Machine Learning Based Corporate Climate Change Disclosure in Integrating Institutional and Resource Approach based on Computational Intelligence Method

There has to be a coordinated effort between academia, government, and business to evaluate and shape the impact of AI and ML on efforts to slow global warming. It’s not clear how ML will affect GHG emissions in the future, but it’s already having an impact now. This is due in part to the fact that assessing and predicting the effects of these emissions is complicated by the fact that their various processes are not well characterised. As a result, we present a structured approach for characterising the consequences of ML on GHG emissions, which is comprised of three levels: (1) compute-related implications; (2) immediate repercussions of using ML; and (3) effects on the whole system. This approach is used to assess and rank the importance of the information and studies required for carrying out an effect evaluation, doing a scenario analysis, or identifying crucial policy levers.