The united states' clothing imports from asian countries along the belt and road: An extended gravity trade model with application of artificial neural network

Danny Chi Kuen Ho, Eve Man Hin Chan, Tsz Leung Yip, Chi Wing Tsang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


In 2013, China announced the Belt and Road Initiative (BRI), which aims to promote the connectivity of Asia, Europe, and Africa and deepen mutually beneficial economic cooperation among member countries. Past studies have reported a positive impact of the BRI on trade between China and its partner countries along the Belt and Road (B&R). However, less is known about its effect on the sectoral trade between the B&R countries and countries that show little support of the BRI. To address that gap, this study examines the changing patterns of clothing imports by the United States (US) from China and 14 B&R countries in Asia. An extended gravity model with a policy variable BRI is built to explain bilateral clothing trade flow. A panel regression model and artificial neural network (ANN) are developed based on the data collected from 1998 to 2018 and applied to predict the trade pattern of 2019. The results show a positive effect of the BRI on the clothing exports of some Asian developing countries along the B&R to the US and demonstrate the superior predictive power of the ANN. More research is needed to examine the balance between economic growth and the social and environmental sustainability of developing countries and to apply more advanced machine learning algorithms to examine global trade flow under the BRI.

Original languageEnglish
Article number7433
JournalSustainability (Switzerland)
Issue number18
Publication statusPublished - Sep 2020


  • Artificial neural network
  • Belt and road initiative
  • Clothing trade
  • Gravity trade model
  • Panel data regression

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

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