Recent studies regarding the impacts of technology spillover and international trade have gained momentum in the emerging economies. Empirical evidences show that some countries gain and other loss to grasp the opportunities of international trade and technological innovation to compete in the global market. This paper examines the effect of export and technology on the economic performance of emerging Asian countries, using the Generalized Method of Moments (GMM) model between the periods 2000–2016. Following the Solow economic growth model, the result identifies a positive and significant effect of export and technology on the economic growth of the emerging Asian economies. Similarly, the long-run estimation ascertains the significant and positive impacts of trade and technology on the economic growth of the countries. The results are robust using alternative dynamic panel models, representing the pivotal role of export and technology to the economic growth of the countries. Thus, we recommend policymakers to device attractive policies that can enhance the advancement of technology and trade to maintain sustained economic growth. This would also fasten the internationalisation process and enable to compete efficiently in the global markets in terms of quality of exports and standardisation.

Climate related shocks cause severe risks to health systems in Ethiopia’s Somali Region, particularly among vulnerable and displaced pastoralist communities. This note discusses how an artificial intelligence (AI) driven evidence synthesis system, co-designed with local stakeholders, could strengthen responses to climate-related health crises in this fragile setting. Stakeholder consultations and participatory co-design workshops were conducted for the development of an AI-enabled evidence synthesis platform (Solace-AI). Participants included vulnerable and displaced communities, regional and national health officials, disaster risk management authorities, and international research partners. Climate catastrophes compounded by decades of conflict-related trauma, mental health problems have become one of the most pressing health issues in the region. Understanding and addressing this interconnected crisis, in which the effects of environmental catastrophe, forced migration, mental illness, and substance misuse are compounded, it is essential for any meaningful intervention in the region. Decision-makers, from national ministries to regional disaster management offices to local health workers, need timely access to evidence to inform their actions. In settings already strained by climate impacts, commissioning formal evidence syntheses is often impossible. AI-enabled evidence synthesis could improve preparedness and responses, thus decision-makers must be capacitated to proceed with whatever information is immediately available, which may be incomplete or not contextualized to their setting. However, effectiveness depends on participatory co-design, ethical governance, sustainability, and local ownership. Without these safeguards, AI risks reinforcing existing inequities rather than alleviating them.

Background: The COVID-19 pandemic has profoundly affected the movement of people across borders in Eastern and Southern Africa. The implementation of border closures and restrictive measures has disrupted the region’s economic and social dynamics. In areas where national authorities lack full control over official and unofficial land crossings, enforcing public health protocols to mitigate health risks may prove challenging. Objective: This study aimed to assess perceived factors that influence the spread and control of COVID-19 among Somali communities living on and near ground crossings in Tog Wajaale, Somali region, Ethiopia. Methods: A community-based cross-sectional study was conducted using a multistage sampling technique. Beliefs and perceptions of the virus’s spread and control were partially adapted from the World Health Organization (WHO) resources, exploring four main perception themes: (1) perceived facilitators for the spread of the virus, (2) perceived inhibitors, (3) risk labeling, and (4) sociodemographic variables. A sample size of 634 was determined using the single proportion formula. Standardized mean scores (0‐100) and SDs categorized perception themes, with mean differences by sociodemographic variables analyzed using ANOVA and t tests. Statistical significance was established with a 95% CI and a P value below .05. The data were analyzed using STATA version 14.1. Results: Factors influencing COVID-19 spread and control include behavioral nonadherence and enabling environments. A total of 81.9% (439/536) did not comply with social distancing, and 92.2% (493/536) faced constraints preventing them from staying home and enabling environments. Misconceptions were prevalent, including beliefs about hot weather (358/536, 66.8%), traditional medicine (36/536, 6.7%), and religiosity (425/536, 79.3%). False assurances also contributed, such as feeling safe due to geographic distance from hot spots (76/536, 14.2%) and perceiving the virus as low-risk or exaggerated (162/536, 30.2%). Only 25.2% (135/536) followed standard precautions and 29.9% (160/536) were vaccinated. Employment, region, income, sex, education, and information sources significantly influenced behavioral nonadherence, myth prevalence, and false assurances. Conclusions: The findings highlight the need for substantial risk communication and community engagement. Only 46.6% (250/536) of individuals adhered to precautionary measures, there was a high perception of nonadherence, and essential COVID-19 resources were lacking. Additionally, numerous misconceptions and false reassurances were noted. Understanding cross-border community behavior is crucial for developing effective, contextually appropriate strategies to mitigate COVID-19 risk in these regions.