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Dutch energy scenarios evaluatedPruiksma, Bieuwe

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CIO, Center for Isotope Research

IVEM, Center for Energy and Environmental Studies

Master Programme Energy and Environmental Sciences

Dutch energy scenarios evaluated:

Trends and Implications

Drawing lessons from an ex-post evaluation of Dutch

energy forecasts and presenting alternative forecasts

Bieuwe Pruiksma

EES 2013-161 T


Training report of Bieuwe Pruiksma

Supervised by: Dr. R.M.J. Benders (IVEM)

Prof.dr. A.J.M. Schoot Uiterkamp (IVEM)


CIO, Center for Isotope Research

IVEM, Center for Energy and Environmental Studies

Nijenborgh 4

9747 AG Groningen

The Netherlands

http://www.rug.nl/fmns-research/cio

http://www.rug.nl/fmns-research/ivem

ACKNOWLEDGMENT

This report is my training thesis for the master Energy & Environmental Sciences of the Univer-sity of Groningen. It took me a lot of effort to fulfill this thesis, because meanwhile I also wrote and finished my master thesis Economics. Consequently, it was sometimes difficult to focus and it took more time to complete this training thesis. In the first place, I really want to thank my supervisor dr. Rene Benders, not only for helping me regarding the subject, but also for his sup-port to finish this research. I want to thank my second supervisor, prof. dr. Ton Schoot Uiter-kamp, for his comments and for the lively student discussion sessions.

Since I did my bachelor at another faculty, I want to thank the rest of the IVEM staff for their lectures and education. I always had interest in environmental studies and I am pleased that I could do this master. I like the multidisciplinary approach and hope to finish the master soon.

TABLE OF CONTENTS

SUMMARY ........................................................................................................................ 5

SAMENVATTING ...............................................................................................................6

1. INTRODUCTION ........................................................................................................... 7

2. LITERATURE OVERVIEW .............................................................................................9

2.1 ORIGIN AND USE OF FORECASTING .................................................................................................... 9 2.2 ORIGIN AND USE OF ENERGY FORECASTS .......................................................................................... 10 2.3 ASSESSMENT OF ENERGY FORECASTING MODELS .............................................................................. 11 2.4 VARIOUS TYPES OF ENERGY FORECASTING MODELS .......................................................................... 13 2.5 PROBLEMS IN FORECASTING ............................................................................................................ 14 2.6 EX-POST EVALUATIONS OF ENERGY FORECASTS ............................................................................... 15 2.7 GENERAL AND ENERGY FORECASTING LESSONS ................................................................................ 18

3. PAST DUTCH ENERGY STUDIES ................................................................................. 21

3.1 HISTORICAL DUTCH ENERGY USE .................................................................................................... 21 3.2 DUTCH ENERGY POLICY .................................................................................................................. 23 3.3 OVERVIEW PAST DUTCH ENERGY STUDIES ...................................................................................... 24 3.4 COMPARISON WITH ACTUAL DEVELOPMENTS .................................................................................... 27

3.4.1 Total Energy Use ............................................................................................................ 28 3.4.2 Energy Use per Sector .................................................................................................... 30 3.4.3 Energy Use per Source .................................................................................................... 31 3.4.4 Assumptions ..................................................................................................................... 31

3.5 TRENDS ......................................................................................................................................... 33 3.6 LESSONS ........................................................................................................................................ 34

4. CURRENT DUTCH ENERGY STUDIES ......................................................................... 37

4.1 FORECASTING SCENARIOS ............................................................................................................... 38 4.2 BACKCASTING SCENARIOS ............................................................................................................... 39 4.3 OVERVIEW CURRENT SCENARIOS .................................................................................................... 40 4.4 CURRENT SCENARIOS ANALYSIS ....................................................................................................... 41

5. ALTERNATIVE FORECASTS ........................................................................................ 43

5.1 TOTAL ENERGY USE ....................................................................................................................... 43 5.2 RENEWABLE ENERGY ..................................................................................................................... 45

5.2.1 Historical Developments ................................................................................................. 47 5.2.2 Marchetti Cycles ............................................................................................................. 49 5.2.3 Transition Theory ........................................................................................................... 50

5.3 COMPARISON OF ALTERNATIVE FORECASTS AND SCENARIOS ............................................................. 52

6. CONCLUSIONS ........................................................................................................... 53

REFERENCES.................................................................................................................. 55

APPENDIX A: HISTORICAL GLOBAL ENERGY USE ......................................................... 65

APPENDIX B: OVERVIEW OF PAST DUTCH ENERGY STUDIES....................................... 67

APPENDIX C: OVERVIEW COMPARISON FORECASTS .................................................... 69

APPENDIX D: GRAPHICAL COMPARISON, RELATIVE FIGURES .................................... 71

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SUMMARY

During the 1970s, many energy scenarios about future energy demand and supply appeared. This can be attributed to various developments, like the increased environmental awareness, the oil crisis of the year 1973, and the introduction of the computer which gave a boost to the fore-casting discipline.

Over time, energy models evolved from simple extrapolations studying only a single en-ergy carrier or industry, to ambitious and complex models that draw upon a wide variety of fields. Nowadays, energy modeling is widely practiced and is of great value for policymakers, planners and the private sector in making policy, planning and investment decisions. These decisions also often have long-term consequences.

However, the practice and value of energy scenarios is subject of discussion in scientific literature. Accordingly, it is useful and important to assess energy studies by looking back and to compare the forecasts with reality. Various studies have examined energy forecasts retrospec-tively, and found that almost all long-term energy forecasts about future energy use and the en-ergy mix do not match real developments. These evaluations have been performed for global, European and US energy scenarios, but not for Dutch energy scenarios. Besides, like forty years ago, in recent years many energy scenarios are presented, containing forecasts about future en-ergy use and the energy mix. Therefore, the main research question is:

“What lessons can be learned from the past 40 years of Dutch energy forecasts, and what are the implications of these lessons for current and future Dutch energy scenarios”.

Hence, this research is twofold: after a study of the literature about energy forecasting and the evaluation of these forecasts, the Dutch case is analyzed by comparing the various past energy studies with actual developments. Second, the results of the first part are used to assess current scenarios and to construct alternative forecasts of total energy use and the share of re-newables.

The results of this research can be reflected by two main conclusions. The first main con-clusion is that energy forecasting is difficult. This is comprehensible, since the energy system is part of a larger complex societal system. Moreover, how to improve energy models and whether these are getting better over time is also difficult to indicate. It is recommended to use a simple and conservative approach, to combine scenarios, and to take long-term trends as a starting point. In addition, a standard procedure can be developed to Dutch evaluate energy scenarios.

Similar to other global, EU and US energy forecast evaluations, the comparison of past Dutch energy scenarios with actual developments identified the following: a mean absolute per-centage error of 36%, an average overestimation of total domestic energy use with 28%, large offsetting errors with respect to energy use per sector and per source, the use of wrong assump-tions, and a large influence of prevailing conditions and short-term trends.

The second main conclusion is that the energy transition takes time. The energy system is slowly changing and a lot of factors (social, institutional, technological, environmental and eco-nomic) influence the transition process. Like the ‘old’ energy scenarios were too optimistic about the contribution of renewable energy, the alternative forecasts indicate that also the current en-ergy scenarios are optimistic about the future share of renewables. On average, the alternative forecasts predict 25% renewable energy in the year 2050, which is lower than the 40% of the current energy scenarios.

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SAMENVATTING

In de jaren zeventig verschenen er vele energiescenario’s over de toekomstige energievraag en –voorziening. Dit kan worden toegeschreven aan verscheidene ontwikkelingen, zoals het toege-nomen milieubewustzijn, de oliecrisis van 1973 en de introductie van de computer welke een impuls gaf aan de wetenschap van het voorspellen. Na verloop van tijd ontwikkelden de energiemodellen zich van simpele extrapolaties van een enkele energiedrager of sector, naar ambitieuze en complexe modellen waarbij meerdere invloeden en relaties een rol spelen. Tegenwoordig worden energiemodellen veelvuldig gebruikt en toegepast, en zijn ze van grote waarde voor beleidsmakers, planners en de particuliere sector voor het maken van beleids-, plannings-, en investeringsbeslissingen. Deze beslissingen hebben ook vaak gevolgen op de lange termijn.

Echter, het gebruik en de waarde van energiescenario's is onderwerp van discussie in de wetenschappelijke literatuur. Derhalve is het nuttig en belangrijk om energiestudies te beoorde-len door terug te kijken en de prognoses te vergelijken met de realiteit. Verschillende studies hebben energie prognoses met terugwerkende kracht onderzocht, en geconstateerd dat bijna alle lange-termijn energie prognoses over het toekomstige energiegebruik en de energiemix niet overeenkomen met de actuele ontwikkelingen. Deze evaluaties zijn uitgevoerd voor mondiale, Europese en Amerikaanse energie-scenario's, maar niet voor Nederlandse energiescenario's. Bovendien zijn in de laatste jaren veel energiescenario’s verschenen met daarin prognoses over het toekomstige energiegebruik en de energie-mix, net als veertig jaar terug. Daarom luidt de centrale onderzoeksvraag:

“Welke lessen kunnen worden getrokken uit de afgelopen 40 jaar van de Nederlandse energiescenario’s, en wat zijn de implicaties van deze lessen voor de huidige en toekomstige Nederlandse energiescenario's".

Dit onderzoek is tweeledig: na een literatuurstudie over energie prognoses en evaluaties van deze prognoses, zijn Nederlandse energiescenario’s geanalyseerd en vergeleken met de feite-lijke ontwikkelingen. Ten tweede, de resultaten van het eerste deel zijn gebruikt om de huidige scenario's te evalueren en om alternatieve prognoses te maken van het totale energieverbruik en het aandeel van hernieuwbare energiebronnen.

De resultaten van dit onderzoek kunnen worden samengevat door twee conclusies. De eerste hoofdconclusie is dat het voorspellen van energie ontwikkelingen moeilijk is. Dit is begrij-pelijk, aangezien het energiesysteem onderdeel is van een groter en complex maatschappelijk systeem. Bovendien is het lastig aan te geven hoe energie modellen kunnen worden verbeterd en of deze beter zijn geworden na verloop van tijd. Het wordt aanbevolen om een eenvoudige en conservatieve benadering te gebruiken, om scenario’s te combineren en om lange termijn trends als uitgangspunt te nemen. Daarnaast zou een standaard procedure ontwikkeld kunnen worden om Nederlandse energie scenario’s te evalueren.

Net als bij andere mondiale, Europese en Amerikaanse evaluaties van energiescenario’s, leidt de vergelijking met de actuele ontwikkelingen tot de volgende constatering: een gemiddelde absolute procentuele afwijking van 36%, een gemiddelde overschatting van het totale binnen-landse energieverbruik met 28%, grote elkaar opheffende afwijkingen met betrekking tot het energiegebruik per sector en per bron, het gebruik van verkeerde veronderstellingen, en een gro-te invloed van heersende omstandigheden en korte termijn trends.

De tweede hoofdconclusie is dat een energietransitie tijd vergt. Het energiesysteem verandert langzaam waarbij veel factoren (sociale, institutionele, technologische, ecologische en economi-sche) invloed hebben op het overgangsproces. Net als de 'oude' energiescenario's te optimistisch waren over de bijdrage van hernieuwbare energiebronnen, duiden de alternatieve prognoses aan dat ook de huidige energiescenario's optimistisch zijn over het toekomstige aandeel van her-nieuwbare energiebronnen. Gemiddeld voorspellen de alternatieve prognoses 25% hernieuwbare energie in 2050, wat lager is dan de 40% van de huidige energiescenario’s.

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1. INTRODUCTION

The awareness about the environment and its vulnerability took shape about forty years ago (Kemp, 2004). One of the most famous publications from this period is the book Limits to Growth by Meadows et al. (1972). This study discussed the potential material fate of mankind and its message was that, unless action was taken, the human population and its economic sys-tem would collapse within 100 years. Turner (2008) finds that data1 of the years 1970-2000 matches with the standard scenario of Limits to Growth, which in the end results in a global col-lapse in the middle of the 21st century. In the end, the underlying cause of this disaster is human activity, leading to disruption and deterioration in the environment. Crutzen (2002) even states that nowadays man is a geological force of importance and that we live in the ‘anthropocene’.

The increased environmental awareness and the related publications in the 1970s led to much concerns about the stock of fossil fuels to supply the world with energy. The oil crisis of the year 1973, sparked by political events, emphasized and enlarged this concern. As a result, the value of energy was increasingly recognized as a vital component in the social and economic well-being of a nation. Simultaneously, the importance of energy in policymaking became appar-ent and subsequently many energy studies were performed (Hoffman and Wood, 1976). For in-stance, these studies tried to model future energy demand and supply by constructing scenarios for the population increase and economic growth and by mapping fossil fuel reserves. Over time, energy models evolved from simple extrapolations studying only a single energy carrier or indus-try, to ambitious and complex models that draw upon a wide variety of fields.

For one thing, energy concerns led to the emergence of energy modeling and subsequent-ly to the adoption of these activities by various planning authorities to make policy decisions. But the oil scarcity and rising energy prices also led to a greater interest in renewable energy and other energy measures. During the last decades, this interest is growing due to the recognition of the greenhouse effect. In recent years, many (inter)national energy and climate forecasts with a long-term view have been published (Martinot et al, 2007). Besides, more and more govern-ments have high ambitions regarding renewable energy and some have agreed upon clear targets. For instance, the Dutch government wants to realize a 16% renewable energy share of total do-mestic energy use by 2020 and 20% less CO2 emissions compared to 1990, and aims for zero carbon emissions by 2050 (EZ, 2011). The goal of the European Union for 2050 is 80% less CO2 emissions compared to 1990 (EC, 2011), while U.S. President Barack Obama mentioned in the 2011 State Of The Union the target to achieve 80% clean electricity in the US by 2035.

Ultimately, energy modeling tries to deal with global problems like fossil fuel depletion, global warming and environmental pollution. Consequently, like Craig et al (2002) stated, “fore-casts have become an essential tool of modern society” (pp. 85).

However, at least of equal importance is the practice to assess energy studies by looking back and comparing the forecasts with the eventual outcomes. Various studies have examined energy forecasts retrospectively, and found that hardly any long-term energy forecast matches real developments. Forecasts of energy demand and supply as well as of the contribution of the different energy sources often have been wrong and have shown large errors. It is interesting to notice that measures suggested in the 1970s to save energy (like insulation) and to use other en-ergy resources (like wind energy), are still being proposed nowadays. For example, solar and wind energy produced less than 1% of the total primary world energy supply in the year 2008 (IEA, 2010).

Reviewing energy forecasts might lead to a better understanding of the forecasting pro-cess, identify trends and sources of inaccuracies and potentially lead to improvements in projec-tions over time (Bezdek and Wendling, 2002; O’Neill and Desai, 2005; Utgikar and Scott, 2006;

1 Regarding changes in industrial production, food production, pollution, population growth and depletion of non-renewable resources. The standard scenario is without policy changes. See also PBL (2009), pp. 23.

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Winebrake and Sakvza, 2006). Since until now mainly global and U.S. energy forecasts have been assessed, this research ex-post evaluates Dutch energy forecasts. To my knowledge, this is the first paper to address the Dutch case. Besides, this research investigates what the implica-tions are for current Dutch energy forecasts. As such, the main research question is:

“What lessons can be learned from the past 40 years of Dutch energy forecasts, and what are the implications of these lessons for current and future Dutch energy scenarios”. In order to answer the main research question, the following sub questions are formulated:

- Have similar types of evaluative studies been performed in the past that ex-post assess the results of energy-modeling activities?

- What kind of forecasting models can be distinguished? - Which Dutch energy forecasts exist, who is the author, what methodology does the study

follow, is it a supply- or a demand-based model, what data is used, which assumptions are made, and what are the results?

- Which variables will be used to compare the Dutch energy forecasts with actual develop-ments?

- How do the forecasting studies perform with respect to actual developments? - What trends can be identified from these studies, and from the comparison with actual

developments? What are the common (false) assumptions and/or elements, and do these correspond to the causes of forecasting errors found in literature?

- What happens if one extrapolates these trends into the future, and how does this perform compared to current Dutch energy scenarios?

This paper presents an overview of Dutch energy scenarios, where only scenarios with a long-term horizon and quantitative forecasts are selected. The following methods are applied during this research: a review of literature, a review of past Dutch energy scenarios, and a review of cur-rent Dutch energy scenarios. Moreover, this report contains an observational study and a model-ing study. The observational study is to determine errors and trends from the comparison of the past scenarios with eventual outcomes. The modeling study is to present alternative forecasts, which are extrapolations by applying determined trends and using results of the first part. Ac-cordingly, this paper consists of two parts. The first part contains the literature review and the observational study of the various Dutch energy forecasts of the 1970s. The second part presents the modeling study and applies the knowledge obtained to the current Dutch energy scenarios. Alternative forecasts are proposed and provide a descriptive picture of future energy use and the share of renewables.

Two periods of about the same length of Dutch energy forecasts are analyzed: the first runs from about 1970 – 2010, although many studies from the seventies focus on the year 2000. The second period comprises the years 2010 – 2050, for which recent studies are examined that preferably focus on the target year 2050. The reason that only energy forecasts are considered that cover two decades or more, is because the energy system is a slowly moving system. Just and Lave (1979) already recognized that energy decisions are difficult because of the long times involved, knowing that a decade is required to install new electricity generation facilities, several decades are required to develop and deploy new energy sources, and capital equipment has a long working life. Consequently, it is better to consider energy forecasts with a long horizon to discern errors and identify trends of energy forecasts, and to minimize the influence of transient phenomena like economic disruptions and other events.

This paper is structured as follows. Chapter 2 contains a literature overview about the field of forecasting, with the focus on energy forecasting. Chapter 3 describes the various Dutch energy forecasts and presents the results of the ex-post evaluation. Chapter 4 analyzes the cur-rent scenarios, whereupon Chapter 5 comprises the trend extrapolation and discusses the impli-cations for the current scenarios. Finally, Chapter 6 presents conclusions.

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2. LITERATURE OVERVIEW

This Chapter reviews various literature regarding forecasting in general and specifically with respect to energy forecasts. The historical development of the field of forecasting is described, and the reasons why people started making energy forecasts are indicated. The maturation of this discipline led to various types of forecasting models and to their assessment. Comparing forecasts with actual developments resulted in an identification of forecasting problems, but also provided valuable lessons to improve forecasts. All these topics are discussed below in the sec-tions 2.1 - 2.7.

2.1 Origin and Use of Forecasting

Forecasting2 is about methods to find out what the future may hold. The discipline includes the study and application of judgment as well as of quantitative or statistical approaches. Forecasts are often made to explore future values of time-series, but also one-off events and the distribu-tion of events are considered (Armstrong, 1985). Forecasting the future is very difficult, if not impossible. A crystal ball alone is not enough. The Danish physicist Niels Bohr (1885 – 1962) already quoted: “Prediction is very difficult, especially if it's about the future.” 3 However, Granger and Jeon (2007) evaluate the forecast The year 2000 by Kahn and Wiener (1967) and conclude that looking ahead thirty years is a difficult task, but not impossible. Moreover, the authors state that long-run forecasting is a good field to participate in, because it takes many years before the forecasts can be assessed.

Reading this, one might wonder what can be expected from forecasting, and what the value is of certain forecasts. Makridakis (1986) answers “it depends” to the question whether the future can be predicted. Some things can be predicted with a high degree of accuracy, illustrated by the exact timing of sunrise tomorrow or a year from now. Moreover, some events are less pre-dictable or entirely uncertain, like the question when the next recession will hit the economy, or when the next major earthquake will hit Japan.

Makridakis (1986) provides a good overview of the historical development of the field of forecasting. Forecasting is a product of the rapid development of technologies during the (post)industrial eras. The scientific foundations of the field had been laid by the late 1930s. WWII gave a boost to both the theory and practice of forecasting, after which the discipline be-came popular in the late 1960s and 1970s. This also had to do with the introduction of the com-puter, which provided welcome computational power and made it possible to construct more complex (and seemingly more realistic) models of the real world. The popularity of forecasting resulted in commercial success, as business and government organizations started to use these methods, but it also gave rise to unrealistic expectations. The 1970s and 1980s have been a learn-ing ground due to a changing context and led to the assessment of forecasting performance.

2 More terms are used to refer to a forecast, like prediction, projection and scenario. They are used inter-changeably in literature, but can also have different meanings. These terms are often distinguished based on positive judgment (what the future will/could look like) and normative judgment (what it should look like). Similarly, some differentiate these terms based on feasibility, from prediction about a certain out-come with the highest confidence till scenario about a range of future development with lowest confidence (Granger Morgan and Keith, 2008). 3 Equivalent proverbs are known in different cultures, like an old Arabic saying goes: “He who predicts the future, lies, even if he tells the truth.” And, a Chinese adage holds: “When men speak of the future, the gods laugh.”

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2.2 Origin and Use of Energy Forecasts

Humans have been curious about the earth and its physical attributes since at least the times of the ancient Greek. Known pioneering studies about environmental issues already appeared some centuries ago.

Malthus (1798), who described the relationship between population growth and food supply, is usually recognized as starting the debate about the capacity of the Earth to support a rapidly growing population. However, his ideas were not new as this subject was already dis-cussed by the Greek historian Herodotus (484 – c. 425 BCE) (Boersema, 2011), and Hale and Petty also considered the increase of mankind in their essays in respectively 1677 and 1682. Nu-merous publications wrote more specifically about the depletion of natural resources, of which Carlowitz (1713) is one of the first. In his book Sylvicultura oeconomica the cutting of whole for-ests for the mining industry in the state Saxony in Germany is treated, and the concept of sus-tainable yield forestry is introduced. One of the first discussing more explicitly resource pollution was Evelyn (1661) in his book Fumifugium. This is known as the first text about the growing air pollution problem in London.

Because of a growing interest in natural science and philosophy environmentalism really developed in the eighteenth and nineteenth century. This had to do with the increasing human impact on the environment caused by the agricultural and later the industrial revolution (Ponting, 2007), and can be attributed to contributions from different disciplines. Not only sci-entists from fields like natural science, geology and biology (like Darwin) were intrigued by the (changing) environment, but also prominent figures from arts, literature and philosophy (like Goethe). As the magnitude of the human influence became clearer, the concern for the environ-ment increased4. Subsequently, many studies appeared that discussed environmental problems at large5, of which Limits to Growth by Meadows et al. (1972) was one of the most famous and controversial.

Until the early 1970s there was no broad awareness of an energy problem, although Hub-bert (1956) already had published his peak oil theory. But by the start of this decade, a new set of political, economic and social conditions had evolved around the world. The oil exporting coun-tries were able to make the price of oil a political determinant. Subsequently, many energy stud-ies were performed, carried out by different interest groups like research institutes and universi-ties, companies and governments. Samouilidis (1980) even states that the first oil crisis of 1973 fathered the energy modeling discipline, using the argument that crises in the history of man-kind often are accompanied by considerable progress in knowledge. This was especially the case in the USA, about which Greenberger and Richels (1979) said that national problems have a way of stimulating the creation of models. Rath-Nagel and Voss (1981) confirm this, observing an ‘explosion’ in the development of energy models followed after the first energy crisis. The au-thors refer to the reviews of energy models published by the International Institute for Applied Systems Analysis (IIASA) that identified 144 different energy models up to 1976.

During the decades before the first oil crisis, energy models focused mainly on the de-mand and supply of a single energy carrier and were intended to support investment decisions related to the capacity to supply energy. Initially, these forecasts were primarily about the use of coal, as coal was the main energy source (Zijlstra, 1977). Zijlstra (1977) further evaluated some energy prognoses of the EU, of which the first was published in 1953. He found error margins of about 10-40% for time horizons of 10-15 years, and stated that this is explained by false assump-

4 See Paehlke (1997) for a good overview of the maturation of the environmental movement. He identified three distinct phases: the ‘first ripples’ that appeared some 200 years ago, the ‘first wave’ extending from 1968 to 1976 and the ‘second wave’ in the environmental movement covering the period 1986 to 1994. 5 Among others, known publications are Man and nature by Marsh (1864), De zelfmoord der menschheid by Teupken (1945), Our plundered planet by Osborn (1948), De aarde betaalt by De Vries (1948), The population bomb by Ehrlich (1968), Resources and man by Cloud et al (1969), A blueprint for survival by Goldsmith (1972), and Only one Earth by Ward and Dubos (1972).

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tions regarding economic growth and the energy intensity. Later on, these studies focused more on the future use of oil, which was initiated by the US car industry However, most work referred to electricity forecasting because of the high investment costs of building a power plant and the time elapsed before one produces electricity (Samouilidis, 1980). The major criticism regarding these models constructed for industrial purposes was the system boundary. Analyzing only a single fuel or sector looks for partial equilibrium and ignores possible interactions and substitu-tions (Rath-Nagel and Voss 1981).

The limitations of the early models were translated into requirements for newer, broader and more complex type of energy models that were constructed after 1973. These models studied the whole energy system (both supply and demand), and later on also incorporated energy-economy interactions (Rath-Nagel and Voss 1981). Accordingly, these studies became more normative by investigating futures under various assumptions and to reveal insights into rela-tionships rather than giving quantitative point forecasts. Some interpreted this change as a way to become immune from criticism, by hiding behind assumptions and assigning responsibility to the forecasting environment (Kraus,1987).

Nowadays, energy modeling is widely practiced and is of great value for policymakers, planners and the private sector in making policy, planning and investment decisions. For in-stance, these studies provide information about future energy demand and supply and the devel-opment and implementation of energy technologies. Energy models also help to better under-stand the complexity of the energy system, which is an integrated set of technical and economic activities operating within a sophisticated societal framework (Hoffman and Wood, 1976). In addition, Craig et al (2002) summarizes seven uses of long-range energy forecasts: as bookkeep-ing devices, as aids in selling ideas or to achieve political ends, as training aids, in automatic management systems, as aids in communication and education, to understand the bounds or limits on the range of possible outcomes and as aids to thinking and hypothesizing.

The significance of long-term energy forecasts in decision-making and planning is re-flected by the fact that these forecasts often influence decisions that also have long-term conse-quences. Many studies indicate the importance of energy forecasts for authorities dealing with long-term energy planning (a.o. Hoffman and wood, 1976; Greenberger and Richels, 1979; O’Neill and Desai, 2005; Winebrake and Sakva, 2006; Utgikar and Scott, 2006). Moreover, it is also relevant from a transnational perspective, as in the end energy modeling helps to deal with global problems like fossil fuel depletion, global warming and environmental pollution.

2.3 Assessment of Energy Forecasting Models

Generally, it is believed that forecasting can be effective in a predictable environment, because energy models are mainly based on past trends (Samouilidis, 1980). Until the first oil crisis, en-ergy forecasting methods were designed in a relatively stable context, characterized by steady economic growth and low energy prices. However, it was already noted that after 1973 the socio-economic and political context changed significantly. Long established trends started to change and discontinuities became more the rule rather than the exception. The unstable patterns and unpredictability that marked this period resulted in less certain and often wrong energy forecasts. Samouilidis (1980) even stated that energy models are not good forecasting devices and that forecasting fallacies can become a source of amusement.

It can be concluded that subsequently the use of forecasts and the credibility of the disci-pline itself was doubted by various authorities. As a result, existing and common forecasting techniques were reconsidered and others were developed. Thus, both the changing socio-economic and political landscape and the proliferation of energy models in the seventies led to attempts to survey and evaluate these models (Labys, 1982).

From the literature, it seems that after the emergence of energy models and the notion that these should be assessed, models were mainly validated by replicating history. This is prob-ably due to the simple fact that at that time, there was no data available to compare with. Often

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the target years of the forecasts made in the 1970s and 1980s were still about the future, since the standard target year was the year 2000. However, historical validation is not necessarily a strong predictor of the future (Greenberger, 1979; Labys, 1982).

Nevertheless, many authors acknowledged the importance of model assessment to sug-gest directions for future model development. Greenberger (1979) stated that “a full comprehen-sion of the model's properties leads naturally to discovery of ways the model can be revised, corrected, extended, simplified, decomposed, linked with other models, and generally improved and made more useful” (pp. 473). Besides, it is mentioned that model assessment is useful for model valuation and to draw attention to model credibility (Hartman, 1979; Labys, 1982).

Later on, when actual data of the target years became available, scientists were able to compare these with forecasts. Figure 1 illustrates that energy forecasting is complex. It shows four different long-term forecasts of global energy consumption for the year 2000 from the 1970s6, compared with the actual global final energy consumption in the year 2000 (IEA, 2010). The most conservative estimate of WAES (1977a), 357 EJ/year in 2000, is still 23% higher than the actual global energy consumption of 290 EJ/year in the year 20007.

Figure 1: Global final energy consumption forecasts for year 2000 compared with actual consumption.

Besides, numerous authors have discussed technological developments in the energy domain8. Some correctly were conservative about the future contribution of new energy sources and ener-

6 Most studies presented a ‘low’ and ‘high’ forecast (except Theyse and Wart) and are depicted in the figure. 7 Primary energy supply was about 400 EJ in 2000 (IEA, 2010). 8 Already in 1865, Jevons predicted the depletion of UK’s coal reserves and considered the feasibility of alternative energy sources. Furthermore, Jevons (1865) demonstrated that more efficient use of fuel did not result in reduced consumption, which has become widely known as the Jevons paradox (this is a re-bound effect, revisited by Khazzoom and Brookes for a society’s energy use, which is known as the Khazzoom-Brookes postulate (Saunders, 1992). The rebound effect can be divided into a direct, substitu-tion effect, an indirect, income effect and an economy wide effect; condition is an elastic price elasticity of demand). Half a century later, Brender a Brandis (1920) saw not much potential in the exploitation of ‘white’ coal (hydro energy) and ‘blue’ coal (tidal energy), but did expect a lot of solar energy. He stated that

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gy savings. For instance, Over and Sjoerdsma (1974) argued that it is highly unlikely that even a crash solar energy development program based on international collaboration could lead to any large-scale introduction before the end of the century. WAES (1977a) stated that renewable sources other than hydro will not become significant before the year 2000. Moreover, many had too rosy expectations, of which Hoog et al. (1976) and Theyse and Wart (1977) were not even the most optimistic. Both stated that the total contribution of all non-conventional energy sources would not be larger than 10% of the world energy demand in the year 2000, while this turned out to be just a few percent excluding traditional biomass and hydropower (IEA, 2010). These results indicate that evaluating forecasts might be useful. A selection of these retrospective ex-aminations is discussed in section 2.6.

2.4 Various Types of Energy Forecasting Models

Due to research and learning, increased computational power and the availability of data the expertise of energy modeling developed over time. The booming of model development and their examination led to the construction of different kinds of energy forecasting models. As men-tioned before, a distinction can be made between energy models from the ex-ante and ex-post period of the first oil crisis. But there are more ways to classify energy models. One can catego-rize these using various dimensions: e.g. the planning horizon, the geographical scope, the com-plexity, the methodology and their character. These dimensions are discussed below.

The planning horizon of energy models can range from days to weeks, months, years and decades. How to classify them as a short, medium or long term model is arbitrary. Armstrong (1985) argues that the duration of “long-range” depends upon the situation. He favours the defi-nition that long-range is the length of time over which large changes in the environment may be expected to occur. Craig et al (2002) state that they consider an energy forecast as long-term when the time horizon covers two or more decades. The geographical scope can vary from re-gional to national and global energy models, and depends upon the purpose of the model (Hoff-man and Wood, 1976).

The complexity of an energy model can be defined by the scope and the level of detail of the model. The scope of an energy model is determined by its system boundaries. This can range from considering only the energy demand or supply for a single energy carrier of a single energy industry (or sector), to studies of the whole energy system with interacting subsystems, to energy models incorporating interrelationships with the economy, society and the environment 9 (Samouilidis, 1980). The level of detail refers to the number of processes and activities included. Simple forecasts, like trend analysis, often rely on a single indicator and are informed by the as-sumed changes in the indicator during the forecast period. These simple methods do not rely on any theoretical foundation and do not explain demand drivers. On the other hand, sophisticated energy models are based on theory and deploy a lot of variables and relationships and often also involve a time component to incorporate dynamics (Bhattacharyya and Timilsina, 2010).

Various forecasting methodologies and techniques exist, called a ‘considerable tool kit’ by Craig et al (2002). The most commonly used long-term forecasting methodologies fall into six categories: trend projections, econometric projections, end-use analysis, combined approaches, systems dynamics and scenario analysis (Craig et al, 2002). The authors discuss each of these together with their strengths and weaknesses, and illustrate them with examples. Econometric models (‘top-down’ approach) were mainly used to represent the demand side, while process or end-use analysis (‘bottom-up’ approach) mostly for the supply side (Greenberger, 1979).

Energy models can also differ in character. Besides describing the current situation, they are deployed for predictive (‘what is likely’) and/or prescriptive (‘what is possible’) purposes

“we are entering the world of fantasy here” while referring to the amount of solar radiation reaching the earth surface and photovoltaic systems (and to build these in the Sahara). 9 Also called E3 models, incorporating the economy, environment and energy system.

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(Hoffman and Wood, 1976). McDowall and Eames (2006) categorize energy future studies in a similar way, and distinguish between descriptive (including forecasts, exploratory scenarios and technical scenarios) and normative studies (including visions, backcasts and road maps). Craig et al (2002) argue that trend projections and econometric methods are best to determine what is likely, whereas end-use, systems dynamics and scenario analysis are most useful to assess what is possible.

2.5 Problems in Forecasting

Various problems are encountered during the forecasting process. Energy forecasting is based on the use of models, which are by definition simplified and imperfect representations of a part of reality. Models try to replicate the essential structure of a real world system and allow to investi-gate its properties. As a result, errors cannot be avoided, in the sense that model outcomes devi-ate from actual developments.

Makridakis (1986) summarized the typical forecasting errors from a large empirical re-search of forecasts from different fields of study. The author presents the mean absolute per-centage error (MAPE) of forecasts, which vary by the time period of data and the forecasting horizon. Forecasting just six years ahead entailed a MAPE of 25%. On average, forecasting as far as 18 periods of time into the future involved a MAPE of 30%. Especially complex and dynamic world systems show large deviations over a longer period of time, since discontinuities and dis-ruptive events that can hardly be modeled are common phenomena. Related to this problem is the recognition and the treatment of uncertainty in key parameters of an forecasting model. Building large models with many interrelated variables to represent and simulate complex sys-tems does not by definition render better results than simple models (Smil, 2000).

The energy system is such a complex system. Energy forecasting models cannot keep pace with the long-term evolution of the real world since energy forecasting is not an exact sci-ence. This is because the energy system is part of the societal system, and is not ruled by univer-sal laws. Besides, the concept of bounded rationality might play a role here (Simon, 1956). This theory implies that the rationality of individuals is limited by the information they have, by their cognitive limits and by the time they have to make decisions. In this respect, Forrester (1971) argues that people have trouble understanding and managing complex systems, and claims that these tend to behave in the reverse of the way human intuition would predict.

However, even in a stable context and for short time horizons energy forecasting models can produce outcomes that differ from reality. This deviation can have several causes, of which the most common are:

- The formulation of unrealistic and incorrect assumptions (Samouilidis, 1980). Sweeney (1980) states that the forecasting accuracy of any model is no better than its assumptions.

- The use of wrong and/or inaccurate data as input for a model (Sweeney, 1980; Rath-Nagel and Voss, 1981);

- The optimism bias. Samouilidis (1980) argues that there is evidence to suggest that mod-els are better in forecasting than humans, being biased forecasters, although the same humans construct these models. Weinstein (1980) demonstrated that people possess and exhibit a systematic tendency to be overly optimistic about the outcome of planned ac-tions, and tend to underestimate uncertainties. This includes overestimating the likeli-hood of positive events and underestimating the likelihood of negative events. Such un-realistic optimism10 may influence the design of forecasting models. For instance, one can

10 Other biases and heuristics in risk assessment may also play a role, such as the primary bias, the gam-bler’s fallacy and the availability heuristic. The primary bias (Lichtenstein et al, 1978) states that people overestimate small probabilities and underestimate large ones. The availability heuristic (Tversky and Kahneman, 1973) implies that people base their judgment about the probability of an event on the ease of

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be too optimistic about the introduction of new energy technologies, while underestimat-ing uncertainties and discontinuities.

- The self-destroying prophecy. It is argued that the internal dynamics of forecasting may lead to its own failure, because measures will be taken when problems are predicted. This reflexive nature of forecasting means that the forecast itself may trigger policy action, leading to shifts in energy production and consumption (Kraus, 1987; Craig et al, 2002; Winebrake and Sakva, 2006).

In addition to the optimism bias of individual modelers and the narrow view these forecasters often adopt with respect to possibilities for change, the external environment of modelers also plays a role in designing forecasting models. While forecasts are often presented as completely rational, objective and value free, every human operates from a set of basic values (Schwartz, 1992) and is influenced by its (scientific) environment. Regarding this, Kraus (1987) states that scientists do not work in a vacuum or isolation but “in a world of paradigms”, which influences the way modelers see the world and thus the design of their models. Moreover, it should also be noticed that energy forecasts are often influenced by sponsoring institutions and interpreted differently by various stakeholders (Craig et al, 2002). Besides, energy forecasts are often used as aids in selling ideas and for partisan purposes by various industrial, political and administrative interests (Midttun and Baumgartner, 1986)11. Craig et al (2002) provide some nice examples, among others about former president of the US Nixon, who announced ‘Project Independence’ within a month of the first oil embargo. This (utopian) energy plan claimed to lead to the reduc-tion of U.S. oil imports to zero by 1980, but was in fact released to create support for policy rea-sons.

2.6 Ex-post Evaluations of Energy Forecasts

Like making forecasts, improving them is difficult. However, the failure to acknowledge imper-fections in forecasting can lead to misjudgments and thus poorly defined policy (Craig et al, 2002). Consequently, examining forecasts in retrospective is an useful task. Below some ex-post evaluations of energy forecasts are discussed and it is investigated whether the assessed forecasts reflect some of the problems identified in the previous section.

This paper already referred to Smil (2000) and Craig et al (2002). Both studies also re-flected on looking far ahead in the energy domain. After the discussion of what these two studies did, an overview is given of other ex-post evaluations.

Smil (2000) examines five distinct areas of long-range energy forecasts: major energy conversions, primary energy requirements, sectoral needs, depletion of energy resource and en-ergy substitutions. Given some nice examples of wrong predictions in these five areas12, the au-thor is pessimistic about the use of long-range energy forecasts. He illustrates this with stating that these forecasts are no more than ‘fairytales’, that these “have missed every important shift of the past two generations”, and that these show a “remarkable extent of individual and collective failure”. However, Smil (2000) also argues that this is not the same as advocating a complete

imagining similar events. Finally, the gambler’s fallacy (Burton et al, 1978) holds that people underesti-mate the chance of a similar event. 11 See for instance Shell Venster (May, June 2011, pp. 11): “Scenarios may be used as communication plat-forms to commit ‘stealth advocacy’, which possibly opens otherwise closed doors.” 12 About overestimations by more than 100% of U.S. primary energy consumption in 2000; about overes-timating the growth of electrical generating capacity in the 1970s and 1980s; about the WAES statement that “the supply of oil will fail to meet increasing demand before the year 2000”; about panick at the U.S. Central Intelligence Agency as the CIA concluded that “the world can no longer count on increases in oil production to meet its energy needs”, and argued for a quick (a matter of months!) transition to alternative energy sources; and finally about wrong forecasts of the contribution of renewables in the year 2000.

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denial of looking into the future. Instead, he recommends the use of scenarios, in order to antici-pate likely realities and to deal with uncertainty, unanticipated events and discontinuities.

Craig et al (2002) examine retrospectively forecasts of energy use for the United States that focus on the year 2000, and consider only those studies with a long-term horizon of two decades or more. They find that most forecasts were systematically too high and underestimated uncertainties, and illustrated this with the failure to foresee the increasing energy efficiency after the first oil crisis. The authors performed this review because of the growing interest in (long-term) global warming forecasts.

One of the most extensive studies conducted within this field is the assessment by Bezdek and Wendling (2002). In hindsight, the authors undertook a careful review of studies that at-tempted to forecast long-term energy developments for the United States and the world. They identified over 100 studies conducted by a variety of organizations between 1952 and 2001, of which 49 are systematically analyzed. By comparing the forecasts with actual developments, it was found that many of the forecasts were inaccurate. The aim of the authors was to discover the main errors made, what lessons could be learned, and what the implications would be for fore-casting. They observed:

- That many forecasts consistently underestimated the size of world energy resources; - That most forecasts underestimated the role of prices and the adaptability of markets and

wrongly assumed long-term changes in population’s behavior; - Too much optimism about innovation of energy technologies, and too much focus on in-

stitutional and political barriers instead of on economic barriers; - Underestimation of unanticipated events.

A number of evaluative studies focus on energy forecasts particularly for the U.S. O’Neill and Desai (2005), Winebrake and Sakva (2006) and the Energy Information Administration (EIA) itself have assessed the accuracy of projections of U.S. energy consumption (the Annual Energy Outlook, AEO, produced by the EIA) over the period 1982–2000. However, these forecasts are not defined as truly long-term according to Craig et al (2002). Instead, they are described as me-dium-term projections, and reflect a period which has been uninterrupted by major crises in the (inter)national energy system (O’Neill and Desai, 2005).

O’Neill and Desai (2005) applied an error decomposition technique to analyze the AEO errors. They calculated the percentage error, absolute percentage error, mean percentage error, and mean average percentage error for energy consumption, GDP growth, and energy intensity for all AEOs available. The authors found that energy consumption projections have tended to underestimate future consumption. Although the average error was only 4% for projections 10-13 years into the future (which is not large given the typical level of forecast accuracy), it was determined that this was masked by much larger, offsetting errors (about 15%) in the projection of GDP (consistently too high) and energy intensity (consistently too low).

Winebrake and Sakva (2006) also adopted an error decomposition method, but they dis-aggregate errors by the major energy sectors commerce/services, industry, residence/households and transport. They conclude that using total energy forecast errors to judge the forecasting quality is misleading. This is because the authors determined that low, aggregate forecast errors conceal much higher sectoral errors that cancel each other out. The industrial sector was overes-timated, while the transportation sector was underestimated (causes not indicated).

The EIA13 not only produces projections (they outline that it are not statements of what will happen but of what might happen) of energy supply and demand each year in the AEO, but also publishes since 1996 the AEO retrospective review (EIA, 2009). This report contains a com-parison between the reference case projections in previous editions of the AEO and realized en-

13 See the website http://www.eia.doe.gov/analysis/reports.cfm. The EIA is the statistical and analytical agen-cy within the U.S. Department of Energy (DOE) and works with an budget of $111 million (2010), of which about 10% is devoted to the National Energy Modeling System (NEMS) that produces the AEOs.

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ergy outcomes, using the average absolute forecast error14. The projections for the total energy consumption with the longest time horizon are those from the years 1986 (for year 2000) and 1991 (for year 2008) and have a forecast error of respectively -8.9% and 5.4%. It is striking that the forecast errors of total energy consumption have been mainly negative until the year 2000 (underestimation), and largely positive afterwards (overestimation).

Nevertheless, the AEO review recognizes that even short-term projections have been wrong. Generally, energy consumption quantities are less volatile and thus forecasted with greater accuracy than energy prices. Furthermore, while acknowledging that external factors like severe weather (e.g. hurricanes), economic events or other disruptions have impact on the pro-jections, these are not anticipated in the AEO.

Pilavachi et al (2008) is one of the first to consider an ex-post evaluation of the results of energy-modeling activities carried out at the European Community level. This paper assesses the “Energy 2000” study from 1985 that combined the demand side model Modèle de prospective de la demande énergétique à long terme (MEDEE) and supply side model Energy flow optimiza-tion (EFOM) and constructed future energy consumption scenarios for the year 2000. The as-sumptions and results are investigated and compared with real statistical data, distinguished by energy source and by country. Population figures were correctly predicted, but average economic growth was slightly overestimated. Although the overall forecast error is small, the authors con-clude that it is difficult to predict assumptions on political decisions, economic incentives and social behaviors in modelling exercises.

Linderoth (2002) assesses forecasts of energy consumption included in the annual publi-cation Energy Policy of IEA Countries. The author compares the forecasts concerning the years 1985, 1990 and 1995 with actual values, and computes the (average) forecast errors and root mean square of forecast errors for total primary energy consumption and energy consumption by energy source, sector and country. The main conclusion of this paper is that observed forecast errors are primarily due to inaccurate growth rate expectations. Furthermore, like Winebrake and Sakva (2006), it was found that significant cancellation effects by sector led to a small fore-cast error for total energy consumption.

The last energy forecast assessment discussed here is the one by Utgikar and Scott (2006). The authors examine the possible reasons of inaccuracy of an energy forecast study con-ducted in 1974 (Smil, 1974). However, this forecast is rather different than many others that are evaluated in retrospective, since it used the Delphi technique15. This study provided predictions for the development of nuclear energy, fossil energy, energy transmission, environmental effects and renewable energy in the years 1985 and 2000. Utgikar and Scott (2006) found that the pre-dictions of the study were highly optimistic and that most of them were not and will not be real-ized. The authors give four possible reasons which might explain the discrepancy: use of improp-er forecasting techniques, (underestimating) technological barriers, and (overlooking) socio-

14 The average absolute forecast error is computed as the simple mean, of all the absolute values of the percent errors, expressed as the percentage difference between the reference case projection and actual historic value, shown for each AEO, for each year in the forecast, for a given variable. This method implies that the average absolute forecast error of the projected total energy consumption AEO1982 – AEO2009 is the absolute average of all forecasts with respect to total energy consumption between the years 1982 and 2009. As a consequence, for instance also the forecasts of total energy consumption of the year 1984 for the year 1985 is included, and the forecast of the year 2007 for the year 2008. The average absolute fore-cast error for total energy consumption is 2.8% over the period 1982 – 2008. One can discuss the signifi-cance of this method, since forecasts only one or a few years ahead are generally more precise in the slow-moving energy system than 10 years or longer ahead. 15 See Wikipedia.org > Delphi method: The Delphi technique is an interactive forecasting method that relies on a panel of experts who are queried about an issue using structured communication. It was devel-oped at RAND corporation in the 1950s when the shortcomings of traditional forecasting methods in the social sciences became apparent. Originally, at the beginning of the Cold War the intention of developing this method was to be able to forecast the influence of technology on warfare.

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political and economic considerations. The final comment by Utgikar and Scott (2006) is that understanding these factors, of which economic considerations may be the most important, will lead to more effective energy forecasting capabilities.

It can be concluded that the above mentioned ex-post evaluations correspond to a large extent with respect to the problems identified in section 2.5. The assessments observed that fore-casters have used wrong data, formulated wrong assumptions and have been too optimistic about various developments. Secondly, small overall forecasting errors can be masked by larger offsetting errors. Finally, the role of uncertainty and unanticipated events is often underestimat-ed or ignored16. In the next section it is investigated whether forecasting lessons from literature deal with the identified forecasting problems.

2.7 General and Energy Forecasting Lessons

Although forecasts often do not match reality retrospectively, they can still serve as a guide for policy action. However, the problems identified in the previous sections indicate that there is room for improvement. A number of forecasting lessons can be derived from literature.

The forecasting expert J. Scott Armstrong17 formulated as much as 139 principles to summarize knowledge about forecasting. The application of these is conditional on the charac-teristics of the situation (Armstrong, 2001). Although it is not relevant to discuss all principles18, Armstrong derived some generalizations from empirically-based comparisons to improve the forecast accuracy (Armstrong, 2005). These generalizations correspond to the conclusions of Makridakis (1986), who performed a large empirical study and reviewed the performance of var-ious forecasts using different methods. The most important lessons are:

- Start with the situation and develop a realistic representation; match the forecasting method to the situation.

- Use domain knowledge from people with experience in the domain; use causal models when good information is available.

- Structure the problem, for instance by breaking it down to manageable pieces. - Use simple quantitative models, as statistical sophistication does not seem to improve

forecasting and complex models are often misled by noise in data. - For longer horizons, it is wise to dampen the trend extrapolation. - Combine forecasts, especially when different forecasting methods are available. Contra-

dictory results can exist, indicating that a specific method is not superior.

16 Comparing energy forecasts with eventual outcomes is often used and relatively easy to determine the performance of these forecasts. However, some criticize the concept of forecast error. Lady (2010) states it is not a proper method because one can expect beforehand that assumptions regarding current laws, regu-lations, policies and technological trends may not hold, due to the influence of geopolitical factors. Kraus (1987) also describes limitations of comparing predictions with reality, like forecasting conditions. Instead he proposes to use the degree to which certain goals are attained instead, assuming that a forecast is pro-duced for some decision purpose. Craig et al (2002) state that long-term energy forecasts are particularly useful for current users, and that its success depends on the intended use of the forecast. They provide four options to examine whether a forecast is successful: if it helps energy planners, if it influences the perceptions of the public or the energy policy community, if it captures the current understanding of un-derlying physical and economic principles, or highlights key emerging social or economic trends. 17 J. Scott Armstrong who works in this field of study since the 1960s created the forecasting principles website (www.forecastingprinciples.com). He is a founder of the Journal of Forecasting, the International Journal of Forecasting, and the International Symposium on Forecasting, and editor of Principles of Forecasting: A Handbook for Researchers and Practitioners. 18 An interesting lesson that Armstrong (2001) concludes with is not one to help forecasters improve fore-cast accuracy, but one that deals with legal aspects of forecasts. He argues that his principles might also protect the forecaster, as some have successfully sued forecasters by showing that they did not adhere to best practice.

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- Methods that give equal weight to all data are less accurate than methods which give more weight to more recent observations.

- Be conservative in case of uncertainty. The above stated list indicates that for long-range forecasting often a simple and conservative approach based on long-term trends is advisable. Granger and Jeon (2007) find that long-term forecasting is likely to be dominated by the simple linear and exponential trend curve, and that the occurrence of future major breaks is the main reason that long-term forecasts are of poor quality.

The lessons try to address the aforementioned problems, although this is not a guarantee that future forecasts are better or will not be wrong. Where Craig et al (2002) conclude that in significant ways long-term forecasts are getting better, others argued that forecasts do not im-prove over time and that the discipline does not show learning (Ascher, 1978; O’Neill and Desai, 2005; Winebrake and Sakva, 2006). Related to this, Smil (2000) states that lessons arising from failures of long-range energy forecasting are largely ignored. Some authors go even further, like House (1979) who argues that policy models are useless to foretell the future, and Smil (2008) who mentions that “long-range energy forecasts are no more than fairy tales” (pp. 154). In this respect, Craig et al (2002) find that long-range energy forecasts, although useful for several ap-plications, are not validatable19.

But Craig et al (2002) do provide lessons particularly for forecasts with longer horizons in the energy domain. Their 10 main insights are:

- Document assumptions; - Link the model design to the decision at hand; - Beware of obsession with technical sophistication; - Watch out for discontinuities and irreversibility; - Do not assume fixed laws of human behavior; - Use scenarios; - Use combined approaches; - Expect the unexpected and design for uncertainty; - Communicate effectively; - Be modest.

Comparing this list with the general forecasting lessons of Armstrong and Makridakis, one can observe that these are quite similar. In addition to the plea for a simple and conservative ap-proach and to deal with uncertainty, both Armstrong (2005) and Craig et al (2002) stress that forecasts should be combined. This is effective when forecasts from different groups using a vari-ety of forecasting methods are available. Furthermore, forecasts should be presented as scenari-os20 to prepare actors for undesirable outcomes (Armstrong, 1985 and 2001), and to avoid new misses in long-range energy forecasting (Smil, 2000).

19 Craig et al (2002) refer to Hodges and Dewar (1992) who distinguish between validatable and nonvali-datable models. Validatable models have the potential to yield predictions of the future in which one can have high confidence, whereas nonvalidatable models are likely to have low precision and unquantifiable errors. Validatable models should meet the following criteria as characterized by Hodges and Dewar (1992): they must be observable; they must exhibit constancy of structure in time; they must exhibit con-stancy across variations in conditions not specified in the model, and; they must permit collection of am-ple and accurate data. Long-range energy forecasts do not meet the second and third criteria and thus are not validatable. 20 Kraus (1987) mentions that Helmer and Rescher (1959) have built the theoretical foundation of the idea of scenarios, and that Kahn and Wiener (1967) developed the original notion of ‘scenario’. According to Kahn and Wiener (1967), a scenario comprises the description of one or more hypothetical chains of

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events, which results from the following questions: (1) How does a hypothetical situation come about, step by step (rational validation)? (2) Which alternatives are possible at each stage (conditionality)?

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3. PAST DUTCH ENERGY STUDIES

This Chapter first presents a brief overview of the Dutch energy situation, after which the various past Dutch energy scenarios are described. Subsequently, the results of the comparison of the scenarios with actual developments and eventual outcomes are described.

3.1 Historical Dutch Energy Use

Long ago, the only energy source humans utilized was manpower and the muscular strength provided by animals. Later, humans started to use a form of renewable energy after they over-came their fear of fire and found out how to make fire. Hunters and gatherers burned wood for lightning, heating, to prevent danger, for hunting and to cook food21, but also as expression of culture. During the Neolithic revolution (about 10,000 – 5,000 BCE), humans started to settle and introduce agriculture by domesticating plants and animals. Via the process of learning by doing, humans gained technical insight and started to develop various tools, which enabled the use of other energy sources. The invention of the watermill and windmill in the last few centuries BCE led to the adoption of water and wind power, although wind power was already in use for sailing (Ponting, 2007).

From the 14th century onwards, in The Netherlands windmills were used mainly to drain wetlands (besides for shipbuilding). This was also applied to lakes created by the digging and later dredging of peat, which was the most important source of fuel since the Middle Ages, and led to the development of the Dutch ‘polders’. Cutting peat and harvesting timber for energy (used as domestic fuel but also for industry like mining and iron and salt production) are largely responsible for the typical Dutch landscape. At the beginning of the 19th century the need for fos-sil fuels increased after the invention of the steam engine. Initially, primarily peat, firewood, lig-nite and coal was used. In the 20th century, the gas and electricity infrastructure was built up. Most cities had their own power stations, which later were removed when these networks got connected. After the introduction of the internal combustion engine the demand for oil starts to increase, but until WWII cars were scarce in the Netherlands (Zijlstra, 1977).

In the second half of the 20th century, Dutch energy consumption increased due to the post-war reconstruction, ongoing industrialization, economic growth and demographic devel-opments. In 1959, a natural gas field was discovered in Slochteren in the province of Groningen. The discovery remained unnoticed for about one year, and it took some years to establish that this field contained a large amount of natural gas (Verbong et al, 200122). Subsequently, the gov-ernment started an utmost active exploitation policy by supplying cheap natural gas to house-holds, industries and foreign countries. The reason behind this policy was that the state feared ending up with a ‘dead’ stock of natural gas, given the developments of other energy sources like nuclear energy (VDEN, 1980). The first nuclear power plant was entered into use in 1969 (Dodewaard), the second in 1973 (Borssele). At the time, it was believed that nuclear energy would play a large role in Dutch future energy supply, especially after the first oil crisis23. There

21 First evidence of cooked food goes back some 1.9 million years. Widespread use of fire started around 50 to 100 thousand years ago, and interestingly, the first known symptoms regarding air pollution are also from this period. See http://en.wikipedia.org/wiki/Fire#Human_control; or Bowman et al (2009). 22 Verbong et al (2001) provide an extensive overview of the history of Dutch energy use and the role of renewable energy. 23 However, already in 1957 (after the Suez crisis of 1956 which already indicated the energy dependency, but before the discovery of domestic gas and the availability of cheap oil), the CPB forecasted that in 1975 about 50% of all electricity would be produced by nuclear power plants (CPB, 1957).

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were even plans for over thirty nuclear power plants, but the position towards nuclear energy changed due to the rise of environmentalism (Verbong et al, 2001)24.

The energy crises of the 1970s and 1980s led to the notion that fossil fuel reserves are fi-nite, encouraged measures to save energy and stimulated the development to harness renewable energy. Figure 2 shows the development over the years of Dutch energy consumption per energy source:

Figure 2: Historical energy use in the Netherlands in MJ per capita per day25

The following can be observed from figure 2: the initial prevalence of renewables (like wood, but also the use of windmills) and peat until the 20th century; the entrance and present dominance of fossil fuels; and the current marginal contribution of renewable energy. This picture largely cor-responds with global energy use regarding the energy mix (see appendix A). Moreover, the enormous increase in Dutch energy use per capita from 1960 till 1980 is striking, and the influ-ence of war and energy and economic crises is noticeable. The development of energy use per sector in the Netherlands is depicted in figure 3:

24 The role of nuclear energy in The Netherlands and the building of new nuclear power plants was dis-cussed in the early 1980s during the ‘broad societal discussion’ on nuclear energy (SMDE, 1984). The gov-ernment did not agree with the majority of the people who were against more nuclear energy, but changed its mind after the Chernobyl disaster. See also http://www.kernenergieinnederland.nl/ and/or http://www.laka.org/index.html. 25 See http://www.deconsult.nl/fr_en_transitie.htm, retrieved January 30, 2012.

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Figure 3: Total and sectoral energy use in the Netherlands, 1960-200326

Again, the large increase in total energy use in the 1960-1980 period is clear, as is the influence of the first energy crisis and the economic crisis of the early 1980s. The energy use increase can be contributed to all sectors, but the energy use of industry almost quintupled due to the growth of the heavy industry. The share of each sector in total energy use is rather constant since the early 1980s, maybe only that of households decreased a bit.

The use of electricity increased significantly after WWII, faster than total energy use. The share of electricity in total energy use increased from about 7% in 1962 (70 PJ of total energy use of 1000 PJ) to about 13% in 2010 (435 PJ of total energy use of 3,492 PJ).

3.2 Dutch Energy Policy

Before the oil crisis of 1973, the objective of Dutch energy policy was to provide a guaranteed and continuous supply for every energy demand, against the lowest possible costs. Initially, it was focused mainly on the exploration and exploitation of domestic resources. Due to the availability of cheap oil and natural gas a long-term energy policy was in fact absent in The Netherlands (Sjoerdsma, 1979)27. After the oil crisis the government realized that fossil fuel reserves are finite and environmental interest groups gained more power, and Dutch energy policy changed. Now the purpose was a guaranteed supply of energy demand, resulting from an efficient as possible energy consumption against the lowest possible social costs, given the context of the (in-ter)national labour and income distribution, ecological boundaries, and safety. The main ele-ments became energy saving, diversification of energy production over various energy sources and suppliers, managing natural gas reserves more from a strategic perspective and the reduc-tion of import dependence (EZ, 1974).

Since the 1970s, the socioeconomic, political and technological landscape has changed considerably. This is partly reflected in current Dutch energy policy. Nowadays, the ob-jective is still to provide a reliable and affordable energy supply, with elements like energy saving. However, more weight is attached to make this energy supply ‘cleaner, smarter and more diversi-fied’. The aim was to realize the energy transition towards a completely sustainable energy sup-ply within 50 years (EZ, 2008)28. The previous government (Rutte I) stated that renewable ener-gy is still relatively expensive and indicated no clear target for 2050. More options towards a carbon poor economy are considered, like emission trading, nuclear energy (“does not lead to CO2 emissions”), CCS and energy saving (EZ, 2011).

26 See http://www.cbs.nl/nl-NL/menu/themas/industrie-energie/publicaties/artikelen/archief/2004/2004-1420-wm.htm, retrieved January 30, 2012. 27 Despite studies that already warned for energy problems, see Uva (1958). 28 See also http://www.rijksoverheid.nl/onderwerpen/energie/energiebeleid-nederland.

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3.3 Overview Past Dutch Energy Studies

Just as energy policy was missing before 197429, there are hardly any energy scenarios from this period besides some incidental extrapolations confined to single fuels. The first known scenario is the CPB forecast of 1957 for the year 1975, which was mainly about coal and underestimated actual primary energy consumption with 70% (Zijlstra, 1977). This was probably due to the lower growth rates before on which the extrapolation was based, due to the discovery of Dutch natural gas and the availability of cheap oil.

One of the first Dutch energy publications addressing the energy problem is Olie op de golven (RPV, 1974). The basic principles of this study were that nuclear energy is unnecessary and unwelcome, to invest in energy saving and efficiency, decrease the use of fossil fuels (using progressive tariffs) and invest in renewable energy sources.

Later on, a lot of energy studies focusing on The Netherlands have been performed in the late 1970s and often appeared in the scientific journal Energiespectrum30. These studies treated both the potentials of energy saving and alternative energy sources. Based on various measures like insulation, heat pumps, CHP, more efficient engines and production processes the energy use of each sector was examined. Besides, the development of primarily solar, wind and nuclear energy was discussed (Energiespectrum, 1977-1981). However, to be able to make a good com-parison between the various energy scenarios and actual developments quantitative studies need to be considered.

Van der Wart (1978) already asked for a systematic comparison of the different energy scenarios and their assumptions to get a good overview of differences and similarities and what causes these. De Man (1987) described most energy scenarios, but obviously could not compare those with actual developments. However, he observed some interesting developments with re-spect to energy forecasting in The Netherlands.

First, three different periods are defined, separated by the first energy crisis and the end of the public energy debate. Before 1974, the few energy forecasts were mainly short term rough extrapolations, based on high growth rates and technological optimism. Between 1974 and 1982, more diverse studies appeared because of the increasing influence of the anti-nuclear opposition. These studies often presented a high and a low forecast for the year 2000 and were based on more sophisticated models and assumed decreasing growth rates. After 1982, the end of the en-ergy (forecasting) debate led to more consensus about the shape of the energy future based on moderate growth rates. De Man (1987) observed that more recent scenarios predicted lower growth of energy use.

Second, there was a predominant role for the CPB in energy forecasting. Although energy forecasts were lacking in the early 1970s, economic forecasting in the Netherlands has a long history and was strongly institutionalized. After the realization of the energy problems, the CPB and its econometric models also were applied to the energy domain.

Third, the energy forecasting establishment in the Netherlands, like in France, Germany and the UK, experienced significant resistance from outside the regular political system. This resistance came from the anti-nuclear and environmental opposition, which challenged the high growth energy forecasts of the early 1970s with energy forecasts that were far below those from government and industry. The establishment wanted to control the variety of information, while the opposition wanted to enlarge it. However, the opposition in The Netherlands had close con-tact with the energy forecasting establishment, which led to a sort of consensus. The result was a compromise that the ecological energy forecast was included as a low scenario in the official en-ergy prognosis, implying convergence of energy forecasts. This raises questions about the au-thenticity of the ‘low’ scenarios (see also Midttun and Baumgartner, 1986).

29 Although power planning was already in general use. 30 Continuation of Atoomenergie en haar toepassingen, from 1977-1992 Energiespectrum, from 1992-1998 Energie- en milieuspectrum, merged into Energieconsulent.

25

It was verified whether these developments can be identified when the various studies are analyzed. All energy studies that are evaluated in retrospective possess the characteristic that their geographic scope is The Netherlands. Besides, the time horizon of all scenarios can be de-fined as long-term, as they look into the future for at least two decades. In this period, large changes in the energy environment can be expected to occur. The various Dutch energy scenarios are from different groups with different interests (business, government, science, political par-ties, interest groups). All available studies are briefly discussed below in chronological order. A concise overview with a characterization of the scenarios can be found in Appendix B.

The first known report describing energy forecasts for The Netherlands for the year 2000 is the first Nota energiebeleid31 (EZ, 1974) written by the government. It presents some extrapo-lations till the year 1985 based on the high growth rates of the decades before, and the initial policy regarding natural gas. Assuming unchanged growth conditions, energy use in the year 1985 will be two and a halve times larger than that of the year 1973. Besides, it is predicted that the contribution of natural gas will decline and that of petroleum will increase significantly. However, the authors recognized that these statements for the future are not realistic. Subse-quently, more realistic forecasts were produced in cooperation with the Dutch CPB (Centraal Planbureau32). These forecasts assumed lower growth rates and incorporated more external in-fluences and developments of that time. This results in a lower total energy use than with un-changed growth, but still about 1.6 times higher than the 1973 value33. The report stated that in the last decades before 1974 few technological developments have occurred in the domain of al-ternative energy sources. Because of this, Dutch energy policy for these forms of energy was un-certain. Therefore the LSEO (Landelijke Stuurgroep Energie Onderzoek34) was installed, which published the first study to be analyzed in this research. However, the Nota energiebeleid (1974) presents some tentative conclusions with respect to alternative energy sources: no significant contribution is expected till the year 1985, whereas solar energy, wind energy and energy from waste can play a substantial role in the year 2000, while nuclear fusion might be a serious option after the year 2000.

The LSEO was established in 1974 to survey energy research in The Netherlands and to formulate a research agenda including some recommendations. Their report Energie 1976 re-flects the results of many different studies, mainly produced by LSEO members, and presents two scenarios for the development of Dutch energy consumption. The LSEO supported both the diversification of energy supply and energy saving, and proposed to establish an energy study centre. In 1976, the ECN (Energieonderzoek Centrum Nederland35) was founded. In fact, this was an expansion of the RCN (Reactor Centre Netherlands) with an energy study centre for more integrated energy analyses.

The energy study by Potma (1977a) intended to present a scenario in which Dutch energy supply was lower and totally secured. This intent corresponds with the later developed ‘Trias Energetica’ and was supported by a.o. the Dutch environmental protection association (Mi-lieudefensie) and the interest group Stop Nuclear Energy. The study is based on The forgotten energy scenario Potma (1977b) and presumes maintenance of employment and welfare and ex-cludes the utilization of nuclear energy. It was one of the first which, besides technical and eco-nomic issues, involved societal problems. Moreover, it considered a different welfare definition

31 Freely translated as Memorandum 1 energy policy. 32 Central Planning Bureau, the Netherlands Bureau for Economic Policy Analysis, one of the three applied policy research institutes of the Dutch government, besides the Netherlands Environmental Assessment Agency (PBL), and the Sociaal en Cultureel Planbureau (SCP). 33 However, in 1973 total domestic energy use was 2,617 PJ, while it was in the year 1985 even lower, 2,558 PJ to be precise. Moreover, consumption of natural gas increased and that of petroleum decreased (CBS Statline). 34 The Steering group for Energy Research. 35 Energy research Centre of the Netherlands.

26

by giving not only attention to production growth but also to the availability of free and envi-ronmental goods. The author thought that the proposed scenario was technically, economically and societally possible, but questioned whether it would be accepted by policymakers. The Pot-ma study was heavily criticized (Boonekamp and Oostvoorn, 1979).

The CPB advises the Dutch government by producing economic analysis and forecasts. To contribute to the exploration of future socioeconomic developments by the WRR (Weten-schappelijke Raad voor het Regeringsbeleid36), in 1977 the CPB for the first time constructed energy scenarios with a planning horizon of 20 years (as before looking ahead 5 years was the maximum). A low and a high scenario of possible developments were considered and were pre-sented at a conference about future Dutch energy supply in 1977 (CPB, 1977). These scenarios are later on used by other organizations.

The energy study by the WAES (Workshop on Alternative Energy Strategies) was pio-neering in the sense that it addressed energy demand and supply of many countries. The authors stated that it “was an experiment in international collaboration”. Moreover, the study also touched upon the global effect of energy use: the issue of the greenhouse gas effect was discussed, and it was stated that the influence of accumulation of carbon dioxide and particulate matter in the atmosphere needed further systematic research. The estimation of energy supply and de-mand for the non-Communist world through the year 2000 took two and a half years of study. Five different scenarios are outlined until the year 1985, of which two are considered in more detail until the year 2000 and computed for The Netherlands (WAES, 1977b; Energiespectrum, 1978, pp. 64)

The WRR provides the government with scientific information on long term social devel-opments and presented in the year 1977 the report De komende 25 jaar37. For this report, the WRR constructed two scenarios in which future social, economic, spatial and environmental developments expected by the committee are described. The scenarios did not anticipate unex-pected, extreme events and the validity of the expectations could not be explained (WRR, 1977).

As a supplier of energy Shell operates in the oil industry and looks forward for strategic planning. In 1978, Royal Dutch Shell stated that they already expected an energy crisis before 1973 (and had warned for it), due to the enormous increase in energy demand at the time38 (Shell, 1978a). Regularly, Royal Dutch Shell presents scenarios which describe a number of pos-sible futures and function as a guide for alternative business strategies. These scenarios are con-structed to make future uncertainties explicit and are updated regularly to take account of recent trends. In 1978, Shell published three different scenarios distinguished by varying social, politi-cal, economic, energy and technological developments: Restructured Business (RB) focuses on prosperity (or Business as it used to be, BU), Restraint as Reaction (RR) (or Realism and Re-straint) on welfare and Frustration and Conflict (FC) on short term tactical considerations (Shell, 1978b). Shell (1983) discussed and evaluated the three socio-political archetypes, RB, RR, and FC, and argues to continue with two newer scenarios: restructured growth (RG) and hard times (HT). This is motivated by Shell using the argument that in the previous years they observed changes in Dutch society supporting economic growth.

The report Gas en electriciteit in Nederland, een toekomstverkenning39 is coordinated jointly by TNO (Toegepast Natuurwetenschappelijk Onderzoek40) and the energy study centre of ECN. This study elaborated on the conclusions of the 1975 TNO report about the potential of hydrogen as an energy carrier after the year 2050, which assumed a shortage of oil and gas start-ing somewhere between the years 2000 and 2050. Two energy scenarios restricted to economic

36 The Scientific Council for Government Policy. 37 Freely translated as The next 25 years. 38 Shell mentions that the ‘oil era’ of sufficient oil reserves is over. 39 Freely translated as Gas and electricity in the Netherlands, an exploration of the future. 40 Netherlands Organization for Applied Scientific Research.

27

and energy developments are described (TNO & ECN, 1979), which largely correspond to those of CPB.

In 1979, the second Nota Energiebeleid41 was presented to the Dutch parliament (EZ, 1979). This report observed that since 1974 the Dutch energy system had not changed signifi-cantly, while the energy context and outlook did and appeared to be structural. As a result, this report emphasized the realization of fossil fuel scarcity, and recommended to address this situa-tion by the formulation of energy policy. Soon decisions were necessary in the light of increasing world energy demand and prices, a relatively energy intensive industry, and because alternative energy sources could be utilized only on the long term. The main message was to implement an extensive energy saving program and diversification of energy sources. In the appendix of the report, two scenarios are presented which are specially produced by the CPB.

The study Energie tot 200042 by Zoutendijk (1979) is produced by the scientific bureau of the Dutch conservative-liberal political party VVD43. The report contains a ‘conservative-liberal’ vision of Dutch energy policy for the 1980s and 1990s and describes two different scenarios.

The association of directors of Dutch electricity companies investigated the future Dutch energy situation (VDEN, 1980). Two energy use scenarios with and without energy saving are discussed, which are mainly based on the economic scenarios of the CPB. The report also pre-sented five other energy scenarios with a different energy mix (mainly gas, oil and diversification with/without nuclear energy).

NCW44 was the Dutch Christian-democratic employers' federation, which merged in 1996 with the liberal labor union VNO45. The NCW wanted to actively participate in the discussion about Dutch future energy supply, because of its value for society and given issues like fossil fuel depletion and increasing demand for energy. Similar to Potma (1977), the NCW thought eco-nomic growth is not the most important goal of a society to aim for, and adopted the view that energy demand does not need to increase. Accordingly, three conservative scenarios are present-ed, based on decreasing economic growth and an extensive energy saving program (NCW, 1981).

The last study that is analyzed is the publication by ESC46 (1983), which presents the four energy scenarios following the public energy debate: a reference scenario, a high and a low sce-nario and a conservation scenario. The economic and energy demand projections of the first three are made by the CPB, whereas the conservation (CE) scenario is developed by the ecologi-cal opposition and evolved from the Potma scenario. The four energy supply scenarios are based on the same linear programming model developed by the ESC.

3.4 Comparison with Actual Developments

The following subsections describe the results of the comparison with respect to total primary energy use, energy use per sector and per source, and regarding the assumptions. Because most scenarios provide forecasts of primary domestic energy use (consumption by all energy users plus energy used for energy transformations), this quantity is considered for the comparative purposes.

The forecast error of past scenarios is analyzed, which simply is the difference between the forecasts and reality. This method is straightforward and transparent. Because total energy consumption forecast errors can mask larger offsetting errors, it is intended to perform an exten-sive analysis of all 29 scenarios. Both the input and output of the various models is compared with actual developments. Constrained by data availability, the assumptions concerning eco-

41 Freely translated as Memorandum 2 energy policy. 42 Freely translated as Energy till the year 2000. 43 Volkspartij voor Vrijheid en Democratie. 44 Nederlands Christelijk Werkgeversverbond. 45 Verbond van Nederlandse Ondernemingen. 46 Energie Studie Centrum of ECN.

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nomic growth, population, number of households, number of cars and energy saving, and the resulting energy demand (also per sector) and supply (also per source) are evaluated.

Appendix C contains a comparison of each scenario regarding the assumptions and ener-gy use per sector and per source with actual developments. Both the forecast and the relative difference with the actual 2000 value is presented.

3.4.1 Total Energy Use

A number of common features can be identified among the various energy scenarios, which largely correspond with the three findings of De Man (1987). Most studies considered two differ-ent scenarios, a ‘high’ and a ‘low’ one to provide an insight into the range of future energy use. The high scenario generally assumed continuing economic growth, while the low scenario mainly assumed lower or even zero growth rates influenced by environmental degradation and natural resource depletion.

One may argue whether the studies are based on energy demand or supply. Most studies are demand-based as the focus was the development of the demand for energy. These studies first considered energy demand (mostly computed per sector based on growth rates and energy efficiency) and assumed accommodating energy supply. Moreover, many studies are based on the economic scenario for energy demand of the CPB and described how energy supply should meet this demand. Energy demand computations were mainly based on (socio)economic as-sumptions about among others economic growth, energy prices and population increase. Energy supply computations were often funded on assumptions with respect to the various energy sources, for instance about Dutch gas policy, the building of nuclear power plants and based on international studies about global reserves of fossil fuels. Most studies mentioned that they were not anticipating big unexpected events.

According to the typology of McDowall and Eames (2006), most scenarios are descriptive (although the studies itself mention that the scenarios are normative), since some scenarios pre-dict likely futures from trends, and because most scenarios both explore the drivers of possible futures and technological possibilities.

As mentioned above, a number of studies are based on the CPB scenarios from 1977 or used them as a starting point, which concerns WRR (1977), TNO& ECN (1979), EZ (1979), VVD 1979, and VDEN (1980).

These scenarios especially correspond with respect to energy demand, which is due to the fact that CPB (1977) contained a detailed socioeconomic scenario. Besides, the CPB discussed economic development with Shell, ECN and the Ministry of Economics. Accordingly, it looks like the different parties have examined and discussed each other’s energy scenarios. This is not nec-essarily wrong, but raises questions about the independence of each study. However, not all as-pects are similar, and projections of energy use differed as can be seen in figure 4:

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Figure 4: Total domestic energy use scenarios in year 2000 of 13 studies and actual value

It can be observed, as might be expected, that almost all scenarios exceed the actual Dutch ener-gy use of 2,482 PJ in the year 1975. But most are also higher than the actual value of 3,067 PJ in the year 2000. In fact, 23 scenarios are higher and 6 lower, with an average energy use of 3,919 PJ. This is 28% higher than the 2000 value, while the mean absolute percentage error is 36%47. It should come as no surprise that five of the six lower scenarios originate from organizations who adopted a different socioeconomic perspective (Potma 1977, NCW 1981 scenarios and the CE scenario of ESC 1983; the sixth is Shell 1983 HT).

Interestingly, later scenarios are closer to the actual value with lower band widths, and in this respect they perform better than the early scenarios. The learning process mainly led to low-er and more conservative scenarios, only the forgotten scenario of Potma is adjusted upwards into the low CE scenario of ESC (1983).

Most ‘high’ scenarios are much too high with an average energy use of 4,798 PJ (which is 58% higher), except those of NCW (1981), ESC (1983) and Shell (1983). Furthermore, most ‘low’ scenarios are also too high with an average energy use of 3,334 PJ (which is 23% high-er), although those from Shell (1978, 1983) almost match actual energy use in 2000.

Four scenarios deviate less than 10% of the actual value of 2000, which are: Shell 1983, HT (5.0% lower); Shell 1978, RR (2.6% higher); ESC 1983, EZ (1.3% higher) and AD (0.5% high-er). Whether the scenarios that are close to the actual value are very good scenarios or suffer from offsetting errors and/or are based on wrong assumptions is described in the next subsec-tion.

47 Most studies also discussed environmental effects, and some (WRR, VDEN, ESC and Shell 1983) pre-sented quantitative forecasts with respect to emissions of sulphur dioxide and nitrogen oxides. Corre-sponding with the overestimation of energy use, it is found that SO2 emissions are grossly overestimated (on average ten times larger, despite the incorporation of emission reduction efforts), while NOx is overes-timated with about 40%.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

PJ

Actual use 1975

1. LSEO, 1976

2. CPB, 1977

3. WAES, 1977

4. Potma, 1977

5. WRR, 1977

6. Shell, 1978

7. TNO/ECN, 1979

8. EZ, 1979

9. VVD, 1979

10. VDEN, 1981

11. NCW, 1981

12. ESC, 1983

13. Shell, 1983

Actual use 2000

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3.4.2 Energy Use per Sector

Figure 5 shows total energy use per sector as far this information is available, including the actu-al values of the years 1975 and 2000:

Figure 5: Total domestic energy use per sector in year 2000, scenarios and actual values48

In general, the sectors industry, households and energy companies are overestimated, with an average difference of respectively 40.3%, 51.2% and 80.0%. The sectors transport and other are generally underestimated, with an average difference of respectively 22.1% and 44.4%. This yields both in absolute and relative terms (See Appendix D).

The scenarios EZ and AD of ESC (1983), although best in overall energy use, show some offsetting errors. Whereas the sector industry closely matches the actual value, the sector transport is about 20% too low, the sector households about 25% too high, the sector other about 50% too low and the sector energy companies about 60% too high.

Again, the very low Potma scenario is striking, but it is interesting to see that this scenar-io overestimated the energy sector. This might be due to the assumption of Potma about increas-ing use of electricity, due to heat pumps and (more) electrical transport.

48 An ‘H’ behind a certain study means ‘High’ scenario, a ‘L’ stands for ‘Low’ scenario. For study 1 and 2 the sector ‘other’ (services, agriculture, fishery, government, and construction) is included in the sector households.

0 1,000 2,000 3,000 4,000 5,000 6,000

Actual values 1975

Actual values 2000

1. LSEO, 1976, L

2. CPB, 1977, H

2. CPB, 1977, L

3. WAES, 1977, C

4. Potma, 1977

8. EZ, 1979, H

8. EZ, 1979, L

12. ESC, 1983, EZ

12. ESC, 1983, IH

12. ESC, 1983, AD

12. ESC, 1983, CE

PJ

Industry Transport Households Other Energy companies

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3.4.3 Energy Use per Source

Figure 6 shows total energy use per energy source as far this information is available, including the actual values of the years 1975 and 2000:

Figure 6: Total domestic energy use per source in year 2000, scenarios and actual values

In general, the sources coal, oil, nuclear and solar and wind are overestimated, with an average difference of respectively 125.8%, 85.2%, 476.2% and 1595.7%. The sources natural gas and other (biomass, waste and heat) are generally underestimated, with an average difference of respec-tively 24.4% and 51.8%. This holds both in absolute and relative terms (See Appendix D).

The scenarios that best predicted total domestic energy use, EZ and AD of ESC (1983), again suffer from significant offsetting errors. Two scenarios from Shell (1978, RR and 1983, HT) seem to be the best with respect to the energy mix.

3.4.4 Assumptions

The finding that the low scenarios were on average even too high indicates that probably wrong assumptions are used. The following assumptions are evaluated: economic growth, population, number of houses and cars and energy saving.

Almost all studies acknowledged that economic growth is the most important determi-nant of energy use. On average, actual economic growth of 2.6% per year is underestimated.

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

Actual values 1975

Actual values 2000

1. LSEO, 1976, H

1. LSEO, 1976, L

2. CPB, 1977, H

2. CPB, 1977, L

3. WAES, 1977, C

4. Potma, 1977

5. WRR, 1977, H

5. WRR, 1977, L

6. Shell, 1978, BU

6. Shell, 1978, FC

6. Shell, 1978, RR

7. TNO/ECN, 1979, H

7. TNO/ECN, 1979, L

8. EZ, 1979, H

8. EZ, 1979, L

9. VVD, 1979, H

9. VVD, 1979, L

12. ESC, 1983, EZ

12. ESC, 1983, IH

12. ESC, 1983, AD

12. ESC, 1983, CE

13. Shell, 1983, RG

13. Shell, 1983, HT

PJ

Coal Crude oil Natural gas Nuclear Solar and Wind Other

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However, the high scenarios assumed too high growth rates (3.1% on average), while the low scenarios too low growth rates (1.6% on average). The first finding may be plausible, but the last needs further investigation, since it is observed that also the low scenarios overestimated energy use.

An explanation might be that the anticipated population increase is large. However, on average population was underestimated with 6.9%, 6.1% for the high scenarios and 8.4% for the low scenarios. Accordingly, the number of houses is also underestimated, with on average 11.6%. Besides, the growth of the number of cars is underestimated with on average 12.3%. Consequent-ly, there must be another explanation for the overestimation of total domestic energy use. This might be found in the observation that most scenarios assumed too low energy savings, where a distinction can be made between a reduction in energy demand (like insulation of houses) and more efficient energy conversion (like more efficient boilers, engines and production processes). On average, total energy savings of about 25% were assumed, while it was in reality about 40%. Retrospectively, energy savings were with circa 2% annually high in the period 1975-1985 (EZ, 1990). This explanation corresponds to the finding of Craig et al (2002) that most long-term en-ergy forecasts were systematically too high because of the failure to foresee the increasing energy efficiency following the first oil crisis. Since 1985, energy savings amount to approximately 1% per year, while the government’s target was and still aims for 2%49. One might hypothesize that energy saving rates were relatively high between 1975-1985 because of various factors: the high growth of energy use before, the realization of finite fossil fuel resources and the economic crisis. Afterwards, energy saving might have been lower due to economic recovery and lower growth of energy use.

Assumptions regarding energy prices, particularly the oil price, are not evaluated due to availability of quantitative information. However, it should be stated that most studies assumed a further increasing oil price, while it in reality did not increase much till the year 2000. If this was known, energy use forecasts of the various scenarios might have been even higher.

It can be concluded that the errors encountered during the comparison of the various Dutch scenarios with actual developments correspond to a large extent with those found in liter-ature described in section 2.6. The mean absolute percentage error is more than 30%, as Ma-kridakis (1986) found for forecasts of at least 18 years ahead. Moreover, all studies show signifi-cant offsetting errors in both energy use per sector and per source.

As for world energy scenarios, the Dutch energy studies suffer from incorrect assump-tions, of which the most important is inaccurate growth rate expectations. Most studies underes-timated global fossil fuel reserves, but this is not visible in the scenarios as it was assumed that there would be no serious supply problems before the year 2000. The impact of events, like in-ternational and economic crises or technological breakthroughs, is also underestimated as all studies indicate that unexpected events will not and cannot be accounted for. However, it is al-most impossible to incorporate this in forecasting models. For instance, the second oil crisis and the economic crises of the early 1980s might have been translated into a lower economic growth rate and higher energy saving assumptions. But it is very difficult to deal with possible future shocks, and may explain why in general more scenarios with different assumptions are con-structed.

49 Energy saving was 0.9% in 2007. Without energy savings over the period 1995-2007, energy use would be about 11% higher in 2007 than actually occurred. Energy saving is based on the change in energy use and by determining the volume and structure effect. Energy saving or efficiency is not the same as energy intensity, as changes in energy intensity can result from both saving and structure effects (ECN, 2001). However, energy intensity also decreased over the period 1975-2000 with about 40%, and is still decreas-ing (IEA, 2009). Energy saving and improvements in energy efficiency are usually interpreted to be the same, although efficiency is expressed in relative terms.

33

It can be argued that the optimism bias is present regarding the future contribution of so-lar and wind energy. Finally, it is hard to prove the self-destroying prophecy, as one may hypoth-esize that the forecasts of high energy use led to energy saving measures.

3.5 Trends

Various trends can be identified from the comparison. Total energy use increased from 2,482 PJ in 1975 till 3,065 PJ in 2000, which implies an average growth rate of about 0.85% per year. Even some ‘low’ scenarios with conservative assumptions overestimated this increase, which might be due to higher than expected energy savings. It is difficult to say whether the observa-tion that later scenarios are closer to the actual total domestic energy use is really due to learning. Of course, energy forecasting methods rapidly developed in the 1970s, but other factors also played a role. First, the time horizons of the later studies were about 5 years shorter. Second, later studies could incorporate more (recent) information and adapt to changing circumstances like the second oil crisis and lower economic growth rates. Finally, De Man (1987) stated that this convergence was partly due to the negotiation processes between the various parties in-volved. The studies (nr. 5 and 7-10 in figure 4) that based energy demand on the CPB (1977) sce-nario can be seen as evidence for this statement. Their scenarios match quite good, where VVD (1979) interestingly focused more on the low scenario. With respect to the energy use per sector and source, this learning process is less clear. Looking at figures 5 and 6, it might be present for energy use per sector but less so for energy use per source.

The sector industry is overestimated for a number of reasons. First, the high growth of the industrial sector of the late 1960s and 1970s, due to cheap natural gas, did not continue (gradual shift of labor-intensive and energy-intensive manufacturing industry to foreign coun-tries). Second, measures to conserve energy and to increase the energy efficiency of production processes probably led to higher than expected energy savings. The same might hold for the sec-tors energy companies and households, despite the fact that in general population and number of houses are underestimated. The sector transport is underestimated because almost all scenar-ios assumed a lower growth of this sector, which is illustrated by the assumptions of the number of cars50. It is difficult to say what caused the underestimation of the sector ‘other’. No clear trends over time are identified with respect to energy use per sector.

The scenarios on energy use per source are in line with the general expectation that the share of coal and nuclear energy would increase because of the future depletion of (in-ter)national oil and gas reserves. However, the scenarios still predicted a large share for oil. This is probably due to the initial underestimation of domestic gas reserves and the sales policy of natural gas. It was also assumed that the contribution of alternative energy sources would re-main limited. Not all studies quantified the use of renewable energy, but they forecasted on aver-age a 5% share (with a range of 0-12%; the highest share in the Potma scenario). Half of this 5% share should come from wind and solar energy, the other half from biomass. However, the actual share of renewables was only 1.7% in 2000, and for solar and wind energy this was even lower (0.1%). This overestimation is not a direct consequence of the overestimation of total energy use, because most scenarios forecasted a relative share of renewable energy.

Some time trends can be observed. Later studies forecast relatively less oil and nuclear energy, and more use of gas and solar and wind energy. This may be due to influence of the envi-ronmental lobby and the start of the broad societal discussion.

It is found that the scenarios with the best forecasts of total domestic energy use (i.e., with the smallest deviation from the actual value), still suffer from offsetting errors and are based on wrong assumptions. Especially economic growth is significantly underestimated by these scenarios. Unfortunately, no study has the ‘right’ assumptions, as it would be interesting to

50 The CPB (1977) scenario, which is used by a number of other studies, also assumed decreasing mileage per car of about 14%, while it increased in reality with about 5% (CBS Statline).

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see how such a study would have performed. Accordingly, it is hard to determine whether studies with relatively good assumptions better predicted actual developments. However, evidence that supports this hypothesis is found by investigating the relationship between the assumptions sep-arately and total energy use. The finding that a higher under- or overestimation leads to a larger deviation of total energy use holds for the assumptions economic growth, population and energy saving, but is less clear for the number of houses and cars.

It is hard to say which type of organization did the best job in forecasting energy use. Again, it turns out that the scenarios Shell 1978 (RR), ESC 1983 (EZ & AD) and Shell 1983 (HT) perform quite well, both with respect to energy use and the assumptions. Consequently, it seems that business (Shell) followed by science produced the best scenarios, implying that utilizing domain knowledge is beneficial.

Moreover, it can be expected that different types of organizations produce different sce-narios because of different interests. As mentioned before, the scenarios of Potma (1977), NCW (1981) and ESC CE (1983) focused more on maintaining wealth than on economic growth and underestimated total energy use. These scenarios were the only to underestimate the share of oil and to overestimate energy savings. Furthermore, they eliminated nuclear energy and overesti-mated the contribution of solar and wind energy, but more scenarios contained such develop-ments. Likewise, Shell scenarios predict the use of more oil as might be expected, but almost all other scenarios forecasted this. No clear trends can be observed regarding the influence of the author on energy use per sector.

Overall, it can be summarized that the evaluated Dutch energy scenarios are a product of their time and are affected by prevailing conditions. Especially the current socioeconomic cir-cumstances have a large influence. Moreover, the assumptions and forecasts were based on trends of say the previous 5-10 years. For instance, no scenario referred to the CPB forecast of 1957 for the year 1975, which significantly underestimated total energy use. It would be interest-ing if one of the scenarios based their forecasts on a longer history.

No clear trends can be determined with respect to the character of the various scenarios. This is because the studies correspond to a large extent regarding system boundary, complexity and methodology (as far this can be assessed). The better scenarios are produced by the more sophisticated models of Shell (1978, 1983) and ESC (1983), but again this may also be due to the changing circumstances of the early 1980s.

3.6 Lessons

In the previous sections it is shown that energy forecasting is complicated. This was expected from the reviewed literature, since it is known that the energy system is part of the societal sys-tem and under influence of many conditions. However, the scenarios are used for decisions with potential long-term consequences. This justifies the question what lessons can be learned from the comparison, and should be kept in mind with respect to new energy scenarios.

The investigated scenarios suffered from incorrect assumptions which resulted in faulty energy use forecasts. The forecasting lessons mentioned in section 2.7 and 2.8 might be of help, but it is difficult to determine whether the Dutch studies already dealt with these lessons in de-signing the scenarios. In general, the studies were not very complex and contained different fore-casts. The scenarios can be improved regarding the documentation of assumptions (in order to evaluate these) and the influence of discontinuities and uncertainty.

Based on the problems and trends described above, it can be argued that longer periods of time should be taken as starting point for scenarios with a long term view. This is not new, as Marchetti (1977) started his famous article with stating that one should look back for at least a century in forecasting energy demand. Such approach can be combined with the lessons of Ma-kridakis (1986) that more weight should be attached to more recent observations, and that trend extrapolations should be dampened.

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A final lesson that can be derived is that new technologies need time to enter and pene-trate the energy system. Despite the knowledge of barriers (technological, economic, political, social, institutional) for new technologies, the contribution of renewable energy sources in the year 2000 was largely overestimated. Marchetti (1977) already observed that for each new ener-gy source it takes about a century to penetrate half of the market. An explanation for this phe-nomenon can be found in transition theory. According to this concept, Rotmans (2010) argues that for the Netherlands renewable energy is still in the pre-development phase of the S-shaped innovation diffusion curve (Rogers, 2003), or might just have arrived at the take-off phase51.

51 A large literature has been built up in the last decade about transition theory, with key concepts like multi-actor involvement, multi-level perspective, multi-phase concept and transition management. This will be introduced and discussed further in subsection 5.2.3.

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4. CURRENT DUTCH ENERGY STUDIES

Besides the goal of this research to evaluate Dutch energy scenarios and to investigate the fore-casting error, the purpose of this research was to analyze whether based on history, large innova-tions in the current energy system can be expected within the next 40 years.

From the literature review, it was found that there were no big changes in the last 40 years. Although the past scenarios were developed given the depletion of fossil fuels (after the first oil crisis) and discussed various energy measures, the energy supply mix has remained ra-ther constant. This justifies the question whether very different energy developments (large en-ergy savings, large share of renewable energy) are likely till 2050 as argued by a number of sce-narios.

This chapter discusses various studies that are recently published and presents scenarios regarding future Dutch energy use for the year 2050. ECN (2002) already provided an overview of nine studies with respect to the future energy infrastructure of the Netherlands, of which three studies look forward to 2050 and are also considered here. Main conclusions were that the sce-narios were very diverse but that all indicated that in the long run substantial changes are need-ed of the energy infrastructure, that future energy demand (and its nature) is highly variable, and that decentralized generation will increase.

However, meanwhile more scenarios have appeared with a time horizon up to 2050. Studies that present quantitative scenarios regarding future energy use and the energy mix (with focus on share of renewable vs. non-renewable energy) are discussed. Some scenarios adopt a similar, more descriptive approach as most ‘past’ scenarios and project forward using various assumptions. Other studies are more normative and are mainly based on the EU goal to reduce GHG emissions in 2050 with about 80-95% compared to 199052. These ‘backcasts’ start with some predetermined end point and describe the pathway(s) leading there. The various scenarios are analyzed given the determined lessons from literature and from the comparison of the past scenarios.

Unlike previous governmental energy reports, the previous Dutch government (Rutte I) presented no quantitative energy forecasts for 2050 because of many uncertainties and seems to prefer economic above environmental interests53. However, the government did state that it aims for zero carbon dioxide emissions in 2050, which is more ambitious than the EU goal54 (EZ, 2011).

52 As agreed by European Heads of State and governments, see the roadmap of the European Commission (EC, 2011), which is consistent with the findings of the IPCC: A 80-95% reduction of GHG emissions in industrialized countries is required by the year 2050 compared to 1990 values to stabilize atmospheric GHG concentrations at 450 ppm CO2 equivalent (globally, emissions need to decrease by 50 to 85% below 2000 levels by 2050 and begin to decrease no later than 2015). This would avoid an increase in global temperature of about 2.1 °C and reduce the likelihood of dangerous anthropogenic interference with cli-mate (IPCC, 2011). 53 The report emphasizes the importance of energy for the Dutch economy (growth, income and jobs). It is conservative with respect to the future contribution of renewable energy and contains statements like ‘en-ergy is economics’, ‘energy policy is international and economic policy’ (EZ, 2011). 54 Energy related goals are sometimes adapted by the government, dependent on who is in office. For in-stance, the program ‘schoon en zuinig’ from 2007 aimed for 30% less CO2 emissions by 2020 compared to 1990, and 20% renewable energy. Recently, these goals are brought back to respectively 20% and 14% (with 7% domestic biomass), in line with EU targets. ECN & PBL (2010) argue that existing policies are insufficient to realize these goals, implying that additional policy is needed.

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4.1 Forecasting Scenarios

The report Energie en samenleving in 205055 (EZ, 2000) is part of the project ‘Long-term vision energy supply’ of the Ministry of Economics. Based on the dimensions economic development and international collaboration, the future Dutch energy system in 2050 is presented for four different worldviews. Rough estimates are given regarding total primary energy use for these worldviews: 2,500 PJ for Free trade, 2,000 PJ for Ecology on local scale, 3,000 PJ for Isolation, and 4,500 PJ for Great solidarity. For all four worldviews, the use of electricity increases in both relative and absolute terms, and offshore wind energy plays an important role.

The scenarios of ECN (2000) are developed given two developments: CO2 reduction goals and the liberalization of the energy market. Three different blueprints are presented (Existing infrastructure or BAU, Hydrogen and Electricity), each with a decentralized and central technol-ogy variant. The report analyzes what the effects are for each blueprint. It is found that a CO2 reduction of 50% in 2050 compared to 1990 can be achieved, utilizing a variety of options: large share of renewable energy, nuclear energy, CCS, and energy saving. The blueprint existing infra-structure is the most likely one, as the other two blueprints require large changes of the energy system.

ECN (2002) partly elaborates on ECN (2000), and is performed on behalf of the General Energy Council in the Netherlands. Focusing on the flexibility of the energy infrastructure, four different scenarios are investigated: BAU, Sustainable Industry, Hydrogen CHP in Built Envi-ronment and Electrical Transport. A CO2 reduction of 50% in 2050 compared to 1990 is only achieved for the Sustainable Industry scenario. The transition towards a sustainable energy sys-tem calls for effective and decisive management and is characterized by incremental innovation (e.g. evolution; not revolution which requires large changes of the energy system and is much more costly).

The study Welvaart en Leefomgeving56 of three planning agencies CPB, MNP, RP (2006) is an exploration of the future state of the physical environment of the Netherlands in 2040, in-cluding the energy system, based on the four different long-term scenarios developed by the CPB (2003): Global Economy (GE), Transatlantic Market (TM), Strong Europe (SE) and Regional Communities (RC). It is concluded that renewable energy continues to be relative expensive, which asks for suitable government policy. The goal of 14% (before 20%) renewable energy in 2020 will not be achieved.

ECN provides a website, maintained by ECN policy studies, where one can retrieve data about various energy related themes (MONITweb57). It is possible to retrieve data about energy use and the energy mix for the year 2040 based on (trend extrapolations of) the program ‘Schoon en zuinig’58 as presented by Balkenende IV in 2007 (VROM, 2007). This program aims for a 30% reduction of GHG emissions by 2020 compared to 1990, energy savings of 2% annual-ly from 2011, and a share of renewable energy of 20% by 2020.

The only energy forecasting scenario from a business organization that was found is from Gasterra, although the company did not build its an own model. Using the Energy Transition Model59, Gasterra produced an energy scenario for the year 2050 that reflects its vision about future energy developments. Gasterra believes that natural gas will play a key role in the energy transition towards a more sustainable energy supply. Energy use in all sectors will decrease ex-cept for the sector industry, and with a share of 40% renewable energy CO2 emissions are re-duced with 47%.

55 Freely translated as Energy and Society in the year 2050. 56 Freely translated as Welfare and the environment. 57 See http://monitweb.energie.nl/.aspx. 58 Freely translated as Clean and careful. 59 See http://www.energietransitiemodel.nl/, developed by Quintel Intelligence.

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4.2 Backcasting Scenarios

DACES (2001) and COOL (2002) both investigate technological options to reduce GHG emis-sions with about 50-80% in 2050 compared to 1990, partly based on the same data sets. DACES (Database Clean Energy Supply 2050) is an initiative of various parties, by order of the Ministry of Housing, Spatial Planning and the Environment, to study the possible technological measures required to reach 80% emission reduction. The report describes three worldviews: Deep Energy Efficiency Improvement, Renewable Energy and Advanced Fossil Fuel, of which only the Renew-able Energy scenario is quantified.

The COOL project (Climate OptiOns for the Long term) had the objective to develop in-sight and recommendations for long-term climate policy in the Netherlands, within an interna-tional context. Participants from the business community, the environmental movement, societal groups and scientific research worked together on this project. Two scenarios are quantified, Global Village and Regions. Besides a diversified energy mix with a significant contribution of renewable energy, material and energy efficiency improvements are important. Treffers et al (2005) describe and summarize the COOL analysis.

SEO (2010) investigates the societal costs and benefits of various possibilities to reduce CO2 emissions with 80% in 2050 compared to 1990. The main conclusion is that the option with a large share of renewable energy is not very different from the option with CCS and nuclear en-ergy in terms of societal costs and benefits. The analysis is performed for two different economic scenarios, one with effective international agreements (Blue Map, 2 options: Nuclear/CCS and renewables) and one without (BAU). The Blue Map scenario involves less costs than BAU and can realize a CO2 emission reduction of 50%; a decrease of 80% is possible but against relative high costs.

TU Delft, KIVI NIRIA (2010) is about the Dutch energy system of today and tomorrow. The report investigates whether 2050 energy demand can be supplied by ‘domestic’ renewable energy, given the aim to reduce CO2 emission in 2050 compared to 1990 with 80%. It is assumed that total energy use decreases due to energy savings and system changes despite increasing de-mand, and that electricity use will triple. The share of renewable energy is 78%, consisting of 35% biomass (using about 5 times Dutch farmland at least), 30% solar (using 6% of land area for PV) and 13% wind energy (using 1.5% of land area for wind turbines on land, and 3,000 km2 on sea).

CE Delft (2010) is a background report of the document Netbeheer (2011) about the Dutch energy infrastructure and specifically the electricity grid of the future. Three different sce-narios are constructed, based on energy demand (extra low or low), energy sources (renewables, natural gas + CCS, or coal + CCS & nuclear), and the degree of decentralized energy production (low, middle or high). These scenarios should reduce CO2 emissions with 30% in 2020 and with 80-95% by 2050 compared to 1990, and achieve 20% renewable energy in 2020 and more in 2050. It is concluded that different pathways are possible, that governmental policy should be changed and that social acceptance needs attention.

The study of ECN & PBL (2011) discusses several routes towards a ‘clean’ economy in 2050, and presents next to the reference case two scenarios: biomass and renewable electricity. It is aimed to reduce CO2 emission in 2050 compared to 1990 with 80%, based on the building stones energy saving, energy from biomass, CCS and clean electricity (wind, solar PV and nucle-ar). It is concluded that large and long-term changes of the energy system are needed, that all building blocks should be utilized, that good innovation policy is needed and an international orientation.

As until now mainly governmental and scientific publications are discussed, it would be nice to add some reports from business or interest groups. However, these are very scarce for energy scenarios focusing solely on the Netherlands. Recently, a group of companies (DONG Energy, EBN, Eneco, GasTerra, Gasunie, GDF SUEZ, and Shell) operating in the energy sector published a report contributing to the energy debate (Energy Forum NL, 2012). To realize overall

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climate ambitions, the group strives for a more long-term, stable energy policy and investment climate in the Netherlands. Dutch primary energy demand is expected to increase from 3,350 PJ in 2010 to 3,800 PJ in 2030; it should then decrease to 3,600 PJ by 2050. Pathways are provid-ed that show how the Netherlands can best contribute to the EU target of 80% CO2 emission re-duction by 2050 compared to 1990. However, the analysis mainly focuses on the year 2030 and on the energy mix of power generation.

4.3 Overview Current Scenarios

In 2010, total primary energy use was 3,492 PJ, the share of renewable energy was 3.7% (129.2 PJ, of which about 97 PJ biomass), final electricity use was 121 TWh (435 PJ, 12.5% of total pri-mary energy use) and GHG emissions were 210 Mt (of which 182 Mt CO2, in 1990 this was re-spectively 215 and 170 Mt, a reduction of 80% implies a ceiling of 43 Mt CO2 equivalent in 2050). Table 1 and 2 present actual values and summarize total energy use, the share of renewable ener-gy and electricity use and CO2 emissions as presented by the forecasting (23 scenarios from 5 studies) and backcasting scenarios (15 scenarios from 6 studies):

Table 1: Overview forecasting scenarios60

Actual values Energy use (PJ) Renewables share Electricity use CO2 Emission (Mt)

Year 1980 2,723 10 PJ; 0.4% 232 PJ; 8.5% 143 Year 1990 2,723 21 PJ; 0.8% 292 PJ; 10.7% 159 Year 2000 3,065 53 PJ; 1.7% 390 PJ: 12.7% 170 Year 2010 3,492 129 PJ; 3.7% 435 PJ; 12.5% 182

Forecasting Scenarios Energy use (PJ) Renewables share Electricity use CO2 Emission (Mt)

ECN, 2000 – BAU, centr. 5,350 2,350 PJ; 44% 2,365 PJ; 44% 214 ECN, 2000 – BAU, decentr. 5,100 2,450 PJ; 48% 2,379 PJ; 47% 214 ECN, 2000 – Hydro, centr. 5,300 1,650 PJ; 31% 2,964 PJ; 56% ≤ 64 ECN, 2000 – Hydro, decentr. 5,600 1,800 PJ; 32% 3,910 PJ; 70% < 64 ECN, 2000 – Electr, centr. 4,650 1,300 PJ; 28% 2,460 PJ; 53% < 64 ECN, 2000 – Electr, decentr. 4,900 1,600 PJ; 33% 2,775 PJ; 57% < 64 ECN, 2002 – BAU 4,250 NA 720 PJ; 18% 217 Idem – low variant 3,550 NA 630 PJ; 19% 185 ECN, 2002 – Base case 4,073 1,515 PJ; 37% NA 128 Idem – low variant 3,409 1,183 PJ; 35% NA 101 ECN, 2002 – Sust. Industry 4,212 2,223 PJ; 53% NA 85 Idem – low variant 3,478 1,537 PJ; 44% NA 80 ECN, 2002 – Hydro-CHP 3,787 1,514 PJ; 40% NA 139 Idem – low variant 3,121 1,183 PJ; 38% NA 112 ECN, 2002 – Electr. Transport 4,607 1,283 PJ; 28% NA 133 Idem – low variant 3,934 955 PJ; 24% NA 107 Planbureaus, 2006 - GE 5,025 70 PJ; 1% 768 PJ; 15% 290 Planbureaus, 2006 – SE 3,540 498 PJ; 14% 582 PJ; 16% 138 Planbureaus, 2006 – TM 4,535 66 PJ; 1% 627 PJ; 14% 230 Planbureaus, 2006 – RC 3,032 278 PJ; 9% 483 PJ; 16% 157 MONITweb, ECN - BAU 3,827 159 PJ; 4% 567 PJ; 15% 225 MONITweb, ECN – ‘Schoon en zuinig’

3,244 541 PJ; 17% 529 PJ; 16% 162

Gasterra – Vision 2050 3,232 1,267 PJ; 39% 455 PJ; 14% 80

Average61 4,256 1,479 PJ; 35% 2,182 PJ; 51% 95 (-44%)

60 Actual values from CBS Statline, except for the year 1980 which is from ESC (1982). Renewable energy and electricity use are expressed both in PJ and as share of total primary energy use, CO2 emissions ac-cording to IPCC definition. See appendix D for a graphical reproduction. 61 The average excludes BAU scenarios and only includes 2050 scenarios.

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Table 2: Overview backcasting scenarios62

Actual values Energy use (PJ) Renewables share Electricity use CO2 Emission (Mt)

Year 1980 2,723 10 PJ; 0.4% 232 PJ; 8.5% 143 Year 1990 2,723 21 PJ; 0.8% 292 PJ; 10.7% 159 Year 2000 3,065 53 PJ; 1.7% 390 PJ: 12.7% 170 Year 2010 3,492 129 PJ; 3.7% 435 PJ; 12.5% 182

Backcasting Scenarios Energy use (PJ) Renewables share Electricity use CO2 Emission (Mt)63

DACES, 2001 – Renewable low 3,000 1,200 PJ; 40% NA -80% DACES, 2001 – Renewable high 3,500 1,575 PJ; 45% NA -80% COOL, 2002 – Global Village 2,000 935 PJ; 47% NA -80% COOL, 2002 – Regions 1,800 1,370 PJ: 76% NA -80% SEO, 2010 – BAU 5,500 0% NA +95% SEO, 2010 – Blue Map 3,350 1,240 PJ; 37% NA -80% Treffers, 2005 - Global Village 1,996 939 PJ; 47% NA -80% Treffers, 2005 - Regions 1,869 1,445 PJ; 77% NA -80% TU Delft, 2010 - Renewables 2,650 2,066 PJ; 78% 1,380 PJ; 52% -80% CE Delft, 2010 - Renewables 1,800 (final) 1,800 PJ; 100% 760 PJ; 42% -90% CE Delft, 2010 – Gas + CCS 2,030 (final) 840 PJ; 41% 910 PJ; 49% -90% CE Delft, 2010 – Coal + CCS 2,575 (final) 640 PJ; 25% 1,665 PJ; 65% -90% ECN, PBL, 2011 - Reference 3,800 400 PJ; 11% NA +0% ECN, PBL, 2011 – Biomass 2,800 650 PJ; 23% NA -80% ECN, PBL, 2011 - Renewable 2,800 1,200 PJ; 43% NA -80%

Average64 (excl. CE Delft) 2,577 1,262 PJ; 49% 1,380 PJ; 54% -80%

Total primary energy use scenarios vary from a decrease of 1,692 PJ (-48%) to an increase of 2,108 PJ (+60%) compared to 2010. On average, it increases with 764 PJ (22%, or about +0.50% per year) in case of the forecasting scenarios, while it decreases with 915 PJ (-26%, or about -0.76% per year) for the backcasting scenarios. The share of renewable energy ranges from 24% to 53% for the forecasting scenarios, and from 23% to 78% for the backcasting scenarios (BAU scenarios not included)65. One can compare this with Martinot et al (2007), who reviewed global, Europe-wide and country-specific energy scenarios, and found 10% to 50% shares of primary energy from renewables by 2050. Consequently, some current Dutch scenarios have quite high shares of renewable energy.

4.4 Current Scenarios Analysis

It is clear that there are two types of current scenarios, forecasting scenarios and backcasting ones. According to the typology of McDowall and Eames (2006), the forecasting scenarios are primarily a combination of exploratory and technical scenarios, as most consider the drivers of possible futures and explore technological possibilities. However, the backcasting scenarios con-sidered also often use these kind of forecasting exercises and thus do not reason entirely back-wards. In general, the scenarios start exploring the social, demographic and economic back-grounds. Then, using various growth figures and an inventory of options and technologies, a quantitative analysis is performed (See for instance Treffers et al, 2005, pp. 1724). In other words, first energy demand is determined based on trends, energy savings and new technologies (often per sector), whereupon the energy supply mix is chosen (constrained by emission targets or not). This is similar to the methods applied by the ‘past’ scenarios.

62 See footnote 51. 63 The CO2 emissions for backcasting scenarios are presented as percentage change relative to the 1990 level, as some studies did not report CO2 emission quantitatively, and other studies considered total GHG emissions. 64 See footnote 52. 65 The difference between the share of renewable energy and CO2 emission reduction for some backcasting scenarios is primarily due to the large-scale application of CCS (and less to the use of nuclear energy).

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Most scenarios assume learning curves regarding some technologies (efficiency im-provements, decreasing costs), while some assume state-of-the-art technologies. Behavior change is not explicitly assumed, but it is more or less accepted given assumptions about high energy savings, and the penetration (and thus acceptance) of various technologies like electrical cars.

As most studies are produced by (collaborations of) research institutes, no influence can be determined with respect to the author on the various scenarios. Besides, no clear time trend can be identified. Like for the ‘past’ scenarios, it is difficult to indicate how the current scenarios perform regarding the forecasting lessons as described in section 2.7. It seems that the current scenarios are better in describing the situation, the problem, the scenarios and the assumptions, but also do not explicitly account for unexpected events. The current scenarios utilize more so-phisticated models than the past scenarios, which might be due to the availability of more knowledge and data. However, no completely different methods (other than trend projections, econometric/regression analysis, input/output analysis, end-use analysis, and these combined in scenarios) are used.

Most important, the current scenarios cannot be characterized as modest regarding total energy use (i.e. high energy savings) and the contribution of renewable energy. The current sce-narios consider a range of options to increase energy saving and to increase the share of renewa-ble energy (primarily via the carrier electricity), like: CHP, heat pump, high efficiency boiler, insulation, electrical transport, CCS, biomass and its applications (e.g. biofuels), solar heat, solar PV, wind energy, and more. Williams et al (2012) summarize these options by defining three major energy system transformations: higher energy efficiency, generation decarbonization, and ‘electrification’. To a large extent, the same technologies already were discussed in the ‘past’ sce-narios, although now more knowledge is available (about technology, costs). However, during the last 40 years, total energy use grew with more than 1% per year, and the share of renewables increased from 0% to merely 3.7% in 2010 in the Netherlands.

Consequently, most current scenarios are quite optimistic. Based on history and the past scenarios, it can be argued that lower energy use and a share of about 40% renewable energy in 2050 are not likely. In this light, one can state that the backcasting scenarios are more wishful thinking. Whether the current scenarios (published between 2000 and 2011) are like the past scenarios a ‘product of the time’ is difficult to say. The average prevailing socioeconomic condi-tions of the last decade were rather positive, which might have contributed to the optimistic bias regarding energy savings and the share of renewables.

The next chapter presents alternative forecasts which are based on the extrapolation of long-term historical trends and are more conservative than most current scenarios.

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5. ALTERNATIVE FORECASTS

From the past scenarios, it was ascertained that actual energy savings were larger than estimated (over the period 1975-2000), but that the contribution of renewable energy was largely overesti-mated. The current scenarios also focus on high energy savings and a significant share of renew-able, but now for the year 2050. Based on historical developments and the evaluation of the past scenarios, most current scenarios are not very likely.

Our aim is to present alternative forecasts using simple methods and given historical trends. Extrapolations are presented of total energy use and of the share of renewable energy. The choice for this method is justified by the findings of the literature review that forecasting models should not be too complex, and by the evaluation of the past Dutch scenarios. According-ly, longer periods of time are considered as starting point for these extrapolations with a long term view. The extrapolations are based on long-term trends to prevent the construction of sce-narios which are a ‘product of the time’, to account for uncertainty due to unanticipated events (like war, disasters), and to incorporate the influence of temporary tendencies (like economic crises)66. Moreover, the extrapolations are modest and of some extrapolations the trend is damp-ened or more weight is attached to recent observations. No constraints are included, like the pos-sible depletion of Dutch natural gas or the need of land area for renewable energy.

Furthermore, Marchetti and transition theory is used to forecast the future contribution of renewable energy. Finally, these alternative forecasts are compared with the current scenarios. It should be remembered that the time horizon of the current scenarios is about twice that of the past scenarios. The past scenarios looked forward for about 20-25 years (1975/1980-2000), while the current scenarios run from about 2000-2010 till the target year 2050.

5.1 Total Energy Use

Total Dutch primary energy use has increased from 2,016 PJ in the year 1970 to 3,250 PJ in 2011. To extrapolate the change in energy use over more years, both the linear increase and the growth can be considered. The growth can be indicated by two different measures: the ‘yearly average change’ and the ‘average growth’67. It is meant to include as many years as possible to capture the long-term trend in Dutch energy use. Over the period 1970-2011, the yearly average change of total energy use has been 1.26%, while the average growth was 1.17%68. The average change per decade was successively 3.23%, 0.06%, 1.22% and 1.35%. These figures clearly indicate the high growth of energy use during the 1970s (due to high economic growth, and due to attraction and expansion of energy intensive industry because of (cheap) natural gas presence), the influence of the economic crisis of the 1980s (due to second oil crisis of 1979) and the relative stable circum-stances during the last two decades. The increase in total energy use has been higher than the growth of Dutch population, which was about 0.61% per year. This implies that energy use per capita also increased: the growth was about 0.55% annually, from 156 GJ/cap/year in 1970 till 195 GJ/cap/year in the year 2011.

66 As unanticipated events and temporary tendencies have occurred in the last 40 years, and it is expected that these also will occur in the coming four decades. However, other unanticipated events and temporary tendencies might take place, which might lead to other long-term trends. To minimize this influence, the longest possible periods of time are considered. 67 The yearly average change is the average of the year to year mutations, and can thus also include nega-tive mutations. The yearly average growth is computed based on the start (1970) and end value (2011). 68 However, the influence of which years are included can be large. For instance, over the period 1970-2010 the yearly average change of total energy use has been 1.47%, while the average growth was 1.38%. This is because Dutch energy use decreased with about 7% from 3,492 PJ in 2010 to 3,250 PJ 2010, due to a rela-tively less cold winter and the economic crisis. Incorporating also the period 1960-1970 will lead to higher average growth rates (see for instance figure 4), but the CBS provides no data of this period.

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To determine total energy use in 2050, one can extrapolate certain developments. A sim-ple trend extrapolation of total energy use captures all effects69. One can also dampen the trend extrapolation as suggested by the forecasting lessons, and for instance decrease the growth rate with 1% per year. Besides, one may attach more weight to recent observations and base the ex-trapolation for instance on the last two decades. Furthermore, a simple but solid method is to found the extrapolation on per capita energy use and population developments as projected by the CBS. A final approach is to simply add the same linear increase over the past to the current value. Again, the period 1970-2011 is considered70. The foregoing leads to 5 extrapolations of Dutch primary energy use in 2050 (which was 3,250 PJ in 2011), see table 3:

Table 3: Trend extrapolations of Dutch primary energy use

Based on Growth rate Energy use 2050

Trend total energy use 1.17% per year 5,116 PJ Idem, trend dampened 1.17%-0.77% per year 4,741 PJ Trend last two decades 1.29% per year 5,358 PJ Energy use/cap; population 0.55% per year; 0.17% per year (or 17.8

million people in 2050) 4,452 PJ

Historical linear increase 30.1 PJ per year 4,424 PJ

A related approach is graphical extrapolation. Figure 7 depicts two main determinants of energy use, population and GDP over the period 1970-2010. It is illustrated that in the past growth of energy use has been a bit higher than population growth, but not as high as GDP growth. Moreo-ver, the figure includes the actual CBS projection of the development of Dutch population till the year 2050, which indicates that population is about to decrease after 2040 (CBS Statline)71. Fi-nally, a linear trend line72 is added for total energy use, which leads to Dutch energy use of about 4,350 PJ in 2050. This is lower than the calculated extrapolations presented above and almost similar to the average of the forecasting scenarios73.

69 Volume effect due to a.o. economic growth, population growth; structure effect due to changes in struc-ture of economy/relative size of sectors; and effect of energy saving. 70 See footnote 59: considering the 1970-2010 period instead leads to a linear increase of 36.9 PJ per year, and results in a total energy use of 4,968 PJ in the year 2050. 71 Although earlier prognoses stated that Dutch population would reach its maximum already in 2035. Accordingly, CBS population projections have been adjusted to above, which might happen again. 72 This trend line is a best-fit straight line, or a simple linear regression based on OLS (ordinary least squares, which minimizes the sum of squared errors of the observations from the fitted line). 73 When the trend line starts at the precise 1970 value (2,016 PJ), then the trend line becomes steeper and leads to Dutch energy use of about 4,850 PJ in 2050. However, this trend line has a lower ‘goodness-of-fit’.

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Figure 7: Total energy use including trend line, population including projection, and GDP

The simple calculated extrapolations are higher than those presented by most current scenarios and higher than the average of the forecasting scenarios, while the graphical extrapolation is similar to this average. The various extrapolations lead to a total energy use in the range of 4,300 – 5,400 PJ in the year 2050 (on average about 4,750 PJ).

5.2 Renewable Energy

The ‘past’ scenarios generally recognized the potential but anticipated a limited contribution of renewable energy by the year 2000. Not all studies quantified or considered the deployment of renewable energy, but on average a 5% share (with a range of 0-12%; the highest share in the Potma scenario) was forecasted. Half of it could be contributed to wind and solar energy, the other half to biomass. This corresponds to other studies exploring renewable energy and studies that appeared in Energiespectrum (1977-1981). For instance, the energy report of EZ (1974), forecasted a future (period 1985-2000) contribution in the range of 2-16%, considering solar, wind, tidal, hydro and geothermal energy and biomass. Van Gool (1976) mentioned that a 5% contribution of wind and solar energy in 2000 should be possible. Some studies focused more on the potential of renewable energy and argued that up till a 50% share renewables was possible in 2000 (Van der Wart, 1978; Best et al, 1982).

In reality, the contribution of renewable energy in 2000 was just 1.7% of total primary energy use in the Netherlands (or 53 PJ, of which mainly energy from biomass waste). Figure 8 shows the final use of renewable energy as a percentage of total final use, and indicates that for this definition74 the share of renewables was even lower in 2000, 1.4%. Of this, waste and bio-mass accounted for 1.2%, while the contribution of wind energy was only 0.13% and that of solar energy not even 0.02% (mainly solar heat instead of solar power).

74 Used for specific data tables about renewable energy by CBS according to EU guidelines (and thus not available in primary energy use); instead of the definition used for the energy balance according to the IEA and Eurostat.

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Figure 8: Final use renewable energy per source, as percentage of total final energy use75

Why so little has happened with respect to the share of renewables cannot be answered univocal-ly. Many factors play a role in this process, as a transition requires changes of the whole energy system which simply costs time. Often mentioned in literature and also by the past Dutch energy scenarios is that barriers to (large-scale) implementation are of technological, practical, social, political and economic nature. Verbong et al (2001) argue that the socioeconomic context has large influence, and that one of the main barriers in the Netherlands has been the societal inte-gration of renewable energy. The authors conclude that it is just a matter of time.

For instance, changing the energy infrastructure is technically complex and challenging (both existing infrastructure needs to be replaced and supporting infrastructure needs to be built up), it takes time (for existing infrastructure with lifetimes of 25-50 years at a rate of 2-4% per year), requires high investments, needs industrial capacity and involves risk and uncertainty. Therefore, difficult (long-term) planning and decisions should be made, accounting for economic and environmental priorities, while dealing with political and social resistance.

The convenience of fossil fuels compared to renewable energy forms is another barrier. For example, solar and wind energy provide an intermittent and variable supply of energy. Other benefits of fossil fuels are the ease of production, transport, storage and distribution. Moreover, the price of oil has not increased much till the year 2000 (although an increasing price was ex-pected; in real terms the price was even lower in 2000 than in 1975, after the temporary price increase during the 1980s) and more stocks of fossil fuels have been discovered than were fore-seen. Although fossil fuels were ‘phenomenally attractive’, it still took more than half a century to bring them into widespread use. Since renewable energy is less attractive (they do not offer new services, involve high costs, unequal geographical distribution of the availability and needs), this energy transition might be slower (Kerr, 2010).

Finally, the ‘fossil fuel regime’ (the oil and gas companies and related industry) had and still has much political and economic power and influence. This incumbent regime is large in the Netherlands, and by its very existence has slowed down the energy transition (Kern and Smith,

75 See: http://www.compendiumvoordeleefomgeving.nl/indicatoren/nl0385-Verbruik-van-hernieuwbare-energie.html?i=6-139. Source: CBS. Retrieved May 14, 2012.

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2008; Rotmans, 201176). A related problem is that both (renewable) energy policy and industry policy is coordinated by the same ministry (economic affairs, agriculture and innovation) in the Netherlands (Verbong et al, 2001).

These and more barriers have prevented a large scale implementation of renewable ener-gy. However, a rational and relevant question is whether and why this will be different in the coming four decades. For instance, still power plants fired by conventional energy sources (mainly coal and natural gas, with possible cofiring of biomass) are being built. This is also the case in the Netherlands, and may lead to overcapacity and export (instead of import) of electrici-ty the coming years (given all plans with a capacity of about 18,000 MW). Accordingly, replace-ment of the Dutch energy infrastructure requires more time.

This has led to a number of studies that do not expect a fast energy transition. For in-stance, Solomon and Krishna (2011) reviewed past transitions, necessary conditions and the time frame. They conclude that it is extremely unlikely (if not technically impossible) that a worldwide transition of energy supply can occur in three decades or less, even if there is international agreement on the need to do so.

On the other hand, it took also some decades to bring fossil fuels into widespread use (Marchetti, 1977). Moreover, like the current scenarios do, one can assume that things and cir-cumstances will change in the coming decades. It is argued that barriers to entry are nowadays more of social (acceptance, convenience, and safety issues), political, and institutional nature (Jacobsen and Delucchi, 2011). In other words, the technology is available and most renewable energy forms in itself are nowadays cost-competitive. Germany shows that a relatively fast tran-sition is possible, with an increase of the share of renewable energy from 3% till 11% (primary consumption) in the last decade, and an increase of renewable electricity from about 7% till 20% (gross consumption) in the last decade (AGEE, 2012)77. Furthermore, the price of fossil fuels is expected to rise further since demand is expected to increase. Besides, this demand increase is nowadays larger than the discovery of new stocks, and the remaining stocks are relatively expen-sive to extract.

Therefore, one may argue that renewable energy technologies will penetrate the energy system, but that this takes time and may not go as fast as presented by the current scenarios. The following subsections put the current scenarios in other perspective by presenting more con-servative forecasts.

5.2.1 Historical Developments

As already mentioned, the past scenarios forecasted a contribution of renewable energy of 5% (of primary energy use) on average (about 200 PJ) for the year 2000 (about half of this would be wind and solar energy, the other half biomass). In reality, this was just 1.7% (or 53 PJ), of which 49 PJ was energy from biomass (largely waste). This implies that renewable energy is overesti-mated with a factor of about 3 till 4, over a time period of 20 years (1980-2000)78.

Based on this finding, one may argue that current scenarios also overestimate the future contribution of renewable energy. Using the same error, this implies that the share of renewables

76 Rotmans even argues that consultancy agencies are influenced by the oil and gas regime. He points to reports from companies like UC partners, that describe that ‘leading’ the energy transition costs a lot of money and threatens the existing energy industry which is so important for the Dutch economy. 77 In Germany, investments in the construction of renewable energy facilities have increased since 2004, and were about €23 billion in 2011 (AGEE 2012). This implies for the Netherlands that investments in renewable energy should relatively be about €5 billion (based on population €4.8 billion, based on GDP €5.2 billion). However, actual investments are about €2 billion per year. To achieve renewable energy (or climate) targets, it is recommended to invest about €10 billion per year (ECN & PBL, 2010; Ecofys, 2010). 78 In more detail, while biomass is overestimated with a factor 2, the contribution of solar and wind energy is overestimated with almost a factor 40! However, the focus is the contribution of renewable energy in total.

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will be about a quarter till a third of 37-44%, resulting in about 10-15%. However, the current scenarios have a time horizon of at least 40 years (2010-2050) which is twice that of the past scenarios. Accordingly, one can also argue that it is more likely that the 2050 share of renewa-bles will be about 20-30%.

Like for total energy use, both the growth trend and the linear increase of renewable en-ergy in the Netherlands can be extrapolated. However, only the linear increase is considered here. This is because the historical (1990-2010 period) exponential growth with a rate of almost 10% per year is not sustainable, as Kramer and Haigh (2009) state that at a certain point growth changes from exponential to linear79. When this point occurs is difficult to indicate, but it has consequences for the extrapolation of the linear increase. The linear growth is extrapolated dur-ing both the whole period for which data is available, 1990-201080, and the period 2000-2010 (assuming that linear growth starts in 2000, and also considering more recent observations). This leads to a renewable energy share of respectively 9.5% (or 345 PJ) and 11.7% (or 435 PJ).

Another option to estimate what the contribution of renewable energy will be in 2050 is a simple (graphical) trend extrapolation based on the historical development of renewables in the Netherlands81. Figure 9 depicts the total contribution of renewable energy in the Netherlands. As well a linear as a quadratic trend line is added, to reflect both the average growth and the trend that the growth rate increases82.

Figure 9: Total contribution of renewable energy, including linear and quadratic trend line

79 This is explained in more detail in subsection 5.2.3. Extrapolating the growth trend would lead to a con-tribution of about 4,000 PJ renewable energy in 2050. The growth rate can be dampened with for example 2 or 3% per year, but then the growth is still exponential. 80 The period 1980-2010 is not considered, as the CBS figures about renewable energy start in 1990. How-ever, the results are about the same (8.1% share or 288 PJ renewable energy) when considering the 1980-2010 period and linear growth. Of course, for exponential growth (about 8-9% growth per year), or using the same trend (multiplication by 10-13 in 30 years) leads to higher shares and contributions in the year 2050 (81% and 3908 PJ, or 49% and 2236 PJ). But it is argued that this is not plausible. 81 Data from the period 1990-2010 is available on CBS Statline, based on different definitions. Here the definition of the IEA and Eurostat is used. Other definitions (like final use, or avoided fossil fuels) lead to similar results. 82 Other trend lines can be applied, but these are not considered realistic. For instance, the exponential trend line leads to very large contributions of renewables (over 5,000 PJ, or a 95% share in 2050).

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Based on the extrapolation of the total contribution of renewable energy, the future contribution ranges from 350-1,000 PJ. The same can be done for the relative contribution, as a percentage of total energy use. This extrapolation leads to a 2050 share that ranges from 10-27%.

These extrapolations can be combined with the trend extrapolation of total energy use (see 6.1), of which the average is 4,800 PJ (see section 6.1). Accordingly, a contribution of 350-1,000 PJ implies a share of 7–21%, while the extrapolated share of 10–27% implies a contribu-tion of 480–1,300 PJ. Almost all these values indicating the future contribution of renewable energy are lower than most current scenarios.

5.2.2 Marchetti Cycles

Using logistic curves, Marchetti (1977) found that primary energy sources exhibited regular long-term trends, by analyzing historical shifts from sources of wood, coal, oil, natural gas, and nucle-ar. He concluded that the penetration process of every new source is slow (it takes about a centu-ry to penetrate half of the market, no clear acceleration of the times is observed) but regular, and that the system dynamics is difficult to influence. If renewables develop according to the Mar-chetti theory, then a 30% share of renewables can be expected in the year 2050 (Devezas et al, 200883). This is based on the observation that it takes about a century to reach a 50% share, as-suming that penetration started in the year 1980.

However, the theory of Marchetti is criticized because of the current (since mid-1980s) ‘flatness problem’ in energy supply. For instance, Smil (2000) states the energy system nowa-days is one of largely stable energy shares with a minimal structural change, and is influenced by major forces since 1973. Moreover, he argues that coal has experienced a world-wide resurgence, while renewables and nuclear power84 have not penetrated as fast as expected. This is also the case in the Netherlands, as can be seen in figure 1085.

83 Devezas et al (2008) come with a different perspective, and argue that increased energy efficiency ac-counts for the observation that the Marchetti theory does not hold for the last 3-4 decades. Also in the Netherlands, energy saving policies were introduced in the late 1970s, which was underestimated by the past scenarios. Even Shell (1979) came with a report about energy saving given the oil crises, the depletion of fossil fuel reserves and the problems surrounding alternative energy sources (nuclear, solar and wind). Shell stated that energy saving is a ‘clean’ substitute. 84 The observation that nuclear energy, once very popular (‘too cheap to meter’) with plans for ‘nuclear islands’, has not become as large as predicted, tempted Shell to state that the same might happen with renewables (Shell, 2011). 85 It is not clear whether the Marchetti curves can be observed regionally (due to trade). Moreover, from figure 10 it looks like coal has just entered the energy system. However, till 1960 coal was the main energy carrier in the Netherlands for about a century long (this can also be derived from figure 2).

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Figure 10: Contribution of energy sources to primary energy supply in the Netherlands

Other mechanisms might account for the introduction of various energy sources, like technologi-cal developments. First mainly wood was used for cooking and heating. The deployment of coal and oil can be contributed to the invention of respectively the steam engine and combustion en-gine. The analysis of Marchetti about the substitution dynamics of energy sources is also based on this perspective, as he was inspired by economic cycles (so-called Kondratiev waves) and the relation with technological innovation. However, it can be argued that the introduction of re-newables is also due to other developments besides technology, like environmental awareness and the recognition that fossil fuels are depletable. Transition theory tries to capture all the fac-tors which influence the introduction and implementation of renewable energy. This theory dis-cussed below.

5.2.3 Transition Theory

A large literature has been built up in the last decade about transition theory, with key concepts like multi-actor involvement, multi-level perspective, multi-phase concept and transition man-agement. Transitions have been described as social transformation processes in which socio-technical systems change structurally over an extended period of time, usually covering more generations. This process runs unsteady and involves besides technological also societal and in-stitutional change (Geels, 2002 and 2004). There is an increasing interest in applying this un-derstanding to address current problems of unsustainability, by investigating how societal change towards more sustainable states may be fostered (Elzen et al., 2004). Some of the current scenarios, often the studies that apply the backcasting method, refer to transition theory.

Building on the S-shaped innovation diffusion curve (Rogers, 1995), four phases of a transition can be identified: pre-development, take-off, acceleration, and stabilization (Rotmans et al. 2001). The energy transition in the Netherlands has arrived at the take-off phase, according to Rotmans (2011) as he signals societal demand for sustainability and many related local initia-tives. This is a critical phase and can be seen as a tipping point, determining whether the transi-tion will continue and accelerate or not. Rotmans (2011) assumes that the take-off and the accel-eration phase last for respectively 10 and 20 years, which implies that the energy transition will enter the stabilization phase in 2040.

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However, Rotmans (2011) does not quantify the future contribution of renewable energy. To be able to say something about the transition progress and the actual contribution of renewa-bles in 2050, one needs to know more about the speed with which new energy technologies can be deployed. ECN & PBL (2011) describe 6 factors that determine the pace of a transition: policy goals, replacement rate of capital, technical complexity, organizational complexity, consumer benefits and the public opinion (see pg. 138). Nevertheless, this pace is also not quantified. Kra-mer and Haigh (2009) state that the rate at which renewable energy can be physically imple-mented is more important than the costs involved. The authors based their analysis on historical growth in energy systems86 and determined two empirical ‘laws of energy-technology deploy-ment’:

1. New energy technologies grow exponentially for about 30 years (corresponding to 26% annual growth) to become ‘material’ (about 1% of world energy mix);

2. After this, growth changes to linear (due to replacement of both old and new stocks) till the technology reaches its share in the energy mix.

These two laws and related deployment curves (similar to the S-curve) are common to many dif-ferent energy technologies.

Knowing this, it can be investigated whether renewable energy sources already account for more than 1% of total energy use. This can be done both on a global scale and for the Nether-lands, but the question is which renewable energy sources should be considered in this analysis. For instance, in 2009 the share of renewables in total world primary energy supply was 13.3%, of which 10.6% biofuels and waste and 1.8% hydro. This share is substantial, but has not increased much since 1973 when it was 12.5%, of which 10.2% biofuels and waste and 2.3% hydro (IEA, 2010). This implies an increase of 0.8% in 36 years, and extrapolating this trend leads to a share of about 14.3% in 2050.

Accordingly, it can be argued that the growth should come from other, newer renewable energy technologies like solar energy (solar thermal and solar PV energy), wind energy, new geo-thermal energy, blue energy and second (or higher) generation biomass. In 2008, the shares of these sources in total world primary energy supply were respectively 0.1% for solar (PV just 0.01%), 0.2% wind, 0.1% geothermal while the share of second generation biomass was negligi-ble (IPCC, 2011). Given a 26% annual growth rate, these sources become ‘material’ between 2015 and 2025, after which growth becomes linear. However, the speed of this linear growth is un-known.

To this end, one can look at historical growth rates of other energy sources. Total primary energy supply of fossil fuels has increased with almost 6 EJ per year (a linear increase of about 4% of the 1965 supply; for coal this was 2.7%, for oil 3.7% and for gas 7.3%) over the period 1965-2009 (BP, 2009). Applying these growth rates to the renewable energy sources results in shares between 5.4%-9.6% on top of the ‘conventional’ renewable energy share of about 12.5%, leading to a total renewable energy share of about 18-22% in the year 2050. However, one can also take nuclear energy as reference, which was almost ‘material’ in the year 1973. This energy source has increased faster (linear increase of about 35% of initial 1973 value) than fossil fuels over the pe-riod 1973-2009. Utilizing this growth rate leads to a total renewable energy share over 40% for the year 2050.

Both extrapolations are based on historical growth rates, but what the growth rate for re-newable energy eventually will be is hard to say. The growth of nuclear energy might be inter-preted as quite high, but the different fossil fuels probably also have experienced these kind of growth rates before87.

86 In fact, this analysis is similar to that of Marchetti, but differs with respect to the substitution of energy sources. 87 There are no statistics about total primary energy supply that go back that far, but this statement can be graphically derived (see for instance figure 2 and figure A.1).

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The same analysis (26% annual growth till ‘materiality’, then linear growth of about 4%) is performed for the Netherlands, but does in total not lead to different results (21.7% renewable energy in the year 2050). When considering renewable energy in total and assuming linear growth, the result is the same as the linear trend extrapolation mentioned above in subsection 5.2.1.

5.3 Comparison of Alternative Forecasts and Scenarios

The preceding two sections presented alternative ‘own’ forecasts about the total and relative con-tribution of renewable energy in the year 2050 for the Netherlands. These forecasts were primar-ily trend extrapolations either based on history, on theoretical observations or on both. The fore-casts are summarized and compared with the current scenarios in table 4:

Table 4: Comparison of current scenarios and alternative forecasts

Current scenarios Total energy use (PJ) Total RE (PJ) Share RE (%)

Forecasting average 4,394 1,610 37

Backcasting average 2,922 1,085 44

Own forecasts

Trend extrapolation 4,300 - 5,400 350 - 1,000 10 - 30

Marchetti theory - - 30

Kramer & Heigh (2009) - - 18 - 22

A few items can be observed from table 3 and 4. First, most current scenarios are lower regard-ing total energy use than the lowest own forecast (4,300 PJ). In more detail, 14 of the 23 fore-casting scenarios are lower, and 14 of 15 backcasting scenarios are lower. Second, most current scenarios are higher regarding the future contribution of renewables than the highest own fore-cast (1,000 PJ or 30%). In absolute terms, 14 of 21 forecasting scenarios are higher, and 8 of 15 backcasting scenarios are higher. In relative terms, 12 of 21 forecasting scenarios are higher, and 11 of 15 backcasting scenarios are higher. These results become stronger when instead of the most extreme forecasts the average of the own forecasts (total energy use 4,800 PJ; share re-newable 24%) is used for comparison. Consequently, using simple and modest extrapolation methods based on historical observations leads to more conservative forecasts. However, only time will tell whether the energy transition really will be slower than presented by most current scenarios.

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6. CONCLUSIONS

This research has been twofold, which is reflected by the main research question: “What lessons can be learned from the past 40 years of Dutch energy forecasts, and what are the implications of these lessons for current and future Dutch energy scenarios”. First, literature about energy forecasting and the evaluation of these forecasts has been reviewed. After this, for the first time the Dutch case is analyzed by comparing the various past energy studies with actual develop-ments. Second, the results of the first part were used to assess current scenarios and to provide alternative forecasts of total energy use and the share of renewables. A number of lessons and implications are derived from both the observational and modeling study, and can be reflected by two main conclusions.

The first main conclusion is that energy forecasting is complex. This results from both the literature review and the analysis of the past Dutch scenarios. Similar to other global, EU and US energy forecast evaluations, the comparison of past Dutch energy scenarios identified the follow-ing: a mean absolute percentage error of 36%, an average overestimation of total domestic ener-gy use with 28%, large offsetting errors with respect to energy use per sector and per source, the use of wrong assumptions, and a large influence of prevailing conditions and short-term trends. Furthermore, no clear influence of the author of the study has been found regarding both the past and current energy scenarios. Just five past scenarios appeared to be less objective. These scenarios adopted a different socioeconomic perspective and forecasted lower total energy use, underestimated the use of oil, and overestimated energy savings and the use of renewables. However, these scenarios, provided by three studies, were mainly based on the Potma study.

The second main conclusion is that the energy transition takes time. As mentioned before, the energy system is slowly changing and a lot of factors (social, institutional, technological, en-vironmental and economic) influence the transition process. Only belief, enthusiasm and wishful thinking is not enough, and energy innovations are not subject to Moore’s law. The energy tran-sition requires very large investments, enormous infrastructural adaptations, a reshaping of so-cial contours and technical foundations, and is a huge organizational challenge (Smil, 2010). This all takes time.

The contribution of renewable energy for the year 2000 was largely overestimated by the past scenarios. Moreover, still the same energy measures are being proposed nowadays as 30-40 years ago, like higher energy efficiency and savings, and the use of renewable energy sources. Furthermore, despite the expected depletion of fossil fuels, energy supply has been relatively stable in the last four decades. Fossil fuels have proved to be persistent and adaptable. Accord-ingly, a relevant question is whether very different energy developments (like large energy sav-ings of about 2% per year, which is very difficult to sustain, and a large share of renewable energy of about 40%) are likely till 2050 as argued by a number of current scenarios.

Based on historical developments, it can be argued that most current scenarios are opti-mistic regarding total Dutch energy use and the contribution of renewable energy in 2050. In general, the backcasting scenarios are more optimistic than the forecasting scenarios of the cur-rent Dutch energy studies.

Alternative forecasts have been presented adopting a simple and conservative approach and based on long-term trends. These extrapolations indicate that total energy use will be higher (on average 4,800 PJ instead of 3,700 PJ) and the share of renewables lower (on average 25% instead of 40%) than the current scenarios. However, things might turn out to be different. This implies that a change of trends is needed, as argued already in the 1970s by Potma. Obviously, this will be not easy, but it is also not impossible.

Given the observed forecasting errors and the optimism bias the use and meaning of en-ergy scenarios can be discussed. Smil (2000) argues that it is better to base policy, planning and investment decisions on normative scenarios than on point forecasts. By considering normative scenarios, decisions can be taken that are good for a range of alternative futures. These decisions

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are fundamentally different from those that are good only if a particular forecast turns out to be correct (Smil, 2000). However, the current Dutch energy studies generally include a BAU sce-nario and one or two visions about the (desired) future state of the energy system. As the non-BAU scenarios are mainly based on climate goals and targets, it can be assumed that policy, planning and investment decisions will be quite different between the BAU and the non-BAU scenarios. It almost seems that energy scenarios are used nowadays more for political and com-munication purposes. This way, the different non-BAU scenarios are used as a vehicle to show what should be done in order to change the BAU scenario, and can be seen as a self-fulfilling prophecy. Without a change of trends this seems more wishful thinking.

Evaluation is an important aspect of using models. However, it is very difficult to indicate how to improve the energy forecasting methods. With hindsight some appropriate approach can be demonstrated, but to suggest what the approach should be for current problems is difficult. This is also because energy forecasting is no exact science, as the energy system is part of a com-plex societal system with many actors and relationships. Besides, energy forecasts need to deal with incomplete information, risk (based on historical errors) and uncertainty (due to lack of information, for instance about unanticipated contingencies).

Whether energy forecasts are getting better over time is not clear and there is no consen-sus about in literature. The past Dutch scenarios do indicate some evidence for ‘learning’, but other factors like shorter time horizon and information availability might also be responsible for this. However, it might be of help if an independent institution with much knowledge about en-ergy (like the ECN) develops and performs a standard procedure to evaluate Dutch energy sce-narios, like the EIA does with the AEO for the United States. For a proper evaluation, it is im-portant that assumptions, data, method and outcomes are well documented.

Other recommendations originating from this study are based on the comparison of the past Dutch energy scenarios with actual developments, and aim to improve forecasts. The follow-ing conclusions are drawn that might lead to better results: the use of a simple and conservative approach, combine forecasts and scenarios, and take long-term trends as starting point for sce-narios since the energy system is slowly moving and changing.

The comparison that is performed in this research is limited by the availability of data of the various energy studies considered. Not all studies provided a clear overview of the model used, the various scenarios, the assumptions, and the outcomes. Moreover, the various studies used different definitions to represent energy quantities (like primary use, gross demand, final demand, including or excluding international transport and bunkers).

This research compared the past Dutch energy scenarios with actual developments. The noted differences in outcome are an indicator of the forecasting performance. Another way to measure this which might be interesting to research, is to validate the various scenarios by repli-cating the used models with some adjustments: first, by using the actual developments (with ‘right’ assumptions) as input; and second, by using the proposed lessons like taking longer peri-ods of time as starting point. Finally, as mentioned before, future research might focus on the development of a standard evaluation for energy scenarios. This evaluation then can eventually asses the quality of the current Dutch energy scenarios and of the alternative forecasts.

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65

APPENDIX A: HISTORICAL GLOBAL ENERGY USE

Figure A.1 depicts the development of global energy use over time. Although in different units, it can be observed that the development of the energy mix is similar to the Dutch situation as de-picted in figure 2 on page 19.

Figure A.1: Historical global energy use in PWh per year88

88See http://www.hydropole.ch/index.php?go=hydrogen_carrier, retrieved January 30, 2012.

67

APPENDIX B: OVERVIEW OF PAST DUTCH ENERGY STUDIES

Table A.1: Overview of the title, year of publication, author, scope, method and scenarios of the 13 past Dutch energy studies

Study Scope Method Scenarios 1. Energie 1976, LSEO (Landelijke Stuurgroep Energie Onderzoek).

Energy demand and supply Scenarios, simple extrapolations for demand, calculations for supply based on basic assumptions (global reserves, future production and demand)

1. Continued growth 2. Decreasing growth, stabiliza-tion within 25 years

2. Een CPB scenario voor Neder-land, 1978, CPB (Centraal PlanBu-reau), led by M. J. Stoffers.

Energy demand and supply, Scenarios, end-use analysis for demand based on economic assumptions, calcu-lations for supply

1. High growth 2. Low growth

3. Energy supply to year 2000: global and national studies, 1977, WAES (Workshop on Alternative Energy Strategies)

Energy demand and supply, global integration.

Scenarios with a number of variables, end-use analysis for demand for 69 eco-nomic sectors, based on supply based on potential supply

5 Scenarios, of which 2 are com-puted for the Netherlands: a low and a high growth.

4. Energiebeleid met minder risiko, 1977, Potma, T.

Energy demand and supply; + social, economic and environmental impact

Energy demand calculations based on nine energy saving measures for the ,ain sectors; Energy supply based on basic assumptions.

1. The forgotten scenario

5. De komende 25 jaar, 1977, WRR (Wetenschappelijke Raad voor het Regeringsbeleid).


Scenarios, Energy demand calculations for main sectors based on economic and energy saving assumptions; Energy sup-ply based on basic assumptions.

A. High growth B. Low growth

6. Kijken naar de toekomst, 1978, Shell.

Energy demand and supply Socio-political assumptions determine economic development. Interplay of three dimensions form basis of energy demand.

1. Restructured Business (RB) 2. Frustration & Conflict (FC) 3. Restraint as Reaction (RR)

7. Gas en electriciteit in Nederland, een toekomstverkenning, 1979, TNO (Toegepast Natuurweten-schappelijk Onderzoek) & ECN (Energieonderzoek Centrum Ne-derland).

Energy demand and supply Scenarios, Energy demand calculations for main sectors based on economic and energy saving assumptions; Energy sup-ply based on basic assumptions.

1. Continued growth 2. Zero growth

8. 2e Nota energiebeleid, 1979, EZ (Ministerie van Economische Za-ken).

Energy demand & supply; and impact on economy and environment.

Scenarios; Energy demand calculations given economic and energy saving as-sumptions for 4 main sectors and for main energy carriers; energy supply based on basic assumptions

1. High growth 2. Low growth

68

9. Energie tot 2000, 1979, prof. mr. B.M. Teldersstichting, Zoutendijk, G.

Energy demand & supply; and environmental impact

Scenarios; simple calculations of energy demand given economic growth, relation economy-energy use and energy savings; Energy supply based on basic assump-tions

1. Low growth 2. High growth

10. Het energievraagstuk, 1981, VDEN (Vereniging van Directeuren van Elektriciteitsbedrijven).

Energy supply; and social and environmental impact.

Scenarios, simple calculations based on assumptions

Given energy demand from CPB (1977) and Energienota deel 1 (1979), five energy supply scenar-ios are presented, each with dif-ferent energy mix.

11. Energie in de toekomst, 1981, NCW (Nederlands Christelijke Werkgeversverbond)

Energy demand Scenarios, based on socioeconomic and energy saving assumptions

Three scenarios: Average, Low and High growth.

12. De energievoorziening in de 4 MDE-scenario's gebaseerd op be-rekeningen met het energiemodel SELPE, 1983, ESC (Energie Studie Centrum van ECN).

Energy demand and supply; and impact on economy and environment.

Scenarios, based on linear programming model SELPE

EZ: Reference scenario IH: High growth AD: Low growth CE: Conservation scenario

13. Scenarios for NL 1982 – 2000, 1983, Shell.


Scenarios; based on system dynamic model

RG: Restructured growth HT: Hard times

69

APPENDIX C: OVERVIEW COMPARISON FORECASTS

Table A.2: Comparison of forecasts with actual developments, absolute figures:

Assumptions Energy use (Total, per sector and per source)

Ec. Growth Population Houses Cars Energy saving

Total Industry Transport Households Other Energy Coal Oil Gas Nuclear Solar & Wind

Actual 2,6 15,9 6,6 6,3 40 3067 1267 462 432 496 408 329 1074 1469 41 3,5

Study

1. LSEO, 1976, H 16,5 7454 931 4825 670 946 82

1. LSEO, 1976, L

25

3400 1530 272

680

933 1298 688 422 79

2. CPB, 1977, H 3,5 15,4 5,6 6,5 13,0 5095 2181 578 862 737 2344 1503 268 243

2. CPB, 1977, L 2,3 14,3 5,6 4,6 11,0

3969 1557 448

716

427 1784 1503 255 0

3. WAES, 1977, C 3,0 28,0 5942 2328 507 938 1097 1072 1063 3207 1026 632 13

3. WAES, 1977, D 1,8

28,0

4133

5. Potma, 1977 0,0 5,3 31,5 1700 427 154 154 441 524 372 745 372 0 154

6. WRR, 1977, H 3,0 14,6 5,9

5543

701

998 2439 1607 333 166

6. WRR, 1977, L 1,5 14,6 5,6 3551 402 817 1562 1030 36 107

4. Shell, 1978, BU 3,5

6,4 6,0

4802

1093 2721 1771 381 0

4. Shell, 1978, FC 2,5 6,1 5,7 3855 574 2370 1599 322 0

4. Shell, 1978, RR 1,5

5,9 5,4

3147

343 2102 1365 29 0

7. TNO/ECN, 1979, H 3,4 5095 2181 578 862 754 2219 1507 461 167

7. TNO/ECN, 1979, L 1,0

3969 1557 448

716

419 2052 1256 251 0

8. EZ, 1979, H 3,0 14,3 5,9 6,1 25,0 4924 2295 502 745 507 875 1118 2412 1277 42 62

8. EZ, 1979, L 2,0 14,3 5,9 5,6 23,0

4041 1679 456 733 457 716

858 1789 1277 42 62

9. VVD, 1979, H 3,0 0,0 4166 649 1675 1465 377 0

9. VVD, 1979, L 2,0

30,0

3789

649 1675 1465 0 0

10. VDEN, 1981, H 3,5 15,4 30,0 4815 -

10. VDEN, 1981, L 2,3 14,3

30,0

3977

-

11. NCW, 1981, A 4,0 30,0 2684 -

11. NCW, 1981, B 3,5

40,0

2541

-

11. NCW, 1981, C 4,0 30,0 2721 -

12. ESC, 1983, EZ 1,3 15,6 6,0 5,6 30,0

3106 1294 359 544 261 646

1011 1075 821 0 24

12. ESC, 1983, IH 2,5 4,6 30,0 3409 1412 436 550 260 751 1045 1186 941 0 24

12. ESC, 1983, AD 1,5

30,0

3083 1254 378 545 219 686

895 1107 843 0 24

12. ESC, 1983, CE 1,0 44,0 2209 826 290 460 189 443 488 831 663 0 34

13. Shell, 1983, RG 1,8

30,0

3609

507 1821 1168 67 46

13. Shell, 1983, HT 0,3 20,0 2914 406 1361 1080 38 29

= Forecast lower than actual value

= Forecast higher than actual value

70

Table A.3: Comparison of forecasts with actual developments, relative figures:

Study Assumptions Energy use (Total, per sector and per source)

Ec.

Growth Population Houses Cars

Energy

saving

Total Industry Transport Households Other Energy Coal Oil Gas Nuclear

Solar &

Wind

1. LSEO, 1976, H 3,8% 143,0% 183,0% 349,3% 54,4% 2207,3% 2242,9%

1. LSEO, 1976, L

43,2%

10,9% 20,8% 41,1%

66,7%

183,6% 20,9% 53,2% 929,3% 2157,1%

2. CPB, 1977, H 34,6% 3,1% 15,2% 3,2% 70,5%

66,1% 72,1% 25,1%

111,3%

124,0% 118,2% 2,3% 553,7% 6842,9%

2. CPB, 1977, L 11,5% 10,1% 15,2% 27,0% 75,0%

29,4% 22,9% 3,0%

75,5%

29,8% 66,1% 2,3% 522,0% 100,0%

3. WAES, 1977, C 15,4%

47,2%

93,7% 83,7% 9,7% 117,1% 121,2% 162,7%

223,1% 198,6% 30,2% 1441,5% 271,4%

3. WAES, 1977, D 30,8%

47,2%

34,8%

5. Potma, 1977 100,0%

19,7%

28,4%

44,6% 66,3% 66,7% 64,4% 11,1% 28,4%

13,1% 30,6% 74,7% 100,0% 4300,0%

6. WRR, 1977, H 15,4% 8,2% 10,6%

80,7%

51,7%

203,3% 127,1% 9,4% 712,2% 4642,9%

6. WRR, 1977, L 42,3% 8,2% 15,2%

15,8%

13,0%

148,3% 45,4% 29,9% 12,2% 2957,1%

4. Shell, 1978, BU 34,6%

3,0% 4,8%

56,6%

232,2% 153,4% 20,6% 829,3% 100,0%

4. Shell, 1978, FC 3,8%

7,6% 9,5%

25,7%

74,5% 120,7% 8,8% 685,4% 100,0%

4. Shell, 1978, RR 42,3%

10,6% 14,3%

2,6%

4,3% 95,7% 7,1% 29,3% 100,0%

7. TNO/ECN, 1979,

H 30,8%

66,1% 72,1% 25,1%

111,3%

129,2% 106,6% 2,6% 1024,4% 4671,4%

7. TNO/ECN, 1979,

L 61,5%

29,4% 22,9% 3,0%

75,5%

27,4% 91,1% 14,5% 512,2% 100,0%

8. EZ, 1979, H 15,4% 10,1% 10,6% 3,2% 35,9%

60,5% 81,1% 8,7% 72,5% 2,2% 114,5%

239,8% 124,6% 13,1% 2,4% 1671,4%

8. EZ, 1979, L 23,1% 10,1% 10,6% 11,1% 41,0%

31,8% 32,5% 1,3% 69,7% 7,9% 75,5%

160,8% 66,6% 13,1% 2,4% 1671,4%

9. VVD, 1979, H 15,4%

100,0%

35,8%

97,3% 56,0% 0,3% 819,5% 100,0%

9. VVD, 1979, L 23,1%

9,1%

23,5%

97,3% 56,0% 0,3% 100,0% 100,0%

10. VDEN, 1981, H 34,6% 3,1%

23,1%

57,0%

10. VDEN, 1981, L 11,5% 10,1%

23,1%

29,7%

11. NCW, 1981, A 53,8%

23,1%

12,5%

11. NCW, 1981, B 34,6%

2,6%

17,2%

11. NCW, 1981, C 53,8%

23,1%

11,3%

12. ESC, 1983, EZ 51,9% 1,9% 9,1% 11,1% 3,2%

1,3% 2,1% 22,3% 25,9% 47,4% 58,3%

207,3% 0,1% 44,1% 100,0% 585,7%

12. ESC, 1983, IH 3,8%

27,0% 3,2%

11,2% 11,4% 5,6% 27,3% 47,6% 84,1%

217,6% 10,4% 35,9% 100,0% 585,7%

12. ESC, 1983, AD 42,3%

3,2%

0,5% 1,0% 18,2% 26,2% 55,8% 68,1%

172,0% 3,1% 42,6% 100,0% 585,7%

12. ESC, 1983, CE 61,5%

41,9%

28,0% 34,8% 37,2% 6,5% 61,9% 8,6%

48,3% 22,6% 54,9% 100,0% 871,4%

13. Shell, 1983, RG 30,8%

3,2%

17,7%

54,1% 69,6% 20,5% 63,4% 1214,3%

13. Shell, 1983, HT 88,5% 35,5% 5,0% 23,4% 26,7% 26,5% 7,3% 728,6%

= Forecast lower than actual value

= Forecast higher than actual value

71

APPENDIX D: GRAPHICAL COMPARISON, RELATIVE FIGURES

Figures A.2 and A.3 depict the domestic energy use per sector and per source in relative figures.

Figure A.2: Relative domestic energy use per sector in year 2000, scenarios and actual value

72

Figure A.3: Relative domestic energy use per source in year 2000, scenarios and actual value

university of groningen dutch energy scenarios evaluated … · acknowledgment this report is my...

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