Building a sustainable peace and development model through data-driven Chinese peacekeeping actions based on UN Global Pulse

Yongjun Yan, Yixin Zhang

Article ID: 2604
Vol 8, Issue 2, 2024

VIEWS - 2161 (Abstract)

Abstract


This paper aims to explore how to build a sustainable peace and development model for China’s peacekeeping efforts through the application of data-driven methods from UN Global Pulse. UN Global Pulse is a United Nations agency dedicated to using big data and artificial intelligence technologies to address global challenges. In this paper, we will introduce the working principles of UN Global Pulse and its application in the fields of peacekeeping and development. Then, we will discuss the current situation of China’s participation in peacekeeping operations and how data-driven methods can help China play a greater role in peacekeeping tasks. Finally, we will propose a sustainable peace and development model that combines data-driven methods with the advantages of China’s peacekeeping efforts to achieve long-term peace and development goals.


Keywords


UN Global Pulse; data-driven approaches; China’s peacekeeping operations; sustainable peace; development models

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References


Bell, E., & Bryman, A. (2006). The Ethics of Management Research: An Exploratory Content Analysis. British Journal of Management, 18(1), 63–77. Portico. https://doi.org/10.1111/j.1467-8551.2006.00487.x Bogic, M., Njoku, A., & Priebe, S. (2015). Long-term mental health of war-refugees: a systematic literature review. BMC International Health and Human Rights, 15(1). https://doi.org/10.1186/s12914-015-0064-9 Bowyer, K. W. (2004). Face recognition technology: security versus privacy. IEEE Technology and Society Magazine, 23(1), 9–19. https://doi.org/10.1109/mtas.2004.1273467 Branigan, T. (2019). China’s Female Peacekeepers Play Vital Role in UN Missions. The Guardian. Available online: https://peacekeeping.un.org/en/troop-and-police-contributors/china (accessed on 6 December 2023). Christin, S., Hervet, É., & Lecomte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution, 10(10), 1632–1644. Portico. https://doi.org/10.1111/2041-210x.13256 Diaz, C., et al. (2019). Towards Privacy-Aware Data Sharing in UN Global Pulse. arXiv preprint arXiv:1903.03925. Fagarasan, C., Cristea, C., Cristea, M., Popa, O., & Pisla, A. (2023). Integrating Sustainability Metrics into Project and Portfolio Performance Assessment in Agile Software Development: A Data-Driven Scoring Model. Sustainability, 15(17), 13139. https://doi.org/10.3390/su151713139 Fan, C., He, W., Liu, Y., Xue, P., & Zhao, Y. (2022). A novel image-based transfer learning framework for cross-domain HVAC fault diagnosis: From multi-source data integration to knowledge sharing strategies. Energy and Buildings, 262, 111995. https://doi.org/10.1016/j.enbuild.2022.111995 Fu J, Zhou J, Li G. (2017). Accuraccuracy analysis of Beidou/pseudosatellite cooperative positioning by CNMC method. Ghaffarian, S. M., & Shahriari, H. R. (2017). Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques. ACM Computing Surveys, 50(4), 1–36. https://doi.org/10.1145/3092566 Ghassemi, M., Naumann, T., Doshi-Velez, F., Brimmer, N., Joshi, R., Rumshisky, A., & Szolovits, P. (2014). Unfolding physiological state. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2623330.2623742 Gleditsch, N. P. (2019). The Data Revolution in Peace and Conflict Research. Journal of Peace Research, 56(2), 159-169. Hatem, F., et al. (2020). Data-Driven Resource Allocation for Peace and Development: A Case Study of South Sudan. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1), 145-153. Hu S. (2013). Killer application in the cloud era: Big Data Massive data analysis (Vol. 233). Common Wealth Magazine Ltd. Joshi, M. (2020). An Institutional Explanation of Troop Contributions in UN Peacekeeping Missions. International Peacekeeping, 27(5), 785–809. https://doi.org/10.1080/13533312.2020.1812392 PAL. (n.d.). Education. Available online: https://www.povertyactionlab.org/education (accessed on 6 December 2023). Kim, C. E., Shin, J.-S., Lee, J., Lee, Y. J., Kim, M., Choi, A., Park, K. B., Lee, H.-J., & Ha, I.-H. (2017). Quality of medical service, patient satisfaction and loyalty with a focus on interpersonal-based medical service encounters and treatment effectiveness: a cross-sectional multicenter study of complementary and alternative medicine (CAM) hospitals. BMC Complementary and Alternative Medicine, 17(1). https://doi.org/10.1186/s12906-017-1691-6 Lee, S., & Aos, S. (2011). Using Cost–Benefit Analysis to Understand the Value of Social Interventions. Research on Social Work Practice, 21(6), 682–688. https://doi.org/10.1177/1049731511410551 Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of Massive Datasets. https://doi.org/10.1017/cbo9781139924801 Li, N. (2016). China’s UN Peacekeeping Role in a Changing World. Strategic Studies Quarterly, 10(1), 59-76. Lucic, M. C., Wan, X., Ghazzai, H., & Massoud, Y. (2020). Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview. Smart Cities, 3(2), 341–361. https://doi.org/10.3390/smartcities3020018 Nordås, R., Cohen, D. K. (2013). Sexual Violence in Armed Conflict: Introducing the SVAC Dataset, 1989–2009. Journal of Peace Research, 50(3), 349-359. Shah, P., et al. (2015). Mapping Poverty in India: A Machine Learning Approach. Science, 359(6379), 72-75. Shaw, I. F. (2003). Ethics in Qualitative Research and Evaluation. Journal of Social Work, 3(1), 9–29. https://doi.org/10.1177/1468017303003001002 https://doi.org/10.1177/1468017303003001002 UN Global Pulse. (2017). Data for Development: A UN Global Pulse Report on Using Big Data for Development. United Nations Global Pulse. United Nations Peacekeeping. (n.d.). China. Available online: https://www.theguardian.com/world/2019/mar/03/china-female-peacekeepers-vital-role-un-missions. (accessed on 6 December 2023) Wang, J., Zhou, B., Liu, W., & Hu, S. (2020). Research progress and development trend of cross-layer energy efficiency optimization in data centers. Scientia Sinica Informationis, 50(1), 1–24. https://doi.org/10.1360/n112018-00293 Zhang, J., Yao, Q., & Tang, P. (2017). Traffic Flow Prediction with Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 18(11), 2814-2823. Zhu, Q., et al. (2018). Data Analytics for Risk Assessment in Peacekeeping Operations. Big Data Research, 12, 1-8.



DOI: https://doi.org/10.24294/jipd.v8i2.2604

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