长江中游地区农业生态效率空间关联网络演化及驱动因素

厘清农业生态效率空间关联的网络特征及驱动因素对践行绿色发展理念、实现区域协同治理具有重要意义。基于2001-2019年长江中游地区38个地级市的面板数据,采用超效率至强有效前沿最近距离(MINDS)模型测度各地级市的农业生态效率,运用引力模型对长江中游地区农业生态效率的空间关联关系进行识别,进而利用社会网络分析方法和二次指派程序(QAP)方法揭示其网络结构演化特征和驱动因素。研究表明:(1)长江中游地区农业生态效率的空间关联关系表现出复杂的网络结构形态,空间关联网络具有高通达性且等级结构趋于松散,但网络稳定性却趋于下降;(2)武汉市和长沙市在空间关联网络中居核心地位,既扮演中心行动者角色又兼具"中介"和"桥梁"功能;岳阳市、黄石市等省际边界地级市在网络中则扮演边缘行动者角色;(3)空间关联网络核心-边缘结构由"十五"时期的"大聚集、小分散"演变为"十三五"时期的"大分散、小聚集",核心区由中部聚集转变为向四周发散;(4)地理空间的邻接、地区农业产业地位、财政支出、经济发展水平的相近以及交通运输水平的差异均有助于空间关联网络的形成。

Evolution and driving factors of spatial correlation network of agricultural eco-efficiency in the middle reaches of the Yangtze River

The middle reaches of Yangtze River are full of agricultural resources and have been contributing greatly to China''s food security. However, in recent years, the green development of agriculture in the middle reaches of Yangtze River has faced great challenges due to the promotion of agricultural modernization. Meanwhile, with the advancement of regional integration, the cross-regional flow of production factors such as technology, capital and labor in the middle reaches of Yangtze River made the inter-regional agricultural economy also present a complex network relationship. Therefore, it is necessary to explore the spatial correlations of agricultural green development in the middle reaches of Yangtze River from a network perspective, which is significant for promoting the green coordinated development of agriculture. In this study, to evaluate the level of agricultural green development in the middle reaches of Yangtze River, the super-efficiency MINDS model was used to measure the agricultural eco-efficiency from 2001 to 2019. Moreover, the gravity model was used to identify the spatial correlation of agricultural eco-efficiency, and the social network analysis method and QAP method were used to reveal its network structure characteristics and driving factors. The results showed that: (1) The spatial correlation of agricultural eco-efficiency in the middle reaches of the Yangtze River showed a complex network structure. The spatial correlation network had high accessibility and the hierarchical structure tended to be loose, but the network stability tended to decline. (2) Wuhan and Changsha occupied the core position in the spatial correlation network, acting as both central actors and as ″intermediaries″ and ″bridges″; the provincial border cities, such as Yueyang, Huangshi, played the role of marginal actors in the network. (3) The core-periphery structure of the spatial correlation network evolved from large aggregation and small dispersion in the Tenth Five-Year Plan period to large dispersion and small aggregation in the Thirteenth Five-Year Plan period, and the core area changed from aggregation in the middle to divergence around. (4) The adjacency of geographical space, the similarity of the status of regional agricultural industry, fiscal expenditure, economic development and the difference of transportation all contributed to the formation of spatial correlation network.