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Environmental Policy, Innovation and Performance: New Insights on the Porter Hypothesis

2011· article· en· W2162704335 on OpenAlexaff
Paul Lanoie, Jérémy Laurent‐Lucchetti, Nick Johnstone, Stéfan Ambec

Bibliographic record

VenueJournal of Economics & Management Strategy · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsPorter hypothesisEnvironmental policyIncentiveCausality (physics)Test (biology)Environmental regulationIndustrial organizationEnvironmental complianceEconomicsBusinessPublic economicsMicroeconomicsEnvironmental economicsPolitical scienceEcologyBiology

Abstract

fetched live from OpenAlex

Jaffe and Palmer (1997) present three distinct variants of the so‐called Porter Hypothesis. The “weak” version of the hypothesis posits that environmental regulation will stimulate environmental innovations. The “narrow” version of the hypothesis asserts that flexible environmental policy regimes give firms greater incentive to innovate than prescriptive regulations, such as technology‐based standards. Finally, the “strong” version posits that properly designed regulation may induce cost‐saving innovation that more than compensates for the cost of compliance. In this paper, we test the significance of these different variants of the Porter Hypothesis using data on the four main elements of the hypothesised causality chain (environmental policy, research and development, environmental performance, and commercial performance). The analysis draws upon a database that includes observations from approximately 4,200 facilities in seven OECD countries. In general, we find strong support for the “weak” version, qualified support for the “narrow” version, but no support for the “strong” version.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.007
Scholarly communication0.0040.006
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.139
GPT teacher head0.198
Teacher spread0.060 · 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 source (direct Gemma or distilled Codex), 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

Citations962
Published2011
Admission routes1
Has abstractyes

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