Audit Trail & Carbon Ledger

Every emission calculation backed by transparent evidence. NetNada's Carbon Ledger provides line-by-line transaction verification, AI confidence scoring, and complete emission factor documentation for audit-ready climate reporting.

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How It Works

The Audit Trail functions as your comprehensive Carbon Ledger—the final verification checkpoint where sustainability managers review individual transactions, validate AI calculations, and maintain the transparent records required for climate financial disclosures.

1

Review Transaction Details

Examine each entry showing date, facility, supplier, and description. Every transaction links back to its original source file, whether uploaded spreadsheet, accounting integration, or manual entry.

2

Verify GHG Classification

Confirm proper Scope classification (1, 2, or 3) and GHG category assignment. The system shows how each transaction maps to GHG Protocol categories with full activity-to-emissions methodology.

3

Check AI Confidence Scores

Colour-coded confidence levels indicate mapping accuracy: Green (≥90%) for strong matches, Yellow (70-89%) for recommended review, Red (<70%) for manual verification needed. Prioritise reviews where they matter most.

4

Inspect Emission Factors

Click into any calculation to see the scientific multiplier used, including source organisation (e.g., ANGA, BEIS, EPA), regional applicability, publication year, and links to original documentation.

5

Filter, Export, and Document

Use multi-facility and date range filters to isolate specific data. Export to spreadsheets for external auditors or create custom pivot tables for detailed analysis.

Why Use NetNada's Audit Trail & Carbon Ledger

Complete Transparency

Every emission figure traces back to source data, calculation methodology, and emission factor. External auditors can verify any number in your climate disclosure without additional documentation requests.

AI Confidence Prioritisation

Don't review every transaction manually. Focus attention on low-confidence items where AI mapping may need human validation. Green scores mean the system is confident; yellow and red need your expertise.

Emission Factor Documentation

See exactly which emission factors were applied and why. Source databases, regional accuracy, vintage year, and original evidence links provide the scientific backing auditors require.

Rapid Anomaly Detection

Unusual transactions surface through confidence scoring and visual review. Catch data entry errors, miscategorised expenses, and outliers before they affect reported emissions.

Flexible Filtering and Export

Slice data by facility, date range, scope, or confidence level. Export filtered results for external audit workpapers or import into third-party analysis tools.

Mandatory Disclosure Ready

Australian climate reporting requirements demand verifiable, auditable emissions data. The Carbon Ledger structure aligns with AASB S2 documentation requirements for Group 1, 2, and 3 entities.

Who Needs Audit Trail & Carbon Ledger

Sustainability Managers Preparing for Audit

Review AI categorisations before external auditors arrive. The confidence scoring helps prioritise where to focus manual verification efforts for maximum audit readiness.

External Auditors and Assurance Providers

Access transaction-level detail with full methodology documentation. Export filtered data sets for testing and verification without requesting additional information from clients.

CFOs Signing Climate Disclosures

Gain confidence that emissions figures are backed by verifiable data. The ledger provides the evidence trail needed for director attestation of climate-related financial disclosures.

Data Quality Analysts

Use confidence scores to identify systematic data quality issues. Low scores often indicate vague descriptions or inconsistent categorisation that can be improved at source.

Consultants Managing Client Accounts

Quickly verify client data quality across multiple accounts. Export audit-ready documentation for client board presentations and regulatory submissions.

Audit Trail & Carbon Ledger Features

Transaction-Level Detail

Four information categories for each entry: Transaction Context (date, facility, supplier, description), Carbon Classification (GHG category, scope, confidence), Activity Data (values, units, tCO₂e), and Scientific Evidence (factor profiles, sources).

AI Confidence Scoring

Colour-coded confidence levels: Green (≥90%) indicates strong AI match confidence, Yellow (70-89%) recommends review, Red (<70%) requires manual verification. Visual indicators help prioritise audit efforts.

Emission Factor Transparency

Click into any calculation to see the emission factor source (ANGA, BEIS, Defra, EPA), regional applicability, publication year/vintage, and links to original evidence documentation.

Source File Links

Every transaction links to its original source file—uploaded spreadsheet, PDF invoice, or accounting integration export. Click through to verify data entry accuracy.

Multi-Facility Filtering

Filter the entire ledger by organisational node. Review data quality at facility level, compare consistency across sites, or isolate specific business units for audit.

Date Range Selection

Customise timeframes for audit review. Match reporting periods exactly, compare year-over-year data quality, or isolate specific quarters for detailed examination.

Spreadsheet Export

Export filtered ledger data for external auditors. Includes all visible columns, active filters, and maintains full traceability to source documentation.

Custom Pivot Tables

Create custom analyses directly in the platform. Group by scope, category, supplier, or facility to identify patterns and verify aggregated dashboard figures.

Real Results from Real Users

See how companies are transforming their sustainability reporting

CHOICE
Sustainability Director, Sustainability Director
"When our external auditors asked for documentation behind our Scope 3 figures, we exported the Carbon Ledger filtered by category. Every transaction, emission factor, and source file was there. The audit went from days to hours."
Impact:
  • Reduced audit preparation time by 80%
  • Zero auditor follow-up questions on data sources
  • Full AASB S2 documentation requirements met
ASI Solutions
Finance Director, Finance Director
"The AI confidence scoring changed how we approach data quality. Instead of reviewing everything, we focus on yellow and red items. Our team fixed 200+ categorisation issues in a fraction of the time."
Impact:
  • Identified 200+ categorisation issues via confidence scores
  • Improved overall data quality score from 3.1 to 4.5
  • Manual review time reduced by 65%
McPhersons Printing Group
Environmental Manager, Environmental Manager
"The emission factor transparency was essential for our Climate Active certification. Auditors could see exactly which factors we used and verify they were appropriate for our industry and region."
Impact:
  • Climate Active certification achieved on first application
  • Emission factor documentation satisfied all auditor queries
  • Complete methodology transparency for stakeholders

Frequently Asked Questions

Everything you need to know about Audit Trail & Carbon Ledger

What does the AI confidence score mean?
The confidence score indicates how certain the AI is about its categorisation of a transaction. Green (≥90%) means high confidence—the AI found a strong match between your data and the emission category. Yellow (70-89%) suggests review is recommended. Red (<70%) indicates manual verification is needed. Low scores often result from vague descriptions or unusual transaction types.
How do I improve low confidence scores?
Low confidence typically results from vague source descriptions. Return to the original data source and enhance detail—instead of 'Office Supplies', specify 'Paper Supplies - A4 Recycled'. More specific descriptions help the AI match transactions to appropriate emission factors accurately.
Can external auditors access the Carbon Ledger?
Yes. You can either export filtered ledger data as spreadsheets for auditor workpapers, or invite auditors as users with view-only access to the platform. The Advisor Portal feature allows external consultants and auditors to review without editing capabilities.
What emission factor sources does NetNada use?
NetNada prioritises Australian National Greenhouse Accounts (ANGA) factors. For categories without Australian factors, we use internationally recognised databases including BEIS (UK), Defra, EPA, and Ecoinvent. The factor source, publication year, and regional applicability are visible for every calculation.
How do I correct a miscategorised transaction?
Click into the transaction to open the detail view. You can manually override the AI-assigned category, scope, or emission factor. All overrides are logged with timestamp and user for audit trail purposes. The system recalculates emissions immediately after changes.
Can I see the original source file for a transaction?
Yes. Every transaction links to its source file—whether uploaded spreadsheet, PDF invoice, or accounting integration export. Click the source file link to view or download the original document for verification.
How does filtering work across the ledger?
Apply multiple filters simultaneously: by facility/node, date range, scope (1, 2, 3), GHG category, confidence level, or supplier. Filters affect the entire view and all exports. Reset filters to return to the complete ledger.
Is the audit trail compliant with Australian mandatory reporting?
Yes. The Carbon Ledger structure provides the documentation required for AASB S2 climate-related financial disclosures. Every emission figure is traceable to source data, calculation methodology, and emission factor—the level of evidence required for Group 1, 2, and 3 entity reporting.

Related Features

GHG Emissions Dashboard

View aggregated emissions data that the Carbon Ledger documents at transaction level. Drill down from dashboard totals to individual entries.

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Custom Emission Factors (EPD/LCA)

Replace default emission factors with supplier-specific EPD or LCA data. All custom factors are documented in the audit trail.

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Climate Compliance Analyzer

Assess your climate disclosure documents against AASB S2 requirements. The analyzer references Carbon Ledger data for compliance verification.

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Carbon Data Uploader

Upload source data that populates the Carbon Ledger. Every uploaded file links to resulting ledger transactions.

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Build Audit-Ready Carbon Accounting Records

Transparent, Verifiable, Compliant

Stop worrying about audit season. NetNada's Carbon Ledger provides the transaction-level transparency, AI confidence scoring, and emission factor documentation that external auditors and regulators require.

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