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This paper explores the significance of digital governance for sustainable rural development in China, emphasizing the collaborative efforts of village administrative organizations, new agricultural business entities, and peasant households. Utilizing an evolutionary game approach, we examine the decision-making behaviors and stability points of these three entities within the context of rural digital governance. Our analysis is grounded in a mechanism of interest linkage among the stakeholders, with numerical simulations used to assess the impact of key variables and parameters on their evolutionary outcomes. The paper reveals that village administrative organizations are highly sensitive to changes in performance gains, special subsidies, penalty losses, and benefit distribution coefficients. Enhancing these variables can significantly motivate these organizations to engage in digital governance. In contrast, new agricultural business entities and peasant households demonstrate a stronger and more consistent willingness to collaborate, minimally affected by variable changes, which suggests a solid economic and social foundation for rural digital governance in China. Our paper underscores the need for positive incentives and a robust fault-tolerance mechanism to foster collaboration among village administrative organizations. It also highlights the importance of integrating new agricultural business entities into the digital governance framework to promote sustainable rural development. These insights provide valuable theoretical and practical implications for policymakers aiming to enhance the efficacy and inclusivity of digital governance in rural China.

The integration of agriculture and tourism has emerged as a potential strategy to boost agricultural productivity and promote sustainable rural development in China. This study investigates the impact of agricultural tourism integration (ATI) on agricultural green total factor productivity (AGTFP) in China, filling a research gap in understanding the complex dynamic nexus. We examine the mediating role of the agricultural industrial structure adjustment index (AISAI) and the threshold effects of ATI and human capital level (HCL) using a panel dataset of 30 Chinese provinces from 2011 to 2021. The empirical results reveal three key findings: (1) ATI has a significant positive impact on AGTFP growth; (2) the AISAI partially mediates the relationship between ATI and AGTFP; and (3) ATI and HCL have a double-threshold effect and a single-threshold effect on the ATI-AGTFP nexus, respectively. These findings suggest that the effectiveness of ATI in promoting AGTFP varies across different stages of integration and levels of human capital development. The empirical results contribute to the literature on rural development strategies by providing an in-depth understanding of how ATI influences agricultural productivity and sustainability. Based on these findings, we propose targeted policy recommendations to optimize ATI strategies and invest in human capital to improve the resilience and sustainability of rural economies in China.

Promoting green and sustainable agriculture is of great significance for ensuring food security and addressing global challenges. Meanwhile, health has increasingly become a global concern. Nutrition and health are the purpose of agricultural production. As two major global issues, how agriculture empowers human health has long been discussed. Based on the provincial panel data of China from 2003 to 2021, this paper studies the impact of agricultural green transformation (AGT) on residents’ health and explores its impact mechanism. The empirical results show that: (1) AGT in China has significantly reduced the average mortality rate and maternal mortality rate and significantly increased the average life expectancy, indicating that AGT in China has significantly improved the health level. The conclusions of robustness testing methods such as replacing AGT indicators and controlling endogeneity are still valid; and (2) The impact of AGT on residents’ health depends on the regional economic level, and there is a threshold effect. Compared with low-income areas, the positive effects of AGT in high-income areas on residents’ health are more pronounced; and (3) Agricultural carbon emissions play an intermediary effect between AGT and residents’ health, and AGT can improve residents’ health by reducing carbon emissions. The level of local education development plays a moderating role in the relationship between AGT and residents’ health. Agricultural policy implications include enhancing the ability to protect and utilize agricultural resources, promoting the green and low-carbon transformation of agriculture, and taking a more scientific and systematic approach towards the complex diversity of health risk factors.

The urgency of enhancing agricultural productivity within the Yellow River Basin cannot be overstated, given its critical role in ensuring food security amidst the challenges posed by climate change, natural disasters, and the increasing demand for food crops. Utilizing panel data from nine provinces within the Yellow River Basin for the period 2001 to 2020, this study examines the temporal characteristics and spatial distribution of Total Factor Productivity (TFP) for key grain crops—namely wheat, corn, and soybean—through the application of the Malmquist index which can be decomposed through the DEA-Malmquist index methodology. The empirical results demonstrate that TFP growth rates for these crops have exhibited significant phase variations, with soybean recording the highest TFP growth rate in the basin. Additionally, this study underscores marked regional disparities in soybean productivity. TFP decomposition reveals that the primary drivers of TFP improvement across these crops are attributed to technical progress, with gains in overall technical efficiency largely due to scale efficiency enhancements, whereas pure technical efficiency has shown limited progress. Regional analysis indicates that Inner Mongolia leads in TFP growth for all crops, while Ningxia, Sichuan, and Shaanxi lag behind in wheat, corn, and soybean. Additionally, our analysis delineates natural disasters as a significant barrier to Total Factor Productivity (TFP), notably obstructing technological advancements in wheat cultivation. The investigation further reveals a positive relationship between regional per capita income and the growth of wheat TFP, in contrast to a negative relationship with the TFP growth of corn and soybeans. Moreover, investing in agriculture, forestry, water management, and road infrastructure supports the growth of wheat TFP, while urbanization levels pose constraints. Conclusively, an uptick in annual rural electricity usage, along with improved per capita postal and telecommunication services, exerts a favorable influence on TFP for corn and soybeans.

Geographical indications (GIs) mitigate information asymmetry in agri-food transactions by providing consumers with origin and quality information. This paper explores the impact of GIs on rural development in China by examining agricultural output and farmers’ income. Utilizing a large county-level dataset and comprehensive official GI information, this study estimates the impact of GIs on agricultural output and rural income using panel-fixed-effects models. The results reveal that GIs significantly boost agricultural added value and rural per capita disposable income. A series of methods, including difference-in-differences, propensity score matching with difference-in-differences, and double machine learning combined with difference-in-differences using random forests verify the robustness of the results. Moreover, by categorizing GIs based on product types, the analysis reveals heterogeneous effects of different GI categories on agricultural growth and income gains for farmers. The research findings in this paper offer valuable insights to inform policymaking aimed at advancing rural development, raising farmers’ incomes, and promoting sustainable agri-food supply chains.

Based on the survey data of five large grain-producing provinces in China, this paper studies the effect of agricultural production trusteeship on agricultural carbon emission reduction by using a propensity score matching method. The empirical results show that the carbon emission of wheat reduces by 7.107 kg/mu, with a decrease rate of 15.5% after participating in agricultural production trusteeship. Among them, chemical fertilizers, manpower input, agricultural chemicals and diesel oil, respectively, reduce with rates of 14.2%, 27.7%, 14.1%, and 6%. However, there are differences in the facilitation effects of different trusteeship services, with the best promotion effect of field management services, followed by cultivation, planting and harvest services, and then agricultural material supply services, for which the average treatment effects on treated (ATT) is −6.160, −5.732 and −5.530, respectively. Meanwhile, there are differences in the promotion effects for farm households with different factor endowments. The promotion effect is better for small farm households with one type of agricultural machinery or less, and an operation scale of 7 mu or less. Therefore, in order to better play the role of agricultural production trusteeship in agricultural carbon emission reduction, the government should vigorously support its development and guide more smallholders to choose agricultural production trusteeship.

Micro-credit for poverty alleviation is an important financial measure of targeted poverty reduction and rural revitalization in China. This paper employs the OLS model and Logit model to empirically test the effect of micro-credit for poverty alleviation on the income level and stability of income growth of farmers based on the field survey data of 458 registered poverty-stricken farmer households in Fuping County and Quyang County of Hebei Province. The results suggest that micro-credit for poverty alleviation can increase farmers’ income, stabilize the growth of their income, and exert significant short-term and long-term effects on income growth and poverty alleviation. The specialized farmer cooperatives, the scale of production and operation, the proportion of family labor force, and the education level of the head of the farmer’s household exert a significantly positive effect on the farmers’ income and the stable growth of their income. There is a significant interaction between micro-credit for poverty alleviation and specialized farmer cooperatives. The physical conditions of family members exert a negative effect on the stable growth of their income, and other financing channels have no significant effect.

The Yangtze River Economic Belt (YREB), an important industrial belt for food security for China, is facing the challenge of decreasing cultivated land in the process of rapid urbanization. In this case, how to improve the cultivated land use efficiency (CLUE) has become the top priority. Based on data from 108 cities of YREB from 2001 to 2019, we measured CLUE using a slack-based measure with undesirable output (SBM-Undesirable). The high-value area of CLUE shows a trend from multi-core agglomeration to two-core agglomeration, mainly concentrated in Chengdu-Chongqing urban agglomeration and the northern part of the YREB. Then the paper examines the spatial effect of urbanization on CLUE using the Spatial Error Model (SEM). The result shows that population urbanization has significantly promoted the improvement of CLUE in YREB during the sample period. With each percentage point increase in population urbanization, CLUE will increase by 2.99%. Land urbanization has a negative impact on CLUE, for each percent increase in the expansion of urban spatial scope, CLUE will decrease by 0.06%. The spatial heterogeneity analysis shows that population urbanization in the lower reaches has significantly promoted CLUE, with a coefficient of 1.053. The population urbanization level in the middle and lower reaches of the region has no obvious effect on CLUE. The coefficient of land urbanization in the downstream region is 0.35, which significantly promotes CLUE. The coefficient in the middle is −0.26, which implies the CLUE decreases by 0.26% for every one percentage point increase in land urbanization. Land urbanization in the upper has no significant impact on the CLUE. Policy implications include improving the quality of the three major urban clusters along the YREB, building an ecologic protective screen in the upper, encouraging a new agricultural management system and detailed regulations related to the cultivated land protection in YREB.

Narrowing the rural–urban income gap is an important challenge in achieving sustained and stable economic and social development in China. The present study investigates the role of farmers’ education and agricultural productivity growth in influencing the rural–urban income gap by applying mediation, interaction, and quantile regression models to provincial panel data of China from 2003 to 2017. Results show that, first of all, China’s agricultural productivity (TFP) continues to improve, and it is mainly driven by technical change (TC), with no significant role of technical efficiency change (TEC) or stable scale change (SC). Improving farmers’ education not only directly narrows the rural–urban income gap but also indirectly improves agricultural productivity to further narrow the rural–urban income gap. Due to differences in income sources of farmers, the corresponding impacts of farmers’ education and agricultural productivity growth on the rural–urban income gap also differ. Policy recommendations include continued investments in farmers’ education and training as well as modernization of agricultural for higher productivity growth.

The goal of this study is to investigate the regulation effect of relationship quality in the process of the omni-channel (OC) model on service supply chain (SSC) collaboration of agricultural products. Furthermore, it is also to explore the intermediary effect of SSC collaboration in the process of service innovation and OC. A questionnaire was developed, research data were gathered from businesses in the agricultural SSC in western China, and an empirical study was carried out by using the AMOS multivariate statistical analysis approach after a thorough review of the literature in recent years. The study demonstrates that the OC model has a considerable impact on service innovation, SSC collaboration has an intermediary effect, and the quality of supply chain (SC) relationships has a regulation effect in the model. The results inspire academics and industry professionals to focus on SSC collaboration, improve the OC model’s administration, and promote service innovation in agricultural SC. Finally, the paper proposes suggestions to promote agricultural product development in western China in terms of enhancing SSC collaboration, OC model, and service innovation.

The traditional Stochastic Frontier Model (SFM) suffers from a very restrictive assumption of independence of its error components and also limited ability to address heterogeneity (inefficiency effects) satisfactorily, thereby leading to potential biases in the estimation of model parameters, identification of inefficiency effect variables influencing efficiency and, ultimately, efficiency scores. This paper aims to investigate the consequences of ignoring any dependency in error components and heterogeneity in the stochastic frontier model, and proposes a copula-based SFM with heterogeneity to resolve such weaknesses based on a simulation study to prove its superiority over the traditional SFM, followed by an empirical application on a sample of rice producers from northern Thailand. We demonstrate that the proposed model, i.e., copula-based SFM with dependent error components and heterogeneity, is unbiased and robust. The simulation experiments show that the traditional SFM can cause biases in parameter estimation and severe overestimation of technical efficiency. The traditional SFM with heterogeneity also has similar consequences. However, just ignoring heterogeneity does not have a great impact on parameter estimation and technical efficiency compared to the consequence of ignoring dependency in error components. The empirical application of the proposed model results show that land, labor and material inputs are all significant drivers of rice production in our copula-based SFM with dependent error components and heterogeneity, whereas in the traditional SFM model only the land variable seems to be a significant driver of rice production. The mean technical efficiency (MTE) score was also overestimated by two points in the traditional SFM, i.e., MTE = 0.88 versus 0.86. Finally, results of the copula-based SFM with dependent error components and heterogeneity reveals that both subsistence pressure and the use of hired labor are significantly associated with technical inefficiency, whereas the traditional SFM could identify the effect of hired labor use only. Therefore, caution is necessary when interpreting results from the conventional SFM as the results may be biased, incomplete and/or inadequate.

Improving energy efficiency and conservation is integral to sustain agricultural growth in emerging economies. This paper investigates the energy efficiency and energy-saving potential of the agricultural sector of 27 emerging economies using a stochastic frontier approach and Shephard distance function, and their determinants are examined using the Tobit quantile regression model. Results revealed that energy efficiency in the agricultural sector fluctuated during the period from 1998 to 2017. The median average energy efficiency was estimated at 0.74, and the cumulative energy-saving potential was estimated at 542.80 million tons of oil equivalent (Mtoe), which can be achieved by eliminating energy inefficiency alone. Differences exist in energy efficiency and energy-saving potential across continents, with higher potential in Asia and lower potential in Europe. Economic structure, urbanization and GDP per capita have negative influences on agricultural energy efficiency. Energy mix and pesticide use are significant drivers of energy efficiency, while the ratio of agricultural land that has varied influences different quantiles. Policy implications include optimization of the energy mix, economic structure and pesticide use.

The core of agricultural development depends on agricultural production efficiency improvement, and total-factor productivity growth is its significant embodiment. Hence, it is essential to address the question of “how to improve China’s agricultural productivity and efficiency in order to achieve growth and sustainability of agriculture in the future”. This paper estimates indices of China’s agricultural technical efficiency (TE) scores, total-factor productivity (TFP), and its two components, technological change/progress (TC) and technical efficiency change (EC), using provincial-level panel data of 30 provinces from 2002 to 2017 by applying a stochastic frontier approach (SFA). The paper also identifies determinants of TE, TC, and TFP using selected indicators from four hierarchical levels of the economy, i.e., farm level, production environment level, provincial level, and the state level, by applying a system-GMM method. Results reveal that agricultural labor, machinery, agricultural plastic film, and pesticides are the significant drivers of agricultural productivity, with no significant role of land area under cultivation. Constant returns to scale exist in China’s agriculture. The agricultural technical efficiency level fluctuated between 80% and 91% with a stable trend and a slight decline in later years, while TFP improved consistently over time, mainly driven by technological progress. Among the determinants, government investment in agricultural development projects significantly drives TC and TE, while the experienced labor force significantly increases TE. The disaster rate significantly reduces TE but promotes TC and TFP. The literacy rate significantly improves TC and TFP. However, government expenditures in “agriculture, forestry, and water” significantly reduce TE, TC, and TFP. Policy recommendations include (1) increased levels of mechanization and agriculture film use while avoiding an increase in pesticide use, (2) a continued increase in government expenditure in agricultural development projects, R&D to improve technological progress, and diffusion of modern agricultural technologies, and (3) investment in education targeted at the farming population in order to continue the growth in the productivity and sustainability of China’s agriculture.