With digital technologies injecting a strong impetus into China’s sustainable agricultural development, the digital village is a new path for Chinese agricultural development. This paper focuses on digital village and sustainable agricultural development, in which the situation of digital village and green and high-quality agricultural development in China from 2010 to 2019 is measured based on the entropy weight–TOPSIS method and coupling coordination model and further explores the spatiotemporal evolution of their coupling coordination. In addition, it studies the factors which influence the coupling coordination of digital village and green and high-quality agricultural development using a geographical detector. The study shows that both digital villages and green and high-quality agricultural development in China show good momentum. In terms of the spatial pattern, cities on the southeast coast witness better development of digital villages, and the southern regions enjoy a higher degree of green and high-quality development in agriculture. The coupling coordination between digital villages and green and high-quality agricultural development shows a fluctuating upward trend in the eastern regions. Some of the influence factors play a significant role in the coupling coordination of digital villages and green and high-quality agricultural development, such as e-commerce, per capita income, innovation, and levels of income and education. On this basis, we suggest that the government should continuously promote the development of digital villages and improve rural governance to bridge the digital divide. In this case, policies to promote green and high-quality agricultural development through digitalization can be introduced according to local conditions, thus enabling sustainable agricultural development with the empowerment of digitalization.

Watershed eco-compensation (WEC) is considered a significant environmental policy instrument for watershed ecological protection and management. However, in the legislation and practice of eco-compensation in China, the development of the WEC mechanism is still in the initial stages. In this paper, the institutional opportunities and challenges of WEC are analyzed from the existing policies, laws, and economical instruments. Theoretically, WEC in China has seen a combination of punitive-based “Watershed Ecological Damage Compensation (WEDC)” and incentive-based “Watershed Ecological Protective Compensation (WEPC)”. Through a comparative analysis of domestic and foreign watershed compensation practices, the results demonstrate that most of China’s WEC projects have an insufficient legal basis, a single compensatory subject, insufficient compensation funds, and an imperfect market-oriented compensation mechanism. To improve watershed eco-compensation in China, it is recommended to strengthen legislation, select diversified eco-compensation approaches, and establish a market-based and systematic eco-compensation mechanism for watersheds.

Enhancing and strengthening food production capacity has always been a top priority in agricultural research, serving as a cornerstone for ensuring national food security and stable economic development. This study, based on panel data spanning from 2011 to 2021 across 30 provinces in China, delves into the mechanism through which the digital economy impacts food production capacity. Employing a double fixed effect model, a mediation effect model, and a panel threshold model, we uncover several key findings: The digital economy significantly boosts food production capacity, with robustness tests affirming the reliability of our results. Mechanism analysis reveals that the digital economy enhances food production capacity by elevating total factor productivity and bolstering agricultural resilience. The threshold effect underscores that urbanization levels exhibit a single-threshold impact, wherein the influence of the digital economy on food production capacity intensifies upon crossing this threshold. Heterogeneity analysis reveals that the digital economy significantly boosts food production capacity in central and primary grain-producing regions, while its impact is comparatively weaker in the eastern and western regions, as well as in non-primary grain-producing areas. In summary, this research sheds light on the pivotal role of the digital economy in augmenting food production capacity, offering valuable insights into regional variations and thresholds in its impact across China.

Urban agglomerations underpin regional economic growth and sustainability transitions, yet the spatial heterogeneity and drivers of land use intensity (LUI) remain insufficiently resolved in inland settings. This study develops a high-resolution framework—combining a 1 km hexagonal grid with XGBoost-SHAP—to (i) map subsystem-specific LUI evolution, (ii) identify dominant drivers and nonlinear thresholds, and (iii) inform differentiated, sustainable land governance in the Guanzhong Plain Urban Agglomeration (GPUA) over 2000–2020. Composite LUI indices were constructed for human settlement (HS), cropland (CS), and forest (FS) subsystems; eleven natural, socioeconomic, urban–rural, and locational variables served as candidate drivers. The results show marked redistributions across subsystems. In HS, the share of low-intensity cells declined (86.54% to 83.18%) as that of medium- (12.10% to 14.26%) and high-intensity ones (1.22% to 2.56%) increased, forming a continuous high-intensity corridor between Xi’an and Xianyang by 2020. CS shifted toward medium-intensity (32.53% to 50.57%) with the contraction of high-intensity cells (26.62% to 14.53%), evidencing strong dynamism (55.1% net intensification; 38.5% net decline). FS transitioned to low-intensity dominance by 2020 (59.12%), with stability and delayed growth concentrated in conserved mountainous zones. Urban–rural gradients were distinct: HS rose by >20% (relative to 2000) in cores but remained low and stable in rural areas (mean < 0.20); CS peaked and stayed stable at fringes (mean ≈ 0.60); FS shifted from an inverse gradient (2000–2010) to core-area recovery by 2020. Explainable machine learning revealed inverted U-shaped relationships for HS (per capita GDP) and CS (population density) and a unimodal peak for FS with respect to distance to urban centers; model performance was strong (HS R² up to 0.82) with robust validation. Policy recommendations are subsystem-specific: enforce growth boundaries and prioritize infill/polycentric networks (HS); pair farmland redlines with precision agriculture (CS); and maintain ecological redlines with differentiated conservation and afforestation (FS). The framework offers transferable, data-driven evidence for calibrating thresholds and sequencing interventions to reconcile land use intensification with ecological integrity in rapidly urbanizing contexts.