National Academies Press: OpenBook

Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports (2019)

Chapter: Appendix F - Climate Risk and Mitigation Numerical Examples

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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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Suggested Citation:"Appendix F - Climate Risk and Mitigation Numerical Examples." National Academies of Sciences, Engineering, and Medicine. 2019. Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports. Washington, DC: The National Academies Press. doi: 10.17226/25497.
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103 Two numerical examples are presented here using the Excel templates described in Appendix E. Simulation results are presented for airports in Myrtle Beach (for the relative sea level rise example) and Phoenix (for the high-temperature weight restriction example). It is important to emphasize that these examples are used only to describe how the methodologies embedded in the templates can be utilized. They are not meant to be accurate descriptions of potential mitigation projects that might be undertaken. Numerical Example for Myrtle Beach International Airport Myrtle Beach International Airport (MYR) is located south of the city of Myrtle Beach, about 2 miles west of the Atlantic coastline. Unfortunately, the ACROS software does not provide any projections related to sea level rise for MYR, so an initial screening analysis is not possible. The Excel template for relative sea level rise can be used to undertake an analysis of potential extreme water level events for MYR. As a first step, the airport can be selected on the User Selections sheet, shown in Exhibit F-1. As seen in the exhibit, the airport elevation is listed as 24.5 ft above MSL, with its lowest run- way being 9.0 ft above MSL. These elevations translate to 21.6 ft and 6.1 ft, respectively, above MHHW, which is the relevant vertical datum measure used in the template. In this example, RCP8.5 (the high-emissions scenario) is selected as the relevant forecast for future SLR. The next step is to specify a potential mitigation project for analysis on the User Selections sheet. But before doing so, one can examine the projected probabilities for flooding events of different heights at MYR under RCP8.5. The first table at the top of the Results sheet presents these results (shown in Exhibit F-2) along with corresponding projected heights for the median event from the simulations as well as the height of the projected 100-year (i.e., top 1%) event. The results shown in Exhibit F-2 do not depend on any specific mitigation project assump- tions; they reflect only the flood event probabilities implied by RCP8.5, taking into account global SLR, the expected localized effects, and the history of extreme water events for MYR. As expected, the probabilities of higher water level events increase gradually over time. Interestingly, the heights of the expected 100-year event are projected to rise from a historical average of 3.15 ft to about 6 ft by 2075, the latter being just above the lowest runway height shown in Exhibit F-1. These results might suggest that the airport may not be in much danger for runway flooding until that time; however, it is important to note that the software cannot take account of any variations in terrain that may exist between the reporting stations and the airport itself, nor can it account for any existing mitigations (such as levies or stormwater systems) that may be operational. A P P E N D I X F Climate Risk and Mitigation Numerical Examples

104 Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports Given this background, Exhibit F-3 shows the mitigation assumptions selected for this example. By design, the mitigation costs and damages with and without the project are defined in a generic way, allowing users to consider specific amounts that would be relevant for however they decide to define the mitigation project. The time horizon for this project is assumed to run from 2020 through 2060. The project takes 1 year for construction, starting in 2020, then mitigation benefits begin to accrue the following year. Initial construction costs are $4 million, with annual maintenance costs of $200,000, plus a rehab cost of $1 million after 20 years. The damage costs listed in the exhibit imply complete mitigation for flooding events of less than 5 ft, and 80% mitigation for events higher than that. Using these assumptions, the template carries out 5,000 Monte Carlo simulations. The results shown in Exhibit F-4 indicate that the project has a very low average benefit–cost ratio and virtu- ally never pays off. This is not surprising given the very low probabilities for flooding events in the early years, as shown in Exhibit F-2. State_Locid SC_MYR Name MYRTLE BEACH INTERNATIONAL Relative to MSL Relative to MHHW Airport Elevation (ft) 24.5 21.6 Lowest Runway Elevation (ft) 9.0 6.1 Historical extreme water levels (EWL) based on: EWL_Station SPRINGMAID PIER EWL_Distance (miles) 1.81 Projected relative sea levels (RSL) based on: RSL_Station SPRINGMAID PIER RSL_Distance (miles) 1.45 RCP_Scenario 8.5 Exhibit F-1. RSL template information for MYR. Water Level Rise above MHHW (ft) Historical 2025 2035 2045 2055 2065 2075 2085 2095 0-1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 1-2 52.10% 11.46% 1.38% 0.08% 0.00% 0.00% 0.00% 0.00% 0.00% 2-3 46.34% 82.16% 81.60% 61.40% 31.78% 11.64% 3.44% 1.00% 0.44% 3-4 1.54% 6.22% 16.62% 36.76% 61.10% 66.66% 53.78% 35.32% 23.18% 4-5 0.02% 0.16% 0.40% 1.70% 6.88% 19.38% 33.76% 45.10% 42.80% 5-6 0.00% 0.00% 0.00% 0.06% 0.24% 2.14% 7.88% 13.12% 21.80% 6-7 0.00% 0.00% 0.00% 0.00% 0.00% 0.14% 0.92% 4.56% 7.92% 7-8 0.00% 0.00% 0.00% 0.00% 0.00% 0.04% 0.20% 0.66% 3.16% 8-9 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.02% 0.14% 0.36% 9+ 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.10% 0.34% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% Median (ft) 1.98 2.35 2.61 2.87 3.19 3.54 3.90 4.25 4.61 100-Yr Event (ft) 3.15 3.49 3.79 4.10 4.59 5.27 6.06 6.93 7.67 MYR Extreme Water Level Event Probabilities from 5,000 Simulations (RCP 8.5) Exhibit F-2. Event probabilities for MYR.

Climate Risk and Mitigation Numerical Examples 105 Analysis_Start_Yr 2020 Analysis_End_Yr 2060 Discount_Rate 7.0% Mitigation_Project_Type Simplified Project_Start_Yr 2020 Mitigation_Start_Yr 2021 Simplified Mitigation Project Costs Construction_Cost $4,000,000 Annual_Maint_Cost $200,000 Rehab_Interval_Yrs 20 Rehab_Cost $1,000,000 Flooding Event Damage Costs EWL above MHHW (ft) Without Project With Project 0-1 $0 $0 1-2 $0 $0 2-3 $0 $0 3-4 $500,000 $0 4-5 $1,500,000 $0 5-6 $2,500,000 $500,000 6-7 $5,000,000 $1,000,000 7-8 $10,000,000 $2,000,000 8-9 $10,000,000 $2,000,000 9+ $10,000,000 $2,000,000 Exhibit F-3. Assumed user inputs for MYR. Mean Std Deviation Avg NPV of Project -$5,185,165 $732,004 Avg B/C Ratio 0.19 0.11 -$9 -$8 -$7 -$6 -$5 -$4 -$3 -$2 -$1 $0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% N PV ($ m ill io ns ) Net Impacts: Baseline vs. Scenario Baseline (Without Project) Scenario (With Project) -$7 -$6 -$5 -$4 -$3 -$2 -$1 $0 $1 $2 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Ch an ge in N PV ($ m ill io ns ) NPV Difference (Scenario - Baseline) Exhibit F-4. BCA and VaR results for MYR 2020 project.

106 Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports The template makes it easy to do what-if comparisons. For example, one could see the effect of delaying the project for 20 years (e.g., starting the project in 2040 and moving the analysis time horizon to 2040 through 2080). These results are shown in Exhibit F-5. The results improve substantially, but the chances of the project paying off are still low at around 17%. Obviously, a viable option might be to wait several years and then undertake a revised analysis when the chances of extreme events are closer in time and when newer and more reliable data for the time period of interest are likely to be available. The impact of the discount rate can also be investigated easily. The choice of discount rate can have a significant effect on the results. Exhibit F-6 shows the results when com- bining the 20-year project delay with a 3% discount rate. Now the results show an average benefit–cost ratio well above 1. However, the VaR results show that the probability of Mean Std Deviation Avg NPV of Project -$1,690,116 $2,730,672 Avg B/C Ratio 0.74 0.43 -$40 -$35 -$30 -$25 -$20 -$15 -$10 -$5 $0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% N PV ($ m ill io ns ) Net Impacts: Baseline vs. Scenario Baseline (Without Project) Scenario (With Project) -$10 -$5 $0 $5 $10 $15 $20 $25 $30 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Ch an ge in N PV ($ m ill io ns ) NPV Difference (Scenario - Baseline) Exhibit F-5. BCA and VaR results for MYR 2040 project. Mean Std Deviation Avg NPV of Project $2,112,055 $6,169,306 Avg B/C Ratio 1.24 0.70 -$100 -$90 -$80 -$70 -$60 -$50 -$40 -$30 -$20 -$10 $0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% N PV ($ m ill io ns ) Net Impacts: Baseline vs. Scenario Baseline (Without Project) Scenario (With Project) -$20 -$10 $0 $10 $20 $30 $40 $50 $60 $70 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Ch an ge in N PV ($ m ill io ns ) NPV Difference (Scenario - Baseline) Exhibit F-6. BCA and VaR results for MYR 2040 project with 3% discount rate.

Climate Risk and Mitigation Numerical Examples 107 a positive payoff from the project is still relatively low (about 56%), so decision makers must consider whether they would be willing to accept the risk of not undertaking the project. All of the results presented have been for illustrative purposes only. However, they show how the template may be used to undertake a Monte Carlo analysis in a straightforward way and how different assumptions can have significant impacts on the reported results. Numerical Example for PHX PHX is a major hub for American Airlines, and it plays an important role in the national avia- tion system. Phoenix of course has a very warm climate, and it is expected to get warmer over the coming decades. As a first step in assessing how future climate change may affect the airport, one can look at the ACROS climate projections for PHX, shown in Exhibit F-7. The incidence of hot days (defined as maximum temperatures at or above 90°F) and very hot days (temperatures above 100°F) is expected to increase fairly significantly by 2060. One potentially important impact of high temperatures at airports is that they increase the required runway distance for takeoffs and reduce climbing performance. Whether a specific operation will be affected depends on the actual temperature, airport elevation, length of the runway, aircraft load, and aircraft being used. In principle, an airline could have multiple options available during hot weather. For example, it could be able to remove some weight from the aircraft, which would lower its minimum takeoff length requirements. This could involve removing passengers (or cargo) from the flight. But if the temperature gets high enough, it may simply choose to cancel the flight altogether. This is a real-world issue at PHX, which has seen heat-related disruptions to operations in the past. For example, extreme heat in June 2017 caused American to cancel more than 40 flights as temperatures reached close to 120°F: A statement from the airline suggested that the maximum operating temperature for a number of aircraft (127°F for an Airbus, 126°F for a Boeing, and 118°F for a Bombardier CRJ regional aircraft) had been reached, or was expected to be reached later in the day (Samuelson 2017). Exhibit F-7. ACROS climate stressor forecast for PHX.

108 Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports For PHX, it is clear that projections for temperatures well above the “very hot days” definition identified in the ACROS projections would be needed to assess the potential impact on aircraft operations at the airport. Using methods described in Chapter 4, the project team obtained LOCA-downscaled projections of daily maximum temperatures for the RCP8.5 climate scenario from 32 different GCM models and four grid points surrounding PHX for the period 2020 through 2089 in order to more fully examine the potential impact of extreme temperatures. The Excel high-temperature template is used for the following example. The temperature projections from RCP8.5 were converted and copied into the Excel template as described in Appendix E and following the specific instructions contained in the template itself. For this example, it was assumed that the airport wished to investigate if and to what degree there might be weight restrictions that could affect certain long-haul routes for which they were trying to gain new service. The relevant information is in the shaded areas at the top of the User Selections sheet, as shown in Exhibit F-8. As shown in the exhibit, the project being considered would extend the runway from its cur- rent 11,500 ft to 14,500 ft. Three long-haul routes (each using a different aircraft type) were specified, along with estimated passengers and number of weekly departures. The results under the Weight Restriction columns show the impact of lengthening the runway in terms of pounds of required weight reduction per flight. The implied counts of affected passengers are shown at the bottom, assuming that departures grow at a 2% annual rate. The next step is to specify the relevant time period for the analysis and costs of the runway extension on the User Selections sheet. But before doing so, one can examine the range of pro- jected high temperatures from the different models. The charts at the top of the Results sheet present these results, shown in Exhibit F-9. The results shown in the exhibit do not depend on any specific mitigation project assumptions; they reflect only the high-temperature projections from the different climate models implied by RCP8.5. As expected, both the counts and uncer- tainties of high temperatures increase gradually over time. The increased variation across the models is not surprising—it reflects the real uncertainty in climate projections many years out. By sampling from all of the models in the analysis, the results will reflect this range of variation.37 The proposed mitigation project for this example is an extension for Runway 08/26, which runs on the north side of the terminal complex. It is constrained by the airport property boundary, the airport Skytrain transit system, and South 44th St. with associated bridge structures and access drives. Beyond those features, there is relatively undeveloped land extend- ing to Route 143 located about 3,600 ft east of the end of Runway 08. For purposes of this example, it was assumed that an extension of Runway 08 to the east by 3,000 ft would be possible. In reality, such an extension might not be feasible since it could well involve the need for property acquisition (depending on whether the airport holds title to the affected land) and either relocating or depressing and bridging over the Skytrain system and South 44th St. In addition, it is possible that additional obstruction removal would be required to maintain obstacle clearance requirements. Also, even if it were physically feasible, there could be other obvious policy or legal reasons why such a project could not be undertaken. Given this background, Exhibit F-10 shows the project assumptions selected for this example. The time horizon for this project was assumed to be from 2020 through 2070. The project would take 1 year for construction, starting in 2020, and then mitigation benefits would begin to accrue the following year. Initial construction costs would be $40 million, with annual maintenance costs of $1,000,000, plus a rehab cost of $10 million after 25 years. Avg_PaxPayload at the bottom of Exhibit F-10 is the average weight in pounds per passenger; Avg_PaxDelay is the assumed aver- age delay in hours that must be incurred by a passenger who is weight-restricted off of a flight; Hourly_DelayCost is the FAA-recommended value of time for commercial airline passengers.

El ev ati on 11 35 ft V al id e le va ti on ra ng e is 0 -4 00 0 ft ; l att er is th e m ax im um e le va ti on fo r w hi ch m od el c an a ut om ati ca lly e sti m at e w ei gh t r es tr ic ti on s. Rw yl en gt h_ Ba se lin e 11 50 0 ft V al id ru nw ay le ng th ra ng e is 4 00 0- 16 00 0 ft . Rw yl en gt h_ Sc en ar io 14 50 0 ft Ro ut e D is ta nc e (n m ) Eq pt Pa ss en ge rs pe r F lig ht W ee kl y D ep ar tu re s 10 0- 10 1 10 2- 10 3 10 4- 10 5 10 6- 10 7 10 8- 10 9 11 0- 11 1 11 2- 11 3 11 4- 11 5 11 6- 11 7 11 8- 11 9 Ba se lin e 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 4 1. 9 3. 4 4. 9 Sc en ar io 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 0. 0 Ba se lin e 3. 5 7. 5 15 .0 19 .0 23 .0 26 .6 30 .6 33 .6 34 .9 36 .2 Sc en ar io 0. 0 0. 0 0. 0 0. 0 2. 6 7. 2 11 .2 14 .2 18 .2 21 .7 Ba se lin e 29 .6 35 .1 46 .1 51 .6 57 .1 63 .1 68 .1 74 .1 79 .7 85 .7 Sc en ar io 0. 0 5. 1 15 .7 21 .7 27 .7 32 .7 38 .7 44 .7 50 .1 56 .1 To ta l D ai ly P as se ng er s A ff ec te d: Pa xd ai ly _B as el in e 12 4. 6 16 0. 7 23 0. 8 26 6. 3 30 2. 4 33 8. 5 37 3. 9 41 3. 7 44 5. 0 47 8. 5 Pa xd ai ly _S ce na ri o 0. 0 19 .4 59 .2 81 .9 11 4. 3 15 0. 4 18 8. 1 22 2. 1 25 7. 7 29 3. 8 45 70 49 99 13 0 19 5 26 0 7 7 7 W ei gh t R es tr ic ti on (0 00 lb ) p er F lig ht a t In di ca te d Te m pe ra tu re s (° F) PH X- LH R 78 7- 8 27 28 1 2 3 PH X- BO G 73 7- 80 0 77 7- 30 0 PH X- N RT R ef re sh C ur re nt S he et Ex h ib it F -8 . H ig h -t em p er at u re t em p la te in fo rm at io n f o r PH X .

110 Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports 0 20 40 60 80 100 120 140 160 180 200 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s A vg A nn ua l D ay s ab ov e 11 0° F Range of Avg Annual Days above 110°F across Models by Decade Max Min Median 0 50 100 150 200 250 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s A vg A nn ua l D ay s ab ov e 10 0° F Range of Avg Annual Days above 100°F across Models by Decade Max Min Median Exhibit F-9. Range of incidence of projected high temperatures for PHX. Analysis_Start_Yr 2020 Analysis_End_Yr 2070 Discount_Rate 7.0% Mitigation_Project_Type Simplified Project_Start_Yr 2020 Mitigation_Start_Yr 2021 Simplified Mitigation Project Costs Construction_Cost $40,000,000 Annual_Maint_Cost $1,000,000 Rehab_Interval_Yrs 25 Rehab_Cost $10,000,000 Damage Parameters Avg_PaxPayload 220 Avg_PaxDelay 2.0 Hourly_DelayCost $44.30 Exhibit F-10. Assumed user inputs for PHX.

Climate Risk and Mitigation Numerical Examples 111 Using these assumptions, the template carries out 5,000 Monte Carlo simulations. The results shown in Exhibit F-11 indicate that the project has a very low average benefit–cost ratio and never pays off. Both net impact curves in the VaR chart on the left are relatively flat, which is consistent with the discussion in Appendix D that both curves will typically change only mod- estly based on temperature variations. Again, the template makes it easy to do what-if comparisons. The impacts of changing the discount rate from 7% to 3% are shown in Exhibit F-12. Now the results show an average benefit–cost ratio well above 1, again showing the dramatic impact that choice of discount rate can have on the analysis. Mean Std Deviation Avg NPV of Project -$18,458,050 $662,973 Avg B/C Ratio 0.64 0.01 -$90 -$80 -$70 -$60 -$50 -$40 -$30 -$20 -$10 $0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% N PV ($ m ill io ns ) Net Impacts: Baseline vs. Scenario Baseline (Without Project) Scenario (With Project) -$25 -$20 -$15 -$10 -$5 $0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Ch an ge in N PV ($ m ill io ns ) NPV Difference (Scenario - Baseline) Exhibit F-11. BCA and VaR results for PHX project. Mean Std Deviation Avg NPV of Project $9,510,258 $1,281,451 Avg B/C Ratio 1.14 0.02 -$160 -$140 -$120 -$100 -$80 -$60 -$40 -$20 $0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% N PV ($ m ill io ns ) Net Impacts: Baseline vs. Scenario Baseline (Without Project) Scenario (With Project) $0 $2 $4 $6 $8 $10 $12 $14 $16 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Ch an ge in N PV ($ m ill io ns ) NPV Difference (Scenario - Baseline) Exhibit F-12. BCA and VaR results for PHX project with 3% discount rate.

112 Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports Mean Std Deviation Avg NPV of Project $9,414,641 $4,237,418 Avg B/C Ratio 1.14 0.06 -$180 -$160 -$140 -$120 -$100 -$80 -$60 -$40 -$20 $0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% N PV ($ m ill io ns ) Net Impacts: Baseline vs. Scenario Baseline (Without Project) Scenario (With Project) $0 $5 $10 $15 $20 $25 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Ch an ge in N PV ($ m ill io ns ) NPV Difference (Scenario - Baseline) Exhibit F-13. BCA and VaR results for PHX project with 3% discount rate and alternate sampling strategy. One can also see the effect of changing the sampling strategy. Both sets of results shown previ- ously use the one-model-per-year strategy, where a different model is used for each year of each simulation. One could select the one-model-per-simulation strategy instead, where the same model is used for all years of any single simulation. These results (also assuming a 3% discount rate) are shown in Exhibit F-13. The alternate sampling strategy has very modest effects on the average NPV and benefit–cost ratios, but it does increase the overall variability in results, as shown by the increased standard deviations, the more variable NPV curves in the VaR chart on the left, and the overall range of NPV differences in the chart on the right.

Next: Appendix G - FAA Guidance on Benefit Cost Analysis »
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TRB’s Airport Cooperative Research Program (ACRP) Research Report 199: Climate Resilience and Benefit–Cost Analysis: A Handbook for Airports provides information on how to apply benefit–cost analysis tools and techniques to improve decision making affecting resilience of airport infrastructure projects in response to potential long-term impacts of climate change and extreme weather events.

The handbook is designed to improve the process by which infrastructure investment strategies are evaluated, with an emphasis on ensuring climate-related resiliency.

Procedures for presenting assumptions and results transparently and for implementing the process are also included so that industry users and decision makers can understand and communicate the outcome of the analytical process.

Based on data availability, the analytical methods included in the handbook focus on two specific areas of climate change likely to affect airports (although these methods can, in principle, be used more widely): (1) the potential for extreme flooding events resulting from storm surge and sea level rise near coastal airports, and (2) the potential for rising temperatures that require weight restrictions on aircraft takeoffs (or possibly full flight delays) at airports with shorter runways in warm climates or at high elevations.

The results available from application of the suggested methodologies do not necessarily make the decision of whether to invest in a mitigation project to combat climate change any easier but, rather, provide a full range of potential outcomes and possibilities for airport planners and managers to consider. Using this methodology, airport decision makers can then determine how much risk from uncertain climate change and extreme weather events they are willing or able to accommodate. Implementation of the methods presented in the handbook can be used to obtain essential quantifiable estimates of those risks, which is of particular value to airport financial professionals.

The handbook is accompanied by a set of Microsoft Excel models to support the decision-making process (one for extreme water rise causing potential flooding events, and the other for high temperatures that may affect weight restrictions on aircraft takeoffs), a video tutorial, a report summary document, and an executive briefing to help decision makers understand the process.

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