The traditional teaching model of landscape architecture education is teacher-centered classroom lectures, which cannot effectively satisfy students’ curiosity and stimulate their creative thinking and computational thinking skills, resulting in students’ low willingness to learn, inattentiveness, and lack of participation in discussions and interactions. This study attempts to combine the BOPPPS teaching structure and Design-Based Learning model to innovate the teaching design, construct the knowledge chain of landscape architecture design modeling and inspire the logic of thinking, with visual programming language, virtual reality parametric modeling and 3D printing hands-on activities while integrating sustainable development goals and related thematic issues to develop practical skills and problem-solving abilities. Through collecting students’ learning performance, examining the quality and effectiveness of teaching and learning, reflecting on the revised teaching content and methods and establishing a practical teaching plan for sustainable landscape architecture education. The results of the study show that the combination of innovative teaching and learning models with SDG-related thematic issues and collaborative group discussions with teaching activities helps to enhance students’ learning effectiveness and bring the learning performances of different learners closer together. In addition, the analysis of learning satisfaction shows that it enhances students’ interest and motivation in learning.

Community home-based care has become China’s main mode of care for the elderly, and the aging of the community public environment has become the focus of attention of all of society. This study uses a questionnaire survey and the fuzzy analytic hierarchy process (FAHP) to (i) obtain the relative weights of indicators in the hierarchy structure of an aging-friendly community public environment and (ii) build a complete indicator evaluation system for the aging-friendly community public environment. The research results show that the quasi-side evaluation index framework of the aging-friendly community public environment is composed of four factors (i.e., community facilities, community road system, community environmental function, and community landscape configuration) and 24 evaluation indexes. The weights of the indicators in descending order are “community road system (w = 0.374)”, “community facilities (w = 0.310)”, “community environmental functions (w = 0.264)”, and “community landscape configuration (w = 0.052)”. The research results show that "community road systems” and “community facilities” are important indicators of the aging-friendliness of a community public environment. “Community environmental function” is an important supplemental factor of the aging-friendliness of a community public environment. “Community landscape configuration” involves improving the construction of the community public environment from the perspective of landscaping. Among all indicator levels, the weights of “Community road floor slip resistance” (w = 0.1795), “Daily health and medical facilities (w = 0.1181)”, and “Provide social interaction functions (w = 0.1067)” are ranked the highest. These results show that ensuring the physical and mental health of the elderly in the community is a core criterion for evaluating the aging-friendliness of a public environment in the community. In this study, an index evaluation weight system is established to clarify the best approach to constructing an aging-friendly community public environment in accordance with previous standard specifications. This system can further clarify the scientific method for evaluating aging-friendly public environments built in the past and can serve as a reference for the practical world.

In order to cope with the rapidly aging society and the Chinese traditional idea of old-age care, community home-based care has become a major mode of care for the aged in China, and the construction of communities suitable for the aged has also become the focus of the whole society. In order to build an objective and scientific evaluation index system of communities suitable for aging, the hierarchical structure and relative important values of the indicators for the assessment were obtained through data text rooted coding and the fuzzy Delphi expert questionnaire survey. The results prove that: the evaluation index system of communities suitable for aging consists of 4 criteria (i.e., humanistic care, public environment, health care, and economic security), 14 sub-criteria, and 48 evaluation indexes. The important values of the four criteria are “health care” (7.35), “public environment” (7.18), “humanistic care” (6.92), and “economic security” (6.87). This shows that with the aging of the elderly and the decline of physical function, health care is the most important material basis for community home care, and is also the core criterion for the construction of communities suitable for aging. Of the 48 important values of the evaluation indexes, “setting up an emergency assistance system” (7.89), “ensuring the accessibility of roads” (7.79), and “handling related affairs” (7.60) ranked the highest. This shows that the protection of the physical and mental health of the elderly and the rights and interests of the elderly are the core values of the construction of communities suitable for aging. This study believes that through constructing an evaluation index system of the communities suitable for aging, the past general constructions suitable for aging according to the standard can be further clarified through the scientific procedure of the ‘community suitable for aging construction evaluation method’, and be a reference for the academic and practical fields.

The primary objective of this study is to investigate the carbon footprint, resilience levels, and optimal landscape area ratios of various parks. Additionally, it explores the relationships between landscape element proportions (LEP), the normalized difference vegetation index (NDVI), resilience indicators (RI), and the carbon reduction benefits associated with carbon neutrality (CN). Six parks were assessed for resilience, NDVI, LEP, and CN values, with Pearson correlation analysis conducted. The results revealed that parks with or without waterbodies exhibited ideal LEP area ratios of 6.5:2:1.5 (Softscape:Waterbody:Hardscape) and 8.3:1.7 (Softscape:Hardscape), respectively. Enhanced Softscape and reduced Hardscape proportions in parks correlated with increased NDVI and CN. NDVI exhibited a positive correlation with Softscape percentage and a negative correlation with Hardscape percentage. Conversely, CN demonstrated a negative correlation with Hardscape percentage and a positive correlation with Softscape percentage. Suggesting Softscape should constitute over 65%, and Hardscape should be under 15% in parks with water bodies. Waterless parks are advised to maintain a Softscape ratio exceeding 83% and a Hardscape ratio below 17%. Finally, the study extended to assess the LEP of 22 additional parks, validating the suitability of the ideal LEP area ratio.

At present, various climate disasters, global warming and other problems feed off of one another. Natural disasters such as earthquakes, typhoons, flooding, and drought as well as human-made disasters such as petrochemical plant safety accidents and steam explosion accidents pose a threat to and affect the development of cities. Therefore, scholars, scientists, and urban planners must study and discuss disasters, so as to work toward important concepts such as resilient cities, resilient landscapes, and disaster prevention parks. This study discusses the resilience park from the perspective of the resilient city. Since the harm caused by natural disasters to parks is mainly due to flooding and high winds, this study focuses on the prevention and control of flood and wind damage. This study is an attempt to explore the indicators of resilience of parks in the context of flood and wind damage, calculate the indicator weight of resilience parks, and establish an indicator evaluation system for resilience parks. The research methods of this study are the Fuzzy Delphi method and the network analysis program method. The results show that the indicators of a resilience park under flood disaster are different types of soil drainage capacity grade (0.199), the lawn to permeable ratio (0.248), the permeable pavement grade (0.125), and the degradation basin grade (0.177). The indicators of a resilience park under high winds are the wind resistance grade of tree species (0.064), the multilayer planting configuration grade (0.053), the tree height and wind resistance grade (0.066), and the bearing capacity grade of trunk and root system (0.067). The weight of each index is calculated. Finally, the park toughness index evaluation system is established. Through this study, we hope to put forward reconstruction suggestions for different parks and provide the evaluation criteria of park toughness so as to improve the disaster resistance of parks.

The Goal of the research is to introducing the principle of Decision Tree that is being used to forecast river pollution, it provides a new method to evaluate the river pollution based on its water quality. We collected monthly monitoring data of water quality from Dezikou River basin of Yilan County, and the data of fish ecology obtained from ecological survey and report, in which to build a water quality and ecology resources database through an actual field investigation. By using data mining software, IBM SPSS Modeler 14.1’s decision tree, conducting the River Pollution Index. Shannon-Weaver diversity, Pielou’s Evenness Index, Margalef’s Species Richness Index, Fish Tolerant Index and Simpson’s Index of Diversity’s classification and prediction, to build a model for river pollution prediction, and to compare this with the Multiple Logistic Regression Analysis. The results showed that the model for river pollution prediction built under the Decision Tree can obtain a better forecast result. The following are the accurate rates of Decision Tree: 88% for CART, 90% for CHAID, 91.67% for C5.0, and 86.11% for Multiple Logistic Regression Analysis. Therefore, the Decision Tree’s algorithm shows a better result in forecasting than the Multiple Logistic Regression Analysis.