中国建设用地错配时空特征、驱动机制及空间效应

doi:10.13284/j.cnki.rddl.003327

Abstract

Abstract:

China's social and economic development is currently transitional. There is great incompatibility between the traditional construction land allocation model and the requirements of social and economic development, marked by a prominent contradiction between the supply and demand of construction land that has severely restricted the sustained and healthy development of China's social economy. Improving the efficiency of land resource allocation has become a new driving force for economic growth. With the limited driving power of this factor input for economic development, it is important to explore the occurrence and mechanism of rare construction land resource misallocation to secure the structural benefits of construction land allocation and sustainable economic development, as correcting construction land misallocation would greatly advance land supply-side reform and improve the efficiency of land resource allocation. To explain the mechanism of construction land misallocation, the article builds models for the measurement of both construction land misallocation and its mechanism, applying methods such as spatial autocorrelation model and the spatial Dubin model (SDM). Using these models, we focus on the space-time characteristics, driving factors, and spatial effects of construction land misallocation in China from 2001 to 2016. The sample data in this article come from 235 prefecture-level cities in China. The results show that the misallocation of construction land in China tends to decrease, followed by an increase, with a spatial distribution high in the West and low in the East. There were obvious spatial agglomeration and association features of construction land misallocation and its influencing factors in China, but the spatial correlation effect has weakened. Land financial dependence, industrial structure optimization, and market development have a significant negative impact on construction land misallocation, while government corruption and economic development have a significant positive impact and exert a space spillover effect. We find that the misallocation of construction land in our country is widespread and shows an upward trend. Changing the mode of economic development, increasing government governance, and improving market mechanisms and regional integration can effectively alleviate the misallocation of regional construction land. This paper is significant in discussing the driving factors of construction land misallocation from both theoretical and empirical perspectives, and in analyzing their spatial effect for the first time. It provides research directions and ideas for quantitatively exploring the problem of land resource misallocation, and indicates a feasible way to correct the misallocation of land resources.

Key words: construction land misallocation, driving mechanism, spatial effect, SDM, China

CLC Number:

F301

Junfeng Zhang, Congcong Wang, Lei Xu, Yuwei Weng. Spatial Effect of Construction Land Misallocation in China: An Empirical Analysis Based on Data o 235 Cities[J].Tropical Geography, 2021, 41(2): 217-228.

Figures/Tables 8

Fig.1

Table 1

Fig.2

Fig.3

Table 2

Table 3

Table 4

Model estimated results of the driving mechanism of construction land misallocation"

变量	空间误差模型（SEM）		空间自相关模型（SAR）		空间杜宾模型（SDM）
	系数	Z值	系数	Z值	固定效应		随机效应
	系数	Z值	系数	Z值	系数	Z值	系数	Z值
土地财政依赖度（lnlfd）	-0.145 0^***	-7.62	-0.131 5^***	-7.68	-0.123 2^***	-6.13	-0.115 7^***	-6.10
政府腐败（lncorr）	0.039 2^***	4.27	0.039 1^***	4.71	0.023 6^**	2.47	0.027 1^***	2.97
经济发展水平（lnpgdp）	0.437 3^***	13.0	0.308 9^***	11.62	0.221 5^**	2.55	0.234 2^***	7.68
产业结构（lnins）	-0.273 8	-1.44	-0.186 4	-1.06	-0.274 3	-1.42	-0.435 6^***	-2.56
市场发育程度（lndfm）	-0.015 3^**	-1.99	-0.014 7	-1.93	-0.014 7^*	-1.91	-0.020 1^***	-3.12
W·lnlfd					-0.030 3	-0.11	0.022 1	0.87
W·lncorr					0.043 8^***	3.34	0.030 7^**	2.42
W·lnpgdg					0.043 4	0.48	0.016 9	0.82
W·lnins					0.399 9	1.43	0.037 8	0.16
W·lndfm					0.003 6	0.29	-0.013 7	-1.43
$ρ$					0.311 1^***		0.339 7^***
R²	0.078 2		0.059 4		0.042 9		0.105 3
Log L	-4 725.4		-4 715.8		-4 708.3		-4 974.8

Table 4

Table 5

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变量名称		变量计算	预期影响
因变量	建设用地错配程度（mcl）	划拨用地数量/建设用地供应总量	自相关
自变量	土地财政依赖度（lfd）	城市土地出让金/财政收入	正向
	经济发展水平（pgdp）	人均国民生产总值	负向
	产业结构（ins）	第二、三产业产值/国民生产总值	负向
	要素市场发育程度（dfm）	实际利用外资/国民生产总值	负向
	政府腐败程度（corr）	审计问题资金额	正向

年份	单变量Moran’s I						双变量Moran’s I
	建设用地错配（mcl）			土地财政依赖度（lfd）			错配程度与土地财政依赖
	Moran’s I	Z值	P值	Moran’s I	Z值	P值	Moran’s I	Z值	P值
2001	0.238 3	6.275 9	0.001	0.474 8	10.380 8	0.001	-0.197 1	-5.412 6	0.001
2002	0.313 4	8.122 8	0.001	0.595 6	12.542 9	0.001	-0.190 7	-5.137 4	0.001
2003	0.111 9	3.569 5	0.007	0.410 4	8.476 9	0.001	-0.255 6	-6.917 3	0.001
2004	0.146 4	3.942 8	0.001	0.382 7	7.985 1	0.001	-0.215 7	-5.582 9	0.001
2005	0.358 3	9.270 2	0.001	0.170 1	3.543 1	0.005	-0.162 8	-4.690 9	0.001
2006	0.297 9	8.181 8	0.001	0.410 0	8.510 7	0.001	-0.127 7	-3.477 2	0.003
2007	0.295 9	7.654 1	0.001	0.643 8	13.928 5	0.001	-0.083 9	-2.259 4	0.014
2008	0.246 4	6.281 6	0.001	0.306 3	6.526 2	0.001	-0.119 7	-3.301 1	0.003
2009	0.146 5	3.902 8	0.001	0.567 6	12.514 7	0.001	-0.245 3	-6.675 2	0.001
2010	0.409 2	10.374 7	0.001	0.353 3	7.701 5	0.001	-0.100 8	-2.782 2	0.004
2011	0.381 0	10.188 4	0.001	0.392 2	8.514 1	0.001	-0.071 0	-2.032 7	0.025
2012	0.426 1	10.696 5	0.001	0.222 0	4.981 8	0.001	-0.120 6	3.318 6	0.001
2013	0.242 9	6.475 9	0.007	0.255 7	5.591 1	0.001	-0.104 9	-2.844 0	0.005
2014	0.355 8	9.001 8	0.001	0.355 8	8.229 5	0.001	-0.037 3	-1.066 4	0.152
2015	0.287 8	7.582 2	0.001	0.358 7	7.761 5	0.001	0.060 8	1.715 2	0.046
2016	0.334 1	8.738 8	0.001	0.267 8	6.032 9	0.001	-0.134 8	-3.742 9	0.001

检验方法	统计值	概率	检验方法	统计值	概率
LR-SEM	63.65	0.000 0	LR-SAR	19.47	0.001 6
Wald-SEM	131.57	0.000 0	Wald-SAR	19.59	0.001 5
Hausman检验结果：chi方正汇总行²_（5）=6.95， p=0.2247

变量	直接效应			间接效应			总效应
变量	系数	Z值	P值	系数	Z值	P值	系数	Z值	P值
土地财政依赖度（lnlfd）	-0.117 8	-6.45	0.000	-0.022 8	-0.75	0.454	-0.140 6	-4.25	0.000
政府腐败（lncorr）	0.030 7	3.46	0.001	0.054 5	3.34	0.001	0.085 2	4.89	0.000
经济发展水平（lnpgdp）	0.243 9	8.43	0.000	0.134 6	4.62	0.000	0.378 5	8.82	0.000
产业结构（lnins）	-0.453 3	-2.66	0.008	-0.156 8	-0.52	0.605	-0.610 1	-1.73	0.084
市场发育（lndfm）	-0.021 7	-3.38	0.001	-0.028 7	-2.35	0.019	-0.050 4	-3.61	0.000

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Spatial Effect of Construction Land Misallocation in China: An Empirical Analysis Based on Data o 235 Cities

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