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Pasishnyk, N. & Lopes, R. J. (2026). Evaluating SDG network models: A network science ontology-based framework . Sustainability. 18 (1)

N. Pasishnyk and R. J. Lopes,  "Evaluating SDG network models: A network science ontology-based framework ", in Sustainability, vol. 18, no. 1, 2026

@article{pasishnyk2026_1772743787404,
	author = "Pasishnyk, N. and Lopes, R. J.",
	title = "Evaluating SDG network models: A network science ontology-based framework ",
	journal = "Sustainability",
	year = "2026",
	volume = "18",
	number = "1",
	doi = "10.3390/su18010100",
	url = "https://www.mdpi.com/journal/sustainability"
}

TY  - JOUR
TI  - Evaluating SDG network models: A network science ontology-based framework 
T2  - Sustainability
VL  - 18
IS  - 1
AU  - Pasishnyk, N.
AU  - Lopes, R. J.
PY  - 2026
SN  - 2071-1050
DO  - 10.3390/su18010100
UR  - https://www.mdpi.com/journal/sustainability
AB  - 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. This framework was applied to 25 studies selected via a scoping review process focused on SDG interactions. Using the proposed framework each paper was coded and classified into A/B/C methodological tiers. 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 another a heterogeneous multilayer architecture. Critically, 96% of these papers 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.
ER  -