Using the DTFM Method to Analyse the Degradation Process of Bilateral Trade Relations between China and Australia

Xiaoyang Han, Sijing Ye, Shuyi Ren and Changqing Song ()
Additional contact information
Xiaoyang Han: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Sijing Ye: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Shuyi Ren: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Changqing Song: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Sustainability, 2023, vol. 15, issue 9, 1-22

Abstract: Quantitative assessment and visual analysis of the multidimensional features of international bilateral product trade are crucial for global trade research. However, current methods face poor salience and expression issues when analysing the characteristics of China—Australia bilateral trade from 1998 to 2019. To address this, we propose a new perspective that involves period division, feature extraction, construction of product space, and spatiotemporal analysis by selecting the display competitive advantage index using the digital trade feature map (DTFM) method. Our results reveal that the distribution of product importance in China—Australia bilateral trade is heavy-tailed, and that the number of essential products has decreased by 68% over time. The proportion of products in which China dominates increased from 71% to 77%. Furthermore, Australia consistently maintains dominance in the most crucial development in trade, and the supremacy of the head product is becoming stronger. Based on these findings, the stability of bilateral trade between Australia and China is declining, and the pattern of polarisation in the importance of traded products is worsening. This paper proposes a novel method for studying Sino—Australian trade support. The analytical approach presented can be extended to analyse the features of bilateral trade between other countries.

Keywords: geographic information science; spatial thinking; heavy-tailed distribution; k-means clustering; co-clustering algorithm; geovisualisation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/9/7297/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/9/7297/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:9:p:7297-:d:1134564

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().