The promotion and development of healthy cities are vital for enhancing human habitats and fostering sustainable economic growth. Based on the core databases of Web of Science, PubMed, Google Scholar, and PsycINFO, and the knowledge graph software, this paper presents a quantitative analysis of the literature related to attention recovery abroad. It is found that in recent years, the research on attention recovery has developed rapidly, the number of related studies has been increasing, and the research content presents the characteristics of interdisciplinary integration. By further analyzing the characteristics of the research literature, research context, and knowledge basis, this paper summarizes the empirical research based on the existing quantitative analysis, reviews the research field based on the mechanism of attention recovery, and analyzes the development process and trend based on the research basis of attention recovery. Due to the change in the modern life style, human health problems are becoming more and more prominent. Attention restoration design provides a new research idea and method to balance the relationship between humans and the urban environment.

The promotion and development of healthy cities are vital for enhancing human habitats and fostering sustainable economic growth. Based on the core database of Web of Science and the knowledge graph software, this paper makes a quantitative analysis of the literature related to attention recovery abroad. It is found that in recent years, the research on attention recovery has developed rapidly, the number of related literature has been increasing, and the research content presents the characteristics of interdisciplinary integration. By further analyzing the characteristics of research literature, research context, and knowledge basis, this paper summarizes the empirical research based on the existing quantitative analysis, reviews the research field based on the mechanism of attention recovery, and analyzes the development process and trend based on the research basis of attention recovery. Due to the change of modern life style, human health problems are becoming more and more prominent. Attention restoration design provides a new research idea and method to balance the relationship between humans and the urban environment.

The current design of commercial service spaces in many communities faces issues like incoherence, irrational resource allocation, and low utilization rates. These challenges contribute to increased energy consumption in communities, hindering the overall sustainable development of cities. As a representative community space in the urban environment, the commercial space within the campus requires continuous energy input. Its energy-efficient layout aligns with the principles of sustainable development. This paper uses the university campus as a case study to examine energy-efficient commercial space layout and community practices for environmental protection. Various factors influence the layout of inter-community commercial spaces, and the parameters for measuring the layout structure are diverse, considering the large sample size. Employing machine learning and big data processing to quantify development indicators across various industries and optimize their structure, resource allocation, and energy use has emerged as a viable tool for sustainable urban planning practices. This research seeks to utilize machine learning and data-driven optimization techniques to formulate a comprehensive framework for the sustainable allocation and design of business service spaces within communities. Firstly, we conduct a comprehensive investigation, which includes data collected by applying questionnaire surveys and field research, to assess and model the factors influencing the spatial layout of commercial services on university campuses. Secondly, the AEL machine learning model is constructed by combining the analytic hierarchy process to determine subjective weights, the entropy weight method to calculate objective weights, and the Lagrange algorithm to determine comprehensive weights. Thirdly, we assess and improve the layout of commercial service spaces. Then, by training and testing the Neural Network Model, we apply cases to ensure the accuracy of the machine learning calculation results. Qualitative analysis elucidates the varying factors influencing the sustainable layout of different commercial spaces. Quantitative analysis indicates that, within university campuses, the distance between commercial service spaces and residence halls is a crucial factor in fostering a more sustainable layout. Other significant factors include their location along major student routes and proximity to teaching areas. This study makes contributions not only to the specific field of optimizing commercial service space in communities but also to the broader discourse on sustainable urban development. It advances our understanding of the complex dynamics involved in crafting urban environments that are both efficient and environmentally friendly. Beyond theoretical considerations, the study provides practical solutions and recommendations applicable to implementing tangible improvements in resource allocation. These contributions aim to foster urban environments that are not only environmentally conscious but also economically viable.

The increasing popularity of high-rise, high-density residential areas in urban environments has brought about problems such as isolation from nature and increasingly depauperate ecological conditions, and consequently, adverse effects on residents’ health and environmental sustainability. Therefore, creating a high-quality biophilic living space environment is key to solving this conflict, considering the health benefits that nature brings to people. However, there are currently no appropriate assessment tools to measure the biophilic living environment of high-rise residential areas. In response, we have developed an environment assessment scale for measuring the Biophilic Experience in High-Rise Residential Areas (BornA) based on resident experiences. Semi-structured interviews were conducted to identify the measurement items relevant to their biophilic living experiences and behaviors. Exploratory factor analysis and confirmatory factor analysis were performed to identify the scale’s structure and examine its reliability and validity. The study resulted in the development of an 18-item BornA, comprising five dimensions: natural landscape, natural interaction, cultural identity, neighborhood interaction, and personal space. The BornA contributes to the assessment of the biophilic living space environment in high-rise residential areas from the perspective of resident behavior and perception, identifying health intervention environments for sample populations and understanding how the residential environment affects residents’ health.

Public participation is a requirement for contemporary urban governance in China, especially in the protection planning of historic districts. In the rapid development stage, many historic districts have lost their functional positioning in cities and are gradually disappearing due to outdated facilities and services. Previous studies have pointed out that protecting historic and cultural districts requires the joint efforts of multiple stakeholders. Integrating multiple stakeholders’ interests and needs is a complex problem in related research fields and planning practices. The traditional forms of public participation, such as questionnaire surveys, interviews, and symposiums, have the problem of high time costs for collection and analysis. At the same time, the information has been translated multiple times, making it challenging to avoid misunderstandings and errors. Therefore, there is an urgent need for an alternative platform for public participation in the renewal of China’s historic districts. Taking Houzaimen Street in Jinan City as an example, this paper uses the Bayesian network to establish a spatial evaluation model of historic districts. Our aims are to translate natural semantics into the design semantics of the architectural field, accurately locate the spatial problems of the historic district, and provide targeted improvement measures. In this paper, a public feedback channel for Houzaimen Street is established to provide a reference for the renewal of the historic district and the advancement of public participation.

Understanding how urban morphology is associated with urban vitality is important for sustainable urban regeneration, yet conventional density-based indicators often fail to capture cross-scale heterogeneity in three-dimensional urban form. This study proposes a morphology-vitality diagnostic framework that integrates elevation-weighted multifractal analysis with explainable machine learning. Taking the main urban area of Jinan, China, as a case study, we derived multifractal metrics from building footprint and height data and constructed a composite vitality index using population, point-of-interest, and road-network indicators. XGBoost models and SHAP interpretation were used to examine the nonlinear association between morphological descriptors and urban vitality, while additional robustness analyses were conducted for the vitality index, feature sets, multifractal parameters, and threshold detection. The results show that the morphological heterogeneity index Δα is a key explanatory variable for vitality variation and remains highly ranked across alternative model settings. Baseline comparisons indicate that multifractal descriptors provide additional predictive information beyond conventional morphological indicators. A partial-dependence-based segmented regression further identifies a stable sensitive interval around Δα ≈ 1.82, with bootstrap and spatial-block robustness tests supporting the consistency of this threshold. Rather than implying direct causality, the proposed mismatch indicator is interpreted as a diagnostic measure of deviation between morphology-based predicted vitality and observed vitality. The framework contributes to SDG-oriented urban regeneration by providing a quantitative tool for identifying areas where morphological structure and functional performance are unevenly aligned.