In recent years, the escalation in emergency occurrences has underscored the pressing need for expedient responses in delivering essential supplies. Efficient integration and precise allocation of emergency resources under joint government–enterprise stockpiling models are pivotal for enhancing emergency response effectiveness and minimizing economic repercussions. However, current research predominantly focuses on contract coordination and cost-sharing within these joint reserve modes, overlooking significant discrepancies in emergency supply classification standards between government and enterprise sectors, as well as the asymmetry in cross-sectoral and cross-regional supply information. This oversight critically impedes the timeliness and accuracy of emergency supply responses. In practice, manual judgment has been used to match the same materials under differing classification standards between government and enterprise reserves. Still, this approach is inefficient and prone to high error rates. To mitigate these challenges, this study proposes a methodology leveraging the BERT pre-trained language model and TextCNN neural network to establish a robust mapping relationship between these classification criteria. The approach involves abstracting textual representations of both taxonomical classes, generating comparable sentence vectors via average pooling, and calculating cosine similarity scores to facilitate precise classification mapping. Illustrated with China’s Classification and Coding of Emergency Supplies standards and Global Product Classification standards, empirical validation on annotated data demonstrates the BERT-TextCNN model’s exceptional accuracy of 98.22%, surpassing other neural network methodologies such as BERT-CNN, BERT-RNN, BERT-BiLSTM, etc. This underscores the potential of advanced neural network techniques in enhancing emergency supply management across diverse sectors and regions.

The challenges in reducing emissions within agricultural supply chains mainly arise from the short-term self-interested behaviors of various stakeholders. To study the impact of the agricultural producer’s dual fairness concerns toward the manufacturer and retailer on profit distribution and emission reduction decisions, this paper develops a centralized model and two decentralized models (with and without dual fairness concerns) for a three-level agricultural supply chain. The paper derives the optimal emission reduction decisions, participant profits, and overall supply chain profits under different decision-making scenarios. The main findings are as follows: First, the centralized model results in higher supply chain profit, emission reduction, and market demand compared to decentralized models, where these factors remain unchanged regardless of fairness concerns. Second, in decentralized decision-making, fairness concerns raise the agricultural producer’s profit while reducing those of the manufacturer and retailer. The manufacturer always earns the highest profit, followed by the retailer. Finally, the agricultural producer’s fairness concerns do not affect emission reduction or overall profit, but they change profit distribution, with increasing concern toward one participant decreasing their profit and increasing the other’s. These findings fill the theoretical gap in existing research and provide valuable theoretical insights for governments and stakeholders in making decisions.

Following major disasters, the urgent demand for supplies often overwhelms the capacity of single reserve points, necessitating the simultaneous coordination of resources from multiple reserve points. While prior studies have focused on evaluating and selecting individual points, the complex dynamics of coordinating multiple reserve points for joint supply remain underexplored.This paper develops a model for optimizing emergency logistics capabilities by determining the most effective sequence of supply point combinations. This study's innovation lies in addressing the dynamic cost variations of the same supply points under different combinations. Factors such as transportation distance, connection costs, and the shifting relative locations with other supply points contribute to diverse outcomes. Additionally, transfer and connection costs among supply points are incorporated to reflect realistic logistical challenges.Another contribution is the Discrete Multi-Strategy Sparrow Algorithm (DMSSA), an enhanced version of the sparrow search algorithm, developed to solve the constructed model and ascertain the optimal supply point combination sequence. Through discretization and feasibility improvements, DMSSA addresses key limitations of the original algorithm, including inadequate global search ability and susceptibility to local optima. By integrating four mutation strategies, experimental results demonstrate that DMSSA significantly improves sophistication and optimization capabilities, consistently outperforming five widely used intelligent algorithms.