Best Data Practices for GHG Inventories
High-quality data is the foundation of a credible greenhouse gas (GHG) inventory. While data availability can vary across emissions sources, applying consistent best practices across all scopes improves accuracy, transparency, and decision-making over time.
There is always an opportunity to improve data quality, and implementing the right systems earlier on can improve your data quality and decision-making over time.
Below are recommended data practices by scope and category, that can be applied across all GHG inventories.
Before we get started, we want to ensure the following is true for the data being quantified into emissions:
Complete: All relevant emissions sources within the chosen boundary are included
Consistent: Methodologies and assumptions are applied uniformly year over year and if there are changed (e.g., better quality data exists, we want to document that change very clearly)
Transparent: Data sources, assumptions, and limitations are clearly documented and available in the event that if auditors, external parties or a new team member joins, they can refer back to the specific documentation around the data points
Accurate: Primary data is prioritized where feasible, with justified use of estimates and estimates are well-documented
Auditable: Calculations can be traced back to source data and emission factors
Scope 1 includes emissions from sources owned or controlled by the organization.
Common Categories
Stationary combustion (e.g., boilers, furnaces, generators)
Mobile combustion (company-owned vehicles, equipment that uses fuel)
Fugitive emissions (e.g., refrigerants, methane leaks)
Process emissions (industry-specific)
Best Data Practices
Use primary activity data wherever possible (fuel invoices, meter readings, maintenance logs)
Track quantities, not just spend (litres of fuel, m³ of gas, lb of refrigerant)
Capture asset-level detail (equipment type, fuel type, refrigerant type)
Align time periods with the reporting year (avoid fiscal/calendar mismatches)
Document estimation methods for fugitive or missing data (e.g., leak rates, top-ups)
Note: Even when estimates are required, consistent estimation methods year over year improve trend reliability.
Scope 2: Purchased Electricity, Steam, Heating & Cooling
Scope 2 covers emissions from purchased energy consumed by the organization.
Best Data Practices
Collect electricity and energy use in kWh, MJ, or GJ, not cost
Use utility-specific data rather than building averages when available
Track location information (country, grid region, utility)
Separate market-based and location-based data where applicable
Retain contracts and certificates (e.g., RECs, EACs) for market-based claims
Best practice is to report both location-based and market-based Scope 2 emissions where required or relevant. Ask the Carbonhound team for support with this if you have any questions!
Scope 3: Value Chain Emissions
Scope 3 often represents the largest share of emissions and the greatest data variability.
General Best Practices Across Scope 3
Prioritize material categories first (based on spend, risk, or relevance)
Start with secondary data, then improve toward primary data over time
Avoid double counting across categories and scopes
Clearly state boundaries and exclusions
Use consistent emission factor sources within each category
Purchased Goods & Services (Category 1)
Prefer supplier-specific activity or emissions data
Ask Carbonhound about our Supplier Engagement Program
When unavailable, use spend-based or average-data methods consistently
Map spend to meaningful categories and continue to map new vendors or spend categories accordingly
Document assumptions around price inflation and currency conversions
Capital Goods (Category 2)
Separate capital goods from operating expenses
Align data with accounting capitalization thresholds
Apply consistent amortization or “year of purchase” approaches
Fuel- and Energy-Related Activities (Category 3)
Ensure Scope 1 and 2 fuel quantities reconcile with Category 3 calculations
Avoid double counting upstream emissions already included elsewhere
Use region-appropriate upstream emission factors
Transportation & Distribution (Categories 4 & 9)
Prioritize distance- and weight-based data over spend where feasible
Capture transport mode (air, ground, rail, sea)
Determine the transport mode specifics (e.g., ferry vs cargo ship, regular dry van vs reefer, electric delivery van vs gas van, etc)
Use actual routes when available (e.g., the departure and destination addresses)
Waste Generated in Operations (Category 5)
Collect waste hauler invoices with weights whenever possible for landfill, recycling, compost, shredding, etc.
Reasonable estimates can be made around consumption if necessary
Business Travel (Category 6)
Use booking or expense system data where possible
Capture distance, class, and mode (especially for flights)
Employee Commuting (Category 7)
Execute an employee commute survey to gather real insights from employees on their commuting and WFH habits
If estimating, document specific work policies (e.g., hybrid work split) and any other details known about employees and apply any other estimates based on regional commuting habits
Clearly document any assumptions and review work policies related to commuting and WFH regularly
Leased Assets (Categories 8 & 13)
Always important if you are leasing spaces with no operational control
Ensure that you try to get as much usage data as possible (i.e. fuel use, energy use etc)
Processing and Use of Sold Products & End-of-Life (Categories 10, 11 & 12)
Define functional units and lifetimes clearly
Align assumptions with product design and customer use cases (Bonus: If you’ve conducted any lifetime of the product assessments or studies, that can be leveraged here)
Document scenarios used (average vs. worst-case vs. best-case)
Revisit assumptions as products evolve
Franchises (Category 14)
Primarily for franchisors, this category accounts for the Scope 1 and 2 emissions (energy use, fuel) of independent franchisees operating under their brand.
Investments (Category 15)
Capture your loan data according to the PCAF standard
Data Improvement Over Time
No organization is perfect from the get-go when it comes to your GHG inventory. New sources may be discovered by key stakeholders and better quality data may be accessible over time, however as your organization continues to track their emissions, remember these key ways of ensuring improving the data over time:
Tracking data quality scores or confidence levels —> label your data as estimates or real usage whenever possible
Setting improvement targets by category —> where can you work with teams to improve the data quality through the implementation of better systems
Engaging suppliers and partners progressively —> start thinking about how you can collaborate with vendors and suppliers to reduce emissions and have better visibility into their operations
Updating methodologies when better data becomes available (with clear disclosures)