Sustainable Finance
Environmental scores for large industrial assets
The study focuses on improving how companies are rated based on their impact on the environment, society, and governance (ESG). Currently, rating these aspects is complicated due to different sources of information and lack of common standards. My research suggests using geospatial data, which includes location-based information, to make the ratings more accurate and consistent. By doing this, financial institutions can take better decisions about investments and risk management, considering a company’s broader impact on the world. This new method aims to simplify the process of assessing a company's commitment to environmental responsibility.
Reference: Rossi et al., “Breaking the ESG rating divergence: an open geospatial framework for environmental scores”, Journal of Environmental Management, 2024.
Global database of cement facilities
I led a project that emphasised the need for accurate and comprehensive location data on physical assets, like factories or power plants, to better assess and manage environment-related risks in the financial system. Currently, the available data is often incomplete or inaccurate, making it challenging for investors and policymakers to make informed decisions. The research introduced a framework that utilises advanced deep learning and Earth observation data to create a global database of pollutant-emitting plants, such as those in cement, iron, and steel industries. This approach aims to enhance sustainable finance decision-making by providing detailed insights into the environmental impacts of companies, enabling the redirection of investments towards more sustainable alternatives.
Reference: Rossi et al., “Detection and characterisation of pollutant assets with AI and EO to prioritise green investments: the Geoasset framework”, IEEE, IGARSS 2022.
