Advancing the science of systemic environmental risk in an era of global change

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.