| Autor: |
Wang YQ; College of Economics & Mana-gement, Northwest A&F University, Yangling 712100, Shaanxi, China.; Research Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, Shaanxi, China., Yao SB; College of Economics & Mana-gement, Northwest A&F University, Yangling 712100, Shaanxi, China.; Research Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, Shaanxi, China., Hou MY; College of Economics & Mana-gement, Northwest A&F University, Yangling 712100, Shaanxi, China.; Research Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, Shaanxi, China., Jia L; College of Economics & Mana-gement, Northwest A&F University, Yangling 712100, Shaanxi, China.; Research Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, Shaanxi, China., Li YY; College of Economics & Mana-gement, Northwest A&F University, Yangling 712100, Shaanxi, China.; Research Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, Shaanxi, China., Deng YJ; College of Economics & Mana-gement, Northwest A&F University, Yangling 712100, Shaanxi, China.; Research Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, Shaanxi, China., Zhang X; College of Economics & Mana-gement, Northwest A&F University, Yangling 712100, Shaanxi, China.; Research Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, Shaanxi, China. |
| Abstrakt: |
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. |
