Low-carbon travel has emerged as a topic of interest in tourism and academia. Studies have offered reasons tourists may engage in low-carbon travel; however, these explanations are scattered throughout the literature and have yet to be integrated into low-carbon travel motivation and constraint constructs. This study develops a low-carbon travel motivation scale (LCTMS) and a low-carbon travel constraint scale (LCTCS). It performs reliability and validity testing to measure the low-carbon travel motives and obstacles. Items were collected primarily by literature review, and, then, by surveys of 382 tourists from low-carbon travel destinations and 390 from non-low-carbon travel destinations. Through a rigorous scale development process, this study identifies six dimensions of the LCTMS (environmental protection, experience-seeking, escape or social connection, industry pleas and measures for environmental protection, low-carbon products, and green transportation) and four dimensions of the LCTCS (intrapersonal constraints, interpersonal constraints, structural constraints, and the not a travel option).

Although consumers generally accept and care about environmental issues, consumers have not adjusted their behavior accordingly. Based on the diamond model theory, this study proposes and tests the direct impact of personal green commitments on low-carbon travel motivation and constraint, and the possibility of subsequent low-carbon travel intention. According to the results of 358 valid questionnaire surveys, this study shows that green commitments positively affect the low-carbon travel motivation and intention, while negatively affecting the low-carbon travel constraint. The low-carbon travel motivation has some mediating effects. The research results can be used as a reference by relevant managers of the tourism industry to make changes in the content of travel services that are more suitable for specific populations.

Under the promotion of combined medical and elderly care (CMEC) policies, the market demand for elder-friendly smart medical products as convenient intelligent healthcare devices is growing. However, most studies on elderly-friendly smart medical products focus on functional enhancement and cost control, and there is a lack of research on the sustainable innovative design of elder-friendly smart medical products from the perspective of elderly emotional needs. Therefore, this paper proposes an integrated framework based on the fuzzy Kano model, Kansei engineering, and theory of inventive problem solving (TRIZ) for mapping the complex and dynamic emotional needs of the elderly to product design parameters to produce innovative solutions, ensuring the sustainability of the design process and the innovativeness of the design solutions of elder-friendly smart medical devices. We verified the effectiveness and applicability of this integrated framework through a case study involving sustainable innovation design of a smart blood pressure device. The results of this study are of considerable theoretical and practical significance for promoting the development of the market for elder-friendly smart medical products under the policy of CMEC, meeting the needs of the elderly for healthcare devices and improving their quality of life.

Hemodialysis mainly relies on the “artificial kidney,” which plays a very important role in temporarily or permanently substituting for the kidney to carry out the exchange of waste and discharge of water. Nevertheless, a previous study on the artificial kidney has paid little attention to the optimization of factors and levels for reducing the solidification of the artificial kidney during the hemodialysis procedure. Thus, this study proposes an integrated model that uses the Taguchi method, omega formula, and back-propagation network to determine the optimal factors and levels for addressing this issue. First, we collected the recommendations of medical doctors and nursing staff through a small group discussion, and used the Taguchi method to analyze the key factors at different levels. Next, the omega formula was used to convert the analysis results from the Taguchi method to assess the defect rate. Finally, we utilized back-propagation network algorithms to predict the optimal factors and levels for artificial kidney solidification, in order to confirm that the key factors and levels identified can effectively improve the solidification rate of the artificial kidney and thereby enhance the effect of hemodialysis. The research finding proposes the following as the optimal factors and levels for artificial kidney solidification: the amount of anticoagulation should be set at 500 units, the velocity of blood flow at 300 ml/min, the dehydration volume at 2.5 kg, and the vascular access type as autologous blood vessels. We obtained 270 sets of data from the patients of End Stage Renal Disease (ESRD) under the setting of the optimal combination of the factors at different levels; the defect rate of artificial kidney solidification is 12.9%, which is better than the defect rate of 32% in the original experiment. Meanwhile, the patient characteristics for physiological status in BMI, serum calcium, hematocrit, ferritin, and transferrin saturation percentage are improved by this study. This conclusion validates the ability of the proposed model in this study to improve the solidification rate of the artificial kidney, thereby confirming the model’s use as a standard operation procedure in the hemodialysis experiment. The ideas behind and the implications of the proposed model are further discussed in this study.