Meinshausen, M. et al. Realization of Paris Agreement pledges may limit warming just below 2 °C. Nature 604, 304–309 (2022).
Google Scholar
Intergovernmental Panel on Climate Change Climate Change 2022: Mitigation of Climate Change (Cambridge Univ. Press, 2023).
Intergovernmental Panel on Climate Change Global Warming of 1.5°C IPCC Special Report (eds Masson-Delmotte, V. et al.) p. 616 (Cambridge University Press, 2018).
National Academies of Sciences, Engineering, and Medicine Negative Emissions Technologies and Reliable Sequestration: A Research Agenda (The National Academies Press, 2019).
Roe, S. et al. Land-based measures to mitigate climate change: potential and feasibility by country. Glob. Change Biol. 27, 6025–6058 (2021).
Google Scholar
Smith, P. et al. Biophysical and economic limits to negative CO2 emissions. Nat. Clim. Change 6, 42–50 (2016).
Google Scholar
Luo, Y. & Weng, E. Dynamic disequilibrium of the terrestrial carbon cycle under global change. Trends Ecol. Evol. 26, 96–104 (2011).
Google Scholar
Hanssen, S. V. et al. The climate change mitigation potential of bioenergy with carbon capture and storage. Nat. Clim. Change 10, 1023–1029 (2020).
Google Scholar
Zamanian, K., Zhou, J. & Kuzyakov, Y. Soil carbonates: the unaccounted, irrecoverable carbon source. Geoderma 384, 114817 (2021).
Google Scholar
Zeng, N. Carbon sequestration via wood burial. Carbon Balance Manag. 3, 1 (2008).
Google Scholar
Zeng, N. et al. Carbon sequestration via wood harvest and storage: an assessment of its harvest potential. Climatic Change 118, 245–257 (2013).
Google Scholar
Zeng, N. & Hausmann, H. Wood vault: remove atmospheric CO2 with trees, store wood for carbon sequestration for now and as biomass, bioenergy and carbon reserve for the future. Carbon Balance Manag. 17, 2 (2022).
Google Scholar
Zeng, N. et al. 3775-year-old wood burial supports “wood vaulting” as a durable carbon removal method. Science 385, 1454–1459 (2024).
Google Scholar
Krause, A. et al. Quantifying the impacts of land cover change on gross primary productivity globally. Sci. Rep. 12, 18398 (2022).
Google Scholar
Consoli, C. Bioenergy and Carbon Capture and Storage (Global CCS Institute, 2019).
Yablonovitch, E. & Deckman, H. Scalable, economical, and stable sequestration of agricultural fixed carbon. Proc. Natl Acad. Sci. USA 120, e2217695120 (2023).
Google Scholar
Cook, F., Knight, J. & Kelliher, F. Modelling oxygen transport in soil with plant root and microbial oxygen consumption: depth of oxygen penetration. Soil Res. 51, 539–553 (2013).
Google Scholar
Luo, Y. & Zhou, X. Soil Respiration and the Environment (Elsevier Academic Press, 2006).
Yi, L., Zhao, H., Yuan, Z., Zhou, D. & Liang, Y. Depression of soil microbial respiration, ammonification and cellulose degradation under the stress of antibiotic residuals. Environ. Eng. Manag. J. 15, 2189–2194 (2016).
Google Scholar
Poulos, H. G. & Bunce, G. Foundation design for the Burj Dubai–the world’s tallest building. In Sixth International Conference on Case Histories in Geotechnical Engineering 14 (Missouri University of Science and Technology, 2008); https://scholarsmine.mst.edu/icchge/6icchge/session_01/14
Kainiemi, V., Kirchmann, H. & Kätterer, T. Structural disruption of arable soils under laboratory conditions causes minor respiration increases. J. Plant Nutr. Soil Sci. 179, 88–93 (2016).
Google Scholar
Alcántara, V., Don, A., Vesterdal, L., Well, R. & Nieder, R. Stability of buried carbon in deep-ploughed forest and cropland soils – implications for carbon stocks. Sci. Rep. 7, 5511 (2017).
Google Scholar
Luo, Y. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Glob. Biogeochem. Cycles 30, 40–56 (2016).
Google Scholar
Ma, M. Bill Gates-backed startup to use old wood to remove carbon from the air. Bloomberg https://www.bloomberg.com/news/articles/2023-11-13/bill-gates-backed-startup-uses-old-wood-to-remove-carbon-from-air (13 November 2023).
Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6, eaba1981 (2020).
Google Scholar
Converting Waste Agricultural Biomass into a Resource-Compendium of Technologies (United Nations Environment Programme, 2009); https://stg-wedocs.unep.org/handle/20.500.11822/7614
Tripathi, N., Hills, C. D., Singh, R. S. & Atkinson, C. J. Biomass waste utilisation in low-carbon products: harnessing a major potential resource. npj Clim. Atmos. Sci. 2, 35 (2019).
Google Scholar
Peng, L., Searchinger, T. D., Zionts, J. & Waite, R. The carbon costs of global wood harvests. Nature 620, 110–115 (2023).
Google Scholar
Hanssen, S. V. & Huijbregts, M. A. J. Assessing the environmental benefits of utilising residual flows. Resour. Conserv. Recycl. 150, 104433 (2019).
Google Scholar
Liu, T. et al. Diagnosing spatial biases and uncertainties in global fire emissions inventories: Indonesia as regional case study. Remote Sens. Environ. 237, 111557 (2020).
Google Scholar
Kaiser, J. et al. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527–554 (2012).
Google Scholar
Van Der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).
Google Scholar
van Wees, D. et al. Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED). Geosci. Model Dev. 15, 8411–8437 (2022).
Google Scholar
Suppression Costs (National Interagency Fire Center, accessed 24 May 2024); https://www.nifc.gov/fire-information/statistics/suppression-costs/
U.S. Billion-Dollar Weather and Climate Disasters (NOAA National Centers for Environmental Information, accessed 24 May 2024); https://www.ncei.noaa.gov/access/billions/
Nowak, D. J., Greenfield, E. J. & Ash, R. M. Annual biomass loss and potential value of urban tree waste in the United States. Urban For. Urban Green. 46, 126469 (2019).
Google Scholar
Construction and Demolition Debris Recycling (CalRecycle, accessed 24 May 2024); https://calrecycle.ca.gov/condemo/
Moreno-García, M., Repullo-Ruibérriz de Torres, M. A., Carbonell-Bojollo, R. M. & Ordóñez-Fernández, R. Management of pruning residues for soil protection in olive orchards. Land Degrad. Dev. 29, 2975–2984 (2018).
Google Scholar
Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).
Google Scholar
Guerrero-Cruz, S. et al. Methanotrophs: discoveries, environmental relevance, and a perspective on current and future applications. Front. Microbiol. 12, 678057 (2021).
Google Scholar
Gorgolewski, A. S. Methane Fluxes from Living and Dead Trees in a Temperate Forest. PhD thesis, Univ. Toronto (2022).
Gurnell, A. M., Gregory, K. J. & Petts, G. E. The role of coarse woody debris in forest aquatic habitats: implications for management. Aquat. Conserv. 5, 143–166 (1995).
Google Scholar
Wojciech, P., Ewa, B. & Jarosław, L. Soil biochemical properties and stabilisation of soil organic matter in relation to deadwood of different species. FEMS Microbiol. Ecol. 95, fiz011 (2019).
Google Scholar
Seibold, S. et al. The contribution of insects to global forest deadwood decomposition. Nature 597, 77–81 (2021).
Google Scholar
Smith, S. et al. The State of Carbon Dioxide Removal 2nd edn (2024); https://www.stateofcdr.org
Lesiv, M. et al. Global forest management data for 2015 at a 100 m resolution. Sci. Data 9, 199 (2022).
Google Scholar
Global Forest Resources Assessment 2020: Main Report (FAO, 2020).
Martin, A. R., Doraisami, M. & Thomas, S. C. Global patterns in wood carbon concentration across the world’s trees and forests. Nat. Geosci. 11, 915–920 (2018).
Google Scholar
Cown, D. J. in Encyclopedia of Materials: Science and Technology (eds Jürgen Buschow, K. H. et al.) 9620–9622 (Elsevier, 2001).
Wood Density Explained, Plus Wood Density Chart (MT Copeland Technologies, 2020); https://mtcopeland.com/blog/wood-density-explained-plus-wood-density-chart/
Doraisami, M. et al. A global database of woody tissue carbon concentrations. Sci. Data 9, 284 (2022).
Google Scholar
Wang, Y., Law, R. & Pak, B. A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences 7, 2261–2282 (2010).
Google Scholar
Lawrence, D. M. et al. The Community Land Model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).
Google Scholar
Dai, Y. et al. Different representations of canopy structure—a large source of uncertainty in global land surface modeling. Agric. For. Meteorol. 269, 119–135 (2019).
Google Scholar
Dai, Y., Zhang, S., Yuan, H. & Wei, N. Modeling variably saturated flow in stratified soils with explicit tracking of wetting front and water table locations. Water Resour. Res. 55, 7939–7963 (2019).
Google Scholar
Lu, X. et al. Full implementation of matrix approach to biogeochemistry module of CLM5. J. Adv. Model. Earth Syst. 12, e2020MS002105 (2020).
Google Scholar
New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part I: Development of a 1961–90 mean monthly terrestrial climatology. J. Clim. 12, 829–856 (1999).
Google Scholar
New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part II: Development of 1901–96 monthly grids of terrestrial surface climate. J. Clim. 13, 2217–2238 (2000).
Google Scholar
Dirmeyer, P. A. et al. GSWP-2: multimodel analysis and implications for our perception of the land surface. Bull. Am. Meteorol. Soc. 87, 1381–1398 (2006).
Google Scholar
Viovy, N. CRUNCEP Version 7 – Atmospheric Forcing Data for the Community Land Model (Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, accessed 1 November 2024); https://doi.org/10.5065/PZ8F-F017
Lin, S. et al. Estimates of net primary productivity and actual evapotranspiration over the Tibetan Plateau from the Community Land Model version 4.5 with four atmospheric forcing datasets. J. Plant Ecol. 17, rtae052 (2024).
Google Scholar
Kim, H. Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1) (Data Integration and Analysis System, 2017).
O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).
Google Scholar
Luo, Y. et al. Data and code used for the main and extended data figures in ‘Large CO2 removal potential of woody debris preservation in managed forests’. Figshare https://doi.org/10.6084/m9.figshare.28824182.v1 (2025).
Xia, J., Wang, J. & Niu, S. Research challenges and opportunities for using big data in global change biology. Glob. Change Biol. 26, 6040–6061 (2020).
Google Scholar
