Energy Transition in Germany, Study

A great number of Sankey diagrams are coming out of Germany, don’t know why that is…

This one is from a study on the (stalling) progress of the ‘energy transition’ (some prefer to call it ‘energy turnaround’, ‘Energiewende’ in German). German Energy Agency (dena) and University of Cologne (EWI institute) have published an intermediate progress report. On climate change, Angel Merkel’s coalition has set the ambitious goal of reducing Germany’s greenhouse gas (GHG) emissions to 55% of the 1990 emission levels by 2030. One pillar of the energy turnaround is the increased use of renewable energy sources.

The study (PDF here, in German) contains a number of Sankey diagrams like this one:

The overall energy consumption of 605 TWh/year in 2015 hasn’t been reduced until 2018, but there is already a noticeable shift away from coal (black streams) and an increase in renewables. On the path to 2030 nuclear energy is to phased out completely and coal an gas are to be reduced significantly in favor of renewables with the overall consumption down to 590 TWh/year, mainly by means of energy efficiency measures.

Sankey Diagrams in Data Analysis Tools

If you are using a big data analysis tool aka business intelligence (BI) visualization tool you are probably aware that many of them have added distribution diagrams (relationship diagrams), a specific subtype of Sankey diagrams. However, it is not always straight forward to produce them.

Qlik Sense users who need some background before producing their own graphs of this type might want to check out this blog article on ‘Visualizing Flows with Sankey’ on the Qlik blog. Or this one from the Qlik healthcare user group.

Users of Tableau can get an idea of how to do Sankey diagrams from this Tableau community board post and some detail background from Yoshi Arakawa’s blog here. I have mentioned the Sigmoid curves here on the blog in a December 2018 post.

Microsoft Power BI also has category relationship diagrams, although they might “not [be] available by default in Power BI Desktop”. The post by Siddharth Mehta at shows the 16 steps to produce your Sankey diagram from data managed in Power BI.

All three visualizations look very similar. This is because all three tools base their implementation on Mike Bostock’s d3-Sankey package.

Aircraft Crashes Cause/Phase Relationship

This one is from a very interesting 2015 blog post titled ‘Visualizing the causes of airline crashes’ by Rick Wicklin on the SAS blog.

The original graphic discussed is from David McCandless’ book ‘Knowledge is Beautiful’. Wicklin, a researcher in computational statistics at SAS has praise for the beauty of McCandless’ infographics, but criticizes the use of a Sankey diagram, points to two main issues with the diagram, and suggests to instead use a mosaic plot to convey the message.

The underlying data is for the time frame 1993 to 2013. The 427 aircraft crashes in that period are broken down in two categories: the cause of the crash (human, mechanical, weather, criminal) and the phase of flight when the crash occured (landing, en route, take off, standing on ground).

In addition to the width of the bands linking the nodes, the size of the nodes themselves are used to represent a percentage share. (This is BTW one of the problems that Wicklin identifies, read more here).

The inset at the top left should also be mentioned, as it shows that the absolute number of aircraft crashes over two decades has a downward trend… maybe a consolation to those that who are afraid of flying…

Brexit Negotiations Outcomes – Outdated!

As Boris Johnson makes a dash to Luxembourg today for negotiations with Juncker on Brexit, this Sankey diagram from a blog post ‘Brexit Negotiation Outcomes using a Sankey Diagram’ by Sean Danaher at Progressive Pulse – less than a year old (Nov 9, 2018) – is soooo outdated. In fact the situation was (and still is) so fluid that any attempt to describe it can be outdated within a week or even a day…

This is a decision-tree diagram, just that the estimated probabilities for a decision are shown as weighted paths. Decision paths can merge again when they lead to the same result.

Note: I try to keep this blog non-political. The only other politics related Sankey diagrams are here (Scottish referendum) and here (Trump).

Dairy Supply Chain GHG Emissions

Rediscovered this Sankey diagram in a 2011 project report ‘Scottish Dairy Supply Chain Greenhouse Gas Emissions’ (Sheane, R., Lewis, K., Hall, P., Holmes-Ling, P., Kerr, A., Stewart, K., Webb, D.: Identifying opportunities to reduce the carbon footprint associated with the Scottish dairy supply chain – Main report. Edinburgh: Scottish Government, 2011).

Flows are in megatonnes of CO2-equivalents (Mt CO2e) greenhouse gas emissions (GHG) in 2007 related to the production of milk and dairy products in Scotland.

A total of 1.657 MtCO2e GHG emissions were caused along the dairy supply chain emissions, which was equivalent to 3% of Scotland’s direct GHG emissions. With a yield of 1.3 billion litres of milk on dairy farms in Scotland in 2007 this corresponds to 1.1 kgCO2e/kg of milk or 1.2 kgCO2e/litre of milk.
If you look at the origins of the emissions in the diagram you will see ‘enteric fermentation’ (aka ‘cow fart’) and liquid manure (‘cow poop’) as the main causes.

Read the full report here.

LatAm BEN – Panama

Skipping the remaining countries in South America (Venezuela, Guyana, Suriname, French Guiana) for the time being, my series of Sankey diagrams depicting the National Energy Balance of Latin American countries continues with Panama.

Data for the Balance Energético Nacional (BEN) is available on the website of the Ministry of Energy (Secretaría Nacional de Energia), but I could only find this visual representation of the Matriz Energético done in Excel.

Arrow lines are all the same width and glued together from horizontal and vertical line segments. To be featured here on the blog, it has to be some kind of Sankey diagram, with the magnitudes of the arrows or bands representing the flow quantity.

I checked the sieLAC page maintained by OLADE, but there I could only find energy flow diagrams up to the year 2010.

So I decided to “translate” this Excel figure (which has all the numbers) into a Sankey diagram. As it turned out, the work wasn’t straightforward, since this figure uses two, actually three different units. Primary energy on the left and consumption per sector is in kbep (kilo barrels of oil equivalents). The pie chart in the middle that represents the electric energy generation is just an insert with data in GWh. The arrows in the Excel figure are labeled in percent. I started out using the kbep scale, but then was unable to convert the quantities for the streams, since actually energy losses are omitted in the figure.

Here is my remake:

I chose to work with the percentage scale for the downstream splits per fuel, setting the magnitude of the energy generated at the same size as the input. We can see that the energy landscape is dominated by imported petroleum derivates consumed for transport. Actually this is almost three times as much energy as is being used for energy generation. Domestic energy production is mainly from renewables with only some coal and natural gas.

Colors and general layout of the remake stick pretty much to the original figure.

Hydrological Cycle

A 2013 booklet ‘The Energy Sustainability Challenge: How will natural resource constraints change the way we produce and use energy?’ published by BP describes scientific findings in the fields of energy and natural resources. In the section on ‘New tools for systems analysis’ they present two Sankey diagrams. “Sankey diagrams are used to visualise how a resource moves from source to use.”

This one shows the hydrological cycle for precipitation over land areas. “Starting at the left, the distribution of rainfall among the continents is illustrated, with the numbers indicating the volume of water measured in km³. Of that water, the majority falls on forests, followed by grasslands, cropland and other land types. The water contributes to the products of these lands – terrestrial ecosystem services, food and other land use.”