Exploring the spatial-temporal variations of agricultural eco-efficiency (AEE) and its driving factors is of vital importance to achieve high-quality agro-ecological development in China. In this study, we used the super efficiency slack-based measure (SBM) model to measure the inter-provincial AEE based on the relevant panel data of 30 provinces/regions/cities in China from 2000 to 2018. Based on the time series analysis, spatial visualization, and trend surface analysis, the geographical detector model was further used to identify the core factors driving the spatial-temporal variations of AEE. The results showed that China's AEE level maintained stable upward progress from 2000 to 2018, which was still at a low level with much room for improvement. The AEE in China exhibited a significant spatial-temporal variation, presenting higher levels in the eastern and western parts but lower in the central part. The spatial variation of AEE was influenced by many factors, including agricultural resource endowment, socioeconomic condition, and the natural ecological environment. There were obvious variations in the influence factors on the spatial-temporal variation of AEE. The interactions among factors would enhance the spatial variation of AEE. Therefore, due to the spatial-temporal variation of AEE, emphasis should be placed on its core driving factors as well as the inter-parts agricultural cooperation in order to achieve high-quality agro-ecological development in China.
研究农业生态效率的时空分异及其影响因素对实现中国农业生态高质量发展具有重要意义。基于2000—2018年中国30个省/区/市的面板数据,采用超效率SBM模型测算省际农业生态效率,在时间序列、空间可视化及趋势面分析揭示农业生态效率时空演变规律的基础上,进一步利用地理探测器模型识别影响农业生态效率空间分异的主导因子及其交互作用。结果表明: 2000—2018年,中国农业生态效率整体呈现稳定上升的趋势,但仍然处于较低水平,存在较大提升空间;中国农业生态效率具有显著的空间分异特征,总体上呈现出东西部地区较高、而中部地区较低的空间分布格局;中国农业生态效率的空间分异受到农业资源禀赋、社会经济、自然生态环境等多种因素的影响,不同因子对农业生态效率空间分异的影响存在明显差异且因子间交互作用会增强其空间分异。综上,要关注农业生态效率时空分异的主导因子,并注重区域间的协同合作,以实现农业的高质量发展。.
Keywords: geographical detector; influencing factor.; spatial-temporal differentiation; agricultural eco-efficiency (AEE).