Journal of Geo-information Science >
Analysis of Spatial-temporal Evolution and Influencing Factors of Green Land Use Efficiency in Central China based on Geographic Detector
Received date: 2020-06-15
Request revised date: 2020-10-12
Online published: 2021-02-25
Supported by
The 65th batch of general projects of China Postdoctoral Science Foundation(2019M652271)
General topics of the Hunan Provincial Social Science Achievement Review Committee in 2019(XSP19YBZ141)
Jiangxi University of finance and economics graduate innovation project in 2019
Copyright
After the Chinese government put forward the Rise of Central China Plan, it rapidly facilitates the economy development of Henan province, Hubei province, Hunan province, Jiangxi province, Anhui province and Shanxi province which has gradually become the fourth growth pole driving national economic growth, has caused the built-up area to expand and arable land to decrease, which not only threaten food security, but also impose resource and environmental constraints. In the context, based on the panel data of 80 prefecture-level cities in six provinces of central China from 2007 to 2018, this paper analyzes the green-efficiency of land use and its evolution, the trajectory of gravity center change, influencing factors of green-efficiency of land use and its influence degree applying Malmquist-Luerberger index, gravity center model, spatial econometric regression model and geographical detector model. The results show that ① the green-efficiency of land use and technological progress in the six provinces of central China from 2007 to 2018 fluctuated frequently and their change pace was basically the same, while the technological efficiency was relatively stable, indicating the green-efficiency of land use was “single-track” driven by technological progress. ② The green-efficiency of urban land use showed obvious spatial differentiation characteristics, and the center of gravity generally moved to the northeast part of Central China. ③ It showed spatial dependence and spatial spillover effects on the green-efficiency of land use at the provincial level and prefecture level, the green-efficiency of land use among the prefecture-level cities is mainly in the high-high and low-low level. ④ In addition to the area of urban construction land, urbanization rate, the advanced level of industrial structure, the level of economic development, and the amount of foreign direct investment all positively affect the green-efficiency of land use in 80 prefecture-level cities of six central provinces. Among them, the influence degree of various factors on the green-efficiency of land use from strong to weak, in order, is the advanced level of industrial structure, the amount of foreign direct investment, the area of urban construction land, the urbanization rate and the level of economic development.
ZHAO Dandan , JIN Shengtian , BAO Bingfei , ZHANG Liguo . Analysis of Spatial-temporal Evolution and Influencing Factors of Green Land Use Efficiency in Central China based on Geographic Detector[J]. Journal of Geo-information Science, 2020 , 22(12) : 2358 -2370 . DOI: 10.12082/dqxxkx.2020.200286
表1 2007—2018年中部六省土地绿色利用效率全局Moran's I指数结果Tab. 1 Global Moran's I index results of land green use efficiency in six provinces in central China from 2000 to 2018 |
年份 | 土地绿色利用效率 | |||
---|---|---|---|---|
Moran's I指数 | 均值 | 标准差 | P值 | |
2007 | 0.0720 | -0.0181 | 0.0605 | 0.0500 |
2008 | 0.1497 | -0.0135 | 0.0619 | 0.0040 |
2009 | 0.0960 | -0.0146 | 0.0653 | 0.0480 |
2010 | 0.2480 | -0.0112 | 0.0681 | 0.0010 |
2011 | 0.1712 | -0.0116 | 0.0638 | 0.0070 |
2012 | 0.2561 | -0.0120 | 0.0695 | 0.0020 |
2013 | 0.2082 | -0.0112 | 0.0672 | 0.0020 |
2014 | 0.1919 | -0.0100 | 0.0663 | 0.0050 |
2015 | 0.1700 | -0.0122 | 0.0680 | 0.0120 |
2016 | 0.1934 | -0.0097 | 0.0678 | 0.0040 |
2017 | 0.3957 | -0.0113 | 0.0672 | 0.0010 |
2018 | 0.2834 | -0.0132 | 0.0665 | 0.0010 |
表2 中部六省土地绿色利用效率影响因素回归结果Tab. 2 Regression results of influencing factors of land green utilization efficiency in six provinces in central China |
变量 | 模型Ⅰ SAR双向固定回归结果 | 模型Ⅱ 稳健性检验① | 模型Ⅲ 稳健性检验② | |||
---|---|---|---|---|---|---|
回归系数 | T值 | 回归系数 | T值 | 回归系数 | T值 | |
X1 | -0.5576*** | -2.9200 | -0.5527*** | -2.8900 | -0.5578*** | -2.9200 |
X2 | 0.0309** | 2.1400 | 0.0303** | 2.0900 | 0.0313** | 2.1700 |
X3 | 0.0670 | 0.2800 | 0.0600 | 0.2600 | 0.0700 | 1.6800 |
X4 | 0.5713* | 1.9400 | 0.5831* | 1.9900 | 0.5725* | 1.9500 |
X5 | 0.0111*** | 5.3900 | 0.0110*** | 5.3300 | 0.0110*** | 5.4200 |
ρ | 0.6254*** | 0.5310*** | 0.4320*** | |||
R2 | 0.3900 | 0.4700 | 0.4800 | |||
LogL | 23.6154 | 23.4366 | 23.3704 | |||
AIC | 61.2308 | 60.8733 | 60.7409 | |||
BIC | 95.2994 | 94.9418 | 94.8094 |
注:*、**、***分别表示在10%、5%、1%水平下通过显著性检验。 |
表3 中部各市土地绿色利用效率影响因素地理探测结果Tab. 3 Geographical exploration results of influencing factors of land green use efficiency in cities in central China |
变量 | 建设用地面积 X1 | 城镇化率 X2 | 产业结构高级化水平 X3 | 经济发展水平 X4 | 外商直接投资额 X5 |
---|---|---|---|---|---|
q | 0.15 | 0.03 | 0.25 | 0.03 | 0.19 |
p | 0.67 | 0.99 | 0.49 | 0.86 | 0.18 |
q排序 | 3 | 4 | 1 | 5 | 2 |
表4 2007—2018年中部各市土地绿色利用效率影响因素变化地理探测结果Tab. 4 Geographical exploration results of change of influencing factors of land green use efficiency in cities in central China from 2007 to 2018 |
年份 | q排序 | ||||
---|---|---|---|---|---|
建设用地面积X1 | 城镇化率X2 | 产业结构高级化水平X3 | 经济发展水平X4 | 外商直接投资额X5 | |
2007 | 1 | 3 | 4 | 2 | 5 |
2011 | 1 | 5 | 2 | 4 | 3 |
2015 | 4 | 2 | 1 | 5 | 3 |
2018 | 3 | 4 | 1 | 5 | 2 |
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