In response to the challenges of climate change, this study explores the use of moringa pod powder as reinforcement in the manufacture of compressed earth bricks to promote sustainable building materials. The objective is to evaluate the impact of African locust bean pod powder on the mechanical properties of the bricks. Two types of soils from Togo were characterized according to geotechnical standards. Mixtures containing 8% African locust bean pod powder at various particle sizes (0.08 mm, 2 mm, and between 2 and 5 mm) were formulated and tested for compression and tensile strength. The results show that the addition of African locust bean pod reduces the mechanical strength of the bricks compared to the control sample without pods, with strengths ranging from 0.697 to 0.767 MPa, compared to 0.967 to 1.060 MPa for the control. However, the best performances for the mixtures were obtained with a fineness of less than 2 mm. This decrease in performance is attributed to several factors, including inadequate water content and suboptimal preparation and compaction methods. Optimizing formulation parameters is necessary to maximize the effectiveness of African locust bean pods. This work highlights the valorization of agro-industrial waste, paving the way for a better understanding of bio-based materials and future research for sustainable construction.

This study investigates the escalating complexity and unpredictability of global supply chains, with a particular emphasis on resilience in the agricultural sector of Antioquia, Colombia. The aim of the study is to identify and analyze the dynamic capabilities, specifically flexibility and adaptability that significantly enhance resilience within agri-food supply chains. Given the sector’s vulnerability to external disruptions, such as climate change and economic volatility, a thorough understanding of these capabilities is imperative for the formulation of effective risk management strategies. This research is essential to provide empirical insights that can inform stakeholders on fortifying their supply chains, thereby contributing to enhanced competitiveness and sustainability. By presenting a comprehensive framework for evaluating dynamic capabilities, this study not only addresses existing gaps in the literature but also offers practical recommendations aimed at bolstering resilience in the agricultural sector.

As one of the key initiatives promoted by the Chinese government, precision poverty alleviation aims to lift information-blocked areas out of poverty and ensure their sustainable economic development. Yunnan Province, characterized by its combination of old, young, border, and poor areas, is the province with the most diverse types of poverty, the widest poverty coverage, and the deepest poverty levels in the country. Yunnan has carried out anti-poverty work in tandem with the national efforts for 42 years in a planned and organized manner, ultimately achieving the goal of zero absolute poverty. In this process, digital rural development has played a very important role. Based on the current experience of digital construction in developed regions, completing regional digitalization requires meeting the needs of information resources, information environment, and information supply and demand. Through keyword search, text analysis, and field visits, we summarized the factors considered by local governments in policy formulation. We also attempted to map out the path for rural governments to build digital villages. With the ultimate goal of bridging the urban-rural gap, the study of digital rural development in Yunnan will provide an effective case.

With its inherent characteristics of decentralization, immutability, and transparency, blockchain technology presents a promising opportunity to revolutionize the South African food supply chains. Blockchain technology, with its decentralized, immutable, and secure nature, offers solutions to these challenges by improving traceability and accountability across the supply chain. This study investigates the role of blockchain technology in enhancing transparency in the food supply chain among small and medium enterprises in South Africa. SMEs form a critical part of the country's agri-food sector but face challenges such as food fraud, inefficient inventory management, and lack of transparency, which impact food safety and trust. The research adopts a mixed-method approach, utilizing the Technology-Organization-Environment framework and Institutional Theory to explain blockchain adoption among SMEs. The results demonstrate that blockchain-enabled practices, such as smart contracts, records traceability, production tracking, and distribution monitoring, significantly enhance supply chain transparency. The findings highlight blockchain's potential to increase operational efficiency, regulatory compliance, and stakeholder trust. This research provides valuable insights for policymakers and practitioners, emphasizing the need for regulatory support and strategic investment in blockchain solutions to promote sustainability and competitiveness in the agri-food sector.

Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.