Abstract
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.
Original language | English |
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Article number | 103995 |
Journal | International Journal of Greenhouse Gas Control |
Volume | 130 |
DOIs | |
State | Published - Dec 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023 The Author(s)
Keywords
- BECCS
- Climate change mitigation
- DACCS
- Greenhouse gas removal
- Machine learning
ASJC Scopus subject areas
- Pollution
- General Energy
- Management, Monitoring, Policy and Law
- Industrial and Manufacturing Engineering