Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH4 Emissions in China

Abstract

Bottom-up CH4 emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed to determine the causes of overestimation in CH4 bottom-up emission inventories across China by applying parameter variability uncertainty analysis to three sets of CH4 emission inventories titled PENG, GAINS, and EDGAR. The top three major sources of CH4 emissions in China during the years 1990–2010, namely, coal mining, livestock, and rice cultivation, were selected for the investigation. The results of this study confirm the concerns raised by inverse modeling results in which we found significantly higher bottom-up emissions for the rice cultivation and coal mining sectors. The largest uncertainties were detected in the rice cultivation estimates and were caused by variations in the proportions of rice cultivation ecosystems and EFs; specifically, higher rates for both parameters were used in EDGAR. The coal mining sector was associated with the second highest level of uncertainty, and this was caused by variations in mining types and EFs, for which rather consistent parameters were used in EDGAR and GAINS, but values were slightly higher than those used in PENG. Insignificant differences were detected among the three sets of inventories for the livestock sector.