calibrate_regional(io::Dict, set::Dict, year::Integer)

Arguments

• d::Dict of DataFrames containing the model data.
• set::Dict of Arrays describing parameter indices (years, regions, goods, sectors, etc.)
• year::Int: year for which to perform calibration

Returns

• d::Dict of DataFrames containing the model data.

_energy_calibration_input(d::Dict, set::Dict, year::Int)
_energy_calibration_input(d::Dict, set::Dict)

This function prepares input for the EEM calibration routine. The indices of the output parameters will include $yr$ only if year is included as an input parameter.

1. Drop "small" values from input data.
2. Calculate additional values for constraints:
   • Define $va_{yr,r,s} = ld_{yr,r,s} + kd_{yr,r,s}$
   • Aggregate regionally: $\tilde{ys}_{yr,s,g}$, $\tilde{x}_{yr,g}$, $\tilde{m}_{yr,g}$, $\tilde{va}_{yr,s}$, $\tilde{g}_{yr,g}$, $\tilde{i}_{yr,g}$, $\tilde{cd}_{yr,g}$. For any parameter $\bar{z}_{yr,r,s,g}$, $\tilde{z}_{yr,s,g} = \sum_{r} \bar{z}_{yr,r,s,g}$
   • Separate $fvs_{yr,r,s}$ for labor (fvs_ld0) and capital (fvs_kd0).
   • Filter $netgen_{yr,r}$ to include only values from SEDS input data.
3. Set electricity imports/exports from/to the national market to/from Alaska and Hawaii to zero.
4. (If T==Dict), fill zeros.
5. Set lower bounds for all variables except for $\bar{ld}_{yr,r,s}$, $\bar{kd}_{yr,r,s}$, $\bar{yh}_{yr,r,g}$, and $\bar{cd}_{yr,r,g}$.