Diversifying models for analysing global change scenarios and sustainability pathways

Abstract

Non-technical summary. Models are increasingly used to inform the transformation of human-Earth systems towards a sustainable future, aligned with the sustainable development goals (SDGs). We argue that a greater diversity of models ought to be used for sustainability analysis to better address complexity and uncertainty. We articulate the steps to model global change socioeconomic and climatic scenarios with new models. Through these steps, we generate new scenario projections using a human-Earth system dynamics model. Our modelling brings new insights about the sensitivity of sustainability trends to future uncertainty and their alignment with or divergence from previous model-based scenario projections. Technical summary. The future uncertainty and complexity of alternative socioeconomic and climatic scenarios challenge the model-based analysis of sustainable development. Obtaining robust insights requires a systematic processing of uncertainty and complexity not only in input assumptions, but also in the diversity of model structures that simulates the multisectoral dynamics of human and Earth system interactions. Here, we implement the global change scenarios, that is, the shared socioeconomic pathways and the representative concentration pathways, in a feedback-rich, integrated assessment model (IAM) of human-Earth system dynamics, called FeliX, to serve two aims: (1) to provide modellers with well-defined steps for the adoption of established scenarios in new IAMs and (2) to explore the impacts of model uncertainty and its structural complexity on the projection of these scenarios for sustainable development. Our modelling shows internally consistent scenario storylines across sectors, yet with quantitatively different realisations of these scenarios compared to other IAMs due to the new model's structural complexity. The results highlight the importance of enumerating global change scenarios and their uncertainty exploration with a diversity of models of different input assumptions and structures to capture a wider variety of future possibilities and sustainability indicators. Social media summary. New study highlights the importance of global change scenario analysis with new, SDG-focused IAMs.

Early systems change necessary for catalyzing long-term sustainability in a post-2030 agenda

“…Similar variations in future projection have been observed among other models 29 (see other model projections in Figures 2 and S1), and this highlights the importance of diversifying models to obtain a broader variety of future possibilities for a robust assessment and better appreciation of the deep uncertainty in future projections. [30][31][32] Our outputs differed from other models mostly in two main areas. First, FeliX projected a faster decline in fossil energy production (e.g., in Fossil-Fueled Development), which resulted from bolder assumptions about fossil fuel and renewable energy production costs.…”

Quantifying the Safe Operating Space for Land‐System SDG Achievement via Machine Learning and Scenario Discovery

“…Without a highly accurate surrogate model, the uncertainty inherent in the original model is compounded by the error in the surrogate model, reducing confidence in the results. Using machine learning to create surrogate models form the projections of multiple diverse land‐use models can be a way forward to perform sustainability analysis addressing the complexity and uncertainty of process based models in a way that is robust to model choice (Moallemi, Gao, et al., 2022).…”

Knowledge Co‐Production Reveals Nuanced Societal Dynamics and Sectoral Connections in Mapping Sustainable Human–Natural Systems

“…Bringing a systems understanding through mapping to multisectoral modeling is key to correctly characterizing interconnections, whether the modeling approach is system dynamics or something else. van Vuuren et al (2012) recognized this as "information exchange," and Moallemi et al (2022) refer to it as "sectoral dynamics," but it is the awareness that human-natural systems are complex and have many underlying drivers that may be outside of the scope of the system being modeled, if that systems understanding is not sufficiently incorporated into the model design. Researcher openness to learning via knowledge coproduction, iterative development, and cross-sectoral scope is required to enable the systems understanding and suitable representation of interconnections.…”

“…Approaches to sustainability assessment include multicriteria analysis (Kain & Söderberg, 2008), indicators and indices, life cycle assessment, and integrated methods including conceptual modeling, systems modeling, and scenario modeling (Ness et al., 2007) which can incorporate complex interactions such as trade‐offs, uncertainty, feedbacks, and pluralism (Bond et al., 2012). In the era of the SDGs, global and national ex ante sustainability assessment is often performed through integrated assessment modeling of scenarios (Allen et al., 2017; Moallemi et al., 2022; Soergel et al., 2021), although the models and scenarios currently used for this are not without their limitations (Moallemi et al., 2022; Soergel et al., 2021). These ex ante approaches are rarely used at the local scale for many reasons including the challenge of understanding heterogeneities on the ground (van Soest et al., 2019), the difficulty of customizing complex models for local case studies (Verburg et al., 2016), and a (misguided) sense that the impact at the local scale is less of a concern (Easterling, 1997).…”