The research team conducted cradle-to-grave life cycle assessments, or LCAs, a methodology that traces environmental burdens from raw material extraction through manufacturing, transport, use, and final disposal. While LCAs have become standard tools in industry for products ranging from automobiles to packaging, they have rarely been applied systematically to laboratory instrumentation. Previous sustainability work in research settings has concentrated on consumables such as pipette tips and plasticware, or on big-picture drivers like building energy consumption and commuting. Equipment, by contrast, has remained something of a blind spot, partly because laboratories own such a bewildering variety of devices, from ultralow temperature freezers to mass spectrometers, each with its own manufacturing footprint and operating profile.

To cut through that complexity, the authors developed a screening approach designed to handle large and heterogeneous inventories of equipment. Rather than attempting a bespoke, data-intensive assessment of every single instrument, their method identifies which categories of equipment carry the highest potential environmental impacts and therefore deserve deeper scrutiny. Applied to the Government of Canada laboratory inventory of over 3,000 items, the approach demonstrates how institutions with sprawling, varied equipment fleets can prioritize where sustainability interventions will actually matter. That prioritization step, the authors argue, is essential if laboratory greening initiatives are to move beyond symbolic gestures and target the genuine hotspots.

The headline finding concerns refrigerators and freezers, the workhorses of sample storage in virtually every scientific discipline. For these appliances, the use stage accounts for approximately 97 percent of life cycle greenhouse gas emissions. The reason is straightforward physics combined with relentless duty cycles: refrigeration equipment runs continuously, often for decades, compressing refrigerants and rejecting heat around the clock. Manufacturing impacts, while real, are dwarfed by the cumulative electricity demand of tens of thousands of operating hours. The implication is striking for procurement officers and lab managers alike. Choosing a marginally more efficient freezer, or consolidating samples to retire an aging unit, can outweigh nearly any consideration about how the device was built.

Yet the study cautions against a one-size-fits-all rule, because the dominance of the use phase is not universal. The life cycle profile of laboratory equipment varies across environmental indicators, including climate change, acidification, smog formation, and ecological toxicity. An instrument that looks relatively benign from a carbon perspective may carry heavier burdens in categories tied to metal mining, chemical manufacturing, or end-of-life treatment. It also varies with usage frequency, measured in hours per year of operation. A device that is energy-hungry but rarely switched on may have a life cycle dominated by its production, whereas the same model running continuously flips the balance toward use. Sustainability strategies, in other words, need to be matched to both the instrument type and the actual pattern of use in a given laboratory.

Scanning electron microscopes illustrate the production-intensive end of the spectrum. These sophisticated instruments, which raster a focused beam of electrons across samples to reveal nanoscale structure, embody substantial material and manufacturing inputs, from precision-machined vacuum chambers to specialized electron optics and detectors. When such equipment is used relatively infrequently, the manufacturing share of its life cycle impacts rises accordingly. For production-intensive devices, the leverage points shift: extending service life, refurbishing and redeploying instruments, and sharing access across research groups become more consequential than shaving kilowatt-hours during operation. The contrast between freezers and electron microscopes encapsulates the study’s central message that equipment-level sustainability requires understanding where in the life cycle each device’s burdens actually sit.

That message leads to the study’s fourth contribution, a deliberate connection between LCA results and the concept of sustainability transformations. Drawing on leverage-point thinking, the authors argue that effective change requires interventions aligned with each equipment category’s life cycle profile, and that these interventions extend well beyond procurement decisions. Procurement matters, particularly for production-intensive instruments and for efficiency specifications on use-intensive ones. But operational practices, maintenance schedules, utilization rates, sample consolidation, and end-of-life management all shape the total footprint. An institution that green-procures a freezer and then leaves its door seals degraded, or runs half-empty ultralow freezers at minus 80 degrees Celsius when minus 70 would suffice, forfeits much of the benefit.

The breadth of the assessment also matters for how laboratories account for their impacts. Much of the existing conversation about sustainable science has centered on greenhouse gas emissions, driven by institutional net-zero commitments. By broadening the scope to acidification, smog formation, and ecological toxicity, the study reveals trade-offs that a carbon-only lens would miss. For example, strategies that reduce electricity use will generally help across multiple categories, since grid electricity feeds many impact pathways. But decisions about materials, refrigerants, and disposal routes can shift burdens between categories in ways that demand a fuller accounting. Cradle-to-grave system boundaries ensure that problems are not simply pushed upstream to mining and manufacturing or downstream to waste streams.

The timing of this work is significant. Scientific institutions worldwide are under growing pressure to align their operations with climate targets, and initiatives to certify sustainable laboratories have proliferated. Yet without equipment-level data, such programs risk optimizing the visible and the easy, such as single-use plastic reduction, while overlooking the slow, continuous emissions of a freezer fleet running at all hours in corridors across the research enterprise. The Canadian inventory approach offers a template: screen the equipment base, identify the use-intensive and production-intensive hotspots, and then deploy the appropriate mix of efficiency upgrades, usage changes, lifetime extension, and informed purchasing.

For the scientific community, the study reframes laboratory sustainability as a problem of matching interventions to life cycle profiles rather than applying blanket rules. Freezers and refrigerators call for energy-focused action during use; microscopes and similar instruments call for stewardship of embodied impacts across decades of service. As laboratories confront their own environmental accounts, the message from this assessment is that the instruments of discovery are not neutral tools but significant environmental actors, and that managing their footprint requires looking at the whole life of the machine, from the mine to the bench to the scrapyard.

Subject of Research: Life cycle assessment of the environmental impacts of laboratory and scientific equipment operation and procurement

Article Title: Environmental impacts of laboratory and scientific equipment: Focus on equipment operation and procurement

Article References: Cimprich, A., Kasaeipour, S., & Young, S. B. (2026). Environmental impacts of laboratory and scientific equipment: Focus on equipment operation and procurement. PLOS Sustainability and Transformation, 5(8), e0000271. https://doi.org/10.1371/journal.pstr.0000271

Image Credits: AI Generated

DOI: 10.1371/journal.pstr.0000271

Keywords: life cycle assessment, laboratory equipment, greenhouse gas emissions, sustainability, refrigerators and freezers, scanning electron microscopy, procurement, use phase emissions, ecological toxicity, Government of Canada laboratories, leverage points, PLOS Sustainability and Transformation

The core of their argument is deceptively simple: AI does not merely respond to existing challenges in long-term care, it actively reshapes them. By determining which data are collected, which risks are classified, and which outcomes are prioritized, AI systems influence how care needs are understood and addressed in the first place. Ethical risks, the authors argue, arise not only from technical performance failures but from something far subtler: the possibility that structural and relational problems in care systems are reframed as individual conditions that can be monitored and managed through technology. When loneliness, neglect, or inadequate staffing become data points on a dashboard, the underlying causes can escape scrutiny entirely.

These risks operate at several interconnected levels, the authors explain. At the individual and relational level, AI may affect autonomy, dignity, privacy, consent, and the relationships between older adults and their caregivers. AI-enabled systems can collect and process sensitive data without sufficient transparency about how that information is used, protected, or retained. Excessive reliance on AI may also weaken the human connection and reciprocity that lie at the heart of care itself. At the level of social justice, biased data, opaque systems, and weak accountability may deepen existing inequalities, particularly for older adults living with frailty, socioeconomic disadvantage, or limited AI literacy. And at the environmental level, scaling data-intensive AI carries material costs through energy consumption, water use, mineral extraction, and the expansion of data infrastructures.

Perhaps the most provocative claim concerns the cultural and epistemic level. Categories such as dependency, risk, efficiency, and quality of care, the authors stress, are not neutral descriptors. They reflect assumptions shaped by medical, administrative, commercial, and policy institutions, and those assumptions can become embedded in datasets, classification systems, platform design, and performance indicators. Decisions about what data to collect and which outcomes to optimize therefore encode particular ideas about normal aging, desirable behavior, appropriate care, and where responsibility for care should lie. The authors warn that these embedded assumptions may reinforce ageist and deficit-oriented representations of older people while marginalizing diverse experiences of aging and social relations. In this process, older adults risk being reduced to data subjects or measurable profiles, while their identities, relationships, life histories, and social belonging receive less and less attention.

To make this concrete, the authors offer a hypothetical scenario that reads like a cautionary tale for care facilities everywhere. Imagine an AI-powered chatbot introduced to reduce loneliness among residents of a long-term care home. In an underfunded facility facing chronic workforce pressures, staff may come to rely on the chatbot to monitor residents’ emotional states, flag signs of social isolation, and initiate conversations. The facility may then treat the technology as a substitute for investment in staffing, communal activities, and sustained human relationships. The system itself may rest on datasets that reflect narrow assumptions about normal social interaction, potentially misinterpreting culturally diverse forms of communication and connection. Its collection of personal data creates privacy and governance risks, while the computing infrastructure behind it carries environmental costs. Loneliness, in this scenario, has been reframed as an individual condition to be detected and technologically managed, while the organizational conditions that produced it remain untouched.

Existing regulation, the authors acknowledge, has made real progress. The European Union’s AI Act has established a risk-based approach to governing artificial intelligence, and China has been building an expanding framework for AI industry standards and technical standardization. These governance frameworks provide important safeguards. Yet compliance-oriented governance, Xu and Teerawichitchainan argue, tends to focus on the capabilities and risks of individual AI systems, framing responsibility mainly in terms of technical compliance rather than institutional responsibility for staffing, social infrastructure, and equitable care. Such approaches may leave unexamined the most important questions of all: why particular AI systems are adopted in the first place, which problems they are expected to solve, and how organizational arrangements, power relations, and sociocultural assumptions shape their use.

The authors’ proposed remedy draws on systems thinking, a framework pioneered by researcher Donella Meadows, whose influential work on leverage points described the places where intervention can alter how an entire system behaves. Systems thinking understands complex challenges as outcomes of dynamic relationships among technical systems, organizational arrangements, professional practices, and societal values. The key insight is that changes to technical parameters, such as improving model accuracy or tightening consent procedures, may reduce particular risks but leave the system’s underlying goals and structures intact. Deeper interventions instead seek to change how problems are defined, whose knowledge carries authority, what organizations are designed to achieve, and who holds the power to redirect the system. Applied to AI-enabled long-term care, this means that the most powerful levers are not in the code but in the context surrounding it.

From this perspective, the authors identify four domains in which deep leverage points may operate. The first encompasses societal paradigms and value systems at the level of policy and public discourse, which define what counts as aging well, technological progress, and quality of care. The second concerns epistemic authority in knowledge production and technical design, determining whose knowledge is used to define care needs, relevant data, and desirable AI outputs. The third involves systemic goals and models of AI-enabled care at the organizational level, which determine whether AI strengthens relational care or primarily advances efficiency, surveillance, and risk management. The fourth addresses distributed decision-making power and user agency, asking whether affected groups can shape, contest, modify, or discontinue the AI systems that touch their lives.

Each domain suggests a corresponding intervention pathway. At the policy level, the authors argue, ethical AI governance requires challenging the assumption that care challenges are best addressed through greater prediction, automation, and individual risk management. Quality of care could instead be defined in terms of dignity, reciprocity, inclusion, sustainability, and opportunities for meaningful social connection. In knowledge production, participation should extend well beyond token consultation: older adults, family caregivers, care workers, and community members should have a meaningful role in defining the problems to be addressed, determining which data are relevant, prioritizing outcomes, and deciding whether an AI application is needed at all. At the organizational level, procurement, funding, and evaluation arrangements should reward uses of AI that strengthen relational care and support the workforce, while facilities should assess whether AI applications redirect resources away from human care, intensify surveillance, or transfer institutional responsibilities onto residents and caregivers. Finally, affected groups need practical mechanisms to question, modify, suspend, or discontinue AI applications.

The article’s ultimate message is both a warning and an invitation. AI is transforming not only how long-term care is delivered but how care needs, social problems, and desirable outcomes are understood. Governance should therefore look beyond whether individual applications are safe or compliant and examine the care contexts into which they are introduced. This demands ongoing multidisciplinary learning about how workforce conditions, organizational priorities, social relationships, and inequalities shape AI implementation, and how AI, in turn, reshapes those conditions. Given the limited evidence on long-term effects, the authors conclude, governance should remain precautionary and adaptive. Most importantly, AI should never be allowed to reframe structural and relational problems as individual conditions to be technologically managed, particularly when doing so may reproduce underinvestment, weaken care relationships, or leave inadequate social infrastructure unaddressed. The future of ethical AI in elder care, in other words, depends less on smarter machines than on wiser choices about what we ask those machines to do.

Subject of Research: Ethical and responsible governance of artificial intelligence in long-term care for older adults

Article Title: Deep leverage points for ethical and responsible AI in long-term care

Article References: Xu, W., & Teerawichitchainan, B. (2026). Deep leverage points for ethical and responsible AI in long-term care. PLOS Aging and Health, 1(3), e0000041. https://doi.org/10.1371/journal.page.0000041

Image Credits: AI Generated

DOI: 10.1371/journal.page.0000041

Keywords: artificial intelligence, long-term care, aging, AI ethics, systems thinking, leverage points, elder care, AI governance, care workforce, ageism, loneliness, health policy

The urgency behind the paper stems from the escalating ecological crisis and the growing recognition that the way humanity values nature shapes how it is protected or destroyed. People value nature in profoundly different ways: instrumentally, for the material benefits it provides; intrinsically, for its existence independent of any human observer; and relationally, through the meanings, identities and social bonds that arise from human–nature interactions. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, or IPBES, emphasized in its 2022 Methodological Assessment of the Diverse Values and Valuation of Nature that most current valuation processes capture only a narrow slice of these values, typically monetary and quantitative ones, which can produce environmental injustices and conflicts while ignoring worldviews of indigenous peoples and local communities.

To translate plural valuation into practice, researchers have identified four values-centered leverage points for transformative change: recognizing the diverse values of nature in valuation methods; embedding those values into decision-making; reforming policies, rights and regulations to internalize nature’s values; and shifting the deeper societal norms, beliefs and goals that underpin how people relate to nature and to each other. Drawing on leverage point theory popularized by Donella Meadows, the authors note that these intervention points cascade in depth—shallower points are easier to act on but yield less change, while deeper points are harder to activate but hold far greater transformative potential when engaged.

The new perspective, the product of two expert workshops hosted by the Open Universiteit in 2022 and 2023, systematically maps how citizen science can strengthen interventions at each of these leverage points. At the first point, recognizing diverse values, citizen science initiatives such as Drinkable Rivers, Curieuze Neuzen in Belgium, and the Dutch Network Ecological Monitoring demonstrate that the questions citizens ask and the species or landscapes they monitor often reflect deeply held values and concerns. Because participants are drawn from varied localities, socio-economic backgrounds and identities, their involvement can widen the spectrum of values that enter formal valuation, though the authors caution that environmental citizen science remains biased toward quantitative data collection, overrepresenting instrumental and intrinsic values while leaving relational values largely implicit.

Measuring relational values—attachments to places, species and nature in general—requires methodological innovation. The researchers point to emerging approaches such as citizens’ written letters to trees, micronarratives and behavioral data on species preferences, which can be combined with ecological measurements to illuminate socio-ecological interactions. They also flag a fundamental trade-off: methods capable of capturing rich detail about people’s values tend to be more complex and time-consuming, reducing the volume of data collected, while high-volume approaches may flatten the very diversity of values that plural valuation seeks to reveal.

At the second leverage point, embedding valuation into inclusive decision-making, the evidence is striking. Citizen-collected air quality data from the Louisiana Bucket Brigade in the United States and the Samen Meten program run by the Dutch national public health institute have shaped governmental policy agendas. In Europe, citizen observatories deliberately link citizens, scientists and decision-makers in shared monitoring of local environments. The authors document cases where citizen science has even entered the courtroom: plastic pollution data gathered by volunteers contributed to a petrochemical company being found liable, and species observations by Swedish citizens have been used in environmental court cases. Participation also reshapes personal decisions, with studies showing that citizen scientists adopt more biodiversity-friendly gardening practices and strengthen their personal connection to nature—effects that ripple outward through the communities and social networks these initiatives create.

Yet the path is not without obstacles. Doubts about the quality of citizen-generated data among scientists and policymakers, persistent power dynamics in decision-making circles, and a lack of agency, skills or resources among decision-makers all constrain uptake. Studies further reveal a persistent demographic skew: citizen science participants tend to be white, highly educated, older and more affluent, which narrows the knowledge produced and limits environmental justice. At the third, deeper leverage point of institutional reform, the authors highlight structural embedding efforts such as the Kunming-Montreal Global Biodiversity Framework’s agreement to involve citizens in knowledge co-creation and the more4nature initiative, which seeks to institutionalize collaboration between citizen science projects and authorities responsible for policy implementation and monitoring. But they also warn that institutionalization carries risks: when data flow into analytical pipelines, ties with the volunteers who collected them can weaken, and the sense of pride and emotional enrichment that motivates citizen scientists can erode if their knowledge becomes an untraceable instrument.

The deepest leverage point—shifting societal norms and paradigms—is where citizen science may hold its most profound potential. Empirical studies show that participation strengthens place attachment, nature connectedness and nature-noticing over both short and long timescales. Communities formed around monitoring projects build shared values and collective action, and so-called citizen science ambassadors inspire pro-environmental attitudes in their social circles. The authors caution, however, that it is methodologically difficult to distinguish values genuinely transformed by participation from values participants already held, and some longitudinal studies find no attitude change because participants began with strongly positive orientations toward nature. Measuring such shifts demands new approaches that explicitly track emotional connectedness and underlying norms.

From this analysis the team distills three overarching recommendations. First, recognize and measure: relational values must be made explicit in citizen science design, supported by new quantitative and qualitative methods, since they are both key motivations for participation and outcomes strengthened by it. Second, embrace diversity: plural valuation calls for plural publics, meaning the full variety of citizen science formats—from top-down governmental programs to grassroots community-led efforts—should be cultivated, with flexible roles that allow citizens, scientists and policymakers to grow into varied responsibilities. Third, pursue structural integration: case-by-case policy engagement is growing, but lasting change requires structural funding, long-term collaborations, storytelling that showcases how citizen science has informed decisions, and open science principles that ease institutionalization into policy processes.

The authors conclude that uniting citizen science and plural valuation is not merely an academic win–win but a necessary foundation for inclusive, effective environmental action and conservation. As governments worldwide struggle to meet biodiversity targets amid widening distrust in institutions, the perspective suggests a practical and democratizing pathway: the millions of volunteers already counting birds, measuring rivers and sensing air pollution are not just collecting data—they are articulating, practicing and fostering a richer, more plural vision of what nature is worth, and in doing so, they may be pulling the deepest levers of transformative change.

Subject of Research: Integrating citizen science with plural valuation of nature to leverage transformative change

Article Title: Uniting citizen science and plural valuation: Potential for leveraging transformative change

Article References: Verploegen, H., Dyke, A. J., Ganzevoort, W., Hugé, J., Jacobs, S., Neuteleers, S., Prats López, M., Verschuuren, B., & Wehn, U. (2026). Uniting citizen science and plural valuation: Potential for leveraging transformative change. Ambio. https://doi.org/10.1007/s13280-026-02458-z

Image Credits: AI Generated

DOI: 10.1007/s13280-026-02458-z

Keywords: citizen science, plural valuation, transformative change, relational values, environmental policy, IPBES, leverage points, biodiversity, nature connectedness, environmental justice, Uniting, citizen