Livia Cabernard

[liviacabernard]

Submitter

I am a postdoc from the Ecological Systems Design Group at ETH Zurich.

Description

Global supply chain analysis of the carbon footprint (GHG emissions) of global plastics production in 2015.

Figure 1 from:
Cabernard, L., Pfister, S., Oberschelp, C. et al. Growing environmental footprint of plastics driven by coal combustion. Nat Sustain 5, 139–148 (2022). 👉Link study

Detailed description:
a–e, The sum of each horizontal bar of the flow chart refers to the carbon footprint of global plastics production in 2015 (1.9 GtCO2e, 100%) and allocates it to the different perspectives in the global value chain: the sectors where GHG emissions are released (a); the processes that release GHG emissions, that is, combustion (88%) and non-combustion (7%) processes of fossil fuels (left side: 95% in total) and other processes (right side: 5 % in total): biogenic emissions (2.7%), hydrofluorocarbon emissions (1.7%) and cement production (0.8%) (b); the regions where GHG emissions are released (production perspective) (c); the regions where plastics are finally used (consumption perspective) (d); and the end products or sectors where plastics are finally used: plastics packaging material is allocated to the end product or sector where it is used (for example, food packaging in the food sector) (e). The flows show the linkages between the perspectives. To enhance clarity, linkages contributing to less than 0.1% of the plastics-related carbon footprint are not shown. The small graphs on the right show the temporal evolution of the carbon footprint of global plastics production for each perspective (a–e) over the past two decades (1995–2015) and in the future (2020–2030) if the world follows the IEA’s projection for a 2 °C or 6 °C scenario34,35 (but not accounting for the decrease in global GHG emissions in 2020 due to the COVID-19 pandemic, as in Liu et al.66). The colours of the graphs on the right correspond to the bars of the flow chart. The red line in each graph represents the global share.

Database(s) used

• Used example data from the contest repository
• Used another database 👉 exiobase for 1995-2015. For the 2° and 6° scenarios we used the future exiobase database from Wiebe et al 2018

Links to the code and visualization

Code

👉 Matlab code to calculate results based on input data from exiobase and Wiebe et al 2018

👉 R and csv input files to create sankey in R with the network D3package

👉 tableau and excel input file to create plots on temporal trends

👉 Compilation and labelling of figures in pptx

Visualizations

👉 Visualization as pdf

Presentation video

👉 Video

License

Creative Commons Attribution 4.0 International

[cmutel]

@liviacabernard Thanks for you entry, it was received in time and meets the criteria for a successful submission. We will have a final jury meeting in mid-March, and you can expect feedback by the end of March.