General concept

This tutorial demonstrates basic concepts of the package.

import bonsai_ipcc
my_ipcc = bonsai_ipcc.IPCC()

Structure

The structure of the package is as followed:

ipcc.<volume>.<chapter>.sequence.<tier_name>
ipcc.<volume>.<chapter>.parameter.<parameter_name>
ipcc.<volume>.<chapter>.dimension.<dimension_name>
ipcc.<volume>.<chapter>.concordance.<concordance_name>
ipcc.<volume>.<chapter>.elementary.<equation_name>

Get information about a tier sequence

help(my_ipcc.waste.incineration.sequence.tier1_co2)

Help on function tier1_co2 in module bonsai_ipcc.waste.incineration.sequence:

tier1_co2(year=2010, region='BG', product='msw_food', activity='open_burn', uncertainty='def')
    Tier 1 method CO2 Emissions.

    Default data to quantify waste generation, composition and management practice
    (requirement: incineration or open burning is not a key category)

    Argument
    ---------
    year : int
        year under study
    region : str
        region under study
    product : str
        Fraction of solid waste.
    activity : str
        Type of waste incineration.
    uncertainty : str
        'analytical', 'monte_carlo' or a property dimension, e.g. 'def'

    Returns
    -------
    VALUE: DataClass
        Inlcudes the results of each step of the sequence.

Inspect the involved parameters

my_ipcc.inspect(my_ipcc.waste.incineration.sequence.tier1_co2)

['b_frac',
 'p_frac',
 'gdp',
 'urb_population',
 'incintype_frac',
 'msw_frac_to_incin',
 'isw_gen_rate',
 'msw_type_frac',
 'dm',
 'msw_gen_rate',
 'fcf',
 'isw_type_frac',
 'of',
 'cf',
 'isw_frac_to_incin',
 'total_population']

Show the table of a parameter

my_ipcc.waste.incineration.parameter.dm.head()

				value	unit
year	region	waste_type	property
2006	World	msw_food	def	0.40	kg/kg
		msw_garden	def	0.40	kg/kg
		msw_paper	def	0.90	kg/kg
		msw_wood	def	0.84	kg/kg
		msw_textiles	def	0.80	kg/kg

help(my_ipcc.waste.incineration.elementary.msw_to_incin)

Help on function msw_to_incin in module bonsai_ipcc.waste.waste_generation.elementary:

msw_to_incin(urb_population, msw_gen_rate, msw_frac_to_incin, msw_type_frac)
    Equation 2.y (not explicit in guidelines, tier 1)

    Calculates the amount of municipal solid waste (MSW) disposed to incineration sites,
    by using default data from chapter 2 (Waste Generation, Composition amd Management).

    Argument
    --------
    urb_population (cap/year) : float
        Urban population of a region in a given year.
    msw_gen_rate (t/cap) : float
        rate of municipal solid waste per capita.
    msw_frac_to_incin (kg/kg) : float
        Fraction of waste disposed to incineration in municipal solid waste.

    Returns
    -------
    VALUE: float
        amount of certain MSW type which is incinerated (tonnes/year)

Check dimension tables of the parameter msw_frac_to_swds

my_ipcc.waste.incineration.dimension.region

	description	region_type
code
AF	Afghanistan	country
AX	Åland Islands	country
AL	Albania	country
DZ	Algeria	country
AS	American Samoa	country
...	...	...
ANT	Antarctica	continent
AFR	Africa	continent
EUR	Europe	continent
ASI	Asia	continent
AUS	Australia and New Zealand	continent

285 rows × 2 columns

my_ipcc.waste.incineration.dimension.year.head()

	description
code
0	place holder for emtpy tables
1900	year in which activity occurs
1901	year in which activity occurs
1902	year in which activity occurs
1903	year in which activity occurs

my_ipcc.waste.incineration.dimension.property

	description	remarks
code
def	default	mean
min	minimum	2.5th percentile
max	maximum	97.5th percentile
abs_max	absolute maximun	theoretical upper bound
abs_min	absolute minimum	theoretical lower bound
dummy	place holder for emtpy tables	NaN

Concordance tables

Concordance tables are used to automated reading parameter values within a tier sequence. When reading the parameter value for a certain dimension, in general, this value is specified in the parameter table.

However, the requirement to define all parameter values for each dimension explicitally in the parameter table would lead to a lot of repetition. To avoid this, the concept of concordance tables is used. Each attribute of a dimension can be hierarchically mapped to others. When using the concordance table for a dimension, the algorithm checks automatically if values for other dimension attributes are available in the parameter table

Let´s have a look to the concordance table for the dimension ‘region’.

my_ipcc.waste.incineration.concordance.region.head()

	unregion	geographicregion	continent	world
country
AF	Southern Asia	NaN	ASI	World
AX	Northern Europe	NaN	EUR	World
AL	Southern Europe	NaN	EUR	World
DZ	Northern Africa	NaN	AFR	World
AS	Polynesia	NaN	AUS	World

When reading a parameter within a sequence for the region Albania (‘AL’), the algorithm would start trying to find the value for ‘AL’. If there is no value for ‘AL’ in the parameter table, it would try to find the value for ‘Southern Europe’, stepwise until to the last item of the column. In this case this is ‘World’, which indicates the validity of the parameter value for all regions.

(‘NaN’ is skipped during the approach)

my_ipcc.waste.incineration.concordance.year.head()

	cat3	cat2	cat1	cat0
cat4
2006	2019	NaN	NaN	NaN
2007	2006	2019.0	NaN	NaN
2008	2006	2019.0	NaN	NaN
2009	2006	2019.0	NaN	NaN
2010	2006	2019.0	NaN	NaN

Look at the additional tutorials how to run a tier sequence.