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Record W3121939310 · doi:10.2866/203304

Finance and carbon emissions

2019· preprint· en· W3121939310 on OpenAlexfundno aff
Ralph De Haas, Alexander Popov

Bibliographic record

VenueEconstor (Econstor) · 2019
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
FundersWageningen University and ResearchUniversity of New South WalesUniversity of BristolQueen's University BelfastImperial College LondonCopenhagen Business SchoolQueen's UniversityLondon School of Economics and Political ScienceEuropean Bank for Reconstruction and Development
KeywordsStock (firearms)Equity (law)Greenhouse gasPer capitaInvestment (military)BusinessPanel dataEquity financingProduction (economics)EconomicsNatural resource economicsMonetary economicsFinanceMacroeconomics

Abstract

fetched live from OpenAlex

We study the relation between the structure of financial systems and carbon emissions in a large panel of countries and industries over the period 1990-2013. We find that for given levels of economic and financial development and environmental regulation, CO2 emissions per capita are lower in economies that are relatively more equity-funded. Industry-level analysis reveals two distinct channels. First, stock markets reallocate investment towards less polluting sectors. Second, they also push carbon-intensive sectors to develop and implement greener technologies. In line with this second effect, we show that carbon-intensive sectors produce more green patents as stock markets deepen. We also document an increase in carbon emissions associated with the production of imported goods equal to around one-tenth of the reduction in domestic carbon emissions. JEL Classification: G10, O4, Q5

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)0.000
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.018
GPT teacher head0.203
Teacher spread0.185 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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