Overall review of distributed photovoltaic development in China: process, dynamic, and theories
Bibliographic record
Abstract
Abstract Non-technical summary DPV systems, typically small to medium-sized solar power installations on buildings, which primarily and directly supply electricity to industrial, commercial, or residential consumers in proximity. DPV is an advocated renewable substation for climate change and energy saving for merits of low installation costs, high energy efficiency, and the ability to provide decentralized power supply. Our research has theoretical significance in explaining and understanding the development and policy evolution of DPV in China and provide valuable suggestions for future industry policies during grid parity. Technical summary Since 2021, China has been phasing out its decade-long feed-in tariff policies, reducing the photovoltaic industry's dependency on subsidies. Despite the challenges posed by declining electricity prices and slowdown in economic growth, the authorities continue to prioritize the development of DPV due to its low investment costs, high energy efficiency, and decentralized power supply, and these technologies have already achieved demand-side parity. Driven by this phenomenon, this study examines the trajectory of DPV diffusion and the evolution of related policies over the last decade. It unravels the dynamic mechanism of DPV investment through theoretical analysis and develops a macro model to identify optimal installation strategies and renewable energy proportions. Our findings highlight the increasing role of green energy and suggest that green finance is crucial for stimulating DPV investment in the era of grid parity. The study concludes with practical recommendations for overcoming DPV challenges in China. Social media summary DPV has become a prominent renewable energy solution in other countries but not in China. We probe the system dynamics modeling to give explanation and solution during grid parity.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".