Built environment and residential blocks carbon emissions: a study using advanced metering infrastructure data
To address the pressure of emissions reduction in urban residential blocks (RBs), this study takes 99 micro-scale RBs in Hongqiao District, Tianjin as the objects, aiming to reveal the driving mechanism of built environmental factors (BEF) on residential blocks carbon emissions (RBCE) and explore pl...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1645402/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849764851890520064 |
|---|---|
| author | Xiaoping Zhang Xiaoping Zhang Zixuan Cui Chaoxian Feng Xin Wen Huabin Xiao Jianbo Ni |
| author_facet | Xiaoping Zhang Xiaoping Zhang Zixuan Cui Chaoxian Feng Xin Wen Huabin Xiao Jianbo Ni |
| author_sort | Xiaoping Zhang |
| collection | DOAJ |
| description | To address the pressure of emissions reduction in urban residential blocks (RBs), this study takes 99 micro-scale RBs in Hongqiao District, Tianjin as the objects, aiming to reveal the driving mechanism of built environmental factors (BEF) on residential blocks carbon emissions (RBCE) and explore planning strategies that balance carbon reduction and health benefits. By integrating spatial statistical analysis and high-precision machine learning models, the system has systematically revealed the spatio-temporal evolution laws, spatial differentiation characteristics and driving mechanisms of BEF on RBCE. Key findings include: (1) From 2021 to 2023, both the RBCE, residential blocks carbon emissions intensity (RBCEI), and average household carbon emissions (RBCE-AH) showed a “first rise then fall” fluctuation, with an overall 5.7% increase, signaling sustained emissions reduction pressure. (2) High emissions areas are spatially concentrated and contagious, while low carbon units are mostly peripheral. Spatial autocorrelation analysis indicates a significant positive correlation and a west-south clustering pattern. (3) Land area (LA) is the main emissions affecting factor, followed by green space ratio (GSR) and Land use mixing degree (LMD), whose inhibitory effect exceeds that of traditional high-intensity development indicators. (4) Targeted planning strategies such as strictly controlling land use expansion, improving GSR, and promoting functional combination were proposed. At the same time, it was suggested that in the future, the heterogeneity of building types and more three-dimensional morphological indicators should be incorporated into the BEF index system, and combined with more refined coupling models, their influence paths should be quantitatively analyzed. These strategies not only provide a basis for the implementation of macro emissions reduction policies, but also offer solutions for micro action plans centered on residents’mental health and cardiopulmonary system protection. Overall, this study provides a scientific basis for low carbon RBs planning and renewal that balances carbon reduction with health benefits. |
| format | Article |
| id | doaj-art-ef42d9100cef434cad9f8b15acdfcf96 |
| institution | DOAJ |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-ef42d9100cef434cad9f8b15acdfcf962025-08-20T03:05:02ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-08-011310.3389/fpubh.2025.16454021645402Built environment and residential blocks carbon emissions: a study using advanced metering infrastructure dataXiaoping Zhang0Xiaoping Zhang1Zixuan Cui2Chaoxian Feng3Xin Wen4Huabin Xiao5Jianbo Ni6College of Architecture and Urban Planning, Tongji University, Shanghai, ChinaSchool of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, ChinaSchool of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, ChinaSchool of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, ChinaSchool of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, ChinaSchool of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, ChinaSchool of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, ChinaTo address the pressure of emissions reduction in urban residential blocks (RBs), this study takes 99 micro-scale RBs in Hongqiao District, Tianjin as the objects, aiming to reveal the driving mechanism of built environmental factors (BEF) on residential blocks carbon emissions (RBCE) and explore planning strategies that balance carbon reduction and health benefits. By integrating spatial statistical analysis and high-precision machine learning models, the system has systematically revealed the spatio-temporal evolution laws, spatial differentiation characteristics and driving mechanisms of BEF on RBCE. Key findings include: (1) From 2021 to 2023, both the RBCE, residential blocks carbon emissions intensity (RBCEI), and average household carbon emissions (RBCE-AH) showed a “first rise then fall” fluctuation, with an overall 5.7% increase, signaling sustained emissions reduction pressure. (2) High emissions areas are spatially concentrated and contagious, while low carbon units are mostly peripheral. Spatial autocorrelation analysis indicates a significant positive correlation and a west-south clustering pattern. (3) Land area (LA) is the main emissions affecting factor, followed by green space ratio (GSR) and Land use mixing degree (LMD), whose inhibitory effect exceeds that of traditional high-intensity development indicators. (4) Targeted planning strategies such as strictly controlling land use expansion, improving GSR, and promoting functional combination were proposed. At the same time, it was suggested that in the future, the heterogeneity of building types and more three-dimensional morphological indicators should be incorporated into the BEF index system, and combined with more refined coupling models, their influence paths should be quantitatively analyzed. These strategies not only provide a basis for the implementation of macro emissions reduction policies, but also offer solutions for micro action plans centered on residents’mental health and cardiopulmonary system protection. Overall, this study provides a scientific basis for low carbon RBs planning and renewal that balances carbon reduction with health benefits.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1645402/fullbuilt environmental factors (BEF)residential blocks carbon emissions (RBCE)Random Forest modelinfluence mechanismTianjin |
| spellingShingle | Xiaoping Zhang Xiaoping Zhang Zixuan Cui Chaoxian Feng Xin Wen Huabin Xiao Jianbo Ni Built environment and residential blocks carbon emissions: a study using advanced metering infrastructure data Frontiers in Public Health built environmental factors (BEF) residential blocks carbon emissions (RBCE) Random Forest model influence mechanism Tianjin |
| title | Built environment and residential blocks carbon emissions: a study using advanced metering infrastructure data |
| title_full | Built environment and residential blocks carbon emissions: a study using advanced metering infrastructure data |
| title_fullStr | Built environment and residential blocks carbon emissions: a study using advanced metering infrastructure data |
| title_full_unstemmed | Built environment and residential blocks carbon emissions: a study using advanced metering infrastructure data |
| title_short | Built environment and residential blocks carbon emissions: a study using advanced metering infrastructure data |
| title_sort | built environment and residential blocks carbon emissions a study using advanced metering infrastructure data |
| topic | built environmental factors (BEF) residential blocks carbon emissions (RBCE) Random Forest model influence mechanism Tianjin |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1645402/full |
| work_keys_str_mv | AT xiaopingzhang builtenvironmentandresidentialblockscarbonemissionsastudyusingadvancedmeteringinfrastructuredata AT xiaopingzhang builtenvironmentandresidentialblockscarbonemissionsastudyusingadvancedmeteringinfrastructuredata AT zixuancui builtenvironmentandresidentialblockscarbonemissionsastudyusingadvancedmeteringinfrastructuredata AT chaoxianfeng builtenvironmentandresidentialblockscarbonemissionsastudyusingadvancedmeteringinfrastructuredata AT xinwen builtenvironmentandresidentialblockscarbonemissionsastudyusingadvancedmeteringinfrastructuredata AT huabinxiao builtenvironmentandresidentialblockscarbonemissionsastudyusingadvancedmeteringinfrastructuredata AT jianboni builtenvironmentandresidentialblockscarbonemissionsastudyusingadvancedmeteringinfrastructuredata |