The smell of space is inseparable from the sustainable development of the living environment. The research on olfactory perception and smell landscape has a positive effect on landscape design and urban planning and contributes to the formation and design optimization of unique urban memory. This study combines urban smell tracking experiments with Internet social media data analysis to classify smells in the old city center of Guangzhou, China, and analyzes the study within the inner ring and six historic districts. Based on the research results, the smell map was drawn, and the reliability of the smell map was tested through social data and semantic analysis. The emotional score heat map of smell and emotion in six regions was constructed, highlighting the impact of smell in key neighborhoods on the environment. In the conclusion to the study, the thematic routes of green urban design are proposed: sightseeing routes, cultural routes, and food routes, as well as improvement strategies to promote the integration of smell and urban operation activities and the sustainable development of urban regional characteristics.

Employing cultural ergonomics concepts to product innovation for cultural education and sustainable development is the target of this study. Atayal culture is famous for its weaving craft and art. The Atayal tribe of Taiwanese aborigines weave on the Atayal loom. Traditional culture is disappearing rapidly due to colonial history and modernization. The Atayal people consider the weaving loom a cultural object when used as a weaving device. This study attempts to transform the ancient Atayal loom into a teaching device and extend Atayal weaving from execution in daily living to cultural experience and cultural heritage development. This study first explores and identifies the meaning of cultural objects and extracts their cultural features. Then, employing cultural ergonomics, a weaving box is redesigned from an ancient part of the loom into a modern weaving box. Finally, the new weaving device is evaluated and discussed. This case study examines a paradigm of how designers interact with cultural heritage and transform cultural objects into new instructional aids. Through cultural product design, it provides an intertwined experience in theoretical and practical approaches to accomplishing the development of cultural sustainability.

Introduction: Smart cities, artificial intelligence (AI) in healthcare, and low-carbon building materials are pivotal to public health, environmental sustainability, and green efficiency. Despite their critical importance, understanding public perceptions and attitudes toward these domains remains underexplored. Additionally, the effective use of advanced technologies like convolutional neural networks (CNN) in predicting and promoting low-carbon solutions in construction is gaining attention.Methods: This study employs a dual approach: (1) A survey of 200 respondents was conducted to gauge public perceptions and attitudes toward smart cities, AI in medicine, and low-carbon building materials. (2) A CNN model was developed and implemented to predict the performance of low-carbon building materials. The model utilized convolutional and pooling layers to capture local features and spatial information from image datasets, with tasks including image classification and segmentation.Results: The survey results indicate high awareness of smart cities (80%), with 60% associating them with environmental protection and green living. For AI in medicine, 70% of respondents are aware of its applications, but only 45% perceive it as environmentally beneficial. Regarding low-carbon building materials, 60% expressed willingness to pay premium prices, and 65% recognized their positive environmental impact. The CNN model demonstrated high prediction accuracy on both training and validation datasets, effectively aiding in the identification of low-carbon materials and reducing building energy consumption and carbon emissions.Discussion: The findings highlight significant public awareness and diverse attitudes toward these critical domains, suggesting the need for improved communication and advocacy for AI’s environmental benefits. The application of CNN models in the construction industry showcases a promising pathway to enhance material selection efficiency and foster sustainable practices. These insights are essential for aligning public understanding with technological advancements to achieve environmental and public health goals.