diff --git a/scripts/A14.R b/scripts/A14.R
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+# create ds object --------------------------------------------------------
+
+ds <- create_dataset(id = "A14")
+
+
+# download the data -------------------------------------------------------
+
+ds <- download_data(ds)
+
+# data cleaning -----------------------------------------------------------
+
+### Filter specific products and indicators
+# NOTE: We only look at tourism as a whole, and not sectors of tourism.
+
+global_products <- c(
+  "Share of tourism of the regional total (in %)",
+  "Total of tourism of the economy"
+)
+
+indicators <- c(
+  "Tourism-connected gross value added (at current prices, in million CHF)",
+  "Tourism-related employment (FTE)"
+)
+
+# Filter relevant factors and cleanup
+ds$data <- ds$data %>%
+  janitor::clean_names() %>%
+  tibble::as_tibble() %>%
+  dplyr::filter(product %in% global_products) %>%
+  dplyr::filter(indicator %in% indicators) %>%
+  dplyr::rename(
+    "value" = regional_indicators_of_the_tourism_satellite_accounts
+  )
+
+# Pivot to separate employment from CHF values
+ds$data <- ds$data %>%
+  tidyr::pivot_wider(names_from = indicator, values_from = value) %>%
+  dplyr::rename(
+    gross_value_added = "Tourism-connected gross value added (at current prices, in million CHF)",
+    employment = "Tourism-related employment (FTE)"
+  )
+
+# Pivot again to separate total from percentage
+ds$data <- ds$data %>%
+  tidyr::pivot_wider(
+    values_from = c(gross_value_added, employment),
+    names_from = product,
+  ) %>%
+  janitor::clean_names()
+
+# Ensure clear column names
+ds$data <- ds$data %>%
+  dplyr::rename(
+    "mio_chf_gross_value_added_of_tourism" = gross_value_added_total_of_tourism_of_the_economy,
+    "total_full_time_employment_of_tourism" = employment_total_of_tourism_of_the_economy,
+    "percent_share_gross_value_added_of_tourism" = gross_value_added_share_of_tourism_of_the_regional_total_in_percent,
+    "percent_share_full_time_employment_of_tourism" = employment_share_of_tourism_of_the_regional_total_in_percent
+  )
+
+# join the cleaned data to the postgres spatial units table ---------------
+
+spatial_map <- ds$data %>%
+  dplyr::select(canton) %>%
+  dplyr::distinct(canton) %>%
+  map_ds_spatial_units()
+
+ds$data %>%
+  dplyr::left_join(spatial_map, by = "canton") %>%
+  dplyr::select(-canton) -> ds$data
+
+## check that each spatial unit could be matched -> this has to be TRUE
+
+assertthat::noNA(ds$data$spatialunit_uid)
+
+
+# ingest into postgres ----------------------------------------------------
+
+### important: name the table as energiebilanz_schweiz_in_tera_joule