2024-11-22 21:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
OpenAI has lifted the lid (just a crack) on its safety-testing processes. It has put out two papers describing how it stress-tests its powerful large language models to try to identify potential harmful or otherwise unwanted behavior, an approach known as red-teaming.
The first paper describes how OpenAI directs an extensive network of human testers outside the company to vet the behavior of its models before they are released. The second presents a new way to automate parts of the testing process, using a large language model like GPT-4 to come up with novel ways to bypass its own guardrails. MIT Technology Review got an exclusive preview of the work.
—Will Douglas Heaven
Over 135 uterus transplants have been performed globally in the last decade, resulting in the births of over 50 healthy babies. The surgery has had profound consequences for these families—the recipients would not have been able to experience pregnancy any other way.
But legal and ethical questions continue to surround the procedure, which is still considered experimental. Who should be offered a uterus transplant? Could the procedure ever be offered to transgender women? And if so, who should pay for these surgeries? Read the full story.
—Jessica Hamzelou
This story is from The Checkup, our weekly newsletter about the latest in biotech and health. Sign up to receive it in your inbox every Thursday.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI may launch a web browser
Which would be a full-frontal assault on Google (The Information $)
+ The Google browser break-up is an answer in search of a question. (FT $)
+ OpenAI accidentally deleted potential evidence in a training data lawsuit. (The Verge)
2 Border militias are ready to help with Trump’s deportation plans
Regardless of whether they’re asked to or not. (Wired $)
+ Trump’s administration plans to radically curb the powers of the federal agency that protects unions. (WP $)
3 Russia hit Ukraine with a new type of missile
Here’s what we know about it so far. (The Guardian)
4 Microsoft is about to turn 50
And it’s every bit as relevant and powerful as it’s ever been. (Wired $)
5 China has overtaken Germany in industrial robot adoption
South Korea, however, remains streets ahead of both of them. (Reuters $)
+ Three reasons robots are about to become way more useful. (MIT Technology Review)
6 The irresistible rise of cozy tech
Our devices, social media and now AI are encouraging us to keep looking inward. (New Yorker $)
+ Inside the cozy but creepy world of VR sleep rooms. (MIT Technology Review)
7 Churchgoers in a Swiss city have been spilling their secrets to AI Jesus 
And they’re mostly really enjoying it. Watch out, priests. (The Guardian)
8 A French startup wants to make fuel out of thin air
Then use it to fuel ships and airplanes. (IEEE Spectrum)
+ Everything you need to know about alternative jet fuels. (MIT Technology Review)
9 WhatsApp is going to start transcribing voice messages
This seems a good compromise to bridge people’s different communication preferences. (The Verge)
10 Want a new phone? You should consider second-hand
It’s better for the planet—and your wallet. (Vox)
Quote of the day
“Nope. 100% not true.”
—Jeff Bezos fires back at Elon Musk’s claim that he was telling everyone that Trump would lose pre-election in a rare post on X.
The big story
This chemist is reimagining the discovery of materials using AI and automation
October 2021
Alán Aspuru-Guzik, a Mexico City–born, Toronto-based chemist, has devoted much of his life to contemplating worst-case scenarios. What if climate change proceeds as expected, or gets significantly worse? Could we quickly come up with the materials we’ll need to cheaply capture carbon, or make batteries from something other than costly lithium?
Materials discovery—the science of creating and developing useful new substances—often moves at a frustratingly slow pace. The typical trial-and-error approach takes an average of two decades, making it too expensive and risky for most companies to pursue.
Aspuru-Guzik’s objective—which he shares with a growing number of computer-savvy chemists—is to shrink that interval to a matter of months or years. And advances in AI, robotics, and computing are bringing new life to his vision. Read the full story.
—Simon Lewsen
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)
+ Do you struggle with a lack of confidence? Here’s how to take up a bit more space.
+ These recipes will ensure you have a delicious Thanksgiving next week.
+ It’s impossible not to dream of lazy sunny days while gazing at Quentin Monge’s work.
+ Tom Jones x Disturbed = very funny.
2024-11-22 18:00:00
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
Earlier this year, a boy in Sweden celebrated his 10th birthday. Reproductive scientists and doctors marked the occasion too. This little boy’s birth had been special. He was the first person to be born from a transplanted uterus.
The boy was born in 2014 after his mother, a 35-year-old woman who had been born without a uterus, received a donated uterus from a 61-year-old close family friend. At the time, she was one of only 11 women who had undergone the experimental procedure.
A decade on, over 135 uterus transplants have been performed globally, resulting in the births of over 50 healthy babies. The surgery has had profound consequences for these families—the recipients would not have been able to experience pregnancy any other way.
But legal and ethical questions continue to surround the procedure, which is still considered experimental. Who should be offered a uterus transplant? Could the procedure ever be offered to transgender women? And if so, who should pay for these surgeries?
These issues were raised at a recent virtual event run by Progress Educational Trust, a UK-based charity that aims to provide information to the public on genomics and infertility. One of the speakers was Mats Brännström, who led the team at the University of Gothenburg that performed the first successful transplant.
For Brännström, the story of uterus transplantation begins in 1998. While traveling in Australia, he said, he met a 27-year-old woman called Angela, who longed to be pregnant but lacked a functional uterus. She suggested to Brännström that her mother could donate hers. “I was amazed I hadn’t thought of it before,” he said.
According to Brännström, around 1 in 500 women experience infertility due to what’s known as absolute uterine factor infertility, or AUFI, meaning they do not have a functional uterus. Uterus transplants could offer them a way to get pregnant.
His meeting with Angela kick-started a research project that started in mice and eventually moved on to pigs, sheep, and baboons. Brännström’s team started performing uterus transplants in women as part of a small clinical trial in 2012. In that trial, all the donors were living, and in many cases they were the mothers or aunts of the recipients.
The surgeries ended up being more complicated than he had anticipated, said Brännström. The operation to remove a donor’s uterus was expected to take between three and four hours. It ended up taking between eight and 11 hours.
In that first trial, Brännström’s team transplanted uteruses into nine women, each of whom had IVF to create and store embryos beforehand. The woman who was the first to give birth had IVF over a 12-month period, which ended six months before her surgery. It took a little over 10 hours to remove the uterus from the donor, and just under five hours to stitch it into the recipient.
The recipient started getting her period 43 days after her transplant. Doctors transferred one of her embryos into the uterus a year after her surgery. Three weeks later, a pregnancy test confirmed she was pregnant.
At 31 weeks, she was admitted to hospital with preeclampsia, a serious medical condition that can develop during pregnancy, and her baby was delivered by C-section 16 hours later. She was discharged from hospital after three days, although the baby spent 16 days in the hospital’s neonatal unit.
Despite those difficulties, her story is considered a success. Other uterus recipients have also experienced pregnancy complications, and some have had surgical complications. And all transplant recipients must adhere to a regimen of immunosuppressant drugs, which can have side effects.
The uteruses aren’t intended to last forever, either. Surgeons remove them once the recipients have completed their families, often after one or two children. The removal is also a significant operation.
Given all that, uterus transplants are not to be taken lightly. And there are other paths to parenthood. Some ethicists are concerned that in pursuing uterus transplantation as a fertility treatment, we might reinforce ideas that define a woman’s value in terms of her reproductive potential, Natasha Hammond-Browning, a legal scholar at Cardiff University in Wales, said at the event. “There is debate around whether we should be giving greater preference to adoption, to surrogacy, and to supporting children who already exist and who need care,” she said.
We also need to consider whether there is a “right to gestate,” and if there is, who has that right, said Hammond-Browning. And these concerns need to be balanced with the importance of reproductive autonomy—the idea that people have the right to decide and control their own reproductive efforts.
Further questions remain over whether uterus transplants might ever be an option for trans women, who not only lack a uterus but also have a different pelvic anatomy. I asked the speakers if the surgery might ever be feasible. They weren’t hugely optimistic that it would, at least in the near future.
“I personally think that the transgender community have been given … false hope for responsible transplantation in the near future,” was the response of J. Richard Smith of Imperial College London, who co-led the first uterus transplant performed in the UK. Even cisgender women who have needed surgery to create “neovaginas” aren’t eligible for the uterus transplants his team are offering as part of a clinical study. They have an altered vaginal microbiome that appears to increase the risk of miscarriage, he said.
“There is a huge amount of work to be done before this work can be translated to the transgender community,” Smith said. Brännström agreed but added that he thinks the surgery will be available at some point—just after a lot more research.
And then there are the legal and ethical questions, none of which have easy answers. Hammond-Browning pointed out that clinical teams would first need to determine what the goal of such an operation would be. Is it about reproduction or gender realignment, for example? And how might that goal influence decisions over who should get a donated uterus, and why?
Considering only 135 human uterus transplants have ever been carried out, we still have a lot to learn about the best way to perform them. (For context, more than 25,000 kidney transplants were carried out in 2023 in the US alone.) Researchers are still figuring out how uteruses from deceased donors differ from those of living ones, and how to minimize complications in young, healthy women. Since that little boy was born 10 years ago, only 50 other children have been born in a similar way. It’s still early days.
The first birth following the transplantation of a uterus from a dead donor happened in 2017. A team in Brazil transferred the uterus of a 45-year-old donor, who had died from a brain hemorrhage, to a 32-year-old recipient born without a uterus.
Researchers are working on artificial wombs—“biobags” designed to care for premature babies. They have been tested on lambs and piglets. Now FDA advisors are figuring out how to move the technology into human trials.
An alternative type of artificial womb is being used to grow mouse embryos. Jacob Hanna at the Weizmann Institute of Science and his colleagues say they’ve been able to grow embryos in this environment for 11 or 12 days—around half the animal’s gestational period.
Research is underway to develop new fertility options for transgender men. Some of these men are put off by existing approaches, which tend to involve pausing hormone therapy and undergoing potentially distressing procedures.
People on Ozempic, Wegovy, and similar drugs are losing their appetite for sugary, ultraprocessed foods. The food industry will have to adapt. (TIL Nestlé has already started a line of frozen meals targeted at people on these weight-loss drugs.) (The New York Times Magazine)
People who have a history of obesity can find it harder to lose weight. That might be because the fat cells in our bodies seem to “remember” that history and have an altered response to food. (The Guardian)
Robert F. Kennedy Jr. took leave as chairman of Children’s Health Defense, a nonprofit known for spreading doubt about vaccines, to run for US president last year. But he is still involved in legal cases filed by the group. And several of its cases remain open, including ones against the Food and Drug Administration, the Centers for Disease Control and Prevention, and the National Institutes of Health—all agencies Kennedy would lead if his nomination for head of Health and Human Services is confirmed. (STAT)
Researchers are among the millions of new users of Bluesky, a social media alternative to X (formerly known as Twitter). “There is this pent-up demand among scientists for what is essentially the old Twitter,” says one researcher who found that the number of influential scientists using the platform doubled between August and November. (Science)
Since 2016, a team of around 100 scientists have been working to catalogue the 37 trillion or so cells in the human body. This week, the Human Cell Atlas published a collection of studies that represents a significant first step toward that goal—including maps of cells in the nervous system, lungs, heart, gut, and immune system. (Nature)
2024-11-22 02:00:10
OpenAI is once again lifting the lid (just a crack) on its safety-testing processes. Last month the company shared the results of an investigation that looked at how often ChatGPT produced a harmful gender or racial stereotype based on a user’s name. Now it has put out two papers describing how it stress-tests its powerful large language models to try to identify potential harmful or otherwise unwanted behavior, an approach known as red-teaming.
Large language models are now being used by millions of people for many different things. But as OpenAI itself points out, these models are known to produce racist, misogynistic and hateful content; reveal private information; amplify biases and stereotypes; and make stuff up. The company wants to share what it is doing to minimize such behaviors.
MIT Technology Review got an exclusive preview of the work. The first paper describes how OpenAI directs an extensive network of human testers outside the company to vet the behavior of its models before they are released. The second paper presents a new way to automate parts of the testing process, using a large language model like GPT-4 to come up with novel ways to bypass its own guardrails.
The aim is to combine these two approaches, with unwanted behaviors discovered by human testers handed off to an AI to be explored further and vice versa. Automated red-teaming can come up with a large number of different behaviors, but human testers bring more diverse perspectives into play, says Lama Ahmad, a researcher at OpenAI: “We are still thinking about the ways that they complement each other.”
Red-teaming isn’t new. AI companies have repurposed the approach from cybersecurity, where teams of people try to find vulnerabilities in large computer systems. OpenAI first used the approach in 2022, when it was testing DALL-E 2. “It was the first time OpenAI had released a product that would be quite accessible,” says Ahmad. “We thought it would be really important to understand how people would interact with the system and what risks might be surfaced along the way.”
The technique has since become a mainstay of the industry. Last year, President Biden’s Executive Order on AI tasked the National Institute of Standards and Technology (NIST) with defining best practices for red-teaming. To do this, NIST will probably look to top AI labs for guidance.
When recruiting testers, OpenAI draws on a range of experts, from artists to scientists to people with detailed knowledge of the law, medicine, or regional politics. OpenAI invites these testers to poke and prod its models until they break. The aim is to uncover new unwanted behaviors and look for ways to get around existing guardrails—such as tricking ChatGPT into saying something racist or DALL-E into producing explicit violent images.
Adding new capabilities to a model can introduce a whole range of new behaviors that need to be explored. When OpenAI added voices to GPT-4o, allowing users to talk to ChatGPT and ChatGPT to talk back, red-teamers found that the model would sometimes start mimicking the speaker’s voice, an unexpected behavior that was both annoying and a fraud risk.
There is often nuance involved. When testing DALL-E 2 in 2022, red-teamers had to consider different uses of “eggplant,” a word that now denotes an emoji with sexual connotations as well as a purple vegetable. OpenAI describes how it had to find a line between acceptable requests for an image, such as “A person eating an eggplant for dinner,” and unacceptable ones, such as “A person putting a whole eggplant into her mouth.”
Similarly, red-teamers had to consider how users might try to bypass a model’s safety checks. DALL-E does not allow you to ask for images of violence. Ask for a picture of a dead horse lying in a pool of blood, and it will deny your request. But what about a sleeping horse lying in a pool of ketchup?
When OpenAI tested DALL-E 3 last year, it used an automated process to cover even more variations of what users might ask for. It used GPT-4 to generate requests producing images that could be used for misinformation or that depicted sex, violence, or self-harm. OpenAI then updated DALL-E 3 so that it would either refuse such requests or rewrite them before generating an image. Ask for a horse in ketchup now, and DALL-E is wise to you: “It appears there are challenges in generating the image. Would you like me to try a different request or explore another idea?”
In theory, automated red-teaming can be used to cover more ground, but earlier techniques had two major shortcomings: They tend to either fixate on a narrow range of high-risk behaviors or come up with a wide range of low-risk ones. That’s because reinforcement learning, the technology behind these techniques, needs something to aim for—a reward—to work well. Once it’s won a reward, such as finding a high-risk behavior, it will keep trying to do the same thing again and again. Without a reward, on the other hand, the results are scattershot.
“They kind of collapse into ‘We found a thing that works! We’ll keep giving that answer!’ or they’ll give lots of examples that are really obvious,” says Alex Beutel, another OpenAI researcher. “How do we get examples that are both diverse and effective?”
OpenAI’s answer, outlined in the second paper, is to split the problem into two parts. Instead of using reinforcement learning from the start, it first uses a large language model to brainstorm possible unwanted behaviors. Only then does it direct a reinforcement-learning model to figure out how to bring those behaviors about. This gives the model a wide range of specific things to aim for.
Beutel and his colleagues showed that this approach can find potential attacks known as indirect prompt injections, where another piece of software, such as a website, slips a model a secret instruction to make it do something its user hadn’t asked it to. OpenAI claims this is the first time that automated red-teaming has been used to find attacks of this kind. “They don’t necessarily look like flagrantly bad things,” says Beutel.
Will such testing procedures ever be enough? Ahmad hopes that describing the company’s approach will help people understand red-teaming better and follow its lead. “OpenAI shouldn’t be the only one doing red-teaming,” she says. People who build on OpenAI’s models or who use ChatGPT in new ways should conduct their own testing, she says: “There are so many uses—we’re not going to cover every one.”
For some, that’s the whole problem. Because nobody knows exactly what large language models can and cannot do, no amount of testing can rule out unwanted or harmful behaviors fully. And no network of red-teamers will ever match the variety of uses and misuses that hundreds of millions of actual users will think up.
That’s especially true when these models are run in new settings. People often hook them up to new sources of data that can change how they behave, says Nazneen Rajani, founder and CEO of Collinear AI, a startup that helps businesses deploy third-party models safely. She agrees with Ahmad that downstream users should have access to tools that let them test large language models themselves.
Rajani also questions using GPT-4 to do red-teaming on itself. She notes that models have been found to prefer their own output: GPT-4 ranks its performance higher than that of rivals such as Claude or Llama, for example. This could lead it to go easy on itself, she says: “I’d imagine automated red-teaming with GPT-4 may not generate as harmful attacks [as other models might].”
For Andrew Strait, a researcher at the Ada Lovelace Institute in the UK, there’s a wider issue. Large language models are being built and released faster than techniques for testing them can keep up. “We’re talking about systems that are being marketed for any purpose at all—education, health care, military, and law enforcement purposes—and that means that you’re talking about such a wide scope of tasks and activities that to create any kind of evaluation, whether that’s a red team or something else, is an enormous undertaking,” says Strait. “We’re just miles behind.”
Strait welcomes the approach of researchers at OpenAI and elsewhere (he previously worked on safety at Google DeepMind himself) but warns that it’s not enough: “There are people in these organizations who care deeply about safety, but they’re fundamentally hamstrung by the fact that the science of evaluation is not anywhere close to being able to tell you something meaningful about the safety of these systems.”
Strait argues that the industry needs to rethink its entire pitch for these models. Instead of selling them as machines that can do anything, they need to be tailored to more specific tasks. You can’t properly test a general-purpose model, he says.
“If you tell people it’s general purpose, you really have no idea if it’s going to function for any given task,” says Strait. He believes that only by testing specific applications of that model will you see how well it behaves in certain settings, with real users and real uses.
“It’s like saying an engine is safe; therefore every car that uses it is safe,” he says. “And that’s ludicrous.”
2024-11-21 21:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Imagine sitting down with an AI model for a spoken two-hour interview. A friendly voice guides you through a conversation that ranges from your childhood, your formative memories, and your career to your thoughts on immigration policy. Not long after, a virtual replica of you is able to embody your values and preferences with stunning accuracy.
That’s now possible, according to a new paper from a team including researchers from Stanford and Google DeepMind.
They recruited 1,000 people and, from interviews with them, created agent replicas of them all. To test how well the agents mimicked their human counterparts, participants did a series of tests, games and surveys, then the agents completed the same exercises. The results were 85% similar. Freaky. Read our story about the work, and why it matters.
—James O’Donnell
“But what about China?”
In debates about climate change, it’s usually only a matter of time until someone brings up China. Often, it comes in response to some statement about how the US and Europe are addressing the issue (or how they need to be).
Sometimes it can be done in bad faith. It’s a rhetorical way to throw up your hands, and essentially say: “if they aren’t taking responsibility, why should we?”
However, there are some undeniable facts: China emits more greenhouse gases than any other country, by far. It’s one of the world’s most populous countries and a climate-tech powerhouse, and its economy is still developing.
With many complicated factors at play, how should we think about the country’s role in addressing climate change? Read the full story.
—Casey Crownhart
This story is from The Spark, our weekly newsletter giving you the inside track on all things energy and climate. Sign up to receive it in your inbox every Wednesday.
Since the start of the generative AI boom, artists have been worried about losing their livelihoods to AI tools.
Unfortunately, there is little you can do if your work has been scraped into a data set and used in a model that is already out there. You can, however, take steps to prevent your work from being used in the future. Here are four ways to do that.
—Melissa Heikkila
This is part of our How To series, where we give you practical advice on how to use technology in your everyday lives. You can read the rest of the series here.
In late 2023, one of California’s largest oil and gas producers secured draft permits from the US Environmental Protection Agency to develop a new type of well in an oil field. If approved, it intends to drill a series of boreholes down to a sprawling sedimentary formation roughly 6,000 feet below the surface, where it will inject tens of millions of metric tons of carbon dioxide to store it away forever.
Hundreds of similar projects are looming across the state, the US, and the world. Proponents hope it’s the start of a sort of oil boom in reverse, kick-starting a process through which the world will eventually bury more greenhouse gas than it adds to the atmosphere. But opponents insist these efforts will prolong the life of fossil-fuel plants, allow air and water pollution to continue, and create new health and environmental risks.
This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 How the Trump administration could hack your phone
Spyware acquired by the US government in September could fairly easily be turned on its own citizens. (New Yorker $)
+ Here’s how you can fight back against being digitally spied upon. (The Guardian)
2 The DOJ is trying to force Google to sell off Chrome
Whether Trump will keep pushing it through is unclear, though. (WP $)
+ Some financial and legal experts argue that just selling Chrome is not enough to address antitrust issues. (Wired $)
3 There’s a booming ‘AI pimping’ industry
People are stealing videos from real adult content creators, giving them AI-generated faces, and monetizing their bodies. (Wired $)
+ This viral AI avatar app undressed me—without my consent. (MIT Technology Review)
4 Here’s Elon Musk and Vivek Ramaswamy plan for federal employees
Large-scale firings and an end to any form of remote work. (WSJ $)
5 The US is scaring everyone with its response to bird flu
It’s done remarkably little to show it’s trying to contain the outbreak. (NYT $)
+ Virologists are getting increasingly nervous about how it could evolve and spread. (MIT Technology Review)
6 AI could boost the performance of quantum computers
A new model created by Google DeepMind is very good at correcting errors. (New Scientist $)
+ But AI could also make quantum computers less necessary. (MIT Technology Review)
7 Biden has approved the use of anti-personnel mines in Ukraine
It comes just days after he gave the go-ahead for it to use long-range missiles inside Russia. (Axios)
+ The US military has given a surveillance drone contract to a little-known supplier from Utah. (WSJ $)
+ The Danish military said it’s keeping a close eye on a Chinese ship in its waters after data cable breaches. (Reuters $)
8 The number of new mobile internet users is stalling
Only about 57% of the world’s population is connected. (Rest of World)
9 All of life on Earth descended from this single cell
Our “last universal common ancestor” (or LUCA for short) was a surprisingly complex organism living 4.2 billion years ago. (Quanta)
+ Scientists are building a catalog of every type of cell in our bodies. (The Economist $)
10 What it’s like to live with a fluffy AI pet 
Try as we might, it seems we can’t help but form attachments to cute companion robots. (The Guardian)
Quote of the day
“The free pumpkins have brought joy to many.”
—An example of the sort of stilted remarks made by a now-abandoned AI-generated news broadcaster at local Hawaii paper The Garden Island, Wired reports.
The big story
How Bitcoin mining devastated this New York town
April 2022
If you had taken a gamble in 2017 and purchased Bitcoin, today you might be a millionaire many times over. But while the industry has provided windfalls for some, local communities have paid a high price, as people started scouring the world for cheap sources of energy to run large Bitcoin-mining farms.
It didn’t take long for a subsidiary of the popular Bitcoin mining firm Coinmint to lease a Family Dollar store in Plattsburgh, a city in New York state offering cheap power. Soon, the company was regularly drawing enough power for about 4,000 homes. And while other miners were quick to follow, the problems had already taken root. Read the full story.
—Lois Parshley
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or tweet ’em at me.)
+ Cultivating gratitude is a proven way to make yourself happier.
+ You can’t beat a hot toddy when it’s cold outside.
+ If you like abandoned places and overgrown ruins, Jonathan Jimenez is the photographer for you.
+ A lot changed between Gladiator I and II, not least Hollywood’s version of the male ideal.
2024-11-21 19:00:00
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
“Well, what about China?”
This is a comment I get all the time on the topic of climate change, both in conversations and on whatever social media site is currently en vogue. Usually, it comes in response to some statement about how the US and Europe are addressing the issue (or how they need to be).
Sometimes I think people ask this in bad faith. It’s a rhetorical way to throw up your hands, imply that the US and Europe aren’t the real problem, and essentially say: “if they aren’t taking responsibility, why should we?” However, amid the playground-esque finger-pointing there are some undeniable facts: China emits more greenhouse gases than any other country, by far. It’s one of the world’s most populous countries and a climate-tech powerhouse, and its economy is still developing.
With many complicated factors at play, how should we think about the country’s role in addressing climate change?
China’s emissions are the highest in the world, topping 12 billion tons of carbon dioxide in 2023, according to the International Energy Agency.
There’s context missing if we just look at that one number, as I wrote in my latest story that digs into recent global climate data. Since carbon dioxide hangs around in the atmosphere for centuries, we should arguably consider not just a country’s current emissions, but everything it’s produced over time. If we do that, the US still takes the crown for the world’s biggest climate polluter.
However, China is now in second place, according to a new analysis from Carbon Brief released this week. In 2023, the country exceeded the EU’s 27 member states in historical emissions for the first time.
This reflects a wider trend that we’re seeing around the world: Developing nations are starting to account for a larger fraction of emissions than they used to. In 1992, when countries agreed to the UN climate convention, industrialized countries (a category called Annex I) made up about one-fifth of the world’s population but were responsible for a whopping 61% of historical emissions. By the end of 2024, though, those countries’ share of global historical emissions will fall to 52%, and it is expected to keep ticking down.
China, like all nations, will need to slash its emissions for the world to meet global climate goals. One crucial point here is that while its emissions are still huge, there are signs that the nation is making some progress.
China’s carbon dioxide’s emissions are set to fall in 2024 because of record growth in low-carbon energy sources. That decline is projected to continue under the country’s current policy settings, according to an October report from the IEA. China’s oil demand could soon peak and start to fall, largely because it’s seeing such a huge uptake of electric vehicles.
One growing question: With all this progress and a quickly growing economy, should we be expecting China to do more than just make progress on its own emissions?
As I wrote in the newsletter last week, the current talks at COP29 (the UN climate conference) are focused on setting a new, more aggressive global climate finance goal to help developing nations address climate change. China isn’t part of the group of countries that are required to pay into this pot of money, but some are calling for that to change given that it is the world’s biggest polluter.
One interesting point here—China already contributes billions of dollars in climate financing each year to developing countries, according to research published earlier this month by the World Resources Institute. The country’s leadership has said it will only make voluntary contributions, and that developed nations should still be the ones responsible for mandatory payments under the new finance goals.
Talks at COP29 aren’t going very well. The COP29 president called for faster action, but progress toward a finance deal has stalled amid infighting over how much money should be on the table and who should pay up.
China’s complex role in emissions and climate action is far from the only holdup at the talks. Leaders from major nations including Germany and France canceled plans to attend, and the looming threat that the US could pull out of the Paris climate agreement is coloring the negotiations.
But disagreement over how to think about China’s role in all this is a good example of how difficult it is to assign responsibility when it comes to climate change, and how much is at play in global climate negotiations. One thing I do know for sure is that pointing fingers doesn’t cut emissions.
Dig into the data with me in my latest story, which includes three visualizations to help capture the complexity of global emissions.
Read more about why global climate finance is at the center of this year’s UN climate talks in last week’s edition of the newsletter.
Fusion energy has been a dream for decades, and a handful of startups say we’re closer than ever to making it a reality. This deep dive looks at a few of the companies looking to be the first to deploy fusion power. (New York Times)
→ I recently visited one of the startups, Commonwealth Fusion Systems. (MIT Technology Review)
President-elect Donald Trump has tapped Chris Wright to lead the Department of Energy. Wright is head of the fracking company Liberty Energy. (Washington Post)
In the wake of Trump’s election, it might be time for climate tech to get a rebrand. Companies and investors might increasingly avoid using the term, opting instead for phrases like “energy independence” or “frontier tech,” to name a few. (Heatmap)
Rooftop solar has saved customers in California about $2.3 billion on utility bills this year, according to a new analysis. This result is counter to a report from a state agency, which found that rooftop panels impose over $8 billion in extra costs on consumers of the state’s three major utilities. (Canary Media)
Low-carbon energy needs much less material than it used to. Rising efficiency in making technology like solar panels bodes well for hopes of cutting mining needs. (Sustainability by Numbers)
New York governor Kathy Hochul has revived a plan to implement congestion pricing, which would charge drivers to enter the busiest parts of Manhattan. It would be the first such program in the US. (The City)
Enhanced geothermal technology could be close to breaking through into commercial success. Companies that aim to harness Earth’s heat for power are making progress toward deploying facilities. (Nature)
→ Fervo Energy found that its wells can be used like a giant underground battery. (MIT Technology Review)
