Data-driven neighborhood-level carbon emission accounting models and decarbonization strategies: Empirical study on Central Shenyang City

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2025-04-02 DOI:10.1016/j.scs.2025.106346

Xiaobin Ye , Zhenyu Wang , Kexin Cui , Shaoxuan Meng , Xin Ning

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引用次数: 0

Abstract

Neighborhood is the basic unit for fine-grained management of urban carbon emissions and the best place to apply low-carbon concepts and technologies. However, accurately calculating carbon emissions at this level remains a challenge, complicating the identification of effective decarbonization strategies. This research proposes utilizing urban land use at the neighborhood level as a decarbonization unit and taking Central Shenyang City as an example to measure the carbon emission characteristics of different sectors by integrating multi-source data. Then, based on the carbon emission intensity of the lands, establishing a baseline for decarbonization and measuring its effectiveness accordingly. The results show that in Central Shenyang City, 90.99 % of CO₂ emissions come from the building and transportation sectors, with carbon sinks offsetting a mere 2.20 % of these emissions. Commercial land has the highest overall level of carbon emissions per unit area, while industrial land records the highest per capita carbon emissions. In addition, there is a significant imbalance in the spatial distribution of carbon emissions among various sectors. Through a graded decarbonization strategy based on the baseline indicators, an overall decarbonization of 19.79 % in carbon emissions is achieved. This study introduces a data-driven model with potential applications in other regions.

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街区是城市碳排放精细化管理的基本单位，也是应用低碳概念和技术的最佳场所。然而，在这一层面准确计算碳排放量仍是一项挑战，使得确定有效的去碳化战略变得更加复杂。本研究以沈阳市中心城区为例，提出利用街区层面的城市土地利用作为去碳化单元，通过整合多源数据，测算不同行业的碳排放特征。然后，根据土地的碳排放强度，建立去碳化基线，并据此衡量去碳化效果。结果显示，在沈阳市中心城区，90.99% 的二氧化碳排放来自建筑和交通部门，碳汇仅能抵消其中的 2.20%。商业用地的单位面积碳排放总量最高，而工业用地的人均碳排放量最高。此外，碳排放量在各部门之间的空间分布也严重失衡。通过基于基线指标的分级去碳化战略，实现了碳排放 19.79% 的总体去碳化。本研究介绍了一个数据驱动模型，该模型有可能应用于其他地区。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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;