Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time
Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time
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DOI：
10.1007/s11027-019-09866-5
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发表时间：
2019-08-01
影响因子：
4
通讯作者：
Zebrowski, Piotr
中科院分区：
文献类型：
--
作者：
Jarnicka, Jolanta;Zebrowski, Piotr
本文讨论了温室气体排放清单中的学习问题，即减少不确定性，不准确和/或不精确。我们分析了每年提交给联合国气候变化框架公约的国家清单报告（NIR）。每一份国家清单报告都载有某一国家某一年的温室气体排放量数据以及对过去几年估计数的修订。我们安排修改，即，将连续国家清单报告中公布的历史排放量估计数编入一个表格，以便每一栏都载有同一年的订正排放量估计数，反映对不确定性的不同认识。我们提出了两个变种的两步程序，调查随着时间的推移的不确定性的变化。在步骤1中，我们评估不准确度的变化，我们认为在每个修订中是恒定的，通过使用拟合到最新修订的平滑样条（方法1）或通过最近修订与先前修订之间的差异（方法2）来消除修订的趋势。步骤2通过分析数据表的列来估计不精确度。我们通过检测和建模不准确性和/或不精确性的下降趋势来评估学习。我们分析了欧盟（EU-15）作为一个整体及其各个成员国的二氧化碳（CO2）排放清单。我们的研究结果表明，尽管仍有改进的余地，但不断努力改进核算方法，减少了国家清单报告中报告的排放估计数的不确定性，这对监测各国减排承诺的实现至关重要。
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.