A consistent and integrated network analysis framework for decoding interlinkages between sustainable development indicators at the global scale
提出了一个集成框架，通过人口加权和区域特异性扩展Kendall相关度量，构建可持续发展指标的有符号加权网络，识别结构分组和系统杠杆点，并引入增强弦图和双准则帕累托前沿选择，为政策制定者提供设计综合干预的工具。
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.