GES 001 演習6

第6週

01/25(TH)　気候変動問題の原因：気候変動と経済活動1

　　　　　　気候変動問題の原因：気候変動と経済活2

第6週、第7週の講義では、気候変動が我々の生活にもたらす影響と対策を議論します。

基本的にIPCCの第6次評価報告書（https://www.data.jma.go.jp/cpdinfo/ipcc/index.html ）

をテキストに使っています。

01/30(TU)　Rでデータサイエンス6：気候変動　 [Main]・[授業]

Environment-Climate　

CO2 emissions (metric tons per capita) ：EN.ATM.CO2E.PC [Link]

Forest area (% of land area)：AG.LND.FRST.ZS [Link]

Renewable electricity output (% of total electricity output)：EG.ELC.RNEW.ZS [Link]

Electricity production from oil, gas and coal sources (% of total)：EG.ELC.FOSL.ZS [Link]

Electricity production from nuclear sources (% of total)：EG.ELC.NUCL.ZS [Link]

IEA:　Energy Statistics Data Browser　データ元

https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=Energy%20supply&indicator=ElecGenByFuel

内容

• CO2 emissions (metric tons per capita) ：EN.ATM.CO2E.PC [Link]

• Forest area (% of land area)：AG.LND.FRST.ZS [Link]

• Renewable electricity output (% of total electricity output)：EG.ELC.RNEW.ZS [Link]

• Electricity production from oil, gas and coal sources (% of total)：EG.ELC.FOSL.ZS [Link]

• Electricity production from nuclear sources (% of total)：EG.ELC.NUCL.ZS [Link]

df_environment <- WDI(
  indicator = c(co2pcap = "EN.ATM.CO2E.PC",
                forest = "AG.LND.FRST.ZS",
                renewable = "EG.ELC.RNEW.ZS",
                fossil = "EG.ELC.FOSL.ZS",
                nuclea = "EG.ELC.NUCL.ZS"
                ), extra = TRUE)

write_csv(df_environment, "data/environment.csv")

df_environment <- read_csv("data/environment.csv")

Rows: 16758 Columns: 17── Column specification ───────────────────────────────────────────────────
Delimiter: ","
chr  (7): country, iso2c, iso3c, region, capital, income, lending
dbl  (8): year, co2pcap, forest, renewable, fossil, nuclea, longitude, ...
lgl  (1): status
date (1): lastupdated
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df_environment_long <- df_environment |> 
  pivot_longer(7:11, names_to = "name", values_to = "value")

df_environment_long |> 
  group_by(year, name) |> drop_na(value) |>
  summarize(num = n()) |> arrange(desc(year))

`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

df_environment_long |> 
  group_by(year, name) |> drop_na(value) |>
  summarize(num = n()) |> 
  ggplot(aes(year, num, col = name)) + geom_line() +
  labs(title = "各指標の年毎のデータ数", y = "データ数", x = "年")

`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

World Development Indicators: [Link]

df_environment_long |> 
  filter(name %in% c("fossil", "renewable", "nuclea")) |>
  filter(iso2c %in% c("1W", "ZF", "T6")) |> drop_na(value) |>
  ggplot(aes(year, value, col = name, linetype = iso2c)) + geom_line()