On the Costs of Reducing GHG Emissions and its Underlying Uncertainties in the Context of Carbon Trading

This paper considers the problem of trading uncertain emissions under the Kyoto Protocol. We analyze a market structure that encourages the reduction of inventory uncertainty, although this option is not explicitly mentioned in the Protocol. According to the setting, parties to the Protocol are allowed to meet their targets by reducing emissions, buying permits, or reducing relative uncertainty. The goal of the paper is to account for the dependence in reductions of both emissions and uncertainty. Although formally a carbon emissions market may be restrained from the convergence to its least cost solution, a numerical experiment shows that it reaches equilibrium on its own. The necessary conditions for cost-effective solutions have been derived for the case of cost functions modeled with quadratic functions

Airflow Resistance of Wheat Bedding as Influenced by the Filling Method

A study was conducted to estimate the degree of variability of the airflow resistance in wheat caused by the filling method, compaction of the sample, and airflow direction. Two types of grain chambers were used: a cylindrical column 0.95 m high and 0.196 m in diameter, and a cubical box of 0.35 m side. All factors examined were found to influence considerably the airflow resistance. Gravitational axial filling of the grain column from three heights (0.0, 0.95 and 1.8 m) resulted in the pressure drops of 1.0, 1.3, and 1.5 kPa at the airflow velocity of 0.3 m/s. Consolidation of axially filled samples by vibration resulted in a maximum 2.2 times increase in airflow resistance. The tests with cubical sample showed that in axially filled samples the pressure drop in vertical direction was maximum 1.5 times higher than in horizontal directions. In the case of asymmetrically filled samples, the pressure drop at the airflow velocity of 0.3 m/s in vertical direction Z was found to be 1.3 of that in horizontal direction X and 1.95 times higher than with horizontal direction Y, perpendicular to X. Variations in airflow resistance in values comparable to that found in the present project may be expected in practice

Preparatory Signal Detection for Annex I Countries under the Kyoto Protocol - A Lesson for the Post-Kyoto Policy Process

In our study we address the detection of uncertain GHG emission changes (also termed emission signals) under the Kyoto Protocol. The question to be probed is "how well do we need to know net emissions if we want to detect a specified emission signal after a given time?" No restrictions exist as to what concerns the net emitter. However, for data availability reasons and because of the excellent possibilityof inter-country comparisons, the Protocols Annex I countries are used as net emitters. Another restriction concerns the exclusion of emissions/removals due to land-use change and forestry (LUCF) as the reporting of their uncertainties is only soon becoming standard practice. Our study centers on the preparatory detection of emission signals, which should have been applied prior to/in negotiating the Kyoto Protocol. Rigorous preparatory signal detection has not yet been carried out, neither prior to the negotiations of the Kyoto Protocol nor afterwards. The starting point for preparatory signal detection is that the Annex I countries under the Kyoto Protocol comply with with their emission limitation or reduction commitments. Uncertainties are already monitored. However, 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. We apply four preparatory signal detection techniques. These are the Critical Relative Uncertainty (CRU) concept, the Verification Time (VT) concept, the Undershooting (Und) concept, and the Undershooting and Verification Time (Und&VT) concepts combined. All of the techniques identify an emission signal and consider the total uncertainty that underlies the countries emissions, either in the commitment year/period or in both the base year and the commitment year/period. The techniques follow a hierarchical order in terms of complexity permitting to explore their robustness. The most complex technique, the Und&VT concept, considers in addition to uncertainty (1) the dynamics of the signal itself permitting to ask for the verification time, the time when the signal is outstripping total uncertainty; (2) the risk (probability) that the countries true emissions in the commitment year/period are above (below) their true emission limitation or reduction commitments; (3) the undershooting that is needed to reduce this risk to a prescribed level; and (4) a corrected undershooting/risk that accounts for detectability, i.e., that fulfills a given commitment period or, equivalently, its maximal allowable verification time. Our preparatory signal detection exercise exemplifies that the negotiations for the Kyoto Protocol were imprudent because they did not consider the consequences of uncertainty, i.e., (1) the risk that the countries true emissions in the commitment year/period are above their true emission limitation or reduction commitments; and (2) detectable targets. Expecting that Annex I countries exhibit relative uncertainties in the range of 5-10 % and above rather than below, excluding emissions/removals due to LUCF, both the CRU concept and VT concept show that it is virtually impossible for most of the Annex I countries to meet the condition that their overall relative uncertainties are smaller than their CRUs or, equivalently, that their VTs are smaller than their maximal allowable verification times. Moreover, the Und and the Und&VT concepts show that the countries committed emission limitation or reduction targets - or their Kyoto-compatible but detectable targets, respectively - require considerable undershooting if one wants to keep the risk low that the countries true emissios in the commitment year/period are above the true equivalents of these targets. The amount by which a country undershoots its Kyoto target or its Kyoto-compatible but detectable target can be traded. Towards installing a successful trading regime, countries may want to also price the risk associated with this amount We anticipate that the evaluation of the countries emission signals in terms of risk and detectability will become reality. The Intergovernmental Panel on Climate Change (IPCC) also suggests assessing total uncertainties. However, a connection between monitored emission and uncertainty estimates for the purpose of an advanced country evaluation, which considers the aforementioned risk as well as detectable targets, has not yet been established. The IPCC has to take up this challenge

Preparatory Signal Detection for the EU Member States under EU Burden Sharing - Advanced Monitoring Including Uncertainty (1990-2002)

This study follows up the authors' collaborative 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 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 use the Protocol's Annex I countries as net emitters and excluded the emission/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 emissions and uncertainty estimates for the purpose of an advanced country evaluation has not yet been established. The authors develop four preparatory signal detection techniques and applied these to the Annex I countries under the Kyoto Protocol. The frame of reference for preparatory signal detection is that Annex I countries comply with their committed emission targets in 2008-2012. In our study we apply one of these techniques, the combined undershooting and verification time (Und&VT) concept to advance the monitoring of the GHG emissions reported by the Member States of the European Union (EU). In contrast to the earlier study, we focus on the Member States' committed emission targets under the EU burden sharing in compliance with the Kyoto Protocol. We apply the Und&VT concept in the standard mode, i.e., with reference to the Member States committed emission targets in 2008-2012, and in a new mode, i.e., with reference to linear path emission targets between the base year and the commitment year (here for 2001). To advance the reporting of the EU we take uncertainty and its consequences 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; and (ii) the detectability of its target. Undershooting the committed EU target or EU-compatible, but detectable, target can decrease this risk. We contrast the Member States' linear path undershooting targets for the year 2001 with their actual emission situation in that year, for which we use the distance-to-target indicator (DTI) introduced by the European Environment Agency. In 2001 only four countries exhibit a negative DTI and thus appear as potential sellers: Germany, Luxembourg, Sweden and the United Kingdom. However, expecting that the EU Member States exhibit relative uncertainties in the range of 5-10% and above rather than below, excluding emissions/removals due to LUCF, the member states require considerable undershooting of their EU-compatible, but detectable, targets if one wants to keep the associated risk low. These conditions can only be met by the three Member States Germany, Luxembourg and the United Kingdom - or Luxembourg, Germany and the United Kingdom if ranked in terms of creditability. Within the 5-10% relative uncertainty class, Sweden can only act as potential high-risk seller. In contrast, with relative uncertainty increasing from 5 to 10%, the emission signal of the EU as a whole 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. We anticipate 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

МОДЕЛЮВАННЯ ТА ПРОСТОРОВИЙ АНАЛІЗ ПРОЦЕСІВ ЕМІСІЇ ПАРНИКОВИХ ГАЗІВ: ТВАРИННИЦТВО ПОЛЬЩІ

The main greenhouse gas emission sources in the Animal subsector in Poland, in particular enteric fermentation of animals, and decomposition, collection, storage and usage of animal manure are analyzed. Mathematical models of emission processes from these sources at the level of elementary objects of fixed size are presented. Using geoinformation system tools, the georeferenced database of statistical information about the number of livestock in Polish regions is formed. In the results of numerical experiments, the estimates of methane and nitrous oxide emissions by type of animals at the level of elementary areas 2 x 2 km in size are obtained. The spatial cadastre of emission are build and presented in the form of digital maps.Проаналізовано основні категорії джерел емісії парникових газів у тваринництві Польщі –кишкова ферментація тварин і розкладення, збір, зберігання та використання гною. Представлено розроблені математичні моделі емісійних процесів від цих джерел на рівні елементарних ділянок заданого розміру. Засобами геоінформаційної системи сформовано георозподілену базу вхідних даних на основі статистичних даних про поголів’я сільськогосподарських тварин у регіонахПольщі. В результаті числових експериментів отримано оцінки емісій метану та закису азоту за видами сільськогосподарських тварин на рівні елементарних ділянок 2 x 2 км. Побудовано просторові кадастри емісій та представлено їх у вигляді цифрових карт

Spatial GHG inventory in the agriculture sector and uncertainty analysis: A case study for Poland

Estimation of uncertainties is an important part of complete inventory of greenhouse gas (GHG) emissions. Information on uncertainty is intended not only to question the reliability of inventory estimates, but to assist in the identifying priority measures to improve the quality of future inventories. This article discusses bottom-up inventory from the agricultural sector in Poland. Accordingly to the developed geoinformation approach area-type sources of emission (arable lands, rural localities) were investigated. In implemented mathematical models for the estimation of GHG emissions from agricultural activity the statistical data on animal and crop production, as well as specific emission factors were used. Methods for the spatial inventory of GHG emissions from agricultural sources, taking into account the specifics of animal nutrition, are described. Monte-Carlo method was applied for a detailed estimation of uncertainty "from category to category," because uncertainties of input parameters (CH4 and N2O emission factors) are large and non-normally distributed (95% confidence interval). The land use map is used to calculate the territorial distribution of GHG emissions. The structure of total GHG emissions on different categories of animal sector and agricultural soils sector by type of GHG is presented and visualised as digital maps. Analysis of uncertainty of GHG inventory results were carried out for voivodeships. Results are presented as sets of numerical values of the bounds of confidence intervals for the main GHGs and at different levels of spatial disaggregation. The improving of knowledge on territories, where emissions took places, enables us to better inventory process and reduce the overall uncertainty

Conditionally autoregressive model for spatial disaggregation of activity data in GHG inventory: Application for agriculture sector in Poland

This report presents a novel approach for allocation of spatially correlated data, such as emission inventories, into finer spatial scales conditional on covariate information observable in a fine grid. Spatial dependence is modelled with the conditional autoregressive structure introduced into a linear model as a random effect. The maximum likelihood approach to inference is employed, and the optimal predictors are developed to assess missing values in a fine grid. The usefulness of the proposed technique is shown for agricultural sector of GHG inventory in Poland. An example of allocation of livestock data (a number of horses) from district to municipality level is analysed. The results indicate that the proposed method outperforms a naive and commonly used approach of proportional distribution

Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources

Greenhouse gas (GHG) inventories at national or provincial levels include the total emissions as well as the emissions for many categories of human activity, but there is a need for spatially explicit GHG emission inventories. Hence, the aim of this research was to outline a methodology for producing a high-resolution spatially explicit emission inventory, demonstrated for Poland. GHG emission sources were classified into point, line, and area types and then combined to calculate the total emissions. We created vector maps of all sources for all categories of economic activity covered by the IPCC guidelines, using official information about companies, the administrative maps, Corine Land Cover, and other available data. We created the algorithms for the disaggregation of these data to the level of elementary objects such as emission sources. The algorithms used depend on the categories of economic activity under investigation. We calculated the emissions of carbon, nitrogen sulfure and other GHG compounds (e.g., CO2, CH4, N2O, SO2, NMVOC) as well as total emissions in the CO2-equivalent. Gridded data were only created in the final stage to present the summarized emissions of very diverse sources from all categories. In our approach, information on the administrative assignment of corresponding emission sources is retained, which makes it possible to aggregate the final results to different administrative levels including municipalities, which is not possible using a traditional gridded emission approach. We demonstrate that any grid size can be chosen to match the aim of the spatial inventory, but not less than 100 m in this example, which corresponds to the coarsest resolution of the input datasets. We then considered the uncertainties in the statistical data, the calorific values, and the emission factors, with symmetric and asymmetric (lognormal) distributions. Using the Monte Carlo method, uncertainties, expressed using 95% confidence intervals, were estimated for high point-type emission sources, the provinces, and the subsectors. Such an approach is flexible, provided the data are available, and can be applied to other countries