[口头报告]资源型城市可持续发展:指标、时空模式及应用

资源型城市可持续发展:指标、时空模式及应用
编号:1074 稿件编号:2015 访问权限:仅限参会人 更新:2024-04-19 11:32:14 浏览:47次 口头报告

报告开始:2024年05月19日 14:40 (Asia/Shanghai)

报告时间:10min

所在会议:[S7] 主题7、遥感与地理信息科学 » [S7-8] 主题7、遥感与地理信息科学 专题7.12、专题7.5(19日下午,303)

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摘要
Resource-based cities serve as crucial strategic bases for energy security in China. However, as urban resources gradually deplete, these cities confront the dilemma of the resource curse, precipitating a severe development crisis and rendering sustainable development attainment more challenging than for other cities. The formulation of Sustainable Development Goals (SDGs) presents a novel framework guiding the trajectory of resource-based cities. Based on the prefecture-level data spanning from 2006 to 2020 in China, this paper used principal component analysis (PCA) to construct a SDGs-oriented sustainable development indicator system for resource-based cities. Further spatial analysis and cluster analysis show that: (1) The sustainable development of resource-based cities show a balanced tendency in space, and regenerative cities have the best performance. (2) A significant positive spatial correlation is observed in the sustainable development of resource-based cities, underscored by a discernible increase in spatial convergence and a concomitant decrease in spatial agglomeration of sustainable development capabilities over time. (3) Despite this, considerable disparities persist across various domains of sustainable development among resource-based cities, with many facing with high carbon emissions. Consequently, the implementation of multifaceted policies and industrial transformation initiatives is imperative to strike a balance in different domains and facilitate carbon reduction. This paper offers an in-depth exploration of the spatiotemporal patterns characterizing resource-based cities, which can provide some new insights conducive to fostering their sustainable development.
 
关键字
resource-based cities; sustainable development; spatiotemporal patterns; principal component analysis(PCA); cluster analysis
报告人
钟琪
硕士研究生 中国矿业大学

稿件作者
钟琪 中国矿业大学
李桂娥 中国矿业大学
LiChunying China University of Mining and Technology
JiaoYangyang China University of Mining and Technology
LIJie China University of Mining and Technology
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