The social cost of greenhouse gases is important in many regulatory impact analyses. However, calculations of the social cost of greenhouse gases are highly complex and periodically revisited. We offer seven recommendations to improve current estimates. These include recommendations to use both country-level and global measures of the social cost of greenhouse gases, to use country-specific values for monetizing climate damages, to represent uncertainties by reporting distributions instead of using only central values, and to conduct a temporal distributional analysis that shows the magnitudes of climate damages across generations. We also provide recommendations for the discount rates that should be used when estimating the social cost of greenhouse gases, and the appropriate discount rates for regulatory impact analyses that include the social cost of greenhouse gases.

The apple buprestid, Agrilus mali Matsumura, that was widespread in north-eastern China, was accidently introduced to the wild apple forest ecosystem in mountainous areas of Xinjiang, China. This invasive beetle feeds on domesticated apples and many species of Malus and presents a serious threat to ancestral apple germplasm sources and apple production worldwide. Estimating the potential area at risk of colonization by A. mali is crucial for instigating appropriate preventative management strategies, especially under global warming. We developed a CLIMEX model of A. mali to project this pest's potential distribution under current and future climatic scenarios in 2100 using CSIRO-Mk 3.0 GCM running the SRES A1B emissions scenario. Under current climate, A. mali could potentially invade neighbouring central Asia and eventually the mid-latitude temperate zone, and some subtropical areas and Pampas Steppe in the Southern Hemisphere. This potential distribution encompasses wild apples species, the ancestral germplasm for domesticated apples. With global warming, the potential distribution shifts to higher latitudes, with the potential range expanding slightly, though the overall suitability could decline in both hemispheres. In 2100, the length of the growing season of this pest in the mid-latitude temperature zone could increase by 1–2 weeks, with higher growth rates in most sites compared with current climate in mid-latitudes, at least in China. Our work highlights the need for strategies to prevent the spread of this pest, managing the threats to wild apples in Tian Shan Mountain forests in Central Asia, and commercial apple production globally. We discuss practical management tactics to reduce the spread of this pest and mitigate its impacts.

Robust design methods have expanded from experimental techniques to include sampling methods, sensitivity analysis and probabilistic optimisation. Such methods typically require many evaluations. We study design and noise variable cross-term second derivatives of a response to quickly identify design variables that reduce response variability. We first compute the response uncertainty and variance decomposition to determine contributing noise variables of an initial design. Then we compute the Hessian second-derivative matrix cross-terms between the variance-contributing noise variables and proposed design change variables. Design variable with large Hessian terms are those that can reduce response variability. We relate the Hessian coefficients to reduction in Sobol indices and response variance change. Next, the first derivative Jacobian terms indicate which design variable can shift the mean to maintain a desired nominal target value. Thereby, design changes can be proposed to reduce variability while maintaining a targeted nominal value. This workflow finds changes that improve robustness with a minimal four runs per design change. We also explore further computation reductions achieved through compounding variables. An example is shown on a Stirling engine where the top four variance-contributing tolerances and design changes identified through 16 Hessian terms generated a design with 20% less variance.

The radiocarbon (14C) dating facility at the Centre for Isotope Research, University of Groningen went through a major upgrade in 2017 and this included installation of a MICADAS accelerator mass spectrometer (AMS). In the first 18 months, we performed 4000 sample and 3000 reference measurements. A careful evaluation of those measurement results is presented, to characterize the various sources of uncertainty and to ultimately assign, for every sample measurement, a realistic expanded uncertainty. This analysis was performed on the measurements of secondary references and sample duplicates in various phases of their processing steps. The final expanded uncertainty includes both the 14C measurement uncertainties and uncertainties originating from pretreatment steps. Where the 14C measurement uncertainty includes straightforward uncertainties arising from Poisson statistics, background subtraction, calibration on Oxalic Acid II and δ13C correction, the uncertainties originating from pretreatment steps are based on the spread of actual measurement results for secondary references and sample duplicates. We show that the 14C measurement uncertainty requires expansion, depending on the number of processing steps involved prior to a 14C measurement, by a maximum factor of 1.6 at our laboratory. By using these expansion (multiplication) factors, we make our reported uncertainty both more realistic and reliable.

There are two units with RBMK-1500 type reactors at the Ignalina Nuclear Power Plant (Ignalina NPP) in Lithuania where graphite was used as a neutron moderator and reflector. These reactors are now being decommissioned, and Lithuania has to find a solution for safe irradiated graphite disposal. It cannot be disposed of in a near surface repository due to large amounts of 14C (radiocarbon, carbon-14); thus, a deep geological repository (DGR) is analyzed as an option. This study had the aim to evaluate 14C migration from the RBMK-1500 irradiated graphite disposed of in a potential DGR in crystalline rocks taking into account the outcomes of the research performed under the collaborative European project CAST (CArbon-14 Source Term) and to identify the potential to reduce the conservatism in the assumptions that was introduced in the lack of data and led in the overestimated 14C migration. The information gathered during the CAST project was used to model 14C transport in the near field by the water pathway and to perform uncertainty analysis. The study demonstrated that more realistic assumptions could reduce the estimated 14C flux from the near field by approximately one order of magnitude in comparison with the previous estimations based on very conservative assumptions.

Sensitivity analysis plays an important role in finding an optimal design of a structure under uncertainty. Quantifying relative importance of random parameters, which leads to a rank ordering, helps in developing a systematic and efficient way to reach the optimal design. In this work, lift prediction and sensitivity analysis of a potential flow around a submerged body is considered. Such flow is often used in the initial design stage of structures. The flow computation is carried out using a vortex-panel method. A few parameters of the submerged body and flow are considered as random variables. To improve the accuracy in lift prediction in a computationally efficient way, a new semi-intrusive stochastic perturbation method is proposed. Accordingly, a perturbation is applied at the linear system solving level involving the inuence coefficient matrix, as opposed to using perturbation in the lift quantity itself. This proposed method, which is partially analogous to the intrusive or Galerkin projection methods in spectral stochastic finite element methods, is found to be more accurate than using perturbation directly on the lift and faster than a direct simulation. The proposed semi-intrusive stochastic perturbation method is found to yield faster estimates of the Sobol’ indices, which are used for global sensitivity analysis. From global sensitivity analysis, the flow parameters are found to be more important than the parameters of the submerged body.

In this study, we conduct a cost-effectiveness analysis of nine air quality regulations recently issued by the U.S. Environmental Protection Agency (EPA). Taking emission reductions in the Regulatory Impact Analyses (RIAs) for these regulations as given, we independently assess uncertainty about the compliance costs of the regulations and the lives the regulations are estimated to save. The latter evaluation is based on a formal uncertainty analysis that integrates expert judgments about the effects of fine particle exposures on mortality risks. These expert judgments were given in an EPA-sponsored elicitation study conducted in 2006. The integrated judgments are used to generate probability distributions for several types of cost-effectiveness ratios, including the gross and net cost per life saved, net cost per life year saved, and net cost per quality-adjusted life year (QALY) gained. The results show that the cost-effectiveness ratios exhibit considerable uncertainty individually and also vary widely across regulations. Within a simulated 90% confidence interval for the gross cost per life saved, for example, there is both the possibility that benefits from lifesavings alone are sufficient to cover the rules’ costs and the possibility that no lives will be saved and cost-effectiveness ratios will be infinite. The wide ranges for the confidence intervals suggest the need for better information about the effects of fine particle exposures on mortality risks.

An understanding of the uncertainty in benefit and cost estimates is a critical part of a benefit–cost analysis. Without a quantitative treatment of uncertainty, it is difficult to know how much confidence to place in the benefit–cost estimates associated with regulatory analysis. In 2002, an NRC report recommended that EPA move toward conducting probabilistic, multiple-source uncertainty analyses in its RIAs with the specification of probability distributions for major sources of uncertainty in the benefit estimates. In 2006, reports by GAO and RFF found that EPA had begun to address the NRC recommendations, but that much remained to be done to meet the NRC concerns. This paper provides a further review of EPA’s progress in developing a quantitative assessment of the uncertainties in its health benefits analyses for the RIAs for four recent rulemakings setting National Ambient Air Quality Standards (NAAQS). In conclusion, EPA’s basic approach to presenting the uncertainty in its health benefits estimates remains largely unchanged. Recent RIAs present the results of uncertainty analysis in piecemeal fashion rather than providing an overall, comprehensive statement of the uncertainty in the estimates. In addition, the uncertainty analysis in recent RIAs continues to focus on the concentration-response relationship and largely fails to address the uncertainty associated with the other key elements of the benefits analysis.

Mathematical models of plant growth are generally characterized by a large number ofinteracting processes, a large number of model parameters and costly experimental dataacquisition. Such complexities make model parameterization a difficult process. Moreover,there is a large variety of models that coexist in the literature with generally anabsence of benchmarking between the different approaches and insufficient modelevaluation. In this context, this paper aims at enhancing good modelling practices in theplant growth modelling community and at increasing model design efficiency. It gives anoverview of the different steps in modelling and specify them in the case of plant growthmodels specifically regarding their above mentioned characteristics.

Different methods allowing to perform these steps are implemented in a dedicated platformPYGMALION (Plant Growth Model Analysis, Identification and Optimization). Some of thesemethods are original. The C++ platform proposes a framework in which stochastic ordeterministic discrete dynamic models can be implemented, and several efficient methodsfor sensitivity analysis, uncertainty analysis, parameter estimation, model selection ordata assimilation can be used for model design, evaluation or application.

Finally, a new model, the LNAS model for sugar beet growth, is presented and serves toillustrate how the different methods in PYGMALION can be used for its parameterization,its evaluation and its application to yield prediction. The model is evaluated from realdata and is shown to have interesting predictive capacities when coupled with dataassimilation techniques.

The behaviour of piston-cylinder operated pressure balances is characterized by thedistortion coefficient λ and zero-pressure effective areaA0 which model the variation of a pressure balance’s area interms of the applied pressure. This paper determines the uncertainties inλ and A0 when utilizing the method ofcross-floating with another pressure balance standard whose parameters and associateduncertainties are known. A limitation that is frequently encountered in many attempts ofthe uncertainty analysis for a pressure balance is that no readily accessible uncertaintyquantification framework for the distortion coefficient is present. As a result theuncertainty in a pressure balance’s area at elevated applied pressures is typicallyunderestimated in the absence of this uncertainty information. We firstly review theuncertainty formulation for a pressure balance generated pressure involving correlationeffects in terms of an implicit multivariate matrix equation approach and then utilizingthe resulting solution present the methodology to consistently perform the uncertaintyanalysis for λ and A0.

Tuberculosis is the leading cause of death due to infectious diseases in the world today,and it is increasing due to co-infection with HIV-1, the causative agent of AIDS. Here, weexamine the impact that HIV-1 infection has on persons with latent tuberculosis. Based on previouswork, we develop a mathematical model of an adaptive immune response in the lung whichconsiders relevant immune effectors such as macrophages, various sub-populations of T-cells, andkey cytokines to predict which mechanisms are important to HIV-1 infection induced reactivationof tuberculosis. Our results indicate that persons latently infected with TB who are subsequentlyco-infected with HIV-1 will suffer reactive TB. The mechanisms that contribute to this are essentiallyrelated to a completely different cytokine environment at the onset of HIV-1 infection dueto the presence of Mycobacterium tuberculosis. Our analysis suggests that macrophages play animportant role during co-infection and decreases in macrophage counts are coupled to a decline inCD4+ T-cells and increased viral loads. These mechanisms are also coupled to lower recruitmentof T-cells and macrophages, compromising protective immunity in the lung and eventually leadingto TB reactivation. These results point to potential targets for drug and vaccine therapies.

Interest is growing in the application of standard statistical inferential techniques to the calculation of cost-effectiveness ratios (CER), but individual level data will not be available in many cases because it is very difficult to undertake prospective controlled trials of many public health interventions. We propose the application of probabilistic uncertainty analysis using Monte Carlo simulations, in combination with nonparametric bootstrapping techniques where appropriate. This paper also discusses how decision makers should interpret the CER of interventions where uncertainty intervals overlap. We show how the incorporation of uncertainty around costs and effects of interventions into a stochastic league table provides additional information to decision makers for priority setting. Stochastic league tables inform decision makers about the probability that a specific intervention would be included in the optimal mix of interventions for different resource levels, given the uncertainty surrounding the interventions.