The Governance pillar of AASB S2 has been receiving the highest maturity rating in ERM’s disclosure analysis (in excess of 90% across all sectors), with most companies generally able to report on the board and management oversight of this important topic. 

Progressive organisations are going one step further, however. Rather than simply viewing climate governance as a disclosure exercise, they are building out their climate capability to inform investment decisions, risk management and strategy.  

Some of our most interesting engagements over the past year have been helping organisations build higher quality data integrity into their disclosure preparation – providing genuine climate insights the organisation can then act on. 

Where do the common data challenges lie? 

For too long now, climate data has been collected in Excel spreadsheets, often with the originator also being responsible for checking their own work! They may have others in the organisation with formally delegated responsibility for data collection, but often this process is not formalised and the rules of the game not written down. In many cases, these processes have evolved organically rather than by design. 

With climate reporting responsibilities typically dispersed across multiple functions, organisations are increasingly recognising the need for a disclosure-ready operating model with clear ownership and accountability.  

The Governance requirements of AASB S2 brings this topic into clearer focus for boards and management teams and for good reason. Anyone who has sat through an emissions audit knows that no matter how well-intentioned the humans involved, errors will creep in. If this is data you’re sharing with your Board, customers or potential investors it clearly needs to be as accurate as possible. Furthermore, if your climate strategy, and risk mitigation or adaptation measures rely on you accurately reading internal and external climate signals, data accuracy simply can’t be left to chance. 

How can AI help? 

Artificial Intelligence (AI) brings an exciting new dimension to this topic. We are now tantalisingly close to the possibility of huge comparable datasets from across the Australian economy, and if your data is out of step, it will stand out and be scrutinised. At ERM, we talk about ‘AI in the loop’ - asking ourselves and our clients, how, by delegating discrete tasks to AI, can we improve data quality and integrity? But we also discuss the humans in the loop, what data rules are they following, and how is data collection built into standard operating procedures and business-as-usual reporting manuals?  

Importantly, we need to understand how consistency is maintained across sites, how work is validated and, increasingly, how accountability for climate information is shared across finance, sustainability, operations and risk teams. Climate reporting is fast becoming a cross-functional business capability rather than a sustainability-led reporting exercise. 

What are the technology limitations? 

Technology can be a powerful enabler, but there’s often no single climate data solution that spans across emissions and climate resilience. Some organisations may need targeted tools to automate activities such as invoice data capture or validation. Others may require enterprise platforms that create a common data environment across functions and sites. The right architecture depends on the organisation’s maturity and use cases. What remains constant is the need for clear ownership, common definitions, embedded controls and traceability from source data, through to decision-making and ultimate disclosure. 

We’re finding that, time and again, the technology platform is only as good as the data governance set up ahead of its implementation. No matter how great the platform, in our experience, there is simply no substitute for establishing a strong climate data governance framework with clearly defined accountability and responsibility for all externally reported data. This requires careful data ‘as is’ mapping and a clear direction for the future state imperatives. Crucially, it also requires executive level sponsorship but with strong feedback loops in place throughout the various functions involved. 

Where’s the commercial upside? 

The value in getting this right? The commercial possibilities can be significant, but the clearest driver has to be insights that the climate strategy can meaningfully rely on. We’ve seen organisations revisit their insurance coverage based on climate risk signals, and we’ve seen others build climate related force majeure into commercial contracts. On the opportunities side, we’ve seen organisations make a compelling business case for energy efficiency measures or support green finance or product claims with transparency and integrity. 

Before investing in another digital tool, organisations should pause and ask a more fundamental question: do we have the governance, operating model and accountability structures needed to generate climate information we can rely on?