Differentiated landscape pattern transformation strategies drive territorial space carbon neutrality: A path exploration based on mixed land use units

IF 12 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Houbao Fan , Xiaobin Jin , Junjun Zhu , Zhouyao Zhang , Chunguang Hu , Han Hu , Xiaoya Du , Yinkang Zhou
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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.
差别化景观格局转化策略驱动国土空间碳中和:基于混合土地利用单元的路径探索
揭示混合土地利用单元下景观格局对碳排放的影响机制,对实现双碳目标和国土空间规划具有重要的启示意义。本研究以江阴市为研究区,开发一套完善的区域性碳预算核算体系。基于土地利用比例和强度数据,应用自组织地图网络模型对混合土地利用单元进行分类。采用可解释机器学习模型分析了多维景观格局对碳排放的影响。结果表明:(1)江阴市碳源排放与碳汇容量存在显著差距。碳源总量为2349.938×104 tC,平均碳源强度为2.986×104 tC/km2,而碳汇总量仅为24983.809 tC。工业用地是碳排放的主要来源,而林地和农田则是主要的碳汇。②高碳区主要集中在北部沿江工业开发区和中东部工业园区。低碳地区主要位于森林覆盖的丘陵和高海拔山区,以及南部的作物种植区。(3)共确定13个混合土地利用单元区划,其中绿色生态保护区碳排放强度最低,农业生产过渡区碳排放强度是农业生产核心区的7.58倍。④景观格局与碳排放的关系在不同混合土地利用单元之间存在显著差异,呈现出复杂的非线性关系。建议建立基于混合土地利用单元的差别化景观格局转换策略,通过调节土地空间配置和增强生态连通性来推进国土空间碳中和。
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来源期刊
Sustainable Cities and Society
Sustainable Cities and Society Social Sciences-Geography, Planning and Development
CiteScore
22.00
自引率
13.70%
发文量
810
审稿时长
27 days
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;
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