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Record W4380050416 · doi:10.54254/2754-1169/6/20220209

Analysis of the Impact of Climate Change on Economic Growth, Financial Development, and Investment — Evidence from the ASEAN Region

2023· article· en· W4380050416 on OpenAlexaff
Tianyang Liu

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsClimate changeForeign direct investmentInvestment (military)Panel dataEconomicsVariable (mathematics)Empirical researchDevelopment economicsGeographyEconomic geographyPolitical scienceMacroeconomicsEconometricsPolitics

Abstract

fetched live from OpenAlex

By using the ASEAN region as a case study, the research attempts to examine how climate change has an impact on financial development, foreign direct investment, and economic growth. ASEAN region is considered among the highest growing region across the world hence this study assesses the climate change consequences by taking into consideration the growth of region. This study conducted an empirical test using regression and focuses on the five nations with the greatest economies in the ASEAN region. These countries are Singapore, Malaysia, Thailand, the Philippines, and Indonesia, and they cover the years 2000–2022. The study collects data from the World Bank. This study used five independent variables while taking one climate change variable (CO2 emission) as independent variable. This study used the data in the panel format and after reviewing multiple literature the hypothesis could be tested. The result states that climate change has had a mixed impact on economic growth, performance, and investment.

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 score0.388
Threshold uncertainty score0.433

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.125
GPT teacher head0.319
Teacher spread0.193 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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