GHG Protocol: Data Requirements

Complete data dictionary for calculating a corporate carbon footprint — what data Dcycle needs, where it typically lives, and how to automate the integration

Use this guide as a reference when planning your data integration with Dcycle. For each emission category, we list the exact fields Dcycle needs, the typical source systems where that data lives, and the recommended integration method.

Who is this for?

This guide is designed for IT and data engineering teams who are planning how to feed data into Dcycle — whether through APIs, bulk CSV uploads, or PDF/OCR processing. Share it with your technical team so they can map their internal data sources to Dcycle’s requirements.

How Dcycle Fits in Your Data Architecture

Dcycle acts as a data processing and calculation engine that sits between your raw data sources and your environmental reporting outputs. You send us raw or semi-structured data, and we handle normalization, enrichment, calculation, and reporting.

┌─────────────────────────────────────────────────────────────────────────────────────────┐
│                          YOUR DATA ARCHITECTURE                                         │
├─────────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                         │
│   DATA SOURCES              DCYCLE                           YOUR OUTPUTS               │
│   ────────────              ──────                           ────────────               │
│                                                                                         │
│   ERP (SAP, Navision...)    ┌─────────────────────────┐                                 │
│   Fuel cards         ──────►│  Ingestion (API / CSV)  │                                 │
│   Utility bills (PDF) ─────►│  OCR & digitization     │     Data Lake (Gold layer)      │
│   Fleet management    ─────►│  Normalization          │──►  Power BI / dashboards       │
│   Travel booking      ─────►│  Enrichment (geocoding, │     Regulatory reports           │
│   HR systems          ─────►│   supplier lookup, etc) │     CSRD / EINF                 │
│   Waste managers      ─────►│  Emission calculation   │     Science-Based Targets       │
│                             │  Quality validation     │                                 │
│                             └─────────────────────────┘                                 │
│                                                                                         │
│   What Dcycle adds automatically:                                                       │
│   • OCR for PDF invoices (electricity, fuel, water)                                    │
│   • Geocoding for logistics (origin/destination → km)                                  │
│   • Supplier enrichment (company name → CNAE/SIC sector code)                          │
│   • Emission factor selection (country + fuel + year → correct EF)                     │
│   • Dual Scope 2 calculation (location-based + market-based)                           │
│   • Scope 3 Cat. 3 (fuel & energy related) — fully automatic from Scope 1 & 2 data    │
│   • Data quality alerts (missing fields, anomalies, outliers)                          │
│                                                                                         │
└─────────────────────────────────────────────────────────────────────────────────────────┘

You don’t need to clean or transform data before sending it to Dcycle.

Send us the rawest data you have — PDFs, CSVs from your ERP, fuel card exports. Dcycle handles OCR, normalization, unit conversion, and emission factor matching. This means your Data Lake integration can sit after Dcycle: pull the enriched, calculated results back via our APIs into your Gold layer.

Integration Methods

Dcycle supports four ways to ingest data. Choose the one that best fits each data source:

Method	Best for	Automation	Setup effort
REST API	ERP integrations, real-time data feeds, programmatic access	Full	Medium
Bulk CSV upload	Periodic data dumps, spreadsheet-based workflows	Semi-auto	Low
PDF / file upload	Invoices, utility bills, fuel receipts	Full (OCR)	Low
Manual entry (App)	One-off data, qualitative estimates, small datasets	Manual	None

Recommended approach for data lake architectures:

1. Configure your Bronze layer ingestion to collect raw data from source systems
2. Send that raw data to Dcycle via API or CSV (no cleaning needed)
3. Pull enriched + calculated results from Dcycle’s APIs back into your Gold layer
4. Build dashboards and reports on top of the Gold layer

This way Dcycle handles all the Silver-layer transformations (OCR, normalization, enrichment, calculation) and you retain full control of the data in your own infrastructure.

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Scope 1: Direct Emissions

Stationary Combustion (Fuel Invoices)

Emissions from burning fuels in fixed equipment: boilers, furnaces, generators, heaters.

📋 Data Fields

Field	Type	Required	Description	Example
type	string	✅	"heat"	"heat"
facility_id	UUID	✅	Facility where fuel is consumed	From facility setup
facility_fuel_id	UUID	✅	Fuel type linked to facility	From GET /facility_fuels/{facility_id}
base_quantity	number	✅	Amount consumed	5000
unit_id	UUID	✅	Unit (m³, kWh, kg, liters)	From GET /units
start_date	date	✅	Consumption period start	"2024-01-01"
end_date	date	✅	Consumption period end	"2024-01-31"
invoice_id	string	✅	Your internal reference	"INV-2024-001"
uploaded_by	UUID	✅	User uploading	User UUID
custom_emission_factor_id	UUID	❌	Override default EF	Custom EF UUID

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Natural gas consumption	Gas utility invoices (PDF or digital)	Monthly billing with m³ or kWh
Diesel/gasoil for generators	Fuel delivery receipts, tank readings	Periodic deliveries in liters
LPG/propane	Supplier invoices	Delivery amounts in kg or liters
Fuel oil	Supplier invoices	Bulk deliveries in liters or tonnes

Automation options:

• PDF upload → Dcycle OCR extracts consumption, dates, and amounts automatically
• API → Push structured data from your ERP or utility data aggregator
• CSV → Bulk upload monthly consumption records

⚡ What Dcycle Calculates Automatically

• Selects correct emission factors based on facility country (MITECO for Spain, IPCC for others)
• Applies fuel-specific factors for CO₂, CH₄, and N₂O
• Converts to CO₂e using IPCC AR6 GWP values
• Handles unit conversions (e.g., m³ → kWh using calorific values)

Refrigerant Recharges (Fugitive Emissions)

Leaks of high-GWP refrigerants from HVAC and cooling systems.

📋 Data Fields

Field	Type	Required	Description	Example
type	string	✅	"recharge"	"recharge"
facility_id	UUID	✅	Facility with equipment	Facility UUID
facility_fuel_id	UUID	✅	Refrigerant type (R-410A, R-134a…)	From facility fuels
base_quantity	number	✅	Amount recharged (= leaked)	15
unit_id	UUID	✅	Unit (typically kg)	kg UUID
start_date	date	✅	Recharge date	"2024-03-15"
end_date	date	✅	Same as start	"2024-03-15"
invoice_id	string	✅	Service invoice reference	"RECHARGE-001"
uploaded_by	UUID	✅	User uploading	User UUID

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Recharge amounts	HVAC maintenance invoices	Amount refilled = amount leaked
Refrigerant type	Equipment register, maintenance contracts	R-410A, R-404A, R-134a, etc.
Equipment inventory	Facilities/maintenance system	List of all cooling units

Automation options:

• API → Push from CMMS (computerized maintenance management system)
• CSV → Annual or quarterly recharge register
• Manual → Enter from HVAC service reports

Mobile Combustion (Company Vehicles)

Fuel consumed by company-owned or controlled vehicles.

📋 Data Fields — Vehicle Registration

Field	Type	Required	Description	Example
name	string	✅	Vehicle identifier	"Delivery Truck 01"
type	string	✅	Vehicle category	"truck", "car", "van"
license_plate	string	✅	License plate	"1234ABC"
country	string	✅	Registration country (ISO)	"ES"
vehicle_fuel_id	UUID	✅	Fuel type	From GET /vehicle_fuels
ownership	string	✅	"owned" or "rented"	"owned"

📋 Data Fields — Fuel Consumption

Field	Type	Required	Description	Example
vehicle_license_plate	string	✅	Identifies the vehicle	"1234ABC"
start_date_recharge	date	✅	Period start	"2024-01-01"
end_date_recharge	date	✅	Period end	"2024-01-31"
quantity	number	✅	Fuel amount	450
unit_measure	string	✅	"l" (liters) or "km"	"l"
organization_id	UUID	✅	Organization	Org UUID

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Vehicle register	Fleet management system, vehicle leasing contracts	Make, model, fuel type, plate
Fuel consumption	Fuel cards (Solred, Repsol, Cepsa…), expense reports	Liters per vehicle per period
Kilometers	Odometer readings, GPS/telematics	For distance-based calculation
EV charging	Charging station data, electricity bills	kWh consumed per vehicle

Automation options:

• CSV → Monthly fuel card export (license plate + liters + dates)
• API → Push from fleet management or telematics systems
• Manual → Enter from fuel receipts

Waste Water Treatment

CH₄ and N₂O from on-site wastewater treatment facilities.

📋 Data Fields (Daily Records)

Field	Type	Required	Description	Example
facility_id	UUID	✅	WWT facility	Facility UUID
name	string	✅	Record identifier	"WWT-2024-01-01"
measurement_date	date	✅	Measurement date	"2024-01-01"
m3_water_in	number	✅	Daily influent (m³)	10000
m3_water_out	number	✅	Daily effluent (m³)	9500
kg_bod_per_m3_wwt_line	number	✅	BOD concentration	0.25
kg_n_per_m3_wwt_line	number	✅	Nitrogen concentration	0.04
kg_sludge	number	✅	Sludge produced (kg)	500
kg_bod_per_kg_sludge_line	number	✅	BOD in sludge	0.6
kg_bod_per_m3_wwd_line	number	✅	BOD in discharge	0.02
kg_n_per_m3_wwd_line	number	✅	Nitrogen in discharge	0.01
m3_biogas_engine	number	❌	Biogas to engine (m³)	200
m3_biogas_flare	number	❌	Biogas flared (m³)	50
m3_biogas_boiler	number	❌	Biogas to boiler (m³)	100

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Flow data	SCADA systems, flow meters	Inlet/outlet m³ per day
BOD/Nitrogen	Laboratory analyses, online sensors	mg/L converted to kg/m³
Sludge	Weighing systems, sludge removal records	kg per day
Biogas	Gas meters on digesters/engines/flares	m³ per day

Automation options:

• API → Push daily records from SCADA/PLC systems
• CSV → Bulk upload from laboratory information system (LIMS)

Process Emissions

Emissions from industrial chemical/physical transformations (not from combustion).

📋 Data Fields

Field	Type	Required	Description	Example
type	string	✅	"process"	"process"
facility_id	UUID	✅	Facility with the process	Facility UUID
facility_fuel_id	UUID	✅	Process source	Process fuel UUID
base_quantity	number	✅	Emission quantity	250
unit_id	UUID	✅	Unit (typically kg)	kg UUID
start_date	date	✅	Period start	"2024-01-01"
end_date	date	✅	Period end	"2024-03-31"
custom_emission_factor_id	UUID	⚠️	Often required for processes	Custom EF UUID

Typical sources: Environmental permits, production records, mass balance calculations.

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Scope 2: Purchased Energy

Electricity

📋 Data Fields

Field	Type	Required	Description	Example
type	string	✅	"electricity"	"electricity"
facility_id	UUID	✅	Facility consuming electricity	Facility UUID
base_quantity	number	✅	kWh consumed	10000
unit_id	UUID	✅	kWh unit	"ba80e6cb-..."
start_date	date	✅	Billing period start	"2024-01-01"
end_date	date	✅	Billing period end	"2024-01-31"
supplier_id	UUID	❌	Electricity supplier (Spain: enables CNMC market-based)	Supplier UUID
custom_emission_factor_id	UUID	❌	Custom EF (non-Spain: enables market-based)	Custom EF UUID
invoice_id	string	❌	Your reference	"ELEC-2024-001"
uploaded_by	UUID	✅	User uploading	User UUID

🏢 Typical Data Sources

Data	Where it typically lives	Notes
kWh consumed	Electricity bills (PDF), smart meters, utility portals	Monthly billing cycle
Supplier name	Electricity contract	Needed for market-based in Spain
Billing period	Invoice dates	Start and end of billing cycle

Automation options:

• PDF upload → Dcycle OCR extracts kWh, dates, and supplier from electricity bills
• Datadis (Spain) → Automatic import from smart meters via Datadis integration
• API → Push from energy management system or building management system (BMS)
• CSV → Bulk upload monthly readings across facilities

⚡ What Dcycle Calculates Automatically

• Dual calculation: Both location-based and market-based are computed simultaneously
• Location-based: Uses country grid EF (Ecoinvent for global, REE for Spain)
• Market-based (Spain): Uses CNMC supplier-specific EFs when supplier_id is provided
• Market-based (other): Uses your custom EF when custom_emission_factor_id is provided
• Scope 3 Cat. 3: Well-to-tank and T&D losses are auto-calculated from the same data

District Heating / Cooling

📋 Data Fields

Field	Type	Required	Description	Example
type	string	✅	"district_heating" or "district_cooling"	"district_heating"
facility_id	UUID	✅	Facility receiving the service	Facility UUID
facility_fuel_id	UUID	✅	Heating/cooling fuel type	Fuel UUID
base_quantity	number	✅	Energy consumed (kWh or MJ)	5000
unit_id	UUID	✅	kWh or MJ	Unit UUID
start_date	date	✅	Service period start	"2024-01-01"
end_date	date	✅	Service period end	"2024-03-31"

Typical sources: District heating/cooling provider invoices, BMS.

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Scope 3: Value Chain Emissions

Category 1: Purchased Goods and Services

Typically 40-80% of total emissions. Three calculation methods available depending on your data quality.

📋 Data Fields — Spend-Based (fastest to start)

Field	Type	Required	Description	Example
description	string	❌	Purchase description	"IT consulting Q1"
sector	string	✅	Exiobase economic sector	"Computer programming, consultancy"
product_name	string	✅	Product/service name	"Computer programming services"
quantity	number	✅	Monetary value (€)	15000
unit_id	UUID	✅	Currency unit (€)	EUR unit UUID
country	string	✅	Supplier country (ISO)	"ES"
purchase_date	date	✅	Purchase date	"2024-03-15"
purchase_type	string	✅	"spend_based"	"spend_based"
supplier_id	UUID	❌	Linked supplier	Supplier UUID

Accuracy: ±50-100%. Use as a starting point, then improve high-impact categories.

📋 Data Fields — Activity-Based (higher accuracy)

Field	Type	Required	Description	Example
sector	string	✅	Product sector	"Manufacture of basic metals"
product_name	string	✅	Specific product	"Aluminium and aluminium products"
quantity	number	✅	Physical quantity	1000
unit_id	UUID	✅	Physical unit (kg, m³, etc.)	kg unit UUID
country	string	✅	Country of origin	"ES"
purchase_date	date	✅	Purchase date	"2024-03-15"
purchase_type	string	✅	"activity_based"	"activity_based"
supplier_id	UUID	❌	Linked supplier	Supplier UUID
recycled	number	❌	Recycled content (0-1)	0.3

Accuracy: ±20-40%. Requires physical quantities from procurement records.

📋 Data Fields — Supplier-Specific (highest accuracy)

Field	Type	Required	Description	Example
quantity	number	✅	Physical quantity	1000
unit_id	UUID	✅	Physical unit	kg unit UUID
purchase_date	date	✅	Purchase date	"2024-03-15"
supplier_id	UUID	✅	Linked supplier	Supplier UUID
custom_emission_factor_id	UUID	✅	From supplier EPD/PCF	Custom EF UUID

Accuracy: ±5-15%. Requires Environmental Product Declarations (EPDs) or Product Carbon Footprints from suppliers.

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Spend by category	ERP (SAP, Navision, A3…), accounts payable	€ per supplier per category
Physical quantities	Delivery notes, bills of materials, inventory systems	kg, m³, units
Supplier details	ERP vendor master, procurement system	Company name, country, tax ID
Supplier CNAE/SIC code	Company registry, Dcycle auto-enriches from company name	Needed for sector classification
EPDs / PCFs	Supplier sustainability reports, EPD databases	For supplier-specific method

Automation options:

• CSV → Export from ERP: supplier name + amount + category + date
• API → Push from procurement system or accounts payable
• Dcycle enrichment → Send supplier name, Dcycle auto-resolves CNAE/SIC sector code

Category 2: Capital Goods

Same data structure as Category 1, but for capital assets (equipment, machinery, buildings).

📋 Key Differences from Category 1

Aspect	Category 1	Category 2
What	Operating expenses, materials	Capital expenditures, fixed assets
Examples	Office supplies, raw materials	Machinery, vehicles (as assets), buildings
Reporting	Same year as purchase	Full in year of purchase, or depreciated
Data source	Operating purchase orders	Capital investment records, asset register

Same API and fields as Category 1 — the classification is done by your accounting team.

Category 3: Fuel and Energy-Related Activities

Fully automatic — no additional data needed.

Dcycle calculates Category 3 (well-to-tank emissions for fuels, upstream electricity, T&D losses) automatically from your Scope 1 and Scope 2 data. Just ensure complete fuel and electricity data.

Category 4 & 9: Upstream and Downstream Transportation

Emissions from transporting goods to/from your facilities.

📋 Data Fields — Logistics Shipments

Field	Type	Required	Description	Example
origin	string	✅	Origin address or city	"Madrid, Spain"
destination	string	✅	Destination address or city	"Barcelona, Spain"
weight	number	✅	Shipment weight (kg)	500
transport_mode	string	✅	Mode of transport	"road", "rail", "air", "sea"
vehicle_type	string	❌	Specific vehicle type	"articulated_truck"
date	date	✅	Shipment date	"2024-03-15"
client_reference	string	❌	Your shipment reference	"SHIP-2024-001"

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Shipment details	TMS (transport management system), logistics ERP	Origin, destination, weight
Carrier/mode	Shipping contracts, carrier invoices	Road, rail, air, sea
Distances	Dcycle auto-calculates from origin/destination	No need to provide km
Supplier deliveries	Purchase orders with delivery addresses	For upstream (Cat. 4)
Customer deliveries	Sales orders with shipping addresses	For downstream (Cat. 9)

Automation options:

• CSV → Export from TMS: origin + destination + weight + mode + date
• API → Real-time push from logistics/shipping system
• Dcycle enrichment → Auto-geocodes addresses and calculates distances

Category 5: Waste Generated in Operations

📋 Data Fields

Field	Type	Required	Description	Example
facility_id	UUID	✅	Facility generating waste	Facility UUID
waste_code	string	✅	LER code (European List of Waste)	"150101" (paper/cardboard)
quantity	number	✅	Waste amount	500
unit_id	UUID	✅	Unit (kg or tonnes)	kg UUID
treatment_code	string	✅	R-code (recovery) or D-code (disposal)	"R03" (recycling)
start_date	date	✅	Period start	"2024-01-01"
end_date	date	✅	Period end	"2024-03-31"
waste_manager_name	string	❌	Treatment company	"Waste Co S.L."
transport_distance	number	❌	Distance to treatment (km)	50

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Waste type (LER code)	Waste manager reports, regulatory filings	6-digit European waste codes
Quantities	Weighing records, waste collection receipts	kg or tonnes per period
Treatment method	Waste manager certificates, transfer notes	R01-R13 or D01-D15 codes
Transport distance	Waste manager contract, facility locations	km to treatment plant

Automation options:

• CSV → Quarterly waste register from waste manager
• API → Push from environmental management system
• Manual → Enter from waste collection certificates (quarterly/annual)

Category 6: Business Travel

📋 Data Fields

Field	Type	Required	Description	Example
origin	string	✅	Departure city/airport	"Madrid"
destination	string	✅	Arrival city/airport	"London"
transport_type	string	✅	Travel mode	"flight", "train", "rental_car"
distance	number	❌	Distance in km (auto-calculated if not provided)	1260
flight_class	string	❌	Cabin class (for flights)	"economy", "business"
round_trip	boolean	❌	Return included	true
start_date	date	✅	Travel date	"2024-03-15"
travelers_count	number	❌	Number of travelers	1

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Flight bookings	Corporate travel agency, booking platform (Amex GBT, BCD…)	Origin, destination, class
Train bookings	Rail booking system, travel agency	Origin, destination
Rental car usage	Rental company reports, expense reports	km driven, fuel type
Hotel stays	Hotel booking platform, expense reports	Nights per city
Expense reports	ERP, expense management tool (SAP Concur, Rydoo…)	May have trip details

Automation options:

• CSV → Export from travel booking platform or expense tool
• API → Push from corporate travel management system
• Manual → Enter from travel itineraries or expense reports

Category 7: Employee Commuting

📋 Data Fields — Employee Setup

Field	Type	Required	Description	Example
name	string	✅	Employee name or ID	"EMP-001"
facility_id	UUID	✅	Office/work location	Facility UUID
work_regime	string	✅	Work arrangement	"on_site", "hybrid", "remote"
days_per_week	number	✅	Days in office per week	4

📋 Data Fields — Commuting Periods

Field	Type	Required	Description	Example
employee_id	UUID	✅	Employee	Employee UUID
transport_mode	string	✅	How they commute	"car_diesel", "train", "bike"
distance_km	number	✅	One-way distance (km)	25
start_date	date	✅	Period start	"2024-01-01"
end_date	date	✅	Period end	"2024-12-31"

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Employee list	HR system (SAP HR, Workday, Factorial…)	Name, work location, work regime
Commute details	Employee survey, HR form	Transport mode, distance
Office days	HR system, attendance tracking	Hybrid/remote schedule
Employee home location	HR records (postal code is enough)	For distance estimation

Automation options:

• CSV → Export from HR system + survey results
• API → Push from HR system
• Survey → Use Dcycle’s survey feature (employees fill in commute details)

Category 11: Use of Sold Products

Downstream emissions from the use phase of energy-consuming products your organization sells. Often the largest Scope 3 category for manufacturers of equipment, appliances, or vehicles.

📋 Data Fields — Product & Sales

Field	Type	Required	Description	Example
name	string	✅	Product name or family	"Industrial Compressor X200"
start_date	date	✅	Period start	"2024-01-01"
end_date	date	✅	Period end	"2024-12-31"
country	string	✅	Country where sold (ISO)	"ES", "DE", "FR"
quantity	number	✅	Units sold in that country	250
unit_id	UUID	✅	Unit of measurement	Unit UUID

📋 Data Fields — Use-Phase Consumption

Field	Type	Required	Description	Example
lifespan	number	✅	Product lifespan	10
lifespan_unit_id	UUID	✅	Temporal unit (day/week/month/year)	Year UUID
consumes_electricity	boolean	✅	Does it consume electricity?	true
electricity_quantity	number	✅*	Amount per frequency	2400
electricity_unit_id	UUID	✅*	Unit (kWh)	kWh UUID
electricity_frequency	enum	✅*	daily/weekly/monthly/yearly/all_life	"yearly"
consumes_combustion	boolean	✅	Does it burn fuel?	false
combustion_fuel_id	UUID	✅*	Fuel type	Natural gas UUID
combustion_quantity	number	✅*	Amount per frequency	1200
combustion_unit_id	UUID	✅*	Unit (liters, m³, kWh)	m³ UUID
combustion_frequency	enum	✅*	Frequency	"yearly"
consumes_water	boolean	✅	Does it consume water?	false
water_quantity	number	✅*	Amount per frequency	10
water_unit_id	UUID	✅*	Unit (m³)	m³ UUID
water_frequency	enum	✅*	Frequency	"yearly"

*Required only when the corresponding consumes_* flag is true.

🏢 Typical Data Sources

Data	Where it typically lives	Notes
Product catalog	PLM, ERP, product management	Product names and families
Units sold by country	ERP, CRM, sales reporting	Quantity per market per period
Product lifespan	Product specs, warranty terms, LCA studies	Expected useful life
Electricity consumption	Product testing, energy labels (EU), spec sheets	kWh per year or per cycle
Fuel consumption	Product testing, performance certificates	Liters/m³ per year
Water consumption	Product testing, water labels	m³ per year or per cycle

Automation options:

• CSV → Upload country sales data via Dcycle App
• API → Push from ERP/CRM for sales; configure use-phase via API
• Manual → Use-phase data typically comes from R&D or product engineering

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Company Structure (Prerequisite)

Before loading emission data, configure your organizational hierarchy.

📋 Organization Setup

Field	Type	Required	Description	Example
company_name	string	✅	Legal entity name	"Holding Corp S.A."
country	string	✅	Country (ISO code)	"ES"
sector	string	❌	Business sector	"Technology"
vat	string	❌	Tax identification	"A12345678"
employee_count_signup	number	❌	Number of employees	200

📋 Facility Setup

Field	Type	Required	Description	Example
name	string	✅	Facility name	"Madrid HQ"
country	string	✅	Country (ISO code)	"ES"
address	string	✅	Full address	"Calle Gran Vía 1, Madrid"
type	string	✅	Facility type	"office", "warehouse", "factory"
surface_m2	number	❌	Floor area (m²)	2000
fuel_types	list	✅	Fuels consumed at this facility	Natural gas, diesel, etc.

Required for: All Scope 1 and Scope 2 calculations. Each invoice/consumption record is linked to a facility.

📋 Parent-Child Relationships (Groups)

Field	Type	Required	Description	Example
share	number	✅	Ownership percentage (0-1)	0.75 (75%)
investment_scopes	list	✅	Scopes to consolidate	[1, 2, 3]
tag	string	❌	Relationship type	"subsidiary", "joint-venture"
status	string	✅	Must be "accepted" to consolidate	"accepted"

Required for: Multi-entity organizations (holding companies, groups with subsidiaries).

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Summary: Data Source Mapping

Use this table to map your internal systems to Dcycle data requirements:

GHG Category	Data Needed	Typical Source	Integration	Dcycle Adds
S1: Stationary combustion	Fuel type + quantity + dates	Utility invoices (PDF), ERP	PDF/API/CSV	EF selection, unit conversion
S1: Refrigerants	Refrigerant type + kg recharged	HVAC maintenance records	CSV/API	GWP lookup
S1: Vehicles	License plate + fuel type + liters/km	Fuel cards, fleet system	CSV/API	EF by fuel and vehicle type
S1: Wastewater	Daily flow + BOD + nitrogen + biogas	SCADA, laboratory (LIMS)	API/CSV	CH₄ + N₂O calculation
S1: Process	Process type + quantity	Production records, permits	API/CSV	Custom EF application
S2: Electricity	kWh + dates + supplier (optional)	Electricity bills, Datadis	PDF/API/CSV	Dual location + market calc
S2: District heat/cool	kWh or MJ consumed	Provider invoices	API/CSV	EF selection
S3.1: Purchases	€ or kg + category + supplier country	ERP, accounts payable	CSV/API	Sector enrichment, EF match
S3.2: Capital goods	Same as S3.1	Asset register, capex records	CSV/API	Same as S3.1
S3.3: Fuel & energy	(automatic from S1 + S2 data)	—	—	Fully automatic
S3.4/9: Transport	Origin + destination + weight + mode	TMS, logistics ERP	CSV/API	Geocoding, km calculation
S3.5: Waste	LER code + kg + treatment method	Waste manager reports	CSV/API	EF by waste + treatment
S3.6: Business travel	Origin + destination + mode + class	Travel agency, expense tool	CSV/API	Distance calc, EF by mode
S3.7: Commuting	Employee + transport mode + km	HR system + survey	CSV/API	EF by mode, work-day calc
S3.11: Use of sold products	Lifespan + energy consumption + units sold by country	Product specs, ERP/CRM sales	CSV/API	Country-specific EF, lifespan calc

Progressive approach: start broad, then deepen

1. Month 1: Upload PDFs (invoices, bills) + basic CSVs from ERP (purchases, fuel cards)
2. Month 2-3: Set up API integrations for high-volume data (logistics, vehicles, electricity)
3. Month 4+: Engage suppliers for EPDs, connect IoT/SCADA for real-time data
4. Ongoing: Pull calculated results back to your Data Lake via Dcycle APIs

You don’t need 100% automation on day one. Dcycle is designed to work with whatever data format you have today and improve over time.

Next Steps

GHG Protocol Tutorial

Step-by-step implementation guide with API examples

API Reference

Full API documentation for all endpoints

Automation & AI

Set up automated pipelines and multi-org management

Custom Emission Factors

Use supplier EPDs and PCFs for highest accuracy