Abstract
Emerging technologies can also be used for governance . This is the case for sand. Sand is a key ingredient for many industries, including concrete, glass, and electronics. In this chapter, sand governance framework is suggested through the applying blockchain technology with the aim of regulating sand extraction and trade. In this case, blockchain is the technology that can be used for distributed concurrency monitoring. Agent-Based Modeling and Simulation (ABMS) is applied to demonstrate the application of the model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, U. S., Wanjari, S. P., & Naresh, D. N. (2017). Characteristic study of geopolymer fly ash sand as a replacement to natural river sand. Construction and Building Materials, 150, 681–688. https://doi.org/10.1016/j.conbuildmat.2017.06.029.
Apte, S., & Petrovsky, N. (2016). Will blockchain technology revolutionize excipient supply chain management? Journal of Excipients and Food Chemicals, 7(3), 910.
Chapron, G. (2017). The environment needs cryptogovernance. Nature, 545(7655), 403–405. https://doi.org/10.1038/545403a.
Doyle, B. C., & Adams, M. R. (2015). Statistical evaluation of shoreline change: A case study from Seabrook Island, South Carolina. Environmental and Engineering Geoscience, 21(3), 165–180. https://doi.org/10.2113/gseegeosci.21.3.165.
Gheorghe, A. V., Vamanu, D. V., Katina, P. F., & Pulfer, R. (2018). Critical infrastructures, key resources, and key assets. Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-69224-1_1.
Jonah, F. E., Agbo, N. W., Agbeti, W., Adjei-Boateng, D., & Shimba, M. J. (2015). The ecological effects of beach sand mining in Ghana using ghost crabs (Ocypode species) as biological indicators. Ocean and Coastal Management, 112, 18–24. https://doi.org/10.1016/j.ocecoaman.2015.05.001.
Katina, P. F., Keating, C. B., Sisti, J. A., and Gheorghe, A. V. (2019). Blockchain Governance, International Journal of Critical Infrastructures, 15(2), 121–135.
Kavak, H., Padilla, J. J., Lynch, C. J., & Diallo, S. Y. (2018). Big data, agents, and machine learning: Towards a data-driven agent-based modeling approach. In Proceedings of the Annual Simulation Symposium (pp. 12:1–12:12). San Diego, CA, USA: Society for Computer Simulation International. Retrieved from http://dl.acm.org/citation.cfm?id=3213032.3213044.
Korpela, K., Hallikas, J., & Dahlberg, T. (2017). Digital supply chain transformation toward blockchain integration. Presented at the Hawaii International Conference on System Sciences, Waikoloa, HI, HICSS. https://doi.org/10.24251/HICSS.2017.506.
Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89. https://doi.org/10.1016/j.ijinfomgt.2017.12.005.
Lai, X., Shankman, D., Huber, C., Yesou, H., Huang, Q., & Jiang, J. (2014). Sand mining and increasing Poyang Lake’s discharge ability: A reassessment of causes for lake decline in China. Journal of Hydrology, 519, 1698–1706. https://doi.org/10.1016/j.jhydrol.2014.09.058.
de Leeuw, J., Shankman, D., Wu, G., de Boer, W. F., Burnham, J., He, Q., et al. (2010). Strategic assessment of the magnitude and impacts of sand mining in Poyang Lake, China. Regional Environmental Change, 10(2), 95–102. https://doi.org/10.1007/s10113-009-0096-6.
Long, Q., & Zhang, W. (2014). An integrated framework for agent based inventory–production–transportation modeling and distributed simulation of supply chains. Information Sciences, Complete, 277, 567–581. https://doi.org/10.1016/j.ins.2014.02.147.
Lu, M., Cheung, C. M., Li, H., & Hsu, S.-C. (2016). Understanding the relationship between safety investment and safety performance of construction projects through agent-based modeling. Accident Analysis and Prevention, 94, 8–17. https://doi.org/10.1016/j.aap.2016.05.014.
Lynch, C. J., Kavak, H., Gore, R., & Vernon-Bido, D. (2017). Identifying unexpected behaviors of agent-based models through spatial plots and heat maps. In 21st Annual Meeting on Agent Based Modeling & Simulation. Suffolk, Virginia: Swarmfest 2017.
Mansour, M. (2015). Develop a strategic forecast of silica sand based on supply chain decomposition. International Journal of Engineering, 9(1), 9–27.
Mascarenhas, A., & Jayakumar, S. (2008). An environmental perspective of the post-tsunami scenario along the coast of Tamil Nadu, India: Role of sand dunes and forests. Journal of Environmental Management, 89(1), 24–34. https://doi.org/10.1016/j.jenvman.2007.01.053.
Mattila, J. (2016). The blockchain phenomenon: The disruptive potential of distributed consensus architectures (ETLA Working Papers No. 38). Helsinki, Finland: The Research Institute of the Finnish Economy. Retrieved from https://ideas.repec.org/p/rif/wpaper/38.html.
Mehta, K. P. (2001). Reducing the environmental impact of concrete. Concrete International, 23(10), 61–66.
Ober, J. A. (2017). Mineral commodity summaries 2017 (USGS Unnumbered Series) (p. 202). Reston, VA: U.S. Geological Survey. Retrieved from http://pubs.er.usgs.gov/publication/70180197.
Ølnes, S., Ubacht, J., & Janssen, M. (2017). Blockchain in government: Benefits and implications of distributed ledger technology for information sharing. Government Information Quarterly, 34, 355–364. https://doi.org/10.1016/j.giq.2017.09.007.
Ponte, B., Sierra, E., de la Fuente, D., & Lozano, J. (2017). Exploring the interaction of inventory policies across the supply chain. Computers & Operations Research, 78(C), 335–348. https://doi.org/10.1016/j.cor.2016.09.020.
Pour, F. S. A., Tatar, U., & Gheorghe, A. (2018). Agent-based model of sand supply governance employing blockchain technology. In Proceedings of the Annual Simulation Symposium (pp. 14:1–14:11). San Diego, CA, USA: Society for Computer Simulation International. Retrieved from http://dl.acm.org/citation.cfm?id=3213032.3213046.
Schieritz, N., & GroBler, A. (2003). Emergent structures in supply chains: A study integrating agent-based and system dynamics modeling. In Proceedings of the 36th Annual Hawaii International Conference on System Sciences, 2003 (9 pp.). https://doi.org/10.1109/HICSS.2003.1174226.
Thornton, E. B., Sallenger, A., Sesto, J. C., Egley, L., McGee, T., & Parsons, R. (2006). Sand mining impacts on long-term dune erosion in southern Monterey Bay. Marine Geology, 229(1), 45–58. https://doi.org/10.1016/j.margeo.2006.02.005.
Tian, F. (2016). An agri-food supply chain traceability system for China based on RFID and blockchain technology. In 2016 13th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1–6). Kunming, China: IEEE. https://doi.org/10.1109/ICSSSM.2016.7538424.
Torres, A., Brandt, J., Lear, K., & Liu, J. (2017). A looming tragedy of the sand commons. Science, 357(6355), 970–971. https://doi.org/10.1126/science.aao0503.
Wilensky, U. (1999). NetLogo. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University.
Wright, A., & De Filippi, P. (2015). Decentralized blockchain technology and the rise of lex cryptographia (SSRN Scholarly Paper No. ID 2580664). Rochester, NY: Social Science Research Network. Retrieved from https://papers.ssrn.com/abstract=2580664.
Author information
Authors and Affiliations
Corresponding author
Final Remarks
Final Remarks
It is time to treat sand like a key resource, on a par with clean air, biodiversity, and other natural endowments that nations seek to manage for the future (Gheorghe et al. 2018). Under this premise, there is need to develop applicable methods, tools, and techniques for addressing risks and vulnerabilities associated with sand. The simulated benefits, clearly, outweigh, continued ‘current’ approach. Moreover, the proposed framework enables the governance of sand through use of blockchain technology along with use of ABM tools. The purpose of this framework is to reduce (and more ambitiously, eliminate the illegal sand mining. This is can be done by monitoring the sand trades through the blockchain technology and geographic imagery provided by satellites and other technologies including unmanned aerial vehicle (e.g., Doyle and Adams 2015). Moreover, the impacts on the environment can be reduced by taking immediate actions for the critical regions which the applied methodology identifies. In addition, it can arm regulators with policy-making of natural resources treatments. Applying the decentralized blockchain system and using influential parties through the ABMS generates major effects from a discrete decision to the cooperative outcome.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Georgescu, A., Gheorghe, A.V., Piso, MI., Katina, P.F. (2019). Governance by Emerging Technologies—The Case for Sand and Blockchain Technology. In: Critical Space Infrastructures. Topics in Safety, Risk, Reliability and Quality, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-030-12604-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-12604-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12603-2
Online ISBN: 978-3-030-12604-9
eBook Packages: EngineeringEngineering (R0)