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Goal 13: Climate action

Emission statement from DCE

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GS Greenhouse gas emission by sector

# 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]
  )

StatBank

Method

Report

Show table

# 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	Nitrous oxide N2O	Methan CH4	Carbondioxid CO2
Total (net emissions)
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
* 1000 tons of CO2 equivalents

# 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]
  )

StatBank

Method

Report

Show table

# 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
Emissions from fuel combustion
Nitrous oxide N2O	2,096	2,039	2,045	2,092	2,093	2,291	2,331
Methan CH4	1,369	1,387	1,378	1,442	1,430	1,501	1,515
Carbondioxid CO2	532,476	520,309	522,563	545,965	542,840	583,722	605,406
Industrial Processes
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
Nitrous oxide N2O	0,001	0,001	0,001	0,001	0,001	0,001	0,001
Methan CH4	0,001	0,001	0,001	0,001	0,001	0,001	0,001
Carbondioxid CO2	0,721	0,949	1,014	0,854	0,715	0,718	0,745
Agriculture
Nitrous oxide N2O	1,628	1,433	2,093	2,207	2,375	4,250	5,035
Methan CH4	7,086	7,234	7,086	7,197	7,225	7,052	6,907
Carbondioxid CO2	0,004	0,004	0,004	0,004	0,004	0,004	0,004
Waste
Nitrous oxide N2O	4,750	5,185	5,337	5,672	5,667	5,712	5,999
Methan CH4	7,477	7,478	7,483	7,488	7,498	7,506	7,509
Carbondioxid CO2	3,385	3,409	3,427	3,450	3,474	3,493	3,511
* 1000 tons of CO2 equivalents

Key figures on greenhouse gas emissions from energy consumption

---

GS Key figures on greenhouse gas emissions from energy consumption

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]
  )

StatBank

Show table

#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