Impact of Wall Paint Solar Absorptance on CO2 Emissions in Residential Buildings: A Case Study from Bangkok
Bibliographic record
Abstract
Electricity consumption in buildings is a significant contributor to greenhouse gas emissions, which drive climate change. Reducing electricity use in residential buildings, which account for approximately 20% of Thailand’s total electricity consumption, represents a key opportunity for lowering greenhouse gas emissions. The aim of this study was to assess the potential reduction in greenhouse gas emissions through the use of appropriate solar absorptance in wall paint, conducted via an energy simulation using a representative residential building model from Bangkok. The DOE2.1E program was employed to simulate a standard two-story house commonly found in Thailand, with an approximate floor area of 120 square meters. The window-to-wall ratios were set at 10% and 20%, and air conditioning usage was modeled for nighttime hours. External wall paint was assigned varying solar absorption coefficients, ranging from 10% to 90%. Greenhouse gas emissions were calculated by multiplying the simulated annual electricity consumption by the emission factor, expressed in kgCO2eq/kWh, provided by the Thailand Greenhouse Gas Management Organization. The results indicated that adjusting wall paint solar absorptance from 10% to 90% led to a 10% variation in both energy consumption and greenhouse gas emissions, potentially reducing CO2 emissions by approximately 411–456 kgCO2eq per house per year. Therefore, implementing regulations that mandate the use of wall paints with appropriate solar absorption coefficients could significantly reduce greenhouse gas emissions and contribute to environmental protection efforts in Thailand.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".