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Record W4212807793 · doi:10.1177/10704965211070034

Climate Finance Effectiveness: A Comparative Analysis of Geothermal Development in Indonesia and the Philippines

2022· article· en· W4212807793 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Environment & Development · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsUniversity of Toronto
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsClimate FinanceRenewable energyDeveloping countryClimate changeFinanceGeothermal gradientScale (ratio)BusinessEconomicsWork (physics)Environmental resource managementNatural resource economicsEconomic growthGeography

Abstract

fetched live from OpenAlex

In light of commitments made under the UNFCCC Paris Agreement and Glasgow Climate Pact, trillions of dollars are needed to fund climate mitigation and adaptation in developing countries. However, few studies have investigated the effectiveness of climate finance or how it impacts barriers to renewable energy development in recipient countries. This article contributes to the literature by investigating climate finance effectiveness through comparative case study analysis of its impacts on geothermal development in Indonesia and the Philippines. The article finds that three mechanisms of climate finance-utility modifier, social learning and capacity building-work interdependently in impacting the financial, regulatory, and technical barriers to geothermal development in Indonesia and the Philippines but are individually insufficient to scale the industry; political will and energy shocks play a significant intervening role. This paper raises policy implications for climate finance effectiveness and renewable energy technology deployment in developing countries.

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.

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)0.004
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 score0.857
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.214
Teacher spread0.204 · 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