Hi @grae.tejada, Please note that this Forum is for technical LSEG API questions only. For answers about content, such as the data item names (e.g.: what is the name of a "ESG KPI" field in the LDL), please use the DIB; failing that, please reach out to my.refinitiv.com.

I looks into the DIB myself and tested out an answer for you with the fields ["TR.TRESGScore","TR.TRESGCControversiesScore"], and came up with:

df = ld.get_history(
    universe="LSEG.L",
    fields=["TR.TRESGScore","TR.TRESGCControversiesScore"],
    start="2020-01-01",
    end="2024-09-01")

df.corr()

This is the output; is it what you're after?

You can go further in your analysis with:

# Assuming df contains the necessary columns
X = df[['TR.TRESGCControversiesScore']]
y = df['TR.TRESGScore']


# Handle any missing values if necessary
X = X.fillna(X.mean())
y = y.fillna(y.mean())


# Initialize and fit the regression model
model = LinearRegression()
model.fit(X, y)


# Print the coefficients
print("Coefficient:", model.coef_[0])
print("Intercept:", model.intercept_)


# Predict ESG Score based on Controversies Score
y_pred = model.predict(X)


from sklearn.metrics import r2_score


r2 = r2_score(y, y_pred)
print("R-squared:", r2)

Don't hesitate to let me know what Data Items you would prefer using, and I can entre them in the code above for you. Alternatively, I would suggest you do so yourself using Codebook.