Circular Economy Indexing with Generative AI and PCA

The circular economy represents a critical pathway toward achieving sustainable development goals through responsible resource production and consumption. Monitoring progress toward implementing a circular economy in supply chains is essential. Therefore, developing indicators to measure circular economy adoption allows organizations and countries to focus and assess their progress effectively. Current methods for creating these indicators, such as linear programming and qualitative approaches, often overlook the variability and complexity inherent in the model. This omission introduces biases that undermine the reliability of circular economy index outcomes. In this study, we propose a circular economy index using Generative Adversarial Networks (GANs) and Principal Component Analysis (PCA) to address these challenges. We utilize this method to evaluate circular economy performance across 23 EU countries, showcasing its effectiveness in identifying potential challenges and opportunities.