SBTi.temperature_score

Module Contents

Classes

ScenarioType

A scenario defines which scenario should be run.

EngagementType

An engagement type defines how the companies will be engaged.

Scenario

A scenario defines the action the portfolio holder will take to improve its temperature score.

TemperatureScore

This class is provides a temperature score based on the climate goals.

class SBTi.temperature_score.ScenarioType

Bases: enum.Enum

A scenario defines which scenario should be run.

TARGETS = 1
APPROVED_TARGETS = 2
HIGHEST_CONTRIBUTORS = 3
HIGHEST_CONTRIBUTORS_APPROVED = 4
static from_int(value) → Optional[‘ScenarioType’]
class SBTi.temperature_score.EngagementType

Bases: enum.Enum

An engagement type defines how the companies will be engaged.

SET_TARGETS = 1
SET_SBTI_TARGETS = 2
static from_int(value) → ’EngagementType’

Convert an integer to an engagement type.

Parameters

value – The value to convert

Returns

static from_string(value: Optional[str]) → ’EngagementType’

Convert a string to an engagement type.

Parameters

value – The value to convert

Returns

class SBTi.temperature_score.Scenario

A scenario defines the action the portfolio holder will take to improve its temperature score.

scenario_type :Optional[ScenarioType]
engagement_type :EngagementType
get_score_cap(self) → float
get_fallback_score(self, fallback_score: float) → float
static from_dict(scenario_values: dict) → Optional[‘Scenario’]

Convert a dictionary to a scenario. The dictionary should have the following keys:

  • number: The scenario type as an integer

  • engagement_type: The engagement type as a string

Parameters

scenario_values – The dictionary to convert

Returns

A scenario object matching the input values or None, if no scenario could be matched

static from_interface(scenario_values: Optional[ScenarioInterface]) → Optional[‘Scenario’]

Convert a scenario interface to a scenario.

Parameters

scenario_values – The interface model instance to convert

Returns

A scenario object matching the input values or None, if no scenario could be matched

class SBTi.temperature_score.TemperatureScore(time_frames: List[ETimeFrames], scopes: List[EScope], fallback_score: float = 3.2, model: int = 4, scenario: Optional[Scenario] = None, aggregation_method: PortfolioAggregationMethod = PortfolioAggregationMethod.WATS, grouping: Optional[List] = None, config: Type[TemperatureScoreConfig] = TemperatureScoreConfig)

Bases: SBTi.portfolio_aggregation.PortfolioAggregation

This class is provides a temperature score based on the climate goals.

Parameters
  • fallback_score – The temp score if a company is not found

  • model – The regression model to use

  • config – A class defining the constants that are used throughout this class. This parameter is only required if you’d like to overwrite a constant. This can be done by extending the TemperatureScoreConfig class and overwriting one of the parameters.

get_target_mapping(self, target: pd.Series) → Optional[str]

Map the target onto an SR15 target (None if not available).

Parameters

target – The target as a row of a dataframe

Returns

The mapped SR15 target

get_annual_reduction_rate(self, target: pd.Series) → Optional[float]

Get the annual reduction rate (or None if not available).

Parameters

target – The target as a row of a dataframe

Returns

The annual reduction

get_regression(self, target: pd.Series) → Tuple[Optional[float], Optional[float]]

Get the regression parameter and intercept from the model’s output.

Parameters

target – The target as a row of a dataframe

Returns

The regression parameter and intercept

_merge_regression(self, data: pd.DataFrame)

Merge the data with the regression parameters from the SBTi model.

Parameters

data – The data to merge

Returns

The data set, amended with the regression parameters

get_score(self, target: pd.Series) → Tuple[float, float]

Get the temperature score for a certain target based on the annual reduction rate and the regression parameters.

Parameters

target – The target as a row of a data frame

Returns

The temperature score

get_ghc_temperature_score(self, row: pd.Series, company_data: pd.DataFrame) → Tuple[float, float]

Get the aggregated temperature score and a temperature result, which indicates how much of the score is based on the default score for a certain company based on the emissions of company.

Parameters
  • company_data – The original data, grouped by company, time frame and scope category

  • row – The row to calculate the temperature score for (if the scope of the row isn’t s1s2s3, it will return the original score

Returns

The aggregated temperature score for a company

get_default_score(self, target: pd.Series) → int

Get the temperature score for a certain target based on the annual reduction rate and the regression parameters.

Parameters

target – The target as a row of a dataframe

Returns

The temperature score

_prepare_data(self, data: pd.DataFrame)

Prepare the data such that it can be used to calculate the temperature score.

Parameters

data – The original data set as a pandas data frame

Returns

The extended data frame

_calculate_company_score(self, data)

Calculate the combined s1s2s3 scores for all companies.

Parameters

data – The original data set as a pandas data frame

Returns

The data frame, with an updated s1s2s3 temperature score

calculate(self, data: Optional[pd.DataFrame] = None, data_providers: Optional[List[data.DataProvider]] = None, portfolio: Optional[List[PortfolioCompany]] = None)

Calculate the temperature for a dataframe of company data. The columns in the data frame should be a combination of IDataProviderTarget and IDataProviderCompany.

Parameters
  • data – The data set (or None if the data should be retrieved)

  • data_providers – A list of DataProvider instances. Optional, only required if data is empty.

  • portfolio – A list of PortfolioCompany models. Optional, only required if data is empty.

Returns

A data frame containing all relevant information for the targets and companies

_get_aggregations(self, data: pd.DataFrame, total_companies: int) → Tuple[Aggregation, pd.Series, pd.Series]

Get the aggregated score over a certain data set. Also calculate the (relative) contribution of each company

Parameters

data – A data set, containing one row per company

Returns

An aggregated score and the relative and absolute contribution of each company

_get_score_aggregation(self, data: pd.DataFrame, time_frame: ETimeFrames, scope: EScope) → Optional[ScoreAggregation]

Get a score aggregation for a certain time frame and scope, for the data set as a whole and for the different groupings.

Parameters
  • data – The whole data set

  • time_frame – A time frame

  • scope – A scope

Returns

A score aggregation, containing the aggregations for the whole data set and each individual group

aggregate_scores(self, data: pd.DataFrame) → ScoreAggregations

Aggregate scores to create a portfolio score per time_frame (short, mid, long).

Parameters

data – The results of the calculate method

Returns

A weighted temperature score for the portfolio

cap_scores(self, scores: pd.DataFrame) → pd.DataFrame

Cap the temperature scores in the input data frame to a certain value, based on the scenario that’s being used. This can either be for the whole data set, or only for the top X contributors.

Parameters

scores – The data set with the temperature scores

Returns

The input data frame, with capped scores

anonymize_data_dump(self, scores: pd.DataFrame) → pd.DataFrame

Anonymize the scores by deleting the company IDs, ISIN and renaming the companies.

Parameters

scores – The data set with the temperature scores

Returns

The input data frame, anonymized