Achieving the United Nations Sustainable Development Goals (SDG) is crucial to addressing global challenges such as poverty, inequality, environmental degradation, and climate change. Yet, their interdependent nature creates complex synergies, trade-offs, and development dilemmas, requiring robust data-driven methods to capture interactions at a granular level. This paper proposes a consistent and integrated framework for analyzing SDG indicator interlinkages at the global scale. We extend the Kendall correlation measure by incorporating population weighting and regional specificities, yielding a signed weighted network of indicators. An optimal clustering method, aligned with eigenvector centrality, identifies structural groupings and systemic leverage points. We also introduce an enhanced chord diagram for improved visualization and a bi-criteria Pareto front selection to prioritize indicators based on influence and urgency. Applied to the SDR 2024 dataset, the framework reveals key synergies and trade-offs, highlighting the roles of governance quality, environmental management, and urban infrastructure. Overall, our approach provides policymakers with a coherent toolset for designing integrated interventions that address development dilemmas while balancing development and sustainability goals at global scale.
Ah-Pine et al. (2026) studied this question.