In the process of urbanization and globalization, urban conservation reinforces the links among past, present, and future, which enhances local identity and is indispensable for urban sustainability. The concept of Historic Urban Landscape (HUL), as both a notion and an approach, has been playing an increasingly important role in urban conservation discourse. Prioritizing actions is one of the pivotal procedures to perform the HUL approach. Instead of simply focusing on values selected and graded by experts, more attention should be paid to local residents’ satisfaction. A collaborative approach involving communities to prioritize actions in urban conservation and regeneration is proposed. Using the data from residents in the old town of Dandong, China, this study pioneers the application of the Asymmetric Impact-Performance Analysis (AIPA) technique in the urban conservation field. HUL attributes are categorized into basic factors, excitement factors, and performance factors based on their potential asymmetric impact on residents’ satisfaction. By taking performance levels of the attributes into consideration, this study further identifies improvement priorities for HUL, which helps planners and city managers to make rational choices in managing historic cities.

Historic cities, rich in heritage values and evocative of collective memories and meanings, also constitute crucial living environments for urban residents. These cities increasingly face challenges from urbanization and globalization, leading to cultural discontinuities and the homogenization of cityscapes. Such developments can diminish residents’ sense of belonging and identification with their cities. Emphasizing local residents’ satisfaction is thus essential to urban conservation. This study, using data from Dandong, China, employs Gradient Boosting Decision Trees (GBDT) to identify factors affecting residents’ satisfaction in historic built environments. The analysis reveals that over half of the variability in satisfaction is linked to distinct features of the historic environment. Among the fourteen key influencers identified, contextual order emerges as the most impactful factor, notable for its significant effects and interactions with other variables. This study also uncovers pronounced non-linear effects and thresholds for physically measured characteristics. For instance, open space markedly boosts satisfaction when exceeding 34%, satisfaction diminishes with travel times to heritage sites longer than 6.7 min, and satisfaction decreases when the entropy index for diversity surpasses 0.758. These findings provide critical insights for guiding urban conservation strategies and promoting a data-driven approach to enhance residents’ satisfaction in historic urban settings.

Subway station entrances serve as crucial links between urban environments and underground transit systems and are particularly vital in cities with cold climates. Specialized design strategies are essential to address user needs and promote safety and comfort, thereby encouraging sustainable travel in harsh winter conditions. This research utilizes data from Harbin and Shenyang, two winter cities in China, to explore the nonlinear influences of subway entrance attributes on passenger satisfaction through the combined use of gradient-boosting decision trees and asymmetric impact-performance analysis. The findings indicate that most key attributes of subway entrances impact passenger satisfaction asymmetrically, highlighting the significance of their hierarchical importance in generating satisfaction. These attributes are categorized into frustrators, dissatisfiers, hybrids, satisfiers, and delighters, based on their asymmetry levels. Considering the current performance of these attributes, the study identifies priority for improvement at Harbin and Shenyang’s subway entrances. This aids urban designers and city managers in making informed decisions for urban development and enhancing the overall commuter experience in winter cities.

The Cross-Regional Cultural Heritage (CRCH) is a continuous, large-spanned, and comprehensive system with a large number and diversity of components. To support the integrated conservation of CRCH, an analytical approach with simple structure and easy operation is needed. Therefore, this paper aims to conduct risk maps to interpret, understand, and manage the slow degradation of built heritage in the daily environment. The hazard factors of slow degradation in the environment are extracted and translated into meteorological data for mapping with the Geographic Information Systems (GIS). A risk map to evaluate the risk level of the heritage is obtained by overlapping the vulnerability and hazard factors. With the case study of the Chinese Eastern Railway (CER), the risk maps revealed the risk condition and spatial distribution pattern of CRCH, based on which a network-connected working platform is developed, and suggestions for solutions are proposed. In terms of the operational plan for conservation works, the graded architectural risk sections are delineated. The priority and extent of intervention are proposed according to the degree of urgency and preventive conservation measures can be implemented in advance by using weather forecast data. In terms of social management strategy, through a level-by-level penetration education model, the social awareness of heritage conservation can be cultivated, the technical methods for restoration and maintenance will be trained, and the operating system for monitoring and inspection can be established. Relevant stakeholders, such as government officials, experts, users, and visitors can participate together in the preventive conservation process of the heritage.

Shared bikes are widely used in Chinese cities as a green and healthy solution to address the First/Last Mile issue in public transit access. However, usage declines in cold regions during winter due to harsh weather conditions. While climate factors cannot be changed, enhancing the built environment can promote green travel even in winter. This study uses data from Shenyang, China, to investigate how built environment attributes impact the travel satisfaction of shared bike users who utilize bikes as a First/Last Mile solution to access public transit in winter cities. By employing machine learning algorithms combined with Asymmetric Impact-Performance Analysis (AIPA) and grounded theory, we systematically identify the key attributes and rank them based on their asymmetric impact and urgency of improvement. The analysis revealed 19 key attributes, 17 of which are related to the built environment, underscoring its profound influence on travel satisfaction. Notably, factors such as the profile design of cycling paths and safety facilities along routes were identified as high priorities for improvement due to their significant potential to enhance satisfaction. Meanwhile, features like barrier-free access along paths and street greenery offer substantial opportunities for improvement with more modest efforts. Our research provides critical insights into the nuanced relationship between built environment features and travel satisfaction for First/Last Mile shared bike users. By highlighting priority improvements, we offer urban planners and policymakers a framework for creating livable, sustainable environments that support green travel even in harsh winter conditions.

Green travel offers significant benefits, including reducing pollution and alleviating traffic congestion. Despite these advantages, green travel is less popular in cold regions, particularly during winter, due to comfort, convenience, and safety concerns. While climate factors are immutable, enhancing the urban environment can promote winter green travel. This paper examines the impact of urban environments on residents’ willingness to engage in green travel in cities located in cold regions during winter. Using data from Shenyang, China, a comprehensive structural equation model based on the stimulus–organism–response (S-O-R) theory was constructed, revealing the causal relationships and underlying structure between environmental factors and green travel willingness. The model demonstrates that social, built, natural, and travel environments collectively shape residents’ willingness to engage in green travel, with the social environment emerging as the most impactful factor. Additionally, this study identified two crucial mediating variables, travel perception evaluation and attitude towards green travel, which indirectly influence green travel willingness. This study also identifies nine critical factors—often underappreciated in traditional analyses—that should be prioritised in urban planning. These findings advance the understanding of green travel behaviour in winter cities by illustrating the complex interplay between environmental factors and individual attitudes while providing actionable guidance for fostering supportive social environments and strategically enhancing built and travel environments to promote green travel in cold climates.