This article investigates the connection between US logistics companies’ commitment to environmental, social and fair governance (ESG) strategy and their performance on the US stock market during the 2007–2022 period. The research considers historical data analysis, CAPM and a comparison of optimised portfolios. According to the results of the analyses, ‘green’ logistics stocks are less volatile, and hence less risky, and more profitable compared to ‘non-green’ logistics stocks. The Great Recession (2007–2009) and the COVID-19 pandemic (2020) had the greatest impact on stock volatility, in terms of the US stock market. Optimised during the time of the Ukrainian crisis, green logistics portfolios were shown to have higher returns, but also risks and Sharpe ratios, than ‘non-green’ ones. The results confirm there to be a connection between companies’ commitment to ESG strategy and enhanced stock performance, which contributes to the importance of the ESG agenda.

This article investigates factors that explain road crash severity levels in Saint Petersburg, Russia, during the 2015–2021 period. The research takes into account factors such as lighting conditions, weather conditions, infrastructure factors, human factors, accident types, and vehicle category and color to assess their influence on crash severity. The most influential accident type is run-off-road crashes, which are associated with an 11.2% increase in fatal accidents. The biggest reason for the increase in fatal accidents due to road infrastructure conditions is road barrier shortcomings (2.8%). Road infrastructure conditions, such as a lack of road lighting, have a significant effect on fatal outcomes, increasing them by 12.6%, and this is the most influential factor in the analysis. The obtained results may serve as a basis for Saint Petersburg authorities to develop new road safety policies.

Introduction. The study continues the approbation of the methodology of cluster identification, developed earlier by the authors and the study of regional industry specialization, within the framework of which the database “Clusters of Russian Regions” was developed. The relevance of the topic is the necessity of the methodology for complex clustering of regions in order to provide further recommendations for the development of industrial sectors. The purpose of the article is to develop and test the methodology for identifying clusters on the territory of Russia based on the synthesis of functional and spatial approaches. Materials and Methods. The analysis of intersectoral relations within the framework of the functional approach consisted in the application of the maximum method, which allows to trace the chain of consumption relative to the main suppliers and main consumers between industries based on the Russian “Input – Output” table of 2016. The spatial approach was implemented by calculating location quotients, determining z-scores, correlation coefficients analysis between clusters’ location quotients to establish regional and interregional links. Results. The results of the article have tested the methods proposed by the authors for the clustering process of regions. The results obtained after applying the methods revealed the localization of the cluster “Chemical Products” in the territories of certain regions of the Russian Federation and its existing significant functional and spatial relationship with the clusters: “Construction”, “Production Equipment” and others. Moreover, it has been determined that the chemical industry has different types of connections: both the functional connection (with the “Metallurgy” cluster) and the presence of spatial communication: interregional (“Construction”), regional (“Production equipment” and others). Therefore, it has been proved that an integrated approach is necessary to identify industrial clusters. Discussion and Conclusion. Considerations of previous studies on regional clustering and our obtained results on the cluster “Chemical products” have confirmed the need to use the complex methodology of regional clustering, which includes the synthesis of functional and spatial approaches, since both approaches separately have their limitations: functional connection does not mean the existence of spatial (analysis of clusters “Chemical products” and “Metallurgy” interconnection) and vice versa. This result will help to comprehensively solve the problem of the chemical industry development in Russia, due to the understanding of the competent placement of enterprises and taking into account the relationship with enterprises of various industries. The materials of the article can be useful both for scientists dealing with the problems of regional economic development, and for governmental bodies whose goals include making managerial decisions in the field of industrial development.

Road accidents are a significant issue, leading to human, material, and economic losses. Many regions and countries have implemented various measures to improve traffic safety. However, these efforts have not eliminated all traffic-related fatalities and severe injuries. Even within a single country, the pattern of road accidents may vary due to spatial and socio–economic differences. Therefore, this research aimed to explore the relationship between regional socio–economic conditions and the dynamics of road accidents, emphasizing the importance of considering regional heterogeneity. The article further investigated the spatiotemporal dynamics of road accidents across 83 Russian regions during the period from 2015 to 2021. The modelling was conducted using Moran’s I spatial autocorrelation index and spatial econometrics models. The dependent variable was also defined as the number of road crashes with injuries per 100,000 people. Analysis of the spatial autocorrelation index showed a positive and significant spatial autocorrelation (at 5% level) in the number of road crashes with injuries per 100,000 people. Furthermore, the Global Moran’s I ranged from 0.152 to 0.278, indicating the presence of regional clustering in road crash rates. According to the Spatial Autoregressive with Fixed Effects (SAC-FE) model, the volume of gross regional product (GRP) per capita, the length of paved public roads per 1,000 people, and the level of innovative activity of organizations showed significance at 1% level both negative direct and indirect (spillover) effects on road accident density. Specifically, a 1% increase in these factors led to a direct decrease of 0.13, 0.04, and 0.024, respectively in road accident density, while the corresponding spillover effects were -0.21, -0.07, and -0.04. The results implied that better socio-economic conditions in the studied regions and the neighboring areas contributed to a reduction in road accidents including injuries. This was attributed to a higher level of responsibility and self-awareness among citizens in more developed regions.