Robust estimation of carbon dioxide airborne fraction under measurement errors

TL;DR

This paper develops robust statistical methods, including generalized Deming regression and instrumental variables, to accurately estimate the CO2 airborne fraction despite measurement errors, providing more reliable results than traditional methods.

Contribution

It introduces a generalized Deming regression, a bootstrap confidence interval approach, and an instrumental variables method for robust estimation of the CO2 airborne fraction under measurement errors.

Findings

IV estimates are around 45%, consistent with OLS.

Deming regression provides robust estimates with confidence intervals.

OLS estimates are less reliable under measurement errors.

Abstract

This paper discusses the effect of measurement errors in the estimation of the carbon dioxide (CO$_2$) airborne fraction. We are the first to present regression-based estimates and standard errors that are robust to measurement errors for the extended model, the preferred specification to estimate the CO$_2$ airborne fraction. To achieve this goal, we add to the literature in three ways: $i)$ We generalise the Deming regression to handle multiple variables. $ii)$ We introduce a bootstrap approach to construct confidence intervals for Deming regression in both univariate and multivariate scenarios. $iii)$ Propose to estimate the airborne fraction using instrumental variables (IV), taking advantage of the variation of additional measurements, to obtain consistent estimates that are robust to measurement errors. IV estimates for the airborne fraction are 44.8%($\pm$ 1.4%; 1$\sigma$) for the simple specification, and 47.3%($\pm$ 1.1%; 1$\sigma$) for the extended specification. We show that these estimates are not statistically different from the ordinary least squares (OLS) estimates, while being robust to measurement errors without relying on additional assumptions. In contrast, OLS estimates are shown to fall outside the confidence interval of the Deming regression estimates.