DEVELOPING LOW CARBON POLICIES FOR ROAD TRANSPORT IN POLAND

Transcription

1 DEVELOPING LOW CARBON POLICIES FOR ROAD TRANSPORT IN POLAND Kok, Robert ECORYS Nederland B.V. & Delft University of Technology Rahman, S. Adnan. ECORYS Nederland B.V. Abstract This paper presents the results of work done for the World Bank to develop low carbon policies for road transport in Poland. Here, we outline the development of Green House Gas (GHG) emissions, develop a Business As Usual (BAU) scenario based on social-economic-, infrastructure-, car market, vehicle stock-, fuel use, and technological developments in Poland, and forecast future GHG emissions to We analyse the potential effect of several policy measures including road pricing, congestion charging, fuel tax policies, eco-driving training, parking policies, and promotion of non-motorized and public transport. If the assumptions included in the BAU scenario remain valid, and there is little reason to assume they will not, Poland will not be able to meet its GHG reduction obligations to the EU as well as under other international obligations. Worse, even if strong policy measures are put in place, Poland will still have substantially higher GHG emissions than what is possible in the EU target growth of 14% by 2020 for non-eu-ets sector such as road transport. 1 Introduction The transport sector is one of the major sources of greenhouse gas emission (GHG) 1 accounting for about 25% of global CO 2 emissions 2. Cars and trucks represent the bulk (about 75% worldwide) of these emissions. Without concerted global action, car ownership worldwide is set to triple, to over two billion by 2050, and trucking activity will double, according to IEA (2009), leading to an even higher share, and more emissions from road transport. In December 2008, in order to combat climate change and increase the EU s energy security, the European Union adopted a climate and energy package. This package ( 3x20% ) includes the following emissions and energy use reduction targets to be met by 2020: 1 2 The primary greenhouse gases produced by the transport sector are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and hydrofluorocarbons (HFC). CO 2-e or carbon dioxide equivalent is a standardized measure of GHG emissions designed to account for differing global warming potentials (GWP) of GHG s. The 100-year GWP s listed in the Intergovernmental Panel on Climate Change s (IPCC, 2007) assessment reports are often applied. In road transport about 98% of the GHG emissions are accounted for by CO 2 emissions.

2 A reduction in EU GHG emissions of at least 20% below 1990 levels 20% of EU energy consumption to come from renewable resources A 20% reduction in primary energy use compared with projected levels, to be achieved by improving energy efficiency. EU Member States agreed to realise the emission reduction target (EC, 2008) by doing two things: A revision and strengthening of the Emissions Trading System (EU ETS) that sets a single EU-wide cap on emission allowances. This cap will be cut annually, reducing the number of emission allowances available to businesses by 21% below the 2005 level in An 'Effort Sharing Decision governing emissions from sectors, such as transport, housing, agriculture and waste that are not covered by the EU ETS,. Under this decision each Member State has agreed to a binding national emissions limitation target for 2020 which reflects its relative wealth. These national targets will cut the EU s overall emissions from the non-ets sectors by 10% by 2020 compared with 2005 levels. The 10% reduction from the effort sharing decision, together with the 21% reduction from the EU ETS during the same period, will accomplish the overall emission reduction goal of the EU Climate and Energy package (20% cut below 1990 levels by 2020). Under the Kyoto Protocol, Poland has a binding target of 6% reduction, relative to 1988 levels, in GHG emissions in the period As an EU Member State, Poland must also contribute to EU efforts to achieve its targets under the EU s Energy and Climate Package. Regarding the Effort Sharing Decision, Poland has agreed to a GHG emission target for non-ets sectors (this includes transport) which allow 14% growth by 2020 relative to the 2005 emissions reference. Poland undertook significant economic and social reforms during the 90s. After its accession to the European Union in 2004, and having implemented the necessary legislative and regulatory changes, it has witnessed rapid GDP growth. For the transport sector, this has led to a dramatic increase in the import of second-hand cars from Western Europe, higher motorisation rates, large investments in road infrastructure (motorways), and a growth in vehicle and freight ton kilometres. While all sectors in Poland show a declining trend in GHG emissions since 1990, the transport sector s GHG emissions have grown by more than 50% since 1990 (see Figure 1).

3 Greenhouse Gas Emissions (GHG) by Sector: Poland Figure 1: GHG emissions by sector in Poland, =1 1,60 1,50 1,40 1,30 1,20 1,10 1,00 0,90 0,80 0,70 0,60 0,50 0,40 Energy Industries Industry Transport Residential Commercial / Institutional Total Source: EU Energy in Figures DG TREN / EEA (2010) Within the Polish transport sector only civil aviation (domestic and international) and road transport show an increase in emissions (over 70%) compared to 1990 levels (see Figure 2). Figure 2: GHG emissions from transport by mode in Poland (including international bunkers), =1 2,20 2,00 1,80 1,60 1,40 1,20 1,00 0,80 0,60 0,40 0,20 0,00 Total Civil Aviation Road Transportation Railw ays Total Navigation Total Source: EU Energy in Figures DG TREN / EEA (2010) In 2007, total CO 2 emissions in Poland were 401 million tons. The transport sector comprised 10% of the total (40.9 M tons) of which road transport was responsible for 92% (37.5 M tons) of the CO 2 e emissions. Given the dominance of road transport in both freight and passenger transport and the

4 trend shown in Figure 3, the road transport sector in Poland faces a serious challenge in reducing its share of GHG emissions. Figure 3: Share of GHG from transport in Poland, % 90% 92% % Transport sector in all sectors 80% 79% % Road transport in total transport 60% 40% 20% 6% 9% 10% 0% Source: EU Energy in Figures DG TREN / EEA (2010) Given that the vehicle fleet in Poland is quite heterogeneous in terms of technology, and a whole range of difficult to measure factors (for example driver behaviour, logistics concepts in use) affect emissions, it is difficult to forecast the potential for reducing emissions, and the costs associated with policy measures for bringing about these reductions. Literature on GHG abatement potential in road transport is biased as in most studies only technical abatement potentials are being considered. Behavioural (e.g. using public transport) and lifestyle changes (e.g. more active transport like walking and cycling) are not largely financial and are therefore often considered difficult to quantify (McKinsey, 2009). This paper outlines an approach for developing policy measures for reducing emissions from the road transport sector. This approach is different from existing approaches in two ways, namely: 1) it is comprehensive in considering alternatives to road transport (public transport and non-motorised modes), and 2) it considers non-technological policy measures and includes measures to stimulate desirable behavioural changes. The approach defines a transport system by defining the boundary of the road transport sector in Poland, identifies exogenous (exogenous to the system) forces (GDP, population, EU emission regulation) that act on and influence and modify this system, defines policy measures, and outcomes-of-interest (GHG reduction potential) to measure the performance of these policy measures in realising the stated policy objectives. Section 2 describes the Polish road transport system and defines the future evolution of this system under the assumptions included in the Business-As-Usual (BAU) scenario. Section 3 identifies the most promising policy measures for reducing GHG

5 emissions and estimates the impacts for each of the policy packages. Section 4 presents the conclusions. 2 Establishing a Business-As-Usual scenario 2.1 Key variables and data sources The BAU scenario is an extrapolation of the current situation into the future, assuming that there are no policy interventions. A BAU scenario is important in that it provides a point of reference for comparing the effects of policy measures and combinations of policy measures. Thus, in order to correctly estimate the magnitude of the policy measures, the BAU scenario must include realistic and plausible assumptions. In the BAU scenario we included assumptions about the following: The demand for road transport The supply of infrastructure for road transport GDP Population Driving conditions (average speeds) on urban and rural roads and on highways The market for second-hand cars (size of the market, age of cars) The market for new cars Motorisation rates (vehicle ownership) Modal split / shares Size and composition of the vehicle fleet (engine size and fuel efficiency) Emission factors (i.e., the diffusion of technology in the vehicle fleet) In this chapter we analyse the development of the above variables in Poland. The BAU scenario is developed using a variety of public and proprietary data sources and forecasts and the assumptions that we made were validated in a series of interviews with experts and policy makers in Poland. 2.2 BAU for passenger car transport Demand for passenger car transport The demand for transport depends on many economic, physical and social/behavioural factors. Examples are the cost of transport (price of cars, fuel prices and taxes, car maintenance costs and tolls for using road infrastructure), the per capita GDP of a country, the supply and quality of a country s infrastructure (motorways, urban roads, rural roads), motorization rates, and peoples preferences for transport modes. In recent years (after accession to the EU), the costs of second hand cars imported into Poland have dropped substantially. Consequently, car ownership has become much less expensive than what it was in the past, the number of cars has increased

6 significantly, and the share of road transport has increased at the cost of other transport modes. The average annual growth of per capita GDP in Poland is expected to be about 3.2% between 2008 and From the available statistics and interviews with GDDKia 4, the kilometres of highways are expected to increase from 765 km in 2008 to 1,953 km in This would result in an average annual increase in the length of the highway network of about 4.4% between 2008 and However, we assume a growth rate of 7.4% per year between 2008 and 2015, and 2.4% per year after 2015 (based on scheduled pipeline projects). In addition, we have assumed that the motorization rate increases from 422 cars per 1,000 inhabitants in 2008, to 605 cars per 1000 inhabitants in The forecast motorization rate is estimated based on National statistics (GUS, 2009) and uses historical growths factors for the motorization rates in Germany and the EU15 as proxy for Poland. Four drivers for the demand for transport are presented in the Table 1 below. The average of the growth rates of the four drivers together with a multiplier of about 1.2 has resulted in the growth of vehicle-km as shown in the Table 1 below. Table 1: Growth assumptions key drivers for transport demand BAU scenario Poland Change p.a. Change p.a. Change p.a. Change p.a. Change p.a. Key drivers for transport demand: (%) (%) (%) (%) (%) GDP per capita 3.6% 3.3% 3.2% 3.1% 3.2% Passenger cars per 1,000 inhabitants 3.0% 1.4% 1.0% 0.5% 1.7% KM motorways infrastructure 7.4% 2.4% 2.4% 2.4% 4.4% Passenger -km cars (million) 6.3% 3.4% 3.4% 3.9% 2.4% Vehicle kilometres (VKM) 6.0% 3.2% 3.0% 2.9% 4.0% Figure 4 below compares our BAU scenario for Poland to the TREMOVE baseline (TML, 2007). The vehicle-kilometres in the BAU scenario are based on the TREMOVE 2009 baseline and updated with respect to the economic growth, growth of infrastructure supply, car ownership and population growth. The average annual growth of vehicle kilometres (vkm) driven between 2010 and 2030 is 2.6% in TREMOVE and 3.8% in the ECORYS BAU scenario. The vkm driven as provided in the TREMOVE baseline increase from 104 billion in 2010 to 172 billion in In our BAU scenario the vkm driven increased from 118 billion in 2010 to 246 billion in This growth rate is based on population size forecasts by GUS Poland statistics office, OECD and PRIMES and GDP growth forecasts by IBS Poland GDDKia, General Directorate for National Roads and Highways

7 Driving conditions In order to analyse emissions from road transport, it is important to know where the vkm are being driven. The fuel consumption of cars, amongst others things, depends on the vehicle speed and driving conditions. Driving conditions are different for urban roads, rural roads and highways. The fuel consumption is highest on urban roads with low speeds and no free-flow driving conditions and the fuel consumption is lowest on rural roads. The fuel consumption on highways is somewhere in between rural and urban roads depending on the type of vehicle and engine size. In this study, we distinguish between two driving categories: urban areas and non-urban areas (highways and rural roads). Different studies use different definitions of urban and non-urban transport resulting in a wide range (10 to 40% for urban transport) of shares of passenger car kilometres in urban and non-urban areas. Table 2 below shows the estimated trends for car transport in Poland. The number of non-urban trips increase faster than urban trips, while the length of urban trips increases faster than the length of non-urban trips. As a result, the total growth of urban and non-urban passenger kilometres are more or less the same. Therefore, the market share remains on average at a level of 24% in urban areas and 76% in non-urban areas between 2005 and Table 2: Passenger kilometres (pkm) and trips by region for Poland 5 Mobility trends Change (%) Average annual change (%) Urban car transport (bn pkm) % 3,6% Non-urban car transport (bn pkm) % 3,6% Total car transport (bn pkm) % 3,6% Urban trips (million) % 2,5% Non-urban trips (million) % 3,1% Total trips (million) % 2,7% Urban trip length (pkm) 5,0 6,8 37% 1,1% Non-urban trips length (pkm) 24,7 28,2 14% 0,5% Total trip length (pkm) 12,8 16,2 26% 0,8% Share urban car transport 23,4% 23,8% 1,8% 0,1% Share non-urban car transport 76,6% 76,2% -0,6% 0,0% Car sales and second-hand imports Following Poland s accession to the European Union (1 May 2004) the import of second hand vehicles increased at an unprecedented rate as it became significantly easier and cheaper to import a car (no customs tariff, lower VAT). 5 Analysis is based on projections in Burnewicz (2008)

8 Before 2004, car imports used be in the range of 50,000 to 250,000 cars per year; after 2004 this increased to 600,000 to 1,100,000 car per year. Imported second-hand passenger cars constitute a large part of the Polish vehicle fleet. Between 2006 and 2010, about 75% of all cars registered for the first time in Poland were second-hand imports and only 25% were new car sales. One important implication of the large number of imported second-hand cars is an older average age of the Polish vehicle fleet, and hence higher emission (given that the technology is older). In 2007, the average age of a passenger car in Poland was 14.3 years. The majority of imports are old, less safe and less environmental friendly than newer cars. In 2004, the share of cars older than 10 years was 73% of all second-hand car imports. Since 2004 this share has been declining and was about 45% in Two trends are expected to contribute to making the Polish passenger car fleet more climate friendly between 2010 and 2030: 1) The increasing share of new car sales compared to second-hand imports, and 2) The decreasing average age of second-hand car imports. Table 3: Evolution of the Polish vehicle stock for passenger cars Characteristics: Fleet size (million) Imported 2 nd hand cars (thousands) New car sales in Poland (thousands) New registrations (import + new sales) ,000 Scrapped cars (thousands) % imports vs. new sales 61%/39% 47%/53% 45%/55% Average age of 2 nd hand imports Average age of new registrations Average age of scrapped cars Size and composition of the passenger cars vehicle fleet The size of the vehicle fleet in Poland is expected to increase to over 22 million cars by Driving this increase are increases in per capita GDP and motorisation rates. Table 4: Overview of vehicle stock and mobility indicators ECORYS BAU Population (million) GDP per capita (PLN 2006 constant prices) 27,069 28,214 33,637 39,622 46,306 54,054 Motorization (per 1,000 inhab.) Vehicle kilometres (billion VKM) Passenger car VKM per capita 2,762 3,113 4,203 4,951 5,772 6,737 Passenger cars (million) VKM per passenger car 6,882 7,218 8,599 9,153 9,913 11,058

9 Besides the size of the vehicle fleet, other relevant factors with a potentially large impact on GHG emissions are the fuels used (petrol, diesel, LPG) in cars and the engine size (cylinder volume). It is expected that the share of diesel cars will increase from 15% in 2010 to 36% by The share of petrol cars will drop from 71% to 56% and the share of LPG cars drop from 14% to 8%. The market share of small cars (below 1.4 litre cylinder) is expected to decrease from 51% in 2010 to 36% in Medium size cars (between 1.4 and 2.0 litre) are expected to increase from 43% in 2010 to 53% in 2030 and the share of large cars will increase from 6% in 2010 to 10% in Emission factors and emission calculation The emissions factors for the vehicle fleet in Poland depend on the age of the cars, the type of fuel, the engine size, fleet renewal and driving conditions. Figure 6 shows the aggregate emissions factors for passenger cars driving in urban and non-urban areas in Poland. As the TREMOVE emissions factors only include the EU emissions performance target of 140 gram CO 2 per vehicle kilometre and no significant emissions reduction afterwards, we have assumed that the long term target of 95 gram CO 2 per vehicle kilometre by 2020 will be realised in 2020 and included it in our BAU scenario. This means that it is only after 2020 that new car sales will comply with this emission target. In 2008, the average type approval (test cycle) CO 2 emissions of new cars in Poland was 153 gram per vehicle kilometre. Please note that the average real world emissions are about 15% 6 higher than the test cycles emissions as referred to in the 95 grams per kilometre target for The emissions curves (real world) as shown in Figure 6 are the aggregation of specific emissions curves for different vehicle types by fuel technology and engine size. The average improvement of energy-efficiency or CO 2 emissions is estimated at about 1.7% per annum. 6 Due to ancillary energy consuming devices like aircon, navigation, and non-optimal driving styles, urbanisation and congestion

10 CO2 emissions factors for passenger cars in urban and non-urban areas in Poland (g CO2 per vkm) Figure 6: Emissions factors for passenger cars in urban and non-urban areas in Poland, TREMOVE 2009 Urban transport TREMOVE 2009 Non-urban transport ECORYS BAU scenario urban transport ECORYS BAU scenario non-urban transport The resulting CO 2 emissions are calculated by multiplying the vehicle kilometres and the corresponding emission factor by each size/fuel class and driving condition. The formula is as follows: vehicle kilometers(vkm)*emissionfactor(ef) D,S, F Where: D S F = Driving condition (urban, non-urban) = Size class of the engine (<1.4L ; 1.4L to 2.0L ; > 2.0L cylinder volumes) = Fuel type (petrol, diesel, LPG) The results in Figure 7 show our BAU scenario for passenger cars, the emissions statistics of Poland and the TREMOVE baseline. The main reasons why the BAU scenario differs substantially from, and has higher CO 2 emissions than the TREMOVE baseline scenario are as follows: ECORYS BAU scenario assumes a larger share of urban transport vehicle kilometres for which the emissions factor is higher ECORYS BAU scenario assumes a larger growth of vehicle kilometres driven, due to economic growth, new motorway infrastructure and motorization. ECORYS BAU includes an updated analysis of the vehicle fleet renewal based on the second-hand car imports. Especially for the timeframe the inflow of large numbers of relatively old and inefficient passenger car imports show a slightly negative impact on the average emission factors as compared to TREMOVE.

11 CO2 emissions (million Ton) from passenger car road transport in Poland Our BAU scenario, however, also includes some assumptions that lead to lower CO 2 emissions than in the TREMOVE baseline scenario. These assumptions include: ECORYS BAU scenario assumes a larger share of diesel and LPG cars in Poland for which the emissions factors are lower than for petrol cars ECORYS BAU scenario assumes a smaller size of the total vehicle fleet of passenger cars ECORYS BAU scenario assumes a further decrease of emissions factors for petrol and diesel cars due to the long terms target of 95 grams CO 2 per kilometre for car manufacturers Figure 7: Road transport CO 2 emissions from passenger cars in Poland, 1995 to TREMOVE 2009 baseline Poland official GHG inventory ECORYS BAU scenario 2.3 BAU for freight transport Basically the same approach as for BAU development for passenger cars was used to establish a BAU scenario for road freight transport (trucking). However, much less data was available regarding the composition and evolution of the truck fleet. The freight transport sector in Poland has witnessed a rapid increase in freight ton kilometres. Especially the trucking sector has grown substantially. The Polish road trucking sector is very competitive both nationally and internationally. It comprises many one-man companies with only one truck. Road haulage is considered much more reliable, flexible and faster than rail transport and an interview with the Ministry of Infrastructure pointed out that even low value bulk materials like coal are increasingly transported by trucks. The ton-km market share of road freight transport in the total surface transport (road, rail and inland waterways) is about 80% and the truck ton-kilometres are expected to increase from 188 billion ton-km in 2010 to 326 billion by

12 CO2 emissions (million Ton) from road freight transport in Poland The estimated market shares (ton-km) by truck size class are 88% by heavy duty trucks (HDT: >16 tons), 10% by medium duty trucks (MDT: 3.5 to 16 tons) and 2% by light duty trucks (LDT: <3.5 tons). The share of light duty vehicles older than 10 years decreased from 40% in 2000 to 20% in The share of heavy duty vehicles older than 10 years decreased from over 65% in 2000 to less than 35% in The resulting development of CO 2 emissions from road truck transport is depicted below. Figure 8: Ultimate CO 2 emissions from road freight transport in Poland, TREMOVE 2009 Baseline for Poland Poland official GHG inventory ECORYS BAU scenario 2.4 Total CO 2 emissions in the BAU scenario The total CO 2 emissions from road transport will increase from 21.3 million ton in 1990 and 34.2 mton in 2005 (EC, 2010) to 65.9 million ton in 2030 which is a growth of 210% and 93% compared to the 1990 and 2005 CO 2 emissions level. This Figure includes trucking, passenger car transport and an additional group comprising emissions from motorcycles, moped, buses and tractors (about 4% of total). 3 Developing low carbon policies 3.1 Screening promising individual policies We first developed a long laundry list of all possible policy measures that could be taken to reduce GHG emissions. This laundry list of measures was screened to reduce the number of policy measures to consider further in the analysis. In order to carry out this screening we used a number of criteria. First, we wanted to make sure that we included a wide range of policy

13 measures covering legislative / regulatory, market-based and educational / informative measures. Then we used the abatement potential, marginal abatement costs (if available) and barriers for implementation to make a preliminary assessment of the attractiveness of the policy measure. The following list of policies and measures were assessed as having a large potential to reduce road transport CO 2 emissions: Road pricing and congestion charging Fuel tax increasing the fuel price by 10% (passenger cars and trucks) Fuel tax proportional to the annual improvement of CO 2 emissions from new cars Promotion of larger and heavier trucks and increased logistics efficiency Eco-driving training Parking policy Promotion of non-motorized and public transport Road pricing and congestion charging This policy measure covers the introduction of electronic tolling on Polish motor and expressways (National policy measure) and a gradual introduction of congestion charging in the larger Polish cities, starting with Warsaw (municipal policy measure). In 2011 about 1,000 km of toll roads is assumed increasing to 3,000 km in 2020 and 5,000 km in This is approximately 80% of the motor- and expressways in Poland. Given the wide range for the price elasticity of transport demand, we have chosen an elasticity of for the whole of Poland. We calculate the impact of a 5% and 10% price increase (Cautious and Proactive, respectively) on toll roads in non-urban areas with an increasing length of roads equipped with the toll system. In addition, a congestion charging scheme will be gradually introduced in the city centers of all major Polish cities (>250,000 inhabitants). The city centers are responsible for generating about 30% of all urban traffic in Poland. The first scheme will be introduced in the city center of Warsaw covering about 800,000 inhabitants 7. Fuel tax increasing the fuel price Impact on passenger car transport: In , fuel prices in Poland were about 10% lower than the European average. We estimate the impact of a fuel price increase of 5% and 10% (Cautious and Proactive, respectively). The reduction of vehicle kilometres, as result of the price increase (fuel tax), is larger in urban areas (-0.3 elasticity) than in non-urban areas (-0.2 elasticity). The impact on the fuel efficiency of cars is corrected for a rebound effect of 20%: people driving more due to the improved fuel economy. The fuel efficiency gain is primarily explained by a shift from gasoline to diesel vehicles, purchase of more fuel efficient vehicles, less demand for (urban) transport and a decrease in congestion levels. 7 Warsaw metropolitan area has 2.7 million inhabitant of which 1.7 million in the city area, the inner city comprises the most central area with 800,000 inhabitants

14 Impact on truck transport: Regarding truck transport, a fuel price increase of 10% is predicted to have a larger impact on heavy duty transport than on light duty transport (Delsalle, 2002). A fuel price increase of 10% is expected to reduce light duty tonkilometres by 0.1% and heavy duty transport by 1%. These impacts confirm the inelastic character of freight transport: the fuel costs are only a small part of the value of the goods transported and these extra costs are passed on to the end-consumer. Nevertheless, in Poland, truck transport currently carries a large proportion of goods like coal with a low density value. It is expected that the transport of these low value goods is more sensitive to a fuel price increase. Less congestion, higher load factors, and more fuel efficient trucks due to the fuel price increase are expected to increase the fuel efficiency by 0.4%. The increased fuel tax will also be responsible for a small modal shift of about 0.4%. Fuel price increase proportional to the annual improvement of CO 2 emissions from new cars In 2010 the average emissions are approximately 150 gram CO 2 per vkm in Poland. If the automotive industry will achieve the target of 95 g per vkm by 2020, emissions of new cars in Poland will decrease from 150 to 95 g per vkm between 2010 and This is a reduction of 36.7% in total or 4.5% annually. This policy measure comprises a proportional annual fuel price increase of 3.2% between 2010 and 2020 (which is an increase of 36.7% between 2010 and 2020) and no rebound effect because the gain in fuel efficiency is diminished by the annual fuel price increase. Larger, heavier truck and more efficient logistic chains A fuel price increase has a limited effect as a policy for reducing GHG emissions from truck transport. Nevertheless, various sources in the literature point at considerable reductions from truck transport due to larger and heavier trucks (den Boer, 2008) and more efficient logistic chains and distribution efficiency (McKinnon, 2007). This policy measure comprises reductions of 10 to 15% (Cautious and Proactive) from allowing and promoting larger and heavier trucks and 5 to 10% (Cautious and Proactive) from promoting more efficient logistic chains in the long run. Eco-driving training Previous studies by TNO (2006) and Wilbers et al (2004) assumed, for existing drivers, an achievable effect of 10%, an effectiveness of 35% and durability of 90%. Consequently the long run efficiency improvement is 3.15% (10%*35%*90%). This policy measure for Poland comprises the introduction of a voluntary eco-driving course from 2010 onwards and a mandatory course for new drivers from 2015 onwards. An estimated annual share of 2.5% new drivers within the total population of drivers in Poland is used. The achievable effect for new drivers is also 10% for new drivers, but the effectiveness and durability are assumed to be 50% and 100% (Cautious and Proactive). Since (voluntary) eco-driving reduces fuel costs, a rebound factor of 20% of the achieved effect is applied.

15 Parking policy Passenger cars and public buses comprise about 95% of the mobility in cities. Projections by Burnewicz (2008) show that by 2033 the market share of buses has decreased by 7 percent points and car transport has increased by 7 percent points. The biggest challenge for Polish cities will be to keep people using public transport and prevent a large modal shift to car transport. Motorists tend to be particularly sensitive to parking price because it is a direct (out-of-pocket) expense. The parking policy in the streets of Warsaw for instance is strictly enforced from Monday through Friday from 8 a.m. to 6 p.m. Metered spaces are free after 6 p.m. and all day on weekends and holidays. The parking price is 2.60 PLN ( 0.65) per hour. The policy measure in this study assumes a parking fee in the entire inner city regions of cities in Poland. In Warsaw this would be an area of 800,000 inhabitants. Parking fees will be increased by 20% in 2010 and another 20% in 2015 (10% in the Cautious scenario). By 2015 the parking price will be 44% higher than in the BAU scenario in Literature shows various elasticities for parking prices and modal shifts from parking fees. In this study we have assumed an elasticity of -0.3 for vehicle-kilometres travelled. A 20% increase (10% in the Cautious scenario) will result in a reduction of 3.8% in vehicle-kilometres in urban areas. We have also assumed that about 60% of the reduced vehiclekilometres will be shifted towards public transport and slow modes (walking an cycling). Promotion of non-motorized and public transport This policy measure promotes non-motorized transport (e.g. walking and cycling) by lowering the speed limit for on roads in urban areas by 10%. This results in increased travel time for cars and a higher relative attractiveness of slow modes. Litman (2009) shows a demand elasticity of travel time of as a European average in the long run (until 2015 in this study) and -0.2 in the short run (after 2015 in this study). 10% longer travel times (5% in the Cautious scenario) are predicted to decrease vkm by 7.2%. In addition, a reduction of the fare cost of public transport by 10% financed by the revenues from demand management interventions result in a short term increase of demand for public transport by 4% and 8% in the long run (modal shift). 3.2 Policy packages and reduction potentials In order to capture the uncertainty inherent in analysing and comparing the potential strategies for mitigation of GHG emissions from the transport sector two policy packages are constructed and their reduction potential, relative to the BAU scenario, estimated. These are a Cautionary policy package (in terms of what can be achieved with respect to reducing GHG emissions) and a Proactive policy package representing the maximum potential reductions in GHG emissions.

16 Table 5: Decomposition of the Cautious and Proactive policy packages for the Polish road transport sector CO 2 emissions (mton) ECORYS BAU Cautious scenario Passenger cars Freight trucks Other Proactive scenario Passenger cars Freight trucks Other % reduction Cautious -8% -12% % reduction Proactive -21% -27% The abatement potential in 2030 is estimated at about 12% and 27% for the the Cautious and Proactive policy packages. Table 6: Decomposition of the Cautious and Proactive policy packages for passenger cars and freight transport CO 2 emissions reduction (mton) Reduction Cautious scenario passenger cars: Road pricing and congestion charging Fuel tax increase Eco-driving Parking policy Promotion non-motorized (public) transport Reduction Cautious scenario freight trucks: Fuel price increase truck transport Larger heavier trucks, logistics efficiency Total reduction Cautious scenario Reduction Proactive scenario passenger cars: Road pricing and congestion charging Fuel tax linked to CO 2 improvements Eco-driving Parking policy Promotion non-motorized (public) transport Reduction Proactive scenario freight trucks: Fuel price increase truck transport Larger heavier trucks, logistics efficiency Total reduction Proactive scenario

17 4 Conclusion Although technological improvements like hybrid and electric vehicles and biofuels have not been explicitly modelled, the 95 gco 2 / km target by 2020 and the higher share of biofuels in transport have been taken into account for estimating the emission factors and evolution of these factors over time. Therefore the BAU scenario takes into account a relatively rapid uptake of vehicle and fuel technologies for increasing the energy efficiency of vehicles and reducing the carbon content of fuels. The abatement potential for road transport in Poland is estimated at about 12% and 27% for the Cautious and Proactive policy packages by Table 7: GHG Policy packages for the Polish road transport sector compared to BAU scenario emissions CO 2 emissions (mton) 1990* ECORYS BAU Cautious package Proactive package % reduction Cautious to BAU 0 0-8% -12% % reduction Proactive to BAU % -27% In 2020, the Cautious and Proactive policy packages still leave emissions 58% and 35% higher than in The binding target for non-ets sectors in Poland is a maximum increase of 14% in 2020 compared to the 2005 GHG emissions. This means that even with the Proactive policy package Poland can expect to exceed its target by 21% (35%-14%). The inescapable conclusion of this is that, if Poland wants to meet its emissions control target of 14%, it cannot only rely on technology. In order to meet this target for the road transport sector, Polish policy makers will have to implement policy measures that promote alternatives to road transport, as well as take steps to slow the growth of road transport. Furthermore, the policy measures that are needed will have to be very stringent, much more so than what we have included in the Proactive policy package, if the 14% maximum emission growth target is to be realised. Of course, another alternative could be that road transport emissions are offset by other non-ets sub-sectors like housing or light industries, or by means of more stringent abatement polices than included in our Proactive scenario. However, whether this is desirable, or realistic is far from clear.

18 CO2 emissions (million Ton) from road transport in Poland Table 8: GHG policy packages for the Polish road transport sector compared to 1990 and 2005 emissions Percentage emissions reduction (%) % 1990 % 2005 % 1990 % 2005 ECORYS BAU 176% 72% 210% 93% Cautious policy package 154% 58% 173% 70% Proactive policy package 117% 35% 125% 40% The emission trajectories are plotted in Figure 9 below. Figure 9: CO 2 emission reduction scenarios for passenger car and truck (LDV, MDV, HDV) transport in Poland Cautionary scenario ECORYS BAU scenario TREMOVE 2009 baseline Proactive scenario EU Energy in figures DG TREN / EEA

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