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Application of Advanced Data Analytics to Climate Risk

SFI is engaging researchers with backgrounds in data analytics and machine learning techniques in analysis and forecasting of potential climate mitigation actions. The forecasts will be essential components in the development of innovative approaches to modelling global mitigation scenarios and probabilities, which in turn are a core component for developing new financial models and products for measuring and managing the physical and transitional risks (policy; technology) of a climate-driven economic transition.

Application of Advanced Data Analytics to Environmental Risk

The growing public awareness to climate change has driven the market for environmentally sustainable businesses and in particular environmental, social, and governance (ESG) data. Today, the financial industry has increasingly embraced new data technologies in order to enhance investment performance and to deliver positive social and environmental impact compared to benchmarks. New fintech companies thus have emerged that calculate companies’ ESG scores, ranking and indices based on alternative data sources (e.g. satellites) and alternative analysis techniques (e.g. artificial intelligence [AI] and machine learning).

SFI’s research on “Application of Advanced Data Analytics to Environmental Risk” focuses on harnessing the potentials of the digital revolution to transform the global financing systems. We suggest the high-impact opportunities of new data technologies for investors and business leaders as these technologies can help us to conduct a deeper analysis and to create investable insights using ESG data. We also investigate the barriers and risks of using these new technologies. Our research projects therefore will provide a clear guidance on how to translate these data into insights for ESG integration and investment.

People:

​Publications:

Project in Progress:

In, S.Y., Y. J. Lee and A. Monk, “Translating Data into Insights: New Technology Enhances ESG Data Analysis and Investing.”