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Dr Victoria Harvey is a researcher and senior carbon consultant who focuses on the UK advertising industry.

Helen Phillips’ book, ‘Hum’, is set in a dystopian near future, in a city suffering the effects of climate breakdown and with dire air quality. Robots (the Hums) press advertising messages during conversations, meetings and even as they carry out medical procedures.

The Hums are vehicles for these ads, which are often for products like cosmetics, sweets or anything that might be relevant during interactions with humans. This advertising is poorly disguised, and merges with sentiments that lean towards concerns of well-being, convincing people how much better off they’ll be if they make a purchase.

While the novel is futuristic, the insidious way advertising permeates daily life is resonant of how adverts show up in our world today. And these ads are directly contributing to the worsening future climate Phillips describes in her book.

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Already by the summer of 2026, Europe had seen a 57% increase in wildfires in just four years with western Europe recording the hottest ever June and July on record. We’re facing droughts and floods, as well as predicted hikes in food costs or even chronic food shortages – and that’s before the expected additional effects of a strong El Niño later this year.

Frequent flying and bigger burgers

A portion of this climate breakdown is fuelled by over-consumption in richer countries, particularly of products that are high carbon – for which advertising can take some of the blame. Research shows that adverts drive citizens to consume about a third more goods and services in general, over and above what they might have purchased.

Yet despite a clear link between promoting high-carbon behaviours and climate breakdown, little advertising regulation exists in the UK. Take frequent flying for instance, one of the most carbon-intensive activities we can partake in.

EasyJet’s latest ad campaign is called “Drop Everything”. It encourages consumers to book cheap flights departing within the next 48 hours for presumably short or weekend getaways. Rather than a specific destination, “Drop Everything” promotes a mindset that encourages indiscriminate consumption of flying. The ads were shown on billboards with clever creative slogans, as well as on digital media and through influencer campaigns.

Airlines risk legal challenges by advertising jet fuel as “sustainable”, NGO warns

Overall, flight numbers are increasing. The UK Civil Aviation Authority reported the highest number of UK passengers in the first quarter of 2026 (more than 61 million, breaking previous records for travel between January and March). It seems we’re still not joining the dots between flying and a worsening climate.

And how about meat consumption? Scientists advocate for less meat-eating, especially beef which has the highest carbon footprint of nearly all foods. Yet adverts from McDonald’s proliferate, helping make it one of the highest-volume sellers of fast-food chain beef burgers. In 2024, the outdoor advertising budget for McDonald’s UK rose to £86 million, an increase of 71% on previous years.

A billboard carrying McDonald’s UK advertising for one of its biggest burgers, which won “Badvert” of the month in May 2026 (Photo: Badvertising) A billboard carrying McDonald’s UK advertising for one of its biggest burgers, which won “Badvert” of the month in May 2026 (Photo: Badvertising) Small share for sustainability

While over half of UK ad professionals feel increasingly queasy about their profession and its effects on the climate crisis, the people running the show – the UK trade bodies – prefer to focus on the growth advertising brings.

In the first three months of 2026, they stated that UK advertising spend increased by 9.3%, reaching a total of £11.7 billion for that quarter, fuelling consumption and market growth.

But how many of those adverts actually promote low carbon goods and services? Kantar’s Sustainable Ads Tracker shows the percentage of ads featuring sustainability messaging in 2026 is around 4.3%. That’s woefully low, and much of this is made up of messaging that promotes recycling.

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Additionally, the industry continues to happily produce adverts for the large oil and gas corporations that are fuelling climate breakdown. These adverts only narrowly pass the Advertising Standards Authorities’ advertising codes, allowing the continued greenwashing of the world’s most polluting brands.

There is essentially no leadership from the UK trade bodies, likely because they are directly funded by the brands and advertisers themselves. They are essentially ‘ad shushing’ – pushing for indiscriminate growth and directing attention to their sustainability awards, while confusingly denying that adverts drive higher consumption overall.

Let’s ‘un-shush’

Where does this leave us as we are subjected to hundreds, if not thousands, of persuasive advertising messages every day that support high-carbon lifestyles? Most ad professionals are unable to push back against this agenda at work, often due to the threat of job loss. The advertising trade bodies won’t take the lead as they work in service to big brands and advertisers.

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So, who can push for the changes we need? Members of the public.

Through pressuring our city officials and governments, we can force through restrictions, such as the watershed bans on unhealthy foods on TV before 9pm in the UK. Through supporting the efforts of organisations such as Ad Free Cities and others, we can help achieve bans on outdoor advertising for fossil fuels, aviation, meat and even single-use plastics in cities and regions such as Amsterdam, The Hague, Edinburgh, Florence, Uppsala and many more.

If we’re serious about climate change and stopping big global brands pushing their high-carbon products onto us, then advertising restrictions are one of the best ways to achieve this. If we don’t want a world like the one Phillips describes in her book, we need to make our voices heard above the advertising noise.

The post Pushing the climate crisis: How advertising fuels high-carbon lifestyles appeared first on Climate Home News.

A new take on a widely used mining technique called froth flotation can recover "virtually all" of the metallic silver from an end-of-life solar panel, according to new research from the University of Newcastle. 

The post Researchers “froth” over new solar recycling method that recovers 99 pct of silver from used panels appeared first on Renew Economy.

This is a guest blog by Rebecca Stoner, Senior US Communications Campaigner at Oil Change International, a research, communications, and advocacy organization working to expose the true costs of fossil fuels and facilitate the ongoing transition to clean energy.

Globally, over 1 billion people lack reliable access to energy they can afford. Across the US and the world, families are being squeezed by rising energy costs. In the U.S. alone, around 80 million people currently struggle with the cost of utilities.  Some even forgo food or medications to keep the lights on. 

Why gas prices are going to rise

New research from Oil Change International (OCI) finds that the Trump administration’s energy and AI dominance agenda, coupled with its attacks on renewable energy, are likely to raise households’ bills even further. It turns out that for working families, Trump’s “drill, baby, drill” agenda actually means “pay, baby, pay.” 

OCI found that the Trump administration’s pro-fossil fuel policies could cause the wholesale price of fossil* gas to double over the next decade. Households will feel the impact of rising wholesale gas prices in multiple ways. Their gas bill will rise. So will their electricity bills, since gas is now the dominant fuel used to generate power for the grid. Businesses are likely to raise prices on food and consumer goods as their electric and gas bills go up. 

A gas power plant in Trumbull County, Ohio. Photo: Credit: Ted Auch, FracTracker Alliance, 2021. Aerial support provided by LightHawk.

The wholesale cost of gas is expected to balloon because the Trump administration has thrown its weight behind a massive expansion of liquefied natural gas (LNG)** exports by lifting a previous pause on  approving new LNG export projects  and backing a massive buildout of AI data centers that largely run on fossil gas, pushing up demand for the fuel to unprecedented heights.  

The primary sources of gas in the US today, the Permian (spanning west Texas and southeastern New Mexico) and Appalachian Basins (centered in Pennsylvania, West Virginia, and Ohio, in the northeastern U.S.), won’t be able to produce enough to meet the surge in demand Trump’s policies create. To fill the gap, producers will need to turn to the Haynesville shale play in Texas and Louisiana, a large underground gas deposit in western Louisiana and eastern Texas that requires fracking: drilling deep wells and injecting a high-pressure mixture to crack open the rock and release the gas trapped inside. Production in the Haynesville shale play in western Louisiana and eastern Texas is expected to soar by over 130%. The problem is, it’s much more expensive to drill in the Haynesville, where gas is harder to extract. This higher cost of drilling will get passed down to consumers. 

A drilling rig next to a flare outside of Midland, Texas. Photo: Oil Change International

Over the past decade, the annual average wholesale price of fossil gas was $3. When gas supply from the Haynesville grows to meet surging demand, prices will rise above $5. This will escalate the energy affordability crisis in the US and the many countries that import American LNG. 

Trump’s attacks on renewables will also raise prices for households and businesses. His administration has blocked solar and wind projects and slashed clean energy tax credits, taking away renewable energy options that could save families money. While we pay, Trump’s fossil fuel industry donors profit from keeping us hooked on gas. 

The health and climate cost of gas

Reliance on fossil gas costs us in more ways than one. Extracting, processing, and transporting gas pollutes our air and water with nitrogen oxides, particulate matter, and hazardous compounds, which are known to cause cancer, respiratory illness, cardiovascular disease, and birth defects with prolonged exposure. 

Greenpeace USA and the Sierra Club estimate that air pollution from currently operating US LNG export terminals causes 60 premature deaths and $957 million in total health costs per year. If all the planned LNG terminals and expansion projects are built in the US, those numbers would increase to 149 premature deaths and $2.33 billion in health costs per year.

The oil and gas industry builds LNG facilities in Black, Brown, Indigenous, and low-income communities that it treats as “sacrifice zones”, areas where the industry and decision-makers allow concentrated pollution and health risks to accumulate, on the assumption that the harm to residents there is an ‘acceptable cost of doing business elsewhere’.  Because of longstanding environmental racism, these communities are often already facing high burdens of pollution. 

Fossil gas is also a major threat to our climate. It’s primarily composed of methane, a superpotent greenhouse gas, and when more is in the atmosphere, extreme wildfires, floods, and hurricanes are more frequent and more ferocious. Fossil gas also displaces renewable energy options from the market. 

What policymakers need to do differently

There’s nothing inevitable about a future of higher energy costs, polluted communities, and climate chaos. The Trump administration and its Big Tech and fossil fuel industry backers are working to bring it into being so they can get even richer at everyone else’s expense. 

But communities across the world are rising up to demand a better future. They’re resisting attempts to build data centers and LNG projects in their communities, which would drive up energy costs and pollute the air, water, and climate. 

Community leaders march through downtown Houston to oppose oil and gas expansion. Photo: Luigi Morris, Climate Defenders

Decision-makers must listen and act. They can protect families from out-of-control energy costs by stopping the buildout of expensive gas projects and AI data centers, and investing in affordable renewable energy instead. Transitioning to a just renewable energy system will also create good green jobs, reduce toxic fossil fuel pollution, and safeguard communities from deadly climate disasters. 

Political choices are driving the energy affordability crisis, and different choices can get us out of it. 

Congress is currently considering a moratorium on new data centers but lawmakers won’t act unless they hear from constituents. Join us in telling your senators to support the AI Data Center Moratorium Act. 

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*Fossil gas, which we’ll usually refer to as simply “gas” in this piece, is not the same as the gasoline that powers vehicles. Fossil gas is the gas used in stoves, for heating homes, and for generating electricity.

**LNG (liquefied natural gas) is the same fossil gas described above, just in a different form. To ship gas overseas, companies cool it down until it turns into a liquid — natural gas that has been cooled to a liquid state, at about -260° Fahrenheit, for shipping and storage. The volume of natural gas in a liquid state is about 600 times smaller than its volume in a gaseous state, which is why it’s called “liquefied.” That shrinking is what makes it possible to load gas onto tankers and sell it to other countries, instead of only piping it to homes and businesses nearby. 

The post Out of Pocket: The real cost of fossil fuels (and AI) on our household bills appeared first on 350.

AFSA remains firm that regulations must be truly scale-appropriate to ensure the survival of small-scale, agroecological and regenerative farming businesses.

The post Australia: The Agroecology & Food Sovereignty Alliance (AFSA) Calls for Appropriate Regulation in Tasmania’s New Horticulture Standards appeared first on La Via Campesina - EN.

The strength of our agriculture lies in its diversity, complementarity and solidarity, as we demonstrated during health crises or the most intense periods of fires.

The post France: Confédération Paysanne Puts Forth 22 Demands to Address the Climate Crises appeared first on La Via Campesina - EN.

From its outset, urban mobility has been built for and by men, mainly with one specific journey in mind: home to work and then back again. Not only does this design marginalise women and ignore their care-driven living patterns, but it also means their safety is endangered in the public space. Across the Balkans, grassroots organisations are mapping inequalities and reimagining urban mobility through a gender-responsive lens.

You cannot easily fit women into a structure that is already coded as male.

– Mary Beard 

The urban environment has never been gender-neutral. In critical discussions of gender inequality, the binary division of space into public and private spheres often emerges as a key theme. Public affairs – and, therefore, the public sphere and mobility – have been dominated by men since antiquity, whereas the private sphere has been viewed predominantly as the domain of women and family life. This understanding of space, according to feminist urban theory, both illustrates and reproduces traditional gender power relations.

Urban mobility dictates when and how people move and carry out their daily routines, as well as who gets to do what. As a system of transport infrastructure that enables movement throughout the urban environment, urban mobility has been conceived, designed, and built by – and for – those who govern the public sphere. As such, urban mobility often ends up preventing the free and safe movement of women. The various elements which make up this infrastructure – such as public transport, street layout, pavements, and lighting – are built according to the needs of men, forcing women daily to try and fit into a space and way of doing things that does not recognise them.

The infrastructure of movement in urban areas is governed by three principles that, throughout history, have been important parameters for the performance of daily tasks traditionally undertaken by men: speed, efficiency, and precision. These principles took the pater familias from point A to point B – from home to work – and back again. Analysing the movements that women make every day, we discovered a much more layered connection between locations and activities – something known as “trip chaining”. This phenomenon represents the practice of connecting the shorter distances that women cover during the day into one “branched” journey which is not linear; rather, it involves visiting additional important locations within an overall pattern of movement.

These points on the journey, where women stop for a short period, are part of a structural issue – namely, caring for the family and the community – involving tasks that are imposed on them, such as taking and collecting children to and from school, going to the gym, grocery shopping, paying bills, taking care of elderly family members, and other care-related and “emotional” work.

Urban mobility dictates when and how people move and carry out their daily routines, as well as who gets to do what.

Globally, women perform 76.2 per cent of unpaid work caring for others and typically choose to work near home so that they can combine paid work with unpaid caregiving responsibilities. Treating household chores and caring responsibilities as “women’s issues” is another mechanism by which gender inequality is reproduced within urban life and women’s spatial movement. Reclaiming their space and freedom of movement often begins in the family.

The gender gap is even visible when we talk about riding a bicycle: globally, women use this mode of transport up to four times less than men. In Europe, specifically, the difference between men and women’s use of cycling as a means of transport is 64 per cent in Spain, 54 per cent in France and the UK, and 42 per cent in Germany. This disparity is another result of the structural obstacles women face: they are more sensitive to traffic risks, fear violence by men, often carry groceries, and take children to and from school or nursery. All of these factors mean that their first choice of transport is usually not to ride a bicycle.

Women’s participation in urban life and their use of urban transport are strongly influenced by a sense of fear. Moreover, the paths and roadways they take every day are determined by which part of the city or which means of transport feels safe, and where there is the least likelihood of being attacked.

Fostering safety

Women shouldn’t have to choose between freedom of movement and safety, and this has been recognised by a number of initiatives in southeast Europe. In places where urban mobility is most risky for women due to a culture of violence or the lack of suitable infrastructure, organisations are working to provide fundamental protection for women. One organisation that has taken up this cause is Space SyntaKs, in Kosovo. It has created a safety map of Pristina based on the experiences of women, who have largely been marginalised in the planning of urban spaces and traffic.

Security in this research is not simply a technical concept, but rather an indicator of what women depend on for their well-being and ability to function. Security starts from the right to a safe environment as a basic human right, which demands that policymakers actively address the problems women face every day in moving around the city. This research pays particular attention to subjective indicators, including feelings such as fear of any type of harassment, theft, and physical threats. It also focuses on how diverse the users of a given public space perceive it to be, and unlike previous research in this area, matches this data with objective indicators. These factors include the presence of street lighting, security cameras, public institutions, and stray dogs.

According to the research, not a single public space in Kosovo’s capital has attained even half of the maximum safety index score. Respondents expressed fear and identified threats such as verbal and physical violence by men, and these were mapped throughout the entire urban core of the city. The maps revealed that Pristina is not a safe place for women, and that the places they move in are virtually never completely safe from start to finish. Gender-responsive planning of urban areas and urban mobility begins with identifying important factors for the safe and free movement of women, as well as mapping locations where intervention can make movement safer.

Similar initiatives have taken place in Belgrade under the initiative “Don’t be afraid of the dark” by the collective Sestre, drugarice (“Sisters, friends”). This campaign is one of the responses to the question of security and the problem of violence by men against women in public spaces and transport, and it aims to map the city of Belgrade according to its “invisible boundaries” – places in which sexual attacks have taken place – and to make these places safer.

Sestre, drugarice has been effective in communicating important and alarming statistics, including that 64 per cent of women have been sexually harassed on the street, with 57.9 per cent of those being child victims aged between 13 and 17. It is particularly striking that 40.5 per cent of harassment took place on public transport. The results of an anonymous questionnaire created as part of this campaign show that public transport and bus stops are among the likeliest locations for sexual harassment incidents. Members of the collective organise women’s activist walks, advise them on how to feel safe in public spaces and on public transport, and call on women to actively help make their environment safer through “micro-actions”. One such initiative, called “Ada is ours”, saw members of the collective and the wider public take part in a feminist activist walk, tying bows around broken streetlights to get them repaired and to send the message that Belgrade belongs to women even after sunset.

According to the collective, and based on answers from respondents, the main forms of violence on public transport include verbal violence, unwanted physical contact, exhibitionism, secondary victimisation, and worse, stalking and attempted rape.1 In order to map and recognise these types of violence, it is important to use all available platforms to inform and educate women about what they mean. Only when such violence becomes visible do we perceive it as systemic, and are thus able to challenge that system.

That said, the very act of creating a mental map of streets and ways of moving as a basis for safety, actively assessing risks, giving out advice on safety to other women, and organising movement based on assumed family obligations all fall under women’s “emotional work”. By recognising this as work, we raise awareness of women’s position, create the possibility of sharing caregiving more equally within the family, place responsibility on the institutions that create public policies related to urban mobility, and preserve women’s emotional and cognitive resources and, ultimately, their lives.

In the sphere of gender-responsive urban planning and urban mobility, there is an ongoing debate about how to create a safe environment for women. Devoting parts of public space and transport infrastructure exclusively to women is one of the short-term solutions adopted by some city administrations to address the issue of male violence against women. This institutional measure has been deemed necessary in social contexts where public transport is the only way for women to move around if they are economically or socially marginalised.

Women’s carriages have been introduced in many countries, from Brazil to Japan. In the latter, sexual violence against women on public transport is so common it even has a special term: chikan. Nevertheless, countries that have responded to the problem of violence on public transport by segregating men and women do not report a significant reduction in the number of attacks on women, indicating that this mechanism does not address the root cause of the problem.

This is precisely the basis for criticising segregation: if women-only train carriages become the norm without any other major changes in public policy – the enforcement of penalties, education, and media campaigns – segregation by gender will place additional pressure on victims, discriminate against non-binary individuals, and send a message to men that their violence will be tolerated and movement and daily life planned around it. Women-only cars and spaces must not be the sole or permanent solution; instead, the city and public transport must be reclaimed, and safety for everyone established within shared spaces.

If we want to achieve the right to freedom of movement and safety for all, we must shine a light on how unsuitable the current urban mobility infrastructure is and how much it marginalises women. Women’s lives and movement patterns show us what complete institutional neglect looks like.

In the case of Podgorica, Montenegro’s capital, apart from the lack of adjustment to gender-sensitive indicators, we can see concrete examples of how unsuitable the transport system is for women with disabilities or mobility impairments. Despite committing more than a decade ago to ensure the accessibility of buildings and streets, Podgorica has utterly failed to do so. The city’s poorly built and badly maintained streets and pavements render movement unsafe for women who predominantly shoulder the responsibility of childcare, push prams, or use wheelchairs to get around. Narrow, uneven pavements full of open manhole covers and drains; inaccessible public transport stops; steep, slippery ramps unsuited to wheelchair tyres; and tactile paving not designed for the variety of shoes women wear – these are just some of the daily obstacles women encounter when getting around. These barriers prevent access to social, healthcare, educational, and professional opportunities, further intensifying the discrimination faced by marginalised groups.

Where women win

This is precisely the basis for criticising segregation: if women-only train carriages become the norm without any other major changes in public policy […] we send a message to men that their violence will be tolerated and movement and daily life planned around it.

Vienna boasts a long tradition of gender-responsive urban mobility. Since 2000, gender equality has been a priority in the Austrian capital’s urban planning. By integrating gender perspectives in all of its policies, the city has implemented projects to improve public lighting; widened pavements to accommodate wheelchairs; enhanced visibility in streets and passageways by installing street mirrors; and added benches and seating at bus stops, along pedestrian routes, and on public transport. This strategy stems from a recognition that women rely more frequently on these ways of moving around and spend more time waiting between short-distance trips.

Vienna’s response to “trip chaining” has been to build housing units as part of the “Women-Work-City” project, designed by women for women. In these units, women who provide care within the family and community can find kindergartens, doctors, and pharmacies all in one place, thereby reducing the time spent on these caregiving tasks. The goal of this project was to facilitate the caregiving work women perform.

Meanwhile, the city of Umeå in Sweden has taken even bolder steps. Widely known as “the most feminist city” in the world, Umeå includes a gender-responsive dimension in all its urban planning. In 2019, a prototype gender-sensitive bus stop was built in the city, designed to address women’s concerns about safety on public transport. Audible and visual signals indicate that the bus is approaching the stop, while rotating wooden booths enhance visibility, allow for monitoring of surroundings, provide shelter from the wind and other weather conditions, and create a sense of security within the public space.

In addition, Umeå has undertaken other feminist-inspired spatial interventions, including benches created for play and socialisation in collaboration with and catered towards teenage girls; constructing well-lit underground pedestrian tunnels featuring large central entrances and exits, as well as rounded wall corners to enhance visibility; prioritising snow removal services based on gender, taking into account that women walk and use public transport more frequently; and clearing pavements and public transport stops before roadways. In addition, the city organises a “Gendered Landscape” tour that connects existing feminist spatial interventions with those still needed to make the city truly a place for everyone. All of this has been reinforced by education, research, the integration of gender perspectives into urban mobility data, advocacy for the equal participation of men and women in caregiving, and women’s active involvement in decision-making.

In the UK, one factor that has improved women’s safety and strengthened a culture of zero tolerance for sexual harassment on public transport is the cooperation among the British Transport Police, the Metropolitan Police, the City of London Police, and Transport for London (TfL). After a 2013 TfL survey showed that 15 per cent of women using public transport had experienced sexual harassment in the previous year, and that about 90 per cent of cases were not reported, training was organised for 2,000 police officers on the public transport network with the support of feminist organisations. Only half a year after the launch of this initiative, the percentage of reported sexual harassment cases increased by 20 per cent, and the number of cases going to trial increased by 32 per cent.

As UN Women has noted, aside from improvements to the public transportation system, the active participation of men is a crucial step in the struggle to eliminate male violence against women in public transport. To that end, in Vietnam’s Ho Chi Minh City, the organisation has also opened so-called Male Advocacy Clubs, which bring men together for the purpose of educating and promoting gender equality in various spheres. One of the media campaigns by the club targeted the problem of sexual harassment of women in public spaces. The campaign’s message about preventing this type of violence reached as many as 8.5 million people.

Mobility for all

To build truly inclusive mobility, we must reclaim our parks, streets, and public transport. Moreover, to liberate women and make their movement safer, we must begin collecting gendered statistics and mapping the intersectional female experience of living in cities. Let’s react to systemic discrimination and violence, shout, write, unite, build a network of solidarity for each other, and create systemic solutions.

As women’s right to free movement is under threat, it is important to imagine a future of equal urban mobility – one where this right is guaranteed and respected, the provision of care is shared equally, and women participate actively in spatial planning and the creation of mobility policies. In such a future, women are healthy and safe, and they can freely socialise, create, educate, and improve their lives.

“What would transportation look like if it were designed by mothers? How would streets be different if they were made for women with disabilities? Which social groups are not represented in our infrastructure, and how can we change that?” – these questions can be the start of constructive conversations about building a fairer city. In addition to marginalised social groups, men must also take responsibility and be active participants in such discussions.

At the centre of these conversations should be a gender-responsive and intersectional understanding of women’s mobility issues. After all, it is from gender studies and feminist urban theory that the criticisms of male-centred urban mobility emerged. Women’s path to reclaiming common spaces passes from there, too.

This article first appeared in Serbian in Omorika. It is republished here with permission. Translated by Alex Melbourne | Voxeurop 

1. H. Cvetičanin Knežević, Koliko je sigurna tvoja vožnja? Istraživanje o seksualnom uznemiravanju, napadima i bezbednosti žena u javnom prevozu u Beogradu (Žensko udruženje Kolubarskog okruga, 2025), p. 5-6. / How safe is your journey? Research into sexual harassment, assault and women’s safety on Belgrade’s public transport (Women’s Association of Kolubara district, 2025), pp. 5-6.  ︎

A recent study from Open Plans and the Transportation Research Program at Hunter College argued that AVs will generate excessive “deadheading,” or empty vehicle miles, in New York City. The concern is fair, but the conclusion was based on a false equivalence: it treats the density of California as directly comparable to New York City. The literature is clear that deadheading depends on demand density. There are fewer empty miles when cars don’t have to travel far to find their next passenger.

Nonetheless, the report raises an important question: how can AVs avoid creating more congestion? It’s a fair question, given the rapid introduction of tens of thousands of personally owned for-hire vehicles more than a decade ago was a shock to the system. While AVs will have a much smaller footprint, there are lessons we all can learn from mistakes made by companies such as Uber and Lyft.

I walk and bike everywhere and I want the same for my kids. That’s why I was thrilled that after a year of advocacy and organizing, the City of Oakland is redesigning one of the city’s most dangerous streets to prioritize bicycle and pedestrian safety. It’s also why I work at Waymo. In my personal advocacy and in my role as the Policy Development and Research Manager at Waymo, I often think about the trade-offs required to build a safe, vibrant, and efficient transportation system. Whether it is bike or bus lanes, curb management, or parking, there is rarely a perfect solution. The conversation about how to most effectively deploy AVs is no different, and curb management is an important place to start.

Cities become more delightful when there are fewer cars circling and less curb space devoted to vehicle storage. Right now, roughly 40 percent of ride-hailing miles are empty, a figure that generally holds for AV fleets. But as a fleet operator, Waymo is incentivized to minimize deadheading because we bear the cost of every single empty mile driven. Every unnecessary mile means additional operating costs, energy consumption, vehicle wear, and risk exposure. Traditional ride-hailing platforms don’t internalize these costs; they are pushed onto the drivers. Waymo, like cities, wants to reduce empty miles as much as possible.

One solution to reducing empty miles is parking between rides. Taxis do this at taxi stands, and the San Francisco County Transportation Authority found ride-hail drivers park an average of 10 minutes between hails. When Waymo asked researchers from UC Berkeley and UC Irvine to model the impact on VMT from AVs staging at the curb, they found that it reduced empty miles by more than 60 percent. And the magnitude of the reduction was striking, particularly because the model strategically accounted for moving empty AVs into off-street lots during low-demand periods.

Staging is also a highly productive use of curb space, according to the researchers. Because AVs spend much of their time providing trips, the cumulative value generated by staging far exceeds that of a curb space occupied by a privately owned vehicle. For a commercial district, this means that local businesses could receive eight to 10 times as many potential customer visits than if the space were designated for private vehicle storage only.

None of this means AVs should have unrestricted access to the curb. Waymo already prioritizes parking in off-street lots, limits itself to one staged vehicle per block face, and avoids cycling through the same spot repeatedly. In a recent report, Open Plans and the Transportation Research Program at Hunter College argued that New York City’s “constrained curb space” makes staging infeasible, leaving AVs with little choice but to drive empty between trips. But the curb isn’t constrained by the laws of physics, it’s constrained by policy. As the Berkeley and Irvine researchers suggested, establishing permitting standards can formalize curbside staging. Deadheading can never be zero, so the real question is how many empty miles will we accept in exchange for the safety, accessibility and other benefits of AVs. Curb management is exactly how cities can organize that tradeoff with clear rules.

Encouragingly, New York is having these conversations at the highest level. Mayor Zohran Mamdani created an Office of Curb Management this year to bring more order to how the city allocates curb space. Officials are weighing ideas from metered spots to residential permits to dynamic pricing. Each plan differs, but the premise is one we agree with: curb space is valuable, and managing it inconsistently doesn’t serve anyone well.

Waymo, city officials, and urbanists have common cause, and we don’t have to wait to start having these conversations. Open Plans has in fact been a leader on reimagining curbs. New York is the first and only city in the nation to implement congestion pricing, which has been a resounding success. Though controversial, there is renewed interest in more visibly holding drivers accountable for blocking street cleaning.

At Waymo, we believe AVs can play a valuable role in making transportation safer and cities more livable. Conversations about curb management and other policies that help responsibly deploy AVs are key to our work with the city officials, advocates, and communities we hope to serve.

• Congressional Democrats are accusing the Federal Transit Administration of intentionally stonewalling transit projects. The Trump administration has yet to issue any grants from a multibillion-dollar capital investment program since the president’s second term started. It’s also tried to cut off funding for already-approved projects in blue states, like the Gateway Tunnel. (New York. Times)
• Transportation Secretary Sean Duffy believes bike lanes cause traffic congestion. In fact, the opposite is true. (Bloomberg Government)
• Despite tariffs, demand for new transit buses in North America remains strong as agencies replace their aging fleets. (Smart Cities Dive)
• Safety and comfort are just as important as distance when it comes to walkability. (The Conversation)
• The privately owned cross-country passenger rail line AmeriStarRail has yet to materialize because the company hasn’t been able to reach an agreement with Amtrak. (USA Today)
• Los Angeles Mayor Karen Bass’ campaign promise to make the L.A. Metro free would cost $1 billion and possibly prevent the agency from improving service. (LAist)
• A Washington, D.C. cycling group is pushing for better walking, biking and transit access around the new RFK stadium. (Greater Greater Washington)
• Portland residents who bought an e-bike using the city’s rebate program say the purchase changed their lives. (Mercury)
• Kansas City is using water-spraying trucks to keep extreme heat from warping streetcar tracks. (Axios)
• The Metro Atlanta Rapid Transit Authority has three finalists for CEO. (Saporta Report)
• A Vision Zero plan in the Seattle suburb of Lynnwood is getting pushback from people who say the city doesn’t need to spend money on traffic safety because it already has relatively few road deaths. (The Urbanist)
• Ann Arbor transit agency The Ride is starting a shuttle service for University of Michigan football games. (MLive)
• Heavy electric vehicles are tearing up China’s roads. (Techy 44)
• A proposal for high-speed rail in Australia would save a lot of jet fuel and help solve Sydney’s affordable housing crisis. (B1M)
• Japanese cities are so livable because everyday needs are within walking distance, and there’s an excellent transit system for longer trips. (BBC)

ALERT’s citizen suit clears the way for safer products during oil spill response

August 13, 2026 – Recently, an Oregon state oil spill response planner asked me, “Have you seen EPA’s new product schedule?” I had not. “Something weird happened to it,” he said. “There’s almost nothing on it.” We both googled it and discovered that EPA’s new list of products for oil spill response contained five products. “See?” the state planner said. “What happened to the other 130 products?”

What happened was the successful lawsuit brought by ALERT and allies in 2020. Our citizen suit changed the rules governing use of dispersants (our focus) and other products – such as surface washing agents, solidifiers, herding agents, and bioremediation agents – during oil spill responses. EPA tightened the protocols for testing products to eliminate more toxic products, required full disclosure of ingredients (no hiding toxic ingredients as “confidential business information”), and significantly, added rules for removing products from the schedule –  an option previously unavailable to citizens. Rules for removal require evidence that technical information supplied to EPA by the manufacturer was incorrect, inaccurate, misleading, or outdated concerning impacts to human health and the environment. In other words, anything less than truthful.

Under court supervision, EPA allowed a lengthy three-year conditional use period, counting the six-months between promulgation of the new rules and when they went into effect on December 11, 2023. [88 FR 38336, June 12, 2023] That conditional use period – which grandfathered in all 130+ products that were listed as of June 12, 2023 – ended on June 10, 2026. The new EPA Product Schedule lists four dispersants and one surface washing agent. Apparently, the manufacturers of the other 130+ products could not or chose not to meet the new requirements.

Was it all smoke and mirrors? Decades of deception that oil spills can be “cleaned up” with chemical products that miraculously create a net benefit instead of more harm than good? Manipulated lab tests? Biased statistics and formulas? Carefully worded labeling that could make black appear to be white? A growing “wealth defense industry” with legions of lawyers who could make thousands of toxic tort cases disappear from workers and the public who were directly exposed to their product? Apparently so.

The famous “Gulf Walrus” are another myth found only in oil spill contingency plans for the Gulf of Mexico at the time of the Deepwater Horizon oil disaster. The error was corrected but the industry’s disrespect for human health and the environment remains.

Case in point: Corexit dispersants were the go-to in the US for oil spill response and comprised 45% of global stockpiles. No more. The former owner of Corexit dispersants, ChampionX, voluntarily pulled the plug on its Corexit product line in November 2022 when EPA’s proposed rules were moving inexorably under court supervision towards promulgation. Then, ChampionX vanished altogether when it was acquired by SLB (Schlumberger) in 2025. Corexit was just one of the 130+ products. The others just disappeared more quietly on June 10, 2026.

We did this – ALERT and the hundreds of concerned citizens over fifteen years who stepped up and signed onto petitions for this rulemaking and the lawsuit. We wiped out 130+ likely toxic products on EPA’s list.

So, where does this leave us?ALERT and the Government Accountability Project filed a noncompliance complaint with OSHA about its less than truthful reporting on technical literature for three of the five dispersants that are currently on EPA’s list. (The other two products were listed after we filed our complaint.) Our complaint is still under active investigation.

At this tipping point, we remain committed to eliminating products that will cause more harm than good during oil spill response. Why? Beyond the obvious, holding the oil industry accountable for multi-organ harm across body systems and species including humans from exposure to its products helps build pressure towards leaving the oil in the ground and transitioning to independence from fossil fuels.

In solidarity,

This is a re-post from The Climate Brink

AI energy use is a huge and controversial topic at the moment. Credible estimates have AI data centers accounting for around 12% US electricity use by 2030. But at the same time consumers have been given reassuringly small numbers about the impact of their own AI use, numbers that seem on their face somewhat inconsistent with the staggering size of their aggregate usage.

In 2025 Google published an article calculating that median Gemini text prompt used only 0.24 watt-hours (Wh), less energy than “watching nine seconds of television”. Around the same time, Sam Altman said that an average ChatGPT query uses about 0.34 Wh, and Epoch AI came out with similar numbers. Writers like Andy Masley and Hannah Ritchie have shown that at these rates an individual using chatbots has a pretty negligible impact, with one prompt only amounting to roughly 1/150,000th of an average American’s daily emissions.

Those numbers are basically right. They are also increasingly divorced from how AI is actually being used today.

The fastest-growing way that software engineers and scientists actually use AI is not typing questions into a chat box. Rather, we use AI agents through tools like Claude Code and Codex that plan, write code, run it, read the results, and iterate on their own. These agents make dozens of model calls per human prompt, and engage in complex reasoning chains that involve attempting and evaluating multiple answers to the same question.

I work for a company in Silicon Valley (Stripe) and admittedly use the latest AI tools more than most people. But I thought it would be instructive to take a deep dive into my own AI use over the past 8 weeks and calculate the actual energy use I was responsible for.

Over the past 8 weeks I typed 1,138 prompts into Claude Code. Those prompts triggered more than 14,000 model calls that processed 3.2 billion tokens. My best estimate is that this used around 170 kWh of data center electricity (with an uncertainty range of roughly 70 to 330 kWh across methods and assumptions). That works out to around 150 Wh per prompt (60 to 290 Wh), which is roughly 600 times (250 to 1,200) the energy of a median chat prompt. A “prompt” is ultimately not a unit of AI use any more than “trips” is a measurement of driving; it’s how far you go that matters.

Agents supercharge AI usage

Part of the impetus for this post is the publication of a new white paper from Watershed (Bistline et al. 2026) proposing a standardized framework for corporate AI emissions accounting. It is the most careful treatment I have seen of why published per-query numbers differ by orders of magnitude (system boundaries, mostly), and it contains a figure that should reframe the whole discussion: electricity per AI task spans more than five orders of magnitude, from thousandths of a watt-hour for text classification to 50-500 Wh for an agentic workflow making 5-50 frontier model calls. As they put it, emissions attributed to one “interaction” may understate the compute actually consumed “by an order of magnitude or more.”

Other researchers have found similar results. Bai et al. (2026) measured coding agents on real software tasks and found they consume roughly 1,000 times the tokens of an ordinary chatbot interaction. And these sort of agents tasks represent the most rapid driver of increased AI usage; Anthropic’s Economic Index found that 97% of their API usage now show “automation-dominant” patterns associated with agents.

To put these values in perspective, the figure below compares published per-prompt and task estimates (blue) with what I measured from my Claude Code use (orange) as well as common benchmarks for energy use (running a microwave, a fridge, or a whole home):

Electricity consumption per AI task, including published estimates (blue) and values computed from my own Claude Code session logs (orange). Measured token counts converted using Bistline (2026) activity-tier energy factors; orange ranges span cache-read energy assumptions of 1% to 25%.

My median Claude Code session uses around 0.6 kWh (0.25 to 1.2 kWh), which is at the top end of Watershed’s generic agentic usage estimate, and fifty times the energy used to charge a cellphone. My average day of Claude Code (3.0 kWh, range 1.2 to 5.9 kWh) uses more electricity than running two refrigerators.

Measuring my own footprint

Claude Code keeps complete local transcripts of every session, including the exact token counts the API reports for every model call.1 This lets me precisely know how much AI usage I was responsible for rather than simply extrapolating it from published benchmarks; its only the step to convert tokens used to energy that requires assumptions.

The first thing I found is that the gap between “prompts” and reality is massive: my 1,138 typed prompts resulted in just over 14,000 distinct model calls (12 per prompt), and each prompt consumed on average 2.9 million tokens. For comparison, typical web-based AI chat exchanges with no reasoning or web searches only use around a thousand tokens.

Over the past the 8 weeks, my Claude Code used 3.2 billion tokens. These overwhelmingly came from the agent re-reading its own working memory. Every time an agent takes a step (e.g. runs a command, reads a file, or calls a tool), the model re-processes its entire accumulated context. The figure below shows the breakdown of how tokens were used and their share of total electricity use.

Token and estimated electricity composition of my Claude Code usage, May 31 to July 25, 2026. “Cache reads” are previously processed context re-read from the key-value cache on each model call; “cache writes” are new context being processed and stored; “output” is text and code generated by the model. Electricity shares use Bistline (2026) factors with cache reads at 10% of the fresh-input energy rate.

The text I actually see (e.g. the model’s output) is only around 0.4% of total tokens processed. Some 96% of the tokens are cache reads where the agent re-reads its own context at each of those 14,000 steps. This matters enormously for the energy estimate, because a cached token is much cheaper to re-read than a fresh one is to process. AI companies charge about 10% of the price for cache reads compared with fresh content, and I use that ratio as my central energy assumption, with 1% and 25% as bounds.2

Since nobody outside of the labs actually knows the true per-token energy of a frontier model (Anthropic has published no per-prompt or per-token figures, something the Watershed paper politely but firmly flags as the field’s biggest data gap), I ran my measured token counts through three independent published methodologies: Watershed’s activity-tier factors, the per-token factors Simon Couch’s estimates derived from Epoch AI’s work, and the claude-carbon tool’s pricing-inferred coefficients.

Estimated electricity consumption for the 3.2 billion tokens I used under three published methodologies: Watershed activity-tier factors under three cache read assumptions, Couch (2026) per-token factors, and claude-carbon per-model coefficients.

Every one of these methodologies gives an answer between roughly 70 and 330 kilowatt-hours over 8 weeks. The estimate is genuinely uncertain, by a factor of ~2 in either direction. But the broader conclusion is not: counting my 1,138 prompts at published per-chat-prompt rates would have suggested about 0.3 kWh, while the reality is 150 to 1,200 times that.

My daily pattern of energy use is shown in the figure below. The day to day variability is huge: my heaviest day (11 kWh central estimate) involved multiple parallel agents churning through a large geospatial analysis, and used more than a third of the total daily electricity of an average US home. This reflects that fact that even within the category of agentic usage, the complexity of the task and the number of simultaneous sub-agents used will greatly influence the resulting energy use.

Estimated daily electricity consumption of my Claude Code usage(bars: cache reads at 10% of input energy; whiskers: 1% to 25%). Reference lines show typical daily electricity use of a refrigerator and of an average US household.

My numbers are a bit higher than some of the other published estimates of agentic use, and it is worth digging in a bit to determine why. Couch estimated that a median Claude Code session uses around 41 Wh, involving 24 model calls and 592k tokens. Andy Masley’s June 2026 calculator puts a 100k-token Claude Opus agent session at ~459 Wh. My median session is ~600 Wh, involving a hundred-plus calls and around ten million tokens including numerous subagents for large data analyses projects. Hannah Ritchie’s hypothetical heavy user (24 agentic queries a day) came out at 2.4 kWh/day, while I measured a central estimate of 3.0 kWh/day (1.2 to 5.9 kWh) for my actual usage.

None of these estimates are necessarily wrong, they just reflect a wide range of actual usage assumptions. Software engineers, researchers, and data analysts (e.g. folks like me) probably lie pretty far down the tail of the usage distribution. At the same time, usage will likely grow over time as more complex agentic tools increasingly become the norm.

What a year of this looks like

If we assume that these 8 weeks are fairly typical, we can estimate that a full year of my agentic Claude Code use would consume roughly 1.1 MWh of data center electricity (0.4 to 2.2 MWh), which is about a tenth of what an average US household uses. Applying the US-average grid intensity, that is roughly 370 kgCO2e per year (150 to 730 kgCO2e).3

Annual emissions of common activities compared with my annualized Claude Code usage. Car: EPA typical passenger vehicle (22.2 mpg, 11,500 mi/yr). EV: 11,500 mi/yr at 0.30 kWh/mi on the California grid. Flight: ICAO-method economy round trip, CO2 only. Home electricity: EIA average US household on the US-average grid. Dryer: typical electric clothes dryer at ~770 kWh/yr (DOE) on the US-average grid.

My personal and professional AI usage now emits a bit more per year than running an electric clothes dryer, and about half as much as driving an electric car 11,500 miles in California or taking one San Francisco to New York round-trip flight in economy.4 It is about 8% of the annual emissions of a typical American gasoline car, and roughly 2% of the average American’s ~18-ton annual greenhouse gas footprint.

This is simultaneously a large emissions source and a relatively modest part of my total carbon footprint. I typically take a round trip flight from San Francisco to the East Coast twice a year to visit my aging parents (not to mention work travel), and I generally don’t lose sleep over that choice. It is also fundamentally a much easier-to-decarbonize end-use than aviation (more on that below). But this also represents a net new source of emissions, at a time when global temperatures are skyrocketing and our emissions reduction goals are increasingly off track.

So what do we do about it?

Having spent most of this post arguing that agentic AI use is hundreds of times more energy intensive than the chatbot numbers suggest, let me be clear that I don’t think the answer is guilt or abstinence. But there are real levers here that we can use to shape the trajectory of AI energy use and emissions going forward.

On the personal side we can try and not be frivolous with agentic tools. There is a real difference between pointing five parallel agents at a hard research problem and doing the same to settle a bar bet (or, in my case, making axolotl-themed games with my daughter). What models you use matters too: sending simple tasks to smaller models uses perhaps 5 to 7 times less energy per token than defaulting to a frontier model,5 and it is what I increasingly do for searches and mechanical work. That said, I don’t want to oversell this. My entire annual AI footprint is a few hundred kilograms of CO2; personal restraint by the small population of heavy users is not going to bend any curves.

The technology lever is more powerful, and it is genuinely impressive. The figure below shows the energy efficiency of NVIDIA’s data center chips over the past decade. The amount of math an AI chip can do per joule of energy has grown roughly 150-fold since 2016, doubling about every two years, per Epoch AI. The latest B300 chips running at their lowest supported precision use about a quarter of the energy per operation of the 2022-era H100s that trained today’s frontier models. This represents a 3.8-fold improvement in energy efficiency in three years. Software gains can make this even faster: Google reports the energy of a median Gemini prompt fell 33-fold in a single year through a combination of better models, better tools, and better hardware.

Peak dense tensor throughput per watt of rated chip power for NVIDIA data center GPUs, by release year and numeric precision. Dashed line shows Epoch AI’s trend of energy efficiency doubling every two years for leading ML hardware.

But if 150-fold efficiency gains were going to reduce AI’s energy use, they would have done it by now. This is the Jevons paradox in action: making compute cheaper per token in turn tends to lead to greater levels of AI use. Efficiency is why my agentic habit costs 170 kWh rather than the 950 kWh it would have used with 2020-era hardware. But efficiency only determines how much intelligence we get per unit of energy, but so far it has so far shown no sign of determining AI’s total energy use.

Which is why the lever that actually matters most is the carbon intensity of the electricity. Every number in this post assumed the US-average grid; run the same workload on largely clean power and my footprint falls by roughly 90%. Unlike aviation, this is an end-use we already know how to decarbonize.

The problem is that we are moving in the wrong direction today: a sizable portion of planned US data center capacity intends to build its own behind-the-meter generation, and nearly three quarters of that is natural gas. AI companies with genuine climate commitments need to do better at finding alternatives: solar plus storage (which I helped lead a study about in 2024), next-generation nuclear and restarts of retired reactors, enhanced geothermal, and siting data centers in regions where both the average and the marginal generation is low-carbon).

There is also a silver linings version of this story where AI demand becomes an asset for decarbonization. Getting to net-zero emissions requires roughly tripling electricity generation by mid-century as we replace nearly all the current uses of fossil fuels with clean electricity. The barriers are mostly not technological, but rather things like interconnection queues, permitting, transmission. The AI buildout is a preview of that world of rapidly increasing electricity demand, backed by companies with enormous capital and unusual urgency. If that money and impatience gets spent speed-running the elimination of those barriers (buying firm clean power, funding transmission, absorbing the early costs of advanced nuclear and geothermal the way early corporate buyers did for wind and solar), the AI boom could leave the grid cleaner than it found it. If it gets spent on behind-the-meter gas turbines, it won’t. That choice is being made right now, and it will matter far more than how many prompts any of us type.

In the interest of full disclosure: the python code underlying the analysis and figures in this post were, naturally, built with the help of Claude Code, but the writing is all mine.6

1 Claude Code records API usage including the amount of uncached input tokens, cache-creation tokens, cache-read tokens, and output tokens, per model call, with model IDs and timestamps. One logging subtlety matters a lot: each API response is written to the log as one line per content block, with every line repeating the message’s full usage object, so a naive line-by-line sum double-counts tokens by a factor of ~2.2. All numbers here count each API message once, deduplicated by message ID.

2 A cache read retrieves already-computed attention states from memory rather than recomputing them, so it is much cheaper than inputting fresh data, but its not free. Cache reads context still makes each output token more expensive to generate at long context. Anthropic prices cache reads at 10% of fresh input, and the claude-carbon and Couch methodologies both adopt ~10% as an energy ratio. Watershed flags cache handling as a known gap in per-token accounting; my 1%-25% band is intended to span the plausible range.

3 Using the eGRID 2024 US-average 341 gCO2e/kWh, since Anthropic does not disclose where their data centers are located and what electricity sources they use. Market-based emissions (counting providers’ clean power purchases) would probably be lower, potentially much lower. This estimate excludes my laptop, which at ~50 W is negligible against 3.0 kWh/day of data center load.

4 Note that the flight estimate here only includes direct CO2 emissions from aviation; including contrails and other secondary factors would probably increase flight emissions by at least 50%.

5 Based on inferring energy use through token pricing, claude-carbon gives ~0.3 J/token for Haiku-class vs ~2 J/token for Opus-class models.

6 In a good example of why you always need to double check work done with AI coding tools, Claude accidentally doubled its original estimate of my token use as all the relevant files were stored twice and it simply added them all up. I only caught it because the numbers seemed too high!

 

Back in 2025, wildlife biologist Kylie Brunette was going through footage from camera traps she’d placed in front of woodcock nests in the forests of West Virginia when something shocking caught...

Ascend Analytics says more than 80% of new large loads seeking interconnection will not have matching generation online by 2030, even as data centers and industrial demand continue to surge.

The New York-based utility company is pursuing growth through electrification, in alignment with city and state climate policies, it said last week.

By Jeremy Brecher,
Senior Strategic Advisor, LNS Co-Founder

Listen to the audio version >>

The startling growth of hyperscale data centers for processing artificial intelligence threatens to exacerbate the climate crisis, the jobs crisis, and the affordability crisis. This commentary presents an overview of the data center explosion and its likely effects. The next commentary in this series will examine the emerging “Data Center Rebellion” and how it can become a major force promoting a Greentech New Deal.

Aerial view of data centers intermingled with other commercial buildings in Loudoun County, near Ashburn, November 26th, 2025. Photo credit: Theodore Christopher, Wikipedia Commons, CC0 1.0 Public Domain.

Data centers are large warehouse-like structures filled with digital electronics that process artificial intelligence (AI). According to the International Energy Agency, “conventional” data centers can use between 10 and 25 megawatts of electricity while “a hyperscale, AI-focused” data center can use 100 megawatts or more. Hyperscale data centers can encompass more than a million square feet. Today there are 4,149 data centers in the US, with 2,788 more under construction or planned. Data center construction rose more than 34 percent between March 2025 and March 2026.

The explosive growth of data centers is already having a significant impact on the energy system. It will have an even greater impact on the transition from climate-destroying fossil fuel energy to climate-protecting Greentech energy in the future. So far, those effects are overwhelmingly negative.

The future of data centers, and AI more broadly, is filled with unknown unknowns. AI is pretty clearly both a technological revolution that will change the way we do things in many spheres of life – but also a technological bubble based on extreme claims that are unproven hype at best. Google CEO Sundar Pichai says artificial intelligence is “more profound than, I dunno, electricity or fire.”

Decisions and investments based on such dubious claims are speculative at best. For example, Chinese AI, with its far cheaper energy system and its cheaper and less energy-intensive strategy, is widely seen as less than a year behind the AI frontier. The AI boom depends on a debt bubble similar to many in the past marked by colossal overinvestment followed by collapse. Tech giants are using other people’s money to make huge investments in hyperscale data centers that have not yet shown they can be even marginally profitable. All of this is developing in the context of Polycrisis 2.0, with its unlimited warfare, unlimited arms races, energy crises, climate crises, galloping inequality, destruction of democracy, and overall prevalence of folly. Finally, the AI bubble is largely driven by the colossal egos of “hyperscalers” like Elon Musk whose megalomania and struggles with each other for dominance will have unpredictable ramifications.

Data centers and jobs

Anti-data-center sentiment in rural Kansas neighborhood, May 24th, 2026. Photo credit: Catboy69, Wikipedia Commons, CC BY 4.0.

AI is clearly affecting employment, but estimates of its employment impact vary wildly. S&P Global’s recent executive survey showed that 42 per cent of organizations abandoned most of their AI initiatives in 2025, compared with 17 per cent in 2024. And a 2024 RAND report indicated that more than 80 per cent of industrial AI projects fail, mainly due to process complexity, poor data quality, and lack of real-world context. The vice president of vehicle hardware engineering at Ford, Charles Poon, explained, “Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that that would produce a high-quality product.” Ford recently brought back 350 experienced engineers it had fired and tried to replace with AI.

The building of data centers will undoubtedly create jobs for a sector of construction workers. Nobody knows how many. Like most construction jobs, these jobs are temporary. In a Cologix data center in Columbus, Ohio, construction lasted on average six and a half weeks, with about 146 workers on site at a time. Jobs building data centers often are taken not by local workers but by workers from all over the country who come in temporarily to take the temporary jobs. The Industrial Development Agency in Genessee County, New York anticipates that 60% of the construction workforce at a proposed Stream US Data Centers Project will be from outside of the 14-county region.

Data centers create few permanent jobs. According to Kartik Hosanagar, codirector of the Wharton Business School’s AI research center, “Most data centers employ about one hundred to 200 people. In fact, when Apple created a $1 billion data center in North Carolina, the news stories reported that there were less than a hundred permanent jobs created as a result.”

A study of data centers in Texas found, not surprisingly, that when a data center opens there is a gross increase in data center jobs in a county. But, more surprisingly, these job increases are “offset by job losses in other sectors.” In other words, “though there are gross job flow changes, there is no discernable net change in jobs associated with the data centers in Texas.”

How many jobs will be destroyed by the AI that data centers power? The answer is, nobody knows. Some of the claims made by the AI industry seem extravagant.

“Dario Amodei, the head of Anthropic, has warned that A.I. could eliminate 50 percent of entry-level white-collar jobs within years. The tech investor Vinod Khosla predicted last year that A.I. would replace 80 percent of jobs by 2030. Elon Musk has said the technology will render work ‘optional.'”

Non-industry accounts indicate that the impact of AI on workers will be extensive. For example, researchers at Boston Consulting Group estimated that more than half of the jobs in the United States would be “reshaped” by artificial intelligence over the next two to three years, though far fewer would be replaced entirely. Amazon delivery driver Jonathan Rosenblum gives a vivid description of what that reshaping can mean:

“When I’m in the Amazon truck, every movement I make is tracked with technology and evaluated by AI programs — where I am, which packages I’ve delivered, and whether it’s keeping pace with the algorithm that Amazon has determined I must meet. Readouts at the end of every shift show how each of my deliveries compared to the timing prescribed by Amazon’s algorithmic standard. We are evaluated every week on whether we took accurate photos on delivery, delivered the packages exactly where the customer requested, and got good or bad customer feedback. Through the system, drivers who don’t “make rate” or who don’t meet Amazon’s prescribed standards don’t stay employed.

“Employers everywhere are seeking to imitate the behemoth’s labor model of exploitation, job instability, and — terrifyingly — the deployment of AI technologies to discipline and disempower workers. This workplace dystopia is being perfected at Amazon, then exported to other employers — in factories, grocery stores, hospitals, restaurants, hotels, construction sites, laboratories, and offices.”

AI-related layoff of tech workers at tech firms have made big headlines. But tens of millions of “back office” jobs are also threatened, such as customer service representatives, bookkeepers, payroll clerks, human resources specialists, and many others. These jobs are predominantly, or overwhelming held by women. Says Molly Kinder, a former researcher on AI at the Brookings Institution, “I worry that A.I. will be to high-school-educated women what deindustrialization was to high-school-educated men.

Whatever the future holds, AI is already destroying jobs right now. Researchers at Stanford University found that employment is already declining for entry-level workers in jobs that were highly exposed to A.I. “Early-career workers (ages 22-25) in AI-exposed occupations experienced 16% relative employment declines.”

Derailing the Greentech revolution?

Roof of a data center featuring cooling towers and backup generators, November, 23rd, 2025. Photo credit: Rsparks3, Wikipedia Commons, CC0 1.0 Public Domain.

Data centers use massive amounts of energy. They already used 448 terawatt hours globally in 2025, more electricity than all but 10 countries. That is on track to double within four years. Some data centers consume more energy than a mid-size city. According to Kartik Hosanagar of the Wharton Business School’s AI research center, in some US states data centers are already consuming up to 5% of all energy used in the state. In two to three years that is projected to be over 10% in most states. By 2030, data centers may consume enough electricity to increase the annual growth in electricity demand nearly five-fold. This July, BloombergNEF’s estimate of projected U.S. data centers’ power capacity by 2035 increased 83% compared to their estimate just half a year earlier. Data centers would account for about 20% of total US electricity consumption by 2035, up from less than 6% today.

US utilities are racing to build new fossil-fuel plants and are keeping ageing gas and coal plants open to meet the swelling requirements of data centers. But often the grid simply can’t keep up. Delays are holding up data centers’ connections to the electric grid by as much as 12 years. So big tech is investing massively to produce its own power by any means available. The biggest growth is in the gas industry, including fracking firms and pipeline companies. Gas companies are building new plants solely to supply data centers, leading to the largest ever construction boom of natural gas-fired power plants.

Some of the demand for electricity is being met by Greentech. For example, Google just developed the world’s largest grid-scale battery to power a data center in Minnesota, and purchased an energy company with which it is expanding renewable development, including a new “off the grid” center in Texas that will include wind, solar, batteries, and gas.

But the new energy for data centers is coming overwhelmingly from fossil fuels. Since 2023, some communities have seen a 48% increase in greenhouse gas emissions because of data centers. An example: Mississippi Power agreed to keep burning coal at one of its plants for roughly a decade longer than planned. In Michigan and other states, data centers have effectively derailed the grid’s planned transitions to renewable energy. Meta plans to build 10 gas power plants across Louisiana for its data centers.

The AI industry and data centers represent an extraordinary concentration of wealth and power.

According to the investment firm Jeffries, Amazon, Google, and Microsoft account for more than half of the country’s data center power capacity. Due to the data center boom, Microsoft consumes nearly four times as much electricity as it did before the pandemic; Google’s electricity use has more than doubled. Amazon’s emissions increased by 16% last year; Google’s increased by 18%; Microsoft’s grew by 25%.

Data centers are dramatically increasing electricity costs for utilities’ residential and business customers. Utilities requested $18.6 billion in electric and gas increases in the first six months of this year, according to a new report from the nonprofit PowerLines. More than $9 billion of those requests were filed in the second quarter of 2026 alone, up 26% from the same period in 2025. On July 14 the nation’s largest electrical grid operator said rising electricity costs would add $6.3 billion to the bills of millions of households and businesses within the next three years as a result of the power demands of data centers. Some reports estimate electricity bills may double by 2039 if data centers continue to be built at current rates.

Unconstrained, the rise of hyperscale data centers threatens to accelerate greenhouse gas emissions and thereby put climate change on steroids. It also threatens the jobs of millions of workers, local environments, neighboring communities, and the affordability of electricity, water, and other necessities of life.

The next commentary in this series will describe the “Data Center Rebellion” that is emerging from the grassroots around the country and presenting an unprecedented challenge to hyperscale data centers and the tech oligarchs who are attempting to impose them on American communities.

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The post Will Data Centers Derail the Greentech Revolution? first appeared on Labor Network for Sustainability.

The Virginia State Corporation Commission said it may use an upcoming docket to weigh whether this policy “could or should” also apply to more upstream transmission costs.

In June, a district court judge rejected PSCo’s effort to acquire roughly 550 feet of easement via eminent domain to connect two nearly complete wind farms totaling more than 1 GW. The utility says the project delays are harming ratepayers.

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An upcoming UK government consultation on weakening targets for electric vehicles (EVs) could cost consumers as much as £3bn a year by 2030, according to Carbon Brief analysis.

It could require the UK to import an extra 17m barrels of oil in 2030, raising expected net imports by 8%, as well as adding 2.5% to national emissions that year, the analysis shows.

After years of fierce lobbying by parts of the car industry – and despite the significant savings on offer for EV drivers – media reports suggest that EV targets could be “watered down”.

Under current rules, battery EVs – BEVs, those which run only on electricity – must make up a rising share of new car sales in the UK.

This policy, known as the “zero-emission vehicles” (ZEV) mandate, was introduced by the previous Conservative government and sets a goal for 33% BEV sales in 2026, rising to 80% in 2030.

(Carmakers are able to use “flexibilities” to help meet their targets, which reduces the effective target under the ZEV mandate to an estimated 25% of sales in 2026.)

Now, the government under new Labour prime minister Andy Burnham is reported to be considering a cut in the BEV target for 2030 to just 50% of new car sales, alongside options for 60% or 70%.

Carbon Brief understands that a consultation on weakening the ZEV mandate is being reviewed by the prime minister’s office in Number 10, ahead of being formally released.

If the mandate is weakened to 50% by 2030 – and if carmakers make more use of “flexibilities” – there could be up to 3m fewer BEVs on UK roads by 2030, according to the NGO T&E.

Previous Carbon Brief analysis found that BEVs are around £1,100 cheaper to run per year than a petrol car, thanks to far lower fuel costs.

Overall, BEVs are more than £1,000 per year cheaper to own than either petrol cars or plug-in hybrids (PHEVs, which can run on petrol or electricity).

This is according to analysis of the “total cost of ownership” by the Energy and Climate Intelligence Unit (ECIU), including purchase price, fuel costs, insurance and proposed pay-per-mile charges.

In total, Carbon Brief analysis shows that UK drivers could be hit with an extra £3bn in annual ownership costs by 2030, if the ZEV mandate is weakened, as shown below.

A weaker ZEV mandate could “put billions of pounds of committed investments at risk”, reports BusinessGreen, including in the EV charging network and battery supply chains.

Industry group Energy UK says that the mandate is “working in the way it was designed to work” and that it is the “single biggest driver of emissions reductions” in government climate plans.

However, Carbon Brief analysis shows that a weaker ZEV mandate could result in an extra 7.4m tonnes of carbon dioxide emissions (MtCO2) in 2030. This would add the equivalent of 2.5% to national emissions in 2030, under the UK’s international climate goal for that year.

In addition, a weaker ZEV mandate could result in the UK needing to import an extra 17m barrels of oil in 2030, equivalent to 8% of projected net imports that year.

Energy UK says that shifting to EVs will help to reduce household energy bills “for everyone”. This is not only through direct cost-of-ownership savings for EV drivers, but also by spreading the costs of upgrading the electricity system across a wider user base.

Car industry group the Society of Motor Manufacturers and Traders claims that its members are spending “blilions…on discounts, finance incentives and marketing support” and that “natural” EV demand is below the level required to meet the current ZEV mandate. Its claims are disputed.

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