Algorithmic Digital Emissions and Their Impact on Modern Carbon Footprint Accounting

Artificial intelligence has become embedded in routine digital activities, operating continuously through background processes such as application optimisation, automated notifications, recommendation systems, and real-time data monitoring. Although individual algorithmic operations require minimal energy, their persistent repetition across billions of connected devices results in a cumulative environmental footprint that remains largely unaccounted for. These dispersed micro-level processes contribute to what this paper describes as algorithmic digital emissions, an emerging category of carbon emissions absent from most modern carbon accounting frameworks. Existing methodologies continue to prioritise visible and industrial scale emission sources, thereby underestimating the environmental consequences of widespread digital automation. This study examines the nature of algorithmic digital emissions, evaluates the limitations of current carbon accounting systems in capturing such emissions, and argues for their formal inclusion in sustainability assessments. Recognizing these emissions is essential if carbon footprint accounting is to remain accurate and relevant in an increasingly AI driven digital ecosystem.