The use of multi criteria decision analysis, an application and evaluation.

The use of multi criteria decision analysis,

an application and evaluation.

Paper:

MSc. Thesis, Technology Management

Version:

Public version

Date: May, 2011

Supervisors: Drs. J.C.L. Paul, University of Groningen

Prof. dr. J. de Vries, University of Groningen

P. Schade, MSc., Nidera Handelscompagnie B.V.

K.M. Arkesteijn, MSc., Nidera Handelscompagnie B.V.

Author: T.P. Smith, BSc.

See also:

Published article in Biofpr:

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. This 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 a number of more academic questions arose: 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.

Each part has been written for a specific audience. Part one has been written for those interested in the content and conclusions of the wood pellet production location analysis. Part two on the other hand has been written for those interested in the theoretical background and evaluation of the MCDA-technique that has been used in the first part. Because of different target groups, both parts are focussed on different content and style. Part one is focussed on the content of the decision analysis and will contain more specific details. Part two has placed more emphasis on the theoretical background and research design. In this part a more academic and abstract writing style was used. Please note that part one has been written as an independent report. As a result some (decision)

General content

Preface ... ii

General introduction ... iii

Part I

Abstract part I ... 1

1. Introduction part one ... 2

1.1

Introduction ... 2

1.2

General energy and biomass developments ... 2

1.3

Nidera Handelscompagnie B.V... 3

1.4

Main problem statement ... 4

1.5

Literature background ... 5

1.6

Summary and problem typology ... 8

2.

Research design ... 9

2.1

Introduction ... 9

2.2

Research structure and research sub-questions... 9

2.3

Methodology ...10

2.4

Summary ...12

3.

Criteria selection ... 13

3.1

Introduction ...13

3.2

Market review ...13

3.3

Selected criteria & measurement ...17

3.4

Summary ...17

4.

Comparison of regions ... 19

4.1

Introduction ...19

4.2

Criteria data overview ...19

4.3

Score overview ...26

4.4

Summary ...27

5.

Overall comparison and scenario analysis ... 28

5.1

Introduction ...28

5.2

Base scenario ...28

5.3

Crosscheck...29

5.4

Alternative scenarios ...31

5.5

Scenario modelling ...35

5.6

Summary ...37

6.

Conclusions ... 38

7.

Discussion ... 40

Part II

Abstract part two ... 1

1.

Introduction part two ... 2

2.

Literature review ... 4

2.1

Introduction ... 4

2.4

Literature summary and refined normative framework ...15

3.

Research design ... 17

3.1

Introduction ...17

3.2

Specific research questions ...17

3.3

Research methodology ...17

4.

Theoretic exploration of MCDA ... 20

4.1

MCDA and Instrumental rationality ...20

4.2

MCDA and procedural rationality ...21

4.3

MCDA and structural rationality ...23

5.

Empiric exploration of MCDA ... 25

5.1

Case introduction ...25

5.2

MCDA and Instrumental rationality ...26

5.3

MCDA and procedural rationality ...28

5.4

MCDA and structural rationality ...32

6.

Conclusions ... 34

7.

Discussion ... 35

Part III

References ... 1

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.

Table 1 The analyzed regions, ranked from highest (5) to lowest (1) location potential

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-firing

in European coal plants can be originated in Brazil or Canada where saw

dust is dried and pelletized before sea transport. Alternatively sawdust can be directly

1 _{Co-firing is the practice of supplementing a base fuel (e.g. coal) with a dissimilar fuel (e.g. wood}

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.

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?”

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.

Considering decisions, Thompson (1964) has characterized two dimensions; the number of

criteria and the number of alternatives. These dimensions result in four modes of decision

making; computation, judgment, compromise and inspiration (table 2). Table 2 also draws

the field of Multiple Criteria Decision Analysis (MCDA), the field of analyzing compromise

and inspiration decision modes.

Decision modes: Choice criteria: single multiple Description of alternatives: certain Computation Compromise

uncertain Judgment Inspiration

Table 2 Modes of decision making (Thompson, 1964)

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

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

2 _{Supply chains and value chains are considered as synonyms based on Feller, Shunk and Callarman}

(2006).

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).

The first location theory has been introduced by Weber, considering single warehouse

locations (Brandeau and Chiu, 1989; Tabari et al. 2008). Throughout the decennia, problems

considered have become more complex considering single facility to multi facility problems.

Techniques have advanced from simple linear quantitative to more advanced quantitative

techniques such as mixed linear programming. Moreover, techniques have been developed

that handled mixed quantitative and qualitative data, such as the weighted sum matrix.

Various advanced techniques have been developed, such as the fuzzy Analytical Hierarchy

Process (AHP) and outranking techniques (e.g. Özerol and Karasakal, 2008)

.

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.

3 _{For a more extensive literature review of the facility location in the context of supply chain}

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

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

criteria performance per region. Chapter five will look into the overall comparison, a

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?

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.

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

developments. This research was intended to aid in the location decision for the

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 routes

. 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

aspects (See e.g. Suurs, 2002; Van Dam, 2009). Conceptually, the supply chain can be

4

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 isolated

. 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

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.

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

Table 5 Criteria considered for supply chain cost comparisons (Hamelinck et al., 2005; Suurs, 2002; Uslu et al. 2008; Van dam, 2009)

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

expenditures and characteristics (capital expenditures, load factor, capacity and energy

consumption) as well as the moisture loss and dry matter loss during the process. Transport

regards distance, mode and load factors. Energy conversion variables are considered when

converting the commodity into energy.

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 apply

.

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%.

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

As depicted in figure 2, four main criteria have been selected and specified in one or more

criteria. The first main criterion has been feedstock. The availability, costs and competitive

pressure regarding feedstock have all been assessed. The second main criterion has been

production. As described earlier, the production process requires significant amounts of

power and costs differ per region. Thirdly, investing in certain regions results in specific

capital costs and capital risks. The fourth main criterion has related the market potential to

the logistical modes and distance. Each of these criteria has been assessed and modelled

using multiple sources and indicators

.

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

7

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.

Figure 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.

Figure 4 Yearly forest residues (UNECE, 2007)

Feedstock costs

Reported residue prices are scarce and sometimes based on small volumes. Therefore, three

different price reports have been taken into consideration. The average price ranges have been

considered as indicator for industrial and forestry residues (figure 5). The figure displays significant

differences in feedstock prices. Regions like France, Germany and Denmark have high feedstock

prices, while Brazil, Canada and Latvia report low prices. Note that prices and resulting costs may

fluctuate rapidly, therefore this research did not rely solely on feedstock costs.

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 after

Yearly forest residues 2008

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

Wood pellet production requires heat and power, especially if the feedstock needs drying. The

industrial power prices show large differences. Reported power price calculations may result in up to

25-30 euro power costs per produced ton pellets (Hansson et al., 2009). Italy has by far the highest

electricity prices, around € 150 per MWh (figure 6). Canada, the USA and Latvia have reported the

lowest prices, around € 45 per MWh.

AVG residue prices

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.

Figure 7 Weighted Average Cost of Capital (Nidera standards, 2009)

Market potential

Figure 8 shows the reported demand of wood pellets. There are regions in which wood pellets are

almost completely used by residential customers. Examples are Austria and the USA, while the

Netherlands has mostly industrial consumption. Note that this research has focussed on supply of

specific industrial markets within the EU.

0 40 80 120 160 B ra zi l Ch il e U SA w es t U SA e as t Ca na da w es t Ca na da e as t So uth A fr ic a R us si a A us tr ia B el gi um Cz ec h R ep . 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 tv ia N eth er la nd s Po la nd Po rtu ga l R om an ia Sl ov ak ia Sl ov en ia Sp ai n Sw ed en U ni te d Ki ng do m Eur o pe r M W h Countries

Power price (excl. VAT)

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-27

.

Figure 8 Wood pellet demand (IEA Bio energy, 2007, Pelletatlas, 2009)

Various reports have analysed 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.

Figure 9 Location of EU-27 coal fired plants included in the CPPD (Hansson et al., 2009)

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).

Germany has by far the largest coal plant capacity, followed by Poland, Spain and the United

Kingdom. However, co-firing capacity potential has not been utilized. Future regulation and support

mechanisms are important drivers for the wood pellet markets.

Coal plant capacity

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; Van Dam et al., 2009). Transportation can consist of truck, train and ship transport. Truck

transport is used for short distances (<100 km), when flexibility is required or rail or water

infrastructure is not available (Hamelinck et al., 2005). On the longer distances rail (100 – 2000km)

and ship transport (500-15.000km) are used.

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

The previous sections have introduced the data used to compare all regions. In order to compare and

allow for tradeoffs, a common way of measurement was needed. Therefore, all the data have been

processed into Likert scaled scores, as prescribed by MCDA. The scores represent a range from

highest (5) to lowest (1) performance per criterion. A detailed description of this process has been

provided in appendix 10. The criteria scores per region have been presented in table 8

. The next

chapter will elaborate more on the criteria performance and their cohesion.

Table 8 Wood pellet location comparison overview, scored from high to low performance (5 to 1) on each of the criteria

4.4 Summary

A concise overview of the data on feedstock, production, capital and market potential and logistics

criteria has been elaborated. This data have been processed into scores. These scores allow for a

comparison of all regions on each of the individual criteria. Thereby, this chapter has formed the

foundation for the overall comparison, addressed in the following chapter.

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.

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

10

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.

Table

10 Main criteria, scored per region on origination potential from high (5) to low (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.

The ranking provided in table 11 indicates a wide range of regions having a medium performance.

Whereas regions such as Russia, Chile and South Africa report low feedstock and power prices, the

logistics and capital characteristics hamper their potential. The low potential of France, Italy, Spain

and Portugal is not caused by their lack of a reachable market potential or their capital

characteristics. Their low potential is caused by a thin spread of feedstock, which may lead to severe

competition on feedstock.

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.

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

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%

, 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:

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:

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 developments. Furthermore, the scenario impact

has been modelled (5.4). The first three 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 feedstock

. 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.

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)

Uslu et al. (2008) have calculated comparable power cost prices for pellets and torrefied pellets

(table 12); respectively €4.6 and €4.8/kWh and about 20% higher power cost prices for fast pyrolysis

(€5.9/kWh). Theoretically, the cofiring characteristics of torrefied pellets are superior to pellets,

especially regarding technical co-fire share limitations. While up to 20 % of the coal can be replaced,

theoretically 100 % can be replaced by torrefied pellets. Since current production costs are

comparable, torrefied pellets do not seem to possess significant added value, but will do so if the

co-firing share of the power plants is surpassing 10-20 %. The processing costs of fast pyrolysis will have

to be significantly lowered before becoming cost competitive. This technology therefore does not

seem relevant in the shorter term.

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 and figure 15 provide 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.

Table 13 Alternatives scenario rankings as result of weight increases of criteria

Figure 15 Scenario overview

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,

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