======================= SISEPUEDE Documentation ======================= SISEPUEDE (**SI**\ mulating **SE**\ ctoral **P**\ athways and **U**\ ncertainty **E**\ xploration for **DE**\ carbonization) is an integrated Python/Julia modeling framework that facilitates exploratory analyses of decarbonization transformations within emissions sectors at the region level. It includes several key components: - Integrated yet separable sectoral models of emissions based on IPCC guidelines for greenhouse gas inventories - Uncertainty specification and trajectory sampling mechanism - A data pipeline management system - Scalable architecture - Customizable variable setup through sector-level categorization Check out the `General Data <../general_data.htm>`_ section to get started. About the Model =============== SISEPUEDE is a compartmentalized, sector-based model of emissions based primarily on two key publications from the IPCC: #. `2006 IPCC Guidelines for National Greenhouse Gas Inventories `_ and #. `2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories `_ These two documents are often abbreviated as **V##, C## IPCC GNGHGI** in attribute tables. In this notation, **V##** gives the volume number, while **C##** gives the chapter number. For example, V5, C6 refers to Volume 5, Chapter 6 (*Wastewaster Treatment and Discharge*). **EXPAND TO DESCRIBE ABSTRACT STRUCTURE** SISEPUEDE and Documentation Terminology --------------------------------------- Subsectors ^^^^^^^^^^ - What are subsectors? Categories ^^^^^^^^^^ - what are categories? Variables and Fields ^^^^^^^^^^^^^^^^^^^^ The SISEPUEDE integrated modeling framework makes use of a generalizable variable schematic to define input variables for models. There are two components to this naming system: #. **Model Variables** These are conceptual variables--for example, `Crop Yield Factor`--that are used to group #. **Variable Fields** These are direct inputs to the SISEPUEDE models, entered as fields in a data frame. For example, the input variables associated with `Crop Yield Factor` include... - variables are abstract groupings of variables for a defined category - some variables represent no categories - some represent all - some represent only a few - the model fundamentally reads in data frames with fields; those fields are defined by the variable construct - reading the variable definition tables - Variable Name - Variable Schema - Categories - Simplex Group (probability simplex) .. note:: SIMPLEX NOTE EXAMPLE Note that the sum of all initial fractions of area across land use categories *u* should be should equal 1 to , i.e. :math:`\sum_u \varphi_u = 1`, where :math:`\varphi_{\text{$CAT-LANDUSE$}} \to` ``frac_lu_$CAT-LANDUSE$`` at period *t*. - Default value - Other attributes Metavariables and Constructing Input Parameters ----------------------------------------------- - Contents -------- .. toc example struct from https://github.com/readthedocs/sphinx_rtd_theme/blob/c9b1bde560d8ee31400e4e4f92f2e8d7a42265ce/docs/index.rst .. https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html .. toctree:: :caption: Getting Started :hidden: installation quick_start sisepuede_concept analytical_parameters .. toctree:: :caption: Variables, Categories, and Data :hidden: general_data afolu circular_economy energy_consumption energy_production ippu socioeconomic .. toctree:: :caption: Defining Strategies :hidden: strategies transformations transformers .. toctree:: :caption: Managing Experiments :hidden: dimensions_of_analysis entering_data running_models sisepuede_database .. toctree:: :caption: Mathematical Specifications :hidden: mathdoc_afolu mathdoc_circular_economy mathdoc_economic_impact mathdoc_energy mathdoc_ippu .. toctree:: :caption: Community :hidden: contribute