MétaCan
Menu
Back to cohort
Record W7009616229

Essais sur les risques physiques du changement climatique pour l'économie et le système financier canadiens

2024· dissertation· en· W7009616229 on OpenAlexaboutno aff

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2024
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicSustainable Finance and Green Bonds
Canadian institutionsnot available
Fundersnot available
KeywordsRail transportationTransportation infrastructureStatistical analysis
DOInot available

Abstract

fetched live from OpenAlex

Cette thèse présente trois papiers de recherche qui visent à mettre en lumière la propagation des chocs des catastrophes naturelles à travers l'économie et le système financier. Étant donné que le changement climatique mondial augmente la fréquence et la gravité des catastrophes naturelles, il est essentiel de comprendre quels sont les agents économiques les plus exposés et comment ces risques sont transférés dans l'économie afin d'élaborer des politiques solides visant à atténuer les effets des catastrophes. Ainsi,le premier chapitre étudie l'effet des catastrophes naturelles sur les marchés du travail et constate un effet significatif sur la croissance des salaires. Le deuxième chapitre examine plus en détail les finances des ménages et révèle une augmentation significative des défauts de paiement des prêts hypothécaires à la suite de feux de forêt. Enfin, le dernier chapitre examine les risques encourus par les prêteurs hypothécaires. Il constate que les risques sont principalement supportés par les ménages, ce qui atténue l'impact sur les prêteurs hypothécaires résidentiels. Toutefois, certains prêteurs dont le portefeuille de prêts est plus concentré géographiquement sont confrontés à un risque accru de catastrophes naturelles.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.200
Teacher spread0.179 · 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.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich)Same topicSustainable Finance and Green BondsFrench-language works237,207