Bingsheng Fu

Bingsheng Fu


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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22 September 2023 in Agriculture
Verified
Journal Article
Open access
PDF file
DLDongkai Lin
Dongkai Lin
BFBingsheng Fu
Bingsheng Fu
KXKexiao Xie
Kexiao Xie
WZWanhe Zheng
Wanhe Zheng
LCLinjie Chang
Linjie Chang
JLJinke Lin
Jinke Lin
In the context of digital intelligence empowerment, the digital literacy level of tea farmers has a significant impact on the intelligent development and transformation of the tea industry. This study extends the original model of the unified theory of acceptance and use of technology (UTAUT) by introducing the personal innovativeness theory and the self-efficacy theory and constructs a new model to explore the influencing factors of moderately scaled tea farmers’ digital literacy improvement behavior. There are a total of 22 research hypotheses. Using structural equation modeling and collecting questionnaire data for analysis, the following research results were obtained. (1) The performance expectancy, social influence, effort expectancy, personal innovativeness, and self-efficacy all significantly positively affected the willingness of tea farmers to improve their digital literacy, according to the path coefficient in descending order: social influence (0.226) > self-efficacy (0.224) > effort expectancy (0.178) > performance expectancy (0.157) > personal innovativeness (0.155). (2) Facilitating conditions and the willingness to improve digital literacy had a significant positive impact on tea farmers’ digital literacy improvement behavior, according to the size of the path coefficient: the willingness to improve (0.271) > facilitating conditions (0.106). (3) The willingness of tea farmers to improve their digital literacy played a complete mediating role between personal innovativeness and self-efficacy on their digital literacy improvement behavior, and was partially mediated between the performance expectancy, social influence, and effort expectancy on their digital literacy improvement behavior. According to the proportion of indirect effects, the order was effort expectancy (27%), performance expectancy (47%), and social influence (49%). (4) The gender and age of tea farmers had a significant positive moderating effect on the impact of performance expectancy on the willingness to improve digital literacy. Age and experience had a significant positive moderating effect on the impact of effort expectancy on the willingness to improve digital literacy. The age of tea farmers had a significant positive moderating effect on the improvement of digital literacy behavior through the facilitating conditions. This study extended the applicability of the UTAUT theoretical model and proposed six strategies to improve the digital literacy of tea farmers, which helps policymakers and industry leaders provide practical guidance for tea farmers to improve their digital literacy and provide reference for research related to farmers’ digital literacy.

13 June 2026 in Land
Verified
Journal Article
Open access
PDF file
BFBingsheng Fu
Bingsheng Fu
GLGuoqing Li
Guoqing Li
DLDongkai Lin
Dongkai Lin
GHGuoxing Huang
Guoxing Huang
JLJinhuang Lin
Jinhuang Lin
YOYouquan Ouyang
Youquan Ouyang
The water–energy–food (WEF) system acts as a critical nexus of social–ecological systems. However, rapid urbanization has intensified the regional imbalance in the supply and demand of ecosystem services (ESs). Clarifying the spatiotemporal matching of ecosystem services supply and demand (ESSD) within the WEF framework and revealing the driving mechanisms behind such imbalances are essential to formulating reasonable zoning schemes and targeted optimization strategies for the coordinated development of the regional WEF system. Taking Zhejiang Province as a case study, this research uses water yield (WY), carbon sequestration (CS), and grain production (GP) to characterize the WEF nexus system. It uses the InVEST model to assess WY and CS, applies spatial allocation methods to characterize GP, and integrates socioeconomic data to quantify the demand for the above three ESs. All indicators were standardized and integrated with equal weights to further clarify the comprehensive levels of ESSD. By integrating the Geodetector and K-Means clustering methods, the study analyzes the supply–demand matching of ecosystem services and its driving mechanisms in Zhejiang Province during this period, thereby exploring ecological management zoning and optimization strategies within the WEF system. The study findings indicate that: (1) From the supply perspective, Zhejiang Province’s WY services demonstrate a trend of elevated activity in the southwest and diminished presence in the northeast; high values for CS services are predominantly found in the vegetation-rich areas of the northwest, while high values for GP services are clustered in the northern Zhejiang Plain; from the demand perspective, high values for all three ESs in Zhejiang Province are primarily located in economically active, densely populated urban areas. (2) The correlation between ESSD within Zhejiang Province’s WEF system exhibits significant spatial heterogeneity and is driven by the combined effects of natural and socioeconomic factors, with the interaction between these two factors often producing a synergistic effect. Specifically, annual average precipitation and population density are the dominant factors influencing WY services, NDVI and human footprint are the dominant factors influencing CS services, and population density and GDP are the dominant factors influencing GP services. (3) From 2000 to 2020, the supply–demand ratio for comprehensive ESs in Zhejiang Province generally followed a pattern of being lower in the east and higher in the west. The supply–demand imbalance of ESs intensified in the core areas of eastern cities, whereas the western regions maintained a relatively sound supply–demand balance. (4) The study classifies the counties in Zhejiang Province into four ecological management zones—ecological stable zones, ecological conservation zones, ecological control zones, and ecological restoration zones—and explores differentiated approaches to optimizing these zones and implementing control strategies.