2nd/10_Wiki/Topic_Programming/AI_and_ML/Sustainability.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-sustainability	Sustainability	10_Wiki/Topics	verified	self	Green Software ESG Carbon Footprint	none	A	0.9	applied	sustainability green-software carbon-footprint esg ai-energy		2026-05-10	pending	language framework python codecarbon

Sustainability

매 한 줄

"매 software / AI 의 carbon-aware design". ESG mandate (EU CSRD 2025+), AI training 의 explosive energy growth (GPT-5 ~15GWh, Claude Opus 4.7 estimates), green coding practice 의 mainstream화. 매 measure → reduce → report 의 cycle.

매 핵심

매 three pillars

• E (Environmental): carbon, water, e-waste.
• S (Social): labor, dataset bias, accessibility.
• G (Governance): transparency, audit, compliance (CSRD, SEC climate rule).

매 software-specific

• Green coding: efficient algorithm, language choice (Rust vs Python), serverless cold-start vs warm.
• Carbon-aware computing: workload scheduling (run when grid is clean — Google "Carbon Intelligent Computing").
• Energy-efficient inference: quantization (INT8, INT4), distillation, MoE sparse routing.
• Hardware: ARM Graviton, Apple Silicon, NVIDIA Blackwell efficiency.

매 AI footprint (2026)

• Training: 매 single Frontier model run ~10-50 GWh.
• Inference: 매 GPT-5 query ~3-10 Wh (vs Google search ~0.3 Wh).
• Aggregate: AI 의 datacenter 가 2030 의 global electricity 의 3-7% 예상.

매 응용

1. CI/CD 의 carbon budget enforcement.
2. Cloud region selection (Quebec hydro vs us-east-1 mixed).
3. Model serving optimization (batch, KV cache reuse).
4. CSRD reporting (EU large company mandate).

💻 패턴

codecarbon (Python tracking)

from codecarbon import EmissionsTracker

tracker = EmissionsTracker(project_name="train_run")
tracker.start()
try:
    train_model()
finally:
    emissions_kg = tracker.stop()
    print(f"Run emitted {emissions_kg:.4f} kg CO2eq")

Carbon-aware scheduler

import requests

def grid_intensity(region: str) -> float:
    # WattTime / Electricity Maps API
    r = requests.get(f"https://api.electricitymaps.com/v3/carbon-intensity/latest?zone={region}",
                     headers={"auth-token": KEY})
    return r.json()["carbonIntensity"]  # gCO2/kWh

def best_region(regions: list[str]) -> str:
    return min(regions, key=grid_intensity)

# usage
target_region = best_region(["us-west-2", "ca-central-1", "eu-north-1"])
schedule_job(region=target_region)

Quantization for inference

from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B", quantization_config=bnb)
# 4-bit quantization → ~75% memory + energy reduction vs fp16

Cloud Run min-instances=0 (cold start tradeoff)

# cloudrun.yaml — 매 idle 시 0 instance, 매 traffic 의 cold start 허용
spec:
  template:
    spec:
      containers:
        - image: gcr.io/proj/api
      containerConcurrency: 80
    metadata:
      annotations:
        autoscaling.knative.dev/minScale: "0"

Carbon budget CI gate

# .github/workflows/carbon.yml
- name: Run with codecarbon
  run: python train.py
- name: Check budget
  run: |
    EMISSIONS=$(jq -r .emissions_kg emissions.json)
    if (( $(echo "$EMISSIONS > 5.0" | bc -l) )); then
      echo "::error::Carbon budget exceeded: ${EMISSIONS}kg > 5kg"; exit 1
    fi

Green model selection

# 매 task 의 simplest sufficient model
from anthropic import Anthropic
client = Anthropic()

def route_query(complexity: int, query: str):
    model = "claude-haiku-4-5" if complexity < 3 else "claude-opus-4-7"
    return client.messages.create(model=model, max_tokens=1024,
                                  messages=[{"role": "user", "content": query}])
# Haiku 의 ~10-20x energy-cheaper than Opus

매 결정 기준

상황	Action
매 training large model	clean-grid region + spot + checkpoint
매 inference at scale	quantize + batch + KV cache
매 simple query	smallest sufficient model (Haiku, Sonnet)
매 reporting mandate	codecarbon + CSRD format
매 datacenter choice	Iceland, Quebec, Norway > us-east-1

기본값: 매 measure first (codecarbon) + 매 model right-size + 매 carbon-aware region.

🔗 Graph

• 부모: ESG
• 변형: Green-Software
• Adjacent: LLM_Optimization_and_Deployment_Strategies · Mixture-of-Experts · Energy-Efficiency

🤖 LLM 활용

언제: 매 model selection (right-size), 매 prompt caching aggressive use (cache hit ~90% energy reduction), 매 batch API. 언제 X: 매 user-facing latency-critical (단, model-route hybrid 가능).

❌ 안티패턴

• 매 항상 Opus 사용: 매 simple task 도 frontier model — 10-20x energy waste.
• Cache 미사용: 매 prompt caching 의 cache miss 가 every call → energy + cost.
• Greenwashing: 매 carbon offset 만 사고 actual reduction X — credibility crash.
• Single region lock-in: 매 dirty grid 의 stuck — multi-region 로 carbon-aware schedule.

🧪 검증 / 중복

• Verified (Green Software Foundation principles 2021+; Patterson et al. 2021 "Carbon Emissions and Large Neural Network Training"; EU CSRD 2024 effective; IEA 2024 datacenter report).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — ESG + AI footprint + green coding patterns