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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, ; Nahorski and Horabik, , J Energy Eng 134(2):47-52, ). 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper we consider the buying/selling prices of carbon dioxide (CO) 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). [ABSTRACT FROM AUTHOR]

pproximately half of human-induced carbon dioxide (CO) 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 CO measurements into the same Bayesian synthesis inverse approach. A special focus is given to sub-continental regions of Europe making use of newly available CO 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. [ABSTRACT FROM AUTHOR]

Many European politicians argue that the EU should set tougher emission targets than what is required by the Kyoto protocol, and moreover, that emission trading with other countries outside EU should be limited so as to keep emission quota prices high. One of the arguments, frequently cited for such a policy, is the need for technological development. However, the literature on climate change and technological innovation does not unambiguously support the need for setting high emission taxes today. In this paper we investigate the relationship between emission taxes and technological change further by modeling innovation activity explicitly. In our model both the amount of R&D and the amount of carbon abatement are decided in a decentralized way by the market as a response to an emission tax. Moreover, we introduce several distinct failures in the market for new innovations, among others, insufficient patent protection and intertemporal knowledge spill-overs. Our findings suggest that governments should under some circumstances set a higher carbon tax today if we have technological change driven by R&D than if we have pure exogenous technological change. Based on numerical simulations these circumstances are (a) positive intertemporal knowledge spillovers and/or (b) weak patent protection. [ABSTRACT FROM AUTHOR]