Generative AI's Carbon Paradox: Balancing Emissions and Climate Mitigation Through Lifecycle Optimization (#458)

Authors

Silva Atencio, Gabriel

Abstract

This study comes up with a new hybrid life cycle assessment (LCA) approach that combines process-based analysis with real-time operating data to solve the carbon riddle of generative AI, which is the tension between the technology's high emissions and its potential to help the climate. Based on an actual review of 12,540 GPU-hours across 36 sector-specific implementations, the study shows that while training models releases 15.2 to 480 tCO₂e, smart application can lead to a net-positive carbon balance over the course of its life. Different types of designs are better at reducing carbon emissions by 2.3 times (p < 0.01) compared to general-purpose models. Task ordering that takes carbon into account and optimizing space also cut working emissions by 28% (±3.2%). To find the best mix between speed, cost, and carbon output, the method uses physics-based neural networks and a multi-objective optimization function. It cuts the average error down to 6.7%, which is 58% better than the old LCA methods. Some important but little-known factors that were looked at in the study are the embodied carbon that comes from making hardware (18.7–38.2% of lifetime emissions) and behavioral return effects, which in industrial settings take away 12.1% (±2.3%) of theoretical gains. As a result, there is good evidence that policy tools such as carbon-aware computer standards and geospatially-tailored green energy incentives can make AI 2.1 to 3.3 times better for the environment.