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Previous studies have examined the relationship between total e-waste generation and its determinants. However, e-waste categories have not received appropriate attention, and thus important information is missing for policymakers. This paper advances the state of knowledge by studying e-waste categories individually. Statistical data of e-waste in the EU28 + 2 countries over the period 2000–2015 is disaggregated into single categories, namely temperature exchange equipment, screens and monitors, lamps, large equipment, small equipment, and small IT and telecommunication equipment. To examine the main driving forces of e-waste in each category, the STIRPAT model (Stochastic Impacts by Regression on Population, Affluence, and Technology) and the environmental Kuznets curve (EKC) hypothesis are applied, using panel quantile regression as main method and pooled OLS to control robustness of findings. Results show that population, renewable energy consumption, trade openness, and urbanization are positively correlated with all e-waste categories. Renewable energy exploitation is a major e-waste driver of large and small equipment, screens and monitors, and small IT. Interestingly, an inverted U-shaped relationship between gross domestic product (GDP) per capita and the quantity of e-waste was found across all e-waste categories for most regressions when using the quantile regression method (28 out of 30 quantiles), and for all regressions when using the pooled OLS method. This confirms the EKC hypothesis and indicates that e-waste of all categories increases with GDP up to a certain level (turning point) but then decreases when GDP continues to grow.

Determinants of e-waste composition in the EU28 + 2 countries: a panel quantile regression evidence of the STIRPAT model