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.

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.

Climate change poses a threat to cities. Geospatial information and communication technology (Geo-ICT) assisted planning is increasingly being utilised to foster urban sustainability and adaptability to climate change. To fill the theoretical and practical gaps of urban adaptive planning and Geo-ICT implementation, this article presents an urban ecosystem vulnerability assessment approach using integrated socio-ecological modelling. The application of the Geo-ICT method is demonstrated in a specific case study of climate-resilient city development in Nanjing (China), aiming at helping city decision-makers understand the general geographic data processing and policy revision processes in response to hypothetical future disruptions and pressures on urban social, economic, and environmental systems. Ideally, the conceptual framework of the climate-resilient city transition proposed in this study effectively integrates the geographic data analysis, policy modification, and participatory planning. In the process of model building, we put forward the index system of urban ecosystem vulnerability assessment and use the assessment result as input data for the socio-ecological model. As a result, the model reveals the interaction processes of local land use, economy, and environment, further generating an evolving state of future land use in the studied city. The findings of this study demonstrate that socio-ecological modelling can provide guidance in adjusting the human-land interaction and climate-resilient city development from the perspective of macro policy. The decision support using urban ecosystem vulnerability assessment and quantitative system modelling can be useful for urban development under a variety of environmental change scenarios.

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.

Urban modeling is being reshaped by advances in artificial intelligence (AI) and data-rich sensing. This review assembles an integrated evidence base connecting spatial dynamic modeling (SDM), planning support systems (PSSs), urban analytics, and governance concerns. We analyze 1290 publications (2000–2025) using a reproducible pipeline that combines structured literature retrieval with retrieval-augmented generation (RAG) for semantic screening and evidence extraction. Bibliometric mapping and a rigorous coding framework structure the synthesis. The results reveal three linked trajectories. First, SDM has progressed from rule-based simulation toward learned spatial representations using deep and multimodal learning. Second, PSS has evolved from static analytical tools to interactive and participatory environments that embed AI for scenario exploration and stakeholder engagement. Third, governance themes such as transparency, fairness, and accountability have gained importance but remain unevenly implemented in modeling workflows. Building on these findings, we advance AI-aligned SDM, which integrates explainability, uncertainty reporting, documentation, and participation into model design to strengthen institutional accountability and evidence-based planning. A forward research agenda emphasizes methodological fusion between simulation and learning, institutional design for continuous model stewardship, and epistemic pluralism connecting local knowledge with AI to advance equitable and transparent urban governance.