Hidden Water Scarcity Risk Paths via Betweenness Centrality
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Solution Overview
Problem
Conventional research has inadequately characterized the cross-regional transmission mechanisms of hidden water scarcity risk, failing to identify critical intermediate nodes and pathways that propagate risks through trade networks, which poses a challenge for timely interventions and resilience in water resources management.
Innovation Solution
A betweenness centrality algorithm is applied to construct a hidden water scarcity risk transfer matrix and network, identifying critical transmission paths and nodes using structural path analysis and betweenness centrality, enabling targeted risk prevention strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional research focuses on water scarcity within specific geographical boundaries, then local water scarcity issues can be addressed, but cross-regional transmission mechanisms of hidden water risk remain inadequately characterized
Solution Approach 1:
The patent segments the water scarcity risk assessment into multiple components: regional water scarcity probability assessment, sectoral water resource dependence analysis, and cross-regional risk transmission modeling. This segmentation allows the complex cross-regional transmission mechanism to be broken down into manageable analytical steps while maintaining comprehensive coverage.
Solution Approach 2:
The patent transitions from traditional single-region water scarcity assessment to a multi-dimensional framework that incorporates spatial (cross-regional), sectoral (multiple industries), and temporal (risk transmission over time) dimensions. This dimensional expansion enables characterization of hidden water risk transmission pathways that were previously inaccessible.
2Loss of information
If source-sink relationships in water scarcity propagation are identified, then risk transmission directions can be determined, but intermediate transmission pathways and pivotal nodes remain inadequately characterized
Solution Approach 1:
The patent introduces intermediate nodes (pivotal nodes) as mediators in the risk transmission pathway. These intermediate nodes represent critical sectors or regions that facilitate the transmission of water scarcity risk from source regions to sink regions. By identifying and characterizing these intermediary elements, the patent fills the information gap between source-sink relationships.
Solution Approach 2:
The patent replaces direct mechanical observation of risk transmission with a computational modeling approach using input-output analysis and network theory. This substitution allows indirect detection and measurement of intermediate transmission pathways that would be difficult to observe directly in the real world.
3Reliability
If numerous critical intermediate nodes act as bridges channeling hidden water scarcity risk, then risk propagation can be understood, but timely interventions before risks spread to downstream sectors become challenging
Solution Approach 1:
The patent performs preliminary identification and characterization of critical intermediate nodes and transmission pathways before water scarcity risks actually propagate to downstream sectors. By using predictive modeling and network analysis to anticipate risk transmission, the framework enables timely interventions at critical nodes before risks materialize in downstream sectors.
Solution Approach 2:
The patent establishes feedback mechanisms through continuous monitoring of water scarcity indicators and risk transmission patterns. This feedback loop allows the system to detect changes in intermediate nodes and adjust intervention strategies in real-time, ensuring timely response to evolving risk conditions.
Data Source
AI summary
The present disclosure discloses a method of reducing regional water scarcity risk. The method includes obtaining the water scarcity probability of each region, and obtaining the water scarcity risk of each sector in each region calculated based on the water resource dependence of each sector in each region and combining the water scarcity probability of each region; according to the water scarcity risk of each sector and based on a multi-regional input-output model, constructing a hidden water scarcity risk transfer matrix; based on the structural path analysis and the hidden water scarcity risk transfer matrix, identifying the critical transmission path of the hidden water scarcity risk, and constructing a hidden water scarcity risk transmission network; identifying a critical intermediate node in the hidden water scarcity risk transmission network based on the betweenness centrality algorithm