Spatio-temporal comprehensive measurement of China’s agricultural green development level and associated influencing factors

Abstract

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.

Research on the Evolution of the Spatial Association Network Structure and Driving Factors of China’s Agricultural Green Development

“…Furthermore, the study finds that the overall connectivity of China's agricultural green development spatial association network is robust, with no isolated entities. This indicates that agricultural green development relationships are tightly interwoven and mutually dependent across the country, exhibiting a clear spatial spillover effect, consistent with findings from other researchers [11,14,60,61]. However, there is significant room to enhance the connectivity and stability of China's agricultural green development network, suggesting a future need to further strengthen regional cooperation and resource sharing.…”

Dynamic evolution and obstacle factor analysis of green development in China's agriculture and rural areas based on entropy-based TOPSIS model

“…A review of existing research has mostly measured the level of GDARA in terms of indicators such as percentage of cultivated land area [ 32 ], fertilizer application intensity [ 33 ] and farmland plastic film coverage [ 34 ]. In addition, there are some studies to construct a comprehensive indicator system of total factor productivity in agriculture to measure GDARA, and the indicators they use are mainly agricultural carbon emissions [ 35 ], pesticide usage [ 19 ], consumption of ecological water resource [ 36 ], total rural domestic energy consumption [ 27 ] and number of patents and technology achievements in agriculture, forestry, animal husbandry, and fishery [ 37 ].…”

Coupling and Coordination Analysis of Digital Economy and Green Agricultural Development: Evidence from Major Grain Producing Areas in China

“…Specifically, the specific variables of the ecological environment and natural resources mainly include "sowing area" [62], "water resources consumption" [63], "agricultural fertilizer" [64], "application of pesticides" [65], and "use of agricultural mulch" [66]. The specific variables of the agricultural product supply dimension mainly include "grain output" [67], "green innovation of agricultural enterprises", "green agricultural product supply" [68], and other related variables.…”