中国中西部地区就近城镇化空间分异格局及机理

doi:10.11821/dlxb202112009

摘要/Abstract

摘要：

就近城镇化是中国中西部地区破除异地城镇化模式弊端、推进新型城镇化健康发展的现实路径选择。通过构建就近城镇化率、贡献力和贡献势指标,从本省、本县和本镇3个城镇化尺度,系统分析2000—2015年中国中西部地区就近城镇化空间分异特征,并深入剖析其驱动机制。研究发现：① 2000—2015年中西部地区就近城镇人口总数从1.96亿增加到3.47亿,就近城镇化率从28.16%上升到46.89%。② 从贡献力看,中西部地区就近城镇化的主要来源是本镇户籍人口,但本镇贡献力呈下降趋势,本县贡献力波动上升,本省贡献力变化不大。③ 从贡献势看,本县贡献势>本省贡献势>本镇贡献势,县域尺度是中西部大部分地区就近城镇化的优势空间单元。④ 中西部地区就近城镇化空间分异是自然环境背景、经济增长和非农产业发展、城市公共服务供给、交通运输条件和地方行政力量等多重因素的综合作用结果。

关键词: 就近城镇化, 贡献力, 贡献势, 空间分异, 机理, 中西部地区

Abstract:

Considering the new-type urbanization background, the nearby urbanization model provides a practical approach toward eliminating the disadvantage of trans-regional urbanization and promoting urbanization. This study establishes the indexes of the nearby urbanization rate (NU), contribution to nearby urbanization (CNU) and trend of contribution to nearby urbanization (TNU), to analyze the spatial differentiation features of nearby urbanization in central and western China from 2000 to 2015. Moreover, this study explores the determinant factors and influencing mechanism of the spatial differentiation of nearby urbanization in the study region. Results show that: (1) From 2000 to 2015, the population of nearby urbanization increased from 196 million to 347 million and the NU increased from 28.16% to 46.89%. (2) Based on the analysis of the CNU, we found that the main source of nearby urbanization was the population registered in the local town, but the contribution of local town to nearby urbanization (CNU_t) gradually declined over the study period. The areas with high CNU_t were widely distributed in central and western China in 2000, but by 2015, they were restricted to Sichuan province. Meanwhile, the contribution of local county to nearby urbanization (CNU_c) increased and the contribution of local province to nearby urbanization (CNU_p) varied slightly. Most areas with high CNU_p were provincial capital cities, resource-based cities or pastural cities. (3) Based on the analysis of the TNU, the trend of the contribution of local county to nearby urbanization (TNU_c) exceeded the trend of the contribution of local province to nearby urbanization (TNU_p), while the trend of the contribution of local town to nearby urbanization (TNU_t) exhibited the least influence. Therefore, examining nearby urbanization based on the county-level spatial unit was advantageous in most regions; however, in the northwestern border regions and provincial capital cities, the provincial level was more applicable. (4) The spatial differentiation of nearby urbanization was determined based on the integrated results of various influencing factors, including natural environment, economic development, non-agricultural industries, urban public services, transportation accessibility, and the influence of local administration.

Key words: nearby urbanization, CNU, TNU, spatial differentiation, influencing mechanism, central and western China

赵美风, 汪德根. 中国中西部地区就近城镇化空间分异格局及机理[J]. 地理学报, 2021, 76(12): 2993-3011.

ZHAO Meifeng, WANG Degen. Spatial differentiation and influencing mechanism of nearby urbanization in central and western China[J]. Acta Geographica Sinica, 2021, 76(12): 2993-3011.

图/表 6

图1

图2

图3

表1

中国中西部地区就近城镇化贡献力空间面板杜宾模型计算结果

模型	普通面板数据模型						空间面板数据模型
因变量	CNU_t		CNU_c		CNU_p		CNU_t		CNU_c		CNU_p
因变量	系数	标准误	系数	标准误	系数	标准误	系数	标准误	系数	标准误	系数	标准误
lnDEM	-2.076^***	0.453	0.949^***	0.300	1.128^***	0.350	-2.094^***	0.579	1.063^***	0.405	1.030^**	0.413
lnSL	0.429	0.578	-0.234	0.370	-0.195	0.405	0.566	0.762	-0.492	0.518	-0.104	0.512
lnPGDP	-10.543^***	1.348	2.524^***	0.884	8.019^***	0.908	-10.572^***	1.270	2.061^**	0.933	8.433^***	0.965
lnFAI	-0.141	0.922	0.085	0.594	0.056	0.645	-0.249	0.863	0.100	0.545	0.162	0.632
lnFIN	-0.744	0.979	-0.141	0.671	0.885	0.572	-0.149	1.041	-0.326	0.681	0.489	0.595
lnINC	0.639	1.069	-0.575	0.644	-0.064	0.853	0.786	1.099	-0.693	0.707	-0.054	0.822
IND	0.190^*	0.057	-0.117^***	0.035	-0.073^*	0.039	0.183^***	0.056	-0.102^***	0.038	-0.078^**	0.040
SEV	-0.146^*	0.060	-0.072^*	0.039	0.217^***	0.040	-0.165^**	0.064	-0.061	0.043	0.228^***	0.039
EDU	-0.179	0.118	-0.007	0.079	0.186^**	0.076	-0.200	0.121	-0.009	0.084	-0.199	0.076
MED	0.103^*	0.050	-0.104^***	0.030	0.000	0.031	0.115^**	0.046	-0.111^***	0.029	-0.005	0.030
lnTRA	0.440	0.432	0.076	0.264	-0.516	0.366	0.448	0.437	0.164	0.296	-0.600	0.374
GOV	-7.638^***	1.739	-1.443^*	0.813	9.081^***	1.591	-6.826^***	1.663	-1.815	1.894	8.939^***	1.722
W×DEM							-0.687	0.889	0.737	0.665	-0.024	0.721
W×SL							2.547^**	1.201	-1.007	0.816	-1.462	1.236
W×PGDP							-3.287	2.148	6.203^***	1.536	-2.236	1.554
W×FAI							2.480^*	1.461	-1.477	1.008	-1.011	1.043
W×FIN							-1.169	1.305	0.343	0.923	0.902	1.023
W×INC							-0.083	2.036	-0.811	1.540	1.137	1.535
W×IND							-0.063	0.093	-0.023	0.064	0.090	0.070
W×SEV							-0.008	0.100	-0.022	0.062	0.058	0.072
W×EDU							-0.063	0.203	0.335^**	0.138	-0.291^**	0.133
W×MED							0.139^*	0.072	-0.076^*	0.043	-0.070	0.043
W×TRA							-1.453^*	0.875	0.993	0.603	0.356	0.619
W×GOV							-0.997	6.215	-5.126	4.062	7.848^*	4.705
ρ							0.101^*	0.053	0.123^**	0.055	-0.012	0.049
R²	0.419		0.423		0.524
Loglikelihood							-1785.983		-1546.444		-1607.727
AIC	3635		3178		3264		3624		3144		3268
BIC	3699		3243		3320		3735		3256		3379
N	531		531		531		531		531		531

表1

表2

图4

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自变量	TNU_t		TNU_c		TNU_p
自变量	系数	T值	系数	T值	系数	T值
lnDEM	-0.414^***	-2.636	0.206^**	2.287	0.203^*	1.835
lnSL	0.259	0.828	-0.152	-0.868	-0.102	-0.021
ΔlnPGDP	-0.158	-1.280	0.155^**	2.185	0.010	0.117
ΔlnFAI	0.108	0.798	-0.141	-0.819	0.033	0.348
ΔlnFIN	0.009	0.076	0.006	0.083	-0.017	-0.194
ΔlnINC	-0.073	-0.222	-0.204	-1.078	0.277	1.194
ΔIND	-0.008	-1.496	0.002	0.583	0.007	0.650
ΔSEV	-0.012^**	-2.084	0.002	0.553	0.010^**	2.554
ΔEDU	0.018^*	1.873	-0.008	-1.377	-0.011	-1.547
ΔMED	0.000	0.093	0.000	0.004	-0.001	-0.311
ΔlnTRA	-0.077	-1.346	0.060^*	1.833	0.018	0.431
GOV	-0.560^**	-2.575	0.257^**	2.057	0.306^**	1.991
W×lnDEM	0.533^***	2.976	-0.270^***	-2.624	-0.257^**	-2.036
W×lnSL	-0.499^***	-2.948	0.237^**	2.438	0.264^**	2.209
W×ΔlnPGDP	-0.429^*	-1.650	0.032	0.214	0.441^**	2.417
W×ΔlnFAI	-0.166	-0.603	0.166	1.051	0.007	0.035
W×ΔlnFIN	0.148	0.688	-0.057	-0.460	-0.101	-0.664
W×ΔlnINC	0.159	0.337	-0.085	-0.311	-0.069	-0.206
W×ΔIND	0.029^**	2.502	-0.016^**	-2.373	-0.014^*	-1.688
W×ΔSEV	0.001	0.091	-0.004	-0.507	0.004	0.402
W×ΔEDU	-0.015	-0.982	0.017^*	1.892	-0.002	-0.147
W×ΔMED	0.022^***	3.633	-0.009^***	-2.670	-0.013^***	-3.056
W×ΔlnTRA	-0.060	-0.534	-0.052	-0.811	0.120	1.521
W×GOV	-0.154	-0.368	0.036	0.148	0.110	0.372
ρ	0.444^***	5.405	0.442^***	5.288	0.384^***	4.381
R²	0.533		0.498		0.451
Loglikelihood	-133.855		-35.823		-71.160
N	177		177		177