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dc.contributor.authorCho, FHT
dc.contributor.authorAglonucci, P
dc.contributor.authorBateman, IJ
dc.contributor.authorLee, C
dc.contributor.authorLovett, A
dc.contributor.authorMancini, MC
dc.contributor.authorRapti, C
dc.contributor.authorDay, BH
dc.date.accessioned2025-02-14T09:15:10Z
dc.date.issued2025-03-03
dc.date.updated2025-02-13T18:20:35Z
dc.description.abstractTo meet decarbonisation targets, nations around the globe have made ambitious commitments to expand forested land. Operationalising these commitments requires choosing a planting strategy: how many trees should be planted, of which species, and where? Given those choices must be made now but have long term consequences, such decisions are plagued by uncertainty. For example, species that are well suited to present conditions may perform poorly under future climates, yet those future climates are themselves highly uncertain. Using the exemplar of the UK, a nation committed to achieving net zero emissions by mid-century, we quantify key uncertainties pertaining to co-evolving climate and economic conditions and examine how modern methods of decision-making under uncertainty can advise on planting choices. Our analysis reveals that the best planting strategy assuming a ‘high-emissions’ future is radically different to that for a future that remains on a ‘near-historic’ path. Planting for the former while experiencing the latter results in substantial net costs to UK society. Assimilating uncertainty into decision-making identifies planting strategies that diversify risk and significantly reduce the probability of high-cost outcomes. Importantly, our research reveals that the scope for mitigating risk through choice of planting strategy is relatively limited. Despite this persistent risk, we find that tree planting remains a highly cost-effective carbon removal solution when compared to alternative technologies, even when those alternatives are assumed to be riskless.en_GB
dc.description.sponsorshipNatural Environment Research Council (NERC)en_GB
dc.description.sponsorshipBiotechnology and Biological Sciences Research Council (BBSRC)en_GB
dc.identifier.citationVol. 122 (10), article e2320961122en_GB
dc.identifier.doi10.1073/pnas.2320961122
dc.identifier.grantnumberNE/T002115/1en_GB
dc.identifier.grantnumberBB/V011588/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/140053
dc.identifierORCID: 0000-0001-7519-5672 (Day, Brett)
dc.language.isoenen_GB
dc.publisherNational Academy of Sciencesen_GB
dc.relation.urlhttps://github.com/LEEP-Modelling-Team/tree-planting-uncertaintyen_GB
dc.rights© 2025 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).en_GB
dc.subjectClimate risksen_GB
dc.subjecttree plantingen_GB
dc.subjectcarbon dioxide removalen_GB
dc.subjectuncertaintyen_GB
dc.subjectportfolio optimisationen_GB
dc.titleResilient tree-planting strategies for carbon dioxide removal under compounding climate and economic uncertaintiesen_GB
dc.typeArticleen_GB
dc.date.available2025-02-14T09:15:10Z
dc.identifier.issn0027-8424
dc.descriptionThis is the final version. Available on open access from the National Academy of Sciences via the DOI in this recorden_GB
dc.descriptionData and Code Availability: Code used to produce analyses in this study is available at https://github.com/LEEP-Modelling-Team/tree-planting-uncertainty. The supporting data used to replicate results will be deposited in an open repository for public access upon publication.en_GB
dc.identifier.eissn1091-6490
dc.identifier.journalProceedings of the National Academy of Sciencesen_GB
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2025-01-13
dcterms.dateSubmitted2023-11-28
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2025-01-13
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2025-02-13T18:20:38Z
refterms.versionFCDAM
refterms.dateFOA2025-03-13T12:53:58Z
refterms.panelCen_GB


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© 2025 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
Except where otherwise noted, this item's licence is described as © 2025 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).