Estimation results of the spatial Durbin model.

Green development is an inevitable trend in the modernization of agriculture and rural areas, and promoting the green development of agriculture has always been an important measure for China’s sustainable growth. However, due to the influence of diverse regional environments and the wide range of landscapes in China, a largely agricultural country, China is facing ongoing challenges in improving the overall level of agricultural green development and narrowing regional differences, which has recently garnered worldwide attention. This study aims to measure and analyze the agricultural green development level of 30 provinces in China (Tibet, Hong Kong, Macao, and Taiwan are not included in the target areas of this research due to a lack of data). Here, we applied GIS technology, an entropy-TOPSIS (technique for order of preference by similarity to ideal solution) model, quantitative analysis methods such as global spatial autocorrelation analysis, coldspot and hotspot analysis, and a spatial Durbin model to construct measurement models and index systems, after which we performed a comprehensive spatiotemporal analysis of China’s agricultural green development level. Furthermore, the present study also analyzed the factors that influence agricultural green development in China. The present study demonstrated that: (i) between 2005 and 2020, China’s overall level of agricultural green development exhibited a fluctuating upward trend, with significant improvement and enhancement in most provinces. However, the overall level of China’s agricultural green development remains low, and differences at the provincial level are particularly prominent, with the main regions displaying the following descending development pattern: Eastern > Central > Western regions. (ii) The level of China’s agricultural green development shows clear signs of spatial aggregation, characterized by spatial dependence and heterogeneity. Although this phenomenon is gradually weakening over time, the high levels of agricultural green development in the eastern regions and low levels in the western regions are likely to persist in the near future. (iii) Green agricultural structure, technology supply, agricultural mechanization level, and arable land area are the key factors influencing China’s level of agricultural green development. Among these factors, technology supply, agricultural mechanization level, and arable land area have the largest direct impact, whereas green agricultural structure has a positive spatial spillover effect on the level of agricultural green development. Technology supply has both a positive direct impact and a negative indirect impact on the level of agricultural green development. Therefore, further improving technology supply and agricultural mechanization level can directly promote China’s agricultural green development.

绿色发展是农业农村现代化的必然趋势，推动农业绿色发展始终是中国实现可持续增长的重要举措。然而，作为农业大国，中国地域环境多样且景观幅员辽阔，在提升农业绿色发展整体水平、缩小区域发展差距方面仍面临持续挑战，这一议题近期已引发全球广泛关注。本研究旨在测算并分析中国30个省份的农业绿色发展水平（因数据缺失，西藏、香港、澳门及台湾地区未纳入本研究的目标区域）。本研究采用地理信息系统（GIS）技术、熵权-逼近理想解排序法（TOPSIS）模型、全局空间自相关分析、冷热点分析等定量分析方法，以及空间杜宾模型（Spatial Durbin Model）构建测算模型与指标体系，随后对中国农业绿色发展水平开展全面的时空分析。此外，本研究还剖析了影响中国农业绿色发展的各类驱动因素。本研究结果表明：（1）2005年至2020年间，中国农业绿色发展整体水平呈现波动上升趋势，多数省份的发展水平得到显著改善与提升。但中国农业绿色发展整体水平仍偏低，省级层面的差异尤为突出，主要区域的发展格局呈如下降序排列：东部地区＞中部地区＞西部地区。（2）中国农业绿色发展水平呈现显著的空间集聚特征，兼具空间依赖性与异质性。尽管该现象随时间推移逐渐减弱，但东部地区农业绿色发展水平较高、西部地区较低的格局在短期内仍将持续。（3）农业绿色结构、技术供给、农业机械化水平与耕地面积是影响中国农业绿色发展水平的关键因素。其中，技术供给、农业机械化水平与耕地面积对农业绿色发展水平的直接影响最为显著，而农业绿色结构对农业绿色发展水平具有正向空间溢出效应。技术供给对农业绿色发展水平同时存在正向直接影响与负向间接影响。因此，进一步提升技术供给与农业机械化水平，可直接推动中国农业绿色发展。