Tilbage

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Delmål 13: Klimaindsats

Emissionsopgørelse fra DCE

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GS Emission af drivhusgasser efter sektor

# Import
ENX1EM1_raw <-
  read_csv(
    paste0("https://bank.stat.gl:443/sq/53af71be-9a37-48a3-983c-e98720f29d40.csv", "?lang=", language),
    locale = locale(encoding = "latin1"))

# Transform
ENX1EM1 <- 
  ENX1EM1_raw %>% 
  rename(
    "gas"    = 1,
    "sector" = 2,
    "time"   = 3,
    "value"  = 4
  ) %>% 
  mutate(
    gas    = gas %>% fct_inorder() %>% fct_rev(),
    time   = time %>% make_date(),
    value  = value / 1000
  )

# Plot
ENX1EM1 %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = gas
  )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title    = sdg13$figs$fig1$title[language],
    subtitle =  ENX1EM1[[2]][1],
    x        = " ",
    y        = sdg13$figs$fig1$y_lab[language],
    fill     = sdg13$figs$fig1$fill[language],
    caption  = sdg13$figs$fig1$cap[language]
  )

Statistikbanken

Metode

Rapport

Vis tabel

# Transform
ENX1EM1 <- 
  ENX1EM1_raw %>% 
  rename(
    "gas"    = 1,
    "sector" = 2,
    "time"   = 3,
    "value"  = 4
  ) %>% 
  mutate(
    gas    = gas %>% fct_inorder() %>% fct_rev(),
    value  = value / 1000,
    value  = round(value, 3)
  ) %>% 
  filter(time >= year(Sys.time()) - 8) %>% 
  spread(1, ncol(.)) %>% 
  arrange(desc(time))

# Table
ENX1EM1 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(year_col = " ") %>% 
  pack_rows(index = table(ENX1EM1[[1]])) %>% 
  add_footnote(sdg13$figs$fig1$foot[language], notation = "symbol")

	SF6	HFC	Lattergas N2O	Metan CH4	Kuldioxid CO2
Samlet nettoemission
2023	0,003	11,49	13,41	15,9	611
2022	0,003	12,20	12,30	16,1	589
2021	0,003	13,02	10,19	16,2	548
2020	0,003	12,94	10,02	16,1	552
2019	0,003	11,14	9,53	16,0	528
2018	0,003	9,76	8,71	16,1	526
2017	0,003	10,09	8,52	15,9	538
* Tusind ton C02-ækvivalenter

# Import
ENX1EM1_raw <-
  read_csv(
    paste0("https://bank.stat.gl:443/sq/ca6d79eb-6230-4f07-ba49-c486f6e1edd4.csv", "?lang=", language),
    locale = locale(encoding = "latin1"))


# Transform
ENX1EM1 <-
  ENX1EM1_raw %>% 
  rename(
    "time"   = 1,
    "gas"    = 2,
    "sector" = 3,
    "value"  = 4
  ) %>% 
  filter(sector != unique(ENX1EM1_raw[[3]])[5]) %>% 
  mutate(
    gas    = gas %>% fct_inorder() %>% fct_rev(),
    sector = sector %>% str_remove_all("[1-5]|\\.") %>% trimws(),
    sector = sector %>% fct_inorder(),
    value  = value / 1000
  )


ENX1EM1 %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = gas
  )) +
  geom_area() +
  facet_wrap(~ sector, scales = "free") +
   scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title   = sdg13$figs$fig2$title[language],
    x       = " ",
    y       = sdg13$figs$fig2$y_lab[language],
    fill    = str_to_title(colnames(ENX1EM1_raw)[2]),
    caption = sdg13$figs$fig2$cap[language]
  )

Statistikbanken

Metode

Rapport

Vis tabel

# Transform
ENX1EM1 <-
  ENX1EM1_raw %>% 
  rename(
    "time"   = 1,
    "gas"    = 2,
    "sector" = 3,
    "value"  = 4
  ) %>% 
  filter(
    sector != unique(ENX1EM1_raw[[3]])[5],
    time >= year(Sys.time())- 8,
    value != 0) %>% 
  mutate(
    gas    = gas %>% fct_inorder() %>% fct_rev(),
    sector = sector %>% str_remove_all("[1-5]|\\.") %>% trimws(),
    sector = sector %>% fct_inorder(),
    value  = value / 1000
  ) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(1, ncol(.)) %>% 
  arrange(sector)

# Table
ENX1EM1 %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(ENX1EM1[[2]])) %>% 
  add_footnote(sdg13$figs$fig2$foot[language],
               notation = "symbol")

	2017	2018	2019	2020	2021	2022	2023
Emission fra energiforbrug
Lattergas N2O	2,096	2,039	2,045	2,092	2,093	2,291	2,331
Metan CH4	1,369	1,387	1,378	1,442	1,430	1,501	1,515
Kuldioxid CO2	532,476	520,309	522,563	545,965	542,840	583,722	605,406
Industrielle processer
SF6	0,003	0,003	0,003	0,003	0,003	0,003	0,003
HFC	10,094	9,761	11,144	12,944	13,023	12,204	11,487
Lattergas N2O	0,001	0,001	0,001	0,001	0,001	0,001	0,001
Metan CH4	0,001	0,001	0,001	0,001	0,001	0,001	0,001
Kuldioxid CO2	0,721	0,949	1,014	0,854	0,715	0,718	0,745
Emission fra landbrug
Lattergas N2O	1,628	1,433	2,093	2,207	2,375	4,250	5,035
Metan CH4	7,086	7,234	7,086	7,197	7,225	7,052	6,907
Kuldioxid CO2	0,004	0,004	0,004	0,004	0,004	0,004	0,004
Emission fra affaldshåndtering
Lattergas N2O	4,750	5,185	5,337	5,672	5,667	5,712	5,999
Metan CH4	7,477	7,478	7,483	7,488	7,498	7,506	7,509
Kuldioxid CO2	3,385	3,409	3,427	3,450	3,474	3,493	3,511
* Tusind ton CO2-ækvivalenter

Nøgletal for emission af drivhusgasser fra energiforbrug

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GS Nøgletal for emission af drivhusgasser fra energiforbrug

ENX6KEY_raw <- 
  statgl_url("ENX6KEY", lang = "da") |> 
  statgl_fetch(
    "key figure" = 16:18,
    time = px_all(),
    .col_code = T
  ) |> 
  as_tibble()

# Transform
ENX6KEY <- 
  ENX6KEY_raw |> 
  rename(key = `key figure`) |> 
  mutate(time = as.numeric(time))



# Plot
ENX6KEY |> 
  ggplot(aes(
    x = time,
    y = value,
    color = key
  )) +
  geom_line(size = 2) +
  facet_wrap(~ key, nrow = 3, scales = "free_y") +
  theme_statgl() +
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg13$figs$fig5$title[language],
    subtitle = sdg13$figs$fig5$sub[language],
    y        = " ",
    x        = " ",
    caption  = sdg13$figs$fig5$cap[language]
  )

Statistikbanken

Vis tabel

#Table
ENX6KEY %>% 
  filter(time >= year(Sys.time()) - 8) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(time, value) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE)

	2017	2018	2019	2020	2021	2022	2023
Faktisk emission i alt [1.000 ton CO2e]	535,7	523,4	525,7	549,4	546,2	587,4	609,1
Faktisk emission i alt pr. indbygger [ton CO2e]	9,5	9,3	9,4	9,7	9,6	10,4	10,7
Faktisk emission pr. BNP-enhed [ton CO2e pr. mio. BNP]	36,3	35,1	34,4	35,6	35,2	NA	NA