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

• The suitability of 182 countries to deploy GGR technologies was assessed. • Use of machine learning minimises the likelihood of human bias in the assessment. • G20 and Sub-Saharan African nations to play key role in GGR deployment. • There is need for regional cooperation and resource sharing among countries. • Countries need to include GGR technologies in their revised NDCs. 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. [ABSTRACT FROM AUTHOR]