AI and Climate Protection: Research Gaps and Needs to Align Machine Learning with Greenhouse Gas Reductions

Bieser, Jan (24 June 2024). AI and Climate Protection: Research Gaps and Needs to Align Machine Learning with Greenhouse Gas Reductions In: 2024 ICT for Sustainability Conference. IEEE

Text Preprint_Bieser_ML Research Gaps_v2.pdf - Accepted Version Restricted to registered users only until 1 July 2026. Available under License Publisher holds Copyright. Download (322kB) | Request a copy

Machine learning (ML) promises to revolutionize our socio-economic landscape, yet its impacts on greenhouse gas (GHG) emissions and strategies to harness ML for climate protection are not well understood. This discussion paper reviews key research on ML’s GHG effects, highlighting significant research gaps and needs for a climate-oriented ML transformation. The results show that research on GHG emissions caused during model development, training, and operation is progressing. However, there is no comprehensive overview of effective measures to reduce them along the entire ML software and hardware life cycle. (Industrial) research on the GHG effects of ML applications focuses mainly on GHG reduction potentials while neglecting the possibility that ML applications also increase emissions. Thus, research in at least three key areas is needed to align ML with GHG reductions. First, robust methods to assess and report the GHG impacts of ML models and applications are required to systematically compare them and identify best practices. Second, comprehensive GHG assessments at every effect level are essential to identify measures to increase the GHG efficiency of ML models and exploit their climate protection potential. Third, analysing ML business models is crucial to propose measures that incentivize ML providers and users to reduce GHG emissions. Addressing these issues is essential for mindfully steering ML toward GHG reductions. Otherwise, there is a risk that the GHG footprint of ML will skyrocket, that ML applications will primarily accelerate GHG-intensive activities, and that an opportunity for decoupling (economic) growth and GHG emissions will be missed.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	Business School > Institute for Public Sector Transformation Business School > Institute for Public Sector Transformation > Data and Infrastructure Business School BFH Centres and strategic thematic fields > Thematic field "Humane Digital Transformation" BFH Centres and strategic thematic fields > Thematic field "Sustainable Development"
Name:	Bieser, Jan0000-0002-6791-6895
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Publisher:	IEEE
Language:	English
Submitter:	Jan Bieser
Date Deposited:	07 Aug 2024 11:13
Last Modified:	03 Sep 2024 09:48
Related URLs:	https://ieeexplore.ieee.org/browse/confe...
Uncontrolled Keywords:	Machine learning Artificial intelligence Greenhouse gas Emissions Climate protection Cimate change
ARBOR DOI:	10.24451/arbor.22067
URI:	https://arbor.bfh.ch/id/eprint/22067