Clarity in Climate Data Assumptions

If climate assumptions rely on judgement… should they be treated any differently from financial estimates? One of the areas I see creating the most friction in practice is how organisations approach judgement in sustainability reporting. The IFRS S1 explainer is particularly useful here, because it makes one thing clear: Climate-related disclosures are not separate from financial reporting discipline. They are built on the same concepts. What stands out when you look at this through an accounting lens: 1. Judgement sits at the core of sustainability disclosures. Companies are required to identify what is material based on what could reasonably affect cash flows, access to finance, or cost of capital 2. Estimation uncertainty is unavoidable. Assumptions used in scenario analysis and risk assessments are inherently uncertain and require clear explanation 3. The same materiality filter applies. Information must be significant enough to influence investor decisions, consistent with financial reporting principles 4. Connectivity is not optional. Assumptions used in sustainability disclosures are expected to align with those used in financial statements What to consider over the next 6-12 months if you sit in finance, risk, or sustainability: (a) Review how climate-related assumptions are developed and challenged. Are they subject to the same level of scrutiny as financial estimates? (b) Assess whether judgement is clearly documented and supportable. Particularly where scenario analysis drives key disclosures (c) Check consistency across reporting outputs. Do assumptions align between sustainability disclosures, budgets, and financial statements? (d) Prepare for increased audit and assurance focus. Judgement and estimation uncertainty will become a central area of challenge This is not about adding more disclosure. It’s about applying the same level of discipline already expected in financial reporting. #ClimateRisk #FinancialReporting #ISSB

📢 New guidance out: GHG emissions, uncertainty and data quality If you’re preparing or assuring climate disclosures, you’ll want to read the latest release from the External Reporting Board in New Zealand. New Zealand is quickly becoming a honeypot of examples and insights for Group 1 reporters. This guidance tackles two of the most anxiety-inducing parts of Scope 3 reporting: uncertainty and data quality. It offers practical info, clear examples, and a refreshing dose of clarity. Some key points: "All GHG emission disclosures contain high levels of estimation and uncertainty. This is a normal part of GHG emissions measurement and disclosure. It is important to remember that one of the main reasons for measuring and disclosing GHG emissions information is to identify areas of risk and opportunity, rather than providing absolute precision." "An entity should work towards obtaining better quality data over time. An entity should prioritise improving areas with high or significant emissions and low data quality, to meet the decision-making needs of its users." "An entity should have the same systems and controls over information to meet their reporting obligations, regardless of whether it is intending to obtain limited or reasonable assurance." "There is a high degree of inherent uncertainty involved in measuring GHG emissions. The GHG emissions disclosures should make the nature and extent of uncertainty clear, and the assurance practitioner may draw attention to relevant disclosures." "The entity’s choice of emission factors is a source of estimation uncertainty. An entity should choose the emission factors that best represent its emission sources, based on considerations such as technology, time, geography, peer review, global warming potential (GWP) and alternate sources. An entity must disclose the source of emission factors used to calculate its GHG emissions. The assurance practitioner will evaluate whether the methods are appropriate and have been applied consistently." "An entity using data from third parties (including suppliers) should understand the method the third party used to calculate that data... Where material, this information should be included in an entity’s methods and assumptions disclosures." "The entity should develop its own systems and controls to ascertain that the supplier data is appropriate to be used as a basis for its disclosures... More material sources of emissions should be subject to a greater level of scrutiny by the entity." Heaps of good stuff to read through - and this is exactly why Sumday asks more questions of suppliers than most, so you can actually disclose the right information if you're using their data. We're also a tech company full of CPAs and ex auditors, for a reason.

✈️ 600% difference in CO₂ estimates — for the same flight. When Scott Gillespie (tClara) and I dug into six leading aviation carbon models, we expected some discrepancies. But what we found shocked even us: estimates varied by up to 6,700 kg per passenger, depending on which model you picked — a gap of over 600%. 📊 On average, more than half of flights showed at least a 200 kg difference, and even long-haul economy flights (where models tend to agree) still varied by ~15%. Why? Models make wildly different assumptions — about load factors, cabin multipliers, fuel burn, even whether to count Well-to-Wake emissions — and rarely document them clearly. Premium cabins and short, low-traffic routes showed the largest inconsistencies. For me, this underlines something I see every day at SQUAKE: we can’t manage what we can’t credibly measure. Corporate travel teams are under growing pressure from CSRD Institute and Scope 3.6 to report emissions — but the “odometer” they’re using is broken. 🌍 The good news? We already have a clear path forward: • Demand transparency in model assumptions (like Travel Impact Model Advisory Committee already provides) • Allocate emissions based on cabin weight, not just floor space • Audit models’ margins of error, just like financial metrics Until then, buyers, TMCs, and airlines must carefully pick and document the methodology they rely on — because business decisions, compliance, and credibility all ride on it. Business travel is full of hard tradeoffs on the road to net zero — let’s not let inconsistent carbon data be one of them. Read the full article here: https://lnkd.in/dkrfbbBf #Aviation #CarbonAccounting #Sustainability #BusinessTravel #Scope3 #CSRD #ClimateAction

As short-term climate risk modelling continues to gain traction, one critical issue remains: how we define and articulate the baseline. We often hear “compared to the baseline,” but how well-defined is the baseline itself? A baseline is not just a reference point—it is the foundation on which all scenario analysis rests. When expanding a firmwide baseline, there are difficult choices to make—particularly when it comes to projecting commodity prices. This step is crucial because a firmwide baseline should be based on projections of actual prices (not deltas). 🔎 Below is the illustration of different approaches to projecting commodity prices in this context; as you may notice — largely inconsistent. Why does this matter? 📌 Multiple baselines may seem like a solution to capture various dynamics, but this approach is counterintuitive. While it might aim to reflect different factors, it often leads to confusion. Implementation teams end up struggling with conflicting data, and risk managers find it difficult to explain how each baseline applies. As a result, the model results are underutilized, and the climate risk integration falls short. 📌 What's more, some of the choices embedded in these models are not immediately visible but are instead implied by the models themselves. These hidden assumptions become apparent only when you start peeling back the layers of assumptions that went into the baseline. Only then do you realize that the choices you thought were objective are, in fact, shaped by underlying assumptions—many of which may be implicit and unnoticed until it's too late. 📌 Baseline in short-term modelling is even more important because it interacts with many critical processes within the firm, such as stress testing, budgeting, and financial reporting. 📌 The trap many firms fall into is maintaining multiple baselines or shadow prices, which only detaches climate risk from real-time decision-making. 🔑Key takeaway: The explicit articulation of a firmwide baseline is crucial for maintaining consistency of assumptions across the organization. This consistency is essential for the credibility of climate stress testing, ensuring that climate risk is genuinely embedded into strategic decision-making processes. #ClimateRisk #ScenarioAnalysis #EnergyMarkets #RiskManagement #CommodityPrices