Calculate Emissions Overview

Dcycle helps you calculate emissions across all aspects of your organization’s operations. This guide explains when and how to use each calculation method.

Emission Calculation Methods

Logistics & Shipments

Calculate transport emissions following ISO 14083 standard

Fleet & Vehicles

Track fuel consumption and vehicle emissions

Facilities & Operations

Monitor energy use and on-site emissions

Supply Chain

Track purchased goods and supplier emissions

Quick Decision Guide

Use this flowchart to determine which method to use:

Are you tracking...

├─ Transportation of goods?
│  └─ Use: Logistics API (ISO 14083)
│     Example: Calculating emissions of transportation and distribution of goods for a client
│
├─ Your company vehicles?
│  └─ Use: Fleet/Vehicles API
│     Example: Calculate the emissions generated by your fleet of vehicles
│
├─ Facility energy consumption?
│  └─ Use: Invoices or Facilities API
│     Example: Office electricity, warehouse heating
│
├─ Purchased products/services?
│  └─ Use: Purchases API
│     Example: Raw materials, office supplies
│
└─ Waste disposal?
   └─ Use: Wastes API
      Example: Recycling, landfill, incineration

Data Requirements

Different calculation methods require different data:

Minimum Data Required

Method	Essential Data	Optional Data
Logistics	Origin, destination, weight	Vehicle type, year
Fleet	Fuel type, quantity	Vehicle specs, odometer
Facilities	Energy type, consumption	Building size, equipment
Purchases	Product category, amount	Supplier, custom factors
Waste	Waste type, quantity	Disposal method, treatment

Data Accuracy Levels

The more specific your data, the more accurate your calculations:

Level 1: Spend-Based (Lowest accuracy)

# Only know the cost
purchase = {
    "category": "office_supplies",
    "spend_amount": 5000,
    "spend_currency": "EUR"
}
# Accuracy: ±50-100%

Level 2: Generic Activity Data

# Know physical quantities
purchase = {
    "category": "paper",
    "quantity": 1000,
    "unit": "kg"
}
# Accuracy: ±20-40%

Level 3: Specific Product Data

# Know exact product and supplier
purchase = {
    "product": "recycled_paper_A4",
    "supplier_id": "supplier-uuid",
    "quantity": 1000,
    "unit": "kg"
}
# Accuracy: ±10-20%

Level 4: Supplier-Verified Data (Highest accuracy)

# Have supplier EPD or custom factor
purchase = {
    "product": "recycled_paper_A4",
    "supplier_id": "supplier-uuid",
    "custom_emission_factor_id": "factor-uuid",
    "quantity": 1000,
    "unit": "kg"
}
# Accuracy: ±5-15%

Common Calculation Patterns

Pattern 1: Bulk Operations

For large datasets, use bulk upload endpoints:

# Upload monthly fleet consumption
csv_url = api.get_bulk_upload_url(type="vehicle_consumptions")

# Upload CSV file
upload_csv_to_s3(csv_url, "fleet_january_2024.csv")

# Poll for results
status = api.check_upload_status(upload_id)

Best for:

Monthly utility bills
Fleet fuel card data
Historical data migration

Bulk Upload APIs →

Pattern 2: Real-Time Calculations

For instant results, use synchronous endpoints:

# Calculate single shipment
result = api.calculate_shipment(
    origin="Madrid, Spain",
    destination="Barcelona, Spain",
    load=1000,
    load_unit="kg",
    toc="van_diesel"
)

# Immediate response with CO2e
print(f"Emissions: {result['co2e']} kg CO2e")

Best for:

Quote calculators
Route optimization
User-facing features

Pattern 3: Batch Processing

For scheduled jobs:

# Process daily orders
for order in daily_orders:
    emissions = api.calculate_shipment(
        origin=order.warehouse,
        destination=order.customer_address,
        load=order.weight,
        # ...
    )

    # Store with order
    order.co2e = emissions['co2e']
    order.save()

Best for:

Nightly batch jobs
Order processing pipelines
Scheduled reports

Combining Multiple Sources

Most organizations need to track emissions from multiple sources:

# Calculate total organizational emissions

# Scope 1: Fleet
fleet_emissions = sum(vehicle_consumptions)

# Scope 2: Purchased electricity
electricity_emissions = sum(electricity_invoices)

# Scope 3: Logistics + Purchases
logistics_emissions = sum(shipments)
purchase_emissions = sum(purchases)

# Total
total_scope1 = fleet_emissions
total_scope2 = electricity_emissions
total_scope3 = logistics_emissions + purchase_emissions

total_emissions = total_scope1 + total_scope2 + total_scope3

Dcycle’s reporting endpoints can aggregate emissions automatically across all sources. See the Logistics Reports API for examples.

Handling Missing Data

When you don’t have complete data, use these strategies:

Strategy 1: Use Defaults

# Don't know vehicle type? Use category default
shipment = api.calculate_shipment(
    origin="Madrid",
    destination="Barcelona",
    load=1000,
    load_unit="kg",
    toc="rigid"  # Generic truck category
)

Strategy 2: Estimate from Similar Activities

# Estimate office electricity from building size
invoice = {
    "type": "electricity",
    "consumption_estimate": building_sqm * 150,  # kWh/sqm/year
    "unit": "kWh",
    "period": "2024-01"
}

Strategy 3: Use Industry Averages

# Use spend-based factor as last resort
purchase = {
    "category": "IT_services",
    "spend_amount": 50000,
    "spend_currency": "EUR"
    # Dcycle applies appropriate spend-based factor
}

Strategy 4: Request Supplier Data

For significant emissions sources, request EPD or LCA data:

# Once you have supplier data, create custom factor
custom_factor = api.create_custom_emission_factor(
    group_id="suppliers-2024",
    ef_name="Aluminum - Supplier ABC",
    emission_factor_values=[
        {"gas_type": "CO2", "value": 2.15},
        {"gas_type": "CH4", "value": 0.008}
    ],
    additional_docs="EPD No. ABC-2024-001"
)

# Use in future calculations
purchase = {
    "product": "aluminum_sheet",
    "custom_emission_factor_id": custom_factor.id,
    "quantity": 1000,
    "unit": "kg"
}

Custom Emission Factors Guide →

Validation and Quality Checks

Before submitting data, validate:

Common Validation Checks

def validate_shipment_data(data):
    """Validate shipment data before submission"""

    # Check required fields
    assert data.get('origin'), "Origin is required"
    assert data.get('destination'), "Destination is required"
    assert data.get('load') > 0, "Load must be positive"

    # Sanity checks
    if data['load'] > 30000:  # 30 tons
        print("⚠️  Warning: Very heavy load, double-check")

    # Distance check (after geocoding)
    if estimated_distance < 10:  # km
        print("⚠️  Warning: Very short distance")

    return True

Response Validation

response = api.calculate_shipment(data)

# Validate response
if response['co2e'] == 0:
    print("❌ Error: Zero emissions calculated")

if response.get('distance_km', 0) == 0:
    print("❌ Error: Zero distance calculated")

# Log for audit
log_calculation(
    input=data,
    output=response,
    timestamp=datetime.now()
)

Next Steps

Choose a tutorial based on your primary use case:

Logistics Tutorial

Calculate transport emissions (ISO 14083)

GHG Protocol Tutorial

Track company vehicle emissions

Facilities Tutorial

Monitor facility energy and emissions

Supply Chain Tutorial

Track purchased goods emissions

Advanced Topics

Custom Emission Factors

Use supplier-specific factors

Core Concepts

Understand emission scopes and factors

Quickstart

Get your API key and start

API Reference

Complete API documentation

Calculate Emissions

GHG Protocol

Facility emissions Tutorial

Supply Chain Emissions Tutorial

Logistics & Transport Emissions Tutorial

Advanced Features

Calculate Emissions Overview

Calculate Emissions Overview

Emission Calculation Methods

Logistics & Shipments

Fleet & Vehicles

Facilities & Operations

Supply Chain

Quick Decision Guide

Data Requirements

Minimum Data Required

Data Accuracy Levels

Common Calculation Patterns

Pattern 1: Bulk Operations

Pattern 2: Real-Time Calculations

Pattern 3: Batch Processing

Combining Multiple Sources

Handling Missing Data

Strategy 1: Use Defaults

Strategy 2: Estimate from Similar Activities

Strategy 3: Use Industry Averages

Strategy 4: Request Supplier Data

Validation and Quality Checks

Common Validation Checks

Response Validation

Next Steps

Logistics Tutorial

GHG Protocol Tutorial

Facilities Tutorial

Supply Chain Tutorial

Advanced Topics

Custom Emission Factors

Core Concepts

Quickstart

API Reference

Calculate Emissions

GHG Protocol

Facility emissions Tutorial

Supply Chain Emissions Tutorial

Logistics & Transport Emissions Tutorial

Advanced Features

​Calculate Emissions Overview

​Emission Calculation Methods

Logistics & Shipments

Fleet & Vehicles

Facilities & Operations

Supply Chain

​Quick Decision Guide

​Data Requirements

​Minimum Data Required

​Data Accuracy Levels

​Common Calculation Patterns

​Pattern 1: Bulk Operations

​Pattern 2: Real-Time Calculations

​Pattern 3: Batch Processing

​Combining Multiple Sources

​Handling Missing Data

​Strategy 1: Use Defaults

​Strategy 2: Estimate from Similar Activities

​Strategy 3: Use Industry Averages

​Strategy 4: Request Supplier Data

​Validation and Quality Checks

​Common Validation Checks

​Response Validation

​Next Steps

Logistics Tutorial

GHG Protocol Tutorial

Facilities Tutorial

Supply Chain Tutorial

​Advanced Topics

Custom Emission Factors

Core Concepts

Quickstart

API Reference

Calculate Emissions Overview

Emission Calculation Methods

Quick Decision Guide

Data Requirements

Minimum Data Required

Data Accuracy Levels

Common Calculation Patterns

Pattern 1: Bulk Operations

Pattern 2: Real-Time Calculations

Pattern 3: Batch Processing

Combining Multiple Sources

Handling Missing Data

Strategy 1: Use Defaults

Strategy 2: Estimate from Similar Activities

Strategy 3: Use Industry Averages

Strategy 4: Request Supplier Data

Validation and Quality Checks

Common Validation Checks

Response Validation

Next Steps

Advanced Topics