Assessing Climate Transition Risks in the Colombian Processed Food Sector: A Fuzzy Logic and Multicriteria Decision-Making Approach

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Détails bibliographiques
Auteurs principaux:	Pérez-Pérez, Juan F., Gómez, Pablo Isaza, Bonet, Isis, Sánchez-Pinzón, María Solange, Caraffini, Fabio, Lochmuller, Christian
Format:	Preprint
Publié:	2024
Sujets:	Artificial Intelligence
Accès en ligne:	Acceder al recurso
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author	Pérez-Pérez, Juan F. Gómez, Pablo Isaza Bonet, Isis Sánchez-Pinzón, María Solange Caraffini, Fabio Lochmuller, Christian
author_facet	Pérez-Pérez, Juan F. Gómez, Pablo Isaza Bonet, Isis Sánchez-Pinzón, María Solange Caraffini, Fabio Lochmuller, Christian
contents	Climate risk assessment is becoming increasingly important. For organisations, identifying and assessing climate-related risks is challenging, as they can come from multiple sources. This study identifies and assesses the main climate transition risks in the colombian processed food sector. As transition risks are vague, our approach uses Fuzzy Logic and compares it to various multi-criteria decision-making methods to classify the different climate transition risks an organisation may be exposed to. This approach allows us to use linguistic expressions for risk analysis and to better describe risks and their consequences. The results show that the risks ranked as the most critical for this organisation in their order were price volatility and raw materials availability, the change to less carbon-intensive production or consumption patterns, the increase in carbon taxes and technological change, and the associated development or implementation costs. These risks show a critical risk level, which implies that they are the most significant risks for the organisation in the case study. These results highlight the importance of investments needed to meet regulatory requirements, which are the main drivers for organisations at the financial level.
format	Preprint
id	arxiv_https___arxiv_org_abs_2404_16055
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Assessing Climate Transition Risks in the Colombian Processed Food Sector: A Fuzzy Logic and Multicriteria Decision-Making Approach Pérez-Pérez, Juan F. Gómez, Pablo Isaza Bonet, Isis Sánchez-Pinzón, María Solange Caraffini, Fabio Lochmuller, Christian Artificial Intelligence Climate risk assessment is becoming increasingly important. For organisations, identifying and assessing climate-related risks is challenging, as they can come from multiple sources. This study identifies and assesses the main climate transition risks in the colombian processed food sector. As transition risks are vague, our approach uses Fuzzy Logic and compares it to various multi-criteria decision-making methods to classify the different climate transition risks an organisation may be exposed to. This approach allows us to use linguistic expressions for risk analysis and to better describe risks and their consequences. The results show that the risks ranked as the most critical for this organisation in their order were price volatility and raw materials availability, the change to less carbon-intensive production or consumption patterns, the increase in carbon taxes and technological change, and the associated development or implementation costs. These risks show a critical risk level, which implies that they are the most significant risks for the organisation in the case study. These results highlight the importance of investments needed to meet regulatory requirements, which are the main drivers for organisations at the financial level.
title	Assessing Climate Transition Risks in the Colombian Processed Food Sector: A Fuzzy Logic and Multicriteria Decision-Making Approach
topic	Artificial Intelligence
url	https://arxiv.org/abs/2404.16055

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