JOURNAL ARTICLE
Report on Sino–Indian Border Disputes: International Law and International Relations Perspectives.
Published In: Chinese Journal of Comparative Law, 2023, v. 11, n. 2. P. 1 1 of 3
Database: Legal Source 2 of 3
Authored By: Lone, Fozia Nazir 3 of 3
Abstract
This article examines the Sino-Indian Border Dispute (SIBD) through insights gathered from two international online symposiums hosted by the City University of Hong Kong's School of Law in 2021. It highlights the complexity of the dispute, rooted in historical colonial legacies, competing territorial sovereignty claims, and geopolitical dynamics involving China, India, and regional actors. The article emphasizes the limited effectiveness of international law—particularly customary international law and adjudicative mechanisms—in resolving the dispute, given both nations' selective acceptance of international legal jurisdiction. It advocates for a multidisciplinary, pragmatic, and incremental approach to conflict resolution that incorporates legal and non-legal factors, including political, cultural, economic, and ethnographic perspectives, and prioritizes bilateral negotiations, confidence-building measures, and cooperation on shared resources. The article also underscores the importance of considering the perspectives of border populations and calls for transparency, de-escalation, and multilateral engagement grounded in the Five Principles of Peaceful Co-existence to foster long-term stability and peace in the region.
Additional Information
- Source:Chinese Journal of Comparative Law. 2023/09, Vol. 11, Issue 2, p1
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2023
- ISSN:20504802
- DOI:10.1093/cjcl/cxad005
- Accession Number:171389203
- Copyright Statement:Copyright of Chinese Journal of Comparative Law is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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