Modelling and Assessing Environmental Impact in Transport to Meet the

Sector’s Climate Goals in 2050

LORENC MALKA1,*, RAIMONDA DERVISHI2, PARTIZAN MALKAJ2,

ILIRIAN KONOMI3, RRAPO ORMENI4, ERJOLA CENAJ2

1Faculty of Mechanical Engineering, Department of Energy,

Polytechnic University of Tirana,

ALBANIA

2Faculty of Mathematical and Physics Engineering,

Department of Mathematical Engineering,

Polytechnic University of Tirana,

ALBANIA

3Faculty of Civil Engineering, Department of Hydraulics & Hydrotechnics,

Polytechnic University of Tirana,

ALBANIA

4Albanian Academy of Sciences,

1000 Tirana,

ALBANIA

*Corresponding Author

Abstract: - The transport sector has had and continues to have an extraordinary impact on the annual final

consumption report based on fossil resources limited by technology and as a result has brought and continues to

present very serious problems for the impact on the environment. The transport sector, particularly in developing

countries, has a critical role in final energy consumption and greenhouse gas emissions reduction strategies.

Albanian transport sector ranks first in terms of total energy consumption and consequently in emissions that may

increase particularly rapidly, and the costs of future retroactive mitigation activities may be prohibitive if no energy

efficiency measures (EEM) are undertaken. The total energy consumption by the end of 2050 is calculated using

multiple variable regression methods driven by the GDP growth rate, and population projections, while the

combination of energy fluxes by fuel type is based on National Energy and Climate Plan (NECP) requirements. In

this study two scenarios are developed using an advanced energy system analysis computer model, EnergyPLAN,

enabling a smart, sustainable, flexible, diversified, and environmentally friendly Albanian transport sector based on

renewable energy sources (RES) and electricity. In this paper the integration of different alternative energy sources

and future expected energy sources driven by two basic criteria: the security of supply, with a minimal

environmental impact toward 2050 goals by using EnergyPLAN are proposed.

Key–Words: - EnergyPLAN, Transport Sector, GHG, Optimization, Smart Energy System, Multivariable

Regression and Statistics.

Received: August 5, 2023. Revised: May 16, 2024. Accepted: June 20, 2024. Published: July 18, 2024.

1 Introduction

The global energy crisis, volatile fuel prices, and the

emerging climate change agenda and geopolitical

issues have provided additional challenges for

countries to carry out their strategies to help better

manage their energy sectors. For most developing

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Rrapo Ormeni, Erjola Cenaj

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countries, transport is usually the major energy

consumer, followed by buildings and industry. As the

largest and fastest growing energy-using sector,

transport was responsible for 23 percent of world

energy-related GHG emissions in 2004, and about 75

percent comes from road vehicles. The transport

sector in Albania totally depends on fossil resources,

which accounted for 100% of its annual energy

demand in 2020. Consequently, the transport sector

ranks first in terms of pollution in the country, [1].

Special support should be given to candidate

countries in establishing clear policies and programs

for a sustainable transport sector based on green

technologies. People are driving on an insufficient

number of poorly maintained roads and unguaranteed

fuel quality, the lack of alternative urban

transportation specifically rail transportation, and

generally lack the money and institutional

determination to fix the problems. The idea of a

carbon-free, sustainable, flexible, and

environmentally friendly transport system based on

RES, and ramping up electrification and biofuels

undoubtedly will play a major role in fully

decarbonize road transport toward the 2050 roadmap.

Thereafter, electrification will be the prominent

lever, with electricity as an energy carrier

representing three-quarters of energy consumption in

road transport by the end of 2050, [2].

Hence, encouraging energy systems with broad

integration of renewable energy sources will be the

dominant energy carrier in the global energy system

and will play a major role in the process of deep

decarbonization of power systems, including the

transport sector [3] preventing the negative effects of

the current transport sector on human health and

using less energy per activity level. In this paper

transport energy demand scenarios that include

biofuel, hydrogen, and EVs are designed based on

demographic, economic, and many other assumptions

and projections. The hypotheses raised in this paper

consider the increase in demand for energy in the

future, and economic growth as well as the problems

of security of energy supply influenced by global

crises and geopolitics, [4].

Therefore, the purpose of this study is to design

policies, integrate and define efficient energy

scenarios in the transport sectors in Albania fully in

line with the requirements of the Energy Strategy for

Albania 2018-2030, as the core strategic document

for the country’s energy sector, is fully coherent with

other national policies and strategies and the

European Green Deal’s objectives with the main aim

in supplying clean, affordable and secure energy;

building and renovating, promoting a cleaner

construction sector; accelerating the shift to

sustainable and smart mobility; eliminating pollution

through measures to cut pollution rapidly and

efficiently and in line with National Energy and

Climate Plan (NCEP) in 2030 and 2050. The NECP

is developed under three main reports such as the

second National Strategy for Development and

Integration (NSDI II) based on the United Nations’

Sustainable Development Goals; the obligations

arising from the signature of the United Nations

Framework Convention on Climate Change

(UNFCCC) and the Energy and Climate Acquis of

the Energy Community. Referring to Sustainable

Development Goals, SDG 7- Affordable and Clean

Energy, calls for ensuring universal access to modern

energy services, improving energy efficiency, and

increasing the share of renewable energy, specifically

in the transportation sector. The progression of

climate change leads to changes in the availability of

renewable energy.

With Albania producing almost 100% of its

electric energy from hydroelectric sources [5], related

changes in the water cycle are of crucial impact,

output from reservoir hydroelectric power plants and

Run-of-river is expected to decrease by 15% and

20% by 2050, while photovoltaics output is expected

to increase by 5%. In a nutshell, import dependence

and high distribution losses in the electricity grid and

reduction of fossil fuel in the total final energy

consumption (TFEC) are challenges to be dealt with

as more than 65 % of TFEC is based on fossil fuel

sources as given in Figure 1 (Appendix). Albania

depends almost exclusively on hydropower for its

electricity generation (98% produced from

hydropower), making it increasingly vulnerable to

unfavorable hydrological conditions in the summer,

especially in view of the predicted effects of climate

change in the Western Balkans region. Apart from

the fact that there is no constant production,

electricity from hydropower is not sufficient to meet

the annual demand.

2 Some Data and Statistics on the

Transport Sector

The most effective scenario is that it integrates new

technologies and provides the best and the cheapest

cost solution. The central problem is that cities in

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developing countries are growing and motorizing at a

very fast rate, even faster than the urbanization rate.

The gross inland consumption in the country is

estimated to be around 2177 ktoe in 2022, while the

final energy consumption results in 1972 ktoe. The

transport sector shares around 38% up to 40 % of the

total final energy consumption (712 ktoe) in the

country. The transport sector is dominated by road

transport sharing 90% of the total energy

consumption within the sector, while the rest belongs

to rail transport with nearly 0% (2.1ktoe), aviation 5

% (36.88 ktoe) and inland navigation 5% (34.30

ktoe). The total number of active vehicles until

December 2023 is 867,765 [6] representing 43 types

of vehicles and 2016 different brands of which

699,337 belong to personal passenger vehicles

(81%).

The vehicle fleet in our country continues to rise

faster. The total number of active vehicles until

December 2023 is 867,765 [6] representing 43 types

of vehicles and 2016 different brands of which

699,337 belong to personal passenger vehicles

(81%). The sector continues to be dominated by

relatively old vehicles compared to other European

countries, with a fleet of vehicles older than fifteen

years.

From the study performed by [7] group that leads

to high levels of pollution in our country is the group

of vehicles manufactured from 1980 to 2002. There

is a slight improvement compared to the 2015 level,

anyway vehicles aged over 15 years still dominate

the vehicle fleet in our country, sharing 70.5 % of the

total numbers as given in the graph in Figure 2

(Appendix). Considering the above factors as well as

the quality of the fuel, which remains another

unresolved issue for the government further

complicates the position of the transport sector in

Albania. According to our forecast, the vehicle fleet

in our country will continue to rise based on annual

average historical growth and the income growth

rate. The final energy consumption by the end of

2050 will be calculated using a multivariable

regression model based on population projections

and GDP growth rate.

As depicted in the graph in Figure 3 (Appendix)

the group of vehicles with an average age of 9 up to

15 shares 22.7 % of the total passenger fleet in the

country, while the group of new cars shares only 2.7

%, [6].

From the data in the chart, as depicted in Figure

4 (Appendix), the number of vehicles registered in

our country until 2023 that use oil as fuel is 513,760

(73.5%) and 114,050 (16.3%) with gasoline, 2,891

electric vehicles (EV) and 64,601 of gasoline-gas

powered engines. The number of electric cars and

other alternative fuels shares a negligible portion in

terms of annual energy balance within the transport

sector with 0.4 %.

So, unless developing countries, including

Albania, make investments today that would curb

their long-term transport sector emissions growth, it

is unlikely that they would follow an emissions

direction and fight GHG much differently than those

exhibited by prosperous countries.

While the other categories of road transport

sector are almost 100% diesel and 100% petrol for

motorcycles. Based on the latest data the distribution

of vehicles of all categories (%) registered for the

first time in the period January-December 2023 by

fuel type is depicted in the graph in Figure 5

(Appendix) showing that diesel and petrol cars

dominate, while electric vehicles share only 2.3%

from the total of 78 157 new registered vehicles

where 82.4% belongs to passenger cars.

The fuel type of other vehicle categories is 100

% fossil fuel. The passenger cars distribution by fuel

type for the newly registered vehicles in the period

January up to December 2023 is given in Figure 5

(Appendix).

As can be seen from the graph in Figure 4 and

Figure 5 (Appendix) an optimistic strategy that

would consider the promotion of locally produced

biofuels, such as: biodiesel, bioethanol, and bio-

methane is considered part of our solution. The

integration of biofuels should consider sustainability

criteria for long-term projections, an option that will

reduce dependence on oil imports and increase

national energy security. The integration of biofuels

will contribute to the improvement of air quality in

urban centers and the most important thing is that it

will help diversify the fuel fluxes within the transport

sector in Albania. The share of electric vehicles (EV)

is at low-rate values bunching only 0.4% within the

sector. The introduction of a series of measures for

smart and sustainable transport, including a revision

of the actual National Energy and Clime Plan

(NECP) [8] and National Energy Strategy 2018-2030

[9] and Law No. 24/2023 on “Promoting the Use of

Energy from Renewable Sources” as given in [10] is

necessary.

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3 Materials and Methods

EnergyPLAN is an advanced energy system

modelling tool, which has been under development

since 1999. The structure of the model is given in

Figure 6 (Appendix) including the whole system in

one control volume. Such structure enables to exploit

the synergies between different sectors as

encapsulated in the smart energy system concept

enabling the experts to easily take a holistic approach

focusing on the analysis of cross-sectoral interaction.

Traditionally diverse demand sectors, such as

buildings, industry, and transport, are linked with

supply technologies through electricity, gas, district

heating, and cooling grids based on a high share of

renewable energy sources. The key objective is to

model a variety of options so that they can be

compared with one another, rather than model one

‘optimum’ solution based on defined pre-conditions.

Using this methodology, it is possible to illustrate a

palette of options for the energy system, rather than

one core solution. This could classify EnergyPLAN

as a simulation tool rather than an optimization tool

[11], even though there is some optimization within

the model. In this way, EnergyPLAN enables the

analysis of the conversion of renewable electricity

into other energy carriers, such as heat, hydrogen,

green gases, and electrofuels, as well as the

implementation of energy efficiency improvements

and energy conservation.

The latest model has improved strongly enough

and includes features such as a better algorithm to

make use of electrolyzers to balance electricity and a

better algorithm to use thermal storage. An option to

enter max and min prices on the external market in

the case of bottlenecks. An option to include HTL

and Pyrolysis in the biomass conversion. An option

to calculate H2 grids and convert them to a 100%

H2 solution. An option to include Biochar from

Pyrolysis. A choice to incorporate other emissions

than CO2 facilitating to compute various emissions

such as N2O, NOx, PM2.5 CH4, and SO2. When

applying the CO2 emission (kg/GJ) of each of the

four fuel types as an input, the model calculates the

CO2 emission simply by multiplying the fuel

consumption by the emission data. The need to

integrate other alternative fuels with zero emission,

based on the internal energy potential and global

trends are the main pillars of the methodology.

Reduced dependence on energy imports will not only

contribute to improving the security of the energy

supply but also to the macroeconomic and political

stability of the country by decreasing the domestic

budget deficit.

4 Scenario Conceptualization

Energy scenarios provide a framework for exploring

future energy perspectives, including various

combinations of technology options and their

implications in the whole system driven by

independent variables such as the economic growth

rate, population, fuel prices, and other limitations

including reduction of GHG and energy importation

level. The energy demand in the transport sector is

increasing constantly, a situation that has increased

the sector's dependence on carbon-intensive fossil

fuels, resulting in high energy-related emissions-and

so is unsustainable and unecological. Our approach

strives for to invigorate and prop up locally produced

biofuels such as biodiesel, bioethanol, and bio-

methane and inject electric vehicles (EV) to reduce

critical excess electricity production (CEEP) in the

cases of high share of renewables into the future

power systems. In this perspective, different fuels

and energy carriers should be combined to assess and

control targets required in our National Energy

Strategy 2018-2030, in the National Sectoral Strategy

for Transport 2016-2020, as well as in the National

Energy and Clime Plan (NECP) as given in Table 1

(Appendix), respectively.

The increase in energy demand in the transport

sector is calculated by applying a forecasting

methodology driven by the historical data of the last

12 years of energy consumption within the transport

sector, economic growth, and other policy factors

given in the current strategic energy documents.

Forecasting, and making predictions about future

energy consumption, especially in the transportation

sector plays a key role in the decision-making

process and measures to maintain or achieve the

desired energy and climate goals. The strategic

priority is to accelerate the integration of Albania's

transport system and create an integrated market that

includes the entire transport infrastructure. Despite

the significant investments and efforts made by

governments in the last decade, to improve the road

infrastructure, the transport sector still does not show

optimistic indicators for economic development in

Albania. In the transport sector, the projected

emission increase results from a continuously

increasing gasoline and LPG use from 2018 to 2050,

which is a function of the projected economic growth

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and increased demand for transport services.

Electrification is expected to pick up towards 2030

but is projected to have the smallest share among all

fuels.

In Table 2 (Appendix) two main scenarios

designed for the transport sector in Albania in the

way toward 2050 goals are introduced. The main

goal of this research paper is to calculate the required

energy carriers to meet demand and reduce emissions

as per NECP requirements. The integration of

alternative fuels, e.g., electricity, H2, renewable fuels

such as synthetic fuels, but also biofuels has been

considered an efficient approach to reduce

conventional fuel use in a well-designed plan.

Electricity can be used in the direct form in all forms

of transport: in the road, rail, and marine transport

sector and to a lesser extent aviation. Electric-based

direct transport is one of the most promising

technologies with high reliability, safety, and

efficiency for transport. The main driver of the

transport sector is the demand for mobility,

forecasting a rising annual demand for per person-km

growth rate, while transport of freight is projected to

undergo growth with a GDP evaluated 3.4% in 2050.

5 Regression Analyses and Energy

Forcasting

The major problem related to energy demand in the

future and the consequent scientific challenge is that

its production greatly depends on available resources,

overall economic and population growth rate,

urbanization rate, efficient and new resources or

technologies that may replace existing energy

systems. The greater the number of independent

variables the more accurately the demand of energy

in a future growing demand would result. Regression

analysis is a statistical technique to set the

relationship among different independent variables

influencing a phenomenon and the mean value of the

corresponding function. Univariate regression is

analyzing the relationship between a dependent

variable         and one independent

variable Y.

5.1 Multiple Linear Regression Model (MLR)

In this study, the historical transport energy data

consumption from 2010 up to 2022, known as

dependent parameter 󰇛󰇜 given in kilo tonne oil

equivalent (ktoe), is a function of many independent

variables and quantities such as activity level (AL) or

simply vehicle numbers, GDP (Billion €), population

projection, and income growth rate (%), are used as

an input to create a multiple regression model with

interaction effects. The interaction effect occurs

when the effect of the independent variables on the

future energy demand in the transport sector changes

subject of the other variables (independent variables).

The regression model with interaction effects is an

extension of the general regression presented as

follows in Eq.1:

       

 

(1)

where  is the intercept and x represent each

independent variable. The other  parameters present

the slope coefficient of the variable. All  parameters

are defined in the model creation process to minimize

the error

. The interaction effects are added to the

general form of the multiple regression model as

follows in Eq.2:

    

 

   

  

(2)

where  is the interaction between two variables

Population (Thousand) and GDP (billion €).

In Table 3 (Appendix) historical energy

consumption, Population and GDP are given for the

period from 2010 up to 2022. The energy data for the

transport sector are provided from the National

Energy Balance report. The transport sector shares

approximately 38 up to 40 % of the total energy

consumption in the country level, while GDP values

after 2022 are calculated using Forecast. Linear

function based on World Bank national accounts

data, and OECD National Accounts data files. The

results of “Forecasted” values on transport energy

consumption (ktoe) and GDP (Billion €) starting

from 2023 up to 2050 using an elasticity coefficient

of 1.05 and 1.2 respectively are given in Table 3

(Appendix). By the end of 2050 the total energy

consumption is expected to rise to a total value of

1773.40 ktoe.

In Table 4 (Appendix) the simulation results of

multi-variable regression for the chosen independent

variable are given. Multiple R value results 0.872,

while R Square is 0.760 and adjusted R Square is

0.733 for a set of observation numbers of 21 as

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shown in the ANOVA test table and results portrayed

by Table 4 (Appendix). The intercept value results in

-370.80, Population coefficient of 0.299, and GDP

coefficient of 27.850 with a p-value less than 0.05

(0.0113).

From the multi linear regression analyses (MLR)

simulation, the energy consumption (ktoe) in

accordance with GDP (billion €) is given in the graph

Figure 7 (Appendix). The correlation between GDP

(billion €) and energy consumption (ktoe) is given by

the mathematical expression in Eq.3:

        

(3)

The coefficient of determination (R2) of this

function is approximately 0.92, which represents a

good correlation, while the consumption based on

population variability is given from mathematical

expression as given in Eq.4:

y = -0.7656x + 2989.4

(4)

The coefficient of determination (R2) value of the

correlation is R² = 0.8609.

Energy demand is a function of income growth

rate, and population. The more the household income

the higher the energy consumption is expected. This

is a universal approach in energy modelling science,

in which the growth in one variable is rated as a

function of the growth in another (independent)

variable.

As can be seen from the plot, energy demand is

strongly dependent on economic development and

population income rate. In the future GDP is

expected to reach a value of 32.69 billion euro by the

end of 2050.

6 Simulation and Results

In this section the simulation results are given per

each scenario considering energy demand from 2020

up to 2050 and the total emission impact for both

scenarios is given. The fuel distribution for the

chosen period between 2020 and 2050 for both cases,

the baseline scenario and mitigation scenario are

depicted in the graph in Figure 8 and Figure 9 in

Appendix.

In the case of the baseline scenario, the total

energy demand in 2050 the corrected fuel

consumption 20.63 TWh splited down as follows:

12.60 of TWh diesel, 4.44 TWh of petrol, 2.8 TWh

of LPG and 0.79 TWh of EV, as it can be seen in the

graph in Figure 8 (Appendix). In this scenario

hydrogen and biofuel, are introduced supported by

high EV penetration. The total energy demand in

2050 results around 19.02 TWh. The baseline

scenario does not consider any change in the

technology branch, using the same distribution and

growth rates from historical data. In this scenario, the

emission impact is expected to deteriorate the

situation of the sector. In the graph in Figure 9

(Appendix) the simulation results for the case of

Mitigation scenario are sketched out. In the case if

Mitigation scenario would be applied then EV and

hydrogen penetration by the end of 2050 must be 2.3

TWh and 0.87 TWh, respectively.

Based on the above projection and fuel types and

distributions, the total emission per scenario using

EnergyPLAN is calculated and given in the graph in

Figure 10 (Appendix). Such simulation is performed

assuming different energy sources with various

specific emission loadings. In Table 5 (Appendix)

specific emissions deriving from burning fossil fuels

such as SO2, PM2.5, NOx, CH4, and N2O are

given.EnergyPLAN arranges the fuel demand for

each technology and multiplies with the selected

defined emission factor that enables to calculation the

total emission per each scenario as given in equation

5-9.

    

   

(5)

      

(6)

      

(7)

      

(8)

      

(9)

The simulation results of the total yearly

emissions and each pollution component are given in

the graph in Figure 10 (Appendix). The model

calculations show that changes in emissions by

scenario are evident as fuel shares and technologies

change, too. In the case of the Mitigation scenario

with the integration of sustainable fuels such as

biofuel, hydrogen, and EV share a significant

reduction of energy due to improved efficiencies and

pollution are evidenced. In the case of the Mitigation

scenario, as it is depicted in the graph given in Figure

10 (Appendix) the reduction level per each

component is substantial. The proposed scenario

reduces the emission level by 71.1% in terms of CO2,

83% less CH4, PM2.5, SO2, and 82.2% less N2O. To

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identify the least cost solutions for the integration of

fluctuating renewable energy sources into current or

future 100% renewable energy supplies one must

take a Smart Energy Systems approach [14], which is

the focus of the next step work.

7 Conclusion

In this research work the total energy demand for the

whole transport sector is calculated based on a

multivariable regression method that includes two

independent variables, X1=GDP growth rate, and

X2=population projections. A strong correlation

between GDP and energy consumption is evidenced,

the strength of the relationship between energy

demand with population is weaker. Based on

historical energy demand and economic data within

the time horizon from 2010 up to 2022 the population

is reduced due to the emigration rate, while on the

other hand GDP is increased. Multiple R-value of the

proposed system that that impact energy demand

results in 0.872. Mixing oil with biodiesel at a level of

35 % is mandatory to meet climate goals in 2050.

The optimal introduction of EV and H2 should be

designed for the overall Albanian energy system,

while Hydrogen as a transportation fuel is forecasted

to cover all freight sub-branch and passengers’

vehicles at a level of 0.87 TWh.

The proposed scenario reduces the emission level

by 71.1% in terms of CO2, 83% less CH4, while

producing 82.2 % less PM2.5, SO2, and N2O. A

combination of energy efficiency measures (EEM)

and low-carbon approaches would reduce transport

energy consumption from 20.64 (TWh) in the case of

baseline scenario 2050 to close to 18 TWh by 2050 if

proposed scenario would be applied.

7.1 Future Work

The design and configuration of a 100 % RES

transport sector must be carefully investigated. Also,

it should be noted that energy savings are extremely

important and can be coupled with fluctuating

resources (wind and solar) and biodiesel.

Acknowledgement:

The authors are thankful to the editor and reviewers

for their valuable comments/suggestions, which

certainly improved the quality and presentation of the

article.

References:

[1] IRENA (2021). Renewables Readiness

Assessment: Albania. International Renewable

Energy Agency, Abu Dhabi, (2021). pp. 1-56,

[Online].

https://www.irena.org/publications/2021/Marc

h/Renewables-Readiness-Assessment-Albania

(Accessed Date: June 7, 2024).

[2] IEA (2023), Road transport, IEA, Paris,

[Online]. https://www.iea.org/reports/road-

transport (Accessed Date: June 7, 2024).

[3] Konomi, I., Prifti, V., & Kërpaçi, A. (2022).

Evaluating Options to Integrate Energy Storage

Systems in Albania. WSEAS Transactions on

Power Systems, 17, pp. 155–168.

https://doi.org/10.37394/232016.2022.17.16

[4] Khadzhynova, O., Simanavičienė, Ž.,

Zherlitsyn, D., Mints, O., & Namiasenko, Y.

(2023). Analysis of the EU Energy

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Enterprise Economic Security. WSEAS

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Development, 19, 290–299.

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[5] ERE, (2023). The Situation of the Power

Sector and ERE Activity during 2022.

Albanian Energy Regulator Authority (ERE),

Tirana, Albania (2023), [Online].

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/Annual_Report_2022.pdf (Accessed Date:

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[6] Open Data DPSHTRR (2024) - Republic of

Albania vehicles fleet, size and distribution.

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6e28b58-ab48-4d13-85b8-

e6155435a9b9/page/RX1xB (Accessed Date:

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[7] Lorenc Malka, & Flamur Bidaj. (2015).

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[8] Ministry of Industry and Energy (2021). “On

the Draft National Energy and Climate Plan of

Albania”, pp. 1-279, Tirane, Albania, [Online].

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content/uploads/2021/11/NECP-

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E-ISSN: 2224-3496

356

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Albania_drafti-shqip.pdf (Accessed Date: June,

7 2024).

[9] Ministry of Industry and Energy, (2018). VKM

480, dated 31.7.2018 "For The Approval of the

National Energy Strategy for the Period 2018-

2030". Ministry of Industry and Energy (MIE).

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"PËR MIRATIMIN E STRATEGJISË

KOMBËTARE TË ENERGJISË PËR

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7, 2024).

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the Use of Energy from Renewable Sources”,

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_use_of_energy_from_renewable_sources.pdf

(Accessed Date: June 07, 2024).

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Sorknæs, P., Skov, I. R., & Mathiesen, B. V.

(2021). EnergyPLAN – Advanced analysis of

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[12] Tarín-Carrasco, P., Im, U., Geels, C., Palacios-

Peña, L., and Jiménez-Guerrero, P. (2022):

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[13] Pellikka, T., Kajolinna, T., & Perälä, M.

(2019). SO2 emission measurement with the

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[14] B.V. Mathiesen, H. Lund, D. Connolly, H.

Wenzel, P.A. Østergaard, B. Möller, S.

Nielsen, I. Ridjan, P. Karnøe, K. Sperling, F.K.

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https://doi.org/10.1016/j.apenergy.2015.01.075

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

- Lorenc Malka, has developed the mathematical

model and has carried out the simulations in

EnergyPLAN computer energy model. He has

collaborated with the formulation, writing and

revision of the manuscript and supervised the

numerical study, made the figures and editing.

- Raimonda Dervishi was responsible for the

entire Statistics and has implemented the

regression analyses and participated to analyze

and synthesize study data.

- Partizan Malkaj was responsible on formulation,

supervising, evolution of overarching research

goals and aims.

- Ilirian Konomi has collaborated with the

formulation, writing and revision of the

manuscript.

- Rrapo Ormeni has collaborated with the writing

and revision of the manuscript.

- Erjola Cenaj has participated to analyze and

synthesize study data.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

This publication was made possible with the financial

support of AKKSHI and supported by Academy of

Sciences of Albania (ASA). Its content is the

responsibility of the author, the opinion expressed in

it is not necessarily the opinion of ASA.

Conflict of Interest

The authors have no conflicts of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en_

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APPENDIX

Fig. 1: Total energy consumption by fuel type (%)

Fig. 2: Distribution of energy consumption by fuel and sector in Albania

0,00 100,00 200,00 300,00 400,00 500,00 600,00 700,00 800,00 900,00 1000,00

Industry

Transport

Residencial& Commerce

Agriculture

Fisheries

Solid fuels Natural Gas

Crude, NGL and Feedstock Biomass (Fuelwood)

Hydro & Electricity Solar Energy

Derived Heat

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Fig. 3: Distribution in (%) of passenger vehicles by manufacture production year

Fig. 4: Distribution of passenger vehicles (Thousand and %) by fuel type

492,807

158,514

29,424

18,592

70,5%

22,7%

4,2%

2,7%

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

100

200

300

400

500

600

Over 15 yrs. 9 up to 15 yrs. 5 up to 8 yrs. 0 up to 4 yrs.

Distribution in (%) of passengers

vehicles by age Χιλιάδες

513,76

114,0564,401 2,8911,736 0,74 0,563 0,501 0,46

73,5%

16,3%

0,4%

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

100

200

300

400

500

600

Diesel Petrol Petrol

+ Gas

EV Petrol

+ EV

Diesel

+ EV

Gas Petrol

+ EV+

Hybrid

Diesel

+ EV+

Hybrid

Distribution of passenger

vehicles by fuel type (%)

Distribution of passenger

vehicles by fuel type Χιλιάδες

Fuel type

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Fig. 5: Distribution of passenger vehicles (Thousand and %) by fuel type

Fig. 6: EnergyPLAN model structure, [11]

67,2% 21,8% 2,3% 1,1% 0,8% 5,5% 0,3%

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

Diesel Petrol EV EV+Petrol EV+Diesel Gas+Petrol Others

Distribution of vehicles of all

categories (%) registered for the

first time in the period January-

December 2023 fuel type.

Fuel type

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Fig. 7: Energy consumption (ktoe) fitted to GDP (billion €)

Fig. 8: Projected energy demand for Baseline scenario by fuel type (TWh)

y = 2,1396x2- 38,852x + 943,26

R² = 0,9203

700,00

900,00

1100,00

1300,00

1500,00

1700,00

3 8 13 18 23 28 33

Consumption (ktoe)

GDP (billion €)

GDP (billion €) Line Fit Plot

Consumption (ktoe)

Predicted Consumption (ktoe)

Πολυωνυμική (Predicted Consumption (ktoe))

5,00

10,00

15,00

20,00

2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050

Energy demand by fuel type (TWh)

Years

Baseline scenario

Electricity

Gasoline

Diesel

LPG

Hydrogen

Biodiesel

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Fig. 9: Projected energy demand for Mitigation scenario by fuel type (TWh)

Fig. 10: Emission impact for each of scenarios. Baseline scenario versus Mitigation scenario

0,01 0,01 0,16 0,31 0,46 0,61 0,76 0,90 1,05 1,20 1,35 1,50 1,65 1,80 1,95 2,10

001,00 1,10 1,20 1,30 1,40 1,50 1,60 1,70 1,80 1,90 2,00 2,10 2,20 2,30

0…

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

202020222024 20262028 2030 20322034203620382040 20422044204620482050

H2 (TWh)

Energy demand by fuel type (TWh)

Year

Mitigation scenario

Electricity

Gasoline

Diesel

LPG

Biodiesel

Hydrogen

2,325 2,502 2,778 3,129

3,376 3,746

4,194

5,899

100

150

200

250

CO2 (Mt)

Tonne

CH4 (t) PM2.5 (t)

NOx (t) N2O (t)

SO2 (t) CO2 (Mt)

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Table 1. National Energy and Clime Plan (NECP) targets

NECP goals

Value

Year

RES SHARE

54.4%

2030

Reduction of CO2

18.7 %

2030

Energy demand reduction

8.4 %

2030

Table 2. Scenarios developed and main parameters drivers affecting energy demand.

Description

Parameters

Baseline

scenario

No changes in the technology

branch

Demand calculated using the latest 12 years of fuel and technology

trends

Mitigation

scenario

Integration of EV, Biofuel, and

Hydrogen

Demand calculated based on new technologies introduced considering

improved efficiencies

GDP

34.63 (billion euro) in 2050

Table 3. Transport energy demand forecasting (ktoe) using multiple variable regression analyses.

Year

Consumption (ktoe)

Population

(Thousand)

GDP (billion €)

2010

751.60

2,913.00

10.42

2011

765.20

2,905.20

10.69

2012

726.80

2,900.40

10.84

2013

789.20

2,895.10

10.95

2014

828.00

2,889.10

11.14

2015

789.20

2,880.70

11.39

2016

766.80

2,876.10

11.76

2017

828.00

2,873.50

12.7

2018

832.80

2,866.40

12.97

2019

860.00

2,862.40

12.54

2020

627.00

2,856.70

13.66

2021

691.00

2,850.50

14.32

2022

712.00

2,843.50

12.2

2023

=FORECAST.LINEAR (x, known y_s,

known x_s) *1.05 =

774.91

2,780.00

=FORECAST.LINEAR (x,

known y_s, known x_s) *1.02

14.16

2024

774.49

2,791.85

14.55

2025

775.74

2,789.43

14.96

2026

769.27

2,785.45

15.38

2027

771.33

2,780.60

15.79

2028

782.70

2,775.18

16.20

2029

792.19

2,769.35

16.59

2030

800.49

2,763.20

17.00

2031

822.80

2,756.81

17.53

2032

854.40

2,750.21

18.11

2033

902.11

2,743.44

18.61

2034

918.40

2,736.52

19.26

2035

941.78

2,729.47

20.06

2036

967.76

2,722.31

20.49

2037

1005.56

2,715.04

21.14

2038

1047.70

2,707.69

21.82

2039

1094.37

2,700.26

22.54

2040

1143.84

2,692.76

23.29

2041

1196.57

2,685.19

24.08

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Year

Consumption (ktoe)

Population

(Thousand)

GDP (billion €)

2042

1253.60

2,677.56

24.91

2043

1314.24

2,669.87

25.76

2044

1377.53

2,662.13

26.63

2045

1445.02

2,654.34

27.54

2046

1518.28

2,646.51

28.49

2047

1601.00

2,638.63

29.46

2048

1688.86

2,630.72

30.48

2049

1782.25

2,622.76

31.58

2050

1773.40

2,614.78

32.69

Table 4. Multivariable regression simulation results.

Table 5. Emission factor used to evaluate emissions from the transport sector in Albania, [12], [13].

g/GJ input fuel

Component

SO2

PM2.5

NOx

CH4

N2O

Value

141

798

2132

0.6

Regression Statistics

Multiple R

0.872083979

R Square

0.760530466

Adjusted R Square

0.73392274

Standard Error

25.4597082

Observations

ANOVA

df SS MS F Significance F

Regression

37054.9043

18527.45215

28.5831

2.58972E-06

Residual

11667.54135

648.1967415

Total

48722.44565

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept

-370.8050769

1231.200559

-0.30117358

0.76673

-2957.461467

2215.851313

-2957.461467

2215.851313

Population (million)

0.299405685

0.393095203

0.761662016

0.45613

-0.526456691

1.125268061

-0.526456691

1.125268061

GDP (billion €)

27.85062522

9.87574319

2.820104238

0.01134

7.102458692

48.59879176

7.102458692

48.59879176

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