Climate Alignment

Equity portfolio managers seeking to meet climate objectives require robust metrics to evaluate the alignment of current and projected emissions associated with their holdings. These indirect impacts are assessed using company-level data. Methodological approaches differ according to the data employed: some rely on economic indicators such as revenues or investments in sustainable activities, others emphasize governance and transition planning, while a third category focuses directly on greenhouse gas (GHG) emissions. The latter, referred to as emissions-alignment methodologies, has seen notable methodological refinements in recent years (ILB, 2024) and constitutes the focus of this functionality.

What is the “carbon overshoot” metric?

Alignment can be expressed through different representations: a binary classification (aligned/not aligned), a carbon overshoot, an implied temperature rise (ITR) score, or a temperature range (ILB, 2024).

The carbon overshoot quantifies the extent to which the projected emissions of a portfolio - defined as the weighted sum of all emission trajectories of its constituent holdings - exceed the emissions pathway prescribed by a chosen reference scenario. The implied temperature rise (ITR) metric translates this overshoot into an equivalent level of global temperature increase, assuming that the global economy were to replicate the portfolio’s emission trajectory.

Why changing parameters?

Despite some methodological variation, portfolio alignment models generally follow five key steps (ILB, 2020; PAT, 2021):

1. Selection of a reference climate mitigation scenario

2. Allocation of a carbon budget to companies

3. Projection of company-level emission trajectories

4. Measurement of company-level alignment

5. Aggregation of company-level results into a portfolio-level metric

As discussed in Bouchet (2025), some design choices underlying these five steps can be grounded in scientific reasoning, while others reflect primarily ethical or normative judgments. A third category of parameters depends on the analytical objective of the exercise.

In this functionality, three parameters are made explicit to the user:

• Level of ambition of the reference scenario (ethical). The ambition of a reference scenario—such as limiting global warming to 1.5°C or 2°C—is inherently an ethical decision tied to intergenerational equity. For investors, the key question is whether to align with a 1.5°C Net-Zero scenario or a 2°C Announced Pledges Scenario, given the increasing improbability of achieving the 1.5°C target. UNEP (2024) reports that global emissions reached 57.1 GtCO₂e in 2023, implying a required annual reduction of 7.5% until 2035 to remain on a 1.5°C pathway, far beyond current policy commitments. Caney (2016) argues that excessively ambitious targets may impose disproportionate burdens on present generations, making a 2°C target more ethically defensible in certain contexts. The difference in remaining carbon budgets is substantial: Lamboll et al. (2023) estimate 250 GtCO₂ for a 1.5°C target (50% probability) versus 1,200 GtCO₂ for 2°C, implying that a 1.5°C-aligned budget would be roughly five times smaller. Hence, the choice between realism and ambition must reflect the purpose of the assessment. A 1.5°C scenario is particularly relevant for assessing past responsibility, when such a target was still plausible, whereas a 2°C scenario may be more appropriate for evaluating future commitments under updated transition plans.

• Horizon (depends on the goal of analysis). The analytical horizon should primarily depend on the objective of the study rather than ethical considerations. The year 2050 is commonly used, as it coincides with global net-zero commitments and long-term climate objectives. However, a 2030 horizon may be preferable for analyses focusing on near-term corporate targets and emissions dynamics within portfolios.

• Metric (ethical). The third key parameter concerns the metric of interest, i.e., whether alignment is assessed in terms of absolute emissions or physical intensity. Since emissions are linked to production and consumption, one may argue that reductions should be evaluated per unit of output rather than in absolute terms. In this perspective, higher emissions could be tolerated for producers of essential goods, implying a focus on output per ton of CO₂ emitted. However, intensity metrics raise consistency challenges: a firm may reduce its emissions intensity while increasing output, resulting in higher aggregate emissions and thus breaching scenario pathways. Conversely, focusing on absolute emissions ensures consistency with global carbon budgets but may penalize production declines unrelated to decarbonization, thereby conflicting with sustainable development objectives. In practice, physical intensity metrics are not available for all sectors; when missing, absolute emissions are used instead.

Data

The alignment model adheres to the five methodological steps outlined above, integrating parameters that reflect key design choices at each stage. The model relies on two types of data: (i) company-level projections of activity and GHG emissions, and (ii) sector-level scenario data.

Company data

The model requires projections of absolute emissions across different scopes and associated intensity metrics (both monetary and physical). These projections are derived from historical emission trends and corporate reduction targets

Historical emissions trends. Absolute emissions for each company are projected using the average annual reduction rate observed over 2015–2023, after excluding extreme variations. Upward variations exceeding 45% (90th percentile of the annual variation distribution) and downward variations exceeding 30% (10th percentile) are removed, as such outliers generally result from exceptional events such as mergers, acquisitions, or divestitures, or from methodological revisions in reporting—particularly for Scope 3 emissions. The resulting reduction rate is then bounded within the ranges reported below.

Notes: The lower bound aligns with the most ambitious pathway for the sector (Net Zero Emissions scenario from the International Energy Agency), while the upper bound follows a business-as-usual trajectory. Since the IEA does not provide a specific business-as-usual scenario, we use an annual increase of +1%, consistent with the global trajectory from the SSP2-baseline scenario from the Intergovernmental Panel on Climate Change for the period 2020–2050.

Corporate reduction targets. For firms with declared emissions-reduction targets, trajectories are adjusted to reflect those targets, provided that the company has achieved an average annual reduction of at least 7% over the previous three years. Adjustments are made separately for Scopes 1, 2, and 3 emissions. Historical emissions and target data are sourced from ISS.

Physical Intensity Projections: A similar procedure is applied to physical intensity data for sectors with homogeneous intensity measures, based on sector-specific scopes (see table below). Historical and target intensity data are sourced from Moody’s alignment dataset.

Notes: For the automotive sector, an assumption of 1.5 passengers per car on average is applied to convert vehicle kilometres into passenger kilometres. Similarly, for the airline sector, an average weight of 100 kg per passenger, including luggage, is used to convert tonne kilometres into passenger kilometres.

Enterprise value. Enterprise value (including cash) at the reference date is used to convert company-level emissions into portfolio-weighted emissions, with data sourced from CIQ.

Scenario data