Forecasting the Environmental Impacts of Future Photovoltaic (PV) Manufacturing with Machine Learning Techniques

This project aims to forecast the environmental impacts associated with future photovoltaic (PV) manufacturing processes, with a focus on emerging technologies like perovskite solar cells, by applying advanced machine learning techniques. As global PV deployment scales up to meet renewable energy targets, understanding and mitigating potential environmental impacts-such as emissions, waste generation, and resource use-becomes increasingly crucial, especially for next-generation materials. By training machine learning models on historical and projected data, the research will develop predictive tools to estimate environmental footprints across diverse manufacturing scenarios, including those unique to perovskite PV. This approach will identify key drivers of environmental impact, support the development of sustainable manufacturing practices, and guide policymakers and industry stakeholders in planning for a sustainable, circular PV economy.