Quantitative modelling is commonly used to assist the policy dimension of sustainability problems. Validation is an important step to make models credible and useful. To investigate existing validation viewpoints and approaches, we analyse a broad academic literature and conduct a survey among practitioners. We find that empirical data plays an important role in the validation practice in all main areas of sustainability science. Qualitative and participatory approaches that can enhance usefulness and public reliability are much less visible. Data-oriented validation is prevalent even when models are used for scenario exploration. Usefulness regarding a given task is more important for model developers than for users. As the experience of modellers and users increases, they tend to better acknowledge the decision makers’ demand for clear communication of assumptions and uncertainties. These findings provide a reflection on current validation practices and are expected to facilitate communication at the modelling and decision-making interface.

Qualitative data is an important source of information for system dynamics modeling. It can potentially support any stage of the modeling process, yet it is mainly used in the early steps such as problem identification and model conceptualization. Existing approaches that outline a systematic use of qualitative data in model conceptualization are often not adopted for reasons of time constraints resulting from an abundance of data. In this paper, we introduce an approach that synthesizes the strengths of existing methods. This alternative approach (i) is focused on causal relationships starting from the initial steps of coding; (ii) generates a generalized and simplified causal map without recording individual relationships so that time consumption can be reduced; and (iii) maintains the links from the final causal map to the data sources by using software. We demonstrate an application of this approach in a study about integrated decision making in the housing sector of the UK.

The 17 Sustainable Development Goals (SDGs) represent a holistic and ambitious agenda for transforming the world towards societal well-being, economic prosperity, and environmental protection. Achieving the SDGs is, however, challenged by the performance of interconnected sectors and the complexity of their interactions which drive non-linear system responses, tipping points, and spillover effects. Systems modelling, as an integrated way of thinking about and modelling multisectoral dynamics, can help explain how feedback interactions within and among different sectors can lead to broader system transformation and progress towards the SDGs. Here, we review how system dynamics, as a prominent systems modelling approach, can inform and contribute to sustainability research and implementation, framed by the SDGs. We systematically analyse 357 system dynamics studies undertaken at the local scale where the most important SDG impacts and their initiators are often located, published between 2015 (i.e. SDGs’ inception) and 2020. We analyse the studies to illuminate strengths and limitations in four key areas: diversity of scope; interdisciplinarity of the approaches; the role of stakeholder participation; and the analysis of SDG interactions. Our review highlights opportunities for a better consideration of societal aspects of sustainable development (e.g. poverty, inequality) in modelling efforts; integrating with new interdisciplinary methods to leverage system dynamics modelling capabilities; improving genuine stakeholder engagement for credibility and impacts on the ground; and a more in-depth analysis of SDG interactions (i.e. synergies and trade-offs) with the feedback-rich structure of system dynamics models.