This is deprecated, please use instead:

• Stable version: https://pypi.org/project/pycirk/
• Unstable version on github: https://github.com/CMLPlatform/pycirk
• Unstable version on bitbucket: https://bitbucket.org/CML-IE/pycirk/

Program for the calculation of Circular Economy policies in input-output analysis starting from multi-regional supply-use tables

start

Initiates the operations to set scenarios and to create IOT from SUT based on prodxprod Industry-Technology assumption both under Market Share Coefficient method and Technical Coefficient method.

From here it is possible to check results and save them in xls files

scenarios

From this .xls file it is possible to set different types of interventions and the analysis to perform:

• direct policy interventions
  • sharing
  • recycling
  • life extension
• market penetration of the direct policy interventions
• indirect effects of policy interventions:
  • rebound effects
  • substituion
• market expansion uncoupled from policy interventions and their indirect effects

These can be set for:

• product category (e.g. basic iron, pulp, raw milk, etc.)
• final demand category (e.g. households, government, etc.)
• primary input category (e.g. employment, etc.)
• emissions extensions
• material extensions
• resource extension

The tables in which it is possible to apply the interventions are:

• total requirement matrix (A)
• final demand (Y)
• primary inputs coefficients (RE)
• emission intermediate extentions coefficients (RBe)
• material intermediate extensions coefficients (RBm)
• resource intermediate extensions coefficients (RBr)
• emission final demand extension coefficients (RYBe)
• material final demand extension coefficients (RYBm)

Additionally it is possible to specify:

• region of the intervention
• whether the intervention affects domestic, import transactions or both

Furthemore, from the analysis sheet you can set the following variables to be compared in the analysis:

• product categories
• primary input categories
• emissions extensions
• material extensions
• resource extensions
• region of interest

results

Class to assemble results for analysis as specified in scenario.xls analysis sheet

• Output product content in other products
• Output results for each scenario
• Output results and all IO tables and extensions

save_

Save class

• Save one scenario results
• Save one scenario results + IOTs
• Save all scenarios + IOTs
• Save all results

apply_policy

Policy interventions class

• Recreate any matrix in IOT from policy interventions listed in the scenarios scenarios.xls

base_n_scen

Calculate IOT for baseline and scenarios from SUTs

SUTtoIO

Assemblying IOTs and Extensions from

• Prod x prod industry technology assumption in market share coefficient method
• Prod x prod industry technology assumption in technical coefficient method

SUTops

Class for fundamental mathematical operations of IOA and SUT

labels

General labels for tables

dirs

Directories and loading primary data sources

Aggregate folder

Contains the following (N.B. module import to be fixed)

• parse_mrSUTs

  • Parse all SUTs from EXIOBASE and outputs them as pickles to facilitate operations.
  • It also adds regional label EU or ROW to faciliate slicing

• agg_MrSUTs

  • aggregates and separates them by EU-27 and ROW.
  • It reapplies multiindexes to be able to sort by region, code, name, abbreviation on all tables

• basic_price_wavg

  • weighted average of products basic prices across the world