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landfill mininghow to explore
an old landfill’sresource potential
master thesisTobias Krüse
2015
!
Tobias Krüse
Landfill Mining How to explore an old landfill’s resource potential Supervisor Associate Prof. Dr. DI Johann Fellner
Technical University of Vienna
Program: MSc Socio-Ecological Economics and Policy Vienna University of Economics and Business
Date of submission: March 2015
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Acknowledgments
I would like to thank Prof. Johann Fellner from the Christian Doppler Laboratory “Anthropogenic
Resources” for the possibility to write my thesis under his supervision at the Technical University of
Vienna. Special thanks go to Andrea Winterstetter, who has always been available on short notice
for my questions and has guided me throughout my research.
I wish to thank my parents J. & R. for their unconditional support – in every way.
To my friends and especially B.: thank you for bearing with me. Whatever is waiting, I hope to share
it with you.
Furthermore I would like to thank:
R.B. for the ‘(‘,
R.C.I for proofreading my thesis,
G. & T. for the last few years,
J.R. for being my ally in SEEP,
my MBP for going through this with me,
the people who invented zotero,
and V. for being home to me.
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Abstract This thesis explores how to assess the resource potential of an old landfill through the use of a case
study in Hechingen, Germany. It aims at classifying the Hechingen landfill as potential mineable
source of materials, based on the United Nations Framework Classification for Natural Resources
and Reserves (UNFC-2009).
Stress on natural resources and the ecosystem due to human activity has been rapidly rising over
the last decades. Although modern waste management has drastically changed, landfilling has been
the predominant form of waste disposal in the past. Previously deposited waste is not only a
potential threat to the environment, but may be a future reservoir for resources. Landfill mining
relates to strategies aimed at recovering materials formerly deposited in landfills – hence reducing
the extraction of primary resources while limiting negative effects of old waste deposits.
While the feasibility of landfill mining projects can be assessed from different perspectives, this thesis
focuses on the economic dimension. Building on site-specific information of the case study, models
for all relevant material, energy and cash flows for a potential landfill mining project were developed.
For this purpose both material flow analysis and net-present value calculations using Monte-Carlo
simulations were applied.
Different assumptions regarding the treatment of the high-calorific fraction and the subsequent use
of the landfill space were accounted for within the analysis. The project was investigated from a
private investors perspective, while the results were embedded into a discussion of external benefits
and costs.
While the findings of the material flow analysis demonstrate technical feasibility, all three investigated
scenarios proved to be unprofitable, however to differing extent. Decreasing costs associated with
the treatment of the high-calorific fraction and rising price levels for energy and metals were
identified as key drivers of the economic performance.
Taking into account insights from the break-even analysis, the investigated scenarios were classified
with respect to their feasibility. Albeit their differences, all scenarios were assigned to the same
classification category. The findings demonstrate the basic applicability of the UNFC-2009 for
classifying anthropogenic resources. However, there is a need for a more standardized method of
interpreting evaluation results with respect to the different classification categories.
External or mediating effects may play an important role for the economic viability of landfill mining
projects. Hence, a comprehensive assessment should be pursued on a case-by-case approach and
needs to take external costs and benefits into account.
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Table of contents Abstract .................................................................................................................................... v!List of Tables ............................................................................................................................ vii!List of Figures ........................................................................................................................... vii!1! Introduction and overview .................................................................................................... 1!2! State-of-the-art in research ................................................................................................. 7!2.1! History of LFM ......................................................................................................................... 7!2.2! Characterization of deposited materials ................................................................................... 9!2.3! Extraction and recovery technologies .................................................................................... 13!2.4! Benefits associated with landfill mining .................................................................................. 15!2.5! Economic evaluation of LFM .................................................................................................. 16!2.6! Summary .............................................................................................................................. 22!2.7! Research focus ..................................................................................................................... 24!3! Methodology and empirical design .................................................................................... 26!3.1! Conceptual framework .......................................................................................................... 26!3.2! Natural resource classification systems and UNFC-2009 ....................................................... 28!3.3! Material flow analysis (MFA) using the software STAN ........................................................... 29!3.4! Net present value calculations and Monte-Carlo simulations .................................................. 32!3.5! Limitations ............................................................................................................................. 34!4! Case Study Kreismülldeponie Hechingen .......................................................................... 36!4.1! Results from the landfill exploration ....................................................................................... 38!4.2! Project and scenario description ........................................................................................... 43!4.3! Model of material flows .......................................................................................................... 45 4.3.a Amount of deposited materials and material composition ............................................................. 47 4.3.b Sorting efficiencies ....................................................................................................................... 48 4.3.c Enery layer ................................................................................................................................... 49 4.4! Case specific investment model ............................................................................................ 49!5! Results .............................................................................................................................. 52!5.1! Material Flow Analysis ........................................................................................................... 52 5.1.a Material flows ............................................................................................................................... 53 5.1.b Energy flows ................................................................................................................................ 55!5.2! NPV calculation results .......................................................................................................... 56 5.2.a Costs and revenues ..................................................................................................................... 58 5.2.b Sensitivity analysis ........................................................................................................................ 61 5.2.c Net costs per ton of excavated and processed waste .................................................................. 62 5.2.d Break-even analysis ..................................................................................................................... 62 6! Non-monetary modifying factors and classification ............................................................ 64!6.1! Monetization of external effects ............................................................................................. 65 6.1.a Monetary valuation methods ..................................................................................................... 66 6.1.b State-of-the-art in research ....................................................................................................... 68 6.1.c Exploring best practices for the evaluation LFM external effects ................................................ 72!6.2! External costs of the LFM project in Hechingen ..................................................................... 74!6.3! Classification under UNFC-2009 ........................................................................................... 75!7! Discussion ......................................................................................................................... 79!8! Conclusion ........................................................................................................................ 82!References .............................................................................................................................. 84!Annex ...................................................................................................................................... 89
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List of Tables Table 1: Classification Urban Mining based on type of resource deposit . ................................................. 2!Table 2: Calorific values – exploration study in Finland. ........................................................................... 11!Table 3: Material composition of landfilled materials – exploration study in Finland .................................. 12!Table 4: Material recovery efficiency for technical sorting – exploration study in Sweden. ........................ 14!Table 5: LFM potential of different thermal treatment options (WtE) ........................................................ 15!Table 6: Efficiency of different WtE plants ............................................................................................... 15!Table 7: Overview of costs and benefits of landfill mining ........................................................................ 17!Table 8: Summary of reviewed landfill exploration studies – material composition ................................... 23!Table 9: Research procedure and connection to UNFC-2009 ................................................................ 29!Table 10: Landfill mass in Hechingen – different scenarios ...................................................................... 38!Table 11: Size composition – exploration study in Hechingen ................................................................. 40!Table 12: Material composition – exploration study in Hechingen ........................................................... 41!Table 13: Average material specific water content – exploration study in Hechingen ............................... 42!Table 14: Calorific values of combustible fration – exploration study in Hechingen .................................. 42!Table 15: Material categories and treatment – assumptions of the case study ....................................... 44!Table 16: Scenario description – case study ........................................................................................... 44!Table 17: Landfill mass and fractions (FM and DM) – assumptions case study ........................................ 47!Table 18: Material composition landfill body – assumptions case study .................................................. 47!Table 19: Transfer coefficients for the sorting process – assumptions case study ................................... 48!Table 20: Transfer coefficients for the RDF preparation process – assumptions case study .................... 49!Table 21: Breakdown of cost and benefits for the investment model – assumptions case study ............. 50!Table 22: Breakdown of material flows – results case study ................................................................... 54!Table 23: Net-electricity production from RDF – results case study ........................................................ 56!Table 24: Expected net present value – results case study ..................................................................... 57!Table 25: Breakdown of costs and income – results case study ............................................................. 60!Table 26: Results from the sensitivity analysis – results case study ......................................................... 61!Table 27: Calculated net costs – results case study ................................................................................ 62!Table 28: Results from the break-even analysis – results case study. ..................................................... 63 Table 29: Overview external effects of LFM projects and applicability to case study ................................ 74 Table 30: Scenario classifcation under UNFC-2009 ................................................................................ 76
List of Figures Figure 1: Procedure for the evaluation of anthropogenic resources ......................................................... 26!Figure 2: Illustration of UNFC-2009 categories and examples of classes ................................................ 28!Figure 3: Basic model of material flows – case study. Energy and material flows ................................... 46!Figure 4: Material flows for on-site scenarios – results MFA. ................................................................... 52!Figure 5: Extractable secondary resources from the landfill body – results case study ............................ 53 Figure 6: Energy flows of the incineration process - results case study ................................................... 54!Figure 7: Illustration of incineration process – case study MFA model ..................................................... 55!Figure 8: Distribution for NPV (SCENARIO A) – results case study .......................................................... 57!Figure 9: Distribution of NPVs (Scenario B) – results case study ............................................................. 58!Figure 10: Distribution of NPVs (Scenario C) – results case study ........................................................... 58!Figure 11: Cash in- and outflows (discounted) for SCNEARIO A, B and C – results case study ............... 59!
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1 Introduction and overview Natural resources have become a central concern in international politics. Attention on resource use,
systems to track natural resource demand and strategies to counteract the environmental burden of
anthropogenic actions has been rapidly rising over the last years. This is due to both (1) the sharp
rise in consumption resulting in excessive demand for natural resources and related growing
environmental pressures, as well as (2) impending resource scarcities and increased concerns about
resource security (Giljum et al., 2011; Wiedmann et al., 2013). Rising prices further fuel economic
and political concerns.
Along with these developments comes increased competition for access to and control of natural
resources. Conflicts over the distribution, however, are not limited to benefits but also relate to
negative environmental and social impacts of exploration and production. Resource politics are
therefore not only an environmental issue but also an issue of economic and political security.
A variety of concepts and initiatives to overcome the resource dilemma have been launched such as
the ‘Raw Materials Initiative’ (hereafter RMI) by the European Commission, aiming at securing
sustainable supplies of non-energy and non-agricultural critical1 raw material. Whilst there has been
a repeated prominent attempt to boost efficiency to reduce resource dependency and
environmental burden of anthropogenic actions, the promotion of recycling has also been picked up
within the RMI. Furthermore, the European Union (EU) has defined managing waste resources as
one of the targets by 2020 in their ‘Roadmap to a Resource Efficient Europe’ and declared landfilling
as a subsidiary option of last resort for waste treatment in Europe, which is supposed to be phased
out completely (European Commission, 2008; European Commission, 2011b).
While concepts such as efficiency and sufficiency aim at reducing the overall material throughput of
natural resources, waste-related strategies can be regarded as a different approach to tackle the
impending resource dilemma. They focus on strategies to recycle already used materials, i.e.
anthropogenic resources2, while conserving natural primary resources (Lederer et al., 2014).
The study of societal metabolism3 plays an important role in this context. Just as organisms who
maintain a continuous flow of materials and energy with their environment to provide for their
functioning, societal systems do in an analogous way. They “convert raw materials into
manufactured products, services and, finally, into wastes (Fischer-Kowalski and Haberl, 1998, pp.
573–574)”, in order to maintain growth and provide for their reproduction. Studying societal
metabolism is not only a helpful framework that enables the discussion of socio-economic and
1 high risk of supply shortage in the next 10 years and which are particularly important for the value chain (European
Commission, 2011a, p. 12) 2 Lederer et al. (2014, p.2) define anthropogenic resources as materials that are no longer located in the natural sphere, but
in the man-made anthroposphere since they were transformed and translocated using human cultural resources such as labour, technology or capital.
3 Origins from biology and refers to the inner processes of a living organism
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cultural explanations to environmental problems, but also helps to identify roads to sustainable
development.
Research has shown, that due to increasing anthropogenic production and consumption, there is an
increasing creation of waste and pollution, that comes along with increasing produced stock of
resources and materials in society. Some studies even suggest that the anthropogenic material
stocks are comparable in size to the remaining natural reserves of certain metals (e.g. aluminium and
ferrous metals; Kapur and Graedel, 2006; Lifset et al., 2002), while at the same time it is assumed
that half of the previously extracted materials are no longer in use (UNEP, 2010). However, these
stocks should not only be regarded as a potential source of pollution and emissions, but as future
reservoirs for resources (Frändegård et al., 2013a). Most importantly, old buildings, hibernating
infrastructure and landfills are mentioned as anthropogenic stocks in this respect.
Rettenberger (2010) demonstrates the relevancy and size of anthropogenic stock for certain
materials based on estimations for German landfills. Ferrous-scrap buried in German landfills is
expected to account for 124% of the annual German demand (57% for copper and 22% for
aluminium respectively; Rettenberger, 2010, p. 40).
As human activity is obviously changing the conditions for mineral extracting, mining will
consequently have to adapt and reorient itself more towards the extraction of previously extracted
materials (Johansson et al., 2013).
Mining the anthroposphere: Urban Mining and Landfill Mining
The increasing attention on secondary stock and its potential to meet future demand for resources
has fostered the creation of a variety of concepts such as urban mining, waste mining, mining above
ground and landfill mining (Johansson et al., 2013).
The most renowned concept is urban mining. It can be referred to as the strategy for mining
secondary resources from the technosphere (old buildings, outdated infrastructure as well as mobile
goods, such as electronic articles and cars) (Brunner and Rechberger, 2004). Despite the fact that
there is no common agreed definition of urban mining yet, in general it focuses on anthropogenic
resources. Therefore, urban mining is by definition not restricted to the urban sphere. A potential
classification is based on the type of resource deposit that is to be exploited. Three distinct
approaches within urban mining can be identified as follows:
Table 1: Classification Urban Mining based on type of resource deposit (Mocker et al., 2009, p. 493).
Recent waste streams Mine piles Buildings and infrastructure
Landfills Stowage Wires/pipes and installations
Urban Mining
Waste Management Mining Industry Construction Sector
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It is evident from the literature that manmade deposits other than in the form of buildings and
infrastructure do exist. Landfills are assumed to hold about 10-20% of overall technospheric metal
stock (Johansson et al., 2013). The related key term for the valorization of the materials formerly
disposed of in landfills is landfill mining (LFM). LFM refers to the process of recovering (extraction,
processing and treatment) materials discarded and deposited in landfills over years, often in
combination with an upgrade to a state-of-the art landfill or site reclamation (Bockreis and Knapp,
2011; Frändegård et al., 2013a; Savage et al., 1993; van der Zee et al., 2004). Although similar to
the other strands of urban mining, LFM originates not primarily from the objective of closing the loop
of resource usage but from waste management. This is reflected by early landfill mining projects,
which were mostly motivated by local pollution issues or lack of storage capacities (Bockreis and
Knapp, 2011; Krook et al., 2012) rather than efforts to recycle secondary resources. However, in
recent years landfill mining has been promoted for the sake of resource recovery and despite any
differences landfill mining and urban mining can be summarized as an attempt ‘where material
agglomerates excluded from ongoing anthropogenic cycles are brought back into societal systems
again’ (Krook and Baas, 2013, p. 2).
Recently, the concept of “Enhanced Landfill Mining” (ELFM) has been put forward in the LFM
discourse. Key to this concept is a change in the way we see landfills. ELFM stresses that landfills
are no longer to be regarded as a final resource deposit, but rather as a temporary storage facility
that serves as a resource as the valorization of embedded materials is pursued (Van Passel et al.,
2013).
The recycling potential of old landfills ranges from cover material, stones, bricks for road
construction over metals such as iron, copper and aluminium for recycling, to paper, cardboard,
plastics and other materials that could applied to energy recovery processes (Hogland, 2002). Both
waste-to-energy (WtE) as well as waste-to-material (WtM) recovery of resources is normally
promoted within LFM activities (Van Passel et al., 2013)4.
There is a vast range of potential technologies that could be applied to tackle the resource potential
of old dumpsites, however, broadly two different strategies need to be distinguished: in-situ and ex-
situ techniques (Jones et al., 2013, p. 46). In-situ valorization relates to recovery strategies within the
landfill storage that do not require the excavation of materials, such as methane extraction. Ex-situ
technologies on the other hand relate to valorization of materials outside the landfill. This thesis will
be focused on ex-situ strategies as they aim to tackle a more substantial share of the resource
potential in old landfills and in-situ strategies are limited to the recuperation of certain materials.
Although landfilling in the EU as a primary tool of waste management seems somehow outdated
from a global perspective landfilling is still the most dominant form of waste removal and has 4 ’Recycling’ in this context refers to the material recovery (WtM), while ‘thermal treatment’ describes the valorization of the
energetic potential of the landfilled materials (WtE). ‘Disposal’ means that there is no further treatment and the materials are re-landfilled. ‘Treatment’ refers to both WtE and WtM.
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historically been a key element of former waste management strategies (Frändegård et al., 2013b).
In Sweden only around 6,000 old landfills continue to exist. Extrapolating this number to Europe
overall leads to a number of 350,000-500,000 old landfills (Hogland et al., 2011). Other estimates
suggest the number of old landfills to be smaller, but still to be about 150,000 (Van Vossen, 2005).
This clearly reveals that the issue at hand is not only of marginal interest and impact.
In ecological terms this seems twofold problematic (Bockreis and Knapp, 2011; Frändegård et al.,
2013a):
(1) by landfilling, products for which both natural resources as well as energy have been used
throughout their production process are wasted
(2) landfills are well-known emitters of greenhouse gas emissions (GHG; especially methane)
and other hazardous substances to air, soil and water
Excavating and processing formerly deposited resources from landfills touches on both the input-
side (exhaustion) as well as on the output-side (pollution) of the resource dilemma. Per se from an
environmental perspective LFM is associated with beneficial effects as it limits future risks of air,
water and soil pollution from landfills as well as reduces stress on primary resources (Bockreis and
Knapp, 2011; Hölzle, 2010). However, these benefits depend upon the feasibility and successful
implementation of landfill mining projects.
Feasibility with respect to LFM projects is related to three distinct areas being the (1) technical, (2)
economic as well as (3) regulatory, social and environmental feasibility (Ford et al., 2013). Knowledge
on quantity and quality of economically interesting materials, technical feasibility of extraction and
valorization as well as economic profitability need to be given, so that a private actor has an
incentive to conduct a LFM project and environmental benefits can become manifest. However, the
regulatory environment as well as social or environmental aspects need to be included into a
comprehensive assessment of the feasibility of LFM projects. Especially when taking into account
that there could be a community commitment to recycling and environmental management,
profitability of LFM may no longer be the primary incentive (US-EPA, 1997).
A thorough assessment of the feasibility of LFM projects is a step towards a deepened
understanding of barriers to landfill mining activities and could serve to inform policy makers on the
design of support mechanisms. Whether LFM is feasible, needs to be answered individually based
on a solid assessment taking into account the specifics of the landfill under investigation and any
economic as well as legal, social and ecological aspects of such a project. The process of evaluating
the potential of LFM is complicated by four main sources of uncertainty (Baas, Leenard et al., 2010;
Frändegård et al., 2013a; Krook et al., 2012):
(1) waste composition, which is crucial for determining the resource potential of any landfill as
well as the expected amount of hazardous materials
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(2) efficiency of processing technologies
(3) market potential for materials recovered from landfills (i.a. transportation needs)
(4) health and environmental risks from excavating landfills
Future contingencies pose a major challenge to LFM. Although some strategies for material
recuperation (e.g. WtE) might be profitable at the moment, it could still be rational to further store the
materials in a landfill as one expects recycling technologies to significantly advance, so that more
profitable techniques become available in future (Van Passel et al., 2013).
In his seminal paper McKelvey (1972) developed a system to classify primary resource deposits as
either ‘resource’ or ‘reserve’, based on the proven economic profitability as well as the degree of
certainty regarding the amount of resources a deposit (landfill) holds. Building on his work, several
natural resource classification5 have been developed aiming at establishing a standard for the
classification of natural resource deposits and their potential for exploitation.
The United Nation Framework Classification for Fossil Energy and Mineral Reserves and Resources
(UNFC-2009) was developed as an international standard for the classification of fossil energy and
mineral deposits located in the earth’s crust. It is based on a three dimensional assessment of a
resource deposit, taking into account the socio-economic feasibility (E), project feasibility (F) and the
level of geological knowledge about the resource deposit (G) (UNECE, 2010). By applying this
primary resource classification system to LFM projects, one could distinguish (1) landfills, that under
current technical, economic and institutional settings are to be mined profitable (referred to as
‘resources’) and (2) landfills that may be profitable to mine in the foreseeable future (‘reserves’) and
other anthropogenic stock.
In light of the potential of LFM, the growing interest in issues of environmental sustainability,
impending resource scarcities and the increasing international gaining importance of resource
security, it’s worthwhile to take a closer look at LFM activities as a strategy to counteract the
resource dilemma. It is especially important to tackle the issue of evaluation techniques for
anthropogenic stocks to enable a profound discussion regarding their potential.
In particular, this thesis will try to answer how one could explore the resource potential of an old
landfill and what necessary building blocks of such an assessment would be. The focus will be on
the economic dimension of LFM, first taking a private investors perspective, before discussing
potential external effects that would need to be considered from a societal perspective and possible
approaches to include them into a more holistic evaluation.
A case-study approach will be applied, investigating a potential landfill mining project at the
Kreismülldeponie Hechingen in Germany. Based on a model of all relevant material and energy flows,
a cash-flow analysis will be conducted.
5 mostly nationally or regionally codes such as PERC (Europe), SME (USA), CIM (Canada)
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The overall aim of the thesis is to assess the economic profitability of the potential LFM project in
Hechingen and relate the findings to the UNFC-2009. By synthesizing the obtained results this paper
attempts to answer the research question: How should the ‘Kreismülldeponie Hechingen’ landfill be classified under UNFC-2009?
The thesis is organized in the following way: after introducing the topic and becoming familiarized
with LFM, the second section will provide an overview on the research related to the topic and deal
with the research focus of the thesis. The third and the fourth section provide an overview of the
research framework and the investigated case study. The fifth and the sixth section describe the
findings of the analysis and review the relevance of external effects for LFM projects. Finally, the
results will be discussed and put into context.
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2 State-of-the-art in research The section will be structured into six parts covering (1) a short résumé of the history of LFM, (2)
studies that focus on the characterization of deposited materials, (3) extraction and excavation
technologies, (4) potential benefits associated with landfill mining activities, (5) a summary of the
discussed research and (6) the research focus of this thesis.
LFM research has often been published in peer-reviewed journals. However an important strand of
the research literature has also appeared in proceedings of conferences such as the International
‘Academic Symposium on Enhanced Landfill Mining’ or the ‘International Waste Management and
Landfill Symposium’ that took place for the fifteenth time in 20146.
2.1 History of LFM
The original report on a landfill mining project dates back to 1953. A project had been designed in
Tel Aviv a aiming to excavate the waste of an old landfill and process it for use as a soil amendment
(Savage et al., 1993). This project remained the single documentation of any landfill mining activity
until the early 1980s.
Rest of the world
Increased concerns related to impending shortages of landfill space in the United States (US)
prepared the stage for further LFM projects as one strategy to regain storage capacities. The first
projects in the US had been carried out in Naples, Florida (from 1986 on) and Edinburgh, New York
(from 1990 onwards). Both were motivated by avoiding and reducing closure costs as well as the
environmental footprint of the landfills (US-EPA, 1997, p. 5). The project in Naples was not only the
first one of a series that followed, but also the first one to incorporate a broad range of resource
recovery strategies into its design: (1) recuperate landfill cover material, (2) using combustible waste
as fuel for a close by waste-to-energy facility and (3) and recuperate recyclable materials. The
project did prove successful. However, only in recovering cover materials. The plant for producing
additional fuel was never developed. By 1990 four LFM projects were already initiated. All these
projects were motivated by both public and private stakeholders (Spencer, 1990). By 1997 almost
50 LFM projects had been initiated in the US and Canada (Hogland et al., 2004).
Besides Israel, there is only one other report of another project in the Middle East, one of the
biggest ever documented. During the relocation of an old landfill in Sharjah (United Arab Emirates)
6.4 million cubic meters of waste was extracted, metals and wood were sorted and the rest was re-
landfilled according to current state-of-the-art technologies at a different location. The regained land
was used for housing development, as due to city-expansion the landfill space had been needed
(Fricke et al., 2012).
6 http://www.elfm.eu/en/symposium.aspx and http://www.sardiniasymposium.it (accessed 15.1.2015)
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Europe
LFM has also been applied in Europe. The first European project that aimed at reducing occupied
landfill volume and served as a pilot project to prove the technical and economic feasibility of LFM
was conducted in Germany (Burghof) in 1993 (Rettenberger et al., 1995). After that a series of series
of other projects in Germany followed, which were motivated by hazard prevention (Hölzle, 2010).
In 1994, the first LFM activities were launched both in Italy (Sardinia) and Sweden (Filbona). These
projects were brought about as a consequence of efforts to reduce impending local risks from poor
installation space shortages due to expanding cities (Cossu et al., 1996).
Despite the fact that first projects in Europe and elsewhere have been pursued, LFM was until now
not commercialized on a large scale. There are a numerous of different reasons why projects had
been launched, but most importantly they were motivated by local pollution problems or hazard
prevention. Resource recovery has only seldom been the driver of LFM in the past, but has recently
gained more importance. In Germany a law was passed in 2005 that forbids the simple relocation of
an old landfill, without recovering resources from the stored content. In Bavaria a public support
scheme has been installed, that subsidizes efforts to explore old landfills and the materials stored
therein – this has lead to a boom in landfill mining activities in 2007 and 2008 (Bockreis and Knapp,
2011).
Until now LFM projects have been pursued for the following reasons, which are also related to the
benefits associated with LFM (see section ‘Benefits associated with landfill mining’; Reno Sam,
2009):
− Expansion of landfill lifetime (conservation of landfill space or increase in storage capacity)
− Pollution prevention and mitigation of existing sources of contamination
− Material and energy recovery
− Reduction of waste management system costs
− Site redevelopment
Austria
Four LFM projects have taken place in Austria until 2011: Donaupark-, Kiener-, Helene Berger- and
Fischer-landfill. However, none of these projects aimed at recycling landfilled materials. They were all
motivated by limiting local pollution problems or by general landfill management (Bockreis and
Knapp, 2011). There are currently several research institutes and projects working on ways to
assess the resource potential of anthropogenic resource deposits (i.a. ‘Christian Doppler Laboratory
for Anthropogenic Resources’ at the Technical University of Vienna, LAMIS at the Montanuniversität
Leoben and researchers from the University of Innsbruck)
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2.2 Characterization of deposited materials
Previously conducted pilot and feasibility studies have shown that resource potentials vary
significantly from landfill to landfill. Most heavily the composition of materials landfilled is influenced
by factors such as the type of landfill, the lifetime of the landfill, meteorological, hydrological
conditions as well as geographical location (country, region) (Quaghebeur et al., 2013). Economic,
cultural and the political context as well as the dynamics of change over time play a crucial role for
the resource composition of a landfill (Gäth and Nispel, 2011).
The content of a landfill could also be described as a product of the of local waste management
practices (Quaghebeur et al., 2013; Sormunen et al., 2008).
Landfills have been the preferred option for the removal for all sorts of waste for decades, both for
municipal solid wastes (MSW) as well as industrial waste (IW). At the same time, however waste
separation of metal, paper, biodegradables or plastics has advanced. Waste incineration has also
become an increasingly used treatment option (Kaartinen et al., 2013). These factors lead to the
assumption that the contents of older landfills (1) are far more heterogeneous than newer landfills
and (2) hold significantly larger stocks of nowadays separated waste streams such as metals,
plastics or paper cardboard.
To assess the recycling potential for any landfill a site-specific in-depth analysis is necessary. In
general there are two distinct approaches to assess the resource potential of a landfill. A top-down
approach that uses data on the composition of deposited materials or incoming waste streams to
calculate the embedded materials, or a bottom-up approach that tries to extrapolate the resource
potential from site-exploration studies (sample drillings or excavations) and the information obtained
thereof. These two strategies are often referred to as either theoretical (top-down) or actual resource
potential (bottom-up) (Gäth and Nispel, 2011).7
In most cases detailed information on composition, volume and storage location of landfilled
materials is not available, making a top-down approach inapplicable. However, the documentation
of the amount of deposited materials in an area in combination with average values on the
composition of certain waste streams such as MSW or IW can be helpful in obtaining an initial rough
estimate of the resource potential in an area. In Tyrol, it has been shown that about 13.7 million tons
of waste were deposited between 1945-2008, while the average composition of MSW in 1998
encompassed a share of roughly 4% metals (Bockreis and Knapp, 2011).
The uncertainty of composition paired with various biological, physical and chemical processes
materials deposited undergo over years results in the fact that the resource and emission potential
7 Unlike Gäth and Nispel (2011) Hölzle (2010) refers to theoretical or actual resource potential as the fraction of materials
that can be technologically recovered compared to the theoretically amount of materials that are encompassed in the landfill.
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of most landfills, unless there is a site-specific investigation, remains unknown (Sormunen et al.,
2008).
Within the research field of LFM waste characterization is the most-covered topic (Krook et al.,
2012). The following section will provide some examples and try to summarize similarities and key
insights.
Måtsalycke and Gladsax (Sweden)
Hogland and Hogland et al. (2002, 2004) performed landfill mining tests in two old Swedish MSW
landfills (Måtsalycke and Gladsax). While the landfill in Måtsalycke was still active upon testing, the
Gladsax was closed in 1975 after more than 30 years of operation. The studies aimed at assessing
the recycling and energy recovery potential of the landfills. Instead of drilling materials were
excavated for further analysis. After separation three size categories (>50 mm, 18-50 mm and <18
mm) of waste material were assigned to different material types.
Soil-type waste was found to account for the largest amount of unsorted waste at Målsycke,
followed by paper, stones and wood. After sorting into size categories and weighing the coarse
fraction (>50mm) accounted for roughly 53% (w/w) in all depths (Hogland et al., 2004).
The dominant material in the coarsest fraction (>50 mm) found was paper (29%) followed by wood
(19%) and miscellaneous (16.9%) (Hogland et all, 2004, p. 121; Hogland, 2002, p. 51).
Miscellaneous referred to fine particles that form a soil-type of material consisting of partially of
decomposed paper and biological wastes and a small component of metal and glass. For the
coarse fraction the metal content was about 5% and higher than in other size fractions.
Hogland concluded that the calorific value of the big sized fraction (>50 mm) is normally high enough
for combustion and the metal fraction could be recovered. Metal recovery would also be possible for
the medium-sized fraction, as well as potential use for digestion/methane gas fermentation due to
organic content. Given the low levels of pollutants the fine fraction could be used as future cover
material for landfills. However, Hogland stressed that the costs and benefits are always case specific
and cannot be generalized.
Kuopio (Finland)
Kaartinen et al. (2013) conducted a case study of a potential landfill mining site in Finland. The landfill
in Kuopio was in operation from 2001 until 2011 and received mixed MSW. Approximately 18.000
square meters were used for storage with a maximum filling height of 30 meters. The research
project of Kaartinen et al. was not limited to assessing the composition of the landfill but also aimed
at evaluating available techniques to process the buried materials. In fact, not only manual but also a
full-fledged technical sorting plant was used in order to compare results of the manual and
automated sorting and assess the feasibility of mining the landfill in Kuopio. Representative samples
were drawn and sorted into different size categories for further analysis, <20 mm (manual sorting)
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and <30mm (mechanical sorting) respectively being the finest fraction. Fraction categories included
metals, plastics, paper and cardboard, textiles, soil, wood, and other (everything that could not be
classified). Like in other studies, also Kaartinen et al. found the fine fraction to be the biggest fraction
accounting for 43% (middle) and 47% (bottom layer) in weight of the overall amount of materials8.
The plastic fraction is the next biggest in weight terms (23-24%), while the share of plastics is
decreasing with fraction size. Compared to other studies levels of plastics found were quite high.
This is probably due to the relatively young age of the landfill and the increasing amounts of plastics
landfilled within more recent years.
Table 2: Calorific values – landfill mining exploration study in Finland. Values for samples from manual (M) and mechnanical (P) sorting. Standard deviations given in parantheses (Source: Kaartinen et al., 2013, p. 62)
Besides the material composition, the calorific values of both hand and mechanical samples were
also assessed. Results from both sorting treatments were similar as seen in Table 2. Calorific values
correspond to class one recovered fuels.
Comparing the results from the manual and mechanical sorting shows, that 40% (w/w) of material vs.
30% (w/w) in case of mechanical sorting could be used as possible fuels. The magnetic fraction of
metals recovered during the mechanical treatment accounted for 1% (w/w), while manual sorting led
to 3% (w/w) potential for metal recovery. Although metals in general were characterized by impurities,
the magnetic fraction was in a cleaner state, increasing chances of being recycled.
Houthalen (Belgium)
Quaghebeur et al. (2013) investigated the composition and valorization potential of the waste stored
at the REMO landfill in Houthalen-Helchteren (Belgium). The landfill has been in operation since the
1970s and has received both MSW and IW, roughly holding 16.5 million tons of waste in 2013. To
assess the valorization potential six waste samples were drawn from different locations. These
locations were subject to previous selection according to information on the stored materials (age,
type). Before sieving and sorting, the waste was dried. Initially a maximum temperature of about 40
degrees Celsius was used in order to not affect the composition of the waste. However, the
temperature was later adjusted to 70 degrees as (1) drying periods were too long using a lower
temperature and (2) the temperature in landfills regularly rises up to 90 degrees.
8 This is consistent with findings from other research projects such as Quaghebeur et al., 2013: 64% fine fraction
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The fine fraction (<10 mm) was sorted out, while the other waste was hand sorted into the following
material categories: plastics, textiles, wood, paper/cardboard, metal, glass/ceramic, stone and other
materials. Furthermore, the chemical and physical properties of the sorted fractions as well as the
non-sorted fine fraction were assessed.
With respect to MSW Quaghebeur et al. (2013) concluded that information on the initial amount of
landfilled materials is a good proxy for most materials, however organic materials cannot be
distinguished after a certain time due to degradation processes that result in a soil-like fraction. The
exploratory study at Houthalen also confirms that the composition of excavated materials changed
over the time the landfill operation time, as recycling procedures have been altered. The moisture
content was about 48 to 66% (w/w) and is comparable in size to other exploratory studies.
Quaghebeur et al (2013) concluded that for the combustible fraction (paper/cardboard, plastics,
wood and textiles) in Houthalen, a waste-to-energy treatment is the most beneficial way of
valorization. This is due to the high level of contamination and material impurities, which makes
recycling impossible. In contrast metals, stones, glass/ceramics and other inert waste might be
applied to a waste-to-material treatment, if materials can be effectively separated out.
The fine fraction could be valorized by three options: (1) waste-to-energy, (2) reuse as filler material,
and/or (3) metal recovery. As previous studies have shown removing the magnetic fraction results in
a reduction of metal concentration of about 50% (w/w) in the fine fraction (Quaghebeur et al., 2013,
p. 80). However, the authors do not provide a straightforward recommendation, but rather base the
valorization on further research regarding its technical feasibility.
Ämmässuo and Kujala (Finland)
Sormunen et al. (2008) studied the internal structure of two Finnish MSW landfills (Ämmässuo and
Kujala). They used grab samples from a various number of boreholes (horizontal sampling) and from
different depths (vertical sampling) of the landfill to assess the internal composition. From the
collected materials representative samples were drawn for sorting and analyzing. During the manual
sorting procedure the excavated materials were assigned into seven (five for Kujala) material
categories (plastics, paper and cardboard, wood, metals, inert materials, textiles and residuals).
Differences in composition did mainly arose between levels not specific regions of the landfills:
Table 3: Material composition of landfilled materials – exploration study in Finland (Kujala/Ämmäsuo) (Source: Sormunen et al., 2008, p. 157)
fraction % of weight fraction % of weightmajor weight fraction inert materials 30-40% residuals 54-75%
smallest fraction paper and cardboard from 2% paper and cardboard from 0.5%
most stable fraction wood 15-16% wood 9-13%
Ämmässuo Kujala
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The authors conclude that extensive sampling is necessary for assessing the resource potential of
any specific landfill, taking into account the size of the landfill and its characteristics (Sormunen et al.,
2008).
Burlington (NJ, USA)
Hull et al. (2005) investigated a landfill in New Jersey (US) that received MSW from commercial,
industrial and residential sources from 1989 to 1999. In total 3.8 million metric tons of waste were
deposited during that time.
Samples were collected during the installation of gas extraction wells. Based on a waste age map,
98 samples were collected in a way to best represent the content of the landfill (selecting waste with
different storage durations). Materials were screened into a fine (< 25.4 mm) and overs fraction. The
overs fraction was later on hand sorted into 14 material categories9.
The fine fraction in every sample accounted for at least 50% (range from 50-58%). The largest
fractions were miscellaneous materials, wood, plastics and paper. The assumed decreased
percentage share of recyclables (glass, ferrous metals, aluminium) in younger deposited materials
was only found for glass.
2.3 Extraction and recovery technologies
The availability of suitable and efficient technologies is a key element for the overall feasibility of
landfill mining. The decision whether materials (WtM) or energy (WtM) are recovered from waste
buried in old landfills is mainly based on the state of technology.
However, before it can be decided if materials are recycled as materials or if the encompassed
energy is recovered, separation and sorting techniques must be available and prove applicable.
Early mining studies have focused on the separation of the soil like materials from other waste
streams, eventually using other techniques for metal recovery (Krook et al., 2012). The applied
technical setups for LFM projects range from the use of simple sieves to sensor-based sorting
systems (Hölzle, 2010).
The first step in the recycling procedure normally uses techniques from open-pit mining for
excavating the buried materials (Savage et al., 1993). After materials are excavated large items are
often removed using hydraulic shovels and wheel loaders (Zobel et al., 2010).
After this initial process, materials are either first dried or directly dumped into different sieves to sort
materials into various size fractions for further treatment. These sorting procedures often take place
9 material categories: paper, cardboard, food and yard waste, polyethylene terephthalate and high density polyethylene
containers, other plastics, galls, ferrous metals, aluminium, other nonferrous metals, textiles/rubber/leather, wood, stone/brick/concrete, miscellaneous items and hazardous items
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using mobile techniques such as coarse sieves, followed by star sieves, before materials for further
treatment are transported into stationary sorting plants (Frändegård et al., 2013b).
Further techniques applied include air classifiers (plastic and wood), magnets (ferrous metals), eddy
current separators (non-ferrous metals) (Bernhard et al., 2011; Ford et al., 2013; Krook et al., 2012).
The choice regarding the technologies applied and the project setup will be dependent on the
nature of the landfill mass (Ford et al., 2013).
Frändegård et al. (2013b) explore the ability of different technical alternatives (mobile vs. stationary
plant) to recover material fractions from a Swedish landfill. They use data from a specialized
recycling company in order to simulate the overall efficiency of material recovery for a LFM project.
Generally they conclude that using a stationary plant almost 80% of all present materials could be
recovered, while using a mobile sorting plant about 60% of the potentially available materials can be
identified. However, the sorting efficiencies vary for different material fractions (see Table 4)
Table 4: Material recovery efficiency for technical sorting – case study in Sweden. Comparison of results from different technical setups (Source: based on Frändegård et al. 2013b, p. 748)
As previously detailed Kaartinen et al. (2013) compare the amount of materials that can be recycled
from landfills using manual and automated sorting. They conclude that about 75% of the
combustible fraction could be recovered using a sorting plant, while the sorting efficiency for metals
is only about 33% compared to manual separation.
Results from trial studies show that the technologies necessary for the extraction and separation of
formerly buried waste into different material streams are available. Therefore, authors like Van
Vossen and Prent (2011) conclude that landfill mining is technically feasible.
While readily available sorting and separation techniques are a necessity for any material treatment,
the efficiency and commercial viability of WtE-technolgies is a further decisive factor for LFM projects.
As recycling for plastics, paper and cardboard or wood is not deemed to be feasible due to the
degree of contamination and/or the associated costs, the efficiency of energy recovery processes
play an important role for the overall goal of material valorization of LFM (Ford et al., 2013;
Quaghebeur et al., 2013). While formerly incineration was used as a means of hazard prevention, it
is nowadays almost always combined with energy recovery (Bosmans et al., 2013). For the purpose
of energy recovery the high calorific fraction of waste is usually converted into refuse-derived fuel
(RDF) in order to reach a more homogenous type of fuel for combustion processes.
ResiduesTotal efficiency
percentage recoverable tons
percentage recoverable tons
percentage recoverable tons
percentage recoverable tons
percentage recoverable tons
Resource Potential 13 - 3 - 30 - 241 - 78 - 0
Stationary Plant 7 53.8% 2 66.7% 8 26.7% 210 87.1% 62 79.5% 75 79%Mobile Plant 6 46.2% 1 33.3% 0 0.0% 188 78.0% 24 30.8% 146 60%
Ferrous metalsNon-ferrous metals Plastiscs
Landfill constr. Material Combustibles
tons
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Bosmans et al. (2013) assess the potential of different thermochemical technologies for energy
recovery from waste streams and compare them against different criteria. Technological efficiency is
only one dimension, since technologies must also be commercially proven viable, so that investors
will be willing to invest them.
Table 5: LFM potential of different thermal treatment options (WtE). Comparsion based on different dimensions (Source: Bosmans et al. 2013, p. 19)
Bosmans et al. (2013) conclude that new technologies like plasma gasification are good prospects
regarding their recovery potential and suitability for different types of fuels, but lack real world
application. Conventional techniques such as incineration are commercially accepted and suitable
for a variety of fuels, however they are not as efficient when it comes to energy recovery.
Efficiency rates of WtE remain a bottleneck to LFM practices. Hölzle (2011) provides rough estimates
on the efficiency rates for energy recovery using different technologies (see Table 6).
Table 6: Energy efficiency of different WtE plants (Source: Hölzle, 2011)
According to Hölzle (2011) treatment efficiencies vary not only significantly between, but also within
the same technologies.
2.4 Benefits associated with landfill mining
Both business analysis as well as economic evaluation of landfill mining projects are intertwined with
the discussion of theoretical benefits associated with landfill mining and the motivations behind
already pursued projects. While most of the cases in Europe were motivated by pollution prevention
and limitation or by initiatives from regional authorities, resource recovery might gain weight as a
driver of future projects (Van Passel et al., 2013). This might also change the perception of benefits
associated with LFM in the context of increasing interest of resource recovery. Indeed, it is important
to keep in mind that the major novelty of LFM activities lies not in excavating the materials per se, as
Minimum Average Maximum
Waste incineration plant 9.4 41.3 78.7Cement incineration plant 18 27 85Biomass power plant 70 80 90RDF plant 22 43.6 82
Efficiency of different WtE plants
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this had been done already before, but in the idea of recovering formerly buried materials (Spencer,
1990).
Almost all LFM related papers touch upon the issue of theoretically associated benefits with LFM
(Krook et al., 2012). There are many possible benefits such as revenues from selling different
recyclables, the valorization of reclaimed land and regained landfill space (Krook and Baas, 2013).
Most articles only mention benefits briefly, and hardly make it a central topic of their research. The
benefits identified largely depend on the focus and definition that is applied to LFM.
Rettenberger (2009) summarizes the most straightforward benefits of LFM:
- increased landfill capacity due to the recycling of certain material fractions (in case of
recycling of light fraction up to 40-55% volume);
- reduction of deposited potentially hazardous materials;
- extraction of high calorific materials for energy recovery;
- extraction of materials for recycling;
- utilization or regained land space.
Other important benefits may include avoided liability through site remediation and reductions in
closure costs (US-EPA, 1997).
Van Passel et al. (2013) stress that external benefits or costs to society are typically not taken into
account when evaluating LFM projects from a business perspective. As are benefits associated with
lower environmental pollution, given the restoration of nature or biodiversity is usually not rewarded
to private investors.
Even though LFM is an attractive alternative to landfill closure, as it prevents costly landfill aftercare
and avoids environmental harm, it is seldom practiced. Hogland et al. (2011) blame this on a lack of
sound economic evaluation of LFM that incorporates a holistic view on the benefits thereof.
2.5 Economic evaluation of LFM
An issue that has been rarely touched upon is the economic dimension of landfill mining activities.
Almost all articles related to landfill mining mention this in some respect with only very general
statements such as that ‘the low recyclability suggests that landfill reclamation is currently not an
economic option under specific circumstances’ (Hull et al., 2005, p. 489). A small number of articles
focus on shedding light on economic benefits and costs that are associated with LFM projects.
Research can be grouped into articles that are either theoretical or case studies. The theoretical
discussion usually relates to the economic dimensions of LFM, for example, methods or data. Case
studies try to use real-world data in order to demonstrate the applicability of resource evaluation
methods to landfills.
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Van der Zee et al. (2004) assess the market potential of landfill mining in the Netherlands. There
research is motivated by the fact that the market size for classical landfilling shrinks and waste
management companies started to look out for new areas to venture. While in 2004 only 30 landfills
were operating, 3,800 landfills had already been closed and would potentially be available for mining
(van der Zee et al., 2004, p. 796).
They aimed to solve the dilemma that waste management companies face when having to select a
project out of a number of potential ones by developing a prospection method. An efficient
approach is needed as an extensive evaluation of every single available project is costly and time
intensive, thus posing an obstacle for firms to engage in landfill mining activities (Kaartinen et al.,
2013).
Van der Zee et al. (2004) (1) identify and categorize costs and benefits associated with LFM before
(2) presenting their approach to assess the profitability of LFM projects.
Van der Zee et al. (2004) apply the same categorization of costs and benefits such as has been
applied in other papers (US-EPA, 1997). Benefits can be grouped into two categories:
(1) benefits associated with the increased efficiency of the landfill operation, and
(2) benefits related to recycling and regained land (van der Zee et al., 2004, p. 798).
Costs can also be grouped either classified as (1) capita costs, or (2) operational costs.
Table 7: Overview of costs and benefits of landfill mining (Source: van der Zee et al. 2004, p. 801)
After the initial step of identifying relevant dimensions of benefits and costs van der Zee et al.
suggest a 4-staged approach when evaluating landfill mining projects. The authors draw from well-
established project decision tools that either use an approach to compare costs and benefits on one
(normally monetary) or on multiple dimensions.
First, generally available information such as region, proximity to highly populated areas, as well as
general characteristics (age, type of landfill) is used as a proxy for the project potential. Based on this
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information landfills are either classified as ‘qualified’ or ‘unqualified’ for further analysis. Hogland et
al. (2011) argue for example that LFM should focus on landfills from the mid-1950s to the mid-
1990s as well as industry-waste landfills. Due to increased waste separation rates afterwards and
reduced metal concentrations in MSW, the recycling potential might be smaller in other cases.
Furthermore, cases should be selected by applying cost-benefit analysis (CBA) based on general
available information. In a next step site-specific information by including experts is obtained. For the
final set of options, a more extensive evaluation of experts as well as a multi-criteria analysis (MCA)
with involved stakeholders should serve as basis for decision-making. MCA also includes
stakeholders such as regional authorities or non-governmental organisations that are likely to pursue
goals besides profitability of LFM.
Van der Zee et al. apply their evaluation approach to a selected sample of 147 landfills in the
Netherlands. By an investment of about € 7,000 they are able to shortlist the number of promising
mining projects to two.
A potential shortfall of their study is that its efficiency is reliant on a big sample of landfills available,
while in reality due to ownership characteristics it might not be realistic for a mining company to
assume that they could buy off landfills for mining easily. However, they make an important
contribution to the research discourse by identifying costs and incorporating the economic/market
dimension of LFM.
Van Passel et al. (2013) take a different approach towards the economics of landfill mining as they
explicitly address both the private and societal dimension. They raise the issue that external costs or
benefits might arise, which are not borne by the private investor and therefore remain unconsidered
in a business analysis. Beneficial effects might include lower environmental pollution, restoration of
nature and biodiversity or reduced import dependency. Hence, this may warrant governmental
correction in favor of forgone landfill mining activities. Van Passel et al. try to identify private as well
as external costs and benefits of ELFM.
Van Passel et al. (2013) discuss several potential economic key indicators for an economic
evaluation of LFM projects such as the net present value (NPV), the payback time, the internal rate
of return (IRR). They argue for the use of IRR as a key performance indicator, as it does not require
assumptions about the discount rate. They generate a comprehensive investment model for a LFM
project assuming a project set-up that includes both WtE and WtM and aims at maximum
valorization of the resource potential. Inputs for their scenario set-up and calculation are derived
from peer-reviewed sources as well as industry sources. Monte-Carlo simulation is used to
determine the impact of key input factors on the business analysis. Efficiency of WtE installations,
the price of CO2-certificates, electricity prices, investment costs of WtE installations, operational
costs of energy production and the support schemes prove to have an important impact on the
economic performance.
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In order to assess the impact of external societal costs, van Passel et al. argue for using CBA that
translates different societal costs and benefits into a single monetary dimension. They calculate the
carbon footprint of the investigated LFM project. By multiplying the amount of CO2 with emissions
certificate prices they derive a ‘societal value’. Van Passel et al. (2013) also present different
monetary valuation approaches for the land that could be regained in the course of the LFM project.
The authors conclude that, given adequate support mechanisms are put in place, there would be an
incentive for private investors to engage in LFM in Flanders (Van Passel et al., 2013). From a societal
perspective LFM in the region of Flanders might be beneficial not only because of the expected
decrease in CO2-emissions compared to a business-as-usual scenario, the land area that would be
freed up and avoided potential water pollution.
Rettenberger (2010, p. 42) delivers some numbers on costs that are associated with LFM projects.
According to his assumptions, landfill mining activities amount to at least 30 EUR/m3 after selling off
ferrous metals and combustibles. As landfill after care costs vary only between 5 and 25 EUR/m3 he
concludes that LFM is not yet profitable. Costs need to further decrease (e.g. reduced gate fees for
combustibles or RDF materials) so that avoided aftercare costs outweigh them.
Rettenberger (2010) further supposes that it is most likely that metals are the only type of material
that can be mined from landfills profitable in the near future. He argues that even though the high-
calorific fraction could be used for incineration, associated costs for recovering the combustible
fraction cannot be offset by benefits of selling them. In fact, currently even the combustible fraction
would need to be paid for upon disposal.
Early case study assessments include the evaluation of mining wood products from landfills (Byrden,
2000) and evaluating LFM as an option against the background of reducing the landfill footprint
(Fisher and Findlay, 1995).
Byrden (2000) considers the economic aspects of mining wood products from an old landfill in the
US as regulatory requirements became more stringent and landfilling more cost-intensive and less
profitable. He develops a phase model to evaluate the feasibility for LFM projects. Steps include
assessing the market potential for recyclables, the associated transportation and land costs, while
also incorporating the potential gains from selling recovered land. The mining option is then
compared to the traditional closure option and the associated costs. Bryden (2000) found one
project in Oregon to be economically viable, a second one proved to be unprofitable, however not
providing detailed information on costs or profit. Differences mainly arose due to transportation
costs and the fact that in one case there was no local market for recycling products.
An important point that Bryden makes is that landfill aftercare costs can be extremely expensive and
long-term projects. Furthermore, he argues that the costs from preventing the occupied land space
from future development should also be taken into account when forecasting closure costs.
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Fisher and Findlay (1995) evaluate LFM as one option to reduce or even eliminate landfill aftercare
costs while limiting environmental impact. They argue that the economic assessment of feasibility
should be conducted in the overall context of solid waste management costs as landfills vary vastly
with respect to their properties, which importantly influence associated costs and potential benefits
of LFM activities.
Fisher and Findlay (1995) assume that landfills mainly consist of soil-like materials and associated
treatment costs would heavily influence the overall expenses of LFM projects (up to 80% of overall
costs). Hence, not taking into account specifics of the soil or the treatment process (e.g. regulatory
requirement for screening on pollutants when intending to reuse landfilled materials as bed material)
might bias the economic assessment and lead to false estimates.
Another discussion point when evaluating LFM projects is to not only compare the mining option in
comparison to the conventional closure costs, but also consider additional benefits such as the
reduced footprint of the landfill and benefits when re-siting the landfill.
Van Vossen and Prent (2011) investigate both the technical and financial feasibility of landfill mining.
Based on data from 60 landfill investigation studies they calculate the waste composition for a
‘standard landfill’. The soil-like material fraction in their estimates includes all materials that have a
smaller diameter than 24 mm. Overall, they find the soil-to-waste ratio to be almost 1:1.
For a complete recovery of all material fractions from the landfill body van Vossen and Prent (2011)
consider a multi-staged model with sequential separation steps. By assigning costs to every
separation step, their cost model is generated. Two basic scenarios are investigated: full and partial
separation of materials. During full separation, all 14 different materials are sorted out. Partial
separation is limited to the recovery of metals from the landfill. Costs for the partial separation
scenario are limited to EUR 17 per ton of waste, while full separation costs are estimated to be
around EUR 45 per ton of waste.
Based upon estimates of standard costs for any landfill mining project (i.e. excavation of materials,
unforeseen costs, preparation works) the profitability of a landfill mining project for a standard landfill
(holding 500,000 tons of waste) is investigated.
For both full and partial separation the cost-benefit analysis results in a deficit. Metal sales are able
to reduce costs by 8.2% for the full separation scenario and 18% for partial recovery.
Van Vossen and Prent (2011) conclude that prospects for the profitability of landfill mining projects
could increase if additional benefits such as from the re-use of freed landfill space or recycling of
plastic can be generated. These benefits however are dependent on the site-specific circumstances.
The article by Van Vossen and Prent (2011) is an important contribution to the economic disocourse
on LFM as it details the estimated financial data on the separation and sorting procedures. They also
specify an estimate of expenses during the project preparation phase for a Dutch case study .
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Ford et al. (Ford et al., 2013) follow a similar approach to van Vossen and Prent (2011) and apply
CBA to a hypothetical standard landfill, however they establish a Scotland specific case. By making
some high level assumptions regarding the average landfill in central Scotland they investigate four
different landfill mining scenarios and their associated costs and benefits (off-site and on-site RDF
combustion and reuse of landfill space for housing development or anewed landfilling). They assume
the average landfill holds about 1.3 million tonnes of waste and a project duration of 10 years. In
absence of specific data on the material composition, they draw average values from the literature.
Their CBA model builds on a variety of different literature and expert sources, including separation
costs from van Vossen and Prent (2010).
Unlike van Vossen and Prent (2010), Ford et al. (2013) not only consider metal recovery as a source
of income, but also estimate costs and benefits for energy recovery for the combustible fraction
(paper and cardboard, plastics, wood, leather and textile). Furthermore, they assess capital costs for
the separation and sorting plant, and the combustion facility. Revenues for the energy recovery are
assumed to be generated by energy sales and incomes from Renewable Obligation Certificates10.
Hence, societal costs as discussed by Van Passel et al. (2013) are also included to some extent.
For simplicity reasons neither cost nor price changes during the project are assumed and sorting
efficiencies are set to 100%. Uncertainty is accounted for within their CBA by developing three
different scenario outcome (best outcome, average outcome and poor outcome) based on the
material concentration and the costs for material separation and treatment.
Results from the CBA suggest that LFM for the average Scottish landfill is not profitable, except for
cases of optimal material concentartion and on-site RDF treatment.
Another general example of CBA regarding landfill mining is presented by Bernhard et al. (2011). As
in van Vossen and Prent (2011) and Ford et al. (2013) the profitability of LFM is also assessed by
Bernhard et al. (2011) on the basis of literature data for the average composition of a landfill, not
relying on site-specific data. They assume the metal fraction to be recycled, while 35% of the
comubstible fraction is used as RDF and 65% are disposed off at a waste incineration plant. As
Bernhard et al. (2011) assume that there is no on-site treatment facility, both RDF and waste
incineration impose some costs. All other materials are relandfilled onsite. Unlike other authors
Bernhard et al. (2011) do not assess costs based on assumptions regarding an average landfill
volume, but calculate costs in EUR per m3. In the baseline scenario net costs amount to EUR 16.85
per m3 treated waste. Assuming a density of 1.1 tons/m3 this translates into costs of EUR 15.2 per
ton of waste. This is half the estimate that Rettenberger (2010) has reported.
In order to assess the influence of uncertainties regarding prices for recyclables and material
concentrations, inputs are varied and the impact on the costs is investigated. Bernhard et al. (2011)
10 Compensation for providers generating energy from renewable sources (biomass fraction of landfill feedstock for
combustion) under the Reweable Obligation, introduced in 2002 in the UK.
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show that even in the case that no combustibles would be present in the landfill, LFM would still be
associated with net costs. LFM would be profitable based on their calculations if the copper content
would be 0.4% of the overall landfill (w/w) or in case the aluminium concentration would be at 1.75%.
Prices for non-ferrous scrap would have to rise by the factor of five compared to the assumed
scenario. The copper price would need to increase threefold for a LFM project to break even. While
changes in the aluminium price, given the assumptions made by Bernhard et al. (2011), almost have
no effect on overall profitability.
Winterstetter et al. (2015; Winterstetter and Laner, 2015) assess the profitability of a landfill mining in
Belgium (Houthalen-Helchteren). They aim to identify critical factors for the economic feasibility of the
LFM project and hence for subsequent classification. The distinct features of their approach
compared to other studies are the incorporation of site-specific composition data, the modelling of
sorting efficiencies based on data from state-of-the-art technologies, taking into account the time
value of money and the application of techniques to represent uncertainty regarding input variables
within the course of the assessment.
By the use of material flow analysis they aim to identify the recoverable fraction given current
technical possibilities. They further calculate the NPV of the mining project (four different scenarios)
by using Monte-Carlo simulations to asses the impact of uncertain input variables on the profitability.
Metals, mineral and stones are assumed to be recycled, while the combustible fraction is subject to
thermal treatment. The residues from the sorting procedure and the fine fraction are redeposited on-
site. Winterstetter et al. (2015) also assess the CO2-balance and associated cashflows, aiming to
represent the societal dimension of the project. All investigated scenarios prove to be unprofitable
given their assumptions.
2.6 Summary
The first report on a LFM project dates back to 1953. Until now, projects have been pursued in
different regions over the world, however interest in the topic has been peaking around the 1990s
and is lately again rising (Krook et al., 2012). While most landfill mining projects until now have been
motivated by pollution prevention or limitation, recently projects have been evaluated on grounds of
their potential for material recovery (Bockreis and Knapp, 2011).
A key finding of the exploration studies, albeit all its differences, is the high share of the fine fraction
compared to the overall landfill body. Across several studies, landfills consist of about 50% of a soil-
type of material (Hull et al., 2005; Kaartinen et al., 2013; Quaghebeur et al., 2013). The soil-type
material not only comprises formerly buried waste materials, but also the cover material used during
the process of landfilling. Furthermore, combustibles (i.e. paper and cardboard, plastics or textiles)
were found to account for 20 to 30% (w/w), while normally landfills also hold a small percentage of
metals (Krook et al., 2012, see Table 8).
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Table 8: Summary of reviewed landfill exploration studies – material composition (Sources: Gäth and Nispel, 2012; Hogland, 2002; Hogland et al., 2004; Hull et al., 2005; Kaartinen et al., 2013; Quaghebeur et al., 2013)
Regarding the calorific values of the combustible fraction, there is solid evidence that it is high
enough to be either directly used or turned into RDF (Kaartinen et al., 2013). However, results
related to the energetic potential are hard to compare, as calorific values were only collected for
specific material fractions such as paper or cardboard (Hull et al., 2005; Quaghebeur et al., 2013).
Hazardous waste in general seems not to pose a challenge to LFM activities as no reviewed
exploration study reported elevated levels of hazardous materials.
Taking into account all of these findings it can be confirmed that there is a certain potential for
material as well as energy recovery from deposited materials in landfills, especially from older landfills.
However, one should keep in mind that there is a difference between the resource potential and the
fraction of the materials that could be recovered during a fully mechanized recovery process. The
technical feasibility of separation and sorting is a prerequisite for any material recovery – in whatever
form.
The share of recoverable materials for a LFM project will depend on the applied project setup.
Recovery efficiencies vary widely in the applied technologies, both for material recovery and energy
recovery (Frändegård et al., 2013b; Hölzle, 2011; Kaartinen et al., 2013). While technical feasibility is
key to the outcome of LFM projects, commercial real-world application of the techniques is an
equally important component of feasibility – especially in the context of WtE-treatment options
(Bosmans et al., 2013).
However, it is assumed that landfill mining is feasible technically (Van Vossen and Prent, 2011).
Despite the fact that there is a range of potential benefits associated with LFM, it is seldom practiced.
Hogland et al. (2011) blame this on a lack of sound economic evaluation.
# of samples 23 4 2Houthalen-Helchteren
(Belgium) (w/w) std Måtsalycke std Gladsax stdGlass 1.3% 0.8% 0.3% 0.1% 0.9% 0.8%
Inert fraction 10.0% 6.0% 13.7% 13.7% 19.1% 10.0%Metal 2.8% 1.0% 1.7% 0.5% 1.4% 0.2%
Textile 6.8% 6.0% 2.3% 1.7% 0.7% 0.6%Wood 6.7% 5.0% 9.9% 3.2% 1.7% 0.8%
Paper/cardboard 7.5% 6.0% 9.7% 7.6% 2.3% 0.8%Plastic/rubber/foam 17.0% 10.0% 5.5% 3.2% 2.1% 2.6%
Soil* 44.0% 12.0% 54.5% 54.5% 71.3% 11.3%Other 3.9% n.a. 2.35% n.a. 0.46% n.a.
*Soil = fine fraction. Varyng from <20mm to <5 mm** Case study used in the course of this thesis
Quaghebeur et al. (2013) Hogland et al. (2002 and 2004) Kaartinen et. al (2013)
Hull et al. (2005)
Gäth and Nispel
(2012)**6 3 34
Kupio (Finland)
Burlington (US)
Hechingen (Germany)
n.a. 0.3% 4.4%n.a. 1.5% 12.2%
4.0% 3.1% 3.4%7.0% 3.6% 7.7%6.0% 8.6% 3.3%6.0% 8.9% 0.3%
24.0% 7.4% 17.4%52%* 53.3% 25.5%
1.00% 8.67% 25.00%
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While almost all research articles on LFM touch upon the economic dimension to some extent, there
is only a limited number of articles focused on it. Major contributions have been made with respect
to identifying potential costs and benefits that occur during a hypothetical LFM project, thereby not
only taking into account the perspective of a private investor but also the general public
(Rettenberger, 2009; US-EPA, 1997; Van der Zee et al., 2004; Van Passel et al., 2013).
Furthermore, different techniques in order to assess the LFM potential for a region, or assess the
financial viability of LFM projects using key indicators have been discussed (van der Zee et al., 2004;
Van Passel et al., 2013). However, most financial assessments apply standard CBA methodology
(Bernhard et al., 2011; Ford et al., 2013; Van Vossen and Prent, 2011; Weißenbach, 2012). For
simplicity reasons these usually assume full efficiency for sorting and separation processes and
refrain from using site-specific data of composition. These assumptions are problematic with respect
to the fact that the resource potential might be overestimated or based on false estimates.
Throughout a range of exploration studies it has been shown that site-specific exploration studies
are a necessity to approximate the resource potential of a landfill (Sormunen et al., 2008). But not
only the material composition varies from landfill to landfill. Benefits are dependent on the local
circumstances as well.
As uncertainties pose a major challenge to the economic evaluation of LFM, the appropriate
representation of risk for a solid financial analysis is essential (Baas, Leenard et al., 2010).
Winterstetter et al. (2015) attempt to tackle the challenges of assessing the economics of LFM. They
classify an potential LFM site in Belgium under UNFC-2009. Their approach includes using site-
specific data, along with uncertainty ranges for input variables as well as state-of-the-art efficiencies
for sorting techniques and accounting for the time-value of money.
Further assessments that follow a similar approach need to be pursued in order to replicate the
results for different circumstances (landfill location and institutional settings) and facilitate a
discussion about the profitability of LFM on solid grounds.
2.7 Research focus
As illustrated above there remains a lack of reliable information regarding the economic feasibility of
landfill mining projects and concrete ways to assess them. Often the economic dimension of landfill
mining is only mentioned as a side topic through referring to current prices for metals, without
discussing the technical prerequisites or the investments that would be necessary to recover those
materials.
This might also be due to the fact that often landfill mining projects in the past have been motivated
by reasons of environmental protection and hazard prevention, which make an economic
assessment irrelevant.
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However, as stated earlier, the profitability of landfill mining might be a driver LFM activities in the
future. Furthermore, an economic assessment of a landfill mining project is a prerequisite for a
classification under natural resource classification systems such as the UNFC. Linking
anthropogenic resources to established classification systems would enable a more profound
discussion of their potential, increases general awareness of the issue and is crucial for raising
commercial interest.
Gaining more insight into the necessary steps of assessing the profitability of any LFM activity as well
as trying to apply developed evaluation techniques to case studies is not only important for the
economic actors of the process, but also the general public as benefits of LFM are dependent upon
its successful implementation.
As there is currently little research aimed at the evaluation of economic profitability of landfill mining
projects both from a private investor’s and a societal perspective, I will attempt to answer the
following research question:
How should the ‘Kreismülldeponie Hechingen’ landfill be classified under UNFC-2009?
Guiding questions for the research are:
(1) What are methods that could be used to assess the resource potential as well as the
economic potential of old landfills?
(2) What factors could influence the profitability of LFM projects?
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3 Methodology and empirical design The thesis applies (1) an extensive literature research on landfill mining, related to different
techniques to explore and assess the resource potential of old landfills. Furthermore, (2) a case
study approach is applied to evaluate the economic profitability of a potential real world LFM project.
The profitability assessment, using different scenarios, will solely focus on a private investor’s
perspective, not taking into account any external effects or the monetization thereof. The business
analysis will be supported by the discussion of potential positive and negative external effects to
landfill mining activities and different approaches for their monetary evaluation. The aim is to place
the findings into perspective against the background of a more holistic economic perspective.
3.1 Conceptual framework
Whereas international classification schemes such as the UNFC-2009 11 aim at categorizing
resources, they do not provide standards and guidelines for the necessary preceding evaluation of
the resource stock itself (UNECE, 2010). The analysis of the Hechingen landfill is therefore based on
the suggested analytical framework for the evaluation of anthropogenic resources by Lederer et al.
(2014), which draws from natural stock resource evaluation, mining exploration techniques and MFA
accounting of anthropogenic resources.
Figure 1: Procedure for the evaluation of anthropogenic resources (Lederer et al., 2014, p. 6)
As this thesis focuses on landfills, in particular on the ‘Kreismülldeponie Hechingen’, the first step of
the approach by Lederer et al. is omitted. Step four is altered, as such the United Nations
Framework Classification on Fossil Energy and Mineral Reserves and Resources (UNFC-2009)
cross-classification is used. Hence the following three-staged approach is applied:
11 was set out to be a generic system to classify natural stock based on three fundamental criteria dimensions: economic
and social viability (E), field project status and feasibility (F) and geological knowledge (G). Economically viable in the definition applied within UNFC-2009 encompasses economic (in the narrow sense) plus other relevant conditions such as „legal/fiscal framework, environmental, social and all other non-technical factors that could directly impact the viability of a development project“ (UNECE, 2010, p. 10; further details see next section)
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(1) Exploration: Assess the resource potential
For assessing the resource potential data on the specific anthropogenic stock is collected. Data
will be derived from released publications on the investigated landfill. Furthermore, the
information will be synthesized into a detailed report that captures the relevant information (i.e.
size of stock, landfill lifetime, history, composition of materials, exploration and mining methods)
for the prospective LFM project.
This stage of research results in a MFA model that delivers site-specific information on the
buried material stock and its resource potential. Uncertainty of input data (i.e. material
concentration) will be incorporated into the calculations.
For the MFA, STAN (software allowing MFA under uncertainty) is used to model the LFM project
and its material potential (Cencic and Rechberger, 2008).
(2) Evaluation: Assessing the project feasibility and the economic viability
For a detailed analysis of the costs and benefits of the LFM project the MFA model has to be
enhanced according to the extraction, treatment and transportation techniques applied. Based
on the efficiencies of the technologies used, the adapted MFA model can be used for a scenario
analysis that allows the comparison of the fractions of resources that actually can be recovered
(given the applied technologies) to the theoretically available stock.
The economic analysis is founded on the results from the first stage of the research (MFA)
combined with financial data. The business evaluation will use NPV calculations. All relevant
price and cost developments need to be forecasted and cash flows discounted over the project
duration in order to calculate the capital value of the project. A sensitivity analysis using Monte-
Carlo simulations is further applied to better understand the main drivers of economic
profitability/unprofitability.
(3) Classification: Anthropogenic resource or reserve
Applying the UNFC-200912 resource classification system, the landfill will either be classified as
resource, reserve or other anthropogenic stock based on the project feasibility, the geological
knowledge on the deposit as well as the socio-economic feasibility. The classification will be
discussed against the background of potential external effects and the monetization thereof.
The following sections provide an overview over the concepts, methods and software programs that
are used during the evaluation.
12 see section XY for further details
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3.2 Natural resource classification systems and UNFC-2009
The UNFC-2009 was developed as a universally applicable evaluation and classification scheme for
fossil energy and mineral deposits located underneath the earth’s surface. It was designed to allow
for the incorporation of national resource classification systems in order to enhance the accuracy
and consistency of data on resource deposits. It further aims to facilitate international
communication and estimation of available stock (UNECE, 2010).
The UNFC-2009 sets a unified standard of reporting on resource deposits, while allowing the
retention of nationally or regionally developed classification and coding systems. It can therefore
either be applied directly or used as harmonizing tool for reporting standards.
Classifications under UNFC-2009 are based on three fundamental criteria (UNECE, 2010):
− The economic and social viability of the project (E): this evaluation dimension considers
market prices as well as relevant regulatory, environmental or contractual conditions in
order to illustrate commercial potential of a mining project based on its socio-economic
background
− Field project status and feasibility (F): concerned with the project status (from early
exploration to already established extraction project) and its maturity
− Degree of geological knowledge (G): displays the level of confidence regarding the
geological potential of the resource deposit and its recoverability
The UNFC-2009 classification approach translates into a three-dimensional classification system
with a numerical and language independent coding scheme:
Figure 2: Illustration of UNFC-2009 categories and examples of classes (UNECE, 2010, p. 5)
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While the UNFC-2009 serves as classification system, it does not provide any underlying standards
for the assessment of the classification dimensions. To assure a certain degree of transparency and
comparability of the exploration and assessment of resource stocks one has to rely on (national)
guidelines, such as the Canadian National Instrument 43-101 for mining projects (Anonymous, 2001).
In fact, the UNFC-2009 was developed in close coordination with the Committee for Mineral
Reserves International Reporting Standards (CRIRCSO) and international/national agencies that have
developed reporting standards, so that findings based on them could be compared on the basis of
UNFC-2009 (Bankes, 2013; UNECE, 2010). CRIRSCO is an international advisory body that
contributes to the establishment of international best practices for the reporting on exploration
results, mineral reserves and resources.
The CRIRSCO family of codes and standards include: JORC (Australasia), NI43-101 & CIM Definition
Standards (Canada), SAMREC/SAMVAL (South Africa), PERC (Europe), SME (United States),
Comisión Minera de Chile (Chile) and NAEN (Russia) (Bankes, 2013).
Relating the research approach UNFC-2009
Although the three-staged research approach applied in this thesis is outlined above, is has not yet
been illustrated how these steps are related to the attempt of classifying the Hechingen landfill under
UNFC-2009. The following table illustrates how the findings of each research phase are related to
the evaluation dimension applied within UNFC-2009:
Table 9: Research procedure and connection to UNFC-2009 (Source: adapted from Winterstetter et al., 2015)
By synthesizing the findings from each research phase a classification of the Hechingen landfill will
be attempted.
3.3 Material flow analysis (MFA) using the software STAN
Material flow analysis (MFA) is a technique to assess the material flows and stocks of a system that
is defined in the dimensions of space and time (Brunner and Rechberger, 2004). By describing the
Research stage Aim UNFC axis Method
ExplorationKnowledge on the geological
composition (composition and quantity)
of the resource deposit
G MFA
Technical feasibility of recovery and
valorization assessing quantity and
quality of extracted materials
F
MFA with process
efficiencies (scenario
analysis)
Assess ecnomic viability given socio-
economic circumstancesE
NPV and sensitivity
analysis
Classification Classify anthropogenic stock under
UNFC-
combination of the
findings gained during
previous stages of
research
Source: adapted from Winterstetter & Laner, 2015, p. XY
Evaluation
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relationship and material fluxes from sources over intermediate deposits to final sinks, it tracks the
material flows and allows for the identification of accumulations or leakages within the system.
MFA builds on the law of conservation of matter, stating that matter can neither disappear nor be
created. Therefore, in a closed system the mass must remain stable over time. This leads to the
balance equation that can be used to control MFA results:
!"#$%& = !"#$"#% − !!ℎ!"#$%!"#$%&
MFA has been applied to a widespread field of studies from industrial ecology, environmental
management and engineering to resource and waste management and serves as methodological
foundation of the ecological footprint (Brunner and Rechberger, 2004; Daniels, 2003).
It has become the primary generic tools for mapping and tracking the metabolism of the human
economy (Daniels, 2002). The basic concept of ‘metabolism’, in this context, means that the
industrial/societal/anthropogenic system is embedded into the biogeosphere. The concept
of ’industrial metabolism’ (Ayres, 1989), ‘metabolism of the anthroposphere’ (Baccini and Brunner,
1991) or ‘societal metabolism’ (Fischer-Kowalski, 1998) came about as a consequence of the rise of
environmental concerns and the related efforts to establish paths of sustainable development
starting in the second half of the last century.
Important concepts applied within MFA can be defined as follows according to Brunner, Rechberger
und Baccini (Baccini and Brunner, 2012; Brunner and Rechberger, 2004):
Process: Processes can be defined as transformation, transport or storage of
materials. Processes can either be of natural origin or manmade.
Flow: Flows link processes with each other. They are defined as mass flow
per time unit through a conductor. They can be distinguished as being
an import or export flow (crossing system boundaries) or as being an
input or output flow into a process.
Stock: Stocks are material reservoirs within processes
System: A MFA system comprises a group of elements (processes and flows)
and describes the interaction and relationship between them.
Alternatively one could say that a system is described by its system
boundary – that defines the system under investigation. The smallest
possible system is a single process. Commonly, MFA is used to
describe complex interactions such as regional economies or modern
waste management systems and comprises several processes and
sub-systems.
Good: A good is a substance or any mixture of substances with an economic
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value, which can be positive or negative.
Substance: Substances are defined as matter of uniform nature. Substances can
be elements, compounds or any other form of.
Material: Materials refer to substances and goods
Transfer Coefficients Throughout processes materials can be separated and assigned to
different output flows. Transfer coefficients are one way to express the
outcome of such partitioning processes. They define the fraction of a
substance/good for a specific output flow in relation to the total input. A transfer coefficient ! with a value of 0,5 for output !1 means that
half of the substance/good under investigation in a hypothetical process ! is allocated to output !1, while the remaining 50% of the
substance input is distributed over any other output flows.
For modeling material and substance flows of the landfill mining project the software program STAN
was used.
STAN (subSTance flow ANalaysis)
The software STAN (subSTance flow ANalaysis) was developed in cooperation with INKA software
by the Vienna University of Technology (Institute for Water Quality, Resources and Waste
Management)13. It enables users to perform MFA according the Austrian standard for MFA with
application to waste management (ÖNORM S 2096) (Cencic and Rechberger, 2008). By the use of
a graphical interface a MFA model can be set up using predefined elements for processes, flows,
subsystems, system boundaries and text fields. STAN allows the user to incorporate sub layers into
the MFA model, tracking the flow of specific substances and energy in the material flow system.
The mathematical model is built from the graphical representation using the following equations
(Cencic and Rechberger, 2008, p. 6):
Balance equation: !"#$%& != ! !"#$"#%! + !!ℎ!"#$!!"!!"#$%!
Transfer coefficient equation: output! = !"#$%&'"!!"#$$%!%#&'!×!output!!
Concentration equation: !"##!"#!$%&'( = !!"##!""# !×!!"#!$#%!"#$%&!"#!$%&'( !!
Stock equation: !"#$%!!! = !"#$%! + !ℎ!"#$!!"!!"#$%! !
Uncertainty ranges can be assigned to input variables that allow for a more realistic representation of
material flows. By means of data reconciliation the accuracy of the MFA model is increased. The
corresponding uncertainties are determined with the method of error propagation.
13 the software is free of charge and can be downloaded from http://stan2web.net
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3.4 Net present value calculations and Monte-Carlo simulations using @RISK
When calculating the NPV, it should be considered that (1) there is uncertainty attached to the input
and output of such a quantitative analysis, and (2) the financial aspects should not be treated
separately from other dimensions such as the environmental or societal.
Consequently, the results of the financial analysis should not be regarded as certain, but as one
potential outcome.
There are several estimation techniques to approximate the profitability of a LFM investment. Most
commonly NPV, IRR and payback time are used (Van Passel et al., 2013). However, NPV
calculations have some advantages such as considering the time value of money and accounting for
all relevant cash flows over the complete project duration, which make them more attractive (Ross
and Ross, 2008). NPV calculations are also applied within this thesis because of its use for primary
resource evaluation Winterstetter et al. (2015).
The logic behind NPV analyses is to calculate the present value of all cash flows of an investment
project (Ross and Ross, 2008). Essentially, the NPV is the difference between initial investment and
discounted cash flows over the project duration. If the NPV is greater than zero, the investment is
regarded as profitable. In the deterministic setup the underlying decision rule is to accept or reject
the project depending on whether its NPV is positive or negative (Hacura et al., 2001). Hence, when
deciding between different options, the project with the highest NPV is chosen.
The NPV can be described by the following formula, where ‘!’ represents the initial investment, ‘!"!’ is the sum of cash inflows, ‘!"!’ is the sum of cash outflows in period ‘!’ and ‘!’ is the interest rate:
!"# = !−! + ! (!"! − !"!)(1 + !)!
!
!!!
Despite its common application there are several limitations and drawbacks to the NPV. Mainly they
are associated with (1) the applied discount rate, and (2) the uncertainty associated with future cash
flows. Choosing the discount rate is crucial to the outcome of the NPV calculations. Often interest
rates are chosen based on the average return of a project with a comparable risk profile. Setting a
discount rate and assuming it to be constant for the overall duration of an investment is a further
simplification that should not be disregarded when evaluating the findings of a NPV calculation.
Another limiting assumption is the availability of capital. Given a positive NPV it is assumed that a
project is pursued, irrespective of its capital requirements. However, there might exist limits in a
companies ability to access sufficient financial means in order to pursue a project with a positive
NPV.
It is argued, that NPV analysis leaves the reader with a false sense of security, when in fact there is
uncertainty related to the most important input variables of the calculation, i.e. the future cash flows
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(Ross and Ross, 2008). Uncertainty refers to the fact that more things can happen than will happen
(Brealey, 2001). It is implausible to assume that cash flows can be forecasted and planned without
any associated uncertainty. Over the lifetime of an investment circumstances can completely change
and lead to inaccurate forecasts. Factors influencing the accuracy of the forecasted cash flows vary
from increased competition over changes in the political environment, to economic malaise or
natural catastrophes.
Hence, there is always some degree of risk associated with the results of an NPV, which needs to
addressed.
Monte-Carlo simulations and sensitivity analysis using @Risk
One option to increase the reliability of NPV calculations in the face of contingencies is to conduct a
risk analysis (Hacura et al., 2001). This involves accurately representing the risk associated with the
defined input variables and the potential outcomes associated with it in the future.
A crucial distinction for this type of analysis is between risk and uncertainty. Whereas risk is a
situation with outcomes that can be characterized by known probabilities, uncertainty refers to a
situation with outcomes that cannot be described by known probabilities. Unlike uncertainty risk can
therefore be assessed and used to evaluate investments. For conducting sensitivity analysis known
probabilities are a prerequisite. While a complete analysis of uncertainty is not possible, using
techniques such as sensitivity analysis can contribute to increased reliability of an investment model.
One technique that can be applied is Monte-Carlo simulation. Kalos and Whitlock describe the
essence of Monte Carlo simulation as “the invention of games of chance whose behavior and
outcome can be used to study some interesting phenomena” (2008, p. 1). Named after the casinos
of Monte Carlo, this technique of statistical sampling is used to approximate solutions to quantitative
problems. In order to perform a Monte-Carlo analysis, a probability distribution for each uncertain
input variable needs to be defined. After the model has been set up, a number of trials (e.g. 10,000)
must be run. Each time the model uses a random set of values from the defined distribution for the
uncertain variables. Through this process a large set of values for the output variable (e.g. NPV) are
generated. The results are normally displayed in the form of a probability distribution of the output
variable, depicting the most-likely scenario.
In the landfill mining context, Monte-Carlo analysis can be most easily be understood as the creation
of multiple of hundred potential future outcomes of the project and deriving information from the
analysis of the generated results.
While Monte-Carlo simulations might provide insights on the potential outcome of a landfill mining
project, a sensitivity analysis can assess what drives these outcomes. Applying a sensitivity analysis
helps to understand how risk based decisions are influenced by uncertain contributing factors. It
illustrates how sensitive the NPV is to changes in the variability of input variables.
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The sensitivity analysis pursued in this thesis uses the results from the Monte-Carlo simulations to
assess how the variability in uncertain input factors impacts on the NPV. Stepwise multiple
regression by using @RISK (for details see next section) is used. Derived coefficients are normalized
to standard deviations14. Through this approach it can be identified which uncertain variables are of
main importance for the profitability of the LFM project.
At the core of a Monte Carlo simulation with sensitivity analysis is a standard NPV calculation (Ross
and Ross, 2008). Steps in a sensitivity analysis include (Hacura et al., 2001, p. 551; Hertz, 1964):
Step 1: Specify the basic model
Defining the mathematical relationships between numerical variables that relate to the
forecasting model – the NPV model
Step 2: Specify the variables associated with risk and define probability distributions
This includes the selection and definition of key variables and specifying their distribution
Step 4: Verification of data input and model
Verify that all the input values and distributions are free from logical errors
Step 4: Performing the experiment
Running the simulation for a predefined number of trials
Step 5: Analysis of results
In this thesis most calculations were completed in Microsoft© Excel. For the risk analysis @Risk form
Palisade Corporation was used. @Risk is an add-on to Microsoft-Excel, which enables the user to
pursue Monte-Carlo simulation, sensitivity analysis and other methods of quantitative risk analyses.
@RISK provides the tools for setting up probability distributions for uncertain input variables,
simulating and viewing results of a risk analysis (Palisade Corporation, 2013). A broad range of
probability distributions are available in order to best represent reality.
@RISK has been widely applied in risk and decision analysis for various purposes in finance and
engineering.
The baseline deterministic model was built in Microsoft Excel, while uncertainties regarding input
variables were incorporated using @RISK 6.0 (academic version).
3.5 Limitations
This thesis is based on a case study investigation of one LFM projects. As such, there are no
general statements to be drawn for other LFM projects. However, the thesis aims at testing the
proposed evaluation framework and serves as an example for other projects.
14 a detailed descritption of the method can be found online on Palisade’s website:
http://kb.palisade.com/index.php?pg=kb.page&id=138. Technical details are made available under http://kb.palisade.com/index.php?pg=file&from=2&id=168 (accessed 15.1.2015)
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A potential drawback of the pursued analysis is its hypothetical nature and the degree of uncertainty
associated with the data. Assumptions must be made about the future, which may or may not be
appropriate. However, this is not a specific drawback of the analysis pursued in this thesis but a
general feature of project evaluation under uncertainty, especially in the field of resource stock
evaluation.
One of the key limitations in this thesis is the inability to accurately verify certain input data (e.g.
material composition of landfill). This is beyond the scope of this thesis due to a lack of expert
knowledge in certain fields. Furthermore, only a limited set of scenarios (technical set-up and
material treatments) are modelled during the analysis. In relation to the evaluation of external effects,
resources and time constraints limit the extent of the analysis.
As stated earlier, the feasibility of a landfill mining project depends on the economic profitability,
technical and the environmental, social and regulatory feasibility. Whereas the economic profitability
is at the core of the thesis, the technical feasibility is assumed based on previous studies and the
regulatory dimension of the landfill mining project in Hechingen is not dealt within the thesis. Clearly
this marks a shortcoming to a holistic evaluation of the landfill mining project.
Finally, it could be argued that the analysis presented is a static assessment and not adaptable to
changes in the future. However, as the model has been established, it can easily be adjusted to
changing input factors.
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4 Case Study Kreismülldeponie Hechingen The Kreismülldeponie Hechingen is located in Hechingen (Baden-Württenberg) in the south of
Germany. The landfill was opened in 1982 as the central waste deposit in the Zollernalbkreis region.
Overall it serves 25 villages and around 185,000 inhabitants (end of 2013) as waste deposit
(Statistisches Landesamt Baden-Württemberg, 2014)). The Hechingen landfill occupies an area of
about 16 ha. It consists of 3 distinct landfill sections and a waste treatment and recycling facility
(Kreismülldeponie Hechingen, 2014):
Landfill Section A: Was the first landfill section that was used for the
deposit of different waste streams. It was opened in 1982 and closed in
2005. It has a capacity of about 2.3 million cubic meters. The maximum
height of the landfill body is 80 meters.
Landfill Section B: The section of the landfill that currently is used for
disposal. It was opened in 2005 and has a capacity of about 1.2 million
cubic meters. As required by German law since June 2005, this landfill
section receives only previously thermal treated residual waste. Currently
(2015) around 10.000 tons of materials are stored per year.
Landfill Section C: From 1989 to 1995 this landfill section was
exclusively used to deposit excavated soil and demolition waste.
In the course of a pilot study (2008-2014) regarding the landfill mining potential of old landfills in
Germany, the material composition of the Kreismülldeponie Hechingen was explored by Prof. Stefan
Gäth and Dr. Jörg Nispel (University of Gießen). The findings until now have been published in
several articles and project reports (Gäth and Nispel, 2012, 2011; Nispel, 2012). The aim of their
research project was to assess/evaluate
- the amount of materials stored in the landfill,
- the material composition of the landfill mass (theoretical and real resource potential),
- techniques that are used for excavating and processing the landfilled materials,
- the hypothetical landfill after care costs,
- ways to valorize different material streams and
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- costs and revenues of a potential landfill mining project.
As stated earlier, landfill practices generally have radically changed over the last decades and older
landfills therefore are generally more promising for landfill mining projects. Both the exploration study
of Gäth and Nispel as well as my own calculations and estimation therefore focus on landfill section
A of the Hechingen landfill. On the one hand, it represents the majority of materials stored in the
landfill overall. On the other hand, it is the oldest part of the landfill, and therefore promises a higher
fraction of materials that could be recovery and valorized in one way or the other. For the remainder
of this paper I will therefore refer only to landfill section A, when presenting any information regarding
the Hechingen landfill.
Important changes in waste treatment practices in Hechingen
In the face of decreasing free landfill capacities in the Hechingen region, a first waste concept was
developed in 1985 (Gäth and Nispel, 2012, p. 63). Since 1985 the separate collection of certain
waste streams (e.g. paper or plastics) have been introduced region-wide. The advances made in the
waste management, have also lead to a continuous reduction in the amounts of materials that are
deposited every year (Kreismülldeponie Hechingen, 2014). The following list presents major steps in
waste management of the Zollnernalbkreis region (Gäth and Nispel, 2012):
1983 mobile collection of hazardous waste 1985 first waste concept 1987 trial collection of organic waste 1988 Introduction of separate collection of refrigerators and other devices for cooling 1989 region-wide collection of scrap 1989 installation of two regional recycling stations 1990 region-wide collection of organic waste 1991 region-wide collection stations for glass and paper 1991 installation of eight further recycling stations 1992 region-wide introduction of the “Biotonne” (separate collection of organic waste) 1992 introduction of the “Gelber Sack” (separate collection of plastics and packaging) 1995 separate collection of scrap wood 1996 separate collection of electronic devices 1996 monthly collection of TVs and screens 2001 installation of weighing facilities at the landfill 2003 region-wide installation of separate paper collection 2006 thermal treatment of residual waste
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4.1 Results from the landfill exploration
Amount of materials deposited
Gäth and Nispel estimate that 1.7-2.6 million metric tons of waste were landfilled in Hechingen.
These figures are based on data derived from the Hechingen landfill and the scenarios developed by
the regional authorities as well as own calculations of Gäth and Nispel (Gäth and Nispel, 2012, pp.
65–66). To obtain the overall landfill mass waste stream specific disposal scenarios where
developed and later aggregated.
Table 10: Landfill mass in Hechingen – different scenarios (minimum, average and maximum amount of materials) (Source: Gäth and Nispel, 2012, p. 91)
On average it is assumed that about 2,133,641 (FM) metric tons have been deposited in the
Hechingen landfill until 2005. The biggest fraction is municipal solid waste (MSW) with roughly
800,000 tons, followed by industrial waste (IW) with 640,000 tons (average scenario). Together
these two streams account for 67% (w/w) of the overall landfill mass fresh matter (FM). This is
especially important as these two streams are normally regarded as important carriers of materials
for recovery (Hogland et al., 2011).
Overall, ‘Landfill material’ is the major fraction of the Hechingen landfill body. ‘Sludges’ and ‘Cover
material’ only account for 240,000 to 320,000 tons in total.
Exploration approach
Generally, a theoretical and an exploration approach to assess the material composition of landfills
can be described15.
Gäth and Nispel (2012, 2011, 2010) both assessed the theoretical as well as the real resource
potential during their research project and later on compared their findings to evaluate the results.
As I am basing my calculations on the real resource potential scenario, I will only discuss the
theoretical approach and its findings against the background of differences between the two
approaches. 15 compare top-down (theoretical resource potential) and bottom-up approach (real resource potential) described p. 9
Sludges Cover material Total
MSW
IW Bulk
y w
aste
Sand
Con
stru
ctio
n w
aste
Slud
ges
Cov
er m
ater
ial
Tota
l
t (FM) t (FM) t (FM) t (FM) t (FM) t (FM) t (FM) t (FM)Scenario MIN 758,905 589,117 95,583 39,071 238,191 155,733 83,223 1,959,823 Scenario AVG 795,374 640,685 99,717 58,384 261,836 164,339 113,307 2,133,641 Secnario MAX 824,421 696,476 104,005 73,952 285,480 175,087 143,390 2,302,811
Landfill material
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Real resource potential (‘Landfill material’)
In the course of the installation of six landfill gas wells (2009, 0.75 m diameter) and three landfill
groundwater monitoring drillings, samples (2011, 0.4 m diameter) of the landfill body were taken.
These samples were sorted and analyzed, to investigate their material composition as well as their
chemical and physical properties. Whereas the location of the drill holes was not decided upon
because of the landfill body exploration, it was attempted to draw samples from different depths of
the landfill body, representing different times of disposal at the landfill (Gäth and Nispel, 2012).
Depending on the location of the drill hole and the height of the landfilled materials 1-9 samples were
drawn at each location. Overall, materials from 34 locations within the landfill body were excavated.
The landfill materials were first sorted into different size fractions before further analysis. In a first step
materials were only separated into two categories: overs fraction (>35 mm) and a fine fraction (<35
mm). As the fraction <35 mm accounted for such a high percentage of the landfill body (65% (w/w)
of fresh matter (FM); further details see ‘Size composition’), a third size category was introduced. In
the final assessment three size categories were used:
- overs fraction: > 35mm in diameter
- medium-sized fraction: 5-35 mm in diameter
- fine fraction: <5 mm in diameter
After this initial classification representative proportions of each drilling sample were collected for all
further evaluations regarding the material composition and physical (size, water content, glowing
loss) and chemical properties.
Both the overs fraction (> 35 mm) and the medium-sized fraction (5-35 mm) were manually sorted
into 14 different material categories (see list below; for a more detailed description see ANNEX). The
material composition of the fine fraction (<5 mm) was not analyzed further. In order to separate the
fine fraction from the medium-sized, the landfilled materials were washed through a 5 mm sieve and
dried before sorting. As the material composition of the overs fraction was assessed from FM the
composition of the fine fraction was adjusted based on the material-specific water content before
calculating the overall composition of the landfill body.
Metals: ‘Ferrous-metals’, ‘Non-ferrous metals’
Combustibles: ‘Paper and cardboard’, ‘Plastics’, ‘Wood’, ‘Organic waste’, ‘Textiles’, ‘Compound packaging’, ‘Materials not defined other’, ‘Sorting rests’
Inert waste: ‘Glass’, ‘Mineral compounds’
Other: ‘Hazardous waste’, ‘Complex products’
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Size composition
Overall, the fraction <35 mm in the Hechingen landfill body accounts for 65% (w/w) of FM. While
35% (w/w) of the landfill body’s FM is made up by materials >35 mm. These values are also in line
with findings from previous studies (Kaartinen et al., 2013; Krook et al., 2012; Quaghebeur et al.,
2013).
Measured in dry matter term, the fine fraction (< 5mm) on average accounted for 25.5% (w/w), the
medium-sized fraction for 40.5% (w/w) and the overs fraction for 34% (w/w) of the deposited
materials.
Table 11: Size composition – exploration study in Hechingen. Average values for the different drillings (DM) (Source: Gäth and Nispel, 2012, p. 99)
There are only minor differences regarding average and median values with respect to size fractions
from the different drillings (2009 and 2011).
Material composition
The material composition of the landfill body is derived by the combination of results from the
material analysis of the different size fractions and drill holes (<5mm, 5-35 mm and >35 mm).
Besides ‘Sorting rests’ and the ‘Fraction <5 mm’, which account for about 50% (w/w) FM of the
overall landfill body, ‘Plastics’ are the biggest fraction with an average mass concentration of about
17% (w/w) FM. The next biggest fractions are ‘Textiles’ (13% [w/w] FM) and ‘Mineral compounds’
(8% [w/w] FM). ‘Metals’, ‘Glass’ and ‘Wood’ have an average concentration of about 3-4% (w/w) FM.
All other fractions account for less then 1% (w/w) FM.
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Table 12: Material composition – exploration study in Hechingen. In (w/w) % of the overall landfill body (FM) (Source: Gäth and Nispel, 2012, p. 156)
When comparing mass concentrations of materials only taking into account the size fraction >35
mm, it becomes evident that the medium-sized fraction holds a substantial amount of materials to
be recovered during a hypothetical landfill mining project in Hechingen.
The mass concentration of ‘Plastics’ in the overall landfill mass rises from 11% (w/w) FM to 17.2%
when taking into account the material fraction 5-35mm.
Comparison: Theoretical Resource Potential There is great consensus on the results from the theoretical and the real resource potential
with respect to the recyclable fractions of ‘Metals’ (<1% (w/w) DM difference) and ‘Glass’.
For the ‘Plastics’ fraction the theoretical resource potential analysis underestimates the real
resource potential by 45%. In contrast to that, ‘Textiles’, ‘Inert waste’ and ‘Sorting rests’ are
overestimated in the theoretical analysis by 60-70%. There is no straightforward connection
between the results from the two analyses with respect to the ‘Organic waste’, ‘Complex
products’ or ‘Compounds’ fraction. This might be due to pitfalls of the measurement both in
the sampling and the theoretical guided approach, such as poor data quality or limited
representativeness due to the comparable small diameter used for the drilling studies.
Water Content
Generally the water content of the landfill body shows an increasing trend with decreasing material
size. On average, the water content of the fraction >35 mm is 34.8% and 42.3% for materials <35
2.8% 0.6% 0.3%
4.4%
17.2%
0.0%
3.2%
7.7%
12.2%
0.1% 0.2% 0.0% 0.6%
25.0% 25.5%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
Ferro
us M
etal
s
Nonf
erro
us M
etal
s
Pape
r and
Car
dboa
rd
Gla
ss
Plas
tics
Org
anic
Was
te
Woo
d
Text
iles
Iner
t Was
te
Pack
agin
g
Com
plex
Pro
duct
s
Haza
rdou
s W
aste
Mat
eria
ls no
t defi
ned
othe
r
Sorti
ng R
ests
Frac
tion
<5m
m
% (w
/w) F
M o
f th
e o
vera
ll la
nd
fill m
ass
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mm. The material specific water content varies from 58.1% (‘Paper and cardboard’) to 1.4%
(‘Ferrous metals’) and can be explained by the different physical properties. ‘Metals’ and ‘Glass’
have average water contents below 2%, whereas ‘Plastics’ and ‘Inert Waste’ have values around
10%. All other fractions show water contents higher than 40%.
Table 13: Average material specific water content – exploration study in Hechingen. (Source: Gäth and Nispel, 2012, p. 109)
Energetic value
‘Plastics’, ‘Paper and Cardboard’, ‘Wood’, ‘Textiles’, ‘Complex Products’, ‘Materials not defined
other’ as well as ‘Sorting rests’ in general could be suitable for waste-to-energy treatment.
According to German law, the lower calorific value threshold for waste is 11,000 kJ/kg.
Table 14: Calorific values of combustible fration – exploration study in Hechingen. Values in kJ per kg. (Source: Gäth and Nispel, 2012, p. 170)
58.13%
10.63%
56.20% 56.20% 52.35%
40.60%
53.30%
41.90%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00% Pa
per a
nd c
ardb
oard
Plas
tics
and
pack
agin
g
Org
anic
was
te
Woo
d
Text
iles
Mat
eria
ls no
t defi
ned
othe
r
Com
plex
pro
duct
s
Sorti
ng rr
ests
wat
er c
onte
nt in
%
4,509
25,625
6,264 6,264 8,026 7,776 7,776
12,134
-
5,000
10,000
15,000
20,000
25,000
30,000
Pape
r and
car
dboa
rd
Plas
tics
and
pack
agin
g
Org
anic
was
te
Woo
d
Text
iles
Mat
eria
ls no
t defi
ned
othe
r
Com
plex
pro
duct
s
Sorti
ng rr
ests
Calo
rific
valu
es (k
J/kg
FM
)
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#43
Gäth and Nispel (2012) calculate the material specific calorific values according to different formulas
from the literature. Especially ‘Plastics and Packaging’ in particular, as well as ‘Sorting rests’ show a
high calorific potential. Assuming material specific water content, the average calorific value of the
combustible fraction is around 15,400 kJ/kg (for further information see ANNEX). Assuming an
average water content for all material fractions the calorific value is reduced to around 14,700 kJ/kg.
Due to its high water content ‘Wood’ has an initial calorific value of roughly 4,500 kJ/kg and
therefore does not reach the minimum threshold of 11,000 kJ/kg for combustion. Assuming a
reduction of the water content to 15%, all material fractions surpass the lower limit for waste-to-
energy treatments. The mean calorific value of the combustible fraction, assuming a water content
of 15%, is 19,900 kJ/kg.
4.2 Project and scenario description
Based on the available information on the Hechingen landfill and insights from other studies on LFM
projects (Frändegård et al., 2013b; Gäth and Nispel, 2012; Van Passel et al., 2013; Van Vossen and
Prent, 2011; Winterstetter et al., 2015) different scenarios for the landfill mining project in Hechingen
have been developed and assessed.
These scenarios are applied throughout the MFA and the discount cash flow analysis to model all
relevant energy and material flows and associated cash flows of the LFM project in Hechingen.
Material treatment
The different scenarios to recover the buried materials in the Hechingen landfill are primarily based
on the results from the investigation study conducted by Gäth and Nispel (2012, 2011, 2010). As
pointed out earlier, two different routes for exploiting the material potential can be distinguished:
WtM and WtE. The decision on whether certain materials can be recycled or thermally treated
depends on their physical-chemical properties as well as the availability of appropriate technologies
and the existence of a market.
In accordance with the European Union waste hierarchy material recovery in the form of recycling or
material re-use were preferred over other forms of treatment such as energy recovery within the
scenario development (European Union, 2008). Disposal was only a chosen subsidiary option of last
resort:
Waste-to-Material
Stones and other mineral compounds could be used as recycled
construction material. Ferrous metals and non-ferrous metals can be
reused in production, as can recycled glass.
Waste-to-Energy
Based on calorific values as well as the content of chloride and heavy
metals material fractions were assigned to the WtE treatment (see
below). According to Gäth and Nispel (2012, p. 171), the combustible
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fraction in Hechingen meets all requirements for a treatment in
German RDF plants.
Disposal
The fraction <5 mm needs to be re-landfilled as the technical
feasibility of material separation for this fraction is not given or would
include substantial investments. Hazardous materials are assumed to
be disposed at an appropriate facility off-site.
Table 15 provides an overview over the assumptions made for different material treatments:
Table 15: Material categories and treatment – assumptions of the case study (Source: own)
Scenario description
While all scenarios assume the same material treatments, the developed scenarios differ with
respect to other aspects of the LFM project, such as if the thermal treatment takes place on-site or
off-site.
On the one hand, the scenarios can be distinguished based on their assumptions about the
recovery techniques and their efficiencies (potential or realistic scenarios). While on other hand, a
distinction can be made regarding the thermal treatment plant and the subsequent use of the landfill
once the project has been completed. The following table provides an overview of the investigated
scenarios:
Table 16: Scenario description – case study (Source: own)
The potential scenarios are developed to display the full material potential of the Hechingen landfill.
Full efficiencies of sorting and incineration processes were assumed for these scenarios (both for
Material fraction TreatmentFerrous metalsGlassNon-ferrous metalsStones and mineralsOrganic wasteOther matterPaper and CardboardPlastic and packagingSorting restsTextilesWood
Fraction < 5mm Landfillsorting is costly and
technically problematic
Reasons
Was
te-t
o-M
ater
ial
Was
te-t
o-En
ergy
realistic potential for recycling (economic and
technical terms)
recycling not possible (purity of material streams and technical feasibility).
WtE due to calorific values and low concentration of
pollutants
On-site RDF Off-site RDF Reusage of landfill space used forPotential On-site pot Off-site pot Landfill pot MFA
RealisticOn site real
SCENARIO AOff-site real
SCENARIO BLandfill real
SCENARIO C MFA and NPV
Process efficiency
Thermal treatment & subsequent use of landfill
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material and energy flows). For the real scenarios, efficiencies from state-of-the-art waste sorting
and incineration plants were used to model material and energy flows throughout the different
processes of the LFM project (further info see MFA model description). By comparing the results of
the realistic and the potential scenario the material loss due to technological inefficiencies is
displayed.
The cash flow analysis focuses solely on the realistic scenarios (SCENARIO A, B & C), as the
potential scenarios are not founded on real-life applicable technologies. Hence the potential
scenarios were not further investigated regarding their profitability.
Regarding the thermal treatment of the combustible fractions both off-site (SCENARIO B & C) and
an on-site (SCENARIO A) monoincineration plant scenarios are investigated. Despite high investment
costs, the on-site scenario is regarded as an attractive option, given energy sales are assumed to be
a highly important cash flow for the overall profitability of the LFM project. On the contrary, the off-
site scenarios seem to be especially relevant as the German market for RDF is characterized by high
capacities and low prices for RDF disposal (Friege and Fendel, 2011; Gäth and Nispel, 2011).
Subsequent use of the landfill void space is a key aspect to LFM. Once the landfill mining project is
completed the regained land space could be used for a variety of purposes including industry
development, development as recreational area, housing or other purposes. Given the fact that
section B of the Hechingen landfill is still operated and is not likely to be closed in the foreseeable
future, potential scenarios for the subsequent use of the adjacent landfill section A are limited.
For SCENARIO A and B it is assumed, that the regained landfill space is not used for any specific
purpose, nor sold off. For SCENARIO C it is assumed, that the void landfill space is refilled with
waste and charging an appropriate gate-fee. New waste will not be deposited at the Hechingen
landfill before the sixth year of the landfill mining project. From year six on, it is assumed that 10% of
the regained landfill space is used for landfilling per year. Hence, income from gate-fees for newly
landfilled materials is assumed to stop in year fifteen of scenario C.
For all scenarios the project duration was assumed to be 10 years.
4.3 Model of material flows
As described earlier physical flows (energy and material) were modeled according to the MFA
methodology of Brunner and Rechberger (2004) using STAN (Cencic and Rechberger, 2008).
Based on the assumptions relating to the treatments for the recovered materials, necessary
processes for the MFA model were identified:
P1: Excavation, Storage and Sorting (ESS)
Before materials can be recycled or treated in another way the landfill body has to be
excavated, sorted and separated. Excavated and separated materials have to be stored
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accordingly to assure processability (e.g. level of humidity) and preventing contamination or
mixture. The sorting and separation procedure is assumed to be similar to the first stage of a
mechanical-biological waste treatment facility (Laner and Brunner, 2008, p. 31).
P2: Preparation of RDF (RDF PREP)
Before thermally treating the combustible fraction, materials are upgraded to RDF.
Appropriate steps include shredding, drying and pelletization of the combustible materials in
order to derive a more homogenous combustion fuel (Bosmans et al., 2013). For this
process technical efficiencies of a state-of-the art RDF preparation plant are assumed (Laner
and Brunner, 2008, p. 32)
P3: Monoincineration (MI)
Based on the available information regarding the suitability of RDF as fuel and its commercial
prove it is assumed that a monoincineration plant is used for the thermal treatment
Hechingen (Bosmans et al., 2013; Winterstetter et al., 2015).
P4: Landfill (LF) All materials that cannot be sorted out or are part of the fraction <5mm need to be re-
landfilled. As such, part of the Hechingen landfill is reused for re-disposal.
The following table provides an overview of the basic MFA model and all material and energy flows:
Figure 3: Basic model of material flows – case study. Energy and material flows (Source: own, output from STAN)
Landfill mining: How to explore an old landfill’s resource potential
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4.3.a Amount of deposited materials and material composition
The amount of material excavated and processed over the project duration is assumed to be fixed.
This assumption is based on the fact that the amount of excavated materials can directly be
influenced. Furthermore, it is assumed that the Hechingen landfill overall holds 2,133,640 (FM) tons
of materials. For reasons of simplicity the dry matter (DM) content was modeled within the MFA – for
the discounted cash flow analysis fresh matter (FM) values were used.
The overall excavated materials sum up to 146,365 metric tons a year (DM) or 1,463,651 tons (DM)
in total. This is a 31% (w/w) reduction to the fresh matter according to the average water content of
the buried materials. The landfill body is further assumed to consist of three different material types:
‘Landfill materials’, ‘Sludges’ and ‘Cover material’.
Table 17: Landfill mass and fractions (FM and DM) – assumptions case study (Source: based on Gäth and Nispel, 2012; own calculations)
The biggest share of processed materials consists of formerly landfilled materials (1.270.287 tons).
Based on the exploration studies by Gäth and Nispel (2012) the following material composition for
‘Landfill material’ fraction is assumed for the analysis:
Table 18: Material composition landfill body – assumptions case study (Source: based on Gäth and Nispel, 2012)
Total amount (tons) FM
Excavated (tons/year) FM
Total amount (tons) DM*
Excavated (tons/year) DM*
Landfill material 1,855,996 185,600 1,270,287 127,029 Sludges 164,339 16,434 92,522 9,252 Cover material 113,307 11,331 100,842 10,084 Sum 2,133,640 213,364 1,463,651 146,365
*based on an average water content of 32% for 'Landfilled material', 11% for 'Cover material' and 44% for 'Sludges' (own calculations)
Note: no uncertainties assumed
Mean values (w/w) +/- Std. (w/w)Fraction <5mm 25.5% 12.5%
Sorting rests 25.0% 12.4%
Plastics 17.4% 8.2%
Inert waste 12.2% 10.9%
Textiles 7.7% 3.9%
Glass 4.4% 7.4%
Metals** 3.4% 1.4%
Wood 3.3% 1.9%
Other materials 0.8% 1.2%
Paper and cardboard 0.3% 0.5%
Hazardous waste 0.04% 0.1%
Organic waste 0.01% 0.0%
*own calculations based on Gäth and Nispel, 2012
**81% ferrous metals and 19% non-ferrous metals (non-ferrous: 45% are
assumed to be aluminium scrap, 50% copper scrap and 5% other non-ferrous
metals)
Mass fractions*
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Uncertainty ranges for each material fraction were calculated based on the 34 samples drawn
throughout the landfill exploration and their material composition (for a more details see ANNEX).
‘Sludges’ are assumed to consist of 100% of particles smaller then 5mm (i.e. ‘Fraction <5mm’),
while ‘Cover material’ are composed of ‘Mineral and stones’. No uncertainty ranges are assumed for
the latter two types of landfill material.
Each material fraction was modeled as a sub-layer within the MFA.
4.3.b Sorting efficiencies
While for the potential scenarios full efficiencies were assumed, realistic scenarios assume
efficiencies of state-of-the-art technologies.
Technical efficiencies for the process ‘ESS’ in the realistic scenarios where based on figures from
the first stage of a state-of-the-art mechanical-biological waste treatment plant. For ‘RDF
Preparation’ the applied efficiencies are derived from a RDF plant using industry like MSW as input
(Laner and Brunner, 2008, p. 31-32).
Sorting efficiencies range for ‘ESS’ from very high levels (≤0.95) for ‘Glass’, ‘Fines’ or ‘Mineral and
stones’ to moderate levels for ‘Hazardous waste’ (0.3). The exact figures are presented in the
following table:
Table 19: Transfer coefficients for the sorting process – assumptions case study. (Source: Laner and Brunner, 2008, p. 31).
Using ‘Glass’ as example it is assumed that 95% of all encompassed ‘Glass’ in the excavated waste
is correctly sorted and separated out within the process ‘ESS’, while the remaining 5% of ‘Glass’ is
falsely assigned to the ‘Combustibles’ fraction. ‘Textiles’ by contrast are identified in 80% of all
cases as ‘Combustibles’, while 20% cannot be assigned to any other material fraction and therefore
are sorted out as ‘Residuals’.
FI Fines <5mmGLASS GlassHZW Hazardous wasteMetals MetalsMI/ST Mineral and stonesOM Other materialsOW Organic wastePAP Paper and cardboardPLA PACK Plastics and packagingSR Sorting restsTEX TextilesWOO Wood
Transfer Coefficients ESS
based on: fist stage of MBA plant (Laner and Brunner, 2008, p. 31)'Combustibles' consist of: 'Other materials', 'Organic waste', 'Paper and cardboard', 'Plastics and packaging', 'Sorting rests', 'Textiles' and 'Wood'*RES = 'Residuals'
FEM FI GLASS HZW MI/ST NFM RES* Combustibles- 0.95 - - - - - 0.05 - - 0.95 - - - - 0.05
0.40 - - 0.30 - - - 0.30 0.60 - - - - 0.10 0.10 0.20 - - - - 0.98 - - 0.02 - - - - - - 0.20 0.80 - - - - - - 0.20 0.80 - - - - - - 0.20 0.80 - - - - - - 0.14 0.86 - - - - - - 0.20 0.80 - - - - - - 0.20 0.80 - - - - - - 0.09 0.91
based on: fist stage of MBA plant (Laner and Brunner, 2008, p. 31)'Combustibles' consist of: 'Other materials', 'Organic waste', 'Paper and cardboard', 'Plastics and packaging', 'Sorting rests', 'Textiles' and 'Wood'*RES = 'Residuals'
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Sorting efficiencies for the process of ‘RDF Preparation’ vary from 0.5 (‘Hazardous waste’) to 0.95
(‘Metals’).
Table 20: Transfer coefficients for the RDF preparation process – assumptions case study (Source: based on Lander and Brunner, 2008, p. 32).
If parts of material fractions are wrongly identified as ‘Combustibles’ (such as ‘Hazardous waste’ or
‘Fines <5mm’) they are distributed over all combustible material flows proportionally to their weight
(see ANNEX for the complete set of transfer coefficients).
For the process ‘Monoincineration’ it was assumed that to facilitate combustion 4% (w/w) of overall
mass input is needed as bed material. Ash contents were adopted from a study on MSW (Kost,
2001, see ANNEX).
4.3.c Energy layer
Energy inputs for the processes ‘ESS’ (35 kWh/ton) and ‘MI’ (4% of total energy input) were based
on values drawn from literature (Rettenberger, 1995; Winterstetter et al., 2015)16.
For the incineration process a cross-electrical efficiency of 30% for the realistic scenarios and of
46% for the potential scenarios was assumed (Kabelac, 2009; Winterstetter et al., 2015).
The calorific values of the combustible fraction were calculated based on the assumption of an
average water content of 34.5% before the RDF treatment and a reduced water content of 15%
afterwards. The average calorific value therefore increased from 14.7 GJ/t to 19.9 GJ/t during the
RDF preparation (further details see ANNEX). Energy input into the monoincineration process is
assumed to be equal to 4% of total RDF energy input (Stubenvoll et al., 2002).
4.4 Case specific investment model
In order to calculate the NPV for the different scenarios values, associated costs and revenues were
drawn from a range of articles from the LFM literature (i.a. Gäth and Nispel, 2010; Kost, 2001;
16 Similar values were also presented in other publications such as Brunner et al. (2001): 23 kWh/t for ‘ESS’
HZW Metals RES* CombustiblesFI 0 0 0.7 0.3
GLASS 0 0 0.8 0.2
HZW 0.5 0 0 0.5
Metals 0 0.95 0.05 0
MI/ST 0 0 0.9 0.1
OM 0 0 0.05 0.95
OW 0 0 0.5 0.5
PAP 0 0 0.05 0.95
PLA PACK 0 0 0.05 0.95
SR 0 0 0.05 0.95
TEX 0 0 0.05 0.95
WOO 0 0 0 1
GlassHazardous waste
MetalsMineral and stones
Woodbased on RDF Preparation: RDF preparation plant (Laner and Brunner, 2008,
p. 32)
*RES = 'Residuals'
Other MaterialsOrganic Waste
Sorting restsTextiles
Plastics and packagingPaper and cardboad
Transfer coefficients RDFFines <5mm
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Rettenberger, 1995; Van Passel et al., 2013; Van Vossen and Prent, 2011; Winterstetter et al.,
2015) and literature on market prices for recyclables (EUWID, 2014, letsrecylce.com, 2014). The
applied costs and revenues varied depending on the analyzed scenario. As pointed out earlier only
realistic scenarios were investigated, in particular three distinct set-ups (SCENARIO A, B & C).
Costs include project preparations, excavation and storage, capital expenses (CAPEX) and
operational expenses (OPEX) for the processes ‘ESS’, ‘Monoincineration’ as well as transportation
costs and gate-fees in case of RDF and hazardous waste disposal.
Revenues are generated by the recycling of materials (‘Glass’, ‘Mineral and stones’ and ‘Metals’),
energy sales from the on-site monoincineration plant and the potential subsequent use of the
regained landfill space.
Energy costs of the excavation and storage of materials are assumed to be covered within
operational expenses. The net-electricity produced from RDF is therefore only calculated from the
total sum energy recovered during the thermal treatment and the energy used for initiating,
controlling and support the combustion process (c.f. Figure 6: ‘Electricity to Grid’ and ‘Energy Input
ind.’ into P3).
It is further assumed that the landfill operator has made a financial provision the landfill aftercare,
which is fully released upon the beginning of the LFM project. Based on the different scenarios by
Gäth and Nispel (p. 187) an amount of EUR 32 millions for the aftercare costs in Hechingen over the
period of 50 years was assumed for the calculations.
For the reuse of the landfill space 20 percent of the obtained gate fee was assumed to be profit,
while the rest is consumed up by operational costs of the landfill and provisions for aftercare
expenses.
Table 21: Breakdown of costs and benefits for the investment model – assumptions case study (Source: own)
Monte-Carlo simulations assessing the NPV for each scenario were applied. For this purpose,
simulations were run using @Risk and Latin Hypercube sampling. @Risk drew a random number for
Costs Revenues
Process Cost Scenario/s Process
Project Preparation TOTAL Project Preparation A,B,C Recycling of Materials
Excavation & Storage OPEX Excavation and Storage A,B,C
Sorting and Separation CAPEX Machinery and Construction A,B,COPEX Sorting and seperation of different fractions A,B,C Energy Sales
Monoincineration CAPEX Monoincineration plant A Avoided Aftercare CostsOPEX Maintenance and operational expenses MI A
RDF Transport B,C Subsequent use of landfill spaceRDF Gate Fee B,C
Landfill OPEX on-site disposal of MSW A,B,C
Disposal of Materials OPEX Disposal of hazardous waste A,B,C
Revenue Scenario/s
Income from Inert Material A,B,CMineral and Stones A,B,CGlass A,B,CIncome from Metal Sales A,B,C
Net-electricity produced from RDF A
Avoided costs from landfill aftercare A,B,C
Income from gate-fees C
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each input variable into the NPV calculation, according to the defined probability distributions. Each
simulation consisted of 10,000 iterations.
Uncertainty ranges and probability distributions were taken from the literature when available or
based on reasonable estimation. For a complete overview of all assumptions regarding the
investment model see ANNEX XY.
Furthermore, sensitivity analyses was used to assess the sensitivity of the output variables (NPVs)
with respect to the modeled inputs (costs and mass flows).
A competitive discount rate of 15% was assumed for the calculations, based on reference projects
and earlier studies (Van Passel et al., 2013).
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5 Results
5.1 Material Flow Analysis
Figure 4 presents the MFA models displaying the material streams (layer ‘Goods’) for the potential
and realistic on-site RDF scenarios:
Figure 4: Material flows for on-site scenarios – results MFA. Figure 4 illustrates material flows for the realistic and potential on-site RDF scenarios (DM). The displayed layer is ‚Goods’. Flows
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are shown in metric tons per year. Uncertainty ranges are shown in standard deviations (% of material flow) (Source: own, STAN output)
As the scenarios, besides the assumptions regarding the process efficiencies (realistic and potential),
only differ with respect to the location of the thermal treatment plant and the subsequent use of the
regained landfill space, the recoverable amount of secondary raw materials (recyclables plus RDF
materials) is the same for all potential as well as for all realistic scenarios.
5.1.a Material flows
While for both scenario groups the input17 of waste per year is equally high at 213 kt FM (146 kt DM),
the sum of extractable secondary raw materials (recyclables plus RDF materials) is smaller for the
realistic scenarios, while a bigger amount of materials needs to be re-landfilled.
Figure 5: Extractable secondary resources from the landfill body – results MFA. Comparison of different scenarios (potential and realistic) for different material fractions (Source: own, based on results from MFA)
Recyclables (‘Metals’, ‘Mineral and Stones’ and ‘Glass’) in the potential scenario account for 49 ± 22
kt (FM), while in the realistic scenario 47 ± 22 kt (FM) of materials can be recovered for recycling.
The small absolute difference regarding the amount of recyclables between the realistic and the
potential scenario is mainly driven by the ‘Mineral and stones’ fraction. While sorting efficiencies for
this material fraction are very high. On the other hand ‘Material and stones’ also account for 72%
(w/w) (FM) of all recyclables buried in the landfill.
The material potential regarding the ‘Metals’ fraction in Hechingen is 5.7 ± 1.7 kt (FM) per year
(realistic scenario). This is about 89% (w/w) (FM) compared to the potential scenario.
The annual potential for the recycling of ‘Glass’ is 7.7 ± 12.4 kt (FM) assuming realistic sorting
efficiencies, while the Hechingen landfill holds a potential of 8.1 ± 13.1 kt (FM, potential scenario).
17 Waste inputs are ‚Landfill materials’, ‚Cover material’ and ‚Sludges’ (see Figure 4)
89 127
105
149 146
213
-
50
100
150
200
Dry matter Fresh matter
Secondary raw materials and overall waste input (scenario comparison) flows are given 1000 metric tons per year
Realisitc scenario Potential scenario Material Input
47
81 86
49
101
64
-
20
40
60
80
100
Recyclables RDF materials Landfilled Materials
Material potential for certain fractions (scenario comparison fresh matter) flows are given 1000 metric tons per year
Realistic Scenario Potential Scenario
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Overall, 101 ± 26 kt per year (FM) of RDF material could potentially be recovered from the
Hechingen landfill. For the realistic scenarios the total amount of RDF materials is 81 ± 20 kt (FM)
per year or about 80% (w/w) of the potential.
The annual material flows of recyclables and RDF materials describe the total secondary raw
material potential of the Hechingen landfill.
Assuming realistic sorting efficiencies extractable secondary raw materials amount to 127 ± 29 kt
(FM) per year. This means that about 60 ± 14% of all materials present in the Hechingen landfill can
be extracted and used under current circumstances. This results mainly from the fact that the fine
fraction accounts for almost 25% (FM) and that about 10% of materials (FM) are sorted out as
residual waste during the excavation and sorting processes because of technological inefficiencies.
While around 60% of all materials from the Hechingen landfill can be recovered as secondary raw
materials (realistic scenarios), about 40 ± 9% need to be either disposed off off-site (‘Hazardous
waste’) or re-landfilled on-site.
The assumed technological efficiencies in the realistic scenarios translate into increased material
flows that are re-deposited (c.f. Figure 4 ‘Fines ESS’, ‘Residues ESS’ and ‘Residuals from RDF
Preparation’). While landfilled materials per year amount to 86 kt ± 19 kt FM assuming realistic
conditions, only 64 kt ± 20 kt (FM) need to be landfilled a year in the potential scenario.
The following table presents a detailed overview regarding the different scenario groups and the
modeled material flows:
Table 22: Breakdown of material flows for the LFM project – results MFA (Source: own, based on results from MFA)
per year +/- per year +/- per year +/- per year +/- 127,349 29,497 89,418 20,188 149,495 34,094 104,647 23,335 FM#Real
Recyclables 46,745 21,679 34,275 14,838 48,503 22,399 35,525 15,330 62RDF materials 80,605 20,002 55,143 13,690 100,992 25,704 69,122 17,592
Landfilled materials 85,985 19,229 56,926 13,161 63,801 19,777 41,671 13,536 Hazardous waste 31 64 21 44 69 191 47 131
213,365 45,933 146,365 31,438 213,365 52,114 146,365 35,668
Recyclables 46,745 21,679 34,275 14,838 48,503 22,399 35,525 15,330 Mineral and Stones 33,329 17,666 25,094 12,091 34,009 18,026 25,606 12,337 Recycled Glass 7,737 12,449 5,295 8,521 8,144 13,105 5,574 8,969 Metals 5,678 1,713 3,886 1,173 6,349 2,245 4,346 1,536
RDF materials 80,605 20,002 55,143 13,690 100,992 25,704 69,122 17,592 Other materials 1,196 1,670 819 1,143 1,552 2,198 1,062 1,504 Organic waste 7 28 5 19 17 69 11 47 Paper and cardboard 452 657 309 450 586 864 401 592 Plastic and packaing 26,634 11,608 18,221 7,945 32,164 14,242 22,014 9,748 Sorting rests 35,786 14,912 24,481 10,206 46,411 19,710 31,765 13,490 Textiles 10,964 5,474 7,500 3,747 14,219 7,211 9,732 4,935 Wood 5,566 3,124 3,808 2,138 6,043 3,434 4,136 2,350
*Flows are in metric tonsSource: own calculations
Realistic Scenarios Potential Scenarios
Secondary raw materials
Sum
FM DM FM DM
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As it is assumed that energy costs, except for the monoincineration (P3), are included in operational
expenses, the energy flows from the MFA model are only relevant for the economic assessment of
the on-site scenarios.
5.1.a Energy flows
Figure 6: Energy flows of the incineration process, realistic scenario– case study MFA model (Source: own, STAN output)
The net electricity that is produced from the combustion of RDF materials equals the balance of the
energy flows ‘Electricity to Grid’ and ‘Energy Input ind.’ of P3 in the MFA model (c.f. Figure 6, see
Annex for a complete model of energy flows). Depending on which water content (i.a. FM or DM) is
modeled and whether the realistic or the potential scenario is investigated the produced amount of
electricity varies by a factor over three. On the one side potential scenarios assume perfect sorting
efficiencies resulting in increased material streams for the ‘Preparation RDF’ and ‘Monoincineration’.
However, the efficiency of the combustion process itself is assumed to be about 13% higher than in
the realistic scenarios.
While the mass flows vary according to the assumption of the water content, calorific values are
fixed for a water content of 15% (after ‘Preparation RDF’).
For the realistic scenarios the net electricity from monoincineration is 70,000 ± 21,000 MWh (DM)
per year. Assuming an average annual energy consumption of 4,187 kWh per household (Statisik
Austria, 2015) this equals the energy demand of more than 16,000 households.
When modeling the FM content (realistic scenario), the produced energy amounts to 102,000 ±
31,000 MWh per year, equaling the energy consumption of over 24,000 households.
However, it is far more likely that the real energy production might be in between the DM and the FM
scenario. This is due to the fact, that during the process ‘Preparation RDF (P2)’ the RDF materials
are dried, however the water content is not further reduced than to 15 percent before combustion.
Assuming material flows with an average water content of 15 percent the net-electricity produced
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from RDF combustion amounts to 87,000 ± 27,000 MWh per year, supplying over 20,000
households in the area with energy (in a realistic scenario)
The following table provides a detailed overview of the energy production for different assumptions
and scenarios:
Table 23: Net-electricity production from RDF – results MFA (Source: own, based on results from the MFA and Statisik Austria, 2015)
5.2 NPV calculation results
As detailed in the previous section, the three realistic scenarios (A, B and C) were used as the
baseline for the NPV simulations. Table 12 contains statistics on each NPV simulation, while Figure
10 gives an overview of the cost and income components for the average scenarios. Table 14
provides the results from the sensitivity analysis.
Results from the Monte-Carlo simulations show that mining the Hechingen landfill is unprofitable for
all investigated scenarios, however to differing extents. The NPVs for all investigated scenarios were
negative at a significance level of 0.05. While SCENARIO A has the highest negative NPV on average,
SCENARIO C shows the least negative NPVs. Net costs ranged from on average EUR 26.4 million
to EUR 49.5 million.
It seems that the benefits from the sale of the on-site produced electricity (SCENARIO A), under the
assumed circumstances, do not offset the investment and running costs associated with the
installation of an on-site RDF plant. Hence, the off-site treatment of the RDF fraction is less costly
than the on-site combustion.
The least negative NPV is found for SCENARIO C, which only differs from SCENARIO B in the reuse
of the landfill space charging an appropriate gate fee. Hence, an additional income stream is
generated in SCENARIO C.
Water content Scenario
DM realistic
DM potential
FM realistic
FM potential
15 percent realistic
Energy flows are given in MWhHousehold energy consumption is assumed to be 4,187 KWh/yearSources: Statistik Austria, 2015 and own calculations
Energy/year +/- Number of households
70,070 21,374 16,735 148,636 40,831 35,499 102,425 31,230 24,462 233,876 59,541 55,857
86,960 26,534 20,769
Energy flows are given in MWhHousehold energy consumption is assumed to be 4,187 KWh/yearSources: Statistik Austria, 2015 and own calculations
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Table 24: Expected net present value – results case study. NPVs for the LFM project in Hechingen – scenario comparison (Source: own, based on results from Monte-Carlo simulations in @Risk)
SCENARIO A: In SCENARIO A, discounted net costs on average amount to EUR 49.5 million.
Maximum costs were not higher than EUR 69.8 million and minimum costs not lower than EUR 29.7
million, assuming a five percent chance of error. The standard deviation was about EUR 10.4 million,
while the chances of breaking even were zero. More detailed information on the probability
distribution for the NPV is presented in Figure 7.
Figure 7: Distribution for NPV (SCENARIO A) – results case study. Values in million EUR. Number of iterations: 10,000 (Source: own, based on results from Monte-Carlo simulations in @Risk)
The 90 percentile value for the NPV in SCENARIO A is EUR -36.0 million. Accordingly, 90 percent of
all observed values from the simulation are below this threshold.
SCENARIO B: The average NPV for SCENARIO B is EUR -30.2 million. Hence, the LFM project in
SCENARIO B is on average EUR 19.2 million less costly than SCENARIO A. The standard deviation
is slightly lower at about EUR 10.4 million. This results in a five percent confidence interval with a
lower threshold of EUR -51.3 million and an upper threshold of EUR -10.2 million.
Statistics SCENARIO A SCENARIO B SCENARIO CMinimum (NPV) -€ 96,200,000 -€ 75,400,000 -€ 70,900,000Maximum (NPV) -€ 6,800,000 € 10,800,000 € 14,400,000Average (NPV) -€ 49,500,000 -€ 30,200,000 -€ 26,400,000Std. dev. € 10,400,000 € 10,400,000 € 10,300,000Probability of breaking even 0.0% 0.1% 0.4%Number of iterations 10,000 10,000 10,000
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Figure 8: Distribution of NPVs (Scenario B) – results case study. Values in million EUR. Number of iterations: 10,000 (Source: own, based on results from Monte-Carlo simulations in @Risk)
SCENARIO C: The average net costs in SCENARIO C amount to EUR 26.4 million. It is the scenario
associated with the lowest net costs: EUR 23.0 million less than for SCENARIO A and EUR 3.8
million less than for SCENARIO B. The standard deviation is almost at the same level as in the other
scenarios. The minimum NPV from the simulations is EUR -70.9 million, the maximum is EUR 14.4
million. 95 percent of all observed NPVs are between EUR -47.4 million and EUR -6.5 million (see
Fig. 10).
Figure 9: Distribution of NPVs (Scenario C) – results case study. Values in million EUR. Number of iterations: 10.000 (Source: own, based on results from Monte-Carlo simulations in @Risk)
5.2.a Costs and revenues
The main cost components of the assumed LFM project are related to the separation and sorting of
the landfilled materials (SCENARIO A, B and C) and the treatment of RDF. The costs from the
disposal of hazardous waste are in general negligible. Excavation and storage of the landfilled
materials account for 4.6-7.0% of overall costs (details see Table 25).
On the income side, avoided landfill aftercare costs are the main source of revenues for all scenarios.
Their fraction of total income varies between 49.6% (SCENARIO A) to 69.4% (SCENARIO B) on
average. Income from the sale of metals is the second most important income source, except for
SCENARIO A in which generated incomes from energy sales are higher.
Both incomes and costs are the highest for SCENARIO A.
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Figure 10 provides an overview of the costs and incomes for the average scenarios.
Figure 10: Cash in- and outflows (discounted) for SCNEARIO A, B and C – results case study. Average values in million EUR. Uncertainty levels are indicated for NPVs (two standard deviations) (Source: own, based on results from Monte-Carlo simulations in @Risk)
Costs associated with the on-site incineration plant (both capital and operation expenses) amount to
EUR 64 million or 55% of the overall discounted costs in SCENARIO A. Incineration costs are largely
driven by capital expenses due to the construction of the on-site treatment facility (EUR 52 million).
As these costs are payable at the start of the project they enter the NPV calculation undiscounted,
unlike revenues from the incineration that are generated throughout the lifetime of the project.
Costs for separating and sorting the landfilled materials account for 39.5% of total discounted
expenses (EUR 46 million) in SCENARIO A. The remaining costs are due to project preparation
expenses (0.9% of total costs), expenses due to the excavation and storage of landfilled materials
(4.6%), as well as fees for the disposal of hazardous waste (0.01%).
SCENARIO B and C do not differ with respect to their cost structure. Separation and sorting
accounts in both scenarios for 59.4% of total costs. The disposal of RDF materials off-site is the
second largest cost component with EUR 25 million (32.4% of total cost) in the two off-site
scenarios.
-49.50 -30.25 -26.40
-120.00
-100.00
-80.00
-60.00
-40.00
-20.00
0.00
20.00
40.00
60.00
SCENARIO A SCENARIO B SCENARIO C
Scenario comparison - costs and revenues (discounted cash flows) values EUR million
NPV
Costs incineration RDF
Costs disposal of hazardous waste Costs disposal of RDF
Costs excavation and storage
Costs separation and sorting
Costs project preparation
Income from subsequent use landfill space Income from energy sales
Avoided aftercare costs
Income from inert materials
Income from metals
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Comparing the average outcomes of the Monte-Carlo simulations, it is about EUR 39 million less
expensive to dispose the RDF-fraction off-site than using an on-site facility. However, this is not
taking into account revenues generated form the sales of energy. One key reason for the differences
in costs is the low level of disposal costs for RDF. Gate-fees for RDF materials are assumed to be
between EUR 45 to 55 per ton in Germany.
As already indicated, the major fraction of income in all scenarios are avoided aftercare costs (on
average EUR 32.8 million). Further, income from metal sales (EUR 13.5 million), energy sales
(SCENARIO A: EUR 19.7 million) and revenues generated from the use of regained landfill space
(SCNEARIO C: EUR 13.5 million) are important sources of income. Cash flows form the recycling of
inert waste only account for 1-2% of total income on average.
Discounted incomes from metals are generated from the non-ferrous fraction (57% of total income).
The price of non-ferrous metals impacts both on the non-ferrous metal fraction that is directly
recovered during the primary sorting and separation, as well as on the metal fraction that is
recovered during the RDF preparation, which also includes non-ferrous metals.
Energy sales generate about 29.4% of total income in SCENARIO A or about EUR 4.3 million
annually over the project duration. Total discounted revenues in SCENARIO A (EUR 67 million) are
about one third higher than for SCENARIO C (EUR 51 million).
Discounted incomes from the subsequent use the restored landfill capacity (SCENARIO C) amount
to EUR 3.8 million. As revenues from the re-usage of freed landfill capacity are not generated before
the sixth year of the project, the impact on the NPV is limited, given the discounting effect. The
undiscounted income from selling the freed landfill space amounts to almost EUR 16 million on
average.
Table 25: Breakdown of costs and income – results case study. Average values in percentage of total cost/income - scenario comparison (Source: own, based on results from Monte-Carlo simulations in @Risk)
Income stream SCENARIO A SCENARIO B SCENARIO CIncome from metals 20.1% 28.5% 26.4%Income from inert materials 1.4% 2.0% 1.9%Avoided aftercare costs 49.0% 69.4% 64.2%Income from energy sales 29.4% 0.0% 0.0%Income from subsequent use landfill space 0.0% 0.0% 7.5%
Sum 100.0% 100.0% 100.0%Cost stream SCENARIO A SCENARIO B SCENARIO CCosts project preparation 0.9% 1.3% 1.3%Costs separation and sorting 39.5% 59.4% 59.4%Costs excavation and storage 4.6% 6.9% 6.9%Costs disposal of RDF 0.0% 32.4% 32.4%Costs disposal of hazardous waste 0.0% 0.0% 0.0%Costs incineration RDF 55.0% 0.0% 0.0%
Sum 100.0% 100.0% 100.0%
Income and cost composition (undiscounted cash flows - fraction of total income/cost)
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5.2.b Sensitivity analysis
Sensitivity analysis for all investigated investment scenarios can be viewed on Table 26. Interactions
between cost and income variables were not taken into account during the analysis.
For all scenarios, the amount of RDF materials showed the highest coefficient. However, while in
SCENARIO A the amount of RDF materials was positively related to NPV, in SCENARIO B and C
RDF materials was found to be the variable having the most negative impact on the NPV. Logically,
for SCENARIO A, income from energy sales is positively linked to the amount of available RDF. For
SCENARIO B and C, increasing amounts of RDF only lead to increased disposal costs, whilst there
are no incomes generated from this material stream.
The analyzed investment scenarios proved equally sensitive to avoided aftercare costs. For all
scenarios this factor showed the second highest sensitivity coefficient. Increasing incomes from
avoided aftercare costs are positively related to the overall NPV outcome. This can be explained by
both (1) the level of the cash flow and (2) the timing of the cash flow (t=0).
Furthermore, the amount of metals, especially of the non-ferrous fraction, was found to be an
important factor positively impacting on the NPV for all scenarios.
For the on-site scenario (A), the energy price (+) and the capital expenses for the incineration plant (-)
were found to be having a strong impact on the NPV.
The interest rate in all scenarios was identified as a factor positively influencing the NPV (+). This
suggests that cash flow balances18 for the investigated scenarios were mostly negative. Only in
SCENARIO C did cash inflows outweigh cash outflows during the last five years of the project. For
this period, gate fees are charged for incoming waste materials, but mining activities are already
completed – hence there are no more expenses.
The fact that the discount rate has less effect on the NPV in SCENARIO A compared to the other
scenarios reveals the differing cash flow structures.
Table 26: Results from the sensitivity analysis – results case study. Scenario comparison (Source: own, based on results from Monte-Carlo simulations in @Risk)
18 cash-inflows minus cash-outflows in one year
Model variable # # overall coefficient* # # overallMaterials for RDF** + 1 1 0.58 - 1 1Avoided costs from landfill aftercare + 2 2 0.38 + 1 2Non-ferrous metals ESS (t/a) + 3 4 0.27 + 2 4Energy Price + 4 8 0.17 Mineral and Stones ESS (t/a) - 1 3 0.30 - - 2 3CAPEX ESS - 2 5 0.27 - - 3 5CAPEX MI - 3 6 0.25 - Plastics ESS (t/a) - 4 7 0.24 - - 4 6Discount rate + 7 19 0.05 + 3 7Metals from RDF Prep (t/a) + 5 10 0.15 + 4 10
*regression coefficient (normalized in std)** MWh/a for SCENARIO A and t/a for SCENARIO B and C
SCENARIO A
not relevant
not relevant
SCENARIO Bcoefficient* # # overall
0.60 - - 1 10.38 + 1 20.27 + 2 4
0.30 - - 2 30.27 - - 3 5
0.24 - - 4 60.23 + 3 80.15 + 4 10
not relevant not relevant
not relevant not relevant
SCENARIO B SCENARIO Ccoefficient*
0.60 - 0.39 0.27
0.30 - 0.27 -
0.24 - 0.19 0.15
not relevant
not relevant
SCENARIO C
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5.2.c Net costs per ton of excavated and processed waste
To further investigate the results from the NPV calculations and assess the effect of the cost-
structure, unit costs were calculated both from undiscounted and discounted cash flows.
Costs were divided by the amount of excavated and processed waste during the LFM project.
Costs were calculated for the average scenario (in this case the 50th percentile), the 10th and the 90th
percentile of the simulated scenarios.
Net costs from discounted cash flows present the results from the NPV calculations in a different
form. Accordingly, SCENARIO C is associated with the lowest net costs. Costs in SCENARIO A are
significantly higher than for SCENARIO C (almost two times higher).
However, if net costs per ton of waste are calculated from undiscounted cash flows, the difference
in costs between SCENARIO A and SCENARIO C is marginal.
Table 27: Calculated net costs – results case study (in EUR per ton of treated waste). Scenario comparison; P10 refers to the 10th percentile of all results from the Monte-Carlo simulations. Respectively P50 stands for the 50th and P90 for the 90th percentile value (Source: own, based on results from Monte-Carlo simulations in @Risk)
5.2.d Break-even analysis
Since NPVs for all investigated scenarios were negative a break-even analysis was conducted,
aiming to investigate necessary changes in key variables of the LFM so that the project could
become cost neutral. As material streams are limited by the resource potential of the Hechingen
landfill, critical values for breaking even (NPV = 0) were only identified for monetary variables. Results
are summarized in Table 28. In general, it can be stated that critical values identified during the
break-even analysis were significantly different than the underlying assumptions. This indicates that
small changes in the underlying cost or price assumptions are not likely to affect overall profitability
of the investigated scenarios.
For the break-even analysis the @Risk ‘goal value function’ was used. Unlike other similar
spreadsheet functions, the results are based on a series of simulations taking into account the
uncertainty regarding the input factors of the investment model, except for the variable under
investigation. The NPV was set to EUR 0 for identifying the corresponding value for the:
− avoided aftercare costs,
− price for electricity,
SCENARIO A SCENARIO B SCENARIO C SCENARIO A SCENARIO B SCENARIO CP10 -€ 29 -€ 20 -€19 P10 -€ 38 -€ 45 -€38P50 -€ 23 -€ 14 -€12 P50 -€ 27 -€ 33 -€26P90 -€ 17 -€ 8 -€6 P90 -€ 16 -€ 23 -€15
undiscounted cash flowsdiscounted cash flowsNet costs of LFM per ton of treated waste (in EUR)
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− price of non-ferrous metals,
− RDF gate fee, and
− charged landfill gate-fee in case of subsequent use of the landfill space.
Table 28: Results from the break-even analysis – results case study. Displayed values in EUR. Underlying assumption: Mean value for NPV is equal to zero for a set of simulations, given the change of the variable under investigation. (Source: own, based on results from Monte-Carlo simulations in @Risk)
Depending on which scenario is investigated, avoided aftercare costs would need to rise by almost
251% percent (SCENARIO A) compared to the assumed value, for the NPV to be equal to zero. For
SCENARIO C the critical value is about 181% percent of the initial assumption. As landfill aftercare
costs were calculated based on an aftercare period of 50 years, critical thresholds can also be
expressed in years of aftercare. An increase in aftercare obligations of 181% translates into 90.5
years of aftercare. However, this is not taking into account potential discounting effects.
The price of the non-ferrous fraction of the landfill would need to increase about six-fold for
SCENARIO A and almost four-fold for SCENARIO C.
The energy price, which was also identified as a major driver of profitability during the sensitivity
analysis would almost need to quadruple (rise by a factor of 3.5) in order for SCENARIO A to be cost
neutral.
In SCENARIO B and C the relevant gate fee for the disposal of the RDF fraction would need to be
negative for the LFM project to break even. However, the identified price level of RDF for SCENARIO
B was so high that it was excluded from further analysis. In SCENARIO C the gate fee for RDF
would need to decrease to -26.63 EUR/ton. This means that RDF plant operators would need to
pay almost 27 EUR for each ton of received RDF.
Also, for SCENARIO C the effect of changes in the income from the subsequent use of the landfill
space were investigated. However, the gate fee for landfill would need to rise to 471 EUR/ton or
increase eight-fold compared to the assumed level.
SCENARIO B SCENARIO C ASSUMED VALUES*Avoided aftercare costs € 82,281,987 € 63,050,791 € 59,277,927 € 32,820,097Energy price € 158 n.a. n.a. € 45Price of non-ferrous metals € 18,599 € 12,259 € 11,335 € 3,047RDF gate fee n.a. ** -€ 27 € 50Gate fee landfill n.a. n.a. € 471 € 60
*average values based on the defined distributions and the results from the Monte-Carlo simulations
**not taken into account as for the extreme value
SCENARIO A
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6 Non-monetary modifying factors and classification While a thorough financial assessment from a private investor’s perspective is a necessary
prerequisite for discussing the feasibility of LFM, it neglects important costs and benefits from such a
project for the broader community. Positive and negative societal effects of LFM are multifaceted
and can often be related back to external costs and benefits initially produced by landfills. Before
proceeding with the synthesis of the obtained results from the business analysis, the following
section discusses the potential costs and benefits of LFM projects from a societal perspective and
ways in which to integrate them into the monetary assessment. The findings will be related to the
specific situation in Hechingen, providing an outlook on how external effects could potentially be
integrated into a more holistic evaluation of the project.
Societal costs with respect to landfills or LFM projects can be clustered into three different groups:
- externalities caused by emissions to air,
- externalities caused by emissions to water and soil, and
- externalities caused by disamenities.
Landfills are often considered to consume a lot of space and pose a threat to water, soil and air, as
well as to human health and ecosystems (European Commission, 2000; Frändegård et al., 2013b).
Once a landfill would be mined and materials recovered, these risk would be permanently eliminated
and unpredictable costs such as long-term groundwater remediation could be avoided (Marella and
Raga, 2014). Furthermore, the regained land space could be restored and used for recreational
purposes, hence creating some value for the community.
Societal costs of landfills can take the form of reduced real estate price in proximity to a landfill
(Eshet, 2005). A LFM project could partially erase this effect and hence correct for this cost.
On the other hand, negative effects may also arise from LFM, which are not normally accounted for
in business analysis. These negative effects may take the form of disamenities for the local
neighbourhood, such as noise, malodor, increased traffic or the presence of pests through the
process of excavating and processing waste materials (Walton et al., 2006).
Substitution effects for primary natural resources may also be created, leading to reduced import
dependency from international trade partners (Van Passel et al., 2013). While independence with
respect to raw materials may be of political value, it is currently not an issue in the globalized
economy. Hence, this aspect is difficult to quantify in monetary terms and therefore not investigated
further.
All the above described effects frequently occur during LFM projects, however they are not regularly
considered in standard CBA. Within the economic literature such disregarded effects are referred to
as externalities. Externalities are uncompensated effects and can be defined as “costs and benefits
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that arise when the social or economic activities of one group of actors (people/firms) affect another
group of actors and the effects are outside (‘external’) the pricing system (Eshet, 2005, p. 488)”. In
the context of the LFM project, this means that costs (i.e. such as noise) or benefits (reduced
emissions) are not born by the perpetrator but the general public – hence limiting the informational
value of standard CBA. External effects from LFM activities may be local (e.g. malodor or soil
pollution), while others are relevant from a global perspective (GHG emissions), adding further
complexity to the issue. It is likely that local short-term negative effects may be created, while from a
societal perspective the benefits are dominating. This might set the stage for local resistance to a
LFM project, which can be framed in the concept of not-in-my-backyard (NIMBY) opposition
(Feldman and Turner, 2010).
6.1 Monetization of external effects
Ideally, the decision whether a LFM project is pursued or not should be based on a systematic
assessment of all relevant costs, i.e. a cost-benefit analysis that also includes the societal dimension
(Dijkgraaf and Vollebergh, 2004). However, in order to do so one must find a way to incorporate
such external effects into the evaluation – with respect to CBA this means monetizing the external
effects.
Discussing methodologies and ways to assess the value of externalities that occur with respect to
LFM activities, is intertwined with the debate about the valuation of ecosystem services (such as
clean air and water). The modern history of ecosystem evaluation originates in the 1970s, mainly
motivated as a means to promote biodiversity conservation, while the focus shifted on methods to
assess the monetary value in the 1990s (Gómez-Baggethun et al., 2010).
Lately, the discussion on pricing ecosystem services is mainly focused around market-based
instruments (MBIs). These instruments, ranging from taxes, tradable permits or other charges, are
increasingly used to influence economic decision-making. The European Union Emission Trading
Scheme (EU ETS) is one prominent example. MBIs are promoted for the reason that environmental
problems are regarded as consequence of the absence of a market for the environmental good
(Neuteleers and Engelen, 2014).
All varieties of MBIs have the objective to assign a monetary price to the environmental goods in
common, thus making it explicit and comparable to what extent a good is preferred over another
(Pirard, 2012). Pirard further points to the fact that this does not mean that the associated price
must reflect the actual benefits or costs from the environmental service, as it can be assessed in
terms of production or opportunity cost. More specifically, MBIs simply assign some monetary value
to nature, mostly for market or any other form of exchange (Pirard, 2012).
Several different techniques to assess the monetary value of non-marketable goods or external
effects have been developed. A distinction between different methodologies can be drawn with
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respect to the source of information. Either monetary valuations are based on information from
revealed preferences (by observation) or stated preferences of economic actors (declared during
questioning) (Neuteleers and Engelen, 2014). Furthermore, cost-based approaches often drawing
from expert knowledge do exist.
6.1.a Monetary valuation methods
The following section provides an overview over the most used methods (Ayalon et al., 2006; Farber
et al., 2002; Liu et al., 2010; Marella and Raga, 2014):
Revealed-preference approaches
Market prices: For some externalities market prices are available due to the creation of a
market or an attempt to internalize costs within the general economic decision-making. The
establishment of a common European market for GHG emissions is the most relevant
example within the LFM discourse. This technique is used when assessing the monetary
value of net emissions due to the LFM project. First, the emission balance is calculated,
which is then further multiplied by the price for CO2-equivalents within the EU ETS.
Hedonic Pricing Method (HPM): This technique is used if the investigated effect directly
impacts on the price of associated market goods. It is then assumed that the willingness to
pay for the related good reflects people’s preferences with respect to the investigated
externality. This technique is mostly used in combination with housing prices. The HPM
calculates the societal costs as a function of changes or differences in housing prices. As
people are willing to pay or to accept lower prices for houses within close proximity to
facilities associated with negative effects (e.g. landfills), it is assumed that these price
differences occur as a consequence of the siting of such a facility.
Following this line of argument, it is assumed that after the completion of a LFM project
housing prices would rise as a result of the increased environmental quality. Hence, the
external costs due to the existence of a landfill could be erased, or assumed to be a benefit
when restoring the site during a LFM project.
However, a necessary precondition for calculating the extent of societal cost or benefit using
the CVM is the existence of a benchmark. Using housing prices, either real estate prices of a
similar location without a landfill in proximity, or prices before and after the landfill was
established or rehabilitated, must be available.
Travel Cost Method (TCM) calculates the price of a ‘service’ or a ‘good’ based on effort to
enjoy or consume the good. The consumption of services such as ‘recreation’ or ‘clean air’
may require traveling, whose costs are further used as a proxy for the value of the service
itself. Willingness to travel is assumed to be an incurred cost that reflects the visitors’
preferences for the good. When applying TCM to landfills or landfill mining, it is normally
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assumed that residents living nearby to the landfill need to travel in order access recreational
activities. If, however, the LFM project would be pursued, it is expected that these trips – at
least to some extent – would not be as necessary. If the potentially restored space from the
LFM project is limited, or only of local importance, this method might not appropriately
capture the value.
Stated preference approaches
Contingent Valuation (CVM): Unlike the approaches for building on revealed preferences,
CVM directly asks people to state their preferences with respect to some ‘good’ or ‘service’.
Price estimates from contingent evaluations are based on questionnaires aiming to reveal
preferences of survey respondents. This technique builds on the respondents’ future
behavior and is able to also value ‘goods’ or ‘services’ that do not yet exist.
By using a random sample of people affected by a negative externality, survey respondents
are asked what they would be willing to pay in order avoid the effects of a negative
externality, or willing to pay in order to enjoy the positive externality (Marella and Raga, 2014).
The contingent valuation method is applied in combination of externalities that are not
related to market goods or any other directly connected price. With respect to LFM, CVM
could be applied to investigate the hypothetical community benefit from the rehabilitation of
a landfill site, after completion of a mining project.
Conjoint or contingent choice method is similar to CVM. It is also based on stated
preferences, however respondents are asked to choose or rank different options or
combination of options. By the comparison of the responses the underlying preference
structure can be inferred.
Stated preference methods might be the only way how to investigate peoples preferences for a non-
market ‘good’ or ‘service’. However, it is questionable if answers to hypothetical scenarios or
consumption activities are able to represent the ‘true’ value associated with an externality.
Cost and income based approaches
A different technique to assess the value of externalities or ecosystem services is applied within
cost-based approaches. Unlike expressing the value of a ‘good’ or ‘service’ in terms of consumer
preferences or utility, these approaches use cost data in order to monetize externalities. There are
different ways to derive a monetary figure based on the following types of costs:
Avoided costs (AC): The ‘service’ or ‘good’ avoids costs that otherwise would need to be
paid by society if the ‘good’ or ‘service’ would not be available. An example is the avoided
damage through the installation of avalanche control.
With respect to landfill mining, AC approaches could be based on avoided costs from
potential environmental pollution.
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Monetary estimates could either be based on clean-up costs or experts’ judgment on
damage costs. In the clean-up approach it is assumed that once the damage is done, the
rehabilitation and restoration costs can be used as a proxy for the value of the damage
(Eshet, 2005).
As it is often difficult to assess the extent of the clean-up costs, especially ex-ante, estimates
are often based on knowledge and intuition of experts in the respective field.
Replacement Cost (RC) is similar to AC approaches, however, it uses the replacement costs
of a ‘service’ or a ‘good’ as a proxy to its value. In case pollution from a landfill would impact
on the ability for local farmers to grow and harvest products from their fields, replacement
costs for substituting local production by third-party production could be used as a proxy for
the societal cost. Furthermore, such societal costs could be turned into a societal benefit in
case a LFM project would erase or decrease the hazard probability.
Income approaches use the same logic as cost-based approaches, but apply the inverse
methodology. The valuation of an externality is based on the impact on economic output
(e.g. increased gains from agrarian production as a consequence of increased soil quality)
Two main criticisms have been raised against approaches to monetize externalities with respect to
environmental services or externalities. First, it has been argued that using a single monetary
dimension for evaluation is reductionist and not able to fully capture all value dimensions (Neuteleers
and Engelen, 2014). Value is not per se an economic concept, and while some aspects (e.g. health
impacts) may be easier converted into monetary terms, for others it will be much more difficult (e.g.
aesthetics).
Second, most approaches build on consumer preferences – while individual consumer choices
might differ from what would be appreciated form a societal perspective. Hence, this approaches
might lead to an under- or overestimating of the true societal value or cost of an externality
(Neuteleers and Engelen, 2014).
6.1.b State-of-the-art in research
Although the monetary assessment of external effects with respect to LFM is a relatively new issue,
some articles have dealt with negative and positive effects associated with landfills or the mining.
Another relevant strand of literature deals with external effects of landfills, which during LFM projects
potentially could – at least partially – be erased.
The following section provides an overview of studies on the issue and relates them to a potential
applicability to the Hechingen landfill.
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Market price approaches
Several studies have attempted to incorporate societal costs from GHG net emissions of LFM
projects into their economic assessment based on market prices from EU ETS emission certificates
(Van Passel et al., 2013; Winterstetter et al., 2015; Winterstetter and Laner, 2015).
Van Passel et al. (2013) calculate the net emissions from a LFM project in Belgium, comparing the
business-as-usual scenario (BAU) to the mining scenario. Emissions from WtM and WtE, as well as
for transport, freight and the necessary installations on-site are taken into account. While the energy
recovery is assumed to result in increased emissions, there is a net saving of emissions with respect
to WtM. Overall, Van Passel et al. (2013) assume a reduction of GHG emissions by 15 percent
compared to the BAU scenario. Interestingly, Danthurebandara et al. (2015) recently reassess the
environmental and economic performance of the project investigated by Van Passel et al. (2013).
Through the use of life-cycle analysis (LCA) they evaluate the environmental impacts of material
recovery in the course of a LFM project. They apply the ReCiPe impact assessment method
(endpoint method, hierarchist) to investigate the environmental effects against a set of different
categories. Although results cannot be directly compared as different methodologies were applied,
the impact category “climate change and human health” can be used as a proxy to the emission
balance. Danthurebandara et al. (2015) obtain vastly different results regarding the emission balance
of the LFM project compared to the BAU scenario. While Van Passel et al. (2013) found the BAU
scenario to cause more pollution, Danthurebandara et al. (2015) report the opposite. The diverging
results are due to differing assumptions related to the avoided emissions. Furthermore, it should be
noted that the impact on climate change is only one environmental impact category out of a range
that are investigated by Danthurebandara et al. (2015). Hence, it should not be concluded that LFM
in general is less favorable then the do-nothing-scenario in environmental terms.
Winterstetter et al. (2015) also investigate the emission balance of a LFM project. Similar to the
results from Van Passel et al. (2013) the WtE processes are the major source of emissions, while
savings are due to avoided emissions from primary steel and energy production. Overall, they
conclude that the LFM project would cause additional emissions compared to the BAU scenario.
Hence, net emissions from the LFM project would need to be incorporated as societal cost into a
holistic assessment.
While Winterstetter et al. (2015) and Van Passel et al. (2013) assess the GHG emissions of a LFM
project as a basis for the monetization, Frändegård et al. (2013b) does not integrate his findings into
any monetary evaluation. Frändegård et al. (2013b) compare different technical set ups (mobile vs.
stationary plant) as well as different types of landfills (e.g. large landfills or landfills with a need for
remediation) with respect to the potential emissions occurring during a LFM project. By means of
Multi-Carlo-simulations they are able to not only calculate an average scenario outcome, but also
assess the probability of a net saving of emissions from the LFM project. They conclude that
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depending on the modeled scenario, between 30 and 50 percent of GHG emissions compared to a
BAU scenario could be saved.
It should be noted that GHG emissions contributing to global warming are not the only emissions
caused by landfills or landfill mining projects. Emissions may also impact on human health and the
ecosystem, therefore causing negative externalities (Eshet, 2005).
Contingent Valuation Method
CVM was applied by Marella and Raga (2014) to monetize the community perceived benefits of a
LFM project in Northern Italy. By the use of a representative sample, peoples’ willingness to pay for
a hypothetical LFM project and for subsequent use of the landfill space as park, was investigated. A
survey was sent to a representative sample of people living close by the landfill. Combining the
results from the two scenarios (LFM and creation of a park), they concluded that the overall
community benefits measured in monetary terms would amount to EUR 1 million. However, Marella
and Raga (2014) assume the value to be higher for more densely populated areas, limiting the
applicability of their findings to other LFM projects.
Ayalon et al. (2006) investigate the economic aspects of landfill rehabilitation and conversion into a
public park in Isreal. While mining or recycling of any materials from the landfill was not investigated,
it is assumed that the effects do not differ greatly for LFM projects. They assess the social benefit
using different methods (CVM, TCM and HP). A survey was conducted amongst 299 residents in
vicinity of the landfill.
Mean willingness to pay for the rehabilitation was about USD 12.7 per annum per person. As Ayalon
et al. (2006) identified 440,000 households to be affected by this project, they projected an annual
societal benefit of USD 5.5 million per year (USD 92.4 million if fully capitalized) from closing the
landfill and rehabilitating the site. The shortfall to this approach is that several beneficial effects such
as (1) elimination of the hazard and (2) the positive effect of construction a public park cannot be
disentangled when analyzing the result.
It should further be noted that results from CVM studies are sometimes presented in extremely large
monetary ranges (e.g. EUR 1-22 million benefits of rehabilitation, Thewys et al., 2000) that might
challenge the overall credibility of a project assessment, if incorporated.
Hedonic Pricing Method
Eshet (2005) reviewed the literature on externalities caused by landfills from the 1990s until 2005. He
summarizes a set of results from studies using the HDP for investigating the effect of disamenities on
the local real estate prices. While all reviewed studies conclude that there is some negative effect on
housing prices in vicinity to a landfill caused by disamenities such as smell, odor or traffic, different
metrics do not enable a comparison or generalization of results. Some studies illustrate the negative
impact in percentage of initial housing prices, others derive an absolute figure in monetary terms.
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Eshet et al. (2007, p. 624) investigate the negative externalities of a waste transfer station in Israel.
They concluded that housing prices were affected up to a distance of 2.8 km in proximity to the
waste facility, with an increase in average house prices by USD 5,000 per km away from the facility.
Cambridge Econometrics (2003, p. 54) assess the impact of a landfill in the UK on real estate prices
in vicinity. They find a reduction of about GBP 5,500 (7%) in house value in a distance from 0-0.4 km
and GBP 1,600 (2%) for 0.4-0.8 km. No effect was found for house prices in further distance from
the landfill.
There is some evidence that disamenity effects of landfills on housing prices are not time consistent.
Usaually, negative effects on housing prices are expected to be larger during the early years of a
landfill (Cambridge Econometrics, 2003; Eshet et al., 2007).
While real estate prices are relatively easy to obtain, there are also drawbacks to using HPM. On the
one hand, heterogeneity of housing markets in vicinity to a landfill might make it difficult to isolate
price effects due to disamenities. However, results are generally site or regional specific, as the
housing market is usually non-comparable from one area to another.
One should also take into account that differences in housing prices might incorporate negative
effects not related to disamenities. Hence, when combining results from different approaches, the
overall social costs and benefits could be over- or underestimated (Eshet et al., 2007).
Travelling cost method
Ayalon et al. (2006) use the travelling cost method to assess the benefits from the transformation of
a landfill into a park. As the project was not realized upon investigating the effects, a different
approach to the classical TCM was applied. Estimated trips to the newly established park were used
in combination with savings of travel time to other recreational sites. Based on the assumption that
the affected 330,000 households, which are within in 0.5 hours travelling distance to the landfill, will
save one hour in travel time once the park is established, Ayalon et al. 2006 calculate an annual
benefit of USD 1.7 million from the landfill rehabilitation. It is further assumed that the recreational
site is visited once every four years. The NPV is expected to be USD 27 million (6% discount rate, 50
years time horizon).
Cost-based approaches
The European Commission (2000) published a review of environmental externalities from landfills and
incineration. Besides emissions to air, the impact from pollution from landfill leachate was also
assessed in monetary terms. They found one study from 1993 to report clean-up cost from leachate
to be on average EUR 0.77 (0-1.54) per ton of waste. Clean-up costs encompass all costs
associated with the leachate.
Another study by Miranda and Hale (1997) found leachate costs from landfills amount to USD 0-
0.98 per ton of waste. However, unlike clean-up costs, these estimates were based on the marginal
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damage function (mortality effects, morbidity effects, materials effects, crop destruction and visibility
impacts; Miranda and Hale, 1997, p. 592).
6.3.c Exploring best practices for the evaluation of LFM external effects
The most important finding from all reviewed studies is that there are ways to monetize external
costs and benefits that emerge in the course of a LFM project – however, techniques differ in terms
of the suitability to assess certain effects. Furthermore, results can rarely be generalized and
transferred to other landfill or LFM sites. The size of the effect and direction may depend on local
specifics, while the measurement is often time consuming and cost intensive. The question arises,
which method is best suited to assess different externalities and which results could be expected.
Emissions to air
As monetary valuation can be costly and time-consuming, using market prices – when available – in
order to assess the external costs/benefits of a landfill or LFM project is the most preferable
approach.
The evaluation of emissions to air from LFM projects need to be broken down into GHG emissions,
contributing to global warming and emissions that are harmful to health and the ecosystem. Using
the carbon price of the EU ETS, the valuation of net emissions occurring during a LFM project
becomes straightforward. However, it is still unclear if the overall emissions balance of LFM is
positive or negative (Frändegård et al., 2013b; Van Passel et al., 2013; Winterstetter et al., 2015). As
is often the case with LCA assessments, net emission or net saving may be a question of system
boundaries. Furthermore, emissions strongly depend on the applied material treatment and the
technical set up of the project.
With respect to emissions that might impact on the health of humans, it is assumed that appropriate
techniques to avoid such negative effects are put in place before a LFM project is pursued, hence
rendering estimates irrelevant.
Emissions to water and soil
Emissions to water and soil cannot be valued at market prices and may be best represented by cost
based approaches. However, there is disagreement whether avoided pollution with respect to soil or
water could potentially be a benefit of LFM projects (especially for the European case).
Generally, there is very limited research on assessing the value of landfill pollution externalities to soil
or water. Results from the studies are ambiguous. While cost-based approaches report clean-up
costs of EUR 0-1.54 per ton, there are several authors arguing that leachate should be considered
negligible in modern landfills (Ayalon et al., 2006; Eshet et al., 2007; European Commission, 2000;
Miranda and Hale, 1997; Rabl et al., 2008).
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Rabl et al. (2008, p. 148) argue that in light of the tight regulations of leachate water from landfills in
place and the limited regional impact, damage costs are unlikely to be significant (except in the case
of a large aquifer being affected). Eshet (2005, p. 494) also concludes that leachate in landfills with
protection liners should be considered negligible. Clean-up costs from leachate might only be
relevant for old landfills with a reported value of of USD 1-2 per ton of waste.
Ayalon et al. (2006) tried to assess the external benefit of leachate treatment in the course of a
landfill rehabilitation project in Israel. Even though the investigated landfill did not entail leachate
collection and treatment they were not able to show negative impacts on the groundwater. Hence,
Ayalon et al. (2006) assumed the external benefit of installing a prevention measure for avoiding
water pollution to be zero.
Besides cost-based approaches, hedonic pricing methods could be applied to value the community
benefit from erasing pollution or potential pollution to water and soil during LFM projects. Thewys et
al. (2000) assessed the societal costs associated with soil pollution of two landfills in Flanders using
HPM. They report an externality cost between EUR 1-22 million. However, the use of techniques
that build on perceived benefits for valuing environmental harm is controversial.
It can be concluded that avoided damage costs from landfill leachate can only be a source of
external benefit for LFM projects in certain cases. For modern landfills that comply with landfill
standards and are not in close proximity to a large aquifer it must be assumed that the hazard
potential to soil and water is generally very low. Hence, benefits from erasing the potential pollution
during LFM projects are also expected to be limited in such cases.
Disamenties
With respect to disamenities or amenities brought about by landfills or landfill mining projects, CVM,
HPM and TCM are the best-suited approaches for monetary valuation. The size of the effect differs
between studies, which could potentially be explained by the local specifics, such as the location of
the landfill site, the maturity, or the aspired subsequent use (e.g. rehabilitation only or creation of
public park).
Monetary estimates for site rehabilitation and conversion into a park using CVM show a positive
value, however to varying degrees. Depending on several intermediating factors results vary from
EUR 1 million (Marella and Raga, 2014) to USD 92 million (Ayalon et al., 2006).
HPM results are comparable to findings from CVM. Throughout different studies negative cost were
identified for landfills, which probably may disappear when mining and restoring the landfill site.
However, these effects vary greatly from one study to the other. Differences are not restrained to
absolute size of the effect, but can also be found with respect to spatial distribution of the effect
(Walton et al., 2006). There is limited data from European studies, but it can be assumed that the
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geographical expansion of the negative effect is similarly limited to a radius of 3-5 km from the landfill
site.
While TCM might be able to only assess the effect of the subsequent use, it is impossible to
disentangle effects from pollution prevention and the recreational value of the rehabilitated site using
HPM and CVM. This clearly marks a shortfall to studies using HPM and CVM, especially when
combining results with findings using other techniques (such as avoided costs from soil pollution).
This could lead to an overestimation of LFM benefits.
6.2 External costs of the LFM project in Hechingen
Whether or not positive or negative externalities arise in the course of a LFM project depends largely
on site-specific circumstances. Table 29 provides an overview of external effects that could be
evaluated with respect to the Hechingen landfill.
Table 29: Overview external effects of LFM projects and applicability to the case study in Hechingen (Source: own)
Assessing the emission balance from the Hechingen project clearly is a way to internalize costs and
benefits associated with the project. It is likely that the LFM project would cause additional
emissions compared to a BAU scenario, hence further contributing to the cost side of the project.
The Hechingen landfill is equipped with a state-of-the-art leachate water filtration system and a
protective landfill layer (Kreismülldeponie Hechingen, 2014). According to the previously presented
literature, leachate costs from modern landfills meeting current landfill standards and featuring
protective layers, are negligible. Hence, using damage cost approaches in this case to assess the
external value of a LFM project is unsuitable. Furthermore, SCENARIO C assumes the subsequent
reuse of the landfill space, which makes the assumption of avoided potential damage through water
and soil pollution unrealistic.
Unlike other LFM projects, subsequent uses of the landfill space in Hechingen are very limited. The
transformation for the regained landfill space into a public park is not possible as there is an adjacent
landfill segment, which is still in operation and not expected to close in the near future. Techniques
Externality Method Source
GHG Emissions Market priceFrändegard, 2013; Van Passel et al., 2013 & Winterstetter et al., forthcoming
Emissions to water and soil CVM, AC
Ayalon et al., 2006; Eshet, 2005; European commission 2000 & Rabl et al., 2008,
Landfill aesthatics /disammenities HPM, CVM
Cambridge Econometrics, 2003; Eshet, 2005; Eshet et al. 2007 & Walton et al., 2006
Recreational value TCM Ayalon et al., 2006
Effect Description Applicability
(+/-)effect unclear - likely to be negative when taking into account all relevant emissions YES
(+/?) negligible for landfills complying with modern landfill standards
NO
(+) & (-)
negative effect of landfills could potentially be elimated if the landfill space is restored to its natural state or upgraded to a park - effect varies with intermediating factors (population densitiy, etc.)
YES, but negative
(+)positive effect in case of restoring the regained landfill as recreational area, however strong dependency on local circumstances
NO
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in order to monetize the value of the recreational value of a potential subsequent use are hence not
applicable to the Hechingen case (TCM, CVM or HPM).
Disamenities that are normally associated with landfills and are erased as a result of mining activities,
can also not be taken into account for the Hechingen project. For the Hechingen case, it should be
expected that traffic and malodour increase during the mining phase, hence causing increased
negative effects for the local community. HPM and CVM approaches still can be applied to estimate
the community costs and benefits of the LFM project in Hechingen. However, it is assumed that the
result would be negative.
A potential different approach to the assessment of avoided pollution damage through LFM projects
can be traced back to Boerboom et al. (2003). Originally developed as a way to assess landfill
aftercare costs, the risk assessment procedure could also be applied to assess external benefits of
LFM projects. Boerboom et al. (2003) present a probabilistic risk assessment method for landfills
that is used to estimate the costs of environmental risks, such as groundwater pollution, soil
pollution or damage of the landfill cover. As this is a site-specific approach it requires input from local
experts. Although, according to Boerboom et al. (2003) the approach was applied in the
Netherlands, no referential values for estimates based on their approach could be found. An
assessment of external costs based on their approach is beyond the scope of this thesis, but may
be an option for future investigation.
Overall, unless the landfill site can be restored for public use, it must be concluded that the
internalization of external effects for the Hechingen landfill would cause additional costs, although
the absolute size of these cost are likely to be relatively small. Benefits are limited to potentially
saved GHG emissions or depend on the closure of the adjacent landfill segment. Furthermore,
disamenities from the LFM project are likely to cause additional costs for the community, though
these costs are not assumed to be critically high due to the landfills remote siting.
For classification under UNFC-2009 it is therefore assumed that external effects for the LFM project
in Hechingen are not a source of additional benefits, but are associated with additional costs caused
by GHG emissions and disamenities. However, the cost level is not assumed to significantly impact
on the classification under UNFC, which is therefore merely based on results from the business and
material flow analysis.
6.3 Classification under UNFC-2009
In order to classify the Hechingen landfill under UNFC-2009 the results must be related to the three
classification dimensions. The results from the classification attempt are presented in Table 30.
With respect to the first axis (E) it has to be decided whether the landfill can be classified as (UNECE,
2010):
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- E1: commercial project
- E2: potentially commercial project
- E3: non-commercial project
While it becomes clear from the NPV results19 that all the investigated scenarios must currently be
considered unprofitable (not falling into E1), the distinction between whether they should be
assigned to E2 or E3 is more difficult.
Table 30: Scenario classification under UNFC-2009 based on results from the analysis. All investigated scenarios are assumed to fall into category 211 (Source: own)
Within UNFC-2009 the distinction between E2 and E3 is based on the fact if the extraction can be
assumed to be economically profitable in the forseeable future, or not. Resources falling under E3
are defined by “extraction and sale is not expected to become economically viable in the foreseeable
future or evaluation is at too early a stage to determine economic viability” (UNECE, 2010, p. 10),
However, this statement leaves room for interpretation, especially with respect to the economic
profitability of LFM that depends on a range of different factors.
The decision as to whether the scenarios are assigned class E2 or E3 is next to the NPV results
based on the findings from the break-even analysis. The critical threshold for identifying a price rise
to be unrealistic was set at five, except for RDF gate fees, as they need to become negative.
For SCENARIO A the price of non-ferrous metals would need to rise by a factor of six, while the
energy price would need to increase more then three-fold. As the critical factor for non-ferrous
19 see section „NPV calculation results“
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metals in SCENARIO A is beyond the threshold of five, it is assumed unrealistic that this scenario
becomes profitable in the foreseeable future due to price rises from non-ferrous metals.
However, taking into account absolute levels of the critical factors (six for non-ferrous and 3.5 for
energy prices) and that potential interdependencies between energy and raw material prices might
exist, it is concluded that SCENARIO A falls into E2 according to UNFC-2009.
For SCENARIO B and C next to non-ferrous metals, RDF gate fees are also critical factors to the
profitability. Non-ferrous metal prices would need to rise between 3.8-4 times for the scenarios to
break even. However, the RDF gate fee would need to become negative (EUR -28 per ton),
compared to the assumed level of EUR 50 per ton so that SCENARIO C would be profitable. For
SCENARIO B the RDF gate fee was not taken into account during the break-even analysis, as the
critical level for the RDF gate fees was too low (<-10,000).
While an increase of the non-ferrous price by a factor of four seems realistic, it is questionable
whether or not the RDF price could realistically fall to EUR -28. However, there are studies, that
assume the RDF price to turn negative within the next decade in Germany (Gäth and Nispel, 2012).
For SCENARIO C the effect of changes in the income from the subsequent use of the landfill space
were investigated. However, the gate fee for the landfill would need to increase eight-fold compared
to the assumed level, which seems unrealistic in the foreseeable future.
Nevertheless, it is assumed for SCENARIO B and C that they could be classified as E2 according to
UNFC-2009. This decision is based on critical non-ferrous price levels for the scenario to break even,
but also on the likely decrease of RDF gate fees and increase of landfill gate fees in the foreseeable
future.
As all the investigated scenarios build on the same basic technologies for excavation and separation,
while incineration is assumed to be equally mature in technical terms, the project feasibility and
status (F-dimension) under UNFC-2009 is assessed simultaneously for all scenarios.
There are four different categories (F1 to F4) with respect to the project feasibility and status. F1
refers to project for which “feasibility of extraction by a defined development project or mining
operation has been confirmed”, while F2 refers to projects for which the feasibility is subject to
further evaluation.
Based on the available information on the applicability of sorting techniques for the purpose of landfill
mining and the estimates for the extractable share (MFA model), it can be assumed that all scenarios
fall into category F1. Although necessary steps for the implementation of a LFM project in Hechingen
have not yet begun, it is assumed that the necessary knowledge and state-of-the-art technologies
could be installed at the landfill site, as there is an ongoing operation of a landfill segment as well as
a material-recycling center at Hechingen.
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The third classification dimension refers to the degree of geological knowledge with respect to the
landfill body. As detailed in the previous sections, extensive exploration studies have taken place in
Hechingen to assess the composition of the deposited waste materials and their physical and
chemical properties. It can therefore be concluded that all scenarios fall into category G1, which is
defined as follows: “quantities associated with a known deposit that can be estimated with a high
level of confidence” (UNECE, 2010, p. 11).
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7 Discussion Based on the available information on the Hechingen landfill a model of all relevant material and energy flows for the LFM project was set up. Results from the MFA suggest that about 60% ± 14%
of all materials buried in the landfill can be extracted and used under realistic assumptions. When
comparing this to the optimal scenario, it becomes apparent that this translates into an average
sorting efficiency of 85%. Compared to other studies (80% on average; reported by Frändegård et
al., 2013b) this seems appropriate. However average sorting efficiencies found within this thesis
might be driven by the fraction of ‘Minerals and stones’ as due to its high weight and sorting
efficiency. Furthermore, it has not been investigated if other technical set ups could lead to even
higher efficiency rates.
What is systematic for the overall assessment is the relatively high uncertainty with respect to the
concentration of certain material fractions (especially for the fraction ‘Paper’, ‘Other materials’ and
‘Organic waste’), which also affects the economic assessment. Even though the exploration in
Hechingen included 34 samples form all over the landfill body, uncertainties persist in the
compositional data (large standard deviations). Hence, this supports the application of an approach
that takes into account uncertainty of input data, as pursued in the course of this thesis.
Given that the amount of recovered materials is crucial for the overall success of the LFM project,
both for realizing the goal of closing the material loop, as well as maximizing economic benefits,
further progress with respect to technical sorting efficiency might still be a key factor for future
success. Progress is especially needed for developing affordable and efficient technologies for
treating the fine fraction. Unlike other projects, it is not assumed during this thesis that metals are
recovered from the fine fraction (Winterstetter et al., 2015). This was decided as compositional data
was missing with respect to the fraction <5mm. Nevertheless, the fine faction accounts for almost
25% (w/w) FM of the landfill body – hence, there is still some material potential left to exploit.
However, it could be demonstrated that the available techniques are able to recover a large fraction
of the materials present in the Hechingen landfill.
Based on the results from the MFA, the investment model, using different scenarios, was developed.
Overall, all developed scenarios resulted in negative NPVs. The amount of materials subject to WtE
treatment was identified as being a crucial cost and benefit driver for all scenarios. This might be
explained by the fact that RDF materials in Hechingen do account for more than 50% (w/w) (FM) of
the landfilled materials.
Avoided aftercare-costs were the second most important driver of profitability in all investigated
scenarios. The assumed costs within this thesis were calculated based on an aftercare period of 50
years. As aftercare periods are regulated by law, a change in regulation may influence the profitability
of LFM projects. If the aftercare obligation would be expended to 100 years, SCEANRIO C is likely to
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be profitable. Hence, regulatory aspects should not be ignored with respect to LFM and avoided
aftercare costs.
Furthermore, the price of non-ferrous metals was found to be of major economic importance. These
findings are in line with the results presented in other studies (Van Passel et al., 2013; Winterstetter
et al., 2015).
An interesting insight is presented by the net costs for the LFM project per ton of waste. While unit
costs from discounted cash flows are by far the lowest for SCENARIO C, there is almost no
difference between costs per ton of waste for SCENARIO C and A, when calculated from
undiscounted cash flows. This suggests that the discount rate has a strong effect on the outcome of
the assessment. The sensitivity analysis did show that the NPV is less sensitive to changes in the
interest rate for SCENARIO A, which can be explained by differences in the cash flow structures.
While SCENARIO B and C are characterized by comparable high negative cash flows throughout the
project, SCENARIO A is distinguished by high upfront investment costs and relatively small negative
cash flows during the project.
If the assumptions regarding the discount rate would be altered drastically, the differing cost
structures could lead to a change in the profitability ranking of the scenarios. This may for instance
be the case if the LFM project in Hechingen would be investigated from a public investor’s
perspective, assuming a lower interest rate.
In general, the net costs per unit found are below the reported values from Rettenberger (2010), but
are similarly to the estimates presented by Winterstetter et al. (2015). The difference in the estimates
might be explained by Rettenberger (2010) assuming only metals will be recovered from the waste
materials.
While conventional CBA did show that none of the investigated scenarios are likely to be profitable, it
neglects potential costs and benefits due to the external effects in Hechingen. Research has shown
that there is a vast range of external effects that should be considered when evaluating LFM projects.
While, for other LFM projects this could be a source of additional income, the result of such
integration for the project in Hechingen is ambiguous.
External benefits that normally do arise in the course of a landfill mining project can be summarized
as avoided emissions (to air, water and soil), a reduction of local effects in the form of disamenities
and potential community benefits from the rehabilitation of the landfill space. However, as a part of
Hechingen landfill is still operated, most benefits associated with LFM activities are irrelevant for this
project. Financial gains from the subsequent use of the landfill space (SCENARIO C) do have a
positive effect on the NPV (approximately EUR 4 million, discounted), however it is likely that
community benefits from the transformation into a park or the restoration of the original natural state
may be greater. By means of applying a contingent valuation assessment, the hypothetical
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restoration benefits could be investigated and hence an alternative scenario for the overall
Hechingen landfill (including the landfill segment still in operation) could be investigated.
GHG emissions should be taken into account for a complete assessment of the LFM project.
Especially since a common market price is available, which facilitates the integration of external
costs, and renders them noncontroversial compared to monetary estimates from other methods
(CVM, HPM and TCM). While monetizing the emission balance of the Hechingen project is not
assumed to drastically change the outcomes of the conventional CBA, the missing integration is a
limitation to this thesis.
As detailed above the MFA and the NPV calculations were both applied to the research question of
how to classify the Hechingen landfill under UNFC-2009 (see Table 9). The MFA proved to be
generally suitable as a means to illustrate and deliver information necessary for the classification
dimension ‘degree of geological knowledge’ (G) and ‘field project status and feasibility’ (F).
By comparing the realistic and the potential scenario outcomes it could be demonstrated that the
material recovery is technically feasible, while allowing for room to identify potential improvements.
While the translation of the case study findings into the corresponding UNFC categories was
unproblematic for the G and F dimension, it was harder to assess the ‘economic and social viability
of the project’ (E).
Issues arose due to the vague definition of key terms such as ‘foreseeable future’, which allows for
various interpretations. However, these problems are not limited to the classification of
anthropogenic resources under UNFC-2009, but do relate more to the overall failure of the
classification framework to establish clear distinctions between different categories.
A common classification is a necessary prerequisite for an informed discussion on material deposits
including anthropogenic resources. However, there is a need for guidance on how the various
dimensions should be assessed and how results from different methodologies should be interpreted
in light of the classification categories.
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8 Conclusion This thesis has explored the resource potential of an old landfill in Germany by applying a multi-
staged research approach with the aim of classifying the investigated project under UNFC-2009.
A conceptual framework for the evaluation of anthropogenic resource deposits was presented.
Through the combination of different methods, various aspects such as the technical and economic
feasibility of a LFM project were assessed and related to the corresponding UNFC-2009 dimensions.
The focus of the analysis was on the economic profitability. Both MFA and NPV were applied, in
order to assess all relevant material, energy and cash flows. Uncertainty of input data was
accounted for within the research approach.
While primarily serving as basis for the investment analysis, results from the MFA suggest that a
substantial fraction of the material potential could be recovered using currently available sorting and
recovery technologies – proving principle technical feasibility of LFM.
The discounted cash flow analysis resulted in negative NPVs for the investigated scenarios, although
to differing extent. From all modeled setups, the scenario combining off-site treatment of RDF
materials with reutilization of the landfill space was found to be the least costly.
Critical levels of certain variables were identified at which the scenarios’ NPV would become zero.
These values were further compared to the baseline assumptions. This procedure explored how
realistic it is to assume that the project could become profitable in the future - an important aspect
when relating the results from the investment analysis to the UNFC-2009.
External factors were not taken into account during the investment analysis, limiting the explanatory
power of the assessment. However, given the fact that potential external benefits that normally arise
in the course of a LFM project seem unlikely in the case of the Hechingen landfill, their impact may
be limited as well. It is not assumed that the overall outcome would have been changed when
accounting for modifying factors. If at all, external effects are assumed to impose further costs for
the Hechingen project.
In summary, it can be stated that the applied research methodology proved a useful tool for
assessing the resource potential of an old landfill. By classifying the Hechingen landfill under UNFC-
2009 it could be shown that there is an economic potential for LFM in Hechingen in the future.
However, future research should take into account external effects on a more comprehensive basis
and try to establish a more standardized way of interpreting project results in light of UNFC-2009.
Overall, LFM seems to be unprofitable in Hechingen for the moment, however, if key variables of the
project change the economic profitability needs to be reassessed. The case-study analysis
demonstrated that site-specific circumstances do play an important role for the overall profitability of
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LFM. Hence, making a case-by-case assessment is a necessity when discussing the economics of
LFM.
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Annex Table of contents: !Annex A:! List of abbreviations ................................................................................................................ ii!Annex B:! Keywords ............................................................................................................................... ii!Annex C:! Used Formulas ....................................................................................................................... iii!Annex D:! Material categories ................................................................................................................ iv!Annex E:! Material composition and standard deviations – Kreismülldeponie Hechingen ........................ v!Annex F:! Landfill mass modeled by water content ............................................................................... viii!Annex G:! Calorific Values ..................................................................................................................... viii!Annex H:! MFA model: Transfer coefficients – realistic scenarios (material flows) .................................... ix!Annex I:! MFA model: Transfer coefficients – potential scenarios (material flows) ................................... x!Annex J:! MFA Model: Ash content ....................................................................................................... xi!Annex K:! MFA Model: MFA Model – Further assumptions ..................................................................... xi!Annex L:! MFA Model: Energy input incineration (P3) ............................................................................ xii!Annex M:! MFA Model: Energy flows realistic on-site scenario ............................................................... xiii!Annex N:! Investment Model: SCENARIO A ......................................................................................... xiv!Annex O:! Investment Model: SCENARIO B .......................................................................................... xv!Annex P:! Investment Model: SCENARIO C ......................................................................................... xvi!Annex Q:! Investment Model: Revenues ............................................................................................... xvii!Annex R: ! Investment Model: Costs .................................................................................................... xviii!Annex S:! Results MFA: scenario comparison different material fractions ............................................. xix!Annex T:! Results NPV: cost and income breakdown (undiscounted cash flows) ................................. xxi!Annex U:! Results NPV: Income and cost composition (undiscounted cash flows) ............................... xxi!
ii
Annex A: List of abbreviations
BAU Business as usual CBA Cost Benefit Analysis CtC Closing the Circle CVM Contingent Valuation Method DOC Degradable organic content DM Dry matter EU European Union FM Fresh Matter HPM Hedonic Pricing Method kJ Kilojoule MBI Market-based instruments MFA Material Flow Analysis MJ Megajoule MSW Municipal Solid Waste STAN Substance Flow Analysis WtE Waste to Energy WtM Waste to Material ELFM Enhanced Landfill Mining LCA Life-Cycle Analysis LFM Landfill Mining NPV Net present value RMI Raw Material Initiative RDF Refuse Derived Fuel TCM Travel Cost Method
UNFC-2009 United Nations Framework Classification for Fossil Energy and Mineral Reserves and Resources 2009
Inert material Inert waste is neither chemically or biologically reactive and will not decompose
Annex B: Keywords
Landfill mining, anthropogenic resources, waste deposits, recycling, resource recovery, circular
economy, urban mining, waste management
iii
Annex C: Used Formulas
Heating value
!!(!"!) = !!! !" ∗ 1 − ! − !2,441 ∗ !"
!!(!") Heating value of dry material (water free) (in !" ∗ !"!!) !!(!"ℎ) Heating value of wet material (in !" ∗ !"!!) !" Water content (!!"#$% ∗ !!"#$%&#!!"#!$%&'(−1 )
Water content
!" = 100!!" −!!"!!"
!" Water content !!" Mass fresh matter !!" Mass dry matter
!!" = ! !!"
(1 −!"100)
!" Water content !!" Mass fresh matter !!" Mass dry matter
Error propagation
! = !! !,!,…
! = !(!,!,… )!
!!! =!"!" !,!
∗ !! +!"!" !,!
∗ !! +⋯
iv
Annex D: Material categories
Ferrous-metals beverage and other cans, other ferrous metals
Nonferrous-metals aluminium cans, aluminium packaging, non-ferrous seals, copper pipes and wires, other non-ferrous metals
Paper and Cardboard
Paper, cardboard, newspaper, books, magazines, used paper, disposable tableware, paper wallpaper, other paper and cardboard
Glass White glass, mixed glass, green glass, glass packaging, glassware, glasses from medical use, flat glass, other glass
Plastics Flies, disposable tableware, pipes, furniture, insulation material, foamed plastic, window frames, hollow containers, other plastics
Wood wood packaging, wood furniture, other wood
Textiles clothing, home ware, other textiles
Inert waste porcelain, pottery, other mineral compounds
Organic waste meat, fish, bones, leftovers, leaves, flowers, hygiene papers, lawn clippings, other organic waste
Hazardous waste Batteries, rechargeable batteries, chemicals, residual oil, drugs, other hazardous waste
Compound Materials and Packaging
paper-compounds, plastic-compounds, aluminium-compounds, beverage packaging, other compound materials
Materials not defined other
Leather, rubber, cork, shoes, diapers, other sanitary products
Complex Products electronic scrap, mattresses, other compound furniture, automobile parts, wood-metal compounds, plastic-metal compounds, wood-metal-textile compounds, upholstered furniture
Sorting rests Other materials
v
Annex E: Material composition and standard deviations – Kreismülldeponie Hechingen
Bl/1/1Bl/1/2Bl/1/3Bl/2/1Bl/2/2Bl/3/1Bl/3/2Bl/3/3Bl/3/4Bl/3/5Bl/3/6Bl/4/1Bl/4/2Bl/4/3Bl/4/4Bl/4/5Bl5/1Bl5/2Bl5/3Bl/6/1Bl/6/2Bll/1/1Bll/1/2Bll/1/3Bll/1/4Bll/1/5Bll/1/6Bll/1/7Bll/1/8Bll/1/9Bll/1/10Bll/2/1Bll/3/1Bll/3/2Bll/3/3
AVG BIAVG BII
Material composition of the Hechingen landfill body (w/w) (FM) - all size fractions
Metals
2.8%3.9%3.4%4.1%3.5%2.4%5.5%5.3%3.8%4.9%3.8%2.2%0.8%2.1%3.2%2.9%1.6%2.4%1.6%3.4%4.3%1.9%3.2%5.9%3.6%1.7%2.0%5.2%4.8%6.9%3.2%2.1%5.5%4.0%1.7%
3.2%3.7%
Material composition of the Hechingen landfill body (w/w) (FM) - all size fractions
Other material
Plastic and packaging
Paper and cardboard Glass
2.3% 42.0% 0.3% 3.5%1.5% 22.9% 2.0% 3.4%0.2% 20.2% 0.0% 3.5%0.9% 18.0% 0.1% 1.1%0.6% 23.7% 0.2% 3.6%3.3% 15.9% 0.8% 4.5%0.2% 17.7% 0.0% 3.1%0.0% 15.7% 0.0% 1.3%0.1% 21.7% 0.0% 3.7%1.2% 22.3% 0.3% 6.0%0.2% 16.5% 0.0% 4.2%4.9% 19.9% 0.0% 10.6%1.6% 6.0% 0.0% 0.5%1.2% 15.6% 0.3% 8.2%0.7% 17.8% 0.6% 5.0%1.7% 25.5% 0.0% 3.1%0.5% 12.3% 0.0% 1.1%0.9% 15.5% 0.7% 7.4%1.0% 16.8% 0.9% 4.2%0.2% 20.9% 0.8% 8.7%0.4% 17.9% 0.0% 4.6%0.3% 3.9% 0.1% 0.5%0.0% 15.9% 0.4% 0.1%0.0% 10.0% 0.7% 0.6%0.0% 13.5% 0.0% 1.6%4.1% 31.2% 0.0% 0.8%0.3% 20.0% 0.0% 1.0%0.0% 15.7% 0.0% 1.2%0.0% 18.0% 0.0% 1.1%0.0% 31.2% 0.1% 0.5%0.9% 14.0% 0.6% 2.5%0.0% 18.9% 0.8% 1.3%0.0% 1.9% 0.0% 44.4%0.0% 0.3% 0.0% 2.1%0.1% 7.9% 1.4% 4.4%
1.1% 19.3% 0.3% 4.4%0.4% 14.5% 0.3% 4.4%
Material composition of the Hechingen landfill body (w/w) (FM) - all size fractions
Organic waste Wood Textiles Mineral and
stones
0.2% 1.8% 5.3% 8.4%0.0% 2.8% 5.0% 10.5%0.0% 2.0% 8.1% 16.5%0.0% 3.7% 12.2% 10.1%0.0% 6.5% 5.8% 6.1%0.0% 1.7% 7.0% 7.3%0.0% 4.4% 9.9% 5.5%0.0% 6.6% 16.7% 8.5%0.0% 3.0% 7.0% 3.6%0.0% 3.4% 6.5% 5.9%0.0% 6.1% 5.4% 9.6%0.0% 2.8% 4.3% 13.8%0.0% 1.9% 1.2% 6.1%0.0% 2.3% 10.7% 10.2%0.0% 5.8% 6.4% 4.5%0.0% 4.3% 9.9% 4.6%0.0% 1.2% 9.5% 16.4%0.1% 3.4% 3.2% 18.4%0.0% 4.4% 11.1% 9.7%0.0% 5.0% 6.6% 18.2%0.0% 7.2% 3.7% 16.4%0.0% 0.5% 11.8% 45.7%0.0% 3.8% 13.8% 9.5%0.0% 2.1% 10.2% 8.8%0.0% 1.3% 4.0% 14.4%0.0% 1.4% 8.1% 3.4%0.0% 5.3% 7.1% 6.0%0.0% 3.3% 5.5% 4.5%0.0% 2.5% 6.5% 3.5%0.0% 4.8% 12.2% 5.4%0.0% 2.2% 11.4% 4.8%0.0% 2.2% 14.4% 24.3%0.0% 0.3% 0.1% 20.8%0.0% 0.2% 0.0% 54.3%0.0% 3.9% 7.5% 11.7%
0.0% 3.8% 7.4% 10.0%0.0% 2.4% 8.0% 15.5%
Material composition of the Hechingen landfill body (w/w) (FM) - all size fractions
Hazardous waste
0.0%0.1%0.0%0.1%0.0%0.0%0.0%0.0%0.1%0.0%0.0%0.5%0.0%0.0%0.0%0.0%0.0%0.0%0.2%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.3%0.0%
0.0%0.0%
Material composition of the Hechingen landfill body (w/w) (FM) - all size fractions
Sorting rests
Fines <5mm
13.2% 20.3%34.3% 13.7%15.3% 31.0%36.0% 13.6%12.2% 37.9%17.0% 40.1%50.0% 3.6%24.3% 21.4%18.2% 39.0%11.6% 38.0%32.8% 21.4%14.4% 26.7%
8.0% 73.9%19.4% 29.8%27.0% 29.1%35.2% 12.9%40.9% 16.6%27.7% 20.4%20.2% 30.2%28.1% 8.1%13.1% 32.4%11.2% 24.2%35.8% 17.6%31.4% 30.2%37.9% 23.7%19.9% 29.5%26.4% 31.7%42.4% 22.3%33.1% 30.4%22.7% 16.1%51.1% 9.5%22.5% 13.5%
1.3% 25.6%5.1% 33.7%
36.2% 25.2%
23.8% 26.7%26.9% 23.8%
Material composition of the Hechingen landfill body (w/w) (FM) - all size fractions
AVG TOTAL 3.4% 0.8% 17.4% 0.3% 4.4% 0.0% 3.3% 7.7% 12.2% 0.0% 25.0% 25.5%Std 1.4% 1.2% 8.2% 0.5% 7.4% 0.0% 1.9% 3.9% 10.9% 0.1% 12.4% 12.5%Amount (DM)*
*tons
43,457 10,617 220,441 4,015 55,740 109 41,366 97,315 155,211 472 317,947 324,184
vi
Bl/1/1Bl/1/2Bl/1/3Bl/2/1Bl/2/2Bl/3/1Bl/3/2Bl/3/3Bl/3/4Bl/3/5Bl/3/6Bl/4/1Bl/4/2Bl/4/3Bl/4/4Bl/4/5Bl5/1Bl5/2Bl5/3Bl/6/1Bl/6/2Bll/1/1Bll/1/2Bll/1/3Bll/1/4Bll/1/5Bll/1/6Bll/1/7Bll/1/8Bll/1/9Bll/1/10Bll/2/1Bll/3/1Bll/3/2Bll/3/3
AVG BIAVG BII
Material composition of the Hechingen landfill body (w/w) (DM) - <35mm
Metals Other material
Plastic and packaging
Paper and Cardboard Glass
2.0% 0.0% 24.8% 0.0% 6.1%0.8% 0.0% 15.8% 0.0% 4.7%3.0% 0.0% 16.9% 0.0% 4.9%0.4% 0.0% 13.0% 0.0% 1.6%1.9% 0.0% 11.8% 0.0% 5.3%1.3% 0.0% 6.1% 0.0% 7.2%2.3% 0.0% 12.0% 0.0% 6.6%4.7% 0.0% 11.8% 0.0% 1.7%1.1% 0.0% 7.4% 0.0% 5.0%1.2% 0.0% 11.6% 0.0% 8.4%1.1% 0.0% 10.3% 0.0% 5.7%0.6% 0.0% 13.8% 0.0% 19.1%0.0% 0.0% 1.4% 0.0% 0.6%0.1% 0.0% 9.2% 0.0% 11.1%1.0% 0.0% 8.5% 0.0% 8.4%2.4% 0.0% 8.0% 0.0% 5.7%0.0% 0.0% 11.3% 0.0% 1.7%0.0% 0.0% 6.5% 0.0% 10.9%1.2% 0.0% 6.7% 0.0% 7.5%2.8% 0.0% 7.7% 0.0% 14.1%2.0% 0.0% 8.6% 0.0% 7.4%0.5% 0.0% 0.3% 0.1% 0.5%0.4% 0.0% 8.2% 0.6% 0.0%3.5% 0.0% 6.3% 0.9% 0.7%1.3% 0.0% 6.7% 0.0% 2.1%0.0% 5.8% 17.3% 0.0% 1.0%0.7% 0.0% 11.4% 0.0% 1.4%1.8% 0.0% 8.3% 0.0% 1.4%1.8% 0.0% 6.9% 0.0% 1.6%2.7% 0.0% 16.1% 0.0% 0.9%0.2% 0.0% 9.5% 0.3% 3.4%1.4% 0.0% 4.1% 0.4% 1.3%4.7% 0.0% 1.7% 0.0% 45.9%1.7% 0.0% 0.1% 0.0% 2.0%1.6% 0.0% 4.8% 1.2% 3.6%
1.4% 0.0% 10.6% 0.0% 6.8%1.6% 0.4% 7.3% 0.3% 4.7%
Material composition of the Hechingen landfill body (w/w) (DM) - <35mm
Organic waste
0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%
0.0%0.0%
Material composition of the Hechingen landfill body (w/w) (DM) - <35mm
Wood Textiles Mineral and stones
0.0% 0.0% 11.1%1.4% 1.2% 12.1%0.7% 2.9% 23.0%2.3% 6.3% 12.7%1.7% 3.4% 7.3%1.0% 2.2% 6.0%2.6% 3.3% 9.5%3.4% 10.1% 10.0%1.9% 6.0% 3.3%0.9% 3.2% 8.3%4.0% 1.0% 11.0%0.0% 0.0% 17.6%0.9% 0.4% 3.3%0.7% 2.3% 11.1%2.1% 1.9% 6.8%2.6% 2.2% 8.0%0.7% 3.5% 15.3%1.9% 1.5% 20.6%2.4% 2.5% 9.3%2.7% 1.4% 26.0%3.4% 2.1% 17.7%0.2% 1.0% 53.7%3.0% 7.0% 0.0%1.7% 5.4% 8.9%1.0% 0.9% 11.9%1.0% 2.1% 2.8%4.0% 1.4% 6.3%3.0% 2.2% 3.0%2.0% 1.9% 1.9%3.5% 8.8% 1.3%2.0% 3.9% 6.5%2.4% 1.1% 21.6%0.2% 0.1% 18.4%0.2% 0.0% 58.2%3.0% 2.1% 5.9%
1.8% 2.7% 11.9%1.9% 2.7% 14.3%
Material composition of the Hechingen landfill body (w/w) (DM) - <35mm
Hazardous Waste
0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%
0.0%0.0%
Sorting Rest
Fines <5mm
19.4% 36.7%41.7% 22.4%
5.1% 43.6%44.7% 19.0%12.8% 55.8%
4.5% 71.8%55.9% 7.9%30.7% 27.6%21.9% 53.5%12.2% 54.2%37.8% 29.2%
0.6% 48.3%7.6% 85.8%
24.5% 40.9%21.6% 49.8%46.4% 24.7%42.0% 25.4%27.9% 30.6%15.4% 55.2%31.9% 13.4%
5.4% 53.3%5.2% 38.6%
52.7% 28.2%36.0% 36.7%45.4% 30.6%27.7% 42.2%30.9% 43.9%49.0% 31.3%40.1% 43.8%38.0% 28.7%60.9% 13.3%
8.9% 58.9%1.4% 27.6%0.0% 37.8%
33.3% 44.5%
24.3% 40.4%30.7% 36.2%
AVG TOTAL 1.5% 0.2% 9.3% 0.1% 6.0% 0.0% 1.8% 2.7% 12.9% 0.0% 26.8% 38.7%Std 1.2% 1.0% 5.2% 0.3% 8.2% 0.0% 1.2% 2.4% 12.6% 0.0% 17.6% 16.5%
vii
Bl/1/1Bl/1/2Bl/1/3Bl/2/1Bl/2/2Bl/3/1Bl/3/2Bl/3/3Bl/3/4Bl/3/5Bl/3/6Bl/4/1Bl/4/2Bl/4/3Bl/4/4Bl/4/5Bl5/1Bl5/2Bl5/3Bl/6/1Bl/6/2Bll/1/1Bll/1/2Bll/1/3Bll/1/4Bll/1/5Bll/1/6Bll/1/7Bll/1/8Bll/1/9Bll/1/10Bll/2/1Bll/3/1Bll/3/2Bll/3/3
AVG BIAVG BII
Material composition of the Hechingen landfill body (w/w) (FM) - >35mm
Metals Other material
Plastic and packaging
Paper and cardboad Glass
1.7% 2.3% 28.3% 0.3% 0.1%3.4% 1.5% 13.3% 2.0% 0.5%1.3% 0.2% 8.2% 0.0% 0.0%3.8% 0.9% 8.7% 0.1% 0.0%2.2% 0.6% 15.7% 0.2% 0.0%1.7% 3.3% 12.5% 0.8% 0.5%4.4% 0.2% 12.2% 0.0% 0.1%1.7% 0.0% 6.6% 0.0% 0.0%3.0% 0.1% 16.3% 0.0% 0.1%4.1% 1.2% 14.2% 0.3% 0.1%3.0% 0.2% 9.0% 0.0% 0.0%1.9% 4.9% 12.3% 0.0% 0.1%0.8% 1.6% 4.8% 0.0% 0.0%2.0% 1.2% 8.9% 0.3% 0.1%2.6% 0.7% 12.8% 0.6% 0.1%1.6% 1.7% 21.3% 0.0% 0.1%1.6% 0.5% 4.9% 0.0% 0.0%2.4% 0.9% 11.2% 0.7% 0.1%0.9% 1.0% 13.1% 0.9% 0.1%1.7% 0.2% 16.2% 0.8% 0.2%3.1% 0.4% 12.7% 0.0% 0.1%1.6% 0.3% 3.7% 0.0% 0.2%3.0% 0.0% 10.8% 0.0% 0.1%3.0% 0.0% 4.8% 0.0% 0.0%2.6% 0.0% 8.3% 0.0% 0.0%1.7% 0.0% 19.1% 0.0% 0.1%1.5% 0.3% 11.8% 0.0% 0.0%3.9% 0.0% 9.8% 0.0% 0.2%3.6% 0.0% 13.2% 0.0% 0.0%5.4% 0.0% 22.2% 0.1% 0.0%3.1% 0.9% 7.2% 0.4% 0.1%1.8% 0.0% 18.0% 0.7% 1.0%1.1% 0.0% 0.3% 0.0% 1.8%2.5% 0.0% 0.2% 0.0% 0.3%0.8% 0.1% 5.2% 0.7% 2.4%
2.3% 1.1% 12.5% 0.3% 0.1%2.5% 0.1% 9.6% 0.1% 0.4%
Material composition of the Hechingen landfill body (w/w) (FM) - >35mm
Organic waste Wood Textiles Mineral and
stones
0.2% 1.8% 5.3% 2.3%0.0% 1.9% 4.3% 3.1%0.0% 1.5% 6.0% 0.1%0.0% 2.1% 7.7% 1.0%0.0% 5.3% 3.5% 1.1%0.0% 1.1% 5.8% 4.0%0.0% 3.2% 8.4% 1.2%0.0% 4.0% 8.9% 0.8%0.0% 1.6% 2.6% 1.2%0.0% 2.8% 4.3% 0.1%0.0% 3.2% 4.7% 1.5%0.0% 2.8% 4.3% 4.1%0.0% 1.1% 0.9% 3.3%0.0% 1.8% 9.0% 2.1%0.0% 4.6% 5.3% 0.5%0.0% 2.9% 8.7% 0.4%0.0% 0.7% 7.2% 6.4%0.1% 2.1% 2.2% 4.7%0.0% 3.1% 9.7% 4.6%0.0% 3.4% 5.8% 2.5%0.0% 5.1% 2.4% 5.7%0.0% 0.4% 11.2% 12.0%0.0% 1.9% 9.4% 9.5%0.0% 0.7% 5.8% 1.5%0.0% 0.5% 3.3% 5.2%0.0% 0.7% 6.6% 1.4%0.0% 2.4% 6.1% 1.4%0.0% 1.2% 3.9% 2.4%0.0% 1.1% 5.2% 2.2%0.0% 2.8% 7.3% 4.7%0.0% 0.8% 8.6% 0.2%0.0% 1.7% 14.1% 19.4%0.0% 0.1% 0.0% 3.7%0.0% 0.0% 0.0% 2.4%0.0% 2.2% 6.3% 8.4%
0.0% 2.7% 5.6% 2.4%0.0% 1.2% 6.3% 5.3%
Material composition of the Hechingen landfill body (w/w) (FM) - >35mm
Hazardous waste
0.0%0.1%0.0%0.1%0.0%0.0%0.0%0.0%0.1%0.0%0.0%0.5%0.0%0.0%0.0%0.0%0.0%0.0%0.2%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.3%0.0%
0.0%0.0%
Sorting rest
Fines <35mm
2.5% 55.3%8.9% 61.0%
11.7% 71.1%4.0% 71.7%3.5% 68.0%
14.5% 55.8%24.5% 45.7%
0.5% 77.4%2.2% 72.9%3.0% 70.1%5.1% 73.3%
14.1% 55.2%1.5% 86.1%1.6% 72.8%
14.4% 58.5%10.9% 52.4%13.4% 65.4%
9.1% 66.6%11.8% 54.7%
8.8% 60.4%9.8% 60.7%7.9% 62.7%3.0% 62.3%1.7% 82.4%2.8% 77.3%0.5% 69.9%4.1% 72.3%7.4% 71.4%5.3% 69.3%1.4% 56.1%7.8% 71.1%
20.5% 22.9%0.0% 92.9%5.1% 89.2%
17.3% 56.7%
8.4% 64.5%6.1% 68.3%
AVG TOTAL 2.4% 0.7% 11.4% 0.3% 0.2% 0.0% 2.1% 5.9% 3.6% 0.0% 7.4% 66.0%Std 1.1% 1.1% 6.1% 0.4% 0.5% 0.0% 1.4% 3.1% 3.9% 0.1% 6.1% 13.1%
viii
Annex F: Landfill mass modeled by water content
Annex G: Calorific Values
Water content (w/w of FM) FM (t) DM (t) DM (t/a)* FM (t/a)* 15 (t/a)**Landfill material 31.56% 1,855,996 1,270,287 127,029 185,600 157,760
Cover material 11.00% 113,306 100,842 10,084 11,331 9,631
Sludges 43.70% 164,338 92,522 9,252 16,434 13,969
Average/sum 31.40% 2,133,640 1,463,651 146,365 213,364 181,359
*assumed project duration: 10 years
**used for the computation of RDF materials in the realistic scenario
1,000,000
1,500,000
2,000,000
Paper and cardboardPlastics and packaging
Organic wasteWood
Textiles
Water content
(w/w) FM
58.1%10.6%56.2%56.2%52.4%
Share combustible fraction
(w/w) FM
0.6%31.9%0.0%6.0%
14.1%
kJ/kg (FM)
4,509 25,625
6,264 6,264 8,026
Calorific valueGJ/t (specific
water content)
4.525.6
6.36.38.0
Calorific valueGJ/T (0.345) - average
water content
8.418.110.610.611.9
Calorific valueGJ/T (0.15) - after
RDF Treatment
11.724.314.514.516.2
Calorific value
Other materialsSorting rests
50.0%41.9%
1.5%46.0%
7,776 12,134
7.812.1
10.914.0
14.918.9
weighted average
Source: own calculations based on Gäth and Nispel (2012, p. 143 & p. 170)
34.5%
Source: own calculations based on Gäth and Nispel (2012, p. 143 & p. 170)
-
Source: own calculations based on Gäth and Nispel (2012, p. 143 & p. 170)
-
Source: own calculations based on Gäth and Nispel (2012, p. 143 & p. 170)
15.4 14.7 19.9
ix
Annex H: MFA model: Transfer coefficients – realistic scenarios (material flows)
FI Fines <5mmGLASS GlassHZW Hazardous wasteMetals MetalsMI/ST Mineral and stonesOM Other materialsOW Organic wastePAP Paper and cardboardPLA PACK Plastics and packagingSR Sorting restsTEX TextilesWOO Wood
Transfer Coefficients ESS
based on: fist stage of MBA plant (Laner and Brunner, 2008, p. 31)
*RES = 'Residuals''Combustibles' consist of: 'Other materials', 'Organic waste', 'Paper and cardboard', 'Plastics and packaging', 'Sorting rests', 'Textiles' and 'Wood'
FEM FI GLASS HZW MI/ST NFM RES* COMB** OM OW PAP PLA PACK SR TEX WOO- 0.95 - - - - - 0.05 0.00 0.00 0.00 0.02 0.02 0.01 0.00 - - 0.95 - - - - 0.05 0.00 0.00 0.00 0.02 0.02 0.01 0.00
0.40 - - 0.30 - - - 0.30 0.00 0.00 0.00 0.10 0.14 0.04 0.02 0.60 - - - - 0.10 0.10 0.20 0.00 0.00 0.00 0.06 0.09 0.03 0.01 - - - - 0.98 - - 0.02 0.00 0.00 0.00 0.01 0.01 0.00 0.00 - - - - - - 0.20 0.80 0.80 - - - - - - - - - - - - 0.20 0.80 - 0.80 - - - - - - - - - - - 0.20 0.80 - - 0.80 - - - - - - - - - - 0.14 0.86 - - - 0.86 - - - - - - - - - 0.20 0.80 - - - - 0.80 - - - - - - - - 0.20 0.80 - - - - - 0.80 - - - - - - - 0.09 0.91 - - - - - - 0.91
based on: fist stage of MBA plant (Laner and Brunner, 2008, p. 31)
*RES = 'Residuals''Combustibles' consist of: 'Other materials', 'Organic waste', 'Paper and cardboard', 'Plastics and packaging', 'Sorting rests', 'Textiles' and 'Wood'
HZW Metals RES* COMB** OM OW PAP PLA PACK SR TEX WOOFI 0 0 0.7 0.3 0.00 0.00 0.00 0.10 0.14 0.04 0.02 GLASS 0 0 0.8 0.2 0.00 0.00 0.00 0.06 0.09 0.03 0.01 HZW 0.5 0 0 0.5 0.01 0.00 0.00 0.16 0.23 0.07 0.03 Metals 0 0.95 0.05 0 - - - - - - - MI/ST 0 0 0.9 0.1 0.00 0.00 0.00 0.03 0.05 0.01 0.01 OM 0 0 0.05 0.95 0.95 - - - - - - OW 0 0 0.5 0.5 - 0.50 - - - - - PAP 0 0 0.05 0.95 - - 0.95 - - - - PLA PACK 0 0 0.05 0.95 - - - 0.95 - - - SR 0 0 0.05 0.95 - - - - 0.95 - - TEX 0 0 0.05 0.95 - - - - - 0.95 - WOO 0 0 0 1 - - - - - - 1.00
GlassHazardous waste
MetalsMineral and stones
Woodbased on RDF Preparation: RDF preparation plant (Laner and Brunner, 2008, p. 32)*RES= ‘Residuals’**COMB= ‘Combustibles’ (consisting of: OM, OW, PAP, PLA PACK, SR, TEX, WOO)
Other MaterialsOrganic Waste
Sorting restsTextiles
Plastics and packagingPaper and cardboad
Transfer coefficients RDFFines <5mm
x
Annex I: MFA model: Transfer coefficients – potential scenarios (material flows)
FI Fines <5mmGLASS GlassHZW Hazardous wasteMetals MetalsMI/ST Mineral and stonesOM Other materialsOW Organic wastePAP Paper and cardboardPLA PACK Plastics and packagingSR Sorting restsTEX TextilesWOO Wood
Transfer Coefficients ESS
*RES = 'Residuals'**COMB = 'Combustibles' (consisting of: 'Other materials', 'Organic waste', 'Paper and cardboard', 'Plastics and packaging', 'Sorting rests', 'Textiles' and 'Wood')
FEM FI GLASS HZW MI/ST NFM RES* COMB** OM OW PAP PLA PACK SR TEX WOO- 1.00 - - - - - - - - - - - - - - - 1.00 - - - - - - - - - - - - - - - 1.00 - - - - - - - - - - -
0.60 - - - - 0.10 - - - - - - - - - - - - - 1.00 - - - - - - - - - - - - - - - - - 1.00 1.00 - - - - - - - - - - - - - 1.00 - 1.00 - - - - - - - - - - - - 1.00 - - 1.00 - - - - - - - - - - - 1.00 - - - 1.00 - - - - - - - - - - 1.00 - - - - 1.00 - - - - - - - - - 1.00 - - - - - 1.00 - - - - - - - - 1.00 - - - - - - 1.00
*RES = 'Residuals'**COMB = 'Combustibles' (consisting of: 'Other materials', 'Organic waste', 'Paper and cardboard', 'Plastics and packaging', 'Sorting rests', 'Textiles' and 'Wood')
HZW Metals RES* COMB** OM OW PAP PLA PACK SR TEX WOOFI 0 0 1 0 - - - - - - - GLASS 0 0 1 0 - - - - - - - HZW 1 0 0 0 - - - - - - - Metals 0 1 0 0 - - - - - - - MI/ST 0 0 1 0 - - - - - - - OM 0 0 0 1 1.00 - - - - - - OW 0 0 0 1 - 1.00 - - - - - PAP 0 0 0 1 - - 1.00 - - - - PLA PACK 0 0 0 1 - - - 1.00 - - - SR 0 0 0 1 - - - - 1.00 - - TEX 0 0 0 1 - - - - - 1.00 - WOO 0 0 0 1 - - - - - - 1.00
*RES= ‘Residuals’**COMB= ‘Combustibles’ (consisting of: OM, OW, PAP, PLA PACK, SR, TEX, WOO)
Paper and cardboadPlastics and packaging
Sorting restsTextiles
Wood
Hazardous wasteMetals
Mineral and stonesOther MaterialsOrganic Waste
Transfer coefficients RDFFines <5mm
Glass
xi
Annex J: MFA Model: Ash content
Annex K: MFA Model: MFA Model – Further assumptions
Energy demand for ESS
Based on Rettenberger (1995): 31.3 kWh / t
Ash content
Kost, 2003
Mass concentrations
Gäth & Nispel, 2012
Energy demand MI
Winterstetter et al., 2015: 4 % of total RDF energy input (Stubenvoll et al., 2002).
Energy Efficiency
Winterstetter et al., 2015: 46 % (based on Kabelac, 2009)
Paper and cardboardPlastics and packagingOrganic wasteWoodTextiles
Ash content (w/w) +/- (w/w)Paper and cardboard 18 6
Plastics and packaging 18 8
Organic waste 21 16
Wood 5 5
Textiles 9 6
Other materialsSorting rests*
*assumption made: same as 'Plastics and Packaging'
Source: Kost (2001, p. 173)
Other materials 21 9.2
Sorting rests* n.a. n.a.
*assumption made: same as 'Plastics and Packaging'
Source: Kost (2001, p. 173)
xii
Annex L: MFA Model: Energy input incineration (P3)
SCENARIO: REALISTIC SCNEARIO (DM)Flow value (GJ/a) +/-
Other Materials (Stoffe ang. and Complex Products) 12,220 17,068 Organic Waste RDF 68 274
Paper RDF 3,647 5,300 Plastics and Packaging 441,858 192,663
Sorting Rest 462,199 192,693 Textiles RDF 121,727 60,811
Wood RDF 55,026 30,896
Flow value (GJ/a) +/-Total Energy Input into Monoincineration 1,096,746 281,464
Energy Demand Monoincineration (4% of Total Input) 43,870 11,259 0.04
SCENARIO: POTENTIAL SCNEARIO (DM)Flow value (GJ/a) +/-
Other Materials (Stoffe ang. and Complex Products) 15,855 22,461 Organic Waste RDF 165 686
Paper RDF 4,733 6,974 Plastics and Packaging 533,843 236,380
Sorting Rest 599,721 254,685 Textiles RDF 157,945 80,096
Wood RDF 59,766 33,963
Flow value (GJ/a) +/-Total Energy Input into Monoincineration 1,372,028 358,974
Energy Demand Monoincineration (4% of Total Input) 54,881 14,359 0.04
SCENARIO: REALISTIC SCENARIO (FM)Flow value (GJ/a) +/-
Organic Waste RDF 99 401 Other Materials (Stoffe ang. and Complex Products) 17,864 24,938
Paper RDF 5,331 7,744 Plastics and Packaging 645,876 281,497
Sorting Rest 675,632 281,541 Textiles RDF 177,938 88,850
Wood RDF 80,429 45,141
Flow value (GJ/a) +/-Total Energy Input into Monoincineration 1,603,170 411,242
Energy Demand Monoincineration (4% of Total Input) 64,127 16,450 0.04
SCENARIO: POTENTIAL SCENARIO (FM)Flow value (GJ/a) +/-
Other Materials (Stoffe ang. and Complex Products) 23,166 32,817 Organic Waste RDF 241 1,002
Paper RDF 6,915 10,189 Plastics and Packaging 779,989 345,371
Sorting Rest 876,242 372,117 Textiles RDF 230,771 117,026
Wood RDF 87,323 49,623
Flow value (GJ/a) +/-Total Energy Input into Monoincineration 2,004,647 524,492
Energy Demand Monoincineration (4% of Total Input) 80,186 20,980
SCENARIO: 15 SCENARIO (FM)Flow value (GJ/a) +/-
Other Materials (Stoffe ang. and Complex Products) 15,166 21,190 Organic Waste RDF 84 340
Paper RDF 4,526 6,580 Plastics and Packaging 548,372 239,191
Sorting Rest 573,603 239,228 Textiles RDF 151,067 75,497
Wood RDF 68,293 38,357
Flow value (GJ/a) +/-Total Energy Input into Monoincineration 1,361,111 349,436
Energy Demand Monoincineration (4% of Total Input) 54,444 13,977
Calculated INPUT FLOW VALUES STAN (GJ/a)
Calculated INPUT FLOW VALUES STAN (GJ/a)
Calculated INPUT FLOW VALUES STAN (GJ/a)
Calculated INPUT FLOW VALUES STAN (GJ/a)
Calculated INPUT FLOW VALUES STAN (GJ/a)
xiii
Annex M: MFA Model: Energy flows realistic on-site scenario
xiv
Annex N: Investment Model: SCENARIO A
Hec
hing
en -
Land
fill M
inin
g P
roje
ct C
alcu
latio
nva
lues
in E
UR
Dis
coun
ted
cash
flow
s1
23
45
67
89
1020
1620
1720
1820
1920
2020
2120
2220
2320
2420
2520
26
13,4
34,9
12
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
895,
454
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
19,6
39,4
11
3,91
3,19
3 3,
913,
193
3,91
3,19
3 3,
913,
193
3,91
3,19
3 3,
913,
193
3,91
3,19
3 3,
913,
193
3,91
3,19
3 3,
913,
193
32,8
20,0
90
32,8
20,0
90
66,7
89,8
67
32,8
20,0
90
6,76
8,54
8 6,
768,
548
6,76
8,54
8 6,
768,
548
6,76
8,54
8 6,
768,
548
6,76
8,54
8 6,
768,
548
6,76
8,54
8 6,
768,
548
Proj
ect P
repa
ratio
n-1
,000
,000
-1
,000
,000
Se
para
tion
and
Sort
ing
-20,
300,
000
-20,
300,
000
Inci
nera
tion
-52,
234,
000
-52,
234,
000
SUM
-73,
534,
000
-73,
534,
000
Exca
vatio
n an
d St
orag
e-5
,354
,123
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
Se
para
tion
and
Sort
ing
-25,
186,
010
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
Inci
nera
tion
-11,
796,
766
-2,3
50,5
30
-2,3
50,5
30
-2,3
50,5
30
-2,3
50,5
30
-2,3
50,5
30
-2,3
50,5
30
-2,3
50,5
30
-2,3
50,5
30
-2,3
50,5
30
-2,3
50,5
30
Dis
posa
l of H
azar
dous
Was
te-7
,755
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
SU
M-1
15,8
78,6
54
-8,4
37,2
60
-8,4
37,2
60
-8,4
37,2
60
-8,4
37,2
60
-8,4
37,2
60
-8,4
37,2
60
-8,4
37,2
60
-8,4
37,2
60
-8,4
37,2
60
-8,4
37,2
60
Cash
Flo
w-4
0,71
3,91
0 -1
,668
,711
-1
,668
,711
-1
,668
,711
-1
,668
,711
-1
,668
,711
-1
,668
,711
-1
,668
,711
-1
,668
,711
-1
,668
,711
-1
,668
,711
NPV
(15
Proz
ent)
-49,
088,
786
€
SCEN
ARIO
A (M
onoi
ncin
erat
ion
Plan
t)
INCOME EXPENSES
OPE
X
CAPE
X
SUM
Inco
me
Met
als
Inco
me
Min
eral
/Sto
nes/
Gla
ssIn
com
e En
ergy
Avo
ided
Aft
er-C
are
cost
s
xv
Annex O: Investment Model: SCENARIO B
Hec
hing
en -
Land
fill M
inin
g P
roje
ct C
alcu
latio
nva
lues
in E
UR
Disc
ount
ed v
alue
s20
1620
1720
1820
1920
2020
2120
2220
2320
2420
2520
26
13,4
34,9
12
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
Non
-fer
rous
Met
als
6,70
3,81
0 1,
335,
748
1,33
5,74
8 1,
335,
748
1,33
5,74
8 1,
335,
748
1,33
5,74
8 1,
335,
748
1,33
5,74
8 1,
335,
748
1,33
5,74
8 Fe
rrou
s M
etal
s2,
965,
563
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
Met
als
from
RD
F Pr
epar
atio
n3,
765,
540
750,
292
750,
292
750,
292
750,
292
750,
292
750,
292
750,
292
750,
292
750,
292
750,
292
895,
454
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
178,
421
32,8
20,0
90
32
,820
,090
47
,150
,457
32,8
20,0
90
2,85
5,35
5 2,
855,
355
2,85
5,35
5 2,
855,
355
2,85
5,35
5 2,
855,
355
2,85
5,35
5 2,
855,
355
2,85
5,35
5 2,
855,
355
Proj
ect P
repa
ratio
n-1
,000
,000
-1
,000
,000
Se
para
tion
and
Sort
ing
-20,
300,
000
-20,
300,
000
SUM
-21,
300,
000
-21,
300,
000
Exca
vatio
n an
d St
orag
e-5
,354
,123
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
Se
para
tion
and
Sort
ing
-25,
186,
010
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
Dis
posa
l of R
DF
-25,
058,
555
-4,9
92,9
69
-4,9
92,9
69
-4,9
92,9
69
-4,9
92,9
69
-4,9
92,9
69
-4,9
92,9
69
-4,9
92,9
69
-4,9
92,9
69
-4,9
92,9
69
-4,9
92,9
69
Dis
posa
l of H
azar
dous
Was
te-7
,755
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
-1
,545
SU
M-7
6,90
6,44
2 -1
1,07
9,69
8 -1
1,07
9,69
8 -1
1,07
9,69
8 -1
1,07
9,69
8 -1
1,07
9,69
8 -1
1,07
9,69
8 -1
1,07
9,69
8 -1
1,07
9,69
8 -1
1,07
9,69
8 -1
1,07
9,69
8
Cash
Flo
w11
,520
,090
-8
,224
,343
-8
,224
,343
-8
,224
,343
-8
,224
,343
-8
,224
,343
-8
,224
,343
-8
,224
,343
-8
,224
,343
-8
,224
,343
-8
,224
,343
NPV
(15
Proz
ent)
-29,
755,
985
EXPENSES
CAPE
X
OPE
X
SCEN
ARIO
B (D
ispo
sal o
f RDF
off-
site)
INCOME
Inco
me
Met
als
Inco
me
Min
eral
/Sto
nes/
Gla
ssA
void
ed A
fter
-Car
e co
sts
SUM
xvi
Annex P: Investment Model: SCENARIO C
Hec
hing
en -
Land
fill M
inin
g P
roje
ct C
alcu
latio
nva
lues
in E
UR
Disc
ount
ed v
alue
s20
1620
1720
1820
1920
2020
2120
2220
2320
2420
2520
2620
2720
2820
2920
3020
3113
,434
,912
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
2,67
6,93
4 2,
676,
934
Non
-ferr
ous M
etal
s6,
703,
810
1,33
5,74
8 1,
335,
748
1,33
5,74
8 1,
335,
748
1,33
5,74
8 1,
335,
748
1,33
5,74
8 1,
335,
748
1,33
5,74
8 1,
335,
748
Ferr
ous M
etal
s2,
965,
563
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
590,
894
Met
als f
rom
RDF
Pre
para
tion
3,76
5,54
075
0,29
2 75
0,29
2 75
0,29
2 75
0,29
2 75
0,29
2 75
0,29
2 75
0,29
2 75
0,29
2 75
0,29
2 75
0,29
2 89
5,45
417
8,42
1 17
8,42
1 17
8,42
1 17
8,42
1 17
8,42
1 17
8,42
1 17
8,42
1 17
8,42
1 17
8,42
1 17
8,42
1 32
,820
,090
32,8
20,0
90
Inco
me
from
subs
eque
nt u
se o
f voi
d sp
ace
3,81
4,06
90
0 0
0 0
1,52
8,55
3 1,
528,
553
1,52
8,55
3 1,
528,
553
1,52
8,55
3 1,
528,
553
1,52
8,55
3 1,
528,
553
1,52
8,55
3 1,
528,
553
50,9
64,5
2632
,820
,090
2,
855,
355
2,85
5,35
5 2,
855,
355
2,85
5,35
5 2,
855,
355
4,38
3,90
8 4,
383,
908
4,38
3,90
8 4,
383,
908
4,38
3,90
8 1,
528,
553
1,52
8,55
3 1,
528,
553
1,52
8,55
3 1,
528,
553
Proj
ect P
repa
ratio
n-1
,000
,000
-1
,000
,000
Se
para
tion
and
Sort
ing
-20,
300,
000
-20,
300,
000
SUM
-21,
300,
000
-21,
300,
000
Exca
vatio
n an
d St
orag
e-5
,354
,123
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
-1
,066
,820
Se
para
tion
and
Sort
ing
-25,
186,
010
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
-5,0
18,3
64
Disp
osal
of R
DF-2
5,05
8,55
5 -4
,992
,969
-4
,992
,969
-4
,992
,969
-4
,992
,969
-4
,992
,969
-4
,992
,969
-4
,992
,969
-4
,992
,969
-4
,992
,969
-4
,992
,969
Di
spos
al o
f Haz
ardo
us W
aste
-7,7
55
-1,5
45
-1,5
45
-1,5
45
-1,5
45
-1,5
45
-1,5
45
-1,5
45
-1,5
45
-1,5
45
-1,5
45
SUM
-76,
906,
442
-11,
079,
698
-11,
079,
698
-11,
079,
698
-11,
079,
698
-11,
079,
698
-11,
079,
698
-11,
079,
698
-11,
079,
698
-11,
079,
698
-11,
079,
698
0 0
0 0
0
Cash
Flo
w11
,520
,090
-8
,224
,343
-8
,224
,343
-8
,224
,343
-8
,224
,343
-8
,224
,343
-6
,695
,790
-6
,695
,790
-6
,695
,790
-6
,695
,790
-6
,695
,790
1,
528,
553
1,52
8,55
3 1,
528,
553
1,52
8,55
3 1,
528,
553
NPV
(15
Proz
ent)
-25,
941,
916
€
EXPENSES
CAPE
X
OPE
X
SCEN
ARIO
C (R
eusa
ge o
f voi
d la
ndfil
l spa
ce)
INCOME
Inco
me
Met
als
Inco
me
Min
eral
/Sto
nes/
Glas
sAv
oide
d Af
ter-
Care
cost
s
SUM
xvii
Annex Q: Investment Model: Revenues
Hec
hing
en -
Land
fill M
inin
g Pr
ojec
t Cal
cula
tion
10Re
venu
es8.
31.
711
.7
Cash
flow
s di
scou
nted
15%
(m
ean
valu
es; i
n EU
R)
Cash
flow
s di
scou
nted
3%
(m
ean
valu
es; i
n EU
R)
Cash
flow
s un
disc
ount
ed
(mea
n va
lues
; in
EUR)
unit
Flow
val
ue+/
-
mod
eled
w
ater
co
nten
t
895,
454
1,
521,
968
1,78
4,21
1
Jo
int
Min
eral
and
Sto
nes
629,
694
1,
070,
266
1,25
4,67
8
5
U
nifo
rm4,
5-5,
5W
inte
rste
tter
et
al.,
2015
t/a
25,0
93.6
12,0
90.7
D
MG
lass
265,
760
45
1,70
2
529,
533
10
N
orm
al8,
3-11
,7av
erag
e 20
10-2
014
lets
recy
cle.
com
t/a
5,29
5.3
8,52
0.7
D
M13
,434
,912
22,8
34,7
89
26
,769
,339
Join
tN
on-f
erro
us M
etal
s6,
703,
810
11,3
94,2
00
13
,357
,479
3,07
4
Jo
int
(uni
form
)
Copp
er 5
060-
5240
(50%
)A
lum
nium
105
8-11
08 (4
5%)
Oth
er/N
FM 2
00-2
50 (5
%)
Gät
h &
Nis
pel,
2012
&
euw
id, 2
014
t/a
434.
6
183.
8
D
M
Ferr
ous
Met
als
2,96
5,56
3
5,
040,
449
5,90
8,94
4
22
5
U
nifo
rm20
0-22
5G
äth
& N
ispe
l, 20
12
&eu
wid
, 201
4t/
a2,
626.
2
1,
104.
2
DM
Met
als
from
RD
F Pr
epar
atio
n3,
765,
540
6,40
0,14
0
7,
502,
916
909
Uni
form
24%
non
-fer
rous
76%
fe
rrou
s m
etal
s G
äth
& N
ispe
l, 20
12 &
eu
wid
, 201
4t/
a82
5.7
34
9.3
DM
GJ
Mon
o-in
cine
ratio
n19
,639
,411
33,3
80,3
31
39
,131
,931
45
Tria
ngle
35-5
5W
inte
rste
tter
et
al.,
2015
MW
h/a
86,9
59.8
26,5
34.3
15
Avoi
ded
Afte
rcar
e Co
sts
32,8
20,0
90
32
,820
,090
32,8
20,0
90
32
,820
,090
Tria
ngle
31.9
22.5
38-4
2.96
3.60
3G
äth
& N
ispe
l, 20
12 p
. 188
Subs
eque
nt u
se
of l
andf
ill3,
814,
069
11,2
47,4
44
15
,285
,534
60
Uni
form
50-7
0
Krei
smül
ldep
onie
H
echi
ngen
, 201
5t
1,27
3,79
4.5
19
2,28
6.6
FM
Inco
me
from
gat
e-fe
es
Proc
ess
Soru
ce o
f rev
enue
Data
from
@Ri
sk
Inco
me
from
Met
al S
ales
Inco
me
from
Iner
t M
ater
ial
Data
from
STA
N
Recy
clin
g of
M
ater
ials
Dist
ribut
ion
Net
-Ele
ctri
city
pro
duce
d fr
om R
DF
(MW
h)
Avo
ided
cos
ts fr
om la
ndfil
l aft
erca
re
Pric
e pe
r uni
t in
EU
R (t
otal
in
com
e or
per
to
n)N
otes
Sour
ce
xviii
Annex R: Investment Model: Costs
Hec
hing
en -
Land
fill M
inin
g Pr
ojec
t Cal
cula
tion
Costs
103
Cash
flow
s di
scou
nted
15%
(m
ean
valu
es; i
n EU
R)
Cash
flow
s di
scou
nted
3%
(m
ean
valu
es; i
n EU
R)
Cash
flow
s un
disc
ount
ed
(mea
n va
lues
; in
EUR)
Uni
tFl
ow v
alue
+/-
mod
eled
w
ater
co
nten
t
Proj
ect P
repa
ratio
nTO
TAL
Proj
ect P
repa
ratio
n1,
000,
000
-
1,00
0,00
0 -
1,
000,
000
-
1,00
0,00
0
Tr
iang
le90
0.00
0-1.
100.
000
Van
Vos
sen
and
Pren
t, 2
011,
p. 6
Exca
vatio
n an
d St
orag
eO
PEX
Exca
vatio
n an
d St
orag
e5,
354,
123
-
9,10
0,19
1 -
10
,668
,200
-
5.00
Nor
mal
4,5-
5,5
4 EU
R/to
n of
exc
avat
ed m
ater
ials
, pr
e-tr
eatm
ent 1
,5 E
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34.1
FM
Cost
s of
RD
F D
ispo
sal
25,0
58,5
55
-
42
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-
49,9
29,6
87
-
jo
int
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spor
t7,
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-
13,4
03,1
00
-
15
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,522
-
0.07
--
0,00
7 EU
R/km
/t. R
DF
plan
t Aßl
ar:
328
kmW
inte
rste
tter
et a
l., fo
rthc
omin
g &
G
oogl
e m
aps
t/a
6843
4.3
1699
5.7
15
RDF
Gat
e Fe
e17
,172
,803
-
29,1
87,9
36
-
34
,217
,165
-
50U
nifo
rm40
-60
Gät
h un
d N
ispe
l, 20
12, P
rogn
os
2014
Gät
h &
Nis
pel,
2012
-Pro
gnos
201
4t/
a68
434.
316
995.
715
CAPE
XM
onoi
ncin
erat
ion
Plan
t52
,234
,000
-
52,2
34,0
00
-
52
,234
,000
-
637
Tria
ngle
574-
700
637
EUR/
tonn
e. A
ssum
ptio
n:
fixed
cap
acity
- 80
% u
pper
th
resh
old
of R
DF
mat
eria
lsFr
iege
& F
ende
l, 20
11, p
. 34
t/a
6843
4.3
1699
5.7
15O
PEX
OPE
X M
onoi
ncin
erat
ion
11,7
96,7
66
-
20
,050
,498
-
23,5
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00
-
5%
--
4,5%
of i
nves
tmen
tFr
iege
& F
ende
l, 20
11, p
. 34
Disp
osal
of h
azar
dous
w
aste
OPE
XD
ispo
sal o
f haz
ardo
us w
aste
7,75
5 -
13,1
80
-
15,4
51
-
50
Uni
form
45-5
5G
äth
and
Nis
pel,
2012
, p. 1
85.
t/a
30.9
64.2
FM
OPE
X
OPE
X
Dat
a fr
om S
TAN
Pric
e pe
r uni
t in
EUR
(tot
al c
ost o
r pe
r ton
/km
)N
ote
Sour
ceD
istr
ibut
ion
Sepa
ratio
n an
d so
rtin
g
RDF
Disp
osal
Mon
oinc
iner
atio
n
Dat
a fr
om @
RISK
Proc
ess
Type
of
cost
Cost
xix
Annex S: Results MFA: scenario comparison different material fractions
41,671
56,926 63,801
85,985
-
20,000
40,000
60,000
80,000
100,000
DM Pot DM Real FM Pot FM Real
mate
rial fl
ow
s p
er
year
in t
ons
Landfilled Materials
69,122
55,143
100,992
80,605
-
20,000
40,000
60,000
80,000
100,000
120,000
DM Pot DM Real FM Pot FM Real
mate
rial fl
ow
s p
er
year
in t
ons
RDF materials
xx
35,525 34,275
48,503 46,745
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
DM Pot DM Real FM Pot FM Real
mate
rial fl
ow
s p
er
yaer
in t
ons
Recyclables
104,647 89,418
149,495
127,349
-
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
DM Pot DM Real FM Pot FM Real
maeria
l flow
s p
er
year
in t
ons
Secondary raw materials
xxi
Annex T: Results NPV: cost and income breakdown (undiscounted cash flows)
Annex U: Results NPV: Income and cost composition (undiscounted cash flows)
-58.19 -71.52 -56.23
!160.00&
!110.00&
!60.00&
!10.00&
40.00&
90.00&
SCENARIO A SCENARIO B SCENARIO C
Scenario comparison - costs and revenues (undiscounted cash flows) values EUR million
NPV
Costs incineration RDF
Costs disposal of hazardous waste Costs disposal of RDF
Costs excavation and storage
Costs separation and sorting
Costs project preparation
Income from subsequent use landfill space Income from energy sales
Avoided aftercare costs
Income from inert materials
Income from metals
TOTAL%INCOME 100.68 61.55 76.84
Income streamIncome from metals 26.6% 43.5% 34.9%Income from inert materials 1.9% 3.1% 2.5%Avoided aftercare costs 32.6% 53.3% 42.7%Income from energy sales 38.9% 0.0% 0.0%Income from subsequent use landfill space 0.0% 0.0% 19.9%
SUM 100.0% 100.0% 100.0%Cost streamCosts project preparation 0.6% 0.8% 0.8%Costs separation and sorting 45.0% 53.7% 53.7%Costs excavation and storage 6.7% 8.0% 8.0%Costs disposal of RDF 0.0% 37.5% 37.5%Costs disposal of hazardous waste 0.0% 0.0% 0.0%Costs incineration RDF 47.7% 0.0% 0.0%
SUM 100.0% 100.0% 100.0%
Income and cost composition (discounted cash flows - fraction of total income/cost)