gcubed.exogenous_adjustments.autonomous_energy_usage_efficiency

This module provides handling of the exogenous projections of autonomous (manna from heaven) energy efficiency improvements that impact on production and consumption.

class AutonomousEnergyUsageEfficiency(gcubed.base.Base):

This class loads and provides access to Autonomous Energy Efficiency Improvements (AEEI) data.

The adjustments are incorporated into the baseline exogenous variable projections.

AutonomousEnergyUsageEfficiency(sym_data: gcubed.sym_data.SymData)

Constructor

Arguments

sym_data: The information about the SYM model definition.

The model configuration is accessible via this argument, thus enabling discovery of the location of the AEEI CSV file.

CSV file format

The default name of the CSV file containing the AEEI data is autonomous_energy_efficiency_improvements.csv.

That name can be changed by setting the ModelConfiguration.AutonomousEnergyEfficiencyImprovementsFile value to the preferred name of the CSV file.

The AEEI CSV file records Autonomous Energy Efficiency Improvements for all regions and all sectors within each region, through the projection years. It also captures these improvements for consumption.

See McKibbin and Wilcoxen (2013) A global approach to energy and environment: the G-Cubed model for details of AEEI.

An example layout for this CSV file is shown below for the 2 region/2 sector model:

2017 2018 2019 .... 2150
AEEI(a01,USA) 0 0 0 0 0
AEEI(a01,USA) 0 0 0 0 0
AEEI(a01,ROW) 0 0 0 0 0
AEEI(a01,ROW) 0 0 0 0 0
AEEIC(USA) 0 0 0 0 0
AEEIC(ROW) 0 0 0 0 0

The first row contains the ordered years as column labels.

Note that the first year can be before the first projection year.

The last year must be the last projection year.

The first column contains row labels. All row labels are made up from three components, in the following order:

  1. The prefix AEEI for production by sectors and AEEIC for consumption.
  2. In brackets, the set combinations that are affected by the autonomous energy efficiency improvements. For sectors, the set combinations are a sector code followed by a region code. For consumption, the set combination is just a region code.

For example, the row labelled AEEI(a01,USA) is the AEEI projections for sector 1 for the United States.

The consumption rows must be the last rows in the file.

The sector rows must be in the SYM-defined sector order.

The rows must also be in the SYM-defined region order as you work down the file.

The data values are the percentage exogenous improvement in energy efficiency for that sector, or for consumption, in the given region for a given year. Thus, a value of 1 is a 1% improvement in energy efficiency in that year.

def sector_energy_usage_efficiency_gains(self, region: str) -> pandas.core.frame.DataFrame:

Overview

Accesses the annual sectoral autonomous energy efficiency gain projections for the given region.

Arguments

region: The region code for which the data is required.

Returns

A dataframe containing the projected autonomous energy efficiency gains for the sectors in the given region.

def sector_cumulative_energy_usage_efficiency_gains(self, region: str) -> pandas.core.frame.DataFrame:

Overview

The cumulative autonomous energy efficiency gain projections for the given region are calculated from the annual gains in the data file.

They are simply a cumulative sum of the annual gains.

Arguments

region: The region code for which the data is required.

Returns

The dataframe of cumulative energy usage efficiency gains for the sectors in the given region.

Each dataframe has a row for each sector.

They are used to populate the projections for the exogenous variables SHL(sector,region).

consumption_energy_usage_efficiency_gains: pandas.core.frame.DataFrame

Overview

Accesses the annual consumption autonomous energy efficiency gain projections for all regions.

Arguments

region: The region code for which the data is required.

Returns

A dataframe containing the projected autonomous energy efficiency gains for consumption with a row for each region and a column for each projection year.

consumption_cumulative_energy_usage_efficiency_gains: pandas.core.frame.DataFrame

Overview

The cumulative autonomous energy efficiency gain projections for consumption in all regions.

They are simply a cumulative sum of the annual gains.

Returns

The dataframe of cumulative energy usage efficiency gains for consumption in each region.

The dataframe has a row for each region.

They are used to populate the projections for the exogenous variables SHEFC(region).

model_includes_consumption_energy_usage_efficiency: bool

True if the input AEEI data include consumption energy usage efficiency projections for each region and False otherwise.

Inherited Members
gcubed.base.Base
zeros
load_data
get_year_labels
log_calling_stack