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Although urban scenario planning is widely applied for exploring various directions of urban development, it often has high requirements on the medium of quantitative information analysis and transformation. Thus, this study establishes a method of combining scenario planning with a spatial dynamic planning support system to predict urban growth. Specifically, a scenario-based spatial dynamic modelling method is integrated with the information module of planning policy for better decision support. The integrated modelling method is applied for an actual urban land use planning case of Nanjing, an evolving city in China. The spatial forms of future urban land use are simulated under four different pre-set policy scenarios. The differences in simulated results under multi-criteria restrictions reveal the effectiveness and practical value of the integration approach. The findings of this study provide policymakers with a process-based approach to test and evaluate ‘what-if’ consequences and help stakeholders reach consensus.

Nature-based solutions (NBS) are essential for carbon-neutral cities, yet how to effectively allocate them remains a question. Carbon neutrality requires city-led climate action plans that incorporate both indirect and direct contributions of NBS. Here we assessed the carbon emissions mitigation potential of NBS in European cities, focusing particularly on commonly overlooked indirect pathways, for example, human behavioural interventions and resource savings. Assuming maximum theoretical implementation, NBS in the residential, transport and industrial sectors could reduce urban carbon emissions by up to 25%. Spatially prioritizing different types of NBS in 54 major European Union cities could reduce anthropogenic carbon emissions by on average 17.4%. Coupling NBS with other existing measures in Representative Concentration Pathway scenarios could reduce total carbon emissions by 57.3% in 2030, with both indirect pathways and sequestration. Our results indicate that carbon neutrality will be near for some pioneering cities by 2030, while three can achieve it completely.

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In the context of accelerated urbanization, socioeconomic development, and population growth, as well as the rapid advancement of information and communication technology (ICT), urban land is rapidly expanding worldwide. Unplanned urban growth has led to the low utilization efficiency of land resources. Also, ecological and agricultural lands are continuously sacrificed for urban construction, which in the long-term may severely impact the health of citizens in cities. A thorough understanding of the mechanisms and driving forces of a city’s urban land use changes, including the influence of ICT development, is therefore crucial to the formation of optimal and feasible urban planning in the new era. Taking Nanjing as a study case, this article attempts to explore the measurable “smart” driving indicators of urban land use change and analyze the tapestry of the relationship between these and urban land use change. Different from the traditional linear regression analysis method of driving force of urban land use change, this study focuses on the interaction relationship and the underlying causal relationship among various “smart” driving factors, so it adopts a fuzzy statistical method, namely the grey relational analysis (GRA). Through the integration of literature research and known effective data, five categories of “smart” indicators have been taken as the primary driving factors: industry and economy, transportation, humanities and science, ICT systems, and environmental management. The results show that these indicators have different impacts on driving urban built-up land growth. Accordingly, optimization possibilities and recommendations for development strategies are proposed to realize a “smarter” development direction in Nanjing. This article confirms the effectiveness of GRA for studies on the driving mechanisms of urban land use change and provides a theoretical basis for the development goals of a smart city.