Academic Journal
Unveiling Individual Climate Behaviors Through Digital Text: A Scoping Review of Natural Language Processing Methods.
| Τίτλος: | Unveiling Individual Climate Behaviors Through Digital Text: A Scoping Review of Natural Language Processing Methods. |
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| Συγγραφείς: | Shabanpour, Negar, Mellouli, Sehl, Roche, Stéphane |
| Πηγή: | Sustainability (2071-1050); Aug2026, Vol. 18 Issue 16, p8297, 19p |
| Περίληψη: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Sustainability (2071-1050) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Unveiling Individual Climate Behaviors Through Digital Text: A Scoping Review of Natural Language Processing Methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shabanpour%2C+Negar%22">Shabanpour, Negar</searchLink><br /><searchLink fieldCode="AR" term="%22Mellouli%2C+Sehl%22">Mellouli, Sehl</searchLink><br /><searchLink fieldCode="AR" term="%22Roche%2C+Stéphane%22">Roche, Stéphane</searchLink> – Name: TitleSource Label: Source Group: Src Data: Sustainability (2071-1050); Aug2026, Vol. 18 Issue 16, p8297, 19p – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Sustainability (2071-1050) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/su18168297 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 8297 Titles: – TitleFull: Unveiling Individual Climate Behaviors Through Digital Text: A Scoping Review of Natural Language Processing Methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shabanpour, Negar – PersonEntity: Name: NameFull: Mellouli, Sehl – PersonEntity: Name: NameFull: Roche, Stéphane IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20711050 Numbering: – Type: volume Value: 18 – Type: issue Value: 16 Titles: – TitleFull: Sustainability (2071-1050) Type: main |
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