| Summary: | Environmental, Social and Governance (ESG) rating agencies have been instrumental in mainstreaming sustainability in the investment industry. Traditionally, they have relied on company disclosure and human analysis to produce their ratings. More recently however, technological innovation in data scraping and Artificial Intelligence (AI) have undercut the traditional approach. Tech-driven Alternative ESG ratings are becoming increasingly influential yet remain critically underexplored in sustainable finance scholarship. Grounded within financial geography and using mixed methods, this paper fills this gap by comparing a set of Traditional ratings, sourced from MSCI ESG, with an Alternative AI-based set of ESG ratings sourced from Truvalue Labs. Our results expand upon recent research on ESG ratings by shedding new light on low commensurability between Traditional and Alternative ESG ratings. Specifically, we show that differences in ratings are driven by four main factors: differences in ESG theorisation based on key issue selection, differences in data sources analysed, differences in weighting structures for rating aggregation, and finally differences in controversy analysis. Our findings are contextualised using participatory observations collected during fieldwork at a leading asset manager in the City of London. Overall, we show that the advantages of Alternative ESG ratings include higher levels of standardisation, a transparent ‘outside-in’ perspective on ratings, a more democratic aggregation process, and rigorous real-time analytics. We argue that these characteristics reflect a geographic reconfiguration of ESG rating construction, expanding from financial agglomerations to technological and digital spaces of innovation. While Alternative ESG ratings make major promises on how technology can reform sustainable investing, we recognise that risks remain.
|
|---|
