carbon intensity

Framework for Carbon-Aware AI Inference Incentives Balancing Accuracy, Latency, and Emissions

This paper proposes an incentive framework for AI inference services that accounts for users' valuation of quality, latency, and environmental consciousness. The core mechanism is a two-tier subscription model where users accept discounted service—lower model quality and higher latency—during high carbon-intensity periods in exchange for reduced costs. The framework formalizes the tradeoff space between carbon emissions and quality-of-experience parameters, giving providers flexibility to shift inference load toward greener operating points.