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Software User Manual 51 Baseline&Scenarios Worksheet In this worksheet, the user can make further changes to the Baseline Forecast and create two different sensitivity cases. Also by selecting the buttons on this worksheet, the user can create confidence bands around the Baseline Forecast that reflect the historic range of error in expec- tations for jet fuel prices and economic growth (based on national GDP). Each of these function- alities is exercised below. Creating a User-Defined Scenario In the Baseline&Scenarios worksheet, the user can create alternative forecasts by simply inputting the data at the top of the worksheet in the columns labeled User Updates. For exam- ple, assume that, starting in 2010, domestic operations and enplanements at ACY were going to grow 5 percent more than indicated by the TAF. The user can easily modify the Baseline Forecast by using simple commands. For instance, in the Domestic Operations column for 2010, the user could input: = 1.05 B11 which would cause domestic operations in 2010 to be 5 percent higher than the TAF projection. The same type of command could be used in following years to increase operations by 5 percent each year. Repeating the same command in the Domestic Enplanements column will cause domestic enplanements to also increase by 5 percent compared to the TAF. The result is a new User-Defined Baseline Forecast as shown in Exhibit II-18 taken from the Baseline&Scenarios worksheet. Notice that the user updates are highlighted in red. These changes in red are now the Baseline Forecast around which sensitivity cases can be created. The user updates created in the Baseline&Scenarios worksheet are the new Baseline Forecast for the model. Any sensitivity cases created subsequently will be based on the user-defined scenario. Exhibit II-18. User updates in the Baseline&Scenarios worksheet. Baseline Forecast for ACY (Based on TAF Air Carrier/Air Taxi Forecast and User Updates of Current Domestic Service) You can change the Default Baseline forecasts by entering new numbers in the User Update columns. Reset User Updates to Baseline Defaults BaselineValues BaselineValues_Int Default Baseline User Updates Default Baseline User Updates Domestic Forecast (changes in red) International Forecast (changes in red) Domestic Domestic Domestic Domestic International International International International Year Operations Enplanements Operations Enplanements Year Operations Enplanements Operations Enplanements 2009 15,084 545,516 15,084 545,516 2009 0 0 0 0 2010 15,233 552,930 15,995 580,577 2010 0 0 0 0 2011 15,384 560,444 16,153 588,466 2011 0 0 0 0 2012 15,535 568,060 16,312 596,463 2012 0 0 0 0 2013 15,689 575,786 16,473 604,575 2013 0 0 0 0 2014 15,846 583,617 16,638 612,798 2014 0 0 0 0

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52 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Creating a Sensitivity Case Using User-Defined Ranges for Key Air Service Drivers In the Baseline&Scenarios worksheet, the user creates sensitivity cases for a forecast. This is the central reason for the creation of the software. The models embedded in the software (described in detail in the following paragraphs) are designed to show how air service may be affected depend- ing on future values for not only key drivers like jet fuel prices and income growth, but also other drivers. The user is free to input whatever range of values for drivers that seems appropriate. When creating scenarios, care should be taken to keep assumptions internally consistent for each sensitivity case. For example, a High Case should reflect an optimistic view of the future, which usually will mean low jet fuel prices, higher income growth, less competition from nearby hubs, and lower inflation. Drivers would move in the opposite direction for a Low Case. Jet Fuel Prices and Local Income The spike in jet fuel prices in 20072008 was largely unanticipated and caused a substan- tial reduction in air services (measured by average daily seat departures); the impact was com- pounded by a severe recession that began in 2008. Creating a Low scenario that anticipates both high fuel prices and low income growth is one of the logical sensitivity tests for any air service forecast. A High scenario would have relatively low jet fuel prices and high income growth. Up-to-date information on oil market expectations is available from the Chicago Mercantile Exchange website link embedded in the software and illustrated in Exhibit II-19. Exhibit II-19. Link to oil futures market in the Baseline&Scenarios worksheet. To get an up-to-the minute view of likely future jet fuel prices, press the link: View the latest Heating Oil futures prices by clicking here This link takes the user to the Chicago Mercantile Exchange (CME) page for heating oil futures prices. These prices are highly correlated with jet fuel prices. Following is a sample page taken from the site on June 28, 2010. Prior Month Last Change Open High Low Volume Settle Jul-10 2.0909 -0.0213 2.1122 2.11 2.1148 2.0801 4,733 Aug-10 2.1144 -0.019 2.1334 2.1386 2.1386 2.1034 14,238 Sep-10 2.1395 -0.0167 2.1562 2.1578 2.1597 2.1293 5,114 Oct-10 2.1646 -0.0158 2.1804 2.1826 2.1826 2.1562 1,688 Nov-10 2.1881 -0.0167 2.2048 2.1858 2.1907 2.1812 556 Dec-10 2.2129 -0.015 2.2279 2.2312 2.2336 2.2039 2,962 Jan-11 2.2368 -0.0142 2.251 2.2325 2.2365 2.2324 377

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Software User Manual 53 Exhibit II-20. Range of values for jet fuel prices and real GDP. Jet Fuel Prices Gallon (nominal) Time Period Average Median High Low 1Q 2000 3Q 2009 $ 1.43 $ 1.33 $ 3.51 $ 0.59 1Q 2005 3Q 2009 $ 2.07 $ 1.94 $ 3.51 $ 1.33 US Real GDP 2000 2009 1.79% 2.27% 3.66% -2.40% 2005 2009 1.15% 2.03% 2.94% -2.40% Sources: ATA and US BEA; EIA The model embedded in the software uses local income growth (measured as the change in real per capita income in the metro- or micropolitan area where the airport of interest is located) as a key economic driver for airport activity. For projections into the future, however, such local income measures may be difficult to obtain, so the baseline projections are based on estimates of national GDP growth. If the user has access to local projections, they can be used in place of the national GDP projections. To assist the user in defining sensitivity cases, Exhibit II-20 reports the high, low, and average values for jet fuel prices and national GDP growth over the past decade. Baseline Inflation Rate The air services model embedded in the software operates on real jet fuel prices. The model takes whatever jet fuel price assumptions are made and converts them to real dollars using the assumed Baseline Inflation Rate forecast. The higher the inflation rate, the lower the real jet fuel price will be; since a lower real price of jet fuel will cause an increase in air services, higher infla- tion is consistent with more air services in the model. Exhibit II-21 reports on the range of annual inflation reported by the GDP deflator in the past decade. Average Aircraft Size In the air services model embedded in the software, aircraft size has a positive effect on seat departures. Aircraft size is a proxy for the cost of producing air services per seat. Because larger aircraft tend to produce lower seat mile costs (all else being the same), in competitive markets these lower costs would be passed onto consumers resulting in more demand and therefore air service. In developing scenarios, users would want to input any known changes in future fleet types used by carriers. Or, it might be appropriate to input a trend in average seat size if it is likely to continue. Seat-Departures from Nearby Large or Medium Hub Airports In the air services model, this variable has a negative impact on air services. An airport operating in the shadow of a large or medium hub airport would tend to have fewer air services Exhibit II-21. Range of annual inflation. US GDP Deflator Time Period Average Median High Low 2000 2009 2.4% 2.9% 3.3% 1.2% 2005 2009 2.6% 2.9% 3.3% 1.2% Source: US BEA

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54 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development than would be the case in the absence of such competition. The software provides informa- tion on the average daily seat-departures at large and medium hubs within 50 miles of the subject facility. Caution: The software is NOT designed to accommodate very large changes in seat-departures at nearby large or medium hubs. Doubling the figures for a nearby hub or setting them to zero may produce nonsensical results. Scenarios showing changes of 10% should be easily accommodated and produce reasonable results. Airport Concentration Index The Herfindahl-Hirschman Index (HHI) is a measure of the level of competition in a defined market. It takes into account the relative size of competitors as well as how many of them there are. As applied in the software, market shares measure the percentage of domestic seat-departures at the airport accounted for by marketing carriers. The HHI is the sum of squares of these shares (measured in percentage points). The equation for the HHI is: HHI = ( share1 ) + ( share 2 ) + ( share 3 ) + . . . + ( share n ) 2 2 2 2 A monopoly carrier with 100 percent of the seats at an airport would have an HHI of 10,000 (= 1002). Five competitors with equal 20 percent shares would have an HHI of 2,000. As the HHI increases, air services decline. The user should input any expected or feasible changes in competitive conditions. For exam- ple, an airport with five competitors that might lose one of them may see a decline in air ser- vices. Adding one or more competitors may cause an improvement in air services. To calculate a new HHI, the user can enter carrier seat-departures in the section at the bottom left of the Baseline&Scenarios worksheet (Exhibit II-22). Exhibit II-23 reports some additional HHI calculations for hypothetical situations ranging from monopoly to equal shares among five carriers. Exhibit II-22. HHI calculator on Baseline&Scenarios worksheet. Airport Concentration Index Calculator Input seat-departures by all carriers at the airport (per day, per week, etc.) Airline Seat-Departures A 10,000 B 5,000 C 2,000 D 500 Airport Concentration Index: E 4,220 F G H I J TOTAL 17,500

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Software User Manual 55 Exhibit II-23. HHI hypothetical market share calculations. Shares of Seat Departures Airline Number of Carriers A B C D E HHI 1 100% 10,000 2 90% 10% 8,200 2 80% 20% 6,800 2 70% 30% 5,800 2 60% 40% 5,200 2 50% 50% 5,000 3 90% 5% 5% 8,150 3 80% 10% 10% 6,600 3 70% 15% 15% 5,350 3 60% 20% 20% 4,400 3 50% 25% 25% 3,750 3 40% 30% 30% 3,400 3 33% 33% 33% 3,327 4 90% 3% 3% 3% 8,133 4 80% 7% 7% 7% 6,533 4 70% 10% 10% 10% 5,200 4 60% 13% 13% 13% 4,133 4 50% 17% 17% 17% 3,333 4 40% 20% 20% 20% 2,800 4 30% 23% 23% 23% 2,533 4 25% 25% 25% 25% 2,500 5 90% 3% 3% 3% 3% 8,125 5 80% 5% 5% 5% 5% 6,500 5 70% 8% 8% 8% 8% 5,125 5 60% 10% 10% 10% 10% 4,000 5 50% 13% 13% 13% 13% 3,125 5 40% 15% 15% 15% 15% 2,500 5 30% 18% 18% 18% 18% 2,125 5 20% 20% 20% 20% 20% 2,000 Sample Sensitivity Case for ACY Returning to the sample runs for ACY, recall that we had input a forecast that assumes that both enplanements and operations will increase 5 percent per year for the five years following 2009. To assess the sensitivity of that new forecast to unforeseen economic circumstances, cre- ate a Low case that assumes $3.50 jet fuel and zero income growth for all five years. For the High case, assume $2.00 jet fuel and 3 percent annual income growth for all five years. Leave the other variables unchanged. To implement this sensitivity case, input the assumptions (shown in Exhibit II-24 in red) into the Baseline&Scenarios worksheet . Examining the Baseline and Sensitivity Cases The new Baseline and Sensitivity cases can be viewed in the Projections worksheet and in the One-Page Report worksheet. The former shows graphs and data for operations and enplanements as well as estimates of airport operating revenues; Exhibit II-25 shows the results for the assump- tions input in Exhibit II-24. By 2014, both operations and enplanements would be about 7 per- cent lower in the Low case than in the Baseline, while the High case would be about 4 percent higher. By 2014, ACY operating revenues are estimated to be 5 percent lower in the Low case and 3 percent higher in the High case. The One-Page Report worksheet shown in Exhibit II-26 was designed to be viewed in combination with the embedded risk analysis options discussed in the following section; it shows the same enplanements and operations graphs as in Exhibit II-25, along with the im- plied High and Low cases for jet fuel and income in the pre-defined risk analysis (see the next section).

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56 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Exhibit II-24. Inputting sensitivity assumptions in the Baseline&Scenarios worksheet. Baseline Forecast for ACY (Based on TAF Air Carrier/Air Taxi Forecast and User Updates of Current Domestic Service) You can change the Default Baseline forecasts by entering new numbers in the User Update columns. Reset User Updates to Baseline Defaults BaselineValues BaselineValues_Int Default Baseline User Updates Default Baseline User Updates Domestic Forecast (changes in red) International Forecast (changes in red) Domestic Domestic Domestic Domestic International International International International Year Operations Enplanements Operations Enplanements Year Operations Enplanements Operations Enplanements 2009 15,084 545,516 15,084 545,516 2009 0 0 0 0 2010 15,233 552,930 15,995 580,577 2010 0 0 0 0 2011 15,384 560,444 16,153 588,466 2011 0 0 0 0 2012 15,535 568,060 16,312 596,463 2012 0 0 0 0 2013 15,689 575,786 16,473 604,575 2013 0 0 0 0 2014 15,846 583,617 16,638 612,798 2014 0 0 0 0 Forecast Drivers for Domestic Scenarios (International Forecast is Fixed) 2005-2009 data are fixed; you may change the Baseline and/or Scenario assumptions below for 2010-2014. If you entered updates to the Baseline Domestic Forecast above, you should ensure that the Baseline assumptions below are consistent with those updates. Reset All Scenarios to Baseline Defaults Calculating... JetFuelValues Set Jet Fuel Scenarios based Please wait InflationValues View the latest Heating Oil futures on Futures Uncertainty prices by clicking here Baseline Price of Jet Fuel Baseline Year (Current Yr $/gal) Scenario 1 Scenario 2 Year Inflation Rate Scenario 1 Scenario 2 2005 $1.622 2005 3.34% 2006 $1.906 2006 3.25% 2007 $2.025 2007 2.87% 2008 $2.938 2008 2.14% 2009 $1.844 $1.199 $3.227 2009 1.18% 2010 $2.174 $3.500 $2.000 2010 1.47% 1.47% 1.47% 2011 $2.258 $3.500 $2.000 2011 1.31% 1.31% 1.31% 2012 $2.499 $3.500 $2.000 2012 1.43% 1.43% 1.43% 2013 $2.719 $3.500 $2.000 2013 1.76% 1.76% 1.76% 2014 $2.888 $3.500 $2.000 2014 1.73% 1.73% 1.73% (Default baseline from 2010 forward based on change in projected price of (Default baseline from 2010 forward based on projected GDP Implicit Price jet fuel from EIA Annual Energy Outlook 2010.) Deflator from EIA Annual Energy Outlook 2010.) Set Income Scenarios based on EIA GDP Uncertainty RealIncomeValu SeatsizeValues Not relevant for Small Hubs Baseline Baseline Local Real Airport Avg Year Income Growth Scenario 1 Scenario 2 Year Seatsize Scenario 1 Scenario 2 2005 0.43% 2005 119.9 2006 0.73% 2006 126.7 2007 0.27% 2007 130.1 2008 0.34% 2008 140.2 140.2 140.2 2009 -2.83% #N/A #N/A 2009 141.9 141.9 141.9 2010 1.07% 0.00% 3.00% 2010 141.9 141.9 141.9 2011 3.52% 0.00% 3.00% 2011 141.9 141.9 141.9 2012 3.64% 0.00% 3.00% 2012 141.9 141.9 141.9 2013 2.80% 0.00% 3.00% 2013 141.9 141.9 141.9 2014 2.46% 0.00% 3.00% 2014 141.9 141.9 141.9 (Default baseline from 2010 forward based on projected US GDP from EIA (Default baseline from 2010 forward equal to 2009 value.) Annual Energy Outlook 2010; 2009 value equal to US GDP growth.) HHIValues Set50Values Not relevant for Small Hubs Baseline Baseline Airport Domestic Daily Seat- Concentration Departures at Index - HHI Lrg/Med Hubs within Year (0-10,000) Scenario 1 Scenario 2 Year 50 Miles Scenario 1 Scenario 2 2005 8,189 2005 0 2006 8,450 2006 0 2007 8,804 2007 0 2008 9,715 9,715 9,715 2008 0 0 0 2009 8,333 8,333 8,333 2009 0 0 0 2010 8,333 8,333 8,333 2010 0 0 0 2011 8,333 8,333 8,333 2011 0 0 0 2012 8,333 8,333 8,333 2012 0 0 0 2013 8,333 8,333 8,333 2013 0 0 0 2014 8,333 8,333 8,333 2014 0 0 0 (Default baseline from 2010 forward equal to 2009 value.) (Default baseline from 2010 forward derived from TAF.)

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Software User Manual 57 Exhibit II-25. Revised baseline and sensitivity example from the Projections worksheet. Projected Annual Operations for ACY 21,000 20,000 19,000 18,000 17,000 16,000 15,000 14,000 Projected Annual Revenues for ACY 13,000 12,000 2008 Act 2009 2010 2011 2012 2013 2014 $27,000,000 Baseline 17,962 15,084 15,995 16,153 16,312 16,473 16,638 Scenario 1 17,962 15,084 15,995 15,484 15,303 15,383 15,589 Scenario 2 17,962 15,084 15,995 16,288 16,573 16,951 17,369 $22,000,000 $17,000,000 Projected Annual Enplanements for ACY $12,000,000 700,000 $7,000,000 650,000 2008 Act 2009 2010 2011 2012 2013 2014 Baseline $21,809,615 $14,419,394 $15,146,637 $15,296,747 $15,447,923 $15,601,112 $15,756,232 600,000 Scenario 1 $21,809,615 $14,419,394 $15,146,637 $14,783,759 $14,673,023 $14,762,465 $14,949,958 Scenario 2 $21,809,615 $14,419,394 $15,146,637 $15,399,595 $15,647,118 $15,964,795 $16,311,881 550,000 500,000 450,000 2008 Act 2009 2010 2011 2012 2013 2014 Baseline 553,177 545,516 580,577 588,466 596,463 604,575 612,798 Scenario 1 553,177 545,516 580,577 564,097 559,593 564,544 574,160 Scenario 2 553,177 545,516 580,577 593,376 606,014 622,098 639,704

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58 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Exhibit II-26. One-Page Report worksheet showing confidence bands.