Helping you understand how our initial estimate of emissions is created
Introduction
The Pathzero Predictive Model enables a projection of your company's or asset’s total emissions based on the industry, region and financial data. The high-level initial estimate is representative of a PCAF-aligned data quality score of 4 or 5. This serves as a starting place for an emissions assessment and can highlight potential emission hotspots by GHG category utilising industry average data and research.
When is Pathzero Predictive Model used
When actual GHG emissions for a specific company (company A) are not available, they can be estimated by reference to industry benchmarks in a specific region / country. In summary, this means that “typical” average emissions for a sector will be scaled appropriately and deemed to be a suitable estimate for company A, until more specific data can be obtained.
When the Pathzero Predictive Model is used as the main data sources to calculate financed emissions, the best possible result will be a PCAF 4 or 5 score.
Required Inputs
The Predictive Model requires the input of the following basic business details:
- Primary country of operation,
- Primary sector of operations, and
- Annual revenue
Additionally, you have the option to provide:
- Number of Full-Time Employees (FTE): This is used to calculate emission intensity per FTE. While it's optional, it can provide more detailed insights into your emissions profile.
How Pathzero Predictive Model works
Creating absolute emissions per asset
The model utilises environmentally extended input-output (EEIO) emissions factors sourced from Exiobase to calculate the initial high-level estimate of a company’s absolute emissions and scope 1, 2 and 3 emissions. These are derived from extensive research and data analysis which calculates greenhouse gas (GHG) emissions from the upstream supply chain activities of different economic sectors per region using industry averages.
EEIO is a tool developed by economists to infer environmental impact indicators, in this case, GHG emissions from financial indicators. It helps bridge the gap between financial information, usually readily available, and GHG emissions, less frequently disclosed.
EEIO data is produced using national or regional economic and environmental information collected or estimated at the industry level. This information is aggregated and averaged to produce a standard GHG emission per million of currency of economic output for each industry. To simplify:
- Assuming company A belongs to sector X
- Assuming sector X economic output (or cumulative revenue for companies of this sector) is $100M
- Assuming sector X emissions for year 20YY are 200 tCO₂e
- Then emissions per $M revenue for companies in sector X are 2 tCO₂e/$M
- Assuming company A’s revenue in year 20YY is $10M, then inferred emissions for company A for that year are 20 tCO₂e
EEIO estimates are the same across companies categorised within this EEIO sector in a given country / region. The specific EEIO data used by Pathzero Navigator is produced by Exiobase, as recommended by the Partnership for Carbon Accounting Financials (PCAF).
Pathzero uses the emissions factors from Exiobase, which represent a combined Scope 1, 2 and an upstream Scope 3 emissions factor, and then applies an "uplift factor" to account for downstream Scope 3 emissions not included in the factors provided by Exiobase. This is based on Pathzero's proprietary research into the emissions impact per sector. This uplift factor is based on the EEIO sector a company is assigned to, and ensures that all PCAF 4 and 5 estimates are conservative.
Allocation of emissions per category
Pathzero finally overlays assumption-based calculations to allocate emissions across the 21-emissions categories as defined by the GHG Protocol. This research is based on a combination of analysis from databases (such as Refinitiv) and published emissions sources. Additionally, Pathzero uniquely enables the continual improvement of category split estimates through the anonymised publishing of emissions data on platform. Therefore as the network grows, so too does the emissions estimation calculations.
This category split provides a detailed breakdown of the attributable source of your emissions. This enables investors to identify the potential focus points for emission reductions.
Considerations of the Model
The Pathzero Predictive Model is a useful tool for enabling an initial estimate for an asset. The methodology is compliant for PCAF 5 or 4 reporting requirements, however as it is an estimation methodology it may not generate an emissions output in line with measured actuals. Notably considerations when utilising the model include:
- The generated outputs are based on industry average data, therefore there is little ability to account for sector variability. This requires than an asset is perfectly representative of the broader sector. Therefore if a company engages in the production of luxury wagyu beef rather than generic cattle farming, this may not be captured.
- Depending on the region of operation; data availability and accuracy (for financial and greenhouse gas reporting) varies . There may be some sectors/ regions combinations which have limited data availability which creates inherent limitations.
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The overall results are averaged, i.e. all companies categorised in the same sector are allocated the same emissions intensity per million of revenue (or asset).
Pathzero is consistently working to update these calculations as new research becomes available or as additional data is published to the platform.
Overall this is an industry-standard emissions estimate based on limited input fields per asset. Notably, this can generally be achieved with financial data that an investment manager typically has to hand. This means an initial estimate can be achieved immediately without having to engage the investee, provided the information is available. Over time it is anticipated that emissions data is measured and these should replace the estimations.
If you need more help, please contact us at support@pathzero.com.