International heritage management approaches have developed into a more inclusive process wherein public participation is identified as a pivotal tool. Thus, determining how to assess public interests and include the public’s ideas in heritage protection has become a technical issue, but relevant research still remains limited. This paper aims to test the digital footprints of social media users as a public participatory tool, with the objectives of identifying industrial heritage landscape attributes and assessing associated values. Targeting the Sanxian industrial heritage landscape of Liangdancheng in China as a case study, in this research the data from user-generated content on social media platforms Ctrip, Weibo, and Meituan were collected and processed with ROST CM 6 and NVivo 12, and content analysis (CA) and importance-performance analysis (IPA) were conducted. Results revealed that the industrial heritage landscape of Liangdancheng encompasses various built, cultural, and natural environmental resources, including both tangible and intangible attributes such as architectural constructions, historic artifacts, cultural events, and plants. These attributes were assessed and categorized into four quadrants of importance–performance characteristics, wherein cultural environmental resources show relatively high performance but built environmental resources need further actions to improve their value perception and interpretation among the public. This research demonstrated that the digital footprints of social media users as a participatory tool can work well in terms of data accessibility, value identification, and public representation, advancing the theoretical framework of Chinese industrial heritage management and global practices.

Despite the growing recognition of heritage risk reduction, a comprehensive framework for multi-risk assessment remains notably absent within the context of historic urban landscapes (HULs) across diverse global contexts. This paper aims to fill this gap by developing an assessment framework to address multiple emerging risks in HUL management, considering climate-related, human-induced, and mixed hazards in UNESCO World Heritage properties. A four-step process is established—hazard identification, exposure categorisation, adaptation capacity-building, and vulnerability monitoring and evaluation. Using content analysis, this framework is applied to official reports from 33 World Heritage HUL cases across 33 countries. The results show that, although various hazards have been acknowledged by state parties, local governments prioritise human-induced or natural hazards more often than mixed hazards, leading to a shortage of comprehensive risk management plans and practical actions in most cases. Regarding heritage adaptation, the factors of capacity and governance are widely addressed, demonstrating the commitment of state parties to formulate strategies and solve problems. However, public participation and education practices remain insufficiently implemented, resulting in a relatively low degree of adaptation capacity-building. The proposed multi-risk assessment framework offers a crucial reference for global urban heritage management and risk reduction.

As urbanization accelerates, megacities are facing challenges such as inefficient land use and traffic congestion, particularly in the context of rail transit-oriented development, where land use optimization remains a significant research gap. Current urban planning still relies heavily on the experience and intuition of government planning departments, without achieving quantitative, intelligent, and scientific decision making. This study takes Panda Avenue Subway Station as a case study to analyze the evolution of land use patterns around subway stations and explore optimization strategies to enhance land development efficiency and spatial utilizationTo fill this research gap, this paper proposes a CNN-AIMatch model based on machine learning algorithm and an enhanced PLUS-Markov prediction model using the increase and decrease of floor area ratio as a control measure, which adopts an increase in plot ratio as a control measure to improve the accuracy of the Kappa coefficient in different plot ratio scenarios and the prediction of 3D urban spatial growth trends. The model effectively overcomes the limitations of the conventional 2D perspective in predicting urban expansion. By simulating urban renewal and ecological preservation scenarios, it provides an innovative solution for land use pattern optimization and plot ratio control at the block level in subway station areas. The goal of this study is to optimize land use and floor area ratio control strategies through the application of this model, intelligently respond to the challenges of high-density development and quality of life assurance, achieve the best use of land, and promote sustainable urban development and the construction of smart cities.

Inefficient land reuse has emerged as a critical pathway for the sustainable development of urban spaces. Efficient land development in megacities’ industrial heritage areas is heavily influenced by the influx of mass passenger flows from new subway stations. To address this issue, a dynamic passenger flow-oriented land use prediction model for subway stations was developed. This model iterates a simulation model for dynamic passenger flow based on tourists and residents with an artificial neural network for land use prediction. By enhancing the kappa coefficient to 0.86, the model accurately simulated pedestrian flow density from stations to streets. Experiments were conducted to predict inefficient land use scenarios, which were then compared with the current state in national industrial heritage areas. The results demonstrated that the AnyLogic-Markov-FLUS Coupled Model outperformed expert experience in objectively assessing dynamic passenger flow impacts on the carrying capacity of old city neighborhoods during peak and off-peak periods at subway stations. This model can assist in resilient urban space planning and decision-making regarding mixed land use.