中国城市物流创新的空间网络特征及驱动机制

孙春晓, 裴小忠, 刘程军, 卜庆军

Spatial network characteristics and driving mechanism of urban logistics innovation in China

SUN Chunxiao, PEI Xiaozhong, LIU Chengjun, BU Qingjun

表5 物流创新联系网络加权出强度与入强度的驱动因素回归结果

Tab. 5 Regression results of the driving factors of the weighted out-intensity and in-intensity of the logistics innovation connection network

解释变量	物流创新联系加权出强度			物流创新联系加权入强度
解释变量	地理权重	经济权重	信息权重	地理权重	经济权重	信息权重
ln(GDP)	0.0478	0.0074	0.0754	0.1580***	0.1500***	0.2000***
ln(GDP)	(0.8500)	(0.1400)	(1.3400)	(2.9900)	(2.9200)	(3.7800)
ln(FAE)	-0.1620***	-0.1890***	-0.2030***	-0.1010**	-0.1190***	-0.1370***
ln(FAE)	(-3.4700)	(-4.5100)	(-4.7100)	(-2.3000)	(-3.0100)	(-3.3600)
ln(PLB)	-0.0017	0.0205	0.0143	-0.0233	-0.0012	-0.0069
ln(PLB)	(-0.0700)	(0.9000)	(0.6100)	(-1.0600)	(-0.0500)	(-0.3100)
ln(MTS)	-0.1310***	-0.1340***	-0.1420***	-0.0988***	-0.0982***	-0.1100**
ln(MTS)	(-3.2600)	(-3.4000)	(-3.4800)	(-2.6100)	(-2.6200)	(-2.8600)
ln(WRS)	0.0006**	0.0129**	0.0185	0.0104	0.0031	0.0116
ln(WRS)	(0.0200)	(0.4800)	(0.6700)	(0.4000)	(0.1200)	(0.4400)
ln(CGS)	0.0786	0.0242	0.0888	0.1170*	0.0744*	0.1330*
ln(CGS)	(1.2300)	(0.3900)	(1.3800)	(1.9400)	(1.2500)	(2.1800)
ln(LEN)	-0.0379	-0.0118	-0.0310	-0.0341	-0.0171	-0.0409
ln(LEN)	(-1.1900)	(-0.3700)	(-0.9600)	(-1.1300)	(-0.5700)	(-1.3400)
ln(SUE)	0.1480***	0.1990***	0.2030***	0.2170***	0.2510***	0.2590***
ln(SUE)	(5.3700)	(8.1300)	(7.9900)	(8.3300)	(10.8000)	(10.8500)
ln(UII)	0.2950***	0.4390***	0.4700***	0.1740***	0.3010***	0.3230***
ln(UII)	(8.3300)	(14.4500)	(15.1300)	(5.2100)	(10.4900)	(11.0500)
W × ln(GDP)	-0.4850	-0.2120	0.3970	-1.4860***	-0.3350**	0.0499
W × ln(GDP)	(-0.9300)	(-1.2900)	(2.5300)	(-3.0200)	(-2.1500)	(0.3400)
W × ln(FAE)	-0.8910***	-0.2920**	-0.5970***	-0.4220*	-0.4300***	-0.5740***
W × ln(FAE)	(-2.9400)	(-2.3900)	(-5.1800)	(-1.4800)	(-3.7300)	(-5.3000)
W × ln(PLB)	0.5712	0.2450	-0.0226	0.1920	0.1540	0.0931
W × ln(PLB)	(1.7300)	(2.6100)	(-0.2700)	(0.6000)	(1.7300)	(1.1900)
W × ln(MTS)	-1.7240***	-0.1620	-0.3340***	-2.2870***	-0.2920**	-0.2980*
W × ln(MTS)	(-3.2600)	(-1.2700)	(-2.6000)	(-4.5300)	(-2.4200)	(-2.4500)
W × ln(WRS)	1.0400***	0.1330*	0.0619	0.6630**	-0.0145	-0.0095
W × ln(WRS)	(3.2000)	(1.7500)	(0.8700)	(2.1800)	(-0.2000)	(-0.1400)
W × ln(CGS)	0.4000	0.3930	-0.2530	1.6130**	0.4000**	0.3160*
W × ln(CGS)	(0.5000)	(1.9500)	(-1.2900)	(2.1400)	(2.1000)	(1.7200)
W × ln(LEN)	1.9810***	0.2390**	-0.0123	0.4190	-0.0294	0.0186
W × ln(LEN)	(4.2100)	(2.1200)	(-0.1900)	(0.9400)	(-0.2800)	(0.3000)
W × ln(SUE)	-0.0005*	0.0515*	0.2070***	0.0447*	0.3220***	0.1980**
W × ln(SUE)	(-0.0000)	(0.6700)	(3.0400)	(0.2100)	(4.3800)	(3.0900)
W × ln(UII)	0.5220*	0.4330***	0.1970**	0.9210***	0.0936*	0.0088
W × ln(UII)	(1.8100)	(4.6700)	(2.1600)	(3.3800)	(1.0500)	(0.1000)
$ρ$	0.3706***	0.2861***	0.0198*	0.2508**	0.2549**	0.0188*
	(3.8100)	(10.6300)	(0.3500)	(2.3400)	(9.3100)	(0.700)
R²	0.5396	0.7514	0.7959	0.4779	0.7387	0.5332
Log-likelihood	5357.6444	5313.8532	5411.6161	5099.0893	5066.2383	5144.4121
Hausman	固定效应	固定效应	固定效应	固定效应	固定效应	固定效应