Exploring neighborhood low-carbon gene based on human cognition.
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| Title: | Exploring neighborhood low-carbon gene based on human cognition. |
|---|---|
| Authors: | Zhao, Guochao1,2 (AUTHOR) zhaoguochao0907@163.com, Yu, Xiaofen1,2 (AUTHOR) chengzhen_1234@163.com, Weng, Ziyou1 (AUTHOR), Zhang, Yifei1 (AUTHOR) |
| Source: | Environment, Development & Sustainability. Jan2026, Vol. 28 Issue 1, p2133-2157. 25p. |
| Subject Terms: | *Green behavior, *Urban pollution, *Climate change, Neighborhoods, Cognition, Memetics, Memes |
| Geographic Terms: | China |
| Abstract: | Since the beginning of the twenty-first century, the trend of global climate change has become increasingly evident. Urban carbon emission is the main factor of climate change, and low-carbon is an inevitable option. As a fundamental building block of the city, the neighbourhood is the basic space to promote the construction of a low-carbon city. Human beings endow neighbourhood with configurations and features, and human cognition of low-carbon could influence the specific participations in low-carbon behaviour, which is largely responsible for a low-carbon neighbourhood. In this study, meme theory was introduced to investigate the cognition of low-carbon stakeholders from a micro perspective of gene. We considered low-carbon cognition as a special type of meme and put forward the concept of the low-carbon gene. Moreover, we put forward the genetic analysis method combined with it. Finally, a neighbourhood in Hubei province of China was selected to illustrate this method. Our practice has proven that the neighbourhood low-carbon gene expands the adaptation of meme theory and provides a quantitative analysis method for the low-carbon stakeholders. [ABSTRACT FROM AUTHOR] |
| Copyright of Environment, Development & Sustainability is the property of Springer Nature 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.) | |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 191807267 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Exploring neighborhood low-carbon gene based on human cognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Guochao%22">Zhao, Guochao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zhaoguochao0907@163.com</i><br /><searchLink fieldCode="AR" term="%22Yu%2C+Xiaofen%22">Yu, Xiaofen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> chengzhen_1234@163.com</i><br /><searchLink fieldCode="AR" term="%22Weng%2C+Ziyou%22">Weng, Ziyou</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yifei%22">Zhang, Yifei</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Jan2026, Vol. 28 Issue 1, p2133-2157. 25p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Green+behavior%22">Green behavior</searchLink><br />*<searchLink fieldCode="DE" term="%22Urban+pollution%22">Urban pollution</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Neighborhoods%22">Neighborhoods</searchLink><br /><searchLink fieldCode="DE" term="%22Cognition%22">Cognition</searchLink><br /><searchLink fieldCode="DE" term="%22Memetics%22">Memetics</searchLink><br /><searchLink fieldCode="DE" term="%22Memes%22">Memes</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Since the beginning of the twenty-first century, the trend of global climate change has become increasingly evident. Urban carbon emission is the main factor of climate change, and low-carbon is an inevitable option. As a fundamental building block of the city, the neighbourhood is the basic space to promote the construction of a low-carbon city. Human beings endow neighbourhood with configurations and features, and human cognition of low-carbon could influence the specific participations in low-carbon behaviour, which is largely responsible for a low-carbon neighbourhood. In this study, meme theory was introduced to investigate the cognition of low-carbon stakeholders from a micro perspective of gene. We considered low-carbon cognition as a special type of meme and put forward the concept of the low-carbon gene. Moreover, we put forward the genetic analysis method combined with it. Finally, a neighbourhood in Hubei province of China was selected to illustrate this method. Our practice has proven that the neighbourhood low-carbon gene expands the adaptation of meme theory and provides a quantitative analysis method for the low-carbon stakeholders. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environment, Development & Sustainability is the property of Springer Nature 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: 10.1007/s10668-024-05080-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 2133 Subjects: – SubjectFull: Green behavior Type: general – SubjectFull: Urban pollution Type: general – SubjectFull: Climate change Type: general – SubjectFull: Neighborhoods Type: general – SubjectFull: Cognition Type: general – SubjectFull: Memetics Type: general – SubjectFull: Memes Type: general – SubjectFull: China Type: general Titles: – TitleFull: Exploring neighborhood low-carbon gene based on human cognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Guochao – PersonEntity: Name: NameFull: Yu, Xiaofen – PersonEntity: Name: NameFull: Weng, Ziyou – PersonEntity: Name: NameFull: Zhang, Yifei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1387585X Numbering: – Type: volume Value: 28 – Type: issue Value: 1 Titles: – TitleFull: Environment, Development & Sustainability Type: main |
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