Emissions from energy use in buildings – Global Homework Experts

Master’s Programme in Energy Technology
Carbon footprint reduction potential in
Finnish residential buildings by
energy efficiency measures
Antti Häggqvist
Master’s Thesis
2021

Aalto University, P.O. BOX 11000, 00076
AALTO
www.aalto.fi
Abstract of master’s thesis
Author
Antti Häggqvist
Title of thesis Carbon footprint reduction potential in Finnish residential buildings by
energy efficiency measures
Master programme Energy Technology Code ENG21
Thesis supervisor Prof. Markku J. Virtanen
Thesis advisor Lara Jasim, MA
Date 21.06.2021 Number of pages 56 Language English
Abstract
Emissions from energy use in buildings have grown steadily, accounting for approximately
36 % of total emissions in the European Union. In line with EU’s commitment to the Paris
Agreement, it aims to be carbon neutral by 2050. Finland as part of the EU has set the
objective to become carbon neutral by 2035, and residential building sector plays an
important role in achieving the goal.
This thesis examines the impacts of various energy efficiency measures on the carbon
footprint of Finnish residential buildings. The energy consumption of residential buildings
and the impacts of various energy efficiency measures were assessed using a building
energy simulation tool IDA ICE. In the life cycle assessment, the emissions caused during
the manufacture stage of the equipment related to energy efficiency measures, and the
operational emission reductions achieved by utilizing them were considered. Manufacture
stage emissions were obtained from the emissions database for construction recently
published by the Finnish Environment Institute.
The carbon footprint analyses carried out in this thesis show that the carbon footprint of
Finnish residential buildings can be reduced by various energy efficiency measures. The
greatest benefits were achieved by replacing the existing heating system with a ground
source heat pump or an air to water heat pump. Other energy efficiency measures can also
be beneficial in reducing the carbon footprint of a building. In general, the older the
building, the greater the carbon footprint reduction potential.
The carbon footprint analysis method created in this study was applied to randomly
selected residential buildings utilizing geographic information system (GIS). For the
selected example buildings, the impacts of different efficiency measures on the carbon
footprint of the buildings were determined. GIS-data could also be utilized on a larger scale.
The carbon footprint analysis method and GIS-data could be used to develop a web-based
service to encourage building owners to implement energy efficiency measures,
accelerating emission reductions in the residential sector on the path to carbon neutral
Finland.
Keywords HVAC technology, energy efficiency, carbon footprint, residential building, GIS
Aalto-yliopisto, PL 11000, 00076 AALTO
www.aalto.fi
Diplomityön tiivistelmä
Tekijä
Antti Häggqvist
Työn nimi Suomalaisten asuinrakennusten hiilijalanjäljen pienentämispotentiaali
energiatehokkuustoimenpiteiden avulla
Maisteriohjelma Energy Technology Koodi ENG21
Työn valvoja Prof. Markku J. Virtanen
Työn ohjaaja Lara Jasim, TaM
Päivämäärä 21.06.2021 Sivumäärä 56 Kieli Englanti
Tiivistelmä
Rakennusten energiankulutuksesta aiheutuvat päästöt ovat kasvaneet jatkuvasti ja niiden
osuus kaikista päästöistä Euroopan Unionin alueella on noin 36 %. Pariisin ilmastosopimuksen mukaisesti Euroopan Unionin tavoitteena on olla hiilineutraali vuoteen 2050
mennessä. Suomen tavoitteena on saavuttaa hiilineutraalius vuoteen 2035 mennessä, ja
asuinrakennussektorilla on merkittävä rooli tavoitteiden saavuttamisessa.
Tässä työssä tarkastellaan energiatehokkuustoimenpiteiden vaikutuksia suomalaisten
asuinrakennusten hiilijalanjälkeen. Asuinrakennusten energiankulutusta ja energiatehokkuustoimenpiteiden vaikutuksia siihen arvioitiin IDA ICE simulointiohjelmalla.
Elinkaaritarkastelussa huomioitiin energiatehokkuustoimenpiteisiin liittyviin
materiaaleihin sitoutuneet päästöt, ja niiden avulla saavutettavat käytön aikaiset päästö-
vähennykset. Materiaaleihin sitoutuneet päästöt perustuvat Suomen ympäristökeskuksen
(SYKE) äskettäin julkaisemaan rakentamisen päästötietokantaan.
Työssä tehtyjen hiilijalanjälkianalyysien perusteella suomalaisten asuinrakennusten
hiilijalanjälkeä voidaan pienentää energiatehokkuustoimenpiteiden avulla. Suurimmat
hyödyt saavutetaan, kun nykyinen lämmitysjärjestelmä korvataan maalämpöpumpulla tai
ilma-vesilämpöpumpulla. Myös muilla pienemmillä toimenpiteillä voidaan saavuttaa
merkittäviä hyötyjä. Yleisesti ottaen mitä vanhempi rakennus on kyseessä, sitä suurempi
sen hiilijalanjäljen pienentämispotentiaali.
Työssä kehitettyä hiilijalanjäljen arviointimenetelmää sovellettiin satunnaisesti valittuihin
asuinrakennuksiin paikkatietojärjestelmää (GIS) hyödyntäen. Valittujen esimerkkirakennusten kohdalla määriteltiin erilaisten energiatehokkuustoimenpiteiden vaikutukset
rakennusten hiilijalanjälkeen. GIS-dataa voitaisiin hyödyntää myös laajemmin.
Hiilijalanjäljen arviointimenetelmän ja GIS-datan avulla voitaisiin kehittää verkkopohjainen palvelu, jonka avulla rakennusten omistajia voitaisiin kannustaa energiatehokkuustoimenpiteisiin ja sitä myöten vauhdittaa asuinrakennussektorin päästö-
vähennyksiä matkalla kohti hiilineutraalia Suomea.
Avainsanat LVI-tekniikka, energiatehokkuus, hiilijalanjälki, asuinrakennus, GIS
Preface
I would like to thank Avoin ry and Otso Valta for offering me the opportunity to
work on such an interesting and timely topic. Thanks to my advisor Lara Jasim
and supervisor Markku J. Virtanen for good cooperation and valuable advice
during the thesis process. I would also like to thank Mika Vuolle for the opportunity
to use IDA ICE simulation program in the thesis.
Otaniemen opiskelukavereista haluan kiittää Aksua ja Joelia suunnattoman
suuresta avusta laskareissa ja harjoitustöissä. Erityiset kiitokset myös Kimmolle,
ilman sinua mikään tästä ei olisi ollut mahdollista.
SIO25 pojille kiitokset unohtumattomista hetkistä, kun Otaniemen opintoni olivat
tauolla. Pari vuotta kanssanne Porissa ovat olleet elämäni parasta aikaa.
Tsemppiä teille kaikille!
Viimeiseksi haluan kiittää vanhempiani ja siskoani kaikesta teiltä saamastanne
tuesta. Rakkaat vaimoni Iina ja suloinen tyttäreni Enni, olette minulle kaikki
kaikessa.
Espoo, 21 June 2021
Antti Häggqvist
Table of Contents
List of abbreviations…………………………………………………………………………………… 1
1 Introduction………………………………………………………………………………………..2
1.1 Background……………………………………………………………………………………2
1.2 Main objectives and scope of the thesis………………………………………………2
1.3 Structure of the thesis ……………………………………………………………………..3
2 Carbon footprint of a building……………………………………………………………….. 4
2.1 Operational & embodied energy and emissions …………………………………..4
2.2 Methodology …………………………………………………………………………………. 5
2.2.1 PAS 2050…………………………………………………………………………………6
2.2.2 ISO 14067………………………………………………………………………………..6
2.2.3 GHG Protocol ………………………………………………………………………….. 7
2.2.4 CEN/TC 350 – Sustainability of construction works ………………………9
2.3 Future prospects ………………………………………………………………………….. 10
3 Operational energy in Finnish residential buildings ……………………………….. 12
3.1 Finnish residential building stock …………………………………………………… 12
3.2 Energy consumption & energy sources ……………………………………………. 13
3.3 Location & climate ……………………………………………………………………….. 16
3.4 Emission reduction potential …………………………………………………………. 18
4 GIS-data based carbon footprint analysis method …………………………………..20
4.1 GIS…………………………………………………………………………………………….. 21
4.2 Standard building types ………………………………………………………………… 21
4.2.1 Building envelope……………………………………………………………………23
4.2.2 Ventilation types, airflows & air handling unit properties ……………..23
4.2.3 Standardized use of the building ……………………………………………….24
4.2.4 Domestic hot water…………………………………………………………………. 25
4.2.5 Main heating systems & heat distribution …………………………………..25
4.3 Major energy efficiency measures ……………………………………………………26
4.4 Additional energy efficiency measures …………………………………………….. 27
4.5 CO
2-emissions & carbon footprint reduction potential ……………………….29
5 Carbon footprint analyses …………………………………………………………………… 31
5.1 Operational energy consumption ……………………………………………………. 31
5.2 Operational CO
2-emissions…………………………………………………………….32
5.3 Operational CO
2-emission reduction potential ………………………………….34
5.4 Carbon footprint reduction potential ……………………………………………….36
6 Exemplary case study analyses……………………………………………………………..40
6.1 Detached houses …………………………………………………………………………..40
6.2 Apartment buildings ……………………………………………………………………..43
7 Discussion………………………………………………………………………………………… 45
7.1 Reliability of the results …………………………………………………………………45
7.2 Further development ……………………………………………………………………. 47
8 Summary ………………………………………………………………………………………….48
References……………………………………………………………………………………………….49

1
List of abbreviations
AAHP
AB
AWHP
BSI
CEN
CFP
CHP
CO
2
CO2e
DH
DHW
EAHP
EIA
EPD
EU
FMI
GIS
GHG
GSHP
HR
HVAC
IDA ICE
IPCC
ISO
LCA
ME
MSE
NV
PAS
PV
SFP
SPF
ST
TC
TRY
UNFCCC
WBCSD
Air to air heat pump
Apartment building
Air to water heat pump
British Standards Institution
European Committee for Standardization
Carbon footprint of a product
Combined heat and power
Carbon dioxide
Carbon dioxide equivalent
Detached house
Domestic hot water
Exhaust air heat pump
U.S. Energy Information Administration
Environmental product declaration
European Union
Finnish Meteorological Institute
Geographic information system
Greenhouse gas
Ground source heat pump
Heat recovery
Heating, ventilation and air conditioning
IDA Indoor Climate and Energy
Intergovernmental Panel on Climate Change
International Organization for Standardization
Life cycle assessment
Mechanical exhaust
Mechanical supply and exhaust
Natural ventilation
Publicly Available Specification
Photovoltaic
Specific fan power
Seasonal performance factor
Solar thermal collector
Technical Committee
Test reference year
United Nations Framework Convention on Climate Change
World Business Council for Sustainable Development
WRI World Resources Institute

2
1 Introduction
1.1 Background
Human-induced climate change caused by increased atmospheric carbon dioxide
(CO
2) concentrations will potentially lead to damaging impacts on earth such as
atmospheric warming and sea level rise (Solomon et al. 2009). There is also
evidence that climate change can be linked to the increased number of extreme
weather events such as droughts, floods, wildfires and tropical storms (Coumou &
Rahmstorf 2012).
To mitigate climate change the European Union (EU) aims to be carbon neutral by
2050 in line with EU’s commitment to the Paris Agreement (European Commission
2018). Finland as a part of the EU has set the objective to become carbon neutral by
2035 and carbon negative soon after that (Finnish Government 2019).
According to the United Nations Environment Programme (2020), the global CO
2
emissions from building sector have been in an upward trend and in 2019 they were
the highest ever recorded. In the EU, energy use in buildings account for 36 % share
of the total CO
2-emissions (Hirvonen et al. 2020). For these reasons, building sector
plays an important role in mitigating climate change and pursuing carbon
neutrality.
1.2 Main objectives and scope of the thesis
The main objective of this thesis is to examine the impacts of various energy
efficiency measures on the carbon footprint of Finnish residential buildings.
Geographic information system (GIS) data is utilized in visualizing the results and
applying them on randomly selected residential buildings in Finland. GIS provides
an opportunity to demonstrate the impact of energy efficiency measures on
individual building level and, when used properly, could motivate building owners
to implement energy efficiency measures and accelerate emission reductions in the
residential sector on the path to common emission reduction targets.
This thesis seeks answers to the following questions:
– How energy efficiency measures affect the operational CO
2-emissions of
residential buildings?
– How energy efficiency measures affect the carbon footprint of residential
buildings?
– How can GIS-data be utilized in carbon footprint analysis of individual
residential buildings?

3
The scope of this thesis is limited to Finnish residential buildings and common
heating, ventilation and air conditioning (HVAC) systems and energy efficiency
measures in Finland.
1.3 Structure of the thesis
This thesis consists of a literature review and a study that introduces a GIS-data
based carbon footprint analysis method.
Chapter 2 includes a literature review of methodologies associated with carbon
footprint of construction products and buildings. The purpose is to review the main
sources of buildings and construction sector CO
2-emissions and to review different
standards and calculation methods for carbon footprint assessment.
Chapter 3 includes a literature review of the factors that affect the operational
energy consumption in Finnish residential buildings. The characteristics of Finnish
residential building stock are reviewed together with most common heat sources,
the impact of location and climate on energy consumption and emissions, and the
emission reduction potential of Finnish residential building stock through various
energy efficiency measures.
The carbon footprint analysis method is introduced in chapter 4. This chapter
describes in detail the methods used to estimate the operational energy
consumption and CO
2-emissions of Finnish residential buildings, as well as how the
impacts of various energy efficiency measures on the carbon footprint of a building
are determined.
The carbon footprint analyses are presented in chapter 5.
In chapter 6, the carbon footprint analysis method and results are applied to six
randomly selected residential buildings utilizing GIS-data. The impacts of various
energy efficiency measures on the carbon footprint of the example buildings are
determined.
The reliability of the results is reviewed in chapter 7. Also, potential applications and
further development of the carbon footprint analysis method are discussed.
Chapter 8 summarizes the contents of this thesis.

4
2 Carbon footprint of a building
Carbon footprint generally stands for CO2 and other greenhouse gases (GHGs)
emitted during a life cycle of a process or a product. It has become one of the most
important environmental protection indicators. (Čuček et al. 2012)
The units commonly used to quantify anthropogenic emissions of greenhouse gases
are mass of carbon dioxide (CO
2) or carbon dioxide equivalent (CO2e). They should
not be confused with each other as CO
2 only considers carbon dioxide emissions
while CO
2e also considers other greenhouse gases converted to the equivalent
amount of carbon dioxide with the same global warming potential (GWP) (Eurostat
2017). The United Nations Framework Convention on Climate Change (UNFCCC)
and its Kyoto Protocol recognize CO
2, methane (CH4), nitrous oxide (N2O),
hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF
6)
as the six main GHGs (UNFCCC 2008).
Buildings usually have a long life cycle, and therefore it is important to apply life
cycle thinking in estimating their environmental impact. A common method for
determining building’s environmental impact is called life cycle assessment (LCA).
According to Norris (2001), LCA is used to evaluate the environmental performance
of a product system over an entire life cycle. It is important to apply LCA as
otherwise we risk focusing only on the environmental issues that require our
immediate attention and might ignore other issues occurring in another place or in
another form (Curran 2013).
2.1 Operational & embodied energy and emissions
Energy consumption and emissions of buildings can be divided into operational and
embodied. Operational energy and emissions are related to the use phase of a
building while embodied energy and emissions are mainly related to building
materials and construction processes (Dixit et al. 2010; Dixit 2017; Ibn-Mohammed
et al. 2013).
Operational energy is the energy consumed to maintain desired indoor environment
including space and water heating, ventilation and air conditioning, lighting,
appliances and other operational activities during the use phase of a building (Dixit
et al. 2010; Dixit 2017). It has contributed to a relatively larger proportion of the
building’s life cycle energy (Dixit et al. 2010). A study by Ramesh et al. (2010) found
that the share of operational energy is typically 80-90 % of the residential and
commercial building’s life cycle energy.
Embodied energy is the energy consumed in building’s life cycle stages other than
operation including manufacturing processes and supply chains of building
materials, construction, assembly, renovation and end-of-life activities along with
any related transportation, administration and services (Dixit et al. 2010; Dixit
2017). Because of its smaller share in the building’s life cycle energy, it has received

5
less attention in the past. However, recent studies have shown the growing
significance of embodied energy especially with the emergence of more energy
efficient buildings (Dixit 2017). Sartori & Hestnes (2007) concluded that while
operational energy typically represents the larger part, the share of embodied energy
can be up to 46 % of the low-energy building’s life cycle energy.
When it comes to embodied and operational emissions, they are not always directly
related to energy consumption. Operational emissions are roughly proportional to
the type of fuel used in energy production. In general, energy produced from fossil
fuels causes higher emissions while the same amount of energy produced from
renewable energy sources may cause much less emissions. However, embodied
emissions can be unrelated to energy use. For example, in cement production, there
are emissions from chemical reactions that are not related to energy consumption,
and on the other hand, timber products even sequester carbon dioxide during their
growth before they are processed into construction products. (Ibn-Mohammed et al.
2013)
The carbon footprint of a building can be affected through operational and
embodied emissions. In general, operational emissions account for a larger share of
the building’s life cycle emissions. However, the less energy a building consumes
during the operational phase, the more important are the emissions embodied to
building materials. Operational emissions can be reduced by improving the
buildings energy efficiency and by utilizing renewable energy technologies (IbnMohammed et al. 2013). Embodied emissions are sensitive to the selection of
building materials. A study by Moschetti et al. (2019) found that using low emission
materials, such as wood, particularly in building components with high demand in
terms of mass can reduce the building’s life cycle embodied emissions significantly.
Operational and embodied emissions should be considered already at early stages
of the building’s design process (Basbagill et al. 2013; Häkkinen et al. 2015;
Pomponi & Moncaster 2018). As the design process progresses, the opportunity for
choices is constantly diminishing which can affect especially the embodied
emissions adversely (Häkkinen et al. 2015). A study by Basbagill et al. (2013) shows
that by applying LCA during the early stages of a design process, the embodied
carbon footprint of a building can be reduced significantly. A low-carbon building is
a joint effort of many different parties and industries.
2.2 Methodology
There is no single internationally accepted method for determining a building’s
carbon footprint (Fenner et al. 2018). Among the factors causing divergence
between calculation methods are differently defined system boundaries, scope,
greenhouse gas units and methodologies (Fenner et al. 2018). Even case studies
using same methodology are difficult to compare since every case has its own
specific properties such as building type, climate, comfort requirements and local
regulations (Chau et al. 2015).

6
System boundaries define which life cycle stages are considered in LCA. Two
common system boundaries among buildings and building products are “cradle-togate” and “cradle-to-grave” (Dixit et al. 2013). Cradle-to-gate considers the early
stages of a product’s life cycle from raw materials acquisition to a point where the
finished product leaves the factory gate (Dixit et al. 2013). Cradle-to-grave also
considers the later stages of the product’s life cycle including the use phase and endof-life activities providing a whole life cycle perspective (Dixit et al. 2013). The used
system boundaries have a great impact in the results of environmental performance
studies and therefore they should be taken into consideration when interpreting the
results.
Fenner et al. (2018), Liu et al. (2016) and Wu et al. (2014) identify PAS 2050, ISO
14067, GHG Protocol and the European standards as the main standards addressing
carbon footprint analysis. These methods are reviewed in the following subchapters.
2.2.1 PAS 2050
Publicly Available Specification (PAS) 2050 is a product-level life cycle GHG
emissions assessment method. It was published by the British Standards Institution
(BSI) in 2008 and updated in 2011. PAS 2050 was developed based on existing LCA
standards established by the International Organization for Standardization (ISO);
the ISO 14040 and ISO 14044. (BSI 2011)
PAS 2050 is intended for organizations assessing the GHG emissions of products
across their life cycle (BSI 2011). When published, PAS 2050 was the first uniform
assessment method for product-level GHG emissions providing significant
clarifications and simplifications to existing LCA requirements (Liu et al. 2016; Wu
et al. 2014). The system boundaries include cradle-to-gate and cradle-to-grave
approaches (BSI 2011).
PAS 2050 addresses a single impact category of global warming. GHG emissions are
converted to CO
2e using the latest 100-year GWP values provided by the
Intergovernmental Panel on Climate Change (IPCC). (BSI 2011)
The advantage of PAS 2050 is that it provides a detailed framework for assessing
GHG emissions of products with little room for interpretation (Fenner et al. 2018).
However, it does not specify any requirements or guidelines for communicating the
results to customers (BSI 2011; Liu et al. 2016). Although it is considered a dominant
carbon footprint standard, there is still demand for additional research applying the
PAS 2050 to construction products (Fenner et al. 2018; Liu et al. 2016).
2.2.2 ISO 14067
As a part of the ISO 14060 family of GHG standards, the ISO 14067 standard is an
assessment method for the carbon footprint of a product (Finnish Standards

7
Association 2018). It was first published in 2013 as a technical specification and in
2018 it was approved as an international standard (Finnish Standards Association
2018; International Organization for Standardization 2018; Liu et al. 2016). ISO
14067 is based on existing international standards on LCA; ISO 14040 and ISO
14044 (Finnish Standards Association 2018).
ISO 14067 is expected to benefit organizations, governments, industry, service
providers, communities and other interested parties by providing clarity and
consistency in quantifying carbon footprint of their products and a better
understanding how to reduce it. The standard specifies principles, requirements and
guidelines for the quantification and reporting of the carbon footprint of a product
(CFP) or a partial CFP based on the selected stages or processes within the life cycle.
The system boundaries include cradle-to-grave approach for CFP and cradle-to-gate
or else for partial CFP depending on which stages or processes of a life cycle are
considered. (Finnish Standards Association 2018)
ISO 14067 addresses a single impact category of climate change. The GHG emissions
are reported in mass of CO
2e per functional unit. Conversion to CO2e is based on the
latest 100-year GWP values by the IPCC. (Finnish Standards Association 2018)
ISO 14067 was the first standard to offer guidelines for comparing carbon labels of
different products (Liu et al. 2016). However, not all CFPs can be compared, as the
calculation of the CFPs of comparable products must follow identical quantification
requirements (Finnish Standards Association 2018). The technical specification of
ISO 14067 also included requirements for communicating the result, but these
requirements were transferred to ISO 14026 before ISO 14067 was published as an
international standard (Finnish Standards Association 2018; International
Organization for Standardization 2018; Liu et al. 2016; Wu et al. 2014).
2.2.3 GHG Protocol
The GHG Protocol consists of various greenhouse gas accounting standards. It was
developed by the World Resources Institute (WRI) and the World Business Council
for Sustainable Development (WBCSD).
The GHG Protocol provides seven standards for different target groups for
accounting and reporting GHG emissions: Corporate Standard, GHG Protocol for
Cities, Mitigation Goal Standard, Corporate Value Chain (Scope 3) Standard, Policy
and Action Standard, Product Standard and Project Protocol.
The Product Standard of the GHG Protocol was published in 2011 and builds on the
framework and requirements established in the ISO 14040, ISO 14044 and PAS
2050 (WRI & WBCSD 2011b; Wu et al. 2014). It aims to provide additional
specifications and guidance for consistent quantification and public reporting of
product life cycle GHG inventories (WRI & WBCSD 2011b). The system boundaries

8
included in the Product Standard are cradle-to-gate and cradle-to-grave (Liu et al.
2016).
Fundamentally the GHG Protocol is based on three scopes for GHG emissions
accounting and reporting purposes, as shown in Figure 1. Scope 1 emissions are
direct GHG emissions that occur from sources that are owned or controlled by the
company. Scope 2 emissions are indirect GHG emissions from the generation of
purchased electricity consumed by the company. Scope 3 emissions are other
indirect GHG emissions that are consequence of the activities of the company but
occur from sources that are not owned or controlled by the company. (WRI &
WBCSD 2004)
Figure 1. The GHG Protocol scopes and sources of emissions (WRI & WBCSD
2011a).
The GHG Protocol requires the accounting and reporting of each greenhouse gas
covered by the UNFCCC Kyoto Protocol separately and in CO
2e using the GWP
values provided by the IPCC (WRI & WBCSD 2004; 2013).
A study by Onat et al. (2014) applying scope-based carbon footprint analysis on U.S.
commercial and residential buildings found that scope 2 indirect emissions had the
highest carbon footprint overall. Scope 3 indirect emissions were found to be higher
than scope 1 direct emissions. Commuting was the most influential activity among
scope 3 emissions before the emissions from construction activities and material
supply chain. (Onat et al. 2014)

9
The GHG Protocol is commonly used among businesses and governments (Fenner
et al. 2018). It is a practical tool for estimating greenhouse gas emissions in the built
environment. The GHG Protocol has been used for example by Ozawa-Meida et al.
(2013) to estimate the carbon footprint of a UK university and Laine et al. (2020) to
evaluate the prospects of carbon neutral cities.
2.2.4 CEN/TC 350 – Sustainability of construction works
The standards for the European Committee for Standardization (CEN) are prepared
by the Technical Committees (TCs). CEN/TC 350 is responsible for the development
of methods assessing the sustainability of construction works including standards
for the EPDs of construction products (CEN 2018). The standards consider the
sustainability of construction products and construction works in terms of
environmental, economic and social performance (CEN 2018).
In the CEN/TC 350 standards the life cycle of a building or a building product is
divided into four stages: product stage, construction process stage, use stage and
end of life stage. These stages are divided into smaller modules as shown in Figure
2. The fifth stage is supplementary information beyond the product’s life cycle
considering possible benefits or loads from recycling or reuse of the materials.
(Finnish Standards Association 2019)
Figure 2. The construction works life cycle stages and modules in the EN 15804
standard (Finnish Standards Association 2019).
CEN/TC 350 has published 12 standards with the EN 15804 and EN 15978 being the
most notable regarding the carbon footprint of building products and buildings
respectively (Fenner et al. 2018).
The EN 15804 standard provides rules for determining and reporting the
environmental effects of construction products. It provides a structure to ensure
that the EPDs of construction products are derived, verified and presented in a
harmonized way. The EPDs can consist of cradle-to-gate or cradle-to-grave
information. The cradle-to-gate type EPDs may include different combination of

10
modules while the cradle-to-grave type EPDs must include all modules from A to D.
(Finnish Standards Association 2019)
The EN 15978 standard provides a calculation method for the environmental
performance of buildings. The whole life cycle of a building is considered, and the
carbon footprint is based on data obtained from the EPDs. If cradle-to-gate type
EPDs are used, they must be supplemented with information to complete all cradleto-grave modules. The purpose of evaluations based on the EN 15978 standard is to
enable environmental performance improvements and comparisons between
different design options, to document the environmental performance of a building
for certification, labelling and marketing, and to support policy development.
(Finnish Standards Association 2012)
The environmental impact indicators in the CEN/TC 350 standards include climate
change, ozone depletion, water use and more. The impact category of climate change
is measured in CO
2e that is based on the 100-year GWP values provided by the IPCC.
(Finnish Standards Association 2012; 2019)
According to Passer et al. (2015) the existence of so many different environmental
claims has created confusion in the building industry inside the EU. The CEN/TC
350 fills the demand for a clarified and harmonized way to assess the environmental
performance of construction products in the EU level.
2.3 Future prospects
The focus has been on reducing operational energy consumption and subsequent
emissions in buildings since the early 2000’s, but embodied emissions have only
received more attention recently. The research field of embodied carbon assessment
is rapidly growing but there is a lack of complete, transparent and comparable
assessment methods. (Pomponi & Moncaster 2018)
The EU has recently developed a method called Level(s) for harmonized assessment
and reporting of sustainability performance of buildings (European Commission
2021). Level(s) framework covers the building’s environmental, social and economic
long-term sustainability and is intended for better understanding and reducing the
impact of built environment on global resources (European Commission 2021).
In Finland, the goal is to regulate the building’s life cycle carbon footprint through
legislation by the mid-2020s (Ministry of the Environment of Finland 2021a). The
work is already well under way. Based on the existing CEN/TC 350 standards and
Level(s) framework, the Ministry of the Environment of Finland has developed a
method for the whole life carbon assessment of buildings that is intended for new
buildings and large-scale refurbishments (Ministry of the Environment of Finland
2019; 2021a). In addition, the Finnish Environment Institute (2021) has developed
an open, free-of-charge emissions database to promote low-carbon construction. It

11
presents average emissions data on construction products, processes and services in
Finland (Finnish Environment Institute 2021).
Another important question for the future is how to measure the potential emissions
reductions from existing buildings. Fenner et al. (2018) state that the current carbon
footprint methodologies can only be applied during the initial design process of new
buildings and there is not an internationally accepted method for measuring,
reporting and verifying the potential reductions in greenhouse gas emissions from
existing buildings.

12
3 Operational energy in Finnish residential buildings
To achieve rapid emission reductions necessary to meet future carbon neutrality
goals in Finland, the most important task is to reduce the energy consumption of
the existing building stock through energy efficiency measures and by developing
less carbon intensive heating solutions (Confederation of Finnish Construction
Industries 2020).
In Finland, the issue is noticed by decision makers, as according to Finnish
Government (2019), the use of fossil oil for heating will be phased out in the public
sector by 2024 and completely by 2030. To accelerate the change, homeowners are
offered financial support to transition from oil-based heating to more sustainable
heating solutions. Those who switch to district heating, ground source heat pump or
air to water heat pump will receive the highest subsidies. (Finnish Government
2019; Ministry of the Environment of Finland 2021b)
This chapter reviews the characteristics of Finnish residential building stock and its
role in the final energy consumption and emissions.
3.1 Finnish residential building stock
Residential buildings account for 62 % share of the gross floor area of Finnish
building stock (Statistics Finland 2020a). Almost 90 % of the residential buildings
are detached houses in terms of building count, but in terms of gross floor area the
share of attached houses and apartment buildings become larger as shown in Figure
3.
Figure 3. Share of residential building types by building count and gross floor area
(Statistics Finland 2020a; 2021a).

13
Most of the floor area of Finnish residential buildings is constructed between 1970
and 1990 (Statistics Finland 2021a). Construction year is an important factor in
building’s operational energy consumption and emissions, as regulations have
required better and better energy performance over the years. The gross floor area
of Finnish residential buildings per construction year is presented in Figure 4.
Figure 4. Gross floor area of residential buildings per construction year (Statistics
Finland 2021a).
3.2 Energy consumption & energy sources
Residential building sector has an important role in potential energy savings and
emission reductions in Finland as approximately 17 % of the total final energy is
consumed in housing (Statistics Finland 2020b, 2020c). Finland’s northern location
and low average annual temperatures increase the heating demand in buildings to
maintain comfortable indoor conditions. Figure 5 shows how the energy
consumption in Finnish households mainly consists of space heating and domestic
hot water heating.

14
Figure 5. Energy consumption in households by use in 2019 (Statistics Finland
2020b).
Heating energy demand is dominant in Finnish households as space heating and
domestic hot water heating share is approximately 82 % of the total energy
consumption. Household appliances share is only 13 %, including cooking, lighting
and other electrical equipment.
The most common heat sources in Finnish apartment buildings are district heat and
electricity (Statistics Finland 2021b). Whereas electricity is available even in remote
areas, the district heat network often extends only to densely populated areas.
According to Vainio et al. (2015), up to 90 % of apartment buildings in Finland are
heated by district heat, while in detached houses the market share of district heat is
only less than 10 %. This leads to building specific heating solutions such as boilers
or heat pumps being more common in detached houses. The most used heat sources
in Finnish detached houses are wood, electricity and heat pumps (Statistics Finland
2021b). The heating energy provided by heat pumps is extracted from the
environment (ground, air, or water). The heating energy consumption in Finnish
households by building type and heat source is presented in Figure 6.

15
Figure 6. Heating energy consumption in households by building type and heat
source in 2019 (Statistics Finland 2021b).
Electricity and district heat are produced centrally in power plants while the other
heating solutions are based on building specific boilers or heat pumps. Electricity is
produced either by combusting fossil or renewable fuels or generated from
emission-free sources such as hydro, solar or wind power (Finnish Energy 2021).
Most of the district heat is generated alongside electricity in combined heat and
power (CHP) plants, but some is also generated separately (Finnish Energy 2020;
Statistics Finland 2020d). In CHP plants the heat that would otherwise be wasted is
captured to provide useful thermal energy for district heating. Also, different heat
recovery methods or heat pump technologies are used in district heat power plants
to reduce their environmental impact (Finnish Energy 2020). However, most of the
electricity and district heat are still generated by fuel combustion, although
emissions from electricity and heat production have been decreasing over the years
(Finnish Energy 2020; 2021).
Emissions caused by district heat or electricity consumption in buildings can be
considered indirect emissions as they occur in power plants (United Nations
Environment Programme 2020). There are regional variations as the emissions are
dependent on the fuel mix used in the power plants. Emissions from building
specific on-site heat production are more predictable because the heat source
remains constant. Those buildings that use electricity and district heat as their heat
source can become carbon neutral regardless of any measures from building owners
if the energy is produced carbon neutrally in the power plants. For example, Fortum
and the city of Espoo aim for carbon neutral district heat production by 2030
(Fortum 2021).

16
3.3 Location & climate
The heating energy demand of buildings is influenced by climatic parameters such
as temperature, solar radiation and wind speed (Kalamees et al. 2012). The annual
variation in temperatures is typical of the Finnish climate, and solar radiation
changes considerably with the seasons and is also very different in the northern and
southern parts of Finland (FMI 2021a; 2021b).
The average annual temperature is approximately 5,5 °C in the southwestern
Finland and decreases towards north being slightly under 0 °C in Northern Lapland
(FMI 2021b). Generally, heating energy demand is highest during winter season, but
temperatures remain so low throughout the year that buildings require heating all
year round. The monthly average temperatures in Finland are shown in Figure 7.
Figure 7. Monthly average temperatures in Finland (FMI 2021c).
Factors that affect the levels of solar radiation include season, geographic location
and cloud cover. In Finland, the highest levels of solar radiation are usually reached
in June when the angle of incidence of the sun is high, and the days are long (FMI
2021b). Around midsummer, the sun does not set below the horizon in locations
north of the arctic circle. Vice versa, around winter solstice the days are shorter, and
the sun even stays below the horizon throughout the day in northern locations.
Although solar radiation does not affect the heating energy demand of buildings as
much as temperature, it does have an impact on the potential and efficient use of
solar systems. In practice, solar systems are least useful in Finland when the energy
demand of buildings is the highest.

17
It is essential to take climate into account when assessing the heating energy
demand of buildings. There are different methods developed for that purpose,
including the concept of heating degree days and weather data sets for building
energy simulation.
Heating degree days are an indication of how cold the outdoor temperature was in
a location during a given period of days (EIA 2020). They are used to describe the
demand for energy needed to heat buildings (FMI 2021d). The heating degree days
“S17” published by the Finnish Meteorological Institute (FMI) are calculated by
adding up the differences of the daily indoor and outdoor temperatures of the whole
month using the measured average outdoor temperatures and a presumed indoor
temperature of 17 °C (FMI 2021d). This method assumes that buildings are not
heated when the average outdoor temperature is above 10 °C in the spring and above
12 °C in the autumn and those days are excluded in the calculations (FMI 2021d).
By using heating degree days, the measured heating energy consumptions can be
standardized to compare between different years and locations. Heating degree days
only consider temperature and not any other climatic parameters.
The use of simulation programs is a common practice in evaluating energy efficiency
of buildings and finding solutions to improve their energy performance (Kalamees
et al. 2012). Weather data sets for buildings energy simulations contain hourly
climatic data in given locations that are used as input parameters for simulation
programs.
Finland is divided into four climate zones for building energy demand calculations,
as shown in Figure 8. Jylhä et al. (2011; 2020) have developed test reference years
(TRYs) that represent statistically typical long-term meteorological conditions in all
four climate zones. Test reference years contain hourly data of temperature, relative
humidity, wind speed and solar radiation. The most recent TRY2020 is based on
weather observations from 1989-2018 and it is an updated version of TRY2012 that
was based on observations from 1980-2009. Due to the climate change, test
reference years should be updated regularly to better represent the current climatic
conditions. For example, the annual temperatures in TRY2020 are 0,17 – 0,36 °C
warmer for different climate zones than in TRY2012. Jylhä et al. (2020) have also
developed test reference years for three future periods based on different projected
climate change scenarios. Future test reference year TRY2030 represents the
immediate future (years 2015-2044), TRY2050 mid-century (years 2035-2064) and
TRY2080 the late century climate (years 2065-2094).

18
Figure 8. Climate zones for energy calculations in Finland (Ministry of the
Environment of Finland 2017a).
3.4 Emission reduction potential
The age of a building is a major influencing factor in energy demand and subsequent
emissions since it affects the structural and HVAC solutions determining the
building’s energy performance. Construction standards have tightened over the
years requiring better energy performance in new buildings. However, most of the
building stock consist of old, low-performance buildings resulting in great potential
to reduce energy demand and emissions. (Hirvonen et al. 2019a)
In Finland, the role of different heating systems in emissions of apartment buildings
is not that important since most of them are heated by using district heating
(Statistics Finland 2021b). On the other hand, in detached houses the range of
different heating systems is wider. The most emitting heating systems such as wood
and oil boilers are more common in old detached houses while energy efficient
heating systems such as heat pumps have become more common recently (Hirvonen
et al. 2019b).
Hirvonen et al. (2019a; 2019b) studied the cost-effective energy renovation
measures on Finnish detached houses and apartment buildings in different age
categories by using simulation and optimization tools. The three main heating
systems studied in apartment buildings were district heating, ground source heat
pump with electric backup heating and exhaust air heat pump with district heating
backup. Additional energy efficiency measures included improvements of building
envelope thermal insulation, solar systems, ventilation type, lower radiator design
temperatures and sewage heat recovery. By using district heating as the main heat

19
source, the emissions could be reduced cost-effectively 24 – 41 % by utilizing the
additional energy efficiency measures. The largest and most cost-efficient emission
reductions, up to 70 – 80 %, could be obtained by switching to ground source heat
pump. The study found that it is possible to reduce emissions even further, but not
cost-effectively. While ground source heat pump was the single most effective way
to reduce emissions, solar systems were useful in all age categories and additional
roof insulation and window retrofits were beneficial in older apartment buildings.
(Hirvonen et al. 2019a)
A study by Hirvonen et al. (2019b) examined the cost-effective energy renovation
measures on Finnish detached houses. The five most common main heating systems
of direct electric heating, wood boiler, oil boiler, district heating and ground source
heat pump were modeled. Additional energy efficiency measures included
improvements of building envelope thermal insulation, solar systems, air to air heat
pump, lower radiator design temperatures and ventilation type. For the whole
building stock of detached houses, a total emission reduction of 55 % was possible
to achieve cost-effectively. The lowest emissions were obtained by using ground
source heat pump as the main heating system. Air to air heat pump was used as an
auxiliary heat source in all optimal scenarios, and solar systems were also useful in
all age categories. Due to the nature of construction standards of past decades,
improving the building envelope thermal insulation was only useful in older
buildings. (Hirvonen et al. 2019b)
In Finland, the largest operational energy related emission reduction potential can
be found in old apartment buildings and detached houses (Hirvonen et al. 2019a;
2019b). While there are many ways to reduce emissions in existing residential
buildings, a large fraction of possible solutions are not economically viable and
therefore it is important to keep economical aspects in mind before applying energy
efficiency measures (Hirvonen et al. 2019a).

20
4 GIS-data based carbon footprint analysis method
The objective of this thesis is to create a method to estimate the annual energy
consumption, CO
2-emissions and the effects of various energy efficiency measures
on the carbon footprint of any randomly selected Finnish residential building. When
the carbon footprint analyses are combined with geographic information of
buildings, the results can be visualized on a map on individual building level.
However, this requires that the necessary basic information of buildings, including
building type, construction year, floor area and existing heating system are included
in the GIS-data.
Building’s CO
2-emissions depend on energy consumption and the energy sources
used. Energy consumption in turn depends on the characteristics of the building,
such as building type, envelope thermal insulation level and HVAC systems. Since
the purpose of this thesis is to create a method that can be applied to any Finnish
residential building, standard building models of detached houses and apartment
buildings are determined to represent the whole Finnish residential building stock.
They are divided into nine age categories based on construction year. The building
models reflect the typical characteristics of residential buildings of different ages
based on current and old Finnish building codes.
The estimated energy consumptions of detached house and apartment building
models in different age categories with different combinations of heating systems
and energy efficiency measures are simulated using a dynamic building energy
simulation tool IDA Indoor Climate and Energy (IDA ICE) by EQUA Simulation AB.
In line with the instructions in decrees on new building’s energy performance
(1010/2017) and building’s energy performance certificate (1048/2017) of the
Ministry of the Environment of Finland (2017a; 2017b), the climatic conditions in
energy simulations are based on weather data set TRY2012 of Helsinki-Vantaa,
which represents the Finnish climate zone I.
The estimated annual energy consumptions obtained from the simulations are
converted into annual CO
2-emissions using average emission factors. The purpose
of energy efficiency measures is to reduce the energy consumption and subsequently
the carbon footprint of a building, but it is also important to consider their
environmental impacts caused during life cycle stages other than operation. The
carbon footprint reduction potential of residential buildings by energy efficiency
measures is estimated based on the emissions caused during the manufacture
process of the equipment, their service life, and the annual operational CO
2
emission reductions achieved by utilizing them.
Finally, the results are attached to GIS-data and the use of the created carbon
footprint analysis method is demonstrated with six randomly selected residential
buildings. For the example cases, the impact of certain energy efficiency measures
on the carbon footprint of the buildings over the service life of the energy efficiency
measures is estimated.

21
4.1 GIS
The estimated CO2-emissions and effects of energy efficiency measures on the
carbon footprint of a building can be allocated to individual buildings by utilizing
GIS-data. In addition to the geographic location of buildings, the other information
relevant to this study are building type, construction year, floor area and the existing
heating system. For example, the city of Tampere provides open access GIS-data
including all the mentioned information from their building stock. This allows to
demonstrate the use of the carbon footprint analysis method created in this thesis
with randomly selected residential buildings. An example of a map showing
buildings in Tampere is presented in Figure 9.
Figure 9. Map showing buildings in Tampere.
4.2 Standard building types
Detached house and apartment building models, presented in Figure 10 and Figure
11 respectively, are created in IDA ICE and used in energy consumption simulations.
Their purpose is to represent the whole Finnish residential building stock and their
shapes are created based on the fact that most of the residential buildings in Finland
are constructed between 1970 and 1989. Basic dimensions such as areas and
volumes of the building models are presented in Table 1. The properties of building
models for different age categories are mainly based on decrees 1010/2017 and
1048/2017 of the Ministry of the Environment of Finland (2017a; 2017b) of which
the latter reflects the historical changes in Finnish building code.

22
Figure 10. Detached house model in IDA ICE.
Figure 11. Apartment building model in IDA ICE.
Table 1. Basic dimensions of building models.

order now
Detached house Apartment building
Floor area, m2 104 1500
Volume, m3 261 4160
Envelope area, m2 317 2056
Window/Envelope 4,6 % 8,8 %

Building models are assumed to maintain a minimum indoor temperature of 21 °C.
Cooling is not considered and the indoor temperature is allowed to rise above the
heating set point. The total energy consumption includes space and domestic hot

23
water heating, electricity of auxiliary HVAC equipment, lighting and other electrical
appliances.
4.2.1 Building envelope
Conduction heat losses through different parts of the building envelope contribute
to the energy demand for space heating. The magnitude of heat losses depends on
the heat transfer coefficients (U-values) of structures and the difference between
indoor and outdoor temperatures. On the other hand, solar radiation transmitted
through windows can reduce the energy demand for space heating in cold weather.
Heat transfer coefficients of different structures for each age category are presented
in Table 2. The total solar transmittance coefficient (g-value) of standard windows
is 0,6. The values are based on decree 1048/2017 of the Ministry of the Environment
of Finland (2017b).
Table 2. Heat transfer coefficients (U-values) of structures in different age
categories, W/(m
2K).

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
External wall 0,81 0,7 0,35 0,28 0,25 0,24 0,17
External slab 0,47 0,4 0,4 0,36 0,25 0,24 0,16
Roof 0,47 0,35 0,29 0,22 0,16 0,15 0,09
Door 2,2 1,4 1,4 1,4 1,4 1,4 1
Window 2,8 2,1 2,1 2,1 1,4 1,4 1

Infiltration also causes energy demand for space heating. Infiltration is driven by
differential pressures across the building envelope due to the temperature
differences, external wind and mechanical ventilation systems (Kauppinen 2011).
Infiltration rate is significantly affected by the airtightness of the building
(Seppänen 2001). The n
50 value describes how many times the air volume of a
building is changed per hour and the q
50 value describes the average infiltration rate
per envelope area with a pressure difference of 50 Pa (Paloniitty 2013). Infiltration
values for each age category are presented in Table 3. The values are based on decree
1048/2017 of the Ministry of the Environment of Finland (2017b).
Table 3. Infiltration values in different age categories.

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
n50, 1/h 6 4
q50, m3/(h,m2) 4

4.2.2 Ventilation types, airflows & air handling unit properties
Mechanical ventilation systems have become more common over the years. The
advantage of a modern mechanical supply and exhaust (MSE) ventilation system is
an effective heat recovery (HR), which collects otherwise wasted heat from exhaust
air and reduces the overall heating energy consumption. Mechanical exhaust (ME)

24
ventilation systems were common before mechanical supply and exhaust ventilation
systems. Natural ventilation (NV) is used in buildings without a mechanical air
handling unit. The ventilation of naturally ventilated building depends on weather
conditions and the airflows cannot be distinguished from infiltration (Seppänen
2001; 2008).
Fans in air handling units consume electrical energy, and specific fan power (SFP)
quantifies their energy efficiency. The energy efficiency of air handling units in
terms of heat recovery and specific fan power has improved continuously.
Ventilation types, airflows per heated floor area and air handling unit properties in
each age category are presented in Table 4. The information and values are derived
from Hirvonen et al. (2019a; 2019b), Seppänen (2008) and decrees 1010/2017 and
1048/2017 of the Ministry of the Environment of Finland (2017a; 2017b).
Table 4. Ventilation types, airflows per heated floor area and air handling unit
properties in different age categories.

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
Detached house
Ventilation type NV ME MSE
Supply air flow, l/(s,m2) 0,4
Exhaust air flow, l/(s,m2) 0,281 0,4
HR annual efficiency 0,3 0,45 0,55
SFP, kW/(m3/s) 1,5 2,5 2 1,8
Apartment building
Ventilation type ME MSE
Supply air flow, l/(s,m2) 0,5
Exhaust air flow, l/(s,m2) 0,281 0,5
HR annual efficiency 0,3 0,45 0,55
SFP, kW/(m3/s) 1,5 2,5 2 1,8

4.2.3 Standardized use of the building
Lighting, appliances and occupants contribute to internal heat gains which slightly
reduce the space heating energy demand. Lighting and appliances are also taken
into account in the total electrical energy consumption of building models.
Occupancy rates and internal heat gains per heated floor area for each building type
are presented in Table 5. They are based on decree 1010/2017 of the Ministry of the
Environment of Finland (2017a).

25
Table 5. Occupancy rates and internal heat gains per heated floor area.

Detached house Apartment building
Occupancy rate:
– Lighting 0,1 0,1
– Others 0,6 0,6
Internal heat gains:
– Lighting, W/m2 6 9
– Appliances, W/m2 3 4
– Occupants, W/m2 2 3

4.2.4 Domestic hot water
The heating energy demand of domestic hot water in the building models consists
of standardized use of hot water and distribution losses. The building models are
equipped with an insulated hot water recirculation system which in practice reduces
water consumption and hot water heating energy demand. Half of the heat losses of
hot water recirculation system are added to internal heat gains. The electrical energy
consumption of hot water recirculation pump is also considered. The properties of
domestic hot water systems for both building types are presented in Table 6. The
values are based on decrees 1010/2017 and 1048/2017 of the Ministry of the
Environment of Finland (2017a; 2017b).
Table 6. Standardized use of domestic hot water.

Detached house Apartment building
DHW heating energy
demand, kWh/(m
2,a)
35 35
Distribution efficiency 0,96 0,97
Distribution losses, W/m2 2,17 2,12
Circulation pump energy
consumption, W/m
2
0,019 0,019

4.2.5 Main heating systems & heat distribution
Heating systems are different in terms of heat sources, heat distribution methods
and efficiencies. The heating systems modelled in this study are selected based on
their popularity in the Finnish residential building stock. The standard main heating
systems in detached houses are district heating, wood boiler, direct electric heating,
electric boiler and light fuel oil boiler and the standard main heating systems in
apartment buildings are district heating, direct electric heating and light fuel oil
boiler. The efficiencies of heating systems and the electrical energy consumption of
auxiliary equipment, presented in Table 7, are slightly different between building
types. The values are based on decree 1048/2017 of the Ministry of the Environment
of Finland (2017b).

26
Table 7. Annual efficiency and auxiliary equipment energy consumption of heating
systems.

Detached house Apartment building
Annual
efficiency
Auxiliary energy,
kWh/(m
2,a)
Annual
efficiency
Auxiliary energy,
kWh/(m
2,a)
District heating 0,94 0,60 0,97 0,07
Wood boiler 0,75 0,77
Direct electric heating 1,00 0,00 1,00 0,00
Electric boiler 0,88 0,02
Light fuel oil boiler 0,81 0,99 0,90 0,24

The two different heat distribution methods considered in this study are waterbased radiators and electric radiators. In district heating, wood boiler, electric boiler
and light fuel oil boiler the heat is distributed to spaces by water-based radiators
with design temperatures of 70/40 °C (supply/return) and in direct electric heating
the heat is distributed to spaces by electric radiators. The heat distribution methods
differ in terms of efficiency and auxiliary equipment energy consumption that are
presented in Table 8. The values are based on decree 1048/2017 of the Ministry of
the Environment of Finland (2017b).
Table 8. Annual efficiency and auxiliary equipment energy consumption of heat
distribution methods.

Annual
efficiency
Auxiliary energy,
kWh/(m
2,a)
Water-based radiators 0,90 2
Electric radiators 0,95 0,5

4.3 Major energy efficiency measures
Ground source heat pump (GSHP) and air to water heat pump (AWHP) are
considered major energy efficiency measures in this study. They are used to
thoroughly replace the existing heating systems in both detached house and
apartment building models. GSHP extracts heat from ground and AWHP from
ambient air. The extracted heat energy is used for space heating and domestic hot
water heating. Heat distribution method is water-based radiators with design
temperatures of 60/40 °C in both systems. In practice, heat pumps operate more
efficiently at lower design temperatures, but the selection of higher temperatures
ensures that the existing water-based radiators do not need to be replaced by low
temperature radiators in the building models.
During the coldest outdoor temperatures, buildings with worse thermal insulation
levels require higher peak power from the heating system to be able to maintain
desired indoor conditions. This affects the required capacity of the heat pump
systems which in turn affects the size of the equipment and the embodied CO
2
emissions. Capacities of the GSHP and AWHP systems in different age categories
are presented in Table 9.

27
Table 9. Capacity of GSHP and AWHP in different age categories, kW.

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
Detached house 16 16 12 12 8 8 8 8 8
Apartment building 160 160 120 120 80 80 60 60 60

The annual performance of a heat pump system is quantified by a seasonal
performance factor (SPF). It describes the fraction of heat energy production per
electricity consumption annually. The SPF-values for GSHP and AWHP with a hot
water supply temperature of 60 °C are based on decree 1048/2017 of the Ministry
of the Environment of Finland (2017b) and they are presented in Table 10.
Table 10. Seasonal performance factors of GSHP and AWHP.

SPF
Ground source heat pump 2,5
Air to water heat pump 2,2

In building models where direct electric heating is replaced by GSHP or AWHP, the
existing electric radiators must be replaced by water-based radiators and new heat
distribution pipe network is required. The quantities of heat distribution pipes and
water-based radiators in these cases are presented in Table 11.
Table 11. Quantities of heat distribution pipes and water-based radiators when direct
electric heating is replaced by GSHP or AWHP in building models.

Water based
radiators, units
Heat distribution
pipes, m
Detached house 12 60
Apartment building 105 600

4.4 Additional energy efficiency measures
Additional energy efficiency measures are used to supplement the existing heating
system. In detached house models, additional energy efficiency measures include
air to air heat pump (AAHP), exhaust air heat pump (EAHP), solar systems and
retrofitted external windows and doors. Additional energy efficiency measures can
also be utilized with GSHP and AWHP. However, in this study only solar systems
and retrofitted external windows and doors are considered with existing GSHP and
AWHP systems since in those cases the application of other heat pumps (AAHP,
EAHP) would only reduce the overall efficiency of the heating system.
Exhaust air heat pump (EAHP)
Exhaust air heat pump is used to supplement the main heating systems in building
models with mechanical exhaust ventilation system. EAHP recovers heat from
exhaust air flow which can be used for space heating with water-based radiators and
domestic hot water heating. In case of electric radiators, the heat energy provided
by EAHP is only used for domestic hot water heating. It is not possible to install

28
EAHP alongside natural ventilation system and it is not feasible with mechanical
supply and exhaust ventilation that already has an effective heat recovery. The
capacity of an EAHP unit, limited by the exhaust air flow, is 1,5 kW in detached
house models and 24 kW in apartment building models. The SPF-value of EAHP is
2,0 which is based on decree 1048/2017 of the Ministry of the Environment of
Finland (2017b).
Air to air heat pump (AAHP)
Air to air heat pump is used to supplement the main heating system in detached
house models only. AAHP is excluded from apartment buildings since it is usually
installed individually by tenants and therefore they affect only individual
apartments and not the whole apartment building. AAHP extracts heat from
ambient air and provides hot air to one space inside a building. In this study, the
capacity of an AAHP unit is 3,5 kW and the SPF-value is 2,8 which is based on decree
1048/2017 of the Ministry of the Environment of Finland (2017b).
Solar systems
Solar systems in the building models include photovoltaics (PV) for electricity
generation and solar thermal collectors (ST) for domestic hot water heating. They
are positioned on the roofs of building models and external shadings such as trees
or other buildings are not considered. An additional hot water tank is used to store
excess heat energy from solar thermal collectors. Volume of the hot water tank is 0,2
m
3 in detached house models and 2 m3 in apartment building models. Electricity
storage is not considered. The total areas and efficiencies of solar systems in building
models are presented in Table 12. The total area of both solar systems covers
approximately 10 % of the roof area of building models.
Table 12. Areas and efficiencies of solar systems in building models.

Detached house Apartment building
Area, m2 Efficiency Area, m2 Efficiency
Photovoltaic 4 0,1 50 0,1
Solar thermal collector 7 0,75 100 0,75

Retrofitted external windows and doors
The purpose of retrofitted external windows and doors is to reduce the building’s
heating energy demand by improving the thermal insulation level of the building
envelope. This is an energy efficiency measure that can benefit especially old
buildings with poor thermal insulation levels. The properties and quantities of
energy efficient windows and doors used in building models are presented in Table
13 and Table 14.

29
Table 13. Properties of energy efficient windows and doors.

U-value, W/m2K g-value
Window 0,6 0,38
Door 0,75

Table 14. Quantities of external windows and doors in building models.

Windows, units Doors, units
Detached house 9 3
Apartment building 84 24

4.5 CO2-emissions & carbon footprint reduction potential
The annual operational energy consumptions of each building model with different
characteristics and combination of heating systems and energy efficiency measures
are simulated separately. Energy consumptions are converted to CO
2-emissions
using average emission factors. Operational emission reduction potentials are
determined by comparing the annual CO
2-emissions of each simulation case and
this information is further used to determine the carbon footprint reduction
potential in building models.
Emission factors for on-site heat energy production such as wood boiler and light
fuel oil boiler are based on “Fuel classification 2021” by Statistics Finland (2021c)
and emission factors for centralized energy production such as electricity and
district heating are based on “Energy 2020 table service” by Statistics Finland
(2021d). It is debatable whether wood should be considered carbon neutral or not,
but in this study the CO
2-emissions caused by combustion of wood are considered
as such. Emission factors used in this study are presented in Table 15.
Table 15. Emission factors for different energy sources.

Emission factor,
kgCO
2/kWh
Wood 0,403
Light fuel oil 0,255
District heat 0,195
Electricity 0,104

The carbon footprint reduction potential in building models determined by
comparing the product stage CO
2e-emissions of the equipment related to energy
efficiency measures and the operational CO
2-emission reductions achieved during
their service life. If the product stage emissions are outweighed by the operational
emission reductions during the service of the energy efficiency measure, the carbon
footprint of the building is reduced. Otherwise, the carbon footprint of the building
is increased. Based on the EN 15804 standard, this approach considers the life cycle
stages A1-A3 (product stage) and B6 (operational energy use) of the energy
efficiency measures. Service lives of energy efficiency measures are presented in
Table 16.

30
Table 16. Service life of energy efficiency measure.

Service life, a
Heat pumps 25
Solar systems 20
Windows and doors 50

Product stage CO2e-emissions are obtained from the emissions database for
construction by the Finnish Environment Institute (2021). Generic product stage
emissions data relevant to this study is currently available for heat distribution pipe,
water radiator, air to air heat pump, solar systems, windows and doors. Product
stage emissions data for ground source heat pump, air to water heat pump and
exhaust air heat pump were not readily available from reliable sources. In this study
they are derived from the product stage emissions data of AAHP, assuming that the
capacity of an AAHP unit is 3,5 kW and that the product stage emissions would be
equal for all heat pumps and scalable in terms of capacity (kW). Product stage CO
2eemissions of equipment related to energy efficiency measures are presented in Table
17.
Table 17. Product stage CO2e-emissions of equipment related to energy efficiency
measures.

Product stage emissions,
kgCO
2e/[unit]
[unit]
GSHP 245 kW
AWHP 245 kW
EAHP 245 kW
AAHP 859 unit
PV 235 m2
ST 75,3 m2
Window 120 unit
Door 34,5 unit
Heat distribution pipe 0,55 m
Water radiator 116 unit

31
5 Carbon footprint analyses
5.1 Operational energy consumption
The annual operational energy consumptions are based on IDA ICE simulations
performed on the building models. The differences between heating systems are
mainly due to differences in their efficiencies and auxiliary equipment energy
consumption. On the other hand, the age of a building generally affects its energy
performance, and new buildings are more energy efficient than the older ones. The
annual operational energy consumption per floor area for detached house models
are presented in Figure 12. The highest value is 455,0 kWh/(m
2,a) in wood boiler
equipped detached houses built in 1975 or before while the lowest value is 104,3
kWh/(m
2,a) in ground source heat pump equipped detached houses built in 2018 or
later.
Figure 12. Annual operational energy consumption of detached house models.
Overall, the simulated energy consumption per floor area is lower in apartment
buildings than detached houses. There are a couple of reasons for this difference.
First, the heating systems have better efficiency in larger scale solutions. Second, the
fraction of envelope area to floor area is smaller in apartment buildings resulting in
lower heat losses through the building envelope and infiltration. Simulated annual
operational energy consumption per floor area for apartment building models are
presented in Figure 13. The highest value is 291,3 kWh/(m
2,a) in light fuel oil boiler
equipped apartment buildings built in 1975 or before while the lowest value is 93,2

32
kWh/(m2,a) in ground source heat pump equipped apartment buildings built in
2018 or later.
Figure 13. Annual operational energy consumption of apartment building models.
5.2 Operational CO2-emissions
The annual operational CO2-emissions are directly proportional to the annual
energy consumption and heat source specific emission factors. Of the different
heating systems and energy sources considered in this study, wood-based solutions
have the highest annual CO
2-emissions by a large margin. Heating solutions based
on electricity are relatively environmentally friendly with GSHP and AWHP
contributing to the lowest annual CO
2-emissions in both building types and all age
categories. The annual energy consumption related CO
2-emissions per floor area in
detached house and apartment building models are presented in Figure 14 and
Figure 15. In detached house models, the values are ranging from 176,2
kgCO
2/(m2,a) in a wood boiler equipped detached house built in 1975 or before to
10,8 kgCO
2/(m2,a) in a ground source heat pump equipped detached house built in
2018 or later. In apartment building models the values are ranging from 68,9
kgCO
2/(m2,a) in a light fuel oil boiler equipped apartment building built in 1975 or
before to 9,7 kgCO
2/(m2,a) in a ground source heat pump equipped apartment
building built in 2018 or later.

33
Figure 14. Annual operational CO2-emissions of detached house models.
Figure 15. Annual operational CO
2-emissions of apartment building models.
34
5.3 Operational CO2-emission reduction potential
Building’s operational energy consumption related CO2-emissions can be reduced
by applying various energy efficiency measures. They can affect the emissions in
many ways. For example, replacing wood boiler, light fuel oil boiler or district
heating with GSHP or AWHP improves the energy efficiency of the heating system,
and the energy source is changed to electricity which has a relatively low emission
factor. EAHP or AAHP reduce the load of the main heating system and they produce
their share of heating energy by using electricity and with a better efficiency. Solar
systems produce electricity for appliances and auxiliary HVAC equipment, and heat
energy for domestic hot water heating from completely emission-free energy
sources. Retrofitted windows and doors reduce the building’s energy demand for
space heating. The magnitude of emission reduction potential achieved by different
energy efficiency measures depends on building type, age category and existing
heating system.
The operational CO
2-emission reduction potentials in detached house models are
presented in Table 18. In the table, existing heating systems are highlighted in grey
and the energy efficiency measures are listed below each of them. Greatest emission
reductions can be achieved by the major energy efficiency measures GSHP and
AWHP. The results show a trend that the lower the efficiency of the existing heating
system and the higher the emission factor of its energy source, the greater the
emission reduction potential of the major energy efficiency measures. The emission
reduction potentials of EAHP and AAHP are limited due to their fixed capacities.
Solar systems provide greater relative emission reduction potential in newer
buildings because the fraction of electricity and domestic hot water heating of the
total energy consumption is larger in newer buildings than in the older ones. On the
contrary, retrofitted windows and doors provide greater emission reduction
potential in older buildings because their current thermal insulation level is poor.
The emission reduction potentials of energy efficiency measures with existing GSHP
and AWHP heating systems are presented in the bottom of the table. In those cases,
only the effects of solar systems and retrofitted windows and doors were modeled.

35
Table 18. Operational CO2-emission reduction potential with different energy
efficiency measures in detached house models.

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
Wood boiler
GSHP* 89 % 89 % 88 % 88 % 87 % 87 % 86 % 87 % 87 %
AWHP* 86 % 86 % 86 % 86 % 85 % 85 % 84 % 84 % 84 %
EAHP** 35 % 38 %
AAHP** 33 % 32 % 30 % 30 % 25 % 26 % 23 % 25 % 26 %
Solar systems** 9 % 10 % 11 % 12 % 14 % 14 % 17 % 16 % 17 %
Retrofitted windows
and doors**
10 % 7 % 8 % 8 % 5 % 5 % 1 % 1 % 1 %
Light fuel oil boiler
GSHP* 81 % 81 % 80 % 80 % 79 % 79 % 77 % 78 % 78 %
AWHP* 77 % 77 % 76 % 76 % 75 % 75 % 74 % 74 % 74 %
EAHP** 32 % 34 %
AAHP** 30 % 30 % 28 % 28 % 23 % 24 % 21 % 23 % 24 %
Solar systems** 9 % 10 % 11 % 12 % 14 % 14 % 16 % 16 % 17 %
Retrofitted windows
and doors**
10 % 7 % 7 % 8 % 5 % 5 % 1 % 1 % 1 %
District heating
GSHP* 72 % 72 % 71 % 70 % 69 % 68 % 67 % 67 % 67 %
AWHP* 65 % 65 % 64 % 64 % 63 % 63 % 61 % 62 % 62 %
EAHP** 28 % 30 %
AAHP** 28 % 28 % 26 % 26 % 21 % 21 % 19 % 21 % 22 %
Solar systems** 9 % 10 % 11 % 12 % 14 % 14 % 16 % 16 % 17 %
Retrofitted windows
and doors**
10 % 7 % 7 % 8 % 5 % 5 % 1 % 1 % 1 %
Electric boiler
GSHP* 52 % 52 % 51 % 50 % 48 % 48 % 46 % 47 % 47 %
AWHP* 41 % 41 % 40 % 40 % 39 % 38 % 37 % 38 % 38 %
EAHP** 20 % 22 %
AAHP** 21 % 21 % 20 % 19 % 15 % 16 % 14 % 15 % 16 %
Solar systems** 9 % 10 % 11 % 12 % 14 % 13 % 16 % 16 % 17 %
Retrofitted windows
and doors**
10 % 7 % 7 % 8 % 5 % 4 % 1 % 1 % 1 %
Direct electric heating
GSHP* 44 % 43 % 41 % 41 % 39 % 39 % 37 % 38 % 37 %
AWHP* 30 % 29 % 28 % 28 % 28 % 28 % 27 % 27 % 26 %
EAHP** 7 % 7 %
AAHP** 18 % 17 % 16 % 15 % 11 % 11 % 10 % 11 % 11 %
Solar systems** 8 % 9 % 10 % 11 % 13 % 13 % 16 % 16 % 16 %
Retrofitted windows
and doors**
10 % 7 % 7 % 8 % 4 % 4 % 1 % 1 % 1 %
Air to water heat pump
Solar systems** 5 % 6 % 7 % 7 % 9 % 9 % 11 % 11 % 12 %
Retrofitted windows
and doors**
9 % 6 % 7 % 7 % 4 % 4 % 1 % 1 % 1 %
Ground source heat pump
Solar systems** 4 % 6 % 6 % 7 % 8 % 8 % 11 % 11 % 12 %
Retrofitted windows
and doors**
9 % 6 % 6 % 7 % 4 % 4 % 1 % 1 % 1 %

* Major energy efficiency measure (replaces existing heating system)
** Additional energy efficiency measure (supplements existing heating system)

36
The operational CO2-emission reduction potentials in apartment building models
are presented in Table 19. The values are slightly lower than in detached house
models but follow the same trends.
Table 19. Annual operational CO2-emission reduction potential in apartment building
models.

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
Light fuel oil boiler
GSHP* 77 % 76 % 76 % 75 % 73 % 73 % 71 % 71 % 70 %
AWHP* 71 % 70 % 70 % 70 % 67 % 67 % 66 % 65 % 65 %
EAHP** 35 % 38 % 42 % 44 %
Solar systems** 11 % 12 % 14 % 15 % 16 % 16 % 19 % 21 % 22 %
Retrofitted windows
and doors**
11 % 7 % 9 % 9 % 5 % 5 % 1 % 2 % 2 %
District heating
GSHP* 68 % 68 % 67 % 66 % 64 % 64 % 61 % 60 % 60 %
AWHP* 60 % 59 % 59 % 58 % 56 % 56 % 54 % 53 % 53 %
EAHP** 31 % 33 % 37 % 38 %
Solar systems** 11 % 12 % 14 % 14 % 16 % 16 % 19 % 20 % 21 %
Retrofitted windows
and doors**
11 % 7 % 8 % 9 % 5 % 5 % 1 % 2 % 2 %
Direct electric heating
GSHP* 41 % 40 % 38 % 38 % 36 % 36 % 32 % 31 % 30 %
AWHP* 25 % 24 % 23 % 23 % 22 % 22 % 20 % 19 % 19 %
EAHP** 7 % 8 % 10 % 10 %
Solar systems** 10 % 11 % 13 % 13 % 14 % 15 % 17 % 19 % 20 %
Retrofitted windows
and doors**
11 % 7 % 8 % 8 % 4 % 4 % 1 % 1 % 1 %
Air to water heat pump
Solar systems** 8 % 9 % 9 % 10 % 10 % 11 % 12 % 13 % 14 %
Retrofitted windows
and doors**
10 % 6 % 7 % 8 % 4 % 4 % 1 % 1 % 1 %
Ground source heat pump
Solar systems** 6 % 7 % 9 % 9 % 10 % 10 % 12 % 14 % 14 %
Retrofitted windows
and doors**
9 % 6 % 7 % 7 % 3 % 3 % 1 % 1 % 1 %

* Major energy efficiency measure (replaces existing heating system)
** Additional energy efficiency measure (supplements existing heating system)
5.4 Carbon footprint reduction potential
The carbon footprint reduction potential in building models consists of the adverse
effects of product stage CO
2e-emissions of equipment related to energy efficiency
measures and the operational CO
2-emission reductions achieved during their
service life. The carbon footprint reduction potentials of detached house and
apartment building models are presented in Table 20 and Table 21. The potential
carbon footprint reductions are calculated over the service lives of the energy
efficiency measures and they are presented in kilograms of CO
2 per heated floor
area. Negative values indicate how much the carbon footprint of a building is

37
reduced and positive values indicate how much it is increased by utilizing the energy
efficiency measures.
In general, older building models have the greatest carbon footprint reduction
potential. Replacing existing heating systems that use carbon intensive energy
sources such as wood boiler or light fuel oil boiler by GSHP or AWHP are the most
beneficial actions while retrofitting windows and doors does not always lead to a
reduced carbon footprint. In those cases, the achieved operational CO
2-emission
reductions are not sufficient to outweigh the product stage CO
2e-emissions of the
installed equipment during their service life.
Table 20. Carbon footprint reduction potential in detached house models during the
service life of energy efficiency measures, kgCO
2/m2.

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
Wood boiler
GSHP* -3880 -3354 -3028 -2785 -2288 -2236 -1772 -1801 -1732
AWHP* -3764 -3254 -2940 -2706 -2225 -2174 -1724 -1753 -1686
EAHP** -1217 -1205
AAHP** -1426 -1229 -1044 -958 -653 -652 -473 -513 -516
Solar systems** -293 -287 -279 -283 -278 -268 -261 -262 -262
Retrofitted windows
and doors**
-875 -515 -510 -508 -255 -240 -33,6 -28,3 -30,8
Light fuel oil boiler
GSHP* -2083 -1798 -1626 -1494 -1230 -1201 -950 -965 -928
AWHP* -1967 -1698 -1538 -1415 -1167 -1140 -902 -918 -882
EAHP** -655 -648
AAHP** -784 -676 -575 -527 -359 -358 -259 -282 -283
Solar systems** -169 -166 -161 -163 -160 -155 -151 -151 -151
Retrofitted windows
and doors**
-508 -297 -295 -293 -145 -136 -14,8 -11,7 -13,1
Electric boiler
GSHP* -500 -425 -390 -358 -296 -288 -223 -227 -218
AWHP* -384 -325 -302 -278 -233 -227 -176 -179 -172
EAHP** -165 -163
AAHP** -205 -177 -153 -140 -92,4 -90,8 -65,6 -71,3 -71,4
Solar systems** -60,4 -59,1 -57,6 -58,3 -56,8 -54,7 -53,5 -53,6 -53,4
Retrofitted windows
and doors**
-189 -107 -107 -109 -48,1 -45,3 1,7 2,5 2,6
District heating
GSHP* -1213 -1044 -946 -868 -717 -700 -551 -560 -538
AWHP* -1097 -944 -858 -789 -654 -638 -504 -512 -492
EAHP** -383 -378
AAHP** -474 -409 -348 -318 -217 -216 -156 -170 -171
Solar systems** -109 -107 -104 -106 -104 -100 -97,5 -97,5 -97,6
Retrofitted windows
and doors**
-331 -192 -190 -189 -91,5 -85,7 -6,0 -3,8 -5,0

38
Table 20 (continued).

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
Direct electric heating
GSHP* -324 -268 -242 -219 -186 -182 -135 -136 -128
AWHP* -208 -167 -154 -140 -123 -120 -87,9 -88,1 -81,6
EAHP** -44,1 -44,2
AAHP** -145 -123 -101 -90,7 -54,5 -54,4 -38,0 -40,7 -40,5
Solar systems** -41,9 -42,0 -42,0 -41,9 -42,4 -41,9 -41,9 -41,9 -41,8
Retrofitted windows
and doors**
-159 -87,5 -90,6 -88,1 -35,5 -35,4 4,7 3,9 3,8
Air to water heat pump
Solar systems** -9,7 -12,5 -11,8 -12,9 -13,8 -13,5 -15,4 -15,1 -15,3
Retrofitted windows
and doors**
-100 -55,1 -58,8 -54,4 -23,0 -22,6 5,8 5,9 6,1
Ground source heat pump
Solar systems** -1,1 -5,5 -4,2 -6,6 -8,4 -8,2 -11,7 -11,4 -11,8
Retrofitted windows
and doors**
-74,5 -39,2 -40,4 -39,7 -13,8 -13,2 7,5 7,6 7,7

* Major energy efficiency measure (replaces existing heating system)
** Additional energy efficiency measure (supplements existing heating system)

39
Table 21. Carbon footprint reduction potential in apartment building models during
the service life of energy efficiency measures, kgCO
2/m2.

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
Light fuel oil boiler
GSHP* -1301 -1159 -992 -941 -820 -812 -656 -596 -554
AWHP* -1197 -1066 -914 -866 -755 -748 -606 -551 -513
EAHP** -592 -579 -561 -554
Solar systems** -139 -138 -136 -135 -132 -131 -129 -129 -128
Retrofitted windows
and doors**
-369 -219 -225 -225 -102 -102 -18,7 -22,3 -22,9
District heating
GSHP* -827 -735 -630 -597 -521 -516 -415 -376 -349
AWHP* -723 -642 -552 -523 -456 -451 -366 -332 -308
EAHP** -378 -369 -357 -353
Solar systems** -97,9 -96,7 -95,2 -94,9 -92,0 -92,0 -90,5 -90,1 -89,6
Retrofitted windows
and doors**
-260 -153 -158 -158 -69,0 -69,4 -11,4 -13,6 -13,9
Direct electric heating
GSHP* -239 -206 -173 -161 -148 -147 -113 -98 -87,6
AWHP* -135 -113 -95 -86,9 -83,3 -82,3 -62,8 -53,6 -47,1
EAHP** -45,9 -46,1 -46,4 -46,5
Solar systems** -41,1 -41,3 -41,4 -41,3 -42,0 -41,9 -42,1 -42,2 -42,3
Retrofitted windows
and doors**
-134 -76,1 -76,0 -75,9 -30,5 -30,3 -1,4 -2,2 -2,5
Air to water heat pump
Solar systems** -18,9 -19,3 -17,7 -17,3 -18,2 -18,1 -19,3 -19,2 -19,2
Retrofitted windows
and doors**
-87,8 -50,6 -50,5 -50,9 -20,9 -20,6 0,7 -0,6 -0,1
Ground source heat pump
Solar systems** -6,3 -8,6 -9,2 -9,9 -11,2 -11,3 -14,0 -14,5 -15,2
Retrofitted windows
and doors**
-62,8 -34,8 -35,6 -35,7 -12,6 -12,6 2,4 1,9 1,9

* Major energy efficiency measure (replaces existing heating system)
** Additional energy efficiency measure (supplements existing heating system)

40
6 Exemplary case study analyses
In this chapter, the previous carbon footprint analysis method and results are
applied to residential buildings in Tampere to present their estimated annual
operational CO
2-emissions on a map and to estimate the carbon footprint reduction
potential in three randomly selected detached houses and apartment buildings.
Geographic location and other basic properties of buildings such as building type,
construction year and existing heating system are obtained from an open access data
portal Open Data Tampere (n.d.) which makes it possible to utilize GIS-data with
the carbon footprint analysis method.
6.1 Detached houses
Figure 16 shows a map containing residential buildings in Tampere that are colorcoded based on their estimated annual operational CO2-emissions, as well as three
randomly selected example detached houses DH1, DH2 and DH3, in which the
impact of energy efficiency measures on the carbon footprint is examined. Buildings
painted in dark grey are either non-residential buildings or residential buildings
without sufficient input data. The basic properties of example detached houses and
selected energy efficiency measures are presented in Table 22.
Figure 16. Residential buildings in Tampere color coded by estimated annual
operational CO
2-emissions, example buildings DH1, DH2 and DH3 highlighted.
41
Table 22. Basic properties of example detached houses.

Construction
year
Floor area,
m
2
Existing heating
system
Applied energy
efficiency measures
DH1 1999 151 Direct electric heating AAHP
DH2 1987 184 District heating GSHP + retrofitted
windows & doors
DH3 1956 105 Light fuel oil boiler AWHP

When the building type, construction year, floor area and existing heating system
are known it is possible to determine the estimated impact of energy efficiency
measures on the carbon footprint of the buildings by using the carbon footprint
analyses presented earlier in Table 20 and Table 21.
Here, the estimated impact of an air to air heat pump on the carbon footprint of the
example building DH1 is determined. Figure 17 highlights how the carbon footprint
reduction potential is determined from Table 20. First, navigate to the correct
existing heating system, which is direct electric heating in this case. Then read the
estimated carbon footprint reduction potential of the selected energy efficiency
measure based on the age of the building. An AAHP leads to an estimated carbon
footprint reduction of -90,7 (kgCO
2/m2) * 151 m2 = -13 700 kgCO2 (or -13,7 tCO2) on
the carbon footprint of the example building DH1 over the 25-year service life of the
heat pump.

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
Direct electric heating
GSHP* -336 -279 -250 -2 27 -192 -188 -141 -142 -133
AWHP* -220 -179 -162 -148 -129 -126 -93,5 -93,7 -87,3
AAHP** -90,7 -54,5 -54,4 -38,0 -40,7 -40,5
EAHP** -45,2 -45,2
Solar systems** -41,9 -42,0 -42,0 -41,9 -42,4 -41,9 -41,9 -41,9 -41,8
Retrofitted windows
and doors**
-159 -87,5 -90,6 -88,1 -35,5 -35,4 4,7 3,9 3,8

-145 -123 -101 Figure 17. Example detached house DH1, determining the carbon footprint
reduction potential of single energy efficiency measure.
In the example building DH2, the existing district heating system is replaced by a
ground source heat pump and additionally the external windows and doors are
replaced by energy efficient ones. Figure 18 shows how to determine the carbon
footprint reduction potential of multiple energy efficiency measures by using Table
20. A GSHP reduces the carbon footprint of the building by -877 (kgCO
2/m2) * 184
m
2 = -161 400 kgCO2 (or -161,4 tCO2) over the period of 25 years and retrofitting
windows and doors provides an additional reduction of -39,7 (kgCO
2/m2) * 184 m2
= -7 300 kgCO2 (or -7,3 tCO2) over the service life of windows and doors which is 50
years.

42

-1975 1976- 1978- 1985- 2003- 2008- 2010- 2012- 2018-
District heating
GSHP* -877 -723 -705 -556 -566 -544
-1224 -1056 -955
AWHP* -1108 -955 -867 -798 -660 -644 -509 -518 -498
AAHP** -474 -409 -348 -318 -217 -216 -156 -170 -171
EAHP** -384 -379
Solar systems** -109 -107 -104 -106 -104 -100 -97,5 -97,5 -97,6
Retrofitted windows
and doors**
-331 -192 -190 -189 -91,5 -85,7 -6,0 -3,8 -5,0
Ground source heat pump
Solar systems** -1,1 -5,5 -4,2 -6,6 -8,4 -8,2 -11,7 -11,4 -11,8
Retrofitted windows
and doors**
-40,4 -39,7 -13,8 -13,2 7,5 7,6 7,7
-74,5 -39,2

Figure 18. Example detached house DH2, determining the carbon footprint
reduction potential of multiple energy efficiency measures.
Figure 19 shows the cumulative carbon footprint reductions in example buildings
DH1, DH2 and DH3 by applying the selected energy efficiency measures during the
first 25 years of their service life. The CO
2-emissions embodied to materials are
outweighed by operational CO
2-emission reductions already in 1-3 years.
Figure 19. Cumulative carbon footprint reductions in DH1, DH2 and DH3.
1.
2.

43
6.2 Apartment buildings
The same analysis is applied to randomly selected example apartment buildings
AB1, AB2 and AB3. The example buildings are presented on a map in Figure 20 and
their basic properties and selected energy efficiency measures are presented in Table
23.
Figure 20. Residential buildings in Tampere color coded by estimated annual
operational CO
2-emissions, example buildings AB1, AB2 and AB3 highlighted.
Table 23. Basic properties of example apartment buildings.

Construction
year
Floor area,
m
2
Existing heating
system
Applied energy
efficiency measures
AB1 2011 2396 District heating GSHP + solar systems
AB2 1949 1518 District heating GSHP + retrofitted
windows and doors
AB3 1938 600 Light fuel oil boiler AWHP

The impacts of energy efficiency measures on the carbon footprint of the example
apartment buildings are determined in a same way as for the example detached
houses, this time using Table 21. The cumulative effects of energy efficiency
measures on the carbon footprint of the example buildings for the first 25 years are
presented in Figure 21. In these example cases, the CO
2-emissions embodied to
materials are outweighed by operational CO
2-emission reductions in less than 2
years.

44
Figure 21. Cumulative carbon footprint reductions in AB1, AB2 and AB3.
45
7 Discussion
Given the EU-level and national CO2-emission reduction targets, the emission
reduction potential of the existing residential building stock is an important and
timely issue. In Finland, buildings heated with oil-based fuels have received more
attention recently, but based on the results of this study, it seems that emission
reductions can be achieved by utilizing various energy efficiency measures in other
buildings as well. However, there are many possible error sources behind the carbon
footprint analysis method created in this study that can affect the reliability of the
results.
7.1 Reliability of the results
In this study the residential buildings were divided into detached houses and
apartment buildings. In addition, each building type was divided into nine age
categories based on construction year. The characteristics of the building models
were mainly determined based on the decree on building’s energy performance
certificate (1048/2017) of the Ministry of the Environment of Finland (2017b). The
characteristics of building models of each age category were selected to meet the
minimum energy efficiency requirements of their era. For this reason, the results
are conservative rather than optimistic. For example, this study does not consider
passive houses or zero-energy buildings which have recently grown in popularity.
The limited amount of building models created in this study were supposed to
represent the whole Finnish residential building stock. Since the energy
performance of each real building is unique, the simulation results are not likely to
be very accurate for individual buildings but can rather be used to identify trends on
a larger scale.
The annual energy consumptions of building models and the effects of various
energy efficiency measures were simulated using a dynamic building energy
simulation tool IDA ICE. The climatic conditions of Helsinki-Vantaa were used in
the simulations, which is why the results are best suited for buildings located in the
southern parts of Finland. The more northerly the location and the colder the
climatic conditions, the greater the actual heating energy demand of a building. The
simulated energy consumption of the building models varied between 104,3 – 455,0
kWh/(m
2,a) in detached houses and between 93,2 – 291,3 kWh/(m2,a) in apartment
buildings, depending on the age category and the heating system. Assessing the
reliability of the simulated energy consumptions by comparing them to actual
measured energy consumption of buildings would be useful, but it is challenging as
the energy consumption data is private. Laitinen et al. (2014) have performed
similar kind of energy consumption simulations based on representative building
types. In their study, the simulated energy demand varied between 108,0 – 300,0
kWh/(m
2,a) in detached houses and between 103,5 – 295,1 kWh/(m2,a) in
apartment buildings of different ages.

46
The simulated annual operational energy consumptions were converted into annual
CO
2-emissions by fuel-specific emission factors and average emission factors for
electricity and district heat production in Finland. The estimated operational CO
2
emissions varied between 10,8 – 176,2 kgCO
2/(m2,a) in detached houses and
between 9,7 – 68,9 kgCO
2/(m2,a) in apartment buildings, depending on the age
category and the heating system. There is a direct link between on-site heating
energy production and the fuel-specific emission factor. However, there is
uncertainty about how buildings contribute to emissions from centralized electricity
and district heat production. Consumption of electricity and district heating in
buildings has an indirect link to CO
2-emissions, because in fact these emissions
occur in power plants and depend on their fuel mixes. For example, a building that
uses only electricity or district heat as its energy sources can be completely carbon
neutral regardless of its operational energy consumption, if the energy is produced
carbon neutrally in centralized power plants. Another question is whether wood
should be treated as a carbon neutral fuel. Wood has a high CO
2-emission factor
although it is a renewable resource and growing forests capture carbon dioxide from
the atmosphere. However, in this study the CO
2-emissions from combustion of
wood were considered as such.
The operational CO
2-emission reduction potential in buildings was determined by
comparing the operational emissions of building models with different
combinations of heating systems and applied energy efficiency measures. Replacing
the existing heating system with a ground source heat pump or an air source heat
pump proved to be the most effective way to reduce operational CO
2-emissions.
According to the results, a ground source heat pump can provide emission
reductions of 37 – 89 % in detached houses and 30 – 77 % in apartment buildings,
depending on the age category and the existing heating system. The results are quite
similar compared to what Hirvonen et al. (2019a; 2019b) found in their studies on
the operational CO
2-emission reduction potential of Finnish residential buildings.
According to Hirvonen et al (2019a; 2019b), the emissions can be reduced costeffectively up to 85 – 92 % in detached houses and up to 68 – 80 % in apartment
buildings of different ages by utilizing combinations of various energy efficiency
measures. The studies by Hirvonen et al (2019a; 2019b) examined the combined
effects of various energy efficiency measures and optimized them in terms of
magnitude, costs and emission reductions. In this study, only the effects of
individual energy efficiency measures were modeled at a time, their magnitude was
predefined, and costs were not considered.
The impacts of energy efficiency measures on the building’s carbon footprint were
assessed based on the product stage CO
2-emissions of equipment related to the
energy efficiency measures and the operational CO
2-emission reductions achieved
by utilizing them. The emissions database for construction by the Finnish
Environment Institute (2021), from which the generic product stage CO
2-emissions
of energy efficiency measures were obtained, is a major step in the right direction
but there are still some gaps especially in the emissions data of HVAC products. A
wider range of product should be available, and it would be useful if the database

47
included emissions data from additional life cycle stages. Based on the EN 15978
standard, product stage and operational energy use related CO
2-emissions were
considered in this study. Other life cycle stages such as construction process stage,
maintenance and repairs during the use stage, and end of life stage were not
considered due to insufficient information. It is difficult to estimate how they would
have affected the results, and this could be determined by conducting further
studies.
GIS-data offers excellent opportunities to look at trends in operational energy
consumption, CO
2-emissions and the carbon footprint of the whole residential
building stock. However, this requires the availability of GIS-data related to
buildings. So far, only a few cities in Finland offer open access to data on their
building stock. For this reason, the application of GIS-data in addition to the carbon
footprint analysis method created in this study is limited to only a small part of the
entire Finnish residential building stock. On the other hand, the available GIS-data
may be outdated or otherwise incomplete. For example, energy efficiency measures
applied in existing residential buildings cannot be detected, except for changes in
heating systems in some cases. As a result of these uncertainties, analysis based on
GIS-data may not give a true picture and may lead to erroneous results for individual
buildings.
7.2 Further development
The results of this study could be utilized in the development of a service based on a
map database, which presents the estimated operational energy consumption, CO
2
emissions and the effects of various energy efficiency measures on the carbon
footprint of individual Finnish residential buildings. To enable this, GIS-data that
contains basic information about buildings, such as building type, construction area,
floor area and heating system, should be readily available throughout the whole
country. To avoid false results and to increase the accuracy of the service, building
owners could be given the opportunity to add or revise information about their own
building. If the service were to reach building owners, it could motivate them to
implement energy efficiency measures and thus accelerate the carbon dioxide
emission reductions in residential building sector.

48
8 Summary
The main objective of this thesis was to examine the impacts of various energy
efficiency measures on the carbon footprint of Finnish residential buildings. The
carbon footprint analysis method and results were applied to randomly selected
residential buildings in Finland utilizing GIS-data. Some gaps in knowledge were
identified and they should be further studied.
The estimated annual operational energy consumption and subsequent CO
2
emissions are slightly higher in detached houses than in apartment buildings. New
buildings are generally more energy efficient than the older ones, and as a result, the
newer the building, the lower its operational CO
2-emissions. Emissions from
Finnish residential buildings are also significantly affected by the energy sources
used.
The highest operational CO
2-emission reductions in residential buildings can be
achieved by replacing the existing heating system with a ground source heat pump
or an air to water heat pump. These major energy efficiency measures can also
reduce the building’s carbon footprint the most, considering the CO
2-emissions
embodied to energy efficiency measures and the operational CO
2-emission
reductions achieved over their service life. The carbon footprint of Finnish
residential buildings can also be reduced through other energy efficiency measures
such as exhaust air heat pump, air to air heat pump, solar systems and by replacing
the external windows and doors with more energy efficient ones. In general, the
older the building, the greater the impact of energy efficiency measure on the carbon
footprint of the building. In some cases, the achieved operational CO
2-emission
reductions might not be sufficient to outweigh the CO
2-emissions embodied to
materials, and the carbon footprint of a building is increased. It is therefore
important to also consider the product stage CO
2-emissions of the energy efficiency
measures.
The carbon footprint analysis method created in this thesis can be applied to
randomly selected Finnish residential buildings. The greatest benefits of the method
are achieved by utilizing GIS-data. The availability and reliability of GIS-data related
to buildings should be improved. In the future, the results of this study and GISdata could be used to develop a web-based service that involves building owners and
motivates them to implement different energy efficiency measures. This could help
accelerate the CO
2-emission reductions in the residential sector on the path to
common emission reduction targets.

49
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