Role of Artificial Intelligence in Enhancing Innovative Environmental and Social Governance (ESG) Reporting and Action Planning

Abstract

As concerns regarding climate action continue to gain momentum, the need to invest smartly has also emerged resulting in the concept of environmental and social governance (ESG) inculcating ESG in the companies and investment life cycle perspective. This calls for action in terms of reporting and action planning so that the companies can remain accountable. This paper is based on the premise of Sustainable Development Goals (SGDs) 9 (innovation and infrastructure) and 13 on (climate action). This presents an opportunity for exploring utilization of Artificial Intelligence (AI)to make the process seamless and effective. Several ESG reporting frameworks have been put forward including Global Reporting Initiatives (GRI), Sustainability Reporting Guidelines (SABS) United Nations Global Compact (UNGC) International Organization for Standardization ISO 26000. Some of the criticism raised in relation to the frameworks is the varied application of the standards, lack of limit and demand placed on economic, environmental and social resources coupled with data inaccuracy. A survey conducted in 2017 indicated that of 4900 companies assessed globally, 60% in all industries report on cooperation responsibility. Though most companies disclose targets on carbon emissions, most of them fail to acknowledge climate risk (KPMG 2017) this could be injurious to efforts on climate action. This shortcoming weighs in on the role of AI in enhancing ESG reporting and action planning. The objectives of this paper are to establish the baseline for incorporating artificial intelligence in ESG reporting among high climate risk companies in Kenya, strengthening and cascading best practices. The population shall be high climate risk enterprises as they pose major environmental risks with high replica effect on climate action. The research shall use the diffusion theory and the Theory of change and adopt the exploratory research design capitalizing on both qualitative and quantitative methodologies.