Accounting researchers say they have uncovered a theoretically possible solution to simplify the tracking of Scope 3 greenhouse gas emissions up and down the value chain using smart contracts and non-fungible tokens on a blockchain platform, easing the carbon reporting process and allowing for increased automation.
Under certain regulations in the European Union, California and other jurisdictions, entities need to report direct emissions from a company’s facilities and vehicles (Scope 1), indirect emissions from the energy used to run its operations (Scope 2), and emissions from upstream suppliers and downstream end users that buy a company’s products (Scope 3), which are generally understood to be the most complex and difficult to track. The accounting researchers—from Auburn University and John Carroll University—believe they have found a technological solution that, theoretically, could make this process easier.
The theoretical solution, outlined in the Accounting Review paper Using Blockchain, Non-Fungible Tokens, and Smart Contracts to Track and Report Greenhouse Gas Emissions, consists of a system that would take the form of a web-based connection that allows companies involved in a value chain to enter their emissions data. This data, with proper permissions, could then be accessed by third parties along the value chain who need to report on not just their emissions but those of their suppliers upstream and their customers downstream.
Emissions rise from smokestacks at the PKN Orlen SA oil refinery in Plock, Poland.
Bartek Sadowski/Bloomberg
More specifically, each component of a tangible asset would have an associated NFT minted by a self-executing smart contract once that component enters the value chain as a blockchain input. This NFT is assigned data showing Scope 1 emissions associated with creating the component; a separate NFT is then minted for the Scope 2 emissions generated to create the component. As these components move through the physical value chain, the corresponding NFTs move between the same firms on the blockchain. When components are combined in manufacturing, smart contracts would “burn” the associated NFTs and mint new ones that represent the updated in-process assets and their aggregated emissions to that point in the value chain. Each one of these updates is recorded onto the blockchain ledger, which the researchers said would be collectively maintained and approved by consortium members.
“This system creates a near real-time cradle-to-grave provenance for tangible assets and their associated emissions as they move through the value chain and allows all emissions to be counted and claimed. Furthermore, all value chain participants can use this system to determine the total emissions associated with a product and their classification as Scope 1, 2, or 3 from their reporting vantage point,” said the paper.
Their system also has the ability to turn on and off different levels of privacy to protect each company’s proprietary information. Participants also can view just the upstream and downstream Scope 3 emissions by categories such as purchased goods or services, transportation and distribution and end-of-life treatment for sold products. When their system is fully built out, according to the paper, any company with authorized access can query the blockchain ledger to see the value of each of their products’ total emissions along all three scopes.
This is in contrast to current practices, which is generally seen as a complex and arduous affair that relies heavily on manual processes.
“In the process of conducting our research, we interviewed one [individual] who works for a large retail company, and he manually enters data from about 4,000 vendors into a spreadsheet and then performs calculations,” said Jenkins, noting that this method is time-intensive and could result in data entry errors and the double-counting of emissions,” said Greg Jenkins, one of the study’s authors.
The paper, however, did not say this technique was a slam dunk. It noted there are many practical hurdles to overcome before such a system could be fully implemented, as well as many risks that must be accounted for. While technologically feasible, experts the researchers ran the concept past pointed to, one, a need for governance and coordination, two, a lack of trust in the blockchain, and, three, blockchain latency. This is on top of other anticipated difficulties such as the challenge of obtaining accurate emissions data to enter into the blockchain in the first place, differences in reporting calendars potentially disrupting coordination, potential exposure of confidential information, and other risks that the technology is meant to address. However, the researchers believe that these challenges can be overcome, and that it will be worth it once they are.
“Notwithstanding the need for future research and refinement, our proposed solution and the prototype we demonstrate can improve the tracking and reporting of value chain emissions. If implemented, it would enable a cradle-tograve provenance for emissions tracking. With its ‘hand-off’ of emissions between firms, the ecosystem allows for tracking emissions as they move between parties, thus alleviating concerns about having to use secondary data sources to estimate upstream and downstream Scope 3 emissions. It would also alleviate concerns around the timing of emissions reporting, by making emissions data available to all value chain participants in near real-time. Finally, the complete and linear provenance of emissions recorded in a verified and secure ledger should help provide a path to higher levels of assurance on emissions disclosures (i.e., reasonable rather than limited assurance),” said the paper’s conclusion.
The emissions tracking technology is protected by a U.S. patent, “System, method, and computer-readable medium for using blockchain, NFTs, and smart contracts to track and report greenhouse gas emissions,” filed in May 2024.
While blockchains have the potential to be very energy intensive themselves, thus creating significant greenhouse gases, Mark Sheldon, another of the study’s authors, noted that specific applications do not necessarily have to be.
“Blockchains use different consensus mechanisms to ensure the various nodes (computers) agree on updates to the underlying ledger. Proof-of-stake, an option to use with our solution, is very energy efficient when compared to proof-of-work which is the one everyone hears about with Bitcoin. In fact, the Ethereum blockchain recently changed from proof-of-work to proof-of-stake and reports to be 99% more energy efficient. There are other factors that also come into play with our specific model, but this is the big one for energy efficiency,” he said in an email.
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.
Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.
Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.
Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.
Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.
This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.
Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.
By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.
Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.