How Dow Built a Carbon Footprint Ledger on Databricks to Accelerate Sustainability at Scale

Summary

Dow built the Carbon Footprint Ledger (CFL) on Databricks to provide a transparent, verifiable way to track and communicate product carbon footprints amid customer Scope 3 commitments and tighter environmental disclosure requirements. The system combines carbon-accounting methodology assured against ISO 14067 and the GHG Protocol Product Standard with a calculation engine and certificate-issuing ledger that maintains residual balances. Within Dow’s Integrated Data Hub, Apache Spark unifies supply-chain, sustainability, operations, and other source data, while Unity Catalog governs access and Delta tables provide ACID transactions, time travel, and efficient upserts. An advanced optimization model, developed and deployed with MLflow, identifies lowest-greenhouse-gas production pathways and sends versioned, monitored results through the pipeline with lineage tracking. The implementation reduced PCF processing from weeks to a fraction of that, enabled portfolio-scale calculations, and supports verifiable certificates and commercial use of decarbonization investments.

Context

Dow needed to calculate product carbon footprints using data scattered across functions and geographies, while computationally intensive optimization made the process take weeks. Customer Scope 3 goals and environmental disclosure requirements increased the need for transparent, verifiable product-level carbon information.

Approach / What changed

Dow built the Carbon Footprint Ledger within its Integrated Data Hub on Databricks. Apache Spark integrates and transforms source data, Unity Catalog provides fine-grained governance, Delta tables persist pipeline stages, and an MLflow-managed optimization model identifies lower-carbon production pathways. The ledger issues Product Carbon Footprint certificates and maintains residual balances.

Takeaways

• The CFL calculation engine follows ISO 14067 and the GHG Protocol Product Standard, while its ledger records certificates and residual balances in a secure, auditable system.
• Apache Spark and Delta tables enabled Dow to unify data at scale and reduce PCF processing from weeks to a fraction of that, with ACID transactions and historical-audit support.
• An MLflow-based optimization service identifies the lowest-greenhouse-gas production pathways across Dow’s manufacturing network and provides versioning, monitoring, and lineage tracking.

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