cost-effectiveness analysis of gains milena stefanova, enea [email protected] bologna, 23...
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Cost-effectiveness analysis of GAINS
Milena Stefanova, [email protected]
Bologna, 23 marzo 2010
UTVALAMB-AIRUnità Tecnica Modelli, Metodi e Tecnologie per le Valutazioni Ambientali – Laboratorio Qualità dell’Aria
Contents
Cost-effectiveness analysis of GAINS: overview
GAINS, RAINS, GAMES/Opera and GAINS-ITALY Uses by IIASA: policy setting and multi-regional
national study
Cost-effectiveness in GAINS
“The GAINS (GHG-Air pollution interactions and synergies) model explores cost effective multi-pollutant emission control strategies that meet environmental objectives on air quality impacts (human health and ecosystems) and greenhouse gasses” (*)
How this translates into methodology:
Cost-effective: minimisation of cost function using linear mathematical optimisation. Multi-pollutant: emissions of many pollutants are considered simultaneouslyEnvironmental objectives on air quality impacts: optimisation constraints are expressed in terms of statistical indicators expressing exposures to concentrations or depositions (PM-loss in life expectancy, O3 – premature mortality; AOT40/fluxes, critical loads for acidification, critical loads for eutrophication; climate impacts: GWP100, Near-term forcing, black carbon deposition). Green-house gasses: considers internally CO2eq-structural measures + indicators expressing radiative forcing as a type of environmental objective.
(*) Last CIAM report
GAINS optimisation• Minimisation of a linear cost function (number of variables
>> 2000). Variables:• Application rates of end-of-pipe measures (app. 2000)• Fuel substitutions (in PP, transport)• Efficiency measures with feedbacks in other sectors
• Constraints: environmental targets expressing effects of air pollution + consistency constraints
• GAMS (general algebraic modelling system, http://www.gams.com/):
• Interface language to optimisation solvers: Cost function and constraints are expressed in specific optimisation-target language, with simplified syntax
• High-performance solvers: implementing standard mathematical algorithms for different kinds of optimisation
GAINS, RAINS, RIAT and GAINS-Italy
Different optimisation methodologies in GAINS/RAINS
• RAINS-mode optimisation: end-of-pipe measures finds an optimal control strategy
• GAINS-mode optimisation: end-of-pipe measures + scenario changing measures finds an optimal scenario (pathway + control strategy)
• RAINS optimisation: end-of-pipe measures + assumption for single-pollutant technologies only - Simplification: marginal cost linear ordering and minimum costs for achieving certain emission (not concentration!) levels (pair wise linear interpolation of the cost function).
GAINS/RAINS versus RIAT (Uni Brescia)
• Multiobjective optimisation: finding an optimum agreement between environmental impacts and costs for their reduction (no fixed environmental targets)
• Different method of using atmospheric dispersion modeling outputs (source-receptor transfer matrices versus neural networks)
• Cost function = RAINS cost function (single-pollutant, end-of-pipe measures)
GAINS, GAINS-Italy and GAMES/Opera
FEATURE GAINS GAINS-Italy RIAT
Costs scenario analysis
End-of-pipe measures YES ? YES
Technical scenario-changing measures
NO/NOT YET (?)
STARTED ?
Non technical and specific regional measures
NO ? (but exists scenario analysis of effectiveness)
NOT YET
Cost effectiveness/Other optimisation-based analysis
End-of-pipe measures YES ? YES
Technical scenario-changing measures
YES ? NOT YET
Non technical and specific regional measures
NO ? (but exists scenario analysis of effectiveness)
?
Uses by IIASA: policy setting and multi-regional national study
Policy setting
(*) Irish NIAM report (2010), “Non-Technical Measures: Consideration of an initial framework for the integrated evaluation of non-technical measures in climate and transboundary air pollution modelling and policy”
Multi-regional national study: GAINS-India
• Optimal control strategy: optimisation with only end-of-pipe measures
• Optimal scenario (control strategy + activity pathway): optimisation with end-of-pipe, structure-changing measures
• Scenario analysis with end-of-pipe measures only: explore benefits of more stringent climate policy on air quality
• Full scenario analysis: not available within published IIASA documents
• Multiregionality: lower national optimisation costs (location of measures more precision).
RAINS-mode optimisation of control strategy •CLE scenario: new large plants (electrostatic precipitators), improved fuels and biomass cooking stoves in DOM (slow penetration), …•ACT scenario (Advanced Control Technology): uniform application of best EoP technologies to all new installations.
RAINS-mode optimisation of control strategy (2)
GAINS-mode optimisation of scenario
Indicator Measure CLE 2005 CLE 2030 GAINS OPTLoss in stat. Life expectance months 24,9 58,8 23,52YOLLS Myears/year 24 102 40,8Disability adjusted life years (indoor) Myears/year 12,8 12,3 4,92Ground-level O3 premature deaths
1000 cases/year 48,2 115,3 46,12
GAINS-mode optimisation of scenario (2)
Measures RAINS-OPT GAINS-OPTPP/Industry EOP 23,9 17,3DOM EOP 4,4 0,5Other EOP 2,2 0,9Fuel switch/REN 14,1PP Savings -16,9EE IND -3,7EE DOM 1,2Fuel Eff. MOB -4,5Total 30,5 8,9
Scenario analysis with end of pipe measures
• Development of an alternative energy scenario with more stringent climate policy measures and the same end-of-pipe control strategy
• Compute emissions, air-quality indicators for base-line and alternative climate scenario for a fixed year
• Compute difference in costs between end-of-pipe measures in baseline and in climate policy scenarios.
Scenario analysis with end of pipe measures
Costs (different pathways, the same control strategy):
Cost comparison end-of-pipe measure of two scenarios: easy