University of Groningen Modelling energy systems Schenk ... · Wiskunde en Natuurwetenschappen aan de Rijksuniversiteit Groningen op gezag van de ... voor de hand liggende richting

University of Groningen

Modelling energy systemsSchenk, Niels Jan

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Modelling energy systems: a methodological exploration of

integrated resource management

ii

Copyright © by N.J. Schenk ISBN 90-367-2730-8 Printed by Febodruk B.V. Enschede Cover-design: Caroline Ellerbeck <http://www.karolina.nl/>

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RIJKSUNIVERSITEIT GRONINGEN

Modelling energy systems: a methodological exploration of

integrated resource management

Proefschrift

ter verkrijging van het doctoraat in de Wiskunde en Natuurwetenschappen aan de Rijksuniversiteit Groningen

op gezag van de Rector Magnificus, dr. F. Zwarts, in het openbaar te verdedigen op

vrijdag 15 september om 14:45 uur

door

Niels Jan Schenk

geboren op 5 november 1973

te Heerenveen

iv

Promotores: Prof. dr. H.C. Moll

Prof. dr. A.J.M. Schoot Uiterkamp

Beoordelingscommissie: Prof. dr. F.G.H. Berkhout Prof. dr. J.C. Hummelen Prof. dr. ir. C. Schweigman

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“The important thing is not to stop questioning.”

Albert Einstein.

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Voorwoord Op een gegeven moment is het schrijven van een voorwoord nog het enige dat een proefschrift scheidt van de drukker. Daardoor word je natuurlijk ook wel een beetje gedwongen terug te kijken op het geheel. En dan vraag je jezelf toch wel af hoe je hier terechtgekomen bent. Energie- en milieukunde was voor mij namelijk niet de meest voor de hand liggende richting om in te promoveren na te zijn afgestudeerd in de chemie. Retroperspectivisch gezien begon dit project toen ik besloot het college ‘geologie voor chemici’ te volgen. De daarbij horende excursie in de Spaanse Pyreneeën heeft mijn denken over het bedrijven van wetenschap sterk beïnvloed. Van professor Peter Westbroek heb ik geleerd hoe fascinerend multi-disciplinair onderzoek kan zijn en hoe te focussen op de grotere, algemene verbanden tussen de dingen die we zien. Deze inzichten zijn bijzonder waardevol gebleken tijdens het onderzoek dat ten grondslag ligt aan dit proefschrift, waarvoor dank.

Just after finishing my masters in chemistry I got the chance to join the ‘Environmental Outlook and Strategy’ project at the Organisation for Economic Co-operation and Development (OECD). The work at the OECD was not only interdisciplinary, but also on the interface of science and policy. Especially the chapters 2 and 6 are based upon insights I gained there. I would like to take this opportunity to thank the people of the OECD Environmental Directorate. Special thanks go to Hoe-Seog Cheong, Lars Mortenson, Helen Mountford, and Rob Visser.

Voorgeschiedenis is leuk en aardig, maar dit proefschrift begon natuurlijk feitelijk pas toen ik bij de IVEM mocht beginnen aan het daadwerkelijk uitvoeren van promotieonderzoek. Ik ben mijn promotores bijzonder dankbaar voor hun begeleiding. Henk Moll en Ton Schoot Uiterkamp hebben mij met enthousiasme, wijsheid en vertrouwen in iedere fase van mijn promotie gesteund. De begeleiding van Henk en Ton heeft het voor mij mogelijk gemaakt om in de huidige academische cultuur, waarbij onevenredig veel belang wordt gehecht aan het in grote getale produceren van artikelen, toch te kunnen genieten van de traditionele academische vrijheid en mij als zelfstandig onderzoeker te kunnen ontwikkelen. Het voelt voor mij als een bijzondere eer de eerste promovendus te zijn waarbij Henk als eerste promotor optreedt.

Promoveren is vaak een nogal solistische bezigheid. Gelukkig word je bij de IVEM bijgestaan door fantastische collegae die je niet alleen bij inhoudelijke problemen bijstaan met hun kennis en kunde, maar die je ook in moeilijkere tijden met raad en daad bijstaan. Enkele IVEM’ers wil ik bij deze bijzonder bedanken: René Benders, Sander Lensink, Henk Mulder, Nicole van Marle en Sanderine Nonhebel.

A special period during my Ph.D. was – of course – the YSSP at the International Institute for Applied Systems Analysis (IIASA) in 2004. The YSSP is the best thing that can happen to a Ph.D. student. I would like to thank the entire ECS group. Special thanks go to Asami Miketa and Leo Schrattenholzer. I also want to thank my fellow YSSP’ers for the great time I had. Prost!

Tenslotte, Hester, wil ik jou bedanken. Toen je me vroeg wanneer ik nu eindelijk naar Nederland zou komen was Groningen niet het scenario dat je in gedachten had. Dat ik

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de eerste twee jaar de helft van de week bij m’n ouders logeerde (waarvoor dank!) was waarschijnlijk ook niet wat je je van samenwonen had voorgesteld. Met engelengeduld heb je al mijn (toen nog vaak belabberde) schrijfsels gelezen en verbeterd. Ik ben heel blij dat jouw verblijf in het Groningsche geresulteerd heeft in een opleidingsplaats voor een schitterend vak zodat ook jij hier aan de Groningse Academie een deel van je opleiding kunt voltooien.

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Contents Voorwoord............................................................................................................. vii

1. General introduction ....................................................................................... 1 1.1. Relevance of natural resource analysis ....................................................... 1

1.1.1. Environmental effects of resource use ................................................ 1 1.1.2. Focus on energy systems .................................................................... 2

1.2. Forecasting ................................................................................................ 3 1.3. Energy models ........................................................................................... 5 1.4. Model building blocks: mental models, empirical data, and theoretical causalities.............................................................................................................. 6 1.5. Model pyramid: a taxonomy of environmental models ............................... 6 1.6. Aim of the thesis: integrating materials in energy models........................... 8 1.7. Guide for the reader ................................................................................... 8

2. Communicating uncertainty in the IPCC’s greenhouse gas emissions scenarios................................................................................................................. 11

2.1. Introduction ............................................................................................. 11 2.2. Description of IPCC emissions scenarios ................................................. 12 2.3. SRES and communicative issues.............................................................. 15

2.3.1. The use of descriptive scenarios to assess a normative problem........ 15 2.3.2. Plausibility, possibility, probability, and pertinence of the scenarios. 19 2.3.3. The risk of simplification ................................................................. 20

2.4. Discussion and conclusions...................................................................... 21 2.4.1. Interdisciplinary science................................................................... 21 2.4.2. Science-policy relation ..................................................................... 22 2.4.3. Transparency.................................................................................... 23

2.5. Concluding remarks ................................................................................. 23 2.6. Acknowledgments ................................................................................... 25

3. Wind energy, electricity and hydrogen in the Netherlands.......................... 27 3.1. Introduction ............................................................................................. 27 3.2. Description of the electricity production system including wind and hydrogen ............................................................................................................. 29

3.2.1. Load types and power plants ............................................................ 29 3.2.2. The effects of high wind energy penetration ..................................... 30 3.2.3. Comparing integrated hydrogen with BaU........................................ 30

3.3. Modelling of the electricity production system including wind and hydrogen 31

3.3.1. Chronological hourly modelling ....................................................... 31 3.3.2. Scheduling optimisation ................................................................... 32 3.3.3. Spinning reserve............................................................................... 32 3.3.4. Capacity credits for wind.................................................................. 32 3.3.5. Part-load distribution........................................................................ 33

3.4. Model implementation ............................................................................. 33 3.4.1. General model dimensions ............................................................... 34 3.4.2. Geographical focus........................................................................... 34 3.4.3. Hourly electricity demand ................................................................ 34 3.4.4. Hourly wind energy electricity production........................................ 34 3.4.5. Primary energy prices....................................................................... 34 3.4.6. Power plant data............................................................................... 34

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3.4.7. Power plant scheduling .................................................................... 35 3.4.8. Part load distribution ........................................................................ 37 3.4.9. Hydrogen production........................................................................ 38

3.5. Results ..................................................................................................... 39 3.5.1. Fossil fuel savings ............................................................................ 40 3.5.2. Hydrogen produced.......................................................................... 42 3.5.3. Stabilising effect of hydrogen production ......................................... 42

3.6. Discussions.............................................................................................. 43 3.6.1. Geographical focus........................................................................... 43 3.6.2. Model assumptions........................................................................... 43 3.6.3. Wind energy growth rate .................................................................. 43 3.6.4. Alternative uses for hydrogen........................................................... 44 3.6.5. Dynamics of electricity generation capital stock ............................... 45 3.6.6. Relieving the grid............................................................................. 45 3.6.7. Policy relevance ............................................................................... 45

3.7. Conclusions ............................................................................................. 46 3.7.1. Hydrogen can be produced from wind.............................................. 46 3.7.2. Relieving the grid............................................................................. 46 3.7.3. Planning new capital is important ..................................................... 46

3.8. Acknowledgements.................................................................................. 47

4. The non-linear relationship between paper recycling and primary pulp requirements.......................................................................................................... 49

4.1. Introduction ............................................................................................. 49 4.2. System and model approach..................................................................... 51 4.3. Non-linearity in resource requirements..................................................... 52 4.4. Effect of non-linear relationship on energy requirements.......................... 55 4.5. Sensitivity Analysis ................................................................................. 60 4.6. Discussion ............................................................................................... 62

4.6.1. On methodology............................................................................... 62 4.6.2. Model simplifications and assumptions ............................................ 62 4.6.3. System boundaries ........................................................................... 63 4.6.4. Changes outside our system.............................................................. 64 4.6.5. Validation of the model: comparing results with others .................... 65 4.6.6. Conclusions...................................................................................... 66

4.7. Acknowledgements.................................................................................. 66 4.8. Appendix A: Solutions of the substance flow model with different numbers of stocks .............................................................................................................. 67 4.9. Appendix B: Sensitivity tests ................................................................... 70

5. The use of physical indicators for industrial energy demand scenarios ...... 71 5.1. Introduction ............................................................................................. 71 5.2. Motivation for developing physical explanatory variables ........................ 72 5.3. Description of a model based on physical explanatory variables............... 74

5.3.1. Model framework............................................................................. 74 5.3.2. Relation between Income and Industrial Physical Output ................. 75 5.3.3. Physical Energy Intensities............................................................... 77

5.4. Model formalisations ............................................................................... 78 5.4.1. Formalisation of the model framework ............................................. 78 5.4.2. Formalisation of the relation between Income and Industrial Physical Output 79

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5.4.3. Formalisation of Physical Energy Intensities .................................... 79 5.5. Data sources and analysis......................................................................... 80

5.5.1. Scenario driving forces..................................................................... 80 5.5.2. General data and dimensions ............................................................ 80 5.5.3. Choice of the physical indicators ...................................................... 81

5.6. Historical data analysis & extrapolation for model parameters ................. 82 5.6.1. Income vs. commodity production: General Observations ................ 83 5.6.2. Income vs. commodity production: Western Europe......................... 84 5.6.3. Income vs. commodity production: Centrally Planned Asia .............. 85 5.6.4. Extrapolation of per capita physical industrial output in a B2 scenario 86 5.6.5. Physical Energy Intensities............................................................... 87

5.7. Energy scenarios...................................................................................... 88 5.7.1. Western Europe................................................................................ 88 5.7.2. Centrally Planned Asia & China....................................................... 89 5.7.3. Comparing the results with other scenarios....................................... 90

5.8. Discussions.............................................................................................. 92 5.8.1. Measurement of income ................................................................... 92 5.8.2. Trade liberalisation........................................................................... 93 5.8.3. Monetary vs. physical approaches .................................................... 94 5.8.4. Directions for further research .......................................................... 94

5.9. Conclusions ............................................................................................. 95 5.10. Acknowledgements.............................................................................. 96

6. Meso-level analysis, the missing-link in energy strategies............................ 97 6.1. Introduction ............................................................................................. 97 6.2. Positioning the meso-level ....................................................................... 98

6.2.1. The macro-level ............................................................................... 99 6.2.2. The micro-level ...............................................................................100 6.2.3. Combined micro- and macro-level approaches ................................100 6.2.4. The meso-level................................................................................101

6.3. Theoretical framework of the meso-level ................................................101 6.3.1. Heterogeneous actors ......................................................................102 6.3.2. Interdependency dynamics ..............................................................102 6.3.3. Taxonomy of systems changes ........................................................103

6.4. Passenger transport .................................................................................104 6.4.1. Technology lock-in .........................................................................105 6.4.2. Needs, opportunity, and ability of heterogeneous actors ..................105 6.4.3. Transition considerations / policy options........................................107

6.5. Electricity production and consumption ..................................................107 6.5.1. The whole differs from the sum of the parts.....................................107 6.5.2. Institutional organisation & electricity consumption increase ..........110 6.5.3. Transition considerations / policy options........................................111

6.6. Conclusions ............................................................................................112 6.6.1. System insights ...............................................................................112 6.6.2. Policy implications..........................................................................113

6.7. Acknowledgements.................................................................................115

7. Summary & conclusions...............................................................................117 7.1. Introduction ............................................................................................117 7.2. Key findings & methodologies................................................................118

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7.3. Over-all conclusion.................................................................................120

Nederlandse samenvatting ...................................................................................123

List of abbreviations.............................................................................................127

References.............................................................................................................129

1

1. General introduction

1.1. Relevance of natural resource analysis

Natural resources1,2 are essential for human societies. Natural resources are crucial for economic development, help to fulfil basic needs such as food and shelter, and contribute to social development by improving education and public health (OECD 2001d). Consequently, many resources are valued directly as inputs in the production of economic goods and services (Johnstone 2001). Natural resources are normally classified according to their ‘type of resource’ and ‘type of use’ as shown in Table 1.1.

Natural resources are – in general – divided into three different types: non-renewable resources, renewable (but exhaustible) resources, and non-exhaustible resources. The resource type has implications for the management of the resource.3 Natural resource use is categorised into energy, materials, and other. Energy is divided into the energy source4 and the energy carrier5 that delivers the final energy service.6 Materials are categorised according to their appearance in national statistics into stocks and flows. Since national statistics are generally published annually, materials are considered stocks when they persist for several years.

1.1.1. Environmental effects of resource use Resource use is associated with environmental effects. Three broad classes of environmental effects are arising from the resource classification presented in Table 1.1. First, ‘resource rents’ are the scarcity effects, arising from resource use, on future potential users. Second, ‘bundled values’ are the role natural resources (particularly forests and freshwater) play in supporting ecosystems and species habitat. Third, ‘environmental externalities’ are the variety of wastes and pollutants (including GHGs) generated by resource use (Johnstone 2001). Changes in resource use efficiency can be attributed to: resource-saving, resource-reusing, and resource-substituting (Johnstone 2001).7

1 “Natural resources are naturally occurring substances that are considered valuable in their relatively unmodified (natural) form. A commodity is generally considered a natural resource when the primary activities associated with it are extraction and purification, as opposed to creation. Thus, mining, petroleum extraction, fishing, and forestry are generally considered natural-resource industries, while agriculture is not. The term was introduced to a broad audience by E.F. Schumacher in his 1970s book Small Is Beautiful.” (<http://en.wikipedia.org/wiki/Natural_resource>, accessed 1 February 2006). 2 In this thesis ‘Natural resources’ are defined more broadly as shown in Table 1.1. 3 Paradoxically scarcity is mainly a problem for renewable resources (Johnstone 2001), and oil is more plentiful now than in 1973 in an economic sense (Watkins 2006). 4 Often referred to as: ‘primary energy’ 5 Often referred to as: ‘final consumption’ 6 The ultimate service that energy consumption provides (OECD 2001d, p362). 7 Additional options: resource-broadening, resource-prolonging, and resource-increase (Johnstone 2001).

2

Table 1.1: natural resource taxonomy

Resource type

Resource use Non-renewable Renewable (exhaustible) Non-exhaustible

Stock

Metallic capital equipment and

consumer durables

Wooden structural materials Limestone

Materials (non-

dissipative)

Flow

Packaging plastics,

aluminium foil, cooling water

(aquifer)

Paper products, agricultural

products, fish products

N2, CO2

Source Crude oil, coal, and natural gas Biomass for fuel Tidal, wind, solar

Energy (dissipative)

Corresponding carrier

Petrol, diesel, electricity

Ethanol , bio-diesel, electricity

Electricity, hydrogen

Other Biodiversity Soil, freshwater

Source: based on (Johnstone 2001; OECD 1999; Tietenberg 1996; Westbroek 1991). Note: The time-frame determines the resource type classification; in the long run solar energy will exhaust and atmospheric nitrogen is a renewable resource.8 'Non-renewable' resources are also referred to as 'depletable' resources.

1.1.2. Focus on energy systems Energy is a natural resource that deserves special attention. Since the beginning of the industrial era9 energy systems allowed mechanisation of all productive sectors, increased productivity and improved labour conditions, while availability of energy services increased the quality of life. The downside of these developments is the ever-increasing dependence on energy. Abrupt increases in oil prices have initiated economic crises in the early 1970s (Doroodian & Boyd 2003), high transport fuel prices have resulted in social unrest and strikes,10 and high energy prices threaten to push the purchasing power of the poorest households in developed countries below socially acceptable levels. Real shortage, for example due to electricity blackouts, weather conditions, strikes and war, deeply affects the life of virtually everyone in the

8 Atmospheric nitrogen is around 2-3% on Venus, Mars, and Earth without life (Lovelock 1988, p9). Life is a key driver of global geochemical cycles on Earth (Westbroek 1991). 9 Before the industrial era windpower and hydropower allowed mechanisations of some sectors. Notably, Jan Adriaanszoon Leeghwater used windpower to create polders in the early 17th century and therefore determined the landscape of large parts of the Netherlands. 10 E.g. French farmers tend to strike when fuel prices are high, Dutch populist politicians often plead for less tax on fuel, and in late 2005 a majority of the Dutch cabinet seemed to be willing to compensate households for the high oil-prices.

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society concerned (IEA 2005a). The search for alternatives for oil is seen as one of the most important scientific quests for this century (Kerr & Service 2005).

Energy systems are also associated with several adverse environmental effects ranging from landscape distortion from wind turbines (as perceived by Dohmen & Hornig 2004) to global climate change from CO2 emissions from fuel combustion (IPCC 2001), and human deaths because of air pollution (OECD 2001b, Ch21). Therefore efficient management of energy systems is beneficial for the wellbeing and ‘sustainable development’11,12 of societies.13

Analysis of these particular energy systems is referred to as ‘energy analysis’ in this thesis. One specific technique to perform energy analysis – and thus to assess energy systems – is by the application of models. Sections 1.3-1.5 give an overview of different types of modelling techniques. Next Section 1.6 introduces the relevance of materials in energy analysis. But first Section 1.2 introduces the need for forecasting.

1.2. Forecasting

The main issues associated with energy resources – security of supply and GHG emissions (see Section 1.1.2) – require long-term planning because of the life-spans of energy-related capital stock (see Figure 1.1), time-lags in the climate system, and etceteras. Therefore information on (possible) future developments is highly important.

Energy related (possible) future developments are associated with a high degree of uncertainty. High-uncertainty and high-stakes science is awkward in the scientific arena and referred to as ‘post-normal’ science (Funtowicz & Ravetz 1993).

High-uncertainty and high-stakes science is one hand complementary with the scientific method – observations and the construction of theories dominate – but on the other hand it lacks the possibility to perform numerous experiments to falsify theories. Therefore, in addition to the ‘normal’ science approach, methods are needed to deal with the associated uncertainty (see Section 2.4.2). ‘Forecasting’ is the appropriate method to deal with high uncertainty and high stakes.

11 ‘Sustainable development’ stems from the ‘Brundtland report’ (WCED 1987), and relies on two key concepts: first, the idea of ‘needs’, and second, the idea of ‘limitations’ on the environment’s ability to meet present and future (generational) needs (OECD 2001d, p38; Pearce 2002). For a historical perspective on sustainable development see: (van Zon 2002). 12 The depletion of exhaustible resources and sustainable development appear to be competing paradigms (Tilton 1996). 13 Energy security and climate change mitigation interfere with each other in such a way that a country will prefer to cut emissions more greatly with those fuels that it imports and less greatly with those that it exports (Huntington & Brown 2004).

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There are several grounds for ‘forecasting’14 in the post-normal scientific arena. First, forecasting provides insights in expected (or likely) future developments. This type of forecasting allows quantification of future consequences of decisions and therefore decision making based on possible future developments. Second, the single scenario can be extended by alternative scenarios that cover a wider range of future developments and help to assess uncertainty. Third, scenarios can be used to change ‘locked-in’ thinking and therefore allow people to escape from conventional worldviews. Break-through solutions can be considered by exploring futures that were never thought of.15 Fourth, scenarios can test the robustness of policies or decisions against a wide variety of possible future developments, in contrast to ‘betting the company’ on the most likely (or desired) development.16

14 In energy and environmental sciences the word ‘forecast’ is used for every systematic attempt to explore the future. Forecasting is defined in this thesis as thinking about possible future developments (Schwartz 1999; Turkenburg 1993), rather than the Oxford definition: “to say in advance what is expected to happen” or “to predict something with the help of information”. Later the word ‘outlook’ came into use, e.g. ‘OECD Environmental Outlook’ (OECD 2001b), and ‘Global Environmental Outlook’ (UNEP 2002). Scenarios are the development paths described by a forecast or outlook. 15 E.g. scenarios for South Africa initiated the peaceful regime change from apartheid to democracy (Schwartz 1999). “[Pierre Wack] was part of a team developing a set of scenarios about the future of apartheid and South Africa for AngloAmerican, the largest South African company. Clem Sunter, one of AngloAmerican's executives, gave a series of public speeches based on these scenarios, called "high road" and "low road." A book of that presentation became a best seller in South Africa.... It is said that De Klerk, the South African president, took these scenarios very seriously, and that they influenced the release of Nelson Mandela.” <http://www.gbn.com/BookClubSelectionDisplayServlet.srv?si=361> 16 Very closely related to the grounds for forecasting are the tree archetypes of scenario analysis: policy optimisation, advocacy and vision building, and strategic orientation (Bakkes 2004).

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0 20 40 60 80 100

Pattern of Transport Links and Urban Development

Building Stock (Residential and Commercial)

Power Stations

Electric Transmis. & distrib., Telecom., Pipelines

Manufacturing Equipment

Commercial Heating and Cooling Equipment

Trucks, Buses, Truck Trailers, Tractors

Cars

Residential Space Heating and cooling Equipment

Residential Water Heating Equipment

Cons. Appliances: stoves, refrig., washers, dryers…

Consumer Electronics: TVs, videos, stereos…

Office Equip.: computers, printers, faxes, copiers…

Light Bulbs fluorescent

Light Bulbs incandescent

Range of expected lifetime (years)

120200

Figure 1.1: average life-spans for selected energy-related capital stock

Source: (OECD 2001d)

1.3. Energy models

Models are simplifications of real-world systems by definition.17 Models are used inside and outside the scientific world to increase understanding of complicated or complex systems.18 Computer aided modelling is used “for representing hitherto hardly accessible complex systems, for simulating their dynamics, and for understanding systems and dealing with them better than before” (Bossel 1994, p2).19 Because models are simplifications, models always have shortcomings (Worrell et al. 2004). Some weaknesses associated with the use of models have been identified:

• Models tend to represent only one point of view about how the future will unfold and can be therefore unnecessarily narrow in view (Alcamo 2001).

• Computerised models can give opinion and subjective judgement an air of robust analysis and formal calculation (Keepin & Wynne 1984).

• Models should not be applied to problems they were not designed to address (Sanstad & Greening 1998). Although disobeying this rule has benefits (Bakkes 2004; Rizzoli & Davis 1999), because the circumstances are favourable for ‘serendipity’.20 Therefore, this rule should not be taken too strictly.

17 “Simplification is the goal and not the restriction” (Schrattenholzer as quoted in van der Sluijs 1996). 18 E.g. ball-on-stick models are used to increase insight in molecular dynamics in chemistry. 19 “Computers are incredibly fast, accurate and stupid. Humans beings are incredibly slow, inaccurate and brilliant. Together they are powerful beyond imagination.” Albert Einstein. 20 The unsought finding (van Andel 1994).

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These weaknesses originate from the anatomy of energy models. Section 1.4 lists the model building blocks, and Section 1.5 shows how different classes of energy models are constructed from these building blocks.

1.4. Model building blocks: mental models, empirical data, and theoretical causalities

Energy models consist of basic building blocks: mental models, empirical data, and theoretical causalities.21

Empirical data is analysed to provide generic properties of the system. Regarding empirical data, the aggregation level is the major factor to distinguish different methods. At the lowest aggregation level, data describes individual processes.22 Process-level data is often favoured because it is the closest to the actual process relevant for energy analysis. At the highest aggregation level, data describes over-all properties of a system, highly aggregated data is often favoured because it provides data wanted by policy makers, e.g. national GHG emissions.

Theoretical causal relations between variables are used to reveal the dynamics of the system by simulation. Theoretical natural resource analysis is relevant in order to understand system behaviour, and thus to predict the systems’ possible responses on stimuli from outside the system (policies). Analogous to empirical data, causal relations can be observed at different aggregation levels.

A mental model is the fuzzy-structured ‘world according to the modeller’. This mental model is constructed from: observations, scientific discipline, culture, values, and etceteras. Formal models are formalisations of mental models, and therefore the mental model of the modeller determines the final model outcome. The dynamic behaviour of the formal model and the accuracy of the formal model to reproduce historical developments may force the modeller to alter his mental model. Consequently, advancement in modelling is an iterative process between mental models, formal models, and real-world data.

1.5. Model pyramid: a taxonomy of environmental models

The era of energy models is squeezed between the modelling building blocks: mental models, empirical data, and theoretical causalities, described in Section 1.4. Figure 1.2 visualises the position of the energy models regarding the building blocks, and the associated methodologies. The model building blocks are represented here as pyramids, because both regarding empirical data, and regarding theoretical causalities, the bases of the pyramids represent heterogeneous data or agents.

21 Also referred to as: ‘idealised systems’. 22 On a slightly more aggregated level one can find data on plant level, or the sub-sectoral level.

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Empiricaldata

Energy models

Theoreticalcausalities

Values

White boxBlack box

Top-down

Bottom-up R

ule-

base

d

Diff

eren

tial e

quat

ionAggregated

data

Heterogeneous data Heterogeneous agents

Homogeneous agents

Verifiablehypotheses

Structured thoughts

Mentalmodels

Figure 1.2: taxonomy of environmental models

Based on: (Farla 2000; Kleijnen 1993; Moll 1993; van Beeck 1999; Wilson 2000; Worrell 1994).

Due to the diversity of modelling approaches, choices need to be made. The choices in modelling are determined by the purpose of the model, or – to be more specific – the question that needs to be answered.

The bottom-up and top-down approach are both associated with empirical data. The bottom-up approach is used to compare energy technologies and to provide parameter input data for predictive models, while the top-down approach is used to monitor long-term trends, to monitor over-all system performance, and to provide input for predictive models.

Rule-based23 models and differential equation models are both associated with theoretical causalities. Rule-based models of individual agents provide insights in the complex dynamic aspects of energy systems, while differential equation models24 provide insights in the over-all functioning of energy systems.

Black- and white-box models are used to aid energy forecasting (see Section 1.2), and therefore referred to as ‘predictive models’. Black-box models emphasise on data, while white-box models emphasise on causal relations between variables. Predictive models are associated with mental models.

23 Also referred to as ‘agent-based’ or ‘stochastic’ models. 24 This type of models is also referred to as ‘system dynamics’ or ‘stock-and-flow’ models.

8

1.6. Aim of the thesis: integrating materials in energy models

Societal metabolism25 requires energy and other resources as inputs (see Section 1.1). Within the societal metabolism framework, energy and other resources do interact with each other, e.g. materials substitution and recycling affects energy use, energy infrastructure like the ‘hydrogen economy’ affects materials requirements. Therefore – in environmental terms – energy and materials resources cannot be examined in isolation (Gielen 1999; Haberl 2001a; Johnstone 2001; Kram et al. 2001; Moll 1993; Sedjo 2002; Tromp 1995b; Wernick & Themelis 1998; Zackrisson 2005).

The aim of this thesis is to model and explore environmentally relevant relations between the management of energy and materials resources (see Table 1.1) with an emphasis on energy systems.26

Because the environmentally relevant relations between the management of energy and materials resources are complicated, they cannot be assessed using a single methodology. Therefore this thesis uses different methodological approaches from Figure 1.2 to assess different case studies.

This research further aims to provide policy-relevant conclusions based on a literature study and three case studies. Together they illustrate energy modelling and the energy and materials interactions, each from a different angle, using a different methodological approach. The insights derived from the case studies are used as input for a policy-oriented study. The next Section gives a brief introduction to the individual Chapters.

1.7. Guide for the reader

Natural resource management is one of the main focuses of this thesis. The 2x3 matrix in Table 1.1 is used to identify possible interactions between types and uses of natural resources. Two of these interactions are considered: ‘renewable energy vs. renewable materials’ and ‘non-renewable energy vs. non-exhaustible energy’.

This thesis starts with a review of – probably – the most comprehensive energy scenario study until present: the IPCC’s special report on emissions scenarios (SRES). Forecasting is an important aspect of energy analysis (see Section 1.2), but also an aspect of energy analysis that is often difficult to communicate. Chapter 2 researches communicative aspects of SRES.

A case study on electricity production is used to study the interactions between non-renewable energy and non-exhaustible energy. Wind energy is an often favoured strategy to reduce GHG emissions in the electricity sector. However, at higher wind energy penetration rates the over-all system-efficiencies decrease because the remaining fossil fuelled plants need to adjust their operating policies, which cause losses. Chapter 3 researches the trade-offs between non-renewable energy and non- 25 For a historical perspective on ‘societal metabolism’, see: (Fischer-Kowalski 1998; Fischer-Kowalski & Hüttler 1998). 26 When focus is on materials resources the trade-offs can be classified according to the materials type: inorganic materials require energy, fossil fuel based materials compete with energy, and bio-based materials compete with food (Huppes, SENSE Core 4, 2004).

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exhaustible energy for the Netherlands. Hydrogen production from wind energy is considered as an option to increase over-all system efficiency.

A case study on waste paper recycling is used to study the interactions between renewable energy and renewable materials. Recycling is a typical way to manage materials and improve efficiency. However, recycling also requires energy. Chapter 4 researches the trade-offs between waste paper recycling and the production from virgin fibre in Western Europe.

A case study on societal materials flows as drivers for industrial energy demand scenarios is used to study the relation between energy and materials from a dynamic perspective. Chapter 5 researches the effects of the use of materials-based indicators for long-term industry energy scenarios for two world regions: Western Europe and China.27

A synthesis of the explorative research of Chapters 3 to 5 is used to provide the reader with general aspects of the level where energy and materials closely interact: the meso-level. Chapter 6 describes policy-relevant aspects of the meso-level of energy systems and finishes with policy recommendations.

Finally, Section 7 (summary & conclusions) discusses the individual chapters based on Figure 1.2 and relates the used methodologies with the modelling elements and modelling approaches.

27 Actually ‘Centrally Planned Asia’.

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2. Communicating uncertainty in the IPCC’s greenhouse gas emissions scenarios28

2.1. Introduction

Forecasting is an important aspect of energy systems research (see Section 1.2). The future is uncertain and impossible to predict. Nevertheless, this uncertainty of the future often needs to be assessed in order to understand the size and nature of environmental threats like climatic change.

Assessing the risk of climatic change differs from old school risk analysis because it is characterised by high risk (EEA 2004), but also by high uncertainty (Grübler & Nakicenovic 2001). Climate change can have huge impacts on ecosystems, agriculture, fisheries, and extreme weather events, and thus affects the lives of practically all people on planet earth. The uncertainty, on the other hand, is enormous. The uncertainty associated with future greenhouse gas emissions has been assessed in the Special Report on Emission Scenarios (Nakicenovic et al. 2000, hereafter SRES) of the Intergovernmental Panel on Climate Change (IPCC). Scenario analysis of a wide spectrum of alternative futures is the main method used to assess the uncertainty and knowledge gaps associated with future GHG emissions (SRES, p65). Complex scientific messages being among the most difficult ones to deliver, the paper focuses on the communication in the framework of SRES.

Because of the high impact of IPCC publications and because long-term forecasting is a relatively attractive scientific subject, SRES received a lot of attention. A critique on the measurement of economic output in combination with a lack of understanding of the concept of scenario analysis lead to the Babel-like confusion of tongues in the ‘Castles & Henderson affair’,29 which reached the non-scientific public (The Economist 2003b; The Economist 2003c) and affected the trust in the findings of the IPCC. The underlying causes of the confusion are twofold.

First, Castles & Henderson appear not to understand the concept of scenario analysis in general and SRES in particular. While statisticians focus on trends, scenario annalists focus on possible trend-breaking events. Scenario analysis in SRES is strategic orientation of what may happen, rather than what is statistically likely to happen. Most of the confusion related to the concept of scenario analysis can be reduced to three communicative issues, i.e. normative elements in the scenarios, probability of the scenarios, and simplifications of the scenarios, which is elaborated in Section 2.3. 28 Co-author: Sander M. Lensink. Provisionally accepted for publication in slightly different form in: Climatic Change. 29 Castles and Henderson put together a critique on SRES. Their main points of criticism were the metric for economic output, Market Exchange Rates (MER) rather than Purchasing Power Parities (PPP), and the plausibility of the scenarios that assume gap-closing between income levels in OECD countries and non-OECD countries. A group of authors connected with SRES responded to the critique, followed by a reply from Castles and Henderson, and again a response from a group of SRES authors (Castles & Henderson 2003a; Castles & Henderson 2003b; Grübler et al. 2004; Nakicenovic et al. 2003). Castles and Henderson argue that, when PPP is used instead of MER, the base-year levels of developing countries are much higher. Consequently the projections of economic output are also much higher.

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Second, SRES authors do not obey the statistical conventions around the use of Purchasing Power Parities (Miketa 2004b).30 They anticipate that the difference in price-levels between OECD countries and non-OECD counties will diminish as the income-gap between OECD countries and non-OECD counties closes. This method was not documented in SRES, however, but first published years later (Manne et al. 2005). The confusion related to the use of MER vs. PPP is funded in methodological issues, i.e. interdisciplinary science, the stakeholder-involvement, and the degree of transparency, which is elaborated in Section 2.4.

This paper aims to contribute to a better communication between scientists and their non-scientific audience. Specifically, the paper reviews scenario literature and SRES in Section 2.2. Section 2.3 identifies the most vulnerable elements in the communication of SRES. The latter elements are commented upon in Section 2.4 to explain them being sources of miscommunication. The concluding remarks should give guidance to reduce the risk of miscommunication and thus offering suggestions to improve future communication of complex messages from scientists to their audience.

2.2. Description of IPCC emissions scenarios

The United Nations Conference on Environment and Development in 1992 in Rio de Janeiro (UNCED 1992) made the curbing of greenhouse gasses (GHG) an important issue on the international political agenda. Most developed countries31 committed themselves to do so in the Kyoto protocol (UNFCCC 1997).

SRES was developed because the IPCC needed new scenarios for their Third Assessment Report (TAR). The reasons for new scenarios were both developments that differed from the assumptions within earlier IPCC scenarios, and new insights in scenario analysis (SRES, p66).32 The objectives of SRES are threefold (SRES, p64):

• To provide input for evaluating climatic and environmental consequences of alternative future GHG emissions in the absence of specific measures to reduce such emissions or enhance GHG sinks.

• To provide input for assessing mitigation and adaptation possibilities, and their costs, in different regions and economic sectors.

• To provide input to negotiations of possible agreements to reduce GHG emissions.

The first two objectives relate to TAR. The third objective suggests a wide use of the scenarios outside the scientific arena.

30 “Purchasing Power Parities (PPPs) are currency conversion rates that both convert to a common currency and equalise the purchasing power of different currencies. In other words, they eliminate the differences in price levels between countries in the process of conversion.” (OECD 2005). PPP is the preferred quantity of economic output among economists (Maddison 2004). 31 The United States are a notable exception. 32 E.g. legislative changes like the Clean Air Act in the US, and the need for gap-closing scenarios (see Section 2.3).

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A specification of SRES is to “cover a wide spectrum of alternative futures to reflect relevant uncertainties and knowledge gaps” (SRES, p65), and therefore SRES assesses uncertainties associated with climate change issues. SRES is, however, limited to so-called non-intervention scenarios because “SRES scenarios do not include any explicit additional climate policy initiatives in accordance with the Terms of Reference” (SRES, p81).33

The method used in SRES is scenario analysis, although elements from other forecasting techniques have also been used. Trend extrapolation34 has been used to identify relations between scenario drivers, e.g. the relation between income and energy intensity (SRES, p125). Analogous thinking35 is not explicitly used; however, SRES assumes a certain consistency of the development paths of non-OECD countries and OECD countries.36 Delphi-like methods37 have been used to construct the storylines. Relevance tree analysis38 has been used in terms of analysis of key driving forces of GHG emissions. Using the latter methods and techniques, SRES first developed four storylines and next developed scenarios based on the storylines.39 Scenarios40 that are based on the same storyline41 are said to belong to the same scenario family (SRES, pp69-71). Based on the four scenario families, six scenario groups were developed.42 There is no traditional trend/Business-as-Usual scenario (Craig et al. 2002; Turkenburg 1993), because the long-term scope of the climate change problem requires strategic orientation on several possible developments. In SRES, there are four so-called scenario families and all scenarios are “equally possible” (SRES, p172). The scenarios “represent pertinent, plausible alternative futures” (SRES, p64).

The scenarios are labeled along two orthogonal axes: 1) Global vs. Regional, and 2) Economic vs. Environmental (SRES, p170; see Figure 2.1). Each of the four scenario families is built on a set of (qualitative) assumptions and GDP projections that form a coherent storyline. Some SRES authors state that the socio-economic variables cannot

33 The authors of this paper cannot find such a restriction in the ‘Terms of Reference’ (SRES, p324-325). 34 Extrapolation of historic trends. 35 Assuming analogies between future developments of non-OECD (developing) countries and past developments of OECD (developed) countries. 36 Future rates of change of indicators like ‘energy intensity’ in non-OECD countries are within the boundaries of historic fast and slow changes in OECD countries. 37 The ‘Delphi method’ was developed by the RAND Corporation in the 1950s as a method for gathering information about the future in order to study future non-surface inter-continental warfare in a broad way. It is based on an iterative process of thesis, antithesis, and synthesis, with synthesis becoming the new thesis. The goal is consensus building among experts (Ringland 1998, p19). 38 Analysis of main drivers of change. 39 A total of 40 scenarios were developed. 40 “Scenario (generic): A plausible and often simplified description of how the future may develop, based on a coherent and internally consistent set of assumptions about key driving forces (e.g., rate of technology change, prices) and relationships. Scenarios are neither predictions nor forecasts and sometimes may be based on a “narrative storyline.” Scenarios may be derived from projections, but are often based on additional information from other sources.” (IPCC 2004). 41 “(Scenario) Storyline: A narrative description of a scenario (or family of scenarios) highlighting the main scenario characteristics, relationships between key driving forces, and the dynamics of their evolution.” (IPCC 2004). 42 A1B, A2, B1, B2, A1T, and A1FI

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be combined freely43 and the SRES authors attempted to combine them based on their interdependency (Grübler & Nakicenovic 2001).

Figure 2.1: relative orientation of the four SRES scenario families

Source: SRES

“SRES was designed to provide insights on uncertainty from a range of plausible scenarios” (Grübler et al. 2004). SRES concludes a high uncertainty of future GHG emissions, ranging from low levels (B1, A1T) to very high levels (A2, A1FI) in 2100 (see Figure 2.2). It should be noted that higher economic growth does not automatically result in higher GHG emissions, and that within one scenario family very different emission paths are possible (see e.g. different A1 scenarios in Figure 2.2). Both low and high emission scenarios are treated as “equally possible” in SRES (p172) and “probabilities or likelihoods are not assigned to individual SRES scenarios” (SRES, p315). Section 2.3.2 elaborates on the plausibility, possibility, and probability of the scenario families.

43 E.g. zero economic growth cannot be combined with rapid technological change and productivity growth (Grübler & Nakicenovic 2001).

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Figure 2.2: CO2 emissions of the four SRES scenario families

Source: SRES

2.3. SRES and communicative issues

The three main communicative issues, the use of descriptive scenarios, plausibility of the scenarios, and simplification of complex messages, are discussed in separate sections below. It should be noted that there appear to be some inconsistencies in SRES and between SRES and separate publications about SRES by SRES-authors regarding the desirability and probability of the scenarios. Regarding the desirability some individual SRES authors state that SRES describes the B1 world as rather desirable (van Vuuren & de Vries 2001),44 while the scenarios are not intended to be desirable (or undesirable) and do not represent preferred developments (SRES, p64). Regarding the probability the scenarios are treated as equally possible (SRES 4.2.1) and thus do not have a Business-as-Usual scenario (SRES, p172), while some individual SRES authors consider the B2 world as Dynamics-as-Usual (Kram et al. 2000; Riahi & Roehrl 2000).45 The inconsistencies shown by the SRES authors46 illustrate the difficulties and pitfalls associated with the SRES message. The question that comes to mind is how policymakers should be able to cope with these issues if even the authors show their struggles to do so.

2.3.1. The use of descriptive scenarios to assess a normative problem Scenarios cannot be value free (SRES, p64). The SRES scenarios are intended to be descriptive, however, not normative (SRES, p64). The use of descriptive scenarios is

44 “a prosperous and fair world (with) a general orientation towards sustainable development” 45 While the audience, by lack of guidance, makes its own choices and is allowed to choose even A1FI as ‘business-as-usual’ (Hillman 2004, p19, p48). 46 IPCC scientists should clarify their position if there is any chance on confusion (Kaiser 2005).

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indeed the common approach for model-oriented science, but it is also associated with possible pitfalls.

Although scenarios are meant to be descriptive, some normative elements have entered into the storylines. This is unavoidable. It is also unavoidable that some scenarios are preferred over others by the scenario developers. The B1 scenario is generally seen as positive and A2 as negative (Castles & Henderson 2003a; Kram et al. 2000, as interpreted by Lomborg 2001, p282; Muskulus & Jacob 2005; Smith et al. 2000; Trnka et al. 2004).

In SRES a strong welfare network (SRES, p182) is added unnecessarily47 to the aggregation of assumptions that led to the optimistic B1 scenario. By adding this politically-coloured element to the optimistic B1 scenario, SRES seems to take position in the debate around welfare networks. Similarly SRES seems to take position against market-based solutions by adding this policy instrument to the aggregation of assumptions that led to the materialistic A1 world and opposite to the one leading to the sustainable development oriented B1 world. Therefore SRES is suggestive in representing the worldview of certain parties, and to favour certain parties’ solutions for problems.48, 49 Remarkably market-based solutions are amongst the most effective policy options to address the main environmental problems and to achieve sustainable development (OECD 2001b, Ch 25). However in SRES, market-based solutions are associated with the A1 scenario that is positioned in contrast to sustainable development.

Some SRES authors state that the scenario variables are interdependent (Grübler & Nakicenovic 2001). In SRES socio-economic variables and policies are aggregated according to their assumed interdependency. However, the combinations of socio-economic variables and policies are based on opinions. Therefore, value-loaded elements are unnecessarily incorporated in scenarios that are intended to be descriptive. The statement in SRES that the variables are interdependent is therefore also a statement that e.g. market-ideology and sustainable development are incompatible.

In SRES the aggregation of the scenario variables is disputable and the impression is given that the aggregation is based on the worldviews of the scenario developers. Political goals and political means are mixed up in such a way that mainly Western European left-wing parties see their worldviews recognisably represented in the SRES scenarios. As for an example, global sustainability is associated with “improved equity” (SRES, p174), and “a strong welfare net” (SRES, p182). In a paper describing the IMAGE-B2 scenario50 some SRES authors associate global sustainability with the market ideology becoming less dominant (de Vries et al. 2000) and SRES associates

47 SRES does not explain why the strong welfare network is added to the B1 scenario, nor does SRES cite literature underpinning the causal relation between strong welfare networks and the sustainable development oriented B1 world. 48 e.g. the RIVM couples worldviews and political parties to SRES-based scenarios (RIVM 2004, p50-51). 49 Reviewers of this paper state that we do not understand SRES by saying that SRES is suggestive in representing the worldview of certain parties. Nevertheless we chose to keep our statement as an illustrative example of a possible misinterpretation of SRES. 50 One of the six marker-scenarios.

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market-based solutions with the seemingly unsustainable A1 world (SRES, p180). By incorporating the left-wing dogmas that sustainable development is associated with planned economies (less market ideology)51 and levelling of income (improved equity and strong welfare net), SRES can be seen as stoutly offensive against some in other parts of the geographical and political spectrum, like US Republicans.52

An obvious normative element in SRES is the income gap closure between OECD countries and non-OECD countries (see Figure 2.3). In the evaluation of previous IPCC emission scenarios it was concluded that scenarios with a significant closure of the income gap between OECD and non-OECD countries was needed (SRES, p66, p123) (Alcamo et al. 1995). There was, however, no scientific reasoning in Alcamo et al. to incorporate such a development path in SRES. Apparently the urge to avoid ‘Being unfair to the South’ (Parikh 1992) was sufficient to include scenarios with implausible high growth rates for developing regions (see Section 2.3.2). Being fair does not seem to be very compatible with descriptive scenario development, to say the least.

SRES is vulnerable because of its attempt to be descriptive.53 The normative elements that entered into SRES made it prone to be misused to justify political opinions, especially because the normative elements in SRES have an air of favouring certain parties and certain parties’ solutions for problems. Most problems with normative elements could be avoided by explicitly labelling them as normative. It is less offending for people who do not agree with normative elements, if these elements are presented as normative rather than subjective science. The subject is too normative to be handled with descriptive scenarios (Swart et al. 2004).

51 Free and fair trade – and thus market ideology becoming more dominant – is a necessity for non-OECD countries to fight poverty (Bhagwati 2004; Hertz 2004). 52 ”The American way of life is not up for negotiation.” the elder George Bush at the first Earth Summit in Rio de Janeiro in 1992 (The Economist 2003a). 53 “The SRES scenarios are descriptive” (SRES, p64).

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Figure 2.3: income in SRES scenarios

Source: calculated from the IPCC website (http://sres.ciesin.org/final_data.html).

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2.3.2. Plausibility, possibility, probability, and pertinence of the scenarios According to SRES, the scenarios “represent pertinent, plausible alternative futures” (SRES, p64)54, 55 which are “treated as equally possible” (SRES, p172),56 and “no single scenario was treated as more or less ‘probable’ than others belonging to the same family” (SRES, p176).57

According to SRES the plausibility of the scenarios is based on an extensive review of the emissions scenarios available in the literature, and has been tested by alternative modeling approaches, by peer review, and by the IPCC review and approval process (SRES, p64). Nevertheless the plausibility, possibility, persistency, and/or probability of the scenarios has been the main topic of dispute in the literature (Allen 2003; Grübler & Nakicenovic 2001; Kriegler & Bruckner 2004; Lempert et al. 2004; Lomborg 2001, p280-287; Pittock 2002; Pittock et al. 2001; Reilly et al. 2001; Schneider 2001; Schneider & Mastrandrea 2005; Webster et al. 2003), especially the Castles & Henderson critique (Castles & Henderson 2003a; Castles & Henderson 2003b; Grübler et al. 2004; Nakicenovic et al. 2003). In this section, the economic development in the A1 AIM marker scenario (see Figure 2.3) is used to illustrate communicative issues linked to the concepts of plausibility, possibility, persistency, and probability.

Historical GDP per capita growth has been as high as 8% for Japan in the period 1950-1973, but in terms of half a century (half the period SRES considers) annual growth rates have not exceeded 3% (Maddison 2001, p126). As Figure 2.3 shows several SRES scenarios include growth rates of over 3% for a hundred years period (see also McKibbin et al. 2004a; McKibbin et al. 2004b). If such scenarios can be considered possible and plausible depends on the context. Unfortunately in SRES context for these high growth scenarios is not sufficiently provided in the sections dealing with economic development (SRES, p92-95, 194-200). In the context of historic developments and scientific knowledge on economic growth, the high growth scenarios are not plausible. Nevertheless scenario developers can alter the context is such a way that high growth scenarios become plausible. High growth scenarios are not impossible, because without restrictions everything is possible.58 SRES authors do, however, restrict the reach of ‘possible’ by stating that: “it is not possible to treat uncertainties of future demographic, economic, and technological developments as independent.” (SRES, p119), and “a ‘free,’ or ‘modeler’s choice,’ numeric combination of scenario indicators is simply not possible.” (SRES, p192). Therefore SRES authors do base the reach of possible on logic derived from past developments. From this particular interpretation of possible, the high growth scenarios seem impossible. Only clear reasoning can provide the context to consider the high growth as possible and plausible. SRES lacks to provide such a context and without a clear given context readers will create their own context.

The plausibility of so-called non-intervention scenarios (no action taken to curb GHG emissions, hereafter ‘baseline scenarios’) is awkward anyway because

54 Pertinent: “relevant to something” (Oxford Dictionary) 55 Plausible: “seeming to be right or reasonable” (Oxford Dictionary) 56 Possible: “that can exist or happen, though not certain to” (Oxford Dictionary) 57 Probable: “likely to happen, exist or be true” (Oxford Dictionary) 58 “Nothing is impossible, just improbable.” (Douglas Adams' Hitchhikers guide to the galaxy)

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implementing no policy at all is not likely to occur. As for an example, the sustainable development oriented B1 scenario covers policies against e.g. acidification (SRES, p65, specification 7) and at the same time implements no GHG policies at all. It is rather unlikely, and not plausible, that action is taken against all environmental treats except for climatic change. Including such policies affect the baseline character of the scenarios.59

The SRES scenario families are not equally plausible. Scenarios that explore the upper or lower boundaries of salient scenario driving forces (Nakicenovic et al. 2003) are differently (not less!) plausible than ‘dynamics-as-usual’ scenarios. In analogy the plausibility of ‘optimistic’ or ‘pessimistic’ scenarios are differently plausible than neutral scenarios. Moreover, as mentioned in footnote 51, free and fair trade – and thus market ideology becoming more dominant – is a necessity for non-OECD countries to develop and overcome poverty according to mainstream economic thought (Bhagwati 2004; Hertz 2004). Therefore, associating high economic growth in non-OECD counties with market ideology becoming less dominant is implausible.

Treating the scenarios as “equally possible” confuses the communication because they are obviously not. Without likelihood assessment scenario analysis looses a lot of weight (Schneider 2001) and, in our opinion, becomes a toothless tiger. Therefore the plausibility needs to be assessed. The assessment however, does not necessarily need to be as quantitative as suggested (Schneider 2001). Quantitative assessment with probability distribution functions, as performed by Mastrandrea and Schneider (Mastrandrea & Schneider 2004), tends to pull the focus of the audience towards dynamics-as-usual scenarios. Assessment of plausibility can alternatively be performed qualitatively by labeling the scenarios explicitly. Experiences from the Shell scenario group tell that likelihood assessment, although mostly qualitative, is a key factor in the communication process (Schwartz 1999). The communication of the scenarios can be improved by naming the scenarios in such a way that the names appeal to the intuition of the audience. Qualitative assessment by explicitly labelling the scenarios according to their plausibility can overcome the plausibility dilemma without incorporating the disadvantages of probability distributions.

Castles & Henderson would probably not have been targeting the plausibility of the SRES scenarios when B1 would have been labelled as ‘extremely optimistic’ regarding the economic development of non-OECD countries. SRES would not have been such an easy target when normative elements would have been labelled explicitly as normative (see e.g. Schneider & Mastrandrea 2005).

2.3.3. The risk of simplification The message of SRES is a complex one with counter-intuitive elements. In a nutshell, SRES concludes that scenarios with different socio-economic drivers can lead to similar cumulative emissions, while similar driving forces can branch out into different categories of cumulative emissions (SRES, p315). According to some authors, SRES contains the message that lower GDP does not automatically leads to lower emissions and that high GDP does not automatically lead to higher emissions 59 It could be argued that this is a false ceteris paribus condition because stringent environmental policies in the absence of GHG policies are not plausible. For a line of reasoning that the “no-control” assumption can be consistent with a global focus to sustainable development see: (de Vries et al. 2000).

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(Grübler et al. 2004). The concept of sustainable development is an integrated part of SRES. The concept of sustainable development does not oppose economic development to the environment.

In the storylines and the 2x2 matrix, economic orientation and environmental orientation are presented as opposites (see Figure 2.1).60 The presentation of the economy and the environment as opposites has potentially a very high impact on the communication of SRES, because it polarises the discussion to pre-sustainable development logic, where economic development and the environment are seen as opposites (Schwartz 1999). Quite the opposite of the message SRES intended to bring. Moreover putting Economy and Environment as opposites on one axis (SRES, p170) implies a choice between economy and environment. It therefore makes the SRES prone to be used to justify political choices without SRES providing any solid justification for those choices. It is considered one of the potential vulnerabilities of scenarios (Keepin & Wynne 1984), especially because opposing ‘Economy’ to ‘Environment’ seems to support the worldview of only a single part of the political spectrum. Alternative ideologies, like the one supported by the current Dutch minister of economic affairs, consider economic growth as a necessary, although not sufficient, condition to implement environmental policies (Brinkhorst 2004). Those politicians see their worldview neglected by the IPCC in the 2x2 matrix, in Figure 2.1.

2.4. Discussion and conclusions

The issues mentioned in the previous Section 2.3 arise from the interdisciplinary character of SRES, the extraordinary relation between science and policy, and lack of transparency of the report. These issues are discussed below.

2.4.1. Interdisciplinary science Performing interdisciplinary science requires scientists from all disciplines that are directly related to the subject. Watson states that the TAR, and thus SRES, is characterized by insufficient involvement of leading macro-economists (Watson 2002). The dispute with Castles & Henderson can be seen as an exponent of this insufficient involvement of leading macro-economists.

The absence of scientists from relevant disciplines is reflected in the choice of the key driving forces identified in SRES. There is a strong emphasis on easily quantifiable drivers like GDP, population, and technology, but other drivers like institutional frameworks, are only vaguely discussed in the storylines. See for comparison the more comprehensive assessment of driving forces in the OECD Environmental Outlook (OECD 2001b) and Global Environment Outlook 3 scenarios (Bakkes et al. 2004).

The insufficient involvement of leading macro-economists (Watson 2002) ended in non-specialist scientists to perform advanced economic tasks, thus ignoring the 60 There is no strong scientific support for both environmental degradation as a consequence of economic growth, and, more important for the concept of sustainable development, reduced economic growth as a consequence of tighter environmental policy (Chua 1999). Moreover, there is no a-priori reason for economic activity to harm the environment, use energy, or emit GHGs (Chua 1999; Craig et al. 2002; Schipper et al. 2001; Smil 2000).

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importance of economics regarding long-term energy scenarios. This apparent attitude disturbs the communication with the economic community, and with one of the stakeholders in the sustainability debate.61 When performing multidisciplinary science, the mono-disciplinary sciences should not be ignored. As for a comparison, in the OECD Environmental Outlook (OECD 2001b) the economic part of the research was conducted by OECD,62 while the environmental pressures were handled by the Stockholm Environment Institute - Boston63 (Kemp-Benedict & Raskin 2001; Schenk 2000). Approval of leading macro-economists is needed to build trust in the economic projections.

2.4.2. Science-policy relation Because of the high stakes and high uncertainties, the science-policy interface of climatic change is the domain of so-called post-normal science (Funtowicz & Ravetz 1993). Moreover, climatic change issues deal with e.g. inter-generational justice, long-term effects, and huge time-lags between cause and effect. Justice is normative, and therefore climate change issues are normative (Schneider & Mastrandrea 2005). The use of descriptive scenarios for a normative problem leads to the conflicts described in Section 2.3.1. The communication can be improved by the development of normative but consistent scenarios, preferably developed in co-operation with relevant stakeholders.

The post-normal science-policy relation requires extension of the peer communities (Funtowicz & Ravetz 1993). Therefore, policy makers should be involved in an early stage of the scientific process. The policy makers should be involved in the model development, similar to the UN ECE acid rain negotiations (Tuinstra et al. 1999). Similarly in the OECD Environmental Outlook and Strategy program (OECD 2001a; OECD 2001b) a steering committee (with policy makers) was involved in order to make sure the scientists answered the questions the policymakers wanted. The steering committee also helped to avoid issues that were too politically sensitive and could potentially block the acceptance of the reports. Additionally a draft of the report received comments during a High Level Segment meeting of the OECD Environmental Policy Committee (EPOC). In the process of the next OECD Environmental Outlook the policy makers from all OECD countries, except the Netherlands, demanded for a single reference scenario instead of several scenarios analogous to SRES (Visser 2004).

The unique science-policy relation urges to go beyond the traditional science-policy relation (Funtowicz & Ravetz 1993). Stakeholder analysis (know who the stakeholders are and what they want) and stakeholder-involved scenario development has a huge potential in climate change issues (van Grinsven 2004). Stakeholder involvement requires, however, a different approach towards scenario development, like stakeholder-involved scenario development. The role of the scientists would be to provide guidance to the stakeholders.

61 E.g. the choice of the SRES authors to disregard the statistical conventions has confused the discussion regarding the use of PPP and MER to a great extent (Miketa 2004b). 62 http://www.oecd.org/ 63 http://www.seib.org/

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2.4.3. Transparency Being transparent is one of SRES’ own specifications (SRES, p65, specification 8). One can argue that SRES itself is insufficiently transparent. While much information could have been presented in a more concise manner, information about the key socio-economic driving forces (population and GDP) is presented only in tables or unclear graphs. Historic developments as well as projections, presented with both absolute levels and average annual changes of key drivers, would allow readers to compare consequences of the different scenarios at a glance.

It is unclear whether the four SRES scenarios sufficiently cover a wide spectrum of alternative futures (SRES, p65, specification 3 & 8) with the division in global vs. regional and economic vs. environmental. SRES does not elaborate on the sufficiency of the scenarios to meet the objective of covering the uncertainties.

SRES aims to assess the uncertainty in driving forces by means of the IPAT identity64 to organise the discussion on GHG emission driving forces (SRES, p105). However, by doing so SRES unwillingly understates the relative importance of key driving forces like institutional frameworks.

The use of six models does not improve transparency. The main document should also explain why specifically the used models (not more, less or other) cover the uncertainty due to models structure. Despite the many pages, the conceptual differences between the models are not transparently described and discussed in the report. The communication and reliability would increase with the use of one official model and several supportive models, analogous to the modeling framework of the UN ECE acid rain negotiations (Tuinstra et al. 1999).

There is much information on general background information, but how exactly storylines were quantified into model input parameters is not transparently presented. Transparent information about the assumptions and the relative importance of the assumptions is needed. Eye-balling the shapes of the carbon dioxide emissions scenarios suggest that fossil resource availability plays a dominant role in the differences between the scenarios. Although uncertainty analysis has been performed (e.g. in van der Sluijs et al. 2001), the transparency could improve by presenting a simplified but representative sensitivity analysis in a transparent way in the main document.

Transparency about normative elements potentially increases the communication. Stakeholder-involved scenario development makes it easier for the scientists to identify normative elements and label them explicitly.

2.5. Concluding remarks

In the communication of GHG emission scenarios through SRES, the weaknesses that have been identified by the authors of this paper are the normative character of climate change assessment (Section 2.3.1), the plausibility of the scenarios (Section 2.3.2), and the risk of simplification of complex messages (Section 2.3.3). The causes

64 I=P*A*T, with I: environmental impact; P: population; A: affluence (GDP/P); T: technology (I/GDP), (Ehrlich & Holdren 1971; Kaya 1990).

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of these communicative issues have been identified as the intrinsic difficulties of interdisciplinary science (Section 2.4.1), the uniqueness of the science-policy relation (Section 2.4.2), and the need for a high degree of transparency (Section 2.4.3).

Our main message to scenario developers is therefore: ‘Keep It Simple, Scientists’ (KISS). Above all the reader needs clear reasoning. Scientific conclusions are to be discussed in scientific journals and the switching to a different modus is required whenever writing reports like SRES. The summaries in SRES are too vague. It matters what the stakeholders need to know, rather than what the scientists think the stakeholder needs to know.

The extraordinary science-policy relation (Section 2.4.2) means extreme caution and changes the way of conducting science (Funtowicz & Ravetz 1993). The use of cultural perspectives is common in environmental sciences (de Vries 2001; Moll 1993; Noorman 1995) but should not be used in post-normal science because one risks the perception of the moral superiority65 of one worldview over the other. In SRES the apparent moral superiority of the B1 scenario over the others disturbs the dialogue with important stakeholders.

The normative character of climate change scenarios (Section 2.3.1) requires explicitly normative emission scenarios. Stakeholder participation in scenario development enables stakeholder groups to obtain sets of scenarios suitable for their specific situation. Experiences with stakeholder participation in scenario development shows the relevance of such an approach (Carlsson-Kanyama et al. 2003). On the other hand, scientists should confront stakeholders with limitations obtained from scientific knowledge. In order not to loose their reliability and credibility, scientists should not give into ridiculous demands from stakeholders, like the non-intervention restriction (SRES, p81).

Key findings

• The (many) simultaneous goals combined in one particular scenario analysis made the analysis too vague. Scenario analysis should be performed with a single and clear goal, rather than with mutually incompatible goals.

• The extraordinary science-policy relations associated with climatic change requires extreme care. GHG scenario development should be performed in different manners depending on the audience. Peers and policy makers require different approaches.

• Normative and politically coloured elements can interfere with the communication of the analysis, unless the normativity is made explicit.

65 During the Athenian democracy in ancient times, many well-known politicians were ostracised because the citizens could not bear their moral superiority (Greene 1998, Ch. 42).

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2.6. Acknowledgments

A significant share of the realisation of this Chapter was done during the IIASA Young Scientist Summer Program (YSSP) 2004. Asami Miketa, Keywan Riahi, Leo Schrattenholzer, and Hal Turton all from the International Institute for Applied Systems Analysis (IIASA) Environmentally Compatible Energy Strategies (ECS) Program, Peter van Grinsven from the IIASA Processes of International Negotiation (PIN) project and Clingendael Institute, Bert de Vries from the Dutch National Institute for Public Health and the Environment (RIVM), Jan Bakkes and Rob Visser of the Organisation for Economic Co-operation and Development (OECD), Winnie Gerbens, Michiel Hekkenberg, Annemarie Kerkhof, Henk Moll, and Ton Schoot Uiterkamp from the Center for Energy and Environmental Studies (IVEM) at the University of Groningen, and the anonymous reviewers of Climatic Change are acknowledged for their contributions to this Chapter.

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3. Wind energy, electricity and hydrogen in the Netherlands66

3.1. Introduction

Non-renewable and non-exhaustible resources have different dynamics because they belong to different resource types (see Table 3.1). The influence of the different dynamics of the resource types on over-all systems performance is studied in this Chapter.

Table 3.1: non-renewable energy vs. non-exhaustible energy

Resource type


Materials (non-dissipative)

Energy (dissipative) Fossil fuels Wind energy

Note: simplified version of Table 1.1

The substitution of fossil fuels by renewable energy sources is a mitigation strategy that is advocated by NGOs (EWEA/Greenpeace 2003),67 research institutes (OECD 2001a), and can be found in concrete policy goals (EC 1997 p10; Rowlands 2005 and references therein). The prospects for increasing the use of renewable energy are much better in the electricity generation sector than other sectors.68 The six main modern renewable energy technologies that produce electricity are: small hydropower, solar photovoltaics, concentrating solar power, biomass, geothermal power, and wind energy (IEA 2003). Wind energy has – in general – benefits over the other technologies because:

• solar photovoltaics is extremely expensive, has high indirect CO2-emissions and a low ‘energy payback ratio’ compared to other renewable energy technologies (Gagnon et al. 2002; Goralczyk 2003; Hondo 2005; IEA 2003),

• concentrating solar power, small hydropower, biomass and geothermal power require special geographical circumstances (IEA 2003) and the global potentials are often limited (Sørensen 2000).69

Therefore wind energy is expected to be the electricity generating renewable energy source with the largest installed capacity worldwide in the near future (IEA 2003).70

66 Co-authors: José Potting, Henri C. Moll, and René M.J. Benders. Submitted in slightly different form to: Energy. 67 An updated version of: (EWEA/Greenpeace 1999). 68 In non-OECD countries biomass is often used for heating and cooking, however with little prospects to increase the share of renewables. In Brazil biomass successfully penetrated the automotive sector. 69 When these special circumstances are present these technologies are often the cheapest options. 70 Currently small hydropower and biomass have more capacity installed, but as mentioned above available land and sites are scarce and slow down growth.

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Wind energy is, however, no reliable energy source since its electricity production depends on the often variable weather conditions. Therefore wind energy has limited options to regulate its load to the grid. When the wind blows and wind energy delivery to the grid increases, power plants – with fuel combustion – decrease their output and (fossil) fuel is saved in this way. This strategy is effective when wind energy penetration rates are low. At higher wind energy penetration rates three classes of problems may arise.

First, when actual wind energy production is lower than expected based on weather forecasts electricity companies may face temporary capacity shortages. Improved forecasting techniques and international trade in electricity contribute significantly in reducing the size of this issue (Giebel 2000). Second, periods with high wind energy may heavily load the high-voltage grid and cause blackouts. Improving the high-voltage grid and improved protocols are options to reduce the risk of blackouts due to over-load of the grid to acceptable levels. Third, over-all system-efficiencies decrease because the remaining fossil fuelled power plants need to adjust their operating policies, which cause losses. This adjustment of operating policies results in less efficient operation of the system hereafter referred to as ‘system losses’. This effect is further explained in Section 3.2.2.

This research focuses on energy inefficiencies due to system losses. There are several potential solutions to (partly) overcome the problem of system losses associated with renewable energy sources. They come in three categories: temporary storage, isolated hydrogen production, and integrated hydrogen production.

Temporary storage. Most solutions are in the direction of temporary storage in times of excessive wind energy supply (Bathurst & Strbac 2003) and the use of hydropower to complement wind energy (Bélanger & Gagnon 2002; Jaramillo et al. 2004). Pumped hydro is often used as an intermediate between peak and off-peak electricity and is an obvious storage medium for wind energy. This solution considers the electricity producing sector as an isolated system.

Isolated hydrogen production. The direct production of hydrogen from wind or solar energy sources is an option often mentioned (even as a core reason to aim to develop a so-called ‘hydrogen economy’). All wind and solar energy is converted to hydrogen and not delivered to the grid. Therefore this solution prevents the ‘unreliable’ energy sources from interfering with the electricity system. Isolated hydrogen production is, however, not an efficient way to utilise renewable energy because the precursor is electricity (Ogden 1999). Electricity produced from wind or solar can be more efficiently utilised by direct delivery to the grid.

Integrated hydrogen production. The production of hydrogen from off-peak electricity stems from scenarios with very high penetration rates of nuclear power (Linden 1996; Ogden 1999). The basic idea is that power plants operate less efficiently at lower loads. Therefore the marginal fuel costs of electricity production decrease at lower loads and consequently the efficiency of hydrogen production from hydrolysis increases. In the system described here wind energy partly delivers electricity to the grid and partly produces hydrogen (see also: Gonzalez et al. 2004).

This research identifies the potential energetic benefits of integrated hydrogen production in electricity systems with high wind energy penetration. A modelling

29

approach is used to compare the over-all efficiency of integrated hydrogen production with the over-all efficiency of the current system where all wind energy is directly delivered to the grid and hydrogen is produced from methane steam reforming. The methodology and model presented in this Chapter is applied to the Netherlands.

This Chapter is structured as follows. Section 3.2 describes the regarded system and section 3.3 describes how the system is modelled. Next, section 3.4 deals with model implementation and addresses technical details of the model (data, equations, algorithms, and etceteras). Finally, section 3.5 shows the results of the model, section 3.6 discusses the interpretation of the model results, and in section 3.7 conclusions are drawn.

3.2. Description of the electricity production system including wind and hydrogen

This section describes the regarded system. Firstly, the characteristics of electricity demand and electricity production are described. Secondly, the effects of high wind energy penetration on the system are explained.

3.2.1. Load types and power plants In electricity production three types of load are can be identified: base-load, mid-load,71 and peak-load (see e.g. Hitchin & Pout 2002). Electricity demand follows a daily pattern during weekdays: low demand during the night, peak demand in the morning, followed by slight decline, peak demand in the evening (after sunset), and again low demand in the late evening.72 As a result electricity production differentiates between different types of load. Base-load is produced continuously over long periods, mid-load starts up in the morning and shuts down during the night and thus runs for a few hours a day, and peak-load covers peak demand and consequently only runs for very short periods. Starting-up and shutting-down power plants is referred to as scheduling in this Chapter.

The total capacity of running power-plants is in practice always higher than the momentary electricity demand. Therefore at least some power-plants have to produce less electricity than their capacity would allow. Increasing or decreasing the output of a single power-plant is in this Chapter referred to as load-regulation.

Different types of power plants were developed because of the different load types and regulation possibilities. The main types used in this research are steam-turbine, gas-turbine,73 combined-cycle,74 and cogeneration.75

Steam-turbine power plants are often large and require quite some time to heat up completely. The possibility for load regulation is more limited than e.g. gas-turbines. A clear benefit is that anything that can produce heat can drive a steam turbine, also nuclear, solar-thermal, and combustible waste.

71 This type of load is also referred to as ‘shoulder’ or ‘cycling’ load. 72 The actual pattern is country dependent. 73 This type of power-plant is also referred to as ‘open-cycle’ power-plant. 74 This type of power-plant is also referred to as STAG (steam and gas) power-plant. 75 This type of power-plant is also referred to as combined heat and power (CHP) power-plant.

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Gas-turbines are less efficient and limited to (expensive) gaseous or light liquid fuels. Therefore gas-turbines are more expensive to operate. Because they have no boiler their start-up time can be very short and load-regulation is rather flexible. Therefore peak-load power-plants are often gas-turbines.

Combined-cycle power plants are a combination of a gas-turbine and a steam-turbine where the hot exhaust-gasses from the gas-turbine are used to produce steam to drive the steam-turbine. Combined-cycle power plants are the most efficient electricity plants. They are, however, limited to gaseous or liquid fuels.76 About 2/3 of the output is delivered by the gas-turbine module and 1/3 by the steam-turbine module (de Biasi 2000). Operating flexibility depends on size and design. Low part-load operation is often possible, but results in significantly lower efficiencies.

Cogeneration is the utilisation of waste heat from power plants for industrial processes or municipal heat.77 The over-all efficiency of cogeneration is very high, but a drawback is that electricity generation is driven by heat demand, which results in low operation flexibility from the electricity generation perspective.

3.2.2. The effects of high wind energy penetration As more wind turbines are installed the fossil fuel plants must adjust their operations strategies in order to deal with the mismatch between actual wind energy supply and electricity demand. This adjustment of the operations strategies results in less efficient production of electricity from fuel combustion because 1) more wind energy leads to less base-load production and more peak-load production, and 2) power plants operating at lower part-loads (Hirst 2002; Jong & Thomann 1983; Kennedy 2005; Lund 2005; Sørensen 2000 p681).

3.2.3. Comparing integrated hydrogen with BaU Figure 3.1 gives a schematic representation of the focus of this research, i.e. the energetic benefits of the production of hydrogen from the ‘system losses’.

System I shows the current situation: wind energy is used to produce only electricity, fossil energy sources are used to produce both electricity and hydrogen.78 System II shows a possible future situation – a part of the renewable energy is used to produce hydrogen, while the rest of the system is not altered. This research reveals the potential fossil fuel reductions as a result of shifting from system I to system II.

In order to be able to compare the systems I and II in Figure 3.1, the fossil production of hydrogen must be taken into account. Methane steam reforming is taken as a reference and hydrogen is only produced from wind when this results in a reduction of the total fossil fuel costs in monetary terms.79 The over-all system efficiency is calculated in terms of avoided primary energy consumption.

76 Coal and biomass can be gasified and then combusted in a combined-cycle plant. 77 This is known as ‘topping’, the opposite – ‘bottoming’ – is the utilisation of the waste heat of very-high temperature industrial processes to produce electricity, which is less common. 78 Currently hydrogen is primary used in the petrochemical industry. 79 Because this is actually a form of part-load distribution, see sections 3.3.3 and 3.4.8.

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Figure 3.1: descriptions of the current system (I) and the integrated hydrogen production system (II)

With: A: wind to grid B: electricity generation from combustible fuels C: steam reforming D: electrolysis

3.3. Modelling of the electricity production system including wind and hydrogen

This section describes the methodology used to model the system described in section 3.2.

3.3.1. Chronological hourly modelling Section 3.2 showed that the fossil fuel savings depend on the interaction between wind energy production and different types of power plants. Daily, weekly and annual dynamics of electricity demand determine power plant scheduling and operating, and thus the over-all system performance. Wind does interfere with the system in a chaotic manner – e.g. not following the same pattern every day – and therefore the appropriate approach to calculate the effects of wind energy on the over-all system

32

efficiency has to be modelled chronologically80 on an hour-to-hour base over a long period (Giebel 2000; van Wijk 1990).81,82

3.3.2. Scheduling optimisation The focus of this model is on energy efficiency. However, when optimising on energy solely, natural gas would be the preferred fuel over coal and the system would not behave realistic. Fuel prices reflect mining-costs, transportation-costs, politics, energy-content, and so forth. These are factors that can be expected to remain relatively constant over time. We chose to optimise on fuel costs rather than energy consumption because this allows the model to behave more closely to real-world behaviour. For the model absolute prices are of no importance, only the relative prices matter.

Scheduling of start-up / shut-down decisions is optimised based on operating costs, start-up costs,83 and shut-down-costs. In practice this means that coal-fired steam-turbines will be scheduled for long periods because of their high start-up costs and low fuel costs. Gas-turbines on the other hand will be scheduled for short periods because of their low start-up costs and high fuel costs. Scheduling of power plants is influenced by the required amount of spinning reserve (Section 3.3.3) and thus the capacity credits for wind energy (Section 3.3.4).

3.3.3. Spinning reserve Because of the high interconnectivity of the electricity supply system, a single power plant breakdown can cause system-wide electricity blackouts far beyond national borders. Therefore, when a power plant breaks down, other power plants have to cover the lost supply directly. In order to do so, the other power plants must have reserve capacity available in case of emergency. This reserve capacity is referred to as spinning reserve and normally equals the largest running power plant. The left-hand side of Figure 3.2 shows how the total running capacity of power plants equals the momentary electricity production plus an amount of spinning reserve.84

3.3.4. Capacity credits for wind Wind energy is not a reliable source of electricity. However, because of the large number of individual wind turbines and their geographical dispersion some reliability can be attributed to wind turbines. This reliability of wind energy is referred to as the “capacity credits” for wind.

Figure 3.2 shows what happens to the electricity production system when wind energy is added to the system. First, electricity production from power plants is lowered,

80 This type of modelling is also referred to as ‘unit commitment’ (for a review see: Sheble & Fahd 1994) 81 Compared to other scheduling and operating optimisation problems. 82 It should be noted that techniques have been developed to assess the output of wind energy with so-called ‘residual load duration curves’ (Kennedy 2005). That method is, however, not precise at high wind energy penetration rates, and not able to determine the potential benefits of hydrogen production. 83 Reflecting the time needed to heat the plant and the energy requirements do so. 84 In actual systems different types of reserve are distinguished depending on respond times (van Asseldonk 2004)

33

resulting in energy savings. Next, spinning reserve is adjusted to the new situation, depending on the capacity credits of wind.

Electricity Production

SpinningReserve

Power plant Capacity

CapacityCredits

FossilSavings

no Wind

withWind

Figure 3.2: electricity output and spinning capacity in situations with and without wind energy

3.3.5. Part-load distribution Due to the requirement of spinning reserve not all power plants will be able to run at full-load. The distribution of the spinning reserve over a set of power plants is called part-load distribution.

Part-load distribution is based on part load efficiencies (PLE) and fuel prices. As a result coal-fired steam-turbines are likely to run at full-load whenever possible, while natural-gas-fired plants are likely to reduce their load sooner.

3.4. Model implementation

This section deals with the implementation of the methodology as described in the previous section. It describes assumptions, data sources, equations, algorithms and etceteras as used in the model.

34

3.4.1. General model dimensions The model is run over a full year (8760 hr) in order to level out seasonal differences in wind energy supply and electricity demand. Seasonal effects tend to be quite strong – regarding both electricity demand and wind energy – in the Netherlands.

This model emphasises energy efficiency optimisation and energy prices are solely included to capture aspects of energy sources that cannot be expressed in Joules, for example differences in combustion properties of gaseous fuels and solid fuels.

3.4.2. Geographical focus This research is limited to the case of the Netherlands because of our knowledge of the Dutch system (Benders 1996; de Vries et al. 1991), data availability, and contacts with energy companies (Battjes & de Kler 2004). The Netherlands lacks ‘special’ energy resources (like mountains for hydro, large area for biomass, sufficient solar, geothermal, etc) to produce renewable energy, and lacks the opportunity to store energy in pumped hydro. Therefore the conditions in the Netherlands are in favour of integrated hydrogen production. Moreover, the Netherlands is considered to have good wind resources (Junginger et al. 2004; Troen & Petersen 1989).

3.4.3. Hourly electricity demand This model is limited to central electricity production. Therefore electricity demand equals central production and net imports. Hourly electricity demand data is calculated from (van Wijk 1990) and scaled-up to represent the 1998 situation (latest available data before liberalisation of the sector). During the scaling process both peak demand and total electricity demand were considered. Peak demand is 12,055 MW, and total demand is 72,062 GWh (Sep 1999).

3.4.4. Hourly wind energy electricity production Hourly wind energy electricity production data is taken from Van Wijk (van Wijk 1990). The dataset represents the Dutch situation with 1GW of wind energy installed. This 1GW of wind energy installed produces 2,043 GWh electricity, equalling a load factor of 23%, while the current load-factor is 23-25% (EWEA/Greenpeace 1999; EWEA/Greenpeace 2003). Runs with alternative amounts of wind energy installed use linear up-scaling of this dataset.

3.4.5. Primary energy prices Prices of primary energy are related to the Dutch situation and based upon (Sep 1996). This model assumes 2.33 �/GJ for coal, and 3.76 �/GJ for natural gas. As mentioned in section 3.3.2 only the ratio of coal and natural gas prices matters for the model. The ratio of prices influences scheduling and part-load distribution.

3.4.6. Power plant data The power plant data is taken from the PowerPlan model (de Vries et al. 1991) and updated (Battjes & de Kler 2004). Load-regulation abilities (see section 3.2.1) are accounted for by minimum-loads. Part load efficiencies (PLE) are constructed from

35

typical Relative Net Plant Efficiency (RNPE)85 values (Chuang & Sue 2005; de Biasi 2000; Kim 2004).

3.4.7. Power plant scheduling Power plant planning consists of two parts: starting-up plants on when required, and shutting-down plants off when no longer needed. In order to be able to make decisions on the planning the planner needs to have some information on the electricity demand and wind energy production in the near future. In this model there is a time-window of 168 hours (one week) wherein both electricity demand and wind energy production are known.86

The spinning reserve required to equal the largest running power plant is 600 MW in this model.

The capacity credits of wind energy are based on a study on the capacity credits of wind energy in the Netherlands. This study finds a capacity credit of 20% for 5% of demand covered and a capacity credit of 13% for 15% of demand covered (Halberg as cited in Giebel 2000 p59).87 This model uses a linear relation between the percentage of demand covered and capacity credits.

The algorithm used to determine which power plant to turn on is straightforward. Figure 3.3 is used to explain how start-up scheduling is optimised. When additional capacity is needed (point A) all non-running power plants are evaluated for start-up. The algorithm is as follows:

• The participation factor in the forecast-window (from point A to B) is determined. This represented by the grey area in Figure 3.3.

• The total costs of starting-up the plant are determined using Equation 3.1. The start-up costs are allocated over the time the power plant is required.

• The cheapest power plant to start-up – based on Equation 3.1 – is selected for start-up.

• The process repeats itself until sufficient power plants are switched on.

ionFactorParticipatFcWindowtsStartupCos

FuelCostsFcPpCosts•

+= Equation 3.1

With: FcPpCosts = Forecasted costs of starting-up the power plant FuelCosts = the fuel costs to generate 1 MWh StartupCosts = the costs per MW capacity to start-up the plant FcWindow = forecast window size; the number of hours taken into consideration ParticipationFactor = the full-load hours of the power plant in the forecast-window divided by the number of hours in the forecast-window, represented by the share of grey in the rectangle between A and B in Figure 3.3.

85 index, full-load = 100% 86 Perfect foresight assumption. 87 For an overview of available literature see: <http://www.drgiebel.de/WindPowerCapacityCreditLit.htm>

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The participation factor enables the model to weight the start-up costs and fuel costs of different power plants with respect to the size of the power plant.

The forecast-window size influences the forecasted fuel costs as can be seen in Equation 3.1. The minimal size of the forecast-window is determined by the dominance of the fuel costs over the start-up costs when the participation factor is one. In other words, when the power plant is expected to run for a long period the power plant with the cheapest fuel should be selected and thus have the lowest forecasted costs. A forecast-window of one week (168 hours) was chosen because of the weekly pattern in electricity demand, and because in our dataset fuel costs are dominant for all power plants regarding start-up decisions when this period is chosen.

Forecast Window Hours not needed

A B DC

Figure 3.3: power plant planning

Shutting-down power plants is a bit more complex than starting them up. Again Figure 3.3 is used to explain how shut-down scheduling is optimised. When more capacity is running than required (point C) all running power plants are evaluated for shut-down. The algorithm is as follows:

• The number of hours the power plant is not needed (from point C to D) is counted.

• The costs of shutting-down the power plant are compared to the costs of not shutting down the power plant using Equation 3.2. The higher FcPpCosts, the higher the refund of shutting-down the plant.

• The power plants are sorted according to the value of FcPpCosts. • The most expensive running plant is shut-down using selection criterion:

FcPpCosts is positive; the refund of shutting-down the power plant must be positive.

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• The process repeats itself until no more power plants are available for shut-down.

stsShutdownCotFuelCostsFcPpCosts −= * Equation 3.2

With: FcPpCosts = forecasted costs of running the powerplant FuelCosts = the fuel costs to generate 1 MWh t = the number of hours the power plant’s capacity is not needed ShutdownCosts = the costs per MW capacity to shut-down the plant

It should be noted that the shut-down costs should not be taken too literally. They (partly) include the costs of starting-up after the shut-down period, because shutting-down implies starting-up and vice versa.

3.4.8. Part load distribution Part-loads are distributed in the most efficient way when the marginal fuel costs of all power plants are equal. This means that power plants with higher fuel costs are regulated down before power plants with lower fuel costs.

Power plants are the most efficient at full-load or just below. Efficiencies decrease at lower loads and power plants also have minimum loads under which operation is not possible. Only fuel costs are considered because this Chapter focuses on energy efficiency. Therefore marginal costs are defined as marginal fuel costs as shown in Equation 3.3.

With: MC = marginal costs (�/MWh) PL = part-load (%) Cap = capacity FC = fuel costs (�)

CapPLFC

PLCapPLFC

PLMC)(

lim)(0

•∂

∂=•∆

∆=→∆

Equation 3.3

The part-load efficiencies are functions of the part-loads; in notation PLE(PL). In reality PLE(PL)’s can be very complex functions, especially when power plants are an assembly of different compounds. E.g. reducing the load in combined-cycle plants – from full load to minimum load – leads to stepwise shutting down individual turbines until only one gas-turbine is running in the end. Therefore the PLE(PL) will look like a sawn-shaped graph rather than a smooth curve (Chuang & Sue 2005). For practical reasons, however, a polynomial of the second order is used to approach the PLE(PL) relationship.

A non-linear PLE(PL) relationship implies that the relation between power plant output and fuel consumption is also not a linear one. Consequently the marginal costs as a function of power plant output are not constant! Marginal costs are lower at lower part-loads and even become zero when minimum loads are reached.

Equation 3.4 shows how the total fuel costs are calculated. Equation 3.5 shows the result of the first derivative of the total fuel costs, which describes the marginal costs as a function of part-loads.

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CapPLRNPEEffCapPLFP

PLPLMC

full ••••••

∂∂=

)(6.3

)( Equation 3.4

( )��

�

�

��

�

�

•∂

∂•−

•••= 2)(

)(

)(1

6.3)(PLRNPEEff

PLRNPEPL

PL

PLRNPEEffFPPLMC

fullfull

Equation 3.5

With: RNPE(PL) = relative net plant efficiency (index, full-load = 100%) Efffull = plant efficiency at full load (%) MC = marginal costs (�/MWh) PL = part-load (%) Cap = capacity FP = fuel price (�/GJ) Notes: Equation 3.5 is only valid when the part-load efficiencies are approached with a polynomial of the second order. The time-frame is 1 hour regarding all formulas. PLE(PL) = Efffull * RNPE(PL)

In the model the marginal costs are increased or decreased until the total electricity production meets total electricity demand. Therefore the power plant’s part-loads need to be a function of marginal costs, i.e. the inverse of Equation 3.5. When RNPE(PL) is approximated with a second order polynomial – as is done in our model – then the inverse of Equation 3.5 becomes unmanageably large. Moreover, these functions would lead to discontinuous relations between marginal costs and part-loads and cause power plants to allocate spinning reserve discontinuously amongst power plants, which is unrealistic behaviour. Therefore the relation between part-load and marginal costs is approximated with a fuzzy-logic approach in this model, which allows fast optimisation. Figure 3.4 shows the approximated relation between marginal costs and part-load.

Point c is determined by taking the average marginal costs associated with increasing the power plant output from minimum-load to full-load. Points b and d are determined by the difference in MC at minimum-load and MC at full-load. On the trajectories between points a and b, and between points d and e the part-loads are kept constant at minimum-load levels and full-load levels respectively.

3.4.9. Hydrogen production Both steam reforming of natural gas and water electrolysis are mature hydrogen production processes (Momirlan & Veziroglu 2002). The energy conversion efficiency of large-scale methane steam reforming is 75-80% HHV (Ogden 1999). In this model 75% efficiency is assumed. Water electrolysers are typically 70-85% efficient on a higher heating-value (HHV) basis (Ogden 1999). In this model 80% efficiency is assumed.

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Marginal Costs

Part Loadba

ed

c

Figure 3.4: approximated relation between marginal costs and part-load

In this model hydrogen is produced when costs to produce hydrogen from electrolysis (based on marginal costs of electricity production) is lower than the costs to produce hydrogen from methane steam reforming.88 The fuel price for gas is assumed to be 3.76 �/GJ (see section 3.4.5). Therefore the benchmark costs of hydrogen are 5.01 �/GJ based on a conversion efficiency of 75% for steam reforming. This benchmark equals 14.44 �/MWhe marginal costs based on a conversion efficiency of 80% for electrolysis. Therefore hydrogen production will only start when electricity prices drop below 14.44 �/MWh.

In this model hydrogen production from electrolysis is modelled as a power plant with negative output. A fuzzy approach was used to regulate hydrogen production similar to the power plants (see section 3.4.8).

3.5. Results

The model was run several times. A first set of model runs, with different wind energy capacities (50MW step-size) and no electrolysis, shows the relation between installed wind energy capacity and net avoided primary energy. A second set of model runs, with different wind energy capacities (1000MW step-size) and different electrolysis capacities, shows the ancillary benefits of hydrogen production.

88 Only fuel costs are considered; capital costs are not included.

40

3.5.1. Fossil fuel savings Figure 3.5 shows the relation between wind energy capacity and avoided fossil energy consumption. On the left-hand side of the graph each added wind-turbine results in a proportional amount of avoided fossil energy consumption because there are no system losses involved. On the right-hand side of the graph each added wind-turbine results in a lower amount of avoided fossil energy consumption than the previous wind-turbine due to the system losses involved. The system losses shown here are low compared to similar analyses in other countries due to the relatively flexible power-plant – very low nuclear and conventional coal share, and high gas-fired combined-cycle – park of the Netherlands.

0

20

40

60

80

100

120

0 2 4 6 8 10 12

Wind Energy Capacity (GW)

Net

avo

ided

prim

ary

ener

gy (P

J) .

Figure 3.5: net energy savings from wind energy

Note: results from model runs with increasing wind energy capacity (step-size = 50MW).

Figure 3.6 shows the relation between wind energy capacity, hydrogen production capacity from electrolysis, and net avoided fossil energy consumption. As can be seen, hydrogen production from system losses is of almost no help at 6 GW or less of wind energy in terms of improving over-all system efficiencies. At higher wind energy penetration rates hydrogen production can significantly contribute to the system. The additional avoided use of primary energy steadily increases as wind energy increases from 7 to 12 GW. As the horizontal line in Figure 3.6 shows, the amount of avoided primary energy use from point from 10 to 11 GW wind is less than going from 10 GW wind to 10 GW wind + 500 MW electrolysis capacity. However, at 6 GW of wind energy capacity – the Dutch policy goal for 2020 – hydrogen production does not significantly increase over-all system efficiency.

41

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12Wind energy capacity (GW)

Avo

ided

prim

ary

ener

gy u

se (P

J) .

0 MW H2500 MW H21000 MW H21500 MW H22000 MW H2

Figure 3.6: net energy savings from wind energy and hydrogen production

Note: the values for no hydrogen production correspond with the values in Figure 3.5

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10 11 12Wind energy capacity (GW)

Hyd

roge

n pr

oduc

ed (P

J)

500 MW H21000 MW H21500 MW H22000 MW H2

Figure 3.7: hydrogen produced

42

3.5.2. Hydrogen produced Figure 3.7 shows the amount of hydrogen produced at different wind energy capacities and different hydrogen production capacities. At 6 GW of wind energy and 1000 MW of electrolysis capacity, the number off full-load hours is 927 hr which corresponds with a load factor of 11%.

0

5

10

15

20

25

30

35

2600 2650 2700 2750 2800 2850 2900 2950 3000 3050 3100

hour

Mar

gina

l cos

ts (�/M

Wh)

Wind = 0GW, H2 = 0MWWind = 6GW, H2 = 0MWWind = 6GW, H2 = 1000MW A

C

D

B

E

Figure 3.8: hourly marginal costs for different scenarios

Note: hour 2600 corresponds with April 19; hour 3100 corresponds with May 10; the 6GW/0MW line overlaps with the 6GW/1000MW line most of the time.

3.5.3. Stabilising effect of hydrogen production Figure 3.8 shows the marginal costs over a period of 500 hours for a situation without wind energy, with wind energy (6 GW), and with wind energy and hydrogen production (1000 MW). As can be seen the production of hydrogen has a dimming effect on the marginal costs when wind energy production is abundant.

Without wind energy the marginal costs follow a daily pattern with narrow canyons during off-peak hours. At A wind energy is almost zero and therefore the MC patterns overlap. At B is shown how wind energy production reduces the MC costs of electricity production during the day. At C is shown how wind energy production deepens the canyon of low MC during the night. At D is shown how wind energy – without hydrogen production – can cause the MC to drop to zero. This happens mostly during the off-peak hours when the power-plants are already running at low part-loads. At D is shown that hydrogen production can dim the effects from high wind energy production during off-peak hours. Finally at E is shown that a hydrogen production capacity of 1000 MW does not always prevent MC against reaching the bottom. Without hydrogen production MC dropped to the bottom during 642 hours; with hydrogen production MC dropped to the bottom during 256 hours, which is a reduction of 60%.

43

As mentioned in section 3.1, wind energy may cause problems by heavily loading the high-voltage grid at times of abundant wind energy. Hydrogen production from hydrolysis has a potential to decrease the load from wind energy to the high-voltage grid.

3.6. Discussions

3.6.1. Geographical focus The Dutch electricity producing system is characterised by relatively low nuclear capacity and relatively high combined-cycle capacity. The potential benefits of integrated hydrogen production are higher in countries with a more rigid power plant park, e.g. more nuclear capacity.

In our model the Netherlands is a closed country, while in reality electricity is imported from and exported to nearby countries. Trade in electricity increases the flexibility of the system and therefore decreases the potential benefits of integrated hydrogen production.

3.6.2. Model assumptions Our model assumes perfect foresight regarding power-plant scheduling. Due to this assumption the running capacity shall often be lower than the real-life situation would be. Therefore, the potential benefits of integrated hydrogen production are underestimated.

Load-regulation abilities of power-plants are limited.89 This limitation is not included in this model because this limitation falls mostly within the resolution of our model. The main reasons are: 1) the Dutch power-plant park is dominated by gas-fired combined-cycle plants who can adjust their load unlimitedly within one hour, the resolution of our model, and 2) operators of a 600MW coal plant assured us that their coal plant can change its output from minimum load to full load within one hour, the resolution of our model.

The energy needed for starting-up power plants is not included in this model. The main reason is that in this model hydrogen production does not influence the power plant scheduling and therefore the number of power plant start-ups is equal with or without hydrogen production. In reality electrolysis capacity will influence power plant scheduling and therefore the potential benefits of integrated hydrogen production are underestimated.

3.6.3. Wind energy growth rate Wind energy capacity in the Netherlands is increasing fast (see Figure 3.9). The implementation of the BLOW-covenant in mid 2002 (EZ 2004) altered the institutional framework and caused a sharp increase in wind energy capacity growth rates.

89 This is also referred to as ‘ramping’.

44

0

200

400

600

800

1,000

1,200

1985 1990 1995 2000 2005

year

Win

d en

ergy

cap

acity

(MW

)

Figure 3.9: total electricity production and wind energy electricity capacity

Source: (CBS 2005; ECN 2005) Note: 2004 is preliminary

The Dutch policy goal for 2000 of 1 GW was not met, in spite of the enormous growth since 1990. The policy goals for 2010 and 2020 are 1.5 GW and 6 GW respectively. Because of successful modification of the institutional framework, the policy goals for 2010 and 2020 are more likely to be met. This implicates that the scenarios modelled cover the near future and beyond in terms of installed wind energy. Therefore measures must be taken to anticipate to these situations.

3.6.4. Alternative uses for hydrogen The hype about the so-called hydrogen economy and the fast growth of wind energy triggered this research. Without doubt the establishment of a hydrogen economy would point focus on the efficient production of hydrogen and on the production of hydrogen from system losses. However, also if the hydrogen economy does not develop, hydrogen production from wind energy would still be a high potential option to aid the exploration of renewable resources. Hydrogen cannot not only be used to fuel fuel-cells, but hydrogen is also an important input for the (petro)-chemical industries (Czuppon et al. 1998). For reason of comparison the amount of hydrogen needed for fertiliser production in the Netherlands is calculated.90

The consumption of nitrogenous fertilisers91 in the Netherlands in 2001 was 290 kilo tonnes (OECD 2004a), which requires 98.6 million kg ammonia (NH3),92 which

90 Data is readily available for fertiliser production. Fertiliser production is normally the largest hydrogen consumer in an economy, followed by oil-refineries (Czuppon et al. 1998). 91 for a comprehensive analysis on fertilisers see: (Davis & Haglund 1999).

45

requires 17.4 million kg of hydrogen.93 This amount of hydrogen represents 2.5 PJ (HHV).94

As Figure 3.7 shows, the amount of hydrogen produced at wind energy capacities of 6 GW and hydrogen electrolysis capacities of 2 GW is less than the amount of hydrogen needed for fertiliser production. At higher wind energy capacities hydrogen production becomes larger than the amount of hydrogen needed for fertiliser production and (finally) larger than the total amount of hydrogen needed for the chemicals industries. Therefore the supply of demand potentially outnumbers the demand. On the other hand it is likely that a vast production of hydrogen will create additional demand for hydrogen (e.g. fuel cells).

3.6.5. Dynamics of electricity generation capital stock The electricity sector is very capital intensive and the lifetime of the capital stock is 25-40 years (de Vries et al. 1991). Therefore the electricity capital stock tends to change very slowly (see e.g. IEA 2002c, p130) and legitimates the use of the current power plant park for scenarios with a time frame of a few decades.

3.6.6. Relieving the grid As shown in section 0, hydrogen production potentially relieves the high-voltage grid from heavy loads due to high production of wind energy. This is very important, because the Dutch high-voltage grid is expected to have a maximum of 3GW wind energy capacity connected (Tennet 2002). Therefore the potential benefits of “integrated hydrogen production” may be both relieving the grid and increasing the over-all system efficiency.

3.6.7. Policy relevance Because of 1) high investment costs, 2) long lifetime of the capital stock, 3) danger of lock-in situations, and 4) high growth rate of wind energy capacity explorative research with a timeline of several decades is relevant for decision makers. Although the electricity markets are liberalised in the Netherlands, the role of the government is still influential. This research directs policy makers to think about the consequences of increasing wind energy and allows them to take action before problems arise.

This research shows that improvement of over-all system efficiency by coupling of currently separated systems can significantly contribute to avoided fossil fuel consumption, which is in line with previous research (Gielen 1995; Kram et al. 2001). As Figure 3.6 shows, the equivalent of avoided fossil fuel consumption from 11 GW wind energy can be met with 10 GW wind energy and 500 MW hydrogen production capacity.

92 1 kg of nitrogenous fertilisers requires 340 g ammonia (Kramer 2000 p50). 93 according to: 3 H2 + N2 (from air) � 2 NH3 (Davis & Haglund 1999). 94 1 kg of H2 � 141.9 MJ (HHV) (Ogden 1999).

46

3.7. Conclusions

3.7.1. Hydrogen can be produced from wind This research shows that the use of system losses for hydrogen production via electrolysis is beneficial in situations with ca. 8 GW or more wind in the Netherlands. Therefore, from a systems-efficiency perspective hydrogen production will only be beneficial at very high wind energy capacities. The 2020 goal of 6 GW will not benefit from hydrogen production in terms of systems-efficiency.

3.7.2. Relieving the grid Relieving the grid is – according to this research – an ancillary beneficial effect of coupling hydrogen production with wind energy. In practice this means that electrolysis capacities should be located near places where large wind energy production facilities – like off-shore wind parks – are coupled to the grid.

3.7.3. Planning new capital is important The planning of new electricity production capital can be important for the over-all efficiency of the system. More flexibility for less design-load efficiency may be profitable in systems with more renewable energy sources.

Diversity of renewable energy sources may also be a sensible tactic. Biomass for example is less favourable in terms of conversion efficiency compared to. However, because biomass conversion routes include gasification it may be used to fire reliable, efficient and flexible combined-cycle plants and thus contributes to the over-all system efficiency.

Key findings

• Increasing wind energy capacity generally results in decreasing fossil fuel consumption.

• The benefits of wind energy suffer from diminishing returns due to losses when the wind blows at times of low electricity demand.

• The production of hydrogen from (discarded) wind energy can help to reduce these losses.

• Reducing the losses would require much electrolysis capacity, and sophisticated regulation.

47

3.8. Acknowledgements

Coos Battjes, Kornelis Blok, Gregor Giebel, David Grote Beverborg, Peter de Jong, René Kleijn, Robert de Kler, Sander Lensink, Sanderine Nonhebel, Bas van Ruijven, Ton Schoot Uiterkamp, and Detlef van Vuuren are acknowledged for their contributions to this Chapter.

49

4. The non-linear relationship between paper recycling and primary pulp requirements95

4.1. Introduction

Renewable resources can be used for energy or materials; wood can be combusted, but also used to produce paper. Materials efficiency is usually increased by increasing recycling. This Chapter studies the effect of waste paper recycling on the use of renewable energy and non-renewable energy. Table 4.1 shows the resources studied in this chapter in the context of the resource matrix.

Table 4.1: renewable energy vs. renewable materials

Resource type


Materials (non-dissipative) Pulpwood

Energy (dissipative) Fossil fuels Bio-energy

Note: simplified version of Table 1.1

An important option for reducing GHG emissions is the substitution of carbon-intensive fossil fuels such as coal or oil by less carbon-intensive, renewable energy sources such as solar, wind or biomass (OECD 2001b). Currently, biomass is the main renewable energy source with the potential to be implemented on a substantial scale because of its relatively low costs and its ability to substitute for coal within the existing electricity infrastructure (Berndes et al. 2003; Hall & Scrase 1998; Klass 1998).

Biomass is a resource that is also widely used for materials purposes, one of which is paper (and related products). About one third (35%) of the total wood production in the European Union is for pulp intended for papermaking (FAOSTAT 2001). The papermaking process can be briefly described as follows: After sawing, roundwood96 is slashed, debarked and chipped. The chips are then pulped,97 whereas the waste products (e.g. sawdust and bark) are used to produce electricity and heat (Genco 1998). The pulp is dried and pressed to form paper. Secondary pulp, produced from waste paper, can substitute for virgin or primary pulp.

95 Co-authors: Henri C. Moll and José Potting. Published in slightly different form in: Journal of Industrial Ecology, 2004, 8(3), 141-161. 96 Roundwood is defined as follows: “ Wood in the rough. Wood in its natural state as felled, or otherwise harvested, with or without bark, round, split, roughly squared or other forms (e.g. roots, stumps, burls, etc.)..” (FAO 2001). 97 Pulping includes various processes. According to Genco (1998), “ The principal wood-pulping processes (…) are stone groundwood, (…) sulphite, and the sulphate or Kraft process (…)” . In the current article, the stone groundwood process is referred to as the mechanical process, and the sulphate or Kraft process is referred to as the chemical process (the sulphite process is not considered in this article).

50

The use of biomass for paper production is intimately connected to waste management choices because waste paper is suitable for recycling back into paper or for energy recovery. If waste paper is recycled, secondary pulp replaces virgin pulp in the paper production process. Therefore, primary resource management and waste management influence each other, making trade-offs and synergies rather complex.

The relative merits of recycling versus incineration with energy recovery (both as alternatives to landfilling) have been the main focus in previous literature about waste paper management. From this literature, the following conclusions can be drawn (Beer et al. 1998; Blum et al. 1998; Finnveden & Ekvall 1998; IIED 1996; Rajotte 2000; Ruth & Harrington 1997b):

• Recycling, compared to incineration, leads to a lower use of biomass (pulpwood)98; and,

• Recycling, compared to incineration, leads to a lower total energy use because the production of secondary pulp from waste paper is less energy-consuming than the production of primary pulp from roundwood; however,

• Recycling, compared to incineration, leads to a higher use of electricity from the public grid which is presently predominantly fossil fuelled; and therefore,

• Recycling leads to higher CO2 emissions when compared to incineration.99 One aspect of waste paper recycling that is not taken into account in the above literature is the fact that fibre recycling is limited due to physical constraints. Re-pulping of waste paper damages the cellulose fibres and decreases the ability of fibres to adhere to each other (Ellis & Sedlachek 1993). Fibres can be shortened as a result of damage during re-pulping and overly short, weak fibres are disposed during the washing process (Borchardt 1998). Typically, a fibre can be reused 3-5 times (Virtanen & Nilsson 1993). Therefore a permanent input of virgin fibre to the system is required (IIED 1996). Because the recycling of a single fibre is physically constrained, one can expect that theoretically, the relationship between recycling rates and resource requirements100 should be represented by a curved line rather than a straight one (Virtanen & Nilsson 1993). Also, analysis of country-level data from CEPI (BUWAL 1996; CEPI 2000) suggests a non-linear101 rather than a linear relationship between virgin fibre consumption and the use of recovered paper (as demonstrated later in Figure 4.2).

In this article, we investigate whether the physical limits on waste paper recycling can be theoretically described with a mathematical model which can be calibrated against available data and will produce the expected non-linear relationship between recycling rates and primary pulp requirements. Next, we explore whether that non-linear relationship leads to an optimal recycling rate with regard to energy consumption; that is, given the physical limits on recycling waste paper, whether an 98 Pulpwood is defined as follows: “ Wood in the rough other than logs – for pulp, particle board or fibreboard.” (FAO 2001). 99 This is in line with the observation that a carbon tax leads to less waste paper recycling (Gielen et al. 2001). 100 "Resource requirements," as used in this article, refers to primary pulp requirements. This term does not refer to land, ecosystem services, or other resources that come into play in the forest product chain. 101 It is common in the natural sciences to refer to the relationship described in this article as “ non-linear” , although alternatively “ curvilinear” is also used to express this relationship.

51

optimum recycling rate can be found where the total energy requirement102 is at a minimum. The study focuses on the pulp and paper industries because almost all energy and resource use for the production of paper from its raw materials is concentrated in the process phase of the life cycle.103 We also investigate the robustness of this optimum and discuss its relevance in the context of future developments both within and without the pulp and paper industries. Our modelling approach contributes to the work of Virtanen and Nilsson (Virtanen & Nilsson 1993) by adding precision to their expert expectation with regard to the energy efficiency of waste paper recycling.

4.2. System and model approach

Our method aims to quantify fibre damage during recycling and investigate whether this leads to a non-linear relationship between recycling rates and virgin fibre requirements. Ultimately, we want to determine whether this non-linearity leads to an optimum wherein the total energy requirement is at a minimum for a certain recycling rate. This was done in three separate stages:

• First, a substance-flow model was built to explore the effects of fibre damage in waste paper recycling in the paper and pulp industries. This model was supplemented with a mathematically-derived equation in which the virgin fibre input depends on the recycling rate and a set of constants. The equation derived from the substance-flow model was then calibrated with country data on recycling rates and wood pulp consumption and the corresponding set of constants was determined.

• Second, in order to estimate the significance of fibre damage during recycling – in terms of the total energy requirement – the consumption of energy resources from the derived equation was used to evaluate whether there is an optimum process energy requirement. The process energy requirement, waste paper consumption, and pulpwood consumption are expressed in terms of primary energy requirements and are presented as a function of the recycling rate.

• Third, the robustness of the optimum recycling rate is investigated by using the upper and lower values of the variables as input variables in order to determine the sensitivity of the model to uncertainties in data.

In our research, we aim to draw generic conclusions about waste paper recycling in the pulp and paper industries. In order to do so, the “ system” was simplified by focusing on energy requirements (in terms of primary energy) of paper manufacturing in the member countries of the Confederation of European Paper Industries (CEPI). In 102 ‘Total energy requirement’ is defined for this article as process energy + feedstocks, both expressed in terms of energy. 103 Typical energy and resource uses in different stages of the life cycle are as follows: <1GJ/t for forestry practices and transportation to plant (excluded), 0.4 GJ/t for capital stock (excluded), ca. 20 GJ/t purchased energy and ca. 2.5 m3/t pulpwood for mechanical pulping and papermaking (included), ca. 15 GJ/t purchased energy and ca. 4.5 m3/t pulpwood for chemical pulping and papermaking (included), and ca. 19 GJ/t purchased energy and ca. 1.5 t/t wastepaper for recycled pulping and papermaking (included) (Berg 1995; BUWAL 1996; de Boer 1998; de Castro 1992; Dielen & Eppenga 2001; EC 2000; Fraanje & Lafleur 1994; Rajotte 2000; Wiselius 1994). Total transportation (including waste paper collection) depends strongly on local conditions and energy use in transportation may go up as well as down under changing recycling scenarios (excluded) (Finnveden & Ekvall 1998).

52

this article, a conceptual framework of fibre length will be used as an indicator for all properties affecting qualitative aspects of fibres. The simplifications made here and their (possible) effects on the results are assessed in the discussion section.

There are several ways to present recycling rates (Berglund et al. 2002). This article defines recycling rates as recovered paper utilization (RPU) rates, that is, the use of recovered paper in a sector as a percentage of the total paper production in that sector (FAO/CEPI 2000). RPU rates are the preferred indicator for recycling where forest protection and energy conservation are concerned (Berglund et al. 2002).104

4.3. Non-linearity in resource requirements

With higher recycling rates, fibres are reused more often, and this increases the chance that they are damaged beyond usability. Higher recycling rates therefore create the need to replace more secondary fibres with virgin fibres. CEPI (CEPI 2000) gives data about virgin fibre consumption and the use of recovered paper as a percentage of the total paper for every CEPI country. The plot of these data in (seen later in Figure 4.2) suggests that the relationship between RPU and virgin fibre inputs is non-linear rather than linear. We therefore further investigated this relationship.

In order to determine the particular kind of non-linear relationship that exists between virgin fibre requirements and recycling rates, waste paper recycling in a closed system was studied with a substance flow model (Kleijn 1999). The basic thought behind this model is that a virgin fibre replaces every fibre that can no longer be recycled. An overview of the model is shown in Figure 4.1.

This diagram assumes a closed economy for the recycling of waste paper in the paper and pulp industries. Virgin fibres enter the system through flow “ z” into stock “ S1” . Next, when the fibre is not recycled, it leaves the system through flow “ F1” . When the fibre is recycled, we assume the fibre to have a certain probability that it will be shortened during the re-pulping process, expressed in the damage rate “ y” . When the fibre is damaged, it leaves “ S1” and enters “ S2” through “ F5” . When the fibre is recycled and not damaged it stays in “ S1” for use and eventually for further recycling.

104 Alternative measurements of waste paper recycling are

• waste paper net recovery rate: the amount of waste paper collected for reuse as a percentage of the adjusted paper and paperboard consumption,

• adjusted waste paper net recovery rate: the amount of waste paper collected as a percentage of the adjusted paper and paperboard consumption from which the non-recoverable paper and paperboard is deducted, and

• waste paper in fiber use rate: the amount of recovered paper used for paper and paperboard as a percentage of the total fiber used for paper and paperboard. (FAO/CEPI 2000).

53

S1

S2

F1

F5

F2

S3

S4

F7

F3

F4

F6

F8

Z

Figure 4.1: Closed-system fibre recycling (example with N=4)

Note: N=4 indicates that the fibres in this model are damaged (and then recycled) four times. The boxes represent stocks of fibres of varying fibre lengths (S1 – S4) and the arrows with bowtie shapes represent flows (F). F1-F4 represent non-recycled fibre flows exiting the system. F5-F8 represent damaged fibre flows. “z” represents the new fibre requirement for the system. Because the system is closed, z = F1+ F2+ F3+ F4+ F8, and the value of “z” in dynamic equilibrium is the model output. The cloud-shaped figures at the end of arrows F2-F4 and F8 represent the boundaries of the system that is modelled. The model equations are given in Appendix A.

The stocks “ S1” to “ S4” represent fibre stocks of different qualities in the system. Note that paper can contain fibres of mixed qualities. A fibre in “ S4” that is recycled but damaged is assumed to be disposed in the washing stage of the recycling process. When the model is run with chosen values of recycling rate (x) and damage rate (y), the value of the virgin fibre requirement (z) evolves in such a way that a dynamic equilibrium is finally achieved. The model is re-run several times with different values of “ x” but a constant value of “ y” . It becomes visible from the calculated “ x” and “ z” that the relationship between the recycling rate (x) and the virgin fibre requirement (z) is non-linear rather than linear (in a theoretic, dynamic equilibrium situation). This non-linearity shows similarities with the empirical country data on recycling rates and (relative) virgin fibre consumption in Figure 4.2. The next obvious step is to determine the exact relationship between “ x” and “ z” from the equations in appendix A and then calibrate that relationship against the data points plotted in Figure 4.2.

54

( )

11

1)(

−��

��

�

+−

−•= Ntot

yxxy

xSxz

Equation 4.1

With: x = recycling rate z(x) = virgin fibre pulp requirement as a function of the recycling rate y = damage rate N = number of stocks Stot = the amount of cellulose fibres in stock as a percentage of the amount of produced paper

Equation 4.1 presents the mathematical relationship between the virgin fibre requirement (z) and the recycling rate (x), with z as a function of x. Appendix A describes how this relationship was derived. A non-linear trend line corresponding with Equation 4.1 is fitted against country data by the least squares method and at a given N. The calculated damage rate (y) and fibre stock (Stot) for different values of N are summarized in Table 4.2; the relationship between “ x” and “ z” corresponding to values of “ y” and “ Stot“ found for N=3 is shown in Figure 4.2.

Table 4.2: Fit of equation 4 to country data for different values of N

Number of stocks (N) 2 3 4 5 Damage rate (y) 0.331 0.565 0.800 1.035 Total fibre stock (Stot) 0.905 0.910 0.913 0.915 Accuracy (r2) 0.883 0.885 0.885 0.886 Recycled fibre share at 100% recycling 85.4% 82.9% 81.4%

It should be noted that the solution of a model with a chain length of five stocks (i.e., five stocks connected analogously to the stocks in Figure 4.1; N=5) is obviously not valid, because the damage rate (y) cannot exceed the value of one (otherwise flows of negative matter occur). The solution of a model with a chain-length of one is also not valid, because that would imply a linear relationship between recycling rates and virgin fibre requirements whereas it was determined earlier that the relationship is not linear, but non-linear. Therefore the only possible integer values of N are 2, 3 and 4.

In the next section the values corresponding to a model with N=3 are used to illustrate how non-linearity affects tradeoffs between waste paper recycling and incineration. In the section on sensitivity analysis we will discuss how the value of N (and corresponding y) affects the outcomes. Figure 4.2 shows the calculated relationship between virgin fibre requirements and the recycling rate described by equation 1 and the fits for N= 3 from Table 4.2. The dotted line represents a fictive situation with no fibre damage during the recycling process. The dots are country data. From the figure it can be seen that for low recycling rates the relationship appears to be approximately linear. Only at higher recycling rates does the relationship start to deviate from the dotted line and become non-linear.

55

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1 1.2

Recycling rate

Virg

in fi

bre

requ

irem

ents

Figure 4.2: Relationship between the recycling rate and the virgin fibre requirement (N=3)

Source: Data taken from (CEPI 2000; FAO/CEPI 2000). Note: N=3 indicates that the fibres in this model are damaged (and then recycled) three times. The dotted line represents a fictive situation with no fibre damage during the recycling process; the trend line is constructed using the least square values method and weighted by actual paper production statistics for member countries of the Confederation of European

Paper Industries, i.e. ( )( )�=

•−m

iiii Pyy

1

2' is minimized (where: y = data values; y’ = values of

the estimated points; P=paper production; i = country index; m = country pool). Accuracy (r2)= 0.885.

4.4. Effect of non-linear relationship on energy requirements

Now that a non-linear relationship between recycling rates and resource requirements has been established (Figure 4.2), the next logical step is to see if this relationship leads to an optimal recycling rate. That is, whether an optimum can be found where the total energy requirement is at a minimum for a certain recycling rate. This section explains how the relationship from Figure 4.2, based on Equation 4.1 with N=3 for the number of stocks (see Table 4.2), was used to express the total energy requirement as a function of recycling rate.

The total energy requirement for paper manufacturing is the sum of process energy requirements (expressed in primary energy) and the caloric value of the materials feedstock (Equation 4.2) (IFIAS 1974). Because this research aims to study the energetic effects of the recycling rate, it is necessary to distinguish between virgin fibre paper and recovered fibre paper as shown in equation 3.

56

Total Energy Requirement = PEtot + FStot Equation 4.2

Total Energy Requirement = PEVFP + PERP + FSVFP + FSRP

Equation 4.3

Where: Total energy requirement = sum of the process energy and the caloric value of the materials feedstock (expressed in terms of primary energy) PE = process energy requirement (expressed in terms of primary energy); FS = energy content of feedstock requirement (expressed in terms of primary energy) VFP = subscript referring to virgin fibre paper RP = subscript referring to recycled paper

Because recycling rates are defined as RPU rates in this article, the amount of waste paper used for recycling can be derived directly from recycling rates. Pulpwood and waste paper are energy sources (they can be burned) and their so-called lower heating values (LHV)105 determine their energy content (IEA 2001; IFIAS 1974). The pulpwood feedstock is given in Equation 4.4, and Equation 4.5 gives the wastepaper feedstock. Equation 4.4 also contains a conversion factor (RWE, roundwood equivalents) to express virgin fibre pulp (z) in pulpwood (Dielen & Eppenga 2001).

FSVFP = z(x) � RWE � LHVRW Equation 4.4

FSRP = x � LHVRP Equation 4.5

Where: z(x) = virgin fibre pulp requirement as a function of recycling rates as given in Equation 4.1 (with N=3; y = 0.565; Stot = 0.910) RWE = roundwood equivalents, i.e. the amount of roundwood (m3) required to produce a tonne of pulp (m3/t) LHV = Lower Heating Value, i.e. the caloric (MJ/m3) RW= subscript referring to roundwood

The conversion factors (RWE and LHV) as used in Equation 4.4 and Equation 4.5 are shown in Table 4.3. It should be noted that they all depend on the pulping process concerned (see footnote 97). The heating values of the pulpwood depend on which tree species are used (Klass 1998; Wiselius 1994). The heating values of waste paper differ because waste paper from mechanical pulp contains more lignin (Niessen 1978).

105 Lower heating value (LHV) is the heating value of a fuel when excluding the vaporization heat for the water vapour, whereas higher heating value (HHV) is the heating value of a fuel when the water in the combustion gases is completely condensed and thus the heat of vaporization is also recovered.

57

Table 4.3: Feedstock conversion factors

minimum maximum typical Unit and reference Roundwood equivalent for chemical pulp (RWEc)

4.0 6.6 4.5

Roundwood equivalent for mechanical pulp (RWEm)

2.4 2.6 2.5

m3/t; (EC 2000; Fraanje & Lafleur 1994)

LHV wood for chemical pulp 7 11 9

LHV wood for mechanical pulp 6 10 8

GJ/m3; (Klass 1998; Wiselius 1994)

LHV wastepaper chemical 11.09 15.68 15.50

LHV wastepaper mechanical 16.32 18.39 17.15

GJ/t; (Beer et al. 1998; Klass 1998; Niessen 1978;

Tillman 1998)

Note: LHV = lower heating value; m3/t = cubic meters per tonne; GJ/m3 = gigajoules per cubic meter; GJ/t = gigajoules per tonne.

Paper manufacturers produce energy from their own waste streams (notably bark and, in the case of the chemical process, also “ black liquor” ), but also purchase external process energy in the form of electricity and heat. Different quantities are needed for producing recycled paper and virgin fibre paper (see Table 4.4).

Heat and electricity are not primary energy sources, but have to be produced from primary energy sources, which involves losses. Efficiencies are calculated for heat and electricity for the studied situation in CEPI countries for 1999 and weighted by the total paper production of the underlying countries. The calculated efficiencies are 36% for electricity and 76% for heat for CEPI countries on average (IEA 2001). The numbers used in the calculations are summarized in Table 4.4.

Table 4.4: Energy requirements for several pulping and papermaking processes

Heat (GJ/t)

Electricity (MWh/t)

Total primary (GJ/t)

min max aver. min max aver. min max aver. Integrated kraft pulp and paper mills 14.0 20.0 17.5 1.2 1.5 1.2 30.4 41.3 35.2

- purchased energy (= total energy minus energy from own waste)

1.0* 6.0* 3.5 0.7* 1.0* 0.7 8.2 17.8 11.6

Integrated mechanical and semi-mechanical pulp and paper mills 0.0 6.0 3.0 1.9 3.0 2.5 18.9 37.8 28.3

Integrated recycled fibre mills 4.0 6.5 5.3 1.0 1.5 1.3 15.2 23.5 19.4 Source: Derived from (EC 2000; IEA 2001). Note: GJ/t = gigajoules per tonne; MWh/t = megawatt-hours per tonne; aver = average; min = minimum; max = maximum; * = estimate

Because recycling is defined as RPU in this research, the share of the processes are not the same as the recycling rate, but rather, have to be derived from feedstock consumption. Equation 4.6 and Equation 4.7 show how process energy depends upon recycling rates.

58

VFPtot

VFP TPES

xzPE •= )(

Equation 4.6

RPtot

totRP TPE

SxzS

PE •−

=)(

Equation 4.7

RPRW

RPtot

totVFP

tot

LHVxLHVRWExz

TPES

xzSTPE

Sxz

xTER

•+••+

•+•=

)(

)()()(

Equation 4.8

Where: TPE = typical process energy (from Table 4.4) TER(x) = total energy requirement (process energy + feedstock), as a function of x

Combining Equation 4.1 to Equation 4.7 gives Equation 4.8, in which the total energy requirement only depends upon the recycling rate (x), variables and conversion factors. Now the variables and conversion factors from Table 4.2-Table 4.4 are used with Equation 4.8 and the total energy requirement was plotted against the recycling rate (x). The results are shown in Figure 4.3. The relationships are non-linear. With regard to paper produced from chemical pulp, an optimum was found at a recycling rate of 93%. Regarding paper produced from mechanical pulp, an optimum was found at a recycling rate of 81%.

30

35

40

45

50

0% 25% 50% 75% 100%

Recycling rateChemical Mechanical

Tot

al e

nerg

y re

quire

men

ts (G

J/t)

Figure 4.3: Total energy requirement (process and feedstock) for paper production at different recycling rates, expressed in terms of primary energy (GJ/t)

Next, the composition of the total energy requirement values were determined (shown in Figure 4.4). The lower (and darker) areas represent external supply of energy, typically fossil fuel based, for both processes. The horizontally-striped areas represent

59

pulpwood consumption and the vertical-striped areas represent waste paper consumption.

A: Chemically processed paper

0

5

10

15

20

25

30

35

40

45

50

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Recycling rate (x)

Tota

l ene

rgy

requ

irem

ents

(GJ/

t)

Feedstock Waste

Feedstock Wood

Process Recycling

Process Virgin

B: Mechanically processed paper

0

5

10

15

20

25

30

35

40

45

50

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Recycling rate (x)

Tota

l ene

rgy

requ

irem

ents

(GJ/

t)

Feedstock Waste

Feedstock Wood

Process Recycling

Process Virgin

Figure 4.4: Composition of the total energy requirement for paper production at different recycling rates

60

4.5. Sensitivity Analysis

The calculations in the previous sections were based upon average or typical values and sensitivity testing is needed in order to gain insight in the robustness of the results and the usability of our approach. The sensitivity of the results was analyzed by substituting the constants and the typical/mean values with a range of different values in Equation 4.8. The results of the sensitivity tests are shown in Section 4.9 (Appendix B), and are further discussed here.

In the previous section, the value of N for the number of stocks was set at 3. Therefore, we started this sensitivity analysis by comparing the results when different values of N were used (and the corresponding values of y and S). The total energy requirement was not affected for small values of x, and just marginally for high values of x. The optimal recycling rate, however, ranged from 87% to 100% for chemical and 76% to 93% for mechanical pulping. The exact optimal recycling rates are of relative unimportance because around the optimums the graphs flatten, implying that any change in the recycling rate around the optimum will result in a relatively low change in total energy requirement.

Next, upper and lower boundary values for process energy requirements (Table 4.4) and feedstock conversion factors (Table 4.3) were used as inputs for the model. The degree of uncertainty (or variation) among input variables appears to be the most important factor affecting the outcomes. Variables and conversion factors regarding virgin fibre paper affect the left hand side of Figure 4.5, while variables and conversion factors regarding recycled paper affect the right hand side of the figure.

Finally, sensitivity tests were performed with more efficient electricity production. Regarding the chemical process, more efficient electricity production leads to a higher optimal recycling rate. Regarding the mechanical process, more efficient electricity production leads to a lower optimal recycling rate. From Figure 4.4 it can be seen that, in the case of the chemical pulping process, total external supply of energy (represented by the lower two areas in Figure 4.4, i.e., process recycling + process virgin) increases with increased recycling rates, while in the case of the mechanical pulping process external supply of energy decreases with increased recycling rates, which explains the results in the sensitivity testing. Heat production is already very efficient and significant increases in efficiency are not to be expected. Therefore, sensitivity analysis was not performed on heat production efficiency.

The general pattern that can be inferred from the sensitivity analysis is that, at low recycling rates, increasing waste paper recycling is energy efficient, but becomes less efficient at higher recycling rates (see Figure 4.5). This general pattern was tested by calculating the total energy requirements at recycling rates 10% higher and lower than the optimum value of x and then comparing these total energy requirements with the optimum total energy requirements. This is also shown in Appendix B. It was found that, close to the optimum total energy requirement, increasing or decreasing the recycling rate does not affect the total energy requirement significantly (�0.3%).

61

A: Chemically processed paper

30

35

40

45

50

0% 25% 50% 75% 100%

Recycling rate

Tota

l ene

rgy

requ

irem

ents

(GJ/

t)

B: Mechanically processed paper

30

35

40

45

50

0% 25% 50% 75% 100%

Recycling rate

Tota

l ene

rgy

requ

irem

ents

(GJ/

t)

Figure 4.5: Sensitivity analysis

Note: Labels A, B1, B2, etc. refer to the sensitivity tests as shown in appendix B

62

4.6. Discussion

4.6.1. On methodology Comparisons of waste paper recycling versus incineration as alternatives for landfills have often been based on energy consumption in an life-cycle assessment (LCA) context (see Section 4.1). The fact, however, that fibre recycling is limited and therefore – on theoretical grounds – tradeoffs are expected to be non-linear is often not explicitly taken into account. By explicitly modelling resource dynamics in the paper and pulp industries, the research in this article was able to demonstrate the existence of a non-linear relationship between virgin paper requirements and recycling rates. The non-linear relationship was first derived, and next was calibrated against empirical data on recycling rates and virgin fibre requirements. Next, the influence of this non-linearity on the total energy requirement for paper production was calculated. The advantage of our analysis is that it is able to mathematically confirm a relationship that has not been confirmed using previous methods. With this relationship being established, it will be easier to understand the effects of policies regarding waste paper.

Both recycling rates and pulpwood consumption data are known on a national level (CEPI 2000), and thus can be compared. An advantage of using data on a national level is that differences in, for example, paper grades, can be assumed to be somewhat averaged-out and therefore generic conclusions can be more easily proposed. Moreover, the difference in fibre content between different grades of paper is quite small. On the other hand, where process energy is concerned, more detailed information is an advantage because process energy requirements may vary for different grades of paper.

4.6.2. Model simplifications and assumptions The pulp and paper industries were modelled in this research in order to be able to draw generic conclusions regarding the issue of waste paper recycling versus incineration. In this section the most important simplifications and assumptions in our modelling framework will be discussed.

This research focuses on graphic paper (newsprint and printing & writing paper). Two grades of graphic paper (chemical and mechanical) are assumed to be representative of graphic paper. In reality, there are not only different grades of graphic paper, but there are also grades of paper other than graphic paper. These different grades of paper are often the result of recycled waste paper which was collected in mixed form. The different stocks in Figure 4.1 hold fibres of different length and could be seen as representing different grades of paper. Improving fibre flows with so-called cascade management (Tromp 1995a) is only implicitly represented in our model, but there is a theoretical relationship between cascade management and the chain length in the model. The consequences of the simplifications mentioned above for the outcome are: 1) focusing on a single type of paper neglects the possibility to use fibres rejected for graphic paper (F8 in Figure 4.1) for other purposes like sanitary paper, and 2) the outflow rates (non-recycled paper) in Figure 4.1 (F1, F2, F3, F4) are in actuality not equal to each other because, in practice, recycling focuses on high quality fibres (S1) rather than on lower quality fibres (S4). The consequences of the simplifications mentioned above for the outcomes are difficult to assess, but are expected not to change the general pattern found in the sensitivity analysis section.

63

In our research, some differences between countries have been averaged. In reality, however, countries are different. An important aspect of differences between countries is that an optimal recycling rate in a country can differ from the optimum recycling rate as calculated in this research. Nonetheless, the general pattern found in the sensitivity analysis section should be valid.

This article focuses on papermaking in so-called integrated pulp and paper mills, because of comparison reasons. All industries in this research are assumed to have implemented best available techniques (BAT) as described within the European Union’ s integrated pollution prevention and control (IPPC) framework (EC 2000) and as summarized in Table 4.3. BAT describes a range of techniques, because efficiencies of plants can differ due to several factors, including plant size, the country where the plant is situated, the type of species used, the grade of paper produced, and so on. However, we are interested in the effects of fibre damage on energy efficiency in a generalized way and for the whole graphic paper sector. Therefore we use the average energy efficiency of the techniques described in BAT. The upper and lower boundaries of BAT values were used in the sensitivity analysis (tests B and C) to explore the effect of plant efficiency on the results.

Energy conversion efficiencies for electricity and heat are averages of CEPI-countries (weighted by the total paper production of each country). In reality CEPI-countries differ significantly (IEA 2001) in the efficiency of their electricity production, and as demonstrated in the sensitivity analysis, these differences influence the outcomes. Because both recycling and virgin fibre pulping profit from increasing efficiencies, the optimum recycling rate is rather robust regarding this aspect. Nonetheless, the optimal recycling rate will differ from country to country.

4.6.3. System boundaries The total energy requirement for paper production is expressed in terms of primary energy, because the amount of wood not used for paper production can, alternatively, be used to produce electricity and thereby replace an equivalent amount of electricity produced by fossil fuels (the primary energy source for conventional electricity from the public grid).

As shown in the introduction, the process phase dominates energy requirements in the life cycle of paper production. Therefore, we focused our analysis on the process phase and therewith avoided the issue of forest management, in which lower levels of wood consumption under a recycling scenario are considered a benefit (as in many LCAs on paper recycling) (IIED 1996, p186). We consider this to have little impact on our overall conclusions because it is common practice in CEPI countries to use only certified roundwood, which is considered the most sustainable method of forest management, and wood from certified forests is considered to be the most sustainable virgin fibre source (Edel 2003). Strictly speaking, however, avoided wood consumption should be treated as an auxiliary benefit. The horizontally-striped areas in Figure 4.4 provide an indication of the amount of wood consumption which takes place under different recycling rates.

This analysis focuses on energy. Therefore, many important environmental impacts related to paper manufacturing are neglected. When other environmental impacts such as emissions are taken into account, recycling is – in line with our results – often

64

favoured over the other processes (EC 2000). One notable exception is that of chlorinated emissions, where the mechanical process for pulp production is favoured over both pulp production through recycling and the chemical process for pulp production (EC 2000). External supply of energy is related to GHG emissions because electricity production is largely based on carbon-intensive fossil fuels. Therefore conclusions regarding GHG emissions can be drawn from this research.

The model focus is on the European region, or, more specifically, member countries of the Confederation of European Paper Industries (CEPI). This is justified by the following arguments:

• Europe is self-sufficient in pulp and paper for over 90% of its paper consumption, which makes the system approximately closed,

• the state of technology does not vary strongly from country to country, • there is enough variation in recycling rates to analyze the effects of different

recycling rates, and • data provided by CEPI is rich and consistent.

Because our theoretic model (Figure 4.1) applies to recycling in a closed system, it is important that not only the total systems, but also the individual countries can be seen as being approximately closed. In reality, the CEPI-countries are not closed systems because of significant trade between of waste paper between these countries (FAOSTAT 2001). This trade results (in theory) in the mixing of waste paper qualities which levels out differences in waste paper qualities between individual countries. Therefore, the relationship that we found between waste paper recycling and virgin fibre requirements is probably in reality less curved than we would expect the relationship to be without trade.

4.6.4. Changes outside our system Our model focuses on changes within the paper production and waste management system. Changes outside of the observed system, however, may affect the system -- as demonstrated in the sensitivity analysis, where the effect of more efficient electricity production was tested.

Our approach is static, implying that variables and conversion factors are constant. In reality, however, technological developments will influence in one way or another how pulp and paper is produced and used (Ruth & Harrington 1997a). Future and new technologies – such as genetic modified organisms (GMO) (Pilate et al. 2002), flexible electronic displays (Chen et al. 2003; Granmar & Cho 2005), alternative oxidizers (Weinstock et al. 2001), the enzymatic pulp bleaching process (OECD 2001e), and the use of xylanase as a pulp brightener (OECD 2001e) – will influence the environmental performance in a way that is beyond the scope our model to represent.

When biomass becomes, in time, more important as an energy source (see introduction), interactions between electricity production and paper manufacturing will increase. Increasing the use of biomass in the electricity sector will cause environmentally preferable recycling rates to shift towards more recycling because of the greater use of forest products for energy purposes.

65

In this research we focus on “ a tonne of paper produced” . When, however, the focus expands to “ a material with certain properties,” materials can substitute for each other, thereby not only changing the outputs and efficiencies of the pulp and paper sector but also influencing other sectors. Other materials can substitute for paper, but paper may also substitute for other materials (Hekkert et al. 2001). When the focus expands even more, e.g. to “ getting the news” (different media such as newspapers, television, and internet can provide this service), substitutions become even more complex (Reichart & Hischier 2002). Therefore, the actual potential for energy savings regarding paper production is higher than our results indicate.

4.6.5. Validation of the model: comparing results with others Based on our results, it should be noted that 0.91 (air-dry) tonne of pulp are required to produce one tonne of paper. This seems to be rather consistent with other sources in the research literature because 15% of the raw materials input in CEPI countries are non-fibrous materials, and the water content of air-dry pulp is approximately 3-5% (CEPI 2000). The Swiss Agency for Environment, Forests and Landscape – BUWAL – concludes that 900 kg of pulp per tonne of product is needed (BUWAL 1996).

In our analysis, the share of reused fibres in paper at a recycling rate of 100% is 83%, meaning that about 17% is too damaged to be reused and needs to be replaced by virgin fibres; Virtanen and Nilson calculated 75% to 80% (Virtanen & Nilsson 1993). Because our results are more directly based on empirical data, we conclude that fibre recycling is more efficient than previously assumed. These results imply that in CEPI countries, fibre can be recycled at least 5 times on average.

In our research the total energy requirement is 49 GJ/t for chemical paper and 47 GJ/t for mechanical paper. We compared these values with previous research. Tromp finds a total energy requirement of 40.2 GJ/t for graphic paper (Tromp 1995b); Castro finds a value of 49.8 GJ/t, though with relatively high figures for transportation (de Castro 1992); BUWAL finds a value of 40 GJ/t for recycled paper, 42 GJ/t for chemically-processed paper and 40 GJ/t for mechanically-processed paper (BUWAL 1996). From these figures one can conclude that our figures are high.

Although it is hard to find the exact reason for these differences, two main sources for the differences have been identified. The first is that the conversion efficiency for electricity from primary sources is a European average, while other research is often based on a single country. For example, BUWAL refers to the Swiss situation where electricity is produced more efficiently than the European average. The second reason is that the caloric values for roundwood we calculated are based on European species-use averages while species-use in a single country can differ from that average. Moreover, the results of our research are difficult to compare with others because of the definition used for recycling. RPU rates refer to inputs, while other research refers to outputs.

The non-linear relationship we found results in approximately 5 GJ/t lower reduction potentials than when a linear relationship is assumed. In our research the maximum energy savings that can be achieved are 8.2 GJ/t (-16.9%) for chemical paper and 5.3 GJ/t (-11.4%) for mechanical paper. These figures are in general lower than other researches conclude (e.g. (Morris 1996) finds energy savings in the range of 14 – 39

66

GJ/t.) Because the non-linear relationship explains only a part of the difference, other factors need investigation.

4.6.6. Conclusions Our research shows that a non-linear relationship exists for paper recycling due to higher average fibre turnover rates at higher recycling rates. Moreover, non-linearity leads to an optimum recycling rate. However, the optimum can in general be found at high recycling rates. Therefore our research concludes that recycling is in general the more energy efficient option rather than incineration, which is in line with previous research (IIED 1996). However, the non-linear relationship between waste paper recycling and virgin fibre requirements results in lower potential energy savings than if there were no fibre damage.

Total external supply of energy (the grey areas in Figure 4.4) is a proxy indicator for CO2 emissions because electricity production is largely based on carbon-intensive fossil fuels. Therefore our research suggests that increasing recycling rates increases CO2 emissions for the chemical process. This is in line with previous research mentioned in the introduction (IIED 1996). On the other hand, our research concludes that increasing recycling rates decreases CO2 emissions for the mechanical process (contrary to previous research). However, it should be noted that the previous studies mentioned in the introduction often focus solely on the chemical pulping processes.

Key findings

• Waste paper recycling is more energy efficient than waste paper incineration.

• The benefits of recycling tend to suffer from diminishing returns.

• Recycling affects the over-all resource mix of paper production: increasing recycling results in increasing use of non-renewable resources and decreasing use of renewable resources.


Irene Edel, Laurie Hendrickx, Sander Lensink, Gert-Jan Nabuurs, Sanderine Nonhebel, Anne Jelle Schilstra, Ton Schoot Uiterkamp, and the anonymous reviewers of the Journal of Industrial Ecology are acknowledged for their contributions to this Chapter.

67

4.8. Appendix A: Solutions of the substance flow model with different numbers of stocks

The Equations corresponding to the model (N=4) as presented in Figure 4.1 are:106

z = F1+F2+F3+F4+F8 F1 = S1�(1-x) F2 = S2�(1-x) F3 = S3�(1-x) F4 = S4�(1-x) F5 = S1�x�y F6 = S2�x�y F7 = S3�x�y F8 = S4�x�y S1 = � z-F1-F5 S2 = � F5-F2-F6 S3 = � F6-F3-F7 S4 = � F7-F4-F8 ΣS = S1+S2+S3+S4 In dynamic equilibrium the stocks (S1 -S4) are constant and therefore the integrals above can be rewritten as: z = F1 + F5 F5 = F2 + F6 F6 = F3 + F7 F7 = F4 + F8 In this appendix, we show how algebraic solutions of the equations corresponding to the model as presented in above are generalized in formula n. However, we do not give proof. First, solutions for N=1, N=2, and N=3 are shown; next a general solution is shown. Let N be the number of stocks, x is the recycling rate (RPU), y is the damage rate, and z is the virgin fibre requirement,

Let �=

=N

iiNtot SS

1, , i.e. the total fibre in stocks.

Then, for N=1:

111 )1()1( SxyxSxySxz •+−=•+•−= (a) And:

11, SS tot = (b)

106 “ x” and “ y” below are model input variables and the value of “ z” in dynamic equilibrium is the model output. x = recycling rate; y = damage rate; z = new fibre requirement.

68

Therefore, substituting S1 in (a) from (b) gives: )1(1, xyxSz tot +−= (c)

Then, for N=2:

xyxxy

SSxyxSxyS+−

=+−•=•1

)1()( 1221 (d)

And:

212, SSS tot += (e)

Therefore: 1

2,1112, 11

1

−

��

��

�

+−+=

+−+=

xyxxy

SSxyx

xySSS tottot (f)

And since formula (a) is valid for every value of N is integer:

��

��

�

+−+

+−•=

xyxxy

xyxSz tot

11

12,

(g)

Then, for N=3:

��

��

�

+−•=��

�

��

�

+−•=

+−•=•+−•=•

xyxxy

SSxyx

xySS

xyxSxySxyxSxyS

1,

1

)1()(),1()(

2312

3221

(h)

And:

3213, SSSS tot ++= (i)

Therefore:

12

3,1

2

1113,

111

11−

��

�

�

��

�

�

��

��

�

+−+��

��

�

+−+•=

��

��

�

+−•+��

�

��

�

+−•+=

xyxxy

xyxxy

SS

xyxxy

Sxyx

xySSS

tot

tot

(j)

And since formula (a) is valid for every value of N is integer:

��

�

�

��

�

�

��

��

�

+−+��

��

�

+−+

+−•=23,

111

1

xyxxy

xyxxy

xyxSz tot

(k)

Then, for N:

1

1

1

11

)1()(−

−

−

��

��

�

+−•=��

�

��

�

+−•=

+−•=•Ni

iNN

NN

xyxxy

Sxyx

xySS

xyxSxyS

(l)

And:

69

11

0,1

1

01

11,

1

1−

−

=

−

===

��

�

�

��

�

�

��

��

�

+−•=

��

��

�

+−•==

�

��

N

i

i

Ntot

N

i

iN

ii

N

iiNtot

xyxxy

SS

xyxxy

SSSS

(m)

Therefore: ( )

11

1

1

1)( ,

1

0

1−��

�

��

�

+−

−•=

��

��

�

+−

+−•=

��

−

=

=N

Ntot

N

i

i

N

ii

yxxy

xS

xyxxy

xyxSxz

(n)

70

4.9. Appendix B: Sensitivity tests

Values that have been changed to perform the sensitivity analysis are underlined. The upper parts of the tables are input variables; below the line are model outputs.

N = the number of times fibres are damaged and then reused; y = damage rate; RWE = roundwood equivalents; LHV = lower heating value

Table 4.5: Chemically processed paper

Index # A B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 G1 G2 H1 H2

N 3 3 3 3 3 3 3 3 3 3 3 2 4 3 3

y 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.33 0.80 0.56 0.56

S 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91

PE recycled 19.4 15.2 23.5 19.4 19.4 19.4 19.4 19.4 19.4 19.4 19.4 19.4 19.4 18.1 20.6

PE virgin 11.6 11.6 11.6 8.2 11.6 11.6 11.6 11.6 11.6 11.6 11.6 11.6 11.6 10.9 12.3

RWE pulp 4.5 4.5 4.5 4.5 4.5 4.0 6.6 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5

LHV waste paper 15.5 15.5 15.5 15.5 15.5 15.5 15.5 11.1 15.7 15.5 15.5 15.5 15.5 15.5 15.5

LHV wood 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 7.0 11.0 9.0 9.0 9.0 9.0

Optimal (%) 93.3% 100% 82.3% 84.3% 93.3% 82.4% 100% 100% 92.5% 66.7% 100% 100% 87.4% 94.5% 92.0%

Maximal (GJ/t) 48.5 48.5 48.5 45.0 48.5 44.4 65.7 48.5 48.5 40.3 56.7 48.3 48.6 47.8 49.2

Minimal (GJ/t) 40.3 37.0 43.4 39.4 40.3 39.3 43.6 35.9 40.5 37.9 41.9 39.7 40.5 39.2 41.4 Possible reduction

with recycling-16.9% -23.8% -10.6% -12.4% -16.9% -11.6% -33.7% -26.0% -16.6% -6.0% -26.2% -17.9% -16.7% -18.1% -15.8%

40.4 37.2 43.5 39.5 40.4 39.4 44.4 36.3 40.6 38.0 42.3 39.9 40.6 39.3 41.5 10% lower recycling

+0.3% +0.5% +0.2% +0.3% +0.3% +0.3% +2.0% +1.2% +0.3% +0.2% +1.0% +0.6% +0.3% +0.3% +0.3%

n/a n/a 43.5 39.5 n/a 39.4 n/a n/a n/a 38.0 n/a n/a 40.6 n/a n/a 10% higher recycling

n/a n/a +0.2% +0.3% n/a +0.3% n/a n/a n/a +0.2% n/a n/a +0.3% n/a n/a

Table 4.6: Mechanically processed paper

Index # A B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 G1 G2 H1 H2

N 3 3 3 3 3 3 3 3 3 3 3 2 4 3 3 y 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.56 0.33 0.80 0.56 0.56

S 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91

PE recycled 19.4 15.2 23.5 19.4 19.4 19.4 19.4 19.4 19.4 19.4 19.4 19.4 19.4 18.1 20.6

PE virgin 28.3 28.3 28.3 18.9 37.8 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 25.9 30.8

RWE pulp 2.5 2.5 2.5 2.5 2.5 2.4 2.6 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

LHV waste paper 17.2 17.2 17.2 17.2 17.2 17.2 17.2 16.3 18.4 17.2 17.2 17.2 17.2 17.2 17.2

LHV wood 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 6.0 10.0 8.0 8.0 8.0 8.0

Optimal 80.9% 91.3% 66.0% 23.9% 100% 78.7% 82.9% 84.7% 75.0% 64.1% 92.2% 93.1% 76.2% 77.2% 84.2% Maximal (GJ/t) 46.5 46.5 46.5 39.8 56.0 45.8 47.3 46.5 46.5 42.0 51.1 46.4 46.6 44.1 49.0

Minimal (GJ/t) 41.2 38.2 44.0 37.0 43.4 41.0 41.4 40.5 42.2 39.7 42.4 41.0 41.3 39.6 42.8 Possible reduction with

recycling-11.4% -18.0% -5.5% -7.1% -22.5% -10.5% -12.3% -12.9% -9.3% -5.5% -17.0% -11.8% -11.4% -10.2% -12.6%

41.3 38.3 44.0 37.0 43.6 41.1 41.6 40.7 42.3 39.7 42.5 41.0 41.4 39.7 42.9 10% lower recycling

+0.3% +0.3% +0.2% +0.1% +0.5% +0.2% +0.3% +0.3% +0.2% +0.2% +0.3% +0.2% +0.3% +0.3% +0.3%

41.3 n/a 44.0 37.0 n/a 41.1 41.6 40.7 42.3 39.7 n/a n/a 41.4 39.7 42.9 10% higher recycling

+0.3% n/a +0.2% +0.1% n/a +0.3% +0.3% +0.3% +0.3% +0.2% n/a n/a +0.3% +0.3% +0.3%

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5. The use of physical indicators for industrial energy demand scenarios107

5.1. Introduction

The interactions between energy and materials resources have – of course – consequences for energy scenarios. Freight transport and industrial production are two sectors that intuitively relate their energy consumption to materials flows. Freight transport scenarios based on materials flows have already been developed. To use this concept on industry energy scenarios is novel, as far as we know.

This Chapter describes a new model that allows formulation of industrial energy demand projections consistent with the assumptions for scenario drivers such as GDP and population. In the model, a level of industrial production is used as a key variable, and we define it in physical units, rather than in monetary units. Throughout the Chapter we discuss the advantage of using physical production indicators over monetary output indicators for the purpose of developing industrial energy demand scenarios.

The aim of this research is to increase insights that come with long-term energy demand scenarios by incorporating physical characteristics of industrial commodity production. Monetary production indicators are often preferred in energy intensity analysis because the production data of different commodities can be readily aggregated. However, energy demand projections based on monetary production indicators fail to take physical limits associated with industrial commodity consumption into account. We aim to examine ways to incorporate such an aspect into scenario building. This research is explorative in two ways, to contribute to scenario building, and to demonstrate the potential and limitations of modelling based upon energy intensities using physical production indicators.

This research focuses on the industry sector. The industry sector is of special interest regarding the use of physical production indicators because its energy consumption can often be directly related with materials processing. Moreover, physical indicators for the industry sector have been widely used as a monitoring tool for energy intensities (see e.g.: Farla & Blok 2000), and therefore extending the approach from monitoring to projecting is logical step (Groenenberg et al. 2005).

This Chapter researches the feasibility of using non-monetary indicators as explanatory variables in long-term energy models. Therefore this Chapter will discuss the pros and cons of physical indicators for energy projections comprehensively. The model discussed in this Chapter is driven by GDP per capita, as rather common in energy models.

Because of the explorative character of this research the aggregation level of the data was as low as possible (depending on data availability). Because the method of

107 Co-author: Henri C. Moll. Published in slightly different form as IIASA Interim Report (IR-06-014). This chapter is scheduled to be resubmitted to Ecological Economics after consideration of reviewers’ comments.

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‘flexible extrapolation’ (as described in section 5.3.2) is labour intensive and full application to all 11 MESSAGE world-regions and all 13 industry sub-sectors requires 11 * 13 = 143 separate analyses, the model is implemented on only two regions: Western Europe (WEU) and Centrally Planned Asia & China (CPA). From these two regions conclusions are drawn regarding future use of physical indicators for energy scenario analysis.

The main research question is to find numerical scenario results for two selected world-regions and thus gaining insights in the size and nature of industrial energy demand under a business-as-usual setting. Emphasis will be on the differences of industrial metabolisms108 between the two regions.

5.2. Motivation for developing physical explanatory variables

Energy analysis and indicators – like ‘energy intensity’ – are based on monetary or physical approaches109 (IPCC 2004, p372; Worrell et al. 1997). Monetary indicators are characterised by the measurement of a factory’ s “ useful output” 110 based upon economic indicators like value added or production value, wile physical indicators are characterised by the measurement of a factory’ s “ useful output” based upon physical indicators like total weight of products (Farla & Blok 2000; Patterson 1996).

Monetary approaches are primarily chosen because data can be aggregated. Moreover, regarding the climate-change issue and GHG emissions from fuel combustion, policymakers’ main interests are the effects of climate change policies on the economy. Therefore the initial focus on monetary indicators is completely rational and justifiable. Nevertheless, some of the society’ s energy consumption is associated with physical flows rather then economic output.. This is where physical indicators come into scope.

Physical approaches are based upon the ‘touchable’ exponents of the society, like the number of tonnes produced of a specific product (Farla 2000), or total material requirement of a nation (Matthews et al. 2000). Also passenger-kilometres – often used to calculate passenger transportation efficiencies – fits in this category. Despite the many benefits of monetary indicators they tend to be (unnecessarily) narrow in view. The use of physical indicators for industry energy scenario analysis offers three distinguished advantages:

• Some research indicates that physical (energy intensity) indicators could be more meaningful than monetary indicators regarding industry output when related to energy consumption, especially with respect to developing countries (Ayres

108 “ The ‘industrial metabolism’ concept refers to the flows of natural resources entering the production side of the economy and the flows of goods and services—to be consumed and/or exported—and of wastes and emissions to the environment leaving the production sectors” (Moll et al. 2005). For application of the ‘industrial metabolism’ concept on energy see: (Haberl 2001a; Haberl 2001b). 109 The term approach refers to the measure of societal behaviour that is assumed to be related with energy consumption. Indicators are tools associated with approaches. 110 “ The 'useful output' of the process need not necessarily be an energy output. It could be a tonne of product or some other physically defined output, or it could be the output enumerated in terms of market prices.“ (Patterson 1996).

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1998; Schipper et al. 2001; Worrell et al. 1997).111 The main reason is that physical indicators are closer related to the processes that require energy than monetary indicators.112 Moreover, several factors distort commodity prices and thus the relation between the monetary output indicator and the physical output indicator.

• Physical indicators provide a possibility for a reality check by incorporating saturation effects of commodity consumption that cannot be revealed by monetary indicators. E.g. the amount of money a person can spend on his dinner is virtually unlimited, while the quantity in physical terms is very much limited.

• Intra-sectoral changes are less disturbing for physical indicators compared to monetary ones, because of the inverse relation between product specificity and product volume (see Figure 5.1)(Fischer-Kowalski & Amann 2001). Regarding physical indicators, structural change leads to 1) decreasing physical output, and 2) decreasing energy consumption. Therefore this relation is not inverse. Regarding monetary indicators, structural change leads to 1) increasing value added, 2) decreasing physical output, and 3) decreasing energy consumption. Therefore this relation is inverse.

Despite the advantages summarised above, physical and monetary indicators should be seen as complementary, rather than substitutes of each other. Insights derived from monetary approaches should be used in physical approaches and vice versa.

111 For a comparative study on monetary and physical indicators for the iron & steel sector see: (Worrell et al. 1997). 112 Similar reasoning can also be found in models for passenger transport (Rühle 2004) and freight transport (Fischer-Kowalski 2004).

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Specialties, catalysts,

pharmaceuticals

Fine chemicals and pseudo

commodities:coatings, dyes, detergents, etc.

Commodities: resins, synthetic materials, etc.

Bulk andpetrochemical

Product value / specificity

Pro

duct

vol

ume

Structural Change

Figure 5.1: segmentation within the chemicals industry

Source: (Venselaar & Weterings 2004) Note: the inverse relation between product specificity and product volume is general for all industry sectors (Fischer-Kowalski & Amann 2001).

5.3. Description of a model based on physical explanatory variables

The model using physical explanatory variables is described in the sections below. In section 5.3.1 the model framework is described and in sections 5.3.2 and 5.3.3 the two main elements in the model framework are described.

5.3.1. Model framework The model framework visualises the information flow starting from scenario driving forces to industry energy consumption. Figure 5.2 shows the model framework, running from GDP and population to industry physical output to industry energy demand. GDP per capita is used as an ultimate driving force in our energy demand projection model. We established a relationship between industrial energy demand and GDP per capita using a 2 step approach to project energy demand: first by relating GDP per capita and industrial production pattern, and second by relating industrial production and industrial energy consumption. We elaborate on the successive steps in sections 5.3.2 and 5.3.3.

75

Income (GDP/capita)

Industry Physical Output Energy Demand

Physical Energy Intensity

Income-Industry Relation

Figure 5.2: model framework using physical energy intensity indicators

5.3.2. Relation between Income and Industrial Physical Output This section starts with background information on the relation between income and industrial (physical) output, and then elaborates on the use of historic relationships for scenario analysis using the “ flexible extrapolation” method.

The use of GDP per capita as an one of the major driving forces for energy scenarios represents the consensus of the energy research field (Burniaux et al. 1992; de Vries et al. 2001; Gritsevskyi 1998; IEA 2004a; Kaya 1990; Nakicenovic et al. 2000; Newman et al. 2001; OECD 2001b). In our model per capita income determines per capita “ Industry Physical Output” . The rationale for this relation stems from the ‘Simple Keynesian Model for a closed economy’ as shown in Equation 5.1. Per capita income determines household savings (and thus business investments), taxes (and thus government spending), and household consumption (Froyen 1996, p85). Household consumption patterns determine industry output and consequently industry energy demand (Curran & Sherbinin 2004; Moll & Groot-Marcus 2002; Vringer & Blok 2000; Wilting 1996).113

GICEY ++≡= Equation 5.1

With: Y = output (GDP) E = aggregated demand C = household consumption I = investments G = government spending Source: (Froyen 1996)

Although household consumption patterns do depend on income, their relation is ambivalent. The consumption of low income households is bounded by their incomes, while the consumption of higher income households is bounded due to saturation effects, and determined by taste and choice (Biesiot & Moll 1995; Curran & Sherbinin 2004; Geyer-Allely & Cheong 2001; Moll et al. 2005; Roca 2003). Therefore increasing per capita income can result in both materialisation (increasing quantity)

113 “ Households use energy directly for many kinds of application, such as heating, lighting and driving. In addition, households use energy in an indirect way. This indirect use of energy concerns, for example, the energy used to manufacture consumption goods, to gather the raw materials for these goods, to transport these goods, or to provide services. “ (Benders et al. 2001).

76

or dematerialisation (increasing quality ) (Agras & Chapman 1999; de Bruyn 2002; Godet 2002).

The relation between per capita income and industry physical output (PhOs) is the core of this industry energy consumption model. The conceptual model needed to describe this relation between per capita income and industry physical output is accomplished by a set of properties that define the ‘flexible extrapolation’ approach:

• An extrapolation model is needed because the aim of this research is to provide numerical output rather than insights in systems dynamics (Kleijnen 1993).

• The model needs to be backed-up by observations, research and common sense. • The extrapolations need to be flexible to a certain extent in order to represent

different scenarios. De Bruyn et al. (1998) identifies four elemental types of relations between income and environmental pressure.114 The most complex one – the N-shaped curve – is rare and therefore not considered in this research. Therefore the minimal complexity of this relation is bounded by the ability to result in an inverted-U-shaped relation between per capita PhOs and income. It should be noted that the inverted-U-shape relations are typically characterised by a Maxwell-Boltzmann distribution shape (Atkins 1990, p726) rather than a symmetrical inverted U shape. An illustration of the model is given in Figure 5.3.

income per capita

PhO

s pe

r ca

pita

A

B

C

Figure 5.3: model of the relation between per capita income and per capita sectoral physical industry output

Based on: (Riahi 2004)

114 In the context of this paper materialisation, and thus physical industry output, is accounted for as an environmental pressure (de Bruyn 2002; de Bruyn & Opschoor 1997).

77

The curves in Figure 5.3 are characterised by three distinguishable stages of development. In stage-A increasing income is associated with increasing industry physical output. However, as income increases the relation between income and industry physical output becomes weaker until it flattens and reaches stage-B. After the peak the industry physical output slowly decreases and may end-up as a constant value or a gentle decreasing slope in stage-C. The OECD regions reached stage-C for some industry sub-sectors, but are in stage A. for other sub-sectors. Non-OECD regions are still in stage-A for all industry sub-sectors.

The relation between per capita income and per capita industry physical output is determined by several effects appearing at different stages of increasing income. Saturation effects, efficiency improvements, dematerialisation, infrastructure development stage, policies, and fashion are among the factors that may lead to inverted-U-shaped relations.115,116 OECD regions have shown peak and decline behaviour for some of the physical properties of industry production, e.g. in the iron and steel sector. The conceptual model shown in Figure 5.3 is supported by research ranging from world level to national level: a systems dynamic model study on world metal use shows an inverted-U-shape relation for metals intensities in monetary terms (van Vuuren et al. 1999) and a consumption-based statistical study on national level also shows this behaviour for some consumption categories (Rothman 1998).

The conceptual model needs to be flexible to a certain extend in order to be able to represent different scenarios. Let stage-A be the trajectory of a developing country in the past decades. In the next 100 years this country develops and may end up in stage-C with a resource intensive economy (upper graph) or a resource extensive economy (lower graph), depending on the type of storyline. Note that both end-states can be reached from a single state for stage-A. Therefore the model must be developed in such a way that it is rigid in stage-A and flexible in stages-B and -C.

The idea that indicator-levels of developing countries with certain qualification move into the direction of indicator-levels of developed countries is hereafter referred to as the “ conditional convergence assumption” , see e.g. (Miketa & Mulder 2005). This assumption is very important regarding industrial energy consumption because, rather than following the linear path derived from stage-A, the indicator-levels can simulate trend-breaking events when certain critical levels of material wealth are achieved.

5.3.3. Physical Energy Intensities Energy intensities are defined as energy consumption per unit of industrial output. In the model they change over time similar to the Autonomous Energy Efficiency Improvements (AEEI) as common practice in long term energy models (Braathen 2001; de Vries et al. 2001; Gritsevskyi 1998; Nakicenovic et al. 2000). A significant difference with monetary-based AEEI’ s is the limitation of efficiency improvements. The use of physical indicators restricts energy efficiency improvements because of the thermodynamic limitations associated with e.g. the production of a tonne crude steel.

115 For an comprehensive overview see de Bruyn (2000). 116 For a ‘mental model’ of the conflicting dynamics causing this relation see (Agras & Chapman 1999).

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5.4. Model formalisations

The model described in section 5.3 is implemented and formalised in this section. This section solely deals with formalisation issues; implementation of the equations is dealt with in Section 5.5. First, Section 5.4.1 formalises the model framework described in Section 5.3.1. Next, Section 5.4.2 formalises the relation between income and industrial physical output described in Section 5.3.2. Finally, Section 5.4.3 formalises the physical energy intensities described in Section 5.3.3.

5.4.1. Formalisation of the model framework The primary scenario drivers for this model are GDP, population, and the composite, income. Decomposition with physical industrial output, analogous to IPAT/Kaya-identity decomposition (Ehrlich & Holdren 1971; Kaya 1990),117 gives Equation 5.2.

s

sss PhO

PECP

PhOPPEC ∗∗=

Equation 5.2

With: P = population, PECs = sectoral primary energy consumption, PhOs = sectoral physical output

Equation 5.2 is however not suitable for energy scenarios because it lacks the key scenario driver GDP. In our model per capita income determines per capita “ Industry Physical Output” (see Section 5.3.2). Equation 5.3 is a formal representation of this relation.

Equation 5.4 defines the sectoral physical energy intensity (PhEIs) as the quotient of sectoral primary energy consumption and sectoral physical output.

��

��

�=P

GDPf

PPhO

ss Equation 5.3

s

ss PhO

PECPhEI =

Equation 5.4

With: PhEIs = sectoral physical energy intensity

Next substitution of Equation 5.3 and Equation 5.4 into Equation 5.2 gives Equation 5.5. Equation 5.5 represents the formalisation of the model chain as shown in Figure 5.2 in Section 5.3.1.

sss PhEIP

GDPfPPEC •�

�

��

�•= Equation 5.5

117

GDPI

PGDP

PTAPI ••=••= ;

with: I = Impact, P = population, A = Affluence, T = technology, GDP = Gross Domestic Product

79

The next logical step is to develop a conceptual model for the function f, and thus for the relation between per capita impact indicators and income.

5.4.2. Formalisation of the relation between Income and Industrial Physical Output

The conceptual model shows two important features: a peak and an asymptote (hereafter called tail). Not only is it desired that a formalization of the model can reproduce the conceptual model, but it is also desired that variables in the formalization represent features of the conceptual model, e.g. the co-ordinates of the peak and properties of the tail. A possible general formula for growth and decline is shown in Equation 5.6 (Riahi 2004).

22 +

•••2=

xP

xPPy

x

xy Equation 5.6

The benefit of Equation 5.6 is that the peak co-ordinates are explicit. However, when this formulation is compared to historic data the fit is not very accurate because with this formulation the origin has to be crossed and historic series often do not show that behaviour. In order to allow the trend line to move away from the origin, Equation 5.6 was extended with an x-axis interception variable (Ix), resulting in Equation 5.7. For practical reasons it was chosen to use separate formulations for the left-hand side and right-hand side of the conceptual model. Equation 5.7 was used to represent the left-hand side of the conceptual model. The right-hand side of the model, the tail actually, needs to be altered in order to be able to determine the height of the tail and the speed of approach. This was done by adding two variables to Equation 5.7, one to set the y-value of the asymptote (Ty), and one to alter x in order to set the speed of approach (Tx), resulting in Equation 5.8.

( ) ( )( ) ( )22

2

xxx

xxxy

IxIP

IxIPPy

−+−−•−••

= Equation 5.7

( )( )( )( ) y

y

yy

Txxx

Txxxy T

P

TP

PxPP

PxPPPy

x

x

+−

•−++

−+•••= 22

2

Equation 5.8

With: Ix = x-intercept Px = X co-ordinate of the peak, Py = Y co-ordinate of the peak Tx = Factor to adjust broadness of the tail, Ty = Hight of the tail Note: It should be noted that Equation 5.8 is in a sense a simplification of Equation 5.7 because now Ix has been left out. Therefore Ix equals zero for regions were only Equation 5.8 is considered.

Section 5.6.1 describes the implementation of Equation 5.7 and Equation 5.8 by means of the “ Flexible Extrapolation” method described in Section 5.3.2.

5.4.3. Formalisation of Physical Energy Intensities Energy intensities are usually modelled based on the assumption of (autonomous and/or induced) annual efficiency improvements (AEI) (see e.g. de Vries et al. 2001), which is formalised in Equation 5.9. When monetary indicators are used there is no a-

80

priori reason to restrict the energy intensity to go below a certain value. On the other hand, when physical indicators are concerned boundaries need to be considered because of the thermo-chemical limitations of industrial processes. Therefore Equation 5.9 was extended with a minimum value of the physical energy intensity, which results in Equation 5.10.

( )ttt AEIEIEI −•= = 10 Equation 5.9

tMintMint AEIPhEIPhEIPhEIPhEI )1()( 0 −•−+= = Equation 5.10

With: PhEIt = Physical energy intensity in year t PhEIMin = Minimum value of PhEI PhEIt=0 = Base-year value of PhEI AEI = Annual efficiency improvement

Section 5.6.5 describes the implementation of Equation 5.10.

5.5. Data sources and analysis

5.5.1. Scenario driving forces The scenario driving forces are kept very basic in this model. Population, GDP, and per capita income drive the industry energy consumption.

Changes in energy use by society can be caused by several factors (e.g. current energy consumption for private transport is related to the fashion to drive SUVs), however in order to research the use of physical indicators under ceteris paribus conditions this research treats population and income scenarios as driving forces.

5.5.2. General data and dimensions The model was developed as a world model with a regional focus consistent with world models from IIASA and WEC (Nakicenovic et al. 1998), which consist of 11 world regions. The timeframe was set from 2000 to 2100 because most data is available until 2000 and the period until 2100 has been determined as the most relevant for climate change issues (Nakicenovic et al. 2000).

The sectoral focus was chosen to be as detailed as possible, because higher disaggregated data is closer to the industrial process itself (Ramirez et al. 2005). Moreover, because of the explorative character of this research, a high level of disaggregating potentially reveals more information about the feasibility of the use of physical indicators. Because the energy analysis was based on IEA datasets, the sectoral focus of this research is the same with 13 industry sectors (IEA 2002a; IEA 2002b).

For this analysis the ‘dynamics-as-usual’ B2 scenario was chosen (Riahi & Roehrl 2000) in order to be able to compare the results with other models. Historical data analysis is limited by data availability and therefore most of the analyses describes the period 1970-2000.

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Past population developments were taken from IEA datasets (IEA 2002a; IEA 2002b). Population scenarios were taken from the medium UN scenario (UN 1999) which are also used for the B2 SRES scenarios. Incomes and GDP are expressed in terms of constant 2000 US$ at Market Exchange Rates (MER). Historical GDP data was taken from (Miketa 2004a). GDP scenarios are based on B2 scenarios and obtained from IIASA (Riahi 2004).

Energy consumption was expressed in terms of primary energy because 1) the character of the research – a long-term energy model – requires macroscopic data, and 2) data availability for world regions is limited to macroscopic data. Energy data was taken from IEA datasets (IEA 2002a; IEA 2002b).

5.5.3. Choice of the physical indicators The choice for the indicators to represent the entire sector depends on several interrelated factors. A first, essential one is data availability. Second, the indicator must be expected robust given the possibilities of intra-sectoral change in order to be representive. Third, the commodities must allow aggregation. The commodities used as indicators to represent the sectors came from the UN industrial statistics database (UNIDO 2002); analogous to (Rothman 1998). The indicators selected to represent the 13 industry sub-sectors are listed in Table 5.1. Due to the diversity of the industry sub-sectors the indicators are subsequently diverse.

Basic industry sub-sectors produce relatively homogenous bulk products. Therefore these indicators can be chosen in line with bottom-up energy indicator studies and represent a measure of industry output volume. Examples are: “ Iron and Steel” , and “ Paper, Pulp and Printing” .

Specified industry sub-sectors produce heterogeneous products. However, regarding several sub-sectors the major materials inputs are relatively homogenous. Therefore regarding these sub-sectors materials inputs are chosen as indicator. Examples are: “ Chemical and Petrochemical” , “ Wood and Wood Products” , “ Construction,” and “ Non-specified industry” .118

Specified industry sub-sectors with heterogeneous inputs are the most difficult to categorise. Regarding these sub-sectors single commodities or several commodities that can be aggregated are chosen to represent the entire sub-sector. Examples are: “ Transport Equipment” , “ Food and tobacco” , and “ Machinery” . These sub-sectors should be observed with extreme caution because intra-sectoral structural changes affect the outcomes.

It should be noted that specific knowledge of the sectors is required in order to select the appropriate commodities. These chosen commodities should be seen as educated-try variables.

118 When only “ Manufacture of rubber and plastics products” is considered.

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Table 5.1: commodities selected to represent industry sub-sectors

Sub-sector Commodities (ISIC Rev. 2 – based code). Iron and Steel119 Crude steel for casting (3710-16), Crude steel, ingots (3710-

19). Chemical and Petrochemical120

Ethylene (3511-10), Naphthalene (3511-11), Propylene (3511-13), Toluene (3511-14), Xylene (3511-15).

Non-Ferrous Metals Copper, primary, refined (3720-041), Copper, secondary, refined (3720-042), Aluminium, unwrought, primary (3720-221), Aluminium, unwrought, secondary (3720-222).

Non-Metallic Minerals Drawn glass and blown glass (3620-01A), Float glass and surface ground or polished glass (3620-04A).

Transport Equipment Passenger cars, produced (3843-10). Machinery Refrigerators, household (3829-58). Mining Iron ores and concentrates (2301-01), Copper ores and

concentrates (2302-01), Nickel ores and concentrates (2302-04), Aluminium ores and concentrates (2302-07), Lead ores and concentrates (2302-10), Zinc ores and concentrates (2302-13), Tin ores and concentrates (2302-16), Manganese ores and concentrates (2302-19), Chromium ores and concentrates (2302-22), Tungsten ores and concentrates (2302-25).

Food and tobacco Meat of bovine animals (3111-01), Meat of sheep or goats (3111-04), Meat of swine (3111-07), Meat and edible offal of poultry (3111-10), Other meat (3111-13).

Paper, Pulp and Printing121 Newsprint, in rolls or sheets (3411-19), Other printing and writing paper (3411-22), Household and sanitary paper (3411-24), Wrapping and packing paper and paperboard (3411-25), Cigarette paper in rolls exceeding 15 cm or in rectangular sheets (3411-28), Other paper and paperboard (3411-31).

Wood and Wood Products Sawnwood, coniferous (3311-04), Sawnwood, broadleaved (3311-07).

Construction Quicklime (3692-01), Portland, aluminous and other hydraulic cements (3692-04), Asbestos-cement articles (3699-01A), Abrasives, agglomerated or not (3699-04).

Textile and Leather Wool yarn, mixed (3211-04), Cotton yarn, mixed (3211-10), Flax, ramie and true hemp yarn (3211-16), Yarn (other than sewing thread) of man-made staple fibres, whether or not put up for retail sale (3211-19), Jute yarn (3211-22), Yarn of other vegetable textile fibres (3211-25).

Non-specified industry122 Polyvinyl chloride (3513-28). Selected from: (UNIDO 2002).

5.6. Historical data analysis & extrapolation for model parameters

As mentioned in Section 5.3.2 intra-regional convergence is an important assumption in this analysis. In this section the Equations from Section 5.4 are fitted against

119 In line with (Farla & Blok 2001; Worrell et al. 1997) 120 Based on (Farla & Blok 2000) 121 In line with (Farla et al. 1997) and Chapter 0. 122 ISIC Divisions 25 (Manufacture of rubber and plastics products), 33 (Manufacture of medical, precision and optical instruments, watches and clocks), 36 (Manufacture of furniture; manufacturing n.e.c.), and 37 (Recycling). (IEA 2002a; IEA 2002b).

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historical data using the “ Flexible Extrapolation” method to incorporate the convergence assumption (see Section 5.3.2).

5.6.1. Income vs. commodity production: General Observations In our approach per capita sectoral industrial physical output is assumed to be a function of income. In our analysis time-series of regional aggregates are considered. Figure 5.4 shows an example of this relation in the iron and steel sector. Other sectors show similar patterns although differences between industries can be huge.

In order to determine the parameters for Equation 5.7 and Equation 5.8 the ‘flexible extrapolation’ approach described in section 5.3.2 is implemented. The regions North America (NAM), Western Europe (WEU), and Pacific OECD (PAO) were first fitted123 against Equation 5.7 to determine the x and y coordinates of the peak (Px and Py) .124 Next, the values for Ty (y-value of the asymptote) in Equation 5.8 were chosen based on their current dynamics (ability to fit to the data-points) and the scenario storyline of B2 (Nakicenovic et al. 2000).

Figure 5.4 illustrates the “ flexible extrapolation” method by applying it to the “ iron and steel” industry. The extrapolations for NAM, WEU, and PAO all show a decreasing steel intensity, although at very different levels. The scenario is rather conservative, and therefore the dematerialisation trend stagnates relatively soon.

123 All fitting is done using least-square values method. 124 Ix was set to zero because it has no meaning for commodities that are ‘over the top’ .

84

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30 35 40

1000 US$ per capita (MER 2000)

met

ric

tons

of c

rude

ste

el p

er th

ousa

nd

peop

le

NAM WEUCPA PAONAM scenario PAO scenarioWEU scenario CPA scenario

Figure 5.4: relation between per capita income and per capita physical industry output for the iron and steel industry

With: NAM = North-Americas, CPA = Centrally Planned Asia & China, WEU = Western Europe, and PAO = Pacific OECD (Australia, New-Zealand, Japan, and South-Korea). Source: (IEA 2002a; IEA 2002b; Miketa 2004a; UNIDO 2002) Notes: Because of the steep decline in the NAM region the fit of Equation 5.8 resulted in extreme values for the top co-ordinates ant therefore this part of the graph is omitted. The markers for WEU and CPA correspond with “steel” in Figure 5.5 and Figure 5.6

The region Centrally Planned Asia & China (CPA) was first fitted against Equation 5.7 to determine an x-axis interception (Ix) and initial values of the Px and Py co-ordinates. Next, the values of the Px and Py co-ordinates were adjusted in such a way that crude steel intensities approach (but not exceed) the values of the most steel intensive region in the world (PAO). After the peak however, there is virtually no dematerialisation.

The same ‘flexible extrapolation’ procedure was used for the other industry sub-sectors in a similar way to determine the relation between per capita income and sectoral physical output. The results for the historic series are described in the sections below.

5.6.2. Income vs. commodity production: Western Europe Figure 5.6 shows average annual changes in per capita GDP growth and per capita physical industrial output for WEU. The figure shows that structural changes took place in the industry sector. Textile declined the most, while particular the chemicals industry and “ Non-specified industry” 125 increased in terms of per capita physical

125 Non-specified Industry: Any manufacturing industry not included above. [ISIC Divisions 25, 33, 36 and 37] Note: Most countries have difficulties supplying an industrial breakdown for all fuels. In these cases,

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output. The figure also shows that per capita industry output more or less stabilised regarding several industries.

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5.6.3. Income vs. commodity production: Centrally Planned Asia Figure 5.6 shows average annual changes in per capita GDP growth and per capita physical industrial output for CPA. The fugure shows that structural changes took place in the industry sector. The figure shows that industries grow at different speeds. Steel is a relatively slow growing sector, several sectors show growth rates similar to GDP, and several other industries grow much faster than GDP.

the non-specified industry row has been used. Regional aggregates of industrial consumption should therefore be used with caution. Please see Country Notes. (IEA 2002a; IEA 2002b)

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Figure 5.6: CPA average annual changes in per capita economic output and per capita physical industrial output

Source: (IEA 2002a; IEA 2002b; Miketa 2004a; UNIDO 2002) Note: 1971-1981 series are incomplete for some sub-sectors.

5.6.4. Extrapolation of per capita physical industrial output in a B2 scenario Figure 5.7 shows the 2000 and 2100 levels of per capita GDP and of per capita physical industrial output relative to WEU 2100 levels. The CPA 2100 levels of per capita physical industrial output are higher than the WEU 2100 levels for all sectors except “ Non-specified industry” .

Figure 5.4 is illustrative for most industries in a sense that levels of per capita physical industrial output are higher in other OECD regions. Therefore CPA projections for 2100 exceed WEU levels for most industries.

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Notes: The per capita PhOs’s are indexed to WEU-2100 values in order to compare convergence in all sub-sectors. WEU is relatively dematerialised compared to other OECD regions. See e.g. Figure 5.4.

5.6.5. Physical Energy Intensities Physical energy intensities were determined by fitting Equation 5.10 to historical data of sectoral energy consumption in terms of primary energy with physical output of that particular sector.

The convergence assumption and constant annual efficiency improvement (AEI) assumption restricted the values of the parameters in Equation 5.10. Moreover the base-year values needed to be calibrated. In an iterative process the values of the parameters in Equation 5.10 were restricted until both assumptions were met.

Figure 5.8 shows the base-year values (PhEIt=0 in Equation 5.10) and the minimum values (PhEIMin in Equation 5.10) of the physical energy intensities for both regions. Convergence is expressed by equal minimum values of PhEI. The convergence assumption is abandoned, however, when data points strongly in other directions and significant differences in industry structure are plausible (e.g. ‘food’ , ‘wood’ , and ‘Non-specified industry’ ).

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In Western Europe all sectors are expected to increase their energy efficiency, although the improvements are low compared to Centrally Planned Asia & China. In WEU the largest efficiency improvements are expected in the ‘chemical’ , ‘machinery’ , ‘mining’ , and ‘textile’ industries. In CPA the largest efficiency improvements are expected in the ‘chemical’ and ‘minerals’ industries, although improvements are significant in all industry sub-sectors.

5.7. Energy scenarios

In this section the previously discussed information is implemented into industry energy demand scenarios. The scenarios for WEU and CPA are shown and compared to the MESSAGE B2 scenario.

5.7.1. Western Europe Figure 5.9 shows the energy scenarios of the individual industries in Western Europe. Notable are the chemicals industry and “ Non-specified industry” , not only because their current level of energy consumption is quite high, but also because of their development patterns. This analysis shows that the non-energy intensive industries (relevant for “ Non-specified industry” ) deserves special attention in OECD countries, which is consistent with a study for the Netherlands (Ramirez et al. 2005).

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5.7.2. Centrally Planned Asia & China Figure 5.10 shows the energy scenarios of the individual industries in Centrally Planned Asia & China. The picture differs from Western Europe in several points. Steel is expected to play an important role in this scenario, while the less energy intensive industries (“ Non-specified industry” ) become dominant in the second half of the century. Chemicals industry is the strongest rising in energy consumption, but in this scenario its share in energy consumption is low compared to the current situation in Western Europe.

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5.7.3. Comparing the results with other scenarios In this section the model described in this Chapter is compared to the industries sector from the B2 MESSAGE scenario. Although one can argue that doing so is comparing apples with oranges, we argue that doing so reveals fundamental aspects of the difference between monetary-based approaches and physical-base approaches.

Figure 5.11 shows an indexed comparison between the results of this research with the B2 MESSAGE scenario for Western Europe. As can be seen the differences in WEU are remarkable: in the first decades this model appears to be optimistic by indicating relatively small increases compared to B2 MESSAGE. In the second half of the model period however, the B2 MESSAGE indicates strong decreases in energy consumption, while this model is more pessimistic. These effects are hard to explain because in first decades one would dynamics as usual models expect to have similar outcomes. The differences at the end may be explained by trend-breaking new technologies in B2 MESSAGE that cannot appear with our simplistic top-down approach.

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Figure 5.12 shows an indexed comparison between the results of this research with the B2 MESSAGE scenario for centrally Planned Asia & China. The differences in CPA are of another nature than in WEU. In the CPA region both model scenarios seem to follow the same path. However, after ca. 2-3 decades this model indicates stagnation in industry energy consumption while B2 MESSAGE indicates undistorted increases. This result indicates that saturation effects may be underestimated in the B2 MESSAGE scenario. Moreover, it indicates that in the B2 MESSAGE scenario physical industry output in CPA region must be about three times as high as expected based on ‘dynamics-as-usual’ trends of industry physical output growth.

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However, it is hard to say if the differences between B2 MESSAGE and this model are fundamental differences between the indicators, or differences in modellers’ interpretations of business-as-usual developments.

5.8. Discussions

The main achievement of this research is the use of physical indicators for energy scenarios. Because of the explorative character this model comes with a wide range of discussion points.

5.8.1. Measurement of income Economic output is measured at constant 2000 US $’ s in Market Exchange Rates (MER) rather than Purchasing Power Parities (PPP). Constant prices are needed for inter-temporal measurement of real output (Maddison 2004). The use of MER needs a bit more explanation, especially because PPP is developed for inter-country comparison (Maddison 2004; The Economist 2004). When GDP is used as a measure of (material) welfare MER is incorrect because commodity prices usually differ from country to country. To convert MER to PPP a commodity basket is used to compare price levels between countries and convert them to a common currency. The appropriate measurement of welfare and the effect on energy scenarios has been discussed and the advantages and disadvantages of both measurements should be kept in mind (Castles & Henderson 2003a; Castles & Henderson 2003b; Grübler et al. 2004; Nakicenovic et al. 2003; Nakicenovic et al. 2000). In this research welfare is expressed in terms of MER.

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The use of MER is not without caveats (see Chapter 2) and attention should be paid to differences in dynamics between goods due to market distortions (trade barriers, exchange rate interference (China!), transportation distances, and etceteras). Moreover, although China is a relatively poor country it showed that it can adopt high technology standards by launching its own space flight program. Therefore the argument that China will have to buy energy technologies like combined-cycle gas turbines at MER prices (Grübler et al. 2004) is probably not true. As energy imports may become strategically undesired fast adoption of energy efficient technologies should be considered in energy scenarios.

5.8.2. Trade liberalisation Trade is not explicitly included in this model (see Section 5.3.2). However, trade and the liberalisation of international trade has major implications for the development of the industries. Characteristics of trade liberalisation are: the steady expansion of the multilateral trading system, the creation of regional trading blocks, the evolution of truly global corporations, the rapid growth in income (particularly in the most dynamic developing countries), the explosive expansion of means of communication, the collapse of Soviet-style communism, and the general acceptance of a liberalising, deregulatory model of economic policy (Brack 2000). The environmental (and energy) impacts can both be negative and positive, depending on the aggregate outcome of a number of effects: scale effects, structural effects, technology effects, product effects, distribution effects, and regulatory effects (Brack 2000).

OECD exports remained dominant particularly in the hi-tech and medium-tech sectors: non-electrical machinery, chemicals and pharmaceuticals, motor vehicles, iron and steel and electrical machinery and aerospace. Non-OECD exports are dominant in low-tech goods and telecommunication and computer equipment (Brack 2000).

In Section 5.3.2 the link between per capita income and per capita “ Industry Physical Output” was explained based upon the ‘Simple Keynesian Model for a closed economy’ as shown in Equation 5.1, which is a model for a closed economy. In real-life economies are open and Equation 5.11 shows the ‘Simple Keynesian Model for an open economy’ (Froyen 1996). Trade (imports and exports) distorts the directness of the relation between per capita income and per capita “ Industry Physical Output” . However, in this research trade is not taken into account.

ZXGICEY −+++≡= Equation 5.11

With: Y = output (GDP) E = aggregated demand C = household consumption I = investments G = government spending X = exports Z = imports Source: (Froyen 1996)

An argumentation for this simplification is the high aggregation level of production and consumption. The higher the aggregation level, the more production patterns

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reflect consumption patterns. Moreover, on the long term imports are roughly in balance with exports.

5.8.3. Monetary vs. physical approaches The physical approach appears to have several benefits compared to the monetary approach. Physical indicators can be used for energy scenarios, although the use is not without caveats. The benefits of the use of physical indicators are not hard to identify: the connection with the real world is much clearer than with monetary units. A clear disadvantage is data: physical indicators are heterogeneous and often not too well documented.

Notable is the similarity between the MESSAGE B2 scenario for CPA (Figure 5.11) and the energy scenarios that were developed in the 1950’ s for Western Europe and Northern America (see e.g. Smil 2000). The CPA scenario from this model shows more similarity with the actual developments in Western Europe and Northern America. These results indicate that monetary indicators may be accurate for developed regions, but for regions in development physical indicators seem to produce more realistic scenarios.

The differences between monetary and physical approaches are stunning when it comes to the ‘limits of growth’ . In monetary terms the output of the industry sector is virtually unrestricted. In physical terms however, the output of the industry sector is restricted. Even in a world where a Hummer is considered a small car, the infrastructure will have a restricting effect on the amount of materials used to construct a car.

Energy intensity is in the monetary approach actually a residue variable with little or no physical meaning (Fischer-Kowalski & Amann 2001). In the approach presented in this Chapter the physical meaning of energy intensities is ambivalent. Regarding sub-sectors where both the industry inputs and the products are heterogeneous (see Section 5.5.3) the physical meaning is as low as with monetary approaches. Regarding other sub-sectors the physical meaning is high and can be comparable with ‘Specific Energy Consumption’ indicators (Farla 2000).

Physical indicators cannot be simply summed to yield an aggregate indicator (Farla & Blok 2000). This problem remains persistent and can only be dealt with by approaching each sub-sector individually and aggregating e.g. the energy demand.126 Further research in this direction should focus on bulk industry inputs and use them as an indicator for industry activity level. Bulk industry inputs can be aggregated (with some caution).

5.8.4. Directions for further research This research indicates that analysing 13 separate industry sectors is probably overdone. Further research in this direction should aim to distinguish between primary manufacturing and final manufacturing. This means that the chemicals and steel

126 Although energy is actually also heterogeneous and even a single form like ‘electricity’ cannot be aggregated because the GHG emissions from peak-production and off-peak-production may differ (Chapter 3).

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industries need to be split-up according to the product specificity, while other sectors should be aggregated.

The obvious direction for further research is to combine insights from energy modelling based on monetary indicators with insights from the Life Cycle Assessment research community and the Industrial Ecology research community.127 An integrated energy and materials modelling approach potentially increases accuracy and reliability of energy scenario analysis.

5.9. Conclusions

This research clearly shows that physical indicators can be used for scenario analysis. The use of physical indicators instead of monetary indicators seems to affect the energy scenarios significantly. As Figure 5.11 shows, however, the differences with monetary indicators are larger in developing regions than in OECD regions. In the CPA-region the industrial energy consumption calculated based on physical indicators is only 1/3 of the calculations based on monetary indicators. Although only in-depth research can reveal the differences between the scenarios, this research points in the direction of measurement of industry output.

We conclude that an integrated energy and materials approach reveals developments that are hardly visible using a monetary approach. Moreover, this research shows the potential and benefits of the use of physical indicators for scenario development.

The ‘reality check’ provided by this approach indicates that, for CPA-region, in order to consume the amount of energy as indicated in the B2 MESSAGE model, the materials consumption must be three times higher then one would expect based on BaU developments of materials flows. Such huge materials flows can be considered unrealistic. This factor three difference indicates the urge to apply integrated energy and materials modelling into energy scenario analysis.

Key findings

• The use of physical indicators for industrial energy demand scenarios tends to produce different outcomes compared to monetary approaches.

• The use of pure ‘black-box’ models allows ‘peak-and-decline’ behaviour that is hard to simulate with ‘white-box’ models.

• Industrial energy demand scenarios based on monetary indicators may imply unrealistically large accomplished materials flows.

127 Especially ‘Materials Flow Analysis’ (MFA) should be considered.

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A significant share of the realisation of this Chapter was done during the IIASA Young Scientist Summer Program (YSSP). Asami Miketa, Keywan Riahi, Leo Schrattenholzer, Gerhard Totschnig, Hal Turton, and the rest of the IIASA-ECS staff, Ernst Worrell from Ecofys in Utrecht, Marina Fischer-Kowalski from Social Ecology (IFF) in Vienna, and Ton Schoot Uiterkamp from the Center for Energy and Environmental Studies (IVEM) at the University of Groningen are acknowledged for their contributions to this Chapter.

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6. Meso-level analysis, the missing-link in energy strategies128

6.1. Introduction

The previous Chapters show that the relevant interactions between energy and materials are at the meso-level, rather than at the micro or macro-level. Therefore, in this Chapter we elaborate on the meso-level dynamics of energy systems.

The increasing knowledge of the effects of the current energy system on human health (OECD 2001b, Ch 21; Or 2000), global climate change (IPCC 2001), and security of supply (Helm 2002; Wit et al. 2003) calls for major changes in energy systems. As a consequence a transition to an energy system that can stand the consequences of major changes in geo-political unstable regions, reduces GHG emissions sufficiently to halt climatic change, and reduces air pollution below (socially) acceptable levels is vital for modern societies.

So far societies have been unable to successfully change their energy systems in a way that adequately addresses environmental and health concerns. Contrariwise, unsustainable129 fossil-fuel based energy is heavily subsidised130 in both OECD and non-OECD countries to secure energy supply and low prices (IEA 1999; OECD 2001b; UNEP/IEA 2002). Consequently greenhouse gas (GHG) emissions are still increasing and the world is more oil-hungry than ever. At the same time industrialised countries attempt to improve air quality and reduce the emissions of GHGs.131 Apparently the policy goal of ‘security of supply’ conflicts with the policy goal of ‘environment and health’ (see also: Section 6.4.2). Policy makers have not been able to successfully cope with both ‘security of supply’ and ‘environment and health’ due to a lack of coherent strategies. There exists no policy consensus regarding long-term energy strategies, resulting in ad hoc policies.

Lack of policy consensus – and the associated ad hoc policies – often resulted in so-called ‘stop-go’ policies of which the ‘Californian wind rush’ (see e.g. Junginger 2000) is an infamous historical example. Stop-go policies are the result of over-enthusiasm (hype) followed-up by reduction or removal of tax incentives, resulting in a retreat of investments, followed-up by the next hype. Stop-go policies have been identified as one of the most important barriers regarding successful energy-transitions (IEA 2004b; Lensink 2005).

The lack of policy consensus and coherent long-term strategies presumably results from several factors, including conflicting interests of energy suppliers and users, and lack of knowledge of energy systems’ meso-level dynamics.

This paper aims to increase understanding of the dynamics of energy systems and to increase insight on the possibilities and limitations of energy policies. Better

128 Co-authors: Henri C. Moll and Anton J.M. Schoot Uiterkamp. In press for publication in slightly different form in: Energy Policy. 129 ‘Sustainable development’ relies on two key concepts: first, the idea of ‘needs’ , and second, the idea of ‘limitations’ on the environment’ s ability to meet present and future needs (OECD 2001d, p38). 130 “ Subsidies continue to distort the energy market in favour of fossil fuels” (EEA 2002, p59) 131 As agreed in the Kyoto protocol (UNFCCC 1997).

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understanding can contribute to increased scientific consensus and consequently political consensus on long-term energy strategies. The meso-level is taken as relevant for the understanding of energy systems, specifically regarding changes in the energy system. It is acknowledged that energy systems need to be known at the micro-level, macro-level, and the meso-level (Lifset 1999; Rotmans et al. 2003). Increased insight into the meso-level of energy systems can contribute to a more consistent and coherent understanding of energy systems, and thus enhances existing energy analysis methods rather than replaces them.

This paper assesses the additional insights that meso-level research can offer in addition to those at the micro-level and macro-level. Focus is on the relevance of two specific meso-level characteristics – interdependencies and heterogeneous actors – for energy policies. Increased insights in energy systems should contribute to establish consensus in both the scientific and policy arenas. Policy consensus is needed to end stop-go policies and to implement effective long-term energy strategies.

Section 6.2 defines the meso-level and elaborates on differences between micro-macro-meso levels. Section 6.3 elaborates on specific characteristics of meso-level analysis and the theory of systems changes, which is relevant for meso-level energy analysis and policy options. Section 6.4 applies meso-level analysis to the transport sector, and Section 6.5 applies meso-level analysis to the electricity sector.132 Section 6.6 concludes with policy recommendations and guidelines for dealing with the meso-level.

6.2. Positioning the meso-level

Energy systems can be assessed on three distinguished levels: micro, macro, and meso. Figure 6.1 shows all three levels and their interrelations.

132 Transport energy use and household electricity consumption are known to be persistently increasing in OECD countries (EEA 2005; IEA 2005a; IEA 2005b; OECD 2001b).

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Source: (Geels 2002).

Energy systems are – in general – considered from the micro- and/or macro-level. We consider the meso-level perspective of energy systems. In the sections below we first elaborate at the micro and macro level. Next we consider the potential contribution of meso-level analysis of energy systems to increased understanding of energy systems.

6.2.1. The macro-level Macro-level perspectives on energy systems regard the energy system at high aggregation levels and are associated with ‘top-down’ analysis. Highly aggregated data is favoured when dealing with general problems that require ‘policy solutions’ . Macro-level energy analysis describes the over-all functioning of systems and is therefore a valuable monitoring and prognostic instrument (see e.g. Focacci 2003; Kaya 1990).

A disadvantage of top-down energy analysis is the lack of structure due to the high aggregation level. Decomposition partly helps to overcome this shortcoming, however, the heterogeneity of the underlying data remains neglected.

As a result of neglecting the heterogeneity of the underlying data and following top-down logic, in maco-level analysis the processes at meso- and micro-levels are determined by macro-level dynamics. As a consequence, macro-analysis is not able to foresee any trend-breaking events, which results in ‘unsurprising’ forecasts (Craig et al. 2002). This is known as the ‘macro-bias’ (Elzen et al. 2002) or ‘economic paradigm’ (van Beeck 1999).

For example, the correlation of energy use with GDP worked with high precision for several decades. Nevertheless, projections based on the assumption that a relation that worked successfully for several decades would continue and that GDP growth would follow historic trends failed. The projections exceeded the actual outcomes because

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economy’ s growth rates slowed down and because the correlation with GDP was not sustained after the 1970s (Craig et al. 2002).

6.2.2. The micro-level Micro-level perspectives on energy systems regard the energy system at low aggregation levels and are associated with ‘bottom-up’ analysis. Disaggregated data is favoured when dealing with specific problems that require ‘engineering solutions’ . Micro-level energy analysis describes the functioning of elements of systems and is therefore a valuable evaluative/assessment instrument for products (see e.g. Damen & Faaij 2003; Hondo 2005; MacLean & Lave 2003).

A disadvantage of bottom-up energy analysis is limited information on the interaction of system elements on the overall system performance, which results in questionable representativeness of data and allocation problems (Benders et al. 2001; Heijungs & Huijbregts 2004; Kok et al. 2001). Both issues introduce uncertainty in the aggregated results. As a result of data uncertainty, a greater level of variability is introduced and therefore bottom-up analysis tends to widely ‘over-forecast’ or ‘under forecast’ at the top-level (Kahn 1998). Because technologies are – generally – implemented where the specific local circumstances are favourable, and because contextual requirements – like infrastructure – are neglected, bottom-up methodology introduces an ‘optimistic bias’ in the data. This ‘optimistic bias’ results in bottom-up approaches overly-optimistic conclusions regarding possible system changes, which is known as the ‘engineering paradigm’ (van Beeck 1999). Therefore, bottom-up analysis is unable to assess changes in the energy system, like the implementation of new technologies or the possibility to save energy. This makes bottom-up analysis not suitable for scenario studies.

For example, an EWEA/Greenpeace study indicates that producing 12% of the worlds electricity from wind energy in 2020 is feasible (EWEA/Greenpeace 2003). This optimistic perspective on wind energy is based on bottom-up extrapolations from non-representative data, e.g. this study assumes increasing capacity factors (mainly based on increasing hub-height), and does not foresee electricity grid limitations (mainly based on experiences in Denmark). Nevertheless, this study overlooks that the ‘success stories’ are the result of specific local circumstances, new turbines are likely to be sited on less windy spots, wind energy in northern Germany faces already the limits of the electricity grid (with about 3% wind energy), and institutional frameworks limit the implementation speed.

6.2.3. Combined micro- and macro-level approaches Top-down and bottom-up models tend to arrive at different conclusions (Unruh 2000; van Beeck 1999). In order close the gap between bottom-up and top-down approaches, and to overcome the shortcomings of the approaches mentioned above, so-called hybrid top-down/bottom-up approaches have been developed, e.g. (Benders et al. 2001; Frei et al. 2003; Jaccard et al. 2004; McFarland et al. 2004). These hybrid approaches improved the understanding of energy systems by linking actual technologies to macroscopic developments. Nevertheless, hybrid approaches generally circumvent rather than cover the meso-level and therefore hybrid approaches do not sufficiently explain energy systems. In-between meso-level analysis is therefore needed to bridge the gap between the macro and micro-level.

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For example, the MESSAGE-MACRO modelling framework is a sophisticated hybrid top-down/bottom-up modelling framework used for medium and long-term energy scenarios (Messner & Strubegger 1995; Messner & Schrattenholzer 2000).133 Nevertheless, the organisation structure (e.g. institutional framework) is not explicitly implemented and actors are considered homogeneous (e.g. by neglecting differences in responses to carbon taxes by different household groups and different cultures).

6.2.4. The meso-level Figure 6.1 shows that the meso-level is wedged between the macro-level and the micro-level. Therefore, the meso-level describes the energy system from an intermediate aggregation level, often the sectoral-level, and this type of analysis acknowledges the mutual coherence of groups of actors.

The meso-level involves the coupling of individual technologies and groups of actors, resulting in interdependencies and regimes. Coupling should not be confused with aggregation (Dopfer et al. 2004). Meso-level analysis focuses on the dynamic behaviour of the interdependencies of individual system elements, rather than on aggregating individual system elements. The dynamic behaviour of the interdependencies of individual system elements may result in complex behaviour of the over-all system. Meso-level analysis is associated with so-called systems-analysis (Battjes 1999), and depends on data acquired from both bottom-up and top-down energy analysis.

Meso-level analysis of energy systems makes energy analysis more consistent and coherent by bridging the gap between the micro and macro-level. In contrast to the hybrid top-down/bottom-up approaches (see Section 6.2.3), the gap between the macro- and micro-level is not circumvented in meso-level analysis. Instead, meso-level analysis focuses on dynamic interactions between individual elements of energy systems as indicated in Figure 6.1. Moreover, meso-level analysis provides additional information on system’ s responses to changes, or – in other words – societies’ responses to energy policies.

6.3. Theoretical framework of the meso-level

Energy systems can be studied at the macro-level and micro level. Meso-level energy analysis is characterised by two typical aspects, i.e. interdependency dynamics, and heterogeneous actors.

Insights in the heterogeneous characteristics of actors allow understanding of the elasticity of energy demand, policy optimisation, and technological diffusion. Section 6.3.1 elaborates on heterogeneous actors from a meso-level perspective.

Insights in the interaction-driven dynamics allow understanding of causalities, trade-offs, feedbacks, natural resource management, and long-term energy strategies. Section 6.3.2 elaborates on system dynamics from a meso-level perspective.

133 See also: http://www.iiasa.ac.at/Research/ECS/docs/models.html

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Insights in system organisation allow understanding of options to change systems. Section 6.3.3 elaborates on the theory of system changes.

6.3.1. Heterogeneous actors Macro-level energy analysis is able to cover all relevant actors. The actors, though, are generally treated as being homogeneous. The effect of simplifying heterogeneous actors to homogeneous actors influences the dynamics of the system, especially regarding initiatives to alter the system (e.g. policies). Socio-economic systems are made up of heterogeneous assemblages of individual actors and cannot be well presented when treated as uniform (Chave & Levin 2003).

Governments, companies, and energy systems themselves differ from country to country and are therefore heterogeneous at the international level. Consumers are yet another heterogeneous group of actors. Consumers may differ in income, educational level, cultural background, habitat (rural or urban), and worldview (see Section 6.4.2). Therefore different groups of actors have to be approached differently in order to achieve efficient policies (see Section 6.4.3).

Regarding technological diffusion, it is useful to look at actors in terms of technology adopters. Relevant actors – like companies, consumers, and governments – can all be considered technology adopters. Different adopter categories can be classified as: innovators, early adaptors, early majority, late majority, and laggards (Rogers 1995, p262). The heterogeneous aspect of consumers is also the driving force for changes. Transitions happen in different phases and critical mass is obtained via early adopters (see Section 6.5.3).

The heterogeneous actors are subjected to the ‘energy dilemma’ , i.e. on one hand governments consider energy a basic need and (are inclined to) subsidise134 the energy production sector substantially, while on the other hand governments consider excessive energy use undesirable and (are inclined to) impose tax135 on energy use (Helm 2002). This apparent schizophrenia in energy policies reflects the diversity and hierarchy in energy needs (Frei 2004). Removal of adverse energy subsidies and changing the tax structure for motor vehicle use – ‘getting the prices right’ – would end the energy policies inconsistency (IEA 1999). Fully applying the Polluter Pays Principle could, however, also limit access of low-income groups to ‘basic energy needs’ and thus implies equity concerns (OECD 2001b, p178).

6.3.2. Interdependency dynamics As stated in Section 6.2.4, the meso-level is determined by ‘interactions’ . One type of ‘interaction’ is feedback. Feedbacks induce complex behaviour and organisation (Prigogine & Stengers 1984).

An example of feedback is the OECD Pressure-State-Response (PSR) framework. Human activities cause ‘pressures’ on the ‘state’ of the environment and natural resources. Autonomous feedback between ‘pressure’ and ‘state’ appears e.g. when increased energy consumption leads to increasing oil-prices. Non-autonomous

134 Dogmatic policy response #1 135 Dogmatic policy response #2

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feedback appears when information about ‘pressure’ and ‘state’ leads to ‘response’ by e.g. governments, non-governmental organisations, industries, consumers, and international institutions. Regarding energy, GHG emissions and concerns about resource depletion initiated complex feedbacks resulting in the Kyoto protocol, renewable energy policies, the first Gulf-war, and the fashion to drive inefficient cars like sport utility vehicles (SUV), all changes on the ‘pressure’ and ‘state’ of the environment and natural resources.

At the macro-level, feedbacks are rarely visible. A well-studied exception in the energy systems is the so-called ‘rebound effect’ of energy efficiency improvement (Bentzen 2004; Birol & Keppler 2000; Grote Beverborg 2001; Noorman 1995). At the meso-level, ‘interactions’ are more persistent and drive the important processes (Chave & Levin 2003). Therefore, meso-level dynamics are often dominated by feedbacks.

As a result of dominant feedbacks, energy systems are at the meso-level characterised by typical aspects. First, due to feedbacks, systems may behave according to “ the whole is more than the sum of its parts” (Heylighen 1992)(see Section 6.5.1), which makes systems difficult or impossible to analyse by reduction (Lovelock 2003). This phenomenon makes energy systems hard to be understood intuitively and hinders the development of coherent policies. Feedbacks may also cause the ‘lock-in’ of inefficient technologies (see Section 6.4.1). Second, past co-evolutionary developments determine the present institutional organisation, ‘institutional lock-in’ . The current institutional organisation is unsustainable in a sense that it encourages increasing energy consumption (see Section 6.5.2).

6.3.3. Taxonomy of systems changes As Section 6.3.2 stated, systems with persistent feedbacks develop structure and organisation. Energy systems are organised as a consequence of their adaptive development in the past. The direct result of system organisation is that options to change the system, have different entries at different levels in the hierarchy of the organisation. Therefore system changes are also hierarchically organised. Figure 6.2 shows the hierarchy of system changes.

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Figure 6.2: hierarchy of system changes

Source: (Bossel 1994, p31)

The way energy systems are organised determines to a large extent the policy options to alter the system. Parameter adaptation, structural re-organisation, purpose evolution, and orientor136 integrity determine the ‘level’ of possible system changes. The higher the level of the change, the more change is possible, but realisation of the change will be harder. ‘High level’ system changes can be considered unrealistic, while ‘low level’ system changes can be considered unambitious. The quest for energy transitions implies therefore finding feasible options for change at the highest ‘level’ possible.

The theories related to aspects discussed in this section: heterogeneous actors, meso-level dynamics, and taxonomy of systems changes are applied to issues associated with passenger transport, and electricity production and consumption in Sections 6.4 and 6.5 respectively.

6.4. Passenger transport

Passenger transport is one of the most difficult sectors to reduce energy use and GHG emissions (van der Wal & Noorman 1998). Despite the advances in efficiency of the internal combustion engine (ICE), passenger transport is an ever-increasing energy user. Efficiency improvements of the ICE are generally offset by increases in transport performance (passenger kilometres per unit of time), increases in mass and the introduction of energy-inefficient comfort enhancements like air-conditioning. This section elaborates on passenger transport from the meso-level perspective. Focus is on passenger car transport, but public transport is discussed in terms of an energy efficient alternative. 136 ‘Orientors’ are system environment properties that force upon systems and must be considered in the orientation of system behaviour and development (Bossel 1994, p233-237).

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6.4.1. Technology lock-in Meso-level dynamics can result in adaptive behaviour due to feedbacks (see Section 6.3.1). Adaptive behaviour can result in organisation of which ‘path dependency’ or ‘technological lock-in’ is a type of organisation relevant for energy technologies (Arthur 1999; Unruh 2000). Passenger transport is ‘locked-in’ to ICE technology and the associated liquid fuels.

Passenger transport efficiency improvements can be achieved at different levels of the systems changes hierarchy (see Section 6.3.3). Besides attention to ICE efficiency improvements, both scientists and policymakers emphasise the environmental performance opportunities associated with alternative fuels. Alternative fuels include: fuels from Fischer-Tropsch synthesis, natural gas, bio-ethanol, methanol (& dimethylether), electricity, and – of course – hydrogen (Gielen & Unander 2005).

Hydrogen as a transport fuel is associated with a meso-level transition barrier, i.e. lock-in of traditional fuels. Traditional fuels are locked-in because fuel stations do not have an incentive to sell hydrogen since no-one owns hydrogen-fuelled cars, and no-one has hydrogen-fuelled cars because fuel-stations do not sell hydrogen. Carbon-based renewable alternatives can be mixed with traditional fuels and fuel ICEs. Therefore these fuels do not suffer from the lock-in of traditional fuels and can potentially replace mineral carbon by renewable carbon. The fuel distributing infrastructure determines the possible technological modes.

6.4.2. Needs, opportunity, and ability of heterogeneous actors Public transport is more energy-efficient than average passenger car transport (van den Brink & van Wee 1997) and e.g. a Volkswagen Lupo needs less than 3 � to drive 100 km. Nevertheless most people in industrialised countries do not use public transport or drive Lupo’ s. Consumer behaviour apparently does not optimise on energy efficiency. The Needs-Opportunity-Ability (NOA) diagram in Figure 6.3 is used as a model of consumer behaviour in contrast to the (macro-level) homo economicus model of consumer behaviour. Therefore a socio-psychological model is used instead of an economic one.

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Technology Economy Demography Institutions Culture

Needsrelations, development, comfort,

pleasure, work, health, privacy, money, status, safety, nature, control,

leisure-time, justice

Opportunityavailability,

advertisement, prices, shops

Abilityfinancial, temporal, spatial,

cognitive, physical

Motivation Behavioral control

Intention

Consumer behavior

Consequences: qualtiy of life, environmental quality

Figure 6.3: the needs-opportunity-ability model of consumer behaviour

Source: (Gatersleben & Vlek 1998; OECD 2001c)

Consumers are heterogeneous in their needs, opportunities, and abilities regarding personal transport.

• Needs. The needs of personal transport are not solely determined by the ability to transport persons, but also by travelling time, comfort, status, perceived safety, and etceteras. Moreover, cars are often equipped for purposes happening only a few times a year, e.g. much of the cars used for travelling daily to work are equipped for family holidays.

• Opportunity. Successive ownership of cars determines much of the opportunity framework of low-income groups. Regarding high-income groups the opportunity framework is set by automobile producers. The incentive for automobile producers to produce efficient cars is almost solely determined by the market.

• Ability. Ones purchasing power determines ones ability to choose a transport mode. Wealthy consumers have more options to choose from, including very efficient, but expensive vehicles like hybrid cars. The less wealthy consumers are often stuck with cheap, old, less efficient, technologies. Next to purchasing power, educational level determines the ability to assess or estimate the life-cycle costs of transport modes.

As successive car ownership is common,137 only a fraction of car buyers give incentives to car producers to modify the composition of the future car fleet. Second-hand buyers can only buy cars that the original buyers are discarding. In order to understand who uses what kind of car, the NOA model should be applied consecutively to separate actor categories. The NOA model clarifies the effects of macro-level policy targeted at car producers and first-hand buyers, on the -broadly

137 E.g. between 1998 and 2004, only 22% of the passenger car sales in the Netherlands were sales of new cars (CBS 2006).

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speaking- quality of life of second-hand car buyers. The NOA model bridges the gap between macro-level policy and meso-level dynamics.

6.4.3. Transition considerations / policy options Transitions to far more efficient transport systems are possible and feasible. Both the technological lock-in and the heterogeneous actors, however, need to be considered.

Energy infrastructure is technologically locked-in and therefore changes in energy infrastructure require investments. The transition to the hydrogen fuelled transport requires huge investments and no clear benefits in terms of energy efficiency improvements. Local harmful emissions, however, are zero in hydrogen fuelled transport modes. Electrical power shows clear benefits for urban transport. Therefore this transition is only sensible in urban areas and can in such areas be achieved without confronting the associated transition barriers.

Passenger transport can also be more energy efficient without transitions to different modes, thus without a so-called modal shift. Road pricing, vehicle and fuel taxes are quite often imposed in policies to discourage automotive fuel consumption and to fully reflect the social and environmental costs of growing motor vehicle use (OECD 2001b, p178). However, different categories of car owners have different needs, opportunities, and abilities to shift to more efficient transport modes. Moreover, the opportunities of one category of car owners are determined by the behaviour of another category of car owners. Consequently, fuel taxes might be considered unfair as to cut-off the basic-need for transport to the lowest income groups. On the other hand energy taxes have little effect on the purchasing power of medium and high income groups. Moreover, tax deductions for employer provided motor vehicles are in favour of relatively large and inefficient new-bought cars, which enter the second-hand market a few years after. Therefore, policies to reduce passenger transport fuel consumption need to be diversified to the car owner categories the policies want to address. New-bought cars in general, and especially employer provided motor vehicles deserve special attention and targeted policies, because this small category determines the opportunities of the other categories. Rather than the ‘one size fits all’ approach of a carbon tax, ‘policy packages’ 138 can approach different groups of actors differently. Policy packages can fulfil the wishes of different actor categories simultaneously and therefore smoothen the path to political consensus.

6.5. Electricity production and consumption

6.5.1. The whole differs from the sum of the parts When (electricity) energy technologies are compared in a LCA-like manner, the functional unit tends to be kWh (Gagnon et al. 2002; Goralczyk 2003; Hondo 2005; Kammen & Pacca 2004; Sims et al. 2003; Sundqvist 2004). This functional unit

138 “ Because of the complexity of many of the most urgent pressures on the environment, (...) single policy instruments will seldom be sufficient to effectively resolve these problems. Instead, combinations of policy instruments will be required which target the range of actors affecting the environment, draw on synergies for realising the different environmental policy objectives and avoid policy conflicts, and which address any social or competitiveness concerns about the policy instruments.” (OECD 2001b, Ch 25).

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makes sense both from the macro-level and the micro-level, but not from the meso-level because multiple interactions dominate electricity production.

Power plants do not operate isolated, but interact with each other in order to meet energy demand. When ‘new’ energy technologies – like wind turbines and solar PV – penetrate the system, conventional energy technologies need to adjust their operating strategies, which results in system losses. Therefore the amount of avoided primary energy from wind turbines or solar PV depends on the way the system of the conventional power plants responds to these technologies. Figure 6.4 shows model results of such system responses applied to the Netherlands.

Total Fractional Coverage (TFC) stands for the amount of renewable electricity produced (wind and solar) divided by the total demand for electricity. In Figure 6.4, the x-axis shows the solar/wind ratio, and the y-axis shows the amount of fossil fuels saved per amount of renewable energy produced.

As shown, solar and wind are relatively efficient at low TFC values. Moreover, mixing solar and wind does not influence the over-all performance strongly. When wind and solar supply is low, conventional power plants can remain faithful to their ‘old’ strategy and save fossil fuels by lowering their output.

At high TFC values solar and wind save relatively less fossil fuels, and mixing solar and wind does influence the over-all system performance strongly. When wind and solar supply is high, conventional power plants have to abandon their ‘traditional’ strategy and lower their output far from their designed part-load operations. At those low part-loads, power plant efficiencies are significantly lower than at full-load, resulting in lower over-all system efficiency. Moreover, specific dynamics of solar and wind do interfere differently with the conventional power plants, and mixing them influences the over-all system performance. As a consequence, ‘the whole differs from the parts’ and therefore average figures are misleading when applied to medium or long-term energy scenarios and energy policies (Hitchin & Pout 2002).

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4

5

6

7

8

0.0 0.2 0.4 0.6 0.8 1.0

Sav

ings

(TJ

/ GW

h)

5% TFC10% TFC15% TFC20% TFC25% TFC

Pure Wind Pure Solar

Figure 6.4: efficiency of solar PV and wind energy in the Netherlands

Source: based on (Schenk et al. 2005). Note: Savings = avoided fossil fuels per amount of renewable electricity produced TFC = Total Fractional Coverage: the % total electricity demand covered by wind & solar electricity.

Figure 6.4 shows that – due to interactions between actors – for energy systems “ the whole differs from the sum” . The latter effect is relevant for long-term energy strategies. The electricity system can be very sensitive to relatively small changes. Long-term energy strategies should not solely focus on efficiency and diversity of primary sources, but also consider the interdependencies of the system elements. Including this aspect affects long-term energy strategies. Without interdependencies included power-plants like Integrated Gasification Combined Cycle (IGCC), and Combined Heat and Power (CHP) are preferred because of their efficiency. Unfortunately they are also very inflexible and thus decrease the flexibility of the electricity producing system, and thus make it harder for large-scale wind energy to be implemented.

Similar reasoning also affects primary energy sources. Primary energy supply is often simply aggregated and renewable energy sources are often compared to traditional energy sources by means of kilowatt-hour prices. These practices are not correct from the meso-level perspective. Wind-energy cannot be compared to gas-turbines, due to the dynamic character of the energy technologies. Gas-turbines are used to cover peak demand in electricity demand, while wind energy is produced depending on the weather and independent on electricity demand. Moreover e.g. mixing wind energy and solar PV saves significantly more fossil energy than using just one of these technologies.

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6.5.2. Institutional organisation & electricity consumption increase Electricity supply is determined by electricity demand. Electricity demand is still increasing in OECD countries and signs of saturation are not visible yet. This section shows how the organisational structure (and also the institutional framework) may explain this phenomenon. The system seems to be ‘designed’ to increase demand. Policies to decrease electricity consumption have not resulted in (long) periods of decreasing consumption until present because the applied policy instruments are not effective.

In the current system, individual households purchase electrical appliances that increase well-being. Housekeeping has never been less time-consuming, due to electrical appliances like micro-wave ovens, dishwashers, and vacuum cleaners. Entertainment and communication is also electricity based by means of personal computers, televisions, audio-equipment, DVD-players, and etceteras. These appliances require households to purchase electricity. Households have also incentives to reduce energy use as it will reduce their energy bill.139

Electricity producers on the other hand, do not have incentives to sell less energy to households. Contrariwise, the more electricity is sold, the higher the profit usually is for the electricity company. Therefore there is little or no incentive from these companies to increase the energy efficiency of the consumers’ energy services. Figure 6.5 shows the conflicting incentives of energy suppliers and energy consumers.

Supplier User Product

Wants to increase volume

Wants to decrease volume

Figure 6.5: conflicting incentives

Source: (OECD 2004b; OECD 2004c)

The conflicting incentives lead to ever increasing energy use because of the asymmetric relation between energy companies and individual households. ‘John Doe’ is no specialist in energy efficiency, nor is the energy use of a new device his major concern. The most remarkable, however, is the long time delay between energy use and bill payment. ‘John Doe’ is never able to understand what parts of his actions eventually resulted in his energy bill. Therefore households with incentives to cut expenditures on their energy bill are virtually paralysed by lack of information. 139 It should be noted that the desire to save energy is often not dominant at the moment of purchasing.

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LCA studies show that – in general – the lion’ s share of energy use is allocated to the user-phase rather than the production-phase. Electrical equipment is, however, designed for comfort, performance, and low production costs. As a result, several apparatus are designed with external transformers and stand-by functions, both consuming electricity non-stop and accounting for 12% of the electricity consumption of the average household (Harmelink & Blok 2004). Producers of electrical equipment have no or little incentive to reduce the life-cycle energy use of their products. As long as incentives are conflicting, energy policies will have ‘red-queen game’ 140 dynamics and energy efficiency will at best compensate increasing demand: ‘running to stand still’ .141

6.5.3. Transition considerations / policy options Electricity production is complex in a sense that “ the whole does not equal the sum” and therefore comparing electricity prices as top-down assessment and LCAs as bottom-up assessment does not provide sufficient information. Reduction of GHG emissions due to renewable electricity production depends strongly on local conditions and therefore path-following of countries like Denmark does not per se result in more sustainable electricity production. Consequently, policies that specifically support distinct renewable energy technologies, like wind or solar PV, are likely inefficient and should be reconsidered.142 Instead policies should focus on GHG emissions and geopolitical circumstances of the energy resources.

The organisational relation between households, energy companies, and producers of electrical devices should be reconsidered. Figure 6.2 shows that the orientor of the system is the highest parameter in the systems hierarchy and in the case of electricity production and consumption the orientor is pointing in the wrong direction. Energy companies should be transformed from energy sellers to service oriented companies.143 Households should not own electrical devices anymore, but obtain services. Figure 6.6 shows how in a service oriented organisation, incentives are aligned towards less energy consumption.

140 Red-queen games are named after the red-queen character in Lewis Carroll’ s “ Through the looking glass” . See for an application of red-queen game concept on economics: (Baumol 2004). 141 Actually, the red-queen said: “ You see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that.” (Carroll 1946, p189). 142 The IEA strongly advices the Netherlands to stop supporting solar PV in the Netherlands (IEA 2004b). In general focus should be on environmental goals rather than subsidising a particular technology in order to encourage innovation (Dobesova et al. 2005). 143 This ‘service-oriented strategy’ has proven to be successful in several industrial sectors (OECD 2004b; OECD 2004c).

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Supplier User Service



Figure 6.6: aligned incentives

Source: (OECD 2004b; OECD 2004c)

A possible option to realise service-oriented household energy systems is to make use of the fact that actors are heterogeneous and fit into different adopter categories (Rogers 1995, p262). Early adaptors should be approached with ‘cool toys’ ,144 e.g. electrical devices equipped with wifi in order to provide direct feedback to the consumer. Moreover, by doing so electrical devices can – in the end – be controlled by the energy company and reduce peak-load demand.

6.6. Conclusions

Assessment of the meso-level of energy systems reveals insights in addition to micro-level and macro-level analysis. These insights allow the design of environmentally and socially sustainable energy policies.

6.6.1. System insights Meso-level analysis provides insights in energy technologies relevant for long-term planning. Meso-level energy analysis is characterised by two typical aspects, i.e. interdependency dynamics, and heterogeneous actors.

Interdependency dynamics may result in quite different figures than those foreseen based on macroscopic indicators, especially when renewable energy sources are considered. For example, Section 6.5.1 shows how “ the whole differs from the sum” regarding electricity production. A main feature of interdependency dynamics is institutional organisation. Institutional organisation can be independent in a flexible organisation, or dependent in a rigid organisation.

Heterogeneous actors result in heterogeneous responses to policies, because the needs, opportunities, and abilities of the relevant actors differ. In order to design environmentally and socially sustainable energy policies, the relevant actors need to be categorised and the interactions between the categories need to be assessed.145

144 Coolness is a necessity! 145 See also: One size fits all? Policy instruments should fit the segments of target groups. C. Egmond, R. Jonkers, G. Kok, Energy Policy, In Press.

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Figure 6.7 presents a diagram summarising the relevant meso-level characteristics, interdependency dynamics, and heterogeneous actors. Both characteristics are relevant regarding energy policies; therefore the meso-level characteristics determine the 2x2 matrix of energy policies. Section 6.6.2 elaborates on the policy implications of the 2x2 matrix of meso-level characteristics.

Actor characteristics

Inte

rdep

ende

ncie

s

DependentRigid organisation

IndependentFlexible organisation

LinearHomogeneous

Complex Heterogeneous

Traditional ‘one-size fits all’

policiesTarget-group

approach

Target-group induced system re-orientation

Redesign system organisation

Figure 6.7: energy policies in relation to meso-level characteristics

Source: based on (Perrow 1984, p327).

6.6.2. Policy implications Meso-level analysis is relevant for energy policies. A consequence of not understanding systems sufficiently is to implement the wrong policy, or ‘to bet on the wrong horse’ . Regarding renewable energy betting on wrong horses followed by drastic changes in policies – so-called ‘stop-go’ policies – is very harmful for the development of renewable energy technologies (Lensink 2005). Policy makers should develop policies that can be expected to remain effective on the long run, rather than subsidising the ‘technology of the week’ .

Stimulating single technologies should be considered as an attempt to change energy-systems on a ‘low’ level of the hierarchy of system changes, whilst changes on ‘higher’ levels allow for more flexibility. Policy targets with too much detail – like prescribed percentage of renewable energy, percentage of biofuels, and waste paper recycling rate – do not per se aid curbing GHG emissions. The focus should be on the institutional framework that can help a transition towards a sustainable energy system, rather than filling in the details.

Target group segmentation makes sense: If one sheep leaps over the ditch, all the rest will follow. C. Egmond, R. Jonkers, G. Kok, Energy Policy, In Press. A strategy and protocol to increase diffusion of energy related innovations into the mainstream of housing associations. C. Egmond, R. Jonkers, G. Kok, Energy Policy, In Press.

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Policies to enhance system changes towards more sustainable energy systems are – in general – focussed on uniform approaches. The heterogeneous-actors aspect of meso-level theory, however, suggests that differential approaches are potentially more effective than uniform approaches. Moreover, differential approaches allow the design of environmentally and socially sustainable energy policies, which provides an escape from the ‘energy dilemma’ mentioned in section 6.3.1.

The adaptive nature of the energy system at the meso-level also explains some of the important transition barriers. The organisation of the system (institutional framework) is the key barrier to transitions.

Environmental policy and policy instruments are: regulations and standards, voluntary agreements, environmental taxes and charges, tradable permits, deposit refund systems, damage compensation, and subsidies146 (Barde 1995). These policy instruments apply best to systems with flexible organisation and when actors can be considered homogeneous. The IEA comes up with three major strategies to reduce electricity consumption quickly: raise electricity prices, encourage behavioural changes, and introduce more efficient technologies (IEA 2005a). Straightforward policies are effective when actors can be treated homogeneously, subsystems are flexibly organised, and interactions between actors are linear. Long term strategies are: minimum energy efficiency levels in building codes, minimum appliance efficiency standards, load management programmes, weatherisation programmes, and general information campaigns to encourage energy conservation (IEA 2005a, p73).

Finally, when organisations are rigid, straightforward policies squeeze actors between the ‘system’ and the ‘policy measure’ . In these situations traditional policy measures are inefficient and redesign of system organisation is needed to achieve paths towards sustainable development. The notion that allows the system to escape from technological lock-in requires high-level system changes like synchronising incentives is shown in Section 6.5.2.

When organisations are rigid and heterogeneous aspects of actors dominate, a combined strategy of target-group induced system re-orientation is required.

146 Subsidies are regarded as inefficient on the long run (Barde 1995; Löfgren 1995; Zylicz 1995).

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Key findings

• Meso-level dynamics do explain phenomena that are hardly seen at micro- or macro-levels.

• Institutional frameworks are a dominant driving force regarding autonomous developments of energy systems and regarding energy policies.

• Considering agents as heterogeneous, rather than homogeneous, results in different over all behaviour, and in different policy responses.

• Persistent energy-related problems require non-traditional policy solutions, i.e. ‘target-group approach’ , ‘redesign system organisation’ , and ‘target-group induced system re-orientation’ .


Sander Lensink is acknowledged for his valuable comments on an earlier draft of this Chapter.

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7. Summary & conclusions

7.1. Introduction

Natural resources are essential for human societies. Natural resources are crucial for economic development, help to fulfil basic needs such as food and shelter, and contribute to social development by improving education and public health. Resource use is, however, associated with environmental effects.

Energy is a natural resource of particular interest. Energy systems allowed mechanisation of all productive sectors, increased productivity and improved labour conditions, while availability of energy services increased the quality of life. Consequently, energy demand has been increasing since the beginning of the industrial era. The main downsides of these developments are the economic dependency on cheap oil, and the arising global climate change due to CO2-emissions from fuel combustion. Energy can, however, not be examined in isolation of other resources. Particularly materials resources are of importance for energy systems.

The issues of security of supply and greenhouse gas emissions require long-term planning. Therefore information on (possible) future developments is highly important. This information can be obtained using a combination of two distinctive approaches: analysis of past and present energy systems, and scenario analysis of future energy systems. Both approaches require energy models.

Energy models consist of three basic building blocks: mental models, empirical data, and theoretical causalities. In most energy models, however, emphasis is on one or two of the building blocks.

The aim of this thesis is to increase insights in energy systems, with the intention to be able to change energy systems in a more sustainable way. Previous research of energy systems at the ‘Center for Energy and Environmental studies (IVEM)’ indicated that crucial knowledge gaps regarding energy systems exists at relations between the management of energy and materials resources. Therefore the main thesis objective is to explore environmentally relevant relations between the management of energy and materials resources with emphasis on energy systems. In order to achieve this objective, the preceding Chapters 3 through 6 consider the subject of energy systems using different methodological approaches. The resulting relations between the individual chapters and the different methodological approaches are visualised in Figure 7.1.

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Empiricaldata

Theoreticalcausalities

White boxBlack box

Top-down

Bottom-up R

ule-

base

d

Diff

eren

tial e

quat

ion

Ch 5

Mentalmodels

Ch 4

Ch 3

Ch 6

Figure 7.1: methods used in chapters 3 through 6

Note: adapted from Figure 1.2.

7.2. Key findings & methodologies

Energy analysis can be performed with an emphasis on one (or two) of the modelling building blocks shown in Figure 7.1. In this section the key findings of the individual thesis chapters are summarised and related to specific methodologies applied.

The review of the IPCC’ s emissions scenarios (Chapter 2) is not explicitly shown in Figure 7.1, because scenario analysis is conceptually broader than modelling. Chapter 2 focuses on the “ storylines” and the communication of the analysis, and does not deal with the modelling itself. Chapter 2 finds that the simultaneous goals combined in one scenario analysis made the analysis vague and fuzzy. Scenario analysis should preferably be performed with a single and clear goal, rather than with mutually incompatible goals. Therefore the main message of Chapter 2 is: keep the analysis as simple as possible (but no simpler). The storylines of the IPCC’ s emissions scenarios contain several politically coloured elements like “ improved equity” , while the scenarios are meant to be descriptive. Not only are the politically coloured elements unnecessary, they also obstruct the communication of the scenarios to the audience. The extraordinary science-policy relation wherein the IPCC operates demands for an extraordinary approach.

The analysis of renewable energy for electricity production and the production of hydrogen from renewable resources (Chapter 3) reveals that although increasing wind energy capacity generally results in decreasing fossil fuel consumption, the benefits of wind energy suffer from diminishing returns due to losses when the wind blows at times of low electricity demand. Moreover it finds that the production of hydrogen

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from (discarded) wind energy can help to reduce these losses. However, this would require much electrolysis capacity and sophisticated regulation. In addition to the numerical results the research revealed that some of the inefficiencies of the electricity sector result from the organisation of that sector. This insight ultimately initiated the research of Chapter 6. The analysis of Chapter 3 was performed with an electricity production simulation on the interface of microscopic data and individual actors as shown in Figure 7.1. The associated methodological approaches are bottom-up data analysis and rule-based simulation modelling. The model simulates the behaviour of individual power plants based on hourly data of electricity demand and available wind energy. The simulated behaviour of the individual power plants is driven by simple decision rules. Aggregation over 8760 hours provides bottom-up produced data of national energy totals.

The analysis of the energy efficiency of waste paper recycling (Chapter 4) reveals that, although waste paper recycling is more energy efficient than waste paper incineration, the benefits of recycling suffer from diminishing returns (Figure 4.3). Chapter 4 further reveals that recycling vs. incineration of waste paper implies a trade-off between fossil (energy) resources and biological (energy and materials) resources (Figure 4.4). Therefore, increasing recycling rates result in increasing CO2-emissions (when the pulp and paper industries are considered separately). The analysis of Chapter 4 was performed using a top-down/bottom-up approach. The top-down data is used to calibrate a white-box materials flow model. The combination of white-box, top-down, and bottom up is visualised with the ellipsoid in Figure 7.1. The main advantage of this approach is that the non-linear relationship between national recycling rates and national virgin fibre requirements is not only based upon their correlation (black-box), but also on theoretical causalities (Figure 4.2).

The industrial energy demand scenario analysis based on physical indicators (Chapter 5) reveals that the use of physical indicators for industrial energy demand scenarios tends to produce different outcomes compared to monetary approaches. This result should, however, be interpreted as complementary to monetary approaches because the physical approach comes with simplifications, like neglecting trade. Chapter 5 further reveals that industrial energy demand scenarios based on monetary indicators may imply unrealistically large accomplished materials flows. Therefore, a potential application of physical indicators is to provide a reality check for industrial energy demand scenarios based on monetary approaches. The analysis of Chapter 5 was performed using a pure ‘black-box’ model as indicated in Figure 7.1. An obvious disadvantage of the use of a black-box model is the lack of insight. The black-box model does not ‘explain’ why certain developments take place, because it focuses on the highest aggregation level. An advantage of a black-box model is, though, that it can project developments that are hard to simulate with high precision using white-box models, like convergence (Figure 5.4), because white-box models tend to be sensitive for variations in input data. The ‘mental model’ that supports the ‘black-box’ model includes key elements from other methodological approaches, e.g. the assumption of commodities saturation is derived from micro-economic analysis. The ‘flexible extrapolation’ method allows the development of different scenarios, while being still in line with historical data.

The meso-level analysis of energy systems (Chapter 6) reveals that meso-level dynamics do explain phenomena that are hardly caught at micro- or macro-levels.

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Chapter 6 further reveals that institutional frameworks are a dominant driving force regarding autonomous developments of energy systems and regarding energy policies. Moreover, considering agents as heterogeneous, rather than homogeneous, results in different over all behaviour, and in different responses tot policies. These conclusions have considerable implications for both energy forecasts and energy policies. The outcomes of energy forecasts differ when it is acknowledged that income is only one explanatory variable for household consumption among many others (rather than a key driver). The effectiveness of energy policies can be improved when policy makers abandon their attempts to cope with the ‘difficult’ sectors for energy policies – i.e. personal transport and electricity consumption – by means of ‘one size fits all’ policies. Non-traditional policy solutions are needed to cope with these sectors. The analysis of Chapter 6 was performed using a ‘mental model’ as indicated in Figure 7.1. An obvious disadvantage of the use of mental models is the lack of quantification. Consequently, mental models are hard to calibrate against real-world observations. The advantage of mental models, however, is that phenomena can be observed that are hard to model quantitatively. Models that simulate meso-level phenomena – like technological lock-in – are sensitive to variations in data input and thus may produce strongly different outcomes when the starting variables are modified slightly. Mental models – like the NOA model of consumer behaviour in Figure 6.3 – can help to understand phenomena that can hardly be captured with quantitative approaches.

7.3. Over-all conclusion

This thesis observes energy systems from different angles, i.e. it considers interactions between different resource types, it considers different aggregation levels, and it considers different methodologies.

Resources of different types and uses (see Table 1.1) do interact with each other within the context of societal metabolism. The interactions between energy and materials resources are generally non-linear. Consequently, diminishing returns and saturation effects are persistent. As a result of diminishing returns the potentials for environmentally benign changes are smaller than observed at first sight. On the other hand, as a result of saturation effects future pressures on resources and the environment may be smaller than expected on linear models. The combination of both diminishing returns and saturation effects make the energy system more difficult to understand intuitively.

The aggregation level may limit and determine the outcome of research. Therefore, energy analysis needs to assess all three levels – micro, macro, and meso – simultaneously. Simultaneous analysis of all three levels, though, is in general not feasible. Nevertheless, single-level energy analysis can be improved significantly by incorporating key elements of the other levels. In this context, the meso-level is of particular interest, because it captures system organisation and institutional frameworks. System organisation and institutional frameworks have also been identified as key driving forces of changes in energy systems, and as key barriers of intended changes of energy systems. Therefore, the meso-level is of particular interest regarding energy policies.

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Closely related to the aggregation level is the applied methodology. Each methodology reveals different aspects of energy systems, but also neglects other aspects of energy systems. All methods displayed in the modelling pyramid (Figure 7.1) are applied in the different chapters. Even more than the aggregation level, the applied methodology limits and determines the outcome of research. Different methodologies (and their associated outcomes) should be interpreted as complementary rather than contradictory. Accordingly, in this thesis energy systems are methodologically triangulated from an integrated resource perspective.

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Nederlandse samenvatting

De mensheid is in sterke mate afhankelijk van natuurlijke hulpbronnen zoals zoet water, bebouwbaar land, productiebos, biodiversiteit, en delfstoffen. Iedere natuurlijke hulpbron kent zijn eigen, specifieke toepassingen en heeft zijn eigen kenmerkende dynamiek.

Het gebruik van natuurlijke hulpbronnen gaat over het algemeen gepaard met één of meerdere bijeffecten. Ten eerste zorgt gebruik in het heden voor schaarste in de toekomst; zo kan het gas van Slochteren maar één keer verstookt worden. Ten tweede vervullen veel natuurlijke hulpbronnen meerdere ‘functies’ ; zo voorziet grasland niet alleen in de voedselvoorziening voor melkvee, maar vormt dit tevens de habitat van weidevogels. Ten derde resulteert het gebruik van natuurlijke hulpbronnen in een productie van afval, uitstoot van schadelijke stoffen en verstoring van landschappen.

Het beheer van natuurlijke hulpbronnen neemt al sinds mensenheugenis een centrale plaats in de cultuur van vrijwel iedere samenleving in, omdat deze hulpbronnen zo cruciaal zijn voor die samenleving en de impact van de nadelige gevolgen zo groot is. Dit proefschrift richt zich voornamelijk op het beheer van één specifieke natuurlijke hulpbron: energie.

Sinds de oliecrisis in de jaren zeventig heeft de afhankelijkheid van energie (uit politiek instabiele gebieden) veel aandacht gekregen. Vanaf de jaren tachtig begon het besef van klimaatverandering gemeengoed te worden. De belangrijkste oorzaak van klimaatverandering is de uitstoot van koolstofdioxide door de verbranding van fossiele brandstoffen. Daarnaast zorgt de verbranding van fossiele brandstoffen voor luchtvervuiling, hetgeen bijzonder schadelijke gevolgen voor de volksgezondheid en de natuur heeft. Afhankelijkheid en beschikbaarheid van energie, klimaatverandering, en volksgezondheid zijn drijfveren om efficiënter met energie om te gaan en om alternatieve energiebronnen te ontginnen. Het is daarom van groot belang om energiesystemen te analyseren met als doel mogelijke toekomstige ontwikkelingen te verkennen. Op die manier krijgt men inzicht in de aard en ontwikkeling van mogelijke problemen en oplossingen.

Het gebruik van natuurlijke hulpbronnen staat niet op zichzelf en daarom is het zinvol om ook andere natuurlijke hulpbronnen bij de energie-analyse te betrekken. In de studies die in dit proefschrift beschreven zijn, ligt de nadruk op het belang van materialen voor energie-analyses. Materiaal- en energiegebruik zijn nauw met elkaar verbonden. Het energiegebruik van de staalindustrie (één van de belangrijkste energie gebruikende industrieën) hangt af van de maatschappelijke behoefte aan staal. Aan de andere kant heeft de materiaalkeuze voor een auto (plastic of staal) invloed op het gewicht van de auto en dus op het energiegebruik.

Om een energiesysteem te analyseren wordt vaak gebruik gemaakt van computermodellen. Een computermodel is een vereenvoudigde weergave van de werkelijkheid, waarbij grif gebruik wordt gemaakt van de rekenkracht van computers. Er zijn verschillende bestaande methodologieën voor energie-analyse met computermodellen. In dit proefschrift wordt een scala aan methodologieën gebruikt om energiesystemen te analyseren. Het doel van het onderzoek is het verkrijgen van

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een evenwichtig beeld van energiesystemen en van de mogelijkheden (en van onmogelijkheden) om energiesystemen te veranderen.

Voordat er oplossingen voor problemen gezocht worden, is het van belang te weten of het probleem er in de toekomst nog steeds zal zijn (of zal zijn verergerd). Hiervoor is een toekomstverkenning van het probleem nodig. Toekomstverkenningen zijn er in vele soorten en maten. Eén van de meest uitgebreide en bekendste toekomstverkenningen is het rapport met emissiescenario's van het ‘International Panel for Climate Change’ (IPCC). Dit proefschrift begint met een beschouwing van dat rapport met emissiescenario’ s (Hoofdstuk 2) met als voornaamste doel inzicht te krijgen in het ontwerpen van scenario's en het gebruik van computermodellen voor energie-analyse. De beschouwing van de IPCC emissiescenario’ s leert dat het interdisciplinaire karakter van energie-analyse één van de potentieel belangrijkste struikelblokken is. Daarnaast blijkt dat een toekomstverkenning een duidelijk, eenduidig doel moet hebben in plaats van meerdere, niet te combineren, doelen.

De toekomstverkenning van het IPCC is bijzonder helder over één ding: klimaatverandering is één van de grootste problemen waarmee we in de 21ste eeuw geconfronteerd zullen worden. Daarom heeft efficiënter met energie omgaan en het gebruiken van alternatieve energiebronnen een hoge prioriteit. Dat in Nederland windenergie een belangrijke duurzame bron is zal weinigen verbazen. Windenergie is echter een ander type natuurlijke hulpbron dan een fossiele energiebron. In Hoofdstuk 3 van dit proefschrift wordt de dynamische interactie tussen deze twee typen hulpbronnen onderzocht. Windenergie wordt gebruikt om elektriciteit op te wekken. Het waait echter niet altijd op het moment dat er vraag naar elektriciteit is. Daarom moeten conventionele elektriciteitscentrales er zorg voor dragen dat er altijd voldoende elektriciteit geproduceerd kan worden. Dit gaat gepaard met energetische verliezen en deze verliezen worden relatief groter naarmate er meer windturbines staan opgesteld. Als windenergie wordt omgezet in waterstof, dan worden de eerder genoemde verliezen geëlimineerd. Maar bij deze waterstofroute treden weer andere verliezen op. Met behulp van een gedetailleerd computermodel is berekend wat de potentiële bijdrage is van de productie van waterstof uit windenergie voor de ‘over-all’ efficiency van het systeem. Het blijkt dat het vanaf een opgesteld vermogen van ca. 6GW aan windturbines voor Nederland efficiënt wordt om waterstof uit windenergie te gaan produceren.

Naast energie vormen materialen een belangrijke categorie natuurlijke hulpbronnen. In hoofdstuk 4 van dit proefschrift wordt de wisselwerking tussen bio-materialen, bio-energie, en fossiele energie onderzocht aan de hand van papierrecycling. Papier kan gerecycled worden, wat energie kost, maar kan ook in een afvalverwerkingscentrale verstookt worden waarbij energie opgewekt kan worden. Als er minder gerecycled wordt, moet er natuurlijk wel meer papier uit bomen geproduceerd worden. En dat kost dan weer bomen die ook in een bio-energiecentrale hadden kunnen worden verstookt. Het is a-priori niet eenvoudig de consequenties van meer of minder papier recyclen op het energiegebruik te overzien. Met modelberekeningen kan dat wel. Uit deze berekeningen blijkt dat het uit energetisch oogpunt efficiënt is om papier te recyclen. Maar ook dat er sprake is van een afnemende meeropbrengst bij toenemende recycling. Het is daarom gerechtvaardigd dat papierrecycling wordt gestimuleerd, maar het is niet zinvol veel hogere recyclingspercentages na te streven dan de huidige.

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Zoals al eerder vermeld heeft het gebruik van materialen invloed op energiegebruik. De materialen die een samenleving gebruikt, noemt men materiaalstromen. Materiaalstromen geven aan waar op welke wijze wat voor materialen in de samenleving gebruikt worden. Materiaalstromen komen een samenleving in als ruwe grondstoffen, verlopen via diverse stadia van bulkproduct tot eindproduct, en verlaten een samenleving als afval. De grootte van materiaalstromen is dan ook deels bepalend voor de hoeveelheid benodigde energie. Grotere materiaalstromen betekenen namelijk meer vrachtverkeer, maar ook meer energiegebruik door de industrieën om deze materiaalstromen te verwerken. In hoofdstuk 5 wordt beschreven hoe materiaalstromen gebruikt kunnen worden bij het maken van scenario’ s voor het energiegebruik van de industriesector. Het gebruik van materiaalstromen als maat van industriële output wijkt af van de standaardmethode waarin de monetaire toegevoegde waarde gebruikt wordt als maat van industriële output. Het blijkt vooral voor ontwikkelingslanden veel uit te maken of de scenario’ s gebaseerd zijn op toegevoegde waarde of op materiaalstromen. Dit onderzoek suggereert dat het toekomstige industriële energiegebruik wel eens significant lager zou kunnen zijn dan de meeste scenariostudies concluderen.

Eén van de kenmerken waarin verschillende methodologieën van elkaar verschillen is het schaalniveau. Energiesystemen kunnen op verschillende schaalniveaus bestudeerd worden. Macroscopische analyse is gericht op data met een hoog aggregatieniveau, zoals bijvoorbeeld nationale statistieken. Microscopische analyse daarentegen, is gericht op data met een laag aggregatieniveau, zoals procesgegevens van een technologie of uitgavepatronen van individuele huishoudens. Uit de studies in dit proefschrift blijkt dat veel van de interessante wisselwerkingen tussen natuurlijke hulpbronnen plaatsvinden op een niveau tussen macro en micro in, het zogenaamde meso-niveau. In hoofdstuk 6 wordt verder ingegaan op het meso-niveau. De dynamiek van het meso-niveau wordt in sterke mate bepaald door terugkoppelingen (E.g. het gebruik van LPG als autobrandstof was lange tijd niet populair mede omdat er weinig tankstations voor LPG waren. En omdat LPG niet populair was was er weinig reden om LPG stations bij te bouwen. Hier is dus sprake van een terugkoppeling tussen de brandstofdistributie en brandstofgebruik). Terugkoppelingen kunnen co-evolutie (e.g. tussen autogebruik en brandstofdistributiesystemen) en organisatie (e.g. huidige brandstofdistributiesysteem) als gevolg hebben. Organisatie kan zowel een katalysator als een belangrijke barrière zijn als het gaat om het verbeteren van energie-efficiëntie. Het beter begrijpen van de organisatie maakt het mogelijk om effectiever beleid te ontwerpen. Er is bijvoorbeeld verschillend beleid denkbaar om er voor te zorgen dat auto’ s gemiddeld zuinig worden. Veel van deze opties, zoals het verhogen van brandstofaccijnzen, resulteren niet in een significant zuiniger wagenpark, maar wel in onbehagen bij automobilisten. Problemen die moeilijk door middel van ‘klassiek’ beleid zijn op te lossen, kunnen wel effectief aangepakt worden door een doelgroep-georiënteerde aanpak of een herinrichting van de organisatie.

In dit proefschrift is er vanuit verschillende invalshoeken naar energie-systemen gekeken. Iedere invalshoek beschouwde interacties tussen verschillende natuurlijke hulpbronnen vanuit verschillende schaalniveaus en gebruikmakend van verschillende methodologieën. Uit dit onderzoek blijkt dat interacties tussen verschillende natuurlijke hulpbronnen vrijwel altijd niet-lineair zijn, wat zich uit in afnemende meeropbrengsten en verzadigingseffecten. Hierdoor zijn energiesystemen intuïtief

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moeilijk te doorgronden. Daarnaast heeft het schaalniveau invloed op de uitkomst van onderzoek en daarom moet energie-analyse op micro-, macro- en meso-niveau uitgevoerd worden. Met betrekking tot energie-beleid (en politiek) is vooral het meso-niveau van belang. Naast de schaalniveaus worden de mogelijkheden van energie-analyse beperkt door de gebruikte methodologie. Iedere specifieke methodologie heeft zijn eigen kenmerkende lacunes en daarom dienen verschillende methodologieën niet als tegenstrijdig, maar als complementair te worden gezien.

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List of abbreviations

BAT best available techniques BaU Business-as-Usual BU Bottom-up BUWAL the Swiss Agency for Environment, Forests and Landscape CEPI Confederation of European Paper Industries Ch Chapter CO2 Carbon dioxide CPA Centrally Planned Asia & China EPOC OECD Environmental Policy Committee GDP Gross Domestic Product GHG Greenhouse gas GMO genetic modified organism HHV higher heating-value IIASA International Institute for Applied Systems Analysis IPCC Intergovernmental Panel on Climate Change IPPC the European Union’ s integrated pollution prevention and control

framework LCA Life-Cycle Assessment LHV lower heating values MER Market Exchange Rates NH3 ammonia NOA Needs-Opportunity-Ability OECD Organisation for Economic Co-operation and Development PLE part load efficiency PPP Purchasing Power Parities RIVM Dutch National Institute for Public Health and the Environment

(Rijksinstituut voor Volksgezondheid en Milieu) RNPE Relative Net Plant Efficiency RPU rates recovered paper utilization rates RWE roundwood equivalents SRES the IPCC’ s special report on emissions scenarios TAR The IPCC’ s Third Assessment Report TD Top-down UNECE United Nations Economic Commission for Europe US United States of America WEU Western Europe YSSP IIASA’ s Young Scientist Summer Program

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