Deloitte Sustainability in a 2017 report titled ‘Blueprint for plastics packaging waste: Quality sorting & recycling’ showed the results of “a quantitative and a qualitative analysis of the main packaging resins (PET, HDPE, LDPE, PP) based on the flows in France, Germany, Italy, Spain and the UK, which represent 70% of the plastic waste generated in Europe”.

The plastic waste streams for the year 2014 are shown as a Sankey diagram on page 16.


The collection rate that year on a European average was at 37% and the recycling rate at 13%. Most of the packaging waste goes to incineration and landfill.

The study also looks at improvement potential in plastics waste collection and recycling. The plastic packaging waste streams for a possible 2025 scenario with a collection rate of 74% and a recycling rate of 55% is also shown as Sankey diagram for comparison.

An interesting blog post titled ‘Cuando las cuentas no cuentan’ (which I would figuratively translate as ‘When the numbers don’t match’) by Sergio Sastre over at the ‘Residuos Profesional’ blog.

Looking in detail at the official municipal solid waste recycling numbers for all 17 autonomous communities in Spain, published by the Environment Ministry (Ministerio para la Transición Ecológica – MITECO) for 2016, Sergio and his team found that there are discrepancies in the data, and that data quality needs improvement.

The overall recycling rate for municipal solid waste (MSW) in Spain is 33.6% … still far from the 2020 goal to reach a 50% recycling rate.

This Sankey diagram shows the breakdown of waste streams.

Flows are in tonnes per year. Of the overall 21.7 million tonnes of MSW generated in Spain, only some 7.2 million tonnes were recycled in 2016 (pink streams). A large chunk if household waste is mixed (grey stream, residuos mezclados, RM), while only a quarter is collected separately (colored streams in the lower part of the figure, recogida selectiva, RS).

Some material can be recovered from the mixed waste stream at sorting facilities and in composting plants or biogas digestors.

In my mini-series on National Energy Balances (Balance Energético Nacional, BEN) of countries in South and Central America, I have reached the Plurinational State of Bolivia.

I couldn’t find any Sankey diagrams on the website of the Ministerio de Hidrocarburos, which apparently is responsible for drawing up the energy balances for Bolivia. However, I was sure they must exist, as a press release for the launch event of the report exists. Finally I found a publication of the ministry for 2000-2009 in the BIVICA library and it has some black/white energy flow diagrams.

There have been newer editions of the report until at least 2015, and here is the BEN Bolivia for 2014 (from the OLADE library), You might remember that OLADE, the inter-governmental Organización Latinoamericana de Energía plays an important role in motivating countries to draw up their BEN and runs a website where BENs are available for many Latin American countries).


The unit of flows is ‘kbep’ (kilo barrels of oil equivalents / miles de barriles equivalentes de petróleo). Now, this Sankey diagram is definitely not to scale: the width of the flow representing 133,902 kbep of gas would have to be almost 6 times wider than the one standing for 23,065 kbep of petroleum. The biomass flow would have to be much thinner in comparison, hence it is over emphasized in the diagram for the reader who is unaware. My feeling is that the person who did this wasn’t acting with bad intentions, but had no technical means or support to do this properly and just glued it together from round rectangles, arrows and other shapes.

Definitely a candidate for a remake, if I find the time…

An analysis of the BEN Bolivia and some background on the data is available (in Spanish) in this paper.

SEEG Sistema de Estimativas de Emissões de Gases de Efeito Estufa (Greenhouse Gas Emissions and Removals Estimates System) is an initiative of the Observatório do Clima (Climate Observatory) in Brazil.

This Sankey diagram on the SEEG web page (in Portuguese) shows greenhouse gas (GHG) emissions in Brazil in 2012.

On the left are the emitters by sector: land transformation, livestock farming, energy generation, industrial processes and waste sector. Emissions are grouped in the middle column by activity: agriculture, industry, transport and other. The third column is a detailed breakdown of the activity sectors.

The agricultural sector contributed 64% of Brazil’s GHG emissions in 2012, with most likely methane (CH4) from livestock breeding and CO2 release from deforestation as the major sources.

Emissions are shown in Mt CO2-e[quivalents], even though the caption says differently. Overall greenhouse gas emissions were 1490 Mt CO2e (or 1.49 bn tonnes CO2e). Detailed data is available on the website, so this can be seen as the consolidated overview of GHG emissions.

More recent GHG data for 2017 from Brazil has been published at an event in November 2018 in São Paulo, but I couldn’t find a Sankey diagram (yet).

This is an interesting kind-of-a-Sankey figure. Back in August I had posted on Nordic Transport Energy in 2050 with two Sankey diagrams from the ‘Nordic Energy Technology Perspectives 2016’ report published by IEA.

The topic of this diagram (taken from the same report) is the energy transmission or transport capacity between different regions in Europe and covering the area of the European Network of Transmission System Operators (ENTSO-E).


© OECD/IEA 2016 Nordic Energy Technology Perspectives 2016, IEA Publishing. Licence: www.iea.org/t&c

To visualize transmission capacity, Europe has been cut into energy regions, and a gap has been inserted between them to be able to distinguish them better. The width of the “bridges” represent the available energy transport capacity between these regions in 2030.

Some countries are divided into several energy producing regions. For example, if you look at Sweden, it is divided into SE_N1, SE_N2, SE_M and SE_S.

The bands are non-directional, so we do not know which region delivers to which region. And probably the energy transport will be able to go in both directions.

Check out the full report here.

As I am cleaning out my office – throwing away old notes, brochures and journals – I came across a November 2011 copy of German ‘UmweltMagazin (Environment Magazine). It has this Sankey diagram in an article on CO2-neutral steam generation for bioethanol production.


This Sankey diagram is in German. Flows are kW. This is for a small-scale plant at an agricultural business and heat is used for a destillation unit. The input feed to the steam boiler is primarily off-heat from a CHP plant fired with biomass. Losses are shown as grey arrows, steam as lilac arrows and heat in red. Condensate recovered at the heat excanger is fed back into the steam boiler (green loop).

Sorry for the quality of the scan. And Happy New Year to all of you! Will be back in 2019 with more Sankey diagrams.

Found an interesting blog post over at TabVizExplorer, a casual blog maintained by Mithun Desai. Data visualization is one focus of his work.

He uses Tableau to draw Sankey charts (I prefer to call them relationship diagrams, alluvial diagrams or even Spaghetti diagrams). Here is a rather simple one, showing the relation between top 20 cricket players and their country of origin.


The diagram has two data categories. The country of origin shown in the left stacked column in no particular order, and the top 20 players ordered according to their ICC ranking score.
In between are the streams or bands (or ‘Spaghettis’ for the sake of it) color coded by country of origin.

Now, it is not up to me criticizing the choice of diagram type for conveying this specific information. The author seems to have chosen the cricket topic just as a sample, to explain how to do Sankey charts in Tableau in general. Actually the colored list of top 20 (right column) already tells us all we need to know and you wouldn’t even need the left column and the streams.

The main reason I am not happy with this diagram is the fact that it does not stick to the most important characteristic of a Sankey diagram. The post itself comes with the definition: “Sankey diagrams are specific type of flow diagram in which the width of the arrows is shown proportionally to the flow quantity.”

So, what is the flow quantity here? I was thinking of net worth in $$$ of each player, or at least a translation of the ranking score to the width of the bands. But then Babar Azam, who ranked 4th with a score of 846 wouldn’t be shown with a band narrower than the one of E.J.G.Morgan coming in 20th with a score of 650. My guess is, that the the widths of the streams are chosen deliberately…

Where the bands merge, they overlap rather than merge to show the sum of the flow quantities. This makes for a very odd visual effect, at least in terms of Sankey diagrams.

The blog article gives away some of the math behind the curves, so called Sigmoid curves, which is interesting.


This capture taken from the embedded Tableau graph shows how the curves are made up and how the width is maintained along the routing of each curve: You do it with cricket balls 😉 … or christmas bulbs.

Other implementations of relationship diagrams use Beziers curves (which sometimes come with another downside, read here). But that’s for another time…

Working my way up the southern cone, here is the Balanço Energético Nacional 2014 for Brazil. Found this on the webpage of Curitiba based consulting firm ACV Brasil.

The national energy balances for Brazil are published annually by the Ministério de Minas e Energia (MME), and newer reports are available (PDF for 2018, large!). However, the energy flows diagrams in these official reports are less refined, so I opted to go with the remake by ACV.


The unit of flow is not shown, but my guess is that it is Mtep. like in the original publication.