kaya-decomposition 0.3.0

Kaya decomposition analysis for integrated-assessment scenario data

kaya-decomposition

Kaya decomposition analysis for integrated-assessment scenario data.

Overview

This library provides tools for computing Kaya decomposition factors from IAMC-format scenario data. The Kaya identity decomposes CO2 emissions into contributing factors: population, GDP per capita, energy intensity, and carbon intensity.

Key features:

• Kaya Decomposition: Compute intermediate variables and decomposition factors
• LMDI Analysis: Logarithmic Mean Divisia Index for scenario comparison
• Cumulative LMDI: Time-series decomposition within a single scenario

Installation

pip install kaya-decomposition

For development:

pip install -e ".[dev]"

Quick Start

Basic Kaya Decomposition

import pyam
from kaya_decomposition import compute_kaya_variables, compute_kaya_factors

# Load your IAMC-format data
df = pyam.IamDataFrame("your_data.csv")

# Compute intermediate Kaya variables
kaya_vars = compute_kaya_variables(df)

# Compute Kaya decomposition factors
factors = compute_kaya_factors(kaya_vars)

LMDI Scenario Comparison

from kaya_decomposition import compute_lmdi

# Compare two scenarios using LMDI decomposition
lmdi_result = compute_lmdi(
    factors,
    ref_scenario=("Model", "Baseline", "World"),
    int_scenario=("Model", "Policy", "World"),
)

Cumulative LMDI (Time-Series Decomposition)

from kaya_decomposition import compute_lmdi_cumulative, compute_lmdi_cumulative_sum

# Decompose changes over time within a single scenario
lmdi_cumulative = compute_lmdi_cumulative(factors, base_year=2020)

# Sum contributions by period
period_sums = compute_lmdi_cumulative_sum(
    factors,
    base_year=2020,
    periods=[(2020, 2050), (2050, 2100), (2020, 2100)],
)
print(period_sums)

All-Sectors Analysis

from kaya_decomposition import compute_all_sectors_lmdi_cumulative

# Complete analysis including non-CO2 gases, industrial processes, and land use
result = compute_all_sectors_lmdi_cumulative(
    input_data,
    base_year=2020,
    scenario=("Model", "Scenario", "Region"),
)

Required Input Variables

Core Kaya Decomposition

The following variables must be present in your input data:

• Population
• GDP|PPP or GDP|MER
• Final Energy
• Primary Energy
• Primary Energy|Coal
• Primary Energy|Oil
• Primary Energy|Gas
• Emissions|CO2|Industrial Processes
• Carbon Sequestration|CCS
• Carbon Sequestration|CCS|Biomass
• Emissions|CO2|Energy and Industrial Processes
• Emissions|CO2|AFOLU
• Carbon Sequestration|CCS|Fossil|Energy
• Carbon Sequestration|CCS|Fossil|Industrial Processes
• Carbon Sequestration|CCS|Biomass|Energy
• Carbon Sequestration|CCS|Biomass|Industrial Processes

All-Sectors Analysis (Optional)

For compute_all_sectors_lmdi_cumulative, additional variables:

• Emissions|CH4
• Emissions|N2O
• Emissions|F-Gases (or individual HFC, PFC, SF6)

Computed Variables

Kaya Variables (Intermediate)

• Primary Energy|Fossil
• Total Fossil Carbon
• Net Fossil Carbon

Kaya Factors

• GNP/P (GDP per capita)
• FE/GNP (Energy intensity of GDP)
• PEDEq/FE (Primary to final energy ratio)
• PEFF/PEDEq (Fossil share of primary energy)
• TFC/PEFF (Carbon intensity of fossil energy)
• NFC/TFC (Net to total fossil carbon ratio)

API Reference

Core Functions

compute_kaya_variables(input_data)

Compute intermediate Kaya variables from input data.

Parameters:

• input_data (pyam.IamDataFrame): Input data with required variables

Returns:

• pyam.IamDataFrame with computed variables, or None if input incomplete

compute_kaya_factors(kaya_variables_frame)

Compute Kaya decomposition factors.

Parameters:

• kaya_variables_frame (pyam.IamDataFrame): Output from compute_kaya_variables

Returns:

• pyam.IamDataFrame with computed factors

LMDI Functions

compute_lmdi(kaya_factors_df, ref_scenario, int_scenario)

Compute corrected LMDI decomposition between two scenarios.

Parameters:

• kaya_factors_df (pyam.IamDataFrame): Output from compute_kaya_factors
• ref_scenario (tuple): Reference scenario (model, scenario, region)
• int_scenario (tuple): Intervention scenario (model, scenario, region)

Returns:

• pyam.IamDataFrame with LMDI contributions for each factor

compute_lmdi_cumulative(kaya_factors_df, base_year, scenario)

Compute cumulative LMDI decomposition for a single scenario over time.

Parameters:

• kaya_factors_df (pyam.IamDataFrame): Output from compute_kaya_factors
• base_year (int): Reference year for comparison (default: 2020)
• scenario (tuple, optional): Scenario to analyze (model, scenario, region)

Returns:

• pyam.IamDataFrame with LMDI contributions at each time point

compute_lmdi_cumulative_sum(kaya_factors_or_lmdi_df, base_year, periods, integration_method, use_corrected)

Sum cumulative LMDI contributions over specified time periods.

Parameters:

• kaya_factors_or_lmdi_df (pyam.IamDataFrame): Kaya factors or LMDI results
• base_year (int): Base year for calculation (default: 2020)
• periods (list of tuples): Periods to sum, e.g., [(2020, 2050), (2050, 2100)]
• integration_method (str): "trapezoidal" (default) or "endpoint"
• use_corrected (bool): Whether to use non-negativity corrected values

Returns:

• pd.DataFrame with factors as rows, periods as columns (values in Gt CO2)

All-Sectors Functions

compute_other_gases_emissions(input_data, fgas_method)

Compute total non-CO2 greenhouse gas emissions in CO2-equivalent.

Parameters:

• input_data (pyam.IamDataFrame): Input data with CH4, N2O, F-gas variables
• fgas_method (str): "aggregate" (default) or "disaggregate"

Returns:

• pyam.IamDataFrame with total other gases in Mt CO2-equivalent/yr

compute_industrial_process_emissions(input_data)

Compute net industrial process carbon emissions (after CCS).

Parameters:

• input_data (pyam.IamDataFrame): Input data

Returns:

• pyam.IamDataFrame with Net Industrial Carbon in Mt CO2/yr

compute_all_sectors_lmdi_cumulative(input_data, base_year, scenario, periods, integration_method, use_corrected)

Compute cumulative LMDI for all emission sectors.

Parameters:

• input_data (pyam.IamDataFrame): Raw input data with all required variables
• base_year (int): Base year for LMDI calculation (default: 2020)
• scenario (tuple, optional): Scenario to analyze (model, scenario, region)
• periods (list of tuples): Periods to sum over
• integration_method (str): "trapezoidal" (default) or "endpoint"
• use_corrected (bool): Whether to use corrected values (default: False)

Returns:

• pd.DataFrame matching the LMDItableRefAllSectors format with rows:
  • Population
  • Economic Activity per Person
  • Energy Intensity of Economy
  • Energy Supply Loss Factor
  • Fossil Fuel Fraction
  • Carbon Intensity of Fossil Energy
  • Industrial Process Carbon Emissions
  • Other Gases
  • Land Use
  • Total Net Emissions

Constants

Access variable name constants for programmatic use:

from kaya_decomposition.constants import (
    input_variables,
    kaya_variables,
    kaya_factors,
    lmdi,
    lmdi_cumulative,
)

# Input variable names
print(input_variables.POPULATION)  # "Population"
print(input_variables.GDP_PPP)     # "GDP|PPP"

# Computed variable names
print(kaya_variables.TFC)          # "Total Fossil Carbon"

# Factor names
print(kaya_factors.GNP_per_P)      # "GNP/P"

# LMDI output names
print(lmdi.Pop_LMDI)               # "Population (LMDI)"

# Cumulative LMDI names (human-readable)
print(lmdi_cumulative.Pop_cumulative)  # "Population"

License

Apache-2.0