中国大城市病典型症状诱发因素的实证分析

doi:10.18306/dlkxjz.2020.01.001

摘要/Abstract

摘要：

论文基于已有研究将大城市病细分为交通拥堵、资源短缺、环境污染3种典型症状,采用2013年全国204个地级市横截面数据,根据夜间灯光数据构建人口空间分布指标,首先运用OLS回归探讨城市规模、空间失衡、建设滞后、产业转型、体制弊病等5种主要致病因素对大城市病的影响;其次,建立门槛效应模型进一步考察不同因素与大城市病之间是否存在非线性关系。实证研究结果表明,大城市病作为一种“综合病”,具体到不同层面对应的主要致病因素也大相径庭。其中,城市总体规模较大、城区人口过度集中分布、交通基础设施不够完善等是造成交通拥堵的主要原因;大城市中并不普遍存在资源匮乏的问题,相反地,产业结构的改善、公共服务的增加、行政权力的集中均有助于吸引和调动资源;此外,不论是城区人口总量的增加,还是分布结构的集中化,均显著不利于消除环境污染,并且非农产业比重的上升和城市道路面积的扩张也会对城市生态环境系统造成破坏。

关键词: 大城市病, 城市人口集中度, 门槛效应模型, 夜间灯光数据, 中国

Abstract:

Based on the results of existing research, typical urban problems can be divided into three categories—traffic congestion, resources shortage, and environmental pollution. This study used cross-section data of 204 Chinese prefecture-level cities in 2013 to build a population spatial distribution index on the basis of nighttime light data for an empirical study. First, ordinary least squares (OLS) regression was applied to examine the impacts of city size, spatial imbalance, lagged infrastructure construction, industrial transformation, and institutional obstacles on urban problems. Second, a threshold effect model was constructed to further investigate possible nonlinear relationships between the influencing factors and urban problems. The empirical results prove that the various aspects of urban problems should be attributed to different influencing factors since these problems are complex. The main reasons leading to traffic congestion are big city size and centralized distribution of urban population, as well as the lack of transportation infrastructure. While resources shortage is not a universal problem in most big cities, in contrast, the improvement of industrial structure, increasing supply of public services, and concentration of administrative power are helpful for big cities to attract and bring in resources. The expansion of urban population and the agglomerated structure of population distribution both hinder environmental pollution control. Besides, the growth in the proportion of non-agricultural industries and expansion of urban road area both can damage urban ecosystems.

Key words: urban problems, urban population concentration degree, threshold effect model, nighttime light data, China

沈洁, 张可云. 中国大城市病典型症状诱发因素的实证分析[J]. 地理科学进展, 2020, 39(1): 1-12.

SHEN Jie, ZHANG Keyun. An empirical analysis of factors leading to typical urban problems in China[J]. PROGRESS IN GEOGRAPHY, 2020, 39(1): 1-12.

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变量	模型一				模型二
变量	y	y₁	y₂	y₃	y	y₁	y₂	y₃
size	0.517^***	0.226^***	0.078	0.535^***	0.552^***	0.251^**	0.088	0.556^***
	(0.115)	(0.093)	(0.150)	(0.163)	(0.123)	(0.129)	(0.148)	(0.149)
concen	0.492^***	0.476^***	0.090^*	0.155^*
	(0.103)	(0.088)	(0.098)	(0.094)
dense					0.249^***	0.051	0.127*	0.287^***
					(0.116)	(0.117)	(0.170)	(0.105)
scnd	-0.359^***	-0.453^***	-1.070^***	0.365^***	-0.143^*	-0.233^***	-1.035^***	0.420^***
	(0.121)	(0.096)	(0.156)	(0.110)	(0.109)	(0.095)	(0.150)	(0.098)
thrd	-0.293^***	-0.370^***	-0.960^***	0.346^***	-0.086^*	-0.126^*	-0.941^***	0.363^***
	(0.126)	(0.102)	(0.164)	(0.113)	(0.124)	(0.110)	(0.155)	(0.107)
road	-0.106	-0.356^**	-0.138^*	0.292^***	-0.205^*	-0.451^***	-0.157^*	0.259^***
	(0.175)	(0.188)	(0.157)	(0.067)	(0.172)	(0.189)	(0.153)	(0.062)
bus	-0.010	-0.217^*	-0.018	-0.233^***	-0.075	-0.275^*	-0.004	-0.207^***
	(0.194)	(0.234)	(0.181)	(0.069)	(0.170)	(0.204)	(0.176)	(0.074)
level	-0.085^***	-0.025^*	-0.142^***	-0.051^*	-0.055^*	-0.016	-0.128^***	-0.018
	(0.036)	(0.031)	(0.050)	(0.034)	(0.042)	(0.034)	(0.053)	(0.035)
_cons	0.658^***	0.812^***	1.608^***	-0.160^**	0.486^***	0.642^***	1.578^***	-0.211^***
	(0.105)	(0.081)	(0.141)	(0.096)	(0.099)	(0.083)	(0.136)	(0.089)
样本量	204	204	204	204	204	204	204	204
R²	0.345	0.398	0.338	0.311	0.218	0.228	0.338	0.319

H₀	H₁	y₁			y₂		y₃
H₀	H₁	size	concen	road	scnd	thrd	scnd
没有门槛效应	存在单一门槛	28.095^***	33.182^***	31.040^***	25.943^***	30.457^***	18.145^**
		(<0.001)	(<0.001)	(<0.001)	(<0.001)	(<0.001)	(0.029)
存在单一门槛	存在双重门槛	17.316	11.086	13.541	20.189^***	13.817	16.877^*
		(0.127)	(0.594)	(0.426)	(0.009)	(0.165)	(0.079)

变量	y₁
	size		concen		road
	size≤0.05	size>0.05	concen≤0.085	concen>0.085	road≤0.154	road>0.154
size	0.752^***	0.644^***	0.182	1.304^***	0.176*	0.746^**
	(0.098)	(0.094)	(0.110)	(0.276)	(0.088)	(0.354)
concen	0.412^***	0.587^***	0.061	0.776^***	0.514^***	1.144^**
	(0.093)	(0.047)	(0.187)	(0.093)	(0.074)	(0.620)
scnd	-0.305^***	-0.300^**	-0.320^***	0.349	-0.377^***	-1.251
	(0.118)	(0.132)	(0.113)	(0.196)	(0.103)	(1.214)
thrd	-0.256^**	-0.158^*	-0.296^**	0.546^*	-0.320^**	-2.048^*
	(0.122)	(0.113)	(0.117)	(0.231)	(0.105)	(1.217)
road	-0.366^***	0.127	-0.850^***	-0.332^*	-1.762^***	-0.787^***
	(0.086)	(0.440)	(0.191)	(0.186)	(0.082)	(0.090)
bus	-0.744^***	0.299^***	0.521^**	-0.889^*	-0.562^*	-0.545^***
	(0.377)	(0.125)	(0.087)	(0.473)	(0.327)	(0.129)
level	0.064	-0.087^***	0.005	0.230	0.034	0.049^***
	(0.064)	(0.025)	(0.032)	(0.136)	(0.036)	(0.011)
样本量	124	80	128	76	168	36
自由度	117	73	121	69	159	27
R²	0.497	0.552	0.427	0.467	0.325	0.629

变量	y₂				y₃
	scnd		thrd		scnd
	scnd≤0.417	scnd>0.417	thrd≤0.559	thrd>0.559	scnd≤0.467	scnd>0.467
size	-0.425^**	0.391^*	0.300^*	-0.441^**	0.255^**	0.816^***
	(0.160)	(0.188)	(0.169)	(0.170)	(0.122)	(0.113)
concen	0.017	0.067	0.032	0.122	0.422^**	0.227
	(0.196)	(0.124)	(0.143)	(0.078)	(0.142)	(0.146)
scnd	-1.015^***	-0.688^***	-0.292^***	-0.024	0.074	0.126^***
	(0.119)	(0.042)	(0.082)	(0.232)	(0.060)	(0.031)
thrd	-0.458^*	0.051	-0.924^***	-0.553^***	0.070	0.056
	(0.249)	(0.103)	(0.055)	(0.098)	(0.131)	(0.092)
road	-0.338^***	-0.217	-0.626^***	0.012	0.369^***	0.202
	(0.111)	(0.331)	(0.232)	(0.220)	(0.067)	(0.206)
bus	0.158^*	-1.461^***	-1.165^***	0.134	-0.259^***	0.255
	(0.102)	(0.423)	(0.341)	(0.113)	(0.051)	(0.283)
level	-0.148^***	-0.157^***	-0.142^**	-0.083	0.107^**	-0.100^**
	(0.067)	(0.067)	(0.062)	(0.056)	(0.045)	(0.045)
样本量	39	165	161	43	52	152
自由度	31	157	153	35	44	144
R²	0.668	0.314	0.366	0.373	0.533	0.327