Final Report DRIVERS AND HEALTH IMPACTS OF AMBIENT AIR POLLUTION IN SLOVAKIA Final Report February 2021 EUROPEAN Funded by the UNION EuropeanSocial European UnionFund Europe & Central Asia Final Report Final Report DRIVERS AND HEALTH IMPACTS OF AMBIENT AIR POLLUTION IN SLOVAKIA Final Report February 2021 1 Final Report © 2021 World Bank Group 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org All rights reserved This volume is a product of the staff of the World Bank Group. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Executive Directors of World Bank Group or the governments they represent. The World Bank Group does not guarantee the accuracy of the data included in this work. 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Introduction.............................................................................................. 10 II. Data description and methodology......................................................... 11 III. Description of the current state.............................................................. 13 Physical estimates of health impacts..................................................................14 Monetary value of health impacts........................................................................20 IV. Reduction scenario after the implementation of the NAPCP and the possible health impacts.............................................................. 25 V. Economic impacts of the NAPCP............................................................. 28 VI. Cost benefit analysis of the NAPCP......................................................... 33 Costs of the NAPCP...................................................................................................33 Benefits of the NAPCP.............................................................................................35 Combining benefits and costs of the NAPCP....................................................39 VII. Conclusions and recommendations........................................................ 42 Annex I: List of districts..........................................................................................................43 Annex II: PM2.5 emissions profiles in the base case and under NAPCP.................44 3 4 Final Report ACKNOWLEDGEMENTS This report is a synthesis report of analytical work carried out by the Institute of Environmental Policy (IEP) of the Ministry of Environment of the Slovak Republic in cooperation with the World Bank. The project was financially supported by the European Commission through the Structur- al Reform Support Programme. Veronika Antalová (IEP, Ministry of Environment, Slovak Republic) and Anil Markandya (Con- sultant, World Bank), are lead authors of the report. The World Bank technical team comprised Klas Sander, Sameer Akbar, Eolina Petrova Milova, and Camilla Sophie Erencin. The team is highly appreciative for the overall guidance and support received from Martin Haluš (IEP, Minis- try of Environment, Slovak Republic), Marianna Bodáczová (IEP), Dušan Štefánik (Slovak Hydro- meteorological Institute), Kseniya Lvovsky (World Bank), and Fabrizio Zarcone (World Bank). The close collaboration and support received from Kaspar Richter and Georgina Georgiou of DG Re- form, European Commission, throughout project implementation is gratefully acknowledged. Review comments provided by Craig Meisner (World Bank), Juan Jose Miranda Montero (World Bank), and Stephen Geoffrey Dorey (World Bank) helped to strengthen the preparation of the final report. Administrative and operational support was provided by Sylvia Stoynova (World Bank), Julie Biau (World Bank), Grace Aguilar (World Bank), and Linh Van Nguyen (World Bank). The team would like to express its sincere appreciation and thanks to the minister and state secretaries of the Ministry of Environment and other partners in Slovakia who participated in discussions and the steering committee, provided information and data, and who facilitated exchanges between regional stakeholders and the team, in particular the air quality depart- ment at the Ministry of Environment and at the Slovak Hydrometeorological Institute. The team would also like to thank the Basque Centre for Climate Change for providing a physical space, creative environment, and technical support during the most important part of the project. 5 Final Report ACRONYMS BCR Benefit to Cost Ratio EU CAFÉ Clean Air For Europe CI Confidence Interval CO2 Carbone dioxide EC European Commission EEA European Environment Agency EU European Union GDP Gross Domestic Produtc GHGs Greenhouse Gases HAD Hospital Admission HRAPIE Health Risks of Air Pollution in Europe MoE SR Ministry of Environment of the Slovak Republic NAPCP National Air Pollution Control Programme NH3 Ammonia NMVOV Non-Methane Volatile Organic Compound NO2 Nitrogen dioxide NOx Nitrogen oxides NPV Net Present Value OECD Organisation for Economic Co-operation and Development PM2.5 Particulate matter with diameter of less than 2.5 μm PM10 Particulate matter with diameter of less than 10 μm RAD Restricted Activity Days PVB Present Value of Benefits PVC Present Value of Costs RR Relative Risk SO2 Sulphur dioxide VAT Value-Added Tax VLYL Value of Life Years Lost VSL Value of Statistical Life WHO World Health Organization WTP Willingness To Pay μg/m 3 Microgram per cubic meter 6 Final Report EXECUTIVE SUMMARY Background and purpose This report, created with the support of the Structural Reform Support Programme of the Euro- pean Commission (EC), provides an estimate of the health impacts of current concentrations of air pollutants in the Republic of Slovakia across its 72 districts (the two biggest cities are count- ed as one each) and evaluates the benefits of measures to reduce concentrations of pollutants relative to the costs of such measures. At the same time, a toolkit is prepared so that similar analyses can be conducted in the future. The study is motivated by the need to better under- stand the extent of the health consequences of the levels of ambient air pollution in Slovakia (which is among the highest in Europe) and to evaluate different actions to improve air quality in terms of their benefits relative to their costs. In this way policy actions to address air pollution can be more cost effective. Methodology The health impacts are measured in terms of increases in premature mortality and increases in the incidence of different morbidities. It is the first time that an analysis of such impacts has been carried out at a granular level for Slovakia. The study goes on to project concentrations in 2030 if the government´s National Air Pollution Control Programme (NAPCP) is implemented. The benefits of the Programme are measured through reduction in the physical health impacts (premature mortality and morbidity), as well as the monetary benefits of such reductions. The benefits are compared to the costs of the Programme, relative to the full economic costs as well as the fiscal costs. Results Current concentrations of PM2.5, PM10 (particulate matter with diameter of less than 2.5 μm and 10 μm respectively) and nitrogen oxide (NO2) are estimated to result in around 1,592 premature deaths every year. What this figure says is that if concentrations were reduced to the guideline value of 10 microgram per cubic meter (μg/m3) for PM2.5 and 20 μg/m3 for PM10, then the num- ber of avoidable premature deaths would fall by this amount. The main source of premature deaths is PM2.5. The uncertainty in the estimate suggests that the figure could lie between 1,143 and 2,013 premature deaths – a range of about +/-27%. Regarding morbidity, main im- pacts take the form of restricted activity days and workdays lost, with some additional cases of chronic bronchitis and asthma. The modelling estimates 2.7 million restricted activity days and 138,000 workdays lost, along with 431 cases of chronic bronchitis and 99 cases of asthma. The monetary cost of these impacts depends significantly on whether a value of statistical life (VSL) approach is taken to evaluate a premature death or a value of life years lost (VLYL). Both approaches have been adopted in European Union (EU) policy discussions. If the VSL method is adopted, the estimated cost of premature mortality is in the range €2.7 and €8.0 billion, with a mean value of €5.3 billion. The VLYL method gives a lower estimate: the median-based figure is €1.1 billion and the mean figure is €2.4 billion. The morbidity costs across all endpoints are around €549 million, or less than half the premature mortality costs based on VLYL (median value) and about 10% of the costs based on VSL (mean value). Taken together, the mortality and morbidity costs amount to €3.0 billion (VLYL) and €5.8 billion (VSL), making them equal to 3.6% to 6.9% respectively of the gross domestic product (GDP) in 2017. The NAPCP has been formulated to meet the air quality and emissions reductions targets for Slovakia by 2030. It consists of several measures to reduce emissions of PM2.5, nitrogen oxides (NOx), sulphur dioxide (SO2) and ammonia (NH3) across transport, residential heating and agri- 7 Final Report culture sectors. The measures will have health benefits over the total implementation period 2021-2030. By 2030, the NAPCP saves about 116 lives, and reduces restricted activity days by 195,000, workdays lost by 92,000 and the number of chronic bronchitis cases by about 81. The value of these health benefits by 2030 are: €397-€1,192 million via VSL and €107-€363 million via VLYL for reduced mortality and €97-€124 million for reduced morbidity. The value of the benefits over the period 2021-2030 is higher as they include gains in the interven- ing years. The estimated present value of the benefits is €2,363 million (VSL) with a range €1,218-€3,280; and €663 million (VLYL) with a range of €504-€1,240. There are two concepts of cost against which the benefit to cost ratio (BCR) can be estimat- ed. One is the economic cost and the other is the fiscal cost. The economic cost measures in monetary terms the value of scarce resources used to implement the project. The fiscal cost is the cost measured in terms of net expenditures required by the government to implement the NAPCP. The analysis has been conducted with respect to both the economic cost as well as the fiscal cost. The estimated figures for the present value of the costs for the 13 components of the NAPCP at a 5% discount rate are: €1,124 million (economic) and €398 million (fiscal). The results indicate the following for the economic costs: a. Under a VSL valuation of premature mortality the NAPCP has a BCR greater than one for the whole range of VSL values. b. Under a VLYL valuation the BCR exceeds one only if the upper end of the range is taken. Under the value that is set by the Slovak legislation to estimate the cost effectiveness of new medications, the ratio is only 0.44 and under a median value it is 0.57. This means that the benefits of the period 2021 to 2030 only represent 44% and 57% of the costs, respectively. c. There is the question of what benefits might remain after 2030. It is reasonable to as- sume there will be some, as the base case without NAPCP cannot be expected to con- verge automatically to the NAPCP level of concentration. However, it is difficult to esti- mate the gap precisely. As an approximation, a sensitivity calculation has been made in the case of economic costs, assuming the gap in 2030 between concentrations under the base case and the NAPCP remains for another ten years. In this case the annual costs of the NAPCP for the period 2031-2040 are estimated as being the same as the maintenance costs for 2030 for each of the programs where such costs are incurred. Extending the analysis to 2040 raises the BCR by about 18%, which is not sufficient to increase the BCR above one in the VLYL analysis. Further sensitivity analysis was carried out using the range of physical health impacts. As stat- ed in Section III, the 95% confidence interval (CI) for the range of impacts is approximately +/- 27%. Applying this range to the BCRs keeps the BCR above one for all cases with the VSL except for the combination of the low VSL value and the lower bound physical impacts. Under the VLYL, however, the BCR only exceeds one with the high VLYL value and under physical impacts at or above the mean. These figures are for benefits until 2030 only. The fiscal costs are €398 billion while the economic costs are €1,124 billion, or 2.8 times as high. Since the benefits are the same, the NAPCP has a higher BCR when judged under these costs. The BCR is now above one and the NPV is positive in all cases. Under VSL, the BCR rang- es from over 3 to over 8 and under VLYL the range is over 1 to over 3. Allowing for the +/-27% physical impacts CI, the BCR remains above unity in all cases. There are additional implications when the findings from the study are viewed in the context of the COVID-19 pandemic. Although the impacts of the pandemic have not been modelled explic- itly here, recent research has shown that particulate matter could create a suitable environment for transporting the virus at greater distances. Furthermore, the health impacts of atmospheric air pollution and associated chronic diseases/NCDs increases the vulnerability to COVID-19. 8 Final Report Both these linkages give greater impetus to immediate action to reduce PM concentrations. To some extent the lockdown measures have reduced PM concentrations in some countries, but the evidence for Slovakia is limited. Some decrease in air pollutant concentrations was seen in March and April 20201, but the long-term impact is not known. Strategies for ‘Building Back Better’ aim to sustain improvements in air quality through measures that combine a reduction in greenhouse gases (GHGs) as well as local air pollutants. Implementation of this strategy in Slovakia could involve accelerating and even strengthening the measures proposed in the NAP- CP and assessed in this study. There are three main recommendations to the Ministry of Environment of the Slovak Republic (MoE SR) of the as a follow-up to this analysis:  Evaluate the health impact of individual air quality interventions within the NAPCP;  Use the models developed within this analysis to assess the impacts of regional policies;  Further improve and regularly update the data used in the toolbox. 1 SHMU: Impact of the first month of COVID-19 related measures on air quality in Slovakia. /www.shmu.sk/sk/?page=2049&id=1054 http:/ 9 Final Report I. Introduction The population of Slovakia faces high concentrations of air pollutants. The levels of pollution in the ambient air cause negative impacts on public health and the environment, with Slovakia having one of the highest mean levels of exposure to PM2.5 (particulate matter less than 2.5 mi- crons in diameter) among the EU member states. These particles contribute to the incidence of asthma, cardiovascular problems, lung disease and consequently to premature death2. Despite some improvements over the past years, the situation in the country remains unsatisfactory, not least because of the insufficient transposition of the EU regulatory framework regarding air quality. This report, created with the support of the Structural Reform Support Programme of the Euro- pean Commission (EC), aims to support Slovakia improving its ambient air quality by strength- ening the understanding of health impacts attributable to air pollution and related economic costs, and, in cooperation with the Ministry of Environment of the Slovak Republic (MoE SR), to increase the public ability to perform cost-efficient interventions and address ambient air pollution. The estimates of policy costs are compared against their benefits in terms of reduced health damages. The net benefits are reported using benefit cost indicators. The results and analyti- cal tools developed can support and facilitate the implementation of the National Air Pollution Control Programme (NAPCP). The project would therefore broaden the knowledge base for fu- ture decision making on issues dealing with transition towards sustainable energy resources as well as policy considerations for major polluting sources. World Bank/IHME (2018). The Cost of Air Pollution: Strengthening the Economic Case for Action. World Bank, 2 Washington DC. 10 Final Report II. Data description and methodology y The data on concentrations of ambient air pollutants were assembled at the district level for ed at the district level for the most recent period This was done for the the71 most recent districts period based on the models of the Slovak Hydrometeorological Institute. This in the country was done for the 71 districts in the country and weighted by the affected population for the tions that are linked to possible health impacts: following concentrations that are linked to possible health impacts: i. PM2.5, Annual Mean ii. PM2.5, Daily Mean iii. PM10, Annual Mean iv. PM10, Daily Mean v. NO2, Annual Mean ne year for concentration vi. data was 2017, NO2,while Maximum 1-Hour for the year 2020 and the year 2030 (scenario lated input data were provided by the National of the A list the each district, reflecting districts uncertainties in and their location is given in Annex I. The baseline year for concentra- tion data was 2017, the basis of the toolkit that will be used by thewhile the two reduction scenarios modelled the pollution concentrations for the year 2020 and tudy. A manual for the toolkit has been created the year 2030 (scenario used to model the full implementation of the NAPCP). All health-related input data were provided by the National Health Information Center. Figures were given as a range for each district, reflecting the uncertainties in measurement. lth impacts. These functions The data give the are expected stored in an interactive file that forms the basis of the toolkit that will be used by o which a baseline population is exposed. They the MoE SR to 3perform future calculations after the completion of this study. A manual for the s of Air Pollution in Europe (HRAPIE) project. o used to estimate air toolkit has pollution created and will be published alongside this report. been across impacts marizes the functions used, giving relative risk dy was limited to PM Theand doseNO 2 on . The data functions response were selected to reflect the main health impacts. These functions ate the impacts on the the level. district give Therefore, expected increase in a given health impact per unit increase in concentration to which not included in this study. a baseline population is exposed. They were taken from the World Health Organization (WHO) Health probabilities, of an adverse Risks health of Air event Pollution in Europe (HRAPIE) project. The same source, which is the most among 3 describes how much the morbidityavailable, up-to-date or mortalitywas also used to estimate air pollution impacts across Europe by the Eu- % confidence interval (CI) shows ropean the range of Environment Agency (EEA)4. Table 1 summarizes the functions used, giving relative risk e for estimation is the pollutant concentration a (RR) estimates for the main health impacts. Coverage in the study was limited to PM and NO2. no health effects exist below this value, but that hose recommended The data by the WHO on atmospheric 6. Since some ozone were not available in sufficient quality to estimate the impacts values but measure on the district impacts level. relative to Therefore, the estimates of health impacts of ozone on the regional level a zero pected (whichever is the highest), were we provide not included in this study. lues to the EEA. Different pollutants are linked e of epidemiological studies. The RR is a measure of the relative risk. It is the ratio of risks, i.e. of probabilities, of an ad- verse health event among the exposed and non-exposed group5. The relative risk in the table 3 WHO, 2013b, Health risks of air pollution in Europe — HRAPIE project: New emerging risks to health from air pollution — Results from the survey of experts, World Health Organization, Regional Office for Europe, Copenhagen 4 EEA (2019). Air Quality in Europe – 2019 Report. EEA: Copenhagen. 5 In the epidemiological literature the relative risk is sometimes related to a concept called the Population Attributable Fraction (PAF). PAF is the proportional increase in population disease or mortality that would occur if exposure to a risk factor were increased from an alternative ideal exposure scenario: Mathematically it is expressed as: ´ ( − ) = where Pi=proportion of population at exposure level i, current exposure and = proportion of population at exposure level i, counterfactual or ideal level of exposure. See: WHO | Metrics: Population Attributable Fraction (PAF) 11 Final Report describes how much the morbidity or mortality would increase if the pollution level increased by 10 μg/m3. The 95% confidence interval (CI) shows the range of RR and our estimates that lie within a 95% CI. The guideline value for estimation is the pollutant concentration a country like Slovakia should aim to achieve. It does not mean that no health effects exist below this value, but that the defined value is a desirable target. The values used here are those recommended Table 1: by the WHO6. Since some other studies, particularly the EEA, do not use the same guideline Dose Response values but measure impacts relative to a zero concentration or a threshold level below which Functions Used a zero impact is expected (whichever is the highest), we provide estimates both relative to the in Making Estimates guideline values as well as similar values to the EEA. Different pollutants are linked to different of Health Impacts health outcomes based on information from a wide range of epidemiological studies. Pollutant Metric Health Outcome RR (95% CI) per 10μg/m3 Guideline Value for Estimation PM2.5 Annual Average All-Cause Mortality. Age 30+ 1.062 (1.040 – 1.083) 10 μg/m3 PM2.5 Annual Average Restricted Activity Days. All Ages 1.047 (1.042 – 1.053) 10 μg/m3 PM2.5 Annual Average Workdays Lost. Ages 20-65 1.046 (1.039 – 1.053) 10 μg/m3 All-Cause Post Neonatal Mortality 1-12 PM10 Annual Average 1.04 (1.02 – 1.07) 20 μg/m3 Months PM10 Annual Average Incidence of Chronic Bronchitis Age 18+ 1.117 (1.040 – 1.189) 20 μg/m3 PM10 Daily Mean Asthma Events. Ages 5-19 1.028 (1.006 – 1.051) 20 μg/m3 Source: WHO (2013). In order to estimate the health effects given the dose response functions the following formula has been used: ( − 1) ( − 0 ) = ∗ ( − 1) ∗ ( − 0 ) = 10 ∗ ∗ 10 ( ) Where C is the concentration in micrograms per cubic meter ( 3) and C0 is the guideline con- 3 centration. B is the exposed population. The baseline number of cases has been derived from local data in most cases. Where this was not possible, estimates of the RR of a particular health effect were taken from the WHO (2013) study for Eastern Europe. In the future these could be substituted with local information and re-estimated to produce a local RR function for Slovakia. The Ministry of Health could help with the design of a survey to collect this epidemiological information. The model was also used to calculate other indicators of pollution effects on the population’s health. However, the available data on average concentrations by district did not show any significant results. These include cardiovascular and respiratory hospital admissions, asthma events in children and all indicators estimating the effects of the NO2 pollution. EEA (2017). Air Quality Standards. EEA: Copenhagen. 6 12 Final Report III. Description of the current state III. Description of the current state III. Description of the current state The estimate of premature deaths is calculated from data on average pollution in the district weighted by the population and the total all-cause mortality in each district. This method gives a rough estimate of air pollution The estimate of premature deaths is calculated from data on average pollution in the district weighted by the impacts but needs to be interpreted carefully. Below is a map of the pollution distribution within the country as well population total and the of mortality in all-cause deaths each district. This method gives a rough estimate of air pollution as the The estimate average premature PM2.5 concentration levels inis region. from data on average pollution in the district calculated each impacts but needs weighted to be by the interpreted population andcarefully. Below the total is a map all-cause of the pollution mortality in eachdistribution within district. This the country gives as well method as the average a rough PM2.5 concentration estimate levels of air pollution in eachbut impacts region. needs to be interpreted carefully. Below is a map Map 1:ofYearly average the pollution of PM2.5 concentrations distribution within the country in Slovakia in 2017 as well as (CMAQ) the average PM concentration levels 2.5 Mapin 1:each region. Yearly average of PM2.5 concentrations in Slovakia in 2017 (CMAQ) Map 1: Yearly average of PM2.5 concentrations in Slovakia in 2017 (CMAQ) Source: SHMU Source: SHMU Averaging air pollution Averaging data diminishes air pollution effects of data diminishes regional effects pollutionpollution of regional hot spots. hot While some spots. Whileareas show Source: some ar- average SHMU eas show average concentrations similar to the ones on Map 1, the population weighted av- level for concentrations Averaging air similar to pollution the data ones on Map diminishes 1, the effects population of regional weighted pollution average hot spots. creates While a someunified pollution areas show average the whole district, concentrations erage which similar creates tocannot the ones a unified by on definition Map pollution foras 1, be level the detailed population wholeweighted the as the map district, above. average which cannot While createsbya for some unified be as level the application pollution definition for detailed concentrations the whole district, whichdistribution cannot bymight be definitionmore be suitable, as for detailed asthe purpose the map of this above. detailed as the map above. While for some application the detailed concentrations distribution study While the for population some weighted application the averages detailed of concentrations might pollutants aredistribution be more suitable, sufficient. might for the be more purpose suitable, of this study for the the purpose of population this study weighted the population averages of pol- weighted averages of pollutants lutants are sufficient. are sufficient. Map 2: Attributable fraction of PM2.5 pollution Map 2: Attributable fraction of PM2.5 pollution Map 2: Attributable fraction of PM2.5 pollution Source: own elaboration Source: own elaboration Source: own elaboration Map 2 shows attributable fraction of all-cause mortality related to PM2.5. This can be explained as the percentage ofMap 2 shows attributable all premature fraction deaths in the of all-cause district mortality that can be 13related attributed to PM to PM 2.5. This can be explained as the percentage 2.5 concentrations. In the most afflicted areas of of all premature deaths in the district that can be attributed to PM2.5 concentrations. In the most afflicted areas of Žilina, Košice, and Ružomberok, more than 5% of all mortality can be attributed to air pollution. Reduced air pollution Žilina, Košice, and Ružomberok, more than 5% of all mortality can be attributed to air pollution. Reduced air pollution in these areas will therefore have the biggest impact on the improvement of public health. Final Report Map 2 shows attributable fraction of all-cause mortality related to PM2.5. This can be explained as the percentage of all premature deaths in the district that can be attributed to PM2.5 con- centrations. In the most afflicted areas of Žilina, Košice, and Ružomberok, more than 5% of all mortality can be attributed to air pollution. Reduced air pollution in these areas will therefore have the biggest impact on the improvement of public health. Physical estimates of health impacts Physical estimates of health impacts Estimates of the physical Impacts are given in Table 2 (Premature Mortality) and Table 3 (Morbidity). These impacts are,Estimates besides air ofpollution the physical Impacts are concentrations, given inon dependent Table 2 (Premature the overall mortalityMortality) in each of and Table 3 The (Mor- the regions. mortality bidity). These impacts are, besides air pollution concentrations, dependent on the data of the population over 30 years is shown in the map below. For the purposes of this study the total overall mor- number of all tality deaths inineach of the the region is regions. The considered, mortality since they are data used of tothe population calculate over the total 30 years number is shown of premature in The deaths. the map below. For the purposes of this study the total number of all deaths in the region National Health Information Centre also creates standardized data that better reflect the population distribution is within the region considered, but would since arebe they not appropriate used for this to calculate thetype of number study. of premature deaths. The Nation- total al Health Information Centre also creates standardized data that better reflect the population Map 3: All-cause distribution mortality within for ages the region butover would 30 not in each for (average of the districts be appropriate this type 2015-2017) of study. Map 3: All-cause mortality for ages over 30 in each of the districts (average 2015-2017) Source: National Health Information Centre Source: National Health Information Centre Table 2 gives the estimated number of premature deaths due to ambient air pollution (PM and NO2) for each district7. The main source of premature deaths is the PM2.5 all-cause dose Table 2 gives the estimated number of premature deaths due to ambient air pollution (PM and NO2) for each district7. Theresponse main source function. are also Theredeaths of premature anPM is the estimated 2-3 neonatal deaths due to PM also concen- 2.5 all-cause dose response function. There are 10 an estimated trations. The total number sums to 2-3 neonatal deaths due to PM10 concentrations. The total number sums to around 1,592 annual prematureif around 1,592 annual premature deaths. This means deaths. concentrations This were reduced means if concentrations to the guideline were reduced valuevalue to the guideline of 10of μg/m 3 10 μg/mfor PM 3 for PM 2.5 and 2.5 and20 20μg/m μg/m 33 for for PM10, PM10 annual , annualwould mortality mortality fall bywould fall by this amount. Athis more detailed A amount. more detailed explanation explanation is included in the box isbelow. includedThe in NO2 all- the mortality cause box below. estimate The NO found is 2 zero in all estimate to be mortality all-cause districts foristhe reference found scenario, to be zero in all as concentrations districts for the of this pollutant, reference when averagedas scenario, the whole districtof forconcentrations area, this appear below the to be when pollutant, for the in WHO guideline averaged all districts whole district8. area, appear to be below the WHO guideline in all districts8. The uncertainty of estimates is shown for the total mortality by combining two sources: the 95% CI for the dose response functions, and The uncertainty the lower and of estimates upper bounds is shown for the fortotal estimates of concentrations mortality by combining stemming from differences two sources: the in the models for pollutant concentrations. The latter gives rise to much bigger variations than the former. Together 95% CI for the dose response functions, and the lower and upper bounds for estimates of con- they suggest that the figure could lie between 1,143 and 2,013 premature deaths – a range of about +/-27%. As for centrations stemming from differences in the models for pollutant concentrations. The latter the regional distribution, premature deaths related to air pollution show the highest impacts in regions that either gives have rise a high to much figure of totalbigger variations mortality, mostly in than the of the south former. Together the country (which they mightsuggest be caused that the figure by factors other than could lie between 1,143 and 2,013 premature deaths – a range of about air pollution) and in the regions with a high level of population-weighted concentration levels of the PM +/-27%. As for the 2.5 (mostly in regional distribution, the north of the country). premature deaths related to air pollution show the highest impacts in These data are mapped to show the variations by district 7 The concentration data are averaged for the total area of the district, which distorts the concentrations close to 8 main NOx pollution sources. This is not avoidable within our methodology, which tends to underestimate the total impacts of NOx pollution. 7These data are mapped to show the variations by district 8The concentration data are averaged for the total area of the district, which distorts the concentrations close to main NOx 14 pollution sources. This is not avoidable within our methodology, which tends to underestimate the total impacts of NOx pollution. 18 Final Report regions that either have a high figure of total mortality, mostly in the south of the country (which might be caused by factors other than air pollution) and in the regions with a high level of population-weighted concentration levels of the PM2.5 (mostly in the north of the country). Why do we use two different guideline values in the study? Physical and economic impacts are estimated using two guideline values. The first refers to a set of recommended maximal values established by the WHO while the second one refers to zero pollution levels that are used by the EEA in the ‘Air Quality in Europe’ reports. The following values are used (in μg/m3): PM2.5 - yearly PM2.5 - daily PM10 - yearly PM10 - daily NO2 - yearly NO2 - max/hour Zero pollution (EEA) 0 0 0 0 20 0 WHO 10 25 20 50 40 200 The impacts of air pollution are estimated as the difference between modelled air pollution level and baseline level. The chart on the right shows the total calculated air pollution impacts in orange. We consider the WHO guidelines to be a better indicator of the total impacts given the fact that it is unlikely to achieve a zero-concentration based on the presence of natural emission sources that are beyond our control. A comparison of the obtained estimates can be made with the EEA (2019) study. The EEA study estimates premature deaths attributable to PM at 5,426 and to NO2 at 13. The main difference between the EEA estimate of PM and the baseline estimate of this study can be explained by the guideline value used. The EEA 2019 study published in the Air Quality in Europe 2019 Report took a zero value for PM as the guideline value and 20 μg/m3 for NO2. In the second sce- nario (zero value) when a guideline value of zero is used for PM in our calculations the resulting premature deaths are 4,375, about 80% of the EEA estimate. The EEA estimate focuses on the year 2016 while we look at 2017, which may explain some part of the difference. The remain- ing difference can be explained by the EEA used model which tends to overestimate pollu- tion concentrations while concentrations provided by the Slovak Hydrometeorological Institute are generally underestimated. Using the zero-guideline value for NO2 concentrations leads to overall results similar to the EEA study. Map 4: Baseline premature mortality related Source: own elaboration to the PM2.5 pollution Source: own elaboration 19 Source: own elaboration 19 15 Final Report Table 2: WHO guideline Zero pollution guideline Estimates of Premature All-cause Neonatal Mortality All-cause Neonatal Mortality Mortality Due District mortality mortality mortality (PM10) (NO2) mortality (PM2,5) (NO2) (PM2,5) (PM10) to Ambient Air Pollution in Total 1 589.60 2.51 0 4 350.99 24.17 0 Slovakia Bánovce nad 12.78 0.01 - 35.03 0.09 - Bebravou Banská 40.32 0.02 - 98.35 0.15 - Bystrica Banská 2.44 0 - 12.73 0.02 - Štiavnica Bardejov 13.84 0 - 51.96 0.47 - Bratislava 17.08 0.11 - 46.03 0.96 - Brezno 14.5 0 - 55.62 0.15 - Bytča 8.62 0 - 26.48 0 - Čadca 21.71 0 - 73.88 0.18 - Detva 6.8 0 - 27.43 0 - Dolný Kubín 8.4 0 - 27.23 0.15 - Dunajská 40.48 0.03 - 109.06 0.23 - Streda Galanta 35.56 0.03 - 92.89 0.29 - Gelnica 3.84 0 - 22.39 0.31 - Hlohovec 15.75 0.01 - 42.36 0.11 - Humenné 22.3 0.03 - 56.86 0.29 - Ilava 19.39 0.02 - 54.79 0.12 - Kežmarok 5.8 0 - 33.65 0.81 - Komárno 45.4 0.04 - 116.88 0.32 - Košice 30.8 0.32 - 65.46 1.5 - Košice - 40.37 0.22 - 101.46 1.68 - okolie Krupina 7.2 0 - 22.5 0 - Kysucké 11.36 0 - 31.64 0.13 - Nové Mesto Levice 48.12 0.07 - 124.46 0.56 - Levoča 5.48 0 - 21.19 0.2 - Liptovský 25.19 0 - 67.56 0.03 - Mikuláš Lučenec 34.83 0.04 - 82.06 0.24 - Malacky 17.75 0.01 - 57.41 0.24 - Martin 43.57 0.04 - 97.34 0.22 - Medzilaborce 2.44 0 - 11.14 0.12 - Michalovce 42.47 0.12 - 104.68 0.86 - Myjava 5.86 0 - 24.48 0.03 - Námestovo 8.37 0 - 32.23 0.19 - Nitra 58.29 0.08 - 150.86 0.54 - Nové Mesto 18.32 0.02 - 58.98 0.2 - nad Váhom Nové Zámky 64.95 0.05 - 165.41 0.41 - Partizánske 17.37 0.01 - 44.27 0.12 - Pezinok 15.34 0.01 - 45.15 0.14 - Piešťany 24 0.02 - 64.26 0.14 - Poltár 7.63 0 - 22.79 0.03 - Poprad 9.98 0 - 58.75 0.61 - Považská 16.01 0.01 - 52.05 0.14 - Bystrica Prešov 59.56 0.27 - 138.8 1.5 - Prievidza 44.38 0.05 - 122.47 0.46 - Púchov 13.56 0.01 - 40.16 0.14 - Revúca 16.65 0.06 - 41.95 0.44 - Rimavská 35.49 0.08 - 87.78 0.62 - Sobota 16 Final Report WHO guideline Zero pollution guideline All-cause Neonatal Neonatal Mortality All-cause Mortality District mortality mortality mortality (PM10) (NO2) mortality (PM2,5) (NO2) (PM2,5) (PM10) Total 1 589.60 2.51 0 4 350.99 24.17 0 Rožňava 21.79 0.03 - 60.79 0.36 - Ružomberok 32.27 0.02 - 68.78 0.12 - Sabinov 11.3 0.03 - 36.91 0.79 - Senec 19.24 0.01 - 52.21 0.14 - Senica 17.85 0.03 - 53.48 0.34 - Skalica 13.95 0.02 - 40.92 0.17 - Snina 11.13 0.01 - 34.42 0.22 - Sobrance 8.69 0.01 - 23.78 0.11 - Spišská Nová 17.35 0.01 - 60.39 0.78 - Ves Stará 5.39 0 - 26.72 0.52 - Ľubovňa Stropkov 5.68 0 - 16.79 0.03 - Svidník 8 0.01 - 25.1 0.19 - Šaľa 20.24 0 - 52.25 0.03 - Topoľčany 27.17 0.04 - 71.34 0.27 - Trebišov 42.18 0.17 - 106.11 1.24 - Trenčín 29.53 0.03 - 93.28 0.24 - Trnava 42.84 0.04 - 114.1 0.32 - Turčianske 4.51 0 - 16.34 0.03 - Teplice Tvrdošín 4.86 0 - 20 0.09 - Veľký Krtíš 17.51 0.01 - 46.84 0.09 - Vranov nad 27.08 0.13 - 67.05 1 - Topľou Zlaté Mora- 14.6 0.02 - 41.34 0.2 - vce Zvolen 22.71 0.02 - 61.12 0.23 - Žarnovica 6.52 0 - 24.85 0 - Žiar nad 12.42 0 - 41.57 0.08 - Hronom Žilina 84.39 0.08 - 173.58 0.47 - Table 3 provides estimates of morbidity effects of ambient air pollution in physical units. The main findings at this stage are the following: a. No effects are found for hospital admissions (HADs) for PM2.5 (daily mean) or NO2 (Max. 1-hour) as concentrations of these pollutants are below threshold or guideline values. b. No effects are found for chronic bronchitis for children due to NO2 (annual mean) as the concentration of this pollutant is below the threshold or guideline value. c. A total of 7.3 million restricted activity days RADs per year are recorded, with Bratislava accounting for about 8%. d. About 431 cases of chronic bronchitis among adults arise annually from PM10, with Košice having the largest number. e. There are 99 cases of asthma among 5–19-year-olds with Košice having the largest number. f. Estimation of the number of workdays lost has been problematic as no baseline figure was available. Absenteeism from work in Slovakia in 2018 was reported at 14.22 days/ employee/year9 based on the Social Insurance Agency of Slovakia data. However, this /gateway.euro.who.int/en/indicators/hfa_411-2700-absenteeism-from-work-due-to-illness-days-per- https:/ 9 employee-per-year/. 17 Final Report data does not differentiate between causes of absenteeism. To obtain a figure of work- days lost due to respiratory illnesses it was necessary to draw on information from other countries. Estimates from the UK suggest that only about 45% of workdays lost are due to illness – the rest being accounted for by other factors10. However, the percentage of those accounted for by illness that are due to air pollution factors is difficult to deter- mine. A US study on reasons for visits to the doctor finds upper respiratory conditions accounting for 22.6% of all factors11. These two sources have been combined to obtain some preliminary estimate of loss of workdays due to air pollution (1.45 days/ employee/ year in Slovakia). As this estimate is based on broad assumptions, it is recommended to the government to collect data on cause of absenteeism in Slovakia. Provisionally the figures from the above sources suggest a loss of about 138,000 workdays attributable to air pollution, with Bratislava having the highest loss of workdays, at nearly 11,000. Table 3: WHO guideline value Zero pollution guideline value Estimates of Morbidity Effects Chronic Chronic Restricted Restricted Due to Ambient Air District activity days Workdays lost bronchitis activity days Workdays bronchitis Pollution in Slovakia (PM2,5) cases in lost (PM2,5) cases in adults (PM2,5) (PM2,5) adults (PM10) (PM10) Total 2 700 222.98 1 273 049.65 430.77 7 342 731.26 3 457 389.91 4 025.05 Bánovce nad 17 898.66 8 491.64 3.73 49 040.23 23 266.08 28.44 Bebravou Banská 65 735.61 31 766.95 11.67 160 350.11 77 489.72 87.99 Bystrica Banská 3 265.21 1 563.07 0 17 052.55 8 163.15 10.18 Štiavnica Bardejov 24 063.00 11 107.05 0.41 90 337.76 41 698.28 50.85 Bratislava 216 080.53 101 365.54 37.9 582 461.29 273 238.43 327.03 Brezno 18 640.86 8 763.69 0 71 486.50 33 608.17 40.32 Bytča 12 684.97 5 944.80 0.84 38 984.54 18 270.07 21.12 Čadca 32 122.69 15 556.82 0.42 109 310.48 52 938.37 60.77 Detva 9 076.02 4 303.89 0 36 599.52 17 355.67 21.81 Dolný Kubín 15 010.27 7 092.93 0.1 48 681.57 23 003.91 26.17 Dunajská 60 938.60 29 727.71 10.57 164 164.96 80 084.70 92.67 Streda Galanta 49 705.67 24 168.90 8.22 129 838.56 63 132.75 72.42 Gelnica 5 609.02 2 454.91 0 32 696.69 14 310.42 18.1 Hlohovec 22 845.24 10 820.88 3.61 61 421.64 29 093.00 34.29 Humenné 34 432.04 16 636.55 5.17 87 791.13 42 418.10 47.77 Ilava 27 818.16 13 542.45 6.56 78 605.72 38 266.88 47.34 Kežmarok 13 222.24 5 729.18 0 76 685.80 33 227.86 37.34 Komárno 55 420.91 26 717.60 10.16 142 669.65 68 778.93 80.73 Košice 181 239.79 85 891.84 43.69 385 167.05 182 535.55 205.57 Košice - 71 800.51 32 469.07 11.88 180 431.79 81 593.47 91.38 okolie Krupina 8 954.95 4 197.26 1.08 27 974.06 13 111.67 15.96 Kysucké Nové 15 755.14 7 531.80 0.76 43 869.71 20 972.06 22.77 Mesto Levice 60 085.57 28 799.56 11.1 155 407.99 74 488.47 87.56 Levoča 10 026.73 4 614.69 0 38 742.61 17 830.85 20.79 Liptovský 36 686.51 17 328.32 0.58 98 385.39 46 470.86 49.85 Mikuláš Lučenec 46 455.83 21 788.75 9.2 109 442.61 51 330.87 58.35 Malacky 27 864.69 13 036.96 2.54 90 126.49 42 167.19 50.31 Martin 66 784.76 31 712.50 13.35 149 200.90 70 847.51 78.94 Medzilaborce 2 875.66 1 310.43 0 13 114.03 5 976.05 7.77 10 /www.timeware.co.uk/download/document/timeware-report-June-2015-absenteeism.pdf https:/ 11 /www.fool.com/investing/general/2013/08/11/the-10-most-common-reasons-people-visit-their-doct.aspx. https:/ 18 Final Report WHO guideline value Zero pollution guideline value Chronic Chronic Restricted Restricted Workdays lost bronchitis Workdays bronchitis District activity days activity days (PM2,5) cases in lost (PM2,5) cases in adults (PM2,5) (PM2,5) adults (PM10) (PM10) Total 2 700 222.98 1 273 049.65 430.77 7 342 731.26 3 457 389.91 4 025.05 Michalovce 64 455.81 29 976.37 11.4 158 884.37 73 892.12 83.44 Myjava 7 154.76 3 381.09 0.31 29 901.10 14 130.23 18.87 Námestovo 18 591.64 8 382.43 0 71 586.54 32 276.31 34.24 Nitra 86 478.50 40 988.54 18.82 223 818.82 106 084.26 127.85 Nové Mesto 24 028.24 11 227.84 4.03 77 337.86 36 138.21 46.69 nad Váhom Nové Zámky 77 304.30 37 044.23 14.48 196 857.98 94 334.37 111.23 Partizánske 25 241.12 12 009.34 4.61 64 318.19 30 601.61 36.27 Pezinok 27 927.74 13 043.43 3.74 82 205.42 38 393.39 45.31 Piešťany 31 957.52 15 051.45 5.52 85 581.02 40 307.21 48.78 Poltár 9 293.06 4 383.72 1.08 27 753.51 13 091.88 15.85 Poprad 18 281.72 8 511.44 0 107 591.11 50 091.31 58.22 Považská 23 757.53 11 604.50 2.37 77 227.50 37 722.21 45.11 Bystrica Prešov 111 722.88 51 480.73 24.33 260 373.69 119 977.45 136.79 Prievidza 65 362.12 31 564.58 10.59 180 380.86 87 109.28 103.85 Púchov 19 297.57 9 289.30 3.09 57 160.09 27 515.24 33.2 Revúca 22 440.45 10 285.15 3.96 56 532.24 25 910.46 29.75 Rimavská 48 817.35 22 352.04 8.46 120 744.37 55 285.31 62.79 Sobota Rožňava 29 702.82 13 824.33 3.38 82 869.16 38 569.07 44.12 Ružomberok 42 821.38 20 143.64 6.53 91 259.17 42 929.29 44.77 Sabinov 22 583.80 9 785.67 1.18 73 754.32 31 958.11 37.36 Senec 42 131.90 19 407.71 6.1 114 319.90 52 660.52 59.25 Senica 25 868.82 12 402.34 3.73 77 518.68 37 164.94 44.69 Skalica 20 770.28 9 878.56 3.53 60 934.81 28 981.23 35.2 Snina 14 920.96 7 247.68 1.33 46 146.11 22 414.93 26.33 Sobrance 11 232.73 5 236.29 1.66 30 717.53 14 319.40 16.87 Spišská Nová 34 201.55 15 267.93 0.51 119 066.40 53 152.49 62.31 Ves Stará Ľubovňa 11 615.48 5 203.89 0 57 544.00 25 780.50 30.9 Stropkov 8 987.15 4 293.90 0.95 26 576.78 12 697.92 14.72 Svidník 13 079.84 6 270.98 1.25 41 055.39 19 683.54 23.18 Šaľa 28 128.15 13 498.91 4.75 72 614.39 34 848.19 40.17 Topoľčany 37 043.87 17 799.96 8.05 97 274.03 46 741.17 56.51 Trebišov 59 430.13 27 494.43 10.55 149 502.01 69 164.80 78.68 Trenčín 45 189.34 21 221.42 12.43 142 746.39 67 035.31 90.14 Trnava 67 497.60 32 215.55 11.53 179 778.49 85 805.46 100.48 Turčianske 5 193.98 2 459.23 0.15 18 815.01 8 908.46 11.16 Teplice Tvrdošín 9 891.11 4 610.98 0 40 684.68 18 966.15 21.5 Veľký Krtíš 22 405.43 10 808.40 3.66 59 926.78 28 908.74 33.77 Vranov nad 46 576.64 21 128.01 7.52 115 327.41 52 314.60 58.22 Topľou Zlaté Moravce 18 940.60 8 991.84 3.19 53 624.31 25 457.56 31.14 Zvolen 34 746.68 16 596.53 5.13 93 533.71 44 675.80 52.25 Žarnovica 7 978.78 3 785.13 0.16 30 403.57 14 423.44 18.16 Žiar nad 17 138.66 8 122.34 0 57 362.04 27 184.99 32.21 Hronom Žilina 126 933.12 60 342.02 23.15 261 080.15 124 113.41 128.16 19 Final Report Monetary value of health impacts The valuation of health impacts is divided into the valuation of premature mortality and the valuation of different morbidity endpoints. For premature mortality the literature values such cases using either the “Value of a Statistical Life” (VSL) or the “Value of Life Years Lost” (VLYL or also called VOLY). The value of statistical life is a measure based on how many individuals would be willing to pay to reduce their risk of death. For example, if a group of 100,000 individuals is willing to pay €10 each for a measure that reduces their risk of death by 1:100,000, the group would pay a total amount of €1 million (i.e. 10x100,000) to save one life. The total amount of one million is called the VSL because it represents the amount people are willing to pay to save one non-specific (i.e. statistical) life. The VLYL is based on a similar argument but now the valu- ation is for a measure that reduces the risk of losing one year of life.12 Recent research on the value of life in the EU28 estimates the VSL at €3,370,891 (mean), with Recent research on the value of life in the EU28 estimates the VSL at €3,370,891 (mean), with a range of a range of €1,685,446 (low) and €5,056,337 (high)13. These values are in 2011 prices. Adjusting €1,685,446 (low) and €5,056,337 (high) . These values are in 2011 prices. Adjusting for inflation to convert them 13 for inflation to convert them into 2019 prices gives the following values: €3,668,844 (mean), into 2019 prices gives the following values: €3,668,844 (mean), €1,834,423 (low) and €5,503,267 (high)14. These €1,834,423 (low) and €5,503,267 (high)14. These values apply for the whole of the EU28. As values apply for the whole of the EU28. As the GDP per capita in Slovakia is below the EU28 average (about 82%) the GDP per capita in Slovakia is below the EU28 average (about 82%) a further adjustment has a further adjustment has been made based on recommendations in the Organisation for Economic Co-operation been made based on recommendations in the Organisation for Economic Co-operation and and Development (OECD, 2012) review15 using the following formula: Development (OECD, 2012) review using the following formula: 15 0.8 = ( ) 28 28 The formula is based on the reasoning that the VSL increases with per capita GDP, reflecting The formula is based on the reasoning that the VSL increases with per capita GDP, reflecting a higher willingness a higher willingness to pay (WTP) to reduce the risk of death in richer countries. The percent to pay (WTP) to reduce the risk of death in richer countries. The percent increase in WTP per one percent increase increase in WTP per one percent increase in per capita income is estimated in the literature in per capita income is estimated in the literature as not being unity but slightly below that – a value of 0.8 is the as not being unity but slightly below that – a value of 0.8 is the most appropriate according to most appropriate according to the OECD. Applying the above formula provides VSL values for Slovakia of the OECD. Applying the above formula provides VSL values for Slovakia of €3,138,572 (mean), €3,138,572 (mean), €1,569,287 (low) and €4,707,859 (high). €1,569,287 (low) and €4,707,859 (high). The VLYL estimates in the literature are €52,000 (median) and €120,000 (mean) for the EU2816. Adjusting these The VLYL values, which are inestimates 2000 prices, for literature in the are €52,000 inflation gives (median) and €71,425 (median) and €120,000 (mean) for €164,827 (mean). the EU28 Adjusting 16 . for further Adjusting the fact that GDP per these capitavalues, which in Slovakia are in is 82% of 2000 the EU28prices, for inflation average gives gives VLYL €71,425 values (median) of €61,101 and and (median) €164,827 (mean). Adjusting further for the fact that GDP per capita in Slovakia €141,002 (mean). An alternative way to estimate VLYL is the figure used for policy purposes in the country. is 82% of theA value EU28 for a life year is average set in thegives SlovakVLYL values of legislation to€61,101 determine (median) how muchanda€141,002 (mean). new medicine can An costalternative per addedway life year. to estimate This benchmark is setVLYL is the at max. figure used 41-times for policy the monthly purposes average in the wage country. As in Slovakia. A value for a lifemonthly year is set the average wage in in the Slovak legislation to determine how much a new medicine can cost 2018 was €1,013, the valuation of an additional year according to the legislation is €41,533, somewhat per added year. than life lower the numbers This benchmark obtained fromistheset at max. 41-times the monthly average wage in Slovakia. As the average literature. monthly wage in 2018 was €1,013, the valuation of an additional year according to the legisla- In order to tion the VLYL somewhat is €41,533, apply lowerdeath to the premature than the numbers estimates the obtained number of from the literature. life years associated with a premature death are required and depend on whether the health impact is acute or chronic. Acute impacts have fewer years than of life lost In chronic order ones. to apply The the EEA VLYL to(2019) uses an estimate the premature of 10.2 years death estimates the for PM2.5 of number all-cause deaths. life years The same associat- figure hased been used with here. a premature death are required and depend on whether the health impact is acute or chronic. Acute impacts have fewer years of life lost than chronic ones. The EEA (2019) uses an The resulting value of estimate oflosses from premature 10.2 years mortality deaths. for PM2.5 all-cause are shown in same The 4. Total Table figure losses has beenfor estimates used here. of premature deaths according to the VSL method lie between €2.7 and €8.0 billion, with a mean value of €5.3 billion. The VLYL method gives a lower estimate: The resulting value of the median-bas losses is €1.1 billion ed figure mortality from premature and the are shown Table figure in mean 4. Totalis losses €2.4 billion. for All are annualof estimatesestimates losses due to deaths premature premature tocaused mortality according the VSLby air pollution method in the form lie between €2.7ofand PM €8.0 and NO 2. billion, In the case of morbidity, a range of valuations are needed, one for each endpoint. The ones that matter for Slovakia 12 https://strata.org/pdf/2017/vsl-full-report.pdf are RADs, cases of chronic bronchitis and workdays lost. For RADs and cases of chronic bronchitis the Clean Air 13 http://old.heatwalkingcycling.org/index.php?pg=requirements&act=vsl&b=1. for Europe (EU CAFÉ) study17 is used. The values for morbidity endpoints in that study have been used extensively 14 A further adjustment could be made to account for growth in per capita GDP between 2011 and 2019 in the EU28. in EU NationalThisAir Control Pollution that is something considered indocuments Programme can be and the revisions to thethe study and figures have not been significantly estimates. updated in15 methodological terms. The estimate for a RAD was €130, and for a case of chronic bronchitis €190,000 OECD (2012), Mortality Risk Valuation in Environment, Health and Transport Policies. OECD: Paris. (with a range 16 of €120,000 http:/ to €250,000). Figures are per day for the EU and in 2000 prices. /en.opasnet.org/w/Value_of_a_life_year_(VOLY)#cite_note-2 Adjusting these figures for inflation and the difference in GDP per capita between the EU28 and Slovakia gives the following estimates: RAD: €172.75; case of chronic bronchitis: 20 €223,255 (lower bound: €141,003, upper bound: €293,756). For workdays lost the average wage in Slovakia has been used giving a cost of €28.36/day18. Final Report with a mean value of €5.3 billion. The VLYL method gives a lower estimate: the median-based figure is €1.1 billion and the mean figure is €2.4 billion. All estimates are annual losses due to premature mortality caused by air pollution in the form of PM and NO2. In the case of morbidity, a range of valuations are needed, one for each endpoint. The ones that matter for Slovakia are RADs, cases of chronic bronchitis and workdays lost. For RADs and cases of chronic bronchitis the Clean Air for Europe (EU CAFÉ) study17 is used. The values for morbid- ity endpoints in that study have been used extensively in EU National Air Pollution Control Pro- gramme documents and the study and figures have not been significantly updated in method- ological terms. The estimate for a RAD was €130, and for a case of chronic bronchitis €190,000 (with a range of €120,000 to €250,000). Figures are per day for the EU and in 2000 prices. Adjusting these figures for inflation and the difference in GDP per capita between the EU28 and Slovakia gives the following estimates: RAD: €172.75; case of chronic bronchitis: €223,255 (lower bound: €141,003, upper bound: €293,756). For workdays lost the average wage in Slo- vakia has been used giving a cost of €28.36/day18. The morbidity costs are presented in Table 5. Total costs across all endpoints are around €549 million, or less than half the premature mortality costs based on VLYL (median value) and about 10% of the costs based on VSL (mean value). RADs account for 75% of the total, followed by chronic bronchitis cases (17%) and asthma in children (6%). Workdays lost make up 2%; these figures, however, may be revised when better data are available. Table 4: Value of Losses from Premature Mortality (Euros Million) WHO guideline value Zero pollution guideline value   Valuations Via VSL Valuations Via VLYL Valuations Via VSL Valuations Via VLYL Lower Upper Slovak Lower Upper Slovak District Mean Median Mean Mean Median Mean Bound Bound Law Bound Bound Law Total 2,498 4,997 7,495 672 989 2,283 6,866 13,732 20,598 1,848 2,719 6,274 Bánovce nad 20.07 40.14 60.21 5.40 7.95 18.34 55.11 110.23 165.34 14.83 21.82 50.36 Bebravou Banská Bystrica 63.31 126.61 189.92 17.04 25.07 57.85 154.57 309.15 463.72 41.61 61.21 141.25 Banská Štiavnica 3.83 7.66 11.49 1.03 1.52 3.50 20.01 40.02 60.03 5.39 7.92 18.28 Bardejov 21.72 43.44 65.16 5.85 8.60 19.85 82.28 164.56 246.83 22.15 32.58 75.18 Bratislava 26.98 53.95 80.93 7.26 10.68 24.65 73.74 147.48 221.22 19.85 29.20 67.38 Brezno 22.75 45.51 68.26 6.12 9.01 20.79 87.52 175.04 262.56 23.56 34.66 79.97 Bytča 13.53 27.05 40.58 3.64 5.36 12.36 41.55 83.11 124.66 11.18 16.45 37.97 Čadca 34.07 68.14 102.21 9.17 13.49 31.13 116.22 232.44 348.66 31.28 46.02 106.20 Detva 10.67 21.34 32.01 2.87 4.23 9.75 43.05 86.09 129.14 11.59 17.04 39.33 Dolný Kubín 13.18 26.36 39.55 3.55 5.22 12.05 42.97 85.93 128.90 11.57 17.01 39.26 Dunajská Streda 63.57 127.14 190.72 17.11 25.17 58.09 171.51 343.01 514.52 46.16 67.91 156.72 Galanta 55.85 111.70 167.55 15.03 22.12 51.04 146.23 292.45 438.68 39.36 57.90 133.62 Gelnica 6.03 12.05 18.08 1.62 2.39 5.51 35.62 71.25 106.87 9.59 14.11 32.55 Hlohovec 24.73 49.46 74.20 6.66 9.79 22.60 66.65 133.30 199.94 17.94 26.39 60.90 Humenné 35.04 70.08 105.13 9.43 13.88 32.02 89.68 179.37 269.05 24.14 35.51 81.95 Ilava 30.46 60.92 91.38 8.20 12.06 27.83 86.17 172.34 258.51 23.19 34.12 78.74 Kežmarok 9.10 18.20 27.31 2.45 3.60 8.32 54.08 108.16 162.23 14.56 21.41 49.42 Komárno 71.31 142.62 213.93 19.19 28.24 65.16 183.92 367.84 551.76 49.50 72.83 168.06 Košice 48.84 97.67 146.51 13.14 19.34 44.63 105.08 210.16 315.24 28.28 41.61 96.02 AEA (2005) Service Contract for carrying out cost-benefit analysis of air quality related issues, in particular in the 17 clean air for Europe (CAFE) programme. Methodology for the Cost-Benefit analysis for CAFE: Volume 2: Health Impact Assessment /countryeconomy.com/national-minimum-wage/slovakia. The data gives a minimum annual wage of https:/ 18 €6,240. It is assumed that 220 days are worked per year. 21 Final Report WHO guideline value Zero pollution guideline value   Valuations Via VSL Valuations Via VLYL Valuations Via VSL Valuations Via VLYL Lower Upper Slovak Lower Upper Slovak District Mean Median Mean Mean Median Mean Bound Bound Law Bound Bound Law Total 2,498 4,997 7,495 672 989 2,283 6,866 13,732 20,598 1,848 2,719 6,274 Košice - okolie 63.70 127.39 191.09 17.14 25.22 58.21 161.86 323.71 485.57 43.57 64.09 147.90 Krupina 11.30 22.60 33.90 3.04 4.47 10.32 35.31 70.62 105.93 9.50 13.98 32.27 Kysucké Nové 17.83 35.65 53.48 4.80 7.06 16.29 49.86 99.71 149.57 13.42 19.74 45.56 Mesto Levice 75.62 151.25 226.87 20.36 29.95 69.10 196.19 392.38 588.58 52.81 77.69 179.28 Levoča 8.60 17.20 25.80 2.31 3.41 7.86 33.57 67.13 100.70 9.03 13.29 30.67 Liptovský 39.53 79.06 118.59 10.64 15.65 36.12 106.07 212.14 318.20 28.55 42.00 96.92 Mikuláš Lučenec 54.72 109.44 164.16 14.73 21.67 50.00 129.15 258.30 387.46 34.76 51.14 118.02 Malacky 27.87 55.74 83.61 7.50 11.04 25.47 90.47 180.94 271.41 24.35 35.82 82.67 Martin 68.44 136.87 205.31 18.42 27.10 62.54 153.10 306.20 459.30 41.21 60.62 139.90 Medzilaborce 3.83 7.66 11.49 1.03 1.52 3.50 17.67 35.34 53.01 4.76 7.00 16.15 Michalovce 66.84 133.67 200.51 17.99 26.47 61.07 165.62 331.24 496.87 44.58 65.58 151.34 Myjava 9.20 18.39 27.59 2.48 3.64 8.40 38.46 76.93 115.39 10.35 15.23 35.15 Námestovo 13.13 26.27 39.40 3.54 5.20 12.00 50.88 101.75 152.63 13.69 20.15 46.49 Nitra 91.60 183.20 274.80 24.65 36.27 83.70 237.59 475.18 712.77 63.95 94.08 217.11 Nové Mesto 28.78 57.56 86.34 7.75 11.40 26.30 92.87 185.74 278.61 25.00 36.77 84.86 nad Váhom Nové Zámky 102.00 204.01 306.01 27.46 40.39 93.21 260.22 520.44 780.66 70.04 103.04 237.79 Partizánske 27.27 54.55 81.82 7.34 10.80 24.92 69.66 139.32 208.98 18.75 27.58 63.66 Pezinok 24.09 48.18 72.27 6.48 9.54 22.01 71.07 142.15 213.22 19.13 28.14 64.95 Piešťany 37.69 75.39 113.08 10.15 14.93 34.44 101.06 202.12 303.19 27.20 40.02 92.35 Poltár 11.97 23.95 35.92 3.22 4.74 10.94 35.81 71.62 107.43 9.64 14.18 32.72 Poprad 15.66 31.32 46.98 4.22 6.20 14.31 93.15 186.31 279.46 25.07 36.89 85.12 Považská 25.14 50.28 75.42 6.77 9.95 22.97 81.90 163.80 245.70 22.04 32.43 74.84 Bystrica Prešov 93.89 187.78 281.67 25.27 37.18 85.80 220.17 440.34 660.51 59.26 87.18 201.19 Prievidza 69.72 139.45 209.17 18.77 27.61 63.71 192.91 385.82 578.74 51.92 76.39 176.28 Púchov 21.30 42.59 63.89 5.73 8.43 19.46 63.24 126.48 189.73 17.02 25.04 57.79 Revúca 26.22 52.45 78.67 7.06 10.38 23.96 66.52 133.04 199.57 17.91 26.34 60.79 Rimavská 55.82 111.64 167.46 15.02 22.10 51.01 138.72 277.45 416.17 37.34 54.93 126.77 Sobota Rožňava 34.24 68.48 102.73 9.22 13.56 31.29 95.96 191.92 287.89 25.83 38.00 87.69 Ružomberok 50.67 101.34 152.02 13.64 20.06 46.30 108.12 216.25 324.37 29.10 42.81 98.80 Sabinov 17.78 35.56 53.34 4.79 7.04 16.25 59.16 118.32 177.49 15.92 23.43 54.06 Senec 30.21 60.42 90.63 8.13 11.96 27.60 82.15 164.30 246.46 22.11 32.53 75.07 Senica 28.06 56.12 84.18 7.55 11.11 25.64 84.46 168.92 253.38 22.73 33.44 77.18 Skalica 21.92 43.85 65.77 5.90 8.68 20.03 64.48 128.96 193.45 17.36 25.53 58.92 Snina 17.48 34.96 52.45 4.71 6.92 15.97 54.36 108.72 163.08 14.63 21.53 49.67 Sobrance 13.65 27.31 40.96 3.67 5.41 12.48 37.49 74.98 112.47 10.09 14.85 34.26 Spišská Nová 27.24 54.49 81.73 7.33 10.79 24.89 95.99 191.99 287.98 25.84 38.01 87.72 Ves Stará Ľubovňa 8.46 16.92 25.38 2.28 3.35 7.73 42.75 85.49 128.24 11.51 16.93 39.06 Stropkov 8.91 17.83 26.74 2.40 3.53 8.15 26.40 52.79 79.19 7.10 10.45 24.12 Svidník 12.57 25.14 37.71 3.38 4.98 11.49 39.69 79.37 119.06 10.68 15.72 36.27 Šaľa 31.76 63.52 95.29 8.55 12.58 29.02 82.04 164.08 246.13 22.08 32.49 74.97 Topoľčany 42.70 85.40 128.10 11.49 16.91 39.02 112.38 224.75 337.13 30.25 44.50 102.69 Trebišov 66.46 132.92 199.38 17.89 26.32 60.73 168.46 336.93 505.39 45.34 66.71 153.94 Trenčín 46.39 92.78 139.16 12.49 18.37 42.39 146.76 293.52 440.28 39.50 58.11 134.11 Trnava 67.29 134.58 201.87 18.11 26.65 61.49 179.56 359.12 538.67 48.33 71.10 164.08 Turčianske 7.08 14.15 21.23 1.90 2.80 6.47 25.69 51.38 77.07 6.91 10.17 23.47 Teplice Tvrdošín 7.63 15.25 22.88 2.05 3.02 6.97 31.53 63.05 94.58 8.49 12.48 28.81 Veľký Krtíš 27.49 54.99 82.48 7.40 10.89 25.12 73.65 147.29 220.94 19.82 29.16 67.30 22 Final Report WHO guideline value Zero pollution guideline value   Valuations Via VSL Valuations Via VLYL Valuations Via VSL Valuations Via VLYL Lower Upper Slovak Lower Upper Slovak District Mean Median Mean Mean Median Mean Bound Bound Law Bound Bound Law Total 2,498 4,997 7,495 672 989 2,283 6,866 13,732 20,598 1,848 2,719 6,274 Vranov nad 42.70 85.40 128.10 11.49 16.91 39.02 106.79 213.58 320.37 28.74 42.29 97.58 Topľou Zlaté Moravce 22.94 45.89 68.83 6.18 9.08 20.97 65.19 130.38 195.56 17.55 25.81 59.57 Zvolen 35.67 71.34 107.01 9.60 14.12 32.59 96.28 192.55 288.83 25.91 38.12 87.98 Žarnovica 10.23 20.46 30.70 2.75 4.05 9.35 39.00 77.99 116.99 10.50 15.44 35.63 Žiar nad 19.49 38.98 58.47 5.25 7.72 17.81 65.36 130.72 196.08 17.59 25.88 59.73 Hronom Žilina 132.56 265.12 397.67 35.68 52.49 121.13 273.13 546.27 819.40 73.52 108.15 249.59 Table 5: Value of Losses from Morbidity (Euros Million) WHO guideline value Zero pollution guideline value Workdays Chronic Chronic Chronic Workdays Chronic Chronic Chronic RADs RADs District Lost Bronchitis Bronchitis Bronchitis Lost Bronchitis Bronchitis Bronchitis (mean) (mean) (mean) (low) (mean) (high) (mean) (low) (mean) (high) Total 413.13 52.2 60.74 96.17 126.54 1123.44 141.75 567.54 898.61 1182.38 Bánovce nad 2.74 0.35 0.53 0.83 1.1 7.5 0.95 4.01 6.35 8.35 Bebravou Banská Bystrica 10.06 1.3 1.65 2.61 3.43 24.53 3.18 12.41 19.64 25.85 Banská Štiavnica 0.5 0.06 0 0 0 2.61 0.33 1.44 2.27 2.99 Bardejov 3.68 0.46 0.06 0.09 0.12 13.82 1.71 7.17 11.35 14.94 Bratislava 33.06 4.16 5.34 8.46 11.13 89.12 11.2 46.11 73.01 96.07 Brezno 2.85 0.36 0 0 0 10.94 1.38 5.69 9 11.84 Bytča 1.94 0.24 0.12 0.19 0.25 5.96 0.75 2.98 4.72 6.2 Čadca 4.91 0.64 0.06 0.09 0.12 16.72 2.17 8.57 13.57 17.85 Detva 1.39 0.18 0 0 0 5.6 0.71 3.08 4.87 6.41 Dolný Kubín 2.3 0.29 0.01 0.02 0.03 7.45 0.94 3.69 5.84 7.69 Dunajská Streda 9.32 1.22 1.49 2.36 3.11 25.12 3.28 13.07 20.69 27.22 Galanta 7.6 0.99 1.16 1.84 2.42 19.87 2.59 10.21 16.17 21.27 Gelnica 0.86 0.1 0 0 0 5 0.59 2.55 4.04 5.32 Hlohovec 3.5 0.44 0.51 0.81 1.06 9.4 1.19 4.84 7.66 10.07 Humenné 5.27 0.68 0.73 1.15 1.52 13.43 1.74 6.74 10.66 14.03 Ilava 4.26 0.56 0.92 1.46 1.93 12.03 1.57 6.68 10.57 13.91 Kežmarok 2.02 0.23 0 0 0 11.73 1.36 5.27 8.34 10.97 Komárno 8.48 1.1 1.43 2.27 2.98 21.83 2.82 11.38 18.02 23.71 Košice 27.73 3.52 6.16 9.76 12.84 58.93 7.48 28.99 45.89 60.39 Košice - okolie 10.99 1.33 1.68 2.65 3.49 27.61 3.35 12.89 20.4 26.84 Krupina 1.37 0.17 0.15 0.24 0.32 4.28 0.54 2.25 3.56 4.69 Kysucké Nové 2.41 0.31 0.11 0.17 0.22 6.71 0.86 3.21 5.08 6.69 Mesto Levice 9.19 1.18 1.57 2.48 3.26 23.78 3.05 12.35 19.55 25.72 Levoča 1.53 0.19 0 0 0 5.93 0.73 2.93 4.64 6.11 Liptovský Mikuláš 5.61 0.71 0.08 0.13 0.17 15.05 1.91 7.03 11.13 14.64 Lučenec 7.11 0.89 1.3 2.05 2.7 16.74 2.1 8.23 13.03 17.14 Malacky 4.26 0.53 0.36 0.57 0.75 13.79 1.73 7.09 11.23 14.78 Martin 10.22 1.3 1.88 2.98 3.92 22.83 2.9 11.13 17.62 23.19 Medzilaborce 0.44 0.05 0 0 0 2.01 0.25 1.1 1.73 2.28 Michalovce 9.86 1.23 1.61 2.55 3.35 24.31 3.03 11.77 18.63 24.51 23 Final Report WHO guideline value Zero pollution guideline value Workdays Chronic Chronic Chronic Workdays Chronic Chronic Chronic RADs RADs District Lost Bronchitis Bronchitis Bronchitis Lost Bronchitis Bronchitis Bronchitis (mean) (mean) (mean) (low) (mean) (high) (mean) (low) (mean) (high) Total 413.13 52.2 60.74 96.17 126.54 1123.44 141.75 567.54 898.61 1182.38 Myjava 1.09 0.14 0.04 0.07 0.09 4.57 0.58 2.66 4.21 5.54 Námestovo 2.84 0.34 0 0 0 10.95 1.32 4.83 7.64 10.06 Nitra 13.23 1.68 2.65 4.2 5.53 34.24 4.35 18.03 28.54 37.56 Nové Mesto nad 3.68 0.46 0.57 0.9 1.19 11.83 1.48 6.58 10.42 13.72 Váhom Nové Zámky 11.83 1.52 2.04 3.23 4.25 30.12 3.87 15.68 24.83 32.67 Partizánske 3.86 0.49 0.65 1.03 1.35 9.84 1.25 5.11 8.1 10.65 Pezinok 4.27 0.53 0.53 0.83 1.1 12.58 1.57 6.39 10.12 13.31 Piešťany 4.89 0.62 0.78 1.23 1.62 13.09 1.65 6.88 10.89 14.33 Poltár 1.42 0.18 0.15 0.24 0.32 4.25 0.54 2.23 3.54 4.66 Poprad 2.8 0.35 0 0 0 16.46 2.05 8.21 13 17.1 Považská 3.63 0.48 0.33 0.53 0.7 11.82 1.55 6.36 10.07 13.25 Bystrica Prešov 17.09 2.11 3.43 5.43 7.15 39.84 4.92 19.29 30.54 40.18 Prievidza 10 1.29 1.49 2.36 3.11 27.6 3.57 14.64 23.19 30.51 Púchov 2.95 0.38 0.44 0.69 0.91 8.75 1.13 4.68 7.41 9.75 Revúca 3.43 0.42 0.56 0.88 1.16 8.65 1.06 4.19 6.64 8.74 Rimavská 7.47 0.92 1.19 1.89 2.48 18.47 2.27 8.85 14.02 18.45 Sobota Rožňava 4.54 0.57 0.48 0.75 0.99 12.68 1.58 6.22 9.85 12.96 Ružomberok 6.55 0.83 0.92 1.46 1.92 13.96 1.76 6.31 9.99 13.15 Sabinov 3.46 0.4 0.17 0.26 0.35 11.28 1.31 5.27 8.34 10.97 Senec 6.45 0.8 0.86 1.36 1.79 17.49 2.16 8.35 13.23 17.41 Senica 3.96 0.51 0.53 0.83 1.1 11.86 1.52 6.3 9.98 13.13 Skalica 3.18 0.41 0.5 0.79 1.04 9.32 1.19 4.96 7.86 10.34 Snina 2.28 0.3 0.19 0.3 0.39 7.06 0.92 3.71 5.88 7.73 Sobrance 1.72 0.21 0.23 0.37 0.49 4.7 0.59 2.38 3.77 4.96 Spišská Nová 5.23 0.63 0.07 0.11 0.15 18.22 2.18 8.79 13.91 18.3 Ves Stará Ľubovňa 1.78 0.21 0 0 0 8.8 1.06 4.36 6.9 9.08 Stropkov 1.38 0.18 0.13 0.21 0.28 4.07 0.52 2.07 3.29 4.32 Svidník 2 0.26 0.18 0.28 0.37 6.28 0.81 3.27 5.17 6.81 Šaľa 4.3 0.55 0.67 1.06 1.39 11.11 1.43 5.66 8.97 11.8 Topoľčany 5.67 0.73 1.14 1.8 2.36 14.88 1.92 7.97 12.62 16.6 Trebišov 9.09 1.13 1.49 2.35 3.1 22.87 2.84 11.09 17.57 23.11 Trenčín 6.91 0.87 1.75 2.78 3.65 21.84 2.75 12.71 20.12 26.48 Trnava 10.33 1.32 1.63 2.57 3.39 27.51 3.52 14.17 22.43 29.52 Turčianske 0.79 0.1 0.02 0.03 0.04 2.88 0.37 1.57 2.49 3.28 Teplice Tvrdošín 1.51 0.19 0 0 0 6.22 0.78 3.03 4.8 6.32 Veľký Krtíš 3.43 0.44 0.52 0.82 1.08 9.17 1.19 4.76 7.54 9.92 Vranov nad 7.13 0.87 1.06 1.68 2.21 17.65 2.14 8.21 13 17.1 Topľou Zlaté Moravce 2.9 0.37 0.45 0.71 0.94 8.2 1.04 4.39 6.95 9.15 Zvolen 5.32 0.68 0.72 1.15 1.51 14.31 1.83 7.37 11.66 15.35 Žarnovica 1.22 0.16 0.02 0.03 0.05 4.65 0.59 2.56 4.05 5.33 Žiar nad Hronom 2.62 0.33 0 0 0 8.78 1.11 4.54 7.19 9.46 Žilina 19.42 2.47 3.26 5.17 6.8 39.95 5.09 18.07 28.61 37.65 24 Final Report IV. Reduction scenario after the implementation of the NAPCP and the possible health impacts The NAPCP for Slovakia has been formulated to meet the air quality and emission reduction targets by 2030 and consists of several measures to reduce emissions of PM2.5, nitrogen oxides (NOx), sulfur dioxide (SO2) and ammonia (NH3) across transport, residential heating and agricul- ture sectors. Table 6 lists the measures in the NAPCP. Table 6: Sector Potential Measures Set of measures Subsidy for replacement of old diesel vehicles  to reduce emissions Introducing subsidies for alternatively-fueled vehicles  of SO2, NOx, NMVOC, Stricter NOx periodical technical controls of vehicles  NH3 and PM2.5 Road Transport Frequency of technical controls of vehicles older than 8 years to be raised from  currently once every two years to once a year Road emission controls for DPF removal – raising frequency of control  Incentives for replacement of unsuitable boilers by using a scrapping scheme  Incentives for replacement of unsuitable boilers: subsidy scheme  Introduction of differentiated registration fees for different categories of heating  devices to promote more environmentally friendly devices Connect households using wood or coal for heating to natural gas  Residential Fuel standards mandating the use of wood that has a moisture content of less  heating than 25% Introduction of a “control system” (based on the Czech model) – each household  that uses solid fuel would have an obligation to have its device regularly inspected Awareness raising campaigns and education  Economic Unification of tax rate for petrol and diesel over a period of 5 years  instruments Agriculture Manure storage and application to soil  Source: World Bank and Ministry of Environment (2019) Report The measures in Table 6 were analyzed in detail in an earlier study regarding reductions in emissions Slovakia can achieve over the period 2020 to 2030, as well as the economic and fiscal costs of the reductions19. That assessment of emissions reductions achievable through the NAP- CP showed that emission reductions would not be sufficient to meet the 2030 Emission Reduction Commitments for PM2.5. as set out in the National Emission Ceilings Directive 2016/2284. While NOx, non-methane volatile organic compound (NMVOC), SO2, and NH3 emissions would be be- low the 2030 target, PM2.5 emissions would be above the 2030 target. Therefore, the impacts of air pollution on health will need to be reduced further even after a full implementation of the NAPCP if the commitments are to be met for all emissions. This section reports the reductions in health impacts achieved by 2030 if the NAPCP is fully im- plemented. These are reported in physical units as well as in monetary terms, using valuations of different health impacts elaborated in previous sections. 19 World Bank and Ministry of Environment: Final Report, Slovak Republic Air Protection Strategy, May 2019. This report is a foundation of the National Air Pollution Control Programme, which has been approved by the government in 2020. 25 Final Report The health impacts of the implementation of the NAPCP have been calculated for all health indicators. Two reduction scenarios have been calculated for each district—one scenario cal- culates the concentrations for the year 2020 and is used to benchmark the no policy case. A second scenario is calculated for the year 2030, which includes the overall impact of all mea- sures proposed in the NAPCP. The total impact of the NAPCP is calculated as the difference between these two scenarios. The physical as well as monetary impacts of these interventions are approximately the same for both guideline systems (WHO maximum recommended con- centrations as well as zero pollution values used by the EEA), with the impact being slightly larger for indicators showing impacts of PM10 pollution with the zero reference case. Table 7 shows the reductions in mortality and selected morbidity indicators by district. Tables 8 and 9 reflects monetary values associated with these reductions. In total, the NAPCP is expected to save about 116 lives in 2030, and reduces restricted activity days by 195,000, workdays lost by 92,000 and chronic bronchitis cases by about 81. Table 7: Mortality reductions Morbidity reductions Estimates Chronic of Premature All-cause Neonatal Mortality Restricted activity Workdays bronchitis District mortality mortality Mortality (PM2,5) (PM10) (NO2) days (PM2,5) lost (PM2,5) cases in adults (PM10) Reductions due to Implementation Total 116.0 0.4 0.0 195133.7 91902.0 80.5 of the NAPCP Bánovce nad Bebravou 1.0 0.0 0.0 1450.7 688.3 0.9 based Banská Bystrica 4.3 0.0 0.0 7018.3 3391.6 2.8 on WHO baseline Banská Štiavnica 0.4 0.0 0.0 553.2 264.8 0.0 values Bardejov 1.0 0.0 0.0 1794.4 828.3 0.0 Bratislava 1.1 0.0 0.0 14333.6 6724.0 7.8 Brezno 2.3 0.0 0.0 2917.4 1371.6 0.0 Bytča 0.8 0.0 0.0 1201.7 563.2 0.0 Čadca 2.4 0.0 0.0 3497.5 1693.8 0.0 Detva 1.0 0.0 0.0 1331.8 631.5 0.0 Dolný Kubín 1.0 0.0 0.0 1876.3 886.6 0.0 Dunajská Streda 1.7 0.0 0.0 2505.6 1222.3 1.4 Galanta 1.9 0.0 0.0 2615.6 1271.8 1.4 Gelnica 0.6 0.0 0.0 927.1 405.8 0.0 Hlohovec 1.0 0.0 0.0 1387.3 657.1 0.9 Humenné 1.0 0.0 0.0 1489.6 719.7 0.9 Ilava 1.3 0.0 0.0 1922.6 935.9 1.4 Kežmarok 0.9 0.0 0.0 2134.4 924.8 0.0 Komárno 1.8 0.0 0.0 2187.4 1054.5 1.4 Košice 1.9 0.0 0.0 10946.9 5187.9 6.0 Košice - okolie 2.5 0.0 0.0 4358.5 1971.0 2.3 Krupina 0.7 0.0 0.0 811.9 380.5 0.5 Kysucké Nové Mesto 1.3 0.0 0.0 1750.7 836.9 0.0 Levice 3.4 0.0 0.0 4289.6 2056.1 2.3 Levoča 0.6 0.0 0.0 1173.3 540.0 0.0 Liptovský Mikuláš 3.0 0.0 0.0 4402.4 2079.4 0.0 Lučenec 2.9 0.0 0.0 3892.4 1825.6 2.3 Malacky 0.7 0.0 0.0 1060.7 496.3 0.5 Martin 3.3 0.0 0.0 5068.4 2406.7 2.8 Medzilaborce 0.2 0.0 0.0 200.9 91.5 0.0 Michalovce 1.4 0.0 0.0 2087.9 971.0 0.9 Myjava 0.5 0.0 0.0 568.8 268.8 0.0 26 Final Report Mortality reductions Morbidity reductions Chronic All-cause Neonatal Mortality Restricted activity Workdays bronchitis District mortality mortality (NO2) days (PM2,5) lost (PM2,5) cases in adults (PM2,5) (PM10) (PM10) Total 116.0 0.4 0.0 195133.7 91902.0 80.5 Námestovo 1.2 0.0 0.0 2701.6 1218.1 0.0 Nitra 4.0 0.0 0.0 5962.5 2826.1 3.2 Nové Mesto nad Váhom 1.2 0.0 0.0 1581.1 738.8 0.9 Nové Zámky 3.6 0.0 0.0 4253.7 2038.4 2.3 Partizánske 1.4 0.0 0.0 2035.1 968.3 0.9 Pezinok 0.8 0.0 0.0 1421.4 663.9 0.9 Piešťany 1.4 0.0 0.0 1895.6 892.8 0.9 Poltár 0.8 0.0 0.0 1018.3 480.3 0.5 Poprad 1.7 0.0 0.0 3132.7 1458.5 0.0 Považská Bystrica 1.7 0.0 0.0 2536.5 1239.0 0.5 Prešov 3.4 0.0 0.0 6310.9 2908.0 3.2 Prievidza 4.3 0.0 0.0 6347.4 3065.3 3.7 Púchov 1.3 0.0 0.0 1798.8 865.9 0.9 Revúca 1.5 0.0 0.0 2006.6 919.7 0.9 Rimavská Sobota 2.9 0.0 0.0 3987.5 1825.8 2.3 Rožňava 2.3 0.0 0.0 3089.5 1437.9 0.9 Ružomberok 3.3 0.0 0.0 4417.6 2078.1 2.3 Sabinov 0.9 0.0 0.0 1841.4 797.9 0.0 Senec 1.0 0.0 0.0 2216.4 1021.0 0.9 Senica 0.7 0.0 0.0 1058.7 507.6 0.5 Skalica 0.5 0.0 0.0 725.2 344.9 0.5 Snina 0.5 0.0 0.0 613.4 298.0 0.5 Sobrance 0.2 0.0 0.0 268.9 125.3 0.0 Spišská Nová Ves 1.9 0.0 0.0 3809.2 1700.5 0.0 Stará Ľubovňa 0.6 0.0 0.0 1333.9 597.6 0.0 Stropkov 0.3 0.0 0.0 459.4 219.5 0.5 Svidník 0.4 0.0 0.0 733.0 351.4 0.5 Šaľa 1.1 0.0 0.0 1493.1 716.5 0.9 Topoľčany 1.8 0.0 0.0 2445.1 1174.9 1.4 Trebišov 1.3 0.0 0.0 1866.8 863.6 0.9 Trenčín 2.2 0.0 0.0 3426.6 1609.2 2.3 Trnava 2.2 0.0 0.0 3493.2 1667.2 1.8 Turčianske Teplice 0.6 0.0 0.0 641.5 303.7 0.0 Tvrdošín 0.7 0.0 0.0 1412.5 658.5 0.0 Veľký Krtíš 1.3 0.0 0.0 1712.3 826.0 0.9 Vranov nad Topľou 1.2 0.0 0.0 2097.9 951.7 0.9 Zlaté Moravce 1.3 0.0 0.0 1695.4 804.9 0.9 Zvolen 2.4 0.0 0.0 3605.2 1722.0 1.4 Žarnovica 0.8 0.0 0.0 1021.0 484.4 0.0 Žiar nad Hronom 1.4 0.0 0.0 1913.9 907.0 0.0 Žilina 6.0 0.0 0.0 8996.1 4276.6 4.6 27 Final Report V. Economic impacts of the NAPCP V. Economic impacts of the NAPCP Tables 8 and 9 Tables 8reflect and 9 monetary values of reduction reflect monetary values in reduction in associated ofconcentrations with the concentrations NAPCP. Map associated with 5 illustrates the the same information NAPCP. Map 5inillustrates form of a map the comparing health costs same information in in form2030ofwith health a map costs in 2020. comparing healthAs costs we discuss in in the next 2030section, withhowever, the NAPCP health costs in 2020. generates benefits As we discuss inin thenext the intervening section, years. Therefore, however, the fullgener- the NAPCP value of the NAPCP is more ates complex benefits than in the this comparison intervening years.suggests. In spite Therefore, of this, the full the of value tables the and NAPCPassociated is more maps are useful complex in indicating the values of suggests. howcomparison than this health impacts for 2020 In spite and the of this, 2030 up against stand and tables each other. associated mapsThis are can usefulbe seen in in Map 5, which depicts indicating how the value the of avoided values premature of health impactsmortality for 2020 reductions and and 2030 in morbidity stand for each up against district each per year. other. The biggest This canvalue becan seen achieved be in Map 5, which the through implementation depicts the value of ofair quality measures avoided premature in mortality districts with andthere-highest current population ductions in exposure morbidity pollutants. to for each district per year. The biggest value can be achieved through the implementation of air quality measures in districts with the highest current population 5: Value to exposure Map of pollutants. Avoided Losses from Premature Mortality and Reduced Morbidity (Euros Million) Map 5: Value of Avoided Losses from Premature Mortality and Reduced Morbidity (Euros Million) Source: own elaboration Source: own elaboration Table 8 reports on the values of reduced mortality (€397-€1,192 million via VSL and €107- €363 million via VLYL). Table 9 reports on the values of reduced morbidity (€97-€124 million). Table 8 reports on the values of reduced mortality (€397-€1,192 million via VSL and €107-€363 million via VLYL). These figures are based on calculations obtained relative to the WHO guideline values. The Table 9 reports on the values of reduced morbidity (€97-€124 million). These figures are based on calculations estimates are similar to using the zero-pollution guideline value for mortality. For morbidity the obtained relative to the WHO guideline values. The estimates are similar to using the zero-pollution guideline value estimates are about 8% higher using a zero-pollution guideline value. for mortality. For morbidity the estimates are about 8% higher using a zero-pollution guideline value. Table 8: Value of Losses from Premature Mortality Avoided Through Implementation of NAPCP (Euros Million) Valuations Via VSL. Valuations Via VLYL District Lower Bound Mean Upper Bound Legislation Median Mean Total 182.8 365.6 548.4 49.2 72.4 167.0 Bánovce nad Bebravou 1.6 3.3 4.9 0.5 0.6 1.5 Banská Bystrica 6.8 13.5 20.3 1.8 2.7 6.2 Banská Štiavnica 0.6 1.3 1.9 0.2 0.3 0.6 Bardejov 1.6 3.2 4.9 0.4 0.6 1.5 Bratislava 1.8 3.6 5.4 0.5 0.7 1.7 Brezno 3.5 7.1 28 10.7 1.0 1.4 3.3 Bytča 1.3 2.6 3.9 0.3 0.5 1.2 Čadca 3.7 7.4 11.1 1.0 1.5 3.4 Detva 1.6 3.1 4.7 0.4 0.6 1.4 Final Report Table 8: Valuations Via VSL. Valuations Via VLYL Value of Losses from Lower Upper District Bound Mean Bound Legislation Median Mean Premature Mortality Avoided Through Total 182.8 365.6 548.4 49.2 72.4 167.0 Implementation Bánovce nad Bebravou 1.6 3.3 4.9 0.5 0.6 1.5 of NAPCP Banská Bystrica 6.8 13.5 20.3 1.8 2.7 6.2 (Euros Million) Banská Štiavnica 0.6 1.3 1.9 0.2 0.3 0.6 Bardejov 1.6 3.2 4.9 0.4 0.6 1.5 Bratislava 1.8 3.6 5.4 0.5 0.7 1.7 Brezno 3.5 7.1 10.7 1.0 1.4 3.3 Bytča 1.3 2.6 3.9 0.3 0.5 1.2 Čadca 3.7 7.4 11.1 1.0 1.5 3.4 Detva 1.6 3.1 4.7 0.4 0.6 1.4 Dolný Kubín 1.7 3.3 4.9 0.5 0.6 1.5 Dunajská Streda 2.6 5.2 7.9 0.7 1.1 2.4 Galanta 2.9 5.9 8.8 0.8 1.2 2.7 Gelnica 1.0 2.0 3.0 0.3 0.4 0.9 Hlohovec 1.5 3.0 4.5 0.4 0.6 1.4 Humenné 1.5 3.0 4.6 0.4 0.6 1.4 Ilava 2.1 4.2 6.3 0.6 0.8 1.9 Kežmarok 1.5 2.9 4.4 0.4 0.6 1.3 Komárno 2.8 5.7 8.5 0.8 1.1 2.6 Košice 3.0 6.0 9.0 0.8 1.2 2.7 Košice - okolie 3.9 7.8 11.7 1.1 1.6 3.6 Krupina 1.0 2.1 3.1 0.3 0.4 0.9 Kysucké Nové Mesto 2.0 4.0 5.9 0.6 0.8 1.8 Levice 5.4 10.8 16.2 1.5 2.2 5.0 Levoča 1.0 2.0 3.0 0.3 0.4 0.9 Liptovský Mikuláš 4.7 9.5 14.2 1.3 1.9 4.3 Lučenec 4.6 9.2 13.8 1.2 1.8 4.2 Malacky 1.1 2.1 3.2 0.3 0.4 1.0 Martin 5.2 10.4 15.6 1.4 2.1 4.7 Medzilaborce 0.3 0.6 0.8 0.1 0.1 0.2 Michalovce 2.2 4.4 6.5 0.6 0.9 2.0 Myjava 0.7 1.5 2.2 0.2 0.3 0.7 Námestovo 1.9 3.8 5.7 0.5 0.7 1.7 Nitra 6.3 12.7 19.0 1.7 2.5 5.8 Nové Mesto nad Váhom 1.9 3.8 5.7 0.5 0.7 1.7 Nové Zámky 5.6 11.2 16.9 1.5 2.2 5.2 Partizánske 2.2 4.4 6.6 0.6 0.9 2.0 Pezinok 1.2 2.4 3.7 0.3 0.5 1.1 Piešťany 2.3 4.5 6.7 0.6 0.9 2.0 Poltár 1.3 2.6 4.0 0.4 0.5 1.2 Poprad 2.7 5.4 8.1 0.7 1.1 2.4 Považská Bystrica 2.7 5.4 8.1 0.7 1.1 2.4 Prešov 5.3 10.7 16.0 1.4 2.1 4.9 Prievidza 6.8 13.6 20.4 1.8 2.7 6.2 Púchov 2.0 4.0 6.0 0.6 0.8 1.8 Revúca 2.3 4.7 7.1 0.6 0.9 2.2 29 Final Report Valuations Via VSL. Valuations Via VLYL Lower Upper District Mean Legislation Median Mean Bound Bound Total 182.8 365.6 548.4 49.2 72.4 167.0 Rimavská Sobota 4.6 9.2 13.8 1.2 1.8 4.2 Rožňava 3.6 7.1 10.7 1.0 1.4 3.3 Ružomberok 5.2 10.5 15.7 1.4 2.1 4.8 Sabinov 1.5 2.9 4.4 0.4 0.6 1.3 Senec 1.6 3.2 4.8 0.4 0.6 1.5 Senica 1.2 2.3 3.5 0.3 0.5 1.1 Skalica 0.8 1.5 2.3 0.2 0.3 0.7 Snina 0.7 1.4 2.2 0.2 0.3 0.6 Sobrance 0.3 0.6 1.0 0.1 0.1 0.3 Spišská Nová Ves 3.0 6.1 9.1 0.8 1.2 2.8 Stará Ľubovňa 1.0 1.9 2.9 0.3 0.4 0.9 Stropkov 0.5 0.9 1.4 0.1 0.2 0.4 Svidník 0.7 1.4 2.1 0.2 0.3 0.6 Šaľa 1.7 3.4 5.1 0.5 0.7 1.6 Topoľčany 2.8 5.7 8.5 0.8 1.1 2.6 Trebišov 2.1 4.2 6.3 0.6 0.8 1.9 Trenčín 3.5 7.0 10.6 1.0 1.4 3.2 Trnava 3.5 7.0 10.5 0.9 1.4 3.2 Turčianske Teplice 0.9 1.7 2.6 0.2 0.4 0.8 Tvrdošín 1.1 2.2 3.3 0.3 0.4 1.0 Veľký Krtíš 2.1 4.2 6.3 0.6 0.8 1.9 Vranov nad Topľou 1.9 3.9 5.8 0.5 0.8 1.8 Zlaté Moravce 2.1 4.1 6.2 0.6 0.8 1.9 Zvolen 3.7 7.4 11.1 1.0 1.5 3.4 Žarnovica 1.3 2.6 3.9 0.4 0.5 1.2 Žiar nad Hronom 2.2 4.4 6.5 0.6 0.9 2.0 Žilina 9.4 18.8 28.2 2.5 3.7 8.6 30 Final Report Table 9: Value of Losses from Morbidity Avoided Through Implementation of NAPCP (Euros Million) WHO guideline value Zero pollution guideline value Chronic Chronic Chronic Chronic Chronic Chronic RADs Workdays RADs Workdays District Bronchitis Bronchitis Bronchitis Bronchitis Bronchitis Bronchitis (mean) Lost (mean) (mean) Lost (mean) (low) (mean) (high) (low) (mean) (high) Total 29.85 3.77 11.33 17.94 23.60 29.85 3.77 15.02 23.78 31.29 Bánovce nad 0.22 0.03 0.12 0.19 0.25 0.22 0.03 0.12 0.19 0.25 Bebravou Banská Bystrica 1.07 0.14 0.40 0.63 0.82 1.07 0.14 0.54 0.86 1.13 Banská 0.08 0.01 - - - 0.08 0.01 0.05 0.07 0.10 Štiavnica Bardejov 0.28 0.03 - - - 0.28 0.03 0.14 0.23 0.30 Bratislava 2.19 0.28 1.14 1.80 2.36 2.19 0.28 1.14 1.80 2.36 Brezno 0.45 0.06 - - - 0.45 0.06 0.23 0.37 0.48 Bytča 0.18 0.02 0.02 0.03 0.05 0.18 0.02 0.09 0.15 0.19 Čadca 0.53 0.07 - - - 0.53 0.07 0.28 0.43 0.57 Detva 0.20 0.03 - - - 0.20 0.03 0.11 0.18 0.23 Dolný Kubín 0.29 0.04 - - - 0.29 0.04 0.14 0.23 0.29 Dunajská 0.38 0.05 0.20 0.32 0.41 0.38 0.05 0.20 0.32 0.41 Streda Galanta 0.40 0.05 0.21 0.33 0.43 0.40 0.05 0.21 0.33 0.43 Gelnica 0.14 0.02 - - - 0.14 0.02 0.07 0.12 0.15 Hlohovec 0.21 0.03 0.11 0.17 0.23 0.21 0.03 0.11 0.17 0.23 Humenné 0.23 0.03 0.12 0.18 0.24 0.23 0.03 0.12 0.18 0.24 Ilava 0.29 0.04 0.16 0.26 0.34 0.29 0.04 0.16 0.26 0.34 Kežmarok 0.33 0.04 - - - 0.33 0.04 0.15 0.23 0.30 Komárno 0.34 0.04 0.17 0.28 0.36 0.34 0.04 0.17 0.28 0.36 Košice 1.67 0.21 0.82 1.31 1.72 1.67 0.21 0.82 1.31 1.72 Košice - okolie 0.67 0.08 0.31 0.49 0.65 0.67 0.08 0.31 0.49 0.65 Krupina 0.12 0.01 0.05 0.07 0.09 0.12 0.01 0.06 0.10 0.14 Kysucké Nové 0.27 0.03 0.00 0.00 0.00 0.27 0.03 0.13 0.20 0.27 Mesto Levice 0.66 0.08 0.34 0.54 0.71 0.66 0.08 0.34 0.54 0.71 Levoča 0.18 0.02 - - - 0.18 0.02 0.09 0.14 0.18 Liptovský 0.67 0.09 - - - 0.67 0.09 0.31 0.50 0.65 Mikuláš Lučenec 0.59 0.07 0.29 0.46 0.61 0.59 0.07 0.29 0.46 0.61 Malacky 0.16 0.02 0.08 0.13 0.17 0.16 0.02 0.08 0.13 0.17 Martin 0.78 0.10 0.38 0.60 0.79 0.78 0.10 0.38 0.60 0.79 Medzilaborce 0.03 0.00 - - - 0.03 0.00 0.02 0.03 0.04 Michalovce 0.32 0.04 0.16 0.24 0.32 0.32 0.04 0.16 0.24 0.32 Myjava 0.09 0.01 - - - 0.09 0.01 0.05 0.08 0.11 Námestovo 0.41 0.05 - - - 0.41 0.05 0.18 0.29 0.38 Nitra 0.91 0.12 0.48 0.76 1.00 0.91 0.12 0.48 0.76 1.00 Nové Mesto 0.24 0.03 0.13 0.21 0.28 0.24 0.03 0.13 0.21 0.28 nad Váhom Nové Zámky 0.65 0.08 0.34 0.54 0.70 0.65 0.08 0.34 0.54 0.70 Partizánske 0.31 0.04 0.16 0.26 0.34 0.31 0.04 0.16 0.26 0.34 Pezinok 0.22 0.03 0.11 0.17 0.23 0.22 0.03 0.11 0.17 0.23 Piešťany 0.29 0.04 0.15 0.24 0.32 0.29 0.04 0.15 0.24 0.32 31 Final Report WHO guideline value Zero pollution guideline value Chronic Chronic Chronic Chronic Chronic Chronic RADs Workdays RADs Workdays District Bronchitis Bronchitis Bronchitis Bronchitis Bronchitis Bronchitis (mean) Lost (mean) (mean) Lost (mean) (low) (mean) (high) (low) (mean) (high) Total 29.85 3.77 11.33 17.94 23.60 29.85 3.77 15.02 23.78 31.29 Poltár 0.16 0.02 0.04 0.06 0.08 0.16 0.02 0.08 0.13 0.17 Poprad 0.48 0.06 - - - 0.48 0.06 0.24 0.38 0.50 Považská 0.39 0.05 0.08 0.12 0.16 0.39 0.05 0.21 0.33 0.44 Bystrica Prešov 0.97 0.12 0.47 0.74 0.98 0.97 0.12 0.47 0.74 0.98 Prievidza 0.97 0.12 0.50 0.79 1.04 0.97 0.12 0.52 0.81 1.07 Púchov 0.28 0.04 0.14 0.23 0.29 0.28 0.04 0.15 0.23 0.31 Revúca 0.31 0.04 0.15 0.23 0.31 0.31 0.04 0.15 0.23 0.31 Rimavská 0.61 0.07 0.29 0.46 0.61 0.61 0.07 0.29 0.46 0.61 Sobota Rožňava 0.47 0.06 0.13 0.21 0.28 0.47 0.06 0.23 0.37 0.48 Ružomberok 0.68 0.09 0.30 0.48 0.63 0.68 0.09 0.30 0.48 0.63 Sabinov 0.28 0.03 0.02 0.04 0.05 0.28 0.03 0.13 0.21 0.28 Senec 0.34 0.04 0.16 0.26 0.34 0.34 0.04 0.16 0.26 0.34 Senica 0.16 0.02 0.09 0.14 0.18 0.16 0.02 0.09 0.14 0.18 Skalica 0.11 0.01 0.06 0.09 0.12 0.11 0.01 0.06 0.09 0.12 Snina 0.09 0.01 0.05 0.08 0.10 0.09 0.01 0.05 0.08 0.10 Sobrance 0.04 0.00 0.02 0.03 0.04 0.04 0.00 0.02 0.03 0.04 Spišská Nová 0.58 0.07 - - - 0.58 0.07 0.28 0.45 0.58 Ves Stará Ľubovňa 0.20 0.02 - - - 0.20 0.02 0.10 0.16 0.21 Stropkov 0.07 0.01 0.04 0.06 0.07 0.07 0.01 0.04 0.06 0.07 Svidník 0.11 0.01 0.06 0.09 0.12 0.11 0.01 0.06 0.09 0.12 Šaľa 0.23 0.03 0.12 0.18 0.24 0.23 0.03 0.12 0.18 0.24 Topoľčany 0.37 0.05 0.20 0.32 0.42 0.37 0.05 0.20 0.32 0.42 Trebišov 0.29 0.04 0.14 0.22 0.29 0.29 0.04 0.14 0.22 0.29 Trenčín 0.52 0.06 0.30 0.48 0.63 0.52 0.06 0.30 0.48 0.63 Trnava 0.53 0.07 0.28 0.44 0.58 0.53 0.07 0.28 0.44 0.58 Turčianske 0.10 0.01 - - - 0.10 0.01 0.06 0.08 0.11 Teplice Tvrdošín 0.22 0.03 - - - 0.22 0.03 0.11 0.17 0.22 Veľký Krtíš 0.26 0.03 0.14 0.22 0.29 0.26 0.03 0.14 0.22 0.29 Vranov nad 0.32 0.04 0.15 0.23 0.31 0.32 0.04 0.15 0.23 0.31 Topľou Zlaté Moravce 0.26 0.03 0.12 0.20 0.26 0.26 0.03 0.14 0.22 0.29 Zvolen 0.55 0.07 0.22 0.34 0.45 0.55 0.07 0.29 0.45 0.59 Žarnovica 0.16 0.02 - - - 0.16 0.02 0.09 0.14 0.18 Žiar nad 0.29 0.04 - - - 0.29 0.04 0.15 0.24 0.32 Hronom Žilina 1.38 0.17 0.62 0.98 1.30 1.38 0.17 0.62 0.98 1.30 32 Final Report VI. Cost benefit analysis of the NAPCP The health benefits of NAPCP can be compared with the costs of implementing the NAPCP through a conventional cost benefit analysis. Ideally, each component of the NAPCP would be evaluated separately to determine whether the benefits it provided exceeded the costs. This was not possible as data on marginal changes in concentrations were not available by measure; but only for the whole NAPCP. Future work on individual elements of the strategy is recommended. The analysis calculates the present value of benefits (PVB) as well as the present value of costs (PVC). PVB is a measure of the sum of benefits received each year from the NAPCP, but with future benefits discounted using an agreed discount rate. Similarly, PVC is the sum of the costs incurred each year to implement the Programme, but with future costs discounted. The choice of the discount rate is explained further below. The difference between the two (PVB-PVC) is the net present value of the NAPCP (NPV). An alternative is the benefit to cost ratio BCR = PVB/PVC. An NPV > 0 or a BCR > 1 is generally considered necessary to justify a program. When funds are limited, governments might ask for a BCR considerably greater than 120. In order to derive the NPV estimates, future costs and benefits are discounted before adding them up to obtain the aggregate figure. The choice of the discount rate is elaborated further below. In this cost benefit analysis, the cost component for the analysis is considered first, followed by the benefit component, before bringing the two together to calculate the NPV and BCR values for the ranges of cost and benefit estimates. Costs of the NAPCP There are two different concepts of cost against which the BCR can be estimated: the eco- nomic cost and the financial cost. The economic cost measures in monetary terms the value of scarce resources used while implementing the project. Where measures involve the use of real resources the full cost of these resources is included, but where measures involve a shift of funds from one agent to another, only the real loss associated with the shift is considered. The financial cost measures monetary flows required to implement the program. In this study we conduct the analysis with respect to both, the economic cost as well as the financial cost, namely the monetary flows required from government sources for the program implementa- tion. This interpretation of the financial cost is also referred to as the fiscal cost of the program. For each component of the program the cost is given with an explanation of the method used to calculate it. For more details on cost benefit analysis for public projects and programmes see: HM Treasury (2018): The Green 20 Book. UK Government: London. A European Commission publication that covers similar material but in a more specific context is: EC Directorate-General for Regional and Urban Policy (2014): Guide to Cost-Benefit Analysis / of Investment Projects Economic appraisal tool for Cohesion Policy 2014-2020: EC: Brussels. Available at: https:/ ec.europa.eu/regional_policy/sources/docgener/studies/pdf/cba_guide.pdf. The guide has been adapted for the /www.minzp.sk/files/iep/cba_metodika.pdf Slovak context by the IEP in 2019, available in Slovak at: https:/ 33 Final Report The costs of the program are incurred over the period 2020 to 2030 (11 years) and are account- ed for in annual terms. Transport: Replacement of Old Diesel Vehicles. The fiscal cost of the program was estimated at €14 million in 2019, made up of a state subsidy of around €33 million, offset by value-added tax (VAT) recovered from the additional sales of €45 and other fees of €1.5 million. The eco- nomic cost, however, is different. Taxes and subsidies are transfers between the government welfare from the reallocation of resources. Figure 1 below representing the demand curve for the product shows and other agents in the economy and do not use up valuable resources. The only economic this loss. The subsidy S lowers the price to the consumer who has a gain in welfare equal to the shaded blue cost occurs because the subsidy results in an inefficient use of funds, whereby there is a loss triangle from an addition of Q1 – Q0 of of welfare from the reallocation new cars. The total subsidy, however, is the rectangle made up of the blue resources. Figure 1 below representing the demand curve triangle for theand the red product one. The shows net cost this loss. Theis therefore subsidy equal to S lowers thethe red to price triangle, which is who the consumer half the hasdirect a gain subsidy if the in welfare equal to the shaded blue triangle from an addition of Q1 – Q0 new cars. The total In that case demand curve is linear. It is also referred to in the literature as the deadweight loss from the subsidy. the economic subsidy, cost amounts however, to €16.5 million. is the rectangle made up Bot h the of fiscal and blue economic triangle andcosts areone. the red a one-off The netitem at the cost is start of the program. therefore equal to the red triangle, which is half the direct subsidy if the demand curve is linear. It is also referred to in the literature as the deadweight loss from the subsidy. In that case the Transport: economic Plug-in Hybrid Electric cost amounts Vehicles. to €16.5 million.This was Both a small fiscal andprogram economic with a €5 are costs million subsidy. a one-off Theat item fiscal cost was estimated the start €5 atof million the and the economic cost, based on the analysis presented above, is about €2.5 million. program. Transport: Plug-in Hybrid Electric Vehicles. This was a small program with a €5 million subsidy. The fiscal cost was estimated at €5 million and the economic cost, based on the analysis presented above, is about €2.5 million. Figure: Economic Cost of a Subsidy Transport: Control of NOx Emissions from Cars. An annual fiscal cost of €1.6 million is incurred. This is also an economic cost as it represents real resources used for monitoring and control. Figure: Economic Cost of a Subsidy Transport: More Frequent Control of Emissions from Old Cars. This case is similar to the previous one, with fiscal and economic costs being the same. The estimate is €6.25 million annually over Transport: Control of NOx the implementation Emissions from Cars. An annual fiscal cost of €1.6 million is incurred. This is also an period. economic cost as it represents real resources used for monitoring and control. Transport: Roadside Emissions Controls. The same applies here with annual costs of €0.16 million. Transport: More Frequent Control of Emissions from Old Cars. This case is similar to the previous one, with fiscal and economicHeating: Residential costs being the same. Subsidies The for Old estimate Boilers. isfiscal The million €6.25cost annually of the overis program the implementation €27 million in theperiod. first year, followed by €54 million in year 4 and 7 and finally €27 million in year 10. In this case the economic Transport: is taken as costEmissions Roadside the same, Controls . Thebased same on the assumption applies that costs here with annual the replacement does of €0.16 million. not provide any additional benefit to the households, for which they would be willing to pay. be an underestimate This mayHeating: Residential Subsidies for OldofBoilers their personal benefit . The fiscal cost of as thethe new boilers program are cleaner is €27 million and in the first year, followed probably easier to use. Without further information, however, it was not possible to estimate by €54 million in year 4 and 7 and finally €27 million in year 10. In this case the economic cost is taken as the same, the value based on the of such benefits. assumption that the replacement does not provide any additional benefit to the households, for which they would be willing to pay. This may be an underestimate of their personal benefit as the new boilers are cleaner and probably easier to use. Without further information, however, it was not possible to estimate the value of such benefits. 34 Residential Heating: Differential Fees for Boilers. In this case the fiscal cost is the gain of revenue by increasing the fee payable to the government on purchase of conventional boilers. The economic cost, however, is less and similar Final Report Residential Heating: Differential Fees for Boilers. In this case the fiscal cost is the gain of revenue by increasing the fee payable to the government on purchase of conventional boilers. The eco- nomic cost, however, is less and similar to the estimate shown in the figure on subsidy, except that in this case there is a tax, also resulting in a deadweight loss. This was calculated in the 2019 analysis of the program as €2.6 million. Residential Heating: Connecting Homes to Gas. The fiscal cost of this program over the period 2020-2030 is €459 million. For the same reasons as given in the case of subsidies for old boilers, this was also taken as the economic cost. Again, there may be some benefit for the conversion to gas that some households may derive, but it was not possible to estimate those. Fuel Standards for Wood Moisture. This program with a cost of €0.1 million a year is the fiscal cost. In addition, there is an increase in the cost of wood to the consumers estimated at €1.04 million a year. Awareness Program for Fossil Fuel Stoves. The program has a fiscal cost of €0.3 million, which is also an economic cost of the resources used in implementing it. Tax Harmonization for Petrol and Diesel. There is a big difference between the fiscal and economic costs of this program. The former is highly negative, with a €552 million gain for the govern- ment. Most of this, of course, is simply a transfer from citizens to the government and is not an economic gain. The latter, calculated in the 2019 analysis of the NAPCP, was estimated at around €1 million a year initially, rising to €75 million by the end of the period. Support for Medium-sized Farms to Adopt NH3 Controls. The economic cost of the program in re- source terms is estimated at €0.49 million a year, with the government picking up €0.39 million (i.e. 80%). Thus, the economic cost is €0.1 million more than the fiscal cost. Residential Heating: Insulation Program and District Heating Connection Programme. These two pro- grams were added to the NAPCP to bring PM2.5 emissions closer to the target level by 2030. The fiscal costs are €154 million (insulation program) and €262 million (DH connection program). For the DH program the fiscal costs are also the economic costs. There is an increase in operat- ing costs, which are included in the fiscal cost figure. For the insulation program the fiscal cost is 72% of the total investment cost, so households bear 28%, leading to a total cost of €214 million. On the other hand, households benefit from the program in form of lower energy bills. Taking account of these reductions decreases the economic cost to 70.5 million. Tables 10 and 11 give the fiscal and economic costs of the NAPCP from 2020 to 2030. At €398 million in NPV terms (with a 5% discount rate) the fiscal cost is considerably lower than the eco- nomic cost, estimated at €1,125 million. The main reason for the lower fiscal costs is the gain in revenue from the tax on diesel, which lowers the fiscal but not the economic cost. Benefits of the NAPCP The gains in benefits from the NAPCP have so far been calculated based on the difference in concentrations of key pollutants in 2020 and 2030 with the NAPCP. However, the benefits from the NAPCP will arise not only in 2030 but in earlier years as well, as pollutant emissions are continuously reduced by NAPCP measures. We may also expect some benefits after 2030 as the NAPCP will lower concentrations from where they would have been in the absence of the NAPCP. On the other hand, the 2019 analysis of emissions under NAPCP and without NAPCP showed a decline in emissions of key pollutants even without NAPCP. In other words, emissions are expected to decline in the base case scenario with no NAPCP, but will decline stronger with the NAPCP. 35 Final Report In order to capture this complex situation, we have used the emission profiles for PM2.5, the only pollutant that affects health, and attributed a percent of the benefits calculated in the comparison between 2020 and 2030 to each year. Furthermore, we have allowed for the base case decline in emissions between 2020 and 2030, so not all benefits for 2030 reported in the previous section are attributed to the NAPCP. Details of emissions are given in the Annex II. The adjustments made are as follows: 1. Reduction in PM2.5 emissions between 2020 and 2030: 6,564 MT 2. Reduction in emissions due to NAPCP: 2,921 MT 3. Percent of gain in benefits in 2030 due to NAPCP: 44.5% For the annual benefits between 2020 and 2030 the percent reduction of the 2030 level was taken to estimate the benefits for each year. The NAPCP-based reductions by year as a percent of the 2030 reduction are given in Table 12. Finally, there is the question of what benefits might remain after 2030. It is reasonable to assume there will be some, as the base case without NAPCP cannot be expected to converge automatically to the NAPCP level of concentration. However, it is difficult to estimate the gap precisely. As an approximation, a sensitivity calculation has been made in the case of economic costs, assuming the gap in 2030 between concentrations under the base case and the NAPCP remains for another ten years. In this case the annual costs of the NAPCP for the period 2031-2040 are estimated as being the same as the maintenance costs for 2030 for each of the programs where such costs are incurred. The benefits profile depends on which valuation of mortality and morbidity are taken from the range estimates in Tables 8 and 9. Tables 13 and 14 summarize estimated benefits covering this range on NPV terms, using a 5% discount rate. Table 13 illustrates the estimates based on VSL and Table 14 the estimates based on VLYL. The range of benefits with VSL mortality valuation and counting benefits only to 2030 is €1.2 billion to €3.2 billion, i.e. with a variation of +/- 45% around the mean. With a VLYL valuation for the same time period the range is €504 million to €1,240 million, the upper bound being 87% greater than the median value and the lower bound, based on Slovak legal data, being about 24% less than the median value. Overall, the VSL approach gives estimates that are approximately 2.5 higher than those from the VLYL approach. Extending the analysis to 2040 on the basis suggested above increases the value of the bene- fits by a factor of about 80%, but this has to be considered speculative. 36 Final Report Table 10: Fiscal Costs of the NAPCP (negative values represent net fiscal revenue) Measure/Year NPV @ 6% 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Replacement of old diesel   -14,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 vehicles Subsidy for PHEV vehicles   5,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Control of NOx emissions   1,60 1,60 1,60 1,60 1,60 1,60 1,60 1,60 1,60 1,60 1,60 from cars More frequent control of 6,25 6,25 6,25 6,25 6,25 6,25 6,25 6,25 6,25 6,25 6,25 emissions from old cars Roadside emissions controls   0,16 0,16 0,16 0,16 0,16 0,16 0,16 0,16 0,16 0,16 0,16 Subsidies for replacement of   27,00 0,00 0,00 54,00 0,00 0,00 54,00 0,00 0,00 54,00 0,00 old boilers (with supplement) Differentiated fees for boilers   6,9 2,59 2,59 2,59 2,59 2,59 2,59 2,59 2,59 2,59 2,59 Connecting homes to gas   20,80 23,40 52,33 54,93 57,53 60,13 62,73 65,33 20,80 20,80 20,80 (With supplement) Fuel standards for wood   0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 moisture Awareness programs for   0,30 0,30 0,30 0,30 0,30 0,30 0,30 0,30 0,30 0,30 0,30 fossil stove users Tax harmonisation for petrol   -43,97 -86,07 -126,45 -165,21 -202,45 -161,12 -86,93 -12,74 61,44 135,63 135,63 and diesel Support for medium-sized   0,39 0,39 0,39 0,39 0,39 0,39 0,39 0,39 0,39 0,39 0,39 farms to adopt NH3 controls Insulation Program   30,81 0,00 30,81 0,00 30,81 0,00 30,81 0,00 30,81 0,00 0,00 DH Connection Program   10,97 16,15 21,33 26,52 31,70 25,92 25,92 25,92 25,92 25,92 25,92 Total € 422,94 52,30 -35,13 -10,59 -18,37 -71,02 -63,68 97,92 89,90 150,36 247,74 193,74 Source: World Bank and Ministry of Environment (2019) Report Table 11: Economic Costs of the NAPCP (Euros Million) Measure/Year NPV @ 5% 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Replacement of old diesel   16,48 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 vehicles Subsidy for PHEV vehicles   2,50 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 Control of NOx emissions   1,60 1,60 1,60 1,60 1,60 1,60 1,60 1,60 1,60 1,60 1,60 from cars More frequent control of 6,25 6,25 6,25 6,25 6,25 6,25 6,25 6,25 6,25 6,25 6,25 emissions from old cars Roadside emissions   0,16 0,16 0,16 0,16 0,16 0,16 0,16 0,16 0,16 0,16 0,16 controls Subsidies for replacement of old boilers (with   27,00 0,00 0,00 54,00 0,00 0,00 54,00 0,00 0,00 54,00 0,00 supplement) Differentiated fees for   2,59 2,59 2,59 2,59 2,59 2,59 2,59 2,59 2,59 2,59 2,59 boilers Connecting homes to gas   20,80 23,40 52,33 54,93 57,53 60,13 62,73 65,33 20,80 20,80 20,80 (With supplement) Fuel standards for wood   1,14 1,14 1,14 1,14 1,14 1,14 1,14 1,14 1,14 1,14 1,14 moisture Awareness programs for   0,30 0,30 0,30 0,30 0,30 0,30 0,30 0,30 0,30 0,30 0,30 fossil stove users 37 Final Report Measure/Year NPV @ 5% 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Tax harmonisation for   1,00 3,85 8,40 14,40 21,85 32,55 43,25 53,95 64,70 75,40 75,40 petrol and diesel Support for medium- sized farms to adopt NH3   0,49 0,49 0,48 0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,46 controls Insulation Program   13,90 0,00 13,90 0,00 13,90 0,00 13,90 0,00 13,90 0,00 0,00 DH Connection Program   10,97 16,15 21,33 26,52 31,70 25,92 25,92 25,92 25,92 25,92 25,92 Total € 1 124,70 105,16 55,92 108,48 162,36 137,49 131,11 212,31 157,71 137,82 188,62 134,62 Source: See Text Table 12: 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Estimated cumulated percent of 2030 Reduction 29% 36% 42% 59% 65% 71% 87% 92% 93% 100% 100% reduction in PM2.5 concentrations in Years 2020 to 2030 Table 13: Lower Bound Middle Case Upper Bound Benefits from NAPCP over the period Mortality Morbidity Total Mortality Morbidity Total Mortality Morbidity Total 2020-2030 with VSL Benefits 978 240 1,218 1,333 320 2,365 2,933 347 3,280 mortality valuation to 2030 (Euros Million) Benefits 1,776 437 2,213 3,552 501 4,053 5,328 630 5,959 to 2040 Notes: 1. The lower, middle and upper bound of mortality values are as explained in the VSL valuation section. 2. The morbidity values for the lower, middle and upper bounds are derived for the range for chronic mortality. 3. Estimates are derived from Tables 8 and 9 using the WHO Guidelines benchmarks. The other benchmark makes very little difference. Table 14: Lower Bound Middle Case Upper Bound Benefits from NAPCP over the period Mortality Morbidity Total Mortality Morbidity Total Mortality Morbidity Total 2020-2030 with VLYL Benefits 263 240 504 387 276 663 893 347 1,240 mortality valuation to 2030 (Euros Million) Benefits 478 436 915 703 501 1,204 1,622 631 2,253 to 2040 Notes: 1. The lower, middle and upper bound of mortality values are, respectively the legal VLYL, the median VLYL and the mean VLYL. 2. The morbidity values for the lower, middle and upper bounds are derived for the range for chronic mortality. 3. Estimates are derived from Tables 8 and 9 using the WHO Guidelines benchmarks. The other benchmark makes very little difference. 38 Final Report Combining benefits and costs of the NAPCP As explained earlier the benefit cost analysis compares the costs against the benefits. In this case, we make one comparison based on the fiscal costs and another based on the economic costs. The benefits are taken as the same in both cases; they consist of the health gains from the reduced concentrations of air pollutants measured in monetary terms. They only represent financial flows in the case of reduced morbidity expenditures, but the mortality benefits are not measured on a financial basis. Hence to that extent, while the analysis based on economic costs is a full economic cost benefit analysis, the one based on fiscal costs is a hybrid, with the costs being net outlays by the public sector and the benefits being full economic benefits. In undertaking the present value calculations, it is necessary to discount future costs and ben- efits at an agreed rate. For this purpose, we have used the EC guidance values for financial and economic cost benefit analysis in cohesion countries, which include Slovakia (see footnote 20). They recommend a discount rate of 4% for financial cost benefit analysis and 5% for economic cost benefit analysis. The summary statistics for each evaluation given here are the NPV, which is the discounted value of the stream of benefits minus the costs of the NAPCP and the BCR, which is the present 2026 2027 2028 value of the 2029by the present benefits divided 2030 value of the costs. 2032 2031 2033 2034 2035 2036 97,92 89,9 150,36 247,74 193,74 193,74 193,74 193,74 193,74 193,74 193,74 219,8 The time profiles of the economic and fiscal costs are shown in Figure 2, along with the134,62 157,71 145,32 188,62 134,62 134,62 134,62 134,62 ben- 134,62 134,62 05,95 325,34 VSL (mean354,03 328,85 efits under 353,59 value) and under 353,59value). 353,59 VLYL (median 353,59that the It should be noted 353,59 pro- 353,59 353,59 39,79 148,65 files from150,25 2030 to 2040161,76 161,56 are estimates 161,56 based on 161,56 a continuation 161,56 of the NAPCP 161,56 beyond 2030. 161,56 161,56 As explained above this is not certain, nor are the details fully determined. The figures show fiscal costs that are well below economic costs initially, even going negative (as tax receipts exceed outlays) but after 2028 they rise above the economic costs. The benefits under VSL are always above both costs but under VLYL they are always below the economic costs and below the fiscal costs after 2027. 400 350 300 250 Figure 2: 200 Time Profile of Costs 150 and Benefits of the NAPCP 100 Source: own elaboration 50 0 -50 -100 Fiscal Costs Economic Costs Benefits VSL Benefits VLYL 39 Final Report Economic Cost Benefit Indicators Table 15 gives the NPV and the BCR for the benefits relative to the economic costs of the NAPCP. Table 15: Benefit to Cost Ratios to 2030 NPVs to 2030 Euros Million NPV and BCR for the Lower Upper Lower Upper NAPCP Based With VSL Mean With VSL Mean Bound Bound Bound Bound on Economic Costs   1.06 1.93 2.84   63.90 1,076.85 2,065.26 Legal Legal With VLYL Median Mean With VLYL Median Mean Value Value   0.44 0.57 1.07   -650.65 -491.52 -0.25 The results indicate the following: a. Under a VSL valuation of premature mortality the NAPCP has a BCR greater than one for the whole range of VSL values. Correspondingly the NPV is positive. This holds for the estimation of benefit to 2030; for an extension to 2040 the BCR rises by about 20%. b. Under a VLYL valuation the BCR exceeds one only if the mean value of the VLYL is taken, with the NPV being positive only in that case. Under the value that is set by the Slovak legislation to estimate the cost effectiveness of new medications, the ratio is only 0.44 and under a median value it is 0.57. This means that the benefits of the whole period 2021 to 2030 only represent 44% and 57% of the costs respectively. Extending the anal- ysis to 2040 means bringing in the benefits of the NAPCP after 2030. Doing that raises the BCR, so where in Table 15 the BCR is 0.57 (median with VLYL) it goes up to 0.67 – i.e. it rises by about 18%. However, even with this extension the BCR still only exceeds one with the mean value of VLYL. Further sensitivity analysis can be carried out using the range of physical health impacts. As stated in Section III, the 95 % CI for the range of impacts is approximately +/-27%. Applying this range to the BCRs leads to Figures 3 below for the VSL valuation and the VLYL valuation. These figures, which are for the benefits to 2030 only, show that allowing for the uncertainty in physical impacts keeps the BCR above one for all VSL cases, except for the combination of the low VSL value and the lower bound physical impact. Under the VLYL, however, the BCR only exceeds one with high VLYL and under physical impacts at or above the mean. Figure 3: BCR Ranges Under BCR Under Range for Physical Impacts (VSL) Different Health Valuation Metrics Source: own elaboration High BCR Range Under Mean 95% CI Physical Impacts with VSL Values Low 0 0,5 1 1,5 2 2,5 3 3,5 4 UB Physical Mean Physical LB Physical 40 UB Physical Mean Physical LB Physical Final Report BCR Under Range for Physical Impacts (VLYL) High BCR Range Under 95% CI Physical Mean Impacts with VLYL Values Low 0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 UB Physical Mean Physical LB Physical Fiscal Cost Benefit Indicators The cost benefit analysis based on fiscal costs is reported in Table 16. As the data on fiscal costs is highly uncertain beyond 2030, no sensitivity analysis for the period is carried out extending the estimation beyond 2030. Table 16: Benefit to Cost Ratios to 2030 NPVs to 2030 Euros Mn. NPV and BCR Lower Upper Lower Upper for the NAPCP Based With VSL Mean With VSL Mean Bound Bound Bound Bound on Fiscal Costs   3.07 5.63 8.27   876.26 1,956.50 2,969.48 Legal Legal With VLYL Median Mean With VLYL Median Mean Value Value   1.27 1.67 3.13   114.23 283.93 765.58 The fiscal costs are considerably less than the economic costs. Tables 10 and 11 shows that the fiscal costs are €398 billion while the economic costs are €1,124 billion, or 2.8 times as high. Since the benefits are the same the NAPCP has a higher BCR when judged under these costs. As the table shows, the BCR is now above one and the NPV is positive in all cases. Under VSL the BCR ranges from over 3 to over 8, and under VLYL the range is over 1 to over 3. Allowing for the +/-27% physical impacts CI, the BCR remains above unity in all cases. 41 Final Report VII. Conclusions and recommendations The study illustrates that the NAPCP has benefits in excess of fiscal costs for a wide range of benefit estimates. The comparison relative to the economic costs suggests that the case is less clear. Some discussion is needed on what method of mortality valuation is appropriate for Slo- vakia before proceeding further. If the VLYL method is chosen, a further review will have to be made of whether methods used to determine the values at the European level are appropriate or whether Slovakia wants to take a conservative value based on Slovak legislation for years of life saved. If the latter is taken, the NAPCP needs further investigation to determine which components are justified on benefit-cost grounds. This can be done as a follow-up to this work, as noted below. The study should be seen as a first step in analyzing the effectiveness of air pollution control measures in terms of benefits and costs. While this study considers the entire NAPCP, a more in-depth evaluation is necessary to consider each component of the NAPCP. As a result, com- ponents can be ranked according to their effectiveness and new ones can be considered where some are found to be particularly ineffective. This requires more air quality modelling than was possible for this initial assessment. The toolkit created as part of this work will allow for such an extension to be undertaken in the future. The granular data assembled here can be used to determine the benefits and costs of regional policies. With information on impacts for each of the 72 districts, local measures can be an- alyzed, such as traffic restrictions and local bans on high emission heating devices, but will require more detailed air quality modelling. Lastly, data used as inputs for the study should be reconsidered and updated regularly. In par- ticular, baseline data on workdays lost is limited and data on other morbidities are taken from default European values. As information becomes available, the initial data used can be replaced by local ones. The datasets used for the calculations of health impacts and their economic val- uations should also be regularly updated to reflect the changes and allow policy makers to use the tool efficiently in the future. 42 Final Report Annex I: of districts Annex I: List List of districts Map of districts (several districts in Bratislava and Košice are merged into one district each) Map of districts (several districts in Bratislava and Košice are merged into one district each) Nº District Nº District Nº District Nº District Nº District Nº District SK0221 Bánovce SK0315 Liptovský Mikuláš SK0418 Sabinov Bánovce nadnad Bebravou SK0221 Bebravou SK0321 Banská Bystrica SK0326 SK0223 Myjava Lučenec SK042BSK0108 Trebišov Senec SK0321 Banská SK0322 Bystrica Banská Štiavnica SK0317 Námestovo SK0106 Malacky Trenčín Senica SK0229SK0215 SK0322 Banská Štiavnica SK0233 Nitra SK0316 Martin SK0217 Trnava Skalica SK0216 SK0411 Bardejov Nové Mesto nad SK0101 Bratislava SK0415 Medzilaborce SK0419 Snina SK0411 Bardejov SK0224 Váhom SK0319 Turčianske Teplice SK0323 SK0101 Brezno Bratislava SK0427 SK0234 Michalovce Nové Zámky SK031ASK0429 Tvrdošín Sobrance SK0323 Brezno SK0311 Bytča SK0225 Partizánske SK0223 Myjava SK032A Veľký Krtíš SK042A Spišská Nová Ves SK0311 Bytča SK0312 Čadca SK0107 Pezinok SK0317 Námestovo SK041D Vranov nad SK041A Topľou Stará Ľubovňa SK0312 Čadca SK0214 Piešťany SK0233 Nitra Zlaté Moravce SK0237SK041B Stropkov SK0324 Detva SK0324 Detva SK0327 Poltár SK032B Zvolen SK0313 Dolný Kubín SK0224 Nové Mesto nad Váhom SK041C Svidník SK0313 Dolný Kubín SK0416 Poprad SK032C Žarnovica SK0211 SK0211 Dunajská Dunajská Streda Streda SK0234 Nové Zámky SK0226 Považská Bystrica SK032DSK0235 Žiar nadŠaľa Hronom SK0212 Galanta SK0212 Galanta SK0417 Prešov SK0225 Partizánske SK031B Žilina Topoľčany SK0236 SK0421 Gelnica SK0421 Gelnica SK0227 Prievidza SK0107 Pezinok SK042B Trebišov SK0213 Hlohovec SK0228 Púchov SK0214 Piešťany SK0229 Trenčín SK0213 Hlohovec SK0412 Humenné SK0328 Revúca SK0412 Humenné SK0327 Poltár SK0217 Trnava SK0222 Ilava SK0329 Rimavská Sobota SK0222 SK0413 Ilava Kežmarok SK0416 SK0428 RožňavaPoprad SK0319 Turčianske Teplice SK0231 Komárno SK0413 Kežmarok SK0318 Ružomberok SK0226 Považská Bystrica SK031A Tvrdošín SK0422 Košice SK0231 Komárno SK0418 Sabinov SK0417 Prešov SK032A Veľký Krtíš SK0426 Košice - okolie SK0108 Senec SK0227 Prievidza SK041D Vranov nad Topľou SK0422 Košice SK0325 Krupina SK0215 Senica SK0426 Košice - okolie SK0228 Púchov SK0237 Zlaté Moravce SK0314 Kysucké Nové Mesto SK0216 Skalica SK0232 Levice SK0325 Krupina Snina Revúca SK0328 SK0419 SK032B Zvolen SK0414 Kysucké Nové Mesto SK0429 Sobrance Levoča SK0314 SK0329 Rimavská Sobota SK032C Žarnovica SK0315 Liptovský SK0232 LeviceMikuláš SK042A Spišská SK0428 Nová Ves Rožňava SK032D Žiar nad Hronom SK0326 Lučenec SK041A Ľubovňa Stará Ružomberok SK0318 SK031B Žilina SK0414 Levoča SK0106 Malacky SK041B Stropkov SK0316 Martin SK041C Svidník SK0415 Medzilaborce SK0235 Šaľa 49 43 Final Report Annex II: PM2.5 emissions profiles in the base case and under NAPCP Emissions are in MT PM2.5 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Base 26 006 25 535 25 108 24 833 24 483 24 120 23 769 23 426 23 070 22 714 22 363 All WAM Measures 25 355 24 770 24 233 23 671 23 209 22 719 22 060 21 587 21 202 20 639 20 291 Plus Supp. Replacement of Conv. 25 355 24 770 24 233 23 545 23 083 22 594 21 809 21 336 20 951 20 387 20 039 Stoves W/Gasification Plus Supp. Replacement of Conv. 25 324 24 709 24 141 23 423 22 931 22 411 21 596 21 093 20 707 20 144 19 796 Stoves W/Condensing Gas Plus Supp. Replacement of Conv. 25 281 24 621 24 010 23 248 22 712 22 192 21 377 20 874 20 489 19 925 19 577 Stoves W/DH Plus Supp. Insulation Program 25 146 24 487 23 875 23 113 22 577 22 057 21 242 20 739 20 354 19 790 19 440 Target for 2030+ 19 125 19 125 19 125 19 125 19 125 19 125 19 125 19 125 19 125 19 125 19 125 Source: World Bank and Ministry of Environment (2019) Report 44 Final Report 45 This final report includes the results of an analytical col- laboration between the Institute for Environmental Pol- icy (IEP) and the World Bank through the project Drivers and health impacts of ambient air pollution. The work on this project was carried out with the support of the European Union through the Instrument for Structural Reforms in cooperation with the European Commission‘s Directorate-General for Structural Reform Support (DG REFORM). The main authors are Veronika Antalová (IEP) and Anil Markandya (World Bank consultant). The material presents the opinions of the authors and the IEP, which do not necessarily reflect the official opinions of the Ministry of the Environment of the Slovak Republic. The aim of publishing IEP comments is to stimulate and improve professional and public discussion on current environmental topics. The citations of the text should therefore refer to the IEP (and not the MoE SR) as the author of these opinions. February 2021 Photo on the cover: www.pixabay.com EUROPEAN Funded by the UNION EuropeanSocial European UnionFund Europe & Central Asia