Filter for companies with Carbon (CO2) emissions.

quirin.braml
quirin.braml Newcomer
in Eikon Data APIs

I wish to filter for U.S. companies with accessible CO2 emissions data. I have implemented this in the screener by establishing filters for, for instance, 5 and 10 years (filtering for GHG Scope 1 emissions > 0). Is there a method to filter for companies that possess emission data spanning the last 10 years (including those with values for the entire decade, as well as those with data available for less than 10 years)?


Is there also a way to obtain the data based on fiscal years beyond 2022, instead of FY 0?



thank you very much for the support

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  • jason.ramchandani01
    jason.ramchandani01 admin
    Answer ✓

    @quirin.braml thanks for your question. So I think one way of achieving this is to use a screener query to get a current universe and then go back in time to see what data is available. Also there are concepts such as Scope 1, Scope 2 and Scope 3 Emissions. 

    import refinitiv.data as rd
    import numpy as 
    import pandas as pd
    rd.open_session()

    Create a screener query for Americas:

    df2 = rd.get_data(["SCREEN(U(IN(Equity(active,public,primary))/*UNV:Public*/), TR.HasESGCoverage==true, IN(TR.HeadquartersRegion,""Americas""), CURN=USD)"],["TR.CommonName;TR.HasESGCoverage;TR.HeadquartersRegion"])

    Define a chunking function - so we remain compliant with API per call limits:

    def chunks(l, n):
        for i in range(0,len(l),n):
            yield l[i:i+n]

    Create routine to loop through our chunks to get the data we need - i included some CO2 energy intensity calcs:

    CEI = Employee Productivity * Energy Intensity * CO2 Intensity

    rics1 = list(chunks(list(df2['Instrument'].values),1000))
    rics1
    maindf= pd.DataFrame()

    for ric in rics1:
        df3 = rd.get_data('0#.SPX',['TR.TRBCEconomicSector','TR.F.SalesPerEmp(Period=FY0)','TR.AnalyticEnergyUse(Period=FY0)','TR.CO2EmissionTotal(Period=FY0)','TR.CO2IndirectScope3(Period=FY0)','TR.EnergyUseTotal(Period=FY0)'])

        df3.columns = ['Instrument','Sector','Revenue/Employee','EnergyUse/Revenue','CO2Emissions','CO2EmissionsScope3','EnergyUse']
        
        df3['CO2Emissions/EnergyUse'] = df3['CO2Emissions'] / df3['EnergyUse']
        df3['CO2Scope12/EnergyUse'] = df3['CO2Emissions'] / df3['EnergyUse']
        df3['CO2Scope123/EnergyUse'] = (df3['CO2Emissions'] + df3['CO2EmissionsScope3']) / df3['EnergyUse']
        df3['CEIScope12'] = df3['Revenue/Employee'] * df3['EnergyUse/Revenue'] * df3['CO2Emissions/EnergyUse']
        df3['CEIScope123'] = d3['Revenue/Employee'] * df3['EnergyUse/Revenue'] * df3['CO2Scope123/EnergyUse']
        if len(df3):
                maindf = pd.concat([maindf, df3], axis=0)

    maindf

    1700654048466.png


    You can then aggregate by sector and plot for example. I hope this can help.

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