Asibor et al. (2023): A machine learning approach for country-level deployment of greenhouse gas removal technologies
Jude O. Asibor, Peter T. Clough, Seyed Ali Nabavi, Vasilije Manovic IN: International Journal of Greenhouse Gas Control, 130, 103995, https://doi.org/10.1016/j.ijggc.2023.103995
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.