How to Align AI Energy Scoring with ESG Reporting Standards

An open-source Python package for tracking the carbon emissions of your compute jobs. It's the de facto standard for measuring energy consumption during AI model training and inference.

• Key Feature: Automatically estimates emissions by tracking CPU/GPU power draw and applying regional carbon intensity data.
• ESG Integration: Outputs structured data that can be mapped to GRI 302 (Energy) and SASB TC-IT-130a.2 (Energy Management) disclosure categories.
• Use Case: Integrate into your training scripts to generate per-experiment emission reports, creating a verifiable data lineage for your AI portfolio's carbon footprint.

Extend the standard ML model lifecycle management platform to capture energy metrics alongside accuracy and performance.

• Key Feature: Use or build plugins to log CodeCarbon outputs as MLflow run parameters and artifacts.
• ESG Integration: Creates a centralized, versioned audit trail of energy consumption per model version, which is essential for CSRD compliance on resource use.
• Use Case: Every model training run automatically records its energy_consumed_kWh and co2e_kg, making it queryable for annual sustainability reporting.

An open-source tool from the Green Software Foundation for visualizing the carbon cost of cloud usage across AWS, GCP, and Azure.

• Key Feature: Translates cloud service usage (like GPU hours) into estimated carbon emissions using the latest carbon intensity data.
• ESG Integration: Provides the Scope 2 emissions data (purchased electricity) required for frameworks like GRI and the Partnership on AI's ML Sustainability Code.
• Use Case: Connect it to your cloud billing exports to get a dashboard of your AI training cluster's monthly carbon impact, broken down by project or team.

Provides embodied carbon data for hardware, critical for a full lifecycle assessment (LCA) of your AI infrastructure.

• Key Feature: Offers APIs to query the carbon cost of manufacturing and transporting servers, GPUs, and other IT equipment.
• ESG Integration: Enables calculation of Scope 3 emissions (upstream activities), a complex but increasingly mandated part of CSRD and advanced ESG reports.
• Use Case: Use the API to calculate the embodied carbon of your on-premise AI cluster or to compare the lifecycle impact of different cloud instance types.

The official resource for the Sustainability Accounting Standards Board (SASB) framework. It defines the precise, industry-specific metrics companies should disclose to investors.

• Key Feature: Provides the exact disclosure topics and metrics for the Technology & Communications sector (e.g., TC-IT-130a.2: Energy management).
• ESG Integration: This is the target schema. Your AI energy data (total consumption, renewable energy percentage) must be formatted to align with these standardized metrics.
• Use Case: Reference this to build the mapping table between your internal AI energy KPIs and the exact SASB metric codes for your annual report.

The comprehensive Global Reporting Initiative (GRI) standards are the most widely used global framework for sustainability reporting.

• Key Feature: GRI 302: Energy is the core standard for disclosing energy consumption, reduction, and intensity.
• ESG Integration: Your AI energy scoring system must produce data that fits GRI's reporting requirements, such as total energy consumption within the organization (GRI 302-1) and energy intensity ratio (GRI 302-3).
• Use Case: Structure your AI energy monitoring system to output data that can be directly fed into the GRI 302 reporting template, ensuring you meet stakeholder expectations for transparency.