| 珠江流域城市工业碳排放效率的空间关联网络特征与影响因素 |
| 摘要点击 1829 全文点击 214 投稿时间：2023-12-12 修订日期：2024-02-29 |
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| 中文关键词 工业碳排放效率 探索性时空数据分析 社会网络分析（SNA） 协同碳减排 空间关联 |
| 英文关键词 industrial carbon emission efficiency exploratory spatiotemporal data analysis social network analysis（SNA） collaborative carbon reduction spatial correlation |
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| 中文摘要 |
| 中国经历了城市化和工业化的快速发展,探索城市工业碳排放及其空间关联特征对推动城市间协同减排,实现“双碳”目标具有重要意义.以珠江流域49个城市为研究对象,运用包含非期望产出的超效率松弛测度模型测算了2010~2020年的城市工业碳排放效率,采用探索性时空数据分析探究其空间格局；并利用社会网络分析和二次赋值过程回归方法探讨城市工业碳排放效率的空间关联网络特征及其影响因素.结果表明,珠江流域城市工业碳排放效率整体呈下降趋势且在空间上存在较强的不平衡性和异质性；在工业碳排放效率空间关联网络中,珠三角地区始终占据主导地位,形成了以核心网为首要组成部分、常规网为辅助部分的多层次复合网络体系；经济发达城市,如广州、深圳和东莞等长期处于网络中心,发挥着显著的中介作用；城市化水平、科学技术水平和工业化水平对碳排放网络的联系强度有愈发显著的正向影响.结合工业碳排放效率网络的空间结构特征、聚类特点和驱动因素,可有效反映城市协同碳减排潜力,可为区域低碳发展战略的制定与实施提供参考. |
| 英文摘要 |
| China has experienced rapid development of urbanization and industrialization, and urban industrial carbon reduction has become the key to green and sustainable development. Exploring urban industrial carbon emissions and its spatial correlation characteristics is of great importance for promoting collaborative cooperation between cities and achieving the “dual carbon” goal. Considering 49 cities in the Pearl River Basin as research objects, the super slacks-based measure model was used to measure the urban industrial carbon emission efficiency from 2010 to 2020, and exploratory spatiotemporal data analysis was used to explore its spatial pattern. On this basis, we used social network analysis and quadratic assignment procedure （QAP） methods to explore the spatial correlation network characteristics and influencing factors of urban industrial carbon emission efficiency. The results indicated that the industrial carbon emission efficiency of the Pearl River Basin showed a downward trend. Additionally, a strong spatial imbalance and heterogeneity was observed. The Pearl River Delta Region always dominated the spatial correlation network of carbon emission efficiency, forming a multi-level composite network system with the core network as the primary component and the conventional network as the auxiliary component. Economically developed cities, such as Guangzhou, Shenzhen, and Dongguan have long been in the center of the network and have extensive connections with other cities, while playing an intermediary role. The level of urbanization, science and technology, and industrialization have an increasingly significant positive impact on the strength of the connection between urban industrial carbon emission networks. The Pearl River Basin industrial carbon emission efficiency network has significant individual differences and hierarchical characteristics. According to the individual centrality, network role, and clustering characteristics, combined with the corresponding influencing factors, the Pearl River Basin urban collaborative carbon emission reduction strategy was formulated. This study can provide reference for the formulation and implementation of regional low-carbon collaborative development strategies. |
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