| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| - time-series-forecasting |
| tags: |
| - synthetic-data |
| - climate-impact |
| - climate-change |
| - ipcc-ar6 |
| - cmip6 |
| - ssp-scenarios |
| - ssp1-1-9 |
| - ssp1-2-6 |
| - ssp2-4-5 |
| - ssp3-7-0 |
| - ssp5-8-5 |
| - climate-projections |
| - temperature-projections |
| - global-mean-surface-temperature |
| - gmst |
| - polar-amplification |
| - enso |
| - pdo |
| - natural-variability |
| - ensemble-spread |
| - extreme-events |
| - heatwave |
| - flood |
| - drought |
| - wildfire |
| - hurricane |
| - compound-events |
| - climate-attribution |
| - fischer-knutti |
| - carbon-emissions |
| - ghg-protocol |
| - scope-1-2-3 |
| - co2 |
| - methane |
| - ch4 |
| - n2o |
| - hfcs |
| - gwp-100 |
| - carbon-budget |
| - net-zero |
| - ngfs |
| - ngfs-scenarios |
| - carbon-pricing |
| - carbon-dioxide-removal |
| - cdr |
| - sea-level-rise |
| - slr |
| - thermal-expansion |
| - ice-sheet |
| - glacier |
| - ocean-acidification |
| - ocean-ph |
| - marine-heatwave |
| - storm-surge |
| - adaptation |
| - maladaptation |
| - benefit-cost-ratio |
| - undrr |
| - world-bank |
| - climate-finance |
| - physical-climate-risk |
| - transition-risk |
| - tcfd |
| - sasb |
| - noaa-storm-events |
| - munich-re-natcat |
| - iea-ghg |
| pretty_name: ENR007 — Synthetic Climate Impact Dataset (Sample) |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: temperature |
| data_files: ENR007_climate_all.parquet |
| - config_name: extreme_events |
| data_files: ENR007_extreme_events_all.parquet |
| - config_name: emissions |
| data_files: ENR007_emissions_all.parquet |
| - config_name: sea_level |
| data_files: ENR007_sea_level_all.parquet |
| - config_name: adaptation |
| data_files: ENR007_adaptation_all.parquet |
| --- |
| |
| # ENR007 — Synthetic Climate Impact Dataset (Sample Preview) |
|
|
| **XpertSystems.ai | Synthetic Data Factory | Energy & Climate Vertical** |
|
|
| A **five-table climate impact dataset** spanning the full physical & transition |
| risk surface: temperature projections with polar amplification and ENSO-like |
| natural variability, extreme weather events (12 hazard types with climate |
| attribution fractions), carbon emissions pathways (6 sectors × 4 GHG species |
| with carbon budgets and pricing), sea level rise (GMSL decomposition into |
| thermal expansion + ice sheets + glaciers, ocean pH, marine heatwaves), and |
| adaptation strategies (12 strategy types with BCR, NPV, maladaptation flags). |
| Calibrated benchmark-first against **IPCC AR6 (2021) WG1 Tables SPM.1 and |
| 9.9**, **CMIP6 model ensemble**, **NOAA Storm Events**, **Munich Re NatCat**, |
| **IEA GHG (2022)**, **Fischer & Knutti (2015) attribution meta-analysis**, |
| **NGFS scenarios**, and **UNDRR/World Bank adaptation literature**. |
|
|
| **All six IPCC SSP scenarios are included in the sample** (SSP1-1.9, SSP1-2.6, |
| SSP2-4.5, SSP3-7.0, SSP5-8.5, Current Policy) — the SSP comparison is the |
| dataset's signature feature. |
|
|
| This is the **sample preview** — 30 locations × 30 years (2020-2049) × all 6 |
| SSP scenarios (~34K total records). The full product covers 500-1000+ |
| locations × 80 years (full IPCC AR6 century horizon to 2100) × all 6 SSP |
| scenarios (~10M+ records) with full geographic detail and complete adaptation |
| strategy coverage. |
|
|
| --- |
|
|
| ## Dataset summary |
|
|
| | Table | Rows (sample) | What it contains | |
| |---|---:|---| |
| | `temperature` | 5,400 | Per location × year × scenario climate projections: GMST anomaly, regional anomaly (with polar amplification), ensemble p10/mean/p90 spread, ENSO-like natural variability, heat index, frost days, growing degree days, Tmax record exceedance, UHI offset | |
| | `extreme_events` | ~22,000 | 12 hazard types (Heatwave, Cold_Snap, Flood, Flash_Flood, Drought, Wildfire, Hurricane, Tornado, Blizzard, Ice_Storm, Storm_Surge, Compound_Event) with severity (Moderate / Severe / Extreme / Exceptional), return period & AEP, damage USD (Pareto tail), insured loss, fatalities, displaced persons, climate attribution fraction, frequency change | |
| | `emissions` | 4,320 | 6 sectors (Energy, Transport, Industry, Agriculture, LULUCF, Waste) × 4 gases (CO2, CH4, N2O, HFCs) × years × 6 scenarios. Includes Scope 1/2/3 decomposition, carbon budgets remaining (1.5°C and 2°C), CDR deployment, per-capita CO2, carbon price trajectories | |
| | `sea_level` | 1,800 | Coastal locations × year × scenario: GMSL with three-part decomposition (thermal expansion + ice sheet + glacier), regional SLR with vertical land motion, ocean pH, marine heatwave days, ocean heat content, storm surge, coastal flood frequency change | |
| | `adaptation` | 864 | 12 adaptation strategy types per location: BCR, NPV of avoided damages, implementation cost & timeline, damage reduction, effectiveness score, maladaptation risk flag, equity score, CO2 mitigation co-benefit (for nature-based solutions) | |
|
|
| All five tables are provided in both **CSV** and **Parquet**. They join on |
| `location_id`, `scenario_id`, and `year`. |
|
|
| --- |
|
|
| ## Calibration sources |
|
|
| All ten validation metrics target named industry / scientific sources: |
|
|
| - **IPCC AR6 (2021) WG1 Table SPM.1** — SSP temperature trajectories |
| (2050 + 2100 medians, p10, p90) |
| - **IPCC AR6 (2021) WG1 Chapter 4** — ensemble spread and climate indices |
| (frost days, growing degree days under warming) |
| - **IPCC AR6 (2021) WG1 Chapter 5** — ocean acidification trajectories |
| (pH drop by SSP scenario) |
| - **IPCC AR6 (2021) WG1 Chapter 9 Table 9.9** — sea level rise projections |
| - **IPCC AR6 (2021) WG1 Chapter 11** — extreme event attribution |
| - **CMIP6 multi-model ensemble** — temperature spread and natural variability |
| - **NOAA Storm Events Database** — extreme event frequencies and damages |
| - **Munich Re NatCat / Swiss Re sigma** — insured loss fractions, damage |
| distributions |
| - **Fischer & Knutti (2015)** — climate attribution meta-analysis (event |
| frequency sensitivity to warming) |
| - **IEA GHG (2022)** — sector-level emissions baselines and decline rates |
| - **GHG Protocol Corporate Standard** — Scope 1/2/3 emissions decomposition |
| - **NGFS Climate Scenarios** — carbon price trajectories by SSP |
| - **UNDRR Global Assessment Report + Schipper (2020)** — maladaptation risk |
| under deep warming |
| - **World Bank / UNDRR** — adaptation cost-benefit literature |
|
|
| --- |
|
|
| ## Validation scorecard (seed = 42) |
|
|
| 10/10 PASS · **Grade A+ (100%)** across all six canonical seeds (42, 7, 123, 2024, 99, 1). |
|
|
| | # | Metric | Observed | Target | Tol | Type | Source | |
| |---|---|---:|---:|---:|---|---| |
| | 1 | `ensemble_p10_le_mean_le_p90_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | IPCC AR6 WG1 Ch 4 | |
| | 2 | `aep_equals_inverse_return_period_rate` | 0.979 | 0.95 | ±0.05 | FLOOR | Probability theory | |
| | 3 | `scope_sum_equals_annual_emissions_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | GHG Protocol | |
| | 4 | `insured_loss_le_economic_damage_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Munich Re NatCat | |
| | 5 | `climate_attribution_fraction_in_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | IPCC AR6 Ch 11 / Fischer & Knutti | |
| | 6 | `carbon_price_2030_matches_scenario_targets_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | NGFS / IPCC AR6 | |
| | 7 | `ocean_ph_in_ar6_band_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | IPCC AR6 WG1 Ch 5 | |
| | 8 | `frost_days_decrease_with_warming` | 1.000 | 1.00 | ±0.01 | FLOOR | IPCC AR6 WG1 Ch 4 | |
| | 9 | `gmsl_warming_ordering` | 1.000 | 1.00 | ±0.01 | FLOOR | IPCC AR6 WG1 Ch 9 Table 9.9 | |
| | 10 | `maladaptation_warming_ordering` | 1.000 | 1.00 | ±0.01 | FLOOR | UNDRR GAR / Schipper (2020) | |
|
|
| The **AR6 scenario warming ordering test** (#9) confirms that GMSL at the |
| terminal year (2049 in this sample) is properly ordered across SSPs: |
| SSP5-8.5 > SSP3-7.0 > Current_Policy > SSP2-4.5 > SSP1-2.6 > SSP1-1.9. |
| |
| --- |
| |
| ## Schema highlights |
| |
| ### `temperature` (5,400 rows × 21 cols, all 6 scenarios) |
| `location_id`, `scenario_id` (SSP1-1.9 / SSP1-2.6 / SSP2-4.5 / SSP3-7.0 / |
| SSP5-8.5 / Current_Policy), `year`, `global_mean_temp_anomaly_C`, |
| `regional_temp_anomaly_C`, `ensemble_mean_C`, `ensemble_p10_C`, |
| `ensemble_p90_C`, `temp_trend_C_per_decade`, `natural_variability_C`, |
| `heat_index_C`, `frost_days_per_year`, `growing_degree_days`, |
| `tmax_record_exceedance_flag`, `urban_heat_island_C`, `latitude`, |
| `longitude`, `region_type` (Polar / Temperate / Mediterranean / Tropical / |
| Arid / Coastal / Island / Mountainous), `is_coastal`, `is_urban`. |
|
|
| ### `extreme_events` (~22,000 rows × 22 cols) |
| `event_id`, `location_id`, `scenario_id`, `year`, `event_type` (12 hazards), |
| `event_severity` (Moderate / Severe / Extreme / Exceptional), |
| `event_start_date`, `event_end_date`, `duration_days`, `affected_area_km2`, |
| `peak_intensity`, `return_period_years`, `annual_exceedance_prob`, |
| `economic_damage_USD`, `insured_loss_USD`, `fatalities`, `displaced_persons`, |
| `infrastructure_damage_score`, `compound_event_flag`, |
| `climate_attribution_fraction`, `event_frequency_change_pct`, `latitude`, |
| `longitude`, `region_type`. |
|
|
| ### `emissions` (4,320 rows × 19 cols) |
| `scenario_id`, `year`, `emission_sector` (Energy / Transport / Industry / |
| Agriculture / LULUCF / Waste), `gas_type` (CO2 / CH4 / N2O / HFCs), |
| `gwp_100yr`, `annual_emissions_MtCO2e`, `cumulative_emissions_GtCO2`, |
| `carbon_budget_remaining_1_5C_GtCO2`, `carbon_budget_remaining_2C_GtCO2`, |
| `emission_intensity_kgCO2_per_GDP`, `per_capita_tCO2e`, |
| `fossil_fuel_combustion_pct`, `land_use_change_MtCO2`, |
| `cdr_deployment_MtCO2yr`, `net_zero_year`, `scope1_emissions_MtCO2e`, |
| `scope2_emissions_MtCO2e`, `scope3_emissions_MtCO2e`, |
| `carbon_price_USD_per_tCO2`, `stranded_asset_risk_USD_B`. |
|
|
| ### `sea_level` (1,800 rows × 17 cols) |
| `location_id`, `scenario_id`, `year`, `global_mean_sea_level_rise_mm`, |
| `regional_slr_mm`, `slr_likely_range_low_mm`, `slr_likely_range_high_mm`, |
| `thermal_expansion_mm`, `ice_sheet_contribution_mm`, |
| `glacier_contribution_mm`, `vertical_land_motion_mm_yr`, |
| `coastal_flood_frequency_change_pct`, `storm_surge_height_m`, `ocean_ph`, |
| `ocean_heat_content_ZJ`, `marine_heatwave_days_per_yr`, `latitude`, |
| `longitude`, `region_type`, `is_coastal`. |
|
|
| ### `adaptation` (864 rows × 15 cols) |
| `adaptation_id`, `location_id`, `scenario_id`, `strategy_type` (Seawall / |
| Managed_Retreat / Urban_Greening / Crop_Diversification / Early_Warning_System |
| / Building_Codes / Water_Harvesting / Reforestation / Insurance_Scheme / |
| Social_Safety_Net / Desalination / Wetland_Restoration), `implementation_year`, |
| `implementation_cost_USD`, `annual_maintenance_cost_USD`, |
| `benefit_cost_ratio`, `damage_reduction_pct`, |
| `co2_mitigation_potential_MtCO2yr`, `adaptation_effectiveness_score`, |
| `maladaptation_risk_flag`, `equity_score`, `implementation_timeline_years`, |
| `avoided_damages_USD_NPV`, `region_type`, `is_coastal`, `latitude`. |
|
|
| --- |
|
|
| ## Suggested use cases |
|
|
| - **SSP scenario comparison ML** — train classifiers / regressors that |
| predict regional climate outcomes given an input SSP scenario; |
| benchmark prediction skill across the 6 SSP trajectories |
| - **Climate attribution ML** — train classifiers for |
| `climate_attribution_fraction` from event_type, severity, location, |
| scenario; useful for litigation / regulatory ML pipelines |
| - **Extreme event return period estimation** — fit Generalized Extreme Value |
| (GEV) models per event_type × region_type and benchmark against the |
| included `return_period_years` |
| - **Catastrophe (CAT) modeling pipelines** — combine `economic_damage_USD`, |
| `insured_loss_USD`, `fatalities`, `affected_area_km2` features for |
| Pareto-tail loss models à la Munich Re / Swiss Re |
| - **Carbon budget exhaustion forecasting** — predict |
| `carbon_budget_remaining_1_5C_GtCO2` trajectories under different |
| policy paths; useful for transition-risk ML for TCFD/SASB |
| - **Scope 1/2/3 emissions decomposition** — train sector-level disaggregation |
| models for corporate emissions reporting |
| - **Net Zero pathway optimization** — model `cdr_deployment_MtCO2yr` and |
| `carbon_price_USD_per_tCO2` joint dynamics for IAM (integrated assessment |
| model) augmentation |
| - **Coastal SLR risk ML** — predict regional SLR from GMSL drivers |
| (thermal_expansion / ice_sheet / glacier components) + vertical land |
| motion; useful for asset-level coastal risk scoring |
| - **Ocean acidification trajectory modeling** — fit pH decay models |
| per scenario for marine ecosystem ML |
| - **Storm surge × SLR compound risk** — model `storm_surge_height_m` as |
| function of baseline surge + regional_slr_mm for flood ML |
| - **Adaptation strategy selection ML** — train recommenders for |
| cost-effective adaptation portfolios given location, scenario, and |
| budget constraints; benchmark BCR and `damage_reduction_pct` |
| - **Maladaptation risk classification** — supervised learning on |
| `maladaptation_risk_flag` from strategy_type, scenario, location |
| - **Climate-economy coupled modeling** — join emissions + adaptation + |
| extreme events for integrated assessment ML pipelines |
| - **Physical climate risk for portfolio analysis** — aggregate |
| location-level extreme event damages + SLR exposure to asset/portfolio |
| level for TCFD physical risk disclosure |
| - **Net-zero year prediction** — classifier for `net_zero_year` given |
| sector decline rates and CDR deployment trajectories |
| |
| --- |
| |
| ## Loading examples |
| |
| ```python |
| from datasets import load_dataset |
|
|
| temp = load_dataset("xpertsystems/enr007-sample", "temperature", split="train") |
| events = load_dataset("xpertsystems/enr007-sample", "extreme_events", split="train") |
| print(temp.shape, events.shape) |
| ``` |
| |
| ```python |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
|
|
| # Compare SSP scenario warming trajectories |
| temp = pd.read_parquet(hf_hub_download( |
| "xpertsystems/enr007-sample", "ENR007_climate_all.parquet", |
| repo_type="dataset", |
| )) |
|
|
| # Mean GMST anomaly by year and scenario |
| trajectory = ( |
| temp.groupby(["scenario_id", "year"])["global_mean_temp_anomaly_C"] |
| .mean() |
| .unstack(level="scenario_id") |
| .round(3) |
| ) |
| print(trajectory.tail(10)) # last 10 years |
| ``` |
| |
| ```python |
| # Extreme event damages by scenario |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| events = pd.read_parquet(hf_hub_download( |
| "xpertsystems/enr007-sample", "ENR007_extreme_events_all.parquet", |
| repo_type="dataset", |
| )) |
| |
| # Total damages by SSP scenario |
| damages = ( |
| events.groupby("scenario_id") |
| .agg( |
| total_damage_billion_USD=("economic_damage_USD", lambda x: x.sum() / 1e9), |
| total_fatalities=("fatalities", "sum"), |
| n_events=("event_id", "count"), |
| ) |
| .round(2) |
| .sort_values("total_damage_billion_USD", ascending=False) |
| ) |
| print(damages) |
| ``` |
|
|
| ```python |
| # Carbon budget trajectories |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| emis = pd.read_parquet(hf_hub_download( |
| "xpertsystems/enr007-sample", "ENR007_emissions_all.parquet", |
| repo_type="dataset", |
| )) |
| |
| # 1.5°C budget remaining by year, per scenario (CO2-only) |
| budget_traj = ( |
| emis[emis["gas_type"] == "CO2"] |
| .groupby(["scenario_id", "year"])["carbon_budget_remaining_1_5C_GtCO2"] |
| .mean() |
| .unstack(level="scenario_id") |
| .round(1) |
| ) |
| print(budget_traj.tail(10)) |
| ``` |
|
|
| ```python |
| # Sea level rise decomposition |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| slr = pd.read_parquet(hf_hub_download( |
| "xpertsystems/enr007-sample", "ENR007_sea_level_all.parquet", |
| repo_type="dataset", |
| )) |
| |
| # Component contributions at terminal year |
| terminal = slr[slr["year"] == slr["year"].max()] |
| print(terminal.groupby("scenario_id").agg( |
| gmsl_mm=("global_mean_sea_level_rise_mm", "mean"), |
| thermal_mm=("thermal_expansion_mm", "mean"), |
| ice_sheet_mm=("ice_sheet_contribution_mm", "mean"), |
| glacier_mm=("glacier_contribution_mm", "mean"), |
| ).round(1)) |
| ``` |
|
|
| --- |
|
|
| ## Limitations and honest disclosures |
|
|
| This sample is calibrated for **structural fidelity, not bit-exact reproduction |
| of any specific CMIP6 model run.** Specifically: |
|
|
| - **Sample covers 30 years (2020-2049), not the full IPCC AR6 century** |
| to 2100. SSP scenario divergence is most pronounced at 2080-2100; |
| this sample shows early-divergence behavior. The full product extends |
| to 2100. Per-scenario benchmarks for 2100 (from `SCENARIOS` dict in |
| the generator) cannot be directly tested at this horizon. |
| - **Climate scenarios use prescribed 2050+2100 median benchmarks** |
| with quadratic interpolation between (line 217-219 of generator). |
| The shape of the trajectory between 2050 and 2100 is a simple |
| quadratic, not an actual coupled climate-carbon cycle simulation. |
| For coupled-system research, use CMIP6 model output directly. |
| - **Polar amplification is a single scalar per region_type** (line 188). |
| Real polar amplification varies with season, depth (ocean), and feedback |
| processes that this synthetic dataset does not model. |
| - **ENSO/PDO natural variability is a single AR(1) process** with φ=0.72 |
| (line 222). Real ENSO is non-Gaussian, has multi-decadal regime shifts, |
| and interacts with PDO/AMO. Use as a low-fidelity natural-variability |
| proxy. |
| - **Extreme event damages use Pareto tail with shape per event_type** |
| (line 339). Compound and tail-risk dependence (e.g., heatwave |
| conditional on drought) is not modeled beyond the single |
| `Compound_Event` category. |
| - **`annual_emissions_MtCO2e` for non-CO2 gases is in native Mt of the |
| gas, not Mt CO2-equivalent.** The generator splits emissions by gas |
| via fixed fractions (0.75/0.17/0.06/0.02 for CO2/CH4/N2O/HFCs) and |
| applies the sector decline rate. The reported value is gas-mass, not |
| CO2e. The `gwp_100yr` column is provided for user-side conversion: |
| CO2e = value × gwp. Don't compare raw `annual_emissions_MtCO2e` totals |
| across gases without that multiplication. |
| - **`cumulative_emissions_GtCO2` accumulates CO2 only** (not CO2e of |
| other gases). Matches AR6 budget convention but means the cumulative |
| total is not all-GHG. The `carbon_budget_remaining_*` columns are |
| CO2-only-budget-compatible. |
| - **`location_id`s do not match between scenarios.** Each scenario run |
| generates a fresh location pool via `generate_locations(seed)`. |
| Same seed → same locations across scenarios. Different seed → fresh |
| locations. When comparing scenarios, group by (scenario_id, year) |
| with aggregation, not on per-location panel. |
| - **Sea level coverage**: at sample scale, locations marked as |
| `is_coastal=True` get full SLR records. The generator falls back to |
| `locations.iloc[:max(1, len/3)]` if no coastal locations exist; |
| at n=30 typically 10-15 coastal locations arise naturally. |
| - **`vertical_land_motion_mm_yr`** is sampled once per location and |
| applied as `vlm * (yr - 2020)` (line 503). Real VLM has spatial |
| correlation (subsidence basins) and can be non-linear over decades. |
| - **Adaptation strategies are independent draws per location** — no |
| portfolio-level interactions or stacking benefits. Real adaptation |
| often shows complementarity (e.g., Seawall + Wetland_Restoration |
| combined is more effective than either alone). |
| - **`benefit_cost_ratio` and `bcr_mu` are scenario-warming-scaled** |
| (line 576-578): higher warming → higher BCR (because there's more |
| damage to avoid). Realistic but should not be interpreted as |
| cost-effectiveness in a vacuum. |
| - **`net_zero_year` is hardcoded per scenario** in the SCENARIOS dict |
| (SSP1-1.9: 2050, SSP1-2.6: 2065, SSP2-4.5: 2095, others: None for |
| scenarios that never reach net zero). Sample data may not visibly |
| show net zero achievement since horizon ends at 2049. |
| - **Carbon price 2030 EXACTLY matches** the scenario target by design |
| (deterministic interpolation in the generator). Real policy paths |
| show stochastic deviations from target prices. |
| |
| The full ENR007 product addresses these by full 2100 horizon, expanded |
| location-pool location consistency across scenarios, multi-model CMIP6 |
| ensemble draws, compound-event tail dependence modeling, and detailed |
| adaptation portfolio interactions — contact us for the licensed |
| commercial release. |
| |
| --- |
| |
| ## Companion datasets in the Energy & Climate vertical |
| |
| - **ENR-001** — Synthetic Power Grid Operations Dataset (transmission |
| bus telemetry, line flows, dispatch, frequency, contingency) |
| - **ENR-002** — Synthetic Renewable Energy Generation Dataset |
| (utility-scale solar/wind/hybrid SCADA, weather, forecast, PCC, BESS) |
| - **ENR-003** — Synthetic Electricity Demand & Load Forecasting Dataset |
| (zone-level demand, multi-horizon forecasts, peak events, EV/DER, TOU) |
| - **ENR-004** — Synthetic Upstream Oil & Gas Production Dataset |
| (well-level production, decline curves, PVT, commodity prices, |
| Subpart W methane) |
| - **ENR-005** — Synthetic Smart Grid Dataset (AMI, DER, OpenADR, feeder |
| power flow, grid edge analytics) |
| - **ENR-006** — Synthetic Wholesale Energy Market Trading Dataset (spot |
| prices, futures, ancillary services, bilateral PPAs, trading risk) |
| - **ENR-007** — Synthetic Climate Impact Dataset (you are here) — **the |
| forward-looking climate forcing companion** to the rest of the |
| Energy & Climate vertical: IPCC SSP scenario inputs that feed |
| emissions intensity (ENR-004), renewable resource trends (ENR-002), |
| demand load patterns (ENR-003), and grid stress events (ENR-001). |
| |
| Use **ENR-004 + ENR-007** together for transition-risk + physical-risk |
| ML on fossil supply chains; combine with **ENR-001 + ENR-002 + ENR-003** |
| for end-to-end climate impact on physical grid + renewables + demand. |
| |
| For subsurface companion data (seismic, well logs, reservoir simulation, |
| geological formations), see the **OIL series** (OIL-001 through OIL-004) |
| in our [Oil & Gas vertical](https://huggingface.co/xpertsystems). |
| |
| For the broader catalog: |
| - [Materials & Energy](https://huggingface.co/xpertsystems) — MAT-001 |
| - [Insurance & Risk](https://huggingface.co/xpertsystems) — 10 SKUs |
| - [Cybersecurity](https://huggingface.co/xpertsystems) — 11 SKUs |
| |
| --- |
| |
| ## Citation |
| |
| ```bibtex |
| @dataset{xpertsystems_enr007_sample_2026, |
| author = {XpertSystems.ai}, |
| title = {ENR007 Synthetic Climate Impact Dataset (Sample Preview)}, |
| year = 2026, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/xpertsystems/enr007-sample} |
| } |
| ``` |
| |
| --- |
| |
| ## Contact |
| |
| - **Web:** https://xpertsystems.ai |
| - **Email:** pradeep@xpertsystems.ai |
| - **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy, |
| Oil & Gas, Energy & Climate, and more |
| |
| **Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) |
| **Full product License:** Commercial — please contact for pricing. |
| |
