Introduction

This document uses the newly available at https://commodityfootprints.earth/ to be used in the first TraseH2O Discussion brief expected to be published in 2022. This data uses a combination of methods developed at SEI-York and are based on key input datasets (Croft et al 2021):

The above indicators are linked to the trade flows obtained using the Input-Output Trade Analysis (IOTA) (Croft et al 2018) modelling framework together with trade statistics. In effect is it a connection between production and trade statistics (FAO) with the consumption behaviour highlighted through the Input-Output data (EXIOBASE 3.8.1, Stadler et al (2021)).

The new “Water Footprint” global dataset

The Tamea et al (2021) data has several different datasets, namely:

The Consumption Footprint data seems to separate green and blue water footprints, but additional information is needed on how this is scale up (presumably using a scaling factor).

Focus on trade

In a first instance we look at results in absolute terms (deforestation, GhG emissions, water use) for the EU27 and focus on products and countries. The main difference between deforestation/GhG emissions and virtual water trade results is that water use will be proportional to trade volume (not an impact indicator per say).

We start by looking at the entire commodity footprint dataset:

in 2017, the absolute values for the EU 27 were as follows (see table above):

The top 14 commodities that in total represent more than 1500 ha of deforestation associated to the product imports into the EU27, also show a few commodities with no virtual water trade:

The total virtual water trade does differentiate differences between blue and green water through a scaling factor:

I took some water footprint values from the WFN database to show some comparisons:

Similar comments as above apply to this new breakdown. In order to get a better sense of the differences across countries, we can do a benchmark analysis of the water footprint of commodities from different countries.

Note that there is no estimate of water use for beef (Cattle and buffalo meat, plus associated co-products) or wood (Industrial roundwood), but there are small amounts of water use elsewhere that are likely too small to show on the graph (e.g. rice).

Crops with largest blue water trade and impact to water scarcity

So far, the above analysis looked at the top commodities and countries imported into the EU with the largest deforestation. Let’s now look at the top irrigated crops imported into the EU and see how they match deforestation.

The above ranking was determined by looking at all imported commodities with > 0.5 Gm3/y and checking their water footprint (m3 ton-1) from the Water Footprint Network before deriving the water scarcity footprint (m3 ton-1) using the characterization factors from Boulay et al 2018. The water scarcity footprints are as follows:

The commodities are ranked based on the largest impact to water scarcity per tonne of product (left). There is a clear mismatch between deforestation and impact to irrigation which I am calling the “missed opportunity” in the Inforbrief.

References

Boulay AM et al (2018) The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE) International Journal of Life Cycle Assessment 23(2): 368-378, doi: 10.1007/s11367-017-1333-8.

Croft S. et al (2021) Technical documentation for an experimental statistic estimating the global environmental impacts of UK consumption. JNCC Report No. 695, JNCC, Peterborough, ISSN 0963-8091.

Kastner T et al (2011) Tracing distant environmental impacts of agricultural products from a consumer perspective Ecological Economics 70(6): 1032–1040, doi: 10.1016/j.ecolecon.2011.01.012

Pendrill F et al (2020) Deforestation risk embodied in production and consumption of agricultural and forestry commodities 2005–2017 (Version 1.0) [Data set] Zenodo: http://doi.org/10.5281/zenodo.4250532

Stadler L et al (2021) EXIOBASE 3.8.1, doi: https://zenodo.org/record/4588235#.YXucsBpBxPY.

Tamea et al (2021) Virtual water trade and water footprint of agricultural goods: the 1961–2016 CWASI database Earth System Science Data 13(5): 2025-2051, doi: 10.5194/essd-13-2025-2021