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The oil embargo in 1973 and the unfamiliar circumstances at that time created the motivation for the development of energy modelling. Early models concentrated mostly on specific sectors, such as the electricity sector or oil sector (Huntington et al. 1982). A second-generation of energy models comprised energy system models that look at the whole energy system from energy supply via energy transformation to energy demand. A further development represented energy-economy models that not only focus on the energy sector but also include economy-wide interactions. Integrated assessment models represent again a more comprehensive category that include interactions across different sectors, such as forestry, agriculture and energy, as well as with the environment, i.e. the impact of rising emissions on the environment and in some cases feedback on the economy through a damage function. Those models try to address the issues of equity across space and time, possible damage costs and uncertainty (see e.g. Rotmans and van Asselt 2001; Stanton et al. 2008).

A modelling approach mainly used in other disciplines is agent-based modelling. These models consider the behaviour and interaction of individual agents and therefore provide insights into the behaviour of organisations and their implications for technology adoption (DeCanio et al. 2001; Worrell et al. 2004, p. 365). This approach tries to challenge the common objective in energy models of cost minimisation or profit maximisation by incorporating a more realistic organisational network structure to examine its overall influence. It can contribute to change implicit assumptions that are generally used when trying to find a solution for environmental problems. Bower et al. (2000) have applied an agent-based model to the UK electricity market.

Energy models can be distinguished according to their planning period. While short- term energy models are used for the portfolio management of single companies and consider a period of days to a year, medium to long-term models also include investment decisions. With the last category of models it is possible to explore questions in energy and environment policy. As this thesis focuses on long-term developments of carbon reduction portfolios the following categorisation focuses on long-term models.

3.2.1 Categorisation

Energy models can be distinguished by many characteristics. Many hybrid types of approaches make a clear distinction impossible and permit only a general categorisation. This is related to the fact that some models were initially built for a specific purpose and were then applied to integrate other aspects. Many models, for example, were developed with a fossil fuel-based energy system in mind that cannot represent intermittent systems based on renewable energy sources. Table 3.1 gives an overview of the possible classifications of energy models. Many researchers have reviewed existing energy models and classified them in various ways (Löschel 2002; Springer 2003; Jebaraj and Iniyan 2006).

The most common separation of such models is into bottom-up and top-down (see e.g. Hourcade et al. 1995). A top-down approach breaks down a system to gain insight into its compositional sub-systems, while a bottom-up approach puts together elements of a system to give rise to grander systems, thus making the original systems sub-systems of the emergent system. In the energy field, bottom-up models are used to describe the current and prospective competition of energy technologies in detail, both on the supply-side (the substitution possibilities between primary forms of energy) and on the demand-side (the potential for end-use energy efficiency and fuel substitution). Typical examples of bottom-up models are energy system models. Top-down models on the other side address the consequences of policies in terms of public finances, economic competitiveness and employment (Hourcade et al. 2006). Typical examples of top-down models are computable general equilibrium (CGE) models. Conventional bottom-up models are known for their technological detail and lack of microeconomic realism, whereas conventional top-down models include economy-wide interactions based on market behaviour, but lack the technological explicitness.

Both model types address the same problem from different perspectives. On the one hand, top-down models are based on historical trends and can therefore only give useful results in the case that historical relationships among key underlying variables remain constant. Bottom-up models, on the other hand, include predominantly only the energy sector and are therefore only suited for analytical purposes when there are no important feedbacks between the energy sector and the other sectors of the economy (van Beeck 1999).

The distinction between top-down and bottom-up models is almost two decades old (Grubb et al. 1993; Wilson and Swisher 1993). Since then it became more difficult to maintain this clear distinction between bottom-up and top-down models because bottom-up models have integrated microeconomic aspects like a price-elastic demand and top-down models have integrated more technological detail into the nested production functions (Hourcade et al. 2006, p. 5f). Moreover, hybrid models have been developed that combine in different ways the top-down and bottom-up approach in one model. Böhringer et al. (2008) distinguish in this context three different types of hybrid models: combination of independently developed bottom-up and top-down models, a bottom-up or top-down model used together with a reduced form representation of the other and a completely integrated model based on solution algorithms for mixed complementarity problems.

Another possibility to divide energy models is according to their treatment of uncertainty, i.e. if they are deterministic or for example stochastic. Many energy models were constructed as deterministic models thus relying on specific input assumptions. In this case uncertainty can only be considered via the variation of input assumptions, i.e. sensitivity analysis. In contrast, stochastic models incorporate uncertainty about technology development, energy prices or other parameters by assigning probabilities to different developments of these input assumptions. This enables the modeller to derive hedging strategies for different scenarios.

According to the time frame one can distinguish energy models into static, dynamic and recursive dynamic. Since many energy models cover several decades, static models, which optimise only one period, are relatively rare. Dynamic models describe states and changes in the system by means of differences and differentials over the course of time. Dynamic models possess perfect foresight, which means that they optimise the system over the whole planning period. Dynamic recursive models, also called myopic models,

do not consider the whole planning period but optimise for a subset of periods, where decisions of earlier periods are inputs to the following period (Keppo and Strubegger 2010).

The mathematical implementation of energy optimisation models can broadly be divided into linear and non-linear with separate integer formulation or mixed integer variants when only a subset of the variables are required to be integers. Linear models need less computational capacities and calculate a global optimum but restrict the modelling to linear relationships, which sometimes approximate non-linear relationships. Non-linear models are in general more time intensive to optimise than a comparable linear model. They allow the consideration of non-linear relationships but may only find one of several local optima rather than a global optimum. One can assume an optimum to be global in the non-linear context only in the case of convex model equations and a convex objective function.

A further well-known differentiation between models is into simulation and optimisation. Optimisation models give an answer to the question of how to achieve a given goal described in an objective function subject to constraints. An example is cost minimisation, where many possible solutions exist and the model chooses the optimal, i.e. the most cost-effective one. One could say that optimisation models simulate some physical aspects of the energy system depending on the degree of endogenisation, i.e. the input parameters, and optimise the rest. Simulation models answer the question: what happens for a set of given conditions? This does not necessarily lead to a full equilibrium or an optimum. It means that these models investigate in an explorative manner the consequences for given options. Mathematically this corresponds to a set of equations with an equal number of variables. In contrast to an optimisation model, where the model chooses the optimum among possible solutions, a simulation model has no degrees of freedom. Optimisation models can also be described as prescriptive models as they give insights on what to do to make the best of a set of conditions, while simulation models can be characterised as descriptive since they clarify what would happen in a specified situation. The advantage of simulation models is that they can better model real, imperfect markets in contrast to optimisation models. Nevertheless, given decision making rules determine the model outcome and interactions between different rules are unclear (Möst and Fichtner 2009, p. 22). In this context, sensitivity

analysis can help to a certain extent to shed some light on these interactions (see section 5.2.1).

The degree of endogenisation, i.e. the degree to which parameters are incorporated into the model, can be another metric for the categorisation of models. Energy models must have at least one external parameter and can have all parameters determined externally, while the majority of models lie in between. Exogenous assumptions include in most cases parameters, such as population growth, economic growth, price elasticity of energy demand and can further include energy demand, supply and existing taxes (van Beeck 1999). The degree of endogenisation tends to be higher in optimisation models compared to simulation models. In recent years several exogenous assumptions have been endogenised in bottom-up models, like price elastic demand curves, use of endogenous technological learning or stochastic programming in order to endogenise uncertainty related to input assumptions (Remme 2006, p. 81). In addition, top-down models endogenise economy wide interactions, while bottom-up models rely on external assumptions in this respect.

Lastly, one can distinguish energy models according to the geographical scope. This includes models on a local, regional, national, continental and global level. In addition, energy models differ according to the sectors they include. Models can be restricted to a single sector, such as electricity generation, the energy system or the whole economy.

Table 3.1: Taxonomy for the differentiation of energy models

The analytical approach: Bottom-up and top-down Treatment of uncertainty: deterministic and stochastic

Treatment of foresight: static, dynamic and recursive dynamic Mathematical implementation: linear and non-linear programming Underlying methodology: optimisation and simulation

Degree of endogenisation: fuel prices, economic growth, taxes, energy demand Geographical scope: local, regional, national, continental and global

3.2.2 Top-down models

This section should give a brief overview of typical types of top-down models. Models in this category can be divided into growth models, CGE models and macroeconometric models.

Growth models are based on modern growth theory maximising aggregated social

welfare, which is discounted over the future. Optimal growth models facilitate the understanding of growth dynamics, i.e. transition paths, over long term horizons under the assumption of what decentralised markets can achieve in the presence of appropriate policy instruments. Global growth is partly explained in terms of research and “learning by doing” affecting the stock of knowledge, which in turn enters the production functions of the model. Important assumptions include representative agents and full employment. In this context growth models can be distinguished as first best models, which implicitly assume perfect markets and optimal policy tools, whilst second best models include market imperfections and sub-optimal policy tools (Edenhofer et al. 2006, p. 62ff).

Examples of growth models are:

 DEMETER (DE-carbonisation Model with Endogenous Technologies for

Emission Reductions) (Gerlagh and van der Zwaan 2004; Gerlagh 2006)

 DICE (Dynamic Integrated Climate-Economy) (Nordhaus 1993)

 FEEM-RICE (Regional Integrated Model of Climate and the Economy) (Bosetti et al. 2006)

The most widely used type of top-down models are CGE models, which are, as their name implies, based on equilibrium theory and thus do not capture short term adjustments but concentrate on the long term. This model type also relies on the assumption of representative agents, but can incorporate the stock of knowledge and can include unemployed labour in contrast to growth models. CGEs optimise over a series of static equilibria, generating insights on how the economy shifts from one equilibrium to another and calculate numerically demand, supply and the resulting price. In these models every sector is mapped with a nested production function, where production factors are substitutable according to a defined elasticity, so that policy responses can be modelled.

Top-down models have been criticised for their dependence on the elasticity of substitution between energy and labour/capital and the autonomous energy efficiency index (AEEI). The elasticity of substitution represent price induced changes in the demand for energy and the AEEI represent the non-price induced energy intensity reduction. Both parameters are used to describe complex behaviour, but are neither

observable nor measurable. Knowing that the rate of non-price induced efficiency improvement has changed historically, it is disputable to assume that it cannot change, or be changed, in the future as is assumed in top-down models (Wilson and Swisher 1993). In effect, this modelling approach assumes that market behaviour remains in line with historical observations, so that institutional innovations as well as technological adjustments beyond current practice aimed at improving energy efficiency are excluded. Provocatively, Wilson et al. (1993, p. 254) stated that top-down models tell us that if it had been expensive to reduce CO2 emissions in the past, and the economy stays the same as it was at that time, it will also be expensive in the future.

That is the reason why top down models have been said to suggest that efforts to reduce carbon emissions are relatively costly, i.e. the economy‟s potential for technological transformation is limited as portrayed by historically-based elasticities (Hourcade et al. 2006, p.4). In recent years, this problem has been recognised and top-down modellers have tried to model induced technological change (ITC) in the presence of ambitious policies (Edenhofer et al. 2006).

In addition, top-down models can consider the rebound effect. This effect describes a phenomenon where efficiency improvements do not lead to the expected reduction in final energy consumption because part of it is compensated by an increase of energy service consumption due to a cheaper energy service (Sorrell 2007). Top-down models take account of the effect in the way that a price decrease results in the recycling of economic savings that leads to increased consumption. However, they do not consider that it can result in the substitution of energy consumption by the consumption of other economic inputs, such as labour.

Lastly, some CGE-models possess the abstract construct of a backstop technology, which can provide infinite energy at a comparably high price and thus set a maximum limit for a CO2 price in the case of a carbon constraint. The reason for the modelling of a backstop technology can be found in the poor technological detail of top-down models.

Examples of CGE models are:

 AIM (Asia-Pacific Integrated Model) (Fujino et al. 2006)

 EPPA (Emissions Prediction and Policy Analysis) (Ellerman and Decaux 1998; Paltsev et al. 2005)

 GCAM (former MiniCAM) (Global Change Assessment Model) (Clarke et al. 2008; Luckow et al. 2010)

 GEM-E3 (General Equilibrium Model for Energy-Economy-Environment) (van Regemorter 2005)

 MERGE (Model for Evaluating Regional and Global Effects of GHG reduction policies) (Manne et al. 1995; Manne and Richels 2006)

 WIAGEM (World Integrated Assessment General Equilibrium Model) (Kemfert 2002)

 WorldScan (Lejour et al. 2006)

A third category of top-down models is macroeconometric models. This model type is also called „neo-Keynesian‟ as it assumes output to be demand determined in contrast to CGE models, which are supply driven. This approach simulates monetary flows between sectors, based on input-output tables. Therefore, a system of equations is created that map the economy. The equations are estimated with the help of statistical techniques, such as regression analysis based on time-series data. Thus, econometric methods are used to extrapolate past market behaviour into the future.

In contrast to CGE models, these models focus on the short to medium term with the focus on the dynamics of adjustment. They can explore the representation of growth pathways. This model type can explore pathways under disequilibrium at a high level of sectoral disaggregation linking investment to historical demand and investment trends. In this way, it enables the analysis of interactions in the economy and of consequences of policy changes, like the introduction of a CO2 tax. Nevertheless, as it is based on historic estimations, this model is not able to integrate intertemporal preferences and structural breaks.

An example of macroeconometric models is:

 E3MG (Energy-Environment-Economy Model of the Globe) (Barker et al. 2006)

3.2.3 Bottom-up models

In contrast to top-down models, bottom-up models are predominantly partial equilibrium models that are limited to a part of the economy, the energy sector. Energy system models (the most common form of bottom-up models) usually derive a cost-

minimum sequence of energy technologies for an exogenously given energy demand using linear programming. The focus is on the technological representation of the energy sector from primary energy through to the level of useful energy or energy service including energy transformation, transport and distribution of final energy. The main advantages of this approach are the detailed depiction of the energy sector and the possibility to base technological change on an engineering assessment of different technologies (Edenhofer et al. 2006). A key aspect of the approach to MAC curves presented in this thesis is the incorporation of technological detail into the representation of the curve. Since bottom-up models possess the technological detail, this type of model is used in the thesis. However, they do not take into account interactions with the wider economy and tend to neglect micro-economic aspects, such as market barriers or rebounds in demand.

The real energy system is represented via the flow of energy carriers and other commodities. Commodities are linked through technical facilities, such as power plants or refineries, which are described with technical and economic parameters. In general, technologies of the real system are aggregated, while the level of aggregation depends on the spatial and sectoral detail. Many energy system models use a network presentation as a mean of representing the real system that is based on a concept, which was developed in the early 1970s at the Brookhaven National Laboratory. This concept, the Reference Energy System (RES), is a physical representation of the energy flows from resources to end use.

The RES includes two types of objects: commodities and processes. The term commodities characterises all quantifiable factors, e.g. energy carriers, gases, services and industrial goods. Processes transform one or more commodities into other commodities. A link represents the flow of a commodity from or into a process, i.e. the produced or consumed good. The process “coal-fired power plant”, for example, consumes the commodity “coal” and produces the commodity “electricity” and “carbon dioxide”. A simplified RES from the UK MARKAL model is given in Figure 3.1. Since the RES is a bipartite graph, commodities and process alternate, so that a process cannot be linked directly to another process and a commodity not to another commodity. The reference contains, in addition to processes and commodities, attributes, which can be divided into process attributes, e.g. life time of a power plant, commodity attribute, e.g. the price of coal, process-commodity attributes, e.g. the variable costs of a power plant,

process-commodity-commodity attributes, e.g. the efficiency of a power plant and lastly global attributes, such as the discount rate (Voß 2009).

Figure 3.1: A simplified reference energy system from the UK MARKAL model

Source: Kannan et al. (2007)1

The RES is designed to permit the assessment of individual technologies, explaining the