1. Climate Impact and Risk Metrics

MARCH 2024

This section describes Scientific Infra & Private Assets’ climate impact and risk metrics. Our methodology explains how our metrics were developed.

The full technical documentation can be downloaded.

The infraMetrics Platform

Besides the financial information used in all our models, the infraMetrics platform offers a range of climate impact and risk metrics developed through the models and methodology we described in the previous chapters. These metrics are available at the index, segment, and asset level and can be used to create benchmarks and analytics to compare the climate impacts and risks of infrastructure companies in each sector. Precisely, we offer three products with different analytical foci: 1) On the index level, we present the climate impact and risk metrics for all market indices and portfolios available on infraMetrics to provide weighted averages for each metric based on the assets included in the respective index. 2) On the sector and market level, the analytics section offers further analysis to compare climate impacts and risks across countries and regions as well as across TICCS pillars (based on business risk, industrial activity, and corporate structure) to build on-demand benchmarks. 3) Lastly, we present all climate metrics on the asset level.

Benchmarks and Proxies

Benchmarks and proxies are common financial concepts that allow comparing and assessing the climate impact and risk performances of individual assets, portfolios, indices, or funds. For Scientific Infra & Private Assets' climate impact and risk metrics, a benchmark is the average of a selected metric for a specific sector, region, or a combination of both. It is important to note that the metrics’ values are modelled estimations (as explained in Part 2 of this documentation) that capture the systematic component of the respective metric. Investors can use these benchmarks to compare climate impact and risk measures against their individual or portfolio-level investments and use the results in their decision-making process.

For a more customised analysis, a proxy is the average of a selected metric based on a set of similar companies for a given asset (e.g., in terms of size, revenues, sector, etc.). Again, the metrics’ values are modelled estimations that capture the systematic component of the respective metric. Investors can use proxies as a “starting point” for climate impact and risk assessment. We refer to these proxies as starting points because they focus on the systematic drivers of the underlying climate metrics. For a complete assessment and precise climate impact and risk estimations, investors can combine these starting points with (idiosyncratic) asset-level characteristics.

Overall, across all three products, our metrics can be differentiated on various levels:

Double materiality: We develop metrics representing climate impact and climate risk, specifically transition and physical risks.
Time horizon: We focus on four time horizons. The baseline climate data offers metrics as of today (updated on an annual basis) based on Oxford Economics’ Baseline scenario that describes the current situation of policies being implemented insufficiently and uncoordinated across countries. The climate scenario data offers metrics at three time horizons – 2030, 2040, and 2050.
Climate Scenarios: We use three climate scenarios from two sources for our metric calculations: a) orderly transition scenarios, including Net Zero 2050 from NGFS and Net Zero from Oxford Economics; b) disorderly transition scenarios, including both Delayed Transition scenarios from NGFS and Oxford Economics; and c) no transition scenarios, including Current Policies from NGFS and Climate Catastrophe from Oxford Economics.

Additionally, our metrics undergo several cross-checks to ensure the quality of our products. For example, we exclude extreme outliers to calculate averages on index and market levels.

The table below provides an overview of the available climate impact and climate risk metrics, and the following pages explain each of the products and respective metrics developed.

_Table:_{Overview of the available metrics}
_Data	_{Index Level}	_{Valuation at Sector Level}	_{Asset Level}
_{CLIMATE IMPACT}
_Baseline_(today)
	_{Carbon Emissions} _{Average financed emissions (tCO2e/USDm) for Scope 1+2 based on: 1. NAV, 2. EVIC}	_{Carbon Emissions} _{Average financed emissions (tCO2e/USDm) for Scope 1+2 based on: 1. NAV, 2. EVIC}	_{Carbon Emissions} _{Absolute carbon emissions: Scope 1, 2, 3, 1+2, 1+2+3 (tCO2e)} _{Financed emissions (tCO2e/USDm) for all 5 absolute carbon emissions based on: 1. NAV, 2. EVIC}
_Climate_Scenarios_{(orderly, disorderly, and no transition for 2030, 2040, and 2050)}
	_{Carbon Emissions} _{Average financed emissions (tCO2e/USDm) for Scope 1+2 based on: 1. NAV, 2. EVIC}	_{Carbon Emissions} _{Average financed emissions (tCO2e/USDm) for Scope 1+2 based on: 1. NAV, 2. EVIC}	_{Carbon Emissions} _{Absolute carbon emissions: Scope 1, 2, 1+2 (tCO2e)} _{Financed emissions (tCO2e/USDm) for all 3 absolute carbon emissions based on: 1. NAV, 2. EVIC}
_{CLIMATE RISK}
_Baseline_(today)
	_{Transition Risk} _{Average carbon intensity (tCO2e/USDm) for Scope 1+2 emissions based on: 1. Revenue, 2. Total assets} _{Average EBITDA-at-risk (in %) for Scope 1+2 emissions} _{Average extreme transition risk (in % NAV loss)} _{Average extreme no-alignment risk (in % NAV loss)} _{Average extreme late-alignment risk (in % NAV loss)}	_{Transition Risk} _{Average carbon intensity (tCO2e/USDm) for Scope 1+2 emissions based on: 1. Revenue, 2. Total assets} _{Average EBITDA-at-risk (in %) for Scope 1+2 emissions} _{Average extreme transition risk (in % NAV loss)} _{Average extreme no-alignment risk (in % NAV loss)} _{Average extreme late-alignment risk (in % NAV loss)}	_{Transition Risk} _{Carbon intensity (tCO2e/USDm) for all 5 absolute carbon emissions based on: 1. Revenue, 2. Total assets} _{EBITDA-at-risk (in %) for Scope 1+2 emissions} _{Extreme transition risk (in % NAV loss)} _{Extreme no-alignment risk (in % NAV loss)} _{Extreme late-alignment risk (in % NAV loss)} _{Physical Risk} _{For 100-, 50-, and 30-year flood, tropical storm, and extratropical storm events: 1. PDaR (in % total assets), 2. PVaR (USDm), 3. Expected annual loss (USDm)} _{Sector exposure ranking for 100-, 50-, and 30-year hazard events} _{Operational revenue loss (USDm) from heat stress for temperature thresholds above 30°, 35°, 40°, and 45° Celsius} _{Extreme physical risk (in % NAV loss)}
_Climate_Scenarios_{(orderly, disorderly, and no transition for 2030, 2040, and 2050)}
	_{Transition Risk} _{Average carbon intensity (tCO2e/USDm) for Scope 1+2 emissions based on: 1. Revenue, 2. Total assets} _{Average EBITDA-at-risk (in %) for Scope 1+2 emissions} _{Average extreme transition risk (in % NAV loss)} _{Average extreme no-alignment risk (in % NAV loss)} _{Average extreme late-alignment risk (in % NAV loss)’}	_{Transition Risk} _{Average carbon intensity (tCO2e/USDm) for Scope 1+2 emissions based on: 1. Revenue, 2. Total assets} _{Average EBITDA-at-risk (in %) for Scope 1+2 emissions} _{Average extreme transition risk (in % NAV loss)} _{Average extreme late-alignment risk (in % NAV loss)} _{Average extreme no-alignment risk (in % NAV loss)}	_{Transition Risk} _{Carbon intensity (tCO2/USDm) for Scope 1, 2, 1+2 emissions based on: 1. Revenue, 2. Total assets} _{EBITDA-at-risk (in %) for Scope 1+2 emissions} _{Extreme transition risk (in % NAV loss)} _{Extreme no-alignment risk (in % NAV loss)} _{Extreme late-alignment risk (in % NAV loss)} _{Physical Risk} _{For 100-year flood, tropical storm, and extratropical storm events: 1. PDaR (in % total assets), 2. PVaR (USDm), 3. Expected annual loss (USDm)} _{Extreme physical risk (in % NAV loss)}