1 / 23

Cost-effectiveness analysis

Cost-effectiveness analysis. Fabian Wagner International Institute for Applied Systems Analysis (IIASA). Cost-effectiveness vs cost-benefit analysis The complexity of the problem Formulation of optimization problem Target setting Implementation of optimization. Agenda. *X. minimize.

macayle
Télécharger la présentation

Cost-effectiveness analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Cost-effectiveness analysis Fabian Wagner International Institute for Applied Systems Analysis (IIASA)

  2. Cost-effectiveness vs cost-benefit analysis • The complexity of the problem • Formulation of optimization problem • Target setting • Implementation of optimization Agenda

  3. *X minimize costs - benefits (1a) Cost-benefit analysis: OR costs (1b) minimize *X benefits Cost-effectiveness vs cost-benefit analysis Cost-effectivenessanalysis: minimize costs (2) such that Environmental targets are met

  4. Cost-effectiveness analysis • Uses (technology) cost-minimization approach • Avoids implicit judgments on • value of environmental benefits • value of human health • differences thereof between countries • Allows independent choices of targets

  5. Environmental targets (I): 1 pollutant, 1 emitter

  6. Environmental targets (II): 2 pollutants, 1 impact, 1 emitter Ozone concentration

  7. Environmental targets (III): 2 pollutants, 1 impact, 2 emitters, 2 receptors Country A Country B Ozone concentration Ozone concentration

  8. See: GAINS optimization module Environmental targets (IV): e.g. 5 pollutants, 4 impact, 40 emitters, 40 receptors

  9. IIASA’s GAINScomputer model PM SO2 NH3 NOx VOC Policy targets for 2020 Multi-pollutant/multi-effect analysisfor identifying cost-effective policy scenarios Health Eutrophication Acidification Ozone

  10. Minimize: costs such that: Constraints are satisfied • Implementation in GAMS (separate from web-interface) • Data on constraints taken from input database • Technologies are explicitly represented • Substitution options can be switched on/off • Effectiveness of multi-pollutant technologies is taken into account GAINS optimization module

  11. Environmental targets are met (e.g. 4 indicators in each of some 40 regions) • Technical potentials are not exceeded • Implied emissions cannot increase • Aggregations are consistent • Useful energy is constant (‘balance equations’) Constraints

  12. Two alternative ‘modes’ of optimization • Activity levels constant – can only change control strategies (‘RAINS mode’) • Both control strategies and some activities can change, e.g. power plants (‘GAINS mode’) • Synergies with CO2 mitigation • More flexibility, lower cost

  13. GAINS single pollutant optimization: With and without Efficiency improvements & fuel substitutions

  14. The GAINS optimization in brief Objective function: Minimization of total emission control costs C for add-on measures (x) and structural changes (y): Application levels(x) and substitution potentials(y) are constrained: Emissions are calculated from activity levels and emission factors: Sectoral emissions of current legislation may not increase: Activity levels plus substitution levels must remain constant to satisfy demand:

  15. Balance equations E.g. energy savings: Demandx + energy_savx = DemandBL Eg: fuel substitution A -> B Fuel(A)x+Θ(A->B)x = Fuel(A)BL Fuel(B)x-λA->B* Θ(A->B)x = Fuel(B)BL

  16. yi,s,f,s’f’: amount of xai,s,f replaced by xai,s’,f’ xai,s’’,f’’• effi,s’’,f’’ xai,s’,f’ • effi,s’,f’ yi,s’’,f’’,s,f yi,s,f,s’f’ xai,s’’,f’’ xai,s’,f’ Italy, power plants, coal Italy, power plants, wind The meaning of balance equations xai,s,f • effi,s,f xai,s,f Italy, power plants, gas

  17. Documentation:

  18. Three concepts for target settingfor PM health effects • Uniform limit value for air quality:Bring down PM2.5 everywhere below a AQ limit value • Gap closure concept:Reduce PM2.5 levels everywhere by same percentage

  19. Base year exposure (2000/1990) 0% Gap used for CAFE analysis 50% 100% Definition of “gap closure”used NEC 2010 ceilings and NEC 2020 ceilings Effect indicator NEC 2010 Baseline 2020 (Current legislation) MTFR from EU25 excluding EURO5/6 MTFR from EU25 MTFR from EU-25 + shipping MTFR from Europe + shipping No-effect level (critical load/level) Zero exposure

  20. Three concepts for target settingfor PM health effects • Uniform limit value for air quality:Bring down PM2.5 everywhere below a AQ limit value • Gap closure concept:Reduce PM2.5 levels everywhere by same percentage • Maximize total health benefits in Europe for a given European budget constraint, disregarding the location of the benefit

  21. Cost-effectiveness of the target setting approachesEmission control costs [billion €/yr] vs. YOLL

  22. Public access Internal Web-interface Database Extract baseline scenario targets Architecture Optimization Upload optimal solution

  23. GAINS uses cost-effectiveness approach – not CBA • This approach helps policy processes: • Objective is clear • No implicit value judgments • The optimization problem is quite complex • Alternative target setting approaches have been explored • We are in the process of making all inputs accessible through web-interface Conclusions

More Related