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Record W3121222528

Did the Invisible Hand Need a Regulatory Glove to Develop a Green Thumb? Some Historical Perspective on Market Incentives, Win-Win Innovations and the Porter Hypothesis

2008· article· en· W3121222528 on OpenAlexaff
Pierre Desrochers

Bibliographic record

VenueSSRN Electronic Journal · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIncentivePorter hypothesisProfit (economics)Government (linguistics)Property rightsCraftProfit motiveLaw and economicsPerspective (graphical)Industrial organizationBusinessEconomicsPublic economicsMarket economyEnvironmental regulationNeoclassical economicsMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

The idea that properly designed environmental regulations can improve a firm s competitiveness while simultaneously contributing to a cleaner environment through the development of so-called win-win innovations (i.e., that reduce environmental damage while simultaneously increasing profits) is usually credited to Porter (1991). Numerous studies have since attempted to assess the validity of the concept, with mixed results. This paper contributes to this debate by surveying a nearly forgotten body of literature written in the late nineteenth and early twentieth century that discussed the impact of market incentives on the development of valuable by-products out of industrial waste. Based on the opinions held by several industrial chemists, engineers, technical journalists and economists, the development of win-win manufacturing practices seems to have been primarily the result of the profit motive, although actual or potential legal actions based on private property rights and/or government regulations occasionally triggered this process. After reviewing some important historical writings on the latter issue, a suggestion is made that the best way to craft well-designed environmental regulations is perhaps to return to a private property rights approach to mitigating pollution problems whenever possible.

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 score0.568
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.185
Teacher spread0.177 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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