Analyzing the impact of agricultural industrial agglomeration (AIG) on agricultural green development (AGD) is of a great significance to realizing the sustainable and high-quality development of agriculture. Panel data of 31 provinces in China from 2009 to 2019 were analysed. For measuring efficiency, a non-parametric DEA approach in the presence of undesirable outputs, a slack-based measure (SBM) was used. From the perspective of the spatial spillover analysis and heterogeneity analysis, Moran’s I index and the Spatial Durbin Model (SDM) were used to empirically analyze the impact of AIG on AGD to alleviate conflicts between agricultural sustainable development and environmental pollution and further explore the regional heterogeneity of AIG on AGD-efficiency due to the vast territory of China. The mediation model is constructed to explore the paths of AIG affecting AGD. The results show that: (1) Chinese efficiency of AGD was raised continuously and the high efficiency was mainly located in the southeastern coastal areas. (2) AIG not only has a significant U-shaped impact on the AGD, but also has a nonlinear U-shaped spatial spillover effect in related regions, which shows that the “siphon effect” will be triggered in the early stage of AIG and the “diffusion effect” will be evoked in the later stage of AIG. (3) From the perspective of heterogeneity analysis, AIG significantly promotes the efficiency of AGD in the central region of mainland China. In the eastern region, the AIG has an inverted U-shaped effect on the efficiency of AGD from positive to negative. On the contrary, the AIG has a U-shaped impact on the efficiency of AGD from negative to positive in the western region. (4) The analysis of the mediation model plays a partial positive mediating role for AGD to persist in promoting technology innovation and increasing the speed of talent agglomeration. Accordingly, suggestions are provided to strengthen the coordination and cooperation in sustainable agricultural development among provinces, to drive the efficiency of science and technology through the scale knowledge spillover effect, and to conduct a scientific layout of agricultural industry development.

Green agriculture is mainstream for the sustainable development of agriculture. Based on the Chinese provincial agriculture panel data from 2010 to 2019, we adopted the slack-based measure (SBM) super-efficiency model, sales force automation (SFA) model, and global malmquist–luenberger (GML) production index to measure the efficiency of agricultural green development (AGD). Moreover, Moran’s I and spatial econometric model were applied to analyze factors influencing AGD. The threshold model was used to analyze the relationship between the scale of AGD and gross domestic product (GDP). The results show that 1) Chinese green agricultural development efficiency is on a rising trend, reducing the impact of environmental factors and random interference on the AGD. 2) The analysis of AGD in the spatial effect showed a direct positive effect from agricultural mechanization, science and technology innovation, industrial agglomeration, income level, and environmental rule and a direct negative effect from agricultural yield structure, farmland pollution, and agricultural disasters. Furthermore, industrial structure optimization and environmental rule evoke a demonstration effect, but technical innovation, income level, and agricultural industrial agglomeration triggered a siphonic effect. 3) The threshold model was used to analyze the scale of AGD to realize sustainable development between agriculture and economy.

Agriculture is the link between sustainable development and human nutrition and health. Increasing carbon emissions from agriculture threaten ecosystems and human living conditions. However, the level of agricultural emissions in China and uncertainties in the global supply chain limit the implementation of more sustainable agricultural policies in China. This paper aims to assess the environmental impacts of Chinese agriculture from the perspective of consumption carbon emissions. This paper adopts a multi-regional environmental input-output model, uses the global supply chain to identify major trade partners and key industries, and estimates China’s domestic agricultural carbon emissions from the perspective of intermediate product input industries. Then, from the perspective of the country (region) and intermediate product input industry, we estimate the embodied carbon of China’s agriculture in international trade. Finally, based on the structural decomposition analysis, we factor out the changes in carbon emissions embodied in Chinese agriculture from the demand and supply sides. The findings underscore that energy supply, crop cultivation, livestock farming, machinery and equipment manufacturing, food production, and electrical equipment manufacturing are the primary drivers behind the growth in carbon emissions. According to the SDA decomposition results of the global supply chain, the major partners of China’s agricultural sector, which are also the top ten carbon emission sources, can be roughly divided into four categories: developed countries with a long geographical distance from China, BRIC countries, Asian neighbors and Taiwan region. The results of the factor decomposition analysis reveal that the surge in carbon emissions is primarily attributable to increased demand and decreased production emission efficiency. In contrast, advancements in industry-wide technology play a key role in reducing carbon emissions. Conclusively, this study provides scientific basis and policy recommendations for formulating sustainable agricultural development strategies and protecting the ecological environment, which is expected to provide an important reference for solving the problem of global climate change.