Multi-Criteria Decision-Making Approaches to Assess SDG Performance in Indian States: A Comparative Evaluation

Abstract

The Sustainable Development Goals (SDGs) provide a universal framework for addressing global developmental challenges, with significant implications for India—a nation of over 1.4 billion people. The Indian government has localized SDG targets, yet the diversity in socio-economic and environmental contexts across states creates disparities in progress. This study evaluates the SDG performance of Indian states and Union Territories (UTs) using four distinct Multi-Criteria Decision-Making (MCDM) methods: Weighted Average Coefficient Method, Hybrid Entropy-GE Matrix, Principal Component Analysis (PCA), and Fuzzy VIKOR. Each methodology offers unique insights. Weighted Average simplifies ranking with aggregated scores but lacks inter-indicator interactions. The Hybrid Entropy-GE Matrix identifies coordination gaps and internal disparities, albeit with computational intensity. PCA reduces dimensionality to uncover latent patterns, though interpretability may be compromised. Fuzzy VIKOR excels in balancing group utility and individual regret but requires precise parameter calibration. The findings reveal consistent top-performing states like Kerala and Tamil Nadu, excelling in health, education, and gender equality, while lagging states like Bihar face challenges in poverty alleviation and infrastructure development. The study concludes by integrating rankings across methods to provide a holistic view of state performance and actionable recommendations for addressing regional disparities. This comparative analysis contributes to the academic discourse on SDG evaluation methodologies and offers policymakers a robust framework for targeted interventions. Despite limitations such as reliance on cross-sectional data and state-level aggregation, the research underscores the value of MCDM techniques in advancing India's SDG agenda.