Transnational trafficking networks of end-of-life vehicles and e-waste

ABSTRACT

Based on case studies and interviews, it appears that the transnational trafficking of various waste types follows distinct paths. However, this information only provides a partial view of the global waste trafficking network, as it has never been studied by combining all the known illegal flows of different waste types. To address this gap, we analysed data from the Basel Convention National Reports to reconstruct networks of countries that engaged in illegal exchanges of end-of-life vehicles, e-waste, or both between 2016 and 2019. Our findings suggest that the structure of these networks and the countries involved in the trafficking vary depending on the waste type, with some similarities. While there are a few reciprocal ties, illegal end-of-life vehicles and e-waste typically move in one direction between countries. Most illegal flows occur from the Global North to the Global South, but trafficking also takes place within each of these regions.

KEYWORDS:

• Waste trafficking
• waste management
• environmental crime
• social network analysis
• e-waste
• end-of-life vehicles

Acknowledgments

The authors wish to thank the anonymous reviewers for their insightful comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Contributions

The present work is the joint product of the work of Serena Favarin, Giulia Berlusconi, Alberto Aziani, Samuele Corradini. Specifically, SF and SC collected and systematised the raw data for the analysis. AA and SF provided the problem formulation. AA, GB and SF defined the empirical method. GB conducted the analysis. AA, GB and SF wrote the paper and reviewed the manuscript. AA and SF provided additional contributions to the Introduction, Discussion and Conclusions. All authors read and approved the final manuscript.

Notes

1. ‘End-of-life vehicles’ category, which is often abbreviated as ELV, predominately includes waste codes 16 01 04, A1160, A1180, B3140 and B1250, which refer to end-of-life vehicles, lead-acid batteries and pneumatic tires. When waste codes are not included in BCNRs we used the specified description to determine the category of waste (e.g. harvesters, work vehicles, car parts, car batteries, tires, damaged vehicles, engines, spare parts of end-of-life vehicles, used cars and used spare parts). The ‘E-waste’ category, which is often abbreviated as WEEE, predominately includes waste codes A1180, B1010, B1130 and A1160 which refer to electrical and metal components, such as catalysts, lead-acid batteries, cables, accumulators or cathode-ray tubes. When waste codes are not included in BCNRs we used the specified description to determine the category of waste (e.g. batteries, household appliances such as refrigerators, cables, electronics, electronic scrap, mobile phones, printed circuit boards, toner cartridges and monitors). We decided to include batteries and accumulators in the e-waste category because Eurostat (2021b Table 6) stated that ‘hazardous components from electrical and electronic equipment may include lead batteries, Ni-Cd batteries, mercury-containing batteries and other batteries and accumulators marked as hazardous; mercury switches, glass from cathode ray tubes and other activated glass, etc’..

2. Cases of illegal waste trafficking are detected by enforcement authorities of countries that are part of the Basel Convention and report information on the country of export, import, waste code, waste type, quantity, reason for illegality, responsibility, and actions taken. Only when the national authority identifies both the country of dispatch and the country of destination of the illegal shipment the case was included in the dataset. However, differences in interception capacity and statistical reporting quality suggest caution in using these data. To address this, we follow other scholars’ examples (Boivin, 2013; Favarin & Aziani, 2020; Giommoni et al., 2017) and focus exclusively on the presence or absence of connections. We use the data to identify pairs of countries involved in waste trafficking and establish each country’s position in the global waste trafficking networks. Our graphs represent the likely most trafficked routes, rather than a detailed schematisation of the actual networks. We assume that connections in the BCNRs are more relevant, on average, than those that do not appear, although others likely exist.

3. Appendix 1, which can be accessed at https://osf.io/4xzk5/?view_only=fe258258dc624f54bed577f4ac3bbc58, reports the node level measures in-degree centrality, out-degree centrality and degree imbalance for each country in each network.

4. In 1980, the Brandt Line was virtually drawn to highlight the disparities and inequalities between the wealthy Global North (e.g. Europe, North America, Australia, and Japan) and the poorer Global South (e.g. Africa, Asia, and Latin America). For many economic, political, social and historical reasons, there is more evidence of continuity than change in the position of the Global South within the international system, so that it is still meaningful to speak about Global North and Global South from an international perspective (Lees, 2021). However, the concept of Global North and Global South has slightly changed over the past forty years. To the authors’ knowledge there is not a well-recognised and generally used updated list of countries belonging to the Global North or to the Global South to be used for scientific purposes. For this reason, in Appendix 2 (available at https://osf.io/4xzk5/?view_only=fe258258dc624f54bed577f4ac3bbc58) we present the classification of countries by Global North and Global South that we used in our analyses.

5. A clique is a subset of nodes of an undirected graph such that every two distinct vertices in the clique are adjacent (Luce & Perry, 1949).

6. The Data and method section explains the procedure used to estimate the ‘degree imbalance’ and its interpretation.

7. Importer, mainly importer, importer/exporter, mainly exporter, and exporter are the five categories in which the values of the ‘degree imbalance’ have been divided to distinguish among the different roles played by the countries in the network. The Data and method section includes additional information about this measure and its classes, as do the notes accompanying Figures 1 and 2.