A product carbon footprint (PCF) is calculated from information about a product that represents activities (e.g., manufacturing, etc.) and emissions factors (EFs) for these activities. The reliability of the PCF requires an assessment of both the activity data and the EFs for relevance and overall quality. HowGood serves its customers PCFs over a wide range of known activity data, from knowing only a product name or UPC all the way down to customers with primary data from suppliers as well as their own operations. Because of this wide range, HowGood cannot assess the activity data for a customer but we do provide an EF quality assessment for each PCF. This, along with a company’s internal activity quality assessment can give a comprehensive data quality assessment of the PCF. Throughout this document, DQS (data quality score) refers to the data quality of the EFs only. No assessment of the activities is included in this rating.

The DQS translates to the Data Quality Rating for each life cycle stage and for PCF rollups, such as cradle-to-manufacturing-gate, based on the following thresholds:

The research team has assessed the emissions factor sources in our database against the criteria in this document. Individual life cycle stage scoring criteria are not on a 100 point scale (see sections below). Each of these is normalized to a 100 point scale prior to calculation in the DQS.

With over 600 data sources in the database, and many sources contributing to a final PCF, we aggregate the source quality based on the EF’s contribution to the final product.

The data quality rating can be used to:

For each life cycle stage, the EF data quality score is computed as a weighted average of each underlying EF’s data quality score and the materiality of that EF to the lifecycle stage. The final EF data quality score is the summation of the EF DQS of each lifecycle stage weighted by the materiality of that stage. See Figure 1 for product life cycle stages (cradle-to-grave isn’t currently offered to customers for use as a public facing PCF and DQS scoring ends with cradle-to-shelf).

Figure 1: HowGood Product Life Cycle Stages

Each life cycle stage DQS criteria and formula are detailed below. The DQS value in each stage’s formula represents the sum of selected option scores, one option per criteria, relevant to the life cycle stage and normalized to a 0-100 scale.

Table 1: Land Management Scoring Criteria

Formula:

For each emissions factor (EF) contributing to the product’s Land Management EF and the DQS of the source used to derive the EF,

Table 2: sLUC Scoring Criteria

Formula:

For each emissions factor (EF) contributing to the product’s sLUC EF and the DQS of the source used to derive the EF,

Note: Due to transportation having an overall small contribution to the final PCF and lack of data granularity for this stage of a product’s life, a single DQS is used for all transportation stages. The score was determined by the criteria below.

Table 3: Transportation Scoring Criteria

Formula:

HowGood calculates processing emissions based on the energy and likely fuel(s) used in the various processes. Because we use this two-pronged approach to calculating emissions during processing, we also used a two-pronged approach to calculating the DQS.

The data quality of the energy required for a given process was scored according to the processing type assessment below. The data quality of the fuels used in the processing steps were scored against the Fuel Assessment criteria below. These two values are combined to give the final DQS along with the EFs themselves.

We take a conservative approach to this DQS. Because the final EF is only as good as the assumptions beneath it, the fuel score acts as a penalty on the processing score. For example, if the fuel score is 80 and the processing type DQS is 67, the final score will be 80/100*67 = 54.

Scoring Criteria:

Table 4: Processing Scoring Criteria

Fuel Assessment:

Each Fuel was assessed against the following criteria. However, when we assessed the prevalence of each fuel within our database we noticed that natural gas and grid energy makes up the majority of energy in our database (see table below).

Table 5: Various fuel contributions to total processing energies

This, along with the fact that non-biogenic emissions from biomass fuel account for only 11% of emissions from biomass, we use a combined fuel score based on a conservative grid fuel DQS and the natural gas DQS only for the Upstream Processing data quality rating.

Table 6: Fuel Scoring Criteria

Formula:

Upstream Processing EF can include the manufacturing emissions if intermediate products are present in the final product. The DQS of the intermediate manufacturing is included in this case by the following formula:

For more details on how the Manufacturing DQS is derived, see the section on Product Manufacturing.

Scoring Criteria:

See Transportation to Processing Criteria

Formula:

Similar to Upstream Processing, we assess both the manufacturing source(s) and the fuel(s) source(s) for Manufacturing. However, all fuels are assessed and used in the DQS calculation for manufacturing. See 2.4 for more information.

Scoring Criteria:

Table 7: Manufacturing Scoring Criteria

Table 8: Fuel Scoring Criteria

Formula:

The manufacturing DQS is based on the fuel(s) score(s) of the fuel(s) used in the manufacturing type and the score of the manufacturing type itself.

Table 9: Packaging Scoring Criteria

Formula:

Scoring Criteria:

See Transportation to Processing Criteria

Formula:

Table 10: Storage/Distribution Scoring Criteria

Formula:

Scoring Criteria:

See Transportation to Processing Criteria

Formula:

Table 11: Retailer Scoring Criteria

Formula:

Biogenic carbon is accounted for in two main areas:

Table 12: Biogenic Scoring Criteria

Biogenic carbon calculations are rolled up into a single value for 2 system boundaries: cradle to gate and cradle to shelf. As such, we also have 2 formulas for the biogenic DQS.

The final PCF data quality score is each life cycle stage’s DQS weighted by that stage’s contribution to the cradle to manufacturing gate EF.

Formula:

The final PCF data quality score is each life cycle stage’s DQS weighted by that stage’s contribution to the cradle to shelf EF.

Formula: