关键词：
Energy optimization;Deep learning;Carbon peak;Carbon neutrality;Enterprise production;Smart manufacturing;Resource allocation;Real-time energy management;Energy-efficient operations
摘要：
Achieving carbon peak and carbon neutrality requires industries to enhance energy efficiency and optimize resource utilization. Traditional energy management methods rely on rule-based or static optimization approaches, which struggle to adapt to dynamic production environments and fluctuating energy demands. These limitations lead to inefficient energy use, increased operational costs, and challenges in meeting sustainability goals. This research introduces Deep Learning-based Green Optimization for Enterprise Production (DeepGreen-Opt), a deep learning-driven framework designed to analyze energy consumption patterns, predict demand, and optimize resource allocation in real time. The DeepGreen-Opt framework integrates Long Short-Term Memory (LSTM) for accurate energy consumption forecasting and Adaptive Hybrid Particle Swarm Optimization (AHPSO) for dynamic energy optimization. A fuzzy logic-based decision system is incorporated to enhance adaptability under uncertain conditions, enabling real-time adjustments to fluctuating energy demands. The DeepGreen-Opt framework was specifically validated across multiple industrial sectors, including automotive manufacturing, steel production facilities, and chemical processing plants, where intelligent energy management demonstrates significant operational improvements. By implementing DeepGreen-Opt, enterprises can achieve cost-effective production while aligning with sustainability objectives. The framework ensures energy-efficient operations, reducing resource waste and improving production efficiency. Experimental validation on industrial datasets demonstrates a 15% increase in energy efficiency and a 12% improvement in overall production performance compared to existing approaches. This research highlights the potential of DeepGreen-Opt in industrial energy management, providing a foundation for future advancements in intelligent and sustainable production processes.
摘要：
BACKGROUND: This study aims to examine the level of coupled and coordinated development between China's digital economy and older adult care services, analyzing their spatiotemporal evolution characteristics and key influencing factors, with the goal of providing feasible recommendations and scientific bases for the development of the digital economy and older adult care services in China. METHODS: This study uses publicly available panel data from China for the years 2015-2022. It employs the entropy method to measure the weights of various indicators in the digital economy and older adult care services. The study analyzes the level of coordinated development between the two using the coupling coordination degree model, and measures the main driving factors using the geographical detector model. RESULTS: (1) The overall level of coupling and coordinated development between China's digital economy and older adult care services shows an upward trend, but the growth rate is uneven, exhibiting an "M-shaped" pattern, with rapid growth followed by gradual slowdown, a bottoming-out rebound, and then a continuous decline. (2) There are significant spatial differences in the coupling and coordinated development of China's digital economy and older adult care services. Coastal areas are developing rapidly, inland areas have great potential, while peripheral areas are relatively lagging behind. Additionally, neighboring regions show regional linkage dynamics. (3) The main factors driving the coupling and coordinated development of China's digital economy and older adult care services include enterprise website ownership, technological contract turnover, the proportion of information technology service income, the building area of older adult care institutions, daily in-house visits, and the number of professional technical personnel. CONCLUSION: To achieve coordinated development between the digital economy and older adult care services, efforts should focus on policy, market, technology, and talent. The government should support technological innovation and new service models, while tailoring strategies to regional market demands. Additionally, accelerating the industrialization of innovations and promoting intelligent upgrades in older adult care services are crucial. Finally, more investment is needed to cultivate composite talents in both the government and older adult care institutions.
摘要：
Under the dual impetus of China's digitalization strategy and rural revitalization initiatives, rural social governance has entered a key phase of digital transformation. As artificial intelligence (AI) and big data technologies are increasingly integrated into governance platforms, the capacity for AI-augmented human-data collaboration is becoming essential to effective multi-stakeholder interaction. Sustainable multi-stakeholder collaboration (SMC) represents both a core requirement and central value of rural digital governance. This study investigates the determinants of SMC and proposes strategies to enhance stakeholder synergy. Through grounded theory analysis of semi-structured interviews with 26 rural digital governance practitioners, six key factors were identified: (1) awareness intensity, (2) technical adaptability, (3) institutional completeness, (4) scenario compatibility, (5) interest relevance, and (6) situational appeal. These factors were validated via structural equation modeling (SEM) using 1,370 questionnaire responses. The results show that all six factors significantly promote SMC (beta = 0.109-0.184, p < 0.001), with awareness intensity and interest relevance having stronger effects. Based on the findings, this study proposes strategies including strengthening publicity and guidance, implementing tiered training, promoting data interoperability, safeguarding public rights, optimizing evaluation mechanisms, and refining institutional frameworks to support sustainable collaboration. This research advances understanding of sustainable governance and provides insights for policy development and implementation of digital technologies in rural China. By highlighting the cognitive and technological dimensions of stakeholder collaboration, it offers an empirical basis for integrating AI-supported human-data interaction into rural governance, paving the way for more adaptive and inclusive digital governance systems. > IMPLICATIONS FOR REHABILITATION center dot Promoting digital inclusiveness: The study highlights the importance of cognitive alignment and technical adaptability in sustaining stakeholder collaboration, suggesting that rehabilitation initiatives involving digital tools should prioritize user-centered design, targeted training, and cognitive accessibility to ensure broad and inclusive participation. center dot Strengthening AI-supported human-data collaboration: The identified factors underscore the need for AI-enhanced platforms to support meaningful interaction among diverse stakeholders. Rehabilitation systems can leverage such platforms to facilitate data-informed decision-making and coordinated service delivery across institutional and community actors. center dot Informing institutional design for sustainable engagement: The emphasis on institutional completeness and scenario compatibility implies that rehabilitation programs must be embedded within flexible but robust governance frameworks, allowing for context-specific adaptation and sustained multi-party cooperation over time.
作者：
Hu, Ji-Xi;Zhu, Yu-Ying;Wang, Wei-Lin;Xiong, Yuan;Tu, Yi
期刊：
环境科学,2025年46(10):6408-6418 ISSN：0250-3301
作者机构：
[Hu, Ji-Xi] College of Public Administration and Law, Hunan Agricultural University, Changsha 410128, China;[Hu, Ji-Xi] Business School of Pingxiang University, Pingxiang 337055, China;[Zhu, Yu-Ying] College of Environment and Ecology, Hunan Agricultural University, Changsha 410128, China;[Wang, Wei-Lin] College of Resources, Hunan Agricultural University, Changsha 410128, China;[Tu, Yi; Xiong, Yuan] School of Materials and Chemical Engineering, Pingxiang University, Pingxiang 337055, China
摘要：
Analyzing and predicting the spatiotemporal evolution of habitat quality caused by land use change is highly significant for optimizing regional ecosystems. In this study， we first examined habitat quality changes along the Jiangxi section of the Gan-Yue Canal from 1980 to 2020 using historical land use data （i.e.， 1980， 1990， 2000， 2010， and 2020） and the InVEST model. We then used the PLUS model to predict habitat quality in 2030 under three scenarios： natural development， ecological protection， and canal development. The key findings follow： ① From 1980 to 2020， forest land， cultivated land， and unused land in the Jiangxi section of the Gan-Yue Canal decreased， primarily in Nanchang's Qingyunpu District， Xihu District， Donghu District， and Honggutan New District， as well as Xunyang District and Lianxi District of Jiujiang City. These areas were converted into construction land. ② The habitat quality index in the Jiangxi section of the Gan-Yue Canal was 0.51 in 2020， with the Nanchang area experiencing the largest decrease. ③ In 2030， scenarios show different trends， with the largest decline in medium-quality habitat and the smallest in low-quality habitat. These results provide data support for future land spatial planning in Jiangxi Province.
通讯机构：
[Jiang, WG ] H;Hunan Agr Univ, Coll Publ Adm & Law, Changsha, Hunan, Peoples R China.
关键词：
impact;spatial spillover effect;agricultural green development;integration of agriculture and tourism;non-linear effect
摘要：
The integration of agriculture and tourism, grounded in agricultural resources, not only offers new development opportunities for the agricultural sector but also steers it towards a greener and more sustainable trajectory. Using panel data from 30 provinces in China from 2011 to 2022, this study quantifies the levels of agriculture and tourism integration (ATL) and agricultural green development (AGD) in each province. Then, the study applies the fixed effects model, the spatial Durbin model, and the panel smooth transition regression (PSTR) model to empirically assess the impact of ATL on AGD, as well as its spatial spillover effect and its nonlinear characteristics. The findings are as follows: (1) Over the study period, both AGD and ATL exhibited steady growth, with marked spatial agglomeration effects; (2) ATL positively influenced AGD, suggesting that the integration of agriculture and tourism contributes to the green development of agriculture; (3) The impact of ATL on AGD exhibited significant spatial spillover effects, meaning that integrated agricultural-tourism development in one region can enhance AGD in its neighboring provinces; (4) The effect of ATL on AGD demonstrated nonlinear characteristics, with the influence of ATL on AGD intensifying as ATL increased. Based on these findings, the study proposes several policy recommendations, including strengthening top-level policy design, improving regional coordination mechanisms, and enhancing human capital cultivation, to foster deeper the integration of agriculture and tourism and to further accelerate the green development of agriculture.
期刊：
Frontiers in Sustainable Food Systems,2025年9:1597500 ISSN：2571-581X
作者机构：
[Liu, Wen; Li, Dexian] College of Public Administration and Law, Hunan Agricultural University, Changsha, China
关键词：
artificial intelligence;Green Food;Quality and safety traceability;Density clustering algorithm;Blockchain technology
摘要：
Introduction: Ensuring the quality and safety of green food throughout the supply chain has become increasingly critical due to growing consumer concerns and the complexity of food systems. Traditional approaches often suffer from information asymmetry and poor traceability. This study addresses these challenges by integrating artificial intelligence (AI) and blockchain technology (BCT) to enhance transparency, traceability, and early warning capabilities in green food supply chains. Methods: A two-part technical framework is developed. First, a production anomaly warning model is constructed using a Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm, allowing real-time detection of deviations in production parameters. Second, a blockchain-based traceability system is implemented using Hyperledger Fabric, recording critical events across procurement, processing, logistics, and distribution nodes. Data from IoT sensors are collected and transmitted through a closed-loop “sensing-analysis-certification” mechanism to ensure data reliability and integrity. Results: The ST-DBSCAN-based warning model outperforms traditional machine learning methods, achieving an accuracy of 0.93, precision of 0.91, recall of 0.90, and F1-score of 0.90. The BCT-based traceability model demonstrates superior performance with an average query time of 2.3 seconds, throughput of 400 requests/second, and latency of 1.15 seconds, significantly surpassing traditional and cloud database systems in response efficiency and stability. Discussion: The proposed AI-BCT integrated model enhances risk identification, real-time monitoring, and traceability across the green food supply chain. Moreover, the study introduces green finance and supply chain financing mechanisms to support small and medium-sized enterprises in adopting intelligent supervision technologies. This research contributes a practical, secure, and scalable approach to advancing digital governance in food safety and building resilient, trustworthy food supply chains.
关键词：
SOR theory;group norms;information sources;online rumor refuting;university students
摘要：
In the digital era, social media proliferation accelerates rumor dissemination. During public health emergencies, such misinformation intensifies social harm. Studying the influencing factors of online rumor refutation behavior thus becomes crucial. This study uses the stimulus-organism-response (SOR) theory as an analysis framework, based on the perspective of information sources and integrating group norms as a moderating factor, to explore the psychological processes affecting Chinese university students' online rumor refuting in public health emergencies. Against the backdrop of the COVID-19 pandemic, a questionnaire survey was conducted on 1017 respondents, and the collected data were analyzed using the structural equation modeling research method. The results indicate that both online and offline information seeking positively influence university students' fear of contracting the COVID-19 virus. University students' fear positively influences their engagement in online rumor refuting. Notably, fear mediates the link between online and offline information seeking and online rumor refuting. Additionally, group norms help strengthen the connection between university students' fear and their involvement in online refuting rumors. These results provide theoretical explanations and practical guidance for university students to refute rumors online.
摘要：
This study explores the relationship between public sector reform (PSR) and the reduction of administrative burdens (ABs), expanding the focus from government-citizen to government-business interactions. It introduces a framework aimed at reducing ABs in business settings, linking PSR to improvements in the business ecosystem. Using enterprise survey data, the study's multivariable regression models demonstrate that government reforms significantly enhance the business ecosystem. Recognising potential endogeneity, the study employs the historical timing of port openings in the ancient China as an instrumental variable. The instrumental variable regression analysis confirms the positive impact of PSRs, even after accounting for endogeneity. This research extends administrative burden theory and offers valuable insights for developing countries like China, suggesting that targeted government reforms can foster economic growth.Points for practitioners This paper introduces a new approach for developing countries like China to enhance the business ecosystem through public sector reform. Reducing administrative burdens is crucial for improving the business ecosystem and guiding targeted policy reforms. Utilising technologies like AI and 5G to reduce administrative burdens is effective, but reforms should be tailored to specific country contexts.
关键词：
Anderson model;Social media;digital health monitoring;disabled elderly;utilisation of care services
摘要：
OBJECTIVE: To examine whether the use of social media can promote the selection of formal care services by disabled elderly and identifies the moderating effects of elderly digital competence and psychological expectations on this relationship. MATERIALS AND METHODS: Based on the modified Anderson model and consumer behaviour psychology, study employs China Health and Retirement Longitudinal Study (CHARLS) data from 2015, 2018 and 2020 to empirically examine the utilisation duration and mechanisms of formal care services for the disabled elderly. RESULTS: The use of social media has significantly increased both the duration of formal care for disabled elderly and the total time of blended family care and formal care. Digital competence and high psychological expectations positively moderate the influence of social media on the utilisation of formal care services. CONCLUSIONS: Enhancing the psychological expectations of disabled elderly regarding formal care services through the information-accessing features of social media and improving their digital competence therefore enhance socialised elderly care security, build a "smart elderly care" platform and conduct "spatial accessibility+social media skills" training, achieve precise coverage of care services.
摘要：
This research explores how individual factors, including intrinsic motivations, capabilities, and specific characteristics, shape both individual disaster preparedness and volunteering, representing private and altruistic coproduction in disasters. Analyzing data from 5,681 respondents in the 2022 US National Household Survey, logistic regression results reveal that group belonging and self-efficacy, identified as citizens’ intrinsic motivations, positively correlate with the likelihood of engaging in disaster preparedness (emergency plans, emergency supplies, emergency alerts, and emergency drills) and volunteering. Regarding individual abilities, disaster experience positively influences the propensity to undertake each preparedness activity and to volunteer, while resource access increases the likelihood of adopting all preparedness actions and volunteering, except for stocking emergency supplies. Concerning individual-specific characteristics, the urban-rural divide shows a negative association with volunteering. The findings contribute to the growing coproduction literature for residents preparing for and volunteering in disasters and highlight individual determinants in building resilient communities.
关键词：
Fragmented growth;Rural housing wealth difference;Land use efficiency;Urban fringes;Suburbanization
摘要：
The dynamic changes in urban fringes have been widely studied in the context of suburbanization, but few studies have disaggregated the fragmented expansion or intrinsic differences in rural housing at the household or village level. Using fine-grained housing data from 66 villages in Shanghai for the period of 1978–2017, we elucidated the fragmented variation in rural housing and the state of wealth differences in urban fringes at the household or village levels, as well as examined the relationships between rural housing expansion and housing wealth differences through the combined use of Gini coefficient decomposition, Shapley decomposition, and quantile regression. The results showed that the disparate increases in rural housing wealth in Shanghai's metropolitan suburbs were largely due to the unapproved and frequent growth of auxiliary rooms in households. Villages adjacent to downtown areas or towns have been able to engage in this form of construction more than those further away, stimulating rural housing differences at the landscape scale. Driven by the opportunity spaces that emerge from government policies and the demand for rentals near sources of urban employment, the unregulated and disorganized dynamic of rural auxiliary rooms have exacerbated existing inequalities and resulted in stratified wealth differentiation within urban fringes. In conclusion, we suggest clarifying the approval system for the construction of rural rooms and implementing effective land use planning to manage fragmented housing growth, thereby leading to more balanced wealth growth and improved land use efficiency.
The dynamic changes in urban fringes have been widely studied in the context of suburbanization, but few studies have disaggregated the fragmented expansion or intrinsic differences in rural housing at the household or village level. Using fine-grained housing data from 66 villages in Shanghai for the period of 1978–2017, we elucidated the fragmented variation in rural housing and the state of wealth differences in urban fringes at the household or village levels, as well as examined the relationships between rural housing expansion and housing wealth differences through the combined use of Gini coefficient decomposition, Shapley decomposition, and quantile regression. The results showed that the disparate increases in rural housing wealth in Shanghai's metropolitan suburbs were largely due to the unapproved and frequent growth of auxiliary rooms in households. Villages adjacent to downtown areas or towns have been able to engage in this form of construction more than those further away, stimulating rural housing differences at the landscape scale. Driven by the opportunity spaces that emerge from government policies and the demand for rentals near sources of urban employment, the unregulated and disorganized dynamic of rural auxiliary rooms have exacerbated existing inequalities and resulted in stratified wealth differentiation within urban fringes. In conclusion, we suggest clarifying the approval system for the construction of rural rooms and implementing effective land use planning to manage fragmented housing growth, thereby leading to more balanced wealth growth and improved land use efficiency.
作者机构：
[Jingyi Ma; Liangwei Zhu; Kequan Gong; Siting Zhan] College of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha 410128, China;Author to whom correspondence should be addressed.;[Qinhao Xiao] College of Public Administration and Law, Hunan Agricultural University, Changsha 410128, China;[Xigui Li] College of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha 410128, China<&wdkj&>Author to whom correspondence should be addressed.
通讯机构：
[Xigui Li] C;College of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha 410128, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
maize crop;spatiotemporal patterns;influencing factors;Hunan Province
摘要：
Maize, one of the world’s three major food crops, plays a vital role in global food security. Analyzing the spatiotemporal patterns of maize cultivation in Hunan Province and their influencing factors contributes to enhancing planting quality and efficiency, optimizing production patterns, and supporting provincial food security initiatives. Utilizing maize cultivation data from Hunan Province (2001–2023), this study employed the standard deviation ellipse, center of gravity shift model, and principal component analysis to examine production patterns and their drivers. Key findings include the following: (1) The maize planting area exhibited an overall increasing trend from 2001 to 2023, with a spatial convergence from the northwest towards the east. Cultivation hot spots were identified in Shaoyang, Loudi, and Changde. Maize cultivation was predominantly concentrated in areas with gentle slopes (0–3°) and gradually shifted eastward towards similar terrain. (2) The provincial maize production center of gravity followed a “Z”-shaped trajectory, moving eastward and southward with Loudi City as its core. While the spatial distribution pattern shifted from “northwest–southeast” to “west–east”, the core concentration area maintained its “northwest–southeast” orientation. Concurrently, the fragmentation of cultivated land within the maize planting landscape increased. (3) Maize planting hot spots expanded from the northwest towards the central and eastern regions, extending southward. Cold spot areas shifted from the central region towards the northeast. By the study’s end, the central region had emerged as the core maize planting area. (4) Agricultural production conditions and policy factors were identified as the main drivers of spatiotemporal changes in maize acreage within Hunan Province.
摘要：
Developing robust ecological networks (ENs) is critical for sustaining ecosystem function and biodiversity in the ecologically vulnerable Loess Hills of the Central Yellow River Basin—a region increasingly fragmented by intensive agriculture and infrastructure expansion. Conventional methods for identifying ecological sources often depend on weighted overlays of ecosystem services (ESs), introducing subjectivity and limiting replicability. To address this, the present study combines a Self-Organizing Map (SOM) neural clustering model with complex network analysis to identify ecological sources and enhance overall network structure. Using a 50 km² threshold to define ecologically functional patches, the analysis identified 42 ecological sources—comprising 23 climate regulation-type sources and 19 agricultural provisioning-type sources—accounting for 26.8 % of the total landscape. These nodes were connected through 91 ecological corridors, which were classified into three types: 26 climate corridors, 29 provisioning corridors, and 36 integrated multifunctional corridors. Following optimization, the network exhibited a 6.5-fold increase in total source area and a 2.7-fold rise in corridor density. Quantitative improvements in structural indices were observed, including increased connectivity (α rising from 0.51 to 0.68), greater complexity (β from 1.81 to 2.34), and higher efficiency (γ from 0.69 to 0.82). Robustness simulations under both random and targeted disturbances demonstrated significant gains in network resilience after the addition of eight strategic corridors guided by node betweenness centrality. This research introduces a transferable, data-driven framework that merges machine learning and systems theory for ecological network construction, with implications for spatial planning and environmental resilience in erosion-prone, agriculturally dominated landscapes.
Developing robust ecological networks (ENs) is critical for sustaining ecosystem function and biodiversity in the ecologically vulnerable Loess Hills of the Central Yellow River Basin—a region increasingly fragmented by intensive agriculture and infrastructure expansion. Conventional methods for identifying ecological sources often depend on weighted overlays of ecosystem services (ESs), introducing subjectivity and limiting replicability. To address this, the present study combines a Self-Organizing Map (SOM) neural clustering model with complex network analysis to identify ecological sources and enhance overall network structure. Using a 50 km² threshold to define ecologically functional patches, the analysis identified 42 ecological sources—comprising 23 climate regulation-type sources and 19 agricultural provisioning-type sources—accounting for 26.8 % of the total landscape. These nodes were connected through 91 ecological corridors, which were classified into three types: 26 climate corridors, 29 provisioning corridors, and 36 integrated multifunctional corridors. Following optimization, the network exhibited a 6.5-fold increase in total source area and a 2.7-fold rise in corridor density. Quantitative improvements in structural indices were observed, including increased connectivity (α rising from 0.51 to 0.68), greater complexity (β from 1.81 to 2.34), and higher efficiency (γ from 0.69 to 0.82). Robustness simulations under both random and targeted disturbances demonstrated significant gains in network resilience after the addition of eight strategic corridors guided by node betweenness centrality. This research introduces a transferable, data-driven framework that merges machine learning and systems theory for ecological network construction, with implications for spatial planning and environmental resilience in erosion-prone, agriculturally dominated landscapes.
摘要：
Modern agricultural technologies are crucial for addressing global food security and environmental sustainability challenges amidst a growing population and climate change. These innovations, including precision agriculture, biotechnology, smart irrigation, automation, vertical farming, and artificial intelligence (AI), significantly enhance productivity and land use efficiency. Precision agriculture, utilizing GPS, drones, and IoT, improves yields by 20-30% and cuts input waste by 40-60%. Biotechnology, with CRISPR and GMOs, delivers drought and pest-resistant crops, stabilizing yields, as seen with Bt cotton reducing pesticide use by 50% in India. Smart irrigation boosts water efficiency by 40-60%, while automation and robotics mitigate labor shortages and reduce costs by 25%. Vertical farming increases yields 10-20 times with 95% less land and water, supporting urban food security. AI analytics enhance decision-making with over 90% accuracy in forecasting and resource allocation. Despite these benefits, high costs, technological illiteracy, and regulatory issues hinder adoption, especially among smallholders. Policy support, public-private partnerships, and training are vital for broader technology access and fair benefits. Integrating renewable energy and circular economy principles into aggrotech presents a path to sustainability. This review highlights the transformative potential of modern technologies for sustainable intensification, increasing productivity without expanding farmland, while lessening environmental impacts. It underscores the need for coordinated efforts to overcome adoption challenges and harness these innovations for global food security and climate resilience.
摘要：
Over the past few decades, China's economic growth and urbanization have driven a significant migration of rural laborers to cities. Recently, however, an increasing number of migrant workers have chosen to return to their hometowns for employment opportunities. Understanding the factors influencing this return migration is crucial but challenging due to the complexity and diversity of these factors and their intricate interrelationships. Moreover, existing research on migrant workers' return lacks a systematic theoretical framework and comprehensive empirical analysis. To address these gaps, our study utilizes the "Push-Pull Theory" from migration theory to develop a comprehensive model. This model investigates how perceived benefits, trust, costs, and both personal and government support affect migrant workers' willingness to return. We employ structural equation modeling (SEM) for empirical analysis, revealing that perceived benefits, trust, and costs significantly influence migrant workers' perception of return support. This perception, in turn, enhances their willingness to return. Additionally, our findings show that government support positively moderates the relationship between perceived benefits and costs with return support. However, it does not significantly affect the relationship between perceived trust and support, indicating that policy incentives alone may not sufficiently build trust in hometowns. Furthermore, emotional factors-such as family and place attachment, community involvement, and quality of life in hometowns-indirectly influence the decision to return by shaping perceived benefits, trust, and costs. This study advances the application of Push-Pull Theory by integrating economic factors with emotional bonds in the context of return migration. It provides novel insights into how both economic incentives and emotional ties drive migrant workers' decisions to return, offering a more nuanced understanding of migration dynamics in China.
关键词：
Cultivated land fragmentation;'no-grain';food security;grain margins;two-way fixed effects model
摘要：
With the continuous growth of the global population, food security has emerged as a critical issue of international concern. Accordingly, this study focuses on the key factor of cultivated land fragmentation, systematically uncovering the underlying mechanisms through which such fragmentation drives the shift away from food crop cultivation. The findings provide a valuable basis for countries worldwide to improve the current state of cultivated land fragmentation. This study utilizes microdata from the national rural fixed observation points (2009-2017) and employs a two-way fixed effects model. Additionally, the study employs IV-2SLS, heterogeneity analysis, and robustness tests to validate the effect of cultivated land fragmentation on this shift. (1) For every one-unit increase in the degree of cultivated land fragmentation, the extent of the shift away from food crop cultivation intensifies by 0.1%, and (2) the profitability of grain cultivation exerts a positive effect of 24.4% on the shift away from food crops. Cultivated land fragmentation exacerbates the shift away from food crop cultivation, thereby undermining food security. Additionally, there is significant heterogeneity in farmer behavior, with part-time farmers and non-farm farmers exhibiting a pronounced tendency towards this shift.
关键词：
community health care integration;government expenditure;health care expenditure;robustness tests;synthetic controls
摘要：
BACKGROUND: Does the implementation of community healthcare integration policies affect fiscal expenditures as a key measure in addressing older healthcare demands and promoting healthy aging? METHODS: This study utilizes the implementation of community healthcare integration in Yantai, a prefecture-level city in China, as a natural experiment to analyze its fiscal expenditure consequences by using the synthetic control approach. RESULTS: Our empirical findings indicate that the program implementation markedly decreased fiscal expenditures in pilot cities. The decrease in expenditure is solely due to policy execution, with no confounding variables detected. The program implementation negatively impacted fiscal expenditures mainly by decreasing government healthcare spending in pilot zones. CONCLUSION: Consequently, in the ongoing effort to enhance community healthcare integration, local governments must devise context-tailored implementation strategies to attain sustainable growth, alleviate fiscal burdens, and improve older adult care services.
关键词：
income inequality;ordered probit model;rural residents;social mentality;subjective well-being
摘要：
Since the initiation of economic reforms and opening-up, China's economy has achieved remarkable growth, leading to a significant improvement in the standard of living for its people. However, the trickle-down effect of this growth has not been equitably distributed across all segments of society. This study attempts to analyze the subjective well-being (SWB) of Chinese rural residents using data from the 2021 Chinese General Social Survey (CGSS). An ordered probit (OProbit) model is constructed to investigate the impact of income inequality on the subjective well-being of Chinese rural residents. The findings reveal three key insights: (1) the benchmark regression demonstrates a significant negative impact of income inequality on the subjective well-being of Chinese rural residents. (2) Social mentality emerges as a critical mediating channel through which income inequality undermines subjective well-being. (3) The impact of income inequality on subjective well-being varied significantly depending on factors such as age, gender, and marital status. As enhancing well-being gains increasing recognition as a central goal in global public health policy, the findings of this study provide valuable insights for designing policies aimed at improving subjective well-being, particularly in rural contexts.
关键词：
SD-LSTM;smart agro-rural development;digital rural construction;high-quality development of agriculture;coupling coordination
摘要：
The rapid advancement of digital information technology in rural China has positioned smart agro-rural development as a key driver of agricultural modernization. This study focuses on the theme of digital rural construction (DRC) and high-quality agricultural development (HAD), combining the two into smart agriculture and rural development. Utilizing panel data from 31 Chinese provinces from 2011 to 2022, a comprehensive evaluation index system is constructed to assess development levels. The entropy weight method and kernel density estimation are employed to evaluate indicator performance and capture dynamic distribution patterns. A coupling coordination model is used to analyze the spatio-temporal evolution of the interaction between the two systems, while a hybrid SD-LSTM (System Dynamics–Long Short-Term Memory) model forecasts coordination trends over the next six years. Results reveal a steady upward trend in both systems, with coordination levels improving from “moderate imbalance” to “moderate coordination.” A distinct spatial pattern emerges, characterized by “high in the east, low in the west” and a mismatch between high coupling and low coordination. Forecasts suggest a continued progression toward “good coordination.” The findings offer policy implications for enhancing digital village initiatives, accelerating rural technological diffusion, and strengthening regional collaboration—providing valuable insights into advancing China’s smart rural transformation and agricultural modernization.
