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10 December 2025

Innovation-Adjusted Dynamics of E-Waste in the European Union: Mathematical Modeling, Stability and Panel EKC Turning Points

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1
Faculty of Management, Bucharest University of Economic Studies, 010374 Bucharest, Romania
2
Faculty of Business Administration in Foreign Languages, Bucharest University of Economic Studies, 010374 Bucharest, Romania
3
Doctoral School Business Administration II, Bucharest University of Economic Studies, 010374 Bucharest, Romania
4
Center of Industrial Software, University of Southern Denmark, Alsion 2, 6400 Sonderborg, Denmark
This article belongs to the Special Issue Computational Economics and Mathematical Modeling

Abstract

The rapid growth of Waste Electrical and Electronic Equipment (WEEE) in the European Union highlights the need for a rigorous understanding of its long-term dynamics and the role of innovation in shaping its trajectory. This study investigates how innovation influences the dynamics of WEEE generation in the European Union. We develop an innovation-adjusted mathematical model of e-waste as a stock flow system and prove the existence and global stability of a unique positive equilibrium. The model analytically generates an environmental Kuznets-type turning point and shows that innovation reduces waste accumulation by accelerating effective depreciation. To link the theoretical results with empirical patterns, we embed the model in a STIRPAT panel specification using annual data for 27 EU member states from 2013 to 2023, where EU Eco-innovation Index (EEI) serves as a composite index which directly captures policy-driven green technology and circular economy activities, aligning precisely with our theoretical framework. We also extend the quasi-demeaning transformation to panels with correlated shocks and establish its consistency under a factor structured error process. The empirical estimates confirm a positive effect of income on WEEE at lower development levels and a negative coefficient on its squared term, consistent with an inverted U pattern, while innovation is associated with lower waste intensity. These findings demonstrate how mathematical modeling can strengthen the interpretation of macro panel evidence on circularity and provide a basis for future optimization of innovation driven sustainability transitions.

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