Greenhouse Gas Inventories

The assessment of greenhouse gases emitted to and removed from the atmosphere is high on the international political and scientific agendas. Growing international concern and cooperation regarding the climate change problem have increased the need for policy-oriented solutions to the issue of uncertainty in, and related to, inventories of greenhouse gas (GHG) emissions. The approaches to addressing uncertainty discussed in this Special Issue reflect attempts to improve national inventories, not only for their own sake but also from a wider, systems analytical perspective—a perspective that seeks to strengthen the usefulness of national inventories under a compliance and/or global monitoring and reporting framework. These approaches demonstrate the benefits of including inventory uncertainty in policy analyses. The authors of the contributed papers show that considering uncertainty helps avoid situations that can, for example, create a false sense of certainty or lead to invalid views of subsystems. This may eventually prevent related errors from showing up in analyses. However, considering uncertainty does not come for free. Proper treatment of uncertainty is costly and demanding because it forces us to make the step from “simple to complex” and only then to discuss potential simplifications. Finally, comprehensive treatment of uncertainty does not offer policymakers quick and easy solutions. The authors of the papers in this Special Issue do, however, agree that uncertainty analysis must be a key component of national GHG inventory analysis. Uncertainty analysis helps to provide a greater understanding and better science helps us to reduce and deal with uncertainty. By recognizing the importance of identifying and quantifying uncertainties, great strides can be made in ongoing discussions regarding GHG inventories and accounting for climate change. The 17 papers in this Special Issue deal with many aspects of analyzing and dealing with uncertainty in emissions estimates.

An uncertainty assessment of the Austrian greenhouse gas inventory provided the basis for this analysis. We isolated the factors that were responsible for the uncertainty observed, and compared our results with those of other countries. Uncertainties of input parameters were used to derive the uncertainty of the emission estimate. Resulting uncertainty using a Monte Carlo approach was 5.2% for the emission levels of 2005 and 2.4 percentage points for the 1990–2005 emission trend. Systematic uncertainty was not assessed. This result is in the range expected from previous experience in Austria and other countries. The determining factor for the emission level uncertainty (not the trend uncertainty) is the uncertainty associated with soil nitrous oxide N₂O emissions. Uncertainty of the soil N₂O release rate is huge, and there is no agreement even on the magnitude of the uncertainty when country comparisons are made. In other words, reporting and use of N₂O release uncertainty are also different between countries; this is important, as this single factor fully determines a country’s national greenhouse gas inventory uncertainty. Inter-country comparisons of emission uncertainty are thus unable to reveal much about a country’s inventory quality. For Austria, we also compared the results of the Monte Carlo approach to those obtained from a simpler error propagation approach, and find the latter to systematically provide lower uncertainty. The difference can be explained by the ability of the Monte Carlo approach to account for statistical dependency of input parameters, again regarding soil N₂O emissions. This is in contrast to the results of other countries, which focus less on statistical dependency when performing Monte Carlo analysis. In addition, the error propagation results depend on treatment of skewed probability distributions, which need to be translated into normal distributions. The result indicates that more attention needs to be given to identifying statistically dependent input data in uncertainty assessment.

Results of research and practical experience confirm that stabilization of GHG concentrations will require a tremendous effort. One of the sectors identified as a significant source of methane (CH₄) emissions are solid waste disposal sites (SWDS). Landfills are the key source of CH₄ emissions in the emissions inventory of Slovakia, and the actual emission factors are estimated with a high uncertainty level. The calculation of emission uncertainty of the landfills using the more sophisticated Tier 2 Monte Carlo method is evaluated in this article. The software package that works with the probabilistic distributions and their combination was developed with this purpose in mind. The results, sensitivity analysis, and computational methodology of the CH₄ emissions from SWDS are presented in this paper.

The Greenhouse Gas Inventory for the United Kingdom currently uses a simple carbon-flow model, CFLOW, to calculate the emissions and removals associated with forest planting since 1920. Here, we aim to determine whether a more complex process-based model, the BASic FORest (BASFOR) simulator, could be used instead of CFLOW. The use of a more complex approach allows spatial heterogeneity in soils and weather to be accounted for, but places extra demands on uncertainty quantification. We show how Bayesian methods can be used to address this problem.

We provide a review description of atmospheric inversion methods for the determination of fluxes of long-lived trace gases based on measurements of atmospheric concentration. Emphasis is given to technical aspects of inversion settings, which are crucial to inter-compare and understand inversion results. We briefly sketch the formalism used in such methods, then provide a summary of major currents in research and contemporary problems. Most attention is given to carbon dioxide (CO₂) which poses the threat of future climate change. Therefore, there is keen interest in better understanding where and when CO₂ emitted by the combustion of fossil fuels is reabsorbed by land ecosystems and oceans. Using the information contained in concentration fields observed from ground-based networks and from upcoming satellite observations in order to constrain the geographic distribution of surface fluxes is an inverse problem; it consists of finding a set of fluxes that optimally matches the observations available. We review the application of inverse methods to quantify the distribution of the sources and sinks of CO₂ at the surface of the Earth based on global measurements of atmospheric concentration and three-dimensional models of atmospheric transport. We describe the use of top–down atmospheric inversion methods in terms of numerical transport modeling and atmospheric observation networks, and detail some of the currently important issues in assigning uncertainties.

Approximately half of human-induced carbon dioxide (CO₂) emissions are taken up by the land and ocean, and the rest stays in the atmosphere, increasing the global concentration and acting as a major greenhouse-gas (GHG) climate-forcing element. Although GHG mitigation is now in the political arena, the exact spatial distribution of the land sink is not well known. In this paper, an estimation of mean European net ecosystem exchange (NEE) carbon fluxes for the period 1998–2001 is performed with three mesoscale and two global transport models, based on the integration of atmospheric CO₂ measurements into the same Bayesian synthesis inverse approach. A special focus is given to sub-continental regions of Europe making use of newly available CO₂ concentration measurements in this region. Inverse flux estimates from the five transport models are compared with independent flux estimates from four ecosystem models. All inversions detect a strong annual carbon sink in the southwestern part of Europe and a source in the northeastern part. Such a dipole, although robust with respect to the network of stations used, remains uncertain and still to be confirmed with independent estimates. Comparison of the seasonal variations of the inversion-based net land biosphere fluxes (NEP) with the NEP predicted by the ecosystem models indicates a shift of the maximum uptake period, from June in the ecosystem models to July in the inversions. This study thus improves on the understanding of the carbon cycle at sub-continental scales over Europe, demonstrating that the methodology for understanding regional carbon cycle is advancing, which increases its relevance in terms of issues related to regional mitigation policies.

While remote sensing is able to provide spatially explicit datasets at regional to global scales, extensive application to date has been found only in the reporting and verification of ecosystem carbon fluxes under the Kyoto Protocol. One of the problems is that new remote sensing datasets can be used only with models or data assimilation schemes adapted to include a data input interface dedicated to the type and format of these remote sensing datasets. In this study, soil water index data (SWI), derived from the ERS scatterometer (10-daily time period with a spatial resolution of 50 km), are integrated into the ecosystem carbon balance model C-Fix to assess 10-daily Net Ecosystem Productivity (NEP) patterns of Europe from the remote sensing perspective on an approximate 1-by-1 km² pixel scale using NDVI-AVHRR data. The modeling performance of NEP obtained with and without the assimilation of remotely sensed soil moisture data in the carbon flux model C-Fix is evaluated with EUROFLUX data. Results show a general decrease of the RRMSE of up to 11 with an average of 3.46. C-Fix is applied at the European scale to demonstrate the potential of this ecosystem carbon flux model, based on remote sensing inputs. More specifically, the strong impact of soil moisture on the European carbon balance in the context of the Kyoto Protocol (anthropogenic carbon emissions) is indicated at the country level. Results suggest that several European countries shift from being a carbon sink (i.e., NEP > 1) to being a carbon source (i.e., NEP < 0) whether or not short-term water availability (i.e., soil moisture) is considered in C-Fix NEP estimations.

In accordance with the concept that only full accounting of major greenhouse gases corresponds to the goals of the United Nations Framework Convention on Climate Change and its Kyoto Protocol, this paper considers uncertainties of regional (national) terrestrial biota Full Carbon Accounting (FCA), both those already achieved and those expected. We analyze uncertainties of major components of the FCA of forest ecosystems of a large boreal region in Siberia (~300 × 10⁶ ha). Some estimates for forests of other regions and Russia as a whole are used for comparison. The systems integration of available information sources and different types of models within the landscape-ecosystem approach are shown to have enabled an estimation of the major carbon fluxes (Net Primary Production, NPP, and heterotrophic respiration, HR) for the region for a single year at the level of 7–12% (confidential interval, CI, 0.9), Net Ecosystem Production (NEP) of 35–40%, and Net Biome Production (NBP) of 60–80%. The most uncertain aspect is the assessment of change in the soil carbon pool, which limits practical application of a pool-based approach. Regionalization of global process-based models, introduction of climatic data in empirical models, use of an appropriate time period for accounting and reporting, harmonization and multiple constraints of estimates obtained by different independent methods decrease the above uncertainties of NEP and NBP by about half. The results of this study support the idea that FCA of forest ecosystems is relevant in the post-Kyoto international negotiation process.

Our research addresses the need to close the gap between bottom-up and top-down accounting of net atmospheric carbon dioxide (CO₂) emissions. Russia is sufficiently large to be resolved in a bottom-up/top-down accounting exercise, as well as being a signatory state of the Kyoto Protocol. We resolve Russia’s atmospheric CO₂ balance (1988–1992) in terms of four major land-use/cover units and eight bioclimatic zones. On the basis of our results we conclude that the Intergovernmental Panel on Climate Change (IPCC) must revise its carbon balance for northern Asia. We find a less optimistic, although more realistic, bottom-up versus top-down match for northern Asia than the IPCC authors. Nonetheless, in spite of the larger uncertainties involved, our research shows that (1) there is indeed an added value in linking bottom-up and top-down carbon accounting because our dual-constrained regional carbon balance is incomparably more rigorous; and that (2) the need persists for more atmospheric measurements, including atmospheric inversion experiments, over Russia.

Our study is a preparatory exercise. We focus on the analysis of uncertainty in greenhouse gas emission inventories. Inventory uncertainty is monitored, but not regulated, under the Kyoto Protocol to the United Nations Framework Convention on Climate Change. Under the Convention, countries publish annual or periodic national inventories of greenhouse gas emissions and removals. Policymakers use these inventories to develop strategies and policies for emission reductions and to track the progress of these policies. However, greenhouse gas inventories contain uncertainty for a variety of reasons, and these uncertainties have important scientific and policy implications. For most countries, the emission changes agreed under the Protocol are of the same order of magnitude as the uncertainty that underlies their combined (carbon dioxide equivalent) emissions estimates. Here we apply and compare six available techniques to analyze the uncertainty in the emission changes that countries agreed to realize by the end of the Protocol’s first commitment period 2008–2012. Any such technique, if implemented, could “make or break” claims of compliance, especially in cases where countries claim fulfillment of their commitments to reduce or limit emissions. The techniques all perform differently and can thus have a different impact on the design and execution of emission control policies. A thorough comparison of the techniques has not yet been made but is needed when expanding the discussion on how to go about dealing with uncertainty under the Kyoto Protocol and its successor.

The focus of this study is on the preparatory detection of uncertain greenhouse gas (GHG) emission changes (also termed emission signals) under the Kyoto Protocol. Preparatory signal detection is a measure that should be taken prior to/during negotiation of the Protocol. It allows the ranking of countries under the Protocol according to their realized versus their agreed emission changes and in terms of both certainty and credibility. Controlling GHGs is affected by uncertainty and may be costly. Thus, knowing whether each nation is doing its part is in the public interest. At present, however, countries to the United Nations Framework Convention on Climate Change (UNFCCC) are obliged to include in the reporting of their annual inventories direct or alternative estimates of the uncertainty associated with these, consistent with the Intergovernmental Panel on Climate Change’s (IPCC) good practice guidance reports. As a consequence, inventory uncertainty is monitored, but not regulated, under the Kyoto Protocol. Although uncertainties are becoming increasingly available, monitored emissions and uncertainties are still dealt with separately. In our study we analyze estimates of both emission changes and uncertainties to advance the evaluation of countries and their performance under the Protocol. Our analysis allows supply and demand of emissions credits to be examined in consideration of uncertainty. For the purpose of our exercise, we make use of the Undershooting and Verification Time concept described by Jonas et al. (Clim Change doi:10.​1007/​s10584-010-9914-6, 2010).

Methodology and geo-information technology for spatial analysis of processes of greenhouse gas (GHG) emissions from mobile and stationary sources of the energy sector at the level of elementary plots are developed. The methodology, which takes into account the territorial specificity of point, line, and area sources of emissions, is based on official statistical data surveys. The spatial distribution of emissions and their structure for the main sectors of the energy sector in the territory of the Lviv region of Ukraine are analyzed. The relative uncertainties of emission estimates obtained are calculated using knowledge of the spatial location of emission sources and following the Tier 1 and Tier 2 approaches of IPCC methodologies. The sensitivity of total relative uncertainty to change of uncertainties in input data uncertainties is studied for the biggest emission point sources. A few scenarios of passing to the alternative energy generation are considered and respective structural changes in the structure of greenhouse gas emissions are analyzed. An influence of these structural changes on the total uncertainty of greenhouse gas inventory results is studied.

The greenhouse gas inventory of the European Communities and its estimation of the uncertainty is built from 15 individual and independent greenhouse gas inventories. This presents a particular challenge and is possible only if homogeneous information is available for all member states and if a proper evaluation of correlation between member states is performed. To this end, we present a methodology that estimates a quantitative measure for the aggregated Tier-level as well as the uncertainty for the main categories in the agriculture sector. In contrast to the approach suggested in the IPCC guidelines, which uses uncertainty estimates for activity data and emissions factors for each source category, the method presented uses quantitative information from individual parameters used in the inventory calculations, in combination with a well defined procedure to aggregate the information. Not surprisingly, N₂O emissions from agricultural soils are found to dominate the uncertainty. The results demonstrate the importance of correlation, if uncertainties are combined for the whole of Europe. The biggest challenge seems to be to conceptually harmonize the uncertainty estimates for the activity data (which tend to be underestimated) and emission factors (which tend to be overestimated).

In this paper we apply a linear regression with spatial random effect to model geographically distributed emission inventory data. The study presented is on N₂O emission assessments for municipalities of southern Norway and on activities related to emissions (proxy data). Taking advantage of the spatial dimension of the emission process, the method proposed is intended to improve inventory extension beyond its earlier coverage. For this, the proxy data are used. The conditional autoregressive model is used to account for spatial correlation between municipalities. Parameter estimation is based on the maximum likelihood method and the optimal predictor is developed. The results indicate that inclusion of a spatial dependence component lead to improvement in both representation of the observed data set and prediction.

The idea of market-based carbon emission trading and carbon taxes is gaining in popularity as a global climate change policy instrument. However, these mechanisms might not necessarily have a positive outcome unless their value reflects socioeconomic and environmental impacts and regulations. Moreover, the fact that they have various inherent exogenous and endogenous uncertainties raises serious concerns about their ability to reduce emissions in a cost-effective way. This paper aims to introduce a simple stochastic model that allows the robustness of economic mechanisms for emission reduction under multiple natural and human-related uncertainties to be analyzed. Unlike standard equilibrium state analysis, the model shows that the explicit introduction of uncertainties regarding emissions, abatement costs, and equilibrium states makes it almost impossible for existing market-based trading and carbon taxes to be environmentally safe and cost-effective. Here we propose a computerized multi-agent trading model. This can be viewed as a prototype to simulate an emission trading market that is regulated in a decentralized way. We argue that a market of this type is better equipped to deal with long-term emission reductions, their direct regulation, irreversibility, and “lock-in” equilibria.

In this paper we consider the buying/selling prices of carbon dioxide (CO₂) emission permits in trading models with uncertainty. Permission prices, although usually omitted from standard models, may significantly influence the trading market. We thus undertook to construct a more realistic trade model and to compare it with the standard one. To do this, we introduced several important changes to the standard model, namely, (1) a new optimized quality function; and (2) transactions with price negotiations between regions. We also enhanced the model using methods described in the literature to allow it to deal with reported emissions uncertainty. Additionally, we used an original method of simulating this kind of market based on a specialized evolutionary algorithm (EA).

Greenhouse gases emission inventories are computed with rather low precision. Moreover, their uncertainty distributions may be asymmetric. This should be accounted for in the compliance and trading rules. In this paper we model the uncertainty of inventories as intervals or using fuzzy numbers. The latter allows us to better shape the uncertainty distributions. The compliance and emission trading rules obtained generalize the results for the symmetric uncertainty distributions that were considered in the earlier papers by the present authors (Nahorski et al., Water Air & Soil Pollution. Focus 7(4–5):539–558, 2007; Nahorski and Horabik, 2007, J Energy Eng 134(2):47–52, 2008). However, unlike in the symmetric distribution, in the asymmetric fuzzy case it is necessary to apply approximations because of nonlinearities in the formulas. The final conclusion is that the interval uncertainty rules can be applied, but with a much higher substitutional noncompliance risk, which is a parameter of the rules.

Interactive resource planning is an increasingly important aspect of emission trading markets. The conferences of Rio de Janeiro, 1992, and Kyoto, 1997, originally focusing on environmental protection at both macro- and micro-economic levels, called for new economic instruments of this kind. An important economic tool in this area is Joint Implementation (JI), defined in Article 6 of the Kyoto Protocol. Sustainable development can be guaranteed only if JI is embedded in optimal energy management. In this contribution we describe and evaluate one international procedure within uncertain markets which helps to establish optimal energy management and interactive resource planning processes within uncertain emission trading markets.