AI for Carbon Accounting

Scope 1 — direct emissions data processing

Scope 1 emissions come from sources your organisation owns or controls: combustion of fuels in boilers and vehicles, process emissions from manufacturing, and fugitive emissions from refrigerants and other gases.

The data is typically the most straightforward of the three scopes because it comes from your own operations. But "straightforward" does not mean "simple" when you have multiple facilities, fuel types, and vehicle fleets.

Here is a prompt template for Scope 1 processing:

You are a carbon accounting specialist. Process the following Scope 1 activity data
for [Company Name], reporting period [Year].

ACTIVITY DATA:
[Paste or attach facility-level fuel consumption data]

TASK:
1. For each facility and fuel type, identify the appropriate emission factor from
   the [DEFRA/EPA/IPCC] database for the reporting year.
2. Convert all fuel quantities to standard units:
   - Natural gas: cubic metres (convert from therms, MMBtu, or kWh if needed)
   - Diesel/petrol: litres (convert from gallons, tonnes, or barrels if needed)
   - Other fuels: specify conversion
3. Calculate CO2, CH4, and N2O emissions separately, then total CO2e using
   AR5 GWP values (or specify: [AR4/AR6])
4. Flag any data points where:
   - Fuel consumption per unit of output differs >20% from prior year
   - Units are ambiguous or potentially misreported
   - Emission factors may not be appropriate for the specific fuel grade

OUTPUT FORMAT:
Table with columns: Facility | Fuel Type | Quantity (original) | Quantity (standard) |
Emission Factor | CO2 (t) | CH4 (t CO2e) | N2O (t CO2e) | Total CO2e | Flags

The key design principles in this prompt: explicit unit conversion instructions, specific emission factor database references, separate greenhouse gas calculations, and systematic flagging of anomalies.

Scope 2 — purchased energy calculations

Scope 2 requires calculating emissions from purchased electricity, steam, heating, and cooling. The critical decision is whether to use location-based or market-based methods — and increasingly, you need to report both.

Location-based uses the average emissions intensity of the grid where your facility is located. This is simpler — you just need consumption data and the grid emission factor.

Market-based reflects the specific electricity you have contracted: renewable energy certificates (RECs/GOs), power purchase agreements (PPAs), or specific supplier tariffs. This method can show zero emissions for a facility on 100% renewable energy, even if the local grid is coal-heavy.

AI is particularly useful for Scope 2 because:

• Grid emission factors vary by country, region, and year — AI can match the correct factor automatically
• Market-based calculations require tracking certificates and contracts alongside consumption — AI keeps these aligned
• Dual reporting (both methods) doubles the calculation workload — AI handles both in a single pass

Process the following Scope 2 activity data for [Company Name], reporting period [Year].

ELECTRICITY CONSUMPTION DATA:
[Facility | Country/Region | Annual kWh | Supplier | Tariff type]

RENEWABLE ENERGY INSTRUMENTS:
[List of RECs, GOs, PPAs, and green tariff contracts with coverage periods and volumes]

TASK:
1. LOCATION-BASED: Match each facility to the correct grid emission factor from
   [IEA/eGRID/AIB] for the reporting year. Calculate emissions.
2. MARKET-BASED: Apply renewable energy instruments to the appropriate facilities.
   For facilities with partial renewable coverage, calculate the residual using
   the residual mix factor. For facilities with no contractual instruments, use
   the grid average.
3. Reconcile: Confirm that total kWh consumed equals total kWh accounted for
   across both methods.
4. Flag: facilities where renewable energy certificates do not cover the full
   reporting period, or where there is a mismatch between consumed volume and
   certificate volume.

OUTPUT: Dual table showing location-based and market-based emissions per facility,
with reconciliation check.

Scope 3 — the value chain challenge

Scope 3 is where AI becomes not just useful but arguably essential. The GHG Protocol defines 15 categories of Scope 3 emissions, covering everything from purchased goods and services (Category 1) to end-of-life treatment of sold products (Category 12) to investments (Category 15).

For most companies, Scope 3 represents 70-90% of their total carbon footprint. It is also the hardest to calculate because the data comes from outside your organisation — from suppliers, customers, logistics providers, and business travel agencies.

The three most common calculation methods for Scope 3, and how AI helps with each:

Spend-based method: Multiply procurement spend by sector-average emission factors (e.g., dollars spent on steel x emission factor per dollar of steel production). This is the least accurate but most feasible when you lack supplier-specific data. AI helps by: classifying procurement line items to the correct sector codes, matching sector codes to emission factor databases, and identifying where spend-based estimates could be replaced with more accurate supplier-specific data.

Average-data method: Use industry-average emission factors per unit of activity (e.g., kg CO2e per tonne of aluminium purchased). More accurate than spend-based but requires quantity data. AI helps by: extracting quantities and material types from procurement records, matching to the correct activity-based emission factors, and flagging where your suppliers' actual intensity likely differs significantly from industry averages.

Supplier-specific method: Use actual emissions data from your suppliers. The most accurate but requires suppliers to report their own emissions. AI helps by: processing supplier-provided emissions data (as covered in Module 3), allocating supplier emissions to your organisation based on your share of their output, and maintaining a hierarchy — using supplier-specific data where available and falling back to average-data or spend-based for the rest.

Calculate Scope 3 Category 1 (Purchased Goods and Services) emissions for [Company Name].

DATA SOURCES:
1. Procurement data: [Supplier | Material/Service | Quantity | Unit | Spend (USD)]
2. Supplier-specific emissions data (where available): [Supplier | Reported Scope 1+2 | Allocation basis]
3. Emission factor database: [DEFRA/EPA/Exiobase/EEIO]

CALCULATION HIERARCHY:
- Use supplier-specific data where available (allocate based on revenue share)
- Use average-data method where quantities are known but supplier data is not
- Use spend-based method as the fallback for remaining categories

FOR EACH LINE ITEM, REPORT:
- Calculation method used (supplier-specific / average-data / spend-based)
- Emission factor applied (with source and year)
- Calculated emissions (tCO2e)
- Data quality score (1-5 per GHG Protocol guidance)
- Recommendation for improving accuracy in next reporting cycle

SUMMARY:
- Total Category 1 emissions by method
- Percentage of emissions calculated by each method
- Top 10 suppliers by emission contribution
- Top 5 opportunities to upgrade from spend-based to supplier-specific

Emission factor matching and unit conversion

Emission factor matching is where human error is most common in carbon accounting — and where AI adds the most precision.

The challenge: emission factor databases contain thousands of factors, organised by fuel type, country, year, sector, and activity. Choosing the wrong factor — using a US grid average instead of a Texas-specific factor, or a 2020 factor when 2023 data is available — can shift your emissions by 10-30%.

AI handles this by matching activity data to factors based on multiple attributes simultaneously:

• Geographic specificity: Use the most granular factor available (city > region > country > global)
• Temporal specificity: Use the factor for the correct reporting year
• Activity specificity: Match the exact fuel grade, material type, or transport mode
• Database hierarchy: Prefer regulatory-specified databases (e.g., DEFRA for UK reporting) over generic ones

You are matching emission factors for the following activity data.

RULES:
1. Always use the most geographically specific factor available
2. Use the emission factor for the reporting year, or the most recent available if
   the current year is not yet published
3. When multiple databases contain a relevant factor, prefer [DEFRA/EPA/IPCC]
   as the primary source for [UK/US/international] operations
4. Document the full factor path: Database > Category > Subcategory > Factor > Unit > Year
5. Flag any activity where no precise factor match exists and you have used a proxy

ACTIVITY DATA:
[Your activity data here]

For unit conversion, the principle is the same: systematic, documented, and traceable. AI should not just convert — it should show the conversion factor used and the source. This matters for assurance: an auditor needs to see not just the result but the path.

Year-over-year comparison and trend analysis

Carbon accounting is not a one-time exercise. You report annually (or more frequently), and stakeholders — regulators, investors, rating agencies — want to see trends. Is the organisation decarbonising? Are targets being met? Where are emissions increasing?

Year-over-year comparison sounds simple but is complicated by:

• Restatements: Changes in methodology, emission factor updates, or corrections to prior-year data
• Organisational changes: Acquisitions, divestments, and restructuring that change the reporting boundary
• Scope changes: Expanding from Scope 1+2 to include Scope 3 categories
• Base year recalculation: Triggers that require restating the base year under the GHG Protocol

AI is excellent at systematic comparison:

Compare [Company Name]'s emissions for [Year 1] and [Year 2].

DATA:
[Year 1 emissions by scope, category, and facility]
[Year 2 emissions by scope, category, and facility]

ANALYSIS:
1. Calculate absolute change and percentage change for:
   - Total emissions (Scope 1 + 2 + 3)
   - Each scope separately
   - Each Scope 3 category
   - Each facility (Scope 1 and 2)
2. Decompose the change into:
   - Activity change (did we use more/less energy, buy more/less material?)
   - Emission factor change (did the grid decarbonise? Were factors updated?)
   - Boundary change (new facilities, new suppliers, new Scope 3 categories?)
   - Methodology change (different calculation approach or data source?)
3. Flag:
   - Any facility or category with >25% year-over-year change (investigate)
   - Any change that is entirely due to emission factor updates (not real reduction)
   - Any Scope 3 category where the supplier data coverage changed significantly
4. Assess progress against targets:
   - [State your reduction targets here]
   - Are you on track based on the required annual reduction rate?

This kind of decomposition analysis — separating real operational changes from methodological and boundary changes — is critical for credible reporting. Investors and regulators increasingly scrutinise whether reported reductions reflect genuine decarbonisation or just accounting changes.