Summary: This is an extract of Chapter 7. After a company has identified the activities to include in its scope 3 boundary, the next step is to collect the necessary data to calculate the company’s scope 3 emissions.
Collecting scope 3 emissions data is likely to require wider engagement within the reporting company, as well as with suppliers and partners outside of the company, than is needed to collect scope 1 and scope 2 emissions data. Companies may need to engage several internal departments, such as procurement, energy, manufacturing, marketing, research and development, product design, logistics, and accounting.
This chapter provides a four-step approach to collecting and evaluating data (see figure 7.1).
Guidance for calculating scope 3 emissions from each scope 3 category is provided in a separate document, Guidance for Calculating Scope 3 Emissions, which is available at www.ghgprotocol.org.
7.1 Guidance for prioritizing data collection efforts
Companies should prioritize data collection efforts on the scope 3 activities that are expected to have the most significant GHG emissions, offer the most significant GHG reduction opportunities, and are most relevant to the company’s business goals. Collecting higher quality data for priority activities allows companies to focus resources on the most significant GHG emissions in the value chain, more effectively set reduction targets, and track and demonstrate GHG reductions over time (see chapter 9).
Companies may use a combination of approaches and criteria to identify priority activities. For example, companies may seek higher quality data for all activities that are significant in size, activities that present the most significant risks and opportunities in the value chain, and activities where more accurate data can be easily obtained. Companies may choose to rely on relatively less accurate data for activities that are expected to have insignificant emissions or where accurate data is difficult to obtain. (See Appendix C for guidance on developing a data management plan, including strategies for obtaining more accurate data over time).
Prioritizing activities based on the magnitude of GHG emissions
The most rigorous approach to identifying priority activities is to use initial GHG estimation (or screening) methods to determine which scope 3 activities are expected to be most significant in size. A quantitative approach gives the most accurate understanding of the relative magnitudes of various scope 3 activities. To prioritize activities based on their expected GHG emissions, companies should:
- use initial GHG estimation (or screening) methods to estimate the emissions from each scope 3 activity (e.g., by using industry-average data, environmentally- extended input output data (see box 7.1), proxy data, or rough estimates); and
- rank all scope 3 activities from largest to smallest according to their estimated GHG emissions to determine which scope 3 activities have the most significant impact.
Calculation methods for each scope 3 category that can be used for screening are provided in a separate document, Guidance for Calculating Scope 3 Emissions, which is available at www.ghgprotocol.org.
Prioritizing activities based on financial spend or revenue
As an alternative to ranking scope 3 activities based on their estimated GHG emissions, companies may choose to prioritize scope 3 activities based on their relative financial significance. Companies may use a financial spend analysis to rank upstream types of purchased products by their contribution to the company’s total spend or expenditure (for an example, see the AkzoNobel case study). For downstream emissions, companies may likewise rank types of sold products by their contribution to the company’s total revenue.
Companies should use caution in prioritizing activities based on financial contribution, because spend and revenue may not correlate well with emissions. For example, some activities have a high market value, but have relatively low emissions. Conversely, some activities have a low market value, but have relatively high emissions. As a result, companies should also prioritize activities that do not contribute significantly to financial spend or revenue, but are expected to have a significant GHG impact.
Prioritizing activities based on other criteria
In addition to prioritizing data collection efforts on activities expected to contribute significantly to total scope 3 emissions or to spend, companies may prioritize any other activities expected to be most relevant for the company or its stakeholders, including activities that:
- the company has influence over;
- contribute to the company’s risk exposure;
- stakeholders deem critical;
- have been identified as significant by sector-specific guidance; or
- meet any additional criteria developed by the company or industry sector (see table 6.1 for more information).
7.2 Overview of quantification methods and data types
There are two main methods to quantify emissions: direct measurement and calculation (see table 7.1). Each requires different types of data.
In practice, calculation will be used most often to quantify scope 3 emissions, which requires the use of two types of data: activity data and emission factors.
Activity data
Activity data is a quantitative measure of a level of activity that results in GHG emissions. Examples of activity data are provided in table 7.2.
Emission factors
An emission factor is a factor that converts activity data into GHG emissions data. Examples of emission factors are provided in table 7.2.
Companies are required to report a description of the types and sources of activity data and emission factors used to calculate the inventory (see chapter 11).
Table [7.1] Quantification methods
| Quantification method | Description | Relevant data types |
|---|---|---|
| Direct measurement | Quantification of GHG emissions using direct monitoring, mass balance, or stoichiometry | Direct emissions data |
| Calculation | Quantification of GHG emissions by multiplying activity data by an emission factor | Activity data, Emission factors |
Table [7.2] Examples of activity data and emission factors
| Examples of activity data | Examples of emission factors |
|---|---|
| Liters of fuel consumed | kg CO₂ emitted per liter of fuel consumed |
| Kilowatt-hours of electricity consumed | kg CO₂ emitted per kWh of electricity consumed |
| Kilograms of material consumed | kg PFC emitted per kg of material consumed |
| Kilometers of distance traveled | t CO₂ emitted per kilometer traveled |
| Hours of time operated | kg SF₆ emitted per hour of time operated |
| Square meters of area occupied | g N₂O emitted per square meter of area |
| Kilograms of waste generated | g CH₄ emitted per kg of waste generated |
| Kilograms of product sold | kg HFC emitted per kg of product sold |
| Quantity of money spent | kg CO₂ emitted per unit of currency spent |
Energy emission factors
Two types of emission factors are used to convert energy activity data into emissions data:
- Combustion emission factors, which include only the emissions that occur from combusting the fuel
- Life cycle emission factors, which include not only the emissions that occur from combusting the fuel, but all other emissions that occur in the life cycle of the fuel such as emissions from extraction, processing, and transportation of fuels
Combustion emission factors are used in the GHG Protocol Corporate Standard to calculate scope 1 emissions (in the case of fuels) and scope 2 emissions (in the case of electricity). Life cycle emission factors are used in the GHG Protocol Product Standard to calculate emissions from fuels and electricity. These two types of emission factors and their use are described in more detail below.
Energy emission factors in scope 1 and scope 2 accounting
Scope 1 and scope 2 emissions are calculated using combustion emission factors following the GHG Protocol Corporate Standard. Scope 1 and scope 2 are defined to avoid double counting by two or more companies of the same emission within the same scope (see table 5.1).
Scope 2 includes emissions from the generation of purchased electricity, steam, heating, and cooling that is consumed by the reporting company. In some regions, electricity emission factors may include life cycle activities related to electricity, such as transmission and distribution of electricity, or extraction, processing and transportation of fuels used to generate electricity. Non-generation activities related to electricity are accounted for in scope 3, category 3 (Fuel- and energy-related activities not included in scope 1 or scope 2), rather than scope 2. As a result, companies should seek (and emission factor developers should provide) transparent, disaggregated electricity emission factors that allow separate accounting of emissions from electricity generation in scope 2 and non-generation activities related to electricity in scope 3. Proper accounting creates consistency in scope 2 accounting and reporting between companies and avoids double counting of the same emission within scope 2 by more than one company. See figure 7.2 for more information on different types of electricity emission factors.
Energy emission factors in scope 3 accounting
Companies should use life cycle emission factors to calculate scope 3 emissions related to fuels and energy consumed in the reporting company’s value chain, except for category 3 (fuel- and energy-related activities not included in scope 1 or scope 2) (see below). Compared to combustion emission factors, life cycle emission factors represent all emissions in the upstream supply chain of fuels and energy. Where possible, companies should use life cycle emission factors that are as specific as possible to the type and source of fuel consumed (e.g., specific to the technology used to produce a fuel).
Emission factors for scope 3, category 3
(Fuel- and energy-related activities not included in scope 1 or scope 2)
Two activities within category 3 require special consideration when selecting emission factors:
- Upstream emissions of purchased fuels (i.e., extraction, production, and transportation of fuels consumed by the reporting company)
- Upstream emissions of purchased electricity (i.e., extraction, production, and transportation of fuels consumed in the generation of electricity, steam, heating, and cooling that is consumed by the reporting company)
To calculate emissions from these activities, companies should use life cycle emission factors that exclude emissions from combustion, since emissions from combustion are accounted for in scope 1 (in the case of fuels), in scope 2 (in the case of electricity), and in a separate memo item (in the case of direct CO2 emissions from combustion of biomass or biofuels).
Global warming potential (GWP) values
Global warming potential (GWP) values describe the radiative forcing impact (or degree of harm to the atmosphere) of one unit of a given GHG relative to one unit of carbon dioxide. GWP values convert GHG emissions data for non-CO2 gases into units of carbon dioxide equivalent (CO2e).
Companies should use GWP values provided by the Intergovernmental Panel on Climate Change (IPCC) based on a 100-year time horizon. Companies may either use the IPCC GWP values agreed to by United Nations Framework Convention on Climate Change (UNFCCC) or the most recent GWP values published by the IPCC. Companies should use consistent GWP values across their scope 1, scope 2, and scope 3 inventory and should maintain consistency in the source of
GWP values used over time (by consistently following guidance provided by either the UNFCCC or IPCC, once selected). Companies that have already developed scope 1 and scope 2 GHG inventories should use the same GWP values for scope 3 to maintain consistency across the scopes. Companies that have not previously developed a corporate GHG inventory should use the most recent GWP values.
Companies are required to disclose the source of GWP values used to calculate the inventory (see chapter 11).
Overview of primary data and secondary data
Companies may use two types of data to calculate scope 3 emissions:
- Primary data
- Secondary data
Table 7.3 provides definitions of these two types of data.
Table [7.3] Types of data
| Data type | Description |
|---|---|
| Primary Data | Data from specific activities within a company’s value chain |
| Secondary Data | Data that is not from specific activities within a company’s value chain |
Primary data includes data provided by suppliers or other value chain partners related to specific activities in the reporting company’s value chain. Such data may take the form of primary activity data, or emissions data calculated by suppliers that are specific to suppliers’ activities. Secondary data includes industry-average data (e.g., from published databases, government statistics, literature studies, and industry associations), financial data, proxy data, and other generic data. In certain cases, companies may use specific data from one activity in the value chain to estimate emissions for another activity in the value chain. This type of data (i.e., proxy data) is considered secondary data, since it is not specific to the activity whose emissions are being calculated.
Table [7.4] Examples of primary and secondary data by scope 3 category
| Category | Examples of primary data | Examples of secondary data |
|---|---|---|
| Upstream scope 3 emissions | ||
| 1. Purchased goods and services | - Product-level cradle-to-gate GHG data from suppliers calculated using site-specific data <br> - Site-specific energy use or emissions data from suppliers | - Industry average emission factors per material consumed from life cycle inventory databases |
| 2. Capital goods | - Product-level cradle-to-gate GHG data from suppliers calculated using site-specific data <br> - Site-specific energy use or emissions data from capital goods suppliers | - Industry average emission factors per material consumed from life cycle inventory databases |
| 3. Fuel- and energy-related activities (not included in scope 1 or scope 2) | - Company-specific data on upstream emissions (e.g., extraction of fuels) <br> - Grid-specific T&D loss rate <br> - Company-specific power purchase data and generator-specific emission rate for purchased power | - National average data on upstream emissions (e.g., from life cycle inventory database) <br> - National average T&D loss rate <br> - National average power purchase data |
| 4. Upstream transportation and distribution | - Activity-specific energy use or emissions data from third-party transportation and distribution suppliers <br> - Actual distance traveled <br> - Carrier-specific emission factors | - Estimated distance traveled by mode based on industry-average data |
| 5. Waste generated in operations | - Site-specific emissions data from waste management companies <br> - Company-specific metric tons of waste generated <br> - Company-specific emission factors | - Estimated metric tons of waste generated based on industry-average data <br> - Industry average emission factors |
| 6. Business travel | - Activity-specific data from transportation suppliers (e.g., airlines) <br> - Carrier-specific emission factors | - Estimated distance traveled based on industry-average data |
| 7. Employee commuting | - Specific distance traveled and mode of transport collected from employees | - Estimated distance traveled based on industry-average data |
| 8. Upstream leased assets | - Site-specific energy use data collected by utility bills or meters | - Estimated emissions based on industry-average data (e.g., energy use per floor space by building type) |
| Downstream scope 3 emissions | ||
| 9. Downstream transportation and distribution of sold products | - Activity-specific energy use or emissions data from third-party transportation and distribution partners <br> - Activity-specific distance traveled <br> - Company-specific emission factors (e.g., per metric ton-km) | - Estimated distance traveled based on industry-average data <br> - National average emission factors |
| 10. Processing of sold products | - Site-specific energy use or emissions from downstream value chain partners | - Estimated energy use based on industry-average data |
| 11. Use of sold products | - Specific data collected from consumers | - Estimated energy use based on national average statistics on product use |
| 12. End-of-life treatment of sold products | - Specific data collected from consumers on disposal rates <br> - Specific data collected from waste management providers on emissions rates or energy use | - Estimated disposal rates based on national average statistics <br> - Estimated emissions or energy use based on national average statistics |
| 13. Downstream leased assets | - Site-specific energy use data collected by utility bills or meters | - Estimated emissions based on industry-average data (e.g., energy use per floor space by building type) |
| 14. Franchises | - Site-specific energy use data collected by utility bills or meters | - Estimated emissions based on industry-average data (e.g., energy use per floor space by building type) |
| 15. Investments | - Site-specific energy use or emissions data | - Estimated emissions based on industry-average data |
7.3 Guidance for selecting data
The quality of the scope 3 inventory depends on the quality of the data used to calculate emissions. Companies should collect data of sufficient quality to ensure that the inventory appropriately reflects the GHG emissions of the company, supports the company’s goals, and serves the decision- making needs of users, both internal and external to the company. After prioritizing scope 3 activities (see section 7.1), companies should select data based on the following:
- The company’s business goals (see chapter 2)
- The relative significance of scope 3 activities (see section 7.1)
- The availability of primary and secondary data
- The quality of available data
Companies may use any combination of primary and secondary data to calculate scope 3 emissions. See table 7.5 for a list of advantages and disadvantages of primary data and secondary data.
In general, companies should collect high quality, primary data for high priority activities (see section 7.1). To most effectively track performance, companies should use primary data collected from suppliers and other value chain partners for scope 3 activities targeted for achieving GHG reductions.
In some cases, primary data may not be available or may not be of sufficient quality. In such cases, secondary data may be of higher quality than the available primary data for a given activity. Data selection depends on business goals. If the company’s main goal is to set GHG reduction targets, track performance from specific operations within the value chain, or engage suppliers, the company should select primary data. If the company’s main goal is to understand the relative magnitude of various scope 3 activities, identify hot spots, and prioritize efforts in primary data collection, the company should select secondary data. In general, companies should collect secondary data for:
- Activities not prioritized based on initial estimation methods or other criteria (see section 7.1)
- Activities for which primary data is not available (e.g., where a value chain partner is unable to provide data)
- Activities for which the quality of secondary data is higher than primary data (e.g., when a value chain partner is unable to provide data of sufficient quality)1
Companies are required to report a description of the types and sources of data (including activity data, emission factors, and GWP values) used to calculate emissions, and the percentage of emissions calculated using data obtained from suppliers or other value chain partners (see chapter 11).
Data quality
Sources of primary data and secondary data can vary in quality. When selecting data sources, companies should use the data quality indicators in table 7.6 as a guide to obtaining the highest quality data available for a given emissions activity. The data quality indicators describe the representativeness of data (in terms of technology, time, and geography) and the quality of data measurements (i.e., completeness and reliability of data).
Companies should select data that are the most representative in terms of technology, time, and geography; most complete; and most reliable. Companies should determine the most useful method for applying the data quality indicators when selecting data and evaluating data quality. One example of applying the data quality indicators is presented in box 7.2.
To ensure transparency and avoid misinterpretation of data, companies are required to report a description of the data quality of reported emissions data (see chapter 11).
Because scope 3 emissions are emissions from activities not under the reporting company’s ownership or control, companies are likely to face additional challenges related to collecting data and ensuring data quality for scope 3 than for activities under the reporting company’s ownership or control. Scope 3 data collection challenges include:
- Reliance on value chain partners to provide data
- Lesser degree of influence over data collection and management practices
- Lesser degree of knowledge about data types, data sources, and data quality
- Broader need for secondary data
- Broader need for assumptions and modeling
These data collection challenges contribute to uncertainty in scope 3 accounting. Higher uncertainty for scope 3 calculations is acceptable as long as the data quality of the inventory is sufficient to support the company’s goals and ensures that the scope 3 inventory is relevant (i.e., the inventory appropriately reflects the GHG emissions of the company, and serves the decision-making needs of users, both internal and external to the company).
For example, companies may seek to ensure that data quality is sufficient to understand the relative magnitude of scope 3 activities across the value chain and to enable consistent tracking of scope 3 emissions over time. See Appendix B for more information on uncertainty.
To facilitate quality assurance and quality control when collecting data, companies should develop a data management plan that documents the GHG inventory process and the internal quality assurance and quality control (QA/QC) procedures in place to enable the preparation of the inventory from its inception through final reporting. For more information, see Appendix C.
Companies should select data that are the most representative in terms of technology, time, and geography; most complete; and most reliable.
Table [7.5] Advantages and disadvantages of primary data and secondary data:
| Data Type | Primary data (e.g., supplier-specific data) | Secondary data (e.g., industry-average data) |
|---|---|---|
| Advantages | - Provides better representation of the company’s specific value chain activities <br> - Enables performance tracking and benchmarking of individual value chain partners by allowing companies to track operational changes from actions taken to reduce emissions at individual facilities/companies and to distinguish between suppliers in the same sector based on GHG performance <br> - Expands GHG awareness, transparency, and management throughout the supply chain to the companies that have direct control over emissions <br> - Allows companies to better track progress toward GHG reduction targets (see chapter 9) | - Allows companies to calculate emissions when primary data is unavailable or of insufficient quality <br> - Can be useful for accounting for emissions from minor activities <br> - Can be more cost-effective and easier to collect <br> - Allows companies to more readily understand the relative magnitude of various scope 3 activities, identify hot spots, and prioritize efforts in primary data collection, supplier engagement, and GHG reduction efforts |
| Disadvantages | - May be costly <br> - May be difficult to determine or verify the source and quality of data supplied by value chain partners | - Data may not be representative of the company’s specific activities <br> - Does not reflect operational changes undertaken by value chain partners to reduce emissions <br> - Could be difficult to quantify GHG reductions from actions taken by specific facilities or value chain partners <br> - May limit the ability to track progress toward GHG reduction targets (see chapter 9) |
Table [7.6] Data quality indicators:
| Indicator | Description |
|---|---|
| Technological representativeness | The degree to which the data set reflects the actual technology(ies) used |
| Temporal representativeness | The degree to which the data set reflects the actual time (e.g., year) or age of the activity |
| Geographical representativeness | The degree to which the data set reflects the actual geographic location of the activity (e.g., country or site) |
| Completeness | The degree to which the data is statistically representative of the relevant activity. Completeness includes the percentage of locations for which data is available and used out of the total number that relate to a specific activity. Completeness also addresses seasonal and other normal fluctuations in data. |
| Reliability | The degree to which the sources, data collection methods, and verification procedures used to obtain the data are dependable. |
Box [7.2] Example of criteria to evaluate the data quality indicators
A qualitative approach to data quality assessment uses rating descriptions for each of the data quality indicators on direct emissions data, activity data, and emission factors as applicable. This rating system has elements of subjectivity. For example, some fuel emission factors have not changed significantly in many years. Therefore, a fuel emission factor that is over 10 years old, which would be assigned a temporal score of poor with the data quality in the table below, may not be different than a factor less than 6 years old (a temporal rating of good). Companies should consider the individual circumstances of the data when using the data quality results as a basis for collecting new data or evaluating data quality.
Representativeness to the activity in terms of:
| Score | Technology | Time | Geography | Completeness | Reliability |
|---|---|---|---|---|---|
| Very Good | Data generated using the same technology | Data with less than 3 years of difference | Data from the same area | Data from all relevant sites over an adequate time period to even out normal fluctuations | Verified data based on measurements |
| Good | Data generated using a similar but different technology | Data with less than 6 years of difference | Data from a similar area | Data from more than 50 percent of sites for an adequate time period to even out normal fluctuations | Verified data partly based on assumptions or non-verified data based on measurements |
| Fair | Data generated using a different technology | Data with less than 10 years of difference | Data from a different area | Data from less than 50 percent of sites for an adequate time period to even out normal fluctuations or more than 50 percent of sites but for a shorter time period | Non-verified data partly based on assumptions, or a qualified estimate (e.g., by a sector expert) |
| Poor | Data where technology is unknown | Data with more than 10 years of difference or the age of the data are unknown | Data from an area that is unknown | Data from less than 50 percent of sites for shorter time period or representativeness is unknown | Non-qualified estimate |
7.4 Guidance for collecting primary data
Primary activity data may be obtained through meter readings, purchase records, utility bills, engineering models, direct monitoring, mass balance, stoichiometry, or other methods for obtaining data from specific activities in the company’s value chain.
Where possible, companies should collect energy or emissions data from suppliers and other value chain partners in order to obtain site-specific data for priority scope 3 categories and activities. To do so, companies should identify relevant suppliers from which to seek GHG data. Suppliers may include contract manufacturers,
materials and parts suppliers, capital equipment suppliers, fuel suppliers, third party logistics providers, waste management companies, and other companies that provide goods and services to the reporting company.
Companies should first engage relevant tier 1 suppliers (see figure 7.3). Tier 1 suppliers are companies with which the reporting company has a purchase order for goods or services (e.g., materials, parts, components, etc.). Tier 1 suppliers have contractual obligations with the reporting company, providing the leverage needed to request GHG inventory data.
To be comprehensive, companies may seek to obtain GHG emissions data from all tier 1 suppliers. However, a company may have many small tier 1 suppliers that together comprise only a small share of a company’s total activities and spending. Companies may develop their own policy for selecting relevant suppliers to target for primary data collection. For example, a company may select suppliers based on their contribution to its total spend (see box 7.3). A company may also seek data from tier 2 suppliers, where relevant (see box 7.5). Tier 2 suppliers are companies with which tier 1 suppliers have a purchase order for goods and services (see figure 7.3). Companies should use secondary data to calculate emissions from activities where supplier- specific data is not collected or is incomplete.
Companies are required to report the percentage of emissions calculated using data obtained from suppliers or other value chain partners (see chapter 11).
It is unlikely that all of a company’s relevant suppliers will be able to provide GHG inventory data to the company. (See table 7.8 for a list of challenges and guidance for collecting primary data from suppliers.) In such cases, companies should encourage suppliers to develop GHG inventories in the future and may communicate their efforts to encourage more suppliers to provide GHG emissions data in the public report. After selecting relevant suppliers, companies should determine the type and level of data to request from suppliers.
Type of data
The type of data that should be collected varies by scope 3 category. For example, companies may send questionnaires to each relevant supplier or other value chain partner requesting the following items:
- Product life cycle GHG emissions data following the GHG Protocol Product Standard
- Scope 1 and scope 2 emissions data5 for the reporting year6 following the GHG Protocol Corporate Standard and according to the hierarchy provided in table 7.7
- The supplier’s upstream scope 3 emissions and/or the types of activities that occur upstream of the supplier (if applicable)
- A description of the methodologies used to quantify emissions and a description of the data sources used (including emission factors and GWP values)7
- The method(s) the supplier used to allocate emissions, or information the reporting company would need to allocate emissions (see chapter 8)
- Whether the data has been assured/verified, and if so, the type of assurance achieved
- Any other relevant information
For more information on types of data to collect by scope 3 category, see the GHG Protocol Guidance for Calculating Scope 3 Emissions, available at www.ghgprotocol.org.
Level of data
Activity data and emissions data may be collected at varying levels of detail and granularity. When collecting primary data from value chain partners, companies should obtain the most product-specific data available (see table 7.7).
Product-level data is more precise because it relates to the specific good or service purchased by the reporting company and avoids the need for allocation (see chapter 8).
In general, companies should seek activity data or emissions data from suppliers that is as specific as possible to the product purchased from the supplier, following the hierarchy in table 7.7. If product-level data is not available, suppliers should try to provide data at the activity-, process-, or production line-level. If activity-level data is not available, suppliers should try to provide data at the facility level, and so on. Collecting more granular data is especially important from diversified suppliers that produce a wide variety of products (see box 7.4). Data collected at the activity, production line, facility, business unit, or corporate level may require allocation. (For guidance, see chapter 8.)
For more guidance on collecting primary data from suppliers, see Guidance for Collecting Data from Suppliers, available at www.ghgprotocol.org.
Quality of supplier data
The quality of supplier data may vary widely and be difficult to determine. Suppliers should use the data- quality indicators in section 7.3 to select data that are most representative of their activities in terms of
technology, time, and geography, and that are the most complete and reliable. Reporting companies should use the data-quality indicators to assess the quality of suppliers’ data. To do so, companies should request that suppliers provide supporting documentation to explain their methodology and the sources and quality of data used. Companies may request that suppliers perform first party or third party assurance of their data to ensure its accuracy and completeness (see chapter 10).
See table 7.8 for a list of challenges and guidance for collecting primary data from suppliers.
Table [7.7]: Levels of data (ranked in order of specificity):
| Data type | Description |
|---|---|
| Product-level data | Cradle-to-gate GHG emissions for the product of interest |
| Activity-, process-, or production line-level data | GHG emissions and/or activity data for the activities, processes, or production lines that produce the product of interest |
| Facility-level data | GHG emissions and/or activity data for the facilities or operations that produce the product of interest |
| Business unit-level data | GHG emissions and/or activity data for the business units that produce the product of interest |
| Corporate-level data | GHG emissions and/or activity data for the entire corporation |
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Table [7.8]: Challenges and guidance for collecting primary data from value chain partners:
| Challenges | Guidance |
|---|---|
| Large number of suppliers | - Target most relevant suppliers based on spend and/or anticipated emissions impact <br> - Target suppliers where the reporting company has a higher degree of influence (e.g., contract manufacturers or suppliers where the reporting company accounts for a significant share of the supplier’s total sales) |
| Lack of supplier knowledge and experience with GHG inventories and accounting | - Target suppliers with prior experience developing GHG inventories <br> - Identify the correct subject-matter expert at the company <br> - Explain the business value of investing in GHG accounting and management <br> - Request data suppliers already have collected, such as energy-use data, rather than emissions data <br> - Provide clear instructions and guidance with the data request <br> - Provide training, support, and follow-up |
| Lack of supplier capacity and resources for tracking data | - Make the data request as simple as possible <br> - Use a simple, user-friendly, standardized data template or questionnaire <br> - Provide a clear list of data required and where to find data (e.g., utility bills) <br> - Use an automated online data collection system to streamline data entry <br> - Consider use of a third-party database to collect data <br> - Engage and leverage resources from suppliers’ trade associations <br> - Coordinate GHG data request with other requests <br> - Follow up with suppliers |
| Lack of transparency in the quality of supplier data | - Request documentation on methodology and data sources used, inclusions, exclusions, assumptions, etc. <br> - Minimize errors by requesting activity data (e.g., kWh electricity used, kg of fuels used) and calculating GHG emissions separately <br> - Consider third-party assurance |
| Confidentiality concerns of suppliers | - Protect suppliers’ confidential and proprietary information (e.g., through nondisclosure agreements, firewalls, etc.) <br> - Ask suppliers to obtain third-party assurance rather than submitting detailed activity data to avoid providing confidential information |
| Language barriers | - Translate the questionnaire and communications into local languages |
7.5 Guidance for collecting secondary data and filling data gaps Collecting secondary data
When using secondary databases, companies should prioritize databases and publications that are internationally recognized, provided by national governments, or peer-reviewed. Companies should use the data-quality indicators in section 7.3 when selecting secondary data sources. The data-quality indicators should be used to select secondary data that are the most representative to the company’s activities in terms of technology, time, and geography, and that are the most complete and reliable. A list of available secondary data sources is available at www.ghgprotocol.org.
Using proxy data to fill data gaps
Companies should use the guidance in section 7.3 to assess the quality of available data. If data of sufficient quality are not available, companies may use proxy data to fill data gaps. Proxy data is data from a similar activity that is used as a stand-in for the given activity. Proxy data can be extrapolated, scaled up, or customized to be more representative of the given activity (e.g., partial data for an activity that is extrapolated or scaled up to represent 100 percent of the activity).
Examples of proxy data include:
- An emission factor exists for electricity in Ukraine, but not for Moldova. A company uses the electricity emission factor from Ukraine as a proxy for electricity in Moldova.
- A company collects data for 80 percent of its production for a given product category, but 20 percent is unknown. The company assumes the unknown 20 percent has similar characteristics to
7.6 Improving data quality over time
Collecting data, assessing data quality, and improving data quality is an iterative process. Companies should first apply data quality indicators and assess data quality when selecting data sources (see section 7.3), then review the quality of data used in the inventory after data has been collected, using the same data quality assessment approach. In the initial years of scope 3 data collection, companies may need to use data of relatively low quality due to limited data availability. Over time, companies should seek to improve the data quality of the inventory by replacing lower quality data with higher quality data as it becomes available. In particular, companies should prioritize data quality improvement for activities that have the following:
- Relatively low data quality (based on the data quality guidance in section 7.3)
- Relatively high emissions
Companies are required to provide a description of the data quality of reported scope 3 emissions data to ensure transparency and avoid misinterpretation of data (see chapter 11). Refer to section 7.3 for guidance on describing data quality; Appendix B for guidance on uncertainty; and section 9.3 for guidance on recalculating base year emissions when making significant improvements in data quality over time.