Case Study 2 - Lab Scientist doing computational work
Last updated on 2026-02-24 | Edit this page
Estimated time: 14 minutes
Overview
Questions
- How does the increasing use of LLMs affect carbon foorprint and energy efficiency?
- What strategies can minimise the carbon footprint of research data storage?
- How does relying on old hardware prevent a modern research lab from being energy efficient?
Objectives
- Introduce a representative case study relating to carbon emissions in typical computational lab workflows
- Identify tools and resources to help estimate emissions associated with daily computational research tasks
- Quantify carbon emissions associated with using LLMs to generate Python scripts
- Quantify carbon emissions associated with storing research data
Introduction
Emma is a researcher in a biology lab and was tasked with analysing genomic sequencing data. While she is an expert in molecular biology, her computational and statistics background is limited. Due to the type and volume of data generated in the lab, she chose to write custom Python scripts to analyse her data. The project Emma is working on is scheduled to run for 5 years.
Emma’s set up:
- Personal laptop: modern and energy efficient laptop (2 years old), which she uses for email and paper writing.
- Lab Desktop: a 15 year old Desktop station, with an outdated version of Linux and no GPUs.
- Data storage: Her research generates approx. 700 GB of raw data every year. She is planning to back up 2 copies of the raw data on different HDDs. In addition, she plans to keep a copy of the processed data on different HDDs (approx 400 GB), which will be used for active analyses.
Emma’s Workflow:
- She uses cloud-based LLMs to write her scripts for processing and analysing data. This often requires many queries and iterations.
- She keeps every version of her raw data on the HDDs, and rarely deletes old files.
- After pre-processing the raw data, she stores a copy of the processed data on different HDDs
- She runs her scripts on the lab Desktop station and scripts often take 12-16 hours to complete. Sometimes Emma leaves the Desktop running 24/7 even over the weekends, so the scripts could finish running.
Emma is interested in reducing her digital carbon footprint and wants to optimise her computational workflow to balance scientific rigour with environmental responsibility.
Challenge 1: Identify Emissions
Sort the items below into Scope 1, Scope 2 or Scope 3 emissions:
- The electricity powering the lab Desktop during a 16-hour run
- The manufacturing of Emmas’s personal laptop
- The energy used by the LLM provider to write the data processing and analysis code
- The energy used by cloud-storage provider to store Emma’s data
- The external monitors used with the lab Desktop
- The electricity powering the lab Desktop (Scope 2)
- The manufacturing of Emmas’s personal laptop (Scope 3)
- The energy used by the LLM provider to write the data processing and analysis code (Scope 3)
- The energy used by cloud-storage provider to store Emma’s data (Scope 3)
- The external monitors used with the Lab Desktop (Scope 2)
Collecting information
Data storage
Emma is considering using differnt storage types after she heard that storing large amounts of data on HDDs might not be the most evironmentally friendly choice. She has heard from other colleagues that she could choose between hard drives (HDD), Solid State Drives (SDD), LTO magnetic tapes or cloud-based storage. However, she is unsure about the enivronmental impacts of these.
She found the following information for the carbon footprint associated with the four storage types and found the following:
- SDDs are the most carbon efficient when in operation, but their manufacturing produces significantly more emissions.
- HDDs have a lifespan of 5-10 years, similar to that of SDDs. Their embodied emissions are significantly lower than that of SDDs but operational emissions are higher.
- Tape storage has a longer lifespan (10-15 years), with modern ones reaching up to 30 years. However, moving and accessing data on a LTO tape is slow.
- Cloud storage’s associated emissions are estimated between 2-40 kg CO₂e/TB/year (according to a WholeGrain report and Greenly), but the value depends heavily on the data center’s efficiency and the region’s power grid. Embodied emissions are hard to estimate and depend on the hardware used by the provider (HDDs or SSDs). They are often included in the operational carbon footprint emissions.
The carbon emissions associated with the four storage types are summarised below:
| Category | SDD | HDD | LTO tape | Cloud |
|---|---|---|---|---|
| Embodied Carbon | High (16-32 kg)1 | Moderate (2-4 kg)1 | Low (~0.07 kg)3 | Difficult to estimate |
| Operational Carbon | Low (2-5 kg)1 | Moderate - High (2-16 kg)1,2 | Low (~0 kg) | Moderate - High (2-40 kg) |
| Lifespan | 5–10 years | 5-10 years | 30+ years | Depends on provider |
* Emissions are in kg CO₂e per TB per year
Emma’s research produces 700 GB of raw data each year, and since her project will run for five years, she will accumulate 3.5 TB of raw data. Because she keeps two copies of all raw data, the total required storage for raw data comes to 7 TB. Beyond that, Emma generates an additional 400 GB of processed data per year, adding up to 2 TB over the duration of the project. Altogether, Emma will need 9 TB of storage to keep both raw and processed data.
Emma works out that storing the 9 TB data on HDDs will have associated carbon emissions approximately equal to 108 kgCO2e in combined embodied and operational emissions, based on the average values within the emissions ranges she identified.
LLMs use
Emma is also concerned about the carbon footprint of her increasing use of LLMs to write the Python code to process and analyse her data. While the exact carbon footprint of using LLMs is hard to quantify, she found the following:
The carbon emissions associated with LLM use come from model training emissions, inference calls (queries) emissions, and infrastructure and hardware emissions.
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When it comes to programming-related queries, Emma found the following data:
- some LLM models emit between 20% and 59% less emissions than human programmers (GPT-4o-mini), while other models can emit 5 to 19 times more carbon than human programmers (GPT4)1
- the number of inference calls (queries) has a high correlation to the amount of carbon emissions 1
Based on her current workflow, Emma uses a reasoning model to write her scripts, often requiring more than 30 queries to the LLM to debug and obtain a script which produces correct results. Using HuggingFace’s Ecologits calculator tool, she finds that queries generating code using GPT-5 model estimate approx. 10.8 gCO2e per query. In her case, running 30 queries generates 0.324 kgCO2e, assuming she only has to do this once.
Emissions from running her scripts
Emma also begins estimating the carbon emissions associated with running her scripts. Since she cannot find the exact specifications of the old desktop, she uses a 0.3 kW power draw, a value she found commonly cited for older computer stations. However, she is still unable to find any information on the embodied carbon cost of the lab Desktop. To estimate operational emissions, she uses data from official UK grid sources (such as EnergyDashboard), and finds a grid carbon intensity of 194 gCO₂/kW on a day with overcast skies and mild winds, typical of the area she works in.
She uses the information gathered to calculate the total emissions associated with running her scripts for 16 to a total of 0.931 kgCO2e. However, this number is probably going to be higher, as Emma is likely to run the script several times throughout the course. Assuming, she runs the scripts once a year, the total carbon emissions would be closer to approx. 4.656 kgCO2e.
Greatest source of carbon emissions
Based on her calculations, Emma concludes that storing her research data and running her Python scripts are the activities with the largest associated carbon emissions. Even so, the emissions linked to using LLMs to help write her code are not insignificant. With this in mind, Emma begins developing an improved research workflow to reduce her digital carbon footprint.
Analysis
She has heard that her institution provides a tape-based cold storage options located in two different campuses and which are intended for data that is not accessed very often. She decides to keep the two copies of the raw data on the LTO-tape based storage provided by her institution, with each copy being stored at a different site. This ensures the data is safe in case something happens with one of the storages. She decides to keep her processed data on HDDs, as she needs easy and fast data access for analyses.
Emma also decides to switch to using her modern laptop to run her scripts to further reduce her carbon emissions. While the carbon footprint of using the LLM to generate her scripts is not as high as that associated with data storage and running her scripts, she decides to switch to a more simple LLM model, which is more suitable for the type of Python code she is generating.
Emma now wants to quantify the difference in carbon emissions between her existing workflow (Scenario 1) and the improved one (Scenario 2).
Scenario 1 (Current Workflow)
- Emma uses a reasoning model to write her scripts, requiring 30 queries to debug.
- She backs up her raw and processed data (9 TB total) on HDDs.
- She runs her script on the old lab Desktop, which takes 16 hours to finish.
Based on the calculations Emma has already done above, the total carbon emissions associated with her current workflow are ~113 kgCO2.
Scenario 2 (Improved Workflow)
- Emma switches to GPT-40-mini, which has a lower carbon footprint per query, and since her computational requirements are fairly light. However, debugging now takes 50 queries.
- She keeps the two copies of raw data (7 TB) in the LTO-tape based facilities provided by her institution. She keeps the processed data (2 TB) on HDDs for active work
- She runs her scripts on her modern laptop, which take 6h to finish.
Given all we know about Emma’s workflow, calculate the emissions associated with the current workflow and the improved workflow.
Using modern laptop instead of old lab Desktop
Emma is using her modern laptop and looks up the specifications for her model to get more more accurate emissions. She finds that her laptop has a Core i6-1145G7 process, with 4 CPU cores and 64 GB memory. She uses the Green-algorithms calculator to find that her computer emits 53.20 gCO2e each time she runs the script for 6 hours. If she runs the script once every year, the total emissions would be 0.266 kgCo2e.
New data storage strategy
Given that magnetic tape has negligible emissions when idle, we can assume that the total emissions from storing data on tape come from embodied emissions, estimated at ~0.07 kgCO₂ per TB. Keeping the two copies of raw data (7 GB) in the institution’s LTO‑tape storage facilities would therefore generate:
\[ E_{tape storage} = 0.07 kgCO₂e/TB/year \times 7 TB \\ E_{tape storage} = 0.49 kgCO₂e/year \]
Keeping the 2 GB of processed data on HDDs would generate:
\[ E_{HDDs} = 3 kgCO₂e/TB/year \times 2 TB + 9 kgCO₂e/TB/year \times 2 TB \\ E_{HDDs} = 24 kgCO₂e/year \]
Therefore, the total costs associated with storing Emma’s research data would be 24.49 kgCO₂e.
Switching to a simpler LLM model
Emma is planning to switch from a reasoning model to a smaller LLM model, GPT4-0-mini, for which emissions are estimated to be around 562 mgCO₂e per query.
\[ E_{LLM} = 0.562 gCO₂e/query \times 50 queries \\ E_{LLM} = 0.028 kgCO₂e \\ \]
The total emissions associated with using the simpler LLM would be approx. 0.028 kgCO₂e.
A comparison of the emissions associated with both scenarios can be found below:
| Scenario 1 (Current Workflow) | Scenario 2 (Improved Workflow) | Change | |
|---|---|---|---|
| Emissions Storage (kgCO₂e) | 108 kg/year | 24.49 kg/year | HDDs only -> LTO tape + HDDs |
| Emissions Computing (kgCO₂e) | 4.656 kg total | 0.266 kg total | old lab Desktop -> modern laptop |
| Emissions LLM (kgCO₂e) | 0.324 kg | 0.028 kg | GPT-5 -> GPT-4-o-mini |
Switching to the new, improved workflow would result in a six-fold reduction in Emma’s carbon emissions. Particularly, moving from storing data on HDDs to a hybrid storing approach that includes both HDDs and LTO-tapes has the greatest impact on lowering emissions.
Steps to reduce emissions
Emma is happy with her carbon footprint after adopting the new workflow. Building on this initial success, she has also identified several additional strategies to further minimise her digital carbon footprint:
- Schedule to run her scripts for then the grid is cleanest
- Use compression technique to further reduce the size of her stored data
- Identify and delete dark data (data that is stored but never used again)
- Process the data before uploading to cloud to reduce storage requirements
- Change which LLMs models she uses based on the task complexity
- Make use of tools such as EcoLogits (open-source Python library to estimate the carbon footprint of inference queries made to LLMs) and online LLM carbon emissions leaderboards