高铁通车对中国城市创业投资网络的影响——基于跨城市创业投资事件的实证研究

doi:10.18306/dlkxjz.2021.10.002

Abstract

Abstract:

Based on the 41692 venture capital investment events in China's mainland from 2001 to 2017, the impact of high-speed railway on the agglomeration, radiation, and intermediary powers of Chinese venture capital network was studied by using the social network analysis model, as well as the heterogeneity of the effect of different city types, radiation radius of the central cities, and four investment stages, in order to research the impact of high-speed railway on Chinese urban venture capital network. The analysis of the time-varying Difference-in-Differences (DID) model shows that high-speed railway can improve the accessibility of cities, reduce the transaction cost of venture capital activities, promote the cross-regional flow of innovation resources, and enhance the abundance of urban innovation elements, thus having a positive impact on the venture capital network development. High-speed railways have a significant promoting effect on the city network of venture capital in both non-central cities and central cities, and the positive effect is stronger in central cities. The impact of high-speed railway on venture capital network is different under different distance scales: the influence is significantly weaker in areas within a radius of 100 km and beyond 200 km than that between 100 km and 200 km. The impact of high-speed railway on the agglomeration power, radiation power, and intermediary power of venture capital network in the expansion and maturity stages (with lower risk, stable return, and higher marketization degree) is clearly more significant than in the seed stage and initial stage.

Key words: venture capital network, high-speed railway, time-varying DID model, social network analysis, China

ZHUANG Delin, LIU Yuchen, WANG Shuai. Impact of high-speed railway on Chinese urban venture capital network: Empirical study based on cross-city venture capital events[J].PROGRESS IN GEOGRAPHY, 2021, 40(10): 1626-1638.

Figures/Tables 8

Tab.1

Tab.2

Fig.1

Tab.3

Tab.4

Tab.5

Tab.6

Tab.7

References 39

[1]	马建堂, 赵昌文. 更加自觉地用新发展格局理论指导新发展阶段经济工作[J]. 管理世界, 2020, 36(11):1-6, 231.
	[ Ma Jiantang, Zhao Changwen. Use the new development pattern theory to guide economic work in the new stage of development. Management World, 2020, 36(11):1-6, 231. ]
[2]	王一鸣. 百年大变局、高质量发展与构建新发展格局[J]. 管理世界, 2020, 36(12):1-13.
	[ Wang Yiming. Changes unseen in a century, high-quality development, and the construction of a new development pattern. Management World, 2020, 36(12):1-13. ]
[3]	黄奇帆. 对加快构建完整的内需体系, 形成国内国际双循环相互促进新格局的思考和建议[J]. 中国经济周刊, 2020(14):94-103.
	[ Huang Qifan. Efforts should be made to create new development pattern whereby domestic and foreign markets can boost each other, with the domestic market as the mainstay. China Economic Weekly, 2020(14):94-103. ]
[4]	钱肖颖, 孙斌栋. 基于城际创业投资联系的中国城市网络结构和组织模式[J]. 地理研究, 2021, 40(2):419-430. doi: 10.11821/dlyj020191023
	[ Qian Xiaoying, Sun Bindong. The spatial structures and organization patterns of China's city network based on inter-city startup investments. Geographical Research, 2021, 40(2):419-430. ]
[5]	庄德林, 王鹏鹏, 许基兰, 等. 中国创业投资城市网络空间结构演变研究: 基于四大投资阶段的分析视角[J]. 地理科学, 2020, 40(8):1256-1265. doi: 10.13249/j.cnki.sgs.2020.08.004
	[ Zhuang Delin, Wang Pengpeng, Xu Jilan, et al. Spatial structure evolution of China's venture capital city network: Based on the analysis perspective of four investment stages. Scientia Geographica Sinica, 2020, 40(8):1256-1265. ]
[6]	Duan L L, Sun W Z, Zheng S Q. Transportation network and venture capital mobility: An analysis of air travel and high-speed rail in China[J]. Journal of Transport Geography, 2020, 88:102852. doi: 10.1016/j.jtrangeo.2020.102852. doi: 10.1016/j.jtrangeo.2020.102852
[7]	Redding S J, Turner M A. Transportation costs and the spatial organization of economic activity[M]// Duranton G, Henderson J V, Strange W C. Handbook of Regional and Urban Economics. Amsterdam, Netherlands: Elsevier, 2015: 1339-1398.
[8]	刘冲, 刘晨冉, 孙腾. 交通基础设施、金融约束与县域产业发展: 基于“国道主干线系统”自然实验的证据[J]. 管理世界, 2019, 35(7):78-88, 203.
	[ Liu Chong, Liu Chenran, Sun Teng. Transportation infrastructure, financial constraint and county industry development: Evidence from China's national trunk highway system. Management World, 2019, 35(7):78-88, 203. ]
[9]	范欣, 宋冬林, 赵新宇. 基础设施建设打破了国内市场分割吗?[J]. 经济研究, 2017, 52(2):20-34.
	[ Fan Xin, Song Donglin, Zhao Xinyu. Does infrastructure construction break up domestic market segmentation? Economic Research Journal, 2017, 52(2):20-34. ]
[10]	孙娜, 张梅青. 基于高铁流的中国城市网络结构特征演变研究[J]. 地理科学进展, 2020, 39(5):727-737. doi: 10.18306/dlkxjz.2020.05.003
	[ Sun Na, Zhang Meiqing. Network structure and evolution characteristics of cities in China based on high-speed railway transport flow. Progress in Geography, 2020, 39(5):727-737. ]
[11]	宣烨, 陆静, 余泳泽. 高铁开通对高端服务业空间集聚的影响[J]. 财贸经济, 2019, 40(9):117-131.
	[ Xuan Ye, Lu Jing, Yu Yongze. The impact of high-speed rail opening on the spatial agglomeration of high-end service industry. Finance & Trade Economics, 2019, 40(9):117-131. ]
[12]	吉赟, 杨青. 高铁开通能否促进企业创新: 基于准自然实验的研究[J]. 世界经济, 2020, 43(2):147-166.
	[ Ji Yun, Yang Qing. Can the high-speed rail service promote enterprise innovation? A study based on quasi-natural experiments. The Journal of World Economy, 2020, 43(2):147-166. ]
[13]	Shao S, Tian Z H, Yang L L. High speed rail and urban service industry agglomeration: Evidence from China's Yangtze River Delta region[J]. Journal of Transport Geography, 2017, 64:174-183. doi: 10.1016/j.jtrangeo.2017.08.019
[14]	董静, 汪江平, 翟海燕, 等. 服务还是监控: 风险投资机构对创业企业的管理: 行业专长与不确定性的视角[J]. 管理世界, 2017(6):82-103, 187.
	[ Dong Jing, Wang Jiangping, Zhai Haiyan, et al. Value-added service or supervisory control? Management patterns of venture capital institutes to entrepreneurial firms: Views from industrial specialty and uncertainty. Management World, 2017(6):82-103, 187. ]
[15]	王兰芳, 胡悦. 创业投资促进了创新绩效吗? 基于中国企业面板数据的实证检验[J]. 金融研究, 2017(1):177-190.
	[ Wang Lanfang, Hu Yue. Does venture capital improve innovation efficiency? Evidence from China's firm-level analysis. Journal of Financial Research, 2017(1):177-190. ]
[16]	汪明峰, 魏也华, 邱娟. 中国风险投资活动的空间集聚与城市网络[J]. 财经研究, 2014, 40(4):117-131.
	[ Wang Mingfeng, Wei Yehua, Qiu Juan. Spatial agglomeration and urban network of venture capital investment in China. Journal of Finance and Economics, 2014, 40(4):117-131. ]
[17]	赵静梅, 傅立立, 申宇. 风险投资与企业生产效率: 助力还是阻力?[J]. 金融研究, 2015(11):159-174.
	[ Zhao Jingmei, Fu Lili, Shen Yu. Venture capital and firms' production efficiency: Helping hand or grabbing hand? Journal of Financial Research, 2015(11):159-174. ]
[18]	杜江, 孟佳, 袁昌菊. 我国风险投资空间分布与退出绩效[J]. 财经科学, 2019(8):26-41.
	[ Du Jiang, Meng Jia, Yuan Changju. Spatial distribution and exit performance of risk investment in China. Finance & Economics, 2019(8):26-41. ]
[19]	陈婧, 方军雄. 高铁开通、经理人市场竞争与高管薪酬激励[J]. 财贸经济, 2020, 41(12):132-146.
	[ Chen Jing, Fang Junxiong. High-speed railway, managerial labor market and executive compensation. Finance & Trade Economics, 2020, 41(12):132-146. ]
[20]	龙玉, 赵海龙, 张新德, 等. 时空压缩下的风险投资: 高铁通车与风险投资区域变化[J]. 经济研究, 2017, 52(4):195-208.
	[ Long Yu, Zhao Hailong, Zhang Xinde, et al. High-speed railway and venture capital investment. Economic Research Journal, 2017, 52(4):195-208. ]
[21]	陆军, 毛文峰. 城市网络外部性的崛起: 区域经济高质量一体化发展的新机制[J]. 经济学家, 2020(12):62-70.
	[ Lu Jun, Mao Wenfeng. The rise of urban network externalities: A new mechanism for the high-quality integrated development of regional economy. Economist, 2020(12):62-70. ]
[22]	胡刘芬, 周泽将. 风险投资机构持股能够缓解企业后续融资约束吗? 来自中国上市公司的经验证据[J]. 经济管理, 2018, 40(7):91-109.
	[ Hu Liufen, Zhou Zejiang. Can venture capital shareholding relieve corporate follow-on financing constraints? Empirical evidence from Chinese listed companies. Business Management Journal, 2018, 40(7):91-109. ]
[23]	诸竹君, 黄先海, 王煌. 交通基础设施改善促进了企业创新吗? 基于高铁开通的准自然实验[J]. 金融研究, 2019(11):153-169.
	[ Zhu Zhujun, Huang Xianhai, Wang Huang. Does traffic infrastructure promote innovation? A quasi-natural experiment based on the expansion of the high-speed railway network in China. Journal of Financial Research, 2019(11):153-169. ]
[24]	杜兴强, 彭妙薇. 高铁开通会促进企业高级人才的流动吗?[J]. 经济管理, 2017, 39(12):89-107.
	[ Du Xing-qiang, Peng Miaowei. Do high-speed trains motivate the flow of corporate highly educated talents? Business Management Journal, 2017, 39(12):89-107. ]
[25]	马光荣, 程小萌, 杨恩艳. 交通基础设施如何促进资本流动: 基于高铁开通和上市公司异地投资的研究[J]. 中国工业经济, 2020(6):5-23.
	[ Ma Guangrong, Cheng Xiaomeng, Yang Enyan. How does transportation infrastructure affect capital flows: A study from high-speed rail and cross-region investment of listed companies. China Industrial Economics, 2020(6):5-23. ]
[26]	蔡宁, 何星. 社会网络能够促进风险投资的“增值”作用吗? 基于风险投资网络与上市公司投资效率的研究[J]. 金融研究, 2015(12):178-193.
	[ Cai Ning, He Xing. Can social networks improve the value-added effect of VC? Based on the relationship of venture capital networks and investment efficiency. Journal of Financial Research, 2015(12):178-193. ]
[27]	同世隆, 赵守国, 王晗. 高铁开通与外资风险投资流入: 基于中国城市面板数据[J]. 中国软科学, 2021(1):32-43.
	[ Tong Shilong, Zhao Shouguo, Wang Han. High-speed rail and foreign venture capital inflow: Evidence from China's urban panel data. China Soft Science, 2021(1):32-43. ]
[28]	张萃. 什么使城市更有利于创业?[J]. 经济研究, 2018, 53(4):151-166.
	[ Zhang Cui. What makes cities more entrepreneurial? Economic Research Journal, 2018, 53(4):151-166. ]
[29]	王春杨, 兰宗敏, 张超, 等. 高铁建设、人力资本迁移与区域创新[J]. 中国工业经济, 2020(12):102-120.
	[ Wang Chunyang, Lan Zongmin, Zhang Chao, et al. High-speed rail construction, human capital migration and regional innovation. China Industrial Economics, 2020(12):102-120. ]
[30]	王晗, 董建卫, 刘慧侠, 等. 高铁开通对城市在位者创投机构网络位置的影响研究[J]. 管理评论, 2020, 32(6):163-174.
	[ Wang Han, Dong Jianwei, Liu Huixia, et al. The impact of high speed rail on incumbent VC firms' network position: Empirical evidence from China's prefecture cities. Management Review, 2020, 32(6):163-174. ]
[31]	周晓艳, 侯美玲, 李霄雯. 独角兽企业内部联系视角下中国城市创新网络空间结构研究[J]. 地理科学进展, 2020, 39(10):1667-1676. doi: 10.18306/dlkxjz.2020.10.006
	[ Zhou Xiaoyan, Hou Meiling, Li Xiaowen. Spatial structure of urban innovation network based on the Chinese unicorn company network. Progress in Geography, 2020, 39(10):1667-1676. ]
[32]	刘军. 整体网分析: UCINET软件实用指南[M]. 2版. 上海: 格致出版社, 2014.
	[ Liu Jun. Lectures on whole network approach: A practical guide to UCINET. 2nd Edition. Shanghai, China: Truth and Wisdom Press, 2014. ]
[33]	阮平南, 郭文静, 杨娟. 中国创业投资区域网络结构与区域合作研究[J]. 科技进步与对策, 2019, 36(14):8-17.
	[ Ruan Pingnan, Guo Wenjing, Yang Juan. Network structure and regional cooperation analysis of regional venture capital in China. Science & Technology Progress and Policy, 2019, 36(14):8-17. ]
[34]	Beck T, Levine R, Levkov A. Big bad banks? The winners and losers from bank deregulation in the United States[J]. The Journal of Finance, 2010, 65(5):1637-1667.
[35]	张克中, 陶东杰. 交通基础设施的经济分布效应: 来自高铁开通的证据[J]. 经济学动态, 2016(6):62-73.
	[ Zhang Kezhong, Tao Dongjie. The economic distribution effect of transportation infrastructure: Evidence from the high-speed railway. Economic Perspectives, 2016(6):62-73. ]
[36]	Fang J W. An analysis of the differentiation rules and influencing factors of venture capital in Beijing-Tianjin-Hebei urban agglomeration[J]. Journal of Geographical Sciences, 2018, 28(4):514-528. doi: 10.1007/s11442-018-1487-8
[37]	Pan F H, Zhao S X B, Wójcik D. The rise of venture capital centres in China: A spatial and network analysis[J]. Geoforum, 2016, 75(7):148-158. doi: 10.1016/j.geoforum.2016.07.013
[38]	刘勇政, 李岩. 中国的高速铁路建设与城市经济增长[J]. 金融研究, 2017(11):18-33.
	[ Liu Yongzheng, Li Yan. High-speed rails and city economic growth in China. Journal of Financial Research, 2017(11):18-33. ]
[39]	谢呈阳, 王明辉. 交通基础设施对工业活动空间分布的影响研究[J]. 管理世界, 2020, 36(12):52-64,161,65.
	[ Xie Chengyang, Wang Minghui. A study on the impact of transportation infrastructure on the spatial distribution of industrial activities. Management World, 2020, 36(12):52-64,161,65. ]

变量名称	含义	计算方法
被解释变量
Indegree	创业投资网络集聚力	点入度
Outdegree	创业投资网络辐射力	点出度
Betwe	创业投资网络联系中介力	中介中心度
解释变量
HSR	投资发生时高铁是否开通	创业投资事件发生时该城市开通高铁则取1,否则取0
控制变量
GDP	人均GDP	各城市人均GDP取对数
SencondaryInd	第二产业占比	第二产业增加值占GDP比重
TertiaryInd	第三产业占比	第三产业增加值占GDP比重
Telecommunication	通信力	移动电话用户数取对数
Innovation	创新力	专利授权数取对数

创业投资企业所在地投资事件分布				创业投资机构所在地投资事件分布
企业所在省份	投资事件数		合计	机构所在省份	投资事件数		合计
企业所在省份	未通高铁城市	高铁通车城市	合计	机构所在省份	未通高铁城市	高铁通车城市	合计
北京	104	8310	8414	北京	0	12728	12728
广东	77	6698	6775	上海	0	9883	9883
上海	44	5453	5497	广东	9	9538	9547
江苏	28	4295	4323	浙江	10	2922	2932
浙江	29	3763	3792	江苏	22	2122	2144
山东	17	1488	1505	天津	0	898	898
四川	9	1355	1364	福建	1	473	474
湖北	7	1216	1223	山东	41	376	417
福建	8	1134	1142	湖北	0	410	410
湖南	5	841	846	湖南	1	273	274
安徽	6	782	788	四川	3	260	263
河南	5	758	763	重庆	0	225	225
天津	3	687	690	西藏	216	0	216
陕西	1	653	654	安徽	3	190	193
河北	1	518	519	陕西	2	188	190
辽宁	2	504	506	江西	2	176	178
重庆	2	471	473	河南	0	148	148
江西	1	311	312	新疆	34	82	116
吉林	1	278	279	河北	1	102	103
云南	3	261	264	辽宁	1	73	74
新疆	3	233	236	云南	0	41	41
内蒙古	4	181	185	黑龙江	0	35	35
贵州	2	178	180	吉林	0	35	35
广西	2	176	178	广西	0	33	33
黑龙江	1	173	174	贵州	0	33	33
山西	0	152	152	海南	1	27	28
海南	0	150	150	内蒙古	13	8	21
宁夏	7	103	110	山西	0	16	16
甘肃	2	107	109	甘肃	0	14	14
青海	0	53	53	宁夏	14	0	14
西藏	0	36	36	青海	0	9	9
总计	374	41318	41692	总计	374	41318	41692

变量	Indegree	Outdegree	Betwe
模型A
HSR	0.175^***	0.103^***	0.032^***
	(0.036)	(0.016)	(0.011)
R²	0.57	0.55	0.35
样本	1580	1580	1580
模型B
HSR	0.143^***	0.085^***	0.027^**
	(0.035)	(0.015)	(0.010)
控制变量	是	是	是
时间固定效应	是	是	是
城市固定效应	是	是	是
R²	0.59	0.59	0.37
样本数	1579	1579	1579

滞后一期				滞后二期
变量	Indegree	Outdegree	Betwe	变量	Indegree	Outdegree	Betwe
HSR(-1)	0.169^***	0.092^***	0.028	HSR(-2)	0.148^**	0.099^***	-0.004
	(0.058)	(0.024)	(0.019)		(0.069)	(0.033)	(0.019)
控制变量	是	是	是	控制变量	是	是	是
时间固定效应	是	是	是	时间固定效应	是	是	是
城市固定效应	是	是	是	城市固定效应	是	是	是
常数	8.682^***	5.111^***	1.670^***	常数	9.167^***	4.826^***	1.663^***
	(1.467)	(0.901)	(0.511)		(1.382)	(0.831)	(0.547)
R²	0.68	0.67	0.44	R²	0.69	0.69	0.44
样本数	875	875	875	样本数	770	770	770
滞后三期				滞后四期
变量	Indegree	Outdegree	Betwe	变量	Indegree	Outdegree	Betwe
HSR(-3)	0.331^***	0.151^***	0.03	HSR(-4)	0.333^***	0.187^***	0.064^**
	(0.079)	(0.04)	(0.024)		(0.100)	(0.050)	(0.029)
控制变量	是	是	是	控制变量	是	是	是
时间固定效应	是	是	是	时间固定效应	是	是	是
城市固定效应	是	是	是	城市固定效应	是	是	是
常数	9.380^***	4.367^***	1.256^**	常数	8.189^***	4.035^***	1.133^**
	(1.528)	(0.917)	(0.549)		(1.579)	(0.836)	(0.522)
R²	0.72	0.72	0.48	R²	0.68	0.73	0.5
样本数	703	703	703	样本数	632	632	632

变量	非中心城市			中心城市
变量	Indegree	Outdegree	Betwe	Indegree	Outdegree	Betwe
HSR	0.143^***	0.085^***	0.027^**	0.404^**	0.352^*	0.351^*
	(0.035)	(0.015)	(0.01)	(0.18)	(0.204)	(0.193)
控制变量	是	是	是	是	是	是
时间固定效应	是	是	是	是	是	是
城市固定效应	是	是	是	是	是	是
R²	0.59	0.59	0.37	0.70	0.78	0.60
样本数	1579	1579	1579	423	423	423

Impact of high-speed railway on Chinese urban venture capital network: Empirical study based on cross-city venture capital events

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 39

Related Articles 15

Metrics

Comments

Recommended 0

变量	Indegree	Outdegree	Betwe
HSR	0.971^***	0.837^***	1.025^***
	(0.159)	(0.155)	(0.197)
HSR*d50 km	-1.294^***	-0.918^***	-1.114^***
	(0.185)	(0.153)	(0.213)
HSR*d100 km	-0.934^***	-0.775^***	-1.263^***
	(0.224)	(0.19)	(0.259)
HSR*d150 km	-0.857^***	-0.854^***	-1.128^***
	(0.228)	(0.173)	(0.241)
HSR*d200 km	-1.175^***	-0.800^***	-1.152^***
	(0.191)	(0.167)	(0.219)
HSR*d250 km	-1.004^***	-0.764^***	-1.291^***
	(0.230)	(0.194)	(0.263)
HSR*d300 km	-1.212^***	-1.003^***	-1.252^***
	(0.191)	(0.161)	(0.230)
控制变量	是	是	是
时间固定效应	是	是	是
城市固定效应	是	是	是
常数	10.627^***	8.994^***	7.302^***
	(1.673)	(1.436)	(1.727)
R²	0.67	0.76	0.59
样本	1678	1678	1678

[1]	GUO Lijia, LI Chang, PENG Hongsong, ZHONG Shi'en, ZHANG Jinhe, YU Hu. Tourism eco-efficiency at the provincial level in China in the context of energy conservation and emission reduction [J]. PROGRESS IN GEOGRAPHY, 2021, 40(8): 1284-1297.
[2]	HAO Lisheng, HE Liye, CHENG Shanjun, LIANG Sujie. Climatic characteristics and monitoring analysis of rainy season in the Haihe River Basin [J]. PROGRESS IN GEOGRAPHY, 2021, 40(7): 1181-1194.
[3]	LI Quanfeng, LI Jicheng, YU Mingpeng, GUO Xinxin. Spatial pattern of gully erosion control urgency in black soil farming area [J]. PROGRESS IN GEOGRAPHY, 2021, 40(7): 1208-1219.
[4]	JIN Zerun, ZHU Shengjun. Technology-introduction pattern of cities in China and its mechanism of change based on technology relatedness and complexity [J]. PROGRESS IN GEOGRAPHY, 2021, 40(6): 897-910.
[5]	SONG Jinyan, LI Xiande, XU Ning. Spatial pattern and underlying factors of new telecommunication equipment ventures in China [J]. PROGRESS IN GEOGRAPHY, 2021, 40(6): 911-924.
[6]	LI Yuyang, GU Renxu, WANG Tengfei, BI Xuecheng. Spatial evolution and mechanism of knowledge gatekeepers in the Yangtze River Delta region [J]. PROGRESS IN GEOGRAPHY, 2021, 40(6): 925-936.
[7]	CHENG Xuelan, FANG Yelin, SU Xueqing, LI Jinglong. Spatial distribution characteristics of network structure of tourism flow in five major urban agglomerations of coastal China [J]. PROGRESS IN GEOGRAPHY, 2021, 40(6): 948-957.
[8]	ZHANG Rong, PAN Jinghu, LAI Jianbo. Characteristics of intercity trip network structure of residents under different traffic modes: A case study of Spring Festival travel rush [J]. PROGRESS IN GEOGRAPHY, 2021, 40(5): 759-773.
[9]	LAN Xue, LIU Chengliang, LUO Rongjing, RUAN Jiaqi, YE Qiuyu, WANG Jie. Spatiotemporal variations and types of the economic power zones of China and the United States [J]. PROGRESS IN GEOGRAPHY, 2021, 40(5): 825-838.
[10]	LIU Zhen, QI Wei, QI Honggang, LIU Shenghe. Spatial distribution of population decline areas in China and underlying causes from a multi-periodical perspective [J]. PROGRESS IN GEOGRAPHY, 2021, 40(3): 357-369.
[11]	ZHAO Lin, LIU Yanxu, CAO Naigang, WU Dianting, JIA Jianqi. Spatiotemporal pattern and spillover effects of inclusive green efficiency in China [J]. PROGRESS IN GEOGRAPHY, 2021, 40(3): 382-396.
[12]	LIU Tingting, WANG Mingfeng, ZHANG Yinghao, WANG Fan, WU Mingyu. Spatiotemporal pattern and determinants of Internet firm survival in China [J]. PROGRESS IN GEOGRAPHY, 2021, 40(3): 410-421.
[13]	CHEN Zhuo, LIANG Yi, JIN Fengjun. Simulation of city network accessibility and its influence on regional development pattern in China based on integrated land transport system [J]. PROGRESS IN GEOGRAPHY, 2021, 40(2): 183-193.
[14]	CHEN Lijin, OU Guoli, FAN Mengyu, SHI Boyuan. Structural characteristics and change of China’s county network from the perspective of high-speed rail flow [J]. PROGRESS IN GEOGRAPHY, 2021, 40(10): 1639-1649.
[15]	XIAO Jiayu, HE Chao, MU Hang, YANG Lu, HUANG Jiayi, XIN Aixuan, TU Peiyue, HONG Song. Spatiotemporal pattern and population exposure risks of air pollution in Chinese urban areas [J]. PROGRESS IN GEOGRAPHY, 2021, 40(10): 1650-1663.