Decarbonisation Technology August 2026 Issue

The fundamental challenge is the effort required to gather, validate, and integrate data from disparate sources. Without a single governed data pipeline, organisations face repeated data handling, inconsistent assumptions, and weak data lineage. As a result, PCF processes are slow, fragile, and resource-intensive. While organisations may produce a PCF, they typically lack the system capability to support continuous, decision-oriented use. The distinction between compliance and decision-making requirements further amplifies this issue. Compliance-driven PCFs require high levels of traceability and verification of historical data, while decision-making requires flexible, forward-looking modelling based on projected scenarios. These demands are often met using the same underlying processes, despite requiring fundamentally different capabilities. The result is an inherent tension between auditability and agility. Systems designed for compliance tend to prioritise control and traceability, while operational use cases require speed and flexibility. Without digital infrastructure to reconcile these needs, organisations are forced into trade-offs that limit the effectiveness of both. Organisational structure often presents a significant barrier. Large companies operating across multiple business units, locations, or subsidiary entities frequently face challenges in coordinating data ownership and system consistency. Siloed responsibilities, combined with legacy systems lacking modern integration

Traditional PCF

Digital PCF

Annual reporting

Real-time insights

Sustainability-led

Cross-functional

Static outputs

Scenario-enabled

Compliance focus

Decision support

Figure 1 Shift from reporting to decision-making

capabilities, further increase the complexity of implementing scalable PCF processes. Ultimately, the issue is whether it can be produced and updated in a way that supports ongoing business decisions. Where PCF remains labour-intensive, its role is largely confined to reporting. Where it becomes automated, integrated, and scenario-enabled, it can begin to function as a decision infrastructure (see Figure 1 ). This growing complexity is not theoretical; it manifests consistently in implementation challenges observed across industries (see Figure 2 ). Lessons from implementation: The five recurring challenges Across multiple industrial implementations, several consistent lessons emerge:  Data confidence beats data volume : Organisations often prioritise collecting large volumes of data but struggle to ensure its reliability. In practice, fewer, higher-quality data

1 Standards

5 Certication

ISO, IMO ISCC CORSIA Regulations such as EU RED Cross-functional teams Roles and responsibilities across various teams (Sustainability, Corporate, Operators , and HSE teams)

ISCC EU, ISCC PLUS, RedCertEU, TÜV Rheinland, Bureau Veritas

Existing digital infrastructure

Optimising existing infrastructure to improve automation

Operators

3 Emissions data

7 Corporate strategy

PCFs are data intensive in nature, meaning they are often resource intensive

Ensuring PCF is used to guide the wider corporate strategy through effective decision - making

4 Modelling software

A variety of ' off the shelf ' licensed software solutions

Sustainability

HSE

Figure 2 Why PCF is so complex