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Diffusion of PV in Japan and Germany-Role of Market-Based Incentive and Research and Development (R&D) Investment

2012· article· en· W3143364369 on OpenAlexvenueno aff
Sanjeeda Chowdhury, Ushio Sumita

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2012
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveInvestment (military)DiffusionEconomicsBusinessMarket economyPolitical sciencePhysicsThermodynamics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.253
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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