Permanent record
RIR-3013 · Logged 13 July 2026
Scalable Urban Energy Modeling for Climate Resilience in Densely Populated Coastal Metropolitan Regions
Urban energy demand is highly sensitive to climate-driven shifts in heating and cooling requirements. This study extends high-performance computing workflows to evaluate energy resilience in diverse urban building stocks.
In plain words
As the climate changes, buildings need different amounts of heating and cooling, which shifts how much energy cities use. A computer modelling method that worked for Nassau County would be tested in other kinds of cities and climates.
Primary research question
How can scalable energy modeling workflows be optimized to inform urban climate resilience planning in diverse metropolitan environments?
Knowledge gap
What remains worth asking
The source suggests that while the workflow is effective for Nassau County, it remains useful to test its transferability to different urban typologies and climate zones.
Potential contribution
Why it may matter
Improved modeling accuracy supports targeted grid decarbonization and energy efficiency policies in urban areas.
Academic placement
Academic fields and topic tags
Study frame: Metropolitan urban building stocks and regional energy grids. · Method signals: Computational simulation, Data modeling, Comparative spatial analysis
Possible study pathways
One question, different levels
Research master’s
Urban energy systems and climate modeling
Doctoral
Computational urban planning and energy policy
- originality
- Moderate
- methodology
- Advanced
- Data access
- Moderate
- ethics
- Accessible
Quality and audit
88/100 quality
Safeguard-based audit was not recorded under the prior standard.
- Requires access to high-performance computing resources.
- 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.
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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
Jalilian, R., & Kamel, E. (2025). Urban-scale building energy modeling under future climate scenarios: a scalable workflow and insights from Nassau County, New York. Frontiers in Energy Research, 13, Article 1683787. https://doi.org/10.3389/fenrg.2025.1683787
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 ↗