Based on the theory of planned behavior, this research examines the influence of different types of information on the behavioral intentions of college students in the context of perceived behavioral control (perceived self-efficacy and perceptual control) as mediating variables. The results showed that: (1) Different types of information intervention factors have different effects on perceptual self-efficacy and perceptual control; the influence degree of economic cost has the strongest effect, followed by group pressure, while the influence degree of publicity and education has the weakest effect. However, policy intervention has no statistically significant effect on both of them (perceived self-efficacy and perceptual control). (2) Two variables, perceived self-efficacy and perceptual control, serve as mediators between information intervention factors and energy-saving behavior intention. (3) Individual characteristic factors have significant moderating effects on each path in the model of information intervention–perceived behavior control–intention. Finally, suggestions are made on how to encourage college students to more effectively save energy.

China has undergone many economic changes in the past four decades, and has seen an imbalance in economic development between rural and urban regions. Meanwhile, it also has an aging population. Using panel data on 3590 elderly residents aged between 60 and 95 in 28 provinces in China in 2015, this study explored older adults’ subjective well-being in terms of income, relative income, and social capital. The ordinary least square (OLS) regression results showed that the effect of actual income on subjective well-being was weak, and relative income and social capital had a significant effect on the happiness of urban and rural residents; however, there were substantial differences between urban and rural areas associated with economic inequality, social context, social security, and demographic characteristics. Expanding income inequality is contributing to decreased happiness of the rural elderly, while higher social trust and social interaction mitigate this negative influence. This paper also verified that relative income has a mediating or moderating effect on income and the subjective well-being of urban and rural elderly people. The evidence indicates that the Chinese government should pay more attention to reducing the income gap between and within urban and rural groups, and take advantage of social capital to improve the subjective well-being of the elderly population.

Artificial intelligence products (AIPs) for older adults enhance the functions of traditional products and improve the quality of their lives. However, AIPs are not popular among this population, and limited attempts have been made to investigate these users’ negative tendencies regarding AIPs. This study explores the causes of avoidance and exit behaviors toward AIPs among older people from both a functional and socio-emotional mismatch perspective. Data were collected from 1102 older AIP users to verify the research model and hypotheses. The results indicate that perceived control and expectation disconfirmation affect the functional mismatch, while public stigma has the greatest impact on the socio-emotional mismatch. Furthermore, the results highlight a mixed influence of the functional and socio-emotional mismatches on negative behaviors. This study explores older people’s negative tendencies toward AIPs, comprehensively considering the functions of AIPs and the socio-emotions they evoke. Thus, it provides new empirical evidence for the systematic relationship between the functional mismatch and the socio-emotional mismatch and fills the research gap on the influence on the subsequent behaviors of older adults. Additionally, this study sheds light on the specific methods of designing, developing, and promoting AIPs.

This study explores a method to improve the site selection for elderly care facilities in an aging region, using Hefei City, China, as the study area. It combines topographic conditions, population distribution, economic development status, and other multi-source spatial big data at a 500 m grid scale; constructs a prediction model for the suitability of sites for elderly care facilities based on integrated learning; and carries out a comprehensive evaluation and feature importance analysis. Finally, it uses trained random forest (RF) and gradient boosting decision tree (GBDT) models to predict preliminary site selection results for elderly care facilities. A second screening that compares three degrees of population aging is conducted to obtain the final site selection results. The results show the following: (1) The comprehensive evaluation indexes of the two integrated learning models, RF and GBDT, are above or below 80% as needed, which is better than the four single learning models. (2) The prediction results of the RF and GBDT models have 87.9% and 78.4% fit to existing elderly facilities, respectively, which indicates that the methods are reasonable and reliable. (3) The results of both the RF and GBDT models indicate that the closest distance to healthcare facilities and the size of the population distribution are the two most important factors affecting the location of elderly care facilities. (4) The results of the preliminary site selection show an overall spatial distribution of higher suitability in the main urban area and lower suitability in the suburban counties. The secondary screening finds that priority needs to be given to the periphery of the main urban area and to Lujiang County and other surrounding townships that have a more serious degree of aging as soon as possible in the site selection of new elderly care facilities.