AI-Driven Valuation of Circular Economy Investments: Implications for Sustainable Real Estate and Resource Management

With the rapid development of technology and increasing material consumption, the efficient management of waste streams has become a critical challenge within the circular economy, particularly in resource-intensive sectors such as electronic waste recycling. This study examines how artificial intelligence can improve the assessment and forecasting of circular economy investment efficiency, with particular attention paid to resource-intensive sectors such as electronic waste recycling. The study reviews data from European Union countries for the period 2010–2024, including economic, technological, and environmental indicators. A machine learning model system based on ensemble predictive methods was developed to assess the effectiveness of circular economy investments. The results show that artificial intelligence-based models have higher forecasting accuracy than traditional econometric methods, and the most important factors determining investment efficiency are the level of automation, recycling efficiency, and the stringency of environmental policies. The study provides a new, data-driven methodological approach to assessing circular economy investments and discusses their implications for sustainable real estate development and resource management.