Introduction

This work organizes information to be presented in an abstract for LCA Food 2026. The objective is to provide a Commodity Supply Mix of beef imports into China and the EU and top beef exports from JBS, Minerva, Marfrig and Mataboi Alimentos, considering GhG emissions (deforestation + enteric methane and manure emissions) and water scarcity footprints.

We use the data generated for TraseH2O available on Zenodo (but saved to s3 as well): https://zenodo.org/records/17392718

## Reading layer `HR_AWAREBR' from data source 
##   `C:\Users\MichaelLathuillière\AppData\Local\Temp\RtmpMNdY4k\file1e542dad250f.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 12 features and 29 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.99064 ymin: -33.75208 xmax: -34.7931 ymax: 5.272317
## Geodetic CRS:  WGS 84

We simplify the datasets with a focus on 2017 imports. We also remove the AGGREGATED municipalities when looking at river basins, since those cannot be linked due to the aggregation.

In all cases, the Functional Unit is 1 tonne of carcass weight (tCW) in 2017 at farm gate.

We can then plot the imports and show the regions (carbon footprint) and the water scarcity (macro basin) sourcing.

Given that we only have space for 2 figures, we consider: + Figure 1: China/EU carbon footprint per category and trader regional sourcing + Figure 2: China/EU water scarcity footprint per category and trader basin sourcing

Global Warming Potential

Countries of import (China and the EU)

For the carbon footprint we need to show:

  • the breakdown per type (deforestation vs. animal)
  • the breakdown per region and importer

These results are slightly different from the cattle emissions which showed greater emissions from deforestation per year, while the emissions are larger when looking at enteric methane over several years. ### Top traders

Now we repeat the above graphs by showing individual top traders that export beef to all countries.

Then we export the combination of China/EU by “type” on the left, and trader regional sourcing on the right as Figure 1 in the abstract

Water scarcity footprint

Countries of import (China and the EU)

We now look at the water scarcity footprint with a focus on importing countries. Note that we remove the AGGREGATED municipality

All results are per tonne of CW.

### Traders

Then we export the combination of China/EU by “type” on the left, and trader regional sourcing on the right as Figure 2 in the abstract