Designing a sequential testing procedure for verifying global CO2 emissions

Following the Paris Agreement, most countries have agreed to reduce their CO2 emissions according to individually set Nationally Determined Contributions (NDCs). However, national CO2 emissions are reported by individual countries, and cannot be directly measured or verified by third parties. This engenders a potential misreporting problem, where nations that are not living up to their Paris commitments could, by underreporting emissions, nevertheless appear to be fulfilling their NDC targets. This paper uses the theory of sequential testing to design a statistical CO2 monitoring procedure that can detect systematic misreportings of CO2 emissions. The data series that we monitor is the so-called carbon budget imbalance, which is a time series derived from reported CO2 emissions and independently measured Earth system data. We show that, when emissions are truthfully reported, the budget imbalance constitutes a stationary process, while, if emissions become systematically misreported, a structural break occurs. Our proposed procedure monitors the budget imbalance data and sequentially tests the null that the budget imbalance is stationary, rejection of the null provides evidence for systematic misreportings of CO2 emissions. By constructing the procedure appropriately, detection time can be made sufficiently fast to help inform the 5 yearly global “stocktake” of the Paris Agreement.