As a crucial component of cultural heritage, industrial heritage possesses considerable cultural, historical, and economic significance. The key challenge for industrial heritage sites is to foster eco-conscious actions among visitors while boosting tourism spending. Based on the S-O-R theory, we constructed a relationship model between authenticity, place attachment and environmental responsibility behavior through structural equation modeling and tested it with the survey data of tourists in Maoming open-pit mine ecological park. Findings reveal that both the object-related authenticity and the existential authenticity of the experience enhance the sense of reliance and belonging to the place, with the former also directly promoting visitors’ eco-conscious actions. In contrast, experiential authenticity does not exert a direct effect on eco-conscious actions. The sense of place reliance and identity are found to be significantly and positively linked to eco-conscious behavior. Furthermore, place reliance and identity act as intermediaries in the relationship between object authenticity, experiential authenticity, and eco-conscious behavior, serving as a sequential mediating factor. The study suggests strategies such as augmenting financial support to preserve the authenticity of the site, prioritizing the enhancement of infrastructure and the creation of cultural offerings to elicit emotional responses, and reinforcing emotional bonds to encourage eco-conscious actions.

Previous studies have demonstrated that the built environment has a significant impact on individual subjective well-being (SWB). However, the majority of these studies primarily examined the impacts of community-level environments on subjective well-being, with limited exploration of the influence of multi-scale environments. This study addresses this gap by utilizing questionnaire surveys and built environment data to examine the effects of built environments at the housing, neighborhood, and community scales on subjective well-being through a multiple regression model (specifically, a hierarchical regression). The results show that environmental variables at the housing scale, neighborhood scale, and community scale can significantly improve the explanatory power of life satisfaction (LS). The findings reveal that the explanatory power of environmental variables on life satisfaction exhibits a diminishing trend from proximate to distal scales. It was found that, in terms of the housing scale, the housing construction environment and quality and the increase in per capita housing area positively contribute to residents’ life satisfaction. At the neighborhood scale, a comfortable environment and enhanced facilities are conducive to improving residents’ life satisfaction, whereas the evaluation of property management and services is linked to reduced life satisfaction. At the community scale, the increase in building density (BD), functional mix density (FMD), road intersection density (RID), and bus stop density (BSD) is not conducive to enhancing residents’ life satisfaction. However, higher bus line density (BLD) is positively correlated with the improvement of residents’ life satisfaction. These findings suggest that in urban community planning and management, attention should be paid not only to housing-scale environmental elements but also to neighborhood-scale environmental elements, and they emphasize the rational planning of community scale environments to enhance residents’ subjective well-being.

In the context of rapid urbanization, accurately identifying the visual factors that influence environmental safety perception is crucial for improving urban transportation environments and enhancing pedestrian safety. With the increase in urban population density and traffic flow, optimizing urban environmental design to elevate residents’ sense of safety has become a key issue in urban planning and management. However, the existing studies face numerous challenges in conducting large-scale quantitative analysis of environmental safety perception in complex scenarios, such as difficulties in data acquisition and limitations in analytical methods. This study addresses these challenges by applying image semantic segmentation and object detection techniques to extract key visual elements from street view images, combined with manual scoring and deep learning methods, to construct a road safety perception dataset. Using a LightGBM model and the SHAP interpretation framework, in this study, we identify the critical visual factors influencing environmental safety perception. An empirical study was conducted in Macau, a modern city where Eastern and Western cultures intersect, and tourism thrives. The findings reveal that: ① The overall environmental safety perception in the eight parishes and surrounding roads of Macau is relatively high, with significant regional differences in safety perception scores around Macau’s parish roads; ② The proportions of buildings, sidewalks, roads, and trees in images are the four primary factors influencing environmental safety perception; ③ The proportions and quantities of visual elements interact with each other, and their reasonable distribution helps form clear spatial visibility and creates conducive activity spaces, thereby enhancing the perception of environmental safety. Through empirical analysis, this study uncovers the mechanisms by which visual elements in urban street scenes affect environmental safety perception, providing scientific evidence for urban planning and transportation environment improvement. The research holds theoretical significance and offers practical references for urban design and management, demonstrating broad application value.

Clarifying the mechanisms by which the micro-scale built environment influences urban vitality is an important scientific challenge, to guide precise urban planning in the context of urban renewal. In this study, we quantify the intensity of human activities through Baidu heat maps, analyze social interaction patterns using social media check-in data, and integrate built environment elements such as road network topology, 3D building morphology, and the spatial distribution of points of interest (POIs). A machine learning technique combining a real-encoded Accelerated Genetic Algorithm-Projective Pathfinding Model (RAGA-PPM) and Shapley Additive Projection for Interpretability (SHAP) for Interpretability Analysis (IPA) was used to investigate the nonlinear mechanisms of 17 factors affecting urban vitality in Macau Peninsula, China. Firstly, the explanatory power of the built environment for comprehensive vitality was significantly better than the other dimensions. Two factors, population vitality and microblogging check-in vitality, contributed the most to the composite vitality value. Secondly, road network density was the most important built environment factor affecting urban vitality in Macau Peninsula (SHAP = 0.025). Finally, the impacts of built environment factors on urban vitality showed nonlinearities, and the threshold effects of the core factors (road network density, spatial fractal dimension, and openness to the sky) showed a consistent neighborhood-level pattern. This study establishes a framework for micro-vitality mechanisms in high-density cities, addressing the limitations of traditional methods in modeling complex nonlinear relationships. The methodological integration of RAGA-PPM and SHAP advances the innovative paradigm of applying interpretable machine learning to the study of urban form.

In order to effectively identify the shortcomings and potential health risks in the construction of healthy cities and achieve sustainable development, relevant improvement strategies have been formulated. According to the National Healthy City Evaluation Index System, with the concept of “Making improvements is more important than reaching standards”, the healthy city construction levels of the first batch of 13 cities in East China were evaluated by combining entropy weight and linear coefficient weighting from the five dimensions of environment, population, society, service, and culture, based on the data of statistical yearbooks, bulletins, and government websites. The results show that Suzhou, Jiading, Wuxi, Hangzhou, Ningbo, Tongxiang, and Zhenjiang are in the first-grade group, Xiamen, Yantai, Jinan, and Weihai are in the second-grade group, and Yichun and Ma’anshan are in the third-grade group. There is also more significant heterogeneity in the healthy environment indicator among the 13 cities; at the same time, there are specific differences in the healthy culture indicator, and there are slight differences in the indicators of healthy population, society, and service. The study reveals the gaps and problems in the construction of healthy cities. It proposes constructive ideas for promoting follow-up improvement of “making up for shortcomings and strengthening the weaknesses”.