# Procedures to combine estimators of greenhouse gases emission factors

## TL;DR

A new method improves accuracy in estimating greenhouse gas emission factors by combining conflicting data using a novel variance estimator.

## Contribution

A new unbiased estimator for variance of emission factors, leading to tighter confidence intervals.

## Key findings

- The new estimator Sa² is unbiased and provides tighter confidence intervals than pooled variance.
- The procedure was successfully applied to estimate methane emissions from rice cultivation.
- The method improves accuracy in greenhouse gas emission factor estimation compared to existing methods.

## Abstract

This article describes a new procedure to estimate the mean and variance of greenhouse gases (GHG) emission factors based on different, possibly conflicting, estimates for these emission factors. The procedure uses common information such as mean and standard deviation usually reported in IPCC (Intergovernmental Panel on Climate Change) database and other references in the literature that estimate emission factors. Essentially, it is a procedure in the class of meta-analysis, based on the computation of \documentclass[12pt]{minimal}
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\begin{document}$${S}_{a}^{2}$$\end{document}Sa2, a new estimator for the variance of the emission factor.

We discuss the quality of this estimator in terms of its probability distribution and show that it is unbiased. The resulting confidence interval for the mean emission factor is tighter than those that would have resulted from using other estimators such as pooled variance and thus, the new procedure improves the accuracy in estimating GHG emissions.

The application of the procedure is illustrated in a case study involving the estimation of methane emissions from rice cultivation.

The estimation of emission factors using \documentclass[12pt]{minimal}
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\begin{document}$${S}_{a}^{2}$$\end{document}Sa2 was demonstrated to be more accurate because it is not biased and more precise than alternative methods.

The online version contains supplementary material available at 10.1186/s13021-024-00250-8.