A machine learning approach for country-level deployment of greenhouse gas removal technologies

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dc.contributor.authorAsibor, Jude Odianosen
dc.contributor.authorClough, Peter T.
dc.contributor.authorNabavi, Seyed Ali
dc.contributor.authorManovic, Vasilije
dc.date.accessioned2023-10-24T12:48:26Z
dc.date.available2023-10-24T12:48:26Z
dc.date.issued2023-10-19
dc.description.abstractThe suitability of countries to deploy five greenhouse gas removal technologies was investigated using hierarchical clustering machine learning. These technologies include forestation, enhanced weathering, direct air carbon capture and storage, bioenergy with carbon capture and storage and biochar. The use of this unsupervised machine learning model greatly minimises the likelihood of human bias in the assessment of GGR technology deployment potentials and instead takes a more holistic view based on the applied data. The modelling utilised inputs of bio-geophysical and techno-economic factors of 182 countries, with the model outputs highlighting the potential performance of these GGR methods. Countries such as USA, Canada, Brazil, China, Russia, Australia as well as those within the EU and Sub-Saharan Africa were identified as key areas suitable to deploy these GGR technologies. The level of certainty of the obtained deployment suitability categorisation ranged from 65 to 98 %. While the results show the need for regional collaboration between nations, they also highlight the necessity for nations to prioritise and integrate GGR technologies in their revised nationally determined contributions.en_UK
dc.identifier.citationAsibor JO, Clough PT, Nabavi SA, Manovic V. (2023) A machine learning approach for country-level deployment of greenhouse gas removal technologies, International Journal of Greenhouse Gas Control, Volume 130, December 2023, Article Number 103995en_UK
dc.identifier.eissn1878-0148
dc.identifier.issn1750-5836
dc.identifier.urihttps://doi.org/10.1016/j.ijggc.2023.103995
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20434
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectGreenhouse gas removalen_UK
dc.subjectBECCSen_UK
dc.subjectDACCSen_UK
dc.subjectMachine learningen_UK
dc.subjectClimate change mitigationen_UK
dc.titleA machine learning approach for country-level deployment of greenhouse gas removal technologiesen_UK
dc.typeArticleen_UK

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