Green Module

Accountability-first, always-on carbon footprint and environmental impact tracking for AI inference.

The Problem

Every LLM inference request has an environmental cost:

  • Carbon emissions from GPU power consumption
  • Water usage for data center cooling
  • Embodied carbon in hardware manufacturing

This cost is largely invisible. Providers don't disclose per-request emissions. Users have no way to understand, track, or reduce their AI environmental footprint.

OpenClaw surfaces this by default — not as guilt, but as accountability and awareness.

Key Features

Feature Description
Always-on tracking Captures carbon data from the first request
Per-request granularity Atomic traces that aggregate into summaries
Conservative estimates Worst-case factors backed by academic research
Confidence scoring Every estimate carries a 0.0–1.0 confidence
Standards compliance GHG Protocol, CDP, TCFD, ISO 14064, SBTi
Multiple interfaces CLI, Gateway dashboard, REST API

Quick Start

# Check your environmental impact
openclaw green status

# View intensity metrics (TCFD)
openclaw green intensity

# Export for GHG Protocol reporting
openclaw green export --format ghg-protocol --period 2025-Q1

Architecture

Green Module Architecture

Next Steps