CleaRIS

The global climate goals are ambitious. Together with tightening sustainability regulations such as the Corporate Sustainability Reporting Directive (CSRD), they put tremendous pressure on companies to get more innovative and leverage new technological developments in CleanTech and ClimateTech to their maximum. The International Energy Agency estimates that half of the greenhouse gas emissions that are to be reduced by 2050 can only be achieved through new technologies that don't yet exist. On the other hand, countries and businesses struggle to get up to speed with climate innovation, as evidenced by more and more cases of climate-related fines (e.g., as imposed by the ECB in May 2024) and international court litigations (e.g., the Swiss “climate seniors” case in April 2024). While it is clear that businesses need to take action, in most cases, they lack the guidance and expertise needed for efficient climate innovation. In this project, we aim to build an intelligence platform that continuously monitors and visualizes the global CleanTech industry. To make these insights actionable, it provides tailored recommendations to businesses about implementing CleanTech innovations, fulfilling regulatory requirements, and communicating CleanTech initiatives to different stakeholders (consumers, investors, etc.). The platform combines traditional predictive AI methods with new developments in generative AI, which allows us to reduce hallucinations and ground recommendations and predictions in a solid database of market insights. These insights are extracted from a large-scale database containing a variety of public sources which are analyzed using predictive AI methods. Recommendations are generated using an open-source Large Language Model (LLM), such as Llama 3.2, which will be fine-tuned to the CleanTech domain. Using Retrieval-augmented Generation (RAG), the system not only has access to a wide range of public CleanTech sources, but can also access company-internal documents of individual clients, thus adapting the insights and recommendations to their unique context and needs.

In this collaboration we aim to pair SDSC’s capacity of delivering innovative, reliable and robust AI products with the collaborators’ broad academic track record in the domains such as ESG factors, CleanTech innovation, greenwashing, and the AI-driven decision-making as well as the extensive experience of delivering LLM-based models for education and research, in order to build this platform that can accelerate the global transition to green economy.