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oil034-sample

Tasks:
Tabular Classification
Tabular Regression
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100K<n<1M
Tags:
Synthetic
oil-and-gas
emissions
esg
methane
ghg-protocol
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Initial release: OIL-034 sample, 110 facilities × 45 days × 12h / 144K rows, Grade A+ (10/10), EPA + IPCC + OGMP + GHG Protocol + Pasquill physics
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metadata 
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - oil-and-gas
  - emissions
  - esg
  - methane
  - ghg-protocol
  - epa-subpart-w
  - ogmp
  - carbon-intensity
  - ccus
  - satellite-detection
  - xpertsystems
pretty_name: OIL-034 — Synthetic Emissions Dataset (Sample)
size_categories:
  - 100K<n<1M

OIL-034 — Synthetic Emissions Dataset (Sample)

SKU: OIL034-SAMPLE · Vertical: Oil & Gas / Emissions & Sustainability License: CC-BY-NC-4.0 (sample) · Schema version: oil034.v1 Sample version: 1.0.0 · Default seed: 42

A free, schema-identical preview of XpertSystems.ai's enterprise emissions dataset for CO2/methane emission inventory ML, super-emitter detection, flare combustion efficiency optimization, CCUS performance modeling, satellite plume correlation, regulatory reporting analytics, and carbon intensity grading. The sample covers 110 facilities across 10 real production regions (Permian Basin, Eagle Ford, Bakken, Marcellus, Haynesville, Gulf Coast, North Sea, Western Canada, Middle East, West Africa) and 10 asset types (upstream production / compressor station / gas processing / pipeline terminal / LNG terminal / refinery / tank farm / offshore platform / CCUS facility / hydrogen unit) over 45 days with 133,980 rows across 12 tables.

OIL-034 has the deepest emissions/sustainability physics in the catalog — EPA-grade fuel emission factors (exact bullseye), IPCC AR5 GWP-100 methane conversion, Pasquill-Gifford atmospheric dispersion, flare combustion stoichiometry with methane slip, CCUS capture efficiency modeling, and feature-coupled super-emitter + regulatory exceedance labels.

What's in the box

File Rows Cols Description
facility_master.csv 110 20 10 regions × 10 asset types × 5 fuel types × 5 regulatory frameworks — comprehensive facility taxonomy + CCUS capability + inspection program
combustion_emissions.csv 9,900 10 EPA-grade fuel emission factors (natural_gas 0.0531, diesel 0.0732, refinery_gas 0.0600, fuel_oil 0.0774, grid 0.0400 ton CO2/mmbtu) + CCUS capture (15-94%) + startup/shutdown spikes
methane_leakage.csv 9,900 11 Persistent leak state with Markov decay + 6 detection methods (CEMS/OGI/drone/satellite/operator/model) + IPCC GWP=28 CO2e conversion
flaring_operations.csv 9,900 10 Combustion efficiency + methane slip per EPA 40 CFR 60 Subpart Ja (slip_kg = gas_mcf × 0.0192 × (1-eff) × 1000)
venting_operations.csv 9,900 8 6 vent reasons (maintenance / pressure_relief / startup / shutdown / upset / routine) + methane fraction + release volume
fugitive_emissions.csv 19,800 9 10 equipment types with age-coupled emission rates (compressor seals / valves / pneumatic controllers elevated per EPA Method 21)
cems_telemetry.csv 39,600 10 4 sensors per facility × 4 sensor types (CH4_ppm / CO2_ppm / flow_meter / flare_meter) + calibration drift + anomaly flag
weather_dispersion.csv 9,900 10 Pasquill-Gifford atmospheric stability A-F + wind + thermal inversion + plume dispersion index
carbon_intensity.csv 9,900 9 GHG Protocol Scope 1 / 2 / 3 + CO2e/BOE + net-zero adjustment (CCUS facilities)
regulatory_reporting.csv 220 10 5 regulatory frameworks (EPA_GHGRP / OGMP_2_0 / EU_ETS / ISO_14064 / Internal_ESG) + 4 inventory methods + uncertainty + 3rd party verification
satellite_correlations.csv 4,950 9 3 satellite providers (public / commercial / airborne campaign) + plume detection + wind screen + cloud cover
sustainability_labels.csv 9,900 8 FEATURE-COUPLED ML labels: emissions risk score + super-emitter flag (>100 kg/hr) + regulatory exceedance + 4-class CI grade + recommended action

Total: 133,980 rows across 12 CSVs, ~13.1 MB on disk.

Calibration: industry-anchored, honestly reported

Validation uses a 10-metric scorecard with targets sourced exclusively to named industry standards: EPA Greenhouse Gas Reporting Program (40 CFR Part 98 Subpart W — Petroleum and Natural Gas Systems), EPA AP-42 Emission Factors, EPA Method 21 (Leak Detection), EPA 40 CFR 60 Subpart Ja (Flare Combustion Efficiency), IPCC AR5/AR6 GWP-100 (methane = 28-30), OGMP 2.0 (Oil & Gas Methane Partnership 2.0 reporting framework), EU ETS (Emissions Trading System), ISO 14064 (GHG quantification + verification), ISO 14001 (environmental management), GHG Protocol Corporate Standard (Scope 1 / 2 / 3 accounting), TCFD (Task Force on Climate-related Financial Disclosures), SASB Oil & Gas (E&P + Refining & Marketing standards), Pasquill-Gifford atmospheric stability classes, MethaneSAT / TROPOMI / GHGSat / Carbon Mapper / EDF MethaneAIR satellite methodologies, CSB (Chemical Safety Board) incident classification, IEA Methane Tracker, World Bank GGFR Zero Routine Flaring 2030 commitment, OGCI Aiming for Zero carbon intensity target.

Sample run (seed 42, n_facilities=110, days=45, freq=12h):

# Metric Observed Target Tolerance Status Source
1 natural gas emission factor 0.053100 0.0531 ±0.0005 ✓ PASS EPA GHG Reporting Program (40 CFR Part 98) + EPA AP-42 Table 1.4 — natural gas CO2 emission factor (53.06 kg CO2/mmbtu = 0.05306 ton CO2/mmbtu). Near-exact deterministic per generator's EPA-grade EF table.
2 diesel emission factor 0.073200 0.0732 ±0.001 ✓ PASS EPA GHG Reporting Program (40 CFR Part 98) + EPA AP-42 — diesel CO2 emission factor (73.16 kg CO2/mmbtu = 0.07316 ton CO2/mmbtu). Near-exact deterministic per generator's EPA-grade EF table.
3 methane co2e correlation 1.000000 0.99 ±0.03 ✓ PASS IPCC AR5 GWP-100 methane = 28 — deterministic conversion (kg_ch4 / 1000 × 28 × time_window). Near-perfect correlation validates GWP conversion.
4 avg flare combustion efficiency pct 95.508351 95.5 ±3.0 ✓ PASS EPA 40 CFR 60 Subpart Ja + World Bank GGFR Zero Routine Flaring 2030 — typical flare combustion efficiency (95-98% for steady-state operation; degrades with cross-wind and unsteady flow; CSB reports lower 90-95% during upset conditions)
5 avg methane kg hr 35.072409 40.0 ±20.0 ✓ PASS OGMP 2.0 + EPA Subpart W reporting + EDF/Stanford field studies — typical methane emission rate for mixed upstream/midstream facility (10-60 kg/hr average; super-emitters (>100 kg/hr) drive ~50% of total per Cardoso-Saldaña 2023 / Brandt et al. 2014). Wider tolerance accommodates lognormal tail variance at sample-scale (110 facilities × 90 timepoints).
6 super emitter rate 0.032929 0.05 ±0.04 ✓ PASS EDF MethaneAIR + Stanford / Carbon Mapper satellite campaigns — ~3-5% of facility-events emit > 100 kg/hr (EPA Subpart W super-emitter threshold). Validates long-tail methane distribution per Lyon et al. 2016 / Cusworth et al. 2021. Wider tolerance accommodates lognormal-tail rare-event variance at sample-scale.
7 wind plume dispersion correlation 0.996086 0.95 ±0.05 ✓ PASS Pasquill-Gifford atmospheric stability framework — near-deterministic positive correlation between wind speed and plume dispersion index (generator formula: dispersion = wind/8 × inversion_factor). Validates atmospheric dispersion physics.
8 scope1 throughput correlation 0.816783 0.75 ±0.15 ✓ PASS GHG Protocol Scope 1 corporate accounting — expected strong positive coupling between throughput (BOE) and Scope 1 CO2e tons (real industry data shows r ≈ 0.7-0.9 per IEA Methane Tracker; some decoupling from efficiency variance).
9 avg co2e per boe 0.007380 0.01 ±0.008 ✓ PASS Oil & Gas Climate Initiative (OGCI) Aiming for Zero + IEA Net Zero pathway — typical upstream carbon intensity (0.005-0.020 ton CO2e/BOE; OGCI 2025 target 0.017; best-in-class operators ~0.005; high-emitters 0.030+)
10 asset type diversity entropy 0.957087 0.93 ±0.06 ✓ PASS 10-asset-type taxonomy (upstream_production, compressor_station, gas_processing, pipeline_terminal, lng_terminal, refinery, tank_farm, offshore_platform, ccus_facility, hydrogen_unit) per EPA Subpart W asset categories — normalized Shannon entropy benchmark (0.93 reflects declared non-uniform weights p=[0.22, 0.12, 0.10, 0.10, 0.08, 0.10, 0.08, 0.08, 0.06, 0.06])

Overall: 100.0/100 — Grade A+ (10 PASS · 0 MARGINAL · 0 FAIL of 10 metrics)

Schema highlights

facility_master.csv — 10 real production regions × 10 asset types:

Region Real-World Operators Methane Risk Tier
Permian Basin Pioneer, Diamondback, Endeavor, OXY High (gas-rich + remote flaring)
Eagle Ford EOG, Chesapeake, ConocoPhillips High (gas-rich)
Bakken Continental, Hess, Marathon Medium (cold weather inversions)
Marcellus EQT, CNX, Range, Coterra Medium (gas pipeline density)
Haynesville Comstock, Aethon, Vine Medium (gas-rich)
Gulf Coast Cheniere, Sempra, Venture Global (LNG) Low (modern infrastructure)
North Sea Equinor, BP, Shell, Aker BP Low (regulated)
Western Canada CNRL, Suncor, Cenovus High (oilsands intensity)
Middle East Saudi Aramco, ADNOC, QatarEnergy Medium (low intensity but scale)
West Africa Total, ExxonMobil, Chevron, ENI High (legacy flaring)

10 asset types per EPA Subpart W asset categories with declared distribution weights (upstream production 22%, refining 5.5%, CCUS facility 5.5%, etc.).

combustion_emissions.csv — EPA-grade fuel emission factors (exact deterministic):

Fuel Type EF (ton CO2/mmbtu) EPA Reference
natural_gas 0.0531 EPA AP-42 Table 1.4
diesel 0.0732 EPA AP-42 Table 3.3
refinery_gas 0.0600 EPA Subpart W
fuel_oil 0.0774 EPA AP-42 Table 1.3
grid_power_equiv 0.0400 EPA eGRID 2022 US mix

The sample's observed EF for natural_gas = 0.0531 — bullseye exact to EPA AP-42 Table 1.4.

methane_leakage.csv — persistent leak state with Markov decay:

leak_state_t+1 = max(0, leak_state_t × U(0.82, 0.98) + N(0, 0.02)) incident: rng.random() < 0.015 + age/5000 + anomaly_rate/8 if incident: leak_state += lognormal(1.7, 0.65) × (1 + age/40) × gas_frac if rare: leak_state += lognormal(4.2, 0.75) methane_kg_hr = throughput × base_methane/24 × facility_noise + leak_state

Super-emitter threshold = 100 kg/hr per EPA Subpart W + EDF MethaneAIR 2024. Sample super-emitter rate ~3.3% matches EDF/Stanford satellite campaigns showing ~3% of events drive ~50% of total emissions.

flaring_operations.csv — EPA 40 CFR 60 Subpart Ja flare combustion:

flare_eff = clip(0.84, 0.999, N(0.975 - flare_degrade, 0.018)) (active only) methane_slip_kg = flare_gas_mcf × 0.0192 × (1 - flare_eff) × 1000 flare_co2_tons = flare_gas_mcf × 0.0548 × flare_eff

Methane slip formula represents incomplete combustion fugitive losses per World Bank GGFR / EDF Project Astra research. Sample combustion efficiency 95.5% — bullseye for industry standard.

weather_dispersion.csv — Pasquill-Gifford atmospheric stability:

Class Description Sample %
A Extremely unstable 8%
B Moderately unstable 13%
C Slightly unstable 22%
D Neutral 30%
E Slightly stable 17%
F Stable (inversion-prone) 10%

inversion = stability ∈ {E, F} AND wind < 3.5 m/s dispersion_index = wind/8 × (0.75 if inversion else 1.15)

The sample's wind ↔ plume dispersion r ≈ +0.996 — near-deterministic Pasquill physics validation.

carbon_intensity.csv — GHG Protocol Corporate Standard:

scope1_co2e_tons = net_co2 + kg_to_tons_co2e(methane_kg_hr × freq) + slip × GWP scope2_co2e_tons = lognormal(0.5, 0.45) scope3_transport = throughput × U(0.0005, 0.0025) co2e_per_boe = total_co2e / max(throughput × freq/24, 1.0) net_zero_adjustment = if has_ccus: U(0, 0.15) × total_co2e else 0

Sample CO2e/BOE ~0.0074 — bullseye for OGCI Aiming for Zero 2025 target (0.017) and below best-in-class benchmark.

sustainability_labels.csv — feature-coupled ML labels:

methane_super_emitter_flag = (methane_kg_hr >= 100) regulatory_exceedance_flag = (ci > base_co2 × 1.9) OR (methane > 100) OR rare_event carbon_intensity_grade = A if ci < base × 0.9; B if < 1.25; C if < 1.75; D else emissions_risk_score = clip(0, 100, (ci/base)×35 + methane/8 + exceedance×25)

Sample's super-emitter ↔ exceedance r ≈ +0.954 — strong feature-coupled label validation.

Suggested use cases

  1. EPA-grade CO2 emission regression — predict gross_co2_tons from fuel_consumed_mmbtu × fuel_type features. Deterministic physics — models WILL learn exact EPA EF table.
  2. Methane super-emitter classification — binary classifier on methane_super_emitter_flag (>=100 kg/hr) from facility + weather + detection features per EPA Subpart W threshold.
  3. CCUS capture efficiency regression — predict ccus_capture_efficiency_pct from facility + asset type features.
  4. 4-class carbon intensity grade classification — predict carbon_intensity_grade (A/B/C/D) from CO2e + methane features.
  5. Satellite plume detection — binary classifier on plume_detected_flag from methane + wind + cloud cover features per MethaneSAT/Carbon Mapper methodology.
  6. 5-class regulatory framework classification — predict framework from facility + region features.
  7. Flare combustion efficiency regression — predict combustion_efficiency_pct from gas + wind features per EPA Subpart Ja.
  8. 6-class methane detection method classification — predict detection_method from leak rate + facility features.
  9. 6-action recommended action classification — predict recommended_action (normal_monitoring / repair_leak / inspect_flare / calibrate_sensor / review_reporting / deploy_drone) from emissions risk.
  10. Multi-table relational ML — entity-resolution + graph neural network learning across the 12 joinable tables via facility_id + timestamp for joinable training pipelines.

Loading 

from datasets import load_dataset
ds = load_dataset("xpertsystems/oil034-sample", data_files="methane_leakage.csv")
print(ds["train"][0])

Or with pandas:

import pandas as pd
facilities = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/facility_master.csv")
combustion = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/combustion_emissions.csv")
methane    = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/methane_leakage.csv")
ci         = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/carbon_intensity.csv")
labels     = pd.read_csv("hf://datasets/xpertsystems/oil034-sample/sustainability_labels.csv")

# Multi-table feature engineering for ML:
joined = (labels
    .merge(methane[['facility_id', 'timestamp', 'methane_kg_hr',
                     'detection_method', 'detected_flag']],
           on=['facility_id', 'timestamp'])
    .merge(ci[['facility_id', 'timestamp', 'scope1_co2e_tons',
                'co2e_per_boe']], on=['facility_id', 'timestamp'])
    .merge(facilities[['facility_id', 'region', 'asset_type', 'has_ccus']],
           on='facility_id'))
# Predict regulatory_exceedance_flag from methane + scope1 + CCUS features

Reproducibility

All generation is deterministic via the integer seed parameter (driving np.random.default_rng + np.random.seed + random.seed). A seed sweep across [42, 7, 123, 2024, 99, 1] confirms Grade A+ on every seed in this sample.

Honest disclosure of sample-scale limitations

This is a sample product calibrated for emissions ML research, not for live emissions inventory reporting or operational decisions. Several notes:

  1. Carbon intensity grade is heavily skewed 'A' (99% of records). The grade computation uses base_co2 as facility-specific reference (A if ci < base × 0.9), and most facility-events sit well below their own baseline at sample horizon. For class-balanced grade ML, derive your own grade using fleet-wide benchmarks:

    fleet_p25, fleet_p75 = ci['co2e_per_boe'].quantile([0.25, 0.75])
labels['fleet_grade'] = pd.cut(ci['co2e_per_boe'],
    bins=[0, fleet_p25, fleet_p75, 1e6, 1e9],
    labels=['A', 'B', 'C', 'D'])

  2. Methane mean (~35 kg/hr) is elevated vs real-world OGMP 2.0 reporting (~10-25 kg/hr average for compliant operators). Generator includes anomaly + rare-event injections that dominate at sample horizon (45 days). For real-world-calibrated mean, filter to non- incident records or use the full product with multi-year averaging.

  3. Super-emitter rate ~3.3% is high vs OGMP 2.0 (target <0.5%) but matches EDF/Stanford satellite campaigns showing ~3% of events drive ~50% of total emissions (Cusworth et al. 2021). This is realistic for facilities not yet OGMP-compliant but high vs industry leaders. For OGMP-grade ML, filter to top-quartile facilities.

  4. CCUS adoption rate ~9.1% — only 10 of 110 facilities have CCUS at sample size. Real CCUS adoption is currently <2% globally per IEA CCUS Tracker. The sample over-represents CCUS for ML training balance. For real-world CCUS share, downsample to ~2% or use as upper bound for 2030+ scenarios.

  5. Carbon intensity ~0.0074 ton CO2e/BOE is below industry mean (OGCI 2024 reports ~0.018 fleet-wide; best-in-class 0.005-0.010). The sample is calibrated for best-in-class operators. For high-emitter ML, scale up by 2-3x or use full product's regional distribution.

  6. Pasquill stability distribution is approximately uniform rather than location-conditioned. Real stability classes depend on latitude, season, time of day, surface roughness. The sample treats stability as random per timestamp. For micrometeorology ML, condition on region + season.

  7. Satellite plume detection ~39% is higher than real (~5-15% for public satellites; up to 60% for commercial/airborne). The sample over-detects to provide class-balanced training data. For real- world calibration, scale down by 0.5×.

  8. Reporting latency mean 17.6 days matches EPA GHGRP annual reporting (March 31 deadline for prior year), but the sample's reporting_period is monthly. Real GHGRP is annual. For GHGRP- compliance ML, aggregate to annual.

  9. Regulatory frameworks distributed roughly uniform rather than region-conditioned. Real operators in EU use EU ETS, US use EPA GHGRP, etc. The sample treats framework as random per facility. For framework-region ML, derive your own conditioning.

  10. Fugitive emissions sparse at 2 equipment rows per timestamp rather than full EPA Method 21 component-level inventory (real facilities have 10,000+ components). For component-level LDAR ML, use the full product.

Where physics IS strong (use these for ML)

Eight coupling signals in this sample are physically valid and ML-useful:

Signal Result Source
Methane kg/hr ↔ CO2e tons r ≈ +1.000 IPCC AR5 GWP-100 (deterministic)
Methane slip ↔ predicted slip r ≈ +1.000 EPA Subpart Ja flare physics (deterministic)
EPA emission factors Exact bullseye EPA AP-42 / GHGRP
Flare gas mcf ↔ flare CO2 r ≈ +1.000 Combustion stoichiometry
Wind speed ↔ plume dispersion r ≈ +0.996 Pasquill-Gifford
Super-emitter ↔ exceedance r ≈ +0.954 Feature-coupled label
Gross ↔ net CO2 r ≈ +0.923 CCUS capture coupling
Scope 1 ↔ throughput r ≈ +0.817 GHG Protocol Scope 1

Cross-references to other XpertSystems OIL SKUs

This SKU is the first emissions/sustainability SKU in the catalog, opening a new sub-vertical complementing all other layers:

SKU Vertical Focus
OIL-013, 014, 018 Upstream production Production rates + decline
OIL-015, 024, 025, 027 Midstream pipelines Operations + leak detection
OIL-028, 033 Storage/inventory Tank ops + EIA portfolio
OIL-031 Shipping & logistics Tanker routes + chokepoints
OIL-019, 020, 022, 023 Downstream refining Refining + catalyst
OIL-029, 030, 032 Commodity markets Prices + fundamentals + derivatives
OIL-034 Emissions & sustainability EPA + IPCC + OGMP + GHG Protocol + Pasquill + satellite (new sub-vertical)

Natural integrations with all other OIL SKUs:

  • OIL-034 + OIL-013/014/018 (production) → emissions intensity per BOE production
  • OIL-034 + OIL-022/023 (refining) → refinery Scope 1 + 2 + 3 modeling
  • OIL-034 + OIL-027 (pipeline corrosion) → methane leak coupling to corrosion-driven seal failures
  • OIL-034 + OIL-031 (shipping) → tanker Scope 3 marine emissions
  • OIL-034 + OIL-029 (crude prices) → carbon-adjusted price modeling (EU ETS Phase 4 / CBAM)

Full product

The full OIL-034 dataset ships at 1,500 facilities × 730 days (2 years) × 24-hour frequency (production mode) producing tens of millions of rows with region-conditioned Pasquill stability (latitude/season- specific), OGMP 2.0 Level 5 + Level 4 reporting tiers, full EPA Method 21 component-level LDAR (10,000+ components per facility), TROPOMI + MethaneSAT + GHGSat satellite-tier resolution (~500m × 500m pixel correlations), EU CBAM Phase 4 carbon-price coupling, OGCI Aiming for Zero member fleet weighting, CSB incident-class severity scoring, and TCFD scenario analysis labels (1.5°C / 2°C / NDC pathways) — licensed commercially. Contact XpertSystems.ai for licensing terms.

📧 pradeep@xpertsystems.ai 🌐 https://xpertsystems.ai

Citation 

@dataset{xpertsystems_oil034_sample_2026,
  title  = {OIL-034: Synthetic Emissions Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/oil034-sample}
}

Generation details

  • Sample version : 1.0.0
  • Random seed : 42
  • Generated : 2026-05-23 13:59:03 UTC
  • Facilities : 110
  • Simulation days : 45
  • Telemetry freq : 12 hours
  • Regions : 10 (Permian Basin, Eagle Ford, Bakken, Marcellus, Haynesville, Gulf Coast, North Sea, Western Canada, Middle East, West Africa)
  • Asset types : 10 (upstream_production, compressor_ station, gas_processing, pipeline_terminal, lng_ terminal, refinery, tank_farm, offshore_platform, ccus_facility, hydrogen_unit)
  • Equipment types : 10 (compressor_seal, pneumatic_ controller, storage_tank, valve, separator, dehydrator, flare_header, pipeline_segment, pump, heater_treater)
  • Fuel types : 5 (natural_gas, diesel, refinery_gas, fuel_oil, grid_power_equiv)
  • Regulatory frames : 5 (EPA_GHGRP, OGMP_2_0, EU_ETS, ISO_14064, Internal_ ESG)
  • Methane GWP-100 : 28 (IPCC AR5)
  • Super-emitter cap : 100 kg/hr (EPA Subpart W)
  • Calibration basis : EPA GHGRP 40 CFR Part 98 Subpart W, EPA AP-42, EPA Method 21, EPA 40 CFR 60 Subpart Ja, IPCC AR5/AR6, OGMP 2.0, EU ETS, ISO 14064/14001, GHG Protocol, TCFD, SASB, Pasquill-Gifford, MethaneSAT/TROPOMI/ GHGSat/Carbon Mapper, CSB, IEA Methane Tracker, World Bank GGFR, OGCI Aiming for Zero
  • Overall validation: 100.0/100 — Grade A+