COMMITTEE ON AVIATION ENVIRONMENTAL PROTECTION (CAEP)

NINTH MEETING

Montréal, 4 to 15 February 2013

Agenda Item 3: Review of technical proposals relating to aircraft noise.

COST-BENEFIT ANALYSIS OF CAEP/9 NOISE STRINGENCY OPTIONS

(Presented by the United States)

SUMMARY

This paper provides results from the U.S. cost benefit assessment of 

environmental impacts compared against economic costs for the CAEP/9 noise 

stringency options, with explicit consideration of interdependencies and 

uncertainties among impacts. Given previous CAEP recognition of the need 

and importance of considering the interdependencies of noise, fuel burn, and 

emissions, the U.S. conducted a cost benefit analysis to more fully assess 

potential interdependencies, and better inform decision making.

1. INTRODUCTION

1.1 This paper provides additional details from a U.S. cost-benefit analysis that assessed the 

interdependent impacts associated with the noise stringency options under consideration for CAEP/9. 

The analysis is in the appendix of this paper, and was used to inform the U.S. position on the noise 

stringency analysis (please see WP/34).

2. BACKGROUND

2.1 Interdependencies

2.1.1 CAEP has increasingly recognized the importance and need to consider the 

interdependencies between noise, fuel burn and emissions and among individual emissions types. The 

current CAEP Work Programme acknowledges the need to take interdependencies into account as part of 

task N.06.02 “to assess interdependency effects of noise stringency options with respect to CO2/fuel burn 

and NOX.”1

 

1 CAEP-SG.20123.IP.001, page SD/5.A2, Task N.06.02

CAEP/9-IP/25

13/12/12

English only
CAEP/9-IP/25 - 2 -

2.1.2 Since 2004, the U.S. has routinely reported to CAEP regarding the development of a suite 

of tools, including the Aviation Environmental Portfolio Management Tool for Impacts (APMT-Impacts), 

to facilitate thorough consideration of aviation's environmental effects. The tool suite has been designed 

and developed by the Federal Aviation Administration in collaboration with the National Aeronautics and 

Space Administration and Transport Canada. The main goal for developing these state-of-the-art tools 

was to be able to characterize and quantify the interdependencies among aviation-related noise and 

emissions, impacts on health and welfare, and industry and consumer costs, under different policy, 

technology, operational, and market scenarios. 

2.1.3 Following CAEP/7 agreement, the U.S. conducted a cost benefit analysis using APMTImpacts to assess the interdependencies associated with the NOx stringency options under consideration at 

CAEP/8. Results of the U.S. analysis, and a comprehensive overview of the approach and methodologies 

used to conduct the analysis, were provided in CAEP/8 IP-30. 

2.2 CAEP/9 Noise Stringency Analysis

2.2.1 CAEP/9 Work Programme directed WG1, WG3, MDG and FESG to conduct analyses 

of the potential noise stringency options under consideration for CAEP/9.

2

 The work conducted 

regarding the stringency options assessed, industry response, environmental benefits and cost 

effectiveness are documented in the MDG and FESG paper to CAEP/9.3 The same data developed 

through the CAEP analysis process were used as inputs to the U.S. cost benefit analysis; specifically data 

from MDG’s AEDT fuel burn and emissions model, the MDG noise models, and FESG’s 

APMT-Economics model.

2.2.2 It is important to note the behaviour of MDG’s environmental results and the FESG costs 

as they feed the cost benefit analysis. The MDG noise results behaved as expected, showing increased 

noise benefits as the noise stringency levels increased. The MDG fuel burn and NOX results did not 

follow the noise data trend.

3. DISCUSSION

3.1 The U.S. conducted a cost benefit analysis to assess the interdependencies associated 

with the noise stringency options under consideration for CAEP/9. The analysis took into consideration 

the environmental impacts of the stringency options, including assessing the health and welfare impacts; 

and, quantified interdependencies in terms of physical and monetary impacts for aircraft-related noise, 

fuel burn and emissions. The analysis followed a similar approach to that used for conducting a 

cost-benefit analysis of the stringency options for the CAEP/8 NOX Stringency Analysis. 

3.2 As delineated in the appendix, the environmental benefits and economic costs associated 

with the CAEP/9 noise stringency options were analyzed for the US domain from 2006 to 2036 with 

policy implementation in 2020. MDG noise, fuel burn and emissions data were used as inputs, along with 

FESG calculated recurring operating costs.

4

 

3.3 All stringency options lead to a noise benefit ranging from 0.5% to 5% reduction in the 

cumulative number of people impacted by noise over the next 30 years.

 

2 Tasks N.06.01, N.06.02, E.10, M.02, and F.02.

3 CAEP/9-WP/8

4

Sensitivity tests incorporating FESG’s non-recurring cost estimates did not change the cost benefit findings.
- 3 - CAEP/9-IP/25

3.4 Stringency 1 (Chapter 4 -3 EPNdB) is shown to be cost beneficial across all assumptions 

considered; however, the associated fuel burn emissions are within the resolution limits of the MDG 

models, indicating that environmental benefits are uncertain and not significant. 

3.5 Stringency 2 (Chapter 4 -5 EPNdB) is shown to be cost beneficial across all assumptions 

considered; provides global reductions in noise, fuel burn and emissions; and, offers the largest reduction 

in direct operating costs relative to the no stringency case. Stringency 2 is the most cost beneficial option, 

irrespective of the combinations of assumptions considered.

3.6 Stringency 3 (Chapter 4 -7 EPNdB) has environmental benefits greater than Stringency 2; 

however, the recurring costs are greater than the monetized environmental benefits across most 

assumptions considered. Only under a very limited set of assumptions5(e.g., indicating a high level of 

impacts for noise, air quality and climate coupled with the highest values of statistical life) could 

Stringency 3 potentially become cost beneficial.

3.7 Stringency 4 (Chapter 4 -9 EPNdB) includes an increase in fuel burn emissions because 

the fleet shifts to smaller, compliant aircraft that require increased operations to maintain seat demand. 

Thus, while Stringency 4 has noise benefits, it also has air quality and climate dis-benefits, and is not a 

sensible stringency consistent with CAEP principles.

3.8 Stringency 5 (Chapter 4 -11 EPNdB) continues the dis-benefit trend in air quality from 

Stringency 4; however, the fleet shifts away from smaller aircraft, and demonstrates climate benefits. This 

stringency is even more inconsistent with CAEP principles.

— — — — — — — —

 

5

See Appendix Tables 4, 5, and 6 respectively for the range of noise, air quality and climate assumptions.
APPENDIX

COST-BENEFIT ANALYSIS OF CAEP/9 NOISE STRINGENCY OPTIONS

1 INTRODUCTION ...........................................................................................................................A-3

2 METHODS FOR ASSESSING TRADE-OFFS AMONG ENVIRONMENTAL & ECONOMIC IMPACTS A-3

2.1 APMT – Impacts.............................................................................................................A-5

2.1.1 Noise Module.....................................................................................................A-5

2.1.2 Air Quality Module............................................................................................A-6

2.1.3 Climate Module .................................................................................................A-6

3 COMMUNICATION OF RESULTS..................................................................................................A-7

3.1 Decision-Making Framework – Lenses..........................................................................A-7

3.2 Timescales ......................................................................................................................A-8

4 NOISE STRINGENCY POLICY ANALYSIS ....................................................................................A-9

4.1 CAEP/9 Noise Stringency Options.................................................................................A-9

4.1.1 FESG Fleet and Traffic Forecast .....................................................................A-10

4.1.2 Emissions Modeling ........................................................................................A-11

4.1.3 Cost of Stringency Options..............................................................................A-11

4.2 APMT-Impacts Modeling Assumptions.......................................................................A-11

4.2.1 APMT-Impacts................................................................................................A-12

4.3 AEDT Noise and Emissions Inputs ..............................................................................A-14

4.4 RESULTS.....................................................................................................................A-19

4.4.1 APMT-Impacts Physical Results.....................................................................A-19

4.4.2 Cost-Benefit Analysis......................................................................................A-23

4.4.2.1 Discount Rate Analysis.......................................................................A-26

4.4.2.2 Lens Analysis......................................................................................A-29

4.4.2.3 Cost Sensitivity Analysis....................................................................A-32

5 SUMMARY AND CONCLUSIONS ................................................................................................A-33

6 REFERENCES.............................................................................................................................A-34

LIST OF TABLES

Table 1: APMT-Impacts Effects Modeled................................................................................................A-5

Table 2: CAEP/9 Noise Stringency Options...........................................................................................A-10

Table 3: Fleet Seat Class Designations...................................................................................................A-10

Table 4: APMT-Impacts Noise Assumptions for the CAEP/9 Noise Stringency Analysis....................A-13

Table 5: APMT-Impacts Air Quality Assumptions for the CAEP/9 Noise Stringency Analysis...........A-13

Table 6: APMT-Impacts Climate Lens Assumptions for the CAEP/9 Noise Stringency Analysis........A-13

Table 7: APMT-Impacts Policy – Baseline Monetized Environmental Benefits ...................................A-26

Table 8: Noise Stringency Cost Benefit Summary, Midrange Lens, 3% Discount Rate ........................A-26

CAEP/9-IP/25

Appendix
CAEP/9-IP/25

Appendix A A-2

LIST OF FIGURES

Figure 1: Aviation Environmental Tool Suite Block Diagram .................................................................A-4

Figure 2: Timescales in Policy Analysis...................................................................................................A-8

Figure 3: Noise Inputs: Baseline Noise Area Exposure..........................................................................A-15

Figure 4: Noise Inputs: Area Exposure Stringency Cumulative Difference from Baseline ...................A-16

Figure 5: Air Quality Inputs; % Change in Fuel Burn Below 3000 feet.................................................A-17

Figure 6: Air Quality Inputs; % Change in NOX emissions below 3000 ft.............................................A-17

Figure 7: Climate Inputs; % Change in Full Flight Fuel Burn................................................................A-18

Figure 8: Climate Inputs; % Change in Full Flight NOX Emissions.......................................................A-19

Figure 9: Percent Change in Cumulative People (in People-Years) Impacted by Noise........................A-20

Figure 10: Baseline Air Quality Yearly Mortalities by PM Species, Midrange Lens ............................A-21

Figure 11: Noise Stringencies - Baseline Air Quality Yearly Mortality Incidences, Midrange Lens ....A-21

Figure 12: Yearly Baseline Climate Physical Impacts, Midrange Lens .................................................A-22

Figure 13: Noise Stringency Yearly Climate Physical Impacts, Midrange Lens....................................A-22

Figure 14: Noise Stringencies - % Change in APMT Physical Metrics, Midrange Lens.......................A-23

Figure 15: Noise Stringency 2 (-5 EPNdB) minus Baseline Impacts and Costs ....................................A-24

Figure 16: Noise Stringency 3 (-7 EPNdB) minus Baseline Impacts and Costs.....................................A-24

Figure 17: Noise Stringencies CBA........................................................................................................A-25

Figure 18: Noise Stringency 2 - Baseline Environmental Impacts Discount Rate Sensitivity ...............A-27

Figure 19: Noise Stringency 3 - Baseline Environmental Impacts Discount Rate Sensitivity ...............A-27

Figure 20: Noise Stringencies - Baseline CBA for Aligned Discount Rates..........................................A-28

Figure 21: Noise Stringency Cost Benefit - Non-Aligned Discount Rates ............................................A-29

Figure 22: Noise Stringency 2 - Baseline Environmental Impacts Lens Sensitivity ..............................A-29

Figure 23: Noise Stringency 3 - Baseline Environmental Impacts Lens Sensitivity ..............................A-30

Figure 24: Noise Stringencies - Baseline Net Cost-Benefit Lens Sensitivity, 3% Discount Rate ..........A-30

Figure 25: Noise Stringency - Baseline Cost Benefit Illustrative Lens, 3% Discount Rate ...................A-31

Figure 26: Cost Sensitivity to Combined Recurring and Non-Recurring Costs.....................................A-32
A-3

CAEP/9-IP/25

Appendix A

1. INTRODUCTION

The International Civil Aviation Organization (ICAO) Committee on Aviation Environmental Protection 

(CAEP) traditionally addressed aircraft noise and emissions impacts independently of each other. Though 

advances have been made in quantifying collateral environmental consequences, ICAO CAEP decisions 

have been based on cost-effectiveness metrics that consider the cost to implement a proposed stringency 

against the benefit in the targeted environmental parameter. While interdependent fuel burn

consequences are accounted for as part direct operating costs, any possible inconsistency between noise 

and NOx consequences can only be noted under the current ICAO CAEP cost-effectiveness analysis 

process. Thus, the current process notes, but does not value environmental trade-offs.

The seventh meeting of the ICAO-CAEP, held in 2007, considered the necessity for comprehensive 

analyses that assess the trade-offs among noise and emissions impacts and economic costs to better 

inform policymaking decisions [1]. As part of the CAEP/8 NOX Stringency analysis, a demonstration of 

the perspective provided by a more complete accounting of societal costs and benefits across different 

impact spheres was demonstrated using the Aviation environmental Portfolio Management Tool for 

Impacts (APMT-I) [2][3].

1.1 Paper Overview

The objective of this paper is to report on the direct assessment of environmental impacts compared 

against economic costs, with explicit consideration of interdependencies and uncertainties among impacts 

for the noise stringency options under consideration for the ninth meeting of the ICAO-CAEP (CAEP/9) 

on 4 to 15 February 2013.

This cost benefit analysis used the Aviation Environmental Portfolio Management Tool for Impacts 

(APMT-Impacts), which is a component of the aviation environmental tool suite. The tool suite has been 

designed and developed by the Federal Aviation Administration (FAA) in collaboration with the National 

Aeronautics and Space Administration (NASA) and Transport Canada (TCCA). The main goal for 

developing these tools is to be able to characterize and quantify the interdependencies among aviationrelated noise and emissions, impacts on health and welfare, and industry and consumer costs, under 

different policy, technology, operational, and market scenarios. In addition to providing environmental 

and economic impact estimates, this work also quantifies uncertainties throughout the policy analysis 

process and explores the sensitivity of results to variability in model inputs and parameters.

2. METHODS FOR ASSESSING TRADEOFFS AMONG 

ENVIRONMENTAL AND ECONOMIC IMPACTS

This Section provides an overview of the models and methods used for the U.S. cost-benefit analysis of 

CAEP/9 noise stringency options.1

Figure 1 is a functional schematic of the portions of the Aviation Environmental Tools Suite as applied to

the subject analysis. The main functional components of the Tools Suite applied in this analysis are 

summarized below.2 Other combinations of tools suite resources have included the Emissions Prediction 

 

1 Additional information on the APMT-I tools and methods is available on-line at http://web.mit.edu/aeroastro/partner/apmt/

2 Additional information is available at http://www.faa.gov/about/office_org/headquarters_offices/apl/research/models/
CAEP/9-IP/25

Appendix A A-4

and Policy Analysis (EPPA) tool for aviation and the Environmental Design Space (EDS); however, they 

were not used in this CAEP/9 analysis.

Figure 1: Aviation Environmental Tool Suite Block Diagram

The Aviation Environmental Tools Suite development was preceded by an extensive survey of guidance 

documents on recommended practices for environmental policy analysis including 

[4][5][6][7][8][9][10][11][12][13] and which were reviewed by the Transportation Research Board of the 

US National Academies [14]. The current tools were built on the foundations laid out by the initial 

survey of economic guidance documents and they continue to evolve to incorporate new knowledge, as 

well as expand modeling capabilities.

For the CAEP/9 analysis reported in this paper, the Aviation environmental Portfolio Management Tool 

(APMT) for Impacts (APMT-I), APMT for Economics (APMT-E) and the Aviation Environmental 

Design Tool (AEDT) were employed. 

 APMT-E models airline and aviation market responses to environmental policy options.

 AEDT models aircraft performance in four-dimensional space and time to produce fuel burn, 

emissions and noise. 

 APMT-I estimates the environmental impacts of aircraft operations through changes in health and 

welfare endpoints for climate, air quality and noise. 

The modular arrangement of the tools is integrated to enable comprehensive cost-benefit and costeffectiveness analyses. As per conventional economics terminology, monetary flows in the aviation 

industry are defined as costs and environmental impacts (e.g. health impacts or noise exposure) as 

benefits. Both costs and benefits can be positive or negative. The remainder of this section includes an 

overview of the modeling methodology adopted in APMT-I.
A-5

CAEP/9-IP/25

Appendix A

2.1 APMT – Impacts

The APMT-Impacts modules assess the physical and socio-economic environmental impacts of aviation 

using noise and emissions inventories as the primary aviation inputs. Socio-economic data and 

projections as well as health and welfare damage valuation schemes, described in the following sections, 

form the basis of code algorithm inputs. Impacts and associated uncertainties are simulated using Monte 

Carlo methods with probabilistic distributions of key variables that have scientific, epistemic, and monostructural uncertainty. APMT-I is sub-divided into three different environmental modules: Noise, Air 

Quality, and Climate. Table 1 lists the effects modeled under each impact area and key corresponding 

metrics.

Table 1: APMT-Impacts Effects Modeled

Impact Type Effects Modeled Primary Impact Metrics

Physical Monetary

Noise Area exposure to noise, population exposure to 

noise, housing value and rental loss

Number of people 

effected

Net present value

Air Quality Primary and secondary particulate matter (PM) Incidences of mortality 

and morbidity

Net present value

Climate CO2

Non-CO2: NOX-O3, NOX-CH4, contrails, 

aviation-induced cloudiness, sulfates, soot, H2O

Globally-averaged 

surface temperature 

change

Net present value

Policy evaluation with APMT-I provides information on the environmental benefits and economic costs 

resulting from the implementation of a policy relative to a baseline scenario. In conveying this 

information to decision-makers, the uncertainties in the quantified impacts and the key assumptions about 

inputs and model parameters are provided with the results.

The Monte Carlo methods used to assess the policy impacts relative to the baseline can be used to account 

for the reduced uncertainties in outputs. When there are many modeling uncertainties common to both 

the policy and the baseline scenario, the difference between two scenarios can be predicted with less 

uncertainty than when considering the baseline impacts in isolation. Therefore, in estimating policy 

impacts, a paired sampling approach is used where the same random draws for model parameters are 

applied to both the baseline and the policy scenarios. The only difference between the two scenarios is 

driven by the effect of the policy, such as a change in the emissions inventory.3

2.1.1 Noise Module

A summary of the behavioral and physiological impacts of aircraft noise exposure is given in He et al. 

[18]. Monetary impacts of noise exposure are commonly attributed to costs from noise-related health 

effects, loss of work productivity, and depreciation of property values around airports [19]. The APMTNoise Module can estimate US and global impacts of aviation noise in terms of both physical and 

monetary metrics by relating noise exposure and city-wide income levels to an expected loss in housing

value. Physical metrics in the Noise Module include estimates of population exposure to a given noise 

level, area exposure to noise around an airport, and the number of people highly annoyed due to aircraft 

noise. The Noise Module then estimates monetary damages from housing value depreciation around 

airports, which is used as an incomplete proxy for the complex set of health and welfare impacts 

associated with aircraft noise. The current method is described by He et al. [18]. The cost-benefit analysis 

 

3 Additional information regarding code assessments and sensitivities are explored in references [15][16][17]
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Appendix A A-6

of CAEP/9 noise stringency options included in this report was limited to consideration of only U.S. 

airports.

2.1.2 Air Quality Module 

The Air Quality Module within APMT-Impacts estimates the health impacts of primary particulate matter 

(primarily soot) and secondary particulate matter (aerosols formed from SOX, NOX, and gaseous 

hydrocarbon emissions) emissions from aircraft for the landing-takeoff cycle. Ozone-related health 

impacts are not considered here as they are estimated to be immaterial relative to PM-related impacts (less 

than ± 10%) by studies within the APMT-I development effort (e.g., [21]), analysis conducted in support 

of the Energy Policy Act of 2005 study [22] [23], and external studies such as the Clean Air for Europe 

Baseline Analysis [24]. APMT-I quantifies PM-related health impacts in terms of incidences of 

premature adult mortality, infant mortality, chronic bronchitis, respiratory and cardiovascular hospital 

admissions, emergency room visits for asthma and minor restricted activity days (MRADs) and their 

associated costs. Rojo [21], Masek [25], Ashok [26] and Brunelle-Yeung [27] provide detailed 

information on the modeling methodology for the APMT-I Air Quality Module (the most complete 

description of the methodology is described in Brunelle-Yeung, while Ashok provides minor updates that 

reflect the latest module).

Limitations of the APMT-I Air Quality module used in this analysis include a restricted scope of 

geographic coverage and consideration of health impacts from only landing and takeoff emissions. Future 

analysis may consider addressing changes in the background atmospheric concentration over time that 

have an impact on aviation‘s contribution to environmental damages, and make aviation emissions more 

significant over time [26][28]. In addition, the contribution of cruise emissions to surface air quality 

impacts may be of future interest, as recent work shows that aircraft cruise emissions may cause 

degradation of air quality over a hemispheric scale [29] [30].

2.1.3 Climate Module 

The APMT-Impacts Climate Module estimates the societal impact of aviation CO2 and non-CO2 

emissions for both physical and monetary metrics. The APMT-I Climate Module adopts the impulse 

response modeling approach based on the work by Hasselmann et al. [31], Sausen et al. [32], Fuglestvedt 

et al. [33], and Shine et al. [34]. Total anthropogenic societal emissions are taken from prescribed 

background scenarios. The climate module determines the climate system response by superimposing or 

convoluting the time series of yearly impulse response curves onto the background emissions. The 

aviation impacts are calculated by taking the difference of total emissions and total emissions not 

including aviation. The temporal resolution of the APMT-I Climate Module is one year while the spatial 

resolution is at the global mean level. The effects modeled include long-lived CO2, the intermediate-lived 

impact of NOX on methane (NOX-CH4) and its associated primary mode interaction on ozone (NOX-O3

long), and short-lived effects including the direct impact of NOX on ozone (NOX-O3 short), the production 

of aviation induced cloudiness, sulfates, soot, and H2O. Long- and intermediate-lived radiative forcing 

impacts associated with yearly pulses of CO2 and NOX emissions decay according to their e-folding times 

or atmospheric lifetimes, while the RF from short-lived effects including the warming NOX-O3 short 

effect is assumed to last only during the year of emissions. A detailed description of the APMT-I Climate 

Module can be found in [35][36][37][38], with the most up to date methodology provided by Wolfe [38].

Limitations of the APMT-Impacts Climate Module include the use of a global spatial scale that does not 

capture regional variations in short-lived aviation climate effects, the lack of consideration of feedbacks 
A-7

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Appendix A

in the climate system that may enhance or mitigate the climate impacts associated with aviation 

emissions, and independent treatment of aviation effects that does not account for interactions among 

some of the different physical and chemical mechanisms. Further, climate impact estimation in APMT-I 

(and the underlying MDG analyses) implicitly assumes that future operational changes involve no 

significant changes in flight routes. 

3. COMMUNICATION OF RESULTS

Given the complex nature of APMT-I with several inputs and model parameters that are influential in 

determining the results of any policy analysis, conveying all the critical policy-relevant information in a 

clear, concise manner becomes a challenging task. An emphasis is placed on relaying three different kinds 

of information: quantified environmental and economic impacts, uncertainties in these impact estimates, 

and the inputs and model parameters that provided the results. 

The assessment activities allow for a distillation of the large amounts of information accumulated through 

multiple Monte Carlo runs. For all components of APMT-I, the assessment results indicate five or six 

inputs and model parameters to which the respective outputs are most sensitive. Based on this condensed 

information, a decision-making framework was developed to enable an interactive application of APMTI, where the user dictates the terms of analysis to be conducted depending on their preferences and 

perspectives. The selection of each of these influential parameters is described through a lens; Section 3.1 

describes the lens concept in further detail.

A second issue of concern with the communication of results is the selection of a time-frame over which 

the impacts of a proposed policy are evaluated. Given the different temporal characteristics of the various 

environmental impacts, not all the impacts from aviation activity are realized in an immediate time-frame. 

CO2 impacts, for instance, tend to accrue over several centuries and that time-span needs to be factored 

into the decision-making process. Section 3.2 delves further into the selection of timescales for policy 

analysis.

3.1 Decision-Making Framework – Lenses

There are about five to six parameters for each APMT-I module that are most influential in determining 

the magnitude of the estimated impacts and associated uncertainties. These influential parameters are 

derived from global sensitivity analyses, and the parameters and their associated values for this analysis 

are presented in Section 4.2.1. Impacts can be represented in physical or monetary terms, with the 

computation of monetary metrics introducing additional influential parameters relative to the important 

parameters for the evaluation of physical effects. Thousands of unique combinations of inputs and model 

parameters that may be of interest in assessing different policy options are possible.

In order to extract meaningful insights about the possible costs and benefits of a policy, it is therefore 

expedient for the analysis options to be synthesized into a set of pre-defined combinations of inputs and 

assumptions. These combinations of inputs and model parameters can be thought of as describing a 

particular point of view or perspective, and are thus designated as lenses. Some example lenses include a 

lens with mid-range environmental and economic impacts; one with worst-case environmental impacts 

and mid-range economic impacts; one focused on short or long-term environmental impacts; or one that 

adopts a conservative perspective for one impact while keeping a mid-range perspective on others. When 

chosen wisely and viewed as a whole, the lenses can be seen as representing the range of scientific 
CAEP/9-IP/25

Appendix A A-8

perspectives of the relevant environmental impact domains. Section 4 demonstrates how the lens 

formulation can be used to illustrate scientific perspectives across the uncertainty domain in the CAEP/9 

Noise Stringency analysis.

3.2 Timescales

Defining timescales over which the policy analysis is conducted and over which the costs and benefits are 

accrued is an important issue in the communication of results. Selection of the analysis timescale can 

significantly alter the conclusions drawn about the efficacy of a proposed policy measure and therefore 

warrants a brief discussion here. There are two timescales embedded in a policy analysis. The first 

timescale is the policy influence time period, which is the duration over which a policy is assumed to 

significantly influence aviation activity. The second timescale is the time period over which the impacts 

of the different environmental effects attributed to the activity persist. As illustrated in Figure 2, in order 

to evaluate a proposed policy measure relative to a baseline scenario, aviation activity is modeled for the 

duration of the assumed policy influence time period (typically 20-30 years in ICAO-CAEP practice).

Figure 2: Timescales in Policy Analysis

The time period over which the impacts of the policy are felt on the environment is typically longer. For 

example, climate change impacts related to changes in aviation activity will persist for centuries. Thus, 

the environmental impacts are modeled for hundreds of years beyond the assumed policy impact period. It 

is important to designate a time-window across which the benefits are measured. The choice of time 

horizon can be influential in determining the expected benefit from a technological or operational change, 

especially when the emissions of shorter-lived species (such as impacts from NOX) are disproportionately 

impacted compared to CO2 [41].

Distinctions between the timescales become important when one wishes to aggregate economic costs and 

environmental benefits resulting from a proposed policy measure relative to a baseline scenario. The time 

period over which the costs and benefits are accrued may change the balance between costs and benefits 

making a policy seem more or less desirable, especially when one considers different discount rates that 

weight the value of long and short term benefits and costs differently. For the policy analysis presented in 
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Appendix A

Section 6 costs and benefits aggregated over the full environmental impacts time period are compared, 

which extends well beyond the policy influence period. The policy influence time period is typically 

chosen to be 30 years which is consistent with the ICAO-CAEP forecasting and analysis practice for 

assessing policy measures, and approximately the same as the time-scale for the development, adoption, 

and significant use of new technology in the fleet.

4. NOISE STRINGENCY POLICY ANALYSIS

On an international level, aircraft certification noise limits have been the primary tool to reduce the 

impact of aviation noise. In 1971, ICAO published Annex 16: Environmental Protection, Volume I: 

International Noise Standards, which has subsequently been updated to reflect newer aircraft 

technologies. International aircraft noise limits were last set in 2001 as part of the CAEP/5 cycle. As part 

of CAEP/7, a formal review of noise certification limits was initiated to be presented at CAEP/8. 

Subsequently, proposed stringency options were developed for consideration at CAEP/9. A first round of 

noise stringency analysis was conducted and presented to CAEP/9 SG2 in September 2011, which 

highlighted a number of areas of concern and led to Steering Group agreement for additional work. To 

accomplish the work, members agreed to establish a Noise Stringency Ad Hoc Group (NoSAHG) with 

representation from WG1, WG3, MDG and FESG. The work of that group led to consensus refinements 

to the assumptions that formed the basis of the second round of noise stringency modeling [42].

4.1 CAEP/9 Noise Stringency Options

At the Steering Group in June 2009, a future work item on a new noise Standard for subsonic jet and 

heavy propeller-driven airplanes was discussed. Working Group 1 (WG1) suggested the following more 

precise wording: ―Review and analyze certification noise levels for subsonic jet and heavy propellerdriven airplanes and, based on the analysis, develop stringency scenarios for impact analysis considering 

open rotor technology.

It was stressed that an analysis be undertaken that would enable CAEP/9 to decide whether a more 

stringent noise standard is technically feasible, economically reasonable and environmentally beneficial. 

WG1 was tasked under Task N.06.01 to review and analyze certification noise levels for subsonic jet and 

heavy propeller-driven airplanes and to develop a range of stringency options up to 10-12 dB cumulative 

margin relative to Chapter 4 standards. A first round of noise stringency analysis was conducted and 

presented to CAEP/9 SG2 in September 2011. This work led to significant refinements of assumptions for 

the fleet evolution process, and a second round of analysis was performed.

Based on this, WG1 recommended a range of noise stringency options for consideration by CAEP/9, 

which were approved at the first meeting of the CAEP Steering Group in November 2010. The five noise 

stringency scenarios under consideration are: -3, -5, -7, -9 and -11 cumulative EPNdB reduction relative 

to Chapter 4 noise limits. Each scenario, as well as a baseline Business-As-Usual scenario, was analyzed 

until 2036, with a single stringency implementation year of 2020 [43].
CAEP/9-IP/25

Appendix A A-10

Table 2: CAEP/9 Noise Stringency Options

Scenario Name Stringency

Baseline Chapter 4 Noise Limits

Stringency 1 (S01) -3 EPNdB

Stringency 2 (S02) -5 EPNdB

Stringency 3 (S03) -7 EPNdB

Stringency 4 (S04) -9 EPNdB

Stringency 5 (S05) -11 EPNdB

Since their inception, the ICAO Annex 16 noise limits have incorporated  ̳flat portions‘, such that below 

maximum take-off weights (MTOW) of between 20.2 and 48.1 tonnes and above weights of 280 or 400 

tonnes the noise constraint limit is constant. The basic shape of these limits have remained constant over 

the two stringency increases since they were established by the ICAO Committee on Aircraft Noise 

(CAN) and reported in their fifth meeting in 1976. WG1 formed a Flat Portion ad-hoc group to study the 

current relevance of low MTOW stringency noise limits. While this significant effort resulted in two 

proposals, no proposal for consideration achieved consensus within WG1. Thus, no change to the shape 

of low MTOW stringency noise limits is considered here [44].

4.1.1 FESG Fleet and Traffic Forecast

The Growth and Replacement (G&R) database was populated with information from WG1/WG3 

(including linking noise and emissions data) and FESG (seat configuration); and, then formed the basis of 

available aircraft used for developing the future fleets. MDG developed a noise stringency options 

spreadsheet, as a subset to the G&R, which identified the aircraft/engine combinations that were 

consistent with each of the five stringency scenarios analysed. This spreadsheet also took into account a 1 

dB supplemental condition. For this noise stringency assessment, the G&R database contained a number 

of project aircraft. Project aircraft are defined as aircraft models that have been designed and tested 

sufficiently to provide noise and emissions estimates but have not yet entered production (e.g. A350). 

Project aircraft were modelled using operation characteristics from the most representative in-production 

aircraft in the G&R database as baseline estimates and then adjusting these estimates based on expert 

input from WG1 and WG3 [44]. Aircraft were split into ―Fleet Seat Class‖ assignments, designated by 

aircraft seat capacity, and fleet replacements were modelled as being consistent across seat classes. Seat 

class designations are shown in Table 3.

Table 3: Fleet Seat Class Designations

Seat Class 

Designation

SC1 SC2 SC3 SC4 SC5 SC6 SC7 SC8 SC9 SC19

Seat Range 20–50 51–

100

101–

150

151–

210

211–

300

301–

400

401–

500

501–

600

601–

650

< 20

In accordance with FESG analysis recommendations, the following forecast assumptions were used: 

(1) CAEP/8 central (traffic and fleet) forecast for passenger services; (2) the CAEP/8 low-growth scenario 

forecast for cargo services (i.e. freighter traffic and fleet); and (3) the CAEP/8 business jet forecast for 

aircraft with less than 20 seats [45][46]. Specific attention was given to conserving projected seat demand 

provided by the FESG CAEP/8 forecasts. This assumption required adjusting the number of operations 

flown when quieter aircraft replaced non-compliant aircraft with a different capacity. Per consensus 

reached through the NoSAHG, the former equal market share approach was replaced with an APMT-
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Appendix A

Economics computed market-driven cost-based modeling approach. Model sensitivity to this assumption 

was explored in APMT-Economics. With FESG and MDG agreement, this revised market share approach 

was modified for Commonwealth of Independent States CIS aircraft, those aircraft built by the Russian 

Federation or the Ukraine, operating in certain countries, as had been done for the equal market share 

approach [47]. Furthermore, market share assumptions were shown to poorly represent expected trends in 

business jet projections. Thus, SC19 was stratified into four different groups based on weight-class.

4.1.2 Emissions Modeling

Modeled emissions are divided into terminal area emissions for air quality and greenhouse gas or full 

mission greenhouse gas emissions for climate change. AEDT provides both AQ and full mission 

emissions [49] [50]. AEDT models aircraft emissions including carbon dioxide (CO2), water (H2O), 

sulfur oxides (SO2), nitrogen oxides (NOX), non-volatile particulate matter (PMNV), total fuel organics 

(PMFO), and total sulfur organics (PMSO) for LTO segments below 3000 ft. In addition, it provides 

NOX, CO2, H2O, and SOX emissions for all flight segments. AQ emissions are modeled using ICAO 

times-in-mode for the taxi, takeoff, climb-out, and approach flight segments below 3000 feet. Full-flight 

emissions and aircraft noise data are provided on a global basis, however due to current modeling 

limitations in the air quality domain, only US results are utilized in the Cost-Benefit analysis. Details on 

the modeling methodology are provided in the AEDT Technical Manual [50].

4.1.3 Cost of Stringency Options

Recurring costs are calculated by APMT-Economics and broken into three functional cost categories: fuel 

costs, capital costs, and other direct operating costs.

All stringency options have been tested for an assumed implementation year of 2020 using the core set of 

assumption values drawing from those used by FESG [39], specifically:

 fuel prices of $3 per U.S. gallon from 2020 to 2036

 Range of Aviation Noise Design Effects Study (ANDES) Fuel Use Impacts. Two variants are 

noted in the response curves defined by CAEP Working Group 1 and the high fuel impact case 

has been used in the tests presented in this report.

 No pass through of increased costs relative to the Baseline, to fares in the Stringency cases and 

hence no demand and seat demand changes between Baseline and Stringency cases.

 Market-driven (relative cost) market shares between aircraft types.

Non-recurring costs are included in a cost sensitivity analysis in Section 4.4.2.3.

4.2 APMT-Impacts Modeling Assumptions

The APMT-I noise stringency analysis presented is limited to US-related impacts in order to align with 

the geographic scope of the APMT-I air quality model and to ensure that the economic costs and 

environmental benefits are compared in a consistent manner. There are several key sources of uncertainty 

involved in conducting an analysis of the CAEP/9 Noise Stringency options. 

a. Uncertainties from APMT-Impacts model inputs. Aleatoric uncertainty from natural variability 

and cycles in the airline industry will constrain the accuracy of future aviation growth scenarios. 
CAEP/9-IP/25

Appendix A A-12

Epistemic and aleatoric uncertainty from emissions and operational impacts of technology 

response both exist and can impact how different stringency options relate to expected 

environmental benefits. Finally, monostrucutural from how emissions and noise projections are 

mapped to APMT-Inputs can have an impact on final societal costs. While many of these 

uncertainties can be explored through the modeling process, the effectiveness of this exploration 

is limited by data availability from the CAEP analysis.

b. Uncertainties from APMT-Impacts model outputs. Environmental impacts models must contend 

with scientific, economic, and modeling uncertainty across the entire operations-impact pathway. 

These uncertainties for each of the APMT-Impacts modules are discussed in detail in 

[27][16][37][15]. The APMT-Impacts Air Quality and the APMT-Impacts Climate modules, 

which include simplified representations of more complex computationally-intensive models, are 

both limited by the structural appropriateness of the underlying complex model and the 

simplification technique.

c. System-level sensitivity and uncertainty. Together the models that produce the inputs to the 

APMT Cost-Benefit Analysis and those models that produce the outputs form a complex system. 

Interactions across the system can propagate uncertainties. System-level assessment is an ongoing subject of research [51]. 

As described in Section 3.1, the lens concept allows analysts and decision-makers to view the impacts of 

a cost-benefit analysis across a range of perspectives where there exists significant uncertainty. The 

following sections describe the lenses selected for conducting a cost-benefit analysis of the proposed 

noise stringencies using the APMT model. The only parameter not grouped in the lens assumptions was 

the discount rate. This was done so that the full range of discount rates could be applied to each result 

regardless of the lens selected for analysis. Further, while the probabilistic parameters modeled in the 

APMT-Impacts modules reflect the range of scientific, economic, and modeling uncertainty, choice of 

discount rate can be seen as a choice of policy-maker preference.

4.2.1 APMT-Impacts

Table 4, Table 5, and Table 6 show the lens assumptions for the APMT-I Noise, Air Quality, and Climate 

Modules respectively. APMT-I noise and air quality impacts are modeled over the 30-year period from 

2006 to 2036. APMT-I climate impacts are modeled over an approximately full time horizon lasting for 

800 years following the 30-year aviation activity period to capture impacts from long-lived effects such as 

CO2. Impacts are expressed in both physical and monetary metrics. Further, discounting effectively 

reduces the period of significant climate monetary impacts to a few hundred years for low and moderate 

discount rates (2%-5%) and to a few decades at high discount rates (7%-9%). For all lenses, zero 

population growth and income growth were assumed, in accordance with the CAEP practices for policy 

analysis.
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Appendix A

Table 4: APMT-Impacts Noise Assumptions for the CAEP/9 Noise Stringency Analysis

Noise Assumptions Low Lens Midrange Lens High Lens

Income Coefficient 0.0013 Mean = 0.0143 SD = 0.0079 0.0272

Income Interaction Term 0.0154 Mean = 0.0170 SD = 0.0094 0.0154

Income Intercept -30.3440 Mean = -37.5292 SD = 207.8134 -30.0440

Background noise level 55 dB Triangular distribution (mode = 52.5, range = 50-55) dB 50 dB

Income growth rate 0 0 0

Significance level 65 dB 52.5 dB 52.5 dB

Contour uncertainty -2 dB Triangular distribution (mode = 0, range = -2 – 2) dB 2 dB

Population growth rate No growth No Growth No Growth

Table 5: APMT-Impacts Air Quality Assumptions for the CAEP/9 Noise Stringency Analysis

Air Quality Assumptions Low Lens Mid Lens High Lens

Population Growth No growth No growth No growth

Emissions Indices

a. Fuel burn

b. SOx (FSC)

c. NOx

d. Non-Volatile PM

a. 0.92

b. 0.0066 (5th

percentile)

c. 0.83

d. 0.52

a. Uniform [0.92 1.12]

b. Weibull 

[mean = 0.0627, std = 1.2683]

c. Uniform [0.83 1.23]

d. Uniform [0.52 2/06]

a. 1.12

b. 0.154 (95th

percentile)

c. 1.23

d. 2.06

Adult Premature Mortality CRF

(% per μg/m3PM2.5

0.6 Triangular distribution

(mode = 1, range = 0.6 – 1.7)

1.7

Value of a Statistical Life (VSL) $6.3 Million $6.3 Million $6.3 Million

Background Emissions NEI 2001 NEI 2001 NEI 1001

Post-Processing SMAT SMAT SMAT

Table 6: APMT-Impacts Climate Lens Assumptions for the CAEP/9 Noise Stringency Analysis

Climate Assumptions Low Lens Midrange Lens High Lens

Climate sensitivity 2K Triangular (2, 3, 4.5) K 4.5K

NOX related effects Stevenson et al. 

(2004)

Discrete Uniform distribution: Wild, 

Stevenson, Hoor (2009)

Wild et al. (2001)

Short-lived effects 

radiative forcing

[AIC, sulfates, soot, 

H2O, contrails]

[11 -29.3 0.56, 0.39, 

5.4] mW/m2

Triangular:

[(11, 33, 87), (-29.3, -4.8, 0.79), (0.56, 3.4, 

20.7), (0.39, 2.8, 20.3), (5.4, 11.8, 25.6)] 

mW/m2

[87, -0.79, 20.7, 

20.3, 25.6] 

mW/m2

Background Scenario SRES A2 SRES B2 SRES A1B

Damage Coefficient 5

th percentile of 

DICE

DICE 2007 Normal Distribution 95th percentile of 

DICE

Mixed layer heat 

capacity

2.52*108J/(K*m2) Triangular: [2.53, 4.41, 6.31] *108 J/(K*m2) 6.31*108J/(K*m2)

Deep ocean heat 

capacity

2.52*1010 J/(K*m2) Triangular: [6.39, 1.26, 25.2]*109J/(K*m2) 6.3*109J/(K*m2)

Mixing Depth 500 m Triangular: [500, 1000 , 2000] m 2000 m

APMT-Impacts models aim to represent the current state of scientific and modeling consensus. However, 

the models have some assumptions that may be considered conservative. An additional illustrative lens 

was included to capture known under-representations in the current modeling assumptions. For this 

illustrative lens, the midrange lens parameterizations formed the basis against which first order 
CAEP/9-IP/25

Appendix A A-14

approximation factors were applied to capture underrepresentation from conservative assumptions. This 

work updates the simplified additional lens explored in the CAEP/8 NOX Stringency analysis [2].

The additional illustrative lens is meant to address the following three areas of importance in air quality 

for this analysis: the impact of cruise emissions, the impact of changing background concentrations over 

time, and the impact of modeling scale and resolution. 

As noted previously, the current APMT-I Air Quality methodology accounts for only the effects LTO 

emissions up to 3000 ft. While this is consistent with other aviation air quality modeling tools and impact 

studies, there is growing evidence that aircraft cruise emissions contribute significantly to air quality 

impacts [29] [30]. 

In addition, while emissions from the aviation sector are expected to grow over time, emissions that 

impact air quality from other sectors are not expected to follow the same trend. Woody et al. [28] estimate 

the impact of aviation emissions against projections of changing future background scenarios taken from 

the IPCC and with reference to domestic air quality control and mitigation strategies. Studies by Ashok 

[26] also indicate that the APMT-I Air Quality methodology currently under-represents impacts.

Finally, the choice of model and resolution can have an impact on estimates of societal damage from 

aviation-attributable particulate matter. The APMT-I Air Quality module models air quality impacts on a 

36km x 36km grid with a domain that covers the continental United States. This resolution may not 

capture the severity of air quality impacts at locations very near to the airport, where particulate matter 

concentrations are higher. Findings by Barrett et al [29] and Lee [52] infer that impacts from LTO 

emissions as quantified by APMT-I may be underestimated.

For these reasons, the additional illustrative lens applies a linear multiplier on Baseline and Policy 

physical impacts that starts at 1 for the baseline year (2006) and grows to 1.6 in (2036). A factor of 1.1 is 

used on Policy-Baseline LTO monetized environmental benefits to account for near-airport impacts not 

accounted for due to model resolution. Finally, a multiplier of 1.7 is used on Policy-Baseline LTO 

monetized environmental benefits to account for US cruise emissions on global mortalities for 

Stringencies 1 through 4, consistent with results from Koo [30]. For Stringency 5, the expected impact of 

full flight emissions is of the opposite sign of the expected impact from the LTO emissions. To account 

for this, a correction factor (CF) scaling the ratio of global mortalities to global fuel burn from Koo [30] is 

used according to the equation:

where LTO represents US LTO fuel burn and LTO_bar represents US total fuel burn less LTO fuel burn.

4.3 AEDT Noise and Emissions Inputs

AEDT noise inputs for this analysis are area impacted by noise contours around 99 US airports expressed 

in terms of the average day-night noise level from 55dB to 75+ dB. These US airports are a part of 185 

AEDT Shell-1 airports worldwide that account for 91% of total global noise exposure (102 of the Shell-1 



CF 

LTO

Mortality RSM

LTO

 LTO

GlobalMortality  Mortality RSM

LTO

Mortality RSM
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Appendix A

airports are located in North America). Figure 3 shows the change in total area exposure to aircraft noise 

across the range of noise impact levels from 2006-2036 for the unconstrained baseline case.

Figure 3: Noise Inputs: Baseline Noise Area Exposure

Projections in 2026 and 2036 of area exposure to noise for the unconstrained baseline case remain 

roughly consistent with previous estimates provided by AEDT [37][16]. However, modeling of the area 

exposure to noise in the datum year is significantly different from previous estimates, with total US 

exposure being increased by 21%. This change leads to an unexpected and unexplained trend of improved 

areal exposure to noise until 2026 even absent policy measures. With expanding operations, area exposure 

is expected to grow from 2026 through 2036.

Figure 4 shows decline in area exposure for the range of stringency options relative to the baseline case 

summed over the 30 years of the scenario. While the short term decrease in exposure followed by a long 

term increase trend is seen across all 5 stringencies, Stringency 4 and Stringency 5 (a -9 EPNdB and -11 

EPNdB reduction from Chapter 4 limits respectively) both have a total area exposure to noise reduction in 

2036 relative to 2006. While percentage decrease in cumulative area exposure is greater for increasing 

noise contour level, absolute reductions in area exposure are largest at the 55 dB DNL level and range 

from 273 mi2 reduction for Stringency 1 to 2711 mi2 reduction for Stringency 5. For this analysis, AEDT 

also provided estimates of population impacted by noise at each airport at each noise level using US 

Census population estimates at the Census Block Group level and 200m x 200m resolution.
CAEP/9-IP/25

Appendix A A-16

Figure 4: Noise Inputs: Area Exposure Stringency Cumulative Difference from Baseline

AEDT inputs to the APMT-Impacts Air Quality Module include fuel burn, emissions of NOX, SOX and 

non-volatile PM below 3000 feet for the landing and takeoff flight segments. Some species, such as SOX

emissions scale directly with fuel burn with an assumed emissions index (EI) of 1.2 g/kg-fuel based on a 

fuel sulfur content of 600ppm. While the relationship between SOX emissions to fuel burn is well 

understood, there remains some uncertainty about the exact EI ratio with the AEDT default EI of 1.16, 

AEDT guidance for use recommendations of 1.1762 [53], and upper-bound estimates of 1.2. Here, the 

upper bound of this limit was conservatively used as the median EI to capture all potential impacts from 

SOX emissions, and uncertainty is capture by a probabilistic multiplier in the APMT-Impacts Air Quality 

model. This assumption is consistent with the AEDT guidance for use recommendations. Here, 100% 

conversion of fuel sulfur to SO2 is conservatively assumed with no emissions as SVI. This assumption 

leads to an approximately 1% increase in total damages over the typical AEDT guidance for use 

recommendation. Figure 5 and Figure 6 show the percent change in fuel burn and NOX across the 

different noise stringencies relative to the unconstrained baseline.

Figure 5 shows the relationship between the noise stringency options and LTO fuel burn. Stringencies 1-3 

all have moderate fuel burn improvements across the lifetime of the policy. However, Stringency 4 shows 

a significant increase in LTO fuel burn while Stringency 5 shows a moderate increase in fuel burn. From 

Stringency 3 to Stringency 4, the number of compliant aircraft in higher fleet seat classes 4 and 6 become 

significantly reduced with the limited number of compliant aircraft having lower seat capacities. 

Therefore, to maintain FESG seat demands, operations must be increased at these seat classes by upwards 

of 7%. While Stringency 5 is also impacted by this increase in operations, at -11 EPNdB, a fuel inefficient 

aircraft in seat class 5 also becomes noncompliant. This is the reason for the moderate fuel burn benefit 

relative to Stringency 4, although it still leads to an increase over the unconstrained case. The resolution 

limit in distinguishing among policies by AEDT has been estimated to be less than 0.05% of fuel burn 

[54]. Only Stringency 1 falls within the estimated AEDT resolution limits throughout the lifetime of the 

policy. No stringency falls outside of the terminal area fuel burn uncertainty estimation.
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Appendix A

Figure 5: Air Quality Inputs; % Change in Fuel Burn Below 3000 feet

Figure 6 shows that all stringency options lead to a reduction in LTO NOX emissions relative to the 

baseline. While the increased fuel burn effect at Stringency 4 occurs due to the expected increase in 

number of operations, the impact is not significant enough to offset engine technology improvements in 

NOX reductions. Stringency 5 has the greatest reduction in expected LTO NOX emissions followed by 

Stringency 3.

Figure 6: Air Quality Inputs; % Change in NOX emissions below 3000 ft.

Inputs for the APMT-Impacts Climate Module include fuel burn, CO2, and NOX emissions. CO2 

emissions scale directly with fuel burn with an EI of 3155g/kg-fuel and are not presented here. Figure 7 

and Figure 8 show the percent changes in full flight fuel burn and NOX emissions across the range of 

noise stringencies relative to the baseline. AEDT results for full flight emissions are provided for North 
CAEP/9-IP/25

Appendix A A-18

America, and US emissions have been scaled from these results assuming that US operations account for 

93% of North American operations. These data also exhibit an expected resolution of limit 0.05% for 

distinguishing changes between policy and baseline cases. This resolution limit is considered by applying 

an uncertainty multiplier to the input inventories in the APMT-Impacts module.

Figure 7 shows that Stringency 1 has an expected fuel burn detriment for full mission flights in 2026, but 

it provides a minor benefit by 2036. However, Stringency 1 is always within the AEDT data resolution 

limits. Stringencies 2 and 3 show increasing fuel burn benefit with increasing Stringency, while 

Stringency 4 once again displays the increased fuel burn effect from increased operations described in the 

Air Quality input data. The benefit from seat class 5 engine efficiency in Stringency 5 results in 

Stringency 5 overcoming the effect seen in Stringency 4 to result in a fuel burn benefit relative to the 

baseline. Stringency 3 remains the most beneficial to fuel burn reduction across the lifetime of the policy. 

Figure 8 indicates that full flight NOX reductions follow the same trend as LTO NOX emissions; however, 

the effect in Stringency 4 produces a net negative impact in full-flight NOX emissions.

Figure 7: Climate Inputs; % Change in Full Flight Fuel Burn
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Appendix A

Figure 8: Climate Inputs; % Change in Full Flight NOX Emissions

For reference, the social cost of carbon values (calculated for the CO2 impacts only) for the low, mid, and 

high lenses, when averaged over the 30-year policy period, were $13/tC, $61/tC, and $511/tC, 

respectively. These are consistent with the range of SCCs estimated by EPA and suggested by the 

Interagency Working Group on Social Cost of Carbon [55][56].

4.4 Results

The goal of the policy analysis presented in this Section is to examine the environmental benefits and 

economic costs of the range of noise stringency options relative to the baseline no stringency case. The 

analysis is conducted using Monte Carlo methods and the results represent the mean of several thousand 

Monte Carlo runs unless otherwise specified.

4.4.1 APMT-Impacts Physical Results

The baseline results provided in this Section are for the mid-range lens model parameters. Physical 

impacts of noise in terms of number of people exposed to noise levels of 55dB DNL and higher are 

presented first.

Figure 9 shows the impact of different stringency options using a physical metrics: net cumulative people 

impacted by noise measured in person-years. Cumulative metrics like those shown in Figure 9 are useful 

in that the earlier the change from the baseline occurs, the larger the final metric. Figure 9 shows a 

nonlinear trend in percent change of area impacted by noise as stringency level increases. Furthermore, 

while increasing stringency leads to increasing benefits at the 55 DNL level, higher stringency reductions 

have disproportionately large benefits at higher noise levels.
CAEP/9-IP/25

Appendix A A-20

Figure 9: Percent Change in Cumulative People (in People-Years) Impacted by Noise

Baseline air quality impacts expressed in terms of yearly incidences of premature mortality attributed to 

exposure to aircraft particulate matter emissions are shown in Figure 10 and Figure 11. Figure 10 shows 

premature mortality due to separate emissions species for the baseline case; incidences of premature 

mortality attributed to particulate matter constitute more than 95% of the total monetized air quality 

health impacts. These impacts are due to aircraft emissions below 3000 feet and do not account for 

impacts of cruise PM emissions. Impacts are apportioned to the different aircraft emissions species 

contributing to changes in ambient particulate matter concentrations. Nitrates dominate the total impacts 

with smaller contributions from sulfates, elemental carbon, particle bound water, and organics. Figure 11 

shows the impact of stringency choice on expected aviation PM attributable mortalities throughout the 

modeled policy time horizon. Stringency 2 and Stringency 3 are shown to both be beneficial with respect 

to air quality physical impacts. Despite reductions to LTO NOX, both Stringency 4 and Stringency 5 

produce significant air quality dis-benefits.
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Appendix A

Figure 10: Baseline Air Quality Yearly Mortalities by PM Species, Midrange Lens

Figure 11: Noise Stringencies - Baseline Air Quality Yearly Mortality Incidences, Midrange Lens

Figure 12 presents baseline climate impacts in terms of changes in globally-averaged surface temperature. 

For the first 30 years, the time horizon over which the policy is modeled, the physical impacts are 

dominated by short-lived species, particularly aviation-induced cloudiness such as cirrus clouds and 

contrails. Longer-lived aviation-related climate impacts such as the warming CO2 effect and the cooling 

effects of NOX-CH4 and NOX-O3-long continue well beyond year 2036, the last year for which aviation 

emissions are modeled. CO2 impacts are dominant by 2040 and contribute to over 98% of total impacts 

(warming and cooling) by 2075. Short-lived effects including NOX-O3 short, aviation-induced cloudiness 

(AIC), sulfates, soot, and H2O decay to negligible impacts within 20 years after the 30-year scenario. For 

noise and air quality impacts, the duration over which the selected policy influences the fleet mix (2006-
CAEP/9-IP/25

Appendix A A-22

2036 in this case) coincides with the time period over which the impacts persist. However, as seen in 

Figure 12, climate impacts persist for several centuries past the last of the scenario.

Figure 12: Yearly Baseline Climate Physical Impacts, Midrange Lens

Figure 13 shows the expected climate physical impacts for the different stringencies studied. The solid 

lines represent mean expected global temperature change and the shaded regions represent the 10th and 

90th percentile ranges of results. The physical impacts follow the trends expected from the fuel burn input 

patterns with Stringencies 2, 3, and 5 showing climate benefits during the second quarter of the century 

and Stringency 4 showing a climate dis-benefit during the same time. Stringency 1 shows little change in 

physical impacts from climate change reflecting its similar emissions profile to the baseline, while 

Stringency 3 shows the most significant benefit.

Figure 13: Noise Stringency Yearly Climate Physical Impacts, Midrange Lens
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Appendix A

Results presented in this section indicate that growth in operations will lead to increasing environmental 

impacts in the future in the absence of new environmental policies. Absent increased stringency, physical 

noise impacts follow an unexpected trend, however it remains unclear whether the first 20 years of this 

trend reflect expectations or are a result of datum modeling assumptions. It can also be seen that different 

stringency levels lead to different environmental impacts. Implementation of the noise stringency leads to 

significant decreases in areas impacted by noise commensurate with the level of the stringency increase. 

On a percentage basis, these benefits increase nonlinearly with increasing noise level. This nonlinearity is 

important as it suggests that showing only area or populations impacted by noise levels from 55-65 dB 

DNL, a practice common in CAEP [42], may under-represent environmental benefits. Stringency 4 results 

in both an air quality and a climate dis-benefit from an increase in flight operations of Seat Classes 4 and 

6. Stringency 5 incurs an air quality dis-benefit, primarily from the same increase in operations seen in 

Stringency 4, but has a resulting climate benefit from a switch to more efficient aircraft in Seat Class 5. 

The next section presents an aggregated cost-benefit analysis comparing the monetized environmental 

benefits and the economic costs of the stringency options relative to the baseline case using monetization 

methods described in Section 2.

4.4.2 Cost-Benefit Analysis

The results in this section present a comparison of costs and benefits across the environmental and 

economic domains. Results are presented here using the midrange lenses as described in Section 4.2.1 and 

monetary impacts are calculated using a 3% discount rate unless otherwise noted. Figure 14 shows the 

impacts of the five stringency levels on physical metrics across all environmental domains. Noise impacts 

are measured cumulative people impacted at the 55dB level and higher, air quality is measured in total 

mortalities, and climate is measured in integrated temperature change out to a 100 year time horizon. The 

physical metrics show that all policies have a larger impact on noise impacts than other environmental 

effects, and that these impacts increase disproportionately with increasing stringency. Lower stringency 

levels have small but significant emissions co-benefits from an increased proportion of efficient aircraft in 

the fleet. While the noise benefits from Stringency 3 to Stringency 4 more than double, there is a reverse 

in the emissions trend for both air quality and climate.

Figure 14: Noise Stringencies - % Change in APMT Physical Metrics, Midrange Lens
CAEP/9-IP/25

Appendix A A-24

However, it is important to examine impacts on a monetized scale as the societal cost of an impact can be 

highly non-linear and because environmental impacts have different baseline expected costs. Example of 

monetized environmental impacts along with industry impacts is shown in Figure 15 and Figure 16. The 

results in Figure 15 represent the difference between Stringency 2 and the baseline business-as-usual case 

while Figure 16 represents the difference between Stringency 3. The net impact for monetized results is 

calculated by summing the three environmental impacts: noise, air quality, and climate, and the FESG 

economic impacts (where 27% of the total recurring costs was estimated to be the US operations costs 

based on previous analyses conducted with APMT-Economics). For these examples, only the midrange 

lens with 3% discount rate of both the environmental and recurring industry costs of one-stringency is 

presented. The uncertainties in the environmental impacts are estimated through Monte Carlo methods. 

(Details on the treatment of uncertainties in the different APMT modules were presented in Section 5.) 

While all these impacts and associated uncertainties have common assumptions and are not entirely 

independent of each other, for a first order estimate it is assumed that they are statistically independent 

effects. All of the mean impacts are summed to get the net impact and all their variances are summed to 

get the variance. The height of the bars indicates the mean value and the error bars represent the 10th and 

90th percentile values. Note that Figure 16 presents policy minus baseline results and therefore a positive 

change is considered detrimental while a negative change is beneficial.

Figure 15: Noise Stringency 2 (-5 EPNdB) minus Baseline Impacts and Costs

(mid lens, 27% recurring costs, 3% costs discount rate, 3% environmental discount rate)

Figure 16: Noise Stringency 3 (-7 EPNdB) minus Baseline Impacts and Costs

(mid lens, 27% recurring costs, 3% costs discount rate, 3% environmental discount rate)
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Appendix A

The costs and benefits are summed for each of the proposed stringencies to demonstrate how total impacts 

and net costs are impacted by stringency level. First, results for environmental benefits at the midrange 

lens are presented with costs and benefits both monetized under a 3% discount rate, as shown in Figure17. 

Section 4.4.2.1 considers the impact of discount rate on net cost benefit while Section 4.4.2.2 explores net 

impacts across different environmental lenses.

Figure 17: Noise Stringencies CBA

(mid lens, 27% recurring costs, 3% costs discount rate, 3% environmental discount rate)

Figure 17 shows that moderate stringency increases (Stringencies 1 and 2) have a net benefit under the 

3% discount rate for the midrange lens. Stringency 3 has a moderate net cost while Stringency 4 and 

Stringency 5 are dominated by recurring costs. Table 7 shows the environmental benefits for each 

environmental impact as well as the total monetized environmental impact by stringency for a midrange 

lens at 3% discount rate. Table 8 shows the environmental impacts and recurring costs leading to the net 

cost or benefit of the stringency.

Table 7 indicates a total environmental benefit for all stringencies. Despite having a much smaller percent 

change in physical metrics relative to noise, climate impacts have the largest monetized benefit for 

Stringencies 2 and 3 due to the nonlinear relationship between temperature change and societal costs. The 

fuel burn increase effect from increased operations in Stringency 4 leads to a small air quality dis-benefit 

and a moderate climate dis-benefit, which are both offset by the noise benefit. Stringency 5, with 

dominating noise benefits, has the largest total environmental impact with a mean benefit of $1.3 Billion 

USD (in 2006 dollars) followed by Stringency 3.
CAEP/9-IP/25

Appendix A A-26

Table 7: APMT-Impacts Policy – Baseline Monetized Environmental Benefits

APMT-I 

Assumptions Stringency

Noise

(2006 US $Billion)

Air Quality

(2006 US $Billion)

Climate

(2006 US $Billion)

Total Impact

(2006 US $Billion)

mean 10-90% mean 10-90% mean 10-90% mean 10-90%

Midrange Lens

3% Discount 

Rate

S01 -0.11 -0.14

-0.08 0.00 -0.010.00 0.00 -0.010.00 -0.12 -0.15-0.08

S02 -0.19 -0.25

-0.14 -0.05 -0.07-0.02 -0.34 -0.57-0.14 -0.58 -0.90-0.30

S03 -0.33 -0.42

-0.23 -0.07 -0.11-0.03 -0.44 -0.73-0.19 -0.83 -1.26-0.45

S04 -0.79 -1.02

-0.56 0.06 0.010.13 0.27 0.120.45 -0.46 -0.890.02

S05 -1.21 -1.56

-0.88 0.18 0.050.36 -0.30 -0.50-0.13 -1.33 -2.00-0.64

Table 8: Noise Stringency Cost Benefit Summary, Midrange Lens, 3% Discount Rate

APMT-I 

Assumptions Stringency

Total Impact

(2006 US $Billion)

Recurring Cost

(2006 US $Billion)

Net Cost Benefit

(2006 US $Billion)

mean 10-90% mean mean 10-90%

Midrange Lens

3% Discount 

Rate

S01 -0.12 -0.15

-0.08 -0.27 -0.39 -0.42-0.35

S02 -0.58 -0.90

-0.30 -1.89 -2.47 -2.79-2.19

S03 -0.83 -1.26

-0.45 2.70 1.87 1.442.25

S04 -0.46 -0.89

0.02 8.64 8.18 7.758.66

S05 -1.33 -2.00

-0.64 14.04 12.7 12.013.4

4.4.2.1 Discount Rate Analysis

The discount rate is the method by which future damages are monetized in comparison to current year 

costs. The discount rate accounts for the fact that individuals have a positive time preference, explained 

because people expect to be wealthier in the future, people are impatient, or from a combination of these 

two factors. Proponents of constant discounting of environmental costs cite the partial Pareto 

improvement criterion: if aggregate welfare is higher under one policy, then a compensation mechanism 

can be implemented to transfer benefits between parties [57]. Opponents of market discount rates cite 

intergenerational inequity due to the long lifetimes of environmental damages, high cost or infeasibility of 

benefit transfer, and the inability to account for irreversible events such as species extinction [58].

The costs and benefits for a range of discount rates are explored, in keeping with US and international 

best practice. Figure 18 and Figure 19 illustrate the effect of the discount rate across the environmental 

impacts for two sample stringencies analyzed for a midrange lens. Climate, which has the longest-lived 

impacts, is most affected by discount rate with monetized impacts decreasing below those of noise at 

discount rates of 5% and higher. For Stringency 2, total mean environmental benefits range from $900 

million USD(2006) to $100 million USD (2006) while for Stringency 3 environmental benefits range 

from $1.2 billion USD (2006) at 2% to just under $200 million USD (2006) at 9%.
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Appendix A

Figure 18: Noise Stringency 2 - Baseline Environmental Impacts Discount Rate Sensitivity

Figure 19: Noise Stringency 3 - Baseline Environmental Impacts Discount Rate Sensitivity

First, the cost and benefit trends across a range of discount rates are examined where recurring costs and 

environmental benefits are computed with aligned discounting assumptions. FESG costs were computed 

at 3%, 7% and 9% discount rates. Figure 20 shows that choice of discount rate does not impact the 

relative ranking of stringency options, and that Stringencies 1 and 2 remain cost beneficial at all three 

discount rates, while Stringencies 3, 4, and 5 all have net societal costs. However, choice of discount rate 

can have a large impact on the net cost as seen in the nearly 7 billion USD difference in net costs between 

a 3% and 7% discount rate on Stringency 5.
CAEP/9-IP/25

Appendix A A-28

Figure 20: Noise Stringencies - Baseline CBA for Aligned Discount Rates

Because of the long lifetime and potential irreversibility of environmental impacts, decisions in the 

present have the possibility of affecting future generations with no opportunity for benefit transfer. Thus, 

it has been argued that non-environmental costs should be discounted with a social discount rate that 

aligns more closely with a market or private investment rate of return, while environmental costs should 

be discounted with a lower discount rate [59]. In the United States, the OMB recommends discount rates 

of between 2.5% and 7% for regulatory analysis but allows for the use of a lower discount rate for 

intergenerational impacts [5]. In past ICAO-CAEP analyses of costs of environmental stringencies, 

discount rates of 9% and 13% have been used to discount future recurring costs [60], while environmental 

benefits have been estimated using discount rates of 2-5% [2]. The cost-benefit analysis of proposed 

stringencies is examined where the environmental benefits are discounted under the 2%, 3%, and 5% 

discount rates, while costs are discounted under a 9% discount rate. Results are shown in Figure 21. With 

costs computed at a 9% discount rate, Stringency 3 becomes cost beneficial at 2% and 3% discount rates 

on environmental benefits. Stringency 4 and Stringency 5 maintain a net societal dis-benefit for all 

discount rates.
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CAEP/9-IP/25

Appendix A

Figure 21: Noise Stringency Cost Benefit - Non-Aligned Discount Rates

(recurring costs at 9% discount rate, midrange lens)

4.4.2.2 Lens Analysis

The sensitivity analysis presented here focuses on variability in results depending on selection of inputs 

and model parameters explored using the lenses described in Section 4.2.1. The sensitivity to lenses is 

shown for two sample stringencies (Stringency 2 and Stringency 3) minus baseline at a 3% discount rate 

in Figure 22 and Figure 23. Monetized benefits from climate change are highly sensitive to lens 

assumptions. This sensitivity is explained by the asymmetric distribution on climate sensitivity and the 

squared relationship between temperature change and damages. Thus, while a midrange of scientific and 

economic expectation indicates moderate climate benefits from Stringency 3, there is a possibility that 

those benefits could be significantly greater.

Figure 22: Noise Stringency 2 – Baseline Environmental Impacts Lens Sensitivity

3% Discount Rate
CAEP/9-IP/25

Appendix A A-30

Figure 23: Noise Stringency 3 - Baseline Environmental Impacts Lens Sensitivity

3% Discount Rate

Figure 24 shows the sensitivity of cost-benefit results to lens assumptions. Stringency 2 remains the most 

cost beneficial policy under all lenses with net benefits ranging from about $1 billion 2006 USD to about 

$7.5 billion USD. Stringency 3 is dominated by costs in the low and mid lenses, but becomes 

significantly cost beneficial under the high lens. Stringencies 4 and 5 maintain a net detriment across all 

lenses, but their relative order changes under the high lens where large climate benefits make Stringency 

5 less detrimental than Stringency 4.

Figure 24: Noise Stringencies - Baseline Net Cost-Benefit Lens Sensitivity, 3% Discount Rate

Because APMT-I contains several conservative assumptions, as described in Section 4.2.1, an additional 

illustrative lens is used to examine the sensitivity of the results to those assumptions. Specifically, this 

lens considers the impact of modeling resolution, cruise emissions, and changing background on air 

quality and the impact of modeling domain on noise damages. Other conservative assumptions, such as 

changes in population over the policy lifetime or the effect of noise and emissions on non-market impacts 

such as public space enjoyment and visibility are not examined in this lens.
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CAEP/9-IP/25

Appendix A

Figure 25 shows the sensitivity of the cost benefit analysis to first order approximations to account for 

these conservative assumptions. As shown in the figure, the cost benefit results are not sensitive to these 

assumptions. For example, for Stringency 3 at a 3% discount rate the illustrative lens has an air quality 

benefit that is 2.9 times greater than that of the midrange lens. However, air quality accounts for less than 

10% of total environmental impacts and less than 3% of the recurring costs. While accounting for these 

conservative assumptions may have compounding effects under high lens or lower discount rates, this 

sensitivity analysis shows that the net cost benefit result for CAEP/9 Noise Stringency is more sensitive 

to choice of discount rate or choice of traditional environmental lens than it is to conservative modeling 

assumptions.

Figure 25: Noise Stringency - Baseline Cost Benefit Illustrative Lens, 3% Discount Rate
CAEP/9-IP/25

Appendix A A-32

4.4.2.3 Cost Sensitivity Analysis

The cost benefit analyses in Sections 4.4.2.1 and 4.4.2.3 consider only the recurring economic costs. This 

section examines a sensitivity test for combined recurring and non-recurring (N-R) costs on the net costbenefit analysis. The scope of N-R costs considered by FESG included various combinations of N-R 

aircraft owner/operator costs and N-R manufacturer costs with low and high estimates. This sensitivity 

study was conducted due to uncertainty associated with cost estimates. For this sensitivity study the midrange environmental assumptions were used with 50%, 75%, and 100% of total costs to test how sensitive 

the cost-benefit results are to cost assumptions. The results are shown in Figure 26. The analysis indicates 

that Stringency 4 and Stringency 5 are the most sensitive to cost assumptions, and that those stringencies 

have a strong net dis-benefit for any consideration of costs. For all cost assumptions considered, 

Stringency 2 is the most cost-beneficial stringency for midrange lens at 3% discount rate. Stringency 1 

remains cost-beneficial across the cost assumptions considered here, but benefits are small and emissions 

are within the estimated data resolution. Stringency 3 has a net cost for all cost sensitivity assumptions.

Figure 26: Cost Sensitivity to Combined Recurring and Non-Recurring Costs

Sections 4.4.2.1, 4.4.2.2, and 4.4.2.3 are designed to show the sensitivity of the results to various 

assumptions. As such, not all modeling assumptions, scientific uncertainty considerations, and policymaker preference combinations are explicitly shown in this paper.
A-33

CAEP/9-IP/25

Appendix A

5. SUMMARY AND CONCLUSIONS

This paper reports on the inclusion of environmental impact assessment and quantification of modeling 

uncertainties to enable a more comprehensive evaluation of CAEP/9 noise stringency options. The 

APMT-Impacts modules were employed to conduct the cost benefit analysis (CBA). This section offers a 

brief summary of the justification and methodology, concluding thoughts based on the work presented in 

this paper. 

While modeling uncertainties grow as one proceeds down the impact pathway, decision-making 

uncertainty decreases as one gains a better understanding of the ultimate impacts of the policy on a human 

health and welfare scale. This work used a CBA to inform the CAEP/9 decision-making practices 

regarding aircraft noise stringency. 

An application of the lens framework was provided through the cost-benefit analysis of the proposed 

CAEP/9 Noise stringencies. Several different lenses reflecting economic, scientific, and modeling 

uncertainties were presented. The environmental benefits and economic costs associated with the CAEP/9 

noise stringency options were analyzed for the US domain from 2006 to 2036 with policy implementation 

in 2020. Operating costs were taken as economic costs and were calculated by APMT-E. All stringency 

options lead to a noise benefit ranging from 0.5% to 5% improvement in the cumulative number of people 

impacted by noise over the next 30 years. 

Stringency 1 (Chapter 4 -3 EPNdB) is shown to be cost beneficial across all assumptions considered; 

however, the associated fuel burn emissions are within the resolution limits of the MDG models, 

indicating that environmental benefits are uncertain and not significant. 

Stringency 2 (Chapter 4 -5 EPNdB) is shown to be cost beneficial across all assumptions considered; 

provides global reductions in noise, fuel burn and emissions; and, offers the largest reduction in direct 

operating costs relative to the no stringency case. Stringency 2 is the most cost beneficial option, 

irrespective of the combinations of assumptions considered. 

Stringency 3 (Chapter 4 -7 EPNdB) has environmental benefits greater than Stringency 2; however, the 

recurring costs are greater than the monetized environmental benefits across most assumptions 

considered. Only under a very limited set of assumptions4(e.g., indicating a high level of impacts for 

noise, air quality and climate coupled with the highest values of statistical life) could Stringency 3 

potentially become cost beneficial.

Stringency 4 (Chapter 4 -9 EPNdB) includes an increase in fuel burn emissions because the fleet shifts 

to smaller, compliant aircraft that require increased operations to maintain seat demand. Thus, while 

Stringency 4 has noise benefits, it also has air quality and climate dis-benefits and is not consistent with 

the CAEP principles of environmental effectiveness, economic reasonableness, and consideration of 

interdependencies. 

Stringency 5 (Chapter 4 -11 EPNdB) continues the dis-benefit trend in air quality from Stringency 4; 

however, the fleet shifts away from smaller aircraft, and demonstrates climate benefits. This stringency is 

even more inconsistent with CAEP principles.

 

4

See Tables 4, 5, and 6 respectively for the range of noise, air quality and climate assumptions.
CAEP/9-IP/25

Appendix A A-34

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