Calculating and measuring financed emissions in multiple asset classes simultaneously.
Organisation details
Name: Varma
Signatory type: Asset owner
HQ: Finland
AUM: €64.3 billion
In 2021, Varma set goals to reduce financed emissions by 25%, 50%, and 75% by 2025, 2030, and 2035, respectively.
However, calculating and measuring financed emissions in multiple asset classes simultaneously is not straightforward. In addition, developments in financed emissions estimation methods may create a misleading picture of actual real-world emission reductions, which may be minimal compared to reported portfolio emissions.
In December 2023, the Net Zero Asset Owner Alliance (NZAOA) published guidance on how breaking down emission calculations into different drivers and components can heighten transparency. We followed that guidance and carried out an attribution analysis, and added drivers related to carbon emission estimation that we believe are important to understand the overall picture, especially in alternative investments.
Drivers of financed emissions
According to the NZAOA’s guidance, drivers of portfolio emission changes include:
New investments that have entered the portfolio during the year and that increase the portfolio’s financed emissions, and investments that are sold from the portfolio during the year, reducing the portfolio’s financed emissions.
Drivers of existing portfolio investments:
- Changes in companies’ reported and verified emissions over time;
- Changes inattribution factor, calculated by dividing the investment size by the company’s enterprise value, including cash (EVIC). Changes in the market value or amount of company equity or debt are directly reflected in the calculation of financed emissions through changes in the ownership share. If emissions have been estimated using emission factors, the attribution factor is the absolute size of the investment (that we measure in euros);
- Interactioneffect: where the company’s reported emissions and attribution factor have changed simultaneously. If emissions have been estimated using emission factors, the driver is the simultaneous change in emission factors and the size of the investment;
- Changes in data coverage: if a company starts reporting GHG emissions , this has either a carbon footprint-increasing or reducing effect depending on the emission factor used in the previous emission assessment. Data quality may also worsen and reporting may stop. The NZAOA’s guidance works well where data coverage is already good and estimation plays a limited role. However, our portfolio includes alternative investments, such as hedge funds and infrastructure, where we have to estimate emissions due to insufficient data. For this reason, we expanded the attribution by including estimation drivers:
1. Changes in emission factors: we use PCAF’s emission factors in our calculations, and changes to these factors have an impact on companies’ financed emissions without changes in actual real-world emissions.
2. Changes in emission estimation methods: improvements in these methods have a significant impact on the calculated emissions of an investment without any changes in real-world emissions.
The last two factors should be isolated to better distinguish between portfolio and real-world decarbonisation.
Calculating our financed emissions
We calculate scope 1 and 2 emissions of our investments in accordance with PCAF standards, where the size of the investment is divided by the company’s EVIC and multiplied by the company’s reported emissions. Where reported data is not available, we, or a third party, estimate the GHGs generated by the investee during the reporting year. In real estate funds, emissions are calculated using either the fund’s own reporting or the PCAF emission factor database, where the underlying property is assigned an emission factor based on property type and location. This is multiplied by the total floor area that we own (measured by sqm).
Our calculation of financed emissions currently covers several asset classes (see Table 1 below). Financed emissions for deposits and cash equivalents are assumed to be zero. For sovereign debt, Varma does not report financed emissions figures in conjunction with other asset classes, to avoid double counting. Direct real estate is part of Varma’s own operational emission calculation (scope 2). Varma also reports a weighted average data quality score on a scale of 1-5 in accordance with PCAF, where the best score (1) reflects verified emission figures and the company’s enterprise value, and the worst score (5) equates to calculations based on the size of the investment, industry, and geographical location of the company. Varma’s weighted average data quality score for GHGs in 2024 was 2.96, and the variation between asset classes ranged from 1.32 to 5.0.
Table 1: Asset classes and their financed emissions
| Asset class | Market value | Share of the portfolio | Scope 1-2 financed emissions (tCo2e) | Vs. 2022 | Carbon footprint (tCo2e/€ million invested) | Data quality |
|---|---|---|---|---|---|---|
| Listed equity | 22.9 | 36% | 633 169 | -38% | 27.6 | 1.32 |
| Corporate bonds | 3.8 | 6% | 185 356 | 15% | 48.8 | 2.13 |
| Hedge funds | 13.1 | 20% | 911 354 | 7% | 69.6 | 5.00 |
| Real estate bonds | 2.7 | 4% | 48 862 | -42% | 18.1 | 2.52 |
| Private equity | 9.7 | 15% | 566 746 | -71% | 57.4 | 3.62 |
| Infrastructure | 3.2 | 5% | 352 164 | -60% | 110.1 | 4.51 |
| Private Debt | 2.1 | 3% | 109 269 | -66% | 52.0 | 4.61 |
| Deposits | 0.0 | 0% | - | - | - | - |
| Total | 57.5 | 89% | 2 796 921 | 46% | 48.6 | 2.96 |
Figure 1: Varma’s attribution analysis for financed emissions (2022-2024)
Varma’s financed emissions decreased by 22% and 46% in 2024 compared to 2023 and 2022 respectively, with the total change from 2021 (the year we set targets) decreasing by 52%.
The two-year attribution analysis above shows that the actual real-world reduction in company reported emissions from 2022 was only 6% of the total change. Note that the figure mainly reflects changes in listed investments, as reported data was not widely available for other asset classes in 2022.
A large part of the actual reduction in emissions is also included in the interaction effect, which arises when our ownership share has changed during the year and reported emissions have changed simultaneously. For example, if Varma has purchased more shares in the company, while the company has issued more shares to which Varma didn’t subscribe in proportion to its original ownership share, or the company has taken on more debt during the year, while emissions reported by the company decreased at the same time.
One weakness of using the EVIC figure as a denominator in financed emissions calculations is its temporal delay, where the size of the investment at the end of the year reflects the situation then, but the EVIC is calculated, as recommended, from the end of the previous year. As a result, the attribution factor may be significantly larger or smaller depending on the development of the share price, or the amount of debt issued during the year.
The most significant driver of Varma’s financed emissions for the past two years has been changes in emission estimation. The impact comes from changes in both the emission factors and the estimation methods used. In all asset classes, in investments where emission data is not available, Varma uses the regularly updated PCAF emission factor database. The change in quality of estimation driver is mostly due to developments in disclosure quality in alternative investments. Varma has improved the data and disclosure quality, especially in private equity, by engaging managers and using side letters.
Conclusion
The attribution analysis of financed emissions helps us better understand the portfolio changes and improved transparency provides a better view of real reductions in GHG emissions. from investments, including what part of the change is due to computational factors.
Our analysis is not without flaws. For example, changes resulting from specific corporate events do not appear in the analysis. Another example: real emission reductions are overestimated in a situation where carbon-intensive business is spun off from a GHG emissions reporting parent company during the year. The impact is then only seen as a reduction in the parent company’s reported emissions, even if no real-world changes have necessarily occurred. The opposite happens if a company acquires a business with a significant carbon footprint, that results in an increase in the reported emissions for the parent company.
The data used in the calculation of financed emissions is not perfect, which makes it difficult to estimate the real starting position, let alone where the portfolio will be positioned in a few decades. Reporting financed emissions simultaneously with the data quality score, however, gives a better overall picture of our portfolio’s emissions than not reporting at all. This also helps us – and other investors – to focus their engagement to improve data quality in asset classes where emission reporting has not yet become widespread. We therefore encourage other investors to report emissions transparently and comprehensively from all asset classes, regardless of the weakness in data quality.
This case study aims to contribute to the debate around topical responsible investment issues. It should not be construed as advice, nor relied upon. It is written by a guest contributor. Authors write in their individual capacity – posts do not necessarily represent a PRI view. The inclusion of examples or case studies does not constitute an endorsement by PRI Association or PRI signatories.
