Objective
In this step, you'll learn how to collect and organize the data needed for your GHG inventory. By the end of this step, you will understand the types of data required for different emission sources, know how to identify data gaps, and have strategies for collecting high-quality data efficiently. This foundation will ensure your emissions calculations are accurate and comprehensive.
Understanding Calculation Approaches
There are a few ways to calculate emissions:
- Estimate emissions (Most Common Method): Multiply activity data (e.g., fuel use records) by the appropriate emission factor. This approach is practical, accessible, and widely adopted by organizations of all sizes.
- Direct Measurement: Monitor GHG concentration and flow rate, such as with a filter on an exhaust pipe. Example: Continuous Emissions Monitoring System (CEMS)
- Stoichiometric Calculation: Measure which elements enter and leave the system. Example: Mass balance approach
Direct Measurement and Stoichiometric Calculation are not readily available to most companies due to the cost and complexity involved. Therefore, estimating emissions using emission factors and activity data is the most commonly used method.
Working with Emission Factors
An Emission Factor (EF) is a value used to convert activity data into greenhouse gas emissions. Different emission factors are used depending on the type of activity and the available data. For instance:
- If you have kWh data for electricity, use an emission factor specific to energy production.
- If the available data is related to fuel use, select an emission factor specific to the fuel type, like diesel or gasoline.
- Emission factors are often region-specific; make sure to choose the appropriate factor based on your geographical location.
Best Practices for Emission Factor Selection
- Public Data Sources: Emission Factors are provided by publicly available data sources such as EPA, BEIS, Exiobase, etc. It is best for a company to select one data source and remain consistent with it throughout reporting.
- Regional Specificity: Users should try to choose EFs from their specific region. However, not every country or region has its own set of emission factors. In such cases, using a proxy from similar regions is acceptable to ensure practicality.
- Consistency Over Time: Users should not change emission factors for the same activity year over year. It is crucial that each emission source uses the same data source for consistency, such as not using EPA in 2020, switching to BEIS in 2021, and then back to EPA in 2022. Consistency helps maintain reliable trend data and makes comparisons more meaningful.
- Use appropriate units for Emission Factors:
- If you have electricity usage in kWh, use a kWh emission factor.
- If your available data is liters of fuel used, use an EF specific to that fuel.
- Data Availability: If an emission factor is only available in specific units, ensure you have data in compatible units or convert your data accordingly.
Leveraging Carbon GPT for Emission Factors
Carbon GPT offers several benefits to help users easily find and select Emission Factors:
- Comprehensive Database: Carbon GPT has compiled the most commonly used Emission Factors from publicly available data sources such as EPA, BEIS, Exiobase, CBAM, and GHG Protocol for you to use. If there are specific datasets you are looking for, please contact our sales team.
- AI Assistance: Carbon GPT uses AI to help you select the appropriate Emission Factor for your specific use case, ensuring accuracy and relevance.
- Custom Emission Factors: Carbon GPT allows you to add your own custom Emission Factor if it is provided by your supplier, giving you flexibility in your carbon accounting.
- Regular Updates: Carbon GPT constantly updates its Emission Factor database, so you always have the latest data available, reducing the risk of outdated information affecting your calculations.
Data Requirements by Emission Scope
Different emission sources require different types of data. Here's a breakdown of common data types needed for each scope:
Scope 1 Data Requirements
| Emission Source | Data Required | Example Units | Potential Data Sources |
|---|---|---|---|
| Stationary Combustion | Fuel consumption by type | Liters, m³, kg, kWh | Utility bills, meter readings, purchase records |
| Mobile Combustion | Fuel consumption by vehicle type | Liters, gallons | Fuel cards, expense reports, odometer readings |
| Process Emissions | Process-specific activity data | Varies by process | Production records, engineering specifications |
| Fugitive Emissions | Refrigerant purchases, top-ups | kg | Maintenance records, purchase invoices |
Scope 2 Data Requirements
| Emission Source | Data Required | Example Units | Potential Data Sources |
|---|---|---|---|
| Purchased Electricity | Electricity consumption | kWh | Utility bills, meter readings, energy management systems |
| Purchased Heat/Steam | Heat or steam consumption | kWh, GJ | Utility bills, meter readings |
| Purchased Cooling | Cooling consumption | kWh, GJ | Utility bills, meter readings |
Scope 3 Data Requirements
| Emission Source | Data Required | Example Units | Potential Data Sources |
|---|---|---|---|
| Business Travel | Distance traveled by mode | km, miles | Travel agency reports, expense claims, booking systems |
| Employee Commuting | Distance traveled by mode | km, miles | Employee surveys, HR records |
| Purchased Goods & Services | Spend data or physical quantities | $, kg | Procurement records, supplier invoices |
| Waste Disposal | Waste by type and disposal method | kg, tonnes | Waste contractor reports, facility records |
| Transportation & Distribution | Distance and weight shipped | tonne-km | Logistics records, shipping documents |
Effective Data Collection Strategies
Data Sources for Common Emission Categories
- Stationary Combustion: Collect fuel consumption records, maintenance logs, or energy bills.
- Mobile Combustion: Gather vehicle fuel records, mileage logs, or telematics data.
- Process Emissions: Review operational records, production reports, or chemical use logs.
- Fugitive Emissions: Look for maintenance records, leak detection surveys, or equipment inspections.
Collaboration with different departments like facilities management, logistics, and production will be crucial to gather all necessary data.
Primary vs. Secondary Data
- Primary Data: Direct measurements or records from your organization's activities (e.g., utility bills, fuel receipts)
- Secondary Data: Estimates, industry averages, or proxy data when primary data is unavailable
Whenever possible, prioritize primary data for greater accuracy. However, secondary data can be valuable for filling gaps or as a starting point for new emission sources.
Collection Methods and Approaches
- Centralized Collection: One team or department is responsible for gathering all data
- Advantages: Consistent methodology, clear accountability
- Challenges: May lack detailed knowledge of all operations
- Distributed Collection: Each department or facility collects their own data
- Advantages: Local expertise, more detailed data
- Challenges: Inconsistent methodologies, requires coordination
- Automated Collection: Using systems to automatically gather and report data
- Advantages: Efficiency, reduced human error, real-time data
- Challenges: Initial setup costs, technical expertise required
Choose the method that best fits your organization's structure, resources, and complexity.
Ensuring Data Quality
Key Principles for Quality Assurance
- Accuracy: Data should reflect the actual emissions as closely as possible
- Completeness: All relevant emission sources should be included
- Consistency: Methods should be consistent across time periods and emission sources
- Transparency: Data sources and methodologies should be clearly documented
- Relevance: Data should be appropriate for the intended use
Addressing Data Gaps
- Identify the gap: Determine which data is missing and why
- Assess significance: Evaluate how important the missing data is to your overall inventory
- Choose an approach:
- Extrapolation from existing data
- Use of proxy data or benchmarks
- Conservative estimates
- Exclusion (with clear documentation of the reason)
- Document your approach: Clearly record how you addressed the gap for transparency
Practical Implementation with Carbon GPT
When using Carbon GPT for data collection:
- Set up data collection templates: Use Carbon GPT's template feature to create standardized data collection forms for each emission source.
- Assign responsibilities: Designate team members responsible for collecting specific data points and set clear deadlines.
- Import data: Upload your collected data into Carbon GPT using the import functionality, which supports various file formats.
- Validate data: Use Carbon GPT's validation tools to identify potential errors, outliers, or inconsistencies in your data.
- Document sources: Record the source of each data point and any assumptions made to ensure transparency and auditability.
Case Study: Manufacturing Company
A medium-sized manufacturing company with facilities in three countries implemented a structured data collection process for their first GHG inventory:
- They created a central data collection team with representatives from each facility.
- The team developed standardized templates for each emission source, ensuring consistency across locations.
- They identified key data sources for each emission category:
- Stationary combustion: Natural gas bills and on-site fuel storage records
- Mobile combustion: Fleet management system and fuel card reports
- Process emissions: Production logs and material consumption records
- Purchased electricity: Utility bills and meter readings
- Where data gaps existed (particularly for one older facility with limited records), they used production-based extrapolation to estimate emissions.
- All data was centrally reviewed for quality and consistency before being entered into Carbon GPT for calculation.
The company discovered that their data collection process not only supported their GHG inventory but also revealed energy efficiency opportunities that could reduce both emissions and costs.
Next Steps
Develop a Data Collection Plan: Create a comprehensive plan that outlines what data needs to be collected, who is responsible for collection, and when it should be collected.
Enter Your List of Emission Sources in Carbon GPT: Begin by entering each emission source identified in Step 4 into the Carbon GPT platform for better tracking and management.
Select the Appropriate Emission Factors for Each Emission Source: Use the features in Carbon GPT to select the correct Emission Factors for each emission source, ensuring consistency and accuracy in your calculations. Emission Sources in Carbon GPT function similarly to templates. Once you've created your Emission Sources, it becomes a lot easier to calculate your emissions moving forward using these templates.
Evaluate Data Gaps: Assess whether you have sufficient data for each identified category and scope. If there are data gaps, develop a plan to collect or estimate this missing information.
Engage Departments for Data Validation: Work with relevant departments (e.g., facilities, finance, operations) to validate the data you've gathered. Their insights will be crucial in ensuring the accuracy of the emission sources and categories you are focusing on.