# Rseng Green Computing > Covers the environmental footprint of research computing: measuring and reporting energy use and carbon emissions of computations (CodeCarbon), reducing them through efficient code, right-sized hardware and carbon-aware scheduling (CATS), the GREENER principles and the Software Carbon Intensity metric. Use when the user asks about the carbon or energy cost of their computations, wants to make workloads greener, mentions sustainability of computing, CodeCarbon, CATS or the Software Carbon Intensity metric. Use PROACTIVELY when planning large training runs, simulations or parameter sweeps - footprint measurement is worthless retrospectively. (Keeping the software project itself alive is rseng-maintenance-sustainability; making code faster is rseng-performance-profiling.) - Skill: `fdiblen/rseng-green-computing` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add fdiblen/rseng-green-computing` - Raw SKILL.md: https://api.skillmd.com/api/skills/fdiblen/rseng-green-computing/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - License: CC-BY-4.0 - Author: fdiblen (https://skillmd.com/u/fdiblen) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/fdiblen/rseng-green-computing --- # Environmentally sustainable research computing Research computing has a real carbon footprint - large simulations, ML training runs and always-on services consume energy at a scale funders and institutions increasingly ask about. The GREENER principles for computational science frame the practice: Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research. For an agent the working core is: measure first, reduce second, schedule smart, report honestly. ## Measure before optimizing Never guess at footprint - estimate it: - CodeCarbon instruments Python code with a few lines (or a CLI wrapper) and estimates energy plus location-adjusted CO2e; add it to representative runs, not every run. - For non-Python or cluster workloads, estimate from job accounting (CPU/GPU hours x hardware power draw x facility PUE x grid carbon intensity) and state the assumptions. - Record estimates alongside results the same way runtimes are recorded, so the cost of a paper's computations is reportable. The Software Carbon Intensity (SCI) specification (an ISO standard) gives a defensible formula when a formal number is needed: operational plus embodied emissions per functional unit. ## Reduce Order interventions by leverage, and quantify the win when possible: 1. Compute less: cache intermediate results, avoid re-running unchanged pipeline stages (rseng-workflows), kill zombie jobs, right-size parameter sweeps before launching them. 2. Compute efficiently: profile first (rseng-performance-profiling) - a 5x speedup is usually a ~5x energy cut; use appropriate precision; prefer vectorized/compiled paths in hot loops (rseng-language-guides). 3. Match hardware to the job: GPUs are more energy-efficient than CPUs for the workloads that suit them (rseng-gpu-computing) and wasteful for the ones that do not; do not reserve more nodes, memory or walltime than the job uses. 4. Store less: data has a footprint too - prune intermediates, compress archives, apply retention rules (rseng-data-management). ## Schedule smart Grid carbon intensity varies by hours and by region. Carbon-aware scheduling shifts flexible batch work to cleaner windows: - CATS (Climate-Aware Task Scheduler) picks the lowest-carbon start time for a job of a given duration on UK-grid data; the same delay-tolerant principle applies anywhere batch work is flexible. - Cloud users can choose lower-carbon regions for flexible workloads; cluster users can prefer off-peak windows where the operator exposes them. ## Report and advocate - Include a brief compute-footprint statement in papers and READMEs for compute-heavy projects (estimated kWh/CO2e and the estimation method) - normalize the practice. - When proposing CI pipelines, keep them lean: cache dependencies, skip redundant matrix entries, avoid scheduled jobs nobody reads (rseng-ci-cd). - Educate while doing: a measured number ("this sweep emitted an estimated 12 kg CO2e") lands better than generic advice. ## Working with this skill This skill is source-independent: its authority is the GREENER principles, the SCI specification and the tool documentation linked below. Learn more (verified): - https://www.nature.com/articles/s43588-023-00461-y - GREENER principles for environmentally sustainable computational science - https://codecarbon.io - CodeCarbon energy/CO2e estimation - https://greensoftware.foundation/standards/sci/ - Software Carbon Intensity specification - https://github.com/GreenScheduler/cats - Climate-Aware Task Scheduler ## Related skills Check whether any of these applies before moving on: - rseng-ci-cd - lean pipelines waste less compute - rseng-data-management - storage retention has a footprint - rseng-gpu-computing - matching hardware to workload efficiency - rseng-hpc-computing - right-sized resource requests save energy - rseng-performance-profiling - speedups cut energy roughly proportionally - rseng-workflows - caching avoids recomputing pipeline stages