Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration
Urban agglomerations with a high economic activity and population density are key areas for carbon emissions and pioneers in achieving carbon peaking and the Sustainable Development Goals (SDGs). This study combines machine learning with an extended STIRPAT (Stochastic Impacts by Regression on Popul...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/23/5899 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850261415596654592 |
|---|---|
| author | Pin Chen Xiyue Wang Zexia Yang Changfeng Shi |
| author_facet | Pin Chen Xiyue Wang Zexia Yang Changfeng Shi |
| author_sort | Pin Chen |
| collection | DOAJ |
| description | Urban agglomerations with a high economic activity and population density are key areas for carbon emissions and pioneers in achieving carbon peaking and the Sustainable Development Goals (SDGs). This study combines machine learning with an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to uncover the mechanisms driving carbon peaking disparities within these regions. It forecasts carbon emissions under different scenarios and develops indices to assess peaking pressure, reduction potential, and driving forces. The findings show significant carbon emission disparities among cities in the Yangtze River Delta, with a fluctuating downward trend over time. Technological advancement, population size, affluence, and urbanization positively impact emissions, while the effects of industrial structure and foreign investment are weakening. Industrially optimized cities lead in peaking, while others—such as late-peaking and economically radiating cities—achieve peaking only under the ER scenario. Cities facing population loss and demonstration cities fail to peak by 2030 in any scenario. The study recommends differentiated carbon peaking pathways for cities, emphasizing tailored targets, pathway models, and improved supervision. This research offers theoretical and practical insights for global urban agglomerations aiming to achieve early carbon peaking. |
| format | Article |
| id | doaj-art-763372674d474e2bb7d36c0718e4389d |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-763372674d474e2bb7d36c0718e4389d2025-08-20T01:55:26ZengMDPI AGEnergies1996-10732024-11-011723589910.3390/en17235899Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban AgglomerationPin Chen0Xiyue Wang1Zexia Yang2Changfeng Shi3School of Economics and Management, Changzhou Institute of Technology, Changzhou 213032, ChinaSchool of Economics and Finance, Hohai University, Changzhou 213200, ChinaSchool of Economics, Fuyang Normal University, Fuyang 236041, ChinaSchool of Economics and Finance, Hohai University, Changzhou 213200, ChinaUrban agglomerations with a high economic activity and population density are key areas for carbon emissions and pioneers in achieving carbon peaking and the Sustainable Development Goals (SDGs). This study combines machine learning with an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to uncover the mechanisms driving carbon peaking disparities within these regions. It forecasts carbon emissions under different scenarios and develops indices to assess peaking pressure, reduction potential, and driving forces. The findings show significant carbon emission disparities among cities in the Yangtze River Delta, with a fluctuating downward trend over time. Technological advancement, population size, affluence, and urbanization positively impact emissions, while the effects of industrial structure and foreign investment are weakening. Industrially optimized cities lead in peaking, while others—such as late-peaking and economically radiating cities—achieve peaking only under the ER scenario. Cities facing population loss and demonstration cities fail to peak by 2030 in any scenario. The study recommends differentiated carbon peaking pathways for cities, emphasizing tailored targets, pathway models, and improved supervision. This research offers theoretical and practical insights for global urban agglomerations aiming to achieve early carbon peaking.https://www.mdpi.com/1996-1073/17/23/5899cluster analysiscarbon peaking pathwaysscenario analysisgrey theoryheterogeneity analysis |
| spellingShingle | Pin Chen Xiyue Wang Zexia Yang Changfeng Shi Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration Energies cluster analysis carbon peaking pathways scenario analysis grey theory heterogeneity analysis |
| title | Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration |
| title_full | Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration |
| title_fullStr | Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration |
| title_full_unstemmed | Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration |
| title_short | Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration |
| title_sort | research on spatial heterogeneity impact mechanism and carbon peak prediction of carbon emissions in the yangtze river delta urban agglomeration |
| topic | cluster analysis carbon peaking pathways scenario analysis grey theory heterogeneity analysis |
| url | https://www.mdpi.com/1996-1073/17/23/5899 |
| work_keys_str_mv | AT pinchen researchonspatialheterogeneityimpactmechanismandcarbonpeakpredictionofcarbonemissionsintheyangtzeriverdeltaurbanagglomeration AT xiyuewang researchonspatialheterogeneityimpactmechanismandcarbonpeakpredictionofcarbonemissionsintheyangtzeriverdeltaurbanagglomeration AT zexiayang researchonspatialheterogeneityimpactmechanismandcarbonpeakpredictionofcarbonemissionsintheyangtzeriverdeltaurbanagglomeration AT changfengshi researchonspatialheterogeneityimpactmechanismandcarbonpeakpredictionofcarbonemissionsintheyangtzeriverdeltaurbanagglomeration |