Global ecosystems are facing challenges posed by warming and excessive carbon emissions. Urban areas significantly contribute to carbon emissions, highlighting the urgent need to improve their ability to sequester carbon. While prior studies have primarily examined the carbon sequestration benefits of single green or blue spaces, the combined impact of urban blue–green spaces (UBGSs) on carbon sequestration remains underexplored. Meanwhile, the rise of machine learning provides new possibilities for assessing this nonlinear relationship. We conducted a study in the Yangzhou urban area, collecting Landsat remote sensing data and net primary productivity (NPP) data at five-year intervals from 2001 to 2021. We applied the LightGBM-SHAP model to systematically analyze the correlation between UBGSs and NPP, extracting key landscape metrics. The results indicated that landscape metrics had varying impacts on NPP. At the patch and type level, the Percentage of Landscape was significantly positively correlated with NPP in green space, while the contiguity index and fractal dimension index favored carbon sequestration under certain conditions. The contribution of blue space was lower, with some indicators exhibiting negative correlations. At the landscape level, the contagion index and aggregation index of UBGS had positive effects on NPP, while the division index and landscape shape index were negatively correlated with NPP. The results enhance the understanding of the relationship between UBGS and carbon sequestration, and provide a reference for urban planning.

The pivotal role of urban greening in landscape design for mitigating climate change and enhancing the thermal environment is widely known. However, numerous evaluations of outdoor thermal comfort are seldom applied within the realm of landscape design scenarios. This study explores the relationship between street design and urban microclimate, aiming to propose a range of design strategies that can significantly improve thermal comfort within the street environment in Shenzhen, China. These design strategies hold immense potential for urban greening implementation and provide valuable insights to enhance the overall thermal quality of streetscapes in subtropical cities. The study employs landscape design and environmental simulation methods to evaluate the different design scenarios for the streetscape. The landscape design encompasses three scenarios with revised interventions: 1. the incorporation of building greening and enhanced pavement material albedo; 2. the introduction of trees and grass at the ground level; and 3. a combination of scenarios 1 and 2. Environmental simulations are utilized to assess the effectiveness of each design scenario. The findings reveal that increasing urban vegetation leads to a reduction in urban heat and significantly improves outdoor thermal comfort. Moreover, the incorporation of shade-providing trees proves to be more efficacious than employing vertical greening in alleviating outdoor thermal discomfort.

Purpose: Large-scale urbanisation has become a significant barrier to the natural migration of tree species, which is being exacerbated by accelerated climate change. Within this context, improving the permeability of urban landscapes is expected to be an effective strategy to facilitate the process of forest migration through cities. The purpose of this paper is to develop a method to assess the permeability of urban green spaces as stepping stones for forest migration, from the perspective of seed dispersal. Design/methodology/approach: The proposed method combines a least-cost path (LCP) model and a graph theory-based approach. The LCP model is applied to map the potential pathways of seed dispersal at multiple spatial and temporal scales, based on which graph theory-based indices are used to quantify the accessibility of urban landscapes for seed dispersers. This method is demonstrated by a case study in the Greater Manchester area, UK. Eurasian jay, Eurasian siskin, coal tit and grey squirrel are selected as the main seed dispersers in the study area. Findings: The results provide a comparison of the landscape permeability maps generated from different seed dispersers and identify key areas likely to facilitate the process of forest migration. Recommendations regarding landscape management for improving permeability are also discussed. Originality/value: This method allows designers to re-visualise highly modified and fragmented urban landscapes as stepping stones for seed dispersal, which in turn allows for a more piecemeal form of landscape design to optimise urban landscapes for climate adaptation.

Cities around the world are promoting tree-planting initiatives to mitigate climate change. The potential of such efforts to assist tree migration has often been overlooked. Due to the urban heat island effect, cities could provide suitable climates for the establishment of outlier populations, serving as propagule sources for poleward tree migration.

Rapid climate change will create extreme problems for the biota of the planet. Much of it will have to migrate towards the poles at a rate far beyond normal speeds. In this context, the concept of assisted migration has been proposed to facilitate the migration of trees. Yet current practices of assisted migration focus on “where tree species should be in the future” and thus have many uncertainties. We suggest that more attention should be paid on the flow of forest migration. Therefore, this study develops a three-step methodology for mapping the flow of forest migration under climate change. Since the migration of trees depends on the activities of their seed dispersal agents, the accessibility of landscapes for dispersal agents is mainly considered in this study. The developed method combines a least-cost path model, a graph-based approach, and a circuit theory-based model. The least-cost path model is applied to map the movement of dispersal agents, based on which graph-based indices are used to evaluate the accessibility of landscapes for dispersal agents, which in turn is used as the basis for circuit theory-based modelling to map the flow of forest migration. The proposed method is demonstrated by a case study in the Greater Manchester area, UK. The resulting maps identify areas with high probability of climate-driven migration of trees.