Package: enbios
ENBIOS2What is ENBIOS 2ENBIOS2 (Environmental and Bioeconomic System Analysis)  is apython-basedsimulation tool for the assessment of environmental impacts and resource requirements of energy system
pathways according to policy scenarios. These pathways are typically calculated by Energy System Optimization Models (
ESOMs). Currently, ENBIOS is coupled with theCalliopeframework, and we are working to couple it with
theTIMESframework.ENBIOS2 is based on the integration of Life Cycle Assessment and the Multi-Scale Integrated Assessment of
Socio-ecosystem framework (MuSIASEM) originally developed by C. Madrid-López
(2019and2020)ENBIOS2 is a ground up new computing implementation of the ENBIOS tool. You can see more information about
this previous version below. Compared to the original version of enbios, this version is more flexible does not make
its own LCA calculations, but usesBrightway2for that.In ENBIOS2 you will implement an experiment. To do this you will need to have at hand:a defined set of activities, that are typically the energy system technologies you would like to inlcude in the
assessmentaccess to a life cycle inventory database that can be imported in Brightway2 (such as Ecoinvent)
or the skills and data to create yoursa MuSIASEM (hierarchical) structuring of your energy system. This must be taylored to the speficis of your assessment
and include the structural components of the system (your activities) and functional components of your systems.a set of non-linear assessment methods such aslife-cycle impact assessment methods (you can use Recipe for example, but you will need to correct its linearity)MuSIASEM methodsa set of ESOM-provided pathways, which contain energy supply, demand or transfer info for the given activitiesIf you like (optional!), you can couple ENBIOS2
withPREMISEfor a prospective definition of
life cycle inventories according to different climate scenarios and integrated assessment models.What you get is results of impacts and resource demands by each activity (structure) and function at each level of the
hierarchy.ENBIOS is developed by theLIVENlab, a research lab of
theSosteniPraResearch group, atICTA-UAB.InstallationWe recommend you to run ENBIOS from a python IDE, such as Pycharm.
But we also have a few Jupyter notebooks for you to use, see below.You first need to create an environment. From your terminal, try this:Windowspython -m venv venvLinuxpython3.9 -m venv venvActivate the environment with(windows)venv\Scripts\activate(linux)source venv/bin/activateInstall enbios2 with(windows)python -m pip install enbios(linux)python3 -m pip install enbiosFundamentalsRead fundamentals about setting up an enbios experimentExperiment configuration json schemaThe json schema for the experiment configuration can be found here:https://github.com/LIVENlab/enbios/blob/main/data/schema/experiment.schema.gen.jsonThis schema, provides the structure of the experiment configuration, which is used to run the experiments.
Note, that there will be additional validations when the experiment is constructed (e.g. existence of brightway project,
databases, activities).Environment variables2 environmental variables are used by enbios2:CONFIG_FILE: This variable can be used to specify the location of the configuration file (json). If experiment is
initialized without any parameter (configuration data or file location), the configuration is read from this path.RUN_SCENARIOS: This variable can be used to specify the scenarios to run whenexperiment_object.run()is called.
Setting the environmental variable overwrites the value set in the configuration file (config.run_scenarios). This
variable should be formatted as a json array, with the aliases of the scenarios that should
run.e.g. '["Scenario 0"]'(indexed default aliases, when no aliases are specified in the configuration file for the
scenarios).Data inputsOutputs from your ESOMA dictionary that connects your ESOM taxonomy with your inventory taxonomylife cycle inventories in .spold formatThe basefile with the hierarchical structuring of the systemyour method fileOutputsFor each system function and structure (activity):Environmental impact indicators from the most used LCIA methods (Recipe2016, CML, AWARE, etc.)Environmental externalization ratesForthcoming: Raw Material Recycling rates and Supply riskFeaturesIntegration of LCA and MuSIASEM evaluation methodsImport of .spold LCA inventory data to a multi-level tree-like settingLibrary of impact assessment methods based on LCIANew impact assessment methods developed for raw materials and circularityConsideration of externalized environmental impactsTakes data from the friendly-data package (other formats under development)High level methods to quickly obtain/refresh analysesDemosThis repository contains a few notebooks (require jupyter notebook) in the demos folder, that can help you get started.
We are updating and commenting these. Please bear with us while we do it and feel free to give us feedback on those (
thanks).Getting startedPlotting resultsSorting the results in alternative hierarchiesSplitting the configurationWorking with treesExperiment with uncertaintiesConvert mermaid diagrams to enbios hierarchyA complete Brightway adaoter configuration objectPeopleRamin Soleymani. -ICTA-UABMiquel Sierra Montoya. -ICTA-UABAlexander de Tomás. -ICTA-UABCristina Madrid-Lopez. -ICTA-UABContactFor questions about the enbios framework, please contact the LIVENlab
leadercristina.madrid@uab.cat.AcknowledgementsThe first ENBIOSThe LCA-MuSIASEM integration that is the core of ENBIOS was born a few years back (2013 seems so far now!).
The first prototype of the python package was built byRafa Nebotin a collaboration
with the Technical Institute of the Canary Islands (ITC) and based on the Nexus
Information System developed within the
Horizon 2020 projectMAGIC-nexusand the LCA-MuSIASEM integration protocol developed in the
Marie Curie projectIANEX. This early development was funded by the
Horizon 2020 project Sustainable Energy Transitions Laboratory (SENTINEL, GA 837089).Current developmentENBIOS2 is in development with funds from the Spanish Research Agency (AEI) and the European Commission (CINEA):SEEDSproject with AEI grant PCI2020-120710-2 funds the ENBIOS 2 build based on the
Brightway2 LCA framework, adding inventory manipulation to match the mixes of the energy scenarios and the connection
with MuSIASEMLIVEN project with AEI grant PID2020-119565RJ-I00 funds the regionalization of the analysis and connection with the
TIMES energy modelETOS project with AEI grant TED2021-132032A-I00 funds the addition of externalizationJUSTWIND4ALLproject with Horizon Europe grant 101083936 funds the development of a higher
resolution module for wind energy assessment, including new wind-specific holistic assessment methods.ReferencesYou can see some more info and results from ENBIOS here:More information on the roots of the framework and version 1 of the software can be found indeliverable 2.2of
theSENTINELproject.An application to the assessment of energy pathway option space (with 260+ pathways modelled with calliope) with
ENBIOS2 can be consulted
indeliverable 2.2of theSEEDSproject.