Is There a Chance to Limit Transport in Slovenia in the Light of the Climate Change? Top Down Approach for Personal Vehicles

Abstract

Slovenia is a quite transport intensive country. Due to its geographic location it attracts a lot of transit traffic, however even bigger issue might be mostly car-oriented development of traffic in the last 50 and more years. The motorisation rate is still increasing, however even smaller cities are facing long congestions. Slovenian National Energy and Climate Plan anticipates large reduction of greenhouse gasses either through switch to sustainable transport or relying on alternative fuels as renewable electricity or synthetic gasses. The paper demonstrates the somewhat ambitious plan dissected to the local community level while taking local specialties into the account.

Full text

Abstract—Slovenia is a quite transport intensive country.
Due to its geographic location it attracts a lot of transit traffic,
however even bigger issue might be mostly car-oriented
development of traffic in the last 50 and more years. The
motorisation rate is still increasing, however even smaller cities
are facing long congestions. Slovenian National Energy and
Climate Plan anticipates large reduction of greenhouse gasses
either through switch to sustainable transport or relying on
alternative fuels as renewable electricity or synthetic gasses.
The paper demonstrates the somewhat ambitious plan dissected
to the local community level while taking local specialties into
the account.
Index Terms—Transport, emission reduction, municipalities,
statistics.
I. INTRODUCTION
The amount of traffic in Slovenia is in perpetual increase
for the last of 20 years. However, this trend can be traced
even decades ago [1], [2] when city planners gradually
decided to phase out not-bus public transportation, state
neglected railway infrastructure and heavily supported road
construction consequently supporting individual transport.
Greenhouse gas emissions in the Slovenian transport sector
have thus been increasing in recent decades as a result of
economic development, country geographical position as a
transit country, structure of settlements and, in most cases,
inferior or even lack of alternative modes of passenger and
freight transport.
Personal transport based on passenger car transport causes
daily congestion in peak times, especially around Ljubljana
[3]. These are noticeably increasing, further contributing to
rising emissions. The increasing external costs caused by
transport call for action beyond the scope of the fight against
climate change.
The lack of integrated planning and, above all, lacking
implementation of the in the past already planned measures
caused the progress of reducing emissions to rely mainly on
the progress of vehicle technology. Furthermore, Slovenia is
crossed by number of international transport corridors, and
being small country it has limited impact on the transit traffic.
Emissions from transport already exceeded the emissions
from 1986 by more than 200% in 2008, subsequently
declining slightly, but transport remains the only sector with
Manuscript received April 20, 2020; revised September 14, 2020. This
research was partially supported by LIFE Programme – LIFE
ClimatePath2050 (LIFE16 GIC/SI/000043).
The authors are with Energy Efficiency Centre, Jožef Stefan Institute,
Ljubljana, Slovenia (e-mail: marko.kovac@ijs.si, matjaz.cesen@ijs.si,
andreja.urbancic@ijs.si, stane.merse@ijs.si).
such high emissions growth. According to the latest 2017
data [4], greenhouse gas (GHG) emissions are 5.54 Mt
CO2eq, which is 25% more than in 2005 (baseline emissions),
with road transport accounting for 99.3% of total emissions
in the transport sector, other transport (rail, air, other) less
than 1%.
Slovenia had put their commitment towards reduction of
GHG emissions into the National Energy and Climate Plan
(NECP) [5]. The efficient plan is hence crucial in addressing
the transport issue. In the first step, Slovenia will favor
long-time neglected rail transport and sustainable mobility
measures to tame the continued growth of road traffic
(passenger and freight), following by strong support to
promote other sustainable mobility options. This will reduce
the carbon footprint in the transport sector and also relieve
heavy traffic, which is quickly becoming unmanageable.
None the less, the main measures that will provide emissions
reductions will be efficiency improvement of vehicles and
increasing the share of alternative drivetrains, mainly
electric.
By 2050, the transport sector needs to be almost fully
decarbonisated with the net GHG emissions close to zero.
This also causes that by 2030, the emissions are expected to
decrease by 10% compared to 2017, however this would still
exceed base emissions by 146%. In addition, by 2050, the
emissions should fall to only 2.4% of base emissions. The
latter data represents as many as two magnitudes smaller
emissions than the present ones, which will undoubtedly be
an extremely challenging feat that will require
comprehensive and, above all, far more ambitious measures
(not only financial but also social and long-time efforts) than
we might imagine today.
Slovenia is combining local and national approaches to
significantly reduce the diesel and gasoline in favour of
electric (including plug-in) or hydrogen vehicles. Local
incentives such as charging infrastructure, special quick lanes,
free parking or no congestion charge and encouraging usage
of public transportation seem to be easier to implement.
The statistical approach described in this paper helps better
predict local specifics and hence improve the effect of
incentives.
II. TOP-DOWN APPROACH
While nation-wide efforts are necessary for
multi-government approach, the majority of transport
problems are, at least in majority cases, quite local experience.
For instance, personal public transport (PPT) in a city solves
local congestion and emissions. At the same time the goals of
Is There a Chance to Limit Transport in Slovenia in the
Light of the Climate Change? Top Down Approach for
Personal Vehicles
Marko Kovač, Matjaž Česen, Andreja Urbančič, and Stane Merše
International Journal of Environmental Science and Development, Vol. 11, No. 11, November 2020t
499
doi: 10.18178/ijesd.2020.11.11.1297

the NECP cannot be directly translated towards the local
level due to local features [6]. Furthermore, local knowledge
and understanding about those problems is usually quite
larger than on regional or even national level, including
already tried but unsuccessful solutions [7].
There were many attempts already addressing the situation:
e.g., strategy for alternative fuels [8], renewable energy
regulation [9] etc. The main problems with those approaches
were that they tend to address only limited problems (e.g.
emissions or traffic congestion etc.) while other related issues
and limitations could be ignored.
The main aim of the presented paper is the transfer of the
national goals related to personal transportation to the level of
municipalities. These enables local authorities to combine
efforts and taking local issues into the account (specific
issues due to development; local & regional, roads density
combined with existing transportation modes, plans for
future development etc). Quite important are also synergies
with other long-time efforts and strategies connected to
traffic (e.g., road safety, population aging etc). Support of the
local efforts is therefore necessary to achieve national wide
goals.
A. Description of Data
For the purpose of this research we used different data
sources in public domain. They are available for each
municipality (214 of them) and consist of [10]:
• Municipality road density
• Categorization and length of the road network
• Motorization rate
• Population with dissection regarding age (active,
younger, older)
• Average monthly pay
• Share of cars with different drive (e.g., ICE, BEV etc.)
TABLE I: THE CORRELATION ANALYSIS ON SOME MUNICIPALITY DATA
BEV share
Motorization
rate
Share of
active
population
Average
monthly pay
BEV share
1.000
Motorization rate
0.116
1.000
Share of active
population
-0.084
0.060
1.000
Average monthly
pay
0.045
-0.055
-0.122
1.000
The first step was to find possible correlations between
mentioned data set. Multiple factor correlation analysis was
performed. The results are shown in Table I.
In addition, Fig. 1 shows correlations of sets of
motorization rate, share of active population, average
monthly pay and share of BEV for all Slovenian
municipalities. The largest Slovenian towns are marked with
labels and linear trendline is shown, however no significant
correlation could be obtained.
The analysis shows only very limited correlations
between those data therefore multi regression analyses was
needed. However, municipalities in Slovenia are quite
heterogeneous, therefore some modal split of municipality
size might be of interest. For instance, municipality size
could be divided into three sizes: large (over 20 000
inhabitants) and small towns (over 10 000 inhabitants) and
smaller municipalities (less than 10 000 inhabitants). For
some parameters, such as weighted road density and average
monthly pay, the size of municipality plays hardly any role,
as shown in Fig. 2.
Fig. 1. Correlations of sets of motorization rate, share of active population,
average monthly pay and share of BEV.
0
1000
2000
3000
4000
5000
1200 1400 1600 1800 2000 2200 2400
Weighted Ro ad Density [m per km2]
Average Monthly Pa y [€]
Larger Towns
Smaller Towns
Smaller Municipalities
Fig. 2. Correlations of sets of BEV share, weighted road density and
municipality size.
R² = 0.0759
R² = 0.0282
R² = 0.0781
0
1000
2000
3000
4000
5000
6000
600 800 1000 1200 1400
Weighted Road Density [m per km2]
Motorizati on Rate [cars per 1000 inha bitants]
Larger Towns Smaller Towns Smaller Municipalities
Fig. 3. Correlations of sets of motorization rate, weighted road density and
municipality size.
Fig. 3 shows correlations between the sets of motorization
rate, weighted road density, keeping the same split on
municipality size. For smaller municipalities the
motorization rate is corelated with road density (this is
common know effect that more roads attract more traffic
especially in areas where public transportation is weak or
none-existent). For larger towns this relation is quite reversed
– higher density causes less motorization rate since part of the
Ljubljana
Maribo r
Kranj
Koper/Ca podistria
Celje
Novo mes to
Domžal e
Vele nje
Nova Gori ca
Kamni k
1 000
1 250
1 500
1 750
2 000
2 250
2 500
0% 2% 4% 6% 8%
Average Monthyl Pay [€]
Share of BEV
Ljubljana
Maribo r Kranj
Koper/Ca podistria
Celje
Novo mesto
Domža le
Vele nje
Nova Gorica
Kamni k
54%
56%
58%
60%
62%
64%
66%
600 800 1 000 1 200 1 400
Share of Active Population
Motorization Rate [cars per 100 inhabitants]
Ljubljana
Maribo r
Kranj
Koper/Capodistria
Celje
Novo mes to
Domža le
Vele nje
Nova Gori ca
Kamni k
1 000
1 250
1 500
1 750
2 000
2 250
2 500
54% 56% 58% 60% 62% 64% 66%
Average Monthly Pay [€]
Share of Active Population
Ljubljana
Maribo r
Kra…
Koper/Capodistria
Celje
Novo mesto
Domža le
Vele nje
Nova Gori ca
Kamni k
600
800
1000
1200
1400
0% 2% 4% 6% 8%
Motorization Rate [cars per 1000
inhabitants]
Share of BEV
International Journal of Environmental Science and Development, Vol. 11, No. 11, November 2020t
500

traffic can be made with public transport and sustainable
mobility.
The same division of municipality by size also effects the
battery electric vehicle (BEV) share, shown in Fig. 4.
Fig. 4. Correlations of sets of BEV share, weighted road density and
municipality size.
Hence, other influences on the BEV share were not
discovered. The main reason for that is probably still low
penetration of BEV in Slovenia. The market share is around
1 %, while the fleet share is 0.1 %. However, both shares are
increasing so better results could be obtained in a reasonable
time.
The influence of the analysed parameters on the BEV share
is summed in Table II.
TABLE II: THE INFLUENCES ON BEV SHARE IN SLOVENIA
Influence
Qualitative
Note
Town/Municipality size
YES
Up to 30%
Motorisation rate
NO
~1%
Share of active population
NO
<1%
Average monthly pay
NO
<1%
Weighted density of road
network
NO
~1%
Vehicle stock replacement dynamics can be partially
connected to current car stock age. Fig. 5 shows age of
current car stock.
020 40
0.00
0.05
0.10
0.15
Frequency [%]
Car age [years]
Fig. 5. Average age of the cars with heat map regarding frequency.
There is a large spike in cars older than 12 years old. This
is the consequence of open market on imports of used cars
from other EU countries after Slovenia joined the EU. This
can be – if necessary – modelled as one-off moment. Some
bumps in cars age can be also contributed to market crises
or/and change of tax laws, which could boost sell of new cars
on particular year by some margin. Therefore, due to large
number of special sales occurrences, the special (i.e.,
car-by-car) model would be an overkill, we relied on
projections as part of NECP.
B. Car Number Growth/Decline
Number of cars Nim for each municipality m by year is
defined by year-to-year basis as:
1(1 )
mi g m
i
N N y y
−
=+
(1)
where i defines the year and 𝛾g is general growth/decline,
while 𝛾g defines municipality specific growth. This is defined
as simple linear function dependend on starting growth 𝛾0
and incremental change Δ𝛾:
,0gi
y r r i= + 
(2)
However, the size-specific growth is defined that at the end
of period (year 2050) the number of the cars is the same as in
case without size-specific growth:
 
2050 , 1,2,3
m
N const m==
(3)
III. RESULTS AND DISCUSSION
Fig. 5 shows projection of number of personal vehicles
until 2050 in Slovenia regarding their energy storage. The
prevailing diesel and gasoline engine will be replaced with
BEV and partially by PHEV and hydrogen vehicles.
However, even PHEV are expected to be phased out latter in
the century. In addition, we expect some decrease on the
number of the whole fleet of personal vehicles after some
positive measure for PPT, sustainable mobility and mobility
as a service are well received.
0
0.25
0.5
0.75
1
1.25
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
Number of personal vehicles [milions]
Year
H2
BEV
Gas
PHEV
Hybrid
Gasoline
Diesel
Fig. 5. Projection of number of personal vehicles until 2050.
-0.5
0
0.5
1
1.5
2
2.5
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
Yearly growth of number of vehicles
Year
Diesel
Gasoline
Hybrid
PHEV
Gas
BEV
H2
Fig. 6. Growth/decline of projected number of cars until 2050.
International Journal of Environmental Science and Development, Vol. 11, No. 11, November 2020t
501

Fig. 6 shows growth/decline of projected number of cars
until 2050 in Slovenia regarding their energy storage (in a
form of
𝛾g
– general growth/decline, see eq. (1)). The data
shows increase of share of cars with less emissions (BEV,
PHEV and hybrid cars), while fossil fuel cars will almost
vanish by the mid of the century.
By taking eq. (2) and eq. (3) into the account, relative
yearly growth of number of BEV cars can be calculated for
three different municipality sizes. Fig. 7 shows the results for
large and small towns and smaller municipalities.
0
0.2
0.4
0.6
0.8
1
2015 2020 2025 2030 2035 2040 2045 2050
Yearly addition of BEV (γ)
Year
Large Towns
Small Towns
Smaller Municipalities
Fig. 7. Relative growth of projected number of BEV cars until 2050.
The growth can then be calculated into BEV car figures or
at least car index, since exact car figures would be different
from one municipally to the next. Fig. 8 therefore shows
indexes of projected number BEV cars in Municipalities of
different sizes based on its size.
0
100
200
300
400
500
600
700
2015 2020 2025 2030 2035 2040 2045 2050
Index of BEV cars (2018 = 1)
Year
Large Towns
Small Towns
Smaller Municipalities
Fig. 8. Index of BEV cars until 2050.
While this prediction looks a bit rough since only relies on
couple of data: municipality size, starting number of BEV
and national wide projection of growth of numbers of BEV
cars, it can still help different municipalities to plan
accordingly.
This sort of information also helps with guiding local
efforts for targets on emission. It seems that emission control
again is a bit simpler in larger towns (where somewhat useful
public transportation exists) than in smaller communities.
Table III shows projection of number of BEV in some
typical municipalities. The representatives for large towns
are Ljubljana or Novo mesto, for small towns Ajdovščina or
Železniki, while smaller municipality is represented by
Borovnica or Vransko.
From the results one can see that there is a large emphasis
of this method on number of BEV already in the municipality,
however this number is not statistical related to other data
(see Table I). Therefore, some additional statistical data is
needed to give a better local evaluation of number of BEV or
other statistics, which might help us to be better prepare for
the future at local level.
TABLE III: PROJECTION OF NUMBER OF BEV IN SOME MUNICIPALITIES
2018
2020
2030
2040
2050
Ljubljana
221
666
36828
134819
155211
Novo mesto
4
12
667
2440
2809
Ajdovščina
8
20
873
4349
5618
Železniki
1
3
109
544
702
Borovnica
2
4
156
993
1405
Vransko
0
1
47
298
421
Fig. 9. Municipality dashboard.
The presented approach can be used to create simple tools
– such like a dashboard, which can be used by municipalities
for quick overview of the situation (prediction of EV share,
emissions etc.) as shown in Fig. 9. This enables quick recall
of local targets and limited what-if scenario to set the
properly timing and extent of the local incentives for
encouraging electrification of transport (such as public
charging infrastructure, parking spaces, congestion charges
[11]).
IV. CONCLUSIONS
The aim of this research was to help municipalities and
local communities to get information about change in their
personal vehicles fleet. This information can enable them to
predict and execute measures in the transport sector, which
are in accordance with National Energy and Climate Plan
(NECP), This will enable efficiently reduce the emissions of
greenhouse gasses on the local and also national level
(bottom-up approach) but also help acting upon raising local
traffic problems (such as congestion, pollution etc.)
The approach showed how the national wide data could be
International Journal of Environmental Science and Development, Vol. 11, No. 11, November 2020t
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used together with additional locally specific data to obtain
more focused projections. This data could in essence help
shape up the local enforcements like replacement of classic
fossil fuels stations with less upsetting charging stations (due
to lack of necessary safety precautions while dealing with
flammables).
For this purpose, we rely on statistical approaches that
helped determined significant parameters (e.g.,
town/municipality size). Another important data, which
might get used in the future, is quite homogenous
non-influence of large other data (e.g., pay height, road
density etc.). It seems that, at least for case of Slovenia, the
most important influences are the national wide-ones, while
local influences are limited.
The results were showed as typical fleet number (i.e.,
index) in representative municipalities (larger and smaller
town, smaller municipality) from now until 2050.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
AUTHOR CONTRIBUTIONS
Andreja Urbančič conceived a general idea and
encouraged Marko Kovač to developed the calculation and
performed the computations. Matjaž Česen is the source of
data and future projections. He also verified the analytical
method. Stane Merše supervised the findings of this work.
Marko Kovač and Matjaž Česen wrote the paper. All authors
had approved the final version.
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Copyright © 2020 by the authors. This is an open access article distributed
under the Creative Commons Attribution License which permits unrestricted
use, distribution, and reproduction in any medium, provided the original
work is properly cited (CC BY 4.0).
Marko Kovač has bachelor’s degree in mechanical engineering from
Faculty of Mechanical Engineering and Ph.D in nuclear engineering from
Faculty of Mathematics and Physics, both of University of Ljubljana.
He is at the moment employed as a senior researcher at Efficiency
Emergency Center of Jožef Stefan Institute. His research interests are
automotive (mostly electric) and photovoltaics.
Dr. Kovač won several rewards by Slovenian Chamber of Commerce and
industrial companies.
Matjaž Česen has a bachelor’s degree in meteorology from Faculty of
Mathematics and Physics of University of Ljubljana, Slovenia.
He is at the moment employed as a Senior researcher at Efficiency
Emergency Center of Jožef Stefan Institute with research interests of
statistical models and general automotive.
Mr. Česen has participated as a reviewer of national communications and
biennial reports under UNFCCC, also as a head reviewer.
Andreja Urbančič has a master’s degree in mathematics from Faculty of
Mathematics and Physics of University of Ljubljana, Slovenia.
She is employed at Efficiency Emergency Center of Jožef Stefan Institute
and works on energy system modelling and analyses in the support of
decision-making in climate and energy policy. She is the project coordinator
of LIFE project Climate Path 2050 – Slovenian Path Towards the
Mid-century Climate Target.
Ms. Urbančič coordinated the European expert group for future activities
within the working group on environment during the period of Slovenian
Presidency of the European Union.
Stane Merše has a master’s degree in electrical engineering from Faculty of
Electrical Engineering of University of Ljubljana, Slovenia.
He is a head of Energy Efficiency Centre of Jožef Stefan Institute since
2008. He is an expert in holistic energy planning and recently especially in
efficient heating and cooling, heat and power cogeneration (CHP) and
district heating.
Mr. Merše is a lecturer at EUREM training and program manager of
annual energy managers conference “Dnevi energetikov”.
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