Models used in energy and environmental economics
In economics we can generally differentiate three types of models for different purposes.
First, we have explanatory models. These models are used to make an argument. Such models are usually theoretical models. That is, they are not fully quantified. Their purpose is to make consistent arguments. For example, what consequences different climate policies might have for technological change. They do not give quantified results, but rather aim at capturing economic mechanisms.
Second, we have simulation models. These models are used to describe plausible scenarios; for example, different pathways towards a renewable energy system or a low carbon society. These models are usually based on simpler economic mechanisms than explanatory models. But they are calibrated so that they yield quantitative insights. Their purpose is to provide a detailed picture of future developments. But, they only show what is possible. They do not make predictions.
Finally, we have prediction models. They are usually econometric models, often with some theoretical model in the background. However, also models approaches similar to the ones for simulation models can be used as well. Their purpose is to predict future developments or the results of policy measures. As this is rather demanding, most models focus on short run predictions and are thus of limited use to most topics relevant in environmental and energy economics.
In this course we will focus on the first two types of models. As each simulation model relies on a conceptual framework (even if it may be simplistic) a solid understanding of explanatory modeling is the basis for good models.
Within the field of energy and environmental modeling, we furthermore observe a large diversity of models in all shapes and sizes. They range from small scale exercises to visualize conceptual findings, over firm and market models focusing on microeconomic interactions, to large scale energy system models and global economy models covering macroeconomic aspects. In addition to the above described general model differentiation, a second classification is often used in energy and environmental modeling: bottom-up (BU) and top-down (TD) models.
As the name suggests, BU models focus on the details of a market or system and aim to capture the technical or environmental details of the modeled system. They are basically disaggregated representations. In contrast, TD models aim to capture the interaction among market actors, several sectors or the whole economy. They are aggregated models in the sense that they work with representative production functions and elasticities to represent substitution between different technologies or products.
Both model clusters have their advantages and drawbacks. The high detail of BU models allows capturing specific market characteristics. In addition, they are well suited for policy impact assessments of a specific sector and for the evaluation of technology or market design shifts. They can be used both for short-term evaluations (sometimes even for predictions) and long-term simulations. BU models have to omit the more general economic interactions and rely on a set of externally defined parameters capturing the elements not represented in the model.
TD models allow capturing cross-sectoral effects, feedback effects across the economy, and macroeconomic impacts like employment, trade and income effects. They cannot represent the same technological detail as BU models. They are suited for long-term evaluations but not for short-term operational simulations. In a sense, also explanatory models can often be seen as a form of top-down modeling as they usually rely more heavily on economic interaction in line with the aggregations needed in large scale TD models.
For real world problems, often the detailed and the aggregated dimensions are important – especially for policy assessments. Therefore, approaches to combine BU and TD models, so called hybrid models, are increasingly emerging. Furthermore, to capture relevant system constraints models often need to account for specific technological (eg, power flows in electricity networks), environmental (eg, diffusion of pollution) or behavioural (eg, agent learning) aspects. Thus, economic modeling of energy and environmental topics is becoming more and more interdisciplinary.
Naturally, there are also other lines of reasoning to cluster model approaches. These include the mathematical nature of the model (ie, whether it is linear or non-linear, has a mixed integer nature, is an optimization or an equilibrium formulation), to the underlying time frame (ie, short- or long-term models), the choice variables (eg, operational or investment models/ static or dynamic models), uncertainty (ie, stochastic or deterministic models), the type of competition (ie, perfect competitive, monopoly, or oligopolistic) or the spatial dimension (eg, network models).
Within this course we will aim to provide a sound understanding of the model basics that are needed for designing explanatory and simulation models. We will also rely to the mathematical aspects of modeling and the differentiation in bottom-up and top-down perspectives, as well as the similarities and differences between optimizations and equilibrium modeling.
To get more familiar with models used for energy and environmental assessments Herbst et al. (2012) and section two of Krysiak and Weigt (2015) are good starting points. They provide comprehensive reviews on existing energy models and model approaches.
Herbst, A. et al. (2012). Introduction to energy systems modelling. Swiss Journal of Economics and Statistics, 148(2), pp. 111-135.
Krysiak, F. C., & Weigt, H. (2015). [The Demand Side in Economic Models of Energy Markets: The Challenge of Representing Consumer Behavior(http://journal.frontiersin.org/article/10.3389/fenrg.2015.00024/full) . Frontiers in Energy Research, 3, p. 24.]
Ventosa et al. (2005) and Weigt (2009) provide model overviews for electricity market models that will be helpful for getting more familiar with numerical simulation models. You may also want to explore the online version of the DICE (Dynamic Integrated Climate-Economy) model of the economics of climate change by Prof. Nordhaus.
Ventosa, M. et al. (2005): Electricity market modeling trends. Energy Policy, 33(7), pp. 897-913.
Weigt, H. (2009). A Review of Liberalization and Modeling of Electricity Markets. Dresden University of Technology.
We will provide further references with specific model examples within the following steps also offering more insights on the functionality of energy markets.
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