how to use the transverse_tagged_databases function in brightway2

Question

I would like to know how to use the transverse_tagged_database method in brightway2. From the documentation is not entirely clear to me. Can we use for, example, to aggregat

Answer 1

The short answer is yes, aggregating impacts by ISIC code in your foreground product system model is exactly the kind of thing you can do using traverse_tagged_databases.

The traverse_tagged_databases function takes advantage of the fact that you can add arbitrary key:value pairs to activities in brightway to let you classify your the activities in your foreground model however you like.

For example, say your activities look like this:

('example_database', 'code_for_bread'):{ 'name': 'Bread', 'code': 'code_for_bread', 'categories':[], 'exchanges':[...], 'location':'GLO' 'unit':'kg', 'database':'example_database', 'isic_code':'1071' 'isic_classifier':'Manufacture of bakery products' },

You can tell traverse_tagged_databases to go through your database looking for a given key (tag), for example 'isic_code', or 'isic_classifier' and aggregate the impact based on these tags.

Say you were modelling a cheese sandwich, you could have the following ISIC codes in your model:

Sandwich: 1079 (Manufacture of other food products n.e.c.)

Bread: 1071 (Manufacture of bakery products)

Cheese: 1050 (Manufacture of dairy products)

Butter: 1050 (Manufacture of dairy products)

You can use traverse_tagged_databases to see the total impact of dairy (cheese and butter) vs bakery (bread).

You use it in a similar way to the LCA function, by specifying a functional unit as a dict and the method as a tuple, with an additional tag argument. Like this:

fu = {('example_database', 'code_for_sandwich'):1} m = ('IPCC 2013', 'climate change', 'GWP 100a') result, tree = traverse_tagged_databases(fu, m, 'isic_classifier')

The function returns two objects (designated result and tree in the line above)

For this analysis, your result will look something like this:

defaultdict(int, {'Manufacture of other food products n.e.c.': 0, 'Manufacture of bakery products': 0.1875, 'Manufacture of dairy products': 0.55})

This is saying that dairy products in the foreground model have an aggregated impact of 0.55 kg CO2-eq, and bakery products have an aggregated impact of 0.1875 kg CO2-eq.

With a bit of post-processing you can turn this data into pie charts, stacked bar charts etc.

You also get a tree, which looks like this:

[{'activity': 'Sandwich' (kg, GLO, []), 'amount': 1, 'tag': 'Manufacture of other food products n.e.c.', 'impact': 0, 'biosphere': [], 'technosphere': [{'activity': 'Bread' (kg, GLO, []), 'amount': 0.75, 'tag': 'Manufacture of bakery products', 'impact': 0, 'biosphere': [{'amount': 0.1875, 'impact': 0.1875, 'tag': 'Manufacture of bakery products'}], 'technosphere': []}, {'activity': 'Butter' (kg, GLO, []), 'amount': 0.05, 'tag': 'Manufacture of dairy products', 'impact': 0, 'biosphere': [{'amount': 0.05, 'impact': 0.05, 'tag': 'Manufacture of dairy products'}], 'technosphere': []}, {'activity': 'Cheese' (kg, GLO, []), 'amount': 0.25, 'tag': 'Manufacture of dairy products', 'impact': 0, 'biosphere': [{'amount': 0.5, 'impact': 0.5, 'tag': 'Manufacture of dairy products'}], 'technosphere': []}]}]

This can look a bit difficult to parse at first, but is essentially a set of nested dictionaries, starting with the root activity (the functional unit = Sandwich), showing techosphere exchanges to other activities, and biosphere exchanges to emissions.

The tree here looks like this (with the amounts of each product in brackets)

Bread +----(0.75 kg)----------+ | | | | Cheese +----(0.20 kg)----------+------(1.00 kg)--------> Sandwich | | | | Butter +----(0.05 kg)----------+

Again, with a bit of post-processing, you can turn this data into stuff like sankey diagrams, or the kind of impact tree diagram you get in SimaPro.