Compliance and emission trading rules for asymmetric emission uncertainty estimates

Abstract

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; 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.

Verification of compliance with GHG emission targets: annex B countries

“…Hence, if a country's reported emissions meet δ mod rather than δ KP , the risk is α that the country's true (but unknown) emissions exceed its true (but unknown) Kyoto target. Nahorski et al (2010) suggest using a risk of greater than 0.3, perhaps even as great as 0.4. Nevertheless, in our study we use 0.1 as a standard (if not otherwise stated) so as to analyze a greater range of possible values.…”

“…The present section deals with the outlook analysis of emission trading based on DTI data. We assume that inventory uncertainty, and its associated risk α, are explicitly accounted for in meeting compliance (see, e.g., Nahorski et al 2007Nahorski et al , 2010, and that these qualifiers are considered under an emission trading scheme. Here, we investigate the influence of uncertainty on the trading of emissions for a given risk α (e.g., 0.1).…”

A Sustainable Industry-Environment Model for the Identification of Urban Environmental Risk to Confront Air Pollution in Beijing, China

“…Above all, uncertainties and their corresponding complexities can increase the risk for policymakers to generate the desired decision alternatives [7]. Previously, many research works such as genetic algorithms, potential economic impact assessments, the asymmetric fuzzy method and the inexact stochastic programming model have been developed for handling inherent uncertainties in the decision process of an IEST issue, which can reduce the difficulties and risk levels of decision-making [8][9][10][11][12][13]. Among them, two-stage stochastic programming (TSP) is a useful method to deal with random problems through rectifying actions (i.e., recourse action with probabilistic event).…”

Uncertainties in greenhouse gases inventories – expanding our perspective

“…It is admitted that the emissions are uncertain and this knowledge affects the trading rules, as presented in earlier studies by Nahorski et al (Nahorski et al 2007) and Nahorski and Horabik (Nahorski and Horabik 2010). These rules lead to more uncertain emissions that are less expensive on the market.…”

“…More appropriate methods use percentiles and critical values, like a so-called undershooting technique which was discussed earlier, e.g. by Nahorski et al (Nahorski et al 2007) and Nahorski and Horabik (Nahorski and Horabik 2010). However, emissions are inventoried usually only once per year, and they are typically not random, so it is difficult to treat them as probabilistic variables.…”