Addressing the Urban Heat Islands Effect: A Cross-Country Assessment of the Role of Green Infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Urban Heat Islands (UHI) effect is a microclimatic phenomenon that especially affects urban areas. It is associated with significant temperature increases in the local microclimate, and may amplify heat waves. Due to their intensity, UHI causes not only thermal discomfort, but also reductions in the levels of life quality. This paper reviews the important role of green infrastructure as a means through which the intensity of UHI may be reduced, along with their negative impact on human comfort and wellbeing. Apart from a comprehensive review of the available literature, the paper reports on an analysis of case studies in a set of 14 cities in 13 countries representing various geographical regions and climate zones. The results obtained suggest that whereas UHI is a common phenomenon, green infrastructure in urban areas may under some conditions ameliorate their impacts. In addition, the study revealed that the scope and impacts of UHI are not uniform: depending on peculiarities of urban morphologies, they pose different challenges linked to the microclimate peculiar to each city. The implications of this paper are threefold. Firstly, it reiterates the complex interrelations of UHIs, heat waves and climate change. Secondly, it outlines the fact that keeping and increasing urban green resources leads to additional various benefits that may directly or indirectly reduce the impacts of UHI. Finally, the paper reiterates the need for city planners to pay more attention to possible UHI effects when initiating new building projects or when adjusting current ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it