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Record W4223893530 · doi:10.3390/g13020032

Endogenous Abatement Technology Agreements under Environmental Regulation

2022· article· en· W4223893530 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGames · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Alberta
FundersJapan Society for the Promotion of Science
KeywordsDuopolySubsidyIndustrial organizationWelfareCournot competitionMicroeconomicsEconomicsOligopolyEnvironmental policyBusinessMarket economyEnvironmental economics

Abstract

fetched live from OpenAlex

In a domestic market, a duopoly produces a homogeneous final good, pollution, pollution abatement, and R&D, which reduces abatement cost. One of the firms (foreign) has superior technology. The government regulates the duopoly by levying a pollution tax to maximize domestic welfare. We consider the potential implementation of three innovation agreements: cooperative research joint venture (RJV), non-cooperative RJV, and licensing. In the cooperative (non-cooperative) RJV, the firms (do not) internalize R&D spillovers. We show that, for the domestic firm, the cooperative RJV dominates, and licensing is the least desirable alternative. Although licensing is dominant for the foreign firm, it is not implementable. Both RJVs are implementable. Implementation of both types of RJVs improves the competitiveness of the domestic firm and welfare. This study yields an important policy prescription: a subsidy policy that induces the foreign firm to accept a feasible cooperative RJV when it strictly prefers a feasible non-cooperative RJV is always welfare improving.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.092
GPT teacher head0.221
Teacher spread0.130 · 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