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.
Highlights
“Two degrees Celsius”, the global temperature target under the Paris Agreement for addressing the climate change problem, represents a significant challenge for all countries
From the analysis based on the variation of only activity data parameters (AD), only emission factor parameters (EF), and all parameters related to emission estimations (All) for the year 2010 in each sector, we found that the rice cultivation sector had the largest uncertainty, at a value of 106%, because of the variation of all parameters in this sector (Figure 3)
From the analysis based on the variation of only activity data parameters (AD), only emission factor parameters (EF), and all parameters related to emission estimations (All) for the year 2010 in each sector, we found that the rpiacreacmuelttievrastiionnthseisctsoerchtoard(tFhigeularerg3e)s.tCuonaclermt(acin)iniLntiygv,earsattnoakcvkeadluseecoofn1d06f%or, because of the variation of all uncertainty with a maximum uncerFtiagiunrtey2o
Summary
“Two degrees Celsius”, the global temperature target under the Paris Agreement for addressing the climate change problem, represents a significant challenge for all countries. Whereas large uncertainties were embedded in these inverse modeling studies, they indicated possibilities that CH4 emissions in East Asia were overestimated in the existing bottom-up emission inventories. We performed an intensive investigation to find the possible causes of overestimations of bottom-up emission inventories across China, which are implied in all top-down inverse model studies [4,5,6]. This was accomplished by examining the values of each parameter used in the existing sets of inventories together with a parameter variability uncertainty analysis, in order to assess the consistency of the results among sets of bottom-up inventories. Three sets of bottom-up CH4 emission inventories consisting of both national and global estimates were used, and these data have been widely referenced by studies on CH4 emissions and used in atmospheric models
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