The purpose of this paper is to apply the Balanced Scorecard (BSC) concept to the sustainable performance evaluation of emerging family farms in Jilin, China. A sustainable performance evaluation system was constructed based on the BSC. A questionnaire survey was used with a sample of 156 emerging family farms involved in the production of planting (grain, horticultural crops) and breeding (animal products) enterprises in Jilin, China. The Analytic Hierarchy Process (AHP)-Fuzzy Comprehensive Evaluation method (FCE) was used for the sustainable performance evaluation by different BSC dimensions, farm types, and regions. This empirical study revealed that the BSC is applicable for the farm sustainable performance evaluation in the Chinese context. The key is selecting suitable indicators for the evaluation index system while considering the particularity of market, resources, management, and personnel. The sustainable performance of the investigated family farms is in the slightly above moderate level as a whole. Financial performance and market performance are above moderate, while internal business process performance is moderate, and learning and growth performance is below moderate. They are facing difficult challenges to upgrade in terms of marketing and financing channels, branding, and organic production. Industrial differences existed in the farms’ sustainable performance. Farms combining planting and breeding have better sustainability, which could be a signal for transformation of the traditional single planting or breeding modes in China. The internal business process performance of grain farms is significantly less, due possibly to long-term policy support and protection with less of an emphasis on ecological outcomes. Subtle regional differences in the overall sustainable performance of surveyed family farms suggest that farm performance depends more on management than on external environment.

Agricultural ecological efficiency (AEE) is an important index to measure the coordinated development of agricultural production, resources, and the environment. This study tries to explore the impact of rural human capital (RHC) on AEE, hoping to provide a reference for promoting the green development of agriculture. Data sets (2006–2021) used in this study were gathered from Official Statistics Panel data in 30 provinces in China. Firstly, the super-efficient slacks-based measure model (SBM) with non-expected output is employed to effectively measure AEE. Secondly, the entropy method is used to measure RHC in three dimensions: education, health, and migration. Finally, this study discusses the impact of RHC on AEE using the panel Tobit model and further verifies the moderating effect of Internet popularization on the effect of RHC on AEE using the moderating effect model. The results show that RHC has a significant positive impact on the promotion of AEE, and this positive impact has obvious regional heterogeneity. Specifically, RHC has a greater promoting role on AEE in the eastern and northeastern regions, while it has a smaller promoting role on AEE in the central and western regions. In addition, Internet penetration plays a positive moderating role in the mechanism of the effect of RHC on the AEE. This study may serve as a reference for improving AEE by providing theoretical guidance and policy suggestions for promoting agricultural green development.

Whether digital village construction can effectively promote agriculture’s green development is essential for modernizing agriculture and rural areas. Using panel data from 30 provinces in China between 2011 and 2022, this study empirically examines the relationship between digital village construction and agroecological efficiency and explores its mechanism of action and threshold effect, contributing to the exploration of agricultural digitization and sustainable development. This study shows that (1) AEE is positively associated with digital village construction; (2) the positive association size varies in regions and construction levels; (3) agricultural land transfer and technological innovation play a mediating role in the positive effect; and (4) there is a single threshold value for the positive effect of digital village construction, and after crossing the threshold value, its marginal effect shows a positive and increasing nonlinear characteristic. This study enhances our comprehension of digital village development to advance agroecological efficiency and offers theoretical insights and policy recommendations for optimizing the rural digital infrastructure and fostering sustainable agricultural growth.

In recent years, there has been rapid development in Chinese small and medium-sized livestock farms, accompanied by an increase in operational risks within the supply chain. To ensure the stability and sustainability of supply chain operations in small and medium-sized livestock farms, it is essential to thoroughly analyze their potential hazards and formulate effective prevention strategies. This paper employs the relevant supply chain theories, combined with previous research and expert interviews, to conduct an empirical analysis of supply chain operation risks in small and medium-sized livestock farms, with a specific focus on pig farms, beef cattle farms, laying hen farms, and broiler farms. The research indicates that the supply chain operation risks faced by small and medium-sized livestock farms primarily stem from external environmental factors and production-related challenges. Furthermore, external environmental risks predominantly arise from market fluctuations and compliance with environmental regulations, while production risk mainly pertains to disease safety. Based on the research findings, this paper proposes specific measures for risk prevention, including the strengthening of external environmental security, driving production innovation capacity, strengthening market forecasting capacity, constantly enhancing environmental awareness, and strengthening epidemic prevention efforts.

Against the backdrop of increasingly severe agricultural labor aging (ALA), the aging process not only threatens food security but also poses challenges to green and sustainable agricultural development. Existing studies have paid insufficient attention to how ALA simultaneously affects grain production efficiency (GPE) and grain eco-efficiency (GEE), and there is a particular lack of systematic investigation into the moderating roles of different crop types and social networks. To address this gap, this study utilizes survey data from 1056 farm households across five major grain-producing provinces in China and employs Tobit regression models to empirically examine the dual effects of ALA on GPE and GEE, while also revealing the moderating mechanisms of formal and informal dual-layer social networks. The main findings are as follows: (1) ALA generally inhibits both GPE and GEE across different grain crops, with a more prevalent negative impact on GEE. (2) The impact of ALA on the two types of efficiency exhibits crop-specific nonlinear characteristics: a positive U-shaped relationship for maize, an inverted U-shaped relationship for rice, and no significant nonlinear relationship for wheat. (3) Social networks play significant linear and nonlinear moderating roles in mitigating the negative effects of ALA, though their effects vary depending on network type, crop system, and efficiency dimension. Based on these findings, it is recommended to implement differentiated intervention strategies tailored to crop characteristics and aging stages, build a multi-tiered social network support system, and strengthen the research, extension, and service support for green technologies targeting middle-aged and older farmers, thereby synergistically enhancing grain production capacity and ecological sustainability.

The rapid expansion of China’s livestock husbandry has boosted the supply of meat, eggs, and dairy products, while concurrently giving rise to environmental pollution issues. Research on the effects of various environmental regulations on the green total factor productivity (GTFP) of the livestock sector and their underlying mechanisms is still lacking, despite the Chinese government’s implementation of corresponding environmental regulatory policies to address this practical challenge. As a key instrument for fostering green economic transformation, examining the relationship between environmental regulation and the green total factor productivity (GTFP) of animal husbandry is crucial for the sector’s sustainable development. In order to estimate the GTFP of China’s livestock sector for the years 2010–2022, this study uses the super-slack-based measure (Super-SBM) methodology. It conducts an empirical analysis to examine the mechanisms through which different environmental regulations influence livestock GTFP, alongside an investigation of regional heterogeneity. The results show that different environmental regulations have different effects on animal husbandry GTFP, with notable regional differences. Specifically, incentive-based environmental regulations enhance livestock GTFP by facilitating technological innovation; however, the level of regional economic development negatively moderates the association between incentive-based environmental regulations and livestock GTFP. The findings confirm that incentive-based environmental regulations are successful in encouraging livestock GTFP through technical innovation. They further emphasize that regions should formulate context-specific environmental regulatory policies to balance environmental protection and industrial development, thereby supporting the green and sustainable growth of China’s livestock industry.