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Machine Learning Predictive Modeling for assessing Climate Risk in Finance

Articolo
Data di Pubblicazione:
2024
Citazione:
Machine Learning Predictive Modeling for assessing Climate Risk in Finance / Ferrara, Massimiliano; Ciano, Tiziana; Capriotti, Alessio; Muzzioli, Silvia. - In: WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT. - ISSN 2224-3496. - 20:(2024), pp. 852-862. [10.37394/232015.2024.20.80]
Abstract:
We investigate how the application of advanced predictive models could help investors to assess and manage climate risk in their portfolios, contributing to the development of more sustainable and resilient investment practices. We highlight the possible applications of predictive analytics as a key tool in climate finance. It emerges how emerging technologies (blockchain and Artificial Intelligence) can improve transparency, efficiency, and climate risk analysis in sustainable investments. Further lines of research are highlighted, focusing on how investors and portfolio managers can develop strategies to manage the risks associated with climate events and the integration of climate risks into the management of Supply Chain Finance to ensure greater resilience and sustainability. Some generalized models are analyzed focusing the most important aspects and features by which modeling Climate risks and related issues in financial frameworks.
Tipologia CRIS:
1.1 Articolo in rivista
Elenco autori:
Ferrara, Massimiliano; Ciano, Tiziana; Capriotti, Alessio; Muzzioli, Silvia
Autori di Ateneo:
FERRARA Massimiliano
Link alla scheda completa:
https://iris.unirc.it/handle/20.500.12318/154986
Link al Full Text:
https://iris.unirc.it//retrieve/handle/20.500.12318/154986/447678/Ferrara_2024_%20WSEAS_Machine%20Learning_editor.pdf
Pubblicato in:
WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT
Journal
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