RESEARCH AND ANALYSIS
Long-term Coordination of
Timber Production and
Consumption Using a
Dynamic Material and Energy
Flow Analysis
Daniel B. Müller, Hans-Peter Bader, and Peter Baccini
Keywords
dynamic modeling
forest products industry
integrated chain management
materials flow analysis (MFA)
resource efficiency
vintage effects
Address correspondence to:
Daniel B. Müller
Yale School of Forestry & Environmental
Studies
205 Prospect Street
New Haven, CT 06511-2189 USA
[email protected]
Summary
A dynamic model for wood and energy flows is used to analyze regional timber management. The model combines a sitequality-dependent forest-growth module with modules for the
timber industry, timber products use, waste management, and
energy supply. The model is calibrated with data of a Swiss
lowland region for the period of 1900–1997. Scenarios are
developed for the period until 2100 in order to discuss possible future roles of domestic timber.
Model simulations show that, with present strategies, timber overproduction will further increase in the twenty-first
century because of an increase in forest site quality in the
second half of the twentieth century, among other reasons.
The increase in building gross floor area of the region by a
factor of 5 during the twentieth century coincides with a reduction of timber use in building construction by a factor of
4.5, from 90 kg/m2 to 20 kg/m2. Increasing timber density in
buildings could address overproduction; however, a strategy of
timber construction could not be accomplished with domestic
timber alone. A balance of production and consumption on
the present level could also be achieved in a scenario in which
the present building stock is gradually exchanged during the
twenty-first century with buildings that exclusively use a combination of solar panels on roofs and domestic firewood and
used wood as heat-energy sources. These replacement buildings would have density typical of late twentieth-century buildings, and they would need to perform on a low-energy standard of not more than 130 MJ/m2/yr.
䉷 2004 by the Massachusetts Institute
of Technology and Yale University
Volume 8, Number 3
http://mitpress.mit.edu/jie
Journal of Industrial Ecology
65
RESEARCH AND ANALYSIS
Introduction
Challenges of Long-term Timber
Management
Not much more than a tree lifetime ago, timber was a scarce resource in many European regions. Concerns about the long-term timber supply led to a variety of measures, such as
promotion of a reduction of wood consumption,
improvement of growth by silvicultural methods,
and reforestation of bare land, for which often
nonindigenous coniferous species were favored
because they were easy to establish and manage
and generated expectations of high volume
growth. In the short run, the wood shortage
could be alleviated with these measures, mainly
as a result of substitution of wood by other resources, such as fossil fuels as the main energy
source and concrete and steel as dominant construction materials, and as a result of decreasing
transportation costs for the shipment of timber
over long distances.
Today, the situation seems to be the reverse:
Many of the coniferous stands planted a century
ago cannot be rejuvenated because timber consumption did not follow the increased production and timber imports increased. This unused
production potential of the coniferous stands has
several undesirable side effects, such as a loss of
biodiversity, a shift to non-site-adapted tree species, and a reduced resistance to damage from
storms, snow, ice, droughts, insects, and fungi
(Spiecker 2003).
From a forest management point of view, long
periods of both forest overuse and underuse are
undesirable. For timber, the time distribution of
the timber harvest has a greater effect on the
ecological damage to the ecosystem as compared
to the time distribution of the production of
other resources such as minerals. That is, evenly
spreading the harvest of a fixed amount of wood
tends to cause significantly less damage than harvesting it all at the beginning or end of an extended time period. By comparison, it makes
much less difference to the overall damage
whether a fixed quantity of a mineral is mined
all at once or evenly distributed over the years.
Compared to other resources such as minerals,
high fluctuations of timber use are particularly
problematic for the ecosystem.
66
Journal of Industrial Ecology
The present situation of a large unused production potential gives rise to the question of
what roles timber could play in the future, as a
construction material, as a raw material for paper
and paperboard production, and as a fuel. If future fluctuations of timber use are to be avoided,
this question needs to be answered in the context
of a coordination between production and consumption.
A precondition for any coordination of production and consumption is a quantification of
the timber flows along the entire life cycle, and
an understanding of how these flows are influenced by different environmental, political, and
economic factors. In this article we introduce a
generic model for long-term timber management. The use of the model is illustrated with a
case study of a Swiss lowland region. The historic
timber flow in this region is analyzed for the
twentieth century. The model is further used to
analyze scenarios for the future production and
consumption of timber in the twenty-first century.
Previous Research
A better understanding of the interactions between production and consumption of timber requires extended information about the distribution of timber in space and time. A variety of
different approaches have been developed to investigate timber management in different sectors, such as forestry (Agren and Axelsson 1980;
Mc Murtrie and Wolf 1983; Mäkelä and Hari
1986; Mohren 1987; Valentine 1990; Sievänen
1992; Bossel 1994; Pretzsch 1997), industry (BfS
and BUWAL 1996, Planconsult 1998), construction inventories and waste management (Mantel
and Schneider 1967; Kroth et al. 1991; Wüest
and Partner 1995; Tolstoy et al. 1998; Kohler et
al. 1999; Steadman 1997; Steadman and Bruhns
2000; Steadman et al. 2000; Johnstone 2001a,
2001b). These models cover different parts of the
timber chain, which is helpful for understanding
a subsystem, such as assuring sustainable forest
management. The models are very limited, however, with respect to supporting a better understanding of the entire timber chain because they
neglect important relationships among the sectors.
RESEARCH AND ANALYSIS
In response to these shortcomings, several research groups have attempted to develop forest
sector models (Randers and Lönnestedt 1979;
Adams and Haynes 1980, 1986; Adams 1985;
Gilles and Buongiorno 1987; Kallio et al. 1986,
1987; Hofstad 1990; Schwarzbauer 1992; Boungiorno et al. 1994; Brooks et al. 1995). Kallio and
colleagues (1987) distinguish four components of
a forest sector model: timber production, timber
processing, demand for timber products, and
trade with other regions. Economic forest sector
models are usually based on econometrics, linear
programming or system dynamics, or combinations of these tools (Buongiorno 1996). Although forest sector models combine forestry and
the timber industry, they neglect the influence of
the construction inventory, waste management,
and recycling on timber demand and supply. Economic forest sector models are therefore not
mass-balance consistent, which limits their accuracy and their capacity to explain the relationship between production and consumption.
A more comprehensive approach was applied
by researchers who analyzed the carbon sequestration capacity of the forest sector. Dewar
(1991) compared carbon storage in old-growth
and managed forests, which imply a carbon stock
in wood products. This study was based on a
theoretical analysis. Karjalainen and colleagues
(1995) analyzed the carbon balance for the Finnish forest sector. They compared the carbon sequestration potential of forest management and
timber product utilization alternatives. The carbon stock in the products was estimated using a
simple model. Pingoud and colleagues (2001)
quantified the carbon stock of wood products, using data of the Finnish building inventory. They
distinguish different building types and age
classes. This model, however, neither considers
the forest inventory nor is designed to discuss
restructuring scenarios for the building stock and
their effects on the timber demand and generation of waste wood, which is essential for a model
aiming at coordinating production and consumption.
Integrating the Partial Models Using a
Dynamic Materials Flow Analysis (MFA)
One could argue that the timber chain could
be described by simply linking different partial
models. But such an attempt would face severe
difficulties because it would require a large
amount of data, implying a long calculation time.
More importantly, it would create serious problems at the interfaces between the partial models.
Different measures for timber—for example,
solid volume with/without bark, round wood
equivalents, stacked volume, wet weight, dry
weight, monetary units—and different modeling
approaches—for example, simple mass balances,
process-based dynamic models, (partial) equilibrium models, linear optimization, simulation
models—are used for the different partial models. The main shortcoming of such an approach
of accumulating partial models, however, would
be the fact that it could not take into account
the relationships among the different parts of the
system, such as feedback and delay.1
We therefore apply a material flow analysis
(MFA) approach (Baccini and Brunner 1991;
Baccini and Bader 1996), which provides a basis
for integrating the physical aspects of the different partial models. It further enables us to describe systems with dynamic or time-dependent
models. A dynamic approach is needed to capture delays caused by the long residence time of
timber in forests and buildings. With the use of
MFA to combine the sector models, it becomes
necessary to translate the partial models, or aspects of them, into MFA language.
Purpose
The purpose of this article is to answer the
following questions:
1. What are the most important factors determining regional timber management?
2. What are the possible future roles of timber as a construction material, as a raw material for paper production, and as a fuel?
3. What are the benefits and limits of the
MFA approach?
This article includes a description of an MFA
model called XYLOIKOS, and an application in
a Swiss lowland region called “Kreuzung
Schweizer Mittelland” (KSM).2 The application of
the model in KSM is relatively brief in this context, however. The focus lies in a methodological
contribution to modeling the timber cycle. The
KSM case study is used here to enhance the un-
Müller et al., Long-term Timber Management Using MFA
67
RESEARCH AND ANALYSIS
derstanding of the model and the system behavior. With this case study, we are therefore neither
attempting to show the full range of application
of the model nor providing an extensive analysis
of the region.
The XYLOIKOS Model
System Definition
Figure 1 shows the XYLOIKOS system, which
describes regional management using 10 main
processes (indicated with boxes) and 27 main
goods or flows (indicated with arrows). The system includes timber flows (straight lines) and
timber-management-related energy flows (dotted
lines), which include wood as well as other fuels,
for example, fossil fuels and solar energy panels
on roofs of houses. The main timber stocks, the
inventories of the forests (process 1), and the
buildings (processes 4 and 5), are divided into
subprocesses of birth cohorts (stands of trees
grouped by the years when they began to grow).
Increases in the quantity of wood in the forests (“wood increments”) I1 is a (net) input into
the system to the process “forests” (process 1).
The increment includes stem wood and does not
include biomass in roots, trunks, branches, and
leaves. The increment varies in time for each
birth cohort because trees grow differently in different growth stages. Accordingly, increment and
harvest are regarded individually for each birth
cohort k. The process “forestry” (process 2) distributes the harvested wood into roundwood
(A2 3) and firewood (A2 10). The timber industry
(process 3) includes the processing and trade of
domestic roundwood, net imports of wood (I3),
and recycling wood (A9 3). The trade involves
roundwood, semi-finished goods, and finished
goods. The timber industry produces four product
types: carpentry and joinery products (A3 4 and
A3 5), other wood products (A3 6), pulpwood (A3
7), and wood residues (A3 10). Carpentry and joinery products are used in buildings. As with the
forests, the buildings are divided into birth cohorts l, representing the different periods of con-
Figure 1 System of the XYLOIKOS model. Boxes represent processes, arrows indicate flows, and dotted
arrows indicate energy flows. Processes with gray boxes (processes 1, 4, 5, and 6) consist of a significant
stock that is considered in the model. Processes with subdivided gray boxes (processes 1, 4, and 5) are
inventories modeled using subprocesses of birth cohorts (vintage structure).
68
Journal of Industrial Ecology
RESEARCH AND ANALYSIS
struction. This allows us to characterize buildings
constructed in different periods as varying in
their timber density. The category “other wood
products” accounts for all wood products that are
not used in buildings, such as packaging, bridges,
fences, and so forth. Pulpwood is used, in combination with recycling paper (A8 7) for the production of paper and paperboard (A7 8) and the
by-product lignin (A7 10). After consumption, paper (including paperboard) is recycled (A8 7),
used for energy production (A8 10), or discarded
as waste paper (O8). These three options for cascading use of paper apply in principle for timber
products also. In some cases, used wood is recycled (A9 3); however, more often it is used for
heat recovery as fuel wood (A9 10) or discarded as
wood waste (O9).
Energy is used in forestry (A10 2), in the timber
industry (A10 3), in the paper industry (A10 7), and
in the buildings (A10 4 and I4). The building energy includes heat energy used for warm water
and room heating, but does not consider the use
of electricity. Energy from the process “energy
supply” involves wood, natural gas, and oil in different combinations. Energy from solar panels is
produced on the roofs of the buildings in different birth cohorts. This allows us to develop scenarios for a combined energy supply of solar panels and wood.
The process “forests” consists of f forest birth
cohorts of 20 years. A simulation from 1900 to
2100 therefore requires f ⳱ 10 birth cohorts. In
order to account for the age structure of the forest
already present at the year 1900, the number of
birth cohorts is extended to f ⳱ 15. In the birthcohort approach, the forest stands (management
units) are grouped according to their year of establishment, for example, all forest stands established between 1900 and 1919, between 1920
and 1939, and so on. Thus, a forest stand remains
in the same subprocess for its entire rotation period. Most traditional forest models use age cohorts or developmental stages instead of birth cohorts. In these approaches, a forest stand passes
through all the subprocesses during a rotation period. In the birth-cohort approach, the forestry
subprocesses are treated as geographically clearly
defined and invariant balance volumes. This allows us to give different geographically related
attributes to the different birth cohorts, for ex-
ample, site quality or tree species composition. It
further provides a simple way to integrate stand
simulation models into regional forest inventories.
In a similar way, the buildings are grouped
into g ⳱ 15 birth cohorts of 20 years. The buildings are sorted into building classes according to
their year of construction. A building remains in
the same birth cohort for its entire lifetime. A
distinction is made between the stocks of joinery
and carpentry wood. Carpentry products, used
for building structures, usually have a longer residence time in buildings than do joinery products, such as furniture, kitchens, and windows.
This distinction allows us to investigate the differences in the dynamics of carpentry and joinery
product use.
The entire system counts a total of 7 Ⳮ f Ⳮ
2g processes and 19 Ⳮ 2f Ⳮ 6g flows. The flows
are divided into 15 Ⳮ 2f Ⳮ 4g material flows and
4 Ⳮ 2g energy flows.
Principles of the Mathematical
Description
The stock and flows are represented mathematically using system variables: M(t) for stock,
A(t) for internal flows, I(t) for input flows, and
O(t) for output flows. The model calculates all
stocks and flows in the system, which counts 26
Ⳮ 3f Ⳮ 8g system variables. The system is therefore determined completely using 26 Ⳮ 3f Ⳮ 8g
independent system equations. Two types of
equations are used:
• 7 Ⳮ f Ⳮ 2g balance equations
• 19 Ⳮ 2f Ⳮ 6g modeling approach equations
For processes 1 to 9, the balance equation is formulated in terms of matter. The balance equation for process 10 (energy supply) is calculated
in terms of energy equivalents. This allows us to
substitute wood for other fuels. The formulation
of the balance equation for each process is
straightforward and therefore omitted.
Modeling Approach
The modeling approach represents the “specific system properties” in mathematical form.
Müller et al., Long-term Timber Management Using MFA
69
RESEARCH AND ANALYSIS
The key characteristics of the system are denoted
by parameter functions.
Forests
The process “forests” represents the relationship between net annual increment (in figure 1
noted as “increment”), inventory (stock), and
harvest (including intermediate and final cutting). The model simulates forests that consist of
stands with even-aged single-species structures,
such as most high forest or irregular shelter-wood
systems. These forest-management systems can
be described using relatively simple models. The
forest submodel could easily be exchanged with
a model for uneven-aged and mixed-species
stands; however, this would significantly increase
the complexity and data requirements.
Most forestry textbooks show that moderate
intermediate cutting has no significant influence
on the overall increment of a forest stand (Assmann 1961; Kramer et al. 1988; Wenk et al.
1990). After moderate cutting activities, the
same increment is distributed among a smaller
number of trees that grow all the more. This observation allows us, in the absence of extreme
interventions, to describe increment and harvest
as independent functions. The net annual increment for all birth cohorts follows the following
formula:
cal law of growth might exist. Thomasius (1965)
has shown that the parameters of the Backmann
function have no biological interpretation. He
argues that the Backmann function is a pure empirical function that often shows a good agreement for growth of even-aged single-species
stands. A model validation in the KSM region
of Switzerland showed, however, that the Backmann function correlated very well with data of
the local yield tables, but not with field data. The
reason for this difference in increment lies, as
XYLOIKOS simulations suggested, in a remarkable increase of the site quality3 since the 1950s.
As a consequence, the Backmann function was
extended with the site-quality index b (Müller
1998):
d(b,t)
⳱ dmax(b)•exp[(ⳮK(b))log2(t/tdmax(b))]
The three site-quality dependent parameters can,
according to the local yield tables—speciesspecific representations of the amount of useable
wood a forest can be expected to produce during
a single rotation based on site index—be well
represented using linear functions of b (Müller
1998).
dmax (b) ⳱ d0 Ⳮ a1 • b(t)
K(b) ⳱ K0 Ⳮ a2 • b(t)
tdmax(b ⳱ t0 Ⳮ a3 • b(t)
f
I1(t) ⳱
兺D
(t)•d(k)(t)•qwood
(k)
for
k ⳱1
(1)
k ⳱ 1, . . . , f (index of birth cohort)
where D(k)
for (t) is the forest area of forest birth cohort k at time t, d(k)(t) represents the net annual
volume increment per area of forest birth cohort
k at time t, and qwood stands for the density of
wood. The volume increment d(k)(t) is modeled
using the Backmann function (Backmann 1942).
d(t) ⳱ dmax•exp[(ⳮK)log2(t/tdmax)]
(1a)
The Backmann function describes volume increment using only three parameters. dmax (for
reasons of simplicity the index k is omitted) is
the maximal volume increment, which is
reached at time tdmax. K is a constant. The accurate, elegant simplicity of the Backmann function led scholars to speculate that a mathemati-
70
Journal of Industrial Ecology
(1b)
(1c)
(1d)
(1e)
Harvest involves intermediate and final cutting, represented by the two terms in the parentheses of the following equation:
A1 2(t)
兺 冢c
f
⳱
k ⳱1
(t) Ⳮ
(k)
冣
D(k)
for, clear(t)
• M(1.k)(t)
(k)
Dfor
(t)
(2)
k ⳱ 1, . . . , f
where c(k)(t) is the volume cutting percentage of
forest cohort k, and D(k)
for,clear(t) is the cleared forest
area of forest cohort k. The yearly rejuvenated or
planted forest area D(k)
for,new(t), the yearly cleared
or harvested area D(k)
for,clear(t), and the area stock
for each birth cohort D(k)
for (t) are determined by
the total forest area Dfor(t) and the length of the
rotation period per forest cohort r(k):
(k)
(k)
D(k)
for,clear(t) ⳱ Dfor,new(t ⳮ r )
(2a)
RESEARCH AND ANALYSIS
D(k)
for (t)
(2b)
t
冮(D
⳱ D0(k) Ⳮ
(k)
for,new
(k)
(t) ⳮ Dfor,clear
(t))dt
0
Forestry
The process “forestry” involves harvesting activities and the distribution of the harvest to firewood and roundwood (including saw logs and
pulpwood). The amount of firewood is a timedependent proportion of the harvest:
A2
10
(t) ⳱ k2 10(t) • A1 2(t)
(3)
where k2 10(t) is the transfer coefficient of firewood production. As a distribution process, forestry itself has no significant timber stock
change:
Ṁ(2)(t) ⳱ 0
(4)
Timber Industry
The process “timber industry” includes all
processing steps from the acquisition of timberbased raw materials to the production of intermediate and final products and trade with these
goods. Net import is assumed to be the dependent variable: Differences between domestic production and domestic consumption are balanced
with imports and exports. Stock change within
the timber industry is regarded as negligible:
Ṁ(3)(t) ⳱ 0
(5)
10
(t) ⳱ k3
10
⳱
兺D
(l)
(t) • mcar
l ⳱ 1, . . . , g
(l)
bui,dem
l⳱1
t
冮
(l)
(l)
Dbui,dem
(t) ⳱ k4.l 9(t,t⬘) • Dbui,new
(t⬘)dt⬘
(9a)
0
(l)
Dbui
(t)
(9b)
t
冮
(l)
⳱ D0(l)(t) Ⳮ (Dbui,new
(t) ⳮ D(l)
bui,dem(t))dt
0
(t) • [A2 3(t)
Ⳮ I3(t) Ⳮ A9 3(t)]
(6)
g
˙ (l) (t) • m(l) l ⳱ 1, . . . , g
Ṁ (t) ⳱ 兺D
bui
car
(7)
兺D˙ bui(l) (t) • m(l)joi l ⳱ 1, . . . , g
(8)
(4)
兺D
(t)
(9c)
(l)
bui
l⳱1
Building Inventory
Changes in timber stock of carpentry and
joinery products held in the building inventory
are calculated as follows:
l ⳱1
g
l ⳱1
(9)
g
Dbui(t) ⳱
where k3 10(t) represents the transfer coefficient
of wood-residues generation.
Ṁ(5)(t) ⳱
A4 9(t)
g
The wood residues are
A3
˙ (l) (t) is the change of the total gross floor area
D
bui
(l)
of building cohort l, and m(l)
car and mjoi are the average densities of carpentry and joinery wood in
building cohort l. The assumption is that the
wood densities vary between different building
cohorts but remain constant during the lifetime
of the buildings.
Waste wood generation from carpentry products in a birth cohort is assumed to be proportional to the yearly demolished gross floor area
in building cohort l, D(l)
bui,dem(t).
Analogous to the forest area (equations 2, 2a,
and 2b), the yearly new built gross floor area
D(l)
bui,new(t), the yearly demolished gross floor area
D(l)
bui,dem(t), and the gross floor area stock of building cohort l, D(l)
bui(t) are determined by the entire
gross floor area Dbui(t) and the lifetime distribution of the buildings k4.l 9(t,t⬘), which is identical
with the lifetime distribution of the carpentry
products.
The transfer function uses t⬘ as input time. This
function is discussed in more detail by Baccini
and Bader (1996), Zeltner et al. (1999), and
Binder et al. (2001). We assume a normally distributed lifetime for the buildings and carpentry
products:
k4.l 9(t,t⬘)
1
(tⳮt⬘ⳮs0)2
⳱
• exp ⳮ
,
N0
2r02
(9d)
with N0 ⳱
冮 exp ⳮ
0
(tⳮs0(t⬘))2
2(r0(t⬘))2
Müller et al., Long-term Timber Management Using MFA
71
RESEARCH AND ANALYSIS
where N0 is a normalization factor, s0 is the average lifetime, and r0 is the variance of the lifetime. Joinery products have a shorter average
lifetime than buildings, and thus are often replaced in buildings. As a result, waste wood generation from joinery products consists of two
components, demolition products A5.l 9,dem(t) and
replacement products A5.l 9,ren(t) produced by
renovations.
A5 9(t) ⳱ A5.l
l ⳱ 1, . . . , g
(t) Ⳮ A5.l
9,dem
9,ren
(t),
(10)
The demolition term is similar to the term describing clear cutting in forests. The renovation
term is described with a transfer function.
A5.l
A5.l
(l)
(t) ⳱ D(l)
bui,dem(t) • mjoi(t)
9,ren(t)
9,dem
(10a)
A3 6(t) ⳱ P(t) • sowp(t)
Waste generation from other wood products assumes a certain delay between input (sales) and
output (waste). This delay corresponds to the
lifetime distribution of the wood products.
t
A6 9(t) ⳱
冮
6 9
(t,t⬘)A3 6(t⬘)dt⬘
k5.l 9(t,t⬘) • A3 5.l(t⬘)dt⬘
(10b)
The renovation term can be interpreted as
follows: All inputs to birth cohort l of times t⬘ < t
contribute to the renovation term by a relative
amount of k5. 9(t,t⬘), where k5.l 9(t,t⬘) is a transfer
function and tR stands for the birth year of the
oldest not yet demolished building. The transfer
function is also assumed to be normally distributed and is identical with equation (9d). tR can
be calculated from the following differential
equation:
dt(l)
R (t)
dt
A5.l
⳱
冤
A3 5.l(t(l)
1ⳮ
R (t))
9,dem
t
(t)
冮k
冥
(t⬘,t(l)
R (t))dt⬘
5.l 9
tR(t)
(10c)
The denominator describes the amount of wood
that is left from input A3 5.l(t) at time t. Equation
(10c) assumes that only the oldest available
buildings in a cohort are demolished.
Consumption of Other Wood Products
Sales of other wood products are dependent
on the population P(t) and the average per capita
sales of other wood products sowp(t):
Journal of Industrial Ecology
(12)
The transfer function k6 9 is also described using a normal distribution of the lifetime, as described in equation (9d).
Used Wood Management
Used-wood management accounts for negligible stock of timber.
Ṁ(9)(t) ⳱ 0
(l)(t)
tR
72
冮k
0
t
⳱
(11)
(13)
Material and energy reuse of used wood are
time-dependent fractions of the total used-wood
generation:
冢
A9 3(t) ⳱ k9 3(t) • A4 9(t)
冣
(14)
冣
(15)
Ⳮ A5 9(t) Ⳮ A6 9(t)
A9
10
冢
(t) ⳱ k9 10(t) A4 9(t)
Ⳮ A5 9(t) Ⳮ A6 9(t)
where k9 3(t) and k9 10(t) are transfer coefficients.
Paper
The stock of paper held by the paper industry
and by paper consumers is assumed to be constant.
Ṁ(7)(t) ⳱ 0
Ṁ(8)(t) ⳱ 0
(16)
(17)
Paper sales are dependent on the population P(t)
and average per capita paper sales sp(t):
A7 8(t) ⳱ P(t) • sp(t)
(18)
Recycling paper depends on the used paper flow
and the recycling rate k8 7 (t):
A8 7(t) ⳱ k8 7(t) • A7 8(t)
(19)
RESEARCH AND ANALYSIS
Lignin production is assumed to be proportional
to the processed pulpwood. Used paper combusted to produce energy is also calculated with
respective ratios:
A7
10
(t) ⳱ k7 10(t) • A3 7(t)
(20)
A8
10
(t) ⳱ k8 10(t) • A7 8(t)
(21)
where k7 10(t) is the transfer coefficient for lignin
production, and k8 10(t) is the transfer coefficient
for energy production from used paper.
Energy
The timber stock held by the energy industry
is assumed to be negligible.
Ṁ(10)(t) ⳱ 0
A10 2(t) ⳱ efor(t) • A1 2(t)
A10 3(t)
⳱ eti(t) • [A2 3(t) Ⳮ I3(t) Ⳮ A9 3(t)]
(23)
(24)
Energy supply for heating buildings is modeled in
XYLOIKOS with fuels (solid, liquid, or gas) plus
solar panels. As the heated area of buildings
equals in first approximation the gross floor area
(Wüest and Gabathuler 1989), the total heat energy consumption of a building cohort can be
calculated with the gross floor area D(l)
bui(t) and
the average heat energy index in building cohort
l e(l)
bui(t).
A10 4(t) Ⳮ I4(t)
(25)
g
兺e
(t) • D(l)
l ⳱ 1, . . . , g
bui
(l)
bui
l⳱1
Energy supply from solar panels for each building
birth cohort is calculated with the average global
radiation in investigation region G(t), the efficiency factor of solar panels per building cohort
g(l)
sp , and the proportion of solar panel area and
gross floor area per building cohort easp/D (t):
(l)
bui
I4.l(t)
g
⳱
兺(D
(l)
bui
l⳱1
(l)
(t) • easp/D • G(t) • gSP
)
l ⳱ 1, . . . , g
(l)
bui
A10 7(t) ⳱ erp(t) • A8 7(t)
Ⳮ enf(t) • A3 7(t)
(27)
Numerical Simulations
Equations (1)–(27) must be solved numerically. All calculations were done using the simulation program SIMBOX (Baccini and Bader
1996).
(22)
Fuel energy consumption for the forestry and the
timber industry are proportional to the amount
of processed timber and their specific energy consumptions, efor(t) and eti(t).
⳱
Energy demand for the paper industry is dependent on the amount of paper recycled and new
fibers processed, and on the energy indexes of
paper recycling and new fiber-based paper production erp(t) and enf(t).
(26)
Data and Calibration
Calibration means finding appropriate parameter functions to describe in an accurate way the
historical development of timber use. The
XYLOIKOS model was calibrated with data of
the Swiss lowland region Kreuzung Schweizer
Mittelland (KSM). This region was chosen for a
case study in the research project SYNOIKOS,4
which was aimed at developing transdisciplinary
methods for the design and planning process of
densely populated areas (Baccini and Oswald
1998). The XYLOIKOS model was used in this
project to evaluate different long-term scenarios
for the restructuring of the forests and the buildings (Müller et al. 1998).
The model was calibrated using data from
1900 to about 1997. Data for the period 1997 to
2100 were based on assumptions for scenarios.
All timber flows were converted into dry matter
equivalents.
The model parameters were determined with
linear and nonlinear regression of data sets. The
primary data were obtained from literature or estimated, for example, using data drawn from
similar regions. The most important primary data
sources were
• Forestry: National area statistics; historic
forest-management plans with data about
age structure, standing volume, and cuttings; local yield tables for spruce; historic
data of age distribution in years 1888,
1920, and 1965
Müller et al., Long-term Timber Management Using MFA
73
RESEARCH AND ANALYSIS
Figure 2a Calibration of parameter functions related to forestry and timber industry. The x-axis
represents calendar years. The circles indicate measuring points between the years 1900 and 2000 used for
the curve fitting. The values between the years 2000 and 2100 are the assumptions used for the standard
scenario.
Note: [inh] ⳱ inhabitants; [ ] ⳱ a unitless value; [ha] ⳱ hectare
• Industry and waste management: National
studies of industrial timber flows, including
wood residues for recycling and thermal
use; studies about cantonal and national
used wood generation, recycling, and thermal use; a German study about specific energy consumption in the timber industry
• Buildings: National population census reports (specified for communities) with data
about population, number of households or
apartments, floor area per apartment; studies of timber content in different buildings
in a Swiss canton; three diploma theses involving field study of timber density in different building elements of demolition objects and new constructed buildings of
different building types; national statistics
for the lifetime of different building ele74
Journal of Industrial Ecology
ments; a study of energy consumption of
different building types
• Paper: Statistics from the Swiss Paper and
Cardboard Industry Association about national paper consumption, paper and lignin
production, and use of used paper for recycling; a study of specific energy consumption at Swiss cellulose and paper manufactories
The parameter functions are presented in figures 2a, 2b, and 2c. The circles indicate measuring points used for the calibration. This allows
the reader to comprehend the differences in the
availability of data and the need to use estimation. In general, the forest data were relatively
comprehensive and had an error factor of 10%
to 30%, whereas the data for the building stock
RESEARCH AND ANALYSIS
Figure 2b Calibration of parameter functions related to use and waste management. The x-axis
represents calendar years, or, in the case of the plots in the third row, numbers of years. The circles indicate
measuring points between the years 1900 and 2000 used for the curve fitting. The values between the
years 2000 and 2100 are the assumptions used for the standard scenario.
Note: [inh] ⳱ inhabitants; [ ] ⳱ a unitless value.
were comparatively poor with a larger error factor of 20% to 40%.
Validation and Sensitivity
Analysis
Where data were sufficient, the calculated
values were compared with measured data. Such
validation was used to understand the quality of
the data and to adjust the parameter functions.
All parameter functions were also varied individually in order to test their influence on all system
variables. In this article, we illustrate the validation and sensitivity analysis with two examples.
Validation of Wood Increment
Increments for all forest birth cohorts were
initially calculated with a constant site-quality
Müller et al., Long-term Timber Management Using MFA
75
RESEARCH AND ANALYSIS
Figure 2c Calibration of parameter functions related to energy. The x-axis represents calendar years. The
circles indicate measuring points between the years 1900 and 2000 used for the curve fitting. The values
between the years 2000 and 2100 are the assumptions used for the standard scenario.
Note: [inh] ⳱ inhabitants; [ ] ⳱ a unitless value.
index b ⳱ 22. The simulation results for the average overall increment showed a slightly decreasing increment during the twentieth century
(figure 3). This can be explained by the extension of the rotation period from 80 to 120 years,
because the increment is declining with increasing forest stand age. The calculated decrease contradicts data of the forest-management plans
(circles in figure 3) that show an increase in volume increment.
The difference between calculated and measured data can occur for different reasons:
• Measuring errors in forestry are usually
around 20% to 30% and are therefore unlikely to explain this difference.
• Tree species are slightly changing from
spruce to beech, which are not considered
in the model. As beech has a lower volume
76
Journal of Industrial Ecology
increment than spruce, we would expect to
observe a decrease in increment, but not
an increase.
• An increase in site quality due to changing
nitrogen emissions and increasing average
temperature could have caused an increase
in increment. The local yield tables, which
were used as a basis for the simulation, do
not reflect changing site qualities. Changing site qualities have been observed for
different locations (Spiecker et al. 1996).
The cause for divergence between measured
and calculated data cannot be determined definitively. An increase in site quality seemed to be
the most plausible reason. Therefore, the model
was recalibrated with a time-dependent sitequality index. Nonlinear regression was used to
calibrate the site-quality index in such a way that
RESEARCH AND ANALYSIS
Figure 3 Validation of the
increment using data from local
forest-management plans. The
forest-management plans report
an increase in increment, whereas
calculations with a constant site
quality lead to a decrease in
increment. A time-dependent site
quality was assumed to fit the
reported increment data.
calculated increments fit with the recorded increment data (figure 3). The calculated parameter function for the site-quality index b is shown
in figure 2a.
This simulation shows that forest site quality
can be an important factor in timber production,
which can be more significant than forestry internal management methods to increase productivity. The model does not give any explanation
for the site-quality increase, however, because it
uses an empirical function to characterize that
quality.
Sensitivity of Carpentry and Joinery
Products Consumption
Timber demand for carpentry and joinery
products is calculated with the model equations
of section 3, which use as input parameters the
population (P(t)), the per capita gross floor area
(Dbui(t)), the timber densities for carpentry and
(l)
joinery products (m(l)
car(t), mjoi (t)), and the lifetime
distributions for carpentry and joinery products
(k4.l 9(t,t⬘), k5.l 9(t,t⬘)). The influence of timber
density in buildings on timber demand is tested
for three variants, distinguishing carpentry and
joinery products (figure 4). All other parameters
are kept the same as in figures 2a, 2b, and 2c. As
carpentry and joinery products have different
lifetimes (here assumed to be 100 Ⳳ 20 and
30 Ⳳ 10 yr, respectively), this example serves as
well to test the influence of the lifetime on timber demand. The three variants are chosen according to the following concept:
1. Standard: This variant uses available data
of timber density in buildings in the KSM
region in the twentieth century. Although
timber density was relatively high at the
beginning of the twentieth century for
both carpentry and joinery products (40
and 50 kg/m2, respectively), these densities
have now both reached a value of about
10 kg/m 2 . The timber density in the
twenty-first century is assumed to stay at
the same level as at the end of the twentieth century.
2. Timber construction: Timber content in
buildings is assumed constant over the
whole simulation period. The values (40
and 60 kg/m2) are based on modern timber
construction, which is comparable to classical Scandinavian building types.
3. Stone construction: Timber content is also
kept constant over the entire simulation
period, and represents typical stone constructions such as Mediterranean buildings, with a timber density of 5 and 5
kg/m2.
The simulation of these three variants
(figure 5) revealed three interesting phenomena:
1. The standard variant shows a relatively
constant timber demand for carpentry and
joinery products which can be verified using data for timber end use. Although the
building inventory has increased by a factor of 5 in the last 100 years, carpentry and
joinery timber sales are still in the same
order of magnitude as a century ago. Decreasing timber densities in buildings have
compensated for this development. The
smooth development of the timber demand for building construction conceals
Müller et al., Long-term Timber Management Using MFA
77
RESEARCH AND ANALYSIS
(l)
Figure 4 Calibration of parameters m(l)car (t) and mjoi
(t) for the three variants. All other parameters are
identical to the standard scenario as defined in figures 2a, 2b, and 2c.
these significant underlying changes. Assuming similar changes of these factors for
the future could lead to significant changes
in timber demand, especially if the different factors are not offsetting one another.
2. As the timber content in buildings is low
compared to the overall use of construction materials, relatively small changes in
the building composition can have a significant impact on timber demand. A consequent realization of a green building policy, as can be deduced from LCA studies
of construction materials,5 using timber instead of concrete or steel would lead to a
sixfold increase in timber demand. Such a
policy could only be accomplished on the
basis of significant timber imports. We calculated that the degree of self-sufficiency
in the KSM region would decrease from
80% to 12%.
3. The timber and stone construction variants show very different behaviors of carpentry and joinery timber demands, even
78
Journal of Industrial Ecology
though timber density is kept constant for
both products. Whereas joinery wood demand stabilizes quickly at a high level, the
demand for carpentry wood shows a strong
oscillation. This effect can be explained
with the assumed stabilization of the building stock and the different product lifetimes. This effect could not yet be verified
with KSM data because the transition from
growth to stabilization is still in its initial
phase.
An important consideration is that the data
available to determine the average timber content are poor. The accuracy of the calculated timber demand is therefore limited. The poor data
do not affect the general principles of mass conservation leading to the phenomena described
above, however. This parameter variation shows
the importance of a better understanding of the
timber content and the lifetime of products, and
how they are influenced by different economic,
social, and political factors.
RESEARCH AND ANALYSIS
Figure 5 Calculated wood demand for carpentry and joinery products (A3 4 (t) and A3 5 (t)) for standard
scenario as defined in figures 2a, 2b, and 2c with varying parameters of wood density in buildings as defined
in figure 4.
Transition Scenarios for a Solar
Energy Supply
Motivation
In this section, we explore the potential and
limits of the KSM region to use domestic timber
as a source for timber construction, paper and
cardboard production, and energy supply. These
future roles of timber are discussed under the
premise that during the twenty-first century, the
present fossil fuels-based energy supply is transformed into an energy supply that is based on
renewable energy, for example, from solar panels,
or photovoltaic or biomass energy sources.
At present, the dominant energy sources for
building heating are fossil fuels, mainly heating
oil and natural gas. Firewood contributes about
5% to building heating in Switzerland (BfE
2002). The potential of firewood to substitute for
heating oil and natural gas is limited by the high
energy consumption of the present building
stock. If all buildings constructed in the future
follow a low-energy (high-efficiency) standard,
however, this substitution potential could gradually increase as the present building stock is exchanged. Such an option in turn could lead to a
trade-off between the use of wood as a construction material and as a fuel. But it would also open
new options for timber down-cycling, for example, if used wood from demolition is used for energy production. A scenario technique was used
to characterize a building type for the twenty-first
century which would allow the region to balance
timber production and consumption in a transition to a building energy supply that is based exclusively on domestic renewable sources of wood
and solar panels.
Choice of Indicators
The model calculates all system variables for
each scenario. Discussing all system variables
Müller et al., Long-term Timber Management Using MFA
79
RESEARCH AND ANALYSIS
would not only exceed the scope of a journal article, but would also provide information that is
not specific enough to interpret the results. For
this reason, we defined four groups of indicators
that condense this information according to the
purpose of the chosen scenarios.
The modeling results are discussed with four
groups of indicators that help us to interpret the
modeling results. All indicators are calculated by
the system variables.
1. Degree of self-sufficiency (DSS) with timber and energy: A DSS less than 1 means
net imports, indicating possible shortages.
A DSS greater than 1 means net exports,
indicating possible surpluses.6
2. Building energy: Energy consumption for
building heating, divided into fossil fuels,
firewood, and solar panels. Other energy
sources such as wind or geothermal energy
are not included in the present version of
XYLOIKOS.
3. Timber stocks per person in forests and
buildings: Indicate resource stocks for future use.
4. Use of construction wood, interior wood,
and total logwood in the region: Data are
quoted as total flows per region, to serve as
indicators for the local industry.
Standard Scenario
The standard scenario assumes a continuation
of the present strategies (according to figures 2a,
2b, and 2c). The increase in site quality is assumed to stabilize in the twenty-first century.
The building lifetime is 100 Ⳳ 20 years for all
birth cohorts. All buildings constructed after
2000 perform at an average heat energy consumption standard of 300 MJ/m2/yr, which is the
present standard for new buildings.
The simulation results of the standard scenario are illustrated in figure 6. The degree of net
self-sufficiency (DSS) for timber decreases during
the period of 1900–1980 from about 1.2 to 0.7.
This decrease is mainly because of an increase in
imports of pulp for paper production. After 1980,
the DSS for timber increases once again during
the entire twenty-first century. This means that
a continuation of the present strategy would lead
80
Journal of Industrial Ecology
to increasing overproduction in the future, even
though all parameters are stabilized in the
twenty-first century. The increase in DSS can be
explained by the decreasing use of timber as a
consequence of the assumed replacement of
buildings with high timber densities by buildings
with lower timber densities (from 90 kg/m2 to 20
kg/m2, respectively). Also, the increase in site
quality in the second half of the twentieth century leads to a delayed increase in harvest, which
will become evident in the twenty-first century.
This delay is determined by the lifetime of the
trees. The DSS for energy also decreases until
1980 and reincreases slightly in the twenty-first
century. These changes are mainly due to the dynamics of the building stock.
After a strong increase in fossil fuel use in the
second half of the twentieth century, a maximum
is achieved around 2000, and it decreases by 50%
in the twenty-first century without specific measures being taken (figure 6). This reduction in
fossil fuel use is a vintage effect and can be traced
to the replacement of the existing building inventory by buildings of the type found in 1995.
Nondependency on fossil fuels in the twenty-first
century is not possible in such a scenario.
Timber stocks per inhabitant, in forest and
building inventories, are almost constant over
the whole period under investigation (figure 6).
Population growth is compensated for by an increase in standing timber stock per area as a consequence of the extension of the rotation period.
The increasing building stock per person is compensated for by a reduction in the average building timber content.
Logwood (roundwood) sales increase in the
second half of the twentieth century, gradually
decreasing toward the end of the century (figure
6). Lower sales of logwood in the first half of the
twentieth century are a result of an extension of
the rotation period from 80 years to 120 years.
The increase seen during the second half of the
twentieth century onward is mainly caused by an
increase in site quality. The sales of pulpwood
peak in the second half of the twentieth century,
and then stabilize at a lower level. This reduction
is due to increased paper recycling. Sales of construction wood in the carpentry sector and interior wood in the joinery sector are increasing
in the growing phase for the building stock, even
RESEARCH AND ANALYSIS
Figure 6 Standard scenario. Forest policy and building policy do not change. All future buildings perform
at the standard of 1995 (300 MJ/m2/yr).
though average timber content in buildings is decreasing. Whereas the use of construction wood
falls as soon as the building stock stabilizes, the
use of interior wood stabilizes at a high level.
This can be explained by the difference in lifetimes of joinery and carpentry wood products.
Restructuring Scenario
This scenario aims at restructuring the
timber-management system in such a way that
the heating energy consumption of buildings can
be accomplished exclusively with renewable domestic resources, while striving for a balance of
production and consumption. The scenario assumes that the present building stock is exchanged between 2000 and 2050 with a highly
energy-efficient building stock. The new buildings use as heating energy sources a combination
of wood and solar panels on the available roof
area. Given the regional climatic conditions and
realistic energy standards, these solar panels provide sufficient hot water and room heat from
about April to September, but current technology does not permit storage of the summer surplus for use in winter. For this reason, firewood
is used in combination with solar panels in this
scenario. We further assume that energetic use of
used wood as fuelwood is increasing from 15% to
50%. This would involve appropriate incineration technology to prevent air pollution, as it is
achieved with some waste incineration plants in
Switzerland, which are combined with a heat distribution system.
The restructuring scenario is calculated for
building types with different timber densities and
energy performances, in order to identify combinations that are balancing domestic timber
production and consumption. This restructuring
scenario assumes a timber density of the new
buildings equal to the buildings constructed at
present. Assuming this timber density in buildings, the threshold for the average building energy performance in this region is identified to
be 130 MJ/m2/yr. The level of this heat energy
index is less than half of the present business
Müller et al., Long-term Timber Management Using MFA
81
RESEARCH AND ANALYSIS
standard, but this level can be achieved with
building technology currently available. Insulation measures to lower energy consumption are
not sufficient to reach this level and are not considered in the model.
The specific parameters of this scenario are
defined in figure 7. All other parameters are identical with the standard scenario (figures 2a, 2b,
and 2c).
In the restructuring scenario (figure 8), DSS
for timber levels off at about the present level.
The overproduction of the standard scenario
could be avoided, but hinterland use could not
be reduced in this scenario where hinterland denotes all nondomestic areas/functions required to
sustain the metabolism of a certain region. The
DSS for energy would increase from about 5% to
about 70%.
The use of fossil fuels to heat buildings is constantly being reduced in the first half of the
twenty-first century (figure 8). Complete nondependency on fossil fuels is achieved in the middle of the twenty-first century, when firewood
(two-thirds) and solar panels (one-third) have
entirely substituted for fuel oil in building heating.
The per capita timber stock in forest and
building inventory are not influenced signifi-
cantly (figure 8). The potential for future resource use are not diminished in such a restructuring scenario.
The sales of construction wood in the carpentry sector during the restructuring phase would
be stabilized at about the present level, compared
to the decrease in the standard scenario (figure
8). The influence on the sales of joinery wood is
smaller because of the shorter product lifetime.
Discussion and Conclusions
MFA models can be used to describe timber
production and consumption in physical terms,
and to develop scenarios for their coordination.
Both production of timber and generation of
used wood as a potential secondary resource are
characterized by delays of several decades,
whereas consumption is determined largely by a
choice of building technology with immediate effects. A coordination of production and consumption therefore needs to integrate long- and
short-term strategies.
Offsetting and Accumulating Effects
Although timber production and consumption have been coordinated very little in the
Figure 7 Standard scenario. Forest policy and building policy do not change. All future buildings perform
at the standard of 1995 (300 MJ/m2/yr).
82
Journal of Industrial Ecology
RESEARCH AND ANALYSIS
Figure 8 Restructuring scenario. Between 2000 and 2050, all buildings are replaced by new buildings that
perform at a low energy standard of 130 MJ/m2/yr.
KSM region, consumption seems to have
adapted to production to some extent.
Model simulations showed that the DSS for
timber decreased in the period of 1900–1980
from 1.2 to 0.7, mainly as a result of increased
imports of pulp. Since 1980, the DSS has been
increasing again. The changes in DSS are relatively smooth compared to the significant
changes of external factors. This relative balance
of production and consumption is achieved because many of these external factors compensated for one another.
Wood increment slightly increased during the
twentieth century, even though the rotation period was extended in this period from 80 to 120
years, which has a decreasing effect on the forest
growth. According to modeling simulations, increment would have increased by 60% to 70% if
no rotation-period extension took place. The
data available suggest that this increase in increment is caused by an increase in site quality. A
site-quality increase could have been caused by
environmental changes such as nitrogen emissions or increase in average temperature. If the
available data are accurate, environmental
changes had a significantly stronger impact on
quantitative timber production than did forestry
internal measures. In order to coordinate production and consumption, it is therefore important to understand in more detail the extent and
causes of site-quality changes for different locations and tree species.
Compared to the forests, changes in the building stock were more distinct. The total gross floor
area of the KSM building stock increased in the
twentieth century by a factor of 5. This growth,
however, coincided with a decrease in average
timber content in the buildings by a factor of 4.5.
Because of the coincidence of these two factors
and their opposing effect on timber use, carpentry and joinery timber consumption were relatively constant. The influence of the building inventory’s stock dynamics has not yet become
evident.
Müller et al., Long-term Timber Management Using MFA
83
RESEARCH AND ANALYSIS
Future Roles of Timber
Model simulations further showed that a continuation of the present strategies would lead to
further increases in DSS, which means increasing
overproduction. An effective measure to avoid
overproduction would be to increase the timber
density in new buildings. A scenario of timber
construction, as suggested on the basis of lifecycle assessment, could not be accomplished
with domestic timber and would cause a significant use of hinterland. This is even true for a
scenario in which the entire building stock is
gradually coming to a steady state, because an
increasing number of old buildings will need to
be replaced.
Another possibility to balance production
and consumption is to gradually exchange the
present building stock in the twenty-first century
with new buildings that use exclusively domestic
wood and solar panels on the roofs for heating
energy. Calculations showed that the DSS could
be stabilized on the present level if the new
buildings have a timber density similar to present
new buildings, but have an energy consumption
per gross floor area that does not exceed 130 MJ/
m2/yr. Modern energy-saving buildings achieve
this value with presently available technology.
The role of wood as a fuel has its largest potential
in the combustion of used wood. The availability
of this source fluctuates with changes in the
building inventory, however.
Coordinating Production and
Consumption
A coordination of timber production and
consumption can only be achieved on the basis
of information about the entire timber chain.
This study revealed that the long-term timber
supply is mainly influenced by the dynamics of
the timber stocks in forests and buildings. Although changes in production are relatively
smooth and occur with a long and predictable
delay, changes in consumption may occur to a
greater extent, more abruptly, and with less predictability. Forests have an unused capacity to
absorb smaller fluctuations in timber demand.
Their potential to react to larger fluctuations is
limited, however, because of the long production
84
Journal of Industrial Ecology
time. The most effective measure to avoid large
fluctuations is to adapt the timber density in new
buildings. This is especially true in regions with
a large building stock and a low percentage of
timber in construction.
Buildings can therefore be regarded as the
motor of the timber cycle. They have a potential
to cause the most significant fluctuations in timber demand and in used-wood generation. At the
same time, they are the weakest link in the timber chain regarding data availability and understanding of the dynamics. The accuracy of the
timber chain model is limited mainly by the poor
information about the timber inventory.
Method
The dynamics of the stocks are usually neglected in conventional economic studies as well
as in life-cycle-oriented studies. The MFA methodology allowed us to model the timber chain
and its stocks in forests and buildings with the
same concepts. The strength of the model lies in
its explanatory power, because it is mass-balance
consistent and considers all relevant processes of
the timber chain. The model is therefore suited
for long-term historical and long-term scenario
analyses. Its accuracy, especially for short-term
changes, is limited, however, because data availability is limited and because the model does not
consider price mechanisms. Further research is
required to explore possibilities to combine the
strengths of economic and material flow-based
approaches.
Acknowledgments
We wish to thank to Prof. Dr. Hans Rudolf
Heinimann from ETH Zurich for providing access to relevant forestry-related resources, Prof.
Dr. Margot Weijnen from TU Delft for her support of this publication within the frame of the
Delft Interfaculty Research Program, “Design
and Management of Infrastructures,” and Miranda Aldham-Breary for language editing support.
Notes
1. Editor’s note: For a discussion of the strengths and
weaknesses of approaches to enlarging the scope
RESEARCH AND ANALYSIS
2.
3.
4.
5.
6.
of LCA models, see the Journal of Industrial Ecology article by Udo de Haes and colleagues (2004).
“Kreuzung Schweizer Mittelland” is the German
expression for “Crossing Swiss Lowlands.” As the
name indicates, this region is located in the intersection of the national highways N1 (eastwest) and N2 (north-south) between Olten,
Oensingen, and Zofingen.
Side quality is the yield potential maximum
quantity of material of a given species that an area
is capable of producing under normal conditions,
so long as the factors of the locality remain unchanged.
SYNOIKOS was a 3-year project carried out by
architects, urban planners, scientists, engineers,
and economists, aimed at developing new methods for the long-term restructuring of urban regions, including buildings, road networks, agricultural areas and forests, among others. The
application of XYLOIKOS as a support tool for
urban design is described by Müller and colleagues (1998).
LCA studies show that the environmental impacts of wood are lower compared with other construction materials such as concrete or steel
(Schari-Rod and Welling 2002).
The KSM region is for many reasons too small to
claim self-sufficiency. Although construction
timber is often used in the production region, fibers and paper products are often shipped over
long distances. It is therefore not possible to find
one scale for self-sufficiency of all products. An
answer to the scale question would imply a multiscale analysis, similar to the multiscale analysis of
copper by Graedel and colleagues (2003). The
units of such a multiscale system would be regions
with imports and exports, as reflected with the
DSS in this model. The basic principles and
mechanisms of coordinating production and consumption remain the same for different scales.
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About the Authors
Daniel B. Müller is a postdoctoral fellow at the
School of Forestry & Environmental Studies at Yale
University in New Haven, Connecticut, USA. HansPeter Bader is a senior research scientist at the Swiss
Federal Institute for Environmental Science and Technology (EAWAG) in Dübendorf, Switzerland. Peter
Baccini is the Chair of Resource and Waste Management at the Swiss Federal Institute of Technology
(ETH) in Zurich, Switzerland.
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