As global temperatures rise, extreme weather events are becoming more intense and more frequent all around the world.
Over the past two decades, the cutting-edge field of extreme weather attribution has sought to establish the role that human-caused warming has played in these events.
There are now hundreds of attribution studies, assessing extremes ranging from heatwaves in China and droughts in Madagascar through to wildfires in Brazil and extreme rainfall in South Africa.
Carbon Brief has mapped every attribution study published to date, revealing that three-quarters of the extremes analysed were made more intense or likely due to climate change.
Along with this explosion of new studies, the different types of attribution studies have evolved and expanded over the past two decades.
For example, the World Weather Attribution service was established in 2015 to provide rapid-response studies, streamlining the process of estimating the human contribution to extreme events in a matter of days.
Meanwhile, a growing community of researchers are developing the “storyline approach” to attribution that focuses more on the dynamics of the specific events being studied.
Other researchers are using weather forecasts to attribute events that have not even happened yet. And many studies are now combining these methods to get the best of all worlds in their findings.
In this detailed Q&A, Carbon Brief explores how the field of attribution science has evolved over time and explains the key methods used today.
- What are the origins of ‘extreme weather attribution’?
- What is ‘probabilistic’ attribution?
- Which weather extremes can scientists link to climate change?
- Why do scientists perform ‘rapid’ attribution studies?
- Can the impacts of extreme weather be linked to climate change?
- How do scientists attribute ‘unprecedented’ events?
- How can weather forecasts be used in attribution studies?
- What are the applications of attribution science?
- What are the next steps for attribution research?
What are the origins of ‘extreme weather attribution’?
The Intergovernmental Panel on Climate Change (IPCC) made its first mention of attribution in its first assessment report (pdf), published in 1990. In a section called “Attribution and the fingerprint method”, the report refers to attribution as “linking cause and effect”.
In these early days of attribution science, experts used statistical methods to search for the “fingerprint” of human-caused climate change in global temperature records.
However, the 1990 report says that “it is not possible at this time to attribute all or even a large part of the observed global mean warming to the enhanced greenhouse effect on the basis of the observational data currently available”.
As the observational record lengthened and scientists refined their methods, experts became more confident about attributing global temperature rise to human-caused climate change. By the time its third assessment report was published in 2001, the IPCC could state that “detection and attribution studies consistently find evidence for an anthropogenic signal in the climate record of the last 35 to 50 years”.
Just two years later, Prof Myles Allen – professor of geosystem science at the University of Oxford – wrote a Nature commentary from his home in Oxford that would open the door for attributing extreme weather events to climate change. The article begins:
“As I write this article in January 2003, the floodwaters of the River Thames are about 30 centimetres from my kitchen door and slowly rising. On the radio, a representative of the UK Met Office has just explained that although this is the kind of phenomenon that global warming might make more frequent, it is impossible to attribute this particular event (floods in southern England) to past emissions of greenhouse gases. What is less clear is whether the attribution of specific weather events to external drivers of climate change will always be impossible in principle, or whether it is simply impossible at present, given our current state of understanding of the climate system.”
Just months after Oxford’s floodwaters began to recede, a now-infamous heatwave swept across Europe. The summer of 2003 was the hottest ever recorded for central and western Europe, with average temperatures in many countries reaching 5C higher than usual.
The unexpected heat resulted in an estimated 20,000 “excess” deaths, making the heatwave one of Europe’s deadliest on record.
In 2004, Allen and two other UK-based climate scientists produced the first formal attribution study, published in Nature, which estimated the impact of human-caused climate change on the heatwave.
To conduct the study, the authors first chose the temperature “threshold” to define their heatwave. They decided on 1.6C above the 1961-90 average, because the European summer of 2003 was the first on record to exceed this average temperature.
They then used a global climate model to simulate two worlds – one mirroring the world as it was in 2003 and the other a fictional world in which the industrial revolution never happened. In the second case, the climate is influenced solely by natural changes, such as solar energy and volcanic activity, and there is no human-caused warming.
The authors ran their models thousands of times in each scenario from 1989 to 2003. As the climate is inherently chaotic, each model “run” – individual simulations of how the climate progresses over many years – produces a slightly different progression of temperatures. This means that some runs simulated a heatwave in the summer of 2003, while others did not.
The authors counted how many times the 1.6C threshold temperature was crossed in the summer of 2003 in each model run. They then compared the likelihood of crossing the threshold temperature in the world with – and a world without – climate change.
They concluded that “it is very likely that human influence has at least doubled the risk of a heatwave exceeding this threshold magnitude”.
A Nature commentary linked to the study called the paper a “breakthrough”, stating that it was the “first successful attempt to detect man-made influence on a specific extreme climatic event”.
In the decade following the heatwave study, more teams from around the world began to use the same methods – known as “probabilistic”, “risk-based” or “unconditional” attribution.
Prof Peter Stott is a science fellow in climate attribution at the UK Met Office and an author on the study. Stott tells Carbon Brief that the basic methods used in this first attribution study are “still used to this day”, but that scientists now use more “up-to-date” climate models than the one used in his seminal study.
What is ‘probabilistic’ attribution?
As the 2004 Nature study demonstrated, probabilistic attribution involves scientists running climate models thousands of times in scenarios with and without human-caused climate change, then comparing the two.
This allows them to say how much more likely, intense or long-lasting an event was due to climate change.
Many studies since have added a third scenario, in which the planet is warmer than present-day temperatures, to assess how climate change may impact extreme weather events in the future.
The figure below shows three distributions of multiple different simulated extreme events. The x-axis (horizontal) represents the intensity of the climate variable – in this instance temperature – with lower temperatures on the left and higher temperatures on the right. The y-axis (vertical) shows the likelihood of this variable hitting certain values.
Each curve shows how the climate variable behaves in a different scenario, or “world”. The red-shaded curve shows a pre-industrial world that was not warmed by human influence, the yellow-shaded curve indicates today’s climate, while the dashed line shows a future, warmer world. The curves shift from left to right as the climate warms.
The peak of each curve shows the most likely temperatures, while likelihood is lowest at the far left and far right of each curve, where temperatures are most extreme. The hatched areas show the temperatures that cross a predefined “threshold” temperature. (In the attribution study on the 2003 European heatwave, this threshold was defined as 1.6C above the 1961-90 average.)
The three curves show how the threshold is more likely to be crossed as the world warms.
Which weather extremes can scientists link to climate change?
In 2011, the American Meteorological Society decided to include a “special supplement” about attribution research in its annual report.
The supplement presented six different attribution studies. It generated significant media interest and the “Explaining Extreme Events” report has been published by Bulletin of the American Meteorological Society almost every year since.
As the research field has grown, so too has the range of different extremes that have been studied.
Heatwaves are generally considered the simplest extreme events to attribute, because they are mainly driven by thermodynamic influences. In contrast, storms and droughts are more strongly affected by complex atmospheric dynamics, so can be trickier to simulate in a model.
The graphic below shows the relative confidence of attributing different types of extreme events.
Attribution studies on extreme heat often assess how much hotter, long-lasting or likely an event was due to climate change. For example, one study finds that the summer heatwave that hit France in 2019 was made 1.5-3C hotter due to climate change and about 100 times more likely.
Heatwaves are the most-studied extreme event in attribution literature, but are becoming “less and less interesting for researchers”, according to a Bloomberg article from 2020.
Assessing extreme rainfall is more complicated – in part because the Earth’s chaotic weather system means that the size and path of a storm or heavy rainfall event has a large element of chance, which can make it challenging to identify where climate change fits in.
Nevertheless, many teams have published studies attributing extreme rainfall events and storms. For example, one study (pdf) found that climate change doubled the likelihood of the intense rainfall that fell in northern China in September 2021.
Scientists also study more complex events, such as drought, wildfires and floods, which are impacted by factors including land use and disaster preparedness.
For example, there are many different ways to define a drought. Some are linked just to rainfall, while others consider factors including soil moisture, groundwater and river flow. Some attribution studies investigating the impact of climate change on drought focus only on rainfall deficit, while others (pdf) study temperature or vapour pressure deficit – the difference between the amount of moisture in the air and how much moisture the air can hold when it is saturated.
A scientist’s decision about which type of drought to study sometimes depends on the available data and the type of impacts caused by the drought. In other cases, the choice may come down to what caused the biggest impact on people.
For example, in late 2022, South America was plagued by a severe drought that caused widespread crop failure. An attribution study on the event, therefore, focused on “agricultural” drought, which captures the response of rainfall on soil moisture conditions and is the most relevant for crop health.
Meanwhile, a study on drought in Madagascar over 2019-21 chose to focus on rainfall deficit. The study says “this was because recent research found rainfall deficits were the primary driver of drought in regions of East Africa with very similar climatic properties to south-west Madagascar”.
Wildfires are affected by conditions including temperature, rainfall, wind speed and land use. While some wildfire attribution studies focus on vapour pressure deficit, others quantify the fire weather index, which looks at the effects of fuel moisture and wind on fire behaviour and spread”.
Tropical cyclones are also complex. There is evidence that climate change can increase the peak “rain rates” and wind speeds of tropical cyclones, and that storm tracks are shifting poleward. There are many aspects of a cyclone that can be analysed, such as rainfall intensity, storm surge height and storm size.
Why do scientists perform ‘rapid’ attribution studies?
As extreme weather attribution became more mainstream, researchers began to produce studies more quickly. However, challenges in communicating the findings of attribution studies in a timely way soon became evident.
After conducting a study, writing it up and submitting it to a journal, it can still take months or years for research to be published. This means that, by the time an attribution study is published, the extreme event has likely long passed.
The World Weather Attribution (WWA) initiative was founded in 2015 to tackle this issue. The team uses a standard, peer-reviewed methodology for their studies, but does not publish the results in formal journals – instead publishing them directly on their website.
(After publishing these “rapid attribution” studies on their website, the team often write full papers for publication in formal journals, which are then peer reviewed.)
This means that rather than taking months or years to publish their research, the team can make their findings public just days or weeks after an extreme weather event occurs.
In 2021, the founders of the initiative – including Carbon Brief contributing editor Dr Friederike Otto, who is a senior lecturer in climate science at Imperial College London’s Grantham Institute – wrote a Carbon Brief guest post explaining why they founded WWA:
“By reacting in a matter of days or weeks, we have been able to inform key audiences with a solid scientific result swiftly after an extreme event has occurred – when the interest is highest and results most relevant.”
The guest post explains that to conduct an attribution study, the WWA team first uses observed data to assess how rare the event is in the current climate – and how much this has changed over the observed record. This is communicated using a “return period” – the expected frequency an event of this magnitude could be expected under a given climate.
For example, the WWA analysed the UK’s record-shattering heatwave of 2022, when the country recorded temperatures above 40C for the first time. They found that the maximum temperature seen in the UK on 19 July 2022 has a 1,000-year return period in today’s climate – meaning that even in today’s climate, 40C heat would only be expected, on average, once in a millennium.
The authors then use climate models to carry out the “probabilistic” attribution study, to determine how much more intense, likely or long-lasting the event was as a result of climate change. They conclude by conducting “vulnerability and exposure” analysis, which often highlights other socioeconomic problems.
Sometimes, the authors conclude that climate change did not influence the event. For example, a 2021 rapid attribution study by WWA found that poverty, poor infrastructure and dependence on rain-fed agriculture were the main drivers of the ongoing food crisis in Madagascar, while climate change played “no more than a small part”.
Other groups are also conducting rapid attribution studies. For example, a group of scientists – including some WWA collaborators – recently launched a “rapid experimental framework” research project called ClimaMeter. The tool provides initial attribution results just hours after an extreme weather event takes place.
ClimaMeter focuses on the atmospheric circulation patterns that cause an extreme event – for example, a low-pressure system in a particular region. Once an event is defined, the scientists search the historical record to find events with similar circulation patterns to calculate how the intensity of the events has changed over time.
Can the impacts of extreme weather be linked to climate change?
A branch of attribution science called “impact attribution” – which aims to quantify the social, economic and/or ecological impacts of climate change on extreme weather events – is also gaining popularity. There are four main types of impact attribution, as shown in the graphic below.
1) Trend-to-trend impact attribution
The first method, called “trend-to-trend” impact attribution, assesses long-term trends in both the climate system and in “health outcomes”. This approach was used in a 2021 study on heat-related mortality around the world, which received extensive media attention.
The authors used data from 732 locations in 43 countries to identify relationships between temperature and mortality in different locations, known as “exposure-response functions”. This allowed them to estimate how many people would die in a given location, if temperatures reach a certain level.
The authors used these relationships to calculate heat-related mortality over 1991-2018 for each location under two scenarios – one with and one without human-caused climate change. The study concluded that 37% of “warm-season heat-related deaths” can be attributed to human-caused climate change.
2) Event-to-event attribution
The second type of study is known as “event-to-event” attribution. In one study using this method, the authors used data on observed mortality rates to determine how many people died in Switzerland during the unusually warm summer of 2022.
They calculated how much climate change contributed to warming during that summer. They then then ran a model to calculate the “hypothetical heat-related burden” that would have been seen during the summer without the warming influence of climate change.
Using this method, they estimate that 60% of the 623 heat-related deaths “could have been avoided in absence of human-induced climate change”.
3) Risk-based event attribution
“Risk-based” event impact attribution – which is demonstrated in a more recent study on the 2003 European heatwave – is the third type of impact attribution. This method combines probabilistic event attribution with resulting health outcomes.
When the paper was published, its lead author, Prof Dann Mitchell – a professor of climate science at the University of Bristol – explained the method to Carbon Brief:
“We have a statistical relationship between the number of additional deaths per degree of warming. This is specific to a certain city and changes a lot between cities. We use climate simulations to calculate the heat in 2003, and in 2003 without human influences. Then we compare the simulations, along with the observations.”
They find, for example, that in the summer of 2003, anthropogenic climate change increased the risk of heat-related mortality in London by around 20%. This means that out of the estimated 315 deaths in London during the heatwave, 64 were due to climate change.
4) Fractional attribution
In the final method, known as “fractional” attribution, the authors combine the results of two independent numbers – an estimation of the total damages caused by an extreme weather event, and a calculation of the proportion of the risk from an extreme weather event for which anthropogenic climate change is responsible, known as the “fraction of attributable risk” (FAR).
The authors of one study used this method to estimate the economic damages linked to Hurricane Harvey.
The authors calculate that “fraction of attributable risk” for the rainfall from Harvey was around three-quarters – meaning that climate change was responsible for three-quarters of the intense rainfall.
Separately, the authors find that according to best estimates, the hurricane caused damages of around US$90bn. From this, the authors conclude that US$67bn of the damages caused by the Hurricane’s intense rainfall can be attributed to climate change.
A study on the 2010 Russian heatwave also used this method. The authors found that the heatwave was responsible for more than 55,000 deaths (pdf), and found an 80% chance that the extreme heat would not have occurred without climate warming. The study concludes that almost 45,000 of the deaths were attributable to human-caused climate change.
However, the fractional attribution method has received criticism. One paper argues that the method “inflates the impacts associated with anthropogenic climate change”, because it “incorrectly assumes” that the event has no impact unless it exceeds the threshold defined by the researchers.
Some of the authors of the Hurricane Harvey paper later wrote a paper advising caution in interpreting the results of FAR studies. They say:
“The fraction of attributable risk (FAR) method, useful in extreme weather attribution research, has a very specific interpretation concerning a class of events, and there is potential to misinterpret results from weather event analyses as being applicable to specific events and their impact outcomes…FAR is not generally appropriate when estimating the magnitude of the anthropogenic signal behind a specific impact.”
Expanding scope
Impact attribution is continuing to expand in scope. For example, studies are now being conducted to assess the impact of climate change on disease transmission.
In 2020, scientists quantified the influence of climate change on specific episodes of extreme ice loss from glaciers for the first time. They found that human-caused climate change made the extreme “mass loss” seen in glaciers in the Southern Alps, New Zealand, in 2018 at least 10 times more likely.
Scientists have also linked climate change to ecosystem shifts. One study focusing on temperature finds that the “extremely early cherry tree flowering” seen in Kyoto in 2021 was made 15 times more likely due to climate change.
Others go even further, linking weather extremes to societal impacts. For example, a 2021 study published in Scientific Reports says:
“By combining an extreme event attribution analysis with a probabilistic model of food production and prices, we find that climate change increased the likelihood of the 2007 co-occurring drought in South Africa and Lesotho, aggravating the food crisis in Lesotho.”
Meanwhile, Imperial College London’s Grantham Institute is working on an initiative to publish rapid impact attribution studies about extreme weather events around the world. Similar to WWA studies, these rapid studies will not be peer reviewed individually, but will be based on a peer-reviewed methodology.
Dr Emily Theokritoff – a research associate at Grantham, who is working on the initiative, tells Carbon Brief that it will be launched “in the near future”. She adds:
“The aim is to recharge the field, start a conversation about climate losses and damages, and help people understand how climate change is making life more dangerous and more expensive.”
How do scientists attribute ‘unprecedented’ events?
An attribution method known as the “storyline approach” or “conditional attribution” has become increasingly popular over the past decade – despite initially causing controversy in the attribution community.
In this approach, researchers first select an extreme weather event, such as a specific heatwave, storm or drought. They then identify the physical components, such as sea surface temperature, soil moisture and atmospheric dynamics, that led to the event unfolding in the way it did. This series of events is called a “storyline”.
The authors then use models to simulate this “storyline” in two different worlds – one in the world as we know it and one in a counterfactual world – for example, with a different sea surface temperature or CO2 level. By comparing the model runs, the researchers can draw conclusions about how much climate change influenced that event.
The storyline approach is useful for explaining the influence of climate change on the physical processes that contributed to the event. It can also be used to explore in detail how this event would have played out in a warmer (future) or cooler (pre-industrial) climate.
One study describes the storyline approach as an “autopsy”, explaining that it “gives an account of the causes of the extreme event”.
Prof Ted Shepherd, a researcher at the University of Reading, was one of the earliest advocates of the storyline attribution approach. At the EGU general assembly in Vienna in April 2024, Shepherd provided the opening talk in a session on storyline attribution.
He told the packed conference room that the storyline approach was born out of the need for a “forensic” approach to attribution, rather than a “yes/no” approach. He emphasised that extreme weather events have “multiple causes” and that the storyline approach allows researchers to dissect each of these components.
Dr Linda van Garderen is a postdoctoral researcher at Utrecht University and has carried out multiple studies using the storyline method. She tells Carbon Brief that, while traditional attribution typically investigates probability, the storyline approach analyses intensity.
For example, she led an attribution study using the storyline method which concluded that the 2003 European and 2010 Russian heatwaves would have been 2.5-4C cooler in a world without climate change.
She adds that it can make communication easier, telling Carbon Brief that “probabilities can be challenging to interpret in practical daily life, whereas the intensity framing of storyline studies is more intuitive and can make attribution studies easier to understand”.
Dr Nicholas Leach is a researcher at the University of Oxford who has conducted multiple studies using the storyline approach. He tells Carbon Brief that probabilistic attribution often produces “false negatives”, wrongly concluding that climate change did not influence an event.
This is because climate models have “biases and uncertainties” which can lead to “noise” – particularly when it comes to dynamical features such as atmospheric circulation patterns. Probabilistic attribution methods often end up losing the signal of climate change in this noise, he explains.
The storyline approach is able to avoid these issues more easily, he says. He explains that by focusing on the dynamics of one specific event, rather than a “broad class of events”, storyline studies can eliminate some of this noise, making it more straightforward to identify a signal, he says.
Conversely, others have critiqued the storyline method for producing false positives, which wrongly claim that climate change influenced an extreme weather event.
The storyline approach has also been praised for its ability to attribute “unprecedented” events. In the EGU session on the storyline method, many presentations explored how the storyline method could be used to attribute “statistically impossible” extremes.
Leach explains that when a completely unprecedented extreme event occurs, statistical models often indicate that the event “shouldn’t have happened”. When running a probabilistic analysis using these models, Leach explains: “You end up with the present probability being zero and past probability being zero, so you can’t say a lot.”
He points to the Pacific north-west heatwave of 2021 as an example of this. This event was one of the most extreme regional heat events ever recorded globally, breaking some local high temperature records by more than 6C.
WWA conducted a rapid attribution study on the heatwave, using its probabilistic attribution method. The heatwave was “so extreme” that the observed temperatures “lie far outside the range” of historical observations, the researchers said.
Their assessment suggests that the heatwave was around a one-in-1,000-year event in today’s climate and was made at least 150-times more likely because of climate change.
Leach and his colleagues used the storyline method to attribute the same heatwave. The methods of this study will be discussed more in the following section.
Leach explains that using the storyline approach, he was able to consider the physics of the event, including an atmospheric river that coincided with the “heat dome” that was a key feature of the event. This helped him to represent the event well in his models. The study concluded that the heatwave was 1.3C hotter and eight times more likely as a result of climate change.
Many experts tell Carbon Brief there was initially tension in the attribution community between probabilistic and storyline advocates when the latter was first introduced. However, as the storyline method has become more mainstream, criticism has abated and many scientists are now publishing research using both techniques.
Van Garderen tells Carbon Brief that storyline attribution is “adding to the attribution toolbox”, rather than attempting to replace existing methods. She emphasises that probability-based and storyline attribution answer different questions, and that both are important.
How can weather forecasts be used in attribution studies?
Forecast attribution is the most recent major addition to the attribution toolbox. This method uses weather forecasts instead of climate models to carry out attribution studies. Many experts describe this method as sitting part-way between probabilistic and storyline attribution.
One benefit of using forecasts, rather than climate models, is that their higher resolution allows them to simulate extreme weather events in more detail. By using forecasts, scientists can also attribute events that have not yet happened.
The first use of “advance forecasted” attribution analysis (pdf) quantified the impact of climate change on the size, rainfall and intensity of Hurricane Florence before it made landfall in North Carolina in September 2018.
The authors, in essence, carried out the probabilistic attribution method, using two sets of short-term forecasts for the hurricane rather than large-scale climate models. The analysis received a mixed reaction. Stott told Carbon Brief at the time that it was “quite a cool idea”, but was highly dependent on being able to forecast such events reliably.
Dr Kevin Trenberth, distinguished senior scientist at the National Center for Atmospheric Research, told Carbon Brief in 2019 that the study was “a bit of a disaster”, explaining that the quality of the forecast was questionable for the assessment.
The authors subsequently published a paper in Science Advances reviewing their study “with the benefit of hindsight”. The authors acknowledged that the results are quite a way off what they forecasted. However, they also claimed to have identified what went wrong with their forecasted analysis.
Problems with the “without climate change” model runs created a larger contrast against their real-world simulations, meaning the analysis overestimated the impact of climate change on the event, they said.
Nonetheless, the study did identify a quantifiable impact of climate change on Hurricane Florence, adding to the evidence from studies by other author groups.
This research team has since published more forecast-based attribution studies on hurricanes. One study used hindcasts – forecasts that start from the past and then run forward into the present – to analyse the 2020 hurricane season. The team then ran a series of “counterfactual” hindcasts over the same period, without the influence of human warming from sea surface temperatures.
They found that warmer waters increased three-hour rainfall rates and three-day accumulated rainfall for tropical storms by 10% and 5%, respectively, over the 2020 season.
Meanwhile, a 2021 study by a different team showed how it was possible to use traditional weather forecasts for attribution. The researchers, who penned a Carbon Brief guest post about their work, found that the European heatwave of February 2019 was 42% more likely for the British Isles and at least 100% more likely for France.
To conduct their study, the authors used a weather forecast model – also known as a “numerical weather prediction” model (NWP).
They explain that a NWP typically runs at a higher resolution than a climate model, meaning that it has more, smaller grid cells. This allows it to simulate processes that a climate model cannot and makes them “more suitable for studying the most extreme events than conventional climate models,” the authors argue.
More recently, Leach and his team carried out a forecast attribution study on the record-breaking Pacific north-west heatwave of 2021, years after the event took place.
The authors defined 29 June 2021 as the start of the event, as this is when the maximum temperature of the heatwave was recorded. They then ran their forecasts using a range of “lead times” – the number of days before the event starts that the model simulation is initialised.
The shortest lead time in this study was three days, meaning the scientists began running the model using the weather conditions recorded on 26 June 2021. The short lead time meant that they could tailor the model very closely to the weather conditions at this time and simulated the event itself very accurately.
By comparison, the longest lead times used in this study were 2-4 months. This means that the models were initialised in spring and, by the time they simulated the June heatwave, their simulation did not closely resemble the events that actually unfolded.
Leach tells Carbon Brief that by lengthening the lead time of the weather forecast, they can effectively “shift the dial” from storyline to probabilistic attribution. He explains:
“If you’re using a forecast that’s initialised really near to your event, then you’re kind of going down that storyline approach, by saying, ‘I want what my model is stimulating to look really similar to the event I’m interested in’…
“The further back [in time] you go, the closer you get to the more probabilistic style of statements that are more unconditioned.”
This combination of storyline and probabilistic attribution allows the authors to draw conclusions both about how climate change affected the intensity and the likelihood of the heatwave. The authors estimate that the heatwave was 1.3C more intense and eight times more likely as a result of climate change.
More recently, Climate Central has produced a tool that uses temperature forecasts over the US over the coming days to calculate a “climate shift index”. This index gives the ratio of how common the forecasted temperature is in today’s climate, compared to how likely it would be in a world without climate change.
The index runs from five to minus five. A result of zero indicates that climate change has no detectable influence, an index of five means that climate change made the temperature at least five times more likely and an index of minus five means that climate change made the temperature at least five times less likely.
The tool can be used for attribution. For example, recent analysis by the group used the index to quantify how climate change has influenced the number of uncomfortably hot nights. It concluded:
“Due to human-caused climate change, 2.4 billion people experienced an average of at least two additional weeks per year where nighttime temperatures exceeded 25C. Over one billion people experienced an average of at least two additional weeks per year of nights above 20C and 18C.”
What are the applications of attribution science?
One often-touted application of attribution studies is to raise awareness about the role of climate change in extreme weather events. However, there are limited studies about how effective this is.
One study presents the results of focus group interviews with UK scientists, who were not working on climate change, in which participants were given attribution statements. The study concludes:
“Extreme event attribution shows significant promise for climate change communication because of its ability to connect novel, attention-grabbing and event-specific scientific information to personal experiences and observations of extreme events.”
However, the study identified a range of challenges, including “adequately capturing nuances”, “expressing scientific uncertainty without undermining accessibility of key findings” and difficulties interpreting mathematical aspects of the results.
In another experiment, researchers informed nearly 4,000 adults in the US that climate change had made the July 2023 heatwave in the US at least five times more likely. The team also shared information from Climate Central’s climate shift index. According to the study, both approaches “increased the belief that climate change made the July 2023 heatwave more likely and is making heatwaves in general more likely as well”.
Meanwhile, as the science of extreme weather attribution becomes more established, lawyers, governments and civil society are finding more uses for this evolving field.
For example, attribution is starting to play an important role in courts. In 2017, two lawyers wrote a Carbon Brief guest post stating “we expect that attribution science will provide crucial evidence that will help courts determine liability for climate change related harm”.
Four years later, the authors of a study on “climate litigation” wrote a Carbon Brief guest post explaining how attribution science can be “translated into legal causality”. They wrote:
“Attribution can bridge the gap identified by judges between a general understanding that human-induced climate change has many negative impacts and providing concrete evidence of the role of climate change at a specific location for a specific extreme event that already has led or will lead to damages.”
In 2024, around 2,000 Swiss women used an attribution study, alongside other evidence, to win a landmark case in the European Court of Human Rights. The women, mostly in their 70s, said that their age and gender made them particularly vulnerable to heatwaves linked to climate change. The court ruled that Switzerland’s efforts to meet its emissions targets had been “woefully inadequate”.
The 2024 European Geosciences Union conference in Vienna dedicated an entire session to climate change and litigation. Prof Wim Thiery – a scientist who was involved in many conference sessions on climate change and litigation – tells Carbon Brief that attribution science is particularly important for supporting “reparation cases”, in which vulnerable countries or communities seek compensation for the damages caused by climate change.
He adds Carbon Brief that seeing the “direct and tangible impact” of an attribution study in a court case “motivates climate scientists in engaging in this community”.
(Other types of science are also important in court cases related to climate change, he added. For example, “source attribution” identifies the relative contribution of different sectors and entities – such as companies or governments – to climate change.)
Dr Rupert Stuart-Smith, a research associate in climate science and the law at the University of Oxford’s Sustainable Law Programme, adds:
“We’re seeing a new evolution whereby communities are increasingly looking at impact-relevant variables. Think about inundated areas, lake levels, heatwave mortalities. These are the new target variables of attribution science. This is a new frontier and we are seeing that those studies are directly usable in court cases.”
He tells Carbon Brief that some cases “have sought to hold high-emitting corporations – such as fossil fuel or agricultural companies – liable for the costs of climate change impacts”. He continues:
“In cases like these, claimants typically need to show that climate change is causing specific harms affecting them and courts may leverage attribution or climate projections to adjudicate these claims. Impact attribution is particularly relevant in this context.”
Dr Delta Merner is a lead scientist at the science hub for climate litigation. She tells Carbon Brief that “enhanced source attribution for companies and countries” will be “critical” for holding major emitters accountable. She adds:
“This is an urgent time for the field of attribution science, which is uniquely capable of providing robust, actionable evidence to inform decision-making and drive accountability.”
Meanwhile, many countries’s national weather services are working on “operational attribution” – the regular production of rapid attribution assessments.
Stott tells Carbon Brief that the UK Met Office is operationalising attribution studies. For example, on 2 January 2024, it announced that 2023 was the second-warmest year on record for the UK, with an average temperature of 9.97C.
New methods are also being developed. For example, groups, such as the “eXtreme events: Artificial Intelligence for Detection and Attribution” (XAIDA) team, are researching the use of machine learning and artificial intelligence for attribution studies.
One recent attribution study uses a machine-learning approach to create “dynamically consistent counterfactual versions of historical extreme events under different levels of global mean temperature”. The authors estimate that the south-central North American heatwave of 2023 was 1.18-1.42C warmer because of global warming.
The authors conclude:
“Our results broadly agree with other attribution techniques, suggesting that machine learning can be used to perform rapid, low-cost attribution of extreme events.”
Other scientists are using a method called UNSEEN, which involves running models thousands of times to increase the size of the datasets used to make it easier to derive accurate probabilities from highly variable extremes.
What are the next steps for attribution research?
The experts that Carbon Brief spoke to for this article have high hopes for the future of attribution science. For example, Stott says:
“Attribution science has great potential to improve the resilience of societies to future climate change, can help monitor progress towards the Paris goals of keeping global warming to well below 2C and can motivate progress in driving down emissions towards net-zero by the middle of this century.”
However, despite the progress made over the past two decades, there are still challenges to overcome. One of the key barriers in attribution science is a lack of high-quality observational data in low-income countries.
To carry out an attribution study, researchers need a Iong, high-quality dataset of observations from the area being studied. However, inadequate funding or political instability means that many developing countries do not have sufficient weather station data.
In a 2016 interview with Carbon Brief, Allen said that “right now there is obviously a bias towards our own backyards – north-west Europe, Australia and New Zealand.”
Many WWA studies in global-south countries mention the challenge of finding adequate data and sometimes this affects the results. A WWA study of the 2022 drought in west Africa’s Sahel region was unable to find the signal of climate change in the region’s rainfall pattern – in part, due to widespread uncertainties in the observational data.
Otto, who was an author on the study, explained at the time:
“It could either be because the data is quite poor or because we have found the wrong indices. Or it could be because there really is no climate change signal…We have no way of identifying which of these three options it is.”
Developing better observational datasets is an ongoing challenge. It is highlighted in much of the literature on attribution as an important next step for attribution science – and for climate science more widely. Merner tells Carbon Brief that scientists also need to work on developing “novel approaches for regions without baseline data”.
Meanwhile, many scientists expect the methods used in attribution science to continue evolving. The Detection and Attribution Model Intercomparison Project is currently collecting simulations, which will support improved attribution of climate change in the next set of assessment reports from the Intergovernmental Panel on Climate Change.
Mitchell says that, over the next decade, he thinks that “we will move away from the more generic attribution methods that have served us well to this point, and start developing and applying more targeted – and even more defensible – methods”.
In particular, he highlights the need for more specific methods for impact attribution – for example, studying the impacts of weather events on health outcomes, biodiversity changes or financial losses.
He continues:
“The interplay of different socioeconomic states and interventions with that of climate change can make these particularly difficult to study – but we are getting there with our more advanced, albeit computationally expensive methods, such as using weather forecast models as the foundation of our attribution statements.”
Stott tells Carbon Brief that incorporating impacts into attribution assessments is a “crucial area for development” in attribution science. He explains that impact attribution is “very relevant to the loss-and-damage agenda and further developments in attribution science are likely to include the ability to attribute the financial costs of storms”.
Stuart-Smith tells Carbon Brief that, “in the coming years, growing numbers of studies will quantify the economic burden of climate change and its effects on a broader range of health impacts, including from vector and water-borne diseases”.
Leach also tells Carbon Brief that it is “important for attribution to move their focus beyond physical studies and into quantitative impact studies to increase their relevance and utility in policy and the media”.
He adds:
“Utilising weather forecasts for attribution would fit neatly with this aim as those same models are already widely used by emergency managers and built into impact modelling frameworks.”
Similarly, Stott tells Carbon Brief that “forecast attribution shows great potential”. He explains that by “progressing that science” will allow this method to be used to attribute more types of extreme weather with greater confidence.
Leach advocates for greater use of weather forecast models for all types of attribution. He says:
“Weather forecast models have demonstrated repeatedly over the past few years that they are capable of accurately representing even unprecedented weather extremes. Using these validated state-of-the-art models for attribution could bring an increase in confidence in the results.”
Many scientists also tell Carbon Brief about the importance of operationalising attribution. The weather services in many countries already have this in place. Stott tells Carbon Brief that groups in Japan, South Korea, Australia and the US are also “at various stages of developing operational attribution services”.
Meanwhile, Otto tells Carbon Brief that “the most important next step for attribution in my view is to really integrate the assessment of vulnerability and exposure into the attribution studies”. She adds:
“In order for attribution to truly inform adaptation it is essential though to go from attributing hazards, as we do now mainly, to disentangling drivers of disasters.”
Mitchell adds that he thinks attribution statements “are absolutely essential for [countries to make] national adaptation plans”.
Meanwhile, another study suggests that extreme event attribution studies could be used by engineers, along with climate projections, to assist climate adaptation for civil infrastructure.
Leach tells Carbon Brief that attribution could be useful in the insurance sector for similar reasons. He adds that many insurance sectors use the same forecasts in their catastrophe models that climate scientists use for forecast attribution, meaning that it should be straightforward to add attribution studies into their pipelines.
The post Q&A: The evolving science of ‘extreme weather attribution’ appeared first on Carbon Brief.
Bemnet Agata is a communications officer at the Tax Justice Network, where Alison Schultz is a research fellow.
We are entering an age of permanent volatility.
Climate change is making extreme weather more destructive. Geopolitical tensions are disrupting energy markets and supply chains. Governments are expected not only to decarbonise their economies, but to protect them against an increasingly unpredictable world. That requires sustained public investment at precisely the moment repeated shocks are placing ever greater pressure on public finances.
Governments are rightly debating how to mobilise the trillions needed for the energy transition. Yet one of the largest untapped sources of climate finance requires neither higher corporate tax rates nor new international funds. It lies in correcting one of the oldest assumptions underpinning the international corporate tax system.
One of the stranger features of the modern economy is that we no longer disagree about what a multinational corporation is—until the conversation turns to tax.
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Investors value Apple as a single global business. Consumers experience it as a single company. Its executives manage it as an integrated enterprise, allocating capital, production and marketing across continents according to commercial strategy rather than national borders. Nobody seriously believes its subsidiaries are independent businesses negotiating with one another as though they were unrelated companies.
Yet this is precisely the legal fiction upon which the international corporate tax system was built—and continues to rest.
That legal fiction does more than misdescribe how multinational businesses operate. It enables profits to be shifted away from the places where real economic activity takes place and into jurisdictions where little or no tax is paid. This not only erodes public revenues, but also undermines the level playing field by giving multinational corporations tax advantages that purely domestic businesses cannot replicate.
$500 billion a year
Taxing multinational corporations as the integrated businesses they actually are could generate around $500 billion in additional corporate tax revenues every year. That’s almost 40% of the $1.3 trillion in annual climate finance that, two years ago, governments agreed should be mobilised by 2035. That is exactly what governments are negotiating this week under the United Nations Framework Convention on International Tax Cooperation in New York.
Imagine Apple sold one million iPhones in Kenya. Few people would dispute that those sales depend on the Kenyan economy. Every iPhone arrives through Kenyan ports, travels on Kenyan roads, is sold by Kenyan workers, connects through Kenyan telecommunications infrastructure and is protected by Kenyan courts. Apple’s success depends not only on its own innovation, but on the public investments and institutions that make economic activity possible.
The negotiations underway under the United Nations Framework Convention on International Tax Cooperation would replace this legal fiction with a system known as unitary taxation with formulary apportionment. Rather than allowing multinational corporations to pay tax where they say their profits arise, it would allocate taxing rights according to where they undertake genuine economic activity—where they employ workers, manufacture goods, provide services and sell to customers. It would replace today’s pay where you say model with one based on pay where you play
This is not about increasing corporate tax rates. It is about deciding where multinational corporations should pay tax on the profits they already earn. Allocating taxing rights in this way would benefit countries across the income spectrum. While higher-income countries would gain the most in absolute terms, lower-income countries would see the largest proportional increases.
France, for example, would collect an additional US$25.5 billion each year, while Kenya would increase its corporate tax revenues by 406%. At a time of mounting climate costs, those revenues could help governments drive the transition to clean energy while investing in the resilience needed to withstand future shocks.
An overdue correction
The strongest argument for reform, however, is not the scale of the projected revenue gains. It is that the proposal corrects a century-old foundational error by bringing international tax rules into closer alignment with how the modern economy actually works.
Every successful market depends on foundations that no company creates alone: public investment, functioning institutions and the participation of millions of workers and consumers. If multinational profits are generated collectively across many countries, the rules governing where those profits are taxed should recognise that reality rather than the legal and accounting artifices that determine where profits appear on paper.
The international tax system remains an outlier. Every other area of economic governance has long since recognised multinational corporations as integrated global businesses. Tax rules remain the last custodian of the legal fiction that multinational corporations are not, in fact, multinational.
The debate taking place in New York is therefore about much more than tax. It is about whether the rules underpinning the global economy still reflect the economy they are meant to govern—and whether they equip governments with the fiscal capacity to confront the defining challenges of the twenty-first century.
Energy sovereignty without fiscal sovereignty is an unfinished transition. Countries cannot build a more secure and resilient future if the wealth generated within their economies continues to escape taxation where it is created.
Recovering those revenues would strengthen public finances, giving governments not only the resources to accelerate the energy transition but also the fiscal capacity to plan, coordinate and sustain it over the long term. In an age of permanent volatility, that capacity may prove to be every country’s most important climate adaptation strategy.
The post A legal fiction blocking billions in climate finance will be challenged this week appeared first on Climate Home News.
A legal fiction blocking billions in climate finance will be challenged this week
Leaders of the Santa Marta coalition – a group of governments, businesses and civil society organisations seeking to transition away from fossil fuels – hope it can withstand the loss of one of its founding members as a far-right, pro-fossil fuel government takes office in Colombia this week.
In April, Colombia hosted 57 governments in the Caribbean city of Santa Marta for the first conference on transitioning away from fossil fuels – a voluntary meeting outside of official UN climate talks. In June, far-right candidate Abelardo de la Espriella won a general election, and is set to take office on Friday.
De la Espriella has pledged to ramp up coal exports and begin fracking for methane gas, reversing a ban on all new hydrocarbon exploration enacted by the current government of Gustavo Petro since 2022. The soon to be environment minister Fabio Arjona said the Santa Marta conference was an “absolute waste of time and money”.
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He will replace Irene Vélez Torres, who co-chairs the Santa Marta coalition. Torres told a press briefing last week that the initiative was created in a way that made sure “it could live without Colombia because we knew [a change in government] was a risk”.
“It’s a coalition of countries but also subnational governments, civil society, scientists… so there is a lot more than just Colombia. It’s a shame that Colombia cannot continue with its international leadership, but it doesn’t mean that what we created as a global legacy will not continue,” she said.
Dutch environment minister Stientje van Veldhoven, also a co-chair in the initiative, told Climate Home News in a statement that “the organization is set-up in a way that progress does not depend on one or two countries”, and highlighted the role of incoming co-chairs Ireland and Tuvalu.
The new co-chairs will officially take the lead after COP31 and are set to host the second Conference on Transitioning Away from Fossil Fuels in Tuvalu next year. Van Veldhoven said the two countries are already involved in preparing for this transition.
Priorities: roadmaps, debt and trade
After meeting in Santa Marta to kickstart work on phasing out fossil fuels, governments agreed to focus on three priorities: developing national roadmaps to phase out fossil fuels, decoupling trade from coal, oil and gas, and reducing global finance’s dependence on fossil fuels.
At last year’s COP30, a group of around 80 countries led a failed push for the UN to adopt a global roadmap to phase out fossil fuels. To keep talks from collapsing, Brazil proposed to draft a voluntary roadmap instead, which has received suggestions from dozens of countries.
In June, Vélez Torres told journalists that Colombia and the Netherlands would seek for COP31 to reflect the work of the Santa Marta coalition, something the co-presidency of Türkiye and Australia was “open” to consider, she added.
Last week, she stressed that the workstreams are also set up independently from the Dutch and Colombian governments, and that each area of focus will have its own “madrina”, which translates as “godmother”, a contact point that will oversee progress and support countries.
Van Veldhoven noted that, while the coalition is open to new members, the current priority is “setting up the organisation with the current involved countries and stakeholders”. The Dutch government noted that “several countries” have expressed interest, but could not disclosed which ones.
Colombia’s fossil fuel shift
While the coalition is set up to withstand changes in government, Colombia’s shift to a pro-fossil fuel government represents an important blow to global initiatives seeking to phase out fossil fuels, said Andreas Malm, author and professor of human ecology at Lund University.
“The gap that we have after this defeat is charismatic political leadership that makes the necessary links and arguments on the global stage. For the moment, I don’t see who could replace Colombia in that role,” he said. “But who knows… perhaps some miracle will happen somewhere in the world and you will have someone to pick up that mantle that is now on the ground.”
Colombia not only leads the Santa Marta coalition, but is also one of the few fossil fuel producers in the group to actually halt new exploration licenses. Coal and oil derivatives account for about a third of the country’s exports, but both industries have followed a downward trend over the last decade.
De la Espriella’s government will also have to start from scratch, as Petro’s government halted all oil and gas exploration pilots in the key Magdalena and Cesar-Ranchería regions. Both areas are also home to indigenous communities who are likely to challenge any projects in court.
Vélez Torres said that halting all new coal, oil and gas exploration licenses “was not easy” and led to “violent reactions” from national elites, including “violent threats”, but that it came with the deep belief that “it is needed, it is urgent, and it cannot be delayed”.
At an international level, she added that more countries need to show “political bravery” to take similar decisions, and that the global discussion to phase out fossil fuels “cannot be delayed” because the time window for humanity to act is shrinking.
“We decided to go against the current. That has been one of the bravest decisions, and I hope that other governments and particularly civil society can get to lead that conversation forward”, she said.
The post Santa Marta coalition tested as co-chair Colombia turns back to fossil fuels appeared first on Climate Home News.
Santa Marta coalition tested as co-chair Colombia turns back to fossil fuels
When heavy storms triggered a fault on a major power line in Indonesia’s Sumatra in late May, blackouts plunged homes and businesses across the island into darkness, leaving millions to cope without power in the humid heat for up to a day.
Failed traffic lights caused chaos on the streets of Medan, one of the country’s biggest cities, and restaurants and shops had to shutter or throw out food after fridges stopped working. Four people were reported to have died from carbon monoxide poisoning from generators.
A power outage caused by damage to cables on a high-voltage transmission line, the first of two to strike Sumatra in a fortnight, highlighted the huge challenge facing Indonesia and much of neighbouring Southeast Asia – the maintenance and upgrading of inadequate grid capacity that industry analysts say is proving an obstacle for billions of dollars in planned clean power investments.
Experts told Climate Home News the Galang–Simangkuk transmission line, which was relatively new and only began operating seven years ago, should have been able to withstand the storms that caused transmission towers to collapse in early June.
“It should not have had these grid failures,” said Wai-Shin Chan, Hong Kong-based head of research at Asia Research & Engagement, a consulting firm, warning that climate change would bring more frequent episodes of extreme weather.
“The grid resilience is really not there,” Chan said.
The Indonesian Air Force helped state-owned utility PT Perusahaan Listrik Negara (PLN) transport emergency power towers to restore electricity supplies within 24 hours, but the two incidents could cause longer-lasting damage to investor confidence – hurting the delivery of much-needed reliable clean electricity supplies.
PLN did not respond to a request for comment.
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Grid bottlenecks and projects stuck on hold
With electrification high on the agenda of the COP31 climate talks later this year, there is growing global focus on the need to bolster grid infrastructure to cope with increased electricity use and more renewables in the power mix.
In Southeast Asia, energy experts say inadequate grid capacity and maintenance is already proving a major factor in the region’s stuttering rollout of new clean energy projects.
About 50% to 60% of renewable energy projects in Vietnam, Thailand and Indonesia were cancelled or stalled between 2021 and 2025, according to a recent report by consultancy Bain & Company and Standard Chartered. In Indonesia, 48% of announced projects were subsequently dropped or delayed during that period.
Progress in the region is also being hampered by issues ranging from unclear power purchase agreement (PPA) structures, a failure of power policies to keep up with investor needs, permitting and licensing approval delays, grid connection constraints, limits to private sector involvement in electricity markets, and policy and tariff uncertainty, energy experts said.
Some renewable energy projects have also faced opposition due to their environmental impact and issues related to land rights.
But Bain researchers found grid infrastructure was the biggest bottleneck for Southeast Asia’s energy transition, with about $18 billion per year needed in investment for modernisation and upgrades.
The International Energy Agency (IEA) has warned that electricity grid and storage investment in the region was higher in 2015 at $15 billion compared with $12 billion in 2025, even as electricity demand and renewable energy growth accelerated.
“It’s a concern for long-term power development in the region,” Chan said.
“If these risks – grid curtailment, policy uncertainty, permitting and PPA – are not adequately addressed, investors just don’t have the confidence to hit the final investment decision button,” he added.
A stuttering energy transition
Ramping up progress on solar, wind, hydro and geothermal projects is vital for Southeast Asian nations to hit their targets on cutting planet-heating carbon emissions.
Indonesia has pledged to reduce emissions by 31.9% by 2030 compared with business-as-usual levels, or by 43.2% with international support, on the way to reaching net zero by 2060.
Renewables accounted for about 18% of Indonesia’s energy mix in April 2026 according to local media reports, falling short of the country’s initial 23% target for 2025, with the majority of its energy needs met by coal, oil and gas. In 2025, a new National Energy Policy postponed achieving the target to 2030.
“The region carries significant weight in global terms, given its share of world population and energy consumption,” said Joseph Jacobelli, an impact investor and author of Asia’s Energy Revolution and Powering the Unstoppable Green Shift.
“Every delay in renewable energy deployment extends dependence on fossil fuels and pushes net zero targets further out of reach,” he said.
There are cost benefits of increasing renewables in the overall power mix, too.
In many parts of the region, new renewable power – especially solar and onshore wind – is cheaper than building new fossil fuel generation. The global energy shock unleashed by the Iran war has highlighted the energy security benefits of renewables, though it also raised concerns about coal backsliding in countries including Indonesia.
Surging oil prices exposed Southeast Asia’s vulnerability to fossil fuel supply disruptions, causing energy prices to soar and widespread fuel shortages that led the World Bank to downgrade the region’s growth projection.
“This situation pushes us to accelerate [the energy transition], we must move faster,” Indonesian President Prabowo Subianto said in March, adding that the government was focused on solar projects that would deliver a total installed capacity of up to 100 GW.
At the same time, progress on moving away from coal has been sluggish. Both Indonesia and Vietnam signed up for Just Energy Transition Partnerships (JETPs) – a funding initiative set up by the G7 to help developing nations shift away from coal – though a lack of favourable financing is holding back these plans.
The US withdrew from its JETP deals with the two countries last year, reflecting President Donald Trump’s wider energy policies, and Indonesia abandoned plans to close a major coal power plant.
Lack of finance, or lack of faith?
But a shortage of financing to bring new renewables projects online is not the cause of foot-dragging in Indonesia, where installed solar capacity reached only about 20% to 30% of the government’s 2020-2025 target, Bain researchers said.
Of an estimated $540 billion in green capital expenditure announced across Southeast Asia’s power and electric vehicle value chains between now and 2030, only about $315 billion is on a credible path towards deployment under current conditions, according to the report.
Between 2022 and early 2026, more than a quarter of the 452 new solar projects announced in Southeast Asian countries were postponed or cancelled, according to Global Energy Monitor‘s Global Solar Power Tracker.
In Indonesia, the Batam Bintan Karimun solar farm was initially expected to come online by 2024 but was cancelled in 2023 for unknown reasons, Kasandra O’Malia, a project manager at Global Energy Monitor, told Climate Home. The project also included plans for Southeast Asia’s largest associated battery storage facility.
Another high-profile Indonesian development that has stalled is a 3,500 MW solar and storage project proposed on Riau Island to export clean electricity to Singapore. While not formally abandoned, there have been few updates to this project since April 2022.
“This execution gap is not really to do with money – there is available capital – but the finance is not being deployed effectively because the risks have not been adequately redressed,” Chan said.
In a bid to foster investor certainty, Indonesia’s government approved a new 2025-2034 Electricity Supply Business Plan (RUPTL) for PLN in May 2025, replacing years of delays over the country’s power development roadmap.
As well as aligning government policy, streamlining permitting, simplifying purchase procedures and targeting 70 GW of new generation, with renewables accounting for the vast majority of additions, the plan includes the construction of about 47,800 kilometres of new transmission lines and substations with a total capacity of 108,000 megavolt-ampere, spread across Indonesia.
The Ministry of Energy and Mineral Resources, several domestic and international renewable energy developers, and the Indonesia Renewable Society, did not respond to requests for comment.
Another way to soothe investors’ nerves would be for governments to use public money to de-risk investments, but there is little appetite for this approach in the region, Chan said.
A more effective tool would be ensuring stable, investment-friendly energy market policies and regulations, said Alnie Demoral, a Manila-based energy analyst at climate think-tank Ember who previously worked with solar developers and investors.
Renewable energy developers, investors and authorities can spend years negotiating the project’s costs, permitting and whether grid connection will be available to bring clean power online, she said.
Often the longest discussions focus on the power pricing tariffs that governments set for renewable energy producers. Changing policies or disagreement on underlying cost assumptions can stall or delay a project before it reaches financial close, she added.
“Governments have to do their part by making sure the investment environment is stable,” Demoral said.
“But this is a two-way process. The private sector and developers must also ensure that their assessments of the project are based on robust assumptions.”
AI data centres add to the strain
At the same time, rapid growth in power-hungry AI data centres is putting extra strain on the region’s overstretched grids.
AI data centres, which use much more power than regular data centres, are becoming one of the largest drivers of new power demand in Southeast Asia as governments in the region jostle for more multibillion-dollar investment in the sector.
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The slow pace of renewable energy deployment and grid modernisation, coupled with ongoing reliance on fossil fuels in the electricity mix, will make it difficult for the region to meet a new, fast-growing source of additional demand without increasing emissions.
Emissions from data centre power use in Indonesia are expected to quadruple between 2024 and 2030, according to Ember.
AI data centres operate around the clock and will often use any power that is available – be it renewables or fossil fuels, said Chan, urging policymakers to first ensure they can meet the power needs before courting data centres.
Many new AI data centres are planned for areas with insufficient high-voltage transmission capacity, according to the Bain report, suggesting that countries should focus on new high-voltage lines, larger substations and stronger interconnections between regions.
The researchers note that AI data centres also typically take about one to three years to build, while major electricity transmission lines and grid updates can take five years or more, adding that power grid investments must happen before renewable energy or AI projects.
“Growth in data centres and AI is already adding pressure to constrained grids,” said Christina Ng, the Kuala Lumpur-based co-founder of Energy Shift Institute, an Asia-focused, independent energy finance think-tank.
“The risk is that new demand is met through high-emitting electricity if clean power and clean grid investment do not keep pace.”
Main image: A technician walks next to solar panels that partially provide electrical power to the Grand Mosque of Istiqlal in Jakarta, Indonesia (Photo: REUTERS/Willy Kurniawan)
The post Southeast Asia’s fragile grids threaten billions in clean energy investment appeared first on Climate Home News.
Southeast Asia’s fragile grids threaten billions in clean energy investment
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