Retire Module

The main retire module provides the core functionality for coal plant retirement analysis.

class retire.retire.Retire[source]

Bases: object

Main analysis class for coal plant retirement strategies.

The Retire class provides access to coal plant data and manuscript results from “Strategies to Accelerate US Coal Power Phaseout Using Contextual Retirement Vulnerabilities” published in Nature Energy. It loads the primary dataset and associated network graph for analysis.

raw_df

The complete US coal plants dataset containing plant characteristics, retirement status, and contextual vulnerabilities.

Type:

pandas.DataFrame

graph

Network graph representing relationships between coal plants based on similarity metrics and contextual factors.

Type:

networkx.Graph

Examples

>>> retire = Retire()
>>> group_analysis = retire.get_group_report()
>>> explanations = retire.get_target_explanations()
__init__()[source]

Initialize the Retire analysis object.

Loads the US coal plants dataset and associated network graph from the package resources. The dataset contains information on plant characteristics, retirement vulnerabilities, and contextual factors.

get_plant_level_analysis(ORISPL: str)[source]

Get detailed analysis for a specific coal plant.

Parameters:

ORISPL (str) – The ORIS plant code (Office of Regulatory Information Systems) identifying the specific coal plant to analyze.

Returns:

Plant-specific analysis results including retirement vulnerabilities, contextual factors, and strategic recommendations.

Return type:

dict or pandas.DataFrame

Notes

This method is currently not implemented and will be added in future versions.

get_group_report()[source]

Load the group-level analysis results from the manuscript.

Returns:

Group analysis results containing aggregated statistics and characteristics for each identified cluster of coal plants with similar retirement vulnerabilities.

Return type:

pandas.DataFrame

Examples

>>> retire = Retire()
>>> groups = retire.get_group_report()
>>> print(groups.columns)
get_target_explanations()[source]

Load plant-level explanations for retirement targeting strategies.

Returns:

Plant-level explanations containing detailed reasoning for why specific plants were identified as priority targets for retirement based on contextual vulnerabilities and strategic factors.

Return type:

pandas.DataFrame

Examples

>>> retire = Retire()
>>> explanations = retire.get_target_explanations()
>>> target_plants = explanations[explanations['priority'] == 'high']

Main Classes

class retire.retire.Retire[source]

Main analysis class for coal plant retirement strategies.

The Retire class provides access to coal plant data and manuscript results from “Strategies to Accelerate US Coal Power Phaseout Using Contextual Retirement Vulnerabilities” published in Nature Energy. It loads the primary dataset and associated network graph for analysis.

raw_df

The complete US coal plants dataset containing plant characteristics, retirement status, and contextual vulnerabilities.

Type:

pandas.DataFrame

graph

Network graph representing relationships between coal plants based on similarity metrics and contextual factors.

Type:

networkx.Graph

Examples

>>> retire = Retire()
>>> group_analysis = retire.get_group_report()
>>> explanations = retire.get_target_explanations()
__init__()[source]

Initialize the Retire analysis object.

Loads the US coal plants dataset and associated network graph from the package resources. The dataset contains information on plant characteristics, retirement vulnerabilities, and contextual factors.

get_plant_level_analysis(ORISPL: str)[source]

Get detailed analysis for a specific coal plant.

Parameters:

ORISPL (str) – The ORIS plant code (Office of Regulatory Information Systems) identifying the specific coal plant to analyze.

Returns:

Plant-specific analysis results including retirement vulnerabilities, contextual factors, and strategic recommendations.

Return type:

dict or pandas.DataFrame

Notes

This method is currently not implemented and will be added in future versions.

get_group_report()[source]

Load the group-level analysis results from the manuscript.

Returns:

Group analysis results containing aggregated statistics and characteristics for each identified cluster of coal plants with similar retirement vulnerabilities.

Return type:

pandas.DataFrame

Examples

>>> retire = Retire()
>>> groups = retire.get_group_report()
>>> print(groups.columns)
get_target_explanations()[source]

Load plant-level explanations for retirement targeting strategies.

Returns:

Plant-level explanations containing detailed reasoning for why specific plants were identified as priority targets for retirement based on contextual vulnerabilities and strategic factors.

Return type:

pandas.DataFrame

Examples

>>> retire = Retire()
>>> explanations = retire.get_target_explanations()
>>> target_plants = explanations[explanations['priority'] == 'high']

Analysis Methods

Retire.get_group_report()[source]

Load the group-level analysis results from the manuscript.

Returns:

Group analysis results containing aggregated statistics and characteristics for each identified cluster of coal plants with similar retirement vulnerabilities.

Return type:

pandas.DataFrame

Examples

>>> retire = Retire()
>>> groups = retire.get_group_report()
>>> print(groups.columns)
Retire.get_target_explanations()[source]

Load plant-level explanations for retirement targeting strategies.

Returns:

Plant-level explanations containing detailed reasoning for why specific plants were identified as priority targets for retirement based on contextual vulnerabilities and strategic factors.

Return type:

pandas.DataFrame

Examples

>>> retire = Retire()
>>> explanations = retire.get_target_explanations()
>>> target_plants = explanations[explanations['priority'] == 'high']