Action to mitigate climate change remains essential. Visualisations of the progress that countries are making towards greener economic growth can help to promote policies that mobilise efforts to achieve net-zero carbon emissions.
Images and graphs – especially those that make effective use of colour – impress emotionally and convey meaning faster and in a stronger and more memorable way. This is widely acknowledged in science, in particular, psychology (see, for example, Gao and Xin, 2006; Wilms and Oberfeld, 2018).
This insight can also help in efforts to tackle climate change. Using colour images of a straightforward indicator that shows progress towards net-zero emissions of carbon dioxide will help non-specialists to see the current picture and the work still to be done.
Colour stripes are an effective way to do this. Visualising a newly proposed indicator, they show how well a country is progressing towards the goal of net-zero emissions, while also taking economic growth into account.
What are greening prosperity stripes – and how can they be used?
‘Greening prosperity’ is a novel measure defined – in a ‘first pass’ or approximation along this line of research – as the ratio of real gross domestic product (GDP) per capita to carbon emissions per capita, based on annual data from the World Bank since 1990 for all countries (Mihailov, 2023).
The measure is then visualised into colourful striped or map images that can improve understanding and raise awareness of environmental pollution, and in turn mobilise policy action.
This greening prosperity indicator by country could be used to track progress towards net zero in a clear and compelling way.
Figure 1: Evidence for global warming
Source: UK government’s Met Office Hadley Centre for Climate Science and Services
Note: Average annual world temperature since 1850, in degrees celsius as deviation from the average (mean) for 1960-90 normalised at zero; temperatures for the northern and southern hemispheres are equally weighted.
Last year, 2023, was the warmest year on record, according to long-run data from the Met Office (see Figure 1). As is clear, there has been a strong upward trend evident since the mid-1960s. By 2016, there had been an almost 1°C increase in temperature compared with the average for the period from 1960 to 1990.
Figure 2 uses the same data but represents them as climate warming stripes – a replication and update of those popularised since 2018 by University of Reading meteorology professor Ed Hawkins.
Figure 2: Replicating the climate warming stripes
Source: UK government’s Met Office Hadley Centre for Climate Science and Services
Note: Same as Figure 1
The greening prosperity stripes, which are inspired by the climate warming stripes, combine environmental pollution data with macroeconomic data, creating comparative visualisations. The colour map stripes show a greening prosperity ratio (GPR) per capita – calculated by dividing real GDP per capita (in constant 2017 US dollars measured at purchasing power parity, PPP) by carbon emissions per capita in metric tons – for all countries.
Given the definition and units of measurement, the GPR per capita might be denoted as real GDP per capita ‘discounted’ by (or ‘cleaned’ from) the degree of carbon emissions – or in brief, cleaned GDP per capita in dollars at PPP.
The main benefit of this new indicator is that colour stripes and maps can easily be interpreted, compared by country and understood even by non-specialists. In addition, the data are sourced by the World Bank, enabling comparability across countries and time.
Further, a greening prosperity indicator is easily applied to track progress towards net zero. Reaching this target would mean a net-zero GPR per capita equal to the numerator of real GDP per capita exactly – that is, with a denominator of carbon emissions per capita of one (kilogram or gram or milligram, depending on any appropriate definition in physical units of zero carbon emissions).
This would indicate clean real GDP per capita (with the denominator of one no longer discounting its value in the numerator). In the visualisations that follow, progress towards net zero shows as increasingly darker green stripes.
What do the greening prosperity stripes tell us about progress towards net zero?
The greening prosperity stripes are created using World Bank data for all countries and country groupings or regions (see Table 1).
Table 1: All countries and country groups in the world, World Bank classification
Source: World Bank
Figure 3 plots, in colour stripes, the evolution of global GDP per capita between 1990 and 2020. It uses the same 16 blue-to-red nuances – see the right-hand side scale – first made popular with the climate warming stripes visualisation created by Ed Hawkins.
Using the same blue-red spectrum is meant to correspond to the conventional terminology in macroeconomics of ‘cooling down’ or ‘heating up’ of the economy.
This colour map visualisation also sticks to the convention established with the climate stripes. The pattern is unique for each country and depicts its GDP per capita data, but it is not directly comparable across countries since the scales on the vertical axes are, by construction, country-specific and, hence, different.
Indeed, the 16 colours are defined between the minimum and maximum value of the respective indicator for each country over time, and so they split the spread, or range, between these minimum and maximum values in 16 equal regions, or stripes, each accounting for 6.25% of the total spread (as seen in the scale bar). In this sense, the colour stripes are standardised and represent a unique pattern by country or country group – here, the global average – similar to those used in product barcodes.
Figure 3: Global GDP per capita
Source: World Bank
Note: The scale measures GDP per capita at PPP in international constant dollars of 2017.
What do we learn from these colour stripe images? First, stripes that move from blue into red but reverse for a period capture recessions. This can be seen at the start of the pandemic in 2020. This evolution is similar to troughs after peaks in standard time-series plots (see Mihailov, 2023, for illustrations and details).
Second, relatively narrow stripes going from blue to red as in 2002-06, and from lighter to darker red as in 2015-18, capture relatively strong growth in GDP per capita.
The same concept can be used to visualise carbon emissions per capita (see Figure 4). Here, we apply a new colour palette that moves from green to brown as a country emits a higher volume of carbon per capita in metric tons (see scale), as is conventional again in the current terminology.
Figure 4: Carbon emissions per capita
Source: World Bank
Note: The scale measures carbon emissions per capita in metric tons.
While in general many individual countries have become greener over the period from 1990 to 2020 (Mihailov, 2023), the world, on average, has become browner. In other words, global carbon emissions per capita have increased (with some reversals seen in the nuances). This is quite a disturbing conclusion.
Next, we turn to the GPR – which combines real GDP and carbon emissions per capita (see Figure 5). This indicator uses the same colour and nuance definitions in 16 stripes as in Figure 4, but the direction is now reversed, following again the logic of colour interpretations, showing brown in the bottom of the scale and green in the top.
Figure 5: Ratio of greening prosperity
Source: Author’s calculations
Note: The scale measures greening prosperity per capita in discounted dollars of 2017.
Using this metric indicates that the world as a whole is going greener – that is, achieving greening prosperity measured in cleaned PPP-dollars since 1990 (steadily, without reversals in the nuances). This is an optimistic trend.
This stripes visualisation does not contain new information relative to the conventional time-series representation, but constitutes another – and more colourful or aesthetic – way of conveying it.
The approach also taps into the stronger sensitivity to colours and images discussed above. These stripe representations are therefore likely to be more striking and could help to mobilise action to mitigate climate change.
How do different countries compare?
To compare the GPR for different countries, the colour definitions are not scaled by the minimum and maximum spread within a country for the period from 1990 to 2020, but instead by the minimum and maximum spread for all countries for the same period. This can be seen by the identical scales in Figures 6-8).
The redefinition generates visualisations that are not as rich in terms of colour and nuance but make the data comparable. GDP per capita at PPP in international dollars (2017 value) in the top panel of Figure 6 ranges between $436.4 for Mozambique in 1992 and $157,602.5 for Macao special administrative region (SAR), China, in 2013.
Against this range, the world on average comes out relatively poor (darker blue) in Figure 6, Switzerland – an example of the richest countries – not that rich (light blue but not going into red) and Mozambique – an example of the poorest countries – unchangeably poor (dark blue).
This is because the GDP per capita of the dozen or so extremely rich and mostly small-island and/or tax-haven countries (mentioned in the cross-section map visualisations below) dwarfs that of even high-income advanced market economies such as Switzerland.
Figure 6: GDP per capita
Source: World Bank
Note: The scale measures GDP per capita at PPP in international constant dollars of 2017.
Carbon emissions per capita range from zero for several small nations in several years to 47.7 metric tons for Qatar in 2004.
Given Qatar’s excessively high emissions in relative terms, it is not surprising that most countries in the sample come out as dominantly green in their prevailing stripe nuances (see Figure 7).
The countries with the darkest shade of green as a single colour – that dominates through the whole period from 1990 to 2020 – are those that pollute the least in terms of carbon emissions per capita. These are, in general, lower-income countries such as Mozambique or rich countries such as Switzerland that are ‘moderate’ polluters, depicted in the relative global scale by two broad mid-green stripes for Switzerland.
Figure 7: Carbon emissions per capita
Source: World Bank
Note: The scale measures carbon emissions per capita in metric tons.
When it comes to the comparable GPRs at a global scale, a few countries – including the polar examples used, Switzerland and Mozambique (with some reversals) – manage somewhat to come out of the dark brown into a lighter brown near the end of the period from 1990 to 2020, while the world as a whole does not.
Indeed, the minimum and the maximum on this indicator range from a global low of about $1,000 discounted dollars (at 2017 value) for many countries to a global high of potentially $120,000 discounted dollars (depending on the availability or not of carbon emissions data for the richest economies). This extremely high level again explains the dominance of brown-nuanced prosperity ratios displayed by most countries.
Figure 8: Greening prosperity ratio
Source: Author’s calculations
Note: The scale measures greening prosperity per capita in discounted dollars (2017 value).
Global comparisons in 2020
We can use similar visualisations to give a cross-sectional view of all countries and country groupings in the world in 2020, this time using maps (see Figure 9). The top panel shows the same 16 colour patterns, now defined against the minimum and maximum in the cross-section of 2020, for GDP per capita for all individual countries in the World Bank dataset (see Table 1) and plotted on the map of the world.
Luxembourg has the highest GDP per capita at nearly $112,000 (2017 value) per capita, followed by Singapore (just below $95,000) and Ireland (just above $91,000). The United States comes 11th, at marginally over $60,000.
As is clear from the figure, there is significant diversity in GDP per capita levels globally. While there are around a dozen super-rich economies, shown in the nuances of red (some tiny on the map and hard to spot), there are plenty that are doing relatively well, in the lighter blue shades. Of course, one can easily see that a number of countries remain in poverty (in dark blue).
Similarly, countries’ levels of carbon emissions per capita in 2020 are quite different (see middle panel of Figure 9). The half a dozen or so brown spots (mostly in the Middle East) show the largest per capita polluters – these include Qatar (with carbon emissions in 2020 of 32 metric tons per capita), Bahrain (22 tons), Brunei Darussalam (21.7 tons), Kuwait (21 tons), and the United Arab Emirates (20 tons). The United States – emitting 13 tons – ranks tenth in this list of largest polluters, following Oman (15.6 tons), Australia (14.8 tons), Saudi Arabia (14.3 tons) and Canada (13.6 tons).
The bottom map of Figure 9 shows the global cross-section of the GPR per capita indicator for 2020. The absence of carbon emissions data for some of the richest small or island economies – often those dominated by financial service sectors that are not polluting very much, such as Bermuda, the Cayman Islands, Hong Kong, Macao and San Marino – means that they are not included in a fairer, more exhaustive calculation of GPR per capita ratios.
These data limitations, which in the near future are likely to be remedied, highlight one of the weaknesses of the GPR per capita indicator, namely to inflate at higher values (due to tiny denominators) – and as seen in the few green nuances on the map – some of the poorest countries in terms of GDP per capita that emit very little carbon.
The Democratic Republic of Congo thus attains the darkest green colour (corresponding to its GPR per capita of just above $32,000 discounted dollars), followed by Somalia (some $28,000), Rwanda (nearly $19,700) and Central African Republic (just above $19,000).
Much richer Western economies that do not have particularly high levels of carbon emissions in relative terms come only a little further down in such a ranking. For example, Switzerland ranks seventh (about $16,800 discounted dollars), and Sweden eighth ($15,800). Ireland ($13,500), Malta ($13,120) and Iceland ($13,080) also rank 14th, 16th and 17th respectively on this measure.
This unrealistic representation of prosperity points to the need for some amendments in the methodology of computing the first approximation of GPR per capita. For example, future research could use life expectancy, real consumption per capita or some composite weighted index of these and real GDP – or, rather, gross national income (GNI) – per capita in the numerator. Using greenhouse gas (GHG) emissions in total and other measures of environmental pollution rather than only carbon emissions, or in combination with it, could also be explored in future work.
In all other countries, the level of greening prosperity remains too low – below $10,000 discounted dollars (2017 value) – shown by the dominant medium-to-dark brown colour shades. This is either because the majority of the world’s economies are considerable carbon emitters or because many economies have relatively low GDP per capita, or both.
Figure 9: GDP per capita (top), carbon emissions per capita (middle) and ratio of greening prosperity (bottom), all countries, 2020
Source: World Bank
Note: The scale measures GDP per capita at PPP in international constant dollars of 2017.
Source: World Bank
Note: The scale measures carbon emissions per capita in metric tons.
Source: Author’s calculations
Note: The scale measures greening prosperity per capita in ‘discounted’ dollars of 2017.
Conclusion
As is often noted, ‘pictures speak louder than words’ – and the use of these colour stripe and map visualisations are an effective way to communicate research on environmental greening.
The visualisations enable comparison of GDP per capita, carbon emissions per capita and the resulting ratio of greening prosperity per capita for all countries and major World Bank country groups across the globe.
Their purpose is to vividly communicate this information and raise awareness of the urgency of climate change mitigation.
The greening prosperity indicator could also be used to track countries’ progress towards net-zero carbon emissions in the years and decades to come. For example, those countries, such as Switzerland, that have lighter brown, and hopefully soon greener, stripes have not sacrificed economic growth while reducing emissions significantly.
This reflects a phenomenon known as decoupling (of economic growth from carbon emissions). Other countries, including the UK, the United States and members of the European Union, are also pursuing this approach (Mihailov, 2023). But decoupling should still come with a caution as it can hide the trend of offshoring polluting industries.
Where can I find out more?
- Greening prosperity stripes across the globe: Discussion paper from November 2023, revised in April 2024 and September 2024.
- Climate stripes: Representing global temperature rise over two centuries.
- #Show YourStripes: Interactive website.
Who are experts on this question?
- Alexander Mihailov
- Ed Hawkins
