Measuring AI’s Carbon Footprint: Challenges and Methodologies

A Rapidly Growing Footprint That’s Still Vastly Underestimated

Building AI systems requires massive infrastructure: always-on data centers, high-performance GPUs running for days or even weeks, and enormous volumes of data to store, process, and retrain. In fact, training a state-of-the-art language model can emit up to 502 metric tons of CO₂.

Worse yet, these numbers are mostly educated guesses. The tech industry lacks transparency, making accurate calculations difficult. Take GPT-3.5 as an example: estimates suggest that queries could generate around 260 tons of CO₂ month.

The Measurement Challenge: Lack of Transparency, Diverse Practices, No Standards

One of the main obstacles to quantifying AI’s carbon footprint is the absence of standardized methodologies. Emissions come from multiple stages—training, inference, data storage, updates—and yet, as the Mozilla Foundation notes, few developers provide clear documentation on their models’ emissions, energy use, or water consumption.

Each company relies on its own metrics, which are often incomplete or inconsistent, making comparisons virtually meaningless. Rarely is the full lifecycle of a model—from design to deployment—publicly disclosed. And when it is, the data is typically fragmented, limited for privacy reasons or fear of backlash. As a result, neither consumers nor policymakers have the tools to truly assess AI’s environmental costs.

Modeling Attempts and Emerging Indicators

Despite these hurdles, some organizations are working to create more objective metrics. Companies like Capgemini are building frameworks to estimate emissions based on variables like algorithm type, model complexity, training duration, GPU usage, and the source of electricity.

There’s also growing interest in expressing AI’s carbon intensity per query or per hour of use—metrics that could help the public better grasp the environmental trade-offs. Some platforms are even considering adding carbon indicators alongside privacy or accessibility notices.

Tech Solutions for More Responsible AI

Measurement is only the first step. Reducing impact is equally crucial. Here are some of the strategies currently being pursued:

• Algorithm optimization: Researchers are designing smaller, more efficient models that require less computing power. Techniques like model distillation and compact architectures significantly reduce energy needs.
• Using renewable energy to power data centers—solar, wind, or hydro—helps cut down on fossil fuel reliance.
• Smarter cooling systems including immersion cooling or the use of outside air, reduce energy waste.
• Heat recovery systems repurpose waste heat from servers to warm buildings or city infrastructure.
• Green standards and certifications (such as ISO environmental labels or digital-specific ratings) are also pushing the industry toward more transparency and accountability.

A Shared Responsibility

As AI accelerates and expands, its environmental footprint can no longer be an afterthought. Rigorous measurement, transparent reporting, and the integration of sustainability metrics into product design must become the norm. Public demand for transparency is rising—users want to know the true cost of the tools they rely on.

Governments, developers, enterprises, and users all need to work toward a shared goal: making AI a driver of progress, not a climate liability. That means choosing energy-efficient technologies and pushing for regulatory frameworks that prioritize sustainability. At Diabolocom, this vision guides our efforts: we design lightweight, task-specific AI that minimizes environmental impact while maximizing operational relevance.

Conclusion: Making AI Smart and Sustainable

AI’s carbon footprint isn’t inevitable—it’s the result of decisions, both technical and political, that we still have time to influence. By making environmental costs visible, measurable, and comparable, we empower all players in the ecosystem to make better choices.

What if tomorrow, every line of code—and every prompt—came with a CO₂ tag? That would be a big leap toward embedding AI into a truly sustainable future. And only then could we genuinely call it intelligent.