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
Abstract
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)…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.