Sensitivity Analysis. A summary of the discounted cash flow results from -------------------- varying key assumptions (such as the discount rate, commodity pricing and/or major operating assumptions); and
Sensitivity Analysis. As discussed, there is a degree of uncertainty associated with the models and data used to devise Australia’s FM reference level. Due to this, the Government’s estimates of emissions and removals from native forests are subject to a significant margin of error and, as the method used here is a replica of the Australian Government’s, it embodies all of the same uncertainties. To account for this, and the potential for future modifications of the method and data sets to alter the FM credit outcomes, sensitivity analysis was undertaken by changing two of the key parameters in FullCAM: the above-ground live biomass yield increment rates and the age-class distribution of the forests subject to harvest. The margin of error associated with the above-ground live biomass yield increment rates was assumed to be ±25%. To account for this range, replica representative plot files were created with +25% and -25% yield increments. The reference and ENGO scenarios were then re-run to test how the lower and higher yield increments affected the credit outcomes. In relation to the uncertainties associated with the age-class distribution of the forests, the estate simulation start date was adjusted ±10 years. In the standard runs, the estate simulation start date was 1 January 1960, meaning that in the sensitivity analysis the simulation start dates were 1 January 1950 and 1 January 1970.
Sensitivity Analysis. In the event of an economic downturn, the business may have a decline in its revenues. Hobby products and supplies are not necessities, and an economic recession may have an impact on the Company’s ability to generate sales as consumers have less discretionary income. However, the business will be able to remain profitable and cash flow positive given its high margins from both retail and online sales.
Sensitivity Analysis. In order to explore the potential impact of a range of variances on the numerical outputs from the option appraisal process, a limited sampling-based sensitivity analysis was conducted. This attempted to understand the main effects of varying key values on the relative prioritisation and scoring of options. The sensitivity analysis conducted considered the variables below: Variable 1: Applying overall (group) scores to amended weightings based on the inclusion / exclusion of the weighting identified by individual stakeholder groups Variable 2: Applying individual stakeholder group scores to agreed overall weightings Variable 3: Excluding single individual stakeholder group scores from agreed overall scores and weightings (using an amended mean score) Variable 4: Applying individual group weightings to the same groups individual scores
Sensitivity Analysis. Develop a spreadsheet based matrix evaluating design parameters and their effect on the transfer station design, operating procedures and load frequency. Parameters to be evaluated include number of loading slots, operating hours, trailer loading time, trailer payload and number of trailer trips per day.
Sensitivity Analysis. Telcordia shall develop a sensitivity analysis to assess the critical factors driving the value of the opportunity and impacts of uncertainties.
Sensitivity Analysis. In conducting a sensitivity analysis, the CONSULTANT will evaluate the sensitivities of different asset classes (e.g., transportation assets, infrastructure, community/emergency facilities, and natural/cultural/historical resources). The objective will be to evaluate the impacts projected to be incurred to each critical asset class and to assign sensitivity ratings based on impact severity.
Sensitivity Analysis. Analyses on the PP population will be performed for the primary and the secondary efficacy endpoints and serve as supportive analyses for the ITT analyses.
Sensitivity Analysis. Sensitivity analysis is a tool that can help risk managers select those controls that best achieve risk management goals. Sensitivity analysis, as a scientific process, shows the effects of changes in various inputs (data or assumptions) on the outcomes of a risk assessment. One of the most useful insights gained from a sensitivity analysis is estimating how much the uncertainty or Risk Analysis variability associated with each input factor contributes to the overall uncertainty and variability in the risk estimate. Input distributions where uncertainty has the greatest impact on the outcome can be identified, and this process also can help set priorities for research to reduce uncertainty.
Sensitivity Analysis. For now we have analyzed the economy- wide effects under full employment of factors (labor and capital), which can be interpreted to be the relevant perspective in phases of an economic boom; this is the standard assumption in CGE models. However, since the economy-wide effects of the expansion of renewable electricity are strongly determined by relative price effects of capital, we carry out additional sensitivity analyses. Instead of assuming full factor employment, we assume that the economy is in a phase of recession (there is an “output gap”), where capital is idle. This means that in the case of an expansion of capital intensive renewable electricity generation, previously idle capital can now be used effectively. This relaxes the strict scarcity assumption, meaning that additional capital is available, and thus capital prices do not rise that strong than is the boom-c ase. In other words, new renewable investment no longer fully crowds out other investment, but the policy actually increases the overall c apital stock of the economy over time, creating an additional production factor supply. Figure 15 shows the ranges of possible welfare effects, spanned by the combinat ion of “boom and high-WACC” (minima) and “recession and low-WACC” (maxima); including integration costs. We see that when renewables are expanded during a recession, the possible positive welfare effects are stronger and that in those cases where negative welfare effects emerged, now also positive effects become possible. Wind PV Wind PV Wind PV Wind PV Wind PV Wind XX XXX WEU AUT EEU SEU GRC +2.0% +1.8% +1.6% +1.4% +1.2% +1.0% +0.8% +0.6% +0.4% +0.2% 0.0% -0.2% -0.4% Figure 15 Ranges of possible welfare effects spanned by combinations of high/low -WACC and boom/recession-assumption (including integration costs). Lower ranges show the effects of “boom and high WACC” combinations, while upper bounds show the effects of a “recession and low WACC” combination.
