Estimates of carbon dioxide emissions based on incomplete condition information: a case study of liquefied natural gas in China

Recent calculations of carbon dioxide (CO₂) emissions have faced challenges because data consist of only partial information, which is called “incomplete information.” According to the emission factor method, energy consumption and CO₂ emission factors with incomplete information may lead to unmatched multiplication between themselves, which affects accuracy and increases uncertainties in emission results. To address a specific case of incomplete information that has not been fully explored, we studied the effects of incomplete condition information on the estimates of CO₂ emissions from liquefied natural gas (LNG) in China. Based on Chinese LNG sampling data, we obtained the specific-country CO₂ emission factor for LNG in China and calculated the corresponding CO₂ emissions. By applying hypothesis testing, regression analysis, variance analysis, or Monte Carlo (MC) simulations, the effects of incomplete information on the uncertainty of CO₂ emission calculations in three cases were analyzed. The results indicate that calorific values have more than a 9.8% impact on CO₂ emission factors and CO₂ emissions with incomplete sample information. Regarding incomplete statistical information, the impact of statistical temperature on CO₂ emissions exceeds 5.5%. Regarding incomplete sample and statistical information, sample and statistical temperatures can individually increase estimate biases by more than 5.2%. Significantly, the impacts of sample temperature and statistical temperature may offset each other. Therefore, the incomplete condition information is quite important and cannot be ignored in the estimation of CO₂ emissions from LNG and international fair comparison.