A reliable and cost-effective electricity system transition requires both the identification of optimal target states and the definition of political and regulatory frameworks that enable these target states to be achieved. Fundamental optimization models are frequently used for the determination of cost-optimal system configurations. They represent a normative approach and typically assume markets with perfect competition. However, it is well known that real systems do not behave in such an optimal way, as decision-makers do not have perfect information at their disposal and real market actors do not take decisions in a purely rational way. These deficiencies lead to increased costs or missed targets, often referred to as an “efficiency gap”. For making rational political decisions, it might be valuable to know which factors influence this efficiency gap and to what extent. In this paper, we identify and quantify this gap by soft-linking a fundamental electricity market model and an agent-based simulation model, which allows the consideration of these effects. In order to distinguish between model-inherent differences and non-ideal market behavior, a rigorous harmonization of the models was conducted first. The results of the comparative analysis show that the efficiency gap increases with higher renewable energy shares and that information deficits and policy instruments affect operational decisions of power market participants and resulting overall costs significantly.
Carbon pricing is a policy with the potential to reduce CO2 emissions in the household sector and support the European Union in achieving its environmental targets by 2050. However, the policy faces acceptance problems from the majority of the public. In the framework of the project Role of technologies in an energy efficient economy–model-based analysis of policy measures and transformation pathways to a sustainable energy system (REEEM), financed by the European Commission under the Horizon 2020 program, we investigate the effects of such a policy in order to understand its challenges and opportunities. To that end, we use a recursive-dynamic multi-regional Computable General Equilibrium model to represent carbon pricing as a cap-and-trade system and calculate its impacts on consumption of energy goods, incidence of carbon prices, and gross income growth for different income groups. We compare one reference scenario and four scenario variations with distinct CO2 reduction targets inside and outside of the EU. The results demonstrate that higher emission reductions, compared to the reference scenario, lead to slower Gross Domestic Product growth, but also produce a more equitable increase of gross income and can help reduce income inequalities. In this case, considering that the revenues of carbon pricing are paid back to the households, the gross income of the poorest quintile grows as much as, or even more in some cases, than the gross income of the richest quintile.
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