Tilbage

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Delmål 7: Bæredygtig energi

Vedvarende energi

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GS Klimakorrigeret andel af affaldsvarme og vandkraft efter sektor

# Import
ENE1ACT_raw <-
  read_csv(
    paste0("https://bank.stat.gl:443/sq/ef08d007-6c33-4385-812c-dc082de93f1e.csv", "?lang=", language),
    locale = locale(encoding = "latin1"))


ENE1ACT <-
  ENE1ACT_raw %>% 
  rename(
    "type"       = 1,
    "use"        = 2,
    "time"       = 3,
    "total"      = 4,
    "waste heat" = 5,
    "hydropower" = 6
  ) %>% 
  mutate(across(4:ncol(.), as.numeric)) %>% 
  mutate_all(~replace(., is.na(.), 0)) %>% 
  mutate(other = total - (`waste heat` + hydropower)) %>% 
  gather(energy, value, 5:ncol(.)) %>% 
  select(-4) %>% 
  mutate(energy = energy %>% fct_inorder())


step <-
  ENE1ACT %>% 
  mutate(
    energy = energy %>% str_replace("waste heat", sdg13$figs$fig3$cols$col1[language] %>% unlist()),
    energy = energy %>% str_replace("hydropower", sdg13$figs$fig3$cols$col2[language] %>% unlist()),
    energy = energy %>% str_replace("other",      sdg13$figs$fig3$cols$col3[language] %>% unlist()),
    energy = energy %>% factor(levels = unique(energy)),
    use    = use    %>% factor(levels = unique(use))
  )

  # Plot
step %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = energy
  )) +
  geom_area(position = "fill") +
  facet_wrap(~ use) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title    = sdg13$figs$fig3$title[language],
    subtitle = ENE1ACT[[1]][1],
    x        = " ",
    y        = " ",
    fill     = sdg13$figs$fig3$fill[language],
    caption  = sdg13$figs$fig3$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Transform
ENE1ACT <-
  ENE1ACT_raw %>% 
  rename(
    "type"       = 1,
    "use"        = 2,
    "time"       = 3,
    "total"      = 4,
    "waste heat" = 5,
    "hydropower" = 6
  ) %>% 
  mutate(across(4:ncol(.), as.numeric)) %>% 
  mutate_all(~replace(., is.na(.), 0)) %>% 
  mutate(other = total - (`waste heat` + hydropower),) %>% 
  gather(energy, value, 5:ncol(.)) %>% 
  select(-4) %>% 
  mutate(energy = energy %>% fct_inorder()) %>% 
  spread(4, 5) %>% 
  filter(time >= year(Sys.time()) - 7) %>% 
  arrange(desc(time))



# Table
ENE1ACT %>% 
  select(-1, -3) %>%
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE,
               col.names = c(" ", colnames(ENE1ACT_raw)[5:6], sdg13$figs$fig3$cols$col3[language])) %>% 
  pack_rows(index = table(ENE1ACT[[1]])) %>% 
  pack_rows(index = table(ENE1ACT[[3]]) %>% rev()) %>% 
  add_footnote(sdg13$figs$fig3$foot[language], notation = "symbol")

	waste heat	hydropower	other
Klimakorrigeret forbrug
2022
Energisektoren	85	1.610	1.156
Handels- og serviceerhverv i alt	0	0	802
Husholdninger	0	0	1.440
Produktionserhverv i alt	0	0	2.947
Transport i alt	0	0	1.847
2021
Energisektoren	105	1.543	1.209
Handels- og serviceerhverv i alt	0	0	808
Husholdninger	0	0	1.446
Produktionserhverv i alt	0	0	2.745
Transport i alt	0	0	1.494
2020
Energisektoren	100	1.496	1.178
Handels- og serviceerhverv i alt	0	0	689
Husholdninger	0	0	1.453
Produktionserhverv i alt	0	0	2.494
Transport i alt	0	0	1.341
2019
Energisektoren	116	1.475	1.124
Handels- og serviceerhverv i alt	0	0	729
Husholdninger	0	0	1.427
Produktionserhverv i alt	0	0	2.474
Transport i alt	0	0	1.821
2018
Energisektoren	93	1.421	1.194
Handels- og serviceerhverv i alt	0	0	671
Husholdninger	0	0	1.312
Produktionserhverv i alt	0	0	2.357
Transport i alt	0	0	1.642
2017
Energisektoren	93	1.343	1.153
Handels- og serviceerhverv i alt	0	0	679
Husholdninger	0	0	1.369
Produktionserhverv i alt	0	0	2.361
Transport i alt	0	0	1.712
* Terajoule

Samlede energiforbrug der udgøres af vedvarende energi

FN 7.2.2 Andel af det samlede energiforbrug, der udgøres af vedvarende energi

# Import
url <- paste0("https://bank.stat.gl:443/api/v1/", language, "/Greenland/EN/EN20/ENX6KEY.px")

ENX6KEY_raw <- 
  url |> 
  statgl_fetch(
    `key figure` = 11,
    time         = px_all(),
    .col_code    = T
  ) |> 
  as_tibble()

# Transform
ENX6KEY <- 
  ENX6KEY_raw |> 
  drop_na()

# Plot
ENX6KEY |> 
  ggplot(aes(
    x     = as.integer(time),
    y     = value,
    color = `key figure`
  )) +
  geom_line(size = 2) +
  scale_y_continuous(labels = scales::percent_format(
    scale        = 1,
    accuracy     = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() +
  scale_color_statgl(reverse = T) +
  labs(
    title = sdg7$figs$fig2$title[language],
    subtitle = ENX6KEY[[1]][1],
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg7$figs$fig2$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Tabel
ENX6KEY |> 
  filter(time >= year(Sys.time()) - 7) |> 
  spread(time, value) |>
  rename(" " = 1) |> 
  statgl_table()

	2017	2018	2019	2020	2021	2022
Vedvarende energi - andel af faktisk energiforbrug [pct.]	16,5	17,6	17,2	18,3	17,5	17,3