The provision of clean drinking water is an important public service as more than 700 million people do not have access to this basic need. When it comes to delivering public services in developing countries, government capacity is a crucial element. This study investigates whether state capacity is a significant determinant in the provision of safe drinking water using panel data from 88 developing countries from 1990 to 2017. The paper applies ordinary least squares and fixed effects regression approaches and uses the Bureaucratic Quality Index and the Tax/GDP ratio as metrics of state capacity. The findings indicate that in developing nations, the availability of clean drinking water is positively correlated with state capacity.

Urban infrastructures and services—such as public transportation, innovation bodies and environmental services—are important drivers for the sustainable development of our society. How effectively citizens, institutions and enterprises interact, how quickly technological innovations are implemented and how carefully new policies are pursued, synergically determine development. In this work, data related to urban infrastructure features such as patents and recycled waste referred to 106 province areas in Italy are investigated over a period of twenty years (2001–2020). Scaling laws with exponents characterizing the above mentioned features are observed and adopted to scrutinize whether and how multiple interactions within a population have amplification effects on the recycling and innovation performance. The study shows that there is a multiplication effect of the population size on the innovation performance of territories, meaning that the dynamic interactions among the elements of the innovation eco-systems in a territory increase its innovation performance. We discuss how to use such approach and the related indexes for understanding metropolitan development policy.

Data literacy is an important skill for students in studying physics. With data literacy, students have the ability to collect, analyze and interpret data as well as construct data-based scientific explanations and reasoning. However, students’ ability to data literacy is still not satisfactory. On the other hand, various learning strategies still provide opportunities to design learning models that are more directed at data literacy skills. For this reason, in this research a physics learning model was developed that is oriented towards physics objects represented in various modes and is called the Object-Oriented Physics Learning (OOPL) Model. The learning model was developed through several stages and based on the results of the validity analysis; it shows that the OOPL model is included in the valid category. The OOPL model fulfils the elements of content validity and construct validity. The validity of the OOPL model and its implications are discussed in detail in the discussion.

This study investigates the intricate relationship between a nation’s GDP growth rate and three key variables: the number of granted patents, research and development (R&D) expenditure, and education expenditure. The purpose of the research is to discern the impact of these factors on GDP growth rates. Drawing on theoretical frameworks, including Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS), and Canonical Correlation Regression (CCR) techniques, the paper employs a robust methodological approach to unveil insights into the dynamics of economic growth. Contrary to conventional assumptions, the results reveal a negative correlation between R&D expenditure and GDP growth rate. In contrast, the number of patents granted and education expenditure shows a positively significant effect on the GDP growth rate, underscoring the pivotal roles of intellectual property creation and education investment in fostering economic growth. The conclusion emphasizes the importance of a nuanced understanding of these relationships for policymakers. The research’s implications highlight the need for balanced investments in innovation and education. The originality and value of this study lie in its unique findings challenging established beliefs about the impact of R&D expenditure on economic growth.

Bibliometric analysis is a commonly used tool to assess scientific collaborations within the researchers, community, institution, regions and countries. The analysis of publication records can provide a wealth of information about scientific collaboration, including the number of publications, the impact of the publications, and the areas of research where collaborations are most common. By providing detailed information on the patterns and trends in scientific collaboration, these tools can help to inform policy decisions and promote the development of effective strategies to support and enhance scientific collaborations between countries. This study aimed to analyze and visualize the scientific collaboration between Japan and Russia, using bibliometric analysis of collaborative publications from the Web of Science (WoS) database. The analysis utilized the bibliometrix package within the R statistical program. The analysis covered a period of two decades, from 2000 to 2021. The results showed a slight decrease in co-authored publications, with an annual growth rate of −1.26%. The keywords and thematic trends analysis confirmed that physics is the most co-authored field between the two countries. The study also analyzed the collaboration network and research funding sources. Overall, the study provides valuable insights into the current state of scientific collaboration between Japan and Russia. The study also highlights the importance of research funding sources in promoting and sustaining scientific cooperation between countries. The analysis suggests that more efforts in government funding are needed to increase collaboration between the two countries in various fields.