How geospatial technologies are transforming urban net-zero energy buildings: A comprehensive review of insights, challenges, and future directions
Bibliographic record
Abstract
Governments worldwide are taking action to alleviate building emissions to achieve “net zero energy targets” by 2050. However, achieving Urban Net-Zero Energy Buildings (UNZEBs) remains challenging due to spatial, temporal and data integration constrain. This study systematically reviews the role of geospatial technologies, particularly GIS and remote sensing , in advancing UNZEBs by analyzing 204 peer-reviewed articles published between 2019 and 2024. Our research focuses on three key objectives: (1) identifying challenges and requirements for urban-scale net-zero energy buildings, (2) evaluating geospatial technologies used in urban building energy and environmental assessments , and (3) assessing their contributions to net-zero energy targets across various spatial scales. The findings reveal that geospatial tools enhance urban energy modeling, renewable energy potential assessments, and policy-driven decision-making. However, challenges such as inconsistencies in data resolution, interoperability issues, and the lack of standardized validation frameworks persist. To address these gaps, we propose a data-driven approach that integrates large-scale remote sensing, GIS, and machine learning to improve urban energy modeling and planning. This review contributes to provide a structured assessment of geospatial technology applications in urban energy transitions, which offers insights for researchers and policymakers to accelerate the shift toward net-zero energy cities. The novelty of this study lies in bridging geospatial data-driven approaches with urban energy sustainability frameworks , and highlights pathways for future advancements in geospatially informed urban planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".