Advertisement

Processing National CO2 Inventory Emissions Data and their Total Uncertainty Estimates

Chapter

Abstract

The uncertainty of reported greenhouse gases emission inventories obtained by the aggregation of partial emissions from all sources and estimated to date for several countries is very high in comparison with the countries’ emissions limitation and reduction commitments under the Kyoto Protocol. Independent calculation of the estimates could confirm or question the undertainty estimates values obtained thus far. One of the aims of this paper is to propose statistical signal processing methods to enable calculation of the inventory variances. The annual reported emissions are used and temporal smoothness of the emissions curve is assumed. The methods considered are: a spline-function-smoothing procedure; a time-varying parameter model; and the geometric Brownian motion model. These are validated on historical observations of the CO2 emissions from fossil fuel combustion. The estimates of variances obtained are in a similar range to those obtained from national inventories using TIER1 or TIER2. Additionally, some regularities in the observed curves were noticed.

Keywords

modelling CO2 emissions nonparametric methods parametric methods geometric Brownian motion estimation of variance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brandes, L. J., Olivier, J. C. J., & van Oorschot, M. H. P. (2004). Validation, verification and uncertainty assessment for improving The Netherlands’ emission inventory. In Proceedings of the workshop ‘Uncertainty in Greenhouse Gas Inventories: Verification, Compliance & Trading.’ (pp. 19–33). Warsaw, Poland: SRI PAS & IIASA. http://www.ibspan.waw.pl/papers/Brandes.pdf.Google Scholar
  2. Debnath, L. (2002). Wavelet transforms and their applications. Switzerland: Birkhäuser.Google Scholar
  3. FCCC (1998). FCCC report of the conference of the parties on its third session, held at Kyoto from 1 to 11 December 1997. Addendum. Document FCCC/CP/1997/7/Add.1, United Nations Framework Convention on Climate Change (FCCC). http://www.unfccc.de/http://unfccc.int/index.html.Google Scholar
  4. FCCC (2001). Implementation of the Buenos Aires plan of action: adoption of the decisions giving effect to the Bonn agreements. Draft decisions forwarded for elaboration, completion and adoption. National systems, adjustments and guidelines under Articles 5, 7 and 8 of the Kyoto Protocol. Document FCCC/CP/2001/L.18, United Nations Framework Convention on Climate Change (FCCC). http://www.unfccc.de/.Google Scholar
  5. Gillenwater, M., Sussman, F., & Cohen, J. (2007). Practical applications of uncertainty analysis for national greenhouse gas inventories (this issue).Google Scholar
  6. Gu, C. (2002). Smoothing spline ANOVA models. Berlin Heidelberg New York: Springer.Google Scholar
  7. Gugele, B., Huttunen, K., & Ritter, M. (2005). Annual European community greenhouse gas inventory 1990–2003 and inventory report 2005. Technical report no. 4/2005. Copenhagen, Denmark: European Environment Agency. http://reports.eea.europa.eu/technical_report_2005_4/en.Google Scholar
  8. Gupta, J., Oltshoorn, X., & Rotenberg, E. (2003). The role of scientific uncertainty in compliance with the Kyoto Protocol to the climate change convention. Environmental Science and Policy, 6, 475–486.CrossRefGoogle Scholar
  9. Hudz, H. (2003). Verification times underlying the Kyoto Protocol: Consideration of risk. Interim report IP-02-066. Austria: International Institute for Applied Systems Analysis (IIASA).Google Scholar
  10. IPCC (1996). IPCC guidelines for national greenhouse gas inventories, vol. 1–3. London: IPCC.Google Scholar
  11. IPCC (2000). Good practice guidance and uncertainty management in national greenhouse gas inventories. In J. Penman, D. Kruger, I. Galbally, T. Hiraishi, B. Nyenzi, S. Emmanuel, L. Buendia, R. Hoppaus, T. Martinsen, J. Meijer, K. Miwa, & K. Tanabe (Eds.), Intergovernmental panel on climate change (IPCC) National gas inventories program. Technical support unit. Hayama, Kanagawa, Japan: Institute for Global Environmental Strategies.Google Scholar
  12. Jonas, M., & Nilsson, S. (2007). Prior to an economic treatment of emissions and their uncertainties under the Kyoto Protocol: scientific uncertainties that must be kept in mind (this issue).Google Scholar
  13. Kroeze, C., Vlasblom, J., Gupta, J., Boudri, C., & Blok, K. (2004). The power sector in China and India: greenhouse gas emission potential and scenarios for 1990–2020. Energy Policy, 32, 55–76.CrossRefGoogle Scholar
  14. Manne, A., & Richels, R. (2004). US rejection of the Kyoto Protocol: the impact on compliance cost and CO2 emissions. Energy Policy, 32, 447–454.CrossRefGoogle Scholar
  15. Marland, G., Boden, T. A., & Andres, R. J. (2006). Global, regional, and national fossil fuel CO2 emissions. In Trends: A compendium of data on global change, carbon dioxide information analysis center. OAK Ridge National Laboratory: U.S. Department of Energy. http://cdiac.esd.ornl.gov/trends/emis/em_cont.htm.Google Scholar
  16. Monni, S., Syri, S., Pipatti, R., & Savolainen, I. (2007). Extension of EU emissions trading scheme to other sectors and gases: consequences for uncertainty of total tradable amount (this issue).Google Scholar
  17. Nahorski, Z., Jeda, W., & Jonas, M. (2003). Coping with uncertainty in verification of the Kyoto obligations. In J. Studziński, L. Drelichowski, & O. Hryniewicz (Eds.), Zastosowania informatyki i analizy systemowej w zarzadzaniu, (pp. 305–317). Warsaw, Poland: IBS PAN.Google Scholar
  18. Nilsson, S., Jonas, M., Obersteiner, M., & Victor, D. (2001). Verification: the gorilla in the struggle to slow global warming. Forestry Chronicle, 77, 475–478.Google Scholar
  19. Nilsson, S., Jonas, M., & Obersteiner, M. (2002). COP 6: a healing shock. Climatic Change, 52, 25–28.CrossRefGoogle Scholar
  20. Riahi, K., Rubin, E. S., Taylor, M. R., Schrattenholzer, L., & Houndshell, D. (2004). Technological learning for carbon capture and sequestration technologies. Energy Policy, 26, 539–564.Google Scholar
  21. Rypdal, K., & Winiwarter, W. (2001). Uncertainty in greenhouse gas emission inventories — evaluation, comparability and implications. Environmental Science and Policy, 4, 104–116.Google Scholar
  22. Wahba, G. (1990). Spline models for observational data. Montpelier, VT, USA: Capital City Press.Google Scholar
  23. Walter, G. G. (1994). Wavelets and other orthogonal systems with applications. CRC Press.Google Scholar
  24. Winiwarter, W. (2007). National greenhouse gas inventories: understanding uncertainties versus potential for improving reliability (this issue).Google Scholar

Copyright information

© Springer Science + Business Media B.V. 2007

Authors and Affiliations

  1. 1.Systems Research InstitutePolish Academy of SciencesWarsawPoland

Personalised recommendations