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

Author

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  • Jolanta Jarnicka

    (Polish Academy of Sciences)

  • Piotr Żebrowski

    (International Institute for Applied Systems Analysis)

Abstract

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.

Suggested Citation

  • Jolanta Jarnicka & Piotr Żebrowski, 2019. "Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 1143-1168, August.
    • Handle: RePEc:spr:masfgc:v:24:y:2019:i:6:d:10.1007_s11027-019-09866-5
    DOI: 10.1007/s11027-019-09866-5
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    References listed on IDEAS

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    1. T. Ermolieva & Y. Ermoliev & M. Jonas & M. Obersteiner & F. Wagner & W. Winiwarter, 2014. "Uncertainty, cost-effectiveness and environmental safety of robust carbon trading: integrated approach," Climatic Change, Springer, vol. 124(3), pages 633-646, June.
    2. Gregg Marland & Khrystyna Hamal & Matthias Jonas, 2009. "How Uncertain Are Estimates of CO2 Emissions?," Journal of Industrial Ecology, Yale University, vol. 13(1), pages 4-7, February.
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    Cited by:

    1. Zakariya Kaneesamkandi & Ateekh Ur Rehman & Yusuf Siraj Usmani & Usama Umer, 2020. "Methodology for Assessment of Alternative Waste Treatment Strategies Using Entropy Weights," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    2. Yi Xiao & Keying Li & Yi Hu & Jin Xiao & Shouyang Wang, 2020. "Combining STRIPAT model and gated recurrent unit for forecasting nature gas consumption of China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1325-1343, October.
    3. Zhibo Zhao & Tian Yuan & Xunpeng Shi & Lingdi Zhao, 2020. "Heterogeneity in the relationship between carbon emission performance and urbanization: evidence from China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1363-1380, October.
    4. Matthias Jonas & Rostyslav Bun & Zbigniew Nahorski & Gregg Marland & Mykola Gusti & Olha Danylo, 2019. "Quantifying greenhouse gas emissions," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 839-852, August.

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