standard deviations are calculated from this database. The default assumptions in the cost data include a 7% discount rate and zero carbon tax rate. The model also incorporates other global assumptions such as average lifetime and lead construction time of different power generation technologies (IEA), CO2 emission coefficient per GWh (IEA), learning rate for renewable technologies (IRENA), and international energy price (IEA). While these global data sets are provided as defaults, users have the flexibility to revise them or use alternative data sources according to their preferences.

Regarding cumulative global power production, the model implements projections from the IEA. Users can select from three scenarios for cumulative global power production and future fossil fuel prices, sourced from the IEA’s World Energy Outlook (2022): the Stated Policies Scenario (STEPS), Announced Pledges Scenario (APS), and Net Zero Emissions (NZE). According to the IEA, these scenarios are linked to global temperature increases of approximately 2.5 ̊C, 1.7 ̊C, and 1.5 ̊C, respectively. Coupled with the learning rate assumptions from IRENA, the global production data in these scenarios are sufficient to describe the dynamics of each technology’s average LCOE in the FTT-FLEX model.

**Where available, domestically sourced technology cost data are preferred to the global estimates,** as these sources tend to better reflect local attributes like labor costs, logistic expenses, and production costs. In cases where a country intends to import equipment and external capacity, utilizing a global cost database becomes an acceptable option. The FTT-FLEX database offers pre-collected global average cost data, serving as an initial reference point for a readily accessible tool. In the next version of FTT-FLEX, users will be able to set their own discount rate to reflect local financing conditions or connect the discount rate to interest rates in a macroeconomic model (see the Discussion section).

In summary, the country specific data required to run FTT-FLEX are:

- Historical data for the production shares of each technology

- Total electricity demand projections

- Production maxima for physically limited technologies like hydroelectricity, wind, and solar

Since the future share dynamics depend on the shares today (this is an important feature of the model dynamics), the historical production shares by technology are the main country specific initial condition of setting up the model.

For some technologies geography places a hard physical limit on the potential for domestic capacity. This is most clearly the case for hydroelectric power, for which potential sites are easily enumerated, but it is also a factor for wind and solar, for which the scope for adding capacity is not boundless. If such data can be obtained, the model implements a cost penalty to the technology as its specific capacity limit is approached.

In the absence of more detailed data, the dynamics of the underlying diffusion theory does most of the heavy lifting. The analysis and conclusions that can be drawn are thus strongly contingent on how appropriate the theory is to the domestic setting (see Limitations section in this paper). The robust empirical support that has been found for these dynamics in Mercure et al., 2021, Mercure et al., 2018b and Semieniuk et al., 2022 in the context of the FTT model suggests that incorporating these dynamics even in low-data contexts is likely preferable to the alternative of assuming static costs.

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