In Kenya’s Laikipia County where temperatures can reach as high as 30 degrees Celsius, a local building technology is helping homes stay cooler while supporting education, creating jobs and improving the livelihoods and resilience of community residents, Climate Home News found on a visit to the region.
Situated in a semi-arid region, houses in Laikipia are mostly built with wood or cement blocks with corrugated iron sheets for roofing. This building method usually leaves the insides of homes scorching hot – and as global warming accelerates, the heat is becoming unbearable.
Peter Muthui, principal of Mukima Secondary School in Laikipia County, lived in these harsh conditions until 2023, when the Laikipia Integrated Housing Project began in his community.
The project uses compressed earth block (CEB) technology, drawing on traditional building methods and local materials – including soil, timber, grass and cow dung – to keep buildings cool in the highland climate. The thick earth walls provide insulation against the heat.
Peter Muthui, principal of Mukima Secondary School in Laikipia County, stands in front of classroom blocks built with compressed earth blocks (Photo: Vivian Chime)
Peter Muthui, principal of Mukima Secondary School in Laikipia County, stands in front of classroom blocks built with compressed earth blocks (Photo: Vivian Chime)
“Especially around the months of September all the way to December, it is very, very hot [in Laikipia], but as you might have noticed, my house is very cool even during the heat,” Muthui told Climate Home News.
His school has also deployed the technology for classrooms and boarding hostels to ensure students can carry on studying during the hottest seasons of the year. This way, they are protected from severe conditions and school closures can be avoided. In South Sudan, dozens of students collapsed from heat stroke in the capital Juba earlier this year, causing the country to shutter schools for weeks.
COP30 sees first action call on sustainable, affordable housing
The buildings and construction sector accounts for 37% of global emissions, making it the world’s largest emitter of greenhouse gases, according to the UN Environment Programme (UNEP). While calls to decarbonise the sector have grown, meaningful action to cut emissions has remained limited.
At COP28 in Dubai, the United Arab Emirates and Canada launched the Cement and Concrete Breakthrough Initiative to speed up investment in the technologies, policies and tools needed to put the cement and concrete industry on a net zero-emissions path by 2050.
Canada’s innovation minister, François-Philippe Champagne, said the initiative aimed to build a competitive “green cement and concrete industry” which creates jobs while building a cleaner future.
Coordinated by UNEP’s Global Alliance for Buildings and Construction, the council has urged countries to embed climate considerations into affordable housing from the outset, “ensuring the drive to deliver adequate homes for social inclusion goes hand in hand with minimising whole-life emissions and environmental impacts”.
Homes built with compressed earth blocks in Laikipia (Photo: Julián Reingold)
Homes built with compressed earth blocks in Laikipia (Photo: Julián Reingold)
With buildings responsible for 34% of energy-related emissions and 32% of global energy demand, and 2.8 billion people living in inadequate housing, the ICBC stressed that “affordable, adequate, resource-efficient, low-carbon, climate-resilient and durable housing is essential to a just transition, the achievement of the Sustainable Development Goals and the effective implementation of the Paris Agreement”.
Compressed earth offers local, green alternative
By using locally sourced materials, and just a little bit of cement, the compressed earth technology is helping residents in Kenya’s Laikipia region to build affordable, climate-smart homes that reduce emissions and environmental impacts while creating economic opportunities for local residents, said Dacan Aballa, construction manager at Habitat for Humanity International, the project’s developers.
Aballa said carbon emissions in the construction sector occur all through the lifecycle, from material extraction, processing and transportation to usage and end of life. However, by switching to compressed earth blocks, residents can source materials available in their environment, avoiding nearly all of that embedded carbon pollution.
According to the World Economic Forum (WEF), global cement manufacturing is responsible for about 8% of total CO2 emissions, and the current trajectory would see emissions from the sector soar to 3.8 billion tonnes per year by 2050 – a level that, compared to countries, would place the cement industry as one of the world’s top three or four emitters alongside the US and China.
Comparing compressed earth blocks and conventional materials in terms of carbon emissions, Aballa said that by using soil native to the area, the process avoids the fossil fuels that would normally have been used for to produce and transport building materials, slashing carbon and nitrogen dioxide emissions.
The local building technology also helps save on energy that would have been used for cooling these houses as well as keeping them warm during colder periods, Aballa explained.
Justin Atemi, water and sanitation officer at Habitat for Humanity, said the brick-making technique helps reduce deforestation too. This is because the blocks are left to air dry under the sun for 21 days – as opposed to conventional fired-clay blocks that use wood as fuel for kilns – and are then ready for use.
Women walk passed houses in the village of Kangimi, Kaduna State, Nigeria (Photo: Sadiq Mustapha)
Traditional knowledge becomes adaptation mechanism
Africa’s red clay soil was long used as a building material for homes, before cement blocks and concrete became common. However, the method never fully disappeared. Now, as climate change brings higher temperatures, this traditional building approach is gaining renewed attention, especially in low-income communities in arid and semi-arid regions struggling to cope with extreme heat.
From Kenya’s highlands to Senegal’s Sahelian cities, compressed earth construction is being repurposed as a low-cost, eco-friendly option for homes, schools, hospitals – and even multi-storey buildings.
Senegal’s Goethe-Institut in Dakar was constructed primarily using compressed earth blocks. In Mali, the Bamako medical school, which was built with unfired mud bricks, stays cool even during the hottest weather.
And more recently, in Nigeria’s cultural city of Benin, the just-finished Museum of West African Art (MOWA) was built using “rammed earth” architecture – a similar technology that compresses moist soil into wooden frames to form solid walls – making it one of the largest such structures in Africa.
David Sathuluri is a Research Associate and Dr. Marco Tedesco is a Lamont Research Professor at the Lamont-Doherty Earth Observatory of Columbia University.
As climate scientists warn that we are approaching irreversible tipping points in the Earth’s climate system, paradoxically the very technologies being deployed to detect these tipping points – often based on AI – are exacerbating the problem, via acceleration of the associated energy consumption.
The UK’s much-celebrated £81-million ($109-million) Forecasting Tipping Points programme involving 27 teams, led by the Advanced Research + Invention Agency (ARIA), represents a contemporary faith in technological salvation – yet it embodies a profound contradiction. The ARIA programme explicitly aims to “harness the laws of physics and artificial intelligence to pick up subtle early warning signs of tipping” through advanced modelling.
We are deploying massive computational infrastructure to warn us of climate collapse while these same systems consume the energy and water resources needed to prevent or mitigate it. We are simultaneously investing in computationally intensive AI systems to monitor whether we will cross irreversible climate tipping points, even as these same AI systems could fuel that transition.
The computational cost of monitoring
Training a single large language model like GPT-3 consumed approximately 1,287 megawatt-hours of electricity, resulting in 552 metric tons of carbon dioxide – equivalent to driving 123 gasoline-powered cars for a year, according to a recent study.
GPT-4 required roughly 50 times more electricity. As the computational power needed for AI continues to double approximately every 100 days, the energy footprint of these systems is not static but is exponentially accelerating.
And the environmental consequences of AI models extend far beyond electricity usage. Besides massive amounts of electricity (much of which is still fossil-fuel-based), such systems require advanced cooling that consumes enormous quantities of water, and sophisticated infrastructure that must be manufactured, transported, and deployed globally.
The water-energy nexus in climate-vulnerable regions
A single data center can consume up to 5 million gallons of drinking water per day – sufficient to supply thousands of households or farms. In the Phoenix area of the US alone, more than 58 data centers consume an estimated 170 million gallons of drinking water daily for cooling.
The geographical distribution of this infrastructure matters profoundly as data centers requiring high rates of mechanical cooling are disproportionately located in water-stressed and socioeconomically vulnerable regions, particularly in Asia-Pacific and Africa.
At the same time, we are deploying AI-intensive early warning systems to monitor climate tipping points in regions like Greenland, the Arctic, and the Atlantic circulation system – regions already experiencing catastrophic climate impacts. They represent thresholds that, once crossed, could trigger irreversible changes within decades, scientists have warned.
Yet computational models and AI-driven early warning systems operate according to different temporal logics. They promise to provide warnings that enable future action, but they consume energy – and therefore contribute to emissions – in the present.
This is not merely a technical problem to be solved with renewable energy deployment; it reflects a fundamental misalignment between the urgency of climate tipping points and the gradualist assumptions embedded in technological solutions.
The carbon budget concept reveals that there is a cumulative effect on how emissions impact on temperature rise, with significant lags between atmospheric concentration and temperature impact. Every megawatt-hour consumed by AI systems training on climate models today directly reduces the available carbon budget for tomorrow – including the carbon budget available for the energy transition itself.
The governance void
The deeper issue is that governance frameworks for AI development have completely decoupled from carbon budgets and tipping point timescales. UK AI regulation focuses on how much computing power AI systems use, but it does not require developers to ask: is this AI’s carbon footprint small enough to fit within our carbon budget for preventing climate tipping points?
There is no mechanism requiring that AI infrastructure deployment decisions account for the specific carbon budgets associated with preventing different categories of tipping points.
Meanwhile, the energy transition itself – renewable capacity expansion, grid modernization, electrification of transport – requires computation and data management. If we allow unconstrained AI expansion, we risk the perverse outcome in which computing infrastructure consumes the surplus renewable energy that could otherwise accelerate decarbonization, rather than enabling it.
With global consensus over climate action faltering on the accord’s 10th anniversary, experts say “coalitions of the willing” should move faster and with more ambition
Rising demand in Southeast Asia and India is expected to prevent coal use from falling significantly this decade, the International Energy Agency predicts
What would it mean to resolve the paradox?
Resolving this paradox requires, for example, moving beyond the assumption that technological solutions can be determined in isolation from carbon constraints. It demands several interventions:
First, any AI-driven climate monitoring system must operate within an explicitly defined carbon budget that directly reflects the tipping-point timescale it aims to detect. If we are attempting to provide warnings about tipping points that could be triggered within 10-20 years, the AI system’s carbon footprint must be evaluated against a corresponding carbon budget for that period.
Second, governance frameworks for AI development must explicitly incorporate climate-tipping point science, establishing threshold restrictions on computational intensity in relation to carbon budgets and renewable energy availability. This is not primarily a “sustainability” question; it is a justice and efficacy question.
Third, alternative models must be prioritized over the current trajectory toward ever-larger models. These should include approaches that integrate human expertise with AI in time-sensitive scenarios, carbon-aware model training, and using specialized processors matched to specific computational tasks rather than relying on universal energy-intensive systems.
The deeper critique
The fundamental issue is that the energy-system tipping point paradox reflects a broader crisis in how wealthy nations approach climate governance. We have faith that innovation and science can solve fundamental contradictions, rather than confronting the structural need to constrain certain forms of energy consumption and wealth accumulation. We would rather invest £81 million in computational systems to detect tipping points than make the political decisions required to prevent them.
The positive tipping point for energy transition exists – renewable energy is now cheaper than fossil fuels, and deployment rates are accelerating. What we lack is not technological capacity but political will to rapidly decarbonize, as well as community participation.
Deploying energy-intensive AI systems to monitor tipping points while simultaneously failing to deploy available renewable energy represents a kind of technological distraction from the actual political choices required.
The paradox is thus also a warning: in the time remaining before irreversible tipping points are triggered, we must choose between building ever-more sophisticated systems to monitor climate collapse or deploying available resources – capital, energy, expertise, political attention – toward allaying the threat.
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