Zhigang Li

Chatbots have become increasingly popular as tools for supporting English language learning. However, how to effectively enhance learning ability through emotion induction during interactions with intelligent chatbots remains an unresolved issue. This study integrates Flow Theory, Perceived Value Theory, and the Unified Theory of Acceptance and Use of Technology (UTAUT) with emotion-induction features of a chatbot to develop and test a theoretical model. Survey data from 388 university students who used DeepSeek for English learning were analyzed using structural equation modeling (SEM). Learning ability was operationally defined as continuance learning behavior and learning engagement or self-rated performance, measured through Likert-scale items. The results show that performance expectancy, effort expectancy, and perceived emotional value were all significantly associated with flow, with effort expectancy exhibiting the most substantial direct effect on flow. Perceived emotional value exerts the most significant total effect on continuance learning behavior by enhancing emotional engagement and reinforcing sustained effort through flow. Positive affective prompts strengthen effects of performance expectancy, effort expectancy, and perceived emotional value on flow. In contrast, negative emotion induction appears to reduce the strength of these associations. These findings specify which cognition–affect pathways matter most for English learning with chatbots and inform the design of emotion-aware prompting in DeepSeek to enhance flow and continuance learning behavior.

To achieve Chengdu's dual-carbon goals and support high-quality development, it is essential to analyze the impact mechanisms of land use patterns on carbon storage in Chengdu. This study aims to provide evidence-based land use planning strategies that synergistically balance ecological security and economic growth for the Chengdu-Chongqing Economic Circle's dual-carbon objectives. This study uses the MCCA - InVEST model to simulate the impact of land use changes on carbon storage in Chengdu City from 2025 to 2035, setting up three scenarios: natural development (ND), ecological conservation (EC), and sustainable development (SD). The MCCA model is used to simulate land use dynamics by integrating 11 driving factors. The InVEST model is then used to assess changes in carbon storage, with a focus on the area evolution and carbon storage response mechanisms of cropland , forests, and impervious land. Land use changes show that under the ND scenario, cropland decreases by an average of 0.82 million hectares per year, impervious land expand by 38.1%, and forest area increases by 20.8%. Under the EC scenario, the rate of cropland reduction slows, with forests growing at an average annual rate of 2.6%, and impervious land expanding by 28.1%. Under the SD scenario, which lies between the two, the reduction in cropland is 0.5 million hectares less than in the ND scenario, forests increase by 25.0%, and impervious land expand by 32.4%, achieving a balance between loss and gain. In terms of carbon storage, the ND scenario results in a net decrease of 9.6×10⁶ tons, the EC scenario results in a net increase of 13.2×10⁶ tons, and the SD scenario remains largely stable. In terms of spatial distribution, high carbon storage areas are concentrated in the Longquan Mountains and the western mountainous regions, with NDVI being the primary driving factor (q=0.7264). The SD scenario can balance ecological conservation and economic development. It is recommended to enhance forest construction in the Longquan Mountains and the western mountainous regions, and to promote 'compact' expansion in the urban core area. The MCCA-InVEST coupling model provides an effective tool for evaluating carbon storage across multiple scenarios. This study offers a scientific basis for Chengdu City to achieve its 'dual carbon' goals and serves as a reference for other cities conducting carbon storage assessments and land use planning

Against the backdrop of the deep integration of chatbots into education, this study, based on Self-Determination Theory (SDT) and the UTAUT model, constructed a model of factors influencing college students’ self-directed learning ability in programming. Through a review of existing literature, six key determinants were identified: learning expectancy, effort expectancy, facilitation conditions, teaching interaction, learning motivation, and learning self-efficacy. The main goal of this study was to develop a model that outlines the interaction of these factors when students use chatbots to learn programming. To this end, a questionnaire survey was conducted among 296 college students who had used chatbots for programming self-study. The questionnaire consisted of a set of rigorously validated items. The collected data were carefully analysed, including the construction and validation of a structural equation model using AMOS software. The results show that learning motivation plays the most important role in the positive influence of chatbots on programming self-learning ability. Its effect was significantly greater than that of other factors, including teaching interaction, learning self-efficacy, learning expectancy, and effort expectancy. In addition, learning motivation was found to mediate the effect of learning self-efficacy on programming self-learning ability. Finally, based on the findings, the study provides a series of recommendations aimed at offering scientific and practical strategies to enhance college students’ ability to learn programming independently.

Short-form video (SFV) marketing is a new technology for farmers in the context of rural revitalization. This study aims to analyze the factors and mechanisms influencing the use behavior of farmers’ SFV marketing of agricultural products from the perspective of government trust. By adding policy perception and government trust as external factors, the conceptual model and hypotheses were proposed based on UTAUT2. SEM was built to analyze the questionnaire data to test the hypotheses. Results showed that (1) Behavioral intention is significantly influenced by performance expectancy, social influence, facilitating conditions, effort expectancy, and price value; (2) The influence of paths varies across different age groups, suggesting the moderating role of age; (3) Policy perception does not directly affect the use behavior, but influences the use behavior through the mediating of government trust; (4) Government trust not only directly affect use behavior, but also indirectly impact the use behavior by the mediating effect of behavioral intention; (5) Use behavior is directly positively affected by government trust, behavioral intention and facilitating conditions; Finally, this study offers suggestions to optimize SFV platform construction and enhance government governance capabilities.

Carbon emission and sustainable development have attracted global attention. Promoting urban green innovation (UGI) in the Yellow River Basin (YRB) will help in lowering the intensity of carbon emissions and improve the safety and sustainability. A SBM-DEA model was constructed to measure carbon emission efficiency (CEE) and the degree of coupling and coordination with UGI was calculated in 73 prefecture-level cities in the YRB. The spatial association network of CEE coupled with UGI is constructed by using a modified gravity model, social network analysis and the quadratic assignment procedure (QAP), to analyze spatial potential energy, network characteristics and clustering characteristics. The study found that: (1) The coupling coordination degree of CEE and UGI in the YRB shows fluctuating growth, mutual promotion and continuous coordinated development. (2) The spatial linkage between CEE and UGI is gradually close, and the potential energy of the spatial linkage increases year by year, with obvious spatial spillover effect, indicating that the radiation and influence between cities are gradually increasing. In contrast to the middle stream, the upstream and downstream regions show a higher percentage of spatial potential energy in the entire network, and their network structure is more intricate and robust. (3) The clustering patterns of the three major urban clusters are examined using the block model, exploring the positioning and functions of various cities in these urban conglomerations, which includes the net spillover, net benefit, two-way spillover and broker plate, so as to strengthen the connection and coordinated development between cities. (4) Factors such as spatial adjacency, industrial structure, population density, digital economy and urbanization level, and energy intensity significantly impact the spatial association network, along with temporal and regional heterogeneity. Therefore, tailored policies are needed in the YRB to strengthen collaboration between CEE and UGI, fostering the development of a circular economy and promoting sustainable development.

The COVID-19 pandemic has heightened the demand for urban pocket parks near residential areas, posing new challenges for environmental governance. However, there is a lack of research on how to engage citizens in pocket-park governance to address both potential and unforeseen risks. This study combines social information processing theory with a norm activation model to develop a framework that identifies the shaping stages and influencing factors of citizens’ intentions to participate in pocket-park governance. Using partial least squares structural equation modeling, this research analyzed the relationships among external factors, attitudes, moral norms, and intentions to participate based on 719 responses from an online survey targeting Chinese citizens in November and December 2023. Results indicate that health risks and pocket-park environmental quality positively affect perceived usefulness. Attitudes and moral norms are indeed important factors mediating the positive effect of the external environment on the intention to participate. The pathway of guiding citizens to participate in pocket-park governance is clarified, which helps bolster the resilience of urban green spaces and improve the quality of life of residents after public crises.

Since China proposed building a national park system in 2017, the establishment of a planning system for nature reserves, with national parks as the main body, is being actively promoted around the country. Among them, scientific ecological space management and control zoning (ESMCZ) is an important link in maintaining the ecological stability of national parks. How to zone national parks and how to improve the precision of zoning has become a new task for national parks. Therefore, this study takes the Giant Panda National Park as the study area, takes ecosystem services and land use/cover change as the research perspective, integrates the InVEST model, PLUS model and bayes belief network (BBN) model, and builds a set of ecological space management and control zoning (ESMCZ) spatial zoning framework based on raster scale, dividing the study area into strictly protected zone, ecological buffer zone, ecological control zone and controlled development zone. The results showed that: (1) The study area showed an increasing trend in water conservation, soil conservation and carbon storage from 2005 to 2020, and the habitat quality index was generally high. The spatial heterogeneity of ecosystem services in the study area was significant, and the effect of a single factor on ecosystem services was most pronounced. (2) Large variation in area for different land uses under natural development scenarios and ecological protection scenarios. In both scenarios, the area of cultivated land, the area of grassland and the area of unused land decrease relative to 2020, and the area of forested land, the area of water and the area of constructed land increase relative to 2020. (3) The Giant Panda National Park is divided into strictly protected zone, ecological buffer zone, ecological control zone and control development zone, of which the strictly protected zone have the largest area and the best ecosystem background condition, and the control development zone have the smallest area and the worst ecosystem background condition. (4) The ecological space management and control zoning (ESMCZ) framework provides a more refined method for the secondary zoning of nature reserves such as the Giant Panda National Park, which is valuable for the implementation of zoning and categorization management for ecological conservation in the Giant Panda National Park.