We summon it with a tap, a prompt, a question whispered into a glowing screen. Answers arrive instantly, as if born from thin air. But nothing on Earth is ever truly weightless. Behind every seamless AI response lies a hidden trail: minerals pulled from deep soil, vast volumes of water sacrificed to cool overheated servers, and electricity drawn from power grids already strained by a warming world.
Artificial Intelligence feels light. Invisible. Almost unreal, with the illusion of weightlessness. While, AI and climate change are often discussed as separate forces shaping our future, but they are not parallel stories unfolding side by side. They are intertwined threads of the same fabric, tightening steadily around the planet. Beneath surface, one crisis erodes ecosystems and livelihoods, while the other promises innovation and efficiency – yet both draw from the same finite Earth, binding technological progress to environmental consequence.
AI And Climate Change: The Hidden Environmental Cost of Artificial Intelligence in an Age of Climate Change
We speak of “the cloud” as if it drifts freely above the Earth – light, clean, untouched by soil or water. Artificial Intelligence is often described the same way: invisible lines of code floating somewhere beyond nature. But nature knows better. Nothing that draws energy, water, and minerals is ever detached from the living world. AI is not weightless. It is grounded – firmly and irrevocably – in land, rivers, forests, and the fragile systems that sustain life – where the climate crisis meets the AI Boom.
In the age of AI and climate change, every digital answer carries a physical cost. Behind each instant response are concrete data centres, humming servers, and vast energy networks pulling electricity from grids already strained by rising temperatures. Water is diverted to cool machines, minerals are extracted to build hardware, and landscapes are reshaped so intelligence can move at the speed of light. What feels effortless on a screen leaves deep impressions on the Earth.
As AI expands across the globe, its environmental footprint grows alongside it, quietly intensifying climate pressures. This growth does not occur in isolation from nature’s limits. It intersects directly with drought, heatwaves, deforestation, and water scarcity – binding the AI revolution to the climate crisis in ways that are rarely acknowledged. AI and climate change are not separate stories unfolding in parallel; they are intertwined forces shaping the same planetary future.
Yet the weight of this transformation does not fall evenly. The environmental costs of AI are borne disproportionately by communities in the Global South – places already living at the edge of climate vulnerability. Regions facing water stress, ecological degradation, and resource extraction now host energy-hungry data infrastructure that competes with people, agriculture, and ecosystems for survival. Through this lens, AI is no longer just a technological phenomenon. It is a question of climate justice, responsibility, and balance.
Early conversations about AI’s environmental impact focused on carbon emissions from training large models. Those numbers were startling, but incomplete. The deeper truth lies in everyday use. Millions of daily AI queries – quiet, constant, and invisible – consume energy and freshwater continuously. Data centres now drink from rivers and aquifers at extraordinary rates, often in landscapes already weakened by climate change. This is not a distant future problem. It is unfolding now, in real places, with real consequences.
As the climate warms and water systems grow more fragile, AI infrastructure increasingly competes with life itself. And as with so many environmental crises, the benefits flow outward while the costs settle locally. AI, despite its digital sheen, is not a cloud floating above nature. It is a landscape – one carved into soil, water, and air. To speak honestly about AI and climate change is to recognise that technological progress cannot be separated from the Earth that sustains it. What we build in code is ultimately written into nature.
Where AI Begins: Beneath the Ground
Every AI system begins long before a single line of code is written. It begins beneath the ground. Despite its digital image, artificial intelligence has a growing physical footprint – one rooted in soil, stone, and extraction.
AI does not start with software or algorithms; it starts with minerals pulled from the Earth. From rare-earth mining to energy-hungry data centres, every stage of the AI lifecycle consumes natural resources and leaves behind emissions. The microchips that power artificial intelligence depend on minerals such as cobalt, lithium, and copper – materials essential to modern computation, yet costly to extract. Their removal scars landscapes, contaminates waterways, and disrupts communities that live closest to the source.
In many parts of the Global South, this extraction carries the heavy legacy of colonial exploitation, now repackaged for the digital age. Nowhere is this more visible than in the Democratic Republic of the Congo, where cobalt mining – critical to AI hardware and batteries – has been repeatedly linked to hazardous working conditions and child labour. For every tonne of rare-earth minerals extracted, thousands of tonnes of toxic waste are often left behind, settling into soil and rivers long after profits have moved on.
AI does not invent these injustices. It accelerates them. As demand for intelligent systems grows, so does pressure on already fragile landscapes. This connection between AI and climate change is often hidden from view, masked by sleek interfaces and abstract language, yet it is written clearly into the Earth.
Early research into AI’s environmental impact focused largely on the carbon emissions released during the training of large models. Those findings were sobering: training a single large language model could emit hundreds of tonnes of carbon dioxide equivalent (CO₂e).
But recent studies reveal a critical shift. The greatest environmental cost no longer lies in training alone, but in AI inference – the everyday use of AI by millions of users across the world.
At the same time, the rapid global expansion of data centres is fueling an emerging water crisis. Cooling AI infrastructure requires vast volumes of freshwater, often drawn from regions already facing drought and rising heat. As climate change tightens its grip on water systems, AI infrastructure increasingly competes with ecosystems, agriculture, and local communities for survival. Here, beneath the ground and behind the screen, the true story of AI and climate change begins – not in code, but in land, water, and lives.
E-Waste: The Silent Afterlife of AI
Once AI hardware becomes obsolete, another injustice begins. On reaching the end of its short life, AI hardware does not disappear. It becomes e-waste – a growing tide of discarded electronics laced with mercury, lead, and toxic chemicals. Much of this waste flows southward, exported from wealthier nations to poorer ones, where informal recycling exposes workers and children to serious health risks.
This offshoring creates layered harms: Villages become dumping grounds, fields turn into scrapyards, and air carries the metallic taste of burning circuits. This is the quiet afterlife of innovation – rarely photographed, rarely discussed. The toxic pollution poses health risks for informal workers, results in unsafe recycling practices, and additional greenhouse gas emissions. Hence, the AI’s environmental footprint does not end when a device is switched off – it lingers in landfills, rivers, and human bodies.
Energy, Fossil Fuels, and Carbon Lock-In
AI needs electricity the way forests need rain. Without it, nothing moves, nothing grows. But where that electricity comes from matters deeply. In the story of AI and climate change, energy is the invisible force shaping consequences far beyond the screen.
Across the world, most data centres still draw heavily from fossil fuels. While some companies in Europe and parts of North America invest in renewable energy and operate on relatively cleaner grids, many facilities in Asia, Africa, and other regions of the Global South remain tied to coal, oil, and gas-based power systems. Access to clean energy is uneven, and so are the emissions that follow.
The same AI service – answering a question, generating an image, and translating a sentence – can produce vastly different carbon footprints depending on where it is hosted. In regions dependent on fossil fuels, every AI interaction locks in additional emissions, quietly reinforcing carbon-intensive energy systems already driving climate instability.
This is where AI’s operational phase introduces another layer of climate injustice. Communities that contribute the least to global emissions often host the infrastructure that produces the most carbon. Energy demand from data centres places additional strain on local grids, sometimes diverting power away from households, hospitals, and essential services. The benefits of AI flow outward, while the environmental and social costs remain rooted in place.
In this way, AI does not merely consume energy – it participates in carbon lock-in, prolonging reliance on fossil fuels at a moment when the planet can least afford it. The connection between AI and climate change is not accidental or abstract. It is built into power lines, fuel sources, and policy choices that determine who bears the burden of progress and who reaps its rewards.
This creates a subtle powerful injustice: The environmental burden of AI is shifted toward regions with fewer choices, weaker protections, and limited access to clean energy. The same AI services can generate higher emissions simply because they are hosted in places already locked into carbon-intensive power systems. At the same time, AI is increasingly used to optimise fossil fuel extraction itself. Within the oil and gas industry, intelligent systems help locate new reserves, improve efficiency, and extend the operational lifespan of existing fields.
While such tools can reduce leaks and short-term waste, they serve a larger purpose – prolonging profitability of fossil fuels at a moment when the world must urgently move beyond them. In this way, AI does more than consume energy. It quietly reinforces the very systems driving climate breakdown, deepening global carbon lock-in and entrenching inequalities already shaped by AI and climate change.
AI Emissions in an Unequal World
Climate change has never been just about carbon. It is about who emits and who suffers. The richest segments of humanity produce the majority of global emissions, while the poorest contribute the least – yet endure the harshest consequences.
Floods, heatwaves, crop failures, and water scarcity strike hardest where resources are already thin. AI deepens this divide. Its benefits flow upward – to corporations, high-income users, and technologically dominant nations. Its environmental costs spread outward – into mining regions, drought-prone communities, and overstressed power grids. This is why AI cannot be discussed without justice.
Why AI Ethics Must Listen to the Earth
Much of AI ethics focuses on fairness in algorithms, bias in data, and transparency in decision-making. These questions matter. But they leave out the ground beneath our feet. A climate justice lens asks different questions: Who supplies the water, who breathes the polluted air, and who loses land so servers can run without interruption? Justice is not only about outcomes, but about voice. Communities most affected by AI’s environmental footprint are rarely invited into decision-making spaces. They are consulted last – if at all.
The Global South’s Quiet Dilemma
For many countries in the Global South, AI offers hope – economic growth, innovation, participation in a digital future. But it also brings difficult trade-offs. India, for example, is becoming a major AI hub while facing intensifying heatwaves, water stress, and energy shortages. Data centres promise investment, yet demand resources that ecosystems and communities can barely spare.
Climate justice does not argue against AI development. It asks for balance: access to clean energy, investment in efficient computing, shared responsibility for emissions, and respect for ecological limits
Conclusion: Remembering That the Earth Is Still Here
AI may feel new, fast, and unstoppable. But the Earth it runs on is ancient, patient, and finite. Rivers cannot be optimised forever, Soil does not regenerate at machine speed, and communities cannot absorb endless extraction without breaking.
Seeing AI through the lens of climate justice does not reject technology. It grounds it – in responsibility, humility, and care. It reminds us that innovation, like nature, must exist within limits. In the end, the question is not whether machines will shape the future. It is whether we will remember the living world that makes them possible.
