Research is an energy-intensive activity. Laboratories run power-hungry equipment around the clock, computing clusters draw enormous amounts of electricity, and the scholarly enterprise is bound together by a culture of international travel to conferences and collaborations. For a long time these costs were invisible in environmental terms — counted, if at all, only as line items in a budget. That is changing. Funders, institutions and researchers are beginning to ask what the carbon footprint of research actually is, and to account for it with something approaching the rigour they already apply to data and finance. This article surveys how greenhouse-gas emissions reporting is taking shape for research institutions, drawing on the sustainable-research domain of the CASRAI Dictionary.
The GHG Protocol and its three scopes
The dominant framework for emissions accounting, in research as everywhere else, is the Greenhouse Gas Protocol. Its central contribution is a way of organising an organisation’s emissions into three scopes, which together prevent both double-counting and convenient omission. Scope 1 covers direct emissions from sources the organisation owns or controls — fuel burned on site, institutional vehicles, and the gases released by certain laboratory processes. Scope 2 covers indirect emissions from the energy the organisation buys, above all purchased electricity — the emissions produced by the power station, attributed to the institution that consumes the power. Scope 3 covers all other indirect emissions across the value chain: business travel, commuting, procured goods and services, waste, and the embodied carbon of the equipment and supplies an institution buys. For most research organisations, Scope 3 is by far the largest and the hardest to measure — and it is where research has some of its most distinctive emissions.
The carbon cost of conferences and travel
Academic culture has long treated frequent international travel as normal and even as a marker of seniority and engagement. Under emissions accounting, that travel becomes visible as a substantial Scope 3 source. Flying researchers across the world to present work and attend meetings carries a real carbon cost, and the recognition of this has driven genuine change in practice: more virtual and hybrid conferences, more deliberate choices about which trips are truly necessary, and policies that encourage rail over short-haul flights where feasible. The point is not to end scholarly exchange — collaboration and the meeting of minds are essential to research — but to make its environmental cost a conscious factor rather than an unexamined habit. Measuring travel emissions is the first step towards managing them.
Laboratory energy and sustainable lab frameworks
The other distinctive source is the laboratory itself. Ultra-low-temperature freezers, fume hoods, autoclaves and specialist instruments make labs among the most energy-intensive spaces in any institution, often consuming several times more energy per unit area than ordinary offices. Two frameworks have become prominent in addressing this. LEAF (the Laboratory Efficiency Assessment Framework) offers laboratories a structured set of actions and an accreditation scheme to reduce their environmental impact — covering energy, waste, water, procurement and sample storage — while also saving money. My Green Lab provides certification and standards for sustainable laboratory practice, working with the scientific community and suppliers to drive improvement. Both turn the abstract goal of a greener lab into concrete, assessable steps, and both give researchers a recognised way to demonstrate that their practice has improved.
The carbon cost of computing
As research becomes ever more computational, the emissions of computing demand their own attention. Large-scale data analysis, simulation and especially the training of machine-learning models can consume very large amounts of electricity, and the associated emissions depend heavily on when and where the computation runs — on the carbon intensity of the electricity grid powering the data centre at that moment. The Software Carbon Intensity (SCI) specification, developed within the Green Software Foundation, provides a methodology for calculating the carbon intensity of a software application, expressing emissions per unit of useful work. For research computing, frameworks like this make it possible to measure and compare the carbon cost of computational work, and they point towards practical responses — running flexible workloads when and where the grid is cleaner, and choosing efficient methods — so that the growth of computational research does not silently inflate its footprint.
Why measurement comes first
Running through all of this is a simple principle: you cannot manage what you do not measure. The value of the GHG Protocol’s scopes, of LEAF and My Green Lab, and of the SCI specification is that they make the carbon cost of research visible and comparable. Once an institution can see that its travel dominates its footprint, or that its freezers or its computing draw disproportionate energy, it can act with proportion rather than guesswork. And once emissions are measured in standard ways, they can be compared across institutions and over time, progress can be demonstrated to funders increasingly interested in sustainability, and good practice can be recognised. Sustainable research is not a matter of gesture; it rests on honest accounting.
A consistent vocabulary for sustainability data
For emissions data to be compared and aggregated across institutions, funders and reporting frameworks, the terms involved must mean the same thing everywhere — what counts as Scope 1, 2 or 3, how travel and procurement emissions are categorised, what a sustainability metric refers to. That consistency is what the CASRAI Dictionary works towards: a shared vocabulary so that the sustainability information flowing through institutional and funder reporting is understood identically wherever it appears. And because sustainable practice is increasingly part of how research is conducted and assessed, the work of greening a lab or reducing computational impact sits alongside the other contributions captured in frameworks such as the CRediT taxonomy and its full set of contribution roles. As research confronts its own environmental impact, the discipline of measurement — the same instinct that produced sound data management — is what turns concern into change. Institutions wanting to integrate this into their operations will find it sits naturally within wider research administration.