Package: pycirk
A python package to model Circular Economy policy and technological interventions in Environmentally Extended Input-Output Analysis starting from SUTs (EXIOBASE V3.3)Documentation:https://pycirk.readthedocs.io/en/latest/readme.html10.5281/zenodo.1492957To cite the use of the software in your research please use the following publication:“Modeling the circular economy in environmentally extended input-output tables: Methods, software and case study”https://doi.org/10.1016/j.resconrec.2019.1045081. Installation1.1. Stable releaseRun in your terminal:0$ pip install pycirk1.2. From sourceClone repository:$ git clonehttps://fdonati@bitbucket.org/CML-IE/pycirk.gitOnce you have a copy of the source, you can install it with:$ python setup.py install1.3 DataYou can download the biregional or multiregional database by following this linkhttp://doi.org/10.5281/zenodo.4695823You need to place the data inside the package
e.g. /home/UserName/.local/lib/python3.6/site-packages/pycirk/data2. Usage2.1. Import packageimport pycirk2.2. Initializemy_work = pycirk.Launch(method, directory, aggregation)2.3. set your scenarios and analysisOpen scenarios.xls in the directory that was specifiedFrom there you can specify interventions and parameters for the analysissave and continue to the following steps2.4. Run scenariosRun one specific scenariomy_work.scenario_results(scen_no, output_dataset)
(0 = baseline)Run all scenariosmy_work.all_results()2.5. save scenariosSave your resultsmy_work.save_results()2.6. Use from command line2.6.1. pycirk –helpUsage: pycirk [OPTIONS]Console script for pycirk. A software to model policy and technological
interventions in Environmentally Extended Input-Output Analysis (EXIOBASE
V3.3, 2011)Options:CommandVariables-tm, –transf_method TEXT0 = PXP ITA_TC; 1 = PXP ITA_MSC-dr, –directory TEXTif left black it will be default-ag, –aggregation1 = bi-regional (EU-ROW)
0 = None (49 regions)-sc, –scenario TEXTall, 1, 2,… accepted - 0=baseline-s, –save TEXTFalse=no, True=yes-od, –output_datasetFalse=no, True=yes–helpShow this message and exit.2.6.2. Command examplepycirk -tm 0 -dr “” -sc “1” -s True -od False3. FeaturesExamples of policies that can be modelled through the software:sharingrecyclinglife extensionrebound effectssubstituionmarket and value added changesefficiencyThe tables in which it is possible to apply changes:total requirement matrix (A)intermediate transactions (Z)final demand (Y)primary inputs (W)emission intermediate extentions (E)material intermediate extensions (M)resource intermediate extensions (R)emission final demand extension (EY)material final demand extension (MY)resource final demand extensions (RY)primary inputs coefficients (w)emission intermediate extentions coefficients (e)material intermediate extensions coefficients (m)resource intermediate extensions coefficients (r)emission final demand extension coefficients (eY)material final demand extension coefficients (mY)resource final demand extensions coefficients (rY)It is possible to specify:region of the interventionwhether the intervention affects domestic, import transactions or both4. Important modules4.1. scenarios.xlsFrom this .xls file it is possible to set different types of interventions and the analysis to perform:matrix = specifies in which matrix of IOT the changes are appliedchange_type = Primary and ancillary are only used to specify the type of intervention from a conceptual levelreg_o or reg_d = Regional coordinates (o=origin or row, d=destination or column)cat_o or cat_d = category (e.g. products or extensions ) coordinates (o=origin or row, d=destination or column)kt = technical coefficient (max achievable technically); a negative value means reduction; unit = %ka = absolute values for additionkp = penetration coefficient (level of market penetration of the policy); unit = %copy = allows you to copy a specific transation to a different point in the matrices (useful for proxy creation)substitution = tells the software whether it needs to substitute values among specified categoriessk = which intervention should be substitutedswk = Substitution weighing factor (how much of the original transaction should be substituted); allows to simulate difference in prices and physical properties between categories; unit = %These can be set for:product category e.g. C_STEL (basic iron), C_PULP (pulp), etc.final demand category e.g. F_HOUS (households), F_GOVE (government), etc.primary input category e.g. E_HRHS (employment highly skilled), T_TLSA (taxes less subsidies), etc.emissions extensions e.g. E_CO2_c (CO2 - combustion)material extensions e.g. NI.02 (Nature Inputs: Coking Coal)resource extension e.g. L_1.1 (Land use - Arable Land - Rice)Furthemore, from the analysis sheet you can set the following variables to be compared in the analysis:product categoriesprimary input categoriesemissions extensionsmaterial extensionsresource extensionsregion of interestimpact categories # Please see the data_validation_list sheet in the scenarios.xls file for the comprehensive list6. CreditsThanks to dr. Arnold Tukker, dr. Joao Dias Rodriguez for the supervision
dr. Arjan de Koning for knowledge support in exiobase
MSc. Glenn Auguilar Hernandez for testingThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-05-11)First release on PyPI.