Quantum Intelligence (QI)-Driven Cross-Level Environmental Monitoring and Governance: Case Studies of Semiconductor Process Emissions, Industrial Wastewater, Urban PM2.5, and Carbon Inventory in Energy Management

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a theoretical framework linking quantum intelligence to environmental governance, offering a new lens for AI-ESG research.

🏢実務担当者:Offers implementation paths and governance mechanisms for integrating advanced analytics into environmental monitoring and disclosure processes.

🏛政策担当者:Highlights the potential of quantum intelligence in enhancing regulatory oversight and data auditability, informing future policy on AI in environmental governance.

Environmental monitoring, driven by ESGs and net-zero policies and increasingly stringent regulations, has transformed from compliance-oriented data collection into a critical infrastructure for cross-departmental and cross-organizational (enterprise–government–supply chain) collaborative governance. However, while traditional artificial intelligence (AI) can support pollution prediction and anomaly detection, it often faces challenges such as processing delays in high-dimensional streaming data, optimization difficulties in multiobjective and multiconstraint governance scenarios, and difficulty in institutionalizing “pattern output” into an “auditable, traceable, and executable” governance loop. In this paper, "QI (quantum intelligence)" is used as a hybrid intelligent decision-making capability that combines quantum optimization and quantum machine learning (QML). From the perspectives of management strategies and governance systems, it explores how QI can reshape the closed-loop process of environmental monitoring from data to decision-making to continuous improvement. This study employs a qualitative multicase study method, focusing on four highly representative scenarios—semiconductor process emissions, industrial wastewater, urban PM2.5, and carbon inventory-energy management—and incorporates relationships between enterprises, government environmental protection agencies, and the supply chain. This paper defines quality intake (QI) from a resource-based value (RBV) perspective as a valuable, scarce, and difficult-to-imitate strategic resource portfolio. It explains how QI enhances sensing, seizing, and transforming using dynamic capability theory. Furthermore, it translates the technical capabilities of QI into implementable process governance, accountability matrices, and auditing mechanisms using PDCA/TQM. The results suggest a “QI-driven cross-level environmental monitoring governance pattern,” indicating that the management value of QI lies not in single-point computing power but in shortening governance cycles, expanding the set of solvable problems, and improving cross-sector collaboration and decision-making auditability. This paper further proposes implementation paths, governance risks (pattern risks, data governance, accountability and disclosure), and key investment assessment points, providing strategic references for enterprises and governments in the platformization and intelligent transformation of environmental governance.

• Research Square https://doi.org/10.20944/preprints202608.0307.v1first seen 2026-08-08 04:32:43