Artificial intelligence (AI) and biomanufacturing are creating new opportunities to reorganize agricultural innovation by linking data-driven prediction with biological design and production. This Commentary examines how multimodal sensing, intelligent breeding, and engineered biological systems can contribute to more adaptive and resource-efficient agriculture, while highlighting constraints related to data interoperability, model transferability, field validation, technology access, and research governance. It proposes a data–biology innovation loop connecting data integration, AI-enabled design, biological production, and field evaluation to guide the responsible translation of emerging technologies into practice.

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High penetration of inverter-interfaced renewable resources is reshaping microgrid dynamics and exposing the limits of stability approaches developed largely for synchronous-machine-dominated systems. Although advances in converter control have improved local regulation, existing approaches still incompletely account for wideband oscillations, nonlinear responses to large disturbances, and interactions across control and energy-management time scales. This Perspective proposes a cyber-physical self-organizing resilience and stability framework that conceptualizes microgrid stability as a system-level property shaped by coupled physical energy dynamics, distributed information exchange, and adaptive control. Its mechanistic foundation links energy-based modeling of converter-dominated networks, including port-Hamiltonian formulations, with distributed sensing, event-triggered communication, and adaptive coordination across control layers. This framework provides a basis for examining how local responses may constrain disturbance propagation, maintain admissible operating conditions, and support recovery under physical and cyber constraints. Key research priorities include developing cross-scale nonlinear stability criteria, designing safe and adaptive distributed control strategies, and validating cyber-physical coordination through digital twins and real-time hardware-in-the-loop experiments. This perspective redirects stability research from isolated controller performance toward the coupled dynamics underlying resilient microgrid operation.

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Rapid artificial intelligence-driven expansion is intensifying the interdependence between digital infrastructure and water systems, while digital technologies are also reshaping water management. This Commentary introduces digital water resilience as the capacity to sustain essential water services while adapting to technology-driven demand and environmental uncertainty. It examines data center water demands, reclaimed-water governance, quantitative microbial risk assessment, and digital monitoring. A three-part framework integrating scientific risk assurance, operational intelligence, and basin-scale resource coordination is proposed to guide resilient and accountable water management.

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This Research Briefing examines how artificial intelligence (AI) can connect intelligent perception, uncertainty-aware forecasting, coordinated control, and resilience enhancement in new-type power systems. It highlights the integration of data-driven learning with physical knowledge as a foundation for trustworthy and adaptive grid operation. The original article was published in Advances in Resources Research, 2026, 6(2), 662–696. https://doi.org/10.50908/arr.6.2_662

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The global energy transition is moving from rapid low-carbon capacity deployment toward the more demanding task of integrating generation, networks, storage, and flexible demand. This Commentary identifies a deployment–integration gap, arising when technological expansion outpaces the ability of infrastructure, markets, and institutions to coordinate increasingly interconnected energy systems. It examines how infrastructure constraints, mismatched technological maturity, temporal flexibility requirements, and digital dependencies shape this gap. A system-integration framework is proposed, emphasizing multi-timescale coordination, adaptive infrastructure, secure artificial intelligence–enabled operation, and integrated techno-economic, environmental, and resilience assessments. Closing this gap is essential to translating installed capacity into reliable, affordable, and sustainable energy services.

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Technological advances across energy, agriculture, food production, and water management are reshaping the water–energy–food nexus. Yet sector-specific efficiency gains do not necessarily produce system-wide benefits when infrastructure, data systems, and governance adapt at different rates. This Commentary introduces systemic coupling lag to describe this mismatch and examines its manifestations in energy transitions, digital agriculture, alternative food production, water infrastructure, and circular resource recovery. It proposes a risk-aware framework combining life-cycle resource accounting, interoperable data, cross-sector stress testing, and adaptive governance to assess whether technological innovation strengthens resource security and system resilience.

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The expansion of artificial intelligence, changing drinking-water regulations, and climate-related resource pressures are reshaping interdependencies among water, energy, and food systems. Although the water–energy–food nexus has clarified cross-sector resource trade-offs, its translation into infrastructure investment, operating rules, and cross-sector accountability remains uneven. This paper advances an infrastructure-governance perspective that links contaminant treatment and residual management, distributed energy and intelligent control, agricultural carbon measurement, and resource recovery from selected brines and wastewater streams. It argues that technological integration creates durable public value only when impacts are evaluated across life cycles and spatial scales and when costs, risks, and benefits are allocated transparently. A governance framework centered on coordinated infrastructure planning, credible environmental measurement, and net-benefit assessment is proposed. This perspective can help align digital expansion and low-carbon development with water security, food-system performance, ecological limits, and equitable access. It also identifies priorities for research on basin-scale coordination, financing mechanisms, and adaptive regulation under technological and climatic uncertainty.

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Environmental economics must reconcile economic efficiency, ecological integrity, and social equity, yet research on climate policy, natural capital, environmental justice, and resource management remains fragmented. This Perspective connects four research domains—climate governance and carbon pricing, natural capital and ecosystem service valuation, environmental justice and just transition, and circular economy and resource efficiency—through externality internalization, ecological valuation, distributional equity, and resource transformation. The synthesis highlights the complementary roles of market instruments, ecological accounting, equity analysis, and life-cycle approaches, while recognizing their institutional and methodological limitations. Monetary valuation cannot fully capture ecological integrity, and resource efficiency does not necessarily reduce absolute environmental pressures. Integrating ecological conditions, economic incentives, and distributional outcomes across scales is essential to reconcile economic development, ecological sustainability, and social inclusion.

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