nach Land – welche Regierung ist verantwortlich?
nach Einwohner – welche Menschen sind verantwortlich?
Quelle
Territorial emissions
Original
CDIAC: Boden, TA, Marland, G and Andres, RJ 2017. Global, Regional, and National Fossil-Fuel CO2 Emissions, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., USA. DOI: 10.3334/CDIAC/00001_V2017.
UNFCCC, 2017. National Inventory Submissions 2017. United Nations Framework Convention on Climate Change.
BP, 2017. Statistical Review of World Energy.
# R
co2pp <- read.csv("/Users/xxx/Desktop/export_20191116_1913.csv",sep = ";", quote = "\"'",)
visited <- colnames(co2)[-1]
register_google(key="???")
ll.visited <- geocode(visited)
ll.visited[, "country"] <- c("Afghanistan","Albania","Algeria","Andorra","Angola","Anguilla","Antigua.and.Barbuda","Argentina","Armenia","Aruba","Australia","Austria","Azerbaijan","Bahamas","Bahrain","Bangladesh","Barbados","Belarus","Belgium","Belize","Benin","Bermuda","Bhutan","Bolivia","Bonaire..Saint.Eustatius.and.Saba","Bosnia.and.Herzegovina","Botswana","Brazil","British.Virgin.Islands","Brunei.Darussalam","Bulgaria","Burkina.Faso","Burundi","Cambodia","Cameroon","Canada","Cape.Verde","Cayman.Islands","Central.African.Republic","Chad","Chile","China","Colombia","Comoros","Congo","Cook.Islands","Costa.Rica","Côte.d.Ivoire","Croatia","Cuba","Curaçao","Cyprus","Czech.Republic","Democratic.Republic.of.the.Congo","Denmark","Djibouti","Dominica","Dominican.Republic","Ecuador","Egypt","El.Salvador","Equatorial.Guinea","Eritrea","Estonia","Ethiopia","Faeroe.Islands","Falkland.Islands..Malvinas.","Fiji","Finland","France","French.Guiana","French.Polynesia","Gabon","Gambia","Georgia","Germany","Ghana","Gibraltar","Greece","Greenland","Grenada","Guadeloupe","Guatemala","Guinea","Guinea.Bissau","Guyana","Haiti","Honduras","Hong.Kong","Hungary","Iceland","India","Indonesia","Iran","Iraq","Ireland","Israel","Italy","Jamaica","Japan","Jordan","Kazakhstan","Kenya","Kiribati","Kuwait","Kyrgyzstan","Laos","Latvia","Lebanon","Lesotho","Liberia","Libya","Liechtenstein","Lithuania","Luxembourg","Macao","Macedonia..Republic.of.","Madagascar","Malawi","Malaysia","Maldives","Mali","Malta","Marshall.Islands","Martinique","Mauritania","Mauritius","Mexico","Micronesia..Federated.States.of.","Moldova","Mongolia","Montenegro","Montserrat","Morocco","Mozambique","Myanmar","Namibia","Nauru","Nepal","Netherlands","New.Caledonia","New.Zealand","Nicaragua","Niger","Nigeria","Niue","North.Korea","Norway",NA,"Oman","Pakistan","Palau","Panama","Papua.New.Guinea","Paraguay","Peru","Philippines","Poland","Portugal","Qatar","Republic.of.South.Sudan","Réunion","Romania","Russian.Federation","Rwanda","Saint.Helena","Saint.Kitts.and.Nevis","Saint.Lucia","Saint.Pierre.and.Miquelon","Saint.Vincent.and.the.Grenadines","Samoa","Sao.Tome.and.Principe","Saudi.Arabia","Senegal","Serbia","Seychelles","Sierra.Leone","Singapore","Slovakia","Slovenia","Solomon.Islands","Somalia","South.Africa","South.Korea","Spain","Sri.Lanka","Sudan","Suriname","Swaziland","Sweden","Switzerland","Syria","Taiwan","Tajikistan","Tanzania","Thailand","Timor.Leste","Togo","Tonga","Trinidad.and.Tobago","Tunisia","Turkey","Turkmenistan","Turks.and.Caicos.Islands","Tuvalu","Uganda","Ukraine","United.Arab.Emirates","United.Kingdom","United.States.of.America","Uruguay","Uzbekistan","Vanuatu","Venezuela","Vietnam","Wallis.and.Futuna.Islands","Western.Sahara","Yemen","Zambia","Zimbabwe")
ll.visited <- as.data.frame(ll.visited)
cnd <- ll.visited$country %in% colnames(co2)
for(i in 1960:2017){
ll.visited[cnd,"CO2"] <- as.vector(t(co2pp[i-1959,ll.visited$country[cnd]]))
p <- ggplot() +
geom_polygon(data = map_data("world"), aes(x=long, y=lat, group=group), fill="grey", alpha=0.2) +
theme_void() +
xlim(-160,190) +
ylim(-60,90) +
geom_point( data=ll.visited, aes(x=lon, y=lat, size=CO2, fill=CO2), alpha=.8, pch=21) +
scale_fill_continuous(low="blue4", high="red", breaks=seq(0,10000,2500), limits=c(0,10000) ) +
scale_size(range = c(.1,35), breaks=seq(0,10000,2500), limits=c(0,10000) ) +
annotate("text", label=i, x=170, y=85, size=8.5) +
scale_size(range = c(.1,14)) +
guides( size = FALSE, fill = guide_colourbar(order = 1, title=expression('t CO'[2]*' per pers '), ) )
fn <- paste("/Users/xxx/Desktop/tmp/",str_pad(i-1959, 3, pad = "0"),".png",sep="")
ggsave(p, file=fn, width = 9, height = 4.5)
}
# ffmpeg -framerate 5 -i /Users/xxx/Desktop/tmp/%3d.png -r 5 -pix_fmt yuv420p -y /Users/wjst/Desktop/X/CO2.mp4
Anmerkung
- Ich weiss nicht, worauf die Differenz zu anderen Angaben beruht.
- Der Plot CO2 pro Kopf hat “capped outliers”, alles was über der Skala lag, wurde auf die obere Grenze gelegt. Dennoch imponieren hier weiterhin Länder wie Curaçao mit einer starken Öl-Ökonomie (Shell…), ebenso auch Katar.
- Eine fixe Skala über die Zeit (die Zeile mit scale_fill_continuous…) hat Vor- und Nachteile. Vorteil – man sieht besser die Entwicklung der Gesamtemission. Nachteil – die relative Entwicklung zu anderen Ländern geht etwas verloren.