Institute for Aviation and the Environment Aviation Integrated Modelling (AIM) Project

Transcription

1 Aviation Integrated ling (AIM) Project ANCAT MITG/12 The Hague, 19 May 2009

2 Background Goal: Develop integrated assessment tool for aviation, environment & economic interactions at local & global levels, now and into the future Assess policies to strike appropriate balances between economic benefits and environmental impact mitigation Independent & transparent tool for mediating between stakeholders Duration: 3-year Phase 1 initiated in October 2006 Funding from: 2

3 AIM Policy Assessment Sample policy: ATC evolution Movement Global Climate Global Environment Sample policy: Regulation Technology & Cost Sample policy: Airport capacity Airport Activity Air Quality & Noise Local Environment Sample policy: Economic instruments Air Transport Demand Regional Economics Local/National Economic 3

4 AIM Detailed Architecture Flight Data Air Traffic Control Data / Engine Characteristics Database Meteorological & Surface Emissions Data (Base Year & Future Projections) Airport Characteristics Database Airborne emissions rates Optimal profiles by class Movement Module Optimal trajectories Optimal Trajectory Airspace Inefficiency Airspace demand Actual trajs Airborne delay Airspace Capacity & Delay Airborne Operations Airborne emissions & path Global Climate Module Combined Emissions Lightning Ground-based Atmospheric Dynamics Advection Convection Diffusion Deposition Atmospheric Chemistry Homogeneous Heterogeneous Photolytic Atmospheric Radiation Radiative Forcing Global Environment Schedule Airborne delay by segment Atmospheric parameters Technology & Cost Module Technology Performance & Emissions Technology characteristics Technology characteristics LTO emissions Fuel use rates Cost cost Total delay Proposed schedule Airline Response O-D flow Constrained schedule & fare Load factor Flight Routing & Scheduling Schedule Airport Activity Module Ground delay Delay Calculator Schedule LTO Operations LTO emissions LTO path LTO times Meteorology Parameterization Source Pre-processing Industry Noise Local Air Quality Dispersion & Noise Module impact parameter Dispersion Source param. Concentrations Atmospheric Chemistry Local Environment Pax/Freight flow and fare by O-D city pair Pax/Freight flow by segment Delays by segment or O-D city pair Noise contours LAQ contours Population / Income / Cost Data and Future Projections Passenger & Freight Demand (Base Year) Schedule / Routing / Segment Data (Base Year) Environmental Cost Parameters Air Transport Demand Module Passenger/ Freight Demand Forecasting Pax/ Freight flows by OD citypair Y Optimise routing? Pax/ Freight flows by citypair segment N Scaled Airport Choice Scaled Routing Pax/freight flow by segment Regional Economics Module Local Employment Noise Costing Noise effects Employment effects LAQ Costing Total Economic Effects LAQ effects Local/National Economic Coefficients of Demand Equation Parameter Estimation Version: 14 September

5 Integration AIM Architecture Benefits Captures interdependencies, data transfer & feedback Examination of trade-offs (e.g. local environment vs. global environment vs. economic impacts) Modularity Resolution of modules tailored to application Subset of modules run independently Substitution of models from other groups Extendability Natural expansion in sophistication or number of modules Policy assessment potential 5

6 AIM Team Core team: Dr. Andreas Schäfer (Principal Investigator) Steven Barrett (Air Quality & Noise) Dr. Lynnette Dray (Air Transport Demand) Antony Evans (Airport Activity) Dr. Helen Rogers (Global Climate) Dr. Maria Vera Morales ( Technology and Cost) IAE co-investigators: Prof. Bill Dawes (Engineering) Dr. Chez Hall (Engineering) Prof. Peter Haynes (DAMTP) Prof. Roderic Jones (Chemistry) Prof. John Pyle (Chemistry) Affiliated researchers: Prof. Rex Britter (Air Quality, MIT) Dr. Marcus Köhler (Global Climate, King s College London) Dr. Tom Reynolds (Air Traffic Control/Management, MIT/Lincoln Labs) Dr. Zia Wadud (Regional Economics, Bangladesh University of Engineering and Technology) 6

7 The First 3 Years Output: Journal publications, Conference papers, PhD theses Collaborations Omega projects (2 lead, 3 partner) PARTNER (2 workshops) MIT IIT Bombay FP7 UK Climate Change Committee Recent Research: Omega Integration Study: Opportunities for Reducing Aviation-Related GHG Emissions a Systems Analysis for Europe PhD Research Antony Evans: ling Airline Responses to Policy Measures and Constraints that may alter the Environmental Impact of Aviation 7

8 Omega Integration Study Omega Project 41, Opportunities for Reducing Aviation-Related GHG Emissions a Systems Analysis for Europe Use modular AIM structure to interface with the results of other Omega projects Goals: Investigate interaction between different technological, operational and economic CO 2 emission mitigation measures Assess which measures would be most useful in achieving aviation CO 2 emission reductions 8

9 How might different policies/scenarios interact? Omega Integration Study Simulate by combining a range of Omega study results with a systems model for European Aviation: Marginal Abatement Costs Fleet Turnover Climate-related ATM Airspace Charging Sustainable Fuels EU ETS Aviation Integrated (Cambridge) Scenario inputs for future population, GDP, carbon/oil price (CCSP) 9

10 Omega Integration Study SESAR Movement Global Climate Global Environment Winglets, Open Rotors, Engine upgrades, Aero/Engine maintenance Technology & Cost Airport capacity Airport Activity Air Quality & Noise Local Environment EU ETS Air Transport Demand Regional Economics Local/National Economic 10

11 European System 2005 and beyond Future Scenarios (CCSP 2007): IGSM: High GDP growth, high oil price MERGE: medium GDP growth and oil price, higher carbon price MiniCAM: low GDP growth, low oil price MERGE

12 European System 2005 and beyond Future Scenarios (CCSP 2007): IGSM: High GDP growth, high oil price MERGE: medium GDP growth and oil price, higher carbon price MiniCAM: low GDP growth, low oil price MERGE

13 ETS-only vs. Reference (no ETS) case Without other mitigation measures, emissions reductions come mainly from lowered demand Reference case demand requires ~doubling in capacity at LHR, CDG to

14 Uptake of abatement measures 14

15 ETS with abatement measures Now include abatement measures airlines can take to reduce emissions Largest effects on emissions from SESAR, biofuels Open rotors in highcost scenarios only Airlines can reduce fuel/carbon costs so RPKM decrease is smaller than ETSonly case 15

16 Conclusions of Study Complex interactions - uptake of one mitigation measure can lower future uptake of other measures Depending on the scenario and assumptions, reductions in airborne CO 2 over reference case seem possible by % (ETS only) 20-30% (ETS+non-biofuel abatement measures) Strongest reduction in lifecycle aviation emissions (under assumptions used here) is ETS+biofuels Lifecycle CO 2 emissions below 2005 levels in 2050 However, noise, local and airborne emissions will be little-changed from reference case Cellulosic biomass fuel land area problems? 16

17 ling Airline Responses Sample policy: ATC evolution Movement Global Climate Global Environment Sample policy: Regulation Technology & Cost Sample policy: Airport capacity Airport Activity Air Quality & Noise Local Environment Sample policy: Economic instruments Air Transport Demand Regional Economics Local/National Economic How would likely airline responses to policy measures and constraints affect the environmental impact of aviation? 17

18 Objectives & Methodology Objective: Develop model of airline responses to changes in cost and demand caused by policy measures and constraints Routing network changes (e.g. avoid congested hubs) Changes in aircraft size Changes in flight frequency Methodology: Select each airline s routing network, flight frequencies, and aircraft to maximize individual profit Simulate game between airlines to capture effects of competition endogenously effects of policies and constraints on airline costs and demand endogenously 18

19 Sample Airline Response ling Flights per day >15 5 airlines in 14 cities / 22 airports / 9 hubs in the domestic US in 2005 SEA SEA SEA BOS BOS BOS SFO OAK DEN ORD MDW DTW LGA EWR JFK PHL IAD DCA SFO OAK DEN ORD MDW DTW LGA EWR JFK PHL IAD DCA SFO OAK DEN ORD MDW DTW LGA EWR JFK PHL IAD DCA LAX ONT ATL LAX ONT ATL LAX ONT ATL DFW DAL DAL DFW DFW DAL IAH HOU IAH HOU IAH HOU MIA MIA MIA Actual Network Operated, 2005 Airline Game Theoretical Equilibrium Network System Optimal Network Tripling of 2005 fuel cost O-D Seats O-D Flt Freq. Segment Flt Freq. Airline Optimal Network System Optimal Network % diff. System R 2 % diff. System R 2 % diff. System R 2 25% high % high % high % low % low % low - 19

20 AIM 2 Greater consideration of supply side effects fleet planning model Global location of aircraft More advanced marginal abatement modelling Integrate airline response modelling Further sophistication of aircraft performance modelling Contrails and regional non-co 2 climate impacts Cruise related air pollutant emissions Further development of economic impacts modelling 20