Swiss Energy Modelling Platform
Different quantitative simulation models exist for the analysis of energy and climate policy in Switzerland. Each model type draws on different fields of knowledge, is routed in different modelling paradigms, and therefore focuses on specific aspects of the economy, technology, and the society when analyzing energy and climate policy options.
The Swiss Energy Modelling Platform aims at bringing together models and modelling teams of different backgrounds in order to analyze and derive insights into a common, overarching policy question of relevance in the context of the Swiss energy transition. It thus seeks to:
- harness the collective capabilities of multiple models to improve the understanding of the implications of energy & climate policies under consideration,
- explain the strengths and limitations of alternative modelling approaches,
- deliver robust answers to major policy tasks, and
- provide guidance for future research efforts.
SEMP aims at improving the use of energy and environmental policy models for making important corporate and government decisions. Moreover, the initiative seeks to strengthen the network of Swiss energy-economic modelers.
The modus operandi for SEMP is to employ a range of models with different characteristics to look at a plausible range of policy scenarios that could emerge in the future. Those models of interest are be able to capture one or more of the following features:
- Geographic coverage includes Switzerland and possible neighboring regions and Europe
- Multi-decade planning horizon
- International trade between the Switzerland and the rest of the world
- Ability to estimate CO2 emissions generated
- Representation of energy markets and processes, ideally with additional detail on electric power
- Ability to model macroeconomic outcomes such as GDP, output and consumption, and sectoral effects
PrinciplesThe work under the modelling platform adheres to the following principles:
- Impartiality: no technology, policy or energy perspective is favored over another
- Disclosure: “Truth-in-modelling” follows from disclosing rather than hiding important assumptions, parameters, judgments and sensitivities.
- Understanding: insights about how markets work are more valuable than precise numerical results