Global governance priorities derived from SDG spatiotemporal dynamics and causal interactions
文献类型：期刊论文
| 作者 | Huan, Yizhong12,13; Ji, Linjiang12; Su, Yiming2,14,15; Kong, Feng12; Lan, Yang1,3; Feng, Zhaohui4; Wang, Siyu5; Liang, Tao2,6; Wang, Mingyuan2,6; Mo, Pengpeng2,14 |
| 刊名 | GONDWANA RESEARCH
 |
| 出版日期 | 2026-03-01 |
| 卷号 | 151页码:242-253 |
| 关键词 | Sustainable development Spatial characteristics Machine learning Causalities Global North Global South Global governance |
| ISSN号 | 1342-937X |
| DOI | 10.1016/j.gr.2025.10.027 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | High-precision assessments of the spatiotemporal dynamics of global Sustainable Development Goal (SDG) performance are needed to guide effective cross-scale governance for sustainable development. However, related studies remain limited, and the contributions and causal interactions of individual goals are unclear, hindering the identification of transformative action priorities. Here, the SDG Index, multiple spatiotemporal analysis models, and a grey forecast model were integrated to develop a new framework for assessing spatiotemporal patterns in global SDG performance from 2000 to 2030. In addition, machine learning was applied to identify the key goals contributing to SDG acceleration and to map their weighted causal interactions across the SDG system. The results showed that global SDG progress stalled after 2020 and is unlikely to be fully achieved by 2030. SDG 2 (zero hunger) significantly lagged behind, while SDG 4 (quality education) was the most influential driver, with particularly strong effects on SDGs 6 (clean water and sanitation) and 17 (partnerships for the goals). Despite strong spatial autocorrelation in SDG performance, notable disparities persist across regions. The spatial center of SDG performance shifted eastward over time, indicating that Asia has become as a key driver of global SDG acceleration, although environmental sustainability challenges persist. Although the Global North-South gap has slightly narrowed, development inequalities remain pronounced, with SDG 1 (no poverty) showing the largest disparity and SDG 2 representing a shared deficiency. This study enhances the understanding of global sustainable development progress and provides new insights applicable for broader global cooperative governance, facilitating the acceleration of the 2030 Agenda. |
| URL标识 | 查看原文 |
| WOS关键词 | SUSTAINABLE DEVELOPMENT ; PROGRESS |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001632297100001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219675]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhou, Guangjin |
| 作者单位 | 1.Beihang Univ, Sch Econ & Management, Beijing, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Nat Resource Coupling Proc & Effects, Minist Nat Resources, Beijing, Peoples R China; 4.Peking Univ, Coll Urban & Environm Sci, Beijing, Peoples R China; 5.Xinjiang Univ, Coll Ecol & Environm, Urumqi, Peoples R China; 6.Univ Chinese Acad Sci, Beijing, Peoples R China; 7.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing, Peoples R China; 8.Minist Ecol & Environm, Satellite Applicat Ctr Ecol & Environm, Beijing, Peoples R China; 9.Minist Ecol & Environm, Key Lab Satellite Remote Sensing, Beijing, Peoples R China; 10.Beijing Normal Univ, Inst Land Surface Syst & Sustainable Dev, Fac Geog Sci, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Huan, Yizhong,Ji, Linjiang,Su, Yiming,et al. Global governance priorities derived from SDG spatiotemporal dynamics and causal interactions[J]. GONDWANA RESEARCH,2026,151:242-253. |
| APA | Huan, Yizhong.,Ji, Linjiang.,Su, Yiming.,Kong, Feng.,Lan, Yang.,...&Wang, Yazhu.(2026).Global governance priorities derived from SDG spatiotemporal dynamics and causal interactions.GONDWANA RESEARCH,151,242-253. |
| MLA | Huan, Yizhong,et al."Global governance priorities derived from SDG spatiotemporal dynamics and causal interactions".GONDWANA RESEARCH 151(2026):242-253. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。