中国新基建发展的时空演变及驱动因素

doi:10.18306/dlkxjz.2023.02.001

Abstract

Abstract:

New infrastructure has become an important support for China's economic transformation and high-quality development in the new era. In order to investigate the temporal and spatial evolution of China's new infrastructure development and its driving factors, this study first constructed a comprehensive evaluation index of the development level of new infrastructure based on the provincial panel data from 2013 to 2020. Then, it used the technique for order of preference by similarity to ideal solution (TOPSIS) entropy method and spatial econometric model to measure the development level of new infrastructure and analyze its driving factors. The results show that: 1) The development level of China's new infrastructure has increased year by year, and there is a trend of balanced development among provinces. 2) There is great spatial heterogeneity in the development level of China's new infrastructure. The new infrastructure development level is much higher in the eastern region than the central, western, and northeastern regions. The overall temporal and spatial evolution pattern showed an east-west expansion trend, and the center of gravity continued to move to the southwest. 3) The development level of new infrastructure is affected by many factors, among which the economic development level, financial development level, innovation foundation, urbanization process, and population size are the key driving factors. By examining the evolution of the spatial pattern of China's new infrastructure development level and its influencing factors, this article may provide a decision-making reference for its optimal layout and development.

Key words: new infrastructure, information infrastructure, convergence infrastructure, innovation infrastructure, development level, China

ZHANG Pei, WANG Jiao'e, XIAO Fan. Spatial evolution and determinants of new infrastructure development in China[J].PROGRESS IN GEOGRAPHY, 2023, 42(2): 209-220.

Figures/Tables 8

Tab.1

Tab.2

Fig.1

Fig.2

Fig.3

Tab.3

Tab.4

Tab.5

References 40

[1]	金凤君. 以“新基建”为牵引促进东北地区融合创新发展[J]. 学习与探索, 2021(1): 120-124.
	[Jing Fengjun. Promoting the integrated and innovative development of Northeast China under the guidance of "new infrastructure". Study & Exploration, 2021(1): 120-124.]
[2]	李晓华. 面向智慧社会的“新基建”及其政策取向[J]. 改革, 2020 (5): 34-48.
	[Li Xiaohua. New infrastructure construction and policy orientation for a smart society. Reform, 2020(5): 34-48.]
[3]	郭朝先, 王嘉琪, 刘浩荣. “新基建”赋能中国经济高质量发展的路径研究[J]. 北京工业大学学报(社会科学版), 2020, 20(6): 13-21.
	[Guo Chaoxian, Wang Jiaqi, Liu Hao-rong. Studies on how new infrastructure empowers high-quality development of China's economy. Journal of Beijing University of Technology (Social Sciences Edition), 2020, 20(6): 13-21.]
[4]	刘艳红, 黄雪涛, 石博涵. 中国“新基建”: 概念、现状与问题[J]. 北京工业大学学报(社会科学版), 2020, 20(6): 1-12.
	[Liu Yanhong, Huang Xuetao, Shi Bohan. China's new infrastructure construction: Concepts, current situations and problems. Journal of Beijing University of Technology (Social Sciences Edition), 2020, 20(6): 1-12.]
[5]	Huang J, Li W, Guo L, et al. Information and communications technology infrastructure and firm growth: An empirical study of China's cities[J]. Telecommunications Policy, 2021, 46(3): 102263. doi: 10.1016/j.telpol.2021.102263. doi: 10.1016/j.telpol.2021.102263
[6]	Qiao L, Li L, Fei J. Information infrastructure and air pollution: Empirical analysis based on data from Chinese cities[J]. Economic Analysis and Policy, 2022, 73: 563-573. doi: 10.1016/j.eap.2021.12.014
[7]	张建伟, 李贝歌, 毕东方, 等. 中国智慧城市发展水平空间差异研究[J]. 世界地理研究, 2017, 26(2): 82-90.
	[Zhang Jianwei, Li Beige, Bi Dongfang, et al. Study on spatial difference of smart city development level in China. World Regional Studies, 2017, 26(2): 82-90.]
[8]	刘鸿雁, 雷磊. 中国智慧产业发展水平综合评价与时空特征[J]. 经济地理, 2017, 37(2): 106-113.
	[Liu Hongyan, Lei Lei. Comprehensive evaluation on development level of regional smart industry in China. Economic Geography, 2017, 37(2): 106-113.]
[9]	Feldman M P, Richard F. The geographic sources of innovation: Technological infrastructure and product innovation in the United States[J]. Annals of the Association of American Geographers, 1994, 84(2): 210-229. doi: 10.1111/j.1467-8306.1994.tb01735.x
[10]	Furman J L, Porter M E, Scott S. The determinants of national innovative capacity[J]. Research Policy, 2002, 31(6): 899-933. doi: 10.1016/S0048-7333(01)00152-4
[11]	吕拉昌, 辛晓华, 陈东霞. 城市创新基础设施空间格局与创新产出: 基于中国290个地级及以上城市的实证分析[J]. 人文地理, 2021, 36(4): 104-113, 125.
	[Lv Lachang, Xin Xiaohua, Chen Dongxia. Urban innovation infrastructure and innovation output: An empirical analysis based on 290 cities at the prefecture level or above in China. Human Geography, 2021, 36(4): 104-113, 125.]
[12]	姚冠辉, 郑晓年. 面向“十四五”谋篇布局统筹推进创新基础设施建设[J]. 中国科学院院刊, 2020, 35(11): 1366-1372.
	[Yao Guanhui, Zheng Xiaonian. Structuring and organizing for 14th Five-year Plan, balancedly promoting the construction of innovation infrastructure. Bulletin of Chinese Academy of Sciences, 2020, 35(11): 1366-1372.]
[13]	伍先福, 黄骁, 钟鹏. 新型基础设施建设与战略性新兴产业耦合协调发展测度及其耦合机制[J]. 地理科学, 2021, 41(11): 1969-1979. doi: 10.13249/j.cnki.sgs.2021.11.010
	[Wu Xianfu, Huang Xiao, Zhong Peng. Measurement and coupling mechanism of the coupling and coordinated development of new infrastructure construction and strategic emerging industries. Scientia Geographica Sinica, 2021, 41(11): 1969-1979.] doi: 10.13249/j.cnki.sgs.2021.11.010
[14]	Wang S H. Fuzzy evaluations applied in analysis of infrastructure development based on national competitiveness perspectives[J]. Journal of Economic Policy Reform, 2014, 17(2): 178-200. doi: 10.1080/17487870.2013.802543
[15]	Chen Z H, Haynes K E. Multilevel assessment of public transportation infrastructure: A spatial econometric computable general equilibrium approach[J]. The Annals of Regional Science, 2015, 54(3): 663-685. doi: 10.1007/s00168-015-0671-3
[16]	陈银娥, 孙琼. 中国基础设施发展水平测算及影响因素: 基于省级面板数据的实证研究[J]. 经济地理, 2016, 36(8): 23-30.
	[Chen Yin'e, Sun Qiong. A study on calculation of development of China's infrastructure and its influencing factors: The emprical research based on provincial panel data. Economic Geography, 2016, 36(8): 23-30.]
[17]	黄金川, 黄武强, 张煜. 中国地级以上城市基础设施评价研究[J]. 经济地理, 2011, 31(1): 47-54.
	[Huang Jin-chuan, Huang Wuqiang, Zhang Yu. Study on infrastructure assessment of cities in China. Economic Geography, 2011, 31(1): 47-54.] doi: 10.2307/142410
[18]	肖凡, 任建造, 伍敏冬, 等. 21世纪以来中国高新技术企业的时空分布和影响机制[J]. 经济地理, 2018, 38(2): 27-35.
	[Xiao Fan, Ren Jianzao, Wu Mindong, et al. The spatiotemporal evolution and the influence mechanism of high-tech enterprise locations in China during the twenty-first century. Economic Geography, 2018, 38(2): 27-35.]
[19]	古恒宇, 肖凡, 沈体雁, 等. 中国城市流动人口居留意愿的地区差异与影响因素: 基于2015年流动人口动态监测数据[J]. 经济地理, 2018, 38(11): 22-29.
	[Gu Hengyu, Xiao Fan, Shen Tiyan, et al. Spatial difference and influencing factors of settlement intention of urban floating population in China: Evidence from the 2015 national migrant population dynamic monitoring survey. Economic Geography, 2018, 38(11): 22-29.]
[20]	赵璐, 赵作权. 基于特征椭圆的中国经济空间分异研究[J]. 地理科学, 2014, 34(8): 979-986. doi: 10.13249/j.cnki.sgs.2014.08.979
	[Zhao Lu, Zhao Zuoquan. Projecting the spatial variation of economic based on the specific ellipses in China. Scientia Geographica Sinica, 2014, 34(8): 979-986.] doi: 10.13249/j.cnki.sgs.2014.08.979
[21]	曹贤忠, 曾刚. 基于熵权TOPSIS法的经济技术开发区产业转型升级模式选择研究: 以芜湖市为例[J]. 经济地理, 2014, 34(4): 13-18.
	[Cao Xianzhong, Zeng Gang. The mode of transformation and upgrading based on the Methods of entropy weight and TOPSIS in case of Wuhu economic and technological development zone. Economic Geography, 2014, 34(4): 13-18.]
[22]	Holtz-Eakin D. Public-sector capital and the productivity puzzle[J]. The Review of Economics and Statistics, 1994, 76(1): 12-21. doi: 10.2307/2109822
[23]	Chen S T, Kuo H I, Chen C C. The relationship between GDP and electricity consumption in 10 Asian countries[J]. Energy Policy, 2007, 35(4): 2611-2621. doi: 10.1016/j.enpol.2006.10.001
[24]	王鹏飞, 李红波. 基于产业结构关联视角的区域经济韧性作用机理研究: 以江苏省为例[J]. 地理科学进展, 2022, 41(2): 224-238. doi: 10.18306/dlkxjz.2022.02.004
	[Wang Pengfei, Li Hongbo. Mechanism of regional economic resilience from the perspective of industrial structure relevance: Taking Jiangsu Province as an example. Progress in Geography, 2022, 41(2): 224-238.] doi: 10.18306/dlkxjz.2022.02.004
[25]	王波, 甄峰, 谢金燕, 等. 智慧社会下的远程通勤: 基于全天和非全天在家办公选择及影响因素的分析[J]. 地理科学, 2021, 41(5): 788-796. doi: 10.13249/j.cnki.sgs.2021.05.006
	[Wang Bo, Zhen Feng, Xie Jinyan, et al. Telecommuting in the e-society: An empirical study of full-day and part-day home-based working adoption and influencing factors. Scientia Geographica Sinica, 2021, 41(5): 788-796.] doi: 10.13249/j.cnki.sgs.2021.05.006
[26]	尹向来, 黄彩虹. 基础设施与城市化水平耦合协调关系演变: 基于285个地级市的实证研究[J]. 世界地理研究, 2018, 27(6): 77-87, 97.
	[Yin Xianglai, Huang Caihong. Research on the evolution of coupling and coordinating relationship between infrastructure and urbannization: Empirical analysis of China's 285 prefecture-level city. World Regional Studies, 2018, 27(6): 77-87, 97.]
[27]	高喆, 顾朝林, 顾江. “新型城镇化”与“乡村振兴”场景下新基建对产业转型的启示[J]. 经济地理, 2021, 41(4): 8-14.
	[Gao Zhe, Gu Chaolin, Gu Jiang. The enlightenment of new infrastructure to industrial transformation under the background of new urbanization and rural revitalization. Economic Geography, 2021, 41(4): 8-14.]
[28]	曹琳剑, 王杰, 王欢欢, 等. 京津冀基础设施建设与人口集聚耦合演进分析: 基于推拉理论解析[J]. 地理科学, 2019, 39(8): 1256-1264. doi: 10.13249/j.cnki.sgs.2019.08.007
	[Cao Linjian, Wang Jie, Wang Huanhuan, et al. Evolutional analysis of coupling between infrastructure and population agglomeration in Beijing-Tianjin-Hebei region: An analysis based on Push-pull theory. Scientia Geographica Sinica, 2019, 39(8): 1256-1264.] doi: 10.13249/j.cnki.sgs.2019.08.007
[29]	袁航, 朱承亮. 国家高新区推动了中国产业结构转型升级吗[J]. 中国工业经济, 2018(8): 60-77.
	[Yuan Hang, Zhu Chengliang. Do national high-tech zones promote the transformation and upgrading of China's industrial structure. China Industrial Economics, 2018(8): 60-77.]
[30]	沈体雁, 于瀚辰. 空间计量经济学[M]. 2版. 北京: 北京大学出版社, 2019.
	[Shen Tiyan, Yu Hanchen. Spatial econometrics. 2nd ed. Beijing, China: Peking University Press, 2019.]
[31]	干春晖, 郑若谷, 余典范. 中国产业结构变迁对经济增长和波动的影响[J]. 经济研究, 2011, 46(5): 4-16, 31.
	[Gan Chunhui, Zheng Ruogu, Yu Dianfan. An empirical study on the effects of industrial structure on economic growth and fluctuations in China. Economic Research Journal, 2011, 46(5): 4-16, 31.]
[32]	宓泽锋, 曾刚. 本地知识基础对新兴产业知识流动的影响: 以中国燃料电池产业为例[J]. 地理学报, 2021, 76(4): 1006-1018. doi: 10.11821/dlxb202104015
	[Mi Zefeng, Zeng Gang. The impact of local knowledge base on knowledge low of emerging industry: A case of China's fuel cell industry. Acta Geographica Sinica, 2021, 76(4): 1006-1018.] doi: 10.11821/dlxb202104015
[33]	王腾飞, 谷人旭, 马仁锋, 等. “集聚—扩散”视角下中国区域创新极及其知识溢出区位[J]. 经济地理, 2021, 41(5): 11-18, 185.
	[Wang Tengfei, Gu Renxu, Ma Renfeng, et al. Regional innovation pole and its location of knowledge spillovers in China from the perspective of "agglomeration diffusion". Economic Geography, 2021, 41(5): 11-18, 185.]
[34]	丁任重, 许渤胤, 张航. 城市群能带动区域经济增长吗? 基于7个国家级城市群的实证分析[J]. 经济地理, 2021, 41(5): 37-45.
	[Ding Renzhong, Xu Boyin, Zhang Hang. Can urban agglomeration drive regional economic growth? Empirical analysis based on seven state-level urban agglomerations. Economic Geography, 2021, 41(5): 37-45.]
[35]	LeSage J, Pace R K. Introduction to spatial econometrics[M]. New York, USA: CRC Press, 2009.
[36]	潘为华, 贺正楚, 潘红玉. 中国数字经济发展的时空演化和分布动态[J]. 中国软科学, 2021(10): 137-147.
	[Pan Weihua, He Zhengchu, Pan Hongyu. Research on spatiotemporal evolution and distribution dynamics of digital economy development in China. China Soft Science, 2021(10): 137-147.]
[37]	张佩, 赵作权. 世界级先进制造业集群竞争力提升机制及启示: 以德国工业4.0旗舰集群为例[J]. 区域经济评论, 2020(5): 131-139.
	[Zhang Pei, Zhao Zuoquan. Competitiveness improvement mechanisms of world-class advanced manufacturing clusters and their implications: Taken the German industry 4.0 flagship cluster as an example. Regional Economic Review, 2020(5): 131-139.]
[38]	刘秉镰, 朱俊丰. 区域市场分割的影响因素及其空间邻近效应分析: 基于1989—2014年中国省际面板数据[J]. 经济地理, 2018, 38(10): 36-45.
	[Liu Binglian, Zhu Junfeng. The analysis on the influencing factors of regional market segmentation and its spatial neighbor effects: Based on the panel data of China's provinces in 1989-2014. Economic Geography, 2018, 38(10): 36-45.]
[39]	焦敬娟, 王姣娥, 程珂. 中国区域创新能力空间演化及其空间溢出效应[J]. 经济地理, 2017, 37(9): 11-18.
	[Jiao Jingjuan, Wang Jiao'e, Cheng Ke. Spatial-temporal evolution and spillover effects of regional innovation ability in China. Economic Geography, 2017, 37(9): 11-18.]
[40]	郭杰, 王珺, 姜璐, 等. 从技术中心主义到人本主义: 智慧城市研究进展与展望[J]. 地理科学进展, 2022, 41(3): 488-498. doi: 10.18306/dlkxjz.2022.03.011
	[Guo Jie, Wang Jun, Jiang Lu, et al. From technocentrism to humanism: Progress and prospects of smart city research. Progress in Geography, 2022, 41(3): 488-498.] doi: 10.18306/dlkxjz.2022.03.011

一级指标	二级指标	三级指标	四级指标
信息基础设施(N₁)	通信网络基础设施(N₁₁)	5G(N₁₁₁) 物联网(N₁₁₂) 工业互联网(N₁₁₃) 卫星互联网(N₁₁₄)	移动电话基站数量(N₁₁₁₁) 物联网相关企业数量(N₁₁₂₁) 工业互联网相关企业数量(N₁₁₃₁) 卫星互联网相关企业数量(N₁₁₄₁)
	新技术基础设施(N₁₂)	人工智能(N₁₂₁) 云计算(N₁₂₂) 区块链(N₁₂₃)	人工智能相关企业数量(N₁₂₁₁) 云计算相关企业数量(N₁₂₂₁) 区块链相关企业数量(N₁₂₃₁)
	算力基础设施(N₁₃)	数据中心(N₁₃₁) 智能计算中心(N₁₃₂)	数据中心相关企业数量(N₁₃₁₁) 国家超级计算中心数量(N₁₃₂₁)
融合基础设施(N₂)	智慧交通基础设施(N₂₁)	城际高速铁路与城际轨道交通(N₂₁₁) 智慧交通(N₂₁₂)	轨道交通里程(N₂₁₁₁) 智慧交通相关企业数量(N₂₁₂₁)
	智慧能源基础设施(N₂₂)	特高压(N₂₂₁) 充电桩(N₂₂₂) 能源生产和利用设施智能化(N₂₂₃)	特高压相关企业数量(N₂₂₁₁) 充电桩相关企业数量(N₂₂₂₁) 智慧能源相关企业数量(N₂₂₃₁₎
	防疫基建(N₂₃)	医疗IT(N₂₃₁) 智慧城市(N₂₃₂)	智慧医疗相关企业数量(N₂₃₁₁) 智慧生活相关企业数量(N₂₃₂₁)
创新基础设施(N₃)	重大科技基础设施(N₃₁)	重大科技基础设施(N₃₁₁)	重大科技基础设施数量(N₃₁₁₁)
	科教基础设施(N₃₂)	学科研究平台(N₃₂₁)	国家重点实验室数量(N₃₂₁₁)
	产业技术创新基础设施(N₃₃)	产业技术创新基础设施(N₃₃₁)	高新技术特色产业基地数量(N₃₃₁₁)

变量	符号	名称	测算方法	单位
因变量	New	新基建发展水平	综合测算指标	—
自变量	PGDP	经济发展水平	人均GDP	万元
	AIS1	产业结构高度化	第三产业增加值/第二产业增加值	—
	AIS2	产业结构合理化	借鉴袁航等^[29]采用Theil测算	—
	FD	金融发展水平	年末全部金融机构各项贷款余额/GDP	—
	INNO	创新基础	研发人员全时当量	万人
	UP	城镇化进程	城镇人口比重	%
	POP	人口规模	总人口	亿人

检验方法	结果
LM_spatial_error	3.137^*
LM_spatial_lag	5.417^**
Hausman	86.49^***
LR_sar	31.55^***
LR_sem	18.04^**
Wald_spatial_error	17.65^**
Wald_spatial_lag	17.13^**

变量	时间固定效应(M1)	个体固定效应(M2)	混合固定效应(M3)
PGDP	0.036^*** (<0.01)	0.102^*** (<0.01)	0.071^*** (<0.01)
AIS1	0.017^*** (<0.01)	0.020^* (0.07)	0.027^*** (0.01)
AIS2	0.145^*** (<0.01)	0.044 (0.18)	0.006 (0.83)
FD	0.035^*** (<0.01)	-0.002 (0.83)	-0.016^** (0.02)
INNO	0.004^*** (<0.01)	0.006^*** (<0.01)	0.006^*** (<0.01)
UP	0.194^*** (<0.01)	-0.540^*** (<0.01)	-0.671^*** (<0.01)
POP	0.077^*** (<0.01)	0.952^*** (<0.01)	0.964^*** (<0.01)
W*PGDP	0.048^** (0.04)	0.022 (0.49)	0.003 (0.93)
W*AIS1	-0.005 (0.59)	-0.036^** (0.05)	0.010 (0.60)
W*AIS2	0.043 (0.33)	0.140^* (0.05)	0.031 (0.64)
W*FD	0.026^** (0.04)	-0.002 (0.90)	-0.056^*** (<0.01)
W*INNO	0.003^*** (<0.01)	0.003^** (0.03)	0.004^*** (<0.01)
W*UP	-0.165^*** (<0.01)	0.988^*** (<0.01)	0.366^*** (0.01)
W*POP	-0.066^*** (<0.01)	-0.031 (0.91)	0.350 (0.16)
ρ	-0.323^*** (<0.01)	-0.012 (0.89)	-0.431^*** (<0.01)
sigma2_e	0.001^*** (<0.01)	<0.001^*** (<0.01)	<0.001^*** (<0.01)
样本数	248	248	248
R²	0.815	0.237	0.192
地区数	31	31	31

变量	直接效应	间接效应	总效应
PGDP	0.035^**	0.028	0.063^***
PGDP	(0.01)	(0.13)	(<0.01)
AIS1	0.017^***	-0.008	0.009
AIS1	(<0.01)	(0.33)	(0.22)
AIS2	0.149^***	-0.002	0.147^***
AIS2	(<0.01)	(0.96)	(<0.01)
FD	0.034^***	0.011	0.045^***
FD	(<0.01)	(0.26)	(<0.01)
INNO	0.004^***	0.002^***	0.006^***
INNO	(<0.01)	(<0.01)	(<0.01)
UP	0.213^***	-0.184^***	0.028
UP	(<0.01)	(<0.01)	(0.36)
POP	0.084^***	-0.074^***	0.010
POP	(<0.01)	(<0.01)	(0.57)

Spatial evolution and determinants of new infrastructure development in China

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 40

Related Articles 15

Metrics

Comments

Recommended 0

[1]	PENG Baoyu, ZHANG Bo, SUN Wei. Evolution and prospect of financial inclusion research in China from the perspective of financial geography [J]. PROGRESS IN GEOGRAPHY, 2023, 42(2): 406-416.
[2]	GONG Qinlin, ZOU Donghan, ZHOU Yi, ZHU Shengjun. Influence of high-level scenic spots on tourism development and its spatial effect [J]. PROGRESS IN GEOGRAPHY, 2022, 41(8): 1364-1377.
[3]	SUN Pingjun, ZHANG Keqiu, HE Tian. Shrinkage effect of urban-rural integration on shrinking cities in the three provinces of Northeast China and mechanism [J]. PROGRESS IN GEOGRAPHY, 2022, 41(7): 1213-1225.
[4]	LI Zhuowei, WANG Shijun, CHENG Lisha, SHI Xiang, GUAN Haoming, SHU Chang. Differences and relationship between population flow and transportation networks in Northeast China [J]. PROGRESS IN GEOGRAPHY, 2022, 41(6): 985-998.
[5]	CHEN Man, HUANG Baishi, LIU Ye. Effects of PM_2.5 concentration on mortality in China: A study based on city-level panel data [J]. PROGRESS IN GEOGRAPHY, 2022, 41(6): 1028-1040.
[6]	CHEN Haixi. Progress and review of research on China-Europe Railway Express in China from the geographical perspective [J]. PROGRESS IN GEOGRAPHY, 2022, 41(6): 1136-1144.
[7]	LI Bo, CAO Gai. Structural resilience change of China’s marine economic network based on the data of sea-related A-share listed companies [J]. PROGRESS IN GEOGRAPHY, 2022, 41(6): 945-955.
[8]	JIANG Sheng, HE Canfei. Effects of related variety and unrelated variety on China’s export market diversification [J]. PROGRESS IN GEOGRAPHY, 2022, 41(5): 770-784.
[9]	LIU Fengshan, GE Quansheng, TAO Fulu, CAI Yangxing, BU Jianchao, BAI Nini. Impacts of maize planting date and variety on canopy temperature on the North China Plain and surface biophysical mechanism [J]. PROGRESS IN GEOGRAPHY, 2022, 41(4): 682-692.
[10]	LI Fengjiao, LIU Jiaming, JIANG Lili. Spatiotemporal change of strategic emerging industries in Northeast China and influencing factors [J]. PROGRESS IN GEOGRAPHY, 2022, 41(4): 541-553.
[11]	SUN Bindong, WANG Yanyan, ZHANG Zhiqiang, LI Wan. The form and evolution of city size distribution and urban growth model in China: An analysis based on Zipf’s Law and Gibrat’s Law [J]. PROGRESS IN GEOGRAPHY, 2022, 41(3): 361-370.
[12]	XU Yan, ZHANG Qingyuan, LU Lin. Tidal tourism flow study: Conceptual framework and research ideas [J]. PROGRESS IN GEOGRAPHY, 2022, 41(3): 521-530.
[13]	LIU Jixiang, XIAO Longzhu, ZHOU Jiangping, GUO Yuanyuan, YANG Linchuan. Non-linear relationships between the built environment and walking to school: Applying extreme gradient boosting method [J]. PROGRESS IN GEOGRAPHY, 2022, 41(2): 251-263.
[14]	WANG Fei, CHEN Baozhang, CHEN Jing, ZHANG Huifang, GUO Lifeng. Comparative study of DLM and CLM5 model simulations at winter wheat-summer maize rotation stations in the North China Plain [J]. PROGRESS IN GEOGRAPHY, 2022, 41(2): 289-303.
[15]	LIU Le, SHENG Kerong, ZHANG Jie, WANG Chuanyang. The impact of financial network embedding on entrepreneurial vitality of cities in China [J]. PROGRESS IN GEOGRAPHY, 2022, 41(12): 2244-2257.