Adv Search
Volume 30 Issue 5
Dec.  2020
Turn off MathJax
Article Contents
Article Navigation >  Chinese Geographical Science  > 2020  >  30(5): 839-854

HUANG Qinshi, ZHU Xigang, LIU Chunhui, WU Wei, LIU Fengbao, ZHANG Xinyi. Spatial-temporal Evolution and Determinants of the Belt and Road Ini-tiative: A Maximum Entropy Gravity Model Approach[J]. Chinese Geographical Science, 2020, 30(5): 839-854. doi: 10.1007/s11769-020-1144-x
Citation: HUANG Qinshi, ZHU Xigang, LIU Chunhui, WU Wei, LIU Fengbao, ZHANG Xinyi. Spatial-temporal Evolution and Determinants of the Belt and Road Ini-tiative: A Maximum Entropy Gravity Model Approach[J]. Chinese Geographical Science, 2020, 30(5): 839-854. doi: 10.1007/s11769-020-1144-x
shu

Spatial-temporal Evolution and Determinants of the Belt and Road Ini-tiative: A Maximum Entropy Gravity Model Approach

doi: 10.1007/s11769-020-1144-x

  • 1. School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China;

  • 2. School of Civil Engineering and Archi-tecture, Zhejiang University of Science and Technology, Hangzhou 310023, China;

  • 3. College of Humanities and Social Development, Nanjing Agricultural University, Nanjing 210095, China;

  • 4. Institute of Regional Agricultural Research, Nanjing Agricultural University, Nanjing 210095, China;

  • 5. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

Funds:

Under the auspices of A Category of Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA20010101)

  • Received Date: 2019-07-17
  • Rev Recd Date: 2019-12-25
  • The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative (BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis of equilibrium points without any reference to when or how to achieve it. In this paper, a dynamic gravity model was established based on the Maximum Entropy (MaxEnt) theory to estimate and monitor the interconnection intensity and dynamic characters of bilateral relations. In order to detect the determinants of interconnection intensity, a Geodetector method was applied to identify and evaluate the determinants of spatial networks in five dimensions. The empirical study clearly demonstrates a heterogeneous and non-circular spatial structure. The main driving forces of spatial-temporal evolution are foreign direct investment, tourism and railway infrastructure construction, while determinants in different sub-regions show obvious spatial differentiation. Southeast Asian countries are typically multi-island area where aviation infrastructure plays a more important role. North and Central Asian countries regard oil as a pillar industry where power and port facilities have a greater impact on the interconnection. While Western Asian countries are mostly influenced by the railway infrastructure, Eastern European countries already have relatively robust infrastructure where tariff policies provide a greater impetus.
  • [1] Alamá-Sabater L, Márquez-Ramos L, Navarro-Azorín J M et al., 2015. A two-methodology comparison study of a spatial gravity model in the context of interregional trade flows. Applied Economics, 47(14):1481-1493. doi: 10.1080/00036846.2014.997929
    [2] Baltagi B H, Egger P, 2016. Estimation of structural gravity quantile regression models. Empirical Economics, 50(1):5-15. doi: 10.1007/s00181-015-0956-5
    [3] Black W, 1983. A generalization of destination effects in spatial interaction modeling. Economic Geography, 59(1):16-34. doi: 10.2307/143759
    [4] Black W R, 1995. Spatial interaction modeling using artificial neural networks. Journal of Transport Geography, 3(3):159-166. doi: 10.1016/0966-6923(95)00013-S
    [5] Casetti E, 1981. A catastrophe model of regional dynamics. Annals of the Association of American Geographers, 71(4):572-579. doi: 10.1111/j.1467-8306.1981.tb01376.x
    [6] Chun Y W, Griffith D A, 2011. Modeling network autocorrelation in space-time migration flow data:an eigenvector spatial filtering approach. Annals of the Association of American Ge-ographers, 101(3):523-536. doi: 10.1080/00045608.2011.561070
    [7] Chung C P, 2018. What are the strategic and economic implications for South Asia of China's Maritime Silk Road initiative?. The Pacific Review, 31(3):315-332. doi: 10.1080/09512748.2017.1375000
    [8] Clarke G, Langley R, Cardwell W, 1998. Empirical applications of dynamic spatial interaction models. Computers, Environment and Urban Systems, 22(2):157-184. doi: 10.1016/s0198-9715(98)00021-0
    [9] Clinch J P, O'Neill E, 2009. Applying spatial economics to national spatial planning. Regional Studies, 43(2):157-178. doi: 10.1080/00343400701808873
    [10] Cuccia T, Guccio C, Rizzo I, 2017. UNESCO sites and perfor-mance trend of Italian regional tourism destinations:a two-stage DEA window analysis with spatial interaction. Tourism Economics, 23(2):316-342. doi: 10.1177/1354816616656266
    [11] Curran D, 2012. British regional growth and sectoral trends:global and local spatial econometric approaches. Applied Economics, 44(17):2187-2201. doi: 10.1080/00036846.2011.562170
    [12] Curry L, 1972. A spatial analysis of gravity flows. Regional Studies, 6(2):131-147. doi: 10.1080/09595237200185141
    [13] Dąbrowski M, Musiałkowska I, Polverari L, 2018. EU-China and EU-Brazil policy transfer in regional policy. Regional Studies, 52(9):1169-1180. doi: 10.1080/00343404.2018.1431389
    [14] Dong S C, Li Z H, Li Y et al., 2015. Resources, environment and economic patterns and sustainable development modes of the silk road economic belt. Journal of Resources and Ecology, 6(2):65-72. doi:10.5814/j.issn.1674-764x. 2015.02.001
    [15] Fan Zhaobin, Zhang Ruohan, Liu Xiaotong et al., 2016. China's outward FDI efficiency along the belt and road:an application of stochastic frontier gravity model. China Agricultural Eco-nomic Review, 8(3):455-479. doi: 10.1108/caer-11-2015-0158
    [16] Fischer M M, Reismunn M, 2002. A methodology for neural spatial interaction modeling. Geographical Analysis, 34(3):207-228. doi: 10.1111/j.1538-4632.2002.tb01085.x
    [17] Fischer M M, Reggiani A, 2005. Spatial interaction models:from the gravity to the neural network approach. Urban Dynamics and Growth:Advances in Urban Economics, 266:319-346. doi: 10.1108/s0573-8555(2005)0000266012
    [18] Fotheringham A S, 1983. A new set of spatial-interaction models:the theory of competing destinations. Environment and Plan-ning A:Economy and Space, 15(1):15-36. doi: 10.1177/0308518x8301500103
    [19] Frachetti M D, Smith C E, Traub C M et al., 2017. Nomadic ecology shaped the highland geography of Asia's Silk Roads. Nature, 543(7644):193-198. doi: 10.1038/nature21696
    [20] Godement F, 2015. Europe scrambles to benefit from China's 21st-Century Silk Road. Global Asia, 10(3):34-38.
    [21] Gordon I, 2010. Entropy, variety, economics, and spatial interac-tion. Geographical Analysis, 42(4):446-471. doi: 10.1111/j.1538-4632.2010.00802.x
    [22] Griffith D A, 1982. Dynamic characteristics of spatial economic systems. Economic Geography, 58(2):177-196. doi: 10.2307/143795
    [23] Gui Q C, Liu C L, Du D B, 2019. The structure and dynamic of scientific collaboration network among countries along the Belt and Road. Sustainability, 11(19):5187. doi: 10.3390/su11195187
    [24] Hafeez M, Yuan C H, Strohmaier D et al., 2018. Does finance affect environmental degradation:evidence from One Belt and One Road Initiative region?. Environmental Science and Pol-lution Research, 25(10):9579-9592. doi: 10.1007/s11356-018-1317-7
    [25] Hermes T R, Frachetti M D, Bullion E A et al., 2018. Urban and nomadic isotopic niches reveal dietary connectivities along Central Asia's Silk Roads. Scientific Reports, 8(1):5177. doi: 10.1038/s41598-018-22995-2
    [26] Hong P F, 2016. Jointly building the ‘Belt and Road’ towards the sustainable development goals. New York:Social Science Electronic Publishing.
    [27] Hu G H, Lau C K M, Lu Z et al., 2018. Why participate in the ‘One Belt and One Road’ Initiative? An income convergence approach. The Singapore Economic Review. doi: 10.1142/S0217590818500297
    [28] Huang Yiping, 2016. Understanding China's Belt & Road Initia-tive:motivation, framework and assessment. China Economic Review, 40:314-321. doi: 10.1016/j.chieco.2016.07.007
    [29] Jing F, 2005. UNESCO's efforts in identifying the World Heritage significance of the Silk Road. In:15th ICOMOS General Assembly and International Symposium:Monuments and Sites in Their Setting-Conserving Cultural Heritage in Changing Townscapes and Landscapes. Xi'an, China.
    [30] Kolosov V A, Dong S C, Portyakov V Y et al., 2017. The Chinese initiative ‘the belt and road’:a geographical perspective. Geography, Environment, Sustanability, 10(1):5-20. doi: 10.24057/2071-9388-2017-10-1-5-20
    [31] LeSage J P, Pace R K, 2008. Spatial econometric modeling of origin-destination flows. Journal of Regional Science, 48(5):941-967. doi: 10.1111/j.1467-9787.2008.00573.x
    [32] Li F, Liu Q, Dong S et al., 2018. Agricultural development status and key cooperation directions between China and countries along ‘the Belt and Road’. IOP Conference Series:Earth and Environmental Science, 190:012058. doi: 10.1088/1755-1315/190/1/012058
    [33] Li Fujia, Liu Qian, Dong Suocheng et al., 2019a. Investment en-vironment assessment and strategic policy for subjects of fed-eration in Russia. Chinese Geographical Science, 29(5):887-904. doi: 10.1007/s11769-019-1051-1
    [34] Li Linyue, Sun Zhixian, Long Xiang, 2019b. An empirical analysis of night-time light data based on the gravity model. Applied Economics, 51(8):797-814. doi: 10.1080/00036846.2018.1523612
    [35] Li Shan, Wang Zheng, Zhong Zhangqi, 2012. Gravity model for tourism spatial interaction:basic form, parameter estimation, and applications. Acta Geographica Sinica, 67(4):526-544. (in Chinese)
    [36] Liao H, Huang X M, Vidmer A et al., 2018. Economic complexity based recommendation enhance the efficiency of the belt and road initiative. Entropy, 20(9):718. doi: 10.3390/e20090718
    [37] Liu C L, Wang J Q, Zhang H, 2018a. Spatial heterogeneity of ports in the global maritime network detected by weighted ego network analysis. Maritime Policy and Management, 45(1):89-104. doi: 10.1080/03088839.2017.1345019.
    [38] Liu C L, Wang J Q, Zhang H et al., 2018b. Mapping the hierar-chical structure of the global shipping network by weighted ego network analysis. International Journal of Shipping and Transport Logistics, 10(1):6386. doi: 10.1504/IJSTL.2018.10008535.
    [39] Liu C L, Xu J Q, Zhang H, 2019. Competitiveness or comple-mentarity? A dynamic network analysis of international agri-trade along the Belt and Road. Applied Spatial Analysis and Policy, 1-26. doi: 10.1007/s12061-019-093075
    [40] Liu Haimeng, Fang Chuanglin, Miao Yi et al., 2018c. Spa-tio-temporal evolution of population and urbanization in the countries along the Belt and Road 1950-2050. Journal of Ge-ographical Sciences, 28(7):919-936. doi: 10.1007/s11442-018-1513-x
    [41] Liu Weidong, 2015. Scientific understanding of the Belt and Road Initiative of China and related research themes. Progress in Geography, 34(5):538-544. (in Chinese)
    [42] Matyas L, 1997. Proper econometric specification of the gravity model. World Economy, 20(3):363-368. doi: 10.1111/1467-9701.00074
    [43] Miller H J, Wentz E A, 2003. Representation and spatial analysis in geographic information systems. Annals of the Association of American Geographers, 93(3):574-594. doi: 10.1111/1467-8306.9303004
    [44] Murphy A B, O'Loughlin J, 2009. New horizons for regional geography. Eurasian Geography and Economics, 50(3):241-251. doi: 10.2747/1539-7216.50.3.241
    [45] Nijkamp P, Reggiani A, 1988. Dynamic spatial interaction models:new directions. Environment and Planning A:Economy and Space, 20(11):1449-1460. doi: 10.1068/a201449
    [46] O'Kelly M E, 2010. Entropy-based spatial interaction models for trip distribution. Geographical Analysis, 42(4):472-487. doi: 10.1111/j.1538-4632.2010.00803.x
    [47] Ord J K, Getis A, 1995. Local spatial autocorrelation statistics:distributional issues and an application. Geographical Analysis, 27(4):286-306. doi: 10.1111/j.1538-4632.1995.tb00912.x
    [48] Parr J B, 2014. The regional economy, spatial structure and re-gional urban systems. Regional Studies, 48(12):1926-1938. doi: 10.1080/00343404.2013.799759
    [49] Pavlićević D, Kratz A, 2018. Testing the China Threat paradigm:China's high-speed railway diplomacy in Southeast Asia. The Pacific Review, 31(2):151-168. doi: 10.1080/09512748.2017.1341427
    [50] Piovani D, Molinero C, Wilson A, 2017. Urban retail location:insights from percolation theory and spatial interaction mod-eling. PLoS One, 12(10):e185787. doi:10.1371/journal.pone. 0185787
    [51] Pooler J, 1994. An extended family of spatial interaction models. Progress in Human Geography, 18(1):17-39. doi: 10.1177/030913259401800102
    [52] Ramasamy B, Yeung M, Utoktham C et al., 2017. Trade and Trade Facilitation along the Belt and Road Initiative Corridors. ARTNeT Working Paper Series, No. 172. Bangkok.
    [53] Ravenstein E G, 1885. The laws of migration. Journal of the Sta-tistical Society of London, 48(2):167-235. doi: 10.2307/2979181
    [54] Rodemann H, Templar S, 2014. The enablers and inhibitors of intermodal rail freight between Asia and Europe. Journal of Rail Transport Planning & Management, 4(3):70-86. doi: 10.1016/j.jrtpm.2014.10.001
    [55] Roy J R, Thill J C, 2003. Spatial interaction modelling. Papers in Regional Science, 83(1):339-361. doi: 10.1007/s10110-003-0189-4
    [56] Sayer R A, 1976. A critique of urban modelling:from regional science to urban and regional political economy. Progress in Planning, 6:187-254. doi:10.1016/0305-9006(76) 90006-4
    [57] Shen S, Chan W, 2018. A comparative study of the Belt and Road Initiative and the Marshall plan. Palgrave Communications, 4(1):32. doi: 10.1057/s41599-018-0077-9
    [58] Sheu J B, Kundu T, 2018. Forecasting time-varying logistics distribution flows in the One Belt-One Road strategic context. Transportation Research Part E:Logistics and Transportation Review, 117:5-22. doi: 10.1016/j.tre.2017.03.003
    [59] Shi K F, Yu B L, Huang C et al., 2018. Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road. Energy, 150:847-859. doi:10.1016/j.energy. 2018.03.020
    [60] Smith T R, Slater P B, 1981. A family of spatial interaction models incorporating information flows and choice set constraints applied to U.S. interstate labor flows. International Regional Science Review, 6(1):15-31. doi:10.1177/0160017681 00600102
    [61] Summers T, 2016. China's ‘New Silk Roads’:sub-national re-gions and networks of global political economy. Third World Quarterly, 37(9):1628-1643. doi: 10.1080/01436597.2016.1153415
    [62] Tekdal V, 2018. China's Belt and Road Initiative:at the crossroads of challenges and ambitions. The Pacific Review, 31(3):373-390. doi: 10.1080/09512748.2017.1391864
    [63] Timmermans H J P, 1981. Multiattribute shopping models and ridge regression analysis. Environment and Planning A:Economy and Space, 13(1):43-56. doi: 10.1068/a130043
    [64] Tong D Q, Murray A T, 2012. Spatial optimization in geography. Annals of the Association of American Geographers, 102(6):1290-1309. doi: 10.1080/00045608.2012.685044
    [65] Tracy E F, Shvarts E, Simonov E et al., 2017. China's new Eura-sian ambitions:the environmental risks of the Silk Road Eco-nomic Belt. Eurasian Geography and Economics, 58(1):56-88. doi: 10.1080/15387216.2017.1295876
    [66] Wang G G, 2017. The Belt and Road Initiative in quest for a dis-pute resolution mechanism. Asia Pacific Law Review, 25(1):1-16. doi: 10.1080/10192557.2017.1321731
    [67] Wang Jinfeng, Xu Chengdong, 2017. Geodetector:principle and prospective. Acta Geographica Sinica, 72(1):116-134. (in Chinese)
    [68] Wang Y, 2016. Offensive for defensive:the belt and road initiative and China's new grand strategy. The Pacific Review, 29(3):455-463. doi: 10.1080/09512748.2016.1154690
    [69] Wang Zhe, Dong Suocheng, Li Zehong et al., 2015. Traffic pat-terns in the silk road economic belt and construction modes for a traffic economic belt across continental plates. Journal of Resources and Ecology, 6(2):79-86. doi: 10.5814/j.issn.1674-764x.2015.02.003
    [70] Wilson A G, 1971. A family of spatial interaction models, and associated developments. Environment and Planning A:Economy and Space, 3(1):1-32. doi: 10.1068/a030001
    [71] Wilson A G, 1975. Some new forms of spatial interaction model:a review. Transportation Research, 9(2-3):167-179. doi: 10.1016/0041-1647(75)90054-4
    [72] Yu Huilu, Dong Suocheng, Li Zehong et al., 2015. Evolution of regional geopolitical pattern and its impact on the regional re-sources cooperation in northeast Asia. Journal of Resources and Ecology, 6(2):93-100. doi:10.5814/j.issn.1674-764x. 2015.02.005
    [73] Zhai Kun, Wang Lina, Liu Xiaowei et al., 2017. A comparative study on the five-connectivity index between China and ASEAN. China-ASEAN Studies, 1:21-43. (in Chinese)
    [74] Zipf G K, 1946. The p1 P2/D hypothesis:on the intercity move-ment of persons. American Sociological Review, 11(6):677-686. doi: 10.2307/2087063
    [75] Zobler L, 1958. Decision making in regional construction. Annals of the Association of American Geographers, 48(2):140-148. doi: 10.1111/j.1467-8306.1958.tb01566.x
    [76] Zou Jialing, Liu Weidong, 2016. Trade network of China and countries along ‘Belt and Road Initiative’ areas from 2001 to 2013. Scientia Geographica Sinica, 36(11):1629-1636. (in Chinese)
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Get Citation
PDF
XML

Article Metrics

Article views(271) PDF downloads(41) Cited by()

Proportional views
Related

Spatial-temporal Evolution and Determinants of the Belt and Road Ini-tiative: A Maximum Entropy Gravity Model Approach

doi: 10.1007/s11769-020-1144-x
  • 1. School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China;
  • 2. School of Civil Engineering and Archi-tecture, Zhejiang University of Science and Technology, Hangzhou 310023, China;
  • 3. College of Humanities and Social Development, Nanjing Agricultural University, Nanjing 210095, China;
  • 4. Institute of Regional Agricultural Research, Nanjing Agricultural University, Nanjing 210095, China;
  • 5. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Funds:

Under the auspices of A Category of Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA20010101)

  • Received Date: 2019-07-17
  • Rev Recd Date: 2019-12-25

Abstract: The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative (BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis of equilibrium points without any reference to when or how to achieve it. In this paper, a dynamic gravity model was established based on the Maximum Entropy (MaxEnt) theory to estimate and monitor the interconnection intensity and dynamic characters of bilateral relations. In order to detect the determinants of interconnection intensity, a Geodetector method was applied to identify and evaluate the determinants of spatial networks in five dimensions. The empirical study clearly demonstrates a heterogeneous and non-circular spatial structure. The main driving forces of spatial-temporal evolution are foreign direct investment, tourism and railway infrastructure construction, while determinants in different sub-regions show obvious spatial differentiation. Southeast Asian countries are typically multi-island area where aviation infrastructure plays a more important role. North and Central Asian countries regard oil as a pillar industry where power and port facilities have a greater impact on the interconnection. While Western Asian countries are mostly influenced by the railway infrastructure, Eastern European countries already have relatively robust infrastructure where tariff policies provide a greater impetus.

HUANG Qinshi, ZHU Xigang, LIU Chunhui, WU Wei, LIU Fengbao, ZHANG Xinyi. Spatial-temporal Evolution and Determinants of the Belt and Road Ini-tiative: A Maximum Entropy Gravity Model Approach[J]. Chinese Geographical Science, 2020, 30(5): 839-854. doi: 10.1007/s11769-020-1144-x
Citation: HUANG Qinshi, ZHU Xigang, LIU Chunhui, WU Wei, LIU Fengbao, ZHANG Xinyi. Spatial-temporal Evolution and Determinants of the Belt and Road Ini-tiative: A Maximum Entropy Gravity Model Approach[J]. Chinese Geographical Science, 2020, 30(5): 839-854. doi: 10.1007/s11769-020-1144-x
shu

    HTML

Reference (76)

Catalog

    Copyright © Editorial office of Chinese Geographical Science

    Tel: +86-0431-85542219   Email: egeoscien@neigae.ac.cn  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return