GDP & emissions - degrowthing or decoupling?

What’s all this about de-growth?

<sigh>

Data

Downloaded from the awesome OurWorldInData.

Code

dt_abs <- data.table::fread(here::here("data", "co2-emissions-and-gdp.csv"))
dt_pc <- data.table::fread(here::here("data", "co2-emissions-and-gdp-per-capita.csv"))

UK: absolute GDP & emissions

First we’ll try production emissions @fig-ukPlotProd.

Code

plotDT <- dt_abs[Entity == "United Kingdom"]

ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP, PPP (constant 2017 international $)`)/1000000000, 
                            x = as.numeric(`Annual CO2 emissions`)/1000000,
                            alpha = Year)) +
  geom_point() +
  labs(x = "Annual CO2 emissions (production-based, gT)",
       y = "GDP $bn")

Figure 1: UK GDP vs production emissions over time.

Next we’ll try consumption emissions @fig-ukPlotCons.

Note that 2020 consumption-based emissions data is missing so you don’t see the downtick

Code

plotDT <- dt_abs[Entity == "United Kingdom"]

ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP, PPP (constant 2017 international $)`)/1000000000, 
                            x = as.numeric(`Annual consumption-based CO2 emissions`)/1000000,
                            alpha = Year)) +
  geom_point() +
  labs(x = "Annual CO2 emissions (consumption-based, gT)",
       y = "GDP $bn")

Figure 2: UK GDP vs consumption emissions over time.

UK: per capita GDP & emissions

Since we’ll be dividing everything pairwise by the same denominator, nothing much should change…

First we’ll try production emissions @fig-ukPlotProdPcc.

Code

plotDT <- dt_pc[Entity == "United Kingdom"]

ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`), 
                            x = as.numeric(`Annual CO2 emissions (per capita)`),
                            alpha = Year)) +
  geom_point() +
  labs(x = "Annual CO2 emissions per capita (production-based, T)",
       y = "GDP per capita $")

Figure 3: UK GDP vs production emissions over time.

Next we’ll try consumption emissions @fig-ukPlotConsPcc.

Note that 2020 consumption-based emissions data is missing so you don’t see the downtick

Code

plotDT <- dt_pc[Entity == "United Kingdom"]

ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`), 
                            x = as.numeric(`Annual consumption-based CO2 emissions (per capita)`),
                            alpha = Year)) +
  geom_point() +
  labs(x = "Annual CO2 emissions per capita (consumption-based, T)",
       y = "GDP per capita $")

Figure 4: UK GDP vs consumption emissions over time.

Data descriptions

Check

Skim the absolute data:

Code

skimr::skim(dt_abs)

Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl`
provided. Falling back to `character`.

Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl`
provided. Falling back to `character`.

Data summary

Name	dt_abs
Number of rows	7795
Number of columns	6
Key	NULL
_______________________
Column type frequency:
character	4
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Entity	0	1.00	4	32	0	255	0
Code	0	1.00	0	8	931	225	0
Annual CO2 emissions	479	0.94	1	21	0	6137	0
GDP, PPP (constant 2017 international $)	1626	0.79	1	21	0	6162	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Year	0	1.00	2005.06	8.930000e+00	1990.0	1997	2005	2013	2020	▇▇▇▇▇
Annual consumption-based CO2 emissions	3730	0.52	976326173.39	3.366486e+09	197201.7	10759533	59975059	290854945	36702502921	▇▁▁▁▁

Skim the per capita data

Code

skimr::skim(dt_pc)

Data summary

Name	dt_pc
Number of rows	7745
Number of columns	6
Key	NULL
_______________________
Column type frequency:
character	2
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Entity	0	1	4	32	0	252	0
Code	0	1	0	8	899	224	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Year	0	1.00	2005.07	8.93	1990.00	1997.00	2005.00	2013.00	2020.00	▇▇▇▇▇
GDP per capita, PPP (constant 2017 international $)	1579	0.80	17879.29	20132.68	436.72	3678.83	10365.78	25832.03	161971.47	▇▂▁▁▁
Annual consumption-based CO2 emissions (per capita)	3680	0.52	6.53	6.92	0.05	1.19	4.30	9.89	57.79	▇▂▁▁▁
Annual CO2 emissions (per capita)	492	0.94	5.11	6.44	0.02	0.77	2.97	7.42	68.72	▇▁▁▁▁

Code

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