Assessment of low carbon measures with a bottom-up energy model in the residential and tertiary sector
Montenegro
April 2014
NATIONAL OBSERVATORY OF ATHENS
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 1
The assessment of low carbon measures in residential and tertiary sector of Montenegro with a bottom-up energy model has been compiled by the National Observatory of Athens (NOA), Greece, and Joanneum Research (JR), Austria in the framework of the project Low Carbon South East Europe (LOCSEE) (SEE/D/0166/2.4/X). The LOCSEE project is co-funded by the South East Europe Transnational Cooperation Programme.
The Report was developed by a team from the Institute for Environmental Research and Sustainable Development (IERSD), NOA and Joanneum Research: . Dr. Elena Georgopoulou, Senior Researcher and LOCSEE Project Coordinator at NOA . Dr. Sebastian Mirasgedis, Senior Researcher . Dr. Yannis Sarafidis, Senior Researcher . Dimitra Koutentaki, Staff Environmental Scientist . Nikos Gakis, External Assistant to NOA for the LOCSEE project . Dr. Vasso Hontou, External Assistant to NOA for the LOCSEE project . Dr. Daniel Steiner, JOANNEUM RESEARCH . Mag. Andreas Tuerk, JOANNEUM RESEARCH . Dr. Hannes Schwaiger, JOANNEUM RESEARCH . Mag. Claudia Fruhmann . DI Johanna Pucker
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 2
CONTENTS
1. INTRODUCTION ...... 4 1.1. GENERAL ...... 4 1.2. METHODOLOGICAL APPROACH ...... 4 2. STRUCTURE OF THE BOTTOM-UP ENERGY MODEL ...... 5 2.1. ENERGY MODELS ...... 5 2.2. MODULE 1: RESIDENTIAL BUILDINGS ...... 6 2.3. MODULE 2: TERTIARY BUILDINGS ...... 9 3. FORMULATION OF THE REFERENCE SCENARIO ...... 11 3.1. INTRODUCTION ...... 11 3.2. BASIC INPUT DATA AND ASSUMPTIONS ...... 11 3.3. GHG EMISSION FACTORS ...... 12 3.4. EMISSIONS PROJECTIONS ...... 13 4. ASSESSING TECHNICAL POTENTIAL OF GHG EMISSIONS REDUCTION MEASURES ... 17 4.1. GHG EMISSIONS REDUCTION MEASURES ...... 17 4.2. TECHNICAL POTENTIAL ...... 19 5. ECONOMIC EVALUATION OF GHG EMISSIONS REDUCTION MEASURES ...... 21 5.1. INTRODUCTION ...... 21 5.2. ECONOMIC EVALUATION OF MITIGATION MEASURES ...... 22 6. SOCIO-ECONOMIC ASSESSMENT OF LOW CARBON MEASURES IN RESIDENTIAL AND TERTIARY SECTOR OF MONTENEGRO ...... 28 6.1. INTRODUCTION ...... 28 6.2. METHODOLOGICAL APPROACH ...... 28 6.3. ELECTRICITY GENERATION - COUNTRY SPECIFIC GHG EMISSIONS AND LOCAL AIR POLLUTANTS ...... 29 6.4. CALCULATION OF LOCAL AIR POLLUTANTS OF LOW CARBON MEASURES ...... 31 6.5. SOCIAL MARGINAL ABATEMENT COSTS OF LOW CARBON MEASURES ...... 31 6.6. MACROECONOMIC IMPACTS ...... 35
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1. INTRODUCTION
1.1. General
Developing, implementing and monitoring low carbon policies and measures is not an easy task as it requires a significant background scientific and technical work in order to collect necessary data, identify mitigation options, develop tools for estimating the GHG emissions abatement potential and selecting the appropriate mix of policies and measures. In order to assist this process, the LOCSEE project (’Low Carbon South East Europe’), funded by the South East Europe (SEE) Transnational Cooperation Programme, was launched in October 2012. A major task of LOCSEE is to develop a methodological framework for assessing the technical and economic GHG emissions abatement potential at sectoral level. The methodology is primarily addressed to SEE countries in the process of joining the EU, but can be applied also by other countries and regions as well. It is based on the long experience of EU Member States in the SEE region which have already developed and implement low carbon policies and measures, and takes into account the real needs, gaps and barriers in the region as communicated by SEE countries during its development. The analysis presented thereafter focuses on residential and tertiary building sector, which is responsible for a large part of GHG emissions of Montenegro. In this context, a bottom-up model for each sub-sector (residential and tertiary) was developed, including all major structural characteristics of the buildings (e.g. energy uses, key technologies, energy sources). The model was first calibrated on the basis of recent energy balances and then utilized for developing a reference scenario for 2030. Next, several mitigation measures were analyzed as regards their GHG emissions abatement potential and their cost-effectiveness. On the basis of the results obtained, a marginal abatement cost curve was constructed in each case, providing quantitative estimations on the technical and economic GHG emissions abatement potential of the sector. 1.2. Methodological approach
The applied methodology for the assessment of GHG emissions mitigation measures in residential and tertiary building sector comprises the following steps: . Formulation of the Reference Scenario. The future energy consumption and the relevant GHG emissions of the residential/tertiary sector are estimated for the period 2010-2030 using an energy model. . Assessing the expected GHG emissions reduction per measure. The potential low carbon measures are identified based on the current situation analysis (e.g. end-uses with a significant influence on sector’s total emissions of greenhouse gases, technical barriers etc.) and good practices examples implemented in other countries. The future energy consumption and GHG emissions after the implementation of each measure are estimated with proper modifications (e.g. technologies and fuels share, technologies efficiency etc.) in the energy model. The difference between the results of the model for these two scenarios shows the energy savings and the GHG emissions abatement potential that can be achieved with the implementation of this measure. . Economic evaluation of measures. Based on the results of the energy model (e.g. technologies share, energy savings per fuel etc.) and market data (e.g. energy
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 4
prices) annual cost and benefits are calculated for each measure. The cost components comprise initial (investment) expenditures and operational / maintenance cost, while the benefit components include potential revenues arising from the operation of the project (such as energy savings in case of energy conservation measures, etc). The cost per unit of CO2eq emission reduction of each measure is then estimated by dividing the net cost (i.e. costs reduced by benefits) with the corresponding GHG emissions reductions, and then marginal abatement cost curves are constructed for residential and tertiary sector respectively.
2. STRUCTURE OF THE BOTTOM-UP ENERGY MODEL
2.1. Energy Models
The detailed modeling of energy intensive economic sectors is the initial step for estimating the energy saving and GHG emissions reduction potential of low carbon technologies. In general energy models can be classified into the following 2 categories: . Top-down energy models which describe in detail the interaction between the energy system and the economy. Top-down energy models do not contain technological detail and in general the technology term is represented by aggregated indicators (e.g. elasticities, energy efficiency index). . Bottom-up energy models which rely on a detailed technical description of the energy system. These models are used to explicitly calculate energy consumption of end- users for each economic sector based on detailed descriptions of fuel shares and technologies efficiency. Top-down energy models can provide consistent scenarios in terms of economic growth, labor productivity, consumption and investment expenditure, government balance, etc. and they estimate the effect of mitigation policies on macro-economic data (GDP, employment and labor activity data, government balances, sectoral values added, etc.), fuel and electricity consumption, and GHG emissions. On the other hand, bottom-up models have a better representation of the technical determining factors of emissions and they incorporate engineering data and technological choices. Specifically: . Optimization models represent the energy system of a country by a detailed set of technology options and therefore are useful for assessing GHG mitigation policies and measures. They project the future evolution of the energy system in question assuming an optimum allocation of energy resources, technologies and relevant services under one or several administrative objectives. However, they neglect feedback effects on the rest of economy and undervalue transaction costs of these policies and measures. Their output comprise fuel consumption per fuel type and sector, energy related GHG emissions, GHG emissions linked to specific processes, load of electricity, plants, unused capacity, shadow prices (marginal production costs) for energy. . Simulation models similar to optimization models represent the energy system by a number of technologies and energy carriers and in their output provide the same information as the optimization models about the future configuration of the energy system in question. Their approach is based on the simulation of the behavior of
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energy consumers and producers under various signals (e.g. prices, incomes, policies), which may not be “optimal”. Typically they use an iterative approach to find market clearing demand-supply equilibrium. . Engineering models usually focus on specific sectors providing a good basis for assessing sector-specific policies and measures. However, the capability for analyzing economic measures and feedback to the rest of economy is limited. . Focus on demand models provides a predefined framework for the development of energy demand scenarios for specific end-users on a disintegrated level. These rely on accounting relationships and they do not consider price effects. More specifically, instead of calculating market shares based on prices and other variables, this type of models simply explores the resource, environment and social cost implications of alternative future “what if” energy scenarios. More recently, hybrid models have been developed combining the properties of top-down and bottom-up models. Depending on the type of the questions that policy makers want to answer, energy models with different characteristics should be used. Specifically, top-down models can be used for: (i) estimating the macroeconomic impact of energy/environmental policies, (ii) choice of policy instruments, (iii) burden sharing of climate target between regions/countries, etc. Bottom-up models as well as hybrid models are often used for: (i) analyzing the impact on the energy system of specific energy and environmental policies, including market-based instruments, (ii) investigating the role of specific technological options, (iii) exploring the impacts of resource constraints, (iv) estimating the market potential of GHG mitigation options, etc. Finally, engineering and focus on demand models are usually used for estimating the technical and economic potential of GHG mitigations and to assess mainly command and control policies. For these reasons, within the framework of LOCSEE project a bottom-up model has been developed for residential and tertiary buildings, which incorporates the basic engineering characteristics of the sector and focuses on the energy demand. The model identifies those energy uses in each sector which generate GHG emissions, and energy consumption per use is analyzed and broken down to specific technologies and energy sources contributing to GHG emissions. The bottom-up energy model includes the following basic modules: . Residential buildings . Tertiary buildings
2.2. Module 1: Residential buildings
In the buildings sector one has to take into account that a detailed assessment of the various low carbon measures needs an aggregated classification of buildings, while at the same time the various characteristics of the building stock need to be accurately represented. Therefore, within the model developed, buildings are classified on the basis of the following criteria: (i) age of buildings, which is related to the thermal characteristics of the building shell, (ii) type of buildings (residential and tertiary) and (iii) size of residential buildings (low rise single dwellings, and high rise apartment buildings). Existing residential building stock in Montenegro can be divided into two periods regarding to the construction year:
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. until 1980 (buildings with lot of reinforced concrete elements, without thermal insulation, coded as Old Buildings in the model) and . since 1980 to present (when first standards of thermal insulation has appeared, coded as New Buildings in the model). Regarding the type of the buildings two main categories may be defined: . low buildings with 1 or 2 floors (detached and semi-detached houses) and . high residential buildings (with multiple apartments/flats) Based on the categories defined above, four main types of residential buildings are defined: . Old detached houses (“Low-Old buildings” in the model), . Old apartments (“High-Old buildings” in the model), . New detached houses (“Low-New buildings” in the model) and . New apartments (“High-New buildings” in the model). Two more categories are added in order to include modern buildings (MB) which will be constructed in accordance to the European standards on Energy Performance of Buildings Directive: . Modern detached houses (“Low-Modern buildings” in the model), . Modern apartments (“High-Modern buildings” in the model), In addition, a large part of total building stock in Montenegro has only a seasonal use or is temporarily not occupied. Therefore an additional type of residential building is defined in the model: . “Seasonal Buildings”. These buildings are assumed to be low buildings with 1 or 2 floors (detached and semi- detached houses) constructed within period 1980-2010 and they are occupied for an average of 3 months per year. The energy consumption of each residential building category is simulated with six end-uses, which include: . Space heating (further categorized in central and individual heating systems) . Hot water . Space cooling . Cooking . Lighting . Electrical Appliances (further categorized in laundries, dish washers, refrigerators, TV and other appliances). Energy demand for each end-use is calculated either by applying methodologies which use typical meteorological data or on the basis of existing information and data from national or international sectoral studies (e.g. energy demand for lighting expressed per m2 of building area). The share of technology and type of fuels used in order to cover energy demand for each end-use (e.g. conventional or low-energy light bulbs, wood fired boiler or district heating
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central heating systems etc.) are the main modelling parameters. Main sources for these variables include databases and reports from Statistical Office and national or international sector studies. The modelling parameters (energy demand, share of technology and fuels etc.) are defined separately for each one of the building type providing greater flexibility and higher accuracy for the model. It must be noted that national databases still lack the statistical data for a comprehensive analysis and assessment of the energy demand of the residential buildings. Therefore, assumptions based on relevant data from other countries are used within the framework of this project. In Table 2.1 the structure of the bottom-up energy model for the residential buildings, developed within the framework of this project, is presented thoroughly. Table 2.1 Residential buildings bottom-up energy model structure.
Building Categories Energy Use Equipment Technology/Fuel Dwelling type Construction Year Old (built until 1980) New (built since 1980 to Low detached houses present) High residential buildings Modern (built according to European Standards) Low detached houses (new) New (built since 1980 to for seasonal use only in present) Montenegro diesel old systems diesel new systems natural gas Central wood Systems Space heating with lignite central heating electricity (heat pumps) systems heavy fuel oil Air conventional (Electricity) Conditioning high efficiency (Electricity) Secondary 7 categories are defined in Montenegro. diesel LPG For each building category six end-uses are defined. Stoves The end-uses are further categorized depending on type Space heating with wood of equipment and technology/fuel used individual heating electricity systems Air conventional (Electricity) Conditioning high efficiency (Electricity) Primary electricity LPG Cooking natural gas wood electricity Hot water Boilers natural gas diesel
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Building Categories Energy Use Equipment Technology/Fuel Dwelling type Construction Year solar collectors Air conventional (Electricity) Space cooling Conditioner high efficiency (Electricity) conventional (Electricity) Lighting Light Bulbs high efficiency (Electricity) Laundries Dish washers Refrigerators
TV conventional (Electricity) Electrical Appliances Other energy class A or higher Dish washers (Electricity) Refrigerators TV Other
2.3. Module 2: Tertiary buildings
In a similar way with residential buildings, 6 building categories are defined in the tertiary sector based on the following assumptions: . Building type: two types (Hotels and accommodation facilities and Offices/Trade stores) simulating the main services subsectors of Montenegro’s economy. . Construction period: the same categorization as in residential buildings is adopted (buildings built until 1980, since 1980 to present and buildings built according to European Standards) The basic input parameter for the modelling of the tertiary buildings is the total area (m2) of the buildings for each category. Since there are no detailed and comprehensive statistical data on the building stock of the tertiary buildings, various assumptions such as area per bed in hotels and area per employee at offices / trade stores and appropriate statistical data (e.g. number of employees, number of hotel beds, etc.) are used as input parameters. The energy consumption of each tertiary building category is analyzed in six end-uses, which include: . Space heating . Hot water . Space cooling . Cooking . Lighting . Electrical Appliances (which consist of refrigerators, elevators, office equipment etc). Energy demand for each end-use is calculated on the basis of existing information and data from national or international sector studies (e.g. energy demand for lighting expressed per
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 9 m2 of building area). In Table 2.2 the structure of the bottom-up energy model for the tertiary buildings, developed within the framework of this project, is presented thoroughly.
Table 2.2 Tertiary buildings bottom-up energy model structure.
Building Categories Energy Use Equipment Technology/Fuel Dwelling type Construction Year Old (built until 1980) New (built since Hotels 1980 to present)
Offices/Trade stores Modern (built according to European Standards) diesel high efficiency diesel natural gas Boilers lignite Space heating residual fuel oil wood electricity Heat pumps geothermal conventional (Electricity) Lighting Light Bulbs high efficiency (Electricity) 6 categories are defined in Montenegro. conventional For each building category six end-uses Heat pumps/ Air (Electricity) are defined. Air Conditioning Conditioners high efficiency The end-uses are further categorized (Electricity) depending on type of equipment and technology/fuel used Ceiling fans electricity electricity natural gas Hot water Boilers diesel solar collectors conventional Electrical (Electricity)
Appliances energy class A or higher (Electricity) electricity Cooking LPG
(only for hotels) natural gas
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3. FORMULATION OF THE REFERENCE SCENARIO
3.1. Introduction
The formulation of the reference scenario with estimations of energy demand in the residential and tertiary buildings is a necessary step for assessing mitigation measures. This reference scenario is more akin to a "frozen technology" scenario, where projections of future energy demand is covered mainly with existing technologies and fuels (with some necessary but minor adjustments), than in a "business as usual scenario" in which significant developments in the energy system may be incorporated within a time horizon of 20 years. Year 2010 is selected as the base year for the model on the basis of existing information and data from official sources. All assumptions and approximations are rechecked and extrapolated back to the year 2006, in order to make sure that the results of the energy model converge at the values of the official energy balances. In estimating the future energy demand, a number of parameters incorporated in the bottom-up energy model have to be defined, namely demographic evolution, economic growth, and renewal of building stock. For the first two of them, data from official sources such as UN demographic research projections were used, while in cases where such data were not available projections were based on past trends. Regarding the renewal rates of the building stock, conservative rates were adopted. The detailed input data and assumptions used for the formulation of the Reference Scenario are presented in the following paragraph. 3.2. Basic input data and assumptions
Montenegro has 314,704 dwellings, 61.860 of which are for seasonal use only according to the 2011 Census of Population, Households and Dwellings in Montenegro [MONSTAT, 2013a]. Data regarding the age of building stock of permanent housing are obtained from Montenegro Statistical Office and they are presented in Table 3.1. According to the 2011 Census of Population, Households, and Dwellings in Montenegro: . 72% of dwellings are positioned in 1st floor of a building or lower . 28% of dwellings are positioned in 2nd floor of a building or higher Data related to the available installations in each household (central heating systems, individual heating systems, air conditioners, hot water boilers, cooking ovens etc) are obtained from: . the 2011 Census of Population, Households, and Dwellings in Montenegro[MONSTAT, 2012b] . the Household Budget Survey 2011 [MONSTAT, 2012a] . the publication “Wood-fuel Consumption for 2011 in Montenegro-New Energy Balances for Woods fuels”, [MONSTAT, 2013b] For the tertiary buildings necessary data (such as number of beds in accommodation facilities and overnights at hotels, number of employees in services sector etc.) is obtained from Statistical Office of Montenegro database (http://www.monstat.org/eng/index.php).
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Table 3.1 Age of the building stock for permanent housing (2011 Census of Population, Households, and Dwellings in Montenegro)
Period of construction No of Dwellings Total m2 Average(m2/dwelling) 1919-1945 13,989 872,744 62.39 1945-1960 23,573 1,374,186 58.29 1961-1980 81,967 5,725,426 69.85 1981-2000 76,526 5,894,959 77.03 2001-2011 44,718 3,412,475 76.31 Unknown 6,581 393,451 59.79 Total 247,354 17,673,241 71.45
The energy consumption for years 2015-2030 is estimated on the basis of the following assumptions: . Annual rate of population growth: 0.16% - medium-growth scenario according to Energy Development Strategy by 2025 [Ministry for Economic Development, 2007] . The average household size will decrease by 2% in each 5-year period . The average dwelling size will increase an annual rate of 0.1% . Residential buildings constructed before 1980 will be demolished/ abandoned at an average annual rate of 0.5% over the entire study period . The number of the residential buildings constructed between 1980 and 2010 will remain constant over the entire study period . All residential buildings constructed after the year 2010 will have diesel central heating systems; heat pumps are gradually installed in 25% of the existing (built by 2010) buildings without central heating . The area of the tertiary buildings’ will increase an annual rate of 3.25% for hotels and 2.5% for offices/trade sector . The area of the tertiary buildings constructed before 2010 remains constant over the entire period . All buildings constructed after the year 2010 are constructed according to European Standards (Directive 2002/91/EC on the Energy Performance of Buildings) . Future climatic conditions will be similar to those of the period 2005-2010. The assumption that the climate is closer to the historical average would ignore the fact the remarkable increase in mean annual temperature during the last fifteen years, and thus would lead to an unjustified increase energy demand for space heating and a probable under-estimated energy demand for air conditioning. 3.3. GHG emission factors
Calculated GHG emissions comprise direct emissions from fuel combustion and indirect emissions from electricity. Direct GHG emissions are calculated according to 2006 IPCC Guidelines Tier 1 fuel-based method on the basis of fuel quantities combusted (as calculated by the bottom-up energy model), and average emission factors from Table 1.4 and Tables 2.4-2.5 of the 2006 IPCC Guidelines, Vol.2 [IPCC, 2006].
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The indirect GHG emissions from electricity and district heating are estimated on the basis of the quantities of heat and electricity used (calculated by the energy model) and average emission factors which depend on the fuel mix for power and heat generation respectively. The average GHG emission factors for electricity are calculated from the following equation:
∑(EFf ,i × f t ) f EL _ EFi,t = (3-1) PCt where
. EL_EFi,t is the emission factor of pollutant i for the year t,
. EFf,i is the emission factor of pollutant i for fuel f used in thermal power plants according to the Table 2.2 of 2006 IPCC Guidelines Vol. 2.
. ft is the quantity of fuel used in year t for power generation according to official Energy Balances
. PCt, is the total electricity consumption (final consumption and energy sector consumption) for the year t according to official Energy Balances 3.4. Emissions projections
Bottom-up model results regarding energy consumption according to the Reference Scenario are presented per end-use and per fuel in Figures 3.1-3.2 for the residential buildings and in Figures 3.3-3.4 for the tertiary buildings. The total energy consumption of residential buildings in Montenegro will increase 16% within 2010-2030 time horizon (from 312 ktoe in 2010 to 361 ktoe in 2030) mainly due to the increased energy demand for space heating and electrical appliances. Space heating is the main end-use as it represents approximately 70% of total consumption with an upward trend which reflects the improvement of household living conditions. It must be noticed that according to the Household Budget Survey of the Statistical Office of Montenegro [MONSTAT, 2012] central heating systems (mainly wood fired boilers) are available only for 7% of households (mainly apartments in high-rise buildings), while the rest of the households use individual heating systems such as stoves and air conditioning units. Energy demands are covered mainly by biomass (~70% mainly for space heating) and electricity (~30% for rest end-uses). Within the time horizon a decrease of biomass consumption and an increase of electricity use is estimated, while greater quantities of diesel will be used in central heating systems due to the installation of diesel fired boilers in new buildings (constructed after 2010).
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Residential Sector (Montenegro) 500
400
300
ktoe 200
100
0 2010 2015 2020 2025 2030 Central Heating Individual Heating Cooking Hot water Space cooling Lighting Electric appliances
Figure 3.1: Energy consumption per end-use in the residential buildings for period 2010- 2030 according to the results of the bottom-up energy model (Reference Scenario).
Residential Sector (Montenegro) 500
400
300
ktoe 200
100
0 2010 2015 2020 2025 2030
Electricity Diesel LPG Solar Wood Lignite
Figure 3.2: Energy consumption per fuel in the residential buildings for period 2010-2030 according to the results of the bottom-up energy model (Reference Scenario). The total energy consumption of tertiary buildings in Montenegro will increase 60% within next twenty years (from 30 ktoe in 2010 to 48 ktoe in 2030) as a result of continued growth of the tourism sector in the economy of Montenegro. Electricity is the main energy source (>80% of total energy use) as it is used used for space heating and cooling (heat pumps) and other end-uses as well and its dominance is expected to continue within the period study.
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Tertiary Sector (Montenegro) 50,00
40,00
30,00
ktoe 20,00
10,00
0,00 2010 2015 2020 2025 2030
Space heating Lighting Space Cooling Hot water Electric Appliances Cooking
Figure 3.3: Energy consumption per end-use in the tertiary buildings for period 2010- 2030 according to the results of the bottom-up energy model (Reference Scenario).
Tertiary Sector (Montenegro) 50,00
40,00
30,00
ktoe 20,00
10,00
0,00 2010 2015 2020 2025 2030
Gas/Diesel oil Electricity Solar LPG Lignite Wood
Figure 3.4: Energy consumption per fuel in the tertiary buildings for period 2010-2030 according to the results of the bottom-up energy model (Reference Scenario). Projected GHG emissions for period 2015-2030 are presented in Figure 3.5 for residential buildings and in Figure 3.6 for tertiary buildings. The total GHG emissions is expected to increase 97% (from 496 kt CO2eq in 2010 to 979 kt CO2eq in 2030) in residential buildings and 62% in tertiary buildings (from 139 kt CO2eq in 2010 to 246 kt CO2eq in 2030) within 20-years period. The sharper estimated increase of emissions compared to the increase of energy consumption in Montenegro is due to the expected substitution of biomass for space heating by electricity and diesel as a result of the improving living conditions. In both sectors more than 80% of total emissions arise from electricity consumption (indirect emissions).
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Residential Sector (Montenegro) 1.000
800
600
kt CO2eqkt 400
200
0 2010 2015 2020 2025 2030 Direct Emissions Indirect Emissions
Figure 3.5: Residential buildings GHG emission projections for period 2010-2030 according to the results of the bottom-up energy model (Reference Scenario).
Tertiary Sector (Montnegro) 250
200
150
kt CO2eqkt 100
50
0 2010 2015 2020 2025 2030
Direct Emissions Indirect Emissions
Figure 3.6: Tertiary buildings GHG emission projections for period 2010-2030 according to the results of the bottom-up energy model (Reference Scenario).
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4. ASSESSING TECHNICAL POTENTIAL OF GHG EMISSIONS REDUCTION MEASURES
4.1. GHG Emissions reduction measures
Space heating is the main energy use and electricity is the main energy source, with an upward trend, for both residential and tertiary buildings according to the Reference Scenario results. Therefore the potential mitigation measures that are analyzed include interventions that: . Reduce energy demand for space heating (Improved insulation, Double glazing and Building Energy Management Systems in tertiary buildings) . Improve efficiency of equipment (efficient lighting, more efficient electrical appliances and heating / cooling devices) . Deploy Renewable Energy Sources (Solar systems for hot water). Nine potential measures in residential buildings and ten measures in tertiary buildings are assessed with the bottom-up energy model. The potential mitigation measures and their basic characteristics (efficiency and penetration rate) are summarized in Table 4.1 for residential buildings and Table 4.2 for tertiary buildings. The low carbon measures are implemented only in permanent housing buildings, since their implementation in seasonal- use dwellings will not result in significant savings. The difference in fuel consumption and GHG emission between the “With Measure Scenario” and the Reference Scenario reflects the technical potential of GHG emissions reduction and energy conservation of this measure.
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Table 4.1: Description and characteristics of GHG reduction measures for residential buildings.
Penetration Measure Description Efficiency Target 2015 2020 2025 2030
Use of double glazing Buildings with central R1 with thermal breaks Uo<2,6 W/(m2*K) 25% 50% 75% 100% heating before 2010 frames Buildings with central R2 Thermal insulation of roof Ur<0,35 W/(m2*K) 25% 50% 75% 100% heating before 2010 Thermal insulation of Buildings with central R3 Uw<0,40 W/(m2*K) 25% 50% 75% 100% external walls heating before 2010 Energy efficient wood +100% of stoves R7 Wood Stoves 25% 50% 75% 100% stoves energy efficiency 60% energy saving R8 Installation of ceiling fans Households with AC units 10% 20% 35% 50% for space cooling Installation of high EER> 3,7 (+48% R9 efficiency air conditioning Households with AC units 25% 50% 75% 100% efficiency) units cover 70% of energy Installation of solar R10 demand for hot All type of households 10% 20% 35% 50% collectors water Promotion of energy R11 75% energy savings All type of households 50% 100% 100% 100% efficient light bulbs Promotion of energy 20%-45% energy R12 efficient household All type of households 25% 50% 75% 100% savings appliances (Class A)
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Table 4.2: Description and characteristics of GHG reduction measures for tertiary buildings
Penetration Measure Description Efficiency Target 2015 2020 2025 2030
Use of double glazing All buildings built T1 with thermal breaks Uo<2,6 W/(m2*K) 25% 50% 75% 100% before 1980 frames Thermal insulation of All buildings built T2 Ur<0,35 W/(m2*K) 25% 50% 75% 100% roof before 1980 Thermal insulation of All buildings built T3 Uw<0,40 W/(m2*K) 25% 50% 75% 100% external walls before 1980 +15% diesel boilers efficiency All type of buildings - Retrofitting of old +23% fuel oli boilers T5 Old diesel, fuel-oil and 25% 50% 75% 100% diesel boilers efficiency coal fired boilers +31% increase of old coal boilers efficiency Trade/Offices, Use of heat pumps Restaurants & Hotels - T6 COP > 2 (+33% efficiency) 25% 50% 75% 100% for space heating Old diesel, fuel-oil and lignite fired boilers Installation of high EER> 3,75 (+50% All type of buildings - T7 efficiency air 25% 50% 75% 100% efficiency) Conventional AC conditioning units Installation of solar cover 50% of energy 6% - 10%- 15%- 20%- T8 Hospitals & Hotels collectors demand for hot water 10% 20% 30% 45% Promotion of energy T9 40% energy saving All type of buildings 50% 100% 100% 100% efficient light bulbs Promotion of energy T10 efficient appliances 10% energy saving All type of buildings 50% 100% 100% 100% (Class A) 20%-30% energy savings 5% - 10%- 10%- 10%- T11 Installation of BMS for heating/cooling, All type of buildings 10% 20% 40% 60% lighting and hot water
4.2. Technical potential
The mitigation potential for the year 2020 (mid-point of the period 2010-2030) is presented in Figures 4.1-4.2 and it amounts to 140 kt CO2eq (-19%) in residential buildings and to 36 kt CO2eq in tertiary buildings (-21%) compared to the Reference Scenario. The real potential of some measures will be lower as the combined implementation of measures will lead to lower energy conservation and thus reduction of GHG emissions.
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Residential Sector (Montenegro) 750,00 21,30 20,49
700,00 19,71 18,27 16,83 650,00 16,66 13,28 kt CO2eq 9,95 3,68 600,00
550,00 R3 R7 R9 R2 R1 R8 R12 R11 R10 Scenario Reference
Figure 4.1: Technical feasible potential of GHG emissions reduction measures in residential buildings of Montenegro up to year 2020. More than 50% of the mitigation potential in dwellings comes from measures associated with more efficient energy equipment (lighting, space cooling and heating systems), while the improvement of the thermal behaviour of buildings (wall & roof insulation and use of double glazing) represents less than 30% of the technically feasible potential. This low share is attributed to the fact that the energy savings from these measures arise only from buildings with central heating systems; according to official data, such systems are applied only to a small number of households (7% in Montenegro). Residential buildings without central heating are considered to be insufficiently heated and thus the implementation of those measures will reduce heat losses and improve internal conditions without energy savings (rebound effect). Regarding renewable energies, 13% of the total GHG mitigation potential in residential buildings derives from the increased penetration of solar collectors for hot water use.
Tertiary Sector (Montenegro) 180,00
14,48 170,00
5,81 160,00 3,75 2,87 kt CO2eq 150,00 2,77 1,92 1,44 1,33 140,00 0,70 0,61
130,00 T9 T3 T7 T6 T5 T1 T8 T2 T10 T11 Scenario Reference
Figure 4.2: Technical feasible potential of GHG emissions reduction measures in tertiary
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 20
buildings of Montenegro up to year 2020.
5. ECONOMIC EVALUATION OF GHG EMISSIONS REDUCTION MEASURES
5.1. Introduction
In general, the economic evaluation of individual GHG emissions abatement measures comprises the following steps: 1. Estimation of measure technical parameters and evaluation assumptions. Based on the results of energy model all the technical characteristics of the measure under evaluation such as penetration (e.g. m2 of roof insulated, number of old diesel-fired boilers retrofitted etc.), fuel savings (in ktoe/year) and GHG emissions reduction (in kt CO2eq/y) are defined in detail. In addition a discount rate is selected in order to reduce the various cost and benefit elements to a common base. 2. Determination of the project cost and benefits components. The analysis involves the recording of all the cost and benefit components, which determine the financial return of the project/measure examined. Specifically, these cost components include initial (investment) expenditures and maintenance and operation cost. On the other hand the benefit components comprise revenues arising from energy savings. Unit costs for each measure (e.g. investment cost per m2 of roof insulated) and energy costs may be determined on the basis of market data and existing experience. The rest data which are necessary for cost calculations such as penetration (e.g. m2 of roof insulated, number of old diesel-fired boilers retrofitted etc.) and fuel savings are derived from the results of the bottom-up energy model. 3. Calculation of the annual net cost. Time allocation of cost and benefit components over the lifecycle of the measure under examination greatly affects the analysis results. An evaluation can only be made by incorporating timing considerations by tracing the incidence over time of costs and benefits and by using an appraisal method that takes this into account. In this context, the initial cost (IC) is annualized over the entire lifetime of the measure (T) by applying the following equation: IC ⋅ r AIC = (5-1) (1− (1+ r)−T ) where AIC is the annualized initial cost (€/y) and r is the discount rate (%). Then the annual net cost C is calculated by subtracting annual revenues R from annual operational and maintenance cost (OM) and annualized initial cost: C = AIC + OM − R (5-2) 4. Evaluation indicators. Indicators used for the economic evaluation of GHG emissions abatement measures are usually based on the criteria applied for financial evaluation of a project. These are the net present value (NPV), the internal rate of return (IRR) and the benefit–cost ratio (B/C). All these indicators basically provide similar project rankings. The NPV determines the present value of net costs by discounting the flows of costs and benefits back to the base year. The IRR is defined as the rate of return on an investment, which will equate the net present value of an investment with zero benefits. The B/C is defined as the ratio of benefits and costs associated with a
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 21
specific project. Both the IRR and B/C indicators are neutral to the GHG emission reductions achieved by the project. On the other hand, the NPV criterion gives a clear view of the overall economic performance of the project without taking into account in this evaluation the reduction of GHG emissions achieved. Therefore in the context of this study the cost per unit of CO2eq emission reduction of a specific measure is used as an evaluation indicator. The unit cost U (€/tn CO2eq) is calculated by dividing the annual net cost (calculated with equation 5.2) with annual emission reduction E as it is estimated with the bottom-up energy model: C U = (5-3) E 5. Construction of the marginal abatement cost curve. Having estimated the cost per unit of CO2eq emission for the potential low carbon measures in residential and tertiary sector, the next step is to construct the marginal abatement cost curve, which relates the quantity of GHG emissions that can be reduced by certain technological or other measures, to the cost per unit CO2eq savings. The starting point for a cost curve construction is the individual mitigation options and specifically the cost and mitigation potential that can be achieved by each of them. There are different ways for integrating abatement measures in cost curves. In the simplest approach the GHG emissions abatement measures are ranked according to their net cost per unit of GHG reduction. The cost curve is built up of partial independent segments, introducing the most cost effective option on the left to the least cost effective and illustrating (in the x axis) the total GHG emissions abated by each individual project. In a more sophisticated approach the interactions between various mitigation options are taken into account. The first step in this approach is a separate ranking of mitigation options as outlined in the previous approach. In the next step we assume that the most valuable option is implemented and the costs and mitigation potentials of all other options are re-estimated. Next, the second most valuable option project is included in the assessment and new calculations performed for all the remaining options, etc. In the final step the cost curve is established. For sake of simplicity and given that the second method is time consuming in the context of this study the simple approach is adopted. A marginal abatement cost curve provides useful insights to decisions makers. In a simple and transparent way highlights projects: (i) with a negative cost per t of CO2eq, meaning that they present a positive NPV within their lifetime (“win-win”) and therefore their implementation should be a priority; (ii) with a low cost which might become viable if for example carbon taxes are introduced or more generally can result in GHG emission reductions at low cost; and (iii) with a high cost which are unlikely to be viable.
5.2. Economic evaluation of mitigation measures
Necessary economic input data derived from a survey of national markets and from relevant projects, while fuel prices are obtained from official databases (e.g. Eurostat, Statistical Office and Energy Regulatory Commission etc) and they are presented in Tables 5.1-5.2. A discount rate of 5% was applied as it reflects the marginal rate of return on similar investments.
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 22
Table 5.1: Investment, operational cost and lifetime period for the various reduction measures in residential and tertiary buildings in Montenegro.
Lifetime U IC (€/U) OM (€/U) (years) Residential Laundries - Energy Class A or higher unit 22,00 10 Dish washers - Energy Class A or higher unit 45,00 10 Refrigerator - Energy Class A or higher unit 36,00 10 TV - Energy Class A or higher unit 50,76 10 Other appliances - Energy Class A or higher household 293,60 10 Ceiling fans unit 315,00 10 Double Glazing m2 230,00 30 Efficient A/C unit 623,75 10 Efficient Wood Stoves unit 2.055,97 20 Efficient Lighting m2 1,48 12 Roof Insulation-Old Buildings m2 40,39 30 Roof Insulation-New Buildings m2 35,39 30 Solar Collectors m2 602,92 6,03 10 Wall Insulation- Old Buildings m2 42,64 30 Wall Insulation- New Buildings m2 37,64 30 Tertiary Energy Class A appliances - Hotel beds beds 397,71 10 Energy Class A appliances - Trade-Offices Area m2 25,80 10 BMS units 3.625,00 181,25 10 Efficient diesel boilers - Hotels boiler 19.654,38 20 Efficient diesel boilers - Trade-Offices boiler 15.074,50 20 Double Glazing m2 130,00 30 Efficient AC unit 623,75 10 Heat pumps - Hotels unit 65.625,00 20 Heat pumps - Trade-Offices unit 10.312,50 20 Efficient Lighting - Hotels m2 1,55 12 Efficient Lighting - Trade-Offices m2 1,55 12 Solar Collectors m2 602,92 6,03 10 Roof Insulation m2 40,39 30 Wall Insulation m2 42,64 30
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 23
Table 5.2: Energy prices in Montenegro.
Fuels Unit Residential Tertiary Electricity(1) €/toe 1.187,21 1.058,14 Diesel(3) €/toe 1.466,43 1.466,43 LPG(3) €/toe 1.245,31 1.245,31 Wood(2) €/toe 232,56 232,56 Lignite(2) €/toe 174,42 174,42 Fuel oil(2) €/toe 488,37 488,37 (1) Eurostat database (http://ec.europa.eu/eurostat/data/database): Electricity prices for domestic consumers 2013 1st Semester all taxes and levies included. Residential: Domestic Consumers with annual electricity consumption in KWh 5000-15000. Tertiary: Domestic Consumers with annual electricity consumption in KWh >15000 (2) Energy Savings International AS, Study on Energy Efficiency in Buildings in the Contracting Parties of the Energy Community, February 2012. Final report prepared for Energy Community Secretariat (available on http://www.energy-community.org/pls/portal/docs/2514181.PDF). (3) Market survey data for year 2013 The results of the economic assessment are presented in details in Tables 5.3-5.4 for residential and tertiary sector respectively. Measures are ranked by increasing unit cost and a marginal cost curve is constructed (Figures 5.1-5.2). As it is shown in the cost curves approximately 50% of the technical potential in residential sector and 27% in tertiary sector can be achieved with the so-called ‘win-win’ measures in the sense that they reduce GHG emissions and at the same time their implementation brings a net economic profit. There are also a number of low cost reduction options which may present a net benefit in case those environmental externalities are also taken into account (and consequently internalized). Insulation of roof (measure R2) and external walls (R3), the replacement of incandescent lamps by energy saving ones (R11) and the retrofitting of old wood stoves (R7) are ‘win-win’ measures in residential buildings, while energy efficient lighting (T9) and insulation of external walls (T3) represent ‘win-win’ measures in the tertiary buildings of Montenegro.
500
400
300
200
100 equivalent 2 0 0 20 40 60 80 100 120 140 -100 €/t CO
-200
-300 kt CO2 equivalent
Figure 5.1: Marginal abatement cost curve for GHG emissions in residential buildings of
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 24
Montenegro (2020).
1.000
800
600
400 equivalent
2 200
0 €/t CO 0 5 10 15 20 25 30 35 40 -200
-400
kt CO2 equivalent
Figure 5.2: Marginal abatement cost curve for GHG emissions in the tertiary buildings of Montenegro (2020).
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 25
Table 5.3: Economic assessment of GHG mitigation measures in residential buildings of Montenegro (year 2020).
GHG emissions GHG abatement IC Annualized IC O&M Cost savings Net Cost Measure reduction (kt cost (€) (€/y) (€/y) (€/y) (€) CO2eq) (€/t CO2eq)
Use of double glazing with thermal breaks R1 101,036,076 6,572,542 0 2,484,706 4,087,836 9.95 410.78 frames R2 Thermal insulation of roof 30,862,940 2,007,679 0 3,338,462 -1,330,783 13.28 -100.21 R3 Thermal insulation of external walls 47,262,631 3,074,502 0 5,299,678 -2,225,176 20.49 -108.59 R7 Energy efficient wood stoves 114,684,604 9,202,589 0 12,395,837 -3,193,248 16.83 -189.77 R8 Installation of ceiling fans 15,637,464 2,025,123 0 870,017 1,155,106 3.68 313.61 Installation of high efficiency air R9 37,543,637 4,862,073 0 3,934,989 927,084 16.66 55.65 conditioning units R10 Installation of solar collectors 37,249,288 4,823,953 372,493 4,315,936 880,510 18.27 48.19 R11 Promotion of energy efficient light bulbs 25,640,233 2,892,870 0 4,656,706 -1,763,837 19.71 -89.47 Promotion of energy efficient household R12 56,833,909 7,360,251 0 5,030,325 2,329,927 21.30 109.41 appliances (Class A)
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 26
Table 5.4: Economic assessment of GHG mitigation measures in residential buildings of Montenegro (year 2020).
IC Annualized IC O&M Cost savings Net Cost GHG emissions GHG abatement cost Measure (€) (€/y) (€/y) (€/y) (€) reduction (kt CO2eq) (€/t CO2eq)
Use of double glazing with thermal breaks T1 21,816,968 1,419,225 0 301,237 1,117,988 1.33 838.17 frames T2 Thermal insulation of roof 5,288,423 344,020 0 136,237 207,783 0.61 338.23 T3 Thermal insulation of external walls 12,256,172 797,282 0 835,064 -37,782 3.75 -10.08 T5 Retrofitting of old diesel boilers 4,339,632 348,223 0 -585,815 934,038 1.44 649.69 T6 Use of heat pumps for space heating 8,554,103 686,403 0 448,917 237,486 1.92 123.63 Installation of high efficiency air T7 14,548,760 1,884,131 0 604,947 1,279,184 2.87 445.18 conditioning units T8 Installation of solar collectors 1,498,946 194,120 14,989 147,056 62,054 0.70 88.84 T9 Promotion of energy efficient light bulbs 3,574,273 403,269 0 1,224,184 -820,915 5.81 -141.18 Promotion of energy efficient appliances T10 56,074,458 7,261,899 0 3,047,768 4,214,130 14.48 291.10 (Class A) T11 Installation of BMS 4,889,523 633,216 122,238 597,461 157,993 2.77 57.10
Modelling of low carbon measures in residential and tertiary buildings: Montenegro 27
6. SOCIO-ECONOMIC ASSESSMENT OF LOW CARBON MEASURES IN RESIDENTIAL AND TERTIARY SECTOR OF MONTENEGRO
6.1. Introduction
At specifying a national GHG abatement strategy the basic step is to quantify the national GHG abatement potential, or in other words, the technically feasible and realistic possibilities within a certain time horizon. However, apart from the GHG abatement potential and the economic performance of each mitigation intervention it is essential for the public debate and public support to be informed about the wider economic impacts going along with it. Questions of what are the total economic impacts of the GHG abatement strategy may arise in the political debate as well as questions regarding economic attractiveness of specific measures in absolute terms as well as in relation to each other. Besides impacts shown on markets like changes in GDP and employment (“internal” or also called macroeconomic impacts) also impacts are included which are not shown on markets – also called “external” effects (e.g. monetized impacts from reduced air pollution). Moreover, the economic attractiveness of specific measures has been evaluated from the viewpoint of a social (societal) perspective additionally to an investor’s perspective shown above. This assists opinion making of how much a society is willing to support certain measures within a strategy or on which measures increased public attention should be laid on.
6.2. Methodological Approach
The socio-economic assessment of low carbon measures in residential and tertiary sector of Montenegro are based on the previous assessment of Marginal abatement costs and underlying penetration rates of measures.
The basic rationale for calculating external economic impacts is to show their magnitude compared to the classic economic indicator GDP. In other words there might be essential economic impacts on the society apart from those shown up in national statistics. Including these external impacts shows also discrepancies between the investor’s view and the society’s view when considering costs for abating GHGs. For this calculation we applied an approach called “Social Benefit Investigation Guide”, also applied in other projects like “Assessing flexibility mechanisms for achieving the Austrian 2020 renewable energy targets” (ReFlex) [Türk et al., 2011].
Social marginal abatement costs of low carbon measures in residential and tertiary sector: Montenegro 28
Figure 6.1. Overview on the methodological approach used for the determination of social marginal abatement costs of low carbon measure.
This approach (Figure 6.1) is divided into three main sections: 1.) Basic data generation; 2.) Environmental assessment; and 3.) Socio-economic assessment.
6.3. Electricity generation - country specific emissions of local air pollutants
For electricity generation country specific GHG emissions and local air pollutants were determined. Table 6.1 shows the investigated emissions for the electricity generation. To determine GHG emissions and air pollutants of the electricity generation in Montenegro the existing electricity generation system and projections for future electricity generation until 2030 were considered. Data about energy savings based on the bottom-up energy model for low carbon measures where combined with technology specific fuel emission factors for certain GHG and air pollutants (CO2, CH4, N2O, SO2, NOx, dust), whereas the electricity sector was modelled country specific as following:
(5) where • Ex,t are the total emissions of pollutant x in year t • TPP_Pi,t is the electricity produced by the thermal power plant i in year t • TPP_EFi,x is the emission factor for the thermal power plant i and the emission x
Social marginal abatement costs of low carbon measures in residential and tertiary sector: Montenegro 29
(6) where • EFx,t is the emission factor of pollutant x in year t • Ex,t are total emissions of pollutant x in year t • Pt is the total amount of electricity produced in year t
Only direct emissions from burning fuels are included. Emissions from the construction of the power plant or from the provision of the fuel are not included in this assessment (no life cycle approach). Therefore in this assessment total GHG emissions and air pollutants of electricity generation are influenced by thermal power plants only. Currently Pljevlja is the only fossil thermal power plant in Montenegro. It is fuelled by lignite. Until 2015 rehabilitation measures and a deSOx-system should be implemented, leading to increased capacity and lower SO2 emissions [Montenegro Ministry of Economy, 2012]. Future projections for thermal power plants are based on the Montenegro Ministry of Economy, 2012. In this assessment the new lignite power plant Pljevlja II is included starting its operation in 2022. Besides lignite power plants the present electricity generation in Montenegro is based on large hydro power plants and a small share of other renewable energy sources (small hydro power, onshore wind power, photovoltaics, solid biomass). For the future projections a low electricity demand and an increase in renewable energy support to an European average level was assumed
Table 6.1: Investigated emissions from burning fuels for electricity generation
Greenhouse gas emissions Air pollutants
Carbon dioxide (CO2) Nitrogen oxide (NOx)
Methane (CH4) Sulfur dioxide (SO2)
Nitrous dioxide (N2O) Particles
By this separate and country specific calculation of the electricity system, the development of emission factors within a certain time period can be shown in detail:
Table 6.2: Emission factors for electricity generation in Montenegro from 2006 to 2030
Pollutant 2006 2010 2015 2020 2025 2030
CO2 [kg/kWh] 0,442 0,433 0,478 0,380 0,501 0,463
CH4 [g/kWh] 0,003 0,003 0,003 0,003 0,003 0,003
N2O [g/kWh] 0,007 0,007 0,008 0,006 0,008 0,007
CO2eq [kg/kWh] 0,444 0,436 0,480 0,382 0,504 0,466
SO2 [g/kWh] 4,923 4,833 5,331 1,835 2,654 2,455
NOx [g/kWh] 0,704 0,691 0,762 0,608 0,892 0,826 Particle [g/kWh] 0,738 0,724 0,799 0,636 0,460 0,426
Social marginal abatement costs of low carbon measures in residential and tertiary sector: Montenegro 30
6.4. Calculation of local air pollutants of low carbon measures
The total local air pollutants of an investigated low carbon measure consist of 1) air pollutants from the direct fuel combustion, and 2) air pollutants from the electricity generation. Emissions from electricity generation are calculated based on the amount of electricity consumed and the emission factors listed in Table 6.2. Emissions from direct fuel combustion are calculated based on the amount of fuel combusted, as calculated in the bottom-up-energy model within the project, and average emission factors listed in Table 6.3. Due to the wide variation of technologies, fuel quality and device maintenance the uncertainty of these emission factors is rather high. The data listed in Table 6.3 is mainly based on the fuel combustion in central heating systems installed in Austria, due to a lack of this type of data for Montenegro. It can be assumed that in Austria the technology level for combustion systems is higher and regulations for air pollutants from fuel combustion in residential and tertiary buildings are stricter than in Montenegro. Therefore savings of air pollutants calculated based on these emission factors might be slightly underestimated.
Table 6.3: Emission factors for different fuels used for stationary combustion
Fuel SO2 NOx Particle Gas/Diesel oil [kg/TJ] 45 37 3 LPG [kg/TJ] 6 34 0,5 Natural gas [kg/TJ] 0,43 34 0,5 Lignite [kg/TJ] 839 71 286 Fuel oil [kg/TJ] 90 115 3 Wood [kg/TJ] 11 107 90 Source: emission factors for lignite [Umweltbundesamt, 2014], emission factors for other fuels [Umweltbundesamt, 2010]
6.5. Social marginal abatement costs of low carbon measures
Combining fuel-, country-, and technology-specific emission factors with data on energy savings or shifts in fuel use provides information of reductions of GHGs and local air pollutants compared to Reference Scenario over a certain time period. Figure 6.2 shows exemplarily for Montenegro emission changes from measures implemented between 2011 and 2030, whereas emission reductions from measures implemented in this time period are effective also beyond 2030. After 2030 emission savings from measures implemented in the period 2011-2030 decline as their technical lifetime is gradually over. When all measures are fully implemented in 2030, GHG emissions in the building sector (both residential and tertiary) are reduced by nearly 457,000 tons CO2eq. Considering local air pollutants emission reductions in 2030 amount to nearly 2,340tons SO2, 1,095 tons NOx and over 709 tons of dust.
Social marginal abatement costs of low carbon measures in residential and tertiary sector: Montenegro 31
(a)
(b) Figure 6.2. Changes in emissions of (a) GHG emissions and (b) local air pollutants in Montenegro For making these physical changes in emission comparable to other economic benefits and costs they need to be monetized – i.e. expressed in monetary values. This is done by combining measure specific emission data with marginal damage costs (MDC) of GHGs and local air pollutants1 (data based on [Watkiss et al., 2005c] and [Energy Community, 2013]. This provides information about the annual monetized external effects (and if summarized, the total external effect). In Montenegro, emission reductions – both of GHGs and local air pollutants – lead to avoided external costs (= external benefits) of ~ € 47 mio. annually at the peak in 2030 (Figure 6.3). Summing up all these annual benefits from measures implemented between 2011-2030 in the building sector leads to external benefits of € 926 mio. (discounted with 2.5 %).
1 For instance calculated by using the Impact-Pathway Approach originally used by Bickel et al., 2005
Social marginal abatement costs of low carbon measures in residential and tertiary sector: Montenegro 32
Figure 6.3. Monetized external benefits from measures implemented between 2011-2020 in the building sector in Montenegro
So, the overall external benefits of reducing GHGs and other local air pollutants by specific measures have been shown above. Now the question arises, what are measure specific costs of reducing GHGs – and local air pollutants going along with GHG reduction. These measure- specific costs are usually expressed in measure specific marginal abatement costs (MAC). The bottom-up energy model (chapter above) provides already data on measure-specific MAC – which expresses the costs from an investor’s viewpoint. Taking up the viewpoint of the society – i.e. balancing the marginal abatement costs with external benefits from emission reductions – leads to social marginal abatement costs (SMAC). These are the marginal abatement costs for mitigation measures faced by the society. Figure 6.4 shows both MAC and SMAC for the building sector in Montenegro. It is demonstrated by these graphs that taking a social viewpoint marginal abatement costs are considerably lower than from an investor’s viewpoint.2
2 As (S)MAC change by time the figures below are provided for (S)MACs in 2020, including a detailed and country-specific modelling of the electricity sector.
Social marginal abatement costs of low carbon measures in residential and tertiary sector: Montenegro 33
a)
b) Figure 6.4. MAC and SMAC of the a.) residential and b.) tertiary building sector in Montenegro
Social marginal abatement costs of low carbon measures in residential and tertiary sector: Montenegro 34
6.6. Macroeconomic Impacts
Beside external effects of mitigation measures, classic economic impacts like effects on GDP and employment are essential contributions to public debates and the decision making process for mitigation strategies. Based on country-specific data of gross value added per job and production per job as well as sector specific import shares, country-specific employment effects can be calculated. Taking into account measure-specific investments (partly changing over time) and sector- specific multipliers for indirect and induced employment effects [based on Kurzmann et al., 2007], the number of jobs created by measure-specific investments can be calculated (compared to Reference Scenario). It has to be considered that employment effects of one annual investment last for many subsequent years (because of indirect and induced effects), whereas the bulk of job effects appears in the first six years [see Kurzmann et al., 2007]. As indirect and induced job effects of one annual investment appear additional to direct (as well as its indirect & induced effects) of investments in subsequent years, annual job effects cummulate by time even if annual investments stay the same. After 2030 – i.e. after the end of the period under consideration – only indirect & induced (but no direct) job effects last for some subsequent years (Figure 6.5).3
Figure 6.5. Employment effects of mitigation measures (Montenegro, tertiary buildings)
Total employment effects directly correspond to total measure-specific investments as well as sector-specific multipliers and import rates. The relatively high amounts of investments for energy efficient household appliances explain this measure’s high employment impacts in the graph above, although highest employment impacts per investment can be expected from roof as well as wall insulation. In total, measures implemented between 2011-2030 lead to employment impacts of nearly 10,000 full-time job years for the exemplary case of Montenegro tertiary buildings. A similar picture arises for GDP impacts of measures (Figure 6.6). Once again, energy efficient household appliances temporarily – due to highest investment amounts – lead to highest GDP impacts. However, especially the measure wall insulation leads to similar GDP impacts by even much lower investment amounts (due to its high domestic production share). Also, the measures roof as well as wall insulation lead the highest GDP impacts per
3 It has to be considered that that provided data on employment and GDP effects are rather high estimates as providing funds for these investments cause negative effects in other economic sectors.
Social marginal abatement costs of low carbon measures in residential and tertiary sector: Montenegro 35
mio. € of investment. In total, all measures lead to positive domestic GDP impacts of € 330 mio. in the long run (compared to investment of around € 250 mio.).
Figure 6.6. GDP effects of mitigation measures (Montenegro, tertiary buildings)
Cost-Benefit-Ratio
For achieving ambitious GHG reduction and energy efficiency targets, certainly all measures are needed to a certain extent. However, countries might wish to put stronger attention on measures where they get most for their money (investments). For a comprehensive assessment of measures’ efficiency it is not sufficient to compare investment costs with avoided negative external effects or macroeconomic impacts like GDP impacts. Rather it is necessary to show the relation of all costs to all benefits. This cost-benefit analysis is based on Steiner, 2006 and its further developments, e.g. by Türk et al., 2010.
Figure 6.7. Cost-benefit ratios of measures (Montenegro, tertiary buildings) The outcoming indicator expresses the overall economic efficiency of measures, where lower indicator values are signals for more cost-efficient measures (“the lower the better”). Figure
Social marginal abatement costs of low carbon measures in residential and tertiary sector: Montenegro 36
6.7 shows the different indicator values for the different mitigation values (once again exemplarily for Montenegro tertiary buildings). It shows that the two most efficient measures (wall insulation, energy efficient lighting) cost ¼ to 1/5 to their total economic benefits. Taking an average of all measures costs are ½ of the measures’ overall economic benefits (macroeconomic + external effects). Thereby costs of around € 250 mio. (excluding interest payments) are in relation to around € 500 mio. of total economic benefits.
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