Journal of Cleaner Production 39 (2013) 329e337
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Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
Economic viability analysis of a construction and demolition waste recycling plant
in Portugal e part II: economic sensitivity analysis
André Coelho, Jorge de Brito*
Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 16 September 2011
Received in revised form
31 March 2012
Accepted 3 May 2012
Available online 21 May 2012
Part I of this paper contained a technological description and economic evaluation of a large-scale highend CDW recycling plant in the Lisbon Metropolitan area. It concludes that economic viability is likely
under the operating conditions considered, but these may and will very probably change in the near
future. The reasons for such assumption have to do with the inherent uncertainty related to CDW
generation (which might vary, for instance, due to socio-economic conditions in the region), such as the
variability of CDW input gate fees and tariffs associated with landfilling rejected materials, which are
market dependent parameters. This made it necessary to perform an (simplified) economic viability
sensitivity analysis, focused on the investment return period and global economic balance. If parameters
such as the plant’s capacity, the CDW input gate fee and landfill fee are varied, the investment return
period is affected in different ways, though its value is generally kept below 8 years, for parameter
variations of 30%. The analysis indicates economic performance for variations in single parameters,
except for the plant’s capacity, which was considered to vary simultaneously with all others. Extreme
best and worst scenarios were also tested in an attempt to define the model’s boundaries.
Ó 2012 Elsevier Ltd. All rights reserved.
Keywords:
CDW fixed recycling plant
Economic analysis
Sensitivity analysis
1. Introduction
Sensitivity analyses have proved to be extremely useful in
scientific studies not only on building analysis (Junnila, 2004;
International Energy Agency, 2005; Palme et al., 2008), but also on
industrial products (Vadde et al., 2007) and CDW recycling operations (Bohne et al., 2008). Although special purpose methods have
been developed to perform sensitivity analysis from a general
mathematical standpoint, as in Saltelli et al. (2004), techniques
such as variance-based methods and Monte Carlo filtering are not
used in the present analysis, which as a consequence is greatly
simplified. Very simple sensitivity analyses are performed regularly in studies which do not require complex probabilistic
reasoning or to which it cannot be applied, because only basic
linear parameter variation impacts are needed, very small sample
sizes are available, or both (de Brito and Gonçalves, 2002; Saari,
2000; Dantana et al., 2004). This has been the case of the present
study, since it was constructed on a simple spreadsheet environment, not compatible with the Monte Carlo filtering method,
which involves: the use of available information or data to establish the set of acceptable model behaviours; in Monte Carlo
* Corresponding author. Tel.: þ351 218443659; fax: þ351 218443071.
E-mail addresses: jbrito@civil.ist.utl.pt, jb@civil.ist.utl.pt (J. de Brito).
0959-6526/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jclepro.2012.05.006
methods parameters randomly vary over a range of values and
generate corresponding sets of model predictions and classification of each simulation as acceptable or unacceptable according to
pre specified behaviour definition (Rose et al., 1991). The application of this method would therefore require an automatic simulation engine, which would perform a set of algorithms (based on
the CDW operation procedures) with randomly varied input
parameters. This would greatly increase the complexity of the
analysis, without introducing an equivalent benefit in the output
results.
The simplified sensitivity analysis used here derived from the
need to see how varying the operating parameters would influence
the return on investment period (and the 60-year operation period
overall economic balance) for the CDW recycling plant studied
(described and characterized in part I (Coelho and de Brito,
submitted for publication)). The parameters in question are: the
plant’s capacity, CDW input gate fee, concrete aggregate selling
price, rejected materials landfill price, percentage of mixed/separated CDW input and CDW input mass rate.
The parameters were varied separately, except for plant
capacity, which was paired with each of the other parameters in
turn. General simultaneous multi-parameter variations and
couplings were not analysed as this would increase analysis time
and complexity, and was not considered crucial. The only simultaneous parameter variation performed was conducted for two
330
A. Coelho, J. de Brito / Journal of Cleaner Production 39 (2013) 329e337
extreme scenarios of best and worst conditions. These two
scenarios were tested for each facility capacity, but whilst it is
highly unlikely that they will occur in the real facility they can be
considered as boundaries to support the study’s framework.
2. Economic viability methodologyevariation parameters
2.1. Variation parameters
As stated in the introduction, several variation parameters were
chosen in order to evaluate the way investment return period and
60-year global economic balance were affected by these variations.
These parameters were chosen initially for their perceived impact
on the resultant performance factors which, except for the concrete
aggregate selling price, were all proven to have moderate to high
impact on the final results. However, other variables could have
been studied, such as energy (electricity and diesel fuel) prices, the
land acquisition cost (even though it is only about 1% of overall 60year costs) or the cost of purchasing and installing equipment
(roughly 3% of overall 60-year costs).
2.1.1. Plant capacity
Plant capacity, in tonne/h, was considered the main variation
parameter, which means that all other variations in other parameters were performed in tandem with it. This was mainly to
account, in a simplified way, for the uncertainty in CDW generation,
in spite of its tendency to rise in the next decade or so (Rose et al.,
1991). The plant was considered to be designed, depending on the
actual CDW generation rate, for the following: 350 tonne/h,
250 tonne/h, 170 tonne/h, and 85 tonne/h. The base case analysed in
part I has capacity of 350 tonne/h, derived from a 416 kg/person.year CDW generation rate applied to the Lisbon Metropolitan area
(Coelho and de Brito, submitted for publication).
2.1.2. CDW input gate fee
Part I showed that the CDW input gate fee benefits would be 86%
of all benefits for a 350 tonne/h capacity plant. This striking figure
naturally makes this parameter a potentially important factor in
determining the plant’s economic viability. For the sensitivity
analysis it was considered to vary by as much as 30%, over or under
the calculated value of the average input gate fee for mixed CDW,
V48/tonne, or V7.8/tonne for separated ceramic and concrete
aggregates (Coelho and de Brito, submitted for publication).
2.1.3. Concrete aggregate selling price
Recycled concrete aggregates comprise around 41% of all input
mass and represent, unlike ceramics, a potential benefit in sales,
with an average price of V2.8/tonne. Recycled ceramics have no
market value at the moment, which is to say they can currently be
delivered free of charge to recyclers/producers (Mimoso, 2011).
Moreover, concrete aggregate represents 15% of all benefits from
materials sold; but it only represents 2.3% of total benefits, which
probably means that any change in the selling price of recycled
concrete aggregate will not greatly affect the plant’s overall return
on investment period.
2.1.4. Rejected materials landfill fee
The average landfill fee was estimated at V114/tonne (taking
figures ranging from V90/tonne to V150/tonne), derived from
a market survey of regional waste operators and contractors. As
concluded in part I, the overall share of landfill fees to be paid
(assumed the same even if the material is delivered to downstream
processors) over the 60 years operation period, is around 80% of all
costs, which makes this parameter a prime candidate for sensitivity
analysis.
2.1.5. Percentage of mixed/separated CDW input
The plant operation was divided into two modes: one accepting
and separating mixed CDW, and another only working with
previously separated concrete and ceramic aggregates. The full
operation mode involves higher operating costs than the simplified
mode, in a proportion that varies according to the plant’s capacity,
ranging from 4.7 through 2.9 more, respectively for case 1
(350 tonne/h capacity) and case 4 (85 tonne/h capacity). On the
other hand, treating fully mixed CDW offers the possibility of
charging a gate fee 6 higher than that for separated concrete and
ceramic aggregates, with which comes large revenue. A third
consequence of varying the percentage of mixed/separated CDW
input is related to the rejected materials landfill fee, since the more
mixed material treated, the more mass is rejected, and therefore
higher costs must be supported.
2.1.6. CDW input mass
CDW input mass is one of the most unpredictable factors
considered and it could have a threefold negative impact, if
reduced. For a given capacity design, a lower average input CDW
flow will lead to sub-optimal operation, implying extra fixed
installation costs not justified by the incoming CDW mass; moreover, lower CDW input, especially mixed CDW, translates into less
benefits from gate fees and, finally, it also means lower output of
materials to be sold. Assuming that a plant’s capacity cannot be
exceeded (or that no surplus capacity can be obtained once the
design has been established), then only reductions in CDW input
flow matter to the analysis, since any incoming input material does
not enter the plant if it is already running at full capacity. Consequently, reductions of 15 and 30% were considered, similar to other
parameter variations. But assuming that this parameter has
a particular effect on profitability and that it is subject to higher
levels of uncertainty than the other variation parameters, a further
reduction of 50% was considered in the analysis.
2.2. Case composition
As noted in 2.1.1, the plant’s capacity was a variation parameter
in all the sensitivity analyses performed. As such, for each design
capacity all other parameters were considered to vary in turn,
which renders this a two-dimensional sensitivity analysis. Table 1
shows the variation for each parameter and labelling each resulting case accordingly; each column’s variation results will therefore
lead to three-dimensional charts, with the investment return
period and global economic balance figures as their vertical axes.
3. Economic viability results and discussion - sensitivity
analysis
Table 2 and Table 3 summarise the main results obtained from
the simplified sensitivity analysis, for each variation parameter. The
investment return period results are given in Table 2 and the global
economic balance over a 60-year period is presented in Table 3, to
depict the potential payback or profit possible within that timeframe for the cases and variations analysed. The 3D charts produced
from these tables are given in Figs. 1 and 2 through Fig. 10. Fig. 11 is
given as an example of the time variation of the overall economic
balance for the four plant capacities considered. It can immediately
be gathered that installed capacity is of primordial importance to
profitability and the return on investment period. It can be seen
that capacity variation alone is responsible for doubling the return
on investment period (2 years for a 350 tonne/h facility and 4 years
for an 85 tonne/h facility), and an almost 6-fold reduction in the
global 60-year economic balance (V386 million for a 350 tonne/h
facility and V66.5 million for an 85 tonne/h facility).
A. Coelho, J. de Brito / Journal of Cleaner Production 39 (2013) 329e337
331
Table 1
Case composition for sensitivity analysis.
Plant capacity e
main variation
parameter
Case 1e350
tonne/h
Case 2e250
tonne/h
Case 3e170
tonne/h
Case 4e85
tonne/h
Variation parameter
CDW input gate fee
Concrete aggregates selling
price
Rejected materials landfill
fee
% Of mixed/separated CDW
input
CDW input mass
Case
designation
Case
designation
Case
designation
Case
designation
Case
designation
% Variation
in relation to
initial value
Case 1_S4
Case 1_S3
Case 1_S0
Case 1_S1
Case 1_S2
Case 2_S4
Case 2_S3
Case 2_S0
Case 2_S1
Case 2_S2
Case 3_S4
Case 3_S3
Case 3_S0
Case 3_S1
Case 3_S2
Case 4_S4
Case 4_S3
Case 4_S0
Case 4_S1
Case 4_S2
30
15
0
15
30
30
15
0
15
30
30
15
0
15
30
30
15
0
15
30
% Variation
in relation to
initial value
Case 1_T4
Case 1_T3
Case 1_T0
Case 1_T1
Case 1_T2
Case 2_T4
Case 2_T3
Case 2_T0
Case 2_T1
Case 2_T2
Case 3_T4
Case 3_T3
Case 3_T0
Case 3_T1
Case 3_T2
Case 4_T4
Case 4_T3
Case 4_T0
Case 4_T1
Case 4_T2
30
15
0
15
30
30
15
0
15
30
30
15
0
15
30
30
15
0
15
30
Case 1_U4
Case 1_U3
Case 1_U0
Case 1_U1
Case 1_U2
Case 2_U4
Case 2_U3
Case 2_U0
Case 2_U1
Case 2_U2
Case 3_U4
Case 3_U3
Case 3_U0
Case 3_U1
Case 3_U2
Case 4_U4
Case 4_U3
Case 4_U0
Case 4_U1
Case 4_U2
3.1. Variations in selected parameters
As shown in Fig. 1, the CDW input gate fee has a pronounced
effect on the investment return period for any installed capacity
(especially for lower capacities, though). For a 30eþ30% variation
in the CDW input gate fee the return period varies by a minimum
factor of 2.5 (170 tonne/h facility) and a maximum of 4 (85 tonne/h
facility).
To guarantee high profitability and avoid unattractive return
periods, gate fees must be kept as high as possible, while competiveness with landfills and other waste processors is maintained.
If plant installed capacity is 85 tonne/h, return on investment
periods may become too long (more than the 8 years taken as
% Variation
in relation to
initial value
30
15
0
15
30
30
15
0
15
30
30
15
0
15
30
30
15
0
15
30
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
Case
1_V4
1_V3
1_V0
1_V1
1_V2
2_V4
2_V3
2_V0
2_V1
2_V2
3_V4
3_V3
3_V0
3_V1
3_V2
4_V4
4_V3
4_V0
4_V1
4_V2
% Variation
in relation to
initial value
30
15
0
15
30
30
15
0
15
30
30
15
0
15
30
30
15
0
15
30
Case
Case
Case
Case
e
Case
Case
Case
Case
e
Case
Case
Case
Case
e
Case
Case
Case
Case
e
% Variation
in relation to
initial value
1_X0
1_X1
1_X2
1_X3
0
15
30
50
e
2_X0
2_X1
2_X2
2_X3
0
15
30
50
e
3_X0
3_X1
3_X2
3_X3
0
15
30
50
e
4_X0
4_X1
4_X2
4_X3
0
15
30
50
e
a reference for economic viability in some regulations (Coelho and
de Brito, submitted for publication)), especially if possible gate fees
remain 15e30% below the averages considered here. This, however,
will depend on how initial fundraising is dealt with and negotiated.
Global economic balance is also strongly affected by the CDW
input gate fee. For a 350 tonne/h facility, a 30% decrease in its value
over 60 years represents a 63% reduction in global economic
balance. That decrease is even sharper for an 85 tonne/h facility,
which ends up with a 67% fall in global economic balance.
Fig. 3 shows that concrete aggregate selling price (coarse or fine,
within a 30% variation range) does not have much influence on the
return on investment period, for any of the capacities evaluated.
Accordingly, influence on the global economic balance over 60 years
Table 2
Results for investment return period, for all analysed situations.
Plant capacity e
main variation
parameter
Case 1e350
tonne/h
Case 2e250
tonne/h
Case 3e170
tonne/h
Case 4e85
tonne/h
Variation parameter
CDW input gate fee
Concrete aggregates selling price
Rejected materials landfill fee
% Of mixed/separated CDW input
CDW input mass
Case
designation
Value,
years
Case
designation
Value,
years
Case
designation
Value,
years
Case
designation
Value,
years
Case
designation
Value,
years
Case 1_S4
Case 1_S3
Case 1_S0
Case 1_S1
Case 1_S2
Case 2_S4
Case 2_S3
Case 2_S0
Case 2_S1
Case 2_S2
Case 3_S4
Case 3_S3
Case 3_S0
Case 3_S1
Case 3_S2
Case 4_S4
Case 4_S3
Case 4_S0
Case 4_S1
Case 4_S2
1
1
2
2
3
1
1
2
2
3
2
2
2
3
4
3
3
4
5
9
Case 1_T4
Case 1_T3
Case 1_T0
Case 1_T1
Case 1_T2
Case 2_T4
Case 2_T3
Case 2_T0
Case 2_T1
Case 2_T2
Case 3_T4
Case 3_T3
Case 3_T0
Case 3_T1
Case 3_T2
Case 4_T4
Case 4_T3
Case 4_T0
Case 4_T1
Case 4_T2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
4
4
4
4
4
Case 1_U4
Case 1_U3
Case 1_U0
Case 1_U1
Case 1_U2
Case 2_U4
Case 2_U3
Case 2_U0
Case 2_U1
Case 2_U2
Case 3_U4
Case 3_U3
Case 3_U0
Case 3_U1
Case 3_U2
Case 4_U4
Case 4_U3
Case 4_U0
Case 4_U1
Case 4_U2
2
2
2
1
1
2
2
2
2
1
3
2
2
2
2
6
4
4
3
3
Case 1_V4
Case 1_V3
Case 1_V0
Case 1_V1
Case 1_V2
Case 2_V4
Case 2_V3
Case 2_V0
Case 2_V1
Case 2_V2
Case 3_V4
Case 3_V3
Case 3_V0
Case 3_V1
Case 3_V2
Case 4_V4
Case 4_V3
Case 4_V0
Case 4_V1
Case 4_V2
1
1
2
2
2
1
2
2
2
2
2
2
2
3
3
2
3
4
5
6
Case 1_X0
Case 1_X1
Case 1_X2
Case 1_X3
e
Case 2_X0
Case 2_X1
Case 2_X2
Case 2_X3
e
Case 3_X0
Case 3_X1
Case 3_X2
Case 3_X3
e
Case 4_X0
Case 4_X1
Case 4_X2
Case 4_X3
e
2
2
2
2
e
2
2
2
3
e
2
2
3
4
e
4
5
6
10
e
332
A. Coelho, J. de Brito / Journal of Cleaner Production 39 (2013) 329e337
Table 3
Results for the global economic balance over 60 years, for all analysed situations.
Plant capacity e Variation parameter
main variation
CDW input gate fee
Concrete aggregates selling price Rejected materials landfill fee % Of mixed/separated CDW input CDW input mass
parameter
Case
Economic
Case
Economic
Case
Economic
Case
Economic
Case
Economic
balance over
designation
balance over
designation
balance over
designation balance over
designation balance over designation
60 years,
60 years,
60 years,
60 years,
60 years,
V 106
V 106
V 106
V 106
V 106
Case 1e350
tonne/h
Case 2e250
tonne/h
Case 3e170
tonne/h
Case 4e85
tonne/h
Case 1_S4
Case 1_S3
Case 1_S0
Case 1_S1
Case 1_S2
Case 2_S4
Case 2_S3
Case 2_S0
Case 2_S1
Case 2_S2
Case 3_S4
Case 3_S3
Case 3_S0
Case 3_S1
Case 3_S2
Case 4_S4
Case 4_S3
Case 4_S0
Case 4_S1
Case 4_S2
591
489
386
284
145
416
343
270
197
123.4
275
225
175
126
75.8
125
99.7
74.8
49.9
25.0
Case 1_T4
Case 1_T3
Case 1_T0
Case 1_T1
Case 1_T2
Case 2_T4
Case 2_T3
Case 2_T0
Case 2_T1
Case 2_T2
Case 3_T4
Case 3_T3
Case 3_T0
Case 3_T1
Case 3_T2
Case 4_T4
Case 4_T3
Case 4_T0
Case 4_T1
Case 4_T2
391
389
386
384
382
273
272
270
268
266
178
177
175
174
173
76.0
75.4
74.8
74.2
73.6
Case 1_U4
Case 1_U3
Case 1_U0
Case 1_U1
Case 1_U2
Case 2_U4
Case 2_U3
Case 2_U0
Case 2_U1
Case 2_U2
Case 3_U4
Case 3_U3
Case 3_U0
Case 3_U1
Case 3_U2
Case 4_U4
Case 4_U3
Case 4_U0
Case 4_U1
Case 4_U2
is also negligible (Fig. 4), as varying this parameter by 30% will not
change it by more than 1.6%, for any of the installed capacities.
The landfill fee for rejected materials has a moderate influence
on the return on investment and global economic balance, as is
apparent from Figs. 5 and 6. As shown, the investment return
period never exceeds 5 years for any installed capacity, even in
particularly adverse conditions (a 30% increase in landfill fees when
operating an 85 tonne/h plant). In fact, variations in the investment
return period never exceed 50% for the 30% variation in the CDW
landfill fee.
294
340
386
432
478
204
237
270
303
336
131
153
175
198
220
52.4
63.6
74.8
86.0
97.2
Case 1_V4
Case 1_V3
Case 1_V0
Case 1_V1
Case 1_V2
Case 2_V4
Case 2_V3
Case 2_V0
Case 2_V1
Case 2_V2
Case 3_V4
Case 3_V3
Case 3_V0
Case 3_V1
Case 3_V2
Case 4_V4
Case 4_V3
Case 4_V0
Case 4_V1
Case 4_V2
604
485
386
306
249
422
338
270
213
170
281
224
175
137
107.4
127
98.4
74.3
55.0
40.4
Case 1_X0
Case 1_X1
Case 1_X2
Case 1_X3
e
Case 2_X0
Case 2_X1
Case 2_X2
Case 2_X3
e
Case 3_X0
Case 3_X1
Case 3_X2
Case 3_X3
e
Case 4_X0
Case 4_X1
Case 4_X2
Case 4_X3
e
386
322
260
175
e
270
224
178
116
e
175
144
113
70.8
e
74.3
58.6
42.8
21.7
e
Meanwhile, differences in the global economic balance for the
cases considered are consistent with the parameter variation range.
So a 350 tonne/h capacity plant would see a 30% reduction in global
economic balance for a 30% reduction in the landfill fee for rejected
materials; for an 85 tonne/h plant this relation is somewhat
different, but still only gives a change of 24% in the 60 years global
economic balance. However, to reduce sensitivity to this factor
while maintaining high profitability, higher installed capacities
must be sought (more than 170 tonne/h and preferably in the
250e350 tonne/h range).
Fig. 1. Return on investment period, years e CDW input gate fee.
A. Coelho, J. de Brito / Journal of Cleaner Production 39 (2013) 329e337
333
Fig. 2. Global economic balance over a 60-year period e CDW input gate fee.
Fig. 4. Global economic balance over a 60-year period e concrete aggregates selling
price.
As for the percentage of mixed/separated CDW input, Figs. 7 and
8 show that the more mixed CDW materials enter the facility
the higher the global economic balance (the more benefits) and the
lower the return on investment period. In fact, except for the
85 tonne/h capacity case, all return on investment periods remain
comfortably below 4 years, even if mixed CDW input gets reduced
to about 50% of the installed capacity (the rest of the input being
composed of separated aggregate). This result is directly linked
with the CDW input gate fee, from which the facility can of course
generate more income if it can charge the higher amount associated
with mixed CDW input, as often as possible.
This parameter has a mild influence on the return on investment
period, when variations of 30% never generate differences of more
than 50% in the result. The 85 tonne/h facility is most sensitive to it,
however, since the return on investment period changes by a factor
of 3 for a 30% variation, while in the 170 tonne/h plant it is a factor
of 1.5 (with the other two capacities exhibiting change factors of 2,
for the same parameter variation range).
Global economic balance is rather more affected by this
parameter, especially for larger amounts of mixed CDW materials.
Actually, if þ30% of the latter is input (reaching 91% of all input
CDW), the global economic balance increased by 71%, compared
with the unchanged situation (70% mixed and 30% separated CDW),
for the 85 tonne/h facility, and 56% for the 350 tonne/h one.
Fig. 3. Return on investment period, years e concrete aggregates selling price.
When the parameter is reduced, however, the response is not
symmetrical, as Fig. 8 shows. In this case a 30% reduction in mixed
CDW input implies a 36% change in global economic balance for the
350 tonne/h unit and a 46% change in the 85 tonne/h capacity one.
This asymmetry is related to the cumulative effect of changing the
input mix: more mixed CDW input entails more benefits gained
due to the higher input rate than benefits lost if the lower rate is
applied more often; also, more mixed CDW input will imply,
although to a lesser degree, higher benefits from output material
sales than benefits lost if it is reduced. This occurs in spite of the fact
that energy, labour and rejected materials dumping costs are higher
when a greater mixed CDW flow enters the facility than when it is
reduced.
Analysing varying CDW input mass, from Fig. 9 it can be seen
that the return on investment period is affected, but especially so
for the 170 and 85 tonne/h installed capacities. For these last two,
reducing the CDW input down to 50% of the designed capacity can
aggravate the return on investment period by a factor of 2 and 2.5,
respectively, tending towards prohibitive values for the 85 tonne/h
plant. Global economic balance shows an even clearer outcome,
since it is reduced by a factor of 2.2 for the 350 tonne/h plant, by
a factor of 3.4 for the 85 tonne/h one. Once again it becomes clear
Fig. 5. Return on investment period, years e rejected materials landfill fee.
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A. Coelho, J. de Brito / Journal of Cleaner Production 39 (2013) 329e337
Fig. 6. Global economic balance over a 60-year period e rejected materials landfill fee.
Fig. 7. Return on investment period, years e percentage of mixed/separated CDW input.
A. Coelho, J. de Brito / Journal of Cleaner Production 39 (2013) 329e337
Fig. 8. Global economic balance over 60-year period e percentage of mixed/separated
CDW input.
that higher capacity outperform lower capacity facilities, both in
absolute profitability and in resilience to the fluctuations of several
operating parameters (this is in tune with other studies, such as
Zhao et al. (2010) and Duran et al. (2006)).
3.2. Best and worst scenarios
Best and worst scenarios were tested for each installed capacity
by assuming all parameters respectively at their most and least
favourable values. Specifically, parameter favourable conditions
are:
CDW input gate fee: þ30%;
Rejected materials landfill fee: 30%;
Percentage of mixed/separated CDW input: þ30%;
CDW input mass: equal to the plant’s capacity.
The concrete aggregate selling price was ignored since, as seen
above, it has a negligible influence on results. Worst parameter
conditions will be the opposite, with a 50% factor applied to the
Fig. 9. Return on investment period, years e CDW input mass.
335
Fig. 10. Global economic balance over 60-year period e CDW input mass.
CDW input mass. Results are summarized in Table 4, where, for
each plant capacity and these best and worst scenarios, the 60-year
global economic balance and investment return period are given.
The time dependent overall economic balance is given in Fig. 12
(best scenario) and 13 (worst scenario), for all the capacities. These
scenarios were tested to determine the facility’s model behaviour
to extreme, though extremely unlikely, conditions.
It can be seen from these results that changing all parameters
simultaneously, in a favourable or unfavourable direction, has
a profound effect on economic results. This “group” variation, when
unfavourable, prevents the lower capacity facilities e 85 tonne/h
and 170 tonne/h e from being economically viable. Even the
250 tonne/h facility, in its worst scenario, can barely be seen as
viable, with an intolerable return on investment period of 28 years.
Changing all specified parameters in an unfavourable direction
sharply reduces operating benefits, with a halving of CDW input
mass, a 30% cut in the input CDW gate fee, halving of the recycled
output mass (i.e. less revenue from selling materials) and less
mixed input CDW, which limits CDW mass chargeable at higher
prices (mixed CDW). At the same time, this worst scenario implies
higher costs from dumping fees, which suffer a 30% increase. As
a result, only the 350 tonne/h facility has a chance of economic
viability (even though 14 years of return on investment period will
probably raise concern among investors), in these theoretical worst
cases.
Fig. 11. Overall economic balance for the 60-year operation period for four different
plant capacities. (350, 250, 170 and 85 tonne/h).
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A. Coelho, J. de Brito / Journal of Cleaner Production 39 (2013) 329e337
Table 4
Best and worst scenarios for each facility capacity.
Plant’s capacity
Case designation
Economic
balance over
60 years, MV
Investment
return period,
years
Case 1e350 tonne/h
Case
Case
Case
Case
Case
Case
Case
Case
1081
41.7
544
6.2
513
3.7
168
14.7
1
8
1
28
1
No return
2
No return
Case 2e250 tonne/h
Case 3e170 tonne/h
Case 4e85 tonne/h
1_Best
1_Worst
1_Best
1_Worst
1_Best
1_Worst
1_Best
1_Worst
Notes.
1. Negative values represent benefits.
2. "No return" means permanent global economic balance deficit or zero return on
investment period over 60 years.
Fig. 13. Overall economic balance for the 60-year operation period for four different
plant capacities e worst scenario.
Table 4 also makes it clear that, where the 60-year overall
economic balance is still positive, with net benefits to the owner,
the resulting value changes by as much as 99% from the best to the
worst case scenarios. In fact, for the 350 tonne/h and 250 tonne/h
facilities, after 60 years of operation global benefits are reduced by
89% and global costs by 67% (decrease in costs due to less material
being rejected e less paid out in dumping fees e and more operating time in the simplified mode, which represents less energy and
labour costs).
This asymmetry means that, in the worst case scenario, global
benefits and costs will be closer than in the best case scenario,
which implies a further reduction in the global economic balance
(in this case, 99%). As a consequence, the overall economic balance
is reduced by roughly two orders of magnitude, from best to worst
case scenarios (Figs. 12 and 13). In the latter case, Fig. 13 also shows
that certain investment moments, particularly after 20 and 40 years
of operation, are much clearer than in best case scenarios (or even
than in the base case scenario, where parameters are at their
original values e Fig. 11).
Due to the sharp reduction in global economic balance in the
worst scenario, the magnitude of the investment required for
equipment replacement, especially at the stated moments, get
closer to the best scenario (which generates the serrated appearance in the global economic balance curves). As operating conditions deteriorate, therefore (economically relevant parameters
worsen), it becomes more important to pay attention to equipment
maintenance and conservation.
If, by investing more time and money in extending all the
equipment’s service life, replacement periods get larger, then that
Fig. 12. Overall economic balance for the 60-year operation period for four different
plant capacities - best scenario.
will be financially more important in unfavourable conditions than
in regular or favourable conditions.
On the best case scenarios side, return on investment period
never rises above 2 years, for all capacities analysed. This is basically due to higher benefits as more mixed CDW enter the facility
and a higher gate fee is charged, but also to reduce costs, as the
dumping fee falls. Of course these conditions are excellent for the
investor, but they are considered unlikely as all parameters reach
best values simultaneously.
4. Conclusions
Following the analysis described in Part I, here we have
explained the economic sensitivity analysis performed on an
advanced CDW recycling plant located in the Lisbon Metropolitan
area, registering the results on the return of investment period and
the global economic balance over a 60-year operation period, given
by varying six parameters: the plant’s capacity (tonne/h), CDW
input gate fee (V/tonne), concrete aggregate sales price (V/tonne),
rejected materials landfill fee (V/tonne), percentage of mixed/
separated CDW input (%) and CDW input mass (tonne). Also a duo
of extreme best and worst case scenarios was tested, in order to
evaluate the facility’s economic performance in those conditions
and establish framework limits.
The following conclusions can be drawn:
In general, return on investment period were under 8 years
(1 or 2 years in favourable conditions), for a wide range of
parameter variations. In fact, this figure is exceeded in only
two particularly aggravated conditions e the 85 tonne/h
plant, functioning with a 30% cut in CDW input fee, and
(separately) the 85 tonne/h plant in which CDW input mass is
reduced by 50%. Given the generally higher return on
investment periods found for the 85 tonne/h capacity plant
and its higher variability (compared with the same analysis
for other plant capacities), this result was expected. As
a consequence, in order to guarantee return periods of less
than 8 years and less sensitivity to changing operating
parameters, design capacities under 170 tonne/h should be
avoided;
The most favourable operating conditions occur, within the
range considered and for single parameter variations, when the
CDW input fee is 30% above the average value determined or
the percentage of mixed/separated CDW input is as close to
completely mixed as possible. Both these parameters influence
A. Coelho, J. de Brito / Journal of Cleaner Production 39 (2013) 329e337
the result equally, as they maximize the benefit from charging
the highest possible CDW input fee; consequently, this fee
must be given top priority and be managed as accurately as
possible (given particular local market conditions), since it has
a strong effect on the facility’s profitability;
The global economic balance for a 60-year operation period is
always beneficial to the owner/manager; given the cost-benefit
conditions of all the analysed situations, even in the worst
scenario a beneficial global outcome is expected after 12 years
of operation. Gains could be as high as V555 million
(350 tonne/h capacity plant, operating at þ30% of the average
CDW input gate fees) and as low as V16.6 million (85 tonne/h
capacity plant, operating at 30% of the average CDW input
gate fees). This is valid for single variation parameters, for all
capacities simulated;
Economic viability for a full-scale high-end CDW recycling
plant is likely to occur for a widely varying operating range of
economic parameters, in pure open market conditions, i.e.,
without government support or specific legislation in favour of
recycling CDW;
Although specific parameter coupling influences, e.g. low CDW
mass input (relative to plant capacity) and lower than average
CDW input gate fee, were not analysed in detail (this kind of
multi-parameter sensitivity analysis can be the object of
further research), extreme conditions of parameters that vary
simultaneously in favourable and unfavourable directions do
have a pronounced effect on economic performance. Best
conditions can lower the investment return period for any of
the capacities studied to under 2 years, and worst case conditions turn smaller facilities (85 and 170 tonne/h) into unprofitable units, and jeopardise the profitability of larger facilities
(250 and 350 tonne/h);
In particularly adverse conditions e 50% less input CDW mass,
30% more separated CDW entering the facility, 30% lower input
CDW gate fee and 30% higher dumping costs e investment in
maintenance and conservation becomes particularly important, as postponing the replacement of equipment can have
significant financial consequences and shorten the return on
investment period or increase the overall economic balance
over the years;
The favourable economic outcome demonstrated by this study
must be strictly related to the conditions implied, which might
benefit from lack of competition (just one large-scale facility
337
for the entire region) and assumption of static conditions over
the years (not accounting for dynamic variations over time).
Acknowledgements
Thanks are due to the FCT (Foundation for Science and Technology) for the research grant awarded to the first author and to the
ICIST e IST research centre.
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