Lala Kasumova

This study provides empirical evidence that CO₂ emissions from transport (total and by transport types) have an impact on countries’ logistics efficiency, both in general (Logistics Performance Index) and in terms of its individual indicators (volumes of freight transport (total and by transport types) and total investments in transport infrastructure). The aim is to empirically demonstrate the impact of CO₂ emissions from different modes of transport on the logistics efficiency of countries, specifically using data from 60 International Transport Forum (ITF) member countries. The Granger causality analysis, based on VAR modeling for six periods evaluated by the World Bank (2007, 2010, 2012, 2014, 2016, 2018), demonstrates that in 50% of the countries, changes in CO₂ emissions from transport are the cause of the shifts in the Logistics Performance Index (increasing emissions can impede logistics efficiency). The sample was reduced to these countries for the regression modeling. Regression modeling (with fixed and random effects, Hausman test, for 2002–2021 on panel data for countries in which direct causality was confirmed) showed that a one-unit increase in total CO₂ emissions from transport (in general and in air transport) is associated with 0.12 and 0.21 decrease in total investments in transport infrastructure. An increase in road CO₂ emissions is associated with a 0.49 decrease in road freight transport (ton-km per thousand units of GDP). An increase in CO₂ rail emissions is associated with a 0.15 increase in total investments in transport infrastructure and a 0.12 increase in total freight transport.

Type of the article: Research Article

Abstract
The banking and financial system of the countries of the world is constantly developing, but at different rates and ways, given their differences in the levels of economic, financial, and innovation development. The purpose of this article is to identify factors that link upper-middle- and high-income countries in terms of banking ecosystem digitalization, based on cluster analysis. The research sample includes 40 countries – 20 top-performing upper-middle-income and 20 high-income economies – based on the 2023 ICT Development Index. The analysis is based on 15 standardized indicators characterizing digitalization in the banking ecosystem, sourced from the International Monetary Fund, the World Bank, and the International Telecommunication Union. These indicators cover ICT development, AI readiness, cybersecurity, GovTech maturity, financial development, banking access, and digital transaction activity. Data standardization was performed in Stata (v19.5) using the built-in function to create new variables with a mean of 0 and a standard deviation of 1. Cluster analysis was conducted using the k-means method in Statgraphics (v19), with silhouette scores computed in Python to determine the optimal number of clusters. Cluster analysis revealed four distinct country groups, demonstrating that similarities in banking ecosystem digitalization transcend income levels. Key convergence factors include ICT development, GovTech maturity, mobile banking adoption, and AI readiness. Some upper-middle-income countries exhibit digitalization patterns comparable to high-income economies, highlighting the role of strategic investment and policy, rather than income, as primary drivers of digital financial advancement.