Scope 3 Verification Readiness: Data, Estimation & Pitfalls

Why Scope 3 Emissions Matter

For the vast majority of organizations, Scope 3 emissions dwarf their direct (Scope 1) and energy-indirect (Scope 2) emissions combined. Research consistently shows that value-chain emissions represent 70 to 90 percent of total corporate greenhouse gas footprints across most sectors. For technology companies, financial institutions, and retailers, the figure often exceeds 95 percent.

This dominance means that any climate strategy focused solely on Scope 1 and 2 reductions addresses only a fraction of the organization's actual environmental impact. Investors, rating agencies, and customers increasingly recognize this. CDP's scoring methodology now heavily weights Scope 3 disclosure completeness, and ISSB's IFRS S2 requires Scope 3 reporting for all entities. Science Based Targets initiative (SBTi) mandates Scope 3 target-setting for companies where Scope 3 represents more than 40 percent of total emissions — which is the case for the majority of reporters.

Yet Scope 3 verification remains the frontier of GHG assurance. Unlike Scope 1 emissions — where combustion data can be traced to meters, fuel invoices, and process logs — Scope 3 data often relies on estimates, proxies, and supplier-provided information of variable quality. This complexity makes verification more challenging but also more valuable. A credible, verified Scope 3 inventory distinguishes organizations that are genuinely managing their value-chain impact from those that are merely disclosing numbers.

The 15 Scope 3 Categories

The GHG Protocol's Corporate Value Chain (Scope 3) Accounting and Reporting Standard divides Scope 3 into 15 categories — eight upstream and seven downstream. Understanding each category is essential for comprehensive verification readiness.

Upstream Categories (1–8)

Downstream Categories (9–15)

Supplier Data Collection Challenges

Collecting primary data from supply chain partners is the single greatest challenge in Scope 3 accounting. The difficulties are both practical and structural.

Why Supplier Data Is Hard to Get

• Volume: Large organizations may have thousands of suppliers, many of which are small or medium enterprises without carbon accounting capabilities.
• Competence: Many suppliers, particularly in developing economies, lack the technical knowledge to calculate their own emissions. Requests for "Scope 1 and 2 data per unit of product supplied" may be meaningless to them.
• Confidentiality: Suppliers may view energy consumption and process data as commercially sensitive and resist sharing it with customers.
• Allocation: Even willing suppliers face the allocation problem — how to divide their facility-level emissions across the different products they manufacture for different customers.
• Timeliness: Supplier emissions data is rarely available in real time. By the time a supplier's annual inventory is verified, the reporting company may already be closing its own reporting period.

Strategies for Better Supplier Data

• Tiered engagement: Classify suppliers by emissions impact (top 20 suppliers often represent 80 percent of Category 1 emissions). Focus primary data requests on high-impact suppliers and use secondary data for the long tail.
• Standardized questionnaires: Use CDP Supply Chain or equivalent platforms to collect comparable data across suppliers in a standardized format.
• Product-level carbon data: Request Environmental Product Declarations (EPDs) or product carbon footprint (PCF) data where available, especially for construction materials, chemicals, and packaging.
• Contractual requirements: Include emissions data provision in procurement terms for new contracts. This signals organizational commitment and builds data collection into business-as-usual processes.
• Capacity building: Provide training or tools to key suppliers to help them measure and report their emissions. This investment pays off in data quality and supplier relationship strengthening.

Estimation Methodologies

Because primary supplier data is rarely available for every emission source, organizations must use estimation methodologies to fill gaps. The GHG Protocol recognizes three primary approaches, each with distinct trade-offs.

Spend-Based Method

The spend-based method multiplies procurement expenditure by sector-average emission factors expressed in units of CO₂e per monetary unit (e.g., kg CO₂e per USD spent on chemicals). Emission factors are typically derived from environmentally extended input-output (EEIO) models such as the US EPA's Supply Chain GHG Emission Factors or the UK DEFRA input-output tables.

Advantages: Easy to apply at scale using existing procurement or accounts-payable data. No supplier engagement required. Covers the full supply chain in a single calculation.

Limitations: High uncertainty (±50% or more). Assumes sector averages apply to specific suppliers, ignoring differences in efficiency, geography, and energy mix. Sensitive to currency fluctuations and inflation. Cannot distinguish between suppliers with different carbon intensities.

When to use: Initial baseline inventory, long-tail suppliers with low individual impact, categories where activity data is unavailable.

Activity-Based Method

The activity-based method uses physical activity data multiplied by emission factors specific to the activity. For example: tonnes of steel purchased × emission factor per tonne of steel produced, or passenger-km of air travel × emission factor per passenger-km.

Advantages: Significantly more accurate than spend-based (±20–30% uncertainty). Reflects actual quantities consumed. Allows meaningful year-over-year tracking of emission intensity reductions.

Limitations: Requires granular activity data that may not be available for all categories. Emission factor selection requires care (technology, geography, and temporal specificity matter). More labor-intensive to compile and maintain.

When to use: High-impact categories, sources where activity data is available from internal systems or supplier reports, categories where year-over-year performance tracking is a priority.

Hybrid Method

The hybrid method combines activity-based data where available with spend-based estimates for remaining gaps. This is the most practical approach for organizations transitioning from initial estimates to higher-quality inventories.

Best practice: Use activity-based data for top suppliers (typically covering 60–80% of Category 1 emissions) and spend-based data for the remaining long-tail suppliers. Document the proportion of each category calculated by each method and target progressive improvement toward activity-based data.

Data Quality Scoring

Verifiers and reporting frameworks increasingly expect organizations to assess and disclose the quality of their Scope 3 data. The GHG Protocol's Scope 3 Technical Guidance provides a data quality scoring framework that evaluates five dimensions.

Five Dimensions of Data Quality

• Data provenance (primary vs. secondary): Primary data from specific suppliers scores highest. EEIO-derived spend-based estimates score lowest. Between these extremes lie industry-average factors, regional factors, and proxy data.
• Temporal representativeness: Data from the same reporting period scores highest. Data more than 3 years old scores lowest. Using 2019 emission factors for a 2025 inventory introduces significant temporal mismatch, especially in rapidly decarbonizing sectors.
• Geographic representativeness: Country-specific or region-specific emission factors score higher than global averages. Using a US grid emission factor for a manufacturing operation in India introduces substantial geographic error.
• Technological representativeness: Emission factors that match the actual production technology score highest. Generic sector averages score lower. For example, using a global average steel emission factor ignores the difference between basic oxygen furnace (BOF) steel and electric arc furnace (EAF) steel — a factor of roughly 3x in emissions intensity.
• Completeness: Does the data source cover all relevant emission sources for the activity? Cradle-to-gate emission factors should include raw material extraction, processing, manufacturing, and inbound transport. Partial coverage introduces underestimation risk.

Scoring Matrix Example

Organizations should calculate a weighted average data quality score for each Scope 3 category and disclose it alongside their emissions figures. Verifiers will compare stated data quality scores with the actual evidence and methodology documentation.

Common Pitfalls in Scope 3 Inventories

Based on our verification experience, the following pitfalls account for the majority of Scope 3 findings.

Pitfall 1: Double Counting

Double counting occurs when the same emissions appear in more than one category or when a reporter's Scope 3 overlaps with their Scope 1 or 2. Common instances include:

• Category 3 overlap with Scope 1/2: Fuel- and energy-related activities (Category 3) covers upstream emissions of purchased fuels and T&D losses — but not the combustion itself (Scope 1) or the grid electricity used (Scope 2). Organizations sometimes include combustion in both.
• Cradle-to-gate factors including transport: Some life cycle databases provide cradle-to-gate emission factors that include inbound transportation. If the organization also separately estimates Category 4 (upstream transport) using its own logistics data, the transport component is counted twice.
• Overlapping Categories 1 and 4: Spend-based estimates for purchased goods and services (Category 1) from EEIO models often include embedded transportation. Organizations that also estimate Category 4 separately double-count the transport component.
• Investments overlap: Financial institutions reporting Category 15 (investments) emissions may double-count if their investee companies are also their suppliers (Category 1) or tenants (Category 13).

Prevention: Document the boundary of each emission factor used (what is included and excluded). Create a source-level boundary map that identifies potential overlaps. Review EEIO factor documentation to understand what activities are embedded.

Pitfall 2: Boundary Gaps

Boundary gaps are the opposite of double counting — emissions that should be included but are missed entirely. Common gaps include:

• Excluding "not relevant" categories without justification: Organizations frequently mark categories as "not relevant" without a screening calculation. Verifiers require a quantitative or qualitative justification for every exclusion.
• Missing sub-categories: Category 1 (purchased goods and services) should include purchased services — IT services, consulting, legal, cleaning, security — not just physical goods. Service procurement is often overlooked because it feels intangible, but it carries a real carbon footprint.
• Excluding subsidiaries or joint ventures: If the organizational boundary includes subsidiaries for Scope 1 and 2 but uses only the parent company's procurement data for Scope 3, the boundary is inconsistent.
• Omitting downstream categories: Manufacturing companies often report upstream categories thoroughly but neglect downstream categories like use of sold products (Category 11) and end-of-life treatment (Category 12), which can be substantial.

Pitfall 3: Outdated Emission Factors

Emission factors evolve as grids decarbonize, manufacturing processes improve, and databases are updated. Using outdated factors can materially misstate emissions.

• Grid emission factors: Electricity grid emission factors can change by 5–15% per year in rapidly decarbonizing regions. Using a 2019 factor for 2025 reporting may overstate or understate emissions significantly.
• EEIO factors: EEIO-derived emission factors are updated infrequently (the US EPA Supply Chain factors are based on the 2016 input-output tables). These factors reflect the economic structure and energy mix of a specific year and may not represent current reality.
• GWP values: Switching between IPCC Assessment Report editions (AR4, AR5, AR6) changes GWP values for methane, N₂O, and fluorinated gases. Consistency with the chosen AR edition must be maintained throughout.

Prevention: Maintain a master emission factor register documenting the source, version, publication year, and geographic scope of every factor used. Review and update factors annually. When newer factors are available, assess the impact on historical comparisons and document any restatements.

Pitfall 4: Insufficient Documentation

Verification is an evidence-based process. Without adequate documentation, even an accurately calculated inventory cannot be verified. Common documentation gaps include:

• No documented methodology explaining how each category was estimated
• No source references for emission factors
• No record of assumptions made (e.g., vehicle occupancy for employee commuting, product lifetime for use-of-sold-products calculations)
• No explanation of how allocation was performed for multi-product suppliers
• No record of data quality assessments

How Verifiers Assess Scope 3

Understanding the verifier's perspective helps organizations prepare more effectively. Here is what a GHG verification team evaluates during a Scope 3 engagement.

Completeness Check

The verifier reviews all 15 categories and checks whether: each category has been screened for relevance, excluded categories have a documented justification, material categories are quantified, and the sum of excluded categories does not represent a significant portion of the likely total.

Methodology Review

For each quantified category, the verifier examines: the estimation method chosen and its appropriateness for the data available, the source and vintage of emission factors, the mathematical accuracy of calculations, the handling of unit conversions (a common source of errors), and whether the methodology is consistently applied across reporting periods.

Data Verification

Verifiers sample underlying data to test accuracy and completeness. This includes: tracing spend data back to financial records, checking activity data against procurement systems or third-party reports, verifying supplier-provided emissions data against the supplier's own published reports, testing recalculations for a sample of emission sources, and checking for anomalies or outliers that may indicate errors.

Boundary and Overlap Assessment

The verifier checks for: consistency between the organizational boundary used for Scope 1/2 and the boundary applied to Scope 3, potential double counting between categories, potential double counting between Scope 3 and Scope 1/2, and alignment between the stated methodology and what was actually done.

From a verifier's perspective, the quality of Scope 3 documentation matters as much as the quality of the numbers themselves. A well-documented inventory with transparent assumptions and acknowledged limitations is far more credible — and easier to verify — than a polished number with no visible methodology behind it.

Practical Steps to Improve Scope 3 Verification Readiness

Improving Scope 3 verification readiness is an iterative process. Organizations should aim for progressive improvement rather than perfection in the first year.

Step 1: Conduct a Materiality Screen

Screen all 15 categories using spend-based estimates or industry benchmarks to identify which categories are likely to be material. This provides a prioritization framework and ensures no significant category is overlooked. Document the screening methodology and results.

Step 2: Improve Data Quality for Top Categories

For the 3–5 categories that represent the largest share of Scope 3 emissions, invest in activity-based data collection. Engage top suppliers, implement data request processes, and replace EEIO-based estimates with activity-based calculations progressively.

Step 3: Build a Methodology Document

Create a comprehensive Scope 3 methodology document covering: the screening approach and results, the estimation method for each category, the emission factors used (with full source citations), all assumptions and their bases, data quality assessments, and known limitations.

Step 4: Implement Quality Controls

Establish internal review processes including: independent recalculation of a sample of emission sources, year-over-year variance analysis (if emissions in a category change by more than 20%, investigate why), cross-check totals against industry benchmarks (if your per-revenue emission intensity is dramatically different from peers, something may be wrong), and version control of emission factor databases.

Step 5: Engage a Verifier Early

Consider engaging a verification body for a limited assurance engagement before pursuing reasonable assurance. A limited assurance engagement (under ISAE 3410 or ISO 14064-3) provides a lower threshold of evidence but still identifies material errors and methodology weaknesses. It is a cost-effective way to build verification readiness progressively.

Step 6: Set a Data Quality Improvement Roadmap

Create a multi-year plan to transition from spend-based to activity-based to supplier-specific data for each material category. Set targets for the proportion of each category calculated using primary data and track progress annually. This demonstrates to verifiers and stakeholders that data quality is improving over time, even if the current inventory relies partly on estimates.

Frequently Asked Questions