In the COVID-19 crisis, many economies suffered from sustainable energy production. The emergence of the COVID-19 crises, extreme volatility in oil prices, limited energy efficiency in energy systems, and weak form of financial stability were the key reasons for it. However, considering these issues, a recent study aims to analyze them. ASEAN countries’ energy efficiency and crude oil price volatility are examined as a solution to how financial conditions might be utilized to handle energy efficiency issues and crude oil price volatility. Extending it, the study aims to identify the influence of financial stability on crude oil price volatility and energy efficiency issues. To do this, GMM is used. According to the study’s findings, environmental mitigation was determined to be important at 18%, and financial stability and carbon risk significant at 21%. Global warming concerns have been raised due to the ASEAN nations’ 19.5% link between financial stability and emissions drift. A country’s financial stability is necessary for implementing green economic recovery strategies, among the most widely accepted measures to reduce energy efficiency and guarantee long-term financial potential on the national scale. The study on green economic growth also provides the associated stakeholders with sensible policy consequences on this importance.

Artificial intelligence is changing the world. The application of the artificial intelligence in logistics has brought a significant reduction in labor costs and huge increase in logistics efficiency. Artificial intelligence will lead the logistics industry truly enter the era of intelligent logistics. Warehousing is one of the most important function of logistics, so the intelligent level of warehousing has great effects on the construction and promotion of intelligent logistics. This article aims to study the applications of artificial intelligence in logistics warehousing, and analyze the limiting factors of artificial intelligence applied in the construction of intelligent warehousing, finally briefly conclude its general situation and prospects of future development.

As the key nodes of the global comprehensive transportation network, ports play a pivotal role in global trade and shipping. With the progress of artificial intelligence technology and the promotion of intelligent port development concept, intelligent port will be the inevitable trend and choice of all countries in the world. This paper analyzes the application status and problems of artificial intelligence in promoting the construction of smart ports, discusses the typical application of artificial intelligence in the intelligent port upgrading, and details the practical application of artificial intelligence in the construction of intelligent port, including container electronic tags, unmanned driving technology, intelligent ship stowage, and intelligent port scheduling. Finally, the corresponding optimization countermeasures of intelligent port are put forward.

E-commerce supply chain links include product procurement, inventory management, distribution channels, logistics and other links. In order to achieve cost reduction and efficiency increase in the supply chain, it is essential to improve each node. This paper takes the development of cost-reduction and efficiency increase in the supply chain of an e-commerce enterprise as an example to analyze its problems and causes. Further, it is proposed that artificial intelligence empowers e-commerce supply chain to reduce cost and increase efficiency is an important measure to achieve stable development of enterprises. This paper proposes to reinforce information exchange through artificial intelligence technology to achieve real-time price adjustment with suppliers. The Automated Guided Vehicle enables e-commerce supply chain warehousing, and meanwhile the application of AI technology enables supply chain inventory, marketing and operation to achieve cost reduction and efficiency increase, and achieve sustainable business development.

Financial time series prediction is extremely challenging due to the intertwined effects of market narratives and complex inter-asset relationships. Traditional prediction models often fail to distinguish similar price patterns driven by different underlying causes, limiting their predictive accuracy in practical scenarios. To address these limitations, this study proposes the Dual-stream Alpha Factor Fusion Network (DAFF-Net), an innovative deep learning framework that integrates event-driven temporal pattern extraction with multi-dimensional relationship-aware channel soft clustering. The event-driven temporal pattern extractor employs an event-aware router to fuse time series data with contextual event information encoded from news, corporate announcements, and macroeconomic data, enabling the model to understand the underlying narratives behind market fluctuations. The multi-dimensional relationship-aware channel soft clustering module constructs a comprehensive asset relationship network through adaptive fusion of frequency-domain, fundamental, and knowledge graph relationships, which is more effective than single-relationship approaches and better captures complex cross-asset dependencies. We validated our approach primarily on Amazon stock data covering the period from 2010 to 2025, with additional cross-asset validation on four stocks from different sectors (healthcare, financial, energy, and electric vehicle sectors). Results demonstrate that DAFF-Net significantly outperforms eight representative baseline models including ARIMA, LSTM, Transformer, and DUET across multiple prediction time horizons. Specifically, compared to the strongest baseline, DAFF-Net achieves 7.4%-15.2% improvement in MSE and 7.0%-21.4% enhancement in $$\text {R}^{2}$$ metrics, showing particularly outstanding advantages in long-term prediction tasks. These results prove the effectiveness of integrating event information and multi-dimensional relationships in financial prediction, providing a new technical paradigm for quantitative investment and risk management applications.