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Greenhouse gas and air pollutionemissions and options for reducing

from the Kosovo transportationsector-dynamic modelling

Skender Kabashi, Sadik Bekteshi and Skender AhmetajFaculty of Mathematical and Natural Sciences, University of Prishtina,

Prishtina, Kosovo

Gazmend KabashiFaculty of Electric Engineering and Computer Sciences, University of Prishtina,

Prishtina, Kosovo

Robert Blinc and Aleksander ZidansekJozef Stefan Institute and Jozef Stefan International Postgraduate School,

Ljubljana, Slovenia, and

Ivo SlausR. Boskovic Institute, Zagreb, Croatia

Abstract

Purpose The purpose of this investigation is the dynamic modelling of greenhouse gas (GHG) and

air pollution emissions, to identify technology and policy options for reducing GHG and air pollution,and to explain how these options might affect the different variables of mobile source emission systemsin Kosovo.

Design/methodology/approach For modelling impacts of the technology and policy options forreducing GHG and air pollution, the model STELLA software has been used. The annual total emissionfor air pollutants (CO, NOx, CHx, SO2 and dust) and GHG (CO2) from the year 2000 up to 2025 iscalculated. 2000 is taken as the base year for emission. Initial data value for vehicle population is takenfrom MEMand from World Bank ESTAPProject for Kosovo. Projection for thetotal number of vehiclesin Kosovo is calculated with the WB Atlas Method, while the projection for emission factors and totalannual emission for Air Pollutants and GHG (CO2) are calculated with US EPA methodology.

Findings From the results obtained using this model, the variables that drive GHG and airpollutant emissions and reduction in transport are identified. This model, predicts high emission of airpollutions and GHG in the short term from 2000 to 2010. After 2015, due to implementing the emission

reduction policies and introducing new technologies in transportation, a continual reduction in airpollution will take place, whereas the CO2 output up to 2025 will be reduced by 25 percent incomparison with the emission values of 2007.

Originality/value Models presented here are the first, together with original data and results, withthe predictions which are regional, but accepted globally. This work is original, since no such analysishas been carried out about mobile source emission systems in Kosovo. The paper provides data andresults on which further research could be carried out.

Keywords Global warming, Air pollution, Transportation

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1477-7835.htm

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Received 4 January 2010Revised 19 May 2010Accepted 10 June 2010

Management of EnvironmentalQuality: An International JournalVol. 22 No. 1, 2011pp. 72-88q Emerald Group Publishing Limited1477-7835DOI 10.1108/14777831111098499


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IntroductionAutomobiles, vans, trucks, tractors and buses population in Kosovo have toppedapproximately 215,000 vehicles registered in 2003 (source: Statistic Office of Kosovo),that is, 172 per cent increasing rate from the pre-conflict (before March 1999) number of

vehicles 125,000 (provided by the Motor Vehicle Registration Authority (MVRA) ofKosovo). These vehicles burn gasoline or diesel fuel in their engines each travelling anaverage of 16,000 km/year, at an average fuel economy of 12 l/100 km. Thistransportation represents a consumption of about 0.5 million tons oil/year (Ministry ofEnvironment and Spatial Planning, http://enrin.grida.no/htmls/kosovo/SoE/energy.htm).

Consumption of this amount of petroleum which causes the emission of airpollutants (CO, oxides of nitrogen NOx, unburned hydrocarbons CHx, dust and airtoxics (e.g. formaldehyde, benzene, or acetaldehyde) and GHG (CO2, NH4, etc.), has itsconsequences on the degradation of the environmental future, because this pollutionleads to increased rate of lung cancer, respiratory illnesses, and other chronic and acutetoxic effects, whereas GHG cause climate change (IPCC, 2007). Annual Kosovo carbondioxide amounts, produced from motor vehicle use is estimated at around 0.8 milliontones or about 15 per cent of total carbon dioxide (5.3 Mt) production in 2003 (both fromtransport and electricity generation). Motor vehicle greenhouse gas and air pollutionemissions is projected to grow up in Kosovo as a result of the increasing number ofvehicles, vehicle kilometres travelled (VKMT) and because Kosovo does not have indisposal standards for emissions (the 90 per cent of registered vehicles in 2003 are ofage.10 year). VKMT is expected to grow up in Kosovo at a double rate of populationgrowth and even faster.

In this paper, the annual total emission for air pollutants (CO, NOx, CHx, SO2 anddust) and GHG (CO2) from the year 2000 up to 2025 is calculated. The base year foremission is taken the year 2000. Initial data value for vehicle population is taken from

Ministry of Environment and Spatial Planning (MESP, n.d.) and Energy SectorTechnical Assistance Project Kosovo (ESTAP, n.d.), and projection for total number ofvehicles in Kosovo is calculated with WB Atlas Method (United Nations Centre forHuman Settlements, 2001), while the projection for emission factors and total annualemission for air pollutants (CO, NOx, CHx, SO2 and dust) and GHG (CO2) are calculatedwith US EPA methodology (EPA, 2000). The model presented here for total emission isa cohort model because we divided the total number of vehicle in Kosovo by their size(light and heavy vehicles) and by age (in five cohorts or groups: 0-2 years, 3-5 years, 6-8years, 9-11 years and .11 years). For each cohort of vehicles we have input scrappagerate rk factor (fraction of vehicles scraped while in the cohort) and increase rate factor(1 pj) (increase of imported vehicles per year). In this model the authors establish thecorrelation between imported vehicles per year (1 pj), scrappage rate factor rk on one

side, and projections for the total number of vehicles and percentage increasing qj(t)which is calculated by WB Atlas Method, on the other side. The authors develop amodel in which they identify technology and policy options for reducing mobile sourceGHGs and air pollution emission based on the European directive on integratedpollution prevention and control in the best available techniques (EuropeanCommission, 1996), and explain how these options might affect the differentvariables of a mobile source emission model to reduce CO2 total annual emission andair pollutants. Modelling is made in STELLA software (ISEE Systems, 2009).

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The mobile sources emission system which is modelled is nonlinear and is presentedwith systems of nonlinear difference equations whichs solution is found by numericalmethods (Eulers method, Runge-Kutta methods) (Beltrami, 1998).

Dynamic modelling of the mobile source emission systems in KosovoThe equation the authors use to calculate emission in this model (Deaton andWinebrake, 2000) is:

Eit j

XVj:VKMTjt:EFijt 1

where:. Ei is the total annual emission of pollutant i (t/year).. Vj is the total number of vehicles of type j on the road during a certain year

(vehicles).. VKMTj is the average annual kilometres travelled for vehicles of type j

(km/vehicle/year).. EFij is the emission factor of pollutant i for vehicle type j (g/km).

Projections of total number of vehicles in Kosovo for the period 2000-2025The commonly used measure of the total forecast number of vehicles type j (j indicatestwo types of vehicles in Kosovo, light and heavy duty vehicles) is an average numberof vehicle per household. The number of vehicles per household depends on manyparameters such as: households income, road infrastructure development, populationdensity and urbanisation rate, environmental and energy policy in Kosovo, etc.

In most of the developing countries vehicle population grows exponentially with the

economic growth; however a correlation can be established between the level ofeconomic development measured as GDP per capita and vehicle population perhousehold. To analyse this correlation, a group of 20 countries somewhat similar withKosovo is selected from eastern and Central Europe and the Former Soviet Union,using the WB Atlas Method for population statistics, GDP per capita, number ofvehicles per 1,000 habitants, the number of vehicles per average household size,number of households for selected countries in 1998 (see Table I).

According to data from Table I, the dependence between the number of vehicles perhousehold and gross domestic product (GDP) per capita can be established:

y 0:4604lnx2 3:4439 2

where x is GDP per capita and y is the numbers of vehicles per household see Figure 1.Based on this correlation, for total number of vehicles (light and heavy vehicles)

depending on projections of GDP per capita and population (number of household)growth in Kosovo, we made this projections for total number of vehicles (light andheavy vehicles) for the period 2000-2025 (see Figure 2).

It should be noted that the projected total number of vehicles in 2003 is very close tothe data provided by the Motor Vehicle Registration Authority (MVRA) of Kosovo(http://enrin.grida.no/htmls/kosovo/SoE/energy.htm).

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Between the total number of vehicles per year Vj(t) and percentage increasing per yearqj(t), equation (3) can be established:

Vjt 1 qjtVjt2 1 3

The values of qj(t) calculated from the data presented in Figure 2, are shown inTable III, and it can be seen that they are very close with WB-ESTAP data for Kosovo.

Figure 1.The number of vehiclesper household and GDP

per capita

Country PopulationNumber ofhouseholds

GDP percapita, USD

dollar

Number ofvehicles per 1,000

habitatsAverage

household

Vehiclesper

household

Albania 3,094,350 633871.8 2,804 28 4.88 0.13Armenia 3,491,896 678640.7 2,072 2 5.14 0.01Azerbaijan 7,580,093 1526847 2,175 47 4.96 0.23Bulgaria 8,356,381 3252232 4,809 226 2.56 0.58Czech Republic 10,244,000 4313964 12,362 322 2.37 0.76Estonia 1,412,802 580487.8 7,682 309 2.43 0.752Georgia 5,007,823 1332277 3,353 92 3.75 0.34Greece 10,538,816 3778135 13,943 295 2.78 0.82Hungary 10,116,449 3954168 10,232 284 2.55 0.72Kazakhstan 16,158,173 5302609 4,378 82 3.04 0.24Latvia 2,385,369 858848.7 5,728 167 2.77 0.46Lithuania 3,655,335 1266144 6,436 226 2.88 0.65Moldavia 4,353,759 1232561 1,947 65 3.53 0.22

Poland 38,532,759 12869912 7,619 273 2.99 0.81Portugal 9,875,000 3561949 14,701 340 2.77 0.94Romania 22,276,486 7797676 5,648 114 2.85 0.32Slovakia 5,354,726 1991563 9,699 219 2.68 0.58Slovenia 1,970,144 705752.1 14,293 381 2.79 1.06Spain 39,471,639 12365127 16,212 425 3.19 1.35TfyrMacedonia 1,995,763 533950.8 4,254 163 3.73 0.60

Sources: United Nations Centre for Human Settlements (2001)

Table I.Population, number of

households GDP percapita, number ofvehicles per 1,000

habitants, averagehousehold, and vehicles

per household in 1998


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Since the percentage increasing per year qj(t) in Kosovo is realized completely from theimport and different types of vehicles of different sizes and ages, the authors haveassumed that the total number of imported vehicles includes the heavy and lightvehicles, which are divided in five groups according to the age: 0-2 years, 3-5 years, 6-8years, 9-11 years and .11 years. Each cohort has its own individual scrappage rate(rk). These rates represent the fraction of vehicles scrapped over the transit time due toaccidents or breakdowns.

Between the total number of vehicles per year Vj(t), increase rate of importedvehicles per year while in the cohort (1 pj(t)), and the scrappage rate rk, this

correlation is established:

Vjt X5

k1

Vjkt X5

k1

1 pjt 12 rk Vjkt2 1 4

where k 1; 2; . . . ; 5 represents the five cohorts.The factor 1 pj(t) does not depend on the age of the automobile, because it is

assumed that the average number of the imported vehicles does not depend on the age,but only on the size (since the number of aged vehicles, for instance in 2000, has been

much greater than the number of new imported vehicles, while the opposite happensfor the years to come).

Assuming that 90 percent of vehicles in the year 2000 have been older than 10 years,and the rest newer than 10 years, the initial number of vehicles is obtained, representedin Table II. Scrappage rate rk is also represented, based on the age, and this factorremains constant over the years till to the year 2025.

From equation (3) and (4) increasing factor for imported vehicles (1 pj(t)) can becalculated, values of which are shown in the Table III.

Figure 2.Projections for increase intotal number of vehicles(light and heavy vehicles)in Kosovo for the period2000-2025

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Total emission projections from the total number of vehicles in Kosovo for the period2000-2025According to US EPA methodology (EPA, 2006) for mobile sources, the equation for thetotal emission for pollutant i emitted from the total number of vehicles is established:

Eit X2

j1

X5

k1

1 pjt12 rk Vjkt2 1 VKMTjt EFijkt 5

where j 1; 2 indicates two types of vehicles: light vehicles and heavy vehicles,i indicates CO2, CO, NOx, SO2 and dust, t 2001, 2002, . . . , 2025 year; t 2000 is theinitial year.

Year q1(t) (%) q2(t) (%) 1 p1(t) 1 p2(t)

2000 0 0 1 12001 9.40 10 1.6 1.592002 7.20 8 1.55 1.542003 6.70 7 1.51 1.49

2004 3.40 7 1.44 1.472005 3.30 7 1.41 1.442006 3.80 7 1.4 1.422007 4.10 7 1.38 1.42008 4 6 1.36 1.372009 3.80 6 1.34 1.352010 4.10 6 1.33 1.342011 4.00 6 1.32 1.332012 4.70 6 1.32 1.322013 4.10 6 1.3 1.312014 4.30 6 1.3 1.32015 4.50 6 129 1.292016 3.00 6 128 1.28

2017 3.00 6 1.26 1.282018 3 6 1.25 1.272019 3 6 1.25 1.262020 3 5 1.24 1252021 3 5 1.24 1242022 3 5 1.23 1.242023 3 5 1.23 1.232024 3 5 1.22 1.232025 3 5 1.22 1.23

Table III.Percentage increasing per

year q(t) and increasingfactor for imported

vehicles per year(1 pj(t))

Age Light vehicles Heavy vehicles Total vehicles Scrap rates

0-2 years 1,500 607 15,607 r1 0.1

3-5 years 7,500 2,000 9,500 r2 0.156-8 years 15,000 3,000 18,000 r3 0.29-11 years 40,000 9,000 49,000 r4 0.25.11 years 75,000 14,500 89,500 r5 0.4Total 139,000 29,107 168,107

Table II.Number of vehicles by

cohort in the year 2000(initial value) and

scrapped rates


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In Figure 3 is presented the cohort model for CO2 emission from heavy vehicles.Many factors have been taken into account to evaluate emission projections from

sources (EIA, 1994, 2003) and (EPA, www.epa.gov/otaq/greenhousegases.htm).

Base year emission and initial data valueFor our calculation of overall emission projections of air pollutants CO, NOx, SO2, dustand CO2, the base year is 2000, the authors used the initial data values for:

. Number of heavy and light vehicles by cohort and scrapped rates, shown in theTable II.

. Increase factor of imported light and heavy vehicles, shown in the Table III.

. Annual average kilometres travelled (a constant value across all cohortsVKMTj 1,6000 km, for both types of vehicles (heavy and light vehicles) isused).

. Emission factors for CO, NOx, SO2, NHx, dust and CO2, are represented inTable IV.

Policy and technology options for reducing air pollution and GHGs from mobile sourcesThere are a variety of policy and technology options that may be employed to reduceemissions from mobile sources. Policies that encourage people to drive less or to usepublic transportation or carpools have been widely instituted throughout manyindustrialized countries (US Department of Transport, 2006).

Such policies will be applied in Kosovo, as well. Some of them are:

Policy options. High-occupancy vehicle lanes this option reduces the number of vehicles on the

road by providing an incentive (faster transit time) to people who carpool.

.

Vehicle scrappage programs and incentives this option reduces the number ofolder model year vehicles on the road, because these vehicles are thought to bethe largest emitters of urban pollutants (this program has partially commencedwith application in Kosovo since 2005).

. Rebates for mass-transit usage this option offers an economic incentive forpeople who use mass transit.

. Corporate average fuel economy (CAFE) standards this option forcesmanufacturers to improve average energy efficiency in vehicles (km/l) so thatless fuel is burned for a given travel distance (which means less air pollutionemission).

. Tolls on certain roadways this option creates a disincentive for driving a

personal vehicle on roadways with tolls.

Technology optionsTechnologies that increase vehicle efficiencies, decrease emissions factors for certainpollutants, or reduce the need to drive altogether have been used worldwide (Fay, 2002;Zidansek et al., 2009) to reduce mobile source emissions. Such technologies areexpected to be applied in Kosovo after 2015, among others:

. high-efficiency vehicles;

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Figure 3.Heavy vehicle (j 2)

cohort model with CO2emission sub model


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. alternative transportation fuels (e.g. natural gas, ethanol, methanol, propane,hydrogen, electric vehicles);

. improved catalytic converters;

.

new forms of efficient mass-transit; and. nanotechnologies.

Application of molecular nanotechnology for energy applications (Gillett, 2002)include: vastly improved efficiency of energy usage (non-thermal energy usage viananostructure devices such as fuel cells, molecularly tailored catalysts for heightenedselectivity and by-product elimination, high-strength materials which will decreasetransportation costs).

Policy- and technology-based projection resultsOwing to the policy and technology options for reducing mobile emissions that have tobe applied in Kosovo in order to comply with the European Union emission standards,

the factors of emission (Figures 4 to 6), and the average value of kilometres travelledper year (Figure 7) change, starting from the year 2010.

Substituting these data which are nonlinear functions of time (Figures 2, 4, 5, 6, and7, and data from Table III) and initial data value (data from Tables II and IV) in theequation (5), for the total emission of CO, NOx, SO2, NHx, dust (t/year) and CO2(Mt/year), we obtain results presented in Figures 8 and 9.

Discussion of resultsEffects of policy and technology options after 2015 can be seen in Figures 4 to 9. Some ofthese political and technological options for reduction have already began to beimplemented in Kosovo. Since 2005 the prohibition of the vehicles more than 8 years oldhas been implemented, which resulted in the increase of the number of new vehicles withlower emission factor. Also, since more than 90 per cent of vehicles in 2000 in Kosovowere more than 10 years old, starting from 2010, most of them will be out of use andconsequently we have a decrease in emission factors as well as in kilometers elapsed.Price rise of oil and gasoline caused that more than 50 per cent of vehicles with gasolinenow has been adapted for use of natural gas instead, which is cheaper and emits less CO2than gasoline and oil, and this trend is going on. This results further in the decrease of gasemission from the vehicles. Since 2008, road infrastructure is under construction,projected to end in 2013. This will result in an unload of urban traffic from small vehicles

EFCO EFNOx EFSO2 EFNHx EFDust EFCO2Age LV HV LV HV LV HV LV HV LV HV LV HV

0-2 years 1.88 4.6 0.5 0.5 0.5 1.3 0.3 0.36 0.3 0.75 138 2703-5 years 2.13 5.9 0.6 0.7 0.6 1.58 0.4 0.4 0.4 0.87 161 2906-8 years 2.38 6.1 0.7 0.85 0.7 1.7 0.5 0.7 0.41 0.9 184 3249-11 years 2.63 6.6 0.8 1.05 0.8 1.9 0.6 0.83 0.43 1.1 208 351.11 years 3.28 7.5 1 1.13 0.9 2.2 0.7 0.9 0.5 1.2 231 378

Note: Emission factors for CO, NOx, SO2, NHx, dust and CO2 (gr/km) of light (LV) and heavy (HV)vehicles initial value

Table IV.Emission factors for airpollution and GHG(gr/km) of light (LV) andheavy vehicles (HV),initial values in year 2000

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Figure 4.Time-dependence changeof CO2 and CO emission

factors while in cohort(light and heavy vehicles)

in Kosovo for the period2000-2025


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Figure 5.Time-dependence changeof NOx and NHx, emissionfactors while in cohort(light and heavy vehicles)in Kosovo for the period2000-2025

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Figure 6.Time-dependence changeof SO2 and dust emission

factors while in cohort(light and heavy vehicles)

in Kosovo for the period2000-2025


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Figure 7.Time-dependence changeof average kilometrestravelled (AVKMT) (lightand heavy vehicles) inKosovo for the period2000-2025

Figure 8.

Total emission of airpollution (CO, NOx, NHx,SO2 and dust) in Kosovofor the period 2000-2025

Figure 9.Total CO2 emission frommobile sources in Kosovofor the period 2000-2025

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and in an increase in the number of heavy vehicles (buses, trains, etc.), which will alsohave impact on the continuous decrease of emission factors and the number of kilometerselapsed, as well as the decrease of total emission of pollutants and greenhouse-gases, after2013. The model presented here, predicts the import in Kosovo, after 2015, of vehicles

working with biodiesel fuel, electric power based vehicles and vehicles withnano-technolgy applied. Road traffic will result in an unload by the use of electrifiedrailroad traffic. Also, taxes for small vehicle circulation on the highways and urban roads,taxes for emission of pollutants and GHG, etc., will be imposed.

From Figures 8 and 9 we see that implementing the emission reduction policies andintroducing new technologies in transportation, after the year 2015, a continualreduction in air pollution will take place, whereas the CO2 output till to the year 2025will be reduced by 25 per cent in comparison with the emission values of the year 2007and will be approximately the same as the values of the year 2001.

Error analysis of model

Uncertainty in predictions of the CO2 and air pollution emissions for transport inKosovoIn Figures 10 and 11 is shown a sensitive analysis (Tailor, 1997; EIA, 2005) of themodel for CO2 emission for transport in Kosovo. For uncertainty in estimation of CO2emission from transport, four scenarios are simulated with uncertainty in vehiclepopulation and imported vehicles per year (high growth 8 per cent, centre case 6 percent, low 4 per cent and our prediction 6.5 per cent) (line 1-4) and estimated mean(line 5).

These uncertainties in estimation in vehicle population and imported vehicles peryear depend on uncertainty in GNP and the number of households which is dependenton uncertainty in population estimates (see Table V) and economic development ofKosovo as well.

The deviation of predictions of the CO2 emission in year 2025 from transport, fromthe estimated mean value for these four scenarios in 2025, ranges from 20.15 Mt to 0.08 Mt with standard deviation of 0.101 Mt (see Table VI).

Uncertainty in air pollution (CO, NOx, NHx, SO2 and Dust) estimated emission inKosovo in 2025, is presented in Table VI.

Figure 10.

Sensitive analysis for CO2emission from transport inKosovo and with

uncertainty on CO2emission factors per km

travelled, vehiclepopulation and imported

vehicles for the period2000-2025


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ConclusionThe modelling is carried out for Kosovo transportation emissions system which is themain emitter of air pollutions (CO, NOx, SO2, Dust) and GHG (CO2, N2O, CH4, etc.)besides electricity generation systems. Total emission of GHG and Air Pollution iscalculated for the year interval t 2000-2025.

From the results of these models, the variables that drive GHG and air pollutantemissions and reduction in transport are identified. Variables of a mobile sourceemission model such as: Total annual number of vehicles, average kilometres travelledand emissions factors for air pollutants and GHGs are nonlinear functions of time and

this nonlinearity depends on many factors (renewable energy potentials, roadstructure, new technology and environmental policies in Kosovo).

This model, according to participation of renewable energy, new renewable energytechnologies and environmental polices in transport, predicts high emission of airpollutions and GHG in short-term from year 2000 to the year 2010. Becauseparticipation of renewable energy in this period is negligible, policy options are statusquo and technology options are old (90 per cent of total vehicles are older than 10years).

Uncertainties CO2(Mt) CO(t) NOx(t) NHx SO2 Dust

Main deviation (MD) 0.072 1,065 185 147 258 80s (standard deviation) 0.101 1,388 242 203 339 114sm (standard deviation of mean) 0.05 694 121 102 170 57

Table VI.Uncertainty inpredictions of the CO2and air pollutionemissions in year 2025from transport in Kosovo

Uncertainities PopulationGDP (Billion

US$)Number ofhouseholds

Vehiclepopulation

Main deviation (MD) 137,466 1.77 43,540 34,000s (standard deviation) 161,402 2.59 61,551 45,000sm (standard deviation ofmean) 93,186 1.29 30,775 23,000

Table V.Uncertainty inpredictions of thepopulation, GNP, numberof households and vehiclepopulations in year 2025in Kosovo

Figure 11.Deviation of CO2 emissionfrom mean value for theperiod 2000-2025

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In middle term, from 2010 to 2015 renewable energy slightly increases by 3-8 percent of the total energy use in transport, stabilization and slightly decrease in GHGemission will be noticed. Emission of sulphur dioxide (SO2), carbon monoxide (CO),nitrogen oxides (NOx) methane (NH4) and dust, generated from mobile sources will be

continually increasing till to the year 2012. In this model after 2012, the authorsincorporate SO2 control technology and policy that would reduce SO2 and other airpollutions emissions from mobile sources.

In long term from year 2015 to 2025 renewable energy gradually increases till to 14per cent of the overall energy use in transport. Policy and technology options forreducing GHG and air pollution will achieve EU standards that would be compatiblewith GHG and air pollution reduction requirements. During this period we will witnessa gradual reduction in CO2 by 25 per cent in comparison with the year 2007 andcontinual reduction in air pollution will take place.

Nomenclature.

GHG: greenhouse gases.. IPCC: International Panel of Climate Change.. ESTAP: energy sector technical assistance project.. MESP: Ministry of Environment and Spatial Planning.. TPP: thermo power plant.. IEA: International Energy Agency.. US EPA: United State Environmental Protection Agency.. CAFE: corporate average fuel economy.. MVRAK: Motor Vehicle Registration Authority of Kosovo..

AVKMT: average kilometres travelled.. GDP: gross domestic product.. EU: European Union.. VKMT: Vehicle Kilometres Travelled.. EIA: Energy Information Agency.. WB: World Bank.

References

Beltrami, E. (1998), Mathematics for Dynamic Modelling, Academic Press, San Diego, CA.

Deaton, M.L. and Winebrake, J.J. (2000), Modelling mobile source air pollution inventories,

in Ruth, M. and Hannon, B. (Eds), Dynamic Modelling of Environmental Systems, SpringerScience Business Media, New York, NY, pp. 142-57.

EIA (1994), Alternatives to traditional transportation fuels, Volume II, Greenhouse GasEmissions, available at: www.eia.doe.gov/cneaf/alternate/page/environment/exec2.html

EIA (2003), Fuel options for reducing gas emissions from motor vehicles, EIA final report,September, available at: www.eia.doe.gov/oiaf/1605/ggrpt

EIA (2005), Uncertainty in emission estimates, March, available at: www.eia.doe.gov/oiaf/1605/transport.html


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Energy Sector Technical Assistance Project (ESTAP) (n.d.), World Bank Grant No.TF-027791,Kosovo, available at: www.ero-ks.org/ESTAP%20I/Executive_Summary.pdf

EPA (2000), Compilation of air pollutant emission factors AP-42, 5th ed., US EnvironmentalProtection Agency, Office of Air Quality Planning and Standards, available at: www.epa.

gov/ttn/chief/ap42/index.htmlEPA (2006), Greenhouse gas emissions from the US transportation sector 1990-2003, EPA 420

R06 003 March 2006, available at: www.epa.gov/otaq/invntory.htm

European Commission (1996), The IPCC Directive, available at: http://ec.europa.eu/environment/air/pollutants/stationary/ipcc/index.htm

Fay, J.A. (2002), Energy and Environment, Oxford University Press, New York, NY.

IPCC (2007), IPCC fourth assessment report: climate change 2007, available at: www.ipcc.ch

ISEE Systems (2009), STELLA, available at: www.iseesystems.com

Ministry of Environment and Spatial Planning (MESP) (n.d.), available at: http://enrin.grida.no/htmls/kosovo/SoE/energy.htm

Stephen, L.G. (2002), Nanotechnology: Clean Energy and Resources for the Future, Foresight

Institute, Palo Alto, CA.Tailor, J.R. (1997), Introduction to Error Analysis, 2nd ed., University Science Books, London.

United Nations Centre for Human Settlements (2001), World Bank: World DevelopmentIndicators (WDI) database, United Nations Centre for Human Settlements (Habitat),available at: www.unhabitat.org

US Department of Transport (2006), Modelling of advanced technology vehicles, final report,available at: http://avt.inel.gov

Zidansek, A., Blinc, R., Jeglic, A., Kabashi, S., Bekteshi, S. and Slaus, I. (2009), Climate changes,bio fuels and the sustainable future, International Journal of Hydrogen Energy, Vol. 34No. 16, pp. 6980-3.

About the authorsSkender Kabashi is based in the Faculty of Natural Sciences, University of Prishtina, Kosovo.Skender Kabashi is the corresponding author and can be contacted at: [email protected]

Sadik Bekteshi is an Assistant Professor of Physics, based in the Faculty of Natural Sciences,University of Prishtina, Kosovo.

Skender Ahmetaj is an Assistant Professor of Physics, based in the Faculty of NaturalSciences, University of Prishtina, Kosovo.

Gazmend Kabashi is a Teaching Assistant in the Faculty of Electric Engineering andComputer Sciences, University of Prishtina, Prishtina, Kosovo.

Robert Blinc is a professor at the University of Ljubljana in Slovenia, currently based at theJ. Stefan Institute in Ljubljana, He is also a member of the European Academy of Sciences and Artsand an honorary member of the Society of Mathematicians, Physicists and Astronomers of Slovenia.

Aleksander Zidansek is an Associate Professor of Physics in the Faculty of Education at theUniversity of Maribor, in Slovenia.Ivo Slaus is Emeritus Professor of Physics, member of the Club of Rome, fellow of the World

Academy of Art and Science, member of the Croatian Academy of Sciences and Arts andfounding member of Academia Europea, President of the International Network of Centres forSustainable Development in South East Europe.

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