Category: 3. Business

Quality analytics has long been seen as key to better understanding benefits utilization and developing strategies that drive up engagement, but getting the analytics hasn’t been easy.

Whether manually crunching data, waiting for partners to provide reports or working with dashboards, extracting and analyzing data can be arduous and time-consuming.

Fortunately, AI agents such as ChatGPT and Len AI, Marsh McLennan’s proprietary AI technology, now allow datasets to be analyzed using natural language so that benefits data can be easily reviewed to uncover valuable insights. Not surprisingly, 50% of employers are already using AI for benefits purposes, and a further 48% plan to use it within the next one to three years. Advanced and predictive analytics, along with personalization, are the main drivers of this trend.^[1]

On a practical level, the reduction in administrative burdens is significant. For example, HR teams can see benefits uptake and engagement rates and identify any utilization issues that could be impacting budgets. This frees up time for HR to focus on more strategic initiatives such as understanding how preventative benefits are impacting claims.

Using AI for data-driven decision making

Predictive analytics presents a compelling use case for AI, especially in the health and well-being arena. For example, by predicting how many people are likely to fall sick within the year, benefits strategies can be optimized to account for medical inflation and budget considerations in the context of ever challenging resource constrained environment. 

This requires building scenarios and simulating the likely outcome of different benefit strategies. For example, if you anticipate a high number of musculoskeletal (MSK) claims, how many are likely to result in longer-term rehabilitation and how might uptake of a preventative benefit change that?

It also requires distinguishing between society-wide issues and those specific to your organization. At Mercer Marsh Benefits (MMB) we can overlay organizational data with huge data sets to help clients identify pockets within their organization that are bucking broader trends.

An even deeper dive can be carried out to identify which clinical pathways, and even healthcare providers, are generating the most successful outcomes. This allows employers to better educate employees on the most effective pathway, hospital, public program or benefit for their needs.

This level of personalization is welcomed by employees, with over two thirds saying they would share their health information with a confidential third party to receive tailored benefits information, or personalized health recommendations.^[2]

Hyper-personalization of benefits

Of course, no benefit strategy will deliver optimal results if employees are not engaged and aware of their options. AI has a meaningful role to play in increasing utilization by replacing one-size-fits-all communications with compelling content automatically generated and tailored to different employee demographics.

Generative AI chat assistants are now being used by benefits platforms like Darwin with the AI Chat Assistant, to allow employees to get answers to benefits questions such as “Can I add my wife to my dental policy?”. Allowing employees to easily access information and execute tasks not only saves them time and boosts engagement but improves the user experience.

On another level, personalization helps employees discover benefits they didn’t know existed or hadn’t considered. For example, individuals who are looking to exercise more and improve their fitness levels can be signposted towards benefits that can help them achieve these goals, such as gym memberships and wellness programs.

To give employees confidence to share relevant, personal data, it’s important to educate them on the ways data is anonymized or de-identified to protect their privacy. This data is often used to build personas to improve the relevance of what is showcased to employees thereby increasing uptake.

Insights generated by any analytics application are only as good as the data that underlies the analysis. Data integrity is critical as is the governance and processes that sit around it. 

Enhanced efficiency and benefits management

Increasing benefits choice used to mean increasing benefits administration, but AI can ingest data more effectively, automate routine tasks, carry out compliance monitoring and reduce human error.

For example, if you had a reimbursement fund to allow people to claim for well-being activities, AI can not only check the receipt to ensure it’s not a duplicate claim, but check the provider actually exists, that the service provided is covered, and that the employee hasn’t gone over their allocation.

Similarly, it can be used to automatically close payroll, enroll new joiners, and run other benefits processes, to significantly reduce administrative overheads. This gives already stretched HR teams more time to focus on more strategic initiatives.

Building the right AI foundation

Any AI initiative is only as good as the underlying dataset and building the right AI foundation to centralize and connect that data is critical to success. 88% of employers who centralized their benefits software in this way say they can respond quickly to change, with 73% of those saying they are on track to achieve their employee engagement targets.^[3]

AI not only has the potential to improve benefits insights and administration for employees and employers, but it also has the potential to transform the employee experience to create a healthier, more satisfied workforce. As such, half of employers are currently using AI to forecast future benefits needs based on workforce trends, and 58% are personalizing benefit recommendations and communications based on AI insights with a further 33% planning to do so by 2026. This shows a significant move towards employers increasingly leveraging data not only for tracking and forecasting, but also for personalization and improving wellbeing.^[4]

Key take-aways for employers

1. Artificial intelligence is transforming how employee benefits are managed, making data analysis and engagement more efficient and effective. Employers who are investing in, and leveraging, AI tools can quickly process complex benefits data to identify trends and opportunities that were previously difficult to uncover.
2. Predictive analytics enables smarter health and well-being strategies to better meet employee needs. Organizations that are using predictive analytics to design targeted wellness initiatives are better able to anticipate potential health risks and tailor programs that proactively support employee wellness.
3. Hyper-personalized communication enhances employee understanding and utilization of benefits. Employers who make use of AI to deliver personalized communication and relevant benefits information to each employee, benefit from increased engagement and ensure that employees make the most of their available options.
4. Automating administrative tasks with AI improves operational efficiency and employee satisfaction. By doing so, HR Leaders can reduce manual workloads and free up their HR teams to focus on strategic initiatives while providing employees with faster, more accurate service.

The entrance to Nike’s swish global headquarters in Beaverton, Oregon is paved with rough cobbles, designed to remind employees to watch their step when they go forward.

Last summer, though, not even the world’s biggest sports brand could stop itself from taking an almighty tumble.

Over the course of one July day in 2024, Nike’s share price plummeted by $28bn – the worst single-day performance in the company’s history – after it revealed that second-quarter sales were down 10%.

There were headlines proclaiming that Nike was in crisis. That it had lost its cool, become either too woke, too safe, too conservative or too cumbersome, depending on their political stripe and reading of the situation. To no one’s surprise, its chief executive, John Donahoe, stepped down.

True, Nike was still worth around $100bn – more than twice the value of its closest rival, Adidas, and even further clear of Puma, Lululemon and New Balance. And, yes, the cost of living crisis didn’t help. But much of the pain was self-inflicted.

Just over a year later I am sitting with Nike’s new president and chief executive Elliot Hill, talking about how he intends to turn the company around with the help of some wild new innovations, including the world’s first motor-powered running and walking shoe.

Soon I will also hear about “mind shoes” that help Erling Haaland focus before matches, a self-inflating winter jacket that Team USA will wear at the Winter Olympics, and technology that aims to help England’s footballers at the World Cup.

Before then, though, I throw Hill, a huge soccer and baseball fan, a curveball. Might Nike’s recent kicking actually turn out to be a good thing? Both for consumers, who now have more choice from upstart brands such as ON and Hoka, and for his company – as it has forced it to raise its game?

Hill laughs, then leans in. “Trust me, I would have preferred not to go through the kicking,” he says. “And this one happens to be a pretty big kicking. But I do think it helps us refocus on why we exist, and is a rallying cry for us as a company.

“I think it helps us get sharper. We’re at our best when we’re competing. And not only competing, but competing and winning.”

Hill’s answer is open, honest and authentic: a face that Nike hasn’t always shown to outsiders. And the 62-year-old is just as direct when it comes to his plans to turn the company round using sport and trusting in new ideas.

“On my first day, I stood up on stage and my first slide said we are a sport company and we are a growth company, and they’re not mutually exclusive,” he says. “And when we invite eight billion consumers into the world of sport, I like our chances of growing.

“I know it sounds simplistic,” adds Hill, who joined Nike as an intern in 1988 and came back last October having retired in 2020. “But the response from around the world was: OK, we’re back to sport. We exist to serve the athletes. And when we do that well, that usually translates into innovative products, emotional storytelling, and then it pays off at retail.”

Internally Nike is signalling that fresh direction with a six-word mission statement: “Create Epic Shit, Make Athletes Better.”

It is provocative and bombastic. In other words, classic Nike. But, as Phil McCartney, Nike’s chief innovation, design and product officer, explains, it is also a tribute to one of its most famous athletes.

“Create epic shit is something that we got from Kobe Bryant,” he says. “He used to walk the halls here and say: ‘Are you creating epic shit?’ It’s a standard of excellence. It has a bit of an attitude to it. And we’re already starting to use it when we look at our innovations. Is it that? If it’s not, then we shouldn’t do it.”

Not so long ago, McCartney, who grew up in Wallsend in the north-east of England and was once a talented Great Britain runner, remembers an acquaintance telling him that the brand had become “big, dumb and slow”.

“And it sort of struck a chord with a lot of us,” he says. “Because that’s not who we are, it’s not how we want to operate. And it’s definitely not our future.”

‘Nobody here is crazy. We just live in the future’

Later that morning I am standing outside Nike’s LeBron James Innovation Centre, a 750,000-square-foot facility which has 400 motion-capture cameras, nearly 100 force plates and male and female sweat-testing dummies, waiting to stare into that future.

First I put on a normal-looking pair of super shoes. Then I attach a prototype device containing a motor to my ankles, calves and the shoes themselves. After that, a senior Nike biomechanical engineer, Alison Sheets, presses a button, and the device starts to whirr and fit more snuggly around my legs.

Sean Ingle tries out the prototype Project Amplify shoes

I feel like Robocop, and initially when I walk I move like him too. But when I start running and Alison ramps up the power via an app, I suddenly turn into Forrest Gump. Despite very little effort, I am running far quicker than my effort level – and my mind – tells me I should be.

It is more than a little weird. But also a whole lot of fun. Incredibly, at one point I jog up the 150-metre ramp on Nike’s campus which has a 15.63% incline – without being remotely out of breath. (If evidence didn’t exist, I wouldn’t blame you for wondering which substances in downtown Portland I had been inhaling.)

“We’re actually putting a robot on your body,” explains Michael Donaghu, Nike’s VP of create the future, emerging sport and innovation. “And we want that robot to be your best friend.”

The Project Amplify shoes

What makes all this even harder to compute is that I happen to have been hobbling around with a heavily bruised little toe. Yet the shoe seems to take most of the impact off the joint, like an anti-gravity treadmill, allowing me to run with only minor pain.

Nike calls this motorised shoe Project Amplify and likens it to an ebike for feet, allowing the general population – rather than serious runners – to run or walk far quicker than they otherwise would do.

How much quicker? In testing, the company’s researchers found that athletes experienced anywhere from a 9-22% improvement in metabolic effort. In other words, like going from a 12-minute mile to a 10-minute mile, using the same effort.

Donaghu says that Nike engineers had been working on various iterations of the device for years, but it was Hill’s return that made it happen. “We sat down, pitched him on what the idea was and unflinchingly, he was like: ‘This is absolutely what Nike should be about,’” he says. “Nobody here is crazy. We just live in the future and have bigger dreams.”

At next summer’s men’s World Cup, Nike’s sponsored teams, including England, the US and Brazil, will be wearing kits with a new Aero-Fit material which – Nike says – will allow players to feel cool even in very hot and humid conditions. For good measure it is also made from 100% textile waste.

Before then it will release the Air Milano, a jacket that inflates in 30 seconds and can generate warmth levels that range from a lightweight hoodie to a mid-level puffer, and which will be worn by Team USA at the Winter Olympics.

Finally there is the Nike Mind, a shoe and slider that contain cushioning nodes which press on the sensors in the feet and – the company says – improve focus when worn.

Haaland, the Manchester City striker, and WNBA basketball star A’ja Wilson have been using the shoe, which took nearly a decade to develop, to prepare for matches. Nike insists it has the science to back up its claims.

“Using tools like EEG, we’ve measured changes in brain activity linked to focus and presence,” says Matthew Nurse, the company’s chief science officer. “That’s the first time we’ve been able to show – and prove – that footwear construction can influence how the mind works.”

However Donaghu, who joined Nike in the late 80s and even worked with the co-founder Bill Bowerman, insists this is just the start.

“We’re like kids in a candy store here,” he says. “And there’s something really magical going on. We sweat getting it right. And worry about making money later. That’s a lesson we learned from Bill Bowerman. Phil Knight [Nike’s other co-founder] really did care about building a company that was solvent and could make money. Bill Bowerman didn’t give a shit about making money. He just wanted to make athletes good. I love having both of those angels on our shoulders.”

Part of the success, he adds, comes from Nike having an innovation kitchen – a place he describes as “very non-hierarchical playground” – where about 30 people from all parts of the company throw ideas around.

“We’re the kids that want to know the things that others just have hunches about,” he says. “Oh, we don’t have a mind scientist? We’re going to hire a mind scientist in the kitchen. That’s how we push the ball forward.”

‘There will be moments when we get it wrong’

Nike has invited the Guardian to Beaverton to unveil its new innovations, but also to show the outside world that it is changing. And so towards the end of our conversation I ask Hill about Allyson Felix, one of America’s greatest Olympians, who in 2019 revealed Nike’s threat to cut her pay by 70% if motherhood affected her future athletic performance. Three months later Nike changed its stance to guarantee athlete’s pay and bonuses for 18 months around pregnancy, and three other brands soon followed suit.

I also mention the former Nike Oregon Project coach, Alberto Salazar, who was given a four-year doping suspension in 2019 and then a lifetime ban for sexual and emotional misconduct, issued by the US Center for Safe Sport.

Every company makes mistakes. But Nike has made a lot down the years without always owning up to them. Will it really be different under his watch?

“I think that consumers today want companies to be more vulnerable, authentic, real, and upfront,” Hill replies. “And here’s what I would say as the leader of this company. The entire world has my commitment to be real – about the situations when we get it right, and then there will be moments when we get it wrong. That’s just inevitable in the world of business. But I think it’s how we respond in those moments when we get it wrong that matters most.”

Hill is more guarded when it comes to discussing Donald Trump’s tariffs, which will cost the company about $1.5bn this year. But is there anything Nike can do to alleviate the burden?

“We have a pretty diverse global supply chain and expenses, and we’re pulling all the levers,” he says. “But my hope is that it doesn’t impact the world of sport.

“Because if you think about it, it’s not just Nike. It is helmet manufacturers, baseball bats, equipment, and it all trickles down to the consumer. The accessibility of sports today is already challenged, and I think it will make it even more challenging for the average American. That’s the part that I’m nervous about.”

Before travelling to the US, I spoke to a couple former Nike employees who both had positive things to say about Hill. One even insisted, somewhat implausibly, that when he was appointed last October, people literally cried with relief.

Certainly something had to be done to turn around the company’s fortunes. For years analysts had warned that Nike was relying too much on colour variations of older favourites – especially Jordans, Dunks and Air Forces – like a classic 80s rock station spinning the same old tunes even when the hype has faded.

Meanwhile Nike put a huge amount of focus on direct-to-consumer sales – meaning that fewer of its products were in stores.

That allowed the likes of Hoka, ON, and others to move in. And when the new wave of runners, often women in their 20s and 30s, flocked to join run crews after the pandemic they often turned to those brands first.

Since returning to Nike, Hill has focused on rebuilding relationships with retailers, with one of its largest customers, JD Sports, saying last month that Nike was “doing all the right things” to revive demand.

Hill has also gone back to having separate departments for each sport, such as running, football and basketball – as opposed to Donahoe, who organised things by men’s, women’s and kids’ categories which led to bottlenecks when it came to innovation. Now, Hill says, teams are quicker to react and get products to market.

He concedes that there is still plenty of work ahead. But in its recent earnings report there were some green shoots, with a number of well-received releases helping the running division grow by over 20% in the last quarter.

‘It didn’t hurt that Spain was an Adidas team’

Football is also a huge passion for Hill, who was raised by a single mum in a blue-collar neighbourhood in Texas. In fact such was his love of the game he made a small investment in the Italian Serie B side Venezia when he initially retired in 2020, only to give it up when he returned to Nike last year.

Hill also remembers being “completely blasted” by some of his European colleagues in 1998 when he suggested they could do more to promote the women’s game. “They were like: ‘No one cares about women’s football,’ And then fast forward to today, almost 30 years later, and it’s phenomenal.”

As we chat he casts his mind back to the Women’s Euros this summer. During the final he sat with Football Association officials with England 1-0 down against Spain and looking like they had nothing left in the tank.

“I was yelling at them: ‘We’ve got to believe,’ he says. “Because you can just see it with the English. They’ve had their hearts broken so many times, especially in those big moments, and never winning one outside of the country, right? You can see it. So I literally started to yell at them: “Come on everybody, let’s go, come on, we got this.’

“And then, of course, when we won – everybody, the joy! And that’s the beauty of sport,” adds Hill, who says he wants Nike to absorb the lessons from the Lionesses’ resilience. “It’s those moments that define an athlete, a team, a country, and in our case, a company. And it didn’t hurt that Spain was an Adidas team.”

Before I leave Oregon I speak to Tony Bignell, a Briton who was a key figure in Eliud Kipchoge’s Breaking2 marathon project and Faith Kipyegon’s Breaking4 mile attempt as well as the game-changing super shoes that powered them, and is now Nike’s chief innovation officer. The company, he insists, is getting back on track despite a few “dramas” in recent years.

“It’s hard sometimes when you’re trying to help an elite athlete as well as somebody like my mum, who wants to walk the dog around the block,” he says. “But under Elliot the handbrake is off, and we feel loose. It’s amazing how one person can make that difference.”

“I wish more people could understand the people in this building because they’re not corporate, whatever you might read about Nike,” he adds. “It’s just a good human place to be.” Somewhat ironically a company that tells stories better than most has often failed to get this across.

Meanwhile to the outsider it also seems like there are multiple Nikes. The sports brand that wants to win at all costs, and sometimes takes it too far. The global multinational with 80,000 employees, watching every flicker of its share price. The company that is trying to stay endlessly cool. And the day-to-day numerous super-smart scientists, engineers, and other employees who will happily shoot the breeze, praise rivals, and be open about the state of the company.

However Hill insists that, when you dig into its soul, Nike remains the company set up in 1972 by Phil Knight (the campus in Beaverton has just been renamed in his honour) and Bill Bowerman. The one that was propelled to glory by elevating athletes such as Steve Prefontaine and Michael Jordan, along with a series of innovations that began when Knight began creating shoes with his waffle maker, and selling them out of his Winnebago.

“Those two started this company on a handshake,” says Hill. “They each put $500 in. And they had a simple purpose: we exist to serve the athlete, and to put them at the centre of everything we do.”

More than five decades much has changed. But one time-worn formula remains the same: Create Epic Shit, Make Athletes Better. Keep consumers in love with the Swoosh. Reap the rewards.

 

23 October 2025 – At the end of Q3’25, global PE deal volume was $1.5 trillion — on pace to reach a four-year high should investment remain steady through Q4’25. The buoyant investment is notable given the significant decline in deal volume — from 15,083 deals in the first three quarters of 2024 to 13,574 in the first three quarters of 2025.

After some pullback in PE investment in Q2’25 — driven largely by geopolitical tensions and uncertainties related to US tariffs — Q3’25 saw global PE investment reach $537.1 billion according to KPMG’s Q3’25 Pulse of Private Equity. The buoyant deal value was helped significantly by three very large public-to-private transactions in the US: Electronic Arts ($54.6 billion), Air Lease ($28.2 billion), and Dayforce ($12.4 billion).

The Americas accounted for 60% of global PE value in Q3’25 ($322.9 billion), and just under half of the total number of deals (1,977). Of this total, the US accounted for $300.2 billion across 1,971 deals. The EMA region came a distant second—with $178.3 billion in PE investment across 1,736 deals during Q3’25, led by the $7.7 billion buyout of UK-based Pension Insurance Corporation — while Asia saw $30.6 billion in PE investment across 253 deals — led by the $2.1 billion buyout of Australia-based Insignia Financial.

At a sector level, the TMT sector attracted the largest share of PE investment globally in the first three quarters of 2025 ($469 billion), although the level of investment was tracking well shy of the $647.3 billion seen in 2024. Meanwhile, PE investment in the infrastructure and transportation space was already $126.3 billion at the end of Q3’25 — far ahead of the $99.4 billion and $98.7 billion seen during 2023 and 2024 respectively. 

The designers of a cryptocurrency launched by the US first lady, Melania Trump, in January were accused in court filings on Tuesday of orchestrating a pump-and-dump scheme.

The $MELANIA coins were released for just a few cents each on 19 January, the day before Donald Trump was inaugurated as US president. In addition to $MELANIA, Donald Trump launched $TRUMP a few hours before his inauguration.

Within hours, the $MELANIA coin’s price soared to $13.73.

However, it then collapsed almost as quickly, and is now only worth about 10 cents – less than 1% of its peak price. $TRUMP traded at a peak of $45.47 and now goes for $5.79, according to Coin Market Cap.

The plaintiffs say the coin’s creators organized the operation knowing that the digital currency’s value would plummet.

Melania Trump herself is not named in the lawsuit. The plaintiffs said they did not believe she was “culpable”, but accused the crypto companies of using her and other familiar faces as “window dressing” for their crimes.

In newly filed court papers, investors accuse the executives of the Meteora cryptocurrency exchange platform, on which $MELANIA was initially traded, of setting up a scheme that allowed them to indirectly purchase large quantities of the virtual coin.

Their accomplices then quickly resold these digital currencies, pocketing substantial profits while causing the price to plummet, according to documents filed on Tuesday in Manhattan federal court.

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The allegations concerning $MELANIA have been added to legal proceedings involving several other cryptocurrencies, which began in April. Meteora did not immediately respond to a request for comment.

The Trump family has pocketed more than $1bn in pre-tax profits from several cryptocurrency-related products and companies over the past 12 months, the Financial Times reported last week.

By Philip van Doorn

Even a slow-growing sector can include rapidly growing companies that are putting up big numbers

Robinhood is expected to increase revenue at a compound annual growth rate of 15.5% from 2025 through 2027, based on consensus estimates among analysts polled by LSEG. But investors seem to have higher expectations based on the stock’s valuation and the company’s annualized revenue-growth rate of 47.4% from 2022 through 2024.

No doubt you have gotten used to the flow of warnings about how expensive the S&P 500 has become. But there are always sectors that trade at low valuations to the full U.S. large-cap benchmark index.

The cheaper sectors reflect investors’ and analysts’ expectations for slower growth than what they expect to continue to see in the information-technology sector. But even in lower-valued sectors there are companies expected to put up big numbers over the next two years.

We are going to screen the three sectors of the S&P 500 that are least expensive based on a commonly used valuation measure. First let’s look at the 11 sectors of the S&P 500 SPX. Here they are, sorted by ascending forward price/earnings ratios, with the full index at the bottom.

   Sector or index           Forward P/E  Forward P/E to 10-year average  Two-year estimated revenue CAGR through 2027  Two-year estimated EPS CAGR through 2027 
   Energy                           14.8                             64%                                          2.4%                                     17.5% 
   Financial                        16.2                            119%                                          5.8%                                     11.2% 
   Healthcare                       17.1                            105%                                          5.7%                                     11.0% 
   Materials                        19.3                            110%                                          5.0%                                     16.5% 
   Utilities                        19.8                            111%                                          5.2%                                      8.9% 
   Communication Services           21.2                            126%                                          7.5%                                     10.4% 
   Consumer Staples                 21.4                            108%                                          4.4%                                      7.5% 
   Industrials                      23.9                            126%                                          6.3%                                     16.0% 
   Consumer Discretionary           28.5                            118%                                          6.7%                                     14.4% 
   Information Technology           29.6                            134%                                         12.7%                                     19.5% 
   Real Estate                      36.4                             90%                                          6.9%                                     11.2% 
   S&P 500 Index                    22.7                            121%                                          6.5%                                     13.9% 
                                                                                                                                                    Source: LSEG 

You might need to scroll the table or flip your screen to landscape to see all of the columns in the table.

The forward price/earnings ratios are based on Wednesday’s closing prices for stocks and consensus 12-month earnings-per-share estimates for companies among analysts polled by LSEG, weighted by market capitalization. The second data column shows the current P/E valuations relative to 10-year average valuations, based on rolling stock prices and 12-month EPS estimates. So the full S&P 500 is trading at a 21% premium to its 10-year average valuation.

In fact, all sectors of the S&P 500 are trading at premium valuations to their 10-year average P/E, except for the energy and real-estate sectors, according to LSEG’s data.

Among the three least expensive sectors based on current forward P/E, the financial sector may appear pricey, since it is trading at a 19% premium to its 10-year average P/E, but it is still the second-cheapest sector based on current P/E. On this basis, the financial sector trades at 71% of the valuation of the full S&P 500. Over the long term, this level of discount for the financial sector to the full index has been typical.

The right-most columns of the table show projected compound annual growth rates (CAGR) for revenue and EPS. The three cheapest sectors by forward P/E (energy, financials and healthcare) all have projected revenue CAGR from 2025 through 2027 lower than the full S&P 500’s projected 6.5%. The energy sector’s projected EPS CAGR of 17.5% exceeds the full index’s projected EPS CAGR of 13.9%. These are both attractive figures and reflect expectations for continuing improvements in efficiency and profit margins. Oil and natural-gas producers in the energy sector have shown discipline during the years following the decline in oil prices form mid-2014 through early 2016 – a period during which U.S. producers suffered in the wake of high production that softened prices. In more recent years, the U.S. oil and gas producers have been careful not to expand production quickly and have focused on increasing dividends to shareholders and on stock buybacks. Reduced share counts resulting from the buybacks boost EPS, and the projected EPS CAGR shows analysts expect this action to continue.

The rapid growth of sales and earnings for the largest technology companies in the S&P 500 has increased the index’s weighting toward a small number of stocks. Success is rewarded in an index weighted by market capitalization, but this has also led to a high level of concentration.

The S&P 500 is now 39.9% concentrated in its largest 10 companies, according to analysts at Ned Davis Research. That is close to the peak concentration of 40.3% in September, which was the highest concentration for the S&P 500 since at least 1972.

Some investors might not realize how much of their portfolios are focused on Big Tech. The $677 billion SPDR S&P 500 ETF Trust SPY tracks the S&P 500 by holding all of its stocks. The ETF is 29.5% concentrated in five companies: Nvidia Corp. (NVDA), Microsoft Corp. (MSFT), Apple Inc. (AAPL), Alphabet Inc. (GOOGL) (GOOG) and Amazon.com Inc. (AMZN).

Screening the cheapest sectors of the S&P 500 for growth stocks

There are index funds tracking each of the sectors of the S&P 500. Among exchange-traded funds, the three sectors we are screening are tracked by the Energy Select SPDR ETF XLE, the Financial Select SPDR ETF XLF and the Health Care Select SPDR ETF XLV. But you might also want to drill down into individual stocks.

To screen these sectors, we combined the S&P 500 energy, financial and healthcare sectors for a list of 157 stocks. Then we cut the list to 151 companies covered by at least five analysts polled by LSEG, and for which consensus revenue and positive EPS estimates were available from the calendar year 2025 through calendar 2027. We used calendar-year estimates as adjusted by LSEG if necessary for companies whose fiscal years don’t match the calendar.

Among the 151 remaining companies in the energy, financial and healthcare sectors, these 10 have the highest projected revenue CAGR from 2025 through 2027 based on consensus estimates among analysts polled by LSEG:

   Company                         Ticker   Two-year estimated revenue CAGR through 2027  Two-year estimated EPS CAGR through 2027  Forward P/E 
   Blackstone Inc.                BX                                               26.1%                                     27.2%         25.8 
   KKR & Co.                      KKR                                              24.4%                                     26.1%         19.0 
   Insulet Corp.                  PODD                                             17.7%                                     24.7%         57.7 
   Apollo Global Management Inc.  APO                                              17.2%                                     19.1%         14.2 
   Eli Lilly & Co.                LLY                                              17.1%                                     27.6%         27.6 
   Fifth Third Bancorp            FITB                                             16.9%                                     16.2%         11.4 
   Brown & Brown Inc.             BRO                                              15.8%                                     11.5%         18.9 
   Robinhood Markets Inc.         HOOD                                             15.5%                                     17.6%         61.5 
   Arthur J. Gallagher & Co.      AJG                                              15.4%                                     17.4%         20.9 
   Dexcom Inc.                    DXCM                                             14.7%                                     22.9%         28.0 
                                                                                                                                   Source: LSEG 

No companies in the energy sector made the list.

All of these companies have projected revenue CAGR more than twice the 6.5% projection for the S&P 500. For EPS, all but Brown & Brown have higher CAGR projections than the S&P 500’s 13.9%.

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The DIFC recently announced that it had enacted an amendment to the Data Protection Law, following an earlier consultation in March.

Summary

The right for data subjects to claim compensation for damage they have suffered by reason of a contravention of their rights under data protection law is established in GDPR based countries, upon which the DIFC Data Protection Law is modelled. Claims of this nature have become increasingly common over the past five or six years in those jurisdictions.

• The introduction of a private right of action through the DIFC courts for data subjects whose rights under the law have been contravened; and
• A widening and clarification of the scope of the application and extraterritorial scope of the law, which applies to:
  • A Controller or Processor who processes personal data and is incorporated in the DIFC, regardless of whether or not the processing takes place in the DIFC; and
  • A Controller, Processor or Sub-processor, processing personal data in the DIFC regardless of their place of incorporation as part of stable arrangements.

Important points to note

Data subjects can claim for mere distress

They do not need to prove that they have suffered a recognised psychiatric injury as a result of the infringement. This reduces the barrier to entry as expert medical evidence is not required in order to issue a claim.

The data subject can claim compensation from both the Controller or the Processor

This is important for Processors to bear in mind as whilst the bulk of the responsibility generally sits with the Controller e.g. notifying the Commissioner and affected data subjects of a personal data breach, this amendment makes clear that Processors will be held liable in circumstances where their unlawful actions, or inappropriate security measures result in harm to data subjects.

A Controller or Processor is not liable if they can prove that they are in no way responsible for the event giving rise to the damage

The burden lies with the Controller or Processor to demonstrate this when seeking an exemption from liability.

For example, if an organisation utilises the services of a third party payment provider, and as a result of a compromise of that payment provider’s systems, the organisation’s customer data is exposed, they may have a defence under Article 64A(4) if they had performed appropriate due diligence before selecting the payment provider (the Processor) and had a valid data processing agreement in place.

In these circumstances the Controller may be able to evidence that the event giving rise to the damage sits squarely with the Processor (albeit the Processor may have their own defence under this Article, for example if this incident was caused by the exploitation of a zero-day vulnerability for which there was no patch yet) and thereby escape liability.

We expect to see a gradual increase in data subject claims as individuals become more informed about their rights and how to exercise them.

We simulate the dynamics of VCG pools using a kinetic simulation that is based on the Gillespie algorithm. In the simulation, oligomers can hybridize to each other to form complexes or dehybridize from an existing complex. Moreover, two oligomers can undergo templated ligation if they are hybridized adjacent to each other on a third oligomer. At each time $t$, the state of the system is determined by a list of all single-stranded oligomers and complexes as well as their respective copy number. We refer to the state of the system at the time $t$ as the ensemble of compounds ${\mathcal{E}}_t$. Given the copy numbers, the rates $r_i$ of all possible chemical reactions $i\in \mathcal{I}$ can be computed. To evolve the system in time, we need to perform two steps: (i) We sample the waiting time until the next reaction, $\tau$, from an exponential distribution with mean $(\sum _{i\in \mathcal{I}}{r}_i{)}^{-1}$, and update the simulation time, $t\to t+\tau$. (ii) We pick which reaction to perform by sampling from a categorical distribution. Here, the probability to pick reaction $i$ equals $r_i/(\sum _{i\in \mathcal{I}}{r}_i)$. The copy numbers are updated according to the sampled reaction, yielding ${\mathcal{E}}_{t+\tau }$. Steps (i) and (ii) are repeated until the simulation time $t$ reaches the desired final time, $t_{\text{final}}$. A more detailed explanation of the kinetic simulation is presented in Göppel et al., 2022; Rosenberger et al., 2021.

Our goal is to compute observables characterizing replication in the VCG scenario based on the full kinetic simulation. In the following derivation, we focus on one particular observable (yield) for clarity. The results for other observables are stated directly, as their derivations follow analogously. Recall the definition of the yield introduced in the Results section,

${\displaystyle {\displaystyle y=\frac{\mathrm{\#}\mathrm{n}\mathrm{u}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{o}\mathrm{t}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{V}\mathrm{C}\mathrm{G}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{r}\mathrm{s}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{l}{\tau }_{\text{lig}}}{\mathrm{\#}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{n}\mathrm{u}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{o}\mathrm{t}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{l}{\tau }_{\text{lig}}}.}}$

As we are interested in the initial replication performance of the VCG, we compute the yield based on the ligation events that take place until the characteristic timescale of ligations ${\tau }_{\text{lig}}=k_{\text{lig}}^{-1}\approx {10}^{12}{t}_0$. In principle, we would like to compute the yield based on the templated ligation events that we observe in the simulation. Unfortunately, for reasonable system parameters, it is impossible to simulate the system long enough to observe sufficiently many ligation events to compute $y$ to reasonable accuracy. For example, for a VCG pool containing monomers at a total concentration of $c_{\mathrm{F}}^{\text{tot }}=0.1\mathrm{m}\mathrm{M}$ and VCG oligomers of length $L=8\mathrm{n}\mathrm{t}$ at a total concentration of $c_{\mathrm{V}}^{\mathrm{t}\mathrm{o}\mathrm{t}}=1\text{μ}\mathrm{M}$, it would take about 1700 hr of simulation time to reach $t=5\cdot {10}^{12}{t}_0$ (Figure 8). Multiple such runs would be needed to estimate the mean and the variance of the observables of interest, rendering this approach unfeasible.

Instead, we compute the replication observables based on the copy number of complexes that could potentially perform a templated ligation, that is complexes in which two strands are hybridized adjacent to each other, such that they could form a covalent bond. We can show analytically that the number of productive complexes is a good approximation for the number of incorporated nucleotides: The number of incorporated nucleotides can be computed as the integral over the ligation flux, weighted by the number of nucleotides that are added in each templated ligation reaction,

${\displaystyle {\displaystyle {\displaystyle (\mathrm{\#}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{n}\mathrm{u}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{o}\mathrm{t}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{l}{\tau }_{\text{lig}})={\int }_0^{{\tau }_{\text{lig}}}\mathrm{d}t\sum _{\text{C}\in {\mathcal{E}}_t}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation}).}}}$

Here, $N(\text{C})$ denotes the copy number of the complex C in the pool ${\mathcal{E}}_t$. $L_{\text{e,1}}$ and $L_{\text{e,2}}$ denote the lengths of the oligomers that undergo ligation, and $\mathbb{1}$ is an indicator function which enforces that only complexes in a ligation-competent configuration contribute to the reaction flux. As only a few ligation events are expected to happen until ${\tau }_{\text{lig}}$, it is reasonable to assume that the ensembles ${\mathcal{E}}_t$ do not change significantly during $t\in [0,{\tau }_{\text{lig}}]$. Therefore, the integration over time may be interpreted as a multiplication by ${\tau }_{\text{lig}}$,

(6)

${\displaystyle {\displaystyle {\displaystyle (\mathrm{\#}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{n}\mathrm{u}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{o}\mathrm{t}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{l}{\tau }_{\text{lig}})\approx {\tau }_{\text{lig}}⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation})⟩,}}}$

where $⟨\dots ⟩$ denotes the average over realizations of the ensembles ${\mathcal{E}}_t$ within the time interval $t\in [{\tau }_{\text{eq}},{\tau }_{\text{lig}}]$. This average corresponds to the average number of complexes in a ligation-competent configuration. Note that, at this point, we made the additional assumption that no templated ligations are taking place between $[0,{\tau }_{\text{eq}}]$. This assumption is reasonable, as (i) the equilibration process is very short compared to the characteristic timescale of ligation, and (ii) the number of complexes that might allow for templated ligation during equilibration is lower than in equilibrium (we start the simulation with an ensemble of single-stranded oligomers). Both aspects imply that the rate of templated ligation is negligible during the interval $[0,{\tau }_{\mathrm{e}\mathrm{q}}]$.

In order to compute the average over different realizations of ensembles $\mathcal{E}$ (as required in Equation 6), we need to sample a set of uncorrelated ensembles that have reached the hybridization equilibrium, which can be done using the full kinetic simulation. The simulation starts with a pool containing only single-stranded oligomers and reaches the (de)hybridization equilibrium after a time ${\tau }_{\text{eq}}$. We identify this timescale of equilibration by fitting an exponential function to the total hybridization energy of all complexes in the system, $\mathrm{\Delta }{G}_{\text{tot}}$ (Figure 9A). In the set of ensembles used to evaluate the average in Equation 6, we only include ensembles for time $t>{\tau }_{\text{eq}}$ to ensure that the ensembles have reached (de)hybridization equilibrium. To ensure that the ensembles are uncorrelated, we require that the time between two ensembles that contribute to the average is at least ${\tau }_{\text{corr}}$. The correlation time, ${\tau }_{\text{corr}}$, is determined via an exponential fit to the autocorrelation function of $\mathrm{\Delta }{G}_{\text{tot}}$ (Figure 9B). Besides computing the expectation value (Equation 6), we are also interested in the ‘uncertainty’ of this expectation value, that is in the standard deviation of the sample mean ${\sigma }_{⟨X⟩}$. (We use $X$ as a short-hand notation for $\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\mathrm{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})$). The standard deviation of the sample mean, ${\sigma }_{⟨X⟩}$, is related to the standard deviation of $X$, ${\sigma }_X$, by the number of samples, ${\sigma }_{⟨X⟩}=(N_s{)}^{-1/2}{\sigma }_X$. Moreover, based on the van-Kampen system size expansion, we expect the standard deviation of $X$ to be proportional to $V^{-1/2}$, such that ${\sigma }_{⟨X⟩}\propto (N_{s}V{)}^{-1/2}$.

Using Equation 6 (as well as an analogous expression for the number of nucleotides that are incorporated in VCG oligomers), the yield can be expressed as

${\displaystyle {\displaystyle {\displaystyle y=\frac{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})⟩}{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})⟩}.}}}$

The additional condition $\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})$ in the numerator ensures that the product oligomer is long enough to be counted as a VCG oligomer, that is at least $L_{\mathrm{U}}$ nucleotides long. Analogously, the expression for the fidelity of replication reads

${\displaystyle {\displaystyle {\displaystyle f=\frac{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation})\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})\mathbb{1}(\text{product correct})⟩}{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation})\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})⟩}.}}}$

Multiplying fidelity and yield results in the efficiency of replication,

${\displaystyle {\displaystyle {\displaystyle \eta =\frac{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\mathrm{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})\mathbb{1}(\text{product correct})⟩}{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\mathrm{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})⟩}.}}}$

The ligation share of a particular type of templated ligation $s(\text{type})$, that is, the relative contribution of this templated-ligation type to the nucleotide extension flux, can be represented in a similar form as the other observables,

${\displaystyle {\displaystyle s(\text{type})=\frac{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation of given type)}⟩}{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation)}⟩}.}}$

As all observables are expressed as the ratio of two expectation values, $\mathcal{Z}=⟨X⟩/⟨Y⟩$, we can compute the uncertainty of the observables via Gaussian error propagation,

${\displaystyle {\displaystyle {\displaystyle {\sigma }_{\mathcal{Z}}=\sqrt{\frac{{\sigma }_{⟨X⟩}^2}{⟨Y{⟩}^2}+\frac{⟨X{⟩}^2{\sigma }_{⟨Y⟩}^2}{⟨Y{⟩}^4}-\frac{2⟨X⟩{\sigma }_{⟨X⟩,⟨Y⟩}^2}{⟨Y{⟩}^3}}.}}}$

Since the variances, ${\sigma }_{⟨X⟩}^2$ and ${\sigma }_{⟨Y⟩}^2$, as well as the covariance, ${\sigma }_{⟨X⟩,⟨Y⟩}^2$, are proportional to $(N_{s}V{)}^{-1}$, the standard deviation of the observable mean, ${\sigma }_Z$, scales with the inverse square root of the number of samples and the system volume, that is ${\sigma }_Z\propto (N_{s}V{)}^{-1/2}$. Therefore, the variance of the computed observable can be reduced by either increasing the system volume or increasing the number of samples used for averaging. Both approaches incur the same computational cost: (i) Increasing the number of samples, $N_s$, requires running the simulation for a longer duration, with the additional runtime scaling linearly with the number of samples. (ii) Similarly, the additional runtime needed due to increased system volume, $V$, also scales linearly with $V$ (Figure 8). One update step in the simulation always takes roughly the same amount of runtime, but the change in simulation time per update step depends on the total rate of all reactions in the system. The total rate is dominated by the association reactions, and their rate is proportional to the volume. Therefore, the change in simulation time per update step is proportional to $V^{-1}$. The runtime, which is necessary to reach the same simulation time in a system with volume $V$ as in a system with volume 1, is a factor of $V$ longer in the larger system. With this in mind, it makes no difference whether the variance is reduced by increasing the volume or the number of samples. For practical reasons (post-processing of the simulations is less memory- and time-consuming), we opt to choose a moderate number of samples, but slightly higher system volumes to compute the observables of interest. The simulation parameters (length of oligomers, concentrations, hybridization energy, volume, number of samples, characteristic timescales) used to obtain the results presented in Figure 2 are summarized in Table 1.

A French oil company engaged in “misleading commercial practices” about the scope of its environmental commitments, a court has ruled.

TotalEnergies, which this month said it aimed to “ramp up production of gas”, was found on Thursday to have probably misled consumers with claims about its climate policies. The civil court in Paris ordered the company to remove messages from its website that said it wanted to reach carbon neutrality by 2050 and be a big player in the energy transition.

The case, brought by NGOs including Greenpeace France and Friends of the Earth France, is the first time the country’s “greenwashing” laws have been applied to a fossil fuel company. Courts in the Netherlands and Germany have already found that airlines misled consumers with vague environmental claims.

The French court gave TotalEnergies a month to take down the misleading statements or face a fine of €10,000 (£8,700) a day. It was also ordered to post the court’s ruling on its website, with the same penalty for noncompliance, as well as to pay €8,000 to each of the three NGOs and €15,000 for their legal costs.

“The French justice system is finally tackling the impunity of fossil fuel greenwashing that Total has enjoyed until now,” said Justine Ripoll, campaigns manager at Notre Affaire à Tous, one of the NGOs that brought the case. “It sends a clear message: climate disinformation is not an acceptable business strategy.”

TotalEnergies has been approached for comment.

The company, which aims to achieve 100 gigawatts of renewable power generation by 2030 but has made fossil gas a “cornerstone” of its strategy, has said it was a multi-energy company aiming to “responsibly, cost-effectively and sustainably produce the energy that we all need in our daily lives”.

The ruling is the result of a legal action brought by NGOs in 2022 in response to a campaign when the company changed its name from Total.

The court ordered TotalEnergies to remove statements that said it placed sustainable development at the heart of its strategy and that it “contributed to the wellbeing of populations” in line with the UN’s sustainable development goals.

Judges dismissed a further accusation of greenwashing over the company’s claims about fossil gas and biofuels. The court found that although the statements contained some disputed claims, they were for informational rather than commercial purposes.

Climate activists and green groups have increasingly taken fossil fuel companies to court for environmental claims that do not align with published climate science.

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In a landmark report in 2022, the Intergovernmental Panel on Climate Change found that the world had enough existing and planned fossil fuel infrastructure to blow past the goal of limiting global heating to 1.5C above preindustrial levels. Meanwhile, the International Energy Agency found that “no new oil and gas” exploration was compatible with its key scenario for keeping planetary heating to that level.

Jonathan White, a lawyer for ClientEarth, which supported the NGOs, said TotalEnergies appeared to be continuing with oil and gas projects despite warnings from climate experts.

“This landmark judgment sends a clear warning shot to other oil and gas majors in Europe and beyond,” he said. “Claiming to be part of the transition while backing new fossil fuel projects comes at a tried-and-tested legal price.”