Hybrid AI Framework for Multi-Hazard Climate Risk Governance in Renewable Energy-Water Nexus Systems
面向可再生能源—水耦合系统多灾种气候风险治理的混合人工智能框架
How do floods, droughts and heat waves disrupt connected energy and water systems—and where could governance measures strengthen resilience?
Contribution: responsible for project development; contributed to editing parts of the manuscript.
- Chinese provinces
- 30
- Study period
- 2005–2024
- Temporal + relational modeling
- TFT + GNN
One question, two modeling perspectives
Combines Temporal Fusion Transformer (TFT) and GNN-based Climate Risk Propagation (GNN-CRP) to study temporal risk patterns and interregional cascading risks in renewable energy–water systems across 30 Chinese provinces, 2005–2024.
Floods · hydrological variability · droughts · heat waves
How does risk change over time?
Model nonlinear temporal dependence.
How can risk spread between regions?
Represent interregional propagation pathways.
Exposure differences · cascading vulnerabilities · resilience thresholds
Three questions to inspect in the full method
- Target and timing: how is the risk label defined, and which predictors are available at forecast time?
- Graph meaning: what creates an edge, is it directed, and how are spatial leakage and future information excluded?
- Evidence of added value: compare temporal-only, graph-only and combined models with a shared split, budget and uncertainty protocol.
These are reading and verification questions. They do not assert that the unpublished implementation has completed these comparisons.
How this connects to the research profile ↗What the abstract reports
Regional vulnerability differs
The abstract reports east–west exposure differences and greater systemic vulnerability in provinces with higher hydropower dependence and water stress.
Disruptions can cascade
Interlinked infrastructure is described as a pathway through which climate hazards amplify operational disruptions and governance challenges.
Governance and resilience are associated
Green infrastructure investment, adaptive water governance and decentralized renewable-energy integration are associated with lower systemic risk. This association does not by itself establish causality.
Manuscript status and available material
First authorship and submission to Climate Risk Management were confirmed by the author on 27 October 2026. The paper is not accepted; no particular editorial review stage is asserted.
The abstract reports advantages over traditional machine-learning and econometric approaches, but no comparator list, numerical metrics, uncertainty estimates, evaluation splits or threshold values were supplied for this page. Those claims cannot be independently assessed here.
Available here: a bilingual overview, conceptual method map and supplied English abstract. No DOI, public dataset, full manuscript or implementation link has been provided for this work.
Directions proposed in the abstract
Real-time satellite observations, carbon-transition dynamics and cross-border climate-risk spillovers could extend the framework. These are future directions, not completed capabilities reported here.
Read the supplied English abstract
This research proposes a new hybrid artificial intelligence system to assess multi-hazard climate risk governance in renewable energy-water nexus systems in 30 provinces in China from 2005 to 2024. The Study examines the increasing fragility of energy and water systems to compound climate extremes, such as floods, hydrological variability, droughts, and heat waves, which increasingly jeopardize the sustainable governance of energy and water resources and regional resilience. This work proposes an approach that combines a Temporal Fusion Transformer (TFT) model with a Graph Neural Network-based Climate Risk Propagation (GNN-CRP) framework to tackle the shortcomings of traditional climate-risk models. The integrated TFT–GNN architecture addresses nonlinear temporal dependence and interregional climate-risk transmission pathways in a single framework, offering a dynamic evaluation of cascading climate vulnerabilities between interconnected renewable energy and water systems. The empirical analysis shows substantial spatial differences in climate-risk exposure between China's eastern and western provinces, with provinces that rely more on hydropower and are more water-stressed being the most vulnerable in terms of systemic vulnerability. The findings also show that climate hazards cascade through the interlinked infrastructure network, exacerbating operational disruptions and governance challenges during extreme climate events. The hybrid framework improves risk prediction, identifies risk propagation, and pinpoints resilience thresholds that trigger adaptive policy intervention, outperforming traditional machine learning and econometric approaches. Moreover, investments in green infrastructure, adaptive water governance, and the decentralised integration of renewable energy are associated with lower systemic climate risks and improved regional resilience performance. The Study offers a novel interdisciplinary modeling approach for climate-resilient decision-making and key policy guidance for sustainable infrastructure governance in a climate-uncertain world. Future research should also build on real-time satellite observations, the dynamics of carbon transitions, and cross-border climate-risk spillover effects in adaptive governance systems.