Data Fusion and Aggregation Methods to Develop Composite Indexes for a Sustainable Future

2025-07-12

AI Summary

This study addresses the challenges of integrating multi-source, heterogeneous indicators and constructing transparent, comparable composite indices for environmental risk assessment under extreme climate events. It systematically compares four methodological approaches—weighted linear aggregation, principal component analysis, fuzzy logic, and data envelopment analysis—evaluating their applicability, underlying assumptions, and limitations. To enhance interpretability and policy relevance, the framework integrates expert knowledge with scenario-based simulation. Empirical validation is conducted using a streamlined dataset and real-world drought cases. Results reveal significant divergence across methods in dimensionality reduction capability, weight sensitivity, and robustness; notably, fuzzy logic and weighted aggregation demonstrate superior suitability for policy-oriented resilience assessment. The study contributes a methodological framework and a reusable, step-by-step guideline for constructing composite indices, supporting evidence-based decision-making in sustainable development and climate adaptation.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Sustainability and climate impact of web technologies

Abstract

Research on environmental risk modeling relies on numerous indicators to quantify the magnitude and frequency of extreme climate events, their ecological, economic, and social impacts, and the coping mechanisms that can reduce or mitigate their adverse effects. Index-based approaches significantly simplify the process of quantifying, comparing, and monitoring risks associated with other natural hazards, as a large set of indicators can be condensed into a few key performance indicators. Data fusion techniques are often used in conjunction with expert opinions to develop key performance indicators. This paper discusses alternative methods to combine data from multiple indicators, with an emphasis on their use-case scenarios, underlying assumptions, data requirements, advantages, and limitations. The paper demonstrates the application of these data fusion methods through examples from current risk and resilience models and simplified datasets. Simulations are conducted to identify their strengths and weaknesses under various scenarios. Finally, a real-life example illustrates how these data fusion techniques can be applied to inform policy recommendations in the context of drought resilience and sustainability.

Problem

• Develop composite indexes for sustainable future using data fusion
• Compare methods to combine multiple environmental risk indicators
• Apply fusion techniques to inform drought resilience policies

Innovation

• Data fusion combines multiple indicators effectively
• Expert opinions enhance key performance indicators
• Simulations test methods under various scenarios