Economic Cost Calculations
Economic Cost Calculations cost calculations go further than all the previously discussed metrics. This metric tries to answer the question: What will the economic costs in year X be due to the expected temperature change from anthropogenic GHG emissions? This metric is more complex because it calculates climate costs and not just radiative forcing or climate response (see Figure 1). Climate cost calculations can be based on GTP or on calculating the sum of temperature change over time (delta T over time); both metrics in turn are based on RF. In addition to all the assumptions made in order to calculate GTP, an additional range of parameters, such as discount rate, economic growth rates, and damage functions need to be determined.
This means that climate cost calculations are more complicated and their results are more value-based than radiative forcing and climate response calculations. Yet the results of climate cost calculations are often more relevant for policy makers (e.g. it is more useful for a policy maker to know the economic impacts than only the physical changes caused by GHG emissions.)
The U.S. Federal Aviation Administration/Aviation Environmental Portfolio Management Tool (FAA/APMT) model calculates the economic costs of the climate impacts due to aviation and is based on delta T over time. The FAA/APMT model looks at the future impacts of current (and future) CO2 and non-CO2 emissions. FAA/APMT looks at marginal air travel impacts by taking into account the background atmospheric GHG concentrations from all anthropogenic emission sources .
Figure 8 shows the FAA/APMT model’s quantified impacts of one year of aviation. The FAA/APMT model is probabilistic in order to capture to the extent possible the impacts of many of the uncertainties. Because some of the behaviors are non-linear, this can be important. The figure shows the mean of the response for each GHG at each point in time.
Figure 8: FAA/APMT Model’s Quantified Climate Impacts of a One-Year Aviation Pulse with Fixed Inputs.
Figure 8(a) shows the impact expressed in change in surface temperature (delta T) over time. The cooling effects of sulfate aerosols, methane decrease (in the figure legend labeled as: NOx-CH4), and long term ozone decrease (in the figure legend labeled as: NOx-03 long.) can be observed. The total impact (x-line) is the sum of all warming effects minus the cooling effects.
Figure 8(b) illustrates the same effects expressed as impact on US Gross Domestic Product (GDP). In order to convert the warming impacts to an economic metric, Marais et al. (2008) assumed a discount rate, economic growth rates, and a damage function, among other parameters. These parameters explain the change in shape of the curves as compared to Figure 8(a). (Baseline damage function are based on, Nordhaus and Boyer (2000). The total impact (x line) merges with the CO2 impact line after a few decades because the short-lived emissions no longer cause warming after a few decades. (Source: Marais et al., 2008)
The parameters in the FAA/APMT model (e.g. the time frame or the discount rate) can be adjusted depending on the policy option that is being researched. The parameters for this model do not have equally strong influences on the results. The relative importance of non-CO2 effects changes depending on the time frame for which they are calculated. Furthermore, the modelers found:
[T]he climate sensitivity, the radiative forcing of different short-lived effects, the choice of emissions scenario and the discount rate have the most significant influence on the output metrics we considered. Other uncertainties were less important. (Marais et al. 2008)
Some of these factors will become more accurate as scientific knowledge improves. Yet others will not: discount rates, for example, cannot be established by scientific analysis because they are dependent on ethical choices and value judgments. They also depend on assumed economic performance in the future.
The FAA/APMT model can also be used to purely express the physical metric of climate impacts by using the integrated delta T-years (the area under the change in temperature graph for each greenhouse gas). In other words, it expresses the changes as a theoretical number that is the sum of all the temperature changes that occur over a given period of time. This result is then neither discounted nor used to make any economic estimates. This is what is shown in Figure 8a.
The FAA/APMT model does not take into account the spatial effects of aviation emissions. It uses a single variable – global-mean surface temperature – to express climate impacts. It therefore has the same shortcomings as Global Temperature Change Potential.
In addition, the number and type of assumptions that have to be made in order to estimate climate costs mean that the results of such economic models are to a large extent value-based. For example, as mentioned in section 4.6, many important climate damages, such as loss of human life, cannot easily be expressed in monetary terms. Economic models frequently express all climate damages through a damage function, assuming a mathematically simple relationship between climate changes (measured by temperature increase) and the total value of associated damages. Yet such damage functions do not reflect the complexities and non-linear behavior of physical, biological and economic systems. The damage function in the FAA/APMT model is based on the climate economics model “DICE,” developed by Nordhaus and Boyer (2000). For a critique of the DICE model and its assumptions, see Ackerman and Finlayson (2006).
Despite their shortcomings, economic models are important because they translate climate change into the currency that is most pertinent to policy makers and businesses: the monetary costs associated with the expected warming. It is therefore vital for evaluating and prioritizing climate mitigation strategies that more sophisticated models which explicitly discuss their underlying assumptions be developed. Any model that calculates climate costs should therefore explicitly state the assumptions that were made for the non-scientific parameters and their associated uncertainties.