High-quality development of agricultural and rural areas is the foundation of national modernization and sustainable development in China. Therefore, the evaluation of the degree of development of modernization of China’s agricultural and rural areas and the summarization of its main characteristics are crucial. However, studies on rural modernization and coordinated development are limited. Here, we developed a statistical evaluation system to effectively identify and assess the levels of agricultural and rural modernization, urban–rural integration, and agricultural rural coordination. We found that agricultural and rural modernization in China has continued to improve, but low scores were attributed to the factors of coordinated development of urban–rural areas, livable ecological environment, farmer prosperity, industrial structure upgrade, and resource allocation efficiency, limiting high-quality agricultural and rural development. Agricultural and rural modernization is lower in the central and western regions, compared to that in the eastern regions, but it is picking up the pace, indicating convergence. Rural modernization is lower than agricultural modernization and the two subsystems are not sufficiently coordinated, especially in undeveloped areas. Furthermore, the regional differences in rural modernization are greater than those in agricultural modernization. The system developed here can provide valuable experience for developing countries to open new paths toward modernization.

Amidst the tremendous evolution of the digital economy and the expedited establishment of a new development paradigm, the use of artificial intelligence (AI) technologies holds significant importance in achieving superior economic development. While much of the previous research focused on the macroeconomic impact of AI, this study examined how AI technology affects food processing firm performance, productivity, and labor skill structure at the food processing firm level. This study utilized panel data from listed food processing enterprises in Shanghai and Shenzhen spanning from 2010 to 2021, performing textual analysis on the annual reports of listed companies and then creating enterprise-level AI indicators to empirically examine the influence of AI applications on enterprise performance and its underlying mechanisms. The findings indicate a substantial improvement in business performance due to the application of artificial intelligence, which is a conclusion corroborated through a series of stability tests. Exploring channels and mechanisms, the analysis revealed that AI-driven advancements in production technologies stimulated the requirement for highly skilled labor, thereby inducing shifts in the labor force’s structure. Further investigation demonstrated that artificial intelligence contributed to enhancing the total factor productivity, consequently bolstering the overall enterprise performance. A heterogeneity analysis showed that firm-level factors, such as the nature of property rights and factor intensity, had an impact on the influence of AI on firm performance. In addition, the geographic location and time of year of a company also had impacts on the productivity benefits of artificial intelligence. This research deepened the cognition and understanding of the role played by AI in the production process at the micro-enterprise level and provided suggestions for promoting the development of artificial intelligence technologies at the micro-enterprise level, which will facilitate the transformation of the labor structure to further augment enterprise efficiency.

While China’s outward foreign direct investment (OFDI) is growing rapidly, few studies have considered the impact of this growth on product quality. This study uses microdata at the enterprise level to investigate the effects of OFDI on product quality and export trade, from the perspective of enterprise heterogeneity. The results denote that (1) OFDI improves product quality, which is caused by the direct promotional effect of OFDI and the productivity effect brought by OFDI. (2) As OFDI of low productivity enterprises creates a productivity effect and leads to a promotional effect on product quality enhancement, the magnitude is significantly higher than that of high productivity enterprises. (3) Product quality enhancement significantly increases the promotional effect on exports induced by OFDI of low productivity enterprises, while simultaneously significantly suppressing the promotional effect on exports induced by OFDI of high productivity enterprises. (4) The promotional effect of OFDI on exports decreases as productivity increases, and OFDI significantly promotes exports of low productivity enterprises and has no significant effect on high productivity enterprises. These results suggest that China should continue to expand the space for international cooperation and encourage enterprises to invest overseas, especially those facing export pressures.

The outflow of the rural labor force has a great impact on the location distribution of agricultural production, which has not attracted enough attention in existing studies. This article describes the mechanism of agricultural production location adjustment and further studies the influence of a regional difference in labor endowment on agricultural production location adjustment. Based on commodity and province-level panel data calculation, the results show that the agricultural production location in China has been adjusted from the East to the Central and then to the West with accelerating transfer speed. Furthermore, it is found that the regional differences in labor endowment are the main driving force for the shift of low alternative crop production. The more labor-intensive the crop varieties are, the more obvious the influence of the regional differences in labor endowment is on the crop production location adjustment. Moreover, regional differences in labor endowment have little effect on the location adjustment of vegetable production, as expanding the vegetable market demand may offset the effect of labor supply shortage on the adjustment of vegetable production location. Therefore, it is necessary to formulate a regional industrial development plan consistent with the trend of agricultural production location adjustment, as well as promote the construction of agricultural circulation facilities and socialized services in less developed areas.

Enhancing mariculture’s green total factor productivity (GTFP) is essential to balance industrial growth with ecology, safeguard global food security, and meet UN Sustainable Development Goal 14 amid mounting marine stress. As a global leading mariculture producer, China provides a typical research sample. This study constructs a mariculture GTFP measurement index system, estimates GTFP in China’s coastal provinces via the global Super-SBM model, identifies root causes of efficiency loss, and explores influencing factors and spatial spillover effects using a spatial econometric model. The results show that the overall mariculture GTFP of China’s coastal provinces exhibits a fluctuating upward trend with significant regional heterogeneity, specifically presenting a distribution pattern of “the highest in the South China Sea Region, followed by the East China Sea Region, and the lowest in the Yellow Sea and Bohai Sea Region”. Meanwhile, mariculture GTFP shows significant positive spatial autocorrelation, with distinct High-High and Low-Low agglomeration characteristics. Excessive resource consumption and undesirable output discharge are the core drivers of efficiency loss. For direct effects, industrial scale, industrial structure, fishermen’s income, transportation accessibility, internet development, technology adoption, and environmental regulation significantly boost local GTFP, while fishery disasters exert a significant negative impact. For spatial spillovers, industrial scale, industrial structure, and internet development show significant positive effects, while fishermen’s income and urbanization present negative effects. Based on these findings, this study proposes targeted multi-stakeholder optimization paths, providing decision support for China’s mariculture green development and replicable experience for global coastal countries.