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Record W4412878812 · doi:10.5539/ijef.v17n9p1

Toward Sustainable Finance and Sustainability: The Imperative of Financial Literacy

2025· article· en· W4412878812 on OpenAlexvenueno aff
Johannes Treu

Bibliographic record

VenueInternational Journal of Economics and Finance · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacySustainabilityFinanceBusinessEconomicsEcology

Abstract

fetched live from OpenAlex

Financial literacy and sustainable finance play a major role in today’s world. Both concepts have gained increasing importance in recent years. More and more countries and international organizations have recognized the importance of measuring and improving the level of financial education. The aim is to achieve political goals such as a more sustainable financial system and the Sustainable Development Goals. Financial literacy is understood as the ability to make financial decisions and understand the associated consequences. Sustainable finance refers to a type of financing that incorporates social, environmental, and ethical considerations into investment decisions. The aim is to promote long-term sustainable development by investing in companies, projects, and products that make a positive contribution to society and the environment. This article uses discourse analysis to analyze the relationship and significance of financial education in promoting sustainable finance and sustainability. The aim is to present arguments for strengthening financial education as a prerequisite for sustainable finance and sustainability. The starting point of the analysis is the assumption that financially educated people are better able to understand the impact of their investment decisions on the environment and society. Through financial education, people can be encouraged to make sustainable financial decisions that are both financially profitable and positive for the environment and society.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.245
Teacher spread0.235 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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