To Main Proceedings Document
Is Ecological living sustainable? - a case study from two Swedish
villages in South Sweden
Hordur Haraldsson & Mats G E Svensson
Lund University Centre for Applied System Dynamics
P O Box 170, SE-221 00 Lund, Sweden
hordur.Haraldsson@ chemeng.Ith.se
mats.svensson@ chemeng.|th.se
ABSTRACT
Sustainable issues need to be investigated on both spatial and temporal scales. Scale level
interactions in different societal sectors suggest that the sustainability concept is ranging
depending on the observed time scale. A western household system has an expected duration
of 50 years. The sustainability time perspective is thus limited to this period, as since
households do not plan generations ahead. Lifestyle patterns, including “ ecological living” is
influenced by several factors, mainly living and consumption. Dynamic simulation can
identify important components of a lifestyle that maintain the highest Ecological Footprint
value through time. Ecological footprints are calculated by converting the living and
consumption to corresponding ecosystems areas required to support the production of the
needed material. System analysis was used to simulate development of the ecological footprint
over time of two lifestyles from two different townships in southern Sweden; an ecological
village; Toarp; and a conventional village; Oxie, both situated in the same region in South
Sweden. The Simulation spans over a 50-year period and included sensitivity analysis of
several scenarios of living and consumption pattern. The construction of ecological houses is
only giving minor reduction in the environmental impact. The result indicate that the “ food”
consumption and space heating, which contribute around 70% of the total ecological
footprint, are the most realistic alternative in order to reduce the environmental impact of
households.
Objectives
The objectives of the study were to analyse and identify the differences between ecological
living and conventional living in south Sweden. Furthermore to identify the most important
driving forces in household consumption in a fifty year perspective and possible alternatives
to reduce its impact.
1.0 Introduction
Much has been debated on how to reach sustainability within cities and what the concept
sustainability implies. For many people sustainability has to do with sustaining human
activities, such as economic growth, jobs or political stability. Others consider sustainability
of cities a combination of meeting people’s needs and the commitment to sustaining the
natural capital. According to Rees (1997) the total land area required to sustain a city can be
considered at least ten times larger than the city’s own boundaries. Industrialised societies
cannot a priori be regarded as sustainable, since they extract resources way beyond local
geographical carrying capacity by importing goods and services from region in other parts of
the world (Wackemagel and Rees, 1996). Reducing environmental impact of certain activities
can have a great effect in reducing symptoms in the short run but have harmful consequences
through the long-term perspective. It has become important to recognise the scale of events,
both the temporal and spatial scale. Sustainable use of resources means evoking “long-term”
stability of the interaction between the society and the natural environment. That can only be
done when taking into account the overall resource metabolism of the city, to what extend it
expands geographically and what kind of resources are being utilised, and its renewability. It
implies that there is a need to keep certain transparency between the design of dwellings, its
use, and overall impact on resource utilisation. In such terms material efficiency of buildings
and their use become a centre focus for planners in terms of forecasting energy use, natural
resource use and durability.
1.1 Ecological living
Creating alternative form of living is seen as one of many means towards sustainable living.
Swedish homes can be considered as typical households of a post industrialised society. In
Sweden, much is debated on what measures are necessary to reduce the environmental impact
of Swedish households. Large shares of these discussions have dealt with “ecological living”,
the importance of building “ecological houses” (eco-houses) and “living ecologically” (eco-
living) (Berge, 1992; Bokalder and Block, 1997; Malbert, 1994). Policy makers are often
faced with the dilemma of choosing between different planning options that do not clearly
demonstrate the environmental perspectives involved. This is understandable since the
underlying processes concerning eco-living are complex and not well known.
The principal idea of eco-living is “self-sufficiency” of materials for constructing a dwelling
and vital necessities for living. It involves a change in lifestyle from being a total receiver of
resources towards production and recycling of resources. People living by ecological
principles extract their own water, grow own food products and utilize local energy source as
well as other materials needed for self sustainment (Boverket, 1992; Gunther, 1989; Gunther,
1995). Building ecologically involves choosing alternative building materials that occur
frequently in nature, are easily recyclable and have minimum health impacts (Boverket,
1992).
1.2 Quantifying ecological living
Quantifying ecological living can be twofold, focusing on material use into dwellings or the
household lifestyle. Numerous studies (Berge, 1992; Berge, 1995; Lundblad and Paulsen,
1996) have been conducted on the environmental impact of building materials, based on the
Life Cycle Assessment principles, focusing mainly on the material cycle and its recycle and
efficiency. Designer and architects have used these studies to improve dwellings and their
performances. Similarly has focus been drawn up on minimisation of household waste, energy
efficiency and alternative sanitation treatment methods, such as dry sanitation, artificial
wetlands and recycle of domestic waste (Ashley et al., 1999; Fittshen and Niemczynowicz,
1997). These studies analyse and describe material flow through the dwelling material cycle
but often lack description on the household lifestyle or the combination of both. Several
studies (Lindén, 1997; Nonami et al., 1997)have focused on ecological lifestyle of dwellers,
but in a more moral or ideological sense where the attitudes and awareness of involved
inhabitants are mapped to reflect on recycling consciousness, choice of products and transport
use. Little has been done on quantifying lifestyle in the term of energy intensity and its impact
on a national and global level. Furthermore has little work been done on combining the two
concepts, ecological impact of building materials and the household consumption.
The Ecological Footprint (EF) is “a one dimensional” projection of several dimensions of
sustainability, energy, mass, structure and time into area. The collapse is stepwise and can
principally be stopped at any level in the down scaling process. This study combines the two
concepts by quantifying the energy and material needs required to construct a dwelling and
the energy used for living.
2.0 The case studies Toarp and Oxie
The design of Toarp followed the definition given by Boverket (1992). The total area is 4.2
hectare, and the village consist of 37 houses, which are positioned on south directed slope.
The houses are constructed from “ecological” materials, with extra pane of glass in windows
and thicker walls for super insulation. For space heating, all houses are installed with heat
exchanger, solar collectors mounted on the roof, and wood stove. The houses are partially
ventilated by natural means. Water is collected from a local well. Grey water is “treaded” by a
local root zone facility (Wiberg, 1998). At the start of the operation, the houses where
installed with dry sanitation (composting) toilets, but the inhabitants faced major technical
and operational problems since the composting toilets where installed without sufficient
knowledge and direction for use. About half of the composting toilets were exchanged for
water toilets. The water toilets are connected to a root zone system and the sludge is collected
by a local farmer. The composted excretory product from the dry sanitation toilets is utilised
by the inhabitants themselves (Fittshen and Niemczynowicz, 1997).
Oxie is a normal south Swedish suburban area. It is chosen as a reference area since it is
situated next to Toarp. Oxie is connected to the national electric grid and to the municipal
water system. Most of the houses in Oxie are heated by electricity but some are equipped with
oil boiler for space heating. Since Toarp has a total area of 4.2 ha, it was necessary to find a
similar site in Oxie, comparable to the eco-village. Houses in the reference area had to be
similar to Toarp regarding; size, garden area, family structure and transport distance to Malmé
city. An area called Kyrkby in Oxie was suitable for the purpose and two streets lying close to
each other “Pilevalsvagen and Backaréngsvagen” were chosen for the analysis. The area can
be considered as typical Swedish households with typical Swedish consumption pattern.
According to Lindén (1997), the lifestyle in Toarp is very similar to the western urban
lifestyle in terms of transport, buying necessities, goods etc. What is contrasting in Toarp
compared to the normal Swedish lifestyle, is the tendency towards more “family centred” and
cooperation between other groups, and more “time-consuming” lifestyle. Furthermore is the
tendency towards living that requires more manual handling (Lindén, 1997).
3.0 Methodology
Three phases of studies were conducted to compare the ecological village Toarp and its
reference village in Oxie;
A. Calculation of material and energy needs. Lifecycle inventory was conducted on the
construction phase of an eco-house from Toarp and a standard house. A sensitivity
analysis of energy use space heating in different housing options.
B. Ecological Footprint analysis (EF) of different lifestyles, one of the eco-village,
Toarp, and its reference village in Oxie.
C. Simulations on consumption trends. A simulation on the ecological footprint was
carried out using the footprint results obtained from the households in Toarp and Oxie.
3.1 Lifecycle inventory of Toarp and Oxie
This assessment was conducted by using lifecycle inventory on the 10 main building materials
of the standard south Swedish house and a house from the ecological village Toarp. Since the
type of construction can vary from different urban areas, a standard south Swedish house was
used as a reference house in the calculations of the construction phase and the assumption was
made that the standard house could be a typical house for Oxie. The information and the data
on the standard house was obtained from the building entrepreneur Skanska AB (Andersson
1998 pers.comm.). Information on the house form Toarp was obtained from PEAB AB
(Larsen 1998 pers.comm.). The basic phases for building a house were identified and system
boundaries were drawn around three basic factors;
1) extraction of resources,
2) manufacturing of materials,
3) transport of materials.
Generally, construction of all dwellings happens to fall within these main categories. The
main focus was put on energy and CO> emissions and data source from Berge (1992, 1995)
and Bokalder and Block (1997)was used for the calculations. Recycling of raw material such
as steel and aluminium is common in the industrial countries and those effects are accounted
for in the calculations. Although in recent years recycling of building materials is increasingly
becoming more important (Heino and Bruno, 1996), it is only recently it has become of some
relevance but this research will focus on dwellings that are constructed from new materials.
3.1.1. Calculating energy to land
In evaluating the energy for different processes, the report from Berge (1995) is giving the
most comprehensive information on primary production of building materials for the Nordic
countries. In the research numbers representing the extraction process and the fabrication
processes are mainly taken from Berge’s report. These numbers represent primary energy use
(PEU) which covers the total energy needed for extraction of the resources and the fabrication
of the building materials. The Transportation phase represents the energy needed to move the
material from the extraction phase to the construction site. Information on this came from
Bokalder and Block (1997) and Berge (1992;1995).
Electricity in Sweden is mainly produced by nuclear- and hydropower and is therefore almost
COz2 emission free. Since nuclear energy has high operational costs and risk of failure, it is
placed on an even basis with coal fired energy (Wackemagel and Rees, 1996). Thus the land
required to sequester CO. from one nuclear energy unit equals one coal energy unit. A matrix
was constructed to compare the material quantities needed for the two villages. The energy
need and CO, emissions were calculated and converted to total “ecological Footprint” value.
The footprint value calculated was divided by the presumed lifetime of the houses, which is
on average considered to be 50 years without heavy maintenance (Heino and Bruno, 1996).
3.2 Ecological Footprint analysis on Toarp and Oxie
A standardised method, developed by Wackernagel & Rees (1996) was used to compare the
consumption levels of the two housing areas, Toarp and Oxie. The method, which is called
Footprint calculations matrix of households, enables one to measure the annual ecological
footprint generated by individuals by looking at the flow of average monthly consumption of
their household. The consumption is divided into six categories, which represent different
aspects of the consumption. After collecting data on consumption from a normal household,
the data was calculated with a program called “ Footprint calculation matrix for households” .
The annual footrprint values were estimated for every household member. Thirty-five
households, were asked to fill in the questionnaire about their monthly consumption. Eighteen
forms were handed out in Oxie, and seventeen in Toarp.
3.3 Simulation of consumption trends
A simulation on the ecological footprint was carried out by using the results obtained from the
households in Toarp and Oxie. The six main consumption categories in the “Footprint
calculation matrix for households”: food, housing, transport, goods, services and waste,
where simulated.
3.3.1 Modelling hypothesis
The model presenting the footprint simulation was based on the hypothesis that two main
actors are behind the increase in the Swedish footprint, the household sector and the
governmental/industry sector. The largest footprint contributors in the household sector are
housing (electricity and space heating) and food consumption. Implementing reduction
strategies concerning energy use, altemative consumption pattern and transport means can
substantially reduce the footprints in households.
3.3.2 Model description
The model consists of two parts, the first represents the fair Earthshare, and the second
represents the Swedish national footprint. The fair Earthshare, which is 2.2 ha/capita, is the
world total available biologically productive land area and sea space per capita in hectare
shared equally between every person on the globe Wackemagel & Rees (1996). The national
footprint is divided into six “sub-categories”, which represent different aspects of the
footprint, food, housing, transport, goods, other household consumption and
governmental/industry. The results from the household footprint calculations were used to
calculate the governmental/industry footprint by subtracting the difference from the national
footprint value. The ratio of each household category within the household sector was
identified and calculated as a ratio from the national value. The national footprint was
initialised at value 1 hectare per person and then simulated by using above ratio. The fair
Earthshare was simulated as an independent variable with a start value of 5.6 ha, which was
the value per capita at the year 1900.
Five different scenarios were run with the model, which are supposed to correspond to
possible implementation strategy towards sustainability. Potentials for decrease in the
household sector were calculated and used in the simulation (see table 4). These scenarios
demonstrate how much is achieved with different actions and what is necessary to reach
sustainability.
3.3.3 The model assumptions
When designing and running the model several assumption were made and are listed as
following:
1) Footprint projection- According to Wackemagel and Rees (1996) the footprint of the
industrialised countries in the beginning of the century was ~1 ha per person. The
assumption is made that Sweden had 1 ha footprint per person at the year 1900 and
that it has increased 1.8% a year to the current national level of 5.9 ha per person (as
calculated in 1993 data). It is also assumed that the behaviour of the footprint will not
be exponential but sigmoidal. Meadows et al. (1992) foresees steep increase in
resource scarcity within the next 50 years and as a consequence nations will have to
either increase efficiency or consume less. Therefore it is assumed that Sweden’s
footprint will not exceed the 11 ha, which is the current footprint value of the US
economy.
2) “Eco- capacity’- According to FAO data (FAO, 1998) world population is expected
to reach 9.4 billion by 2050 and stabilise around 10 billions in following decades
thereafter. According to Wackernagel et al. (1998) the available amount of all
ecological productive land on Earth per person is today 2.2 ha. This is known as fair
Earthshare. The fair Earthshare has been steadily decreasing from 5.6 ha in the year
1900 to 2.2 ha in 1998 and is expected to slide down to 1.2 ha per capita by 2050. This
study assumes that the world population will stabilise around 10 billion and with fair
Earthshare of 0,9 ha in 2080.
3) Itis assumed that the population growth is responsible for 2/3 of the decrease in fair
Earthshare and environmental degradation responsible for 1/3 of the decrease.
Following strategies are implemented in the model:
1. Reducing footprint of food consumption by 50% in households by consuming 50%
less meat. Scenario implemented over 10 years.
2. Reducing footprint of lighting and space heating in households by 90%. Can
possibly be done through factor 10 (lighting and appliances efficiency, super
insulation and renewable energy). Scenario implemented over 15 years.
3. Reducing footprint of transport, goods, services and waste by 50%. This would
mean less dependency on fossil fuels, drive less, more train and bus commuting, as
well as waste reduction. Scenario implemented over 15 years.
4.0 Results
4.1 Lifecycle inventory of the Eco-house and the standard house
This study extracted two types of data (see table 1). The first set of data describes the use of
energy needed to produce the ten most common materials, and the energy needed for
transportation of the materials. The second set of data describes CO» emissions, which are
released from production and transportation of the same materials. All energy values were
converted to “the” hectare forest, corresponding area that can produce the energy according to
Wackernagel’s methodology (Wackernagel and Rees, 1996). When all the energy and the
emission data were collected and assessed, it was converted to corresponding hectare land
needed to support the energy. According to Wackemagel & Rees (1996), the consumption of
80-100 Gj fossil fuel per year corresponds to the use of one-hectare biological productive
land. The value ~80 Gj*ha’*year'' was used in this assessment.
Table 1: Calculation matrix of the 10 most common building materials in the standard house and the
eco- house.
units in ton ton EF EF EF
kg per lenergy transp. total C02 |coz |co2 |eecr. foros. —|ronsp.
[Standart house _|100m?_|p.e.u. |My total KWh]|kWhim? [Dist km rans, [kWh |kWhim? |o/kg [ow Juans. loves. [phase _lonase
steel 26 «1536 a7 43) 1000 460 46 250 0,064 0,008) 0,010 0,04 0,004
aluminium 5 58 266 74 0,7) 5000 78 0,8 1900 0,009 0,001) 0,003 0,00 0,000
brick 12385 224771 6881 68,8] 500 9977 998 160 1,982 0,713] 0,155 1,10 0,396
concrete 37156 06 22294 6193 61,9] 500 15482 1548 120 4,459 2,140/ 0,139 2.48 1,189]
ipsum 3761 518807 5224 52,2] 300 5788 57,9 330 1,241 0,130) 0,118 0,69 0,072
alass 13807963 268 27] 600 309 3,2 600 0,083 0,010) 0,006 0,05 0,005
mineral woo! 527, 115793 1609 16) 500 1741 17,4770 (0,405 0,030] 0,036 0,23 0,017
lose m/wool 11511112660 3517 35,2) 500, 3804 «38,0 880 1,013 0,066] 0,079 0,56 0,037
paper 190 3,6 685 190 4,9) 200 202 20 0 0,000 0,003) 0,004 0,00 0,001
wood 938 32813 71 7,8 200 838 ©8450. 0,087 0,013) 0,018 0,03 0,007
plastics 505 75 «37844 «© 10512 105,1| 3000 10770 107,7 2000 1,009 0,059] 0,237 0,56 0,033}
lexpanded clay block | _1564 23128 69___8,7|_800 1495149 230 0,360 0,144) 0,020 0,20 0,080
total 131560 36544365. 50943509 li 3] 082 593 1,84
units in} kWh/ ton |ton |EF EF EF
kg per energy transp. |tonn |total CO. |CO2 [COZ |erecw. pros. |rransp.
Toarp house _|100m*_|P.£.u. [MJ total KWh]kWhim? [Dist km lrans, [kWh |kWhim? |o/kg [ow Janse. loves. [phase _lonase
steel 20.6 1557 43343] 1000 0,13 466 47 250 0,065 0,008] 0,010 0,04 0,004
aluminium o 58 0 © 00} sooo 0,17 © 60 1900 0,00 0,000) 0000 0 0
brick 13841227682 7689 -76,9| 500 0,5 11150-1225 160 2,21 0,797| 0,173 1,230 0,443
concrete 3114206 18685 5190 549) 500 05 12976 1298 120 3,74 1,794] 0,117 2,076 0,997
ipsum 2595 = '5_:12976 3604 36,0) 300-05 «3994 «38,9 330 0,86 0,090] 0,081 0.476 0,050
alass 4330073028 84184) 600-0571 7 600 0,26 0,030 0,019 0,144 0,017
mineral woo! 234112569 n4 if 500 05 772:« 7770-018 0,013) 0,016 0,100 0,007
lose m/wool 848 119325 2590 25,9] 500 0,5 ©2802 28,0 880 0,75 0,049) 0,058 0,414 0,027
paper 260 3,6 934 260 26) 200 0,3 «2758 = 00,000,004] 0,006 0,000 0,002
wood 865 32595 m1 72} 200-03 «773, = F580. 0,048 0,012) 0,016 0,024 0,007
plastics 1475 1038 288 29) 3000 0,17 295 +30 2000 0,030,002] 0,006 0,015 0,001]
expanded caybock | 6055 _2 1211 3364 33,6] 8000.5 5786 57,9230 139 0,558] 0,076 0774 0,310
tal 92500 25694 257 40260 403 10 3| 058 529 1,86
The following sources and methods were used to analyse the difference between the two buildings:
PEU- Primary energy use, data from Berge (1995): Bygningsmaterialer for en baerkraftig utvikling,
NKB. Numbers are in MJ*kg'' produced. The numbers are values from production within the Nordic
countries. (In this research it is assumed that production and transport is relative low cost factor within
the Nordic countries and well competitive with material from central Europe). PM- refers to “primary
material” or the raw material.
KWh/ ton transport- According to Berge (1992) the energy consumption ratio per unit transport is
following: kWh*1000kg*km’, large trucks 0,5, trucks w/(trailer) 0.30-0.35, electric trains .11-
Freighters 0.17, Flight 9.8.
0.13,
Transport distances and transport methods of the building materials are from Berge (1992) and
Adalberth (1999).
C02 g*kg material *ton CO2 transport’- calculated from Berge (1992), Berge (1995) and Bertilsson
(1995).
EF electricity production- This is footprint from electricity production in Sweden, which is 50%
nuclear and 50% hydro. Nuclear energy if incorporating high operation costs and risk of failure is on
even basis with fossil fuel. In that term nuclear energy equals fossil fuel energy ratio (80 Gj*ha’*year
rt
).
EF production phase- This comes from CO, emissions from producing the materials, e.g. producing
aluminium will result in CO2 emissions from the smelting process. One-hectare land of average forest
can approximately sequester 1.8 tons of CO2.
EF transport phase- the CO, emissions data from different transport sources is converted to hectare
productive land needed to sequester the gas. The ratio 1.8 tons/ha is used. Data is from Bertilsson
(1995).
Data on the south Swedish standard house was given by Bertil] Anderson at Skanska AB Malm, the
building method and the materials very typical for south Sweden. Calculations are based on 218m?
house.
Data on the Eco- house in Toarp comes from the building entrepreneur PEAB AB Malmé, this
particular house is 115.6m” (Larsen 1998 pers. comm.).
Table 2: summary on each house per 100 m?
Comparison p/100m2_GJ P.E.U. GJ P.E.U.&transp. tons CO, prod. tons CO2transp. COz total footprint in hectare
standard 131,6 183,4 10,7 33 14,0 86
Toarp 92,5 14,9 95 33 12,8 11
difference % 29,7 21,0 10,8 “1,2 7,9 10,0
These results do only reflect the material use and their transport to the building site. It does
not take into consideration the activities of the building entrepreneurs during the building
process. Berge (1992) estimates that these activities can raise the total energy use by 10%.
According to Heino and Bruno (1996) the approximate lifetime of a family house before
being subjected to large maintenance is 50 years. By dividing the footprint value from the
construction phase with 50 years, the annual footprint from the building material is obtained.
This annual footprint value for the construction phase is added to the total footprint number
obtained from the households in Toarp and Oxie. Since there is only 10% difference between
the two building methods, used in construction of the two residential areas considered, the
averages EF number from both examples can be used as a common footprint value for
building materials in Toarp and Oxie. In that case the impact from the building process is on
average 0,16 ha/year during 50 years.
4.2 Results from footprint calculation of Oxie and Toarp
Out of total 35 forms that were given to families in Toarp and Oxie, a total of twenty forms
were received from both places, ten answers came in from Oxie and ten from Toarp. The
answers were run through the Footprint calculation matrix for households, for calculations.
The average family size in the households were 3.9 persons in Toarp (20 adults and 19
children) and 2.1 persons in Oxie (20 adults and 10 children).
In some cases, people did not fill out completely the household form and left thus some
entries with question mark or other remarks to indicate that the knowledge of that particular
item was not at hand. To compensate for that, an average value from that particular village
was calculated and used. An average hectare value for each village was obtained. Since the
samples where randomly taken from each town, the alphabetic order does not mean
comparison between individual houses (table 3).
Table 3: The footprint value from each household category is displayed in m? biological
roductive land and also in average ha.
foarp (mr) a B c D E F G H T 7 average ha
Food 8423 7410 7834 5397 5 269 13244 10972 17129 11002 6 553 0,93)
Housing 10627 11488 11585 7859 13 569 10813 11188 15592 10570 11221] 1,15]
|Transport 1213 5 063 1555 2234 5 832 6 108 5970 6477 4596 3218 0,42)
Goods 542 1533 1604 551 179 420 1226 1769 6014 1537 0,15)
Services 247 256 283 246 1882 532 520 529 3907 383} 0,09)
Waste 504 342 478 145 193 305 360 210 784 725 0,04)
total hectars 2,2 2,6 2,3 16 27 31 3,0 42 3] 24 2,8)
foxte_tm") a B c D E F Gc D T J average ha
Food 18537 15 223 20 850 6 789 12 688 10 862 16 182 7350 10 869 16 910} 1,36]
Housing 13777 16 938 26 726 7564 10677 22 483 12.167 4078 6875 19721] 1,41)
|Transport 8 823 7573 232 5139 1896 9287 8314 1738 3435 2938 0,49)
Goods 2197 6 399 1008 1242 1051 4263 6158 1754 962 3160 0,28)
Services 381 561 427 824 835 459 1977 279 483 595) 0,07]
Waste 537 2478 186 441 948 568 1322 498 208 883 0,08)
[total hectars 44a 49 49 22 28 48 46 16 23 44] 3,7)
If we include the embodied footprint from the building process, (see table 2) the numbers will
increase slightly: Toarp: 3.0 ha*y’! Oxie: 3.9 ha*y’.
The construction phase contributes less than 5% of the total footprint flow in Toarp and Oxie,
given that the footprint level in the households will hold through the lifetime of the house.
There was no significant statistical difference in lifestyle between the Toarp and Oxie areas
(Mann-Whitney U-test, Z=,n=10 p=0.05). Toarp had an average of 3.7 (SD=1.2) and Oxie an
average of 2.8 (SD=0.8).
4.2.3 footprint values
The following figures reveal which categories of the household consumption differ in both
villages.
1,60
141
1,40 136 DToarp
BOxie
1,20 43s
uw 1.00] 093
ir]
i 0,80
0,60 aaa
0,42
0,40
0.28
0,20 os
0.09 9,07 004,208
0,00 = ae |
Foo HOUSING TRANSPORTATION GOODS SERVICES waste
Categories
Figure 1: The average footprint in hectare per person from each consumption category in
both villages.
5,0
48 ei OToarp
% a “EF from building materials ae
: i 0,16 halyear wOxie
35 35 “x
35
.
4 30 33
25
20
15 ane,
10 07
os
00 |
FooD HOUSING TRANSPORTATION GOODS SERVICES WASTE
Categories
Figure 2: Annual footprint in hectare per household is compared between the towns.
The largest difference between the villages is the food category (figure 2). Oxie has ~30%
larger footprint in food per person than residents in Toarp. The footprint in the housing
category is ~18% lower in Toarp than Oxie. If we compare the footprint per household (figure
2) the difference is only marginal in all categories except for housing. There, Toarp has ~20%
larger footprint than Oxie. Other categories do not show much difference. The embodied
ecological footprint from construction is included as black box on top of the housing columns
(0.16 hectare per year over 50 year period).
4.3 Simulations of the Swedish footprint
This section will simulate the Swedish footprint and compare it to available footprint globally.
The study focuses on trend in development of footprint in an average Swedish household and
for total Sweden. Following simulation graph shows scenarios run from the year 1900 to
2080. The scenarios presented here, all run from the year 2000 and onwards. Following are
the proportions each household category contributes to the total national footprint:
* The housing sector contributes 21.7%
* The food sector contributes 19.4%
¢ The transport sector contributes 7.8%
* The goods sector contributes 3.7%
¢ Other household sectors contribute 2,3%
¢ The governmental/industry sector contributes ~45%
Table 4 presents the average value calculated from Toarp and Oxie. The mean value was used
to simulate the Swedish footprint.
Table 4: Potentials for decreasing the ecological footprint in households
‘arp and Oxle combined
‘Mean EF value [Decrease of EF Final EF
Food 1,15 0,57 0,57
Housing 1,28 1,15 0,13
\Transport 0,46 0,37 0,09
Goods 0,22 0,11 0,11
Services 0,08 0,04 0,04
|Waste 0,06 0,03 0,03
‘otal ha 3,24 2,27 0,97
The table 4 shows what potential exists in decreasing the footprint in the household sector if;
consumption is decreased by 50% (services, goods, waste), communal transport is chosen
instead of private car which reduces impact by. ~80% and for the housing, reduce footprint by
90% (known as factor 10).
4.3.2 Graphical display of scenarios
Following six scenarios demonstrate possible outcomes by carrying out different strategies.
12.0u
6.004
_—2 1
0.00.
1900.00 1945.00 1990.00 2035.00 2080.00
10
Figure 3: Scenario 1- No changes. It is assumed that footprints cannot increase forever, thus
when the global available footprints per capita decreases in the next century, the general
consumption will also reach some upper limits (line 2). The footprint is assumed to have S-
shaped behaviour. The global share of footprint (“fair Earthshare”) is expected to shrink from
2.2 hain 1998 to 1.2 ha in 2050 and 0.9 in 2080 (line 1).
12.009
6.004"
d 4
wee
oo 1900.00 1936.00 1972.00 2008.00 2044.00 2080.00
Figure 4: Scenario 2- Reduction potentials in households. This diagram presents what
potentials there are for decreasing the footprint. Line 1 shows the footprint level as it is today.
Line 2 shows the footprint level after reducing meat consumption in households by 50%. Line
3 shows potential in reducing footprint in housing, by implementing factor 10 and reduction
of meat consumption by 50%. Line 4, includes all above and demonstrates as well reduction
in transport, services, goods and waste by 50%.
12.004
6.0047
ea
0.00
1900.00 1936.00 1972.00 2008.00 2044.00 2080.00
Figure 5: Scenario 3- Household scenario. Different implementation strategies are tried for
households. Line 1, is unchanged scenario. Line 2 is 50% decrease in meat consumption over
a fifteen-year period. In scenario Line 3, same as no 2 but also the energy use in the housing
sector is decreased by 90% over a fifteen-year period. Line 4, factor 10 is implemented in
housing and food consumption is decreased, plus additionally is the mean of transport
changed to more communal one, services and consumption of goods changed so it decreases
11
its footprint by 50%. This could lead to some 70% of total decrease in footprint per capita for
households. The graph shows proportional reduction in the footprint per category households,
it is assumed that the governmental/ industry sector is passive towards any actions.
12.004
6.004
1
0.00
1900.00 1945 00 1990 00 2035 00 2080.00
Figure 6: Scenario 4- Governmental, industry 50% and household, 70%. In addition to
the energy and consumption savings made by the households (total 70%), the
governmental and industrial sector reduces its footprint by 50% These changes are
stretched on a time period of 40 years. Note that the Swedish national level (line 2) is 2.2
hectare per person, but is still above the fair Earthshare, which is expected to be 0.9
hectare per person in 2080 (line 1).
12.00 quer
6.00 4.
——————
0.00
+
1900.00 1945.00 1990.00 2035.00 2080.00
Figure 7: Scenario 5- Government, industry 90% and household 70%. In the idealistic
scenario, the governmental and the industry sector implements factor 10 policy which would
lead to reduction of footprint by 90%. This scenario will reduce the over all footprint of
Sweden by 85% and probably only possible if major changes are made in the society. These
changes run over 40-year period (line 2). Although this scheme is carried out, it does not
become sustainable in the long run, since the fair Earthshare falls below the Swedish national
footprint due to world population increase and environmental degradation (line 1).
12
5.0 Discussions
5.1 Comments on the construction phase
The construction phase contributes less than 5% of the total footprint flow in Toarp and Oxie,
given that the footprint level of the household will hold constant through the lifetime of the
house. According to Berge (1992) the total energy needed to construct a conventional 100m?
house is between 360 and 540 kWh/m? in Scandinavian climate. Although the lifecycle
inventory covered only 10 building materials and only energy and CO» emissions, the results
fall well within Berge’s (1992) definition on energy intensity in housing construction (Toarp
house 403 kWh/m? and standard house 509 kWh/m’). The difference between the two houses
is 106 kWh/m’, which can be related to the quantities of plastics used in the standard house
(see table 1). Constructing an eco-house requires larger quantities of materials such as bricks
and expanded clay blocks, but the total energy needed to produce the building materials is still
20% lower than from the standard house. Using plastics is very energy intensive, and this
raises the total energy needed for the standard house and as a consequence its footprint value.
Building ecologically is rather recently occurring in Sweden. The technology associated is
more expensive than conventional methods. Much of the extra expenditure has been allocated
on “green” technology such as local water and waste management, greenhouses and technical
aspects of the buildings (Lundbeck, 1991). This could result projects to be economically
constrained and limited in their environmental performance. According to Lundbeck (1991),
most projects that are ecologically oriented have not been supported financially by the
government similarly to conventional building projects. In Sweden most community housing
projects are tenancy right oriented which strains the entrepreneurs expenditure on projects.
New alternative methods require experience that may prove costly in the beginning especially
if they are technologically oriented. Making economic tradeoffs for lower building costs is
beneficial for the entrepreneur and the consumer but not the environment. To some extend
this could explain why there is currently so marginal difference in the footprint between
ecological and conventional building processes.
5.2 Footprint analysis
Comparing the ecological footprint between Toarp and Oxie reveals the following: The
difference per person (figure 1) between the households is explained by more people residing
per household in Toarp than Oxie. It is observed in figure 3 that both households have similar
footprint distribution, which could indicate that the consumption pattem is fairly similar in
both places. Even if households in Toarp use 50-70% less electricity than their neighbours in
Oxie, they still generate larger footprint in the housing category (figure 2). This difference can
be explained by the extensive use of firewood for space heating in Toarp. The Swedish
national electricity grid provides space heating and lighting in Oxie. Using wood for space
heating generates larger footprint than using electricity produced by hydropower
(Wackernagel and Rees, 1996). This alone increases the footprint of the Toarp households by
~23% compared to Oxie (figure 2). If the footprint from the building materials is included in
the housing category the footprint value is slightly higher (see figure 2).
The most important consumption categories are the food and housing categories. They
represent for roughly 75% of the total footprint in both Toarp and Oxie. Although transport is
a large factor in the household, it contributes only ~14% to the total footprint. Goods,
services, and waste amount for 11% of the total footprint. As observed in the Toarp sample,
one fact can be considered, more people per household decrease the total footprint per person.
This is important because it indicates that large houses that are only resided by 2 persons are
13
very inefficient in terms of footprints. It would be recommendable to switch to renewable
energy sources, increase efficiency, or increase dweller per m? house. If the footprint from the
building materials is included in the housing category the value is increased by 5%, which is
low compared to the lifestyle.
The six simulation scenarios show the potentials that exist to decrease the Swedish national
footprint per capita in the 21 century. The results show that if Sweden manages to decrease
current footprint levels by 85% over the next 40 years and the international community
reverses environmental degradation in the same period, Sweden can have footprint value that
is below the fair Earthshare. This can be accomplished if the governmental/industry sector
implement factor ten policy and households reduce its footprint by 70% through improved
housing and change in consumption pattern.
It is likely that the Swedish footprint will not develop to 11-12 hectare per capita. Actually
such discussion is irrelevant for the modelling purposes, since the model shows the potentials
in decrease of the Swedish footprint. If Sweden will develop high consumer, it will be much
harder to reduce the footprint than if a lower economic development course is taken. In that
sense the model can be used to predict certain scenarios which can partially be used to predict
certain assumptions. For instance, in the model it is assumed that the footprint of the food
category in the Swedish households can be decreased 50% by reducing meat consumption.
The scenario indicates that this factor does not affect the total national footprint as much as
believed but allows us to distinguish better between different footprint contributors. Food
products are high energy demanding and claim over 30% of the total energy used in a typical
industrial country: 10% of this energy is consumed by agriculture, and the rest, 90% is used in
preparation, packaging, transport, etc (Heilig, 1993). By reducing meat consumption, arable
land is freed up for other purposes and energy can be saved. Cereals production is more
efficient than meat production and requires only around 1/5 land area per footprint unit
(Cowell and Clift, 1996; Wackernagel and Rees, 1996).
This brings us to the arguments of energy and externalities. Since the environmental
degradation is not yet included in the consumer price, consumers do not feel the need for
changes. Since energy use is one of the largest contributors to the Swedish footprint, it should
be a priority thing to be addressed. Generally in western countries, energy (especially
electricity) is so heavily subsidised by the government that the consumer never pays the right
price for the energy but does it through other taxes in the society (Lovins, 1996). Decreasing
the footprint has a lot to do with saving energy, which can be clearly observed in the housing
categories in Toarp and Oxie.
5.3.1 Ecological living in Toarp and Sweden
there is no significant difference between the two observation places in the household
comparison and only 10% difference in the comparison of building materials. But Toarp has
smaller average footprint per person, due to larger family sizes per household. Thus the more
people there are per household the smaller the footprint becomes. This was observed in Toarp
and in some households in Oxie. According to Wiberg (1998 pers. comm.) dwellings in
apartment houses are usually smaller and the space heating is somewhat more efficient per
person. If households from a newly built apartment house would partake in a comparison
study with Toarp, it is likely that conventional lifestyle could reveal even less footprint than
Toarp.
14
What can be considered the most important factors in ecological living? The largest
contributors to the ecological footprint in households are the housing and the food categories.
The attention should thus be focused on these two categories. For instance, if permaculture
(permanent-agriculture) would be seriously considered in eco-villages, as Gunther (1995)
suggests, the energy savings in the local food production could be as much as 80% compared
to the conventional lifestyle. This would certainly reduce the footprint of Toarp if
implemented. Toarp has good potentials to become more sustainable (in terms of footprint),
by concentrating on local energy production (wind energy) and shifting food consumption
towards more vegetarian food and local production.
The study concludes that no significant difference exists in the ecological footprint between
the ecological living and the conventional living in the form as presented today in Sweden.
6.0 Refernces
Adalberth, K. (1999). Energy Use in Multi-Family Dwellings during their Life Cycle. Lund,
Lund Institute of Technology- Report, 118p.
Ashley, R. M., Soute, N., Butler, D., Davies, J., Dunkerley, J., & Hendry, S. (1999)
Assessment of the sustainability of alternatives for the disposal of domestic sanitary
waste. Water Science & Technology 39: 251-258.
Berge, B. (1992) Bygnings materialenes dkologi. Universitetsforlaget, Oslo.
Berge, B. (1995) Bygningsmaterialer for en beerekraftig utvikling. Nordisk komité for
bygningsbestemmelser, NKB. Arbeidsgruppen for dkologisk byggning, Nordiska
ministerradet.
Bertilsson, F. (1995) Carbon Dioxide A batement for Reducing an Anthropogenic Greenhouse
Effect. Departement of Chemical Engineering. Lund Institute of Technology, Lund.
Bokalder, V., & Block, M. (1997) Att bygga sunda hus, Bygg-ekologi I. AB Svensk
Byggtjanst, Stockholm.
Boverket (1992) Ekobyar- introduktion. Boverket.
Cowell, S. J., & Clift, R. (1996) Farming for the future- An Environmental Perspective. WP-
RASC.
FAO (1998) Food requirements and population growth 1998.
http://www. fao.org/wfs/final/e/volume! /t4-e.htm#PO PULA TIONCHANGES
(accessed in October 1998).
Fittshen, I., & Niemczynowicz, J. (1997) Experience with dry sanitation and greywater
treatment in the ecovillage Toarp; Sweden. Departement of Resource Engineering-
University of Lund, Lund.
Gunther, F. (1989) Ekobyar, Ekologiskt anpassad och resurssnal bebyggelse. Institute of
Energy and Environmental Studies, Lund.
Gunther, F. (1995) Livsmedelssystemet: Samverkande ldsningar for miljé, ekonomi och
minskad sarbarhet. Kungliga Sog- och Lantbruksadademien 136: 41-49.
Heilig, G. K. (1993) Lifestyles and Energy use in human food chains. IIASA, 28p.
Heino, E., & Bruno, E. (1996) Bygg- och rivningsavfall in Norden. Nordic commitee on
building- NBK working group for ecological construction, Nordic Ministry.
Lindén, K. P. (1997) Ekology och vardagsliv. Lund University Press, Lund.
Lovins, A. (1996) Technology is the answer (but what was the question?). In Miller, ed.
Living in the environment, principles, connections and solutions Wadsworth
Publishing Company, NY.
Lundbeck, B. (1991) Ekobyboende- ett privilegium for de rika. Energy & Miljo 91: 31-36.
15
Lundblad, D., & Paulsen, J. (1996) Ramverk for miljsbedémning av byggnadsprodukter, med
LCA metoder som verktyg. Avdelning for Byggnadsmaterial KTH, Stockholm.
Malbert, B. (1994) Ecology-based planning & construction in Sweden. The Swedish Council
for Building Research, Stockholm.
Meadows, D. H., Meadows, D. L., & Randers, J. (1992) Beyond the limits: confronting global
collapse, envisioning a sustainable future. Chelsea Green Publishing Company.
Nonami, H., Sugiura, J., Ohnuma, S., Yamakawa, H., & Hirose, Y. (1997) The roles of
various media in the decision making processes for recycling behaviour: A path
analysis model. Japanese Journal of Psychology 68: 264-271.
Rees, W. (1997) Urban ecosystems: the human dimension. Urban Ecosystem 1: 66-75.
Wackermagel, M., Onisto, L., Linares, A., Falfan, I., Guerrero, J., & Guerrero, M. (1998)
Ecological Footprint of Nations, How much nature do they use 1998.
http://www.ecouncil.ac.cr/rio/focus/report/english/footprint/.
Wackeragel, M., & Rees, W. (1996) Our Ecological Footprint, Reducing human impact on
the Earth. New Society Publishers.
Personal communication:
Andersson, B. (1998) Interview at Skanska AB, Malm6, June 1998.
Larsen, R. (1998) Received letter from, PEAB AB, Malmo in July 1998.
Wiberg, K. (1998) Interview at Institute of Archtecture, Lund University, October 1998.
16
Appendix I: Model preferences
== FairEarthshare QI
jopulation ines)
ner Swedish footprint
Available footpint globally
household FP
‘decrease in footprint
footprint date
footprint increases
‘wedi National Footpnt
housng sector
housing decrease
FP increase fom 1900
food sector
goodsdecrease
C
FP increase from 1900,
8.
otherdectease
Govemmental footprint
decrea:
17
Appendix Il: Model formulas
Available footmint_globally(t) = Available footmpint_globally(t- dt) + (- decrease in footprint) * dt
INIT Available footrpint globally = 5.6
OUTFLOWS:
secre foie = ttt oon nose Avail han (A walle obi. boca erin) +
(population increase* Available footrpint_globaly
Lge narnia reine yin cyan
INIT Tood sector =0.191 {1.14 {57% of the total swedish Footprint 0.382409}
INFLOWS:
inflow =food_sector*FP increase from 1900
OUTFLOWS
decrase H_& F =if ime>=starting year then (food sectortfood decrease) else 0
Goods sectortt) = Goods sectortt dt) + inflow6 -outflow6) * dt
INIT Goods sector = 0.03817 {0.076359}
INFLOWS:
inflow6 = Goods sector*FP increase from 1900
OUTFLOWS
‘outflow6 = if time>=starting_yearthen (Goods sector* goods decrease) else 0
Govemmental_footprint(t) = Governmental footprint{t- dt) + (inflow2- decentralisation) * dt
INIT Governmental footprint = 0.43 {0.86}
INFLOWS:
inflow2 = Governmental footprint*FP_increase from_1900
OUTFLOWS:
decentralisation =if time>=starting year then (Governmental footprint*Factor_10_and_recycling) (decrease by_50%*Governmental footprint) else 0
hhousing sector{t) =housing sectortt- dt) + (inflow -outflowd) * dt
INIT housing sector=0.24T {0.482862}
INFLOWS:
inflow = housing sector* FP_increase_from_1900
OUTFLOWS:
outow4 =f tine>=starting year then housing. seta housing decrease) el 0
other sectorst) = other sectors(t- dt) + (inflow - outflow?) *
INIT other sectors =0.02287 {0.045741}
INFLOWS:
inflow’ = other sectorstFP_increase_from_1900
OUTFLOWS:
outflow’ =f time>=starting year then (other sectors* other decrease) else 0
transport_sector(t) = transpoit_sectont- dt) + {inflow5 - outflows) * at
(0763 {0.152671}
inflow5 = transport_sector*FP_increase from 1900
OUTFLOW:
‘outflow5 = if time>=starting year then (transport sector*transport_decrease) else O
footprint date = 2050
FP increase_from_1900 =f time >= starting_year then sum_all else 0.0183 {0.0183 intial}
houschold_ FP = food_sector + Goods. sector housing sector + transport. sector + other sectors
2000
igmoidal curve-(F_sigm d+H_sigm d4T_sigm d 246 sigm d 340 sigm_d 4)
Swedish footprint =food sector + Goods sector + Governmental footprint + housing sector + other sectors + transport sector
decrease_by_50% =GRAPHitime)
(2000, 0.00), (2005, 0.0105), (2010, 0.0165), (2015, 0.022), (2020, 0.023), (2025, 0.023), (2030, 0.021), (2035, 0.016), (2040, 0.0105), (2045, 0.00), (2050, 0.00)
ecosystem decradation = GRAPH(time {0.01decrease per month 0.000833}
trea, se 06), 1813, 0.0000), (1825, 00004) {18a8,€OO07), 18S, 0.00), (1963, 0.001), (1975, 0.02), (1988, 000278), 2000, 0.00835), (2013, 0.037, 2025, ©0040), (2038,
0.00415), (2050, 0.00415)
Factor 10 and recycling = GRAPH(time)
{2000.0 2005002), (2010, ©0405), (205, 0.0585, (202, 0.075), 2025, 0.09, 2030, 0.0), (2035, 0.065) (20400032), (245,0.00, (250, 000)
food decrease = GRAPI
(2000, 0071s), (2001, C0715), (2002, 0.0715), (2003, 0.075), (204, 0.078), (005, 0.0718), (2007, 06718), 2008, ©0715), 2008, 0.0715), 201, 0.0715), (211, 0.00)
footprint_increases = GRAPH( time)
(2000, 0.014), (2005, 0.0136), (2010, 0.0132), (2015, 0.0121), (2020, 0.0109), (2025, 0.00922), (2030, 0.00742), (2035, 0.00558), (2040, 0.00396), (2045, 0.00202), (2050, 0.00)
F sigm d =GRAPHitime)
(2000, 0.00176), (2008, 0.00173), (2016, 0.00168), (2024, 0.00147), (2032, 0.00112), (2040, 0.000756), (2048, 0.000522), (2056, 0.000324), (2064, 0.000162), (2072, 0.00), (2080, 0.00)
goods decrease = GRAPHtime)
{boca, 8.056), (2002 0.057), (2003, 0.057), (2005, 0058), 2006, 0.058, 2008, 0.06), (2009, 0.0), (201, 0.0), (2042, 0.057, 2014, 0.0), (2015, 0.00025)
RA
(0.00075), (200, 0.0074, (2016, 0900675, (2024, 000056) (2022, ©0004) 2040, 0.00095), 2048, 0.00258), 2056, 0.00013, (2064, 3 e005, 2072000, (208,000)
housing decrease =GRAPH(time)
(po, 0.15), aon 0.15), (2003, 01), (2005, 0148), 2006, 0.148, 2008, 0.149), 2010, 0.148), (2011 0.148), (2013, 0148) (204, 0.147, (206, 0.001)
PH(tie)
sign
(2, 6.00398), 2005, 6.00), (2016, 00), (2028, 000362), (2032, 000205, 2040, 0.00242, 208, 0.0182), 2086, 0.00126), 206, 0.0008), (2072, 00002), (nen, 00
‘other ‘decrease = GRAPH(timé)
{2000, 0051), (2002, 00528), (2003, 0.0505, (2005, 0.0615), (200, 0.0525), 2008, 0.0525), (2008, 00528), (2011, 0.0525, (2012, 00525), (204, 0.0525), 2015, 0.00)
PH time
sig
(200g, 2000205), (2008, 000785, 2016, 000028, (2024, 0.00027, {203 0.0023), 2040, 0.00019), 2048, 0.0002), (2056, 756005, (2064, 3-05), (2072 0.0), (2080, 0.00)
ypulation increase = GRAPH(time {0.008decrease per month 0.000753)
(1800, 0.005), 1815, 004), (1830, 0.0073}, (1948, 0.0085, (1960, 06108, (1975, 0.011, (1880, 0.011), 2005.01), (2020, 009) (235,007, (2050, 000)
sigmoidal curve = GRAPH( time
(Suc, 0.18), (2008, 017), (2016, 0157), (2024, 00112), (2032, 000801, (2080, 0.00477), 208, 0.0297), (2
transport_ decrease = GRA PH(time)
(2000, 0.105)
0.0018), (2064, 0.00072), (2072, 0.00018), (2080, 0.00)
(2002, 0.103), (2003, 0.103), (2005, 0.106), (2006, 0.106), (2008, 0.104), (2010, 0.105), (2011, 0.105), (2013, 0.105), (2014, 0.106), (2016, 0.00)
T sigm d 2 = GRAPH(time
(2000, 6.001), (2008, 0.001), (2016, 0.001), (2024, 0.000995), (2032, 0.000675), (2040, 0.00047), (2048, 0.000325), (2056, 0.00019), (2064, 7.5e-005), (2072, 0.00), (2080, 0.00)
18
