Introduction: Beef consumption

This document looks at the combined indicators of consumption related to beef exports from Brazil. Information on production is linked to the supply chains of Trase (Brazil soy and beef) to look at actor performance (companies and countries).

We look at 2015-2017 trade.

Importing country average

We first look at the benchmark of key actors (China and the EU) linking specific Brazilian municipality of export with the average beef WF and the composition of green and blue WF (in m3 per tonne). At this stage there is no Brazilian beef domestic consumption data in the trase data. We basically calculate a Commodity Supply Mix.

Recall that the trase data that is imported here has the cattle deforestation 5y total exposure already embedded. Given that it is a 5y sum, you cannot sum across the years of interest and can only show individual years.

We need to keep in mind that there are unknown flows (which need to be reported as %total) for which we can:

  • not present a benchmark (should be a small percentage)
  • provide a country-wide benchmark (probably not worth it)

First we present the amount of unknowns in the data:

## # A tibble: 3 × 4
##   year      vol  vol_tot pct_vol
##   <chr>   <dbl>    <dbl>   <dbl>
## 1 2015  140848. 2018264.    0.07
## 2 2016  137313. 2028075.    0.07
## 3 2017  142190. 2215283.    0.06

The number of unknowns are less than 10%.

We then check the beef water benchmark for imports into the EU and China by looking at both estimates of drinking water use from Zanetti et al (2019). We also focus exclusively on the Anualpec estimate of cattle population.

First we need to derive state-specific factors so that we can embed information on trade flows that are linked to BR-XX-AGGREGATED municipalities. This is because the results do not give us the actual municipality of production and so we can provide a state-wide values. We can still use the database results to add the deforestation and ghg emissions where results are available.

The performance seems to be roughly the same across the indicators when looking at things spatially explicitly, but need to dig deeper into it.

Big numbers assigned to importing countries

## Linking trade to production benchmarks

We revisit the cattle production benchmarks and then check which municipalities are supplying each consumer country, and the associated metrics

For now we focus exclusively on the top deforestation and top water scarcity footprint (and overlap)

The results below need to be checked when you’ve removed issues with NAs

Then, we need to relate the to the EU and China focused benchmarks which we calculate here.

We then connect to the “flags” that we assigned to beef production which included whether beef:

  • Was linked to a municipality above or below the 80th percentile cutoff (a municipality with no beef deforestation is included in the “< cutoff”)
  • the level of total water footprint (80%tile or not)
  • whether soy was irrigated or not

We place China and EU imports within these categories and also determine the volume of soy that would fall within each of the categories.

Note that we need to deal with the “AGGREGATED” municipalities which do not have a benchmark (since they are aggregated at the state level). These represent a small amount of beef imported in the EU and China:

  • ~1000-2000 tonnes CW for China in 2015-2017 (or <1%)
  • ~189-253 tonnes CW for EU in 2015-2017 (<1%)

There are some off municipalities from which beef is being imported but for which we do not have a flag. Again this also represents a small amount of beef imports.

We can then provide details on the types of beef that are being imported into the respective countries. We note that there is a small amount of volume that does not have any flags assigned:

  • China: 0.8 tCW (2015), 4.6 tCW (2016), 8.8 tCW (2017)
  • EU: 8.7 tCW (2015), 8.7 tCW (8)2016), 6.2 tCW (2017)

The majority of imports come from areas that are below the 80th percentile of deforestation, WF and WSF. This makes sense, since the largest consumption is assigned to Brazil:

  • China: 0.3 MtCW (72%) (2015), 0.4 MtCW (74%) (2016), 0.53 MtCW (74%) (2017)
  • EU: 0.1 MtCW (72%) (2015), 0.1 MtCW (70%) (2016), 0.07 MtCW (68%) (2017)

What is interesting is the next largest flags:

  • China: 0.044 MtCW (11%) below 80th percentile for def and WSF, but above WF benchmark (2015); same for 0.045 MtCW (8%) (2016); 0.067 MtCW (9%) (2017)

  • China: 0.040 MtCW (10%) above the 80th percentile deforestation, but below WF and WSF (2015); same for 0.050 MtCW (9%) (2016); 0.059 MtCW (8%) (2017)

  • EU: 0.0018 MtCW (10%) below 80th percentile deforestation, but above WF and WSF (2015); same for 0.002 MtCW (12%) (2016); 0.02 MtCW (15%) (2017).

The above curve can be used to highlight specific municipalities and volumes where beef is being sourced with the highest WSF. The idea is to superimpose the volumes of the above benchmark with the production benchmark and then show the following:

  • the locations where the beef with the highest WSF is being sourced from by China and the EU within the context of all the WSF of beef production
  • provide information on deforestation that is also associated with this production and highlight the river basins where impact is highest
  • See if we can link to the categories created in the production analysis (80%tile, etc. )

Below is an example of the sourcing for China and the EU in 2017.

You need to check the results of the benchmark as there are some municipalities that import beef from places that don’t produce and so the WF == NA.

The above graphs gives the positioning and size of imports from the municipalities with the largest impacts to water scarcity. We can now show this spatially.

We can also calculate the volume of soy that falls within the largest water scarcity footprint, regardless of deforestation.

## # A tibble: 6 × 4
##   year  economic_bloc vol_sum def_exp_ha
##   <chr> <chr>           <dbl>      <dbl>
## 1 2015  CHINA          82227.     31923.
## 2 2015  EU             36528.      2855.
## 3 2016  CHINA         111105.     50528.
## 4 2016  EU             36130.      3350.
## 5 2017  CHINA         144309.     64398.
## 6 2017  EU             32677.      1659.

Plot WSF for China in 2015:

Plot WSF for China in 2016:

Plot WSF for China in 2017:

Plot WSF for EU in 2015:

Plot WSF for EU in 2016:

Plot WSF for EU in 2017:

Calculate water savings

Then we can calculate the potential water savings based on application of a median beef WF in cases where the WF is within the cutoff (now 80th percentile). So we carry out the following:

## # A tibble: 6 × 6
##   economic_bloc tot_km3_from_median_wf tot_km3_ref median_wf km3_savings year 
##   <chr>                          <dbl>       <dbl>     <dbl>       <dbl> <chr>
## 1 CHINA                          0.283       0.489     4456        0.206 2015 
## 2 EU                             0.164       0.359     4456        0.195 2015 
## 3 CHINA                          0.330       0.586     4503        0.256 2016 
## 4 EU                             0.174       0.386     4503        0.212 2016 
## 5 CHINA                          0.449       0.768     4410.       0.319 2017 
## 6 EU                             0.165       0.371     4410.       0.206 2017

Sankey representation (figure 3)

We can then link this Sankey to the summary of resource use (i.e. total water use, total deforestation, etc.).

Since we do not have spatially explicit domestic consumption for beef, we derive it at the country level by:

When creating the Sankey, just add the footprints of production using CW, rather than LW (or add both)

The Sankeys currently don’t have the corrected Brazilian domestic consumption since the Unknown flows were not remove. Rather look at the table values.

Then look at the resource use and emissions assigned to Brazil domestic consumption. Note that the values are corrected for unknown flows, so that domestic consumption is represented by Mtcw_br_corr.

## # A tibble: 5 × 10
##   year  Mtcw_br Mha_def_br blue_km3_br green_km3_br Mtco2_h_br Mtco2_l_br
##   <chr>   <dbl>      <dbl>       <dbl>        <dbl>      <dbl>      <dbl>
## 1 2015     8.77       3.13        46.3        5528.      1738.      1593.
## 2 2016     8.65       3.91        45.3        5301.      1743.      1594.
## 3 2017     8.63       3.76        43.8        5146.      1710.      1562.
## 4 2018     0          0            0             0          0          0 
## 5 2019     0          0            0             0          0          0 
## # ℹ 3 more variables: Mtco2_def_br <dbl>, vol_unk <dbl>, Mtcw_br_corr <dbl>

Statistical analyses

We can now run some statistics on the numbers, with potentially two tests to carry out:

Compare beef imports into China and the EU with and without deforestation and check if there are statistical differences in:

Here we check the hypothesis:

The beef imports into China and the EU have the same WF and WSF whether or not the beef is linked to deforestation

Note that the code should be reviewed to confirm the results. At this stage we will not include this analysis in the paper.

Summary of results:

Add a summary when you revisit these results (if needed) for publication.

## [1] "saved at C:/Users/MICHAE~1/AppData/Local/Temp/RtmpsRlF0D/file4c904ecf3966/beef_trase_countries_2025-01-27.csv"