crossrefEnergies2026-05-04Cited by 0

Nonlinear Dynamics and Spatial Correlation Pattern of the Digital Economy on Energy Efficiency: Evidence from Ensemble Learning and Spatio-Temporal Graph Neural Network

Rui Cao, Chenjun Zhang, Xiangyang Zhao, Yanan Deng

Achieving synergy between the digital economy and energy efficiency is pivotal for realizing high-quality development under the “Dual Carbon” targets. However, traditional econometric methods struggle to capture the complex nonlinear and spatio-temporal dependencies inherent in this relationship. To address this issue, this study develops a two-stage framework using Chinese provincial panel data. It combines LightGBM/CatBoost and SHAP for critical factor identification, and employs STGNN for capturing nonlinear and spatial correlation patterns, to systematically decode the driving mechanisms of the digital economy on energy efficiency. The results reveal three key findings: (1) Complex Nonlinearity: The impact manifests in distinct U-shaped, inverted U-shaped, and weak correlation patterns, accompanied by significant spatial clustering. (2) Structural Heterogeneity: The dimensions of the digital economy show differential associations with energy efficiency. Industrial digitization and infrastructure are associated with more direct improvements in efficiency, whereas digital industrialization functions primarily through indirect technological supply. (3) Spatial Correlation Pattern: Higher levels of digital development correspond to higher local energy efficiency and are linked to positive predicted adjustments in neighboring regions, with notable regional heterogeneity. Combining machine learning-based feature selection with deep learning-based spatiotemporal modeling provides a scientific basis for formulating location-specific digital economy strategies and coordinated energy-saving policies.

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An Integrated CFD–Machine Learning Framework for Flow Assurance Risk Assessment During Hot Oil Commissioning of Subsea Jumpers

To mitigate flow assurance risks during the hot oil commissioning of deepwater jumpers, this study develops a transient displacement risk assessment framework integrating CFD–machine learning surrogate models. A 3D numerical model using VOF and conjugate heat transfer simulated h…

Alternative Thermal Technologies for Industrial Process Heat: Barriers and Opportunities

Energy scarcity and subsequent global fuel market shocks have become a significant concern for the United States. Process heating in industry accounts for over half of all industrial energy usage and is almost entirely (>95%) supplied by natural gas, coal, and byproduct fuels.…

Prediction of Waterflooding Performance with a New Machine Learning Method by Combining Linear Dynamical Systems with Neural Networks

Machine learning methods have gained significant attention in forecasting waterflooding performance in recent years, but their accuracy often remains insufficient for practical field applications. This study proposes a hybrid framework that integrates a linear dynamical system (L…

Machine Learning-Based Lifetime Prediction of Lithium Batteries: A Comparative Assessment for Electric Vehicle Applications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aim…

Rotating Electric Machine Fault Diagnosis with Magnetic Flux Measurement Using Deep Learning Models

This paper presents new techniques for electric machine diagnostics that combine advanced signal processing and artificial intelligence (AI)-based techniques using magnetic flux measurements acquired under various operating conditions. Developing an effective electric machine dia…