Integrating urban building energy modeling (UBEM) and urban-building environmental impact assessment (UB-EIA) for sustainable urban development: A comprehensive review
Bibliographic record
Abstract
Rapid urbanization has increased energy demand and environmental impacts in urban buildings, highlighting the need to understand building interactions and energy transfer. This has led to various methodologies for large-scale building assessment, such as Urban Building Energy Modeling (UBEM) and Urban-Building Environmental Impact Assessment (UB-EIA). Both have been separately and widely studied to support urban planning and building design, develop sustainable and smart cities, and enable stakeholders to optimize resource use and make informed decisions. However, a detailed comparative analysis of various UBEM and UB-EIA methodologies and their integrations have not been thoroughly reviewed in current existing research. To fill this research gap, this comprehensive review systematically investigated 157 articles to understand the evolution, methodologies, challenges of UBEM and UB-EIA. This review provides a holistic understanding, highlighting complementary strengths and identifying opportunities for integration to enhance urban building sustainability assessments. The findings of the review found that integrating UBEM and UB-EIA holds significant potential for enhancing urban sustainability usually through a comprehensive, data-driven approach. UBEM can serve as a foundation for UB-EIA by using similar data and methods, improving validation, and addressing gaps. UB-EIA's reliance on Building Information Management (BIM) can be enhanced by UBEM's detailed 2D and 3D models for precise EIA. This integration fosters more informed decision-making, promoting resilient and sustainable urban development by accurately reflecting complex urban interactions. Further research should explore the social and economic impacts of urban buildings using integrated UBEM and UB-EIA strategies for thorough and robust assessments. • Evaluates current UBEM and UB-EIA, assessing their strengths and limitations. • Promotes the need for integration and validation for effective UBEM and UB-EIA. • Highlights opportunities to enhance data transparency in UBEM and UB-EIA. • Reviews data collection, modeling, and validation for both UBEM and UB-EIA. • Provides insights for integrating environmental and energy profiles in urban buildings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".