Impacts of urban heat island effect on critical urban infrastructure: a review of studies published between 2012 and 2022
Bibliographic record
Abstract
The urban heat island (UHI) effect has become a prominent urban characteristic in the last few decades and brings significant changes to the local urban climate. Such changes have severe impacts on the lifetime efficiency and performance of “ critical urban infrastructure” (CUI). As CUI forms the backbone of vital urban systems and socio-economic processes, it becomes important to understand the various impacts of UHI on different CUI elements. The impacts of UHI on CUI have consequently become a prominent study area within the urban research domain. This study presents a systematic bibliometric review of 118 relevant articles published within the last decade (2012–2022), selected from a variety of indexed, scholarly databases. The articles mainly focused on developed regions and large urban areas. The review shows a consistent upward trend in the annual number of publications on UHI effects with a peak reached in 2020. Of the four major CUI groups studied for UHI impacts, built form and energy and communication (with a strong focus on increased energy consumption) are the most prominent topics in the current literature, followed by transportation, and water and sanitation. Research on other CUI elements is still quite sparse, and significant efforts would be needed to identify the nature of UHI's impacts on these factors. This review highlights that the UHI impact on CUI is a developing research area that requires further attention and illustrates the state of knowledge and gaps present in current research. These findings provide a clear direction for future UHI impact studies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.030 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".