Key Points

Why sustainable digital research matters

ICT currently contributes around 4.1% of global carbon emissions, with projections reaching 14% by 2040.
Digital research in the UK alone generates an estimated 75 kilotons of CO₂e per year, split roughly equally between large-scale compute facilities and smaller devices.
Net zero goes further than carbon neutral by targeting all greenhouse gases and requiring offsets that genuinely absorb emissions rather than merely compensating elsewhere.
Mindful computing is a conscious approach to planning, running and managing digital tasks to ensure scientific progress does not produce more emissions than necessary.
Researchers have a responsibility to consider the environmental impact of their digital activities and take steps to reduce them.

Carbon intensity of electricity varies throughout the day and across the year depending on the energy mix, which is driven by demand, weather, and fuel costs.
Demand shifting — scheduling computationally intensive work during periods of low carbon intensity — is a practical strategy to reduce operational emissions.
Embodied carbon covers emissions from the full lifecycle of a product including manufacture, transport, and disposal, not just operational electricity use.
The GHG Protocol categorises emissions into Scope 1 (direct), Scope 2 (purchased energy), and Scope 3 (value chain), providing a framework for comprehensive accounting.
Taking a broad view across all three scopes avoids underestimating the true carbon footprint of digital research activities.

For consumer devices, embodied emissions typically outweigh operational emissions, making extending device lifetime the most impactful sustainability action for personal computing hardware.
Data centres are generally more carbon efficient than equivalent local computing setups due to higher utilisation, better cooling efficiency, and shared infrastructure.
Choice of storage technology significantly affects carbon emissions; LTO tape is preferable for cold or archival data, while SSDs suit frequently accessed data.
Research data management practices — such as deleting unused data, using compression, and adopting tiered storage — can substantially reduce storage-related emissions.
Generative AI emissions scale with model size, query count, and response length; selecting the smallest model appropriate to the task reduces unnecessary emissions.
Carbon-aware computing — shifting workloads in time or location to periods or regions with lower carbon intensity — is an effective strategy for reducing operational emissions from computational workloads.

Carbon emissions in digital research map to specific research roles and workflows, meaning reduction strategies need to be tailored to context.
Identifying and quantifying emissions is the essential first step before designing effective reduction measures.
Different research roles face distinct sustainability challenges; insights from one context may still be transferable to others.

Research software development can have significant environmental impacts.
Measuring and estimating carbon emissions from research software development is important for identifying areas for improvement.
GitHub Actions and LLMs should be used judiciously to avoid unnecessary emissions.
Performance profiling can be a useful tool for identifying areas of code that could be optimised to reduce emissions.
Providing good user documentation and support can help users to make more efficient use of software, reducing emissions from usage.

Storing large amounts of research data can have significant environmental impacts.
Having a good data management plan and using appropriate storage medium can reduce the carbon emissions associated with storing data.
Not all tasks require the most advanced LLM model. Switching from a reasoning model to a less powerful model for simple data processing and analysis scripts can also contribute to lowering carbon emission associated with digital research.
While Emma’s improvements are substantial, they represent only one piece of a larger puzzle. For a life scientist, the total work-related emissions typically range from 4 to 15 tCO2e annually ². These numbers are driven by carbon intensive activities, such as international travel, laboratory heating, ventilation and AC systems, and the heavy use of chemical reagents and single-use equipment.

Data storage is often the dominant source of carbon emissions for researchers working with large datasets and long retention requirements.
Adopting a tiered storage strategy — using LTO tape for archival data and HDDs or SSDs for actively accessed data — can reduce storage emissions by an order of magnitude.
Choosing a smaller, task-appropriate LLM model can reduce AI inference emissions dramatically compared with using the largest available model.
Defining a data management plan before a project begins helps avoid unnecessary data collection, replication, and long-term storage.
Contextualising digital emissions alongside other research activities such as lab equipment and travel helps identify where reductions will have the greatest impact.

Tracking actual resource usage across HPC facilities is essential before attempting to measure or reduce associated emissions.
Wasted computation from failed or misconfigured jobs is a significant and avoidable source of emissions; validating job inputs before submission reduces this waste.
Different HPC clusters vary in carbon efficiency; benchmarking workloads across available facilities helps identify where to direct them for lowest impact.
Benchmarking parallel job resource allocation can reveal configurations that reduce energy use with only a modest speed penalty.
Sharing findings on emissions and efficiency with colleagues and facility operators multiplies the impact of individual actions across a research community.

Prior training run data and local benchmark timings can be combined to estimate emissions before committing to an expensive full training run.
Naive ML training workflows often contain significant avoidable computation — training from scratch when transfer learning is possible is a common and costly example.
ML-specific optimisations (transfer learning, early stopping, mixed precision, model pruning) can reduce both training time and GPU memory requirements, often by an order of magnitude.
Maximising GPU utilisation through right-sized batch sizes improves energy efficiency; a partially-occupied GPU is disproportionately less efficient than a fully-occupied one.
Cloud training offers lower operational carbon through data centre efficiency but removes the option to demand-shift; local training restores that flexibility at the cost of higher embodied emissions per unit of compute.

Digital research infrastructure is a growing contributor to global carbon emissions, but individual researchers can take meaningful action to reduce their impact.
Measuring or estimating emissions is the critical first step before designing effective reduction strategies.
Many emission reduction measures align with established best practices in software engineering, data management, and research methodology, providing additional motivation for their adoption.
Sustainable digital research requires ongoing monitoring and adaptation as workflows, tools, and infrastructure evolve over time.