and ll mining - publik.tuwien.ac.at

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

Landfill mining: How to explore an old landfill’s resource potential

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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.


#10

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


#12

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


#14

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





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


#15

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


#16

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.


#17

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


#18

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.


#19

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.


#20

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 .


#21

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?


#26

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


#33

(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.


#34

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.


#36

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


#37

- 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


#38

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


#39

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’


#40

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.


#41

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

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aste

Mat

eria

ls no

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Sorti

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ests

Frac

tion

<5m

m

% (w

/w) F

M o

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vera

ll la

nd

fill m

ass


#42

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

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and

pack

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Org

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was

te

Woo

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Com

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Sorti

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ests

wat

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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

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Plas

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and

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Calo

rific

valu

es (k

J/kg

FM

)


#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


#44

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


#45

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


#46

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)


#47

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*


#48

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


#56

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


#57

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%


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%


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%


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 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%


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


AVG BIAVG BII

Material composition of the Hechingen landfill body (w/w) (DM) - <35mm

Metals Other material


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%


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%


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%


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


AVG BIAVG BII

Material composition of the Hechingen landfill body (w/w) (FM) - >35mm

Metals Other material


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%


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%


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%


-


-


15.4 14.7 19.9

ix

Annex H: MFA model: Transfer coefficients – realistic scenarios (material flows)



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


Plastics and packagingPaper and cardboad


x

Annex I: MFA model: Transfer coefficients – potential scenarios (material flows)



*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


Wood

Hazardous wasteMetals

Mineral and stonesOther MaterialsOrganic Waste


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) +/-




Wood RDF 59,766 33,963



SCENARIO: REALISTIC SCENARIO (FM)Flow value (GJ/a) +/-

Organic Waste RDF 99 401 Other Materials (Stoffe ang. and Complex Products) 17,864 24,938



Wood RDF 80,429 45,141



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



Wood RDF 87,323 49,623


Energy Demand Monoincineration (4% of Total Input) 80,186 20,980

SCENARIO: 15 SCENARIO (FM)Flow value (GJ/a) +/-




Wood RDF 68,293 38,357


Energy Demand Monoincineration (4% of Total Input) 54,444 13,977

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

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934

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934

895,

454

178,

421

178,

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421

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421

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178,

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178,

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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,

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548

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768,

548

Proj

ect P

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para

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000

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300,

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nera

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SUM

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Exca

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64

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18,3

64

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18,3

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18,3

64

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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

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50,5

30

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Dis

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15,8

78,6

54

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37,2

60

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60

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60

-8,4

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Cash

Flo

w-4

0,71

3,91

0 -1

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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

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com

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ergy

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Aft

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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

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1620

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1920

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2120

2220

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26

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292

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292

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292

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Dis

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,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

UR/

ton

Van

Vos

sen

and

Pren

t, 2

011

&V

an

Pass

el e

t al.,

201

3, p

.9-1

0t/

a

CAPE

XM

achi

nery

and

Con

stru

ctio

n20

,300

,000

-

20,3

00,0

00

-

20

,300

,000

-

20,3

00,0

00

Tr

iang

le13

.500

0-27

.000

0

alte

rnat

ive

scen

ario

(equ

ipm

ent

rent

al (1

6.-1

9.00

0 U

SD/m

onth

) (R

enoS

am, 2

009)

Ford

et a

l. 20

13 p

. 28

Sepe

ratio

n an

d So

rtin

g25

,186

,010

-

42,8

07,6

66

-

50

,183

,644

-

join

t

Met

als

ESS

641,

145

-

1,

089,

729

-

1,27

7,49

4 -

20

.12

Nor

mal

18-2

2

20,1

2 EU

R N

on-f

erro

us M

etal

s (2

4% M

etal

le) 3

0 EU

R/Fe

rrou

s M

etal

s 17

(76%

)V

an V

osse

n &

Pre

nt, 2

011,

p. 7

t/a

6349

.426

85.8

FMFi

nes

ESS

4,48

2,83

3 -

7,

619,

294

-

8,93

2,13

7 -

14

N

orm

al13

-15

Van

Vos

sen

& P

rent

, 201

1, p

. 7t/

a63

801.

019

776.

6FM

Gla

ss E

SS1,

430,

572

-

2,43

1,48

7 -

2,

850,

445

-

35

Nor

mal

32-3

9V

an V

osse

n &

Pre

nt, 2

011,

p. 7

t/a

8144

.113

104.

6FM

Haz

ardo

us W

aste

ESS

4,82

5 -

8,20

1 -

9,61

4 -

14

Nor

mal

13-1

5A

ssum

ptio

n: s

ame

as 'O

ther

'V

an V

osse

n &

Pre

nt, 2

011,

p. 7

t/a

68.7

191.

4FM

Min

eral

and

Sto

nes

ESS

5,97

3,99

7 -

10

,153

,767

-

11,9

03,3

12

-

35

N

orm

al32

-36

Van

Vos

sen

& P

rent

, 201

1, p

. 7t/

a34

009.

518

026.

1FM

Org

anic

ESS

2,93

4 -

4,98

7 -

5,84

6 -

35

Nor

mal

32-3

6V

an V

osse

n &

Pre

nt, 2

011,

p. 7

t/a

16.7

69.3

FMO

ther

Mat

eria

ls E

SS10

9,02

1 -

185,

298

-

21

7,22

6 -

14

Nor

mal

13-1

5V

an V

osse

n &

Pre

nt, 2

011,

p. 7

t/a

1551

.621

98.1

FMPa

per

ESS

70,6

44

-

120,

070

-

14

0,75

9 -

24

Nor

mal

22-2

6V

an V

osse

n &

Pre

nt, 2

011,

p. 7

t/a

586.

586

4.2

FMPl

astic

s an

d Pa

ckag

ing

ESS

5,64

9,91

3 -

9,

602,

934

-

11,2

57,5

68

-

35

N

orm

al32

-39

Van

Vos

sen

& P

rent

, 201

1, p

. 7t/

a32

164.

514

242.

1FM

Sort

ing

Rest

s ES

S3,

260,

975

-

5,54

2,55

0 -

6,

497,

559

-

14

Nor

mal

13-1

5A

ssum

ptio

n: s

ame

as 'O

ther

'V

an V

osse

n &

Pre

nt, 2

011,

p. 7

t/a

4641

1.1

1970

9.6

FMTe

xtile

s ES

S2,

497,

633

-

4,24

5,12

8 -

4,

976,

586

-

35

Nor

mal

32-3

9V

an V

osse

n &

Pre

nt, 2

011,

p. 7

t/a

1421

8.8

7210

.5FM

Woo

d ES

S1,

061,

519

-

1,80

4,22

1 -

2,

115,

098

-

35

Nor

mal

32-3

9V

an V

osse

n &

Pre

nt, 2

011,

p. 7

t/a

6043

.134

34.1

FM

Cost

s of

RD

F D

ispo

sal

25,0

58,5

55

-

42

,591

,036

-

49,9

29,6

87

-

jo

int

RDF

Tran

spor

t7,

885,

751

-

13,4

03,1

00

-

15

,712

,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

05,3

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


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


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


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)

and ll mining - publik.tuwien.ac.at

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