Environmental pollution and ecological damage caused by human activities have attracted widespread attention in recent years, and while citizens’ environmental awareness and intentions have increased, their actions may not necessarily change accordingly. This study aims to understand the intention–behavior gap, based on the theory of planned behavior (TPB), by exploring the relationship between intention and pro-environmental behavior on the new waste sorting policy in China. The structural model of extended TPB was tested using sample data from 3113 residents of Changsha, each of whom was asked to complete a two-stage survey. Results demonstrated that perceived policy effectiveness and actual behavioral control positively affect behavioral intention, implementation intention, and pro-environmental behavior. Among them, the actual behavioral control of residents was found to be the most influential factor on behavioral intention and implementation intention, followed by residents’ perceived policy effectiveness. Moreover, behavioral intention and implementation intention mediate the relationship between antecedents and pro-environmental behavior. These findings imply that people with high-level perceptions of policy effectiveness, strong control over actual behavior, strong behavioral intentions, and strong implementation intentions are more likely to engage in pro-environmental behavior. The findings suggest that factors such as perceived policy effectiveness and actual behavioral control should be considered when implementing new policies and campaigns for waste sorting and management.

Residents’ behavior is the result of the combined effect of external environment factors and internal psychological factors. Based on the theory of planned behavior (TPB) and the attitude–behavior–condition (ABC) theory, this study aims to explore the impact of policy support on residents’ psychological factors and proenvironmental behavior. This study developed an extended TPB and ABC model and replaced the behavioral intention in the TPB model with implementation intentions to enhance the ability of the variables to explain and predict proenvironmental behavior. The longitudinal research method was adopted to collect data through a two-stage questionnaire survey of 1145 Shanghai residents. Results demonstrated that perceived policy effectiveness has a significant and positive impact on attitude, implementation intention, and proenvironmental behavior. This means that proenvironmental behavior tends to appear in people with a high perception of policy effectiveness, positive attitude, and strong implementation intention. Moreover, this study points out for the first time that high waste management knowledge weakens the relationship between perceived policy effectiveness and attitude. For residents with high waste management knowledge, the effect of simple policy publicity is limited. The findings suggest that the government should increase the breadth and depth of policy support and policy publicity to cover the entire waste management process.

Nowadays, many high‐tech SMEs have gradually focused on innovation‐orientation and have adopted various measures to create an organizational climate, stimulate knowledge workers’ enthusiasm for innovation, and promote individual innovation behavior, but met with little success. Based on the stimulus organism response (SOR) model, the objective of this study is to explore the mediating factors that affect knowledge workers’ innovative work behavior, and construct a three‐level model of the external environment, psychological factors, and individual behavior. This study selected 575 valid samples from 24 high‐tech SMEs in Zhongguancun, which is a technology hub in Haidian District, Beijing, China. Through the convenience sampling method, This study analyze the data and perform structural equation modeling (SEM) using AMOS 23.0. The external environment (i.e., organizational climate, innovation orientation) and psychological factors (i.e., intrinsic motivation, psychological safety) have a significant positive impact on innovative work behavior. Psychological factors have the most significant impact on innovative work behavior. Besides, psychological factors also mediate the relationship between the external environment and innovative work behavior. The findings indicated that high‐tech SMEs need to formulate scientific innovation‐orientation when implementing strategies, and continue to shape a harmonious and equal organizational climate. Furthermore, high‐tech SMEs should encourage knowledge workers to speak the truth, express different voices, and stimulate work enthusiasm to improve psychological safety and intrinsic motivation, thereby enhancing knowledge workers’ innovative work behavior.

Usually, both external environmental factors and internal psychological factors affect the self-efficacy of athletes returning to sports after an injury. Based upon COR theory, this study investigated mindfulness interventions’ effects on competitive state anxiety and burnout in injured athletes who are returning to sports. The study was conducted in South China from March to April 2022. The snowball and convenience sampling methods were used to select high-level sports teams’ injured athletes returning to sports, and a questionnaire survey was administered, from which 433 valid samples were obtained. Amos v. 26 was used to analyze the data. The results showed that mindfulness has a significant negative effect on competitive state anxiety and burnout, such that after strengthening the mindfulness intervention, athletes’ competitive state anxiety and burnout decreased and regulatory emotional self-efficacy increased. Further, this study indicated that athletes are prone to negative emotions after injury, and among athletes who returned to sports after injury, those with mindfulness interventions reported lower levels of competitive state anxiety and burnout. Hence, the study demonstrated that mindfulness can improve regulatory emotional self-efficacy in injured athletes who are returning to sports by reducing competitive state anxiety and burnout.

Coping with the relation between the increase in carbon emissions and energy consumption in the transportation sector is a pressing issue today. Machine learning and deep neural networks were used in this study to explore the influential factors and trends in future transportation carbon emissions. First, the least absolute shrinkage and selection operator (LASSO) regression was adopted to screen out the key influencing factors in transportation carbon emissions. Second, the prediction performance of the long short-term memory (LSTM) network, generalized regress neural network, and back propagation (BP) network were compared, and an improved LSTM optimized by the sparrow search algorithm was proposed. Third, LASSO-SSA-LSTM was used to predict the transportation sector’s future carbon emissions trends under different scenarios. The results suggested that transportation carbon emissions in China presented a trend of ‘rapid increase—fluctuating decrease—continuous increase’ from 2010 to 2019. Although the main determinant in curbing the rising rate of carbon emissions effectively is the continuous development of renewable energy technology, the variation in transportation carbon emissions in China under eight scenarios showed significant differences. Generally, systemic changes and innovations are crucial to accommodate China’s future low-carbon and sustainable transportation development.

The normalization of epidemic prevention and control has exacerbated nurses’ physical and mental stresses. The important role of physical activity in relieving nurses’ physical and mental stresses has received extensive attention from researchers in recent years. The purpose of this study was to investigate the influence of physical activity on the regulatory emotional self-efficacy, resilience, and emotional intelligence of nurses and explain their interactions. The present study adopted the cluster sampling method. From April to May 2022, a total of 500 nurses in six municipal hospitals in Changsha City were selected. Finally, 402 valid data samples were obtained. Afterward, AMOS 23.0 (by maximum likelihood estimation) was used to process the collected data and analyze the proposed hypotheses by using 5,000 bootstrap samples to test the mediating effects of the structural equation model. The results demonstrated that there are positive correlations between physical activity and resilience (standardized coefficients = 0.232, p < 0.001), resilience and regulatory emotional self-efficacy (standardized coefficients = 0.449, p < 0.001), and emotional intelligence and regulatory emotional self-efficacy (standardized coefficients = 0.330, p < 0.001). The positive influence of physical activity on emotional regulation self-efficacy is completely mediated by emotional intelligence and resilience (standardized indirect effect = 0.237, p < 0.01), and this explanatory power is far higher than any previous study (R² = 0.49). The positive emotions generated by an individual’s physical activity have an important explanatory role for individuals who want to establish more emotional regulation self-efficacy, emotional intelligence, and psychological resilience.