Community With a Shared Future: Spatial Correlation Network Analysis of Economic Linkages Between China and ASEAN

Cheng Qin & Xueyi Wang

International Regional Science Review2026https://doi.org/10.1177/01600176261426489article
ABDC B
Weight
0.50

Abstract

With the deepening of economic cooperation between China and ASEAN, economic linkages exhibit pronounced regional differentiation. Taking Chinese provinces as the analytical scale, this study employs a modified gravity model, spatial correlation network analysis, and the quadratic assignment procedure (QAP) to examine the structural characteristics and driving factors of the spatial economic network between Chinese provinces and ASEAN countries. The results show that: (1) from 2012 to 2023, economic linkages among Chinese provinces strengthened, while linkages among ASEAN countries weakened; Chinese coastal and border provinces maintained stronger economic ties with ASEAN than inland and northern provinces; (2) the density of the China–ASEAN regional economic network increased, while its hierarchical concentration declined markedly, indicating a transition from a unipolar structure to a multi-centered nested structure; and (3) differences in economic development and industrial structure exert dual effects on the spatial correlation network. Based on these findings, this study argues that policy efforts should focus on leveraging the multi-centered nested spatial structure, strengthening connections between core and non-core regions, improving the stability and connectivity of the regional economic network, and enhancing the matching of industrial and factor conditions to mitigate the constraining effects of regional disparities, thereby promoting China–ASEAN economic cooperation toward broader coverage and higher quality.

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https://doi.org/https://doi.org/10.1177/01600176261426489

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@article{cheng2026,
  title        = {{Community With a Shared Future: Spatial Correlation Network Analysis of Economic Linkages Between China and ASEAN}},
  author       = {Cheng Qin & Xueyi Wang},
  journal      = {International Regional Science Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/01600176261426489},
}

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Evidence weight

0.50

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F · citation impact0.50 × 0.4 = 0.20
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R · text relevance †0.50 × 0.4 = 0.20

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