AI Emissions: The Hidden Carbon Cost of Intelligence and How to Manage It Strategically

AI systems like ChatGPT, while transformative, are not free from consequence. Behind the frictionless digital interface lies an energy-intensive infrastructure that carries a measurable carbon cost. As companies scale their use of AI, emissions related to model training, deployment, and usage are rapidly compounding. These are not marginal figures. The carbon impact of AI, particularly large language models (LLMs), is now a material issue for sustainability and ESG leaders.

For context, training a single large model such as GPT-3 has been estimated to emit over 500 metric tons of CO₂e, equivalent to one person taking 500 roundtrip flights from New York to San Francisco. Since then, model complexity and usage volume have only increased. While these figures are widely cited, they barely scratch the surface of how AI-driven emissions play out across operations, supply chains, and Scope 3 disclosures.

Why AI Emissions Are Now a Board-Level Issue

Three converging factors have elevated AI emissions from a niche concern to a core ESG risk:

• Explosive adoption of generative AI tools across enterprises

• Mounting regulatory pressure around emissions disclosure and digital infrastructure

• Shareholder scrutiny linking ESG maturity with tech governance

In April 2024, the European Commission proposed mandatory carbon transparency for AI developers, requiring companies to disclose emissions tied to model training and deployment. In the US, the SEC’s Climate Rule, though temporarily delayed in the courts, has triggered proactive action by publicly listed firms to inventory all material emissions sources—including cloud computing and AI infrastructure.

Moreover, recent data from the International Energy Agency shows that data center electricity demand is projected to more than double by 2026, with AI as a primary driver. These shifts represent more than operational risks. They introduce compliance exposure, reputational liability, and strategic tradeoffs in how firms deploy AI at scale.

Hidden Emissions in AI Value Chains

Most emissions linked to AI use do not sit within the company that uses the model, but upstream with the hyperscalers that host and train these systems. This introduces a Scope 3 dilemma. Companies leveraging AI in their workflows are indirectly responsible for significant energy and carbon usage, but often lack clear line of sight.

In our experience, organizations that attempt to account for AI emissions without deeper coordination across procurement, IT, and sustainability functions struggle to achieve traceability or set meaningful targets. Many over-rely on cloud provider ESG reports, which often aggregate emissions data in ways that obscure the marginal impact of specific AI workloads.

A practical starting point for firms is to conduct a materiality assessment of digital emissions, identifying which AI tools and processes carry disproportionate energy loads. This should be followed by supplier engagement around workload-specific carbon intensity metrics. Some leading cloud providers now offer granular emissions dashboards, but their adoption remains uneven and rarely maps cleanly onto Scope 3 frameworks.

The Strategic Imperative: Aligning AI Deployment with ESG Strategy

AI is not inherently unsustainable, but its deployment must be guided by strategic carbon-aware design. We are beginning to see clients implement AI governance frameworks that include emissions thresholds, model selection based on energy efficiency, and alignment with internal carbon pricing. These frameworks go beyond IT cost efficiency—they link technology decisions with ESG outcomes.

Case in Point: Emissions Hidden in AI Marketing Workflows

In a recent article on ANA.net, one of our partners, Arif, detailed an emissions audit for a European consumer electronics client. The company’s marketing team used AI tools like Jasper and Google Cloud automation for content generation and segmentation. The analysis revealed that background processes and default platform settings were responsible for more AI emissions than the company’s entire business travel footprint during the quarter. With targeted changes such as disabling unused automation, adjusting data center regions, and shifting some tasks back to human teams, AI-related marketing emissions dropped by 22 percent with no impact on campaign performance. This example illustrates how default AI workflows can introduce unnecessary carbon costs and why intentional configuration is essential.

As AI becomes further embedded in core business operations—from customer service to product design—organizations must treat its emissions as strategically relevant, not merely technical or marginal.

Operationalizing AI Emissions Management: Where to Focus Next

Despite growing awareness, most firms remain underprepared to quantify and manage AI-related emissions across the lifecycle. This gap is not due to lack of intent but rather the complexity of emissions attribution in AI systems. Energy use is influenced by variables such as model architecture, compute hardware, data center location, power mix, and frequency of use.

For instance, inference—the act of using an already trained model—can account for more total emissions than training when deployed at scale. This is particularly relevant in industries with high-volume automation, such as e-commerce, fintech, and customer support. Yet most sustainability reporting frameworks are not designed to capture these dynamic, usage-based emissions.

Firms should prioritize three foundational steps to build maturity in this space:

• Establish AI-specific emissions baselines, distinguishing training vs inference vs idle energy

• Engage vendors and cloud partners for transparent reporting aligned with GHG Protocol Scope 3 guidance

• Embed carbon intensity into AI procurement and model selection criteria

In our consulting experience, we’ve found that emissions baselining efforts often fail without a cross-functional taskforce that includes IT, procurement, and sustainability. Without technical fluency in both AI architecture and ESG reporting, it is easy to misattribute emissions or underreport significant hotspots.

Policy Signals and Regulatory Direction

Regulators are catching up. The EU AI Act, passed in 2024, includes provisions for energy efficiency and transparency in high-risk AI systems. Meanwhile, the OECD AI Principles are being reinterpreted through a climate lens, encouraging voluntary disclosures on emissions performance.

In the United States, while federal guidance is still fragmented, the Department of Energy has initiated pilot programs to benchmark emissions from large-scale model training. At the state level, California’s SB 253 (Climate Corporate Data Accountability Act) will compel many AI-heavy firms to disclose Scope 3 emissions starting in 2027, further elevating the issue.

These emerging requirements create both risk and opportunity. Early movers that integrate AI emissions into ESG strategy will not only reduce exposure, but also differentiate in markets where digital sustainability is becoming a competitive signal.

A Missed Opportunity: Underutilized Levers for AI Carbon Efficiency

Even among advanced organizations, several levers remain underutilized. These include:

• Model distillation, which compresses large models into smaller, more efficient variants

• Time-of-day optimization, aligning inference to grids with higher renewable penetration

• Geographic workload placement, choosing regions with lower carbon intensity for compute

While technically feasible, these optimizations often sit outside the sustainability team’s scope. Aligning sustainability goals with IT architecture decisions requires a governance reset, supported by updated KPIs, incentives, and collaboration models.

The Road Ahead: A Strategic Lens on AI Carbon Impacts

As AI becomes a core enabler of business transformation, sustainability leaders must ensure their organizations are not caught off-guard by the emissions it introduces. The question is not simply how to minimize AI’s carbon footprint, but how to balance AI’s transformative benefits with its environmental costs—and how to do so in a way that strengthens compliance, reputation, and long-term resilience.

Many organizations are still in the early stages of recognizing AI emissions as material. But the shift is underway. Competitive advantage will accrue to those who act now to embed emissions intelligence into AI strategy, procurement, and operations.

The tools exist. What’s often missing is a strategic blueprint and the right cross-functional coordination.

Let’s Talk

Our team at Gasilov Group supports organizations in aligning AI strategy with ESG mandates, developing tailored frameworks for emissions attribution, governance, and optimization. We do not offer one-size-fits-all answers. Instead, we partner with your teams to build practical, measurable, and forward-looking solutions.

Reach out to explore how we can help your organization navigate the intersection of AI performance and sustainability leadership.

Frequently Asked Questions (FAQ): Artificial Intelligence (AI) Emissions

What are the main sources of emissions from AI systems like ChatGPT?

The primary emissions come from two phases: model training and inference. Training involves high compute usage in data centers, often over several weeks, while inference generates emissions every time the model is used. These processes consume large amounts of electricity, contributing to carbon emissions depending on the grid’s energy mix.

How can companies measure AI-related emissions within Scope 3 reporting?

Organizations should work with cloud providers to obtain workload-specific emissions data and align it with Scope 3 Category 1 (Purchased Goods and Services) or Category 2 (Capital Goods) depending on the use case. Engaging IT and procurement teams is essential for accurate attribution.

Are there regulations that require disclosure of AI carbon emissions?

While not always explicit, several policies are converging on digital emissions. The EU AI Act and California’s SB 253 both create pressure for transparency. The SEC Climate Rule also pushes companies to consider emissions from IT infrastructure, including AI deployments.

Can companies reduce AI emissions without compromising performance?

Yes. Techniques like model pruning, using smaller distilled models, and deploying AI workloads in regions with cleaner energy grids can lower emissions without major performance loss. Strategic workload scheduling and carbon-aware procurement are also effective levers.

What is the role of internal carbon pricing in managing AI emissions?

Internal carbon pricing helps organizations incorporate the environmental cost of AI use into business decisions. By assigning a price to carbon-intensive AI models or deployments, firms can make more informed trade-offs and align technology choices with net-zero goals.

Written by: Gasilov Group Editorial Team

Reviewed by: Rafael Rzayev, Partner – ESG Policy & Economic Sustainability

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