Mapping carbon accumulation potential from global natural forest regrowth

Abstract

To constrain global warming, we must strongly curtail greenhouse gas emissions and capture excess atmospheric carbon dioxide1,2. Regrowing natural forests is a prominent strategy for capturing additional carbon3, but accurate assessments of its potential are limited by uncertainty and variability in carbon accumulation rates2,3. To assess why and where rates differ, here we compile 13,112 georeferenced measurements of carbon accumulation. Climatic factors explain variation in rates better than land-use history, so we combine the field measurements with 66 environmental covariate layers to create a global, one-kilometre-resolution map of potential aboveground carbon accumulation rates for the first 30 years of natural forest regrowth. This map shows over 100-fold variation in rates across the globe, and indicates that default rates from the Intergovernmental Panel on Climate Change (IPCC)4,5 may underestimate aboveground carbon accumulation rates by 32 per cent on average and do not capture eight-fold variation within ecozones. Conversely, we conclude that maximum climate mitigation potential from natural forest regrowth is 11 per cent lower than previously reported3 owing to the use of overly high rates for the location of potential new forest. Although our data compilation includes more studies and sites than previous efforts, our results depend on data availability, which is concentrated in ten countries, and data quality, which varies across studies. However, the plots cover most of the environmental conditions across the areas for which we predicted carbon accumulation rates (except for northern Africa and northeast Asia). We therefore provide a robust and globally consistent tool for assessing natural forest regrowth as a climate mitigation strategy.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Variation in carbon accumulation among biomes and previous land use/disturbance.
Fig. 2: Mapping carbon accumulation potential.
Fig. 3: Predicted rates compared to IPCC defaults.

Data availability

The literature-based dataset (both raw and filtered) and detailed descriptions of the environmental covariates are all available at https://github.com/forc-db/groa, where GROA stands for Global Restoration Opportunity Assessment. Data are also archived on Zenodo at https://doi.org/10.5281/zenodo.3983644). The Supplementary Information includes metadata for the literature-derived dataset (Supplementary Table S3, Supplementary sections 4 and 5). We also include data on country-level estimates (see Supplementary Data 1). Spatial data for both aboveground carbon accumulation rates and uncertainty (scaled and unscaled by mean pixel value), as well as belowground carbon accumulation rates can be downloaded from Global Forest Watch (http://www.globalforestwatch.org). S.C.C.-P. and N.H. welcome discussions around potential collaborations, and the data are freely available. Source data are provided with this paper.

Code availability

We include code for constructing the global maps and assessing uncertainty at https://github.com/forc-db/groa.

References

  1. 1.

    Rogelj, J. et al. Paris Agreement climate proposals need boost to keep warming well below 2 °C. Nat. Clim. Chang. 534, 631–639 (2016).

    CAS  Google Scholar 

  2. 2.

    Masson-Delmotte, V. et al. (eds) Global Warming of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (IPCC, 2018).

  3. 3.

    Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).

    ADS  CAS  PubMed  Google Scholar 

  4. 4.

    Requena Suarez, D. et al. Estimating aboveground net biomass change for tropical and subtropical forests: refinement of IPCC default rates using forest plot data. Glob. Chang. Biol. 25, 3609–3624 (2019).

  5. 5.

    Dong, H., MacDonald, J. D., Ogle, S. M., Sanz Sanchez, M. J. & Rocha, M. T. (eds) 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use (IPCC, 2019).

  6. 6.

    Grassi, G. et al. The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Chang. 7, 220–226 (2017).

    ADS  Google Scholar 

  7. 7.

    International Union for Conservation of Nature infoFLR https://infoflr.org/ (IUCN, accessed 20 June 2018).

  8. 8.

    Lamb, D., Erskine, P. D. & Parrotta, J. A. Restoration of degraded tropical forest landscapes. Science 310, 1628–1632 (2005).

    ADS  CAS  PubMed  Google Scholar 

  9. 9.

    Seddon, N. et al. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Phil. Trans. R. Soc. Lond. B 375, 20190120 (2020).

    Google Scholar 

  10. 10.

    Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).

    ADS  CAS  PubMed  Google Scholar 

  12. 12.

    Lewis, S., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Regenerate natural forests to store carbon. Nature 568, 25–28 (2019).

    ADS  CAS  PubMed  Google Scholar 

  13. 13.

    Romijn, E. et al. Assessing change in national forest monitoring capacities of 99 tropical countries. For. Ecol. Manage. 352, 109–123 (2015).

    Google Scholar 

  14. 14.

    United Nations Adoption of the Paris Agreement https://unfccc.int/files/essential_background/convention/application/pdf/english_paris_agreement.pdf (UN, 2015).

  15. 15.

    Holl, K. D. & Brancalion, P. S. Tree planting is not a simple solution. Science 368, 580–582 (2020).

    ADS  CAS  PubMed  Google Scholar 

  16. 16.

    Gilroy, J. J. et al. Cheap carbon and biodiversity co-benefits from forest regeneration in a hotspot of endemism. Nat. Clim. Chang. 4, 503–507 (2014).

    ADS  Google Scholar 

  17. 17.

    Chazdon, R. L. Landscape restoration, natural regeneration, and the forests of the future. Ann. Missouri Botan. Gardens 102, 251–257 (2017).

    Google Scholar 

  18. 18.

    Veldman, J. W. et al. Tyranny of trees in grassy biomes. Science 347, 484–485 (2014).

    ADS  Google Scholar 

  19. 19.

    Meli, P. et al. A global review of past land use, climate, and active vs. passive restoration effects on forest recovery. PLoS One 12, e0171368 (2017).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Crouzeilles, R. et al. Ecological restoration success is higher for natural regeneration than for active restoration in tropical forests. Sci. Adv. 3, e1701345 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Jones, H. P. et al. Restoration and repair of Earth’s damaged ecosystems. Proc. R. Soc. Lond. B 285, 20172577 (2018).

    Google Scholar 

  22. 22.

    Shimamoto, C. Y., Padial, A. A., Da Rosa, C. M. & Marques, M. C. M. Restoration of ecosystem services in tropical forests: a global meta-analysis. PLoS One 13, e0208523 (2018).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Reid, J. L., Fagan, M. E. & Zahawi, R. A. Positive site selection bias in meta-analyses comparing natural regeneration to active forest restoration. Sci. Adv. 4, eaas9143 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Betts, R. A. Climate science: afforestation cools more or less. Nat. Geosci. 4, 504–505 (2011).

    ADS  CAS  Google Scholar 

  25. 25.

    Nave, L. E. et al. Reforestation can sequester two petagrams of carbon in US topsoils in a century. Proc. Natl Acad. Sci. USA 115, 2776–2781 (2018).

    CAS  PubMed  Google Scholar 

  26. 26.

    Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. (eds) The Global Assessment Report on Biodiversity and Ecosystem Services https://ipbes.net/global-assessment (IPBES, 2019).

  27. 27.

    Bonner, M. T. L., Schmidt, S. & Shoo, L. P. A meta-analytical global comparison of aboveground biomass accumulation between tropical secondary forests and monoculture plantations. For. Ecol. Manage. 291, 73–86 (2013).

    Google Scholar 

  28. 28.

    Tuomisto, H. L., Ellis, M. J. & Haastrup, P. Environmental impacts of cultured meat production. Environ. Sci. Technol. 45, 6117–6123 (2014).

    Google Scholar 

  29. 29.

    Arneth, A. et al. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, And Greenhouse Gas Fluxes In Terrestrial Ecosystems https://www.ipcc.ch/srccl/ (IPCC, 2019).

  30. 30.

    Griscom, B. W. et al. We need both natural and energy solutions to stabilize our climate. Glob. Change Biol. 25, 1889–1890 (2019).

    ADS  Google Scholar 

  31. 31.

    Field, C. B. & Mach, K. J. Rightsizing carbon dioxide removal. Science 356, 706–707 (2017).

    ADS  CAS  PubMed  Google Scholar 

  32. 32.

    Goldstein, A. et al. Protecting irrecoverable carbon in Earth’s ecosystems. Nat. Clim. Chang. 10, 287–295 (2020).

    ADS  CAS  Google Scholar 

  33. 33.

    Erb, K.-H. et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553, 73–76 (2018).

    ADS  CAS  PubMed  Google Scholar 

  34. 34.

    Paul, K. I. & Roxburgh, S. H. Predicting carbon sequestration of woody biomass following land restoration. For. Ecol. Manage. 460, 117838 (2020).

    Google Scholar 

  35. 35.

    Anderson-Teixeira, K. J. et al. ForC: a global database of forest carbon stocks and fluxes. Ecology 99, 1507 (2018).

    PubMed  Google Scholar 

  36. 36.

    Powers, J. S., Corre, M. D., Twine, T. E. & Veldkamp, E. Geographic bias of field observations of soil carbon stocks with tropical land-use changes precludes spatial extrapolation. Proc. Natl Acad. Sci. USA 108, 6318–6322 (2011).

    ADS  CAS  PubMed  Google Scholar 

  37. 37.

    Stocker, T.F. et al (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2013).

  38. 38.

    Zahawi, R. a., Holl, K. D., Cole, R. J. & Reid, J. L. Testing applied nucleation as a strategy to facilitate tropical forest recovery. J. Appl. Ecol. 50, 88–96 (2013).

    Google Scholar 

  39. 39.

    Ashton, M. S. et al. Restoration of rain forest beneath pine plantations: a relay floristic model with special application to tropical South Asia. For. Ecol. Manage. 329, 351–359 (2014).

    Google Scholar 

  40. 40.

    Teixeira, A. M. G., Soares-Filho, B. S., Freitas, S. R. & Metzger, J. P. Modeling landscape dynamics in an Atlantic rainforest region: implications for conservation. For. Ecol. Manage. 257, 1219–1230 (2009).

    Google Scholar 

  41. 41.

    Sloan, S., Goosem, M. & Laurance, S. G. Tropical forest regeneration following land abandonment is driven by primary rainforest distribution in an old pastoral region. Landsc. Ecol. 31, 601–618 (2016).

    Google Scholar 

  42. 42.

    Chazdon, R. L. Second Growth: The Promise of Tropical Forest Regeneration in an Age of Deforestation (Univ. of Chicago Press, 2014).

  43. 43.

    Speed, J. D. M., Martinsen, V., Mysterud, A., Holand, O. & Austrheim, G. Long-term increase in aboveground carbon stocks following exclusion of grazers and forest establishment in an alpine ecosystem. Ecosystems 17, 1138–1150 (2014).

    CAS  Google Scholar 

  44. 44.

    Reid, J. L. et al. How long do restored ecosystems persist? Ann. Missouri Botan. Gardens 102, 258–265 (2017).

    Google Scholar 

  45. 45.

    Paquette, A. & Messier, C. The role of plantations in managing the world’s forests in the Anthropocene. Front. Ecol. Environ. 8, 27–34 (2010).

    Google Scholar 

  46. 46.

    Smyth, C. E. et al. Quantifying the biophysical climate change mitigation potential of Canada’s forest sector. Biogeosciences 11, 3515–3529 (2014).

    ADS  Google Scholar 

  47. 47.

    Cao, S. Why large-scale afforestation efforts in China have failed to solve the desertification problem. Environ. Sci. Technol. 42, 8165 (2008).

    ADS  Google Scholar 

  48. 48.

    Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).

    Google Scholar 

  49. 49.

    Bond, W. J. Ancient grasslands at risk. Science 351, 120–122 (2016).

    ADS  CAS  PubMed  Google Scholar 

  50. 50.

    Crouzeilles, R., Ferreira, M. S. & Curran, M. Forest restoration: a global dataset for biodiversity and vegetation structure. Ecology 97, 2167 (2016).

    PubMed  Google Scholar 

  51. 51.

    Deng, L., Shangguan, Z. P. & Sweeney, S. ‘Grain for Green’ driven land use change and carbon sequestration on the Loess Plateau, China. Sci. Rep. 4, 7039 (2015).

    Google Scholar 

  52. 52.

    Bárcena, T. G. et al. Soil carbon stock change following afforestation in Northern Europe: a meta-analysis. Glob. Change Biol. 20, 2393–2405 (2014).

    ADS  Google Scholar 

  53. 53.

    Marín-Spiotta, E. & Sharma, S. Carbon storage in successional and plantation forest soils: a tropical analysis. Glob. Ecol. Biogeogr. 22, 105–117 (2013).

    Google Scholar 

  54. 54.

    Deng, L., Zhu, G., Tang, Z. & Shangguan, Z. Global patterns of the effects of land-use changes on soil carbon stocks. Glob. Ecol. Conserv. 5, 127–138 (2016).

    Google Scholar 

  55. 55.

    Zhang, K., Dang, H., Zhang, Q. & Cheng, X. Soil carbon dynamics following land-use change varied with temperature and precipitation gradients: evidence from stable isotopes. Glob. Change Biol. 21, 2762–2772 (2015).

    ADS  Google Scholar 

  56. 56.

    Becknell, J. M., Kissing, L. & Powers, J. S. Aboveground biomass in mature and secondary seasonally dry tropical forests: a literature review and global synthesis. For. Ecol. Manage. 276, 88–95 (2012).

    Google Scholar 

  57. 57.

    Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 1–15 (2016).

    Google Scholar 

  58. 58.

    Guo, Q. & Ren, H. Productivity as related to diversity and age in planted versus natural forests. Glob. Ecol. Biogeogr. 23, 1461–1471 (2014).

    Google Scholar 

  59. 59.

    Krankina, O. NPP Boreal Forests: Siberian Scots Pine Forests, Russia, 1968–1974, R1 http://daac.ornl.gov (Oak Ridge National Laboratory, 1995).

  60. 60.

    Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).

    Google Scholar 

  62. 62.

    Chew, S. T. & Gallagher, J. B. Accounting for black carbon lowers estimates of blue carbon storage services. Sci. Rep. 8, 2553 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    James, J., Devine, W., Harrison, R. & Terry, T. Deep soil carbon: quantification and modeling in subsurface layers. Soil Sci. Soc. Am. J. 78, S1–S10 (2014).

    Google Scholar 

  64. 64.

    Aalde, H. et al. Forest land. In 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use (eds Paustian, K. et al.) Ch. 4 (IPCC, 2006).

  65. 65.

    Aalde, H. et al. Generic methodologies applicable to multiple land-use categories. In 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use (eds Paustian, K. et al.) Ch. 2 (IPCC, 2006).

  66. 66.

    Russell, M. B. et al. Quantifying carbon stores and decomposition in dead wood: a review. For. Ecol. Manage. 350, 107–128 (2015).

    Google Scholar 

  67. 67.

    Pribyl, D. W. A critical review of the convential SOC to SOM conversion factor. Geoderma 156, 75–83 (2010).

    ADS  CAS  Google Scholar 

  68. 68.

    Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root:shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006).

    ADS  Google Scholar 

  70. 70.

    Jobbágy, E. G. & Jackson, R. B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10, 423–436 (2000).

    Google Scholar 

  71. 71.

    Swedish National Forest Inventory Sample Plot Data https://www.slu.se/en/Collaborative-Centres-and-Projects/the-swedish-national-forest-inventory/listor/sample-plot-data/ (SNFI, 2019).

  72. 72.

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    MATH  Google Scholar 

  73. 73.

    Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).

  74. 74.

    Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).

    MATH  Google Scholar 

  75. 75.

    Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).

    CAS  PubMed  Google Scholar 

  76. 76.

    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V. & Thirion, B. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    MathSciNet  MATH  Google Scholar 

  77. 77.

    Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).

    ADS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Shono, K., Cadaweng, E. A. & Durst, P. B. Application of assisted natural regeneration to restore degraded tropical forestlands. Restor. Ecol. 15, 620–626 (2007).

    Google Scholar 

  79. 79.

    Nieuwenhuis, M. Terminology of forest management. In International Union of Forest Research Organizations World Series Vol. 9-en (IUFRO, 2000).

  80. 80.

    Winrock International AFOLU Carbon Calculator. The Agroforestry Tool: Underlying Data and Methods (USAID and Winrock International, 2014).

  81. 81.

    Vieira, D. L. M., Holl, K. D. & Peneireiro, F. M. Agro-successional restoration as a strategy to facilitate tropical forest recovery. Restor. Ecol. 17, 451–459 (2009).

    Google Scholar 

Download references

Acknowledgements

We thank the Children’s Investment Fund Foundation, COmON Foundation, the Craig and Susan McCaw Foundation, the Doris Duke Charitable Foundation, the Good Energies Foundation, and Microsoft’s AI for Earth program for financial support. This paper was also developed with funding from the Government of Norway, although it does not necessarily reflect their views or opinions. We thank J. Adams, E. Brolis, A. Hector, J. Ghazoul, M. Hamsik, S. Lewis, B. Luraschi, R. Thadani, B. Tsang and A. Yang for the initial idea development at an Oxford University workshop in 2017. We thank G. Domke and B. Walters (USDA Forest Service) for providing fuzzed FIA plot data, J. Fridman (Swedish National Forest Inventory) for providing Swedish data, and H. Xu for providing raw biomass data from Jainfengling Nature Reserve (Hainan Island, China).

Author information

Affiliations

Authors

Contributions

S.C.C.-P., B.W.G., N.L.H., D.G., K.L., S.S. and L.X. designed the study with input from all authors. S.C.C.-P. contributed to and led all other facets of the study. S.M.L., K.J.A.-T., R.D.B., P.W.E., H.P.G., K.D.H., C.L., R.L., K.P., S.R., S.A.W., C.E.W., W.S.W. and B.W.G. contributed to database compilation, analyses and manuscript preparation. N.L.H., K.L., D.G., T.W.C., D.R., S.S., L.X. and J.v.d.H. constructed the global maps and contributed to manuscript preparation. G.L., R.L., V.H., K.P. and S.R. contributed to database compilation and manuscript preparation. R.L.C., R.A.H., Y.M., P.M., A.P. and J.D.P. contributed to manuscript preparation. S.C.C.-P. is the corresponding author, handling requests for reprints and materials not included in the data repository.

Corresponding author

Correspondence to Susan C. Cook-Patton.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Michael Ryan, Edzo Veldkamp and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Variation in carbon accumulation among biomes.

Observed variation in total plant carbon accumulation rates and soil carbon accumulation rates (mean ± 95% confidence intervals) from the literature-derived dataset. We did not have plant biomass data for subtropical and tropical conifer forests. Source data

Extended Data Fig. 2 Accumulation of coarse woody debris and litter carbon through time.

We did not find studies describing litter (black) or coarse woody debris (grey) pools in temperate savannas, or coarse woody debris in tropical savannas. Source data

Extended Data Fig. 3 Variation in carbon stocks among biomes.

Carbon pools (mean ± standard error) in coarse woody debris (grey) and litter (black). Source data

Extended Data Fig. 4 Effect of disturbance intensity on carbon accumulation.

Carbon accumulation in plots with high intensity disturbance (black circles, black line) versus low intensity disturbance (grey circles, grey line). The most disturbed categories had lower residual biomass at the initiation of regrowth (for example, 0 Mg C ha−1 versus 28 Mg C ha−1 in the least disturbed category; Welch’s t-value = 5.9, P < 0.0001), suggesting that the higher rate in the most disturbed category is due to standard sigmoidal growth rates in forests. Source data

Extended Data Fig. 5 Map of extent of extrapolation per pixel across all covariate layers.

A value of 1 indicates that 100% of pixels fall within the sample range (that is, there is no extrapolation).

Extended Data Fig. 6 Fine-scale variation in rates.

a, Map of predicted carbon accumulation rates in Colombia, as an example. b, Map of predicted rates filtered to the area of opportunity in Griscom et al.3 to demonstrate where these rates might apply.

Extended Data Fig. 7 Coverage of field data.

Distribution of sites after final filtering of the literature-based dataset (blue) and inclusion of the field inventory data (green). We compiled data from forest (dark grey) and savanna biomes (light grey). We restricted savanna data to portions of these grassland-forest matrices with forest cover >25%.

Extended Data Table 1 General approaches for restoring forest or tree cover
Extended Data Table 2 Effect of disturbance intensity on carbon accumulation

Supplementary information

Supplementary Information

This supplementary information file includes additional methodological details (Table S1 and S2), metadata for the literature-derived dataset (Tables S3, S4 and S5), and a list of all publications included in the literature-derived dataset.

Supplementary Data

This supplementary dataset includes country-level summaries of carbon accumulation rates (Mg C ha−1 yr−1) and mitigation potential from natural forest regrowth (Tg C yr−1) under two scenarios for natural forest regrowth. The first scenario represents a biophysical maximum3 and the second is based on national commitments12. Mitigation estimates are illustrative only, based on assumptions of new forest area. Note that the national commitments scenario includes commitments around “forest restoration”, which may or may not describe areas of new forest. The rate column includes rates from pixels that overlap with area of opportunity pixels in Griscom et al3. We only list countries that are a million hectares or larger.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cook-Patton, S.C., Leavitt, S.M., Gibbs, D. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020). https://doi.org/10.1038/s41586-020-2686-x

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing