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Tasks 12-14: Related work, citations, complementary tool links

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Task 12: Add Related Work section (Section 21) to methodology covering
EcoLogits, CodeCarbon, AI Energy Score, Green Algorithms, Google/Jegham
published data, UNICC framework, and social cost research.

Task 13: Add specific citations and links for cognitive deskilling
(CHI 2025, Springer 2025, endoscopy study), linguistic homogenization
(UNESCO), and algorithmic monoculture (Stanford HAI).

Task 14: Add Related Tools section to toolkit README linking EcoLogits,
CodeCarbon, and AI Energy Score. Also updated toolkit energy values to
match calibrated methodology.
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claude 2026-03-16 10:43:51 +00:00
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impact-toolkit/README.md
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@ -40,7 +40,8 @@ The hook fires before Claude Code compacts your conversation context.
It reads the conversation transcript, extracts token usage data from
API response metadata, and calculates cost estimates using:
- **Energy**: 0.003 Wh/1K input tokens, 0.015 Wh/1K output tokens
- **Energy**: 0.1 Wh/1K input tokens, 0.5 Wh/1K output tokens
(midpoint of range calibrated against Google and Jegham et al., 2025)
- **PUE**: 1.2 (data center overhead)
- **CO2**: 325g/kWh (US grid average for cloud regions)
- **Cost**: $15/M input tokens, $75/M output tokens
@ -48,13 +49,32 @@ API response metadata, and calculates cost estimates using:
Cache-read tokens are weighted at 10% of full cost (they skip most
computation).
## Related tools
This toolkit measures a subset of the costs covered by
`impact-methodology.md`. For more precise environmental measurement,
consider these complementary tools:
- **[EcoLogits](https://ecologits.ai/)** — Python library that tracks
per-query energy and CO2 for API calls to OpenAI, Anthropic, Mistral,
and others. More precise than our estimates for environmental metrics.
- **[CodeCarbon](https://codecarbon.io/)** — Measures GPU/CPU energy for
local training and inference workloads.
- **[Hugging Face AI Energy Score](https://huggingface.github.io/AIEnergyScore/)** —
Benchmarks model energy efficiency. Useful for choosing between models.
These tools focus on environmental metrics only. This toolkit and the
methodology also cover financial, social, epistemic, and political costs.
## Limitations
- All numbers are estimates with low to medium confidence.
- Energy-per-token figures are derived from published research on
comparable models, not official Anthropic data.
- Energy-per-token figures are calibrated against published research
(Google, Aug 2025; Jegham et al., May 2025), not official Anthropic data.
- The hook only runs on context compaction, not at conversation end.
Short conversations that never compact will not be logged.
- This toolkit only works with Claude Code. The methodology itself is
tool-agnostic.
- See `impact-methodology.md` for the full methodology, uncertainty
analysis, and non-quantifiable costs.