Calendar An icon of a desk calendar. Cancel An icon of a circle with a diagonal line across. Caret An icon of a block arrow pointing to the right. Email An icon of a paper envelope. Facebook An icon of the Facebook "f" mark. Google An icon of the Google "G" mark. Linked In An icon of the Linked In "in" mark. Logout An icon representing logout. Profile An icon that resembles human head and shoulders. Telephone An icon of a traditional telephone receiver. Tick An icon of a tick mark. Is Public An icon of a human eye and eyelashes. Is Not Public An icon of a human eye and eyelashes with a diagonal line through it. Pause Icon A two-lined pause icon for stopping interactions. Quote Mark A opening quote mark. Quote Mark A closing quote mark. Arrow An icon of an arrow. Folder An icon of a paper folder. Breaking An icon of an exclamation mark on a circular background. Camera An icon of a digital camera. Caret An icon of a caret arrow. Clock An icon of a clock face. Close An icon of the an X shape. Close Icon An icon used to represent where to interact to collapse or dismiss a component Comment An icon of a speech bubble. Comments An icon of a speech bubble, denoting user comments. Ellipsis An icon of 3 horizontal dots. Envelope An icon of a paper envelope. Facebook An icon of a facebook f logo. Camera An icon of a digital camera. Home An icon of a house. Instagram An icon of the Instagram logo. LinkedIn An icon of the LinkedIn logo. Magnifying Glass An icon of a magnifying glass. Search Icon A magnifying glass icon that is used to represent the function of searching. Menu An icon of 3 horizontal lines. Hamburger Menu Icon An icon used to represent a collapsed menu. Next An icon of an arrow pointing to the right. Notice An explanation mark centred inside a circle. Previous An icon of an arrow pointing to the left. Rating An icon of a star. Tag An icon of a tag. Twitter An icon of the Twitter logo. Video Camera An icon of a video camera shape. Speech Bubble Icon A icon displaying a speech bubble WhatsApp An icon of the WhatsApp logo. Information An icon of an information logo. Plus A mathematical 'plus' symbol. Duration An icon indicating Time. Success Tick An icon of a green tick. Success Tick Timeout An icon of a greyed out success tick. Loading Spinner An icon of a loading spinner.

Aligning financial portfolios to net zero: what, how, and why

Dollar bills.

Portfolio alignment metrics (PAMs) are measurement systems that financial firms can use in order to assess the progress of their investments to decarbonise, as well as inform new investment activities to decarbonise their own portfolio and meet net zero targets.

However, PAMs are still a relatively nascent tool and the financial community has yet to consolidate a common understanding of what works best when it comes to measuring the decarbonisation progress of their portfolios.

There are many metrics to choose from

In theory, GFANZ recommends that PAMs should be “simple to use, transparent, science-based, broadly applicable, aggregable, and incentive optimal”. GFANZ outlines four categories of PAMs available today, listed in order of increasing complexity:

  1. Binary metrics: focuses on measuring alignment based on the proportion of portfolio companies aligned with net zero emission reduction targets.
  2. Maturity scale metrics: arranges portfolio companies into alignment categories on a scale of “aligned”, “aligning”, “committed to aligning”, or “not aligned”.
  3. Benchmark divergence metrics: includes the calculation of how much a portfolio company is over or under a net zero-aligned benchmark.
  4. Implied Temperature Rise (ITR) metrics: translates the over/undershoot of a portfolio company’s net zero goal into a science-based, end-of-century global warming outcome.

There are pros and cons to each type of PAM. For example, a binary metric is simple to deploy, but does not give the full picture of real world decarbonisation progress, whereas an ITR metric is much more complex to use, but provides the most context and incentive for companies to decarbonise.

The proliferation of so many different metrics and methodologies has led to a divergence of company level results across different portfolios, as one metric will yield a different result compared to another metric. There is thus a need for convergence to create an even playing field to accurately measure and compare progress of different companies and portfolios.

However, the current PAMs available still do not cover the full suite of issues that they should cover in order to provide a clear overview of net zero alignment. PAMs should be enhanced to cover issues such as the accurate representation of climate solutions, the suitability for use in broader asset classes such as private equity, and the phase-out of high-emitting assets.

Enhancing the PAMs available could lead to an uptick in adoption according to GFANZ, as there is still uncertainty regarding methodologies and how they can impact investment decisions. The report states that “practitioners have raised concerns that scenarios used to construct an alignment benchmark lack sector and regional granularity, which might prevent appropriate alignment outcomes and result in perverse incentives for capital allocation”.

“Measuring portfolio alignment is essential for financial institutions to understand their progress in reaching net zero and allocation capital to transition finance opportunities for the real economy”, explained David Blood, a senior partner at sustainable investment firm Generation Investment Management.

Future-proofing financial portfolios

While there may be a moral and environmental incentive to improve the adoption of PAMs to green the financial sector, there is also a major economic incentive. Climate risks will have a major impact on the financial sector, and investors want to ensure they are in the best position to mitigate these risks by building a portfolio that can survive, and even thrive, physical and transitional risks catalysed by climate change.

Assessing climate risk depends both on having accurate data on current and future emissions to better understand how the climate is changing, as well as having a clear overview of the policies, regulations, trends, business models, etc that are emerging to decarbonise the world’s economy to analyse where their may be potential transitional risks and opportunities.

Having widely adopted and standardised PAMs can be one way to better inform the modelling of climate risk scenarios by filling in the data gaps. However, even with this data there is still much uncertainty in regards to how to accurately calculate how emissions and net zero policies will impact the financial sector over the long-term.

New ways to calculate climate risk to inform portfolio management

In a recent paper, the IMF explains that climate risk analysis is crucial to raise awareness of the risk, and inform adaptation needs and opportunities for a more resilient financial system. Yet, they warn that current methodologies to assess climate risk are not where they need to be to accurately inform decision making long-term.

But a new study published by the Rotterdam School of Management has proposed a novel approach to tackling this methodology dilemma to inform long-term portfolio choice by deploying Bayesian mathematics and the temperature long-run risk (LRR-T) model developed in 2019.

Since there is limited historical data on the impacts of climate change, climate risk scenarios inevitably must make assumptions. The new methodology approach takes its assumptions from the LRR-T model, in which “rising temperatures influence asset prices by increasing the likelihood of future climate-driven natural disasters that lower economic growth”. Because of these future concerns from investors, climate risk can actually already be reflected in current asset prices “even though the impact of climate change in historical data is limited”.

The new methodology takes these theoretical assumptions to analyse the risk-return trade-off over long time horizons, using temperature as a predictor in their model to proxy climate change. This inherent flexibility of this climate risk analysis allows for it to take into account different views and uncertainty on the economic impact of climate change.

Effective portfolio metrics translate long term risk to real asset price

Essentially, the methodology translates the perceived riskiness in the long-term of a certain portfolio choice to real world asset prices now and in the long-term, which can help the financial sector better manage their portfolios.

Although the methodology is still theoretical and has its limitations as well, it is a novel way to package both physical and transition risks by taking stock of the varying perceptions and concern on the economic impacts of climate change.

Deploying more accurate and long-term methodologies to further assess climate risks on portfolios will be crucial as financial firms rearrange their portfolios to stay on track to net zero and ensure their resilience with so much uncertainty around climate change impacts.

More from SG Voice

Latest Posts