Dan Harple brings more than 30 years of experience as a technology entrepreneur, inventor and engineer to his role as Chairman & CEO of Context Labs BV. We sat down with Dan to discuss how technology can make product-level carbon accounting easier to adopt and use, the potential role of open-source software, and why Context Labs sees Carbon Measures as an important part of that effort.

Context Labs is one of Carbon Measures' first technology members. What made the coalition a valuable initiative for you to join, and what perspective do you see Context Labs bringing to its mission?

Context Labs joined as one of Carbon Measures’ first technology members to contribute the data infrastructure that will support the implementation of product-level carbon accounting that can be applied and verified across supply chains and satisfy market compliance, disclosure, and regulatory reporting across industries. By applying financial accounting principles to product-level emissions, Carbon Measures aims to establish a basis for differentiating lower-carbon products that can scale to government and inter-governmental product carbon intensity standards. We see Context Labs’ contribution addressing the data infrastructure and digital enablement component: linking data and calculations for products across complex supply chains and enabling them to test and deploy the method. By deploying an open data protocol, each contributing company will be able to retain sensitive data within its own environment while enabling third parties to verify the reports. 

Context Labs already works with energy and industrial companies to connect operational data and calculate product-level carbon intensity. What lessons from that experience could help Carbon Measures develop a practical approach?

Our work with energy and industrial operators shows that the reliability of product carbon intensity data depends as much on data traceability as on the calculation. Across many operators, we have observed that most of the required data is already collected in meters, laboratory results and ERP systems, but held in separate systems without documented provenance. When that provenance must be reconstructed manually at each transfer of custody, cost and the risk of error increase. For Carbon Measures, the implication is that the method will require provenance to be captured where the data originates and to remain linked to the product through each transaction. Practically, this will increase the security and reliability of the method for its users.

Advances in technology, including AI, are creating new opportunities for product-level carbon accounting. Where do you see the greatest potential, and what needs to be in place for companies to use these tools with confidence?

AI implementation can reduce cost and accelerate time to deployment, provided that the data it processes is verifiable. The clearest applications are reconciling records across systems and performing calculations at scale that can process the high volume a supply chain generates. The precondition for this to be trusted and secure is that every value must be traced to its source and cryptographically signed by its issuer, so any alteration is detectable. Without that, AI produces values that appear precise but cannot be defended under audit, and the buyer or lender relying on them carries risk.

What opportunities do you see emerging from the open-source software you are developing in collaboration with Carbon Measures, and how would it enable the ecosystem?

The opportunity is adoption at scale. Open-source software lowers the cost of applying the Carbon Measures framework, so participation is no longer limited to companies with dedicated carbon accounting staff. This will increase access: participants can join and test the method without requiring large investment and commitment to new systems or third-party service providers. Shared code and data rules allow a carbon intensity value to pass between companies without recalculation, increasing interoperability between organizations. The result is a framework that can extend across an entire value chain without sacrificing verifiability.