A groundbreaking study emerging from the University of Gothenburg has shed new light on the persistent problem of improper waste disposal, revealing that the emotional response of disgust plays a critical role in shaping public behavior in shared environments. Traditionally, waste management failures have been attributed largely to social norms and carelessness. However, this new research emphasizes the powerful influence of sensory and emotional perceptions, particularly disgust sensitivity, on how individuals interact with waste disposal spaces.

The conventional wisdom posits that people’s waste disposal habits are mainly influenced by the behaviors of those around them—if littering is common, individuals are more likely to follow suit. While this social contagion effect is well-documented, it overlooks a vital psychological component: the visceral reaction humans have to unclean environments. When people perceive a space, such as a waste disposal room, as dirty or revolting, their discomfort and aversion can drive them to avoid engaging in proper disposal behavior, ironically exacerbating the original problem.

Dr. Jacob Sohlberg, a political scientist spearheading this research, explains that disgust—a fundamental human emotion designed to protect us from contamination—can paradoxically undermine environmental cleanliness. “People sensitive to disgust may actively avoid spending time in waste disposal areas if these spaces are perceived as repugnant, increasing the likelihood of haphazard waste disposal elsewhere,” Sohlberg notes. This new perspective shifts waste management research beyond the realm of pure social compliance and into the intricate interplay of human emotion and environmental cues.

The study focused on disadvantaged neighborhoods in Sweden, Finland, and Denmark, areas where littering is notably problematic and causes significant concern among residents. Prior empirical evidence uncovered that in these communities, residents view littering as a problem as severe as crime and unemployment, issues typically regarded as more pressing societal challenges. This underscores the urgency of addressing waste disposal inefficiencies comprehensively, taking into account not only social policies but human psychological tendencies.

The research team proposed three pivotal hypotheses. First, that unclean waste disposal environments heighten the incidence of improper waste disposal. Second, that individuals with heightened disgust sensitivity are disproportionately likely to dispose of waste incorrectly. Third, that the adverse effect of dirty surroundings on waste disposal behavior is magnified in those with high disgust sensitivity. These hypotheses guided a multifaceted research design involving field intervention, experimental manipulation, and large-scale surveys.

In a hands-on field study conducted over three weeks in Gothenburg, researchers allied with a local municipal housing company to observe waste disposal behavior in real time. Two waste stations were meticulously cleaned daily, while eight stations served as controls with no intervention. The results were revealing: stations subjected to extra cleaning saw a marked decrease in littering and erroneous waste disposal. Conversely, control stations showed no significant change, highlighting the tangible benefits of environmental maintenance on public behavior.

To directly examine the psychological mechanisms at play, the team designed a controlled experiment involving more than 300 residents from a disadvantaged Gothenburg neighborhood. Participants were exposed to images of either a pristine or a filthy waste disposal station. Those who viewed the dirty environment reported a significantly lower willingness to use the waste station properly, particularly among those scoring high on a disgust sensitivity scale. This experimental approach confirmed a causal link between perceived environmental cleanliness, disgust, and waste disposal intentions.

Expanding on these results, a third study reached over one thousand participants across socioeconomically challenged neighborhoods in Sweden, Denmark, and Finland through an online experiment that mirrored the earlier design. The data robustly supported the preliminary findings: perceptions of dirty waste disposal spaces increased self-reported intentions to mismanage waste, with disgust sensitivity intensifying this effect. Such consistency across different populations and methodologies affirms the generalizability of the emotional response’s role in waste behavior.

From a policy standpoint, this research translates into actionable strategies. Municipal authorities and housing agencies aiming to mitigate littering and improve waste management efficacy should prioritize the cleanliness and aesthetic quality of waste disposal areas. A well-maintained waste station not only encourages proper disposal but also fosters a community-wide perception of care and order, potentially creating a virtuous cycle of environmental stewardship and social norm adherence.

The societal implications of these findings extend beyond mere environmental tidiness. Cleaner waste disposal areas improve residents’ quality of life, enhancing neighborhood attractiveness and reducing public health risks associated with waste mismanagement. Moreover, better-managed waste systems facilitate the achievement of broader sustainability goals, lowering contamination risks and enhancing recycling efficacy.

Researchers anticipate that integrating psychological insights such as disgust sensitivity into urban planning and public health campaigns will refine waste management interventions. This emotionally informed approach moves beyond traditional messaging and enforcement, incorporating environmental design considerations that shape unconscious behavioral drivers effectively.

Ultimately, the research from the University of Gothenburg propels the discourse on waste disposal into new dimensions, showcasing the synergy between human psychology, environmental conditions, and collective action. It serves as a reminder that solving public sanitation issues necessitates nuanced understanding of both societal structures and the fundamental, innate emotional systems governing human behavior.

As cities worldwide grapple with mounting waste challenges, the integration of emotion-focused research provides a promising avenue to foster healthier public spaces. Keeping waste disposal environments not only clean but also psychologically inviting may very well be the key to reducing littering and promoting sustainable waste habits in vulnerable urban communities.

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Subject of Research: Waste disposal behavior and disgust sensitivity in socioeconomically disadvantaged public environments.

Article Title: How Disgust Sensitivity Shapes Waste Disposal Behavior in Everyday Public Environments: Experimental and Difference-in-Differences Studies in the Nordic Countries

News Publication Date: 28-Apr-2026

Web References:
DOI Link

Image Credits: Photo: Emelie Asplund, featuring Jacob Sohlberg, political scientist at University of Gothenburg.

Keywords: Disgust sensitivity, waste disposal behavior, littering, public environment, environmental psychology, socioeconomically disadvantaged neighborhoods, waste management, recycling, behavioral intervention, urban sanitation.

A recent cohort study conducted across numerous U.S. households with children sheds light on a critical factor influencing family well-being: the burden of high out-of-pocket medical expenses. This study reveals that such financial strain extends beyond the immediate challenge of covering healthcare costs, potentially undermining the ability of families to meet other essential health-related social needs. These needs encompass access to nutritious food, the capacity to pay essential bills, and securing adequate, quality housing—all foundational elements contributing to both physical and psychological health.

The research underscores a complex and cascading effect where substantial medical expenditures diminish disposable income available for these crucial necessities, exposing families to a heightened risk of adverse health outcomes. This multifaceted relationship highlights the interconnectivity between healthcare costs and social determinants of health, effectively portraying how economic hardship in medical spending can destabilize broader aspects of a household’s life.

By examining data from diverse households, the study articulates a nuanced perspective on how chronic financial pressure from healthcare payments impinges upon the ability of families to maintain food security. Nutrition, a critical pillar of health, becomes compromised when families face choices between procuring medications or purchasing groceries. Such dilemmas can exacerbate existing health conditions or contribute to new health challenges, thereby perpetuating a vicious cycle of poor well-being.

Equally important, the findings draw attention to the impact of medical expenses on a family’s capacity to pay routine bills, including utilities and other fixed costs necessary for sustaining a stable living environment. Disruptions in paying bills not only cause immediate discomfort but can also trigger longer-term economic instability, which is intrinsically linked to stress and mental health disorders.

Furthermore, the study posits that the quality of housing is often deprioritized in the face of mounting medical bills. When forced to allocate substantial funds for health services, households might settle for lower-quality housing or face housing insecurity. Housing inadequacies—such as overcrowding, poor ventilation, or unsafe neighborhoods—are known contributors to significant health disparities, amplifying the social costs of medical financial burdens.

The implications of these findings resonate profoundly within the healthcare policy domain. The study suggests that attempts to curtail high out-of-pocket costs, through policy reform or insurance redesign, could have far-reaching benefits beyond immediate medical affordability. By alleviating financial stress due to healthcare, families might retain or regain their ability to secure other health-promoting resources.

In this context, the study raises important questions about the design and structure of health insurance coverage and the broader social safety net. It indicates the need for more comprehensive approaches that incorporate support for social determinants of health alongside medical care. Such integration could inform future strategies targeting health equity and chronic disease management.

Moreover, it is noteworthy that this relationship between out-of-pocket costs and social needs is not merely correlational but potentially causal through mechanisms related to income allocation and financial decision-making. Families juggling expensive medical bills are more likely to experience trade-offs that adversely affect their health and social stability, evidencing a systemic vulnerability that demands interventions beyond clinical care.

Importantly, the cohort study focuses particularly on households with children, a demographic where the stakes of unmet health-related social needs are exceptionally high. Children’s development and long-term health trajectories are intimately tied to stable nutrition, housing, and economic security. Disruption in any of these domains can have lasting consequences throughout the lifespan.

This comprehensive research also contributes to growing evidence that tackling healthcare costs in isolation cannot fully address health disparities. Instead, it emphasizes a holistic understanding of health economics that encompasses the synergy between medical expenses and social conditions.

For healthcare providers, policymakers, and advocates, these findings underscore the critical role of integrating social support mechanisms with medical treatment plans. Addressing out-of-pocket costs alone, while crucial, must be paired with broader efforts to enhance social needs assistance in order to improve overall population health outcomes.

The evidence from this study invites stakeholders to reconceive health interventions through a multidisciplinary lens, where economic, social, and clinical factors are unified considerations. This paradigm shift is essential for designing effective solutions that mitigate the multifactorial risks posed by healthcare costs on the well-being of vulnerable families.

In summary, this important cohort study enriches our understanding of how high out-of-pocket medical costs can profoundly impair families’ access to essential social supports, risking a cascade of negative health consequences. Its findings advocate for a reformed healthcare system that advances affordability and integrates social determinants to foster healthier communities nationwide.

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Subject of Research: Impact of high out-of-pocket medical costs on affordability of health-related social needs in U.S. households with children
Article Title: Not provided
News Publication Date: Not provided
Web References: Not provided
References: (doi:10.1001/jamanetworkopen.2026.16485)
Image Credits: Not provided
Keywords: Health care costs, Out-of-pocket medical expenses, Social determinants of health, Food security, Housing quality, Health disparities, U.S. households with children

In American politics, cursing and "four-letter words" are no longer confined to hot mics or hidden behind closed doors. Politicians and pundits are increasingly using so-called "bad words" in speeches, social media posts and campaign ads. Benjamin Bergen, professor of cognitive science, and Pamela Ban, associate professor of political science, both from UC San Diego's School of Social Sciences, examine why swearing among politicians is on the rise and what it reveals about persuasion, emotion and modern public discourse.

In American politics, cursing and "four-letter words" are no longer confined to hot mics or hidden behind closed doors. Politicians and pundits are increasingly using so-called "bad words" in speeches, social media posts and campaign ads. Benjamin Bergen, professor of cognitive science, and Pamela Ban, associate professor of political science, both from UC San Diego's School of Social Sciences, examine why swearing among politicians is on the rise and what it reveals about persuasion, emotion and modern public discourse.

Three seconds. That is roughly how long a cheetah needs to go from a dead stop to about 60 miles an hour.  The Cheetah Conservation Fund goes a little further, citing acceleration to a top speed past 110 km/h in just over three seconds.

Numbers like these tend to get pressed into a familiar comparison: the cheetah out-accelerates a sports car. The comparison is not wrong but it often leaves out the fact that the animal sustains this only for about half a minute before it has to stop.

Why the supercar comparison holds, and where it breaks

On the acceleration figure alone, the cheetah genuinely keeps pace with fast machinery. A three-second sprint to 60 mph sits in the same range as a great many high-performance cars, and beats most ordinary ones outright.

For context, a Toyota GR Supra 3.0 can do 0–60 mph in 3.9 seconds, while Car and Driver note that a 2025 Porsche 911 Carrera reaches that speed in 3.1 seconds. Sure, the quickest performance cars are now faster — the BMW M3 Competition xDrive at 2.8 seconds to 60 mph — but that only makes the comparison stranger: a wild animal is operating in the same acceleration conversation as serious modern machinery.

The comparison breaks down on duration. A supercar can hold its top speed for as long as the road and the fuel allow. A cheetah cannot. As put by the Cheetah Conservation Fund, “Prey must be caught within about 30 seconds, as maximum speed can only be maintained briefly”. The engine and the chassis are not the same thing as the fuel tank, and in a cheetah the tank is small.

There is a second wrinkle. The headline top speeds, the 110-plus figures, mostly come from captive or estimated conditions. When researchers actually measured wild cheetahs at work, the picture changed.

What the wild data showed

In 2013, Alan Wilson and colleagues at the Royal Veterinary College published a Nature study that fitted five wild cheetahs in Botswana with custom GPS-and-motion collars and recorded 367 hunting runs. The fastest run they captured was striking but earthbound: a top speed of about 93 km/h, or 58 mph. Most hunts involved only moderate speeds. Note that this figure is the top speed in this sample of wild animals, not the species ceiling.

The more telling number is the average. Most runs in the study were well below that record, with the typical chase topping out around 33 mph. The cheetahs were not maxing out. They were managing.

The 30-second ceiling, and the myth around it

So why does the sprint end so soon? For decades the textbook answer was overheating. The cheetah, the story went, hits a thermal ceiling and has to stop before it cooks itself. That figure, a body temperature of 40.5 C, traced back to a single 1973 treadmill experiment in which cheetahs ran at only about 30 km/h.

The physiologist Robyn Hetem put the problem plainly. Hetem noted that the long-standing overheating theory traced back to that single early study. Her 2013 work on free-living cheetahs measured body temperature minute by minute and found it averaged just 38.4 C when chases ended, well below the supposed limit. The animals stopped, but they were not overheating.

If not heat, then what? That question is not fully settled. Hetem’s own candidate is energy, and she keeps it hedged: the cheetahs “may just run out of energy after 30 seconds of sprinting.” Oxygen debt and the sheer cost of anaerobic effort sit somewhere in that explanation. 

Built for the moment, not the chase

What emerges from the data is a different animal than the speedometer suggests. The cheetah’s gift is not sustained velocity. It is the explosive opening, the burst. Its impressive top speed is something it can reach but rarely needs to hold.

The three-second sprint is real. What the collars added is the part the comparison to cars leaves out: the animal is engineered around a window it cannot hold open for long, and almost everything it does in a hunt is an attempt to finish before that window shuts.

The post A cheetah can go from a standstill to about 60 miles an hour in roughly three seconds, out-accelerating many sports cars, but it can’t hold that speed for long appeared first on Space Daily.

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Newly released NASA satellite images reveal the extent of recent wildfire damage on Santa Rosa Island in vivid detail, showcasing the impact of the largest Channel Islands fire on record.

The images, obtained with NASA satellite observation platforms that include the Fire Information for Resource Management System (FIRMS) and the Fire Event Explorer, reveal fire damage to nearly half of the island’s southeastern side.

The fire was initially spotted on May 15, 2026, and containment efforts began as the blaze spread across the island over the following days.

Now, the new NASA imagery is revealing the extent of the damage caused by the historic fire, which officials say came close to endangering one of our nation’s rarest species.

18,000 Acres Scorched on Santa Rosa Island

Current damage estimates indicate that close to one-third of the island was impacted, constituting more than 18,300 acres on the island, which is part of California’s Channel Islands National Park.

Comparisons with past NASA imagery of Santa Rosa Island, made possible with Landsat satellite images, reveal a sharp contrast between once verdant regions of the island, which are now scorched by fire, shown in reddish brown in the more recent images (see below).

Fortunately, Channel Islands National Park officials reported that the fire had been 97 percent contained by May 26, after burning its way through chaparral and grassland covering large portions of the island.

Endangering One of America’s Rarest Species

The Channel Islands serve as a unique and extremely diverse habitat for a range of species of both plants and animals. Among the species threatened during the recent fires were Torrey pines (Pinus torreyana), recognized as our nation’s rarest pine tree, which only grows on Santa Rosa Island and in a preserve in urban San Diego.

Fortunately, most of the island’s Torrey pine forest remains intact, although some damage was reportedly discernible in surveys by firefighters on the island and in drone imagery of the scorched areas.

According to island officials, the fire appears to have burned its way inland at lower intensity, making its way through pine areas that burned ground-dwelling vegetation while leaving the overlying canopy largely unaffected.

Damage from the Largest Channel Island Fire

Park officials say that some smaller areas of forest did sustain significant damage, as conditions in those pockets allowed a greater burn intensity.

Closer to the fire’s northern boundary, Santa Rosa’s cloud forests—the wooded areas comprised mostly of oak and pine growth surrounded by chaparral, whose name is derived from the island fog that sustains them—were successfully preserved by firefighting crews who worked ahead of the fire to cool areas where combustible vegetation grows.

Based on recent local reports, the fire that consumed large portions of Santa Rosa Island’s vegetation is the largest known to have impacted any of the Channel Islands. Fortunately, many of the island’s indigenous trees and other vegetation are resilient enough to withstand fire, since they do not rely on it as part of their growth cycles like many mainland plant species.

Additional information about the fires can be found here, and more imagery of the recent damage has been made available at NASA’s Earth Observatory page.

Micah Hanks is the Editor-in-Chief and Co-Founder of The Debrief. A longtime reporter on science, defense, and technology with a focus on space and astronomy, he can be reached at micah@thedebrief.org. Follow him on X @MicahHanks, and at micahhanks.com.

North Carolina’s blueberries may have a beetle problem. For the first time, scientists in the Tarheel State have documented the presence of Prionus imbricornus eating blueberry bushes. This longhorn beetle and its larvae can chomp their way through the state’s valuable blueberry fields. The findings are described in a study published this week in the Journal of Integrated Pest Management. 

Blueberries are native to North Carolina, but were not cultivated until 1935. The state is the sixth largest blueberry producer in the United States, and the blueberry industry is valued at roughly $70 million. Protecting the plants from pests is crucial, as blueberries are considered one of North Carolina’s most valuable and desirable crops. 

Several species including the blueberry maggot (Rhagoletis mendax), plum curculio (Conotrachelus nenuphar), and cranberry fruitworm (Acrobasis vaccinii Riley) can threaten blueberry crops. The long-horned beetle P. imbricornus may now join their ranks. P. imbricornus is known for their long antennae and are considered wood-boring beetles. The adult females typically lay their eggs in the soil near the roots of hardwood trees. The larvae then eat and destroy the roots. These larvae can grow up to five inches long and potentially kill trees, since the adults don’t feed. 

North Carolina is the first state to report that P. imbricornus is actively feeding on blueberry bushes. However, reports of unidentified larvae from the Prionus beetle genus feeding on and damaging blueberry bush roots go back to 2010. In the 16 years since, identifying the specific species responsible has been difficult since the larvae live near the roots of the plants. Different types of longhorn beetle larvae also look very similar, and not identifying a species can harm efforts to combat harmful bugs. 

“Before now, researchers often just assumed the species of Prionus on their commodities based on adult identification,” Kenneth Geisert, a study co-author and NC State graduate student, said in a statement. “If that guess was incorrect, it could mean using a treatment strategy that did not line up with the problem and incorrectly associating species and their hosts.”

For example, P. imbricornus attacks roots, but another longhorn beetle species may go after a tree’s dead branches or trunk. 

“Without knowing which species of beetle you’re dealing with and their ecology, incorrect management can cause adverse effects on non-target insects,” Geisert added.

For this study, the team used a series of black panel traps scented with sex pheromones to attract and gather adult beetles. The traps were placed at six farms across Pender, Sampson, Bladen, and New Hanover counties. The team then used a technique called genetic barcoding on the larvae to analyze small, standardized segments of their DNA to identify the species. They then compared the unknown larval sequences with the same genetic segments from known Prionus adults.

They matched the P. imbricornus with 98 to 99 percent accuracy. According to the team, this result is both good and bad news for farmers.

“On one hand, it’s very important that we know which species we’re dealing with,” said Lorena Lopez, a study co-author and entomologist at NC State. “On the other, North Carolina was the first state to ever report Prionus infestation in blueberries, and there are no insecticides currently labeled against this pest in blueberries.”

To address this shortfall, Lopez has begun insecticide trials. Pinpointing effective insecticides and timing during P. imbricornis reproductive cycles can potentially limit larval development. Fewer larvae could help prevent major root damage and provide blueberry farmers with an effective management tool to protect their crops. 

The post A ‘mystery beetle’ is devouring North Carolina’s precious blueberries appeared first on Popular Science.

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The figure for a single email comes from a 2025 peer-reviewed paper in Communications of the ACM by Pengfei Li, Shaolei Ren, and colleagues at the University of California, Riverside. The paper, titled “Making AI Less Thirsty,” sets out the methodology by which the per-query water footprint of large language models can be estimated. The figure for the 100-word email is approximately 519 millilitres, which is close enough to the volume of a standard bottle of water for the bottle to be the practical comparison. The number includes both the direct water used to cool the data centre’s servers and the indirect water used to generate the electricity those servers consume.

The 519 millilitre figure assumes a single response. Most users do not send a single response. They have conversations.

The same research group estimates that a single sustained conversation with a chatbot, defined as somewhere between ten and fifty exchanges, consumes approximately the same 500-millilitre order of magnitude. The figure scales by a factor of one each time the conversation extends.

Why AI needs water at all

Data centres generate heat. The servers processing AI queries are essentially small radiators running at high intensity for as long as the workload continues. The chips at the heart of contemporary AI training and inference, the high-end graphics processing units manufactured primarily by Nvidia, dissipate between 300 and 700 watts each, depending on the model. A single training run for a large language model uses tens of thousands of these chips simultaneously, for weeks or months at a time. The heat has to go somewhere.

The most common method for moving that heat out of a data centre is evaporative cooling. Water is pumped through pipes that run alongside or directly across the heat-producing equipment, absorbs the heat, and is then exposed to the air. A portion of the water evaporates, carrying the heat into the atmosphere as water vapour. The remaining water cycles back through the system. Approximately 80 per cent of the water drawn into an evaporative cooling system is lost to evaporation. The rest returns to local water systems, sometimes at higher temperatures and with chemical residues from the cooling process.

The newer generation of data centres built specifically for AI workloads are larger, more dense, and more thermally intense than the data centres built for general cloud computing in the 2010s. A single large hyperscale AI campus can now consume more water in a day than a town of ten thousand people uses for everything: drinking, washing, cooking, sanitation, agriculture, and irrigation combined.

This video explains exactly what big tech promised and how AI is doing the opposite of that.

How much, in actual numbers

Google’s most recent Environmental Report, covering the 2024 financial year, sets out the water consumption of the company’s global operations in detail. The combined figure for 2024 was approximately 8.1 billion gallons, of which approximately 95 per cent was used at data centres. The 2024 figure was an 8 per cent increase on 2023. The 2023 figure had been a 17 per cent increase on 2022. The 2022 figure had been a 20 per cent increase on 2021. The cumulative result is that Google’s water consumption nearly doubled between 2021 and 2024, with the company itself naming AI workload growth as the primary driver in successive environmental reports.

Microsoft’s figures are similar in shape, smaller in absolute scale. The company reported water consumption of approximately 1.7 billion gallons in 2022, a 34 per cent year-on-year increase. The growth has continued. The independent investigative reporting on Microsoft’s data centre cluster in West Des Moines, Iowa, where the GPT-4 training runs were conducted in 2022, has documented that a single training run consumed 11.5 million gallons of water in July 2022 and another 13.4 million gallons in August. The same cluster has, in subsequent years, expanded to five separate facilities collectively drawing 68.5 million gallons annually from the West Des Moines municipal water system, more than any other industrial user in the metropolitan area.

Meta consumed approximately 813 million gallons globally in 2023, with 95 per cent of that volume used at data centres. Amazon, which operates the largest cloud infrastructure in the world, does not publish aggregate water consumption figures.

The Lawrence Berkeley National Laboratory’s 2024 Data Center Energy Usage Report, prepared for the United States Department of Energy under the Energy Act of 2020, estimated that data centres in the United States consumed approximately 17.4 billion gallons of water directly through cooling in 2023. The same report estimated that an additional 211 billion gallons of water were consumed indirectly through the electricity required to power the same data centres. The indirect figure is approximately twelve times larger than the direct figure. The report projects that the direct figure could double or quadruple by 2028. The indirect figure scales in the same proportion.

Where the water comes from

The Li and Ren paper projects that global AI demand will require somewhere between 4.2 and 6.6 billion cubic metres of water withdrawal annually by 2027. The lower estimate is approximately the total annual water withdrawal of four Denmarks. The higher estimate approaches half the total annual water withdrawal of the entire United Kingdom. Both estimates assume current trajectories of AI workload growth and current water-efficiency practices. Neither estimate accounts for the possibility that AI demand continues to grow faster than the modelled trajectory.

The water has to come from somewhere. In Microsoft’s 2023 sustainability report, the company acknowledged that approximately 42 per cent of its water consumption that year came from regions classified as “water-stressed” under the World Resources Institute’s standard rating system. Google’s equivalent figure for 2023 was 15 per cent of freshwater withdrawals from regions of “high water scarcity.” Both figures, on the trajectory of the past three years, are likely to increase rather than decrease.

The concrete consequences of those abstract percentages are now visible in specific locations. In September 2024, Google announced it was pausing its planned 200-million-dollar data centre in Cerrillos, near Santiago, Chile, after a Chilean environmental court partially reversed the project’s original 2020 permit. The court ruled that the company had not adequately accounted for the impact on the Central Santiago Aquifer in a country that had been in a continuous drought for fifteen years and had begun rationing residential water in 2022. The project is now under revision.

In Querétaro, Mexico, where 32 new data centres are currently planned, the state suffered its worst drought in a century in 2024, with seventeen of eighteen municipalities affected and the drinking water supply for thousands of families at risk. Microsoft has secured rights to approximately 25 million litres of water annually from a local aquifer that is currently running a 60-million-litre annual deficit. In Uruguay, currently experiencing its worst drought in 70 years, Google’s proposed data centre in Canelones would, in its first operational phase, consume approximately 7.6 million litres of water per day, equivalent to the daily residential water needs of 55,000 people. In Goodyear and Buckeye, Arizona, a 14-billion-dollar data centre project was withdrawn in 2024 after local resident organisations successfully pressed elected officials to deny the necessary rezoning. In Aragón, Spain, multiple data centre projects are advancing in regions where agricultural water rights are already contested.

The pattern, on the available evidence, is that the cooling infrastructure for global AI is being built preferentially in regions where freshwater is cheap, regulatory oversight is loose, and the local population is least positioned to negotiate.

What companies don’t disclose

The figures cited above are the figures the companies have made public. The full water footprint of the AI industry is, by every available assessment, larger than the figures voluntarily disclosed in sustainability reports.

Three specific gaps recur across the disclosure landscape. The first is the gap between water withdrawal, which is the volume drawn from local sources, and water consumption, which is the volume permanently lost to evaporation. Most corporate reports name only one of these figures, and the choice between them can shift the apparent footprint by a factor of three or more depending on which is reported. The second is the gap between direct cooling water and indirect electricity-generation water. Almost no corporate report includes the indirect figure, despite the Lawrence Berkeley estimate that the indirect figure is approximately twelve times the direct one. The third is the gap between aggregate global figures and facility-level figures. A company-wide annual total tells a stakeholder nothing about whether the company’s data centre in a drought-stressed Arizona town is straining the local aquifer.

The reasons for the disclosure gaps are several. Some are methodological: the per-facility water footprint of a data centre depends on cooling technology, local climate, electricity-grid mix, and seasonal demand variation, none of which the company necessarily measures with precision. Some are competitive: detailed facility-level water disclosure could give competitors useful intelligence about a company’s infrastructure plans. Some are reputational: a company that discloses its full water footprint and is then criticised for the size of it is exposed to public-relations risk in a way that a company reporting only aggregate figures is not.

The Li and Ren paper’s contribution to the literature is, in significant part, that it produces credible estimates of the gaps. The figures that the AI industry has not been willing to publish are figures that academic researchers, using publicly available proxies for cooling efficiency and electricity-grid water intensity, are now able to estimate within reasonable bounds.

What is at stake

The global infrastructure for processing AI queries is being built faster than any new technology infrastructure in modern history, on a financing trajectory that McKinsey has projected at approximately 5.2 trillion US dollars by 2030. The physical buildings the trillion-dollar investment is producing are, in their fundamental operational requirements, large industrial-scale evaporative cooling systems with computing equipment inside them.

Each query is small. The aggregate is not.

Half the United Kingdom’s annual water withdrawal, evaporating into the atmosphere from cooling towers across the world’s data centres by 2027, is not a marginal correction to a global water balance that is otherwise stable. Global freshwater scarcity is increasing on every measured trajectory. Approximately one-quarter of the world’s population, by United Nations projections, will face severe water stress by 2030. The water the AI industry is now drawing from aquifers, rivers, and reservoirs, increasingly in the regions least able to spare it, is competing directly with that population.

The technologies the AI industry is developing have, by any reasonable analysis, the potential to contribute to solving some of the same water-management problems they are now exacerbating, through better climate modelling, more efficient irrigation, more accurate weather prediction, and more sophisticated drought response. Whether the contribution arrives at scale faster than the consumption does is the open question that determines whether the trade-off, on the long view, is worth it.

On the present trajectory, the answer is unclear.

What the trajectory will look like by 2027 depends on decisions being made, in board rooms and government offices and local zoning meetings, now.

The post Writing a single 100-word email with ChatGPT consumes approximately the volume of a standard bottle of water, the global infrastructure processing AI queries is projected to use the equivalent of half the United Kingdom’s annual water withdrawal by 2027, and much of that water is being drawn from regions already experiencing severe drought. appeared first on Space Daily.

A removable patch delivered immune signals and sped healing in mice and pigs.

A new study finds pollution from satellite megaconstellations could account for 42% of the space sector’s climate impact by 2029.