Evolution of structural properties and its determinants of global waste paper trade network based on temporal exponential random graph models

Helian Xu, Lianyue Feng, Gang Wu and Qi Zhang

Renewable and Sustainable Energy Reviews, 2021, vol. 149, issue C

Abstract: As an important recyclable and reusable resource, waste paper is traded in millions of dollars around the world every year. Global waste paper trade not only addresses resource scarcity issues, alleviates environmental pressures and brings substantial economic gains, but also contributes to the development of global circular economy. Using complex network methods, bilateral waste paper trade data, and temporal exponential random graph models (TERGM), we construct global waste paper trade networks (GWPTNs) during 2000–2018, and examine their structural evolution and determinants. We report that: (I) GWPTNs display obvious features of small-world network, low reciprocity, heterogeneity and disassortativity; (II) Asia is the leading recipient of global waste paper, while Europe and North America are the main exporters; (III) China and India dominate the waste paper import markets while the United States is the largest exporter, and Germany plays an essential role in both importing and exporting hubs of waste paper; (IV) The evolution of the GWPTNs is significantly influenced by endogenous reciprocity, transitivity and preferential attachment, and economies with more partners or within the same continents are more likely to trade waste paper. Economies with higher urbanization rates, more per capita income, stricter environmental regulation, or lower industrialization rates are more likely to export waste paper; conversely, they are more likely to be the importers. Economy-pairs sharing a language or religion, being a former colony of the same colonizer, historical colonial relationship or a border, or signing regional trade agreements are more likely to trade waste paper.

Keywords: Waste paper; Global trade network; Complex network; Temporal exponential random graph model (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (12)

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DOI: 10.1016/j.rser.2021.111402

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