling within this range. Discrepancies arise from each approach using different definitions for FLUC, the spatial extent of managed land and including degradation and environmental effects to varying degrees. As a result, each approach accounts for different fluxes and land areas. Uncertainties within individual estimates are attributed to quality of land-use data, observational constraints and incomplete process consideration. These uncertainties can be reduced through better separation of anthropogenic and natural CO2 fluxes, including the effects of anthropogenically driven ecosystem degradation and improving model parameterizations. Thus, future research should prioritise unambiguous and consistent definitions and conducting systematic evaluations against each other to improve the translation and harmonization of FLUC estimates, which is essential to support effective climate policies and optimize land-based climate change mitigation.

Key points

  • Bookkeeping model estimates of CO2 fluxes from land use and land-use change (FLUC), which average 1.3 (0.8–1.5) PgC yr−1 between 2000 and 2023, can be improved by better accounting for environmental effects and spatial heterogeneity in carbon densities and comprehensively including land management and anthropogenic degradation.

  • FLUC estimates from dynamic global vegetation models (1.9 ± 0.6 PgC yr−1, 2000-2023) can be improved by excluding unrealized carbon uptake in cleared forests, implementing ecosystem demography and providing CO2 flux estimates for specific drivers.

  • National Greenhouse Gas Inventory FLUC estimates (−0.9 ± 0.2 PgC yr−1, 2000–2023) can be improved by disaggregating specific management activities and comprehensively including fluxes from all land uses and carbon pools, including soils. Enhanced support for the compilation of inventories in resource-constrained countries is also needed.

  • Earth observation approaches (with FLUC estimates ranging between −1.0 PgC yr−1 in 2001–2023 and 1.1 PgC yr−1 in 2000–2015) can be improved through enhanced separation of natural and anthropogenic CO2 fluxes and improved representation of forest degradation and regrowth via long-term time series of biomass and land cover.

  • FLUC estimates from atmospheric inversions (−0.7 ± 0.3 PgC yr−1, 2000–2023) can be improved through expanding in situ and satellite observations of atmospheric CO2, further developing atmospheric transport models and enhancing the spatial resolutions of inversion systems.

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Fig. 1: Global FLUC estimates from different assessment approaches.
Fig. 2: Summary of land use and land-use change flux assessment approaches.
Fig. 3: Factors contributing to uncertainty and differences in FLUC estimates.
Fig. 4: Priority improvements to reduce uncertainties in FLUC estimation approaches.
Fig. 5: FLUC estimates for the period 2000–2023.

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Acknowledgements

We acknowledge the use of resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bm0891. In addition, I.T.L. thanks the Netherlands Organisation for Scientific Research (VI.Vidi.213.143). P.C. thanks the European Space Agency in the Climate Space Program funded RECCAP2 project and the French national research agency (ANR) funded CLAND Convergence Institute and the AI4FOREST projects. V.H. thanks the European Union (grant agreement no. 101059548) funded CGIAR MITIGATE+ project, the WRI Land and Carbon Lab and the Open EarthMonitor Project. We thank M. Urbazaev, B. Brede and M. Herold (GFZ Helmholtz Centre for Geosciences) for providing feedback on an earlier version. V.H. acknowledges the ongoing honorary status at the University of Bristol, UK. The views expressed are those of the authors and might not in any circumstances be regarded as stating an official position of the European Commission.

Author information

Author notes

  1. These authors contributed equally: Wolfgang A. Obermeier, Clemens Schwingshackl.

Authors and Affiliations

  1. Department of Geography, Ludwig-Maximilians-Universität München, München, Germany

    Wolfgang A. Obermeier, Clemens Schwingshackl, Raphael Ganzenmüller & Julia Pongratz

  2. European Commission, Joint Research Centre, Ispra, Italy

    Giacomo Grassi

  3. Remote Sensing and Geoinformatics, German Research Centre for Geosciences, Potsdam, Germany

    Viola Heinrich

  4. School of Geographical Sciences, University of Bristol, Bristol, UK

    Viola Heinrich

  5. Meteorology and Air Quality Department, Wageningen University and Research, Wageningen, The Netherlands

    Ingrid T. Luijkx

  6. Institute for Earth System Science and Remote Sensing, Leipzig University, Leipzig, Germany

    Ana Bastos

  7. Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France

    Philippe Ciais

  8. Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

    Stephen Sitch

  9. Max Planck Institute for Meteorology, Hamburg, Germany

    Julia Pongratz

Authors
  1. Wolfgang A. Obermeier
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  2. Clemens Schwingshackl
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  3. Raphael Ganzenmüller
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  4. Giacomo Grassi
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  5. Viola Heinrich
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  6. Ingrid T. Luijkx
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  7. Ana Bastos
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  8. Philippe Ciais
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  9. Stephen Sitch
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  10. Julia Pongratz
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Contributions

W.A.O. and C.S. contributed equally to writing and data synthesis. All authors reviewed and edited the manuscript.

Corresponding authors

Correspondence to Wolfgang A. Obermeier or Clemens Schwingshackl.

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Competing interests

The authors declare no competing interests.

Peer review

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Nature Reviews Earth & Environment thanks Xin Zhao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

LULUCF data hub: https://forest-observatory.ec.europa.eu/carbon/fluxes

Supplementary information

Glossary

Afforestation

Forest establishment on land that has been treeless for a long time period (≥50 years treeless according to IPCC and FAO).

Bottom-up approaches

Methods for estimating FLUC on the basis of process-based modelling or using land-use activity data combined with empirical estimates of carbon stock changes.

Carbon dioxide emissions from land use and land-use change (E LUC)

A synonym for FLUC used in the context of bookkeeping models and highlighting that global FLUC are emissions if considering the bookkeeping model definition of FLUC.

Carbon dioxide fluxes from land use and land-use change

(FLUC). Net sum of carbon dioxide emissions and removals from land use and land-use change, with fluxes from land use and land-use change, from land-use change and forestry and from land use and land-cover change, sometimes used as synonyms.

Environmental contributions to F LUC

FLUC changes due to altered environmental conditions (increasing atmospheric carbon dioxide concentration, climate change and nitrogen availability) relative to preindustrial environmental conditions.

Global Carbon Budget

(GCB). Annually updated and peer-reviewed assessment of the global carbon budget produced by the Global Carbon Project with the support of more than 100 researchers from 70 organizations in 18 countries.

Land cover and land-cover change

(LCLCC). All types of land cover and land-cover change with no distinction between anthropogenic and natural drivers, thus differing from land use and land-use change, which only considers anthropogenic activities.

Land use and land-use change

(LULUC). All types of land use and land-use change, that is, anthropogenic activities on land, including land management, often also called land-use change.

Land-use activity

Any human-induced activities on land comprising reforestation, afforestation, deforestation, forest management, cropland management, pasture management and wetland management.

Land-use change

(LUC). All types of land use and land-use change, often also called land use and land-use change.

Land-use transition

The conversion from one land-use state to another (for example, forest to cropland).

Loss of additional sink capacity

(LASC). Carbon dioxide fluxes in response to environmental changes interacting with land-use change comprising replaced sinks and sources and environmental contributions to FLUC.

Nationally determined contributions

National climate action plans for mitigating greenhouse gas emissions and adapting to climate impacts, as required by the Paris Agreement.

Reforestation

Forest establishment on land that has temporarily been treeless.

Replaced sinks and sources

(RSS). Part of the loss of additional sink capacity that refers to replaced (lost) carbon dioxide sinks due to anthropogenic land-use changes like deforestation and gained sinks (replaced sources) from land-use changes like afforestation.

Subgrid-scale transitions

Land-use transitions that cover only a fraction of a grid cell, mostly related to deforestation and subsequent regrowth in shifting cultivation cycles.

Top-down approaches

Methods for estimating FLUC using atmospheric transport models to estimate land–atmosphere carbon dioxide fluxes that best fit the observed atmospheric carbon dioxide concentrations.

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Obermeier, W.A., Schwingshackl, C., Ganzenmüller, R. et al. Differences and uncertainties in land-use CO2 flux estimates. Nat Rev Earth Environ 6, 747–766 (2025). https://doi.org/10.1038/s43017-025-00730-6

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