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Learn more: PMC Disclaimer | PMC Copyright Notice editorial Fundam Res . 2026 Jan 21;6(2):575–576. doi: 10.1016/j.fmre.2026.01.013 Search in PMC Search in PubMed View in NLM Catalog Add to search Advancing the science of systemic environmental risk in an era of global change Guoqiang Wang Guoqiang Wang a Advanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China Find articles by Guoqiang Wang a, ⁎ , Shijie Cao Shijie Cao b School of Architecture, Southeast University, Nanjing 210096, China c Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control, Southeast University, Nanjing 210096, China Find articles by Shijie Cao b, c, ⁎ , Jin Wu Jin Wu a Advanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China Find articles by Jin Wu a Author information Article notes Copyright and License information a Advanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China b School of Architecture, Southeast University, Nanjing 210096, China c Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control, Southeast University, Nanjing 210096, China ⁎ Corresponding authors. [email protected] [email protected] Collection date 2026 Mar. © 2026 The Authors. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13069867 PMID: 41971836 The intensifying convergence of climate change, biodiversity loss, pollution, and land-system transformation is giving rise to environmental risks that are no longer local, linear, or isolated. Instead, they manifest as systemic threats—emergent, nonlinear, and cascading across ecological, economic, and institutional boundaries. These risks arise from feedback loops, threshold effects, and hidden interdependencies within tightly coupled human–environment systems, rendering conventional risk assessment frameworks inadequate. Yet, despite decades of progress in environmental monitoring and regulation, dominant risk management paradigms remain largely hazard-centric, built to quantify individual stressors, extrapolate past trends, and respond after exceedances occur. They therefore tend to fail precisely where stakes are highest: in anticipating abrupt regime shifts, cross-sectoral spillovers, and compound crises that transcend spatial, temporal, and governance boundaries. Addressing this challenge demands a paradigm shift: from single-hazard analysis toward integrated, cross-scale, and anticipatory science capable of capturing complexity, propagation, and surprise. This special issue of Fundamental Research brings together eight original contributions that collectively pioneer new frontiers in systemic environmental risk research, centering on key aspects including identification, emergence, propagation, and impact. Spanning from molecular processes to continental-scale networks, and from ecological tipping points to socio-economic spillovers, these studies exemplify the interdisciplinary convergence essential for understanding and governing complex environmental futures. Together, these papers trace the full life cycle of systemic environmental risk—from early detection and network-mediated propagation to socio-economic amplification and technological mitigation. Early detection and continuous monitoring constitute the informational backbone of systemic risk science, providing the situational awareness needed to identify emerging vulnerabilities before they escalate. Wang et al. [ 1 ] develop an AI-enhanced framework using multi-source satellite data to identify large-scale environmental anomalies—such as abrupt vegetation decline or wetland desiccation—with high spatial and temporal resolution, enabling near real-time risk surveillance. Complementing this, Wang et al. [ 2 ] establish a remote sensing–based indicator system to quantify biodiversity degradation across heterogeneous landscapes, transforming static assessments into dynamic tracking of ecological vulnerability. These contributions move risk governance upstream—from post hoc reporting toward proactive, decision-relevant risk intelligence. If monitoring provides the signals, network-based perspectives explain how risk spreads and amplifies through connectivity. Gong et al. [ 3 ] reconstruct ecological interaction networks in mangrove ecosystems under sea-level rise stress, revealing how the loss of structurally central species can disproportionately disrupt energy flow and resilience—a vivid illustration of risk emerging from network topology rather than disturbance magnitude alone. Hu et al. [ 4 ] propose an ecological node–food web framework that integrates toxicological data from key species with food web modeling to capture ecosystem-wide pollutant responses, reduce data demands, and expose community-structure-mediated mechanisms for deriving robust ecological risk thresholds. In the socio-economic domain, Wang et al. [ 5 ] map virtual energy flows embedded in China’s interprovincial trade network, demonstrating how localized environmental constraints (e.g., water scarcity or emission limits) can propagate as systemic shortages across supply chains, exposing deep couplings between ecological carrying capacity and economic stability. Taken together, these contributions underscore that systemic resilience often depends less on disturbance magnitude and more on network structure and functional connectivity. Anticipating irreversible regime shifts remains a central scientific frontier. Zhang et al. [ 6 ] integrate time-series analysis with dynamical systems theory to detect early warning signals—such as rising autocorrelation and variance—in long-term lake monitoring data, providing a robust methodology to forecast eutrophication-driven collapse before it occurs. Similarly, Jiang et al. [ 7 ] investigate transboundary algal blooms in the Yangtze River Basin, showing how nutrient runoff from agriculture, altered hydrological regimes, and urban discharge synergistically generate water quality crises that transcend administrative borders, necessitating coordinated, basin-wide governance. Technological innovation also plays a vital role in risk mitigation. While the preceding studies focus on detecting, characterizing, and anticipating systemic risk propagation, effective governance ultimately depends on interventions that reduce underlying drivers and avoid creating new operational risks. Ma et al. [ 8 ] reviewed emerging technologies for risk control of new contaminants in drinking water, emphasizing the importance of integrated, end-to-end (from source to tap) collaborative risk management across the entire water supply chain. Yang et al. [ 9 ] apply machine learning to accelerate the molecular design and process optimization of amine-based CO₂ capture systems—from quantum chemical screening to pilot-scale simulation—thereby enhancing both carbon removal efficiency and operational safety. This work directly addresses systemic climate risk by bridging molecular engineering with industrial decarbonization pathways. Together, these seven studies [ [1] , [2] , [3] , [4] , [5] , [6] , [7] ] chart a coherent trajectory: systemic environmental risk cannot be understood through disciplinary silos. It requires unifying ecological dynamics, network theory, Earth observation, data science, and engineering design into predictive and adaptive frameworks that can anticipate nonlinearity, track propagation pathways, and identify leverage points for intervention. The insights herein underscore the need for early-warning systems that detect not just hazards, but emergent vulnerabilities; for governance that transcends jurisdictional boundaries; and for technologies that mitigate root causes while avoiding unintended consequences. As guest editors, we express our sincere gratitude to all authors for their exceptional contributions, to the reviewers for their rigorous and constructive evaluations, and to the editorial office for its steadfast support throughout the publication process. We hope this special issue serves not only as a milestone, but also as a catalyst—accelerating integration across natural and social sciences and enabling science-informed action to navigate systemic uncertainty in an era of global change. Declaration of competing interest The authors declare that they have no conflicts of interest in this work. Biographies Guoqiang Wang is a professor at the Beijing Normal University. He specializes in interdisciplinary research at the intersection of environmental engineering, remote sensing science and technology, hydrology, and ecology. Shijie Cao is a professor at the Southeast University. His research primarily revolves around directions such as rapid prediction and intelligent regulation of the built environment, collaborative regulation of urban environmental heat and pollution, as well as urban renewal and enhancement of ecological functions. Footnotes Peer review under the responsibility of Editorial Board of Fundamental Research. Contributor Information Guoqiang Wang, Email: [email protected]. Shijie Cao, Email: [email protected]. References 1. Wang L., Yinglan A., Wang G., et al. Building a global early-warning system for environmental risks with remote sensing. Fundam. Res. 2026;6(2):580–582. [ Google Scholar ] 2. Wang L., Yinglan A., Gong M., et al. Early detection of biodiversity degradation risks: The critical role of remote sensing. Fundam. Res. 2026;6(2):577–579. [ Google Scholar ] 3. Gong M., Yu K., Huang Q., et al. Understand systemic risk from mangrove ecosystem through network analysis. Fundam. Res. 2026;6(2):636–646. [ Google Scholar ] 4. Hu H., Ding R., Gao H., et al. The “ecological node-food web” based ecological risk assessment. Fundam. Res. 2026;6(2):587–591. [ Google Scholar ] 5. Wang H., Li H., Xie Y., et al. Virtual energy shortage risk to trade network in China. Fundam. Res. 2026;6(2):647–658. [ Google Scholar ] 6. Zhang H., Li H., Huo S., et al. Navigating tipping points: A complex systems framework for anticipating lake ecosystem collapse. Fundam. Res. 2026;6(2):592–603. [ Google Scholar ] 7. Jiang Q., Huang H., Wu Z., et al. Transboundary algal migration-induced systemic environmental risk in the Yangtze River Basin, China. Fundam. Res. 2026;6(2):583–586. [ Google Scholar ] 8. Ma B., Qi J. Risk control technologies for emerging contaminants in drinking water: A review and perspective. Fundam. Res. 2026;6(2):620–635. [ Google Scholar ] 9. Yang P., Yu X., Stylianou K.C., et al. Accelerating amine-based CO2 capture with machine learning: From molecular screening to process optimization. Fundam. Res. 2026;6(2):604–619. [ Google Scholar ] Articles from Fundamental Research are provided here courtesy of The Science Foundation of China Publication Department, The National Natural Science Foundation of China ACTIONS View on publisher site PDF (268.4 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top