Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time

16123 9 88601 2 253936 document 88601 Jarnicka-Żebrowski2019_Article_LearningInGreenhouseGasEmissio.pdf application/pdf 6dcca600f988b90b120ccb67b2324011 MD5 2512232 2019-10-25 12:31:57 https://pure.iiasa.ac.at/id/eprint/16123/1/Jarnicka-%C5%BBebrowski2019_Article_LearningInGreenhouseGasEmissio.pdf 16123 1 1 application/pdf text en public cc_by_4

Jarnicka-Żebrowski2019_Article_LearningInGreenhouseGasEmissio.pdf

published 88603 2 253953 document 88603 lightbox.jpg image/png aa820a4fa0f3405d821cecfe25fe6065 MD5 39769 2019-10-25 12:42:15 https://pure.iiasa.ac.at/id/eprint/16123/2/lightbox.jpg 16123 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/88601 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/88601 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/88601 archive 2 disk0/00/01/61/23 2019-10-25 12:34:47 2021-08-27 17:32:14 2019-10-25 12:34:47 article show Jarnicka J. Żebrowski P. 2086 0000-0001-5283-8049 Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time pub prog_asa Uncertainty; Inaccuracy; Imprecision; GHG emission inventory; Learning; Regression model This paper addresses the problem of learning in greenhouse gas (GHG) emission inventories understood as reductions in uncertainty, i.e., inaccuracy and/or imprecision, over time. We analyze the National Inventory Reports (NIRs) submitted annually to the United Nations Framework Convention on Climate Change. Each NIR contains data on the GHG emissions in a given country for a given year as well as revisions of past years’ estimates. We arrange the revisions, i.e., estimates of historical emissions published in consecutive NIRs into a table, so that each column contains revised estimates of emissions for the same year, reflecting different realizations of uncertainty. We propose two variants of a two-step procedure to investigate the changes of uncertainty over time. In step 1, we assess changes in inaccuracy, which we consider constant within each revision, by either detrending the revisions using the smoothing spline fitted to the most recent revision (method 1) or by taking differences between the most recent revision and the previous ones (method 2). Step 2 estimates the imprecision by analyzing the columns of the data table. We assess learning by detecting and modeling a decreasing trend in inaccuracy and/or imprecision. We analyze carbon dioxide (CO2) emission inventories for the European Union (EU-15) as a whole and its individual member countries. Our findings indicate that although there is still room for improvement, continued efforts to improve accounting methodology lead to a reduction of uncertainty of emission estimates reported in NIRs, which is of key importance for monitoring the realization of countries’ emission reduction commitments. 2019 10.1007/s11027-019-09866-5 343 public Mitigation and Adaptation Strategies for Global Change 24 6 1143-1168 TRUE 1381-2386 FALSE info:eu-repo/semantics/article