淮河流域农业生态效率时空演变与影响因素研究

Spatiotemporal evolution and influencing factors of agricultural eco-efficiency in the Huaihe River Basin

  • 摘要: 以2001—2020年淮河流域35个地级市为研究对象,融合面源污染与碳排放指标,采用超效率EBM模型、泰尔指数及面板Tobit回归模型,揭示农业生态效率(AEE)的时空演变与驱动机制。结果表明:(1)时序上,AEE呈波动上升趋势,但仍存较大优化空间;(2)空间上,效率差异持续收敛,分布由分散向均衡优化转变;高效率区向豫皖鲁三省交界带显著集聚,形成“边缘崛起”,而河南中部与安徽北部出现块状效率塌陷区;省内差异是区域分异主导来源,其中皖、豫两省内部分化最为突出;(3)城镇化、复种指数、粮食种植、灌溉条件及规模经营通过资源整合提升效率,而财政支农(补贴高碳要素)、机械化(化石能源依赖)及科技支出结构(忽视绿色技术)因政策导向偏离产生显著抑制效应。未来需深化跨省制度壁垒的量化解析与治理路径创新。

     

    Abstract: Taking 35 prefecture-level cities in the Huaihe River Basin from 2001 to 2020 as the research objects, non-point source pollution and carbon emission indicators were integrated, and the super-efficiency EBM model, Theil index, and panel Tobit regression model were adopted to reveal the spatiotemporal evolution and driving mechanisms of agricultural ecological efficiency (AEE). The results showed that:(1) Temporally, AEE exhibited a fluctuating upward trend, but substantial space for optimization remained;(2) Spatially, the efficiency gap continued to converge, with the distribution transforming from dispersion to balanced optimization; high-efficiency areas were significantly agglomerated in the junction zone of Henan, Anhui, and Shandong provinces, forming an "edge rise", while block-shaped efficiency depressions emerged in central Henan and northern Anhui; intra-provincial differences were the dominant source of regional differentiation, among which the internal differentiation within Anhui and Henan provinces was the most prominent; (3)Regarding the influencing factors, the urbanization, multiple cropping index, grain cultivation, irrigation conditions, and large-scale operation improved efficiency through resource integration, while fiscal support for agriculture (subsidizing high-carbon factors), mechanization (reliance on fossil energy), and the structure of scientific and technological expenditure (neglect of green technologies) exerted significant inhibitory effects due to deviations in policy orientation. In the future, it is necessary to deepen the quantitative analysis of inter-provincial institutional barriers and innovate governance paths.

     

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