Search Results (40)

This study addresses the challenge of designing low-carbon supply chain pathways in the global seaborne metallurgical coal sector by developing an enhanced Ant Colony Optimisation (ACO) algorithm. This quantitative approach bridges operations research and sustainability science by identifying optimal supply pathways to minimise transportation-related carbon emissions. The enhanced framework incorporates coal-specific maritime logistical constraints and maintains Pareto efficiency across a comprehensive global dataset encompassing 201 mines, 11 exporting nations, and 72 destination ports in 26 importing countries. Computational analysis demonstrates that the proposed algorithm achieves a 25% reduction in transportation carbon intensity (from 38.2 to 28.6 kg CO₂eq/t) relative to the 2022 baseline. To evaluate supply chain resilience, scenario analyses incorporating geopolitical disruptions, such as the Russian coal sanctions, provide quantitative insights into the trade-offs between policy interventions and emission reduction objectives. Extending projections to 2050 under various demand trajectories yields cumulative emission reductions of 35–70 Mt CO₂eq (an average of 53 Mt), representing additional mitigation beyond the 230 Mt of reductions identified in prior research. These findings demonstrate that mathematical optimisation can deliver near-term environmental benefits without requiring capital-intensive technological breakthroughs, thereby supporting global climate mitigation targets. Full article

Background/Objectives: Although therapeutic use of medicinal marijuana by patients in Poland became legal in 2017, there remains doubt among primary care physicians (PCPs) related to prescribing medicinal marijuana to their patients. In this study, we aimed to investigate the attitudes of family physicians and the systemic barriers that influence doctors’ therapeutic decisions with respect to prescribing medicinal marijuana. Methods: A 28-question survey was administered to a representative group of PCPs in the Lublin province of Poland. Statistical analysis of the answers of 293 (out of 301) respondents enabled us to determine the PCPs’ levels of knowledge about medicinal marijuana and their willingness to prescribe this type of therapy for their patients. Results: Only 32.3% of the surveyed PCPs had encountered patients who experienced symptoms associated with medicinal marijuana use. The two groups of symptoms most frequently reported by these PCPs were emotional agitation or playfulness (50.8%) and psychomotor retardation, drowsiness, and catatonia (25.4%). Only 41.0% of the surveyed PCPs perceived risks associated with prescribing medicinal marijuana to their patients, including the possibility of patients abusing medicinal marijuana, leading to addiction; sanctions from national regulatory bodies; trade in prescriptions (so-called “counterfeit prescriptions”); a lack of control over the resale of drugs by patients; and the absence of recommendations or guidelines for the use of medicinal marijuana. Our findings also demonstrate that only 5.2% of the surveyed PCPs had already prescribed medicinal marijuana in their professional practices. Conclusions: Limited willingness among PCPs to prescribe medicinal marijuana is primarily due to insufficient knowledge among physicians about the therapeutic effects of medicinal marijuana, its potential adverse effects, the legal framework for prescribing medications, and associated uncertainties. Full article

Geopolitical disruptions such as trade wars, sanctions, and political instability threaten global supply chain (SC) resilience. As a result, multinational corporations face financial losses, operational delays, and strategic uncertainties, creating an urgent demand for innovative risk management and scenario-planning strategies. Traditional risk management methods struggle to keep pace with the complexity of these events. This study explores the metaverse, combining VR, AR, digital twins, AI, and blockchain, as a tool for enhancing SC risk management. By enabling immersive scenario planning, real-time risk visualization, and collaborative decision-making, the metaverse supports agile and resilient supply chains. This research proposes a conceptual framework integrating key fourth industrial revolution (4IR) technologies to address geopolitical SC disruptions systematically. This model fosters digital preparedness, simulation-based learning, and adaptive coordination. While technological, organizational, and regulatory challenges persist, the study demonstrates that metaverse-enabled systems can support future-ready SC resilience strategies. Full article

Background: Ongoing challenges such as geopolitical conflicts, trade disruptions, economic sanctions, and political instability have underscored the urgent need for large manufacturing enterprises to improve resilience and reduce dependence on global supply chains. Integrating regional and local Small- and Medium-Sized Enterprises (SMEs) has been proposed as a strategic approach to enhance supply chain localization, yet barriers such as limited visibility, qualification hurdles, and integration difficulties persist. Methods: This study proposes a comprehensive knowledge graph driven framework for representing and discovering SMEs, implemented as a proof-of-concept in the U.S. BioPharma sector. The framework constructs a curated knowledge graph in Neo4j, converts it to Resource Description Framework (RDF) format, and aligns it with the Schema.org vocabulary to enable semantic interoperability and enhance the discoverability of SMEs. Results: The developed knowledge graph, consisting of 488 nodes and 11,520 edges, enabled accurate multi-hop SME discovery with query response times under 10 milliseconds. RDF serialization produced 16,086 triples, validated across platforms to confirm interoperability and semantic consistency. Conclusions: The proposed framework provides a scalable, adaptable, and generalizable solution for SME discovery and supply chain localization, offering a practical pathway to strengthen resilience in diverse manufacturing industries. Full article

This paper develops a nonlinear optimization model for the optimal allocation of labor and investment resources in a three-sector open economy. The model is based on the Cobb–Douglas production function and incorporates sectoral interdependencies, capital depreciation, trade balances, and import quotas. The resource allocation problem is formalized as a constrained optimization task, solved analytically using the Lagrange multipliers method and numerically via the golden section search. The model is calibrated using real statistical data from Kazakhstan (2010–2022), an open resource-exporting economy. The results identify structural thresholds that define balanced growth conditions and resource-efficient configurations. Compared to existing studies, the proposed model uniquely integrates external trade constraints with analytical solvability, filling a methodological gap in the literature. The developed framework is suitable for medium-term planning under stable external conditions and enables sensitivity analysis under alternative scenarios such as sanctions or price shocks. Limitations include the assumption of stationarity and the absence of dynamic or stochastic features. Future research will focus on dynamic extensions and applications in other open economies. Full article

In the context of the globalization process, the interplay between geopolitical dynamics and international trade fluctuations has had significant effects on global economic and business stability. Recent crises, such as the US–China trade war, the invasion of Ukraine, and the COVID-19 pandemic, have highlighted how changes in the structure of international trade can amplify the risks of business failure and reshape global competitiveness. This study aims to analyze in depth the transmission of business failure risk within the global trade network by assessing the sensitivity of industrial sectors in different countries to disruptive/critical/significant events. Through the integration of data from sources such as the World Trade Organization, national customs, and international relations research centers, a quantitative, exploratory, and descriptive approach based on graph theory, random forest, multivariate regression models, and neural networks is developed. This quantitative system makes it possible to identify patterns of risk propagation and to evaluate the degree of vulnerability of each country according to its commercial and financial structure. The mechanisms that relate geopolitical factors, such as trade sanctions and international conflicts, with the oscillations in the global market are analyzed. This study not only contributes to our understanding of how the macroeconomic environment influences business survival, but also provides analytical tools for strategic decision making. By providing an empirical and theoretical framework for early risk identification, it brings a novel perspective to academia and business, facilitating better adaptation to an increasingly volatile and uncertain business environment. Full article

Shadow fleets, operating covertly in global maritime commerce, have emerged as a significant challenge to international regulatory frameworks and trade policies. This paper introduces a novel conceptual framework that distinguishes between ‘dark fleets’ and ‘gray fleets’, offering a more nuanced understanding of these clandestine maritime activities. Through a comprehensive methodological approach integrating a literature review, case studies, and data analysis, we examine the characteristics, operational strategies, and implications of shadow fleets. Our research reveals that shadow fleets have expanded rapidly, now accounting for approximately 10% of global seaborne oil transportation. We identify key indicators of shadow fleet operations, including disabled Automatic Identification System (AIS) transmitters, inconsistent vessel information, unusual behavior patterns, obscure ownership structures, and the use of aging vessels. This paper highlights the economic disruptions caused by shadow fleets, their role in circumventing international sanctions, and the significant environmental and safety risks they pose. The study underscores the regulatory challenges in addressing shadow fleets, particularly their exploitation of flags of convenience and complex ownership structures. We propose a multifaceted approach to tackling these challenges, emphasizing the need for advanced technological solutions, enhanced international collaboration, and adaptive ocean governance frameworks. This research contributes to the evolving field of maritime security and policy, offering insights for policymakers, industry stakeholders, and researchers into developing strategies to mitigate the risks posed by shadow fleets in global maritime commerce. Full article

Political scientists and economists often assert that they understand how economic sanctions function as a foreign policy tool and claim to have backed their theories with compelling statistical evidence. The research puzzle that this article addresses is the observation that despite almost four decades of empirical research on economic sanctions, there is still no consensus on the direction and magnitude of the key variables that theoretically determine the success of economic sanctions. To address part of this research puzzle, we conducted a meta-analysis of 37 studies published between 1985 and 2018, focusing on three key determinants of sanction success: trade linkage, prior relations, and duration. Our analysis examines the factors contributing to the variation in findings reported by these primary studies. By constructing up to 27 moderator variables that capture the contexts in which researchers derive their estimates, we found that the differences across studies are primarily influenced by the data used, the variables controlled for in estimation methods, publication quality, and author characteristics. Our results reveal highly significant effects, indicating that sanctions are more likely to succeed when there is strong pre-sanction trade, when sanctions are implemented swiftly, and when they involve countries with better pre-sanction relationships. In our robustness checks, we consistently confirmed these core findings across different estimation techniques. Full article

Despite the fact that a decade has elapsed since the establishment of the Eurasian Economic Union (EAEU), the impact of the EAEU on the economic development of its member states remains a subject of ongoing debate. This article examines the mutual trade in services between the Eurasian Economic Union (EAEU) countries, with the aim of assessing the impact of membership on it. The difference-in-difference model has been applied for impact assessment. The model utilizes data from five EAEU member countries—Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia—capturing periods both before and after their EAEU membership, spanning 17 years in total. The results show that membership in the EAEU has significantly affected the exports of services from Russia and Belarus and has a less significant impact on the exports of services from Kazakhstan to the EAEU. At the same time, it has no significant effect on the exports of services from Kyrgyzstan and Armenia to other EAEU countries. In order to ascertain the challenges that exist, expert surveys among service exporters from Armenia have been conducted. Representatives of companies exporting various services to the EAEU have been selected as experts. The survey results indicate the presence of various barriers, including legal, logistical (for cargo transportation companies), and cultural challenges. These barriers encompass licensing difficulties, technical obstacles related to VAT refunds, a ban on cash payments, and difficulties with financial transfers due to sanctions against Russia. The findings of this research are of practical importance and can serve as a guideline for policymakers in the EAEU. Full article

This research determines the contribution of Corporate Social Responsibility (CSR) to reducing financial risks and, consequently, to the sustainable development of companies in different sectors of the economy and at different phases of the economic cycle (using Russia as an example). The informational and empirical base comprises data on the dynamics of stock prices of sectoral indices of the Moscow Exchange’s total return “gross” (in Russian rubles): oil and gas, electricity, telecommunications, metals and mining, finance, consumer sector (retail trade), chemicals and petrochemicals, and transportation, as well as the “Responsibility and Openness” index in 2019 (before the crises), in 2020 (COVID-19 crisis), 2022 (sanction crisis), and 2024 (Russia’s economic growth). Economic–mathematical models, compiled through regression analysis, showed that the contribution of CSR to reducing the financial risks of companies is highly differentiated among economic sectors and phases of the economic cycle. The research presents a new sectoral perspective on counter-cyclical management of the financial risks of companies through CSR, enabling a deeper study of the cause-and-effect relationships of such management for the sustainable development of companies from different economic sectors. This is the theoretical significance of this research, its novelty, and its contribution to the literature. The research has practical significance, revealing previously unknown best practices for the sustainable development of companies from different economic sectors of Russia across different phases of the economic cycle. The systematized experience will be useful for forecasting the financial risks of companies during future economic crises in Russia and improving the practice of planning and organizing the financial risk management of Russian companies through CSR. The authors’ conclusions have managerial significance because they will help enhance the flexibility and efficiency of corporate financial risk management by considering the sectoral specifics and cyclical nature of the economy when implementing CSR. Full article

The potency of economic sanctions imposed on nations depends on demand and supply adjustment possibilities. Adverse GDP impacts will be maximal when import, export, production, distribution and finance are inflexible (universal non-substitution). This paper elaborates on these conditions and quantifies the maximum GDP loss that Western sanctions could have inflicted on Russia in 2022–2023. It reports the World Bank’s predictions, contrasts them with the results and draws inferences about the efficiency of Russia’s workably competitive markets. This paper shows that Russia’s economic system exhibits moderate universal substitutability and is less vulnerable to punitive discipline than Western policymakers suppose. The likelihood that economic sanctions will compel the Kremlin to restore Ukraine’s territorial integrity ceteris paribus is correspondingly low, even though war reduces Russia’s quality of existence. Western economic sanctions serve narrow geostrategic ends that are reconcilable with Pareto-efficient free trade and globalization, if precision-targeted, but as the Russo-Ukrainian war intensifies, an expanded array of novel and dubiously legal sanctions is degrading free trade, and spurring de-globalization and anti-Western coalitions. If this armed combat is prolonged, the goals of free trade and globalization could be set back for decades. Full article

In 2022, the Russian forest sector was severely affected by the government’s ban on the export of unprocessed timber and trade sanctions imposed by several countries. It is generally recognized that the regions of the Russian North-West are the most affected by trade barriers that have emerged. Against this background, the impact of bilateral trade restrictions on timber companies in the Asian part of Russia is not discussed. Nevertheless, the forest industry is an important sector of the Siberian economy that has an economic, social and environmental impact on the life of local communities. This paper analyzes the differences among Siberian timber companies in their response to the crisis depending on three factors: industrial specialization, scale of revenue and regional location. The results show that in 2022 the highest median revenues and net profits were generated by small firms that were focused on the domestic market and benefited from reduced competition due to sanctions. There is also evidence that spatial heterogeneity in the response to the crisis may be due to the different support measures of regional authorities and the proximity of the region to border points. We argue that the current conditions may become a new driver for the timber industry development, aimed at the growth of added value and expansion of domestic demand for wood products. Full article

Since 2014, economic sanctions between Russia and Western nations have significantly altered the global seafood trade. The consequent decline in bilateral trade also had spillover effects on the rest of the world (ROW). According to earlier studies, economic sanctions appear to negatively impact bilateral trade and income. However, few studies examine how Russian sanctions affect the world as a whole and estimate their effects on the fisheries industry. This study seeks to close this gap by quantifying the extent to which Russian sanctions have impacted trade in terms of trade deflection, trade destruction, trade depression, and trade creation. To this end, panel data from 185 countries were created for the years from 2005 to 2020. With trade policy variables that account for changes in trade channels, a structural gravity trade model was specified. Based on calculations using the Poisson pseudo-maximum likelihood (PPML) fixed effect model, economic sanctions led to a 119.28% surge in Russia’s seafood imports from the rest of the world (ROW), alongside a 39% decline in imports from Western countries. The extent of trade deflection, which includes the exports of Western nations diverted from Russia to the ROW markets, increased by 5.49%. The results demonstrate that trade between sanctioned states, as well as global trade, is significantly impacted by economic sanctions. Full article

Ardabil Plain, which holds significant political and economic importance in agricultural production in Iran, has faced various challenges including climate change, economic sanctions, and limited access to global trade. Ensuring food security has become a key priority for the region. The main objective of this research is to identify a suitable crop for this critical region with regard to future climate change conditions. This study employs a new framework of the system dynamics model (SDM) and the Hybrid Weighted Averaging (HWA) method to assess the Water–Energy–Food (WEF) nexus and resource sustainability in the Ardabil Plain under different climate change scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). The research addresses current and future water challenges, emphasizing the need for additional energy and selecting optimal crops. Using the SDM, the study analyzes the impact of water supply fluctuations on agriculture, economic gain, and energy consumption from 2021 to 2050. The results indicate that barley is the most suitable crop for the Ardabil Plain in the near future, based on the overall ranking derived from the HWA method, which is as follows: barley > wheat > soybeans > potatoes > pears. The study highlights the significant challenges in energy supply for agriculture due to declining water levels and the increased force required by pumps to supply water to farms. These findings provide valuable insights for policymakers and stakeholders to make informed decisions in addressing water scarcity and rising energy demands in the Ardabil Plain. Full article

Given the prevalence of the digital world, artificial intelligence (AI) stands out as one of the most prominent technologies for demand prediction. Although numerous studies have explored energy demand forecasting using machine learning models, previous research has been limited to incorporating either a country’s macroeconomic characteristics or exogenous elements as input variables. The simultaneous consideration of both endogenous and exogenous economic elements in demand forecasting has been disregarded. Furthermore, the stability of machine learning models for energy exporters and importers facing varying uncertainties has not been adequately examined. Therefore, this study aims to address these gaps by investigating these issues comprehensively. To accomplish this objective, data from 30 countries spanning the period from 2000 to 2020 was selected. In predicting oil demand, endogenous economic variables, such as carbon emissions, income level, energy price, gross domestic product (GDP), population growth, urbanization, trade liberalization, inflation, foreign direct investment (FDI), and financial development, were considered alongside exogenous factors, including energy sanctions and the COVID-19 pandemic. The findings indicate that among the input variables examined in demand forecasting, oil sanctions and the COVID-19 pandemic have had the most significant impact on reducing oil demand, while trade liberalization has proven to be the most influential factor in increasing oil demand. Furthermore, the support vector regression (SVR) model outperforms other models in terms of lower prediction error, as revealed by the error assessment of statistical models and AI in forecasting oil demand. Additionally, when comparing the stability of models in oil exporting and importing countries facing different levels of demand uncertainty, the SVR model demonstrates higher stability compared to other models. Full article