Path-breaking transformative change is needed in human-environment relations to move towards more sustainable development trajectories at local, national and global scales. Crises may trigger transformative change and learning in the short and in the long term. However, in particular, a short-term response to crises may also be reactive, strengthening established unsustainable practices and further perpetuating vulnerability and inequality rather than supporting transformative change towards a more sustainable path. To understand the nature and response to a crisis in the context of sustainability transformations, this paper elaborates on the following aspects of a crisis: What are the characteristics of a crisis? What and who shapes the narrative(s) of a crisis? What and who shapes the nature of the response to a crisis? Do responses to crises trigger higher levels of learning? Conceptual synthesis is complemented with an exploratory comparative analysis of the Cape Town water crisis and of the COVID-19 pandemic in South Africa. To this end the paper analyzes the interplay between mobilizing individual, collective and relational agency and navigating and transforming power relations to challenge and profit from already weakened unsustainable structures. This approach proves to be promising to understand the role of crises in catalysing and supporting transformative learning to eventually replace unsustainable structures.@en

The paper reports on a comparative study of three different cases on vision and strategy development for climate change adaptation planning in (i) The South African Breede–Overberg Catchment, (ii) The Mississippi Estuary-New Orleans region and (iii) The Dutch Rhine-Meuse Estuary. The objective of the paper is twofold: to develop a better understanding of such processes and to further develop the Backcasting-Adaptive Management (BCAM) methodology. A framework for case evaluation is developed using six dimensions: (i) inputs and resources, (ii) future vision, (iii) stakeholder engagement, (iv) methodological aspects, (v) pathway development and (vi) impact. Major conclusions based on a cross-case comparison and testing propositions are (i) participatory vision development is a strong tool for climate change adaptation planning in different governance contexts and shows considerable diversity in its application in these contexts; (ii) a single, shared future vision is not a prerequisite for vision and pathway development and endorsement; (iii) broad stakeholder engagement enriches strategy development, but the involvement of marginal groups requires additional efforts and capacity building; (iv) multiple pathways and robust elements are useful but require novel expertise; and (v) more institutional embeddedness and support for participatory processes lead to better implementation of the outcomes of these processes.

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In this paper we compare 10 established frameworks for analyzing social-ecological systems. We limited ourselves to frameworks that were explicitly designed to be used by a wider community of researchers and practitioners. Although all these frameworks seem to have emerged from the need for concepts that permit structured, interdisciplinary reasoning about complex problems in social-ecological systems, they differ significantly with respect to contextual and structural criteria, such as conceptualization of the ecological and social systems and their interrelation. It appears that three main criteria suffice to produce a classification of frameworks that may be used as a decision tree when choosing a framework for analysis. These criteria are (i) whether a framework conceptualizes the relationship between the social and ecological systems as being uni- or bidirectional; (ii) whether it takes an anthropocentric or an ecocentric perspective on the ecological system; and (iii) whether it is an action-oriented or an analysis-oriented framework.@en

The complexity and uncertainty inherent in environmental models must be considered and managed in an appropriate manner. This chapter presents a conceptual approach to deal with uncertainty, which considers the context of the purpose for which the model is developed. The four major modelling purposes identified - prediction, exploratory analysis, communication and learning - each focus on different modelling characteristics and roles of uncertainty. The notion of uncertainty is broadened, from being an attribute associated with the quality of information to also comprise modellers' beliefs and experiences. The chapter proposes ways in which uncertainty should be handled for each of the four modelling purposes and presents examples that illustrate the concepts. Various sources of uncertainty are considered relevant when modelling complex systems, and each source manifests differently in the data, structure or frame of the model.@en