1. Cost-effectiveness analysis November 2002 Aude Lenders, CESSE – ULB

2. November 26th, 20022 Cost-Effectiveness Analysis 1.Introduction 2.Results a)Presentation b)Benefit indicators c)Short-term versus long-term d)Cost variations e)Effectiveness variations 3.Conclusions

3. November 26th, 20023 1. Introduction Cost-effectiveness analysis spreadsheet: Inputs: –Population exposure per scenario (« Instantaneous benefit ») as an output from EURANO or from the Extrapolation module. –Programme : set of noise reduction measures (=scenario) + implementation schedule (within a 10-years period). –Parameters : lifetime, costs, discount rates

4. November 26th, 20024 Outputs: –Net Present value of the Benefits = Number of persons who have gained a noise reduction thanks to the measures applied. [Persons*years]  “Effectiveness” People exposed to noise above 60dB(A) Annoyed people Weighted people (  factor) –Net Present Value of the Costs in Euros –Efficiency = Present Benefits / Present Costs

5. November 26th, 20025 Benefit Function PB = Net Present Value of  Benefits of each measure + Interactions between measures  For each year of the modeled period Interpolation of EURANO output: evolution of the benefits when supplementary units of the measure are implemented.

6. November 26th, 20026 Cost Function PC = Net Present Value of Investment years 1 to 10 + Maintenance during lifetime of the measure + Removal at the end of the lifetime

7. November 26th, 20027 2. Results Costs without windows insulation Best efficiency Worst efficiency 14’000’000 Persons > 60dB 700’000

8. November 26th, 20028 b) Two indicators for the benefits : same results Same results for different indicators

9. November 26th, 20029 Variations in the ranking of the programmes Third indicator : number of people weighted (noise level and noise reduction) Same ranking of the programmes for ≠ weightings Uncertainties : the costs of the measures

10. November 26th, 200210 c) Two different approaches

11. November 26th, 200211 d) Costs variation according to the number of freight wagons (-25%)

12. November 26th, 200212 d) Variation according to the ratio “number of wagons/ km urban areas”

13. November 26th, 200213 e) Benefits variation: % freight trains & distribution of people

14. November 26th, 200214 Conclusions Despite these small variations, all the graphs have generally the same appearance  the results seem reliable. Further study : –Other noise reducing measures –Other scenarios (combination of measures)