The use of multi criteria decision analysis,
an application and evaluation.
Paper: MSc. Thesis, Technology Management Date: June, 2010
Supervisors: Drs. J.C.L. Paul, University of Groningen Prof. dr. J. de Vries, University of Groningen P. Schade, MSc., Nidera Handelscompagnie B.V.
Main content
Part I:
A location analysis of wood pellet production, using a MCDA
modelling approach.
Part II:
Preface
This thesis serves as the final research project of the Master of Science Technology
Management of the University of Groningen. A significant part of this research consisted out of a five month internship at Nidera H.B.V., an international commodity trading organization. During my internship I have learned a lot regarding international production development, trade, biomass and (bio-) energy markets as well as conducting an extensive research project with both pragmatic and academic goals.
I would like to thank Louk Paul, Jan de Vries, Pjotr Schade and Karlijn Arkesteijn for their support and useful criticism on my thesis’ contents, methodology and structure.
Additionally, I would like to thank Marleen Vermeulen, Bald Sinke, Peter-Paul Schouwenberg and all other people involved from the Nidera who have helped me familiarize with the organization and the biomass and bio energy market.
I have attempted to produce a valuable contribution to the organization and its business unit energy as well as interesting and renewed academic insights regarding the use of MCDA. I hope to provide the reader a useful and inspiring read.
General introduction
This thesis consists of two parts. The general introduction will elaborate the origin and relation of both parts. Because both parts have been written for a different audience, the differences in focus and writing style will also be addressed.
Bio energy commodity markets are developing rapidly and market actors are trying to gain market share and exploit the arising opportunities. One of the bio energy commodities is the wood pellet. Production and global trade of this commodity has developed quickly and has attracted numerous competitors to the market. Nidera, an organization working in the bio energy market was the starting point of this thesis. During a five month internship, I was asked to aid the origination of wood pellets, i.e. securing the long term supply of wood pellets, by developing and investing in assets, long term supply and off take contracts. During this research numerous questions were raised: Which regions would prove most profitable for origination of wood pellets in the long run? Should we produce in distant regions with cheap resources or produce near our markets? How to cope with uncertain growth prospects as subsidies or regulations may change radically, for example affecting demand in regions? Such specific challenges are common for most businesses. They then also raise a number of more academic questions: How should organizations cope with such challenges and corresponding decisions? How can available decision analysis techniques aid in these challenges and what are their merits?
This thesis has therefore covered two topics. In part one, a location analysis of the
production of wood pellets can be found. The second part has reflected on the analysis of part one and explored the strengths and weaknesses of applying Multi Criteria Decision Analysis in complex and political contexts such as a location analysis.
General content
Preface ... iii
General introduction ... iv
Part I Abstract part I ... 2
1. Introduction part one ... 3
1.1 Introduction ...3
1.2 General energy and biomass developments...3
1.3 Nidera Handelscompagnie B.V. ...4
1.4 Main problem statement ...5
1.5 Literature background...6
1.6 Summary and problem typology ...9
2. Research design ... 11
2.1 Introduction ... 11
2.2 Research structure and research sub-questions ... 11
2.3 Methodology ... 12
2.4 Summary ... 14
3. Criteria selection ... 15
3.1 Introduction ... 15
3.2 Market review ... 15
3.3 Selected criteria & measurement ... 19
3.4 Summary ... 20
4. Comparison of regions ... 21
4.1 Introduction ... 21
4.2 Criteria data overview ... 21
4.3 Score overview ... 28
4.4 Summary ... 30
5. Overall comparison and scenario analysis ... 31
1. Introduction part two ... 3
2. Literature review ... 7
2.1 Introduction ...7
2.2 Concepts ...7
2.3 Characteristics of decision making in political and complex contexts ... 14
2.4 Literature summary and refined theoretic framework ... 20
3. Research design ... 22
3.1 Introduction ... 22
3.2 Specific research questions ... 22
3.3 Research methodology ... 23
4. Case evaluation ... 27
4.1 Introduction ... 27
4.2 MCDA and Instrumental rationality ... 27
4.3 MCDA and procedural rationality ... 29
4.4 MCDA and structural rationality ... 33
4.5 Summary and conclusions ... 35
5. Conclusions ... 37
6. Discussion ... 39
Part III References ... 2
Part I:
Abstract part I
Purpose: Part one of this thesis has been written to aid the energy origination desk of Nidera Handelscompagnie B.V. Pragmatically, this part has identified the most potential production regions for wood pellets. Academically, this analysis has also served as a case for part two of this thesis in which the analysis tool that has been used has been evaluated.
Approach: A Multi Criteria Decision Analysis approach (MCDA) has been applied in this research. By reviewing literature related to facility location decisions, decision aiding
processes and a market overview, a selection of key criteria has been made. Subsequently, a number of regions have been compared and scenarios have been analyzed and ranked. Findings: Four main criteria have been selected and analyzed: feedstock, production, capital and market potential and logistics. A balanced ranking of regions has been depicted in table 1. The most potential regions possess abundant feedstock at low price levels and most are located near harbours for cheap and flexible transport. Furthermore, investment climate proved to be an important factor.
Implications: The ranking formed in this research aids Nidera in focussing on certain regions that show potential for origination of wood pellets. The ranking acts as a benchmark tool to quickly scan new project proposals and aid in the construction of a long term strategic location planning. The constructed model allows for analysis of new scenarios and may act as a communication tool.
Research limitations: The ranking has to be seen as a rough benchmark comparing generalized regions, not specific sites. Moreover, the biomass market is still emerging, thereby limiting the preservability of the used data, weights and ranking.
Keywords: Wood pellets, biomass, MCDA, facility location selection, decision analysis.
1. Introduction part one
1.1 Introduction
As noted in the general introduction, part one will address the facility location analysis for the production of wood pellets. In this first chapter of part one, a concise introduction of the biomass market, an introduction of the organization and the central research question will be provided. After this introduction, the next chapter will cover the methodology and research design of part one. In chapter three and four, the performed analysis will be described. Finally, chapter five will elucidate the conclusions and implications.
1.2 General energy and biomass developments
Global primary energy demand has grown in the past decades and this trend is expected to continue. Coal, oil and gas have been major sources to meet this demand and will remain so in the coming decade. Along the fossil fuel sources there are nuclear and renewable energy sources (RES). The RES are developing, driven by politics, environmental considerations, high fossil fuel prices and technological advancements (Resch et al, 2008).
The four most used RES are solar, hydro, wind and biomass. Of these types, biomass has been especially interesting to create base load capacity because of storage capabilities, securing continuous supply. It is expected that the biomass contribution to energy supply is to grow to 11.5% of the RES by 2030, while total RES for electricity generation will more than double (Resch et al., 2008). This growth, especially in Europe, is driven by legislation (e.g. mandates, subsidies and emission-rights) (Resch et al., 2008).
Bio energy is the energy extracted from biomass or any biological material which has stored sunlight into chemical energy, excluding fossil fuels. Biomass such as sawdust, logs, straw, pulp and paper waste can originate from various regions. In addition, different technologies and processes can be used to improve logistical feasibility and utilization of biomass such as drying, torrefication, fermentation, pyrolysis, and pelletizing. For example, biomass which is used for co-firing1 in European coal plants can be originated in Brazil or Canada where saw
1 Co-firing is the practice of supplementing a base fuel (e.g. coal) with a dissimilar fuel (e.g. wood
dust is dried and pelletized before sea transport. Alternatively sawdust can be directly incinerated in a dedicated (100%) biomass boiler near the biomass producing facilities such as sawmills.
This research will confine itself to industrial wood pellets, a commodity produced from forestry (by-) products and industrial residues. These industrial wood pellets are mainly used for co-firing in coal plants. Industrial wood pellets are easy to handle, transport, store and convert into energy, which makes it one of the most successfully traded biomass
commodities (Heinimö and Junginger, 2009).
1.3 Nidera Handelscompagnie B.V.
This research has been written on behalf of Nidera H.B.V., which is an international
commodity trading and shipping organization which started in the grain sector in the 1920’s. At the time, several European grain merchants founded the company in Rotterdam. Nidera, a name which resembled the initials of the six agricultural trade regions were they focussed their activities: Netherlands, Italy, Deutschland (Germany), England, Russia and Argentina. Since then, Nidera has expanded its global network of facilities and offices and employs around 3000 people. The firm has diversified into other agricultural commodities and recently, the company became active in the biomass and bioenergy markets. The growing biomass markets offered the organization growth and diversification. Currently, almost 20 million tons of grains, oilseeds, vegetable oils, oilseed meals, feedstock and bioenergy products are traded.
To increase their activities in the biomass and bioenergy markets, the energy origination desk was founded. This desk is developing long-term (3 to 15 years) origination projects for biomass and bioenergy, by developing and investing in assets, long term supply and off take contracts. These activities aim at securing supply of commodities at cost price and improving profit margins of the trading activities.
coordinated in the origination projects. The energy origination desk also works closely with the departments of risk, finance and legal.
1.4 Main problem statement
Nidera had set ambitious targets for growth in the biomass markets and had identified the trade of wood pellets as a promising way to reach these targets. In order to support the trade of wood pellets, the energy origination desk was responsible for development of new projects that result in long term supply of wood pellets.
However, the market of wood pellets was still emerging, requiring careful tradeoffs of the location of new projects. The market can develop in multiple ways and the location decision could prove crucial for the success of such projects. In addition, at the time, there was insufficient knowledge of the market and the viability of originating wood pellets from various regions.
This has led to the formulation of the following management problem (red. Challenge): “Nideras’ business unit energy wanted to strategically expand its wood pellet origination projects, but had insufficient knowledge for the selection of locations.”
Globally there are numerous regions where origination projects can be developed. The market and competition is rapidly developing, but key location criteria had not been analyzed comprehensively by Nidera yet. Commitment to a specific location would be long term and the location choice posed a challenge critical for success (see e.g. Lorentz, 2008). This analysis was performed in order to identify the strategic regions, posing long term potential for the origination of wood pellets.
The main question described in this case was formulated as:
“Where could an organization like Nidera strategically locate its wood pellet origination projects?”
research attempted to guide the origination desks’ search and selection of origination projects as well as contribute to the insights in the dynamics of the strategic location decision for producing wood pellets.
A number of research boundaries were set. First, the specific feedstock types considered were forests, forestry by-products and industrial residues. Second, a specific number of regions have been considered, depicted in appendix 2. Third, the time scope was limited to about ten years (2020), as the analysis aimed to contribute to production facility decisions on the short term.
1.5 Literature background
A number of literature themes will be addressed based on a similar research project of Lorentz (2008). In his research, Lorentz has related the regional production location comparison to supply chain management issues, facility location analysis and multi criteria decision analysis. Each of these themes will be touched upon and in addition, decision analysis (processes) and concepts of operations strategy will be elaborated. These themes and interlinks have provided an integrated and comprehensive base of reasoning for the research design, provided in the next chapter.
Decisions, decision processes and multi criteria decision analysis
Bocean (2006) has described decision making as the cognitive process of selecting a course of action among multiple alternatives. In this way this research can be seen as a decision making process in which the final choice is a region or specific area to locate a wood pellet project.
Decision modes: Choice criteria: single multiple Description of alternatives: certain Computation Compromise
uncertain Judgment Inspiration
Table 2 Modes of decision making (Thompson, 1964)
Decisions are solved using decision making processes. Balasubramaniam and Voulvoulis (2005) have described that this process is initiated by characterization of a decision problem and definition of the analysis contexts. Subsequently a scoring and analysis phase leads to a result, which may be a ranking and preference. Finally, the results are reviewed and a decision is made.
According to Balasubramaniam and Voulvoulis (2005), MCDA is aligned with general decision making processes because of various features. MCDA suits decision contexts involving multiple criteria and participants. MCDA allows the objectives of each of the decision makers to be incorporated in the decision process. Furthermore it allows transparency, facilitates analysis and making trade-offs among decision makers. Finally, the MCDA-process allows compromises required by the non-dominated nature of the set of alternatives, i.e. there are no alternatives that outperform the other alternatives on all criteria (Zeleny, 1982).
Supply chain management
Supply chain management and design involve decisions regarding customers, products, manufacturing processes as well as the establishment / closure of facilities (Lorentz, 2008). Such decisions require careful analysis, because of long term investments and impact on profitability (Lorentz, 2008). The field of supply chain management has been well elaborated in literature2. Kaplinsky and Morris (2001) have defined supply chain
2 Supply chains and value chains are considered as synonyms based on Feller, Shunk and Callarman
management as the description of activities which are required to bring a product or service from conception, through the different phases of production, delivery to the final consumer and final disposal after use.
Supply chain analysis has been considered increasingly useful by Kaplinsky and Morris (2001) because of three reasons. First, the growing division of labour and global dispersion of components have increased systemic competitiveness. Second, supply chain analysis has been able to support the relevance of efficiency in production. Third, entry in global markets which allowed for sustained income growth required understanding of the dynamic factors within the whole supply chain. Moreover, strategic supply chain decisions have become especially relevant for industries where supply chain functionality and management have been the main sources of competitiveness (Lorentz, 2008).
Location decisions
Decisions regarding facility locations are often based on limited knowledge of the current and projected conditions (Current, Ratick and Revelle, 1997; Goetschalckx, vidal and Dogan, 2002). Moreover, the business environment is posed to a high rate of change, imperfect and incomplete knowledge of the market and high levels of uncertainty and risk, especially in emerging markets (Lorentz, 2008). MacCarthy and Atthirawong (2003) have addressed that the decision process of selecting international locations is difficult because of (1) the many factors involved (red. multiple criteria), (2) the right information and right experts to obtain, (3) management issues and (4) the relation of new location and existing manufacturing resources/technology. In order to deal with decisions, rationality has a mediating impact and results in a better quality decisions (Nooraie, 2008).
Various advanced techniques have been developed, such as the fuzzy Analytical Hierarchy Process (AHP) and outranking techniques (e.g. Özerol and Karasakal, 2008)3.
Altogether, the field of location theory has evolved to more comprehensive techniques, useful in the context of this research. A mix of quantitative and qualitative criteria and combining techniques attempt to provide a more comprehensive understanding and higher quality decision making.
According to Slack and Lewis (2008) location decision criteria may relate to the operation resources or the market requirement, which need to be aligned accordingly. MacCarthy and Atthirawong (2003) have presented an overview of the quantitative and qualitative decision criteria associated with location decisions.
Operations strategy and location decisions
Operation strategy is also related to facility (location) decisions. Slack and Lewis (2008) have defined the location choice as part of operations strategy. More specifically: “... the total pattern of decisions which shape the long-term capabilities of any type of operation and their contribution to overall strategy through the reconciliation of market requirements with operational resources.”(p.18) According to Slack and Lewis (2008), market understanding need alignment with the design of resources and processes of the organization.
1.6 Summary and problem typology
This first chapter has introduced the context and problem statement of this research. Nidera is expanding its activities in the wood pellet market. In order to expand these activities, a secured flow of wood pellets is required. This requires developing or investing in production facilities and careful tradeoffs when choosing locations.
Furthermore, this section has provided a concise literature overview. Different decision types, the process to cope with them and the field of MCDA have been reviewed. The facility location decisions have been placed in the context of supply chain decisions. Finally, the
3 For a more extensive literature review of the facility location in the context of supply chain
characteristics of location decisions and the developments in solving location decision problems have been addressed.
2. Research design
2.1 Introduction
Based on the central research question and the literature background provided in the introduction, the research design will be drawn up. This chapter commences with the outline of the structure and the research sub-questions (2.2). Then the methodology will be
addressed (2.3).
2.2 Research structure and research sub-questions
The central research question formulated in the introduction has been divided in three sub-questions. In order to identify the strategic regions for the production of wood pellets, the generic MCDA steps have been used as described by Yang and Lee (1997). These include: (1) the problem identification and analysis context, (2) the identification of relevant criteria, (3) development of criteria weights, (4) the analysis of comparative results, (5) the collection of data and ranking of criteria and (6) identification of preferred alternatives. Each of these MCDA steps has been transposed in three sub-questions.
The problem identification has already been covered in the introduction. The first sub-question that has been formulated addressed the identification and selection of relevant criteria (table 3). Sub-question two involved the steps of scoring, assigning weights and comparison of the regions on each of the chosen criteria and as a whole. Finally, the third sub-question has covered to the comparison by analyzing possible scenarios.
Table 3 Research questions derived from the MCDA steps of Yang and Lee (1997)
The remainder of part one will be structured along these sub-questions. The criteria that have been selected will be elaborated in chapter three. Chapter four will cover the specific
Sub-questions: Addressed in:
1 What are the relevant location decision criteria in the context
of the international wood pellet market?
Chapter 3
2 How do the possible wood pellet production regions
compare?
Chapter 4 & 5
3 What are relevant scenarios for the international wood pellet
market and what is their impact on the location comparison?
criteria performance per region. Chapter five will look into the overall comparison, a
scenario analysis and crosscheck. Then, the final chapter of part one, chapter six will present the management implications and conclusions.
2.3 Methodology
This section will elaborate the methodology of the MCDA. It will look into the overall methodology. The specific methodological considerations of each sub-question will be addressed in the introductions of the following chapters.
Research strategy
Yin (2003) has described three conditions that distinguish the suitable research strategies. First, the type of research question posed. Second, the required extent of control an investigator has over behavioural events and third, the degree of focus on contemporary as opposed to historical events. This research aimed at analysis of a ‘where’ question, did not require control over actual behavioural events and was focussed on contemporary events. According these conditions and their favoured research strategy, this research has made use of both archival analysis as well as a survey strategy (interviews).
Data collection
The assessment of biomass markets is challenging, particularly for global areas (Siemons, 2002). Siemons has addressed two main reasons; the definition of available data and the reliability. Biomass sources are varied and disparate and many biomass residues have (had) no market and are informally traded. As a result, few data records are kept. Although, in recent years (2002-2009) more and more studies and databases have been published, addressing the feedstock potential, logistics, trade flows, regulation and sustainability. Kaplinsky and Morris (2001) have indicated that the availability of data by country level is often best described (table 7). Therefore this research has compared the regions using country data and where necessary only feasible parts have been considered.
Bioenergy task 40. The references in this literature have been used to identify other relevant sources. Multiple experts within Nidera as well as external to Nidera have been interviewed. Lastly, a significant amount of data has been gathered from publicly accessible databases. Data analysis strategy and MCDA technique
The literature review in the introduction mentioned a variety of MCDA techniques. Considering data reliability, market dynamics and significant number of regions, this research made use of a simple and broadly applied method of modelling. Although more advanced modelling methods existed, such as fuzzy techniques, the relative simple weighted sum method was selected. There were five arguments for this selection. Different data types needed to be integrated in the analysis, ranging from quantitative to qualitative. The
available data on wood pellets was limited, so techniques that were able to process detailed data did not seem beneficial. The emphasis was placed on gathering useful data, rather than sophisticated manipulation of the data. The weighted sum score method had proven general applicability (Özerol and Karasakal, 2008) and facilitated scenario analysis and integration of different expert views.
In order to compare all of the selected regions, a number of criteria have been analyzed per region. These criteria have been scored into a score weighted sum matrix or
multi-parametric decomposition (see e.g. Zeleny, 1982). Validity
Four types of validity have been mentioned by Yin (2003): construct validity, internal and external validity and finally reliability. Each of these forms has been concisely addressed. In order to improve the construct validity of this research, multiple data sources have been triangulated. Additionally, multiple market experts, both within the organization as well as external market analysts, have reviewed the contents of this research design, the analysis and the outcomes.
According to Yin (2003), internal validity is related to the causal relationships drawn up in this research. The outcomes of this research are not intended to predict market
organization by exploration of possible scenarios. However, the comparison has still been compared with current production developments in order to identify possible correlations. External validity has been optimized by usage of information that was not specific to Nidera. In this way, the analysis represented the location dynamics for both Nidera and other market actors.
The reliability of this research was enhanced by using publicly available data and providing detailed methodological considerations. In order to minimize biases, multiple data sources were inquired from publicly available databases. In case of qualitative judgements, the rationale for appointing scores has been elaborated.
Although the research design was intended to optimize validity, validity itself has been difficult to assess. There are few researches to benchmark the results of this research with. Possible limitations of validity have been elaborated in the discussion of part one (chapter 7).
2.4 Summary
3. Criteria selection
3.1 Introduction
RQ1: What are the relevant location decision criteria in the context of the international wood pellet market?
This chapter will cover the first step in answering the main question of this research, the selection of criteria. In order to select the relevant criteria, a location criteria overview of MacCarthy and Atthirawong (2003) has been used as an outline for the criteria
identification. In order to identify and select the specific criteria relevant in this context, biomass and bioenergy literature and wood pellet market characteristics have been evaluated. A selection of criteria has been made based on relevancy, the ability to
differentiate regions for origination potential, significant cost impact and long term impact on potential. After selection and definition, the data sources and data processing have been described.
3.2 Market review
Biomass to bio energy routes and supply chain
The first chapter elaborated the relevancy of supply chain considerations. Therefore the main biomass supply chain characteristics have been described in this market review. First the overall biomass supply chains have been covered, followed by the specific supply chain of wood pellets.
Biomass is processed and converted into energy (thermal, electric or kinetic), following a number of conversion routes4. Oil crops are mainly utilized for transport and electricity, while solid and wet biomass is usually converted into heat or electricity. These heat and electricity generation technologies and systems are known for their complexity and dynamics (Li, 2008).
The international biomass to bio energy supply chain offers various configurations. Various regional supply chain comparisons have been published, analyzing costs and sustainability
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aspects (See e.g. Suurs, 2002; Van Dam, 2009). Conceptually, the supply chain can be expressed as in figure 1. Biomass is produced at a site locally or distant to the utility, up to thousands of kilometres away. Optionally, wet biomass can be pre-treated to improve the logistic characteristics. After production and optional treatment it is transported to the utility, where it receives further treatment and is incinerated. Transport consists of modes such as truck, train, ship or a combination and may pass various terminals.
S e a / riv e r Pretreatment / conversion Distant biomass Moisture Density Energy content Utility Heat Power Local biomass P o w e r Pretreatment / conversion Distant biomass Moisture Density Energy content P o w e r S u b s id ie s S u b s id ie s S u b s id ie s
Figure 1 Conceptual overview of the biomass supply chain
The supply chain of biomass is not isolated5. The wood pellet supply chain overlaps with for example the pulp for paper industry, which competes on the same feedstock upstream. Downstream, wood pellets compete with other biomass commodities, such as wood chips and waste wood. Supply chain overlapping is still ill described, although the impact of only considering the ‘focal’ chain can be very costly (Hertz, 2006). Hertz has also illustrated that certain stages in the chain development seems to have significant impact on the effect of the overlap. For example; the younger woody biomass supply chain may benefit from the older timber wood supply chain, because of residual resources (e.g. saw dust from saw
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mills). However, in later development stages, competition on wood pellets is likely to increase feedstock costs affecting both markets. This development has been addressed in the scenario analysis (chapter 5).
The following pre-treatment steps considered for the production of wood pellets are storing, drying, sizing and densification. The energy can also be converted using gasification,
pyrolysis, and torrefication; latter technologies have been considered in the scenario analysis (chapter 5). The treatment of biomass results in various biomass commodity types with specific characteristics, which are shown in table 4. These characteristics have been used to align the data in chapter four later on.
Biomass characteristics Moisture (%) Density (kg / m3)
Net Caloric Value (GJ / t wet) Energy density (GJ / m3) Ash content (%) Coal (lignite) 6 1000 25 25 13
Round wood (logs) 50 450 - 600 9.5 5.1 - 5.7 0.2
Wood chips 20 - 50 210 - 320 9.5 - 15.2 3.0 - 5.7 0.2 - 0.3
Wood pellets 7 600 17.5 10.5 <3
Table 4 Biomass characteristics (EUBIA, 2009; Suurs, 2002; Hamelinck, Suurs and Faaij, 2005)
Wood pellet supply chain cost comparisons
Various papers have been published calculating and comparing wood pellet and other biomass commodity supply chains. Table 5 shows an overview of the relevant variables considered by scholars.
The biomass feedstock costs and harvesting window are considered for biomass production. Pre-treatment of biomass, in this case pelletizing, involves a mixture of equipment
Operation step Variable
Biomass production Biomass costs
Harvesting window
Pre-treatment Equipment capacity
Capital an operations and maintenance
Energy consumption (power, fuel, heat)
Load factor
Dry matter loss
Moisture loss
Transport Transport distance
Transport mode
Load factor
Energy conversion Conversion efficiency
Capital an operations and maintenance
Load factor
Table 5 Criteria considered for supply chain cost comparisons (Hamelinck et al., 2005; Suurs, 2002; Uslu et al. 2008; Van dam, 2009)
Co-firing economics: subsidies and emissions
Subsidy and emission regulation resulting from the Kyoto protocol are major drivers for the biomass and bioenergy markets (IEA Bioenergy, 2007). For biomass co-firing in coal plants and dedicated biomass incineration, different subsidies apply6.
For wood pellets, the main industrial users are co-firing plants. Except from the United Kingdom, Belgium and the Netherlands, no co-firing subsidies exist (Heinimö and Junginger, 2009). The other drivers for co-firing are the coal price, carbon emission price and the price of the wood pellets itself. Other forms of biomass can be co-fired as well, but the
characteristics of wood pellets are often favourable (e.g. grinding characteristics and moisture content).
The economic feasibility of co-firing biomass depends mostly on the comparative costs of coal and biomass and the incentives for the mitigation of CO2 emissions (De and Assadi, 2009). The amount of carbon emission rights allocated is decreasing, enforcing
implementation of cleaner and less carbon emitting technologies. The standard technical potential of co-firing depends on the technology and ranges between 10 – 20%.
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The European Union has allocated the allowed carbon emission restrictions per country. National governments have allocated these emissions to the specific industries, such as energy. Since the energy sectors in various countries have been allocated independently, the pressure to buy extra emission rights differs per country.
3.3 Selected criteria & measurement
This section will elaborate the criteria that have been selected based on market literature and expert interviews. The selection was guided by the facility selection criteria overview of McCarthy and Atthirawong (2003) and selection was based on three aspects; the ability to differentiate regional production or market potential, reliability on the longer term and significant cost impact. The criteria that have been selected are presented in figure 2.
Feedstock
Production
Feedstock availability
Available logistics modes Capital costs
Market potential and
logistics Proximity to market Reachable market potential Feedstock competition Feedstock costs
Capital
Power costs
Main criteria: Criteria:
Figure 2 Selected criteria
the logistical modes and distance. Each of these criteria has been assessed and modelled using multiple sources and indicators7.
3.4 Summary
In line with sub-question one, a selection of relevant criteria has been made. A market review has been performed to identify and select the relevant criteria. The first main criterion selected, covered aspects of feedstock for the production of wood pellets. Availability, prices and competition on feedstock have been selected as specific criteria in order to assess the feedstock potential of various regions. The production of wood pellet requires significant amounts of power, which is the second criterion placed under the main criterion of production. The prices of power differ per region, resulting in different power consumption costs per region. The third main criterion that has been selected is capital. Investing in different regions involves for examples different interest rates and currency risks. The fourth main criterion that has been constructed is related to the market potential and logistics. This main criterion has considered logistic modes, distances and the potential of reachable markets. The following chapter will address the next step in this MCDA analysis; the comparison on the selected criteria.
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4. Comparison of regions
4.1 Introduction
RQ2: How do the possible wood pellet production regions compare?
The second research sub-question has been covered in both this chapter and the next. This chapter will present the regional data used to compare each of the selected criteria
individually by assigning scores. The next chapter will continue by weighting the individual criteria into an overall comparison and scenarios.
The data used for the criteria will be discussed in the following order: feedstock, production, capital, market potential and logistics. Only part of the data has been presented in this chapter, more figures have been placed in the appendix 9. Section 4.3 will summarize the appointed scores for each of the compared regions.
4.2 Criteria data overview
Feedstock availability
Standing tree volume is the most basic indicator for feedstock availability. Standing tree volume depends on the forest surface and density of the forest. Scandinavian countries for example have more forest surface, while central European countries have higher forest density. Known large wood processing countries such as Brazil, USA, Canada and Russia have huge volumes available, although large parts are (yet) inaccessible.
Yearly industrial residues available
0 20 40 60 80 B ra zil C h ile U SA w es t U SA e as t C an ad a C an ad a So u th A fr ic a R u ss ia A u st ria B elg iu m C ze ch R ep . De n m ar k Es to n ia Fin la n d Fr an ce G er m an y H u n ga ry It aly La tv ia N et h er la n d s P o la n d P o rt u ga l R o m an ia Slo va kia Slo ve n ia Sp ain Sw ed en U n it ed Countries M ill ion m 3 Yearly industrial residues logs, chips sawdust (excl. bark), volume available afterFigure 3 Industrial residues available in 2007 (FAOSTAT, 2009)
Since total volume of available industrial residues is often insufficient for the biomass demand, forest residues are another import feedstock source (figure 4). To indicate the yearly forest residues, the surface angle of the landscape as well as forest density influences the share which is technically available (UNECE, 2007). The technically available forest residues ratio shows the density being highest in Czech Republic and the Baltic countries. Since forest residues availability has only been reported for EU countries, average residue ratios have been assumed for non-EU countries.
Yearly forest residues 2008
0 10 20 30 40 A u st ria B elg iu m C ze ch R ep . De n m ar k Es to n ia Fin la n d Fr an ce G er m an y H u n ga ry It aly La tv ia N et h er la n d s P o la n d P o rt u ga l R o m an ia Slo va kia Slo ve n ia Sp ain Sw ed en U n it ed K in gd o m Countries M ill ion m 3 Yearly total volume of forest residues Yearly technically available volume of forest residues
Figure 4 Yearly forest residues (UNECE, 2007)
Feedstock costs
that prices and resulting costs may fluctuate rapidly, therefore this research did not rely solely on feedstock costs.
AVG residue prices
0 10 20 30 40 50 60 B ra zil C h ile U SA w es t U SA e as t C an ad a w es t (B .C .) C an ad a ea st ( Q + N .S. ) So u th A fr ic a R u ss ia A u st ria B elg iu m C ze ch R ep . De n m ar k Es to n ia Fin la n d Fr an ce G er m an y H u n ga ry It aly La tv ia N et h er la n d s P o la n d P o rt u ga l R o m an ia Slo va kia Slo ve n ia Sp ain Sw ed en U n it ed K in gd o m Countries Eur o pe r tonne Prices industrial residues Price Forest residues
Figure 5 Average prices (VIEWLS, 2005; Siemons, 2002; EUBIONET, 2007)
Feedstock competition
The third feedstock indicator has been competition on feedstock. In some regions nearly all feedstock is used for the production of pulp and paper, while other regions mainly serve the panelboard production. In addition, there are differences in the amount of available
feedstock in relation to the competition, which has been used as an indicator for the competition on feedstock. To indicate the amount of competition, the volume consumed by the alternative consumers of feedstock and conversion rates have been processed into ratios. These ratios indicate significant differences in the type of competition on feedstock. Regions such as South Africa showed minor competition on forestry volumes, while Brazil, Russia, Canada and Latvia showed least competition on round wood (logs).
Production
Figure 6 Industrial power prices (Europe’s energy portal, 2009; AEI, 2009)
Capital
Developing a production facility requires a significant investment. The capital costs and risks differ per country and have been compared by using a Weighted Average Cost of Capital (WACC), considered internally by Nidera (figure 7). The difference or risk premium in the WACC is higher in less developed and less stable regions.
0 4 8 12 16 20 Br az il C h ile U SA w e st U SA e as t C an ad a w e st C an ad a e as t So u th A fr ic a Ru ss ia A u st ria B e lg iu m C ze ch Re p . De n m ar k Es to n ia Fin la n d Fr an ce G e rm an y H u n ga ry It aly La tv ia N e th e rla n d s P o la n d P o rt u ga l R o m an ia Slo va kia Sl o ve n ia Sp ai n Sw e d e n U n it e d K in gd o m % Countries
WACC
2009Figure 7 Weighted Average Cost of Capital (Nidera standards, 2009)
Market potential
Total co-fired biomass potential EU-27 demand is estimated at 500-940 PJ per year (Hansson et al., 2009). Assuming 500 PJ per year produced by burning industrial wood pellets would result in a consumption of about 30 million tons of industrial wood pellets for the EU-278.
Figure 8 Wood pellet demand (IEA Bio energy, 2007, Pelletatlas, 2009)
Various reports have analyzed and compared supply chain costs and routes (e.g. Hamelinck, 2005; Suurs, 2002; Van Dam, 2009). The following current industrial wood pellet markets have been reported: Sweden, Belgium, Netherlands, Denmark, Germany and Finland. The United Kingdom has also been identified as a market (IEA Bioenergy, 2007), but no demand data was available.
Industrial wood pellets are mainly used for co-firing in coal plants. However, coal plants have been unevenly spread over Europe (figure 9). The coal capacity per country is shown in figure 10. This figure shows the majority of the coal fired plants are located in Germany, United Kingdom, Poland and Spain.
8
Figure 9 Location of EU-27 coal fired plants included in the CPPD (Hansson et al., 2009)
Coal plant capacity
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 Au st ri a Be lg iu m Cz ec h Re p. D en m ar k Es to ni a Fi nl an d Fr an ce G er m an y H un ga ry It al y La tvi a N et he rl an ds Po la nd Po rt ug al Ro m an ia Sl ova ki a Sl ove ni a Sp ai n Sw ed en U ni te d Ki ng do m Countries GW Steam + lignite capacity 12 % cofiring capacity potential
Figure 10 Coal plant and co-firing capacity (Chalmers Power Plant Database, 2009)
In addition to the distribution of coal plants, the size and technology per installation differ. Fluidized bed boilers (15%) can technically co-fire more than grate fired or pulverized coal boilers (10%) (Hansson et al, 2009; Berggren et al, 2008; Leckner, 2007). Some advanced boilers in Denmark apply co-firing up to 20% (IEA Bioenergy, 2007).
Figure 11 shows the allocated emissions of coal plants per region. Not surprisingly, regions with numerous coal power plants have been allocated with more emissions rights. However, the ratio of emission rights that has been allocated to the coal sector differs among regions. For example, the emission rights to the coal sector are tighter in Sweden than they are in Hungary (10:1). As has been elaborated earlier, tighter emissions allocations result in higher pressure to avoid emissions, for example by utilizing biomass.
Figure 11 Emissions allocated to coal sector
In 2009, only the United Kingdom, Belgium and The Netherlands subsidize co-firing of biomass. Other countries do have subsidies for biomass, but those are restricted to dedicated (100%) biomass incineration and are often limited to 5MW, which is relatively small.
Logistics
Several studies on biomass and bio energy elaborate on logistics (e.g. Suurs, 2000;
(Hamelinck et al., 2005). All these effects can pose significant regional differences in costs and are therefore difficult to generalize.
To assess the logistic position, the rough costs of transport are of interest. Distant markets are served via long haul sea transportation. The associated costs have been expressed in table 6.
Sea transport costs, wood pellets, ARA € / t Ship capacity
Brazil 38
South Africa 38
North America, east 19 - 29 56 - 22kt
North America, west 23 - 34 56 - 22kt
Table 6 Long haul sea transport costs (Nidera freight department, 2009)
A European assessment of the transport costs shows strong preference for short sea
shipping, followed by internal water way and train transportation also indicated by Hansson (2009) and Van Dam (2009) (table 7).
Transport type Fixed costs € Variable costs € / km
Short sea 7.4 0.0055
Internal water way 7.4 0.022
Train 0.38 0.046
Table 7 Transport cost per ton wood pellet estimations (adapted from van Dam et al., 2009)
Van Dam et al. (2009) concluded that regions connected to sea are most attractive for long distance trade, followed by ‘land locked’ regions having a suitable inland water network and train networks.
4.3 Score overview
have been presented in table 89. The next chapter will elaborate more on the criteria performance and their cohesion.
Regions / criteria Fe e ds toc k A v a ila b ili ty o f s ta n d in g w o o d A v a ila b ili ty o f in d u s tr ia l re s id u e s A v a ila b ili ty o f fo re s tr y r e s id u e s In d u s tr ia l re s id u e s c o s ts Fo re s tr y r e s id u e s c o s ts C o m p e ti ti o n o n s ta n d in g w o o d C o m p e ti ti o n o n i n d u s tr ia l re s id u e s C o m p e ti ti o n o n f o re s tr y r e s id u e s P rod uc ti on P o w e r C a pi ta l W A C C M a rk e t po te nt ia l & l og is ti c s A v a ila b le l o g is ti c m o d e s P ro x im it y t o m a rk e t R e a c h a b le m a rk e t p o te n ti a l SA Brazil 4 1 3 5 5 5 2 5 3 4 3 1 4 Chile 2 2 3 5 5 2 2 3 4 4 3 1 4 NA USA west 3 2 3 4 5 3 2 3 4 5 4 1 4 USA east 3 2 3 4 5 3 2 3 5 5 4 2 4 Canada west 3 1 3 5 5 4 2 4 5 5 4 1 4 Canada east 3 1 3 5 5 4 2 4 5 5 4 2 4 A South Africa 1 1 3 5 5 3 2 5 4 1 4 1 4 RUS Russia 2 1 3 5 5 5 4 5 4 2 3 2 3 EU Austria 5 5 5 4 4 1 3 1 3 5 3 5 3 Belgium 3 3 4 3 3 1 1 1 3 5 5 5 5 Czech Rep. 4 5 5 4 4 2 4 2 4 2 4 4 5 Denmark 1 3 1 3 2 2 5 1 4 5 5 4 5 Estonia 4 5 4 5 4 2 3 2 5 2 5 3 4 Finland 3 3 5 2 3 1 2 2 4 5 4 4 4 France 2 2 2 3 1 2 2 2 4 5 4 3 3 Germany 3 5 3 2 2 2 5 2 2 5 5 5 5 Hungary 2 1 1 3 3 3 1 2 3 2 4 3 2 Italy 2 1 1 2 4 2 1 1 1 5 4 3 1 Latvia 3 4 3 5 4 4 5 4 5 2 5 3 3 Netherlands 2 2 1 3 3 2 2 1 3 5 5 5 5 Poland 2 3 2 3 5 2 3 1 4 2 3 4 4 Portugal 2 1 2 3 5 1 1 1 3 5 5 3 2 Romania 3 3 2 3 5 4 5 2 3 2 3 3 1 Slovakia 4 4 3 4 4 2 3 2 3 2 4 3 1 Slovenia 5 2 2 3 5 3 1 1 4 2 4 4 1 Spain 1 1 1 3 4 1 1 1 3 5 4 3 3 Sweden 4 4 5 1 3 1 2 2 4 5 4 5 4 United Kingdom 1 2 2 3 4 1 2 2 3 5 4 4 3
Table 8 Wood pellet location comparison overview, scored from high to low performance (5 to 1) on each of the criteria
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4.4 Summary
5. Overall comparison and scenario analysis
5.1 Introduction
RQ2: How do the possible wood pellet production regions compare? (Continued)
RQ3: What are relevant scenarios for the international wood pellet market and what is their impact on the location comparison?
So far, all regions have been scored on each of the criteria. This set of scores shows that some regions perform well on certain criteria and worse on others. In order to compare the potential of the regions, the MCDA prescribes setting weights on each of the criteria. By weighting the all scores into a combined score, the second research sub-question will be covered.
This chapter will also address the third sub-question related to scenarios. First a base scenario will be set up and an overall comparison will be presented (5.2). A crosscheck will be addressed in (5.3). Then, four alternative scenarios will be considered (5.4) and a model output will be compared with the base scenario (5.5).
5.2 Base scenario
Three experts have independently indicated the criteria weights (table 9). A fourth set of weights has been derived from biomass literature. Hereby, the criteria and their relative impact have been inquired and processed into weights.
Criteria weights (%) Expert 1 Expert 2 Expert 3 Literature Average
Feedstock 60 45 63 65 58
Production 10 5 5 5 6
Capital 5 10 2 5 6
Logistics & market potential 25 40 30 25 30
Table 9 Main criteria and weights by source10
The weights of the criteria indicated by the experts and the literature review showed
moderate consensus. The experts agreed that feedstock was critical, followed by logistic and market potential. The production and capital criterion have been assigned small weights.
10
Table 10 has depicted the performance of each of the regions per main criterion, expressed in a score from 5 to 1 (highest to lowest performance). The overview shows that there are no regions that dominate on all main criteria. Combining the overall average weights has resulted in the base scenario output, depicted in table 11. The regions in table 11 have been sorted from the highest score to the lowest scores.
B ra zi l Ch il e U SA w e st U SA e ast Ca n ad a w e st Ca n ad a e ast So u th A fr ic a R u ssi a A u st ri a B e lg iu m Cz e ch R e p . D e n m ar k Est o n ia Fi n la n d Fr an ce G e rm an y H u n ga ry It al y La tv ia N e th e rl an d s Po la n d Po rt u ga l R o m an ia Sl o va ki a Sl o ve n ia Sp ai n Sw e d e n U n it e d K in gd o m Best feedstock 5 4 4 4 4 4 4 5 5 2 5 1 5 2 1 2 1 1 5 1 3 2 4 4 3 1 2 2 Best production 3 4 4 5 5 5 4 4 3 3 4 4 5 4 4 2 3 1 5 3 4 3 3 3 4 3 4 3 Best capital 4 4 5 5 5 5 1 2 5 5 2 5 2 5 5 5 2 5 2 5 2 5 2 2 2 5 5 5
Best logistics & market potential 2 2 2 3 2 3 2 2 3 5 4 5 4 4 3 5 2 2 3 5 3 3 1 2 2 3 4 3
Table 10 Main criteria, scored per region on origination potential from high (5) to low (1)
Est o n ia Cze ch R e p . La tv ia Ca n ad a e ast A u st ri a Ca n ad a w e st U SA e ast B ra zi l B e lg iu m G e rm an y U SA w e st R u ss ia Ch il e Sw e d e n So u th A fr ic a Fi n la n d N e th e rl an d s D e n m ar k Po la n d Sl o va ki a Sl o ve n ia U n it e d K in gd o m R o m an ia Po rt u ga l Fr an ce Sp ai n H u n ga ry It al y Base scenario 5 5 5 5 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 2 2 1 1
Table 11 Base scenario, ranked on origination potential score from high (5) to low (1)
The base scenario shows higher potential for those regions with high feedstock volumes at lower prices. This was expected based on the weights that have been assigned to the feedstock criteria. However, some of these regions are located in eastern regions of Europe (e.g. Estonia, Czech Republic and Latvia), while others are in North America. Both sets of regions seem to rely either on a better logistic positioning, or on a better investment climate. In addition some better scoring regions have a central positioning in their markets such as Austria, Belgium and Germany.
5.3 Crosscheck
The MCDA model has scored and ranked the regions in a base scenario. In order to test and improve the validity of this base scenario. The regions have been compared to wood pellet production increases data. Also, an in depth session with multiple experts was held, in which the findings and rankings have been discussed.
During the meeting, the experts indicated the financial investment climate had been underweighted in the base scenario. This would be a possible explanation of the high potential of Estonia, Czech Republic and Latvia, whereas Northern America and some west European countries showed the most significant production growth. The experts have recommended increasing the weight of the capital criterion. The initial capital criterion weight was adjusted by +20%11, leading to a refined base ranking. Figure 12 and 13 have depicted both the initial and refined ranking and scores, combined with the known production developments in the market.
Figure 12 Wood pellet production related to initial regional ranking (production data sources: Pelletatlas, 2009; IEA Bioenergy task 40, 2007)
11
As in the case of the scenario an absolute change of 16% is achieved
Figure 13 Wood pellet production related to the refined regional ranking
(production data sources: Pelletatlas, 2009; IEA Bioenergy task 40, 2007)
The produced output indicates that in highly ranked regions, the production volumes seem (somewhat) correlated with growing wood pellet production. Especially in the refined ranking, the preferred regions show significant production volume and growth. Austria, Brazil and Czech Republic show high potential, but production is stable and/or volumes are smaller. Canada, USA, Germany and Sweden are showing sharp increases in produced volume while also identified as highly potential.
Although the aim of this research has not been focused on explaining past production increases, current theoretic potential and actual market developments seem to align. Thereby the validity of the results has been strengthened.
5.4 Alternative scenarios
In an emerging and developing market it has been particularly useful to analyze the effects of various scenarios on the base comparison. A selection of those scenarios relevant has been made based on market literature, followed by a qualitative description of the scenario conditions and effects. Four scenarios have been analyzed: feedstock competition increase, increased logistical competition, policy and consumption changes and technological
scenarios have also been modelled by increasing the weights of the relevant criteria by two intensities12. The specific scenario circumstances were simulated by altering these weights. To further improve reliability, the results have been compared with reported wood pellet production developments. Finally, an in depth discussion has been organized to further improve the accuracy of the ranking.
Scenario 1: feedstock competition increase
Demand from the various competing value chains consuming woody feedstock can increase and force the industry to consume more costly forms of feedstock13. Increased competition on feedstock drives the market to ‘upstream’ residues and energy crops, to secure the supply of feedstock. This increase has already led to the import of wood pellets from distant countries with abundant feedstock, such as Canada.
The criteria in the model have already considered multiple types of feedstock to assess the effect of feedstock competition. In this scenario model; the influence of increased feedstock competition, the overall feedstock weight is increased and spread evenly over the sub-criteria. The outcomes will be addressed in section 5.4.
Scenario 2: Critical logistics
Logistical costs have increased significantly in years of strong economic and trade growth resulting in higher fuel and transport demand. This is illustrated in figure 14, showing the Baltic Dry Index. This index shows the tariffs of the 25 busiest sea transport routes and shows sharp fluctuations over the years. These developments can decline profit margins quickly as in 2003. In 2003, the wood pellets produced in western Canada could not be delivered to Europe at competitive prices (IEA Bioenergy, 2007).
12
The relevant weight increases of 30 and 50 have been equally distributed over the affected criteria. This results in 23% and 33% weight changes (130/130 *100 = 23%, 150/150 *100 = 33%).
13
Figure 14 Baltic Dry Index and S&P 500 (Seekingalpha, 2009)
Some of the regions considered have to travel long distances and bear more risk to these price increases than others. To explore the effects, the weight of proximity to market has been increased in the model (see section 5.4).
Scenario 3: Policy changes & consumption
Subsidies and regulations have significant impact on the biomass co-firing markets (see e.g. De and Assadi, 2009 and 3.2). Renewable energy targets are seen as a cornerstone for sustainable and supply security. The European Union has set the goal of 20% overall
renewable energy demand from renewable sources by 2020. Co-firing is perceived by many as one of the most significant and sustainable ways to reach that goal, in both short and long term (see e.g. Wahlund, Yan and Westermark, 2004; Kangas, Lintunen and Uusivuori, 2009). However, current policy makers promote more often the use of domestic biomass resources used in small scale power plants. Subsidizing co-firing is seen by some stakeholders as extending coal plant life-time, slowing down the development of other bioenergy options (Hansson et al., 2009). The debate is still ongoing and the national policies may pose strong consequences for the biomass market development.
Obviously, regions close to those that would implement more attractive co-firing policies would benefit, as well as those with an attractive logistic connection. Although these
markets in reach provide a producing region more flexibility regarding changing co-firing policies. In the scenario of market changes, the weight of the mentioned criterion was increased. This has provided some insight in regions that are more flexible in their markets, although no specific market changes have been considered.
Scenario 4: Technologic developments: torrefication and pyrolysis
Because of the emerging market characteristics, careful consideration of the technology portfolio might prove to be an important success factor in future organizational
performance. Technological pre-treatment technologies can have significant impact on the bio energy supply chain logistics (Uslu, Faaij and Berman, 2008). Transportation, storage and conversion characteristics differ when using these different pre-treatments. Significant differences in simulated costs have been described in literature (e.g. Suurs, 2002; Uslu et al., 2008). However, torrefication has not yet been commercially demonstrated and pyrolysis process costs have been considered too high for co-firing in power plants.
CIF harbor (€ / GJ) Power (Cofired) €/kWh Caloric value GJ/ton
Torrefied pellets 3.3 4.6 20.4
Pellets 3.9 4.8 17.5
Fast pyrolysis 4.7-7.0 5.9 17.2
Fuel oil 42.7
Table 12 Cost of chains delivering fuel and power (co-fired) (Uslu et al., 2008)
electricity prices. Since Usla et al (2008) confirm that pelletization is considered as the dominant option in the coming years, only one scenario with a small increase of the power criterion has been considered.
5.5 Scenario modelling
The four scenarios elaborated in the previous section have been modelled. The weights have been altered and the effects on the ranking have been analyzed. Table 13 provides an overview of the scenarios and their impact. Although the scenarios have been simulated in the model, most of those scenarios that have been classified in the base scenario as highly potential remain so in the other scenarios. Still, overall the base scenario seemed to be quite stable, since the weight increases have been strong.
C a n a d a e a s t A u s tri a C a n a d a w e s t U S A e a s t B e lg iu m G e rm a n y U S A w e s t E s to n ia B ra z il C z e c h R e p . S w e d e n L a tv ia F in la n d N e th e rl a n d s D e n m a rk C h ile U n it e d K in g d o m P o rt u g a l R u s s ia F ra n c e S p a in P o la n d S lo v a k ia S o u th A fri c a S lo v e n ia R o m a n ia It a ly H u n g a ry Base scenario 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 2 2 2 2 2 2 1 Feedstock 23%+ 5 5 5 5 4 4 4 4 4 4 4 4 4 3 3 4 3 3 4 2 2 3 3 3 2 2 2 1 Feedstock 33%+ 5 5 5 5 4 4 4 5 5 5 3 5 3 3 3 4 3 3 4 2 2 3 3 3 3 3 1 1 Logistics 23% + 4 5 4 4 5 5 3 4 3 4 4 4 4 5 4 2 3 3 2 3 2 2 2 2 2 1 2 1 Logistics 33% + 4 4 3 3 5 5 3 4 2 4 4 4 4 5 4 2 3 3 2 2 2 2 2 1 2 1 2 1
Policy & consumption +23% 5 4 5 5 5 5 4 4 4 5 4 4 4 5 5 4 3 3 3 3 3 3 1 3 1 1 1 1
Policy & consumption +33% 5 4 5 4 5 5 4 4 4 5 4 3 4 5 5 4 3 2 3 3 3 3 1 3 1 1 1 1
Power 23%+ 5 4 5 5 4 3 4 4 3 4 4 4 4 3 4 4 3 3 3 3 3 3 2 3 3 2 1 1
Table 13 Alternatives scenario rankings as result of weight increases of criteria
Feedstock competition favoured regions such as Brazil and Russia, both owning major unutilized feedstock volumes. Also Latvia had gained potential in this scenario because of the lower feedstock prices and the abundance of industrial residues. Regions such as Austria, USA, Belgium and Germany were more likely to loose potential, because of their feedstock characteristics. According literature, this is one of the most critical and likely scenarios (see e.g. Hamelinck et al., 2005; Uslu et al. 2008; Van dam, 2009). Although some shifts in potential have been addressed, the base scenario already considered feedstock as one of the most important criteria.
poses a threat for the competitiveness of regions such as Canada, USA and Brazil.
Simultaneously, regions such as Belgium, Germany and the Netherlands gain in this scenario because of their position, close to the markets.
Policy changes may have a significant impact on the demand in certain regions. Although, which regions will change their policies and in what extent is impossible to predict. A central position and/ or flexible logistics will reduce the risk of an unfeasible location. Regions such as Germany and Belgium seem to become more attractive in this scenario. Overall, sea located regions are more flexible in their destinations, and thereby more likely to adapt to policy changes. One more important general shift can be seen in the scenario modeling; Nearly two thirds of the regions indicate a rounded score of 4 or higher. It seems that in this scenario, a broad range of regions may perform equally well.
Intensified consumption of energy in case of producing torrefied pellets or using pyrolysis has an impact on the power costs. Regions with lower power prices such as Canada, USA and Estonia seem to profit from these developments. Regions such as Germany, Belgium and Brazil are less preferable, since their power price levels may turn out to be more expensive. Although strongly adjusting the weights and some shifts in the ranking, the base scenario shows quite a robust ranking. The four modelled scenarios showed a similar selection of regions with more potential. Regions such as those in Central and Eastern Europe, North America, Belgium and Germany show strong origination potential under all scenarios. Of the four alternative scenarios that were explored, the feedstock and logistic scenario seemed to limit the regions that are to be seen as attractive. This while the policy scenario has favoured an extended mix of regions, including regions such as Chile, Sweden, South Africa, The Netherlands and Denmark.
5.6 Summary
6. Conclusions
The main purpose of this research has been to aid the location selection of wood pellets origination projects. A MCDA-model was constructed, which has rationally expressed why certain regions seem more suitable than others. Criteria on feedstock, investment climate, production, market potential and logistics were assessed in this comparison. Canada, Austria, USA and Germany have been concluded as most potential, mainly because of their feedstock availability and flexibility to reach market demand. Additionally, regions within North and Central Europe have been concluded as higher potential (e.g. Estonia, Sweden, Latvia and Czech Republic). These regions indicate higher amounts of feedstock at lower prices and have relatively short distances to and flexibility towards their markets. The various scenarios that have been evaluated indicate that the potential of the mentioned regions are quite robust.
Depending on yet uncertain circumstances, some regions have been judged as more potential and higher flexibility to rise to these new circumstances. The identification of highly potential regions can be seen as a first step towards extending the origination activities of Nidera. This research has evaluated the risks and uncertainties involved in the selection of a production location. An emerging market requires actions that will limit risks, while retaining competitive and flexible activities. This research has indicated regions that are better able to cope with different scenarios. Nidera is recommended to conduct a more extensive risk analysis of specific sites and plan their response in advance. Risk
diversification may be achieved by spreading investments over multiple facility locations. A possible mix could consist of distant regions that perform well under feedstock shortages and regions that are located near and central to potential markets.
A dynamic and emerging market such as the wood pellet market necessitates close
monitoring and focussing efforts. The constructed model may act as a guiding benchmark to select projects and quickly raise key questions to assess passing projects. The insights of this research may aid Nidera in enhancing its strategic vision of the wood pellet market and ways to become successful in multiple scenarios.
