diff --git a/.buildlibrary b/.buildlibrary
index f394e15..8128f65 100644
--- a/.buildlibrary
+++ b/.buildlibrary
@@ -1,4 +1,4 @@
-ValidationKey: '1019439'
+ValidationKey: '1201560'
 AutocreateReadme: yes
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'
@@ -6,3 +6,4 @@ AcceptedWarnings:
 AcceptedNotes: 'Undefined global functions or variables:'
 allowLinterWarnings: yes
 enforceVersionUpdate: no
+skipCoverage: no
diff --git a/.github/workflows/check.yaml b/.github/workflows/check.yaml
index f6ea5d4..d85a316 100644
--- a/.github/workflows/check.yaml
+++ b/.github/workflows/check.yaml
@@ -23,14 +23,14 @@ jobs:
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: |
-            any::lucode2
-            any::covr
-            any::madrat
-            any::magclass
-            any::citation
-            any::gms
-            any::goxygen
-            any::GDPuc
+            lucode2
+            covr
+            madrat
+            magclass
+            citation
+            gms
+            goxygen
+            GDPuc
           # piam packages also available on CRAN (madrat, magclass, citation,
           # gms, goxygen, GDPuc) will usually have an outdated binary version
           # available; by using extra-packages we get the newest version
@@ -63,6 +63,6 @@ jobs:
         shell: Rscript {0}
         run: |
           nonDummyTests <- setdiff(list.files("./tests/testthat/"), c("test-dummy.R", "_snaps"))
-          if(length(nonDummyTests) > 0) covr::codecov(quiet = FALSE)
+          if(length(nonDummyTests) > 0 && !lucode2:::loadBuildLibraryConfig()[["skipCoverage"]]) covr::codecov(quiet = FALSE)
         env:
           NOT_CRAN: "true"
diff --git a/CITATION.cff b/CITATION.cff
index 593037b..e0a85ef 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -2,8 +2,8 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'reporttransport: Reporting package for edgeTransport'
-version: 0.5.1
-date-released: '2024-09-23'
+version: 0.6.0
+date-released: '2024-10-30'
 abstract: This package contains edgeTransport-specific routines to report model results.
   The main functionality is to generate transport reporting variables in MIF format
   from a given edgeTransport model run folder or REMIND input data.
diff --git a/DESCRIPTION b/DESCRIPTION
index 3db5ee6..904708a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Type: Package
 Package: reporttransport
 Title: Reporting package for edgeTransport
-Version: 0.5.1
-Date: 2024-09-23
+Version: 0.6.0
+Date: 2024-10-30
 Authors@R:
     person("Johanna", "Hoppe", , "johanna.hoppe@pik-potsdam.de", role = c("aut", "cre"))
 Description: This package contains edgeTransport-specific routines to
diff --git a/R/aggregateVariables.R b/R/aggregateVariables.R
index e37ed3d..1b76eba 100644
--- a/R/aggregateVariables.R
+++ b/R/aggregateVariables.R
@@ -75,6 +75,7 @@ aggregateVariables <- function(vars, mapAggregation, weight = NULL) {
   # Test for duplicated entries to prevent double counting in the aggregation
   test <- copy(vars)
   test[, value := NULL]
+
   if (anyDuplicated(test)) stop("Variables for aggregation contain duplicates.
                                 Check reportEdgeTransport() to prevent double counting")
 
@@ -96,6 +97,14 @@ aggregateVariables <- function(vars, mapAggregation, weight = NULL) {
     setnames(weight, "value", "weight")
   }
 
+  # Exclude vars that are not aggregated and create solely the variable entry
+  exclude <- c("Load factor", "Load factor raw", "Preference|FV", "Preference|S1S", "Preference|S2S1",
+               "Preference|S3S2", "Preference|VS3", "TCO sales Operating costs (total non-fuel)", "TCO sales Fuel costs", "TCO sales Capital costs", "Time value costs",
+               "Annual mileage", "Energy intensity sales", "Energy intensity (raw)", "Purchase Price", "Load factor (raw)")
+
+  aggregatedvars <- createVariableEntry(vars[variable %in% exclude | grepl(".*Iteration.*", variable)], aggrOrder)
+  vars <- vars[!(variable %in% exclude | grepl(".*Iteration.*", variable))]
+
   # Aggregate each level of the decision tree --------------------------------------------------------------------
   for (i in seq(0, length(aggrOrder) - 1)) {
     aggrvars <- copy(vars)
@@ -135,7 +144,7 @@ aggregateVariables <- function(vars, mapAggregation, weight = NULL) {
     }
   }
 
-  exclude <- c("Sales", "Vintages", "Stock", "Load factor")
+  exclude <- c("Sales", "Vintages", "Stock")
   varsForFurtherAggregation <- vars[!variable %in% exclude]
 
   # Aggregate Pass with bunkers --------------------------------------------------------------------
@@ -283,8 +292,8 @@ aggregateVariables <- function(vars, mapAggregation, weight = NULL) {
   aggregatedvars <- rbind(aggregatedvars, aggrvars)
 
   if (anyNA(aggregatedvars)) stop("Output variable contains NAs.
-                                  Please check reportAndAggregatedMIF()")
+                                  Please check aggregateVariables()")
   if (anyDuplicated(aggregatedvars[, c("region", "period", "variable")])) stop("Output variable contains Duplicates.
-                                         Please check reportAndAggregatedMIF()")
+                                         Please check aggregateVariables()")
   return(aggregatedvars)
 }
diff --git a/R/convertToMIF.R b/R/convertToMIF.R
index c32fec2..01292bd 100644
--- a/R/convertToMIF.R
+++ b/R/convertToMIF.R
@@ -22,6 +22,7 @@ convertToMIF <- function(vars, GDPMER, helpers, scenario, model, gdx,  isTranspo
   applyReportingNames <- function(vars, mapNames) {
 
     rename <- function(columns, mapNames) {
+
       dt <- data.table(name = c(columns))
       dt[, rownum := .I]
       dt <- merge(dt, mapNames, by = "name", allow.cartesian = TRUE, all.x = TRUE)
@@ -29,6 +30,7 @@ convertToMIF <- function(vars, GDPMER, helpers, scenario, model, gdx,  isTranspo
       setkey(dt, "rownum")
       dt <- dt[!is.na(reportName), name := reportName]
       dt[grepl(".*tmp", name), name := NA]
+      dt[name == "", name := NA]
       dt[!is.na(name), name := paste0("|", name)]
 
       return(dt$name)
@@ -45,42 +47,46 @@ convertToMIF <- function(vars, GDPMER, helpers, scenario, model, gdx,  isTranspo
     return(varsNew)
   }
 
-  # Use ES demand as weight to aggregate over modes-------------------------------------------
-  varsToMIFext <- vars$ext[!names(vars$ext) %in% c("GDPppp", "population")]
-  noAggregationvars <- vars$ext[c("GDPppp","population")]
-  varsToMIFext <- rbindlist(varsToMIFext, fill = TRUE, use.names = TRUE)
-  varsToMIFint <- rbindlist(vars$int, fill = TRUE, use.names = TRUE)
-
-  # Prepare vars that are not aggregated over modes
-  if (!is.null(noAggregationvars$GDPppp)){
-    noAggregationvars$GDPppp[, variable := "GDP|PPP"][, value := value * 1e-3][, unit := "billion constant 2017 Int$PPP"]
-    noAggregationvars$population[, variable := "Population"][, unit := "million"]
-    noAggregationvars <- rbindlist(noAggregationvars, fill = TRUE, use.names = TRUE)
-  }
-
-  # Apply variable naming convention----------------------------------------------------------
-  varsToMIFext <- applyReportingNames(varsToMIFext, helpers$reportingNames)
-  varsToMIFint[, fuel := NA]
-  varsToMIFint <- applyReportingNames(varsToMIFint, helpers$reportingNames)
+  noAggregationvars <- rbindlist(vars$int[c("GDPpcPPP", "GDPpcMER")], fill = TRUE, use.names = TRUE)
+  varsToMIFint <- rbindlist(vars$int[!names(vars$int) %in% c("GDPpcPPP", "GDPpcMER")], fill = TRUE, use.names = TRUE)
+  varsToMIFext <- rbindlist(vars$ext[!names(vars$ext) %in% c("GDPppp", "population", "GDPMER")], fill = TRUE, use.names = TRUE)
 
   # Regional aggregation----------------------------------------------------------------------
   ## Aggregation to world is always supplied
-  mapWorld <- unique(varsToMIFext[, c("region")])[, aggrReg := "World"]
-  worldDataExt <- as.data.table(aggregate_map(varsToMIFext, mapWorld, by = "region"))
-  if (!is.null(noAggregationvars$GDPppp)) worldDatanoAggregationvars <- as.data.table(aggregate_map(noAggregationvars, mapWorld, by = "region"))
-  weight <- copy(varsToMIFext[variable == "ES"])
-  weight[, c("variable", "unit", "fuel") := NULL]
+  mapWorld <- unique(vars$ext[[1]][, c("region")])[, aggrReg := "World"]
+  worldDataExt <- lapply(vars$ext, function(x, mapWorld) as.data.table(aggregate_map(x, mapWorld, by = "region")), mapWorld)
+
+  weight <- copy(vars$ext$fleetESdemand)
+  weight[, c("variable", "unit") := NULL]
   setnames(weight, "value", "weight")
-  weightedInt <- merge(weight,varsToMIFint, by = intersect(names(varsToMIFint), names(weight)), all.x = TRUE)
-  byCols <- names(weightedInt)
-  byCols <- byCols[!byCols %in% c("region", "value", "weight")]
-  weightedInt[, sum := sum(weight), by = eval(byCols)]
-  weightedInt[sum == 0, weight := 1, by = eval(byCols)][, sum := NULL]
-  worldDataInt <- weightedInt[, .(value = sum(value * (weight / sum(weight)))), by = eval(byCols)]
-  worldDataInt[, region := "World"]
-  varsToMIFint <- rbind(varsToMIFint, worldDataInt)
-  varsToMIFext <- rbind(varsToMIFext, worldDataExt)
-  if (!is.null(noAggregationvars$GDPppp)) noAggregationvars <- rbind(noAggregationvars, worldDatanoAggregationvars)
+
+  #split shareweights
+  if (!is.null(vars$int$scenScpecPrefTrends)) {
+    Preferences <- list(
+      PrefrenceFV = vars$int$scenScpecPrefTrends[variable == "Preference|FV"],
+      PrefrenceS1S = vars$int$scenScpecPrefTrends[variable == "Preference|S1S"],
+      PrefrenceS2S1 = vars$int$scenScpecPrefTrends[variable == "Preference|S2S1"],
+      PrefrenceS3S2 = vars$int$scenScpecPrefTrends[variable == "Preference|S3S2"],
+      PrefrenceVS3 = vars$int$scenScpecPrefTrends[variable == "Preference|VS3"]
+    )
+    vars$int <- vars$int[!names(vars$int) == "scenScpecPrefTrends"]
+    vars$int <- append(vars$int, Preferences)
+  }
+
+  worldDataInt <- lapply(vars$int, function(x, weight) {
+    byCols <- names(x)
+    #sharweights include empty columns
+    emptyColumns <- names(x)[sapply(x, function(x) all(is.na(x) | x == ""))]
+    byCols <- byCols[!byCols %in% c("value") & byCols %in% names(weight) & !byCols %in% emptyColumns]
+    weight <- weight[, .(weight = sum(weight)), by = eval(byCols)]
+    weightedInt <- merge(x, weight, by = intersect(names(x), names(weight)), all.x = TRUE)
+    byCols <- names(weightedInt)
+    byCols <- byCols[!byCols %in% c("region", "value", "weight")]
+    weightedInt[, sum := sum(weight), by = eval(byCols)]
+    weightedInt[sum == 0, weight := 1, by = eval(byCols)][, sum := NULL]
+    worldDataInt <- weightedInt[, .(value = sum(value * (weight / sum(weight)))), by = eval(byCols)]
+    worldDataInt[, region := "World"]
+    return(worldDataInt)}, weight)
 
   ## Additional regions
   ## if regionSubsetList != NULL -> gdx provides 21 region resolution
@@ -99,33 +105,52 @@ convertToMIF <- function(vars, GDPMER, helpers, scenario, model, gdx,  isTranspo
       tmp <- data.table(region = regionSubsetList[[i]], aggrReg = namesReg[i])
       regSubsetMap <- rbind(regSubsetMap, tmp)
     }
-    regSubsetDataExt <- as.data.table(aggregate_map(varsToMIFext[region %in% unique(regSubsetMap$region)],
-                                                 regSubsetMap, by = "region"))
-    if (!is.null(noAggregationvars$GDPppp)) regSubsetDataNoAggregationVars <- as.data.table(aggregate_map(noAggregationvars[region %in% unique(regSubsetMap$region)],
-                                                                  regSubsetMap, by = "region"))
-    weightedInt <- merge(varsToMIFint, regSubsetMap, by = intersect(names(varsToMIFint), names(regSubsetMap)), all.y = TRUE)
-    weightedInt <- merge(weight, weightedInt, by = intersect(names(weightedInt), names(weight)), all.y = TRUE)
-    byCols <- names(weightedInt)
-    byCols <- byCols[!byCols %in% c("region", "value", "weight")]
-    weightedInt[, sum := sum(weight), by = eval(byCols)]
-    weightedInt[sum == 0, weight := 1, by = eval(byCols)][, sum := NULL]
-    regSubsetDataInt <- weightedInt[, .(value = sum(value * (weight / sum(weight)))), by = eval(byCols)]
-    setnames(regSubsetDataInt, "aggrReg", "region")
-
-    regSubsetDataExt <- as.data.table(aggregate_map(varsToMIFext[region %in% unique(regSubsetMap$region)],
-                                                    regSubsetMap, by = "region"))
-
-    varsToMIFint <- rbind(varsToMIFint, regSubsetDataInt)
-    varsToMIFext <- rbind(varsToMIFext, regSubsetDataExt)
-    if (!is.null(noAggregationvars$GDPppp)) noAggregationvars <- rbind(regSubsetDataNoAggregationVars, noAggregationvars)
+    regSubsetDataExt <- lapply(vars$ext, function(x, regSubsetMap) {
+      as.data.table(aggregate_map(x[region %in% unique(regSubsetMap$region)],
+                                                 regSubsetMap, by = "region"))}, regSubsetMap)
+    regSubsetDataInt <- lapply(vars$int, function(x, regSubsetMap, weight) {
+      weightedInt <- merge(x, regSubsetMap, by = intersect(names(x), names(regSubsetMap)), all.y = TRUE)
+      byCols <- names(x)
+      #sharweights include empty columns
+      emptyColumns <- names(x)[sapply(x, function(x) all(is.na(x) | x == ""))]
+      byCols <- byCols[!byCols %in% c("value") & byCols %in% names(weight) & !byCols %in% emptyColumns]
+      weight <- weight[, .(weight = sum(weight)), by = eval(byCols)]
+      weightedInt <- merge(weight, weightedInt, by = intersect(names(weightedInt), names(weight)), all.y = TRUE)
+      byCols <- names(weightedInt)
+      byCols <- byCols[!byCols %in% c("region", "value", "weight")]
+      weightedInt[, sum := sum(weight), by = eval(byCols)]
+      weightedInt[sum == 0, weight := 1, by = eval(byCols)][, sum := NULL]
+      regSubsetDataInt <- weightedInt[, .(value = sum(value * (weight / sum(weight)))), by = eval(byCols)]
+      setnames(regSubsetDataInt, "aggrReg", "region")
+      return(regSubsetDataInt)}, regSubsetMap, weight)
+
+    noAggregationvars <- rbind(noAggregationvars,
+                               rbindlist(regSubsetDataInt[c("GDPpcPPP", "GDPpcMER")], fill = TRUE, use.names = TRUE),
+                               rbindlist(worldDataInt[c("GDPpcPPP", "GDPpcMER")], fill = TRUE, use.names = TRUE),
+                               rbindlist(vars$ext[c("GDPppp", "population", "GDPMER")], fill = TRUE, use.names = TRUE),
+                               rbindlist(regSubsetDataExt[c("GDPppp", "population", "GDPMER")], fill = TRUE, use.names = TRUE),
+                               rbindlist(worldDataExt[c("GDPppp", "population", "GDPMER")], fill = TRUE, use.names = TRUE))
+
+    varsToMIFint <- rbind(varsToMIFint,
+                          rbindlist(regSubsetDataInt[!names(regSubsetDataInt) %in% c("GDPpcPPP", "GDPpcMER")], fill = TRUE, use.names = TRUE),
+                          rbindlist(worldDataInt[!names(worldDataInt) %in% c("GDPpcPPP", "GDPpcMER")], fill = TRUE, use.names = TRUE))
+
+    varsToMIFext <- rbind(varsToMIFext,
+                          rbindlist(regSubsetDataExt[!names(regSubsetDataExt) %in% c("GDPppp", "population", "GDPMER")], fill = TRUE, use.names = TRUE),
+                          rbindlist(worldDataExt[!names(worldDataExt) %in% c("GDPppp", "population", "GDPMER")], fill = TRUE, use.names = TRUE))
   }
 
+  # Apply variable naming convention----------------------------------------------------------
+  varsToMIFext <- applyReportingNames(varsToMIFext, helpers$reportingNames)
+  varsToMIFint[, fuel := NA]
+  varsToMIFint <- applyReportingNames(varsToMIFint, helpers$reportingNames)
+
   # Aggregate variables-----------------------------------------------------------------------
   toMIFext <- aggregateVariables(varsToMIFext, helpers$reportingAggregation)
   weight <- varsToMIFext[variable == "ES"]
   toMIFint <- aggregateVariables(varsToMIFint, helpers$reportingAggregation, weight)
 
-  if (!is.null(noAggregationvars$GDPppp)) {
+  if (!is.null(noAggregationvars)) {
     toMIF <- rbind(toMIFint, toMIFext, noAggregationvars)
   } else {toMIF <- rbind(toMIFint, toMIFext)}
 
diff --git a/R/reportAnalyticsVarSet.R b/R/reportAnalyticsVarSet.R
index 2c89dfc..1fff7f9 100644
--- a/R/reportAnalyticsVarSet.R
+++ b/R/reportAnalyticsVarSet.R
@@ -13,19 +13,52 @@ reportAnalyticsVarSet <- function(data, timeResReporting) {
   updatedEndogenousCosts <- list()
   policyMask <- list()
   rawEndogenousCost <- list()
+  allCostsFV <- list()
+  allCostsVS3 <- list()
+  allCostsS3S2 <- list()
+  allCostsS2S1 <- list()
+  allCostsS1S <- list()
 
-  for (i in seq_along(data$endogenousCostsIterations)) {
-    updatedEndogenousCosts[[i]] <- data$endogenousCostsIterations[[i]]$updatedEndogenousCosts
-    policyMask[[i]] <- data$endogenousCostsIterations[[i]]$policyMask
-    rawEndogenousCost[[i]] <- data$endogenousCostsIterations[[i]]$rawEndogenousCost
+  endogenousCostsData <- data[names(data)[grepl("endogenousCostsIteration.*", names(data))]]
+  for (i in 1:length(endogenousCostsData)) {
+    updatedEndogenousCosts[[i]] <- endogenousCostsData[[i]]$updatedEndogenousCosts
+    policyMask[[i]] <- endogenousCostsData[[i]]$policyMask
+    rawEndogenousCost[[i]] <- endogenousCostsData[[i]]$rawEndogenousCosts
   }
 
   updatedEndogenousCosts <- rbindlist(updatedEndogenousCosts)
   policyMask <- rbindlist(policyMask)
   rawEndogenousCost <- rbindlist(rawEndogenousCost)
-  fleetVehNumbersIterations <- rbindlist(data$fleetVehNumbersIterations)
+  fleetVehNumbersIterations <- rbindlist(data[names(data)[grepl("fleetVehNumbersIteration[0-9]+", names(data))]])
+  costsDiscreteChoiceData <- data[names(data)[grepl("costsDiscreteChoiceIteration[0-9]+", names(data))]]
+  cols <- names(data$helpers$decisionTree)
+  for (i in 1:length(costsDiscreteChoiceData)) {
+    lapply(costsDiscreteChoiceData[[i]], function(dt, cols){
+      colsdt <- names(dt)
+      missingCols <- cols[!cols %in% intersect(colsdt, cols)]
+      if (!length(missingCols) == 0) dt[, eval(missingCols) := ""]
+    }, cols)
+    allCostsFV[[i]] <- costsDiscreteChoiceData[[i]]$allCostsFV
+    allCostsVS3[[i]] <- costsDiscreteChoiceData[[i]]$allCostsVS3
+    allCostsS3S2[[i]] <- costsDiscreteChoiceData[[i]]$allCostsS3S2
+    allCostsS2S1[[i]] <- costsDiscreteChoiceData[[i]]$allCostsS2S1
+    allCostsS1S[[i]] <- costsDiscreteChoiceData[[i]]$allCostsS1S
+  }
 
-  analyticsData <- list(updatedEndogenousCosts, policyMask, rawEndogenousCost, fleetVehNumbersIterations)
+  allCostsFV <- rbindlist(allCostsFV)
+  allCostsVS3 <- rbindlist(allCostsVS3)
+  allCostsS3S2 <- rbindlist(allCostsS3S2)
+  allCostsS2S1 <- rbindlist(allCostsS2S1)
+  allCostsS1S <- rbindlist(allCostsS1S)
 
+  analyticsData <- list(updatedEndogenousCosts = updatedEndogenousCosts,
+                        policyMask = policyMask,
+                        rawEndogenousCost = rawEndogenousCost,
+                        fleetVehNumbersIterations = fleetVehNumbersIterations,
+                        allCostsFV = allCostsFV,
+                        allCostsVS3 = allCostsVS3,
+                        allCostsS3S2 = allCostsS3S2,
+                        allCostsS2S1 = allCostsS2S1,
+                        allCostsS1S = allCostsS1S)
   return(analyticsData)
 }
diff --git a/R/reportEdgeTransport.R b/R/reportEdgeTransport.R
index b11fae0..daf4390 100644
--- a/R/reportEdgeTransport.R
+++ b/R/reportEdgeTransport.R
@@ -42,78 +42,82 @@ reportEdgeTransport <- function(folderPath = file.path(".", "EDGE-T"), data = NU
                                 isTransportExtendedReported = FALSE, isAnalyticsReported = FALSE,
                                 isREMINDinputReported = FALSE, isStored = TRUE, ...) {
 
-  # If you want to change timeResReporting to timesteps outside the modeleled timesteps,
-  # please add an interpolation step
-  timeResReporting <-  c(seq(2005, 2060, by = 5), seq(2070, 2110, by = 10), 2130, 2150)
+  applyReportingTimeRes <- function(item, timeRes) {
+    if (typeof(item) %in% c("character", "double") | "decisionTree" %in% names(item)) return(item)
+    else if (is.data.table(item) & ("period" %chin% colnames(item))) item <- item[period %in% timeRes]
+    else if (typeof(item) == "list" & !is.data.table(item)) {
+      item <- lapply(item, applyReportingTimeRes, timeRes)
+    }
+    return(item)
+  }
 
   #########################################################################
   ## Load data for reporting if data is not supplied in function call
   #########################################################################
+  # collect data supplied in the function
   args <- list(...)
   if (is.null(data)) data <- list()
   data <- append(data, args)
 
-  # load files needed for all
+  # Load necessary data that is not yet available
+  ## Define which files are needed for the configuration of switches
+  filesToLoad <- c("hybridElecShare",
+                   "helpers",
+                   "combinedCAPEXandOPEX",
+                   "scenSpecEnIntensity",
+                   "scenSpecLoadFactor",
+                   "fleetSizeAndComposition",
+                   "ESdemandFVsalesLevel")
+  if (isREMINDinputReported) {
+   add <- c("annualMileage", "timeValueCosts", "scenSpecPrefTrends", "initialIncoCosts")
+   filesToLoad <- c(filesToLoad, add[!add %in% filesToLoad])
+  }
+  if (isTransportReported) {
+    add <- c("upfrontCAPEXtrackedFleet")
+    filesToLoad <- c(filesToLoad, add[!add %in% filesToLoad])
+
+    if (isTransportExtendedReported) {
+      add <- c("population", "GDPppp", "GDPpcPPP", "GDPpcMER","GDPMER", "annualMileage", "energyIntensityRaw",
+               "loadFactorRaw", "CAPEXother", "nonFuelOPEXother", "nonFuelOPEXtrackedFleet", "subsidies",
+               "timeValueCosts", "scenSpecPrefTrends", "initialIncoCosts")
+      filesToLoad <- c(filesToLoad, add[!add %in% filesToLoad])
+    }
+    if (isAnalyticsReported) {
+      add <- c("fleetVehNumbersIteration[0-9]+", "endogenousCostsIteration[0-9]+", "costsDiscreteChoiceIteration[0-9]+")
+      filesToLoad <- c(filesToLoad, add[!add %in% filesToLoad])
+    }
+  }
+  filesToLoad <- c(filesToLoad[!filesToLoad %in% names(data)])
+
+  # Load data
   if (is.null(data$SSPscen)) {
     cfg <- readRDS(file.path(folderPath, "cfg.RDS"))
     data <- append(data, cfg[names(cfg) %in% c("SSPscen", "transportPolScen", "demScen")])
   }
   if (is.null(data$scenarioName)) data$scenarioName <- paste0(data$transportPolScen, " ", data$SSPscen)
   if (is.null(data$modelName)) data$modelName <- "EDGE-T"
-  if (is.null(data$hybridElecShare)) data$hybridElecShare <- readRDS(file.path(folderPath, "1_InputDataRaw", "hybridElecShare.RDS"))
-  if (is.null(data$helpers)) data$helpers <- readRDS(file.path(folderPath, "1_InputDataRaw", "helpers.RDS"))
-  if (is.null(data$combinedCAPEXandOPEX)) data$combinedCAPEXandOPEX <- readRDS(file.path(folderPath, "2_InputDataPolicy", "combinedCAPEXandOPEX.RDS"))
-  if (is.null(data$scenSpecEnIntensity)) data$scenSpecEnIntensity <- readRDS(file.path(folderPath, "2_InputDataPolicy", "scenSpecEnIntensity.RDS"))
-  if (is.null(data$scenSpecLoadFactor)) data$scenSpecLoadFactor <- readRDS(file.path(folderPath, "2_InputDataPolicy", "scenSpecLoadFactor.RDS"))
-  if (is.null(data$fleetSizeAndComposition)) data$fleetSizeAndComposition <- readRDS(file.path(folderPath, "4_Output", "fleetSizeAndComposition.RDS"))
-  if (is.null(data$ESdemandFVsalesLevel)) data$ESdemandFVsalesLevel <- readRDS(file.path(folderPath, "4_Output", "ESdemandFVsalesLevel.RDS"))
-
-  # load files for standard and extended transport reporting
-  if (isTransportReported) {
-    if (is.null(data$upfrontCAPEXtrackedFleet) & length(list.files(folderPath, "upfrontCAPEXtrackedFleet.RDS", recursive = TRUE, full.names = TRUE)) > 0)
-      data$upfrontCAPEXtrackedFleet <- readRDS(file.path(folderPath, "2_InputDataPolicy", "upfrontCAPEXtrackedFleet.RDS"))
-    if (is.null(data$population) & length(list.files(folderPath, "population.RDS", recursive = TRUE, full.names = TRUE)) > 0)
-      data$population <- readRDS(file.path(folderPath, "1_InputDataRaw", "population.RDS"))
-    if (is.null(data$GDPppp) & length(list.files(folderPath, "GDPppp.RDS", recursive = TRUE, full.names = TRUE)) > 0)
-      data$GDPppp <- readRDS(file.path(folderPath, "1_InputDataRaw", "GDPppp.RDS"))
-    if (is.null(data$gdxPath)) {
-      gdxPath <- list.files(path = folderPath, pattern = "\\.gdx$", full.names = TRUE)
-      # Check if any files were found
-      if (length(gdxPath) > 1) {
-        gdxPath <- gdxPath[1]
-        cat("More than one gdx file found. The following one was chosen\n")
-        cat(gdxPath, sep = "\n")
-      } else if (length(gdxPath) == 0) {
-        stop("No gdx files found in the specified directory.\n")
-      }
-      data$gdxPath <- gdxPath
-    }
-  }
-  if (isAnalyticsReported) {
-    # load files for analytic purposes
-    if (is.null(data$fleetVehNumbersIterations)) {
-      fleetFilesIterations <- list.files(path    = file.path(folderPath, "4_Output"),
-                                       pattern = "fleetVehNumbersIteration.*", full.names = TRUE)
-      if (length(fleetFilesIterations) > 0) {
-        data$fleetVehNumbersIterations <- lapply(fleetFilesIterations, readRDS)
-      }
-    }
-    if (is.null(data$endogenousCostsIterations)) {
-      endogenousCostFilesIterations <- list.files(path       = file.path(folderPath, "4_Output"),
-                                                  pattern    = "endogenousCostsIteration.*",
-                                                  full.names = TRUE)
-      if (length(endogenousCostFilesIterations) > 0) {
-        data$endogenousCostsIterations <- lapply(endogenousCostFilesIterations, readRDS)
-      }
+
+  if (is.null(data$gdxPath)) {
+    gdxPath <- list.files(path = folderPath, pattern = "\\.gdx$", full.names = TRUE)
+    # Check if any files were found
+    if (length(gdxPath) > 1) {
+      gdxPath <- gdxPath[1]
+      cat("More than one gdx file found. The following one was chosen\n")
+      cat(gdxPath, sep = "\n")
+    } else if (length(gdxPath) == 0) {
+      stop("No gdx files found in the specified directory.\n")
     }
+    data$gdxPath <- gdxPath
   }
 
-  if (isREMINDinputReported) {
-    # load files for REMIND input data only reporting
-    if (is.null(data$annualMileage)) data$annualMileage <- readRDS(file.path(folderPath, "1_InputDataRaw", "annualMileage.RDS"))
-    if (is.null(data$timeValueCosts)) data$timeValueCosts <- readRDS(file.path(folderPath, "1_InputDataRaw", "timeValueCosts.RDS"))
-    if (is.null(data$scenSpecPrefTrends)) data$scenSpecPrefTrends <- readRDS(file.path(folderPath, "2_InputDataPolicy", "scenSpecPrefTrends.RDS"))
-    if (is.null(data$initialIncoCosts)) data$initialIncoCosts <- readRDS(file.path(folderPath, "2_InputDataPolicy", "initialIncoCosts.RDS"))
+  if (length(filesToLoad) > 0) {
+    filePaths <- list.files(folderPath, recursive = TRUE, full.names = TRUE)
+    pathFilesToLoad <- unlist(lapply(filesToLoad, function(x) {filePaths[grepl(paste0(x, ".RDS"), filePaths)]}))
+    itemNames <- basename(pathFilesToLoad)
+    itemNames <- sub("\\.RDS$", "", itemNames)
+    addFiles <- lapply(pathFilesToLoad, readRDS)
+    names(addFiles) <- itemNames
+    data <- c(data, addFiles)
   }
   #########################################################################
   ## Report output variables
@@ -123,22 +127,62 @@ reportEdgeTransport <- function(folderPath = file.path(".", "EDGE-T"), data = NU
   reporting <- baseVarSet
   outputVars <- baseVarSet
 
+  #########################################################################
+  ## Report REMIND input data
+  #########################################################################
+  if (isREMINDinputReported) {                                                                                          # nolint: object_name_linter
+
+    timeResReporting <- c(seq(1900,1985,5),
+                          seq(1990, 2060, by = 5),
+                          seq(2070, 2110, by = 10),
+                          2130, 2150)
+    REMINDinputData <- reportREMINDinputVarSet(fleetESdemand        = baseVarSet$ext$fleetESdemand,                     # nolint: object_name_linter
+                                               fleetFEdemand        = baseVarSet$ext$fleetFEdemand,
+                                               fleetEnergyIntensity = baseVarSet$int$fleetEnergyIntensity,
+                                               fleetCapCosts        = baseVarSet$int$fleetCost[variable == "Capital costs"],
+                                               combinedCAPEXandOPEX = data$combinedCAPEXandOPEX,
+                                               scenSpecLoadFactor   = data$scenSpecLoadFactor,
+                                               scenSpecPrefTrends   = data$scenSpecPrefTrends,
+                                               scenSpecEnIntensity  = data$scenSpecEnIntensity,
+                                               initialIncoCosts     = data$initialIncoCosts,
+                                               annualMileage        = data$annualMileage,
+                                               timeValueCosts       = data$timeValueCosts,
+                                               hybridElecShare      = data$hybridElecShare,
+                                               demScen              = data$demScen,
+                                               SSPscen              = data$SSPscen,
+                                               transportPolScen     = data$transportPolScen,
+                                               timeResReporting     = timeResReporting,
+                                               helpers              = data$helpers)
+
+    reporting <- REMINDinputData
+    if (isStored) storeData(outputFolder = folderPath, REMINDinputData = REMINDinputData)
+  }
+  #########################################################################
+  ## Report transport variables
+  #########################################################################
+  # If you want to change timeResReporting to timesteps outside the modeleled timesteps,
+  # please add an interpolation step
+  timeResReporting <-  c(seq(2005, 2060, by = 5), seq(2070, 2110, by = 10), 2130, 2150)
+  # Apply time resolution that should be reported
+  data <- lapply(data, applyReportingTimeRes, timeResReporting)
+  baseVarSet <- lapply(baseVarSet, applyReportingTimeRes, timeResReporting)
+
   if (isTransportReported) {
     transportVarSet <- reportTransportVarSet(data             = data,
-                                             baseVarSet       = baseVarSet,
-                                             timeResReporting = timeResReporting)
+                                             baseVarSet       = baseVarSet)
     outputVars <- transportVarSet
+
     if (isTransportExtendedReported) {
       extendedTransportVarSet <- reportExtendedTransportVarSet(data             = data,
-                                                               baseVarSet       = baseVarSet,
-                                                               timeResReporting = timeResReporting)
+                                                               baseVarSet       = baseVarSet)
+
       outputVars$ext <- append(outputVars$ext, extendedTransportVarSet$ext)
       outputVars$int <- append(outputVars$int, extendedTransportVarSet$int)
     }
     if (isAnalyticsReported) {
-      if (!is.null(data$endogenousCostsIterations)) {
+      if (!is.null(data$endogenousCostsIteration1)) {
         analyticsVarSet <- reportAnalyticsVarSet(data = data, timeResReporting = timeResReporting)
-        outputVars$analytic <- analyticsVarSet
+        outputVars$int <- c(outputVars$int, analyticsVarSet)
       } else {
         message("Analytics data not stored in the run folder. Analytics reporting is skipped.")
       }
@@ -161,31 +205,5 @@ reportEdgeTransport <- function(folderPath = file.path(".", "EDGE-T"), data = NU
     if (isStored) write.mif(reporting, file.path(folderPath, "Transport.mif"))
   }
 
-  #########################################################################
-  ## Report REMIND input data
-  #########################################################################
-  if (isREMINDinputReported) {                                                                                              # nolint: object_name_linter
-    REMINDinputData <- reportREMINDinputVarSet(fleetESdemand        = baseVarSet$ext$fleetESdemand,                     # nolint: object_name_linter
-                                               fleetFEdemand        = baseVarSet$ext$fleetFEdemand,
-                                               fleetEnergyIntensity = baseVarSet$int$fleetEnergyIntensity,
-                                               fleetCapCosts        = baseVarSet$int$fleetCost[variable == "Capital costs"],
-                                               combinedCAPEXandOPEX = data$combinedCAPEXandOPEX,
-                                               scenSpecLoadFactor   = data$scenSpecLoadFactor,
-                                               scenSpecPrefTrends   = data$scenSpecPrefTrends,
-                                               scenSpecEnIntensity  = data$scenSpecEnIntensity,
-                                               initialIncoCosts     = data$initialIncoCosts,
-                                               annualMileage        = data$annualMileage,
-                                               timeValueCosts       = data$timeValueCosts,
-                                               hybridElecShare      = data$hybridElecShare,
-                                               demScen              = data$demScen,
-                                               SSPscen              = data$SSPscen,
-                                               transportPolScen     = data$transportPolScen,
-                                               timeResReporting     = timeResReporting,
-                                               helpers              = data$helpers)
-
-    reporting <- REMINDinputData
-    if (isStored) storeData(outputFolder = folderPath, REMINDinputData = REMINDinputData)
-  }
-
   return(reporting)
 }
diff --git a/R/reportExtendedTransportVarSet.R b/R/reportExtendedTransportVarSet.R
index 5705e68..3526f35 100644
--- a/R/reportExtendedTransportVarSet.R
+++ b/R/reportExtendedTransportVarSet.R
@@ -14,15 +14,11 @@ reportExtendedTransportVarSet <- function(data, baseVarSet, timeResReporting) {
 
   constrYear <- variable <- period <- . <- value <- NULL
 
-  # Switch from mixed time resolution to the reporting time resolution for all vars------------
-  loadFactor <- copy(data$scenSpecLoadFactor)[period %in% timeResReporting]
-  fleetFEdemand <- baseVarSet$ext$fleetFEdemand[period %in% timeResReporting]
-  fleetCost <- baseVarSet$int$fleetCost[period %in% timeResReporting]
-  population <- data$population[period %in% timeResReporting]
-  GDPppp <- data$GDPppp[period %in% timeResReporting]
+  outputVarsExt <- list()
+  outputVarsInt <- list()
 
   # Report useful energy-----------------------------------------------------------------------
-  fleetUEdemand <- reportUE(FEdemand = fleetFEdemand,
+  fleetUEdemand <- reportUE(FEdemand = baseVarSet$ext$fleetFEdemand,
                                 helpers = data$helpers)
 
   # Report vintages (stock without sales)-------------------------------------------------------
@@ -30,17 +26,64 @@ reportExtendedTransportVarSet <- function(data, baseVarSet, timeResReporting) {
   vintages[, variable := "Vintages"][, constrYear := NULL]
   cols <- names(vintages)
   vintages <- vintages[, .(value = sum(value)), by = eval(cols[!cols %in% c("value", "constrYear")])]
-  vintages <- approx_dt(vintages, timeResReporting, "period", "value", extrapolate = TRUE)
-  loadFactor <- merge(loadFactor, data$helpers$decisionTree,
-                      by = intersect(names(loadFactor), names(data$helpers$decisionTree)))
+  vintages <- approx_dt(vintages, unique(fleetUEdemand$period), "period", "value", extrapolate = TRUE)
+
+  # Report annualized TCO per pkm/tkm
+  aggregatedCAPEX <- data$combinedCAPEXandOPEX[grepl(".*Capital.*", variable)]
+  aggregatedCAPEX <- aggregatedCAPEX[, .(value = sum(value)), by = c("region", "period", "univocalName", "technology", "unit")]
+  aggregatedCAPEX[, variable := "Capital costs"]
+  combinedCAPEXandOPEX <- rbind(data$combinedCAPEXandOPEX[!grepl(".*Capital.*", variable)],
+                                aggregatedCAPEX)
+  combinedCAPEXandOPEX <- merge(combinedCAPEXandOPEX, data$helpers$decisionTree,
+                                         by = intersect(names(data$combinedCAPEXandOPEX),
+                                                        names(data$helpers$decisionTree)))
+  combinedCAPEXandOPEX[, variable := paste0("TCO sales ", variable)]
+
+  if (!is.null(data$GDPppp)) {
+    data$GDPppp[grepl("mil\\..*", unit), value := value * 1e-3][, unit := gsub("mil\\.", "billion", unit)]
+    outputVarsExt <- c(outputVarsExt, list(GDPppp = data$GDPppp))
+  }
+  if (!is.null(data$GDPpcPPP)) {
+    data$GDPpcPPP[grepl("mil\\..*", unit), value := value * 1e-3][, unit := gsub("mil\\.", "billion", unit)]
+    outputVarsInt <- c(outputVarsInt, list(GDPpcPPP = data$GDPpcPPP))
+  }
+  if (!is.null(data$GDPMER)) {
+    data$GDPMER[grepl("mil\\..*", unit), value := value * 1e-3][, unit := gsub("mil\\.", "billion", unit)]
+    outputVarsExt <- c(outputVarsExt, list(GDPMER = data$GDPMER))
+  }
+  if (!is.null(data$GDPpcMER)) {
+    data$GDPpcMER[grepl("mil\\..*", unit), value := value * 1e-3][, unit := gsub("mil\\.", "billion", unit)]
+    outputVarsInt <- c(outputVarsInt, list(GDPpcMER = data$GDPpcMER))
+  }
+  if (!is.null(data$population)) {
+    outputVarsExt <- c(outputVarsExt, list(population = data$population))
+  }
+
+  # Report transport input data if available
+  inputData <- c("timeValueCosts", "annualMileage", "scenSpecLoadFactor",
+                 "loadFactorRaw", "scenSpecEnIntensity", "energyIntensityRaw")
+  inputData <- inputData[inputData %in% names(data)]
+
+  if (!is.null(inputData)) {
+    inputData <- lapply(copy(data[inputData]), function(item, decisionTree) {item <- merge(item, decisionTree,
+                                                                            by = intersect(names(item),
+                                                                            names(decisionTree)), allow.cartesian = TRUE)},
+                                                                            data$helpers$decisionTree)
+    outputVarsInt <- c(outputVarsInt, inputData)
+  }
+
+
 
   # Split extensive and intensive variables ---------------------------------------------------
-  outputVarsExt <- list(fleetUEdemand = fleetUEdemand,
-                        vintages      = vintages,
-                        population    = population,
-                        GDPppp        = GDPppp)
-  outputVarsInt <- list(loadFactor = loadFactor,
-                        fleetCost  = fleetCost)
+  outputVarsExt <- c(outputVarsExt,
+                     list(fleetUEdemand = fleetUEdemand,
+                          vintages      = vintages)
+  )
+
+  outputVarsInt <- c(outputVarsInt,
+                     list(scenScpecPrefTrends = data$scenSpecPrefTrends[, level := NULL],
+                          combinedCAPEXandOPEX = combinedCAPEXandOPEX)
+  )
   outputVars <- list(ext = outputVarsExt,
                      int = outputVarsInt)
   return(outputVars)
diff --git a/R/reportLiquidsAndGasesComposition.R b/R/reportLiquidsAndGasesComposition.R
index 4e77365..c518285 100644
--- a/R/reportLiquidsAndGasesComposition.R
+++ b/R/reportLiquidsAndGasesComposition.R
@@ -3,7 +3,6 @@
 #' @param dtFE Final energy data for liquids and gases
 #' @param gdxPath Path to REMIND gdx, which contains the share of the various production routes
 #'                for liquid and gaseous energy carriers
-#' @param timeResReporting Time resolution for variable reporting
 #' @param helpers List of helpers
 #'
 #' @returns Final energy for liquids and gases split into fossil|bio|hydrogen
@@ -13,13 +12,13 @@
 #' @import data.table
 #' @export
 
-reportLiquidsAndGasesComposition <- function(dtFE, gdxPath, timeResReporting, helpers) {
+reportLiquidsAndGasesComposition <- function(dtFE, gdxPath, helpers) {
 
   all <- value <- Fossil <- Biomass <- Hydrogen <- variable <- type <- from <- bioToSynShareOverall <-
     synToBioShareOverall <- fuel <- technology <- univocalName <- share <- emiSectors <- period <-
       to <- from <- sumbio <- sumsyn <- . <- region <- unit <- NULL
 
-  calcSplit <- function(REMINDsegment, dataREMIND, splitOverall, timeResReporting) {                                     # nolint: object_name_linter
+  calcSplit <- function(REMINDsegment, dataREMIND, splitOverall) {                                     # nolint: object_name_linter
 
     # Final energy carrier types liquids and gases consist of the following secondary energy carrier types in REMIND
     mixedCarrierTypes <- c("seliqfos", "seliqbio", "seliqsyn", "segafos", "segabio", "segasyn")
@@ -78,8 +77,6 @@ reportLiquidsAndGasesComposition <- function(dtFE, gdxPath, timeResReporting, he
       shares <- sharesLiquids
     }
 
-    # apply low time resolution
-    shares <- approx_dt(shares, timeResReporting, "period", "value", extrapolate = TRUE)
     shares[, sum := sum(value), by = c("region", "period", "technology")]
 
     if (anyNA(shares) | nrow(shares[(sum < 0.9999 | sum > 1.0001) & sum != 0])) {
@@ -152,12 +149,13 @@ reportLiquidsAndGasesComposition <- function(dtFE, gdxPath, timeResReporting, he
   splitTransportOverall <- list(liqBioToSyn = liqBioToSyn, gasesBioToSyn = gasesBioToSyn)
 
   REMINDsegments <- c("LDVs", "nonLDVs", "bunker")                                                                     # nolint: object_name_linter
-  splitShares <- sapply(REMINDsegments, calcSplit, demFeSector, splitTransportOverall, timeResReporting,               # nolint: undesirable_function_linter
+  splitShares <- sapply(REMINDsegments, calcSplit, demFeSector, splitTransportOverall,               # nolint: undesirable_function_linter
                         simplify = FALSE, USE.NAMES = TRUE)
 
   # Make sure that only Liquids are supplied
   dtFE <- copy(dtFE)
   dtFE <- dtFE[technology %in% c("Liquids", "Gases")]
+  splitShares <- lapply(splitShares, approx_dt, xdata = unique(dtFE$period), xcol = "period", ycol = "value", extrapolate = TRUE)
   splittedCarriers <- rbindlist(lapply(REMINDsegments, applySplit, dtFE, splitShares, helpers))
   splitShares[["LDVs"]][, variable := paste0(variable, "Transport|LDV")]
   splitShares[["nonLDVs"]][, variable := paste0(variable, "Transport|Other")]
diff --git a/R/reportREMINDinputVarSet.R b/R/reportREMINDinputVarSet.R
index c0fa008..280a11e 100644
--- a/R/reportREMINDinputVarSet.R
+++ b/R/reportREMINDinputVarSet.R
@@ -44,11 +44,6 @@ reportREMINDinputVarSet <- function(fleetESdemand,
 
   DEM_scenario <- GDP_scenario <- EDGE_scenario <- value <- sumES <- variable <- univocalName <- ESdemand <- NULL
 
-  timeResReporting <- c(seq(1900,1985,5),
-    seq(1990, 2060, by = 5),
-    seq(2070, 2110, by = 10),
-    2130, 2150)
-
   ## Input data for transport module GAMS code----------------------------------------------------------------------------
 
   # See needed inputs in REMIND/modules/35_transport/edge_esm/datainput.gms
diff --git a/R/reportTransportVarSet.R b/R/reportTransportVarSet.R
index 74e6bc3..0a8ae57 100644
--- a/R/reportTransportVarSet.R
+++ b/R/reportTransportVarSet.R
@@ -2,34 +2,22 @@
 #'
 #' @param data List that contains the model results to report the detailed transport variable set
 #' @param baseVarSet Basic output variable set
-#' @param timeResReporting Timesteps to be reported
 #'
 #' @returns Detailed transport output variable set
 #' @author Johanna Hoppe
 #' @import data.table
 #' @export
 
-reportTransportVarSet <- function(data, baseVarSet, timeResReporting) {
+reportTransportVarSet <- function(data, baseVarSet) {
 
   fuel <- variable <- value <- constrYear <- period <- technology <- . <- ESdemand <- unit <- NULL
 
-  # Switch from mixed time resolution to the reporting time resolution for all vars------------------------
-  data$ESdemandFVsalesLevel <- data$ESdemandFVsalesLevel[period %in% timeResReporting]
-  data$fleetSizeAndComposition <- lapply(data$fleetSizeAndComposition,
-                                         FUN = function(x) x <- x[period %in% timeResReporting])
-  data$upfrontCAPEXtrackedFleet <- data$upfrontCAPEXtrackedFleet[period %in% timeResReporting]
-  fleetFEdemand <- baseVarSet$ext$fleetFEdemand[period %in% timeResReporting]
-  fleetESdemand <- baseVarSet$ext$fleetESdemand[period %in% timeResReporting]
-  fleetCost <- baseVarSet$int$fleetCost[period %in% timeResReporting]
-  fleetEnergyIntensity <- baseVarSet$int$fleetEnergyIntensity[period %in% timeResReporting]
-
   # Report liquids and gases split----------------------------------------------------------------------
-  varsFEcomposition <- fleetFEdemand[technology %in% c("Liquids", "Gases")]
+  varsFEcomposition <- baseVarSet$ext$fleetFEdemand[technology %in% c("Liquids", "Gases")]
   mixedCarrierSplit <- reportLiquidsAndGasesComposition(dtFE = varsFEcomposition,
                                                             gdxPath = data$gdxPath,
-                                                            timeResReporting = timeResReporting,
                                                             helpers = data$helpers)
-  fleetFEdemandsplittedCarriers <- copy(fleetFEdemand[!technology %in% c("Liquids", "Gases")])
+  fleetFEdemandsplittedCarriers <- copy(baseVarSet$ext$fleetFEdemand[!technology %in% c("Liquids", "Gases")])
   fleetFEdemandsplittedCarriers[, fuel := NA]
   fleetFEdemandsplittedCarriers <- rbind(fleetFEdemandsplittedCarriers, mixedCarrierSplit$splittedCarriers)
 
@@ -56,16 +44,17 @@ reportTransportVarSet <- function(data, baseVarSet, timeResReporting) {
   # Report vehicle sales-----------------------------------------------------------------------------------
   sales <- copy(data$fleetSizeAndComposition$fleetVehNumbersConstrYears[period == constrYear])
   sales[, variable := "Sales"][, constrYear := NULL]
-  sales <- approx_dt(sales, timeResReporting, "period", "value", extrapolate = TRUE)
+  sales <- approx_dt(sales, unique(fleetEmissions$period), "period", "value", extrapolate = TRUE)
 
   # Report yearly investment costs-------------------------------------------------------------------------
-  fleetES <- copy(fleetESdemand)
+  fleetES <- copy(baseVarSet$ext$fleetESdemand)
   fleetES[, c("variable", "unit") := NULL]
   setnames(fleetES, "value", "ESdemand")
-  fleetYrlCosts <- merge(fleetCost, fleetES,
-                         by = intersect(names(fleetCost), names(fleetES)))
+  fleetYrlCosts <- merge(baseVarSet$int$fleetCost, fleetES,
+                         by = intersect(names(baseVarSet$int$fleetCost), names(fleetES)))
   fleetYrlCosts[, value := value * ESdemand][, unit := "billion US$2017/yr"][, ESdemand := NULL]
   fleetYrlCosts[variable == "Capital costs", variable := "Annualized fleet investments"]
+
   fleetYrlCosts[variable == "Operating costs (total non-fuel)",
                 variable := "Operating costs fleet (total non-fuel)"]
   fleetYrlCosts[variable == "Fuel costs",
@@ -80,20 +69,23 @@ reportTransportVarSet <- function(data, baseVarSet, timeResReporting) {
 
   # Report upfront capital cost for vehicle sales
   if (!is.null(data$upfrontCAPEXtrackedFleet)) {
-    data$upfrontCAPEXtrackedFleet <- copy(data$upfrontCAPEXtrackedFleet)
-    data$upfrontCAPEXtrackedFleet <- merge(data$upfrontCAPEXtrackedFleet, data$helpers$decisionTree,
+    upfrontCAPEXtrackedFleet <- data$upfrontCAPEXtrackedFleet
+    upfrontCAPEXtrackedFleet <- upfrontCAPEXtrackedFleet[, .(value = sum(value)), by = c("region", "period", "univocalName", "technology", "unit")]
+    upfrontCAPEXtrackedFleet[, variable := "Purchase Price"]
+    upfrontCAPEXtrackedFleet <- merge(upfrontCAPEXtrackedFleet, data$helpers$decisionTree,
                                          by = intersect(names(data$upfrontCAPEXtrackedFleet),
                                                         names(data$helpers$decisionTree)))
   }
+
   # Split extensive and intensive variables ---------------------------------------------------
   outputVarsExt <- list(FEsplittedCarriers = fleetFEdemandsplittedCarriers,
-                        fleetESdemand = fleetESdemand,
+                        fleetESdemand = baseVarSet$ext$fleetESdemand,
                         fleetEmissions = fleetEmissions,
                         sales = sales,
                         stock = data$fleetSizeAndComposition$fleetVehNumbers,
                         fleetYrlCosts = fleetYrlCosts)
-  outputVarsInt <- list(upfrontCAPEXtrackedFleet = data$upfrontCAPEXtrackedFleet,
-                        fleetEnergyIntensity = fleetEnergyIntensity)
+  outputVarsInt <- list(fleetEnergyIntensity = baseVarSet$int$fleetEnergyIntensity)
+  if (!is.null(data$upfrontCAPEXtrackedFleet)) outputVarsInt <- c(outputVarsInt, list(upfrontCAPEXtrackedFleet = upfrontCAPEXtrackedFleet))
   outputVars <- list(ext = outputVarsExt,
                      int = outputVarsInt)
 
diff --git a/R/storeData.R b/R/storeData.R
index 46b3862..50f8f6e 100644
--- a/R/storeData.R
+++ b/R/storeData.R
@@ -28,6 +28,9 @@ storeData <- function(outputFolder, varsList = NULL, ...) {
                        "timeValueCosts",
                        "subsidies",
                        "GDPppp",
+                       "GDPMER",
+                       "GDPpcMER",
+                       "GDPpcPPP",
                        "population",
                        "f29_trpdemand",
                        "helpers")) subfolder <- "1_InputDataRaw"
@@ -42,6 +45,7 @@ storeData <- function(outputFolder, varsList = NULL, ...) {
     if (varName %in% c("histPrefs")) subfolder <- "3_Calibration"
     if (varName %in% c("fleetSizeAndComposition",
                        "vehSalesAndModeShares",
+                       "costsDiscreteChoiceIterations",
                        "fleetVehNumbersIterations",
                        "endogenousCostsIterations",
                        "endogenousCosts",
@@ -120,6 +124,13 @@ storeData <- function(outputFolder, varsList = NULL, ...) {
     }
     vars <- vars[!names(vars) %in% c("endogenousCostsIterations")]
   }
+  if (!is.null(vars$costsDiscreteChoiceIterations)) {
+    for (i in seq_along(vars$costsDiscreteChoiceIterations)) {
+      saveRDS(vars$costsDiscreteChoiceIterations[[i]], file.path(outputFolder, "4_Output",
+                                                             paste0("costsDiscreteChoiceIteration", i, ".RDS")))
+    }
+    vars <- vars[!names(vars) %in% c("endogenousCostsIterations")]
+  }
 
   # store REMIND inputdata if provided
   if (!is.null(vars$REMINDinputData)) {
diff --git a/R/supportFunctions.R b/R/supportFunctions.R
index 3855356..727cbf2 100644
--- a/R/supportFunctions.R
+++ b/R/supportFunctions.R
@@ -83,3 +83,6 @@ checkForNAsAndDups <- function(dt, varname, codePosition) {
   }
 
 }
+
+
+
diff --git a/README.md b/README.md
index 0b2bb28..a3cb277 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Reporting package for edgeTransport
 
-R package **reporttransport**, version **0.5.1**
+R package **reporttransport**, version **0.6.0**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/reporttransport)](https://cran.r-project.org/package=reporttransport)  [![R build status](https://github.com/pik-piam/reporttransport/workflows/check/badge.svg)](https://github.com/pik-piam/reporttransport/actions) [![codecov](https://codecov.io/gh/pik-piam/reporttransport/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/reporttransport) [![r-universe](https://pik-piam.r-universe.dev/badges/reporttransport)](https://pik-piam.r-universe.dev/builds)
 
@@ -41,7 +41,7 @@ In case of questions / problems please contact Johanna Hoppe <johanna.hoppe@pik-
 
 To cite package **reporttransport** in publications use:
 
-Hoppe J (2024). _reporttransport: Reporting package for edgeTransport_. R package version 0.5.1, <https://github.com/pik-piam/reporttransport>.
+Hoppe J (2024). _reporttransport: Reporting package for edgeTransport_. R package version 0.6.0, <https://github.com/pik-piam/reporttransport>.
 
 A BibTeX entry for LaTeX users is
 
@@ -50,7 +50,7 @@ A BibTeX entry for LaTeX users is
   title = {reporttransport: Reporting package for edgeTransport},
   author = {Johanna Hoppe},
   year = {2024},
-  note = {R package version 0.5.1},
+  note = {R package version 0.6.0},
   url = {https://github.com/pik-piam/reporttransport},
 }
 ```
diff --git a/inst/compareScenarios/cs_06_input_parameters.Rmd b/inst/compareScenarios/cs_06_input_parameters.Rmd
new file mode 100644
index 0000000..848a26f
--- /dev/null
+++ b/inst/compareScenarios/cs_06_input_parameters.Rmd
@@ -0,0 +1,587 @@
+# Model input parameters
+## Raw input data
+```{r}
+library(dplyr)
+library(ggplot2)
+
+showTransportInputMultiLinePlots <- function(data, vars, scales = "free_y", nrowNum = 1, mainReg = getOption("mip.mainReg")) {
+  
+  # Validate function arguments
+  stopifnot(is.character(vars))
+  stopifnot(is.character(scales) && length(scales) == 1)
+  stopifnot(is.character(mainReg) && length(mainReg) == 1)
+  
+  # Data preprocessing
+  d <- data %>%
+    filter(variable %in% vars) %>%
+    droplevels()
+  
+  dMainScen <- d %>%
+    filter(region == mainReg, scenario != "historical") %>%
+    droplevels()
+  
+  dMainHist <- d %>%
+    filter(region == mainReg, scenario == "historical") %>%
+    droplevels()
+  
+  dRegiScen <- d %>%
+    filter(region != mainReg, scenario != "historical") %>%
+    droplevels()
+  
+  dRegiHist <- d %>%
+    filter(region != mainReg, scenario == "historical") %>%
+    droplevels()
+  
+  regions <- levels(dRegiScen$region)
+  
+  # Check if there is any data to plot
+  if (nrow(dMainScen) == 0) {
+    warning("Nothing to plot.", call. = FALSE)
+    return(invisible(NULL))
+  }
+  
+  label <- paste0("(", paste0(levels(d$unit), collapse = ","), ")")
+  
+  p2 <- dRegiScen %>%
+    ggplot(aes(x = period, y = value, color = region)) +
+    geom_line(aes(linetype = scenario)) +
+    geom_point(data = dRegiHist, aes(shape = model)) +
+    geom_line(data = dRegiHist, aes(group = paste0(model, region)), alpha = 0.5) +
+    facet_wrap(~ variable, scales = scales, nrow = nrowNum) +
+    theme_minimal() +
+    scale_color_manual(values = plotstyle(regions)) +
+    expand_limits(y = 0) +
+    ylab(label)
+  
+  print(p2)
+  cat("\n\n")
+}
+```
+
+### Load factor
+#### Pass other
+```{r}
+vars <- as.character(unique(data$variable))
+items <- vars[grepl("Load factor (raw)\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & !grepl(".*Four Wheelers.*|.*Two Wheelers.*|.*Non-motorized.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("(Load factor raw|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit) / 5))
+```
+
+#### Pass Four Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit) / 6))
+```
+
+#### Pass Two Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Two Wheelers\\|", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```
+
+#### Freight other
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & !grepl(".*Truck.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("(\\|Transport|\\|Pass|\\|Bunkers)", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```
+
+#### Freight Trucks
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", .) & grepl(".*Truck.*", .)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Road\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit) / 6))
+```
+
+### Energy intensity
+#### Pass other
+```{r}
+items <- vars[grepl("Energy intensity (raw)\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & !grepl(".*Four Wheelers.*|.*Two Wheelers.*|.*Non-motorized.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("(Energy intensity (raw)|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit) / 5))
+```
+
+#### Pass Four Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit) / 6))
+```
+
+#### Pass Two Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Two Wheelers\\|", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```
+
+#### Freight other
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & !grepl(".*Truck.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("(\\|Transport|\\|Pass|\\|Bunkers)", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```
+
+#### Freight Trucks
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & grepl(".*Truck.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Road\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit) / 6))
+```
+
+### Annual mileage
+#### Cars
+```{r}
+items <- vars[grepl(".*Annual mileage.*", vars)]
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit) / 6))
+```
+
+#### Busses
+```{r}
+itemsSplit <- items[grepl(".*Bus.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Road\\|", "", variable))
+itemsSplit <- unique(plotData$variable)
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit) / 6))
+```
+
+#### Trucks
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & grepl(".*Truck.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Road\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+### Purchase Price Cars, Trucks and Busses
+#### Cars
+```{r}
+items <- vars[grepl(".*Purchase Price.*", vars)]
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+#### Busses
+```{r}
+itemsSplit <- items[grepl(".*Bus.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Road\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+#### Trucks
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & grepl(".*Truck.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Road\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+### Time value costs
+#### Pass other
+```{r}
+items <- vars[grepl("Time value costs\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & !grepl(".*Four Wheelers.*", items) & !grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("(Time value costs|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+#### Pass Four Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+#### Pass Two Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Two Wheelers\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+## Scenario specific input data
+### Annualized TCO for new sales
+#### Capital costs
+##### Pass other
+```{r}
+items <- vars[grepl("TCO sales Capital costs\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & !grepl(".*Four Wheelers.*", items) & !grepl(".*Two Wheelers.*", items)& !grepl(".*Non-motorized.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("(TCO sales Capital costs|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+##### Pass Four Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+##### Pass Two Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Two Wheelers\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+##### Freight other
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & !grepl(".*Truck.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("(\\|Transport|\\|Pass|\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+##### Freight Trucks
+```{r}
+itemsSplit<- items[grepl(".*Freight.*", items) & grepl(".*Truck.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Road\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+#### Operating costs (non-fuel)
+##### Pass other
+```{r}
+items <- vars[grepl("TCO sales Operating costs \\(total non-fuel\\)\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & !grepl(".*Four Wheelers.*", items) & !grepl(".*Two Wheelers.*", items)& !grepl(".*Non-motorized.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("(TCO sales Operating costs \\(total non-fuel\\)|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+##### Pass Four Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+##### Pass Two Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%
+  filter(variable %in% itemsSplit) %>%
+  mutate(variable = gsub("^.*\\|Two Wheelers\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+##### Freight other
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & !grepl(".*Truck.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(\\|Transport|\\|Pass|\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+##### Freight Trucks
+```{r}
+itemsSplit<- items[grepl(".*Freight.*", items) & grepl(".*Truck.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Road\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+#### Fuel costs
+##### Pass other
+```{r}
+items <- vars[grepl("TCO sales Fuel costs\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & !grepl(".*Four Wheelers.*", items) & !grepl(".*Two Wheelers.*", items)& !grepl(".*Non-motorized.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(TCO sales Fuel costs|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+##### Pass Four Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+##### Pass Two Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Two Wheelers\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+##### Freight other
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & !grepl(".*Truck.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(\\|Transport|\\|Pass|\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+##### Freight Trucks
+```{r}
+itemsSplit<- items[grepl(".*Freight.*", items) & grepl(".*Truck.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Road\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+### Scenario specific load factor
+#### Pass other
+```{r}
+vars <- as.character(unique(data$variable))
+items <- vars[grepl("Load factor\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & !grepl(".*Four Wheelers.*", items) & !grepl(".*Two Wheelers.*", items)& !grepl(".*Non-motorized.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(Load factor|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+#### Pass Four Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+#### Pass Two Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Two Wheelers\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+#### Freight other
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & !grepl(".*Truck.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(\\|Transport|\\|Pass|\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+#### Freight Trucks
+```{r}
+itemsSplit<- items[grepl(".*Freight.*", items) & grepl(".*Truck.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Road\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+### Scenario specific energy intensity
+#### Pass other
+```{r}
+items <- vars[grepl("Energy intensity sales\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & !grepl(".*Four Wheelers.*", items) & !grepl(".*Two Wheelers.*", items)& !grepl(".*Non-motorized.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(Energy intensity sales|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+#### Pass Four Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Four Wheelers.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Four Wheelers\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+#### Pass Two Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Two Wheelers\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+#### Freight other
+```{r}
+itemsSplit <- items[grepl(".*Freight.*", items) & !grepl(".*Truck.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(\\|Transport|\\|Pass|\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+#### Freight Trucks
+```{r}
+itemsSplit<- items[grepl(".*Freight.*", items) & grepl(".*Truck.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Road\\|[A-Za-z]+\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+### Preference trends
+#### sector-Subsector 1
+##### Pass
+```{r}
+items <- vars[grepl("Preference\\|S1S\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(Preference\\|S1S|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+##### Freight
+```{r}
+items <- vars[grepl("Preference\\|S1S\\|.*", vars)]
+itemsSplit <- items[grepl(".*Freight.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(Preference\\|S1S|\\|Transport|\\|Freight\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+#### Subsector 1 - Subsector 2
+```{r}
+items <- vars[grepl("Preference\\|S2S1\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & grepl(".*Bus.*|.*LDV.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(Preference\\|S2S1|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit)/5))
+``` 
+
+#### Subsector 2 - Subsector 3
+```{r}
+items <- vars[grepl("Preference\\|S3S2\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & grepl(".*LDV.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(Preference\\|S3S2|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit)/5))
+``` 
+
+#### Subsector 3 - Vehicle
+##### Pass
+```{r}
+items <- vars[grepl("Preference\\|VS3\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items)& grepl(".*LDV.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|LDV\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+##### Freight
+```{r}
+items <- vars[grepl("Preference\\|VS3\\|.*", vars)]
+itemsSplit <- items[grepl(".*Freight.*", items)& grepl(".*Road.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Road\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+#### Vehicle - Fuel
+#### Pass other
+```{r}
+items <- vars[grepl("Preference\\|FV\\|.*", vars)]
+itemsSplit <- items[grepl(".*Pass.*", items) & !grepl(".*Four Wheelers.*", items) & !grepl(".*Two Wheelers.*", items)& !grepl(".*(Walk|Cycle).*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(Preference\\|FV|\\|Transport|\\|Pass\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+##### Pass Two Wheelers
+```{r}
+itemsSplit <- items[grepl(".*Two Wheelers.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("^.*\\|Two Wheelers\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = 2)
+```  
+
+#### Freight other
+```{r}
+items <- vars[grepl("Preference\\|FV\\|.*", vars)]
+itemsSplit <- items[grepl(".*Freight.*", items) & !grepl(".*Road.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("(Preference\\|FV|\\|Transport|\\|Freight\\||\\|Bunkers)", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/5))
+```  
+
+##### Freight Trucks
+```{r}
+itemsSplit<- items[grepl("Preference\\|FV\\|.*", items) & grepl(".*Truck.*", items)]
+plotData <- data %>%   filter(variable %in% itemsSplit) %>%   mutate(variable = gsub("Preference\\|FV\\|Transport\\|Freight\\|Road\\|", "", variable))
+itemsSplit <- as.character(unique(plotData$variable))
+showTransportInputMultiLinePlots(plotData, itemsSplit, nrowNum = ceiling(length(itemsSplit )/6))
+```  
+
+### GDP
+```{r}
+items <- vars[grepl(".*GDP.*", vars)]
+showMultiLinePlots(data, items, nrowNum = 2)
+```
diff --git a/inst/compareScenarios/cs_08_transportRemindInputfiles.Rmd b/inst/compareScenarios/cs_08_transportRemindInputfiles.Rmd
new file mode 100644
index 0000000..77278eb
--- /dev/null
+++ b/inst/compareScenarios/cs_08_transportRemindInputfiles.Rmd
@@ -0,0 +1,186 @@
+---
+title: "Untitled"
+author: "Johanna Hoppe"
+date: "2024-09-05"
+output: html_document
+---
+
+```{r}
+library(ggplot2)
+library(dplyr)
+
+timeResReporting <-  c(seq(2005, 2060, by = 5), seq(2070, 2100, by = 10))
+axisScaling = "free_y" #fixed
+paths = params$mifScen
+paths <- gsub("Transport.mif", "5_REMINDinputData", paths)
+
+files <- list(f29_trpdemand = "f29_trpdemand",
+              f35_demByTech = "f35_demByTech",
+              f35_esCapCost = "f35_esCapCost", 
+              f35_fe2es = "f35_fe2es")
+
+# Load files from all paths
+loaded_files <- lapply(files, function(file_name) {
+  lapply(paths, function(path) {
+    # Load the data using read.csv
+    file_path <- file.path(path, paste0(file_name, ".csv"))
+    if (file.exists(file_path)) read.csv(file_path)
+  })
+})
+
+# Clean the loaded data by applying the required transformations
+loaded_files <- lapply(loaded_files, function(file_list) {
+  lapply(file_list, function(df) {
+    # If the column V1 exists, remove it
+    if ("V1" %in% colnames(df)) df <- df %>% select(-V1)
+    return(df)
+  })
+})
+
+# Change scenario name to mifScenNames if applicable
+if (!is.null(params$mifScenNames)) {
+  loaded_files <- lapply(loaded_files, function(file_list) {
+    for (i in 1:length(params$mifScenNames)) {
+      file_list[[i]] <- file_list[[i]] %>% mutate(EDGE_scenario = params$mifScenNames[i])
+    } 
+    return(file_list)
+  })
+}
+
+# Combine the corresponding datasets across paths
+combined_files <- setNames(
+  lapply(files, function(file_name) {
+    bind_rows(loaded_files[[file_name]])
+  }),
+  files # This will set the names of the resulting list to the filenames
+)
+
+# Select relevant data for different files, based on "tall" column being in "timeResReporting"
+f29_trpdemand <- combined_files[["f29_trpdemand"]] %>% filter(tall %in% timeResReporting)
+f35_fe2es <- combined_files[["f35_fe2es"]] %>% filter(tall %in% timeResReporting)
+f35_esCapCost <- combined_files[["f35_esCapCost"]] %>% filter(tall %in% timeResReporting)
+f35_demByTech <- combined_files[["f35_demByTech"]] %>% filter(tall %in% timeResReporting)
+```
+
+# REMIND input data rom EDGE-T standalone
+
+## Energy service demand on CES node level
+```{r}
+unique_all_in <- unique(f29_trpdemand$all_in)
+
+# Loop through each unique value of all_teEs and create a plot
+for (allin in unique_all_in) {
+
+  # Filter data for the current technology/service
+  filtered_data <- f29_trpdemand %>% filter(all_in == allin)
+  
+  # Create the ggplot for the current allin
+  p <- ggplot(filtered_data, aes(x = tall, y = value, color = EDGE_scenario)) +
+    geom_line() +
+    facet_wrap(~ all_regi, scales = axisScaling) +  # Facet by region/country
+    labs(
+      title = paste("Energy service demand for:", allin),
+      x = "Time",
+      y = "Value",
+      color = "EDGE Scenario"
+    ) +
+    theme_minimal() +
+    theme(legend.position = "bottom")
+  
+  # Print the plot on a new page
+  print(p)
+  cat("\n\n")
+}
+
+```
+
+
+## Energy efficiency per energy service technology
+```{r}
+unique_teEs <- unique(f35_fe2es$all_teEs)
+
+# Loop through each unique value of all_teEs and create a plot
+for (teEs in unique_teEs) {
+
+  # Filter data for the current technology/service (all_teEs)
+  filtered_data <- f35_fe2es %>% filter(all_teEs == teEs)
+  
+  # Create the ggplot for the current teEs
+  p <- ggplot(filtered_data, aes(x = tall, y = value, color = EDGE_scenario)) +
+    geom_line() +
+    facet_wrap(~ all_regi, scales = axisScaling) +  # Facet by region/country
+    labs(
+      title = paste("Energy efficiency for:", teEs),
+      x = "Time",
+      y = "Value",
+      color = "EDGE Scenario"
+    ) +
+    theme_minimal() +
+    theme(legend.position = "bottom")
+  
+  # Print the plot (or save it if you want to store the plots)
+  print(p)
+  cat("\n\n")
+}
+```
+
+
+## Capital cost per energy service technology
+```{r}
+unique_teEs <- unique(f35_esCapCost$all_teEs)
+
+# Loop through each unique value of all_teEs and create a plot
+for (teEs in unique_teEs) {
+  
+  # Filter data for the current technology/service (all_teEs)
+  filtered_data <- f35_esCapCost %>% filter(all_teEs == teEs)
+  
+  # Create the ggplot for the current teEs
+  p <- ggplot(filtered_data, aes(x = tall, y = value, color = EDGE_scenario)) +
+    geom_line() +
+    facet_wrap(~ all_regi, scales = axisScaling) +  # Facet by region/country
+    labs(
+      title = paste("Capital cost for:", teEs),
+      x = "Time",
+      y = "Value",
+      color = "EDGE Scenario"
+    ) +
+    theme_minimal() +
+    theme(legend.position = "bottom")
+  
+  # Print the plot (or save it if you want to store the plots)
+  print(p)
+  cat("\n\n")
+}
+
+```
+
+## Final energy demand per energy service technology
+```{r}
+unique_teEs <- unique(f35_demByTech$all_teEs)
+
+# Loop through each unique value of all_teEs and create a plot
+for (teEs in unique_teEs) {
+  
+  # Filter data for the current technology/service (all_teEs)
+  filtered_data <- f35_demByTech %>% filter(all_teEs == teEs)
+  
+  # Create the ggplot for the current teEs
+  p <- ggplot(filtered_data, aes(x = tall, y = value, color = EDGE_scenario)) +
+    geom_line() +
+    facet_wrap(~ all_regi, scales = axisScaling) +  # Facet by region/country
+    labs(
+      title = paste("Final energy for:", teEs),
+      x = "Time",
+      y = "Value",
+      color = "EDGE Scenario"
+    ) +
+    theme_minimal() +
+    theme(legend.position = "bottom")
+  
+  # Print the plot (or save it if you want to store the plots)
+  print(p)
+  cat("\n\n")
+}
+
+```
diff --git a/man/reportLiquidsAndGasesComposition.Rd b/man/reportLiquidsAndGasesComposition.Rd
index 882e5ff..762542e 100644
--- a/man/reportLiquidsAndGasesComposition.Rd
+++ b/man/reportLiquidsAndGasesComposition.Rd
@@ -4,7 +4,7 @@
 \alias{reportLiquidsAndGasesComposition}
 \title{Report the split of liquids and gases into fossil|bio|hydrogen}
 \usage{
-reportLiquidsAndGasesComposition(dtFE, gdxPath, timeResReporting, helpers)
+reportLiquidsAndGasesComposition(dtFE, gdxPath, helpers)
 }
 \arguments{
 \item{dtFE}{Final energy data for liquids and gases}
@@ -12,8 +12,6 @@ reportLiquidsAndGasesComposition(dtFE, gdxPath, timeResReporting, helpers)
 \item{gdxPath}{Path to REMIND gdx, which contains the share of the various production routes
 for liquid and gaseous energy carriers}
 
-\item{timeResReporting}{Time resolution for variable reporting}
-
 \item{helpers}{List of helpers}
 }
 \value{
diff --git a/man/reportTransportVarSet.Rd b/man/reportTransportVarSet.Rd
index 315dae7..8280074 100644
--- a/man/reportTransportVarSet.Rd
+++ b/man/reportTransportVarSet.Rd
@@ -4,14 +4,12 @@
 \alias{reportTransportVarSet}
 \title{Report detailed transport variable set}
 \usage{
-reportTransportVarSet(data, baseVarSet, timeResReporting)
+reportTransportVarSet(data, baseVarSet)
 }
 \arguments{
 \item{data}{List that contains the model results to report the detailed transport variable set}
 
 \item{baseVarSet}{Basic output variable set}
-
-\item{timeResReporting}{Timesteps to be reported}
 }
 \value{
 Detailed transport output variable set