Analyzing the relationship between three-dimensional architectural landscapes and urban carbon emissions using machine learning approaches
Bibliographic record
Abstract
Detecting the relationship between urban architectural landscapes and carbon emissions is crucial for achieving China’s carbon-peaking and carbon-neutrality goals. This study aims to investigate the relationship between 3D architectural landscapes and carbon emissions in Qingdao City, based on building 3D information extracted from high-resolution satellite images and carbon emission data from the Center for Global Environmental Research for the year 2020. First, key architectural landscape factors impacting carbon emissions were identified utilizing the Pearson correlation test and Random Forest (RF). A predictive relationship model between architectural landscapes and carbon emissions was built using Support Vector Machine Regression (SVR) and further optimized through the Chaotic Particle Swarm Optimization (PSO) algorithm. The results revealed strong correlations between carbon emissions and factors such as building density, building number, shape, and height. Floor area ratio had the highest impact on carbon emissions, contributing 45.5%, followed by building number, landscape shape index, building coverage ratio, Shannon’s diversity index, and building shape coefficient (BSC). The optimized PSO-SVR model achieved a higher coefficient of determination (R 2 ) in the training dataset (77.32%) and test dataset (76.14%) compared to the SVR model (70% and 64.48%), along with lower mean absolute error (MAE) and mean relative error (MRE). Overall, the PSO-SVR model demonstrated enhanced accuracy in predicting carbon emissions and provided valuable insights for carbon reduction through targeted urban planning and architectural design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".