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

Jude O. Asibor*, Peter T. Clough, Seyed Ali Nabavi, Vasilije Manovic

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 languageEnglish
Article number103995
JournalInternational Journal of Greenhouse Gas Control
Volume130
DOIs
StatePublished - Dec 2023
Externally publishedYes

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

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