Quantifying Thermal Impacts of Green Infrastructure: Review and Gaps
Bibliographic record
Abstract
Green infrastructure, that is, wet weather management approaches and technologies that infiltrate, evapotranspire, capture, and reuse stormwater to maintain or restore natural hydrologies, can affect temperature in different aspects of the urban environment. Restoration of natural landscape features and local installations of rain gardens, green roofs, green walls, infiltration planters, permeable pavement, or trees and tree boxes can have beneficial effects that reduce (1) stormwater runoff temperatures, (2) heat loss and heat gain in buildings, and (3) the urban heat island effect (UHIE). Rainfall and streamflow in urbanized areas pick up heat from unshaded, exposed man-made surfaces like pavement and rooftops and deliver the excess heat to downstream surface waters to the detriment of stream habitat. Green infrastructure techniques minimize local pavement and rooftop thermal absorption through increased shading and evaporative cooling. These practices also decrease the volume of runoff flowing across heated surfaces and slow the delivery of runoff, allowing more time for heat to dissipate prior to conveyance to surface water bodies. In addition to the insulative properties of green roofs, the increased shading and evaporative cooling result in cooler interior temperatures for buildings during warm weather periods. The insulative properties of green roofs also mitigate heat loss in winter, resulting in reduced energy needs throughout the year. When technologies such as green roofs and green walls are integrated into the design process, greater energy savings for buildings can be realized through reduced heating, ventilation and air conditioning (HVAC) installation size. When green infrastructure is applied at a greater scale in neighborhoods and cities, it tends to reduce the UHIE in understandable though not well-quantified ways, including reductions in exposed heat-absorbing surfaces, increased evaporative cooling, and decreased heat due to reduced HVAC system use. The thermal benefits of green infrastructure can extend well beyond the future city, as reduced cooling needs lower the demand for power during peak loading periods and result in fewer greenhouse gas emissions and less water consumption for power generation. This paper reviews current knowledge on the thermal benefits associated with the use of green infrastructure, identifies gaps in knowledge, and indicates possible directions for future research. Understanding and quantifying the thermal benefits of green infrastructure from the perspectives of emissions avoidance and economic benefits will aid the (re)design of cities to handle energy
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".