Risk assessment based on fuzzy synthetic evaluation method

Area of Science:

• Environmental Science
• Climate Change Adaptation
• Risk Assessment

Background:

• The Intergovernmental Panel on Climate Change (IPCC) assesses climate risks by combining hazards with vulnerability and exposure.
• Current IPCC methods rely on expert opinions for indicator placement, introducing subjectivity and uncertainty in risk map generation.
• This can lead to multiple, potentially conflicting risk maps for the same area, hindering clear decision-making.

Purpose of the Study:

• To compare the effectiveness of the Fuzzy Synthetic Evaluation (FSE) method against IPCC assessment methods (TAR and AR5) for climate risk mapping.
• To evaluate the uncertainty associated with different IPCC methodologies in defining risk zones.
• To determine if FSE offers a more objective and reliable approach to climate risk assessment.

Main Methods:

• Applied the Fuzzy Synthetic Evaluation (FSE) method to generate climate risk maps.
• Utilized two IPCC assessment methods, specifically the third assessment report (TAR) and the fifth assessment report (AR5), for comparison.
• Created a variant AR5 risk map by reassigning an indicator from the exposure to the sensitivity domain to test methodological sensitivity.

Main Results:

• Statistically significant differences were observed in risk zones generated by different IPCC methods.
• This variation highlights the inherent uncertainty and subjectivity in IPCC risk mapping approaches.
• The FSE method produced a single, consistent risk map without requiring expert domain distribution for indicators.

Conclusions:

• The Fuzzy Synthetic Evaluation (FSE) method provides a more certain and objective approach to climate risk mapping compared to IPCC methods.
• FSE's binary logic for indicator placement eliminates the subjective perceptions inherent in expert-driven IPCC assessments.
• The single, consistent risk map generated by FSE can be considered a more reliable representation of true risk for a given set of indicators.