Houbao Fan , Xiaobin Jin , Junjun Zhu , Zhouyao Zhang , Chunguang Hu , Han Hu , Xiaoya Du , Yinkang Zhou
{"title":"Differentiated landscape pattern transformation strategies drive territorial space carbon neutrality: A path exploration based on mixed land use units","authors":"Houbao Fan , Xiaobin Jin , Junjun Zhu , Zhouyao Zhang , Chunguang Hu , Han Hu , Xiaoya Du , Yinkang Zhou","doi":"10.1016/j.scs.2025.106865","DOIUrl":null,"url":null,"abstract":"<div><div>Revealing the mechanism of landscape pattern on carbon emissions under the mixed land use units provides important insights for implementing dual carbon goals and territorial space planning. This study takes Jiangyin as the study area to develop a refined territorial carbon budget accounting system. Based on the data of land use proportion and intensity, the self-organizing map network model was applied to classify the mixed land use units. The interpretable machine learning model was used to analyze the impact of multidimensional landscape pattern on carbon emissions. The results show that: (1) There is a significant gap between carbon source emissions and carbon sink capacity in Jiangyin. The total amount of carbon source was 2349.938×10<sup>4</sup> tC, with an average carbon source intensity was 2.986×10<sup>4</sup> tC/km<sup>2</sup>, while the total carbon sink was only 24983.809 tC. Industrial land is the major contributor to carbon emissions, whereas forestland and cropland serve as the primary carbon sinks. (2) High-carbon regions are mainly concentrated in the northern industrial development zone along the Yangtze River and the industrial parks in the central eastern area. Low-carbon regions are mainly located in forest covered hilly and mountainous areas at higher elevations, as well as in crop cultivation areas in the southern part. (3) Thirteen zones of mixed land use units were identified, among which the carbon emission intensity of green ecological protection zone was the lowest, and the carbon emission intensity of agricultural production transition zone was 7.58 times that of agricultural production core zone. (4) The relationship between landscape patterns and carbon emissions varies significantly across different mixed land use units, exhibiting complex nonlinear relationships. This study recommends establishing differentiated landscape pattern transformation strategies based on mixed land use units and advancing the territorial space carbon neutrality by regulating land spatial configuration and enhancing ecological connectivity.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"133 ","pages":"Article 106865"},"PeriodicalIF":12.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670725007383","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Revealing the mechanism of landscape pattern on carbon emissions under the mixed land use units provides important insights for implementing dual carbon goals and territorial space planning. This study takes Jiangyin as the study area to develop a refined territorial carbon budget accounting system. Based on the data of land use proportion and intensity, the self-organizing map network model was applied to classify the mixed land use units. The interpretable machine learning model was used to analyze the impact of multidimensional landscape pattern on carbon emissions. The results show that: (1) There is a significant gap between carbon source emissions and carbon sink capacity in Jiangyin. The total amount of carbon source was 2349.938×104 tC, with an average carbon source intensity was 2.986×104 tC/km2, while the total carbon sink was only 24983.809 tC. Industrial land is the major contributor to carbon emissions, whereas forestland and cropland serve as the primary carbon sinks. (2) High-carbon regions are mainly concentrated in the northern industrial development zone along the Yangtze River and the industrial parks in the central eastern area. Low-carbon regions are mainly located in forest covered hilly and mountainous areas at higher elevations, as well as in crop cultivation areas in the southern part. (3) Thirteen zones of mixed land use units were identified, among which the carbon emission intensity of green ecological protection zone was the lowest, and the carbon emission intensity of agricultural production transition zone was 7.58 times that of agricultural production core zone. (4) The relationship between landscape patterns and carbon emissions varies significantly across different mixed land use units, exhibiting complex nonlinear relationships. This study recommends establishing differentiated landscape pattern transformation strategies based on mixed land use units and advancing the territorial space carbon neutrality by regulating land spatial configuration and enhancing ecological connectivity.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;