Toward more accurate Scope 3 emissions accounting

From the January-February 2025 edition of Supply Chain Management Review.

January-February 2025

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By Josué C. Velázquez Martinez, Ken Cottrill

Accurately calculating Scope 3 emissions—indirect greenhouse gas (GHG) emissions generated by sources not owned or controlled by a company—is challenging given the extensive data required from external parties such as suppliers. Moreover, multiple emissions categories fall under the Scope 3 umbrella, the largest ones usually associated with the purchase of goods and services and inbound/outbound transportation.

Scope 3 accounting poses two core challenges: dealing with different data sources and suppliers’ carbon tracking methodologies. Complexities like these often create data outliers and inaccuracies.

Yet complying with emissions regulations and developing effective sustainability strategies rely on accurate Scope 3 assessments of suppliers’ carbon footprints.

The good news is that more accurate assessments are becoming available. Machine learning techniques that drive data improvements and research on improved ways to track emissions could bring clarity to Scope 3 calculations.

Flawed spend approach

Many companies address the complexity of Scope 3 emissions reporting by using the spend-based method for calculating carbon footprints when direct emissions data from suppliers is unavailable.

In this method, the value of purchased goods or services is multiplied by industry-average emissions factors related to this monetary value. If a company accounts for, say, 10% of a supplier’s total sales, it is allocated 10% of that supplier’s GHG emissions.

The approach is fraught with inconsistencies. Consider, for example, a situation where a company encourages a supplier to adopt carbon capture technology for the items it supplies. The supplier may choose to install the technology only for the items it sells to this customer, making it difficult to develop cohesive sustainability solutions. Moreover, to recoup the cost of its investment in its fragmented carbon-reduction efforts, the supplier may increase the price of the target items. Consequently, the emissions associated with these products could, paradoxically, increase because the pricier products now represent a larger share of the supplier’s total revenue. In effect, the spend-based methodology rewards cost decreases more than emissions decreases for the target products.

To illustrate these shortcomings, consider the following case discussed in Kosolpatanadurong and Gupta (2024) regarding the analysis of carbon emissions associated with a single hypothetical product that accounts for 10% of a company’s revenue. If the target product’s emissions decrease by 25%, the spend-based accounting method shows a decrease of only 3%. When the product price increases by 25%, the product’s emissions allocation increases by 25%. Conversely, if the product price decreases, the GHG allocation falls. The spend-based method is mainly sensitive to the cost of the product as opposed to the actual emissions.

Inaccuracies like these distort the emissions picture, undermining a company’s sustainability strategy. Also, erroneous emissions reporting can discourage enterprises from investing in sustainability.

 

Other pros and cons

Companies have several other methods for calculating Scope 3 emissions. These are generally more accurate than the popular spend-based option but have drawbacks.

Average data method. Users of this option gather relevant data and multiply it by the industry average lifecycle assessment. It provides a standardized, relatively straightforward measure of emissions. However, the method does not account for supplier-specific emissions information or suppliers’ efforts to adopt more sustainable practices.

Hybrid method. Here, emissions are quantified by collecting suppliers’ activity data and making assumptions about these activities if supplier-specific data is unavailable. Using actual supplier information provides more precise emissions estimates. However, suppliers’ privacy concerns can limit the data available to buyers.

Embodied carbon modeling. This method uses advanced modeling of product manufacturing and supplier-specific data where possible to calculate emissions. The method is relatively complex and requires a lot of supplier information to produce an accurate result.

Lifecycle method. The most accurate approach, this option requires comprehensive information on each supplier, encompassing assessments of each product component from the extraction stage to final use. Collecting such granular information is usually extremely difficult if not impossible.

Another issue companies face when accounting for Scope 3 emissions is the need for more standardization in calculating and submitting related information.

Emissions profiles differ from industry to industry. As a result, each business sector requires its own method for tracking and sharing Scope 3 emissions information in a way that does not violate privacy concerns. Moreover, regulatory authorities’ data submission requirements can vary. This lack of standardization is hugely problematic for companies when assessing Scope 3 emissions.

Approaches to carbon tracking can also introduce errors in emissions accounting. Traditional methods often rely on recurring reporting and estimates, which can be out-of-date and inaccurate.

Advances on the horizon

Better methods for quantifying greenhouse gas emissions are being developed.

For example, in collaboration with the CDP and other partner organizations, the MIT Sustainable Supply Chain Lab has established a consortium of leading enterprises to tackle the challenges related to Scope 3 emissions. This includes addressing the challenges of managing a large number of tiers of suppliers, accurately accounting for this category, and developing effective strategies to meet carbon reduction goals. The alliance is called the STEC (Scope Three Emissions Challenge) Consortium. This program will enable the lab to formulate recommendations for standardized methodologies in data collection and reporting (Companies interested in joining STEC can find out more here: sustainable.mit.edu/contact-us). Through collaborative investment in decarbonization initiatives, organizations can prevent redundancy in their efforts. This research will improve CDP systems, helping customers and many sectors requiring monitoring of Scope 3 emissions in the coming years.

The Lab is also focused on enhancing the performance of GHG emissions tracking devices to improve the accessibility of lifecycle emissions methods.

The application of machine learning in emissions standards compliance is particularly promising. Algorithms that process massive amounts of real-time data from multiple information streams can identify pollution sources and emissions patterns. They can deliver almost real-time updates, enhancing the accuracy and reliability of emissions inventory estimates and enabling companies to make timely interventions to mitigate environmental impacts.

Machine learning algorithms can reveal complex data relationships that conventional analytical methods may overlook. They can highlight connections between disparate data elements affecting emissions, such as weather patterns, industrial activity, and topographical features. These insights provide a deeper understanding of a company’s emissions profile, promoting more targeted, effective reduction strategies. The integration of many data sources also facilitates continuous monitoring of GHG emissions.

AI and machine learning also trigger emissions tracking and reporting innovation.

For example, a recent MIT CTL capstone research project (Kosolpatanadurong, D. and Gupta, H. (2024) Supply Chain Emission Hotspot and Allocation Method Analysis) developed an algorithm for detecting outliers in emissions data.  Outliers—a supplier providing information that is significantly different from the previous year is an example—can drastically skew results, rendering emissions data inaccurate. The algorithm creates a graphical representation of the data that helps companies identify potential outliers. MIT CTL is working on incorporating the algorithm into emissions reduction and assessment processes.

The combination of improvements in carbon data tracking and machine learning in emissions accounting represents a powerful sustainability tool. Given the problems hampering precise Scope 3 accounting, these advances can’t come soon enough.

• This article is based on findings from the 2024 State of Supply Chain Sustainability Report published by MIT CTL and the Council of Supply Chain Management Professionals. See the full report here: sustainable.mit.edu/sscs-report.

About the authors:

Josué C. Velázquez Martinez, is director, MIT Sustainable Supply Chain Lab and can be contacted at [email protected]. Ken Cottrill is editorial director, MIT Center for Transportation & Logistics and can be contacted at [email protected].

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