This study investigates the impact of non-agricultural employment on food consumption upgrading among rural residents and examines the underlying mechanisms. Based on data from the 2020 China Rural Revitalization Survey (CRRS), the analysis employs a binary logit model, mediation analysis, an instrumental variable approach, and propensity score matching to empirically assess how non-agricultural employment influences improvements in food consumption structure and quality. The results demonstrate that non-agricultural employment exerts a significant positive effect on both structural and qualitative aspects of rural residents' food consumption, a finding that remains robust across multiple sensitivity tests. Mediation analysis reveals that this effect is transmitted through enhanced health awareness and improved information acquisition capacity-two key pathways linking employment to dietary improvement. Heterogeneity analysis further indicates that the magnitude of this promoting effect varies significantly across age groups and educational levels, highlighting differential responsiveness within the rural population. These findings suggest that expanding non-agricultural employment opportunities for rural laborers, strengthening public education on nutrition and food safety, improving rural information infrastructure and market service systems, and implementing targeted guidance strategies can effectively unlock the latent potential for food consumption upgrading. Such measures are essential for advancing dietary modernization and fostering integrated rural revitalization.

Smart forestry, the strategic use of digital tools, Earth Observation (EO) systems, drones, mobile GIS applications, and integrated data platforms and is becoming increasingly important for effective forest governance in Kenya. Forest ecosystems across the country face persistent pressures from illegal logging, charcoal production, encroachment, and climate-driven disturbances, creating a need for faster, more transparent, and data-driven management. In response, the Kenya Forest Service (KFS) and partners have adopted a suite of digital innovations including satellite-based forest-cover monitoring, drone-supported reconnaissance, mobile data-collection tools, and a national Forest Information System (FIS) that integrates licensing, real-time inventory, and automated alerts. These digital systems have already yielded measurable gains, including a documented reduction in illegal logging cases between 2020 and 2024 and greater participation of Community Forest Associations (CFAs) through mobile reporting of forest health, fire outbreaks, and encroachment. Pilot deployments in Kericho and Baringo show that drones enhance coverage of inaccessible sites and reduce operational costs by up to 35 percent, while emerging digital marketplaces and blockchain-style traceability systems are improving value chains for legal timber and non-timber forest products. Despite this progress, structural barriers—such as weak rural Information, Communication and Technology (ICT) infrastructure, high device and data costs, and low digital literacy—continue to limit widespread adoption. The paper recommends targeted investments in digital infrastructure, public–private partnerships, capacity-building programs, and policy support to scale up smart forestry nationwide. Integrating digital tools into routine operations offers a transformative pathway toward sustainable forest management, enhanced accountability, a stronger forest-based economy, and progress on Kenya’s climate, restoration, and biodiversity commitments.

The imbalance between supply and demand of rural information is a prominent problem faced by rural informatization. The lack of an effective supply of rural information is the biggest cause of this problem. The innovation of rural accurate information service mode has important practical significance in improving the contradiction between the supply and demand of rural information. This study analyzes the advantages and disadvantages of the existing rural information service modes at home and abroad. Based on the existing problems of rural information service model in China, a rural accurate information service model based on an improved FP Tree algorithm is proposed. The effectiveness of the mode is verified by simulation experiments, and the specific implementation path is given. The results show that the main problems of the rural information service model in China are lack of pertinence, low information quality of farmers, high information cost, and imperfect long-term mechanism; the rural accurate information service model based on improved FP Tree algorithm overcomes the above problems in terms of organization mode, service content, communication mode, benefit distribution, and technical support Under the same data scale and tolerance, the improved FP Tree algorithm is better than the traditional Apriori algorithm.

ABSTRACT Rural information systems initiatives (RISIs) are ICT‐based projects intended to drive institutional change and reduce inequalities in rural communities. However, the extant information systems literature shows that many such initiatives paradoxically aggravate inequalities in other domains, but the literature provides little theoretical explanation of the mechanisms involved. This study addresses this gap from an affordance‐based institutional logics perspective. Specifically, it conceptualizes a RISI as an institution that embodies a dominant institutional logic, which shapes how technology affordances are perceived and enacted. This lens is applied to an interpretive case study of an e‐governance initiative implemented in village municipalities (i.e., village councils) in Nepal aimed at reducing knowledge inequalities in the municipalities. The initiative introduced a self‐assessment logic for evaluating local development work, which amplified existing knowledge and power inequalities over time. The case study surfaced two mechanisms through which RISIs may aggravate inequalities: (1) by impeding logic hybridization and (2) by activating discriminatory logics. These findings contribute to the theoretical understanding of RISIs and provide critical insights into the design and governance of ICT‐based initiatives in rural contexts.

This study proposes an AI-driven information system framework to address data fragmentation and low engagement in preserving intangible cultural heritage handicrafts. By integrating multimodal data (image, motion, 3D) and intelligent recommendation via knowledge graphs, the system enhances semantic modeling, interoperability, and user interaction. Evaluation shows improved accuracy (75%→91%), data integration (65%→89%), and user satisfaction (70%→90%). Results demonstrate its potential for effective information management and dissemination. The authors recommend explainable AI and inclusive, service-oriented design. The framework is adaptable to agricultural and environmental systems requiring traceability, knowledge integration, and public participation. Its modular architecture supports scalable deployment and integration into existing digital heritage or rural information infrastructures.

In the context of rural informatization and sustainable development, more and more people are accessing global agricultural knowledge systems, environmental policy frameworks, and digital extension services through English based information platforms. However, the limited English proficiency of rural populations poses a significant obstacle to their participation in these knowledge ecosystems. The authors propose an innovative framework based on multimodal data fusion and intelligent feedback mechanism to improve the English listening comprehension ability of rural students, enabling them to participate in digital agriculture and environmental information systems. Through comparative experiments, the research results indicate that the improvement of English listening skills directly enhances the ability of rural learners to acquire, interpret, and apply knowledge from global agricultural and environmental portals. This article contributes to the broader goal of rural revitalization that integrates education, digital infrastructure, and sustainable agriculture.

ABSTRACTThis study explores the distinct information needs of urban and rural populations by analyzing six months of Q&A posts on Naver's Knowledge‐iN in South Korea. Using Latent Dirichlet Allocation (LDA), KoBERT, and Non‐negative Matrix Factorization (NMF), we compared major themes within urban and rural posts. Our findings show that both groups share interests and concerns regarding dental healthcare, transportation, education, and food. Urban posts emphasized daily life services and mobile technology, reflecting interests in convenience and connectivity. In contrast, rural posts focused on regional welfare, local spots, and family or emotional concerns, suggesting possible service gaps and unique social dynamics. Topic distributions varied across the three topic modeling methods: LDA revealed broader categories, NMF highlighted more specific segments, and KoBERT captured context‐rich, nuanced themes. Overall, this comparative analysis underscores region‐specific information needs and demonstrates the complementary benefits of multiple topic modeling techniques for understanding social and digital inequalities.

ABSTRACT Balancing marine economic development with ecological protection has been a common challenge for coastal countries. Taking the construction of marine ecological civilization demonstration zones as an example, we conducted a study based on panel data from 42 coastal cities in China between 2003 and 2022. Using a difference‐in‐differences model, we examined its impact effects and mechanisms on marine fisheries, aiming to offer valuable insights for promoting the development and conservation of marine resources. The results indicate that, first, the establishment of these zones inhibited the growth of local marine fishery economic efficiency, and this finding remains robust across a series of checks. Second, the policy effects show clear heterogeneity; coastal cities adjacent to the South China Sea, with greater governmental attention to environmental protection and higher levels of rural education, experienced milder transitional pressures in the fishery sector. Third, improvements in agricultural innovation and the advancement of rural informatization helped reduce short‐term economic costs in pilot cities and enhanced their performance in marine cultivation and fishery sustainability. Based on a global perspective and China's practice, we proposed three management implications: (1) establish an inclusive and resilient marine governance framework; (2) implement differentiated development paths based on regional comparative advantages; (3) build a multi‐stakeholder, collaborative governance model.

Farmers’ green production behaviors are of great significance in promoting the green development of agriculture and enhancing the level of rural ecological civilization construction. Based on the research data from four cities and counties in Hainan Province, this study explored the role mechanisms between publicity media use, ecological policy cognition and farmers’ green production behaviors using Ordered Logit model and Mediated effect model, addresses the underexplored differential impacts of traditional publicity media use and new publicity media use on farmers’ green production behaviors. The results show that (1) publicity media use and ecological policy cognition have a significant positive effect on farmers’ green production behaviors, (2) ecological policy cognition plays a mediating role in publicity media use on farmers’ green production behaviors, (3) there is a difference in the effect of different types of publicity media use on farmers’ green production behaviors, with new publicity media use having a stronger direct effect, while the traditional publicity media influence is more mediated by ecological policy cognition. Based on these findings, the government should improve the level of rural information infrastructure, emphasize the role of media in improving farmers’ ecological policy cognition, and strengthen the training of new publicity media use.

As deep learning and reinforcement learning technologies advance, intelligent rural information management is transforming substantially. This article presents an innovative framework, the evolutionary bidirectional long short-term memory deep Q-network (EBLM-DQN), which integrates evolutionary algorithms, reinforcement learning, and bidirectional long short-term memory (Bi-LSTM) networks to significantly improve the accuracy and efficiency of rural information management, particularly for recognizing and classifying information relevant to farmers. The proposed framework begins with data preprocessing using disambiguation techniques and data complementation, followed by temporal feature extraction via a Bi-LSTM layer. It then employs a deep Q-network (DQN) to adjust and optimize weights dynamically. After feature extraction and weight optimization, evolutionary algorithms are used to select the optimal weights, enabling precise recognition and classification of conditions encountered by farmers seeking assistance. Experimental results indicate that the EBLM-DQN framework outperforms existing frameworks on public datasets and real-world applications, providing higher classification accuracy. This framework offers valuable technical support and a reference for future optimization and development of rural information management systems.

PurposeThis study aims to investigate the behaviors and barriers of information poverty (IP) among heterogeneous rural groups in Guizhou Province, Southwest China, using Dervin’s sense-making theory (SMT) and Chatman’s theory of IP.Design/methodology/approachEmploying a mixed-methods approach, we conduct K-medoids clustering to analyze 3,864 respondents and perform an inductive analysis of 132 participants (2018–2021). The study identifies distinct rural groups: the “traditional group” (TG), “mainstream group” (MG), “backbone group” (BG) and “new generation group” (NG), along with the pre-existing “village cadre group.”FindingsDiverse behaviors and IP barriers are observed across rural groups. TG and MG exhibit self-behaviors such as avoidance, reluctance, and easy-contentment. Conversely, NG and BG utilize multiple sources and demonstrate persistence, particularly in seeking rural policy information. All groups exhibit behaviors such as foregoing insurance and seeking information outside rural settings, notably concerning education. Interactive behaviors vary significantly – from providing information facilities to supporting decision-making – with varying acceptance levels. Moreover, village cadres, when providing information services, often inadvertently perpetuate the IP barriers that affect these rural groups.Originality/valueThis study introduces a new analytical framework by integrating SMT with IP. It validates and expands existing IP theories by demonstrating that different rural groups exhibit unique but overlapping small-world behaviors. The study proposes behavioral phenomena including “lower expectations of compromise and belief in connection,” “varying levels of interactive behavior” and “measurable assessments of rural information services.”

Abstract Popularizing low‐carbon production practice (LCPP) is essential for addressing climate change and achieving sustainable agricultural development. However, farmers' engagement in such practices remains suboptimal. The impact of the Internet, as a facilitative tool in the network economy era, has not been comprehensively explored. Utilizing survey data from 595 grain growers in China, this study employs a Poisson model to assess the direct effect, heterogeneity, and mechanism of Internet use on LCPP among farmers. First, the analysis reveals a positive correlation between Internet use and LCPP; specifically, each unit increase in Internet use boosts the likelihood of engaging in LCPP by 3.6 percentage points. Second, this effect is more pronounced among new‐generation farmers, those in large‐scale operations, and those in economically developed villages. Third, Internet use enhances LCPP by improving capital endowment, including cultural, social, and economic capital. Consequently, the government should enhance rural information infrastructure and provide training on Internet use to help farmers build capital endowment and promote LCPP.

Under the background of the information age, emerging technologies are constantly affecting all aspects of people’s lives, especially in the field of rural education. Through the impact of rural education informatization on rural classrooms, it is found that there is still a big gap between the basic construction of rural informatization teaching and the teaching of teachers and cities. This paper analyzes the challenges faced by rural classrooms under the background of the information age by combining the practice cases of informatization, and thus puts corresponding suggestions.

This research examined the effects of edge computing in enhancing cloud-based records management for rural information retrieval in Akwa Ibom State, Nigeria. The objective of the study was to evaluate the state of information availability, consider the possible advantages of using edge computing and identify the problems associated with implementing such systems. The study used a survey research method while adopting a quantitative technique to analyse data gathered from 333 respondents. Findings of the study show that edge computing greatly improves data processing rate, latency, and system reliability in rural environments. The integration of edge computing enabled the enhancement of performance, experiencing a 60.5% increase in the rate of data processing, which is a 68.4 percent reduction in latency, and an increase in system availability from what was recorded to be 87.5% to 98.2%. However, impediments to implementation that were noted were power fluctuations, infrastructural constraints, and costs. The study advances the literature on effective technological solutions to enhance information availability in the developing regions while also offering valuable guidelines for policymakers and practitioners.

Background of the study: Fisherfolk are often exposed to physical hazards, environmental changes, economic fluctuations, and policy/regulatory changes while contributing to food security and economic development. Purpose: The study aimed to explore challenges in and ways to strengthen health information dissemination methods for fisherfolk in rural Ghana. Method: This study utilised a case study design with 26 participants from three rural fishing communities in Ghana's Keta Municipal District. Qualitative data was collected through interviews with five public health workers as well as focus group discussions with 21 fisherfolk. The study employed purposive and convenience sampling methods for the selection of participants. Data collected from the participants were analysed through the use of thematic analysis. Findings: The study revealed that fisherfolk depended on interpersonal and media sources to meet their multidimensional health information needs. Public health officials also leveraged interpersonal approaches to disseminate information to these communities. However, the fisherfolks encountered infrastructural and other barriers that hindered their ability to obtain health information. Conclusion: The study highlights the need to tailor health information dissemination approaches to the needs and preferences of fishing communities while strengthening the rural information infrastructure by the Ghana Health Service to more effectively disseminate and expand access to health information in these settings.

This study aims to address the limitations of traditional data processing methods in predicting agricultural product prices, which is essential for advancing rural informatization to enhance agricultural efficiency and support rural economic growth. The RL-CNN-GRU framework combines reinforcement learning (RL), convolutional neural network (CNN), and gated recurrent unit (GRU) to improve agricultural price predictions using multidimensional time series data, including historical prices, weather, soil conditions, and other influencing factors. Initially, the model employs a 1D-CNN for feature extraction, followed by GRUs to capture temporal patterns in the data. Reinforcement learning further optimizes the model, enhancing the analysis and accuracy of multidimensional data inputs for more reliable price predictions. Testing on public and proprietary datasets shows that the RL-CNN-GRU framework significantly outperforms traditional models in predicting prices, with lower mean squared error (MSE) and mean absolute error (MAE) metrics. The RL-CNN-GRU framework contributes to rural informatization by offering a more accurate prediction tool, thereby supporting improved decision-making in agricultural processes and fostering rural economic development.

The quality of rural informatization construction is often constrained by the state of rural information infrastructure. Therefore, effectively promoting the construction of rural information infrastructure is crucial for enhancing the level of rural informatization. However, due to the numerous and complex technical indicators of rural information infrastructure, there is a lack of study specifically focusing on which indicators should be prioritized for construction and which indicators can be temporarily postponed to save costs. This study adopts a method of literature synthesis and comparison to address these issues. Through the study, it is found that the technical indicators that researchers generally focus on are Data Transmission Rate and Network Coverage; while the technical indicators that researchers pay less attention to mainly include Network Management Level, Data Management Level, and Online Service Level, among others. Additionally, the study explores other related issues. The findings of this study can provide fundamental decision-making support for many countries' rural information infrastructure construction.

Tourism consumption as a typical representative of service consumption, has strong comprehensiveness and driving force, continuously expanding new consumption upgrade space. In the context of the digital economy, the development of the mobile internet has opened up new opportunities for the tourism industry acting as an important factor influencing the consumer behaviour of the residents. Based on 9007 resident survey data from the China General Social Survey (CGSS) in 2017, this paper explores the relationship between Internet use, access to information and residents' travel consumption behavior by means of probit model and mediating effect model. First, according to the findings, Internet use has a positive impact on residents' travel consumption behavior, increasing residents' tourism expenditure. Second, the mechanism suggests that access to information plays a partially mediating role between Internet use and travel consumption behavior. Third, further analysis revealed that, for the retired population, the more frequently they use the Internet, the more likely they are to spend on travel. Compared to those with lower incomes, those with higher incomes will spend less on travel after retirement. With this in mind, in order to promote residents' tourism consumption, speed and fee reduction should be continuously promoted, urban and rural information infrastructure should be strengthened, and accessibility of tourism information for residents should be improved. At the same time, there is a need to innovate the way tourism products are promoted, improve the types of tourism products and launch diversified tourism products.

The overuse of pesticides has led to serious ecological and environmental degradation, largely due to the lack of effectiveness of agricultural-extension services. In recent years, an increasing number of farmers have tended to acquire technical information from the Internet. In this context, the present study analyzes the impact of Internet-based information acquisition on pesticide use and the mediating role of farmers’ technical knowledge. For this purpose, the treatment-effects model and survey data covering 1113 rice farmers in Guizhou, Hubei, Jiangsu, and Zhejiang provinces in China were utilized. The results indicate that Internet-based information acquisition could significantly reduce the intensity of pesticide use by 2.036 kg/ha, accounting for the self-selection issue. Further analysis illustrates that farmers’ technical knowledge plays a significant mediating role in the negative impact of Internet-based information acquisition on pesticide-use intensity. In addition, the impacts of Internet-based information acquisition on pesticide-use intensity are heterogeneous across different education levels or rice-sown areas. Thus, the present study suggests that efforts should be made to accelerate the construction of rural information infrastructure networks to broaden smallholder farmers’ access to technical information from the Internet platforms, promote “Internet plus” agricultural-extension services, and improve farmers’ skills in using the Internet.

The history of the internet, whether in China or elsewhere in the world, is often told through the stories of successful and often publicly listed companies, state policy initiatives and chronology, or prominent individuals and groups such as tech company founders and influential social and political activists. This short essay tells a different story of the Chinese internet history in the past 30 years from a social reproductive perspective, centering on rural e-commerce and families caught in between the rural-urban divide, against the wider currents of the state’s rural informatization campaign, Chinese Big Techs’ expansion into the countryside, and ongoing transformation in urbanization and rural-urban conversion. The goal is to move beyond a techno-determinist, individualized, and male-centric framework of analyzing digital media and the internet to adopt a materialist, feminist, and substantivist approach.