Scope 3 emissions often account for 70–90% of an organization's total carbon footprint, yet they remain among the most difficult emissions to measure, manage, and reduce. As regulatory pressure and investor scrutiny rise, organizations are increasingly turning to AI to improve emissions visibility, supply chain transparency, and decarbonization efforts across the value chain.

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What are GHG emissions?

Greenhouse gas (GHG) emissions are gases that trap heat in the atmosphere and result in rising temperatures. GHGs have always been produced by Earth's natural systems but are now primarily generated by humans and businesses, by burning fossil fuels for electricity and heat generation, industrial processes, transportation, and other activities that have become integral to our modern economy. The most common greenhouse gases are carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O).

As the relationship between rising atmospheric CO₂ concentrations and global temperatures becomes increasingly clear and following the adoption of the Paris Agreement in 2015, businesses have worked to improve the management of their GHG emissions. Under the Greenhouse Gas Protocol, the "gold standard" for corporate emissions accounting, emissions are categorized into three scopes based on an organization's level of operational control:

• Scope 1 — Direct emissions from sources owned or controlled by the organization
• Scope 2 — Indirect emissions generated through purchased energy consumption
• Scope 3 — Indirect emissions produced across an organization's value chain, including activities such as transportation and purchased goods and services

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Why are Scope 3 emissions garnering so much attention?

Scope 3 emissions have become a major area of focus because, without them, organizations are often significantly understating the true scale of their carbon footprint.

While Scope 1 and 2 emissions capture direct operations and purchased energy use, Scope 3 emissions account for indirect emissions generated across the broader value chain, through both upstream and downstream business activities. As a result, Scope 3 emissions often represent the largest portion of an organization's carbon footprint, making them a critical focus for organizations seeking to reduce their environmental impact. However, due to limited oversight across supplier networks and complex value chains, lack of data availability, and minimal direct control over external activities, they are the most difficult and consequential emissions category for organizations to measure and manage.

Despite these challenges, evolving regulatory requirements and shifting investor expectations are placing growing pressure on organizations to measure, disclose, and reduce Scope 3 emissions:

• Emerging legislation and reporting standards, such as the Corporate Sustainability Reporting Directive (CSRD), California Climate Disclosure laws, and IFRS S2, are pressuring and, in many cases, requiring organizations to measure, disclose, and manage Scope 3 emissions.
• Investors are placing greater emphasis on Scope 3 transparency when assessing organizational performance, climate-related risk exposure, and long-term capital allocation decisions. The Climate Action 100+ and Net Zero Asset Manager initiatives, for example, aim to enhance climate-risk related disclosure and net-zero pathways within the financial services industry.

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Scope 3 measurement strategies are hitting a roadblock

Organizations typically rely on one of three measurement approaches to calculate Scope 3 emissions and gain visibility into value chain activities:

• Supplier-specific — Uses primary emissions data directly reported by suppliers, including product-level carbon footprint information combined with supplier-specific emission factors to calculate emissions.
• Activity-based — Calculates emissions using measurable supplier operational data, such as fuel consumption, electricity use, travel distance, or material quantities, which are then mapped to corresponding emission factors.
• Spend-based — Estimates emissions by multiplying the amount spent on goods or services by industry-average emission factors. This method is commonly used when supplier-specific or operational data is unavailable because it relies primarily on existing financial and procurement records.

Although organizations have several approaches available for measuring Scope 3 emissions, many continue to rely heavily on spend-based methodologies and industry-average assumptions, as more granular activity-based and supplier-specific emissions data are often limited. However, as this changes and emissions data becomes increasingly available, new challenges emerge related to collecting, storing, standardizing, and managing large volumes of supply chain data. Developing the systems and infrastructure needed to support these data ecosystems often requires considerable time, coordination, and financial resources, creating another significant barrier to effective Scope 3 emissions management.

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Where can AI be leveraged?

Organizations are increasingly turning to AI to advance Scope 3 emissions measurement, management, and reporting. As organizations face growing pressure to better understand and reduce value chain emissions, AI technologies are emerging as a tool to improve data availability, operational efficiency, and emissions transparency across complex supply chains. Three key use cases where AI can support Scope 3 decarbonization include:

Scalable Operational Efficiency

AI can support organizational efficiency and optimization, especially through data management, monitoring, and reporting. AI technologies can compile and organize fragmented data from all lines of business, products, and suppliers in an efficient data ecosystem that requires significant time, resources, and stakeholder engagement efforts for an organization to develop on its own. By centralizing and better organizing this data, AI can generate meaningful insights through predictive modeling, data analysis, and scenario planning informing companies on their energy usage, operational performance, and supply chain logistics in real time.

With greater visibility into their data, companies can easily identify and address inefficiencies and continually find ways to improve their practices, streamline operations, and enhance supply chain coordination. According to Microsoft, small to medium-sized businesses have seen substantial results in productivity through AI implementation with organizations reporting up to a 40% improvement in efficiency and a 30% reduction in operational costs.

Overall, these improvements can reduce energy consumption, lower operational costs, and decrease greenhouse gas (GHG) emissions across the value chain while also improving the quality and availability of Scope 3 emissions data.

Supply chain visibility

AI-powered tools can automate outreach and data collection efforts across hundreds of suppliers simultaneously, increasing access to supply chain emissions data at a scale that would otherwise be difficult to manage manually. Using advanced analytics, AI systems can map and model multi-tier supply chains, helping organizations identify emissions hotspots, supplier risks, and areas where engagement efforts may have the greatest impact.

Organizations such as Blue Yonder and Dow Chemical have additionally started to develop their own multi-agentic supply chain systems allowing them to review and detect supply chain discrepancies, monitor transportation systems, and view inventory demand in real-time providing meaningful insight to organizational strategy. This enhanced visibility enables companies to prioritize supplier collaboration initiatives and develop more targeted strategies to reduce supply chain emissions.

Automated measurement & reporting

AI technologies can integrate and analyze data from multiple business functions, including procurement, logistics, operations, and corporate reporting systems. Natural language processing (NLP) capabilities also allow AI systems to extract relevant emissions information from unstructured data sources such as supplier disclosures, regulatory filings, invoices, and product specifications. When primary emissions data is unavailable, AI can generate more informed estimates and assumptions than traditional methods. As a result, organizations can produce emissions inventories that are more dynamic, granular, and timely than conventional spend-based accounting approaches.

This increased data availability can help organizations strengthen decarbonization strategies, improve transition planning, and make more informed decisions regarding Scope 3 emissions reductions. IBM Envizi, Sweep, and Persefoni provide a few examples of technology platforms setting the standard for AI automation in GHG management and reporting.

Environmental considerations of AI adoption for emissions management

While AI offers meaningful potential to improve Scope 3 emissions measurement and management, it also introduces important considerations related to its own environmental footprint. AI data centers consume significant amounts of electricity to power increasingly complex systems, further contributing to global emissions. In 2024, the International Energy Agency estimated that data centers accounted for approximately 4% of U.S. electricity consumption, with associated emissions totalling roughly 182 million tons of CO₂, the equivalent to the annual emissions of 26 million homes.

Importantly, a portion of these emissions are classified as Scope 3 emissions for organizations using AI services, as they occur outside of direct operational control but are embedded within value chain activities. As AI adoption expands, this dynamic adds complexity to how organizations account for and manage their overall emissions profiles. Rather than limiting adoption, this emphasizes the need for greater transparency, standardized reporting, and improved emissions accounting mechanisms to ensure AI-driven solutions are fully integrated into broader decarbonization strategies.

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Organizations that have started using AI to support reduction of their Scope 3 emissions

Case Study 1: Walmart

Walmart has developed its own proprietary AI software, Route Optimization, to optimize driving routes, schedule deliveries, and plan inventory pickups, in support of creating a more efficient supply chain and business transport system. Through the use of this tool, Walmart has been able to avoid 94 million pounds of CO₂ emissions by optimizing over 110,000 inefficient travel routes, eliminating over 30 million unnecessary miles driven as of 2024. Due to the large-scale success of the tool, Route Optimization is now available to other businesses as a software as a service (SaaS) solution.

Case Study 2: Reckitt

Reckitt, a multinational consumer goods company, partnered with CO2 AI and Quantis to leverage generative AI to enhance data availability and improve Scope 3 emissions accounting. By using this technology platform, Reckitt gained access to more detailed product-level emissions data and collected over 300,000 data points across its value chain. This enabled the company to develop a more accurate and dynamic carbon footprint, offering clearer insights into where emissions reduction efforts should be prioritized. Within four months of implementing CO2 AI and Quantis's tool, Reckitt was able to obtain emissions data for more than 25,000 products across its value chain, improving the accuracy of its emissions footprint by 75%.

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A pathway towards smarter Scope 3 decarbonization

Scope 3 emissions remain one of the most significant and complex challenges in corporate decarbonization due to limited visibility across fragmented and multi-tier supply chains. As organizations work toward net-zero commitments and respond to increasing regulatory and investor expectations, the ability to accurately measure and manage these emissions has become essential to credible climate action.

AI is emerging as a powerful enabler in addressing this challenge by improving data availability, enhancing supply chain visibility, and enabling more dynamic and scalable emissions measurement and reporting. However, this opportunity is accompanied by a critical trade-off: the growing use of AI introduces its own environmental footprint, much of which falls within an organization's Scope 3 emissions impact. AI is, therefore, both a solution to Scope 3 challenges and a contributor to them – and companies will need to carefully weigh its environmental costs and benefits.

Moving forward, the effectiveness of Scope 3 decarbonization strategies will depend on how well organizations integrate AI-driven capabilities with strong governance, transparency, and accountability frameworks. Those that can leverage AI responsibly, while fully accounting for its environmental impact, will be better positioned to advance credible, data-driven progress toward net-zero targets.