Research on Framework for and Strategies of Green Energy Consumption Based on Unsupervised Machine Learning.
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| Title: | Research on Framework for and Strategies of Green Energy Consumption Based on Unsupervised Machine Learning. |
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| Authors: | Lyu, Jun1 (AUTHOR) jlv@dbm.ecnu.edu.cn, Shu, Yu1,2 (AUTHOR), Wang, Shuo1,2 (AUTHOR) |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 11, p2733. 21p. |
| Subject Terms: | *Semantic network analysis, *Machine learning, *Human behavior models, *Sustainable consumption, *Environmental, social, & governance factors, *Renewable energy sources, *Thematic analysis, *Renewable energy transition (Government policy) |
| Abstract: | Documentary videos on green energy consumption are widely distributed via platforms such as YouTube, yet the verbal framing strategies embedded in their subtitle transcripts remain systematically understudied. This study applies the Analysis of Topic Model Networks (ATMN)—an unsupervised machine learning approach combining LDA topic modeling, semantic network analysis, and hierarchical clustering—to subtitle transcripts extracted from 60 YouTube green energy consumption documentaries. Three distinct framing communities are identified: (1) the Technological Supply Frame, which foregrounds zero-carbon resources, renewable generation, smart grid systems, and AI-enabled energy management as the technical foundation of decarbonization; (2) the Socioeconomic Transition Frame, the most thematically expansive, which positions the energy transition simultaneously as an economic opportunity, a behavioral imperative, and a systemic industrial transformation spanning green investment, end-use substitution, industrial decarbonization, and green mobility; and (3) the Ecological Governance Frame, which integrates ecological co-benefits with international climate commitments to construct the transition as a globally mandated planetary responsibility. Together, these frames reveal a richer and more multi-dimensional verbal framing landscape than previously documented in the green energy communication literature, extending beyond techno-optimism or environmentalism to encompass financial, governance, and behavioral dimensions within a single integrated corpus. The identified framing strategies offer actionable guidance for policymakers, energy enterprises, and media producers seeking to accelerate green energy consumption transition through targeted, evidence-based video communication. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194588121 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Research on Framework for and Strategies of Green Energy Consumption Based on Unsupervised Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lyu%2C+Jun%22">Lyu, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jlv@dbm.ecnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Shu%2C+Yu%22">Shu, Yu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Shuo%22">Wang, Shuo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2733. 21p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Semantic+network+analysis%22">Semantic network analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Human+behavior+models%22">Human behavior models</searchLink><br />*<searchLink fieldCode="DE" term="%22Sustainable+consumption%22">Sustainable consumption</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental%2C+social%2C+%26+governance+factors%22">Environmental, social, & governance factors</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br />*<searchLink fieldCode="DE" term="%22Thematic+analysis%22">Thematic analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+transition+%28Government+policy%29%22">Renewable energy transition (Government policy)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Documentary videos on green energy consumption are widely distributed via platforms such as YouTube, yet the verbal framing strategies embedded in their subtitle transcripts remain systematically understudied. This study applies the Analysis of Topic Model Networks (ATMN)—an unsupervised machine learning approach combining LDA topic modeling, semantic network analysis, and hierarchical clustering—to subtitle transcripts extracted from 60 YouTube green energy consumption documentaries. Three distinct framing communities are identified: (1) the Technological Supply Frame, which foregrounds zero-carbon resources, renewable generation, smart grid systems, and AI-enabled energy management as the technical foundation of decarbonization; (2) the Socioeconomic Transition Frame, the most thematically expansive, which positions the energy transition simultaneously as an economic opportunity, a behavioral imperative, and a systemic industrial transformation spanning green investment, end-use substitution, industrial decarbonization, and green mobility; and (3) the Ecological Governance Frame, which integrates ecological co-benefits with international climate commitments to construct the transition as a globally mandated planetary responsibility. Together, these frames reveal a richer and more multi-dimensional verbal framing landscape than previously documented in the green energy communication literature, extending beyond techno-optimism or environmentalism to encompass financial, governance, and behavioral dimensions within a single integrated corpus. The identified framing strategies offer actionable guidance for policymakers, energy enterprises, and media producers seeking to accelerate green energy consumption transition through targeted, evidence-based video communication. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194588121 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19112733 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 2733 Subjects: – SubjectFull: Semantic network analysis Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Human behavior models Type: general – SubjectFull: Sustainable consumption Type: general – SubjectFull: Environmental, social, & governance factors Type: general – SubjectFull: Renewable energy sources Type: general – SubjectFull: Thematic analysis Type: general – SubjectFull: Renewable energy transition (Government policy) Type: general Titles: – TitleFull: Research on Framework for and Strategies of Green Energy Consumption Based on Unsupervised Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lyu, Jun – PersonEntity: Name: NameFull: Shu, Yu – PersonEntity: Name: NameFull: Wang, Shuo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 11 Titles: – TitleFull: Energies (19961073) Type: main |
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