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Cities and urbanized regions are complex, dynamic, and highly integrated systems linking social, ecological, and technical infrastructure domains in ways that create deep challenges for good governance, policymaking, and planning. The combination of impacts from climate change in cities, air pollution, rapid population growth, multiple sources of development pressure and overall urban system complexity make it difficult for decision-makers to develop and guide development trajectories along more livable, equitable, and at the same time, more resilient pathways. Advancing urban sustainability and resilience agendas requires expanding the scope of inter- and trans-disciplinarity approaches, moving beyond the historically separate social–ecological and socio-technical approaches to jointly study social–ecological–technical infrastructure systems in cities. We take urban complexity as a given and suggest that in both research and practice we need to better capture and understand feedbacks, interdependencies, and non-linearities which create uncertainties and challenge the efficacy of governance practices to achieve normative goals for society. Here, we explore new methods, tools, and approaches to advance our understanding of urban system complexity through a series of journal special issue articles that examine urban structure–function relationships, urban sustainability transitions, green space availability, social–ecological memory, functional traits, and urban land use scenarios. [ABSTRACT FROM AUTHOR]

Automated decision making and predictive analytics through artificial intelligence, in combination with rapid progress in technologies such as sensor technology and robotics are likely to change the way individuals, communities, governments and private actors perceive and respond to climate and ecological change. Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring. Investments into applications of these technologies in agriculture, forestry and the extraction of marine resources also seem to be increasing rapidly. Despite a growing interest in, and deployment of AI-technologies in domains critical for sustainability, few have explored possible systemic risks in depth. This article offers a global overview of the progress of such technologies in sectors with high impact potential for sustainability like farming, forestry and the extraction of marine resources. We also identify possible systemic risks in these domains including a) algorithmic bias and allocative harms; b) unequal access and benefits; c) cascading failures and external disruptions, and d) trade-offs between efficiency and resilience. We explore these emerging risks, identify critical questions, and discuss the limitations of current governance mechanisms in addressing AI sustainability risks in these sectors. • There is a growing interest in AI-technologies for sustainability. • Increased uses of these technologies could create new types of systemic risks. • We show a global uptake of AI-technologies in farming, forestry and marine management. • We explore gaps in principles and governance of AI from a sustainability perspective. [ABSTRACT FROM AUTHOR]

The narrative of urban sustainability transformations epitomises the hope that urban governance can create the conditions to plan and govern cities in a way that they contribute to local and global sustainability and resilience. So far, urban governance is not delivering: novel governance approaches are emerging in cities worldwide, yet are unable to transform conventional policymaking and planning to allow for innovative, co-beneficial and long-term solutions and actions to emerge and institutionalise. We present a capacities framework for urban transformations governance, starting from the need to fulfil distinct output functions ('what needs to happen') for mobilising and influencing urban transformation dynamics. The framework helps to diagnose and inform urban governance for responding to disturbances (stewarding capacity), phasing-out drivers of path-dependency (unlocking capacity), creating and embedding novelties (transformative capacity) and coordinating multi-actor processes (orchestrating capacity). Our case study of climate governance in New York City exemplifies the framework's applicability and explanatory power to identify conditions and activities facilitating transformation (governance), and to reveal gaps and barriers of these vis-à-vis the existing governance regime. Our framework thereby functions as a tool to explore what new forms of urban transformation governance are emerging, how effective these are, and how to strengthen capacities. • Existing urban governance is unable to address persistent problems. • Effective governance support of transformation lies in collective capacities. • We develop a framework of four capacities for urban transformation governance (UTG). • We use the framework to explain the development of UTG capacities in New York City. • The framework enables explaining, evaluating and supporting UTG. [ABSTRACT FROM AUTHOR]