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Record W7117117436 · doi:10.1016/j.eneco.2025.109109

Market structure and technology adoption in renewable energy

2025· article· en· W7117117436 on OpenAlexaff
Gaurav Doshi, Sarah Johnston

Bibliographic record

VenueEnergy Economics · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsUniversity of Calgary
FundersOffice of the Vice Chancellor for Research and Graduate Education, University of Wisconsin-MadisonUniversity of Wisconsin-MadisonWisconsin Alumni Research Foundation
KeywordsRestructuringRenewable energyCompetition (biology)Market structureWind powerMarket competitionFrontier

Abstract

fetched live from OpenAlex

We study the effect of market structure on technology adoption in the U.S. solar and wind power industries. We compare adoption across two market types: restructured markets, which are designed to promote competition, and regulated markets, which are dominated by regulated monopolists. Solar projects in restructured markets are 32 percent less likely to adopt frontier technology. We also find negative effects of restructuring on adoption for wind projects. We provide evidence that this negative relationship between competition and technology adoption is explained by differences in financing costs across the two market types. • We find that solar and wind projects located in restructured markets, which are designed to promote competition, are less likely to adopt frontier technology than comparable projects in regulated markets. • Solar projects in restructured markets are 32 percent less likely to adopt axis-tracking panels and wind projects use 2.4 meters smaller turbines than comparable projects in regulated markets. • We find evidence that higher financing costs in restructured markets explain lower technology adoption in these markets. • Our results suggest that reducing financing costs in restructured markets can help promote technology adoption by renewable projects.

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)0.000
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 score0.207
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.171
Teacher spread0.165 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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