Circular Economy – Comparative Study Within the EU of Five Countries on The Spectrum Based on Eurostat and Artificial Intelligence Methods. Case study: Germany, Holland, France, Romania and Poland

This paper is a short presentation of concepts related to a circular economy, such as those depicted by Paul Ekins, the butterfly model introduced by Ellen Mac Arthur, the 9 R’s introduced by European Union in 2017, to denote the strategies for a circular economy, the indicators of a circular economy proposed by Teresa Domenech such as the consumption material use, recycling, energy use, consumption, renewable energy, air quality, water consumption; well-being – health, social cohesion, as well as the indicators of a circular economy proposed by Peterborough such as the amount of renewable electricity available to each household, CO2 emissions per capita, percentage household waste recycled, percentage circular jobs, and so on. North America and Europe, as well as Japan are the first promoters of this kind of economy throughout the globe. Inside Europe, the differences situate themselves between western Europe, more advanced on the implementation of the roadmap for a circular economy, while the Eastern Europe is designing its roadmap and moving towards its implementation. A brief presentation is made on three major western European countries – Germany, Netherlands and France, by considering them as role models into the future circular economy. Additionally, it is presented how far they are onto this path, the programs they have implemented, and what they still need to achieve. On the other hand, is also made a short study on eastern and central European strategy plan for a circular economy and bio- economy. Special focus is given to Romania and Poland, considering their efforts involved in some programs such as recycling electronics, and construction materials and also highlighting a lack of initiative for other programs, such as recycling municipal wastes, reinserting back into the economy raw materials and the production of renewable biological resources for the conversion into food by innovative and efficient technologies. The research paper is based on EUROSTAT charts, graphs, roadmap and tables. It uses two artificial intelligence deep learning methods, a Multi-Layer Perceptron and a Long Term Short Memory, to estimate and forecast threetime series based on the data set collected from 2013 to 2022 for 2023-2027.