Study identifies ‘fingerprints’ of energy models exploring emissions mitigation scenarios

Over the past decades, environmental scientists and engineers have attempted to design effective solutions to reduce greenhouse gas emissions and mitigate the harmful effects of climate change. This has led to the creation of various energy models, frameworks that can be used to study emissions mitigation scenarios in hopes of meeting the goals set out by the Paris Agreement.

Some of these energy models are more detailed than others, and their structure, parameters and objectives may vary. When used to estimate what would happen following the introduction of specific climate policies and interventions, models may therefore produce different results.

Researchers from the Dutch Environmental Assessment Agency PBL, Utrecht University and other European institutes have recently set out to reliably quantify the differences in the results produced by leading energy models. Their article, published in Natural energyidentifies the “fingerprints” of energy patterns (patterns describing their unique characteristics).

“In many of our research projects, we intentionally include multiple energy modeling teams because the community is already aware of the importance of different perspectives,” said Mark Dekker, one of the researchers who led the study , at Phys.org. “Model differences can be quite significant in our field and this needs to be well explained before interpreting our results.”

The recent study by Dekker and colleagues is part of a larger project called the European Climate and Energy Modeling Forum (ECEMF H2020). The key objective of this project is to inform the development of energy and climate policies that could be implemented across Europe or at national level.

Before starting to carry out calculations to assess potential pathways for Europe to reach net zero emissions, the researchers decided to carry out a series of diagnostic tests to compare the results obtained using of different energy models. These tests were quite elaborate and time consuming, but they produced interesting results.

“The idea of ​​expressing diagnostic results as “diagnostic indicators”, some of which come from two previous articles published in Environmental Research Letters And Technological forecasts and social change” said Dekker.

“The idea of ​​creating model “fingerprints” rather than simply comparing these indicators individually (as has been done in previous literature) came to me during my own analysis of the results. The unique behavior of a model in one dimension sheds new light on its behavior in one dimension. another, which is why we sought to combine many dimensions into a simple framework and ultimately succeeded. »

In their tests, Dekker and his colleagues attempted to define diagnostic indicators for energy models by focusing on five key dimensions. These dimensions included a model’s responsiveness and proposed mitigation strategies, as well as estimated energy supply, energy demand, and mitigation costs/efforts.

“Energy models are crucial to understanding the future of our economy and our climate: they give us insight into where our future energy comes from, how it is used and what policy levers can be used,” explained Dekker.

“However, the differences between these models make this information difficult to navigate, both for fellow scientists and policy makers. This paper marks an important step in understanding our energy projections by mapping where each model behaves uniquely and where they match.”

Dekker and his colleagues run their diagnostic tests on eight energy models, which they applied to 10 potential scenarios for mitigating greenhouse gas emissions in Europe. These tests allowed them to create “fingerprints” for these models, which are essentially diagrams that uniquely represent them, in the same way that fingerprints or DNA uniquely identify human beings.

“The most important practical implication of our study is that people can now put modeling studies in context, especially those that rely on a single model,” Dekker said. “The bias or behavior of this model is now explained in relation to other models. For example, the model can generally project more renewable energy than other models, which is important to know when reading its projections on renewable energies.”

The work of this team of researchers could guide future studies comparing the predictions of different energy models or attempting to contextualize their estimates. Collectively, these research efforts could help better predict the outcomes of climate policies and energy-related interventions, which could in turn inform the work of the European Commission and other European policymakers.

“We are currently investigating the role of model differences in general and how we can find structures in large scenario databases, which can sometimes appear like ‘spaghetti’ to a general user,” Dekker added. “Categorizing our scenario into groups of scenarios each of which tells a different story can help better understand the future of energy and climate.”

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