Conference Programme
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available). Note that the schedule is subject to changes.
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Parallel Session 07: Carbon Emission Targets, Measures & Verification Location: Seminar Room 6 (Level 2) Session Chair: Sadat Reza, Nanyang Technological University | |
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Artificial intelligence and the voluntary disclosure of Scope 3 carbon emissions 1: University of Regensburg, Germany; 2: San Diego State University, CA, USA; 3: Freie Universität Berlin, Germany Measuring Scope 3 carbon emissions (S3E) is complex and data intensive. We examine whether, and how, artificial intelligence—specifically, machine learning (AI-ML)—enhances firms’ ability to measure S3E for disclosure purposes. Using a novel measure of firms’ AI-ML adoption intensity based on job postings, we find that AI-ML adoption intensity is positively associated with voluntary disclosure of S3E. An instrumental variable design exploiting plausibly exogenous variation in talent supply supports a causal interpretation. We also find that AI-ML adoption intensity is positively associated with the quality of S3E disclosures. Path analysis reveals that these results are partly mediated by firms’ environmental value chain management practices, but that these mediation effects are secondary. Overall, our findings highlight the role of technology in enabling the disclosure of complex and difficult-to-measure carbon accounting metrics, offering insights for managers, regulators, and other stakeholders engaged in corporate climate accountability. | |