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Addressing uncertainty and bias in land use, land use change, and forestry greenhouse gas inventories

Author

Listed:
  • Emily McGlynn

    (University of California, Davis)

  • Serena Li

    (ClimateWorks Foundation)

  • Michael Berger

    (California Environmental Associates)

  • Meredith Amend

    (Industrial Economics, Incorporated)

  • Kandice Harper

    (ClimateWorks Foundation)

Abstract

National greenhouse gas inventories (NGHGIs) will play an increasingly important role in tracking country progress against United Nations (UN) Paris Agreement commitments. Yet uncertainty in land use, land use change, and forestry (LULUCF) NGHGHI estimates may undermine international confidence in emission reduction claims, particularly for countries that expect forests and agriculture to contribute large near-term GHG reductions. In this paper, we propose an analytical framework for implementing the uncertainty provisions of the UN Paris Agreement Enhanced Transparency Framework, with a view to identifying the largest sources of LULUCF NGHGI uncertainty and prioritizing methodological improvements. Using the USA as a case study, we identify and attribute uncertainty across all US NGHGI LULUCF “uncertainty elements” (inputs, parameters, models, and instances of plot-based sampling) and provide GHG flux estimates for omitted inventory categories. The largest sources of uncertainty are distributed across LULUCF inventory categories, underlining the importance of sector-wide analysis: forestry (tree biomass sampling error; tree volume and specific gravity allometric parameters; soil carbon model), cropland and grassland (DayCent model structure and inputs), and settlement (urban tree gross to net carbon sequestration ratio) elements contribute over 90% of uncertainty. Net emissions of 123 MMT CO2e could be omitted from the US NGHGI, including Alaskan grassland and wetland soil carbon stock change (90.4 MMT CO2), urban mineral soil carbon stock change (34.7 MMT CO2), and federal cropland and grassland N2O (21.8 MMT CO2e). We explain how these findings and other ongoing research can support improved LULUCF monitoring and transparency.

Suggested Citation

  • Emily McGlynn & Serena Li & Michael Berger & Meredith Amend & Kandice Harper, 2022. "Addressing uncertainty and bias in land use, land use change, and forestry greenhouse gas inventories," Climatic Change, Springer, vol. 170(1), pages 1-25, January.
  • Handle: RePEc:spr:climat:v:170:y:2022:i:1:d:10.1007_s10584-021-03254-2
    DOI: 10.1007/s10584-021-03254-2
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    References listed on IDEAS

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    1. Giacomo Grassi & Jo House & Werner A. Kurz & Alessandro Cescatti & Richard A. Houghton & Glen P. Peters & Maria J. Sanz & Raul Abad Viñas & Ramdane Alkama & Almut Arneth & Alberte Bondeau & Frank Dent, 2018. "Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks," Nature Climate Change, Nature, vol. 8(10), pages 914-920, October.
    2. Karl-Heinz Erb & Thomas Kastner & Sebastiaan Luyssaert & Richard A. Houghton & Tobias Kuemmerle & Pontus Olofsson & Helmut Haberl, 2013. "Bias in the attribution of forest carbon sinks," Nature Climate Change, Nature, vol. 3(10), pages 854-856, October.
    3. Giacomo Grassi & Jo House & Frank Dentener & Sandro Federici & Michel den Elzen & Jim Penman, 2017. "The key role of forests in meeting climate targets requires science for credible mitigation," Nature Climate Change, Nature, vol. 7(3), pages 220-226, March.
    4. Stephanie Roe & Charlotte Streck & Michael Obersteiner & Stefan Frank & Bronson Griscom & Laurent Drouet & Oliver Fricko & Mykola Gusti & Nancy Harris & Tomoko Hasegawa & Zeke Hausfather & Petr Havlík, 2019. "Contribution of the land sector to a 1.5 °C world," Nature Climate Change, Nature, vol. 9(11), pages 817-828, November.
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    1. Radu Lucian Pânzaru & Daniela Firoiu & George H. Ionescu & Andi Ciobanu & Dragoș Mihai Medelete & Ramona Pîrvu, 2023. "Organic Agriculture in the Context of 2030 Agenda Implementation in European Union Countries," Sustainability, MDPI, vol. 15(13), pages 1-31, July.

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