Thesis
Reference
Mixing Technological and Behavioural Data in the Development of Energy Policies
KANALA, Roman
Abstract
Optimisation models for environmentally compatible energy planning based on the concept of economic equilibria share a common flaw that stems from their neoclassic roots: the hypothesis of a perfect information and the hypothesis of perfect economic rationality. One of the possible ways how to circumvent this issue, the method we propose first, consists in soft-linking data from sociological surveys that determine technical coefficients for the energy system optimisation model, creating a hybrid approach of coupling a deductive engineering model with typical inductive methods of social sciences. Consumer behaviour is described via usual technological attributes and used in virtual process technologies, keeping the model and data format compatible with all the tools and user interfaces already developed for energy planning models, including International Energy Agency (IEA) supported software platforms.
The approach, called Social MARKAL and demonstrated as an example on the technology of lighting bulbs, can be extended to all demand sectors and to all models based on the concept of economic equilibria. The second proposed [...]
KANALA, Roman. Mixing Technological and Behavioural Data in the Development of Energy Policies. Thèse de doctorat : Univ. Genève, 2018, no. Sc. 5225
DOI : 10.13097/archive-ouverte/unige:106924 URN : urn:nbn:ch:unige-1069245
Available at:
http://archive-ouverte.unige.ch/unige:106924
Disclaimer: layout of this document may differ from the published version.
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Mixing Technological and Behavioral Data in the Development of Energy Policies
Roman Kanala 2 July 2018
Remerciements
La première personne à laquelle je dois toute ma gratitude est Emmanuel Fragnière, professeur à la HES-SO, Haute école spécialisée de Suisse occidentale à Sierre, ancien- nement à la HEG, Haute école de gestion de Genève pour ses nombreuses idées et pour la possibilité d’effectuer les enquêtes sociologiques dans le cadre du LEM, Laboratoire d’Etude des Marchés, qu’il a co-dirigé, puis dirigé.
Je dois beaucoup à Walter Wildi, professeur aux Sciences de la Terre de l’Université de Genève, qui a accepté de m’accompagner pendant ma thèse. Sans son soutien, sa bien- veillante attention et ses encouragements, je n’aurais pas surmonté les nombreux pièges et tracasseries administratives et c’est à ses conseils avisés que je dois l’aboutissement de ce travail. Je remercie également le professeur Urs Schaltegger pour avoir accepté de jouer ce rôle pour me conduire à la soutenance en tant que co-directeur de thèse et les professeurs Bastien Chopard et Sonia Yeh pour avoir accepté d’être membres du jury de thèse.
Je remercie toutes les personnes qui m’ont permis de bénéficier d’un contrat de travail pendant les années passées à l’Université de Genève et à la Haute Ecole de Gestion de Genève: les professeurs Bernard Giovannini, Alain Haurie, Jean-Philippe Vial, Jacques Vicari, Hubert Greppin, Claude Raffestin, Rémi Baudoui et Emmanuel Fragnière. C’est grâce à cela que j’ai pu me consacrer au travail scientifique.
C’est aussi grâce à Madame Geneviève Auroi-Jaggi et au Service Formation Continue qu’elle a dirigé que j’ai pu devenir un professionnel en informatique et un expert en design des interfaces Web sur les bases de données, comme le projet swissuni.ch en témoigne, et gagner ma vie en tant qu’informaticien pendant les nombreuses années qui ont suivi.
Je remercie mes collègues, assistants doctorants et chercheurs à l’Université de Genève, pour leur soutien et leur amitié. Bernard Aebischer, Dominique Pain, Jean-Luc Bertho- let au CUEPE, Emmanuel Fragnière, Francesco Moresino, Olivier Epelly, Olivier Bahn, Jacek Gondzio, Olivier du Merle, Angela Cadena, Robert Sarkissian aux HEC, ainsi que de nombreux autres amis qui m’ont aidé, encouragé et soutenu à divers stades de mes
travaux.
J’ai gardé de bons souvenirs de la collaboration avec mes collègues du Centre univer- sitaire d’écologie humaine, Robert Degli Agosti, Ewa Mariéthoz et Marika Bakonyi avec lesquells j’ai pu collaborer à de nombreuses occasions. A l’ISE, Institut des sciences de l’environnement, j’ai eu la chance de connaître des collègues comme Stéphane Goyette, Hatem Fekkak, Marie Isabel Haroon, Stéphanie Girardclos, Francisco Marzoa, le pro- fesseur Jérôme Kasparian qui a lu et commenté ma thèse et nombreux autres personnes pour lesquelles je garde le plus grand estime. Sans leur soutien, ce travail n’aurait pas pu être achevé.
Je dois aussi beaucoup aux partenaires de recherche, collègues chercheurs des autres universités rencontrés aux conférences de l’ETSAP, surtout à Denis Lavigne, Jean-Philippe Waaub, Nadia Maizi, Maurizio Gargiulo, Rocco de Miglio, Gary Goldstein, Amit Kanudia, Socrates Kypreos, GianCarlo Tosato et de nombreux autres, pour les discussions fructueuses et les remarques avisées qui ont permis d’élaborer et préciser les concepts présentés dans cet ouvrage.
Abstract
Optimisation models for environmentally compatible energy planning ba- sed on the concept of economic equilibria share a common flaw that stems from their neoclassic roots: the hypothesis of a perfect information and the hypothesis of perfect economic rationality. One of the possible ways how to circumvent this issue, the method we propose first, consists in soft-linking data from sociological surveys that determine technical coefficients for the energy system optimisation model, creating a hybrid approach of coupling a deductive engineering model with typical inductive methods of social sci- ences. Consumer behaviour is described via usual technological attributes and used in virtual process technologies, keeping the model and data format compatible with all the tools and user interfaces already developed for energy planning models, including International Energy Agency (IEA) supported software platforms.
The approach, called Social MARKAL and demonstrated as an example on the technology of lighting bulbs, can be extended to all demand sectors and to all models based on the concept of economic equilibria.
The second proposed approach proposed goes even further by applying the methodology called Conjoint Analysis to find the "Willingness To Pay".
The method can be applied on immaterial goods like insurance or air ticket pricing and here it is applied on energy services considered as a service, far from usual method of considering it as investment.
Both methods eliminate the systematic error on the demand side where the efficiency of demand-side management measures is over-optimistic, which may lead to inaccurate decisions and poor policies. The improved model is thus better suited to build long term policies that are not solely based on technology progress but also taking into account social change.
The most important innovation is that the energy consumer behaviour has been brought to the same mathematical optimisation platform and evaluated together and as a complement to technology change.
Résumé
Les modèles de planification énergétique basés sur le concept d’équilibres économiques partagent le même défaut provenant de ses origines néoclas- siques: les hypothèses sous-jacentes de l’information parfaite et de la ra- tionalité économique parfaite. Une des façons de contourner ce problème, la première méthode que nous proposons, est d’utiliser l’approche du cou- plage du modèle d’optimisation par données extérieures avec les sondages so- ciologiques pour déterminer les coefficients techniques du modèle MARKAL, créant ainsi un modèle hybride issu de l’association d’un modèle déductif d’ingénieur avec la méthode inductive typiquement utilisée dans les sciences sociales. Le comportement des utilisateurs est décrit par des attributs tech- nologiques habituels et utilisé dans la description des technologies virtuelles.
Ce procédé garde le modèle et le format des données compatibles avec les outils déjà existants, y compris ceux développés pour la planification énergé- tique sous l’égide de l’AIE.
L’approche, appelée MARKAL Social, illustrée par l’exemple des tech- nologies utilisées pour les ampoules électriques peut être étendue sur d’autres secteurs de la demande et peut également être généralisée à d’autres modèles basés sur le concept d’équilibres économiques.
La deuxième méthode proposée va encore plus loin en appliquant la métho- dologie appelée Analyse Conjointe pour trouver WTP, Willingness To Pay.
La méthode peut être appliquée sur les biens immatériels comme les contrats d’assurance ou la tarification des billets d’avion et ici elle est appliquée sur les services d’énergie, loin de leur représentation habituelle en tant qu’un in- vestissement.
Les deux approches éliminent l’erreur systématique du côté de la demande, où les attentes concernant l’efficacité des mesures d’économie s’avèrent sou- vent trop optimistes, ce qui peut conduire aux décisions peu adéquates et des politiques non réalistes. Le modèle proposé dans le cadre de cette thèse vise
à améliorer les politiques à long terme qui ne sont pas basées uniquement sur les améliorations technologiques mais qui prennent également en compte les changements sociaux.
L’innovation la plus importante est que le comportement des consomma- teurs des services énergétiques a été ramené à la même plate-forme d’optimisation mathématique et évalué ensemble avec les changements technologiques comme un complément aux améliorations technologiques.
Contents
1 Context of Approaches to the Development of Energy Policies 1
2 Literature Review, Energy Modelling 13
2.1 Introduction . . . 13
2.2 Energy models, types and classification . . . 25
2.3 Top down models and econometric models . . . 27
2.4 Bottom-up technico-economic models . . . 29
2.5 Hybrid and other models . . . 36
2.6 Sociology of energy consumption . . . 42
2.7 New trends in operations research: Introducing behaviour in modelling . . 56
3 Methodology & Tools 63 3.1 MARKAL . . . 64
3.2 TIMES . . . 79
3.3 OSeMOSYS . . . 89
3.4 Sociological surveys . . . 92
3.4.1 Qualitative sociological surveys . . . 92
3.4.2 Ethnomethodology . . . 94
3.4.3 Quantitative surveys . . . 94
3.4.4 Stated vs. Revealed preferences . . . 95
3.5 Linking models . . . 96
3.6 Research design . . . 99
4 Including Behavioral Data in Mathematical Programming Framework109 4.1 Inclusion of behaviour into energy optimisation models . . . 118
4.2 Social MARKAL Nyon . . . 123
4.3 Social TIMES Romania . . . 132
4.4 TIMES with travel time incorporated . . . 138
4.5 COCHIN-TIMES, a simulation model coupled with TIMES . . . 139
4.6 MoCho-TIMES, modal choice TIMES . . . 143
4.7 A Share-of-Choice model integrated with OSeMOSYS . . . 146
4.8 A comparison of the presented modelling approaches . . . 154
5 Main Results and Findings from Sociologic Surveys 157 5.1 Hypotheses obtained from ethnomethodology approach . . . 157 5.2 Hypotheses from classical qualitative sociological surveys . . . 158 5.3 Descriptive statistics and hypotheses testing using quantitative sociologi-
cal surveys . . . 164 5.4 Remarks on sociological surveys . . . 167
6 Conclusions 169
6.1 Findings . . . 170 6.2 Limitations . . . 172 6.3 Further research . . . 172 7 Annex - Sociological surveys forms and questionnaires 177 7.1 Survey about lighting bulbs, French version (original) . . . 177 7.2 Survey about bulbs, English version . . . 184 7.3 Survey about bulbs, Romanian version . . . 190 8 Annex - Detailed procedure to set up MARKAL Nyon residential light-
ing model 201
9 Abbreviation list - glossary 217
List of Figures
1.1 A diagram of components of sustainable development. . . 3
1.2 Climax of planetary availability of non-renewable energy. . . 5
2.1 Expected Theorical and Actual Observed Energy Consumption Reduction 15 2.2 Components of an energy-economic model. . . 25
2.3 Survival functionβ(τ). . . 30
2.4 Schematic diagram of LEAP model. . . 33
2.5 Technologies in the MARKAL model. . . 35
2.6 Energy flux in the MARKAL model. . . 35
2.7 POLES model structure. . . 39
2.8 Classical Economic Equilibrium. . . 44
2.9 Proposed Formulation of Economic Equilibrium. . . 44
2.10 A schema of the Theory of Planned Behaviour. . . 46
2.11 A schema of the Habitual Behaviour. . . 47
2.12 A schema of the Stern’s Attitude - Behaviour - Context (ABC) Model. . . 47
2.13 A schema of Triandis’ Theory of Interpersonal Behaviour. . . 49
2.14 Another schema of Triandis’ Theory of Interpersonal Behaviour. . . 49
2.15 A schema of the Motivation-Opportunity-Ability Model. . . 50
2.16 A schema of the Value-Belief-Norm (VBN) theory. . . 51
2.17 Yet another schema of the Value-Belief-Norm (VBN) Model. . . 51
2.18 Another schema of the Value-Belief-Norm (VBN) Model. . . 52
2.19 Relationship citizen-consumer. . . 53
2.20 Behaviour depending on awareness stages. . . 53
3.1 Schematic diagram of model, data and instance. . . 64
3.2 A determinist problem. . . 66
3.3 A stochastic problem. . . 67
3.4 Diagram of sources, conversion and process technologies, demand devices and useful demand in MARKAL. . . 68
3.5 The form of the MARKAL problem matrix. . . 74
3.6 Integer programming (IP) polytope with linear programming (LP) relaxation 75 3.7 Equilibrium of the supply and demand according to neoclassic theory. . . 75
3.8 Equilibrium in the original MARKAL model with inelastic demand. . . . 76
3.9 MARKAL equilibrium in the modified model with elastic demands. . . 76
3.10 Schema of hard-linked MARKAL-MACRO model. . . 77
3.11 Schematic diagram of TIMES inputs and outputs. . . 81
3.12 VEDA-FE Navigator interface. . . 84
3.13 Invoking VEDA-FE Case Manager. . . 86
3.14 VEDA-FE Case Manager interface. . . 87
3.15 VEDA-BE Scenario select dialog box. . . 88
3.16 OSeMOSYS ’blocks’ and levels of abstraction. . . 90
3.17 OSeMOSYS logical blocks. . . 91
3.18 Degrees of integration of linked models. . . 97
3.19 Schema of a minimalist MARKAL with just two technologies in competition.100 3.20 A screenshot of the ANSWER interface: useful demand residential lighting.102 3.21 A screenshot of the ANSWER interface: adding energies. . . 103
3.22 A screenshot of the ANSWER interface: tangible demand technology RLD1.103 3.23 A screenshot of the ANSWER interface: virtual demand technology RLD3. 104 3.24 A screenshot of the ANSWER interface: RES for lighting demand. . . 104
3.25 A screenshot of the ANSWER interface: virtual technology MRKP3. . . . 105
3.26 Awareness stages modified by information campaign. . . 106
4.1 Model scale, centralised vs. decentralised decisions, number of decision makers and subjectivity . . . 110
4.2 Classical Economic Equilibrium . . . 111
4.3 Proposed Formulation of Economic Equilibrium . . . 112
4.4 Attributes, utility, choice set and choice. . . 112
4.5 Explanatory variables, utility and choice. . . 113
4.6 Latent variables and indicators. . . 114
4.7 Explanatory variables, latent variables and indicators. . . 114
4.8 Choice model with explanatory variables, utility, latent variables and in- dicators. . . 115
4.9 Behavioural framework with motivation, information, attitude, percep- tion, choice . . . 115
4.10 Technical and sociological parameters in energy consumption: interrela- tions and interdependence . . . 118
4.11 Hierarchy of nested systems. . . 120
4.12 RES for Nyon residential lighting with competition of tangible technologies 125 4.13 RES for residential lighting including both tangible and virtual technologies126 4.14 Unbound model immediately eliminates all incandescence bulbs. . . 128
4.15 Use of virtual technologies eliminates both the systematic error and mod- eller’s subjectivity. . . 128
4.16 Sensibility analysis with lowering investment cost of information campaign as a virtual process technology. . . 130
4.17 Schema of final energy forms, demand devices and useful demands in TIMES Romania residential sector. . . 133
4.18 Initial installed capacities of technologies in TIMES Romania. . . 137
List of Figures xi
4.19 Classical transport technologies within modes in TIMES. . . 139
4.20 Transport technologies with intermodal competition in TIMES-TTI. . . . 140
4.21 Results of reference case (as percentage of vehicle sales) in TIMES. . . 141
4.22 Results of reference case (as percentage of vehicle sales) in Cochin-TIMES. 142 4.23 RES of "classical" TTI-TIMES. . . 144
4.24 RES of MoCho-TIMES. . . 145
4.25 Share of Choice survey process . . . 149
4.26 Card system questionnaire unveiling the price, life and efficiency. . . 150
4.27 Second card system questionnaire showing the yearly cost. . . 150
4.28 RES of the Utopia model with fluorescent lighting RL4 added. . . 151
4.29 Bulbs penetration according to the different model instances . . . 152
6.1 Schema of tangible and virtual technologies for lighting . . . 173
6.2 Schema of tangible and virtual technologies for heating . . . 174
6.3 Schema of tangible and virtual technologies for passenger cars . . . 175
List of Tables
2.1 Energy Returned on Energy Invested (ERoEI) . . . 18
2.2 Energy Returned on Energy Invested (ERoEI) according to more recent sources. . . 19
2.3 Instrument table, match of instruments and determinants . . . 55
3.1 Overview of the MARKAL family models. . . 78
3.2 Similarities and differences between MARKAL and TIMES models . . . . 82
3.3 A comparison of global models based on MARKAL / TIMES. . . 83
3.4 An example of a typical five-level Likert scale. . . 96
3.5 Nyon resident population 1850 - 2015 . . . 99
3.6 Structure of jobs in Nyon 2008. . . 99
3.7 A review of surveys performed within the framework of this research. . . . 105
4.1 Level of integration of the transport system. . . 120
4.2 A comparison of energy and transportation models incorporating behaviour.122 4.3 Behavioural features in transportation sector and available modelling tools to address them. Inspired by Venturini (2015). . . 124
4.4 Energy carriers and technologies in Socio MARKAL, units, descriptions. . 127
4.5 Key technologies for Social-MARKAL Nyon in ANSWER format . . . 131
4.6 Periods, timestep, milestones in Social TIMES Romania . . . 132
4.7 Commodities definition in Social TIMES Romania residential demand sector.134 4.8 Processes definitions in Social TIMES Romania residential demand sector. 135 4.9 Import prices of commodities in TIMES Romania . . . 136
4.10 Demand commodities in TIMES Romania . . . 136
5.1 A review of ethnomethodologic, qualitative and quantitative surveys 2009- 2014. . . 158
5.2 Frequencies: Q3 and Q5 . . . 162
5.3 Test statistics: Q3 and Q5 . . . 162
5.4 χ2 statistics for questions Q3 / Q5 . . . 163
5.5 Values ofχ2 for common p values . . . 163
5.6 Test statistics: Q6/Q17. . . 163
5.7 Economic and extra-economic reasons to turn the lights on . . . 164
5.8 Most important characteristics of a lighting bulb . . . 165
5.9 Proportion of low consumption bulbs among all the bulbs . . . 165
5.10 Possible reasons to change the consumer’s consumption of electricity. . . . 166
5.11 Survey on lighting bulbs (2010), if you were better informed, would you be ready to abandon incandescence bulbs ? . . . 166
5.12 Parts of people claiming to have no particular reflex to save light, hot water, or heat . . . 166
8.1 Definitions of sets for Social-MARKAL Nyon, ANSWER format. . . 202
8.2 Useful demand “Residential Lighting” for Social-MARKAL Nyon . . . 202
8.3 Technologies and Energy Carriers in Social-MARKAL Nyon . . . 202
8.4 Residential light demand RLD in MARKAL Nyon. . . 203
8.5 Investment cost for technology RLD4, Existing low consumption bulbs. . . 204
8.6 MRKP2 Marketing moderate use (virtual process technology) . . . 205
8.7 MRKP3 Marketing technology switch (virtual process technology) . . . . 206
8.8 Frequency of positive answer 15.6 (Nothing will change my behaviour). . . 211
8.9 Results (yes, no, missing answer) for Question 3. . . 211
8.10 Results (yes, no, missing answer) for Question 13. . . 213
8.11 Results (yes, no, already did, missing answer) for Question 14. . . 213
8.12 Virtual technologies for Social-MARKAL Nyon in ANSWER format . . . 215
8.13 Virtual technologies for Social-TIMES Romania in ANSWER format . . . 215
1 | Context of Approaches to the Development of Energy Policies
In this introductory chapter, the subject of this work, importance of human behaviour, is put into context of the most important environmental issues.
Statements contained in this chapter are obvious and matter of public record, so we do not feel necessary to document every single statement with a citation. Only these that bring an unexpected or new insight are supported by a citation.
The two major issues linked to energy in today’s finite world are:
1. depletion of non-renewable resources
2. rapid fossile fuel use releasing greenhouse gases linked to global changes
Energy and related issues are among the most important challenges that the society has to face today. While the world population and resource consumption per capita are growing, non-renewable resource reserves depletion is leading to exploitation of more re- mote sources of difficult access with increased costs and shrinking energy yield. Resource consumption always placed a burden on the environment, but until the 20th century, the environmental charge remained within the possibilities of natural regeneration of the planet. Today, the sustainability of the planet’s ecosystem is already threatened by the consumption of resources. Environmental charges due to the extraction of resources are also growing and are further added to the total environmental burden. On the other side, the planet’s size does not change, and its regeneration capacity is further diminished by human activities like deforestation, land use pattern changes and ocean pollution. All these factors mean that the mankind has reached the limits that cannot be overcome by any human action as they are exogenous, given only by the Nature and the planet Earth.
Among the consequences of the use of fossil hydrocarbons, climate change is one of the issues which has to be assessed and addressed with a high priority given its impact on human activities but also on the underlying environment that the humans are part of and cannot survive without. Global warming is considered today to be a well established fact and the reduction of anthropogenic emissions of greenhouse gases is an important issue.
Supply chain disequilibrium between places of extraction and consumption is grow- ing.
An other issue is energy and the economic dependence linked to the supply chain.
For fossile energy resources in particular, the resource supply chain has evolved in a way that the consumption is located mainly in developed countries and emerging markets while resource extraction and transformation is not necessarily geographically bound to the place of the consumption.
Thus there is a twofold objective as 1. to orient the supply chain toward decarboni- sation of the activities and 2. to minimise the risk of resource dependence from unstable regions, legislations and political regimes. On one side, finding new or different sources and finding alternatives to traditional resources, on the other side, reducing the resource intensity of economic activities, are two ways how to address these issues while preserving the economic development.
Replacement of fossil fuels by nuclear power, then replacement of the nuclear power generation by renewable sources of energy, exploration of shale gas sources as an alter- native to the traditional natural gas extraction are examples of strategies considered to switch the primary energy supplies to reach the objectives of decarbonisation and of re- ducing the energetic dependence on unstable regions. Reducing the specific consumption of the new cars while keeping or increasing their power, increasing the offer of public transportation to assist the drivers to decide for a transport modus switch and aban- don the individual transport, also phasing out the incandescence bulbs in profit of low- consumption lighting sources, or thermal insulation of buildings to reduce their energy intensity, as well as the Swiss initiative "Société 2000 Watts" (Bauer, 2007; Bretschger et al., 2010; Jochem, 2004) are examples of addressing the issue to reduce the energy consumption while preserving the quality of life and economic development.
These objectives can be achieved principally in two ways: by technology progress or by behavioural and social change, or, typically, by some mixture of the two ingredients.
So far, there have been research activities in both senses but they remained distinct and far away from each other both in terms of subject of research and of methodology which is specific to the relative disciplines. There is a rich literature on this topic but with a methodological dichotomy: technology progress is studied with engineering models, while the behaviour using the methodology of social sciences. The same when it comes specifically to energy systems optimisation models that so far included behaviour only in a form that is compatible with the normative model. For example, modal choice in the transportation sector.
This research is a part of a more general endeavour. The process of capturing eco- nomic development, social justice and the global environmental issues into one single process is calledsustainable development. A graphical representation of the concept
3 is drawn in Figure 1.1.
Resolving the world’s environmental, social and economic problems will require so- lutions that take all three aspects into account at the same time. In addition, the sustainability requirement means that resources have to be used in a way to meet human needs while preserving the environment so that these needs can be met not only in the present, but also for generations to come1.
Components to address the challenge are:
1. technology progress 2. consumer behaviour
Economic Growth
Environmental Viability Social
Nurturing community
Sustainable economic
develop- ment Equitable social
development
Sustainable natural& built
environment Sustainable development
Figure 1.1: One of the most frequent graphic representations of sustainable development.
Sustainable development is thus the central concept of every sound policy and should be taken into account, if possible, already at the stage of setting up the model.
Technology progress is sound if it is compatible with the concept of the sustainable development. New tech- nologies are the key of the progress of the society to less resource intensive ways of life. However behaviour has also to be part of the equa- tion. Behavioural change as a part of structural changes is fully compatible with the concept of sustainable devel- opment.
Today’s civilisation is undergoing globalisation that creates global issues which are real, visible, important and urgent. Global prognoses are not taken seriously by most of people including politicians whose interests are not compatible with in-depth under- standing of these important phenomena. The system is seeking to protect itself in the first place; therefore the most popular solutions are those that do not touch the insti- tutional basis of the existing system. The existing predator-prey schema that humans developed toward the Nature and the philosophy of endless exponential growth came to
1The most often-quoted definition of sustainable development is the one from Brundtland Commission Report: "development that meets the needs of the present without compromising the ability of future generations to meet their own needs."
(Brundtland, 1987; UN General Assembly Resolution 42/187, 1987).
their limits and continuing on this path is putting in danger the very existence of the human race.
The failure of the Copenhagen United Nations Climate Change Conference in Decem- ber 2009 has shown the dichotomy between the emergency coined by the scientists who claim that the temperature increase by 2◦ Celsius is the maximum bearable to maintain the state of the planet, while for the politicians, it was perceived as the maximum target that could perhaps be reached but they still preferred business as usual. Poorer countries behave as if the problem did not exist at all (Baker, 2009). Day-to-day problems and short-term survival behaviour are leaving less time to address important challenges. The action still has to be preceded by awareness of emergency character of measures to be taken. The temperature increase by just 2◦ Celsius means that the environment will be irreversibly affected by depletion of ocean life and reduction of biodiversity. Economi- cally, from this level of warming, a significant part of the global GDP starts to be used not for production but for damage prevention and repair, such as rising the dikes in Netherlands, in London, New York City and New Orleans, or relocation of millions of people in Bangladesh who lost their habitat because of sea level rise. Tens of millions of people will lose access to drink water and hundreds of millions of people will be added to the number of starvating populations as their purchase power is below the production cost of the food.
Burke et al. (2015) have shown that overall economic productivity is non-linear in temperature for all countries, with productivity peaking at an annual average tempera- ture of 13◦C and declining strongly at higher temperatures. The relationship is globally generalizable, unchanged since 1960, and apparent for agricultural and non-agricultural activity in both rich and poor countries. These results provide the first evidence that economic activity in all regions is coupled to the global climate and establish a new em- pirical foundation for modelling economic loss in response to climate change.
Emphasising on behavioural change as part of the solution is compatible with the emission reduction targets. Behavioural change supposes an increased environmental and humanitarian awareness as well as recognising the existence of a link between the two.
Elements blocking the needed change are:
1. myth of infinite growth and the definition of growth itself 2. blocking structural changes
Economic development is one of the components of overall human development. Eco- nomic development is a positive evolution composed of both economic growth and of structural changes, related to a geographic territory or to a population. Often, economic development is linked to increase of wealth of people and improvement of their living conditions and this is the reason why economic development is associated to progress.
The word development became an approximative synonym of economic development that
5 Figure 1.2: Climax of planetary
availability of non-renewable energy. Natural energy limit of exploitability depends on the state of technology and on existing social order but also remains bound by the laws of the Nature. After exhausting the non-renewable energy resources, if there is no substitution, the total human energy consumption will have to shrink to near zero values.
With substitution, it may further increase up to the maximum given by natural limit. Nuclear fusion is omitted here.
Source: Elliot (2003). Adapted
from Gustav R. Grob (2009). BC0 1000 2000 3000 4000
years 100
1000 Energy
[ PWh ]
Renewable energy:
Solar direct /indirect Wind power
Hydro /Tidal /Wave power Ocean /Geothermal power Biomass /Biogas Ambient energy Muscle power Novel energy systems Non-renewable energy
200
Total usable energy on Earth
Depletion of f energy resources
inite
is interchanged with economic growth, or simply growth. There is a widespread belief that growth is good. Growth is often reduced to one single parameter, the GDP growth.
Indeed, in 19thand 20thcentury, GDP growth has brought progress in many domains like health care, education, technology and production, as well as in social domain. But today, economic growth and structural changes can be de-connected.
The modern economy is based on a duality relationship of GDP growth versus debt management. Debt is used to foster growth and growth is necessary to reduce debt. The total debt of the world represents about 225 % of world GDP and is growing faster than GDP itself. Both parameters are hostile to sustainable development.
There even are examples that economic growth is not profitable to people. In Nige- ria, during many years, GDP has grown by 5 to 7 % per year, inflation was below 9
%, with quickly developing banking and telecommunication sector. As soon as petrol prices dropped, the economy suffered. Life of simple citizen did not improve during the economic boom. Economy has grown but did not develop. No progress in infrastruc- ture, education, health care or other parameters of the quality of life has been observed.
The reasons are weak institutions, thriving corruption and lack of political willingness to implement changes. Similar evolution can be found in case of Venezuela, Equatorial Guinea, Zimbabwe, Sierra Leone or Russia.
In this regard, Acemoglu and Robinson (2013) complete the suggestions of Landes (1999), who is mentioning as the main reason of the dichotomic economic evolution the industrial revolution, in which some countries made the leap to industrialisation and became rich, while other countries failed to adapt and remained poor. Factors such as climate, natural resources, and geography would not be as important as certain cultural traits such as work, thrift, honesty, patience, and tenacity that affect the ability to ef- fect an industrial revolution. On the other side, Acemoglu and Robinson, instead of cultural traits, identify the political system as being the main factor of economic suc- cess and suggest that under an authoritarian regime (extractive political institutions), the economic developments is slower than in democracies (inclusive political institutions).
GDP is a parameter which is well measurable as the sum of the value of all the produced goods expressed in monetary units, values that are thoroughly documented as one of the most tightly followed statistical data. Using GDP as the principal indicator, or, even worse, the only indicator, is widespread but utterly incorrect. There are more elaborate indexes of progress such as index of human development, or indexes of social inequality and so on but they are not so simple and straightforward.
There is worse: the reductionist approach of interchanging economic development and GDP growth leads to a fallacious perceived relationship between economic develop- ment and increase of the energy consumption because spending on energy is increasing the GDP. To go back to the original meaning of economic development, one has to take into account the structural changes. Examples of Japan, Sweden, Switzerland show that economic growth can be achieved without energy consumption increase because most eco- nomic growth comes from dematerialised production associated with high added value in a post-industrial society. This is driven by technological progress but again, behavioural change and increased awareness are part of the solution. Behavioural change is part of the needed structural changes.
Energy consumer behaviour has interesting properties from energetic and economic points of view. Energy returned on energy invested (ERoEI) is the ratio of the amount of usable energy acquired from a particular energy resource to the amount of energy expended to obtain that energy resource (Murphy and Hall, 2010). Hall, Lambert and Balogh (2013) are bringing a similar, yet different-wording definition of ERoEI as the ratio of amount of energy delivered, to energy required to deliver that energy.
ERoEI has to be greater than 1 to be energetically viable and to maintain its sustain- able effectiveness. This value varies from 0.6-1.3 for ethanol corn produced in the US to 100 or more for hydro power dams. In order to trigger a behavioural change by informa- tion or marketing campaign or via persuasion and example of neighbours or family, no direct energy consumption is necessary. The value of ERoEI for behavioural change tends to infinity which is the best energy investment one can imagine. The concept of ERoEI is further discussed in Chapter 2, Literature Review, Energy Modelling, on page 21.
7
Cost/benefit analysis can also be favourable for behavioural change, however this relationship is not obvious. Depending on the information vector, the cost of an informa- tion campaign may become important, for example a TV ad is expensive but can reach a large public with some efficiency. The investment cost is known in advance but the yield and exact figures for cost efficiency have to be determined through sociological surveys.
Importance of declining ERoEI can be seen from historic parallels. The reason of existence of human societies is to share the burden of solving problems. Societies col- lapse when they are no more able to address problems and thus lose the reason of their existence. During their existence, societies become more complex with the organisation of production toward task specialisation. Anthopologist and historian Joseph A. Tain- ter (Tainter, 1990) exposes the usual theories of disappearances of societies (because of internal reasons like absence of adequate response face to shortage of natural resources, soil degradation and decline in agricultural production, or because of external force such as a natural disaster or invasion of barbarians). Then he draws his own theory of societal decline: as societies become more complex, the costs of meeting new challenges increase, until there comes a point where marginal productivity of sociopolitical change is declining and resources devoted to meeting new challenges first produce diminishing, then negative returns. At this point, societies become less complex as they collapse into smaller soci- eties where marginal returns of complexification are higher. For Tainter, social problems leading to collapse are originating from problems of recruiting enough energy resources to feed the complexification of society which is necessary to solve ever-newer problems.
Obviously, this is not just a question of technology progress but also of operating modes and of intensity of use of natural resources, e.g. a behavioural issue.
Homer-Dixon (2006) is examining the ancient Roman empire from an unusual point of view: thermodynamics of empire. And he draws parallels with modern days. Rome’s success depended on its ability to extract energy surpluses, mostly in form of food, ex- tracted from peripheral territories and concentrated at the centre. Over centuries, it enabled the development of a the of organisational complexity. EROI of imperial energy tributes declined over time to the point where the complexity of the centre could no longer be maintained and "the empire could no longer afford the problem of its own exis- tence". Homer-Dixon identifies five major stresses: population growth, energy depletion and declining EROI, environmental degradation, climate change and financial instability.
In addition, two potential multipliers, the escalating destructive power of small groups and the rising speed and connectivity of our socioeconomic system, can trigger the po- tential for negative synergy between them. Our managerial approach is adding layers of complexity to an already rigid and dysfunctional governance system declining in re- silience.
Other authors, such as Jared Diamond, are bringing similar insights. Different stages of development of human societies around the world and in different points in history is
explained by differences of geography and natural resources availability such as energy sources, animals that are suited for domestication, raw materials that brought knowledge how to manufacture metals and use them in increasingly complex social setup (Diamond, 1999).
In his next book (Diamond, 2004), the author is dealing with reasons of disappear- ance of human societies where he is underlining the role of degradation of environment and shortage of usable resources in collapsing societies, and on the other side, the role of sustainability in long-term survival of some social orders. Broad analyses of both books were controversial and a special session was held at the American anthropological society annual conference in San Jose in 2006.
The proceedings of the conference (McAnany and Yoffee, 2009) contain contributions that confirm most of the author’s findings. Some societies, such as Vikings from Groen- land, Mayas from southern Yucatan or Indians from south-west USA did not die, just relocated and are living in a broad neighbourhood of their original locations. Survival behaviour of many societies leads to frenetic depletion of resources that is incompatible with the concept of sustainability. Again, this is a behavioural issue when men face the scarcity of resources and choose a bad strategy that is leading to their ultimate depletion.
Beyond qualitative assessment of historians of human development, there are specific research methodologies to study and describe energy consumption, energy choices, and energy conscious behaviour of consumers. For decades, in the energy research, there are two approaches to assess behaviour. From the side of social sciences, many models of ecological behaviour with increasing complexity and explanation power brought better understanding of the way how to define variables to get the best correlation and explana- tory power concerning the ecologic behaviour.
On the engineering side, including behaviour into normative models is an emerging issue that, if it is done only with engineering approach, may address some details in certain demand sectors such as transportation. There is clearly a need for an universal methodology that would allow the modeller to address any behaviour in any demand sec- tor, be it by coupling the two methodologies, technico-economic models with inductive methodology used in social sciences.
A system is a regularly interacting or interdependent group of items forming a unified whole2, a set of things working together as parts of a mechanism or an interconnecting network; a complex whole3. An energy system is a group of things that are used together to produce energy4, an interrelated network of energy sources and stores of energy, con-
2definition by Merriam-Webster Dictionary
3definition found on google.com
4definition by Cambridge Dictionary
9 nected by transmission and distribution of that energy to where it is needed5.
Energy system modelling is the process of building computer models of energy sys- tems in order to analyse them. Such models often employ scenario analysis to investigate different assumptions about the technical and economic conditions at play. Outputs may include the system feasibility, greenhouse gas emissions, cumulative financial costs, nat- ural resource use, and energy efficiency of the system under investigation. However the most frequent output is the total energy demand for some energy agent belonging to the class of useful energy. Useful energy is an energy form such as electricity or gasoline that can be directly used in a demand device to satisfy some type of useful demand such as transportation, light or heat. A wide range of techniques are employed, rang- ing from broadly economic (top-down econometric models, input-output matrix, general equilibrium models) to broadly engineering (bottom-up accounting models, energy sys- tems optimisation models). Mathematical optimisation is often used to determine the least-cost in some sense.
Mathematical optimisation (or mathematical programming) is a class of mathemati- cal problems that can be formulated in the following way:
Given is a function f :A→R,
whereA is some set andR is the set of real numbers ("objective function").
Sought is an element x0 ∈A
such thatf(x0)≤f(x) for all x∈A ("minimisation"), or such thatf(x0)≥f(x) for all x∈A ("maximisation").
In energy systems optimisation, the most frequent objective function is the global aggregated cost of the energy system and the extreme sought is the minimum global cost of the entire energy system.
In this case, the research gap was stemming from the difference of the approaches used in engineering models and in social sciences that had pursued different research objectives and were methodologically incompatible.
This research has been conducted to address the research gap and its purpose is to create ahybrid model uniting the deductive method of normative engineering models with inductive methods of social sciences, to develop a tool that allows the modeller to mix technology choices and behavioural change on the same platform to offer the decision makers a comprehensive concept for holistic management of energy choices including social change and to set up efficient and coherent economic public poli- cies. Such a tool is a step toward sustainability resulting from public policies that will better take into account both components of the problem: progress through technology and behavioural changes.
5http://environ.andrew.cmu.edu/m3/s3/all_ene_sys.htm
Our research question is:
• How to mix the two approaches, progress in energy efficiency due to technology change on one side, and behavioural and social changes on the other side, in a coherent way, to be able to bring them to the same optimisation platform? Be- havioural change then can enter into competition with technology change and allow new insights when designing public policies.
This research question is an original scientific contribution as a new brick to the bridge between distant disciplines but also as an element contributing to the solution of global change issues. Obviously it is a procedural question, yet it remains pertinent from the scientific point of view. This is a methodological contribution and not a consultancy where the result counts. In this work, the result is a method.
A procedural task involves performing a procedure, which is a sequence of activities to achieve a goal. Synonyms include method, technique, skill, and rule. Soft-linking data originating from different methodologies used in distant disciplines is more than just a procedure, it is an original methodological step in construction of meta-models.
Including consumer behaviour into normative engineering models is an entirely new ap- proach that already has followups in literature, mostly in the transportation section using TIMES model, with behavioural data both endogenous and exogenous. We are describ- ing two methodologies: virtual technologies that result from coupling energy models with sociological data, and coupling linear programming energy model with a non-linear op- timisation willingness-to-pay model.
A "procedural" research question is not necessarily less scientific than a "usual" re- search question seeking just for results by applying usual routine methods. From the point of view of scientific contribution, the least interesting works propose some consul- tancy, which is defined as an application of well established routine to obtain new results.
In the best case, existing routines are being applied to unusual situation or context.
More interesting are works that bring a methodological contribution or new concepts.
And the top contributions bring a new paradigm.
As our contribution precisely is a new methodology, the research question formulation
"how-to" is fully legitimate.
In order to address the research question, several subsidiary problems had to be dealt with:
• What are the specificities of theperceptionof consumers when it comes to demand of different useful energy forms?
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• What is the general attitude of the consumers toward consumption of various energy forms?
• How consumers behave when they deal with different useful energy forms?
• What are the limitsof the different modelling approaches?
This document is organised as follows:
The context of development of energy policies and the research question is described in Chapter 1, "Context of Approaches to the Development of Energy Policies". The re- search question is how to include behavioural data together with technology data in order to formulate a hybrid model that takes into account both technology progress as well as the behavioural and social change, so that such model can serve as basis to elaborate comprehensive and coherent public policies on energy system configuration, policies that are not flawed by over-optimistic expectations toward energy savings coming from tech- nology changes alone. The requirement is to address the systematic error coming from hypothesis of perfect information and the hypothesis of perfect economic rationality that are no more fulfilled when extrapolating the original MARKAL model out of its original context: a country-level model with emphasis on the supply side.
The state of the art of all the available tools both on engineering and sociological sides that can be used to address the research question are listed in Chapter 2, "Literature Review, Energy Modelling".
Then, the tools chosen to address the research question are described in Chapter 3,
"Methodology & Tools" as well as the research design and the roadmap of how we came to the results. The model used is a MARKAL model constructed for the city of Nyon, TIMES6Romania country model, OSeMOSYS7UTOPIA8for the Share-of-Choice model as well as sociological surveys conducted between 2009 and 2014 (one ethnomethodolog- ical, several qualitative and quantitative ones).
The methodological contribution is drawn in Chapter 4, "Including Behavioral Data in Mathematical Programming Framework", where we describe the structure of the new hybrid model and its properties, as well as the way we obtained technical coefficients from sociological surveys. The behaviour of consumers is not corresponding to the expected theoretic behaviour, i.e. the computed one when using the optimisation model without correction for consumer behaviour on the demand side. But the actual behaviour can be measured using sociological surveys, as well as factors that could influence that be- haviour. Most frequently measured parameters are: information, attitude and behaviour.
6TIMES, The Integrated Markal Efom System energy systems optimisation framework, see sec- tion 3.2 TIMES on page 79
7OSeMOSYS, The Open Source Energy Modeling System
8UTOPIA: 1. A ficticious island society imagined by Sir Thomas More, 1516; 2. a generic case model provided with energy modelling frameworks to perform a standard test of the system.
All these can be influenced by targeted information and marketing campaigns aimed on triggering a change in operating modes and promoting a technology switch.
Instead of modelling these phenomena as additional bounds on technology activities, that would introduce further subjectivity, or via the economic function, that would in- troduce non-linearity, we have chosen to model consumer behaviour as virtual process technologies. That way, they may enter into competition with real technologies and appear in the optimal solution, if the usual criteria are met: the least aggregated cost over the entire programming period. This is the concept behind the corrected MARKAL model that we call Socio MARKAL. Included are the scenarios of the MARKAL runs, and a sensitivity analysis to verify a sound behaviour of the system.
Still in chapter 4, we also mention the concurrent or followup approaches. Soft-linking a linear energy optimisation model with a market share simulation model can give an insight on real market share of competing technologies without the "winner takes it all"
feature which is common to optimisation models (Ramea et al., 2013). An alternative is to introduce a new variable "travel time investment" that, in addition to a competition within transport modes, puts in competition the modes themselfs (Daly et al., 2014). In the Share of Choice method the consumer preferences are hard-linked with the optimi- sation model, introducing binary variables (Moresino, Fragnière & Kanala).
The main results and findings of sociologic surveys are listed in the Chapter 5, "Main Results and Findings from Sociologic Surveys", including a few interesting hypotheses resulting from ethnomethodologic or classical qualitative sociological surveys that either have already been tested or that still are to be tested using a special dedicated quanti- tative survey.
Finally, Chapter 6, "Conclusions", resumes the conclusions, lists the limits of the Social MARKAL / Social TIMES approach and the recommendations for further research directions.
2 | Literature Review, Energy Modelling
There is an extensive literature on the technology issues and the supply channel as energy consumption was considered for a long time to be a purely technical question. There is an emerging literature on consumer’s behaviour when it comes to energy services. Also, there are literature sources on the sociology of the energy consumption and related social models. Nevertheless the idea of mixing the two approaches by quantifying the relative effects and integrating them to the same optimisation platform, as presented in this doc- ument, is new.
In this chapter, we first describe the basic concepts of energy modelling, as well as the issues that have to be addressed when constructing an energy model in general. Then, we deal with energy models, types and classification of these. We explain top-down, bottom- up and hybrid models and then we introduce energy modelling from the sociological point of view. Social psychology deals more with attitude and behaviour of consumers when facing investment or behavioural decisions in everyday life. Finally, we briefly look at the new trend in Operations Research that consist in examining the behavioural issues.
2.1 Introduction
Energy consumption and the energy supply chain can be modelled using several types of tools. Econometric models are top-down models used to compute the final energy consumption using a few aggregated explicative variables, usually descriptors of macro- economic trends. Top-Down Computable General Equilibrium (CGE) models are well suited for a large range of problems, like the choice of a development strategy, income dis- tribution, trade policy, structural adjustments to external shocks, fiscal policy modelling including government expenditure policies with subsidies and taxes, long-term growth and structural changes, as well as, since 1980’s, for analysis of environmental policy and resource management issues.
Engineering models, also called technical-economic models, arebottom-up models that take details of technical parameters as input and work up to the higher conceptual level, in our case energy consumption, as a sum of contributions from various kinds of energy consumption devices and from different energetic sectors: households, industry, transportation, services.
Bottom-up models can be accounting models that simply add contributions from different technologies for a given period of time, usually a year, to compute the total energy consumption in a sector or region. Bottom-up models based on economic equi- libria are optimisation models where, under a set of assumptions, hypotheses and constraints, the point of economic equilibrium is sought that corresponds to the optimal configuration of the energy system with minimum cost. Economic equilibrium is the state of economy where economic forces are balanced and which will remain unchanged in absence of external influences. Economic equilibrium is the point where supply equals demand for a product and the equilibrium price is where the supply and demand curves intersect. Accounting bottom-up models bring all the technology details, technical ex- plicitness and, in case of optimisation models, include also economic parameters giving them micro-economic pertinence.
Hybrid economic models (Jaccard and Dennis, 2006; Rivers et al., 2006; Rivers and Jaccard, 2005) unite the technological explicitness of the bottom-up approach with the microeconomic realism and macroeconomic completeness of the topdown approach.
Sociology of energy consumption can be considered as a part of environmental sociol- ogy, the sociological study of societal-environmental interactions; although this definition immediately presents the problem of separating human cultures from the rest of the en- vironment that even humans biologically remain part of and cannot survive without.
But sociology of energy consumption can be approached also from the technology side, focusing on the interactions and interdependences between the society and the technol- ogy that defines the material reality and in the inverse sense, the degree of progress of a society. As we see, already at the stage of definitions, sociology issues are described by more verbose, more complicated constructions compared to engineering approach.
Interactions between energy consumption models and the sociology is a very new field of research as so far, these two research directions were separated by their respec- tive methodologies and approaches but also by the result set. On one side, well quantified and detail-rich specifications of technologies, on the other side, qualitative and not al- ways well quantified descriptions of trends and tendencies, a matter from where it is difficult to construct a sound public policy. Yet a well positioned public policy has to include both technology and social aspects because the technology alone cannot give the complete answer as the behavioural aspects have to be part of the solution.
The problem can be quickly resumed on the Figure 2.1 showing the expected and obtained improvements in energy consumption reduction (O’Leary et al., 2009). The curve of expected technical evolution is a result from an engineering model. The curve of observed behaviour is measured and takes into account behavioural effects such as rebound effect, intensity of use and operating modes, that are companion effects of tech- nology improvement: people pay less attention to savings if the equipment is becoming more efficient. If a policy is designed to take into account only the technical aspects, the
Introduction 15 model will be over-optimistic, a common sin of engineering models, and if it serves as basis to a public policy, such policy will be poorly designed.
Figure 2.1: Expected theorical and actual ob- served energy consumption reduction (ODEX in- dex, 1995=100) in residential sector in Ireland 2000-2010. Only half of the expected consump- tion reduction has been actually achieved.
The term "energy efficiency gap" de- scribing the phenomenon that expected and actual energy consumption reduc- tion do not match has been used for the first time by Eric Hirst and Mar- ilyn Brown (1990). Various barriers that prevent the society from successfully closing energy efficiency gap can be di- vided into two categories: structural bar- riers and behavioural barriers. Struc- tural barriers result from the public poli- cies and are usually beyond the con- trol of the individual energy end con- sumer. Behavioural barriers, if identi- fied, can be addressed by every energy user.
Among the structural barriers, the authors mention (Hirst and Brown, 1990):
• Distortion in fuel prices - end user prices do not include environmental and social externalities.
• Uncertainty about future fuel prices - prevent the users from making rational pur- chase decisions when investing into new devices and energy-using systems.
• Limited access to capital - explains high discount rates when making individual tradeoffs between the capital invested and savings from reduced fuel and operating costs in the future1.
• Government fiscal and regulatory policies - tend to support and encourage energy consumption, rather than energy efficiency.
• Codes and standards - often lag behind the development of technologies and may become a barrier for energy efficiency technological innovation.
• Supply infrastructure limitations - deployment of energy efficiency technologies is highly restricted by factors such as geography, infrastructure and human resources2
1An extreme form of undercapitalised environment may lead to survival behaviour - capital is spent only on the most vital expenditures, barring any maintenance or innovation, as seen in our research on Romania.
2For example, development of alternative renewable sources featuring stochastic character of produc- tion such as solar or wind electricity is limited by the capacity of the electric grid. Replacing the steady current from baseload sources by production plagued by random power peaks requires more reserve.
Behavioural barriers that can be removed by individual action are:
• Attitudes toward energy efficiency - awareness affects energy-related purchase and consumption behaviors.
• Perceived risk of energy-efficiency investments - risk aversion in investing in energy efficiency technologies comes from uncertainties of fuel prices and high discount rate for operating costs.
• Information gaps - consumers tend not to change their energy consumption be- haviour if little information is provided about performances of new technologies and economic advantages of energy-saving behaviour.
• Misplaced incentives - lack of life-cycle thinking on costs and savings and passivity coming from principal - agent problem raise barriers to energy conservation.
Jaffe and Stavins place a bigger emphasis on the customer information in repeated oligopolistic market and categorise the barriers differently. Market failures are due to imperfect information, the public good attributes of information and information asym- metry. Non-market failures include heterogeneity of customers and their behavioural inertia, and uncertainty about future energy prices that influence the return on invest- ment from energy efficiency investments (Jaffe and Stavins, 1994).
In the recent literature, Alcott and Greenstone mention investment inefficiences that could cause an energy efficiency gap, namely the imperfect information and inattention, which is a special type of investment inefficiency that hits decision makers who are well informed, yet they make a suboptimal investment (Allcott and Greenstone, 2013).
Gillingham and Palmer further develop on the market vs. non-market categorisation.
According to the authors, the energy efficiency gap could be overestimated because of hidden costs, consumer heterogeneity, uncertainty, overestimated savings, and rebound effect. Among market failures, the authors mention the imperfect information, principal - agent issues, credit (liquidity) constraints, learning-by-using, and regulatory failures.
Behavioural anomalies and failures include non-standard preferences such as self-control problems (taking long-term view of decisions, but as the future approaches, increasing the discount rate used to evaluate decisions), or reference-dependent preferences (deci- sion making under uncertainty where the consumer’s utility from any outcome depends on the outcome’s relationship to a particular reference point, for example, loss aversion).
Other behavioural anomalies include non-standard beliefs, and non-standard decision making such as limited attention, framing of choices, and suboptimal decision heuristics.
Proposed energy efficiency policies should include measures such as matching the policies to market and behavioural failures, and build on empirical evidence such as information strategies, economic incentives and efficiency standards (Gillingham and Palmer, 2014).
Measures narrowing the energy efficiency gap include
• Information campaigns - help to overcome the lack of information
Introduction 17
• Energy labels - establish a scale, and are the first step to introduction of standards
• Minimum equipment efficiency standards - eliminate inefficient equipment by ad- ministrative ban
• Taxes and subsidies - help to internalise external costs and to overcome capital shortage for investments in public interest
In economy, an externality or external cost is the additional cost associated with activities or with a consumption of certain goods which is supported by someone else than the consumer himself. Usually, it is a broader circle of payers or collectivities like in case of noise, or polluted air and water, or degraded space from road construction.
Externalities may be internalised by subsidies or taxes that have to be well designed to target only the external cost and nothing else.
EROI (Energy Return on Investment) is the ratio of how much energy is gained from an energy production process, compared to how much of that energy or its equivalent from some other source is required to extract or grow a new unit of the energy in ques- tion (Murphy and Hall, 2010). A different EROI will thus be associated with each energy form. As EROI definition is somewhat fuzzy, it allows one to think of it as energy return on financial investment. But the simple definition for EROI is covering much complexity under the hood.
One issue is that there is a range of EROIs in the literature for each energy source, depending on the methodology the researcher is using. There is a good indicator of controversy when it comes to figures, Wikipedia. It can be edited by anybody with administrative rights that can be modulated collectively by a panel of personalities. De- pending on the orientation of the collective bias of editors, there can be different contents for different language versions dedicated to the same term. Figures in tables 2.1 and 2.2 are witnessing that the same matter can be treated in a very different ways.
Also, there is no single accepted way of calculating EROI, because it depends in part on the definition of boundaries, on what is being counted as an input. Two most fre- quent types of EROI are "net energy ratio" and "external energy ratio". For many energy forms, the difference is small but in case of tar sands, it is huge. The "net energy ratio"
counts all inputs, including diesel fuel for the giant trucks or the tar sands themselves.
The "external energy ratio" only counts the external energy put into the process and not the resource itself, and therefore is higher than the "net energy ratio". For tar sands, many sources use only the "external energy ratio", however, often, for conventional oil only the "net energy ratio" is available (Inman, 2013).
Finally, there are uncertainties in any EROI estimate that come from imprecise statis- tics of energy companies. Publishing precise numbers could reveal commercial secrets.
In our case precise figures do not count much as this work is not about ERoEI. We
