中国东部沿海五大城市群创新效率、影响因素及空间溢出效应

doi:10.11821/dlyj020181124

地理研究 ›› 2020, Vol. 39 ›› Issue (2): 257-271.doi: 10.11821/dlyj020181124

中国东部沿海五大城市群创新效率、影响因素及空间溢出效应

盛彦文¹, 骆华松², 宋金平¹(), 赵金丽³, 张学波⁴

^1. 北京师范大学地理科学学部,北京 100875
^2. 云南师范大学中国西南地缘环境与边疆发展协同创新中心,昆明 650500
^3. 山东师范大学地理与环境学院,济南 250358
^4. 曲阜师范大学地理与旅游学院,日照 276826

收稿日期:2018-10-16 修回日期:2019-06-08 出版日期:2020-02-20 发布日期:2020-05-20
通讯作者: 宋金平
作者简介:盛彦文（1990- ）,男,湖南株洲人,博士研究生,研究方向为城市与区域发展。E-mail: shengyanwen31@163.com
基金资助:
国家自然科学基金项目(41271132);中国西南地缘环境与边疆发展协同创新中心开放课题

Evaluation, influencing factors and spatial spillover of innovation efficiency in five major urban agglomerations in coastal China

SHENG Yanwen¹, LUO Huasong², SONG Jinping¹(), ZHAO Jinli³, ZAHNG Xuebo⁴

^1. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
^2. Collaborative Innovation Center for Geopolitical Setting of Southwest China and Borderland Development, Yunnan Normal University, Kunming 650500, China
^3. College of Geography and Environment, Shandong Normal University, Jinan 250358, China
^4. School of Geography and Tourism, Qufu Normal University, Rizhao 276826, Shandong, China

Received:2018-10-16 Revised:2019-06-08 Online:2020-02-20 Published:2020-05-20
Contact: SONG Jinping

摘要/Abstract

摘要：

作为经济发展的战略核心区域,东部沿海城市群的创新效率关系到创新驱动发展战略的实施和创新型国家建设。基于东部沿海五大城市群创新投入和产出面板数据,引入随机前沿生产函数测度2001—2015年五大城市群的创新效率,并利用空间Durbin模型定量分析创新效率的空间溢出效应。结果表明：① 2001年以来,五大城市群的创新效率均呈现稳定增长的趋势;② 京津冀创新效率增长缓慢,山东半岛城市群创新效率的提升幅度最大,长三角创新效率的提升幅度和均值仅次于山东半岛和京津冀城市群,而城市群核心城市虽然创新资源投入较多,但创新效率偏低;③ 经济发展水平、集聚经济、外商投资、劳动力素质、政府资助、基础设施、产业结构和高新技术产业对城市群创新效率有直接作用和溢出效应。

关键词: 创新效率, 随机前沿生产函数, 空间溢出效应, 空间Durbin模型, 东部沿海城市群

Abstract:

As the strategic core regions of national economic development, the urban agglomerations in the eastern coastal area of China has played a critical role in the implementation of innovation-driven development strategy and construction of the innovative country. Based on the dataset of innovation input and output spanning the 2001-2015 period, the SFA method and spatial Durbin model are applied to measure and examine the innovation efficiency and its influencing factors and spatial spillover effect in the five major urban agglomerations. Our results suggest that the innovation efficiency of five major urban agglomerations showed a steady increase over the studied period. Among the five urban agglomerations, the innovation efficiency in the Beijing-Tianjin-Hebei region rises slowly while that of Shandong Peninsula has increased relatively fast. The growth rate and average of innovation efficiency in the Yangtze River Delta fell behind the Shandong Peninsula and Beijing-Tianjin-Hebei region. Meanwhile, innovation efficiency in core cities of urban agglomerations is low, although they have high-input in innovation resources. Moreover, there is a significantly positive spatial spillover effect of innovation efficiency between cities in the five urban agglomerations. Economic development, agglomeration economies, FDI, the quality of labor force, the financial support by government, infrastructure, secondary industrial ratio and high-tech industries have direct and indirect effects on innovation efficiency in the urban agglomerations.

Key words: innovation efficiency, SFA, spatial spillover effect, spatial Durbin model, urban agglomerations of eastern coastal area of China

盛彦文, 骆华松, 宋金平, 赵金丽, 张学波. 中国东部沿海五大城市群创新效率、影响因素及空间溢出效应[J]. 地理研究, 2020, 39(2): 257-271.

SHENG Yanwen, LUO Huasong, SONG Jinping, ZHAO Jinli, ZAHNG Xuebo. Evaluation, influencing factors and spatial spillover of innovation efficiency in five major urban agglomerations in coastal China[J]. GEOGRAPHICAL RESEARCH, 2020, 39(2): 257-271.

图/表 11

图1

表1

图2

图3

图4

图5

表2

表3

空间Durbin模型估计结果"

	地理距离权重矩阵		经济距离权重矩阵		相互作用权重矩阵
		t-statistic		t-statistic		t-statistic
Ln GDP	0.153	(9.711)^***	-0.001	(-0.156)	0.160	(10.126)^***
Ln URB	0.060	(5.060)^***	-0.009	(-2.873)^***	0.067	(5.777)^***
Ln FDI	-0.001	(-0.130)	-0.004	(-2.173)^**	-0.003	(-0.301)
Ln GOV	0.008	(0.661)	0.005	(2.820)^***	0.008	(0.711)
Ln LAB	-0.037	(-4.129)^***	-0.005	(-1.422)	-0.044	(-4.875)^***
Ln BAS	0.093	(5.996)^***	0.003	(1.409)	0.088	(5.715)^***
Ln INDU	0.154	(3.955)^***	0.012	(1.068)	0.129	(3.334)^***
Ln HIGH	-0.016	(-6.366)^***	-0.003	(-4.416)^***	-0.015	(-6.025)^***
W $?$ Ln GDP	0.015	(0.613)	0.043	(4.709)^***	-0.025	(-1.097)
W $?$ Ln URB	0.071	(3.996)^***	-0.003	(-0.546)	0.071	(3.924)^***
W $?$ Ln FDI	-0.086	(-6.560)^***	-0.012	(-3.557)^***	-0.099	(-7.269)^***
W $?$ Ln GOV	-0.017	(-1.128)	-0.009	(-4.121)^***	-0.012	(-0.833)
W $?$ Ln LAB	-0.035	(-2.244)^**	-0.008	(-1.520)	-0.044	(-2.740)^***
W $?$ Ln BAS	-0.038	(-1.646)	0.001	(0.302)	-0.024	(-1.117)
W $?$ Ln INDU	-0.637	(-10.921)^***	-0.012	(-0.746)	-0.510	(-9.585)^***
W $?$ Ln HIGH	0.003	(0.482)	0.001	(1.417)	0.011	(1.904)^*
$ρ$	0.128	(2.899)^***	0.759	(45.847)^***	0.093	(2.129)^**
R²	0.426		0.992		0.423
log-likelihhod	269.199		2047.535		267.893

表3

表4

表5

空间滞后、空间误差及因变量滞后一期的空间Durbin模型回归结果"

	地理距离权重矩阵			经济距离权重矩阵			相互作用权重矩阵
	SAR	SEM	SDM	SAR	SEM	SDM	SAR	SEM	SDM
Ln GDP	0.087^***	0.090^***	0.155^***	0.009	-0.004	-0.005	0.109^***	0.114^***	0.162^***
Ln URB	0.043^***	0.044^***	0.055^***	-0.005^*	-0.006^**	-0.007^**	0.1^***	0.098^***	0.062^***
Ln FDI	-0.044^***	-0.045^***	0.001	-0.005^***	-0.002	-0.004^**	-0.037^***	-0.032^***	-0.001
Ln GOV	0.022^**	0.029^***	0.006	0.004^**	0.006^***	0.006^***	0.013	0.012	0.007
Ln LAB	-0.046^***	-0.047^***	-0.039^***	-0.003	-0.003	-0.001	-0.036^***	-0.037^***	-0.045^***
Ln BAS	0.048^***	0.051^***	0.09^***	0.003	0.002	0.002	0.1^***	0.108^***	0.085^***
Ln INDU	-0.165^***	-0.152^***	0.16^***	0.02^*	0.013	0.029^**	0.13^***	0.151^***	0.134^***
Ln HIGH	-0.007^**	-0.007^**	-0.015^***	-0.003^***	-0.003^***	-0.003^***	-0.013^***	-0.014^***	-0.015^***
W $?$ Ln GDP			0.008			0.061^***			-0.033
W $?$ Ln URB			0.069^***			-0.006			0.069^***
W $?$ Ln FDI			-0.088^***			-0.014^***			-0.102^***
W $?$ Ln GOV			-0.013			-0.008^***			-0.009
W $?$ Ln LAB			-0.035^**			-0.003			-0.043^***
W $?$ Ln BAS			-0.04^*			0.001			-0.023
W $?$ Ln INDU			-0.608^***			-0.001			-0.483^***
W $?$ Ln HIGH			0.004			-0.001			0.012^**
$ρ$	0.215^***		0.130^***	0.783^***		0.757^***	0.153^***		0.093^***
$λ$		0.173^***			0.8^***			0.156^***
R-squared	0.259	0.220	0.413	0.992	0.969	0.993	0.327	0.308	0.412
log-likelihood	153.021	144.501	256.275	2035.774	2034.869	1975.161	197.734	195.529	256.076

表5

表6

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	京津冀城市群	山东半岛城市群	长三角城市群	海峡西岸城市群	珠三角城市群
常数项	-1.547	3.439	-3.041	0.051	1.288
	(-4.424)^***	(1.012)	(-2.408)^**	(0.071)	(3.769)^***
LnL	0.028	0.229	0.315	0.182	0.071
	(0.675)	(2.158)^**	(1.892)^*	(1.450)	(0.980)
LnK	0.462	0.413	0.233	0.204	0.309
	(4.639)^***	(3.848)^***	(1.258)	(1.702)^*	(4.190)^***
LnKS	0.295	-0.145	0.492	0.384	0.283
	(3.337)^***	(-0.671)	(3.133)^***	(3.612)^***	(4.961)^***
σ²	0.590	0.587	0.587	0.889	0.697
	(2.263)^**	(2.694)^***	(2.397)^**	(2.704)^***	(2.083)^**
γ	0.828	0.733	0.767	0.700	0.939
	(10.476)^***	(1.792)^*	(7.909)^***	(6.227)^***	(30.471)^***
η	0.019	0.102	0.070	0.059	0.050
	(2.197)^**	(2.949)^***	(3.600)^***	(5.010)^***	(9.568)^***
LR	95.828^***	68.592^***	231.125^***	144.006^***	229.821^***

	地理距离权重矩阵		经济距离权重矩阵		相互作用权重矩阵
		P值		P值		P值
LM-lag test	36.636	0.000	11.511	0.001	21.600	0.000
R-LM-lag test	34.102	0.000	14.833	0.000	25.371	0.000
LM-err-test	14.040	0.000	3.817	0.051	6.425	0.011
R-LM-err-test	11.505	0.001	7.138	0.008	10.195	0.001
Moran's I	0.087	0.000	0.052	0.033	0.060	0.006
Wald-lag test	261.209	0.000	58.481	0.000	270.393	0.000
LR-lag test	232.302	0.000	52.387	0.000	237.997	0.000
Wald-err test	210.702	0.000	136.845	0.000	232.282	0.000
LR-err test	249.412	0.000	279.729	0.000	254.098	0.000
Hausman test	613.427	0.000	60.083	0.000	698.885	0.000

	地理距离权重矩阵		经济距离权重矩阵		相互作用权重矩阵
	直接效应	间接效应	直接效应	间接效应	直接效应	间接效应
Ln GDP	0.154	0.040	0.019	0.156	0.160	-0.011
	(9.846)^***	(1.518)	(2.462)^**	(8.015)^***	(10.187)^***	(-0.454)
Ln URB	0.062	0.088	-0.014	-0.036	0.069	0.083
	(5.421)^***	(5.010)^***	(-3.026)^***	(-1.731)^*	(5.883)^***	(4.491)^***
Ln FDI	-0.004	-0.096	-0.011	-0.054	-0.005	-0.108
	(-0.450)	(-7.005)^***	(-4.139)^***	(-4.472)^***	(-0.577)	(-7.518)^***
Ln GOV	0.007	-0.018	0.003	-0.019	0.008	-0.013
	(0.570)	(-1.060)	(1.607)	(-3.457)^***	(0.706)	-(0.833)
Ln LAB	-0.038	-0.045	-0.010	-0.041	-0.045	-0.052
	(-4.239)^***	(-2.701)^***	(-2.493)^***	(-2.536)^***	(-4.829)^***	(-3.081)^***
Ln BAS	0.092	-0.030	0.005	0.013	0.088	-0.016
	(5.995)^***	(-1.241)	(1.520)	(0.864)	(5.817)^***	(-0.756)
Ln INDU	0.135	-0.689	0.011	-0.012	0.118	-0.540
	(3.568)^***	(-10.891)^***	(0.752)	(-0.209)	(3.041)^***	(-9.604)^***
Ln HIGH	-0.016	0.001	-0.003	-0.002	-0.015	0.011
	(-6.457)^***	(0.111)	(-3.351)^***	(-0.516)	(-5.778)^***	(1.641)

中国东部沿海五大城市群创新效率、影响因素及空间溢出效应

Evaluation, influencing factors and spatial spillover of innovation efficiency in five major urban agglomerations in coastal China

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