c) company-specific views on revenue forecast

The above models of total assets and revenues reflect their global cross-sectional relation with the macroeconomic variables. However, each infrastructure sector and company has their own unique characteristics. To address such company-level specifics, we integrate Scientific Infra & Private Assets’ company-level revenue forecasts into the above projections. Scientific Infra & Private Assets forecasts revenue for every company in the index universe (e.g., the infra300 index) and routinely updates the forecasts to reflect the latest developments of the company and its sector. However, these revenue forecasts do not incorporate climate risks but rather capture the current economic and sector trends. Accordingly, we align our scenario projections of each company’s revenue growth to Scientific Infra & Private Assets’ revenue forecasts by assuming that their forecasts correspond to the NDC scenario in NGFS and the Baseline scenario in Oxford Economics (which are equivalent). We then keep the differences between each scenario and NDC or Baseline as given by the regression model above. Following this step, the projected values of total assets and revenues can be used in the factor models for CFADS, debt service, retention rate, and risk premium for any climate scenario.

Thus, the above approach successfully integrates climate risk at a macro level. However, companies face additional climate risks at the micro level (based on their geolocation, industrial sector, and carbon emissions), which will translate into additional costs and affect companies’ cash flows. The next section describes these asset-specific climate risks and the further adjustments made to CFADS to reflect their future impact on assets under the various climate scenarios.