Improving agricultural eco-efficiency (AEE) plays a critical role in fulfilling agriculture sustainable development goals (SDGs). China’s agriculture-led Rural Industrial Integration (RII) seeks to synergize rural industrialization with agricultural sustainability, yet its impact on AEE remains underexplored. Using a 2008–2022 panel of 285 prefecture-level cities in China, this study uses a series of econometric methods to empirically verify the impact of RII on AEE. The coefficient of RII under the fixed effect model is 0.366, indicating that it has a significant positive impact on AEE, which remains valid after robustness tests such as the instrumental variable method and the use of the “Rural Industrial Integration Development Demonstration County” pilot as a quasi-natural experiment. Mechanism tests show that rural labor transfer, agricultural technology innovation, and agricultural carbon emissions play an important role in mediating the impact of RII on AEE. RII has a negative spatial spillover effect on AEE, with a coefficient of −2.280. In addition, the impact of RII on AEE also varies under the heterogeneity of regions and development models. This study provides new evidence that China’s RII practices can promote sustainable agricultural development, deepens theoretical understanding of the impact of RII on AEE, and provides a reference for future policy implementation.

Against the backdrop of rapid modernization and tightening agricultural resource constraints, coordinating urbanization and grain production is a key challenge for China. Using panel data from 30 Chinese provinces from 2004 to 2023, this study applies the coupling coordination degree (CCD) model and a panel vector autoregression model to examine the spatiotemporal coupling characteristics and interaction mechanisms among new-type urbanization (NTU), cultivated land use efficiency (CLUE), and food security (FS). The results show that these three systems have gradually evolved toward coordinated development, with major grain-producing regions consistently leading and entering a moderate coordination stage earlier than other regions. Spatially, CCD exhibits significant positive spatial autocorrelation, characterized by stable “High–High” agglomeration in Northeast China and “Low–Low” agglomeration in southern provinces. Dynamic analysis indicates that system fluctuations are mainly driven by internal inertia, while inter-system interactions are also significant; NTU promotes CLUE, and CLUE and FS exhibit bidirectional causality with complex feedback effects. This study argues for promoting urban–rural factor mobility, advancing green and technology-enabled land use, implementing region-specific development strategies, and establishing a “human–land–grain” early-warning mechanism to safeguard food security during urban expansion.

Industrial agglomeration (IA) is an important factor in promoting forestry development, which has a notable impact on green total factor productivity (GTFP). IA can generate a “scale effect”, but excessive agglomeration may also bring a “crowding effect”, ultimately leading to an inverted U-shaped impact of IA on GTFP. How do these two effects work? From the perspective of agglomeration externalities, this study explores the intermediate role of labor pooling, input sharing, and knowledge spillover to clarify the mechanism between IA and GTFP. This study calculates forestry GTFP of Chinese provinces from 2004 to 2021 and empirically tests the inverted U-shaped relationship between IA and GTFP. It further examines the mediating and moderating effects of agglomeration externalities. The findings reveal that most provinces are still in the “scale effect” stage, but as IA intensifies, the “crowding effect” gradually becomes increasingly evident. Additionally, “crowding effect” is most significant in the eastern region and forestry industrialization areas. Therefore, this study proposes policy measures based on regional differences to promote the green development of the forestry sector.