资源科学 ›› 2021, Vol. 43 ›› Issue (10): 1947-1960.doi: 10.18402/resci.2021.10.02

• 资源经济 • 上一篇    下一篇

中国省域林业生产技术效率的空间收敛性及分异特征

杨旭1(), 屈志光2, 邓远建3()   

  1. 1.湖南大学经济与贸易学院,长沙 410079
    2.中南财经政法大学信息与安全工程学院,武汉 430073
    3.中南财经政法大学工商管理学院,武汉 430073
  • 收稿日期:2020-09-10 修回日期:2021-01-14 出版日期:2021-10-25 发布日期:2021-12-25
  • 通讯作者: 邓远建,男,四川广安人,博士,副教授,研究方向为农业生态经济、农村绿色发展。E-mail: dyj_scga@163.com
  • 作者简介:杨旭,男,山东泰安人,博士研究生,研究方向为资源与环境经济、林业经济地理。E-mail: 523351804@qq.com
  • 基金资助:
    国家自然科学基金项目(71673302);国家自然科学基金青年项目(71804196);中央高校基本科研业务费专项资金资助项目(2722021BX018)

Spatial convergence and differentiation of forestry production technology efficiency in 30 provinces of China

YANG Xu1(), QU Zhiguang2, DENG Yuanjian3()   

  1. 1. School of Economics and Trade, Hunan University, Changsha 410079, China
    2. School of Information and Security Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
    3. School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China
  • Received:2020-09-10 Revised:2021-01-14 Online:2021-10-25 Published:2021-12-25

摘要:

林业既是国民经济的重要生产部门,也是生态文明建设的重点领域,提高林业生产技术效率是高效利用林业资源的关键环节。本文采用超效率SBM模型测算了中国30个省(直辖市、自治区)2004—2018年的林业生产技术效率,在考虑空间因素的前提下,基于空间条件 β收敛模型对其收敛趋势及时空分异特征展开分析。研究发现:①从林业生产技术效率的测度结果来看,全国层面林业生产技术效率总体水平不高,存在较大提升空间;林区层面由高到低依次为西南、南方、东北、华北、西北林区;省份之间差距同样明显,但同一林区内省份间差距小于不同林区的省份间差距。②就林业生产技术效率的收敛性而言,我国林业生产技术效率具有显著的空间条件 β收敛趋势,且空间因素的纳入使得收敛周期缩短了约4年。林业对外开放程度、林业收入水平和林业产业结构对林业生产技术效率向高值收敛具有促进作用,而技术市场环境对其具有抑制作用,地区经济发展水平、林业人力资本水平和自然环境条件则影响不显著。③就林业生产技术效率收敛性的分异特征而言,空间上,五大林区均存在“俱乐部收敛”现象,且收敛率普遍高于全国平均水平,其中华北林区收敛率最高,东北、西南、西北林区次之,南方林区最低;时间上,2012—2018年收敛率高于2004—2011年,地区经济发展水平、林业对外开放程度等因素对林业生产技术效率的作用方向或力度在前后两个时期均有明显差异。为此,本文建议进一步完善林业体制机制,明确区域定位,因地制宜制定林业发展措施。

关键词: 林业生产技术效率, 空间收敛性, 分异特征, 条件β收敛模型, SDM模型, 中国

Abstract:

Forestry is not only an important production sector of the national economy, but also a key area of ecological civilization construction. Improving the production technology efficiency (forestry PTE) is a key link in the efficient use of forest resources. In this article, the super-efficiency slacks-based measure (SBM) model was used to calculate the forestry PTE of 30 provinces (municipalities, autonomous regions) in China from 2004 to 2018. Considering spatial factors, the convergence trend and spatial-temporal differentiation characteristics were developed based on the spatial conditional β convergence model. The study found that: (1) The overall level of forestry PTE at the national level was not high, and there was a large room for improvement. The forestry PTE at the major forest region level ranks from high to low in southwest, south, northeast, North China, and northwest forest region. The gap between the provinces was equally obvious. But the gap between provinces in the same forest regions is smaller than the gap between provinces in different forest regions. (2) At the national level, forestry PTE showed a significant spatial conditional β convergence trend, and the inclusion of spatial factors shortens the convergence period by about 4 years. The degree of forestry opening up, the level of forestry income, and the structure of forestry industry were positively correlated with the convergence to high values, while the technological market environment had a restraining effect. The level of regional economic development, the level of forestry human capital, and the natural environment had insignificant impacts. (3) From the perspective of forest regions, the five major forest regions showed a “club convergence” phenomenon, and the convergence rate was generally higher than the national average. The North China forest region had the highest convergence rate, followed by the northeast, southwest, and northwest forest regions, and the southern forest region have the lowest convergence rate. In terms of time periods, the convergence rate from 2012 to 2018 was higher than that from 2004 to 2011. The regional economic development level and the degree of forestry opening up to the outside world had a different impact in the direction or strength of forestry PTE in the two periods. For this reason, this article proposes to further improve the forestry system and mechanism, clarify the regional positioning, and formulate forestry development measures according to local conditions.

Key words: forestry production technology efficiency, spatial convergence, differentiation characteristics, conditional β convergence model, SDM model, China