Factory Energy Emissions Tracking

This dataset provides granular, time-stamped records of energy consumption and emissions across manufacturing processes, equipment, and factory locations. It is designed for sustainability and ESG reporting, enabling benchmarking of energy efficiency, monitoring of environmental impact, and identification of improvement opportunities within factory operations.

  • last updated 8 Dec 2025
  • by GoMask
The brief that made it

Benchmarking energy efficiency across factories and processes

Sample rows

preview · 8 of 144 rows · all 24 columns
record_idstringenergy_sourcestringemissions_ch4_kgfloatoutput_unit_typestringfactory_idstringfactory_namestringfactory_street_addressstringfactory_citystringfactory_statestringfactory_postal_codestringfactory_countrystringprocess_idstringprocess_namestringequipment_idstringequipment_namestringenergy_consumed_kwhfloatemissions_co2e_kgfloatemissions_n2o_kgfloatmeasurement_start_datetimedatetimemeasurement_end_datetimedatetimeunit_outputfloatreported_bystringreporting_datedatenotesstring
REC-00001coal12.8tonsF001GreenSteel Ltd1801 Foundry AvePittsburghPA15222USAP1001SmeltingEQ-8001Blast Furnace Alpha24500.519800.48.12024-02-03T06:00:002024-02-03T18:00:0012000jdoe_steel2024-02-04Max output, high emissions period
REC-00002natural_gas2.8kgF002Classic Chemicals44 Chamber StHoustonTX77009USAP1002Reaction ChamberEQ-9001Reactor Omega8200.64100.20.92024-03-10T08:00:002024-03-10T16:00:003800chem_team_a2024-03-11Standard batch, moderate emissions
REC-00003electricity0blankF001GreenSteel Ltd1801 Foundry AvePittsburghPA15222USAP1003Cooling Phaseblankblank3200.2220.102024-02-03T18:00:002024-02-03T22:00:00blankjdoe_steel2024-02-04No output produced during cooling
REC-00004electricity0kgF003Nordic Paper12 MillstrasseGothenburgVästra Götaland41123SwedenP1004PulpingEQ-7102Pulper Delta9800670.402024-04-15T07:00:002024-04-15T15:00:0044000nordic_ops2024-04-16High volume, low emissions
REC-00005diesel4.6tonsF004Arctic Metals99 Mine RoadWhitehorseYTY1A 5V8CanadaP1005Ore CrushingEQ-6504Crusher Titan18500.37890.12.32024-05-20T06:30:002024-05-20T18:30:009000arctic_admin2024-05-21Boundary record for max output
REC-00006solarblanktilesF005SolarTiles Pty17 Sunlight WayMelbourneVIC3000AustraliaP1006Tile MoldingEQ-4301Hydraulic Press1500blank2024-01-18T09:00:002024-01-18T17:00:00500solar_ops2024-01-19Solar only, zero emissions
REC-00007electricity0litersF002Classic Chemicals44 Chamber StHoustonTX77009USAP1007DistillationEQ-9002Distiller Beta4100.7280.502024-03-10T16:00:002024-03-10T19:00:00850chem_team_b2024-03-11Auxiliary phase, low emissions
REC-00008other1.1vasesF006Tokyo Ceramics2-3-5 GinzaTokyoTokyo Prefecture104-0061JapanP1008Kiln FiringEQ-5607Kiln Gamma1100190.20.52024-06-14T10:15:002024-06-14T16:45:00120ceramic_shift2024-06-15Biogenic methane observed

What the 144 rows show

from the 144-row sample

Coal (energy source) stands out: mean emissions_ch4_kg is 8.5, against 0.70 for the rest.

  • 0.05median emissions_ch4_kg
  • 14factory countries
  • 1,650median energy_consumed_kwh
  • 405.4median emissions_co2e_kg
  • 0.05median emissions_n2o_kg
  • 1,700median unit_output
Mean emissions_ch4_kg by energy_source132 rows
05100.03elec…69 ro…1.9natu…21 ro…0.0solar3 rows0.0wind6 rows3.1dies…11 ro…8.5coal13 ro…0.77other9 rows
emissions_ch4_kg132 rows, in bands of 2
055110103135332210816emissions_ch4_kg →

Median 0.05, from 0.0 to 15.2.

output_unit_type117 rows with a value · 27 left blank
  1. liters20
  2. kg14
  3. panels13
  4. tons12
  5. tiles12
  6. pieces11
  7. vases9
  8. blocks9
  9. panes7
  10. battery7
24 columns by typefrom the column list below
  • string 16
  • float 5
  • date 1
  • datetime 2

Columns

24 columns in three groups
blueprint · 24 columns
columntypedescriptionexample
Text 16 columns
record_idstringUnique identifier for each energy emission recorduniqueREC-00001
factory_idstringUnique identifier for the factory where the record was collectedF001
factory_namestringName of the factoryGreenSteel Ltd
factory_street_addressstringStreet address of the factoryoptional1801 Foundry Ave
factory_citystringCity where the factory is locatedoptionalPittsburgh
factory_statestringState or province where the factory is locatedoptionalPA
factory_postal_codestringPostal code of the factory locationoptional15222
factory_countrystringCountry where the factory is locatedoptionalUSA
process_idstringUnique identifier for the manufacturing process or areaP1001
process_namestringName of the manufacturing process or areaSmelting
equipment_idstringUnique identifier for the equipment or machineryoptionalEQ-8001
equipment_namestringName of the equipment or machineryoptionalBlast Furnace Alpha
energy_sourcestringType of energy source used (e.g., electricity, natural gas, solar)7 valuescoal
output_unit_typestringType of output unit (e.g., pieces, kg, liters)11 types · optionaltons
reported_bystringName or identifier of the person or system reporting the dataoptionaljdoe_steel
notesstringAdditional comments or notes about the recordoptionalStandard tile production
Numbers 5 columns
energy_consumed_kwhfloatAmount of energy consumed in kilowatt-hours (kWh)0 or more24500.5
emissions_co2e_kgfloatTotal CO2 equivalent emissions in kilograms0 or more19800.4
emissions_ch4_kgfloatMethane (CH4) emissions in kilograms0 or more · optional12.8
emissions_n2o_kgfloatNitrous oxide (N2O) emissions in kilograms0 or more · optional8.1
unit_outputfloatUnits of product output during the measurement period0 or more · optional12000
Dates and times 3 columns
measurement_start_datetimedatetimeStart date and time of the measurement period2024-02-03T06:00:00
measurement_end_datetimedatetimeEnd date and time of the measurement period2024-02-03T18:00:00
reporting_datedateDate when the record was reported2024-02-04

Use it for

  • median emissio…0.05132 rowsmean emissions ch4 kg…0.03elec…1.9natu…0.0solar0.0wind

    A manufacturing dashboard

    Emissions_ch4_kg by energy_source and a breakdown of output_unit_type. Excel, Power BI or Tableau.

  • Why do the 13 coal rows have a mean emissions_ch4_kg of 8.5?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable records with factory_id, factory_name and factory_street_address to fill a screen in front of a buyer.

Not quite right?

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This dataset144 rows24 columns
Yours1,000 rows24 columnsfactory_street_address: UK only

blueprint · factory-energy-emissions-tracking

Behind this dataset

Same schema. As many rows as you need.

These 144 rows came out of a blueprint — 24 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Each row represents a discrete equipment asset for a single shift
  • Energy usage and emissions data must be recorded for both electricity and natural gas sources
  • Emissions values must be calculated using standardized conversion factors
  • Flag equipment that exceeds emissions thresholds set by company ESG policy
  • Include reason codes for abnormal spikes in energy usage
  • Data entries must include timestamp, facility location, and asset type
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
factory-energy-emissions-tracking

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