  Corporate carbon emissions data is disclosed by approximately 65% of large
and mid-sized companies globally, despite being a key indicator of corporate
climate performance. With investors increasingly looking to integrate climate
risk into their investment strategies and risk reporting, this creates demand
for robust prediction models that can generate reliable estimates for missing
carbon disclosures. However, these estimates lack transparency and are
frequently used in the investment decisions process with the same confidence as
corporate reported data. As disclosures remain mostly voluntary and the
propensity to disclose is shaped by several factors (e.g. size, sector,
geography), missing emissions data should be assumed to be missing not at
random (MNAR). However, widely used estimation methods (e.g. linear regression
models) typically do not correct for MNAR bias and do not accurately reflect
the uncertainty of estimated data. The objective of this paper is to address
these issues: (1) account for the uncertainty of the missing data and thus
obtain regression coefficients by multiple imputation (MI) (2) correct for
potential bias by using MI algorithms based on Heckman's sample selection model
introduced by Galimard et al. (3) estimate missing carbon disclosures with
linear models based on MI and report on the uncertainty of predicted values,
measured as the length of the prediction interval. In the simulation, our
approach resulted in an accuracy gain based on root mean squared error of up to
30%, and up to a 40% higher coverage rate than the existing models. When
applied to commercial data, the results suggested up to 20% higher coverage for
proposed methods.
