Heat Island Mitigation Assessment and Policy Development for the Kansas City Region
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
Lawrence Berkeley National Laboratory partnered with Mid-America Regional Council (MARC) to quantify the costs and benefits from the adoption of urban heat island (UHI) countermeasures in the Kansas City region (population 1.5 million), and identify the best regional implementation pathway for MARC. The team selected cool (high-albedo) roofs and increased vegetation as the two countermeasures to evaluate. For vegetation, there were two strategies: (1) planting new trees to shade building surfaces, and (2) increasing urban irrigation (a surrogate for the use of vegetation to manage stormwater) to increase evapotranspiration. Using the Weather Research and Forecasting (WRF) model we simulated selected weeks during summer time, across five years (2011 2015) representing a range of normal summer conditions. We also simulated six of the most intense heatwaves that occurred between 2004 and 2016. We found under typical summer conditions (non-heatwave) average daytime (07:00 19:00 local standard time) regional near-ground air temperature reductions of 0.08 and 0.28 C for cool roofs and urban irrigation, respectively. We calculated the building electricity, electricity cost, and emission savings that result from the reduction in outdoor air temperature (indirect savings) and found maximum regional annual indirect electricity savings of 42.8 GWh for cool roofs and 85.6 GWh for urban irrigationyielding maximum regional annual indirect electricity cost savings of $5.6M ($0.05/m2 roof) and $11.1M ($0.01/m2 irrigated land), respectively, and maximum regional annual CO2 savings of 43.4 kt and 80 kt, respectively.We next evaluated the building energy, energy cost, and emission savings from reducing direct absorbed radiation on the building surfaces using cool roofs and shade trees (direct savings). For cool roofs, we found regional annual direct energy cost savings of $10.9M ($0.15/m2 roof) with regional annual CO2 savings of 66.4 kt. For shade trees, the regional annual direct energy cost savings were $21M ($21/tree) with regional annual CO2 savings of 126 kt. We investigated cool roof cost premiums (the additional cost for selecting a cool roof product in lieu of a conventional roof product, estimated to be zero to $2.15/m2) and shade tree first costs (assumed to be $100 per tree). The regional cool roof cost premium was calculated using the regional roof area per roofing material type and the range of cool roof product premiums for each material type. The extra cost of selecting cool roofs across the region ranged from $4.33M to $87.1M, while the additional shade trees planted across the region were assumed to cost $102M. When we compared the regional annual direct cost savings to the regional cool-roof cost premium and the regional shade-tree first cost, we found regional simple payback times up to 8.0 years for cool roofs and 4.9 years for trees, respectively.Since this comprehensive assessment of UHI countermeasures is a valuable methodology for other local governments to apply, we developed a step-by-step guide for others to follow. Based on the benefits and costs of the UHI countermeasures, MARC will pursue the inclusion of these countermeasures in existing regional plans where they can complement other regional priorities for transportation, climate resiliency, clean air, and hazard mitigation. They hosted a local workshop in 2016 for stakeholders to introduce the topic and will continue to share these resources to further appropriate adoption of UHI countermeasures.
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 | 0.000 |
| 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