xpertsystems/oil034-sample

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.

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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.

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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)

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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.

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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.

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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

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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.

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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.

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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

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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)

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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

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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+