Research on the Characteristics and Influencing Factors of Virtual Water Trade Networks in Chinese Provinces

Guangyao Deng (), Siqian Hou and Keyu Di
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Guangyao Deng: School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, China
Siqian Hou: School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, China
Keyu Di: School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, China

Sustainability, 2025, vol. 17, issue 15, 1-36

Abstract: Promoting the sustainable development of virtual water trade is of great significance to safeguarding China’s water resource security and balanced regional economic growth. This study analyzes the virtual water trade network among 31 Chinese provinces based on multi-regional input–output tables from 2012, 2015, and 2017, using total trade decomposition, social network analysis, and exponential random graph models. The key findings are as follows: (1) The total virtual water trade volume remains stable, with Xinjiang, Jiangsu, and Guangdong as the core regions, while remote areas such as Shaanxi and Gansu have lower trade volumes. The primary industry dominates, and it is driven by simple value chains. (2) Provinces such as Xinjiang, Heilongjiang, and Jiangsu form the network’s core. Network density and symmetry increased from 2012 to 2015 but declined slightly in 2017, with efficiency peaking and then dropping, and the clustering coefficient decreased annually. Four economic sectors exhibit distinct interactions: frequent two-way flows in Sector 1, significant inflows in Sector 2, prominent net spillovers in Sector 3, and key brokers in Sector 4. (3) The network evolved from a core-periphery structure with weak ties to a stable, heterogeneous, and resilient system. (4) Influencing factors, such asper capita water resources, economic development, and population, significantly impact trade. Similarities in economic levels, population, and water endowments promote trade, while spatial distance has a limited effect, with geographic proximity showing a significant negative impact on long-distance trade.

Keywords: virtual water; trade in value added; social network analysis; multi-regional input–output modeling; exponential random graph modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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