Research on Framework for and Strategies of Green Energy Consumption Based on Unsupervised Machine Learning
教師なし機械学習に基づくグリーンエネルギー消費のフレームワークと戦略に関する研究 (AI 翻訳)
Jun Lyu, Yu Shu, Shuo Wang
🤖 gxceed AI 要約
日本語
本研究は、YouTube上のグリーンエネルギー消費ドキュメンタリー60本の字幕に教師なし機械学習（LDAトピックモデル、意味ネットワーク分析、階層的クラスタリング）を適用し、技術供給、社会経済的移行、生態的ガバナンスの3つのフレーミング戦略を特定。脱炭素化の多様な議論を示し、政策立案者やメディア制作に示唆を与える。
English
Using unsupervised machine learning (LDA topic modeling, semantic network analysis, and hierarchical clustering) on subtitle transcripts from 60 YouTube green energy documentaries, this study identifies three framing communities: Technological Supply, Socioeconomic Transition, and Ecological Governance. The analysis reveals a multidimensional framing landscape beyond techno-optimism, offering actionable guidance for accelerating green energy consumption through targeted video communication.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本研究成果は、日本のGX政策におけるグリーンエネルギー普及のためのメディア戦略に示唆を与える。特に、技術供給フレームと社会経済的移行フレームは、日本のエネルギー基本計画やGX推進戦略と整合性が高い。
In the global GX context
This paper offers a systematic framework for analyzing green energy consumption communication, relevant for governments and organizations worldwide seeking to promote energy transition. The identified framing strategies can be applied to enhance messaging in corporate sustainability reports and public campaigns aligned with global climate goals.
👥 読者別の含意
🔬研究者:GX researchers can adopt the ATMN method for analyzing framing strategies in other sustainability communication contexts.
🏢実務担当者:Corporate sustainability teams can use the identified framing strategies to craft more effective green energy messages for consumers and stakeholders.
🏛政策担当者:Policymakers can leverage these framing insights to design communication campaigns that accelerate green energy adoption.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/en19112733first seen 2026-06-07 05:03:39
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