An Amazon Web Services data center in Ashburn, Virginia, in 2024
Nathan Howard/Bloomberg
Tech companies’ relentless push into artificial intelligence is coming at an undisclosed cost to the planet. Amazon, Microsoft and Meta are concealing their actual carbon footprints, buying credits tied to electricity use that inaccurately erase millions of tons of planet-warming emissions from their carbon accounts, a Bloomberg Green analysis finds.
Recently Microsoft reported that its emissions are 30% higher today than in 2020, when it set a goal to become carbon negative. Other tech companies’ emissions are rising, too. However, Microsoft and other AI leaders insist that the increase is because of the carbon-intensive materials used to build data centers — cement, steel and microchips — and not because of the massive amount of energy AI requires. That’s because they have said the power is mostly or all from zero-carbon sources, such as solar and wind.
Is AI being powered exclusively by clean energy? “There is no physical reality for that claim,” said Michael Gillenwater, executive director of the Greenhouse Gas Management Institute.
Companies are buying credits — called unbundled renewable energy certificates — that can make it seem that power consumed from a coal plant came from a solar farm instead. Amazon, Microsoft and Meta rely on millions of unbundled RECs each year to claim emission reductions when making voluntary disclosures to CDP, a nonprofit that runs a global environmental reporting system.
The current carbon accounting rules allow for the use of these credits for calculating a company’s carbon footprint. However, work that many academics have done shows the accounting rules need to be updated in order to accurately reflect greenhouse-gas emissions.
That’s because these carbon savings on paper are not actual emissions reductions in the atmosphere. If companies didn’t count unbundled RECs, Amazon could be forced to admit that its 2022 emissions are 8.5 million metric tons of CO2 higher than reported — that’s three times what the company disclosed and matches Mozambique’s annual impact. Microsoft’s sum could be 3.3 million tons higher than the reported tally of 288,000 tons. And Meta’s reported footprint could grow by 740,000 tons from near zero. (See below for methodological details.)
“Companies shouldn’t be allowed to use unbundled RECs to claim emissions reductions,” said Silke Mooldijk, who focuses on corporate climate responsibility at the nonprofit NewClimate Institute. “It’s misleading to consumers and investors.”
Not all tech companies have gobbled up unbundled RECs to obscure the rising emissions that have resulted from the hotly-contested AI race. Alphabet Inc.’s Google phased out its use of unbundled RECs several years ago after acknowledging that it doesn’t amount to real emissions reductions. “Studies have raised legitimate questions about whether [these credits] displace fossil-powered generation,” said Michael Terrell, senior director of energy and climate at Google.
Amazon relied on unbundled RECs for 52% of its renewable energy in 2022, making it the most dependent of the four on the instruments. A spokesperson for Amazon said the number of unbundled RECs the company uses is expected “to decrease over time” as more of its directly contracted renewable energy projects come online. Microsoft, which relied on unbundled RECs for 51% of its renewable energy, also plans “to phase out the use of unbundled RECs in future years,” a company spokesperson said.
A spokesperson for Meta, which relied on unbundled RECs and power from utilities labeled “green” for 18% of its renewable energy, said the company takes “a thoughtful approach” and that the “majority” of the company’s “renewable energy efforts” are focused on projects that “would not have otherwise been built.”
The thousands of companies using Amazon-powered AI for their customer chat bots, Microsoft’s AI Copilot for summarizing meetings, or Meta’s Llama for generating images may assume there are few or no energy emissions from relying on these models. It’s a powerful marketing tool for these big tech companies, helping to allay concerns of potential customers who are themselves likely under pressure from users and investors to lower their own carbon footprints. In reality, it’s creating a cascading impact of misreported emissions and growing demand for energy-intensive AI products.
“If consumers do not understand what the climate impact of AI is, because tech companies do not transparently report on it, then there’s no incentive for consumers to change their behavior and change to a different AI model,” said Mooldijk.
It’s a concern across finance, too. Banks and investors which tend to stuff big tech in sustainable funds too often take emissions claims at face value. “At the moment, there’s just not a sophisticated understanding of this issue,” said Gerard Pieters, a director at Tierra Underwriting that helps banks on clean-energy deals. “We’re still in a period where people make claims quite easily and they’re just copied and accepted as fact.”
Tech companies are the largest buyers of unbundled RECs in the world. Whether or not they continue buying these credits to make climate claims matters a great deal as more corporations look to cut their carbon footprint and green their credentials.
Back to the source
To understand how the companies’ use of RECs works, consider the origins of the power generated on a grid. Usually it comes from a mix of sources: from coal and gas to wind and solar. Climate-conscious companies are increasingly looking to secure power exclusively from sources that generate the least planet-warming emissions.
One way to do this is to sign a contract for clean power directly with the supplier through a power-purchase agreement, where a tech company is signing a long-term contract and thus taking on some of the risk for a period of 10 or 15 years. That, in turn, makes it easier for the developer to acquire the financing to build the solar or wind farm.
To help tech companies trace the source of that power, renewable-energy producers also issue energy attribute certificates, or RECs, which are a type of tracking instrument. However, RECs can also be bought on their own, separate from an electricity purchase. The idea behind these so-called ‘unbundled’ RECs is that there’s value in renewable energy generation beyond simply the electrons produced and sold — its lack of emissions also has a value. So since renewable energy generators produce two things of value — energy and, specifically, low-emissions energy — they should be able to get paid not just for producing electricity but also for being green.
This idea — and the calculation that sprang from it — was developed when renewable energy was expensive to produce and not price-competitive with fossil fuels. The thinking was that the extra money renewable energy developers would receive in the form of a REC might work as an incentive to produce more wind and solar development than would have been otherwise and thus be “additional.”
Studies as far back as 2010 showed that unbundled RECs weren’t delivering on that theory of stimulating the production of renewables. But that inconvenient fact was mostly ignored, and the enthusiasm for RECs led to a quirk in emissions reporting rules that allows companies to buy unbundled RECs and then deduct the emissions from their CO2 accounts. This means companies can report reduced emissions from their electricity use even if their actual use has not changed in any way (and may still come from a coal power plant).
Solar and wind power have now become cheaper than the fossil-fuel alternative, and a growing body of evidence shows that most unbundled RECs aren’t what those who count emissions call “additional.” That is, they don’t spur new wind or solar farms and thus there is no second value producers should be paid for, and certainly no emissions reductions for the buyer.
“The widespread use of RECs … allows companies to report on emissions reductions that are not real,” Anders Bjorn, assistant professor at the Technical University of Denmark, and a team of researchers, wrote in a paper published in the scientific journal Nature in June 2022. After adjusting for companies’ use of RECs, they found 40% no longer showed alignment of their activities with the Paris Agreement goal of keeping global warming to within 1.5C.
Last month, Amazon claimed that it had reached 100% renewable energy use in 2023 using its own accounting methodology and thus will have no emissions from electricity use. The company has not yet reported the details underpinning its 2023 renewable energy consumption, but Bloomberg Green’s analysis suggests that the claim likely relies on the use of unbundled RECs. In response, an Amazon spokesperson said, “It can take several years for the projects we invest in to come online, so we sometimes utilize unbundled RECs — a fundamental part of the global renewable energy market — to temporarily bridge the gap to a project’s operational date.”
Like Amazon, Google claims to be 100% renewable powered on an annual global basis. In lieu of using unbundled RECs, Google purchases more clean energy than it consumes in some places, like Europe, and less in others, like Asia-Pacific, depending on the availability in those locations. Google, however, makes clear that it does not consume carbon-free energy on an hourly and location-specific basis. That’s now “our ultimate goal,” said Terrell.
Amazon, Microsoft, Meta and Google are following the accounting rules set out under the Greenhouse Gas Protocol that was first developed in 2001. Those disclosures underpin the analyses that investors rely on to make decisions about what counts as a green company. While the protocol has received small updates over the years, it’s due for a big update and experts are working to propose changes. All the big tech companies are now involved in lobbying on those changes.
“Standards need to evolve, because measuring carbon emissions isn’t an exact science,” said Google’s Terrell. “It’s continuing to improve and we’re committed to helping improve it.”
The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.
The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.
Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.
Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.
Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.
Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.
Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.
Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.
Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.
In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.
Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.
Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.
Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.
Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.
Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.
The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.
In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.
AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.
When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.
Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.
This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.
Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.