| 陕西省县域碳排放空间关联网络结构及驱动因素 |
| 摘要点击 248 全文点击 4 投稿时间：2025-05-05 修订日期：2025-07-23 |
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| 中文关键词 碳排放 空间关联网络 社会网络分析 指数随机图模型（ERGM） 陕西省县域 |
| 英文关键词 carbon emissions spatial correlation network social network analysis exponential random graph model （ERGM） county-level regions of Shaanxi Province |
| DOI 10.13227/j.hjkx.202505015 |
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| 中文摘要 |
| 准确把握县域碳排放空间关联网络结构特征及其影响因素，对促进区域协同碳减排和低碳可持续发展具有重要意义. 基于陕西省县域碳排放数据，在定量评估碳生态承载系数的基础上，通过社会网络分析法和指数随机图模型系统考察陕西省县域碳排放空间关联网络的结构特征，并对网络形成的关键驱动因素进行分析. 结果表明：①碳排放空间网络具有高度连通性，稳定性逐渐增强，但空间关联性不高，仍存在结构松散且冗余等问题；②高陵区和凤县等县域在碳排放空间关联网络中占据关键节点位置，阎良区及凤县等县域发挥“中心行动者”作用，不易受其他县域影响或控制，阎良区与杨陵区等县域中介作用明显，对网络中其他县域碳排放的控制能力较强；③陕南、关中和陕北区域碳排放空间关联均呈现“板块间强于内部”的特征，普遍存在“碳排放转移”现象，关中存在复杂空间溢出关系，陕南关联不紧密需加强联动，陕北跨区域关联显著且相互影响明显；④发现自组织调节效应随时间减弱，从双向互锁转向单向主导. 碳汇压力、经济发展、能源效率、产业结构、环保力度、技术水平和城镇化率等社会选择行为因素对碳排放关联的影响呈现阶段性变化：早期碳汇压力和经济因素主导，后期环境保护和产业结构起关键作用，但技术推动力仍不足，同时地理距离和县域邻接关系的制约效应逐渐减弱. |
| 英文摘要 |
| Accurately grasping the structural characteristics of the spatial correlation network of county-level carbon emissions and its influencing factors is of great significance for promoting regional collaborative carbon emission reduction and low-carbon sustainable development. Based on carbon emissions data in county-level regions of Shaanxi Province， this study quantitatively evaluates the carbon ecological carrying coefficient， systematically examines the structural characteristics of the spatial correlation network of carbon emissions in county-level regions of Shaanxi Province using social network analysis and the exponential random graph model and analyzes the key driving factors behind the network formation. The results show that： ① The spatial carbon emission network exhibited high connectivity and gradually increasing stability， but the spatial correlation remained weak， with persistent issues such as structural looseness and redundancy. ② Key nodes in the spatial correlation network of carbon emissions were occupied by counties such as Gaoling District and Fengxian County， while regions like Yanliang District and Fengxian County played the role of “central actors” being less susceptible to influence or control by other counties. Counties such as Yanliang District and Yangling District demonstrated significant intermediary roles， exerting strong control over carbon emissions in other counties within the network. ③ The spatial correlations of carbon emissions in southern Shaanxi， Guanzhong， and northern Shaanxi all exhibited the characteristic of “inter-plate connections being stronger than intra-plate connections，” with widespread “carbon emission transfer” phenomena. Guanzhong displayed complex spatial spillover relationships， southern Shaanxi lacked tight connections and requires enhanced coordination， while northern Shaanxi showed significant cross-regional correlations and pronounced mutual influences. ④ The self-organizing regulatory effect was found to weaken over time， shifting from bidirectional interlocking to unidirectional dominance. The influence of social selection behavior factors─such as carbon sink pressure， economic development， energy efficiency， industrial structure， environmental protection efforts， technological level， and urbanization rate─on carbon emission correlations exhibited phased changes： Carbon sink pressure and economic factors dominated in the early stage， while environmental protection and industrial restructuring played prominent roles in the later stage， though the driving force of technology remains insufficient. Meanwhile， the constraining effects of geographical distance and county adjacency relationships gradually diminished. |
