Implementation of ExternE Methodology in Eastern ... - ExternE project

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Nov 30, 2004 - Calculate the corresponding damage costs in Czech Republic, Hungary ..... Data for Hungary were collected by an external partner CAAG (Clean Air ... hard coal ..... Data on inventory of materials and repair costs have being ...
ExternE-Pol Externalities of Energy: Extension of Accounting Framework and Policy Applications (Contract N° ENG1-CT-2002-00609)

Final Report on Work Package 7

Implementation of ExternE Methodology in Eastern Europe Jan Melichar, Miroslav Havranek, Vojtech Maca, and Milan Scasny, Charles University Prague, Czech Republic and Mariusz Kudelko, MEERI, Krakow, Poland 30 November 2004 Abstract We apply the ExternE method, based on impact pathway approach, utilizing the newest updates and improvements to the method. The external costs attributable to energy transformation and transportation caused by air pollution are assessed. External costs for the energy sector in the Czech Republic, Hungary and Poland are estimated. In addition, externalities for transport sector are also quantified for the Czech Republic. Power plants and vehicles used are inventoried. The assessment of the external costs required, in addition to the collection of data, the adaptation of certain parts of the EcoSense software. Monetary valuation of health end points is also analysed and possible valuation for other endpoints is discussed. This WP was carried out by research institutes located in the Czech Republic (Charles University Environment Center - CUP/CUEC) and in Poland (Mineral and Energy Economy Research Institute of Polish Academy of Sciences - MEERI). The approach followed these project tasks:  Review ExternE methodology  Provide information on costs of health effects  Discuss of data on costs and inventory of materials possible damaged by air pollution  Modify the EcoSense software as appropriate  Obtain data on inventory ofpower plants, cars, buses, and railways, and on their emission of pollutants  Calculate the corresponding damage costs in Czech Republic, Hungary and Poland.

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Content: Introduction ................................................................................................................................................................ 3 Tasks description .................................................................................................................................................. 3 Application of Externe Methodology .................................................................................................................. 3 Data collection ........................................................................................................................................................... 5 Data on pollutants ................................................................................................................................................. 5 Meteorological data .............................................................................................................................................. 5 Data on energy production and other technical parameters ............................................................................... 6 Data on transport inventory.................................................................................................................................. 8 Costs of health effects......................................................................................................................................... 14 Non-market valuation of health impacts ........................................................................................................... 15 Buildings and materials ...................................................................................................................................... 16 Forestry................................................................................................................................................................ 19 Implementation of ExternE methodology in the Czech Republic, Hungary and Poland.................................... 22 Electricity production in CEEC ......................................................................................................................... 22 The fossil fuel cycles .......................................................................................................................................... 24 Technical characteristics of the reference power plants in the Czech Republic............................................. 24 Technical characteristics of the reference power plants in Poland .................................................................. 25 Technical characteristics of the reference power plants in Hungary............................................................... 27 Quantification of damages for the Czech Republic .......................................................................................... 29 Case study: External costs for CHP lignite for the Czech Republic................................................................ 30 Quantification of damages for Hungary ............................................................................................................ 32 Quantification of damages for Poland ............................................................................................................... 33 Results for Central European Countries ............................................................................................................ 34 Case Study: External costs for transport in the Czech Republic ..................................................................... 39 Externality internalisation and policy options ....................................................................................................... 42 Options for externality reduction ....................................................................................................................... 42 Internalization of externalities ........................................................................................................................... 43 Conclusions .............................................................................................................................................................. 46 Reference:................................................................................................................................................................. 47

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Introduction External cost calculation has quite a long tradition in Western Europe. However in the acceding countries of Central and Eastern Europe, external costs of energy and transport have not previously been calculated in comprehensive and complex form. Doing so has become timely with the extension of the EU. We apply the ExternE method, based on impact pathway approach, utilizing the newest updates and improvements to the method. The external costs attributable to energy transformation and transportation caused by air pollution are assessed. External costs for the energy sector in three new EU Member States are estimated. In addition, externalities for transport sector are also quantified for the Czech Republic. Power plants and vehicles used are inventoried. The assessment of the external costs required, in addition to the collection of data, the adaptation of certain parts of the EcoSense software. Monetary valuation of health end points is also analysed and possible valuation for other endpoints is discussed. This WP was carried out by research institutes located in the Czech Republic (Charles University Environment Center - CUP/CUEC) and in Poland (Mineral and Energy Economy Research Institute of Polish Academy of Sciences - MEERI). Description of Tasks Two objectives were to be achieved ExternE-Pol Work Package 7. The first objective was to build up scientific capacity in Eastern Europe for analysis of external costs; the second, to provide external cost estimates attributable to energy use in the Czech Republic, Hungary and Poland. The approach followed these project tasks:  Review ExternE methodology  Provide information on costs of health effects  Discuss of data on costs and inventory of materials possible damaged by air pollution  Modify the EcoSense software as appropriate  Obtain data on inventory ofpower plants, cars, buses, and railways, and on their emission of pollutants  Calculate the corresponding damage costs in Czech Republic, Hungary and Poland. Application of ExternE Methodology The ExternE methodology (EC, 1998), including the newest updates and method improvements, was usedto calculate external cost in CEEC countries. Within the framework of ExternE methodology,we used LCA (Life Cycle Assesment) and IPA (Impact Pathway Analysis). LCA was used mainly for upstream processes, which include mining, transport and preparation of fuel, etc. We followed a “bottom-up” approach, namely the impact pathway approach (IPA) – the core of the ExternE methodology.

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Box 1: “Top-down” versus “Bottom-up” approach The “top-down” approach starts with total damage expressed in monetary terms for the entire economy or sector. This total is then disaggregated among all particular producers of the externality. The “top-down” approach is constituted by several steps: firstly, total aggregated damage caused by entire economy or sector is estimated; secondly, all relevant causes such as emissions responsible for the damages are inventoried (consequently can be weighted by related toxicity factors); thirdly, the share of particular plant on total damage caused by these the causes (emissions) is estimated; lastly, the damage on production unit is derived. The “bottom-up” approach, in contrast with the “top-down” approach, allows consideration of local conditions, spatial and time factors. It starts with defining a particular technology operating within specific climate environment and under local conditions. Beginning with primary firm emission and production data, it then models their impact on particular receptors, considering atmospheric deposition and modelling. Site, time, and technology-specific externalities can be further aggregated for the entire sector or economy by using appropriate algorithm.

For power generation we used the IPA approach, which includes technology specification, air pollution, dispersion of pollutants, and quantification of impacts in physical and monetary units. We calculated and evaluated impacts on human health (public health – mortality and morbidity), crops (nitrates, sulfates, and ozone), building materials (corrosion by acid deposition) and climate change (abatement cost to reach Kyoto). We did not take in account damages on forestry, ecosystems, accidents, noise or aesthetic value. This is mainly because there are no robust dose-response functions available or no scientifically justifiable causality frame to base our assumptions upon. The ExternE method has been modified and improved by implementing results from NewExt (YOLL – years of life lost -- values for chronic and acute mortality), as well as by improvements made by other WPs of ExternE-Pol project (dose-response function in area of human health and agriculture). The contribution of the current WP was mainly as an application for Eastern Europe conditions, and testing of methodology for energy sector of particular countries. In applying ExternE methodology, we used several software packages. We initially used UWM software to provide us with rough estimation of external costs, though later we recalculated results using more precise software. We used EcoSense (version 4.1) to calculate external damage caused by heat and electricity producers, calculating only regional impacts. For local dispersion and estimation of transport externalities, we used RiskPoll (version 1.5.1). We did not apply more specialized software like EcoSense multisource or EcoSense Transport.

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Data collection Data on pollutants CUP used several data sources to compile an emission inventory. First, emissions data for the energy sector and for the entire economy were collected These data were easily accessible from public sources, mainly from the Czech Statistical Office and the , Czech Hydrometeorological Institute (CHMI). Second, we obtained predictions about emissions development for energy sector and total emissions. For the Czech Republic, we obtained these predictions from public sources, mainly from documents of Ministry of Environment. The third and most important part was specific pollution data for particular energy transformation technologies. We chose several plants that are important from several points of view (position, energy production, fuel type etc.). We then contacted energy companies directly and asked for data. In accepting the data, we had to sign an agreement that we would not publish the names of the plants concerned. As crosscheck, we also gained access to the Registry of Air Pollution Sources (REZZO). This is official database run by CHMI. The REZZO data we obtained were more or less in agreement with the data we obtained from energy companies. Thus we used the REZZO data for calculation, mainly because this is an official source and by Czech law, polluters pay according to their emissions as recorded in REZZO. We were able obtain data on classical pollutants (SO2, NOX, PM, CO) and also for various heavy metals and specific organic pollutants. However, in our calculations, we included only those pollutants for which we have robust dose-response function. MEERI obtained data for Poland by similar means. Data for total country overview and projection were obtained from national as well as international sources (EEA, IEA). Data for specific sources were also obtained directly from plants. Data for Hungary were collected by an external partner CAAG (Clean Air Act Group). Those data were obtained mainly from private energy companies, and partly from Hungarian Energy Office. Meteorological data Meteorological data were required to calculate local external costs of air pollution. As previous results have shown, local impact is unimportant (2-6% of total impact) for large sources of air pollution when the emission stack is high. For transport, the situation is reversed, and local impact is very important (70-90% of total). Data for the Czech Republic were obtained from the Czech Hydrometeorological (CHMI) office. CHMI employs a network of meteorological measuring station and automatic immission station measuring network (AIM). Data from meteorological networks were expensive, and beyond financial scope of this project. However, AIM also gathers some meteorological data, with much better data availability than from meteorological stations. Another advantage of AIM was that its stations were mostly located near large air pollution sources, so we could use truly site specific data for calculation. We were not able to obtain

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precise hourly data for all parameters required by the EcoSense model, but we were able to obtain data required for RiskPoll in its most detailed calculations The data obtained include hourly temperature, wind speed, wind direction. Acquisition of meteorological data for Poland was similar to that for Czech Republic. Data was obtained from Polish meteorological institute. Again there was problem with sufficient data availability to run EcoSense, but we were able to obtain data necessary to run RiskPoll (average stability classes). Both because of difficulties with obtaining meteorological data and minimal impact of local effects from power plant emissions on the total value of external cost we didn’t collect meteorological data for Hungary. Data on energy production and other technical parameters Energy statistics and energy inventory for national level were obtained from international resources, mainly OECD and IEA statistics. We used national resources (Energy Regulatory Offices, Statistical Offices) to complement these international sources. Site-specific data for selected reference power plants were obtained directly from energy companies and verified by statistics from regulatory bodies of the particular country. For example, in the Czech Republic we contacted the energy company directly to obtain data. As noted above, we had to sign that we would not publish names and specific details of the selected plants. For crosscheck, we also gained access to data from the Registry of Air Pollution Sources (REZZO) and from Energy Regulatory Office (ERU), which also includes some data about technology used. Tables 1, 2 and 3 below summarize inventory of energy technologies and their electricity generating capacity for the Czech Republic, Hungary, and Poland respectively Tab. 1: Specification of Czech power plants, 2002 Net electricity generating Classification by fuel capacity (GW) Combustible fuels, of which 9.27 hard coal 0.96 brown coal 6.73 lignite 0.1 hard coal/natural gas 0.69 brown coal/natural gas 0.42 coal gas/natural gas 0.37 Nuclear 2.76 Hydro 2.01 Wind 0.01 Total capacity 14.04 Type of generation Steam Combined cycle

9.03 0.23

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Number of power plants with 100 MW 2 12 1 2 2 1 2 5

Source: IEA/OECD 2004, CEA 2003

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Tab. 2: Specification of Hungarian power plants, 2002 Net electricity generating Classification by Fuel capacity (GW) Combustible fuels, of which 6.4 hard coal 1.03 brown coal 0.8 oil 0.41 oil/natural gas 3.96 natural gas 0.2 Nuclear 1.87 Hydro 0.05 Total capacity 8.32 Type of generation Steam Internal combustion Gas turbine Combined cycle

Number of power plants with 100 MW 7 1 2 5 2 1 1

4.95 0.2 0.41 0.84 Source: IEA/OECD 2004, CEA 2003

Tab. 3: Specification of Polish power plants, 2002 Net electricity generating Classification by fuel capacity (GW) Combustible fuels, of which 26.7 hard coal 17.1 brown coal 9.2 natural gas 0.4 Hydro 2.21 Wind 0.03 Total capacity 28.94 Type of generation Steam Gas turbine

Number of power plants with 100 MW 31 5 3 4

26.3 0.4 Source: IEA/OECD 2004, CEA 2003, GUS 2002

Data on transport inventory Data on transport inventory were gathered from different statistical sources at different levels of detail. We relied on officially published statistics provided by Central Statistical Office for Hungary, Statistical Yearbook of Transport published by Czech Ministry for transport, Polish Central Statistical Office, and the Road Transport Institute in Warsaw. Additional data were obtained from Transport Research Centre in Brno (Czech Republic), CD (Czech Railways) and Czech Ministry for the Environment. Relatively detailed data are available for road transport, but much less detail is available for railways. Table 4 below summarizes the road vehicle inventory for all three countries

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Hungary

Czech Republic

Tab. 4: Road vehicle inventory in CEEC, number of vehicles (2000) Personal car Buses total number 3 438 870 18 259 up to 2 years 250 535 1 437 2-5 years 479 357 2 262 5-10 years 687 773 3 138 over 10 years 2 021 205 11 422 total number up to 1 year 1-2 years 3-5 years 6-15 years 16 and over

2 364 706 130 397 232 288 230 653 1 088 795 682 573

Lorries 275 617 33 762 66 925 69 223 105 707

17 855 648 1 335 1 485 10 173 4 214

328 202 27 687 52 729 46 262 149 275 52 249

Poland

total number 7 813 000 57 400 1 266 000 up to 9 years 4 341 000 16 400 598 000 10-14 years 1 712 000 26 600 326 000 15 and over 1 760 000 13 900 522 000 Sources: MDCR Statistical Yearbook of Transport 2002, Hungarian Central Statistical Office, Road Transport Institute Tab. 5: Road vehicle inventory, mileage and fuel consumption (Poland, 2000) Number (thousand)

Average year mileage (km) rural

Passenger cars (petrol) "urban class" without CC 2007 "urban class" with CC 580 "other classes" without CC 2042 "other classes" with CC 1930 PC (LPG) 440 PC two-stroke (petrol) 145 PC (diesel) 669 LDV (light duty vehicle) delivery vans (petrol) without CC 454 delivery vans (petrol) with CC 106 delivery vans (LPG) 40 delivery vans (diesel) 329 Lorries lorries above 3,5t (diesel) 265 track-tractor (diesel) 68 Buses long-distance buses, type 1 15,3 long-distance buses, type 2 20 urban buses (diesel) 11,9 Special cars above 3,5t (diesel) 45,2 Note: CC – catalytic converter Source: Road Transport Institute, additional calculation MEERI

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urban

Average consumption l/100 km rural urban

5133 4400 7830 7800 5255 3895 7513

5220 6600 4321 5200 7883 974 4781

5.7 4.8 7.1 5.7 5.2 8.5 4.7

7.6 6.6 11.0 9.4 8.3 11.0 7.3

7608 8800 9900 10725

5061 7200 8100 8272

12.8 6.2 5.2 7.6

14.8 8.7 8.3 9.7

29557 50159

7399 2835

21.7 30.9

25.5 42.5

9035 60327 7757

1004 6703 69811

24.1 24.0 30.2

32.6 31.5 39.2

3702

8638

23.8

29.1

The most detailed inventory data are provided by Hungarian Central Statistical Office. These data can be easily sorted according to age, fuel and cylinder capacity (PC – personal cars), age, fuel and number of seats (buses), and age and load capacity (lorries). The same data categories exist for the Czech Republic, however, the two sources cannot be directly compared. MEERI gives interrelated data on vehicle inventory, mileage and fuel consumption in Poland based on statistics from the Road Transport Institute in Warsaw (Tab. 5). We compiled an inventory of locomotives and rail cars for Poland and the Czech Republic. The transport performance in the Czech Republic was 42.5 bln. gross tkm for electric and 8 bln. gross tkm for diesel traction. Tab. 6: Railway inventory (2002) Locomotives Rail cars electric diesel steam electric units diesel railcars Poland 1811 2314 25 1182 Czech Rep 940 1 510 26 170 798 Sources: Statistical Yearbook of Transport 2002, Polish Central Statistical Office

Aggregated data on overall transport performance in Hungary and the Czech Republic are given in the following table. Tab. 7: Transport performances (2002) Passenger (mil. passenger km) Rail transport Public bus transport Urban public transport Total public transport Individual road passenger transport

Czech Republic 7 299 10 200 15 209 32 708 65 500*

Hungary 10 531 12 097 9 684 32 312 n. a.

Freight (mil. tonne km) Rail transport 16 880 Road transport for hire or reward 34 210 Road transport for own account 6 050 Sources: Statistical Yearbook of Transport 2002, Hungarian Central Statistical Office

7 752 14 142 3 001

* Expert estimation

These statistics provide overall emissions from transport for all three countries. Table 8 gives comparison of total [ ] emissions from transport for six pollutants, for the Czech Republic. Tab. 8. Emission factors of transport modes in the Czech Republic (2002) Individual road passenger transport Public road passenger transport Road freight transport Urban transport – buses Railway transport – motor traction

CO

NOx

VOC

SO2

PM

CO2

g/pkm

2.285

0.405

0.403

0.030

0.004

97.581

g/pkm g/tkm g/pkm g/tkm

0.879 1.511 0.422 0.561

1.272 0.974 0.547 1.001

0.207 0.364 0.099 0.130

0.033 0.029 0.012 0.046

0.090 0.066 0.035 0.044

111.921 99.536 47.792 38.739

Source: Statistical Yearbook of Transport 2002, additional calculation CUP

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More detailed, specific emissions for different transport modes were obtained for the Czech Republic, and emissions from road transport on a regional scale were obtained from Transport Research Centre in Brno. Fig. 1: Total emissions from transport (2001, in thousand tons) Czech Rep

SO2

Hungary

PM

Poland

NOx NMVOC CO N2O CH4 CO2 0

100

200

300

400

500

600

700

Sources: Statistical Yearbook of Transport 2002, Hungarian Central Statistical Office, Polish Statistical Office Notes: Hungary data for 2000, CO2 in million tons

Relatively detailed emission factors are available for road vehicles in Poland. However they do not allow vehicles to be categorized according to EURO (or ECE) nomenclature. Tab. 9: Emission factors for road vehicles in Poland (2000), in g per kg NM CO2P CO2 R CH4 N2 O CO NOx PM SO2 VOC Passenger cars PC (petrol) without CC 3153 2680 1.70 0.10 211.5 43.0 34.6 0 1.65 PC (petrol) with CC 3153 3056 0.32 0.30 43.7 8.9 7.1 0 1.65 PC (LPG) 2985 2602 1.70 0.10 170.0 30.3 32.0 0 0.00 PC two-stroke 3153 2150 2.25 0.02 246.5 191.8 9.7 0 1.65 PC (diesel) 3153 3103 0.10 0.13 11.9 3.4 9.6 5.5 3.40 LDV delivery vans (petrol) 3153 2702 1.70 0.10 206.2 38.5 31.7 0 1.65 delivery vans (LPG) 2985 2602 1.70 0.10 170.0 30.3 32.0 0 0.00 delivery vans (diesel) 3153 3081 0.10 0.13 19.1 6.9 12.6 5.5 3.40 Lorries above 3.5t (diesel) 3153 3040 0.30 0.16 32.5 12.5 55.0 6 3.40 track-tractor (diesel) 3153 3040 0.30 0.16 32.5 12.5 55.0 6 3.40 Buses long-distance buses 3153 2993 0.35 0.16 55.7 15.8 57.1 6 3.40 urban buses 3153 2993 0.35 0.16 55.7 15.8 57.1 6 3.40 Special cars above 3.5t (diesel) 3153 3040 0.30 0.16 32.5 12.5 55.0 6.0 3.40 Note: CC – catalytic converter Source: Road Transport Institute, additional calculation MEERI

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For the transport case study, we used specific emission factors as published by Ministry for the Environment in their MEFA database (Sebor et al 2002). This is a model for calculation of emission factors for motor vehicles developed using the HBEFA database and adjusted according to particular emissions of typical vehicles in the fleet. As to trends, the following tables give an overview of the growth in number of motor vehicles and catalytic converters in the Czech Republic over the last twelve years. Tab. 10: Changes in vehicle stock in the Czech Republic (in thousands) 1990 1 159 2 411 156 26 1%

Motorcycles PCs and LDVs up to 3,5 t HDVs Buses share of vehicles with CC

1995 1 142 3 043 203 23 14%

2000 958 3 439 276 18 32%

2001 915 3 530 296 18 37%

2002 760 3 647 323 21 42%

Source: Statistical Yearbook of Transport 2002

Note: CC – catalytic converter

The growth of vehicle fleet and its increasing utilization counterweighs emissions reductions stemming from improvement of technologies, as can be seen in Figure 2, which shows trends in emissions from transport from 1990-2001 in the Czech Republic. Fig. 2: Changes in emissions from transport (Czech Republic; 1990=100)

350 300 250

CO2

CO

NO2

N2O

CH4

VOC

SO2

Pb

PM

1990

1992

200 150 100 50 0 1988

1994

1996

1998

2000

2002

Source: Transport Research Centre

Unfortunately, virtually no scenarios of future development of emissions in transport exist in any of these countries. For Poland, existing forecasts of domestic transport development from The National Policy of Transport predict high growth trends for both passenger and goods transport up to 2020. These increases will influence the total emissions of pollutants. Some positive things like low emissions cars could of course partially offset these increases. . The Czech Integrated National Policy on Emissions Reduction (2004) gives brief insight in future development of transport sector. Based on estimates from Infrastructure Operational

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Program, they forecast that transport of freight will increase by 6.7% and individual transport by 31.2% by 2010. Implementation of EURO IV and V standards will further reduce NOX and VOC emission rates. This decrease will presumably outweigh the influence of growth of transport on the overall emissions in mid term. The following figures show the prognosis for particulate and NOX emissions as predicted by DHV CR, without taking account of hypothetical vehicle fleet structural changes. Fig. 3: Prediction of particulate emissions (tons per year)

6000 5000 4000 3000 2000 1000 0 2001

2002

2005

Individual road passenger transport Road freight transport Railway transport – motor traction

2010

2015

Public road passenger transport Urban transport – buses Total

Source: Czech Ministry for the Environment Fig. 4: Prognosis of NOx emissions (tons per year)

140 000 120 000 100 000 80 000 60 000 40 000 20 000 0 2001

2002

2005

Individual road passenger transport Road freight transport Railway transport - motor traction Total

2010

2015

Public road passenger transport Urban transport - buses Air transport

Source: Czech Ministry for the Environment

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Costs of health effects One task for this project was to provide information on monetary valuation of respiratory and cardiovascular health effects related to air pollution. For this task, we gathered relevant data for Poland and the Czech Republic. In both countries we had to rely on data for part of the population only. This was because there were no overall statistics on unit or even average costs for specific medical treatment. Even though these costs are not unassailable we assume them to be close proxy of real market costs. For Poland, all data except the cost of treating asthma attack and the cost of sick leave were collected from Malopolska Health Fund’s unpublished statistics on cost, which were based on contracts signed with hospitals and with out-patient care providers. These data were for 2003. Malopolska Health Fund’s coverage is limited to southern Poland, but the National Health Fund in Warsaw verified the reliability of the statistics. The cost of treating asthma attack s was estimated on the basis of information on retail costs published on the internet (pharmacies, medical services etc.). The cost of sick leave (monthly sick allowance) was estimated as 80% of average month salaries, less 18.71% for social insurance, as set by official regulation. Average length of incapacity for work was calculated as amount of average days missing in one year per amount of employees in economy (that is, not to number of sick persons) retrieved from Statistics of the Social Insurance Fund 2002. Tab. 11: Costs of health effects (in EUR)

general practitioner

Czech Republic

Poland

3 4 17 6

6

Cost of doctor visit adults children

Cardiology Pulmonary Inhalatives antiasthmatics

Cost of treating asthma attack per package 13-17 per package 8.3

16-33

general respiratory cardiovascular

Cost of hospitalization per day per day per day

70 64 114

62 40 105

per case per case average length of incapacity for work (days)

Cost of sick leave respiratory cardiovascular respiratory cardiovascular

165 636 16.3 60.5

308 (per month) 14.7 (in general)

the cost of a doctor visit in the Czech Republic was derived from Yearbook of the General Health Insurance Company for the year 2001. General Health Insurance Company’s coverage is around 67% of total population, with moderate overrepresentation of elderly people. As the system of in-patient care payment is prevailingly based on fixed payment tariffs, we derived average costs per one hospitalization in 2001 and than multiplied it by the relative weight of cardiovascular or respiratory diseases as shown by unpublished diagnosis related system (DRG) statistics of General Health Insurance Company. The cost of treating an asthma attack

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is set to the price of medication for the treatment, as reported by the State Institute for Drug Control for relevant ATC category for 2001. The average amount of sick leave is based on a calculation formula set by the law for average monthly salary in 2001, retrieved from Czech Statistical Office. The average length of incapacity for work was retrieved from statistics on incapacity for work provided by Institute of Health Information and Statistics for 2001. Overall the data for Poland and Czech Republic were quite similar. The only significant difference encountered was between costs of medicaments for treating asthma attack but this may be partly due to different period recorded (2001 vs. current) and different coverage of medicaments (all sold in Czech Republic vs. selected in Poland). The costs of outpatient care are obviously significantly lower than costs of inpatient care. The average length of hospitalization in the Czech Republic in 2001 was 7 days for respiratory diseases, and almost 10 days for cardiovascular diseases. Moreover, hospitalization for cardiovascular diseases counts 1.63 or 1.69 times higher than general cost of hospitalization, less than the factor of 1.92 reported as EU average in CAFE project. We were not able to obtain health cost data for Hungary. We contacted several Hungarian institutions, including Hungarian National Institute for Strategic Health Research (ESKI), but have as yet received no reliable information from them. Non-market valuation of health impacts There has been long tradition in health impacts valuation by applying non-market valuation methods in CEEC countries. We reviewed all non-market valuation applications conducted in Hungary, Poland, and Czech Republic during last 15 years (see Scasny and Melichar 2005). We found more than 30 of such studies, mostly applied in the area of waste management, air protection and landscape (or public goods provided by agriculture or forestry). Most of them applied CVM (contingent valuation method). However, none of them, up to year 2002, was connected directly with the externalities estimation. Research on externality-related valuation in the CEE has become more widely developed in recent years, particularly in Poland and the Czech Republic. There are two studies dealing with valuation of air pollution effects in particular including human health (Dziegielewska and Mendelsohn 2002; author forthcoming). These studies were focused on the WTP (willingness to pay) of Polish citizens to harmonize Polish air pollution standards with EU standards. The total value in Poland was estimated as 0.77% of GDP per capita for 25% pollution reduction, and 0.96% of GDP per capita for 50% reduction1. Mortality effects due to car accidents were estimated in the Czech Republic (Seda et Kutacek 2004). Thanks to the ExternE-Pol project, we were able to build capacity that led to 1

These results were not different enough to pass the scope test. However, respondents supported a more unielastic response when given sequential valuation questions. These results suggested that doubling reductions would lead to doubling WTP. The WTP is by far the highest for the mortality component. The values for bronchitis, asthma, minor health effects and visibility reduction consecutively decrease. The value reaches its lowest point for material damages and then rises for damages to historical heritage and ecosystems. The results showed that historical heritage and ecosystem damages are important and that the literature underestimated total damages by omitting them. In the Polish case, if these components were not valued, the results would be underestimated by 13 to 16 percent. The study provides different estimates of the relative value of mortality and morbidity than for instance the American estimates (USEPA, 1999); they find that mortality is less important but morbidity is more important.

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development of many damage estimates based on non-market valuation methods. Most of them are strongly embedded in and linked to the ExternE methodology. For instance, the survey on eliciting WTP for reducing own risk of dying from cardiovascular and respiratory diseases in the Czech Republic (partly funded by World Health Organization, thanks to EC funded project cCASHh) was conducted in the mid of year 2004. Value of Statistical Life (VSL)was firstly estimated for the first time for New Member States (see Alberini, Scasny et al. forthcoming for preliminary results). Research on mortality valuation will be extended in Hungary, Poland, and Czech Republic within the Integrated Project NEEDS. We can expect the results for VSL and VOLY in the mid of 2006. Research on the valuation of adults and infant morbidity is being explored in the Czech Republic (2004-2005).. Moreover, the new method will be developed and relevant survey will be conducted for child mortality valuation also, in the Czech Republic (with in VERHI project, planned to be funded by the European Commission since the beginning of the year 2005; see also Scasny et Melichar 2005 for the brief project description) Buildings and materials Data on inventory of materials and repair costs have being continuously analysed in the Czech Republic by National Research Institute for Protection of Materials (SVUOM)2. As this research is typically very time- and cost-intensive, we did not conduct independent research on this topic under ExternE-Pol. However, we reviewed relevant literature and examinedcost data. Table 12 reports on the inventory of occupied dwelling (single-family houses and multifamily buildings) according to materials used for supporting walls, number of floors and age for the Czech Republic and Prague. Tab. 12: Inventory of occupied dwellings in the Czech Republic Year of construction Houses