INDC technical background document
Albania
(Version 25.08.2015.)
“This publication has been produced with the assistance of the European
Union. The contents of this publication are the sole responsibility of
ECRAN and can in no way be taken to reflect the views of the European
Union.”
“GIZ support to this project is part of the International Climate Initiative
(IKI). The German Federal Ministry for the Environment, Nature
Conservation, Building and Nuclear Safety (BMUB) supports this initiative
on the basis of a decision adopted by the German Bundestag.”
Contents
1
2
3
4
Introduction .......................................................................................................................................... 3
1.1
INDC process ................................................................................................................................. 3
1.2
This document .............................................................................................................................. 4
1.3
Acknowledgement ........................................................................................................................ 5
Choice of INDC type .............................................................................................................................. 6
2.1
Type of contribution ..................................................................................................................... 6
2.2
Choice of gases............................................................................................................................ 10
2.3
Treatment of LULUCF .................................................................................................................. 10
2.4
Use of market mechanisms......................................................................................................... 10
2.5
Choice of sectors ......................................................................................................................... 11
2.6
Fairness and ambition ................................................................................................................. 11
Baseline emission projection and methodology ................................................................................. 13
3.1
Introduction ................................................................................................................................ 13
3.2
Sectoral data and assumptions ................................................................................................... 13
3.3
Results of the baseline emission projection ............................................................................... 21
3.4
Uncertainties ............................................................................................................................... 23
Cost-Benefit Analysis of Mitigation Options for Albania .................................................................... 24
4.1
Introduction to the cost-benefit analysis work........................................................................... 24
4.2
Key sectors and baseline emissions for Albania ......................................................................... 25
4.3
Mitigation Potential in 2030 and Investment Costs ................................................................... 33
5
Consolidation ...................................................................................................................................... 37
6
Annex 1 – INDC template for Albania ................................................................................................. 38
7
Annex 2 – Summary of existing projections........................................................................................ 41
7.1
Description of existing modelling exercises ................................................................................ 41
7.2
Assessment of the modelling methodology ............................................................................... 42
7.3
Assessment of the scenarios ....................................................................................................... 50
7.4
Presentation of model results by sector ..................................................................................... 60
8
Annex 3 – Submitted INDCs ................................................................................................................ 67
9
Annex 4 – List of abbreviations ........................................................................................................... 69
2
1 Introduction
1.1 INDC process
Climate change is one of the major challenges of the world today if not the biggest one. The 5th
Assessment Report of the Intergovernmental Panel on Climate Change stressed that warming of the
climate system is unequivocal, and since the 1950’s, many of the observed changes are
unprecedented over decades to millennia. The report underlines that anthropogenic greenhouse
gas emissions have increased since the pre-industrial era, driven largely by economic and population
growth, and are now higher than ever. This has led to atmospheric concentrations of carbon dioxide,
methane and nitrous oxide that are unprecedented in at least the last 800,000 years. Continued
emission of greenhouse gases will cause further warming and long-lasting changes in all parts of the
climate system, increasing the likelihood of severe, pervasive and irreversible impacts for societies
and ecosystems. – According to IPCC, global net emissions of CO2 are required to decrease to zero
and in the coming decades there should be constraint on the annual emissions as well.
The international community acts via the United Nations Framework Convention on Climate Change
to address the challenge of climate change. Its ultimate objective is to stabilise atmospheric
greenhouse gas concentrations at a level which would prevent dangerous anthropogenic
interference with the climate system.
Within this framework a need for a comprehensive
agreement applicable for all is present since 2007. Current efforts to establish an overarching global
regime comes from a mandate given at the Conference of Parties in 2011. The mandate is to develop
a protocol, another legal instrument or an agreed outcome with legal force under the Convention
applicable to all Parties, which is to be completed no later than 2015 in order for it to be adopted at
the twenty-first session of the Conference of the Parties (COP) and for it to come into effect and be
implemented from 2020. Within the preparations of this mandate, learning from earlier experiences
a formal invitation was put forward for all the Parties of the UNFCCC at the COP in 2013, in Warsaw
to initiate or intensify domestic preparations for their intended nationally determined contributions
(INDCs) towards achieving the objective of the Convention in a manner that facilitates the clarity,
transparency and understanding of the INDC.
The call for pledges in this case is different from the Kyoto Protocol and the UNFCCC itself as this call
for all Parties recognises that every country needs to make efforts to avoid dangerous climate
change taking into account the common but differentiated responsibilities and capabilities of
different countries.
The call for submission of INDCs is defined by two decisions adopted by two consequent
Conferences of Parties: COP 19 (Warsaw, 2013) and COP 20 (Lima, 2014).
The Conference of the Parties (COP), by its decision 1/CP.19, invited all Parties to initiate or intensify
domestic preparations for their INDCs towards achieving the objective of the Convention as set out
in its Article 2, without prejudice to the legal nature of the contributions, in the context of adopting a
protocol, another legal instrument or an agreed outcome with legal force under the Convention
applicable to all Parties.
The COP, by its decisions 1/CP.19 and 1/CP.20, invited all Parties to communicate to the UNFCC
Secretariat their INDCs well in advance of COP 21 (by the first quarter of 2015 by those Parties ready
to do so) in a manner that facilitates the clarity, transparency and understanding of the INDC. While
the deadline of this call passed, it should be noted that INDCs submitted before 1 October 2015 will
be summarized in the technical document to be prepared before COP 21 in Paris, thus able to inform
the negotiations. By the date of writing this report 18 INDCs were submitted representing 46
3
countries. The European Union submitted its INDC on the basis of the 2030 climate and energy
framework on 6th March 2015.
The information provided shall make INDCs transparent, understandable and clear in order to be
able to quantify and compare INDCs – to avoid the challenges which made the Copenhagen COP
failing to bring an international agreement. It is necessary to aggregate them internationally and
provide the information basis for analysis. At COP 20 Parties decided that the information Parties
communicate together with their INDC may include (as appropriate), inter alia (see 1/CP.20, para.
14):





quantifiable information on the reference point (including, as appropriate, a base year),
time frames and/or periods for implementation,
scope and coverage,
planning processes,
assumptions and methodological approaches including those for estimating and accounting
for anthropogenic greenhouse gas emissions and, as appropriate, removals, and
 how the Party considers that its intended nationally determined contribution is fair and
ambitious, in light of its national circumstances, and how it contributes towards achieving
the objective of the Convention as set out in its Article 2.
In the decision 1/CP.20 the COP also invited all Parties to consider communicating their undertakings
in adaptation planning or consider including an adaptation component in their intended nationally
determined contributions.
The submission of INDC is new for many countries which had no previous mitigation commitment or
goal either internationally or nationally, as it brings a new factor into consideration of the
development of various sectors of the economy and defines a development pathway. This pathway
of decarbonisation, appears unavoidable having in mind the magnitude of negative impacts of
climate change. The mitigation target pledged with the INDC will be the basis for countries/Parties at
COP 21 in Paris and will serve as countries’ pledges later on. This way it is recommended that this
international commitment is considered and approved by governments on the highest level and
then conveyed to the international community as a submission to the UNFCCC secretariat. While the
submission itself is a short document, it has the weight of demarking the future development path of
a given country for the coming decades.
The relevant COP decisions do not specify the time interval for which the INDC should serve as an
emission reduction goal, however, the general thinking is that the political agreement should set the
framework for the 2020-2030 period.
1.2 This document
This document is constructed as follows:
o
Chapter 2 gives account of possible types of INDC and provides recommendations for
Albania. The choice of options is based on consultations with government stakeholders
considering the available and optimal choices
o
Chapter 3 gives the results of the calculations of the baseline scenario of emissions for
Albania
o
Chapter 4 analyses emission reduction options and associated costs and benefits
o
The above findings are supported by the following annexes:
4
o
Annex 1 provides the recommended INDC template for Albania. The final figures will
be based on the selected emission reduction target compared to the baseline
scenario
o
Annex 2 is the summary of the review of existing modelling work for Albania and the
assessment of their applicability.
o
Annex 3: For reference purposes the already submitted INDCs are presented (Status:
10 August 2015)
o
Annex 4 presents the list of abbreviations used in the document.
1.3 Acknowledgement
This document was prepared by a combined team of experts from ECRAN and Ricardo:
Ágnes Kelemen (ECRAN)
Heather Haydock (Ricardo)
József Feiler (ECRAN)
Dun Craig (Ricardo)
Guy Whiteley (Ricardo)
Imre Csikós (ECRAN)
Having a very ambitious timeline and significant challenges for the preparation of this report it
would not be been possible without the dedicated support of several experts and government
officials. The authors of this report would like to express their gratitude for their support. Special
thanks to Mirela Kamberi (UNDP) and Besim Islami (UNDP) having the substantial work of UNDP for
the draft Third National Communication made available, Artan Leskoviku (AKBN) for making the
LEAP based model, developed with the support of USAID made available for the baseline scenario
calculations , Paolo Guglielminetti and his transport policy team (PWC, Italy) making their interim
report on interurban transport futures available, the team of the Polytechnic University of Tirana
which worked on the Promitheas4 project, making their LEAP model available. We also thank Merita
Meksi (GIZ), JakobDoetsch (GIZ) and Alken Myftiu (USAID) for their support for the work preparing
this technical report.
5
2 Choice of INDC type
Information from the UNFCCC process regarding the format and content of the INDC is limited only
to key elements, but these elements give the choice of potential INDCs which the countries can
choose from. This part of the document analyses the choices of the type of INDC which is
recommended to Albania.1
There are few basic questions which help to define the INDC. They are related to the type of INDC,
choice of greenhouse gases and sectors covered by the INDC, choice of actions included or the
choice of the way expressing the target. The time frame of the INDC is important and in the case of
mitigation outcome, the choice of target level has to be considered. The final question in case of all
INDCs is about the quantity of GHG emission reduction it delivers as a contribution to the global
effort.
The INDC submission should inform the reader about the self-assessment, how the INDC contributes
to fairness as a basic principle, and also it should be demonstrated how it provides ambition beyond
the business as usual case. Transparency in preparation and details of the INDC as well the way it is
to be implemented are to have been given account of. Specific focus questions for the INDC are how
it treats land use, land use change and forestry and whether the Party submitting the INDC plans to
use future market based mechanism to buy or sell carbon credits.
Figure 1.
Types of mitigation contributions for the INDC – Source: Designing and preparing
intended nationally determined contribution (INDCs
Source: WRI, UNDP 2015.
2.1 Type of contribution
1
The choice of the type of INDC was discussed with government stakeholders on 14 July 2015 and the final
choice of the type of INDC reflects this discussion.
6
The primary choice is between (1) a greenhouse gas mitigation actions based INDC and (2) a GHG
mitigation outcome based INDC, although it is possible to select only one of those elements or to
have a combination of them.
GHG mitigation action based INDCs mean the use of a specific policy, sectoral strategy or other
action, project, which will have an emission reduction potential and constitutes a pledge towards the
international community for the implementation of such action. It can take the form of a list of
various activities one by one which will result in an aggregate number of avoided emissions or
emission reductions within a given time-frame or by a given year. The emission reduction realised as
the impact of these activities can be quantitative – for example, how much GHG emissions are
avoided/reduced by a given policy or strategy expressed in Mt CO2eq or how much capacity of
specific renewable electricity generation is installed. Such actions can include also planned NAMAs.
Qualitative objectives of actions can be related to the removal of various barriers or to the triggers
of activities which result in an emission reduction. It is to be noted that for the indicated actions,
their ambitious nature should be demonstrated.
Criteria for selecting a GHG mitigation action based INDC should take into consideration the GHG
reduction potential of the given action, the feasibility of the action, involved benefits and costs of
the action, among other considerations.
The GHG mitigation outcome based INDCs are looking into the delivered aggregate effect of actions
in the economy as a whole or selected sector(s). Typically it is emission reduction relative to a
historical base year or projected future emissions or emission intensity reductions and this is the
more common type of pledge in the international climate regime.
The outcome based INDC can be classified into five sub-categories based on the way it represents
the achieved emission reduction:
1. Base year GHG emission target - reduction in greenhouse gas emissions relative to a
historical base year
2. Fixed level outcome: a reduction in greenhouse gas emissions to a fixed, absolute level e.g.
carbon neutrality
7
3. Base year GHG intensity target: a reduction in GHG intensity relative to a historical base
year (e.g. GHG reduction in carbon intensity per unit of GDP by 2030 compared to 2005
levels)
4. Baseline scenario outcome: a reduction in GHG emissions relative projected future emissions
(e.g. x% GHG reduction below Business As Usual by a specific date)
5. Trajectory target – reduction of the greenhouse gas emissions to specified quantities in
multiple target years of periods over a longer period. This allows for the carbon budget
approach and scenarios with peaking, stagnating and declining emission parts
8
For the choice of the sub-categories available data, information, plans and potential impacts of the
scenarios have to be taken into consideration as well as the choice which fits the best to the
economic development plans of the country, keeping in mind the “ambitious” criteria of the INDC.
The outcome based targets can apply to the whole economy or specific sector(s) of the economy.
An action based INDC requires to have a full inventory of policies up till 2030 and detailed
greenhouse gas emission calculations for those individual policies. Not having such a framework on
all areas of GHG emissions and more importantly not having a precise impact assessment of such
policies regarding emissions, excludes this choice as an option. Thus it is recommendedthat Albania
to choosean outcome based INDC, which is also the more generally used form of INDC.
From the above mentioned five types of outcome based targets, the fixed level outcome can be
eliminated as it is typical for a reduction to an absolute level – e.g. to the level of carbon neutrality
on the long term. Also the trajectory based target is a type which can be eliminated from the choice
of target types, as it requires detailed planning to set and implement multi-year carbon budget
consequentially – for which the typical example is the United Kingdom’s framework for carbon
budgets.
HG intensity targets are typical to those developing countries where there is very little information
available about greenhouse gas emission levels and the development of the country foresees
decoupling of emissions from development. For Albania, the choice between the intensity, base year
GHG emission and baseline scenario targets depends on the availability of information on sectoral
emissions and trends. The base year scenario target or the trajectory target are not recommended
as more precise information would be necessary to have this type of target without having the risk
of over or underestimating the effort which will be committed.
The choice between intensity target and baseline scenario target is having the emission reduction
expressed in the change of carbon intensity of the economy or drawing different development
pathways with different emission profiles. In the current situation, where uncertainty of available
information regarding current sectoral situation and emissionsis prevalent, having the difference of
two different development pathways considered, contains less of uncertainty than a target which is
an absolute target in its nature.
Having considered the above mentioned considerations it is recommended that Albania takes a
baseline scenario target.
Having a base line GH emission target where 2016 is chosen as starting point of the emission
reduction efforts and 2030 as the end point of the scenarios is the best, as it takes into consideration
all the emission reduction efforts compared to a later start (i.e. 2020) and it is suitable to a baseline
scenario target.
9
2.2 Choice of gases
Similarly, as indicated above there can be a choice of greenhouse gases, for which the target applies
– for developed countries with solid greenhouse gas inventory data and detailed information about
emissions in all sectors, it is usually the basket of all greenhouse gases not controlled by the
Montreal Protocol:
 Carbon Dioxide (CO2)
 Methane (CH4)
 Nitrous Oxide (N2O)
 Hydrofluorocarbons (HFCs)
 Perfluorocarbons (PFCs)
 Sulphur hexafluoride (SF6)
 Nitrogen trifluoride (NF3)
The greenhouse gas inventory of Albania is has complete time series until 2000 and inventory data
was made available for the years 2005 and 2009 as part of the preparations for the 3rd National
Communication. Not having the time series until 2012 and having a relative high uncertainty of
accounting of other gases than CO2, it is recommended for Albania to choose only CO2 as the gas in
which its INDC is expressed. However, Albania can announce to expand its INDC to other gases at a
later stage having improved data quality which will enable such expansion/inclusion of other GHGs.
2.3 Treatment of LULUCF
Another choice to be considered is the approach for land use, land-use change and forestry
emission. Net emissions from these sectors can change the emission scenarios for the country
significantly as some parts of these sectors can absorb carbon-dioxide emissions, thus reducing the
overall amount of net emissions. However, the data demand for adequate and precise
understanding the trends of net emissions from these sectors is significant.
In case of Albania the greenhouse gas inventory contains a significant uncertainty of removals
connected to the land use, land use change and forestry thus it is recommended that the INDC
would not contain accounting for the LULUCF. It is recommended to include a provision in the INDC
which reserves the right of Albania to include forestry in the INDC in the future, but before the start
of the foreseen “commitment period”, 2020.
2.4 Use of market mechanisms
The feasibility of reaching the targets usually depends on the availability of financial resources.
Some countries already indicated targets which they are willing to achieve but they do not see
domestic sources for securing these achievement, thus they make them conditional of international
funding. Thus it is possible to provide unconditional and conditional targets and a mixture of them,
provided there is a transparency of what is conditional and what is unconditional.
Use of the market mechanism(s) can mean also additional funding for realizing the INDC and
supporting the sustainable development of Albania. It should be also necessary to note that the
rules of the future market mechanisms will be known only in the coming years, as the result of
UNFCCC climate negotiations, thus details of future market based mechanisms) are not seen yet. It is
recommended for Albania to signal its intent to use the market mechanism(s) if they have
environmental integrity leaving the choice for using the mechanism(s) open depending on its
details and conditions to be established during the UNFCCC negotiations.
10
2.5 Choice of sectors
Deciding on the choice of sectors is a decision to take into consideration two aspects. One of them is
aiming to have an as comprehensive coverage of sectors as possible, thus having the most
comprehensive INDC as possible. The other consideration is the data quality regarding various
sectors. It is a factor which limits what is possible to cover.
Ideally the whole economy would be covered by the INDC, which is expressed in the GHG inventory
in five main sectors: energy, industrial processes, agriculture, land use change and forestry and
waste.
Data availability limitations can be managed by two restrictions on the INDC:
-
Not to include land use change and forestry sector as it is discussed in section 2.3
Not to include other GHGs than CO2 as recommended in section 2.2, which also
automatically excludes agriculture and waste, as the inventory does not contain GHG related
emissions regarding agriculture and waste
Having LULUCF and other than CO2 excluded, limits the emissions covered by the INDC to less than
60% of GHG emissions in the inventory. This is a serious limitation of the INDC, however, including
other gases would have very high uncertainty.
At this stage it is recommended that for the purpose of Albania’s INDC the focus will be on the
Energy and Industrial Processes sectors as they are defined in the GHG inventory. However, it is
recommended for Albania to expand its INDC to other economic sectors and greenhouse gases as
well before 2020, if data quality of inventory and projections will be improved regarding those gases.
2.6 Fairness and ambition
Assessment of fairness and ambition is a difficult issue because it involves a multi-criteria
assessment when there are a large number of factors that can be chosen from and considered with
different weights. Fairness and ambition are both connected to distributive justice, to the division of
contributions for reducing global greenhouse emissions to a very high extent and sufficient pathways
to be provided to achieve this, with the participation of all countries. Fairness and ambition are
further complicated with another term – vulnerability to climate change. This influences resources
which can be allocated to decarbonisation, putting a strain on those resources, thus impacting
fairness. This third criterion emerges especially for the developing countries where resource scarcity
deems climate vulnerability much larger compared to a small or non-existent response capacity.
The main approaches which surfaced in the international climate negotiations for fairness are the
following:

Egalitarian: each human being has an equal right to use the atmosphere; this translates into
schemes based on per capita entitlement
 Sovereignty and acquired rights: all countries have a right to use the atmosphere and
current emissions constitute a ‘status quo right’; this translates into schemes based on
grandfathering entitlements.
 Responsibility / polluter pays: the greater the contribution to the problem, the greater the
share in the mitigation / economic burden.
 Capability: the greater the capacity to act or ability to pay, the greater the share in the
mitigation / economic burden
Generally, the equity principles need to be distinguished both from equity criteria or indicators and
from specific rules or formulas that may be used in order to compute practical commitments or
targets.
11
As Albania is a candidate country in accession to the EU, which means that in the period of the
currently considered INDC it will likely accede to the EU, it is advisable to take the levels of ambition
expressed in EU targets and political goals, and their characteristic into consideration when applying
the above mentioned principles except of the one of acquired rights. It should be also noted, that
the EU targets and political commitments are in line with long term global decarbonisation according
to the ultimate objective of the UNFCCC.
Per capita emissions in Albania are less than one-third of EU average per capita emissions, which can
be partly attributed to its development status and partly to the decarbonized electricity generation
sector. Having these two factors considered, it can be foreseen that Albania’s greenhouse gas
emissions will grow, accommodating the development needs of the country, however, these
modernization trends can follow a low carbon pathway, decoupling economic growth from increase
of emissions.
1990
2000
2010
38,232,170.06
40,563,437.00
50,911,113.68
5,636,933.47
5,103,281.75
4,834,156.78
3,382,160.08
475,160,781.00
486,958,178.00
503,234,845.00
518,499,055.00
11.86
10.48
9.61
6.52
Population of Albania
3,286,000
3,196,130
3,150,143
Albania’s emission Gg
CO2eq
4,341.02
6,774.54
8,687.002
1.32
2.44
2.76
0.011
0.017
0.017
World Total emission
Gg
EU 28
EU 28 population
EU emission/capita
Albania’s
emission/capita
Albania's emissions in %
of world emissions
2030
Data sources: JRC, Eurostat, Instat, Primes, inventory data (NC1, NC2, draft NC3)
Having the pathway for Albania with limited emission increase in the coming two decades, it can be
assumed without risk that the pathway foreseen for Albania enables reaching the 2 tons per capita
GHG emission level by 2050. This is an emission reduction ambition level which is comparable to the
EU 2050 political goal and it is possible to achieve with high confidence with the foreseen increase of
emissions followed by decoupling and slow decrease of emissions.
Regarding capability – Albania is a developing country, having USD 4919 (2014) per capita GDP on
current prices. This enables it to take more efforts to limit emissions than poorer countries, but less
efforts than countries with higher GDP. The level of GDP is in the category that potential climate
impact might impair Albania’s capacity to reduce emissions with modernisation of its economy and
increasing its efficiency, thus vulnerability concerns are to be taken into consideration to some
extent. However, it should be noted that the emission profile of Albania is already highly
decarbonized, thus such impacts are likely limited when it concerns further emission reduction. One
notable exposure is the availability of precipitation for hydro power based electricity generation,
which is the most significant source of electricity production in Albania.
2
2009 inventory data is used as proxy data for 2010 as there is no GHG inventory data available for Albania for 2010.
12
3 Baseline emission projection and methodology
3.1 Introduction
During the assessment of modelling work conducted to date for future energy and climate
mitigation scenarios it was found that despite different strengths of different exercises, no single
model or data source seems to adequately reflect the current or likely future baseline emissions in
Albania, or mitigation potential. Therefore it was agreed in a meeting held on 14 July 2015 in Tirana
with government officials and other stakeholders that the preparation of the INDC would take place
based on the following information, building on the strengths of different sources of information:
1) Development of a baseline scenario until 2030:
a) emissions for a 2012 base year (and corresponding fuel mix data) are to reflect emissions in
NC3 modelling;
b) As a default, trends from the PRIMES model are to be used for projections of energy
intensity and activity levels;
c) A contribution from other sectoral models (in particular SLED electricity and buildings sector
models if results are available in time);
2) Development of a mitigation scenario:
a) assess potential measures and their costs and propose a set of low cost measures to go
beyond efforts contained in the baseline.
It was agreed that the INDC would cover CO2 emissions only, due to the higher inherent uncertainty
in estimating emissions of other GHGs. It was also agreed that LULUCF emissions and removals
would not be included in the projection.
In addition to the above, an attempt was made to align the transport sector energy consumption
and emission projections with the EBRD study which will serve as a background to the Sustainable
Transport Strategy.
Based on this information, a demand side model was prepared in LEAP, with a separate model for
the supply side (electricity sector), the SLED model. The assumptions of the two modelling exercises
and their results are presented in the following chapters.
3.2 Sectoral data and assumptions
In the sections below the assumptions used for various sectors are provided in detail.
3.2.1 Service sector

The service sector is divided into the public and commercial buildings sector. Energy demand
is proportional with floor area. It is assumed that floor area growth in the commercial sector
is 2% annually, and in the public sector it is 1%.

Electricity demand in the commercial sector grows faster than floor space as energy
intensity per unit floor area increases.
3.2.2 Residential sector
General assumptions

Population growth: reduction to 2.79 million in 2030 from 2.9 million in 2012;
13

Change in household size: Annual decrease in household size by 0.0425 persons per
household, based on EUROSTAT data from other Balkan countries (Bulgaria, Romania,
Macedonia, Croatia, Slovenia). Household size decreases from 2.96 persons per household in
2012 to 2.19 persons in 2030.

Household size and population together determine number of households, therefore due to
decrease in household size there is an increase in the number of households;

Residential floor area of inhabited buildings grows from 25m2 to 35 m2 per capita by 2030
(OECD value is 38 m2);
Space heating

Only space heating and air conditioning are divided into climate zones, other uses are not.

It is assumed that there is only partial heating (heating in one room) in some dwellings, see
Table 2. Based on this information, the share of total heated space has been estimated. It is
assumed that the share of partial heating decreases by 20% in climate zones A and B
compared with 2012, and reaches 100% in climate zone C by 2030. No international trends
were found to support the exact number, but it is highly likely that increased income will
result in increased heated area over time.
Table 3.
Partial vs full heating in the different climate zones in 2012
Climate zone
Share of full heating
Share of partial heating
Share of total space heated
Zone A
17.50%
82.50%
44.8%
Zone B
47.50%
52.50%
64.8%
Zone C
77.50%
22.50%
84.9%
Source: AKBN, last column estimated

As mentioned previously, space heating is partly driven by the increase in floor area per
person from 25m2 in 2012 to to 35m2 in 2030.

Data on space heating needs reflect a very low value compared with other Southern
European countries with comparable climates, as space heating needs in Albania according
to this data are around 25 kWh/m2/year.3 In addition, if we take into account distribution of
floor space among climate zones and differences in shares of heated floor space (which is
higher in climate zone C than in the other two climate zones) then energy use per heated m2
is lowest in the coldest climate zone. We therefore consider that there is very high
uncertainty in the figures related to residential heating. A source of uncertainty may be that
use of fuel wood may be considerably underestimated. There are also other potential
sources of uncertainty. However, the source of the data is AKBN and therefore this has been
used as a starting point, allowing for increased biomass use in the model to obtain realistic
energy demand per floor area, and realistic differences between climate zones.

The fuel mix for space heating for the years 2012 and 2013 is shown in Table 3. It is assumed
that fuel mix for space heating is unchanged in the baseline.
3
The space heating requirement is “60-90 kWh/m2 in southern countries with lower heating needs (Malta,
Spain, Bulgaria, Greece and Croatia)” (http://www.odyssee-mure.eu/publications/br/energy-efficiency-trendspolicies-buildings.pdf, p. 30).
14
Table 4.
Fuel mix of urban and rural space heating by climate zone (ktoe)
2012
2013
Electricity
31.13
38.91
LPG
8.12
8.69
Wood
13.48
13.48
Diesel
1.50
1.50
Electricity
22.68
30.76
LPG
7.84
8.17
Wood
25.60
25.60
Diesel
0.70
0.70
Electricity
13.32
18.65
LPG
3.64
3.74
Wood
26.10
26.10
Diesel
0.00
0.00
Electricity
67.13
88.32
LPG
19.60
20.60
Wood
65.18
65.18
Diesel
2.20
2.20
Zone A (Ktoe)
Zone B (Ktoe)
Zone C (Ktoe)
National (Ktoe)
Source: AKBN
Water heating

Energy consumption for water heating in comparison to other uses is already quite high in
Albania. According to statistics received from AKBN it is approximately 20% of household
energy use, and two thirds the size of energy used for space heating. Energy use for this end
use is already at around 60% of the EU average value, while overall energy use per dwelling
is around 35% of the EU value, despite high differences in income. For this reason, and
taking into account water saving measures which will reduce the volume of heated water, a
low growth rate was assumed for energy use for water heating, at 1% a year.

The fuel mix for water heating for the years 2012 and 2013 is shown in Table 3. It is assumed
that fuel mix for water heating is unchanged in the baseline.
15
Table 5.
Fuel mix of urban and rural water heating by climate zone (ktoe)
2012
2013
Electricity
32.55
40.68
LPG
4.76
5.00
Wood
6.55
6.55
Solar
3.88
3.96
Electricity
17.64
23.92
LPG
4.76
4.88
Wood
10.88
10.88
Solar
1.00
1.06
Electricity
6.66
9.32
LPG
2.38
2.44
Wood
9.86
9.86
Solar
0.50
0.51
Electricity
56.85
73.93
LPG
11.90
12.32
Wood
27.29
27.29
Solar
5.38
5.52
Zone A (Ktoe)
Zone B (Ktoe)
Zone C (Ktoe)
National (Ktoe)
Source: AKBN
Cooking

The fuel mix for energy use for cooking is shown in Table 5. It is assumed that fuel mix for
cooking is unchanged in the baseline.

Energy used for cooking in terms of absolute value per household based on data received
from AKBN is twice as high in Albania as it is in EU countries (where around 6% of 1.4
toe/household is used for cooking). Therefore, no increase in the level of energy used for
cooking can be expected.
Table 6.
Fuel mix of urban and rural cooking (ktoe)
National (Ktoe)
2012
2013
Electricity
54.18
68.69
LPG
38.50
40.86
Wood
68.04
67.78
Source: AKBN
Lighting
16

Lower energy intensity for compact fluorescent has been assumed than for incandescent
lighting, so the ratio of energy intensity is 4:1 which reflects real world efficiencies.4 Total
household energy use for lighting on average is assumed to be 275 kWh/household (235 for
incandescent households and 69 for CFL) in the base year.5
Appliances

Energy use of appliances is 477 kWh per household in 2012 and increases to 1107 kWh per
household in 2030. A 5% annual growth rate in electricity use for appliances is assumed and
Appliance energy use is represented as an aggregate in the model. The figure for appliance
energy use in the EU on average is around 1900 kWh per household.
Air conditioning
Table 7.
Share of households with air conditioning in each climate zone (%)
zone A
Urban
40
Rural
15
zone B
Urban
25
Rural
10
zone C
Urban 8.6
15
Rural
5
Source: AKBN

295 GWh figure for total final energy use for air conditioners was kept from the NC2 LEAP
model. However, given the low penetration of ACs overall, this would mean close to 1500
kWh electricity consumption for each household that has an AC, which seems to be on the
high end given income levels in Albania.
4
see e.g. http://www.designrecycleinc.com/led%20comp%20chart.html
This seems reasonable as the average consumption for lighting of 200 kWh/household is the figure for
Romania and Bulgaria. (see: http://www.odyssee-mure.eu/publications/br/energy-efficiency-trends-policiesbuildings.pdf
5
17
Total base year energy demand in the residential sector

The total aggregate energy demand in the residential sector – include here a table in word
format with data for 2012 only
Table 8.
Total residential energy demand (ktoe)
Zone 1
Electricity
Zone 2
Zone 3
Total
141.5
84
37
262.5
28
28
14
70
Wood
28.5
64
58
150.5
Diesel
1.5
0.7
0
2.2
Solar
3.88
1
0.5
5.38
Oil
0
0
0
0
Natural Gas
0
0
0
0
203.38
177.7
109.5
490.58
LPG
Total
Source: AKBN
3.2.3 Industry
At this time it is not possible to model energy demand in a detailed way in the industry sector based
on individual technologies, therefore energy intensity and GVA are used. GVA values were provided
by AKBN and energy intensity (toe per GVA) figures were calibrated so that statistical data on total
energy demand of each subsector could be reproduced from GVA and energy intensity.
Trends for changes in energy intensity and GVA (2012=100%) are taken from the PRIMES model. The
model produces results at 5 year time steps, so these have been annualised by assuming constant
growth rates within 5 year periods. The tables below show the PRIMES model assumptions on GVA
for each industrial sector and the outputs on energy intensity until 2030.
Fuel mix data for each industrial subsector was provided by AKBN.
18
Table 9.
Sectoral Gross Value Added in industrial subsectors 2010-2030 (2010=100)
2010
2015
2020
2025
2030
Iron and steel
100.0
117.33
136.88
158.99
181.44
Non ferrous metals
100.0
110.99
138138.
4
163.44
191.77
Chemicals
100.0
124.55
159.33
189.11
217.55
Non metallic minerals
100.0
103.77
125.99
150.22
177.11
Paper and pulp
100.0
103.66
134.77
163.22
193.99
Food, drink and tobacco
100.0
112.88
140.88
170.0
203.88
Engineering
100.0
112.88
147.33
182.55
226.77
Textiles
100.0
108.66
116.44
117.55
120.22
Other industries
100.0
109.33
133.88
158.44
187.77
Source: PRIMES model
Table 10.
Sectoral energy intensity in industrial subsectors 2010-2030 (2010=100)
2010
2015
2020
2025
2030
Iron and steel
100.00
98.55
94.33
90.44
86.88
Non ferrous metals
100.00
104.77
102.55
98.33
95.66
Chemicals
100.00
109.44
103.99
96.11
89.33
Non metallic minerals
100.00
110.11
101.44
99.33
98.99
Paper and pulp
100.00
101.66
9797.5
92.44
87.66
Food, drink and tobacco
100.00
98.99
91.88
8585.7
79.77
Engineering
100.00
99.55
95.11
91.00
86.00
Textiles
100100.
0
98.77
93.66
88.44
83.44
Other industries
100.00
103103.
5
104.66
103.88
102.66
Source: PRIMES model
19
13.17
Solar
0
0
Wood
0
0
Heat
0
0
3.2
LPG
0
1.5
2.2
Diesel
0.81
Fuel Oil
0
Oil (unspecified)
0.55
0.05
0.2
0
0.2
5.3
0.23
Coke
7.66
0
7.3
50.27
0
15
0
Lube
0
Coal
0
0
8.16
0.8
15.98
5.75
20.06
13.11
8.22
4.87
0.4
0.9
0.29
0
0.56
Total
Other industries
Paper and
printing
Engineering &
other metal
industry
Textile, leather &
clothing industry
Food, drink &
tobacco industry
Electricity
Natural gas
5.01
Chemical
industry
Glass, pottery &
building mat.
industry
Ore-extraction
industry
Non-ferrous
metal industry
Fuel mix in the industrial sector in 2012 (ktoe)
Iron & steel
industry
Table 11.
8.18
102.51
0
0.4
10
10
3.2
0.9
2.5
6.2
0.66
0
5.73
0
0.83
0.83
0.19
0.79
0
7.5
0
0.25
4.7
85.18
7.8
7.8
0.81
0
0
77.71
77.71
2.7
1
3.7
Source: AKBN
3.2.4 Transport
An attempt was made to use data from the EBRD Sustainable Transport Study to develop a baseline
transport scenario. However, due to the limited scope of the study (it covers only interurban
transport), there was a need to complement the study with data from other sources. However, the
information contained in the UNDP model on transport performance and energy intensities (also
separately received from AKBN) could not be reconciled with the EBRD study. Specifically, relying on
information on both total fuel use and pkm/tkm from AKBN resulted in specific energy use values in
terms of MJ/pkm which are not realistic, vkm of cars in the EBRD study exceeds pkm of cars in the
LEAP model, and data on shares of vehicle types and fuel mixes could not be reconciled.
Data on transport activity levels expressed in pkm and tkm for passenger and freight transport
respectively were provided by AKBN. Total fuel use was also provided by AKBN. The share of each
transport mode was taken from PRIMES. Energy intensity was calibrated to arrive at statistical data
on total energy use calculated from energy intensity and activity data.
The PRIMES model was used to project trends for activity levels (in pkm and tkm) and energy
intensity values (2012=100%) for the transport sector.
Base year emission and fuel mix data were provided by AKBN.
3.2.5 Agriculture
The PRIMES model shows a decreasing energy demand until 2025 followed by an increase until 2030
for this sector. Emissions in 2030 in the PRIMES model are at the same level in 2030 as in 2010. As
there is essentially no difference between using PRIMES model assumptions from using the UNDP
20
LEAP model assumptions, the latter has been retained for scenario development, which also shows
stable energy related CO2 emissions in the agriculture sector between 2012 and 2030.
3.2.6 Energy branch
Emissions for the energy branch (oil refining, central heat and crude oil production) were taken from
the LEAP model for 2012. The PRIMES model growth rates for emissions were assumed for these
sectors.
3.2.7 Electricity sector
The following scenarios were modelled using the EEMM model of the SLED project:

REF scenario: Demand growth according to NREAP until 2020, followed by 3.1% growth until
2030. RES-E penetration according to NREAP until 2020, then 25 % of the growth of the AMB
scenario until 2030.
 CCP scenario: harmonised to the Energy Efficiency Natural Gas (EE-NG) Scenario of Low
Emissions Strategies and Clean Energy Development (LESCED). RES-E penetration according
to NREAP until 2020, then 50 % of the growth of the AMB scenario until 2030.
 AMB Scenario: harmonised to the Renewable Natural Gas (RES-NG)Scenario of LESCED. RES
penetration according to NREAP until 2020, then RES-NG LESCED until 2030.
)New gas plants: 200+160 MW installation are assumed by 2025.
Table . New power generation investments in the different scenarios
Investment cost, €/kW
Natural gas
Coal
Hydro
Geothermal
Solar
Wind
Biomass
Total
1 000
2 000
2 500
4 000
1 100
1 000
3 000
-
New capacity, MW
REF
CPP
AMB
360
360
360
0
0
0
909
1 296
2 068
0
0
0
77
124
218
100
170
310
19
38
75
1 465
1 988
3 032
Investment cost, m€
REF
CPP
AMB
360
360
360
0
0
0
2 273
3 239
5 170
0
0
0
85
137
240
100
170
310
56
113
226
2 875
4 018
6 306
The results of the SLED electricity model show that although the new gas-fired power plants are
assumed to be commissioned in 2020 (200 MW) and 2025 (160 MW), they will produce only in 2030.
In 2030 the utilization rate of these power plants is low, around 0.5% in the reference scenario. The
corresponding CO2 emissions arelow, the highest value is 7 kt in 2030 in REF, which decreases to 2.5
kt in AMB scenario.
The results are explained by the fact that the expected significant mainly coal based power plant
capacity expansion in the region, together with the relatively high price of gas-based generation
compared with coal based generation will result in lower price of imports than of gas based
generation in Albania.
3.3 Results of the baseline emission projection
Note: The calculations, with base year fuel consumption data being based primarily on data from the
UNDP LEAP model and data received from AKBN, has not been able to reproduce the level of
emissions in the 2009 year inventory, despite 13% higher fuel use levels in the 2012 base year. At
the same time, the aggregate energy demand results for 2012 are largely in line both with the
21
INSTAT energy balance for 2012 and the IEA energy balance for the same year. Further consultation
is therefore needed with the inventory experts and AKBN to explain differences in results.
TA demand side model was prepared in LEAP, with a separate model for the supply side (electricity
sector), the SLED model. Efforts were made to harmonise data with energy balance data received
from AKBN, but at the same time be internally consistent in the model. The assumptions of the two
modelling exercises and their results are presented below.
.
Energy related non-biogenic CO2 emissions in the demand sectors (Gg CO2)
Sector
2012
2015
2020
2025
2030
Residential
195.00
207.28
222.02
237.23
256.32
Service
55.70
61.63
71.02
82.33
96.04
Industry
770.27
837.01
953.13
1,101.46
1,273.59
2,237.56
2,431.10
2,681.75
2,897.72
3,197.43
AFF
242.98
275.18
307.32
341.04
310.96
Non Energy Use
100.67
100.67
100.67
100.67
100.67
3,602.18
3,912.86
4,335.90
4,760.44
5,235.01
Sectors
2012
2015
2020
2025
2030
Industry
210
227
290
433
758
Total
210
227
290
433
758
Transport
Total
Process emissions (Gg CO2)
Total CO2 emissions (Gg CO2), non-biogenic
Sector
2012
2015
2020
2025
2030
Residential and service
251
269
293
320
352
Industry
980
1064
1243
1534
2032
Transport
2,238
2,431
2,682
2,898
3,197
Agriculture
243
275
307
341
311
Non Energy Use
101
101
101
101
101
Energy branch
139
142
155
159
175
0
0
0
0
7
3,952
4,282
4,781
5,353
6,174
Electricity production
Total
22
3.4 Uncertainties
Despite best efforts to prepare a baseline scenario and mitigation options for Albania, a number of
uncertainties remain in the analysis. There pertain to the following factors:

large uncertainties in base year data, including energy consumption data at sectoral level,
activity data (e.g. related to the output of industrial subsectors or the performance of the
transport sector), and inconsistencies between different data sources;

uncertainties in future evolution of emissions are particularly high in cases where high
economic growth and ongoing structural and socio-economic change may impact emissions
in unexpected ways;

uncertainties related to developments in emissions that may be caused by a single large
installation which may have a significant impact on the emissions of a small country;

the limited amount of time available to address these uncertainties and the lack of resources
to verify existing data sources in order to improve quality.
23
4 Cost-Benefit Analysis of Mitigation Options for Albania
4.1 Introduction to the cost-benefit analysis work
International experts from Ricardo-AEA joined the INDC support team on 12th July 2015, shortly
before a series of meetings in Tirana with Government representatives and other stakeholders.
They have been focusing on cost-benefit analysis of different mitigation options for Albania, with
funding from Deutsche GesellschaftfürInternationaleZusammenarbeit (GIZ) as part of the
International Climate Initiative (IKI).
The objectives of the cost-benefit analysis work were:
 To conduct a cost-benefit assessment of different GHG mitigation scenarios using Marginal
Abatement Cost Curves (MACCs)
 To suggest optimal mitigation scenario(s) to be used for submission as Albania’s economywide mitigation contribution in its Intended Nationally Determined Contribution (INDC)
 To support with drafting Albania’s INDC on mitigation.
This chapter briefly summarises the cost-analysis methodology used, the results obtained and the
implications for the level of mitigation contribution that could be included in Albania’s INDC. It also
presents an estimate of the investment costs (capital costs) associated with mitigation options in the
residential, services, industry and transport sectors.
4.1.1 Use of Marginal Abatement Cost Curves to inform mitigation potential and costs
Marginal Abatement Cost Curves (MACCs) provide a simple visualisation of the mitigation options
available in a given year against a baseline, the mitigation potential from each option (in tonnes of
emissions per year) and the cost-effectiveness of the option (in cost per unit emission reduction per
year). This is illustrated below – Figure 4.1 shows how a MACC provides a snapshot of mitigation
potential ranging from no mitigation (baseline) to full technical potential.
GHG emissions (MtCO2)
Baseline/
Reference
Mitigation
scenario 1
All technically
possible
2000
13.072015
2010
2020
2030
Cost-benefit assessment of Albania INDC
Figure 4.1: How a MACC can provide a snapshot of mitigation potential in a given year
24
Abatement cost (€/tC)
Figure 4.2 provides an example of a MACC. The width of each measure on the x-axis in the MACC
indicates the mitigation potential of that option while the position on the y-axis indicates costeffectiveness of the measure. The measures on the left hand side of the illustrative MACC in Figure
2are cost-effective (negative €/tCO2), the measures in the middle are low cost (low positive €/tCO2),
and the measures on the right hand side are higher cost (high positive €/tCO2).
Abatement potential 2020 (MtC)
Figure 4.2: Illustrative MACC for CO2 mitigation
MACCs for individual sectors or for the economy as a whole can be used to inform the appropriate
level of a country’s ambition in its INDC or national climate change policy planning. For example,
sector MACCs were used extensively in the development of the UK’s climate change strategy and
carbon budgets system in 2008, along with MARKAL and other economic modelling to capture
interactions between sectors and wider economic impacts.
The main advantages of MACCs are that they are more transparent and easily understood than
many other modelling approaches, and they can be developed with relatively limited data and then
improved and refined as further data become available. A key limitation is that they do not take
account of interactions between sectors, e.g. efficiency savings or fuel switching in the power sector
changing the emissions factor for electricity use in other sectors. This is perhaps less of an issue for
Albania than many other countries since the emissions factor for electricity is so low due to the high
proportion of renewable energy in the Albanian electricity mix.
4.2 Key sectors and baseline emissions for Albania
TCO2 emissions from the energy-related sectors, including transport, represent about half of total
GHG emissions for Albania, with near-zero CO2 emissions associated with the power sector. It is
therefore appropriate for analysis of mitigation options and potential for Albania’s INDC to focus
primarily on CO2 emissions from the energy sector. The emissions inventories and projections for
energy-related CO2 are also less uncertain than the inventories and projections for non-CO2 GHG
emissions, and less uncertain than the CO2 emissions associated with the Agriculture, Forestry and
Land Use (AFOLU) sector.
25
This baseline or Business as Usual (BaU) scenario confirms that the transport, industry and
residential sectors are the key emitting sectors, with services, transport and industry emissions
growing significantly over the period to 2030 driven by economic growth. This is shown in Figure 4.3
below.
Figure 4.3: Energy sector emissions in 2012 and 2030 (Gg CO2)
4.2.1 Buildings sector: mitigation options and costs
The following data sources and models were used for the buildings sector. Data specific to Albania
was used wherever possible.
Third National Communication: The Draft Third National Communication (TNC) (UNDP, 2015)
contains a list of mitigation measures, their cost effectiveness and their mitigation potential. The
mitigation potential and cost analysis has been based on the GACMO model which has been made
available to Ricardo-AEA as part of this project. The baseline scenario assumes that most of energy
demand will be met through imported gas and hydro energy. Heating demand will increase in
households with no improvements in efficiency.
The National Energy Efficiency Action Plan (NEEAP): The National Energy Efficiency Action Plan
(NEEAP) (Republic of Albania, 2010) was launched in 2010. It contains long term vision (2010-2018)
for energy efficiency actions in the country.
Energy Efficiency in Buildings in the Contracting Parties of the Energy Community: This report
supports governments to meet their commitments under the Energy Community Treaty (ECT) to
increase efficient use of energy in buildings (Energy Savings International AS, 2012). The energy
efficiency target (energy saving) in the buildings sector has been assumed to be 1% a year which
accumulates to 9% by 2020. The report analyses the building stock inventory, roughly estimates the
potential for energy savings and the most cost effective energy saving measures. These are
presented for each building stock area. All profitable measures (positive NPV) were included in the
26
packages considered. The baseline considered is total energy consumption at the time that the
analysis was undertaken in 2012.
GACMO: The GACMO model that has been circulated as a template provides approximate mitigation
costs and potential for mitigation measures informing the INDC process. The model is not set up to
be a detailed, highly country specific model. The model has been designed for ease of use with
minimal data required. Our initial analysis of the model suggests fuel mix, or power generation
assumptions have little impact on the model. A key driver of the model is the assumption of the
implementation of measures in future scenarios which determine the abatement potential of each
measure considered and the emissions factor that is included in the model. The baseline assumes
many different technology choices are currently meeting energy demand, these technologies have
to be assessed carefully to understand the validity of the model.
SLED - Refurbishment of Buildings: The SLED project provided the team with the expected costs and
energy savings expected with two levels of refurbishment packages (standard and ambitious) on the
existing building stock in Albania.
Table 4.3 shows a selection of mitigation measures identified in these various reports, and the costs
and mitigation potential associated with each. We have selected those measures that have a
significant mitigation potential, which in effect means prioritising measures that reduce the use of
fossil fuels since reducing electricity consumption would not have a significant effect on Albania’s
CO2 emissions, due the very low emissions factor of electricity.
Table 4.3: Selected CO2 mitigation measures and costs for the buildings sector
Source
Comment
Excluded a number of
measures because a poor
methodology appeared to be
used in their calculation in the
GACMO model.
Third National
Communication
NEEAP
Energy
Efficiency in
Buildings in the
Contracting
Parties of the
Energy
Community
Perhaps an underestimate of
mitigation potential as
renewable energy is modelled
and reduces emissions where
renewable energy is not
possible.
Values listed are for
mitigation in 2018. All
measures have a very small
impact on emissions as
contained in the NEEAP.
Approximate cost of meeting
Energy Efficiency Targets in
the Energy Community. Used
LEAP and PRIMES data to get
energy split and household
growth.
Measures Include
Cost of Package
of Measures
US$/tonne
Mitigation
Potential in
2030
(kt/year)
thermal insulation
of householdswood
-31
12
thermal insulation
of households-LPG
-79
37
thermal insulation
of householdskerosene
-81
1
thermal insulation
of households-DH
-48
0
District Heating-DH
-77
38
Central HeatingCH
-65
26
Package of
measures that
implement the
Energy Efficiency
Action Plan
n/a
0
Cost effective
refurbishment of
buildings
All measures are
cost effective
203
Assumed to be rolled out to
27
all buildings listed in the
building inventory.
GACMO
Used the GACMO model with
a 2030 estimate of emission
factors from the SLED model.
Cost effective numbers are so
high because emissions
savings are so small but costs
savings (driven by electricity
savings) are high
Efficient domestic
lighting with CFLs
-475,968
0
Solar water heater,
residential
-366,223
0
Efficient office
lighting with CFLs
-356,888
0
Efficient
refrigerators
-217,840
0
Efficient domestic
lighting with LEDs
-52,557
0
- 189 Standard
539
-154 Ambitious
640
Only cost effective measures
taken assumed to be applied.
SLED
Refurbishment
Assumed that 1%-5% of
properties are destroyed each
year, and 5% of existing
buildings are retrofitted each
year. 5% discount rate.
Electricity emissions factor
assumed to be from SLED in
2030.
Insulation of
internal, external
walls and roofs
Notes:





Third National Communication (TNC): A number of measures were excluded from what is presented in
the TNC. It is believed the GACMO model that the TNC is based on assumes that energy savings are made
on the basis of an electricity grid that is dominated by coal. Those measures listed do not assume coal as a
baseline technology. The GACMO model that went into the TNC reduces all measures by proportion of
renewable energy on the grid, even where renewable energy is not applicable. Therefore those measures
listed may represent an underestimate of mitigation potential.
NEEAP: The measures contained in the NEEAP present savings in total across all sectors of only
approximately 0.3kt CO2 in 2018. This seems very low and may indicate an error in the units presented or
in the calculation of impact. Therefore there islow confidence in this mitigation contribution.
Energy Efficiency in Buildings in the Contracting Parties of the Energy Community: The report presents
an estimate of savings from enacting certain packages of cost-effective retrofit measures in residential and
services buildings. . Measures are assumed to be rolled out to all the building inventory in the report and
therefore likely to be an overestimate of impact.
GACMO: The mitigation potential in GACMO is driven by the assumptions of the penetration of the
measure by 2030. We have taken the penetration of measures assumed in the TNC. The extremely high
cost effectiveness of some measures is driven by the low emission factor provided by the SLED model.
Large electricity cost savings and very small emissions savings result in a high figure for cost effectiveness
(as a result of very high penetration of close to zero CO2 electricity production in Albania by 2030).).
SLED Refurbishment: Emissions savings in 2030 are greater than emissions calculated for entire residential
sector. This is due to the extremely high energy demand in kWh per m2 (mostly 10 times higher per
building higher than that contained in the LEAP model) which was presented as an output of the SLED
model. It has not been possible to check or validate these figures as the SLED modelling project is ongoing.
Many of the benefits of the measures detailed above are not captured through energy cost savings
or emissions savings.
 Such benefits can include the health impacts of warmer homes in Albania resulting from a
programme of insulation as a result of refurbishment or improved building codes. This could
be tied to productivity benefits from work spaces.
28

There is a large potential for job creation in undertaking retrofitting of houses.
4.2.2 Industry sector: mitigation options and costs
Table 4.4 shows a selection of mitigation measures identified in the Third National Communication
(TNC), NEEAP and GACMO model, and the costs and mitigation potential associated with each. As
for the buildings sector, we have selected those measures that have a significant mitigation
potential, which in effect means prioritising measures that reduce the use of fossil fuels since
reducing electricity consumption would not have a significant effect on Albania’s CO2 emissions, due
the very low emissions factor of electricity.
Table 4.4: Selected CO2 mitigation measures and costs for the industry sector
Source
Comment
Excluded a number of
measures because a poor
methodology appeared to be
used in their calculation in the
GACMO model.
Third National
Communication
Perhaps an underestimate of
mitigation potential as
renewable energy is modelled
reduces emissions where
renewable energy is not
possible.
NEEAP
Values listed are for mitigation
in 2018. All measures have a
very small impact on emissions
as contained in the NEEAP.
GACMO
Used the GACMO model with a
2030 estimate of emission
factors from the SLED model.
Cost effective numbers are so
high because emissions savings
are so small but costs savings
(driven by electricity savings)
are high
Measures Include
Cost of Package
of Measures
US$/tonne
Mitigation
Potential in
2030
(kt/year)
R
Efficient boilers
fuel oil-diesel
-86
128
1
Efficient boilers
coal
-68
97
0
Industry
Na
0
1
Efficient Electric
Motors -Industry
-352,429
0
0
Efficient Electric
Motors –Services
-390,627
0
0
Clinker
replacement
8
164
5
Notes:

See notes for the Third National Communication, NEEAP and GACMO under Table 4.3
In addition to the measures listed in Table 4.4, we have considered whether there may be scope for
CO2 reduction through fuel switching from coal or oil to less CO2-intense fuels in the industry sector.
Table 4.5 shows where there may be potential for fuel switching, noting that the potential would be
much greater if natural gas were available as a fuel for industry in Albania by 2030.
Table 4.5: Energy consumption in total and from fossil fuels by sector in 2014 (ktoe)
Sector
Iron and steel
Mitigation potential from fuel switching
Some potential for switching to natural gas if/when
available. Unlikely for high temperature
processing, e.g. any blast furnaces.
29
Non ferrous metals
None
Chemicals
Potential for switching to natural gas if/when
available. Depends on nature of sector processes
and products.
Non metallic minerals
Potential for fully or partially switching to
renewables or natural gas if/when available.
Depends on nature of sector processes and
products.
Paper and pulp
Low
Food, drink and tobacco
Good potential for switching to renewables or
natural gas if/when available. Likely to be lower
temperature processes.
Engineering
Low
Textiles
Low
Other industries
Low
As a first estimate, perhaps 20% of fossil energy use in 2030 could be switched to lower carbon fuels
cost-effectively, i.e. as part of scheduled replacement of equipment. Natural gas has a CO2 emission
factor 10% lower than that of oil and 30% lower than that of coal, so switching to natural gas would
reduce emissions by about 20% overall. The potential for switching to renewables is lower, but it
would achieve 100% emissions saving. As a ball-park figure, therefore, it may be possible to costeffectively reduce emissions from the industry sector by 20% x 25% = 5% in 2030 through fuel
switching. Note this percentage reduction cannot be simply added to the itemsin Table 4.4since
there will be an interaction between them – you cannot replace a coal-fired boiler with a gas-fired
boiler and also count the CO2 reduction from improving the efficiency of the coal-fired boiler.
There would be wider economic and social benefits associated with growing industrial output while
improving the efficiency of production and reducing air pollutant emissions associated with oil and
coal use in the Albanian industry sector. These benefits have not been quantified as yet.
4.2.3 Power sector: mitigation options and costs
The model comparison work undertaken by the EC ECRAN project (see Annex 2) shows a range of
different possible baseline electricity mixes for the Albania power sector in 2030, depending which
model is used and which baseline scenario within that model. The results are repeated below in
Figure 4.4, for ease of reference.
30
Figure 4.4: Electricity mix projections under different models and scenarios for 2030
All of these baseline scenarios assume a large proportion of electricity supply from hydro, as is the
case now, and some scenarios include significant introduction of natural gas before 2030. There is
no uptake of new coal or oil power generation in any baseline scenario, and so if the total power
generated in Albania is not sufficient to meet the total demand it is assumed that the difference will
be made up by imported electricity from neighbouring countries. This imported electricity is
expected to be high carbon, because it is largely generated from lignite, but these emissions would
not be counted towards the Albania inventory or INDC baseline.
There are expected to be wider energy security and economic benefits associated with the
introduction of natural gas into Albania, as well as offering the potential for emissions reduction
through fuel switching from oil and coal to natural gas in the buildings and industry sectors. There
may also be potential to use natural gas in transportation. However from the perspective of a costeffectiveness or MACC analysis, the introduction of gas is part of the baseline and so should not be
considered as a power sector mitigation measure. Likewise the further uptake of additional
renewable energy, such as solar PV or wind, would not reduce Albania’s CO2 emissions, although it
would provide benefits in terms of global GHG reduction by reducing the demand for high carbon
imported electricity.
4.2.4 Transport sector: mitigation options and costs
The analysis below is based on international data and expert judgement on measures that might be
applicable to Albania tailored to the informationreceived from the EBRD project on the Albanian
vehicle fleet. We have no Albania-specific information on the applicability or costs of different
mitigation options and do not expect to receive such information within the timeframe of this
project.
Table 4.7 below shows all the transport sector measures which have been considered. When
analysing the potential of each of the following measures, only passenger cars, buses, trucks and taxi
31
modes will be considered. These modes contribute over 80% of transport emissions in Albania and
therefore focus (in terms of policy intervention) should focus here.
Table 4.7: Proposed mitigation measures in the transport sector
Category of mitigation measure
Disaggregated mitigation measure
Increased efficiency to petrol and diesel fleet
Modern vehicles will include two or three suitable
additive technologies aimed at increasing vehicle
efficiency.
Uptake of alternatively fuelled road vehicles
Alternative fuels - Petrol HEV (hybrids)
Alternative fuels - Diesel HEV (hybrids)
Alternative fuels – EV (Electric vehicles)
Alternative fuels – CNG (Gas fuelled vehicles)
Modal shift
Shift from private vehicles to public transport (bus
rapid transit scheme)
Biofuels
10% biofuel penetration by 2030 (5% by 2025)
The results of analysis of these abatement measures and associated costs are shown in the form of a
MACC for 2030 in Figure 4.8 below, and summarized by type of measure in Table 4.9.
32
Figure 4.8: 2030 Marginal abatement cost for transport sector
Table 4.9: 2030 MACC Summary for transport sector
2030 MACC summary
All measures
0.557
Technical
15.4%
Behavioural
2.1%
Total
17.4%
Cost effective measures only
Total
MtCO2 Abated
Reduction
116
MtCO2 Abated
3.197
MtCO2 (BAU)
3.6%
Reduction
4.3 Mitigation Potential in 2030 and Investment Costs
Table 4.10 below summarises the absolute and percentage reductions from the baseline CO2
emissions that could be achieved in 2030 by implementing different packages of measures:
a. All cost-effective mitigation measures identified in the Third National Communication (TNC)
and National Energy Efficiency Action Plan (NEEAP), except district/central heating
b. Cost-effective mitigation measures for the buildings sector identified in the Energy
Efficiency in Buildings in the Contracting Parties of the Energy Community (CPEC) report plus
cost-effective mitigation measures in the industry sector from the TNC and NEEAP
33
c. All cost-effective mitigation options identified in the TNC and NEEAP reports (as a)plus other
mitigation options for the transport and industry sectors based on international experience
d. All cost-effective mitigation options (as c) plus the introduction of 10% biofuels into the
transport fuel mix by 2030.
Table 4.10: Estimated cost-effective mitigation potential for Albania in 2030, MtCO2
Packages of measures
ktCO2
saving in
2030
Percentage
reduction from BaU
CO2 emissions in
2030
% of overall baseline
a) TNC and NEEAP
- Buildings
- Industry
- Total
b) Package a) with CPEC instead of TNC
measures for buildings
- Buildings
- Industry
- Total
c) Package a) plus additional measures based on
international experience
- Buildings
- Industry
- Transport
- Total
d) Package c) plus 10% biofuels in transport fuel
- Buildings
- Industry
- Transport
- Total
50
-
225
-
275
4.5%
106
-
225
-
331
5%
50
-
277
-
116
-
443
7.2%
50
-
277
-
381
-
708
11.5%
We have not included any measures in the power sector as the baseline emissions are very low in
this sector, even assuming natural gas is introduced for energy security reasons and natural gas
power stations are operating before 2030, and additional renewable energy would displace
imported electricity so not reduce Albania’s CO2 emissions.
Table 4.11 provides an estimate of the investment cost associated with each mitigation measure, i.e.
the marginal capital expenditure (capex). Although most of the mitigation measures identified are
cost-effective over their lifetime, there will be an upfront marginal cost associated with, for example,
installing more efficient boilers or retrofitting buildings, followed by cost savings from reduced fuel
34
use later. These investment costs do not include the costs of designing and administering policies to
implement these measures, which would typically be 2-5% of the capital expenditure.
Table 4.11: Investment costs for mitigation measures by 2030 (m€)
Mitigation
ktCO2
Investment
cost m€
Sector
Measure
Buildings
Thermal insulation
50
21
Albania-specific cost data from the Third
National Communication
Industry
More efficient
boilers
225
15
From GACMO model used for Third
National Communication
0
Assuming no additional costs for natural
gas fuelled equipment vs coal/oil fuelled
equipment, and that natural gas is
available to main industrial sites under
the baseline.
52
Source/assumptions
Industry
Fuel switching
Transport
Cost-effective
measures
116
195
All cost-effective measures in transport
MACC; EU cost data, assumes natural gas
is available to vehicle depots with no
additional costs for gas distribution.
Transport
10% biofuels
265
456
Assumes a 100 million litre capacity
biofuel plant costs $250m (UK data)
Total
All above measures
708
687
The measures in Table 4.11 include fuel switching to natural gas in both the industry and transport
sector, which is consistent with the assumption that natural gas will be introduced to Albania before
2030, initially for use in the power sector. We have not included any investment costs associated
with the construction of a gas distribution network connecting the gas pipeline to main industrial
sites and vehicle depots since this is assumed to be built under the baseline scenario.
35
Figure 4.12. Baseline and mitigation scenario emissions, package C (Gg CO2)
The CPEC report identifies a further 56 ktCO2 of cost-effective mitigation potential from the buildings
sector with additional investment costs of €308m. This is likely to be an overestimate of mitigation
potential, since it appears to be based on a retrofit programme for all buildings in Albania.
In setting an appropriate level of ambition for the economy-wide mitigation contribution for Albania
in 2030 we recommend that the Albanian Government applies the following considerations.
1. The potential for cost-effective mitigation is likely to be understated in the TNC and NEEAP,
and also in our preliminary estimate of potential from the transport sector based on
international measures and vehicle stock information from the EBRD report.
2. It may not be appropriate to set a target of achieving all cost-effective mitigation by 2030
since there would be considerable investment costs and other barriers associated with, for
example, retrofitting a large proportion of the building stock.
3. There are significant uncertainties in the baseline as well as in the mitigation potential for all
sectors, and so any INDC mitigation contribution should be suitably caveated, allowing the
possibility to re-base once further information becomes available.
36
5 Consolidation






Choice of gases: The emissions covered by the INDC reflects less than 60% of GHG emissions in
the inventory as per National Communications. Although this is a serious limitation of the INDC,
the inclusion of other gases would have very high uncertainty. Not having the time series and
having a relative high uncertainty of accounting of other gases than CO2, for Albania only CO2has
been listed as the GHG in which its INDC is expressed;
Treatment of LULUCF: In case of Albania, the greenhouse gas inventory contains a significant
uncertainty of removals connected to the land use, land use change and forestry thus the INDC
would not contain accounting for the LULUCF. It is recommended to include a provision in the
INDC which reserves the right of Albania to include forestry in the INDC in the future, but before
the start of the foreseen “commitment period”, 2020.
Use of Market Mechanisms: It is recommended for Albania to signal its intent to use the market
mechanism(s) if they have environmental integrity leaving the choice for using the mechanism(s)
open depending on its details and conditions to be established during the UNFCCC negotiations.
Choice of Sectors:For the purpose of Albania’s INDC the focus has been on the Energy and
Industrial Processes Sectors.However, it is recommended for Albania to expand its INDC to
other economic sectors and greenhouse gases as well before 2020, if data quality of inventory
and projections will be improved regarding those gases.
Fairness and ambition: It is recommended that Albania takes an emission reduction ambition
level which is comparable to the EU 2050 political goal. It is possible to achieve this with high
confidence with the foreseen increase of emissions followed by decoupling and a slow decrease
of emissions.
Proposed target:The INDC of Albania is a baseline scenario target: it commits to reduce CO2
emissions compared to the baseline scenario in the period of 2016 and 2030 by(11.5)%. This
reduction means 708kT greenhouse gas emission reduction in 2030. The emission trajectory of
Albania allows to have a smooth trend of achieving 2 tons of greenhouse gas emissions per
capita by 2050, which can be taken as a target for global contraction and convergence of
greenhouse gas emissions.
37
6 Annex 1 – INDC template for Albania
Intended Nationally Determined Contribution (INDC) of Albania following decision
1/CP.19 and decision 1/CP.20
This document presents Albania’s Intended Nationally Determined Contribution following
decision 1/CP.19 and decision 1/CP.20 of the United Nations Framework Convention on
Climate Change (UNFCCC), which invited Parties to communicate the UNFCCC Secretariat
their INDCs, with the aim to achieve the ultimate objective of the UNFCCC as set out in Article
2 of the Convention.
Albania is a developing country with a per capita GDP of 10 thousand USD. It’s total
greenhouse emissions are relatively low (8,4 M tons in 2009, of which roughly 60% is of the
CO2 emissions) it is aiming to take its fair share from the efforts to avoid dangerous climate
change. The country has unique emission profile as its electricity generation is based on
renewable source generation at currently, with hydro power providing dominant part of it.
Unfortunately, this hydro power capacity is vulnerable to climate change impacts. The unique
electricity mix of Albania is positive in the sense that electricity system is on a level of
decarbonisation what other countries aim for only on the long term, but it also means that
there is limited opportunity for further policies and measures in this sector to reduce
emissions. Maintaining the low greenhouse gas emission content of the electricity generation
and decoupling growth from increase of greenhouse gas emissions in other sectors are the
primary drivers of the country regarding mitigation contribution as its INDC.Having high
uncertainty of data regarding non CO2 greenhouse gases results that Albania is to provide its
INDC regarding CO2. If data quality of non-CO2 greenhouse gases improves, Albania intends to
expand itsINDC to other greenhouse gases as well.
The INDC of Albania is a baseline scenario target: it commits to reduce CO2 emissions
compared to the baseline scenario in the period of 2016 and 2030 by (11.5 %). This
reduction means (708kT) greenhouse gas emission reduction in 2030.
The emission trajectory of Albania allows to have a smooth trend of achieving 2 tons of
greenhouse gas emissions per capita by 2050, which can be taken as a target for global
contraction and convergence of greenhouse gas emissions.In the following additional
information is provided regarding the INDC in order to facilitate clarity, transparency and
understanding.
Type
Gases covered
Target year
Baseline
Mitigation contribution of GHG emissions
Baseline scenario target: a reduction in GHG emissions relative
projected future emissions
Carbon Dioxide (CO2)
2030
Business As Usual scenario of emissions projections based on
38
Sectors covered
economic growth in the absence of climate change policies,
starting from 2016
The INDC covers the following sectors of the greenhouse gas
inventory:


Energy
Industrial processes
Planning process
Planning process of the INDC included the review of available data
and modelling work applicable to greenhouse gas reduction
pathway as well as consultations with government stakeholders as
well as with the public.
The scenarios for the INDC were developed taking into
consideration draft of the 3rd National Communication of Albania
and all available scenario development work related to greenhouse
gas emissions.
Within the preparation process of the INDC it became clear that
significant data uncertainty exist regarding the emissions of
greenhouse gases other than CO2 and in sectors outside of sectors
covered by the INDC. Improvements were made on existing
modelling work and the scenarios presented are result of this
work.
Participation in
Albania intends to sell carbon credits during the period until 2030
international market
to contribute to cost-effective implementation of the low emission
mechanism
development pathway and its sustainable development. Albania
foresees that for the utilization of international market mechanism
is conditional on having effective accounting rules developed
under the UNFCCC to ensure the environmental integrity of the
mechanisms.
Fairness, equity, ambition and Means of Implementation
Fairness, equity and
Albania is a developing country, highly vulnerable to the effects of
ambition
the climate change. National emissions of the greenhouse gases
represent only 0,017 % of global emissions and the net per capita
GHG emissions Albania was 2.76 tCO2e which is less the a quarter
of emissions of high-income countries. .
Albania will take into account the ultimate objective of the UNFCCC
in its future development and committed to decouple greenhouse
gas emissions from its economic growth and embarks on a low
emission development pathway.
The INDC submitted by Albania is fair and ambitious because it
aims to secure limited increase of its greenhouse gas emissions
while it the country pursues a strong economic development
pathway. Moreover, the pathway allows on long term for the
convergence of Albania’s per capita emissions to the 2 ton/capita
level.
Means of
The results of the preparation of the INDC will be reflected in the
implementation
Third National Communication of Albania and also will form the
basis of the Environmental and Climate Change strategy which is in
39
preparation. Development of the strategic directions for energy
and transport sectors will take into consideration the INDC.
Coordination of activities in relation to the strategy is foreseen to
be coordinated by the Ministry of Environment which is the chair
of the inter-ministerial body on Climate Change.
Albania also transposes and implements parts of the EU legislation,
including legislation on climate change and builds capacity for its
implementation which supports its ability to reduce greenhouse
gas emissions.
Albania is a contracting party of the Energy Community Treaty
which aims to extend the EU internal energy market to South East
Europe and beyond on the basis of a legally binding framework.
The overall objective of the Energy Community Treaty is to create a
stable regulatory and market framework which also includes
legislation aiming to reduce greenhouse gas emissions.
Key Assumptions
Metric Applied
The metric used for the GHG emissions is the Global Warming
Potential on a 100 year timescale in accordance with the IPCC’s
2nd Assessment Report
Inventory methodology
IPCC 2006 Guidelines
Approach to accounting
Greenhouse gas emissions and removals from agriculture, forestry
for agriculture, forestry
and other land uses are currently not included in the accounting.
and other land uses
Emissions and removals from these sectors can be included in the
INDC at a later stage when technical conditions allow for that.
Having relatively high uncertainty regarding emission data in the LULUCF sector and non-CO2
greenhouses gas emissions and removals Albania reserves its right to review its INDC until
2020 upon the availably of more accurate data and improved technical conditions regarding
land use, land use change and forestry as well as non-CO2 greenhouse gases and include it in
its nationally determined contribution.
If the agreement or related COP decisions are amended before their entry into force in such a
way that they include rules or provisions that in effect alters the assumptions under which
this INDC has been developed, Albania reserves the right to revisit the INDC.
Albania requests the UNFCCC Secretariat that this submission is published on the UNFCCC
webpage and that our INDC is included in the synthesis report to be prepared by the
Secretariat.
40
7 Annex 2 – Summary of existing projections
This section provides a summary of existing emission and energy projections for Albania. The aim of
the overview is to provide information to support the selection of one or more models as the
analytical basis for the preparation of Albania’s INDC.
In order to provide a sound basis for an INDC, the modelling method has to be theoretically sound,
the data used for the base year have to reflect available statistical information while data on
projected trends needs to be underpinned by sound analysis, and the scenarios analyse have to
reflect a likely outcome as well as a policy framework which is widely accepted by stakeholders and
decision makers. Therefore, this section provides an overview of existing projections focusing on the
following aspects:

modelling methodology,

data used, including base year data and trends, and

scenariosanalysed.
7.1 Description of existing modelling exercises
7 existing modelling exercises which cover Albania have been identified. These are shown in Table. 2.
Sections 2.2-2.4 provide a technical summary of these modelling exercises, including assessment of
the modelling methodology, accuracy and reliability of base year input data, and assessment of the
scenarios modelled.
Table 2.
Name
project
NC3 UNDP
Existing modelling exercises for Albania
of Organisation/Developers
Website
National experts
Third National Communication not uploaded to
UNFCCC website yet
LOCSEE
National Observatory of http://www.locsee.eu/modeling_reports.php
Athens (NOA), Greece
Joanneum Research (JR),
Austria
EU Reference/ E3MLab/ICCS at National http://www.e3mlab.ntua.gr/e3mlab/index.php?optio
PRIMES
Technical
Universityof n=com_content&view=category&id=35%3Aprimes&It
Athens
emid=80&layout=default&lang=en
PROMITHEAS4 Polytechnic University of http://www.promitheasnet.kepa.uoa.gr/Promitheas4
Tirana
/index.php/library
National
and
KapodistrianUniversity of
Athens
SLED
Regional Centre for Energy http://www.rec.org/project.php?id=184
Policy Research (REKK)
(electricity)
Institute
for
Climate
Protection, Energy and
Mobility (IKEM) (buildings)
USAID
Center for Renewable none
41
Energy Sources and Saving
(CRES)
Reports were received for all models mentioned in Table 2. However, access to the model itself was
not possible for the PRIMES and the SLED electricity model. It is expected that access to the
PROMITHEAS4 model will be possible, but the model has not yet been received. For the PRIMES
model excel files were provided with the results and the values of some input variables. Accessing
the modelling work including the models themselves and reports would have been essential for all
models to verify information which is often missing from the materials provided (e.g. information on
the level of detail which was available in each model, information on how the impacts of policies
were translated into modelling, etc.). The SLED buildings sector model is not yet included in the
report as calibration of the model is ongoing.
7.2 Assessment of the modelling methodology
A summary of the different models, focusing on the modelling methodology used is provided in this
section. The summary includes objective descriptors of the models in Table 3 (sectors covered,
geographic and temporal coverage).
NC3 UNDP and LEAP,
USAID
MARKAL
GACMO, Energy
supply
and
demand
(residential,
service,
industrial,
transport, agriculture) and nonenergy related emissions of CO2, CH4
and N2O
EU Reference/ PRIMES
Energy supply/transformation and
PRIMES
demand
(industry,
residential,
tertiary, transport) and non-energy
related emissions of all GHGs
SLED
European Electricity Electricity, residential buildings
Market Model and
residential buildings
stock model using
LEAP platform
LOCSEE
LEAP
Road transport
PROMITHEAS4
Temporal
coverage
Gases
covered
of
Sectors
covered
Name
project
Summary of the scope of models
Name of
model/m
odelling
platform
Table 3.
CO2, CH4, 2030
N2O
CO2
(energy
and
process)
CO2
(energy)
CO2
(energy),
CH4, N2O
Model
developed Energy (including transformation and CO2
using LEAP platform demand - industry, residential, (energy)
commercial and public, transport,
agriculture) and non-energy use of
fuels
2050
2030
2030
2050
42
To put it simply, an ideal emission projection model has attributes which enable it to reflect the
most important real world developments which influence emissions. These attributes have been
identified by researchers as technological explicitness, behavioural realism and macro-economic
feedbacks. These attributes are necessary to ensure that model results are realistic and reliable to
the extent that this is possible given the state of the art in energy/climate policy modelling.
It is outside the scope of this technical paper to discuss these aspects of an ideal model in detail, but
a short definitions of these three terms are provided in the paragraph below to enable a better
understanding of the model descriptions which follow and the assessment contained in Table 4.
However, the following paragraph can be skipped by less inclined readers.
Technological explicitness means that the models reflect the characteristics of current and future
technologies, e.g. by describing efficiencies, fuel mixes, network losses, etc. in an explicit way,
reflecting real world (present and future) technology performance. Behavioural realism implies that
models depict agents (e.g. private companies, households, etc.) in a way which reflects their real
world decision-making processes. This generally means that models should take into account costs
(investment, operating and maintenance, etc.) and benefits related to a particular technology and
only depict widespread use of a particular technology if it is viable given the existing (current and
future) commercial and policy framework. For example, in the energy sector a model should depict
decisions which are sensitive to costs of fuels and technologies, as well as labour needed to operate
these technologies, etc. Behavioural realism also implies that models take into account barriers to
cost-minimising/benefit maximising behaviour. In the real world such barriers would include e.g.
non-financial barriers such as lack of information, uncertainty relating to the performance of a new
technology, etc. as well as financial barriers relating to e.g. income constraints coupled with high
investment costs. Finally, macro-economic feedbacks relates to the impact that energy/climate
policies have on the wider economy, and the impacts that these in turn have on emissions. For
example, investment in energy efficiency in buildings may result in increased employment in the
construction and building materials sectors, which will result in increased aggregate household
income and increased consumption, which will in turn increase energy demand and emissions.
43
Table 4.
Assessment of models
NC3
USAID
Quality of data
Scenarios
Microecono
-mic
realism
Macroecon
o-mic
feedbacks
Cost
information
of
Technological
explicitness
Modelling method
Name
project
and Yes,
No
not all
sectors
No
Yes
LOCSEE
Yes
No
No
Yes
PRIMES
Yes
Yes
No
Yes
PROMITHEAS4
n.a.
No
No
No
SLED
Yes
Yes
No
Yes
Data from national sources, some
input data contained in report.
Relatively high coherence with NC3
inventory and INSTAT data.
Baseline scenario with
very high drivers and no
efficiency improvement
Mitigation
scenarios
seemingly not in line
with national policy
Some data on vehicle stock not Scenarios very narrowly
available for AL taken from Croatian defined with focus on
statistics, but overall coherence with vehicle fleet
inventory data on total emissions
Data from international statistical Only 1 draft scenario
sources, not completely coherent available, not in line with
with inventory.
EnC commitments
Data from international statistical Unclear how model takes
sources and domestic sources, could into account current
not yet be reviewed
policies
Base year data not received
Baseline scenario drivers
as USAID and NC3
Participation,
consultation
Unclear
documents
from
Unclear
documents
from
Consultation
national
administration
Unclear
documents
with
public
from
Involvement of local
experts
and
consultation
with
ministries
44
7.2.1 NC3 UNDP and USAID
For NC3 the modelling work is based on 3 models using LEAP, GACMO and MARKAL. The LEAP model
provides the core of the modelling. For USAID the modelling is based on the LEAP model. Although this
is not immediately apparent from the reports on the two models, on receiving the LEAP files of the
models themselves it became apparent that the two models are identical in their structure, data,
assumptions and scenarios, and therefore also produce the same results.
The models cover all relevant energy demand sectors as well as electricity generation, transmission and
distribution, and district heating/cental heat production. The models are bottom-up simulation model
(i.e. costs do not influence decisions of agents and agent behaviour is not depicted). The activity levels
drive emissions (in addition to technology choice); these activity levels (e.g. GDP, number of households,
population, floor area of buildings driving heating needs, transport activity levels and gross value added
in industrial sectors) are exogenous in the model. The model does not depict macro-economic
feedbacks.
Technologies are explicit in the model; the degree of detail varies among sectors. For example,
electricity generation technologies are explicitly represented through efficiency of individual power
plants. However, for the demand sectors the model contains significantly less technology detail, and
uses energy intensity variables to describe the evolution of the energy performance in these sectors.
The value of these variables is difficult to verify. Industrial process technologies are depicted through
energy intensity values (decreasing over time), space heating needs in buildings are not modelled
explicitly, and appliance energy consumption is depicted by values relating to energy use per household.
For the transport sector energy intensity per pkm represents the efficiency of individual technologies,
the value of which increases over time for all vehicle types.
7.2.2 EU Reference/ PRIMES
The EU energy transport and GHG emission reference scenario is modelled using as main models the
PRIMES model for energy and CO2 emissions and the GAINS model for non-CO2 emissions. For this
exercise, a draft PRIMES energy and CO2 reference scenario for Albania as sent out mid-March 2015 for
consultation with Albanian authorities was used.
The model was not received and the description of the model is based on publicly available reports.
It is an advanced and complex model which uses submodels for different sectors, covering all relevant
sectors of the economy in the EU, EFTA countries Western Balkans and Turkey. The model depicts
markets and solves to find an equilibrium between supply and demand in all markets. It models
behaviours of agents based on microeconomic foundations where agents minimise costs/maximise
utility (i.e. it is an optimisation model), taking into account perceived costs and uncertainties through
premiums on discount rates. Investment in technologies (including new equipment and retrofitting) and
purchase of fuels is endogenous in the model. Technology uptake is a function of decisions on
technology selection decisions of agents and is constrained by infrastructure. Technology learning and
economies of scale are taken into account in the model. Investment in network infrastructure is
exogenous in the model.
The model can depict secondary impacts in other markets, but impacts on macro-economic variables are
not taken into account in the model, and drivers of demand such as GDP are exogenous, as are some
prices (e.g. fuel prices).
45
The model is technologically explicit and takes into account expected future evolution of technologies. A
large number of technologies and sub-sectors are considered in the model. For example, 72 existing
power plant types and 150 new power plant technologies are considered by the model. For some
sectors technology representation is more limited.
The strength but also a weakness of the model is its focus on the entire EU, Western Balkans, EFTA and
Turkey. This makes it possible to take into account interactions with EU markets which is an advantage.
At the same time, real stakeholder involvement at country level during modelling is limited due to the
large geographic scope of the modelling exercise. This issue is discussed further in the section
addressing the (single) scenario of the PRIMES model which was received for Albania.
7.2.3 SLED
The SLED (Support for Low-Emission Development in South Eastern Europe) project supported the
development of two models, one for the buildings and one for the electricity sector.
The electricity sector model is composed of two models which have been linked, a market model
representing electricity production which satisfies demand, and a network model. In addition, a gas
network model has been used to project gas prices which have been input into these models.
The European Electricity Market Model (EEMM) is Excel based and was originally commissioned by the
JRC. It is a partial equilibrium model of the European electricity sector which includes around 5000
power plants. The model is an optimisation model, it minimises the cost of supplying electricity given
the constraints related to generation capacity and capacity of cross border interconnectors, taking into
account the cost categories of fuel cost, variable OPEX, excise tax and CO2 costs where applicable. This
cost minimisation results in production levels being determined by the model for each power plant, in
parallel with imports and exports of electricity. It models 3 types of agents: consumers, producers and
electricity traders. In the model energy demand is calculated in a simplified way based on exogenous
variables, and the electricity prices resulting from balancing supply and demand in the EU, EFTA (without
Iceland) and the Western Balkan countries are calculated endogenously. Exogenous electricity prices are
assumed for neighbouring countries (e.g. Russia and Turkey).
The EKC network model is based on the software package PSSE. It performs steady state and
contingency analyses by assessing steady state AC load flow, security (n-1) and performing a voltage
profile analysis. It also performs an evaluation of total net transfer capacity, and transmission grid losses
with and without a level of energy production from renewable sources.
The SLED buildings sector model is not yet included in this report because calibration of the model is
ongoing.
7.2.4 LOCSEE
The model addresses the transport sector only. The model uses a very simplified approach and has
several shortcomings which result in low reliability of the model, including unreliable base year data on
vehicle stock, future projections and emission reduction calculations.
The drivers of transport activity are not examined in any detail, and are assumed to be a function of
stock*mileage*pkm, or for stock*mileage*tkm for the passenger and freight transport sectors
respectively. The stock evolves for all vehicle types in one of two ways: either it is assumed to remain
constant over the 20 year period (buses, coaches, heavy duty vehicles) or to increase at the same
46
linearly decreasing rate (passenger cars, light duty vehicles and power 2 wheeler). Mileage is unchanged
over time for all individual vehicles within a vehicle type (although there is some shift in technologies).
pkm and tkm are also unchanged over time. Ultimately therefore the emissions depend on two factors:
the very simplified description of the evolution of thetotal vehicle stock and the shift between EURO
categories within the vehicle stock.
7.2.5 PROMITHEAS4
The model or model description was not received and a model description is not publicly available. The
model uses the LEAP software. Further analysis of the modelling methodology is not possible until the
model can be reviewed.
7.2.6 Assessment of base year data and trend assumptions
7.2.7 Base year data
Table 5 contains a summary of the base year CO2 emissions data and final energy demand data for the
different models and sectors and a comparison with data from the Third National Communication
Inventory. It is not part of this assignment to verify the data and methodology used to develop the latest
inventory. The inventory data contains relatively significant changes from the previous inventory which
was submitted to the UNFCCC. (There is a more than 10% decrease in emissions for year 2000, which is
the only overlapping year between the two inventories, in the NC3 inventory compared with the NC2
inventory.)
As can be seen from Table 5, there is much higher agreement between different models on total final
energy demand than there is regarding emissions. For CO2 emissions for the year 2010, the highest
estimate is 50% higher than the lowest estimate for total emissions, while the difference between
lowest and highest final energy demand is only 11%. There is also significantly higher agreement on total
final energy demand than there is on sectoral energy demand; it seems that allocating statistical data on
final energy demand to sector level poses some challenges that were addressed in different ways by
different modellers.
In addition, even for the sector where emissions and energy consumption seem to be of highest
certainty, i.e. the transport sector, some serious issues regarding data reliability exist. The preliminary
data collection undertaken by funding from the EBRD for the Sustainable Transport Strategy of Albania
shows very different results. Emissions are assessed as 1.25 million kt CO2 on average over the period
2009-2014 for the transport sector in comparison with the more than 2 million kt assumed by all models
reviewed for this analysis. Energy consumption of the transport sector is 367 ktoe according to the
Sustainable Transport Strategy, or 4.27 TWh, compared with more than 8 TWh assumed by all reviewed
models. The difference in estimates is even higher if one takes into account the fact that the initial
figures for the Sustainable Transport Strategy include energy use and emissions from aviation and
maritime transport.
47
Table 5.
Comparison of base year data with the Third National Communication inventory data
(CO2 emissions in 2010, kt)
NC3 Inventory
365
Final energy
demand, TWh
(2010)*
n.a.
NC3 UNDP and USAID
NC3 Inventory
PRIMES
222
1.13
1287
563
n.a.
9.34
NC3 UNDP and USAID
290
7.76
NC3 Inventory
PRIMES
NC3 UNDP and USAID
NC3 Inventory
LOCSEE
PRIMES
NC3 UNDP and USAID
NC3 Inventory
INSTAT
703
1007
654
2301
2266
2231
2239
4969
n.a.
n.a.
4.15
3.61
n.a.
8.59
8.67
8.57
n.a.
22.89
NC3 UNDP and USAID
3639
21.43
PRIMES
3868
22.16
Sector
Agriculture
Buildings
Industry
Transport
Total
Model
CO2 emissions, kt
(2010)*
* emissions for the inventory are from year 2009, emissions for NC3 UNDP and USAID are for year 2012
** agriculture and waste
It is not clear at this moment what factors account for the large differences, and the issue of most
reliable data needs further investigation. All results presented in Table 5 are for CO2 emissions only and
do not include emissions of other GHGs. Where models have included emissions from biomass these
have been taken to be zero. For most models the data for some or all sectors except transport diverge
significantly from data in the NC3 inventory. As the electricity sector in Albania is virtually zero
emissions, the difference cannot be explained by the accounting of direct and indirect emissions in the
base year. For now, it is assumed that the models which have results closest to INSTAT data and the NC3
inventory are most reliable. However, the inventory has not been reviewed and therefore there is no
guarantee that this is indeed the case.
7.2.8 Trend assumptions
A comparison of some of the most important drivers of energy demand are shown in Table 6 below.
Such data is not contained in the SLED electricity model or the LOCSEE model. For energy demand levels
the SLED electricity model used the assumptions of the USAID model at the request of ministry officials,
thereby implicitly reflecting population and economic growth assumed by the USAID model.
The assumptions regarding drivers diverge significantly. Some of the assumptions of the models with
respect to drivers of emissions seem to be in line with international analyses of Albania’s expected
48
performance, while other models seem to rely on drivers of emissions which are significantly lower or
higher. Based on recent trends, over the short term a strong growth in either population or household
numbers is highly unlikely. At the same time, high GDP growth can be expected given Albania’s
economic performance over the past 10 years and the further growth that EU membership is likely to
trigger. The World Bank projects annual GDP growth between 3-3.5% for Albania for the short term
(2015-2017)6, while the European Commission projected lower growth for 2014, but 3-3.5% growth
rates for the years 2015-2016.7 Both the low and medium variant of the UN population projections
foreseen a decrease in population numbers in Albania.8
The higher assumptions for the USAID model compared with other models may have a strong role in
explaining the steep rise in emissions and energy demand in both the baseline and mitigation scenario
compared with other models (these are contained in section 3.5 for all sectors).
6
http://www.worldbank.org/en/publication/global-economicprospects/data?variable=NYGDPMKTPKDZ&region=ECA
7
http://ec.europa.eu/economy_finance/eu/forecasts/2015_winter/cc_albania_en.pdf
8
http://esa.un.org/wpp/Demographic-Profiles/pdfs/8.pdf
49
Table 6.
Drivers of energy demand in the various models (absolute values and average annual
growth rates)
2020
2025
2030
2035
2040
2045
2050
AGR
(%)
17.0
1
21.6
0
989
1581
23.0
1
26.5
1
29.8
1
3.08
804
966
14.5
0
15.7
6
936
1397
19.8
3
10.2
5
847
1091
12.2
8
12.2
9
891
1235
2.86
2.95
3.10
3.26
3.42
3.15 3.20
PRIMES
* Base year for NC3 and USAID is 2012
3.24
3.28
3.31
Drivers
GDP, bn
EUR
Model
PRIMES
NC3 &USAID
Hholds,
'000
PRIMES
NC3 &USAID
Pop,
million
NC3 &USAID
2010
2015
8.87
9.84
9.50
4.67
1051
1130
1234
1343
1.29
2.77
1.00
3.30
3.25
3.18
3.09
-0.04
Economic growth as well as population and household numbers are more likely to be correctly reflected
by the PRIMES model. At the same time, given the large number of people who have migrated away
from Albania, their return on improvement of the economic situation cannot be excluded, and therefore
more optimistic population growth scenarios such as assumed by the NC3 and USAID models cannot be
excluded.
7.3 Assessment of thescenarios
This section contains an assessment of the scenarios. Ideal scenarios should adequately consider policies
which impact GHG emissions, including national policies, Energy Community commitments, and EU
policies (e.g. carbon price) after the expected date of accession. It needs to be emphasised that EU
policies will entail not only further commitments for Albania beyond those already undertaken within
the framework of the Energy Community, but also additional opportunities, in particular through
possibly significant amounts of EU funding geared towards climate change mitigation.
The baseline and mitigation scenarios, if these are to serve as a basis for Albania’s INDC, should also
reflect the Lima text. This means that the INDC should make a contribution towards the aim of the
United Nations Framework Convention on Climate Change, i.e. it should contribute to the achievement
of a 2 degree target, in line with IPCC recommendations on the level of ambition that reaching such a
target requires. It should also be fair, taking into account a country’s national circumstances. Fairness
may also be interpreted as relating to variables such as emissions per capita. Finally, the contribution
should be ambitious, i.e. it should achieve more than “current undertakings”.
Translated into operational terms, this means that a technical analysis (prior to a political decision on an
INDC) should recommend a baseline scenario which already takes into account all existing policies and
measures (i.e. current undertakings) and is not artificially inflated to accommodate fictitious emission
reductions. A technical analysis should also identify a mitigation scenario with the aim of going beyond
the level of ambition of the baseline scenario. The extent to which the mitigation scenario should go
beyond the level of ambition of the baseline scenario depends on national circumstances, in which
context the principle of common but differentiated responsibilities and respective capabilities of the
UNFCCC can be recalled. Based on these principles, it is clear that Albania, with low GHG emissions per
50
capita, an already high renewable energy share, and capabilities which are constrained by low national
income should not be expected to reduce current emissions significantly. However, an attempt needs to
be made to keep current low emission levels in check while achieving economic growth. The existing
modelling exercises are reviewed through this conceptual framework; the scenarios are assessed against
the criteria of policy coherence, level of ambition and fairness of the contribution.
In addition to the following general overview of the scenarios, a more specific overview is provided for
each sector scenario in section 3.6 for the specific models which are recommended as a basis for the
sectoral commitment.
7.3.1 NC3 UNDPand USAID
The draft report of the Third National Communication containing the energy and transport mitigation
analysis states that the baseline scenario is in line with the updated draft National Energy Strategy. It is
also stated that not all measures foreseen in the national action plan are assumed to be implemented in
the baseline scenario. The report states that the mitigation scenario assumes the implementation of a
number of policies and measures going beyond the baseline scenario. The report also states that the
policies considered in the RES scenario include the first and second NEEAP, the first REAP, and national
building codes in line with the EPBD. In addition to the policies, a large number of measures are also
considered. These include measures such as use of efficient refrigerators and lighting in the residential
sector, introduction of efficient motors for industrial consumers, reconstruction of poor quality roads,
promotion of solar thermal up to a capacity of 400-420 GWh per year.
On examination of the LEAP model, the statements of the report could not be verified. It has not been
possible to assess how the consistency with national policies was ensured in the definition of the
scenarios. It is not clear from the text how these measures relate to the mentioned policies, and if they
serve the implementation of these policies, or if they are additional to them. Based on the model it
seems that the consistency of the modelled scenarios with existing or planned policies is not ensured,
and that the baseline and the mitigation measures assumed are rather arbitrary.
In total 4 scenarios were modelled:

Baseline scenario: The scenario assumes that total final energy demand grows at the same rate
as GDP. Due to the change in fuel mix, emission growth is faster than GDP growth. There is
significant hydro capacity expansion, with an approximate doubling of hydro production from
2012 to 2030, and a small share of oil based production. Despite the capacity expansion of
hydro, unmet requirements (net imports) in the electricity sector make up around half of energy
demand in 2030.

Energy efficiency scenario: Energy efficiency measures across all sectors result in slightly lower
growth of energy demand than in the baseline scenario. The energydemand growth under this
scenario is 90% until 2030 compared with 2012, while GDP growth is 127%.

Combined RES-EE scenario: Through a combination of supply and demand side measures, 40%
RES in gross final consumption is reached by 2020. Energy demand growth is the same as in the
efficiency scenario. In reality, this is the scenario which delivers the renewable energy targets of
Albania resulting from its obligations under the Energy Community Treaty and related
legislation.
51

Coal scenario: Demand growth under this scenario is the same as for the baseline scenario, total
emissions are almost 30% higher.
The first 3 scenarios have two variants: one assuming that gas is unavailable, the other assuming
substantial gas imports via the TAP pipeline which is used in electricity generation and final demand. All
scenarios result in substantial (1-1-5 Mtoe) increase in imports of natural gas by 2030 compared with
the baseline scenario. The increased role of gas based electricity generation results in decreased
electricity imports.
7.3.2 EU Reference/ PRIMES
Only draft results for the reference scenario are available from the European Commission for the
purpose of preparing this report. This is a big constraint, as the reference scenario does not correspond
to a more advanced mitigation scenario, and therefore despite the advanced modelling approach it is
difficult to build on the results of the PRIMES model.
No report was received on details of the choices made with respect to e.g. the country specific
assumptions and their consistency with national strategic goals (e.g. energy security). Exchanges with
European Commission officials and the output tables of model results allowed us to draw some
conclusions.
In the baseline scenario the model does not account for the impact of EU policies such as ETS, as can be
read from the tables received, where the ETS carbon price is assumed to be zero throughout the
modelling timeframe. The model does not consider obligations under the Energy Community Treaty as
such as part of the baseline, in relation to RES or energy efficiency targets,, but only to the extent they
are reflected innational legislation. Therefore the EnC RES target of 38% by 2020 is not achieved in the
baseline scenario and neither is the indicative energy efficiency target of 9% by 2018 for the non-ETS
sector (without international aviation).
However, some policy for RES and energy efficiency is assumed. RES support is considered to be part of
energy policy, for example expressed by a RES value level at a level of EUR 34/MWh in 2020. The
shadow price of energy efficiency (which covers those efficiency policies which are not directly modelled
and can be interpreted as the price of white certificates) is also assumed to be positive from around
2015, as can be seen in Table 7.
The model takes into account infrastructure constraints, therefore it considers that Albania is not
connected to a gas network and only assumes gas in the electricity mix starting from 2025 onwards,
with a starting share of 11.5% of electricity production. The model also accounts for existing capacities.
Table 7.
Reference scenario policy variables for Albania in the PRIMES model
Policy variables
Albania: draft Reference scenario
Year
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
21.6
95.0
125.0
125.0
105.7
105.5
62.5
62.5
0.0
0.0
0.0
3.4
3.2
2.8
2.5
2.1
1.8
1.4
Carbon value (€'10/ t of CO2)
ETS sectors
non-ETS sectors
Average efficiency value
(€'10/toe)
Renewables value (€'10/MWh)
52
CO2 Emissions Index (1990=100)
136.8
133.2
150.1
166.5
193.1
217.5
242.7
271.6
298.5
325.4
RES in transport (%) (B)
RES in gross final energy demand
(%) (B)(C)
Gross Electricity Generation in
TWhe
RES in gross electricity demand
(%, normalized) (B)(C )
0.3
0.0
0.0
1.3
1.3
1.4
1.4
1.5
1.5
1.6
61.3
39.4
31.4
34.1
31.9
29.6
28.3
26.4
25.3
23.9
5.4
7.6
4.5
6.3
7.7
8.3
8.9
9.5
10.1
10.5
190.3
99.9
75.2
80.9
80.0
75.7
73.9
71.4
69.1
67.3
The model results show that even in the reference scenario the role of RES (mainly hydropower with
some wind capacity coming online around 2020, and solar from 2025) in electricity generation remains
dominant through 2050, as can be seen in Tables 8 and 9. The model results seem to be in line with the
energy policy priority to reduce the share of imports, as the level of imports drops to around 10% by
2025 and its share decreases further.
Table 8.
Draft reference scenario projection for the power generation fuel mix (TWh net) for
Albania in the PRIMES model
Albania:draftReference
scenario
From Solids
From Oil
From Gas
From Nuclear
From RES
Total Power Generation
2005
0.00
0.07
0.00
0.00
2010
0.00
0.01
0.00
0.00
2015
0.00
0.02
0.00
0.00
2020
0.00
0.02
0.00
0.00
2025
0.00
0.00
0.88
0.00
2030
0.00
0.00
1.16
0.00
2035
0.00
0.00
1.55
0.00
2040
0.00
0.00
1.97
0.00
2045
0.00
0.00
2.41
0.00
2050
0.00
0.00
2.78
0.00
5.36
7.56
4.52
6.29
6.81
7.14
7.31
7.45
7.61
7.69
5.43
7.57
4.54
6.31
7.69
8.29
8.87
9.42
10.02
10.47
53
Table 9.
Draft reference scenario projection for power sector capacity mix (GW net) for Albania
in the PRIMES model
Albania:Reference scenario
Solids
Oil
Gas
Nuclear
RES
Total Power Generation
excl.generation for RES
storage
2005
0.00
0.18
0.00
0.00
2010
0.00
0.03
0.00
0.00
2015
0.00
0.13
0.00
0.00
2020
0.00
0.15
0.06
0.00
2025
0.00
0.15
0.26
0.00
2030
0.00
0.15
0.35
0.00
2035
0.00
0.12
0.47
0.00
2040
0.00
0.12
0.58
0.00
2045
0.00
0.12
0.69
0.00
2050
0.00
0.10
0.77
0.00
1.47
1.48
1.53
1.96
2.35
2.53
2.60
2.69
2.78
2.84
1.65
1.51
1.66
2.17
2.76
3.03
3.20
3.40
3.59
3.71
The PRIMES model results show lower growth in total final energy demand and CO2 emissions than the
NC3 UNDP and USAID models. The PRIMES model results seem to show that this more modest growth in
emissions and energy demand can be achieved without compromising economic growth; the model
shows significant increase in activity levels (e.g. passenger transport activity increases almost threefold
by 2050) and in GDP (which is 3.4 times higher in 2050 than in 2010). This is achieved through a
decrease in energy intensity for all sectors, including the industry, residential, tertiary and transport
sectors. The model shows an increase in carbon intensity for electricity, industry and the residential
sector. (see Table 10)
Table 10.
Draft energy and carbon intensity indicators for Albania in the PRIMES model
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
Energy intensity indicators (2000=100)
Industry (Energy on Value added)
72.1
100.0
103.9
97.9
96.4
94.9
90.8
88.9
86.5
82.8
Residential (Energy on Private Income)
146.2
100.0
110.2
103.3
92.3
83.9
79.2
71.0
65.3
60.2
Tertiary (Energy on Value added)
131.0
100.0
93.7
83.2
68.2
61.7
53.4
50.5
46.1
41.5
Transport (Energy on GDP)
144.4
100.0
103.6
91.5
83.8
79.0
74.2
70.3
65.9
63.9
Carbon Intensity indicators
Electricity and Steam production (t of
CO2/MWh)
0.03
0.00
0.00
0.00
0.04
0.05
0.06
0.07
0.08
0.09
Final energy demand (t of CO2/toe)
1.96
1.98
1.98
1.96
2.03
2.07
2.11
2.15
2.18
2.21
Industry
2.10
2.59
2.57
2.54
2.58
2.62
2.64
2.65
2.65
2.66
Residential
0.39
0.45
0.51
0.53
0.61
0.62
0.68
0.70
0.72
0.76
Tertiary
1.57
1.09
0.96
0.94
0.97
0.96
0.95
1.00
1.00
0.98
Transport
3.04
3.06
3.04
3.03
3.03
3.03
3.03
3.03
3.03
3.03
7.3.3 SLED
The SLED project produced two models, one for the electricity sector (the European Electricity Market
Model, EEMM, coupled with the EKC Network Model) and one for the buildings sector.
Three scenarios have been modelled, a reference scenario which reflects the development of the
electricity system based on already accepted regulations/measures and policy decisions, a CPP scenario
which is defined on the basis of measures/policy decision that already appear in official policy
documents of the government as planned (e.g. in the Energy Strategy). The instruments to achieve these
targets are not necessary in implemented currently and will be introduced in the future but the
54
likelihood if their implementation is high. The Ambitious scenario is a vision with more ambitious GHG
reduction targets than the CPP scenario. The aim of this scenario is to assess whether more ambitious
policies are possible, and their associated cost.
The electricity model has defined scenarios which are in line with the scenarios of the USAID model in
terms of energy demand by end use sectors, at the request of ministry officials. Electricity production
and imports have been modelled to satisfy this demand. Electricity capacity development has also been
modelled according to requests from ministry officials. In line with this, new gas-based power
production capacities are 660 MW, and new hydro capacity is 2040 MW. In the ambitious scenario,
biomass, PV and wind capacities are also installed with a total of 610 MW. This is shown in Table 11
together with investment costs.
Table 11.
New electricity capacities installed in Albania in the SLED model
Natural gas
Coal
Hydro
Geothermal
Solar
Wind
Biomass
Total
New capacity, MW
REF
CPP
AMB
0
660
660
0
0
0
2,040
2,040
2,040
0
0
0
0
0
220
0
0
310
0
0
80
2,040
2,700
3,310
Investment cost, m€
REF
CPP
AMB
0
660
660
0
0
0
5,100
5,100
5,100
0
0
0
0
0
242
0
0
310
0
0
240
5,100
5,760
6,002
The extent to which these (new and existing) capacities are used is determined by the model based on
cost-minimisation, taking into account domestic production costs as well as the cost of imports. As the
model is an optimisation model, and significant capacity developments in the region ensure adequate
supply, the gas power plant installed under the CPP and AMB scenarios is virtually not put in operation
in the presence of options with lower marginal production cost, as can be seen from Table 12.
55
Table 12.
Electricity production mix in Albania in the SLED model
Coal and lignite
Natural gas
Hydro
Wind
Biomass
HFO, LFO
PV
Geothermal
Net import
REF
2015
CPP
AMB
REF
2020
CPP
AMB
REF
2025
CPP
AMB
REF
2030
CPP
AMB
0
0
4886
0
0
0
0
0
3007
0
0
4886
0
0
0
0
0
3007
0
0
4886
0
0
0
0
0
3007
0
0
6490
0
0
0
0
0
2746
0
0
6490
0
0
0
0
0
2746
0
0
6490
36
136
0
58
0
2515
0
0
8493
0
0
0
0
0
2276
0
0
8493
0
0
0
0
0
2276
0
0
8493
294
546
0
176
0
1259
0
0
10337
0
0
0
0
0
2125
0
13
10337
0
0
0
0
0
2112
0
12
10337
570
546
0
323
0
673
The electricity production mix in Albania under the different scenarios shows that net imports can be
reduced significantly as a proportion of total demand even under the reference scenario. The reduction
in absolute value of imports is much smaller. The most significant reduction of imports is possible under
the ambitious scenario, where net imports make up only around 5% of total electricity consumption by
2030.
7.3.4 LOCSEE
The model is excel based. It covers the transport sector (passenger and freight) only. It is a simulation
model. It includes information on costs, but the cost of measures do not influence whether they are
implemented. The adoption of measures is determined exogenously by the modeller, and costs are
determined ex post. (For more information on which measures have been implemented and to what
extent, please see section 3.4.)
A baseline scenario is modelled where evolution of total transport demand is assumed to be dependent
on socio-economic factors. However, no further information is provided on this in the report. Following
the development of the baseline scenario, the model develops a mitigation scenario through estimation
of the impacts of specific measures on emissions and subtracting emission reduction of the measures
from the baseline.
The model has a very narrow scope, focusing on the transport sector only, and within that sector mostly
on the evolution of the technology of the vehicle stock, and a small number of measures related to
speed/eco-driving and renewable energy. The model does not look at wider policies which address the
modal shift of transport or the drivers of transport (e.g. infrastructure development, etc.).
The specific measures considered to reduce emissions:

Driving speed, eco-driving: Public transport- increase in bus speed, eco-driving;

Vehicle stock changes: penetration of CNG busses, hybrid cars, electric cars penetration,
renewal of Light Duty Vehicles, renewal of Heavy Duty Vehicles, renewal of gasoline passenger
cars and renewal of diesel passenger cars;

Renewables: Biodiesel penetration (5% in 2020 and 15% in 2030).
The mitigation potential of the considered measures is contained in Table 13.
56
Table 13.
Emission reduction measures and their mitigation potential considered in the LOCSEE
transport model
7.3.5 PROMITHEAS4
Three scenarios were developed: the Business as Usual scenario, and the Optimistic and Pessimistic
scenarios. Based on information contained in the report (the model itself was not made available) the
baseline assumption for energy demand for all sectors assumes that it follows GDP growth (close to 3%
annually). The fuel mixes are also unchanged for all sectors in the baseline scenario. The baseline
scenario is therefore essentially a frozen efficiency scenario, which is unlikely, given that even without
any policies and measures some autonomous improvement in energy efficiency is likely to take place
resulting from replacement of obsolete equipment and infrastructure with new.
BaU scenario:

Household sector: The energy consumption in the residential sector will be reduced by 22,8%
by2018 (note: from total energy consumption figures it seems that no EE in household sector,
check model)

All other demand sectors frozen efficiency

Transformation: For the BAU scenario no modernization is planned for the power grid, and a
growth of transmission and distribution losses (including technical and commercial losses) is
assumed according to historical trends. Several new hydropower capacities will be added,
existing capacities will not be modernized, and no thermal or other renewable capacities will be
added.
Optimistic scenario

Household sector: 5% reduction in the energy consumption by 2020 compared to 2010 due to
insulation, 10% reduction in energy consumption by 2020 compared to 2010 due to energy
57
labelling of appliances, 5% reduction in the energy consumption by 2020 compared to 2010 due
to performance standards. Solar energy is used for water heating by 5% share up to 2020.

Agriculture: 2% biofuels

Industry: 20% reduction of the energy consumption of the sector by 2020 compared to that of
year 2010,

Commercial sector same as household sector,

Transport: energy consumption of the sector is reduced by 10% until 2020 compared to that of
year 2010, and 8% increase in the use of biofuels.

Transformation: Capacities: Oil – 150MW by 2020; Wind – 2000MW by 2020. For the Optimistic
scenario electricity production will be based mainly on hydro power plants, small share of
biomass. Total electricity generation is doubled by 2050 compared with the BaU scenario, from
around 1.1 to around 2.2. Mtoe.
Pessimistic scenario:

Households: 2,5% reduction in the energy consumption 2020 compared to 2010 due to
insulation, 5% reduction in energy consumption by 2020 compared to 2010 due to energy
labeling of appliances and 2,5% reduction in the energy consumption by 2020 compared to
2010 due to performance standards. Solar energy is used for water heating by 2,5% up to 2020.

All other sectors as BaU (follow GDP growth rate with unchanged fuel shares).

Transformation: Electricity generation from the hydropower plants for the Pessimistic scenario
has almost the same trend as for the BAU scenario.There is only one thermal power plant with
installed capacity of 97 MW. For this scenario this plant will be operational and no other thermal
power plant capacities will be added in the system.For the Pessimistic scenario the trend is the
same as for the BAU scenario. In the Pessimistic scenario the electricity production will be based
mainly on hydro power plants.
There is little difference between the results of each scenario by 2050, as can be seen from Table 14,
although the optimistic scenario results in significant emission reductions by 2020. This is due to
investment in gas power plants in the optimistic scenario (compared with 100% RES in the BaU scenario)
offsets efficiency gains.
Table 14.
Total GHG emissions (MtCO2eq) under the 3 scenarios of the PROMITHEAS4 model
Table 15.
sector
GHG emissions (MtCO2eq) under the 3 scenarios of the PROMITHEAS4 model by
58
With regards to the policies and scenarios considered, the model has some weaknesses:

Despite a long description of the current situation in Albania with respect to policies which
influence emissions in the introductory section of the PROMITHEAS4 report, it is unclear how
the national laws and regulations have been translated into emission reductions in the
modelling. For example, the report states that for the BaU scenario feed-in-tariffs (only available
for small HPPs) remain stable. However, it is not clear how the modellers have modelled the
decisions of power sector stakeholders to invest in RES based on this information. Some of the
assumed policy impacts (e.g. 2.5% and 5% energy reduction due to insulation in different
scenarios or 5% energy reduction due to energy labelling) seem rather arbitrary, and not
connected to existing or planned policies in Albania, or underpinned by analysis of physical or
economic feasibility.

Some relevant policies (e.g. adoption of the provisions of the Energy Efficiency Directive and
Energy Performance of Buildings Directive) seem not to be considered in any way. For example,
no measures relating to new buildings seem to be assumed under the optimistic scenario, as
would be required by the EPBD.

Although the model makes assumptions related to some global trends (e.g. crude oil price or
price of CERs) impact demand or supply of energy in the different scenarios. Due to the
modelling method used, it can be deduced that that these factors had no impact on results.
59

The model does not take into account EU accession and the impact that EU policies will have on
emissions.
7.4 Presentation of model results by sector
The following section contains a series of figures depicting CO2 emission pathways and energy use
pathways for different models and scenarios which have been reviewed.
7.4.1 Agriculture
Figure 3.
CO2 emissions from agriculture (kt)
Figure 4.
Final energy demand of agriculture (TWh)
60
61
7.4.2 Buildings
Figure 5.
CO2 emissions of the buildings sector (kt)
Figure 6.
Final energy demand of the buildings sector (TWh)
62
7.4.3 Industry
Figure 7.
CO2 emissions from industry (kt)
Figure 8.
Final energy demand of industry (TWh)
63
7.4.4 Transport
Figure 9.
CO2 emissions from transport (kt)
Figure 10.
Final energy demand of the transport sector (TWh)
64
7.4.5 Total demand
Figure 11.
Total CO2 emissions
Figure 12.
Total final energy demand (TWh)
65
7.4.6 Supply
Figure 13.
Electricity mix projections under different models and scenarios for 2020
Figure 14.
Electricity mix projections under different models and scenarios for 2030
66
8 Annex 3 – Submitted INDCs
Benin - 7/8/15 - Avoiding cumulative emissions of 120 million tonnes of carbon dioxide equivalent
between 2020 and 2030, compared to business as usual. Of this, 5MtCO2e would be avoided in the
energy sector and 115MtCO2e from land and forests.
Trinidad and Tobago - 6/8/15 - By 2030, an unconditional 30% reduction in business-as-usual CO2,
methane and nitrous oxide emissions from transport, power and industry. A conditional 45% reduction
is also on the table.
Former Yugoslavian Republic of Macedonia - 6/8/15 - A 30 or 36% reduction in energy-related carbon
dioxide emissions by 2030, compared to business as usual. These targets are equivalent to increases
against a 1990 baseline of 20 or 31%. Macedonia will consider the use of carbon markets.
Monaco - 29/7/15 - A 50% reduction in greenhouse gas emissions by 2030 on 1990 levels, without the
use of carbon credits if possible, but without ruling them out. Includes a section on adaptation.
Kenya - 24/7/15 - A reduction in emissions of 30% by 2030 relative to a business-as-usual scenario of
143 MtCO2e. This is subject to financial and technological international support. "Does not rule out" use
of international market mechanisms. Includes plan for adaptation actions.
Marshall Islands - 21/7/15 -A 32% reduction in emissions below 2010 levels by 2025, with a further
indicative target to reduce emissions by 45% below 2010 levels by 2030, "with a view to achieving net
zero GHG emissions by 2050, or earlier if possible". The Marshall Islands could increase its target when it
is reviewed in five years' time. There are no conditions attached to the submission, but it says that many
of its proposed actions will depend on the availability of international support.
Japan - 17/5/15 -A 26% reduction in emissions on 2013 levels by 2030. Includes precise information on
how it will generate its power by 2030.
New Zealand - 7/7/15: A 30% reduction by 2030 on 2005 levels, which translates to an 11% reduction
on 1990 levels. New Zealand says its INDC is conditional upon confirmation of accounting rules in Paris
that will allow it "unrestricted access" to global carbon markets.
Singapore - 3/7/15: A 36% reduction in emission intensity by 2030, compared to 2005 levels, with
emissions peaking "around 2030". Singapore intends to achieve this without international market
mechanisms, though will continue to study their potential.
Iceland - 30/6/15: Intends to take part in the EU's collective effort to reduce emissions across the region
by 40% on 1990 levels by 2030. The precise commitment it will take on as part of this effort sharing
approach has yet to be decided; if no agreement is reached, Iceland will submit a new INDC.
South Korea - 30/6/15: A 37% reduction on business-as-usual emissions by 2030. Its INDC estimates that
Korea's BAU emissions in 2030 will be 850.6 megatons of carbon dioxide equivalent. Korea will decide
whether or not to incorporate its land use sector, "at a later stage". It will partly use carbon credits to
achieve its target.
67
China - 30/6/15: A peak in carbon dioxide emissions by 2030, with "best efforts" to peak earlier. China
has also pledged to source 20% of its energy from low-carbon sources by 2030 and to cut emissions per
unit of GDP by 60-65% of 2005 levels by 2030, potentially putting it on course to peak by 2027.
Serbia - 30/6/15: A 9.8% reduction on 1990 levels by 2030. Serbia has also included a section on loss
and damage - extreme climate and weather conditions have cost the country €5bn since 2000.
Adaptation measures implemented between 2000 and 2015 have cost around $68m, it adds.
Ethiopia - 10/6/15: A 64% reduction on business as usual emissions by 2030, equivalent to a 3%
reduction against a 2010 baseline.
Morocco - 5/6/15: An unconditional 13% reduction on business as usual emissions by 2030, with a
conditional 32% reduction if Morocco receives "new sources of finance and enhanced support".
Canada - 15/5/15: A 30% reduction on 2005 greenhouse gas emissions, by 2030. This includes possible
use of international emissions credits. It also includes the land sector and forestry.
Andorra - 1/5/15: A 37% reduction in greenhouse gas emissions from a business-as-usual scenario by
2030.
Liechtenstein - 23/4/15: A 40% reduction on 1990 levels by 2030. This includes the possibility to achieve
emissions reductions abroad, but with the primary focus on domestic emissions.
Gabon - 1/4/15: At least a 50% reduction in greenhouse gases by 2025 compared to a business as usual
scenario. This would mean emissions would hit roughly the same levels as they were in 2000. They also
include a national adaptation strategy focused on coastal areas.
Russia - 31/3/15: 25-30% domestic reduction in greenhouse gases by 2030 compared to 1990 levels.
The Russian pledge includes "maximum possible account" of the land sector
US - 31/3/15: 26-28% domestic reduction in greenhouse gases by 2025 compared to 2005, making its
"best effort" to reach the 28% target. This includes the land sector and excludes international credits "at
this time".
Mexico - 30/3/15: Unconditional 25% reduction in greenhouse gases and short lived climate pollutants
from a business-as-usual scenario by 2030, which would rise to 40% subject to the outcome of a global
climate deal. For the unconditional pledge, this means peaking net emissions by 2026 and reducing
emissions intensity per unit of GDP by around 40% from 2013 to 2030.
Norway - 27/3/15: At least a 40% reduction in greenhouse gases by 2030 compared to 1990 levels,
including use of EU carbon credits.
EU - 6/3/15: At least a 40% domestic reduction in greenhouse gases by 2030 compared to 1990 levels.
Switzerland - 27/2/15: 50% reduction in greenhouse gases by 2030 compared to 1990 levels, partly
using carbon credits from international mechanisms.
68
9 Annex 4 – List of abbreviations
AKBN
National Agency of Natural Resources, Albania
BAT
Best Available Technology
BaU
Business As Usual
COP
Conference of Parties
EEMM
European Electricity Market Model
EFTA
European Free Trade Agreement
EnC
Energy Community Treaty
EU ETS
European Union’s Emission Trading System
GACMO
Greenhouse gas Abatement Cost Model
GDP
Gross Domestic Product
GHG
Greenhouse gas
GVA
Gross Value Added
INDC
Intended Nationally Determined Contribution
IPCC
Intergovernmental Panel on Climate Change
IPPC
Integrated Pollution Prevention and Control
LEAP
Long Range Energy Alternatives Planning
LULUCF
Land Use, Land Use Change and Forestry
NAMA
Nationally Appropriate Mitigation Action
NC
National Communication
ODS
Ozone Depleting Substance
OPEX
Operational expenditures
PRIMES
Price-Induced Market Equilibrium System (model)
RES
Renewable energy supply
SLED
Support for Low Emission Development in South East Europe
TSO
Transmission system operator
UNFCCC
United Nations Framework Convention on Climate Change
WAM
With Additional Measures
69
WEM
With Existing Measures
70