Recent Modelling Approaches in Applied Energy Economics
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The necessary alignment with other sustainable development goals opens the question of the technological, social and economic implications of NDCs and mid-century strategies. National strategies are also largely interdependent through, inter alia, energy markets, trade, foreign investment, etc. The aim of the Summer School is to foster scientific exchanges between participants and faculty members about these issues and the relevant applied modelling methods to address the different dimensions of the problem, including energy models, Computable General Equilibrium CGE models, Integrated Assessment Models IAMs.
The Summer School is aimed at advanced graduates PhD and master and post-doctoral students in environmental and energy economics and modelling. Participants will be asked to present a version of their research work and will receive valuable feedback from fellow students and faculty members. Additional activities include group project exercises, reading sessions and conferences. First, techno-economic BU models designed as linear or linear mixed-integer optimization problems.
They are required to take the demand side as a fixed input and thus cannot capture price or budget feedbacks. Changes in the demand side can only be incorporated as shifts of the load level, for example, via a new hourly demand profile, due to demand-side management technologies; an increasing demand level, due to economic growth; or different demand scenarios based on energy efficiency assumptions.
Furthermore, the linear structure leads to a classical cost optimal result that corresponds to a perfect competitive market framework, whereas imperfect competition cannot easily be captured within this model framework. Second, BU models designed as complementarity problems or non-linear optimization problems incorporate demand-side functionalities, typically a relation between demand and price.
Non-linear optimization problems can include welfare maximization instead of a pure cost minimization as the objective.
This captures the price interaction but still keeps the models limited to perfect competitive benchmark outcomes. In addition, BU models using the equilibrium framework allow the representation of multiple agents with individual optimization rationales and thereby facilitate the simulation of strategic firm behavior, imperfect competition, or the impact of structural changes. Similar to the linear type BU models, the demand functionalities need to be externally defined, especially regarding the price elasticities.
Consequently, general economic interrelations, such as budget effects or substitution-effects across markets, cannot be captured directly. However, the endogenous price formation makes it possible to cover direct price-quantity effects within the respective sector. Top-down models aim at representing the whole economy instead of only energy sectors and thereby capture the feedback effects across the economy.
This modeling approach requires a high degree of aggregation and cannot represent the same technological detail as BU models. The most prominent macroeconomic model approach in energy economics are computable general equilibrium CGE models. Those models have a highly aggregated representation of the energy system and the other sectors of an economy. The equilibrium concept ensures that all modeled markets clear supply equals demand on each market , given supply and demand characteristics. This equilibrium is obtained by endogenous price adjustments following the microeconomic rational of utility maximizing agents and profit maximizing firms.
However, the agents in CGE models are highly aggregated; most often, a representative household is used. Due to their aggregation level and equilibrium concept, CGE models are well suited for long-term evaluations of changes in the policy or market frame and not for short-term operational simulations. Due to the high abstraction level of CGE models, the production technology process is transferred into production functions with constant elasticities of substitution ESUB.
The different inputs and outputs are linked via nesting structures; that is, the energy input into a production function is itself an aggregate of different energy types, like electricity and fossil fuels that can be substituted for each other. As these elasticities determine the degree of substitution between inputs, they are thereby an important driver of the effects of policy changes.
To capture the effect of technological change in the energy sector — basically a shift of the production functions — exogenous shift parameters are often used, like the autonomous energy efficiency index AEEI. The AEEI represents a price-independent energy efficiency increase, which is sometimes used to carry out sensitivity analyses. The same logic is applied to the demand side of CGE models. Figure 2 shows an exemplary demand-side structure for a CGE model with detailed energy specifications.
Demand is derived from maximizing the utility function of a representative household, given a budget restriction.
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Consumption itself is split into direct energy use and consumption of other goods. The energy needed for the other goods, the embedded energy, is obtained by a similar structure on the production side. This allows CGE models to capture indirect energy effects due to changes in consumption. There exist a large number of CGE models that address different economic aspects. Bergman provides a general introduction to CGE models and a review on different environmental- and resource-related CGE models. Those models can be broadly clustered into global, multi-regional, and single-country CGEs.
Top-down computable general equilibrium models are well suited to capture price-based demand side effects across different sectors via budget effects. This is particularly important for the estimation of rebound effects that result from such indirect effects. They also are well suited for public finance evaluations of taxes and other instruments. This poses two challenges: first, data and estimations on both ESUBs, and particular AEEI, are incomplete, and second, estimates based on past and present data do not necessarily have to be an accurate description of future behavior making TD models less suited for the analysis of extensive system shifts in comparison to BU models.
Due to the limitations of both BU and TD approaches, researchers are developing methods to merge both lines of models for policy analyses. This approach faces the challenge of consistency of the disaggregated and aggregated results; that is, the electricity generation of different power plant types of a BU electricity model run need to match the aggregated fuel consumption of the electricity sector in the TD model. Second, a reduced form version of one model is incorporated into another model, as, for example, in Bosetti et al.
From a demand perspective, hybrid approaches facilitate the combination of detailed sectoral effects, like shifts in demand profiles, with general macroeconomic feedbacks, such as indirect rebound effects. This is of particular relevance for energy efficiency evaluations. Furthermore, the potential to model higher temporal resolutions in BU approaches makes it possible to combine short- and long-term economic feedbacks. In addition to CGE models and optimization and partial equilibrium BU models, there are a number of additional model approaches in energy economics [see Catenazzi and Herbst et al.
These include input—output models, system dynamics approaches, and econometric models. The latter often include multiple consumer groups [i. But due to their reliance on historic data, they are not well suited to analyze significant system shifts. On the BU side, there are furthermore simulation models and agent-based models. The former are often more technology driven and can represent whole energy systems with great detail; see, for example, the LEAP model Heaps, The latter results from a relatively new model approach in energy economics [e.
Instead of a closed mathematical market formulation, individual market participants are modeled as agents with autonomous behavior that interact with each other. This makes it possible to model different behavior of the market participants and thereby capture choice related aspects. Summarizing the different existing energy model approaches, we see that they are typically designed to capture supply side related market aspects while demand-side aspects are much less detailed.
This is partly a result of the underlying computational structure but also a result of the historic market development; for a long time, electricity and natural gas systems were regulated markets in which cost optimal energy supply was the main focus. Furthermore, most of the recent energy-related developments took place on the supply side, such as, the emergence of renewable energy technologies.
It is thus not surprising that existing models typically lack endogenous demand-side influences beside price-quantity relations. Furthermore, most models treat the demand side as an aggregate with little detail on specific consumer aspects and differentiated consumers.
Despite these problems, existing models are well suited to analyze small, price-induced changes on the demand side as well as the effects of pre-defined scenario-based changes to energy consumption on markets and energy supply. In particular, it will be necessary to develop models that capture consumer choices with respect to energy provision and that can describe the relation between changes in individual behavior and demand-side policies. As discussed above, most applied economic models describe energy demand as being a function of prices and income only. From a theoretical perspective, this is warranted by the basic microeconomic model of consumer choice, where an individual maximizes her utility U e, x over a bundle of energy goods e and other goods x subject to the condition that total expenditure does not exceed income y for a given vector of energy prices z and other prices p :.
The above basic setup is useful to describe the response of energy demand to price changes, in particular, the effects of changes in energy markets or of some policy instruments, such as energy taxation. Furthermore, it can be used to examine simple indirect phenomena, like the above discussed rebound effects. However, to assess other types of demand-side policies or more general effects, the model lacks structure. A simple but powerful extension is to consider heterogeneous consumers, for example, groups of consumers that differ regarding their income or preferences.
Such an extension makes it possible to assess the distributive impacts of energy policies. Furthermore, such a model can be used to assess potential benefits of group-specific interventions. But even with this extension, the model does not capture many effects that have been found to be relevant in field studies. In the next subsections, we will discuss how the above model can be adjusted in simple ways to capture the potential relevance of information, social interactions, and changing preferences. To make room for potential effects of information-based approaches to steer energy demand, a necessary assumption is that consumers are not perfectly aware of all options for changing their energy demand.
For example, they might not know which energy-efficient appliances exist, what quality and prices they have, and where they can be bought. Thus, if they want to change their behavior, they need to search for new solutions. There is a long tradition of search models in economics, with applications mostly to labor markets, explaining price dispersion, and innovation. Chandra and Tappata use such a model to explain differences in gasoline prices among stations; Kortum as well as Makri and Lane use a search model to explain how firms find new technological solutions. To transfer the main insights of these models to individual energy demand, it is useful to assume that consumers need to invest in appliances some goods x , in our above notation to alter their ability of adjusting energy use e.
However, they are not aware of the properties of the relevant goods x and thus need to spent time or money searching for an appliance that meets their requirements. From a modeling perspective, we could assume that consumers know a distribution of possible characteristics of appliances, that is, they know which qualities, costs, usage characteristics, and energy reductions are technically feasible.
However, without gathering information, they do not know which appliance has which properties. Thus consumers can either buy an appliance without this information or invest time modeled via fixed opportunity costs S to ascertain the characteristics of one appliance they randomly draw an appliance from the overall distribution and learn its properties.
If they invest in this search, they can afterward decide to buy this good or to research another one. This decision will be made based on the overall distribution of possible characteristics, that is, on their knowledge what is feasible; whenever the good comes sufficiently close to having the preferred characteristics among all feasible goods, a consumer will not invest in a new search the probability of finding a better solution is too small and rather buy this good.
Such a model is able to describe some interesting effects. First, changing energy consumption induces one-time costs search costs. Thus potential gains in energy efficiency will only be reaped, if these gains compensate for the search costs, in other words, small changes to energy prices will have little, but somewhat larger changes might have substantial effects. Furthermore, the model explains why different consumers will resort to different solutions in the short run and thus explain technological variety, e. Finally, and most importantly, the model can describe an impact of information-based policies.
Such policies would lead to a reduction of search costs, implying an earlier start of the search process and thus making it easier to reap small gains in energy efficiency. Thus in the context of this framework, information-based policies will be ineffective; if consumers do not reduce their energy consumption, because the individual gains savings from using less energy do not cover the individual costs in terms of expenses or reduced quality of life.
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A different way of influencing individual behavior is to provide information about the behavior of others or implicit information about social norms regarding energy consumption. This approach has been found to be effective in a number of studies.
Again, there is some tradition in other fields of economics of modeling social norms. Other contexts where this modeling approach is used are the explanation of tipping behavior, see, for example, Azar , and green consumption, as in Nyborg et al. In a general framework, this can be modeled by a slight extension of the above basic model. To this end, assume that the utility of individual i out of n individuals depends not only on her consumption e i , x i but also on a social norm N :.
The social norm is in turn a result of the behavior of all individuals in the society which might, however, have different influence on norm formation :. In such a model, changes in individual behavior can result in adjustments of social norms, which in turn will lead to further changes in individual behavior.
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Furthermore, it is possible that there are several equilibria, for example, an equilibrium with low and one with high energy consumption, which are each stabilized via the endogenously formed social norm Lindbeck et al. An information-based policy could be described either as manipulating the social norm or as making people more aware of an existing norm. In the first case, it might be possible to suggest that the norm is low energy consumption, which could move the system to an equilibrium with lower energy consumption if multiple equilibria exist.
In the second case, the policy could increase the disutility from not being close to the norm, which would induce both a direct change in behavior and an according adjustment of the norm. Such increases in disutility could be achieved by providing information about the behavior of other consumers. An example is given in Traxler , who shows how changing the beliefs of tax payers regarding the incidence of tax evasion and thus their disutility from not meeting a social norm can change overall outcomes rather drastically.
A slightly more elaborate version of the above model would not use a single social norm but rather a set of group-specific norms, whose formation may be interrelated. This would facilitate the modeling of social interactions or peer pressure within groups.
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However, a major problem is the quantification of the effects that social norms have on individual decisions. Some authors argue [see, e. Others, such as Levitt and List , are more critical and point out that questions regarding a limited transferability of experimental situations to every-day-behavior have particular relevance for the case of adherence to social norms. Field experiments provide another option, see, for example, Shang and Croson , who study the influence of social information on public good provision.
However, as field experiments are rather costly, this option usually implies a transfer across contexts and countries, as it is not possible to implement a field experiment in every situation where the influence of social norms on energy use needs to be assessed. Another, much discussed, approach toward reducing energy consumption is sufficiency.
This term is used in the literature in different ways [see, Oikonomou et al. Most importantly, sufficiency needs to be disentangled from efficiency, which is not trivial, as the economic concept of efficiency covers both changes in technology and changes in behavior. Often, sufficiency is considered to be an enforced or voluntary frugal way of living Oikonomou et al.
In case of enforced frugality, this might imply reduced individual well-being. In contrast, if sufficiency is to be chosen voluntarily, an individual has to get a sufficient recompense for the reduced consumption, which might take the form of an increased self-esteem, utility from contributing to a socially desirable outcome, or an increase in leisure time due to be able to cope with less income. However, a salient question is if sufficiency gains exist, why have they not yet been fully reaped? This would reduce the problem of modeling sufficiency to the cases discussed in the preceding subsection and interventions toward sufficiency would need to address social norms.
A different approach to sufficiency would be to remain on the individual level and to assume that individuals can only assess the quality of life in situations that they have already experienced.
Thus they know how to live in the way they are currently living and how to react to small shocks. However, there might be different ways of living that reduce energy consumption without sacrificing well-being that the individual has not yet experienced and thus does not know. In terms of modeling, we could assume that preferences consist of a set of local preferences each defined in a neighborhood of a given consumption bundle out of which an individual knows only one her current local preferences.
The other preferences i. Once a new way of living has been tried, the respective local preferences become known. If the driving force of the change vanishes energy prices come down again , the consumer might either maintain this way of living or switch back to her original consumption pattern. The benefit of this approach is that it captures much of the essence of the sufficiency concept and introduces an effect into the energy economic modeling that is not present so far: a one-time intervention can have lasting effects for some but not all parts of the population.
For example, an oil price shock might initially increase the number of people not using cars. However, once oil price go down again, some consumers might switch back to their original way of living, whereas others have experienced a new and preferred lifestyle, which they voluntarily maintain. However, it should be noted that if sufficiency gains are to be depicted in a model, this model cannot use per capita consumption, GDP, or total costs to assess demand-side policies.
Rather, a measure of welfare has to be used that is based on individual utility and that captures either utility derived from adhering to social norms or the above mentioned uncertainty. Whereas this is common in theory, it is hard to implement in numerical models, as the necessary data is lacking. Obviously, existing numerical energy models will need adjustments and extensions to address the challenges in relation to increased energy efficiency and demand-side policies.
For all changes, a necessary first step is the inclusion of heterogeneous consumers into the existing model structures. For CGE models, this basically refers to a more disaggregated structure on the demand side of the market transferring the oftentimes single representative household into several household types; for example, households representing different income classes that differ in their demand elasticities for specific goods.
Detailed data on different household types, their income, the split of income across sources, and consumption choices would be needed.
For BU models, such a disaggregation is possible but will only result in a differently shaped aggregate demand function without much impact on overall computational model structure. Again, the main bottleneck for such a development is data availability like sufficient spatial or temporal resolution. Including sufficiency or search processes would be much more difficult, as this requires the inclusion of uncertainty, which is hard to achieve in large-scale numerical models.
For BU models, a stepwise or time-dependent model structure as used in dynamic investment models, unit commitment models, or rolling planning models can be used as starting point. Within a period t the consumption decision is derived from externally defined parameters including, for example, norm driven aspects.
Whether this influence is handled outside the model, that is, by adjusting the demand function accordingly, or within the model depends on the scope and structure of the model. The former should easily be accommodated by most BU model approaches, including linear optimization problem following a myopic logic. The latter introduces dynamic elements similar to path dependent investment aspects which increases the model complexity. However, the proposed concepts require a quantification of their effects before they can be included into numerical models. Given our current knowledge on energy demand and particular on non-price driven influences this represents a significant non-modeling challenge.
Consequently, to properly address those aspects in economic models we will first need a better understanding of the fundamental drivers of consumers energy demand. Overall, this paper has two main messages. First, most of the currently available applied energy models do not use sophisticated approaches to describe the demand side. In fact, most models cannot describe or assess demand-side interventions apart from price changes. However, the second part shows that this is not a restriction imposed by the general economic approach to modeling consumer behavior.
Much richer models are feasible and are used in other fields of economics. In particular, it is feasible to model many effects, such as social norm or social interactions that have been found to be relevant in field studies. In our view, there are two reasons why these approaches are currently not used in energy modeling. First, there is a lack of demand. For decades, energy policy has focused on the supply side; whereas billions have been spent to enact changes in energy supply, demand-side policies have typically a small budget.
Second, applied modeling requires not only concepts but also data. Whereas data on energy supply is abundant, there is a lack of data regarding the structure of energy consumption and its main determinants apart from prices and technologies. Few countries have a micro census that includes more than some elementary energy-related items, so that projects aiming for a better description of the demand side have to collect their own data. Given the different foci of such projects, there is little chance of combining their data to a sufficiently broad database. As energy strategies in many countries are based on a strong reduction in per capita energy consumption, the first reason will vanish rather rapidly; the need for more qualified assessments of demand-side policies will strongly increase within the next years.
However, the second bottleneck missing data will not dissolve in a likewise manner. Thus if better models of energy consumption are desirable, generating the necessary data should be the main priority. The need for detailed data also extends to a more general lack of understanding the fundamental drivers and mechanisms of energy demand beyond the technological layer.
Overcoming this knowledge gap will require fundamental research in social and political science as well as psychological and consumer behavior research and the transfer of those insights into the economic model community. How such an integrated interdisciplinary framework could be achieved is addressed in Burger et al. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank Jan Abrell and Sebastian Rausch for helpful input. Abrahamse, W. A review of intervention studies aimed at household energy conservation. Alcott, B. The sufficiency strategy: would rich-world frugality lower environmental impact? Allcott, H.glycepotmer.tk
Recent Modelling Approaches in Applied Energy Economics
Social norms and energy conservation. Public Econ. Azar, O. What sustains social norms and how they evolve? The case of tipping.