期刊：
International Journal of Production Economics,2025年:109761 ISSN：0925-5273
通讯作者：
Zhiqiao Xiong
作者机构：
[Shuangcheng Luo; Qi Kang] School of Economics, Hunan Agricultural University, Changsha, 410128, China;[Qiulan Qian] Hunan Vocational College Of Commerce, Changsha 410205, China;[Jie Cheng; Zhiqiao Xiong] School of Economics and Management, Changsha University of Science and Technology, Changsha, 410076, China
通讯机构：
[Zhiqiao Xiong] S;School of Economics and Management, Changsha University of Science and Technology, Changsha, 410076, China
摘要：
In light of the prevailing circumstances of global warming and the increasing frequency of extreme climates, China has put forth the "dual carbon goals" policy, which establishes more rigorous standards for the advancement of traditional manufacturing industries. This paper employs a difference-in-differences model to explore the influence of intelligent manufacturing on ESG and sustainable development, drawing upon the Intelligent Manufacturing Pilot Demonstration Policy (IMPDP) as a point of reference. The results reveal that: (1) Intelligent manufacturing enhances the ESG performance of businesses, and this effect remains consistent even when subjected to serial robustness tests, PSM-DID, and placebo tests. (2) The three primary mechanisms that mediate the impact of intelligent transformation on ESG are the reduction of information asymmetry, the alleviation of financing constraints, and the promotion of green innovation. (3) Intelligent manufacturing has a more pronounced effect on ESG of capital-intensive industries, non-heavily polluting sectors, and companies located in the eastern region. (4) Further analysis using the two-stage model reveals that after intelligent manufacturing improves ESG performance, it can improve the environmental and financial performance, thereby achieving sustainable development. The conclusions of the research provide valuable insights for modernizing and transforming traditional manufacturing sectors, as well as for the long-term, sustainable growth of businesses.
In light of the prevailing circumstances of global warming and the increasing frequency of extreme climates, China has put forth the "dual carbon goals" policy, which establishes more rigorous standards for the advancement of traditional manufacturing industries. This paper employs a difference-in-differences model to explore the influence of intelligent manufacturing on ESG and sustainable development, drawing upon the Intelligent Manufacturing Pilot Demonstration Policy (IMPDP) as a point of reference. The results reveal that: (1) Intelligent manufacturing enhances the ESG performance of businesses, and this effect remains consistent even when subjected to serial robustness tests, PSM-DID, and placebo tests. (2) The three primary mechanisms that mediate the impact of intelligent transformation on ESG are the reduction of information asymmetry, the alleviation of financing constraints, and the promotion of green innovation. (3) Intelligent manufacturing has a more pronounced effect on ESG of capital-intensive industries, non-heavily polluting sectors, and companies located in the eastern region. (4) Further analysis using the two-stage model reveals that after intelligent manufacturing improves ESG performance, it can improve the environmental and financial performance, thereby achieving sustainable development. The conclusions of the research provide valuable insights for modernizing and transforming traditional manufacturing sectors, as well as for the long-term, sustainable growth of businesses.
期刊：
Finance Research Letters,2025年83:107689 ISSN：1544-6123
通讯作者：
Lin Ni
作者机构：
[Yang, Yingjun] School of Economics, Lanzhou University of Finance and Economics, Lanzhou 730000, China;[Cheng, Lihong] Lanzhou Open University, Lanzhou 730000, China;[Ni, Lin] Science and Technology Daily, Beijing 100000, China;[Xu, Xin] School of Economics, Hunan Agricultural University, Hunan 410000, China
通讯机构：
[Lin Ni] S;Science and Technology Daily, Beijing 100000, China
摘要：
This study examines listed real estate companies in China from 2008 to 2023, focusing on the impact of debt financing costs and patient capital on corporate resilience. The findings indicate that increasing debt financing costs significantly suppress resilience, exhibiting a U-shaped dynamic trend of initially decreasing and then increasing effects. There is also a significant negative correlation between patient capital and resilience. In addition, patient capital has an important moderating effect on the relationship between debt financing costs and resilience, with different effects found between firms with standard and non - standard audit opinions.
This study examines listed real estate companies in China from 2008 to 2023, focusing on the impact of debt financing costs and patient capital on corporate resilience. The findings indicate that increasing debt financing costs significantly suppress resilience, exhibiting a U-shaped dynamic trend of initially decreasing and then increasing effects. There is also a significant negative correlation between patient capital and resilience. In addition, patient capital has an important moderating effect on the relationship between debt financing costs and resilience, with different effects found between firms with standard and non - standard audit opinions.
摘要：
The purpose of this paper is to analyze the impact of agricultural carbon emissions on China's agricultural economic development, which is of great significance to the modernization of China's agricultural sector. Based on the panel data of 31 provinces in China from 2019 to 2022, this paper selected 10 carbon emission-related indicators and comprehensively adopted a pooled regression model, a fixed effect model, and a random effect model to evaluate the impact. It also passes the Hausman test and Tobit model stability test. The results show that limiting agricultural carbon emissions in China has a significant impact on agricultural economic development; the total power of agricultural machinery (TPAM), rural electricity consumption (REC), amount of agricultural chemical fertilizer applied (AACF), and amount of plastic film used in agriculture (APF) have a significant positive impact on the output of agricultural economy (OAE), while cultivated land (CA) has no significant impact. Chinese agriculture is currently on the left side of the inverted "U" shape of the environmental Kuznets curve. Therefore, there is a need for more research and development of agricultural biotechnology and agricultural policy support to strengthen farmers' knowledge of how to reduce carbon emissions using the above indicators, which can promote agricultural economic growth and achieve high-quality agricultural development in China.
通讯机构：
[Long, F ] H;Hunan Agr Univ, Coll Econ, Changsha 410128, Peoples R China.
关键词：
farmers' digital literacy;green transition of food production;outsourcing of services;green perceptions of farmers;finite mixture modeling
摘要：
Digital literacy plays a significant role in aiding farmers' shift towards green production methods and enhancing their income. This study utilizes household surveys to gather data from 1,182 Chinese grain farmers to empirically analyze the impact and mechanism of farmers' digital literacy on the transition toward green food production. The study demonstrates that enhancing farmers' digital literacy positively impacts the green transition of food production. This effect remains consistent even after endogeneity analysis and robustness testing. The mechanism test shows that farmers' digital literacy promotes the green transition of food production mainly through the mediating roles of improving farmers' agricultural skill level, increasing service outsourcing participation, and improving farmers' green cognition. The extension study reveals that the impact of digital literacy on the green transition of food production is particularly pronounced when farmers possess the traits of risk preference, primarily engage in rice cultivation, and operate on a small scale. Therefore, future efforts should take into account the differences in farmers' endowments and focus on enhancing digital literacy globally while promoting the integration of digital technologies into agricultural systems worldwide, contributing to the green transformation of food production and supporting the achievement of the global Sustainable Development Goals (SDGs).
期刊：
Environment, Development and Sustainability,2025年:1-37 ISSN：1387-585X
通讯作者：
Wenjing Li
作者机构：
College of Economics and Management, Huazhong Agricultural University, Wuhan, China;Hubei Rural Development Research Centre, Wuhan, PR China;[Juan Liu] College of Agricultural Economics and Rural Development, Renmin University of China, Beijing, China;College of Economics, Hunan Agricultural University, Changsha, China;[Shilong Zhao; Wenjing Li] College of Economics and Management, Huazhong Agricultural University, Wuhan, China<&wdkj&>Hubei Rural Development Research Centre, Wuhan, PR China
通讯机构：
[Wenjing Li] C;College of Economics and Management, Huazhong Agricultural University, Wuhan, China<&wdkj&>Hubei Rural Development Research Centre, Wuhan, PR China
关键词：
New agricultural operating entities;Fertilizer reduction;Fertilizer efficiency;Food security;Sustainable agricultural development
摘要：
Ensuring food security and sustainable agricultural production in developing countries, particularly China, necessitates reducing fertilizer application and enhancing its efficiency. While extensive research has explored pathways to achieve these goals from the perspective of smallholder farmers, the role of new agricultural operating entities (NAOEs) remains underexplored. This study addresses this gap by analyzing county-level and household-level panel dataset to estimate the impact of NAOEs on fertilizer application. The empirical findings suggest that NAOEs, including family farms, farming companies, and agricultural cooperatives, significantly contribute to both the reduction and efficiency enhancement of fertilizer application, the results remaining robust across various robustness checks. The effects are primarily achieved by technological innovation effect and scale effect, where family farms exert a greater technological innovation effect, and farming companies show a more pronounced scale effect. Furthermore, the impact of NAOEs on fertilizer application varies depending on regional agricultural resource endowments and prevailing policy environments. These findings underscore the potential of NAOEs as a key driver for mitigating excessive fertilizer use, thereby ensuring food security and advancing sustainable agricultural development.
通讯机构：
[Liu, H ] H;Hunan Agr Univ, Sch Econ, Changsha 410128, Peoples R China.
关键词：
availability of credit;large-scale operations;agricultural machinery;productivity;China's rural areas
摘要：
Rural finance provides financial support for agricultural production. Agricultural credit, as the most important rural financial resource, is designed to regulate rural economic activity and guide the rational adjustment of the rural economy and industrial structure. However, the relationship between the availability of credit to farmers and their choice of cropping behavior in the agricultural production process remains unexplored in depth. To fill this gap, this study constructs an analytical framework for 'Agricultural credit-production factor allocation-planting structure decision-making behaviour'. Using data from a large-scale rural survey in China, this paper empirically examines the impact of agricultural credit on the specialization and 'grain-oriented' of farm households' planting structure using the OLS model, the mediated effects model, and the 2SLS model. In addition, this study explores the mechanism of the allocation of agricultural production factors in this process. This has enriched the theoretical and practical research on rural finance for agricultural development. Studies have shown that agricultural credit contributes significantly to the specialization and 'grain-oriented' of the planting structure. The findings of the study also show that agricultural credit promotes cropping restructuring among farmers through large-scale operations, technological advancement, and green production. In addition, there are differences in the impact of agricultural credit on the planting structure depending on the type of food-producing area, the scale of operation, the development of digital infrastructure, the education of the head, and the source of credit. These findings suggest that increasing rural financial support and promoting the restructuring of land improvement, agricultural machinery, and green production factors may be an effective path to optimizing the cropping structure and improving the efficiency of production factor utilization.
关键词：
Digital economy;ESG;resource-based cities;broadband China
摘要：
Against the high-risk backdrop of 'climate penalty' in resource-based cities, enhancing the ESG (Environmental, Social, and Governance) performance of local enterprises is of paramount importance, yet relevant research is scarce. Using the BCDC (Broadband China Demonstration City) policy as a natural shock to the regional digital economy, the DID (Difference-in-Differences) method is used to examine the influence of the digital economy on ESG performance. Findings reveal that the digital economy contributes to improved ESG performance of businesses located in resource-based cities, which still holds true after robustness tests such as placebo and PSM-DID, and the result is mainly reflected in non-state-owned enterprises and digital enterprises. In addition, enhancing green innovation, improving productivity and reducing transaction costs are key mechanisms. The conclusions provide useful insights for breaking the resource curse and thus realizing sustainability.
期刊：
Land Degradation & Development,2025年36(9):3037-3050 ISSN：1085-3278
通讯作者：
Yang, YP
作者机构：
[Han, Chengji] Hunan Agr Univ, Econ Coll, Changsha, Peoples R China.;[Li, Tong] Univ Queensland, Sch Agr & Food Sustainabil, St Lucia, Qld, Australia.;[Yang, Yuping] Acad Natl Food & Strateg Reserv Adm, Tech & Econ Res Inst Grain Ind, Inst Grain Ind Technol & Econ, Beijing, Peoples R China.;[Han, Feng] Natl Forestry & Grassland Adm, Dev Res Ctr, Beijing, Peoples R China.
通讯机构：
[Yang, YP ] A;Acad Natl Food & Strateg Reserv Adm, Tech & Econ Res Inst Grain Ind, Inst Grain Ind Technol & Econ, Beijing, Peoples R China.
关键词：
data envelopment analysis;Qinba Mountain areas;SDGs;sustainable wellbeing
摘要：
Under the global Sustainable Development Goals initiative, the pursuit of well-being is gradually shifting from wealth to sustainable development. Re-examining the contribution of regional economic, ecological, and social development to the common creation of well-being, analyzing their deep connections, will help us understand the multidimensional concepts and processes of development, and provide ideas for further promoting the construction of a more equitable and sustainable world. China is moving from comprehensive prosperity to common prosperity, and the continuous improvement of sustainable well-being provides effective samples for our research. This study focuses on 46 counties in the Qinba Mountains Areas of China and constructs a coupled performance indicator system for sustainable well-being in mountain villages. The Super SBM model is used to evaluate the matching performance of input and output factors, with Economic capital, Ecological capital, and Social capital as explanatory variables and Sustainable Happiness Index as the expected output. Research has found that: (1) There is a mismatch between the input and output factors of sustainable well-being in the Qinba Mountain Areas, and management techniques are a key factor hindering the improvement of the coupling performance level of sustainable well-being in the Qinba Mountain Areas; (2) The coupling performance level of sustainable happiness in the Qinba Mountain Areas is showing a downward trend, and only adjusting the input–output relationship by about 1.02% can achieve optimization and growth in performance level; (3) There is a significant shortage of input factors, with 87% of counties experiencing insufficient economic capital, about 76% experiencing insufficient arable land, and 41% experiencing insufficient social capital. In order to solve the above problems, it is necessary to strengthen the level of sustainable management in the ecological, economic, and social integration of the counties, respectively. Continuously promote capital investment in the mountain economy, such as logistics, industry, consumption, and public services, in order to upgrade the economy. Strict use of arable land and optimization of the land use structure, and implementation of arable land protection policies. Strengthening the level of social governance and enhancing the satisfaction of residents, thereby raising the level of sustainable well-being. This research will provide a useful reference for achieving sustainable development goals in similar regions of the world.
摘要：
Research has paid much attention to climate change and natural resource management while overlooking a critical area of harvested cropland, land degradation, and agricultural growth. Nonetheless, the global population is rapidly increasing, and China, as the most populated economy globally, could face the issue of land degradation and scarcity of agricultural products. It is crucial to recognize the factors determining agriculture growth in the region. In this regard, this research intends to analyze the influence of land degradation, agriculture cropland, and agricultural growth. China has increased its green energy production and consumption climate change abatement, and water utilization for industrial and agricultural purposes. Therefore, these factors are also considered along with the socioeconomic conditions and technological advancement. Covering the quarterly data from 1980Q1 to 2023Q4, this research uses time series cointegration tests, which validate the presented of long-run association. Following the mixed order of integration on variables, this uses the autoregressive distributed lag approach. The results mention that harvested cropland, water resources, and green energy are the significant drivers of agriculture growth in the short and long run. The study recommends investing in modern agriculture technology, implementing policies to improve socioeconomic conditions, enhancing circular economy and maintaining food security.
关键词：
cropland non-grain transformation;spatial distribution;dynamic evolution;remote sensing technology;sub-pixel segmentation model
摘要：
With the rapid economic development and accelerated urbanization in China, the phenomenon of cropland non-grain transformation has become increasingly prominent, posing a significant threat to national food security. Cropland non-grain transformation refers to the conversion of land originally designated for grain production into non-grain purposes, such as industrial or urban construction land. This trend not only undermines the sustainability of grain production but also negatively impacts the ecological functions of land resources. Scientifically identifying the spatial distribution characteristics and dynamic evolution trends of cropland non-grain transformation is essential for providing decision-makers with valuable insights and supporting the formulation of farmland protection policies. Remote sensing technology, due to its efficiency and extensive applicability, has emerged as a crucial tool in the study of cropland non-grain transformation. Existing research, both domestic and international, has primarily focused on identifying spatial distribution patterns and analyzing evolutionary trends. However, most studies rely on single-pixel scale methods, which struggle to accurately capture subtle spatial differences. Additionally, traditional dynamic evolution analysis methods often overlook the spatiotemporal heterogeneity inherent in cropland non-grain processes and fail to comprehensively consider complex influencing factors. To address these limitations, this study employs an optimized sub-pixel segmentation model to enhance the precision of spatial identification and proposes a novel dynamic evolution analysis framework to uncover the spatiotemporal evolution patterns of cropland non-grain transformation across different regions in China.
期刊：
Frontiers in Sustainable Food Systems,2025年9:1607156 ISSN：2571-581X
通讯作者：
Yin, N
作者机构：
[Xia, Yulian; Yin, Ning] Hunan Agr Univ, Coll Econ, Changsha, Peoples R China.;[Luo, Sichang] Chinese Acad Agr Sci, Inst Agr Econ & Dev, Beijing, Peoples R China.
通讯机构：
[Yin, N ] H;Hunan Agr Univ, Coll Econ, Changsha, Peoples R China.
关键词：
Water rights trading;Policy effect;Green efficiency;Grain production;Water Resource
摘要：
Introduction Since water is an essential input in agricultural production, the reform of water rights system can pose significant influence on water resource allocation and utilization within agri-food systems. Water rights trading has been recognized as an innovative market incentive strategy to promote sustainable water use. This study investigates the role of China's water rights trading policy played in promoting the green water use efficiency of grain production.Methods A three-stage DEA method is adopted to evaluate the green water use efficiency with a provincial panel data from 2006 to 2020. A PSM-DID model, based on the quasi-natural experiment of China's water rights trading pilots in 2014, is employed to analyze policy effects and to explore the role of market mechanism.Results Results have shown that the green water use efficiency of grain production has been significantly improved after the implementation of China's water rights trading policy, and technological innovation is found to exert a mediating effect. The policy effect appears more pronounced and robust in national water rights trading pilots than in provincial ones. Heterogeneity is also detected from the perspective of water resource endowment. The efficiency improvement effect is more pronounced in areas with higher per capita water endowment or in China's main grain-selling areas.Discussion The findings reveal benefits of utilizing market mechanisms to improve the water use efficiency considering environmental constraints. This study gives reference for regions aiming to implement resource conservation and environmentally friendly policies, and also provides inspiration for fostering the sustainable development of grain production in developing countries facing resource scarcity.
摘要：
Clarifying the key driving factors behind the adoption of manure resource utilization technology and promoting its widespread application are crucial for achieving the high-quality development of animal husbandry. This study analyses survey data from 412 large-scale dairy farms across 23 provinces in China. The Cov-AHP method is used to measure the adoption intensity of technology and analyse its mechanisms and underlying logic. The results indicate that value perception, particularly economic value perception, is the strongest driver of adoption intensity. Although the direct effect of environmental regulation is limited, it significantly amplifies the influence of value perception—particularly economic value perception—on technology adoption intensity, especially in large-scale farms. Furthermore, incentive-based regulations (e.g., government subsidies) markedly promote the adoption of manure resource utilization technologies, whereas constraint-based measures (e.g., fines) exert stronger effects on small-scale farms. Additionally, demonstration farms serve as critical catalysts for disseminating best practices and accelerating technology adoption. This study suggests that policies should integrate value perception with targeted financial subsidies and regulatory measures to improve technology adoption, especially with support for small-scale farms. By leveraging demonstration farms to promote successful experiences, the comprehensive adoption of manure resource utilization technologies across the industry can be further improved.
关键词：
agricultural insurance;agricultural agglomeration;industrial integration;agricultural resilience;sustainable agricultural systems
摘要：
Agricultural insurance has achieved rapid development in China, but its role in enhancing agricultural resilience remains unclear. This article aims to fill this research gap by investigating the impact and mechanism of agricultural insurance on agricultural resilience from the perspectives of agricultural agglomeration and industrial integration. The empirical results demonstrate that agricultural insurance exerts a significant and positive influence on the resilience of agriculture, which remains valid even after accounting for endogenous factors through the application of two IV sets. Further mechanism analysis reveals that agricultural insurance primarily boosts agricultural resilience by encouraging horizontal agricultural agglomeration rather than vertical industrial integration. Nevertheless, the influence of agricultural insurance on agricultural resilience differs among regions. Specifically, its effect on agricultural resilience is markedly more pronounced in the eastern and central regions compared to the less-developed western regions. Moreover, its effect on the agricultural resilience in the main grain-producing areas is obviously stronger than that in the main grain-selling area and those with a balance between production and marketing.
期刊：
Environment, Development and Sustainability,2025年:1-33 ISSN：1387-585X
通讯作者：
Chen, JY;He, M
作者机构：
[Zhou, Zhiyu] Sichuan Univ, Sch Econ, Chengdu 610065, Peoples R China.;[Chen, Jinyu] Cent South Univ, Sch Business, Changsha 410083, Peoples R China.;[Chen, Jinyu] Cent South Univ, Inst Met Resources Strategy, Changsha 410083, Peoples R China.;[He, Meng; He, M] Hunan Agr Univ, Sch Econ, Changsha 410128, Peoples R China.;[Liu, Tianqi] Xiamen Natl Accounting Inst, Xiamen 361000, Peoples R China.
通讯机构：
[He, M ] H;[Chen, JY ] C;Cent South Univ, Sch Business, Changsha 410083, Peoples R China.;Cent South Univ, Inst Met Resources Strategy, Changsha 410083, Peoples R China.;Hunan Agr Univ, Sch Econ, Changsha 410128, Peoples R China.
关键词：
Green fiscal policy;Anti-driving effect;Resource compensation effect;Corporate green innovation
摘要：
To support the carbon neutrality goal, China introduced a green fiscal policy in 2011, with a primary focus on energy conservation and emission reduction. However, there is a notable gap in empirical research regarding the incentive effects of this policy on corporate green innovation. Using a multi-period difference-in-difference model, this study examines whether the green fiscal policy drives green innovation in heavily polluting enterprises. The results indicate that: (1) the green fiscal policy sustainably enhances the green innovation capacity of heavily polluting enterprises in both quantity and quality. The results remain robust after a series of tests. (2) Mechanism analysis shows that the main paths for the green fiscal policy to promote corporate green innovation are through the anti-driving effect of higher external media pressure and internal environmental attention, and the resource compensation effect by increasing corporate environmental investment. (3) The green innovation-driven effect of the green fiscal policy is more significant among state-owned enterprises, enterprises with higher internal control quality and located in regions with stricter environmental regulations. Our findings highlight the critical role of green fiscal policies in driving corporate green transformation and sustainable economic development in developing countries.
摘要：
Family farms are crucial for promoting the green transformation of China's agriculture, but their green production (GP) level remains low under the traditional sales model. Rural e-commerce (RE), facilitated by the “Internet +”, has the potential to overcome this challenge; however, its theoretical and empirical efficacy remains unproven. This article aims to verify the impact of RE on family farms' GP. To do so, we establish a theoretical framework, and use endogenous switching regression (ESR) to investigate the impact based on survey data collected from 836 farms in Hunan province, China. We find that participation in RE can significantly promote GP on family farms, with an average treatment effect of 1.175, indicating a 24.05 % improvement in GP. The finding remains robust after a series of tests. The analysis of heterogeneity shows RE is more effective in improving the GP of family farms with a social e-commerce model, large-scale operation, growing food crops, and cooperative membership. It is also more conducive to promoting GP in family farms without product certification, atypical demonstration, and low land tenure security. Further mechanism analysis results show the impact is mainly driven through five mechanisms: expected price incentive, transaction cost saving, mitigation of credit constraint, convenient information access, and quality reputation guarantee. These results suggest that the government enhances investment in rural e-commerce infrastructure, actively promote social e-commerce, develop differentiated support policies, and facilitate practical channels for e-commerce to drive green transformation.
Family farms are crucial for promoting the green transformation of China's agriculture, but their green production (GP) level remains low under the traditional sales model. Rural e-commerce (RE), facilitated by the “Internet +”, has the potential to overcome this challenge; however, its theoretical and empirical efficacy remains unproven. This article aims to verify the impact of RE on family farms' GP. To do so, we establish a theoretical framework, and use endogenous switching regression (ESR) to investigate the impact based on survey data collected from 836 farms in Hunan province, China. We find that participation in RE can significantly promote GP on family farms, with an average treatment effect of 1.175, indicating a 24.05 % improvement in GP. The finding remains robust after a series of tests. The analysis of heterogeneity shows RE is more effective in improving the GP of family farms with a social e-commerce model, large-scale operation, growing food crops, and cooperative membership. It is also more conducive to promoting GP in family farms without product certification, atypical demonstration, and low land tenure security. Further mechanism analysis results show the impact is mainly driven through five mechanisms: expected price incentive, transaction cost saving, mitigation of credit constraint, convenient information access, and quality reputation guarantee. These results suggest that the government enhances investment in rural e-commerce infrastructure, actively promote social e-commerce, develop differentiated support policies, and facilitate practical channels for e-commerce to drive green transformation.
期刊：
APPLIED WATER SCIENCE,2025年15(8):1-13 ISSN：2190-5487
通讯作者：
Tariq, A;Ullah, S
作者机构：
[Jie, Yu] Hunan Agr Univ, Sch Econ, Changsha 410128, Hunan, Peoples R China.;[Panezai, Sanaullah] Univ Balochistan, Dept Geog & Reg Planning, Quetta 87300, Pakistan.;[Tariq, Aqil] Mississippi State Univ, Coll Forest Resources, Dept Wildlife Fisheries & Aquaculture, Mississippi State, MS 39762 USA.;[Abdullah-Al-Wadud, M.] King Saud Univ, Coll Comp & Informat Sci, Dept Software Engn, Riyadh 11543, Saudi Arabia.;[Ullah, Sajid] Nangarhar Univ, Dept Water Resources & Environm Engn, Nangarhar 2600, Afghanistan.
通讯机构：
[Ullah, S ] N;[Tariq, A ] M;Mississippi State Univ, Coll Forest Resources, Dept Wildlife Fisheries & Aquaculture, Mississippi State, MS 39762 USA.;Nangarhar Univ, Dept Water Resources & Environm Engn, Nangarhar 2600, Afghanistan.
关键词：
Geospatial technique;Multi-influencing factor (MIF);Groundwater potential zones;Remote sensing (RS);Geographic information system (GIS)
摘要：
Climate change has affected groundwater resources worldwide. Consequently, Pakistan is ranked in the world’s top ten climate change-affected countries and is experiencing a water stress situation. Remote sensing and geographic information systems (RS and GIS) play important roles in preserving water resources. This study was carried out in one of the most climate-affected provinces of Pakistan to delineate potential groundwater resources. This study has integrated RS, GIS, and multi-influencing factor (MIF) techniques for delineating groundwater potential zones (GWPZs). Various groundwater influencing thematic layers, including geology, soil, land use, land cover, slope, etc., were employed in the GIS domain. All these thematic layers were assigned weights and ranks using the MIF technique through weight overlay analysis in ArcGIS 10.8.2. The study area was classified into four groundwater potential zones (GWPZs) very low, covering an area of 1367.96 km2 (22.0%); low with an area of 3046.82 km2 (49.0%); moderate, with an area of 994.88 km2 (16.0%), and 808.34 km2 (13.0%) of the study area fall under ‘high’ GWPZs. Lastly, the model produced through RS, GIS, and MIF techniques was validated using water table depth data from the existing tube wells in the study area. However, in the present study, the overall accuracy of the produced model is more than 90%. The produced model is helpful for water management authorities for the future sustainable use of groundwater resources in the study area.
关键词：
Industrial progress;agriculture development;growth;renewable energy use;CO 2 emission
摘要：
This study aims to reveal the influence of industrial progress and agricultural value addition on environmental harm and explores how the unique moderating role of institutional quality handles this association; using the panel ARDL approach on a panel of South and East Asian economies from 1980 to 2020. The baseline model proposes that for each 1% increase in agricultural added value and industrial progress, carbon dioxide emissions may fluctuate considerably positively by 0.563% and 0.758% respectively. In the long term, the plausibility of the upturned U-shaped association between economic growth and carbon dioxide emissions in South and East Asian economies has been confirmed in the analysis. The use of renewable energy considerably condenses long-term emission levels. Institutional quality is found to have a strong moderating effect on the link between the main model regressors (renewable energy use, industrial growth, GDP per capita, and agricultural value addition) and CO 2 emissions. Institutional quality supports the enhancement of industrial and agricultural capabilities and the improvement of environmental worth. Finally, the threshold results indicate that the influence of the main regression variables of the model (renewable energy utilization, industrial growth, per capita GDP, and agricultural added value) on carbon dioxide emissions has fully penetrated the institutional quality level. Strong, high-quality institutions, rather than weak institutions, can improve the aptitude of industry and agriculture to mitigate environmental degradation. In this pursuit, it becomes increasingly important to develop policies that support renewable energy based on institutional values.
作者机构：
[Yu, Zetian; Peng, Hua; Dong, Xiaoxia] Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China;[Liu, Hao] College of Economics, Hunan Agricultural University, Changsha, Hunan 410128, China
通讯机构：
[Xiaoxia Dong] A;Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
关键词：
Digital transformation;Digital technology investment;Economic performance;Technical efficiency
摘要：
The objective of this study is to examine the impact of digital transformation on the economic performance of dairy farms. Using survey data from 251 dairy farms in China, we apply stochastic frontier analysis (SFA) and regression models to assess how digital transformation, quantified by digital investments, affects farm performance. The results show that, in general, digital transformation significantly improves technical efficiency, with a 1% increase in digital investment leading to an efficiency gain of 0.029%. After addressing potential endogeneity and conducting robustness checks, the positive effects remain significant, with a 1% increase in digital investment leading to an efficiency gain of between 0.015% and 0.085%. Heterogeneity analysis reveals that medium and small farms (herd size <3,000), non-state-owned farms, and farms in the eastern and western regions experience greater benefits from digital investment. Moreover, digital investment reduces costs and enhances output. Specifically, it leads to a significant reduction in feed costs and a modest decrease in labor costs, while intermediate costs remain unaffected. These findings underscore the role of digitalization in promoting efficient dairy production, particularly in developing economies.
The objective of this study is to examine the impact of digital transformation on the economic performance of dairy farms. Using survey data from 251 dairy farms in China, we apply stochastic frontier analysis (SFA) and regression models to assess how digital transformation, quantified by digital investments, affects farm performance. The results show that, in general, digital transformation significantly improves technical efficiency, with a 1% increase in digital investment leading to an efficiency gain of 0.029%. After addressing potential endogeneity and conducting robustness checks, the positive effects remain significant, with a 1% increase in digital investment leading to an efficiency gain of between 0.015% and 0.085%. Heterogeneity analysis reveals that medium and small farms (herd size <3,000), non-state-owned farms, and farms in the eastern and western regions experience greater benefits from digital investment. Moreover, digital investment reduces costs and enhances output. Specifically, it leads to a significant reduction in feed costs and a modest decrease in labor costs, while intermediate costs remain unaffected. These findings underscore the role of digitalization in promoting efficient dairy production, particularly in developing economies.
摘要：
Introduction Improving agricultural efficiency while ensuring environmental and economic sustainability remains a global challenge.Methods This study introduces the Integrated Agro-Economic Sustainability Framework (IAESF), a novel architecture that fuses multi-source remote sensing data-including satellite, UAV, and ground sensors-with multi-objective optimization and real-time feedback mechanisms. IAESF leverages predictive analytics and adaptive resource allocation to balance profitability with sustainability metrics such as carbon emissions, water usage, and biodiversity preservation. The framework is evaluated across four benchmark datasets (GF-FloodNet, SSL4EO-L, OpenSARShip, TimeSen2Crop) covering spatial, temporal, and spectral variability.Results Experimental results show significant improvements in segmentation accuracy (IoU up to 91.34%) and yield forecasting precision (RMSE reduced by 29.5%) over state-of-the-art models. Scalability is demonstrated through deployment across both smallholder and industrial-scale simulations, supported by dynamic optimization and lightweight model design.Discussion IAESF aligns with global sustainability goals (e.g., SDG 2, SDG 13) and offers actionable insights for precision agriculture policy and planning. This work advances a transparent, interpretable, and resilient decision-making paradigm for sustainable agricultural systems.
