S3. Category 7: Employee Commuting

Understanding Scope 3 Category 7

Employee commuting covers emissions from the transportation of employees between their homes and worksites in vehicles not owned or operated by your organization. According to the GHG Protocol Scope 3 Standard, Category 7 includes:

Daily commuting: Regular home-to-work travel
All transport modes: Car, public transit, cycling, walking
Remote work: Zero emissions for work-from-home days
Carpooling: Shared vehicle emissions (divided among passengers)

┌─────────────────────────────────────────────────────────────────────────────┐
│              SCOPE 3 CATEGORY 7: Employee Commuting                         │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  EMPLOYEE HOMES                               WORKPLACE                     │
│  ──────────────────                           ──────────                    │
│                                                                             │
│  ┌──────────────┐                            ┌──────────────┐               │
│  │   🏠 Home    │◄─── 🚗 Car ────────────►   │   🏢 Office  │               │
│  └──────────────┘                            │              │               │
│                                              │              │               │
│  ┌──────────────┐                            │              │               │
│  │   🏠 Home    │◄─── 🚂 Train ──────────►   │              │               │
│  └──────────────┘                            │              │               │
│                                              │              │               │
│  ┌──────────────┐                            │              │               │
│  │   🏠 Home    │◄─── 🚌 Bus ────────────►   │              │               │
│  └──────────────┘                            │              │               │
│                                              │              │               │
│  ┌──────────────┐                            │              │               │
│  │   🏠 Home    │◄─── 🚲 Bike ───────────►   │              │               │
│  └──────────────┘                            │              │               │
│                                              │              │               │
│  ┌──────────────┐                            │              │               │
│  │   🏠 Home    │──── 💻 Remote ───────────  │   (0 km)     │               │
│  └──────────────┘                            └──────────────┘               │
│                                                                             │
│  DAILY ROUND TRIP:                                                          │
│  • Distance (km) × 2 (round trip)                                           │
│  • × Working days per week                                                  │
│  • × Emission factor (by transport mode)                                    │
│                                                                             │
│  INCLUDED:                             NOT INCLUDED:                        │
│  • Daily commute                       • Business travel (Category 6)       │
│  • All transport modes                 • Company vehicles (Scope 1)         │
│  • Remote work tracking                • Customer/visitor travel            │
│  • Carpooling emissions                                                     │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Category 7 vs Category 6

Category 7 (Employee Commuting): Daily travel between home and work
Category 6 (Business Travel): Trips for business purposes (meetings, conferences, site visits)

Prerequisites

Before starting, ensure you have:

Dcycle API credentials (get them here)
Completed Step 1: Company Structure
Employee commuting data: home location, transport mode, working days
Basic knowledge of Python or JavaScript

Using the Dcycle App?You can track employee commuting through our web interface:

Send employee surveys (ES) - Collect data via email
Manual entry for individual employees
CSV bulk upload for HR data

Data Map: Category 7 Requirements Overview

┌─────────────────────────────────────────────────────────────────────────────────────────┐
│                   CATEGORY 7 DATA REQUIREMENTS OVERVIEW                                  │
├─────────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                         │
│  ┌─────────────────────────────────────────────────────────────────────────────────┐   │
│  │ EMPLOYEE RECORD                                                                 │   │
│  ├─────────────────────────────────────────────────────────────────────────────────┤   │
│  │                                                                                 │   │
│  │  Required Fields              Optional Fields                                   │   │
│  │  ──────────────────           ──────────────────                                │   │
│  │  • origin OR total_km         • email                                           │   │
│  │  • destination OR total_km    • name                                            │   │
│  │  • transport_type             • vehicle_size                                    │   │
│  │  • weekly_travels             • fuel_type                                       │   │
│  │                               • renewable_energy                                │   │
│  │                               • carpool (true/false)                            │   │
│  │                                                                                 │   │
│  └─────────────────────────────────────────────────────────────────────────────────┘   │
│                                                                                         │
│  ┌─────────────────────────────────────────────────────────────────────────────────┐   │
│  │ COMMUTING PERIOD (Employee Historic)                                            │   │
│  ├─────────────────────────────────────────────────────────────────────────────────┤   │
│  │                                                                                 │   │
│  │  Required Fields              Optional Fields                                   │   │
│  │  ──────────────────           ──────────────────                                │   │
│  │  • commuting_type             • situation                                       │   │
│  │    ("in_itinere")                                                               │   │
│  │  • start_date                                                                   │   │
│  │  • end_date                                                                     │   │
│  │  • daily_trips (default: 1)                                                     │   │
│  │                                                                                 │   │
│  └─────────────────────────────────────────────────────────────────────────────────┘   │
│                                                                                         │
│  ┌─────────────────────────────────────────────────────────────────────────────────┐   │
│  │ CALCULATION FLOW                                                                │   │
│  ├─────────────────────────────────────────────────────────────────────────────────┤   │
│  │                                                                                 │   │
│  │  Origin + Destination ──► Distance (km) [one-way]                               │   │
│  │                                │                                                │   │
│  │  Weekly Travels ──────────────┼──► Working days in period                       │   │
│  │                               │                                                 │   │
│  │  Transport Type ──────────────┼──► Emission Factor (kg CO₂e/km)                 │   │
│  │                               │                                                 │   │
│  │  CO₂e = Distance × Days × Daily_Trips × EF × 2 (round trip)                     │   │
│  │                                                                                 │   │
│  │  If carpool: CO₂e ÷ 3 (carpool factor)                                          │   │
│  │                                                                                 │   │
│  └─────────────────────────────────────────────────────────────────────────────────┘   │
│                                                                                         │
└─────────────────────────────────────────────────────────────────────────────────────────┘

Weekly Travels (Working Days)

The weekly_travels field defines which days of the week the employee commutes:

Day	Value	Description
Monday	`0`	Included if present in array
Tuesday	`1`	Included if present in array
Wednesday	`2`	Included if present in array
Thursday	`3`	Included if present in array
Friday	`4`	Included if present in array
Saturday	`5`	Included if present in array
Sunday	`6`	Included if present in array

Examples:

Pattern	`weekly_travels`	Description
Full week (Mon-Fri)	`[0, 1, 2, 3, 4]`	Traditional 5-day week
3 days/week	`[0, 2, 4]`	Mon, Wed, Fri only
Hybrid (2 days)	`[1, 3]`	Tue, Thu in office
Full remote	`[]`	Empty = no commuting

Remote Work / TeleworkSet weekly_travels: [] (empty array) for fully remote employees. Dcycle will calculate zero commuting emissions for these employees.

Transport Types

Transport Type	Description	Typical EF Range
`car`	Personal vehicle	0.1-0.2 kg CO₂e/km
`bus`	Public bus	0.05-0.1 kg CO₂e/km
`train`	Commuter rail	0.03-0.05 kg CO₂e/km
`metro`	Urban subway	0.02-0.04 kg CO₂e/km
`tram`	Light rail	0.02-0.04 kg CO₂e/km
`motorbike`	Motorcycle	0.08-0.12 kg CO₂e/km
`bicycle`	Cycling	0 kg CO₂e/km
`walk`	Walking	0 kg CO₂e/km

Vehicle options for cars:

Vehicle Size	Fuel Type	Description
`small`	`petrol`, `diesel`, `electric`, `hybrid`	Compact cars
`medium`	`petrol`, `diesel`, `electric`, `hybrid`	Sedans
`large`	`petrol`, `diesel`, `electric`, `hybrid`	SUVs

CarpoolingWhen carpool: true, emissions are divided by 3 (average carpool occupancy). This encourages shared transportation and accurately reflects the lower per-person emissions.

Emission Factor Sources

Dcycle uses commuting emission factors from Ecoinvent:

Ecoinvent Database

Ecoinvent 3.8+ Cut-off

Lifecycle emission factors by transport mode
Regional variations where available
Covers cars, public transit, and active transport
Well-to-Wheel (WTW) factors including fuel production

Distance Calculation

Google Maps API:

Actual road distances between home and work
Accounts for real routes, not straight-line distance
If origin/destination unavailable, use total_km directly

Step 1: Create Employee Records

📋 Data Map: Employee Record

Field	Type	Required	Description	Example
`origin`	string	⚠️	Home address/city	`"Madrid, Spain"`
`destination`	string	⚠️	Office address	`"Company HQ, Madrid"`
`total_km`	number	⚠️	One-way distance	`15`
`transport_type`	string	✅	Commute mode	`"car"`
`weekly_travels`	array	✅	Days in office [0-6]	`[0, 1, 2, 3, 4]`
`email`	string	❌	Employee email	`"john@company.com"`
`name`	string	❌	Employee name	`"John Smith"`
`vehicle_size`	string	❌	For cars	`"medium"`
`fuel_type`	string	❌	For cars	`"petrol"`
`carpool`	boolean	❌	Shared vehicle	`false`

⚠️ Conditional requirements:

Either origin + destination OR total_km must be provided

Where to get this data:

Home location: HR records, employee surveys
Transport mode: Employee surveys
Working days: HR/scheduling systems

Track individual employee commuting:

import requests
import os

headers = {
    "Authorization": f"Bearer {os.getenv('DCYCLE_API_KEY')}",
    "Content-Type": "application/json",
    "x-organization-id": os.getenv("DCYCLE_ORG_ID"),
    "x-user-id": os.getenv("DCYCLE_USER_ID"),
}

# Example: Employee commuting by car (5 days/week)
employee = {
    "email": "john.smith@company.com",
    "name": "John Smith",
    "origin": "Residential Area, Madrid",
    "destination": "Company HQ, Madrid Business District",
    "transport_type": "car",
    "vehicle_size": "medium",
    "fuel_type": "petrol",
    "weekly_travels": [0, 1, 2, 3, 4],  # Mon-Fri
    "carpool": False,
}

response = requests.post(
    "https://api.dcycle.io/v1/employees",
    headers=headers,
    json=employee
).json()

print(f"✅ Employee created")
print(f"   ID: {response['id']}")
print(f"   Email: {employee['email']}")
print(f"   Commute: {employee['origin']} → {employee['destination']}")
print(f"   Transport: {employee['transport_type']}")
print(f"   Days/week: {len(employee['weekly_travels'])}")

# Create commuting period
employee_id = response['id']

commuting_period = {
    "employee_id": employee_id,
    "commuting_type": "in_itinere",  # Home-work commute
    "start_date": "2024-01-01",
    "end_date": "2024-12-31",
    "daily_trips": 1,  # 1 round trip per day
    "origin": employee['origin'],
    "destination": employee['destination'],
    "total_km": 15,  # One-way distance
    "transport_type": employee['transport_type'],
    "vehicle_size": employee.get('vehicle_size'),
    "fuel_type": employee.get('fuel_type'),
    "weekly_travels": employee['weekly_travels'],
    "carpool": employee.get('carpool', False),
}

period_response = requests.post(
    "https://api.dcycle.io/v1/employees_historic",
    headers=headers,
    json=commuting_period
).json()

print(f"   Period: {commuting_period['start_date']} to {commuting_period['end_date']}")
print(f"   Distance: {commuting_period['total_km']} km (one-way)")
print(f"   CO₂e: {period_response.get('co2e', 'Calculating...')} kg")

const axios = require('axios');

const headers = {
  'Authorization': `Bearer ${process.env.DCYCLE_API_KEY}`,
  'Content-Type': 'application/json',
  'x-organization-id': process.env.DCYCLE_ORG_ID,
  'x-user-id': process.env.DCYCLE_USER_ID
};

// Example: Employee commuting by car (5 days/week)
const employee = {
  email: 'john.smith@company.com',
  name: 'John Smith',
  origin: 'Residential Area, Madrid',
  destination: 'Company HQ, Madrid Business District',
  transport_type: 'car',
  vehicle_size: 'medium',
  fuel_type: 'petrol',
  weekly_travels: [0, 1, 2, 3, 4],
  carpool: false
};

const response = await axios.post(
  'https://api.dcycle.io/v1/employees',
  employee,
  { headers }
).then(res => res.data);

console.log('✅ Employee created');
console.log(`   ID: ${response.id}`);
console.log(`   Email: ${employee.email}`);
console.log(`   Transport: ${employee.transport_type}`);
console.log(`   Days/week: ${employee.weekly_travels.length}`);

Common Commuting Scenarios

# Example employees with different commuting patterns

employees = [
    # Full-time office worker (car)
    {
        "email": "alice@company.com",
        "name": "Alice Johnson",
        "total_km": 20,
        "transport_type": "car",
        "vehicle_size": "medium",
        "fuel_type": "diesel",
        "weekly_travels": [0, 1, 2, 3, 4],
        "carpool": False,
    },
    # Hybrid worker (public transit, 3 days)
    {
        "email": "bob@company.com",
        "name": "Bob Williams",
        "total_km": 12,
        "transport_type": "train",
        "weekly_travels": [1, 2, 3],  # Tue, Wed, Thu
        "carpool": False,
    },
    # Eco-commuter (bike)
    {
        "email": "carol@company.com",
        "name": "Carol Davis",
        "total_km": 5,
        "transport_type": "bicycle",
        "weekly_travels": [0, 1, 2, 3, 4],
        "carpool": False,
    },
    # Remote worker
    {
        "email": "david@company.com",
        "name": "David Brown",
        "total_km": 0,
        "transport_type": "walk",  # Placeholder
        "weekly_travels": [],  # Fully remote
        "carpool": False,
    },
    # Carpooler
    {
        "email": "emma@company.com",
        "name": "Emma Wilson",
        "total_km": 25,
        "transport_type": "car",
        "vehicle_size": "large",
        "fuel_type": "petrol",
        "weekly_travels": [0, 1, 2, 3, 4],
        "carpool": True,  # Shares ride with colleagues
    },
    # Electric car commuter
    {
        "email": "frank@company.com",
        "name": "Frank Miller",
        "total_km": 30,
        "transport_type": "car",
        "vehicle_size": "medium",
        "fuel_type": "electric",
        "weekly_travels": [0, 1, 2, 3, 4],
        "carpool": False,
    },
]

# Create all employees
for emp in employees:
    response = requests.post(
        "https://api.dcycle.io/v1/employees",
        headers=headers,
        json=emp
    ).json()
    
    days = len(emp['weekly_travels'])
    remote = " (Remote)" if days == 0 else ""
    carpool = " 🚗👥" if emp.get('carpool') else ""
    
    print(f"   {emp['name']}: {emp['transport_type']} | {days} days/week{remote}{carpool}")
    print(f"      Distance: {emp['total_km']} km | CO₂e: {response.get('co2e', 'TBD')} kg/year")

print(f"\n✅ Created {len(employees)} employee records")

Step 2: Send Employee Surveys

Collect commuting data directly from employees via survey:

import requests
import os

headers = {
    "Authorization": f"Bearer {os.getenv('DCYCLE_API_KEY')}",
    "Content-Type": "application/json",
    "x-organization-id": os.getenv("DCYCLE_ORG_ID"),
    "x-user-id": os.getenv("DCYCLE_USER_ID"),
}

# Send survey to employees
survey_request = {
    "emails": [
        "alice@company.com",
        "bob@company.com",
        "carol@company.com",
    ],
    "survey_type": "commuting",
    "message": "Please complete this short survey about your daily commute.",
}

response = requests.post(
    "https://api.dcycle.io/v1/employees/survey",
    headers=headers,
    json=survey_request
)

if response.status_code == 200:
    print(f"✅ Survey sent to {len(survey_request['emails'])} employees")
else:
    print(f"❌ Failed: {response.text}")

Survey QuestionsDcycle’s commuting survey asks employees:

Home location (address or postal code)
Primary transport mode
Vehicle details (if car)
Days worked in office per week
Carpooling (yes/no)

Step 3: Bulk Upload Employee Data

For organizations with many employees, use CSV upload:

CSV Format

email,name,origin,destination,total_km,transport_type,vehicle_size,fuel_type,weekly_travels,carpool
alice@company.com,"Alice Johnson",,,20,car,medium,diesel,"[0,1,2,3,4]",false
bob@company.com,"Bob Williams",,,12,train,,,"[1,2,3]",false
carol@company.com,"Carol Davis",,,5,bicycle,,,"[0,1,2,3,4]",false
david@company.com,"David Brown",,,0,walk,,,"[]",false
emma@company.com,"Emma Wilson",,,25,car,large,petrol,"[0,1,2,3,4]",true
frank@company.com,"Frank Miller",,,30,car,medium,electric,"[0,1,2,3,4]",false

CSV Notes:

total_km is one-way distance (home to office)
weekly_travels use format "[0,1,2,3,4]" (JSON array as string)
Empty "[]" for remote workers
carpool: true or false

Upload CSV

import requests
import os

headers = {
    "Authorization": f"Bearer {os.getenv('DCYCLE_API_KEY')}",
    "x-organization-id": os.getenv("DCYCLE_ORG_ID"),
    "x-user-id": os.getenv("DCYCLE_USER_ID"),
}

# Upload CSV file
with open("employee_commuting.csv", "rb") as f:
    files = {"file": ("employee_commuting.csv", f, "text/csv")}
    
    response = requests.post(
        "https://api.dcycle.io/v1/employees/bulk/csv",
        headers=headers,
        files=files
    )

if response.status_code == 200:
    result = response.json()
    print(f"✅ Uploaded {result.get('records_processed', 0)} employee records")
else:
    print(f"❌ Upload failed: {response.text}")

Step 4: Query Category 7 Emissions

Get Employee Commuting Totals

import requests
import os

headers = {
    "Authorization": f"Bearer {os.getenv('DCYCLE_API_KEY')}",
    "Content-Type": "application/json",
    "x-organization-id": os.getenv("DCYCLE_ORG_ID"),
    "x-user-id": os.getenv("DCYCLE_USER_ID"),
}

# Get all employees
response = requests.get(
    "https://api.dcycle.io/v1/employees",
    headers=headers,
    params={
        "enabled": True,
    }
).json()

print(f"📊 Scope 3 Category 7: Employee Commuting (2024)")
print("=" * 60)

total_employees = len(response.get('items', []))
total_co2e = 0
remote_count = 0

for emp in response.get('items', []):
    co2e = emp.get('co2e', 0) or 0
    total_co2e += co2e
    
    weekly_travels = emp.get('weekly_travels', [])
    if not weekly_travels:
        remote_count += 1

print(f"   Total employees: {total_employees}")
print(f"   Remote workers: {remote_count} ({remote_count/total_employees*100:.0f}%)")
print(f"   Total CO₂e: {total_co2e:,.0f} kg ({total_co2e/1000:.1f} tonnes)")
print(f"   Avg per employee: {total_co2e/total_employees:,.0f} kg/year")

Analyze by Transport Mode

from collections import defaultdict

# Group by transport type
transport_summary = defaultdict(lambda: {"count": 0, "co2e": 0})

for emp in response.get('items', []):
    transport = emp.get('transport_type', 'unknown')
    co2e = emp.get('co2e', 0) or 0
    
    transport_summary[transport]["count"] += 1
    transport_summary[transport]["co2e"] += co2e

print("\n📊 Category 7 by Transport Mode:")
print("-" * 50)

transport_icons = {
    "car": "🚗",
    "train": "🚂",
    "bus": "🚌",
    "metro": "🚇",
    "tram": "🚊",
    "bicycle": "🚲",
    "walk": "🚶",
    "motorbike": "🏍️",
}

for transport, data in sorted(transport_summary.items(), key=lambda x: x[1]['co2e'], reverse=True):
    icon = transport_icons.get(transport, "🚀")
    percentage = (data['co2e'] / total_co2e * 100) if total_co2e > 0 else 0
    avg = data['co2e'] / data['count'] if data['count'] > 0 else 0
    
    print(f"   {icon} {transport.capitalize()}:")
    print(f"      Employees: {data['count']}")
    print(f"      Total CO₂e: {data['co2e']:,.0f} kg ({percentage:.1f}%)")
    print(f"      Avg per employee: {avg:,.0f} kg/year")
    print()

Analyze Remote vs Office

# Calculate remote work impact
office_co2e = 0
remote_co2e = 0
office_count = 0
remote_count = 0

for emp in response.get('items', []):
    weekly_travels = emp.get('weekly_travels', [])
    co2e = emp.get('co2e', 0) or 0
    
    if weekly_travels:
        office_co2e += co2e
        office_count += 1
    else:
        remote_co2e += co2e
        remote_count += 1

print("\n📊 Remote vs Office Impact:")
print("-" * 50)
print(f"   🏢 Office workers: {office_count}")
print(f"      Total CO₂e: {office_co2e:,.0f} kg")
print(f"      Avg per employee: {office_co2e/office_count if office_count else 0:,.0f} kg/year")
print()
print(f"   🏠 Remote workers: {remote_count}")
print(f"      Total CO₂e: {remote_co2e:,.0f} kg (should be ~0)")
print()

# Calculate potential savings
if office_count > 0:
    avg_office_co2e = office_co2e / office_count
    potential_savings_1day = avg_office_co2e / 5  # 1 day remote per week
    potential_savings_2day = avg_office_co2e * 2 / 5  # 2 days remote per week
    
    print(f"   💡 Potential savings per employee:")
    print(f"      1 remote day/week: {potential_savings_1day:,.0f} kg/year")
    print(f"      2 remote days/week: {potential_savings_2day:,.0f} kg/year")

Best Practices

1. Track Working Patterns Accurately

# Validate weekly_travels data
def validate_commuting_data(employee):
    """Validate employee commuting data"""
    
    issues = []
    
    weekly_travels = employee.get('weekly_travels', [])
    
    # Check for valid day values
    for day in weekly_travels:
        if day not in range(7):
            issues.append(f"Invalid day value: {day}")
    
    # Check for duplicates
    if len(weekly_travels) != len(set(weekly_travels)):
        issues.append("Duplicate days in weekly_travels")
    
    # Check distance is reasonable
    total_km = employee.get('total_km', 0)
    if total_km and total_km > 150:
        issues.append(f"Unusually high distance: {total_km} km")
    
    return issues

# Validate all employees
for emp in response.get('items', []):
    issues = validate_commuting_data(emp)
    if issues:
        print(f"⚠️ {emp.get('email')}: {', '.join(issues)}")

2. Encourage Low-Carbon Commuting

# Identify high-emission commuters
high_emitters = []

for emp in response.get('items', []):
    co2e = emp.get('co2e', 0) or 0
    
    # Threshold: employees above average
    if co2e > (total_co2e / total_employees) * 1.5:
        high_emitters.append({
            "email": emp.get('email'),
            "transport": emp.get('transport_type'),
            "co2e": co2e,
            "distance": emp.get('total_km', 0),
        })

print(f"\n🎯 High-Emission Commuters ({len(high_emitters)}):")
for emitter in high_emitters[:10]:
    print(f"   {emitter['email']}")
    print(f"      Transport: {emitter['transport']} | Distance: {emitter['distance']} km")
    print(f"      CO₂e: {emitter['co2e']:,.0f} kg/year")
    
    # Suggest alternatives
    if emitter['transport'] == 'car' and emitter['distance'] < 10:
        print(f"      💡 Consider cycling or e-bike")
    elif emitter['transport'] == 'car' and emitter['distance'] < 30:
        print(f"      💡 Consider public transit or carpooling")

3. Calculate Intensity Metrics

# Calculate emissions per employee per workday
def calculate_commuting_intensity():
    """Calculate kg CO₂e per employee per workday"""
    
    total_co2e = 0
    total_workdays = 0
    
    for emp in response.get('items', []):
        co2e = emp.get('co2e', 0) or 0
        weekly_travels = emp.get('weekly_travels', [])
        
        total_co2e += co2e
        # Estimate workdays per year (52 weeks × days per week)
        total_workdays += len(weekly_travels) * 52
    
    if total_workdays > 0:
        return total_co2e / total_workdays
    return 0

intensity = calculate_commuting_intensity()
print(f"\n📊 Commuting Intensity: {intensity:.2f} kg CO₂e per workday")

# Benchmark
if intensity > 5:
    print("   ⚠️ High intensity - promote alternatives")
elif intensity > 2:
    print("   📈 Moderate - encourage public transit")
else:
    print("   ✅ Good - maintain sustainable commuting")

Reduction Strategies for Category 7

Remote work policy: Allow 2+ days WFH per week
Public transit subsidies: Incentivize train/bus use
Cycle-to-work schemes: Provide bike facilities and subsidies
Carpooling programs: Connect employees on similar routes
Electric vehicle charging: Install workplace chargers
Shuttle services: Provide company buses for major routes
Flexible hours: Avoid peak traffic (lower congestion emissions)

Troubleshooting

Issue: Distance Not Calculated

# Provide distance directly if geocoding fails
employee = {
    "email": "test@company.com",
    "total_km": 15,  # Provide direct one-way distance
    "transport_type": "car",
    "weekly_travels": [0, 1, 2, 3, 4],
}

Issue: Zero Emissions for Non-Remote Worker

# Check common causes
def diagnose_zero_emissions(employee):
    """Diagnose why an employee has zero emissions"""
    
    issues = []
    
    # Check if fully remote
    if not employee.get('weekly_travels'):
        issues.append("weekly_travels is empty (remote worker)")
        return issues
    
    # Check if zero distance
    if not employee.get('total_km'):
        issues.append("total_km is missing or zero")
    
    # Check if zero-emission transport
    if employee.get('transport_type') in ['bicycle', 'walk']:
        issues.append(f"Zero-emission transport: {employee.get('transport_type')}")
    
    # Check if missing commuting period
    if not employee.get('employees_historic'):
        issues.append("No commuting period (employees_historic) created")
    
    return issues

for emp in response.get('items', []):
    if emp.get('co2e', 0) == 0:
        issues = diagnose_zero_emissions(emp)
        if issues:
            print(f"⚠️ {emp.get('email')}: {', '.join(issues)}")

Issue: Category 7 Seems Too Low

# Common reasons for underreported commuting emissions
checks = {
    "Missing employees": "Ensure all employees are in the system",
    "Remote misclassification": "Check remote workers aren't marked as office",
    "Missing transport details": "Ensure transport_type is specified",
    "Missing distance": "Provide total_km or origin/destination",
    "Missing periods": "Create employees_historic records",
    "Inactive employees": "Check enabled=True filter",
}

print("🔍 Checklist for Category 7 completeness:")
for issue, action in checks.items():
    print(f"   ☐ {issue}")
    print(f"      → {action}")

Next Steps

Category 6: Business Travel

Track business trip emissions

Category 1: Purchased Goods

Track upstream product emissions

Employee Surveys

Collect commuting data from employees

Scope 3 Overview

Back to all Scope 3 categories

Calculate Emissions

ISO 14064

GHG Protocol

GLEC v3.0 Framework

Use Case Tutorials

Life Cycle Assessment (LCA)

Own Workforce (Social Data)

ISO 14001 Management Systems

Advanced

Automation & AI

Understanding Scope 3 Category 7

Prerequisites

Data Map: Category 7 Requirements Overview

Weekly Travels (Working Days)

Transport Types

Emission Factor Sources

Step 1: Create Employee Records

Common Commuting Scenarios

Step 2: Send Employee Surveys

Step 3: Bulk Upload Employee Data

CSV Format

Upload CSV

Step 4: Query Category 7 Emissions

Get Employee Commuting Totals

Analyze by Transport Mode

Analyze Remote vs Office

Best Practices

1. Track Working Patterns Accurately

2. Encourage Low-Carbon Commuting

3. Calculate Intensity Metrics

Troubleshooting

Issue: Distance Not Calculated

Issue: Zero Emissions for Non-Remote Worker

Issue: Category 7 Seems Too Low

Next Steps

Category 6: Business Travel

Category 1: Purchased Goods

Employee Surveys

Scope 3 Overview

​Understanding Scope 3 Category 7

​Prerequisites

​Data Map: Category 7 Requirements Overview

​Weekly Travels (Working Days)

​Transport Types

​Emission Factor Sources

​Step 1: Create Employee Records

​Common Commuting Scenarios

​Step 2: Send Employee Surveys

​Step 3: Bulk Upload Employee Data

​CSV Format

​Upload CSV

​Step 4: Query Category 7 Emissions

​Get Employee Commuting Totals

​Analyze by Transport Mode

​Analyze Remote vs Office

​Best Practices

​1. Track Working Patterns Accurately

​2. Encourage Low-Carbon Commuting

​3. Calculate Intensity Metrics

​Troubleshooting

​Issue: Distance Not Calculated

​Issue: Zero Emissions for Non-Remote Worker

​Issue: Category 7 Seems Too Low

​Next Steps

Category 6: Business Travel

Category 1: Purchased Goods

Employee Surveys

Scope 3 Overview

Understanding Scope 3 Category 7

Prerequisites

Data Map: Category 7 Requirements Overview

Weekly Travels (Working Days)

Transport Types

Emission Factor Sources

Step 1: Create Employee Records

Common Commuting Scenarios

Step 2: Send Employee Surveys

Step 3: Bulk Upload Employee Data

CSV Format

Upload CSV

Step 4: Query Category 7 Emissions

Get Employee Commuting Totals

Analyze by Transport Mode

Analyze Remote vs Office

Best Practices

1. Track Working Patterns Accurately

2. Encourage Low-Carbon Commuting

3. Calculate Intensity Metrics

Troubleshooting

Issue: Distance Not Calculated

Issue: Zero Emissions for Non-Remote Worker

Issue: Category 7 Seems Too Low

Next Steps