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Record W3112595426 · doi:10.3390/ijerph17249161

Trends in Social Acceptance of Renewable Energy Across Europe—A Literature Review

2020· review· en· W3112595426 on OpenAlexaff
Marco Segreto, Lucas Principe, Alexandra Desormeaux, Marco Torre, Laura Tomassetti, Patrizio Tratzi, Valerio Paolini, Francesco Petracchini

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typereview
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsCarleton University
Fundersnot available
KeywordsSocial acceptanceRenewable energyOpposition (politics)BusinessQualitative propertyEnvironmental economicsPolitical sciencePublic economicsPsychologySocial psychologyEconomicsEngineeringComputer sciencePolitics

Abstract

fetched live from OpenAlex

Social acceptance has proven to be a significant barrier in the implementation of renewable energy systems (hereinafter "RES"). While a general acceptance of RES is high, low local acceptance has hindered the development of renewable energy projects (hereinafter "REP"). This study assesses the determinants of local and general social acceptance of REP across Europe through a qualitative analysis from 25 case studies of the most significant social drivers and barriers that include all European countries. These case studies contain qualitative and quantitative analyses of the main factors for social acceptance of many representative groups including residents, stakeholders, and experts. Understanding the influences of social acceptance enables us to create strategies that will promote the development of REP by mitigating any public opposition.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.012
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.110
GPT teacher head0.470
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations229
Published2020
Admission routes1
Has abstractyes

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