English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Uncertainties surrounding the energy transition often lead modelers topresent large sets of scenarios that are challenging for policymakers tointerpret and act upon. An alternative approach is to define a few qualitativestorylines from stakeholder discussions, which can be affected by biases andinfeasibilities. Leveraging decision trees, a popular machine-learningtechnique, we derive interpretable storylines from many quantitative scenariosand show how the key decisions in the energy transition are interlinked.Specifically, our results demonstrate that choosing a high deployment ofrenewables and sector coupling makes global decarbonization scenarios robustagainst uncertainties in climate sensitivity and demand. Also, the energytransition to a fossil-free Europe is primarily determined by choices on theroles of bioenergy, storage, and heat electrification. Our transferrableapproach translates vast energy model results into a small set of criticaldecisions, guiding decision-makers in prioritizing the key factors that willshape the energy transition.

Streamlining Energy Transition Scenarios to Key Policy Decisions