A Network-Based Framework to Identify Synergies and Trade offs among SDG Indicators
Achieving the United Nations Sustainable Development Goals (SDGs) requires an understanding of the complex interlinkages that exist among their underlying indicators. While most existing research examines these interconnections at the goal level, policy interventions are typically designed and implemented at the indicator level, where synergies and trade-offs most directly emerge. This study addresses this gap by proposing a network-theoretic framework to assess indicator-level interactions in a systematic and data-driven manner. We introduce two complementary measures, the positive strength and negative strength of an indicator, which jointly capture the balance between synergistic and conflicting interactions within a national SDG indicator network. Based on these measures, indicators are classified as synergy- and trade-off-dominated according to their net systemic interaction structure. To move beyond classification, we further examine the structural drivers of synergy dominance using an explanatory regression framework, focusing on the roles of direct positive interactions and indirect network embeddedness. This analysis shows that indicators classified as synergy-dominated are typically characterized by a high concentration of direct synergies and additional support from indirect pathways through the network, allowing positive effects to extend beyond immediate neighbors. The framework is applied to two national case studies, India and Italy, to illustrate how the classification of indicators varies across development contexts. Overall, the proposed methodology provides a transparent and scalable tool for identifying the structural conditions under which indicator-level synergies emerge, thereby supporting a more nuanced understanding of how development actions can generate reinforcing effects across the SDG system.