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An unsupervised model reduces the influence of human bias by assessing deployment potential holistically from bio-geophysical and techno-economic inputs for 182 countries, with outputs indicating performance and categorisation certainty from 65 to 98%.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Control 130 (2023) 103995  \nContents lists available at ScienceDirect  \nInternational Journal of Greenhouse Gas Control  \njournal [homepage:](homepage: www.elsevier.com/locate/ijggc)[ www.elsevier.com/locate/ijggc](homepage: www.elsevier.com/locate/ijggc)  \n| A machine learning approach for country-level deployment of greenhouse gas removal technologies\u003Cbr>*\u003Cbr>Jude O. Asibor , Peter T. Clough , Seyed Ali Nabavi , Vasilije Manovic\u003Cbr>School of Water, Energy and Environment, Cranfield University, Bedfordshire MK43 0AL, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Greenhouse gas removal BECCS\u003Cbr>DACCS\u003Cbr>Machine learning Climate change mitigation |  | The 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. |\n\n1. Introduction  \nTo keep global warming below 1.5 ◦ C by the end of the century, the adoption of greenhouse gas removal (GGR) technologies, sometimes referred to as negative emission technologies (NETs), has been recognised as a crucial and essential strategy (IPCC, 2022). Under this scenario, global net anthropogenic CO2 emissions is expected to be reduced by about 45 % from 2010 levels by 2030 with net zero achieved by 2050 (IPCC, 2018). Attaining this mitigation target will require substantial efforts ranging from proactive emission reduction solutions to thereactive GGR methods. These GGR technologies include forestation, habitat restoration, enhanced weathering (EW), soil carbon sequestration (SCS), ocean fertilisation, biochar, direct air carbon capture and storage (DACCS), bioenergy with carbon capture and storage (BECCS), and ocean alkalinity, among others. While they are currently at various levels of technological readiness (Haszeldine et al., 2019; The Royal Society, 2018), their performance and future deployment is influenced by techno-economic, bio-geophysical and socio-political variables (Asibor et al., 2021; Fuss et al., 2018; Hepburn et al., 2019; Rueda et al., 2021). While GGR technologies such as forestation, DACCS and BECCS are only focussed on carbon dioxide removal (CDR) which is the dominant greenhouse gas, others such as Biochar have the co-benefit  \nand additional potential to reduce other greenhouse gas emissions especially when applied for soil amendment (Woolf et al., 2010).  \nWith the exception of forestation and some land management measures, the nationally determined contributions (NDCs) that nations have made to the United Nations Framework Convention on Climate Change (UNFCCC) show a low intent to use these GGR technologies (Pozo et al., 2020), thus, making the 1.5 ◦ C objective difficult to achieve (Roe et al., 2019; Rogelj et al., 2016; Schiermeier, 2015). There is therefore a great need for a global country-level assessment of these GGR technologies for potential de","cbCaivM7SHINDDi3","https://ap.wps.com/l/cbCaivM7SHINDDi3","pdf",4674539,11,"English","# Introduction\n## Greenhouse gas removal and climate mitigation context\n## Nationally determined contributions and deployment gaps\n## Need for country-level assessment\n## Prior literature and limitations","[{\"question\":\"Which greenhouse gas removal technologies are evaluated in the study?\",\"answer\":\"The study assesses forestation, enhanced weathering, direct air carbon capture and storage (DACCS), bioenergy with carbon capture and storage (BECCS), and biochar.\"},{\"question\":\"How does the study model country suitability?\",\"answer\":\"It uses a hierarchical clustering unsupervised machine learning approach, drawing on bio-geophysical and techno-economic inputs.\"},{\"question\":\"What countries or regions are identified as key areas for deployment suitability?\",\"answer\":\"The results highlight countries including the USA, Canada, Brazil, China, Russia, and Australia, along with regions within the EU and Sub-Saharan Africa.\"}]","A machine learning approach for country-level deployment of greenhouse gas removal technologies - Research report | PDF",28]