Las energías en Europa

23/12/22

Contexto

Con nuestro trabajo pretendemos obtener una visión general de la situación energética actual en Europa.

Sobre todo porque, con el calentamiento global y la guerra de Ucrania, las energías se han convertido últimamente en un tema muy controvertido. Los Estados intentan cada vez más reducir su consumo y utilizar energías renovables.

Así, examinaremos varios puntos:

Introducción

El consumo de las energías
Los tipos de energías utilizadas
La producción de energías
Los precios de las energías

Código

#Paquetes utilizados en el trabajo 
library(readr)
library(tidyverse)
library(plotly)
library(knitr)
library(eurostat)
library(gganimate)


#Datos utilizados
table_data <- "nrg_bal_s"
table_datar <- "nrg_ind_ren"
table_fosil <- "nrg_ind_ffgae"
table_combustible <- "nrg_inf_epc"
table_rw <- "nrg_inf_epc"
table_renewables<- "nrg_inf_epcrw"
table_consumo <- "nrg_cb_sff"
table_consumoR <- "nrg_cb_rw"
table_gas <- "nrg_pc_202"  #DESDE 2007 (GAS)
table_gas07 <- "nrg_pc_202_h"  #ANTES DEL 2007 (GAS)
table_elec <- "nrg_pc_204"  #DESDE 2007 (ELECTRICIDAD)
table_elec07 <- "nrg_pc_204_h"  #ANTES DE 2007 (ELECTRICIDAD)
table_consumoG <- "nrg_cb_gas"  #GAS
table_consumoE <- "nrg_cb_e"  #ELECTRICIDAD
table_inflation <- "tec00118"  #INFLACION de 2010 a 2021

#transformamos los datos
data <- get_eurostat(table_data, time_format = 'raw', keepFlags = TRUE)
df_namesD <- names(data)
data <- label_eurostat(data, code = df_namesD, fix_duplicated = TRUE)
df_dicc <- pjpv.curso.R.2022::pjp_dicc(data)
df_uniques <- pjpv.curso.R.2022::pjp_valores_unicos(data)
data[data == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
data <- data %>% 
  filter(nrg_bal == "Total energy supply",
         geo %in% paises,
         siec == "Total",
         unit == "Thousand tonnes of oil equivalent") %>%
  select(time, geo, values) %>%
  mutate(time = as.numeric(time))
colnames(data) <- c("year", "country", "value")


datar<- get_eurostat(table_datar, time_format = 'raw', keepFlags = TRUE)
df_namesDr <- names(datar)
datar <- label_eurostat(datar, code = df_namesDr, fix_duplicated = TRUE)
df_dicc <- pjpv.curso.R.2022::pjp_dicc(datar)
df_uniques <- pjpv.curso.R.2022::pjp_valores_unicos(datar)
datar[datar == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
datar <- datar %>% 
  filter(nrg_bal == "Renewable energy sources") %>%
  filter(geo %in% paises) %>% 
  select(time, geo, values) %>% 
  mutate(time =  as.numeric(time))
colnames(datar) <- c("year", "country", "value")


dfFosil <- get_eurostat(table_fosil, time_format = 'raw', keepFlags = TRUE)
df_namesFosil <- names(dfFosil)
dfFosil <- label_eurostat(dfFosil, code = df_namesFosil, fix_duplicated = TRUE)
df_dicc <- pjpv.curso.R.2022::pjp_dicc(dfFosil)
df_uniques <- pjpv.curso.R.2022::pjp_valores_unicos(dfFosil)
dfFosil[dfFosil == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
str(dfFosil) #Esta como numero VALUES

tibble [1,209 × 10] (S3: tbl_df/tbl/data.frame)
 $ unit_code  : chr [1:1209] "PC" "PC" "PC" "PC" ...
 $ geo_code   : chr [1:1209] "AL" "AT" "BA" "BE" ...
 $ flags_code : chr [1:1209] NA NA NA NA ...
 $ time_code  : chr [1:1209] "2020" "2020" "2020" "2020" ...
 $ values_code: num [1:1209] 58.1 66.8 80.5 76.5 62.9 ...
 $ unit       : chr [1:1209] "Percentage" "Percentage" "Percentage" "Percentage" ...
 $ geo        : chr [1:1209] "Albania" "Austria" "Bosnia and Herzegovina" "Belgium" ...
 $ flags      : chr [1:1209] NA NA NA NA ...
 $ time       : chr [1:1209] "2020" "2020" "2020" "2020" ...
 $ values     : num [1:1209] 58.1 66.8 80.5 76.5 62.9 ...

Código

dfFosil <- dfFosil %>% mutate(time =  as.numeric(time))
dfFosil <- dfFosil %>% 
  select(time, geo, values) %>% 
  filter(geo %in% paises)
#paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")


df <- get_eurostat(table_combustible, time_format = 'raw', keepFlags = TRUE)
df_names <- names(df)
df <- label_eurostat(df, code = df_names, fix_duplicated = TRUE)
df_dicc <- pjpv.curso.R.2022::pjp_dicc(df)
df_uniques <- pjpv.curso.R.2022::pjp_valores_unicos(df)
rm(df_dicc, df_uniques)
df[df == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
dfEngFos <- df %>% filter (siec == c("Nuclear fuels and other fuels n.e.c.", "Combustible fuels"),
                   geo %in% paises) %>%
  select(time,geo,unit, values, operator) %>%
  mutate(time = as.numeric(time)) %>%
  group_by(geo, time) %>%
  summarise(Total = sum(values)) %>% 
  filter(Total != 0)
colnames(dfEngFos) <- c("pais", "anyo", "fosiles")



df_rw<- get_eurostat(table_rw, time_format = 'raw', keepFlags = TRUE)
df_namesrw <- names(df_rw)
df_rw <- label_eurostat(df_rw, code = df_namesrw, fix_duplicated = TRUE)
df_dicc <- pjpv.curso.R.2022::pjp_dicc(df_rw)
df_uniques <- pjpv.curso.R.2022::pjp_valores_unicos(df_rw)
rm(df_dicc, df_uniques)
df_rw[df_rw == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
eng_rw <- c("Tide, wave, ocean", "Solar photovoltaic",
            "Solar thermal", "Wind", "Geothermal",
            "Pumped hydro power", "Mixed hydro power",
            "Pure hydro power", "Hydro")
dfEngR <- df_rw %>% filter (siec %in% eng_rw,
                           geo %in% paises) %>%
  select(time,geo,unit, values, operator) %>%
  mutate(time = as.numeric(time)) %>%
  group_by(geo, time) %>%
  summarise(Total = sum(values)) %>% 
  filter(Total != 0)
colnames(dfEngR) <- c("pais", "anyo", "fosiles")


df_consumo<- get_eurostat(table_consumo, time_format = 'raw', keepFlags = TRUE)
df_namesC <- names(df_consumo)
df_consumo <- label_eurostat(df_consumo, code = df_namesC, fix_duplicated = TRUE)
df_diccC <- pjpv.curso.R.2022::pjp_dicc(df_consumo)
df_uniquesC <- pjpv.curso.R.2022::pjp_valores_unicos(df_consumo)
rm(df_diccC, df_uniquesC)
df_consumo[df_consumo == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
df_consumo <- df_consumo %>% select(time, geo, values, siec)
df_consumo <- df_consumo %>% mutate(time =  as.numeric(time))
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
df_consumo <- df_consumo %>%
  filter (geo %in% paises) %>%
  group_by(geo, time) %>%
  summarise(Total = sum(values))
colnames(df_consumo) <- c("pais", "anyo", "consumo total de energias no renovables")



df_consumoR <- get_eurostat(table_consumoR, time_format = 'raw', keepFlags = TRUE)
df_namesCR <- names(df_consumoR)
df_consumoR <- label_eurostat(df_consumoR, code = df_namesCR, fix_duplicated = TRUE)
df_diccCR <- pjpv.curso.R.2022::pjp_dicc(df_consumoR)
df_uniquesCR <- pjpv.curso.R.2022::pjp_valores_unicos(df_consumoR)
rm(df_diccCR, df_uniquesCR)
df_consumoR[df_consumoR == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
df_consumoR <- df_consumoR %>% select(time, geo, values, siec)
df_consumoR <- df_consumoR %>% mutate(time =  as.numeric(time))
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
df_consumoR <- df_consumoR %>%
  filter (geo %in% paises) %>%
  group_by(geo, time) %>%
  summarise(Total = sum(values))
colnames(df_consumoR) <- c("pais", "anyo", "consumo total de energias renovables")



dfGas <- get_eurostat(table_gas, time_format = 'raw', keepFlags = TRUE)
df_namesGas <- names(dfGas)
dfGas <- label_eurostat(dfGas, code = df_namesGas, fix_duplicated = TRUE)
df_dicc <- pjpv.curso.R.2022::pjp_dicc(dfGas)
df_uniques <- pjpv.curso.R.2022::pjp_valores_unicos(dfGas)
rm(df_dicc, df_uniques)
dfGas[dfGas == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
dfGas <- dfGas %>% mutate(time =  as.numeric(time_code))
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
dfGas <- dfGas %>%
  filter(tax == "Excluding taxes and levies",
         currency == "Euro",
         unit == "Gigajoule (gross calorific value - GCV)",
         geo %in% paises) %>%
  mutate(fecha = lubridate::yq(time_code)) %>% 
  mutate(periodo = lubridate::year(fecha)) %>%
  select(periodo,geo, geo_code,unit, currency, values, consom) %>% 
  group_by(geo, periodo, geo_code) %>%
  summarise(Total = sum(values)) %>%
  filter(periodo > 2007)


dfGas07 <- get_eurostat(table_gas07, time_format = 'raw', keepFlags = TRUE)
df_namesGas07 <- names(dfGas07)
dfGas07 <- label_eurostat(dfGas07, code = df_namesGas07, fix_duplicated = TRUE)
df_dicc07 <- pjpv.curso.R.2022::pjp_dicc(dfGas07)
df_uniques07 <- pjpv.curso.R.2022::pjp_valores_unicos(dfGas07)
rm(df_dicc07, df_uniques07)
dfGas07[dfGas07 == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
dfGas07 <- dfGas07 %>% mutate(time =  as.numeric(time_code))
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
dfGas07 <- dfGas07 %>%
  filter(tax == "Excluding taxes and levies",
         currency == "Euro",
         unit == "Gigajoule (gross calorific value - GCV)",
         geo %in% paises) %>%
  mutate(fecha = lubridate::yq(time_code)) %>% 
  mutate(periodo = lubridate::year(fecha))  %>%
  select(periodo,geo, geo_code,unit, currency, values, consom) %>% 
  group_by(geo, periodo, geo_code) %>%
  summarise(Total = sum(values)) %>%
  filter(periodo < 2008)
 



dfElec <- get_eurostat(table_elec, time_format = 'raw', keepFlags = TRUE)
df_namesElec <- names(dfElec)
dfElec <- label_eurostat(dfElec, code = df_namesElec, fix_duplicated = TRUE)
df_dicc <- pjpv.curso.R.2022::pjp_dicc(dfElec)
df_uniques <- pjpv.curso.R.2022::pjp_valores_unicos(dfElec)
rm(df_dicc, df_uniques)
dfElec[dfElec == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
dfElec <- dfElec %>% mutate(time =  as.numeric(time_code))
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
dfElec <- dfElec %>%
  filter(tax == "Excluding taxes and levies",
         currency == "Euro",
         consom != "Consumption of kWh - all bands",
         geo %in% paises) %>%
  mutate(fecha = lubridate::yq(time_code)) %>% 
  mutate(periodo = lubridate::year(fecha)) %>%
  select(periodo, geo, geo_code, unit, currency, values, consom) %>% 
  group_by(geo, periodo, geo_code) %>%
  summarise(Total = sum(values)) %>%
  filter(periodo > 2007)



dfElec07 <- get_eurostat(table_elec07, time_format = 'raw', keepFlags = TRUE)
df_namesElec07 <- names(dfElec07)
dfElec07 <- label_eurostat(dfElec07, code = df_namesElec07, fix_duplicated = TRUE)
df_dicc <- pjpv.curso.R.2022::pjp_dicc(dfElec07)
df_uniques <- pjpv.curso.R.2022::pjp_valores_unicos(dfElec07)
rm(df_dicc, df_uniques)
dfElec07[dfElec07 == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
dfElec07 <- dfElec07 %>% mutate(time =  as.numeric(time_code))
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
dfElec07 <- dfElec07 %>%
  filter(tax == "Excluding taxes and levies",
         currency == "Euro",
         consom != "Consumption of kWh - all bands",
         geo %in% paises) %>%
  mutate(fecha = lubridate::yq(time_code)) %>% 
  mutate(periodo = lubridate::year(fecha)) %>%
  select(periodo, geo, geo_code, unit, currency, values, consom) %>% 
  group_by(geo, periodo, geo_code) %>%
  summarise(Total = sum(values)) %>%
  filter(periodo < 2008)


df_consumoG <- get_eurostat(table_consumoG, time_format = 'raw', keepFlags = TRUE)
df_namesG <- names(df_consumoG)
df_consumoG <- label_eurostat(df_consumoG, code = df_namesG, fix_duplicated = TRUE)
df_diccCG <- pjpv.curso.R.2022::pjp_dicc(df_consumoG)
df_uniquesCG <- pjpv.curso.R.2022::pjp_valores_unicos(df_consumoG)
rm(df_diccCG, df_uniquesCG)
df_consumoG[df_consumoG == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
df_consumoG <- df_consumoG %>% mutate(time =  as.numeric(time))
df_consumoG <- df_consumoG %>% 
  filter(unit == "Terajoule (gross calorific value - GCV)") %>%
  select(time, geo, values, siec, unit) %>%
  filter(geo %in% paises)  %>%
  group_by(geo, time) %>%
  summarise(Total = sum(values))



df_consumoE <- get_eurostat(table_consumoE, time_format = 'raw', keepFlags = TRUE)
df_namesE <- names(df_consumoE)
df_consumoE <- label_eurostat(df_consumoE, code = df_namesE, fix_duplicated = TRUE)
df_diccCE <- pjpv.curso.R.2022::pjp_dicc(df_consumoE)
df_uniquesCE <- pjpv.curso.R.2022::pjp_valores_unicos(df_consumoE)
rm(df_diccCE, df_uniquesCE)
df_consumoE[df_consumoE == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
df_consumoE <- df_consumoE %>% mutate(time =  as.numeric(time))
df_consumoE <- df_consumoE %>%
  select(time, geo, values, siec, unit) %>%
  filter(geo %in% paises)  %>%
  group_by(geo, time) %>%
  summarise(Total = sum(values))



df_inflation <- get_eurostat(table_inflation, time_format = 'raw', keepFlags = TRUE)
df_namesInf <- names(df_inflation)
df_inflation <- label_eurostat(df_inflation, code = df_namesInf, fix_duplicated = TRUE)
df_dicc <- pjpv.curso.R.2022::pjp_dicc(df_inflation)
df_uniques <- pjpv.curso.R.2022::pjp_valores_unicos(df_inflation)
rm(df_dicc, df_uniques)
df_inflation[df_inflation == "Germany (until 1990 former territory of the FRG)"] <- "Germany"
paises <- c("Spain","France", "Sweden", "Denmark", "Italy", "Norway", "Germany")
df_inflation <- df_inflation %>% mutate(time =  as.numeric(time))
df_inflation0 <- df_inflation %>%
  select(time, geo, geo_code, values) %>%
  filter(geo %in% paises)
colnames(df_inflation0) <- c("Anyo", "Pais", "geo_code","Inflacion")

El consumo de las energías globales

La evolución del consumo de energías

Comparación del consumo de las energías para cada año

Países que más consumen

year	country	value
2020	Germany	280169.38
2020	France	221095.55
2020	T<fc>rkiye	146134.49
2020	Italy	140100.48
2020	Spain	110218.90
2020	Poland	102521.54
2020	Ukraine	86551.03
2020	Netherlands	69719.52
2020	Belgium	50241.27
2020	Sweden	44863.79
2020	Czechia	40096.94
2020	Romania	32163.21
2020	Austria	31910.03
2020	Finland	31833.37
2020	Norway	28288.90
2020	Hungary	26051.55
2020	Portugal	20863.49
2020	Greece	20093.12
2020	Bulgaria	17698.18
2020	Slovakia	16417.67

El consumo según los tipos de energías

Las energías renovables

Las energías fósiles

Comparación energías renovables/fósiles

La producción según los tipos de energías

Energias fósiles

Energías renovables

Los precios de las energías

El gas

La electricidad

La correlación precios/inflación

Gas
Electricidad

Conclusíon

Aunque el consumo global de energía se ha mantenido relativamente estable durante los años, los tipos de energías consumidas han combiado. El consumo y la producción de energías renovables han aumentado, mientras que el consumo de combustibles fósiles ha disminuido un poco. Esto es muy positivo para el futuro, pero aún los países tienen que hacer esfuerzos, sobre todo en la producción de combustibles fósiles, que sigue siendo importante.
No observamos una relación entre el consumo y la producción de combustibles fósiles. Esto se debe a que la producción es muy grande, por lo que hay muchas importaciones y exportaciones. Sin embargo, esto es muy diferente en el caso de las energías renovables. Los países suelen consumir su propia producción porque es baja.
En cuanto a la relación precio/inflación, la relación parece bastante clara para la mayoría de países. Y es que cuando aumenta la inflación, aumentan los precios, ya sean de gas o de electricidad, y durante uno o dos años, esto se ajustan los precios. Caso contrario si cae la inflación.