Emissions Reporting Maturity Model: supporting cities to leverage emissions-related processes through performance indicators and artificial intelligence
Victor de A. Xavier, Felipe M. G. França, Priscila M. V. Lima
TL;DR
The paper tackles fragmented city-level emissions reporting and stagnation in GHG reductions by introducing the Emissions Reporting Maturity Model (ERMM) and a parallel Performance Indicator Development Process (PIDP). It combines AI-driven clustering (ClusWiSARD) with qualitative methods (Grounded Theory, Case Study) to identify reliable KPIs from emissions data, using CDP city data to ground the analysis. The ERMM framework integrates ISO/IEC TS 33061 and CMM/DMMM concepts to produce an Emissions Reporting Maturity Level (ERM-L, 0–5) and a data-management context for cities, with demonstration across 814 CDP cities. The work outlines practical pathways for AI-assisted decision support in e-government and highlights future directions, including IoT-enabled data streams and broader domain applications in energy, transport, and employment reporting.
Abstract
Climate change and global warming have been trending topics 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 critical component of GHG reduction policy and is therefore the focus of this work. The main goal of this work is two-fold: (i) to propose an emission reporting evaluation model to leverage emissions reporting overall quality and (ii) to use artificial intelligence (AI) to support the initiatives that improve emissions reporting. Thus, this work presents an Emissions Reporting Maturity Model (ERMM) for examining, clustering, and analysing data from emissions reporting initiatives to help the cities to deal with climate change and global warming challenges. The Performance Indicator Development Process (PIDP) proposed in this work provides ways to leverage the quality of the available data necessary for the execution of the evaluations identified by the ERMM. Hence, the PIDP supports the preparation of the data from emissions-related databases, the classification of the data according to similarities highlighted by different clustering techniques, and the identification of performance indicator candidates, which are strengthened by a qualitative analysis of selected data samples. Thus, the main goal of ERRM is to evaluate and classify the cities regarding the emission reporting processes, pointing out the drawbacks and challenges faced by other cities from different contexts, and at the end to help them to leverage the underlying emissions-related processes and emissions mitigation initiatives.