Package 'edgeTransport'

Title: Prepare EDGE Transport Data for the REMIND model
Description: EDGE-T is a fork of the GCAM transport module https://jgcri.github.io/gcam-doc/energy.html#transportation with a high level of detail in its representation of technological and modal options. It is a partial equilibrium model with a nested multinomial logit structure and relies on the modified logit formulation. Most of the sources are not publicly available. PIK-internal users can find the sources in the distributed file system in the folder `/p/projects/rd3mod/inputdata/sources/EDGE-Transport-Standalone`.
Authors: Johanna Hoppe [aut, cre] , Alois Dirnaichner [aut], Marianna Rottoli [aut], Jarusch Muessel [aut]
Maintainer: Johanna Hoppe <[email protected]>
License: GPL-3
Version: 2.12.3
Built: 2024-12-20 09:25:00 UTC
Source: https://github.com/pik-piam/edgeTransport

Help Index

Generate EDGE-Transport Input Data for the REMIND model, madrat interface.

Description

calcEdgeTransportSA() is a wrapper for toolEdgeTransportSA() to make use of madrat caching.

Usage

calcEdgeTransportSA(
  SSPscen,
  transportPolScen,
  isICEban = FALSE,
  demScen = "default",
  gdxPath = NULL,
  outputFolder = NULL,
  isStored = FALSE,
  isTransportReported = TRUE,
  isTransportExtendedReported = FALSE,
  isREMINDinputReported = FALSE,
  isAnalyticsReported = FALSE
)

Arguments

SSPscen

SSP or SDP scenario
transportPolScen

EDGE-T transport policy scenario
isICEban

optional enabling of ICE ban
demScen

Demand scenario, used to apply reduction factors on total demands from the regression
gdxPath

Path to a GDX file to load price signals from a REMIND run
outputFolder

Path to folder for storing output data
isStored

Optional saving of intermediate RDS files
isTransportReported

Optional transport reporting in MIF format
isTransportExtendedReported

Optional extension of transport reporting providing more detailed variables
isREMINDinputReported

Optional reporting of REMIND input data
isAnalyticsReported

Optional reporting of analytics data (e.g. variables over iterations)

Value

Transport input data for REMIND

Author(s)

Jarusch Muessel, Johanna Hoppe

Calculate shares based on discrete choice model.

Description

Function works for the use of generic preferences as well as for inconvenience costs. If no preferences are provided the function sets them to one which is equivalent to pure description by inconvenience costs.

Usage

calculateShares(totPrice, lambda, pref = NULL)

Arguments

totPrice

total price of an option in a branch of the decision tree
lambda

exponent that determines the price sensitivity of the decision model
pref

optional use of generic preference fators

Value

share of option in a branch of the decision tree

Author(s)

Johanna Hoppe

Check a data.table for NAs and duplicates and throw an error if needed

Description

Check a data.table for NAs and duplicates and throw an error if needed

Usage

checkForNAsDups(dt, varname, codePosition)

Arguments

dt

data.table to be checked
varname

name of the variable
codePosition

position in the code to find the bug

Author(s)

Johanna Hoppe

Creates RDS files for iterative REMIND-EDGE runs from csv input files. Existing files are overwritten silently. Does not return anything.

Description

Creates RDS files for iterative REMIND-EDGE runs from csv input files. Existing files are overwritten silently. Does not return anything.

Usage

csv2RDS(filename, inputPath, SSPscenario, demScenario, transportPolScenario)

Arguments

filename

name of the file
inputPath

the path to the folder containing the input (csv-) files
SSPscenario

SSP scenario
demScenario

demand scenario
transportPolScenario

Transport policy scenario

List associated univocalNames for any entry of the decision tree

Description

List associated univocalNames for any entry of the decision tree

Usage

getFilterEntriesUnivocalName(categories, decisionTree)

Arguments

categories

vector of categories to filter
decisionTree

decision tree that contains the univocalNames associated to the category

Value

list of categories and their associated univocalNames e.g. trn_pass as list entry containing a vector c("Compact Car", "HSR", ..)

Author(s)

Johanna Hoppe

EDGE-Transport iterative

Description

Run in the REMIND output folder in between iterations

Usage

iterativeEdgeTransport()

Author(s)

Johanna Hoppe

Apply ICE ban on vehicle types that feature preference factors

Description

Apply ICE ban on vehicle types that feature preference factors

Usage

toolApplyICEbanOnPreferences(preferenceTab, helpers)

Arguments

preferenceTab

data.table including preferences for all levels of the decision tree
helpers

list of helpers

Value

Preferences in accordance to the ICE ban policy

Author(s)

Johanna Hoppe

toolApplyMixedTimeRes

Description

Applies two different temporal resolutions on a data.table object in the edgeTransport data structure and do a linear approximination for the highRes data that is not available

Usage

toolApplyMixedTimeRes(data, helpers, idcols = NULL)

Arguments

data

data.table containing data in in the edgeTransport data structure (at least featuring univocalName, period, value)
helpers

list containing several helpers used throughout the model. It includes dtTimeRes, a data.table containing the temporal resolution for different univocalNames
idcols

optional supply of idcols for using approx_dt

Value

data.table

Author(s)

Johanna Hoppe

Apply scenario specific adjustments to the preference trends

Description

Apply scenario specific adjustments to the preference trends

Usage

toolApplyScenPrefTrends(
  baselinePrefTrends,
  scenParPrefTrends,
  GDPpcMER,
  policyStartYear,
  GDPcutoff,
  helpers,
  isICEban
)

Arguments

baselinePrefTrends

Baseline preference trends
scenParPrefTrends

Scenario parameters to be applied on the preference trends
GDPpcMER

Per capita GDP based on market exchange rate
policyStartYear

Year from which the scenario parameters are applied on the baseline preference trends
GDPcutoff

Threshold used to categorize countries into different mitigation groups based on their GDP
helpers

List containing several helpers used throughout the model
isICEban

Switch to turn on ICE phase out policies

Value

Scenario specific preference trends

Author(s)

Johanna Hoppe

Apply demand scenario specific adjustments to the energy intensity

Description

Apply demand scenario specific adjustments to the energy intensity

Usage

toolApplyScenSpecEnInt(enInt, scenParEnergyIntensity, policyStartYear, helpers)

Arguments

enInt

Energy intensity input data supplied by mrtransport
scenParEnergyIntensity

Transport policy scenario specific energy intensity improvement factors
policyStartYear

Year from which scenario specific transport policies are applied
helpers

List with helpers

Value

data.table with scenario specific energy intensity input data

Author(s)

Johanna Hoppe

Apply demand scenario specific adjustments to the load Factor

Description

Apply demand scenario specific adjustments to the load Factor

Usage

toolApplyScenSpecLoadFactor(
  loadFactor,
  scenParLoadFactor,
  policyStartYear,
  helpers
)

Arguments

loadFactor

load factor input data supplied by mrtransport
scenParLoadFactor

Scenario specific parameters to be applied don the baseline load factor
policyStartYear

Year when scenario differentiation sets in
helpers

List with helpers

Value

Scenario specific load factor

Author(s)

Johanna Hoppe

Calculate annuity for different vehicle types

Description

Calculate annuity for different vehicle types

Usage

toolCalculateAnnuity(annuityCalc, helpers)

Arguments

annuityCalc

input data for interest rate and service Life from edgeTransport
helpers

list with helpers

Author(s)

Johanna Hoppe

Calculate composition of the fleet

Description

This function calculates the fleet composition based on the energy service demand, the composition of new sales each year and the service life of vehicles. With the help of the annual mileage, the absolute numbers of vehicles is calculated. If the remaining stock is sufficient to meet the energy service demand, 10

Usage

toolCalculateFleetComposition(
  ESdemandFVsalesLevel,
  vehDepreciationFactors,
  vehSalesAndModeShares,
  annualMileage,
  loadFactor,
  helpers
)

Arguments

ESdemandFVsalesLevel

Energy service demand on fuel vehicle level for new sales
vehDepreciationFactors

Factors for the depreciation of vehicles in each year of the maximum service Life
vehSalesAndModeShares

Vehicle sales and mode shares for the decision tree
annualMileage

Annual kilometers driven by a vehicle
loadFactor

Persons or tons loaded on each vehicle
helpers

List of helpers

Value

list of fleet composition data

Author(s)

Johanna Hoppe

toolCalculateFS3share

Description

Calculates fuel subsector L3 shares

Usage

toolCalculateFS3share(
  endoCostData,
  timesteps,
  timeValue,
  preferences,
  lambdas,
  helpers
)

Arguments

endoCostData

data.table containing all cost components on technology level
timesteps

years for which to calculate FS3 shares
timeValue

data.table containing mode specific time value costs based on speed and gdp
preferences

data.table containing preference trends
lambdas

data.table containing exponents for discrete choice calculation
helpers

list containing helpers like mappings, decisionTree etc.

Value

data.table containing all cost components on technology level and their respective FS3 shares

Author(s)

Johanna Hoppe

Calculate eneryg service demand on fuel vehicle level

Description

Calculate eneryg service demand on fuel vehicle level

Usage

toolCalculateFVdemand(
  sectorESdemand,
  salesAndModeShares,
  helpers,
  histESdemand = NULL,
  baseYear = NULL
)

Arguments

sectorESdemand

energy service demand on CES node level
salesAndModeShares

vehicle sales and mode shares
helpers

List with helpers
histESdemand

historcial energy service demand for iterative EDGE-T
baseYear

ned year of historical energy service demand

Value

data.table

Author(s)

Johanna Hoppe

Apply regional differences for inconvenience cost start values based on ICE cost differences

Description

Apply regional differences for inconvenience cost start values based on ICE cost differences

Usage

toolCalculateInitialIncoCost(
  combinedCost,
  incoCostStartVal,
  annuity,
  loadFactor,
  annualMileage,
  helpers
)

Arguments

combinedCost

total cost of ownership
incoCostStartVal

start values for inconvenience costs
annuity

calculated annuity for different vehicle types
loadFactor

load factor data
annualMileage

annual mileage data
helpers

list with helpers

Value

data.table including initial inconvenience costs from 1990-2020 for LDV 4W US$/(p|t)km

Author(s)

Johanna Hoppe

Calculate vehicle depreciation factors for fleet tracking

Description

Calculate vehicle depreciation factors for fleet tracking

Usage

toolCalculateVehicleDepreciationFactors(annuityCalc, helpers)

Arguments

annuityCalc

parameters for annualization
helpers

list with helpers

Value

data.table including vehicle depreciation factors over service life#'

Author(s)

Johanna Hoppe

Calibrate the logit share weights to historical data.

Description

Calibrate the logit share weights to historical data.

Usage

toolCalibrateHistPrefs(
  combinedCosts,
  histESdemand,
  timeValueCost,
  lambdas,
  helpers
)

Arguments

combinedCosts

Annualized total cost of ownership
histESdemand

Historical energy service demand data
timeValueCost

Time value cost for passenger transport modes
lambdas

Exponents for discrete choice function
helpers

list with helpers

Value

data.table with calibrated historical preferences

toolCheckAllLevelsComplete

Description

Checks whether data is complete for all levels of decision tree

Usage

toolCheckAllLevelsComplete(data, decisionTree, name)

Arguments

data

data.table containing data in all levels format that should be checked
decisionTree

data.table containing full edgeTransport decision Tree
name

name of variable to be checked

Value

data.table

Author(s)

Johanna Hoppe

Function that converts CAPEX and OPEX into US$/(p|t)km and provides them combined in a structured format

Description

Function that converts CAPEX and OPEX into US$/(p|t)km and provides them combined in a structured format

Usage

toolCombineCAPEXandOPEX(
  CAPEXtrackedFleet,
  nonFuelOPEXtrackedFleet,
  CAPEXother,
  nonFuelOPEXother,
  fuelCosts,
  subsidies,
  energyIntensity,
  loadFactor,
  annualMileage,
  annuity,
  helpers
)

Arguments

CAPEXtrackedFleet

CAPEX data for vehicle types that feature fleet tracking: Cars, trucks, busses
nonFuelOPEXtrackedFleet

non-fuel OPEX data for vehicle types that feature fleet tracking: Cars, trucks, busses
CAPEXother

CAPEX data for other vehicle types
nonFuelOPEXother

non-fuel OPEX data for other vehicle types
fuelCosts

fuel cost data
subsidies

purchase price subsidy data
energyIntensity

energy intensity data
loadFactor

load factor data
annualMileage

annual mileage data
annuity

calculated annuity for different vehicle types
helpers

list with helpers

Value

data.table including total costs of ownership in US$/(p|t)km

Energy service demand projection

Description

Energy service demand projection

Usage

toolDemandRegression(
  historicalESdemand,
  GDPperCapitaPPP,
  POP,
  genParDemRegression,
  scenParDemRegression,
  scenParRegionalDemRegression,
  scenParDemandFactors,
  baseYear,
  policyStartYear,
  helpers
)

Arguments

historicalESdemand

Historical energy service demand
GDPperCapitaPPP

GDP per capita based on purchase power parity
POP

Population data
genParDemRegression

General regression factors
scenParDemRegression

Scenario specific general regression factors
scenParRegionalDemRegression

Scenario specific regionally differentiated regression factors
scenParDemandFactors

Demand scenario factors
baseYear

End year of historical energy service demand data
policyStartYear

Start year of scenario differentiation
helpers

list with helpers

Value

Scenario specific energy service demand for all model years on CES level

Author(s)

Johanna Hoppe

Calculate vehicle sales shares and mode shares for all levels of the decisionTree.

Description

Function that traverses each level of the decision tree and calculates the shares according to the discrete choice approach

Usage

toolDiscreteChoice(input, generalModelPar, updatedEndoCosts, helpers)

Arguments

input

dataset for discrete choice module
generalModelPar

general model parameter
updatedEndoCosts

updated endogenous costs
helpers

list of helpers

Value

calculated shares

Author(s)

Johanna Hoppe

Energy Demand Generator (EDGE)- Transport Model

Description

The Edge Transport Model includes the transport specific input data preparation, a choice model to determine transport mode and technology shares, a demand regression and a fleet tracking for cars, busses and trucks

Usage

toolEdgeTransportSA(
  SSPscen,
  transportPolScen,
  isICEban = FALSE,
  demScen = "default",
  gdxPath = NULL,
  outputFolder = NULL,
  isStored = TRUE,
  isTransportReported = TRUE,
  isTransportExtendedReported = FALSE,
  isREMINDinputReported = FALSE,
  isAnalyticsReported = FALSE
)

Arguments

SSPscen

SSP or SDP scenario
transportPolScen

EDGE-T transport policy scenario
isICEban

optional enabling of ICE ban
demScen

Demand scenario, used to apply reduction factors on total demands from the regression
gdxPath

Path to a GDX file to load price signals from a REMIND run
outputFolder

Path to folder for storing output data
isStored

Optional saving of intermediate RDS files
isTransportReported

Optional transport reporting in MIF format
isTransportExtendedReported

Optional extension of transport reporting providing more detailed variables
isREMINDinputReported

Optional reporting of REMIND input data
isAnalyticsReported

Optional reporting of analytics data (e.g. variables over iterations)

Value

Transport input data for REMIND

Author(s)

Johanna Hoppe, Jarusch Müßel, Alois Dirnaichner, Marianna Rottoli

Read and build the complete structure of the edgeTransport decision tree

Description

Read and build the complete structure of the edgeTransport decision tree

Usage

toolLoadDecisionTree(regionAggregation = "iso")

Arguments

regionAggregation

choose one of the different options for regional aggregation (iso|regionCode21|regionCode12)

Value

data.table of full spatially extended edgeTransport decision tree

Author(s)

Johanna Hoppe

Load all inputs that are required to run the model

Description

Load all inputs that are required to run the model

Usage

toolLoadInputs(SSPscen, transportPolScen, demScen, gdxPath, hybridElecShare)

Arguments

SSPscen

SSP or SDP scenario
transportPolScen

EDGE-T transport policy scenario
demScen

Demand scenario, used to apply reduction factors on total demands from the regression
gdxPath

Path to a GDX file to load price signals from a REMIND run
hybridElecShare

Share of electricity in Hybrid electric vehicles

Value

list with different input data sets

Author(s)

Johanna Hoppe

Load iterative inputs

Description

Load iterative inputs

Usage

toolLoadIterativeInputs(
  edgeTransportFolder,
  inputFolder,
  inputFiles,
  numberOfRegions,
  SSPscenario,
  transportPolScenario,
  demScenario
)

Arguments

edgeTransportFolder

transport folder
inputFolder

the path to the folder containing the input (csv-) files
inputFiles

names of the input files
numberOfRegions

regional resolution
SSPscenario

SSP scenario
transportPolScenario

Transport policy scenario
demScenario

demand scenario

Load GDP and Population data from mrdrivers

Description

Load GDP and Population data from mrdrivers

Usage

toolLoadmrdriversData(SSPscen, helpers)

Arguments

SSPscen

SSP scenario for which the mrdrivers input data shall be loaded
helpers

list containg several helpers used throughout the model. It includes dtTimeRes, a data.table containing the temporal resolution for different univocalNames

Value

list of data.tables containing mrdrivers input data

Load data from mrremind

Description

Load data from mrremind

Usage

toolLoadmrremindData(helpers)

Arguments

helpers

list containg several helpers used throughout the model. It includes dtTimeRes, a data.table containing the temporal resolution for different univocalNames

Value

list of data.tables containing mrremind input data

Load input data from the mrtransport package

Description

Load input data from the mrtransport package

Usage

toolLoadmrtransportData(SSPscen)

Arguments

SSPscen

SSP scenario for which the mrtransport input data should be loaded

Value

list of data.tables with the mrtransport input data

Load input data from the edgeTransport package and choose data according to SSP and transport policy scenario

Description

Load input data from the edgeTransport package and choose data according to SSP and transport policy scenario

Usage

toolLoadPackageData(SSPscenario, transportPolScenario, demScenario = NULL)

Arguments

SSPscenario

SSP scenario for which the package data should be loaded
transportPolScenario

transport policy scenario for which the package data should be loaded
demScenario

demand scenario for which the package input data should be loaded

Value

list of data.tables with the package input data

Load REMIND energy service demand

Description

Load the energy service demand from a REMIND fulldata.gdx on sector level (CES level) in [bn (p|t)km/yr] and the requested temporal resolution

Usage

toolLoadREMINDesDemand(gdxPath, helpers)

Arguments

gdxPath

path to REMIND fulldata.gdx
helpers

list of helpers

Load fuel prices from a REMIND fulldata.gdx in [US$/MJ] and map them on to the edgeTransport decision tree. The output is provided in the same spatial resolution as the transferred gdx file and the temporal resolution is set according to the param yrs.

Description

Load fuel prices from a REMIND fulldata.gdx in [US$/MJ] and map them on to the edgeTransport decision tree. The output is provided in the same spatial resolution as the transferred gdx file and the temporal resolution is set according to the param yrs.

Usage

toolLoadREMINDfuelCosts(gdxPath, hybridElecShare, helpers)

Arguments

gdxPath

path to REMIND fulldata.gdx
hybridElecShare

Share of electricity in Hybrid electric vehicles
helpers

list with helpers

Value

fuel costs on technology level

Normalize preferences so that the maximum in each branch of the decision tree equals 1

Description

Normalize preferences so that the maximum in each branch of the decision tree equals 1

Usage

toolNormalizePreferences(preferenceTab)

Arguments

preferenceTab

data.table including preferences for all levels of the decision tree

Value

Normalized preferences

Author(s)

Johanna Hoppe

toolOrderandCheck

Description

sort data.table according to edgeTransport data structure and check for NAs If checkCompletness is activated it is further checked, if the full data set is provided. In case of fleetVars == TRUE the full data set is reduced to the vehicle types that feature fleet tracking

Usage

toolOrderandCheck(
  data,
  decisionTree,
  yrs = NULL,
  checkCompleteness = FALSE,
  fleetVars = FALSE
)

Arguments

data

data.table containing data in all levels format that should be checked
decisionTree

data.table containing full edgeTransport decision Tree
yrs

temporal resolution
checkCompleteness

if activated it is further checked, if the full data set is provided
fleetVars

if activated the full data set is reduced to vehicle types that feature fleet tracking

Value

data.table

Author(s)

Johanna Hoppe

Format all cost components

Description

Format all cost components

Usage

toolPrepareDataEndogenousCosts(inputData, lambdas, helpers)

Arguments

inputData

List containing inputData
lambdas

exponents for discrete choice calculation
helpers

List with helpers

Value

data.table including all cost components

Author(s)

Johanna Hoppe

Apply transport scenario specific adjustments to input data

Description

Apply transport scenario specific adjustments to input data

Usage

toolPrepareScenInputData(
  genModelPar,
  scenModelPar,
  inputDataRaw,
  policyStartYear,
  GDPcutoff,
  helpers,
  isICEban
)

Arguments

genModelPar

General model parameters
scenModelPar

Transport scenario (SSPscen + demScen + polScen) specific model parameters
inputDataRaw

Raw input data
policyStartYear

Year when scenario differentiation sets in
GDPcutoff

GDP cutoff to differentiate between regions
helpers

List with helpers
isICEban

optional enabling of ICE ban

Value

List of data.tables with scenario specific input data

Author(s)

Johanna Hoppe

Calculate data for the next higher level of the decision tree.

Description

Function that aggregates the data for the next higher level of the decision tree

Usage

toolTraverseDecisionTree(data, upperLevel, decisionTree)

Arguments

data

data.table containing the data of the lower level
upperLevel

name of the upper level in the decision tree
decisionTree

full edgeTransport decision tree

Value

data.table

Author(s)

Johanna Hoppe

toolUpdateEndogenousCosts

Description

Provides updates for endogenous cost components e.g. inconvenience costs for cars

Usage

toolUpdateEndogenousCosts(
  dataEndoCosts,
  depreciationFactors,
  scenParIncoCost,
  policyStartYear,
  timeValue,
  preferences,
  lambdas,
  helpers,
  isICEban,
  vehiclesPerTech = NULL
)

Arguments

dataEndoCosts

data.table containing all cost components for cars over the full range of policy years in a yearly resolution. Exogenous CAPEX and OPEX are provided over the full range. Endogenous cost components and FS3 shares are provided until policyStartYear. Rest is filled with NA.
depreciationFactors

data.table containing vehicle depreciation factor for each year of service Life
scenParIncoCost

data.table containing scenario specific parameters for inconvenience costs policy mask
policyStartYear

year from which scenario-specific differentiation begins
timeValue

data.table containing mode specific time value costs based on speed and gdp
preferences

preference factor trends
lambdas

data.table containing exponents for discrete choice calculation
helpers

list containing helpers like mappings, decisionTree etc.
isICEban

optional enabling of ICE ban
vehiclesPerTech

data.table containing total number of vehicles for all years and regions

Value

list containing data.table with endogenous cost components over the full time span and additional data.tables for model behavior analysis

Author(s)

Johanna Hoppe