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Sustainability within the Construction Sector. CILECCTA – Life Cycle Costing and Assessment.

Authors:
  • Fraunhofer IBP and University of Stuttgart
www.cileccta.com
CILECCTA Life Cycle Cosng and Assessment
Sustainability within the
Construcon Sector
Sustainability within the Construcon Sector
CILECCTA – Life Cycle Cosng and Assessment
Authors: The CILECCTA partners
Editor: SINTEF Building and Infrastructure
ISBN 978-82-536-1343-7 (pdf)
© Copyright SINTEF Academic Press 2013 (Oslo, Norway)
The material in this publicaon is covered by the provisions of
the Norwegian Copyright Act. Without any special agreement
with SINTEF Academic Press any copying and making available
of the material is only allowed to the extent that this is permit-
ted by law or allowed through an agreement with Kopinor, the
Reproducon Rights Organisaon for Norway. Any use contrary
to legislaon or an agreement may lead to liability for damages
and conscaon, and may be punished by nes or imprisonment.
www. cileccta.com
Project Coordinator
Jens Kroepelien (Holte as)
Email: jens.kroepelien@holteconsulng.com
Consorum Manager
Rick Hartwig (Designtech)
Email: Rick.Hartwig@designtech.se
Technical Manager
Frode Eek (Holte as)
Email: frode.eek@holte.no
CILECCTA
«A user-oriented, knowledge-based suite of Construcon Indu-
stry LifE Cycle CosT Analysis soware for pan-European deter-
minaon and cosng of sustainable project opon»
The research leading to the results found in the CILECCTA project
has received funding from the European Union Seventh Frame-
work Programme [FP7/2007-2013] under grant agreement no.
229061.
Any publicity made by the beneciaries in respect of projects
funded by the European Union reects only the author’s views
and the European Union is not liable for any use that may be
made of the informaon contained therein.
Sustainability within the Construcon Sector
CILECCTA – Life Cycle Cosng and Assessment
SINTEF Academic Press
2013
Editors Note
Sustainability is a word that is being increasingly used. We use it
in the context of nature, where we want biological systems to re-
main diverse and healthy. Sustainability is used to talk of how we
can live within the means provided by our planets resources. Hu-
manly speaking sustainability is measured in culture, polics, the
ecology and economic terms. An underlying driver to improved
sustainability is change which implies new and innovave think-
ing, which will in turn drive our behaviour.
CILECCTA is about change. Two very dierent disciplines are
brought together into a single process. Life Cycle Costs and Life
Cycle Indicator Results work together to advance into Life Cycle
Cosng and Assessment (LCC+A). World wide nancial and envi-
ronmental data can be integrated and shared via a single plaorm.
And another important thing: we move from determinisc to pro-
balisc thinking.
CILECCTA has been co funded by the European Commission’s FP7
programme. The 15 partners have worked together to develop
something unique. A decision making tool that incorporates prob-
abilisc thinking. Thank you to everyone for your contribuon.
I hope you, the reader, enjoy this book as much as we have en-
joyed wring it.
Change is inevitable in the context of sustainability. CILECCTA will
help you make more informed decisions whether you look ve or
even 50 years ahead.
Rick Hartwig
Exploitaon and Disseminaon Manager
CILECCTA is a large-scale colla bo -
r ave project co-nanced by the
Euro pean Commission under the 7th
Frame work Programme Cooperaon.
The CILECCTA con sor um comprises
15 partners from 7 European coun-
tries. All partners bring their indi-
vidual experse to the project and
each is necessary for its execuon.
In short, the consorum forms com-
plementary enterprises that have
a broad reach across all aspects of
the construcon industry; from ar-
chitects (Cambridge Architectural
Research Limited, APIA XXI) to large
enterprises in the infrastructure
(ACCIONA), from hotels and resorts
(TUI), Research Instutes (Fraun-
hofer-IBP, SINTEF) and an associa-
on represenng their members in
the industry (Norsk Teknologi) to
service providers (Holte as, BSRIA,
PE Internaonal, TechnoBee, ASM,
DesignTech). In addion, the Uni-
versity of Stugart and the Luleå
University of Technology provide a
direct route to higher educaon in
the building sector.
Contents
1 About CILECCTA ............................................................7
2 Life Cycle Costing ......................................................... 11
3 Life Cycle Assessment .................................................. 15
4 Combining LCC and LCA: LCC+A ................................21
5 Advances in LCC+A ...................................................... 25
6 Price Banks and Life Cycle Indicator Results ............... 31
7 CILECCTA Software ..................................................... 35
8 Case Studies ................................................................41
9 Training and e-learning ................................................. 55
10 Seminars,PapersandScienticArticles .......................61
11 Input to Standards ......................................................... 67
12 CILECCTA Business Plan ..................................................... 71
Partners .........................................................................74
Participants ...................................................................75
6
The CILECCTA project has developed a bridge between life cycle think-
ing connected to both economics and the environment, and has created
demonstraon soware based on this.
When a decision is made in the construcon sector, it is oen made on the basis of
an economic evaluaon of various alternaves. Oen these calculaons are based
on investment costs, not considering outlays on future maintenance or waste
treatment, and neglecng the lifeme of the system components. Life Cycle Costs
(LCC) is a calculaon method taking these issues into account. The same can be
done for environmentally damaging emissions using the method of Life Cycle
Assessment (LCA). The CILECCTA soware combines the two methods, thus creat-
ing a new term: Life Cycle Cosng and Assessment (LCC+A).
1 About CILECCTA
Figure 1.1
Combining LCC and LCA
8
The CILECCTA soware is an innovave tool developed to be a deci-
sion making tool which can be used for sustainable planning with-
in all kinds of construcon projects. How to handle uncertainty,
together with the possibility of implemenng exible systems, are
issues which are dealt with in a unique way.
Future uncertainty
Tradional LCC and LCA soware does not take uncertainty into
consideraon in the analysis. Current LCC or LCA tools require pre-
cise data input for all variables throughout the chosen study pe-
riod. With this precise input data the tools generate precise values
for life-cycle cost and environmental impact, known as the deter-
minisc approach to LCC/LCA.
With a typical study period of 20 years plus, many factors are
certain to change, e.g. the price of fuels or energy carriers, build-
ing products, the service life of building products, refurbishment
measures, the usage of buildings and infrastructure etc. Each has
an impact on the cost and environmental eect of an item’s life-
cycle.
It is therefore preferable for the variables in LCC/LCA tools to be
given ranges of values rather than precise values. This is the pro
babilisc approach to LCC /LCA. In this way it is possibe to make
analysis based on dierent scenarios. As a result, the data values
are dened as ranges, typically «three-point esmates» (low-
est conceivable value, most likely value and highest conceivable
value).
Flexibility
A further important feature of CILECCTA is the ability to model
exibility. Flexibility answers fundamental quesons like «how
much does it cost me to invest a lile extra when seng up the
construcon, compared to the cost of wanng to change the con-
strucon at a later point in me». Since they provide the analy-
sis with cost and environmental data, these two scenarios can be
compared. This kind of combined analysis has not been possible
with any other tools.
Integration of LCC and LCA
The integraon of economic-oriented LCC and environmentally-
oriented LCA presents signicant challenges as they are quaned
in dierent units. In addion they originated in very dierent con-
texts – investment eciency for LCC and environmental conserva-
on for LCA – and adopt dierent assumpons. Time preference
(the increased weight given to costs or benets that occur in the near
Life Cycle Thinking
When making a holisc evaluaon of
a systems impact or costs it is oen
dened as life cycle thinking. By con-
suming a product or service, a series
of associated acvies are required
to make it happen. Raw material ex-
tracon, material processing, trans-
portaon, distribuon, consumpon,
maintenance, reuse/recycling, and
disposal are all examples of the phas-
es a product can go through. Looking
at it as one system it is seen as the life
cycle of the product.
9
future compared to those that are further in the future) is formalised
through a discount rate in LCC, but in LCA a discount rate is not used.
In connecon with this issue, together with making it possible to
import data from Price Banks (PBs) and Life Cycle Indicator Results
(LCIRs) across Europe, a mapping tool has been developed using
the Internaonal Framework for Diconaries (IFD). This is a stand-
ard for ontology based on internaonally accepted open stand-
ards. For CILECCTA this means that each potenal data provider
will implement a common interface that accepts a standardized
input and generates a standardized set of return values. The map-
ping approach guarantees that the dierent material categories
are globally unique and therefore the interface to each data pro-
vider will be kept very simple. In this way the user can be sure that
apples and pears are not being compared when data from dier-
ent databases are used.
The CILECCTA e-handbook
The booklet in which you are reading these lines is the «CILEC-
CTA e-handbook». The book aims to give you an introducon to
the methodology behind CILECCTA, together with informaon
about the project and how you can use CILECCTA to your benet.
Chapters 2, 3, 4 and 5 explain the LCC, LCA and LCC+A methods.
Chapters 6 and 7 provide knowledge of how the soware is built
up including informaon regarding Price Banks and Life Cycle In-
dicator Results.
Figure 1.2
Screenshotofthenished CILECCTA software showing a comparison of two different heating systems
10
For an industry dominated by small enterprises (SMEs) it is im-
portant that CILECCTA features are applicable for all enterprises
regardless of their size. All kinds of construcon projects, as well
as project stages can be analysed. In order to make the soware
more tangible and gather experience, the soware has been test-
ed using three demonstraon examples:
decision making on road construcon with regard to layout
and changing mobility.
decision making on using phase change materials for applica-
on in residenal housing.
decision making on what energy system to implement at a re-
sort in Mallorca.
Chapter 8 presents the case studies and experiences gathe red
from these.
An e-learning program together with educaonal courses has been
constructed to give potenal users the possibility to learn about
life cycle methodology and how the CILECCTA soware works. In
chapters 9 and 10, Training and Elearning and Seminars, Papers
and Scienc Arcles you can learn more about CILECCTA and the
topic of LCC+A.
The demo version of CILECCTA soware is a solid plaorm for fur-
ther add-ons or applicaons to address specic cases according to
specic enterprise’s needs. Chapters 11 and 12 are about the me
aer the CILECCTA project – How to use CILECCTA in standardisa-
on work and how you can use CILECCTA for your benet.
Figure 1.3
CILECCTA provides an innovative
tool to improve decision-making for
construction projects. Photo: Mette
Langeid, SINTEF Academic Press
Life cycle cosng (LCC) is the economic appraisal of a potenal investment
or exisng asset taking into account immediate and longer term costs and
benets. Its purpose is to assist decision-making and it can be applied to
individual products or components, to complete building services systems,
or to enre construcon or refurbishment projects.
The principles of life cycle costing
Life cycle analysis is an iterave process, as can be seen from gure 2.1. The steps
start with need to thoroughly understand the problem that has iniated the anal-
ysis, and also to formulate a range of possible technical soluons to that prob-
lem. Since LCC is essenally a method for evaluaon of costs aspects along the life
2 Life Cycle Costing
Applicaons of LCC
Life cycle cosng has two principal applicaons. Firstly it is used to compare dierent design solu-
ons to a technical problem – in this case it is the relave dierences between the calculated life
cycle costs that are assessed. Secondly it is used to esmate budget expenditure in future years
for the selected design.
Comparave LCCs can also be used to priorise projects compeng for limited funds, by calculat-
ing how much benet each gives per unit of investment.
These two applicaons are quite dierent. For a comparave study any common factors that ap-
ply across all the designs can be ignored, since it is only the relave costs that are appropriate.
This means that underlying inaon and any items of cost that are duplicated across all designs
need not be considered.
12
cycle, it is important that those soluons/designs provide the
same levels of performance or output. For example, comparing
dierent lighng schemes will require that each scheme provides
the same level and quality of workplace or task illuminaon, or
dierent heang systems must deliver the same heat output.
Once a range of alternave designs have been set up, each one is
modelled according to its costs and benets over the same me
period. For some LCC applicaons, the me period is pre-dened,
whereas other applicaons will require a sensible me period to
be chosen. Each model captures the complete prole of costs over
me inial installaon, maintenance, equipment replacement,
energy use and other operaonal costs, and also any end-of-life
costs such as decommissioning or waste treatment.
The LCC data from the models is calculated turning each costs
and benets prole into a single number, which is the net present
value of the design. This requires the use of a single discount rate
across all models. The choice of this discount rate is a fundamental
part of the LCC process. Within CILECCTA, the discount rate can be
adapted by the user to represent individual preferences.
Figure 2.1
The iterave process of decision making
13
The system boundary
Just like LCA every LCC analysis has a system boundary within
which are contained all the relevant acvies that contribute to
the cost and benet dierences. This may be more widespread
than at rst thought. For example, the life cycle analysis compar-
ing an innovave plasterboard with phase change material against
a standard product will also need to include the eects on the
heang and cooling system in the building. This is because the
principal benet of the innovave plasterboard is to reduce the
peaks and troughs in room temperature and therefore reduce the
energy consumpon of the building because of less need for heat-
ing or cooling.
The system boundary needs to be set in discussion with the client,
to make sure that the assumpons contained in the analysis are
appropriate and reasonable in terms of the level of design detail
when the LCC is carried out, and in terms of the available sources
of input data.
Iteration is a key part of the process
Iteraon in LCC occurs at two disnct levels. Once the inial LCC
models have been created and calculated, the inputs can be
tweaked to assess the impact of uncertaines in any of the data.
This might be something very specic such as the life expectancy
or energy consumpon of a parcular component, or something
much broader such as the likely future movement in energy costs,
or even the mescale for which the LCC is calculated.
The second level of iteraon can occur when the overall results
are interpreted, as this may indicate some assumpons in the
original scenario need to be invesgated or challenged, or some
changes made to the set of designs being modelled (new ideas to
be included or exisng designs to be modied or removed).
Modelling uncertainty in LCC
Uncertainty in data can be modelled using a range of techniques.
Simple variaon in one or two variables can be assessed by alter-
ing the model(s), rerunning the calculaons and then comparing
the results. At its simplest this might be done by selecng high and
low esmates for the variable and seeing if there is any change
to which alternave produces the most favourable life cycle cost.
However, mulple variables or more complicated probability dis-
tribuons need to be analysed using Monte Carlo simulaon. The
models are recalculated many hundreds or thousands of mes us-
ing sets of input data chosen at random according to the prede-
ned probability distribuons. This funconality in the CILECCTA
soware is further described in chapter 4.
Sources of LCC data
LCC data is required in two main
forms. Firstly there are the es-
mates of cost for dierent project
acvies, and secondly there are
the esmates of life expectancy and
maintenance intervals that dictate
when equipment replacement and
maintenance needs to happen.
Cost data can be obtained from
exisng in-house records, supply
chain contacts, from industry price
books or online services. Life ex-
pectancy data for building services
equipment can be obtained from
estate records, from equipment
manufacturers or from published
sources.
You can read more about price
banks in chapter 6.
14
The results from all the calculaons can then be analysed stas-
cally. This gives mean and median values which indicate «aver-
age» values and also standard deviaons to show how the results
vary. The results can also be shown graphically – see fFigure 2.2.
In this case, it can be seen that there is a large area of overlap be-
tween the LCC results from two compeng designs.
Precision of results
One of the natural implicaons of uncertain input data, even if it
is not explicitly modelled, is that there is a limit to how precise the
results of LCC analysis can be. For example, life cycle costs carried
out at the early stages of design should really be interpreted with
a margin of error of ± 15 %, and even those carried out at detailed
design stage should have a margin of error of at least ± 5 %.
For the analyst this means that it is meaningless to quote life
cycle costs to their calculated precision. Only three signicant
gures need be given 103,000 rather than £ 102,731.28), and
somemes only two signicant gures (£ 100,000 in the above
case).
The CILECCTA soware addresses this by generang scaer plots
of cost and environmental impact for the alternaves being mod-
elled, so that the variety of results for the various alternaves can
be easily seen and interpreted. You can read more about this in
chapter 4 and 7.
Figure 2.2
Frequency distribution of results from LCC Monte Carlo simulation
Life cycle assessment (LCA) is a structured method for calculang the en-
vironmental impact of goods and services. It can be applied to any prod-
uct or process and may include manufactured products, processes, assem-
blies, enre HVAC systems, or even whole buildings within construcon
and building services applicaon. This might sound complicated but in fact
the principles are simple.
Different meanings of «life cycle»
The assessment can represent many dierent life cycle stages of the goods or ser-
vices – see gure 3.1. The two main ones are «cradle to grave», where goods are
modelled from raw material extracon through manufacture, delivery, use and dis-
posal, and «cradle to gate», where goods are modelled from raw material extracon
3 Life Cycle Assessment
Applicaons of LCA
Life Cycle Assessment can be used in many dierent ways, by a wide range of stakeholders.
Policy makers at regional, naonal and internaonal level can use LCA to inform policy decisions
in environmental protecon.
Manufacturers of goods and suppliers or services can use LCA to show how new products or tech-
nologies can be less environmentally damaging than exisng products, and thereby support sus-
tainable growth and development.
Purchasers of goods and services can use LCA to compare alternave products and soluons, to
inform their decision-making and to support any goals they have to favour lower-impact designs.
16
through to manufacture and packaging ready to be shipped to a
wholesaler, retailer or end-customer. A third life cycle is «gate to
design» which is used for modelling processes just within a sin-
gle manufacturing organisaon, say taking a set of o-the-shelf
products and assembling them into a new product.
Environmental Impact Categories and
EcoPoints
Environmental impacts are organised into categories that repre-
sent dierent types of impact. At face value these dierent cate-
gories cannot be directly compared or combined. For example, a
material with a high impact in non-renewable resource depleon
but a low global warming potenal cannot be directly compared
with an alternave material that has a low resource depleon
but a high global warming potenal.
LCA soware packages usually present their results according
to each impact category. For example, global impact catego-
ries include ozone depleon as well as the resource depleon
and global warming potenal menoned above. Regional im-
pact cate gories include water use, land use, eutrophicaon and
photo chemical oxidaon.
Figure 3.1
Typical lifecycles within LCA
17
In addion to this detailed and comprehensive list of impacts,
CILECCTA will also oer the possibility of a «Combined Analysis
Chart». This aggregates the environmental impacts to a single
value and then sets it in relaon with the economic aspects of
a soluon. While losing the objecvity of the assessment, this
oers an intuive way to support decision making.
More than just carbon
The impacts covered in LCA are very broad. They go far beyond
carbon dioxide emissions or other greenhouse gases. For ex-
ample, the standard impact categories include non-renewable
resource use, acidicaon of soils leading to forest decline,
eutrophicaon of water bodies leading to sh decline and oth-
ers. Greenhouse gases are more correctly covered under global
warming potenal, where the global warming eects of dier-
ent gases are appropriately factored and combined into a single
gure expressed as Carbon Dioxide equivalent (CO2 e). The same
is done for other impact categories that are expressed in other
reference units.
Complex relationship between diverse
in- and outputs
The range of goods and services that contribute to the manu-
facture or use of a single product can be unexpectedly compli-
cated, or even circular. The complexity of life cycle inventories,
where the list of constuents for a given product are stored, can
be demonstrated by the case of the mineral wool that might
be used as an insulaon material for ductwork and pipework.
The inventory for this product contains many dierent inputs of
products, recycled materials and natural resources including ba-
salt, limestone, coke, electricity, water, rail transport, and many
dierent outputs of wastes including waste heat, parculates,
and municipal solid waste, and dierent emissions to air, water
and soil including carbon dioxide. All of these inputs themselves
have inventories and their inputs will also have inventories, and
so on. The ulmate aim is to compile the full list of outputs and
emissions in sucient detail for the analysis.
In fact, loops can oen be found within the life cycle inventory
analysis, where the impacts of all resource, material and process
ows that cross the system boundary are analysed. For example,
a product that has steel as a raw material means that coal is in-
cluded as this is one of the inputs for manufacturing steel. But
coal is mined and processed using equipment that includes steel.
The reliance on inventory data and the complexity of its analysis
means that soware is usually needed to carry out LCA.
Sources of LCA inventory
data
LCA inventory data is crical to cal-
culate environmental impacts. Each
inventory is a detailed list of the in-
puts (e.g. resources, raw materials)
and outputs (e.g. emissions, waste)
arising from the lifecycle of a given
material, product, system or service.
There are some wellknown LCA in-
ventory datasets including the con-
strucon sector (Covering dierent
regions in the EU and worldwide).
These are available for a license fee
or may be included in the price of
LCA soware packages.
GaBi (English/ German)
Ecoinvent (English/Japanese/
German)
DuboCalc (Dutch/English)
IVAM LCA Data (Chinese/ Eng-
lish).
In addion, inventory data has been
compiled by a range of internaonal
trade and material federaons. Most
of these datasets are free.
American Plascs Council
European Aluminium Associa-
on
Internaonal Iron and Steel
Associ aon
Plascs Europe.
Based on those databases, product
systems are modeled and their over-
all environmental impact can be ex-
pressed in an objecve way.
Read more about Life Cycle Invento-
ries in chapter 6.
18
Figure 3.2
Framework for life cycle assessment (based on ISO14040:2006)
The functional unit
The descripon of the mineral wool inventory, above, raises an-
other important issue – that of the funconal unit. As well as its
ability to idenfy environmental hot-spots, LCA is also a compari-
son tool where one technical design can be compared with one
or more alternaves to nd out which is the best. To make the
comparison valid, each soluon has to be analysed on the basis
of its performance. For example, two dierent insulaon materi-
als for pipework would be analysed on the basis of the U-value
they provide, as this expresses their insulaon performance. This
would then need to be analysed to understand how much of the
insulaon would be needed in each case – for example, insulaon
for a 10 m length of 50 mm pipe might require x kg of mineral
wool and y kg of expanded foam. The inventories stored in the da-
tabase would both be compiled on the basis of 1 kg of the insula-
on material and the various inputs and outputs would be scaled
accordingly.
This gives rise to the possibility that 1 kg of a new material produc-
es much higher environmental impacts than 1 kg of a tradional
material, but because the new material is so much more eecve
in its performance, the impacts to deliver a required level of per-
formance actually turn out to be much less.
19
The system boundary and the LCA process
The product being analysed, the hierarchy of inputs and the
level of detail in the life cycle all go to dene the system bound-
ary. The denions of the system boundary and of the funconal
unit are fundamental tasks in seng the goal and scope of a Life
Cycle Assessment. From this the inventory is constructed and then
analysed for the environmental impacts see gure 3.2. At each
stage, the analysis is interpreted and checked to make sure that
the required levels of model accuracy and levels of compleon
and precision of the inventory are all achieved.
20
The life cycle of buildings or products is becoming more important as
more people learn that investment in the construcon stage can have a
signicant eect over the lifeme. To take account of these concerns in
construcon decision-making, we need tools that look at all the phases a
material or product goes through. With the two dierent focuses as LCC
and LCA have, it can be dicult to see what the best overall soluon is.
According to CILECCTA the answer is to combine these two approaches
into one single method: LCC+A.
While there may be one parameter that is more inuenal in making a decision,
it could help to make a beer overall decision to present the whole picture to the
decision-maker. For example the nancial director would only be concerned with
the cost of the project whereas the environmental manager or person in charge
of the corporate environmental policy would be interested in the environmental
impacts. In the world we live in today, both factors have their place and should be
considered.
Current approaches are based on two separate assessments. An LCC study will be
conducted to assess the discounted cost of dierent scenarios or opons, while an
LCA will be carried out to quanfy environmental impacts. This is typically done
in dierent soware tools. While there are special LCA and LCC tools, there are
no sophiscated tools for an integrated assessment of both environmental and
economic aspects. When creang two separate models in dierent tools, there is
always a danger of neglecng aspects in one study that is not included in the other,
which can result in comparing scenarios that are supposed to be idencal, but
actually have a dierent scope and/or system boundaries. CILECCTA and its under-
lying LCC+A approach allow the modelling of both economic and environmental
aspects in a single model, thereby ensuring idencal scope and system boundaries
4 Combining LCC
and LCA: LCC+A
22
for all scenarios or alternaves to be assessed. Furthermore, the
impacts of uncertainty on both result dimensions can be included
in the assessment.
To show this graphically, a simple XY plot allows the reader to see
how the cost and environmental impact interact. The gures ob-
tained from LCC and LCA calculaons may represent probabilies
rather than absolute gures due to sensivity or unknowns. In
some cases the potenal scaer in the results will overlap.
In the Combined Analysis Chart, cost and environmental results of
analysis are shown. The centre of the ellipses represents the mean
values while the width and height represent standard deviaons.
It is the boom le-hand corner that we are interested in, as this is
the part of the chart indicang least cost and least environmental
impact.
A simplied example is ploed in gure 4.1 showing the possible
cost and equivalent CO2 impacts to build 1m2 of wall out of stand-
ard construcon materials such as brick, concrete block, steel, and
reinforced concrete.
As can be seen, in this case the reinforced concrete has both the
highest cost and environmental impact. The size of the mark-
ers gives an indicaon of the potenal scaer in the values, so
brick could have a higher or lower environmental impact than
block. This is an oversimplied example; if full analysis was un-
dertaken then the insulaon material, mortar, nancial as-
pect of me to build, etc. would have to be considered to give
the full picture of which is the best soluon for both cost and
environmental impact, but it does demonstrate how the cost and
impacts can be easily compared for dierent alternaves.
Figure 4.1
LCC+A results of a wall of 1 m3
23
If the ming of the wall acvity is not known, such as in the case
where an extension to an exisng building will be constructed at
some future date, then probabilies can be applied. This eec-
vely increases the scaer of the various numerical results for
each dierent wall material. So, using the example of a company
needing expansion someme over a 10-year period and needing
to build an extension, with the probability of it happening of 10%
at yearly intervals, then we can use Monte Carlo simulaon (see
fact box) to see the eect. The midpoint is the average and the
range (diuseness) of the spot shows the results of the simula-
on, in this case run 1000 mes. There’s a vercal diuseness
(= sensivity to environmental impacts) and a horizontal diuseness
(= sensivity to costs aspects). Now we can see that steel is more
sensive to costs, than to environmental aspects and that it touch-
es the cost range of both brick and reinforced concrete. This sort
of plong can be produced via the soware development within
the CILECCTA project.
While this way of interpreng the data should not detract from the
individual LCC and LCA reports, it does however show to the less
technically knowledgeable decision-makers in these areas that
somemes a small increase in building costs can have a signicant
eect on their environmental impact, or conversely where there
is very lile dierence in techniques for environmental impact the
cost variaon may be dramac.
As we connue into a future where the environmental impact will
play a more signicant role in policy, the understanding of both
the cost and environment will become a more important part of
decision-making.
Figure 4.2
LCC+A results of a wall of 1 m3 with Monte Carlo simulations
Monte Carlo simulaon
Monte Carlo simulaons rely on re-
peated random sampling to obtain
numerical results. By running simula-
ons many mes over the probability
of an occurrence can be calculated.
This can be compared to actually
playing and recording your results
in a real casino situaon: hence the
name.
24
Any approach to the life cycle evaluaon of buildings and building com-
ponents involves looking into the future. But the future is uncertain. The
CILECCTA soware for LCC+A provides new ways of taking account of
future uncertainty.
Impossibility of prediction
Even though it is impossible to predict the future, many exisng soware systems
for LCC and LCA require precise data inputs for every year of the project study
period, typically 20–60 years. The data inputs are in fact esmates based on
today’s assumpons about the future. Because the assumpons are subject to
uncertainty, the input values also have a margin of uncertainty. For example, the
future CO2 emissions from the electricity used in a building will depend on both the
amount of electricity used and the carbon content of the electricity, but neither of
these factors can be accurately predicted.
When the input data for an LCC or LCA study consists of precise values, or «single-
point» esmates, the output is also a precise number that looks like a predicon
– but it is actually an uncertain esmate.
Instead of ignoring uncertainty, CILECCTA treats it as an integral part of LCC+A.
Uncertainty can be pictured by a «fan» diagram as done in gure 5.1. Time runs
from le to right – the le-hand side represents «now». If we know today’s value
for a system of interest, such as the price of natural gas or the embodied CO2 of
concrete, there is a single point on the «now» line. In situaons where the future
is totally predictable, such as the movement of the sun, there is a single line from
today’s certain value to certain values in the future. In situaons with uncertainty
there is a range of possible values in the future, shown on the right hand side of the
diagram. The greater the uncertainty, the wider the range of values.
5 Advances in LCC+A
26
Describing uncertain data
It is more realisc (and also easier) to specify what will happen
in the future using a range of possible values, rather than a «sin-
gle-point» esmate. A «three-point» esmate is a good way of
doing this. The three points are: a) the lowest conceivable value,
b) the most likely value, and c) the highest conceivable value.
Three-point esmates are widely applicable; for example, to es-
mate the service life of the carpet in a hotel bedroom, the three
points might be: a) 1 year (lowest conceivable value), b) 5 years
(most likely value) and c) 10 years (highest conceivable value). Us-
ers of the CILECCTA soware for LCC+A can enter input data as
three-point esmates.
With the input data specied by data ranges, the CILECCTA so-
ware gives a range of possible LCC+A output values. Using the
method of Monte Carlo simulaon, CILECCTA generates a large
number of trial runs – maybe 1,000, 10,000 or even 100,000 runs.
In each trail run the values for the uncertain inputs are randomly
selected from the data ranges. When the output values from all
the runs are combined they form a probability distribuon – the
probabilisc esmate of LCC+A (gure 5.2).
Figure 5.1
Illustrating uncertainty
27
Using probabilistic results
When CILECCTA presents the results of LCC+A as a range of values,
the user gains addional informaon compared to the single value
given by convenonal methods of LCC and LCA. The average LCC+A
value, at the peak of the probability distribuon, is important, but
the shape of the probability distribuon is also signicant. It in-
dicates how likely it is that that the true value will be higher or
lower than the average. A steeply peaked probability distribuon
suggests that the true value will be close to the average; but with
a aer probability distribuon the true value can vary consider-
ably from the average. This tells the user how much condence to
place on the average value, and how much allowance should be
made for dierent outcomes.
Future decisions and exibility
The most common use of LCC and LCA is to help in todays deci-
sions between alternaves for a construcon project. However,
during the service life of the project many other decisions are
made, including decisions about the replacement of components
that have a shorter life than the study period.
Figure 5.2
Illustrating the pro babilistic estimate of LCC+A
28
Figure 5.3 b
At the time of replacement there is a new decision and a previously rejected alternative might be chosen
Figure 5.3 c
Some alternatives can be expected to disappear during the service life, and new ones become available
Figure 5.3 a
Illustration of a decision which is repeated like-for-like throughout the study period
29
In convenonal LCC and LCA it is assumed that the rst decision
is repeated like-for-like throughout the study period (gure 5.3 a).
This is unrealisc. At the me of replacement there is a new
decision and a previously rejected alternave might be cho-
sen (gure 5.3 b). Also, some alternaves can be expected to
disappear during the service life, and new ones become available
(gure 5.3 c).
These future decisions allow a construcon project to adapt to
unfolding events that are uncertain at the me of design. For ex-
ample, in a parcular project we may know that a wood-pellet
boiler is a good decision today, but we cannot know whether like-
for-like replacement will also be a good decision in 2030. But the
decision-makers in 2030 will know, and decide accordingly. There-
fore, future decisions improve life-cycle performance.
To maximise the benet from future decisions, construcon pro-
jects should be designed with many opportunies for future de-
cision-making – that is, they should be exible. This is not a new
idea, but the CILECCTA method of LCC+A is the rst that can model
exible designs.
Evaluating exible strategies
CILECCTA describes exibility by specifying the alternaves that
can be selected by future decision-makers. In the Monte Carlo
simulaon runs for a exible strategy the most favourable alterna-
ve is chosen at each replacement cycle, and over many simula-
on runs the LCC+A performance of the exible strategy is estab-
lished. It can be compared to the LCC+A performance of other,
non-exible strategies. Because there is oen an addional cost
for exibility, the benet given by exibility over the life-cycle
must be compared to its inial cost to determine whether the ex-
ible strategy is a good idea. This evaluaon of exibility is a unique
feature of the CILECCTA soware for LCC+A.
30
It is a clear objecve of the CILECCTA development that the soware will
be able to access a number of Pan-European data sources. A task was
therefore established to develop a deep understanding of the two types
of data sources, namely Price Banks (PBs) and Life Cycle Indicator Results
(LCIRs) that would support CILECCTA’s development of an integrated tool.
The CILECCTA team’s objecve were to idenfy PBs and LCIRs, and to:
Analyse the data presented, the architecture and mechanism of access for each
dataset.
Benchmark the exisng PBs and LCIRs against best pracce at a global level.
Build on idened best pracce to design a generic architecture and access
mechanism for both Price Banks and Life Cycle Indicator Results.
Ensure compliance with exisng ISO standards and begin the process of encap-
sulang them in new standards if appropriate.
6 Price Banks and
Life Cycle Indicator
Results
32
Characteristics of identied Price Banks
Both data base types are generally region or country specic and
therefore the CILECCTA team were tasked to idenfy Price Banks
in each of the four European regions (the North, East, South and
West) and if possible, at least one in each EU state. A United States
based PB was included to compare with our ndings. A total of 32
price banks from 13 countries were idened.
Feature Finding
Accessibility The programmatic accessibility is as a major performance characteristic, which could be
achieved with direct access via an API or export capability.
The majority of the databases have no API available. API’s existed where a close relation-
ship the dataset and software.
However there are many PBs in simple electronic formats which can be imported by any
customer–CD’sspreadsheetandpdfles.
Codication TheclassicationcodingisofkeyimportanceforCILECCTAasthereisaneedtomapa
unitsbetweendatabasesandwiththeirrespectivei»ofcodication.Many«internallycodi-
ed»databasesarebasedonnationalstandard.
Weneedtonotethat thecodicationof elementshasagrowing importanceinthe soft-
ware industry due to the tendency to integrate design, measurement and tender or budget
procedures.
However many of the databases are based on several national standards often enriched
withinternalcodication. Our study showed no relationshipwithanyinternationalEuro-
pean wide standard.
Integration In response to the question ‘What relation does the database hold with software pro-
grams?Generallydatasetsexistasseparateentitiesorstand-aloneles.Thesehavetheir
origin in old provided by engineering associations or institutions that are not linked to any
specicsoftwareapplication.
Databasesthatreportedacloserelationwithspecicsoftwaregenerallyusedonlinetools.
Classication A hierarchical structure characteristic, related to the granularity held or used in the data-
bases with more than three levels found.
A Price Bank is a database that in-
cludes product and cost data which is
used to calculate life cycle (nancial)
cost of a structure.
Life Cycle Indicator Results is a data-
base which is used to evaluate and
quanfy the energy and material in-
ows and oulows during the life of
a structure. Data from LCIRs are used
to assess the environmental impact
that a parcular product or process
has throughout its enre life cycle.
33
Characteristics of identied LCIR databases
Life Cycle Indicator Results (LCIR) databases is a new term dened
in CILECCTA. Life Cycle Inventories (LCIs) are the basis for the cal-
culaon of LCIRs.
The Life Cycle Inventory invesgaons revealed two categories of
LCI database, namely «pure LCI databases» and «indicator based
LCI databases» – 23 and 13 databases respecvely. The dierenc-
es can be summarized as follows:
A «pure LCI database» oers datasets which contain pure LCI
informaon on dierent processes, regardless if unit or aggre-
gated processes are displayed. By the help «characterizaon
factors» and using «Life Cycle Impact Assessment» methodo-
logy, input and output ows are transferred into potenal en-
vironmental impacts. If this type of database is used, a tool for
conducng the LCIA is essenal and the outputs are not easily
incorporated into calculaons.
An «indicator-based LCI database» contains already «charac-
terized informaon», also known as environmental Life Cycle
Indicator Results (LCIR), on potenal environmental impacts
for dierent processes. Informaon on mass and energy ows
may be only partly reected.
Few provided datasets exists specically for the use in the con-
strucon sector or the building industry, as most LCIs provide raw
output data (e.g., CO2, SO2) of dierent producon processes.
Nine of the LCIs contain or are indicator based LCIR data. With
regard to a European construcon database on a European level,
the applicaon of an already standardized XML data format and
an electronic data transfer has been established. The ILCD format
technology only requires a web-editor to access data.
We found «pure LCI databases» not appropriate for use, whereas
LCIR databases or results (e.g. of environmental product declara-
ons) provide a beer soluon.
Invesgaon of price banks and LCIs constutes the one of the
rst steps for achieving the broader aim of integrang LCC and
LCA for the assessment of sustainable and economic opons in
the construcon industry.
34
CILECCTA data classication
All Price Bank and Life Cycle Indicator Results databases that have
been idened use some form of classicaon and codicaon
system to idenfy where an item’s nancial and environmental
data belongs. The CILECCTA vision is to have a single tool dealing
with data from PBs and LCIRs from all over Europe there will be
many dierent classicaons to take into consideraon.
There is, however, the need to store all in standardized dened
concepts and their meaning in a central repository that all data
users can access. There are many ways of developing such a tool.
However, there has already been done a lot of work by the build-
ingSmart community to accommodate soluons that use these
ideas in the IFD Library. IFD stands for «Internaonal Framework
for Diconaries» which is simply a standard for terminology librar-
ies or ontologies.
Read more about IFD and data mapping in chapter 7.
Feature Finding
Availability – 40% of the LCIR databases have full online access
– 29% is available in CD/DVD.
Languages Majority available in language of host country
Nature There are many university and consultancy-based LCI databases which character-
ize particular industrial sectors and product groups. They are generally very diverse
and fragmented, with a low level of cross-national harmonisation. Germany, Swe-
den, and Switzerland lead the way in LCI data development.
Data LCI databases provide raw data which may be compared for different options in
LCC+A.
Assume that there are two materials, A and B, which can be used interchangeably
during construction. When comparing the environmental impact of producing those
materials, the material that with the least amount of is selected.
For material A, a raw LCI database provides an output that 0.3 kg SO3 will be
produced
For material B, the output is 0.4 CO2. In this case, it is not clear which one should
be selected since the output is expressed in different units.
However, indicator based databases express the output as a number for each
material production, global warming potential would be expressed as GWP g CO2
= 100 000
We concluded that we should use LCIR databases
Classication Classication system indicates how the data is indexed in a database and which
standard is used. Data for building elements, materials, equipment and labour prices
isclassied onnational standardsandabout 20%ofthemarebased onorganiza-
tion-specicinternalstandards.
The main objecve of CILECCTA has been to develop soware that a) goes
beyond state of the art, as far as exisng avai lable tools and methodologies are
concerned, and b) includes and combines cost data and environmental data in
construcon industry decision making. This chapter explains how CILECCTA deals
with issues like building product classicaon, uncertainty, probabilisc cost
modeling and environmental impact categories.
IFD Library – from products to concepts
In the process of mapping cost data and environmental data, in general or for a
specic case, it is of vital importance that data providers nd products and con-
cepts that are similar. In order to allow data providers from all over Europe to map
their domain data against comparable building concepts, CILECCTA have to follow
a common building product classicaon (see fact box). To achieve this, the part-
ners decided to lean on the work currently under development by the building-
Smart community – The IFD Library.
The IFD Library is a building SMART development project and are basing its solu-
ons on the framework provided by ISO 120063:2007 Framework for object ori-
ented informaon.
7 CILECCTA Software
36
Building product
classicaon
The benets of using an interna-
onally developed building-product
classicaon are many:
One gains access to already de-
ned concepts from other mem-
bers
It is possible to compare prod-
ucts with the same properes
across borders
The IFD community has rules
that will make sure concepts are
dened at the same levels
One could contribute to the
community by dening/ adding
concepts to the constantly grow-
ing concepts database.
Figure 7.2
The structure of the IFD Library im-
plemented in the software
Figure 7.1
Screenshotofthemappingtoolusedtocreatemappingles
37
Uncertainty on input values
Current pracce in performing LCC analysis is to discount the fact
that the future is uncertain. The CILECCTA tool oers several tools
to model various types of uncertainty; Three Point Esmate, Bino-
mial Tree and Expression.
Figure 7.3
Screenshot of the possibility of using Three Point Estimate
Using three point estimates on data input will provide the decision-makers
with output results that are more realistic than just discounting uncertain-
ty. This is a commonly used technique when making a probabilistic LCC
analysis.
Figure 7.4
Screenshot of the possibility of using Binominal tree
In the binomial tree approach the uncertainty of the value of the underly-
ing asset is assumed to follow binomial variation. Therefore the binomial
tree is an explicit statement of the underlying uncertainty of the value of
the asset.
Binominal tree
Three Point Esmates
Mathemacal Expressions
Figure 7.5
Screenshot of the possibility of using Mathematical Expressions
Users are also allowed to add their own mathematical expressions to
express how the input values should be represented.
38
Figure 7.6
Screenshot of the presentation of the results with probability included
Probabilistic cost modelling
Included in the soware is the possibility to use Monte Carlo
simulaon. Monte Carlo is a widely used approach for probabi-
lisc cost modeling. The approach is relavely simple in terms of
use, understanding and reporng, and can handle many dierent
formulaons of uncertainty, overcoming some of the limitaons
of other approaches.
When uncertainty is described in terms of a probability distribu-
on, the Monte Carlo approach randomly samples from this prob-
ability distribuon and simulates the response of the system to
this changing, uncertain parameter (e.g. switch to a cheaper en-
ergy source, develop a building, or «wait and see»). This process
is repeated mulple mes, and the results of each sample run are
aggregated to give probability distribuons of the opon value –
which can be used to inform decision-making. Figure 7.6 shows a
screenshot of the presentaon of the results when probability is
included into the analysis.
When modelling probability and uncertainty, the output data will
be shown with standard deviaon. The output is Net Present Val-
ues (discount rates on input values over a given study period) and
not actual costs. The cost output, shown as NPV-values, includes
one-o costs together with ongoing costs during the life me
(gure 7.7).
39
Environmental Impact Factor (EIF)
To be able to compare costs against more than one environmental
impact category (e.g Global Warming Potenal, unit: kg CO2 eq.)
the term Environmental Impact Factor (EIF) has been dened. In
this way the results presented by the soware can be two-dimen-
sional, but sll considering more than one environmental impact
category at once. The Environmental Impact Factor is weighing
the dierent environmental categories against each other result-
ing in one nal factor. The user of the soware can decide what
emphasis she will make on each impact category depending on
what the assessment shall enlighten. See gure 7.8.
Figure 7.8
Weighting factors in the CILECCTA software (screenshot)
Figure 7.7
Screenshot of the presentation of the results with probability included
Figure xx Weighting
factors in the CILEC-
CTA software
40
Figure 7.9
ScreenshotofthenalversionofCILECCTA software
Version 0.1 of the CILECCTA software
The CILECCTA soware v 0.1 has been systemacally tested and
evaluated by the demo projects and the project parcipants
during the project period. The feedback formed the basis of
making new specicaon for improved versions of the soware.
Figure 7.9 shows a screenshot of the nal version as we see it to-
day. The alternaves which are to be compared are presented to
the le and the graphs illustrang the results to the right. Chapter
8 demonstrates the soware through real cases.
In order to test the features of the CILECCTA soware tool, three demon-
straon projects were selected. The demonstraon projects represent a
wide range of scenarios in the construcon sector and have allowed in-
depth tesng of the CILECCTA soware.
The three demonstraon projects are
The MESSIB Demonstraon House
Economic and environmental impacts of thermal storage systems in build-
ings are assessed for the MESSIB demo-house in Greece. Responsible partner:
Acciona.
The Heang System Case
Dierent scenarios for heang systems are assessed for an employee house
related to a resort in Mallorca, Spain. Responsible partner: TUI.
The Road Case
Opmal specicaons for road construcons are idened based on uncertain
development of trac in Spain. Responsible partner: APIA XXI.
Apart from providing actual case studies for the general applicaon of the so-
ware tool, each case study also has a slightly dierent scope. The MESSIB demo
case focuses on the assessment of newly developed construcon materials, such
as phase-change insulaon material. The Heang System Case has a special focus
on the result presentaon and interpretaon. Finally, the road case has been used
to improve the uncertainty analysis part of the CILECCTA tool.
In the following, the demo projects are described in detail, with their tesng scope,
experiences and lessons learned during the tesng and feedback of the tool use.
8 Case Studies
42
The MESSIB demonstration house
MESSIB (Mul-source Energy Storage System Integrated in Build-
ings) was a four-year project nanced by the European Commis-
sion under the 7th Framework Programme from 2009 to 2013.
It has been dedicated to developing, evaluang and demonstrat-
ing the mul-source energy storage systems in buildings, based
on new materials, technologies and advanced intelligent control
systems to manage energy demand in buildings. Energy storage is
the way to conserve energy (thermal or electric) in one form and
release it when needed in the same or another form.
One of the forms of thermal energy storage is realised via special
latent heat storage in Phase Change Materials (PCM) (gure 8.1).
These materials can be integrated in the building structures, such
as: walls, windows, ceilings or oors.
CILECCTA is used to assess the paran-based PCM system in-
stalled in the plasterboards of external and internal walls of the
MESSIB demo house which is a residenal building in Amphilochia,
Greece (gure 8.2).
Plasterboard is a common lining material used in steel-framed
construcons, such as the MESSIB demo-house. Replacing it with
PCM plasterboard provides greater heat capacity, and more en-
Figure 8.1
Gypsum Plaster Board with PCM.
© BASF SE, 2013
43
ergy can be stored in the building´s envelope, which helps to re-
duce energy consumpon for cooling or heang in a passive and
sustainable way.
Two alternaves have been assessed with the CILECCTA tool:
Lower energy consumpon related to the «MESSIB building»
with PCM plasterboards
Higher energy consumpon related to a tradional building
with tradional plasterboards.
Within this demo project, CILECCTA has been used to quanfy the
saving potenal of new materials and the capability of CILECCTA
to assess newly developed materials. Furthermore, the idenca-
on of the inuence of single components such as the PCM on the
enre house has been assessed with CILECCTA.
Figure 8.3 and 8.4 shows results from the tesng. PCM plas-
terboard seem to be more environmentally friendly than the
convenonal material. In the result of sensivity analysis
(gure 8.4), the target price of the PCM plasterboard 30 mm
should be lower than 27€/m² to get lower cost of the MESSIB
building in the inial stage of its life cycle. Figure 8.2
MESSIB demo-house in Amphilo-
chia (Greece)
44 Figure 8.3
Analysis Chart illustrating the results from the testing of MESSIB demonstration case (screenshot)
Figure 8.4
Sensitivity analysis on life cycle costs for the MESSIB demonstration case (screenshot)
45
Tesng experience and lessons learned
«The CILECCTA soware is easy to use and gives exibility in evaluaon of the
economic aspect in the construcon sector. For example probabilisc analy-
sis that helps to calculate the degree of uncertainty of a construcon invest-
ment. At present me it doesn´t have direct competors in the market, which
makes CILECCTA soware unique.
From the early tesng we learned that the process of integraon of PBs
and LCIRs with the CILECCTA soware was tedious and me-consuming. It
required the use of another mapper program to have access to the LCIRs.
Moreover, in the case of the MESSIB demo-house some new construcon ma-
terials had been installed, which do not exist in any available data-base, which
made it challenging to do the analysis properly.
This was all important experiences for further developments of the so-
ware.»
Ewa Alicja Zukowska, Acciona
46
The Heating System Case
Dierent heang systems were considered for the Robinson
Club Cala Serena, a TUI resort in Mallorca. In 2010, a biomass
heater using wood pellets as fuel was integrated in a sta house.
This was taken as a real-life case basis for the dierent scenarios
conducted, as renewable energy sources are being considered for
the enre club.
It was decided to assess the measure ulizing the CILECCTA so-
ware and to compare it with alternave renewable heang sys-
tems. This was done to idenfy both economic and environmen-
tally reasonable systems and combinaons of heang systems in a
life-cycle perspecve. In parcular, there were four systems being
assessed:
S1: a biomass heang system, current status of the building,
which supplies all the heat demand.
S2: a solar thermal heang system, which supplies 60% of the
domesc heated water demand, combined with a natural gas
condensing boiler, which provides the balance of the heated
water.
S3: a biomass heang system, responsible for the domesc
heated water demand, combined with a water-water heat
pump and a suboor heang installaon to heat the building.
Figure 8.5
Robinson Club Cala Serena, Mal-
lorca
47
Figure 8.6
Comparison of heating system
scenarios S1-S5 (screenshot)
S4: a solar thermal heang system, which supplies 60% of the
domesc heated water demand, combined with a water-water
heat pump which provides the rest of the domesc heated
water and a suboor heang installaon to heat the building.
A scenario S5 was also included to compare the scenarios to a
system with a duel fuel boiler using fossil fuels.
Figure 8.6 shows some of the simulaon results were the heang
systems are compared. As can be seen, the biomass system (S1)
is the alternave with the least normalized Environmental Impact
Factor and are here used as reference with Environmental Impact
Factor (EIF) = 1. The solar collector and heat pump system (S4) is a
bit cheaper, but has over 40% larger EIF.
Deterministic versus probabilistic approach
Figure 8.7 shows a simulaon where a determinisc approach
is used to compare the alternaves against kg Sb equivalents.
When comparing this gure with gure 8.8, where uncertain en-
ergy prices are taken into consideraon leading to a probabilisc
approach, it becomes clear why a probabilisc approach can give
beer and more realisc results.
On the other hand, the costs of the future are also uncertain. The
CILECCTA soware gives therefore the percentage probability
of costs for the dierent scenarios. The result can be studied in
gure 8.9.
48
Figure 8.8
Probabilistic approach, taking into account uncertain energy prices (screenshot)
Figur 8.7
Deterministic approach (screenshot)
49
Tesng experiences and lessons learned
«While in general economic and environmental impacts of heang systems
can be calculated separately with other soware tools, an integrated assess-
ment of both dimensions with exisng tools is currently not possible. Fur-
thermore, CILECCTA allows assessment of the implicaons of the uncertain
development of energy prices, among other factors.
The CILECCTA tool is based on an easy-to-understand modular modelling
approach that enables the user very quickly to get rst results, but on the
other hand to model very complex situaons. Especially the possibility of
including probabilisc scenarios enhances the global picture of a project by
far, and helps the aempt to achieve a noon of future trends. It allows you
to systemacally support decision-making processes for all types of construc-
on-related decisions from both an environmental and an economic point of
view.
Apart from regular feedback concerning the use of soware and minor
comments related to improvement, the TUI demo case found the presenta-
on of the results to be a key factor in the wider use of the CILECCTA tool.
CILECCTA processes a large amount of data, both from an environmental and
economic point of view. This data has to be presented in a user-friendly way
and should be easily understandable.»
Dieter Semmelroth, TUI
Figure 8.9
Graph illustrating the probability of costs for the different scenarios (screenshot)
50
The Road Case
A complete road design project includes detailed topograph-
ic studies, geotechnical surveys, analysis and characterizaon
throughout the enre length of the road, and the design of dier-
ent structures (pavement, drainage, bridges, retaining walls, etc.).
The engineering process includes many variables and a long term
project analysis, making it possible to take into account upgrade
situaons depending on trac evoluon or even nancing capa-
bilies. The current road demonstraon case focuses on the de-
sign and selecon of the pavement structure for a generic square
meter of the road, based on the same parameters used on a real
case by the civil engineers.
The design of pavement structures is a science that involves study-
ing the constraints of the road paths, the supporng capacity and
material characteriscs of the available substrates, as well as the
climate, period and above all the trac usage planned for the in-
frastructure. The case study follows the standards used in Spain to
select and size the structural secon of the pavements depending
on two main parameters: the available foundaons for the «sub-
grade», and the «trac category» the road must serve during its
life cycle. As the future development of trac is subject to uncer-
tainty, CILECCTA can contribute to the decision making process by
analyzing the associated real opons and oering the economical
and environmental evaluaon of dierent alternaves, including
Figure 8.10
Road base pavement structure detail
51
Figure 8.11
Economical sensitivity analysis of the road case alternatives when the discount rate varies from 4% to 14%
aspects of the construcon phase, the O&M phase and the po-
tenal upgrades. Therefore, the focus related to the road case has
been the applicaon of the Life Cycle Opon assessment module
of the CILECCTA tool. One example is the evaluaon of the im-
pact the discount rate uncertainty has when deciding if a exible
alternave is more advantageous than a xed non-upgradable
one. Figure 8.10 shows the combined environmental and eco-
nomical evaluaon comparing several road pavement structures,
with dierent surface course, base course and subgrade combina-
ons (E1 212, E2 122 etc.), as well as the possible upgrade paths
(from a 232 to a 132, etc.), while gure 8.11 shows the resulng
economical evaluaon and turning points when the discount rate
is changed. In this use case there was also uncertainty related to
trac growth (evaluated as a three point esmate). Figure 8.12
shows that with low trac growths the exible alternaves oer a
much beer economical result in the long term, while when high-
er trac growths appear high specicaon roads are preferred.
The sensivity analysis proved to be a very powerful tool in or-
der to understand the context dependency of the choices to take.
Since the road case showed a big dependency of two main pa-
rameters, both related to the economy, the discount rate and the
trac growth, it was logical to progress the analysis into a two di-
mensional sensivity analysis. In order to allow a two dimensional
sensivity analysis there must be a selecon done among the al-
ternaves based on a certain criteria; therefore we must dene a
52
Figure 8.12
Economical sensitivity analysis of the road case alternatives when keeping the initial conditions, but varying the
typicaltrafcgrowthfrom0%to4.5%
decider associated to a certain unit within the calculaons. For the
road case the lowest LCC expected value was kept as selecon cri-
teria, while varying the discount rate from 4% to 14% and adding
the uncertainty to the trac intensity growth (from a typical 0% to
a 4.75%), geng the next combined graph (gure 8.13).
The general trend is that for low discount rates and high trac
growth rates, high specicaon soluons perform best, whereas
for high discount rates and low trac growth rates, low specica-
ons with exibility for upgrading perform best.
CILECCTA should not be treated or measured as a future predicon
crystal ball, but instead a present wide range evaluaon tool that
unhides the uncertaines, and is capable of successfully providing
context aware updatable decision roadmaps. The process involves
modelling the basic premises, with the capability to introduce the
associated uncertaines for each variable, and provide environ-
mental and economical life cycle evaluaons of the alternaves
that can help you argument the value of not only the straight
forward soluons, but also the exible real opons alternaves.
CILECCTA allows the construcon sector to eciently analyse and
reason any decision making process by oering a clear picture of
where the alternaves might change, allowing for wiser and more
sustainable decisions.
53
Tesng experiences and lessons learned
«For the tesng process the dierent road design alternaves need to be
clearly dened and modelled, based on the dierent units and parameters of
the materials to use of each layer and work to perform in order to construct
it. As a general experience this task takes a signicant poron of the design
phase, but has the advantage that once the model is in place results will be
possible for a wide range of scenarios. Furthermore one of the greatest add-
ed values of the tool is to be able to take uncertainty parameters, like the
future road trac category, and use it to simulate possible alternave changes
– road pavement upgrades- that can provide some very interesng decision
taking assessments on the most opmal soluon for the current project.»
Ignacio Robles Urquijo, APIA XXI
Figure 8.13
Combined economical sensitivity analysis of the road case alternatives when the discount rate is varies from 4% to
14%andthetrafcgrowthchangesfrom0%toa4.75%
54
Parallel with the CILECCTA soware development, the partners in the con-
sorum have been developing training courses and e-learning modules.
This has been done to explain the topics of sustainability, LCC, LCA, LCC+A,
together with the CILECCTA soware. The courses combine knowledge
from research as well as experience from the demo-projects.
Norwegian Technology has developed in-house training modules and vocaonal
courses, BSRIA has constructed industrial courses, and USTUTT, together with LTU,
has had the responsibility for development of university courses.
In-house training
To allow training of the in-house parcipants in the project, a series of e-learning
courses has been developed and divided into three modules:
Module 1 Basic training contains general theory about LCC and LCA, and users are
presented with the concepts of exible design. The purpose of this training course
is to give the project parcipants basic knowledge and understanding of the princi-
ples the CILECCTA soware is based on. These modules will form the basis for vo-
caonal training. You can take Module 1 e-learning course by clicking on gure 9.1.
9 Training and
e-learning
56
Module 2 is the in-house training course for the CILECCTA so-
ware v0.1. The purpose of this training course is go give project
parcipants’ basic training in trying out the soware to provide
feedback to the developers of the soware. This module is avail-
able to project parcipants on the CILECCTA website and requires
login.
Module 3 is more advanced training in the use of the CILECCTA
soware. The purpose of this training course is to show what the
soware is capable of, and will focus on how changes in input data
will aect the results of the calculaon. Parts of this module will
also be included in vocaonal training.
Figure 9.1
Module 1 Basic training. Click on
theguretotakethecourse
Figure 9.2
e-learning Module 3. Three anima-
tion videos are made illustrating
each demonstration case: The
MESSIB Demonstration House, The
Heating System Case and The Road
Case (picture)
57
CILECCTA education possibilities
You are able to do courses within the environmental and econom-
ic topics that CILECCTA is built on. Educaon within CILECCTA is
divided into three levels; vocaonal, industrial and higher educa-
on.
Vocational training
Vocaonal training will be an e-learning course using Module 1
Basic training as basis. In addion it will show an example from the
CILECCTA soware and focus on the results from the calculaon
and how to understand them. This training course will be avail-
able on the CILECCTAwebsite. It is planned to oer this course for
example in summer school.
Industrial training
Industrial training aims at potenal applicants of the CILECCTA
soware from industry. Therefore, the course will focus more
strongly on everyday aspects of LCC, LCA and LCC+A. Special focus
will be on model generaon, modelling guidelines and interpreta-
on of results, while scienc background is not at the core of
training. The course will be oered to industrial applicants through
members of the CILECCTA consorum.
Figure 9.3
CILECCTA training
Photo: Taran Gjoeystdal
58
Industrial training has been divided into three courses. Two of
these courses are in a classroom style that is aimed at industrial
personnel who wish to learn how to understand or carry out life
cycle cosng or life cycle analysis. The third course will be delivered
via webinar and covers LCC+ A. This course is aimed at decision-
makers within industry so that they can understand how to use
both LCC and LCA in the decision-making process.
Course 1
Advanced Life Cycle Cosng – a oneday course
(This is an addon to a 2day course on Life Cycle Cosng).
Life Cycle Cosng enables project sponsors and delivery teams to
evaluate the combined capital and operang costs of construc-
on work. This is important to make sure that the client is geng
long term value for money from the project. This training course
presents some advanced topics such as probabilisc analysis, «the
opons-based approach», the principles of deferring decisions,
the Binomial Tree method of calculang costs or benets of de-
ferring decisions, and the impacts of these approaches. Pricing a
deferred decision enables a client to take a realisc view of the
nancial benets of incorporang exibility into a design. Exam-
ples might include deliberately designing a new building so that it
can be converted from commercial to domesc use and vice versa
as cheaply and easily as possible, or deliberately designing a heat-
ing system that can use mulple fuels so that the primary fuel can
be switched as prices change.
Course 2
Life Cycle Assessment – a two day training course
Life Cycle Assessment is becoming a key tool in understanding the
environmental impact of a product or process. LCA enables pro-
ject teams to make informed design decisions as to which opons
have the lowest impact on the environment across their whole
lifeme. A full LCA covers the cradle-to-grave scenario, analysing
the impacts of the raw materials used, manufacturing processes,
use of the product and nally disposal processes. Outputs can be
various, not just the carbon emissions, thus providing a full pic-
ture of the predicted impact on the environment. The course will
cover the key processes in LCA as outlined in ISO 14040:2006
Environmental Management – Life Cycle Assessment Principles
and Framework, and ISO 14044:2006 – Environmental
Management – Life Cycle Assessment – Requirements and Guide-
lines.
Course 3
Combining life cycle cosng and life cycle assessment – Webinar
Life cycle cosng is becoming more widely used to help project
teams understand the long-term cost implicaons of the design
implicaons they make, for example whether a higher equipment
specicaon is worthwhile in terms of savings from increased en-
59
ergy eciency or equipment life expectancy. Life cycle assessment
is also being used to highlight the environmental impacts of de-
sign decisions such as choice of construcon materials, or passive
vs. acve heang and cooling.
These two analyses are usually reported separately to cost consul-
tants and to sustainability consultants respecvely. But to make
the best overall decision it is necessary to combine the economic
and environmental performance into a single set of results. This
webinar will explain how the results of lifecycle cosng and life
cycle assessment can be brought together in a praccal way, to
help clients, designers, contractors and specialist suppliers make
beer decisions. The webinar will also explain some of the pialls
that clients and project teams should avoid.
If you are interested, please contact Ian Wallis at BSRIA (ian.wal-
lis@bsria.co.uk). More informaon about the courses can also be
found at www.bsria.co.uk.
Figure 9.4
CILECCTA education
Photo: Taran Gjoeystdal
60
Higher education
The probabilisc approach of the soware allows benets com-
pared with determinisc approaches, as it is possible to evaluate
the future outcome connected to uncertainty. With these possibil-
ies there is a need of knowledge among students. The university
courses have been constructed as general decision making cours-
es and are suitable for both economics and engineering students.
The entry requirements are general; no other university course
is necessary before taking the rst course. In total, there are four
courses: a Sustainability course, an LCC course, an LCA course and
an LCC+A course.
The sustainability course focuses on what sustainability actually is
and why it is relevant to industry and society today. It also intro-
duces the students to the concept of life cycle thinking (LCT).
Among the topics in the LCC course are the advantages and dis-
advantages of dierent economic methods for cost analysis and
dierent life-cycle cost models.
Life Cycle Assessment or LCA describes a method of quanfying
the environmental impacts of products, processes or services
along their enre life cycle. Within this segment of the course, the
background methodology of LCA, as well as its applicaons, is de-
scribed.
The LCC+A course focuses on the analysis and evaluaon of in-
put and output for the soware. The students will learn and use
dierent concepts such as real opon alternaves and muldi-
mensional decision criteria.
LCC+A Course
The LCC+A course at university level is
available free of charge for students
and university employees, as well as
for members of non-prot research
bodies. This course is based on the
outcomes of the CILECCTA project
with major contribuons by BSRIA,
the University of Luleå and the Uni-
versity of Stugart.
If you are interested in this course,
please contact Hannes Krieg at the
University of Stugart (hannes.
krieg@lbp.uni-stugart.de)
Results and ndings from the CILECCTA project have been made available
to the public through seminars, papers and arcles in scienc journals.
From January to May 2013, CILECCTA seminars were held in eastern, western,
northern and southern Europe. The seminars were aiming to give an introducon
to the methodology behind CILECCTA, together with informaon about the so-
ware and how it can give benets to the construcon sector.
In January a seminar was held in Poznań, Poland. The seminar took place at the
BUDMA Construcon Fair, which is considered the most important trade meeng
in Poland. In April two training events connected to the CILECCTA demonstraon
cases were organised, focusing on assessment capabilies for the soware. One
was held in Madrid, Spain, and another one in Hannover, Germany.
In May CILECCTA was represented by a stand at the Elfack trade fair in Gothenburg,
Sweden. Here the tool was presented by an animaon video and a quiz was held to
challenge the audience to see how much they know about life cycle assessments
and CILECCTA.
A full-day seminar took place in London in February. In total 47 feedback forms
were received at the end of the event. 65% of all delegates completed a form. This
is a very high return, given that the feedback forms where not submied by some
of the CILECCTA partners present and actual speakers who felt that their views
might be biased.
Figure 10.1 shows that building owners, consulng engineers and FM and mainte-
nance providers comprised three quarters of all those aending.
10 Seminars, Papers
and Scientic
Articles
62
Figure 10.3
Question: Where your objectives met?
Figure 10.1
Attending delegates
Delegates were asked for their objecves for aending the semi-
nar. From the 45 responses answering this queson, life cycle in
general was a key objecve, accounng for 56% of responses. As
for the overall dierence between LCA (environmental) and LCC
(nancial) implicaons, there was no overall dierence with 20%
each (gure 10.2).
As can be seen in gure 10.3, an very high percentage (93%) of
respondents stated their objecves had been met.
Figure 10.2
Question: What were your objectives
for attending this event?
63
Figure 10.4
The presentations from the semi-
narwerecapturedonlm
The presentaons from the seminar in London were captured on
lm. Just click on gure 10.4 and you will be guided to these ve
presentaons:
Life Cycle Cosng: David Churcher, BSRIA
Life Cycle Assessment: Hannes Krieg, University of Stugart
Evaluang Life Cycle Opons: William Fawce, Cambridge
Architectural Research
Road Construcon – Ulizing Trac: Ignacio Robles, APIAXXI
Low Carbon Heang Systems: Hannes Krieg, University of
Stugart
Figure 10.5
Seminar in London, England
64
Figure 10.8
Elfack trade fair in Gothenburg, Sweden
Figure 10.9
CILECCTA event in Madrid, Spain
Figure 10.7
CILECCTA event in Hannover, Germany
Figure 10.6
Seminar in Poznan , Poland
65
Papers and scientic articles
Many papers and scienc arcles have been produced during the four years of
CILECCTA. Selected examples are listed below.
2013
Title Integrated environmental and economic assessment in the construc-
on sector
Authors Hannes Krieg, Stefan Albrecht, J. Gantner, William
Fawce
Where SIM conference, June 26-29, Lisbon
Title Time Preference and Risk Aversion Among Development and Construc-
on Professionals and Managers
Authors Ian Ellingham, William Fawce, and Peter Wallström
Where The Internaonal Associaon of People-Environment Studies (IAPS),
25-28 June, A Coruna, Spain
Title
Whole-life carbon analysis: integrang the analysis with design
Author
William Fawce
Where
Ecobuild 2013, London, 7 March
2012
Title Flexible strategies for long-term sustainability under uncertainty
Authors William Fawce, Marn Hughes, Hannes Krieg, Stefan Albrecht &
Anders Vennström
Where Building Research & Informaon, Volume 40, Issue 5, Routledge
Title Quanfying the benets of open building
Authors William Fawce and Marn Hughes
Where Internaonal Conference on Open Building 18th, 19–21 Nov, Beijing
Title
Embodied carbon: an overview of measurement techniques and cur-
rent material labelling
Author
William Fawce
Where
Ecobuild 2012, London, 21 March 2012
2011
Title Invesng in exibility: The Lifecycle opons synthesis
Authors William Fawce
Where Projecon, Volume 10, MIT
Link hp://web.mit.edu/dusp/projecons/projecons10web/Projec-
ons10_fawce.pdf
66
Title Sustainiable construcon projects: case study of
exible strategies for long-term sustainability under uncertainty
Authors William Fawce, Hannes Krieg, Marn Hughes, Stefan Albrecht &
Anders Vennström
Where SB11 conference, November, Helsinki
Title Using Life Cycle thinking approaches for energy price sensivity analy-
sis along the value chain
Authors Stefan Albrecht, Hannes Krieg, Thilo Kupfer, Jan Paul Lindner
Where SIM2011 – Sustainable Intelligent Manufacturing
ISBN : 978-989-8481-03-0
2010
Title Determinaon and cosng of sustainiable construcon projects: opon
based decision support
Authors Anders Vennström, Thomas Olofsson, William Fawce, Ala Dikbas
Where CIB W78 – Applicaons of IT in the AEC Industry&
Accelerang BIM Research, 27th Internaonal Conference 16-19 No-
vember, Cario
Title CILECCTA Herramientas de análisis de ciclo de vida, costes y opciones
Authors Juan José González Méndez, Ingacio Robles Urquijo
Where SB10mad Sustainable construcon. Revitalizaon and rehabilitaon of
neighborhoods, 28th of April, Madrid
Link hp://www.sb10mad.com/ponencias/archivos/c/C059.pdf
The CILECCTA project was iniated to try to combine methodologies relat-
ed to Life Cycle Cosng and Life Cycle Assessment. The aim was to develop
soware which will make it possible to take both costs and environmental
impact into consideraon before making important decisions. In pracce,
this means going beyond «state of the art» and established pracces.
The results developed by CILECCTA are innovave. Most standards in the building
industry are not based on innovave thinking. They are the result of exisng prac-
ces over me. Output from CILECCTA would naturally constute input to guid-
ance documents, training-courses or specicaons that could make the industry
adopt the new approaches and perhaps work dierently than they do today.
Over the last 10–15 years a lot of specicaons and standards have been draed
and developed in order to increase and improve the ow of building informaon
in building projects. Organizaons like buildingSMART Internaonal have made it
possible to organize projects providing standardizaon bodies with new innovave
specicaons and standards. Results from CILECCTA may also constute input to
buildingSMART or relevant standardizaon commiees.
11 Input to Standards
68
Recommendations for existing and new
standards
The CILECCTA project has provided relevant standardizaon
commiees with technical report containing recommendaons
to exisng and new standards. Experiences concerning data-
mapping and development of mapping tools are topics discussed,
together with recommendaons related to the following topics:
New terms and methods
LCC+A: describing both cost and environmental aspects of
a project
Flexible alternaves: alternaves modelled with real opon
techniques
Probabilisc instead of determinisc thinking
Case-use examples
The 3 demo projects in CILECCTA could all constute informa-
ve annexes to standards showing how it is possible to use
probabilisc techniques when analyzing costs and environ-
mental impacts on projects. This is not something exisng
standards promote today.
Uncertainty
Exisng standards are what one could call a result of how the
building industry thinks Life Cycle Cosng should be dealt with.
Most analyses are done based on historical data and probabil-
isc analyses are seldom used. Exisng standards barely men-
on this as a possibility.
A real improvement to exisng standards would be to imple-
ment, as an alternave to tradional determinisc analyses,
simple workows and techniques describing how to perform
various probabilisc analyses. Examples in the informave sec-
on of the standard could show the benets of carrying out
analyses in this way.
69
Figure 11.1
Construction work at Bjørvika in Oslo, Norway. Photo: Mette Langeid, SINTEF Academic Press
70
The purpose of CILECCTA is to oer decision support when modeling vari-
ous construcon related scenarios start up through operaon, mainte-
nance, change of use and demolion phases of a construcon project.
The core of the CILECCTA soware is the calculaon engine that can be accessed
through a web enabled interface. The soware will connect to external data bas-
es of nancial and environmental data. Alternavely a user has the opon of up-
loading proprietary data.
The CILECCTA team is oering customers the opon of developing unique tem-
plate for the running of the soware. Theses applicaon templates will be devel-
oped at a price to be agreed with a user. They will have the opon of making these
applicaons available through a library, for at a fee which will generate an income
stream for the applicaon owner.
It is also possible to consider customizaon if a customer would like to directly con-
nect the CILECCTA core engine or applicaons with their exisng system.
12 CILECCTA Business
Plan
72 Regional exploitation strategies
In addion to this main overall strategy the CILECCTA partners
have explored other possibilies on a more regional or local level.
Several of the partners have monitored other ongoing projects,
possible future projects or business opportunies throughout the
CILECCTA project.
The new CILECCTA webpage
The consorum will be launching a new commercial page. Please
go to www.cileccta.com for more informaon on the CILECCTA
soware.
This is the most important contact point for aracng new part-
ners who might be interested in CILECCTA as «Soware as a Ser-
vice», or addional services like applicaons development servic-
es, customizaon services, training and support.
You can also nd us on
Facebook: hps://www.facebook.com/cileccta
Twier: hps://twier.com/cileccta
Google + :
hps://plus.google.com/110518289328581828009
Pinterest: hp://pinterest.com/cileccta/
Slideshare: hp://www.slideshare.net/cileccta
The possibilies are numerous to use CILECCTA knowledge as a
basis for further developments in the years to come.
Figur 12.1
Illustration of the CILECCTA busi-
ness model
73
Partners
CAMBRIDGE
ARCHITECTURAL
RESEARCH
LIMITED
ASM-CENTRUM BADAŃ I ANA-
LIZ RYNKU Sp. z o.o. (Poland)
www.asm-poland.com.pl
Holte as (Norway)
www.holte.no
Cambridge Architectural
Research Ltd (UK)
www.carltd.com
BSRIA (UK)
www.bsria.co.uk
Luleå University of Technology
(Sweden)
www.ltu.se
Fraunhofer Instute for Build-
ing Physics (IBP) (Germany)
www.ibp.fraunhofer.de
University of Stugart
(Germany)
www.lbp.uni-stugart.de
TechnoBee (Turkey)
www.technobee.com.tr
Designtech (Sweden)
www.designtech.se
PE INTERNATIONAL AG
(Germany)
www.pe-internaonal.com
Norsk Teknologi (Norway)
www.norskteknologi.no
SINTEF Building and
Infrastructure (Norway)
www.sintef.no
TUI AG (Germany)
www.tui-group.com
ACCIONA Infraestructuras S.A.
(Spain)
www.acciona.com
APIA XXI S.A. (Spain)
www.apiaxxi.es
ACCIONA Edith Guedella Bustamante ES
ACCIONA Ewa Alicja Zukowska ES
APIA XXI Ignacio Robles ES
APIA XXI Israel Pinto ES
ASM Agnieszka Kowalska PL
ASM Katarzyna Stachurska PL
ASM Michał Jabłoński PL
BSRIA David Churcher UK
BSRIA Ian Wallis UK
BSRIA Peter Tse UK
CARLTD William Fawce UK
CARLTD Ian Ellingham UK
CARLTD Marn Hughes UK
CARLTD Anthony Waterman UK
Designtech Johan Falk SE
Designtech Patrik Svanerudh SE
Designtech Rick Hartwig SE
Fraunhofer IBP Katrin Lenz DE
Fraunhofer IBP Mahias Fischer DE
Holte as Aleksander Bjaaland NO
Holte as Frode Eek NO
Holte as Jørgen Wang Svendsen NO
Holte as Lars Mikalsen NO
Holte as Per Kveim NO
LTU Peter Wallström SE
LTU Thomas Olofsson SE
Norsk Teknologi Svein Harald Larsen NO
Norsk Teknologi Åge Lauritzen NO
PEI Alexander Forell DE
PEI Johannes Kreissig DE
PEI Siegrun Kielberger DE
PEI Viviana Carrillo DE
SINTEF Kari Sørnes NO
SINTEF Kari Thunshelle NO
TBEE Ala Dikbas TR
TUI Dieter Semmelroth DE
TUI Ingo Woltmann DE
USTUTT Hannes Krieg DE
USTUTT Johannes Gantner DE
USTUTT Stefan Albrecht DE
Participants
www.cileccta.com
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