Frontiers in Sustainable Food Systems (Jan 2025)
Spatio-temporal differentiation and influencing factors of high-quality development of agriculture and rural areas in China
Abstract
High-quality development refers to an efficient, equitable and sustainable development aimed at meeting the growing needs of individuals for a better life, and is an inevitable requirement for agriculture and rural areas development. Based on data at the provincial level, this study used the entropy method, Dagum Gini coefficient decomposition, and spatial Markov chain to estimate the spatio-temporal differentiation of high-quality development of agriculture and rural areas (HQDAR) in China from 2008 to 2022. Additionally, the geographically and temporally weighted regression was employed to analyze the spatial heterogeneity of influencing factors. The results showed that: (1) During the study period, the level of HQDAR in China increased steadily, and the spatial distribution showed a pattern of highest in the eastern region, followed by the center and the lowest in the west. (2) The HQDAR in China had obvious spatial agglomeration characteristics. Hot spots were primarily situated in some eastern and central provinces, while cold spots were primarily situated in the western regions. (3) There were clear spatial differences in the HQDAR in China, mainly due to inter-regional differences, followed by intra-regional differences and hypervariable density. (4) The HQDAR in China maintained the stability of the original state and demonstrated an obvious trend of overall upward transfer. The neighborhood background had an important influence on the change in its transfer, and the influence of varying neighborhood backgrounds was different. (5) The influencing factors showed significant spatio-temporal heterogeneity. The urbanization level, transportation infrastructure, scientific and technological innovation ability, and Internet penetration rate mainly had positive effects, while industrial structure and rural population aging mainly had negative effects. These findings can provide relevant policy recommendations for improving the quality of agriculture and rural areas.
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