Every organisation has its ESG ambitions on paper by now: CO2 reduction targets, a CSRD roadmap, a sustainability report. EU policy is clear about the order of priorities: reduce and reuse before repair, repair before recycle. The European Green Deal, the Circular Economy Action Plan and the Ecodesign for Sustainable Products Regulation all push in the same direction.

For lab-heavy organisations, most of that impact sits in one place: the equipment. And that is exactly where ambition tends to stall.

The gap between the boardroom and the lab

ESG targets are decided at the highest level. A board commits to a reduction, a sustainability officer turns it into a plan, and the plan lands on the desk of a lab manager, a core facility coordinator or a workshop lead. They are the people who have to make the changes.

And they face a simple question: “Where do I start?”

Which of your 200 machines draw the most energy? Which sit idle most of the week? Which have passed the point where keeping them costs more than replacing them? Does the purchase request on your desk duplicate a machine two floors up that nobody uses?

In most labs, nobody knows. Usage lives in paper logbooks, Outlook calendars and pivot tables, if it is recorded at all. Energy is measured per building, not per instrument. The people responsible for delivering the target are asked to steer without instruments.

You can’t steer what you can’t see.

Estimates don’t change behaviour

Without data, ESG reporting falls back on estimates: a flat emissions factor, an assumed utilisation rate, an extrapolation from last year. Most organisations under CSRD obligation still report this way.

Auditors question estimates, but that is the smaller problem. The bigger one is that an estimate gives the people in the lab nothing to act on. It can’t tell you which machine to switch off, which run to move or which purchase to stop. Sustainability becomes a reporting exercise instead of an operational one.

Sustainability shouldn’t slow research down

In R&D, sustainability often sounds like doing less: fewer runs, fewer instruments, longer queues. That is the wrong goal. Research and innovation shouldn’t slow down because of an ESG target.

The real opportunity is making better use of the machines you already have. The machine that stands idle in one lab is often exactly what another lab is waiting for. When you bring more experts to the same equipment and stop buying duplicates of machines that sit unused elsewhere, you reduce your footprint while doing more research, not less.

Start with visibility

A pragmatic approach is the best way to get moving. You don’t need a five-year transformation programme. You need to know what actually happens on your machines. Start with one lab and the equipment that matters most.

Step one is visibility. A unit on each machine records every session automatically: who used it, when, for how long and how much energy it drew. No logbooks, no self-reporting. Actual use, not booked time.

Once you can see, the next steps become concrete, and the people in the lab can take them themselves.

Share before you buy. Utilisation data exposes idle and duplicate equipment across teams and sites. Manufacturing lab equipment typically has a far bigger footprint than using it, so every machine you don’t need to buy is embodied carbon that never gets created.

Maintain on actual use. Issue reporting at the machine and maintenance based on real operating hours catch small problems before they become breakdowns. Assets last longer, with up to 72% less downtime along the way.

Run when the grid is clean. Belgium’s grid swings from around 100 gCO2/kWh in the cleanest window to roughly 210 g late in the evening. A week-ahead forecast shows teams the cleanest hours to run energy-hungry equipment, and session data shows whether they did. In one recorded set of sessions, shifting runs cut the footprint by about 40% compared with a flat emissions factor, without changing the work itself.

Keep or replace on the numbers. Repair before replace is the right default, but not always the right answer. An ageing machine that draws far more power than a current model keeps costing energy and CO2 every day it runs. Take energy consumption into the equation and a higher investment today can become a saving over the machine’s lifetime, both in euros and in emissions. That saving has to outweigh the footprint of building a new machine. A keep-or-replace simulation per machine, combining energy draw, maintenance, downtime and resale value, shows which way the balance tips. Sometimes the answer is replace, sometimes it’s keep and extend. Either way, the decision rests on data, not on the age of the unit.

One data layer, all three letters

Visibility is not only an environmental story. The unit that logs usage also enforces trained access, so a machine won’t unlock for someone who hasn’t completed the training. Every session, access change and maintenance action is timestamped and attributed to a named user, giving an immutable audit trail.

And it rolls up. Each lab keeps its autonomy, but everything reports in the same format. The board gets measured, instrument-level figures for CSRD and ESRS disclosures, and the objective measurement that LEAF and My Green Lab certification ask for. The loop between the boardroom and the lab finally closes.

The bonus: faster innovation

Breakthroughs rarely happen behind locked doors. They happen when people with different backgrounds collide over a new question, and a shared machine is often where that collision starts. Every team that opens up an instrument to another group creates a connection no formal programme could have planned.

Sharing doesn’t just mean fewer machines and a smaller footprint. It means more people, more disciplines and more ideas around the same equipment.

Working more sustainably isn’t pulling the handbrake. It can even help you move faster.