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Record W2476994262 · doi:10.1111/ropr.12183

Trade and Industrial Policy as Levers for Sustainable Energy Technology Adoption? Experiences from Urban<scp>L</scp>atin<scp>A</scp>merica

2016· article· en· W2476994262 on OpenAlex
Alexandra Mallett

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

VenueReview of Policy Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsCarleton University
FundersLondon School of Economics and Political Science
KeywordsSustainable energyBusinessEmpirical evidenceEnergy (signal processing)Clean energyEconomicsIndustrial organizationEnvironmental economicsRenewable energyEngineering

Abstract

fetched live from OpenAlex

Abstract Debates abound regarding the link between trade and industrial policy and the adoption of sustainable energy technologies in developing countries. Some purport that open trade regimes support technology diffusion, while others indicate that more interventionist regimes are more conducive. This paper uses empirical evidence from Mexico City and São Paulo to argue that sustainable energy technology uptake can be more prevalent in settings with partially open trade policy regimes. These regimes have afforded countries more opportunities to develop local capabilities, which, in turn, has had knock‐on effects on sustainable energy technology uptake. Specifically, having more local technology sources (equipment, expertise) brought quicker access to these technologies, created more perceptions of technology “ownership,” fostered more effective mobilization, and helped create well‐established standards, which in turn contributed positively to sustainable energy technology uptake, while taxes and tariffs were less influential.

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)0.001
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.062
GPT teacher head0.363
Teacher spread0.302 · 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