In earamendia/EROITools: EROITools

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(dplyr)
library(EROITools)

In this vignette, we show how EROIs can be aggregated by fossil fuel group at the useful stage by respecting a given breakdown, which would usually be a breakdown by final demand sector or end-use category.

Domestic perspective

We start by constructing the dummy final-to-useful efficiencies for each energy product in a similar way than in the vignette demonstrating the aggregation process:

# Constructing final-to-useful efficiencies data frame
length_to_use <- tidy_AB_erois_dta %>%
  dplyr::select(Country, Method, Energy.type, Year, Product) %>%
  dplyr::distinct() %>%
  nrow()

tidy_FU_efficiencies_dta <- tidy_AB_erois_dta %>%
  dplyr::select(Country, Method, Energy.type, Year, Product) %>%
  dplyr::distinct() %>%
  dplyr::mutate(
    Average_Efficiency_Col = seq(0.15, 1, 0.85/(length_to_use-1))
  ) %>% 
  dplyr::glimpse()

Now, supposing that the analyst has access to different final-to-useful efficiencies for three different end-uses; "heating", "mechanical work", and "other end-uses"; let's suppose that the final-to-useful efficiencies for mechanical work are half of those of heating, and those of other end-uses a third of those of heating. We can then construct the data frame of end-use specific final-to-useful efficiencies as follows:

# Making the data frame end-use specific
tidy_FU_efficiencies_dta_end_uses <- tidy_FU_efficiencies_dta %>% 
  tidyr::expand_grid(End_Use = c("Heating", "Mechanical Work", "Other end-uses")) %>% 
  dplyr::mutate(
    Average_Efficiency_Col = dplyr::case_when(
      Average_Efficiency_Col == "Mechanical Work" ~ Average_Efficiency_Col / 2,
      Average_Efficiency_Col == "Other end-uses" ~ Average_Efficiency_Col / 3,
      TRUE ~ Average_Efficiency_Col
    )
  ) %>% 
  dplyr::glimpse()

Then, we can calculate the useful stage EROIs at the product level using the push_to_useful_erois, which will respect the different end-uses.

# Constructing useful stage EROIs data frame
  tidy_useful_erois_dta_end_uses <- push_to_useful_erois(
    .tidy_io_erois = tidy_AB_erois_dta,
    tidy_FU_efficiencies = tidy_FU_efficiencies_dta_end_uses,
    eroi_calc_method = "dta"
  ) %>% 
  dplyr::glimpse()

Note that likewise, a data frame containing the final demand sector specific final-to-useful efficiencies could have been conducted, and a final demand sector specific of useful stage EROIs could have been conducted.

Last, note that the aggregate_useful_stage_erois function does not yet allow to aggregate EROIs respecting the breakdown. Such a feature will be developed in the near future.

Multi-regional perspective

Similarly, we construct the data frame containing the final-to-useful efficiencies.

# Constructing final-to-useful efficiencies data frame
length_to_use <- tidy_AB_erois_gma %>%
  dplyr::select(Country, Method, Energy.type, Year, Product) %>%
  dplyr::distinct() %>%
  nrow()

tidy_FU_efficiencies_gma <- tidy_AB_erois_gma %>%
  dplyr::mutate(
    Country = stringr::str_extract(Product, "\\{.*\\}") %>%
      stringr::str_remove("\\{") %>% stringr::str_remove("\\}"),
    Product = stringr::str_remove(Product, "\\{.*\\}_")
  ) %>%
  dplyr::select(Country, Method, Energy.type, Year, Product) %>%
  dplyr::distinct() %>%
  dplyr::mutate(
    Average_Efficiency_Col = seq(0.15, 1, 0.85/(length_to_use-1))
  ) %>%
  dplyr::glimpse()

Next, we make this data frame end-use specific assuming some specific final-to-useful efficiencies by end-use: the average efficiency of mechanical work being half of the one of heating, and the one of other end-uses being a third of the one of heating, and this for all energy products.

# Making the data frame end-use specific
tidy_FU_efficiencies_gma_end_uses <- tidy_FU_efficiencies_gma %>%
  tidyr::expand_grid(End_Use = c("Heating", "Mechanical Work", "Other end-uses")) %>%
  dplyr::mutate(
    Average_Efficiency_Col = dplyr::case_when(
      Average_Efficiency_Col == "Mechanical Work" ~ Average_Efficiency_Col / 2,
      Average_Efficiency_Col == "Other end-uses" ~ Average_Efficiency_Col / 3,
      TRUE ~ Average_Efficiency_Col
    )
  ) %>%
  dplyr::glimpse()

Finally, we construct the useful stage EROIs data frame using the push_to_useful_erois function.

# Constructing useful stage EROIs data frame
tidy_useful_erois_gma_end_uses <- push_to_useful_erois(
  .tidy_io_erois = tidy_AB_erois_gma,
  tidy_FU_efficiencies = tidy_FU_efficiencies_gma_end_uses,
  eroi_calc_method = "gma"
  ) %>%
  dplyr::glimpse()