Python based tools for working with LCA foreground model disclosures
Disclosure framework based on "Disclosure of product system models in life cycle assessment: achieving Transparency and Privacy" by Brandon Kuczenski in J Ind Ecol. (doi: 10.1111/jiec12810)
The lca_disclosures repository supports the generation of a disclosure specification from the following software:
- Brightway2
- lcopt
- lca-tools (* pending migration)
To use the disclosure framework, instantiate the appropriate Disclosure subclass and supply it with the software-specific input data. The disclosure object can be used to answer questions about the model:
- listing foreground flows, background flows, and emissions;
- identifying cutoff flows (flows that exit the model boundary with no impacts);
- performing an aggregation of the model into a unit process
The disclosure object can also be serialized to JSON or saved as an Excel spreadsheet.
pandas, to write to excelscipyandnumpy, to perform matrix operations
Generally, any At the moment, the disclosure class requires brightway2 and lcopt to be installed in order to run tests. However, this could probably be phased out.
A disclosure has six parts, including three lists of flows:
- i An ordered list of foreground flows, beginning with the functional unit of the system (length = p);
- ii An ordered list of flows from background processes or activities (length = n);
- iii An ordered list of environmental or social "emissions" (length = m).
And three sparse matrix specifications:
- iv A list of nonzero entries in the p x p sparse matrix defining the foreground;
- v A list of nonzero entries in the n x p sparse matrix defining the foreground's dependency on the background;
- vi A list of nonzero entries in the m x p sparse matrix defining the foreground's direct emissions.
A visual representation of the disclosure is found in Figure 3 of the above paper:
The base Disclosure class is abstract, and a subclass must be created for each supported software type. A subclass must populate the following methods:
_prepare_efn()returns the evaluated filename used to store disclosure serializations._prepare_disclosure()returns a 6-tuple corresponding to the 6 disclosure elements, in the order listed above.__init__()must be written to handle input data and then call the superclass__init__, which computes_prepare_disclosure()and stores the output.