Measure AI's Carbon Footprint with Purpose
Extending the ISO-certified Software Carbon Intensity (SCI) methodology to measure the carbon emissions of AI systems throughout their lifecycle.
AI systems have become increasingly resource-intensive, yet there's no consistent way to measure their environmental impact. SCI for AI changes that by building on ISO/IEC 21031:2024 — the world's first ISO standard for software carbon measurement — to provide a consensus-based standard that makes AI's carbon footprint transparent, comparable, and actionable.
Developed with consensus from GSF member organisations
What is SCI for AI?
Unlike simple energy metrics or carbon offsets, SCI for AI creates a comprehensive score that incentivizes real emission reductions. By making the true carbon cost of AI transparent and comparable, it transforms sustainability from an abstract goal into a measurable, optimizable metric that drives innovation in efficient AI architectures and influences strategic decisions across industries.
Why SCI for AI Matters
As AI becomes ubiquitous across industries, its environmental footprint grows exponentially, but measurement remains fragmented and inconsistent.
Industry Impact
SCI for AI provides the first consensus-based, standardized approach to measuring AI's environmental impact. This standardization drives innovation in efficient AI architectures, influences procurement decisions, and helps organizations meet sustainability commitments. By revealing the true carbon cost of AI development and deployment, it enables meaningful comparisons between different systems and approaches, transforming how organizations think about AI investments.
Business Benefits
Reduce operational costs through improved computational efficiency and optimized cloud resource consumption
Prepare for future carbon pricing and regulatory requirements with ISO-compatible measurement standards
Gain a competitive advantage through transparent sustainability metrics for AI products and services
Make informed trade-offs between model performance and environmental impact with clear, actionable data
Build stakeholder trust through demonstrable commitment to responsible AI development
Environmental Impact
SCI for AI directly addresses the growing carbon footprint of AI systems by providing metrics that incentivize real reductions rather than offsets. The specification reveals the full picture of AI emissions, from data preparation through training to inference, exposing impacts in early stages that often dwarf inference costs. This visibility encourages practices like model optimization, efficient architectures, and carbon-aware computing that significantly reduce AI-related emissions by enabling informed choices about when, where, and how AI systems operate.
Understanding AI's Carbon Lifecycle
SCI for AI measures emissions across every stage of AI development and deployment, revealing optimization opportunities throughout the entire lifecycle.
Inception
Scoping the problem and setting constraints
Design and Development
Where major emissions accumulate through training
Deployment
Integrating AI into production systems
Operation and Monitoring
Inference, orchestration, and ongoing maintenance
End of Life
Decommissioning systems and handling data
AI's Hidden Emissions
Traditional approaches often focus solely on inference costs, missing the significant carbon footprint of training and data preparation. SCI for AI provides comprehensive lifecycle coverage, including often-overlooked stages like data engineering and system integration. This holistic view enables organizations to identify and address the true sources of AI emissions.
Transformative Capabilities
SCI for AI brings unprecedented clarity to AI sustainability through innovative features designed for real-world application.
Comprehensive Coverage
Measures emissions from data preparation through end-of-life, capturing impacts others miss
AI-Native Design
Supports all AI paradigms: ML, deep learning, generative AI, and emerging technologies
Clear Boundaries
Precise definitions for measuring different AI systems with appropriate functional units
Engineering Focus
Incentivizes direct optimizations rather than relying on carbon offsets
Industry Consensus
Developed with input from major players with royalty-free IPR for broad adoption
A New Architecture for Energy Intelligence
Building on Industry Collaboration
In early 2025, AI experts from GSF member organizations participated in a series of workshops hosted by the Software Standards Working Group. These sessions were designed to define the GSF approach to AI measurement as well as evaluate existing metrics. The outcomes laid the groundwork for creating the SCI for AI specification.
“The purpose of this specification is to assist AI practitioners in understanding and reducing the carbon footprint of AI systems. By making informed choices about model design, computational efficiency, and deployment strategies, practitioners can minimize emissions while maintaining performance.”
Navveen Balani
Software Standards Working Group Chair
Accenture / Green Software Foundation
Explore Further
Deep dive into SCI for AI methodology and related resources
Development Timeline
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Q4 2024
Proposal
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Q2 2025
Pre-Draft
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Q3 2025
Draft
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Q4 2025
Consistency Review
SC Ratification -
Q1 2026
Publication
ISO-readiness approval -
Q2 2026
ISO Submission
Get Involved
Your expertise and experience can help refine this transformative standard
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