Calculating Scope 3.1 Emissions with Climatiq’s Procurement Endpoint
Spend-based emission calculations (opens in a new tab) are a common way to estimate emissions for purchased goods and services (GHG Protocol Category 3.1) when detailed activity data is not available. Popular sources of spend-based emission factors include the UK-specific BEIS, the US Environmental Protection Agency (EPA), and EXIOBASE. These sources offer valuable data for calculating carbon footprints, each with unique methodologies to cater to a variety of data availability scenarios and calculation requirements.
These datasets are often derived from large-scale economic models known as Environmentally Extended Input-Output Models (EE IOT) (opens in a new tab). These models account for all upstream emissions associated with a product and capture emissions from resource extraction to production and transportation. However, the methods these sources use to extract emission factors from the IOTs vary and therefore also demand different input expenditure data; for instance, while some models might need basic product prices (EXIOBASE), others may require prices as they appear on retail shelves (BEIS).
The basic price refers to the price of a product at the factory gate. Table 1 summarizes what is included in the basing and in the purchaser price.
Table 1. What Basic and Purchaser prices include
| Sales Tax (deductible, e.g VAT/GST) | Tax (non-deductible) | Trade Margin (Retail, Wholesale) | Freight and insurance charges (invoiced separately) | |
|---|---|---|---|---|
| Basic Price (opens in a new tab) | Excluded | Excluded | Excluded | Excluded |
| Purchaser's Price (opens in a new tab) | Excluded | Included | Included | Included |
[Basic Price] + [Taxes] + [Trade Margin] + [Transportation] = [Purchaser’s Price]
When using emission factors from the following datasets, several key considerations must be accounted for:
- EPA (opens in a new tab) is specific to the US and offers Emission Factors (EFs) derived from the USEEIO model. It provides two sets of emission factors; the first is designed for use with basic prices of products at the factory gate, while the second is compatible with purchaser prices, excluding taxes. At Climatiq, we utilize the latter set of factors.
- BEIS (opens in a new tab) is specific to the UK and provides emission factors intended for use with expenditure data exclusive of Value Added Tax (VAT).
- EXIOBASE (opens in a new tab) is the most commonly used resource for spend-based emission factors. By establishing a relationship between economic activity and emissions rates, it services 200 products across 49 regions. The EFs based on EXIOBASE require the provision of expenditure data in basic prices.
Adjusting your expenditure for inflation is an important aspect to consider when using spend-based emission factors. Detailed EE MRIOT models like EXIOBASE are typically updated every few years, e.g. the current version concluding observed data in 2019. The key to maintaining accuracy in such cases lies in adjusting for inflation. By converting the expenditure values to 2019 prices, you can ensure calculation accuracy by aligning your spend data with the context in which these emission factors were developed. This inflation adjustment guarantees that the spend-based emission calculations remain robust and truthful, even when applied beyond the timeframe of the data model. Refer to the section below for details on the implementation of inflation adjustments in the Procurement endpoint.
Climatiq’s Procurement calculation endpoint: streamline calculations using EXIOBASE
As explained above, and due to the nature of the underlying EXIOBASE model, the emission factors derived from it should be applied in conjunction with spend data provided in basic prices. Practitioners often use EXIOBASE emission factors incorrectly, using the purchaser’s price (total purchase price incl. tax, trade, and transport margins) when making the conversion. This approach leads to an overestimation of calculated footprints, which can sometimes be significant.
To align your calculations to the EXIOBASE model and ensure the coherence and precision of your spend-based carbon footprint assessments, it is crucial to deduct taxes, trade margins, and freight costs from the expenditure sum, followed by an adjustment for currency-based inflation.
Climatiq has developed the Procurement calculation endpoint (opens in a new tab) to eliminate the need for handling these complex calculations, including inflation adjustments and determining basic prices. This endpoint streamlines the calculation of your purchased goods and services carbon footprint (GHG Protocol Category 3.1) by automatically deriving basic (factory-gate) prices, adjusting for currency conversions (based on UN Treasury (opens in a new tab), the IRS, and the World Bank), and correcting for inflation. The endpoint provides the flexibility to provide your own margin rates in the API query, or, if unknown, to default to margins derived from EXIOBASE. This enhances the precision of your spend-based emission assessments, providing a more accurate starting point for your environmental audits.
For the development of the Procurement endpoint, we collaborated with Prof. Richard Wood (opens in a new tab), a key developer of EXIOBASE. Prof. Wood is a member of our scientific advisory board (opens in a new tab), where he provides important guidance for further development of our solutions.
Inflation adjustments
When applying EXIOBASE's spend-based emission factors, it's crucial to adjust for inflation. Aligning expenditure values to the year of the model's data ensures precise calculations, even when dealing with timeframes beyond the model's original scope.
The compound inflation rate, represented as the CIR (Compound Inflation Rate), reflects the cumulative inflation rates over multiple years. To calculate the inflation-adjusted spend amount, the spend should be divided by the CIR for the year following the emission factor's year (e.g., if the spend occurred in 2021 and the factor is from 2019) and then multiplied by the CIR for the year preceding the emission factor's year (e.g., if the spend occurred in 2017 and the factor is from 2019).
Our endpoint uses two sources of inflation data:
- Sector-specific inflation data: available for a number of European countries and obtained from the European Central Bank (opens in a new tab).
- Country-specific inflation data: rates provided by the World Bank (opens in a new tab) for those countries where sector-specific inflation rates were unavailable.
Margin calculations
Climatiq’s Procurement API endpoint supports three calculation scenarios:
Margins unknown:
Where precise margins are not at hand, the API will automatically use the default EXIOBASE margins.
Request
curl --request POST \--url https://api.climatiq.io/procurement/v1/spend \--header "Authorization: Bearer $CLIMATIQ_API_KEY" \--data '{ "activity": { "classification_code": "12", "classification_type": "isic4" }, "spend_year": 2023, "spend_region": "DE", "money": 100, "money_unit": "eur"}'Response
{ "estimate": { "co2e": 4.562, "co2e_unit": "kg", "co2e_calculation_method": "ar5", "co2e_calculation_origin": "source", "emission_factor": { "name": "Tobacco products", "activity_id": "consumer_goods-type_tobacco_products", "id": "919992fc-1373-4fc9-a728-77bcfe2949d9", "access_type": "public", "source": "EXIOBASE", "source_dataset": "EXIOBASE 3", "year": 2019, "region": "DE", "category": "Food/Beverages/Tobacco", "source_lca_activity": "unknown", "data_quality_flags": [] }, "constituent_gases": { "co2e_total": 4.562, "co2e_other": null, "co2": null, "ch4": null, "n2o": null }, "activity_data": { "activity_value": 16.55, "activity_unit": "eur" }, "audit_trail": "enabled" }, "calculation_details": { "tax_margin": 0.6744582649, "trade_margin": 0.1223713536, "transport_margin": 0, "inflation_applied": 0.22778602554608013 }, "notices": [], "source_trail": [ { "data_category": null, "name": "Average tax margins for spend type", "source": "EXIOBASE", "source_dataset": null, "year": null, "region": "DE", "region_name": "Germany" }, { "data_category": null, "name": "Average trade margins for spend type", "source": "EXIOBASE", "source_dataset": null, "year": null, "region": "DE", "region_name": "Germany" }, { "data_category": null, "name": "Average transport margins for spend type", "source": "EXIOBASE", "source_dataset": null, "year": null, "region": "DE", "region_name": "Germany" }, { "data_category": null, "name": "Industry-specific inflation rates", "source": "EUROSTAT", "source_dataset": null, "year": null, "region": "DE", "region_name": "Germany" }, { "data_category": "emission_factor", "name": "Tobacco products", "source": "EXIOBASE", "source_dataset": "EXIOBASE 3", "year": "2019", "region": "DE", "region_name": "Germany" } ]}Actual margins available:
Providing precise margins (trade, tax, and transport) results in the most accurate estimation of spend-based calculations.
Request
curl --request POST \--url https://api.climatiq.io/procurement/v1/spend \--header "Authorization: Bearer $CLIMATIQ_API_KEY" \--data '{ "activity": { "classification_code": "12", "classification_type": "isic4" }, "spend_year": 2023, "spend_region": "DE", "money": 100, "money_unit": "eur", "tax_margin": 0.2, "trade_margin": 0.1, "transport_margin": 0.04}'Response
{ "estimate": { "co2e": 14.82, "co2e_unit": "kg", "co2e_calculation_method": "ar5", "co2e_calculation_origin": "source", "emission_factor": { "name": "Tobacco products", "activity_id": "consumer_goods-type_tobacco_products", "id": "919992fc-1373-4fc9-a728-77bcfe2949d9", "access_type": "public", "source": "EXIOBASE", "source_dataset": "EXIOBASE 3", "year": 2019, "region": "DE", "category": "Food/Beverages/Tobacco", "source_lca_activity": "unknown", "data_quality_flags": [] }, "constituent_gases": { "co2e_total": 14.82, "co2e_other": null, "co2": null, "ch4": null, "n2o": null }, "activity_data": { "activity_value": 53.76, "activity_unit": "eur" }, "audit_trail": "enabled" }, "calculation_details": { "tax_margin": 0.2, "trade_margin": 0.1, "transport_margin": 0.04, "inflation_applied": 0.22778602554608013 }, "notices": [], "source_trail": [ { "data_category": null, "name": "Industry-specific inflation rates", "source": "EUROSTAT", "source_dataset": null, "year": null, "region": "DE", "region_name": "Germany" }, { "data_category": "emission_factor", "name": "Tobacco products", "source": "EXIOBASE", "source_dataset": "EXIOBASE 3", "year": "2019", "region": "DE", "region_name": "Germany" } ]}Actual margins partially available:
Complement your incomplete data with default EXIOBASE margins to fill the gaps.
Request
curl --request POST \--url https://api.climatiq.io/procurement/v1/spend \--header "Authorization: Bearer $CLIMATIQ_API_KEY" \--data '{ "activity": { "classification_code": "12", "classification_type": "isic4" }, "spend_year": 2023, "spend_region": "DE", "money": 100, "money_unit": "eur", "tax_margin": 0.2}'Response
{ "estimate": { "co2e": 15.22, "co2e_unit": "kg", "co2e_calculation_method": "ar5", "co2e_calculation_origin": "source", "emission_factor": { "name": "Tobacco products", "activity_id": "consumer_goods-type_tobacco_products", "id": "919992fc-1373-4fc9-a728-77bcfe2949d9", "access_type": "public", "source": "EXIOBASE", "source_dataset": "EXIOBASE 3", "year": 2019, "region": "DE", "category": "Food/Beverages/Tobacco", "source_lca_activity": "unknown", "data_quality_flags": [] }, "constituent_gases": { "co2e_total": 15.22, "co2e_other": null, "co2": null, "ch4": null, "n2o": null }, "activity_data": { "activity_value": 55.19, "activity_unit": "eur" }, "audit_trail": "enabled" }, "calculation_details": { "tax_margin": 0.2, "trade_margin": 0.1223713536, "transport_margin": 0, "inflation_applied": 0.22778602554608013 }, "notices": [], "source_trail": [ { "data_category": null, "name": "Average trade margins for spend type", "source": "EXIOBASE", "source_dataset": null, "year": null, "region": "DE", "region_name": "Germany" }, { "data_category": null, "name": "Average transport margins for spend type", "source": "EXIOBASE", "source_dataset": null, "year": null, "region": "DE", "region_name": "Germany" }, { "data_category": null, "name": "Industry-specific inflation rates", "source": "EUROSTAT", "source_dataset": null, "year": null, "region": "DE", "region_name": "Germany" }, { "data_category": "emission_factor", "name": "Tobacco products", "source": "EXIOBASE", "source_dataset": "EXIOBASE 3", "year": "2019", "region": "DE", "region_name": "Germany" } ]}EXIOBASE margins are elements of a broad-scale economic model. They were derived from observed statistical data for the year 2007 from official governmental sources of regions. The EXIOBASE margins may, however, carry substantial uncertainties due to inconsistencies in reporting across countries, averaging of data across different industries to match the NACE industry scheme, absence of observed data, and the fact that they represent average margins across the entire economy.
While EXIOBASE margins provide a feasible solution in the absence of known data, the use of actual, specific margins when available is advisable for the most accurate carbon footprint assessments.
In practice, data on margins can often be incomplete or missing. To cater to such scenarios of data availability, our endpoint also offers the flexibility to use a default margin when specific values are unknown, and the option to override the default ones when users have known values at hand.
Note that while trade and transport margins cannot take negative values, tax margins can indeed be negative. This situation typically arises when there are subsidies applied to a specific product.
While analyzing EXIOBASE's margin data, a number of missing values and discrepancies within the data were encountered by our science team, namely margins that were either erroneous or unreflective of actual conditions, and outliers. To rectify missing data and maintain the integrity of our dataset, we filled the gaps using industry-specific averages for the particular region and industry sector associated with the emission factor. Outliers have been handled utilizing the DBSCAN algorithm. However, some residual outliers may be present in the dataset and users are encouraged to inspect the audit trail when in doubt.