RBC GAM: Decoding weighted average carbon intensity

Organisation details:

RBC Global Asset Management

Signatory type:  Investment manager

HQ: Toronto, Canada

Covered in this case study:

Asset class:  Equities

AUM:  US$484.7 billion, as at 31 December 2024 (global assets managed across all affiliated entities)

RBC GAM recognises the importance of the global goal of achieving net-zero emissions by 2050 or sooner, in order to mitigate climate-related risks. Climate change poses systemic risks that may materially affect issuers and the economies, markets, and societies in which issuers operate. We describe our beliefs and actions related to responsible investment, including climate change, in Our Approach to Responsible Investment.

The role of carbon emissions in identifying climate-related risks and opportunities

Carbon emissions are often the basis for analysis of an investment’s exposure to climate-related risks. This analysis can identify areas of risk due to increased costs from carbon pricing or changes in revenue due to shifting consumer preferences. A common metric used to assess a portfolio’s emissions is the Weighted Average Carbon Intensity, by Sales (WACI). The WACI metric measures the carbon efficiency of the portfolio by taking the carbon intensity of each portfolio company (scope 1 + 2 emissions ÷ US$M sales) and computing the weighted average based on portfolio weights. The pros of this metric are that it is intuitive and useful in identifying exposure to carbon-intensive issuers. The cons are that it is backwards-looking, can be influenced by non-climate factors (e.g., business cycle and inflation), is sensitive to outlier values, and an intensity based on revenues is not perfectly comparable across sectors.

Tracking fluctuations in WACI over time

Looking at variations in the WACI over time may provide useful insights into market trends. The chart below shows a 20% decline in the WACI of the MSCI ACWI from 31 March 2023 to 31 March 2024. At first glance, this may appear like progress toward economy-wide decarbonisation. To determine whether this is the case, it is important to dissect the underlying drivers of that change.

Figure 1: WACI, by sales, of the MSCI ACWI Index, March 2023-March 2024

Source: RBC GAM analysis based on MSCI ESG research

Decoding the underlying drivers of change in a portfolio

The following analysis aims to identify the drivers of changes in portfolio WACI over time, whether due to issuers’ reduction in emissions, fluctuation in financial variables, or shifts in weights and holdings. In the chart below, a positive value indicates the factor is driving an increase in the portfolio’s carbon intensity, while a negative number indicates a decrease. This analysis shows that most of the decline in carbon intensity over the period was due to changes related to existing issuers in the MSCI ACWI (responsible for a -30.2 tCO₂eq/sales reduction in WACI). For existing issuers, both changes in issuer weight and issuer carbon intensity were substantial contributors to overall emission reductions. The analysis also shows that the decline in issuers’ emissions intensity was exclusively driven by increasing sales rather than decreasing emissions, likely due to rising commodity prices and inflation during that period. Meanwhile, issuers’ total emissions contributed to an increase in the overall WACI (of 2 tCO₂eq /sales).

Figure 2 : Decoding the underlying drivers of portfolio changes

Source: RBC GAM analysis, based on MSCI ESG research. Data is from March 31, 2023 to March 31, 2024 for the MSCI ACWI

Decoding an issuer’s carbon intensity

Since the analysis above relies on the issuer’s carbon intensity values (e.g., emissions and sales), it is important to recognise factors that may affect the accuracy and interpretation of those values. Typically, carbon intensity metrics are sourced from third-party data providers, and there are generally two types of issues that may arise: errors in the values reflected by third-party data providers (e.g. due to incorrect accounting of a corporate action at an issuer, misalignment of reporting periods between emissions and sales); and issues with methodologies that don’t take into consideration differences in the accounting of emissions and revenue for issuers in specific industries or with distinct business models. These types of challenges are common across all data providers.

At RBC GAM, we seek to source accurate and transparent ESG data for our climate-related analysis and reporting, while recognising that climate-related data and methodologies are still relatively nascent and evolving. We use ESG data from multiple vendors and engage regularly on issues of data quality, accuracy, and methodology. The case study below describes an example of misaligned methodology that we identified and aimed to address with the third-party vendor.

Carbon intensity: where two worlds collide

Reporting an issuer’s carbon intensity brings together data from two distinct frameworks: the Greenhouse Gas (GHG) Protocol for emissions (e.g. emissions based on financial control or equity share ), and accounting standards for sales (e.g. impacted by the company’s role as principal or agent in the transaction – see example below). For most issuers, the methodology for calculating emissions aligns with the methodology for calculating sales, but there are notable instances of misalignment that can lead to an inaccurate measure of emissions intensity.

Example: Hotel industry

The hotel industry has increasingly adopted an asset-light business model, divesting ownership of physical properties and concentrating efforts on managed properties (owned by a third party but operated by the hotel company) as well as franchised properties (owned and operated by a third party under the hotel company’s brand). From the market’s perspective, this can be a more capital efficient business model and helps address delays associated with construction of hotels and allows focus on operations. While the emissions of hotel chains and revenue generation model should reflect its operations, the following case describes how an asset-light business model produces a misalignment of these factors.

The hotel chain in this example reported its emissions in accordance with the GHG Protocol’s financial control approach, whereby emissions from corporate-owned and managed properties were reported as scope 1 and 2 emissions, and emissions from franchised properties were reported under scope 3 .

Based on accounting standards for owned properties, the hotel is a principal and reflects the total gross revenue on financial statements, whereas for managed properties, the hotel is an agent and only reflects their ‘take rate’ or ‘management fee’, which is a percentage of gross revenue.

The asset-light business model means hotel chains are increasingly operating as agents. See below for an illustration of how emissions and revenue allocation impact carbon intensity values of a hotel chain, depending on the degree to which its hotels are corporate-owned or managed. This simplified example assumes that each underlying hotel generates the same level of absolute emissions and gross revenue. This shows how the carbon intensity of a hotel chain is heavily dependent on whether it is an asset-light (more managed hotels) or asset-heavy (more owned hotels) business model; the carbon intensity is not based on the carbon emissions of the hotels themselves.

Figure 3: Differences between asset-light and asset-heavy business models for calculating hotel emissions

Source: RBC GAM analysis for illustrative purposes only. As at 18 July 2025.

Our recommendation is to apply a consistent approach for the allocation of emissions and sales, which is illustrated in the example below. This approach accounts for scope 1 and 2 emissions from corporate-owned and managed hotels (and offices) alongside gross revenue from corporate-owned and managed hotels.

As seen in the calculations below, the implication for the hotel chain’s emissions intensity is significant (74% lower with the revised approach), accounting for the extent to which the issuer has shifted its business model to an asset-light approach. While the revised approach has been adopted by our third-party data provider based on a methodology review, there can be challenges with this approach as some hotel chains may not report gross revenue data.

Figure 4: Calculating carbon intensity value by sales

Hotel company: Previous approach

Hotel company: Revised approach

Note: Another example by RBC GAM as to how it recommends handling holding companies for the purpose of assessing and reporting carbon intensity is available in this PRI workshop summary.

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.