Corporate Reporting of Scope 3 Greenhouse Gas Emissions and the Future of Artificial Intelligence: Current Situation, Issues, and Prospects

Abstract

The current state of corporate greenhouse gas (GHG) emissions reporting has undergone a massive transformation over the last several years as the Scope 3 emissions are the most challenging and enormous part of organizational carbon footprints that often comprises more than 70 percent of overall emissions by industry. Although these regulatory and stakeholder pressures to incorporate full emissions disclosure in their reporting have positively impacted their reporting, there are still gaps in their standardization, data quality of Scope 3 reporting frameworks and methodological consistency. The paper provides a summary of the current standards and gaps in the research field, a new analysis of the Scope 3 greenhouse gas emissions reporting problem and technologies, which can transform the practice of GHG emission reporting. The studies include the findings of more than 15 other peer-reviewed articles and industry reports published in the last 5 years and investigate the extent of current reporting systems, industry-specific variations in emissions reporting, and the major ambiguities that could impede effective Scope 3 reporting, relying on the holistic dataset of more than 100 companies across industries. It presents a novel AI-based emissions estimation regression model, which shows high predictive accuracy, especially of S3 Category 1 emissions, and network analysis shows new importance of investment-related Scope 3 emissions groups. Such outcomes positively respond to the necessity to develop standardized strategies, enhance the quality control of the information, and instill technologies of artificial intelligence into the Scope 3 reporting system that would serve plausible policy suggestions which can be followed by policy-makers, sustainability specialists, and business decision-makers in their practice of introducing more transparency and accountability of emissions to the system.