Hi Reader, I think we await a small revolution in terms of sustainability in science.
Just think back: how many peer-reviewed publications about laboratory sustainability have you read?
The scientific system has built a robust system of peer review, citability, method reporting, and archiving data, but the sustainability field?
We need to fix that in order to progress further. Let me explain. I will take you on a tour through the history:
Today's Lesson: How Our Data Evolves
The how reporting sustainable practices changed
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In 1998, John Warner, in collaboration with Paul Anastas, developed the 12 Principles of Green Chemistry. These principles were introduced in their seminal book Green Chemistry: Theory and Practice, which has since become a foundational text. It was the first formalized work that focused on sustainable practice, providing a framework for designing chemical products and processes more sustainable.
1998
How Sustainability Data Develops
The core challenge is that sustainability claims are only perceived as valid insofar as they are supported by precisely measured data.
This graph stems from Sala et al., titled "The sustainability publication gap and its implications".
However, this data must be created, formalized, and transparently shared – have we reached that point yet?
Phase 1: Ignorance
Most new fields—or innovations—do not begin with a revolution but with a change in perspective.
Scientists have always been aware of their item, chemical, and equipment use—but mainly from a viewpoint that considered affordability and availability.
What was missing was the perspective that these activities carry a significant environmental impact.
You can read the publication here - published in 2013 and given the variability in PhD-related work, take the numbers with a grain of salt.
When the first few individuals adopted this view, they faced an immediate problem: there was no data for anything.
The absence of data essentially means that no single step is possible.
This is because missing data poses five key limitations:
Claims cannot be defended against critics.
The scale of a problem cannot be estimated.
Prioritization of actions is impossible because there is no basis for decision-making.
(Future) impact cannot be determined – i.e., implications are missing.
Improvements cannot be traced or quantified.
On top of this, without a medium to communicate, like-minded individuals cannot find each other, learn from others, and replication is impossible.
Phase 2: A New Spark
When people lack what they need, they become creative. So, early adopters began to measure consumption—not to test hypotheses, but to highlight what they intuitively sensed was a problem.
The main driver here was a mix of urgency and frustration from being unheard. The call for data generation grew.
This is one of the oldest sustainability-related pieces that is still available online. It is so funny because Mauricio Urbina is not a big name in the field – apart from this publication – I doubt the authors are aware of their fame :D
Among the blog posts and collections of data points, more precise case studies with solid quantification began to appear.
Remember, at the time, it was not as easy to access energy consumption figures for instruments (i.e., before the ACT-label), and less information on environmental impacts & alternatives was available.
This is one of the few published examples of energy as well as environmental impact analyses – in this case by Global Cooling Inc. themselves, introducing their Stirling technology as their new innovation (hence their current name, Stirling Ultracold). Published in MDPI’s Sustainability in 2012. And here is one of the oldest papers on energy savings overall.
Additionally, methodological transparency and scientific rigor were often lacking, giving many critics room for doubt.
Phase 3: Becoming More Adept
As interest grew, people wanted more than just awareness—they wanted options for action. But before optimization could be achieved, better strategies, data and a fundamental understanding was needed.
This publication outlines for the first time how sustainability within science should be conceptualized — combining environmental protection, data quality, and workflow efficiency.
To provide answers, researchers had to standardize how measurements were taken and communicate them openly.
Moreover, not just consumption, but potential savings had to be quantified.
This paper quantified hospital steam sterilizer resource consumption and how it can be reduced. You can read it here.
Eventually, collaborative papers emerged. Data on specific instruments became available. Quantified savings in money, material, and CO₂e could be calculated.
As a result, more and more institutions began to open sustainability manager positions, funding bodies required sustainability-related data reports, and legislation changed.
Nowadays, we reached a point where one can find meta-analyses on LCAs, several papers on laboratory sustainability as well as preprints on quantifications of footprints like this one by Farley et al.
Phase 4: Struggling Once Again
When moving from getting an idea to precise numbers, new challenges become apparent: Lab workflows differ significantly.
Comparisons depend on what stage of technology is being examined. Additionally, is a study from 10 years ago still valid today?
Moreover, many early references came from blog articles, posters, and internal reports with little to no methodology. Some of these websites are no longer online. Mistakes couldn’t be ruled out.
And finally, as more stakeholders joined the conversation—companies, institutions, staff—the variety of perspectives grew.
We have achieved a lot, but now we need to organize and refine it.
Applying The Knowledge
The original call for “more data” has shown its flip side.
Take bioplastics—some studies show bioplastics are clearly more sustainable, while others indicate impacts can be worse than petrochemical plastics.
This shows data from an LCA approach for Bio vs Fossil Based Plastics. We previously discussed this publication, and if you like to read more, you can do so here. PLA: Polylactic Acid; TPS: Thermoplastic Starch; HDPE: High-Density Polyethylene; LDPE: Low-Density Polyethylene; PET: Polyethylene Terephthalate; PP: Polypropylene; PS: Polystyrene.
Why? Because there is no unified system for designing studies, measuring, or reporting sustainability data.
Try reading a few LCAs on a single topic; it’s often unclear which impacts were included and what boundaries were used.
Therefore, just as an organic chemist must learn to assess a metabolism study critically, sustainability-minded scientists will have to develop skills to judge data validity—peer review alone won’t be enough.
There is no single correct way to conduct an LCA (but there probably is one for reporting it)
Overstating or understating impacts depends on the subject basis
Results vary depending on application and context.
In essence, yes, we’ve reached a point where we have precise, peer-reviewed data.
Articles and blogs like this certainly have their justification — like this one reporting on an attempt to use old plastics to mold them into new lab tools. However, we must stop citing such articles in papers, as too many websites that are not Science or Nature do not maintain the data long enough, and data generation is often non-transparent
However, the next step is investing in replicating and advancing the blog-based data and find a way to share lab practices in a trustworthy and understandable manner.
And if you’ll allow me to briefly dream: maybe we will be able to create a common assessment basis that people trust, much like peer review, without forgetting to make up their own minds.
Upcoming Lesson:
Sustainability Education in Classrooms and Labs
How We Feel Today
References
Anastas, P. T. et al., Green Chemistry: Theory and Practice. Oxford University Press (2000; online edn, 2023). https://doi.org/10.1093/oso/9780198506980.001.0001
Sala, O. E. et al., The sustainability publication gap and its implications. Current Opinion in Environmental Sustainability39, 39–43 (2019). https://doi.org/10.1016/j.cosust.2019.06.006
Penndorf, P. et al., A new approach to making scientific research more efficient – rethinking sustainability. FEBS Letters (2023). https://doi.org/10.1002/1873-3468.14736
McGain, F. et al., Steam sterilisation’s energy and water footprint. Australian Health Review41(1), 26–32 (2016). https://doi.org/10.1071/AH15142
Achten, W. M. J. et al., Carbon footprint of science: More than flying. Ecological Indicators34, 352–355 (2013). https://doi.org/10.1016/j.ecolind.2013.05.025
Berchowitz, D. M. et al., Environmental Profiles of Stirling-Cooled and Cascade-Cooled Ultra-Low Temperature Freezers. Sustainability4(11), 2838–2851 (2012). https://doi.org/10.3390/su4112838
Farley, M. et al., The carbon footprint of science when it fails to self-correct. bioRxiv (2025). https://doi.org/10.1101/2025.04.18.649468
Brizga, J. et al., The Unintended Side Effects of Bioplastics: Carbon, Land, and Water Footprints. One Earth3(1), 45–53 (2020). https://doi.org/10.1016/j.oneear.2020.06.016
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