Measuring spatial access to recovery networks for WEEE: The Bavarian case

Effective management of Waste Electrical and Electronic Equipment (WEEE) is essential for advancing environmental sustainability and achieving the EU’s circular economy objectives. However, like other EU member states, Germany faces significant challenges in meeting the recycling targets set by EU legislation. Crucially, WEEE collection performance depends on a variety of factors, including an effectively designed collection infrastructure and, presumably, the geographical proximity of dedicated collection facilities. To evaluate the importance of this latter factor, this study applies the approach developed by an Italian study on WEEE collection to the German federal state of Bavaria. In detail, the study at hand quantifies spatial accessibility and availability indicators for collection centres and correlations between these indicators and collection rates. This serves to answer (1) whether accessibility and availability of collection centres correlate with collection rates, (2) whether spatial discrepancies or clusters in indicators and collection rates exist in Bavaria, and (3) whether the conclusions drawn for Italy can also be drawn for the case at hand. The results hint at differences in municipal waste management structures, particularly in northern Bavaria. However, contrary to expectations and the Italian case, the findings indicate that neither spatial accessibility nor availability significantly influences collection performances. This suggests that policymakers should focus on alternative strategies to enhance WEEE collection rates in Germany (e. g., public awareness, education, or monetary incentives), and, in line with similar recent literature, hints at opportunities to improve the efficiency of the e-waste collection landscape. • Transfer of the methodology of Bruno et al. (2021) to the case of Bavaria • Assessment of the spatial access to Bavarian collection centers for WEEE • Quantification of five indicators using GIS-based census data • Identification of local clusters using Moran’s I and Local Moran’s I • Spearman’s correlation between accessibility / availability and collection rate