Sweet spots or dark corners? An environmental sustainability view of big data and artificial intelligence in ESG
Abstract: This chapter examines environmental aspects of ESG and risks and opportunities for using big data (BD) and artificial intelligence (AI) to capture these in ESG ratings. It starts by outlining the difference between relative and absolute sustainability and what this means for delivering on globally agreed upon targets, such as the Sustainable Development Goals. We then look at what the state-of-the-art climate and Earth System science has to offer investors interested in absolute environmental sustainability. Next, we discuss the risks associated with a blurring of concepts relating to sustainability and materiality, and examine and contrast conventional ESG rating procedures with new approaches informed by BD and AI to understand what this new generation of tools can offer investors interested in sustainability. We note a current misalignment between stated ambitions of investors, and the ability to deliver on stated goals through the use of current ESG metrics and ratings. We therefore finish with suggestions for how to better align these and how those interested in ESG can become more ‘sustainability savvy’ consumers of such ratings.
Citation: Crona, B. and E. Sundström. 2022. Sweet spots or dark corners? An environmental sustainability view of big data and artificial intelligence in ESG. In: T. Rana, J. Svanberg, P. Öhman and A. Lowe (eds.) Handbook of Big Data and Analytics in Accounting and Auditing. Singapore Springer Nature, Singapore, pp. 105–131.
Keywords: environmental, ESG, risks, challenges, big data, machine learning, opportunities, absolute sustainability, environmental controversies