gcubed.model_parameters

This module contains the Parameter calibration system for G-Cubed.

TECHNOLOGY_ADVANCEMENT_RATE: float = 0.0

TECHNOLOGY_CATCHUP_RATE: float = 2.0

TECHNOLOGY_GAP: float = 100.0

AUTONOMOUS_ENERGY_EFFICIENCY_IMPROVEMENT_RATE: float = 0.0

ADAPT: float = 0.35

INT_ELAST: float = -0.6

GDPSHARE: float = 0.19

PHI_Y: float = 4.0

PHI_Z: float = 4.0

WAGE_P: float = 0.4

WAGE_Q: float = 0.35

DELTA: float = 0.1

DELTA_Y: float = 0.0

DELTA_Z: float = 0.025

FOREI: float = 0.3

FORE_C: float = 0.3

SIGMA_OR: float = 1.1

SIGMA_OH: float = 0.8

SIGMA_ER: float = 0.5

SIGMA_EH: float = 0.5

SIGMA_MR: float = 1.0

SIGMA_MH: float = 1.0

SIGMA_ED: float = 2.0

ALPH_REX: float = 0.2

MRULE_R: float = 1.0

MRULE_EX: float = 0.0

MRULES_OTHER: List[float] = [1.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

def generate_user_parameters(sym_data: gcubed.sym_data.SymData) -> pandas.DataFrame:

Generate model-aware default user parameters in SYM region order.

Include a decimal government spending share of 0.19 for each region when the compiled model declares the gdpshare parameter.

Arguments

sym_data : SymData

The SymData object containing model data such as regions and sectors.

Returns

The generated template user parameters DataFrame with regions matching the current model.

def generate_technology_advancement_rates( advancement_rate: float, sectors: List[str], years: List[str]) -> pandas.DataFrame:

Generate technology advancement deviations for all sectors and years.

Arguments

advancement_rate : float

The deviation in percentage points from configured steady-state log
growth. Zero follows the configured frontier rate. These cells must
be exactly zero for the final 50 projection years when loaded.

sectors : List[str]

The list of sectors in the model.

years : List[str]

The list of projection years in the model.

Returns

A DataFrame of technology advancement rates with sectors as the index and years as the columns.

def generate_technology_catchup_rates( long_run_catchup_rate: float, regions: List[str], sectors: List[str], years: List[str]) -> pandas.DataFrame:

Generate a DataFrame of technology catchup rates for all regions, sectors, and years.

Arguments

long_run_catchup_rate : float

The long-run catchup rate (e.g., 2 for 2%).

regions : List[str]

The list of regions in the model.

sectors : List[str]

The list of sectors in the model.

years : List[str]

The list of projection years in the model.

Returns

A DataFrame of technology catchup rates with a MultiIndex of (region, sector) and years as the columns.

def generate_technology_gaps( regions: List[str], sectors: List[str], gap: float = 100.0) -> pandas.DataFrame:

Generate a DataFrame of technology gaps for all regions and sectors.

Arguments

regions : List[str]

The list of regions in the model.

sectors : List[str]

The list of sectors in the model.

Returns

A DataFrame of technology gaps sector rows and region columns.

def generate_autonomous_energy_efficiency_improvements( regions: List[str], sectors: List[str], years: List[str], rate: float = 0.0) -> pandas.DataFrame:

Generate a DataFrame of autonomous energy efficiency improvements for all regions, sectors, and years.

Arguments

regions : List[str] The list of regions in the model.

sectors : List[str] The list of sectors in the model.

years : List[str] The list of projection years in the model.

rate : float, optional The annual improvement rate (default is 1.0, meaning 1 percent improvement).

Returns

A DataFrame of autonomous energy efficiency improvements with a MultiIndex of (region, sector) and years as the columns.

def remove_template_data_files_and_user_parameters(data_directory: pathlib.Path):

Remove the template data files and user parameters CSV files if they exist.

def write_generated_user_parameters( *, generated_user_parameters: pandas.DataFrame, output_file_path: pathlib.Path, preserve_existing_user_parameters: bool = False) -> None:

Overview

Write generated user parameters, optionally preserving existing user-owned rows while removing the obsolete runtime-derived capital-return family. Add a missing gdpshare row when it is present in the generated table; preserve any existing regional values.

Arguments

generated_user_parameters: Complete generated parameter table.
output_file_path: Destination user_parameters.csv path.
preserve_existing_user_parameters: When true and the destination exists, preserve every non-derived row and cell exactly, adding only the missing generated gdpshare row.

Returns

None.

Exceptions

Raises ValueError for duplicate rows, a changed header, or a partial obsolete capital-return family. Propagates filesystem errors.

def generate_baseline_data_files_and_user_parameters( model_configuration: gcubed.model_configuration.ModelConfiguration, overwrite: bool = False, preserve_existing_user_parameters: bool = False) -> None:

Generate the template baseline data files and user parameters CSV files.

Arguments

model_configuration : ModelConfiguration

The ModelConfiguration object containing model configuration details.

overwrite : bool, optional Whether to overwrite existing files (use the standard names for the files). Default is False. If this is set to False, the files will be saved with "_template" appended to their names.

preserve_existing_user_parameters : bool, optional Whether an overwrite should preserve existing user-owned values while removing obsolete runtime-derived rows and adding a missing generated gdpshare row. Defaults to false.

Returns

None.