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Primary vs. Secondary Data in LCA and Carbon Footprinting: Understand the key differences 

Emma Valli

Marketing Coordinator

Primary vs. secondary data in carbon footprint calculations

Life Cycle Assessment (LCA) and carbon footprint calculations are tools to evaluate the environmental impact of products, processes, and services. They help you understand the environmental consequences of different operations and guide you towards more sustainable practices. To calculate carbon footprints, you must collect and use data, which can be categorised into primary vs. secondary data.

This article enlightens the differences and helps you to choose primary vs. secondary data in LCA and carbon footprint calculations with examples. Let’s dive in!

Primary Data 

Gathering primary data in the context of LCA and carbon footprinting involves actively conducting specific measurements or surveys to obtain information about a product, process, or activity. Primary data sources include suppliers within the supply chain, monitoring equipment in production facilities, laboratory experiments, and consumers using your products.

Examples of Primary Data: 

  • Measurement of energy consumption from monitoring equipment in a manufacturing facility. 
  • Conducting surveys with raw material suppliers to determine the type of transportation vehicles used and the distance from the supplier to your facility. 
  • Laboratory experiments to determine the emissions generated during a specific chemical reaction. 
  • Conducting customer surveys to determine how consumers use your product. 

👍 Advantages of Primary Data: 

High accuracy, representativeness and relevance
Primary data is specific to the process or activity, which environmental impact is being calculated. Data collected directly from the original sources often represents the activities more accurately than, e.g., statistical data based on averages.  

Calculating emissions with primary data allows you to set specific emission reduction targets and track the process throughout the years.  

The data collection process is under your control, minimizing potential biases.

    👎 Disadvantages of Primary Data: 

    Time and resource-intensity
    Collecting primary data can be time-consuming and costly, especially for extensive studies. 

    Limited availability
    Primary data may not always be readily available, making data collection challenging. 

    Potential for bias
    If you don’t collect it carefully, it can be influenced by human errors.

    Secondary Data 

    On the flip side, secondary data refers to data collected and published by others. Secondary data sources encompass scientific literature, published reports, and databases provided by statistical authorities or other entities. This readily available data serves various purposes, including LCA and carbon footprint assessments. Secondary data finds use in situations where collecting primary data proves impractical or unnecessary.

    Examples of Secondary Data: 

    • Emission factor databases. 
    • Published scientific papers providing data on the carbon footprint of specific materials. 
    • Scientific articles describing the process inputs for a specific manufacturing process. 
    • Industry reports on energy consumption in the manufacturing sector. 

    👍 Advantages of Secondary Data: 

    Secondary data is readily available and can save time and resources compared to collecting primary data. 

    Broad applicability
    You can access a wide range of existing data sources for diverse applications. 

    Good overview
    Utilizing secondary data can be a company’s first step in conducting carbon footprint assessments. Calculations based on secondary data can provide a comprehensive overview of the total emissions of a product or company and help identify the most significant emission sources.

      Primary and secondary data in carbon footprinting

      👎 Disadvantages of Secondary Data: 

      Lack of specificity
      Secondary data may not precisely match the requirements of a specific study, leading to potential inaccuracies. 

      Limited control
      You have limited control over the quality and collection methods of secondary data. 

      Data quality concerns
      The reliability and accuracy of secondary data can vary, and it may be outdated or incomplete.

        Sources of Primary and Secondary Data 

        For primary data, sources may include in-house measurements, surveys, experiments, or data collected specifically for a calculation project. The primary data collection phase should follow rigorous data collection protocols to ensure accuracy and reliability. 

        Secondary data can be provided by multiple different sources, such as government agencies, academic institutions, industry associations, and reputable databases. Some valuable sources of secondary data include: 

        • U.S. Environmental Protection Agency (EPA) – Provides emissions factors, environmental databases, and reports: 
        • International Energy Agency (IEA) – Offers global energy statistics and reports: 
        • Scientific journals and publications – Contain a wealth of research findings on environmental impacts and carbon footprints. 
        • Industry-specific reports and publications – Industry associations often publish data related to their sectors. 
        • Ecoinvent database – Provides data for environmental assessments. 

        Many of these sources are either built-into or used in digital carbon footprint calculators. It makes the calculation process a lot easier.  


        In LCA and carbon footprint assessments, primary and secondary data play both important roles in the calculations. While primary data offers high accuracy and customization, the data collection phase might be resource intensive.

        In contrast, secondary data is often cost-effective and readily available but may lack specificity. Therefore, it is important to carefully select the appropriate data sources and consider the advantages and disadvantages of each option to ensure the desired level of accuracy. Combining both primary and secondary data sources often yields the most robust and informative results in the pursuit of sustainability.

        As a general guideline, primary data should be used when assessing the most emission intensive operations. Alternatively, secondary data can be applied for smaller emission sources. In the contest between primary vs. secondary data, primary data prevails in terms of data accuracy, while secondary data excels in scalability.

        Digital carbon footprint calculators like Biocode have a built-in database containing secondary data emission factors. They also offer the option to incorporate primary data, such as actual production data and information collected from suppliers. Discover more about Biocode’s tools for collecting data from the primary production.

        Practical examples of when to use primary vs. secondary data

        Example case Primary vs. Secondary data
        The emission factor for broiler meat when calculating the carbon of chicken soup. Primary Assumably makes up a substantial portion, e.g., 60-70% in the recipe. It also has a significant emission factor.
        The emission factor for curry powder when calculating the carbon of chicken soup. Secondary Comprises a small portion (e.g., 0.5-1% of the total recipe), a rough estimate is adequate.
        The first Scope 1-3 emissions assessment of a food company with an aim to find the emission hotspots. Secondary Secondary data can help to create an emission overview and to identify significant emissions sources.
        The Scope 1-3 assessment of a food company with an aim to set the SBTi targets. Primary When setting specific targets, it's beneficial to use data from your own operations. This also enables monitoring of your emission reductions.
        The effects of land use changes on the carbon footprint of wheat cultivation. Primary/Secondary Soil type, land use practices, and changes in the land use practices over the past 20 years have an impact on the soil carbon storage. Calculations based on national averages do not reflect the effects of measures taken by individual farmers

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