How AI Transforms Real-Time Tracking and Reduction of Scope 3 Emissions in Supply Chains

Scope 3 emissions account for up to 90% of a company’s total emissions, but tracking them is complex. Increasingly, companies are turning to AI to measure and reduce these indirect emissions along the supply chain in real time, leveraging advanced digital tools to address data gaps and operational inefficiencies.

Key Takeaways:

• What are Scope 3 emissions?: Indirect emissions from raw material sourcing, transportation, product use, and disposal, often representing the largest share of a company’s carbon footprint.
• Challenges: Poor data quality, complex supply chains, and lack of resources make accurate tracking difficult, as highlighted by the fact that only 9% of companies can comprehensively monitor these emissions (Deloitte).
• AI Solutions:
  • Real-time monitoring with IoT sensors enables granular, continuous data collection across distributed operations.
  • Blockchain for transparent and tamper-proof data storage, ensuring auditability and trust in reported figures.
  • AI-powered forecasting for strategic decision-making, allowing companies to proactively address emissions hotspots and optimize logistics.
• Success Stories: Companies like Microsoft and Chartwells have reduced emissions by up to 15% using AI, while others, such as UPS, have achieved significant operational and environmental gains through AI-driven route optimization (UPS).

Why This Matters:

Only 9% of companies accurately track Scope 3 emissions. AI offers the opportunity to measure emissions more efficiently, automate reporting, and make supply chains more sustainable. As regulatory and investor scrutiny intensifies, digital solutions become essential for compliance and competitive advantage (World Economic Forum).

AI-Driven Supply Chain Transformation

AI Systems for Tracking Supply Chain Emissions

The use of artificial intelligence (AI) in emissions management is transforming how companies monitor and control their Scope 3 emissions. According to studies, 89% of companies are using or seeking digital solutions to track their Scope 3 emissions (Deloitte). This trend is supported by practical examples from the business world, where AI-driven platforms are enabling unprecedented transparency and actionability.

Connecting Data Sources with AI

AI systems link data streams across the entire supply chain, automate data collection, and ensure precise and consistent emissions measurement. For instance, Chartwells Higher Ed, in partnership with HowGood, discovered that 96–97% of their supply chain emissions fell under Scope 3, prompting a rigorous, data-driven approach to procurement and menu design.

"Scope 3 emissions literally make up maybe 96–97% of our supply chain, including food production, transportation, and waste. Every decision we made about what to purchase, how to design our menu, and which alternatives to choose had to go through a truly rigorous, data-driven validation process."
– Monalisa Prasad, Vice President of Sustainability at Chartwells Higher Education

24/7 Emissions Monitoring

By networking data, AI enables not only collection but also real-time monitoring of emissions. Microsoft has implemented this with its AI-powered Sustainability Calculator for Azure services. Using IoT sensors and advanced analytics, the company monitors the energy consumption of its global data centers in real time. These measures have led to an annual reduction in data center emissions of over 12% (Microsoft).

AI-Based Emissions Forecasting

AI goes beyond real-time monitoring and also provides forecasts for future emissions trends. This enables companies to act strategically and proactively. For example, a global adhesive manufacturer optimized its distribution logistics using an AI-powered CO₂ Scope 3 solution, achieving a 15% reduction in emissions and 14% cost savings.

The World Economic Forum estimates that AI-based energy efficiency solutions and smart grids could unlock up to $1.3 trillion in economic potential by 2030, underscoring the financial and environmental incentives for digital transformation.

Technology Stack for Emissions Tracking

Real-time emissions tracking relies on the interplay of three key technologies: IoT, blockchain, and AI analytics. These technologies work hand in hand to make the process from data collection to analysis efficient and reliable.

IoT Networks in the Supply Chain

IoT sensors allow continuous collection of environmental and consumption data, enabling precise measurement of emissions. Companies that use IoT and advanced analytics in their supply chains can increase operational efficiency by 20% and reduce CO₂ emissions by 15% at the same time (McKinsey).

A great example is Hapag-Lloyd, which uses smart containers to monitor transport conditions and associated emissions in real time. While IoT data forms the foundation for these processes, blockchain technology ensures transparency and security.

Blockchain-Based Emissions Tracking

Blockchain technology plays a central role in verifying and documenting emissions data. Thanks to its immutable and transparent structure, it ensures high data integrity and trust among stakeholders.

An example of this technology in action is the Sustainability Data Platform from SDFA, which enables secure exchange of sustainability data between financial institutions and their clients (World Economic Forum).

Supplier Analysis with AI

AI-based analytics tools give companies the ability to assess the sustainability performance of their suppliers in detail and identify emission-intensive areas in the supply chain. The collaboration between Omdena and SustainLab demonstrates how AI-powered ESG benchmarking can scale from hundreds to tens of thousands of reports, enabling a 5–10% reduction in supply chain greenhouse gas emissions (Omdena).

These three technologies—IoT, blockchain, and AI—form the foundation for a data-driven, sustainable transformation of supply chains and are a central component of modern ESG strategies.

Implementing AI Emissions Management

The integration of IoT, blockchain, and AI technologies demonstrates concrete application possibilities in emissions management, from automated data capture to actionable insights.

Data Standards and Protocols

Unified data standards are the foundation of successful AI-powered emissions management. The Open Footprint Data Model ensures consistency in data labels, units of measurement, and data relationships (Open Footprint Forum).

High-quality data is essential for the effectiveness of AI solutions. Janina Bauer, Global Head of Sustainability at Celonis, emphasizes:

"Emissions factors must be of outstanding quality and detail, ideally supported by a scientific committee or a dedicated team with whom we can validate the calculations. They also need to be presented in a way that is accessible and understandable for our customers."

Collaboration with Suppliers

A key component of AI-driven emissions management is close collaboration with suppliers. Scope 3 emissions, which often account for over 90% of the total CO₂ footprint, make this alignment essential. Yet in the EU, only 37% of these emissions are actively addressed through decarbonization measures (McKinsey).

A successful example of cooperation is Yamato Holdings, which, together with Fujitsu, has developed a platform for sustainable supply chains. The company plans to reduce greenhouse gas emissions by 42% and labor costs by 65% by the end of fiscal 2025 (Fujitsu).

Automated Emissions Reporting

Collaboration with suppliers also facilitates automated collection and reporting of emissions data. AI-powered systems offer several advantages:

• Efficiency: Manual effort can be reduced by up to 40% (Deloitte).
• Data Quality: Anomalies are detected and corrected in real time, based on international standards such as the GHG Protocol and ISO 14064.
• Compliance: Adherence to current reporting standards is ensured, which is especially important as 89% of investors consider ESG factors in investment decisions (Morgan Stanley).

Next Steps in AI Emissions Technology

The future of AI in emissions management is set for exciting developments. Building on current solutions, forecasts show that the global market for AI in the energy sector, which reached €8.75 billion in 2023, will grow at an impressive annual rate of 30.1% by 2030 (Statista).

AI for Analyzing Incomplete Data

Incomplete emissions data presents a major challenge that requires innovative approaches. Professor Elsa A. Olivetti from MIT points out:

"We need a more contextual approach to systematically and comprehensively understand the impact of new developments in this field. Due to rapid improvements, we have not had the opportunity to keep pace with our ability to measure and understand the trade-offs."

A practical example of advanced AI systems in action is UPS. Their AI tool ORION optimizes up to 55,000 delivery routes daily, processes massive amounts of data, and saves 37.8 million liters of fuel and 100,000 tons of CO₂ annually (UPS).

With AI solutions now addressing data gaps, quantum computing is emerging as the next revolutionary step.

Quantum Computing: A Look into the Future

Quantum computing holds enormous potential for climate protection. By 2035, quantum technologies could help save up to 7 gigatons of CO₂ annually (BCG). However, this vision is not without hurdles. The IEA warns:

"It is important to note that there is currently no momentum to ensure widespread adoption of these AI applications. Therefore, their overall impact could still be marginal in 2035 if the necessary framework conditions are not established."

Nevertheless, companies should not wait but act now by:

• Building partnerships within their value chains
• Identifying disruptive use cases for their industry
• Launching development projects, even though fault-tolerant quantum computers are still years away

Challenge: The Energy Appetite of AI

Rising energy demand is one of the biggest challenges. Estimates suggest that by 2028, the electricity consumption of AI systems could reach a level equivalent to 22% of the total energy use of all US households (IEA). These figures make it clear that, alongside technological breakthroughs, energy efficiency must become a greater focus to achieve sustainable progress.

Summary: AI Tools for Supply Chain Emissions

The AI solutions presented here make one thing clear: Artificial intelligence brings transparency to supply chains and enables precise tracking of Scope 3 emissions. Currently, only 9% of companies comprehensively monitor their emissions, while 86% still rely on manual spreadsheets (Deloitte).

A remarkable example comes from a leading electronics manufacturer that reduced its Scope 3 emissions by 20% within a year using AI. Another example is the Spanish food retailer Ametller Origen, which impressively demonstrates the benefits of smart systems:

"We are making our way toward CO₂ neutrality by 2027. RELEX's smart replenishment solution has enabled us to significantly reduce our environmental impact through more efficient processes. The high quality of their demand forecasts has led to a reduction in fresh produce shrinkage, improving sustainability throughout our supply chain. We look forward to building on this strong start to continuously improve our sustainability efforts while improving key outcomes throughout our supply chain."
– Roberto Gómez, Chief Operations Officer, Ametller Origen

Challenges and AI Solutions

AI enables not only precise measurements but also proactive action. Predictive models allow emission trends to be identified early and supply chains to be strategically optimized. Intelligent algorithms close data gaps and increase the reliability of assessments.

AI is much more than just a tool for increasing efficiency—it actively shapes sustainable supply chains. Through standardized data models and specialized algorithms, its ability to track and reduce emissions is continuously expanding.

FAQs

How do AI and IoT sensors support companies in real-time monitoring of Scope 3 emissions?

Artificial Intelligence and IoT Sensors: Real-time Data for Scope 3 Emissions

With the help of artificial intelligence (AI) and IoT sensors, companies can monitor their Scope 3 emissions in real time. These technologies continuously collect data on energy consumption, transportation, and material flows throughout the entire supply chain. The result? Accurate insights that make it possible to better analyse and actively manage indirect emissions.

AI-powered analytics help uncover inefficient processes. Once these weak points are identified, companies can take targeted measures to reduce emissions. This not only supports compliance with ESG requirements but also actively contributes to sustainability and decarbonisation goals.

How does blockchain technology help secure data integrity in emissions tracking?

The Role of Blockchain Technology in Data Security

Blockchain technology is a crucial component when it comes to ensuring data integrity in emissions tracking. With its secure, transparent, and immutable system, it ensures that once data is recorded, it cannot be manipulated. This builds trust in the accuracy of emissions reports and minimises the risk of errors or intentional manipulation.

Another advantage of blockchain is its ability to seamlessly connect data from different sources. This significantly improves traceability and accountability in greenhouse gas emissions reporting. Companies receive precise and reliable information that helps them achieve their climate goals more efficiently.

What are the benefits of using AI to better predict and reduce Scope 3 emissions in the supply chain?

Using AI to forecast and reduce Scope 3 emissions offers companies numerous advantages. Firstly, AI enables much faster and more precise analysis of emissions data, greatly simplifying the creation of reports. Secondly, it improves decision-making by leveraging historical and real-time data to generate reliable forecasts. In addition, companies can make their processes more efficient and allocate resources more effectively, resulting in long-term cost savings.

AI also helps ensure compliance with ESG requirements by providing transparent and traceable data. This not only strengthens a company’s sustainability strategy but also its competitiveness, as innovative approaches to reducing the carbon footprint can be leveraged. By using AI, companies not only achieve their climate targets more effectively but also secure a stronger market position.