The explosive growth of artificial intelligence is creating an unprecedented demand for computing power. As tech giants and startups race to build massive data centers to train and run increasingly complex AI models, a critical question emerges: What would be the environmental impact of powering this digital revolution with renewable energy versus fossil fuels? The answer involves complex trade-offs across energy production, materials science, supply chains, and carbon emissions that will shape our climate future.

The AI Energy Crisis

The numbers are staggering. AI workloads consume 10-100 times more energy than traditional computing tasks. A single training run for a large language model can emit as much carbon as five cars over their entire lifetimes. With AI adoption accelerating across industries, data center electricity consumption is projected to double by 2026, potentially reaching 8-10% of global electricity demand by 2035.

“We’re witnessing a fundamental shift in computing economics,” says Dr. Elena Sharma, climate scientist at the Global Energy Institute. “The energy footprint of AI doesn’t follow traditional computing efficiency curves. As models grow more complex, their energy demands scale exponentially.”

Renewable vs. Non-Renewable: The Decade Ahead

Industry analysts project that AI computing capacity will increase by 8-10x in the next decade. Under a business-as-usual scenario with the current energy mix, this would result in an additional 250-300 million metric tons of CO2 emissions annually by 2035. But what if the industry pivoted entirely to renewables?

Energy Production Impact

A 100% renewable path would prevent approximately 2.5-3 billion metric tons of CO2 emissions over the decade compared to current energy mix scenarios. However, this transition presents its own challenges.

Wind and solar capacity would need to increase by an estimated 150-180 GW beyond current renewable expansion plans to accommodate AI’s growing appetite. This would require approximately $200-250 billion in additional investment, though these costs continue to fall as renewable technologies mature.

Hydropower, already powering several major data centers in the Nordic countries, offers another renewable option with high reliability, though geographic limitations restrict its scalability compared to wind and solar.

Materials and Supply Chain Considerations

The environmental equation extends beyond operational emissions to the materials and manufacturing required for both computing infrastructure and energy systems.

For renewable infrastructure, mining rare earth elements for wind turbines and manufacturing photovoltaic cells creates significant environmental impacts. Solar panel production is energy-intensive and generates toxic waste, while wind turbines require substantial amounts of concrete, steel, and composite materials.

Likewise, AI hardware demands rare minerals like cobalt, lithium, and gallium. Under a business-as-usual scenario, researchers estimate mining for these materials could increase water pollution by 25-30% and generate 8-10 million tons of e-waste annually by 2035.

“The rare earth supply chain is the hidden environmental cost in both scenarios,” notes Dr. Kenji Watanabe, materials scientist at the Technology Sustainability Institute. “Whether you’re building solar panels or GPUs, you’re increasing pressure on these critical minerals. The difference is that renewable energy infrastructure has a longer operational lifespan and generates clean energy throughout.”

Interestingly, renewable-powered data centers would ultimately require less total mining activity over the decade. While they demand more minerals initially for renewable infrastructure, they avoid the continuous resource extraction required for fossil fuel operations.

Water Usage and Thermal Management

Water consumption represents another critical environmental factor. Traditional data centers use enormous quantities of water for cooling—up to 3-5 million gallons daily for a hyperscale facility. Non-renewable energy generation further compounds this issue, with coal and natural gas plants requiring substantial water for cooling towers.

Renewable-powered facilities often implement advanced cooling technologies like ambient air cooling and closed-loop systems that reduce water usage by 80-90%. When combined with renewable energy sources that use minimal water during operation (particularly wind and solar), the water footprint difference becomes substantial—an estimated 1.2-1.5 trillion gallons saved over the decade.

Land Use Considerations

Land usage presents a more complex picture. Solar farms require 5-10 acres per megawatt, while wind farms need 30-140 acres per megawatt (though most of this land remains available for agriculture). In contrast, fossil fuel infrastructure requires less direct land but creates more environmental degradation through extraction, processing, and waste disposal.

“The land use question isn’t just about quantity but quality,” explains environmental economist Dr. Sarah Jenkins. “Fossil fuel extraction permanently degrades ecosystems in ways that are difficult to restore. Well-planned renewable installations can coexist with natural habitats and even enhance biodiversity when implemented thoughtfully.”

The Carbon Math

When all factors are considered—construction, operation, supply chains, and decommissioning—the renewable path would reduce net carbon emissions by approximately 65-80% compared to fossil-fuel-powered expansion over the decade.

The most significant savings come from operational emissions, where renewable energy eliminates the continuous carbon output from coal, natural gas, and other fossil fuels. Construction emissions are higher initially for renewable infrastructure but are quickly offset by operational savings, typically within 2-4 years.

“The compounding effect of renewable energy is powerful,” says climate modeler Dr. Marcus Chen. “Each year of operation widens the emissions gap between the two scenarios, with the difference becoming more pronounced over time.”

The Nordic Advantage: DataEnergy’s Hydropowered Revolution

Against this backdrop, innovative companies are pioneering sustainable approaches to AI infrastructure. DataEnergy represents the vanguard of this movement in the Nordic region by harnessing Norway’s abundant hydropower resources and cold climate to create a new paradigm for AI computing infrastructure.

The company is developing a network of strategically placed data centers with direct connection to Norwegian hydropower infrastructure, enabling 100% renewable energy sourcing with N+2 power redundancy designs that minimize or eliminate the need for fossil-fuel backup generators. This approach addresses both the operational carbon footprint and the reliability concerns that have traditionally plagued renewable energy adoption in mission-critical computing.

“We’re fundamentally redesigning the complete infrastructure adapted for new data centers with high power density compute needed for AI,” explains Rune Skow, DataEnergy’s CTO, who brings extensive experience from telecommunications infrastructure and data center development. “Our concept represents long-term significant cost savings in terms of materials used as well as energy savings in operations—fully green and sustainable.”

What distinguishes DataEnergy’s approach is its multi-faceted engineering strategy. Beyond simply using renewable energy, the company employs advanced thermal management systems that leverage the ambient Nordic climate for free cooling, targeting a Power Usage Effectiveness (PUE) of less than 1.1. To accommodate the extreme thermal density of modern AI workloads—which can reach 30-100kW+ per rack—DataEnergy is transitioning from traditional air cooling to more efficient liquid cooling systems.

Perhaps most innovative is the company’s circular economy approach to energy utilization. Their facilities incorporate engineered systems for waste heat capture and reuse, employing heat exchangers to transfer thermal energy rejected by IT equipment to secondary applications such as district heating networks, agriculture (greenhouses), or aquaculture (land-based fish farming). This approach is quantified using metrics like the Energy Reuse Factor (ERF).

“We’re offering clean zero carbon footprint from hydro-powered data centers in Norway and the Nordics, close to production, with access to running water, with a minimum of grid loss,” notes Øivind Oberg Magnussen, DataEnergy’s CFO. “The re-use of energy for other businesses establishes a full circle of usage.”

The company’s modular and scalable design allows for deployments in increments (from 2.5 MW to 40+ MW) with significantly reduced build times of 18-24 months. In partnership with MIT research scientists, DataEnergy is creating digital twins to optimize their designs and validate environmental performance across the infrastructure lifecycle.

In collaboration with Planet Home, DataEnergy is working to address not just operational emissions but the entire environmental footprint of AI infrastructure, including hardware lifecycle management, water usage, and land impact. Their approach represents a holistic rethinking of data center design for the age of AI—one that recognizes computing as a major energy sector requiring sustainable reinvention from the ground up.

The Path Forward

The environmental math is clear: A renewable-powered AI infrastructure would dramatically reduce the climate impact of our digital future. However, this transition requires intentional policy support, technological innovation, and industry commitment.

“The decisions made today about AI infrastructure will lock in environmental impacts for decades,” warns climate policy expert Dr. James Wilson. “We need to recognize that computing is becoming a major energy sector in its own right and plan accordingly.”

As companies like DataEnergy demonstrate, combining renewable energy with innovative approaches to computing architecture, cooling systems, and circular material flows can create a foundation for sustainable AI development. The challenge ahead is scaling these solutions rapidly enough to meet explosive demand growth.

For an industry built on exponential thinking, the message is straightforward: The environmental cost of AI’s expansion is not inevitable but a choice—one that will significantly shape our collective climate future in the critical decade ahead.