IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v10y2023i1d10.1057_s41599-023-02234-4.html
   My bibliography  Save this article

From the coast to the interior: global economic evolution patterns and mechanisms

Author

Listed:
  • Xiaoming Jin

    (Dalian Maritime University)

  • Weixin Luan

    (Dalian Maritime University)

  • Jun Yang

    (Northeastern University
    Liaoning Normal University)

  • Wenze Yue

    (Zhejiang University)

  • Shulin Wan

    (Dalian Maritime University)

  • Di Yang

    (Dalian Maritime University)

  • Xiangming Xiao

    (University of Oklahoma)

  • Bing Xue

    (Institute of Applied Ecology, Chinese Academy of Sciences)

  • Yue Dou

    (University Twente)

  • Fangzheng Lyu

    (University of Illinois at Urbana-Champaign)

  • Shaohua Wang

    (Aerospace Information Research Institute, Chinese Academy of Sciences
    Aerospace Information Research Institute, Chinese Academy of Sciences)

Abstract

It is well established that nighttime light brightness value, which is measured from satellites, correlates with economic prosperity across the globe. Researchers have diverged over whether economic factors cluster in coastal areas or move to interior areas. By using nighttime light data and applying the random forest algorithm to measure the proportion of global “near regions” GDP, it was seen that global GDP decreased from 67.25% in 2000 to 63.02% in 2018. This research reveals that under the continuous promotion of economic globalization, there is still a spatial imbalance of economic development between global “near regions” and “far regions”; however, economic factors are gradually shifting to interior areas and forming a “coastal remoteness” evolution pattern. Within the intercontinental range, there are obvious differences in the evolution patterns and spatial structure of economic development between the sub-regions. The reduction of overseas transportation costs and diseconomies of scale are the primary reasons for the evolution of “coastal remoteness” in global economic development. Our findings can facilitate future policymaking and the management of global coastal and interior areas, as well as establish new horizons for relevant research topics within the context of land and marine-coordinated development.

Suggested Citation

  • Xiaoming Jin & Weixin Luan & Jun Yang & Wenze Yue & Shulin Wan & Di Yang & Xiangming Xiao & Bing Xue & Yue Dou & Fangzheng Lyu & Shaohua Wang, 2023. "From the coast to the interior: global economic evolution patterns and mechanisms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
  • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02234-4
    DOI: 10.1057/s41599-023-02234-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-023-02234-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-023-02234-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Martin Visbeck, 2018. "Ocean science research is key for a sustainable future," Nature Communications, Nature, vol. 9(1), pages 1-4, December.
    2. Head, Keith & Ries, John & Swenson, Deborah, 1995. "Agglomeration benefits and location choice: Evidence from Japanese manufacturing investments in the United States," Journal of International Economics, Elsevier, vol. 38(3-4), pages 223-247, May.
    3. J Vernon Henderson & Tim Squires & Adam Storeygard & David Weil, 2018. "The Global Distribution of Economic Activity: Nature, History, and the Role of Trade1," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 357-406.
    4. Johannes Stephan & Oliver Stegle & Andreas Beyer, 2015. "A random forest approach to capture genetic effects in the presence of population structure," Nature Communications, Nature, vol. 6(1), pages 1-10, November.
    5. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    6. Yichu Wang & Xiabin Chen & Alistair G. L. Borthwick & Tianhong Li & Huaihan Liu & Shengfa Yang & Chunmiao Zheng & Jianhua Xu & Jinren Ni, 2020. "Sustainability of global Golden Inland Waterways," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    7. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    8. Chengpeng Wan & Zaili Yang & Di Zhang & Xinping Yan & Shiqi Fan, 2018. "Resilience in transportation systems: a systematic review and future directions," Transport Reviews, Taylor & Francis Journals, vol. 38(4), pages 479-498, July.
    9. Nicholas D. Ward & J. Patrick Megonigal & Ben Bond-Lamberty & Vanessa L. Bailey & David Butman & Elizabeth A. Canuel & Heida Diefenderfer & Neil K. Ganju & Miguel A. Goñi & Emily B. Graham & Charles S, 2020. "Representing the function and sensitivity of coastal interfaces in Earth system models," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    2. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    3. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    4. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    5. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    6. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    7. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    8. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    9. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    10. Yi Cao & Xue Li, 2022. "Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    11. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    12. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
    13. Jorge Antunes & Peter Wanke & Thiago Fonseca & Yong Tan, 2023. "Do ESG Risk Scores Influence Financial Distress? Evidence from a Dynamic NDEA Approach," Sustainability, MDPI, vol. 15(9), pages 1-32, May.
    14. Lyudmyla Kirichenko & Tamara Radivilova & Vitalii Bulakh, 2018. "Machine Learning in Classification Time Series with Fractal Properties," Data, MDPI, vol. 4(1), pages 1-13, December.
    15. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    16. José A. Ferreira, 2022. "Models under which random forests perform badly; consequences for applications," Computational Statistics, Springer, vol. 37(4), pages 1839-1854, September.
    17. Villacis, Alexis & Badruddoza, Syed & Mayorga, Joaquin & Mishra, Ashok K., 2022. "Using Machine Learning to Test the Consistency of Food Insecurity Measures," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322472, Agricultural and Applied Economics Association.
    18. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
    19. Raman Pall & Yvan Gauthier & Sofia Auer & Walid Mowaswes, 2023. "Predicting drug shortages using pharmacy data and machine learning," Health Care Management Science, Springer, vol. 26(3), pages 395-411, September.
    20. Nametso Matomela & Tianxin Li & Peng Zhang & Harrison Odion Ikhumhen & Namir Domingos Raimundo Lopes, 2023. "Role of Landscape and Land-Use Transformation on Nonpoint Source Pollution and Runoff Distribution in the Dongsheng Basin, China," Sustainability, MDPI, vol. 15(10), pages 1-19, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02234-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.