デジタルテキストによる個人の気候行動の解明：自然言語処理手法のスコーピングレビュー
Unveiling Individual Climate Behaviors Through Digital Text: A Scoping Review of Natural Language Processing Methods (原題)
negar shabanpour, Sehl Mellouli, Stéphane Roche
🤖 gxceed AI 要約
日本語
家計消費は世界の排出量の約72%を占め、個人行動の解明が重要だが、従来の調査はコストやバイアスが課題。本研究はPRISMA-ScRに基づくスコーピングレビューで、NLPを用いた個人の気候行動分析の研究を系統的にマッピングした。2015年から2026年までの文献から10件を抽出し、トピックモデリングとトランスフォーマーが主流で、感情分析と組み合わせられることを示した。NLPはサーベイの補完としてスケーラブルで、気候介入の基盤となる。
English
Household consumption accounts for ~72% of global emissions, yet traditional surveys are costly and biased. This PRISMA-ScR scoping review maps NLP methods for analyzing individual climate behaviors, screening 2,580 records and including 10 studies from Twitter/X, Weibo, Reddit, and e-commerce reviews. Topic modeling and transformer models dominate, often combined with sentiment analysis. NLP offers a scalable complement to surveys and a foundation for targeted climate interventions.
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
日本では、個人の気候行動データは主にアンケートに依存しており、NLPによるテキスト分析は、自治体や企業の脱炭素施策の効果測定や、行動変容を促すコミュニケーション戦略の改善に貢献し得る。SSBJ開示やTCFD対応で求められる社会影響の把握にも応用可能。
In the global GX context
Globally, this review supports the use of NLP to analyze user-generated content for climate behavior insights, complementing traditional surveys. It aligns with growing interest in using alternative data for sustainability reporting and climate risk assessment, offering scalable methods for understanding consumer behavior and informing climate policy.
👥 読者別の含意
🔬研究者:Provides a systematic map of NLP techniques applied to individual climate behaviors, highlighting gaps and methodological trends.
🏢実務担当者:Offers insights into using social media and review text to gauge consumer climate attitudes and tailor sustainability communications.
🏛政策担当者:Suggests scalable, cost-effective methods for monitoring public climate behavior and designing targeted interventions.
📄 Abstract（原文）
Climate change is one of the most serious global challenges, and greenhouse gas emissions continue to rise despite mitigation efforts. Household consumption accounts for approximately 72% of global emissions, indicating the central role of individual behaviors. Traditional measurement instruments, such as surveys and interviews, are costly, time-consuming, and subject to response biases. User-generated textual content provides an alternative source of evidence, and natural language processing (NLP) enables its analysis at scale. Despite this potential, existing reviews have not systematically mapped its use for individual-level climate behaviors. The main goal of this research is to address this gap through a scoping review following PRISMA-ScR guidelines. Systematic searches were performed in Web of Science, Engineering Village, and Google Scholar, covering January 2015 to April 2026. A total of 2580 records were screened, and ten studies met the inclusion criteria. These studies analyzed Twitter/X, Sina Weibo, Reddit, and e-commerce reviews, covering behaviors from green transportation to waste management. The findings demonstrate that topic modeling and transformer-based models are the dominant techniques, typically combined with sentiment analysis. Recent studies extend beyond describing climate discourse toward explaining behavior. NLP-based text analysis constitutes a scalable complement to surveys and a foundation for targeted climate interventions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18168297first seen 2026-09-03 05:02:43
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