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UTVALAMB-AIR Unità Tecnica Modelli, Metodi e Tecnologie per le Valutazioni Ambientali – Laboratorio Qualità UTVALAMB-AIR Unità Tecnica Modelli, Metodi e Tecnologie per le Valutazioni Ambientali – Laboratorio Qualità dell’Aria Cost-effectiveness analysis of GAINS Milena Stefanova, ENEA milena. [email protected] it Bologna, 23 marzo 2010

Contents § Cost-effectiveness analysis of GAINS: overview § GAINS, RAINS, GAMES/Opera and GAINS-ITALY § 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 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, O 3 – premature mortality; AOT 40/fluxes, critical loads for acidification, critical loads for eutrophication; climate impacts: GWP 100, Near-term forcing, black carbon deposition). §Green-house gasses: considers internally CO 2 eq-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). 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 optimisationtarget language, with simplified syntax • High-performance solvers: implementing standard mathematical algorithms for different kinds of optimisation

GAINS, RIAT and GAINS-Italy GAINS, RIAT and GAINS-Italy

Different optimisation methodologies in GAINS/RAINS • RAINS-mode optimisation: end-of-pipe measures finds an optimal control 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 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, endof-pipe measures)

GAINS, GAINS-Italy and GAMES/Opera FEATURE GAINS-Italy RIAT End-of-pipe measures YES ? YES Technical scenario-changing GAINS, GAINS-Italy and GAMES/Opera FEATURE GAINS-Italy RIAT 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 Costs scenario analysis 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 Uses by IIASA: policy setting and multi-regional national study

Policy setting (*) Irish NIAM report (2010), “Non-Technical Measures: Consideration of an initial framework 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-ofpipe measures • Multi-regional national study: GAINS-India • Optimal control strategy: optimisation with only end-ofpipe 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 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 Eo. P technologies to all new installations. 125 120 115 110 105 100 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 115. 3 102 65. 9 58. 8 CLE 2005 46 CLE 2030 26. 5 ACT 2030 12. 3 Loss in stat. Life expectance YOLLS 13 2. 7 1. 4 Disability Ground-level Crop loss -Rice adjusted life O 3 premature years (indoor) deaths 8. 4 Crop loss Wheat 0. 5 0. 2 Crop-loss Soybean

RAINS-mode optimisation of control strategy (2) RAINS-mode optimisation of control strategy (2)

GAINS-mode optimisation of scenario Indicator Measure Loss in stat. Life expectance months YOLLS Myears/year GAINS-mode optimisation of scenario Indicator Measure Loss in stat. Life expectance months YOLLS Myears/year Disability adjusted life years (indoor) Myears/year Ground-level O 3 premature 1000 deaths cases/year CLE 2005 CLE 2030 GAINS OPT 24, 9 24 58, 8 102 23, 52 40, 8 12, 3 4, 92 48, 2 115, 3 46, 12 140 120 100 80 CLE 2005 60 CLE 2030 GAINS OPT 40 20 0 Loss in stat. Life expectance YOLLS Disability adjusted Ground-level O 3 life years (indoor) premature deaths

GAINS-mode optimisation of scenario (2) Measures RAINS-OPT GAINS-OPT PP/Industry EOP 23, 9 17, 3 GAINS-mode optimisation of scenario (2) Measures RAINS-OPT GAINS-OPT PP/Industry EOP 23, 9 17, 3 DOM EOP 4, 4 0, 5 Other EOP 2, 2 0, 9 Fuel switch/REN 14, 1 PP Savings -16, 9 EE IND -3, 7 EE DOM 1, 2 Fuel Eff. MOB -4, 5 Total 30, 5 8, 9 35 30 25 20 15 End of Pipe 10 Structural 5 0 -5 -10 -15 RAINS-OPT GAINS-OPT

Scenario analysis with end of pipe measures • Development of an alternative energy scenario Scenario analysis with end of pipe measures • Development of an alternative energy scenario with more stringent climate policy measures and the same end-ofpipe 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 35000 30000 25000 20000 15000 10000 5000 Scenario analysis with end of pipe measures 35000 30000 25000 20000 15000 10000 5000 0 Baseline Climate policy 70 60 50 40 30 20 10 0 Baseline Climate policy Loss of statisctical life expectancy (months) SO 2 NOx PM 2. 5 CO 2 Costs (different pathways, the same control strategy): 16. 8 16. 3 15. 8 Costs in 2030 (billion euro) 15. 3 Costs in 2030 (billion euro) 14. 8 14. 3 13. 8 16. 3 14. 9 Cost comparison end -of-pipe measure of two scenarios: easy