Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time
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Bartels R (1982) The rank version of von Neumann’s ratio test for randomness. J Am Stat Assoc 77(377):40–46
Brandt S (2014) Data analysis: statistical and computational methods for scientists and engineers, 4th edn. Springer, New York
Bun A, Hamal K, Jonas M, Lesiv M (2010) Verification of compliance with GHG emission targets: annex B countries. Clim Chang 103(1–2):215–225. https://doi.org/10.1007/s10584-010-9906-6
Cowan G (1998) Statistical data analysis. Clarendon Press, Oxford
Cox DR, Stuart A (1995) Some quick tests for trend in location and dispersion. Biometrika 42(1/2):80–95
Ermolieva T, Ermoliev J, Jonas M, Obersteiner M, Wagner F, Winiwarter W (2014) Uncertainty, cost-effectiveness and environmental safety of robust carbon trading: integrated approach. Clim Chang 124(3):663–646. https://doi.org/10.1007/s10584-013-0824-2
Hamal K (2010) Reporting GHG emissions: change in uncertainty and its relevance for detection of emission changes. Interim Report IR-10-003. IIASA, Laxenburg
Hocking RR (2013) Methods and applications of linear models: regression and the analysis of variance. In: Wiley series in probability and statistics, 3rd edn. John Wiley & Sons, Inc., Hoboken
IPCC (2000) Good practice guidance and uncertainty management in national greenhouse inventories, http://www.ipccnggip.iges.or.jp/public/gp/english/ . Accessed 28 May 2019
IPCC (2006) Guidelines for national greenhouse gas inventories, http://www.ipcc-nggip.iges.or.jp/public/2006gl/ Accessed 13 Nov 2018
Jarnicka J, Nahorski Z (2015) A method for estimating time evolution of precision and accuracy of greenhouse gases inventories from revised reports. Proc. 4th Intl Workshop on Uncertainty in Atmospheric Emissions, Kraków, Poland, 2015, pp. 97–102, available at http://www.ibspan.waw.pl/unws2015/images/publications/4thWorkshopProceedings.pdf . Accessed 28 May 2019
Jarnicka J, Nahorski Z (2016) Estimation of temporal uncertainty structure of GHG inventories for selected EU countries. In: Ganzha M, Maciaszek L, Paprzycki M (eds) Proceedings of the 2016 FedCSiS Conference ACSIS, vol 8. IEEE, pp 459–465. https://doi.org/10.15439/2016F318
Jonas M, Gusti M, Jęda W, Nahorski Z, Nilsson S (2010) Comparison of preparatory signal analysis techniques for consideration in the (post-)Kyoto policy process. Clim Chang 103(1–2):175–213. https://doi.org/10.1007/s10584-010-9914-6
Marland G, Hamal K, Jonas M (2009) How uncertain are estimates of CO2 emissions? J Ind Ecol 13:4–7. https://doi.org/10.1111/j.1530-9290.2009.00108.x
Myers RH (1990) Classical and modern regression with applications, 2nd edn. Duxbury Press, Belmont
Nahorski Z, Jęda W (2007) Processing national CO2 inventory emission data and their total uncertainty estimates. Water Air Soil Pollut Focus 7:513–527. https://doi.org/10.1007/s11267-006-9114-6
Ryan TP (2008) Modern regression methods, 2nd edn. Wiley Series in Probability and Statistics, John Wiley & Sons, New York
Soong TT (2004) Fundamentals of probability and statistics for engineers. John Wiley & Sons, New York
Żebrowski P, Jonas M, Rovenskaya E (2015) Assessing the improvement of greenhouse gases inventories: can we capture diagnostic learning? Proc. 4th Intl Workshop on Uncertainty in Atmospheric Emissions, Kraków, Poland, 2015, pp. 90–96, available at: http://www.ibspan.waw.pl/unws2015/images/publications/4thWorkshopProceedings.pdf . Accessed 28 May 2019