Project on integrated assessment model-based scenarios for sustainable development objectives
In 2015, nearly all nations agreed on a set of ambitious Sustainable Development Goals (SDGs) to ensure environment protection and human development. Accounting for the coherence of the overall set of goals presents a major scientific challenge as there are many complex relationships and feedbacks between them and across various scales (local to global, near- and long-term). However, in fact very little scientific information exists on how to achieve such a diverse set of goals simultaneously. In the proposed research, I will address the scientific gap by developing a novel set of model-based scenarios that explore the efforts required to achieve a set of key sustainable development targets by 2050 simultaneously (i.e. using a backcasting approach). These targets are based on the SDGs and other international agreements, and cover key sustainability issues such as food production, energy and land use, climate change, water scarcity, and nutrient cycles. 

The purpose of the analysis is to identify 1) transformation processes needed for achieving these targets (including timing), 2) key synergies and trade-offs among sustainability issues, and 3) the relationships between different geographic scales. In PICASSO, I will use the integrated assessment model IMAGE 3.0 to develop such scenarios and explore the key linkages. For this purpose, important model improvements will be made to better cover the feedbacks and response options relevant for the SDG targets. Additional project activities will cover uncertainty analysis, and questions related to implementation (the role of different actors in these transitions). Stakeholder interaction is vital and will be formalised in a project forum with key stakeholders that will meet on a regular basis. The scenarios may thus be used to implement and evaluate the SDGs, and they will also result in new scientific understanding of integrated response strategies that can be used by other researchers in more detailed analysis.