Obtaining Granularity in Scope 3 Data Collection and Tools for Ingesting Scope 3 Footprint

We use the internet for everything from entertainment, communication, research, and it has completely transformed the way we work. Most people don’t realize that emissions from internet and cloud usage are quickly exceeding the amount of carbon from other industries. In 2023, cloud computing accounts for around 3% of all global emissions, which is more than the airline industry, shipping, and food processing.

Understanding the full extent of your environmental impact is critical to obtain corporate sustainability. This includes not only the emissions your company controls directly but more importantly, emissions one step—or several steps—removed within your supply chain. Most of the emissions are indirect and often outweigh the carbon footprint a company has. However, unlocking this data is a task easier said than done. This almost represents an impossible challenge, considering the quality of present supply chains and the high variability of both quality and availability of the data. But what if technology could turn the tide in our favor?

Enter AI, a tool that’s revolutionizing the way we approach Scope 3 emissions data collection.

The Complexity of Scope 3 Data Collection

At first glance, tasks like collecting information on Scope 3 emissions seem fairly simple: just get the info from your suppliers. If only it were that easy. In fact, procurement and sustainability managers face a labyrinth of problems:

Diverse Supply Chains: Many companies are now operating within an international marketplace as they source materials and services from a complex web of suppliers. If at all reported, each likely possesses a set of emissions reporting standards and practices unique to that link of the chain.

Data quality and consistency: Data quality varies with regard to its granularity, format, and definitions. It is extremely difficult to try and aggregate the existing data for analysis.

Lack of Transparency: It will be likely that suppliers do not want to share such data or do not have proper tracking mechanisms in place to share data on emissions.

Lack of Response: Suppliers may not even respond to surveys and view them as additional time-consuming bureaucracy.

AI as the Solution

Artificial intelligence (AI) has great potential to solve these challenges of Scope 3 data collection by breaking these barriers and removing data reporting discrepancies.

Automated Data Aggregation: From pooling in data from across sources, AI algorithms can aggregate information. It uses the parsing of supplier reports, industry databases, and can even make use of satellite information to approximate cases where emissions are not available directly.

Improved Data Analysis: After the collection of data, the use of AI might be applicable in the analysis of consistency, completion, and finally, accuracy of the data. The machine learning model will continue to improve and learn from the patterns in the data, hence identifying any anomaly or gap.

Predictive Insights: AI could offer the predictive insights that forecast the future trend of emissions on many factors. This will enable companies to take an informed decision on the kind of suppliers they would like to engage with or take up sustainability initiatives they would like to drive.

Real-World Applications: Now, only companies that are forward in their thinking will be able to improve the collection of data for Scope 3 emissions. Leading automotive manufacturers have rolled out AI-enabled tools to map the emissions of their supplier network and identify key areas of emissions reduction. This helps in targeting sustainability initiatives at suppliers.

In conclusion, the road toward sustainability is full of many challenges, but perhaps the most vexatious is Scope 3 emissions data collection. That has only recently changed, however, with the development of AI technology that offers a solution. Companies will thread through their supply chain complexities to bring about a granularity in their emission reporting, which was unthinkable earlier. This will not only meet the regulatory requirements to a higher level but will make a groundbreaking step into a more sustainable and ecologically responsible business.


Q: Is AI technology accessible to all companies, regardless of size? 

A: While the technology of AI could be very expensive at its adoption, the beginning of cloud-based AI solutions is offering it increasingly to small, medium, and big corporations. This, in essence, justifies the costs at the start because long-term benefits, basically increased efficiency and sustainability impacts, more often than not. Q: Can AI completely replace manual data collection efforts? 

A: While it may hugely reduce the work of collecting data by hand, AI does still remain in need of the direction of human control to interpret its findings and derive strategies for deciding ways forward by the insights derived. So, embracing AI in pursuit of sustainability would mean that companies make a leap from how they relate their duty toward the environment. Advancements in technology increasingly present the potential for AI to transform our Scope 3 emissions data collection relation and change the way we experience the world.

DitchCarbon uses AI to collect thousands of primary company emission disclosures and calculates company-specific Scope 3 emissions.

Get in touch with us to see how we can do the same for your company!

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