Calculation of indicators of envitonmental pressure caused by

Luxembourg: Office for Official Publications of the
European Communities, 2003
ISBN 92-894-5515-2
ISSN 1725-0803
Cat. No. KS-AU-03-001-EN-N
2003 EDITION
S T U D I E S
A N D
P A P E R S
W O R K I N G
COPYRIGHT
© European Communities, 2003
Calculation of
Indicators of
Environmental
Pressure caused by
Transport
Main report
E U R O P E A N
COMMISSION
8
THEME 8
Environment
and
energy
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Luxembourg: Office for Official Publications of the European Communities, 2003
ISBN 92-894-5515-2
ISBN 1725-0803
© European Communities, 2003
Table of Contents
1
2
3
Introduction........................................................................................................................... 1
Summary................................................................................................................................ 3
Project overview.................................................................................................................... 4
3.1 Outline of the approach for road transport.......................................................................... 4
3.2 Outline of the approach for railways................................................................................... 5
3.3 Outline of the approach for maritime and inland shipping ................................................. 6
3.4 Outline of the approach for aviation ................................................................................... 7
3.4.1 Air traffic source data ................................................................................................. 7
3.4.2 Future emissions from IFR flights .............................................................................. 8
3.4.3 TRENDS/aviation methodology................................................................................. 8
3.5 Outline of the transport activity balance (TAB) module .................................................... 9
3.6 Outline of the noise study ................................................................................................. 11
4
Basecase scenario ................................................................................................................ 12
4.1 Overview........................................................................................................................... 12
4.2 Results per mode............................................................................................................... 12
4.2.1 Fleet data ................................................................................................................... 12
4.2.2 Vehicle emissions ..................................................................................................... 15
4.3 Results – Total .................................................................................................................. 26
4.3.1 Fleet data ................................................................................................................... 26
4.3.2 Vehicle emissions ..................................................................................................... 28
4.3.3 Contribution of each mode to the total EU15 emissions .......................................... 32
4.3.4 Emission factors........................................................................................................ 34
5
TRENDS - Auto Oil II comparison ................................................................................... 40
5.1 Activity data...................................................................................................................... 40
5.1.1 Road transport........................................................................................................... 40
5.1.2 Maritime.................................................................................................................... 43
5.1.3 Railways.................................................................................................................... 46
5.2 Emission results ................................................................................................................ 49
6
Spatial disaggregation ........................................................................................................ 62
6.1 Road Transport.................................................................................................................. 62
6.1.1 HigHway emissions ................................................................................................... 62
6.1.2 Urban emissions........................................................................................................ 63
6.1.3 Rural emissions......................................................................................................... 63
6.1.4 Production of GIS maps............................................................................................ 63
6.2 Maritime shipping............................................................................................................. 67
6.3 Inland shipping.................................................................................................................. 69
6.4 Railways............................................................................................................................ 72
6.4.1 Attributing Intraplan-nodes to GISCO railway segments ......................................... 72
6.4.2 Attributing railway segments to NUTS regions........................................................ 76
7
Temporal disaggregation – road transport ...................................................................... 80
7.1 Data availability ................................................................................................................ 80
7.2 Methodology ..................................................................................................................... 80
7.3 Results............................................................................................................................... 81
8
Problems and Shortcomings of the present system.......................................................... 87
8.1 Road transport module...................................................................................................... 87
8.2 Railway, maritime and inland shipping modules.............................................................. 88
8.3 Air module ........................................................................................................................ 88
9
Future Developments.......................................................................................................... 89
References .................................................................................................................................... 91
Appendix A: Seasonal distribution of CO2, NOx and PM emissions...................................... 92
Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
1 INTRODUCTION
The purpose of this study was to develop a system for calculating a range of environmental
pressures due to transport within a PC-based MS Access environment (TRansport and
ENvironment Database System - TRENDS). These environmental pressures include air emissions
from the four main transport modes, i.e. road, rail, ships and air. In addition, waste generation and
noise emissions from road transport were also addressed. Finally, the system provides an option
for simple scenario analysis including vehicle dynamics (such as turnover and evolution) for all
EU15 Member States.
The final aim of this study was to produce a range of transparent, consistent and comparable
environmental pressure indicators caused by transport. These indicators were calculated directly
from the activity levels and reflect the potential change in the state of the environment, or the risk
of specific environmental impacts which any changes in policy might have.
The TRENDS project was funded by the European Commission, Directorate-General for
Transport and Energy and conceived and managed by Graham Lock in the Environment and
Sustainable Development Unit of Eurostat. The project was developed in the framework of a
collaboration between members of the following institutes and organisations:
•
Laboratory of Applied Thermodynamics, Aristotle University, Greece (LAT)
•
Department of Energy Engineering, Denmark Technical University (DTU)
•
Ψ A -Consulting, Austria (PSIAMTK)
•
INFRAS, Bern, Switzerland (INFRAS)
The Laboratory of Applied Thermodynamics (LAT), Aristotle University of Thessaloniki, Greece,
was the co-ordinator of this study team and responsible for the administration of the project.
The project was completed in three phases, starting at 1997 as follows:
Phase I: December 1997 - December 1998 (EC contract: E1-B97-B2-7040-SIN 7674-SER) Final Report of Phase I, December 1998
Phase II: March 1999 - March 2000 (EC contract: B99-B2704010-S72.7941-RE1 9930 SER.STAT) - Final Report of Phase II, February 2000
Phase III: November 2000 - June 2002 (EC contract: B2000-B27040B-SI2.198159-SER
ARISTOTLE) – Main Report and Detailed reports, October 2002
This is volume 1 and the main report of the project. It summarises a series of detailed reports and
provides the basic conclusions of the work. The other detailed reports on which the main report
is based are the following:
2. Road Transport
3. Maritime and Inland Shipping
4. Railways
5. Aviation
6. Waste
7. Noise
8. Transport Activity Balance (TAB)
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Study Teams
Laboratory of Applied Thermodynamics – Aristotle University Thessaloniki (LAT/AUTh)
Zissis Samaras
Myrto Giannouli
Charis Kouridis
Evelina Tourlou
Theodoros Zachariadis
Aris Babatzimopoulos
Department of Energy Engineering, Denmark Technical University (DTU)
Spencer Sorenson
Aliki Georgakaki
Robert Coffey
Ψ A -Consulting, Austria (PSIAMTK)
Manfred Kalivoda
Monika Kurdna
INFRAS, Bern, Switzerland (INFRAS)
Mario Keller
Peter deHaan
Roman Frick
René Zbinden
Philipp Wüthrich
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2 SUMMARY
The main parameters investigated in the framework of this project can be summarised as follows:
Air emissions from the following transport modes:
•
Road (including all types of passenger and goods transport)
•
Rail (including electrical trains, passenger and goods transport)
•
Shipping (maritime and inland, passenger and goods transport)
•
Air (national and international, passenger transport)
Pollutants covered: carbon monoxide; carbon dioxide; non-methane volatile organic compounds;
methane; nitrous oxide; xxides of nitrogen; oxides of sulphur; lead, particulate matter (PM10)
•
Waste production from road transport
•
A feasibility study was conducted on noise emissions from road transport.
•
Spatial resolution: The geographical distribution includes the EU15 Member States, as well
as cities, regions and different classes of infrastructure (e.g. urban and rural roads,
motorways).
•
Temporal resolution: Annual air emissions were disaggregated into seasonal emissions.
•
Time span. The study provides time series of indicators for every year from 1970 to 2020.
•
System dynamics, projections and forecasting: Extrapolations were conducted for future
years, based on simple assumptions. Main emphasis was given on specific requirements for
vehicle fleet dynamics (turnover, mean age, technology split etc.).
An important aspect of the project was to obtain feedback on data gaps, in particular where these
gaps had a significant influence on the reliability of the outputs.
The calculation system including the methodologies and related databases was transferred in a
computer model within a PC-based MS Access97 environment.
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3 PROJECT OVERVIEW
3.1 OUTLINE OF THE APPROACH FOR ROAD TRANSPORT
The road transport module developed in the framework of the TRENDS project produces both
analytical and aggregated results for the EU15 countries and for a time-span of 50 years. More
specifically, the road transport module calculates various transport-related parameters, such as the
annual mileage, vehicle population, average age, vehicle emissions and fuel balance, for all
vehicle categories considered by COPERT. Additionally, temporal and spatial disaggregation of
the estimated vehicle emissions was conducted for the target year 1995.
For the estimation of air pollutant emissions from road transport a top down approach was
considered to be the most appropriate. Focus of the calculation was the annual air emissions of a
Country (each EU15 Member State). The time range was set from 1970 to 2020, with 1995
defined as the base year for the calculations.
For air emissions and fuel consumption the COPERT III calculation module was applied. After
annual air emissions were estimated on country basis, a spatial disaggregation module allocated
the above annual air emissions to the different parts of the countries, using the initial COPERT
estimates for urban, rural and highway split of the emissions for the different vehicle categories.
At a final step, temporal disaggregation of vehicle emissions was conducted for each country,
using appropriate patterns.
A detailed description of the methodological steps of the calculation for road transport follows:
Step 1: Creation of the appropriate databases for the calculation modules. All available Eurostat
databases such as TRAINS and SIRENE were used in order to construct the appropriate
input for the calculations. In this respect, data concerning vehicle stocks, vehicle new
registrations, vehicle usage indicators (such as tonne-kilometres, passenger-kilometres,
etc.) as well as fuel consumption for transport were used.
In addition to Eurostat, other sources of information were also incorporated (with main
emphasis on COPERT [1], TRAP [2] and MEET [3])) which provided additional data
not found in Eurostat. The information derived from these databases included usage data
such as technology splits of vehicle fleets for certain years, annual mileage for different
vehicle categories, vehicle representative speeds, split of the annual mileage to different
road classes, etc. Moreover, national data were also examined in order to fill gaps but
also to make comparisons and to calibrate the existing data.
Step 2: A system dynamics module was established in order to attain the following objectives:
(a) Extrapolation of the main vehicle categories into the future using data of the past.
This was conducted using a sigmoid-type Gompertz function, which simulates the
evolution of vehicle density. [3] The results of the extrapolation were combined with
Eurostat population forecasts per country in order to produce estimates of vehicle
stocks per country.
(b) Simulation of the vehicle turnover for the main vehicle categories. This was achieved
using appropriate lifetime functions, which were developed by means of a Weibullbased function. The approach was calibrated on the basis of Eurostat data for the
evolution of vehicle stock and new registrations.
(c) The above were supplemented with corresponding data on emissions technology
parameters which were introduced via a number of suitable implementation tables per
country, including simultaneous introduction of different legislation, scrappage
schemes, etc.
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Step 3: The data resulting from the aforementioned processes were adapted in such a way as to
produce the input tables for the calculation of annual air emissions required by the
methodology of COPERT. These input tables were produced for the entire calculation
period, i.e. from 1970 to 2020. Especially as regards the future emission estimates, it was
necessary to amend the legislation implementation tables with future estimates referring
to the dates of introduction and to the effects of future legislation.
Step 4: Spatial disaggregation was performed using the basic annual estimates of COPERT and
their split in urban, rural and highway modes, as follows:
• Highway emissions were directly allocated to the highway networks of the countries.
To this aim, selected traffic counts from different types of highways were used in
order to produce appropriate traffic allocation patterns.
• Urban emissions were allocated to cities above a certain threshold (all settlements
with 20 000 or more inhabitants were considered as cities) of the different countries.
The allocation was conducted using mainly the population data of the Eurostat/New
Cronos database REGIO, but also complemented with other data, such as fuel
consumption and/or vehicle densities of the different countries, mainly in order to
reflect differences between different regions of countries.
• The rural emissions produced by COPERT were allocated over the whole non-urban
area of the EU15 countries, depending on the population density and regional GDP of
each area.
Step 5: Temporal disaggregation: As Eurostat data on seasonal variation of transport activities
were scarce, other sources of information were investigated. The only source of temporal
data discovered, was a project conducted in Austria [4], which contains a study of the
traffic load for different types of roads, depending on various time-related parameters.
The monthly variations of the traffic load provided by this source were used in order to
produce the required seasonal variations of vehicle emissions.
Within the road transport module, a “waste from road transport” module was developed in order
to forecast the total waste production originating from end-of-life road transport vehicles.
The waste from road transport database produces “waste factors”. These waste factors represent
the amount of waste for a given material or vehicle component as a function of activity, in
analogy to the emission factors for atmospheric pollutants. Waste factors were produced not only
for passenger cars, but also for light and heavy-duty vehicles as well as for motorcycles.
The waste factors within the database can be divided in two major categories:
•
Waste produced during operation of road transport vehicles (in-use waste factors, expressed
as a function of the veh-km travelled)
•
Waste produced when the vehicle was finally taken from the road and shredded (so-called
end-of-life waste factors, expressed per scrapped vehicle).
All waste factors depend on the technology stage (EURO-I, -II, etc.) of the vehicle, in order to
reflect the rapid change in technology and in the materials used over the last decades.
3.2 OUTLINE OF THE APPROACH FOR RAILWAYS
The purpose of the railway module was to establish a database that provides indicators for
railway transport in EU15 countries, between the years 1970 and 2020. In this study, only the
energy consumption of tractive movements and the consequent emissions of airborne pollutants
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were considered. Other activities such as maintaining infrastructure and vehicle stock, or
environmental factors such as noise and vibration, were not examined.
The indicators produced were determined for both diesel and electric energy sources, as well as
for freight and passenger traffic. Results for energy consumption, CO2, SOx and NOx emissions
were plotted for all EU15 Member States for the year 1995.
In order to develop the railway database, traffic data provided by Eurostat were used, based on
the Eurostat New Cronos Rail Database, UIC and national sources.
A database was then constructed, which estimates emissions and energy consumption of railway
transport from the year 1970 to the present day and provides projections up to the year 2020. The
database was constructed in such a way that it may be updated or adapted with relative ease,
should improved information become available.
A detailed database was also constructed for the base year 1995 by combining UIC data and data
provided by the INTRAPLAN study [5]. The spatial resolution of the detailed database is on a
network level. The resulting factors were attributed to the TEN railway corridors and to NUTS
zones. The temporal range for the detailed database was limited to the year 1995, as this is the
only year for which data was available from the INTRAPLAN study.
With some correction in terms of the specific energy consumption of passenger trains and using
empirical results for freight trains, the energy consumption and emissions calculated in the
detailed database were estimated to within 30%, of published figures for national networks, with
most estimates lying within 20%.
Recommended measures to improve the estimation of indicators were given. These include the
need:
•
to record the gross hauled tonne-kilometres of passenger and freight train movements at a
network level
•
to divide passenger traffic into categories on the basis of service
•
to identify power sources in all traffic measurements
3.3 OUTLINE OF THE APPROACH FOR MARITIME AND INLAND
SHIPPING
The TRENDS study of maritime shipping aimed to estimate the environmental pressures caused
by the world’s commercial shipping fleet attending EU15 countries. According to the Lloyds
register [6] there are currently around 83 000 vessels operating in the world’s oceans with a total
gross tonnage of 491 million tonnes. The register excludes vessels under 100 GT as well as
naval, pleasure, unpowered craft or those restricted to canal, river or harbour service. It should be
noted that the military fleet consists of around 20 000 vessels [7]. These are on average smaller
than their commercial counterparts and were not considered by this investigation.
Only shipping movements that involved contact with EU15 countries such as the delivery or
receipt of goods were considered. Ships passing through European waters without contact with
these countries were not considered by the study.
In terms of maritime transport, the structure and method of a detailed database was constructed
within MS Access, which included all stages of the emissions and energy consumption
calculations. The major technical assumptions were established and the necessary technical
factors were incorporated within the database. This database was designed to operate on detailed
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statistical data provided by Eurostat. However, data was available only at port level, so a bottomup approach was employed.
As the statistical data collection was not calibrated towards emission modelling, problems were
encountered in using the data successfully for that purpose. Since this bottom-up approach could
only be conducted for a few years, aggregated data on a country level were used in order to
provide time series calculations through a second database.
The database for goods transport by inland waterways was completed within MS Access in terms
of both structure and method. However, further work is required in order to support some of the
assumed operational parameters such as loading factors and average speed. The nature of statistics
at country level does not allow for great detail in this database.
3.4 OUTLINE OF THE APPROACH FOR AVIATION
Increasing numbers of flights and still unknown effects of exhaust gases on the high atmosphere
have drawn attention on air traffic and its emissions. In Europe, many institutions work in this
area, collecting traffic and emission data, creating emission inventories and assessing effects.
That leads to some work done in parallel while using different databases and methodologies,
which often lead to results that cannot be compared or matched.
For EU purposes, scenarios of future emissions need to be carried out centrally using a common
method and harmonised data sets. For that reason, Eurostat developed methods for estimating
emissions based on a single data set provided by Eurocontrol.
Eurocontrol is the European Organisation for Safety of Air Traffic. At the moment it has 28
Member States, including the EU15 countries, with the exception of Finland. Eurocontrol
provides annual flight statistic data for a special area covered by its Member States. Although the
data does not include all the current EU Member States, it is indicative of the rate of change
throughout Europe.
3.4.1 AIR TRAFFIC SOURCE DATA
Air traffic in IFR (Instrument Flight Rules) flights is controlled by air traffic control services that
report each flight to Eurocontrol.
Eurocontrol provided data on the profile flown and the aircraft type used for the 7 Mio. flights
that were conducted in Eurocontrol area in 1997. This enabled the use of a bottom-up approach
for the estimation of emissions produced by aviation.
Detailed information on air traffic is only available for civil aviation and more specifically for
IFR flights. For that reason, military aviation was not addressed in this study and IFR flights
were considered to be responsible for about 95% of air transport emissions.
Eurocontrol provides for the area covered by its Member States two detailed movement
databanks:
•
CRCO and
•
CFMU
Records from these databases giving information on the flight profile were linked to emission
data from aviation, provided by Eurostat.
Data from the AEA database (AEA technology) were also considered. These data cover the time
period 1975-1995 and are available for passenger-kilometres, tonne-kilometres, seat-kilometres
and vehicle per kilometre for each country and year. These data also distinguish between
passenger and freight transport.
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3.4.2 FUTURE EMISSIONS FROM IFR FLIGHTS
In order to forecast the annual number of flights Eurocontrol adopts a method in which extremes
and a baseline are analysed. Figure 3-1 is an adaptation of a figure that was published in air
traffic statistics and forecasts of Eurocontrol (June 1998).
EURO 88 - annual number of IFR flights (in thousands)
18000
16000
Number of flights
14000
12000
10000
High Scenario
Baseline Scenario
8000
Low Scenario
6000
4000
2000
19
74
19
77
19
80
19
83
19
86
19
89
19
92
19
95
19
98
20
01
20
04
20
07
20
10
20
13
20
16
20
19
0
Year
Figure 3-1: Air traffic forecast for the Eurocontrol area
Eurocontrol produced forecasts of air traffic up to and including 2015, based on three different
growth scenarios (high, low and baseline). According to these estimates, the number of flights in
the Eurocontrol area is expected to increase, from less than 6 million in 1998, to more than 10
million in 2015 (see Figure 3-1).
The original chart produced by Eurocontrol, showed traffic statistics and forecast up to and
including 2015. The remaining five-year forecast was extrapolated to give an indication of the
traffic until year 2020.
Emission scenarios are an important factor in the estimation of aircraft emission factors. Future
emissions from aviation depend on the balance between improvements in technology (producing
more efficient and less polluting aircrafts) and the growth in air transport. New and improved
technologies were briefly reviewed in this study and predictions of future levels of traffic were
examined. On the basis of this information, a number of future scenarios for aircraft emissions
were produced [8].
3.4.3 TRENDS/AVIATION METHODOLOGY
In order to produce emission forecasts for the time period 2002-2020, the traffic increase rates of
2002 – 2009 predicted by Eurocontrol (according to the baseline scenario) were extrapolated
until the year 2020.
As mentioned in section 3.4.1, one source that publishes passenger-kilometres as well as tonnekilometres is AEA. The passenger data provided by AEA were used to crosscheck the
TRENDS/Aviation extrapolation. Unfortunately, this comparison revealed that the AEA data
seem to underestimate passenger-kilometres significantly (by a factor of 40-100).
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Since the discrepancy between freight data (tonne-kilometres) provided by Eurocontrol and the
respective AEA data was considerable, only Eurocontrol data were used for the final calculation
of air emissions. As a consequence, a significant deviation is expected between the emissions
produced by TRENDS/Aviation and international statistical data.
The split between passenger and freight traffic was not possible due to the lack of freight data.
As mentioned before, AEA passenger data were considered unsuitable and no other source of
freight data was available for assessing the quality of AEA tonne-kilometre data. For that reason,
freight data were not included in the TRENDS aviation database. As a result, all emissions from
aviation were allocated to passenger transport.
An MS Access computer tool was finally created, called AvioPOLL, which employs the MEET
and AvioMEET methodologies in order to produce flight data. This tool enables the calculation
of emissions for pairs of regions (departure region-destination region). The calculations are
conducted quarterly, from quarter 1 in 1996, until the first quarter of 2002.
Moreover, a database was produced, which provides air emissions, including forecasts for the
time period 1970 to 2020. Emissions were generated per year according to the Eurocontrol split
into:
•
Short haul (SH)
•
Medium haul (MH)
•
Long haul western (LH)
For each region considered, emission data were also generated for movements, passengerkilometres and vehicle-kilometres.
AvioPoll is a purely analysing tool, based on activity data provided by Eurocontrol for the years
1996 till 2002. Combining actual (to be more precise actual flight plan) data with emission
factors makes it possible to:
•
Create an emission and fuel consumption inventory
•
Analyse emissions and fuel consumption on a spatial disaggregated level
•
Analyse emissions and fuel consumption for different aircraft types
•
Create environmental indicators from emissions and passenger-kilometres and vehiclekilometres
The activity data, which were incorporated into AvioPoll, represent aggregated number of flights
per origin/destination pairs per aircraft type groups. It was not foreseen to allow the user to
change any of this activity data in AvioPoll. Thus, it is not possible for example to change on a
given origin/destination pair actual aircraft type in order to assess the impact on environment. It
is also not possible to use AvioPoll in order to estimate air emissions for any years other than the
time period 1996-2002.
3.5 OUTLINE OF THE TRANSPORT ACTIVITY BALANCE (TAB)
MODULE
A particular task within TRENDS deals with the “balance of the overall transport activity data”.
This so called “transport activity balance” module (TAB) can be considered as a synthesis of
TRENDS since it allows to present the main data of all modes of TRENDS in a comparable way
– in particular the traffic activity and the emissions associated with it. TAB also allows the user
to perform a simple scenario analysis by assessing the effects of different assumptions about key
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factors like lower or higher overall transport activity evolution, modal shifts, different emission
standards etc.
However, it was understood that this scenario analysis should be kept on a comparatively low
level of complexity. In particular, TAB was not designed to elaborate sophisticated socioeconomic scenarios. It is rather the understanding that the “base case” (or “reference case”)
scenario which was defined within the individual modules of TRENDS represents a commonly
accepted development.
Varying some key factors leads to the creation of alternative scenarios. It is up to the user to
define “reasonable” variations of the assumptions. This should be possible for the time period
from 1970 up to 2020, (on a yearly basis) according to the time frame covered by TRENDS. The
appropriate level of spatial allocation is the country level or “EU15”, i.e. the aggregation of all 15
countries of the European Union.
The results produced by TAB can be divided into two main categories: traffic activity and
emission results. These results are given per country (and EU15 as a total) for all the years
considered by TRENDS. A large number of options are available to the user for implementing
these results. Data produced by TAB can be displayed according to the traffic type
(passenger/freight), according to the vehicle type and the vehicle technology. Figures 3-2 and 3-3
present the different options provided for displaying the traffic activity and emission results
respectively.
Figure 3-2: TAB menu for displaying the traffic activity
Figure 3-3: TAB menu for displaying emission results
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3.6 OUTLINE OF THE NOISE STUDY
Noise is the subjective description of sound. The perception of noise is dependent on the
frequencies, the sonar energy, its duration and regularity. Several methods have been developed
to represent these variables with one single indicator. The most commonly used unit is dB(A).
This unit therefore is taken as the indicator for assessing the disturbance of the population by
noise.
At very high noise levels (>120 dB(A)), noise can cause physical damage. The noise levels
reached by the various means of transport are, in general, much lower. Nevertheless, noise is a
significant source of annoyance and might lead to long-term psychological or physical damage.
According to recent German studies about 2% of all heart attacks are caused by road noise. In
addition, transport noise is a main source of disturbance of sleep and communications.
According to UBA [9], in Germany for example, 70% of the population perceive the noise from
road traffic as annoyance, air traffic is second with 55%. This indicates that noise is indeed a
major concern.
In the framework of the TRENDS project, a feasibility study was conducted on noise emissions
due to transport. The objective of the feasibility study was to evaluate ways and means of how
the disturbance by traffic noise can be measured and monitored. While a certain method for the
calculation of air pollutants exists, the assessment of noise and its monitoring creates new and
different types of questions since noise is a local problem. Therefore, in the case that noise is
treated on an aggregated level, the classical treatment is likely to become obsolete and alternative
approaches have to be investigated. This noise study was an attempt to sketch and evaluate
different possibilities to address the problems associated with noise.
There are various methods for measuring, calculating or monitoring noise and the annoyance
caused by noise. The methods can be classified in three main categories:
•
Engineering approach
•
Survey approach
•
LCA (life cycle assessment) approach
Since the TRENDS project focuses in principle on vehicle emissions, it was considered
consistent to apply the same approach for noise as well. Thus, noise emissions can be calculated
as noise indicators, using data produced by TRENDS whenever possible.
A very important element in the calculation of noise indicators is data availability. For that reason,
it was suggested that the same data sets that were used for the development of the various
modules should also be used for deriving the noise indicators. Finally, a methodology was
proposed for estimating noise emissions for three of the main transport modes: road, rail and air.
Noise emissions from shipping were considered to be negligible.
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4 BASECASE SCENARIO
4.1 OVERVIEW
As mentioned in section 3.5, the “Transport Activity Balance” (TAB) module allows a simple
scenario analysis by assessing the effects of different assumptions about key factors like lower or
higher overall transport activity evolution, modal shifts or changes in technology mixes (e.g.
petrol / diesel) etc.
Within the TAB module the user has the ability to change different parameters concerning the
traffic activity within TAB. The software then calculates the traffic activity and emission results
on different levels of detail (e.g. per vehicle class, per mode, or total emissions).
The data incorporated in TAB were produced from the different mode-specific modules. These
modules provided traffic activity data as well as emissions for the time period 1970-2020. These
data represent the reference or basecase scenario.
The traffic activity data included in the reference scenario were based on statistical results
provided by Eurostat and other sources. In order to obtain complete sets of timeseries, available
data were either extrapolated to missing years or kept constant over the time period 1970-2020.
An example of this is the share of diesel, gasoline and LPG vehicles in road transport. In order to
evaluate this share, statistical data provided by Eurostat were used, (available only until the years
1995-97) referring to both new registrations and total fleet. From these data, values of the vehicle
split were obtained for all EU15 countries, which were kept constant over the entire calculation
period. This stability in diesel/gasoline/LPG shares may not reflect the actual situation in Europe.
For example, some countries (e.g. France, Germany, Austria) recently exhibited a tendency
towards increasing diesel share. These tendencies were not considered in the basecase scenario.
In the future, additional scenarios can be created in order to account for such effects.
The following sections provide examples of traffic activity and emission results produced
according to the basecase (reference) scenario. All data were obtained from the TAB module
version 04h.
4.2 RESULTS PER MODE
4.2.1 FLEET DATA
Figures 4-1 to 4-5 show the annual vehicle-kilometres predicted by each mode (i.e. aviation,
maritime, railway, road transport and inland shipping) for all EU15 countries. These results
distinguish between freight and passenger vehicle-kilometres for the time period 1970-2020.
From these figures it can be observed that all transport modes exhibit an increase in vehiclekilometres, as expected.
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EU 15 Air Veh Km
25,000
mio Veh Km
20,000
15,000
Passenger
Freight
10,000
5,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-1: EU15 vehicle-kilometres for passenger and freight transport predicted by the air
module from 1970 to 2020
EU 15 Rail Veh Km
4,000
3,500
mio Veh Km
3,000
2,500
Passenger
Freight
2,000
1,500
1,000
500
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-2: EU15 vehicle-kilometres for passenger and freight transport predicted by the railway
module from 1970 to 2020
eurostat
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EU 15 Maritime Veh Km
2,000
1,800
1,600
mio Veh Km
1,400
1,200
Passenger
Freight
1,000
800
600
400
200
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-3: EU15 vehicle-kilometres for passenger and freight transport predicted by the
maritime shipping module from 1970 to 2020
EU 15 Road Veh Km
4,500,000
4,000,000
mio Veh Km
3,500,000
3,000,000
2,500,000
Passenger
Freight
2,000,000
1,500,000
1,000,000
500,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-4: EU15 vehicle-kilometres for passenger and freight transport predicted by the road
transport module from 1970 to 2020
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EU 15 Inland Veh Km
250
mio Veh Km
200
150
Passenger
Freight
100
50
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-5: EU15 vehicle-kilometres for passenger and freight transport predicted by the inland
shipping transport module from 1970 to 2020
4.2.2 VEHICLE EMISSIONS
Figures 4-6 to 4-25 represent the annual CO, NOx, HC and CO2 emissions produced by each
mode for all EU15 countries. The results are presented in terms of passenger and freight
emissions for the time period 1970-2020.
From these figures the following observations can be made:
•
Emissions from air transport increase steadily throughout the entire calculation period.
However, an anomaly can be detected in the curve between the years 1996 and 2001. This is
due to the fact that actual movement data were used for the calculation of emissions during
that period, while the emissions produced for the remaining years are mostly the result of
extrapolations.
•
Rail emissions (with the exception of CO2) present a slight decrease from 1970 to 2020, even
though the respective vehicle-kilometres increase during this period (cf. Figure 4-2). This
effect is probably due to the increasing use of electric trains, which do not produce air
emissions but contribute to the overall energy consumption.
•
Maritime emissions present a considerable increase, as expected, since maritime vehiclekilometres also increase significantly between the years 1970 and 2020. (cf. Figure 4-3)
•
Road transport emissions rise considerably until the years 1985-1990. After this time,
emissions from road transport drop rapidly until they reach very low levels. This is due to the
introduction of improved technologies (e.g. catalysts) and to the administration of more
stringent legislation measures. The exception to this tendency is CO2 emissions, which
increase steadily. This is a direct consequence of the increasing road activity observed in
EU15 countries (cf. Figure 4-4)
•
Emissions produced by inland shipping present a slight upward trend without any significant
variations, in agreement with the respective vehicle-kilometre results (Figure 4-5)
eurostat
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EU15 CO Air Emissions
180,000
160,000
140,000
Tons
120,000
100,000
Passenger
Freight
80,000
60,000
40,000
20,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-6: CO emissions [t] for EU15 countries produced by passenger and freight air transport
from 1970 to 2020
EU15 CO Rail Emissions
30,000
25,000
Tons
20,000
Passenger
Freight
15,000
10,000
5,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-7: CO emissions [t] for EU15 countries produced by passenger and freight railway
transport from 1970 to 2020
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EU15 CO Maritime Emissions
600,000
500,000
Tons
400,000
Passenger
Freight
300,000
200,000
100,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-8: CO emissions [t] for EU15 countries produced by passenger and freight maritime
shipping from 1970 to 2020
EU15 CO Road Emissions
50,000,000
45,000,000
40,000,000
35,000,000
Tons
30,000,000
Passenger
Freight
25,000,000
20,000,000
15,000,000
10,000,000
5,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-9: CO emissions [t] for EU15 countries produced by passenger and freight road
transport from 1970 to 2020
eurostat
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EU15 CO Inland Emissions
4,500
4,000
3,500
Tons
3,000
2,500
Passenger
Freight
2,000
1,500
1,000
500
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-10: CO emissions [t] for EU15 countries produced by passenger and freight inland
shipping from 1970 to 2020
EU15 NOx Air Emissions
1,000,000
900,000
800,000
700,000
Tons
600,000
Passenger
Freight
500,000
400,000
300,000
200,000
100,000
0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Year
Figure 4-11: NOx emissions [t] for EU15 countries produced by passenger and freight air
transport from 1970 to 2020
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EU15 NOx Rail Emissions
180,000
160,000
140,000
Tons
120,000
100,000
Passenger
Freight
80,000
60,000
40,000
20,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-12: NOx emissions [t] for EU15 countries produced by passenger and freight rail
transport from 1970 to 2020
EU15 NOx Maritime Emissions
6,000,000
5,000,000
Tons
4,000,000
Passenger
Freight
3,000,000
2,000,000
1,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-13: NOx emissions [t] for EU15 countries produced by passenger and freight maritime
shipping from 1970 to 2020
eurostat
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EU15 NOx Road Emissions
7,000,000
6,000,000
Tons
5,000,000
4,000,000
Passenger
Freight
3,000,000
2,000,000
1,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-14: NOx emissions [t] for EU15 countries produced by passenger and freight road
transport from 1970 to 2020
EU15 NOx Inland Emissions
90,000
80,000
70,000
Tons
60,000
50,000
Passenger
Freight
40,000
30,000
20,000
10,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-15: NOx emissions [t] for EU15 countries produced by passenger and freight inland
shipping from 1970 to 2020
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EU15 HC Air Emissions
80,000
70,000
60,000
Tons
50,000
Passenger
Freight
40,000
30,000
20,000
10,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-16: HC emissions [t] for EU15 countries produced by passenger and freight air
transport from 1970 to 2020
EU15 HC Rail Emissions
8,000
7,000
6,000
Tons
5,000
Passenger
Freight
4,000
3,000
2,000
1,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-17: HC emissions [t] for EU15 countries produced by passenger and freight rail
transport from 1970 to 2020
eurostat
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EU15 HC Maritime Emissions
180,000
160,000
140,000
Tons
120,000
100,000
Passenger
Freight
80,000
60,000
40,000
20,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-18: HC emissions [t] for EU15 countries produced by passenger and freight maritime
shipping from 1970 to 2020
EU15 HC Road Emissions
7,000,000
6,000,000
Tons
5,000,000
4,000,000
Passenger
Freight
3,000,000
2,000,000
1,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-19: HC emissions [t] for EU15 countries produced by passenger and freight road
transport from 1970 to 2020
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EU15 HC Inland Emissions
4,500
4,000
3,500
Tons
3,000
2,500
Passenger
Freight
2,000
1,500
1,000
500
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-20: HC emissions [t] for EU15 countries produced by passenger and freight inland
shipping from 1970 to 2020
EU15 CO2 Air Emissions
300,000,000
250,000,000
Tons
200,000,000
Passenger
Freight
150,000,000
100,000,000
50,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-21: CO2 emissions [t] for EU15 countries produced by passenger and freight air
transport from 1970 to 2020
eurostat
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EU15 CO2 Rail Emissions
30,000,000
25,000,000
Tons
20,000,000
Passenger
Freight
15,000,000
10,000,000
5,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-22: CO2 emissions [t] for EU15 countries produced by passenger and freight rail
transport from 1970 to 2020
EU15 CO2 Maritime Emissions
250,000,000
200,000,000
Tons
150,000,000
Passenger
Freight
100,000,000
50,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-23: CO2 emissions [t] for EU15 countries produced by passenger and freight maritime
shipping from 1970 to 2020
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EU15 CO2 Road Emissions
1,200,000,000
1,000,000,000
Tons
800,000,000
Passenger
Freight
600,000,000
400,000,000
200,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-24: CO2 emissions [t] for EU15 countries produced by passenger and freight road
transport from 1970 to 2020
EU15 CO2 Inland Emissions
4,500,000
4,000,000
3,500,000
Tons
3,000,000
2,500,000
Passenger
Freight
2,000,000
1,500,000
1,000,000
500,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-25: CO2 emissions [t] for EU15 countries produced by passenger and freight inland
shipping from 1970 to 2020
eurostat
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4.3 RESULTS – TOTAL
4.3.1 FLEET DATA
Figures 4-26 and 4-27 present the annual vehicle-kilometres produced by each mode during the
time period 1970-2020, for passenger and freight transport respectively. From these figures it is
clear that vehicle-kilometre road transport values are considerably higher than the predicted
vehicle-kilometres for all other modes, mainly due to the large number of road transport vehicles
in the EU.
Figures 4-28 and 4-29 show the annual passenger-kilometres and tonne-kilometres respectively,
produced by each mode during the time period 1970-2020. From Figure 4-28 it can be observed
that the predominant means of passenger transport are air and road, while according to Figure 429, the transportation of goods is mainly conducted by sea and in a smaller degree by road.
EU15 Veh Km from Passenger Transport
3,500,000
3,000,000
mio Veh Km
2,500,000
Air
Maritime
Inland
Road
Rail
2,000,000
1,500,000
1,000,000
500,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-26: Annual vehicle-kilometres produced by passenger transport for all EU15 countries
from 1970 to 2020
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EU15 Veh Km from Freight Transport
1,200,000
mio Veh Km
1,000,000
800,000
Air
Maritime
Inland
Road
Rail
600,000
400,000
200,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-27: Annual vehicle-kilometres produced by freight transport for all EU15 countries
from 1970 to 2020
EU15 Pas Km
9,000,000
8,000,000
mio Pas Km
7,000,000
6,000,000
Air
Maritime
Inland
Road
Rail
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-28: Annual passenger-kilometres predicted by TRENDS for all EU15 countries from
1970 to 2020
eurostat
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EU15 Ton Km
25,000,000
mio Ton Km
20,000,000
Air
Maritime
Inland
Road
Rail
15,000,000
10,000,000
5,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-29: Annual tonne-kilometres predicted by TRENDS for all EU15 countries from 1970
to 2020
4.3.2 VEHICLE EMISSIONS
Figures 4-30 through 4-37 present the annual CO, NOx, HC and CO2 emissions produced by
passenger and freight transport for all modes, during the time period 1970-2020. From these
figures it can be seen that emissions from passenger transport are mostly produced from the road
and air modes, while emissions from the transport of goods are mainly produced by road and
maritime. These results are in agreement with the passenger-kilometre and tonne-kilometre data
presented in the previous section.
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EU15 CO Passenger Emissions
50,000,000
45,000,000
40,000,000
35,000,000
Air
Maritime
Inland
Road
Rail
Tons
30,000,000
25,000,000
20,000,000
15,000,000
10,000,000
5,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-30: CO emissions [t] produced by passenger transport, as predicted by TRENDS, for
all EU15 countries from 1970 to 2020
EU15 CO Freight Emissions
5,000,000
4,500,000
4,000,000
3,500,000
Air
Maritime
Inland
Road
Rail
Tons
3,000,000
2,500,000
2,000,000
1,500,000
1,000,000
500,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-31: CO emissions [t] produced by freight transport, as predicted by TRENDS, for all
EU15 countries from 1970 to 2020
eurostat
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EU15 NOx Passenger Emissions
5,000,000
4,500,000
4,000,000
3,500,000
Air
Maritime
Inland
Road
Rail
Tons
3,000,000
2,500,000
2,000,000
1,500,000
1,000,000
500,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-32: NOx emissions [t] produced by passenger transport, as predicted by TRENDS, for
all EU15 countries from 1970 to 2020
EU15 NOx Freight Emissions
8,000,000
7,000,000
6,000,000
Tons
5,000,000
Maritime
Inland
Road
Rail
4,000,000
3,000,000
2,000,000
1,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-33: NOx emissions [t] produced by freight transport, as predicted by TRENDS, for all
EU15 countries from 1970 to 2020
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EU15 HC Passenger Emissions
6,000,000
5,000,000
Tons
4,000,000
Air
Maritime
Inland
Road
Rail
3,000,000
2,000,000
1,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-34: HC emissions [t] produced by passenger transport, as predicted by TRENDS, for
all EU15 countries from 1970 to 2020
EU15 HC Freight Emissions
1,000,000
900,000
800,000
700,000
Air
Maritime
Inland
Road
Rail
Tons
600,000
500,000
400,000
300,000
200,000
100,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-35: HC emissions [t] produced by freight transport, as predicted by TRENDS, for all
EU15 countries from 1970 to 2020
eurostat
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EU15 CO2 Passenger Emissions
1,000,000,000
900,000,000
800,000,000
700,000,000
Air
Maritime
Inland
Road
Rail
Tons
600,000,000
500,000,000
400,000,000
300,000,000
200,000,000
100,000,000
0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Year
Figure 4-36: CO2 emissions [t] produced by passenger transport, as predicted by TRENDS, for
all EU15 countries from 1970 to 2020
EU15 CO2 Freight Emissions
800,000,000
700,000,000
600,000,000
Tons
500,000,000
Air
Maritime
Inland
Road
Rail
400,000,000
300,000,000
200,000,000
100,000,000
0
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
Year
Figure 4-37: CO2 emissions [t] produced by freight transport, as predicted by TRENDS, for all
EU15 countries from 1970 to 2020
4.3.3 CONTRIBUTION OF EACH MODE TO THE TOTAL EU15 EMISSIONS
Figures 4-38 to 4-41 exhibit the contribution of each mode to the total CO, NOx, HC and CO2
emissions produced in the EU during the year 1995. From these figures it can be observed that
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road transport is the main source of CO and HC emissions. Road transport is also responsible for
the greatest part of NOx and CO2 emissions. However, air and maritime emissions also present a
significant contribution towards the production of NOx and CO2 emissions in the EU.
EU15 1995 CO Emissions [Tons]
Rail
Road
Inland
Maritime
Air
Figure 4-38: Comparison between the CO emissions [t] predicted by all modes for the year 1995
for EU15 countries
EU15 1995 NOx Emissions [Tons]
Rail
Road
Inland
Maritime
Air
Figure 4-39: Comparison between the NOx emissions [t] predicted by all modes for the year
1995 for EU15 countries
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EU15 1995 HC Emissions [Tons]
Rail
Road
Inland
Maritime
Air
Figure 4-40: Comparison between the HC emissions [t] predicted by all modes for the year 1995
for EU15 countries
EU15 1995 CO2 Emissions [Tons]
Rail
Road
Inland
Maritime
Air
Figure 4-41: Comparison between the CO2 emissions [t] predicted by all modes for the year
1995 for EU15 countries
4.3.4 EMISSION FACTORS
Figures 4-42 to 4-50 present annual emission factors (g/vehicle-kilometre) produced by all
modes for passenger and freight transport from 1970 to 2020.
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From these figures it can be observed that emission factors (g/vehicle-kilometre) produced by
road transport decrease considerably over the years. This tendency is consistent with the
observed decrease in annual road transport emissions (see section 4.2.2) as well as with the
increase of road transport vehicle-kilometres (cf. Figure 4-4).
EU15 CO Passenger Emission Factors
180
35
160
30
140
g / Veh Km
25
120
100
20
80
15
60
Inland (Sec Axis)
Maritime
Rail (Sec Axis)
Road (Sec Axis)
Air (Sec Axis)
10
40
5
20
0
1970
1980
1990
2000
2010
0
2020
Year
Figure 4-42: CO emission factors [g/vehicle-kilometre] produced by passenger transport for
EU15 countries from 1970 to 2020
eurostat
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EU15 CO Freight Emission Factors
25
350
300
20
g / Veh Km
250
15
200
150
10
Maritime
Air (Sec Axis)
Rail (Sec Axis)
Road (Sec Axis)
Inland (Sec Axis)
100
5
50
0
1970
1980
1990
2000
2010
0
2020
Year
Figure 4-43: CO emission factors [g/vehicle-kilometre] produced by freight transport for EU15
countries from 1970 to 2020
EU15 NOx Passenger Emission Factors
2000
90
1800
80
1600
70
g / Veh Km
1400
60
1200
50
1000
40
800
30
600
20
400
10
200
0
1970
Road (Sec Axis)
Maritime
Rail (Sec Axis)
Inland (Sec Axis)
Air (Sec Axis)
1980
1990
2000
2010
0
2020
Year
Figure 4-44: NOx emission factors [g/vehicle-kilometre] produced by passenger transport for
EU15 countries from 1970 to 2020
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EU15 NOx Passenger Emission Factors
90
2.5
80
2
g / Veh Km
70
60
1.5
50
40
Rail
Air
Road (Sec Axis)
Inland (Sec Axis)
1
30
20
0.5
10
0
1970
1980
1990
2000
2010
0
2020
Year
Figure 4-45: Detail of Figure 5-44, showing NOx emission factors [g/vehicle-kilometre]
produced by passenger transport for EU15 countries from 1970 to 2020
EU15 NOx Freight Emission Factors
90
3500
80
3000
70
g / Veh Km
2500
60
2000
50
1500
40
30
Inland
Maritime
Air
Rail (Sec Axis)
Road (Sec Axis)
1000
20
500
0
1970
10
1980
1990
2000
2010
0
2020
Year
Figure 4-46: NOx emission factors [g/vehicle-kilometre] produced by freight transport for EU15
countries from 1970 to 2020
eurostat
37
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Calculation of Indicators of Environmental Pressure Caused by Transport
EU15 HC Passenger Emission Factors
60
10
9
g / Veh Km
50
8
7
40
6
30
5
4
20
Inland (Sec Axis)
Maritime
Rail (Sec Axis)
Road (Sec Axis)
Air (Sec Axis)
3
2
10
1
0
1970
1980
1990
2000
2010
0
2020
Year
Figure 4-47: HC emission factors [g/vehicle-kilometre] produced by passenger transport for
EU15 countries from 1970 to 2020
EU15 HC Freight Emission Factors
100
25
90
80
20
g / Veh Km
70
60
15
50
40
10
Maritime
Air (Sec Axis)
Rail (Sec Axis)
Road (Sec Axis)
Inland (Sec Axis)
30
20
5
10
0
1970
1980
1990
2000
2010
0
2020
Year
Figure 4-48: HC emission factors [g/vehicle-kilometre] produced by freight transport for EU15
countries from 1970 to 2020
38
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Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
EU15 CO2 Passenger Emission Factors
235
80000
70000
230
g / Veh Km
60000
225
50000
220
40000
30000
215
Rail
Inland
Maritime
Air
Road (Sec Axis)
20000
210
10000
0
1970
1980
1990
2000
2010
205
2020
Year
Figure 4-49: CO2 emission factors [g/vehicle-kilometre] produced by passenger transport for
EU15 countries from 1970 to 2020
EU15 CO2 Freight Emission Factors
140000
478
120000
476
474
g / Veh Km
100000
472
80000
470
60000
468
40000
466
20000
0
1970
Rail
Inland
Maritime
Air
Road (Sec Axis)
464
1980
1990
2000
2010
462
2020
Year
Figure 4-50: CO2 emission factors [g/vehicle-kilometre] produced by freight transport for EU15
countries from 1970 to 2020
eurostat
39
Main Report
Calculation of Indicators of Environmental Pressure Caused by Transport
5 TRENDS - AUTO OIL II COMPARISON
A comparison was conducted between TRENDS estimates and data produced by the Auto Oil II
study (basecase scenario) [10] in order to assess the quality of traffic activity and emission results
predicted by TRENDS.
The Auto Oil II study provides data for nine EU countries. From these countries, the following
countries were considered for this comparison: Finland, Germany, Italy, Netherlands and UK.
The Auto Oil II database contains traffic activity and air emission data for the years 1990-2020.
For that reason, the time period 1990-2020 was selected for this comparison.
Emission results from the Auto Oil II study, are only available for air emissions produced by road
transport. However, activity data are available for road transport, as well as for waterways and
trains.
5.1 ACTIVITY DATA
5.1.1 ROAD TRANSPORT
Figures 5-1 to 5-5 represent a comparison between TRENDS and Auto Oil II (AOII) vehiclekilometres produced by passenger road transport for the aforementioned countries. From these
figures it can be observed that in general, there is a satisfactory agreement between TRENDS and
AOII traffic activity data for road transport. The difference between the results produced by the
two sources is as low as 3-5% in some countries (cf. Figure 5-3). Large deviations can be
observed mostly in future years (2015-2020) and in some cases they reach values as high as 3040% (cf. Figure 5-4)
Comparison of road veh km produced by passenger transport for Finland
70,000
TRENDS
Annual veh km (million)
60,000
Auto Oil II
50,000
40,000
30,000
20,000
10,000
20
20
20
18
20
16
14
20
12
20
10
20
20
08
20
06
20
04
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-1: Annual road vehicle-kilometres produced by passenger transport for Finland
40
eurostat
Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
Comparison of road veh km produced by passenger transport for Germany
800,000
TRENDS
Annual veh km (million)
700,000
Auto Oil II
600,000
500,000
400,000
300,000
200,000
100,000
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
00
20
98
19
96
19
94
19
92
19
19
90
0
Year
Figure 5-2: Annual road vehicle-kilometres produced by passenger transport for Germany
Comparison of road veh km produced by passenger transport for Italy
700,000
600,000
Annual veh km (million)
TRENDS
500,000
Auto Oil II
400,000
300,000
200,000
100,000
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
00
20
98
19
96
19
94
19
92
19
19
90
0
Year
Figure 5-3: Annual road vehicle-kilometres produced by passenger transport for Italy
eurostat
41
Main Report
Calculation of Indicators of Environmental Pressure Caused by Transport
Comparison of road veh km produced by passenger transport for
Netherlands
140,000
TRENDS
120,000
Annual veh km (million)
Auto Oil II
100,000
80,000
60,000
40,000
20,000
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
00
20
98
19
96
19
94
19
92
19
19
90
0
Year
Figure 5-4: Annual road vehicle-kilometres produced by passenger transport for Netherlands
Comparison of road veh km produced by passenger transport for UK
600,000
TRENDS
Annual veh km (million)
500,000
Auto Oil II
400,000
300,000
200,000
100,000
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
00
20
98
19
96
19
94
19
92
19
19
90
0
Year
Figure 5-5: Annual road vehicle-kilometres produced by passenger transport for the UK
42
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Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
5.1.2 MARITIME
Figures 5-6 to 5-10 present a comparison between TRENDS and AOII vehicle-kilometres
produced by maritime freight transport for the aforementioned countries. From these figures it
can be observed that in most countries (Finland, Germany, UK) there is not great difference
between the results produced by the two sources. However, there is a significant deviation
between TRENDS and AOII data in the cases of Italy and Netherlands.
Comparison of maritime veh km produced by freight transport for Finland
30
25
Annual veh km (million)
TRENDS
Auto Oil II
20
15
10
5
20
20
20
18
16
20
20
14
12
20
20
10
08
20
20
06
04
20
02
20
00
20
98
19
96
19
94
19
92
19
19
90
0
Year
Figure 5-6: Annual maritime vehicle-kilometres produced by freight transport for Finland
eurostat
43
Main Report
Calculation of Indicators of Environmental Pressure Caused by Transport
Comparison of maritime veh km produced by freight transport for Germany
350
300
Annual veh km (million)
TRENDS
250
Auto Oil II
200
150
100
50
20
20
18
20
16
20
20
14
20
12
20
10
20
08
20
06
04
20
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-7: Annual maritime vehicle-kilometres produced by freight transport for Germany
Comparison of maritime veh km produced by freight transport for Italy
250
Annual veh km (million)
200
TRENDS
Auto Oil II
150
100
50
20
20
18
20
16
20
20
14
20
12
20
10
20
08
20
06
04
20
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-8: Annual maritime vehicle-kilometres produced by freight transport for Italy
44
eurostat
Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
Comparison of maritime veh km produced by freight transport for
Netherlands
300
Annual veh km (million)
250
TRENDS
Auto Oil II
200
150
100
50
20
20
18
20
16
20
20
14
20
12
20
10
20
08
20
06
04
20
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-9: Annual maritime vehicle-kilometres produced by freight transport for Netherlands
Comparison of maritime veh km produced by freight transport for UK
300
250
Annual veh km (million)
TRENDS
Auto Oil II
200
150
100
50
20
20
18
20
16
20
20
14
20
12
20
10
20
08
20
06
04
20
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-10: Annual maritime vehicle-kilometres produced by freight transport for the UK
eurostat
45
Main Report
Calculation of Indicators of Environmental Pressure Caused by Transport
5.1.3 RAILWAYS
Figures 5-11 to 5-15 show TRENDS and AOII vehicle-kilometres produced by passenger rail
transport. It should be noted here that AOII results refer to all trains including metro, while the
estimates of TRENDS do not include data for metro. From figures 5-11 to 5-15 it can be
observed that in some cases (Finland, Netherlands, UK) the discrepancies between the data
produced by TRENDS and AOII are within reasonable limits. In the case of Germany and Italy
however, there is considerable difference between the predictions of the two sources. These
differences indicate that additional comparisons with other sources are required in order to assess
the validity of the results produced by TRENDS. Ultimately, some of the results of
TRENDS/Rail as well as the assumptions behind these results might be reconsidered.
Comparison of rail veh km produced by passenger transport for Finland
50
45
TRENDS
Annual veh km (million)
40
Auto Oil II
35
30
25
20
15
10
5
20
20
20
18
16
20
20
14
12
20
20
10
08
20
20
06
04
20
02
20
00
20
98
19
96
19
94
19
92
19
19
90
0
Year
Figure 5-11: Annual rail vehicle-kilometres produced by passenger transport for Finland
46
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Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
Comparison of rail veh km produced by passenger transport for Germany
1,800
TRENDS
1,600
Auto Oil II
Annual veh km (million)
1,400
1,200
1,000
800
600
400
200
20
20
18
20
16
20
20
14
20
12
10
20
20
08
20
06
04
20
02
20
20
00
19
98
96
19
94
19
92
19
19
90
0
Year
Figure 5-12: Annual rail vehicle-kilometres produced by passenger transport for Germany
Comparison of rail veh km produced by passenger transport for Italy
350
TRENDS
Annual veh km (million)
300
Auto Oil II
250
200
150
100
50
20
20
18
20
16
20
20
14
20
12
20
10
20
08
20
06
04
20
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-13: Annual rail vehicle-kilometres produced by passenger transport for Italy
eurostat
47
Main Report
Calculation of Indicators of Environmental Pressure Caused by Transport
Comparison of rail veh km produced by passenger transport for
Netherlands
250
TRENDS
Auto Oil II
Annual veh km (million)
200
150
100
50
20
20
18
20
16
20
20
14
20
12
20
10
20
08
20
06
04
20
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-14: Annual rail vehicle-kilometres produced by passenger transport for Netherlands
Comparison of rail veh km produced by passenger transport for UK
800
700
Annual veh km (million)
TRENDS
600
Auto Oil II
500
400
300
200
100
20
20
18
20
16
20
20
14
20
12
20
10
20
08
20
06
04
20
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-15: Annual rail vehicle-kilometres produced by passenger transport for UK
48
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Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
5.2 EMISSION RESULTS
Figures 5-16 to 5-40 show a comparison between TRENDS and AOII air emissions produced by
road passenger transport. The comparison was conducted for the years 1990-2020 and emission
results were produced for the following pollutants: CO, NOx, HC, CO2 and PM.
From these figures it is apparent that in general, road transport emissions predicted by TRENDS
correspond well with the respective emissions produced by the Auto Oil II study. In most cases,
the results of TRENDS not only coincide numerically with the results of AOII, but they also
follow a similar trend during the time interval considered. Significant differences are only
observed in PM emissions for Finland and Germany (cf. Figures 5-36 and 5-37). In these cases,
PM emissions predicted by TRENDS exceed those produced by AOII.
Comparison of road CO emissions produced by passenger transport for
Finland
600,000
Annual CO emissions (t)
500,000
TRENDS
Auto Oil II
400,000
300,000
200,000
100,000
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
00
20
98
19
96
19
94
19
92
19
19
90
0
Year
Figure 5-16: Annual CO emissions (t) produced by road transport for Finland
eurostat
49
Main Report
Calculation of Indicators of Environmental Pressure Caused by Transport
Comparison of road CO emissions produced by passenger transport for
Germany
9,000,000
Annual CO emissions (t)
8,000,000
TRENDS
7,000,000
Auto Oil II
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-17: Annual CO emissions (t) produced by road transport for Germany
Comparison of road CO emissions produced by passenger transport for
Italy
6,000,000
Annual CO emissions (t)
5,000,000
TRENDS
Auto Oil II
4,000,000
3,000,000
2,000,000
1,000,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-18: Annual CO emissions (t) produced by road transport for Italy
50
eurostat
Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
Comparison of road CO emissions produced by passenger transport for
Netherlands
1,400,000
Annual CO emissions (t)
1,200,000
TRENDS
1,000,000
Auto Oil II
800,000
600,000
400,000
200,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-19: Annual CO emissions (t) produced by road transport for Netherlands
Comparison of road CO emissions produced by passenger transport for UK
8,000,000
Annual CO emissions (t)
7,000,000
TRENDS
6,000,000
Auto Oil II
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-20: Annual CO emissions (t) produced by road transport for the UK
eurostat
51
Main Report
Calculation of Indicators of Environmental Pressure Caused by Transport
Comparison of road NOx emissions produced by passenger transport for
Finland
90,000
Annual NOx emissions (t)
80,000
70,000
TRENDS
Auto Oil II
60,000
50,000
40,000
30,000
20,000
10,000
20
20
20
18
20
16
14
20
12
20
10
20
20
08
20
06
20
04
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-21: Annual NOx emissions (t) produced by road transport for Finland
Comparison of road NOx emissions produced by passenger transport for
Germany
1,000,000
900,000
Annual NOx emissions (t)
800,000
TRENDS
Auto Oil II
700,000
600,000
500,000
400,000
300,000
200,000
100,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-22: Annual NOx emissions (t) produced by road transport for Germany
52
eurostat
Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
Comparison of road NOx emissions produced by passenger transport for
Italy
1,200,000
1,000,000
Annual NOx emissions (t)
TRENDS
Auto Oil II
800,000
600,000
400,000
200,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-23: Annual NOx emissions (t) produced by road transport for Italy
Comparison of road NOx emissions produced by passenger transport for
Netherlands
200,000
180,000
Annual NOx emissions (t)
160,000
TRENDS
140,000
Auto Oil II
120,000
100,000
80,000
60,000
40,000
20,000
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
00
20
98
19
96
19
94
19
92
19
19
90
0
Year
Figure 5-24: Annual NOx emissions (t) produced by road transport for Netherlands
eurostat
53
Main Report
Calculation of Indicators of Environmental Pressure Caused by Transport
Comparison of road NOx emissions produced by passenger transport for
UK
1,400,000
Annual NOx emissions (t)
1,200,000
TRENDS
1,000,000
Auto Oil II
800,000
600,000
400,000
200,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-25: Annual NOx emissions (t) produced by road transport for the UK
Comparison of road HC emissions produced by passenger transport for
Finland
90,000
Annual HC emissions (t)
80,000
70,000
TRENDS
Auto Oil II
60,000
50,000
40,000
30,000
20,000
10,000
20
20
20
18
20
16
14
20
12
20
10
20
20
08
20
06
20
04
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-26: Annual HC emissions (t) produced by road transport for Finland
54
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Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
Comparison of road HC emissions produced by passenger transport for
Germany
1,200,000
1,000,000
Annual HC emissions (t)
TRENDS
Auto Oil II
800,000
600,000
400,000
200,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-27: Annual HC emissions (t) produced by road transport for Germany
Comparison of road HC emissions produced by passenger transport for
Italy
1,200,000
Annual HC emissions (t)
1,000,000
TRENDS
Auto Oil II
800,000
600,000
400,000
200,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-28: Annual HC emissions (t) produced by road transport for Italy
eurostat
55
Main Report
Calculation of Indicators of Environmental Pressure Caused by Transport
Comparison of road HC emissions produced by passenger transport for
Netherlands
200,000
180,000
Annual HC emissions (t)
160,000
TRENDS
140,000
Auto Oil II
120,000
100,000
80,000
60,000
40,000
20,000
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
00
20
98
19
96
19
94
19
92
19
19
90
0
Year
Figure 5-29: Annual HC emissions (t) produced by road transport for Netherlands
Comparison of road HC emissions produced by passenger transport for UK
1,400,000
Annual HC emissions (t)
1,200,000
TRENDS
1,000,000
Auto Oil II
800,000
600,000
400,000
200,000
20
20
20
18
20
16
14
20
20
12
10
20
20
08
06
20
04
20
20
02
00
20
19
98
19
96
19
94
92
19
19
90
0
Year
Figure 5-30: Annual HC emissions (t) produced by road transport for the UK
56
eurostat
Calculation of Indicators of Environmental Pressure Caused by Transport
Main Report
Comparison of road CO2 emissions produced by passenger transport for
Finland
12,000,000
TRENDS
Auto Oil II
Annual CO2 emissions (t)
10,000,000
8,000,000
6,000,000
4,000,000
2,000,000
20
20
20
18
16
20
20
14
12
20
20
10
20
08
06
20
20
04
20
02
00
20
19
98
19
96
19
94
19
92
19
90
0
Year
Figure 5-31: Annual CO2 emissions (t) produced by road transport for Finland
Comparison of road CO2 emissions produced by passenger transport for
Germany
160,000,000
TRENDS
Auto Oil II
Annual CO2 emissions (t)
140,000,000
120,000,000
100,000,000
80,000,000
60,000,000
40,000,000
20,000,000
20
20
20
18
20
16
14
20
12
20
20
10
08
20
06
20
20
04
20
02
00
20
19
98
19
96
94
19
92
19
19
90
0
Year
Figure 5-32: Annual CO2 emissions (t) produced by road transport for Germany
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Comparison of road CO2 emissions produced by passenger transport for
Italy
120,000,000
Annual CO2 emissions (t)
100,000,000
TRENDS
Auto Oil II
80,000,000
60,000,000
40,000,000
20,000,000
20
20
20
18
20
16
14
20
12
20
20
10
08
20
06
20
20
04
20
02
00
20
19
98
19
96
94
19
19
19
90
92
0
Year
Figure 5-33: Annual CO2 emissions (t) produced by road transport for Italy
Comparison of road CO2 emissions produced by passenger transport for
Netherlands
20,000,000
TRENDS
18,000,000
Auto Oil II
Annual CO2 emissions (t)
16,000,000
14,000,000
12,000,000
10,000,000
8,000,000
6,000,000
4,000,000
2,000,000
20
20
20
18
16
20
14
20
20
12
10
20
20
08
20
06
20
04
20
02
20
00
98
19
19
96
19
94
92
19
19
90
0
Year
Figure 5-34: Annual CO2 emissions (t) produced by road transport for Netherlands
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Comparison of road CO2 emissions produced by passenger transport for
UK
140,000,000
Annual CO2 emissions (t)
120,000,000
TRENDS
Auto Oil II
100,000,000
80,000,000
60,000,000
40,000,000
20,000,000
20
20
20
18
20
16
14
20
12
20
20
10
08
20
06
20
20
04
20
02
00
20
19
98
19
96
94
19
92
19
19
90
0
Year
Figure 5-35: Annual CO2 emissions (t) produced by road transport for the UK
Comparison of road PM emissions produced by passenger transport for
Finland
1,800
Annual PM emissions (t)
1,600
1,400
TRENDS
Auto Oil II
1,200
1,000
800
600
400
200
20
20
18
20
16
20
20
14
20
12
10
20
20
08
20
06
04
20
02
20
20
00
19
98
96
19
94
19
92
19
19
90
0
Year
Figure 5-36: Annual PM emissions (t) produced by road transport for Finland
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Comparison of road PM emissions produced by passenger transport for
Germany
35,000
Annual PM emissions (t)
30,000
TRENDS
25,000
Auto Oil II
20,000
15,000
10,000
5,000
20
20
20
18
20
16
14
20
12
20
10
20
20
08
20
06
20
04
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-37: Annual PM emissions (t) produced by road transport for Germany
Comparison of road PM emissions produced by passenger transport for
Italy
25,000
Annual PM emissions (t)
20,000
TRENDS
Auto Oil II
15,000
10,000
5,000
20
20
20
18
20
16
14
20
12
20
10
20
20
08
20
06
20
04
02
20
00
20
98
19
19
96
19
94
19
92
19
90
0
Year
Figure 5-38: Annual PM emissions (t) produced by road transport for Italy
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Comparison of road PM emissions produced by passenger transport for
Netherlands
4,500
Annual PM emissions (t)
4,000
3,500
TRENDS
Auto Oil II
3,000
2,500
2,000
1,500
1,000
500
20
20
18
20
16
20
20
14
20
12
10
20
20
08
20
06
04
20
02
20
20
00
19
98
96
19
94
19
92
19
19
90
0
Year
Figure 5-39: Annual PM emissions (t) produced by road transport for Netherlands
Comparison of road PM emissions produced by passenger transport for UK
16,000
Annual PM emissions (t)
14,000
TRENDS
12,000
Auto Oil II
10,000
8,000
6,000
4,000
2,000
20
20
20
18
20
16
20
14
12
20
10
20
20
08
20
06
20
04
20
02
20
00
19
98
19
96
94
19
19
92
19
90
0
Year
Figure 5-40: Annual PM emissions (t) produced by road transport for the UK
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6 SPATIAL DISAGGREGATION
6.1 ROAD TRANSPORT
Road transport emissions are normally estimated for three distinct driving modes: urban, rural
and highway driving. For that reason, spatial allocation of emissions was conducted using a
different method for each type of driving. These methods are outlined below.
6.1.1 HIGHWAY EMISSIONS
The UN-ECE Census of Motor Traffic contains figures of measured traffic volume (annual
average number of vehicles per day) in most E-roads of Europe for each main vehicle category
for the year 1995. Additionally, it provides information on the fraction of vehicle-kilometres
driven in E-roads over the total vehicle-kilometres in each country. In cases where data were not
available from the UN-ECE Census of Motor Traffic database either the APUR database or other
sources were used.
All highway roads were located and distinguished from the rest road types. Information on the
traffic volume was obtained separately for light duty and heavy-duty vehicles. On the basis of
these data, a file was prepared for each country, which contains all highways and their
corresponding traffic volumes.
With the aid of these data, estimated highway emissions per country were allocated to each
highway as follows:
•
Total highway vehicle-kilometres (a) and emissions (e) for each vehicle category in a country
were produced by COPERT.
•
For each E-road segment, the annual vehicle-kilometres (b) were obtained as: annual average
daily traffic volume × 365 × length of road segment.
•
The fraction (c) of vehicle-kilometres driven in E-roads over total highway vehiclekilometres was provided by the UN-ECE Census.
•
Thus, annual emissions x, in a specific E-road segment can be calculated as follows:
x = e × (b × c / a)
The annual country highway emissions were allocated to the specific highways and the enhanced
files were introduced in the GIS system, in order to convert the emission values into geographical
information. Figure 6-1 illustrates the highways in Italy and Germany.
Figure 6-1: Highways in Italy (left) and Germany (right).
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6.1.2 URBAN EMISSIONS
Assuming that there are no significant differences in vehicle ownership and vehicle use between
regions of the same country, it is reasonable to allocate the total urban emissions of a country to
each urban area according to its population. As an additional criterion, the GDP of a city or
region can be used to distribute vehicle emissions in urban areas. This method was applied in
order to allocate emissions to all cities of EU15 countries in the GISCO database. According to
GISCO, this included all areas with population over 20 000.
6.1.3 RURAL EMISSIONS
With the exception of a few dual carriageways, the rural road network is not available in GISCO.
It was therefore proposed to allocate national rural emissions from road transport over the whole
non-urban area of each country (at NUTS II level), using population density and regional GDP as
criteria. These data at NUTS II level are available from New Cronos, so the information exists
and can be used directly for this purpose.
6.1.4 PRODUCTION OF GIS MAPS
Emission results were projected on maps by means of the GIS system, for urban and rural areas,
as well as for highways. The pollutants considered were the following:
•
CO
•
NOx
•
NMVOC
•
CO2
•
PM
•
CH4
•
Pb
Vehicle emissions were distributed in NUTS areas using the following data set:
♦ nuec1mv6 → \nuts NUTS boundaries V6 1 Million, obtained from the GISCO database
In order to allocate vehicle emissions to highways, the following data were used:
♦ rdeu1mv4 → roads, obtained from the APUR database
Vehicle emissions were also projected in cities using the data set:
♦ steugg.e00 → \st Settlements, obtained from the GISCO database
The Lambert Azimuthal projection was used in order to project the data. This projection is
recommended by Eurostat since it is suitable for a large area, preserving as much as possible the
shape of the continent. It is a planar projection, which means that map data are projected onto a
flat surface. This projection preserves the area of individual polygons while simultaneously
maintaining a true sense of direction from the centre.
The GISCO Lambert Azimuthal Equal Area projection is characterised by the following
parameters:
Units : meters
Spheroid : sphere
Parameters: Radius of sphere of reference 6378388
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Longitude of centre of projection 09°00’00”
Latitude of centre of projection 48°00’00”
False easting 0.0
False northing 0.0
Examples of the maps produced by the procedure described above are given in Figures 6-2 and
6-3, for Germany and Greece respectively.
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200 Miles
Road Emissions CO tn/year/km
0 - 53
53 - 97
97 - 150
150 - 209
209 - 306
Settlements CO tn/year
631 - 7635
#
7636 - 20964
#
20965 - 48119
#
48120 - 78821
# 78822 - 198334
#
NUTS CO tn/year/km²
0.1 - 0.33
0.33 - 0.46
0.46 - 0.75
0.75 - 1.5
1.5 - 163.62
N
#
W
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Figure 6-2:Annual (1995) urban, rural and highway CO emissions for Germany
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0 - 2372
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# 65129 - 279199
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Road Emissions NOx tn/year/km
7.846 - 152.396
152.396 - 376.31
376.31 - 685.376
685.376 - 1177.936
1177.936 - 2933.088
No Data
NUTS CO tn/year/km²
0.17
0.17 - 0.24
0.24 - 0.32
0.32 - 0.51
0.51 - 15.44
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Figure 6-3:Annual (1995) urban, rural and highway CO emissions for Greece
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6.2 MARITIME SHIPPING
The TRENDS maritime database can provide air pollutant emission results for a type of vessel or
the total amount of traffic per port, country or maritime coastal area. Due to the fact that in the
data provided by EUROSTAT the link between ships and cargo is not maintained, discrepancies
were observed in the results concerning some types of vessels. This does not allow for any
conclusions to be drawn from the results. However, as it was one of the aims of the project to
find a way of representing the results spatially, this was achieved using the existing data.
The GISCO database contains an extensive number of major and minor ports both on inland and
coastal areas. The number of ports to which emissions could be attributed was significantly
smaller for a number of reasons:
•
Inland ports were not considered, as they would be a part of inland shipping for which traffic
data exists only on a country level.
•
EUROSTAT does not have data for all ports as some countries (i.e. Italy) and a number of
minor ports do not report to them in time (or at all)
•
The port-to-MCA distance table is not complete and therefore ports, which are not accounted
for, are also excluded from the calculation.
•
In the GISCO database not all ports are provided with a LoCode, which is the only link to the
emission results. As a consequence, only 190 out of the 320 ports for which emission results
exist can actually be represented.
Figure 7-4 displays the SOx emission caused from freight traffic in the year 2000 attributed to
EU15 ports. Results can be obtained and represented at a more detailed as well as more
aggregated level. Figure 6-5 shows the SOx emission induced by bulk carriers in the Baltic area
for the same year.
The emissions from maritime shipping can also be attributed to NUTS by spatially joining the
Port and NUTS layers. This exercise was only pursued at an experimental level as the amount of
ports displayed and the fact that the year was different than the base year 1995 rendered the
information useless for further aggregation with other modes of transport.
For future successful representation of the results of the detailed TRENDS database, the GISCO
database should be updated to include a larger number of LoCodes – at least as many as the Portto-MCA distance database.
Should actual routes be presented in the GISCO database the emissions could be attributed to
linear sources instead of ports. This would be mostly useful for short sea and national shipping,
involving coastal routes within EU boundaries.
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Figure 6-4: SOx emissions from maritime freight traffic at port level for EU15 countries in the
year 2000 (tonnes)
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Figure 6-5: SOx emissions from dry bulk carrier traffic for the Baltic area in the year 2000
(tonnes)
6.3 INLAND SHIPPING
In the case of inland shipping, neither the traffic data nor the necessary geographical data for a
detailed representation was available. The traffic data provided by EUROSTAT refers to a
country total of tonne-kilometres per year and the results obtained by the simple TRENDS model
are tonnes of pollutants per country per year. Even if the traffic data were available, however, the
GISCO database does not include a map of the navigable waterways of EU15 countries so the
pollution could not be attributed to linear sources as in the case of railway.
The aim of the GIS part of TRENDS was to sum the pollution from all modes of transport for
each of the individual NUTS region. In this case, the results obtained for inland shipping per
country would have to be divided by the amount of NUTS1 per country before being attributed.
However, lack of consistent data to create a base year scenario has lead to this aim being
unattainable, and the representation for inland shipping was kept at country level.
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The results produced for all EU15 countries for a specific year were connected to the NUTS layer
using the country code. In this way all areas within the same country were attributed with the
same value irrespective of their containing navigable waterways.
There was no reason to retain the NUTS segmentation in this situation so shape-merge was used
to produce figure 6-6 which displays the SOx emission from inland shipping in EU15 countries
for the years 1970, 2000 and 2010 (projection) respectively.
Except for the change in Germany (year 2000) and Sweden (year 2010), no further differences
are observed in the maps, under the present scale scheme, for the years 1970 to 2020.
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Year 1970
Year 2000
Year 2010
Figure 6-6: SOx emissions from inland shipping traffic in the EU15 countries (tonnes)
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6.4 RAILWAYS
The spatial disaggregation of railway emissions was based on the results of the detailed database.
6.4.1 ATTRIBUTING INTRAPLAN-NODES TO GISCO RAILWAY SEGMENTS
The Intraplan [5] traffic database was prepared with the aim to be geographically represented, so
a base map was also prepared by Intraplan during the study in the form of a node database.
The node database contains all the important junctions in the rail network, consisting of about
1 600 nodes. Each node was defined with a unique number established by the consultants, a
name and a set of co-ordinates in the Lambert-Azimuth projection. The co-ordinates were
defined based on the GISCO map for railways and therefore no inconsistency was expected in
projecting data from the two databases together. The problem, however, was that Intraplan used a
different segmentation and encoding to the one encountered in GISCO without providing a
complete set of co-ordinates for the “nodes” used for the database in their report.
Thus, though the co-ordinates and representation fit, in none of the segmentations present in
GISCO are the nodes the same as the ones in Intraplan. What is more, the encoding is completely
different so that there can be no manipulation into connecting the two codes. Furthermore,
neither INTRAPLAN nor Eurostat were able to provide a link-table when requested, claiming
that the relevant data were no longer present in either their archives. Obviously, an alternative
method of connecting the databases was required.
After discussions with the GIS team of the Commission, colleagues in Eurostat have attempted to
solve the problem by matching the country code and the two station names (origin, destination)
in the emission result tables. In Arc View the two tables were matched on the concatenated
“names” with a 95% hit rate. The rest 5% of the entries, still a fair amount, had to be treated
manually.
These data-points were treated manually due to the name of the station containing characters that
were not recognised by the database. An effort was made to replace these with English characters
specific to country groups before matching, but was not successful in all cases, especially since
errors existed already.
For example, many problems were created with the Danish links since the special characters æ,
ø, å were not taken into account in the substitutions done by Eurostat. Most of these links had to
be treated manually.
Once the connecting table was established, the emission results for each particular link between
two nodes could be displayed. The following sets of results were spatially attributed for each of
the pollutants examined:
− Figure 6-7 : Total emissions from rail traffic activity
− Figure 6-8 : Emission from passenger rail traffic activity – both electric and diesel
− Figure 6-9 : Emission from freight rail traffic activity – both electric and diesel
− Figure 6-10 : Emission from diesel train traffic activity – both passenger and freight
− Figure 6-11 : Emission from electric train traffic activity – both passenger and freight
The possibility for more detailed representation exists, for example by splitting passenger traffic
to diesel and electric and even further to locomotives, railcars and high-speed trains for each
engine type.
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Figure 6-7: CO2 emissions from train traffic – both passenger and freight – in EU15 countries
for the year 1995 (1 000 tonnes)
The above procedure, however, was not successful in connecting the entire Intraplan database
and the calculated emission results to the GISCO network. In fact, a comparative examination of
the country totals before and after the connection shows that in most cases a considerable amount
of data is lost in the process. The respective fraction based on the results of energy consumption
for each country is shown in Table 6-1.
Three main reasons are identified for the discrepancies in the data:
1. Despite the effort made, the connecting table may not be complete
2. Intraplan have recorded traffic on ferry links that do not “belong” to a specific country and on
links between countries that are attributed to a non-EU15 country when summing
3. Intraplan have recorded traffic data for lesser railway links that do not belong to the main
arteries portrayed in the TEN corridors. Traffic on these links is discarded during the
connection process as no station match exists on the main artery layer.
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Figure 6-8: CO2 emission from passenger train traffic in EU15 countries for the year 1995
(1 000 tonnes)
Figure 6-9: CO2 emissions from freight train traffic in EU15 countries for the year 1995 (1 000
tonnes)
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Figure 6-10: CO2 emissions attributed to diesel train traffic in EU15 countries for the year 1995
(1 000 tonnes)
Figure 6-11: CO2 emissions attributed to electric train traffic in EU15 countries for the year
1995 (1 000 tonnes)
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Table 6-1: Fraction of the actual energy consumption that can be displayed
Country Fraction Country Fraction Country Fraction
AT
0,66
FI
0,91
LU
1,04
BE
0,84
FR
0,77
NL
0,82
DE
0,78
GR
0,83
PT
0,61
DK
0,63
IE
0,71
SE
0,94
ES
0,56
IT
0,67
GB
0,80
A more detailed network exists in the GISCO database as well, however, a connection between
the two was not possible as, contrary to the attributes of the major arteries, no station names were
included in this layer. An attempt was made to spatially join the layers, but the result was not
satisfactory.
In addition to the loss of data discussed above, limitations on the quality of data provided by
Intraplan do not allow any conclusions to be drawn on the basis of the presented railway results.
For example, the data for freight traffic in Germany, Great Britain, the Netherlands, Sweden and
Finland were based on assumptions since no data was supplied by the countries in question.
Whatever the quality of traffic data, however, it is important to observe that the emission
representation follows, by large, the traffic representation in the Intraplan maps. In other words,
the procedure of putting the emissions on the map is accurate enough, if provided with reliable
data.
Still some observations can be made. Links with heavy traffic can be acknowledged though the
difference in train energy consumption between the countries and the different types of trains
prevent the relationship from being linear. However, the emission of CO2 is directly related to
energy consumption, while other pollutants are more dependent on other factors, the type of
power plant for example in the case of SOx.
6.4.2 ATTRIBUTING RAILWAY SEGMENTS TO NUTS REGIONS
As previously mentioned the goal was to attribute emissions from rail traffic to NUTS
administrative regions in order for them to be added up with emission from other transport
modes. In this way, an emission profile was created for each of the administrative regions.
As in the case of attributing the emissions to the railway network the problem was the lack of a
connecting table stating which administrative region the rail traffic link belonged to. Such a table
was created by spatially joining the NUTS (polygon) and the railway links (arc) layers in
ArcView with the layer containing railway stations (point) thus creating a link between the two.
This action was necessary since a direct spatial join between the two layers did not have a
satisfactory effect. The resulting data table had to be further processed for double entries before
emission values could be attributed.
However, another issue had to be dealt with, as NUTS regions contained more than one railway
link, or links extended over more than one NUTS region so that the emission values had to be
summed or split accordingly. To make sure that emission values were not taken into account
more than once, the original values were divided by the number of times the respective railway
link appeared in the connecting table before being attributed to NUTS regions. Consequently,
emission values were summed per NUTS region to produce the final data table that would be
projected.
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This method of transferring emissions from network to NUTS level was very effective as can be
seen from Table 6-2 that displays the difference in the country totals calculated on a NUTS and
network level. With the exception of Luxembourg the error is not significant given all the other
assumptions involved.
Table 6-2: Ratio between the NUTS and network country totals based on energy consumption
values.
Country
AT
BE
DE
DK
ES
Fraction
1,06
1,00
0,99
1,00
0,98
Country
FI
FR
GR
IE
IT
Fraction
1,02
0,99
1,00
1,00
1,01
Country
LU
NL
PT
SE
GB
Fraction
0,70
1,03
1,05
1,00
1,00
Figures 6-12 and 6-13 show the energy consumption and SOx emission over the EU15 NUTS
regions respectively. The effect of the fuel and type of power plant used is evident, especially in
the case of countries such as France where a high energy consumption is not translated into high
SOx air pollutant emission due to electric trains powered by nuclear stations.
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Figure 6-12: Total energy consumption by rail traffic activities in the EU15 countries attributed
to the NUTS administrative regions – Year 1995
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Figure 6-13: Total SOx emissions by rail traffic activities in the EU15 countries attributed to the
NUTS administrative regions (tonnes) – Year 1995
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7 TEMPORAL DISAGGREGATION – ROAD TRANSPORT
7.1 DATA AVAILABILITY
One of the aims of this project was to attempt to quantify temporal variations of vehicle emissions
and to distribute the annual emissions (for 1995) of EU15 countries on a seasonal basis. Due to
lack of relevant data, this procedure was conducted only for the road transport mode.
In order to enable the disaggregation of emissions (via activity data disaggregation) to seasonal
levels, appropriate patterns were collected, analysed and consolidated. As Eurostat data on
seasonal variation of transport activities were scarce, other sources of information were
investigated. Finally, the temporal distribution of vehicle emissions was conducted using
statistical data from a project conducted by the University of Graz [4]. This project contains a
study of the traffic load for different vehicle and road types, depending on various time-related
parameters. More specifically, the traffic load of both urban roads and highways was recorded on
a weekly and daily basis. The study also discriminated between passenger cars and heavy-duty
vehicles. Weekly variations of the traffic load provided by this source were used in order to
produce the required seasonal variations of vehicle emissions.
7.2 METHODOLOGY
Figure 7-1 gives an example of the recorded variations of the weekly traffic load over an entire
year. Each weekly variation in Figure 7-1 is represented as a deviation from an average traffic
load.
Seasonal values were determined by calculating the average value of the deviation over the 13
weeks that correspond to each season. Since traffic load data were available for several urban
roads the final seasonal variation factors were obtained by averaging over the values of the
various roads. Finally, CO2, NOx and PM emissions produced by TRENDS for all EU15
countries were multiplied with the seasonal deviation factors in order to obtain the required
distribution of vehicle emissions.
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Figure 7-1: Example of the weekly traffic load variations measured at an urban road over an
entire year.
7.3 RESULTS
Figures 7-2 to 7-4 show the seasonal variation of CO2, NOx and PM emissions respectively, for
Germany and Greece. Figures 7-5 to 7-7 and Figures 7-8 to 7-10 show the seasonal distribution
of the aforementioned pollutants in the case of heavy-duty vehicles and highways respectively.
The seasonal distribution of CO2, NOx and PM emissions for all EU15 countries is presented in
Tables A-1 to A-4 of Appendix A.
The large deviation between the annual emissions in Germany and Greece that can be observed
from these figures is due to the difference in vehicle populations between the two countries.
From Figures 7-2 to 7-10 it is also apparent that the levels of vehicle emissions are higher during
the summer, which is to be expected since there is an increase in transportation during the
summer holidays.
From Figure 7-4 it can be observed that the yearly PM emissions in Germany are considerably
higher than the respective emissions in Greece. The difference in annual emissions between the
two countries is more pronounced in the case of PM emissions than in the case of other
emissions. This is due to the fact that the number of diesel PCs in Greece is significantly lower
than that of Germany, because according to the Greek legislation, diesel PCs are only allowed for
use as taxis. Since PM emissions are produced almost entirely by diesel vehicles, PM emissions
for PCs in Greece are extremely low.
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Comparison of seasonal CO2 emissions for PCs between Germany and
Greece
35.000
30.000
CO2 [1000 t]
25.000
20.000
Germany
Greece
15.000
10.000
5.000
0
Spring
Summer
Autumn
Winter
Figure 7-2: Seasonal distribution of annual (1995) CO2 emissions for PCs in Germany and
Greece
Comparison of seasonal NOx emissions for PCs between Germany and
Greece
200.000
180.000
160.000
NOx [t]
140.000
120.000
Germany
Greece
100.000
80.000
60.000
40.000
20.000
0
Spring
Summer
Autumn
Winter
Figure 7-3: Seasonal distribution of annual (1995) NOx emissions for PCs in Germany and
Greece
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Comparison of seasonal PM emissions for PCs between Germany and
Greece
8.000
7.000
6.000
PM [t]
5.000
Germany
Greece
4.000
3.000
2.000
1.000
0
Spring
Summer
Autumn
Winter
Figure 7-4: Seasonal distribution of annual (1995) PM emissions for PCs in Germany and
Greece
Comparison of seasonal CO2 emissions for HDVs between Germany and
Greece
14.000
12.000
CO2 [1000 t]
10.000
8.000
Germany
Greece
6.000
4.000
2.000
0
Spring
Summer
Autumn
Winter
Figure 7-5: Seasonal distribution of annual (1995) CO2 emissions for HDVs in Germany and
Greece
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Comparison of seasonal NOx emissions for HDVs between Germany and
Greece
140.000
120.000
NOx [t]
100.000
80.000
Germany
Greece
60.000
40.000
20.000
0
Spring
Summer
Autumn
Winter
Figure 7-6: Seasonal distribution of annual (1995) NOx emissions for HDVs in Germany and
Greece
Comparison of seasonal PM emissions for HDVs between Germany and
Greece
14.000
12.000
PM [t]
10.000
8.000
Germany
Greece
6.000
4.000
2.000
0
Spring
Summer
Autumn
Winter
Figure 7-7: Seasonal distribution of annual (1995) PM emissions for HDVs in Germany and
Greece
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Comparison of seasonal CO2 emissions for highways between Germany
and Greece
16.000
14.000
CO2 [1000 t]
12.000
10.000
Germany
Greece
8.000
6.000
4.000
2.000
0
Spring
Summer
Autumn
Winter
Figure 7-8: Seasonal distribution of annual (1995) CO2 emissions for highways in Germany and
Greece
Comparison of seasonal NOx emissions for highways between Germany
and Greece
140.000
120.000
NOx [t]
100.000
80.000
Germany
Greece
60.000
40.000
20.000
0
Spring
Summer
Autumn
Winter
Figure 7-9: Seasonal distribution of annual (1995) NOx emissions for highways in Germany and
Greece
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Comparison of seasonal PM emissions for highways between Germany and
Greece
6.000
5.000
PM [t]
4.000
Germany
Greece
3.000
2.000
1.000
0
Spring
Summer
Autumn
Winter
Figure 7-10: Seasonal distribution of annual (1995) PM emissions for highways in Germany and
Greece
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8 PROBLEMS AND SHORTCOMINGS OF THE PRESENT SYSTEM
8.1 ROAD TRANSPORT MODULE
Quantity and quality of data
•
Data on load factors and occupancy rates are scarce (not available for all EU countries, nor
for all years; corresponding data for urban-rural-highway driving conditions are not
available), thus not allowing for an accurate estimation of specific emissions (indicators). The
accuracy of the data on LF and OR obtained so far is ambiguous
•
The input data for some countries are poor. Consistency checks on the basis of fuel
consumption data are required (following the example of COPERT, these checks could be
introduced in the module instead of being performed externally).
•
Statistical data for the temporal disaggregation (seasonal, monthly, diurnal profiles) of
emissions are not available
•
Waste calculation: Validation of emission factors is still required
Technical issues
The system is unable to handle the introduction of new technologies (e.g. post Euro V vehicles).
In this sense, scenarios based on alternative technologies cannot be simulated.
In addition, scenarios including changes in the vehicle fleet composition and the life time
function parameters (e.g. scrappage schemes) can only be performed by expert users (either by
changing the LTF parameters, or by introducing vehicle fleet data for specific years)
Geographical coverage
The software was designed to calculate road transport parameters for the EU15 Member States
only and does not allow the introduction of new countries. Thus, if new countries are to be
included (e.g. Candidate countries, cf. ETC/ACC requirements) the structure of the system
requires significant modifications
Moreover, the module does not calculate EU totals. This function is performed externally,
through an Excel-based module
Lack of flexibility
All output values should be easy to handle. Export facilities for obtaining the data in predefined
formats are required. If TRENDS is to be used as a source of data for ETC/ACC or other
activities, then the user requirements must be specific from the very beginning. The options of
either producing one (or more) exports per country and a “total” export for all countries must be
available.
The software operates only under a specific version of Microsoft Access (Access 97). Moreover,
it exhibits occasional failures and errors during the data calculation
The system was designed for expert users only. Changes in the input data are rather complex
(although there should be options to allow users to enter other than the “default” values at every
step of the calculation). Minor alterations and additions in the software require radical changes in
the input tables
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8.2 RAILWAY, MARITIME AND INLAND SHIPPING MODULES
Quantity of input data
•
The traffic data available for all modes is limited to a small number of years
•
Projections for future years are not available
Quality of input data
•
The data available is more of statistical interest than suitable for calculations
•
Discrepancies in the data are very common
Lack of evaluation
•
There is very limited information as to the accuracy of the model
Requires expert user
•
The management and use of the database requires an experienced user that is familiar with
the data and system limitations
Access has proved unstable and “difficult” as a platform
•
Lack of flexibility means that minor changes may require major restructuring in the database
•
Software may not run smoothly in all systems
8.3 AIR MODULE
Quantity of input data
•
Detailed traffic data is only available for IFR flights in Europe from 1996 onward
•
Projections for future years are not available in the same degree of detail
Quality of input data
•
Data available consist of flight plan information not actual flights, which can lead to
discrepancies mainly due to congestion, changes in schedule etc.
•
No information is available on number of passengers or cargo carried per flight
Lack of knowledge on several components
•
No information is available on PM10 and PM2.5 emissions
•
Only draft estimates are available on components like CH4, NH3, N2O
•
Only estimates are available for additional ground emissions like engine start and auxiliary
power unit, which are not covered by the standard LTO cycle
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9 FUTURE DEVELOPMENTS
Further development is aimed at:
Focusing the role of TRENDS on the production of TERM indicators
•
The outline of the TRENDS program was designed prior to TERM, with the intention of
providing indicators related mainly to air pollution and energy consumption. TRENDS is
now regarded as the main production tool for TERM indicators. How could TRENDS be
better adapted to the needs of TERM? In particular what other TERM indicators could be
produced within TRENDS, and what data-sets would be needed for their calculation?
Improving the efficiency of the software
•
The current system is based on MS Access. It has proved difficult to revise coding. Other
alternatives should be investigated and a more appropriate software package should be
considered in the future.
Expanding or reducing coverage
•
TERM is currently being extended to cover non-EU countries. The development of TRENDS
should be considered in order to include the countries of the European Economic Area
(Iceland, Liechtenstein, Norway), Switzerland, and the Candidate countries (Bulgaria,
Cyprus, Czech Republic, Estonia, Hungary, Lithuania, Latvia, Malta, Poland, Romania,
Slovakia, Slovenia and Turkey). A critical factor here is the existence of compatible data.
•
TRENDS is linked to a GIS system which provides a regional disaggregation of emissions, as
well as a split between urban, rural and highway areas for one base year. Is it appropriate and
useful for TRENDS to attempt such regional disaggregation? Or should this link be
developed?
•
The aviation module covers only the years 1996-2001. The calculating tool needs to be
expanded to produce estimates for all years in the time frame 1970-2020. In order to evaluate
air emissions for the missing years, additional data from Eurostat or other sources are
required.
•
The TRENDS program currently covers waste from road transport. The development of
TRENDS to cover waste from other modes should be considered.
•
Would it be feasible to introduce life-cycle analysis (e.g. as within the STEEDS and ASTRA
programmes [11]) and calculations of external costs (e.g. as developed within ExternE [12])
and by INFRAS [13] based on coefficients derived from other programmes, particularly at
EU level?
Data issues: better fitting statistics to methods
•
Has the optimum use been made of existing data? Are there other sources which were not
exploited? Should Eurostat establish a new data collection?
•
How can data be more efficiently pre-processed, either externally or within TRENDS?
•
What should be endogenous to TRENDS, and what should be exogenous? What sources
should be used for exogenous variables? Possible links to other EU projects should be
explored, especially as regards baseline forecasts of transport activity (cf. Scenes [14]).
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The modelling of road vehicle turnover uses an engineering approach as used in emission
models. It has not been adapted for estimating waste, and does not consider scrappage
schemes or imports and exports of used vehicles. It is therefore inadequate for estimating
end-of-life vehicles and waste from this stream. What are the alternatives to the current
method?
TAB: Revision of basecase scenario - production of additional scenarios
•
The assumptions made for the basecase scenario should be discussed and modifications
should be made, if required.
•
Sensitivity runs are required in order to assess the effect of various input parameters on the
traffic activity and emission results produced.
•
Apart from the basecase scenario, additional scenarios should be created, in order to take into
account various effects, such as the increase of diesel share in recent years, (see section 4.1)
which were not considered in the reference scenario.
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REFERENCES
1) L. Ntziachristos and Z. Samaras, COPERT III: Computer programme to calculate emissions
from road transport, Methodology and emission factors (Version 2.1), Technical report No
49 (2000)
2) LAT, TUV, KTI, TRAP: Study on Transport-Related Parameters of the European Road
Vehicle Stock, 1999
3) N. Kyriakis, Z. Samaras and A. Andrias, MEET: Methodologies for Estimating Air Pollutant
Emissions from Transport - Road Traffic Composition, Task 2.2 - Deliverable 16, LAT
Report No 9823, 1998
4) Technical University of Graz, KFZ-Emissionskataster Steiermark, Final report January 1992,
Report No 7/92 – Stu 1992 02 18
5) Intraplan, Tetraplan, Transport Flows on the European Railway Network, INRETS (1997)
6) Lloyds Register of Shipping (1994), Register of Ships, 1994-1995, London
7) Corbett JJ, P Fischbeck (1997), Emissions from ships, Science, Vol 278, pp 823-824
8) Sorenson S.C., T. Kalivoda, M. Kudrna and P. Fitzgerald Future non-road emission factors
DTU report, (1998) DEL MEET 25
9) UBA Berlin, Umweltbundesamt, Jahresbericht 1995, Berlin
10) European Commission, Standard and Poor’s DRI and K.U. Leuven, Auto-Oil II CostEffectiveness Study, Draft final report, 1999
11) http://www.cordis.lu/transport/src/astra.htm.
12) http://externe.jrc.es.
13) http://www.infras.ch.
14) http://www.iww.uni-karlsruhe.de/SCENES
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APPENDIX A: SEASONAL DISTRIBUTION OF CO2, NOX AND PM EMISSIONS
Table A-1: CO2, NOx and PM vehicle emissions for spring
Table A-2: CO2, NOx and PM vehicle emissions for summer
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Table A-3: CO2, NOx and PM vehicle emissions for autumn
Table A-4: CO2, NOx and PM vehicle emissions for winter
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