- What are the benefits and challenges of using AI in weather and climate forecasting?
- What are some other current uses of AI in climate science research?
- How is AI being used to advance climate policy and action?
- How could AI be used in climate research over the coming years?
- Are there any misconceptions about the use of AI in climate science?
- Could AI improve or exacerbate inequalities in climate research?
- What are some other risks and challenges of AI in climate change research?
EU-regulated “green” funds are investing in some of the world’s biggest coal companies that are expanding their operations in contrast to a 2021 UN agreement for countries to reduce their use of the dirty fossil fuel.
European investors hold shares worth at least $65 million in major coal firms across China, India, the United States, Indonesia and South Africa within funds designated as “promoting environmental and social” goals under EU rules, an analysis by Climate Home and media partners found.
Taken together, these companies emit around 1,393 million tonnes of carbon dioxide (CO2) into the atmosphere every year, putting them among the world’s top five polluters if they were a country.
The investments are owned by major financial firms including BlackRock, Goldman Sachs and Fideuram, a subsidiary of Italy’s largest bank Intesa Sanpaolo. Most firms analysed are signatories of the Glasgow Financial Alliance for Net Zero (GFANZ), whose members pledge to align their portfolios with climate-friendly investment.
The asset managers told Climate Home their coal holdings do not contradict EU green policies or the 2015 Paris Agreement to tackle climate change.
At the COP26 UN climate summit in Glasgow in 2021, countries agreed for the first time to accelerate efforts “towards the phase-down of unabated coal power”. “Unabated” means power produced using coal without any technology to capture, store or use the planet-heating CO2 emitted during the process.
But rather than shrinking, global coal capacity has grown since the signing of the Glasgow Climate Pact with a fleet of new coal plants firing up their boilers, primarily in China, India and Indonesia. Coal miners in those countries have also boosted their operations to keep up with the increasing demand.
European leaders have heavily opposed this, with EU president Ursula von der Leyen saying the bloc is “very worried” about coal expansion in China.
“Light green” funds
The investments analysed by Climate Home have been made by funds classified under Article 8 of the EU’s Sustainable Finance Disclosure Regulation (SFDR), which the European Commission hoped would discourage greenwashing and promote sustainable investments when it was introduced in 2021.
Article 8 – known as ‘light green’ – refers broadly to a fund that has “environmental and social characteristics”, while the ‘dark green’ Article 9 refers more directly to sustainability.
The rules were also intended to offer members of the public more clarity on where asset managers invest their money and enable them to make an informed decision on whether they want their savings or pension pots to prop up climate-harming activities.
Workers shovel coal onto a truck at a coal yard near a coal mine in Huating, Gansu province, China. REUTERS/Thomas Peter
But a group of European financial market watchdogs warned this month the rules are having the opposite effect and called for an overhaul of the system.
“Status as ‘Article 8’ or ‘Article 9’ products have been used since the outset in marketing material as ‘quality labels’ for sustainability, consequently posing greenwashing and mis-selling risks,” they said in a joint opinion to the European Commission.
“The general public is still being misled when it comes to sustainable funds,” Lara Cuvelier, a sustainable investments campaigner at Reclaim Finance, told Climate Home. “The regulations are very weak and there is no clear criteria as to what can or cannot be included. It’s still in the hands of investors to decide that for themselves.”
Funding coal expansion
Climate Home identified investments in the biggest-polluting companies in the coal sector as part of a wider investigation led by Voxeurope, which tracked holdings by funds that disclose information under the EU’s sustainable finance directive.
These “green” funds include investments in mining companies like Coal India and China Shenhua – the respective countries’ top coal producers – and Indonesia’s Adaro Energy, as well as in giant coal power producers such as NTPC in India and China Resources Power Holdings.
All of these companies are planning large-scale expansions of their coal output, according to the influential Global Coal Exit List compiled by German NGO Urgewald.
No new coal mines, mine extensions or new unabated coal plants are needed if the world is to reach net zero emissions in the energy sector by 2050 and keep the 1.5C warming limit of the Paris Agreement “within reach”, according to projections by the International Energy Agency (IEA).
State-owned Coal India is the world’s largest coal producer, with fast-growing output topping 773 million tonnes in the latest financial year. It is targeting 1 billion tonnes of annual coal production by 2025-26 by opening new mines and expanding dozens of existing ones.
IEA calls for next national climate plans to target coal phase-down
In its latest annual report, Coal India cited “pressure of international bodies like [the] UN to comply with [the] Paris Agreement” as one of the main threats to its business. Coal India’s share value has more than doubled over the last 12 months on the back of stronger coal demand in the country, as extreme heatwaves have fuelled the use of air-conditioning among other factors.
State-run mining and energy giant China Shenhua plans to invest over $1 billion in 2024 to expand its fleet of coal power stations and build new coal mines. “We will keep a close eye on climate change to improve the clean and efficient use of coal,” its latest annual report said.
Big investors
The funds with stakes in those coal-heavy companies are managed by Fideuram, an arm of Italy’s largest bank Intesa Sanpaolo, US-based AllianceBernstein and Mercer, a subsidiary of the world’s largest insurance broker Marsh McLennan.
Coal investments in Fideuram’s Article 8 funds – worth at least $16 million – also appear to breach the company’s own coal exclusion policy, designed to rule out holding shares in certain coal firms.
Two of its flagship “emerging markets” funds claim to promote environmental and social characteristics including “climate change prevention” and the “reduction of carbon emissions”, according to information disclosed under EU rules. To achieve their ‘green’ objectives, the funds claim to exclude any investment in companies “deriving at least 25% of their revenues” from the extraction, production and distribution of electricity connected with coal.
But Climate Home found the funds include investments in at least six major coal companies exclusively or primarily involved in coal mining or power generation.
A coal-fired power plant under construction in Shenmu, Shaanxi province, China, in November 2023. REUTERS/Ella Cao
Fideuram did not answer Climate Home’s questions about the funds’ apparent breach of their own policy. But a company spokesperson said in a written statement that “investments in sectors with high-carbon emissions do not conflict with the objectives of the SFDR, which concern the transparency of sustainability investments, nor with the Paris Agreement, which promotes a transition to a low-carbon economy”.
A spokesperson for Mercer said its Article 8 fund, which holds shares in NTPC and China Resources Power Holdings. has an exclusion policy to avoid investing in companies that generate more than 1% of their revenue from thermal coal extraction. “Based on the data provided by ISS [a provider of environmental ratings], no groups involved breach the 1% threshold, and therefore, the fund is not in violation of its SFDR commitments,” they added.
AllianceBernstein did not respond to a request for comment.
Coal-hungry steelmaking
While excluding investments in so-called thermal coal used for electricity generation, several ‘green’ funds put their money in companies producing coking coal – or metallurgical (met) coal – which is used to make steel.
Goldman Sachs’ Article 8 funds hold shares worth several million dollars in Jastrzebska Spolka Weglowa, Europe’s largest coking coal producer, and Shanxi Meijin in China. BlackRock offers exchange-traded funds (ETFs) tracking indexes that include investments in SunCoke, a leading met coal producer in the US and Brazil, Alabama-based Warrior Met and Shanxi Meijin.
Reclaim Finance’s Cuvelier said that, up until recently, the focus has been on pushing thermal coal out of investor portfolios because the alternatives to met coal in steel production were “less developed”.
“There are now increasing calls on financial institutions to cover met coal as well in their exclusion policies as alternatives exist,” she added. “It’s becoming very important because there are new projects under development that should be avoided”.
A spokesperson for BlackRock said: “As a fiduciary, we are focused on providing our clients with choice to meet their investment objectives. Our fund prospectuses and supporting material provide transparency as to the methodology and investment objectives of each fund”.
Goldman Sachs did not reply to a request for comment.
Reforms on the horizon
At the end of 2022, the European Commission began a review of the SFDR’s application with a view to updating its sustainable finance rules.
Future reforms may include changes to the ways funds are categorised. “There are persistent concerns that the current market use of the SFDR as a labelling scheme might lead to risks of greenwashing… partly because the existing concepts and definitions in the regulation were not conceived for that purpose,” the Commission said in a consultation paper released last year.
It also indicated that the existing categories under Articles 8 and 9 could either be better defined or scrapped entirely and replaced with a different system. The new Commission, yet to be formed following last month’s elections, will decide if and how to move forward with the reform process.
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Separately, the EU’s market supervisory authority, ESMA, has recently issued guidelines to prevent funds from misusing words like “sustainability”, “ESG” – environmental, social and governance – or “Paris-aligned” in their names. A handful of the funds with coal investments analysed by Climate Home have used those labels.
Under the new guidelines, asset managers wanting to slap climate-friendly labels on their funds will have to exclude companies that derive more than a certain percentage of revenues from fossil fuels.
Climate Home produced this article with data analysis contributions from Stefano Valentino (Bertha Fellow 2024) and Giorgio Michalopoulos. This article is part of an investigation coordinated by Voxeurop and European Investigative Collaborations with the support of the Bertha Challenge fellowship.
(Reporting by Matteo Civillini; additional reporting by Sebastián Rodríguez; editing by Sebastián Rodríguez, Megan Rowling and Joe Lo)
The post EU “green” funds invest millions in expanding coal giants in China, India appeared first on Climate Home News.
EU “green” funds invest millions in expanding coal giants in China, India
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Artificial intelligence (AI) is developing at a rapid pace and is increasingly being used by scientists to better understand climate change.
AI is perhaps best-known for its use in “large language models”, such as ChatGPT, Claude and Gemini.
However, the field is vast.
From weather forecasting to analysing satellite images, climate scientists are using various types of AI to develop faster and more efficient ways to study the warming planet.
However, the technology comes with no shortage of risks and challenges, including overreliance on unverified results, intensive energy use and exacerbating existing inequalities in climate science.
Carbon Brief spoke to 10 experts working across a range of climate-related fields – from developing climate forecasts to analysing changes in the energy grid – about how AI is being used.
Their answers are set out below, grouped by theme.
- What are the benefits and challenges of using AI in weather and climate forecasting?
- What are some other current uses of AI in climate science research?
- How is AI being used to advance climate policy and action?
- How could AI be used in climate research over the coming years?
- Are there any misconceptions about the use of AI in climate science?
- Could AI improve or exacerbate inequalities in climate research?
- What are some other risks and challenges of AI in climate change research?
What are the benefits and challenges of using AI in weather and climate forecasting?
Dr Doug McNeall, science lead in data-driven climate modelling at the UK Met Office:
One of the most significant recent developments has been the success of AI weather-prediction models, which learn from large volumes of weather data to predict the next few hours ahead and then repeatedly feed those predictions back into themselves to generate forecasts up to two weeks into the future.
These models have demonstrated that AI can provide accurate predictions while requiring far less computing power than existing numerical models [which rely on mathematical equations to simulate the climate].
One of the main challenges is that AI weather-prediction models have been developed for relatively short timescales, where large amounts of observational data [for example, temperature, rainfall or sea ice extent data] is available and model performance can be evaluated quickly and rigorously.
Climate prediction is fundamentally different. To provide useful climate information, we need to make projections for future climates that lie outside the observational record.
One promising route forward is, therefore, to use AI to emulate or accelerate these existing models, rather than relying solely on purely data-driven approaches. This blended approach could make climate risk information far more accessible and affordable to produce, while retaining the physical understanding [based on mathematical and scientific principles] that underpins confidence in climate projections.
Prof William Collins, professor of AI for climate science at the University of East Anglia:
We’re seeing a really exciting renaissance in both weather and climate science…Machine learning [a subset of AI that uses algorithms to let computers learn from data and make predictions without being explicitly programmed] has had an enormous impact on numerical weather prediction.
So, for example, the European Center for Mid-Range Weather Forecasts is now deploying an AI forecast system as part of their official communication of weather predictions to the European community, alongside the traditional forecast system. AI systems have become good enough that they’re absolutely competitive with the world’s leading numerical weather prediction…So that’s been quite remarkable.
Much of machine learning and AI involves learning from data and climate represents a future for which we do not have data. We’re literally trying to predict the future. We don’t have observations of it. So, where does the data come from to inform the models? That is, at the moment, the central challenge that the climate community is attempting to overcome.
Dr Amirpasha Mozaffari, researcher in Earth artificial intelligence at the Barcelona Supercomputing Centre:
AI forecast models do well on typical weather, but underperform physics-based models on record-breaking and unprecedented extremes – precisely the events that matter most for adaptation [to climate change].
Solving that – whether through hybrid models [which combine AI with traditional models], physical constraints [in which rules are implemented based on the laws of physics to help the models make realistic forecasts] or new ways of evaluating models on what they have not seen – is the central open problem for the field.
What are some other current uses of AI in climate science research?
Dr Stefano Materia, researcher in climate variability and change at the Barcelona Supercomputing Centre:
Besides the climate modelling and prediction framework, the proliferation of Earth-observation satellites has created a massive influx of geospatial data (for example, images showing deforestation or sea ice melt). AI is used to translate raw pixel data into actionable climate intelligence.
Methane tracking and ecosystem monitoring are two examples of this kind of application. Methane is a potent greenhouse gas, but leaks are largely invisible and hard to track. AI algorithms now automatically scan hyperspectral imagery [which uses data from across the electromagnetic spectrum] from satellites to pinpoint specific industrial methane leaks globally, alerting operators in near real-time.
At the same time, machine-learning models process thousands of satellite images daily to detect illegal logging, track land-use changes and monitor glacier retreat long before humans could manually review the footage.
Dr Clare Eayrs, polar research scientist at the Environmental Fluid Dynamics Lab at New York University:
In sea ice research, machine learning is now used across the sea ice modelling workflow…The most mature application is extracting more information from observations, for example, mapping fine-scale sea ice deformation from ship radar and classifying snow layers in micropenetrometer profiles [which measure the hardness or softness of different layers of snow].
Forecasting is the most visible application in this field. Ice Net [a sea ice forecasting system] was an early demonstration that a deep-learning system [which uses models inspired by the layers of a human brain to process information] could outperform a leading physical model in seasonal Arctic forecasts. Machine-learning systems are now used to predict [climate] variables such as sea ice concentration, thickness and drift.
Prof Cristiana Stan, vice-chair of the World Climate Research Programme (WCRP):
AI now appears across all major parts of the WCRP. [For example,]…the ”weather prediction model intercomparison project” [an initiative to create a centralised database of physics- based, machine-learning and hybrid model forecasts] includes physics-based numerical weather prediction models, hybrid [models] and AI weather prediction models. Because it is focused on operational models, the project offers a direct comparison between state-of-the art systems. [It] is intended to accelerate the deployment of the next generation of operational models and contributes to development of metrics needed to assess relative strengths and weaknesses of models.
Downscaling is another area that is harnessing AI power to refine the coarse-resolution model output into high-resolution.
Dr William Collins:
The Intergovernmental Panel on Climate Change (IPCC) is performing its seventh assessment report (AR7)…This time, for the very first time, there’s a model intercomparison project just for models using AI [for weather and climate forecasting]. So that is a real milestone…It’s a very exciting proof of principle or proof of concept and, I think, the harbinger of many more developments of this kind to come.
[For more on how model intercomparison will inform IPCC AR7, see Carbon Brief’s recent guest post.]
Dr Amirpasha Mozaffari:
A few years ago, it took two or three years for a new architecture to make its way from the machine-learning community into weather and climate work. Today it is a matter of months: diffusion models, foundation models and generative ensembles have all arrived in our field almost as soon as they appeared elsewhere. That speed opens up things that were previously out of reach.
And the cost of running a global forecast has dropped by orders of magnitude…which, in principle, changes who can produce weather and climate information at all.
How is AI being used to advance climate policy and action?
Prof Priya Donti, assistant professor at the Massachusetts Institute of Technology and co-founder of Climate Change AI:
As we introduce more solar, wind and other renewable energy into the power grid, we need to rethink how we actually manage the various sources on the power grid…
AI is used in a few different ways here. One of them is just trying to get better predictions of solar and wind power production in order to reduce that uncertainty and allow us to plan with a little more foresight.
Another big one is helping to actually optimise and control the various components we have on power grids – the power generators, the batteries, flexible loads like industrial loads or the batteries in your electric vehicles or heating and cooling systems.
Dr Carlos Rodriguez-Pardo, postdoctoral researcher at Politecnico di Milano, affiliated scientist at the European Institute on Economics and the Environment and at the Euro-Mediterranean Center on Climate Change:
AI is already useful at every stage of the climate policy cycle.
The