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Introducing stochastic data envelopment analysis models for evaluating circular economy performance in European
Behrouz Arabi1,2, Mehdi Toloo3,4, Hajar Farnoudkia5
1School for Business and Society, University of York, York, UK.
Achieving circular economy (CE) efficiency requires balancing economic output and waste management. Waste treatment efficiency is crucial for overall CE performance, not just economic size.
Area of Science:
- Environmental Economics
- Operations Research
- Sustainability Science
Background:
- Circular economy (CE) research lacks robust methods for evaluating efficiency under uncertainty.
- Interconnectedness of economic production and waste treatment subsystems is complex.
- Data uncertainties and stochastic variables impact traditional efficiency assessments.
Purpose of the Study:
- Introduce a novel methodological framework integrating Network Data Envelopment Analysis (NDEA) with chance-constrained programming.
- Provide robust CE efficiency estimates by accounting for system dynamics and uncertainties.
- Guide policymakers and practitioners in improving CE performance.
Main Methods:
- Developed a hybrid NDEA and chance-constrained programming model.
- Applied the framework to data from 26 European Union countries (2013-2020).
- Analyzed the interplay between economic production and waste treatment subsystems.
Main Results:
- CE efficiency hinges on a balanced approach to economic production and waste management.
- Waste treatment efficiency significantly influences overall CE performance.
- Economic size does not guarantee circular efficiency; large economies may struggle with waste management.
Conclusions:
- The proposed framework offers a robust empirical tool for CE assessment.
- Policy interventions should focus on enhancing waste management and resource recovery.
- Targeted incentives and regulations are needed to boost circular activities and efficiency across the EU.
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