A review of systems modelling for local sustainability

Abstract

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.

Diversifying models for analysing global change scenarios and sustainability pathways

“…FeliX is based on the system dynamics approach (Moallemi et al, 2021; Sterman, 2000) with a resolution set at a global scale and with annual timescale over a long-term period (1900–2100). The model has been used as a policy assessment tool in exploring emission pathways (Walsh et al, 2017), evaluating sustainable food and diet shift (Eker et al, 2019), and analysing socio-environmental impacts on Earth observation systems (Rydzak et al, 2010).…”

“…A diverse set of models has been used to inform sustainable development (Verburg et al, 2016), including input–output models (Wiedmann, 2009), macro-economic and optimisation models (DeCarolis et al, 2017), computational general equilibrium models (Babatunde et al, 2017), system dynamics models (Moallemi et al, 2021; Pedercini et al, 2019), and bottom-up agent-based models (Hansen et al, 2019). Modelling applications have spanned different aspects of the SDGs such as food and diet (Bijl et al, 2017; Eker et al, 2019), climate adaptation (JGCRI, 2017; Mayer et al, 2017; Small & Xian, 2018), land-use (Doelman et al, 2018; Gao & Bryan, 2017), energy (Rogelj et al, 2018a; Walsh et al, 2017), transportation (Moallemi & Köhler, 2019), and biodiversity conservation (Mace et al, 2018).…”

Acting on Sustainable Development Goal (SDG) synergies and trade-offs requires policy-focused systems tools

“…The understanding of interaction archetypes and linking causal effects to their underlying configurations can provide valuable information and inform model-based sustainability analyses (27). They provide a dynamic hypothesis that can help capture the co-evolving nature of socioeconomic and environmental processes, which could be simplified in the model structure.…”

“…These interaction archetypes, adopted from systems science (Methods), can be seen as the building blocks or unit of analysis in SDG synergy and trade-off studies, which can occur at any scale or in any context. They can also be combined to explain a more complex representation of multiple interacting synergies and trade-offs (27). We illustrate the utility of the archetypal patterns of interaction with some simple multidisciplinary examples of the most frequent SDG synergies and trade-offs that have been observed and reported at a global scale (3,4).…”

“…Despite the increasing number of analyses of SDG interactions, no research has so far provided a system-level understanding of underlying causal mechanisms and insights about their generated patterns of behaviour. Addressing this gap requires the adoption of systems science tools (27)(28)(29)(30), which can move beyond the study of interactions as individual linear links, and frame them as recurring patterns of connected feedback loops, whose reinforcing and stabilising effects can cause nonlinearity in progress towards the SDGs (e.g., acceleration, disruption, step changes, and tipping points).…”