Acta Geographica Sinica ›› 2018, Vol. 73 ›› Issue (11): 2168-2183.doi: 10.11821/dlxb201811009

• Ecosystem and Carrying Capacity • Previous Articles     Next Articles

Spatial-temporal evolution and trend prediction of agricultural eco-efficiency in China: 1978-2016

HOU Mengyang1,2(),YAO Shunbo1,2()   

  1. 1. College of Economics & Management, Northwest A& F University, Yangling 712100, China;
    2. Research Center for Resource Economics and Environment Management, Northwest A& F University, Yangling 712100, China;
  • Received:2017-12-08 Online:2018-11-25 Published:2018-11-22
  • Supported by:
    Special Fund for Scientific Research of Forestry Commonwealth Industry, No.201504424; Key Fund for Humanities and Social Sciences of the Ministry of Education, No.14JJD790031; National Natural Science Foundation of China, No.71473195

Abstract:

Based on the panel data of 30 provinces in China from 1978 to 2016, the super efficiency SBM model was used to measure the inter-provincial agricultural eco-efficiency in our study. On the basis of time series analysis and spatial correlation analysis, traditional and spatial Markov probability transfer matrices were constructed to explore the spatial and temporal evolution of agricultural eco-efficiency of China, and the long-term trends were also predicted. The result shows that: (1) The agricultural eco-efficiency in China presents a "double-peak" distribution with stable rise in fluctuation, and the gap between peak heights is narrowing, but the overall level is still relatively low. Therefore, there is still room for improvement in agricultural eco-efficiency. Besides, the agricultural eco-efficiency improvement in the eastern region is more significant than that in the central and western regions. (2) The trend of China's agricultural eco-efficiency shifting to a higher level is significant, but the evolution of agricultural eco-efficiency has maintained the stability of the original state, and it is difficult to achieve a leap-forward shift. The geospatial structure plays an important role in the spatial-temporal evolution of agricultural eco-efficiency and the spatial agglomeration is significant. The provinces with higher agricultural eco-efficiency have positive spillover effects, while those with lower agricultural eco-efficiency have negative spillover effects. As a result, the "club convergence" phenomenon of "high agglomeration, low concentration, high radiates low, and low inhibits high" has been gradually formed in the spatial pattern. (3) From the long-term trend prediction, the agricultural eco-efficiency in most provinces gradually shifts upward to a relatively high level, and gradually evolves from a low-to-high incremental pattern. In the context of the low agricultural eco-efficiency, its long-term stable evolution is manifested as a "partial unimodal" distribution; while under the geographical background of higher agricultural eco-efficiency, it has evolved into a "double-peak" distribution of higher-level agglomeration for a long time. Finally, we analyze the shortcomings and what needs to be improved for current research. What's more, we propose that controlling agricultural pollution emissions, inter-regional agro-ecological policy linkages, and strengthening inter-regional agro-ecological cooperation, exchange, and learning can effectively improve China's agricultural eco-efficiency and narrow the gap between provinces.

Key words: agricultural eco-efficiency, spatial-temporal evolution, trend prediction, super efficiency SBM model, spatial Markov chain, China