unit shift from US2005 to US2017

pik-piam · Sep 6, 2024 · c01cc1a · c01cc1a
1 parent 7967bb0
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diff --git a/.buildlibrary b/.buildlibrary
@@ -1,4 +1,4 @@
-ValidationKey: '4793280'
+ValidationKey: '48112567972'
 AutocreateReadme: yes
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'

diff --git a/CITATION.cff b/CITATION.cff
@@ -2,7 +2,7 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'edgeTransport: Prepare EDGE Transport Data for the REMIND model'
-version: 2.4.0
+version: 2.4.0.9001
 date-released: '2024-09-06'
 abstract: 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.

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: edgeTransport
 Title: Prepare EDGE Transport Data for the REMIND model
-Version: 2.4.0
+Version: 2.4.0.9001
 Authors@R: c(
     person("Johanna", "Hoppe", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0009-0004-6753-5090")),
@@ -30,7 +30,7 @@ Imports:
     gdxrrw,
     zoo,
     gdxdt,
-    reporttransport (>= 0.3.0)
+    reporttransport 
 Suggests:
     testthat (>= 3.0.0),
     knitr,

diff --git a/R/iterativeEDGETransport.R b/R/iterativeEDGETransport.R
@@ -78,20 +78,20 @@ iterativeEdgeTransport <- function() {
 
   ## Load REMIND fuel cost
   REMINDfuelCost <- toolLoadREMINDfuelCosts(gdx, hybridElecShare, helpers)
-  # Convert fuel costs from US$2005/MJ to US$2005/vehkm
+  # Convert fuel costs from US$2017/MJ to US$2017/vehkm
   # Merge with energy intensity
   energyIntensity <- copy(RDSinputs$scenSpecEnIntensity)
   energyIntensity[, c("variable", "unit") := NULL]
   setnames(energyIntensity, "value", "energyIntensity")
   REMINDfuelCost <- merge(REMINDfuelCost, energyIntensity, by = c("region", "univocalName", "technology", "period"))
-  REMINDfuelCost[, value := value * energyIntensity][, unit := "US$2005/vehkm"][, energyIntensity := NULL]
-  # Convert fuel costs from US$2005/vehkm to US$2005/(p|t)km
+  REMINDfuelCost[, value := value * energyIntensity][, unit := "US$2017/vehkm"][, energyIntensity := NULL]
+  # Convert fuel costs from US$2017/vehkm to US$2017/(p|t)km
   loadFactor <- copy(RDSinputs$scenSpecLoadFactor)
   loadFactor[, c("variable", "unit") := NULL]
   setnames(loadFactor, "value", "loadFactor")
   REMINDfuelCost <- merge(REMINDfuelCost, loadFactor, by = c("region", "univocalName", "technology", "period"))
   REMINDfuelCost[, value := value / loadFactor][, loadFactor := NULL]
-  REMINDfuelCost[, unit := ifelse(univocalName %in% c(helpers$filter$trn_pass, "International Aviation"), "US$2005/pkm", "US$2005/tkm")]
+  REMINDfuelCost[, unit := ifelse(univocalName %in% c(helpers$filter$trn_pass, "International Aviation"), "US$2017/pkm", "US$2017/tkm")]
 
   pathFuelCosts <- list.files(file.path(".", edgeTransportFolder), "REMINDfuelCostIterations.RDS", recursive = TRUE, full.names = TRUE)
   if (length(pathFuelCosts) > 0) {

diff --git a/R/toolCalculateFS3share.R b/R/toolCalculateFS3share.R
@@ -15,7 +15,7 @@
 
 toolCalculateFS3share <- function(endoCostData, timesteps, timeValue, preferences, lambdas, helpers){
 
-  #time costs in [$2005/pkm] for traveling with mode X in region Y
+  #time costs in [US$2017/pkm] for traveling with mode X in region Y
   timeValueCosts <- merge(timeValue, unique(helpers$decisionTree[, -c("technology")]), by = c("region", "univocalName"), all.x = TRUE)
   timeValueCosts[, type := "Travel time"][, c("unit", "univocalName", "variable") := NULL]
   if (length(timesteps) > 1) {

diff --git a/R/toolCalculateInitialIncoCost.R b/R/toolCalculateInitialIncoCost.R
@@ -8,7 +8,7 @@
 #' @param annualMileage annual mileage data
 #' @param helpers list with helpers
 #' @import data.table
-#' @returns data.table including initial inconvenience costs from 1990-2020 for LDV 4W US$2005/(p|t)km
+#' @returns data.table including initial inconvenience costs from 1990-2020 for LDV 4W US$2017/(p|t)km
 
 toolCalculateInitialIncoCost <- function(combinedCost, incoCostStartVal, annuity, loadFactor, annualMileage, helpers) {
 
@@ -65,13 +65,13 @@ toolCalculateInitialIncoCost <- function(combinedCost, incoCostStartVal, annuity
   incoCostStartValReg <- merge(decTree, incoCostStartValReg, by = c("region", "univocalName", "technology"),
                                all.x = TRUE, allow.cartesian = TRUE)
 
-  incoCostStartValReg[, unit := "US$2005/veh/yr"]
+  incoCostStartValReg[, unit := "US$2017/veh/yr"]
   setnames(incoCostStartValReg, "incoCostType", "variable")
 
-  # convert to US$2005/pkm
-  # Annualize and discount to convert to US$2005/veh/yr
+  # convert to US$2017/pkm
+  # Annualize and discount to convert to US$2017/veh/yr
   annualizedincoCostStartVal <- merge(incoCostStartValReg, annuity, by = "univocalName", allow.cartesian = TRUE)
-  annualizedincoCostStartVal[, value := value * annuity][, unit := "US$2005/veh/yr"][, annuity := NULL]
+  annualizedincoCostStartVal[, value := value * annuity][, unit := "US$2017/veh/yr"][, annuity := NULL]
 
   loadFactor <- copy(loadFactor)
   loadFactor[, c("variable", "unit") := NULL]
@@ -85,9 +85,9 @@ toolCalculateInitialIncoCost <- function(combinedCost, incoCostStartVal, annuity
   annualizedincoCostStartVal <- merge(annualizedincoCostStartVal, annualMileage,
                                       c("region", "univocalName", "technology", "period"), all.x = TRUE)
   annualizedincoCostStartVal[, value := value / (annualMileage * loadFactor)][, c("loadFactor", "annualMileage") := NULL]
-  #unit US$2005/pkm for passenger and unit US$2005/tkm for freight
+  #unit US$2017/pkm for passenger and unit US$2017/tkm for freight
   annualizedincoCostStartVal[, unit := ifelse(univocalName %in% c(helpers$filter$trn_pass, "International Aviation"),
-                                              "US$2005/pkm", "US$2005/tkm")]
+                                              "US$2017/pkm", "US$2017/tkm")]
 
   if (anyNA(annualizedincoCostStartVal) == TRUE) {
     stop("Inconvenience cost start values contain NAs")

diff --git a/R/toolCombineCAPEXandOPEX.R b/R/toolCombineCAPEXandOPEX.R
@@ -1,4 +1,4 @@
-#' Function that converts CAPEX and OPEX into US$2005/(p|t)km and provides them combined in a structured format
+#' Function that converts CAPEX and OPEX into US$2017/(p|t)km and provides them combined in a structured format
 #'
 #' @param CAPEXtrackedFleet CAPEX data for vehicle types that feature fleet tracking: Cars, trucks, busses
 #' @param nonFuelOPEXtrackedFleet non-fuel OPEX data for vehicle types that feature fleet tracking: Cars, trucks, busses
@@ -12,7 +12,7 @@
 #' @param annuity calculated annuity for different vehicle types
 #' @param helpers list with helpers
 #' @import data.table
-#' @returns data.table including total costs of ownership in US$2005/(p|t)km
+#' @returns data.table including total costs of ownership in US$2017/(p|t)km
 
 
 toolCombineCAPEXandOPEX <- function(CAPEXtrackedFleet,
@@ -33,42 +33,42 @@ toolCombineCAPEXandOPEX <- function(CAPEXtrackedFleet,
                               value), by = c("region", "period", "value")]
 
   # Tracked fleet (LDV 4W, Trucks, Busses)
-  # Annualize and discount CAPEX to convert to US$2005/veh/yr
+  # Annualize and discount CAPEX to convert to US$2017/veh/yr
   # Include subsidies on LDV 4 Wheelers
-  upfrontCAPEXtrackedFleet <- rbind(CAPEXtrackedFleet, subsidies) # in US$2005/veh
+  upfrontCAPEXtrackedFleet <- rbind(CAPEXtrackedFleet, subsidies) # in US$2017/veh
   cols <- names(upfrontCAPEXtrackedFleet)
   cols <- cols[!cols %in% c("value", "variable")]
   upfrontCAPEXtrackedFleet[, .(value = sum(value)), by = cols][, variable := "Upfront capital costs sales"]
   annualizedCapexTrackedFleet <- merge(upfrontCAPEXtrackedFleet, annuity, by = "univocalName", allow.cartesian = TRUE)
-  annualizedCapexTrackedFleet[, value := value * annuity][, unit := "US$2005/veh/yr"][, annuity := NULL]
+  annualizedCapexTrackedFleet[, value := value * annuity][, unit := "US$2017/veh/yr"][, annuity := NULL]
   # Combine with non Fuel OPEX
   CAPEXandNonFuelOPEXtrackedFleet <- rbind(annualizedCapexTrackedFleet, nonFuelOPEXtrackedFleet)
-  # Merge with annual mileage to convert to US$2005/vehkm
+  # Merge with annual mileage to convert to US$2017/vehkm
   annualMileage <- copy(annualMileage)
   annualMileage[, c("variable", "unit") := NULL]
   setnames(annualMileage, "value", "annualMileage")
   CAPEXandNonFuelOPEXtrackedFleet <- merge(CAPEXandNonFuelOPEXtrackedFleet, annualMileage, by = c("region", "univocalName", "technology", "period"))
-  CAPEXandNonFuelOPEXtrackedFleet[, value := value / annualMileage][, unit := "US$2005/vehkm"][, annualMileage := NULL]
+  CAPEXandNonFuelOPEXtrackedFleet[, value := value / annualMileage][, unit := "US$2017/vehkm"][, annualMileage := NULL]
 
-  # Combine with other modes of transport provided in US$2005/vehkm
+  # Combine with other modes of transport provided in US$2017/vehkm
   CAPEXandNonFuelOPEX <- rbind(CAPEXandNonFuelOPEXtrackedFleet, CAPEXother, nonFuelOPEXother)
 
-  # Convert fuel costs from US$2005/MJ to US$2005/vehkm
+  # Convert fuel costs from US$2017/MJ to US$2017/vehkm
   # Merge with energy intensity
   energyIntensity <- copy(energyIntensity)
   energyIntensity[, c("variable", "unit") := NULL]
   setnames(energyIntensity, "value", "energyIntensity")
   fuelCosts <- merge(fuelCosts, energyIntensity, by = c("region", "univocalName", "technology", "period"))
-  fuelCosts[, value := value * energyIntensity][, unit := "US$2005/vehkm"][, energyIntensity := NULL]
+  fuelCosts[, value := value * energyIntensity][, unit := "US$2017/vehkm"][, energyIntensity := NULL]
   combinedCAPEXandOPEX <- rbind(CAPEXandNonFuelOPEX, fuelCosts)
 
-  # Convert all cost components from US$2005/vehkm to US$2005/(p|t)km
+  # Convert all cost components from US$2017/vehkm to US$2017/(p|t)km
   loadFactor <- copy(loadFactor)
   loadFactor[, c("variable", "unit") := NULL]
   setnames(loadFactor, "value", "loadFactor")
   combinedCAPEXandOPEX <- merge(combinedCAPEXandOPEX, loadFactor, by = c("region", "univocalName", "technology", "period"))
   combinedCAPEXandOPEX[, value := value / loadFactor][, loadFactor := NULL]
-  combinedCAPEXandOPEX[, unit := ifelse(univocalName %in% c(helpers$filter$trn_pass, "International Aviation"), "US$2005/pkm", "US$2005/tkm")]
+  combinedCAPEXandOPEX[, unit := ifelse(univocalName %in% c(helpers$filter$trn_pass, "International Aviation"), "US$2017/pkm", "US$2017/tkm")]
 
   # add zeros for active modes (time value costs are treated seperately)
   # use dummy that does not feature fleet tracking

diff --git a/R/toolEdgeTransportSA.R b/R/toolEdgeTransportSA.R
@@ -34,7 +34,7 @@ toolEdgeTransportSA <- function(SSPscen,
                                 isAnalyticsReported = FALSE){
 
   # set GDP cutoff to differentiate between regions
-  GDPcutoff <- 25000 # [constant 2005 US$MER]
+  GDPcutoff <- 30800 # [constant 2017 US$MER]
   # Year when scenario differentiation sets in
   policyStartYear <- 2021
   # last time step of historical data

diff --git a/R/toolLoadREMINDfuelCosts.R b/R/toolLoadREMINDfuelCosts.R
@@ -1,4 +1,4 @@
-#' Load fuel prices from a REMIND fulldata.gdx in [US$2005/MJ] and map them on to
+#' Load fuel prices from a REMIND fulldata.gdx in [US$2017/MJ] and map them on to
 #' the edgeTransport decision tree. The output is provided in the same spatial resolution
 #' as the transferred gdx file and the temporal resolution is set according to the param yrs.
 #'
@@ -26,10 +26,10 @@ toolLoadREMINDfuelCosts <- function(gdxPath, hybridElecShare, helpers){
    fuelCosts <- fuelCosts %>% lowpass() %>% magpie2dt()
    setnames(fuelCosts, c("all_regi", "ttot"), c("region", "period"))
    fuelCosts <- fuelCosts[, c("region", "period", "all_enty", "value")]
-   # convert from TerraUS2005$ per TWyear to US2005$ per EJ
+   # convert from TerraUS$2017 per TWyear to US$2017 per EJ
    tdptwyr2dpgj <- 31.71  # TerraDollar per TWyear to Dollar per GJ
    GJtoMJ <- 1e-3 # dollar per GJ to dollar per MJ
-   fuelCosts[, value := value * tdptwyr2dpgj * GJtoMJ] # US$2005/MJ
+   fuelCosts[, value := value * tdptwyr2dpgj * GJtoMJ] # US$2017/MJ
    # map on EDGE-T structure
    fuelCosts <- merge(fuelCosts, mapEdgeToREMIND, by = "all_enty", all.y = TRUE, allow.cartesian = TRUE)[, all_enty := NULL]
    # calculate price for hybrids
@@ -49,7 +49,7 @@ toolLoadREMINDfuelCosts <- function(gdxPath, hybridElecShare, helpers){
      print("Values are filtered out and are interpolated from other timesteps.")
    }
 
-   fuelCosts[, variable := "Fuel costs"][, unit := "US$2005/MJ"]
+   fuelCosts[, variable := "Fuel costs"][, unit := "US$2017/MJ"]
 
    # get right temporal resolution
    fuelCosts <- toolApplyMixedTimeRes(fuelCosts, helpers)

diff --git a/R/toolLoadmrdriversData.R b/R/toolLoadmrdriversData.R
@@ -12,10 +12,10 @@ toolLoadmrdriversData <- function(SSPscen, helpers) {
   years <- unique(helpers$dtTimeRes$period)
 
   GDPMERmag <- calcOutput("GDP", aggregate = TRUE, regionmapping = "regionmapping_21_EU11.csv",
-                            unit = "constant 2005 US$MER")[, , paste0("gdp_", SSPscen)]|> time_interpolate(years)
+                            unit = "constant 2017 US$MER")[, , paste0("gdp_", SSPscen)]|> time_interpolate(years)
   GDPMER <- magpie2dt(GDPMERmag, yearcol = "period", regioncol = "region")[, variable := NULL]
   GDPpcMERmag <- calcOutput("GDPpc", aggregate = TRUE, regionmapping = "regionmapping_21_EU11.csv",
-                         unit = "constant 2005 US$MER")[, , paste0("gdppc_", SSPscen)]|> time_interpolate(years)
+                         unit = "constant 2017 US$MER")[, , paste0("gdppc_", SSPscen)]|> time_interpolate(years)
   GDPpcMER <- magpie2dt(GDPpcMERmag, yearcol = "period", regioncol = "region")[, variable := NULL]
 
   GDPpppMag <- calcOutput("GDP", aggregate = TRUE, regionmapping = "regionmapping_21_EU11.csv")

diff --git a/R/toolLoadmrremindData.R b/R/toolLoadmrremindData.R
@@ -13,9 +13,6 @@ toolLoadmrremindData <- function(helpers) {
   setnames(subsidies, "variable", "technology")
   #average between legal and private entities
   subsidies <- subsidies[, .(value = mean(value)), by = c("region", "period", "technology")]
-  # exchange rate 2020: 1 euro = 1.12 dollar
-  # conversion from EUR2020 to US$2005 : inflation/exchange rate = 1.3504/1.12 = 1.205714286
-  subsidies[, value := value / 1.205714286] # in 2005 USD
   subsidies[, value := - value] # count subsidies negative
   completeSub <- unique(subsidies[, c("region", "technology")])[, temporal := "all"]
   temporal <- data.table(period = yrs)[, temporal := "all"]
@@ -32,7 +29,7 @@ toolLoadmrremindData <- function(helpers) {
   subsidies <- merge(unique(helpers$decisionTree[subsectorL3 == "trn_pass_road_LDV_4W",
                                          c("region", "univocalName", "technology")]), subsidies,
                      by = c("region", "technology"), all.x = TRUE, allow.cartesian = TRUE)
-  subsidies <- subsidies[!is.na(value)][, variable := "Capital costs subsidy"][, unit := "US$2005/veh"]
+  subsidies <- subsidies[!is.na(value)][, variable := "Capital costs subsidy"][, unit := "US$2017/veh"]
   #Q: How to include phase out of the incentives? Is that needed at all?
 
 return(list(

diff --git a/R/toolLoadmrtransportData.R b/R/toolLoadmrtransportData.R
@@ -20,22 +20,22 @@ toolLoadmrtransportData <- function(SSPscen) {
                                     regionmapping = "regionmapping_21_EU11.csv", subtype = "loadFactor", SSPscen = SSPscen)
   loadFactorRaw <- magpie2dt(loadFactorMagpieobj)[, variable := paste0(variable, " (raw)")]
 
-  # CAPEX for the tracked fleet (cars, trucks, busses) [US$2005/veh]
+  # CAPEX for the tracked fleet (cars, trucks, busses) [US$2017/veh]
   CAPEXtrackedFleetMagpieobj <- calcOutput(type = "EdgeTransportSAinputs", aggregate = TRUE, warnNA = FALSE,
                                            regionmapping = "regionmapping_21_EU11.csv", subtype = "CAPEXtrackedFleet", SSPscen = SSPscen)
   CAPEXtrackedFleet <- magpie2dt(CAPEXtrackedFleetMagpieobj)
 
-  # non-fuel OPEX for the tracked fleet (cars, trucks, busses) [US$2005/veh/yr]
+  # non-fuel OPEX for the tracked fleet (cars, trucks, busses) [US$2017/veh/yr]
   nonFuelOPEXtrackedFleetMagpieobj <- calcOutput(type = "EdgeTransportSAinputs", aggregate = TRUE, warnNA = FALSE,
                                                  regionmapping = "regionmapping_21_EU11.csv", subtype = "nonFuelOPEXtrackedFleet", SSPscen = SSPscen)
   nonFuelOPEXtrackedFleet <- magpie2dt(nonFuelOPEXtrackedFleetMagpieobj)
 
-  # CAPEX other [US$2005/vehkm]
+  # CAPEX other [US$2017/vehkm]
   CAPEXotherMagpieobj <- calcOutput(type = "EdgeTransportSAinputs", aggregate = TRUE, warnNA = FALSE,
                                     regionmapping = "regionmapping_21_EU11.csv", subtype = "CAPEXother", SSPscen = SSPscen)
   CAPEXother <- magpie2dt(CAPEXotherMagpieobj)
 
-  # non-fuel OPEX other [US$2005/vehkm]
+  # non-fuel OPEX other [US$2017/vehkm]
   nonFuelOPEXotherMagpieobj <- calcOutput(type = "EdgeTransportSAinputs", aggregate = TRUE, warnNA = FALSE,
                                           regionmapping = "regionmapping_21_EU11.csv", subtype = "nonFuelOPEXother", SSPscen = SSPscen)
   nonFuelOPEXother <- magpie2dt(nonFuelOPEXotherMagpieobj)
@@ -45,7 +45,7 @@ toolLoadmrtransportData <- function(SSPscen) {
                                        regionmapping = "regionmapping_21_EU11.csv", subtype = "annualMileage", SSPscen = SSPscen)
   annualMileage <- magpie2dt(annualMileageMagpieobj)
 
-  # Time value costs [US$2005/pkm]
+  # Time value costs [US$2017/pkm]
   timeValueCostsMagpieobj <- calcOutput(type = "EdgeTransportSAinputs", aggregate = TRUE, warnNA = FALSE,
                                       regionmapping = "regionmapping_21_EU11.csv", subtype = "timeValueCosts", SSPscen = SSPscen)
   timeValueCosts <- magpie2dt(timeValueCostsMagpieobj)

diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 # Prepare EDGE Transport Data for the REMIND model
 
-R package **edgeTransport**, version **2.4.0**
+R package **edgeTransport**, version **2.4.0.9001**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/edgeTransport)](https://cran.r-project.org/package=edgeTransport)  [![R build status](https://github.com/pik-piam/edgeTransport/workflows/check/badge.svg)](https://github.com/pik-piam/edgeTransport/actions) [![codecov](https://codecov.io/gh/pik-piam/edgeTransport/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/edgeTransport) [![r-universe](https://pik-piam.r-universe.dev/badges/edgeTransport)](https://pik-piam.r-universe.dev/builds)
 
@@ -46,7 +46,7 @@ In case of questions / problems please contact Johanna Hoppe <johanna.hoppe@pik-
 
 To cite package **edgeTransport** in publications use:
 
-Hoppe J, Dirnaichner A, Rottoli M, Muessel J (2024). _edgeTransport: Prepare EDGE Transport Data for the REMIND model_. R package version 2.4.0, <https://github.com/pik-piam/edgeTransport>.
+Hoppe J, Dirnaichner A, Rottoli M, Muessel J (2024). _edgeTransport: Prepare EDGE Transport Data for the REMIND model_. R package version 2.4.0.9001, <https://github.com/pik-piam/edgeTransport>.
 
 A BibTeX entry for LaTeX users is
 
@@ -55,7 +55,7 @@ A BibTeX entry for LaTeX users is
   title = {edgeTransport: Prepare EDGE Transport Data for the REMIND model},
   author = {Johanna Hoppe and Alois Dirnaichner and Marianna Rottoli and Jarusch Muessel},
   year = {2024},
-  note = {R package version 2.4.0},
+  note = {R package version 2.4.0.9001},
   url = {https://github.com/pik-piam/edgeTransport},
 }
 ```
diff --git a/inst/extdata/genParDemRegression.csv b/inst/extdata/genParDemRegression.csv
@@ -1,18 +1,17 @@
-* Description: Income elasticities used in the demand regression for time steps before the scenario differentiation is applied ;;;
 variable;sector;GDPpcPPP;value
-Income elasticity;trn_pass;10000;0.8
-Income elasticity;trn_pass;20000;0.75
-Income elasticity;trn_pass;30000;0.5
-Income elasticity;trn_pass;40000;0.1
-Income elasticity;trn_aviation_intl;10000;1.1
-Income elasticity;trn_aviation_intl;20000;1.3
-Income elasticity;trn_aviation_intl;30000;0.8
-Income elasticity;trn_aviation_intl;40000;0.3
-Income elasticity;trn_freight;10000;1.5
-Income elasticity;trn_freight;20000;0.9
-Income elasticity;trn_freight;30000;0.4
-Income elasticity;trn_freight;40000;0.16
-Income elasticity;trn_shipping_intl;10000;0.5
-Income elasticity;trn_shipping_intl;20000;0.3
-Income elasticity;trn_shipping_intl;30000;0.2
-Income elasticity;trn_shipping_intl;40000;0.1
+Income elasticity;trn_pass;12314;0.8
+Income elasticity;trn_pass;24628;0.75
+Income elasticity;trn_pass;36941;0.5
+Income elasticity;trn_pass;49255;0.1
+Income elasticity;trn_aviation_intl;12314;1.1
+Income elasticity;trn_aviation_intl;24628;1.3
+Income elasticity;trn_aviation_intl;36941;0.8
+Income elasticity;trn_aviation_intl;49255;0.3
+Income elasticity;trn_freight;12314;1.5
+Income elasticity;trn_freight;24628;0.9
+Income elasticity;trn_freight;36941;0.4
+Income elasticity;trn_freight;49255;0.16
+Income elasticity;trn_shipping_intl;12314;0.5
+Income elasticity;trn_shipping_intl;24628;0.3
+Income elasticity;trn_shipping_intl;36941;0.2
+Income elasticity;trn_shipping_intl;49255;0.1