基于市场潜能的城市经济增长空间格局及溢出效应

doi:10.13249/j.cnki.sgs.2017.11.002

摘要/Abstract

摘要：

基于新经济地理学理论,定量分析中国285个地级市的市场潜能及其空间格局,并根据其强度划分为3个等级市场潜能区,运用探索性空间数据分析工具,揭示其空间相关性随地理距离的变化规律。引入市场潜能变量作为测度经济增长溢出效应的指标,建立空间计量模型测度中国经济增长的空间溢出效应及其范围,结果显示：① 中国市场潜能空间分布呈“中心-外围”结构,具有从沿海向内陆阶梯状递减的趋势;② 市场潜能的空间自相关性随距离增加先上升后下降,市场潜能的强度不同,其空间自相关范围也有所差异;③ 经济增长溢出效应强度随距离增加呈“∽”型趋势变化,中国地级市经济增长的有效溢出范围为200 km以内,市场潜能越高,经济活动空间集聚性越强,溢出范围越大。

关键词: 市场潜能, 经济增长, 空间自相关, 空间溢出效应

Abstract:

With the process of reform and opening up, the market questions are becoming more and more important to Chinese economic growth, especially the questions on the relationships of market potential and economic growth. The measurement of market potential which reflected the the first law of geography is widely used by theories of new economic geography and regional econometrics. This article constructed the theoretical model of market potential and economic growth based on the theory of new economic geography. The article also analyzed the market potential and of Chinese 258 prefecture-level cities quantitatively and explored the spatial distribution of market potential. According to intensity of market potential, the research areas could be divided into three grades of market potential areas. The first market potential zone is the area which owns the highest market potential and the values in the second market potential zone are lower than that. The third market potential zone obtains the lowest values of market potential. This article revealed the characteristics of market potential’s spatial autocorrelation with using exploratory spatial data analysis tools. Moreover, this article introduced the market potential variable as the index of economic spillover effects, because the conception of market potential could reflect the spatial relationships of different areas to a certain extent, especially spatial dependence and spillover effects. So the spatial effects could not be ignored in this article. Taking the spatial effects out of consideration could lead to the results of estimation biased and inconsistent. Therefore, this article established the spatial econometric models (including spatial lag model and spatial error model) to measure the scope of economic spillover effects in China. The results are as follows: 1) The spatial distribution of market potential in China presented the ‘core-edge’ structure, which showed a trend of cascade decrease from the coast to inland areas. 2) The spatial autocorrelation of market potential increases first and then fall with geographical distance. What’s more, for the market potential areas of different intensity, the scope of their spatial autocorrelation is different. 3) Economic growth spillover effects showed a trend of "∽" type with geographic distance. The economic growth effective spillover of Chinese 258 prefecture-level cities is in the range of 200 km. For the market potential areas of different intensity, the scope of the economic growth spillover effects is different. The area that market potential are higher, the spatial agglomeration of economic activities is stronger, and the scope of economic growth spillover effects is broader.

Key words: market potential, economic growth, spatial autocorrelation, spatial spillover effect

中图分类号:

F061.5

王雪辉, 谷国锋. 基于市场潜能的城市经济增长空间格局及溢出效应[J]. 地理科学, 2017, 37(11): 1617-1623.

Xuehui Wang, Guofeng Gu. Spatial Pattern and Spillover Effects of Urban Economic Growth Based on Market Potential[J]. SCIENTIA GEOGRAPHICA SINICA, 2017, 37(11): 1617-1623.

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检验	统计量	概率值
残差Moran’s I	4.787	0.000
L Mlag	6.808	0.009
R-LMlag	4.304	0.038
L Muerr	20.326	0.000
R-LMerr	17.822	0.000

	OLS			SLM			SEM
	全国	一、二等级	二、三等级	全国	一、二等级	二、三等级	全国	一、二等级	二、三等级
mp	0.185^***	0.276^**	0.250^***	0.238^***	0.274^**	0.294^***	0.131^**	0.175^***	0.194^**
	（3.104）	（2.278）	（3.244）	（3.837）	（2.313）	（3.673）	（1.995）	（3.313）	（2.339）
k	0.665^***	0.615^***	0.641^***	0.667^***	0.618^***	0.645^***	0.647^***	0.603^***	0.636^***
	（20.428）	（14.561）	（16.455）	（20.899）	（14.959）	（16.852）	（19.132）	（14.051）	（15.741）
dl	0.132^***	0.115^***	0.094^***	0.126^***	0.106^***	0.096^***	0.106^***	0.083^***	0.081^***
	（5.758）	（4.509）	（3.055）	（5.567）	（4.222）	（3.188）	（4.566）	（3.335）	（2.688）
l	0.019	0.065^***	0.022	0.026	0.066^***	0.023	0.027	0.078^***	0.028
	（ 1.020）	（2.677）	（0.998）	（1.424）	（2.801）	（1.082）	（1.540）	（3.501）	（1.318）
ρ/λ				0.028^***	0.019^**	0.016^*	0.323^***	0.365^***	0.217
				（2.613）	（2.034）	（1.734）	（4.265）	（4.215）	（1.528）
R-squared	0.759	0.775	0.704	0.764	0.779	0.708	0.778	0.798	0.717
Log-L	80.494	55.097	28.748	70.098	50.134	27.243	92.759	75.192	25.484