Our climate future is unknown territory. Still, financial institutions want to plot a course to its coolest fringes. Hence the buzz over portfolio alignment metrics (PAMs) — the measurement systems firms can use to understand what investment, lending, and underwriting activities support a 1.5°C warming pathway.
Banks, asset managers, asset owners, and insurers want PAMs because they can show how in-sync their portfolios are with global climate goals. This is especially important for those firms in the Glasgow Financial Alliance for Net Zero (GFANZ), the umbrella group of climate finance initiatives dedicated to decarbonizing the economy in line with the aims of the Paris Agreement.
Many of these institutions have experimented with PAMs as way of honoring their commitment to the alliance and to mark their progress toward its overarching objective. Third-party vendors have also built specialist products to meet the growing demand for PAMs. MSCI and Moody’s are two major players in this space.
As a result, there now exists an exotic menagerie of PAMs addressing various use cases and built using all kinds of forward-looking methodologies. The problem is that the evolution of these tools has outpaced efforts to organize, standardize, and validate them.
GFANZ is working to change this. On Tuesday, it published a report on portfolio alignment metrics that includes guidance for designing and implementing effective PAMs. The alliance’s goal is to bring about convergence on best practice, improve the transparency on the assumptions that underpin PAMs, and broker agreement on their methodological frameworks.
The stakes are high. Flawed PAMs may lead financial institutions to inadvertently increase their exposure to climate transition risk, for example by investing in carbon-intensive companies that aren’t taking appropriate steps to lower their emissions. Industry-wide, they could disrupt the flow of capital toward the net-zero economy — in the process elevating the likelihood of a disorderly transition, which could bring systemic risks in its wake.
Taming the metrics zoo, therefore, is no small matter.
GFANZ makes a start on this task by sorting PAMs into four categories:
- Binary metrics determine the percentage of a financial institution’s portfolio companies that have net-zero aligned emissions reduction targets.
- Benchmark divergence metrics calculate a company’s alignment with benchmark emissions pathways developed using forward-looking climate scenarios.
- Implied Temperature Rise (ITR) metrics convert a company’s and/or portfolio’s (mis)alignment with benchmark emissions pathways into a temperature score that describes the likely climate outcome should the global economy replicate the same behavior as the company/portfolio in question.
- Maturity scale alignment metrics grade a company on the extent to which it is aligned with net-zero using qualitative and quantitative factors. These can include their climate targets, past emissions reduction performance, climate-related disclosure practices, and governance. GFANZ notes that these metrics have gained popularity recently thanks in part to the influence of the Institutional Investors Group on Climate Change, which last year published a ‘Net Zero Investment Framework’ — complete with a rubric for scoring the alignment of certain types of assets with net-zero pathways.
The variety of PAMs in circulation reflects the range of use cases they serve, as well as the dispersion of technical skills and climate data availability across institutions. Certain metrics are, after all, more difficult to put together than others. Given this, the fact that no single metric dominates the space is not surprising. However, it wasn’t very long ago that the Task Force on Climate-related Financial Disclosures (TCFD) — the premier framework for climate risk reporting — appeared to favor the widespread use of ITRs over other kinds of metric. In fact, in a draft version of its 2021 implementation guidance, the group recommended that financial institutions “measure and disclose the alignment of their portfolios consistent with a 2°C or lower temperature pathway,” a task arguably best suited for ITR-style PAMs.
Certain stakeholders pushed back against this in a subsequent consultation. The Transition Pathway Initiative, for instance, argued that the TCFD’s endorsement of ITRs would “create pressure on investors to invest time and effort in providing such disclosures.” Others spoke out against what they saw as the TCFD picking winners in the blossoming competition among PAM providers. Following the consultation, the TCFD pared back its support for ITRs, and simply recommended that financial institutions use “whichever approach or metrics best suit their organizational context or capabilities” to show their alignment with a well below 2°C scenario.
GFANZ does not make the same mistake as the TCFD by declaring a preference for one PAM over another. On the contrary, the report explains that many firms do not want to limit themselves to a single metric, but prefer instead a “dashboard approach” which makes use of a variety of backward- and forward-looking metrics. The guidance featured in the report, therefore, is intended to apply to the whole spectrum of PAMs.
In this instance, GFANZ is reflecting the will of its signatories. However, promoting the whole gamut of PAMs may not best serve the overarching mission of the alliance. After all, a single type of PAM used industry-wide would make it easier to assess the financial system’s overall alignment with net-zero goals. GFANZ is not a standard-setter, though, and its influence depends on the support of its member institutions. Alienate them by imposing unwanted standards and the alliance could break apart.
Still, while it does not favor one metric over another GFANZ does highlight the pros and cons of each PAM, perhaps to nudge institutions towards what the evidence suggests are the higher-quality and more decision-useful versions. For instance, the report notes that benchmark divergence metrics are “[c]omplex to use and interpret”, are “not meaningful to aggregate at the portfolio level”, and that their decision-usefulness is “highly dependent on an appropriate level of scenario granularity by sector and geography.” This suggests it’s a flawed metric for conducting the kind of portfolio-level analysis GFANZ members are keen on.
The guidance itself is organized around a conceptual framework established by the Portfolio Alignment Team, a group of climate finance professionals who published reports on designing effective PAMs in 2020 and 2021. This framework consists of nine “key design judgments” — specific questions that financial institutions and metric providers should consider when building PAMs — and best practice recommendations for answering each one. GFANZ’s hope is that by adhering to the ‘gold standard’ design judgments, financial institutions and third-party providers will be able to produce decision-useful PAMs.
The “key design judgments” are:
- What type of benchmark should be built?
- How should benchmark scenarios be selected?
- Should you use absolute emissions or intensity?
- What scope of emissions should be included?
- How should emissions baselines be quantified?
- How should forward-looking emissions be estimated?
- How should alignment be measured?
- How should alignment be expressed as a metric?
- How do you aggregate counterparty-level metrics into a portfolio-level score?
It’s a long list of questions, and there are multiple credible responses for each one. However, in its guidance GFANZ does try to nudge institutions toward specific answers. For example, on the first judgment — “what type of benchmark should be built?” — GFANZ proposes that firms use a single-scenario benchmark built using a fair-share carbon budget approach where possible, and a convergence-based approach if not.
Recommendations like these are supposed to help bring about the convergence around best practices the alliance is looking for, and foster some degree of PAM standardization. The question is to what extent member institutions are willing to follow them and amend the PAMs they have in use accordingly. For some firms it would be risky to change their metrics to align with GFANZ’s answers to the key design judgments.
For example, GFANZ recommends in respect to judgment 4 — “what scope of emissions should be included?” — that Scope 3 emissions be factored into company-level alignment for certain priority sectors, including oil and gas and electric utilities. A fund that has been using a PAM that only considers Scope 1 and 2 emissions for these sectors may today be able to show that its portfolio is aligned with a 1.5°C pathway. However, if the PAM were amended to incorporate Scope 3 emissions the metric’s output may show it is actually closer to a 2°C or even 3°C warming trajectory. This discrepancy, if publicly disclosed, would expose the fund manager to accusations of greenwashing. It could also have significant business implications. Out of a desire to quickly re-align its portfolio with 1.5°C, the fund may choose to rapidly divest from those companies with high Scope 3 emissions, catalyzing a fire-sale that ripples through financial markets.
GFANZ’s big challenge, therefore, is to find ways to cajole member institutions into embracing its guidance while minimize this kind of ‘PAM transition risk.’ One way to address the first half of this problem would be to encourage better disclosure around PAMs, so that financial institutions and metric providers have to explain their choices against the nine “key design judgments.” This would help identify flawed PAMs and give stakeholders the evidence they need to push for changes.
In order to ameliorate ‘PAM transition risk’, meanwhile, institutions could be advised to publish standardized, backward-looking metrics alongside their PAMs that adhere to a simple, standardized methodology. Doing so would provider stakeholders with a fallback disclosure they can use to anchor their understanding of the forward-looking PAMs. The simplest such metric for this purpose may be financed emissions, presented according to the Partnership for Carbon Accounting Financials methodology. Firms could also disclose their portfolio alignment under old and new iterations of their chosen PAMs side-by-side to head off accusations of greenwashing.
This may result in more lengthy disclosures. But firms seem to be moving in this direction anyway, given their professed preference for ‘climate dashboards’ that include multiple PAMs and backward-looking metrics. What may be lost in terms of accessibility and the ability to easily compare portfolio alignment across firms would be more than made up for through greater transparency.
The hard truth is that mapping financial portfolios onto desired climate futures is complex, and the output of PAMs will always be plagued by uncertainty. This being the case, a ‘keep it simple, stupid’ approach may be unhelpfully reductive. GFANZ has marked a path to improving PAMs that may require some financial institutions and metrics vendors to go back to the drawing board when it comes to their own portfolio tooling. Though this may be a tough process for some institutions, it promises to be worthwhile — both for them and the wider net-zero financial system.