Human-caused global warming has just nudged past 1.5°C, according to a new method we have developed. That’s approaching 0.2°C higher than previously thought.

But this does not mean the goal of keeping warming below 1.5°C is dead, as the Paris agreement and the UN climate summits are based on different methodology.

This additional warming comes out of how we define what was pre-industrial, with our method using bubbles of air buried in Antarctic ice to gather data reaching back well before the industrial era. Current methods exclude some of the early warming. We also radically improve how well we know these numbers.

There are two steps to measuring human-caused warming. The first requires us to compare temperature measurements with their pre-industrial counterpart – we call this the pre-industrial baseline. The second step involves separating the human contribution from the part humans are not responsible for, such as volcanic eruptions, El Niño, or random weather events – we call this natural variability.

The Intergovernmental Panel on Climate Change (IPCC) chose the period 1850-1900 as the pre-industrial baseline as that’s when we started meaningfully measuring the temperature around the world, even if the Industrial Revolution actually began earlier. Warming since this period is what negotiators would have had in mind when setting up the Paris agreement. Climate models and statistical analysis are then used to tease out the volcanoes and short-term weather fluctuations in the data, to leave just the human-caused bit.

Using these methods, by 2023 there had been 1.31°C of human-caused warming since 1850-1900. However, there is considerable uncertainty in figures like this, and the reality could be somewhere between 1.1°C and 1.6°C. So although we are likely to be around 0.2°C below the 1.5°C limit, using these previous approaches we cannot be certain that we are not already past it.

Unfortunately, the 1850-1900 pre-industrial baseline probably has human-caused warming baked into it because the Industrial Revolution started significantly before then. As a result, the human-caused warming we are currently negotiating on is an underestimate.

A new approach

Fortunately, our new method means we can make significantly more accurate estimates. That’s because of a simple but previously overlooked relationship between the CO₂ concentration we measure in the atmosphere and the temperature change we see.

We treat this relationship as a straight line, meaning a certain amount of additional atmospheric carbon will always be associated with the same amount of warming. This is somewhat controversial, but allows us to do a number of very useful things.

First, it allows us to build from a pre-industrial baseline well before 1850. This is because unlike global temperature measurements we have ice core CO₂ data stretching back thousands of years, well before the start of the Industrial Revolution. This data is gathered by drilling down through the Antarctic ice cap and harvesting the air trapped in the bubbles in the ice. The further you drill down, the older the air.

That data tells us that CO₂ concentrations in the atmosphere were pretty constant for two millennia at 280 parts per million, before they started rising from about 1700. We can then estimate the temperature change associated with that additional carbon, which tells us how much warming was baked into the 1850-1900 baseline currently used by the IPCC.

Second, the CO₂-temperature straight line relationship also allows us to separate the human-caused warming from the natural variability, because the warming trend we are after is so closely related to increases in CO₂ we measure.

A more accurate estimate

Using our new method we can estimate human-caused warming either from our pre-1700 baseline or from the IPCC’s 1850-1900 baseline. Using the 1850-1900 baseline we estimate human-caused warming for 2023 at 1.31°C, agreeing with the IPCC-based best guess. However, our estimate is three times better defined. Although we have experienced record warming in 2024, we can be sure human-caused warming has not yet passed 1.5°C if measured from 1850-1900.

When measured from the pre-1700 baseline, humans have caused warming that almost hit 1.5°C in 2023, and as of October 2024 is at 1.53°C (within a range of 0.11°C). This captures a fuller picture of the warming caused by centuries of human activity as rising levels of deforestation, farming and early industries all contributed to increases in carbon dioxide levels. This result tells us there is approaching 0.2°C of warming baked into the 1850-1900 baseline from ignoring the effects of the early emissions released before the temperature records began.

1.5, dead or alive?

As the Paris agreement is based on science that used 1850-1900 as a baseline, the additional early warming we flag may not in the end be counted towards the temperature goals. So it’s unfair to say the 1.5°C limit has been breached by our latest estimate. Yet even if we stick to the 1850-1900 pre-industrial baseline, 1.5°C of human-caused warming is less than a decade away at current warming rates. The 1.5°C Paris limit is certainly critically ill.

But perhaps this is not the right way to see 1.5’s role. The agreed aim is to hold the temperature increase “well below 2 degrees”, and the super tanker that is the global economy will need something strong to pivot from to change course. Keeping 1.5 in reach is currently that anchor point. Knowing precisely where we are in relation to this anchor will be critical. And maybe this is where our research will help most.

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A new type of porous material called a covalent organic framework quickly sucks up carbon dioxide from ambient air

October 23, 2024

Capturing and storing the carbon dioxide humans produce is key to lowering atmospheric greenhouse gases and slowing global warming, but today’s carbon capture technologies work well only for concentrated sources of carbon, such as power plant exhaust. The same methods cannot efficiently capture carbon dioxide from ambient air, where concentrations are hundreds of times lower than in flue gases.

Yet direct air capture, or DAC, is being counted on to reverse the rise of CO₂ levels, which have reached 426 parts per million (ppm), 50% higher than levels before the Industrial Revolution. Without it, according to the Intergovernmental Panel on Climate Change, we won’t reach humanity’s goal of limiting warming to 1.5 °C (2.7 °F) above preexisting global averages.

A new type of absorbing material developed by chemists at the University of California, Berkeley, could help get the world to negative emissions. The porous material — a covalent organic framework (COF) — captures CO₂ from ambient air without degradation by water or other contaminants, one of the limitations of existing DAC technologies.

“We took a powder of this material, put it in a tube, and we passed Berkeley air — just outdoor air — into the material to see how it would perform, and it was beautiful. It cleaned the air entirely of CO₂. Everything,” said Omar Yaghi, the James and Neeltje Tretter Professor of Chemistry at UC Berkeley and senior author of a paper that will appear online Oct. 23 in the journal Nature.

“I am excited about it because there’s nothing like it out there in terms of performance. It breaks new ground in our efforts to address the climate problem,” he added.

According to Yaghi, the new material could be substituted easily into carbon capture systems already deployed or being piloted to remove CO₂ from refinery emissions and capture atmospheric CO₂ for storage underground.

UC Berkeley graduate student Zihui Zhou, the paper’s first author, said that a mere 200 grams of the material, a bit less than half a pound, can take up as much CO₂ in a year — 20 kilograms (44 pounds) — as a tree.

“Flue gas capture is a way to slow down climate change because you are trying not to release CO₂ to the air. Direct air capture is a method to take us back to like it was 100 or more years ago,” Zhou said. “Currently, the CO₂ concentration in the atmosphere is more than 420 ppm, but that will increase to maybe 500 or 550 before we fully develop and employ flue gas capture. So if we want to decrease the concentration and go back to maybe 400 or 300 ppm, we have to use direct air capture.”

COF vs MOF

Yaghi is the inventor of COFs and MOFs (metal-organic frameworks), both of which are rigid crystalline structures with regularly spaced internal pores that provide a large surface area for gases to stick or adsorb. Some MOFs that he and his lab have developed can adsorb water from the air, even in arid conditions, and when heated, release the water for drinking. He has been working on MOFs to capture carbon since the 1990s, long before DAC was on most people’s radar screens, he said.

Two years ago, his lab created a very promising material, MOF-808, that absorbs CO₂, but the researchers found that after hundreds of cycles of adsorption and desorption, the MOFs broke down. These MOFs were decorated inside with amines (NH₂ groups), which efficiently bind CO₂ and are a common component of carbon capture materials. In fact, the dominant carbon capture method involves bubbling exhaust gases through liquid amines that capture the carbon dioxide. Yaghi noted, however, that the energy intensive regeneration and volatility of liquid amines hinders their further industrialization.

Working with colleagues, Yaghi discovered why some MOFs degrade for DAC applications — they are unstable under basic, as opposed to acidic, conditions, and amines are bases. He and Zhou worked with colleagues in Germany and Chicago to design a stronger material, which they call COF-999. Whereas MOFs are held together by metal atoms, COFs are held together by covalent carbon-carbon and carbon-nitrogen double bonds, among the strongest chemical bonds in nature.

As with MOF-808, the pores of COF-999 are decorated inside with amines, allowing uptake of more CO₂ molecules.

“Trapping CO₂ from air is a very challenging problem,” Yaghi said. “It’s energetically demanding, you need a material that has high carbon dioxide capacity, that’s highly selective, that’s water stable, oxidatively stable, recyclable. It needs to have a low regeneration temperature and needs to be scalable. It’s a tall order for a material. And in general, what has been deployed as of today are amine solutions, which are energy intensive because they’re based on having amines in water, and water requires a lot of energy to heat up, or solid materials that ultimately degrade with time.”

Yaghi and his team have spent the last 20 years developing COFs that have a strong enough backbone to withstand contaminants, ranging from acids and bases to water, sulfur and nitrogen, that degrade other porous solid materials. The COF-999 is assembled from a backbone of olefin polymers with an amine group attached. Once the porous material has formed, it is flushed with more amines that attach to NH₂ and form short amine polymers inside the pores. Each amine can capture about one CO₂ molecule.

When 400 ppm CO₂ air is pumped through the COF at room temperature (25 °C) and 50% humidity, it reaches half capacity in about 18 minutes and is filled in about two hours. However, this depends on the sample form and could be speeded up to a fraction a minute when optimized. Heating to a relatively low temperature — 60 °C, or 140 °F — releases the CO₂, and the COF is ready to adsorb CO₂ again. It can hold up to 2 millimoles of CO₂ per gram, standing out from other solid sorbents.

Yaghi noted that not all the amines in the internal polyamine chains currently capture CO₂, so it may be possible to enlarge the pores to bind more than twice as much.

“This COF has a strong chemically and thermally stable backbone, it requires less energy, and we have shown it can withstand 100 cycles with no loss of capacity. No other material has been shown to perform like that,” Yaghi said. “It’s basically the best material out there for direct air capture.”

Yaghi is optimistic that artificial intelligence can help speed up the design of even better COFs and MOFs for carbon capture or other purposes, specifically by identifying the chemical conditions required to synthesize their crystalline structures. He is scientific director of a research center at UC Berkeley, the Bakar Institute of Digital Materials for the Planet (BIDMaP), which employs AI to develop cost-efficient, easily deployable versions of MOFs and COFs to help limit and address the impacts of climate change.

“We’re very, very excited about blending AI with the chemistry that we’ve been doing,” he said.

The work was funded by King Abdulaziz City for Science and Technology in Saudi Arabia, Yaghi’s carbon capture startup, Atoco Inc., Fifth Generation’s Love, Tito’s, and BIDMaP. Yaghi’s collaborators include Joachim Sauer, a visiting scholar from Humboldt University in Berlin, Germany, and computational scientist Laura Gagliardi from the University of Chicago.

RELATED INFORMATION

• Carbon dioxide capture from open air using covalent organic frameworks (Nature)
• Omar Yaghi’s website

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When you think of what makes us human, would you say it’s our powers of prediction?

I probably wouldn’t have, at least not until my conversation with Mark Miller, a philosopher of cognition and research fellow at both the University of Toronto and Monash University in Melbourne. He studies how new ideas about the mind can provide insight into human well-being.

Prediction is clearly useful: Being able to anticipate the future helps us strategize in the present.

But too much predictive power is usually the stuff of dystopian sci-fi stories, where being creative and unpredictable are the hallmarks of humanity, while the power of prediction — like the trope of an all-knowing algorithm — is cast as the weapon of technology.

And yet, one of the latest big theories in neuroscience says that humans are fundamentally creatures of prediction, and not only is creativity not at odds with that, but it actually goes hand in hand with improving our predictive power. Life itself, in this view, is one big process of creatively optimizing prediction as a survival strategy in a universe otherwise tending toward chaos.

Miller’s work starts with this big idea known as predictive processing, which says that your experience of the world is like a dream — a simulated model constructed by your brain. We’re not observing the world through open windows in our skulls. Rather, in our brain’s pursuit to plan, survive, and achieve our goals, it has learned how to guess what the world is actually like based on incoming sensory data. Those predictions are always uncertain, at least to a degree, which is why the goal of predictive processing is often described as minimizing that uncertainty.

But an optimal relationship with uncertainty calls for a balance. Through a predictive lens, Miller argues, uncertainty can help us snap out of harmful loops, like depression or addiction. And in general, it turns out that one of the best ways to become healthier, more adaptive creatures is to regularly expose ourselves to different kinds of uncertainty.

Miller’s work goes on to use this idea to explain the value of everything from art and horror movies to meditation and psychedelics. In each case, we’re brought to “the edge of informational chaos,” where our predictive models begin to break down. Surprisingly, he sees creativity and optimizing our predictive powers as complementary forces that help sustain life itself.

So I invited Miller as the next guest for The Gray Area’s series on creativity to discuss the paradox of how we humans survive thanks to prediction but need chaos in order to thrive.

“All of life is this resistance to entropy,” Miller said. “As the universe expands and entropy is inevitable, life is that single force that’s defying that gradient.”

The following excerpt has been edited for length and clarity. Listen to the full Gray Area interview here.

Right now, I’m looking out my window and I see a particular scene and, naively, it seems to me like the light is coming in from the outside, into my body, reaching my brain, and that’s what I’m seeing. What you’re telling me is actually what I’m seeing is the model being predicted by my brain. What happens, though, when the light actually does get passed through my body? Am I experiencing that at any point, or when do we switch from experiencing our predictions of the world to raw sensory data?

Probably never. That’s just not what you’re built to do. And actually you don’t need access to it. What you need is the driving signal from the world to be making sure that the models that you’re generating are elegant, sophisticated, and tracking real-world dynamics.

This does get dizzying the more you think about it. But this is a huge claim: that my experience of the world is not a direct experience of objective reality. It is my brain’s best guess of the world outside of my skull. How early-stage is predictive processing as a theory?

Well, not that early. I don’t think it’s irresponsible to say that it’s the preeminent theory today in all sorts of communities, computational psychiatry, computational psychology, neuroscience. I mean, if it’s not the foremost theory, it’s adjacent. So I guess it’s a mix. It’s younger than the other, it is the new kid on the block in a way, but it’s a very popular new kid and very exciting.

You wrote a paper about how this predictive framework can explain a lot about what makes us humans happy. So tell me about that. What is the predictive account of happiness?

The human system starts predicting for one reason or another that the world is some way. And then the trouble looks like when that prediction becomes strong enough and divergent enough from the way things actually are. So we call it sticky — it has a sticky quality to it.

Just think about depression. You’ve installed the belief for whatever reason that you just can’t fit with the world, that either it’s because you are not good enough or the world isn’t good enough. But for some reason you can’t resolve this difference between the way that you want the world to be and the way the world actually is, either because of something on your side or something on the world’s side. One thing that marks depression is that that belief persists even if the conditions were to change. Even if you were to change the situation entirely, there’s a sticky quality to these pathologies.

So let me ask you then about swinging back to the positive dimension, happiness in particular. That’s a picture of depression and psychopathology and mental illness. So what does this predictive framework say about the feeling of happiness itself?

Well, I’m going to say two things. There’s a difference between momentary subjective happiness and well-being, like having a good life. Just in case anybody doesn’t know what these are, the momentary subjective being well-being is like hedonic well-being. That’s just the feeling good stuff.

Is that like pleasure?

Exactly. Overall well-being doesn’t look like it’s exactly identical with that because to have a really rich, meaningful, good life may mean you’re in pain quite a lot. Momentary subjective well-being is a reflection, at least in part, of predicting better than expected. So we have this idea that valence is that good or bad feeling that comes as part of your embodied system telling you how it’s going. So when you feel good, that’s your body and nervous system and brain telling you, “I’ve got it. Whatever’s happening right now, I’m on top of it. I’m predicting it for us. I’m predicting it well. I’m managing uncertainty really well.” And when you feel bad, that’s an indicator: “I don’t understand something here.”

How does creativity fit into this story?

I think a starting point for thinking about creativity using this model is to start by maybe showing a puzzle. Why would a predictive system that looks like it’s trying to reduce uncertainty be attracted to situations and indeed make those situations where it’s bumping into uncertainty? Like why do we build roller coasters? Why do we go to horror movies?

Part of the answer is that too much certainty is a problem for us, especially when that certainty drifts from real-world dynamics. So in order to protect our prediction engine, our brain and nervous system, from getting into what we’ve called the bad bootstrap, that is from getting very, very certain about something that’s wrong, it really behooves us to occasionally inject ourselves with enough uncertainty, with enough intellectual humility to be uncertain about your model enough that you can check to see whether or not you’ve been stuck in one of these bad bootstraps.

If you’re with me to there, then we have a wonderful first-principles approach to thinking about the benefit of creativity and art, especially provocative art that calls you to rethink who you are. Because as far as we’ve seen, the research just keeps pointing in this direction, anything that gets you out of your ordinary mode of interacting with the world so that you can check to see how good it is or how poor it is, is gonna be a benefit for us. It’s gonna protect us from those bad siloed opportunities. I think art does that, right?

You can go somewhere, see something grand, see something beautiful, see something ugly and horrible. If you let yourself be impressed by it, it can be an opportunity for you to be jostled out of your ordinary way of seeing the world, which would let the system check to see whether or not it’s running optimal models or not.

So it sounds like you’re likening creativity to this injection of the right kind of uncertainty into our experience of the world. And in your paper on horror movies, you used a term that I think captures a lot of this. It’s a thread that seems to run through everything so far: art, creativity, horror movies, even meditation and psychedelics. You wrote that the brain evolved to seek out the “edge of informational chaos” — a place where our predictive models begin to break down, and in those uncertain zones, we actually have much to learn.

It sounds to me like this edge of chaos actually explains at least one perspective on why art, why creativity, why play, why all these things benefit us. Because that edge is a really healthy place to be. So I wanted to ask you about this framing of the edge of informational chaos and why that’s a place that our brains would want to go.

Where are we gonna learn the most? If you are a learning system, and this is amazing, right from the lab, we see that animals and us, we get rewarded, not only when we get fed and watered and sexed, we get rewarded when we get better information. Isn’t that amazing to acknowledge?

If you get better information, my system is treating it like I’ve been fed. That’s how important good information is for us. And in fact, in lots of situations, it’s more rewarding for us than the food itself because one bit of food is one thing. Information about how to get food over time, that could be much, much more important. So where do we learn the most?

Well, we don’t learn where our predictive models are so refined that everything is just being done by rote. And we’re not learning the most way out in deep volatility, unexpected uncertainty environments. That’s like where not only do you not know what’s going on, but you don’t know how to get to knowing what’s going on. That’s why we sometimes have culture shock if we move somewhere else.

So where do we learn the most? We learn at this Goldilocks zone, which is that healthy boundary between order and chaos, right at the edge where our predictive models necessarily break down. And the hope there is that in breaking down, new, better models are possible.

We’ve talked about how art and creativity can bring us to that edge of chaos, but you’ve also said elsewhere that meditation can do a similar kind of thing. Which is confusing at first because meditation looks pretty different from watching a horror movie. In meditation, I’m sitting there very quietly, in what looks like the opposite of chaos. So how do you understand what meditation is doing in this predictive framework, and how does that relate to creativity and these beneficial kinds of uncertainty?

This idea is common now, especially, in the West, that meditation might be more about relaxation, or maybe addressing stress. But that’s not the meat of the program. The center of that program is a deep, profound, and progressive investigation about the nature of who we are and how our own minds work. It’s a deep investigation about the way our emotional system is structured and the nature of our unconscious experience. What are we experiencing? Why are we experiencing it? What does that have to do with the world?

And then we can adjust, progressively and skillfully, the shape of who and what we are so that we fit the world better, so that we are as close as possible to what’s real and true, so that we can be as serviceable as possible.

Ultimately, you can do everything that we’ve been talking about, including all the stuff that psychedelics do for the predictive system, all the stuff that horror and violent video games do, you can do it all contemplatively, in a way that’s better for you.

So you’re saying that one way to find that thread that puts meditation and horror movies in the same vein of practice is thinking about meditation and psychedelics as injecting uncertainty into our experience of the world. Is that the common currency there?

You’ve got it. Absolutely.

Let me ask you this. After this whole story we’ve unpacked, there’s still a tension that leaves me a little bit uncomfortable. It feels like we’re saying that creativity is just kind of an input or a means toward juicing the powers of prediction. And part of me pushes against that. It almost feels reductive, right? Is creativity really just this evolutionary strategy that makes us better predictive creatures? Does that make creativity feel less intrinsically valuable?

Because when I think about creativity, at least in part it doesn’t just feel like a tool for survival that evolution has honed. Sometimes it feels like it’s that which makes life worth living, that it has intrinsic value of its own. Not as a tool for the predictive powers in my brain or the algorithms or whatever. So I’m curious if you feel this tension at all, and how you think about creativity being framed in the service of prediction.

So two things. One, even though we are excited by this new framework, I don’t think we need to be afraid of it being overly reductionistic. I mean, in a way, it’s radically reductionistic. We’re saying that everything that’s happening in the brain can be written on a T-shirt, basically.

But the way that it actually gets implemented in super complex, beautiful systems like us, it shouldn’t make us feel like all of the wonderful human endeavors are simply explainable in a sort of overly simplified way. I don’t have any worry like that. I think if it turned out that life was operating over a simple principle of optimization — that’s the most beautiful thing I’ve ever heard, first of all, that all of life is about optimization. All of life is this resistance to entropy. That’s just what it is to be alive, is just your optimal resistance to entropy. As the universe expands and entropy is inevitable, life is that single force that’s defying that gradient. That’s so beautiful.

Two, when it comes to art, I want to even be careful to say that art is only about finding this critical edge. I think that’s one really interesting way of thinking about it. It’s one way that we’ve been thinking about it, if you consider movies and video games as forms of art also.

Another central reason that this kind of system might benefit from artistic expression that we didn’t cover but that’s completely relevant for our discussion is that art creates this wonderful opportunity for endless uncertainty and uncertainty management. And not very many things do that.

And as you progressively create dancing, painting, singing, whatever, the enthusiasm of that literally being in the spirit of that creative endeavor, is you managing uncertainty in a new and remarkable way that it’s never been done before in all of existence through all time. Nobody has ever encountered and resolved that uncertainty in particular. So it should be endlessly rewarding, fascinating.

No wonder we find it so beautiful. It might be by its very nature the purest expression of uncertainty generation and management. That would make it intrinsically valuable for an uncertainty-minimizing system like us.

Phasing out coal power is the most important step the world can take to curb climate change.

Coal, the most polluting fossil fuel, supplied 36% of electricity generation in 2022. This must drop to 4% by 2030 and then 0% by 2040 if the world is to limit global warming to 1.5 degrees C (2.7 degrees F) and prevent the most catastrophic impacts of the climate crisis. (That's according to a high-ambition scenario from Climate Action Tracker.)

Removing coal from power generation is also critical to decarbonizing other sectors of the economy. It will help ensure that green technologies that run on electricity, like electric vehicles and heat pumps, are powered by cleaner energy sources.

While the share of coal used in the global power sector has been gradually ticking down in recent years, it needs to start declining much faster. Developed countries should reach zero coal the soonest, as they’re in a stronger financial position than developing nations and are already, for the most part, less reliant on coal. But all countries will require an incredibly swift transition.

Phasing out coal at the speed needed will be extremely challenging, but a handful of countries are already proving that a rapid, sustained shift is possible. While each one must chart its own path forward, other coal power-reliant countries can learn from these leaders.

Which Countries Have Reduced Coal Power the Fastest?

The world has eight years to scale down its use of coal power from 36% of electricity generation in 2022 to less than 4% in 2030. To explore how such a quick phase-down might be achieved, we analyzed the 10 countries that have reduced coal power the fastest over any eight-year period since 2000. Greece and the U.K. achieved the fastest coal power reductions — moving at a quicker pace than what’s needed globally — followed by Denmark, Spain, Portugal, Israel, Romania, Germany, the United States and Chile.

Of the top 10 countries, only Portugal has reached zero coal power already. Some other countries, such as Austria and Belgium, have also eliminated coal power entirely, but did not make the top 10 as they either used very little coal to begin with or phased it out over a longer time span.

Greece reduced coal power faster than any other country in the world over an eight-year span, from 51% in 2014 to 10% in 2022, replacing it with a combination of gas and renewables. At number two, the United Kingdom reduced coal power from 39% in 2012 to 2% in 2020, replacing it mostly with wind and bioenergy but also some gas. Denmark was third fastest and is notable as the only country on the list where the reduction in coal power was replaced by 100% zero-carbon power sources.

While many of these leading countries are European, there are positive examples from other areas of the world as well. The United States cut its coal power use in half between 2014 and 2022, replacing it with a combination of gas, solar and wind. In Chile, coal plants were booming as recently as a decade ago, but the country has quickly reversed course; it is now supporting early retirement of coal plants and replacing them mainly with solar and wind power.

Similarities and Differences Among the Top 10 Countries

On the whole, the countries with the fastest coal phase-outs are high income, with relatively small populations, less growth in electricity demand than average, and coal plants already nearing the age of retirement. Nine of the 10 countries have announced coal phase-out targets and eight have implemented some form of national carbon pricing. All of these factors can work in favor of a clean energy transition.

However, key differences among the top 10 countries demonstrate that phasing out coal is possible in a variety of circumstances.

While all 10 countries are currently high income by the World Bank’s definition, Chile and Romania have only recently reached that level. At the time these countries started their steepest coal reductions, GDP per capita ranged from $9,000 per year in Romania (2012) and $15,000 in Chile (2014) to $55,000 per year in the United States (2014).

Seven out of the top 10 countries have populations below 50 million people and thus have lower electricity demand, making for an easier energy transition — although the United States (with 330 million people) and Germany and the United Kingdom (with 84 million and 66 million people, respectively) have proved that a rapid transition can be feasible even with a bigger energy grid.

Electricity demand in most of these countries was either falling or growing slower than the global average during their fastest transition periods, making it easier to retire coal plants. By contrast, electricity demand in Chile, Israel and Spain was growing at a rate near or slightly above the world average, showing that coal phase-out is possible even in places with rising electricity needs. But Israel’s case also illustrates a major challenge in these scenarios: The country’s demand grew so much that even though coal fell as a share of all power, total coal power use remained fairly steady.

The nature of each country’s coal industry matters, too. Six of the 10 countries relied mainly on coal imports, so shifting to other power sources like renewables or domestically available gas could benefit their energy security without causing major economic impacts. However, the U.S., Germany, Romania and Greece were able to reduce coal power dramatically despite having large coal mining sectors and relying on mostly domestic coal production.

Finally, nearly all of these countries have retired older coal plants which were already close to or above the typical retirement age of 37 years. The decision will be more difficult for nations with younger coal plants, which investors expected to operate and generate revenue for decades to come. The biggest exception is Chile, which has so far retired coal plants that were 29 years old on average and plans to close many that are younger still.

2 Countries Achieving Success in Coal Phase-out

Let’s dive deeper into the United Kingdom and Chile, two countries that have been successful in phasing down coal in very different contexts, to learn from their experiences. (We profiled Denmark’s energy transition in another recent article.)

The United Kingdom has had two periods of rapid coal phase-down

Coal fueled the industrial revolution in the United Kingdom, but two separate periods of rapid phase-down in recent decades have pushed its coal power use to near zero. Today the country has only one coal power station left which is scheduled to close in September 2024.

The U.K.’s first period of coal power decline, in the 1990s, was driven by market forces. New EU regulations in 1991 allowed gas to be used for electricity; when coupled with the high cost of mining in the U.K. and the development of cheap, abundant gas in the North Sea, this led to a rapid shift from coal- to gas-fired power plants. Coal tumbled from about 65% of the power mix in 1991 to around 30% in 1999.

After remaining relatively steady in the early 2000s, the U.K.’s coal power use plummeted again in the 2010s. And this time the reduction in coal was replaced not by gas but by renewables.

Around this time, the EU and U.K. enacted a series of policies aimed at phasing out coal. The EU introduced tighter limits on local coal plant pollution in 2008 which would require expensive upgrades for old coal plants. With the average coal plant in the U.K. already nearing retirement age, many chose to close rather than comply.

In 2013, the U.K. introduced a carbon price floor which went above and beyond the EU’s existing carbon price, then raised it multiple times in subsequent years. This drove up the cost of coal power so that it was more expensive than gas and much more expensive than renewables. By 2015, coal plants were no longer profitable and were being shut down in droves.

The government set a target in 2015 to fully phase out coal by 2025; this has since been moved up to 2024. With tightening regulations, clear policy signals from the government and unfavorable economics — as well as strong public support for climate action — the country’s remaining coal plants quickly closed. Meanwhile, falling costs and supportive policies helped trigger a boom in renewable power, especially wind energy, which made up for most of the reduction in coal.

The U.K. also benefitted from having electricity interconnections with neighboring countries like France, which can help balance supply and demand as renewable power fluctuates. And its overall electricity demand was going down at the time, thanks to increasing energy efficiency standards, technological improvements and deindustrialization.

Chile has reversed course on coal, even as electricity demand continues to rise

Unlike in the U.K., Chile’s electricity demand has been rising. From 2009 to 2019 the country built 14 new coal power plants with the goal to meet electricity demand and increase energy security. But the tide began to turn against coal even before these had finished construction. In recent years, Chile’s solar and wind production have so far outpaced demand growth that it's been able to shut down coal plants. Coal fell from a high of 46% of the electricity mix in 2013 to only 23% in 2022.

Chile has benefitted from some of the best solar and wind availability in the world. To leverage this advantage, the government passed a renewable energy quota in 2008 and enacted other supportive laws which made it easier for new companies to enter the renewable energy industry. Falling costs have made solar and wind competitive without any subsidies needed, and Chile’s copper mining companies (a mainstay of the economy) are increasingly demanding and funding renewable energy. The country is also planning to become a major exporter of green hydrogen produced with renewable power. Thanks to these developments, it’s ranked as the most attractive emerging economy for clean energy investments.

At the same time, Chile has taken active steps to phase out coal. The government implemented several policies that made coal power less competitive, like pollution and efficiency standards on power plants and a small carbon tax. And in 2018, on the back of a new government decarbonization strategy, Chile’s Ministry of Energy convened a working group to decide how to phase out coal power. This included governmental agencies, coal companies, the national electricity coordinator, environmental NGOs and the coal workers union. The following year, its members announced a voluntary plan to close several coal plants in the near term and all units by 2040.

Since then, Chile has accelerated its near-term timetable and companies have often closed plants ahead of schedule. Of the country’s 28 coal power plants, 8 have already retired and another 12 are set to retire by 2025. Seven of the coal plants scheduled to shutter will be 15 years old or less when they are decommissioned, setting an example for other countries facing retirement of younger coal fleets.

Looking ahead, Chile must work to retire the final eight coal plants that will remain after 2025 — which experts say needs to happen by 2030 rather than by 2040 as originally planned to stay in line with global climate goals. This will require integrating increasingly high levels of renewable power on the grid as well as upgrading transmission lines, building out energy storage, and developing a smart grid to maximize grid flexibility and reliability.

Coal-reliant Countries Can Learn from These Examples

Other countries reliant on coal power can learn from the success of these leaders and adapt the lessons for their national circumstances.

The United Kingdom’s example is especially relevant for some developed countries. For example, Poland and Russia’s coal plants are nearing retirement age like the U.K.’s were. Japan and South Korea’s coal plants are generally younger, but they have even less domestic coal mining that the U.K. did — relying essentially 100% on imports — which will lessen the domestic economic impacts of the transition.

Meanwhile, Chile’s example is most relevant for developing countries with relatively young coal plants and growing electricity demand. Turkey, Malysia and Vietnam, for example, are in a similar position today to where Chile was in 2014, with rising demand and coal at around 40% of their electricity mix.

However, countries that rely more heavily on coal will face much steeper challenges — like China (61% coal power share), Indonesia (62%), India (74%), and South Africa (85%). To meet global climate targets, these countries will need to decrease their coal power use far faster than has ever been achieved before.

Some, Like China and India, Will Face Steeper Challenges

China and India together are responsible for two-thirds of the world’s coal power generation today, so much of the success of global coal phase-out hinges on them.

These countries’ sheer amount of coal capacity — around 1,100 GW for China and 240 GW for India — will make the transition difficult. Electricity demand in both countries also increased by roughly 50% from 2014-2022, more than twice as fast as the world average. While the share of coal power has been falling slightly in China and remained fairly flat in India, both countries continue to approve and build new coal plants to meet growing demand, so coal power use is still rising overall. Renewable power has been growing exponentially in both countries, breaking records, but it will need to accelerate ever faster to meet demand growth.

China and India also have far larger domestic coal mining industries than any of the top 10 countries. China is currently home to 3.4 million coal miners and 740,000 coal power plant workers (more people than the entire population of Croatia), while India has 1.4 million coal miners and 600,000 coal power plant workers. This will increase both the cost and the difficulty of ensuring a just transition for all coal-dependent communities.

While challenging, however, transitions of this magnitude have happened before. China already lost more than 2 million coal jobs from 2012-2018 as production slowed and became more efficient, leading the government to create a transition fund. China and India can also draw learnings from other countries and communities that have successfully transitioned away from fossil fuels.

China is predicted to reach peak coal in 2025 and its government has pledged to reduce coal over the 2026-2030 period, but it has not yet committed to a full phase-out. India has no plans to retire any coal plants before 2030 and has indicated that coal will play a substantial role for decades to come. Both governments will need to step up their efforts — but they shouldn’t have to do it alone.

Ending Coal Power Globally Will Require More International Support

Asking developing countries to phase down coal power at a faster rate than developed countries have ever achieved raises important ethical questions. In order to give more time for developing countries, developed countries should take the lead and completely phase out coal power by 2030 or earlier. But, while critical, this would not provide much leeway for developing countries that are large emitters. Developing countries will still need to phase out the vast majority of their coal power by 2030 and reach zero by 2040, replacing it with clean energy.

For this to be possible, developed countries must seriously step up international finance and support for developing countries’ transitions.

The G7 group of countries (Canada, France, Germany, Italy, Japan, the U.K. and the U.S.) have already launched Just Energy Transition Partnerships (JETPs) with countries like South Africa, Indonesia and Vietnam to support their coal phase-out efforts. So far $47 billion has been pledged, but that is not nearly enough. The G7 and other developed countries should expand these partnerships and increase finance, preferably in the form of grants instead of loans. They should also supply technical assistance to help countries develop policies that are conducive to clean energy investment; develop public-private partnerships to leverage private capital; and explore innovative financial mechanisms to de-risk private investments. Multilateral Development Banks like the World Bank should also stand true to their commitments to stop funding coal and shift fully to funding renewables.

Governments, development banks, and companies must also plan ahead to support the workers and communities that will be most affected by the energy transition. This could include shifting coal workers to other careers in energy or industry, offering unemployment or relocation compensation, and providing financial support to regions that have lost coal revenue. Just transition policies cannot be implemented overnight and are not one-size-fits-all, so planning needs to begin now.

Keeping the End of Coal Power in Sight

Just because a transition this rapid and far-reaching has never happened before doesn’t mean it’s not possible. Unlike most previous energy transitions, which happened gradually over time, there is a clear deadline for coal phase-out that countries are deliberately trying to meet. What’s more, renewable power is now more cost effective than fossil fuels in the majority of countries. And it is a positive trend that many countries which had planned to expand coal — like Turkey, Vietnam and Bangladesh — have cancelled most of those plans. The number of coal plants in construction or planned for construction is half what it was in 2017 and less than a quarter of what it was ten years ago.

The world will need urgent efforts and close cooperation to achieve this shift, but the end of coal is nearer than it’s ever been. It has to be.

Unless otherwise noted, national electricity data used in this article is from Ember’s yearly electricity data as of November 9, 2023.

This article is the third in a series of deep-dive analyses from Systems Change Lab examining countries that are leaders in transformational change. The first two articles analyzed countries rapidly scaling up renewable power and electric vehicles. Systems Change Lab is a collaborative initiative — which includes an open-sourced data platform — designed to spur action at the pace and scale needed to limit global warming to 1.5 degrees Celsius, halt biodiversity loss and build a just and equitable economy.