Chapter 3. Digitalization of crisis management remediation: assessment of implementation and development prospects

Authors

Bohdan Cherniavskyi
State University of Applied Sciences in Konin
https://orcid.org/0000-0001-9174-6139
Keywords: remediation, digitalization, crisis management, recovery, Internet of Things (IoT), artificial intelligence (AI), digital twins, geographic information systems (GIS), unmanned aerial vehicles (UAVs), blockchain, post-war recovery of Ukraine, multi-criteria decision analysis (MCDA), adaptive management, digital transformation, sustainability, Monte Carlo simulation, resilience

Synopsis

Economy in the era of digital transformation: trends, opportunities and perspectives

The first quarter of the 21st century has been marked by the growth in the scale and complexity of emergencies, ranging from global climate disasters to full-scale military conflicts. All of this has created an acute need to shift from traditional crisis management methods to intelligent digital systems capable of responding rapidly, processing vast arrays of heterogeneous data, and coordinating the actions of all participants in real time. In this context, remediation in the modern world has already been established as a multifunctional process that combines the restoration of the ecological environment with the revival of socio-economic activity on the cleaned, reconstructed, and restored territory. Today, successful remediation serves not only an environmental purpose but also stimulates the return of the population, the development of entrepreneurial entities, the attraction of investments, and the strengthening of a country's international reputation. In the case of Ukraine, the digitalization of crisis management of remediation processes plays the role of a critically important factor for the efficiency and speed of post-war recovery. In the author's research, the historical and theoretical aspects of the development of the remediation concept are revealed, and a methodological framework for assessing the effectiveness of digital management based on multicriteria models and Monte Carlo simulation is presented. Particular attention is paid to the integration of IoT, AI, UAVs, digital twins, GIS, and blockchain technologies to achieve a comprehensive environmental, social, and economic recovery effect. Recommendations are formulated for the application of digital solutions in the practice of territorial remediation, with an emphasis on the prospects for maximizing Ukraine’s potential.

Downloads

Download data is not yet available.

Author Biography

Bohdan Cherniavskyi, State University of Applied Sciences in Konin

PhD, Adjunct
Department of Economics and Technical Sciences
https://orcid.org/0000-0001-9174-6139
Corresponding author
bohdan.cherniavskyi@konin.edu.pl

References

Cherniavska, T., Cherniavskyi, B. (2023). Architecture-oriented agent-based model (AOAM) for optimizing transport evacuation management and emergency medical assistance in the context of the war in Ukraine: challenges and prospects. Birmingham.

Cherniavska, T., Cherniavskyi, B., Sanikidze, T., Sharashenidze, A., Tortladze, M., Buleishvili, M. (2023). Optimization of medical logistics with bee colony algorithms in emergency, military conflict and post-war remediation settings. Birmingham.

Trusova, N. V., Tanklevska, N. S., Cherniavska, T. A., Prystеmskyi, O. S., Yeremenko, D. V., Demko, V. S. (2020). Financial Provision of Investment Activities of the Subjects of the World Industry of Tourist Services. Journal of Environmental Management and Tourism, 11 (4), 890–902. https://doi.org/10.14505//jemt.v11.4(44).13

Russia-Ukraine War: Environmental Impact 2024 (2024). Top Lead. Available at: https://toplead.eu/en/works/id/war-environmental-impact-308/

Cherniavska, T., Tanklevska, N., Cherniavskyi, B. (2024). A decision-making system for managing the remediation of water resources in the Kherson region: agent-oriented modeling in the context of post-war economic recovery. Transformations of National Economies under Conditions of Instability. Tallinn: Scientific Route OÜ, 223–256. https://doi.org/10.21303/978-9916-9850-6-9.ch8

Ang, M. L. E., Owen, J. R., Gibbins, C. N., Lèbre, É., Kemp, D., Saputra, M. R. U. et al. (2023). Systematic Review of GIS and Remote Sensing Applications for Assessing the Socioeconomic Impacts of Mining. The Journal of Environment & Development, 32 (3), 243–273. https://doi.org/10.1177/10704965231190126

Yu, D., He, Z. (2022). Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities. Natural Hazards, 112 (1), 1–36. https://doi.org/10.1007/s11069-021-05190-x

Charfeddine, L., Umlai, M. (2023). ICT sector, digitization and environmental sustainability: A systematic review of the literature from 2000 to 2022. Renewable and Sustainable Energy Reviews, 184, 113482. https://doi.org/10.1016/j.rser.2023.113482

Wang, X., Li, R., Tian, Y., Zhang, B., Zhao, Y., Zhang, T., Liu, C. (2022). A Computational Framework for Design and Optimization of Risk-Based Soil and Groundwater Remediation Strategies. Processes, 10 (12), 2572. https://doi.org/10.3390/pr10122572

Harrison, R. L. (2010). Introduction to Monte Carlo Simulation. AIP Conference Proceedings, 1204, 17–21. https://doi.org/10.1063/1.3295638

Rubinstein, R. Y., Kroese, D. P. (2016). Simulation and the Monte Carlo method. John Wiley & Sons. https://doi.org/10.1002/9781118631980

Li, X., Yi, S., Cundy, A. B., Chen, W. (2022). Sustainable decision-making for contaminated site risk management: A decision tree model using machine learning algorithms. Journal of Cleaner Production, 371, 133612. https://doi.org/10.1016/j.jclepro.2022.133612

Melnichuk, Iu. (2024). Vosstanovlenie selskokhoziaistvennykh zemel: Ukraina ne mozhet zhdat, poka zakonchitsia voina. Zerkalo nedeli. Avaialble at https://zn.ua/ariculture/vosstanovlenie-selskokhozjajstvennykh-zemel-ukraina-ne-mozhet-zhdat-poka-zakonchitsja-vojna.html

Cherniavskyi, B. (2024). Digital technologies as an accelerator of remediation: a strategic vector for the post-war revitalization of Ukraine’s territory. Transformational Economy: Theoretical and Practical Aspects. Riga: Baltija Publishing, 653–675. https://doi.org/10.30525/978-9934-26-494-8-29

Russell, S., Norvig, P. (2020). Artificial intelligence: A modern approach. Pearson Education.

Copernicus platform. The Earth Observation. European Union’s space programme. Available at: https://defence-industry-space.ec.europa.eu/eu-space/copernicus-earth-observation_en

Cherniavskyi, B.; Slavinska, O., Danchuk, V., Kunytska, O., Hulchak, O. (Eds.) (2025). Integration of Drones and Dio-Inspired Algorithms into Intelligent Transportation Logistics Systems for Post-war Remediation of Ukraine. Intelligent Transport Systems: Ecology, Safety, Quality, Comfort. Cham: Springer, 426–437. https://doi.org/10.1007/978-3-031-87379-9_39

Lyu, J., Zhou, S., Liu, J., Jiang, B. (2023). Intelligent-Technology-Empowered Active Emergency Command Strategy for Urban Hazardous Chemical Disaster Management. Sustainability, 15 (19), 14369. https://doi.org/10.3390/su151914369

Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilovic, A. et al. (2019). FactSheets: Increasing trust in AI services through supplier’s declarations of conformity. IBM Journal of Research and Development, 63 (4/5), 6:1–6:13. https://doi.org/10.1147/jrd.2019.2942288

Carlon, C., Pizzol, L., Critto, A., Marcomini, A. (2008). A spatial risk assessment methodology to support the remediation of contaminated land. Environment International, 34 (3), 397–411. https://doi.org/10.1016/j.envint.2007.09.009

Gu, Z. (2022). Complex heatmap visualization. IMeta, 1 (3). https://doi.org/10.1002/imt2.43

Britchenko, I., Cherniavska, T. (2019). Blockchain technology in the fiscal process of ukraine optimization. Economic Studies, 28 (5), 134–147.

Britchenko, I., Cherniavska, T., Cherniavskyi, B. (2018). Blockchain technology into the logistics supply chain implementation effectiveness. Development of small and medium enterprises: the EU and east-partnership countries experience. Tarnobrzeg: Wydawnictwo Państwowej Wyższej Szkoły Zawodowej im. prof. Stanisława Tarnowskiego w Tarnobrzegu, 307–318.

Tanklevska, N., Povod, T., Ostapenko, A., Borovik, L. (2021). Crowdfunding Development Trends: Foreign Experience and Ukrainian Realities in the Digital Economy. The Importance of New Technologies and Entrepreneurship in Business Development: In The Context of Economic Diversity in Developing Countries, 897–908. https://doi.org/10.1007/978-3-030-69221-6_69

Connected Commercial Drones Report 2025 – The Number of Connected Commercial Drones Reached 2.8 Million Units Worldwide in 2024 and is Set to Reach 4.5 Million Units by 2029 – ResearchAndMarkets.com. (2025). Business Wire, Inc. Available at: https://www.businesswire.com/news/home/20250425414769/en/Connected-Commercial-Drones-Report-2025---The-Number-of-Connected-Commercial-Drones-Reached-2.8-Million-Units-Worldwide-in-2024-and-is-Set-to-Reach-4.5-Million-Units-by-2029---ResearchAndMarkets.com

Digital Twin Market Size & Trends (2025). Grand View Research. Available at: https://www.grandviewresearch.com/industry-analysis/digital-twin-market

Blockchain Market. Overview (2024). MarketsandMarkets. Available at: https://www.marketsandmarkets.com/Market-Reports/blockchain-technology-market-90100890.html

Sinha, S. (2024). State of IoT 2024: Number of connected IoT devices growing 13 % to 18.8 billion globally. IoT Analytics. Available at: https://iot-analytics.com/number-connected-iot-devices/

Is There A Rapid Increase in IoT Adoption? – Manufacturing & IoT in 2025 (2025). Ubisense. Available at: https://ubisense.com/a-rapid-increase-in-iot-adoption-manufacturing-iot-in-2023/ja-vojna.html

Cover for Chapter 3. Digitalization of crisis management remediation: assessment of implementation and development prospects