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

CO₂ Emission vs GDP - Decoupled Economy

Our World in Data (OWID) shows some examples from a country that was able to decoupled their economy from CO2 emission. Decoupling economy is an economy where 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 quoting a paragraph that interest me to write this article.

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

The narrative implies that you can grow your economy without emitting emission - a rather different statement considering the other chart from Figure 2 where the GDP (representing the economy strength) 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

A country needs energy to grow their economy, the higher the energy consumption the higher the CO₂ emission would be. The narrative that a country emits more CO₂ because they are rich can be misleading.

It is 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 not to undermine the impact of CO₂ emission to our global temperature, rather a proposal – 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 a dataset, contains emission from different countries, and to see correlation and infer some causality (if any) between economic growth and emissions. Hopefull would shed some lights on the final question:

What allows these countries to decouple their economy from emissions?

Data Importing and Cleaning

Exploring the CO₂ emission dataset provided by the OWID (Our World in Data). The focus will be on the CO₂ emission and it’s impact to GDP of a country. The 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	0.157	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	0.156	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	0.156	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	0.155	0.000	0.000	0.000	0.0	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
47410	Zimbabwe	2018	ZWE	15052191.0	2.678627e+10	0.558	0.037	10.715	1.419	15.265	...	NaN	0.114	0.001	0.001	0.002	0.0	116.76	29.37	-0.088	-0.825
47411	Zimbabwe	2019	ZWE	15354606.0	2.514642e+10	0.473	0.031	9.775	-0.939	-8.765	...	NaN	0.113	0.001	0.001	0.002	0.0	116.03	28.70	0.143	1.463
47412	Zimbabwe	2020	ZWE	15669663.0	2.317871e+10	0.496	0.032	7.850	-1.926	-19.700	...	NaN	0.112	0.001	0.001	0.002	0.0	113.20	25.99	0.818	10.421
47413	Zimbabwe	2021	ZWE	15993525.0	2.514009e+10	0.531	0.033	8.396	0.547	6.962	...	NaN	0.110	0.001	0.001	0.002	0.0	NaN	NaN	1.088	12.956
47414	Zimbabwe	2022	ZWE	16320539.0	2.590159e+10	0.531	0.033	8.856	0.460	5.477	...	NaN	0.110	0.001	0.001	0.002	0.0	NaN	NaN	NaN	NaN

47415 rows × 79 columns

There are over 70 columns in the original dataset, 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
232	Austria	1998	7975856.0	8.390	12.345	32570.340809
1125	El Salvador	1995	5748201.0	0.870	1.154	4369.935718
2768	New Zealand	2006	4179986.0	8.931	9.530	31297.629066
3606	Spain	1999	40542232.0	7.352	7.783	25446.336118
3207	Portugal	2016	10332751.0	4.874	5.276	25365.954418

Combining it with the Gapminder dataset¹, and with the previously curated CO₂ dataset we just created - we can have countries to add more context.

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	2839.372990	Europe
2	Albania	1992	3303738.0	0.762	2647.205908	Europe
3	Albania	1993	3300715.0	0.708	2919.471012	Europe
4	Albania	1994	3294001.0	0.584	3216.791504	Europe
...	...	...	...	...	...	...
3078	Zimbabwe	2017	14751101.0	0.630	1733.837618	Africa
3079	Zimbabwe	2018	15052191.0	0.712	1779.559752	Africa
3080	Zimbabwe	2019	15354606.0	0.637	1637.711652	Africa
3081	Zimbabwe	2020	15669663.0	0.501	1479.209026	Africa
3082	Zimbabwe	2021	15993525.0	0.525	1571.891677	Africa

3083 rows × 6 columns

Expanding on the GDP per Capita, we can infer from which income class is a certain country belongs to. Using a rough classification by world-bank (may not be the best representation as income class is not the same every year²), but good enough for the purpose of this writing.

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	2839.372990	Europe	lower-middle
2	Albania	1992	3303738.0	0.762	2647.205908	Europe	lower-middle
3	Albania	1993	3300715.0	0.708	2919.471012	Europe	lower-middle
4	Albania	1994	3294001.0	0.584	3216.791504	Europe	lower-middle
...	...	...	...	...	...	...	...
3078	Zimbabwe	2017	14751101.0	0.630	1733.837618	Africa	lower-middle
3079	Zimbabwe	2018	15052191.0	0.712	1779.559752	Africa	lower-middle
3080	Zimbabwe	2019	15354606.0	0.637	1637.711652	Africa	lower-middle
3081	Zimbabwe	2020	15669663.0	0.501	1479.209026	Africa	lower-middle
3082	Zimbabwe	2021	15993525.0	0.525	1571.891677	Africa	lower-middle

3083 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 - Decoupling Countries

Recalling 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.

Decoupling concept itself is based on a premise of decreased from the previous data point. The low-income countries, cannot possibly have lower data point - it is already low! Meanwhile, for rich countries, fonce it gets saturated - their only way is to go down. 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 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=480,
)#.interactive()

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

The Figure 5 shows that 20,000 is a tipping point - where some affluent countries started to be able to decoupling 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. As context, 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.

These are affluent countries, what about the rest of the countries with less GDP per capita. Do we see similar tipping point? To answer that, we need to do some tinkering with the data as below.

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		7404.844274
1	Albania	1991	3302087.0	1.299	2839.372990	Europe	lower-middle		7404.844274
2	Albania	1992	3303738.0	0.762	2647.205908	Europe	lower-middle		7404.844274
3	Albania	1993	3300715.0	0.708	2919.471012	Europe	lower-middle		7404.844274
4	Albania	1994	3294001.0	0.584	3216.791504	Europe	lower-middle		7404.844274
...	...	...	...	...	...	...	...	...	...
3078	Zimbabwe	2017	14751101.0	0.630	1733.837618	Africa	lower-middle		-389.441120
3079	Zimbabwe	2018	15052191.0	0.712	1779.559752	Africa	lower-middle		-389.441120
3080	Zimbabwe	2019	15354606.0	0.637	1637.711652	Africa	lower-middle		-389.441120
3081	Zimbabwe	2020	15669663.0	0.501	1479.209026	Africa	lower-middle		-389.441120
3082	Zimbabwe	2021	15993525.0	0.525	1571.891677	Africa	lower-middle		-389.441120

3083 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		7404.844274	0.026
1	Albania	1991	3302087.0	1.299	2839.372990	Europe	lower-middle		7404.844274	0.026
2	Albania	1992	3303738.0	0.762	2647.205908	Europe	lower-middle		7404.844274	0.026
3	Albania	1993	3300715.0	0.708	2919.471012	Europe	lower-middle		7404.844274	0.026
4	Albania	1994	3294001.0	0.584	3216.791504	Europe	lower-middle		7404.844274	0.026
...	...	...	...	...	...	...	...	...	...	...
3078	Zimbabwe	2017	14751101.0	0.630	1733.837618	Africa	lower-middle		-389.441120	-0.826
3079	Zimbabwe	2018	15052191.0	0.712	1779.559752	Africa	lower-middle		-389.441120	-0.826
3080	Zimbabwe	2019	15354606.0	0.637	1637.711652	Africa	lower-middle		-389.441120	-0.826
3081	Zimbabwe	2020	15669663.0	0.501	1479.209026	Africa	lower-middle		-389.441120	-0.826
3082	Zimbabwe	2021	15993525.0	0.525	1571.891677	Africa	lower-middle		-389.441120	-0.826

3083 rows × 10 columns

With this new dataset, we can confirm our exploratory analysis before,

Are there decoupling countries with less than 20,000 USD GDP Per Capita?

Figure 6 shows a distribution of countries where the their GDP per Capita increases between 1990-2018, while their CO₂ emissions were decreased (decoupling countries). As shown, not every country created equally, as different country has different tipping point.

However, as can be seen all countries were sitting above upper-middle income class at around 4,500 USD GDP per Capita.

Show Code

import numpy as np
import altair as alt

#country with increase gdp, but decreased co2
source=merged_df.query(" gdp_diff>0 & co2_diff<0 & population >= 5_000_000")

alt.Chart(
    source,
    title=alt.Title(
        "Decoupling Countries",
        subtitle=["At least 5 Million population,", 
                  "Increased GDP while Reducing 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=list(np.linspace(1990,2020,10, dtype=int))), title='Year'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=480,
)#.interactive()

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

One of the factor to differentiate between these countries in tipping point is population among other things. Kaya identity, listed four factors affecting the emission of a country. One of them is population.

In addition, cumulatively-speaking, emission from populous countries are important to be considered. My country Indonesia, has roughly 280 Million people, and I would imagine it as Youtuber CEO used to say with a hint of aforementioned context;

Problem at Youtube (populous countries) is problem at Scale

Are there any country that was able to decouple their economy, while having high population (at least 100 Million)?

Show Code

import altair as alt

#country with increase gdp, but decreased co2
source=merged_df.query(" gdp_diff>0 & co2_diff<0 & population >= 100_000_000")

alt.Chart(
    source,
    title=alt.Title(
        "Decoupling Countries",
        subtitle=["At least 100 Million population,", 
                  "Increased GDP while Reducing 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=[3,30])
        ),
    y=alt.Y(
        'gdp_per_capita:Q', title='GDP per Capita',
        scale=alt.Scale(type="log",domain=[1000, 100_000])
        ),
    color=alt.Color('country', title='Country'),
    size=alt.Size('year:O', scale=alt.Scale(domain=list(np.linspace(1990,2020,10, dtype=int))), title='Year'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=480,
)#.interactive()

Figure 7: Countries with low CO2 Emission and increase GDP (countries with minimum 100 Million population)

There are, three countries, in the entire world - that was able to decoupled, when the population is at least 100 million.

The interesting part is, all of them is way above the previous tipping point of upper-middle income class (4,500 USD GDP per Capita). These countries tipping point is about 10,000 USD before they can start to decoupled their economy from emissions.

To conclude.

No Countries in the last decades was able to decouple without being rich at least above 4500 USD GDP per Capita for less populous countries (5 Million), and at least 10,000 USD GDP per capita for populous countries (100 Million).

Country with Increased CO2 Emission & Increased GDP

This is probably the category where it gets tricky. These countries, not only among the fastest growing countries in the world, biggest emitter in the last decades, but also among the most populous countries in the world. Critical to asses their approach to emissions, relative to their carbon budget, historical aspect, and their tipping point (income level). As shown in Figure 8, these countries are all on the increasing trend, their GDP per capita is increasing but at the same time they were emitting CO₂.

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=list(np.linspace(1990,2020,10, dtype=int))), title='Year'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=480,
)#.interactive()

Figure 8: Countries with increased CO2 Emission and GDP

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=list(np.linspace(1990,2020,10, dtype=int))), title='Year'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=480,
)#.interactive()

Figure 9: 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 10 for more details.

Figure 10: Top 5 Overemitting and Underemitting Countries with respect to Net Zero scenario (Fanning and Hickel 2023)

Country with Decreased CO₂ Emission & Decreased GDP

This is part where we should allow their CO₂ emission to grow because it is directly related to the their economic growth. Figure 12 shows country with worse condition in 2018 than they are in 1990, Zimbabwe. Another important concept is carbon budget, where each country, depending on their population should have quota / “budget” of CO₂ they were allowed to use. And in this case, Zimbabwe carbon budget is still below their fair share, as shown in Figure 11, a night and day difference compared to UK carbon share³.

Figure 11: 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=list(np.linspace(1990,2020,10, dtype=int))), title='Year'),
    tooltip=['country', 'population', 'co2_per_capita', 'gdp_per_capita', 'year']
).properties(
    width='container',
    height=480,
)#.interactive()

Figure 12: Countries with low CO2 Emission and low GDP

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 13 and Figure 11) to do so.

Figure 13: 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{wijaya2023,
  author = {Wijaya, A.A.},
  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:

Wijaya, A.A. 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 - Decoupled Economy

Data Importing and Cleaning

Data Exploration and Illustration

Country with Decreased CO2 Emission & Increased GDP - Decoupling Countries

Country with Increased CO2 Emission & Increased GDP

Country with Decreased CO2 Emission & Decreased GDP

Closing Words

References

Footnotes

Citation

CO₂ Emission vs GDP - Decoupled Economy

Country with Decreased CO₂ Emission & Increased GDP - Decoupling Countries

Country with Decreased CO₂ Emission & Decreased GDP