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Urban living labs have emerged as spatially embedded arenas for governing urban transformation, where heterogenous actor configurations experiment with new practices, institutions, and infrastructures. This article observes a nascent shift towards experimentation at the precinct scale and responds to a need to further investigate relevant processes in urban experimentation at this scale, and identifies particular challenges for urban planning. We tentatively conceptualise precincts as spatially bounded urban environments loosely delineated by a particular combination of social or economic activity. Our methodology involves an interpretive systematic literature review of urban experimentation and urban living labs at precinct scale, along with an empirical illustration of the Net Zero Initiative at Monash University in Melbourne, Australia, which is operationalising its main campus into a living lab focussed on precinct-scale decarbonisation. We identify four processual categories relevant to precinct-scale experimentation: embedding, framing, governing, and learning. We use the empirical illustration to discuss the relevance of these processes, refine findings from the literature review and conclude with a discussion on the implications of our article for future scholarship on urban planning by experiment at precinct scale.

The need for challenge-led innovation policies to address grand societal challenges is increasingly recognised at various policy levels. This raises questions how to overcome a variety of 'failures' prohibiting innovations to flourish. A key-line of thought in theory and policy emerged since the late 1990s on the role of system failures, next to more conventional market-failure thinking. More recently, scholarly work introduced the notion of 'transformational failures', which implies an even broader perspective on innovation failures as resting in challenges related to transforming entire systems of production and consumption. This paper combines the literature on Technological Innovation Systems (TIS) with literature on multi-level approaches to sustainability transitions to make a contribution to this debate. In particular, this paper argues that the current literature, so far, has failed to explore how different kinds of policies, or policy mixes, can overcome transformational failures. The paper uses a simulation model (i.e. a system dynamics model) and illustrative examples on electric vehicles to explore relations between transformational failures and (mixes of) policy interventions. A key conclusion is that, in particular in the case where an emerging TIS is in a competitive relation with an incumbent system, overcoming transformational failures can be realised either by directly addressing the incumbent system, for instance by taking away its resources (which may be political challenging). Alternatively, the model results show that a clever mix of policy interventions elsewhere in the system may lead to sufficient performance improvements of the emerging TIS so that it can challenge the incumbent system on its own – albeit with a need for substantial additional resources.

The inability of global governments to meaningfully progress sustainable development over the past three decades is deeply concerning. AI is increasingly framed as a solution for achieving such outcomes, sometimes uncritically. We argue that: 1) for AI to improve public decision-making, the conditions and factors influencing public decisions must be better understood and considered; 2) to mainstream AI-enabled insights, transformations of those conditions and factors are necessary; and, 3) critical governance questions about those transformations must be addressed. To develop our arguments we draw on: original research identifying factors shaping public decision-making; ongoing interdisciplinary research exploring conditions that influence the use of AI for sustainable development policy; and, conceptual framings from literature concerned with transitions, earth system governance, leverage points and policy entrepreneurship - all sharing ambition to understand transformative change. In so doing, we seek to advance critical knowledge on the, potentially, transformative implications of AI in public decision-making.