Diffusion of PV in Japan and Germany-Role of Market-Based Incentive and Research and Development (R&D) Investment
Bibliographic record
Abstract
The goals of increasing the use of PV energy face significant obstacles. Regulatory requirements can be used to mandate the adoption of renewable energy, but market-based incentive mechanisms can also achieve the same results by inducing voluntary behavior from stakeholders. Variations in terms of both design and implementation of market-based incentives can have meaningful effects on the outcomes of incentive programs. We examine Japan and Germany in which PV energy accounts for a relatively high portion of total net electricity energy consumption. Germany FITs were originally linked to the spot electricity price, but a fixed tariff was adopted in 2000, and revised in 2004. A grant program also funds a portion of construction costs for new PV systems. The country has experienced rapid uptake of renewables over the past decade, making it a world leader in solar PV at the end of 2008. The purpose of this study is to analyze the PV diffusion in Japan and Germany during 1990-2011. Germany chooses an effective market-based incentive mechanism which is long term and more generous than Japanese incentive program. The termination of incentive policy is the main blocking factor of the decline of PV market in Japan.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".