Influence of Structural Change in GHG Emissions on Total Uncertainty

It is important to understand the change in uncertainty in reporting greenhouse gas (GHG) emissions to improve the communication of uncertainty and to facilitate the setting of emission targets. Uncertainty in GHG emissions varies over time due to the effects of learning, as well as structural change. This report provides examples of change in uncertainty due to structural change in GHG emissions considering EUs "20-20-20" targets. We estimate uncertainty for the year 2020 for various scenarios of energy pathways assuming today's knowledge. We apply an emissions-change-uncertainty analysis technique (called Und&VT) developed in IIASA to calculate modified emission targets for the EU

Preparatory Signal Detection for the EU-27 Member States Under EU Burden Sharing - Advanced Monitoring Including Uncertainty (1990-2007)

This study follows up IIASA Interim Report IR-04-024 (Jonas et al., 2004), which addresses the preparatory detection of uncertain greenhouse gas (GHG) emission changes (also termed emission signals) under the Kyoto Protocol. The question probed was how well do we need to know net emissions if we want to detect a specified emission signal after a given time? The authors used the Protocol's Annex B countries as net emitters and referred to all Kyoto GHGs (CO2, CH4, N2O, HFCs, PFCs, and SF6) excluding CO2 emissions/removals due to land-use change and forestry (LUCF). They motivated the application of preparatory signal detection in the context of the Kyoto Protocol as a necessary measure that should have been taken prior to/in negotiating the Protocol. The authors argued that uncertainties are already monitored and are increasingly made available but that monitored emissions and uncertainties are still dealt with in isolation. A connection between emission and uncertainty estimates for the purpose of an advanced country evaluation has not yet been established. The authors developed four preparatory signal analysis techniques and applied these to the Annex B countries under the Kyoto Protocol. The frame of reference for preparatory signal detection is that Annex B countries comply with their agreed emission targets in 2008-2012. The emissions path between base year and commitment year/period is generally assumed to be a straight line, and emissions prior to the base year are not taken into consideration. An in-depth quantitative comparison of the four, plus two additional, preparatory signal analysis techniques has been prepared by Jonas et al. (2010). This study applies the strictest of these techniques, the combined undershooting and verification time (Und&VT) concept to advance the monitoring of the GHG emissions reported by the 27 Member States of the European Union (EU). In contrast to the study by Jonas et al. (2004), the Member States' agreed emission targets under EU burden sharing in compliance with the Kyoto Protocol are taken into account, however, still assuming that only domestic measures will be used (i.e., excluding Kyoto mechanisms). The Und&VT concept is applied in a standard mode, i.e., with reference to the Member States' agreed emission targets in 2008-2012, and in a new mode, i.e., with reference to linear path emission targets between base year and commitment year. Here, the intermediate year of reference is 2007. To advance the reporting of the EU, uncertainty and its consequences are taken into consideration, i.e., (i) the risk that a Member State's true emissions in the commitment year/period are above its true emission limitation or reduction commitment (true emission target); and (ii) the detectability of the Member State's agreed emission target. This risk can be grasped and quantified although true emissions are unknown by definition. Undershooting the agreed target or the compatible but detectable target can decrease this risk. The Member States' undershooting options and challenges as of 2007 are contrasted with their actual emission situation in that year, which is captured by the distance-to-target-path indicator (DTPI; formerly: distance-to-target indicator) initially introduced by the European Environment Agency. This indicator measures by how much the emissions of a Member State deviate from its linear emissions path between base year and target year. In 2007, fourteen EU-27 Member States exhibit a negative DTPI and thus appear as potential sellers: Belgium, Bulgaria, Czech Republic, Estonia, France, Germany, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Sweden, and the United Kingdom. However, expecting that all of the EU Member States will eventually exhibit relative uncertainties in the range of 5-10% and above rather than below (excluding LUCF and Kyoto mechanisms), the Member States require considerable undershooting of their EU-compatible but detectable targets if one wants to keep the said risk low that the Member States' true emissions in the commitment year/period fall above their true emission targets. As of 2007, these conditions can only be met by ten (nine new and one old) Member States (ranked in terms of credibility): Latvia, Lithuania, Estonia, Romania, Bulgaria, Slovakia, Hungary, Poland, the Czech Republic and the United Kingdom; while four Member States, Germany, Belgium, Sweden and France, can only act as potential sellers with a higher risk. The other EU-27 Member States do not meet their linear path (base year-commitment year) undershooting targets as of 2007 (i.e., they overshoot their intermediate targets), or do not have Kyoto targets at all (Cyprus and Malta). The relative uncertainty, with which countries report their emissions, matters. For instance, with relative uncertainty increasing from 5 to 10%, the 2008/12 emission reduction of the EU-15 as a whole (which has jointly approved, as a Party, an 8% emission reduction under the Kyoto Protocol) switches from detectable to non-detectable, indicating that the negotiations for the Kyoto Protocol were imprudent because they did not take uncertainty and its consequences into account. It is anticipated that the evaluation of emission signals in terms of risk and detectability will become standard practice and that these two qualifiers will be accounted for in pricing GHG emission permits

COMPARATIVE PSYCHOMETRIC ANALYSIS OF COGNITIVE FUNCTIONS IN PATIENTS WITH HYPERTENSIVE DISEASE AND HYPOTHYROIDISM

Aim: The aim of the study was to determine whether certain cognitive domains exist in the assessment of cognitive functions in HD patients, patients with hypothyroidism and HD patients with concomitant hypothyroidism. Material and methods: The patients were divided into 3 groups according to nosology: Group I – 21 patients with hypertensive disease (HD); Group II – 18 patients with hypothyroidism, Group III – 19 hypertensive patients with concomitant hypothyroidism. Results: It was revealed that patients with HD had a decrease in memory according to the test proposed by A.R. Luria for learning 10 words, (p<0.05), as well as Digit span from Mattisse scale, (p<0.05). In patients with hypothyroidism, a short span of attention was revealed, according to the method of “Selectivity of attention” (G. Munsterberg test), (p<0.05). The analysis of the results showed that considering the interaction of factors (HD and hypothyroidism), the most affected cognitive domains are memory, executive functions and optical-spatial functions, respectively, (p<0.05). Conclusions: To diagnose CI in patients with HD who have problems with the domain of cognitive function memory, it is advisable to use a test for learning 10 words according to the method proposed by A.R. Luria and Digit span from Mattisse scale. In patients with hypothyroidism, attention and executive functions should be determined using the Schulte Tables and the “Selectivity of Attention” method (G. Munsterberg test). With the combined pathology, HD patients with a concomitant hypothyroidism should use Schulte Tables, test for learning 10 words by A.R. Luria and Clock Drawing Test

Geoinformation Technologies and Spatial Analysis of GHG Emissions in Polish Regions Bordering Ukraine

The specificity of territorial distribution of the GHG emission sources has been analyzed for polish regions bordering Ukraine. Mathematical models and geoinformation technology for spatial analysis of GHG emissions in the Energy sector that consider the territorial distribution of GHG emission sources and the structure of statistical data for Polish regions Lublin and Subcapathian are developed. The results of spatial analysis for the Lublin and Subcapathian voivodeships are presented.Проаналізовано специфіку територіального розміщення джерел емісії парникових газів в польських регіонах, що межують з Україною. Розроблено математичні моделі емісії парникових газів в енергетичному секторі з врахуванням структури статистичної інформації та відповідні геоінформаційні технології для здійснення просторової інвентаризації в польських воєводствах: Люблінському та Підкарпатському. Представлено результати просторового аналізу для цих двох воєводств.Проанализирована специфика территориального размещения источников эмиссии парниковых газов в польских регионах, граничащих с Украиной. Разработаны математические модели эмиссии парниковых газов в энергетическом секторе с учетом структуры статистической информации и соответствующие геоинформационные технологии для осуществления пространственной инвентаризации в польских воеводствах: Люблинском и Подкарпатском. Представлены результаты пространственного анализа для этих двух воеводств

Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line

Geoinformation technologies and methods of spatial analysis of emissions in the border regions have been developed and GIS based software has been created for estimating mass of carbon dioxide (CO2) emissions that goes through border line. Described mathematical models of processes of CO2 emissions in the energy sector in the border regions take into account the meteorological data. Spatial analysis of carbon dioxide transport processes has been done for Ukrainian – Polish border zone in consideration with wind rose.Описаны геоинформационные технологии и методы пространственного анализа эмиссий парниковых газов в приграничных регионах и создано программное обеспечение для численного моделирования процессов переноса диоксида углерода через границу. Предложенные математические модели процессов эмиссии углекислого газа в энергетическом секторе западных регионов Украины для вычисления перемещения атмосферных масс учитывают метеорологические условия, а именно – розу ветров. Пространственный анализ эмиссий углекислого газа был сделан для украинско-польской пограничной полосы.Описано геоінформаційні технології та методи просторового аналізу емісій парникових газів в прикордонних регіонах та створено програмний засіб для числового моделювання процесів переносу діоксиду вуглецю через лінію кордону. Запропоновані математичні моделі процесів емісії вуглекислого газу в енергетичному секторі західних регіонів України для обчислення переміщення атмосферних мас враховують метеорологічні умови, а саме – розу вітрів. Просторовий аналіз емісій вуглекислого газу зроблено для українсько-польської прикордонної смуги

Copernicus Global Land Cover Layers—Collection 2

In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation

A spatial assessment of the forest carbon budget for Ukraine

The spatial representation of forest cover and forest parameters is a prerequisite for undertaking a systems approach to the full and verified carbon accounting of forest ecosystems over large areas. This study focuses on Ukraine, which contains a diversity of bioclimatic conditions and natural landscapes found across Europe. Ukraine has a high potential to sequester carbon dioxide through afforestation and proper forest management. This paper presents a new 2010 forest map for Ukraine at a 60 m resolution with an accuracy of 91.6 ± 0.8% (CI 0.95), which is then applied to the calculation of the carbon budget. The forest cover map and spatially distributed forest parameters were developed through the integration of remote sensing data, forest statistics, and data collected using the Geo-Wiki application, which involves visual interpretation of very high-resolution satellite imagery. The use of this map in combination with the mapping of other forest parameters had led to a decrease in the uncertainty of the forest carbon budget for Ukraine. The application of both stock-based and flux-based methods shows that Ukrainian forests have served as a net carbon sink, absorbing 11.4 ± 1.7 Tg C year−1 in 2010, which is around 25% less than the official values reported to the United Nations Framework Convention on Climate Change

Comment on “The extent of forest in dryland biomes”

Bastin et al. (Reports, 12 May 2017, p. 635) claim to have discovered 467 million hectares of new dryland forest. We would argue that these additional areas are not completely “new” and that some have been reported before. A second shortcoming is that not all sources of uncertainty are considered; the uncertainty could be much higher than the reported value of 3.5%

Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map

Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART). We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs