Modelling the Emission Offseting Potential of Rooftop Solar Panels
Bibliographic record
Abstract
This study empirically investigates the emission-offsetting potential of rooftop solar panels in a group of cities across three continents. By use of experimental big data from the Google Environmental Insights Explorer (GEIE) collected over two months in early 2023 as well as additional official data sources, the link between the potential rooftop solar (RPV) offset ratio and a set of city characteristics are estimated by use of linear regression methods. The data consist of 352 observations from a large group of cities in Australia, Canada, the United Kingdom, and the United States between 2018 and 2021. The main independent variable is population density, and additional control variables are the availability of public mass transportation, country, topography, and year.\n\nQuantile regressions (Q 0.5), considering the skewed distribution of the dependent variable, reveal that population density is linked to the potential RPV offset ratio at the one per cent significant level and with a negative sign. Countries where the RPVs are installed are also significant, with the largest offsetting potential in Australia compared to the reference country Canada. The years 2020 and 2021 are also significant, indicating that reduced transport emissions due to lockdowns, travel restrictions, and the aftermath of the global COVID-19 pandemic relate to the offsetting potential. A robustness analysis shows that the negative relationship with population density, in principle, does not appear until beyond approximately 2000 inhabitants per square kilometre.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".