From Complex SDG Systems to Network Models: an Ontology-Based Eight-Step Framework

With only 18% of Sustainable Development Goals (SDGs) on track for 2030, systems-based approaches to understanding their interdependencies are essential. Network science can reveal leverage points and guide prioritisation, yet it is often applied without sufficient domain integration, obscuring rather than clarifying sustainability dynamics. We present an eight-step framework for evaluating network science applications in SDG research, applied to 25 studies. The analysis reveals two dominant patterns: semantic/expert-based approaches (11 studies) and indicator/statistical approaches (12 studies). Beyond these, one study implements a multiplex design and one a heterogeneous multilayer architecture. Critically, 96% focus on formal SDG structures rather than the actors, processes, and mechanisms through which targets are achieved, limiting practical utility. The framework makes explicit how modelling choices encode theoretical assumptions and supports like-with-like comparison, meta-analysis and evidence synthesis. As AI-enabled knowledge synthesis proliferates, such transparency steers SDG modelling toward implementation-relevant representations that preserve contextual factors shaping real-world transformations.