Decoupling GDP from CO2 Emission - Only If You’re Rich Enough?

CO₂ Emission vs GDP

Our World in Data (OWID) shows some examples from a country that was able to decoupled their economy from CO2 emission. Meaning that they still able to increase their GDP while at the same time reducing CO2 emission. UK is the example used in Figure 1.

Figure 1: United Kingdom Decoupled CO₂ Emission vs Economic Growth

You can read the full article, but I am going to quote paragraph that interest me to write this article.

“These countries show that economic growth is not incompatible with reducing emissions.”

This narrative means that you can grow your economy without emitting more emission - a bold statement considering the other chart from Figure 2 showed the opposite, where the GDP of a country is strongly related to the CO₂ emission.

Even OWID themselves explained:

“Historically, CO₂ emissions have been strongly correlated with how much money we have. This is particularly true at low-to-middle incomes. The richer we are, the more CO₂ we emit. This is because we use more energy – which often comes from burning fossil fuels.” (source: Our World in Data)

Figure 2: CO₂ Emission per Capita vs GDP per Capita

Here is where It got me concerned. There should be a big asterix on a narrative that countries can grow their economy while also reducing emission - Only when they are rich.

The premise is simple, country needs energy to grow their economy, the higher the energy consumption the higher the CO₂ emission would be. I think the narrative that a country emits more CO₂ because they are rich can be misleading. I think it is the other way around.

So not because we are rich we emit more CO₂, but we are rich because we emit more CO₂ used for energy, to grow the economy.

This is of course no to undermine the impact of CO₂ emission to our global temperature, but rather to manage our expectations and a reality check on what can really be done. Some questions about “can we reduce our CO₂ emission but still maintaining economy growth”? Or “do we have to increase our CO₂ to raise our economic growth?” are some fair questions to be addressed in more detail.

Despite some articles pointed out the fact that some countries were able to decoupled their economy from CO₂ emission as shown in Figure 1, it is important to understand the context, in which these countries were positioned compared to rest of the world.

This article will explore that question.

What allows these countries to decouple their economy from their emissions?

Data Importing and Cleaning

I explored the CO₂ emission dataset provided by the OWID (Our World in Data). I will be focusing mainly on the CO₂ emission and it’s impact to GDP of a country. My premise stays the same, that a country must burn the energy to grow their economy, and to do that they will have to emit CO2, since more than 80% of energy (see Figure 3) in the world still comes from fossil-fuels (oil, gas, coal).

The dataset is downloaded from the provided link in the code block below.

Show Code

#importing dataset

import pandas as pd
import warnings

# Ignore future warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.options.mode.chained_assignment = None  # default='warn'

co2_raw = pd.read_csv('https://raw.githubusercontent.com/owid/co2-data/master/owid-co2-data.csv')
co2_remark = pd.read_csv('https://raw.githubusercontent.com/owid/co2-data/master/owid-co2-codebook.csv')
co2_raw

	country	year	iso_code	population	gdp	cement_co2	cement_co2_per_capita	co2	co2_growth_abs	co2_growth_prct	...	share_global_other_co2	share_of_temperature_change_from_ghg	temperature_change_from_ch4	temperature_change_from_co2	temperature_change_from_ghg	temperature_change_from_n2o	total_ghg	total_ghg_excluding_lucf	trade_co2	trade_co2_share
0	Afghanistan	1850	AFG	3752993.0	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Afghanistan	1851	AFG	3767956.0	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	0.165	0.000	0.000	0.000	0.0	NaN	NaN	NaN	NaN
2	Afghanistan	1852	AFG	3783940.0	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	0.164	0.000	0.000	0.000	0.0	NaN	NaN	NaN	NaN
3	Afghanistan	1853	AFG	3800954.0	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	0.164	0.000	0.000	0.000	0.0	NaN	NaN	NaN	NaN
4	Afghanistan	1854	AFG	3818038.0	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	0.163	0.000	0.000	0.000	0.0	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
50593	Zimbabwe	2017	ZWE	14751101.0	2.194784e+10	0.469	0.032	9.596	-0.937	-8.899	...	NaN	0.114	0.001	0.001	0.002	0.0	115.00	27.71	0.910	9.486
50594	Zimbabwe	2018	ZWE	15052191.0	2.271535e+10	0.558	0.037	11.795	2.199	22.920	...	NaN	0.114	0.001	0.001	0.002	0.0	116.76	29.37	0.771	6.537
50595	Zimbabwe	2019	ZWE	15354606.0	NaN	0.570	0.037	11.115	-0.681	-5.772	...	NaN	0.113	0.001	0.001	0.002	0.0	116.03	28.70	0.978	8.795
50596	Zimbabwe	2020	ZWE	15669663.0	NaN	0.570	0.036	10.608	-0.507	-4.559	...	NaN	0.112	0.001	0.001	0.002	0.0	113.20	25.99	1.006	9.481
50597	Zimbabwe	2021	ZWE	15993525.0	NaN	0.570	0.036	11.296	0.688	6.488	...	NaN	0.111	0.001	0.001	0.002	0.0	NaN	NaN	NaN	NaN

50598 rows × 79 columns

There are over 70 columns in the original dataset, so we will only use columns that we are interested in, mainly related to GDP and CO2 emission of a country, referenced by year. Some data cleaning (removing any null rows in GDP per Capita, or CO₂ consumption per Capita, etc.).

Show Code

#selecting dataset
co2 = co2_raw[[ 'country', 'year','population', 'gdp', 'co2_per_capita', 'consumption_co2_per_capita' ]]

#adding gdp per capita column
co2['gdp_per_capita'] = co2['gdp']/ co2['population']

# dropping any rows with null consumption_co2_per_capita
co2 = co2[~co2.consumption_co2_per_capita.isnull()].reset_index(drop=True)

#drop gdp column
co2 = co2.drop(columns='gdp')

#removing any incomplete data
co2 = co2.query(" gdp_per_capita>0 & co2_per_capita>0")
co2.dropna().sample(5)

	country	year	population	co2_per_capita	consumption_co2_per_capita	gdp_per_capita
614	Burkina Faso	2015	18718022.0	0.198	0.260	1401.644279
2089	Kyrgyzstan	2002	5026646.0	0.979	1.118	3216.956624
2751	Nigeria	2013	174726128.0	0.666	0.645	5493.963030
1663	Hong Kong	2010	7132437.0	5.617	11.312	41909.730236
3942	United Arab Emirates	2015	8916909.0	24.266	27.123	77553.611172

I would like to make graphic where I can colored a country based on its continent, just for an additional context to the data. We took that from Gapminder dataset¹, and combine it with the previously curated CO₂ dataset we just created.

Show Code

#importing gapminder
gapminder=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv')
gapminder = gapminder[['country', 'continent']]
gapminder = gapminder.drop_duplicates().reset_index(drop=True)

#merging with original co2 dataset
co2 = pd.merge(co2, gapminder, on='country', how='inner')

#drop consumption per capita
co2 = co2.drop(columns='consumption_co2_per_capita')

#sanity check
co2

	country	year	population	co2_per_capita	gdp_per_capita	continent
0	Albania	1990	3295073.0	1.675	3929.457471	Europe
1	Albania	1991	3302087.0	1.299	2839.369269	Europe
2	Albania	1992	3303738.0	0.762	2647.207768	Europe
3	Albania	1993	3300715.0	0.708	2919.476286	Europe
4	Albania	1994	3294001.0	0.584	3216.795546	Europe
...	...	...	...	...	...	...
2786	Zimbabwe	2014	13855758.0	0.862	1531.673864	Africa
2787	Zimbabwe	2015	14154937.0	0.866	1485.520916	Africa
2788	Zimbabwe	2016	14452705.0	0.729	1450.371681	Africa
2789	Zimbabwe	2017	14751101.0	0.651	1487.877848	Africa
2790	Zimbabwe	2018	15052191.0	0.784	1509.106089	Africa

2791 rows × 6 columns

I would like to also see depending on the GDP per Capita, from which income class is a certain country belongs to. I am using a rough classification by world-bank. I know this may not be the best representation as income class is not the same every year², and I am not exactly using exact number here, but I think this is good enough to put some contexts for data illustration.

Show Code

#creating income class category based on gdp per capita. 

bins= [0.00001,1000,4000,12000,1000000] #setting up the group based on bmi bins 
labels = [
         'lower',
         'lower-middle',
         'upper-middle',
         'upper'
         ] #setting up the label on each group

co2['income_class']= pd.cut(
   co2['gdp_per_capita'], 
   bins=bins, 
   labels=labels,
   include_lowest=False
   )

co2

	country	year	population	co2_per_capita	gdp_per_capita	continent	income_class
0	Albania	1990	3295073.0	1.675	3929.457471	Europe	lower-middle
1	Albania	1991	3302087.0	1.299	2839.369269	Europe	lower-middle
2	Albania	1992	3303738.0	0.762	2647.207768	Europe	lower-middle
3	Albania	1993	3300715.0	0.708	2919.476286	Europe	lower-middle
4	Albania	1994	3294001.0	0.584	3216.795546	Europe	lower-middle
...	...	...	...	...	...	...	...
2786	Zimbabwe	2014	13855758.0	0.862	1531.673864	Africa	lower-middle
2787	Zimbabwe	2015	14154937.0	0.866	1485.520916	Africa	lower-middle
2788	Zimbabwe	2016	14452705.0	0.729	1450.371681	Africa	lower-middle
2789	Zimbabwe	2017	14751101.0	0.651	1487.877848	Africa	lower-middle
2790	Zimbabwe	2018	15052191.0	0.784	1509.106089	Africa	lower-middle

2791 rows × 7 columns

Data Exploration and Illustration

The first exploration of the data is to see how much change every country experiencing with over the course of 28 years from 1990-2018, and how the relationship between CO₂ emission per Capita vs GDP per Capita look like.

Figure 4 shows the time-lapse between years, annotated to some countries from low (e.g. Ethiopia, Bangladesh), middle (e.g. Indonesia, India) to high (Singapore, USA, UK, etc.) GDP per CO₂ Ratio.

The position is relatively stable especially for high-income countries like US, UK, and Germany, but drastic change for low-middle income countries.

Show Code

import plotly_express as px

source = co2

#selected countries to annotate
highlighted_countries = ['United States', 'Germany', 'United Kingdom',
                         'China', 'Singapore','Mexico', 'India', 'Indonesia', 
                         'Nigeria', 'Vietnam', 'Bangladesh', 'Ethiopia'
                        ]

# Create a new column for text values based on the condition
source['text_value'] = source['country'].apply(lambda country: country if country in highlighted_countries else '')

fig = px.scatter(data_frame=source, 
           x="co2_per_capita", 
           y="gdp_per_capita", 
           animation_frame="year", 
           animation_group="country",
           size="population", 
           color="continent", 
           hover_name="country", 
           log_x = True, log_y=True,
           size_max=80,
           width=700,
           height=800,
           # text_baseline='bottom',
           range_x=[0.01,100], 
           range_y=[400,90000],
           text='text_value'
          )


fig.update_layout(
    # title='CO2 Emission vs GDP of Countries',
    xaxis_title='CO2 Emission per Capita (tonnes)',
    yaxis_title='GDP per Capita (USD)',
    legend=dict(
    orientation="h",
    yanchor="bottom",
    y=1.02,
    xanchor="right",
    x=1
)
)

fig.show()

Figure 4: 1990-2018 Time-lapse Chart of CO2 Emission and GDP per Country

Country with Decreased CO₂ Emission & Increased GDP

If we recall some articles from OWID, where they pointed out some countries such as United Kingdom, where the economic growth still happening while at the same time, reducing the CO₂ emission as shown in Figure 1. It sounds impressive, but from the Figure 5 below, it is quite clear on why some countries like United Kingdom, Germany or USA were able to decoupled their economy from their CO₂ emissions.

Because they are already rich.

It is easy to ignore the fact that those countries were sitting on top of other countries in terms of income level, with GDP per Capita at the high-income class countries, consistently above 20,000 USD ever since 1990.

The narrative that a country can really keep increasing their GDP per Capita without producing more CO₂ emission is really an oversimplification of the whole set of conditions that allow a country to do so.

Show Code

import altair as alt

highlighted_countries = ['India', 'Indonesia', 'United Kingdom', 'Germany', 'United States']

source=co2[co2['country'].isin(highlighted_countries)]

alt.Chart(
    source,
    title=alt.Title(
        "GDP per Capita vs CO2 Emission",
        subtitle=["Different CO2 vs GDP rate in different Countries"],
        anchor='middle',
        offset=10, fontSize=16, 
    )
    ).mark_point(size=90, filled=True, opacity=0.7).encode(
    x=alt.X(
        'co2_per_capita:Q', title='CO2 per Capita',
        scale=alt.Scale(type="log", domain=[0.3, 30])
        ),
    y=alt.Y(
        'gdp_per_capita:Q', title='GDP per Capita',
        scale=alt.Scale(type="log")
        ),
    color=alt.Color('year', title='Year'),
    shape=alt.Shape('country', title='Country'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=400,
).interactive()

Figure 5: CO2 vs GDP per Capita for some countries with different trend

A country needs to be rich first, at around 20,000 USD GDP per Capita before they can decouple their CO₂ emission from GDP. To be rich, a country will have to use more energy, and likely to produce more CO₂ emissions because of that. Even using a modest estimation of 5% GDP growth, and 2% inflation rate, it will take 7 years, before Indonesia can reach 20,000 USD per Capita level.

Another view is to see if there is country where the CO₂ emission is reduced, but at the same time, their economy halted, and their GDP per Capite is reduced as well. To do that, let’s create a new sets of columns, of the difference in GDP per Capita per country.

Show Code

# create a column for gdp changes between 1990-2018

# Pivot the DataFrame to have years as columns
gdp_df = co2.pivot(index='country', columns='year', values='gdp_per_capita')
# pivoted_df

# Calculate the difference between GDP values for years 1980 and 2018
gdp_df['gdp_diff'] = gdp_df[2018] - gdp_df[1990]

# Reset the index to convert the DataFrame back to the original format
gdp_df.reset_index(inplace=True)

# Merge the calculated difference back to the original DataFrame
merged_df = pd.merge(co2, gdp_df[['country', 'gdp_diff']], on='country', how='left')

merged_df

	country	year	population	co2_per_capita	gdp_per_capita	continent	income_class	text_value	gdp_diff
0	Albania	1990	3295073.0	1.675	3929.457471	Europe	lower-middle		7891.497683
1	Albania	1991	3302087.0	1.299	2839.369269	Europe	lower-middle		7891.497683
2	Albania	1992	3303738.0	0.762	2647.207768	Europe	lower-middle		7891.497683
3	Albania	1993	3300715.0	0.708	2919.476286	Europe	lower-middle		7891.497683
4	Albania	1994	3294001.0	0.584	3216.795546	Europe	lower-middle		7891.497683
...	...	...	...	...	...	...	...	...	...
2786	Zimbabwe	2014	13855758.0	0.862	1531.673864	Africa	lower-middle		-659.894783
2787	Zimbabwe	2015	14154937.0	0.866	1485.520916	Africa	lower-middle		-659.894783
2788	Zimbabwe	2016	14452705.0	0.729	1450.371681	Africa	lower-middle		-659.894783
2789	Zimbabwe	2017	14751101.0	0.651	1487.877848	Africa	lower-middle		-659.894783
2790	Zimbabwe	2018	15052191.0	0.784	1509.106089	Africa	lower-middle		-659.894783

2791 rows × 9 columns

Do the same for CO₂ Emissions between 1990-2018.

Show Code

# create a column for co2 changes between 1990-2018

# Pivot the DataFrame to have years as columns
co2_df = co2.pivot(index='country', columns='year', values='co2_per_capita')
# pivoted_df

# Calculate the difference between GDP values for years 1980 and 2018
co2_df['co2_diff'] = co2_df[2018] - co2_df[1990]

# Reset the index to convert the DataFrame back to the original format
co2_df.reset_index(inplace=True)

# Merge the calculated difference back to the original DataFrame
merged_df = pd.merge(merged_df, co2_df[['country', 'co2_diff']], on='country', how='left')
merged_df

	country	year	population	co2_per_capita	gdp_per_capita	continent	income_class	text_value	gdp_diff	co2_diff
0	Albania	1990	3295073.0	1.675	3929.457471	Europe	lower-middle		7891.497683	0.057
1	Albania	1991	3302087.0	1.299	2839.369269	Europe	lower-middle		7891.497683	0.057
2	Albania	1992	3303738.0	0.762	2647.207768	Europe	lower-middle		7891.497683	0.057
3	Albania	1993	3300715.0	0.708	2919.476286	Europe	lower-middle		7891.497683	0.057
4	Albania	1994	3294001.0	0.584	3216.795546	Europe	lower-middle		7891.497683	0.057
...	...	...	...	...	...	...	...	...	...	...
2786	Zimbabwe	2014	13855758.0	0.862	1531.673864	Africa	lower-middle		-659.894783	-0.754
2787	Zimbabwe	2015	14154937.0	0.866	1485.520916	Africa	lower-middle		-659.894783	-0.754
2788	Zimbabwe	2016	14452705.0	0.729	1450.371681	Africa	lower-middle		-659.894783	-0.754
2789	Zimbabwe	2017	14751101.0	0.651	1487.877848	Africa	lower-middle		-659.894783	-0.754
2790	Zimbabwe	2018	15052191.0	0.784	1509.106089	Africa	lower-middle		-659.894783	-0.754

2791 rows × 10 columns

With this new dataset, we can confirm our exploratory analysis before, what countries that can decoupled their CO₂emission from economic growth (GDP)?

Figure 6 shows a distribution of countries where the their GDP per Capita increases between 1990-2018, while their CO₂ emissions were decreased. One interesting fact is all countries were sitting above upper-middle income class at around 4,500 USD GDP per Capita, and some are still on the increasing trend (GDP and CO₂ emission increase) for the past 10 years e.g. Columbia.

As shown, not every country created equally, as different country has different tipping point when they started to be on decreasing trend on CO₂ emission while still increasing their GDP per Capita.

Show Code

import altair as alt

#country with increase gdp, but decreased co2
source=merged_df.query(" gdp_diff>0 & co2_diff<0 & population >= 2_000_000")

alt.Chart(
    source,
    title=alt.Title(
        "GDP per Capita vs CO2 Emission for Countries",
        subtitle=["Countries with minimum 2 Million population,", 
                  "increased GDP and reduced CO2 Emissions"],
        fontSize=16, offset=10
    )
    ).mark_point(size=90, filled=True, opacity=0.6).encode(
    x=alt.X(
        'co2_per_capita:Q', title='CO2 per Capita',
        scale=alt.Scale(type="log", domain=[0.5,50])
        ),
    y=alt.Y(
        'gdp_per_capita:Q', title='GDP per Capita',
        scale=alt.Scale(type="log")
        ),
    color=alt.Color('country', title='Country'),
    size=alt.Size('year:O', scale=alt.Scale(domain=[1990,1995,2000,2005,2010,2015,2020]), title='Year'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=400,
).interactive()

Figure 6: Countries with low CO2 Emission and increase GDP (countries with minimum 2 Million population)

Country with Decreased CO₂ Emission & Decreased GDP

This is part where we should expect some of the poorest country in the world, where their CO₂ emission should not be halted because it is directly related to the their economic growth. Figure 8 shows country with worse condition in 2018 than they are in 1990, Zimbabwe. This country needs help, and if that means emitting more CO₂ emission, so be it. In fact their carbon budget is still below their fair share, as shown in Figure 7, a night and day difference compared to UK carbon share³.

Figure 7: Cumulative emission on UK and Zimbabwe compared to their respective Carbon Budget (Fanning and Hickel 2023)

Show Code

import altair as alt

#country with decreased CO2 and GDP
source=merged_df.query(" gdp_diff<0 & co2_diff<0 ")

alt.Chart(
    source,
    title=alt.Title(
        "GDP per Capita vs CO2 Emission for Countries",
        subtitle=["Countries with decreased GDP and CO2 Emission"],
        fontSize=16, offset=10
    )
    ).mark_point(size=90, filled=True, opacity=0.6).encode(
    x=alt.X(
        'co2_per_capita:Q', title='CO2 per Capita',
        scale=alt.Scale(type="log", domain=[0.5,50])
        ),
    y=alt.Y(
        'gdp_per_capita:Q', title='GDP per Capita',
        scale=alt.Scale(type="log")
        ),
    color=alt.Color('country', title='Country'),
    size=alt.Size('year:O', scale=alt.Scale(domain=[1990,1995,2000,2005,2010,2015,2020]), title='Year'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=400,
).interactive()

Figure 8: Countries with low CO2 Emission and low GDP

Country with Increased CO2 Emission & Increased GDP

This is probably the category where everyone is trying to judge on, country that is trying to grow their economy and in doing so- increase their CO₂ emission. As shown inf Figure 9, these countries are all on the increasing trend, their GDP per Capita is increasing but at the same time they were emitting CO₂, which is understandable.

Show Code

import altair as alt

#country with increased CO2 and GDP
source=merged_df.query(" gdp_diff>0 & co2_diff>0 ")

alt.Chart(
    source,
    title=alt.Title(
        "GDP per Capita vs CO2 Emission for Countries",
        subtitle=["Countries with Increased GDP and CO2 Emission"],
        fontSize=16, offset=10
    )
    ).mark_point(size=90, filled=True, opacity=0.6).encode(
    x=alt.X(
        'co2_per_capita:Q', title='CO2 per Capita',
        scale=alt.Scale(type="log",)
        ),
    y=alt.Y(
        'gdp_per_capita:Q', title='GDP per Capita',
        scale=alt.Scale(type="log")
        ),
    color=alt.Color('country', title='Country'),
    size=alt.Size('year:O', scale=alt.Scale(domain=[1990,1995,2000,2005,2010,2015,2020]), title='Year'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=500,
).interactive()

Figure 9: Countries with increased CO2 Emission and GDP

If we are looking at some countries such as Indonesia, Vietnam, India, it is clear that these countries are trying to grow their economy from below 4,000 USD GDP per Capita in 1990, to hopefully near 20,000 USD GDP per Capita (just like any other rich countries in Figure 5), in the foreseeable future.

Show Code

import altair as alt

#country with increased CO2 and GDP
source=merged_df.query(" gdp_diff>0 & co2_diff>0 & population > 50_000_000 ")

alt.Chart(
    source,
    title=alt.Title(
        "GDP per Capita vs CO2 Emission for Countries",
        subtitle=["Countries with Increased GDP and CO2 Emission", " with population more than 50 Million "],
        fontSize=16, offset=10
    )
    ).mark_point(size=90, filled=True, opacity=0.6).encode(
    x=alt.X(
        'co2_per_capita:Q', title='CO2 per Capita',
        scale=alt.Scale(type="log",)
        ),
    y=alt.Y(
        'gdp_per_capita:Q', title='GDP per Capita',
        scale=alt.Scale(type="log")
        ),
    color=alt.Color('country', title='Country'),
    size=alt.Size('year:O', scale=alt.Scale(domain=[1990,1995,2000,2005,2010,2015,2020]), title='Year'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=400,
).interactive()

Figure 10: Countries with increased CO2 Emission and GDP

On the one hand, some developed countries, the one that were referred as an example for a country that successfully decoupled their CO2 emission from their economic growth are proved to be still overemitting. In fact, the top 5 countries such as United States, Germany and United Kingdom are still in the excess shares with respect to the net-zero scenario (Fanning and Hickel 2023).

On the other hand, some developing countries like India, Indonesia, Pakistan, and even China still have their carbon share to be used, as they are sacrificing their fair shares with respect to the net-zero scenario. See Figure 11 for more details.

Figure 11: Top 5 Overemitting and Underemitting Countries with respect to Net Zero scenario (Fanning and Hickel 2023)

Closing Words

Some developed countries are at a better position to afford moving towards decreasing CO2 emission while still flourishing in their economic growth. Some developing countries are still trying to go to the level of those developed countries, while at the same time emitting CO2 emission, which in some conditions could still be within their carbon budget (Figure 12 and Figure 7) to do so.

Figure 12: Indonesia Carbon Budget vs The Cummulative Emission (Fanning and Hickel 2023)

References

Fanning, Andrew L., and Jason Hickel. 2023. “Compensation for Atmospheric Appropriation.” Nature Sustainability, June. https://doi.org/10.1038/s41893-023-01130-8.

Footnotes

Gapminder is an independent educational non-proﬁt ﬁghting global misconceptions. Complete website is available here ↩︎
The low income countries is classified as country with income about 1,000 USD or lower, middle is divided to two: lower-middle income is roughly between 1,000 USD - 4,000 USD, upper middle income is between 4,000 USD and 13,000 USD, and high income countries is any country with more than 13,000 USD income.↩︎
Website to display the cumulative emission with respect to their fair shares on carbon budgets. Link can be found here ↩︎

Citation

BibTeX citation:

@online{arie wijaya2023,
  author = {Arie Wijaya, Aditya},
  title = {Decoupling {GDP} from {CO2} {Emission} - {Only} {If} {You’re}
    {Rich} {Enough?}},
  date = {2023-07-23},
  url = {https://adtarie.net/posts/20230723-co2-growth},
  langid = {en}
}

For attribution, please cite this work as:

Arie Wijaya, Aditya. 2023. “Decoupling GDP from CO2 Emission - Only If You’re Rich Enough?” July 23, 2023. https://adtarie.net/posts/20230723-co2-growth.

CO2 Emission vs GDP

Data Importing and Cleaning

Data Exploration and Illustration

Country with Decreased CO2 Emission & Increased GDP

Country with Decreased CO2 Emission & Decreased GDP

Country with Increased CO2 Emission & Increased GDP

Closing Words

References

Footnotes

Citation

CO₂ Emission vs GDP

Country with Decreased CO₂ Emission & Increased GDP

Country with Decreased CO₂ Emission & Decreased GDP