Carbon accounting – data governance

Data governance is a key challenge for businesses looking to create and maintain an accurate view of their carbon footprint.

As a new field and responsibility for most businesses, there are systems that need to be introduced to create a carbon inventory and improve it over time.

This short guide provides some things for you to consider about carbon data collection and governance.

For more information please see the Greenhouse Gas Protocol corporate accounting and reporting standard.

Establishing a data collection protocol

To build a carbon footprint inventory, different types of information need to be collected and stored. A data collection protocol stores information about:

data to be collected including units
footprint calculation approach – activity based, spend based, SAAS based, actual data, estimation
emission factor source
frequency of data collection
person responsible for collecting data
data sources
assumptions
some recommendations about data improvement for each category of emissions.

Any organisation can follow steps to develop an overall carbon inventory quality management system.

This diagram from the Greenhouse Gas Protocol outlines the high-level steps to introduce a quality management system and ensure your data is accurate, including feedback loops to keep it up to date.

Inventory quality management system. Source: GHG Protocol

Generic quality management measures can include the check points outlined below.

Data gathering, input, and handling activities: Check a sample of input data for transcription errors; Identify spreadsheet modifications that could provide additional controls or checks on quality; Ensure that adequate version control procedures for electronic files have been implemented; Others
Data documentation: Confirm that bibliographical data references are included in spreadsheets for all primary data; Check that copies of cited references have been archived; Check that assumptions and criteria for selection of boundaries, base years, methods, activity data, emission factors, and other parameters are documented; Check that changes in data or methodology are documented; Others
Calculating emissions and checking calculations: Check whether emissions units, parameters, and conversion factors are appropriately labelled; Check if units are properly labelled and correctly carried through from beginning to end of calculations; Check that conversion factors are correct; Check that data processing steps (for example, equations) in the spreadsheets; Check that spreadsheet input data and calculated data are clearly differentiated; Check a representative sample of calculations, by hand or electronically; Check some calculations with abbreviated calculations (back of the envelope calculations); Check the aggregation of data across source categories, business units, and so on; Check consistency of time series inputs and calculations; Others

Data types

Based on the GHG Protocol there are 2 types of data from which you can choose to calculate scope 3 emissions.

1. Primary data:  data from specific activities within a company’s value chain

Provided by suppliers or other value chain partners related to specific activities in your company’s value chain.

2. Secondary data:  activities outside a company’s value chain

Secondary data includes industry-average data, for example, from published databases, government statistics, literature studies and industry associations. It also includes financial data, proxy data and other generic data. In some instances, you can use data from one activity in your value chain to estimate emissions for another activity. This type of data – proxy data – is considered secondary data because it’s not specific to the activity whose emissions are being calculated.

Here are some examples of primary and secondary data by scope 3 categories.

Upstream scope 3 emissions

Category	Examples of primary data	Examples of secondary data
Purchased good and services	Product level cradle-to-gate GHG data from suppliers calculated using site specific data Site specific energy use of emissions data from suppliers	Industry average emissions factors per material consumed from life cycle inventory databases
Capital goods	Product level cradle-to-gate GHG data from suppliers calculated using sire-specific data Site specific energy use or emissions data from capital good suppliers	Industry average emission factors per material consumer form life cycle inventory databases
Fuel and energy related activities (not included in scope 1 or scope 2)	Company specific data on upstream emissions (for example, extraction of fuels) Grid specific T&D loss rate Company specific power purchase data and generator specific emissions rate for purchased power	National average data on upstream emissions (for example, from life cycle inventory database) National average T&D loss rate National average power purchase data
Upstream transportation and distribution	Activity specific energy use of emissions data from third-party transportation and distribution suppliers Actual distance travelled Carrier specific emissions factors	Estimated distance travelled by mode based on industry average data
Waste generated in operations	Site specific emissions data from waste management companies Company specific metric tons of waste generated Company specific emissions factors	Estimated metric tons of waste generated based on industry average data Industry average emissions factors
Business travel	Activity specific data from transportation supplies (for example, airlines) Carrier specific emission factors	Estimated distance travelled based on industry average data
Employee commuting	Specific distance travelled and mode of transport collected from employees	Estimated distance travelled based on industry average data
Upstream leased assets	Site specific energy use data collected by utility bills or meters	Estimated emissions based on industry average data (for example, energy use per floor space by building type)

Using business travel as an example, you should have access to primary data from a travel agent or through collecting data from your employees about the trips they’ve taken. Secondary data, which uses the industry-average in Australia, could be used if flights hadn’t been tracked.

There are advantages and disadvantages of primary and secondary data to consider.

Advantages and disadvantages of primary and secondary data

	Primary data (for example, supplier specific data)	Secondary data (for example, industry average data)
Advantages	Provide better representation of the company’s specific value chain activities 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 Expands GHG awareness, transparency, and management throughout the supply chain to the companies that have direct control over emissions Allows companies to better track progress toward GHG reduction targets	Allows companies to calculate emissions when primary data is unavailable or of insufficient quality Can be useful for accounting for emissions from minor activities Can be more cost effective and easier to collect Allows companies to more readily understand the relative magnitude of various scope 3 activities, identity hot spots, and prioritise efforts in primary data collection, supplier engagement and GHG reduction efforts
Disadvantages	May be costly 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 Does not reflect operational changes undertaken by value chain partners to reduce emissions Could be difficult to quantify GHG reductions from actions taken by specific facilities or value chain partners May limit the ability to track progress towards GHG reduction targets

Similarly, there are also challenges when collaborating with value chain partners to collect primary data.

Challenges when collaborating with value chain partners to collect primary data

Challenges	Examples of primary data
Large number of suppliers	Targets most relevant suppliers based on spend and/or anticipated emissions impact Targets suppliers where the reporting company has a higher degree of influence (for example, contract manufactures or suppliers where the reporting company accounts for a significant share of the supplier’s sales)
Lack of supplier knowledge and experience with GBG inventories and accounting	Target suppliers with prior experience developing GHG inventories Identify the correct subject matter expert at the company Explain the business value of investing in GHG accounting and management Request data suppliers already have collected, such as energy use data, rather than emissions data Provide clear instructions and guidance with the data request Provide training, support, and follow up
Lack of supplier capacity and resources for tracking data	Make the data request as simple as possible Use a simple, user friendly, standardised data template or questionnaire Provide a clear list of data required and where to find data (for example, utility bills) Use an automated online data collection system to streamline data entry Consider use of third party database to collect data Engage and leverage resources from suppliers’ trades associations Coordinate GHG data request with other requests Follow up with suppliers
Lack of transparency in the quality of supplier data	Request documentation on methodology and data sources used, inclusions, exclusions and assumptions Minimise errors by requesting activity data (for example, kWh electricity used, kg of fuels used) and calculating GHG emissions separately Consider third part assurance
Confidentiality concerns of suppliers	Protect suppliers’ confidential and proprietary information (for example, through nondisclosure agreements and firewalls) 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

When collecting secondary data, companies need to assess data quality and consider the best approach to fill any data gaps.

Prioritise databases and publications that are internationally recognised, provided by national governments or peer reviewed.
If quality data is unavailable, consider using proxy data to fill the 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 customised to be more representative of the given activity, for example, partial data for an activity extrapolated or scaled up to represent 100% of the activity.

Examples of proxy data:

An emissions factor exists for electricity in Ukraine, but not for Moldova. A company uses the electricity emissions factor from Ukraine as a proxy for electricity in Moldova.
A company collects data for 80% of its production in a given product category but 20% is unknown. The company assumes the unknown 20% has similar characteristics to the known 80% so applies a linear extrapolation to estimate 100% of the production data.

Overall, remember the importance of gradually improving your data quality over time.

Better data leads to better insights, decisions and results.

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