Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid

2025-11-06

AI Summary

Generative AI (GenAI) poses significant climate risks due to its rapidly growing energy demand and associated carbon emissions, yet it remains inadequately integrated into mainstream climate risk assessment frameworks. Method: We propose G-TRACE—a novel, region-aware, cross-modal carbon accounting framework—that combines empirical data with micro-level simulation to quantify energy consumption and carbon intensity by output modality (text/image/video) and deployment geography. We further introduce a seven-tier AI Sustainability Pyramid model linking digital behavior to actionable governance pathways. Contribution/Results: Empirical analysis reveals that a trend in Studio Ghibli–style image generation consumed 4,309 MWh and emitted 2,068 tons of CO₂, underscoring how decentralized inference amplifies systemic environmental impact. G-TRACE enables granular, auditable carbon accounting for GenAI, advancing both theoretical understanding and practical policy intervention for AI-related climate risk identification, measurement, and mitigation.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Philosophy and Ethics of AI: Artificial General Intelligence
• Machine Learning: Efficient ML / Green AI

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Sustainability and climate impact of web technologies

Abstract

Generative Artificial Intelligence (GenAI) represents a rapidly expanding digital infrastructure whose energy demand and associated CO2 emissions are emerging as a new category of climate risk. This study introduces G-TRACE (GenAI Transformative Carbon Estimator), a cross-modal, region-aware framework that quantifies training- and inference-related emissions across modalities and deployment geographies. Using real-world analytics and microscopic simulation, G-TRACE measures energy use and carbon intensity per output type (text, image, video) and reveals how decentralized inference amplifies small per-query energy costs into system-level impacts. Through the Ghibli-style image generation trend (2024-2025), we estimate 4,309 MWh of energy consumption and 2,068 tCO2 emissions, illustrating how viral participation inflates individual digital actions into tonne-scale consequences. Building on these findings, we propose the AI Sustainability Pyramid, a seven-level governance model linking carbon accounting metrics (L1-L7) with operational readiness, optimization, and stewardship. This framework translates quantitative emission metrics into actionable policy guidance for sustainable AI deployment. The study contributes to the quantitative assessment of emerging digital infrastructures as a novel category of climate risk, supporting adaptive governance for sustainable technology deployment. By situating GenAI within climate-risk frameworks, the work advances data-driven methods for aligning technological innovation with global decarbonization and resilience objectives.

Problem

• Quantifying climate risks from Generative AI energy consumption and emissions
• Measuring AI carbon footprint across different regions and output modalities
• Translating emission metrics into governance models for sustainable AI deployment

Innovation

• Region-aware carbon accounting framework for AI emissions
• Cross-modal measurement of training and inference emissions
• Seven-level governance model linking metrics to policy