Cost-effectiveness analysis of GAINS Milena Stefanova, ENEA Bologna, 23 marzo 2010 UTVALAMB-AIR Unità 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 simultaneously   Environmental 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, 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 FEATUREGAINSGAINS-ItalyRIAT Costs scenario analysis End-of-pipe measuresYES? 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 measuresYES? 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 IndicatorMeasure CLE 2005CLE 2030GAINS OPT Loss in stat. Life expectancemonths24,958,823,52 YOLLSMyears/year ,8 Disability adjusted life years (indoor)Myears/year12,812,34,92 Ground-level O3 premature deaths 1000 cases/year48,2115,346,12

GAINS-mode optimisation of scenario (2) MeasuresRAINS-OPTGAINS-OPT PP/Industry EOP23,917,3 DOM EOP4,40,5 Other EOP2,20,9 Fuel switch/REN 14,1 PP Savings -16,9 EE IND -3,7 EE DOM 1,2 Fuel Eff. MOB -4,5 Total30,58,9

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