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Record W2183411446 · doi:10.2175/193864710798285381

Quantifying Thermal Impacts of Green Infrastructure: Review and Gaps

2010· article· en· W2183411446 on OpenAlex
Corrie Clark, Brian Busiek, Peter Adriaens

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the Water Environment Federation · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsnot available
FundersArgonne National LaboratoryUniversity of GuelphUniversity of Minnesota
KeywordsGreen infrastructureBusinessEnvironmental scienceNatural resource economicsEnvironmental resource managementEconomics

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.200
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it