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Record W4411225367 · doi:10.1016/j.renene.2025.123750

Exploring the economic and non-economic determinants of investments in renewable energy

2025· article· en· W4411225367 on OpenAlexaff
Gazi Salah Uddin, Md. Bokhtiar Hasan, Donghyun Park, Md. Sumon Ali, Christoffer Wadström

Bibliographic record

VenueRenewable Energy · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsImpact
FundersUniversitas IndonesiaLinköpings Universitet
KeywordsRenewable energyNatural resource economicsEconomicsBusinessEnvironmental economicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

ABSTRACT Amid growing concerns about climate change and fossil fuel depletion, the global shift toward sustainable energy has become increasingly urgent. Despite significant investments in renewable energy (RE), there remains a large gap in meeting global sustainability targets. This study is the first to analyze the key factors driving RE investments, using a comprehensive panel dataset of 36 countries over 21 years (2000-2020), comparing developed and developing/emerging nations. The results show that in developed countries, factors like industrial growth, environmental taxes, social globalization, and climate vulnerability drive RE investments, while inflation and political instability hinder progress. In contrast, developing countries benefit from environmental taxes, social globalization, environmental technologies, and climate vulnerability, but industrial growth and oil prices negatively impact RE investments. These differences underscore the need for tailored strategies. Quantile-based assessments further highlight variations across countries, offering a nuanced understanding of the determinants of RE investments. This research provides valuable insights for policymakers, helping to shape more effective strategies to accelerate the transition to RE and meet sustainability goals worldwide.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.209
Teacher spread0.178 · 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 designSimulation or modeling
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

Citations5
Published2025
Admission routes1
Has abstractyes

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