Enhancing the Circular Economy in the European Union with Machine Learning

For the European Union, the 2030 Agenda represents a comprehensive framework that aims to achieve objectives such as sustainable development, promoting economic growth, social inclusion, and environmental protection by the year 2030. One of the main strategies of the European Union’s Agenda 2030 is to implement a circular economy (CE) with the aim of supporting elements such as sustainable development, emphasizing resource efficiency, waste reduction, and a shift towards renewable materials. One of the most important tools that can help the circular economy achieve its objectives is Machine Learning (ML). Machine Learning can transform the economy by driving innovation, improving efficiency, and enabling data-driven decision-making across industries.

Circular economy and machine learning intersect by leveraging data-driven insights to optimize resource use, improve recycling processes, and enhance product life cycles for greater sustainability.

The research paper’s purpose is to highlight the role that the use of ML can have in the implementation of the principles and approaches specific to the circular economy. The paper describes the specific aspects of the circular economy, types of Machine Learning algorithms and the support that ML can offer EC. A data analysis using a specific ML algorithm is also performed. However, it is important to note that the research is limited by the choice of specific methods and datasets, without extending the analysis to various economic sectors or to the political and social influences that may affect the integration of these technologies. The research also does not address the possible ethical and security challenges associated with the use of machine learning algorithms in the circular economy.