Environmental Science and Pollution Research

, Volume 26, Issue 9, pp 8847–8861 | Cite as

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

  • Lingyue Li
  • Jing Yang
  • Yan Cao
  • Jinhu WuEmail author
Research Article


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.


CO2 emissions Incomplete information LNG China Statistics analysis Monte Carlo simulation 


Funding information

This work was supported by the Strategic Pilot Scientific & Technological Project of CAS (No. XDA05010305).


  1. Abrahams LS, Samaras C, Griffin WM, Matthews HS (2015) Life cycle greenhouse gas emissions from U.S. liquefied natural gas exports: implications for end uses. Environ Sci Technol 49:3237–3245CrossRefGoogle Scholar
  2. Anderson M, Salo K, Fridell E (2015) Particle- and gaseous emissions from an LNG powered ship. Environ Sci Technol 49:12568–12575CrossRefGoogle Scholar
  3. API (2015) Liquefied natural gas (LNG) operations consistent methodology for estimating greenhouse gas emissions. American Petroleum Institute, USAGoogle Scholar
  4. Arteconi A, Brandoni C, Evangelista D, Polonara F (2010) Life-cycle greenhouse gas analysis of LNG as a heavy vehicle fuel in Europe. Appl Energy 87:2005–2013CrossRefGoogle Scholar
  5. Bengtsson S, Andersson K, Fridell E (2011) A comparative life cycle assessment of marine fuels: liquefied natural gas and three other fossil fuels. P I Mech Eng M-J Eng 225:97–110Google Scholar
  6. Brandt AR, Sun Y, Vafi K (2015) Uncertainty in regional-average petroleum GHG intensities: countering information gaps with targeted data gathering. Environ Sci Technol 49:679–686CrossRefGoogle Scholar
  7. CSP (2015) China energy statistical yearbook 2015. China Statistics Press, Beijing (in Chinese)Google Scholar
  8. Grubb MJ (1989) On coefficients for determining greenhouse gas emissions from fossil fuel production and consumption. Energy and Environmental ProgrammeGoogle Scholar
  9. Hardisty PE, Clark TS, Hynes RG (2012) Life cycle greenhouse gas emissions from electricity generation: a comparative analysis of Australian energy sources. Energies 5:872–897CrossRefGoogle Scholar
  10. Hiete M, Berner U, Richter O (2001) Calculation of global carbon dioxide emissions: review of emission factors and a new approach taking fuel quality into consideration. Global Biogeochem Cy 15:169–181CrossRefGoogle Scholar
  11. IEA (2004) Energy statistics manual. International Energy Agency, FranceGoogle Scholar
  12. IPCC (1997) Revised 1996 IPCC guidelines for national greenhouse gas inventories. Intergovernmental Panel on Climate Change, Mexico CityGoogle Scholar
  13. IPCC (2000) Good practice guidance for land use, land-use change and forestry. Intergovernmental Panel on Climate Change, JapanGoogle Scholar
  14. IPCC (2006) 2006 IPCC guidelines for national greenhouse gas institute for inventories. Intergovernmental Panel on Climate Change, JapanGoogle Scholar
  15. IPIECA, API (2015) Addressing uncertainty in oil and natural gas industry greenhouse gas inventories. International Petroleum Industry Environmental Conservation Association; American Petroleum Institute, USAGoogle Scholar
  16. Jaramillo P, Griffin WM, Matthews HS (2007) Comparative life-cycle air emissions of coal, domestic natural gas, LNG, and SNG for electricity generation. Environ Sci Technol 41:6290–6296CrossRefGoogle Scholar
  17. Keeling CD (1973) Industrial production of carbon-dioxide from fossil fuels and limestone. Tellus 25:174–198CrossRefGoogle Scholar
  18. Kumar S, Kwon HT, Choi KH, Lim W, Cho JH, Tak K, Moon I (2011) LNG: an eco-friendly cryogenic fuel for sustainable development. Appl Energy 88:4264–4273CrossRefGoogle Scholar
  19. Liu Z (2016) National carbon emissions from the industry process: production of glass, soda ash, ammonia, calcium carbide and alumina. Appl Energy 166:239–244CrossRefGoogle Scholar
  20. Liu M, Meng J, Liu B (2014) Progress in the studies of carbon emission estimation. Trop Geogr 34:248–258 (in Chinese)Google Scholar
  21. Liu Z, Guan D, Wei W, Davis SJ, Ciais P, Bai J, Peng S, Zhang Q, Hubacek K, Marland G, Andres RJ, Crawford-Brown D, Lin J, Zhao H, Hong C, Boden TA, Feng K, Peters GP, Xi F, Liu J, Li Y, Zhao Y, Zeng N, He K (2015) Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524:335–338CrossRefGoogle Scholar
  22. Liu Z, Zheng B, Zhang Q (2018) New dynamics of energy use and CO2 emissions in China. arXiv preprint:1811.09475Google Scholar
  23. Marland G, Rotty RM (1984) Carbon dioxide emissions from fossil fuels: a procedure for estimation and results for 1950-1982. Tellus Ser B Chem Phys Meteorol 36:232–261CrossRefGoogle Scholar
  24. O'Donoughue PR, Heath GA, Dolan SL, Vorum M (2014) Life cycle greenhouse gas emissions of electricity generated from conventionally produced natural gas systematic review and harmonization. J Ind Ecol 18:125–144CrossRefGoogle Scholar
  25. Okamura T, Michinobu FB, Ishitani H (2007) Future forecast for life-cycle greenhouse gas emissions of LNG and city gas 13A. Appl Energy 84:1136–1149CrossRefGoogle Scholar
  26. Ou XM, Zhang XL, Zhang X, Zhang Q (2013) Life cycle GHG of NG-based fuel and electric vehicle in China. Energies 6:2644–2662CrossRefGoogle Scholar
  27. Pustišek A, Karasz M (2017) Natural gas: a commercial perspective. SpringerGoogle Scholar
  28. Ramirez A, de Keizer C, Van der Sluijs JP, Olivier J, Brandes L (2008) Monte Carlo analysis of uncertainties in the Netherlands greenhouse gas emission inventory for 1990-2004. Atmos Environ 42:8263–8272CrossRefGoogle Scholar
  29. Ritter K, Lev-On M, Shires T (2010) Understanding uncertainty in greenhouse gas emission estimates: technical considerations and statistical calculation methods. Paper presented at the 19th Annual International Emission Inventory Conference, SanAntonio, TexasGoogle Scholar
  30. Safaei A, Freire F, Henggeler Antunes C (2015) Life-cycle greenhouse gas assessment of Nigerian liquefied natural gas addressing uncertainty. Environ Sci Technol 49:3949–3957CrossRefGoogle Scholar
  31. Seo Y, Kim SM (2013) Estimation of greenhouse gas emissions from road traffic: a case study in Korea. Renew Sust Energ Rev 28:777–787CrossRefGoogle Scholar
  32. Shan Y, Guan D, Zheng H, Ou J, Li Y, Meng J, Mi Z, Liu Z, Zhang Q (2018) China CO2 emission accounts 1997-2015. Sci Data 5:170201CrossRefGoogle Scholar
  33. Shi WY, Su LJ, Song Y, Ma MG, Du S (2015) A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability. Ecol Evol 5:4480–4491CrossRefGoogle Scholar
  34. Steinmann ZJN, Venkatesh A, Hauck M, Schipper AM, Karuppiah R, Laurenzi IJ, Huijbregts MAJ (2014) How to address data gaps in life cycle inventories: a case study on estimating CO2 emissions from coal-fired electricity plants on a global scale. Environ Sci Technol 48:5282–5289CrossRefGoogle Scholar
  35. Tang M, Xia C (1998) Natural gas calculation of calorific values, density, relative density and wobbe index form composition. China National United Oil Corporation, Sichuan (in Chinese)Google Scholar
  36. Tong LI, Chang CW, Jin SE, Saminathan R (2012) Quantifying uncertainty of emission estimates in national greenhouse gas inventories using bootstrap confidence intervals. Atmos Environ 56:80–87CrossRefGoogle Scholar
  37. Tu X, Yang Y, Xu J (2013) Evaluation of difference between LNG and diesel heavy-duty commercial vehicle’s life cycle environmental emission. China Mech Eng 24:1525–1530 (in Chinese)Google Scholar
  38. Vafi K, Brandt AR (2014) Uncertainty of oil field GHG emissions resulting from information gaps: a Monte Carlo approach. Environ Sci Technol 48:10511–10518CrossRefGoogle Scholar
  39. Venkatesh A, Jaramillo P, Griffin WM, Matthews HS (2011) Uncertainty in life cycle greenhouse gas emissions from United States natural gas end-uses and its effects on policy. Environ Sci Technol 45:8182–8189CrossRefGoogle Scholar
  40. Verbeek RP, Kadijk G, Mensch P, van Wulffers CA, Beemt CBPJ, van den Fraga FS (2011) Environmental and economic aspects of using LNG as a fuel for shipping in the Netherlands. DutchGoogle Scholar
  41. Vidas H (2013) U.S. LNG exports impacts on energy markets and the economy. American petroleum institute, USAGoogle Scholar
  42. Wu QW (2014) Current status of supply and demand in LNG markets and LNG price trend in and outside China. Nat Gas Technol Econ 8:60–64 (in Chinese)Google Scholar
  43. Yang J, Wu JL, He T, Li LY, Han DZ, Wang ZQ, Wu JH (2016) Energy gases and related carbon emissions in China. Resour Conserv Recycl 113:140–148CrossRefGoogle Scholar
  44. Zhang G (2014) The LNG industry is in the ascendant, and all parties need to work together to promote. China Energy News. Accessed 24 September 2018 (in Chinese)
  45. Zhang YJ, Qin CK (2012) Analysis on reference condition for natural gas conversions including wobbe index and superior calorific value calculations. Gas Technol 7:13–17 (in Chinese)Google Scholar
  46. Zhao Y, Nielsen CP, McElroy MB (2012) China’s CO2 emissions estimated from the bottom up: recent trends, spatial distributions, and quantification of uncertainties. Atmos Environ 59:214–223CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdaoPeople’s Republic of China
  2. 2.School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.Institute for Combustion Science and Environmental Technology, Department of ChemistryWestern Kentucky UniversityBowling GreenUSA

Personalised recommendations