With rapid urbanization, enhancing the quality of public spaces is critical to residents’ well-being and sustainable urban development. However, user perceptions of these spaces remain insufficiently quantified. This study introduces a perception-based evaluation framework encompassing four dimensions: service, spatial, cultural, and aesthetic. A three-dimensional importance-performance analysis (3-D IPA) model is applied to assess two multifunctional public spaces in Wuhan—the Citizens’ Home (CH) and the Creative World Industrial Park (CWIP)—with the aim of identifying user-prioritized attributes that inform sustainable design interventions. The findings reveal the following: (1) At CH, spatial perception (importance = 3.93; performance = 4.02) received the highest ratings, particularly for openness and ecological pavement, highlighting areas for green infrastructure improvement. (2) At CWIP, cultural perception (importance = 3.75; performance = 3.73) dominated, with a need to enhance the signage systems and cultural integration for greater place identity. (3) Optimization priorities included energy-efficient lighting, entrance enhancements, and recreational layout improvements at CH, and thematic diversity and wayfinding systems at CWIP. (4) The 3-D IPA framework effectively identifies user-perceived priorities and supports experience-driven, resource-conscious spatial improvements. This study provides a user-centered, data-informed approach for evaluating and optimizing urban public spaces, offering practical strategies to align spatial quality with long-term sustainability goals.

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable urban development. Specifically, the research quantitatively evaluates how building distribution and orientation influence building energy consumption and photovoltaic power generation through a comprehensive simulation model approach, employing tools, such as LightGBM, for the enhanced predictability and optimization of urban forms. Our simulations reveal that certain urban forms significantly enhance solar energy utilization and reduce cooling energy requirements. Notably, an optimal facade orientation and building density are critical for maximizing solar potential and overall energy efficiency. This study introduces novel findings on the potential of machine learning techniques to predict and refine urban morphological impacts on solar energy efficacy, offering robust tools for urban planners and architects. We discuss how strategic urban and architectural planning can significantly contribute to sustainable energy practices, emphasizing the application of our results in diverse climatic contexts. Future research should focus on refining these simulation models for broader climatic variability and integrating more granular urban morphology data to enhance precision in energy predictions.

The reconstruction of old communities makes an outstanding contribution to, and holds practical significance for, livelihoods and ecological civilization in the urban renewal context. Clarifying the renovation intentions of residents is conducive to the effective implementation of reconstruction projects. This paper takes a typical old neighborhood in Wuhan as an example to survey residents’ living satisfaction and renovation intention. Multiple linear regression analysis, factor analysis, and correlation analysis were used to quantitatively screen, judge, and process sample data. The results show that residents’ living satisfaction and willingness to renovate were different under the dimensions of resident age and property rights, respectively. Most residents were dissatisfied with the living state of the neighborhood. Residents over 61 years old or those who held real estate certificates had a strong willingness to renovate and tended to accept a unified management manner after the renovation of the old neighborhood. Moreover, resident satisfaction with the internal road traffic, infrastructure, and building state significantly affected the residents’ overall satisfaction with the living environment in the old neighborhood, which should be given more attention to improve the residents’ willingness to support the renovation items. Furthermore, it was found that resident satisfaction with building conditions, infrastructure, internal road traffic, and public environment significantly impacted their agreement with the renovation in the old neighborhood. This investigation could provide a basis and guidance for the reconstruction and design of old communities.

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities.

The bell tower of Nanking University, built in 1888, is a milestone of modern educational architecture in Nanjing. It fully introduced Western architectural concepts and is an important historical witness to the cultural exchange between China and the West. At present, there are few studies on the conservation and reuse of the building, so the authors conducted the project. In this paper, we conducted research by combining qualitative and quantitative methods. The qualitative analysis includes analyses of historical maps, photos, text, surveys, mapping of buildings, and a comparative analysis. The quantitative analysis includes statistics on the distribution of the urban school architectural heritage, the number of buildings at Nanking University, and China’s school building design standards. Through the historical research and value analysis of the building, we explore its real information from the aspects of architectural style, space, structure, material, and construction technology, and propose a reasonable conservation method. According to the current urban planning policy and architectural heritage data, the functional upgrading and renovation of the building will be carried out. This paper explores authenticity and integrity-based restoration methods, as well as the adaptive-reuse strategy for China’s architectural heritage, which can provide a paradigm for regional development and historic campus renewal.

In the context of an increasingly extreme climate, Urban Heat Island (UHI) mitigation of communities through ventilation has recently attracted more attention. To explore the impact mechanisms of different morphological renovation schemes on its wind and thermal environment, this paper selected the Laozheng Community as a case study and: (1) analyzed measured data to quantitatively investigate the UHI within the community; (2) established the CIM-WTEPS system to construct community information models and to conduct wind environment parametric simulation for seven micro-renovation schemes across three levels; (3) performed correlation analyses between morphology indicators and wind environment indicators; (4) conducted the thermal environment parametric simulation of the community under different schemes. The results reveal that: (1) the Laozheng Community exhibits the Urban Heat Island Intensity (UHII) of up to 6 °C; (2) apart from the “ Hollowing “ scheme, which deteriorates the community wind environment, all other schemes optimize it, potentially increasing the average wind speed by up to 0.03m/s and in the renovated area by up to 0.42 m/s; (3) building density is highly correlated with the average wind speed and the proportion of calm wind area, with correlation coefficients of −0.916 (p < 0.01) and 0.894 (p < 0.01), respectively; (4) the adding of shading facilities can enhance the proportion of areas with lower Universal Thermal Climate Index (UTCI) without adversely affecting the optimization effects of the wind environment, achieving an maximum increase of 3.1%. This study provides a reference for optimizing the community’s microclimate through morphological micro-renovations and detailed operations, aiding designers in better controlling community morphology for in future community renewal and design planning, thereby creating a more hospitable outdoor environment.