The problem the researchers set out to solve is rooted in the way carbon accounting frameworks classify emissions. Electricity consumed by a laboratory falls under so-called scope 2 emissions, the indirect emissions generated when power is produced elsewhere and delivered to the building. For energy-intensive facilities such as laboratories, which run freezers, incubators, centrifuges, and high-performance computing equipment around the clock, scope 2 accounting can represent a substantial share of the total carbon footprint. Yet despite the rapid rollout of auditing processes such as the Laboratory Efficiency Assessment Framework, known as LEAF, and shifting institutional policies, easy-to-use and accurate reporting tools for this category of emissions have not been widely available to individual researchers.

WillCO2st takes an unusually pragmatic approach to closing that gap. The program draws on real-time, region-specific conversion factor data that are freely available through resources such as the Carbon Intensity API and the Electricity Maps API. These services track the carbon intensity of electricity grids as it fluctuates throughout the day, reflecting the changing mix of wind, solar, nuclear, and fossil fuel generation feeding the grid at any given moment. By combining this live grid data with metadata extracted from saved experimental files, the software can produce experimental carbon footprint estimates that reflect not just how much electricity an experiment consumed, but when that electricity was used and how clean the grid happened to be at that time.

The workflow is deliberately simple. Researchers can click and drag their data files directly into WillCO2st, and the program generates an experimental sustainability audit within minutes. That speed matters in a field where sustainability reporting has often been delegated to specialist sustainability officers or outsourced to consultants, adding time and cost that many research groups cannot spare. By automating the conversion of raw experimental metadata into a formatted report, the tool eases what the authors describe as the time and resource burden on researchers, allowing them to produce documents suitable for institutional submissions without specialized training in carbon accounting.

One of the most intriguing features of the software is what the authors call live nudging. Because the tool knows the carbon intensity of the local grid at different times of day, it can identify windows during which running the same experiment would produce fewer emissions. The researchers demonstrated this capability by quantifying the retrospective, theoretical reduction in electricity-driven emissions that would have been achieved simply by shifting experimental timing to periods of lower carbon intensity. In grids where solar generation peaks at midday or wind output surges overnight, the difference between running an energy-hungry protocol at a high-carbon hour and a low-carbon hour can be significant, and the software makes those differences visible and actionable.

The significance of this timing-based approach extends beyond individual laboratories. Electricity grids around the world are becoming more dynamic as renewable generation expands, which means the carbon cost of a kilowatt-hour is no longer a fixed number but a moving target that varies by hour and by region. Traditional carbon accounting often relies on annual average conversion factors, which smooth over these fluctuations and obscure opportunities for demand shifting. By using real-time, region-specific data instead, WillCO2st aligns research emissions reporting with the way modern grids actually operate, and it positions scheduling as a legitimate emissions-reduction strategy alongside equipment upgrades and energy-efficiency measures.

The broader context for the tool is a wave of institutional change sweeping through the research sector. Funding bodies and universities are making sustainability a focal point, and auditing frameworks such as LEAF are being rolled out rapidly across laboratories in the United Kingdom and beyond. These processes create demand for sustainability accounting at the level of the individual researcher, not just the institution. Until now, meeting that demand has often required researchers to assemble estimates manually, piecing together electricity consumption figures, conversion factors, and reporting templates on their own. WillCO2st serves, in the authors’ words, as a model for how researchers can effectively and accurately engage with research sustainability auditing.

The authors are careful to describe the tool’s estimates as experimental, an honest caveat that reflects the inherent uncertainty in combining file metadata with live grid data. Real-time carbon intensity figures themselves carry uncertainties, since grid operators model the fuel mix rather than measure every generator directly, and the electricity consumption inferred from experimental files may not capture every device involved in a workflow. Even so, the approach represents a meaningful advance in accessibility. A researcher who previously had no practical way to estimate the scope 2 emissions of an experiment can now drag a file into a program and receive a formatted, region-aware audit, with all of the underlying conversion data drawn from freely available public sources.

What makes WillCO2st potentially viral among laboratory scientists is the combination of zero cost, minimal effort, and immediate output. Sustainability reporting has traditionally been perceived as an administrative chore imposed from above, but a tool that produces a report in minutes from files a researcher already has lowers the barrier to participation to nearly nothing. The live-nudging feature adds an element of gamification as well, inviting scientists to compete against their own past emissions by rescheduling work toward cleaner hours of the grid. If widely adopted, such behavioral shifts could aggregate into meaningful demand-side reductions, particularly in research clusters located on grids with strong daily swings in renewable output.

The study ultimately frames sustainability auditing not as a burden but as an opportunity for the research community to lead by example. By demonstrating that accurate, real-time carbon footprinting can be fast, free, and built on open data, Smith and Pulver offer a template that other disciplines and institutions could adapt. As funders continue to tie sustainability performance to research assessment, tools of this kind are likely to move from novelty to necessity, and the laboratories that adopt them early may find themselves ahead of both the regulatory curve and the climate one.

Subject of Research: Real-time carbon intensity forecasting for research laboratory electricity emissions reporting

Article Title: WillCO 2 st: leveraging freely available real-time carbon forecasting for research emissions reporting

Article References: Smith, W. V., & Pulver, S. R. (2026). WillCO2st: leveraging freely available real-time carbon forecasting for research emissions reporting. PLOS Sustainability and Transformation, 5(7), e0000257. https://doi.org/10.1371/journal.pstr.0000257

Image Credits: AI Generated

DOI: 10.1371/journal.pstr.0000257

Keywords: carbon accounting, scope 2 emissions, laboratory sustainability, carbon intensity, electricity maps, real-time forecasting, research emissions, LEAF framework, open-source software, energy demand shifting, grid decarbonization, sustainability reporting

The research, published in the journal Discover Sustainability, was conducted by Yi Huan Tan, Lai Ti Gew, Raja Affendi Raja Ali and Pei Boon Ooi, drawing on teams from Sunway University’s Jeffrey Cheah Sunway Medical School and School of Psychology, together with the Gut Research Group at Universiti Kebangsaan Malaysia. The team set out to identify the determinants of sustainable single-use plastic waste management behaviour among active laboratory users in Malaysia. Rather than simply cataloguing what people throw away, the researchers wanted to understand the mental machinery behind the decision: the attitudes, social pressures, sense of control and environmental awareness that combine to shape whether a scientist sorts, reuses or properly disposes of plastic consumables.

To do this, the study leaned on one of the most widely used frameworks in behavioural science: the Theory of Planned Behaviour, or TPB. Developed originally to explain how human actions flow from intention, the theory proposes that the single best predictor of a behaviour is the intention to perform it, and that intention itself is driven by three factors. The first is attitude, meaning the individual’s evaluation of the behaviour as beneficial or harmful. The second is subjective norms, the perceived social pressure from colleagues, supervisors and peers to behave in a certain way. The third is perceived behavioural control, the person’s belief that they are actually capable of carrying out the behaviour given the resources, opportunities and obstacles they face.

The Malaysian team extended this classic model by adding a fourth psychological ingredient: environmental awareness, the depth of a person’s understanding of environmental problems and the consequences of their own actions. They also included a practical control variable, namely whether a sustainable waste management protocol was present in the respondent’s workplace, to test whether formal institutional rules shape outcomes independently of psychology. This combination allowed the researchers to weigh the relative power of internal beliefs against external structures, a question that matters enormously for anyone designing sustainability policies for research institutions.

Data came from a cross-sectional survey of 245 active laboratory users, recruited through an online questionnaire. The sample captured people who work at the bench day to day and therefore generate and handle laboratory plastic waste directly. The researchers then analysed the responses using Partial Least Squares Structural Equation Modelling, a statistical technique known as PLS-SEM that is well suited to testing complex networks of relationships between psychological constructs. Rather than examining each factor in isolation, PLS-SEM allows scientists to build a model in which attitudes, norms, control beliefs and awareness feed into intention, and intention and control in turn feed into actual reported behaviour, with the strength of each pathway estimated simultaneously.

The results were striking in several respects. Attitude, perceived behavioural control and environmental awareness all significantly predicted behavioural intention, confirming that laboratory users who view sustainable plastic management as worthwhile, who feel capable of doing it, and who understand the environmental stakes are more likely to intend to act. But the single strongest predictor was perceived behavioural control. In other words, what mattered most was not whether scientists cared, and not even whether they felt social pressure from those around them, but whether they believed they had the practical ability, time, facilities and institutional support to manage their plastic waste sustainably.

Just as telling was what failed to predict intention. Subjective norms, the social pressure component of the Theory of Planned Behaviour, showed no significant relationship with behavioural intention in this sample. This is a genuinely counterintuitive result, because in many environmental behaviour studies the expectations of peers and colleagues are a powerful lever. The finding suggests that in laboratory settings, simply encouraging a culture of sustainability, or relying on colleagues to nudge one another, may not be enough on its own. Scientists, the study implies, may respond less to what everyone around them says and more to what the workplace actually enables them to do.

The model’s downstream pathways reinforced this control-centred picture. Both behavioural intention and perceived behavioural control positively predicted sustainable waste management behaviours, meaning that good intentions do translate into action, but only when people also feel empowered to act. The practical implication is hard to overstate: institutions that want greener laboratories should focus less on persuasion and more on infrastructure. Accessible recycling streams, clear protocols, adequate bins, training and time built into workflows may accomplish what awareness campaigns alone cannot, because they directly strengthen the sense of control that the data identify as the dominant driver.

The presence of a sustainable waste management protocol as a control variable adds an organisational dimension to the findings. Formal protocols represent the institutional scaffolding within which individual psychology operates, and their inclusion acknowledges that laboratory behaviour is never purely a matter of personal choice. Sterility requirements, biosafety rules and experimental demands constrain what is possible at the bench, and a written, enforced protocol can either widen or narrow the space for sustainable action. By testing this factor alongside the psychological variables, the study offers a template for future research that treats individual motivation and organisational structure as interacting forces rather than competing explanations.

The broader context makes the study timely. Scientific research generates an enormous and growing stream of single-use plastic waste, and the sector’s reliance on disposable consumables has proven difficult to reconcile with sustainability goals. Yet laboratories are also places of unusually high expertise and, often, strong environmental values, which makes the gap between attitude and action especially frustrating. By showing that perceived behavioural control outweighs both attitude and social norms in this population, the Malaysian findings point toward a specific, actionable strategy: give laboratory users real options, clear procedures and genuine capability, and sustainable behaviour is likely to follow. The authors present their work as a contribution to the growing discourse on sustainable laboratory practices, offering recommendations for policy and organisational initiatives aimed at reducing single-use plastic waste in research settings. If the message spreads, the humble pipette tip box may finally get the recycling infrastructure its users have long intended to give it.

Subject of Research: Psychological determinants of sustainable single-use plastic waste management behaviour among laboratory users

Article Title: Sustainable single-use plastic waste management among laboratory users using theory of planned behaviour

Article References: Tan, Y. H., Gew, L. T., Raja Ali, R. A., & Ooi, P. B. (2026). Sustainable single-use plastic waste management among laboratory users using theory of planned behaviour. Discover Sustainability. https://doi.org/10.1007/s43621-026-04891-2

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04891-2

Keywords: theory of planned behaviour, single-use plastic, laboratory waste, sustainable waste management, PLS-SEM, environmental awareness, perceived behavioural control, behavioural intention, Malaysia, environmental psychology, laboratory sustainability, waste management protocol