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Record W4285465188 · doi:10.32920/ryerson.14639670

Foreign Direct Investment and the Choice of Environmental Policy

2021· preprint· en· W4285465188 on OpenAlexaff
Paul Missios, Halis Murat Yildiz, Ida Ferrara

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsForeign direct investmentPollution haven hypothesisProfit (economics)IncentiveInternational economicsEnvironmental policyEnvironmental pollutionInternational tradeBusinessOligopolySubsidyHavenOrder (exchange)EconomicsNatural resource economicsMarket economyEnvironmental protectionWelfareMacroeconomicsMicroeconomics

Abstract

fetched live from OpenAlex

We use a simple two-country oligopoly model of intra-industry trade to examine the implications of foreign direct investment for the pollution haven hypothesis and environmental policy. Countries which lower environmental standards to be more competitive in world markets generate pollution havens if environmental policy is exogenous. However, if FDI is a viable option as a mode of entry, profit-shifting considerations weaken in favour of environmental considerations and FDI recipients tighten environmental policy, reducing incentives to relocate production. Interestingly, when countries are sufficiently similar in their environmental awareness, "grey" countries can become greener than originally "green" countries but firms in the latter still engage in FDI in the former, in spite of the stricter standard they face, in order to level the playing field. We derive conditions under which FDI-receiving countries have incentives to manipulate their environmental standards to prevent or attract FDI, potentially eliminating or creating pollution havens.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
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 head0.022
GPT teacher head0.203
Teacher spread0.181 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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