Performance indicators for emissions reporting based on artificial intelligence

Abstract

Climate change and global warming have been a trending topic worldwide since the Eco-92 conference. However, little progress has been made in reducing greenhouse gases (GHGs). The problems and challenges related to emissions are complex and require a concerted and comprehensive effort to address them. Emissions reporting is a key component of GHG reduction policy and is therefore the focus of this work. This work presents a method for examining, clustering, and analysing data from emissions reporting initiatives. Using artificial intelligence clustering technologies, performance indicator concepts and qualitative analysis approaches, the proposed method is implemented through a performance indicator development process (PIDP), which aims to search for performance indicators (PIs) among data selected from emissions databases. During the implementation of the PIDP, the results showed that a new model is essential to deal with emission reporting information. Therefore, this study proposes a new model to evaluate emissions reporting processes implemented by cities, which is based on concepts inherited from the capability maturity model (CMM). The main objective of this model is to help cities address the challenges of emission reduction by leveraging the areas and processes associated with emission reporting. This model and how it can be used in the context of emissions reporting is described in detail in the methodology, as are the experiments and other results obtained during the development of this study.