Structure of the Global Virtual Meat Protein Trade Network

Huang Zhuofan; He Zhenglei

doi:10.12052/gdutxb.250145

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Structure of the Global Virtual Meat Protein Trade Network

更新时间：2026-08-04

- Structure of the Global Virtual Meat Protein Trade Network
- Vol. 43, Issue 4, Pages: 58-66(2026)
- 作者机构：
  
  广东工业大学生态环境与资源学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.250145
  CLC： X192
- Published：2026
- 稿件说明：
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黄卓帆, 何正磊. 全球肉类虚拟蛋白质贸易网络结构研究[J]. 广东工业大学学报, 2026, 43(4): 58-66.
黄卓帆, 何正磊. 全球肉类虚拟蛋白质贸易网络结构研究[J]. 广东工业大学学报, 2026, 43(4): 58-66. DOI： 10.12052/gdutxb.250145.

DOI： 10.12052/gdutxb.250145

摘要

全球肉类养殖资源分布不均导致蛋白质供需错配，且目前肉类贸易背后承载的虚拟蛋白质流动机理及其演变模式仍不清晰。本文构建了1995~2022年全球肉类虚拟蛋白质贸易网络，旨在深入剖析其结构演变、地理格局及不平等性。本文将全球肉类贸易数据转化为虚拟蛋白质流，并运用复杂网络分析，全面考察了网络的拓扑指标(平均度、图密度、聚类系数、路径长度、社区数量和模块化)。同时，引入基尼系数量化进出口不平等性。结果显示，全球肉类虚拟蛋白质贸易总量显著增长，禽肉类增速尤为突出。贸易格局高度集中，中国、美国和德国合计占全球虚拟蛋白质进口量的25.3%。贸易关系从区域性向全球化演变，新兴经济体角色日益重要。网络结构分析表明其连通性增强，效率和集群性动态变化，社群划分渐趋清晰。基尼系数证实，全球肉类虚拟蛋白质贸易存在显著不平等性，出口端尤甚。本文研究首次构建全球肉类虚拟蛋白质贸易网络，并从营养价值流动与网络结构耦合角度揭示贸易集中性与不平等机制。

Abstract

Given the uneven global distribution of livestock resources leading to a mismatch in protein supply and demand

and the current lack of clarity regarding the mechanisms and evolution of virtual protein flows embedded within meat trade

this study constructs a global virtual meat protein trade network for the period 1995~2022. Our aim is to deeply analyze its structural evolution

geographical patterns

and inherent inequalities. We converted global meat trade data into virtual protein flows and employed complex network analysis to comprehensively examine the network's topological indicators

including average degree

graph density

clustering coefficient

path length

number of communities

and modularity. Additionally

the Gini coefficient was introduced to quantify import and export inequalities. Results reveal a significant increase in the total volume of global virtual meat protein trade

with poultry experiencing particularly rapid growth. The trade pattern is highly concentrated

with China

the United States

and Germany accounting for 25.3% of global virtual protein imports. Trade relations have evolved from regional to global

with emerging economies playing an increasingly pivotal role. Network structure analysis indicates enhanced connectivity

dynamic changes in efficiency and clustering

and clearer community delineation. The Gini coefficient confirms a significant inequality in global virtual meat protein trade

especially pronounced in exports. This research is the first to construct a global virtual protein trade network for meat and reveals the mechanisms of trade concentration and inequality from the perspective of the coupling of nutritional value flow and network structure.

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references

XU Z, LI Y, CHAU S N, et al . Impacts of international trade on global sustainable development[J ] . Nature Sustainability, 2020, 3(11): 964-971.

WIEDMANN T, LENZEN M. Environmental and social footprints of international trade[J]. Nature Geoscience, 2018, 11(5): 314-321.

ZHAO K, GAINES S D, GARCíA MOLINOS J, et al . Effect of trade on global aquatic food consumption patterns[J ] . Nature Communications, 2024, 15(1): 1412.

CHUNG M G, KAPSAR K, FRANK K A, et al . The spatial and temporal dynamics of global meat trade networks[J ] . Scientific Reports, 2020, 10(1): 16657.

KOZICKA M, HAVLíK P, VALIN H, et al . Feeding climate and biodiversity goals with novel plant-based meat and milk alternatives[J ] . Nature Communications, 2023, 14(1): 5316.

GOVONI C, CHIARELLI D D, LUCIANO A, et al . Global assessment of land and water resource demand for pork supply[J ] . Environmental Research Letters, 2022, 17(7): 074003.

BOAKES E H, DALIN C, ETARD A, et al . Impacts of the global food system on terrestrial biodiversity from land use and climate change[J ] . Nature Communications, 2024, 15(1): 5750.

LIANG S, YU Y, KHARRAZI A, et al . Network resilience of phosphorus cycling in China has shifted by natural flows, fertilizer use and dietary transitions between 1600 and 2012[J ] . Nature Food, 2020, 1(6): 365-375.

MAYOR P, EL BIZRI H R, MORCATTY T Q, et al . Wild meat trade over the last 45 years in the Peruvian Amazon[J ] . Conservation Biology, 2022, 36(2): e13801.

ANDREI J V, CHIVU L, CONSTANTIN M, et al . Economic aspects of international agricultural trade and possible threats to food security in the EU-27: a systematic statistical approach [M ] . Shifting patterns of agricultural trade: The protectionism outbreak and food security. Springer. 2021: 229-261.

VAN VLIET N, MUHINDO J, NYUMU J, et al . Understanding factors that shape exposure to zoonotic and food-borne diseases across wild meat trade chains[J ] . Human Ecology, 2022, 50(6): 983-995.

WANG X, QIANG W, LIU X, et al . The spatiotemporal patterns and network characteristics of emissions embodied in the international trade of livestock products[J ] . Journal of Environmental Management, 2022, 322: 116128.

FANG X C, LIU B W, ZHANG W F. Population aging and China's monetary policy interest rate channel and credit channel: theoretical analysis and empirical evidence[J]. Journal of East China Normal University (Philosophy and Social Sciences Edition) , 2025, 57(3): 131.

XU Y L, ZHAO M Y, LI K, et al . Review and prospect of research progress on agricultural adaptation to climate change[J ] . Chinese Journal of Eco-Agriculture, 2023, 31(8): 1155-1170.

YANG M, LIANG S, WU X, et al . Mitigating forage-livestock conflicts in China through economic structure transition[J ] . Cell Reports Sustainability, 2024, 1(3): 100033.

TIAN-XIANG L I, XING-YU L I U, RONG-BO W, et al . Dynamics of global pork trade networks during 2000-2019 and its implications for China: a complex network analysis[J ] . Journal of Natural Resources, 2021, 36(6): 1557-1572.

SUN Q, HOU M, SHI S, et al . The influence of country risks on the international agricultural trade patterns based on network analysis and panel data method[J ] . Agricult ure, 2022, 12(3): 361.

GUTIéRREZ-MOYA E, ADENSO-DíAZ B, LOZANO S. Analysis and vulnerability of the international wheat trade network[J]. Food Security, 2021, 13(1): 113-128.

TU C, SUWEIS S, D’ODORICO P. Impact of globalization on the resilience and sustainability of natural resources[J]. Nature Sustainability, 2019, 2(4): 283-289.

SUN C Z, BAI T J, HAN Q. Regional and structural equilibrium analysis of China's gray water footprint based on the Gini coefficient[J]. Journal of Natural Resources, 2016, 31(12): 2047-2059.

SUN X, MI Z, DU H, et al . Impacts of poverty eradication on carbon neutrality in China[J ] . Science Bulletin, 2024, 69(5): 648-660.

杨月欣. 中国食物成分表 2004 [M]. 北京: 北京大学医学出版社, 2004.

LI X, ZHANG Y, WANG S. Deconstruction and analysis of global biodiversity loss transfer network based on the social network analysis method[J]. Environmental Science and Pollution Research, 2025, 32(3): 1375-1392.

CHEN Y, CHEN M. Evolution of the global phosphorus trade network: a production perspective on resilience[J]. Journal of Cleaner Production, 2023, 405: 136843.

HUANG Z, HE Z, XING Z, et al . Dataset of biodiversity loss in global staple food trade, 1995—2022[J ] . One Ecosystem, 2026, 11: e159884.

GINI C. Measurement of inequality of incomes[J]. The Economic Journal, 1921, 31(121): 124-125.

KLER R, GANGURDE R, ELMIRZAEV S, et al . Optimization of meat and poultry farm inventory stock using data analytics for green supply chain network[J ] . Discrete Dynamics in Nature and Society, 2022, 2022(1): 8970549.

王木香, 陶红军. 我国猪肉进口物流管理制度, 短板及改进[J]. 中国猪业, 2021, 15(6): 23-32.

PUMA M J. Resilience of the global food system[J]. Nature Sustainability, 2019, 2(4): 260-261.

PUMA M J, BOSE S, CHON S Y, et al . Assessing the evolving fragility of the global food system[J ] . Environmental Research Letters, 2015, 10(2): 024007.

FALKENDAL T, OTTO C, SCHEWE J, et al . Grain export restrictions during COVID-19 risk food insecurity in many low-and middle-income countries[J ] . Nature Food, 2021, 2(1): 11-14.

GAUPP F. Extreme events in a globalized food system[J]. One Earth, 2020, 2(6): 518-521.

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