A Network-Based Framework to Identify Synergies and Trade offs among SDG Indicators

TL;DR

This paper presents a network-based framework to identify synergies and trade-offs among SDG indicators at the indicator level, addressing the gap left by goal-focused analyses. It constructs country-specific complete weighted networks using edge weights $w^{(k)}_{ij}=\rho^{(k)}_{ij}$ and defines indicator-level strengths $S^+_{ki}$ and $S^-_{ki}$ to classify indicators as synergy- or trade-off-dominated. A logistic regression with direct ($X^d_{ki}$) and indirect ($X^h_{ki}$) network effects elucidates structural drivers of synergy dominance, revealing a strong role for direct positive interactions and meaningful indirect embeddedness. Case studies of India and Italy demonstrate context-dependent patterns, while global results show a majority of synergy-dominated indicators across countries; the framework offers a transparent, scalable tool for informing policy actions that harvest cross-indicator spillovers, with limitations including reliance on correlation-based relationships and potential gains from causal and dynamic extensions.

Abstract

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.

Figures (7)

• Figure 1: Illustration of a complete weighted network $G_k$ with four nodes, its star subgraph $G_{k1}$ corresponding to node $1$, and its partition into $G^{+}_{k1}$ and $G^{-}_{k1}$ based on postive and negative weights. Green-coloured edges represent the positive weighted edges, while red-coloured edges represent the negative weights.
• Figure 2: Confusion matrix for the testing dataset
• Figure 3: Spearman correlation heatmap of 80 sustainable development indicators for India, where dark red indicates strong negative correlation, dark green indicates strong positive correlation, and white indicates no correlation.
• Figure 4: Distribution of synergy and trade-off–dominated SDG indicators across the 17 Sustainable Development Goals for India. Green bars represent the number of indicators that are synergy-dominated, while red bars represent trade-off–dominated indicators. Bar lengths indicate indicator counts, shown symmetrically around the vertical zero line, with SDG icons aligned at the center for visual reference.
• Figure 5: Spearman correlation heatmap of 75 sustainable development indicators for Italy, where dark red indicates strong negative correlation, dark green indicates strong positive correlation, and white indicates no correlation.