The model elements we discussed so far were focused on representing perfect systems: companies take market prices as given, policies are easy to implement, and we possess perfect information on all relevant aspects. Reality is different!

If you want to answer real world challenges and design a credible model for those questions you will also need to account for the imperfections in the world. Theoretic first-best results are always a good place to start your analysis. Especially, if you want to test whether a specific mechanism can achieve a desired outcome in the first place (if it does not work under the best of circumstances chances are small it works under more realistic assumptions).

But often you want to test whether a specific market or policy design still performs as intended when companies have the potential to abuse market power. For example, if large incumbents can keep competitors out of the market. Another common problem is the fact that we seldom possess perfect information on all relevant aspects. For example, firms typically have better knowledge on their production and abatement costs than regulators setting environmental policies.

Those market failures and limitations are common in energy markets and when deciding about environmental policies and as a modeller you need to address those restrictions. This is where we leave the theoretical first-best world and start to aim for second-best solutions.

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