Stochastic techniques for the design of robust and efficient emission trading mechanisms

The assessment of greenhouse gases (GHGs) emitted to and removed from the atmosphere is highon both political and scientific agendas internationally. As increasing international concern and cooper- ation aim at policy-oriented solutions to the climate change problem, several issues have begun to arise regarding verification and compliance under both proposed and legislated schemes meant to reduce the human-induced global climate impact. The issues of concern are rooted in the level of confidence with which national emission assessments can be performed, as well as the management of uncertainty and its role in developing informed policy. The approaches to addressing uncertainty that was discussed at the 2nd International Workshop on Uncertainty in Greenhouse Gas Inventories 1 attempt to improve national inventories or to provide a basis for the standardization of inventory estimates to enable comparison of emissions and emission changes across countries. Some authors use detailed uncertainty analyses to enforce the current structure of the emissions trading system while others attempt to internalize high levels of uncertainty by tailoring the emissions trading market rules. In all approaches, uncertainty analysis is regarded as a key component of national GHG inventory analyses. This presentation will provide an overview of the topics that are discussed among scientists at the aforementioned workshop to support robust decision making. These range from achieving and report- ing GHG emission inventories at global, national and sub-national scales; to accounting for uncertainty of emissions and emission changes across these scales; to bottom-up versus top-down emission analy- ses; to detecting and analyzing emission changes vis-a-vis their underlying uncertainties; to reconciling short-term emission commitments and long-term concentration targets; to dealing with verification, com- pliance and emissions trading; to communicating, negotiating and effectively using uncertainty

Taking advantage of the UNFCCC Kyoto Policy Process: What can we learn about learning?

Learning is difficult to anticipate when it happen instantaneously, e.g. in the context of innovations [2]. However, even if learning is anticipated to happen continuously, it is difficult to grasp, e.g. when it occurs outside well-defined lab conditions, because adequate monitoring had not been put in place. Our study is retrospective. It focuses on the emissions of greenhouse gases (GHGs)that had been reported by countries (Parties) under the Kyoto Protocol (KP) to the United Nations Framework on Climate Change (UNFCCC). Discussions range widely on (i) whether the KP is considered a failure [6] or a success [5] ; and (ii) whether international climate policy should transit from a centralized model of governance to a 'hybrid' decentralized approach that combines country-level mitigation pledges with common principles for accounting and monitoring [1] . Emissions of GHGs - in the following we refer to CO2 emissions from burning fossil fuels at country level, particularly in the case of Austria - provide a perfect means to study learning in a globally relevant context. We are not aware of a similar data treasure of global relevance. Our mode of grasping learning is novel, i.e. it may have been referred to in general but, to the best of our knowledge, had not been quantifed so far. (That is, we consider the KP a success story potentially and advocate for the hybrid decentralized approach.) Learning requires 'measuring' differences or deviations. Here we follow Marland et al. [3] who discuss this issue in the context of emissions accounting: 'Many of the countries and organizations that make estimates of CO2 emissions provide annual updates in which they add another year of data to the time series and revise the estimates for earlier years. Revisions may reflect revised or more complete energy data and ... more complete and detailed understanding of the emissions processes and emissions coefficients. In short, we expect revisions to reflect learning and a convergence toward more complete and accurate estimates.' The United Nations Framework Convention on Climate Change (UNFCCC)requires exactly this to be done. Each year UNFCCC signatory countries are obliged to provide an annual inventory of emissions (and removals) of specified GHGs from five sectors (energy; industrial processes and product use; agriculture; land use, land use change and forestry; and waste) and revisit the emissions (and removals) for all previous years, back to the country specified base years (or periods). These data are made available by means of a database [4]. The time series of revised emission estimates reflect learning, but they are 'contaminated' by (i) structural change (e.g., when a coal-power plant is substituted by a gas-power plant); (ii) changes in consumption; and, rare but possible, (iii)methodological changes in surveying emission related activities. De-trending time series of revised emission estimates allows this contamination to be isolated by country, for which we provide three approaches: (I) parametric approach employing polynomial trend; (II) non-parametric approach employing smoothing splines; and (III) approach in which the most recent estimate is used as trend. That is, after de-trending for each year we are left with a set of revisions that reflect 'pure'(uncontaminated) learning which, is expected to be independent of the year under consideration (i.e., identical from year to year). However, we are confronted with two non-negligible problems (P): (P.1) the problem of small numbers - the remaining differences in emissions are small (before and after de-trending); and (P.2) the problem of non-monotonic learning - our knowledge of emission-generating activities and emission factors may not become more accurate from revision to revision

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

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

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