Benchmarking the Performance of Solar Installers and Rooftop Photovoltaic Installations in California
Bibliographic record
Abstract
Rooftop photovoltaic (PV) systems are rapidly proliferating around the world. Whether the PV systems have been efficiently installed is an issue of utmost importance for both solar installers and policymakers. However, the impact of solar installers on PV performance is not well understood. In this paper, we investigate the performance of rooftop PV installations and the solar installers using a dataset of 1035 projects developed by 213 installers in California. Based on data envelopment analysis (DEA), our study takes the PV system capacity, electricity generation, cost, modules, solar irradiance, and ambient temperature into account simultaneously to construct a unified measure for the efficiency of PV installations. We analyze the relationship between installer characteristics and PV system performance. We find PV installations with the installer also being the module manufacturer, exhibit significantly better performance than other installations. PV installations by subsidiaries of oil firms have inferior performance. PV installations by large installers on average do not perform better than the installations by small installers. Geographic diversification of an installer’s operations is significantly and inversely related to the performance of installations. We demonstrate the aforementioned findings have significant implications for policymakers and the solar installation industry.
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.
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 | 0.001 | 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 | 0.001 |
| 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 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".