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

TL;DR

This paper presents a fuzzy logic and multicriteria decision-making approach to assess climate transition risks in Colombia's processed food sector, highlighting key risks like price volatility and regulatory costs.

Contribution

It introduces a novel fuzzy logic-based framework for classifying and analyzing vague climate transition risks in the food industry.

Findings

Price volatility and raw material availability are the most critical risks.

Regulatory and technological changes pose significant financial challenges.

Fuzzy logic improves risk description and assessment accuracy.

Abstract

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.