共 50 条
Spatial network characteristics of carbon balance in urban agglomerations- a case study in Beijing-Tianjin-Hebei city agglomeration
被引:1
|作者:
Ren, Xuezhen
[1
]
Xiong, Rui
[2
]
Ni, Tianhua
[1
]
机构:
[1] Nanjing Univ, Sch Geog & Ocean Sci, 163 Xianlin Ave, Nanjing 210023, Jiangsu, Peoples R China
[2] Minist Water Resources, Water & Soil Conservat Monitoring Ctr, 2,Lane 2,Baiguang Rd, Beijing 100053, Peoples R China
关键词:
Carbon balance;
Spatial association network;
Urban agglomeration;
Gravity model;
Social network analysis;
ENERGY-CONSUMPTION;
CO2;
EMISSIONS;
ASSOCIATION;
POLICY;
CHINA;
D O I:
10.1016/j.apgeog.2024.103343
中图分类号:
P9 [自然地理学];
K9 [地理];
学科分类号:
0705 ;
070501 ;
摘要:
The cities within city clusters have strong trade connections, resulting in spatial heterogeneity and correlation of carbon balances. Previous studies have not deeply explored the characteristics of the spatial association network (SAN) of carbon balance in urban agglomerations, particularly regarding network structure, node effects, and spatial-temporal inhomogeneities. This study investigated the spatial network characteristics of carbon balance in the Beijing-Tianjin-Hebei (BTH) region from 2000 to 2019, employing a modified gravity model and social network analysis (SNA). The findings revealed the following: 1) Carbon emissions increased by 106.42%, and carbon sinks increased by 31.06%, displaying spatial-temporal heterogeneity and forming a multi-level, multinodal SAN of carbon balance centered around Beijing. 2) The carbon balance was mainly influenced by spatial spillovers occurring at different nodes, typically moving from lower-tier to higher-tier nodes. 3) Cities assumed four roles: bidirectional spillover, net spillover, primary beneficiary, and agent, with these roles being dynamic. 4) The spatial correlation of carbon balance was primarily influenced by economic development (max 0.602), spatial distance (max 0.331), and per capita ecological land (max 0.445). This approach would refine carbon management policies and deepen the understanding of the SAN and its influencing factors, providing insights for optimizing carbon emission reduction management.
引用
收藏
页数:11
相关论文