# Designing a statistical procedure for monitoring global carbon dioxide emissions

## TL;DR

This paper develops a statistical sequential testing method to detect under-reporting of national CO$_2$ emissions, aiming to improve verification in global climate agreements like the Paris Agreement.

## Contribution

It introduces a novel sequential testing procedure for monitoring CO$_2$ emission reports, addressing the challenge of verifying self-reported data.

## Key findings

- Test correctly sizes when emissions are faithfully reported
- Detection can be rapid under under-reporting
- Method can inform global emission verification processes

## Abstract

Following the Paris Agreement of $2015$, most countries have agreed to reduce their carbon dioxide (CO$_2$) emissions according to individually set Nationally Determined Contributions. However, national CO$_2$ emissions are reported by individual countries and cannot be directly measured or verified by third parties. Inherent weaknesses in the reporting methodology may misrepresent, typically an under-reporting of, the total national emissions. This paper applies the theory of sequential testing to design a statistical monitoring procedure that can be used to detect systematic under-reportings of CO$_2$ emissions. Using simulations, we investigate how the proposed sequential testing procedure can be expected to work in practice. We find that, if emissions are reported faithfully, the test is correctly sized, while, if emissions are under-reported, detection time can be sufficiently fast to help inform the $5$ yearly global "stocktake" of the Paris Agreement. We recommend the monitoring procedure be applied going forward as part of a larger portfolio of methods designed to verify future global CO$_2$ emissions.