Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
From Smart Data to Sustainable Decisions: Intelligent Technologies in Environmental Monitoring and Resource Management
Authors
-
Xu Han
Hainan International College, Communication University of China, Lingshui 572400, China
DOI:
https://doi.org/10.30564/jees.v8i8.13287Abstract
The rapid advancement of intelligent technologies, such as machine learning, optimization algorithms, and decision support systems (DSS), presents significant opportunities for improving environmental monitoring and resource management. Such technologies are able to handle large, complicated datasets to deliver actionable insights that are able to support sustainable decision-making. Nonetheless, data quality, model reliability, model uncertainty, and stakeholder participation are important issues that should be considered carefully during the transformation of raw environmental data into informed decisions. The review describes the application of intelligent systems to different contexts of environmental decisions, such as compliance and enforcement, early warning systems, resource allocation, and conservation planning. We argue that these systems must be reliable and fair, highlighting the importance of data governance, transparency, and ethical considerations. We also discuss the practical demands of being deployed in the real world, including the interoperability of the data, adapting the model to new circumstances, and the financial and technical constraints of deploying the more sophisticated technologies. Based on case studies, we determine not only the achievements but also the drawbacks of the existing applications, providing information on how intelligent technologies could be better combined into the decision-making process. In conclusion, we present a research and policy agenda wherein we would focus on integrating the system, assessing the model continuously, and involving a wide range of stakeholders to make sure that these technologies are used in a way that enhances environmental sustainability and social equity.
Keywords:
Intelligent Technologies; Environmental Decision-Making; Data Governance; Machine Learning; SustainabilityReferences
[1] Shobeirinejad, A., Bernus, P., Trevathan, J., 2016. The application of decision making models in sustainable management of environmental systems. Journal of Statistics and Management Systems. 19(4), 541–566.
[2] Nittel, S., 2009. A survey of geosensor networks: Advances in dynamic environmental monitoring. Sensors. 9(7), 5664–5678.
[3] Li, J., Pei, Y., Zhao, S., et al., 2020. A review of remote sensing for environmental monitoring in China. Remote Sensing. 12(7), 1130.
[4] Bibri, S.E., 2020. The eco-city and its core environmental dimension of sustainability: Green energy technologies and their integration with data-driven smart solutions. Energy Informatics. 3(1), 4.
[5] Olatunbosun, A., Ukasoanya, F.C., Eleshin, M.A., 2025. Artificial intelligence in climate change mitigation and adaptation: A review of emerging technologies and real-world applications. Global Journal of Engineering and Technology Advances. 24(2), 235–250.
[6] Ranger, N., 2011. Adaptation as a Decision Making under Deep Uncertainty: A Unique Challenge for Policymakers? In: Linkov, I., Bridges, T. (Eds.). Climate: Global Change and Local Adaptation. Springer: Dordrecht, The Netherlands. pp. 89–122.
[7] Phillips-Wren, G., 2013. Intelligent decision support systems. In: Doumpos, M., Grigoroudis, E. (Eds.). Multicriteria Decision Aid and Artificial Intelligence: Links, Theory and Applications. John Wiley & Sons, Ltd.: Hoboken, NJ, USA. pp. 25–44.
[8] Fu, L., Li, J., Chen, Y., 2023. An innovative decision making method for air quality monitoring based on big data-assisted artificial intelligence technique. Journal of Innovation & Knowledge. 8(2), 100294.
[9] Cortès, U., Sànchez-Marrè, M., Ceccaroni, L., et al., 2000. Artificial intelligence and environmental decision support systems. Applied Intelligence. 13(1), 77–91.
[10] Leikas, J., Koivisto, R., Gotcheva, N., 2019. Ethical framework for designing autonomous intelligent systems. Journal of Open Innovation: Technology, Market, and Complexity. 5(1), 18.
[11] Ascough II, J., Maier, H.R., Ravalico, J.K., et al., 2008. Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecological Modelling. 219(3–4), 383–399.
[12] Roadknight, C.M., Ball, G., Mills, G.E., et al., 1997. Modeling complex environmental data. IEEE Transactions on Neural Networks. 8(4), 852–862.
[13] Ning, M.A., 2024. Artificial Intelligence-Driven Decision Support Systems for Sustainable Energy Management in Smart Cities. International Journal of Advanced Computer Science & Applications. 15(9).
[14] Klassen, R.D., Whybark, D.C., 1999. Environmental management in operations: The selection of environmental technologies. Decision Sciences. 30(3), 601–631.
[15] Hough, J.R., White, M.A., 2003. Environmental dynamism and strategic decision-making rationality: An examination at the decision level. Strategic Management Journal. 24(5), 481–489.
[16] Cook, C.N., Inayatullah, S., Burgman, M.A., et al., 2014. Strategic foresight: How planning for the unpredictable can improve environmental decision-making. Trends in Ecology & Evolution. 29(9), 531–541.
[17] Reed, M.S., 2008. Stakeholder participation for environmental management: A literature review. Biological Conservation. 141(10), 2417–2431.
[18] Koontz, T.M., Steelman, T.A., Carmin, J., et al., 2010. Collaborative Environmental Management: What Roles for Government-1. Routledge: New York, NY, USA.
[19] Pacheco-Vega, R., Murdie, A., 2022. When do environmental NGOs work? A test of the conditional effectiveness of environmental advocacy. In: Hayes, G., Jinnah, S., Kashwan, P. (Eds.). Trajectories in Environmental Politics. Routledge: London, UK. pp. 177–198.
[20] Yearley, S., 2017. Communication strategies of environmental NGOs and advocacy groups. In: Nisbet, M.C., Schäfer, M.S., Markowitz, E., et al. (Eds.). Oxford Research Encyclopedia of Climate Communication. Oxford University Press: New York, NY, USA.
[21] Rashed, A.H., Shah, A., 2021. The role of private sector in the implementation of sustainable development goals. Environment, Development and Sustainability. 23(3), 2931–2948.
[22] Collin, R.W., Collin, R.M., 1998. The role of communities in environmental decisions: Communities speaking for themselves. Journal of Environmental Law and Litigation. 13, 37.
[23] Klug, H., Kmoch, A., 2015. Operationalizing environmental indicators for real time multi-purpose decision making and action support. Ecological Modelling. 295, 66–74.
[24] Wang, X., Tian, S., Zhou, Y., et al., 2025. Climate Policy Uncertainty, Environmental Risk Aversion, and Import Structure Adjustment. Journal of Asian Economics. 102, 102096.
[25] Digkoglou, P., Papathanasiou, J., 2025. Application of multiple criteria decision aiding in environmental policy-making processes. International Journal of Environmental Science and Technology. 22(8), 6967–6982.
[26] Farooq, O., Khan, A., 2025. The Impact of Machine Learning on Climate Change Modeling and Environmental Sustainability. Artificial Intelligence and Machine Learning Review. 6(1), 8–16.
[27] Evans, J.P., 2006. Lost in translation? Exploring the interface between local environmental research and policymaking. Environment and Planning A. 38(3), 517–531.
[28] Ncube, M.M., Ngulube, P., 2024. Enhancing environmental decision-making: A systematic review of data analytics applications in monitoring and management. Discover Sustainability. 5(1), 290.
[29] Fang, S., Xu, L.D., Zhu, Y., et al., 2014. An integrated system for regional environmental monitoring and management based on internet of things. IEEE Transactions on Industrial Informatics. 10(2), 1596–1605.
[30] Kumar, A., Kim, H., Hancke, G.P., 2012. Environmental monitoring systems: A review. IEEE Sensors Journal. 13(4), 1329–1339.
[31] O’Grady, M.J., Muldoon, C., Carr, D., et al., 2016. Intelligent sensing for citizen science: Challenges and future directions. Mobile Networks and Applications. 21(2), 375–385.
[32] Alam, F., Mehmood, R., Katib, I., et al., 2017. Data fusion and IoT for smart ubiquitous environments: A survey. IEEE Access. 5, 9533–9554.
[33] Sta, H.B., 2017. Quality and the efficiency of data in “Smart-Cities”. Future Generation Computer Systems. 74, 409–416.
[34] Kanevski, M., Maignan, M., 2004. Analysis and Modelling of Spatial Environmental Data, Vol. 6501. EPFL Press: New York, NY, USA.
[35] Goodchild, M.F., 2003. Geographic information science and systems for environmental management. Annual Review of Environment and Resources. 28(1), 493–519.
[36] Biber, E., 2013. The challenge of collecting and using environmental monitoring data. Ecology and Society. 18(4), 68.
[37] Bena, Y.A., Ibrahim, R., Mahmood, J., et al., 2025. Big data governance challenges arising from data generated by intelligent systems technologies: A systematic literature review. IEEE Access. 13, 12859–12888.
[38] Almulhim, A.I., Yigitcanlar, T., 2025. Understanding smart governance of sustainable cities: A review and multidimensional framework. Smart Cities. 8(4), 113.
[39] Singh, J., Cobbe, J., Norval, C., 2018. Decision provenance: Harnessing data flow for accountable systems. IEEE Access. 7, 6562–6574.
[40] Rajabifard, A., 2010. Critical issues in global geographic information management with a detailed focused on data integration and interoperability of systems and data. In Proceedings of the 2nd Preparatory Meeting of the Proposed UN Committee on Global Geographic Information Management, New York, NY, USA, 10–11 May 2010.
[41] Bigdeli, A.Z., Kamal, M.M., De Cesare, S., 2013. Electronic information sharing in local government authorities: Factors influencing the decision-making process. International Journal of Information Management. 33(5), 816–830.
[42] Matei, S.A., Reed, K.P., 2025. Mission Command’s Asymmetric Advantage through AI-Driven Data Management. The US Army War College Quarterly: Parameters. 55(4), 8.
[43] Bokde, N.D., 2025. Data-Driven Sustainability: Methods and Evidence across Energy, Policy, and Industry. Energies. 18(23), 6164.
[44] Hughes, S., Giest, S., Tozer, L., 2020. Accountability and data-driven urban climate governance. Nature Climate Change. 10(12), 1085–1090.
[45] Vardon, M., Castaneda, J., Nagy, M., et al., 2018. How the system of environmental-economic accounting can improve environmental information systems and data quality for decision making. Environmental Science & Policy. 89, 83–92.
[46] Bibri, S.E., Alexandre, A., Sharifi, A., et al., 2023. Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: An integrated approach to an extensive literature review. Energy Informatics. 6(1), 9.
[47] Gregory, R., Failing, L., Harstone, M., et al., 2012. Structured Decision Making: A Practical Guide to Environmental Management Choices. John Wiley & Sons: Hoboken, NJ, USA.
[48] Sun, A.Y., Scanlon, B.R., 2019. How can Big Data and machine learning benefit environment and water management: A survey of methods, applications, and future directions. Environmental Research Letters. 14(7), 073001.
[49] Musostov, Z., Berkaeva, A., Basnukaev, I., 2022. Natural hazards and environmental decision-making. Reliability: Theory & Applications. 17(4), 249–255.
[50] Sham, F.A.F., El-Shafie, A., Jaafar, W.Z.W., et al., 2025. Advances in AI-based rainfall forecasting: A comprehensive review of past, present, and future directions with intelligent data fusion and climate change models. Results in Engineering. 27, 105774.
[51] Sathvara, P.K.B., Sathvara, B., Anuradha, J., et al., 2024. Prediction models for climate change using artificial intelligence. In: Ghai, D., Rawal, K., Dhir, K., et al. (Eds.). Artificial Intelligence Techniques for Sustainable Development. CRC Press: Boca Raton, FL, USA. pp. 359–379.
[52] Wiens, J.A., Hobbs, R.J., 2015. Integrating conservation and restoration in a changing world. BioScience. 65(3), 302–312.
[53] Zhu, X.X., Tuia, D., Mou, L.C., et al., 2017. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine. 5(4), 8–36.
[54] Uslu, S., Kaur, D., Rivera, S.J., et al., 2024. Causal inference to enhance AI trustworthiness in environmental decision-making. In Proceedings of the International Conference on Advanced Information Networking and Applications, Kitakyushu, Japan, 17–19 April 2024.
[55] Forootan, M.M., Larki, I., Zahedi, R., et al., 2022. Machine learning and deep learning in energy systems: A review. Sustainability. 14(8), 4832.
[56] Villano, F., Mauro, G.M., Pedace, A., 2024. A review on machine/deep learning techniques applied to building energy simulation, optimization and management. Thermo. 4(1), 100–139.
[57] Polasky, S., Carpenter, S.R., Folke, C., et al., 2011. Decision-making under great uncertainty: Environmental management in an era of global change. Trends in Ecology & Evolution. 26(8), 398–404.
[58] Kavetski, D., Qin, Y., Kuczera, G., 2018. The Fast and the robust: Trade‐offs between optimization robustness and cost in the calibration of environmental models. Water Resources Research. 54(11), 9432–9455.
[59] Kiker, G.A., Bridges, T.S., Varghese, A., et al., 2005. Application of multicriteria decision analysis in environmental decision making. Integrated Environmental Assessment and Management. 1(2), 95–108.
[60] Siddiqui, F., Rabbani, H., Akhtar, N., et al., 2025. Scalable Environmental Monitoring System Using Iot and Edge Computing. International Journal of Environmental Sciences. 11(11s), 932–943.
[61] Taherdoost, H., Mohebi, A., 2024. Using SMART method for multi-criteria decision making: Applications, advantages, and limitations. Archives of Advanced Engineering Science. 2(4), 190–197.
[62] Alotaibi, E., Nassif, N., 2024. Artificial intelligence in environmental monitoring: In-depth analysis. Discover Artificial Intelligence. 4(1), 84.
[63] Poch, M., Comas, J., Rodríguez-Roda, I., et al., 2004. Designing and building real environmental decision support systems. Environmental Modelling & Software. 19(9), 857–873.
[64] Smith, P., Ashmore, M.R., Black, H.I.J., et al., 2013. The role of ecosystems and their management in regulating climate, and soil, water and air quality. Journal of Applied Ecology. 50(4), 812–829.
[65] Conrad, C.T., Daoust, T., 2008. Community-based monitoring frameworks: Increasing the effectiveness of environmental stewardship. Environmental Management. 41(3), 358–366.
[66] Liu, W., Zhao, J., Du, L., et al., 2021. Intelligent comprehensive evaluation system using artificial intelligence for environmental evaluation. Environmental Impact Assessment Review. 86, 106495.
[67] Vermesan, O., Friess, P., 2013. Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. River Publishers: Gistrup, Denmark.
[68] Kollmuss, A., Agyeman, J., 2002. Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? Environmental Education Research. 8(3), 239–260.
[69] Agarwal, P., Mittal, M., Ahmed, J., et al., 2022. Smart Technologies for Energy and Environmental Sustainability. Springer: Cham, Switzerland.
[70] Ahmed, E.S.A., Yousef, M.E., 2019. Internet of things in smart environment: Concept, applications, challenges, and future directions. World Scientific News. 134(1), 1–51.
[71] Tadejko, P., 2017. Environmental monitoring systems using internet of things—Standards and protocols. Ekonomia i Środowisko. 63(4), 102–116.
[72] Agbeyangi, A., Suleman, H., 2024. Advances and challenges in low-resource-environment software systems: A Survey. Informatics. 11(4), 90.
[73] Marzhan, Y., Talshyn, K., Koshekov, K., et al., 2022. Smart technologies of the risk-management and decision-making systems in a fuzzy data environment. Indonesian Journal of Electrical Engineering and Computer Science. 28(3), 1463–1474.
[74] Glynn, P.D., Voinov, A.A., Shapiro, C.D., et al., 2017. From data to decisions: Processing information, biases, and beliefs for improved management of natural resources and environments. Earth's Future. 5(4), 356–378.
[75] Solís, P., Vanos, J.K., Forbis, R.E., 2017. The decision‐making/accountability spatial incongruence problem for research linking environmental science and policy. Geographical Review. 107(4), 680–704.
[76] Yigitcanlar, T., Mehmood, R., Corchado, J.M., 2021. Green artificial intelligence: Towards an efficient, sustainable and equitable technology for smart cities and futures. Sustainability. 13(16), 8952.
[77] Layode, O., Naiho, H.N.N., Adeleke, G.S., et al., 2024. Data privacy and security challenges in environmental research: Approaches to safeguarding sensitive information. International Journal of Applied Research in Social Sciences. 6(6), 1193–1214.
[78] Jankowski, P., 2009. Towards participatory geographic information systems for community-based environmental decision making. Journal of Environmental Management. 90(6), 1966–1971.
[79] Camilleri, M.A., 2024. Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems. 41(7), e13406.
[80] Rath, P., Jindal, M., Jindal, T., 2021. A review on economically-feasible and environmental-friendly technologies promising a sustainable environment. Cleaner Engineering and Technology. 5, 100318.
Downloads
How to Cite
Han, X. (2026). From Smart Data to Sustainable Decisions: Intelligent Technologies in Environmental Monitoring and Resource Management. Journal of Environmental & Earth Sciences, 8(8), 181–200. https://doi.org/10.30564/jees.v8i8.13287
Issue
Article Type
Review
License
Copyright © 2026 Xu Han
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
