Low-carbon travel is considered as one of the most important strategies to reduce transportation carbon emissions, and its success is decided by the active participation of residents. Based on the theory of planned behavior (TPB) and Attitude-Behavior-Context theory (ABC), this study explores the influencing factors and formation paths of individual low-carbon travel behavior, and analyzes low-carbon travel behavior regarding both daily commuting from residence and tourism destinations. This study collects a sample of 506 respondents and uses Mplus 8.0 to examine the hypotheses. Empirical research results indicate that: (1) A certain gap exists in the individuals’ low-carbon travel behavior between daily residence and tourism destination. Differences exist in direct effects, mediating effects and moderating effects. (2) Low-carbon travel behavioral intention plays a significant mediating role in both daily residence and tourism destination, especially the former. Regarding daily residence, individuals’ attitude, subjective norms and perceived behavioral control have a positive effect on behavior through behavioral intention. Regarding tourism destination, only the attitude-low-carbon travel behavioral intention-behavior path is significant. (3) Situational factors play a significant positive moderating effect on the relationship between low-carbon travel behavioral intention and behavior, especially in tourism destination. This study reveals the internal mechanism of individuals’ low-carbon travel behavior and the differences between travel in daily life and tourism, helping to deepen understanding of individuals’ low-carbon travel behavior and providing guidance for promoting individuals’ low-carbon travel.

As an important transportation infrastructure and transportation backbone in China, high-speed rail (HSR) plays a critical role in promoting the development of green and low-carbon transportation. Calculating the CO₂ emissions reduction performance of HSR will be conducive to promote the CO₂ emissions reduction work of the railway. Based on the Dalkic HSR CO₂ emissions reduction performance model, by adjusting HSR CO₂ emission factor (CEF_HSR), annual times of departures (T) and other parameters, this study develops China HSR CO₂ emissions reduction performance model. Taking the Beijing-Shanghai HSR as the research object, this study conducts a questionnaire survey to explore the substitution effect and demand effect of HSR on different transportation modes, collects data such as passenger volume, average electricity use, and annual times of departures of Beijing-Shanghai HSR in 2019, and calculates the CO₂ emissions reduction performance of the Beijing-Shanghai HSR. This study has two main results: (1) Build China HSR CO₂ emissions reduction performance model based on substitution effect and demand effect. (2) In 2019, the CO₂ emissions of Beijing-Shanghai HSR is 2898 233.62t, the CO₂ emissions reduction performance of Beijing-Shanghai HSR is 17 999 482.8t, the annual CO₂ emissions of Beijing-Shanghai line in ‘No HSR’ case is as 7.2 times as in " HSR" case, and PKT of HSR is 10.2 g/pkm. Based on the research results, this study proposes three CO₂ emissions reduction policy suggestions. This study would be helpful for further HSR CO₂ emissions reduction research and departments related to railway transportation management to make CO₂ emissions reduction policies.

As a vital component of urban tourism, urban theme parks increasingly face experience homogenization and intensifying competition. Accordingly, the implementation of refined digital marketing and operational strategies based on visitor digital behavior has become increasingly essential. In this context, tourists’ social media sharing has become a crucial link between destination marketing and visitors’ experience construction. Within the SOBC (Stimulus–Organism–Behavior–Consequence) framework, this study examines how theme park servicescapes (S) shape sharing motivations (O), which, in turn, influence multimodal sharing intentions (B—text, image + text, video) and subsequently contribute to memorable theme park experience (C). A two-stage, mixed-method design was employed, and the study considered visitors to Beijing Universal Studios and Shanghai Disney Resort. Semi-structured interviews and grounded analysis identified five motivations: altruism, self-presentation, affective expression, hedonic motivation, and community identification. Testing was performed using a survey (N = 604), along with structural equation modeling. The findings indicate that the staff-related social environment exerts significant positive effects on all five motivations, whereas the effects of the physical environment are more selective. Motivations differentially predict modal intentions: text aligns with altruism and affective expression; image + text aligns with altruism, community identification, and self-presentation; and video aligns with self-presentation, hedonism, community identification, and affective expression. All three intentions positively affect memorable theme park experience. These results clarify how motivations map onto content forms and validate a support SOBC framework from servicescapes to memorable experience, offering actionable implications for experience design and digital marketing.

This study examines how tourist perceptions in theme park settings are shaped by three modalities of user-generated content: text, image-text, and video. Drawing on Media Richness Theory, we analyze posts from Weibo and RedNote related to Universal Beijing Resort and Shanghai Disneyland using topic modeling and deep learning sentiment classification. Seven perceptual themes emerge, with clear cross-modal differences: text emphasizes service evaluation, image-text highlights aesthetics and symbolic elements, and video captures immersive, process-based narratives; sentiment distributions also differ by modality. Theoretically, the findings refine media-task fit by showing stable correspondences between modality cues and the focus of perceptual expression. Methodologically, the study demonstrates a scalable pipeline for mining multimodal perceptions from large, real-world corpora. Practically, the results translate into concrete measures: deploy text monitoring to surface operational issues and reply with concise guidance; standardize visual presets and required hashtags to strengthen brand visibility; and curate first-person video with authentic on-site sound to amplify atmosphere around characters and rides.