Permanent record
RIR-2090 · Logged 13 July 2026
Applying Deep Leverage Points to Decision Making Under Deep Uncertainty for Climate Transformation
The paper reviews decision-making techniques under deep uncertainty (DMDU) for low-carbon transitions, identifying a lack of focus on structural uncertainty and deep leverage points. It recommends future research to explicitly incorporate these elements into infrastructure decision-making models.
In plain words
Building the infrastructure for a low-carbon society means making big decisions when the future is very unclear. This study asks how planning tools could also allow for doubt about how the whole system works, and for the deep points where a change can reshape it.
Primary research question
How can structural uncertainty and deep leverage points be effectively integrated into DMDU frameworks for climate transformation?
Knowledge gap
What remains worth asking
The source suggests that current models often neglect structural uncertainty and deep leverage points, and it remains useful to test new modeling approaches that address these gaps.
Potential contribution
Why it may matter
This research will improve the accuracy and effectiveness of interventions aimed at achieving transformative climate change.
Academic placement
Academic fields and topic tags
Study frame: Infrastructure decision-making for low-carbon societal transformation. · Method signals: System dynamics modeling, Structured literature review, Scenario analysis
Possible study pathways
One question, different levels
Professional master’s / MBA
Strategic decision-making for sustainability
Doctoral
Systems modeling and climate policy
- originality
- Advanced
- methodology
- Advanced
- Data access
- Moderate
- ethics
- Accessible
Quality and audit
92/100 quality
Safeguard-based audit was not recorded under the prior standard.
- Focus on technical model development
- Requires strong quantitative background
- Open-access scholarly source and DOI metadata verified
This older record can be checked against the current standard on request. It changes only if every academic gate and independent safeguard passes.
Sign-in required · uses one AI creditProvenance
Research Idea curation
- DOI and bibliographic metadata independently resolved
- Open-access status verified
- The research direction is transparently marked as AI-inferred
The public contributor code contains no name or account email.
APA 7 source
Few, S., Bonjean Stanton, M. C., & Roelich, K. (2023). Decision making for transformative change: exploring model use, structural uncertainty and deep leverage points for change in decision making under deep uncertainty. Frontiers in Climate, 5, Article 1129378. https://doi.org/10.3389/fclim.2023.1129378
Paper abstract and discussion context; AI-inferred direction
This is an AI-inferred direction. The source authors did not state it as a recommendation.
Open source ↗