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Workshop on the use of GAINS model for the revision of the Gothenburg Protocol Focus on key measures to improve air quality in Europe and the role of EECCA and Balkan countries in that improvement 20-21 June 2011 IIASA, Laxenbrg.
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Workshop on the use of GAINS model for the revision of the Gothenburg Protocol Focus on key measures to improve air quality in Europe and the role of EECCA and Balkan countries in that improvement 20-21 June 2011 IIASA, Laxenbrg
Methodology for emission calculations and estimating of mitigation potentials Janusz Cofala (IIASA), Stefan Åström (IVL) The GAINS model
Outline Background Method Some illustrative results
Background GAINS The GAINS model is an updated version of the RAINS model Originally, the RAINS model was developed to support the UNECE CLRTAP GAINS makes it possible to propose effect-oriented international policies to reduce transboundary air pollution These policies are cheaper than 'uniform cap' policies. The GAINS model is today also including greenhouse gas calculations The GAINS model provides support to the work with air pollution in the EU and CLRTAP, as well as EU efforts to reduce emissions of Greenhouse gases in the EU.
Background GAINS The model calculates cost efficiency from a 'techno-economical' perspective (macro-economic feedbacks are not included) The model does not put an economic value on health and environmental impacts from air pollution. Cost-benefit analysis can be done as a separate task by other research team (AEA Technology) The model exists in several versions: The abatement cost minimizing offline version run by IIASA The Greenhouse Gases Mitigation Efforts Calculator (for Annex I countries) online scenario analysis tools for: Europé (within EMEP), East Asia (with China), South-Asia, Rest of the World Ireland, Italy, The Netherlands, Sweden, Russia (national implementations)
GAINS methodology – REMINDER!! A model provides a simplified version of reality which can be used to show complex interactions
GAINS methodology – REMINDER!! • The scenario technique • A scenario is the description of a possible, consistent future development of a system (e.g. the energy, transport and agricultural system) • The purposes of scenarios are • the presentation and quantification of different pathways of future development of technical systems and analysis of the consequences of these pathways (costs, environmental impacts) • the analysis of changes in the system caused by changed exogenous parameters Friedrich, 2010
Simulation/ “Scenario analysis” mode, available online Building blocks of GAINS Energy/agricultural projections PRIMES, POLES, CAPRI,IEA, nat. projections Emission control options Emissions Costs Atmospheric dispersion Air pollution impacts, GWP of GHG emissions
OPTIMIZATION Environmental targets The GAINS optimization mode Energy/agricultural projections PRIMES, POLES, CAPRI,IEA, national projections Emission control options Emissions Costs Atmospheric dispersion Air pollution impacts, GWP of GHG emissions
Models help to separate policy and technical issues Amann, 2009
Aggregation of energy- related sources • Primary sectors: • Power plants • Other energy production and conversion • Industry • Domestic • Transport • Non-energy use • Further divided into secondary sectors • Fuel categories: • Coal • Oil • Gas • Biomass • Renewables • Nuclear • Electricity • Heat • Different types and grades included
Aggregation of transport sources • Road transport • Cars, light-duty trucks • Heavy-duty trucks, buses • Motorcycles and mopeds (2-stroke, 4-stroke) • Non-road mobile sources • Rail, Air, Inland waterways • National sea traffic and national fishing • Mobile machines – construction and industry, agriculture • Other (households, gardening, forestry, military) • For each source vehicle numbers and fuel consumption assessed. For road transport – also vehicle-kilometers
Aggregation of process sources • Storage and handling of bulk products: • Coal • Agricultural products • Metal ores • Fertilizers • Other… • Construction activities • Waste treatment and disposal • Production of raw materials: • Steel • Aluminum • Other metals • Cement • Glass • Oil and natural gas • Oil refining • Fertilizers…
Options to control emissions of air pollutants: Stationary sources: • SO2: • Use of low sulfur fuels • In-furnace control • Flue gases desulphurization • Process emissions controls • NOx: • Combustion modification • Catalytic and non-catalytic reduction • NH3: • Dietary changes • Animal housing adaptation and air purification • Manure storage and application techniques • Urea substitution • VOC: • Basic management techniques • Solvent substitution • End-of-pipe measures • PM • Cyclones, ESP, other Filters • Cleaner industrial processes • Improved boilers and stoves • Good practices • Mobile sources: • EURO standards • Non-road EURO equivalents
GAINS data base on emission control options SO2: 180 options NOx: 400 options PM: 850 options (same as NOx for transport sector sources) NH3: 110 options VOC: 500 options GHG: 300 (ca), incl structural measures for CO2, options for CH4, N2O, F-gases Wagner, Klimont, 2009
GAINS methodology - scenarios By using a data base containing information on: Emission factors for unabated emissions Dispersion of air pollution over Europe Ecosystem sensitivity, Population distribution Technologies and options for reducing emissions, specified with respect to: emission removal efficiency cost of implementation And by using scenario specific estimates (projections regarding: Activities causing pollution Implementation of emission reducing technologies
GAINS results - scenarios The GAINS model can calculate the following results: Emissions in a country The impact on the environment and human health caused by the emissions The costs for reducing emissions in countries With respect to that: Some technologies used to reduce pollutants might increase emissions of other pollutants Emissions in some countries have a larger impact on human health and the environment than other countries’ emissions
GAINS methodology – Calculating emissions Klimont, 2009
Cost calculations in GAINS • All costs in constant Euro 2005 • Net of taxes • Annual costs method • Costs based on international investment and operating experience • For developing countries – local components in investment costs included • Three levels of discount rate • 4% (social) • 10% (business) • 20% (private)
Cost components • Common (the same for all countries) • - unit investment costs, • - fixed O+M costs, • extra demand for labor, energy, and materials • Country-specific • - size of installation, • - plant factors, • - prices for labor, electricity, fuel and other materials, • - cost of waste disposal
Calculating abatement costs • Cost components: • common (the same for all countries) • - annualized unit investment costs, Iann • - fixed O+M costs, OMfix • - extra demand for labor, energy, and materials, OMvar • country-specific, OMvar • - size of installation, • - plant factors, • - prices for labor, electricity, fuel and other materials, • - cost of waste disposal C = Iann + OMfix + OMvar Cofala, 2009
Calculating dispersion of pollutants(Source-receptor relationships for PM2.5 - from the EMEP Eulerian model) PM2.5j Annual mean concentration of PM2.5 at receptor point j I Set of emission sources (countries) J Set of receptors (grid cells) pi Primary emissions of PM2.5 in country i si SO2 emissions in country i ni NOx emissions in country i ai NH3 emissions in country i αS,Wij, νS,W,Aij, σW,Aij, πAij Linear transfer matrices for reduced and oxidized nitrogen, sulfur and primary PM2.5, for winter, summer and annual
Air pollution impacts Damage to human health: • loss in life expectancy from PM2.5 • mortality from ground-level ozone Damage to vegetation: • effects of acidification and eutrophication for • forests, semi-natural ecosystems, Natura 2000 areas Emissions of greenhouse gases
Current and future (2020) emissions of air pollutants in Europe, kilotons
An example cost curve for SO2 Cost curves describe how pollution control costs increase with increasing levels of emission reductions.
Loss in life expectancy attributable to fine particles [months] • 2020 2020 • CAFE baseline Maximum technical Current legislationemission reductions Loss in average statistical life expectancy due to identified anthropogenic PM2.5Calculations for 1997 meteorology
Excess acid deposition to forests • 2020 2020 • CAFE baseline Maximum technical Current legislationemission reductions Percentage of forest area with acid deposition above critical loads, Calculation for 1997 meteorology
More information Documentation http://www.iiasa.ac.at/rains/gains-methodology.html?sb=10 Presentations http://www.iiasa.ac.at/rains/meetings/GAINS-tutorial/presentations.html