Search Results (186)

Small and medium-sized enterprises (SMEs) represent the majority of EU businesses and a disproportionate share of its environmental impact, yet longitudinal evidence on their resource efficiency behaviour remains scarce. This study examines whether SME adoption of resource efficiency practices increased between 2017 and 2024 and whether the size gradient changed across a period spanning several major and concurrent institutional and economic events: the European Green Deal (2019), the Circular Economy Action Plan (2020), the COVID-19 pandemic and its economic aftermath (2020–2021), the energy price shock that intensified in 2021 and was amplified by the Russia–Ukraine conflict in 2022, and the early implementation phase of the Corporate Sustainability Reporting Directive (2022–2024). These events overlapped substantially in time and cannot be disentangled with the present empirical design. Using microdata from three Flash Eurobarometer waves (N = 38,165; EU27), we construct a harmonised eight-item adoption index and estimate a weighted Poisson regression with cluster-robust standard errors and wave × firm-size interactions. Adoption increased substantially: SMEs reporting no resource efficiency action fell from 10.5% to 4.2%, and renewable energy use more than doubled (+15.3 pp). More substantively, the population-level gap in resource efficiency adoption between medium-sized enterprises (50–249 employees) and microenterprises (1–9 employees) narrowed by 81%, from +0.89 practices in 2017 (out of a maximum of 8) to a statistically non-significant +0.13 and +0.17 practices in 2021 and 2024 respectively (interaction IRR ≈ 0.81–0.82, p < 0.001 in both waves), robust to nine checks. The size gradient in environmental behaviour appears not to be structural but a dynamic feature of the institutional landscape, one that narrowed substantially over a period coinciding with intensifying regulatory ambition and energy price shock—though the present repeated cross-sectional design cannot establish which mechanisms drove this compression. Full article

This paper examines whether prolonged sanctions and major geopolitical episodes associated with financial fragmentation alter exchange-rate dynamics and weaken the explanatory power of domestic macroeconomic channels and strengthen external financial dominance of traditional exchange-rate transmission mechanisms. Standard exchange rate theories are based on the concepts of Purchasing Power Parity (PPP) and Uncovered Interest Parity (UIP). However, the application of continuous sanctions could weaken this explanatory power and change exchange rate dynamics. The present study applies a combined framework of Autoregressive Distributed Lag (ARDL), Error Correction Modeling (ECM), Vector Autoregression (VAR) and structural break analysis to study the exchange-rate behavior in response to repeated geopolitical shocks using monthly data for Russia from 2005 to 2025. The results indicate that external variables such as the US dollar index and oil prices are important determinants of exchange rates, while inflation and interest rate differentials associated with PPP and UIP have little explanatory power. Structural break tests detect major regime shifts associated with the Global Financial Crisis, Crimea-related sanctions episode, COVID-19 pandemic and Russia–Ukraine conflict. The error correction process indicates that the speed of adjustment to equilibrium is slow, which means that traditional exchange-rate relationships will continue to diverge. In general, the results suggest a regime-dependent exchange rate environment in which external financial factors tend to dominate domestic adjustment mechanisms. Our study contributes to the literature on exchange rates, sanctions and financial fragmentation by providing evidence on how geopolitical shocks shift the relative importance of domestic and external determinants in a highly sanctioned economy. Full article

Russia’s invasion of Ukraine has posed serious risks to global food security, by causing substantial crop yield losses since 2022. Accurate yield estimation helps policymakers to plan compensation, yet modelling yields in conflict regions remains challenging due to significant non-meteorological disruptions. This study proposes a novel framework to quantify war-related crop yield losses by comparing estimations derived from meteorological data, representing weather-driven yield variability, with those based on Earth observation (EO) data, reflecting actual crop conditions influenced by both weather and conflict. Thus, meteorologically based yield estimates are expected to exceed those derived from EO data, with the difference indicating war-related losses. Both, meteorological- and EO-based models, are developed using transfer learning (TL) to estimate yields of maize, winter wheat, and spring barley. Models are initially trained on EU country data and subsequently finetuned with Ukrainian data. Their performance is compared to two non-TL approaches: Extreme Gradient Boosting (XGB) and Artificial Neural Network (ANN) to test their reliability. Results show crop yield losses for maize; however, since we do not detect losses in the other crops, we conclude that simply comparing meteorological- and EO-based models proves insufficient to fully isolate conflict effects due to strong interactions of EO and meteorological data. Nevertheless, TL substantially enhances prediction accuracy (R² around 0.7), exceeding alternative models by 0.05–0.2 across crops. These findings demonstrate the value of TL for yield modelling in data-scarce environments and underscore the need for improved methodologies to quantify conflict-induced agricultural losses. Full article

Despite advances in computational social science, online conflict discourse is still commonly analyzed through sentiment, stance, or topic models, leaving limited insight into the social and psychological mechanisms embedded in digital war narratives. This study develops a theory-informed probabilistic framework for detecting discourse-level indicators of Social Identity Theory, Moral Foundations Theory, Threat Appraisal, Cognitive Distortion, and Deindividuation in Russia–Ukraine war discourse. The empirical design uses 48,201 tweets in total: 10,815 tweets collected between 1 January and 28 June 2022 for model development and primary analysis, as well as and an external validation corpus of 37,386 Russia–Ukraine cyberwar-related tweets—collected from 30,706 users across 54 languages between October 2022 and April 2023—for temporal robustness assessment. The primary corpus contained 10,815 unique tweet identifiers, 10,229 unique textual records, 586 repeated textual items, a textual uniqueness rate of 94.58%, 6646 English tweets (61.45%), and 32,260 retweet engagements. Methodologically, the framework combines contextual language representations, theory-aligned linguistic cues, temporal signals, engagement features, and graph-based indicators. These signals are used to infer latent constructs and are evaluated through calibration, ablation testing, human validation, and cascade comparison. Empirically, Deindividuation was the dominant construct (1654 posts, 15.29%), followed by Cognitive Distortion (525, 4.85%) and Threat Appraisal (503, 4.65%). Co-activation analysis showed the strongest overlap between Deindividuation and Cognitive Distortion (Jaccard = 0.26). Validation diagnostics indicated internal lexical consistency ($r=0.88$ for Deindividuation), 93% rumor calibration, 91% bootstrap stability, and improved baseline performance (F1 = 0.72; Brier = 0.12; cascade log-likelihood = −865). The findings demonstrate that theoretically grounded probabilistic modeling can provide scalable, interpretable, and temporally validated insight into psychological patterns in digital conflict discourse. Full article

In light of growing concerns about sustainable development and green innovation, the green market has progressively taken center stage in the financial markets. From the nonlinear information transmission angle, we look into the interconnectedness between the global banking sectors and the green markets using transfer entropy causal networks, containing the Dow Jones Green Bond Index (SPGB), Dow Jones Sustainability Index (DJSI), The S&P Global Clean Energy Index (SPCL), and MSCI World ESG Leaders Index (ESGL). We observe significant bidirectional causal relationships between two markets. The banking industries of developed nations and emerging economies like South Korea, Indonesia, and India are the most important, while four green markets are vital. Furthermore, using the CEEMDAN-SE-KM approach, this study also investigates the two markets’ heterogeneous performance at various time scales. The causal relationships between two markets exhibit heterogeneity at time scales, and that is most noticeable at the short-term scale. Additionally, after the COVID-19 pandemic and the conflict between Russia and Ukraine, there is an increase in the causal relationships between the two markets and a higher efficiency of information transmission. These results help regulatory bodies and green market players have a more thorough understanding of and dynamic regulation of the green market. Full article

This paper provides a comparative, descriptive assessment of macroeconomic adjustment dynamics within the euro area in response to the inflationary and crisis-related disturbances of the 2015Q1–2024Q4 period, with particular attention to the COVID-19 pandemic and the geopolitical shock associated with the Russia–Ukraine conflict. Using a Vector Error Correction Model (VECM) framework estimated separately for the euro-area aggregate and four representative core and peripheral economies (Germany, France, Spain, and Greece), the analysis characterizes long-run equilibrium relationships and short-run adjustment dynamics among output, inflation, public debt, and unemployment. Rather than identifying structurally causal transmission, the study interprets the estimated cointegration structures, error-correction speeds, and impulse-response patterns as reduced-form indicators of how differently national systems absorb common disturbances. The comparative evidence points to substantial heterogeneity: core economies display comparatively more contained and coordinated adjustment, whereas peripheral economies exhibit stronger fiscal sensitivity, more persistent labor-market adjustment, and greater macroeconomic interdependence. Read together, these patterns suggest that resilience within the monetary union is better understood not solely as equilibrium restoration, but as the persistence, coordination, and stability of the broader adjustment process—and that asymmetric adjustment structures persist despite a common monetary framework. Full article

This study examines the evolution of dependence structures and tail risk transmission among technology equities, Bitcoin, Gold, and Crude Oil during 1 January 2016–1 January 2026. The analysis focuses on NVIDIA (NVDA), AMD, Tesla (TSLA), Bitcoin (BTC), Gold and Oil, covering major disruptions including the COVID-19 pandemic and the Russia–Ukraine conflict. An integrated methodological framework combines DCC-GARCH modeling, R-vine copulas, tail dependence analysis, complexity measures and machine learning-based forecasting techniques. The findings reveal volatility persistence and time-varying correlations, especially between technology equities and BTC during crisis periods. Regime analysis reveals that dependence structures are not stable in time. Lower-tail dependence intensifies during periods of market stress, indicating increased downside risk transmission. Gold remains weakly connected to the other assets, while Bitcoin has the strongest exposure to extreme downside co-movements. Complexity analysis based on the Scale-Dependent Lyapunov Exponent (SDLE) indicates heterogeneous dynamics across scales, characterized by local divergence and stabilization at broader scales. Forecast results based on Random Forest and XGBoost models provide limited predictive gains over benchmark specifications, suggesting that dependence and tail risk modeling offer better insight than short-horizon return predictions. These results are important for monitoring tail risk transmission for financial stability policies. Full article

This paper analyses the dynamics of connectedness among technology-oriented assets, such as fintech, blockchain, cybersecurity, internet, and disruptive technology indices, on the effect of political signal quality on the transmission of spillovers. Applying the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with frequency-based connectedness, the paper explores dynamic, horizon-dependent spillovers in the interconnection of innovation-based financial markets from January 2015 to April 2025. The findings show consistently high interconnectedness among 4IR assets, but this level increases significantly during the COVID-19 outbreak and the Russia–Ukraine conflict. It is also found that disruptive technology and fintech indices dominate shock transmission among interconnectedness networks. Based on the frequency decomposition approach, it is evident that spillovers arise from short-run dynamics, indicating that 4IR financial systems respond quickly to uncertainty shocks and to synchronized investor behavior. The regression and quantile regression analyses indicate a conditional effect of political signal quality on connectedness, especially during crisis periods marked by higher market uncertainty and stress. Specifically, it is evident that a deterioration in political signal quality increases spillover effects due to information uncertainty and expectation-based investor behavior. This means that, in an innovation-driven financial system, uncertainty is not just transmitted through macroeconomic and financial factors, but also through political communication and information uncertainty. In summary, this paper adds to the existing literature on connectedness by considering political information quality uncertainty in analyzing the 4IR financial system and by identifying how technological integration makes the financial market vulnerable during crises. Full article

Amid intensifying extreme climate events, geopolitical conflicts, and sudden trade policy disruptions, the resilience and vulnerability of global staple food trade systems have emerged as pressing governance concerns. This study constructs directed weighted trade networks for wheat, maize, and rice from 2015 to 2024 and evaluates their vulnerability and resilience evolution using a three-dimensional structural resilience framework and underload cascading failure models. The results reveal that all three networks display scale-free and disassortative properties. The wheat network gradually recovered following the Russia–Ukraine conflict, whereas structural imbalance continues to deepen in the maize network, and the rice network faces persistent resilience pressure arising from excessive dependence on core exporters. Cascading failure simulations indicate that targeted attacks on key exporting countries can trigger large-scale network collapse. Introducing cross-crop substitution effects markedly enhances the resilience of individual food trade networks through cross-layer substitution and supplementation; yet under simultaneous attacks, crop substitution effects instead serve as a conduit for cross-layer cascading failure propagation, and even a minimal willingness to substitute can weaken network resilience. Accordingly, this study proposes policy recommendations to strengthen the resilience of the global staple food trade network. Full article

Thematic analysis of large-scale political discourse remains a challenge due to semantic complexity and overlapping policy areas and changing diplomatic vocabulary. Although large language models (LLMs) offer promise for scalable thematic classification, their reliability in politically sensitive contexts requires systematic validation against expert human annotations. We evaluate LLM-based thematic classification of United Nations General Assembly (UNGA) speeches across a decade (2014–2023), using 7680 human-annotated themes mapped into 12 policy domains. Our results show that DeepSeek R1 achieves the highest accuracy 77% (F1 = 0.73), followed by ChatGPT, Gemini and LLaMA, with strong performance in lexically stable domains but substantial degradation in semantically overlapping categories such as governance and international cooperation. A unique dimension of our work is timeline analysis, which shows that the performance of LLMs over the years varies strongly and the precision decreases during times of rhetorical transformation, including pandemic-related discussions and the discourses of cooperation determined by the Russia–Ukraine conflict. By linking domain-level ambiguity and geopolitical shifts to temporal instability, this study introduces a dynamic robustness perspective for evaluating LLMs in computational political discourse analysis. Full article

Using the recently created contemporaneous and lagged R²-decomposed connectedness paradigm, this study examines the dynamics of return transmission between large South African banks and two top international banks, J.P. Morgan and BNP Paribas. The analysis makes a distinction between delayed (liquidity-driven) propagation mechanisms and instantaneous (information-driven) spillovers, using daily stock returns from 2015 to 2024. With a Total Connectedness Index of 44.14%, which is driven mostly by contemporaneous transmission, the results demonstrate a high degree of systemic interdependence and rapid assimilation of global information across banking stocks. We find smaller lagged spillovers which become much more intense during stressful events like COVID-19 in 2020, the conflict between Russia and Ukraine in 2022, and the banking instability involving the United States and Switzerland in 2023. These findings are conditioned by funding pressures, liquidity limits, and slow portfolio rebalancing. In the South African financial system, Standard Bank and Nedbank consistently act as net transmitters of shocks, whereas J.P. Morgan and BNP Paribas primarily act as net receivers, indicating asymmetric cross-border contagion pathways. However, their spillover transmission roles switch during crises. Overall, the results offer fresh empirical insight on how global shocks are absorbed and retransmitted by emerging-market banking systems, providing policy-relevant information for cross-border supervisory coordination, macroprudential design, and systemic risk monitoring. Full article

Understanding how information flows across financial segments during global crises is crucial for analyzing complex and highly interconnected markets. This study investigated the dynamic information flow between cryptocurrencies, commodities, stock market indices of G10 countries, five-year sovereign CDS spreads, ten-year government bond yields, foreign exchange market variables, and technology company stocks using daily return data spanning from 1 January 2018 to 24 March 2026. Transfer Entropy is estimated using two alternative approaches: directly from the original variables and from independent components obtained via Independent Component Analysis (ICA), which reduces noise and uncovers latent relationships. A sliding-window framework is employed to capture time-varying directional information flow and to assess changes across major global events, including the COVID-19 pandemic, the Russia–Ukraine conflict, and the Middle East tensions. The results indicate that the magnitude and direction of information flow change significantly during crisis periods, revealing an event-sensitive and dynamically evolving connectivity structure between financial segments. Overall, the integration of ICA and Transfer Entropy provides a clearer and more reliable representation of directional interactions in multidimensional financial systems under the conditions of heightened uncertainty. Full article

This study investigates the roles gold and the US dollar play as safe-haven, hedging, or diversifier assets relating to six important financial stock market indices: the S&P 500, FTSE 100, Hang Seng, CAC 40 (Paris), Shanghai Composite Index, and Nikkei 225. This paper applies the bivariate dynamic copula technique and the DCC-GARCH econometric advanced methods from January 2013 to July 2024 by focusing on four serious market crashes: the Chinese stock market meltdown (2015–2016), the trade war between the US and China (2018–2020), the COVID-19 pandemic (2020–2022), and the conflict between Russia and Ukraine (2022–2024). The results show that the US dollar displays reliable hedging and safe-haven characteristics with strong evidence mainly for its role as a safe-haven asset against the FTSE 100, Hang Seng, and S&P 500. Our findings support the idea that the US dollar serves consistently as a safe-haven asset. In contrast, gold showcased a twofold function, serving as a hedge for the FTSE 100 and the S&P 500 during crisis times and acting as a diversifier for the CAC 40 and the Shanghai Composite Index in times of market stability. This dynamic was specifically noticeable in the COVID-19 period, when gold’s hedging properties were outstanding and its role as a diversifier became more pronounced in the Paris and Shanghai markets. Our results suggest that the consistent reliability of the US dollar as a safe-haven asset combined with gold’s dual role presents a compelling argument for including both in well-diversified portfolios. This strategy enables investors to mitigate risk and safeguard their wealth, especially during periods of financial market volatility. Full article

This paper investigates the long-run relationship and short-run price dynamics between the German electricity and natural gas markets to assess market efficiency, with a focus on the impact of the Russia–Ukraine conflict. Employing Johansen cointegration tests and a Vector Error Correction Model (VECM) on weekly data from 2018 to 2025, we find a stable long-run equilibrium between the two prices. The results show that while the electricity market exhibits a self-correcting mechanism, indicating a certain degree of efficiency, this efficiency significantly deteriorated following the conflict’s outbreak. The natural gas market lost its error-correction capability post-conflict, and momentum effects became pronounced, suggesting impaired price discovery and weakened market efficiency under severe geopolitical stress. The findings provide empirical evidence supporting the reform of marginal pricing models in Europe to enhance resilience against geopolitical shocks. Full article

This article deals with the attitudes of residents of the border village of Ubľa toward the arrival of foreign nationals in the Slovak Republic, with a particular focus on individuals who left Ukraine because of the international armed conflict between Russia and Ukraine. The aim of this research is to assess the impact of this migratory movement on the perceived quality of life of local inhabitants living near the border crossing and to identify potential measures for improvement. This study is based on a review of the current state of the issue in both national and international contexts, serving as a theoretical foundation for the empirical part of this study. This study was conducted using the Computer-Assisted Web Interviewing (CAWI) method to examine residents’ attitudes toward foreign nationals in general, toward arrivals from Ukraine specifically, and toward the management of the crisis declared in 2022 in response to their arrival. Data were analyzed using descriptive and analytical statistics. The results indicate significant differences in respondents’ attitudes depending on their level of education, with university-educated respondents being approximately twice as likely to express more positive attitudes toward the arrival of foreign nationals and refugees from Ukraine compared to respondents with secondary education, who tended to hold more negative views. Full article