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

Juan F. Pérez-Pérez, Pablo Isaza Gómez, Isis Bonet, María Solange Sánchez-Pinzón, Fabio Caraffini, Christian Lochmuller

TL;DR

The paper tackles climate transition risk assessment for Colombia's processed food sector by integrating a fuzzy logic risk matrix with comparisons to multiple MCDM methods. It develops a 25-rule Mamdani fuzzy inference system to convert expert judgments on probability and impact into risk levels, and uses TOPSIS-derived weights to inform the fuzzy model. The study identifies RM2 (raw materials price/availability), RT3 (technological change/stranded assets), Rreg2 (carbon tax), and RT1 (shift to low-carbon production) as the most critical risks, illustrating how fuzzy classification can provide actionable risk levels beyond simple rankings. The approach supports regulatory compliance and investment planning by translating vague climate risks into interpretable, categorized risk indices and highlights future work to unify vulnerability, exposure, and resilience in the impact criterion and expand expert input across the organization.

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.

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

TL;DR

The paper tackles climate transition risk assessment for Colombia's processed food sector by integrating a fuzzy logic risk matrix with comparisons to multiple MCDM methods. It develops a 25-rule Mamdani fuzzy inference system to convert expert judgments on probability and impact into risk levels, and uses TOPSIS-derived weights to inform the fuzzy model. The study identifies RM2 (raw materials price/availability), RT3 (technological change/stranded assets), Rreg2 (carbon tax), and RT1 (shift to low-carbon production) as the most critical risks, illustrating how fuzzy classification can provide actionable risk levels beyond simple rankings. The approach supports regulatory compliance and investment planning by translating vague climate risks into interpretable, categorized risk indices and highlights future work to unify vulnerability, exposure, and resilience in the impact criterion and expand expert input across the organization.

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.
Paper Structure (21 sections, 1 equation, 6 figures, 7 tables)

This paper contains 21 sections, 1 equation, 6 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Fuzzy systems in the risk assessment domain
  4. Multicriteria decision-making (MCDM) methods
  5. Materials and methods
  6. Risk matrix construction
  7. Variables selection (climate transition risk identification)
  8. Multicriteria decision making methods and their comparison
  9. Data collection
  10. Data set preparation
  11. Design considerations on the data collection form
  12. The proposed fuzzy system
  13. Fuzzy risk matrix sets definition (fuzzification)
  14. Fuzzy inference system
  15. Experimental phase and results discussion
  16. ...and 6 more sections

Figures (6)

  • Figure 1: Risk matrix example. Green is used to indicate low risk, yellow to indicate medium risk, orange to indicate high risk and red to indicate critical risk.
  • Figure 2: Section 1: transitional climate risk criteria rating.
  • Figure 3: Section 2: regulatory risks.
  • Figure 4: membership functions.
  • Figure 5: Correlation matrix
  • ...and 1 more figures