AI systems have become increasingly resource-intensive, yet there's no consistent way to measure their environmental impact. SCI for AI changes that by providing the first consensus-based standard that makes AI's carbon footprint transparent, comparable, and actionable. Help refine this transformative specification with your feedback and experience as we revolutionize how we build and deploy sustainable AI.
Leading organizations collaborating to standardize AI carbon measurement
As AI becomes ubiquitous across industries, its environmental footprint grows exponentially, but measurement remains fragmented and inconsistent.
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.
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
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.
SCI for AI measures emissions across every stage of AI development and deployment, revealing optimization opportunities throughout the entire lifecycle.
Scoping the problem and setting constraints
Where major emissions accumulate through training
Integrating AI into production systems
Inference, orchestration, and ongoing maintenance
Decommissioning systems and handling data
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.
SCI for AI brings unprecedented clarity to AI sustainability through innovative features designed for real-world application.
Measures emissions from data preparation through end-of-life, capturing impacts others miss
Supports all AI paradigms: ML, deep learning, generative AI, and emerging technologies
Precise definitions for measuring different AI systems with appropriate functional units
Incentivizes direct optimizations rather than relying on carbon offsets
Developed with input from major players with royalty-free IPR for broad adoption
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.
Software Standards Working Group Chair
Accenture / Green Software Foundation
Deep dive into SCI for AI methodology and related resources
Your expertise and experience can help refine this transformative standard
Proposal
Pre-Draft
Draft
Consistency Review
SC Ratification
Publication
ISO-readiness approval
ISO Submission
SCI for AI provides the first standardized methodology to measure and reduce the carbon emissions of artificial intelligence systems. Whether you're developing AI, making procurement decisions, or driving sustainability strategy, join us in making AI's environmental impact transparent and actionable.
