SISEPUEDE Documentation

SISEPUEDE (SImulating SEctoral Pathways and Uncertainty Exploration for DEcarbonization) 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 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:

  1. 2006 IPCC Guidelines for National Greenhouse Gas Inventories and

  2. 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:

  1. Model Variables These are conceptual variables–for example, Crop Yield Factor–that are used to group

  2. 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. \(\sum_u \varphi_u = 1\), where \(\varphi_{\text{$CAT-LANDUSE$}} \to\) frac_lu_$CAT-LANDUSE$ at period t.

  • Default value

  • Other attributes

Metavariables and Constructing Input Parameters

Contents