Instructions for Finding and Documenting Supplier Resource Mix

This document provides comprehensive, step-by-step instructions for analysts to identify, verify, and document the resource mix for each qualifying supplier-region pair (e.g., from a spreadsheet of utilities/suppliers by country/state). The process is designed for the Granular Registry SSS Reporting platform, aligning with GHG Protocol Scope 2 updates (as of September 13, 2025). Resource mix refers to the composition of a supplier's electricity generation sources in the region, expressed as percentages by fuel type (e.g., coal: 40%, renewables: 30%), including subcategories (e.g., wind vs. solar), associated emissions factors (gCO2e/kWh), and any SSS-specific allocations (e.g., tied to regulated cost recovery). Focus on public data for the most recent years (2023–2025), prioritizing supplier-specific over aggregated data.

Analysts must process one pair at a time, documenting in a standardized template (see Section 6). Aim for >70% coverage of the supplier's regional operations; flag incomplete data. Re-evaluate annually or upon events like regulatory changes (e.g., post-IRA extensions). Allocate 2–4 hours per pair, depending on region.

1. Preparation

• Review Pair Details: From the spreadsheet, note supplier name, region (e.g., Duke Energy - North Carolina), qualifying SSS categories (from Step 2), and any prior data (e.g., from Step 3 outputs).

• Define Scope: Target generation mix (not consumption); include owned/controlled assets and purchased power if allocated to SSS. Exclude non-electricity (e.g., steam unless Scope 2-relevant).

• Gather Tools: Use web browsers, PDF readers, and analysis software (e.g., Excel for aggregation, Python/Pandas for modeling if needed). No proprietary tools.

• Ethical Note: Rely solely on public sources; do not contact suppliers or access paywalled data without approval.

2. Initial Search and Data Identification

• Step 2.1: Keyword Formulation: Craft targeted queries, e.g., "[Supplier] electricity generation mix [Region] 2025" or "[Supplier] fuel sources emissions factor [Year]". Include variants: "resource portfolio", "power supply mix", "integrated resource plan (IRP)".

• Step 2.2: Primary Source Search:

  • Start with supplier's website: Navigate to sustainability/ESG reports, IRPs, or investor filings (e.g., search "sustainability report 2025").

  • Check regulatory bodies: E.g., U.S. PUC/FERC for tariffs/IRPs; EU national regulators for disclosures.

• Step 2.3: Secondary Database Query:

  • Use global aggregators for context: IEA Data and Statistics (iea.org/data-and-statistics) for country-level mixes; Ember Global Electricity Review 2025 (ember-energy.org/latest-insights/global-electricity-review-2025) for source trends.

  • If supplier-specific unavailable, use as proxy but note limitations.

• Step 2.4: Explore Alternatives: If initial searches fail, try semantic variations (e.g., "energy portfolio" for non-U.S.); check news/academic sources for indirect data (e.g., via Google Scholar for studies on supplier mix).

3. Regional-Specific Guidance

Adapt searches to regional data ecosystems; prioritize supplier-level where possible.

• United States:

  • Primary: EIA Electricity Data Browser (eia.gov/electricity/data/browser/) for plant-level generation; eGRID (epa.gov/egrid) for subregional mixes and emissions (2023 data released 2025).

  • Forms: EIA-860 (annual generator data) and EIA-861 (sales/utility-specific) at eia.gov/electricity/data.php.

  • State PUC sites (e.g., dsireusa.org for RPS ties); EEI Industry Data (eei.org/resources-and-media/industry-data) for aggregates.

  • Verification: Cross-check with Annual Energy Outlook 2025 (eia.gov/outlooks/aeo/).

• European Union:

  • Primary: ENTSO-E Transparency Platform (transparency.entsoe.eu) for generation by unit/operator (hourly granularity; export as CSV).

  • Eurostat Energy Balances (ec.europa.eu/eurostat/web/energy/data) for country mixes; AIB for attribute data.

  • National: E.g., Ofgem (U.K.) or BNetzA (Germany) for utility reports.

  • Note: Post-RED III (2025), more granular disclosures expected.

• China:

  • Primary: National Energy Administration (nea.gov.cn) for provincial data; CEC (China Electricity Council) reports.

  • Aggregators: IEA China profiles; Ember for trends.

  • Challenges: Less supplier-specific; use state-owned enterprise reports (e.g., State Grid sustainability PDFs).

• India:

  • Primary: Central Electricity Authority (cea.nic.in) for monthly reports; POSOCO for grid data.

  • Our World in Data (ourworldindata.org/electricity-mix) for stacked charts; IRENA Country Profiles.

  • State utilities: E.g., DISCOM annual reports.

• Brazil:

  • Primary: ANEEL (aneel.gov.br) for generation concessions; ONS (ons.org.br) for system data.

  • EPE (epe.gov.br) for energy planning reports.

• Australia:

  • Primary: AEMO (aemo.com.au) for market data; Clean Energy Regulator (cleanenergyregulator.gov.au) for renewables.

  • AER (aer.gov.au) for state reports.

• Other Regions (e.g., Southeast Asia, Africa): Default to IEA/IRENA country profiles; national ministries (e.g., Indonesia's PLN reports). For emerging markets, use World Bank Energy Data (databank.worldbank.org).

• Global Fallbacks: Energy Institute Statistical Review (energyinst.org/statistical-review); REN21 GSR 2025 (ren21.net/gsr-2025); RFF Global Energy Outlook (rff.org/publications/reports/global-energy-outlook-2025).

4. Data Extraction and Analysis

• Step 4.1: Extract Raw Data: Download PDFs/CSVs; parse mixes (e.g., % by fuel). Note units (GWh vs. %).

• Step 4.2: Calculate if Needed: If raw generation provided, compute percentages (e.g., coal GWh / total GWh). Use tools like Excel formulas or Python (e.g., df['percentage'] = (df['generation'] / df['generation'].sum()) * 100).

• Step 4.3: Link to SSS: Allocate portions to categories (e.g., RPS-funded renewables under Non-Bypassable Charges).

• Step 4.4: Emissions Factors: Derive from mix using standard factors (e.g., IPCC for coal: ~820 gCO2e/kWh); or source from eGRID/IEA.

5. Verification and Quality Assurance

• Multi-Source Cross-Check: Compare at least three sources (e.g., supplier report vs. EIA vs. IEA). Flag discrepancies >5% (e.g., due to imports).

• Mathematical Validation: Verify totals sum to 100%; perform sensitivity analysis (±10% on renewables).

• Challenge Assumptions: Assume data is self-reported—check for audits (e.g., search "[Supplier] mix audit 2025"). Consider improbables: Hidden fossil subsidies skewing mix? Verify via IMF reports.

• Triple-Verify: Re-search independently; use alternative methods (e.g., satellite data from Carbon Monitor if ground-truthing needed). Document uncertainties (e.g., "2024 data preliminary; 2025 estimates based on trends").

• Logical Scrutiny: Review for biases (e.g., overreported renewables in EU); seek counter-evidence (e.g., NGO critiques like Ember).

• Final Reconsideration: After drafting, re-process the pair from Step 2 to confirm no oversights.

6. Documentation

Use this template for each pair (e.g., in Excel/Google Sheets):

Field	Description	Example
Supplier	Name	Duke Energy
Region	State/Country	North Carolina
Qualifying SSS Categories	From Step 2	Regulated Cost Recovery
Resource Mix Breakdown	Table of % by source (2023–2025 avg.)	Coal: 35%, Gas: 40%, Nuclear: 15%, Renewables: 10% (Wind: 6%, Solar: 4%)
Emissions Factor	Avg. gCO2e/kWh	450 (location-based)
Data Vintage	Years covered	2023–2024 (2025 projected)
Sources	List with URLs	EIA eGRID (epa.gov/egrid, accessed 09/13/2025); Duke Sustainability Report (duke-energy.com/sustainability, PDF p.45)
Uncertainties/Notes	Gaps, assumptions	Excludes imports (10% of mix); Pending IRA impacts may increase renewables by 5% in 2026
Completeness Score	% coverage	85% (supplier-specific)

Compile into a master report; include visuals (e.g., pie charts).

7. Risks and Mitigations

• Risk: Data Unavailability: Emerging markets lack granularity. Mitigation: Use national averages as proxy; flag and recommend advocacy for disclosures.

• Risk: Inaccuracy/Staleness: Pre-2025 data may not reflect transitions. Mitigation: Prioritize latest reports; average last two years.

• Risk: Regional Variations: Definitions differ (e.g., hydro as renewable). Mitigation: Standardize per GHG Protocol (include hydro in low-carbon).

• Risk: Overlaps/Multi-Region Suppliers: Double-counting if supplier operates multi-state. Mitigation: Allocate by regional sales (from EIA-861).

• Pitfalls Addressed: Assumed universal access—mitigated by public-only rule. Logical gaps (e.g., ignoring T&D losses)—adjust via IEA factors (5–15%). Oversights (e.g., biomass classification)—cross-check IPCC guidelines.

This process ensures robust, verifiable resource mix data to support SSS reporting and Scope 2 integrity.