| ORGANISATION DETAILS | |
|---|---|
| Name | VidaCaixa |
| Signatory type | Asset owner |
| Region of operation | Spain and Portugal |
| Assets under management | €93bn in 2019 |
| COVERED IN THIS CASE STUDY | |
|---|---|
| Asset class | Mainly fixed income including some green bonds (GBs) |
| Geography | Organisation for Economic Cooperation and Development (OECD) |
| Environmental objective | Mitigation |
| Economic activity | All |
VidaCaixa considers the publication of the first part of the taxonomy as a major advance towards increased transparency and homogeneity in terms of the environmental impacts of investment activities. We also believe the technical criteria published by the Technical Expert Group (TEG) is very helpful in determining a common definition of which thresholds in each sector are considered good practice. Implementing the taxonomy with other Principles for Responsible Investment (PRI) members has been very useful to help us identify the main difficulties and challenges the taxonomy may bring about and their potential solutions.
Other aspect you would like to mention?
Our main focus was to apply the technical criteria on a generalist fixed income portfolio (insurance product). We only applied tests on mitigation aspects.
Taxonomy implementation
Principles, criteria, thresholds
Initially, we tried to match the technical criteria from activities identified in the portfolio with data reported by the issuers and/or estimated by ESG data providers. We did not find comparable Key Performance Indicators (KPIs) for most of the criteria. Therefore, we decided to focus on only a small number of activities in the portfolio, such as electricity generation, where there were more KPIs (for example, CO2/kWh), even though they were not sufficiently granular or systematized (for example, most utilities did not have breakdown of carbon intensity by asset).
Our conservative approach meant that the final taxonomy-aligned percentage of the fund was not a comprehensive figure.
We considered outstanding GBs as aligned, after ensuring they had a valid second party opinion and complete use-of-proceeds.
Do no significant harm assessment
We opted for a simplified approach based on an analysis of all environment-themed controversies identified by the TEG Excel tool. As we were not yet able to isolate a specific share of turnover affected by a controversy, we considered all issuer activities as non-aligned when facing these kinds of controversies.
Social safeguards assessment
We adopted a similar ‘controversy’ approach to the do no significant harm (DNSH) assessment.
Where we identified severe controversies related to breaches of human rights principles, we determined all issuer activities non-aligned, as we were not able to isolate a specific share of turnover affected.
Turnover/capex/opex alignment
Since data reported and/or estimated by ESG providers and issuers was not sufficient to fully implement the taxonomy, we mainly used data based on turnover.
Additional comments
The most significant challenge we faced was identifying activities compliant with the technical criteria. We are uncertain how to apply this specific filter in a systematic and comprehensive way on portfolios comprising multiple components. Given the lack of data, the only way to apply this filter would be to carry out an ad hoc, issuer-by-issuer in-depth analysis. However, it would be very demanding to implement the taxonomy in this way across a broad range of portfolios, and it does not constitute a solution for non-European issuers which are not affected by the taxonomy disclosure requirements.
Alignment results
We think the results from this case study are inconclusive as we were only able to ‘technically screen’ selected sectors, which were not representative of the whole portfolio. Results varied considerably, depending on how the alignment percentage was interpreted.
On the denominator of the ratio, should investors include the whole funds outstanding, including those that are not within the taxonomy scope (such as public debt for example). In that case, the alignment would be < 1%. Or should we compare it only with credit amounts (result: < 5%)?
Should we also exclude the amounts corresponding to activities from NACE sectors, which are not yet analysed by the TEG?
Conclusive alignment figures depend on which approach is adopted. Indeed, resolving these questions will ultimately benefit clients’ understanding of the portfolio as a whole.
Challenges and solutions
| NO. | CHALLENGE | SOLUTION |
|---|---|---|
| 1 | Application of technical criteria in a systematic and automatized way to a portfolio with multiple lines, given the lack of data required by the TEG. | We decided to focus our analysis on a small number of activities that were (partially) comparable with the criteria defined by the TEG and for which KPIs were available. Therefore, results were partial and conservative. |
| 2 | The process of analysing issuer’s activities as a percentage of their turnover against the list of NACE sub-sectors provided in the TEG Excel tool was not as simple as we expected. The TEG Excel tool is not comprehensive (some sub sectors are not specified) and the identification of activities as percentage of turnover was not always available. |
We chose to simplify the first taxonomy ‘filter’ by applying a “1 issuer = 1 NACE sector” rationale. In the second step, we analysed in more detail the issuers that complied with the first filter, including a breakdown of their activities. We only considered the share of their revenue that complied with both the TEG NACE list and the technical criteria as taxonomy-aligned. |
| 3 | We found it challenging to reach a conclusive result: Should it be an alignment percentage of the whole outstanding portfolio or should it only express the share of activities already analysed by the TEG? | We calculated differing “alignment percentages” with distinct denominators. We believe the regulator should be more specific. |
Recommendations
We recommend testing the application of the taxonomy on a portfolio with limited lines to facilitate an in-depth and issuer-by-issuer analysis.
We would then focus on compliance analysis of some sectors/activities where the level of disclosure on environmental aspects is higher and more likely to match the requirements of TEG criteria.
One of the main difficulties we identified was the significant discrepancy between the detail disclosed by companies (even those considered as optimal) compared to the detail required by the taxonomy’s technical criteria. Issuers require time to align with this level of detail and engagement will be an important tool.
