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Morning Rundown: Baltimore battles ship owners over bridge collapse, concerns about Biden university speech, and why the perception of 'old age' may be changing

Scientists push new paradigm of animal consciousness, saying even insects may be sentient

Bees play by rolling wooden balls — apparently for fun . The cleaner wrasse fish appears to recognize its own visage in an underwater mirror . Octopuses seem to react to anesthetic drugs and will avoid settings where they likely experienced past pain. 

All three of these discoveries came in the last five years — indications that the more scientists test animals, the more they find that many species may have inner lives and be sentient. A surprising range of creatures have shown evidence of conscious thought or experience, including insects, fish and some crustaceans. 

That has prompted a group of top researchers on animal cognition to publish a new pronouncement that they hope will transform how scientists and society view — and care — for animals. 

Nearly 40 researchers signed “ The New York Declaration on Animal Consciousness ,” which was first presented at a conference at New York University on Friday morning. It marks a pivotal moment, as a flood of research on animal cognition collides with debates over how various species ought to be treated. 

The declaration says there is “strong scientific support” that birds and mammals have conscious experience, and a “realistic possibility” of consciousness for all vertebrates — including reptiles, amphibians and fish. That possibility extends to many creatures without backbones, it adds, such as insects, decapod crustaceans (including crabs and lobsters) and cephalopod mollusks, like squid, octopus and cuttlefish.

“When there is a realistic possibility of conscious experience in an animal, it is irresponsible to ignore that possibility in decisions affecting that animal,” the declaration says. “We should consider welfare risks and use the evidence to inform our responses to these risks.” 

Jonathan Birch, a professor of philosophy at the London School of Economics and a principal investigator on the Foundations of Animal Sentience project, is among the declaration’s signatories. Whereas many scientists in the past assumed that questions about animal consciousness were unanswerable, he said, the declaration shows his field is moving in a new direction. 

“This has been a very exciting 10 years for the study of animal minds,” Birch said. “People are daring to go there in a way they didn’t before and to entertain the possibility that animals like bees and octopuses and cuttlefish might have some form of conscious experience.”

From 'automata' to sentient

There is not a standard definition for animal sentience or consciousness, but generally the terms denote an ability to have subjective experiences: to sense and map the outside world, to have capacity for feelings like joy or pain. In some cases, it can mean that animals possess a level of self-awareness. 

In that sense, the new declaration bucks years of historical science orthodoxy. In the 17th century, the French philosopher René Descartes argued that animals were merely “material automata” — lacking souls or consciousness.

Descartes believed that animals “can’t feel or can’t suffer,” said Rajesh Reddy, an assistant professor and director of the animal law program at Lewis & Clark College. “To feel compassion for them, or empathy for them, was somewhat silly or anthropomorphizing.” 

In the early 20th century, prominent behavioral psychologists promoted the idea that science should only study observable behavior in animals, rather than emotions or subjective experiences . But beginning in the 1960s, scientists started to reconsider. Research began to focus on animal cognition, primarily among other primates. 

Birch said the new declaration attempts to “crystallize a new emerging consensus that rejects the view of 100 years ago that we have no way of studying these questions scientifically.” 

Indeed, a surge of recent findings underpin the new declaration. Scientists are developing new cognition tests and trying pre-existing tests on a wider range of species, with some surprises. 

Take, for example, the mirror-mark test, which scientists sometimes use to see if an animal recognizes itself. 

In a series of studies, the cleaner wrasse fish seemed to pass the test . 

The fish were placed in a tank with a covered mirror, to which they exhibited no unusual reaction. But after the cover was lifted, seven of 10 fish launched attacks toward the mirror, signaling they likely interpreted the image as a rival fish. 

After several days, the fish settled down and tried odd behaviors in front of the mirror, like swimming upside down, which had not been observed in the species before. Later, some appeared to spend an unusual amount of time in front of the mirror, examining their bodies. Researchers then marked the fish with a brown splotch under the skin, intended to resemble a parasite. Some fish tried to rub the mark off. 

“The sequence of steps that you would only ever have imagined seeing with an incredibly intelligent animal like a chimpanzee or a dolphin, they see in the cleaner wrasse,” Birch said. “No one in a million years would have expected tiny fish to pass this test.”

In other studies, researchers found that zebrafish showed signs of curiosity when new objects were introduced into their tanks and that cuttlefish could remember things they saw or smelled . One experiment created stress for crayfish by electrically shocking them , then gave them anti-anxiety drugs used in humans. The drugs appeared to restore their usual behavior.

Birch said these experiments are part of an expansion of animal consciousness research over the past 10 to 15 years. “We can have this much broader canvas where we’re studying it in a very wide range of animals and not just mammals and birds, but also invertebrates like octopuses, cuttlefish,” he said. “And even increasingly, people are talking about this idea in relation to insects.”

As more and more species show these types of signs, Reddy said, researchers might soon need to reframe their line of inquiry altogether: “Scientists are being forced to reckon with this larger question — not which animals are sentient, but which animals aren’t?” 

New legal horizons

Scientists’ changing understanding of animal sentience could have implications for U.S. law, which does not classify animals as sentient on a federal level, according to Reddy. Instead, laws pertaining to animals focus primarily on conservation, agriculture or their treatment by zoos, research laboratories and pet retailers.

“The law is a very slow moving vehicle and it really follows societal views on a lot of these issues,” Reddy said. “This declaration, and other means of getting the public to appreciate that animals are not just biological automatons, can create a groundswell of support for raising protections.” 

State laws vary widely. A decade ago, Oregon passed a law recognizing animals as sentient and capable of feeling pain, stress and fear, which Reddy said has formed the bedrock of progressive judicial opinions in the state.  

Meanwhile, Washington and California are among several states where lawmakers this year have considered bans on octopus farming, a species for which scientists have found strong evidence of sentience. 

British law was recently amended to consider octopuses sentient beings — along with crabs and lobsters .

“Once you recognize animals as sentient, the concept of humane slaughter starts to matter, and you need to make sure that the sort of methods you’re using on them are humane,” Birch said. “In the case of crabs and lobsters, there are pretty inhumane methods, like dropping them into pans of boiling water, that are very commonly used.”

Evan Bush is a science reporter for NBC News. He can be reached at [email protected].

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Drug companies often do one-on-one outreach to doctors. A new study finds these meetings with drug reps lead to more prescriptions for cancer patients, but not longer survival. Chris Hondros/Getty Images hide caption

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Perspective

Which scientists get mentioned in the news mostly ones with anglo names, says study.

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Once lost to science, these "uncharismatic" animals are having their moment

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This medieval astrolabe has both Arabic and Hebrew markings. Here's what it means

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Oil and gas companies emit more climate-warming methane than EPA reports

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What we know about long COVID — from brain fog to physical fatigue

March 13, 2024 • "Long COVID has affected every part of my life," said Virginia resident Rachel Beale said at a recent Senate hearing. "I wake up every day feeling tired, nauseous and dizzy. I immediately start planning when I can lay down again." Beale is far from alone. Many of her experiences have been echoed by others dealing with long COVID. It's a constellation of debilitating symptoms that range from brain fog and intense physical fatigue to depression and anxiety. But there's new, promising research that sheds light onto some symptoms. NPR health correspondent Will Stone talks with Short Wave host Regina G. Barber about the state of long COVID research — what we know, what we don't and when we can expect treatments or even cures for it. Have more COVID questions you want us to cover? Email us at [email protected] — we'd love to hear from you.

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The "shocking" tactic electric fish use to collectively sense the world

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The "shocking" tactic electric fish use to collectively sense the world

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Science News

These are the most-read science news stories of 2021.

The International Space Station spotted the origins of a bizarre type of upside-down lightning called a blue jet (illustrated) zipping up from a thundercloud into the stratosphere in 2019. The discovery ranked among  Science News ' most-read stories of 2021.

DTU SPACE, DANIEL SCHMELLING/MOUNT VISUAL

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By Science News Staff

December 23, 2021 at 9:00 am

Science News drew over 21 million visitors to our website this year. Here’s a rundown of the most-read news stories and long reads of 2021.

Top news stories

1. space station detectors found the source of weird ‘blue jet’ lightning.

Instruments on the International Space Station detected the origins of an odd type of lightning called a blue jet. The bizarre bolt is sparked by a “blue bang” — a flash of bright blue light that may be brought on by the turbulent mixing of oppositely charged regions within a thundercloud ( SN: 2/13/21, p. 14 ).

2. A newfound quasicrystal formed in the first atomic bomb test

The first atomic bomb test, in 1945, forged a peculiar, glassy material called trinitite — and within it, a rare form of matter called a quasicrystal . Quasicrystals’ atoms are arranged in an orderly structure like normal crystals, but the structure’s pattern doesn’t repeat ( SN: 6/19/21, p. 12 ).

3. An Indigenous people in the Philippines have the most Denisovan DNA

The Ayta Magbukon people in the Philippines set the record for the highest known level of Denisovan ancestry — about 5 percent of their DNA comes from the ancient hominids . The finding suggests that several Denisovan populations independently reached Southeast Asia and interbred with Homo sapiens groups that arrived thousands of years later ( SN: 9/11/21, p. 16 ).

4. Astronomers may have seen a star gulp down a black hole and explode

In a first, astronomers caught a glimpse of a rare double cosmic cannibalism : A star swallowed a black hole or neutron star, which then gobbled that star from within, resulting in an astonishing explosion ( SN: 10/9/21 & 10/23/21, p. 6 ).

5. Frog skin cells turned themselves into living machines

Skin stem cells plucked from frog embryos organized themselves into miniature living robots, dubbed “xenobots,” that can swim, move around debris and even self-heal . Xenobots may one day serve a useful purpose, but ethical questions need to be considered ( SN: 4/24/21, p. 8 ).

Favorite video

Acrobatic rabbits bewitched online readers in our most-viewed YouTube video posted this year. The video — accompanying the story “ A gene defect may make rabbits do handstands instead of hop ” ( SN: 4/24/21, p. 13 ) — shows a sauteur d’Alfort rabbit walking on its front paws (below). Such hop-less bunnies may have adopted the odd gait because of a mutation in a gene called RORB , scientists discovered.

Top feature stories

1. new drugs that block a brain chemical are game changers for some migraine sufferers.

A class of drugs that inhibits a neurotransmitter called calcitonin gene-related peptide is helping some patients who suffer from chronic, debilitating migraines ( SN: 3/27/21, p. 16 ).

2. Einstein’s theory of general relativity unveiled a dynamic and bizarre cosmos

Albert Einstein’s general theory of relativity has served as the bedrock of our understanding of the cosmos. In the last 100 years, scientists have confirmed its most radical predictions, including black holes, gravitational waves and an expanding universe ( SN: 2/13/21, p. 16 ).

3. Chemists are reimagining recycling to keep plastics out of landfills

No matter people’s dedication to sorting and recycling plastics, most still end up in landfills because the materials are too difficult to transform into useful new products. Some chemists are trying to change that ( SN: 1/30/21, p. 20 ).

4. Psychology has struggled for a century to make sense of the mind

In the last 100 years, psychologists and other social scientists have dug into the muddy “science of us” and developed conflicting theories about human thought and behavior. From the messy, contentious research bloomed insights into what makes humans tick ( SN: 8/14/21, p. 18 ).

5. Fossils and ancient DNA paint a vibrant picture of human origins

From the Taung Child to Lucy, the last century of paleoanthropology has sketched a rough timeline of how humans came to be. Scientists now agree that human evolution has its roots in Africa, but many mysteries in our history remain to be solved ( SN: 9/25/21, p. 20 ).

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Discover recent technology news articles on topics such as Nanotechnology ,  Artificial Intelligence , Biotechnology ,  Graphene , Green Tech , Battery Tech , Computer Tech , Engineering , and Fuel-cell Tech featuring research out of MIT , Cal Tech , Yale , Georgia Tech , Karlsruhe Tech , Vienna Tech , and Michigan Technological University . Discover the future of technology with SciTechDaily.

Technology April 23, 2024

How a New 3D Printer Automatically Masters Diverse Sustainable Materials

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AI Index: State of AI in 13 Charts

In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.

This year’s AI Index — a 500-page report tracking 2023’s worldwide trends in AI — is out.

The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year’s report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.

Don’t have an afternoon to pore through the findings? Check out the high level here.

A Move Toward Open-Sourced

This past year, organizations released 149 foundation models, more than double the number released in 2022. Of these newly released models, 65.7% were open-source (meaning they can be freely used and modified by anyone), compared with only 44.4% in 2022 and 33.3% in 2021.

But At a Cost of Performance?

Closed-source models still outperform their open-sourced counterparts. On 10 selected benchmarks, closed models achieved a median performance advantage of 24.2%, with differences ranging from as little as 4.0% on mathematical tasks like GSM8K to as much as 317.7% on agentic tasks like AgentBench.

Biggest Players

Industry dominates AI, especially in building and releasing foundation models. This past year Google edged out other industry players in releasing the most models, including Gemini and RT-2. In fact, since 2019, Google has led in releasing the most foundation models, with a total of 40, followed by OpenAI with 20. Academia trails industry: This past year, UC Berkeley released three models and Stanford two.

Industry Dwarfs All

If you needed more striking evidence that corporate AI is the only player in the room right now, this should do it. In 2023, industry accounted for 72% of all new foundation models.

Prices Skyrocket

One of the reasons academia and government have been edged out of the AI race: the exponential increase in cost of training these giant models. Google’s Gemini Ultra cost an estimated $191 million worth of compute to train, while OpenAI’s GPT-4 cost an estimated $78 million. In comparison, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900.

What AI Race?

At least in terms of notable machine learning models, the United States vastly outpaced other countries in 2023, developing a total of 61 models in 2023. Since 2019, the U.S. has consistently led in originating the majority of notable models, followed by China and the UK.

Move Over, Human

As of 2023, AI has hit human-level performance on many significant AI benchmarks, from those testing reading comprehension to visual reasoning. Still, it falls just short on some benchmarks like competition-level math. Because AI has been blasting past so many standard benchmarks, AI scholars have had to create new and more difficult challenges. This year’s index also tracked several of these new benchmarks, including those for tasks in coding, advanced reasoning, and agentic behavior.

Private Investment Drops (But We See You, GenAI)

While AI private investment has steadily dropped since 2021, generative AI is gaining steam. In 2023, the sector attracted $25.2 billion, nearly ninefold the investment of 2022 and about 30 times the amount from 2019 (call it the ChatGPT effect). Generative AI accounted for over a quarter of all AI-related private investments in 2023.

U.S. Wins $$ Race

And again, in 2023 the United States dominates in AI private investment. In 2023, the $67.2 billion invested in the U.S. was roughly 8.7 times greater than the amount invested in the next highest country, China, and 17.8 times the amount invested in the United Kingdom. That lineup looks the same when zooming out: Cumulatively since 2013, the United States leads investments at $335.2 billion, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion.

Where is Corporate Adoption?

More companies are implementing AI in some part of their business: In surveys, 55% of organizations said they were using AI in 2023, up from 50% in 2022 and 20% in 2017. Businesses report using AI to automate contact centers, personalize content, and acquire new customers. 

Younger and Wealthier People Worry About Jobs

Globally, most people expect AI to change their jobs, and more than a third expect AI to replace them. Younger generations — Gen Z and millennials — anticipate more substantial effects from AI compared with older generations like Gen X and baby boomers. Specifically, 66% of Gen Z compared with 46% of boomer respondents believe AI will significantly affect their current jobs. Meanwhile, individuals with higher incomes, more education, and decision-making roles foresee AI having a great impact on their employment.

While the Commonwealth Worries About AI Products

When asked in a survey about whether AI products and services make you nervous, 69% of Aussies and 65% of Brits said yes. Japan is the least worried about their AI products at 23%.  

Regulation Rallies

More American regulatory agencies are passing regulations to protect citizens and govern the use of AI tools and data. For example, the Copyright Office and the Library of Congress passed copyright registration guidance concerning works that contained material generated by AI, while the Securities and Exchange Commission developed a cybersecurity risk management strategy, governance, and incident disclosure plan. The agencies to pass the most regulation were the Executive Office of the President and the Commerce Department. 

The AI Index was first created to track AI development. The index collaborates with such organizations as LinkedIn, Quid, McKinsey, Studyportals, the Schwartz Reisman Institute, and the International Federation of Robotics to gather the most current research and feature important insights on the AI ecosystem. 

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April 23, 2024 | Mikala Kane - Neag School of Education

Two Neag School Faculty Named Outstanding Reviewers by AERA

Alyssa Hadley Dunn and Suzanne Wilson were recognized at the American Educational Research Association’s 2024 Annual Meeting

Alyssa Hadley Dunn, left, and Suzanne Wilson were both named Outstanding Reviewers for 2023 by the American Educational Research Association. (Submitted photo, Neag School photo)

Two UConn Neag School of Education faculty members have been named Outstanding Reviewers for 2023 by the American Educational Research Association (AERA). Suzanne Wilson , Neag Endowed Professor of Teacher Education, received the award for her work with the Review of Educational Research, while Alyssa Hadley Dunn , director of teacher education and professor, was recognized for her service to the American Educational Research Journal.

Both faculty officially received their awards earlier this month at the AERA 2024 Annual Meeting in Philadelphia, as the Outstanding Reviewer awards are always celebrated at the following year’s meeting. The awards are presented by AERA journal editors, recognizing the invaluable contribution of volunteer reviewers who have provided exemplary, thorough, and timely reviews of manuscripts received by their respective journals.

“Congratulations to Dr. Wilson and Dr. Dunn,” Dean Jason G. Irizarry says. “The peer-review process is vital to creating a vibrant, empirically rigorous body of research to inform education policy and practice. Their contributions in this regard have been profound, and I am thrilled that they were recognized for this important work.”

The Review of Educational Research publishes critical, integrative reviews of research literature bimonthly. Such reviews typically include conceptualizations, interpretations, and syntheses of literature and scholarly work in a field broadly relevant to education, but the journal also encourages submissions from other disciplines if the research is relevant to education.

“Reviewing for RER has been a privilege, allowing me to learn about new research,” Wilson says. “Reading the reviews of other researchers and the editors also allows one to witness how manuscripts get stronger through the review process. It also teaches one to be a much better critic of one’s own scholarship.”

The American Educational Research Journal is AERA’s flagship journal. It features articles that advance the empirical, theoretical, and methodological understanding of education and learning. It publishes original, peer-reviewed analyses that span the field of education research, all levels of education, and all forms of learning.

“I appreciate the opportunity to serve as a peer reviewer for a foundational journal like AERJ,” Dunn says. “It is a great chance to learn about new, exciting research from colleagues around the world who are seeking to publish their findings and advance the field. I’m grateful to AERA for this recognition.”

To learn more about Neag School-affiliated awards and presentations from the 2024 American Educational Research Association Annual Meeting, visit education.uconn.edu/aera .

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Teens and social media: Key findings from Pew Research Center surveys

For the latest survey data on social media and tech use among teens, see “ Teens, Social Media, and Technology 2023 .” 

Today’s teens are navigating a digital landscape unlike the one experienced by their predecessors, particularly when it comes to the pervasive presence of social media. In 2022, Pew Research Center fielded an in-depth survey asking American teens – and their parents – about their experiences with and views toward social media . Here are key findings from the survey:

Pew Research Center conducted this study to better understand American teens’ experiences with social media and their parents’ perception of these experiences. For this analysis, we surveyed 1,316 U.S. teens ages 13 to 17, along with one parent from each teen’s household. The survey was conducted online by Ipsos from April 14 to May 4, 2022.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, which is an independent committee of experts that specializes in helping to protect the rights of research participants.

Ipsos invited panelists who were a parent of at least one teen ages 13 to 17 from its KnowledgePanel , a probability-based web panel recruited primarily through national, random sampling of residential addresses, to take this survey. For some of these questions, parents were asked to think about one teen in their household. (If they had multiple teenage children ages 13 to 17 in the household, one was randomly chosen.) This teen was then asked to answer questions as well. The parent portion of the survey is weighted to be representative of U.S. parents of teens ages 13 to 17 by age, gender, race, ethnicity, household income and other categories. The teen portion of the survey is weighted to be representative of U.S. teens ages 13 to 17 who live with parents by age, gender, race, ethnicity, household income and other categories.

Here are the questions used  for this report, along with responses, and its  methodology .

Majorities of teens report ever using YouTube, TikTok, Instagram and Snapchat. YouTube is the platform most commonly used by teens, with 95% of those ages 13 to 17 saying they have ever used it, according to a Center survey conducted April 14-May 4, 2022, that asked about 10 online platforms. Two-thirds of teens report using TikTok, followed by roughly six-in-ten who say they use Instagram (62%) and Snapchat (59%). Much smaller shares of teens say they have ever used Twitter (23%), Twitch (20%), WhatsApp (17%), Reddit (14%) and Tumblr (5%).

Facebook use among teens dropped from 71% in 2014-15 to 32% in 2022. Twitter and Tumblr also experienced declines in teen users during that span, but Instagram and Snapchat saw notable increases.

TikTok use is more common among Black teens and among teen girls. For example, roughly eight-in-ten Black teens (81%) say they use TikTok, compared with 71% of Hispanic teens and 62% of White teens. And Hispanic teens (29%) are more likely than Black (19%) or White teens (10%) to report using WhatsApp. (There were not enough Asian teens in the sample to analyze separately.)

Teens’ use of certain social media platforms also varies by gender. Teen girls are more likely than teen boys to report using TikTok (73% vs. 60%), Instagram (69% vs. 55%) and Snapchat (64% vs. 54%). Boys are more likely than girls to report using YouTube (97% vs. 92%), Twitch (26% vs. 13%) and Reddit (20% vs. 8%).

Majorities of teens use YouTube and TikTok every day, and some report using these sites almost constantly. About three-quarters of teens (77%) say they use YouTube daily, while a smaller majority of teens (58%) say the same about TikTok. About half of teens use Instagram (50%) or Snapchat (51%) at least once a day, while 19% report daily use of Facebook.

Some teens report using these platforms almost constantly. For example, 19% say they use YouTube almost constantly, while 16% and 15% say the same about TikTok and Snapchat, respectively.

More than half of teens say it would be difficult for them to give up social media. About a third of teens (36%) say they spend too much time on social media, while 55% say they spend about the right amount of time there and just 8% say they spend too little time. Girls are more likely than boys to say they spend too much time on social media (41% vs. 31%).

Teens are relatively divided over whether it would be hard or easy for them to give up social media. Some 54% say it would be very or somewhat hard, while 46% say it would be very or somewhat easy.

Girls are more likely than boys to say it would be difficult for them to give up social media (58% vs. 49%). Older teens are also more likely than younger teens to say this: 58% of those ages 15 to 17 say it would be very or somewhat hard to give up social media, compared with 48% of those ages 13 to 14.

Teens are more likely to say social media has had a negative effect on others than on themselves. Some 32% say social media has had a mostly negative effect on people their age, while 9% say this about social media’s effect on themselves.

Conversely, teens are more likely to say these platforms have had a mostly positive impact on their own life than on those of their peers. About a third of teens (32%) say social media has had a mostly positive effect on them personally, while roughly a quarter (24%) say it has been positive for other people their age.

Still, the largest shares of teens say social media has had neither a positive nor negative effect on themselves (59%) or on other teens (45%). These patterns are consistent across demographic groups.

Teens are more likely to report positive than negative experiences in their social media use. Majorities of teens report experiencing each of the four positive experiences asked about: feeling more connected to what is going on in their friends’ lives (80%), like they have a place where they can show their creative side (71%), like they have people who can support them through tough times (67%), and that they are more accepted (58%).

When it comes to negative experiences, 38% of teens say that what they see on social media makes them feel overwhelmed because of all the drama. Roughly three-in-ten say it makes them feel like their friends are leaving them out of things (31%) or feel pressure to post content that will get lots of comments or likes (29%). And 23% say that what they see on social media makes them feel worse about their own life.

There are several gender differences in the experiences teens report having while on social media. Teen girls are more likely than teen boys to say that what they see on social media makes them feel a lot like they have a place to express their creativity or like they have people who can support them. However, girls also report encountering some of the pressures at higher rates than boys. Some 45% of girls say they feel overwhelmed because of all the drama on social media, compared with 32% of boys. Girls are also more likely than boys to say social media has made them feel like their friends are leaving them out of things (37% vs. 24%) or feel worse about their own life (28% vs. 18%).

When it comes to abuse on social media platforms, many teens think criminal charges or permanent bans would help a lot. Half of teens think criminal charges or permanent bans for users who bully or harass others on social media would help a lot to reduce harassment and bullying on these platforms. 

About four-in-ten teens say it would help a lot if social media companies proactively deleted abusive posts or required social media users to use their real names and pictures. Three-in-ten teens say it would help a lot if school districts monitored students’ social media activity for bullying or harassment.

Some teens – especially older girls – avoid posting certain things on social media because of fear of embarrassment or other reasons. Roughly four-in-ten teens say they often or sometimes decide not to post something on social media because they worry people might use it to embarrass them (40%) or because it does not align with how they like to represent themselves on these platforms (38%). A third of teens say they avoid posting certain things out of concern for offending others by what they say, while 27% say they avoid posting things because it could hurt their chances when applying for schools or jobs.

These concerns are more prevalent among older teen girls. For example, roughly half of girls ages 15 to 17 say they often or sometimes decide not to post something on social media because they worry people might use it to embarrass them (50%) or because it doesn’t fit with how they’d like to represent themselves on these sites (51%), compared with smaller shares among younger girls and among boys overall.

Many teens do not feel like they are in the driver’s seat when it comes to controlling what information social media companies collect about them. Six-in-ten teens say they think they have little (40%) or no control (20%) over the personal information that social media companies collect about them. Another 26% aren’t sure how much control they have. Just 14% of teens think they have a lot of control.

Despite many feeling a lack of control, teens are largely unconcerned about companies collecting their information. Only 8% are extremely concerned about the amount of personal information that social media companies might have and 13% are very concerned. Still, 44% of teens say they have little or no concern about how much these companies might know about them.

Only around one-in-five teens think their parents are highly worried about their use of social media. Some 22% of teens think their parents are extremely or very worried about them using social media. But a larger share of teens (41%) think their parents are either not at all (16%) or a little worried (25%) about them using social media. About a quarter of teens (27%) fall more in the middle, saying they think their parents are somewhat worried.

Many teens also believe there is a disconnect between parental perceptions of social media and teens’ lived realities. Some 39% of teens say their experiences on social media are better than parents think, and 27% say their experiences are worse. A third of teens say parents’ views are about right.

Nearly half of parents with teens (46%) are highly worried that their child could be exposed to explicit content on social media. Parents of teens are more likely to be extremely or very concerned about this than about social media causing mental health issues like anxiety, depression or lower self-esteem. Some parents also fret about time management problems for their teen stemming from social media use, such as wasting time on these sites (42%) and being distracted from completing homework (38%).

Note: Here are the questions used  for this report, along with responses, and its  methodology .

CORRECTION (May 17, 2023): In a previous version of this post, the percentages of teens using Instagram and Snapchat daily were transposed in the text. The original chart was correct. This change does not substantively affect the analysis.

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• Published: 26 June 2023

GREENER principles for environmentally sustainable computational science

• Loïc Lannelongue   ORCID: orcid.org/0000-0002-9135-1345 1 , 2 , 3 , 4 ,
• Hans-Erik G. Aronson   ORCID: orcid.org/0000-0002-1702-1671 5 ,
• Alex Bateman 6 ,
• Ewan Birney 6 ,
• Talia Caplan   ORCID: orcid.org/0000-0001-8990-1435 7 ,
• Martin Juckes   ORCID: orcid.org/0000-0003-1770-2132 8 ,
• Johanna McEntyre 6 ,
• Andrew D. Morris 5 ,
• Gerry Reilly 5 &
• Michael Inouye 1 , 2 , 3 , 4 , 9 , 10 , 11  

Nature Computational Science volume  3 ,  pages 514–521 ( 2023 ) Cite this article

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The carbon footprint of scientific computing is substantial, but environmentally sustainable computational science (ESCS) is a nascent field with many opportunities to thrive. To realize the immense green opportunities and continued, yet sustainable, growth of computer science, we must take a coordinated approach to our current challenges, including greater awareness and transparency, improved estimation and wider reporting of environmental impacts. Here, we present a snapshot of where ESCS stands today and introduce the GREENER set of principles, as well as guidance for best practices moving forward.

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Scientific research and development have transformed and immeasurably improved the human condition, whether by building instruments to unveil the mysteries of the universe, developing treatments to fight cancer or improving our understanding of the human genome. Yet, science can, and frequently does, impact the environment, and the magnitude of these impacts is not always well understood. Given the connection between climate change and human health, it is becoming increasingly apparent to biomedical researchers in particular, as well as their funders, that the environmental effects of research should be taken into account 1 , 2 , 3 , 4 , 5 .

Recent studies have begun to elucidate the environmental impacts of scientific research, with an initial focus on scientific conferences and experimental laboratories 6 . The 2019 Fall Meeting of the American Geophysical Union was estimated to emit 80,000 metric tonnes of CO 2 equivalent (tCO 2 e), equivalent to the average weekly emissions of the city of Edinburgh, UK 7 (CO 2 e, or CO 2 -equivalent, summarizes the global warming impacts of a range of greenhouse gases (GHGs) and is the standard metric for carbon footprints, although its accuracy is sometimes debated 8 ) The annual meeting of the Society for Neuroscience was estimated to emit 22,000 tCO 2 e, approximately the annual carbon footprint of 1,000 medium-sized laboratories 9 . The life-cycle impact (including construction and usage) of university buildings has been estimated at ~0.125 tCO 2 e m −2  yr −1 (ref. 10 ), and the yearly carbon footprint of a typical life-science laboratory at ~20 tCO 2 e (ref. 9 ). The Laboratory Efficiency Assessment Framework (LEAF) is a widely adopted standard to monitor and reduce the carbon footprint of laboratory-based research 11 . Other recent frameworks can help to raise awareness: GES 1point5 12 provides an open-source tool to estimate the carbon footprint of research laboratories and covers buildings, procurement, commuting and travel, and the Environmental Responsibility 5-R Framework provides guidelines for ecologically conscious research 13 .

With the increasing scale of high-performance and cloud computing, the computational sciences are susceptible to having silent and unintended environmental impacts. The sector of information and communication technologies (ICT) was responsible for between 1.8% and 2.8% of global GHG emissions in 2020 14 —more than aviation (1.9% 15 )—and, if unchecked, the ICT carbon footprint could grow exponentially in coming years 14 . Although the environmental impact of experimental ‘wet’ laboratories is more immediately obvious, with their large pieces of equipment and high plastic and reagent usage, the impact of algorithms is less clear and often underestimated. The risks of seeking performance at any cost and the importance of considering energy usage and sustainability when developing new hardware for high-performance computing (HPC) was raised as early as 2007 16 . Since then, continuous improvements have been made by developing new hardware, building lower-energy data centers and implementing more efficient HPC systems 17 , 18 . However, it is only in the past five years that these concerns have reached HPC users, in particular researchers. Notably, the field of artificial intelligence (AI) has first taken note of its environmental impacts, in particular those of the very large language models developed 19 , 20 , 21 , 22 , 23 . It is unclear, however, to what extent this has led the field towards more sustainable research practices. A small number of studies have also been performed in other fields, including bioinformatics 24 , astronomy and astrophysics 25 , 26 , 27 , 28 , particle physics 29 , neuroscience 30 and computational social sciences 31 . Health data science is starting to address the subject, but a recent systematic review found only 25 publications in the field over the past 12 years 32 . In addition to the environmental effects of electricity usage, manufacturing and disposal of hardware, there are also concerns around data centers’ water usage and land footprint 33 . Notably, computational science, in particular AI, has the potential to help fight climate change, for example, by improving the efficiency of wind farms, by facilitating low-carbon urban mobility and by better understanding and anticipating severe weather events 34 .

In this Perspective we highlight the nascent field of environmentally sustainable computational science (ESCS)—what we have learned from the research so far, and what scientists can do to mitigate their environmental impacts. In doing so, we present GREENER (Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research; Fig. 1 ), a set of principles for how the computational science community could lead the way in sustainable research practices, maximizing computational science’s benefit to both humanity and the environment.

The GREENER principles enable cultural change (blue arrows), which in turn facilitates their implementation (green arrows) and triggers a virtuous circle.

Environmental impacts of the computational sciences

The past three years have seen increased concerns regarding the carbon footprint of computations, and only recently have tools 21 , 35 , 36 , 37 and guidelines 38 been widely available to computational scientists to allow them to estimate their carbon footprint and be more environmentally sustainable.

Most calculators that estimate the carbon footprint of computations are targeted at machine learning tasks and so are primarily suited to Python pipelines, graphics processing units (GPUs) and/or cloud computing 36 , 37 , 39 , 40 . Python libraries have the benefit of integrating well into machine learning pipelines or online calculators for cloud GPUs 21 , 41 . Recently, a flexible online tool, the Green Algorithms calculator 35 , enabled the estimation of the carbon footprint for nearly any computational task, empowering sustainability metrics across fields, hardware, computing platforms and locations.

Some publications, such as ref. 38 , have listed simple actions that computational scientists can take regarding their environmental impact, including estimating the carbon footprint of running algorithms, both a posteriori to acknowledge the impact of a project and before starting as part of a cost–benefit analysis. A 2020 report from The Royal Society formalizes this with the notion of ‘energy proportionality’, meaning the environmental impacts of an innovation must be outweighed by its environmental or societal benefits 34 . It is also important to minimize electronic waste by keeping devices for longer and using second-hand hardware when possible. A 2021 report by the World Health Organization 42 warns of the dramatic effect of e-waste on population health, particularly children. The unregulated informal recycling industry, which handles more than 80% of the 53 million tonnes of e-waste, causes a high level of water, soil and air pollution, often in low- and middle-income countries 43 . The up to 56 million informal waste workers are also exposed to hazardous chemicals such as heavy metals and persistent organic pollutants 42 . Scientists can also choose energy-efficient hardware and computing facilities, while favoring those powered by green energy. Writing efficient code can substantially reduce the carbon footprint as well, and this can be done alongside making hardware requirements and carbon footprints clear when releasing new software. The Green Software Foundation ( https://greensoftware.foundation ) promotes carbon-aware coding to reduce the operational carbon footprint of the softwares used in all aspects of society. There is, however, a rebound effect to making algorithms and hardware more efficient: instead of reducing computing usage, increased efficiency encourages more analyses to be performed, which leads to a revaluation of the cost–benefit but often results in increased carbon footprints. The rebound effect is a key example of why research practice should adapt to technological advances so that they lead to carbon footprint reductions.

GREENER computational science

ESCS is an emerging field, but one that is of rapidly increasing importance given the climate crisis. In the following, our proposed set of principles (Fig. 1 ) outlines the main axes where progress is needed, where opportunities lie and where we believe efforts should be concentrated.

Governance and responsibility

Everyone involved in computational science has a role to play in making the field more sustainable, and many do already, from grassroots movements to large institutions. Individual and institutional responsibility is a necessary step to ensure transparency and reduction of GHG emission. Here we highlight key stakeholders alongside existing initiatives and future opportunities for involvement.

Grassroots initiatives led by graduate students, early career researchers and laboratory technicians have shown great success in tackling the carbon footprint of laboratory work, including Green Labs Netherlands 44 , the Nottingham Technical Sustainability Working Group or the Digital Humanities Climate Coalition 45 . International coalitions such as the Sustainable Research (SuRe) Symposium, initially set up for wet laboratories, have started to address the impact of computing as well. IT teams in HPC centers are naturally key, both in terms of training and ensuring that the appropriate information is logged so that scientists can follow the carbon footprints of their work. Principal investigators can encourage their teams to think about this issue and provide access to suitable training when needed.

Simultaneously, top–down approaches are needed, with funding bodies and journals occupying key positions in both incentivizing carbon-footprint reduction and in promoting transparency. Funding bodies can directly influence the researchers they fund and those applying for funding via their funding policies. They can require estimates of carbon footprints to be included in funding applications as part of ‘environmental impacts statements’. Many funding bodies include sustainability in their guidelines already; see, for example, the UK’s NIHR carbon reduction guidelines 1 , the brief mention of the environment in UKRI’s terms and conditions 46 , and the Wellcome Trust’s carbon-offsetting travel policy 47 .

Although these are important first steps, bolder action is needed to meet the urgency of climate change. For example, UKRI’s digital research infrastructure scoping project 48 , which seeks to provide a roadmap to net zero for its digital infrastructure, sends a clear message that sustainable research includes minimizing the GHG emissions from computation. The project not only raises awareness but will hopefully result in reductions in GHG emissions.

Large research institutes are key to managing and expanding centralized data infrastructures and trusted research environments (TREs). For example, EMBL’s European Bioinformatics Institute manages more than 40 data resources 49 , including AlphaFold DB 50 , which contains over 200,000,000 predicted protein structures that can be searched, browsed and retrieved according to the FAIR principles (findable, accessible, interoperable, reusable) 51 . As a consequence, researchers do not need to run the carbon-intensive AlphaFold algorithm for themselves and instead can just query the database. AlphaFold DB was queried programmatically over 700 million times and the web page was accessed 2.4 million times between August 2021 and October 2022. Institutions also have a role in making procurement decisions carefully, taking into account both the manufacturing and operational footprint of hardware purchases. This is critical, as the lifetime footprint of a computational facility is largely determined by the date it is purchased. Facilities could also better balance investment decisions, with a focus on attracting staff based on sustainable and efficient working environments, rather than high-powered hardware 52 .

However, increases in the efficiencies of digital technology alone are unlikely to prove sufficient in ensuring sustainable resource use 53 . Alongside these investments, funding bodies should support a shift towards more positive, inclusive and green research cultures, recognizing that more data or bigger models do not always translate into greater insights and that a ‘fit for purpose’ approach can ultimately be more efficient. Organizations such as Health Data Research UK and the UK Health Data Research Alliance have a key convening role in ensuring that awareness is raised around the climate impact of both infrastructure investment and computational methods.

Journals may incentivize authors to acknowledge and indeed estimate the carbon footprint of the work presented. Some authors already do this voluntarily (for example, refs. 54 , 55 , 56 , 57 , 58 , 59 ), mostly in bioinformatics and machine learning so far, but there is potential to expand it to other areas of computational science. In some instances, showing that a new tool is greener can be an argument in support of a new method 60 .

International societies in charge of organizing annual conferences may help scientists reduce the carbon footprint of presenting their work by offering hybrid options. The COVID-19 pandemic boosted virtual and hybrid meetings, which have a lower carbon footprint while increasing access and diversity 7 , 61 . Burtscher and colleagues found that running the annual meeting of the European Astronomical Society online emitted >3,000-fold less CO 2 e than the in-person meeting (0.582 tCO 2 e compared to 1,855 tCO 2 e) 25 . Institutions are starting to tackle this; for example, the University of Cambridge has released new travel guidelines encouraging virtual meetings whenever feasible and restricting flights to essential travel, while also acknowledging that different career stages have different needs 62 .

Industry partners will also need to be part of the discussion. Acknowledging and reducing computing environmental impact comes with added challenges in industry, such as shareholder interests and/or public relations. While the EU has backed some initiatives helping ICT-reliant companies to address their carbon footprint, such as ICTfootprint.eu, other major stakeholders have expressed skepticism regarding the environmental issues of machine learning models 63 , 64 . Although challenging, tech industry engagement and inclusion is nevertheless essential for tackling GHG emissions.

Estimate and report the energy consumption of algorithms

Estimating and monitoring the carbon footprint of computations is an essential step towards sustainable research as it identifies inefficiencies and opportunities for improvement. User-level metrics are crucial to understanding environmental impacts and promoting personal responsibility. In some HPC situations, particularly in academia, the financial cost of running computations is negligible and scientists may have the impression of unlimited and inconsequential computing capacity. Quantifying the carbon footprint of individual projects helps raise awareness of the true costs of research.

Although progress has been made in estimating energy usage and carbon footprints over the past few years, there are still barriers that prevent the routine estimation of environmental impacts. From task-agnostic, general-purpose calculators 35 and task-specific packages 36 , 37 , 65 to server-side softwares 66 , 67 , each estimation tool is a trade-off between ease of use and accuracy. A recent primer 68 discusses these different options in more detail and provides recommendations as to which approach fits a particular need.

Regardless of the calculator used, for these tools to work effectively and for scientists to have an accurate representation of their energy consumption, it is important to understand the power management for different components. For example, the power usage of processing cores such as central processing units (CPUs) and GPUs is not a readily available metric; instead, thermal design power (meaning, how much heat the chip can be expected to dissipate in a normal setting) is used. Although an acceptable approximation, it has also been shown to substantially underestimate power usage in some situations 69 . The efficiency of data centers is measured by the power usage effectiveness (PUE), which quantifies how much energy is needed for non-computing tasks, mainly cooling (efficient data centers have PUEs close to 1). This metric is widely used, with large cloud providers reporting low PUEs (for example, 1.11 for Google 70 compared to a global average of 1.57 71 ), but discrepancies in how it is calculated can limit PUE interpretation and thus its impact 72 , 73 , 74 . A standard from the International Organization for Standardization is trying to address this 75 . Unfortunately, the PUE of a particular data center, whether cloud or institutional, is rarely publicly documented. Thus, an important step is the data science and infrastructure community making both hardware and data centers’ energy consumption metrics available to their users and the public. Ultimately, tackling unnecessary carbon footprints will require transparency 34 .

Tackling energy and embodied impacts through new collaborations

Minimizing carbon intensity (meaning the carbon footprint of producing electricity) is one of the most immediately impactful ways to reduce GHG emissions. Carbon intensities depend largely on geographical location, with up to three orders of magnitude between the top and bottom performing high-income countries in terms of low carbon energies (from 0.10 gCO 2 e kWh −1 in Iceland to 770 gCO 2 e kWh −1 in Australia 76 ). Changing the carbon intensity of a local state or national government is nearly always impractical as it would necessitate protracted campaigns to change energy policies. An alternative is to relocate computations to low-carbon settings and countries, but, depending on the type of facility or the sensitivity of the data, this may not always be possible. New inter-institutional cooperation may open up opportunities to enable access to low-carbon data centers in real time.

It is, however, essential to recognize and account for inequalities between countries in terms of access to green energy sources. International cooperation is key to providing scientists from low- and middle-income countries (LMICs), who frequently only have high-carbon-intensity options available to them, access to low-carbon computing infrastructures for their work. In the longer term, international partnerships between organizations and nations can help build low-carbon computing capacity in LMICs.

Furthermore, the footprint of user devices should not be forgotten. In one estimate, the energy footprint of streaming a video to a laptop is mainly on the laptop (72%), with 23% used in transmission and a mere 5% at the data center 77 . Zero clients (user devices with no compute or storage capacity) can be used in some research use cases and drastically reduce the client-side footprint 78 .

It can be tempting to reduce the environmental impacts of computing to electricity needs, as these are the easiest ones to estimate. However, water usage, ecological impacts and embodied carbon footprints from manufacturing should also be addressed. For example, for personal hardware, such as laptops, 70–80% of the life-cycle impact of these devices comes from manufacturing only 79 , as it involves mining raw materials and assembling the different components, which require water and energy. Moreover, manufacturing often takes place in countries that have a higher carbon intensity for power generation and a slower transition to zero-carbon power 80 . Currently, hardware renewal policies, either for work computers or servers in data centers, are often closely dependent on warranties and financial costs, with environmental costs rarely considered. For hardware used in data centers, regular updates may be both financially and environmentally friendly, as efficiency gains may offset manufacturing impacts. Estimating these environmental impacts will allow HPC teams to know for sure. Reconditioned and remanufactured laptops and servers are available, but growth of this sector is currently limited by negative consumer perception 81 . Major suppliers of hardware are making substantial commitments, such as 100% renewable energy supply by 2030 82 or net zero by 2050 83 .

Another key consideration is data storage. Scientific datasets are now measured in petabytes (PB). In genomics, the popular UK Biobank cohort 84 is expected to reach 15 PB by 2025 85 , and the first image of a black hole required the collection of 5 PB of data 86 . The carbon footprint of storing data depends on numerous factors, but based on some manufacturers’ estimations, the order of magnitude of the life-cycle footprint of storing 1 TB of data for a year is ~10 kg CO 2 e (refs. 87 , 88 ). This issue is exacerbated by the duplication of such datasets in order for each institution, and sometimes each research group, to have a copy. Centralized and collaborative computing resources (such as TREs) holding both data and computing hardware may help alleviate redundant resources. TRE efforts in the UK span both health (for example, NHS Digital 89 ) and administrative data (for example, the SAIL databank on the UK Secure Research Platform 90 and the Office for National Statistics Secure Research Service 91 ). Large (hyperscale) data centers are expected to be more energy-efficient 92 , but they may also encourage unnecessary increases in the scale of computing (rebound effect).

The importance of dedicated education and research efforts for ESCS

Education is essential to raise awareness with different stakeholders. In lieu of incorporating some aspects into more formal undergraduate programs, integrating sustainability into computational training courses is a tangible first step toward reducing carbon footprints. An example is the ‘Green Computing’ Workshop on Education at the 2022 conference on Intelligent Systems for Molecular Biology.

Investing in research that will catalyze innovation in the field of ESCS is a crucial role for funders and institutions to play. Although global data centers’ workloads have increased more than sixfold between 2010 and 2018, their total electricity usage has been approximately stable due to the use of power-efficient hardware 93 , but environmentally sustainable investments will be needed to perpetuate this trend. Initiatives like Wellcome’s Research Sustainability project 94 , which look to highlight key gaps where investment could deliver the next generation of ESCS tools and technology, are key to ensuring that growth in energy demand beyond current efficiency trends can be managed in a sustainable way. Similarly, the UKRI Data and Analytics Research Environments UK program (DARE UK) needs to ensure that sustainability is a key evaluation criterion for funding and infrastructure investments for the next generation of TREs.

Recent studies found that the most widely used programming languages in research, such as R and Python 95 , tend to be the least energy-efficient ones 96 , 97 , and, although it is unlikely that forcing the community to switch to more efficient languages would benefit the environment in the short term (due to inefficient coding for example), this highlights the importance of having trained research software engineers within research groups to ensure that the algorithms used are efficiently implemented. There is also scope to use current tools more efficiently by better understanding and monitoring how coding choices impact carbon footprints. Algorithms also come with high memory requirements, sometimes using more energy than processors 98 . Unfortunately, memory power usage remains poorly optimized, as speed of access is almost always favored over energy efficiency 99 . Providing users and software engineers with the flexibility to opt for energy efficiency would present an opportunity for a reduction in GHG emissions 100 , 101 .

Cultural change

In parallel to the technological reductions in energy usage and carbon footprints, research practices will also need to change to avoid rebound effects 38 . Similar to the aviation industry, there is a tendency to count on technology to solve sustainability concerns without having to change usage 102 (that is, waiting on computing to become zero-carbon rather than acting on how we use it). Cultural change in the computing community to reconsider how we think about computing costs will be necessary. Research strategies at all levels will need to consider environmental impacts and corresponding approaches to carbon footprint minimization. The upcoming extension of the LEAF standard for computational laboratories will provide researchers with tangible tools to do so. Day to day, there is a need to solve trade-offs between the speed of computation, accuracy and GHG emissions, keeping in mind the goal of GHG reduction. These changes in scientific practices are challenging, but, importantly, there are synergies between open computational science and green computing 103 . For example, making code, data and models FAIR so that other scientists avoid unnecessary computations can increase the reach and impact of a project. FAIR practices can result in highly efficient code implementations, reduce the need to retrain models, and reduce unnecessary data generation/storage, thus reducing the overall carbon footprint. As a result, green computing and FAIR practices may both stimulate innovation and reduce financial costs.

Moreover, computational science has downstream effects on carbon footprints in other areas. In the biomedical sciences, developments in machine learning and computer vision impact the speed and scale of medical imaging processing. Discoveries in health data science make their way to clinicians and patients through, for example, connected devices. In each of these cases and many others, environmental impacts propagate through the whole digital health sector 32 . Yet, here too synergies exist. In many cases, such as telemedicine, there may be a net benefit in terms of both carbon and patient care, provided that all impacts have been carefully accounted for. These questions are beginning to be tackled in medicine, such as assessments of the environmental impact of telehealth 104 or studies into ways to sustainably handle large volumes of medical imaging data 105 . For the latter, NHS Digital (the UK’s national provider of information, data and IT systems for health and social care) has released guidelines to this effect 106 . Outside the biomedical field, there are immense but, so far, unrealized opportunities for similar efforts.

The computational sciences have an opportunity to lead the way in sustainability, which may be achieved through the GREENER principles for ESCS (Fig. 1 ): Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research. This will require more transparency on environmental impacts. Although some tools already exist to estimate carbon footprints, more specialized ones will be needed alongside a clearer understanding of the carbon footprint of hardware and facilities, as well as more systematic monitoring and acknowledgment of carbon footprints. Measurement is a first step, followed by a reduction in GHG emissions. This can be achieved with better training and sensible policies for renewing hardware and storing data. Cooperation, open science and equitable access to low-carbon computing facilities will also be crucial 107 . Computing practices will need to adapt to include carbon footprints in cost–benefit analyses, as well as consider the environmental impacts of downstream applications. The development of sustainable solutions will need particularly careful consideration, as they frequently have the least benefit for populations, often in LMICs, who suffer the most from climate change 22 , 108 . All stakeholders have a role to play, from funding bodies, journals and institutions to HPC teams and early career researchers. There is now a window of time and an immense opportunity to transform computational science into an exemplar of broad societal impact and sustainability.

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Acknowledgements

L.L. was supported by the University of Cambridge MRC DTP (MR/S502443/1) and the BHF program grant (RG/18/13/33946). M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). M.I. was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1). This work was supported by core funding from the British Heart Foundation (RG/13/13/30194; RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland) and the British Heart Foundation and Wellcome.

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Lannelongue, L., Aronson, HE.G., Bateman, A. et al. GREENER principles for environmentally sustainable computational science. Nat Comput Sci 3 , 514–521 (2023). https://doi.org/10.1038/s43588-023-00461-y

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