东北三省城际技术转移网络的空间演化及影响因素

doi:10.11821/dlxb201910010

Abstract

Abstract:

Interurban technology transfer becomes an essential channel for regions or cities to obtain external knowledge. Based on patent transaction data among cities during 2005-2015, this study investigates the interurban technology transfer network of Northeast China, aiming to explore spatial evolution of technology transfer network in this region from national to local perspectives based on social network analysis (SNA). A negative binomial regression analysis further reveals the factors of interurban technology transfer network. The results of the study are as follows: (1) From the national perspective, the interurban technology transfer network of Northeast China presents a core-periphery structure. The spatial pattern of "divergence in the northeast region" and "convergence in the coastal areas" has been formed. (2) From the local perspective, the technology transfer network of Northeast China shows a centripetal contraction situation, and its four hubs, namely, Harbin, Changchun, Shenyang and Dalian, play the role of technology gatekeeper. The interurban technology transfer flows present the characteristic of strengthening nationalization and weakening localization, which are more likely to emerge between the Northeast-Southeast China rather than among the Northeast China. (3) Both path-dependence and path-creation exist in the spatial dynamics of intercity technology flows in Northeast China. From the national perspective, technology flows from Northeast China to the Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta urban agglomerations with Beijing, Shanghai and Shenzhen as the core respectively, while the local intercity technology transfer in Northeast China presents a mixed diffusing mode including hierarchical, contagious and jump diffusions. In addition, the local network mainly focuses on intra-provincial technology flows which centered on Haibin, Changchun, Shenyang and Dalian. (4) Some drivers, such as geographical proximity, the similarity of industrial structure, economic differences, the similarity of innovation capability, technology absorptive capacity, foreign direct investment, are evidenced to play a significant or determining role in interurban technology transfer of Northeast China.

Key words: intercity technology transfer, technology transfer network, national and local perspectives, spatial evolution, factors, Northeast China

LIU Chengliang, NIU Caicheng. Spatial evolution and factors of interurban technology transfer network in Northeast China from national to local perspectives[J].Acta Geographica Sinica, 2019, 74(10): 2092-2107.

Figures/Tables 15

Tab. 1

The indicators of directed weighted network and their topological meanings

指标	计算公式	公式解释	意义
度中心性	$C D (i) = ∑ j = 1 N x ij (i ≠ j) N - 1$ （2）	节点i与其他节点关联数与可能N-1节点最大连接数的比值	城市与其他城市产生技术联系的能力
网络中心势	$C = ∑ j N (D max - D j) N - 1 D max - 1$ （3）	最大度值与其他度值的差值总和与差值总和最大可能值的比值	衡量网络中点的集中趋势 (星形网络的中心势为1)
加权入度	$S i in = ∑ j = 1 N a ij$ （4）	以节点i为终点的所有弧的权重和	城市转入专利的数量
加权出度	$S i out = ∑ j = 1 N a ij$ （5）	以节点i为起点的所有弧的权重和	城市转出专利的数量
加权度	$S i = S i in + S i out$ （6）	与节点i直接相连的所有弧的权重和	城市专利转移总量
净流入度	$S i net = S i in - S i out$ （7）	节点i的加权入度与加权出度之差	城市在技术转移网络中发挥作用

Tab. 1

Tab. 2

The variables definition and interpretation

变量名称	符号	指标解释或公式	预期符号
被解释变量
专利转移数量	trans	$trans = Coun t ij$ （9）城市i转移到城市j的专利数量。
解释变量
地理距离接近度	geo	$geo = 1 - ln d ij max d ij$ （10）d_ij为城市i和城市j间的地理距离。	+
产业结构相似度	ins	$ins = ∑ k = 1 K p i, k - p i ˉ p i, k - p j ˉ ∑ k = 1 K (p i, k - p i ˉ) 2 ∑ k = 1 K (p j . k - p j ˉ) 2, k = 1,2, 3$ （11） $p i, k$ 是城市i中产业k的产值占总产值的比例。	+
经济发展水平差距	ecgap	$ecgap = 1 - ln (gd p min gd p max)$ （12） $gd p min$ 和 $gd p max$ 分别是城市i和j的GDP中较小值和较大值。	-
创新能力相似度	ino	$ino = p min p max p min + p max 2$ （13） $p min$ 和 $p max$ 分别是城市i和j的专利申请量中较小值和较大值。	-
技术吸收能力	ac	以转入城市的专利申请量代表城市的技术吸收能力。	+
外商直接投资	fdi	以转入城市的FDI代表城市的海外技术引进水平。	-
控制变量
输出城市为副省级城市	key city	构建虚拟变量,输出城市为副省级城市赋值为1,地级市赋值为0。

Tab. 2

Fig. 1

Tab. 3

Fig. 2

Fig. 3

Tab. 4

Tab. 5

Fig. 4

Tab. 6

Fig. 5

Tab. 7

Fig. 6

Tab. 8

Tab. 9

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2005年			2010年			2015年
城市	度中心性	净流入度	城市	度中心性	净流入度	城市	度中心性	净流入度
沈阳市	0.325	-33(24/57)	北京市	0.307	25(77/52)	哈尔滨市	0.459	-58(75/133)
北京市	0.300	16(21/5)	长春市	0.250	30(58/28)	大连市	0.336	-74(56/130)
哈尔滨市	0.175	2(5/3)	大连市	0.227	-16(16/32)	长春市	0.260	-20(48/68)
长春市	0.175	-11(7/18)	沈阳市	0.216	-26(19/45)	北京市	0.253	64(215/151)
大连市	0.150	-2(3/5)	上海市	0.170	-14(16/30)	沈阳市	0.240	-4(73/77)
深圳市	0.150	4(11/7)	哈尔滨市	0.170	-50(9/59)	上海市	0.110	17(37/20)
上海市	0.125	21(40/19)	大庆市	0.068	-5(6/11)	深圳市	0.089	27(45/18)
大庆市	0.075	3(4/1)	武汉市	0.057	1(3/2)	大庆市	0.089	-24(8/32)
盘锦市	0.050	-4(1/5)	吉林市	0.057	-5(5/10)	青岛市	0.082	8(25/17)
广州市	0.050	-2(0/2)	天津市	0.057	0(4/4)	广州市	0.082	10(25/15)

		2005年		2010年		2015年
		转出比例(%)	转入比例(%)	转出比例(%)	转入比例(%)	转出比例(%)	转入比例(%)
专利分类	发明	38.80	46.15	51.45	48.44	51.67	62.01
	实用	49.00	42.31	46.89	47.92	42.26	34.64
	外观	12.20	11.54	1.66	3.65	6.07	3.35
主体分类	企业	60.87	68.63	43.72	91.15	49.16	75.42
	高校	0.00	0.00	11.63	0.00	10.25	5.87
	研究所	2.17	0.00	5.12	0.00	2.51	1.12
	个人	36.96	31.37	39.53	8.85	38.08	17.60

年份	节点数	边数	网络密度	平均度	平均加权度	加权度	中心势
2005年	18	17	0.056	1.889	1.111	40	0.111
2010年	25	36	0.060	2.880	5.520	276	0.120
2015年	28	74	0.098	5.286	8.857	496	0.196

2005年			2010年			2015年
城市	度中心性	净流入度	城市	度中心性	净流入度	城市	度中心性	净流入度
哈尔滨市	0.294	2(4/2)	沈阳市	0.500	-2(13/15)	沈阳市	0.889	-51(17/68)
长春市	0.235	0(2/2)	哈尔滨市	0.375	10(19/9)	哈尔滨市	0.630	-24(10/34)
沈阳市	0.235	-3(1/1)	大连市	0.292	13(34/21)	大连市	0.630	2(27/25)
大连市	0.176	1(2/2)	长春市	0.250	-4(4/8)	长春市	0.481	-27(13/40)
鞍山市	0.176	2(2/2)	绥化市	0.125	-3(0/3)	通化市	0.222	7(15/8)
佳木斯市	0.118	-1(1/1)	牡丹江市	0.125	-15(1/16)	大庆市	0.185	2(12/14)
辽阳市	0.118	-3(0/0)	通化市	0.125	0(2/2)	吉林市	0.185	7(12/5)
本溪市	0.059	1(1/1)	大庆市	0.125	1(2/1)	丹东市	0.185	2(6/4)
白山市	0.059	-1(0/0)	鞍山市	0.125	-39(2/41)	本溪市	0.185	1(7/8)
通化市	0.059	1(1/1)	佳木斯市	0.083	1(6/5)	营口市	0.185	1(4/3)

		2005年		2010年		2015年
		转移比例(%)		转移比例(%)		转移比例(%)
专利分类	发明	32.37		47.63		54.49
	实用	59.71		48.51		39.53
	外观	7.91		3.87		5.98
		转出比例(%)	转入比例(%)	转出比例(%)	转入比例(%)	转出比例(%)	转入比例(%)
主体分类	企业	45.71	67.14	30.76	83.30	49.49	81.23
	高校	2.86	2.86	6.85	5.98	8.59	4.86
	研究所	3.57	1.43	2.64	1.23	4.86	0.47
	个人	47.86	28.57	59.75	9.49	37.07	13.45

Spatial evolution and factors of interurban technology transfer network in Northeast China from national to local perspectives

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References 53

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模型	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
解释变量
geo		0.148^***	0.695^***	0.706^***	0.671^***	0.919^***	0.914^***	0.478^***	0.892^***	0.685^***
		(3.59)	(6.62)	(6.79)	(6.81)	(8.89)	(9.16)	(4.43)	(7.84)	(6.27)
ins			-2.169^***	-1.953^***	-2.900^***	-1.268^***	-2.148^***	-3.178^***	-1.110^**	-1.685^***
			(-5.91)	(-5.33)	(-8.57)	(-3.89)	(-7.02)	(-9.09)	(-3.02)	(-5.03)
ino				-0.134^**	-0.258^***	-0.0926^*	-0.226^***	-0.190^***	-0.0130	-0.133^**
				(-3.04)	(-5.53)	(-2.07)	(-4.85)	(-3.96)	(-0.28)	(-2.85)
ecgap						-0.845^***	-1.027^***	-1.492^***	-1.018^***	-1.202^***
						(-6.31)	(-7.65)	(-10.17)	(-6.97)	(-8.35)
lnac								0.376^***	0.373^***	0.502^***
								(8.16)	(7.77)	(10.68)
lnfdi									-0.284^***	-0.375^***
									(-6.91)	(-9.58)
控制变量
key	0.812^***				1.879^***		2.034^***	2.329^***		2.627^***
	(6.49)				(9.45)		(10.25)	(11.61)		(13.61)
N	12342	12342	12342	12342	12342	12342	12342	12342	12342	12342

模型	(1)	(2)	(3)	(4)	(5)	(6)
geo	0.0178	-0.255	0.117	0.452^***	0.516^***	0.896^***
	(0.14)	(-1.47)	(0.59)	(3.39)	(2.60)	(3.90)
ino	-0.261^***	-0.0639	0.0901	-0.166^**	-0.190^***	-0.179^***
	(-5.08)	(-1.46)	(1.59)	(-3.28)	(-3.05)	(-2.82)
ecgap	-1.075^**	-2.130^***	-1.565^**	-1.490^*	-1.110^*	-1.117^*
	(-2.42)	(-4.41)	(-3.00)	(-2.39)	(-1.75)	(-1.81)
lnac		0.307^***	0.525^***		0.635^***	0.594^***
		(2.88)	(5.10)		(4.35)	(4.40)
lnfdi		-0.0730	-0.136		-0.475^***	-0.248^**
		(-0.82)	(-1.77)		(-4.17)	(-2.14)
ins			-3.744^***			-3.840^***
			(-4.89)			(-4.07)
N	7719	7719	7719	7719	7719	7719

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