The exploitation of urban subsurface space in urban inventory planning is closely connected to the quality of urban environments. Currently, the construction of underground pedestrian streets is characterised by inefficiency and traffic congestion, making them insufficient for fulfilling the demand for well-designed and human-centred spaces. In the study of spatial quality, traditional evaluation methods, such as satellite remote sensing and street maps, often suffer from low accuracy and slow updating rates, and they frequently overlook human perceptual evaluations. Consequently, there is a pressing need to develop a set of spatial quality evaluation methods incorporating pedestrian perspectives, thereby addressing the neglect of subjective human experiences in spatial quality research. This study first quantifies and clusters the characteristics of underground pedestrian spaces using spatial syntax. It then gathers multidimensional perception data from selected locations and ultimately analyses and predicts the results employing machine learning techniques, specifically Random Forest and XGBoost. The research results indicate variability in pedestrians’ evaluations of spatial quality across different functionally oriented spaces. Key factors influencing these evaluations include Gorgeous, Warm, Good Ventilation, and Flavour indicators. The study proposes a comprehensive and applicable spatial quality evaluation model integrating spatial quantification methods, machine learning algorithms, and multidimensional perception measurements. The development of this model offers valuable scientific guidance for the planning and construction of high-quality urban public spaces.

The complex spatial environment of underground commercial street spaces will affect users’ behavior and information needs. As a medium to coordinate the interaction between the underground commercial street space environment and people, guide signs can provide useful information for users. However, the visual saliency of guide signs is the fundamental premise for determining the transmission of information to users. Based on field research, this study identified and examined the factors influencing the significance of guide signs in underground commercial streets from the user’s perspective using the order relation analysis method (G1 method) and with the help of screen-based eye tracking and virtual reality (VR) eye-tracking technology, In addition, we explored the design relationship between critical influencing factors and the space between underground commercial streets, and the visual significance of the differences between each important influencing variable. The study showed that the set position, set height, and design color of underground commercial street guide signs are essential factors in their visual prominence. The prominence was more significant when the position of guide signs was located in the middle and upper area of the space, and the prominence was more significant when the set height was 2.56~2.75 m and 3.12~3.31 m. This study can provide data and theoretical support for the visual saliency design of underground commercial street guide signs and provide a reference for the humanized design of underground commercial street guide signs for public facilities in cities.

Developing and utilizing underground space is a vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards more significant, more terrific refinement, applying architectural theories and excessively subjective designs has resulted in a deficient human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with the perception evaluation, and a regression analysis was employed to identify appropriate scale ranges, which were superimposed with the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces was established, providing a reference for the human-centered design of these environments.

In the context of low-carbon construction, reducing carbon dioxide emissions from town construction land is the key to mitigating the problems caused by global warming. The influence of spatial form on carbon emissions has been generally recognized, but its influence at the level of town construction land is less explored. Therefore, in this study, in order to investigate the relationship between the spatial form of town construction land and carbon emissions, the relationship between them was analyzed, taking Macheng town of Bengbu city as the research object, selecting spatial form elements and quantifying them, and characterizing and accounting for the carbon emissions from the town construction land by each building’s energy consumption. The study demonstrates that the spatial form elements such as building area and building storeys are important factors affecting the carbon emissions of residential land. Likewise, the building area, building shape coefficient, and floor area ratio are crucial factors impacting the carbon emissions of public lands. This research offers spatial form optimization strategies from a carbon reduction perspective by delving into the inherent relationship between spatial form and carbon emissions in town construction land. Consequently, it provides valuable scientific guidance for quantifying spatial planning and formulating carbon reduction strategies within a low-carbon framework.

Building skin can provide comprehensive functions of energy production, daylighting, and shading with an integrated transmissive solar-concentrating panel. In this study, Rhino Grasshopper parametric modeling, Ladybug tool performance simulation, and Octopus multiobjective optimization platforms are used to carry out experimental research. This study establishes the optimal relationship between the conflicting objectives of light environment creation and energy production efficiency of solar-concentrating skin by controlling three variables, namely the size of the solar-concentrating module, the rotation angle, and the number of modules, aiming to design the optimal solution and build a multiobjective optimization technology framework for the solar-concentrating skin of an office space. A comparison and analysis of the scenarios indicate a dynamic concentrating skin that can effectively reduce the daylight glare probability (DGP) by 70% and increase the useful daylight illuminance (UDI) by 10%, while achieving energy production. The correlation between the variables and the performance indices of the solar-concentrating skin was obtained as angle > width > length > amount, and the optimal design interval for each parameter variable. This study reveals the laws of how parameter changes affect individual indicators, which can provide ideas for the design of dynamic concentrating skins and building integration, methods for improving the balanced design of indoor light environments and building capacity, and a technical framework for multiobjective optimization processes.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.