Heat stress amplified by climate change causes excessive reductions in labor capacity, work injuries, and socio-economic losses. Yet studies of corresponding impact assessments and adaptation developments are insufficient and incapable of effectively dealing with uncertain information. This gap is caused by the inability to resolve complex channels involving climate change, labor relations, and labor productivity. In this paper, an optimization-based productivity restoration modeling framework is developed to bridge the gap and support decision-makers in making informed adaptation plans. The framework integrates a multiple-climate-model ensemble, an empirical relationship between heat stress and labor capacity, and an inexact system costs model to investigate underlying uncertainties associated with climate and management systems. Optimal and reliable decision alternatives can be obtained by communicating uncertain information into the optimization processes and resolving multiple channels. Results show that the increased heat stress will lead to a potential reduction in labor productivity in China. By solving the objective function of the framework, total system costs to restore the reduction are estimated to be up to 248,700 million dollars under a Representative Concentration Pathway of 2.6 (RCP2.6) and 697,073 million dollars under RCP8.5 for standard employment, while less costs found for non-standard employment. However, non-standard employment tends to restore productivity reduction with the minimum system cost by implementing active measures rather than passive measures due to the low labor costs resulting from ambiguities among employment statuses. The situation could result in more heat-related work injuries because employers in non-standard employment can avoid the obligation of providing a safe working environment. Urgent actions are needed to uphold labor productivity with climate change, especially to ensure that employers from non-standard employment fulfill their statutory obligations.

This paper aims to explore how to build a sustainable peace and development model for China’s peacekeeping efforts through the application of data-driven methods from UN Global Pulse. UN Global Pulse is a United Nations agency dedicated to using big data and artificial intelligence technologies to address global challenges. In this paper, we will introduce the working principles of UN Global Pulse and its application in the fields of peacekeeping and development. Then, we will discuss the current situation of China’s participation in peacekeeping operations and how data-driven methods can help China play a greater role in peacekeeping tasks. Finally, we will propose a sustainable peace and development model that combines data-driven methods with the advantages of China’s peacekeeping efforts to achieve long-term peace and development goals.

Taking Xinjiang Agricultural University as an example, based on Rain Classroom and Dingding platform, the linear algebra course changes the current situation of "emphasizing theory and ignoring application" in traditional mathematics classrooms, adding applied teaching cases with the background of industry and agriculture, using online and offline The blended teaching mode, through inquiry-based and case-based teaching methods and students' autonomous learning and discussion methods, develops from a teaching mode focusing on "teaching" to focusing on "learning". The teaching mode has been comprehensively reformed, and satisfactory results have been achieved.

This study investigates the integration of sustainability principles into educational curricula, focusing on the gap between theoretical knowledge and practical application. Through a mixed-methods approach, the research identifies key institutional barriers, including outdated policies, insufficient teacher training, and limited resources. These barriers hinder the effective incorporation of sustainable development principles into education. The study reveals that while some educational systems struggle to adopt sustainability, examples from progressive institutions show that integrating these principles enhances student awareness and equips them with skills essential for sustainable development. The findings suggest that substantial changes are needed in existing educational frameworks to better support sustainability in curricula. Recommendations for future research include conducting longitudinal studies to assess the long-term impact of curriculum changes on sustainability outcomes and exploring the role of technology in advancing sustainable education. Policy recommendations emphasize the need for advocacy and the implementation of actionable strategies, such as industry collaborations for pilot projects and real-world applications. Furthermore, institutional support for teacher professional development is crucial, with structured programs that combine theoretical knowledge and practical skills in sustainability. Enhancing partnerships between educational institutions and industries, including co-designed curriculum modules and internship opportunities, is also essential for aligning education with the Sustainable Development Goals. This study highlights the importance of transforming educational practices to better address the challenges of sustainable infrastructure development, ultimately preparing students to contribute to a more sustainable future.