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Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations

Juan F. Pérez-Pérez Orcid Logo, Isis Bonet Orcid Logo, María Solange Sánchez-Pinzón, Fabio Caraffini Orcid Logo, Christian Lochmuller Orcid Logo

International Journal of Intelligent Systems, Volume: 2024, Start page: 3334263

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1155/int/3334263

Abstract

Addressing climate change represents one of the most pressing challenges for organisations in developing nations. This is particularly relevant for companies navigating the shift towards a low-carbon economy. This research leverages artificial intelligence (AI) methodologies to evaluate the financia...

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Published in: International Journal of Intelligent Systems
ISSN: 0884-8173 1098-111X
Published: Wiley 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa68573
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last_indexed 2025-01-09T20:33:51Z
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spelling 2025-01-03T13:20:36.3594612 v2 68573 2024-12-16 Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2024-12-16 MACS Addressing climate change represents one of the most pressing challenges for organisations in developing nations. This is particularly relevant for companies navigating the shift towards a low-carbon economy. This research leverages artificial intelligence (AI) methodologies to evaluate the financial implications of climate transition risks, encompassing both direct and indirect energy usage, including expenditures on electricity and fossil fuels. Advanced machine learning (ML) and deep learning (DL) models are employed to predict electricity and diesel consumption trends along with their associated costs. Findings from this study indicate an average prediction accuracy of 90.36%, underscoring the value of these tools in supporting organisational decision making related to climate transition risks. The study lays a foundation for comprehending not only the added costs linked to climate risks but also the potential advantages of transitioning to a low-carbon economy, particularly from an energy-focused perspective. Additionally, the proposed climate transition risk adjustment factor offers a framework for visualising the financial impacts of scenarios outlined by the Network for Greening the Financial System. Journal Article International Journal of Intelligent Systems 2024 3334263 Wiley 0884-8173 1098-111X Artifcial intelligence; climate scenarios; climate transition risk; prediction 23 12 2024 2024-12-23 10.1155/int/3334263 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2025-01-03T13:20:36.3594612 2024-12-16T09:51:35.4978641 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Juan F. Pérez-Pérez 0000-0002-5650-2451 1 Isis Bonet 0000-0002-3031-2334 2 María Solange Sánchez-Pinzón 3 Fabio Caraffini 0000-0001-9199-7368 4 Christian Lochmuller 0009-0008-7322-5311 5 295 true 10.5281/zenodo.10991563 false
title Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations
spellingShingle Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations
Fabio Caraffini
title_short Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations
title_full Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations
title_fullStr Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations
title_full_unstemmed Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations
title_sort Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Juan F. Pérez-Pérez
Isis Bonet
María Solange Sánchez-Pinzón
Fabio Caraffini
Christian Lochmuller
format Journal article
container_title International Journal of Intelligent Systems
container_volume 2024
container_start_page 3334263
publishDate 2024
institution Swansea University
issn 0884-8173
1098-111X
doi_str_mv 10.1155/int/3334263
publisher Wiley
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
description Addressing climate change represents one of the most pressing challenges for organisations in developing nations. This is particularly relevant for companies navigating the shift towards a low-carbon economy. This research leverages artificial intelligence (AI) methodologies to evaluate the financial implications of climate transition risks, encompassing both direct and indirect energy usage, including expenditures on electricity and fossil fuels. Advanced machine learning (ML) and deep learning (DL) models are employed to predict electricity and diesel consumption trends along with their associated costs. Findings from this study indicate an average prediction accuracy of 90.36%, underscoring the value of these tools in supporting organisational decision making related to climate transition risks. The study lays a foundation for comprehending not only the added costs linked to climate risks but also the potential advantages of transitioning to a low-carbon economy, particularly from an energy-focused perspective. Additionally, the proposed climate transition risk adjustment factor offers a framework for visualising the financial impacts of scenarios outlined by the Network for Greening the Financial System.
published_date 2024-12-23T02:55:08Z
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score 11.04748

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