The United Nations(UN) has always been a core force in promoting global sustainable development, drawing research interest from scholars in various academic fields. The UN General Debate Corpus (UNGDC) is widely adopted in research on diplomatic affairs and economic topics, but few studies have used it to track how sustainability-related topics have changed over time. This research addresses this research gap by using keyword analysis and word collocation network methods to explore how sustainable development has been discussed in UNGDC speeches across fifty years (1972–2024). The research results show obvious thematic changes divided into four clear stages: the early stage (1972–1992) focusing on peace maintenance and institutional fairness; the post-Cold War stage (1992–2000) aiming to build global governance rules, resolve international conflicts and incorporate basic environmental concerns into global issues; the Millennium Development Goals(MDGs) period (2000–2015) focusing on measurable goals for poverty elimination, public health improvement and climate protection; and the latest stage (2015–2024) featuring an integrated development model that combines climate action, public health safety and Sustainable Development Goals (SDGs). One major research result is that global sustainability research and practice have formed a systematic thinking mode, in which discussions on climate action, SDGs and health security are closely connected with each other. This discovery of thematic integration provides a clear reference for policy makers to formulate unified policies to deal with complicated and interrelated global problems.

To improve procurement efficiency, reduce procurement costs, and shorten procurement cycles, universities often entrust procurement projects with relatively small amounts to the procurement management department based on actual circumstances. But due to the high professional requirements of procurement, lengthy procurement processes, and the involvement of multiple departments, numerous problems often arise in practice. Based on an analysis of the current state of self-conducted procurement in universities, this paper combines practical situations to seek corresponding countermeasures, aiming to enhance the standardization and quality of self-conducted procurement in universities.

The Sichuan-Chongqing region is the core bearing area of the Chengdu-Chongqing Computing Power Hub under the national "East Data, West Computing" project, boasting the largest hydropower cluster in China and a complete natural gas industrial chain. However, it is undeniable that natural gas power generation has long been plagued by problems such as lagging installation progress, low unit utilization rate, and difficult project profitability. In contrast, the year-round stable base load demand of AI computing power centers can naturally and efficiently fill the core shortcoming of idle natural gas power generation units during the wet season. Therefore, based on the actual operation data of the natural gas power generation industry in Sichuan-Chongqing from 2021 to 2025 and combined with the value reconstruction theory of integrated energy systems, this paper systematically and rigorously clarifies the development dilemmas of the industry, quantitatively analyzes the profitability effect of the integration model, supplements risk analysis and multi-agent collaboration mechanisms, and accordingly puts forward a development path for the integrated development of natural gas power generation and computing power suitable for the Sichuan-Chongqing region. The most powerful and clear conclusion is that the integrated integration model can increase the utilization hours of natural gas power generation units from 1520 hours to 3200 hours, turning the project from a loss to a profit, thus providing an excellent solution for the sustainable development of natural gas power generation and the energy security of computing power hubs in Sichuan-Chongqing.

This paper uses several approaches of machine learning to predict if the customers will buy the term deposit by telemarketing of the bank. The data is collected from the phone calls of a Portuguese banking institution from 2008 to 2012. The paper compared five models: logistic regression, k-nearest-neighbors, support vector machine, decision tree and random forest. The analysis revealed several key insights. The predictive significance of variables like duration, p-days, and previous was confirmed by their important effect on the desired outcome. The result of this study is focused on the accuracy, precision, recall, the F1-score, and the Area Under the Receiver Operating Characteristic (ROC) Curve. The logistic regression method has the best result among all the five models, because it has the highest accuracy and the largest Area Under ROC Curve (AUC). This shows a high level of ability to distinguish if the clients will subscribe to the term deposit. This study will give a valuable reference to the bank for precision marketing to their future customers.

With the global economy accelerating toward a low-carbon and green transformation, the Environmental, Social, and Governance (ESG) framework has gradually become a core standard for measuring sustainable development. Green finance, as a bridge connecting capital and sustainable projects, aims to promote resource allocation toward environmentally friendly projects to achieve balanced development in the economy, environment, and society. This paper analyzes the current status of sustainable development in green finance under the ESG framework, explores trends in the development of the green finance market, the extent of the adoption of ESG investment concepts, policy implementation, and challenges faced in financial product innovation, and proposes relevant countermeasures and recommendations.

With the advancement of big data technology, machine learning has found widespread application in financial market forecasting due to its exceptional data processing and predictive capabilities. Upon reviewing recent research on stock prediction based on machine learning, it is found that under different market environments, Long Short-Term Memory significantly reduces prediction errors compared to other single-model methods. Therefore, to further explore the potential of LSTM models, this paper reviews recent research on stock price prediction based on improved LSTM models, focusing on the basic principles and characteristics of five models: BiGRU-LSTM、 VMD-CSSA-LSTM、 DMD-LSTM、 SF-GET-LSTM、 Doc-W-LSTM. Comparative analysis is conducted with other advanced models. Meanwhile, through comparative analysis of the literature, it is found that current research faces issues such as insufficient interpretability, limited research markets, and inadequate verification methods. It is proposed that introducing methods such as SHapley Additive exPlanations, causal inference, and time-series cross-validation may address these shortcomings. Finally, future research may endeavour to integrate the five models, constructing a multimodal adaptive and explainable intelligent financial forecasting system. This would provide theoretical and technical support for intelligent financial decision-making, driving further optimisation and innovation in deep learning within the forecasting domain.

Amid rapid advances in digital technologies, the cultural tourism sector is experiencing profound ecological restructuring and innovation-driven transformation. This study investigates the digital evolution of Yongqingfang, a historic district in Guangzhou, and proposes a three-dimensional driving framework encompassing technology empowerment, content innovation, and ecological synergy. It systematically analyzes the roles of artificial intelligence, blockchain, and extended reality in enriching cultural experiences, optimizing operational efficiency, and enhancing user engagement. The results indicate that Yongqingfang has realized a value-chain upgrade—from activating cultural resources to transforming consumption models—through the development of branded intellectual property, the rise of influencer-led digital economies, and the digital expression of intangible cultural heritage. In addition, policy support and multi-stakeholder collaboration have facilitated a sustainable integration of heritage conservation, cultural communication, and economic value creation. This case offers a replicable model for the digital regeneration of historic districts and provides practical insights for the global cultural tourism industry.

The first-price sealed-bid auction (FPSB) is a prevalent mechanism for selling high-value assets. However, its susceptibility to inefficiencies like the winner’s curse and strategic underbidding can deter market participation and lead to revenue fluctuations, as observed in recent global auction market trends. This paper aims to derive optimal bidding strategies that approximate the Bayesian Nash Equilibrium (BNE), thereby mitigating these inefficiencies. Through a case study of a 1961 Alaska oil and gas lease auction, this study identifies the format’s advantage in deterring collusion alongside its problems of pricing inaccuracy and resource misallocation. To solve these problems, two solutions are provided in this paper. Provided the cumulative distribution expected value of bidders follows the cubic function, a function that can achieve the Bayesian Nash Equilibrium is provided. This paper also provides different strategies based on the genre of the auction item. Bidders can shade more when bidding for artworks and antiques, and shade less when bidding for real estate and gas fields.

Against the backdrop of rapid global expansion in green credit and the increasing impact of environmental risks on financial stability, Chinese commercial banks are accelerating the integration of green factors into their credit decision-making processes. However, the associated risk effects exhibit significant heterogeneity. This study employs a full-sample regression and a grouping regression design to examine the moderating effect of listing status on the risk effects of green credit. The findings reveal that green credit significantly reduces the risk-taking of listed banks. Conversely, for non-listed banks, green credit appears ineffective and actually intensifies scale risks, indicating an institutional divergence. Based on these results, this paper proposes a differentiated regulatory framework. It suggests optimizing synergistic rule effectiveness for listed banks, while focusing on building regional technical platforms and providing corresponding fiscal and tax incentives for non-listed institutions. The aim is to achieve the synergistic evolution of green risk management within the banking system.

With the rapid development of digital economy, as a new core factor of production, the economic value and importance of data elements have become increasingly prominent, playing a key role in promoting economic growth, enhancing the competitiveness of enterprises and optimizing the allocation of resources. However, the economic characteristics of data elements make the identification of assets face many challenges, including the vague definition of property rights and the difficulty of value evaluation. In the context of the marketization of data elements, this paper analyzes the economic characteristics of data elements and combines with the reality of China to provide a theoretical framework and practical path for enterprises to construct the identification and confirmation of enterprise data elements assets. Starting from the theoretical basis of data element asset identification, this paper analyzes the development status and challenges of data element asset identification in China by combining qualitative analysis with quantitative analysis, and explores possible coping strategies, which provides a reference for building a scientific and rational data element asset identification system and a practical path for enterprises to identify data element assets.