UTVALAMB-AIR Unità Tecnica Modelli Metodi e Tecnologie per

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. stefanova@enea. it Bologna, 23 marzo 2010

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, 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). 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

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, endof-pipe measures)

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

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 • 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 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)

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 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 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 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