The scientific management and protection of intangible cultural heritage (ICH) cannot be separated from continuous innovation and the public’s active participation. The evolution of the value of local cultural heritage can help prevent the loss of valuable cultural identity and cultural heritage. This study is divided into two stages. In the first stage, the innovative practice of heritage education in colleges and universities is explored through the case studies of “She costumes culture” and “Quanzhou traditional embroidery skills”. In the second stage, a questionnaire survey on creative products and activities of technical, intangible cultural heritage is conducted. SPSS and AMOS are used for statistical analysis of the questionnaire results, and a total of 26 indicators are obtained, as well as six dimensions: heritage education, the local value of heritage, cultural participation, cultural identity, cultural and tourism integration, and cultural reflection. Among these are a covariant relationship between cultural tourism integration and heritage education, which shows that they play an equally significant role. Sustainable ICH education requires the government and local communities to make corresponding adjustments upon clarifying their own roles and functions in preserving cultural heritage. Ultimately, practical innovation in ICH requires more citizens to change their inherited cultural values. In particular, inheritors achieve sustainable protection of ICH through self-management.

Urban space constitutes a complex system, the quality of which directly impacts the quality of life for residents. In high-density cities, factors such as the green coverage in street spaces, color richness, and accessibility of services are crucial elements affecting daily life. Moreover, the application of advanced technologies, such as deep learning combined with street view image analysis, has certain limitations, especially in the context of high-density urban streets. This study focuses on the street space quality within the urban fabric of the Macau Peninsula, exploring the characteristics of the street space quality within the context of high-density urban environments. By leveraging street view imagery and multi-source urban data, this research employs principal component analysis (PCA) and deep-learning techniques to conduct a comprehensive analysis and evaluation of the key indicators of street space quality. Utilizing semantic segmentation and ArcGIS technology, the study quantifies 16 street space quality indicators. The findings reveal significant variations in service-related indicators such as the DLS, ALS, DCE, and MFD, reflecting the uneven distribution of service facilities. The green coverage index and color richness index, along with other service-related indicators, are notably influenced by tourism and commercial activities. Correlation analysis indicates the presence of land-use conflicts between green spaces and service facilities in high-density urban settings. Principal component analysis uncovers the diversity and complexity of the indicators, with cluster analysis categorizing them into four distinct groups, representing different combinations of spatial quality characteristics. This study innovatively provides a quantitative assessment of street space quality, emphasizing the importance of considering multiple key factors to achieve coordinated urban development and enhance spatial quality. The results offer new perspectives and methodologies for the study of street space quality in high-density urban environments.

(1) Background: China is beginning to see increasingly complex real estate development dynamics as urbanization, industrialization and globalization advance. As a key driver of economic and social development in China’s cities, real estate has created prosperity while facing the risk of capitalization and a “hard landing”, making it increasingly difficult to bring it under control. (2) Methods: a new approach that integrates “evolution dynamics–driving mechanism–policy design” is constructed based on the Boston Consulting Group matrix, exploratory spatial data analysis, GIS and Geodetector, and this paper empirically studies the dynamics and driving mechanism of real estate development based on the case study of small county-level cities in Gansu, China. (3) Results: Firstly, real estate development in Gansu is characterized by significant spatial differentiation, heterogeneity and autocorrelation, and its distribution pattern comes into being from unsynchronized macroeconomic, population, social, industrial, institutional and policy development interweaved with the real estate control. Secondly, the real estate is diversified in spatiotemporal evolution models, and the cold and hot cities of different models are in quite different geographical patterns with high spatial agglomeration. Thirdly, there are many driving factors affecting the distribution patterns in real estate. These factors are in complex relationships and they are classified into three categories of “Scale–Contribution–Comprehensive”-oriented driving factor and three sub-categories of “Key–Important–Auxiliary” factors. Fourthly, the factors show large differences in the interaction effects, with the real estate industry scale influencing factors being dominated by bifactor enhancement and the economic contribution influencing factors being dominated by non-linear enhancement. Notably, factors such as permanent resident population, urbanization and government revenue have a strong direct influence on the industry scale and economic contribution of real estate, and factors such as expenditure, output value of industry, urbanization rate and number of secondary schools all have a strong interactive influence. (4) Conclusions: The cities are divided into four policy areas of comprehensive development, contribution improvement, scale growth and free decision. Furthermore, differentiated and adaptive measures are proposed for each zoning, which significantly improves the accuracy and synergy of urban real estate management.

Background: The precise allocation and efficient use of industrial land are necessary for the development and optimization of urban production space; however, the mismatches between urban industrial land consumption and the growth of manufacturing are becoming more serious and has become the primary obstacle to sustainable urban development. Methods: Based on a combination of the Boston Consulting Group matrix, spatial mismatch model, decoupling index, GIS, and Geodetector tools, this paper conducts an empirical study on the Yangtze River Delta region in an attempt to reveal the spatio-temporal evolution of the mismatch between urban industrial land changes and the growth of manufacturing and provide a basis for spatial planning and land management in the new era. Results: The distribution of urban industrial land is characterized by high heterogeneity and agglomeration, the coexistence of expansion and contraction, and increasingly complex and diversified changes. Gross domestic product, government revenue, the added value of tertiary industry, and government investment in science and technology indicate that the goal orientation and scale effect of economic growth play a decisive role in the allocation of urban industrial land and that the influence of industrial structures and technological innovation is rapidly increasing. The interaction between the different factors is a bifactor enhancement, for example, land used for logistics and storage, utilities, commercial and other services, and the import and export trade, which have a strong synergistic enhancement effect. The mismatches between urban industrial land changes and the growth of manufacturing are still within a reasonable degree but there is an increasing number of cities with negative mismatches, making it necessary to implement a differentiated spatial adjustment and management policy. Conclusions: Compared with the mismatches of mobile resources such as labor, finance, and capital, the mismatches of immovable land resources have an increasing impact with more serious consequences and it is harder to make optimizations and corrections. However, the academic community has limited knowledge about land resource mismatches. By quantitatively assessing the mismatches between industrial land consumption and the growth of manufacturing in YRD cities, this paper argues that the mismatches can be rectified through spatial and land use planning and suggests the establishment of a zoning management and governance system to achieve the optimal allocation of urban industrial land resources through the implementation of a “standard land + commitment system” and industrial land protection lines.

By decoding the behavioral mechanisms underlying material perception, this study pioneers a sensory nudging strategy to accelerate the adoption of sustainable materials. This study, grounded in the Stimulus-Organism-Response (S-O-R) model, investigates the factors influencing the purchase intention and loyalty of wood-plastic composites (WPCs), specifically examining the impact of material stimuli, surface treatment processes, product carriers, and environmental stimuli on consumers’ perceptions of uniqueness and emotional responses. A total of 335 valid questionnaires were collected and analyzed using Structural Equation Modelling (SEM), with the results indicating that material stimuli and product stimuli were the strongest predictors of perceived uniqueness and emotional responses. Moreover, emotional response had a significantly stronger effect on purchase intention compared to uniqueness perception. Additionally, material familiarity positively moderated the relationship between emotional response and purchase intention. This study provides theoretical support for the marketing of WPCs, emphasizes the significance of integrating material properties, surface treatments, and usage environments in product design, and suggests new avenues for future research, particularly regarding the relationship between multisensory experiences and consumer behavior.