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Commit ec9d0d05 authored by Ben Anderson's avatar Ben Anderson
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Merge branch 'master' into 'master' 

updating the emissions & GDP decoupling analysis

See merge request !21
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1 merge request!21updating the emissions & GDP decoupling analysis
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gdpAndEmissions/GDP_emissions_trends.html
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gdpAndEmissions/GDP_emissions_trends.qmd
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......@@ -30,9 +30,21 @@ One of a [series](https://dataknut.github.io/fridayFagPackets/)...
\<sigh\>
A follow-up to https://twitter.com/tom_rushby/status/1577752402798821410
Updated after https://twitter.com/JKSteinberger/status/1587562447795167233
# Data
Downloaded from the awesome [OurWorldInData](https://ourworldindata.org/grapher/co2-emissions-and-gdp?country=~GBR).
Downloaded from the awesome [OurWorldInData](https://ourworldindata.org/grapher/co2-emissions-and-gdp?country=~GBR):
- absolute and per capita GDP
- absolute and per capita Greenhouse Gas emissions in two flavours:
- production ('territorial') and
- consumption
```{r}
#| label: loasdData
......@@ -42,17 +54,29 @@ dt_pc <- data.table::fread(here::here("data", "co2-emissions-and-gdp-per-capita.
```
Who do we care about?
Where do we care about?
> Change these at will and for your purposes...
>
> This would make a really nice [shiny app](https://shiny.rstudio.com/gallery/)
```{r}
#| label: selectEntities
entities_of_interest <- c("United Kingdom", "United States", "New Zealand",
"China")
"China", "India", "Norway", "Nigeria")
# check valid
dt_pc[Entity %in% entities_of_interest, .(nObs = .N,
meanAnnualPerCapTerr = mean(`Annual CO2 emissions (per capita)`, na.rm = TRUE),
meanAnnualPerCapCons = mean(`Annual consumption-based CO2 emissions (per capita)`,na.rm = TRUE),
meanAnnualGDPperCap = mean(`GDP per capita, PPP (constant 2017 international $)`,na.rm = TRUE)), keyby = .(Entity)]
```
# Absolute GDP & emissions
First we'll try production emissions.
## Absolute production emissions
First we'll try production ('territorial') emissions. We are not controlling for population size so countries with large populations will visually dominate.
```{r}
#| label: fig-SelectedProdPlot
......@@ -61,18 +85,18 @@ First we'll try production emissions.
plotDT <- dt_abs[Entity %in% entities_of_interest]
ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP, PPP (constant 2017 international $)`)/1000000000,
x = as.numeric(`Annual CO2 emissions`)/1000000,
ggplot2::ggplot(plotDT, aes(x = as.numeric(`GDP, PPP (constant 2017 international $)`)/1000000000,
y = as.numeric(`Annual CO2 emissions`)/1000000,
colour = Entity,
alpha = Year)) +
geom_point() +
labs(x = "Annual CO2 emissions (production-based, gT)",
y = "GDP $bn (constant 2017 $)")
labs(y = "Annual CO2 emissions (production-based, gT)",
x = "GDP $bn (constant 2017 $)")
```
Next we'll try consumption emissions.
## Absolute consumption emissions
Note that 2020 consumption-based emissions data is missing so you don't see the downtick
Note that 2020 consumption-based emissions data is missing so you don't see the 2020 COVID-19 downtick. Absolute emissions are falling in some places even as GDP is still increasing...
```{r}
#| label: fig-SelectedConsPlot
......@@ -80,18 +104,20 @@ Note that 2020 consumption-based emissions data is missing so you don't see the
#| warning: false
plotDT <- dt_abs[Entity %in% entities_of_interest]
ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP, PPP (constant 2017 international $)`)/1000000000,
x = as.numeric(`Annual consumption-based CO2 emissions`)/1000000,
ggplot2::ggplot(plotDT, aes(x = as.numeric(`GDP, PPP (constant 2017 international $)`)/1000000000,
y = as.numeric(`Annual consumption-based CO2 emissions`)/1000000,
colour = Entity,
alpha = Year)) +
geom_point() +
labs(x = "Annual CO2 emissions (consumption-based, gT)",
y = "GDP $bn (constant 2017 $)")
labs(y = "Annual CO2 emissions (consumption-based, gT)",
x = "GDP $bn (constant 2017 $)")
```
# Per capita GDP & emissions
Since we'll be dividing everything pairwise by the same denominator, nothing much should change... but the plots should be much clearer as we've removed the affect of population size
Since we'll be dividing everything pairwise by the same denominator, nothing much about the shapes should change... but the plots should be much clearer as we've removed the affect of population size. Countries with large populations will no longer visually dominate...
## Production emissions
First we'll try production emissions.
......@@ -104,17 +130,39 @@ entities_of_interest <- c(entities_of_interest, "World")
plotDT <- dt_pc[Entity %in% entities_of_interest]
ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`),
x = as.numeric(`Annual CO2 emissions (per capita)`),
p <-ggplot2::ggplot(plotDT, aes(x = as.numeric(`GDP per capita, PPP (constant 2017 international $)`),
y = as.numeric(`Annual CO2 emissions (per capita)`),
colour = Entity,
alpha = Year)) +
geom_point() +
labs(x = "Annual CO2 emissions per capita (production-based, T)",
y = "GDP per capita (constant 2017 $)")
geom_line() +
labs(y = "Annual CO2 emissions per capita (production-based, T)",
x = "GDP per capita (constant 2017 $)")
p
```
Let's repeat that. Hover over the dots to see which is which. This is [plotly](https://plotly.com/ggplot2/) in action #YMMV.
```{r}
#| label: fig-SelectedProdPlotPccPlotyly
#| fig-cap: GDP vs production emissions over time (selected entities)
#| warning: false
p <-ggplot2::ggplot(plotDT, aes(x = as.numeric(`GDP per capita, PPP (constant 2017 international $)`),
y = as.numeric(`Annual CO2 emissions (per capita)`),
colour = Entity)) +
geom_point() +
geom_line() +
labs(y = "Annual CO2 emissions per capita (production-based, T)",
x = "GDP per capita (constant 2017 $)")
plotly::ggplotly(p)
```
Let's repeat that for all countries. Hover over the dots to see which is which #YMMV
Now let's have a look at all the countries but without the lines.
Quite interesting, Qatar for example. Perfect place for a winter sport's World Cup. Trinidad and Tobago also worth a look. Macao looks like the place to be?
```{r}
#| label: fig-ProdPlotPcc
......@@ -124,21 +172,23 @@ Let's repeat that for all countries. Hover over the dots to see which is which #
#plotDT <- dt_pc[Entity %in% entities_of_interest]
plotDT <- dt_pc # for fun
p <- ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`),
x = as.numeric(`Annual CO2 emissions (per capita)`),
p <- ggplot2::ggplot(plotDT, aes(x = as.numeric(`GDP per capita, PPP (constant 2017 international $)`),
y = as.numeric(`Annual CO2 emissions (per capita)`),
colour = Entity,
alpha = Year)) +
geom_point() +
theme(legend.position = "none") +
labs(x = "Annual CO2 emissions per capita (production-based, T)",
y = "GDP per capita (constant 2017 $)")
labs(y = "Annual CO2 emissions per capita (production-based, T)",
x = "GDP per capita (constant 2017 $)")
plotly::ggplotly(p)
```
## Consumption emissions
Next we'll try consumption emissions.
Note that 2020 consumption-based emissions data is missing so you don't see the down-tick
Note that 2020 consumption-based emissions data is missing so you don't see the down-tick.
```{r}
#| label: fig-SelectedConsPlotPcc
......@@ -146,16 +196,19 @@ Note that 2020 consumption-based emissions data is missing so you don't see the
#| warning: false
plotDT <- dt_pc[Entity %in% entities_of_interest]
ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`),
x = as.numeric(`Annual consumption-based CO2 emissions (per capita)`),
ggplot2::ggplot(plotDT, aes(x = as.numeric(`GDP per capita, PPP (constant 2017 international $)`),
y = as.numeric(`Annual consumption-based CO2 emissions (per capita)`),
colour = Entity,
alpha = Year)) +
geom_point() +
labs(x = "Annual CO2 emissions per capita (consumption-based, T)",
y = "GDP per capita (constant 2017 $)")
geom_line() +
labs(y = "Annual CO2 emissions per capita (consumption-based, T)",
x = "GDP per capita (constant 2017 $)")
```
Let's repeat that for all countries. Hover over the dots to see which is which #YMMV
Let's repeat that for all countries. Hover over the dots to see which is which #YMMV.
Well this time Luxembourg is out in the lead...
```{r}
#| label: fig-ConsPlotPcc
......@@ -165,34 +218,34 @@ Let's repeat that for all countries. Hover over the dots to see which is which #
#plotDT <- dt_pc[Entity %in% entities_of_interest]
plotDT <- dt_pc # for fun
p <- ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`),
x = as.numeric(`Annual consumption-based CO2 emissions (per capita)`),
p <- ggplot2::ggplot(plotDT, aes(x = as.numeric(`GDP per capita, PPP (constant 2017 international $)`),
y = as.numeric(`Annual consumption-based CO2 emissions (per capita)`),
colour = Entity,
alpha = Year)) +
geom_point() +
theme(legend.position = "none") +
labs(x = "Annual CO2 emissions per capita (consumption-based, T)",
y = "GDP per capita (constant 2017 $)")
labs(y = "Annual CO2 emissions per capita (consumption-based, T)",
x = "GDP per capita (constant 2017 $)")
plotly::ggplotly(p)
```
# Efficiency
How about looking at efficency gC02e per \$ GDP?
How about looking at efficiency: gC02e per \$ GDP?
Following <https://timjackson.org.uk/earth-vs-growth/> ...
```{r}
#| label: selectProduction
dt_abs[, `Emissions intensity` := `Annual CO2 emissions`/`GDP, PPP (constant 2017 international $)`*1000*1000]
dt_abs[, `Emissions intensity (production-based)` := `Annual CO2 emissions`/`GDP, PPP (constant 2017 international $)`*1000*1000]
```
```{r}
#| label: selectAndPlotProduction
plotDT <- dt_abs[Entity %in% entities_of_interest]
ggplot2::ggplot(plotDT, aes(Year, `Emissions intensity`, colour = Entity)) +
ggplot2::ggplot(plotDT, aes(Year, `Emissions intensity (production-based)`, colour = Entity)) +
geom_line() +
labs(y = "gC02 per $ GDP",
colour = "Country",
......
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