Course structure and recommendations for the course
Modeling can be structured into four main phases:
- First, we have to understand our problem: What question do we want to answer? Do we want to analyse an argument or a real world setting? What economic mechanisms may be important in our answer? What kind of results do we expect our model to yield?
- Second, we have to build the model. To this end, we have to transfer our problem into a formal structure by combining different model parts so-called ‘building blocks’. We have to decide what has to be included in our model and how to include it. Furthermore, we have to be able to show that our model is a credible description of the problem at hand.
- Third, we need to solve our model. We have to use our model equations to get results that answer our questions. We have to find necessary data if we want to evaluate a real world setting. Furthermore, we have to check whether our results are robust.
- Finally, we have to interpret the results and use the insights gained in the model to make an argument. In particular, we have to discuss why and to what extent our results can be transferred to reality and what implications they might have. Thus we embed our model in a ‘story’.
It is important to note that in most cases, we cannot simply go through these phases step-by-step. Rather, we usually have to go back and forth until we have found the right model that yields interesting results that help us to answer our questions.
Most importantly, we will often iterate between phase 1 and 2 and between phase 2 and 3. The first iteration is useful, because by modeling a situation, we get to know it better and thus might want to adjust our original question or include new promising economic mechanisms. The second iteration is necessary, because not all models can be solved. We often have to make our first modeling attempts simpler to get a solution or we have to make them more complex to get a solution that is actually interesting.
This course is mostly structured along the above pathway of building a model. During this first week, we have discussed what economic models are and what we have to consider when building models so that our models are indeed useful.
During the next two weeks, we will cover phase 1 and 2. You will see how to set up theoretical (Week 2) and numerical (Week 3) models for answering questions from environmental and energy economics.
As modeling can only be learned by doing it, we will also offer you assignments at the end of Week 2 in which you are asked to build your own model using the tools that you see in this course.
After covering phase 2, we will use the preliminary work of selected groups (our own Case Study Groups from the University of Basel) on these assignments to discuss with you how to fit a model to a problem and how to build a model so that it covers the mechanisms that might be important in this problem. This will be done in Week 4, which will include a live event, where we discuss the work of these groups.
In the following two weeks, we will cover phase 3 and 4: ‘Solving a model and interpreting the results’. We will do this first for numerical (Week 5), then for theoretical models (Week 6).
During these weeks, you will have time to work on the assignments. In Week 7, we will then again use the work of the groups as case studies to discuss the results of the assignments, showing possible ways of solving them and highlighting how to interpret the results gained from the models.
Finally, we will use the last week of the course to discuss how to check model results for robustness and how to embed them in a storyline, leading to a compelling and consistent way of exploring possible futures.
Additional learning resources
During this course, you will benefit strongly from participating in the assignments and by reading some papers that use environmental or energy economic models.
For environmental economic modeling work, a good starting point is to look at the papers and the working papers listed under this topic in RePEc. Papers that can accessed free of charge have a green download symbol. Look for interesting titles and then see whether the paper uses a formal model. In addition, you might look at the work of some environmental economists that use models similar to the ones taught in this course, such as Michael Hoel, Martin Weitzman, Carolyn Fischer, Scott Taylor, or Till Requate. Again, a green download symbol shows papers that you can directly access.
For numerical modelling in energy economics, a good starting point is the respective working paper series on [RePEc] (https://ideas.repec.org/n/nep-ene/), with the possibility to subscribe in order to get a monthly update, or the subject matter listings on SSRN: Politics & Energy eJournal and Renewable Energy eJournal. Those working papers are free access publications. Naturally, not all of those publications will include models.
If you want to get more familiar with different energy models you can also visit the wiki of the openmod initiative. They provide an overview on different models that are at least partly accessible. Furthermore, they also provide information on how to obtain energy data. Similarly, the Simulation Lab of the Swiss Competence Center for Research in Energy, Society and Transition provides an overview on the different energy models used for socio-economic energy research in Switzerland.
Finally, we would like to mention that you might benefit from using scientific software for doing the assignments. The theoretical assignment can be done just using pen and paper, but using tools such as Mathematica or Maple might reduce the time spent on calculations (if you already have some experience with these software packages). For doing the numerical assignment, you could use scientific software, such as GAMS or Matlab. However, often spreadsheet software (like Excel) is sufficient and the model development will also only require pen and paper.
For most scientific software packages, test or student versions are offered at no or strongly reduced costs, so it might be useful to have a look at these tools.
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