Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model
Residential land is the basic unit of urban-scale carbon emissions (CEs). Quantifying and predicting CEs from residential land are conducive to achieving urban carbon neutrality. This study took 84 residential communities in Susong County, Anhui Province as its research object, exploring the nonline...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/14/3/440 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850090610530189312 |
|---|---|
| author | Congguang Xu Wei Xiong Simin Zhang Hailiang Shi Shichao Wu Shanju Bao Tieqiao Xiao |
| author_facet | Congguang Xu Wei Xiong Simin Zhang Hailiang Shi Shichao Wu Shanju Bao Tieqiao Xiao |
| author_sort | Congguang Xu |
| collection | DOAJ |
| description | Residential land is the basic unit of urban-scale carbon emissions (CEs). Quantifying and predicting CEs from residential land are conducive to achieving urban carbon neutrality. This study took 84 residential communities in Susong County, Anhui Province as its research object, exploring the nonlinear relationship between the urban built environment and CEs from residential land. By identifying CEs from residential land through building electricity consumption, 14 built environment indicators, including land area (LA), floor area ratio (FAR), greening ratio (GA), building density (BD), gross floor area (GFA), land use mix rate (Phh), and permanent population density (PPD), were selected to establish an interpretable machine learning (ML) model based on the XGBoost-SHAP attribution analysis framework. The research results show that, first, the goodness of fit of the XGBoost model reached 91.9%, and its prediction accuracy was better than that of gradient boosting decision tree (GBDT), random forest (RF), the Adaboost model, and the traditional logistic model. Second, compared with other ML models, the XGBoost-SHAP model explained the influencing factors of CEs from residential land more clearly. The SHAP attribution analysis results indicate that BD, FAR, and Phh were the most important factors affecting CEs. Third, there was a significant nonlinear relationship and threshold effect between built environment characteristic variables and CEs from residential land. Fourth, there was an interaction between different dimensions of environmental factors, and BD, FAR, and Phh played a dominant role in the interaction. Reducing FAR is considered to be an effective CE reduction strategy. This research provides practical suggestions for urban planners on reducing CEs from residential land, which has important policy implications and practical significance. |
| format | Article |
| id | doaj-art-af5e76f874a842ddbee968340ef651e0 |
| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-af5e76f874a842ddbee968340ef651e02025-08-20T02:42:32ZengMDPI AGLand2073-445X2025-02-0114344010.3390/land14030440Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP ModelCongguang Xu0Wei Xiong1Simin Zhang2Hailiang Shi3Shichao Wu4Shanju Bao5Tieqiao Xiao6Science Island Branch, University of Science and Technology of China, Hefei 230026, ChinaScience Island Branch, University of Science and Technology of China, Hefei 230026, ChinaAnhui Provincial Key Laboratory of Building Earthquake Disaster Mitigation and Green Operations, Anhui Institute of Building Research & Design, Hefei 230088, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241000, ChinaSchool of Architecture and Planning, Anhui Jianzhu University, Hefei 230009, ChinaResidential land is the basic unit of urban-scale carbon emissions (CEs). Quantifying and predicting CEs from residential land are conducive to achieving urban carbon neutrality. This study took 84 residential communities in Susong County, Anhui Province as its research object, exploring the nonlinear relationship between the urban built environment and CEs from residential land. By identifying CEs from residential land through building electricity consumption, 14 built environment indicators, including land area (LA), floor area ratio (FAR), greening ratio (GA), building density (BD), gross floor area (GFA), land use mix rate (Phh), and permanent population density (PPD), were selected to establish an interpretable machine learning (ML) model based on the XGBoost-SHAP attribution analysis framework. The research results show that, first, the goodness of fit of the XGBoost model reached 91.9%, and its prediction accuracy was better than that of gradient boosting decision tree (GBDT), random forest (RF), the Adaboost model, and the traditional logistic model. Second, compared with other ML models, the XGBoost-SHAP model explained the influencing factors of CEs from residential land more clearly. The SHAP attribution analysis results indicate that BD, FAR, and Phh were the most important factors affecting CEs. Third, there was a significant nonlinear relationship and threshold effect between built environment characteristic variables and CEs from residential land. Fourth, there was an interaction between different dimensions of environmental factors, and BD, FAR, and Phh played a dominant role in the interaction. Reducing FAR is considered to be an effective CE reduction strategy. This research provides practical suggestions for urban planners on reducing CEs from residential land, which has important policy implications and practical significance.https://www.mdpi.com/2073-445X/14/3/440residential landmachine learningXGBoost modelSHAP algorithmcarbon emissionsbuilt environment factors |
| spellingShingle | Congguang Xu Wei Xiong Simin Zhang Hailiang Shi Shichao Wu Shanju Bao Tieqiao Xiao Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model Land residential land machine learning XGBoost model SHAP algorithm carbon emissions built environment factors |
| title | Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model |
| title_full | Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model |
| title_fullStr | Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model |
| title_full_unstemmed | Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model |
| title_short | Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model |
| title_sort | research on the nonlinear relationship between carbon emissions from residential land and the built environment a case study of susong county anhui province using the xgboost shap model |
| topic | residential land machine learning XGBoost model SHAP algorithm carbon emissions built environment factors |
| url | https://www.mdpi.com/2073-445X/14/3/440 |
| work_keys_str_mv | AT congguangxu researchonthenonlinearrelationshipbetweencarbonemissionsfromresidentiallandandthebuiltenvironmentacasestudyofsusongcountyanhuiprovinceusingthexgboostshapmodel AT weixiong researchonthenonlinearrelationshipbetweencarbonemissionsfromresidentiallandandthebuiltenvironmentacasestudyofsusongcountyanhuiprovinceusingthexgboostshapmodel AT siminzhang researchonthenonlinearrelationshipbetweencarbonemissionsfromresidentiallandandthebuiltenvironmentacasestudyofsusongcountyanhuiprovinceusingthexgboostshapmodel AT hailiangshi researchonthenonlinearrelationshipbetweencarbonemissionsfromresidentiallandandthebuiltenvironmentacasestudyofsusongcountyanhuiprovinceusingthexgboostshapmodel AT shichaowu researchonthenonlinearrelationshipbetweencarbonemissionsfromresidentiallandandthebuiltenvironmentacasestudyofsusongcountyanhuiprovinceusingthexgboostshapmodel AT shanjubao researchonthenonlinearrelationshipbetweencarbonemissionsfromresidentiallandandthebuiltenvironmentacasestudyofsusongcountyanhuiprovinceusingthexgboostshapmodel AT tieqiaoxiao researchonthenonlinearrelationshipbetweencarbonemissionsfromresidentiallandandthebuiltenvironmentacasestudyofsusongcountyanhuiprovinceusingthexgboostshapmodel |