International Green AI initiatives
There is currently a need to understand the environmental costs of AI to ensure deployment at scale for all countries and to build an AI ecosystem respectful of our planetary boundaries. The goal of the Coalition is to ensure that AI becomes an essential lever for addressing global environmental challenges while minimizing its ecological footprint. Various initiatives will enable this vision, starting with methods and standards to measure and minimize this impact along the value chain.
To ensure that the use and development of AI is in line with planetary boundaries, this inventory of initiatives brings visibility to Green AI collaborative projects that aim to reduce or minimize the environmental impacts of AI.
These projects were selected following consultation with members of the Coalition for Sustainable AI. They are organised into sub-categories according to how they address AI environmental issues.
Standards
Roadmap for AI environmental sustainability standardization
According to France, ITU, IEEE, and ISO, this roadmap aims to ensure efficient resource use, reduce confusion, and promote consistent measurement of AI’s environmental impact. It encourages collaboration among international standardization bodies to adopt best practices and minimize overlapping or conflicting standards.
Environmental Impacts of Artificial Intelligence Working Group
According to the Institute of Electrical and Electronics Engineers (IEEE), this standard defines a framework for reporting environmental indicators for AI systems, including compute intensity and associated impacts such as CO₂ emissions and water use. It provides methodologies to distinguish AI-specific compute from general-purpose compute in data centers.
Metrics and Guidelines for the Environmental Impact of AI
According to the European Committee of Standardization (CEN), this document outlines principles and a framework for measuring the environmental impact of AI systems and services. It provides harmonized calculation methods, reporting guidelines, and best practices to reduce environmental impacts throughout the AI lifecycle.
Guidelines for assessing the environmental impact of AI Systems
According to the International Telecommunication Union (ITU), this report uses global and regional models, datasets, and consultations to project AI’s electricity consumption over the next decade, likely energy sources, and implications for energy security, emissions, innovation, and affordability.
The iMasons
Climate Accord
The iMasons Climate Accord coalition works towards the industry adoption of an open standard to report carbon in data center power, materials, and equipment and a maturity model to report participant progress.
Software Carbon Intensity
for AI
According to the Green Software Foundation, this specification helps AI practitioners reduce the carbon footprint of AI systems. It develops a methodology to calculate a carbon emissions rate (SCI score) to guide design, efficiency, deployment, and progress tracking across the AI lifecycle.
Best practices
Engagement Kit
for Frugal AI
According to the French Ministry of Environment, with Hub France IA and AFNOR Standardisation, a list of best practices on frugal AI is published for public and private stakeholders. It promotes reasoned AI development, efficient systems, and provides actionable guidance for organizations to adapt and reference in AI or ethics charters.
Sustainable Datacenters
& ICT
The project is led by the Copenhagen Centre on Energy Efficiency and serves as a global platform to share best practices of energy efficient solutions in data centres, provide policy recommendations to policy makers and foster transformational collaboration among various public and private stakeholders in the global data centre industry.
Library of
compressed algorithms
Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead. It provides a comprehensive suite of compression algorithms including caching, quantization, pruning, distillation, and compilation techniques
Climate Neutral Data Centres Pact
According to CNDCP, over 100 data centre operators and trade associations are committed to the European Green Deal and climate neutrality. They agree to make data centres climate neutral by 2030, supporting Europe’s goal of achieving greenhouse gas reductions and a climate-neutral future by 2050.
Measuring & Reporting tools
Code Carbon
CodeCarbon is a lightweight Python package that estimates CO₂ emissions from cloud or personal computing resources. It helps developers reduce emissions by optimizing code or using cloud regions powered by renewable energy.
Green Algorithms
The Green Algorithms project aims at promoting more environmentally sustainable computational science. It regroups calculators that researchers can use to estimate the carbon footprint of their projects, tips on how to be more environmentally friendly, training material, past talks etc.
Ecologits
EcoLogits tracks the energy consumption and environmental impacts of using generative AI models through APIs. The main goal of the EcoLogits Calculator is to raise awareness on the environmental impacts of LLM inference.
CarbonAI
(python package)
This project aims at creating a python package that allows you to monitor the power consumption of any python function.
Carbon Footprint Calculator
for AI
AI carbon footprint calculator allows users to easily assess the environmental impact of a business’s AI marketing activities.
Net Carbon Impact Assessment Methodology and case Study Calculators
According to the European Green Digital Coalition, with support from the European Commission, science-based methods were developed to estimate the net environmental impact of digital solutions across six sectors. Phase I produced a methodology for net climate impact, Case Study Calculators, and sector-specific guidelines for green digital transformation.
BoAmps
According to Boavizta, this project provides a standard data model and an open data space to collect and share AI energy consumption measures. Its goal is to assess the environmental impacts of AI system use systemically, enabling multi-criteria evaluations and life cycle assessments based on user-specific references.
Energy and Environmental Optimization of Data Centers
According to Energy4Climate Center and Revaia, this project analyzes and anticipates data centers’ global impact on energy use and CO₂ emissions, focusing on dashboards for site and tech choices, thermal modeling, global energy and carbon mapping, and optimizing workloads across hyperscale data centers.
Zeus: Deep Learning Energy Measurement and Optimization
According to ML Energy, Zeus is a library for measuring and optimizing the energy consumption of deep learning workloads. It provides low-overhead measurement in Python or the command line and multiple optimizers for workload and GPU tuning.
Transparency
Energy & AI Observatory
According to the International Energy Agency (IEA), a Global Observatory on Energy and AI is being launched to provide data and analysis on AI’s impact on the energy sector, in collaboration with industry and scientific experts.
Observatory of AI Environmental Impact
According to Ecole normale supérieure (ENS), with support from Capgemini and the AI and Society Institute, the ENS Foundation has launched an Observatory to analyze and mitigate AI’s environmental impacts across its lifecycle. The initiative aims to establish a shared methodology to promote sustainable AI use.
Expert Group on AI Compute & Climate Change
According to the OECD.AI Expert Group, the AI Compute and Climate initiative develops a framework to understand, measure, and benchmark AI computing capacity by country and region. The group works with key AI players to assess energy consumption from AI compute.
Green Digital Action Working Group on Sustainable AI
According to the International Telecommunication Union (ITU), the Green Computing pillar of the Green Digital Action initiative is conducting a three-phase project to develop standardized metrics and assess AI’s environmental footprint. Click on the “learn more” to read the differnt phases.
AI Energy Score
According to Hugging Face, the AI Energy Score initiative, co-led with Salesforce and in partnership with Cohere and Carnegie Mellon University, aims to establish a standardized framework for evaluating the energy efficiency of AI model inference. The project addresses the urgent need to assess and mitigate AI’s environmental impact as energy consumption is projected to rise significantly.
Impact AI
According to the Alexander von Humboldt Institute for Internet and Society Greenpeace, and Economy for the Common Good, the Impact AI project develops a transdisciplinary auditing method to measure social and ecological impacts and promote sustainable AI use.
ML.ENERGY Leaderboeard
According to ML. Energy, using the Python library Zeus, ML. Energy created a leaderboard for LLM chat energy consumption to highlight trade-offs between energy use, system performance, and user experience.
List a new initiative
The initiatives identified as part of the coalition are brought forward by its members and supporters and listed in this “Hub”. They have to be open, collaborative and internationally-oriented. Members and supporters of the Coalition do not endorse the listed initiatives. However, they can participate or provide support to the listed initiatives at their own discretion. If you wish to list a new initiative, please use the contact form.
