In this study, the influence of the gymnasium building form on energy consumption and photovoltaic (PV) potential was investigated to address its high energy consumption and carbon emissions issues. Five cities in different climate zones in China (Harbin, Beijing, Shanghai, Guangzhou, and Kunming) were selected as case study environments to simulate and calculate the energy use intensity (EUI), photovoltaic power generation potential (PVPG), and CO₂ emission (CE) indicators for 10 typical gymnasium building forms, while also assessing the impact of building orientation. This study found that changes in gymnasium building orientation can cause a 0.5–2.5% difference in EUI under the five climatic conditions, whereas changes in building form can cause a 1.9–6.4% difference in EUI. After integrating a building-integrated photovoltaic (BIPV) system on the roof, changes in building orientation and form can lead to a 0–14.4% and 7.6–11.1% difference in PVPG and a 7.8–68.1% and 8.7–72.0% difference in CE. The results demonstrate that both the choice of form and orientation contribute to a reduction in carbon emissions from BIPV gymnasiums, with the rational choice of form having a higher potential for carbon savings than orientation. These research findings can guide the initial selection of gymnasium designs to pursue low-carbon goals.

The visual perception of landscape spaces between residences in cold regions is important for public health. To compensate for the existing research ignoring the cold snow season’s influence, this study selected two types of outdoor landscape space environments in non-snow and snow seasons as research objects. An eye tracker combined with a semantic differential (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out the gaze characteristics in the landscape space, and reveal the design factors related to landscape visual perception. In the snow season, the spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlations with the landscape visual perception scores (W). In the non-snow season, in addition to the above three factors, the roof height difference (RHD), tall-tree height (TTH), and hue contrast (HC) also markedly influenced W. The effects of factors on W were revealed in immersive virtual environment (IVE) orthogonal experiments, and the genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimize the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82–2.15, RHD: 10.81–20.09 m, BS: 48.53–61.01, TTH: 14.18–18.29 m, GP: 0.12–0.15, and HC: 18.64–26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22–2.54, BS: 68.47–82.34, and GP: 0.1–0.14.

Restoring campus pedestrian spaces is vital for enhancing college students’ mental well-being. This study objectively and thoroughly proposed a reference for the optimization of restorative campus pedestrian spaces that are conducive to the mental health of students. Eye-tracking technology was employed to examine gaze behaviors in these landscapes, while a Semantic Difference questionnaire identified key environmental factors influencing the restorative state. Additionally, this study validated the use of virtual reality (VR) technology for this research domain. Building height difference (HDB), tree height (HT), shrub area (AS), ground hue (HG), and ground texture (TG) correlated significantly with the restorative state (ΔS). VR simulations with various environmental parameters were utilized to elucidate the impact of these five factors on ΔS. Subsequently, machine learning models were developed and assessed using a genetic algorithm to refine the optimal restorative design range of campus pedestrian spaces. The results of this study are intended to help improve students’ attentional recovery and to provide methods and references for students to create more restorative campus environments designed to improve their mental health and academic performance.

The visual perception of leisure spaces between residences in cold regions is important for public health. To compensate for the existing researches` lack of ignoring the cold snow season`s influence, this study selected two types of outdoor leisure space environments in non-snow and snow seasons as research objects. An eye tracker combined with a Semantic Difference (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out leisure gaze characteristics, and reveal the environmental factors related to leisure visual perception. In snow season, spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlation to the leisure visual perception scores (W). In non-snow season, apart from the above three factors, roof height difference (RHD), tall tree height (TTH) and color contrast (HC) also influenced W obviously. The effects of factors on W were revealed in immersive virtual en-vironment (IVE) orthogonal experiments and genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimized the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82‒2.15, RHD: 10.81 m‒20.09 m, BS: 48.53‒61.01, TTH: 14.18 m‒18.29 m, GP: 0.12‒0.15, and HC: 18.64‒26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22‒2.54, BS: 68.47‒82.34, and GP: 0.1‒0.14.

The vision-based restoration of pedestrian space on campus is of great significance to the mental health of college students. In order to make up for the lack of objectivity and research depth of the existing research on the restoration of campus space, the outdoor pedestrian space of students' main activities is taken as the research object in this study. An eye tracker was used to explore gaze patterns in the landscape space, and SD questionnaire was used to screen the environmental factors related to the restorative state. The feasibility of the application of virtual reality technology was verified. The experimental results showed that, height different of building (HDB), height of tree (HT), area of elevation in the field of view (AS), hue of ground (HG) and texture of ground (TG) had a strong correlation with restorative state (ΔS). A virtual pedestrian experiment was conducted by setting up VR scenes with different parameters to reveal the influence mechanism of five environmental factors on ΔS. After training and comparing three prediction models for the restorative state, t GA was applied for restorative design optimization. The optimal thresholds were as follows: HDB: 20m∼28m, HT: 8.5m∼15.5 m, AS: 12%∼15%, TG: 0.20m2∼0.50 m2, and HG: 140° ∼245 °.

In the winter environment of cold regions, the visual perception of inter-house leisure space in settlements has an important impact on the physical and mental health of residents. The study used an eye-tracking device combined with a semantic differential (SD) questionnaire to screen the visual attention elements and attributes of winter residents in cold regions, including the spatial aspect ratio (D/H), residential elevation saturation (RES), and the percentage of lawn in the field of view (POL). Orthogonal experiments were established in an immersive virtual environment to reveal the influence mechanism of visual perceptual environmental factors of cold regions settlements on residents' leisure space evaluation in winter environment. The study trained and compared four visual perception machine learning agent models, combining genetic algorithms (GA) with k-nearest neighbor algorithms (KNN), resulting in optimized threshold ranges of D/H: 2.22-2.54, RES: 68.47-82.34, and POL: 10%-14% in winter environments.