Cost-effectiveness analysis of GAINS

Milena Stefanova, ENEAmilena.stefanova@enea.it

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