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Record W2335465906 · doi:10.5539/jsd.v9n2p181

Analysis of Linkages between Environmental Policy Instruments and Innovation: A Case Study of End-of-Life Vehicles Technologies in Japan

2016· article· en· W2335465906 on OpenAlexvenueno aff
Rajeev Kumar Singh, Helmut Yabar, Rie Murakami-Suzuki, Noriko Nozaki, Randeep Rakwal

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveExternalityOrder (exchange)Environmental policyBusinessControl (management)Porter hypothesisEnvironmental pollutionIndustrial organizationPublic economicsEnvironmental economicsEconomicsEnvironmental protectionFinanceMicroeconomicsEnvironmental science

Abstract

fetched live from OpenAlex

<p>Environmental policies are designed to deal with externalities either by internalizing environmental costs or imposing specific standards for environmental pollution. This study aims to examine the impact of environmental regulations related to End-of-Life Vehicles (ELV) on innovation in Japan. We determined whether there is any statistical difference in patent activity comparing the periods before and after the regulations were enacted. In order to control for exogenous factors such as business cycles, we also analyzed the ratios of ELV and total environmental patents during the same periods. Results showed that environmental regulations drive innovations and the number of ELV-related patents were larger even after controlling for such exogenous factors. We concluded that environmental policy for ELV in Japan was effective in inducing innovation. However, we also found that the weakness in these types of command and control policy is the lack of incentives for further innovation.</p>

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.001
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 head0.023
GPT teacher head0.232
Teacher spread0.209 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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