Data center energy capacity is expected to accelerate five times more, from 25 GW to 120 GW by 2030. At the same time, global investments in data center infrastructure will significantly increase, amounting to trillions of dollars, which will create a challenging environment for carbon accounting. In Europe, carbon data has started being treated the same way it is with financial data, resulting in developments that show how companies should track their environmental footprint. Not as an optional process but more like an essential business requirement.
As data becomes messier, with audit-grade outputs, AI carbon accounting has taken its fair share in this space, but it has also created a new class of risks. Regulations are behind this strong demand under the CSRD and the EU Carbon Border Adjustment Mechanism (CBAM) that aims to equalize the carbon price paid by EU industries under the Emissions Trading System (ETS) with that of non-EU producers.
In this piece, you will find out how AI can make accounting emissions a little easier, main challenges that need to be tackled but you will mostly understand how adequately trained teams can handle massive data and audit pressure.
The previous understanding of carbon accounting, which involved rough calculations, is no longer applicable to companies. Starting in 2026, the new rules will require precision in measuring emissions, along with data that can be checked and verified.
Older methodologies on carbon accounting have confirmed how incorrectly many companies were calculating their emissions; most of them had a significant deviation of almost 30%–40%. When calculations go wrong, it is not difficult to end up taking poor decisions that can also shake investors’ trust, among others. Now that the carbon audits are becoming the norm, companies are reconsidering whether their data usage should expose any inaccuracies. On the contrary, audits can verify carbon records that stick to reliable data and follow known standards like the GHG Protocol.
According to CSRD and the companies that fall under its scope, all information that relates to climate and emissions needs to be disclosed using the ESRS. ESRS 1 specifically asks for disclosure. Some companies have already published their reports last year of gross Scope 1, 2, and 3 emissions and totals, putting Scope 3 firmly on the agenda.
What is considered the main change for 2026 is the Commission’s regulation to prevent carbon leakage by putting a carbon fee on carbon-intensive goods imported into the EU via the Carbon Border Adjustment Mechanism. Importers must systematically gather and compute embedded emissions across covered goods and suppliers at scale. The CBAM period started early this year.
In the second semester of 2026, the EU AI Act will be fully applicable. Even when carbon accounting tools aren’t “high-risk” systems, organizations want to use AI in reporting.
By using AI carbon accounting, companies are not just calculating their footprint; they are taking advantage of multiple capabilities to automate processes that make operations more viable and effective. AI systems can accelerate work without compromising accuracy. Features like machine learning can take raw data from invoices, bills, docs, and supplier PDFs and turn it into useful information with the least amount of errors. They can also map materials and logistics activity to correct emissions categories.
But most importantly, AI carbon accounting creates a clear trail that permits auditing, starting with the source documents and the activity data, continuing with calculations, and ending up with a disclosure line item. The result from this technology is to solve the hardest problem: a complete record that connects every reported number back to its source.
Understanding AI carbon accounting starts with the three emission scopes defined by the GHG Protocol. The global expansion of data centers to meet AI computation needs requires organizations to track various emission types throughout their operations.
Scope 1 emissions include direct greenhouse gas emissions from owned or controlled sources. AI operations generate these emissions mainly through on-site fuel combustion. Several AI companies have built new gas-powered turbines to power their data centers. These emissions show the immediate carbon footprint from company-owned infrastructure and backup generators that keep operations running during grid outages.
Scope 2 covers indirect emissions from purchased electricity, steam, heating, and cooling. Electricity consumption dominates this category in AI operations. Data centers currently contribute about 1% of global energy-related greenhouse gas emissions and rank among the fastest-growing sources. The GHG Protocol standards require companies to report Scope 2 emissions using both location-based methods and market-based methods. Tech giants reduce their market-based footprint through renewable energy purchases, though questions about whether these purchases truly add new clean energy to grids continue.
Scope 3 includes all other indirect emissions throughout a company’s value chain. AI operations generate these emissions through hardware manufacturing, data center construction, and supply chain activities, and these are the emissions that make up more than 90% of a company’s total footprint. Companies struggle to track these emissions, citing insufficient supplier data as their greatest challenge.
As mentioned earlier, in January 2026 it started the definite period for CBAM. Throughout the year there will be measurements so that certificate sales can begin the following year (in 2027). Compliance with CBAM can be helped through AI applications as they create a manageable pipeline through the collection and analysis of complex supply chain data. AI technology can accurately calculate carbon footprints while also enabling a more careful evaluation during suppliers’ monitoring. Besides the ability for automation, companies that use AI platform tools can lay out optimization strategies for lower emissions and reduce their operational costs.
Sustainability reporting might have received a simplification push from Omnibus; however, it hasn’t derailed from its original direction of staying in line with structured climate disclosures and assurance-readiness. Companies in 2026 are expected to keep up with ways to professionalize carbon accounting with data owners, controls, and methodologies, facilitating AI’s scope of action for automation.
When the AI Act becomes enforceable (starting August 2026), procurement will become more involved in governance questions on model classifications and data use and retention, making AI carbon accounting structurally important for controlled automation.
AI carbon accounting can only support things up to a certain point. For example, in Scope 3, if suppliers are not properly engaged to have primary supplier data in place, AI alone can neither create nor improve it. Effective prioritization, along with monitoring and estimation, is what works best.
It is important to acknowledge that, during auditing, the methodologies used must be fully understandable. They should not rely only on prediction techniques without clear lineage of input and output results. Otherwise, AI tools can backfire on the final disclosure. That is why it is still often stressed in such discussions around ESG reporting how the human oversight for AI should remain active. Automation enables risks, especially when people over-rely on it.
Carbon footprint work in 2026 requires teams to face it as a continuing operating process that requires evidence with clear methods, data owners, and controls. In practice, that means building the footprint on recognized standards (e.g., the GHG Protocol Corporate Standard for organizational inventories and the Scope 3 Value Chain Standard for supplier and value-chain emissions) so boundaries and categories are consistent year to year and aligning measurement and documentation to verification-ready frameworks such as ISO 14064-1.
As European disclosure expectations (including ESRS climate requirements) continue to emphasize structured Scope 1–3 reporting and traceability (efrag.org), high-performing teams increasingly use AI to scale the not-so-fun work of collecting activity data, mapping spend and SKUs to emissions factors, quality-grading supplier inputs, and flagging anomalies, while keeping humans responsible for the decisions that matter. Professionals who understand how to deploy AI safely in their Sustainable/ESG practices, along with a carbon reduction business strategy that helps them translate the footprint into action, can handle operations more efficiently and take smarter decisions.
AI isn’t meant to replace carbon accounting expertise. It raises the bar: companies need people who can govern AI, challenge outputs, and turn complex value-chain data into audit-ready disclosures.
