diff --git a/R/calcIEA_ETP.R b/R/calcIEA_ETP.R
index a3ef5a5b..88619468 100644
--- a/R/calcIEA_ETP.R
+++ b/R/calcIEA_ETP.R
@@ -11,7 +11,6 @@
 #' @export
 
 calcIEA_ETP <- function() {
-
   mapping <- toolGetMapping("Mapping_IEA_ETP.csv", type = "reportingVariables", where = "mrremind") %>%
     filter(!is.na(!!sym("REMIND")), !!sym("REMIND") != "") %>%
     mutate("Conversion" = as.numeric(!!sym("Conversion"))) %>%
@@ -52,11 +51,25 @@ calcIEA_ETP <- function() {
   x <- aggregate(value ~ region + year + model + variable, x, sum, na.action = na.pass) %>%
     as.magpie()
 
+  .customAggregate <- function(x, rel, to = NULL) {
+    out <- toolAggregate(x, rel = rel, to = to)
+
+    # remove cement and industry production for all regions except for GLO
+    if ("GLO" %in% getItems(out, dim = 1)) {
+      out[setdiff(getItems(out, dim = 1), "GLO"), ,
+          c("Production|Industry|Cement (Mt/yr)", "Production|Industry|Steel (Mt/yr)")] <- NA
+
+    } else {
+      out <- out[, , c("Production|Industry|Cement (Mt/yr)", "Production|Industry|Steel (Mt/yr)"), invert = TRUE]
+    }
+    return(out)
+  }
+
   return(list(
     x = x,
     weight = NULL,
+    aggregationFunction = .customAggregate,
     unit = c("EJ/yr", "Mt CO2/yr", "Mt/yr", "bn pkm/yr", "bn tkm/yr"),
     description = "IEA ETP projections as REMIND variables"
   ))
-
 }
diff --git a/R/filter_historical_mif.R b/R/filter_historical_mif.R
deleted file mode 100644
index c86d3f27..00000000
--- a/R/filter_historical_mif.R
+++ /dev/null
@@ -1,71 +0,0 @@
-#' Filter improperly aggregated regional data from historical.mif
-#'
-#' Load a `historical.mif` file, remove designated data, write back to file.
-#'
-#' For all combinations of scenario, model, and variable (and all years), either
-#' all regions listed in `exclude_regions` in the `filter_table` are removed, or
-#' all regions _except_ those listed in `include_regions` in the `filter_table`
-#' are removed.
-#' Defining both `include_regions` and `exclude_regions` on the same row of
-#' `filter_table` will throw an error.
-#' Scenarios, models, and variables are matched precisely, not partially or via
-#' regular expressions.
-#'
-#' @md
-#' @param path Path to `historical.mif` (or any mif for that matter), defaults
-#'   to `historical.mif` in the currently configured
-#'   `getConfig('outputfolder')`.
-#' @param filter_table Data frame with columns `scenario`, `model`, `variable`,
-#'   `include_regions`, and `exclude_regions`.  Defaults to the built-in
-#'   `./inst/extdata/historical_mif_filter_table.csv` of the `mrremind` package.
-#'
-#' @importFrom quitte cartesian
-
-#' @export
-filter_historical_mif <- function(path = NULL, filter_table = NULL) {
-    if (is.null(path))
-        path <- 'historical.mif'
-
-    if (is.null(filter_table))
-        filter_table <- read.csv(
-            file = system.file('extdata', 'historical_mif_filter_table.csv',
-                               package = 'mrremind', mustWork = TRUE),
-            colClasses = 'character')
-
-    # combine scenario, model, and variable columns to string for indexing a
-    # magpie object
-    scenario_model_variable <- sapply(
-        seq_len(nrow(filter_table)),
-        function(i) {
-            Reduce(f = cartesian,
-                   x = filter_table[i, c('scenario', 'model', 'variable')])
-        })
-
-    # check for conflicting include/exclude definitions
-    include_and_exclude <- ( '' != filter_table[['include_regions']]
-                           & '' != filter_table[['exclude_regions']])
-
-    if (any(include_and_exclude))
-        stop('Both include and exclude regions defined for:',
-             paste(c('', scenario_model_variable[include_and_exclude]),
-                   collapse = '\n'))
-
-    h <- read.report(file = path, as.list = FALSE)
-
-    # process each row of the filter table
-    for (i in seq_len(nrow(filter_table))) {
-        if ('' != filter_table[[i, 'include_regions']]) {
-          excludedRegions <- setdiff(getItems(h, dim = 'region'),
-                                     filter_table[[i, 'include_regions']])
-        } else {
-          excludedRegions <- filter_table[[i, 'exclude_regions']]
-        }
-
-        h_include <- h[, , scenario_model_variable[i]]
-        h_include[excludedRegions, , ] <- NA
-
-        h <- mbind(h[, , scenario_model_variable[i], invert = TRUE], h_include)
-    }
-
-    write.report(h, file = path)
-}
diff --git a/R/fullVALIDATIONREMIND.R b/R/fullVALIDATIONREMIND.R
index c0974067..249fedc6 100644
--- a/R/fullVALIDATIONREMIND.R
+++ b/R/fullVALIDATIONREMIND.R
@@ -259,7 +259,4 @@ fullVALIDATIONREMIND <- function(rev = 0) {
     try = FALSE, years = years,
     writeArgs = list(scenario = "historical", model = "INDSTAT2")
   )
-
-  # filter variables that are too imprecise on regional level ----
-  filter_historical_mif()
 }
diff --git a/inst/extdata/historical_mif_filter_table.csv b/inst/extdata/historical_mif_filter_table.csv
deleted file mode 100644
index 557c8b1f..00000000
--- a/inst/extdata/historical_mif_filter_table.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-scenario,model,variable,include_regions,exclude_regions
-historical,IEA ETP 2DS,Production|Industry|Cement (Mt/yr),GLO,
-historical,IEA ETP 2DS,Production|Industry|Steel (Mt/yr),GLO,
-historical,IEA ETP B2DS,Production|Industry|Cement (Mt/yr),GLO,
-historical,IEA ETP B2DS,Production|Industry|Steel (Mt/yr),GLO,
-historical,IEA ETP RTS,Production|Industry|Cement (Mt/yr),GLO,
-historical,IEA ETP RTS,Production|Industry|Steel (Mt/yr),GLO,