The US Securities and Exchange Commission is preparing to mandate Scope 3 disclosures covering indirect carbon emissions across the value chain. CDP, a not-for-profit, estimates that these emissions account for an average of 75% of an enterprise’s total emissions. Neo4j’s Dr. Michael Moore, Ph.D. explains how graph technology can model complex carbon footprint data to meet Scope 3 reporting requirements

 The value chain has become a major source of climate risk for enterprises, from carbon emissions from the transportation of raw materials to the energy requirements in manufacturing. At the same time, customers and consumers have become more aware of their carbon footprint. Meanwhile, enterprises are under more pressure from shareholders to report on sustainability.

Enterprises need to monitor the ESG (environmental, social, and governance) impact of their ‘Scope 1 and 2’ activities. These include emissions that an enterprise controls directly, such as fuel used in fleets of vehicles and indirect greenhouse gas emissions from purchased or acquired energy to heat or cool buildings, for example.

Enterprises also need to meet the more onerous Scope 3 disclosure requirements. This covers all the emissions the enterprise is indirectly responsible for up and downstream of the value chain. This includes everything from transportation and distribution to waste generated in operations to the usage of sold products and their end-of-life cycle. These emissions are harder to keep track of as they are outside the enterprise’s direct control but usually represent the most significant portion of a greenhouse gas inventory.

Tackling the Scope 3 challenge

Scope 3 is currently not mandatory, but the regulatory landscape is changing. Scope 3 has recently come under the spotlight as the US Securities and Exchange Commission (SEC) has issued a deadline for the fiscal year 2024 for Scope 3 reporting. The Scope 3 compliance requirements are as broad as they are challenging, especially for industries with complex supply chains such as retail, logistics, and manufacturing. It has therefore been dubbed the new Sarbanes Oxley.

Enterprises are understandably concerned about pulling all these data strands together, from sourcing raw materials in India to manufacturing in China down to delivering the finished product to the consumer. It is a long and convoluted journey.

Tracking these billions of interactions along the route is incredibly challenging using traditional SQL-based relational database management systems (RDBMS) because they are ineffective at modelling and querying hierarchical and deeply connected data.

There is an answer, however, in the form of graph-based digital twins that can handle a dynamic value chain environment, simplifying data capture, measurement, and aggregation while clarifying the modelling of complex processes.

Graph-based digital twins can understand complex supply chains

 Digital twins built on knowledge graph technology allow enterprises to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets while understanding the various interactions in its makeup.

 Digital twins are virtual representations of physical reality, which are as extensive and detailed as the reality they look to capture. They offer the only possible way today of modelling large real-world and real-time systems with the appropriate level of reliability and robust support analytics for smart decision-making. And the best data backbone for a digital twin is a graph database, representing the full complexity of the networks of connected elements found in the digital twin.

Graph technology provides a flexible, high-performance data foundation for analysing complex digital twin data. The beauty of a modern native graph database is that it can handle billions of relationships at scale. All the relationships between data points are stored on disk and in memory, creating a fabric of fully connected data that can be queried almost instantaneously.

Graphs can accurately map data for Scope 1, 2, and 3 compliance

Regarding emissions analysis, a graph-based digital twin can pull together networks of connected assets with data from various sources, including real-time IoT sensor data, supplier reports, and third-party estimates. Graph queries can accurately determine patterns of non-compliance, critical dependencies, optimal routings, infrastructure alerts, and root causes of excess emissions within an enterprise value chain.

Understanding the sources of where emissions are coming from is pivotal to managing an enterprise’s carbon footprint. With digital twins, enterprises can visually trace where the data comes from and exactly how processes are connected.

 As well as putting data into context for Scope 1,2 and 3 reporting, results generated by graph-based digital twins can be used to improve efficiencies in an enterprise. Improving sustainability, reducing risk, and bolstering the bottom line.