In a dynamic market marked by disruptions like pandemics and recessions, organizations face significant challenges in efficiently managing logistics processes and activities. The primary objective of this article is to propose an integrated four-phase model for assessing the efficiency of retail distribution warehouses based on principal component analysis-data envelopment analysis-improved fuzzy step-wise weight assessment ratio analysis-combined compromise solution (PCA-DEA-IMF SWARA-CoCoSo). The model provides a synergistic effect of all positive sides of the considered methods. PCA-DEA methods are used to reduce the number of variables and to identify efficient warehouses. IMF SWARA is applied to determine criteria weights, while the CoCoSo method is employed in the last phase for ranking efficient warehouses. The model incorporates 18 inputs and 3 outputs, derived from both literature and real-world systems. The proposed model identifies the most efficient warehouses, which can serve as benchmarks for improving the performance of less efficient ones. After implementing PCA-DEA, only seven warehouses were identified as efficient. Subsequently, fixed and variable costs are identified as the two most important criteria. Results of the considered case study indicate that warehouse A4 emerges as the best one, whereas A6 is the least preferred warehouse. This research offers valuable insights and practical implications for organizations operating in dynamic markets, assisting them in achieving operational excellence and improving their supply chain performance.

Aquaculture is flourishing in the Nordic region, namely in Norway and Denmark since their coastline geographic positioning. Conversely, despite the potential for utilizing marine resources and wind energy through extensive research and development (R&D), these countries nonetheless face ongoing environmental challenges due to large-scale aquaculture and maritime operations. Nevertheless, there has been insufficient focus on comprehending the consequences of aquaculture production, marine shipping, wind energy generation, and blue R&D on greenhouse gas (GHG) emissions so far. Thus, this work aims to address this research gap in the context of Norway and Denmark between 2000Q1 and 2021Q4 by employing "quantile-on-quantile regression (QQR) and wavelet coherence (WC)". The QQR results demonstrate that aquaculture production in Denmark harms the environment at lower quantiles but becomes positive at higher quantiles, indicating that the aquaculture operation leads to an increase in GHG emissions. Aquaculture production in Norway has a detrimental effect on GHG emissions across several quantiles. The results can be attributed to the stringent regulation and promotion of sustainable aquaculture techniques in Norway. Maritime shipping exhibits a positive effect on the emissions of GHG in both nations. Moreover, blue R&D appears to be more significant in Norway compared to Denmark when it comes to decreasing GHG emissions. Finally, wind energy generation has varying effects on GHG emissions across different quantiles for both countries. It is recommended that both countries encourage the implementation of sustainable aquaculture practices by making significant investments in blue R&D for a healthier environment.

To accurately capture the dynamic changes and patterns of supply chain logistics demand, a prediction algorithm based on BiLSTM-AM is proposed. After collecting relevant data on supply chain logistics demand, outliers were removed using the Local Outlier Factor (LOF) to enhance data quality. From the cleaned dataset, features such as the supply chain logistics demand growth rate, inventory turnover rate, seasonal index, customer order volume, supplier delivery cycle, and transportation efficiency were extracted to construct a time series feature set for supply chain logistics demand. Based on the Long Short-Term Memory (LSTM) neural network model, a bidirectional structure was introduced, combined with an Attention Mechanism (AM), to establish a BiLSTM-AM-based supply chain logistics demand forecasting model, which was then trained using the gradient descent method. The obtained time series of supply chain logistics demand features were input into the trained forecasting model, and its output represented the forecasted supply chain logistics demand results. The experiment shows that the algorithm can accurately predict the demand for supply chain logistics. After applying this algorithm, the on-time delivery rate for each month is above 95%, and the decrease in logistics costs ranges from 0.3 to 0.5, indicating strong application value.

With the rapid development of technologies such as big data, cloud computing, and artificial intelligence, the digital economy is transforming the operational models of traditional industries, providing new opportunities for the transformation, upgrading, and resilience enhancement of industrial chains. As the world's second-largest economy, China faces a complex and ever-changing domestic and international economic environment, where the stability and competitiveness of its industrial chains are directly linked to the sustainable and healthy development of its economy. To promote the modernization of industrial chains and improve their resilience, it is a key element and crucial step. The resilience of industrial chains is reflected in their ability to resist risks and recover, which requires systematic efforts such as filling gaps, extending chains, consolidating chains, and strengthening chains. However, at present, there are still challenges in enhancing China's industrial chain resilience, such as insufficient innovation capacity, a shortage of professional talent, and weak industrial foundations. Therefore, China should leverage the digital economy to accelerate the digital upgrade of its industrial chains, focus on breakthroughs in core technologies, and promote the development of more complete industrial chains, which is crucial for driving the high-quality development of China's industrial chains.

With the continuous deepening of China's reform and opening up, it has brought new opportunities for the development of China's logistics industry, while also exposing some problems, and the concept of the green logistics system has emerged. The so-called green logistics system is derived based on the concept of green development. Starting from the development of the logistics industry, the operation of enterprises, energy conservation, resource conservation, pollution reduction and sustainable development involved in the logistics activities, the green logistics system is integrated throughout the entire operation process of logistics enterprises to achieve the sustainable development of logistics enterprises. To meet the requirements of the concept of sustainable development, the green logistics system is a new direction for the development of China's logistics industry and an inevitable requirement for the implementation of the new development concept. However, with the rapid development of the economy and society, higher requirements have been put forward for the development of the logistics industry. Relevant departments should adjust the current development concept based on the actual development situation, incorporate the green logistics system, promote the transformation of China's logistics industry, and provide a useful reference for promoting the sustained and healthy development of China's green logistics.