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.

Human-induced urban growth and sprawl have implications for greenhouse gas (GHG) emissions that may not be included in conventional GHG accounting methods. Improved understanding of this issue requires use of interactive, spatial-explicit social–ecological systems modeling. This paper develops a comprehensive approach to modeling GHG emissions from urban developments, considering Stockholm County, Sweden as a case study. GHG projections to 2040 with a social–ecological system model yield overall greater emissions than simple extrapolations in official climate action planning. The most pronounced difference in emissions (39% higher) from energy use single-residence buildings resulting from urban sprawl. And this difference is not accounted for in the simple extrapolations. Scenario results indicate that a zoning policy, restricting urban development in certain areas, can mitigate 72% of the total emission effects of the model-projected urban sprawl. The study outcomes include a decision support interface for communicating results and policy implications with policymakers.

Nature-based solutions (NbS) are recognized as widely available and cost-effective mechanisms for sequestering carbon and offsetting carbon emissions. Realistic NbS implementations for carbon neutrality need to be effective at the global level and also appropriate for the socio-economic and physical conditions prevailing at the local level. This paper presents a framework that can help stakeholders identify demands, locations, and types of NbS interventions that could maximize NbS benefits at the local scale. Key processes in the framework include (1) interpolating carbon emissions data at larger spatial scales to high-resolution cells, using land use and socio-economic data; (2) assessing NbS effects on carbon reduction and their location-related suitability, through qualitative literature review, and (3) spatially allocating and coupling multiple NbS interventions to land use cells. The system was tested in Stockholm, Sweden. The findings show that the urban center should be allocated with combinations of improving access to green spaces and streetscapes, while the rural and suburban areas should prioritize preserving and utilizing natural areas. Our proposed method framework can help planners better select target locations for intended risk/hazard-mitigating interventions.

Artificial intelligence (AI) has become a transformative force across various disciplines, including urban planning. It has unprecedented potential to address complex challenges. An essential task is to facilitate informed decision making regarding the integration of constantly evolving AI analytics into planning research and practice. This paper presents a review of how AI methods are applied in urban studies, focusing particularly on carbon neutrality planning. We highlight how AI is already being used to generate new scientific knowledge on the interactions between human activities and nature. We consider the conditions in which the advantages of AI-enabled urban studies can positively influence decision-making outcomes. We also consider the importance of interdisciplinary collaboration, responsible AI governance, and community engagement in guiding data-driven methods and suggest how AI can contribute to supporting carbon-neutrality goals.