Crossmark

Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques

Crossref DOI link: https://doi.org/10.1007/s10584-023-03529-w

Published Online: 2023-06-16

Published Print: 2023-07

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Li, Pei-Hao https://orcid.org/0000-0002-2988-2399
Pye, Steve

Keppo, Ilkka

Jaxa-Rozen, Marc

Trutnevyte, Evelina
Funding

Funding for this research was provided by:

Horizon 2020 Framework Programme (821124)

UK Energy Research Centre (EP/S029575/1)

Engineering and Physical Sciences Research Council (EP/S031898/1)

University of Geneva
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Text and Data Mining valid from 2023-06-16

Version of Record valid from 2023-06-16
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Article History

Received: 26 April 2022

Accepted: 7 April 2023

First Online: 16 June 2023

Declarations

:

: Not applicable because this study does not involve humans and/or animals. Consent to participate is not applicable since this study does not report the results of studies involving humans and/or animals.

: Not applicable because this study does not report the results of studies involving humans and/or animals.

: The authors declare no competing interests.

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