Research Summary: Toward a Time-Aware Assessment Framework for the Carbon Cost of AI-Enabled Decarbonization

A new framework has been proposed to assess the carbon cost of Artificial Intelligence (AI) solutions used for decarbonization, particularly within the built environment. This framework addresses critical gaps in existing assessment methods by accounting for both AI-induced carbon emissions and the temporal mismatch between when AI costs are incurred and when decarbonization benefits are realized. It emphasizes a time-aware approach to model these emissions and benefits over a defined period.

Why it matters

This framework is strategically important because it provides a more accurate and holistic method for evaluating the true environmental impact of AI-driven decarbonization initiatives. It enables organizations to make better-informed investment decisions, ensuring that AI solutions genuinely contribute to sustainability goals rather than inadvertently increasing overall carbon footprints. Understanding the temporal dynamics of emissions and benefits is crucial for long-term strategic planning in climate mitigation.

Key insights

• AI is increasingly deployed to aid decarbonization efforts in the built environment.
• Current assessments of AI-supported decarbonization often overlook the energy consumption and CO2e emissions generated by AI's development, training, and operation.
• Existing evaluations frequently fail to account for the time difference between the occurrence of AI-related costs and the materialization of decarbonization benefits, especially in large-scale infrastructure projects.
• A new time-aware assessment framework is introduced to model avoided emissions and AI-induced emissions as discrete-time streams over a finite time horizon.
• The framework aims to support temporal decision-making by providing a more comprehensive view of the net carbon impact.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.18029