Are role model states really green?

Compare ecological footprint data of 100+ countries and explore in which countries people consume at the largest ecological scale

Photo credit by Hello I’m Nik (2020)

Worst of all

Do you know which the top three most ecologically harmful nations are? Make your bets!

3…

2…

1…

The top harmers are Qatar, Luxembourg, and the United Arab Emirates. I claimed “nations” based on per capita view of their ecological footprint (EF). Living at their consumption level would require circa 8 Earths to sustain.

You can check the latest data available (that of 2016) yourself in the dashboard prepared by the Global Footprint Network here, in the picture below, or by continuing reading. I’d suggest the latter as I added some extra data visualization features to the following original version.

Image of the dashboard published by the Global Footprint Network (as of 2019)

While the original dashboard unambiguously has its own merits, e.g.

  • one can check different years and measures
  • or the rank of individual countries
  • and discover some atypical ecological footprint compositions

the underlying dataset possesses further unexploited potential. This notion also made the viz becoming subject of a MakeoverMonday challenge in 2018.

Visual features added

I gave it a try to check the opportunities myself with the aim of both keeping the key merits of the original and extending it in some respects.

  • First of all, I built a data story of zooming in and out of the data. I thought it would help understand patterns such as similarities between countries or diverging tracks of development.
  • Second, I extended the geographic granularity of the dataset by adding the layer of regions. It was essential to making high-level observations with relative ease.
  • Third, given that a story comes with multiple pages I had a chance to utilize several types of charts. E.g. box-and-whiskers help understand how countries within a region disperse along a measure whilst treemaps enable differentiating countries by two different measures in parallel. I also added time-series views as this was a fundamental characteristic missing from the original dashboard.
Within-region differences in ecological footprint by country (2016)
  • Finally, by exploiting the capabilities of Tableau I enabled further interactive engagement with the data thus allowing for thorough individual exploration. E.g. one can zoom into a world heatmap, select the preferred length and range of countries’ ranking or play with geographic filtering.
Dashboard for the identification of possible regional differences (2016)

Disappointing Scandinavians, worsening oil-rich Middle-East

Compared to the green image of Scandinavian countries it might be embarrassing that three of them made it to the worst 20 of 2016: Denmark, Sweden, and Finland. Each has a large footprint in carbon. Danish and Swedish people consume relatively large amounts of animal products, mirrored by large EF per capita for grazing land. Moreover, the Danish have an outstanding footprint for fishing whilst the Swedish are to be shamed for their forest hectare needs.

Four of the worst five countries are Middle-Eastern countries that are rich in oil resources: Qatar, the United Arab Emirates, Bahrain, and Kuwait. It is observable in their timeline how their footprint per capita has climbed for the last few decades.

Caveats not to be covered

Certainly, there is still much room for improvement in the visualization of the footprint dataset.

  • I was focusing solely on the consumption-based type of ecological footprint measure although allowing. The available dataset, however, consists of production based accounts as well.
  • For meaningful insights, it would be essential to add socioeconomic data. Thus conclusions could be at least partially cleaned from part of the fundamental underlying factors of EF differences.

Biases are probably also present in the data visualizations and conclusions which are drawn from these at the level of data processing and data insights.

  • Outliers are present in the dataset (see e.g. the box-and-whiskers chart on countries). However, studying these might be crucial for the identification of relevant policy solutions to counteract the environmental harm caused by excessive consumption.
  • Countries constituted a normal distribution neither in terms of total nor of per capita EF. This condition is rather an asset for making arguments based on this, i.e. that certain countries should be questioned for their natural resource utilization.
  • Biases in data insights, however, can still be prevented. This requires that one does not assume a causal relationship between any of the features presented (such as region, geographic size, or the sum of EF) as potentially observed correlations do not imply causality. Thus one can avoid confirmation bias. One also should keep in mind that several features are absent from the presented visual analysis which indeed might be the cause of underlying correlations (such as a nation’s wealth, for example), i.e. confounding variables.

Are you going to save the world then?

You can check the full storybook, including figuring out the rank of your country in the list of shame.

As a consumer one could always make an extra effort to make environmentally more conscious choices. There are plenty of ground-to-earth resources available on how to commit ourselves to reasonable goals in this respect.

As for a citizen — as one can figure out from this data as well — there is still room for demanding environmentally more ambitious policies and implemented regulations from politicians.

P.s. I’d suggest the answer to the question in the title is no. Europe, at least, should really perform better.

Senior advisor in Technology and Project Advisory at a Big 4 company, aiming at excelling in various data & analytics domains.