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author:

Chen, Z. (Chen, Z..) [1] | You, X. (You, X..) [2] | Wang, C. (Wang, C..) [3] | Wu, W. (Wu, W..) [4] | Wei, Y. (Wei, Y..) [5] | Shi, K. (Shi, K..) [6] | Yu, B. (Yu, B..) [7]

Indexed by:

Scopus

Abstract:

Assessing the promotion of economic growth between China and the world is challenging given data limitations and the lack of consideration of time delay. Based on the time-series nighttime light (NTL) data from satellites covering more than 100 months, we found that the world is lighting in most countries, even after a sudden jump due to the outbreak of COVID-19. Then, we conducted the convergent cross-mapping before COVID-19 (2014.1-2019.12) and during/after COVID-19 (2020.1-2022.11), we found that the influences from China to the world economic growth in these two phases are widely expanded to more and more economies, the number of which increases from 65 to 73, with a longer time lag, which averagely increased from 4.6 to 6.6 months. On the contrary, the number of economies, which can influence China’s economy, is increasing from 48 to 55 but the average lag time is stable at around 4.5 months. Meanwhile, a regional multilateral economic network related to China’s economy was found. The economies with mutual causalities with China were mostly concentrated in Asia, Africa, and Europe. China indeed can warm the world economy partially, especially in developing countries and has also benefited from the multilateral economy. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Keyword:

China convergent cross-mapping Economic growth nighttime light remote sensing

Community:

  • [ 1 ] [Chen Z.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou, China
  • [ 2 ] [Chen Z.]The Academy of Digital China, Fuzhou University, Fuzhou, China
  • [ 3 ] [You X.]School of Geographical Sciences, Fujian Normal University, Fuzhou, China
  • [ 4 ] [Wang C.]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China
  • [ 5 ] [Wang C.]School of Geographic Sciences, East China Normal University, Shanghai, China
  • [ 6 ] [Wu W.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou, China
  • [ 7 ] [Wu W.]The Academy of Digital China, Fuzhou University, Fuzhou, China
  • [ 8 ] [Wei Y.]School of Geographical Sciences, Northeast Normal University, Changchun, China
  • [ 9 ] [Shi K.]Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Province, School of Geography and Tourism, Anhui Normal University, Wuhu, China
  • [ 10 ] [Yu B.]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China
  • [ 11 ] [Yu B.]School of Geographic Sciences, East China Normal University, Shanghai, China

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Source :

International Journal of Digital Earth

ISSN: 1753-8947

Year: 2024

Issue: 1

Volume: 17

3 . 7 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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