Navigating the corporate disclosure gap: Modelling of Missing Not at Random Carbon Data

TL;DR

This paper develops a novel multiple imputation approach to estimate missing corporate carbon emissions data, accounting for bias and uncertainty, thereby improving prediction accuracy and reliability for climate risk assessment.

Contribution

It introduces a multiple imputation method based on Heckman's sample selection model to address MNAR bias in corporate carbon data estimation.

Findings

Up to 30% accuracy improvement in simulation

Up to 40% higher coverage rate in simulation

Up to 20% higher coverage in real data

Abstract

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.