JOURNAL ARTICLE

Evolution of international pork trade patterns: A complex networks perspective.

  • Published In: International Journal of Modern Physics C: Computational Physics & Physical Computation, 2026, v. 37, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ma, Jinlong; Feng, Jiale; Hu, Jiahao 3 of 3

Abstract

In the context of economic globalization, international pork trade has emerged as a crucial mechanism for balancing food demand across complementary countries or regions. In this paper, we construct the international pork trade networks based on available trade data from 1991 to 2021, and explore the evolution of international pork trade patterns for three perspectives: global, regional and national. Our key findings are as follows: First, the international pork trade network reached its maximum scale in 2017, with trade relationships and volumes among countries continuing to grow. Second, there is a distinct globalization trend in the pork trade network, evident in the decreasing heterogeneity and the blurring of community structures over time. Third, the network exhibits scale-free characteristics, where a small proportion of countries (less than 10 volume), highlighting the pivotal role of core countries in network stability. Lastly, geographic proximity is a significant factor shaping the community patterns within the network. Notably, China, Poland, Italy, Germany and Japan are the primary pork importing countries, while European and American nations such as Germany, France, Denmark, New Zealand, and Brazil dominate global pork exports. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics C: Computational Physics & Physical Computation. 2026/02, Vol. 37, Issue 2, p1
  • Document Type:Article
  • Subject Area:Nutrition and Dietetics
  • Publication Date:2026
  • ISSN:0129-1831
  • DOI:10.1142/S0129183125500640
  • Accession Number:189477025
  • Copyright Statement:Copyright of International Journal of Modern Physics C: Computational Physics & Physical Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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