From activity data to report
Every tool follows the same four steps, and every choice you make along the way is kept with the result.
- Step 1
Choose the boundary
A single IPCC sector, the National Reporting Suite, a company (CarbonTrack) or a city (CityCarbon).
- Step 2
Enter activity data
Fuel, production, livestock, land area or waste in the module’s own units. Default factors come from the library with their source shown.
- Step 3
Set tier and GWP
Tier 1, 2 or 3 where the module supports it, and AR4, AR5 or AR6. Both are recorded against the result.
- Step 4
Review and report
Check the breakdown, then export. Each line keeps its activity data, factor, source, tier and GWP basis.
Standards stack
CarbonTiers is built on three layers of internationally recognised methodology. They are complementary: IPCC defines the science, GHG Protocol defines the boundaries, ISO defines the reporting format.
IPCC 2006 Guidelines
Volumes 1-5 covering Energy, IPPU, AFOLU, Waste with sectoral equations, default emission factors, and uncertainty ranges. Refined by IPCC 2019.
GHG Protocol Corporate
Scope 1 (direct), Scope 2 (location- and market-based), Scope 3 (15 categories). Sets organisational and operational boundaries for corporate inventories.
ISO 14064-1:2018
Six-category indirect emission classification. Replaces the older Scope 1/2/3 split for ISO-conformant inventories. Verifiable per ISO 14064-3:2019.
Tier 1 → Tier 3 progression
The IPCC tier framework defines methodological complexity and accuracy. CarbonTiers records the tier used for every line item, so verifiers see exactly which factor came from which source.
IPCC default factors
Global default emission factors and parameters from IPCC 2006 Guidelines. Suitable when activity data is limited or for first-time inventories.
- Fuel combustion: IPCC default EF by fuel type
- Enteric fermentation: regional default EF (kg CH₄/head/yr)
- Cement: 0.51 t CO₂ / t clinker default
Country-specific factors
Jurisdiction-specific factors from official transmission operators, energy agencies, and national inventory reports. Higher accuracy than Tier 1.
- Grid electricity: TEIAS, EPA eGRID, DEFRA, UBA, RTE
- Cattle: country-specific feed digestibility & live weights
- Industrial process: national-average plant data
Plant-level / measured
Direct measurement, continuous emission monitoring (CEMS), or facility-specific factors. Highest accuracy, required for CBAM and verified ETS reporting.
- CEMS-derived stack CO₂ / NOx
- Plant-specific clinker chemistry (CaO content)
- Process model output (e.g., DAYCENT for soils)
GWP: AR4 / AR5 / AR6
Global Warming Potentials are not constants - each IPCC assessment report updates the values. CarbonTiers lets you pick AR4, AR5, or AR6 (100-year horizon) and applies it consistently. The selected version is recorded on every CO₂e output for verification.
| Gas | AR4 (2007) | AR5 (2013) | AR6 (2021) |
|---|---|---|---|
| CO₂ | 1 | 1 | 1 |
| CH₄ (fossil) | 25 | 28 | 29.8 |
| CH₄ (biogenic) | — | — | 27 |
| N₂O | 298 | 265 | 273 |
| HFC-134a | 1,430 | 1,300 | 1,530 |
| HFC-23 | 14,800 | 12,400 | 14,600 |
| SF₆ | 22,800 | 23,500 | 24,300 |
| NF₃ | 17,200 | 16,100 | 17,400 |
| CF₄ | 7,390 | 6,630 | 7,380 |
100-year time horizon. UNFCCC reporting required AR4 until 2020; most national inventories now use AR5; AR6 adoption is growing for voluntary disclosure (CDP, SBTi).
Sectoral methodology
Each of the five IPCC sectors has its own equations, default factors, and tier rules. The platform implements them volume-by-volume, chapter-by-chapter, with the canonical reference next to each line.
Energy
Fuel combustion (stationary + mobile), fugitive emissions from oil/gas/coal, and carbon capture & storage.
- •Stationary Combustion (40+ fuels)
- •Mobile Combustion (road, rail, air, water)
- •Fugitive (oil, gas, coal mining)
- •Reference Approach (apparent consumption)
- •CCS (CO₂ transport & storage)
E = AD × EF (Tier 1)E = AD × EF country-specific (Tier 2)E = AD × EF plant-specific, with CO₂ oxidation factor (Tier 3)IPPU
Industrial Processes and Product Use. Mineral (cement, lime, glass), chemical (ammonia, nitric acid), metal (iron & steel, aluminium), and 15 fluorinated gases.
- •2A Mineral (cement, lime, glass, soda ash)
- •2B Chemical (ammonia, HNO3, adipic, carbides)
- •2C Metal (iron & steel, aluminium, lead, zinc)
- •2D Non-Energy Products from Fuels
- •2E Electronics (integrated circuits, TFT)
- •2F F-gases (15 HFC/PFC, SF6, NF3)
E_CO2 = M_clinker × EF_clinker × CKD surcharge (2A1)E_N2O = M_HNO3 × EF_process (2B2)E_HFC = M_product × EF_product × GWP (2F)Agriculture
Enteric fermentation, manure management, rice cultivation, managed soils (direct + indirect N₂O), and field burning of agricultural residues.
- •Enteric Fermentation (20+ animal categories)
- •Manure Management (11 systems × 3 climate bands)
- •Rice Cultivation (7 water regimes)
- •Managed Soils (direct + indirect N₂O)
- •Biomass Burning (residues)
Enteric CH₄: E = (N_animal × EF) / 1000 (eq. 10.19)Manure CH₄: E = VS × Bo × MCF (eq. 10.23)Direct soil N₂O: E = (Σ(F_SN + F_ON + F_CR + F_SOM) × EF1) × 44/28Rice CH₄: E = A × t × EF_i × SFw × SFp × SFoLULUCF
Land Use, Land-Use Change and Forestry. Stock-difference and gain-loss methods across forest land, cropland, grassland, wetlands, settlements, and harvested wood products.
- •Forest Land (12 ecological zones)
- •Cropland SOC
- •Grassland SOC
- •Wetlands & Peatlands
- •Settlements
- •Other Land
- •HWP (5 approaches: SCA/AFA/PA/SDA/FOD)
Stock-difference: ΔC = (C_t2 - C_t1) / (t2 - t1)Gain-Loss: ΔC = Σ(ΔC_AB + ΔC_BB + ΔC_DW + ΔC_LI + ΔC_SO)HWP (FOD): C(t) = e^(-k) × C(t-1) + ((1-e^(-k))/k) × Inflow(t)Waste
Solid waste disposal (FOD model), biological treatment, incineration, and wastewater (domestic + industrial pathways).
- •Solid Waste Disposal (FOD, 7 types × 6 SWDS × 5 climate zones)
- •Biological Treatment (composting, anaerobic digestion)
- •Waste Incineration (7 technology types)
- •Wastewater (14 domestic + 10 industrial pathways)
SWDS (FOD): CH₄ = Σ ((1-f) × MSW × DOC × DOC_f × MCF × F × 16/12 - R) × (1-OX)Wastewater: CH₄ = (TOW - S) × EF - RIncineration: CO₂ = SW × dm × CF × FCF × OF × 44/12Scope mapping & ISO categories
The platform classifies every line by both the GHG Protocol scope and the ISO 14064-1:2018 indirect category. This dual mapping lets you produce both reporting formats from the same inventory without rework.
GHG Protocol scopes
ISO 14064-1:2018 categories
- 1Direct GHG emissions and removals (= Scope 1)
- 2Indirect GHG emissions from imported energy (= Scope 2)
- 3Indirect from transportation
- 4Indirect from products used by the organization
- 5Indirect associated with the use of products from the organization
- 6Indirect from other sources
Scope 3 - 15 categories
Scope 2 dual reporting
GHG Protocol Scope 2 Guidance (2015) requires both a location-based and market-based total. The platform calculates both automatically.
Location-based
Average grid intensity for the country/region in which the consumption physically occurs.
E = kWh × EF_grid_countryMarket-based
Contractual instruments (PPAs, RECs, GoOs) reflecting your specific energy supplier choices.
E = Σ(kWh_i × EF_supplier_i) + (residual × EF_residual)PCAF - financed emissions
For financial institutions, Scope 3 Category 15 (Investments) is calculated per the PCAF Global GHG Accounting and Reporting Standard.
Financed emissions = Σ (Outstanding amount_c / EVIC_c) × Emissions_cFor listed equity & corporate bonds: attribution factor = outstanding amount ÷ EVIC (Enterprise Value Including Cash). For project finance, business loans, mortgages, motor vehicle loans, commercial real estate, and sovereign debt, the platform implements the asset-class-specific PCAF formulas.
Uncertainty & data quality
Each line item carries a Data Quality Indicator (DQI) score. The platform aggregates uncertainty using IPCC Approach 1 (error propagation) and supports Monte Carlo (Approach 2) on Pro plans.
Scenario modelling math
Project emissions forward against five built-in pathways or design custom scenarios from the 81+ mitigation options across the five sectors. All scenarios share the same baseline year and end year for clean comparison.
Business as Usual (BAU)
E(t) = E(t₀) × (1 + g)^(t - t₀)Project current activity growth forward at constant rate g. Used as the no-action reference baseline.
IPCC 1.5 °C (AR6)
Linear path to net-zero by 2050; -7% / yr global reductionAR6-aligned trajectory. Roughly 50% reduction by 2030 vs. 2019 baseline, net-zero CO₂ by mid-century.
IPCC 2 °C
Net-zero CO₂ by ~2070; -3% / yrParis-compatible likely-below 2 °C trajectory. Less aggressive than 1.5 °C path.
EU ETS Fit-for-55
LRF 4.3% (2024-27) → 4.4% (2028-30); -62% by 2030 vs 2005Linear Reduction Factor schedule from EU ETS Directive. Used for installations under EU ETS or CBAM.
Custom mitigation
E(t) = E_BAU(t) - Σ(M_i × A_i(t))Stack 81+ mitigation options across the 5 sectors with start year, ramp curve, and abatement potential. Compare against BAU and IPCC pathways side-by-side.
Five interpolation methods
Audit traceability
Every tonne of CO₂e on the platform traces back through a five-step chain. Verifiers receive a single inventory summary that maps to GHG Protocol scopes and ISO 14064 categories simultaneously.
Source citation
Every emission factor links to its publisher (TEIAS, IPCC table ref, DEFRA dataset etc.).
Version pinning
GWP version, factor publication year, and IPCC volume reference are stored on every line.
Export formats
Excel, PDF, Word, CSV, XML and chart images, depending on the tool. CRT tables for national inventories.
Grid factor library
Multi-country grid emission factors sourced from official transmission operators and energy agencies. Used for Scope 2 location-based calculations.
Grid Emission Factor Library
208 countries and regions · 213 distinct vintage entries · sourced from national authorities.
| Country / region | Latest year | EF (tCO₂/MWh) | Source | Vintages |
|---|---|---|---|---|
| Turkey | 2025 | 0.4360 | ÇŞİDB | 6 |
| EU-27 average | 2022 | 0.2760 | IEA | 1 |
| Germany | 2022 | 0.3850 | UBA | 1 |
| France | 2022 | 0.0560 | RTE | 1 |
| United Kingdom | 2024 | 0.2070 | DEFRA | 1 |
| United States | 2022 | 0.3860 | EPA eGRID | 1 |
| Japan | 2022 | 0.4700 | MoE Japan | 1 |
| China | 2021 | 0.5810 | NDRC | 1 |
| India | 2022 | 0.7080 | CEA | 1 |
| Austria | 2023 | 0.1100 | Ember | 1 |
| Belgium | 2023 | 0.1370 | Ember | 1 |
| Bulgaria | 2023 | 0.3720 | Ember | 1 |
| Croatia | 2023 | 0.1440 | Ember | 1 |
| Cyprus | 2023 | 0.5730 | Ember | 1 |
| Czechia | 2023 | 0.4020 | Ember | 1 |
| Denmark | 2023 | 0.1450 | Ember | 1 |
| Estonia | 2023 | 0.4270 | Ember | 1 |
| Finland | 2023 | 0.0620 | Ember | 1 |
| Greece | 2023 | 0.3180 | Ember | 1 |
| Hungary | 2023 | 0.1900 | Ember | 1 |
| Ireland | 2023 | 0.2810 | Ember | 1 |
| Italy | 2023 | 0.2680 | Ember | 1 |
| Latvia | 2023 | 0.1320 | Ember | 1 |
| Lithuania | 2023 | 0.1450 | Ember | 1 |
| Luxembourg | 2023 | 0.0980 | Ember | 1 |
| Malta | 2023 | 0.4100 | Ember | 1 |
| Netherlands | 2023 | 0.2680 | Ember | 1 |
| Poland | 2023 | 0.6100 | Ember | 1 |
| Portugal | 2023 | 0.1280 | Ember | 1 |
| Romania | 2023 | 0.2430 | Ember | 1 |
| Slovakia | 2023 | 0.1080 | Ember | 1 |
| Slovenia | 2023 | 0.2080 | Ember | 1 |
| Spain | 2023 | 0.1520 | Ember | 1 |
| Sweden | 2023 | 0.0320 | Ember | 1 |
| Norway | 2023 | 0.0300 | Ember | 1 |
| Switzerland | 2023 | 0.0410 | Ember | 1 |
| Iceland | 2023 | 0.0280 | Ember | 1 |
| Serbia | 2023 | 0.6800 | Ember | 1 |
| Bosnia and Herzegovina | 2023 | 0.7000 | Ember | 1 |
| North Macedonia | 2023 | 0.6000 | Ember | 1 |
| Albania | 2023 | 0.0240 | Ember | 1 |
| Montenegro | 2023 | 0.4000 | Ember | 1 |
| Kosovo | 2023 | 0.9000 | Ember | 1 |
| Moldova | 2023 | 0.5200 | Ember | 1 |
| Ukraine | 2023 | 0.2300 | Ember | 1 |
| Russia | 2023 | 0.3600 | Ember | 1 |
| Georgia | 2023 | 0.1500 | Ember | 1 |
| Canada | 2023 | 0.1250 | Ember | 1 |
| Mexico | 2023 | 0.4230 | Ember | 1 |
| Brazil | 2023 | 0.0980 | Ember | 1 |
| Argentina | 2023 | 0.3340 | Ember | 1 |
| Chile | 2023 | 0.3360 | Ember | 1 |
| Colombia | 2023 | 0.1960 | Ember | 1 |
| Peru | 2023 | 0.2340 | Ember | 1 |
| Australia | 2023 | 0.5480 | Ember | 1 |
| New Zealand | 2023 | 0.1040 | Ember | 1 |
| South Korea | 2023 | 0.4370 | Ember | 1 |
| Indonesia | 2023 | 0.6780 | Ember | 1 |
| Vietnam | 2023 | 0.3970 | Ember | 1 |
| Thailand | 2023 | 0.4720 | Ember | 1 |
| Malaysia | 2023 | 0.5500 | Ember | 1 |
| Philippines | 2023 | 0.6110 | Ember | 1 |
| Pakistan | 2023 | 0.4000 | Ember | 1 |
| Bangladesh | 2023 | 0.5200 | Ember | 1 |
| Kazakhstan | 2023 | 0.7600 | Ember | 1 |
| South Africa | 2023 | 0.7090 | Ember | 1 |
| Egypt | 2023 | 0.4460 | Ember | 1 |
| Morocco | 2023 | 0.6100 | Ember | 1 |
| Nigeria | 2023 | 0.3860 | Ember | 1 |
| Kenya | 2023 | 0.0960 | Ember | 1 |
| Ghana | 2023 | 0.4120 | Ember | 1 |
| Ethiopia | 2023 | 0.0260 | Ember | 1 |
| Tanzania | 2023 | 0.3950 | Ember | 1 |
| Saudi Arabia | 2023 | 0.6290 | Ember | 1 |
| United Arab Emirates | 2023 | 0.3960 | Ember | 1 |
| Qatar | 2023 | 0.4900 | Ember | 1 |
| Israel | 2023 | 0.5310 | Ember | 1 |
| Jordan | 2023 | 0.4300 | Ember | 1 |
| Azerbaijan | 2023 | 0.5100 | Ember | 1 |
| Afghanistan | 2024 | 0.1172 | Ember | 1 |
| Antigua and Barbuda | 2024 | 0.6081 | Ember | 1 |
| Armenia | 2025 | 0.2440 | Ember | 1 |
| Angola | 2024 | 0.1851 | Ember | 1 |
| American Samoa | 2024 | 0.6222 | Ember | 1 |
| Aruba | 2024 | 0.5490 | Ember | 1 |
| Barbados | 2024 | 0.6009 | Ember | 1 |
| Burkina Faso | 2024 | 0.5609 | Ember | 1 |
| Bahrain | 2024 | 0.9025 | Ember | 1 |
| Burundi | 2024 | 0.1837 | Ember | 1 |
| Benin | 2024 | 0.5792 | Ember | 1 |
| Brunei Darussalam | 2024 | 0.8930 | Ember | 1 |
| Bolivia | 2025 | 0.4814 | Ember | 1 |
| Bahamas | 2024 | 0.6524 | Ember | 1 |
| Bhutan | 2024 | 0.0237 | Ember | 1 |
| Botswana | 2024 | 0.8525 | Ember | 1 |
| Belarus | 2025 | 0.3163 | Ember | 1 |
| Belize | 2024 | 0.1702 | Ember | 1 |
| Congo (DRC) | 2024 | 0.0276 | Ember | 1 |
| Central African Republic | 2023 | 0.0214 | Ember | 1 |
| Congo | 2024 | 0.7156 | Ember | 1 |
| Cote d'Ivoire | 2024 | 0.4056 | Ember | 1 |
| Cook Islands (the) | 2024 | 0.3500 | Ember | 1 |
| Cameroon | 2024 | 0.2262 | Ember | 1 |
| Cuba | 2024 | 0.6429 | Ember | 1 |
| Cabo Verde | 2024 | 0.4635 | Ember | 1 |
| Djibouti | 2024 | 0.4300 | Ember | 1 |
| Dominica | 2023 | 0.5733 | Ember | 1 |
| Dominican Republic | 2025 | 0.5380 | Ember | 1 |
| Algeria | 2024 | 0.6328 | Ember | 1 |
| Ecuador | 2025 | 0.1588 | Ember | 1 |
| Eritrea | 2024 | 0.5911 | Ember | 1 |
| Fiji | 2024 | 0.2757 | Ember | 1 |
| Falkland Islands (Malvinas) | 2023 | 0.7000 | Ember | 1 |
| Faroe Islands | 2023 | 0.3612 | Ember | 1 |
| Gabon | 2024 | 0.5254 | Ember | 1 |
| Grenada | 2024 | 0.6583 | Ember | 1 |
| French Guiana | 2023 | 0.2571 | Ember | 1 |
| Greenland | 2024 | 0.1383 | Ember | 1 |
| Gambia | 2024 | 0.6588 | Ember | 1 |
| Guinea | 2024 | 0.1814 | Ember | 1 |
| Guadeloupe | 2023 | 0.4934 | Ember | 1 |
| Equatorial Guinea | 2024 | 0.6430 | Ember | 1 |
| Guatemala | 2024 | 0.3015 | Ember | 1 |
| Guam | 2024 | 0.6054 | Ember | 1 |
| Guinea-Bissau | 2024 | 0.6625 | Ember | 1 |
| Guyana | 2024 | 0.6420 | Ember | 1 |
| Hong Kong (SAR of China) | 2024 | 0.6756 | Ember | 1 |
| Honduras | 2024 | 0.3220 | Ember | 1 |
| Haiti | 2024 | 0.5395 | Ember | 1 |
| Iraq | 2024 | 0.6831 | Ember | 1 |
| Iran | 2025 | 0.6560 | Ember | 1 |
| Jamaica | 2024 | 0.5624 | Ember | 1 |
| Kyrgyzstan | 2025 | 0.0726 | Ember | 1 |
| Cambodia | 2025 | 0.4988 | Ember | 1 |
| Kiribati | 2024 | 0.5000 | Ember | 1 |
| Comoros | 2023 | 0.6571 | Ember | 1 |
| Saint Kitts and Nevis | 2024 | 0.6043 | Ember | 1 |
| North Korea | 2024 | 0.3406 | Ember | 1 |
| Kuwait | 2025 | 0.6347 | Ember | 1 |
| Cayman Islands | 2024 | 0.6324 | Ember | 1 |
| Lao PDR | 2024 | 0.2321 | Ember | 1 |
| Lebanon | 2024 | 0.3882 | Ember | 1 |
| Saint Lucia | 2024 | 0.6425 | Ember | 1 |
| Sri Lanka | 2025 | 0.3677 | Ember | 1 |
| Liberia | 2024 | 0.3140 | Ember | 1 |
| Libya | 2024 | 0.8269 | Ember | 1 |
| Madagascar | 2024 | 0.4337 | Ember | 1 |
| Mali | 2024 | 0.5374 | Ember | 1 |
| Myanmar | 2024 | 0.5023 | Ember | 1 |
| Mongolia | 2025 | 0.8218 | Ember | 1 |
| Macao (SAR of China) | 2024 | 0.4744 | Ember | 1 |
| Martinique | 2023 | 0.5265 | Ember | 1 |
| Mauritania | 2024 | 0.5150 | Ember | 1 |
| Montserrat | 2024 | 0.7000 | Ember | 1 |
| Mauritius | 2024 | 0.6422 | Ember | 1 |
| Maldives | 2024 | 0.6153 | Ember | 1 |
| Malawi | 2024 | 0.0568 | Ember | 1 |
| Mozambique | 2024 | 0.1294 | Ember | 1 |
| Namibia | 2024 | 0.0524 | Ember | 1 |
| New Caledonia | 2024 | 0.5641 | Ember | 1 |
| Niger | 2024 | 0.6705 | Ember | 1 |
| Nicaragua | 2024 | 0.3004 | Ember | 1 |
| Nepal | 2024 | 0.0240 | Ember | 1 |
| Nauru | 2024 | 0.5400 | Ember | 1 |
| Oman | 2025 | 0.5444 | Ember | 1 |
| Panama | 2024 | 0.2203 | Ember | 1 |
| French Polynesia | 2024 | 0.4403 | Ember | 1 |
| Papua New Guinea | 2024 | 0.5161 | Ember | 1 |
| Saint Pierre and Miquelon | 2023 | 0.6600 | Ember | 1 |
| Puerto Rico | 2025 | 0.6561 | Ember | 1 |
| Palestine (State of) | 2024 | 0.4172 | Ember | 1 |
| Paraguay | 2025 | 0.0248 | Ember | 1 |
| Reunion | 2023 | 0.3987 | Ember | 1 |
| Rwanda | 2024 | 0.3619 | Ember | 1 |
| Solomon Islands | 2024 | 0.6000 | Ember | 1 |
| Seychelles | 2024 | 0.5603 | Ember | 1 |
| Sudan | 2024 | 0.1543 | Ember | 1 |
| Singapore | 2025 | 0.4970 | Ember | 1 |
| Sierra Leone | 2024 | 0.0571 | Ember | 1 |
| Senegal | 2024 | 0.5401 | Ember | 1 |
| Somalia | 2024 | 0.5302 | Ember | 1 |
| Suriname | 2024 | 0.3230 | Ember | 1 |
| South Sudan | 2024 | 0.6464 | Ember | 1 |
| Sao Tome and Principe | 2023 | 0.5889 | Ember | 1 |
| El Salvador | 2025 | 0.1113 | Ember | 1 |
| Syria | 2024 | 0.7062 | Ember | 1 |
| Eswatini | 2024 | 0.1197 | Ember | 1 |
| Turks and Caicos Islands | 2024 | 0.6370 | Ember | 1 |
| Chad | 2024 | 0.6297 | Ember | 1 |
| Togo | 2024 | 0.4211 | Ember | 1 |
| Tajikistan | 2025 | 0.0726 | Ember | 1 |
| Turkmenistan | 2024 | 1.3062 | Ember | 1 |
| Tunisia | 2025 | 0.5602 | Ember | 1 |
| Tonga | 2024 | 0.5714 | Ember | 1 |
| Trinidad and Tobago | 2024 | 0.6813 | Ember | 1 |
| Taiwan (China) | 2025 | 0.6336 | Ember | 1 |
| Uganda | 2024 | 0.0585 | Ember | 1 |
| Uruguay | 2025 | 0.0802 | Ember | 1 |
| Uzbekistan | 2025 | 1.0000 | Ember | 1 |
| Saint Vincent and the Grenadines | 2024 | 0.5733 | Ember | 1 |
| Venezuela | 2024 | 0.0859 | Ember | 1 |
| Virgin Islands (British) | 2023 | 0.6588 | Ember | 1 |
| Virgin Islands (U.S.) | 2023 | 0.6397 | Ember | 1 |
| Vanuatu | 2023 | 0.5000 | Ember | 1 |
| Samoa | 2024 | 0.4000 | Ember | 1 |
| Yemen | 2024 | 0.5914 | Ember | 1 |
| Zambia | 2024 | 0.1258 | Ember | 1 |
| Zimbabwe | 2024 | 0.3837 | Ember | 1 |
References & sources
All standards, datasets, and grid factor sources used by the platform.
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