Guest post by: Sean Daley, Director of Sustainability Consulting at Sphera
Sustainability reporting is increasingly demanding more from businesses, shifting the conversation away from announcing commitments toward demonstrating progress, including how those commitments are actually measured and verified. Organisations are increasingly expected to show that their climate targets are supported by strong data that can withstand scrutiny from auditors, regulators, investors, and customers alike. At the centre of this shift toward demonstrating progress sits Scope 3.
Scope 3 GHG emissions encompass all indirect greenhouse gas emissions occurring within a company’s value chain, both upstream and downstream. They often represent the largest share of a company’s climate footprint, but they are also the hardest to both measure and reduce. Quantifying them requires organisations to work across complex supplier networks, procurement systems, and operational boundaries that were never designed with carbon accounting in mind; and reducing them involves getting an accurate picture of where your emissions hotspots lie in your broader value chain.
Why Scope 3 remains so difficult
Our 2026 Sphera Scope 3 Report illustrates how structural constraints continue to shape this challenge. The research surveyed 1,034 sustainability leaders across fifteen industries and three global regions. It found that transparency is moving in the right direction: voluntary Scope 3 disclosure has risen to 73%, up from 49% in Sphera’s 2024 report, but significant barriers remain.
Many organisations are trying to build credible reporting programs with relatively small teams. More than a quarter of respondents said their sustainability function consists of ten people or fewer. Governance structures are still evolving, with relatively few organisations reporting directly to a dedicated Chief Sustainability Officer. Respondents also indicate that confidence in Scope 3 data remains fragile. Nearly half of the leaders we surveyed said they have limited confidence in the accuracy of their reported figures. This presents challenges to organisations looking to make decisions regarding their investments into decarbonization activities throughout their value chain.
These issues are compounded by technical realities. The data required to quantify Scope 3 emissions rarely sits in one place. It is scattered across procurement systems, enterprise resource planning platforms, and supplier spreadsheets that vary widely in format and detail. Suppliers report information in different units and with different levels of precision. Emission factors shift depending on geography, production methods, and the products themselves. Even methodological decisions, such as whether to use spend-based or activity-based calculations, can significantly influence results. The process can quickly become an exercise in reconciling imperfect information instead of building a robust, transparent, and accurate Scope 3 inventory with clearly defined decarbonization opportunities.
Why organisations now treat Scope 3 as an immediate compliance and risk issue
Many factors are driving more urgency surrounding these efforts. Regulatory expectations are tightening, and disclosure frameworks continue to evolve, pushing organisations to move faster. In our survey, eighty percent of respondents said new or emerging regulations had accelerated their reporting activities. At the same time, voluntary disclosure is rising sharply. Nearly three-quarters of organisations now publish emissions data even where it is not yet mandatory, a significant increase compared with just two years ago. Looking ahead, most respondents expect to expand the breadth of their Scope 3 reporting.
The reasoning is straightforward: Scope 3 is increasingly viewed through the lenses of business risk and sustainability performance. Procurement teams are scrutinizing supplier emissions more closely as part of broader resilience and due diligence efforts. Finance functions are evaluating potential exposure to emerging mechanisms such as carbon border adjustments. At the same time, product teams must interpret new compliance requirements tied to lifecycle impacts and material transparency, while investors and customers are asking organisations to substantiate climate claims with credible evidence. Within this environment, Scope 3 reporting has become embedded in broader conversations about operational risk and corporate transparency.
How weak data quality is limiting both reporting and the effective use of AI
Many organisations are turning to analytics and artificial intelligence to help manage this complexity. The potential is real, but technology alone cannot compensate for weak data foundations. AI systems rely on structured inputs. When supplier information is inconsistent or when master data is incomplete, automated analysis can increase errors rather than resolve them.
While AI adoption is moving quickly, data confidence is not keeping pace. More than half of organisations in our survey are already experimenting with AI for sustainability tasks, yet nearly half still report limited confidence in the accuracy of their Scope 3 data.
Poor data quality creates a cascade of problems. Baselines become unreliable, decision-making slows as teams question whether the numbers are dependable, and decarbonization opportunities go unrecognized. It also complicates assurance. Regulators and auditors expect transparent methodologies and traceable sources, which becomes difficult when the underlying data is fragmented or inconsistent.
Where AI can help – and where human oversight is essential
Even with these limitations, AI can play a valuable role when applied carefully. Automation is particularly effective in areas where sustainability teams have historically relied on manual, repetitive work like data transformation, as an example. Machine learning can interpret supplier data submitted in different formats, standardise units, and align procurement categories with emissions factors, while pattern recognition helps flag anomalies and data gaps that require follow-up.
Where supplier data is unavailable, AI also provides value through probabilistic modelling that can estimate emissions using verified lifecycle datasets and indicate uncertainty ranges. Analytical tools can also highlight the suppliers, product categories, or lifecycle stages responsible for the largest share of emissions, helping organisations prioritise engagement and data collection where it will have the greatest impact.
Technology, however, does not replace human judgment. Effective Scope 3 programs still depend on clear governance and methodologies that remain consistent over time. Outputs must be traceable to verified datasets if they are to stand up to regulatory scrutiny or external assurance. AI is most effective when it supports experienced sustainability professionals who can interpret results in context.
Practical steps to move from compliance to confidence
For many organisations, progress begins with strengthening the underlying data foundations. Clear, standardised product and supplier master data significantly improve the accuracy of emissions calculations and allow automated tools to operate more effectively. Many organisations also benefit from incorporating independently verified lifecycle datasets and emission-factor libraries, which help reduce uncertainty and support auditability.
Supplier engagement is equally important. Establishing clearer workflows for data sharing enables suppliers to provide more consistent information and gradually strengthen their own emissions reporting capabilities.
Scope 3 reporting remains challenging because it exposes the most complex parts of global supply chains. Yet the steady rise in disclosure shows organisations are moving from intention toward execution, and as reporting expands, confidence in the underlying data becomes critical.
When organisations combine stronger data governance with verified datasets and carefully applied technology, Scope 3 reporting can evolve beyond compliance. It begins to reveal operational risks and support more informed strategic decisions.
Execution ultimately depends on trust in the data behind the numbers. Building that trust requires ongoing investment in systems, partnerships, and oversight. With those foundations in place, organisations can turn reporting into meaningful action and measurable progress.
