This research analyses the effects of openness, telecommunications, and institutional nexus on economic growth in African countries using a panel model with data from 16 landlocked countries from 1996 to 2021 and employing the pooled mean group estimation technique that mitigates bias from country heterogeneity and discerning short-term and long-term equilibrium dynamics and two-step system-generalized method of moments (GMM) estimation for robustness check. The empirical findings indicate that openness exerts a significantly positive effect on economic growth in the models. This supports the neoclassical model, suggesting that being landlocked should not impede economic growth, but rather, growth should depend on opportunities available to each country. However, institutions and telecommunications show a mixed correlation with economic growth. These findings can guide landlocked developing countries in enhancing their exports and fostering skill acquisition to attract advanced technology. In conclusion, policymakers should improve macroeconomic policies, telecommunications infrastructure, and institutional structure to strengthen the sustainability of economic growth in African landlocked countries.

The role of technology in stimulating economic growth needs to be reexamined considering current heightened economic conditions of Asian developing Economies. This study conducts a comparative analysis of technology proxied by R&D expenditures alongside macroeconomic variables crucial for economic growth. Monthly time-series data from 1990 to 2019 were analyzed using a vector error correction model (VECM), revealing a significant impact of technology on the economic growth of India, Pakistan, and the Philippines. However, in the cases of Indonesia, Malaysia, Thailand, and Bangladesh, macroeconomic indicators were found more crucial to their economic growth. Results of Granger causality underlined the relationship of R&D expenditures and macroeconomic variables with GDP growth rates. Sensitivity analyses endorsed robustness of the results which highlighted the significance and originality of this study in economic growth aligned with sustainable development goals (SDGs) for developing countries.

This study explores benefits, barriers and willingness to pay for bike-sharing service in tourism context. Based on a sample of 800 individuals who visited Da Nang, Vietnam between July and August 2023, trends in the barriers and benefits related to bike-sharing service from tourists’ point-of-view were explored. The results show that bike-sharing is appreciated for many reasons, notably for its fun/relaxing, cost saving, ease of city exploration, and promotion of better physical and mental health. However, bike-sharing services are considerably less likely to be viewed as options for faster transportation to a destination or reducing traffic hazards. Notably, eighty-six percent of non-riders indicated contentment with their existing transportation options and a lack of interest in bike-sharing services, a proportion significantly higher than any other group. Predictably, barriers related to the availability of bike-sharing and infrastructure, such as lack of sufficient number of shared bikes, far destination, and poor road conditions were notably more likely to be selected by one-time riders. The results are also evident that a significant portion of tourists is willing to pay to enhance their tourist experience with a bike-sharing service. On average, tourists were willing to pay $0.92 per hour (with a standard deviation of $0.24). This amount reflects the tourists’ recognition of the value added to their mode experience. These findings suggest that bike-sharing service play a significant role in fulfilling an essential transportation niche and have the potential to contribute to enhance tourists’ experience. Efforts aimed at addressing barriers associated with bike-sharing usage could further enhance their contribution to improve tourist satisfaction and boost attraction demand.

The Intellectual Property (IP) chapter of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is recognized for its extensive coverage, encompassing a broad range of innovation areas such as patents, trademarks, geographical indications, and copyright. This chapter sets a new global benchmark for IP protection, posing significant challenges to the existing legal frameworks of member countries and necessitating rapid adaptation, particularly for developing members like Vietnam, Malaysia, and Mexico. These nations have undertaken comprehensive revisions to their IP laws to align with the international standards established by the CPTPP. Despite their unique national contexts, the legal amendments reflect distinct strategies and methodologies in meeting international standards. This paper conducts a qualitative analysis of Vietnam, Malaysia, and Mexico, comparing their law amendment strategies, contents, and techniques across three dimensions. It highlights the distinctive characteristics and impacts of their legal revisions, offering valuable insights for other prospective developing members within the CPTPP framework on the practice of IP law reform.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.